Current Protocols in Pharmacology

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FOREWORD

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harmacology has come a long way since the Egyptian Ebers Papyrus, the first pharmacopoeia, was written over 3500 years ago. The era of folklore medicine that the papyrus ushered in was believed to be a description of how almost everything in nature could be put to good use as a means of providing relief from distress. Our ancestors seem to have used everything and anything they could get their hands on. Herbs, weeds, thorns, woods, barks, roots, juices, stones, minerals, animal appendages of every imaginable kind, and much more were drummed into service. Incredible as it may seem from a present-day perspective, this era of folklore medicine lasted until the middle of the 19th century. Modern pharmacology grew out of a deepening understanding of animal physiology and organic chemistry. For nearly 100 years, pharmacology was a descriptive science—a description of the effects of drugs on physiological systems. Pharmacological techniques were essentially the physiological techniques needed to describe the acute effects of drugs on blood pressure, respiration, urine flow, acid secretion, and so on. At the same time, developments in organic chemistry led not only to the isolation and characterization of natural products, but also to the synthesis of derivatives and analogues of these compounds. Early studies in structure-activity relationships helped pharmacology become a serious scientific discipline distinct from physiology. During this century, growth in biochemical knowledge and in understanding of the nature of metabolism allowed pharmacology to develop gradually as an analytical science. Therapeutics continued to be based on the pharmacological description of the actions of drugs on physiological systems. However, the development of analytical pharmacology— that is, the attempt to explain the effects of drugs at the physiological level in terms of molecular interactions occurring at the biochemical level—not only turned pharmacology into an exploratory science, but also led to the beginnings of so-called rational drug design. The great achievement of analytical pharmacology has been to discover how often a selective drug action at the physiological level can be interpreted as a selective action at a biochemical site that is normally the interactive site of a natural molecule. The techniques used in pharmacological analysis are the same biochemical techniques that were used to discover these chemical sites in the first place. In addition to direct biochemical measurements, pharmacologists have manufactured a whole library of mathematical models that they use to interpret drug actions at the physiological level. Pharmacology has become progressively more reductionist in its analytical approach. Pharmacology is now studied at every conceivable level of complexity: populations (in clinical trials), conscious people (in clinical pharmacology), intact animals (including intact, surgically modified, and transgenic animals), intact isolated tissues (including perfused whole organs and pieces of architecturally intact tissues sustained in vitro), cells in tissue culture, intracellular transducers and genetic messages, pure proteins, free radicals, and protons (although not yet as far down as quarks!). Each level has its own appropriate techniques and protocols, many of which are detailed in Current Protocols in Pharmacology. At the same time, modern pharmacology has become intensely multidisciplinary in its approach. Under pressure from the sheer power and universality of molecular biology, reinforced by the decisions of funding bodies and institutional directors, the boundaries between the traditional subjects of anatomy, physiology, biochemistry, pharmacology, immunology, and genetics are becoming blurred, even disappearing altogether in some Current Protocols in Pharmacology

Contributed by Sir James Black Current Protocols in Pharmacology (1998) i-ii Copyright © 1998 by John Wiley & Sons, Inc.

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universities and research organizations. More and more, biomedical researchers are being expected to become technical polymaths. Hence the importance of this new book of up-to-date methods in pharmacology, written by specialists for their colleagues. I have no doubt this book will satisfy a huge and growing need. Sir James Black Dulwich, London

Foreword

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PREFACE

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o identify new therapeutic agents and define the mechanism of action of existing drugs requires the use of a wide array of ever-more-sophisticated methodologies. Pharmacology is an integrative science that requires a broad working knowledge of a host of disciplines, including cellular and molecular biology, biochemical pharmacology, physiology, anatomy, immunology, neuroscience, toxicology, and endocrinology as well as compound pharmacokinetics and metabolism. As it enters its second century, experimental pharmacology continues to grow by adapting the methodological advances in other fields of research to its own ends: defining and refining knowledge of disease etiology, and elaborating on the structure, activity, and concentration/dose-response relationships of new compounds in both in vitro and in vivo model systems. The latter provide the natural meeting point between the disciplines of pharmacology and medicinal chemistry that has revolutionized biomedical research. In recent years, pharmacological research has focused at the molecular level to more precisely define sites of drug action in an attempt to develop selective, and presumably safer, therapeutics. Even with this emphasis, it is still necessary to assess the pharmacological actions of potential drug candidates in a hierarchical manner at the cellular, tissue, organ, and whole-animal levels before they advance to clinical trials. This requires that pharmacologists be able to design and interpret data from experiments aimed at characterizing the biochemical, physiological, and behavioral effects of chemical agents. Thus, in addition to its rich history of drug discovery, pharmacology is characterized by the diversity of the analytical techniques and experimental methodologies that are applied to meet these objectives. Contributions to the present volume were assembled with the goal of providing the reader with a practical, working knowledge of some of the more common methods currently used in pharmacology and drug discovery. While most of the basic approaches presented are not unique to pharmacology, examples have been selected to demonstrate how each procedure can be used in identifying new drug candidates or defining mechanism(s) of action. In certain instances, overviews of particular topics explain more fully the theoretical context and basis for a particular methodological approach or the mathematical principles used for data analysis. For the most part, however, the text consists of a series of detailed protocols designed to facilitate the establishment and implementation of particular procedures in appropriately equipped research laboratories. This volume is intended for academic and industrial scientists, from research associates to students, postdoctoral fellows, and laboratory directors, interested in pursuing pharmacological research. It is presented on a level designed to ensure that anyone with a background in biological research will be able to establish, execute, and validate these assays. The protocols have been prepared by individual researchers who have used them extensively and, in some instances, are the original developers of the methodologies. Pitfalls and troubleshooting techniques are described to aid the experimenter in identifying and correcting technical difficulties and alternative procedures are presented to provide options for addressing a particular issue. The number of different assays utilized by pharmacologists makes it virtually impossible to provide a comprehensive, detailed, and current description of them all in a single volume published at a given point in time. Current Protocols in Pharmacology is unique among manuals in the field in that it is part of a proven series that has been carefully Current Protocols in Pharmacology

Contributed by S.J. Enna, Michael Williams, John W. Ferkany, Terry Kenakin, Roger D. Porsolt, and James P. Sullivan

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designed to allow for the timely revision and addition of new material on a regular basis. Given the ever-expanding list of pharmacological assays driven, in part, by the expanding number of molecular targets for potential drugs (e.g., receptors, enzymes, and transcription factors), there is no lack of important material to include in these supplements. In this regard, the editors welcome comments and suggestions from readers with respect to prioritizing topics for subsequent publication. Just as methods development is crucial to move a discipline forward, so it is hoped that by enhancing the research capabilities of the Current Protocols in Pharmacology reader, this series may catalyze the development of the new technologies and assays critical to the advancement of research in pharmacology and allied fields of biomedical research. HOW TO USE THIS MANUAL Format and Organization This publication is available in both looseleaf and CD-ROM format. For looseleaf purchasers, a binder is provided to accommodate the growth of the manual via the quarterly update service. This format allows easy insertion of new pages, units, and chapters that are added. The index and table of contents are updated with each supplement. CD-ROM purchasers receive a completely new disc every quarter and should dispose of their outdated discs. The material covered in the two versions is identical. Subjects in this manual are organized by chapters and sections, and protocols are contained in units. Protocol units, which constitute the bulk of the book, generally describe a method and include one or more protocols with listings of materials, steps and annotations, recipes for unique reagents and solutions, and commentaries on the “hows” and “whys” of the method. Other units present more general information in the form of explanatory text with no protocols. Overview units contain theoretical discussions that lay the foundation for subsequent protocols. Other discussion units present more general information; for example, Chapter 1 contains a unit on analysis and interpretation of radioligand binding data (including the underlying theory behind the practical equations that are used for this purpose), to which the reader may turn to gain a greater understanding of experimental results. Page numbering in the looseleaf version reflects the modular arrangement by unit; for example, page 1.2.3 refers to Chapter 1 (Receptor Binding), UNIT 1.2 (Receptor Theory), page 3 of that particular unit. Many reagents and procedures are employed repeatedly throughout the manual. Instead of duplicating this information, cross-references among units are used extensively. Cross-referencing helps to ensure that lengthy and complex protocols are not overburdened with steps describing auxiliary procedures needed to prepare raw materials and analyze results. Introductory and Explanatory Information

Preface

Because this publication is first and foremost a compilation of laboratory techniques in pharmacology, we have not provided exhaustive instructive material. We have, however, included explanatory information where required to help readers gain an intuitive grasp of the procedures. Chapter 1 provides an overview of receptors and of the theory that forms the foundation of ligand/receptor analysis, to help the reader plan for all aspects of an experimental study. Some subsequent chapters begin with special overview units that describe the state of the art of the topic matter and provide a context for the procedures that follow. Chapter and unit introductions describe how the protocols that follow connect

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to one another, and annotations to the actual protocol steps describe what is happening as a procedure is carried out. Finally, the Commentary that closes each protocol unit describes background information regarding the historical and theoretical development of the method, as well as alternative approaches, critical parameters, troubleshooting guidelines, anticipated results, and time considerations. All units contain cited references and many indicate key references to inform users of particularly useful background reading, original descriptions, or applications of a technique. Protocols Many units in the manual contain groups of protocols, each presented with a series of steps. One or more basic protocols are presented first in each unit and generally cover the recommended or most universally applicable approaches. Alternate protocols are provided where different equipment or reagents can be employed to achieve similar ends, where the starting material requires a variation in approach, or where requirements for the end product differ from those in the basic protocol. Support protocols describe additional steps that are required to perform the basic or alternate protocols; these steps are separated from the core protocol because they might be applicable to other uses in the manual, or because they are performed in a time frame separate from the basic protocol steps. Reagents and Solutions Reagents required for a protocol are itemized in the materials list before the procedure begins. Many are common stock solutions, others are commonly used buffers or media, while others are solutions unique to a particular protocol. Recipes for the latter solutions are provided in each unit, following the protocols (and before the commentary) under the heading Reagents and Solutions. It is important to note that the names of some of these special solutions might be similar from unit to unit (e.g., SDS sample buffer) while the recipes differ; thus, make certain that reagents are prepared from the proper recipes. On the other hand, recipes for commonly used stock solutions and buffers are provided once in APPENDIX 2A. These universal recipes are cross-referenced parenthetically in the materials lists rather than repeated with every usage. Commercial Suppliers Throughout the manual, we have recommended commercial suppliers of chemicals, biological materials, and equipment. In some cases, the noted brand has been found to be of superior quality or it is the only suitable product available in the marketplace. In other cases, the experience of the author of that protocol is limited to that brand. In the latter situation, recommendations are offered as an aid to the novice in obtaining the tools of the trade. Experienced investigators are therefore encouraged to experiment with substituting their own favorite brands. Addresses, phone numbers, and facsimile numbers of all suppliers mentioned in this manual are provided in the SUPPLIERS APPENDIX. Use of Proprietary Compounds The editors of Current Protocols in Pharmacology make every attempt to ensure that all the materials described for use in each unit are publicly available, either from commercial sources or by individual request to the company of origin. There are instances, however, where the cutting-edge nature of the research is such that the only compounds that could be used in these protocols as target-selective reference standards have not been identified and are not available in the public domain. While the peer-review process would normally

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indicate that such protocols should not be published until such compounds and the information about them are available, the removal of data on unidentified compounds would limit the practical utility of the protocol to the extent that it would not be useful and would preclude others from using the unit. The editors review such protocols on an individual basis to ensure that they have inherent value even when compounds are neither identified nor available. This is intended to provide the readership of Current Protocols in Pharmacology with the most up-to-date methods in breaking areas of pharmacological research. In these instances, the authors of the units will clearly indicate that, at the time of writing, the compounds used are for example purposes only and are not available in the public domain. Safety Considerations Anyone carrying out these protocols may encounter the following hazardous or potentially hazardous materials: (1) radioactive substances, (2) toxic chemicals and carcinogenic or teratogenic reagents, and (3) pathogenic and infectious biological agents. Check the guidelines of your particular institution with regard to use and disposal of these hazardous materials. Although cautionary statements are included in the appropriate units, we emphasize that users must proceed with the prudence and precaution associated with good laboratory practice, and that all materials must be used in strict accordance with local and national regulations. Another source for use and disposal guidelines can be found in APPENDIX 2A and APPENDIX 2B of Current Protocols in Molecular Biology. Animal Handling Many protocols call for use of live animals (usually rats or mice) for experiments. Prior to conducting any laboratory procedures with live subjects, the experimental approach must be submitted in writing to the appropriate Institutional Animal Care and Use Committee (IACUC) or must conform to appropriate governmental regulations regarding the care and use of laboratory animals. Written approval from the IACUC (or equivalent) committee is absolutely required prior to undertaking any live-animal studies. Some specific animal care and handling guidelines are provided in the protocols where live subjects are used, but check with your IACUC or governmental guidelines to obtain more extensive information. Reader Response Most of the protocols included in this manual are used routinely in our own laboratories. These protocols work for us; to make them work for you we have annotated critical steps and included critical parameters and troubleshooting guides in the commentaries to most units. However, the successful evolution of this manual depends upon readers’ observations and suggestions. Consequently, a self-mailing reader-response survey can be found at the back of the manual (and is included with each supplement); we encourage readers to send in their comments. ACKNOWLEDGMENTS

Preface

This manual is the product of dedicated efforts by many of our scientific colleagues who are acknowledged in each unit and by the hard work by the Current Protocols editorial staff at John Wiley and Sons. We are extremely grateful for the critical contributions by Gwen Taylor (Series Editor) who kept the editors and the contributors on track and played a key role in bringing the entire project to completion. Other skilled members of the Current Protocols staff who contributed to the project include Janet Blair, Michael Gates, Scott Holmes, Demetra Kagdis, Alice Ro, Brennan Travis, Joseph White, and Kathy

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Wisch. The extensive copyediting required to produce an accurate protocols manual was ably handled by Rebecca Barr, Lisa Christenson, Elizabeth Harkins, Karen Hopkin, Monte Kendrick, Kathy Morgan, Connie Parks, Allen Ranz, and Kristine Templeman, and electronic illustrations were prepared by Gae Xavier Studios. S.J. Enna, Michael Williams, John W. Ferkany, Terry Kenakin, Roger D. Porsolt, and James P. Sullivan

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CHAPTER 1 Receptor Binding INTRODUCTION n today’s era of molecular biology, where reports of new receptor families appear on an almost weekly basis, it is often forgotten that the concept of “receptors” as the primary mediators of neurohormonal transmission is over a century old. Although it was first postulated by Ehrlich and Langley in the late 19th century, the identification of receptors as discrete molecular entities did not proceed to certainty until some 75 years later, coincident with the introduction and the rapid widespread utilization of receptor binding techniques. Both the theoretical and practical aspects of this methodology are addressed in this chapter. The placement of receptor binding as the introductory chapter speaks to the fundamental importance of the technique in the pharmacological sciences.

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The first three units provide the broad strokes necessary to understand the basics of receptor binding from a historical, theoretical, and practical perspective. UNIT 1.1 offers a brief history of receptors and radioligand interactions as well as a discussion of the underlying importance of receptors to biomedical research in both academic and industrial research laboratories. UNIT 1.2 details the basic mathematical underpinnings of receptor theory and radioligand binding. This unit is of central importance to the understanding of experimental data, as it is now apparent that receptor-compound interactions can result in a myriad of events well beyond the simple original concept of receptor activation or blockade. This concept is now more thoroughly described in UNIT 1.21 (Receptor Allosterism). UNIT 1.3 reconciles theory to the “bench,” focusing in particular on the practical and analytical pitfalls that may lead to misinterpretation of results. The majority of units are devoted to describing individual methods for measuring the interaction of radioligands with specific receptors in vivo. Methods are presented to investigate the three major categories of receptor families including G-proteinlinked receptor-binding small molecules (UNITS 1.4, 1.5, 1.6, 1.9, 1.19, 1.23, 1.26, 1.28, 1.29, & 1.30; opioid receptors, adrenoceptors, dopamine receptors, purinoceptors, histaminergic, serotonergic, cannabinoid, melanocortin, neurotensin, and CGRP receptors, respectively), receptors that bind proteins, peptides, or chemokines (UNITS 1.10, 1.11, 1.12, 1.13, 1.15, & 1.24; angiotensin, neuropeptide Y, cholecystokinin, CRF, tachykinin, and chemokines, respectively), and receptors composed of multiple subunits forming membrane-spanning ion channels (UNITS 1.7, 1.8, 1.14, 1.17, 1.18, 1.20, 1.25, & 1.27; GABA, nicotinic acetylcholine, wild-type excitatory amino acid, potassium channels, picrotoxin, the MK-801 binding site of the NMDA receptor, calcium channels, and NMDA glycine-related sites, respectively). UNIT 1.21 introduces in some detail the concept of receptor allosterism, and practical examples are provided in UNITS 1.16 (benzodiazepine binding to GABAA receptors) and 1.22 (muscarinic acetylcholine receptors). Receptor proteins that function in the translocation of neurotransmitters across membranes are introduced in UNIT 1.32. This unit provides a protocol to evaluate GABA transport in cells lines transiently transfected with GAT-1 or other GAT isoforms. Procedures are tailored for both small-scale and high-throughput (96-well microtiter plate) approaches. Every effort has been made to achieve commonality among the protocols with regard to units of measurement, buffers, equipment names, and approaches Receptor Binding Contributed by John W. Ferkany Current Protocols in Pharmacology (2005) 1.0.1-1.0.2 C 2005 by John Wiley & Sons, Inc. Copyright


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to bench techniques. Although there are obvious overlaps among methods, each protocol should be read individually, as no two receptor binding assays are identical in nature, performance, or likely experimental outcome. Where recombinant protein(s) are commercially available, protocols are described using these materials. Using the family of cytokines as an example, UNIT 1.31 introduces methods to identify receptor ligands when traditional receptor-binding approaches may be problematic. Since functional cytokine receptors are heteromeric and typically have ultra-high affinities for the cognate ligand, the use of standard receptor-binding assays to identify antagonists for these important therapeutic targets has proven difficult. Investigators have instead relied on more diverse approaches, including measurement of the variation in cytokine levels in response to drugs, cell-based effector systems, and in vivo models. In this respect, UNIT 1.31 presents an interface between the concepts of receptor binding discussed in Chapter 1 and those of signal transduction described in Chapter 2. The five overviews and 27 protocol units presented are diverse and should allow readers to develop and validate methods for other receptors not immediately addressed. The overviews and protocols of Chapter 1 should not be read in isolation, as the editors have made every attempt to link receptor binding to progressively more complex systems. The presentations in Chapter 1 serve to introduce concepts and procedures assessing drug effects on signal transduction (Chapter 2), isolated tissue preparations (Chapter 4) and animal models of disease (Chapter 5). Other chapters and publications are also relevant, including those discussing molecular biology (Chapter 6 and Ausubel et al., 2005), receptor/enzyme localization (Chapter 8), drug discovery technologies (Chapter 9), electrophysiological techniques (Chapter 11), and in vitro cellular assays (Chapter 12). The methods, strategies, and outcomes described in each of these chapters remains fundamentally based on a thorough knowledge of ligand-receptor interactions.

LITERATURE CITED Ausubel, F.A., Brent, R., Kingston, R.E., Moore, D.D., Seidman, J.G., Smith, J.A., and Struhl, K. (eds.) 2005. Current Protocols in Molecular Biology, John Wiley & Sons, New York.

John W. Ferkany

Introduction

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Receptors as Drug Targets Receptors are typically envisaged as cell surface recognition sites for endogenous hormones, neurotransmitters, and neuromodulators. They are coupled to various signal transduction systems located both within the membrane and intracellularly, and can therefore regulate responses to the cellular/tissue microenvironment. Compounds that bind to a receptor are known as ligands. These may be conceptualized as “keys” that fit into a specific “lock” on the cell surface, the latter being the receptor, to modify cellular activity at the biophysical, biochemical, and/or genomic level. While major advances have been made with respect to understanding the structure and function of receptors, the “lock and key” hypothesis proposed by Ehrlich and Langley over a century ago remains the conceptual molecular model for understanding the etiology of human disease states, for defining drug action, and for targeting and designing new therapeutic agents. Receptors can be defined in terms of their selectivity, the saturability and reversibility of ligand binding, and functionality. The definition of a receptor in both pharmacological and physiological terms requires that it has specific interactions with ligands that belong to a given pharmacological class. Thus for an adrenergic receptor, it is important that the receptor recognize with high affinity (i.e., low dissociation constant, in the nanomolar to micromolar range) agonists such as epinephrine and norepinephrine as well as antagonists such as phenoxybenzamine (α-adrenoceptor) and propranolol (β-adrenoceptor). In contrast, an adrenoceptor should show minimal affinity for other ligand classes (e.g., selective calcium entry blockers, angiotensin converting enzyme— ACE— inhibitors, and glutamate receptor ligands). This provides a structure-activity relationship (SAR) for the interaction of a receptor with its ligands. The ability of a receptor to distinguish between enantiomers of a selective ligand, its stereoselectivity, is a critical feature of a receptor versus a ligand-binding site; the latter lacks the ability to initiate a functional response. However, not all receptors discriminate between enantiomers of all ligands. For instance, while the α4β2 subtype of the nicotinic cholinergic receptor labeled by [3 H]cytisine clearly distinguishes between the R- and

UNIT 1.1

S- enantiomers of nicotine, it has no stereoselectivity for the enantiomers of the novel nicotinic analgesic, epibatidine. The reasons for this are unknown. The saturability of a receptor relates to the ability of a ligand to fully occupy a finite receptor population in a tissue. A lack of saturability reflected in a radioligand binding assay can indicate the presence of low-affinity, “nonspecific” sites that are not receptors. Ligand binding to a receptor should also be reversible, reflecting the dynamic nature of a chemical transmission process that reaches equilibrium when the ligand association rate is equal to the dissociation rate. Reversible binding is not always easy to demonstrate in vitro, especially in the case of peptide ligands. A physiologically relevant receptor should also be shown to mediate a functional response that can be blocked by a selective antagonist. The term “receptor” is now used in a broader sense to describe any recognition site for a drug-like compound. Thus, enzymes, uptake sites, ligand-binding proteins (e.g., CRF and acetylcholine-binding proteins), voltagegated ion channels, and intracellular targets (e.g., NFκB, bcl2 ) are also considered as receptors along with the more traditional receptors: ligand-gated ion channels (LGICs) and G protein-coupled receptors (GPCRs). Strictly speaking, the term “drug” describes a chemical substance that alters tissue function for the benefit of organism function. The term drug can also be used colloquially to describe recreational substances like cocaine, amphetamine, marijuana, and to a lesser extent, caffeine, alcohol, and nicotine, substances that alter perceptional and/or attentional states and can cause habituation or addiction. These substances also produce their effects by acting at receptors, either directly, e.g., marijuana interacting with cannabinoid receptors, or indirectly, as with cocaine-induced stimulation of dopamine receptors by blocking dopamine uptake into nerve terminals. Chemical substances used as research tools to define biological systems are termed “new chemical entities” (NCEs), “compounds,” or simply ligands, rather than as drugs. The latter term is reserved for those NCEs active in humans. The manner in which NCEs specifically affect cellular function to produce beneficial effects is an area of evolving knowledge. The Receptor Binding

Contributed by Michael Williams and Rita Raddatz Current Protocols in Pharmacology (2006) 1.1.1-1.1.18 C 2006 by John Wiley & Sons, Inc. Copyright


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majority of drugs in use today, many of which were identified before their molecular targets were identified, are receptor antagonists or enzyme inhibitors. This suggests that many diseases result from excess receptor activity or enzyme activation. Indeed, recent findings suggest that receptors are not static entities but rather may be constitutively active, i.e., the receptor may be activated even in the absence of endogenous substances that stimulate the recognition site. The concept of constitutively active receptors (Kenakin, 1996; UNIT 9.5) suggests there may be naturally occurring regulators of intrinsic receptor function (i.e., inverse agonists) whose malfunction or absence may contribute to the etiology of some diseases. Receptors are complex proteins with multiple potential ligand recognition sites, including sites that may be distinct from the endogenous agonist recognition site and may actually reside on distinct proteins that are part of the receptor complex. Such receptor modulatory sites may represent novel drug targets, e.g., allosteric or modulatory sites (UNIT 1.21). The effect of benzodiazepines (BZs) on GABAA receptor function illustrates the conceptualization of ancillary drug targets and the elusive nature of the proposed endogenous modulator, presumed to be a “BZ-like” substance. The dynamic nature of the molecular events underlying a variety of disorders, such as bipolar illness and septic shock, can be illustrated in the sequential activation, amplification, and induction of multiple modulatormediated events. In septic shock, the induction of a toxic cytokine receptor-mediated cascade has significantly complicated the search for new drugs to treat this condition. This emphasizes the need to define key targets in critical pathways rather than attempt to treat their sequelae. It is possible that many diseases are the result of multifactorial events that vary during the pathophysiological course of the illness. For instance, >32 discrete gene loci have been associated with schizophrenia. Therefore, targets that are downstream from key points in the disease transduction pathway may not be the optimal targets for treating the disorder.

RECEPTOR CLASSIFICATION AND NOMENCLATURE

Receptors as Drug Targets

New receptors are identified on a regular basis as scientists screen the various genomic databases for novel sequences. These receptors are frequently designated in an apparently haphazard manner, often leading to confusion as identical receptors are given different names

by different groups. Kenakin (1993) recommended receptor parsimony as an approach to receptor classification, since in his view, “a new receptor subtype should not be invoked until absolutely necessary.” Despite the pragmatism of this approach, receptor nomenclature remains a challenge. In an attempt to bring order to this process, the International Union of Pharmacology (IUPHAR) established a Receptor Nomenclature Committee with standing subcommittees assigned for each receptor superfamily. As data become available, these subcommittees make recommendations for developing a systematic nomenclature for each receptor family. These recommendations are published in Pharmacological Reviews and are collated in the Sigma-RBI Handbook (see Internet Resources), which is a valuable and definitive online source.

RECEPTOR STRUCTURE AND MOTIFS Receptors are large macromolecules composed of proteins, lipids, and carbohydrates with molecular weights that vary from 30 to >300 kDa. Receptor proteins also undergo posttranslational modification, including phosphorylation, prenylation, glycosylation, palmitoylation, sulfation, and dimerization, that contributes to their distinct functional and pharmacological properties. Receptors are currently divided into four major classes.

G Protein–Coupled Receptors (GPCRs) GPCRs are structurally represented by a motif of seven hydrophobic membranespanning amino acid helices (Fig. 1.1.1A), each of which is between 20 and 30 amino acids in length. This motif is used to describe this group of proteins as seven-transmembrane (7TMs or R7 G) receptors (Kenakin, 1996; Strosberg, 1996). The complex signaling pathways modulated by GPCRs offer a variety of potential therapeutic targets (Liebmann, 2004). GPCRs are coupled to various members of the G protein superfamily, so named because of their functional dependence on the hydrolysis of the purine nucleotide, GTP, for activity. The functional unit of GPCRs is the receptor/G protein/effector. The effector is one of a large family of intracellular proteins that include adenylyl cyclase, the MAP (mitogen-activated protein) kinases, phospholipases A2 , C, and D, phosphatidylinositol-3 kinase (PI3K), pp60Src , p21, K+ and Ca2+

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Figure 1.1.1 Structural motifs of various receptor classes. (A) GPCR with seven membranespanning regions; (B-E) LGICs: (B) glutamate receptor, (C) P2X receptor, (D) nAChR, and (E) VGIC K+ -rectified inward (Kirs) receptor; (F) STAT receptor; (G) PTK growth factor receptor; (H) neutrophin receptor (trk).

channels, and other signaling proteins. Although most cellular responses following activation of a GPCR are thought to be mediated by G proteins, a growing number of GPCRmediated effects, such as src kinase activation, have been shown to be G protein–independent, suggesting this family of receptors would more accurately be termed 7-TM receptors (Hall et al., 1999). The subunits of the G protein superfamily are comprised of three heterologous subunits, α, β, and γ. Eighteen distinct Gα, five Gβ, and twelve Gγ isoforms have been identified to date. The α subunits are divided into four subgroups based on their major effector interactions: Gs , which stimulate adenylyl cyclase; Gi/o , which are inactivated by pertussis toxin (PTX) and inhibit adenylyl cyclase; Gq/11 , which are PTX-insensitive and mediate phospholipase C activation, and G12/13 , which mediate cellular responses via Rho- and RacGEFs (guanine nucleotide exchange factors). The Gβγ subunits also regulate effector function, including βARK-1, PLA2 , adenylyl cyclase types II and IV, N-type Ca2+ channels, and K+ channels. The heterotrimeric G proteins are inactive when bound to GDP, and

GDP/GTP exchange is catalyzed by interaction with activated GPCRs. The active G protein dissociates into α and βγ subunits that interact with effector molecules and are rapidly inactivated by the intrinsic GTPase activity of the α subunit (responsible for hydrolyzing GTP back to GDP) and the resulting reconstitution of the heterotrimeric Gα, β, γ protein complex. The lengths of the extracellular and intracellular loops that connect the transmembrane helices vary between different GPCRs, including those that are members of the same receptor superfamily. The amino acid composition of these loops determines the nature of the ligand-binding site and aids in defining the interactions of a given receptor with the various G proteins. The second and third intracellular domains and the C-terminal tail confer G protein coupling specificity. GPCRs can also be grouped into subfamilies that include the rhodopsin/adrenoceptor, secretin/VIP, metabotropic glutamate, and protease-activatable families, among others. The endogenous ligands for these include small molecules, lipids, metals, peptides, and, in the case of rhodopsin, light.

Receptor Binding

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Ligand-Gated and Voltage-Gated Ion Channel (ICs)

Receptors as Drug Targets

ICs are composed of several distinct protein subunits. These are usually in the form of a tetramer or pentamer that constitutes a functional ion channel through which conduction is modulated by various ligands (Catterall, 2000). The ICs, unlike GPCRs, mediate fast responses in cells. There are numerous ligandbinding sites that contribute to the modulation of channel function located on and between the component subunits. Some of these sites are in the pore. Strictly speaking, only the ligandgated ion channels (LGICs) can be considered to be receptors, since they bind conventional receptor ligands. Voltage-gated channels can, however, be included in this family because of the broader definition of receptors as ligand targets. Such compounds can modulate the function of voltage-gated channels (e.g., charybdotoxin). The ICs include several functionally characterized receptor families that have different structural motifs (Fig. 1.1.1B-E). LGICs include GABAA , glutamate, serotonin-3 (5HT3 ), P2X, and nicotinic cholinergic (nAChR) receptors. VGICs include K+ -rectified inward (Kir’s) and K+ -rectified outward (Kv) ion channels, calcium-activated potassium channels (KCa ), as well as Na+ -, Cl− -, Ca2+ -, cyclic nucleotide-, and ATP-gated ion channels. The ICs are very complex proteins having many splice variants of different subunits that, in turn, vary in composition. In the LGIC family, each subunit of the channel can have either two (e.g., P2X) or four (e.g., nAChR) hydrophobic membrane-spanning domains (Fig. 1.1.1C and D), termed M1 to M4. M2, which forms the lining of the pore, is conserved throughout all known LGICs and is responsible for controlling ion selectivity. The ligand specificity of LGICs is determined by the nature of the component subunits. For example, the binding site for nicotine on neuronal α4β2 nAChRs is formed by, and between, two subunits. Homomeric ion channels, those comprised of multiples of a single type of subunit, are generally considered to be exceptions in nature, with heterologous ion channels like the α4β2 nAChR being the norm. For many ion channel receptors, the number of possible combinations and permutations of constituent subunits and associated proteins is in the thousands. At present, it is unknown which represent naturally occurring functional receptors. LGICs are modulated by a series of distinct ligands, each interacting with its own

discrete recognition site on one of the proteins associated with the channel. In the case of the GABAA receptor, a major modulator site is represented by the BZ receptor. For the N-methyl-D-aspartate (NMDA) subtype of the glutamate receptor family, component sites include receptors for glycine, MK-801, and various polyamines. VGICs are also multimeric protein complexes that vary in composition between the major families. Voltage-gated potassium channels comprise twelve families, Kv1 to Kv12, and have six putative membrane-spanning domains, termed S1 to S6, in an α subunit, plus an associated β subunit that is not membrane-spanning (Grissmer, 1997). Voltage-gated sodium channels have an α subunit with four homologous domains I through IV, each of which has six putative membranespanning regions and an associated β subunit. Voltage-gated calcium channels consist of a large α1 subunit that incorporates the pore, the voltage sensor, and sites of known ligand interactions plus three or four additional subunits that help modulate expression and electrophysiological properties of the channel. The ten identified α1 subunits have been classified into three major types; Cav1-3 and demonstrate a similar topography to the VG sodium channel α subunits, with four homologous domains each containing six putative membranespanning regions. While the structural motifs of the various classes of mammalian ICs vary considerably (Fig. 1.1.1B-E), some show a high degree of homology with gene products found in lower organisms such as C. elegans and Drosophila melanogaster. These include the ced family of apoptotic genes. Defining the function of proteins encoded by similar genes in simpler organisms is often a useful strategy for establishing the function of the mammalian gene products.

Transcription Factor Receptors The superfamily of transcription factor receptors (also called nuclear hormone receptors) includes intracellular ligand-dependent transcription factors that regulate gene expression upon binding of their cognate ligands. The steroid hormone receptors are the best characterized members of the transcription factor receptor superfamily and include the glucocorticoid (GR), progesterone (PR), estrogen (ER), mineralcorticoid (MR), and androgen (AR) receptors. Both agonists, e.g., estrogen, and antagonists, e.g., tamoxifen, of these receptors are used clinically. Also included in the

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transcription factor receptor superfamily are the thyroid hormone (TR), vitamin D3 (VDR), retinoic acid (RAR), retinoid x (RXR), peroxisome proliferators–activated receptor (PPAR), ecdysone (EcR) receptors, as well as a number of orphan receptors with structural homology to the family. Many members of this superfamily heterodimerize with a RXR and the complex then binds to specific response elements within the DNA promoter regions. In addition, other families of transcription factors, such as the signal transducer and activator of transcription (STAT) proteins (Ihle, 1996), may be considered as receptors in that they may be targets for bioactive ligands. The STATs are latent cytoplasmic transcription factors that are structurally defined by a phosphotyrosine binding domain, the SRC homology 2 (SH2), which is required for STAT dimerization and for subsequent interactions with the JAK (Janus protein-tyrosine kinases) family. This family of kinases phosphorylates STATs and leads to activation of the JAKSTAT signaling pathway. This family also includes other signal transduction elements like the low-molecular-weight G protein family, the transcription factors AP-1, NFκB, and NFAT, p48, the SMAD family of tumor suppressor proteins, CSF-1 and the Bcl-2 and p53 families. These signaling elements are typically activated downstream of other receptors, including GPCRs and tyrosine kinase receptors. The hormone response elements (HREs) on DNA themselves may also be targeted by ligands to block interaction with transcription factors and thereby block subsequent gene transcription.

Enzyme-Associated Receptors The enzyme-associated receptor superfamily (including growth factor receptors) consists of single- or multi-subunit proteins that contain a subunit with a single transmembrane domain. The largest groups within this superfamily are the protein-tyrosine kinase (PTK) receptors (Fig. 1.1.1G) that include PDGF, EGR, FGF, IGF, HGF, VEGF, and neurotrophin (trk; Fig. 1.1.1H), and contain kinase domains within their protein structure. Also included in the superfamily are the multimeric complexes that utilize kinases, such as the JAK-type kinases, for their signal transduction. This family includes the Class I and Class II cytokine receptor families; the tumor necrosis factor (TNF) receptor family; antigen receptors (TCR, BCR); and the type II serine/threonine kinase receptor family that

includes TGFβR (transforming growth factorβ) and ActR. Members of the Class I cytokine family are characterized by the presence of multiple fibronectin type III–like motifs and include receptors for the interleukins IL-2, IL-3, IL-5, IL-6, IL-7, IL-9, and IL-11; EPO, prolactin, and ciliary neurotrophic factor (CNTF); and granulocyte/macrophage and granulocyte colony-stimulating factors (GMCSF and G-CSF). The Class II cytokine receptor family includes IL-10R and the interferon receptors IFN-α/βR, IFN-γ-Rα, and IFNγ Rβ. The receptors pp60src and p56lck represent two classes of tyrosine kinase that are not activated by traditional receptor ligands. The delineation of functions of ICs, GPCRs, transcription factor receptors, and enzyme-associated receptor families is not always clear. Cytokines can activate STATs (Leaman et al., 1996), as can the GPCR for angiotensin II. GPCRs also modulate channel function, while some LGICs produce their effects in association with G protein–coupled receptor systems. Because of this, the receptor motif remains the most critical element in assigning a receptor to a particular superfamily, and the associated signal transduction mechanism is secondary.

RECEPTOR LIGANDS Compounds that interact with receptors are characterized in terms of two basic properties: affinity and efficacy (Kenakin, 1996; Kenakin and Onaran, 2002). The affinity of a compound is described in terms of its dissociation constant (Kd ) with respect to the receptor. Accordingly, affinity describes the strength of the attraction between the receptor and the ligand. The units for the dissociation constant, derived from the Law of Mass Action, are molar (M). A typical neurotransmitter or hormone-receptor ligand will have a Kd value in the range of 0.1 to 10 nM. The lower the Kd value, the higher the affinity of the ligand for the receptor. Efficacy describes the capacity of a ligand to elicit a biological response. Those ligands that produce a positive response, such as stimulation of adenylyl cyclase or activation of an ion channel, mimic the actions of the endogenous ligand for the receptor and are said to have positive efficacy and, therefore, are known as agonists. By definition, a full agonist is a ligand that produces the same maximal effect (100% or unity) as that observed with the endogenous ligand in a given functional system. Intrinsic activity (IA) is a proportionality factor introduced by Ariens (1954) to describe

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Figure 1.1.2

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Ligand efficacy spectrum.

the ability of a ligand to elicit a response in relation to the maximal observable response in a given tissue. The concept of IA assumes a linear relationship between receptor occupancy and tissue response, and is a function of both the efficacy and the affinity of a ligand. In practical use, IA is used to define relative maximal responses when comparing a series of ligands in a particular tissue. Intrinsic efficacy of a ligand is a proportionality factor that defines the stimulus per receptor molecule that is produced by an agonist. Recently, agonists have been identified with intrinsic activities (IA) of >100%. While these were initially termed “super agonists,” such agents are, in reality, full agonists. The lesser activity of the standard full agonist reflects the fact that it is either a partial agonist or causes a rapid desensitization of the receptor. In many instances, the relationship between the stimulus and an agonist response is nonlinear, indicating that the receptor/transduction process can amplify receptor stimulation. Compounds that bind to the receptor that have no effect on their own but which block the actions of endogenous and exogenous agonists have zero efficacy and are termed neutral antagonists. Compounds with effects opposite to those of an agonist display negative efficacy and are known as inverse agonists or positive antagonists. This type of compound has an efficacy value of −1. Thus, the spectrum of ligand activity can, by definition, range from −1 to +1 (Fig. 1.1.2). Accordingly, the effects

of an inverse agonist are blocked by a neutral antagonist. Ligand efficacy is a complex, poorly understood, and actively debated concept, especially in the context of the in vivo effects of drugs. Efficacy encompasses a number of physiochemical factors including affinity, tissue (i.e., receptor) access (distribution), promiscuous receptor coupling to different transduction mechanisms, metabolism, and plasma protein binding, among others. The concept has become increasingly complex as more is learned about the molecular aspects of receptor activation and signal transduction, requiring an integrative, iterative systems approach for both receptor and ligand characterization. Thus, ligand binding and functional data derived from a transfected human receptor system must be compared with data obtained with the receptor in its native, “wild” state and in different tissue systems.

CONSTITUTIVELY ACTIVE RECEPTORS An important new concept is that of constitutively active receptors (UNIT 9.5). These are receptors, currently confined to GPCRs, which are active in the absence of a ligand. Conceptually, it is thought that G protein– activating sections of the 7TM receptor structure are in contact with the relevant G protein, leading to signal transduction. Ligand efficacy has therefore been redefined as “the

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property of a [compound] that modifies subsequent interaction of the 7TM receptor with other membrane proteins” (Kenakin, 1993). Furthermore, Kenakin (1996) has postulated that the inactive form of a GPCR is a specialized case. This raises the possibility of endogenous inhibitory factors that modulate constitutive receptor activity. Indeed, the agouti-related protein (AgRP) is released from neuronal terminals and acts as both an inhibitor of α-MSH-induced activity and as an endogenous inverse agonist of melanocortin MC(3) and MC(4) receptors (Adan and Kas, 2003). Additionally, accessory proteins could modulate the interaction between the receptor and the G protein and therefore may represent important new drug targets. Evidence for this concept comes from work with PD 81,723, a putative allosteric modulator of the adenosine A1 GPCR (Kollias-Baker et al., 1997). In addition to effects enhancing the binding and actions of the endogenous ligand, adenosine, PD 81,723 appears to increase constitutive receptor activity. Constitutive receptor activity adds yet another layer of complexity to the characterization of receptors and ligands. The potential existence of endogenous modulators of constitutively active receptor function may make redundant the theoretical concept of spare receptors per se, as the latter may simply reflect the degree of endogenous modulation of the receptor-G protein interaction in a given tissue. The assumption that the receptor (and enzyme) population of a given tissue system is static is no longer a viable concept. The levels of both receptors and enzymes can be altered (Donaldson et al., 1997) as a result of trauma (e.g., hypoxic, ischemic, or physical damage), disease (e.g., inflammation, viral, or bacterial infection), or development (Clifford et al., 1997). The concept of diseaserelated, inducible receptors and enzymes adds yet another layer of complexity and opportunity to the drug discovery process. Viruses have been identified that activate dormant genes in host cells that express constitutive chemokine receptors or promote incorporation of the virus genome into the host cell genome (Arvanitakis et al., 1997). The ability of human cytomegalovirus (CMV) to encode a β-chemokine receptor related to the human chemokine receptors CCR5 and CXCR4 as a means to facilitate HIV entry provides a pathophysiological rationale for the effect of viruses on host cell chemokine expression (Pleskoff et al., 1997). Similarly, the finding

that chronic pain can induce spinal cord nociceptive neurons to express the algesic neurokinin substance P (Neumann et al., 1996) represents another major conceptual advance in understanding receptor function in the context of pain processing. Some drugs have been in use for many years and served as ligands to define receptor families long before endogenous agonists for those receptors were discovered. An example of this is the opiate receptor family. While morphine and related agents were known to produce their analgesic effects by interacting with receptors, it was not until the 1970s, with the identification of the enkephalins and endorphins as endogenous ligands for these sites that a bona fide target was identified. More recently, the tetrapeptide endomorphins have been found to be potent agonists for the µ opiate receptor (Zadina et al., 1997). Similar efforts targeted towards identifying the endogenous ligand for cannabinoid receptors resulted in the identification of anandamide (Devane et al., 1992). In a diseased or traumatized tissue, receptor-mediated responses are frequently modified to the detriment of the organism, as noted above in the case of viral infections. Examples include constitutive β-adrenoceptor activation in hypertension, decreased insulin production in diabetes, and hyperactivation of glutamate receptors during stroke-related hypoxia and ischemia. A drug may be used to restore normal function by mimicking the effects of an endogenous ligand or by blocking the actions of an endogenous agonist. It is also possible that while the level of the endogenous ligand is normal, the number or sensitivity of receptors is increased. An antagonist may be of therapeutic benefit in such circumstances reflecting a diminution in constitutive modulator activity. To better define the pathophysiology of human disease states at the molecular level, new technologies must be applied, including differential display in tissues from in vitro and in vivo disease models and positional and functional cloning techniques using human genomic databases.

LIGAND-RECEPTOR INTERACTIONS Ligands (L) are thought to interact with the receptor (R) in a reversible, competitive, and saturable manner in accordance with the Law of Mass Action:

Equation 1.1.1

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The formation of an RL complex imparts to the receptor an ability to alter cellular function by interaction with transmembrane signaling proteins or, in the case of ion channels, by a change in ion flux. Receptor isolation, cloning, and point mutation studies have confirmed that the binding of a ligand to its receptor is an event distinct from that of receptor coupling to the second messenger systems. The RL complex formation thus results in either an alteration in the spatial relationship of the receptor and the transmembrane signaling proteins or, alternatively, a change in the steric conformation of the ligand, thermodynamically favoring the transduction process. For enzymes, the situation is similar to that for RL complex formation, except that the enzyme-substrate (ES) complex results in the formation of a product as shown in the following equation:

Equation 1.1.2

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The substrate (S) undergoes a catalytic conversion, whereas both the receptor and ligand are unchanged at the end of the transduction process described by the first equation above. With the ES complex, the reaction can be driven backward by feedback inhibition of excess product, depending upon the concentrations of the various components at equilibrium. For an analogous situation with a receptormediated event, a receptor response can be diminished by receptor downregulation (e.g., internalization or phosphorylation), a decrease in receptor number, or a functional uncoupling of the signal transduction mechanism. While the Law of Mass Action may be extrapolated from enzymes to receptors, the use of Michaelis-Menten kinetics to describe receptor behavior is an approximation rather than a strict extrapolation of enzyme theory. As discussed in UNITS 1.2, 1.3 & 2.1, receptors may be studied in the context of their concentrationor dose-dependent effects on radioligand binding, or in terms of a biochemical or physiological tissue response. Receptor-ligand (RL) interactions are described in vitro by a number of terms. In binding studies, the dissociation constant (Kd ), a measure of receptor affinity, and receptor density (Bmax ) are derived from a saturation isotherm (Fig. 1.1.3A). In this type of experiment, the receptor concentration is held constant while the radioligand concentration is increased until the radioligand saturates all the specific binding sites on the receptor.

Historically, binding data have been analyzed using the Scatchard equation, which plots radioligand bound (B) versus B divided by the amount of free radioligand (F). The Kd and Bmax can be derived from the plot of B versus B/F (Fig. 1.1.3B). However, the Scatchard plot is limited in that B is present on both the ordinate and the abscissa, constraining the data towards linearity. To overcome this limitation, nonlinear regression analysis using computer programs (e.g., GraphPad Prism, EBDA, or Ligand) can be employed to analyze data derived from binding site saturation analyses, to assess whether the data fit a single- or multiplebinding-site model, and to derive the corresponding Kd and Bmax values. The Bmax should always be expressed in terms of milligrams of protein, not gram tissue weight, since the latter can vary considerably depending on the individual tissue preparation. Concentration-response curves (Fig. 1.1.3C) are used to derive an IC50 value, the concentration of an antagonist required to inhibit 50% of the radioligand binding. Since the IC50 value is a function of receptor affinity and the concentration of radioligand used, it is a relative value. A more absolute value is the Ki , which is derived using the Cheng-Prusoff equation: Ki = IC50 /(1 + c/Kd ), where c = the concentration of radiolabeled ligand and Kd = the affinity constant of the ligand for the receptor. The Ki value corrects for differences in the concentration of radioligand and the receptor affinity. As a result, the affinity of a compound in a series of different binding assays can be appropriately compared. The EC50 value is the concentration of an agonist that produces a response that is half the maximal response produced by a saturating concentration of the ligand (Fig. 1.1.3D). The EC50 value for a full agonist (A) is half the maximal response that can be observed in the system. For a partial agonist (B), the EC50 may be derived either as the EC50 for half the maximal response achieved by agonist B, or as the concentration of agonist B that produces the same half-maximal response as that found for agonist A. The difference between the two EC50 values reflects the intrinsic efficacy of each compound. When evaluating a weak partial agonist like C, the response may never approach even half the response observed with the full agonist. The generation of concentration-response curves in the absence or presence of an antagonist is used to derive a pA2 value, which is defined as the negative logarithm of the molar concentration of an antagonist that

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Figure 1.1.3 (A) Schematic of a saturation binding curve: total, nonspecific, and specific binding (see UNIT 1.3). (B) Scatchard derivation of specific binding saturation isotherm. (C) Ligand displacement curve showing IC50 relationship. (D) Ligand efficacy and EC50 derivation. The EC50 for a partial agonist (EC50 B) can be determined as the concentration at which a similar response to that of a full agonist (EC50 A) is observed. Alternatively, the EC50 for a partial agonist can be determined as the concentration at which 50% of the maximal response to the partial agonist is determined (EC50 C). Clearly, using the latter approach in the absence of any measure of ligand potency (receptor affinity) can provide misleading data. (E) Dose-response relationship in the presence of increasing concentrations (X-Z ) of an antagonist. The antagonist produces a classical dose-dependent rightward of the agonist response. (F) Schild derivation of the data in E to derive a pA2 value (see UNIT 1.2).

produces a two-fold rightward shift of the agonist concentration-response curve (Fig. 1.1.1E). This dose ratio (dr) represents the increase in agonist concentration needed to achieve a given response in the presence of the antagonist. The pAx relationship was identified by Schild in 1949 and is derived from a Schild plot or Schild regression (Fig. 1.1.3F). The pA2 is equivalent to the pKB , which is the negative logarithm of the equilibrium dissociation constant for the antagonist-receptor complex.

ORPHAN RECEPTORS As new receptors are cloned from genomic databases (Hopkins and Groom, 2002). their sequences are usually compared to those of other known receptors to see whether they belong to an established family. Sequence ho-

mology within a family can vary between 25% and 100%, and often the individual transmembrane (TM) sequences are used to identify homology. There are no absolute rules regarding the degree of homology required to assign a new protein to a particular receptor family. By this process, a large number of orphan receptors have been identified in both the GPCR and transcription factor receptor families. The identification of new proteins that belong to a particular receptor family on the basis of their motif, sequence homology, and in some instances, effector mechanisms, has resulted in the discovery of a number of orphan receptors. These are defined by the absence of any known, endogenous agonist ligand. One of these is the opioid-like orphan receptor,

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LC132. While this putative receptor shows sequence homology to the cloned µ-, δ-, and κ-opioid receptors, no known opioid receptor ligands interact with this site with high affinity. A heptadecapeptide termed orphanin FQ (OFQ), or nociceptin, was isolated from brain that had high affinity (∼0.2 nM) for LC132. Accordingly, the receptor was termed OFQ/NR or NOP (Ardati et al., 1997). Although sequence homology can often help categorize receptors into known families and subtypes, changes in a single key amino acid in receptors such as the 5-HT1B receptor (Oksenberg et al., 1992), an ∼450-aminoacid GPCR, can markedly affect the pharmacology of the rodent as compared to the human form of the receptor. The 5-HT1B receptor was originally identified in the rat, but a human homolog was not readily found. Although a human brain gene sharing 93% of the deduced protein sequence of the rat receptor was identified, its pharmacology, as assessed by radioligand binding assays, was very different. Thus, methysergide, which has a Ki value of 1823 nM at the rat 5-HT1B receptor, has a Ki value of 130 nM at the human homolog of the receptor. Similarly, (-)-propranol, which has a Ki value of 57 nM at the rat 5-HT1B receptor, has a Ki value of 8100 nM at the human homolog. These discrepancies suggest that the two receptors are clearly different from a pharmacological standpoint. However, replacing the threonine residue at amino acid 355 in the human version of the 5-HT1B receptor with an asparagine (T355N) to correspond with the rat receptor made the human receptor identical to the rat in terms of its pharmacological selectivity. The fact that a single amino acid, ∼0.2% of the total protein in this GPCR, can cause such a dramatic change in the pharmacology of the receptor provides a cautionary note regarding extrapolation of radioligand binding data from laboratory animals to human receptors. It also highlights the possibility of phenotypic differences in receptor recognition site characteristics even when the proteins are as much as 93% identical. Thus, when receptor homology is based on 50% to 70% shared identity, there is a need for some caution in extrapolating pharmacological and signal transduction properties from one homolog to another. The identification of a new protein that belongs to a known receptor family represents only the first step in its use in defining function and disease etiology—a fact that is sometimes lost in the excitement of cloning. Because a protein can be assigned to a family does not mean that its function is identical to that of

other members of that group. In Alzheimer’s research, two key proteins associated with familial forms of the disease, the presenilins 1 and 2, have structural motifs indicating membership in the 7TM family, although these are intracellular proteins that exist in large protein complexes with aspartyl protease activity (gamma secretases). Even though their structure has been known for several years, there is still ongoing debate about their function and importance as potential targets in Alzhemier’s disease therapies. The transition from structure to function is a major challenge for receptor research that should not be underestimated, especially with the increased focus on the novel proteins identified by genomics. As previously discussed, endogenous agonists for some orphan receptors may not have been identified to date because their regulation may occur via endogenous inverse agonists, such as the agouti-related protein. There is also the possibility that some of these 7TM motif orphan receptors represent endogenous modulators of constitutive receptor activity or “decoy” proteins whose function is not that of a typical ligand-recognition site, but rather maintain tone in the signal transduction cascade or act as G protein “sinks” that regulate the activity of other receptors in the cell. Some orphans may remain un-liganded due to a need for accessory proteins as was seen with one previous orphan receptor that requires coexpression of RAMP1 (receptor-activity modifying protein 1) to become a functional CGRP receptor complex (Poyner et al., 2002). Recent studies have demonstrated a novel role for the GPCR Mas receptor as an antagonist of the AT1 (angiotensin II type 1) receptor when they form a heterodimer (Kostenis et al., 2005). The complexity of functional roles receptors may play in vivo makes identification of ligands and functions for orphan receptors a complex undertaking.

NEUROTRANSMITTERS, NEUROHORMONES, AND NEUROMODULATORS Chemical communication between cells and tissues has several hierarchical levels that are defined on the basis of the temporal effects of the different natural effector classes for the receptor (Bloom, 1988). Neurotransmitters are released close to their target receptors and cause rapid and specific effects that result in a rapid depolarization or hyperpolarization of the neuron. The consequences of this action are integrated postsynaptically to

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determine the spatial and temporal phenotype of subsequent neurotransmission processes from the target neuron or cell. This may result in the integration of the effects of two or more neurotransmitters or, as in the case of chronic pain input (Neumann et al., 1996), a major change in the type of neurotransmitter being produced in the target neuron. Neurotransmitter effects are short lived, typically in the millisecond timeframe. Neuromodulators, on the other hand, have a more prolonged duration of action and serve to regulate the effects of neurotransmitters. While neuromodulators are released in a manner similar to neurotransmitters, they are not restricted to the nerve terminal immediately proximal to the target receptor. The GPCRs are involved in many neuromodulatory processes since, in addition to their effects on biochemical and ion channel–related events, they can induce immediate early genes, which may extend the consequences of their actions by days or weeks (Laduron, 1992). Neuropeptides are classical neuromodulators. Hormones such as growth hormone and insulin are longer-acting entities, whose effects are usually produced at a site distant from their release. Their target site(s) are accessed via the systemic circulation. Hormones are essential for growth, development, reproduction, regulation of intermediary metabolism, and the overall homeostasis of the organism. They produce their effects over periods of days to years. Alterations in hormone levels and function result in puberty, menopause, and aging. Decreases in estrogen levels have been associated with the time to onset of Alzheimer’s disease and breast cancer. Paracrine hormones are peptides produced by endocrine glands and in the intestine. Included in this group are gastrin and somatostatin. Autacoids are substances that are produced locally in response to tissue injury and include serotonin, histamine, bradykinin, the eicosanoids (prostaglandins, leukotrienes, and thromboxanes), platelet-activating factor (PAF), and lymphokines. The response to these agents is rapid, although they have often been described as local hormones. However, since they do not use the systemic circulation to reach their site of action, but rather act locally at the site of the inflammatory insult, they are distinguished from hormones.

ALLOSTERIC LIGANDS Allosteric ligands are compounds that indirectly modulate receptor function by interacting with sites on the receptor that are different

from those binding the endogenous ligands or with sites on proteins associated with a receptor. The concept of allosterism has evolved from the classical model of Monod et al. (1965) and the sequential model of Koshland et al. (1966), which were derived from knowledge of the subunit interactions of hemoglobin, the enzyme pyruvate dehydrogenase, and the Torpedo electroplax nicotinic cholinergic receptor. An established group of allosteric ligands are the BZs that modulate GABAA receptor function. These drugs are effective and safe anxiolytics, anticonvulsants, and hypnotics. Because of the superior safety profile of BZs compared to direct-acting receptor ligands, it has been postulated that allosteric modulators have an advantage over receptor agonists and antagonists as drug candidates. All types of LGICs have allosteric modulators, and some share common recognition sites for compounds like MK 801. It is only recently, however, that allosteric modulators of GPCRs have been identified (May et al., 2004). In retrospect, it is now appreciated that the first of these were the various cations and anions known to modulate ligand affinity at these receptors and their signal transduction processes. Other types of ligands found to disrupt G protein interactions with GPCRs were mastoparan and the peptide adenoregulin. Moreover, compounds such as PD 81,723, which acts at the adenosine A1 receptor, and obidoxime and alcuronium, which act at m2 muscarinic receptors, are also selective allosteric modulators. Allosteric modulators often demonstrate subtype selectivity amongst related GPCRs that has not been achieved for ligands acting at the orthosteric site. pH can also affect receptor function, which may be an important allosteric effect related to the pH changes involved in tissue trauma and ischemia. In this context, nitric oxide (NO) sequestered in hemoglobin (Hb) enhances delivery of oxygen to tissues where oxygen tension is low, since under this condition, Hb releases NO to cause vasorelaxation and increase Hb flow and oxygenation (Stamler et al., 1997).

HUMAN RECOMBINANT RECEPTORS In the effort to understand disease etiology and discover new drugs, the ultimate focus is on human receptors. Traditionally, the study of receptor function was initiated with the more easily accessible rodent (at either the membrane or whole-animal level) or with

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immortalized human cell lines that are usually derived from tumors. While both approaches have been extremely useful, they are limited in that neither reflects the native, wild-type human receptors. Indeed, in some cases, it has been found that these receptor sources do not even approximate the wild-type human receptor, leading to considerable complications in the transition of compounds from preclinical animal models to human trials. This has been found with respect not only to efficacy and selectivity, but also to safety. The use of transfected human receptors in cell lines that do not spontaneously express the receptor of interest (“null” cell lines) has been made possible by the ability to clone human receptors. Human gene expression in such cells has become a critical tool in receptor research, although it is limited by a number of factors that are not always considered when interpreting data from transfected cells (Kenakin, 2003). Ideally, when human DNA is incorporated into the genome of a cell, the receptor is a permanent feature of the phenotype of the transfected cell as it replicates (stable transfection). In many instances, due to problems with the vector or other experimental parameters, the human receptor is only transiently expressed, appearing as part of the cell phenotype for a single or a limited number of cell cycles. While this is better than no expression at all and provides a means to assess receptor function using electrophysiological or biochemical techniques, it creates problems in obtaining reproducible data. Because gene transfection and expression are a function of the individual experiment, the receptor may be underexpressed, normally expressed, or overexpressed in any given study. Since the functional response to a ligand is related to the ligand efficacy, receptor density, and receptor interactions with endogenous signal transduction mechanisms, this variability can lead to compounds being characterized as partial or full agonists not because of their intrinsic properties, but because of characteristics of the experimental system. For example, with overexpression of GPCRs, there is a potential for the receptor to be promiscuous and activate different members of the G protein superfamily that are not normally affected by receptor activation. This effect may explain differences in tissue and regional responses to a given receptor ligand. Depending on the array of signal transduction mechanisms and their relative abundances, the tissue response to a ligand may vary from full agonist to an

inverse agonist. Under normal conditions, a natural ligand may be capable of achieving selectivity by virtue of its differing spectrum of signaling efficacy in a number of distinct tissue systems. Therefore, an exogenous ligand may produce a different pharmacological profile from the endogenous ligand (or known antagonist) by activating (or inhibiting) systems differently. In developing a structure-activity relationship (SAR) for a new series of chemicals, the more that is known regarding the efficacy profile and receptor selectivity of a ligand across a number of tissue systems, the easier it is to interpret the in vivo animal and human data collected with these compounds. Much of the tissue target selectivity of new compounds that is ascribed to “pharmacokinetics” may reflect a lack of understanding of the molecular properties of a compound in different tissue systems. Cyproheptadine, widely used as a serotonin receptor antagonist, is also a potent antagonist of histamine and acetylcholine receptors. Likewise, the serotonin uptake blocker fluoxetine, one of the most widely used antidepressants, has recently been found to block nicotinic receptors at therapeutic doses (Garcia-Colunga et al., 1997). As new compounds are advanced, receptor binding profiles that include the assessment of their activities at >80 GPCR, ion channel, enzyme, and uptake-site assays in vitro can provide important information about their value as experimental tools and therapeutic agents. A negative finding about a compound in one of ten or fifteen assays should focus attention on what parameters were being investigated in the one assay rather than calling into question the other nine or fourteen assays. Furthermore, selectivity is a relative term, with many examples where in vitro activity does not have in vivo consequences. A further complication in ligand characterization is related to the potential cell cycle dependence of receptor coupling and the effects of the receptor environment on the ligand response to any one component in the presence of others. This relates to both receptor interactions or dimerization (e.g., dopamine D1 and D2 receptors; adenosine A2A and dopamine D2 receptors) and interactions between different signaling cascades. For example, it is necessary to interpret with caution data derived from the use of intact tissue systems where a cholinergic tone is either induced or removed to enhance observation of the response evoked by another class of receptor ligand. This underlines the need for an integrative

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systems approach to ligand characterization. In the absence of stably transfected cells that have binding characteristics similar to the wild-type human receptor, it is imperative that interesting new ligands also be defined using tissue systems containing the native receptor whenever possible (Chapter 4). An example of the caution required in interpreting data from transfected cell lines is the report of a cloned P2 purinergic GPCR designated as P2Y7 (Akbar et al., 1996). While this receptor was shown to be sensitive to ATP, it was subsequently found to be identical in primary structure to a leukotriene B4 (LTB4 ) receptor known as BLTR (Yokomizo et al., 1997). Further examination of the Cos-7 cell line used to identify the P2Y7 receptor showed that it contained intrinsic purinergic receptors and, in this system, the P2Y7 /BLTR receptor responded to 1 µM ATP, an endogenous ligand for the P2 receptor, and resulted in an increase in intracellular calcium levels. However, when BLTR was transfected into C6-15 glioma cells, which have negligible levels of P2 purinergic receptors, ATP had no effect on intracellular calcium at concentrations up to 300 µM, while 10 nM LTB4 was fully efficacious. Receptor characterization using a series of ligands is dependent on a logical SAR that provides information on the recognition site. When a large number of diverse structures are found to interact with a binding site, efforts must be made to ensure that the site being studied is a biologically relevant receptor rather than merely a radioligand binding site. As new receptors are identified, the search for novel ligands represents a major challenge. Black (1989) outlined his Nobel Prizewinning work regarding β-adrenoceptor and histamine H2 receptor blockers based on modifications of the natural agonist. However, this approach is not always successful, and in many instances years after a receptor has been identified the only known ligands are close structural analogs of the natural agonist. For over a decade, antagonists for the tachykinin receptor family remained undiscovered until, in the space of 6 to 9 months, high-throughput screening efforts at Sanofi, Rhone Poulenc Rorer, Sterling Drug, and Pfizer resulted in a series of selective, nonpeptide, neurokinin antagonists (Williams and Gordon, 1996). For several systems, receptor classification is based on the rank order potency of a limited series of agonists due to the paucity of effective and selective antagonists. Receptor characterization at the functional level is then severely limited, especially when the avail-

able ligands lack bioavailability or appropriate pharmacokinetics. While as many as 17 receptors in the P2 purinergic receptor superfamily have been identified by cloning, the pharmacological classification of these receptors is based on a series of analogs of ATP and ADP that may also function as ectonucleotidase inhibitors. Antagonists for the P2 receptor family include a limited number of modified ATP analogs, suramin (a compound that directly interacts with G proteins) and a group of dyes. Advances in the area of P2 receptors are limited, not by the techniques available or the diversity of the receptors identified, but by the lack of suitable ligands that would permit characterization and assignment of function to the different molecular forms of the receptor.

RECEPTOR MUTATIONS AND CHIMERAS Receptor pharmacology has traditionally involved defining the SAR of a series of ligands or the relative activities of a number of different pharmacophores. Since the tools of molecular biology can be used to selectively alter the amino acid composition of a protein, it is possible to modify the amino acid composition of the natural, wild-type receptor to identify those constituents that are involved in ligand binding and signal transduction. As already noted, alteration of a single amino acid in the human 5-HT1B receptor radically alters its pharmacology (Oksenberg et al., 1992). Receptor chimeras also aid in mapping functional domains in receptors. α2 and β2 adrenoceptors are both activated by epinephrine but differ in their G protein interactions; the α2 adrenoceptor interacts with Gi while the β2 adrenoceptor interacts with Gs . Using differing chimeric constructs of α2 and β2 adrenoceptors, Kobilka et al. (1988) showed that the receptor motif extending from the N-terminus of transmembrane domain V (TM-V) to the C-terminus of transmembrane domain VI (TM-VI) was responsible for coupling to Gs and activation of adenylyl cyclase. Chimeric proteins combining GPCRs with G protein α subunits have also been important for investigating structural determinants of both receptor and G protein activation and selectivity (Ward and Milligan, 2004). Chimeric nicotinic-serotonergic receptors have been prepared that show that the distinct subunits from the 5-HT3 receptor and the nicotinic α7 receptor, which are functionally distinct LGICs, can couple together in a functionally significant manner (Elsele et al., 1993). In this

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case, the α7-V201-5HT3 chimera is sensitive to nicotinic agonists, but not to serotonin. The change in receptor function resulting from point mutations is of more than scientific interest, as a number of diseases have been linked to receptor mutations (Coughlin, 1994). Loss-of-function mutations can lead to retinitis pigmentosa, color blindness, congenital nephrogenic diabetes insipidus, and familial hypocalciuric hypercalcemia, while constitutively activating mutations have been associated with hyperfunctioning thyroid adenomas and familial male precocious puberty.

ASSESSING RECEPTOR FUNCTION Radioligand Binding

Receptors as Drug Targets

The evaluation of NCEs for activity at receptors, uptake sites, or enzyme active sites is done at a number of levels. These activities may be predicted based on existing knowledge of the diversity and function of the molecular target. Before 1970, efforts to characterize NCEs, and to understand disease etiology, were directed at highly empirical tissue or animal models that indirectly measured receptor function as changes in the physical or biochemical properties of a tissue or overt behavior in an animal. That this pragmatic approach was successful is evidenced by the continued stream of drugs that resulted. However, since their precise mechanism of action was unknown, it was nearly impossible to define the factors responsible for efficacy of these NCEs as distinct from those causing unwanted side effects. The ratio between these two factors, the therapeutic index (TI), was therefore poorly understood, making the search for compounds free of major side effects difficult. The development of radioligand binding assays in the early 1970s by Cuatrecasas and Roth at the NIH, Lefkowitz at Duke University, and Snyder at Johns Hopkins University provided a rapid means to evaluate compounds directly for their ability to interact with a receptor or enzyme, independent of efficacy (Williams et al., 2005). As new assays were developed, it became possible to assess the activity of a compound against a battery of binding sites to obtain an in vitro radioligand binding profile for the compound. Binding assays are currently available for >100 different classes of receptors and enzymes and are routinely used to identify new receptor classes and subtypes. Radioligand binding assays and the many receptor reporter systems they have spawned (UNITS 2.1 & 6.2) have provided biochemists

with a rapid, cost-effective way of establishing an SAR for a series of compounds. Because only minimal quantities of the NCE are needed (1 to 5 mg), the information derived from these assays is invaluable in the design and synthesis of potent and selective agents. The development of this screening technology and its adaptation for high-throughput screening (HTS) has contributed significantly to the drug discovery process and the development of combinatorial chemistry. However, since binding assays measure only the recognition site on the receptor, the intrinsic activity and efficacy of the ligand are not revealed by this approach.

Functional Receptor Assays The functional activity of a ligand is determined by its ability to evoke a response. Thus, an NCE may mimic the effects of an endogenous agonist like acetylcholine (ACh) or insulin by causing a muscle to contract or blood sugar levels to fall. In such instances, the degree to which the compound elicits a response similar to that obtained from the natural ligand can reveal whether it is a full or partial agonist. Conversely, a compound that blocks the actions of either ACh or insulin would be considered an antagonist. When using a tissue preparation to define such activity, it is extremely important to delineate between pharmacological and functional antagonism. Pharmacological antagonism describes the situation where a ligand antagonizes the effect of an agonist by blocking it directly at its site of action. Functional antagonism can yield data identical to those of a pharmacological antagonist, except that a functional antagonist does not affect RL-complex formation but rather acts at a site distinct from the receptor. An example is a hypothetical system involving a GABAA receptor innervated by a dopaminergic pathway that itself is under the control of a cholinergic neuron (Fig. 1.1.4). From a pharmacological perspective, any response ascribed to GABAA receptor activation that is blocked by a compound could be ascribed to GABAA antagonist activity. However, a dopamine receptor antagonist or nicotinic receptor antagonist would produce a similar effect, thereby indirectly serving as a functional antagonist of the GABAA site. Failure to appreciate this concept may lead to a nicotinic antagonist being mistakenly characterized as a GABAA receptor antagonist. This example illustrates a major limitation of in vivo compound evaluation in the absence of in vitro data. Again, the hierarchical nature of

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Figure 1.1.4 Pharmacological versus functional antagonism. (A) GABAA receptor activation produces a signal (GABA release) which causes neuron B to produce a response. (B) Pharmacological antagonism: blockade of the GABAA response at neuron B by a GABAA antagonist is a direct competitive effect. (C) Functional antagonism: The same GABAA response on neuron B can be blocked in vivo by a nicotinic or dopaminergic antagonist via interactions with upstream events in a pathway. In the absence of any further data on the putative nicotinic or dopaminergic antagonist, they could be classified as GABAA antagonists.

compound evaluation, defining its selectivity in vitro followed by the application of this information for interpretation of intact tissue and in vivo studies, is the main reason for defining the NCE site of action. Functional activity may also be measured biochemically by assessing ligand-induced changes in receptor-linked second messenger systems or gene-reporter constructs (UNIT 2.1). This could include measurement of changes in fluorescence of pH-, membrane potential- or calcium-sensitive fluorophores (for LGICs or GPCRs) or of melanophore-induced pigment changes or luciferase-dependent light formation in systems linking these signals to second messengers (for GPCRs). For enzymes, similar radiometric and spectrophotometric methods are used to assess either the catalytic disappearance of substrate or the appearance of product (UNIT 6.2). Once an NCE is found to have functional activity, it is important that the in vitro selectivity be assessed in appropriate systems. For instance, many different types of receptors are known to alter cyclic AMP formation, as can

compounds that inhibit phosphodiesterase activity. Thus, while a compound may be identified as selective at a particular receptor from its binding profile, there is always the possibility it may alter second messenger production by more than one mechanism. The use of appropriate antagonists or agonists may help to clarify this situation. It is crucial to link the functional activity of an NCE with the pathophysiology of the disease being targeted. Increased phosphatidylinositol (Pl) turnover is a measure of muscarinic cholinergic M1 receptor activation, a target that has been associated with enhanced cognitive performance in certain animal models. However, the hypothesis that M1 receptor agonists enhance cognitive function must be proven in appropriate animal models since it cannot be assumed that the biochemical measure absolutely predicts behavioral activity. Similarly, while phosphodiesterase inhibitors alter cardiac cyclic nucleotide metabolism, thereby increasing contractile force, their use as positive ionotropic agents for the treatment of congestive heart failure must be assessed in an intact

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animal model. In addition to validating the hypothesis, evaluation of compounds in animal models provides a measure of their bioavailability. The lack of activity of a compound in vivo is more likely due to problems with bioavailability and pharmacokinetics (PK; see UNIT 7.1) than to a mechanism of action unless the former is ruled out. Frequently, compounds for which no PK or bioavailability data are available are administered to animals and assessed in models where there is clear inconsistency between the activity being measured and the half-life of the compound. Measuring the anxiolytic potential of a peptide 15 min after administration is a flawed approach when the half-life of the compound is measured in seconds. The inability of an NCE to reach its site of action due to lack of absorption, firstpass metabolism, or lack of penetration across the blood-brain barrier can limit its potential as a drug candidate unless alternative dosage forms can be used to overcome these obstacles. While some of the factors affecting bioavailability are understood (Navia and Chaturvedi, 1996), to date, there are no hard-and-fast rules that can be applied across a chemical series. A similar caveat exists when addressing toxicity.

Animal Models

Receptors as Drug Targets

There are several animal models that provide a reasonable approximation of what will occur in humans when a compound enters clinical trials. The spontaneously hypertensive rat (SHR; UNIT 5.1) is a very useful model in predicting the hypotensive activity of a compound in humans, provided the compound is bioavailable. Animal models of pain (e.g., mouse hot plate; UNIT 5.7) and renal function (UNIT 5.21) can predict activity in clinical trials. However, in general, animal models of disease states are generally retrospective, empirical tests for measuring the effects of known active compounds, rather than the disease state per se. For example, the rat catalepsy model used to assess potential antipsychotics is in fact an in vivo model of dopamine receptor blockade. Likewise, the induction of septic shock in mice using lipopolysaccharide is only an approximation of the human condition and is open to criticism since several compounds active in the mouse model subsequently have been shown to be ineffective in the clinic. This probably relates to the complexity of the cytokine cascade in humans. Transgenic animal models of human disease states represent a potentially important

approach in the effort to increase the reliability of animal test procedures (Zambrowicz and Sands, 2003). By introducing a foreign gene into chromosomal DNA, a transgenic or “knockout” animal can be produced in which the expression of the native gene is inhibited. The function of the targeted gene may then be assessed in the living animal and may provide a model of the human condition. In several instances, the phenotype of the transgenic animal appears unchanged, indicating that the expression of the altered gene is not related to the disease despite genomic evidence to the contrary. In others, because of the critical nature of the targeted gene, the animals do not survive when expression of the targeted gene is inhibited. Alternatively, since the transgenic approach requires that the altered gene be present at birth, the animal may have sufficient functional redundancy in the targeted system to overcome any deficiency that results from the transgene modification. Newer approaches to creating animal genetic models of human disease involve the transfection of foreign genes into the germ cell line that are subsequently only activated in specific tissues (e.g., the tyrosine hydroxylase Cre-loxP construct in striatum; Gelman et al., 2003). Others are present in a quiescent state in the host genome but can be activated selectively once the animal has reached adulthood to better approximate the onset of the human disorder. Despite some limitations, transgenic models can provide important information about the potential disease etiology. For instance, overexpression of adenosine A1 receptors increases myocardial resistance to ischemia, suggesting that this receptor may be deficient in damaged heart muscle (Matherne et al., 1997).

Literature Cited Adan, R.A. and Kas, M.J. 2003. Inverse agonism gains weight. Trends Pharmacol. Sci. 24:315321. Akbar, G.K.M., Dasarai, V.R., Webb, T.E., Ayyanathan, K., Pillarisetti, K., Sandhu, A.K., Athwal, R.S., Daniel, J.L., Ashby, B., Barnard, E.A., and Kunapuli, S.P. 1996. Molecular cloning of a novel P2 receptor from human erythroleukemia cells. J. Biol. Chem. 271:18363-18367. Ardati, A., Hennigsen, R.A., Higelin, J., Reinscheid, R.K., Civelli, O., and Monsma, F.J. Jr. 1997. Interaction of [3 H]orphanin FQ and 125 I-tyr14-orphanin FQ with the orphanin FQ receptor: Kinetics and modulation by cations and guanine nucleotides. Mol. Pharmacol. 51:816-824.

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Ariens, E.J. 1954. Affinity and intrinsic activity in the theory of competitive inhibition. Arch. Int. Pharmacodyn. Ther. 99:32-49.

Kenakin, T. 1996. The classification of seven transmembrane receptors in recombinant expression systems. Pharmacol. Rev. 48:413-465.

Arvanitakis, L., Geras-Raaka, E., Varma, A., Gershengorn, M.C., and Cesarman, E. 1997. Human herpes virus KSHV encodes a constitutively active G-protein-coupled receptor linked to cell proliferation. Nature 385:347-350.

Kenakin, T. and Onaran, O. 2002. The ligand paradox between affinity and efficacy: Can you be there and not make a difference? Trends Pharmacol. Sci. 23:275-280.

Black, J.W. 1989. Drugs from emasculated hormones: The principle of syntopic antagonism. Science 245:486-492. Bloom, F.E. 1988. Neurotransmitters: Past, present and future directions. FASEB J. 2:32-41. Catterall, W.A. 2000. From ionic currents to molecular mechanisms: The structure and function of voltage-gated sodium channels. Neuron 26:13-25. Clifford, E.E., Martin, K.A., Dalal, P., Thomas, R., and Dubyak, G.R. 1997. Stage-specific expression of P2Y receptors, ecto-apyrase and ectoS -nucleotidase in myeloid leukocytes. Am. J. Physiol. 273:C973-C987. Coughlin, S.R. 1994. Expanding horizons for receptors coupled to G proteins: Diversity and disease. Curr. Opin. Cell Biol. 6:191-197. Devane, W.A., Hanus, L., Breur, A., Pertwee, R.G., Stevenson, L.A., Griffin, G., Gibson, D., Mandelbaum, A., Etinger, A., and Mechoulam, R. 1992. Isolation and structure of a brain constituent that binds to the cannabinoid receptor. Science 258:1882-1884. Donaldson, L.F., Hanley, M.R., and Villablanca, A.C. 1997. Inducible receptors. Trends Pharmacol. Sci. 18:171-181. Elsele, J.-L., Betrand, S., Galzi, J.-L., DevilliersThiery, A., Changeux, J.-P., and Bertrand, D. 1993. Chimaeric nicotinic: Serotonergic receptor combines distinct ligand binding and channel specificities. Nature 366:479-483. Garcia-Colunga, J., Awada, J.N., and Miledi, R. 1997. Blockage of muscle and neuronal nicotinic acetylcholine receptors by fluoxetine (Prozac). Proc. Natl. Acad. Sci. U.S.A. 94:2041-2044. Gelman, D.M., Noain, D., Avale, M.E., Otero, V., Low, M.J., and Rubinstein, M. 2003. Transgenic mice engineered to target Cre/loxP-mediated DNA recombination into catecholaminergic neurons. Genesis 36:196-202. Grissmer, S. 1997. Potassium channels still hot. Trends Pharmacol. Sci. 18:347-350.

Kenakin, T. 2003. Predicting therapeutic value in the lead optimization phase of drug discovery. Nat. Rev. Drug Discov. 2:429-438. Kobilka, B.K., Kobilka, T.J., Daniel, K.W., Regan, J.W., Caron, M.G., and Lefkowitz, R.J. 1988. Chimeric α2 -, β2 -adrenergic receptors: Delineation of domains involved in effector coupling and ligand specificity. Science 240:13101316. Kollias-Baker, C.A., Ruble, J., Jacobson, M., Harrison, J.K., Ozeck, M., Shyrock, J.C., and Belardinelli, L. 1997. Agonist-independent effect of an allosteric enhancer of the A1 adenosine receptor in CHO cells stably expressing the recombinant human A1 receptor. J. Pharmacol. Exp. Ther. 281:761-768. Koshland, D.E. Jr., Nemethy, S., and Filmer, D. 1966. Comparison of experimental binding data and theoretical models in proteins containing subunits. Biochemistry 5:365-381. Kostenis, E., Milligan, G., Christopoulos, A., Sanchez-Ferrer, C.F., Heringer-Walther, S., Sexton, P.M., Gembardt, F., Kellett, E., Martini, L., Vanderheyden, P., Schultheiss, H.P., and Walther, T. 2005. G-protein-coupled receptor Mas is a physiological antagonist of the angiotensin II type 1 receptor. Circulation 111:1806-1813. Laduron, P.M. 1992. Towards genomic pharmacology: From membranal to nuclear receptors. Adv. Drug Res. 22:107-148. Leaman, D.W., Leung, S., Li, X., and Stark, G.R. 1996. Regulation of STAT-dependent pathways by growth factors and cytokines. FASEB J. 10:1578-1588. Liebmann, C. 2004. G protein-coupled receptors and their signaling pathways: Classical therapeutical targets susceptible to novel therapeutic concepts. Curr. Pharm. Des. 10:1937-1958. Matherne, G.P., Linden, J., Byford, A.M., Gauthier, N.S., and Headrick, J.P. 1997. Transgenic A1 adenosine receptor overexpression increases myocardial resistance to ischemia. Proc. Natl. Acad. Sci. U.S.A. 94:6541-6546.

Hall, R.A., Premont, R.T., and Lefkowitz, R.J. 1999. Heptahelical receptor signaling: Beyond the G protein paradigm. J. Cell Biol. 145:927-932.

May, L.T., Avlani, V.A., Sexton, P.M., and Christopoulos, A. 2004. Allosteric modulation of G protein-coupled receptors. Curr. Pharm. Des. 10:2003-2013.

Hopkins, A.L. and Groom, C.R. 2002. The druggable genome. Nat. Rev. Drug Discov. 1:727-730.

Monod, J., Wyman, J., and Changeux, J.-P. 1965. On the nature of allosteric transitions. J. Mol. Biol. 12:88-118.

Ihle, J.N. 1996. STATs: Signal transducers and activators of transcription. Cell 84:331-334.

Navia, M.A. and Chaturvedi, C. 1996. Design principles for orally bioavailable drugs. Drug Disc. Today 1:179-189.

Kenakin, T. 1993. Pharmacologic Analysis of DrugReceptor Interaction, 2nd ed. Raven Press, New York.

Neumann, S., Doubell, T.P., Leslie, T., and Woolf, C.J. 1996. Inflammatory pain hypersensitivity

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mediated by phenotypic switch in myelinated primary sensory neurons. Nature 384:360-364. Oksenberg, D., Marsters, S.A., O’Dowd, B.F., Jin, H., Havlik, S., Peroutka, S.J., and Ashkenazi, A. 1992. A single amino-acid difference confers major pharmacological variation between human and rodent 5HT1B receptors. Nature 360:161-163. Pleskoff, O., Treboute, C., Brelot, A., Heveker, N., Seman, M., and Alizon, M. 1997. Identification of a chemokine receptor encoded by human cytomegalovirus as a cofactor for HIV-1 entry. Science 276:1874-1878. Poyner, D.R., Sexton, P.M., Marshall, I., Smith, D.M., Quirion, R., Born, W., Muff, R., Fischer, J.A., and Foord, S.M. 2002. International Union of Pharmacology. XXXII. The mammalian calcitonin gene-related peptides, adrenomedullin, amylin, and calcitonin receptors. Pharmacol. Rev. 54:233-246. Stamler, J.S., Jia, L., Eu, J.P., McMahon, T.J., Demchenko, I.T., Bonaventura, J., Gernert, K., and Piantadosi, C.A. 1997. Blood-flow regulation by S-nitrosohemoglobin in the physiological oxygen gradient. Science 276:2034-2037. Strosberg, A.D. 1996. G protein coupled R7 G receptors. Cancer Surv. 27:65-83. Ward, R.J. and Milligan, G. 2004. Analysis of function of receptor-G-protein and receptor-RGS fusion proteins. Methods Mol. Biol. 259:225-247. Williams, M. and Gordon, E.M. 1996. Drug discovery: An overview. In A Textbook of Drug Design and Development, 2nd ed. (P. KrogsgaardLarsen, T. Liljefors, and U. Madsen, eds.) pp.1-34. Harwood Academic Publishers, Chur, Switzerland. Williams, M., Mehlin, C., Raddatz, R., and Triggle, D.J. 2005. Receptor targets in drug discovery and development. In Burger’s Medicinal Chemistry and Drug Discovery, 7th Edition. Vol. 2, Drug Discovery and Development (D. Abraham, ed.), pp. 319-355. John Wiley & Sons, Hoboken, N.J.

Zadina, J.E., Hackler, L., Ge, L.J., and Kastin, A.J. 1997. A potent and selective endogenous agonist for the mu-opiate receptor. Nature 386:499-502. Zambrowicz, B.P. and Sands, A.T. 2003. Knockouts model the 100 best-selling drugs—Will they model the next 100? Nat. Rev. Drug. Discov. 2:38-51.

Key References Kenakin, T. 2004. A pharmacology primer: Theory, application and methods. Elsevier, Inc., London, UK. An absolutely indispensable and comprehensive review of the current state of receptor theory. Required reading for anyone interested in receptor theory and pharmacology. Moss, S.J. and Henley, J. 2002. Receptor and IonChannel Trafficking: Cell Biology of LigandGated and Voltage Sensitive Ion Channels. Oxford, London. Compendium on ion channels. Nature Reviews Drug Discovery GPCR Questionnaire Participants. 2004. The state of GPCR research in 2004. Nat. Rev. Drug Discov. 3:577626. Twenty GPCR experts answer questions relevant to drug discovery now and projecting into the future.

Internet Resources http://www.sigmaaldrich.com/Area of Interest/ Life Science/Cell Signaling/Sigma RBI Handbook2.html The Sigma-RBI Handbook of Receptor Classification and Signal Transduction.

Contributed by Michael Williams and Rita Raddatz Worldwide Discovery Research Cephalon, Inc. West Chester, Pennsylvania

Yokomizo, T., Izumi, T., Chang, K., Takuwa, Y., and Shimizu, T. 1997. A G-protein-coupled receptor for leukotriene B4 that mediates chemotaxis. Nature 387:620-624.

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Receptor Theory The equations and models that describe the interaction of chemicals with receptors comprise what is generally known as drug-receptor theory. Although largely adapted from enzyme kinetics, the principles necessarily differ from those of enzyme-substrate interactions in that they deal with chemicals that are not changed by the receptor, but rather interact in a reversible manner to produce a change in the state of the receptor that is then transmitted to the cellular host. Theories to describe this biochemical phenomenon have evolved since the turn of the century. This unit describes the evolution of drug receptor theory from that point.

ORIGINS OF RECEPTOR THEORY The physiologists John Newport Langley (1878) and Paul Ehrlich (1909) are credited with the first discussion of receptors (Parascandola, 1986). In a classic study on the antagonism of pilocarpine by atropine, Langley, a Cambridge University student, described a “… substance … with which both atropine and pilocarpine are capable of forming compounds.…” Ehrlich was conducting experiments on biological staining and went on to discuss “receptive side chains.” The first formulations that used mathematics to describe drug-receptor interactions were given some years later by another Cambridge student, A.J. Clark. His equations (Clark, 1933, 1937) described what is referred to as the occupancy theory, which describes processes in which a biological response ensues from the occupancy of a receptor by an agonist.

OCCUPANCY THEORY Receptor occupancy theory describes the quantitative relationships between drug concentrations and the responses that result from the interaction of those drugs with receptors. The first process to be considered is the production of a response by an agonist.

Agonism An agonist is a compound that binds to a receptor and induces a biological response. The interaction of an agonist (A) with an unbound receptor (R) to form a complex (AR) is shown in Equation 1.2.1,

A+R

Ka

Û AR

Equation 1.2.1

UNIT 1.2 where Ka is the equilibrium association constant defined in Equation 1.2.2. Ka =

[AR] [A] × [R]

Equation 1.2.2

The conservation equation for receptors, which accounts for the state of the total number of receptors ([Rt]), is given as [Rt] = [R] + [AR]. If it is assumed that the response emanates only from an agonist-occupied receptor (AR), then the response can be given as a fraction of the total receptor population as in Equation 1.2.3. response =

[AR]

[R t ]

=

[AR]

[ R ] + [AR]

Equation 1.2.3

Using a rearrangement of Equation 1.2.2 that yields the expression [R] = [AR]/([A] × Ka), the response from Equation 1.2.3 can be expressed as in Equation 1.2.4, which is the equation used in Clark’s 1937 treatise. response =

[A] [A] + 1 Ka

Equation 1.2.4

This equation uses the equilibrium association constant, Ka; however, the equilibrium dissociation constant (KA), which is the reciprocal of Ka, is a much more common and useful replacement because it has units of concentration. It should be noted that KA has a chemical meaning and is unique to the agonist-receptor pair, making it useful for characterizing receptors in any host cell system. It is also the concentration of agonist that occupies half of the available receptor population. Making the substitution of KA for 1/Ka, Clark’s equation takes on the familiar form of Equation 1.2.5. response =

[A] [A] + K A

Equation 1.2.5

Clark’s equations describe many phenomena and formed the basis of drug-receptor theory. The relationships between agonist concentration, receptor occupancy, and tissue response Receptor Binding

Contributed by Terry Kenakin Current Protocols in Pharmacology (1998) 1.2.1-1.2.27 Copyright © 1998 by John Wiley & Sons, Inc.

1.2.1

could produce a maximal tissue response at extremely low receptor occupancies (far less than maximal). Such data suggested the existence of nonlinear hyperbolic relationships between occupancy and response. The failure of Equation 1.2.5 to model the effects of some agonists led Ariens (1954, 1964) to introduce a proportionality factor to scale the maximal response of weak agonists. Termed intrinsic activity (α), it allowed for agonists to produce a maximal response that is lower than the maximal tissue response. Under these circumstances, the response to an agonist is described by Equation 1.2.6.

are shown in Figure 1.2.1 according to Clark’s formulations. Although Equation 1.2.5 describes the effects of certain drugs in some systems, it has shortcomings that result from two major assumptions: (1) that the maximal response to the drug is equal to the the maximal tissue response, and (2) that the relationship between occupancy and response is linear and direct. The first assumption led to the prediction that all agonists would produce the same maximal response, which is now known not to be the case. The second assumption was also found to be incorrect some years later when Nickerson (1956) showed that agonists such as histamine

A

100

Maximal response (%)

Stephenson Clark

Ariens

50

α = 0.4

0 0

50

100

Receptor occupancy (%)

Response occupancy

B Clark

Ariens

Stephenson

response

occupancy

response

occupancy

response

occupancy

log[A]

Receptor Theory

Figure 1.2.1 Occupancy-response relationships. (A) The relationship between receptor occupancy by an agonist (as percent maximal occupancy) and tissue response (as percent maximal response) is shown as described by variations of occupancy theory. Clark related occupancy to response with a direct linear relationship. Ariens maintained the linear relationship, but introduced a proportionality factor termed the intrinsic activity (α) that allowed a partial agonist to produce a reduced response at maximal receptor occupancy. Stephenson related occupancy to response with a hyperbolic relationship. (B) Response and receptor occupancy as a function of agonist concentration (dose-response curves), according to Clark, Ariens, and Stephenson.

1.2.2 Current Protocols in Pharmacology

response =

relationship. The response to an agonist is then defined by Equation 1.2.7.

α[ A ]

[A] + K A



e[ A ]

   [A] + K A 

response = f 

Equation 1.2.6

If α = 0.4, the agonist produces only 40% of the maximal tissue response at full receptor occupancy. Agonists that produce a maximal response lower than the tissue maximum are termed partial agonists. The magnitude of α ranges from zero (for antagonists that produce no tissue response but occupy the receptor) to unity (for agonists that produce the maximal tissue response). Agonists with a value of α = 1 are termed full agonists. Although Ariens’ definition of intrinsic activity improved the descriptive power of the occupancy theory, it did not resolve the problems associated with the assumption of a linear relationship between receptor occupancy and tissue response (see Fig. 1.2.1). Receptor occupancy theory was revolutionized with the introduction of the concept of stimulus (Stephenson, 1956). Agonists were assumed to stimulate the system by occupying receptors, and the strength of that stimulus was defined by a property of the agonist termed efficacy (e). The tissue response was assumed to be a monotonic function of the stimulus (f), and was referred to as the stimulus-response

Equation 1.2.7

This equation allows for nonlinear relationships between receptor occupancy and tissue response. Stephenson used a hyperbolic function (response = stimulus/[stimulus + 1]) to model the responses to muscarinic agonists. Using this model, the value of e can range from zero to any positive number. The relationship between receptor occupancy and tissue response according to Stephenson’s formulations is compared to those modeled by Clark and Ariens in Figure 1.2.1. Stephenson’s equations were modified some years later by Furchgott (1966, 1972), who dissociated the tissue component of efficacy from the agonist-receptor component. This was extremely useful because relative efficacy then became a chemical term characteristic of the agonist and receptor, which allowed it to be used across different receptor systems. Intrinsic efficacy (ε) was defined as the unit stimulus per occupied receptor, using the relationship e = ε × [Rt]. Under these circumstances, the equation for agonist response is written as Equation 1.2.8.

Maximal response (%)

100

guinea pig trachea rat left atria cat left atria cat papillary guinea pig left atria guinea pig e.d.l.

50

0 0

10

20

Receptor occupancy (%)

Figure 1.2.2 Tissue-specificity of the relationship between receptor occupancy and maximal tissue response. The β-adrenergic receptor was activated with the agonist isoproterenol in a variety of isolated tissues (e.d.l., extensor digitorum longus muscle). Data redrawn with permission from Kenakin and Beek (1980).

Receptor Binding

1.2.3 Current Protocols in Pharmacology

Table 1.2.1

Terms Used in Drug-Receptor Occupancy Theory

Term

Description

Properties of drug or receptor Equilibrium association constant (in liter/mol) defined as affinity Ka

KB

Equilibrium dissociation constant of an agonist-receptor complex (KA = 1/Ka; in mol/liter); also defined as the concentration producing half-maximal agonist-receptor occupancy Equilibrium dissociation constant of an antagonist-receptor complex (KB = 1/Kb; in mol/liter)

ε

Intrinsic efficacy; unit stimulus imparted to a single receptor as a result of agonist occupancy

KA

Properties of receptor system [Rt] f Stimulus α β ED50 (also EC50)

Concentration of total receptors in the system Stimulus-response relationship; a monotonic function, usually a hyperbola, describing the resultant of the cellular effectors that translate receptor stimulus to tissue response Receptor-mediated signal that initiates cellular response Intrinsic activity, defined as the maximal response to an agonist and expressed as a fraction of the maximal tissue response A fitting constant for the hyperbolae used to model stimulus-response functions (f). β is inversely proportional to the efficiency of receptor coupling Dose of agonist that produces 50% of the maximal response to the agonist; referred to as a dose (ED50 for in vivo studies) or a concentration (EC50 for in vitro studies)

Property of drug and receptor system e Efficacy (as defined by Stephenson), encompassing both the ability of the agonist to produce a response and the ability of the system to amplify the stimulus and produce the response

 ε[ A ][ R t ]  response = f    [A] + K A  Equation 1.2.8

Receptor Theory

To relate agonist receptor occupancy and tissue response, some formulation for the stimulus response is required. As noted above, Stephenson chose the function response = stimulus/(stimulus + 1). This defined a hyperbola relating receptor occupancy and tissue response (see Fig. 1.2.1). However, in practice there are a number of such relationships for different tissues, reflecting variations in the efficiency of coupling between receptors and their effectors in different tissues. In Figure 1.2.2, a stimulus-response relationship is shown connecting β-adrenergic receptor occupancy to the response to isoproterenol in different tissues. Although differences between Stephenson’s model and experimental data are evident, a hyperbola can be used to characterize the stimulus-response relationships (Kenakin and Beek, 1980). A more versatile relationship for the link between receptor occupancy and

tissue response is the function shown in Equation 1.2.9, response =

stimulus stimulus + β

Equation 1.2.9

where β is a fitting parameter that inversely reflects the efficiency of coupling between the receptor and the cytosolic response elements. As the magnitude of β decreases, the coupling efficiency increases (i.e., fewer receptors must be occupied to produce a response). Occupancy theory thus predicts a response that is calculated as: response =

([A]

ε[ R t ][ A ] K A

K A )(ε[ R t ] + β) + β

Equation 1.2.10

A summary of the terms used in occupancy theory to describe agonism is shown in Table 1.2.1. In Figure 1.2.3, an array of dose-response curves is shown for different receptor coupling efficiencies (β = 0.001 to 10). There are essen-

1.2.4 Current Protocols in Pharmacology

ε = 3.0

100

Maximal response (%)

β = 0.001 0.01 0.1 1

50

10

0 0

–5

–4

–3

–2

–1 K A

log([A]/K A )

Figure 1.2.3 The effect of varying receptor coupling efficiency on the response to an agonist. Values for β indicate different efficiencies of receptor coupling (highest efficiency here is β = 0.001). Efficacy (ε) = 3; [Rt] = 1. The dashed hyperbola illustrates that the half-maximal response increases (relative to the half-maximal tissue response) as β decreases, but approaches KA as β increases. The shaded bar on the logarithmic concentration axis represents the difference between the affinity of the agonist for the receptor (KA) and the observed ED50. There is less than a three-fold difference between the ED50 and the KA when receptor coupling is such that the maximal response to the agonist is lower than the tissue maximum.

tially two sets of dose-response curves. In the first few curves beginning from the far right, as coupling efficiency increases, the maximal response to the agonist also increases, but the location parameter (i.e., the position of the curve along the x-axis) in this region remains relatively unchanged. However, as the receptor coupling efficiency increases further, a constant maximal response is obtained (the maximal tissue response) and the curves begin to shift to the left. The change in maximal response is illustrated in Figure 1.2.3 as a dashed hyperbola representing the change in halfmaximal response. The upper limit of the maximal response results from the saturation of some element in the stimulus-response mechanism of the cell. Increases in receptor density can increase the likelihood of receptor activation by the agonist, however, increasing the probability of agonist-receptor interaction and producing further leftward shifts in the dose-response curve.

This model explains a phenomenon observed with all high-efficacy agonists: the dissociation between the potency of the agonist (quantified as the ED50, the concentration of agonist producing half-maximal response) and the affinity (defined by KA, the concentration of agonist producing half-maximal occupancy). As shown in Figure 1.2.3, at high receptor coupling efficiencies (e.g., β = 0.01), the concentration producing a 50% response is considerably lower than the concentration occupying 50% of the receptors (KA). This can be plainly seen in Equation 1.2.11, which describes the observed location parameter of the dose-response curves (ED50). ED 50 KA

=

β

ε[ R t ] + β

Equation 1.2.11

The more efficiently the receptors are coupled (i.e., lower β) or the higher the efficacy

Receptor Binding

1.2.5 Current Protocols in Pharmacology

(i.e., larger ε), the greater is the differential between the affinity of the agonist (KA) and the potency (ED50). The maximal response (i.e., when [A]/KA → ∞) is given by Equation 1.2.12. maximal response =

ε[ R t ]

ε[ R t ] + β

Equation 1.2.12

This shows that the higher the efficacy or the more efficient the receptor coupling, the greater the likelihood that the agonist will produce the maximal tissue response (maximal response →1). The frequently observed phenomenon of receptor reserve (also referred to as spare receptors) describes the situation where an agonist must occupy only a fraction of the existing receptor population to produce the maximal tissue response. Although the term receptor reserve connotes a property of the receptor system, it in fact depends upon both the receptor system and the agonist. As shown in Equation 1.2.12, there will be a receptor reserve if the efficacy of the agonist (ε) is high or the receptors are efficiently coupled (low β). Thus, a given tissue may have a receptor reserve for a high-efficacy agonist, but no receptor reserve for a low-efficacy agonist. The relationship between the maximal response of a receptor system to the ED50 value

A

of an agonist is also shown in Figure 1.2.3. When receptor coupling is such that a submaximal response is produced by the agonist, the ED50 value is near the affinity constant (KA) of the agonist. The power of this relationship is that it holds for all receptor systems (the complete range of receptor efficiencies) and also for all agonist efficacies. Thus, if a given agonist produces a submaximal tissue response, then the ED50 value is a reasonable approximation of the KA. As this is a chemical term, it describes a characteristic independent of tissues and receptor systems, and therefore is a particularly valuable measurement.

Antagonism Another pharmacological phenomenon mediated by receptors is the inhibition of agonistinduced actions by drugs that produce no tissue response themselves. Such agents are referred to as antagonists. Gaddum and coworkers (1955) described two general classes of antagonism, surmountable and insurmountable, based on the effect of the antagonist on the dose-response curve to agonists. Surmountable antagonism is characterized by a constant maximal tissue response (i.e., the normal maximal response can be reached in the presence of antagonist by increasing the concentration of the agonist). A parallel rightward shift of the dose-response curve to the agonist is usually observed under this circumstance (Fig. 1.2.4A). In contrast, insurmountable antago-

B

Maximal response (%)

100

50

0 log[A]

Receptor Theory

log[A]

Figure 1.2.4 Surmountable versus insurmountable receptor antagonism as described by Gaddum and coworkers (1955). (A) Surmountable antagonism is characterized by a parallel rightward shift of the dose-response curve with no diminution of the maximal response to the agonist. (B) Insurmountable antagonism is characterized by a depression of the maximal response to the agonist that may or may not be accompanied by a rightward shift of the dose-response curve. [A], agonist concentration.

1.2.6 Current Protocols in Pharmacology

A

B

Maximal response (%)

100

common binding site

50

[B] = 0 3 10 30 100 KB

0 log[A]

Figure 1.2.5 Simple competitive antagonism. (A) The molecular mechanism for this type of antagonism consists of a competition between two ligands for the same binding site on the receptor. (B) The result of competition between the agonist and the antagonist is a parallel rightward shift of the dose-response curve to the agonist A. The magnitude of the shift is dependent upon the concentration of the antagonist B.

nism is characterized by a depression of the maximal tissue response to the agonist, and a distinctly nonparallel displacement of the doseresponse curve (Fig. 1.2.4B). Competitive reversible antagonism Competitive reversible antagonism is defined as a mass-action competition between the agonist and the antagonist for the same site on the receptor (Fig. 1.2.5A). As drugs continually bind to and diffuse away from receptors, the probability of the agonist binding to a receptor temporarily vacated by an antagonist molecule increases with increasing concentration of agonist. The maximal response to the agonist is attained in the presence of the antagonist by increasing the concentration of the agonist. Therefore, if dissociation is sufficiently rapid, competitive antagonism should always result in surmountable antagonism. Competitive antagonism is described in Equation 1.2.13; the system consists of an agonist (A), a competitive antagonist (B), and a receptor (R).

The conservation equation for receptors is changed by the addition of the antagonist-receptor complex: [Rt] = [R] + [AR] + [BR]. Assuming that a response emanates only from AR, the equation for the response to an agonist in the presence of a competitive reversible antagonist is given in terms of receptor occupancy by the agonist ([AR]/[Rt]), as shown in Equation 1.2.14.

[AR]

=

[A]

KA

[R t ] [A] K A + [B] KB + 1 Equation 1.2.14

AR

Equation 1.2.14 is known as the Gaddum equation (derived by Sir John Gaddum, 1937, 1957). It forms the basis for the classification of drugs and receptors using competitive antagonists. This equation predicts that a reversible competitive antagonist will produce rightward parallel shifts of agonist dose-response curves with no diminution of the maximal response (see Fig. 1.2.5B). This equation also forms the basis of the Schild analysis, which is used to quantify the equilibrium dissociation constant of the antagonist-receptor complex (KB; UNIT 4.1).

Equation 1.2.13

Noncompetitive (allotopic) antagonism Another mechanism by which an antagonist blocks the effects of an agonist is by attaching

B +

A +R

Ka

Kb BR

Receptor Binding

1.2.7 Current Protocols in Pharmacology

A

B

Receptor occupancy (%)

100

50

0 log[A]

Figure 1.2.6 Allotopic (or noncompetitive) antagonism. (A) The molecular mechanism of this type of antagonism involves binding of the antagonist at a site that is separate from that of the agonist. Antagonist binding then causes a conformational change in the receptor (conceptually represented here by the zigzag line) that inhibits the response to the bound agonist. (B) The effect of an allotopic antagonist on agonist receptor occupancy. It is assumed that the antagonist does not affect the affinity of the receptor for the agonist, but rather prevents the activation of the receptor by the agonist. Thus, the dose-response curve shows a depressed response without a horizontal shift.

to a site separate from the agonist binding site and, in so doing, locking the receptor into a conformation that does not allow the agonist to transmit a stimulus (see Fig. 1.2.6A). This is referred to as allotopic or noncompetitive antagonism. The scheme for this type of interaction is shown in Equation 1.2.15.

B +

A +R

B Ka

γKb

Kb A + BR

+

AR

γKa

ARB

indicating that its affinity is altered by the binding of the first ligand. Thus, the agonist will bind with affinity Ka to an unbound receptor (R), and will bind with affinity γKa to the antagonist-bound receptor (BR). This complies with the principle of microreversibility, which states that there must be a pathway whereby the drug-bound receptor can bind additional drug at the second site (Wyman, 1975). Three equilibrium equations are used to describe noncompetitive antagonism.

[ARB] = γ [B]K b [AR] [BR] =

Equation 1.2.15

The receptor can bind either agonist (A) or antagonist (B) with equilibrium association constants Ka and Kb, respectively. The receptor can then bind the other ligand, since A and B bind to separate sites; however, if the antagonist is present on the receptor, no stimulus is transmitted to the system (i.e., AR can signal, but ARB and BR cannot). A modifying factor (γ) is added to the binding of the second ligand,

[R] =

[AR][B]K b [ A ]K a

[AR] [ A ]K a

Equation 1.2.16

The conservation equation for total receptors is [Rt] = [R] + [AR] + [BR] + [ARB]. Therefore, the response, which is again expressed as [AR]/[Rt], is given in Equation 1.2.17.

Receptor Theory

1.2.8 Current Protocols in Pharmacology

[AR]

[R t ]

response =

=

[A] K A K 1 + γ A [ ] [B] K B ) + (1 + [B] ( A )(

([A] KB )

Equation 1.2.17

1 + γ [ B] K B

The ED50 value of the agonist may or may not change, depending upon whether the binding of the antagonist affects the binding of the agonist, as shown in this modified equation for the ED50 value. KA

=

Equation 1.2.20

maximal response =

ε

ε + β(1 + γ [ B] K B )

Equation 1.2.21

1

Equation 1.2.18

ED 50

]

K A ) ε + β(1 + γ [ B] K B ) + β(1 + [ B] K B )

and the maximal response becomes:

Equation 1.2.18 shows that the maximal occupancy for the agonist decreases when a noncompetitive antagonist is bound to the receptor. maximal occupancy =

[

ε [A] K A

1 + [ B] K B 1 + γ [ B] K B

Equation 1.2.19

If the antagonist has no effect on the binding of the agonist (i.e., if γ = 1), then the agonist receptor occupancy curve will not shift from the control value and the curves will simply be depressed (Fig. 1.2.6B). Receptor occupancy (as defined by a signaling AR complex) can be depressed by a noncompetitive antagonist without any concomitant effect on the maximal agonist response. This occurs with high-efficacy agonists, where the maximal response is obtained with submaximal receptor occupancy. For example, if only 5% of the receptors must be activated by a particular agonist to achieve maximal response, blockade of 75% of the receptors by a noncompetitive antagonist will not depress the maximal response to this agent. However, the dose-response curves will shift to the right, because the probability of the agonist attaching to a receptor free of antagonist decreases with increasing concentrations of antagonist. This effect is modeled by building a series of stimulus-response hyperbolic functions that relate the receptor occupancy by the agonist (Eqn. 1.2.17) and a nonlinear stimulus-response function for the step relating receptor occupancy and tissue response (Eqn. 1.2.9). The response equation then becomes:

Thus, if receptors are efficiently coupled (i.e., if β is very small) or if the efficacy (ε) is very high, then concentrations of noncompetitive antagonist that occupy considerable portions of the receptor population will not produce a measurable depression of the maximal response to that agonist. In Figure 1.2.7, the effects of a noncompetitive antagonist on the responses to a high-efficacy agonist in an efficiently coupled receptor system are shown. As illustrated, considerable displacement of the dose-response curve occurs as the maximal response to the agonist is depressed.

Advances and Limitations of Occupancy Theory Occupancy theory, beginning with Clark’s description in the 1930s and continuing with Furchgott’s modification in the late 1960s, was an extremely useful tool for characterizing receptors and quantifying agonist activity. It enabled the recognition of two major factors in the definition of agonist activity: the drug-specific parameters of affinity and intrinsic efficacy. Additionally, tissue-specific (or receptor system–related) parameters, such as receptor density and the efficiency of coupling between receptors and the effector mechanisms of the tissue, could be dissociated from agonist-related parameters. The separation of these influences allowed for the measurement of parameters that are independent of the tissue and, instead, characterize the molecular interaction between the agonist and the receptor. However, the concept of efficacy was still encased in a dimensionless proportionality constant with no correlate to molecular mechanism. Also, receptor theory had not yet incorporated the known fact that proteins adopt numerous conformations according to thermal energy and are not the static entity denoted by R in the above equations. To study this phenomenon, a means was required to monitor

Receptor Binding

1.2.9 Current Protocols in Pharmacology

Maximal response (%)

100

50

0 log[A]

Figure 1.2.7 The effect of a noncompetitive antagonist on the response to an agonist (A) in a tissue where there is highly efficient coupling of the response to the receptor stimulus. A system is shown where there is a receptor reserve for this agonist (i.e., maximal response can be attained by activation of a small percentage of the receptors). Blockade of receptors by a low dose of the noncompetitive antagonist causes the dose-response curve to shift to the right, with little effect on the maximal response. However, as the dose of antagonist rises (as indicated by the arrow), concomitant shifts and depressions of the maximal response are observed.

different protein conformations and to quantify their relative frequency of existence. This was achieved by electrophysiological studies of open and shut ion channels, which resulted in the description of the two-state theory.

TWO-STATE THEORY Agonism By monitoring the flow of ions through open ion channels and noting the lack of current flowing when channels are shut, various protein conformations can be studied. Katz and Thesleff (1957) described a system whereby a channel (denoted as a receptor) could exist in either an open, active state (Ra) or a shut, inactive state (Ri). The relative quantities of the two species are controlled by an allosteric constant (L), as shown in Equation 1.2.22. When L is high (i.e., L >1), there is a high degree of spontaneous receptor activation (i.e., the equilibrium is shifted towards Ra).

L

Û Ra

Ri

Equation 1.2.22

A drug may have distinct affinities for the two conformational states: Ka and γKa, where γ is the difference in affinity caused by the change in receptor state. The scheme for a drug (A) that interacts with a two-state receptor system is given by Equation 1.2.23.

A

A

+

Ri

L

+

Ra

γKa

Ka AR i

γL

ARa

Equation 1.2.23

Three equilibrium equations describe this scheme.

Receptor Theory

1.2.10 Current Protocols in Pharmacology

[AR i ] = [R i ] =

[AR a ] γL

[AR a ]

γL[ A ]Ka

[R a ] =

[AR a ]

γ [ A ]K a

Equation 1.2.24

The conservation equation for the receptor is [Rt] = [Ra] + [Ri] + [ARa] + [ARi]. If it is assumed that response emanates from the active receptor, the response is given in Equation 1.2.25.

[R a ] + [AR a ] = [R t ] [AR a ](1 γL[A]Ka + 1 γ [A]Ka + 1 γL + 1) Equation 1.2.25

When association constants are converted to dissociation constants (inverted), this simplifies to Equation 1.2.26.

[R t ]

ED 50 KA

=

1+ L 1 + γL

Equation 1.2.27

[AR a ](1 + 1 γ [A]Ka )

[R a ] + [AR a ] =

of active-state receptor (from Ra to Ra+ARa), resulting in response. The effects of an agonist on a two-state receptor system when γ > 1 are shown in Figure 1.2.8A for different values of L. As L increases, the basal response of the system increases beyond zero due to the spontaneous formation of Ra, and the agonist effect is superimposed on the elevated basal response. Also, the potency of the agonist increases with increasing L, as measured by a decreased number of receptors bound at 50% maximal response (ED50). This is shown in Equation 1.2.27, which is derived by modifying Equation 1.2.26 to express the ED50.

([A]

γL [ A ] K A + L

K A )(1 + γL ) + L + 1

Equation 1.2.26

Parameters describing two-state receptor theory are summarized in Table 1.2.2. The description of a two-state receptor system differs from that of standard occupancy theory in two respects. First, the setpoint of the system (i.e., the relative spontaneous activity of the system, in terms of constitutive Ra present in the absence of ligand) becomes a variable that can modify (and in some cases override) the effects of an agonist. Second, the setpoint of the system can lead to a basal constitutive response that results from receptor activation, but occurs in the absence of agonist. Two-state theory also offers a molecular mechanism for efficacy in terms of selective affinity for the two receptor states. If the agonist has a higher affinity for the active state of the receptor (Ra; e.g., γ > 1), then the binding of the agonist to Ra shifts the equilibrium toward Ra beyond what would be produced according to the allosteric constant (L). The net result will be an increase in the amount

Therefore, potency is dependent not only on the differential affinity of the agonist for the two receptor states (γ), but also on the setpoint of the system (i.e., the magnitude of L). This is illustrated for a range of agonists in Figure 1.2.8B, where a 100-fold differential in the affinity of the agonist for the active state (γ = 100) results in a maximum of a 100-fold increase in the potency of the agonist (over the affinity KA), an effect that is dependent upon the magnitude of the allosteric constant (L). This figure also illustrates that higher-efficacy agonists require lower values of L for increased potency than do lower-efficacy agonists. Similarly, the setpoint of the system can control the maximal response to an agonist. The expression for maximal response (from Equation 1.2.26) is: maximal response =

γL 1 + γL

Equation 1.2.28

If the allosteric constant is very low (i.e., if the system is very unresponsive, with a highenergy barrier to production of the maximal response), then the maximal response to the agonist is correspondingly low. Conversely, if the system spontaneously produces a great deal of active receptor (high L), then the basal response will already be at the tissue maximum and the agonist will not produce an added response. The effects of the allosteric constant on the maximal response for a range of agonist efficacies (γ) is shown in Figure 1.2.8C. The in-

Receptor Binding

1.2.11 Current Protocols in Pharmacology

Table 1.2.2

Term

Terms Used in Two-State Receptor Theory

Description

Properties of drug or receptor Equilibrium dissociation constant (KA = 1/Ka); also defined as the concentration producing KA half-maximal agonist-receptor occupancy KB

Equilibrium dissociation constant of an antagonist-receptor or inverse agonist-receptor complex (KB = 1/Kb; in mol/liter)

β

Efficacy of an inverse agonist, defined as its differential affinity for the different affinity states Efficacy; a multiplicative factor defining both the effect of agonist binding on L, and the relative affinity of the agonist for the different receptor states

γ

Properties of receptor system [Ra] Concentration of active receptors (receptors that can mediate a pharmacological response) [Ri] L pA2

Concentration of inactive receptors (receptors that cannot mediate a pharmacological response) Allosteric constant defined as the ratio of active to inactive receptors (L = [Ra]/[Ri]). High values of L indicate a system that is spontaneously activated in the absence of agonist. Negative logarithm of the molar concentration of antagonist that produces a dose-ratio of 2 for an agonist (i.e., a 2-fold rightward shift of the agonist dose-response curve)

creased response to an agonist depends upon the setpoint of the system (magnitude of L), and this dependence produces a bell-shaped curve. At very low levels of L, there is insufficient transduction to produce an increased response; at high levels of L, the basal response is increased to a level where an agonist can produce little additional increase. Note also that the range of L at which an increased maximal response can be seen is dependent upon agonist efficacy. Thus, as γ decreases, the peak for each of the four curves shifts to the right. Because of this effect, there could be systems where high-efficacy agonists produce a response but low-efficacy agonists do not. In contrast, the basal response depends entirely upon the magnitude of the allosteric constant. basal response =

L 1+ L

Equation 1.2.29

Antagonism

Receptor Theory

The effect of competitive antagonists on two-state systems can be predicted by derivation of the expression for their observed potency. This is quantified by pA2, the logarithm of the molar concentration of antagonist that produces a two-fold rightward shift of the agonist dose-response curve. Under certain

conditions, this approximates the molar concentration of antagonist that occupies 50% of the receptor population. Thus, it can be a characteristic chemical constant unique to the antagonist-receptor pair. The pA2 is related to the equilibrium dissociation constant of the antagonist-receptor complex (KB, which is equal to 1/Kb), as shown in Equation 1.2.30,

 K (1 + L )  pA 2 = − log  B   1 + βL  Equation 1.2.30

where β is the differential multiplicative affinity of the antagonist for the active state of the receptor (i.e., KB is the equilibrium dissociation constant for BRi, and βKB is the corresponding constant for BRa). This shows that the setpoint of the two-state system is immaterial to the observed potency of competitive antagonists unless the antagonist can discern the active and inactive states of the receptor. The efficacy of an antagonist is thus defined by its ability to recognize different states of a receptor.

Inverse Agonism Two-state receptor theory introduces another new concept into drug receptor mechanisms: the concept of negative efficacy. In the early treatments of receptor theory, quiescent

1.2.12 Current Protocols in Pharmacology

L = 30

50

1

0.1

0.3

0.03 0.01

0

2

–3 –2 –1

0 1 log L

2

3

E L = 300

3 1 0.3 0.1

50

0.03 γ = 0.1

0

0 –2 –1 0

1

2

3

log([A]/KA)

100 γ = 100 30

50

10 3

0 –4 –2

0

2 log L

F 0.01

100 100 30

10

ED50 /KA

Maximal response (%)

10 30 γ = 100

0

D

50

3

0.5

–3 –2 –1 0 1 2 log([A]/KA)

100

Increased maximal response (%)

1.0

10 3

Decreased maximal response (%)

100

C

B ED50 /KA

Maximal response (%)

A

0

1

2

3

4

0 0.5 0.3 0.1

–50

0.03 γ = 0.01

–1 0 0 –2 0 2

log L

4 log L

Figure 1.2.8 The effect of drugs on two-state receptor systems. (A) The effects of a positive agonist (γ = 10) in a two-state receptor system under various levels of spontaneous receptor activation (values of L). At low levels of activation (L = 0.01), submaximal responses are observed. At higher levels of activation (i.e., L = 0.3, 1), both basal activity and agonist activity increase. At high values of L (i.e., L = 10), basal activity nearly supplants agonist activity. (B) The effect of spontaneous receptor activation on observed agonist potency (measured as a multiple of the affinity). As spontaneous receptor activation (L) increases, the observed affinity of agonists increases. (C) The effect of spontaneous receptor activation on the observed maximum response (calculated as the maximal response to the agonist minus the basal response) to agonists of varying efficacy. (D) Effects of an inverse agonist (γ = 0.1) on elevated basal responses due to spontaneous receptor activation. (E) Effect of spontaneous receptor activation on the observed affinity of inverse agonists of varying efficacy. (F) The effect of spontaneous receptor activation on the negative maximal responses to inverse agonists of varying efficacy.

systems that did not have resting elevated basal responses were utilized in pharmacologic experiments. In this setting, drugs which produced a visible response (i.e., agonists) and drugs which produced no intrinsic response but interfered with the response to agonists (antagonists) were used to delineate receptor function. However, the two-state receptor scheme predicts that a drug might have a higher affinity for the inactive state of the receptor (i.e., γ < 1), shifting the prevailing equilibrium toward Ri. In most quiescent systems where equilibrium in the absence of a drug is already shifted toward Ri, a drug with γ < 1 produces no directly visible effect, but will block the effects of agonists. However, if there is a measurable spontaneous effect due to spontaneously formed Ra, then this type of drug will block the spontaneous basal response. This has been ob-

served in both recombinant and natural systems. Such drugs are called inverse agonists. This phenomenon is illustrated in Figure 1.2.8D, which shows the effects of an inverse agonist on basal response at different values of L. When there is no resting basal response (i.e., no spontaneously formed Ra), then no direct effect of the inverse agonist is observed, and the inverse agonist is indistinguishable from a classical competitive antagonist. However, as the resting basal response increases, the inverse agonist produces inhibition towards zero response. As with positive agonists, the system may be so spontaneously activated (very high L) that an inverse agonist cannot overcome spontaneous receptor activation, in which case no effect is observed. The affinity of an inverse agonist can be calculated from Equation 1.2.27. As with posi-

Receptor Binding

1.2.13 Current Protocols in Pharmacology

tive agonists, the system can affect the observed potency of the inverse agonist, which is inversely proportional to the level of spontaneous activation (L), as illustrated in Figure 1.2.8E. Thus, an inverse agonist with a 100-fold increase in affinity for Ri can have an observed potency 100-fold less than the affinity of the agonist for Ra (if a considerable proportion of the receptors are spontaneously in the active state). The figure also illustrates that the dependence of potency on the level of spontaneous receptor activation varies with the level of agonist efficacy (γ). Figure 1.2.8F shows the dependence of the maximal depressant effects of an inverse agonist on the system setpoint (L). As with positive agonists, the maximum change in the basal response is biphasic. At low levels of spontaneous activation, the basal response is low and an inverse agonist causes a small change in maximal response. At high levels of spontaneous receptor activation the basal response is high, but agonists must overcome an equilibrium of receptors that is highly skewed toward the active state. This effect competes with the ability of the agonist to reverse the activated receptor system and the maximal response diminishes.

Advances and Limitations of Two-State Theory

Receptor Theory

Two-state theory was introduced to describe the behavior of ion channels (Katz and Thesleff, 1957) and was later applied to autonomic and neurotransmitter receptors (Karlin, 1967; Colquhoun, 1973; Thron, 1973). It introduced three new ideas into the existing receptor theory. 1. Efficacy was conceived as being the selective affinity of the agonist for the active over the inactive states of the receptor. 2. The idea of a basal response in the absence of agonist was introduced in terms of spontaneously formed active receptor resulting from favorable values of the allosteric constant (L). 3. The idea of negative efficacy was introduced in the form of drugs that selectively bound to the inactive state of the receptor and thus reversed spontaneous basal receptor activity. The two-state theory is limited, however, to giving an isolated view of the agonist and receptor. New discoveries about the mechanisms of seven-transmembrane-domain receptors led to the introduction of a new and important component in receptor systems: G proteins. This led to the ternary complex model, an

important expansion of receptor theory that includes aspects of the cell’s effector mechanisms.

TERNARY COMPLEX MODEL OF RECEPTORS A large body of experimental data suggest that receptors translocate within the two-dimensional space of cell membranes. The original model of a receptor that floated in the lipid bilayer of membranes and interacted with other membrane-bound components was given by Cuatrecasas (1974). DeLean et al. (1980) described the first ternary complex model of receptors.

Simple Ternary Complex Model The components of the system are receptors (R), drugs (A), and membrane-bound protein couplers. In the case of seven-transmembranedomain receptors, the couplers are G proteins (G). It is assumed that tissue response emanates from the activation of G proteins by the active receptor. The system is shown in Equation 1.2.31.

G +

A +R Kg A + RG

G Ka

+

AR

γKg γKa

ARG

Equation 1.2.31

In this scheme, γ is the factor that controls the effect of the agonist on the receptor–G protein interaction (i.e., it is related to the efficacy of the agonist). The three equilibrium equations that describe the ternary complex system are:

[AR] = [ RG] = [R] =

[ARG]

γ [ G ]K g

[ARG] γ [ A ]K a

[ARG] γ [ G ]K g [ A ]K a

Equation 1.2.32

The conservation equation for receptors is [Rt] = [R] + [AR] + [RG] + [ARG]. From this, the equation for the species producing a pharmacological response is:

1.2.14 Current Protocols in Pharmacology

Table 1.2.3

Terms Used in the Ternary Complex Model of Receptors

Term

Description

Properties of drug or receptor Equilibrium dissociation constant (KA = 1/Ka; in mol/liter); also defined as the concentration KA producing half-maximal agonist-receptor occupancy KB

Equilibrium dissociation constant of an antagonist-receptor or inverse agonist-receptor complex (KB = 1/Kb; in mol/liter)

β

Efficacy of an inverse agonist, a multiplicative factor defining both the effect of antagonist binding on the formation of the receptor–G protein complex, and the relative affinity of the antagonist for the receptor and the receptor–G protein complex Efficacy; a multiplicative factor defining both the effect of agonist binding on formation of the receptor–G protein complex, and the relative affinity of the agonist for the receptor and the receptor–G protein complex

γ

Properties of receptor system [G] [RG]

Concentration of G protein in the system Concentration of receptors spontaneously bound to G protein

[ARG] KG

Concentration of ternary complex formed between the agonist, receptor, and G protein Equilibrium dissociation constant for the receptor/G protein complex

[ RG] + [ARG]

[R t ]

=

([G] KG ) × ( γ [A] K A + 1) ([A] K A )(1 + γ [G] KG ) + (1 + [G]

KG )

Equation 1.2.33

where the equilibrium association constants, Kg and Ka, are converted to their reciprocal equilibrium dissociation constants, KG and KA, respectively. The parameters describing the ternary complex model are given in Table 1.2.3. Figure 1.2.9A shows the effects of various concentrations of G protein on the dose-response curve to an agonist. As the amount of G protein increases in the system, so too does the basal response (in the absence of agonist), the potency of the agonist, and the maximal response to the agonist. The equation for the ED50 value of an agonist is given by Equation 1.2.34. ED 50 KA

=

1 + [G ] K G 1 + γ [G ] K G

Equation 1.2.34

This equation demonstrates that the observed potency of an agonist depends upon its affinity and its efficacy (i.e., its effect on the interaction

between R and G, denoted by γ) as well as on the amount of G protein in the system. Figure 1.2.9B shows the effect that the amount of G protein in a receptor system has on the potency of an agonist. As with two-state systems, the maximal effect on potency equals the magnitude of the differential affinity of the agonist for the G protein–bound and –unbound receptor. The sensitivity of this effect varies with the concentration of G protein. Thus, high-efficacy agonists require lower levels of G protein than do low-efficacy agonists. The maximal response of a G protein–coupled receptor (GPCR) to an agonist is given in Equation 1.2.35. maximal response =

γ [G ] K G 1 + γ [G ] K G

Equation 1.2.35

The effect of G protein concentration on the maximal response to agonists of various efficacies is shown in Figure 1.2.9C. Agonists produce low levels of maximal responses in systems with low concentrations of G protein. However, at high G protein levels, the increased maximal response to agonists becomes limited as a result of an elevated basal response. Therefore, there is a bell-shaped relationship between G protein content and the increased maximal response to an agonist. As with two-state sys-

Receptor Binding

1.2.15 Current Protocols in Pharmacology

A

B 100

1.0

3 ED50/KA

Maximal response (%)

10

1 50

0.3 0.1

0.5

γ =10

3

2

0.03 [G]/KG = 0.01

0 –3

0 –3

–2

–1

0

1

2

3

–2

log([A]/KA)

–1

0

1

2

log([G]/KG)

C

D 100 Maximal response (%)

Increased maximal response (%)

5

γ =300 100 30 10

50

3

100

50

0

0 –4

–3

–2

–1

0

1

2

3

log([G]/KG)

–3

–2

–1 0 1 log([G]/KG)

2

3

Figure 1.2.9 The effects of agonists in ternary complex receptor systems. (A) The effects of a positive agonist (γ = 10) on maximal response at various levels of G protein. (B) The effect of G protein concentration on the observed potency of agonists with different efficacies. (C) The effect of G protein concentration on the observed maximal response (calculated as the maximal effect of the agonist minus the basal response) to agonists of varying efficacy. (D) The effect of G protein concentration on the basal response of a G protein–linked receptor system.

tems, the sensitivity of the maximal response to G protein levels varies with the efficacy of the agonist. The ternary complex model also predicts that basal responses are elevated in the absence of agonist by spontaneous association of receptors and G proteins. Thus, the basal response in the ternary complex model is calculated as in Equation 1.2.36. basal response =

Receptor Theory

[G ] K G 1 + [G ] K G

absence of agonist. It also introduces the notion that a factor other than receptor density controls the sensitivity of the system to agonists, namely the G protein. The dependence of basal response on the concentration of G protein in a receptor system is shown in Figure 1.2.9D. An expression for the observed potency of competitive antagonists (pA2) in a ternary complex model is derived as in Equation 1.2.37.



pA 2 = − log  K B



1 + [G ] K G   1 + β [G ] K G 

Equation 1.2.36

Equation 1.2.37

As with the two-state theory, the ternary complex model allows for basal response in the

The potency of a competitive antagonist will not be affected by the presence of G proteins as

1.2.16 Current Protocols in Pharmacology

long as the antagonist does not distinguish between the receptor and the receptor–G protein complex (i.e., β must equal unity). If the antagonist has efficacy (β ≠ 1), then the relative receptor–G protein stoichiometry will affect the agonist’s observed potency in the receptor system. Thus, with the advent of the ternary complex model, drug-receptor theory was modified by the addition of a third nonreceptor component to the receptor system, which affected the observed properties of drugs interacting with these sites. In this sense, a synoptic view was taken of a receptor system as opposed to a receptor in isolation.

[AR a ] = [R a G] = [R i ] =

[R a ] =

Samama and colleagues (1993) introduced a formal modification of the ternary complex model to account for certain experimental observations that could not otherwise be accommodated. Specifically, it had been shown that receptors could spontaneously become active and could in turn activate G proteins in the absence of agonists. The extended ternary complex model is the melding of the twostate model and the original ternary complex model, and is shown schematically in Equation 1.2.38,

A + Ri

L A + Ra +

Ka

γKa

ARi

+

G

A + RaG

γφL[G ]K g [ A ]K a

[AR a G]

γφ[G ]K g [ A ]K a

[AR a G]

γφL[G ]K g

ρ= L [ G ] K G ( γφ [ A ] K A + 1)

([ A ]

K A )[1 + γL(1 + φ [ G ] K G )] + [1 + L(1 + [ G ] K G )]

Equation 1.2.40

φKg

γφKa

γφ[ A ]K a

The conservation equation for receptors is [Rt] = [ARaG] + [ARa] + [ARi] + [Ra] + [Ri] + [RaG]. The relationship between agonist concentration and the response-producing species (ρ) can be described as ρ = ([ARaG] + [RaG])/[Rt]. This is rewritten in Equation 1.2.40, using the equilibrium and conservation equations, and converting equilibrium association constants to equilibrium dissociation constants.

G

Kg

[AR a G]

Equation 1.2.39

γL ARa

φ[G ]K g

[AR a G]

[AR i ] =

Extended Ternary Complex Model

[AR a G]

ARaG

Equation 1.2.38

where φ represents the effect of agonist binding on the interaction of Ra and G. In this model, receptors exist spontaneously in either the active (Ra) or inactive (Ri) form, ligands interact with either form, and G proteins interact with the active form whether or not it is occupied by ligand. A summary of relevant terms is given in Table 1.2.4. The equilibrium equations for this model are shown in Equation 1.2.39.

The location parameter of dose-response curves is given in Equation 1.2.41. ED 50 KA

=

1 + L(1 + [G ] K G ) 1 + γL(1 + φ [G ] K G )

Equation 1.2.41

This equation reveals that potency is dependent upon the efficacy (γ), the ability of the receptors to spontaneously become active (L), and the availability of G protein in the system. The maximal response of the agonists is shown in Equation 1.2.42. maximal response =

γφL [G ] K G 1 + γL(1 + φ [G ] K G )

Equation 1.2.42

This equation illustrates that the maximal response to an agonist depends upon the same

Receptor Binding

1.2.17 Current Protocols in Pharmacology

Table 1.2.4

Term

Terms Used in the Extended Ternary Complex Model

Description

Properties of drug or receptor Equilibrium dissociation constant of the agonist-receptor complex (KA = 1/Ka; in mol/liter) KA KB γ β φ

Equilibrium dissociation constant of the antagonist-receptor or inverse agonist-receptor complex (KB = 1/Kb; in mol/liter) Effect of receptor activation on the affinity of the agonist for the receptor; alternatively, the effect of agonist binding on receptor activation Effect of receptor activation on the affinity of the antagonist for the receptor; alternatively, the effect of antagonist binding on receptor activation Effect of ligand binding on the interaction of activated receptor and G protein

Properties of receptor system [Rt] [G]

Concentration of total receptors in the system Concentration of G protein in the system

KG L

Equilibrium dissociation constant of the receptor–G protein complex (KG = 1/Kg; in mol/liter) Allosteric factor defined as the ratio of active versus inactive receptors (L= [Ra]/[Ri]). High values of L indicate a spontaneously activated system in the absence of agonist.

factors as the potency (Equation 1.2.41), and also upon the effect of agonist binding on the interaction of receptor and G protein (φ). The basal response (in the absence of agonist) is given in Equation 1.2.43. basal response =

L [G ] K G

1 + L(1 + [G ] K G )

Equation 1.2.43

This equation is similar to Equation 1.2.36 (basal response according to the simple ternary complex model), except that here the spontaneous activation of the receptors (L) plays a role in the basal response. If L is low, little spontaneous response will be produced even in the presence of a high concentration of G protein, because the receptors remain in the inactive state. The observed potency of a competitive antagonist (pA2) is described as



pA 2 = − log  K B



1 + L(1 + [G ] K G )   1 + βL(1 + φ [G ] K G ) 

Equation 1.2.44

Receptor Theory

where β refers to the difference in affinity of the antagonist for the active versus the inactive receptor, and φ is the difference in affinity of the antagonist for the G protein–coupled versus uncoupled receptor. If the antagonist is devoid of efficacy and does not detect receptor activa-

tion or G protein association, then β = φ = 1 and the observed pA2 is immutable with respect to the G protein content and activation state of the system. Interestingly, it can be shown that Equation 1.2.44 also corresponds to the concentration of inverse agonist that produces a 50% reduction in constitutive receptor activity. Thus, if the ligand is an inverse agonist, the concentration that decreases the basal receptor activity by 50% equals the concentration that produces a two-fold rightward shift of a doseresponse curve produced by a positive agonist. Ostensibly, the extended ternary complex model appears to be a formal joining of the two-state and the ternary complex models. However, the insertion of the factor φ formally indicates that there are thermodynamic differences between the unbound receptor (activated and inactivated), the ligand-bound receptor, and the G protein–bound receptor. Thus, this no longer qualifies as a two-state receptor system. Although the extended ternary complex model describes a great deal of receptor pharmacology, it is incomplete from a thermodynamic standpoint. In statistical terms, there must be a thermodynamic energy pathway between all species in the system. It should be stressed that all of these interactions need not take place to an appreciable extent at equilibrium, but the mechanism for their formation must exist.

1.2.18 Current Protocols in Pharmacology

A

B A

A

R

R G

C

D A

Ri

A

Ra

G

Ri

Ra

G

Figure 1.2.10 The evolution of receptor theories for seven-transmembrane-domain receptors. (A) Occupancy theory states that the agonist binds to and activates the receptor. (B) The ternary complex model builds on this model by adding that the activated receptor mediates its response through the activation of a G protein. (C) In the extended ternary complex model, the receptor can be activated spontaneously or by an agonist; the activated receptor still mediates a response through activation of a G protein. (D) In the cubic ternary complex model, both the active and the inactive forms of the receptor can interact with G proteins, albeit with varying efficiency.

The conceptual steps that lead from the early formulations of Clark’s occupancy theory, through the ternary complex model, and the extended ternary complex model are shown in Figure 1.2.10. The thermodynamically complete version is also shown (Fig. 1.2.10D); the principle of microscopic reversibility dictates that the G protein be able to interact with both the active and inactive forms of the receptor, and is upheld in this model. The thermodynamic arguments for this requirement apply to all tripartite systems and can be discussed in such terms.

Tripartite Multistate Receptor Systems A tripartite receptor system refers to a condition whereby three elements combine to form a reaction. An obvious tripartite system is the ternary complex of an agonist, receptor, and G protein, but any three elements qualify. When the condition of two states is added to the system, the thermodynamics of the interactions are best described by a cube. For example, noncompetitive allotopic antagonism of an agonist in a two-state receptor can be drawn as a sandwich diagram, as in Figure 1.2.11A.

However, the principle of microscopic reversibility dictates that receptor states be able to interconvert when ligands are bound, although the energetics of doing so may be unfavorable. Therefore, a cubic scheme is a more accurate way to represent the tripartite two-state model of two ligands interacting on one receptor, as shown in Figure 1.2.11B. The same argument can be made for the ternary complex model in which the receptor exists in two conformational states. The thermodynamically complete model is given in Figure 1.2.11C. Note that the G protein can interact with both the active and inactive states of the receptor, in both the presence or absence of agonist. It should be stressed that the accumulation of [RiG] is not a prerequisite for this model; however, an energy pathway through this species is required (Wyman, 1975). This type of model, while statistically complete, is considerably more complex because the effect of each element on the interaction of the other two elements must be accounted for (Weiss et al., 1996a,b). This is accomplished by defining a factor, as was done for the preceding models, that defines the modification of interaction of

Receptor Binding

1.2.19 Current Protocols in Pharmacology

A

B BRa

BRa

ABRa

BRi

ABRi

BRi

C

δγφL

ARiG

γL

ηKa

δηγKa ARa

ARi RiG γKa

Kg

Ri

ARaG

δηφKg

ηKg

Ka

ABRi

ARi

Ri

ARi

Ri

ARa

Ra

ARa

Ra

ABRa

L

φL

RaG φKg

Ra

Figure 1.2.11 Tripartite receptor systems. (A) Schematic for the interaction of two allotopic ligands binding to separate sites on the receptor. The receptor can exist in two conformations (Ra and Ri); thus, the ligands have two separate targets with which to interact. This scheme is derived from Equation 1.2.15; the top layer represents the active receptor and the bottom layer represents the inactive receptor. (B) Due to the principle of microscopic reversibility, there must be an energy-driven pathway through all possible species in the system. Thus, although some transformations may not be energetically favorable, a cube structure is required to account for all possible interconversions within the system. (C) The cubic ternary complex model of a ligand interacting with a receptor and a G protein.

Receptor Theory

two components that is brought on by a third component. Thus, γ defines the effect of receptor activation on ligand binding in the two-state model and the ternary and extended ternary complex models. A factor φ accounts for the effect of receptor activation on G protein binding. Another factor, η, is added to account for possible effects of ligand binding on the interaction of receptor and G protein. Realistically, many of these factors may be insignificant, in which case they would equal unity. A summary of these various factors and terms for the cubic ternary complex model is given in Table 1.2.5. In measuring drug effects and affinities in such complex systems, the interactive nature of the various species must be considered. Thus, unless a ligand views each of the receptor species as being equivalent, it will bind to them selectively and, in so doing, will alter their relative proportions (according to the ideas dis-

cussed previously; see Two-State Theory). If the various effects of activation, G protein binding, and ligand binding are interactive, then a redistribution of receptor species will result, and the observed effects of ligands may be deceptively simple. For example, if a ligand selects various receptor species in a receptor system, then its observed affinity will be dependent upon not only the nature of the receptor, but also the system’s parameters, such as the level of spontaneous receptor activation and the G protein content. The observed affinity of a ligand in a cubic ternary complex model receptor system is described by Equation 1.2.45 (Weiss et al., 1996a).

K Aobs KA

=

1 + L + [G ] K G (1 + φL )

1 + γL + η[G ] K G (1 + δγφL ) Equation 1.2.45

1.2.20 Current Protocols in Pharmacology

Table 1.2.5

Term

Terms Used for a Multistate Tripartite Receptor System (the Cubic Ternary Complex Model)

Description

Properties of drug or receptor Equilibrium dissociation constant of the ligand-receptor complex (KA = 1/Ka; in mol/liter) KA KB γ η δ

Equilibrium dissociation constant of an antagonist-receptor or inverse agonist-receptor complex (KB = 1/Kb; in mol/liter) The effect of receptor activation on the affinity of the ligand for the receptor, and the effect of ligand binding on receptor activation The effect of ligand binding on the interaction of the activated receptor and G protein Factor describing possible synergy between the binding of ligand, receptor activation, and G protein binding

Properties of receptor system [Rt] [G]

Concentration of total receptors in the system Concentration of G protein in the system

KG

Equilibrium dissociation constant between the inactivated receptor and G protein. (Assumed to be very small to insignificant in the extended ternary complex model; KG = 1/Kg.) The effect of receptor activation on the affinity of the receptor for the G protein. Alternatively, the effect of G protein binding on receptor activation. Allosteric factor defined as the ratio of active to inactive receptors (L = [Ra]/[Ri]). High values of L indicate a very spontaneously activated system in the absence of agonist.

φ L

KAobs (the observed KA) will equal KA only if γ = η = φ = δ = 1. For this to occur, the ligand must recognize all of the receptor species (Ri, Ra, RiG, and RaG) with equal affinity—i.e., the ligand must have no efficacy. Considering that drugs have specific affinities for receptor proteins and that these proteins change conformations under physiological conditions, drug-receptor parameters measured in tripartite multistate systems may not be immutable in terms of the experimental conditions of the system. It is interesting to note that all previous models of seven-transmembrane-domain receptor function are subsumed by the cubic ternary complex model. Various schemes described within receptor theory are shown in Figure 1.2.12, with accompanying references. The portions of the cubic receptor system in which these models reside is shown in Figure 1.2.13.

Operational Model The operational model is an alternative and innovative model of drug-receptor interaction that was developed by Black and Leff (1983). It makes no assumptions about the nature of efficacy and the relationship between stimulus and response. This theory is based on the experimental finding that the relationship between agonist concentration and tissue re-

sponse is hyperbolic in nature. It can be shown mathematically that the product of successive hyperbolae is a single hyperbola. Therefore, the relationship between agonist concentration ([A]) and tissue response (Ea) can be expressed as in Equation 1.2.46, Ea =

[ A ]E m [A] + c

Equation 1.2.46

where Em is the maximal tissue response and c is a fitting parameter that comprises both agonist-specific elements (efficacy) and tissuespecific components (efficiency of transduction from initial receptor stimulation to tissue response). From this, [A] can be calculated as shown in Equation 1.2.47.

[A] =

Ea c Em − Ea

Equation 1.2.47

The equation for receptor occupancy derived within occupancy theory can be used to formulate an equation relating agonist concentration ([A]) and the concentration of the agonist-reReceptor Binding

1.2.21 Current Protocols in Pharmacology

Classical

A+R

AR

Simple ternary complex

A+R

AR + G

ARG Ternary complex

Simple two-state

R+A + G

AR + G

RG + A

ARG

R+A

AR

AR* Full two-state

Extended ternary complex

R+A

AR

R* + A

AR*

R+A

AR

R* + A + G

AR* + G

R*G + A

AR*G

Figure 1.2.12 Models of drug receptor systems. References: classical model, Langley (1878), Hill (1909), Clark (1933), Ariens (1954), Stephenson (1956), Gaddum (1957); simple ternary complex, MacKay (1987, 1990), Mayo et al. (1989), Ross (1989), Birnbaumer (1990), Bourne et al. (1990); ternary complex, Jacobs and Cuatrecasas (1976), Boeynaems and Dumont (1977), DeLean et al. (1980), Wregget and DeLean (1984), Ehlert (1985), Lee et al. (1986), Neubig et al. (1988), Minton and Sokolovsky (1990), Costa et al. (1992); simple two-state, Del Castillo and Katz (1957), Katz and Thesleff (1957), Changeux et al. (1967), Karlin (1967), Thron (1973); full two-state, Karlin (1967), Podleski and Changeux (1970), Colquhoun (1973), Ross et al. (1977), Heidenreich et al. (1980), Iyengar et al. (1980), Birnbaumer et al. (1990); extended ternary complex, Lefkowitz et al. (1993); Samama et al. (1993).

Receptor Theory

1.2.22 Current Protocols in Pharmacology

ARaG

ARiG ARi

ARiG ARi

ARa RiG

Ri

Ri

simple two-state ARiG ARi

ARaG ARa

RiG Ri

ARi RiG

Ra

Ri

classical

ARi

Ra

Ri

ARiG ARi

ARa

Ra

ARaG ARa

RiG

RaG Ri

ternary complex

simple ternary complex 2

ARi

ARaG

RiG

RaG

ARiG

Ra

ARiG

ARa

RaG

RaG

full two-state

ARaG

ARiG

ARa RiG

RaG Ra

ARaG

ARi

extended ternary complex

ARa RiG

RaG Ra

ARaG

ARiG

Ri

RaG Ra

cubic ternary complex

ARaG ARa

RiG Ri

RaG Ra

simple ternary complex 1

Figure 1.2.13 A range of receptor theories as subsets of the cubic ternary complex model. The black circles indicate the relevant components of each model; the shaded areas represent the sphere of interaction of these components.

ceptor complex ([AR]), as shown in Equation 1.2.48.

[A] =

[ A ]K A [R t ] − [AR]

Equation 1.2.48

and tissue response ensues. This agrees with experimental data and thus provides a reasonable assumption for the theoretical framework of this theory. Under these circumstances, [AR] is related to tissue response by the following hyperbolic function. Ea

Combining Equations 1.2.47 and 1.2.48 gives the following equation for tissue response under the operational model. Ea =

[AR]K A Em

[AR]( K A − c) + [ R t ]c Equation 1.2.49

Several mathematical possibilities for this equation should be noted. If KA < c, then tissue response becomes infinite and the equation has no equivalent in physiology. If KA = c, then Equation 1.2.49 results in a linear relationship between receptor occupancy and tissue response. In practice, this is an exceedingly rare occurrence. However, if KA > c, then a hyperbolic relationship between receptor occupancy

Em

=

[AR] [AR] + K E

Equation 1.2.50

KE describes the efficiency of the interaction between the activated receptor and the stimulus-response machinery of the tissue (in the process of producing a response). In this sense, the term KE is thought of as the location parameter (analogous to β in Equation 1.2.9) of the hyperbolic function relating receptor occupancy by the agonist to the tissue response. Thus, KE has properties that are unique both to the tissue (where low values of KE indicate efficient receptor-effector coupling) and also to the agonist (where low values of KE indicate high agonist efficacy). Receptor Binding

1.2.23 Current Protocols in Pharmacology

Table 1.2.6

Term

Terms Used in the Operational Model of Drug Response

Description

Properties of drug or receptor Equilibrium dissociation constant (KA = 1/Ka; in mol/liter); also defined as the concentration KA producing half-maximal agonist-receptor occupancy Properties of receptor system [Rt] Concentration of total receptors in the system Em

The maximal response of the tissue. This may or may not be the same as the maximal response produced by the agonist.

Properties of drug and receptor system Efficiency of response produced upon receptor occupancy. One component is a molecular KE property of the agonist (i.e., efficacy); another component is a property of the system (i.e., the efficiency of the system to generate a response from receptor activation). τ

Defined as [Rt]/KE

The relationship between agonist concentration and tissue response (expressed as a fraction of the maximal tissue response) can then be expressed as in Equation 1.2.51. Ea Em

=

[A][ R t ]

[A]([ R t ] + K E ) + K A K E Equation 1.2.51

Black and Leff (1983) then defined a constant (τ = [Rt] / KE) to quantify both the efficacy of the agonist and the ability of the tissue to translate agonist stimulation of the receptors into tissue response. Although KE superficially resembles Stephenson’s efficacy term e, which also has tissue- and agonist-specific properties, there is a fundamental difference in these constants. Whereas Stephenson introduced an artificial proportionality factor into the Langmuir adsorption isotherm to account for efficacy, the operational model has no such ad hoc constant for efficacy. Thus, the main equation within operational theory for the production of agonist response is given as: Ea Em

[A]τ = A τ ( [ ] + 1) + K A

Equation 1.2.52

The constants used in operational theory are given in Table 1.2.6. In Figure 1.2.14, the relationship between [A] and effect, [AR] and effect, and [A] and AR-complex formation are shown as described by the operational model. Receptor Theory

The observed potency of agonists is given by Equation 1.2.53. ED 50 KA

=

1 1+ τ

Equation 1.2.53

According to operational theory, the ED50 of an agonist in any tissue depends upon the affinity of the agonist for the receptor, receptor density, the efficiency of transduction of the stimulus by the tissue (a component of KE), and the efficacy of the agonist, which can be thought of as the drug-related property that causes the receptor to interact with the stimulus-response mechanism to initiate a response (i.e., a drug component of KE). The maximal response to an agonist is described in Equation 1.2.54. maximal response Em

=

τ 1+ τ

Equation 1.2.54

As with other treatments of the maximal response to agonists, the maximal response under operational theory depends upon the intrinsic efficacy of the agonist (contained within the drug-specific elements of KE) and the ability of the system to translate receptor stimulation into tissue response (i.e., the magnitude of [Rt] and the tissue-specific elements of KE). Operational theory is extremely useful for comparing the activity of agonists and antagonists by functional methods. Since its introduction in 1983, it has been increasingly utilized

1.2.24 Current Protocols in Pharmacology

Em

E

[

] AR

ED50 KE

log [A]

Figure 1.2.14 The operational model of drug action. The right plot shows the relationship between response (E) and agonist concentration (standard dose-response curve). The left plot shows the response as a function of receptor occupancy. The bottom plot shows the relationship between drug concentration and receptor occupancy.

in the characterization of drug-receptor activity in both isolated tissues and cellular systems.

SUMMARY The equations and models that comprise what is known as “drug-receptor theory” are an amalgam of different ways to represent systems where molecules repeatedly produce changes in proteins without being changed themselves. Due to the lack of precise knowledge about drug-receptor mechanisms, null procedures have been developed that measure drug activity both operationally (i.e., relative efficacy) and chemically (measurements of affinity). The use of recombinant receptor systems has begun to furnish data that more specifically guide refinements of drug-receptor models. Perhaps the most valuable contribution of drug-receptor theory to pharmacology is that it allows doseresponse phenomenology to be considered in molecular terms.

LITERATURE CITED Ariens, E.J. 1954. Affinity and intrinsic activity in the theory of competitive inhibition. Arch. Int. Pharmacodyn. Ther. 99:32-49. Ariens, E.J. 1964. Molecular Pharmacology: The Mode of Action of Biologically Active Compounds. Academic Press, San Diego. Birnbaumer, L.G. 1990. G proteins in signal transduction. Annu. Rev. Pharmacol. Toxicol. 30:675-705. Birnbaumer, L.G., Yatani, A., VanDongen, A.M.J., Graf, R., Codina, J., Odabe, K., Mattera, R., and Brown, A.M. 1990. G protein coupling of receptors to ionic channels and other effector systems. In G-Proteins and Signal Transduction (N.M. Nathanson and T.K. Harden, eds.) pp. 169-183. Rockefeller University Press, New York. Black, J.W. and Leff, P. 1983. Operational models of pharmacological agonist. Proc. R. Soc. London B Biol. Sci. 220:141-162. Boeynaems, J.M. and Dumont, J.E. 1977. The twostep model of ligand-receptor interaction. Mol. Cell. Endocrinol. 7:33-47. Bourne, H.R., Sanders, D.A., and McCormick, F. 1990. A conserved switch for diverse cell functions. Nature 348:125-132. Receptor Binding

1.2.25 Current Protocols in Pharmacology

Changeux, J.-P., Thiery, J., Tung, Y., and Kittel, C. 1967. On the cooperativity of biological membranes. Proc. Natl. Acad. Sci. U.S.A. 57:335-341. Clark, A.J. 1933. The Mode of Action of Drugs on Cells. Edward Arnold, London. Clark, A.J. 1937. General pharmacology. In Heffner’s Handbuch der Experimentaellen Pharmacokogie, Erganzungswerk Band 4. SpringerVerlag, Berlin. Colquhoun, D. 1973. The relationship between classical and cooperative models for drug action. In Symposium on Drug Receptors (H.P. Rang, ed.) pp. 149-182. University Park Press, Baltimore. Costa, T., Ogino, Y., Munson, P.J., Onaran, H.O., and Rodbard, D. 1992. Drug efficacy at guanine nucleotide-binding regulatory protein-linked receptors: Thermodynamic interpretation of negative antagonism and of receptor activity in the absence of ligand. Mol. Pharmacol. 41:549-560.

Heidenreich, K.A., Weiland, G.A., and Molinoff, P.B. 1980. Characterization of radiolabeled agonist binding to β-adrenergic receptors in mammalian tissues. J. Cyclic Nucleotide Res. 6:217-230. Hill, A.J. 1909. The mode of action of nicotine and curare, determined by the form of the contraction curve and the method of temperature coefficients. J. Physiol. (Lond.) 39:361-373. Iyengar, R., Abramowitz, J., Bordelon-Riser, M., and Birnbaumer, L. 1980. Hormone receptormediated stimulation of adenylyl cyclase systems. Nucleotide effects and analysis in terms of a simple two-state model for the basic receptoraffected enzyme. J. Biol. Chem. 255:3558-3564. Jacobs, S. and Cuatrecasas, P. 1976. The mobile receptor hypothesis and “cooperativity” of hormone binding: Applications to insulin. Biochim. Biophys. Acta 433:482-495.

Cuatrecasas, P. 1974. Membrane receptors. Annu. Rev. Biochem. 43:169-214.

Karlin, A. 1967. On the application of a “plausible model” of allosteric proteins to the receptor for acetylcholine. J. Theor. Biol. 16:306-320.

Del Castillo, J. and Katz, B. 1957. Interaction at end-plate receptors between different choline derivatives. Proc. R. Soc. London B Biol. Sci. 146:369-381.

Katz, B. and Thesleff, S. 1957. A study of the ‘desensitization’ produced by acetylcholine at the motor end-plate. J. Physiol. (Lond.) 138:6380.

DeLean, A., Stadel, J.M., and Lefkowitz, R.J. 1980. A ternary complex model explains the agonistspecific binding properties of adenylate cyclase coupled β-adrenergic receptor. J. Biol. Chem. 255:7108-7117.

Kenakin, T.P. and Beek, D. 1980. Is prenalterol (HI33/80) really a selective β-1 adrenoceptor agonist? Tissue selectivity resulting from differences in stimulus-response relationships. J. Pharmacol. Exp. Ther. 213:406-412.

Ehlert, R.J. 1985. The relationship between muscarinic receptor occupancy and adenylate cyclase inhibition in the rabbit myocardium. Mol. Pharmacol. 28:410-421.

Langley, J.N. 1878. On the physiology of salivary secretion. J. Physiol. (Lond.) 1:339-369.

Ehrlich, P. 1909. Veber den jetzigen Stand der Chemotherapie. Berl. Dtsch. Chem. Ges. 42:1747. Furchgott, R.F. 1966. The use of β-haloalkylamines in the differentiation of receptors and in the determination of dissociation constants of receptor-agonist complexes. In Advances in Drug Research, Vol. 3 (N.J. Harper and A.B. Simmonds, eds.) pp. 21-55. Academic Press, San Diego. Furchgott, R.F. 1972. The classification of adrenoreceptors (adrenergic receptors). An evaluation from the standpoint of receptor theory. In Handbook of Experimental Pharmacology, Catecholamines, Vol. 33 (H. Blaschko and E. Muscholl, eds.) pp. 283-335. Springer-Verlag, Heidelberg. Gaddum, J.H. 1937. The quantitative effects of antagonistic drugs. J. Physiol. (Lond.) 89:7P-9P. Gaddum, J.H. 1957. Theories of drug antagonism. Pharmacol. Rev. 9:211-218. Gaddum, J.H., Hameed, K.A., Hathway, D.E., and Stephens, F.F. 1955. Quantitative studies of antagonists for 5-hydroxytryptamine. Q. J. Exp. Physiol. 40:49-74.

Lee, T.W.T., Sole, M.J., and Wells, J.W. 1986. Assessment of a ternary model for the binding of agonists to neurohumoral receptors. Biochemistry 25:7009-7020. Lefkowitz, R.J., Cotecchia, S., Samama, P., and Costa, T. 1993. Constitutive activity of receptors coupled to guanine nucleotide regulatory proteins. Trends Pharmacol. Sci. 14:303-307. MacKay, D. 1987. Use of null equations, based on classical receptor and ternary models of drug action, to classify receptors and receptor-effector systems. In Perspectives on Receptor Classification (J.W. Black, D.H. Jenkinson, and V.P. Gerskowitch, eds.) pp. 193-206. Alan R. Liss, New York. MacKay, D. 1990. Agonist potency and apparent affinity: Interpretation using classical and steady-state ternary-complex models. Trends Pharmacol. Sci. 11:17-22. Mayo, K.H., Nunez, M., Burke, C., Starbuck, C., Lauffenberger, D., and Savage, C.R. Jr. 1989. Epidermal growth factor receptor binding is not a simple one-step process. J. Biol. Chem. 264:17838-17844. Minton, A.P. and Sokolovsky, M. 1990. A model for the interaction of muscarinic receptors, agonists, and two distinct effector substances. Biochemistry 29:1586-1593.

Receptor Theory

1.2.26 Current Protocols in Pharmacology

Neubig, R.R., Gantzoz, R.D., and Thomsen, W.J. 1988. Mechanism of agonist and antagonist binding to α2-andrenergic receptors: Evidence for a precoupled receptor guanine nucleotide protein complex. Biochemistry 27:2374-2384.

Stephenson, R.P. 1956. A modification of receptor theory. Br. J. Pharmacol. 11:379-393.

Nickerson, M. 1956. Receptor occupancy and tissue response. Nature 178:697-698.

Weiss, J.M., Morgan, P.H., Lutz, M.W., and Kenakin, T.P. 1996a. The cubic ternary complex receptor occupancy model. I. Model description. J. Theor. Biol. 178:151-167.

Parascandola, J. 1986. The development of receptor theory. In Pharmacological Methods, Receptors & Chemotherapy, Vol. 3. (M.J. Parnham and J. Bruinvels, eds.) pp. 12-158. Elsevier/North Holland, Amsterdam. Podleski, T.R. and Changeux, J.-P. 1970. On the excitability and cooperativity of electroplax membrane. In Fundamental Concepts in DrugReceptor Interaction (J.F. Danielli, J.F. Moran, and D.J. Triggle, eds.) pp. 93-119. Academic Press, New York. Ross, E.M. 1989. Signal sorting and amplification through G protein-coupled receptors. Neuron 3:141-152. Ross, E.M., MaGuire, M.E., Sturgill, T.W., Biltonen, R.L., and Gilman, A.G. 1977. Relationship between the β-adrenergic receptor and adenylate cyclase. J. Biol. Chem. 252:57615775. Samama, P., Cotecchia, S., Costa, T., and Lefkowitz, R.J. 1993. A mutation-induced activated state of the β2-adrenergic receptor: Extending the ternary complex model. J. Biol. Chem. 268:46254636.

Thron, C.D. 1973. On the analysis of pharmacological experiments in terms of an allosteric receptor model. Mol. Pharmacol. 9:1-9.

Weiss, J.M., Morgan, P.H., Lutz, M.W., and Kenakin, T.P. 1996b.The cubic ternary complex receptor occupancy model. II. Understanding apparent affinity. J. Theor. Biol. 178:169-182. Wregget, K.A. and DeLean, A. 1984. The ternary complex model: Its properties and application to ligand interactions with the D2-dopamine receptor and the anterior pituitary gland. Mol. Pharmacol. 26:214-227. Wyman J. 1975. The turning wheel: A study in steady states. Proc. Natl. Acad. Sci. U.S.A. 72:3983-3987.

Contributed by Terry Kenakin Glaxo Wellcome Research and Development Research Triangle Park, North Carolina

Receptor Binding

1.2.27 Current Protocols in Pharmacology

Practical Aspects of Radioligand Binding

UNIT 1.3

Radioligand binding assays have greatly facilitated the characterization of receptors and the ligands (substrates) that interact with them. In this context, the term “receptor” is used to describe any protein of biological interest that interacts with a ligand that can be (1) radiolabeled or (2) engineered as part of a reporter system. Radioligand binding can be used to: (1) characterize receptors in their natural environment as well as those transfected into cell lines; (2) study receptor dynamics and localization; (3) identify novel chemical structures that interact with receptors; and (4) define ligand activity and selectivity in normal and diseased tissues. This unit reviews procedures for developing a binding assay. A number of other articles describing practical and theoretical aspects of ligand binding are available (UNIT 1.1; Kenakin, 1993; Limbird, 1996; Motulsky and Neubig, 1997; Williams et al., 2005). For many binding assays, a suitable radioligand and a crude homogenate of a tissue known to contain the receptor are required. The homogenate and the radioligand are mixed, and at an appropriate time (empirically determined), the unbound radioligand (L*, or free) is rapidly separated from the ligand bound to the receptor (L*R, or bound), usually by filtration. Alternatively, technologies that do not require a separation step, such as homogeneous binding assays, can be used. In this case the signal measured is generated by bound ligand (L*R) with little interference from free ligand (L*) (Nelson, 1987). Tissue sources for radioligand binding include tissue slices, subcellular fractions, or intact cellular preparations including native, immortalized, or transfected cells. Ligands can also be labeled with fluorescent probes, such as fluorescein, for fluorescence detection. As fluorescence detection is influenced by the environment of the fluorophore, such ligands are useful for probing receptor conformational changes as well as for direct ligand binding assays (Zuck et al., 1999). This approach also makes it possible for fluorescence polarization (FP) to be used for detecting ligand-receptor interactions. While radioactive labeling produces a more sensitive binding assay and is less likely to interfere with function of the ligand due to the relatively small changes in the chemical structure of the molecule, nonradioactive labels are less costly and, because they are less hazardous, are more useful for use in high throughput (HTS; UNIT 9.4) binding assays. Detection methods, depending on the type of label, nature of the labeled ligand, the principles for calculating results, and interpreting the findings, are the same for all ligand-binding assays. Historically, the existence of receptors, or specific ligand-binding sites, was inferred from pharmacological data (UNIT 1.1). The biochemical demonstration that these lowabundance proteins actually existed required the development of radioligand binding assays and/or their cloning. Until the advent of molecular cloning techniques, knowledge of receptor structure, function, and pharmacology was dependent on classical tissue and whole-animal studies and radioligand binding. In addition to their use in HTS assays, radioligand binding assays can also be used to examine new chemical entities (NCEs) in multiple (80 to 140) assays to assess their selectivity for other receptors, enzymes, and signal-transduction targets (Ator and Williams, 2005). This approach is a valuable starting point for assessing functional and in vivo activity, and can provide information on potential side effect liabilities for a drug candidate. Radioligand binding can be used in combination with autoradiography to visualize receptors in situ. Thin microtome-generated tissue sections can be labeled and juxtaposed to X-ray film to produce photographic images of the radioligand bound to the receptor. Contributed by Michael McKinney and Rita Raddatz Current Protocols in Pharmacology (2006) 1.3.1-1.3.42 C 2006 by John Wiley & Sons, Inc. Copyright


Receptor Binding

1.3.1 Supplement 33

Ligands labeled with short-lived isotopes, e.g., 11 F, 13 C, or 99 Tc, can be used in vivo using positron emission tomography (PET) to visualize ligand bound to receptor in living tissue. This technique is used to measure receptor dynamics in various disease states, especially in the central nervous system (CNS), to study drug distribution, and to measure receptor occupancy in real time. Such information makes it possible to titrate the amount of drug administered to individual patients, helping to minimize side effects (Meyer et al., 2004). The criteria established for validating a binding assay are as follows (Cuatrecasas and Hollenberg, 1976): 1. Specific binding must be saturable, indicating a finite number of receptor sites, although in some instances, nonspecific binding can appear saturable as well (see Binding Specificity for a discussion of nonspecific binding behavior). 2. The binding affinity, defined as the dissociation constant (Kd ), should be consistent with physiological values established for the receptors (e.g., 100 pM to 10 nM). 3. Binding should be reversible, consistent with a physiological mechanism for terminating the effect of a ligand at the receptor. 4. The tissue and subcellular distribution of the specific binding should be consistent with what is known about the proposed physiological effects of the endogenous ligand, and with what is known about the localization of the receptor. 5. The substrate selectivity of binding for both agonists and antagonists should be consistent with the pharmacology of the natural ligand in functional and whole-animal tests. Conversely, ligands known to be inactive at the targeted receptor should not affect radioligand binding. 6. There should be a correlation between the binding and concentration-response data in identical tissue preparations. 7. Activity in a binding assay should be predictive of activity in an established animal model of receptor function. In general, items 1 through 5 are part of the process of characterizing a binding assay, whereas items 6 and 7 address, more specifically, properties of compounds tested in the assay. Radioligand binding assays only measure the affinity and density of a ligand binding site. The efficacy, pharmacodynamic, and pharmacokinetic properties of the ligand are not revealed in a binding assay, but rather, must be assessed using functional in vivo and in vitro analyses. For 7-TM receptors, it is possible to predict whether a ligand is an agonist or an antagonist (UNITS 1.1 & 1.2) by conducting a GTP shift experiment (Childers and Snyder, 1980) by using a reporter (UNIT 6.2) or intact tissue system (see Chapter 4).

FUNDAMENTALS OF RADIOLIGAND BINDING ASSAYS The binding of a radioligand to a receptor is analogous to a bimolecular reaction according to the Law of Mass Action. That is, the radioligand (L*) combines with the receptor (R) to form a complex (L*R).

Practical Aspects of Radioligand Binding

Equation 1.3.1

1.3.2 Supplement 33

Current Protocols in Pharmacology

The rate of the forward reaction (left to right) is determined by the concentrations of L* and R, and by the forward rate constant (k+1 ), as follows:

Equation 1.3.2

The constant k+1 has units of (time−1 × concentration−1 ). Generally, this reaction is reversible, with the L*R complex dissociating to reform L* and R. The rate of the reverse reaction is dependent on the amount of L*R and the magnitude of the reverse rate constant (k−1 ). The constant k−1 is expressed in units of time−1 .

Equation 1.3.3

At equilibrium, the forward and reverse reactions are equal in rate, meaning the amounts of L*, R, and L*R remain constant. Like a bimolecular chemical reaction, the ratio of the rate constants in a radioligand binding reaction is equal to the thermodynamic equilibrium binding constant (Kd ).

Equation 1.3.4

The Kd is expressed in molar units of concentration (e.g., nanomolar or picomolar). The binding affinity of a receptor for a ligand is a molecular consequence of its structure, with the Kd used to identify and classify receptors based on this affinity. Therefore, the determination of Kd is a primary goal in developing a binding assay once the optimal conditions for specific binding (see Binding Specificity) are established. In the assay, the species measured is the bound ligand (i.e., the L*R complex). The receptor-ligand complex, which is embedded in the plasma membrane, is readily isolated from the aqueous reaction mixture by filtration. By quantifying the radioactivity recovered on the filter, the amount of radioligand bound to the tissue is measured. The radioligand attached to the receptor is considered the specific binding component. Some radioligand will be nonspecifically trapped with the lipid membrane or other constituents of the assay mixture. Nonspecific binding is defined as radioactivity detected in the tissue sample that is not bound to the receptor of interest and is quantified by measuring the amount of radioligand associated with the tissue in the presence of very high concentrations of an unlabeled ligand, whereas specific binding is the radioactivity displaced by saturating concentrations of an unlabeled ligand selective for the receptor being studied. In equilibrium binding assays, unbound and bound ligand are separated from each other after the forward and reverse binding reactions reach equilbrium. In kinetic binding assays, the reaction is interrupted at various times during the formation or dissociation of the L*R complex. The Kd value can be determined with either type of assay. If the binding is bimolecular, the Kd will be similar using the two different approaches. The kinetic binding assay also allows for the determination of the association and dissociation rate constants (k+1 and k−1 , respectively). With a saturation binding experiment, assays are performed using a series of radioligand concentrations, ranging up to a concentration at which virtually all of the receptors are occupied with ligand. An example is shown in Figure 1.3.1, which describes the binding of [3 H]N-methylscopolamine ([3 H]NMS), a muscarinic cholinergic receptor antagonist,

Receptor Binding

1.3.3 Current Protocols in Pharmacology

Supplement 33

Figure 1.3.1 Saturation binding to muscarinic receptors on N1E-115 mouse neuroblastoma cells. Six concentrations of [3 H]N-methylscopolamine ([3 H]NMS), with or without 10 µM unlabeled NMS, were incubated with ∼300,000 intact cells/tube for 45 min at 15◦ C before rapid filtration was performed to separate bound from free. The total binding is the sum of the specific and nonspecific binding. Nonspecific binding is defined as the amount of binding found in the tube containing both the radioligand and unlabeled NMS.

to muscarinic receptors in N1E-115 mouse neuroblastoma cells. The concentration of [3 H]NMS is plotted on the abscissa, with the amount of radioligand bound to the filters at each concentration plotted on the ordinate. Displayed in this figure are total, specific, and nonspecific binding, where total binding is the sum of specific and nonspecific binding components (see Binding Specificity). While a certain amount of nonspecific binding is always present, it should represent only a minor (20) are needed to obtain a curvilinear Scatchard plot that could reliably reveal the second group of binding sites (19% of the total). Direct fitting of the association data shown in Figure 1.3.12 using iterative nonlinear fitting yields a k+1 value of 0.699 nM−1 min−1 . A more accurate determination of binding parameters is possible if the data are directly fitted with a mathematical model rather than being transformed and analyzed on a manual plot, even though, in this instance, the k+1 value determined from the plot (0.56 nM−1 min−1 ) is similar to the computer-derived value (0.699 nM−1 min−1 ).

Figure 1.3.12 Association of [3 H]NMS to muscarinic receptors on N1E-115 cells. The radioligand concentration was 0.56 nM, 300,000 cells/tube were used, and the temperature was 37◦ C. Panel (A) is a plot of the untransformed specific binding measured at various times (t) after starting the incubations. The plot in panel (B) is a logarithmic transform of the ratio of the amount bound at equilibrium (Beq ) to that remaining unbound (Beq – Bt ) at any time (t).

Receptor Binding

1.3.33 Current Protocols in Pharmacology

Supplement 33

Use of Computer Modeling Techniques There are many computer programs for the analysis of binding data. Most spreadsheet or graphical programs (e.g., Lotus, GraphPad PRISM) incorporate iterative-fitting techniques for data. In many instances, the user can formulate the mathematical model. The choice of model and the method of fitting to the data should be defensible on both biological and statistical grounds. The simplest hypothesis should be selected unless there is information to support a more complex model (Kenakin, 1993). For example, the addition of a third binding site to a two-site model may be inappropriate if only two sites are known to actually exist in the tissue. While the addition of parameters to a receptor-ligand binding model always allows a closer fit of the model to the data to produce a lower sum-of-squared residuals, the question then becomes whether the increase in parameters has significantly reduced the variance. To permit statistical discrimination between alternate models, the data must be of sufficient quality and quantity to permit confident assessment of model parameters. This means the variance should be as low as experimentally possible and there should be a sufficient number of data points. A measure of variance is calculated by determining the differences between the data and the fitted curve at each point, and then squaring these differences and summing them. Variance can be minimized by performing replicates of the binding assay and, up to a point, the statistical power may be strengthened by increasing the number of data points in the assay. Receptor models Generally, the receptor models used in computer fitting of equilibrium binding data utilize equations that describe the binding to one site or to multiple, noninteracting sites. The derivation of these models and their interpretation with regard to their appearance in the Scatchard plot can be traced back to Feldman (1972). With the simplest model of multiple, independent sites (two binding sites), the Scatchard plot is concave and the tangents to the extremities of the curve describe two major populations of receptors, high-affinity and low-affinity. Because these tangents are difficult to draw accurately, the Scatchard plot should not be used. Examples of the more commonly encountered equations are shown below with a description of their use. While in all such cases there is only one independent variable ([L*] or [D]) and one dependent variable ([L*R]), two or more parameters must be determined. These parameters are constants in the equations to be solved by the computer using the data sets (pairs of [L*], [L*R] or [D], [L*R]) to iteratively refine parameter estimates until the model equation best fits the data. The parameters are thus adjusted so that the differences between the values of [L*R] actually determined in the experiment and the corresponding values calculated from the model are minimized. Normally, when a computer is used to determine binding parameters, certain constraints are applied, such as allowing only nonnegative values. A common case encountered in binding experiments is the presence of two independent binding sites (usually two different receptor molecules). In a saturation equilibrium binding assay with a radioligand (L*) that distinguishes two sites, the amount of specifically bound radioligand [L*R] is

Practical Aspects of Radioligand Binding

Equation 1.3.33

1.3.34 Supplement 33

Current Protocols in Pharmacology

where B1 and B2 are the capacities of the two sites (i.e., the Bmax values for each receptor), and K1 and K2 are the equilibrium binding dissociation constants for the radioligand at the respective sites. With this equation, the independent variable is [L*], the dependent variable is [L*R], and there are four parameters to be determined (B1 , B2 , K1 , and K2 ). If the radioligand recognizes both sites with the same affinity, then the equation simplifies to one term on the right-hand side, and only two parameters must be determined. Conversely, if more than two independent binding sites are present, additional analogous terms may be added to the two right-hand terms shown in Equation 1.3.33. Moreover, a term describing nonspecific binding can be added ([L*] × KNS ), in which the amount of nonspecific binding is constrained to be linearly dependent on [L*]. With iterative computer-based minimization techniques, the addition of this term is a more accurate way to obtain an estimate of the level of nonspecific binding. When a radioligand (L*) is competitively displaced from two independent sites that bind it with the same Kd and that also bind an unlabeled competitor (D) with differing affinities, the following equation is used:

Equation 1.3.34

where Kd refers to the dissociation constant for the radioligand, usually determined in a separate saturation experiment, and K1 and K2 are the two binding constants for the unlabeled drug D at the two independent binding sites that have respective capacities of B1 and B2 . In this case, the independent variable is [D] and the dependent variable is [L*R]. [L*] is fixed at a known value and this value is inserted into the model before iterative fitting to the data. The Kd for the radioligand is determined in independent experiments by analysis of saturation curves (either with the computer or by Scatchard analysis). The six parameters on the right-hand side of Equation 1.3.34 are thus reduced to four, which can be estimated iteratively by the computer. Constraints are applied during the calculation of the four parameters so that the computer does not attempt to fit the data with negative parameter values. An alternate form of Equation 1.3.34 uses affinity binding constants (the inverse of the dissociation constant):

Equation 1.3.35

in which KA , K1  , and K2  are the affinity constants (expressed in liter/mol) for the radioligand and displacing agent, respectively. Multiplicity of binding sites can occur in a single receptor population if the receptor changes conformation and consequently binds the ligand with a different affinity. Formally, the two binding sites are not independent because they interconvert. An example of this is the ternary complex model (DeLean et al., 1980; Wreggett and DeLean, 1984;

Receptor Binding

1.3.35 Current Protocols in Pharmacology

Supplement 33

UNIT 1.2), which describes a mechanism by which multiple binding sites result from the interaction of the agonist-receptor complex with GTP-binding proteins (Tolkovsky and Levitzki, 1981). Agonist binding to GTP-coupled receptors in situ is complex, with concave Scatchard plots and Hill slopes 10,000

>10,000

>10,000

Sigma-Aldrich

Diphenoxylate

54

310

>1000

Sigma-Aldrich

Diprenorphine

0.27

0.31

0.20

Sigma-Aldrich

13

310

900

Sigma-Aldrich

5 -Guanidinonaltrindole (GNTI)

25

34

0.069

Tocris

GR89696

31

76

0.51

Sigma-Aldrich

Levorphanol

2.3

8.6

7.7

Sigma-Aldrich

Morphine

19

220

230

Sigma-Aldrich

Nalbuphine

6.0

140

50

Sigma-Aldrich

Naloxone

2.3

24

12

Sigma-Aldrich

Naloxone benzoylhydrazone

0.54

4.1

1.0

Sigma-Aldrich

51

7.2

0.042

Sigma-Aldrich

>10,000

>10,000

16

Sigma-Aldrich

>1000

0.47

>1000

Spiradoline

150

>1000

2.3

Sigma-Aldrich

d

0.15

50

75

Janssene

350

0.16

650

Tocris

3228

0.31

>1000

>1000

>1000

4.2

Sigma-Aldrich

720

>1000

12

Sigma-Aldrich

a

Compound

b

Dextrorphan

Fentanyl


Nor-binaltorphimine Salvinorin A

c

SNC-80 Sufentanyl

TAN67 (SB 205607) TIPPψ

f

U-50488H U-69593

Compound source

Tocris

McGill Universitye

a Abbreviations: BUBU, H-Tyr-D-(O-t-Bu)-Gly-Phe-Leu-Thr(O-t-Bu)-OH; BW373U86, (±)-4-((α-R*)-α-((2S*,

5R*)-4-allyl-2,5-dimethyl-1-piperazinyl)-3-hydroxybenzyl)-N,N-diethylbenzamide; CTOP, H-D-Phe-Cys-Tyr-D-TrpOrn-Thr-Pen-Thr-NH2 ; DAMGO, (D-Ala2 , N-Me-Phe4 , glycinol5 )enkephalin; GR89696, methyl 4-[(3,4dichlorophenyl)acetyl]-3-(1-pyrrolidinylmethyl)-1-piperazinecarboxylate fumarate; SNC-80, (+)-4-[(α-R)-α-((2S, 5R)-4-allyl-2,5-dimethyl-1-piperazinyl)-3-methoxybenzyl]-N,N-diethylbenzamide; TAN-67, (4aS*,12aR*)-4a-(3hydroxyphenyl)-2-methyl-1,2,3,4,4a,5,12,12a-octahydropyrido[3,4-b]acridine; U-50488H, trans-3,4-dichloro-Nmethyl-N-[2-(1-pyrrolidinyl)-cyclohexyl]benzeneacetamide; U-69593, (5α,7α,8β)-N-methyl-N-[7-(1-pyrrolidinyl)1oxaspiro[4,5]dec-8-yl]benzeneacetamide. b Data from Gacel et al. (1988). c Data from Roth et al. (2002). d Data from Raynor et al. (1994). e Not commercially available. f Data from Schiller et al. (1993).

Receptor Binding

1.4.37 Current Protocols in Pharmacology

Supplement 29

Table 1.4.11 Affinity Constants (Ki Values) of Reference Agents for the Cloned ORL1 Receptor Determined in the Filtration Assaya

Compound

Ki values (nM)

Buprenorphine

Compound source

840

Sigma-Aldrich

)-dynorphin A DAKLI [(Arg (1-13)-gly-5-amino-pentylamide]

150

Bachem

Dynorphin A (1-10) amide

510

Bachem

Dynorphin A (1-11) amide

120

Bachem

Dynorphin A (1-13) amide

61

Bachem

Dynorphin A (1-17) amide

110

Bachem

J-113397

7.6

Banyu Pharmaceuticalb

JTC-801

64

JT, Inc.b

11,13

Naloxone benzoylhydrazone

1100

Sigma-Aldrich

NNC63-0532

130

Novo Nordiskb

Nociceptin/OFQ

2.3

Bachem

0.16

Bachem

Nociceptin/OFQ (1-13) amide 1

2

[Phe -ψ(CH2 -NH)-Gly ]-nociceptin (1-13) amide

15

Sigma-Aldrich

Ro-64-6198

21

Hoffmann-La Rocheb

a Abbreviations:

J-113397, 1-[(3R,4R)-1-cyclooctylmethyl-3-hydroxymethyl-4-piperidyl]-3-ethyl-1,3-dihydro-2Hbenzimidazol-2-one; JTC-801, [N-(4-amino-2-methylquinolin-6-yl)-2-(4-ethylphenoxymethyl) benzamide HCl; NNC63-0532, (8-naphthalen-1-ylmethyl-4-oxo-1-phenyl-1,3,8-triazaspiro[4.5]dec-3-yl)acetic acid methyl ester; Ro-64-6198, [(1S,3aS)-8-(2,3,3a,4,5,6-hexahydro-1H-phenalen-1-yl)-1-phenyl-1,3,8-triazaspiro[4.5]decan-4-one]. b Not commercially available.

Table 1.4.12 Potencies and Efficacies of Reference Agonists at the Cloned Human µ, δ, and κ Opioid Receptors in Opioid Receptor–Mediated [35 S]GTPγS Binding using FlashPlatesa

Compound

Receptor

Potency (EC50 , nM)

Efficacy (% maximum relative stimulation)

Loperamide

µ

27

100%

BW373U86

δ

0.37

100%

U-50488H

κ

11

100%

Morphine

µ

180

51%

TAN-67 (SB 205607)

δ

6.8

73%

Buprenorphine

κ

6.1

42%

a Abbreviations: BW373U86, (±)-4-((α-R∗ )-α-((2S∗ , 5R∗ )-4-allyl-2,5-dimethyl-1-piperazinyl)-3-hydroxybenzyl)-N,N-

diethylbenzamide; TAN-67, (4aS*,12aR*)-4a-(3-hydroxyphenyl)-2-methyl-1,2,3,4,4a,5,12,12a-octahydropyrido[3,4b]acridine; U-50488H, trans-(±)-3,4-dichloro-N-methyl-N-[2-(1-pyrrolidinyl)cyclohexyl]benzeneacetamide.

Table 1.4.13 Potencies of Reference Antagonists at the Cloned Human µ, δ, and κ Opioid Receptors in Opioid Receptor-Mediated [35 S]GTPγS Binding using FlashPlates

Characterization of Opioid and ORL1 Receptors

Compound

Receptor

Potency (IC50 , nM)

Naloxone

µ

7.3

Naltrindole

δ

5.5

Nor-binaltorphimine

κ

4.2

1.4.38 Supplement 29

Current Protocols in Pharmacology

Time Considerations The amount of time required to perform an opioid receptor binding assay depends on the type of experiment, the time to reach equilibrium for that particular radioligand under the assay conditions employed, and the number of data points tested and analyzed. After assay validation is complete, at least 64 titration curves can usually be completed in 1 day and, for general screening at a single concentration of drug, at least 800 data points can be generated in 1 day when assays are performed using a 96-well format. These estimates do not assume automated liquid handling, but do assume the data are transferred electronically to a data analysis program. Automated liquid handling with an eight-tip system does not necessarily enable more to be accomplished in a day, but does allow the experimenter to perform other tasks, such as analyzing an earlier experiment or preparing reagents, while the routine dilutions and additions are being performed. Systems with independent tip movements also enable the selection, from previously tested plates of compounds, of actives for retesting and titration (“hit-picking”) that is more reliable than manual “hitpicking,” which requires a great deal of effort. Automated liquid handling systems with a 96tip capability increase assay capacity by twoto three-fold and make the limiting factors: addition of scintillation cocktail to the plates, sealing the plates, and counting the plates. A relatively inexpensive method for dispensing scintillation cocktail in a 96-well format is the Corning Transtar-96 adjustable volume pipettor. This system uses disposable cartridges that must be changed every day because components of the cartridge are not resistant to the solvents in the scintillation cocktail. A more robust, and more expensive, alternative is the PXR-96 from Brandel, which is designed to dispense viscous fluids, such as scintillation cocktail, into 96-well plates. Automated plate sealers are also available. Of all of the enhancements to throughput in filtration binding assays, the most valuable is the use of 96-well harvesting and scintillation counting rather than vials. The cost savings in scintillation cocktail (30 to 40 µl rather than 3 to 5 ml) and other supplies pays for the 96-well scintillation counter in less 10,000

>10,000

aAll compounds listed are available from RBI (see SUPPLIERS APPENDIX).

Characterization of GABA Receptors

1.7.4 Current Protocols in Pharmacology

MEASUREMENT OF GABAA RECEPTOR BINDING IN RAT BRAIN MEMBRANES USING [3H]GABA

ALTERNATE PROTOCOL 1

Basic Protocol 1 uses [3H]muscimol to measure binding to the GABAA receptor. While that method has a high degree of specificity, the radioligand can be quite expensive. An alternative presented in this protocol is to use the cheaper, but less specific, [3H]GABA to determine binding. Specificity is increased by treating the membranes with detergent. Incubation with Triton X-100 and multiple resuspensions and centrifugations destroys neuronal GABA uptake sites that may bind [3H]GABA and rids the tissue of endogenous GABA, which competes for binding sites with the radioligand. Detergent treatment is less important when [3H]muscimol is used to label GABAA receptors, since muscimol has a low affinity for the GABA transporter (Krogsgaard-Larsen et al., 1983). Additional Materials (also see Basic Protocol 1) 10% Triton X-100 in Tris citrate buffer (see Basic Protocol 1 for buffer) [3H]GABA (NEN Life Sciences) Tissue solubilizer (e.g., BTS-450, Beckman) Scintillation cocktail compatible with organic solvents 15-ml polypropylene centrifuge tubes Prepare GABAA receptors 1. In 50-ml polypropylene centrifuge tubes, resuspend previously frozen tissue or brain membranes in 50 vol ice-cold Tris citrate buffer using the tissue homogenizer (midpoint setting for ∼30 sec). 2. Centrifuge homogenate 10 min at 50,000 × g, 4°C. 3. Resuspend the resultant pellet in sufficient Tris citrate buffer to yield a concentration of 1 mg protein/ml. Protein concentration may be measured using Bradford, BCA (Pierce), Lowry, or other suitable assay (see APPENDIX 3A) with BSA as reference standard.

4. Add to the tissue suspension sufficient 10% Triton X-100 in Tris citrate buffer to yield a 0.05% (v/v) concentration of detergent in the suspension. 5. Incubate 20 min in a 37°C water bath. 6. Centrifuge the tissue suspension 10 min at 50,000 × g, 4°C. 7. Resuspend and centrifuge the tissue two additional times as in steps 1 and 2. 8. Using the tissue homogenizer, resuspend the pellet in sufficient Tris citrate buffer to yield a final concentration of ∼0.5 mg protein/ml. Measure [3H]GABA binding to GABAA receptors 9a. For competition assays: In separate 15-ml polypropylene tubes on ice, assemble the following components in 1-ml volumes, diluted with Tris citrate buffer: 8.0 nM [3H]GABA (to determine total binding); 8.0 nM [3H]GABA + [200 µM (−)-bicuculline methiodide or 20 µM muscimol] (to determine nondisplaceable binding); 8.0 nM [3H]GABA + various concentrations of unlabeled competitor (test compound). Perform all assays in duplicate or triplicate. Final concentrations of [3H]GABA, bicuculline methiodide, and muscimol in the final 2-ml incubation volume will be 4 nM, 100 ìM, and 10 ìM, respectively. The unlabeled bicuculline methiodide or unlabeled muscimol is used to define nonspecific binding (blank)

Receptor Binding

1.7.5 Current Protocols in Pharmacology

which, when subtracted from total binding (tissue in tubes containing [3H]GABA alone), reveals the amount of radioligand bound to the GABAA receptor.

9b. To generate binding site saturation data by displacement: Prepare 1-ml solutions in tubes as described in step 9a but containing: 8.0 nM [3H]GABA; 8.0 nM [3H]GABA + various concentrations (2.0 to 1000 nM) of unlabeled GABA. As described in Basic Protocol 1 with [3H]muscimol, this assay may be used as a general screen for assessing the affinity of unlabeled compounds for the GABAA receptor binding site.

10. To begin the assays, add 1 ml of the tissue suspension to each of the chilled tubes. Gently vortex each tube to mix the contents. The final tissue concentration in the 2-ml incubation volume will be ∼0.25 mg protein/ml, which is within the tissue linearity range for [3H]GABA binding to GABAA receptors.

11. Incubate 5 min in an ice-water bath (4°C) to achieve binding equilibrium. 12. Terminate the binding reaction by centrifuging the mixture 10 min at 50,000 × g, 4°C. To accurately measure the low-affinity GABA binding site, centrifugation rather than filtration is used to terminate the [3H]GABA binding assay to minimize loss of bound radioligand during the more thorough rinsing procedure associated with filtration. Since, with the Triton wash, the Kd for high-affinity [3H]GABA binding is ∼20 nM or less, the filtration procedure can be used with this radioligand if the higher-affinity site is the primary target.

13. Discard the radioactive supernatant, then rinse the tissue pellets rapidly and superficially three times with 5 ml ice-cold Tris citrate buffer. Caution must be exercised to ensure the tissue pellets, or portions of them, are not dislodged from the bottom of the tubes during the rinse procedure. The buffer should be sprayed against the wall of the tube opposite the tissue so the pellet is not exposed to the full force of the fluid.

14. Gently dry the inside of each tube with tissue paper or cotton swabs to remove any residual rinse buffer, taking care not to touch the pellet. 15. Place 1 ml tissue solubilizer into each tube, ensuring that the pellet is submerged. 16. Allow tissue to dissolve in solubilizer at room temperature, or by incubating the tubes in a 37°C water bath. 17. Once the tissue is dissolved, add 4 ml organic solvent–compatible scintillation cocktail. The tissue solubilizer contains toluene.

18. Transfer the contents of each tube into individual scintillation vials, then quantify radioactivity using liquid scintillation spectrometry. 19. Perform data analysis. UNIT 1.3 provides details on plotting and analyzing concentration-response curves. Sample

results obtained for [3H]GABA binding to GABAA receptors in rat brain tissue are shown in Figure 1.7.2. Table 1.7.2 lists IC50 values determined for competitors of GABAA binding determined by displacement of [3H]GABA. Characterization of GABA Receptors

1.7.6 Current Protocols in Pharmacology

A [3H]GABA bound (pmol/mg protein)

2.0

1.0

0 0

0.75

1.5

[3H]GABA concentration (µM)

B Bound/ free (×1000)

14.0 Kd = 14 nM Bmax = 0.13 pmol/mg protein Kd = 343 nM Bmax = 4.4 pmol/mg protein

7.0

0 0

1.0

2.0

[3H]GABA bound (pmol/mg protein) Figure 1.7.2 Analysis of specific sodium-independent [3H]GABA binding to rat brain synaptic membranes treated with 0.05% Triton X-100 (Enna and Snyder, 1977). (A) Saturation of specific [3H]GABA binding with increasing concentrations of [3H]GABA. (B) Scatchard plot of specific [3H]GABA binding from data show in panel A. Dissociation constant (Kd) and maximum binding (Bmax) values for high- and low-affinity [3H]GABA binding sites were calculated using LIGAND.

MEASUREMENT OF GABAB RECEPTOR BINDING IN RAT BRAIN MEMBRANES USING [3H]GABA

BASIC PROTOCOL 2

This protocol describes an in vitro assay for labeling GABAB receptors in rat brain membranes using [3H]GABA as the labeling ligand. It is still the preferred ligand for GABAB sites, because it yields more consistent and robust data. Incubation with Triton X-100 and multiple resuspensions and centrifugations destroy neuronal GABA uptake sites that may bind [3H]GABA and rids the tissue of endogenous GABA, which competes for the binding site with the radioligand. There is an absolute requirement for calcium in the incubation medium for [3H]GABA to attach preferentially to the GABAB receptor. In addition, isoguvacine is used as a selective GABAA receptor agonist, which is added in excess to prevent attachment of [3H]GABA to this site. Materials Frozen membrane preparation (see Support Protocol) 50 mM Tris⋅Cl (pH 7.4 at 25°C; APPENDIX 2A)/2.5 mM CaCl2 Triton X-100 0.05 M Tris citrate buffer (pH 7.1 at 4°C; adjust pH of 1 M Tris base with a concentrated solution of citric acid at 4°C, then dilute 1:20)

Receptor Binding

1.7.7 Current Protocols in Pharmacology

Isoguvacine (ICN Biomedicals) [3H]γ-Amino-n-butyric acid (GABA; 25 to 40 Ci/mmol; NEN Life Sciences) (±)-Baclofen or GABA (unlabeled; Sigma, RBI, or ICN Biomedicals) Test compound: unlabeled competitor (optional) Tissue solubilizer (e.g., BTS-450, Beckman) Scintillation cocktail compatible with organic solvents 50- and 15-ml polypropylene centrifuge tubes Tissue homogenizer (e.g., Polytron, Brinkmann; Tissumizer, Tekmar) Refrigerated centrifuge (Sorvall RC-5 with SS-34 or SM-24 rotors, or equivalent) 37° and 25°C water baths Liquid scintillation counter and vials NOTE: Be sure to adjust buffers to the proper pH at the temperatures indicated, as the pH of Tris buffers varies significantly with temperature. Prepare GABAB receptors 1. In 50-ml polypropylene centrifuge tubes, resuspend frozen tissue or brain membranes in 100 vol Tris⋅Cl/2.5 mM CaCl2 using the tissue homogenizer (midpoint setting for 30 sec). 2. Centrifuge the homogenate 10 min at 1000 × g, 4°C. 3. Pour the supernatant into a fresh 50-ml polypropylene centrifuge tube. 4. Add sufficient Triton X-100 diluted in Tris citrate buffer (see Alternate Protocol 1, step 1) to yield a final concentration of 0.03% (v/v). 5. Incubate the supernatant 30 min in the 37°C water bath. 6. Centrifuge the supernatant 10 min at 50,000 × g, 4°C. 7. Resuspend the resultant tissue pellet with the tissue homogenizer in the same volume of buffer as in step 1, then centrifuge the homogenate 10 min at 50,000 × g, 4°C. 8. Repeat step 7. 9. Resuspend the pellet with the tissue homogenizer in sufficient buffer to yield a final concentration of ∼1 mg protein/ml. Protein concentration may be measured using Bradford, BCA (Pierce), Lowry, or other suitable assay (see APPENDIX 3A) with BSA as reference standard.

Measure [3H]GABA binding to GABAB receptors 10a. For competition assays: In separate 15-ml polypropylene centrifuge tubes on ice, assemble the following components in a small volume (10 to 20 µl) and dilute to 100 µl with Tris⋅Cl/2.5 mM CaCl: 100 nM [3H]GABA + 400 µM isoguvacine (to determine total binding); 100 nM [3H]GABA + 400 µM isoguvacine + [1 mM (±)-baclofen or 1 mM unlabeled GABA] (to determine nondisplaceable binding); 100 nM [3H]GABA + 400 µM isoguvacine + various concentrations of unlabeled competitor (test compound). Perform all assays in duplicate or triplicate. Final concentrations in the 1-ml incubation volume will be 40 ìM isoguvacine and 10 nM [3H]GABA.

Characterization of GABA Receptors

The unlabeled (±)-baclofen or unlabeled GABA is used to define nondisplaceable binding (blank), which when subtracted from total binding (in tubes containing only [3H]GABA and isoguvacine) reveals the amount of specific binding to the GABAB receptor.

1.7.8 Current Protocols in Pharmacology

10b. To generate binding site saturation data by radioligand displacement: Prepare 100-µl solutions in tubes as described in step 10a but containing the following: 100 nM [3H]GABA + 400 µM isoguvacine; 100 nM [3H]GABA + 400 µM isoguvacine + various concentrations of unlabeled GABA (0.1 to 100 µM). The high-affinity GABAB binding site may also be characterized using increasing concentrations of [3H]GABA in the presence and absence of a saturating (100-ìM) concentration of unlabeled GABA (Fig. 1.7.3). As described in step 5b of Basic Protocol 1 for [3H]muscimol binding to GABAA receptors, this assay may be used as a general screen for assessing the affinity of unlabeled compounds for the GABAB receptor binding site. Table 1.7.3 lists IC50 values for competitors of GABAB substrates determined by displacement of [3H]GABA.

11. Add 900 µl of the tissue suspension to each tube and gently vortex to mix the contents. The tissue concentration in the incubation medium will be slightly less than 1.0 mg protein/ml, which is within the tissue linearity range for [3H]GABA binding to GABAB receptors (Bowery et al., 1985).

A [3H]GABA bound (pmol/mg protein)

2.0

1.0

0 0

1.95 [3H]GABA

B

3.9

concentration (µM)

Bound/free (×1000)

0.03 Kd = 19 nM Bmax = 0.50 pmol/mg protein

0.015

Kd = 1147 nM Bmax = 1.94 pmol/mg protein

0 0

1.0 [3H]GABA

2.0

bound (pmol/mg protein)

Figure 1.7.3 Analysis of specific [3H]GABA binding to rat brain synaptic membranes (Bowery et al., 1985). (A) Saturation of specific [3H]GABA binding with increasing concentrations of [3H]GABA. (B) Scatchard plot of specific [3H]GABA binding from panel A. Dissociation constant (Kd) and maximum binding (Bmax) values for high- and low-affinity [3H]GABA binding sites were calculated using LIGAND.

Receptor Binding

1.7.9 Current Protocols in Pharmacology

Table 1.7.3 Activity of Ligands at GABAB Receptors in Rat Brain Membranesa

IC50 (nM)

Compoundb

[3H](−)-Baclofen

[3H]GABA

(−)-Baclofen

50

100

(+)-Baclofen GABA

22,000 22

>100,000 54

Muscimol Isoguvacine

5,000 >100,000

5,000 >100,000

Bicuculline methiodide

>100,000

>100,000

aData based on Bowery et al. (1985). bAll compounds listed are available from RBI (see SUPPLIERS APPENDIX).

12. Incubate the mixture 10 min at 25°C to achieve binding equilibrium. 13. Terminate the binding reaction by centrifuging 10 min at 50,000 × g, 4°C. 14. Discard the supernatant, then rinse the tissue pellets rapidly (3 to 5 sec) and superficially three times with 5 ml ice-cold Tris/CaCl2 buffer. Caution must be exercised to ensure the tissue pellets, or portions of them, are not dislodged from the bottom of the tube during the rinsing procedure. The buffer should be sprayed against the wall of the tube opposite to the tissue so that the pellet is not exposed to the full force of the fluid.

15. Gently dry the inside of each tube with tissue to remove any residual rinse buffer, taking care not to touch the tissue pellet. 16. Place 1 ml of tissue solubilizer into each tube, ensuring that the pellet is submerged. 17. Allow tissue to dissolve in the solubilizer at room temperature or by incubating the tubes in a 37°C water bath. 18. Once the tissue is dissolved, add 4 ml organic solvent–compatible scintillation cocktail. The tissue solubilizer contains toluene.

19. Transfer the contents of each tube into individual liquid scintillation counting vials, then quantify radioactivity using liquid scintillation spectrometry. 20. Perform data analysis. UNIT 1.3 provides details on plotting and analyzing concentration-response curves. Sample

results obtained for [3H]GABA binding to GABAB receptors in rat brain tissue are shown in Figure 1.7.3.

Characterization of GABA Receptors

1.7.10 Current Protocols in Pharmacology

MEASUREMENT OF GABAB RECEPTOR BINDING IN RAT BRAIN MEMBRANES USING [3H]BACLOFEN Baclofen, a selective agonist for the GABAB site, may be used as a radioligand instead of GABA. Although it should be more specific for this site, it does not yield as consistent or robust data. Table 1.7.3 lists IC50 values for competitors of GABAB binding determined by displacement of [3H]baclofen.

ALTERNATE PROTOCOL 2

Additional Materials (also see Basic Protocol 2) [3H](−)-Baclofen (30 to 50 Ci/mmol; NEN Life Sciences) [3H](−)-Baclofen binding assay for GABAB receptors 1. In 50-ml polypropylene centrifuge tubes, resuspend previously frozen whole tissue or brain membranes in 100 vol Tris⋅Cl/2.5 mM CaCl2 using the tissue homogenizer (midpoint setting for 30 sec). 2. Centrifuge homogenate 20 min at 20,000 × g, 4°C. 3. Repeat steps 1 and 2 with the tissue pellet three additional times. A thorough washing of the tissue is essential to rid it of endogenous GABA, which competes with the binding of the radioligand.

A [3H](−)-Baclofen bound (pmol/mg protein)

2.0

1.0

0 0

1.95 [3H](−)-Baclofen

B

3.9

concentration (µM)

Bound/ free (×1000)

0.03 Kd = 22 nM Bmax = 0.48 pmol/mg protein

0.015

Kd = 327 nM Bmax = 1.4 pmol/mg protein

0 0

1.0 [3H](−)-Baclofen

2.0

bound (pmol/mg protein)

Figure 1.7.4 Analysis of [3H](−)-baclofen binding to rat brain synaptic membranes (Bowery et al., 1985). (A) Saturation of specific [3H](−)-baclofen binding with increasing concentrations of [3H](−)baclofen. (B) Scatchard plot of specific [3H](−)-baclofen binding from panel A. Dissociation constant (Kd) and maximum binding (Bmax) values for high- and low-affinity [3H](−)-baclofen binding sites were calculated using LIGAND.

Receptor Binding

1.7.11 Current Protocols in Pharmacology

4. Resuspend the pellet with the tissue homogenizer in sufficient buffer to yield a final concentration of ∼1 mg protein/ml. Protein concentration may be measured using Bradford, BCA (Pierce), Lowry, or other suitable assay (see APPENDIX 3A) with BSA as the reference standard.

5. Measure binding to receptors and perform competition assays: see Basic Protocol 2 (GABAB receptor binding), steps 10 to 20, and follow the procedure described, except using 50 nM [3H](−)-baclofen in place of 100 nM [3H]GABA. The final concentration of in [3H](−)-baclofen the 1-ml assay is 5 nM. Sample results obtained for [3H](−)-baclofen binding to GABAB receptors in rat brain tissue are shown in Figure 1.7.4. BASIC PROTOCOL 3

MEASUREMENT OF GABAC RECEPTOR BINDING IN RAT BRAIN MEMBRANES USING [3H]GABA This protocol describes an in vitro assay for labeling GABAC receptors in rat brain membranes using [3H]GABA (Drew and Johnston, 1992). Unlike GABAA and GABAB sites, which are found in abundance throughout the neuroaxis, GABAC receptors are most enriched in retina and cerebellum. Accordingly, cerebellum is the tissue of choice for this assay. While an assay utilizing [3H]cis-4-aminocrotic acid has been published (Drew and Johnston, 1992), it is not described here since the radioligand is not available commercially and is also highly toxic. As described in Basic Protocol 1 for [3H]muscimol binding to GABAA receptors, this assay may be used as a general screen for assessing the affinity of unlabeled compounds for the GABAC receptor binding site. Materials Frozen cerebellar membrane preparation (see Support Protocol) 50 mM Tris⋅Cl (pH 7.4 at 20°C; APPENDIX 2A) Isoguvacine (ICN Biomedicals) [3H]γ-Amino-n-butyric acid (GABA; 25 to 40 Ci/mmol; NEN Life Sciences) GABA (unlabeled; Sigma, RBI, or ICN Biomedicals) Test compound: unlabeled competitor (optional) Scintillation fluid compatible with organic solvents 50-ml polypropylene centrifuge tubes 20°C shaking water bath Tissue homogenizer (Polytron, Brinkmann; Tissumizer, Tekmar) Refrigerated centrifuge (Sorvall RC-5 with SS-34 and SM-24 rotors, or equivalent) Liquid scintillation counter and vials NOTE: Be sure to adjust buffer to the proper pH at 20°C, as the pH of Tris buffers varies significantly with temperature. Prepare GABAC receptors 1. In 50-ml polypropylene centrifuge tubes, resuspend cerebellar membranes in sufficient 50 mM Tris⋅Cl to yield a final concentration of ∼8.0 mg protein/ml using the tissue homogenizer (midpoint setting for ∼30 sec). Protein concentration may be measured using Bradford, BCA (Pierce), Lowry, or other suitable assay (see APPENDIX 3A) with BSA as reference standard.

2. Incubate the tissue suspension 45 min at 20°C in a shaking water bath. Characterization of GABA Receptors

3. Centrifuge the tissue suspension 10 min at 8000 × g, 4°C.

1.7.12 Current Protocols in Pharmacology

4. Resuspend the resultant pellet using the tissue homogenizer in the same volume of buffer as in step 1. 5. Incubate the tissue suspension 15 min in the 20°C shaking water bath. 6. Centrifuge the tissue suspension 10 min at 8000 × g, 4°C. 7. Repeat steps 4 to 6 two additional times. Multiple resuspensions and centrifugations rid the tissue of endogenous GABA, which competes with [3H]GABA for attachment to the GABAC receptor binding site.

8. Resuspend the final tissue pellet in sufficient buffer containing 40 µM isoguvacine to yield a tissue concentration of ∼3 mg protein/ml. 9. Allow suspension to stand 10 min at room temperature before initiating the binding assay. The 10-min delay allows sufficient time for the isoguvacine to block GABAA receptors.

Measure [3H]GABA binding to GABAC receptors 10a. For competition assays: In separate 1.5-ml microcentrifuge tubes on ice, assemble the following components in a 900-µl volume, diluted with 50 mM Tris⋅Cl, pH 7.4 (but calculating the concentrations for a 1000-µl final volume): 5 nM [3H]GABA + 40 µM isoguvacine (to determine total binding); 5 nM [3H]GABA + 40 µM isoguvacine + 300 µM unlabeled GABA (to determine nondisplaceable binding); 5 nM [3H]GABA + 40 µM isoguvacine + various concentrations of unlabeled competitor (test compound). Perform all assays in duplicate or triplicate. Isoguvacine is a selective GABAA receptor agonist that is added in excess to prevent binding of [3H]GABA to this site. The unlabeled GABA is used to define nondisplaceable binding (blank) which, when subtracted from total binding (tissue in tubes containing [3H]GABA and isoguvacine alone), reveals the amount of specific binding to the GABAC receptor.

10b. To generate binding site saturation data by ligand displacement: Prepare 900-µl solutions in tubes as described in step 10a, but containing the following (again calculating the concentrations for a 1000-µl final volume): 40 µM isoguvacine + 5 nM [3H]GABA; 40 µM isoguvacine + 5 nM [3H]GABA + various concentrations of unlabeled GABA (5 nM to 5 µM). 11. Add 100 µl of the tissue suspension (300 µg protein) to each tube and gently vortex to mix the contents. The final tissue concentration in the assay medium (∼300 ìg/ml) is within the linearity range for binding to receptors (Drew and Johnston, 1992).

12. Incubate the mixture 10 min in the 20°C shaking water bath to achieve binding equilibrium. 13. Terminate the binding reaction by microcentrifuging the samples 5 min at 10,000 × g, 20°C. 14. Rinse the pellets rapidly and superficially three times with 1.0 ml ice-cold distilled water. Caution must be exercised to ensure the tissue pellets, or portions of them, are not dislodged from the bottom of the tube during the rinsing procedure. The ice-cold water should be added slowly to the tube, directing the spray away from the tissue sample so it is not exposed to the full force of the fluid.

Receptor Binding

1.7.13 Current Protocols in Pharmacology

15. Add 1 ml ice-cold distilled water to the microcentrifuge tube, submerging the pellet. 16. Leave samples 24 hr at room temperature. 17. Vortex each sample, then transfer to scintillation vials. 18. Add 4 ml organic solvent–compatible scintillation cocktail. 19. Quantify radioactivity using liquid scintillation spectrometry. 20. Perform data analysis. UNIT 1.3 provides details on plotting and analyzing concentration-response curves. Sample

results obtained for [3H]GABA binding to GABAC receptors in rats are shown in Figure 1.7.5. Table 1.7.4 lists IC50 values for competitors of GABAC substrates determined by displacement of [3H]GABA. Table 1.7.4 Substrate Specificity of [3H]GABA Binding to GABAC Receptors in Rat Cerebellar Membranesa

Compound

IC50 (nM)

GABA Baclofen

80 >100,000

aData from Drew and Johnston (1992).

A [3H]GABA bound (pmol/mg protein)

17.0

8.5

0 0

1.0

2.0

[3H]GABA

B

3.0

4.0

5.0

6.0

concentration (µM)

Bound/ free (×1000)

20 Kd = 24 nM Bmax = 3.4 pmol/mg protein

10

Kd = 1080 nM Bmax = 12.9 pmol/mg protein

0 0

9.0 [3H]GABA

Characterization of GABA Receptors

18.0

bound (pmol/mg protein)

Figure 1.7.5 Analysis of specific [3H]GABA binding to rat cerebellar synaptic membranes in the presence of 40 µM isoguvacine (Drew and Johnston 1992). (A) Saturation of specific [3H]GABA binding with increasing concentrations of [3H]GABA. (B) Scatchard plot of specific [3H]GABA binding from panel A. Dissociation constant (Kd) and maximum binding (Bmax) values for high- and low-affinity [3H]GABA binding sites were calculated using LIGAND.

1.7.14 Current Protocols in Pharmacology

PREPARATION OF MEMBRANES For all five protocols, membranes can be prepared directly from whole tissue samples that have been stored frozen. Virtually any tissue can be examined in this way, although GABA receptor binding sites are not abundant outside the CNS. For GABAA and GABAB receptor binding at least, the receptors appear stable with tissue frozen at −80°C for up to 3 months. In some cases it is advantageous to prepare a crude synaptosomal membrane fraction from fresh brain tissue prior to freezing. This protocol provides a method for doing so.

SUPPORT PROTOCOL

Materials Fresh brain sample 0.32 M sucrose, ice cold Potter-Elvehjem glass homogenizer with Teflon pestle Refrigerated centrifuge (Sorvall RC-5 with SS-34 and SM-24 rotors or equivalent) Tissue homogenizer (Polytron, Brinkmann; Tissumizer, Tekmar) Prepare the membranes 1. Place fresh brain tissue into 15 vol ice-cold 0.32 M sucrose in a Potter-Elvehjem glass homogenizer fitted with a Teflon pestle, and homogenize. 2. Centrifuge the homogenate 10 min at 1000 × g, 4°C. 3. Discard the resultant pellet and centrifuge the supernatant 20 min at 20,000 × g, 4°C. 4. Resuspend the pellet in 20 ml ice-cold distilled water using a tissue homogenizer (midpoint setting for 30 sec). 5. Centrifuge the suspension 20 min at 8000 × g, 4°C. 6. Gently agitate the tube by hand to suspend the soft buffy coat surrounding the pellet into the supernatant without disturbing the pellet itself. 7. Discard the pellet and centrifuge the suspension 20 min at 48,000 × g, 4°C. 8. Discard the supernatant and store the pellet (crude synaptic membrane pellet) at least 18 hr at −20°C prior to use for a GABA receptor binding assay. When prepared and stored in this way the tissue retains binding activity for at least 3 months. For assay, the pellet is thawed and homogenized in buffer as described in step 1 of each of the individual protocols (see Basic Protocols 1 to 3 and Alternate Protocols 1 and 2).

COMMENTARY Background Information It has been estimated that up to 30% of the neurons in the central nervous system utilize GABA as a neurotransmitter. Given its high concentration and ubiquitous distribution, GABA appears to be the predominant inhibitory neurotransmitter in the brain. Because modifications in GABAergic transmission are likely to occur in many, if not most, disorders of the central nervous system, there is a great deal of interest in discovering or designing drugs capable of selectively regulating this neurotransmitter system. A primary target for these efforts is the GABA receptor, a plasma membrane protein that mediates the action of this neurotransmitter.

Among the three generally recognized categories of GABA binding sites, the GABAA binding site is located on a ligand-gated chloride ion channel receptor, is inhibited by bicuculline, and, in some cases, is regulated by benzodiazepines. The GABAB receptor is coupled to a G protein, which regulates the formation of cyclic AMP, is selectively activated by baclofen, and is not inhibited by bicuculline. Like GABAA, GABAC binding sites are located on a ligand-gated chloride channel, but are insensitive to bicuculline and baclofen, and selectively activated by cis-4-aminocrotonic acid. In general, activation of GABA receptors causes hyperpolarization of the cell. A significant contribution to this endeavor

Receptor Binding

1.7.15 Current Protocols in Pharmacology

Characterization of GABA Receptors

was made with the development of ligand binding assays for GABA receptors (Enna and Snyder, 1975, 1977; Bowery et al., 1985; Drew and Johnston, 1992). Besides providing a technically simple and rapid means for determining whether a chemical has any affinity for these sites, and therefore potential as a therapeutic agent, this methodology made it possible to examine the biochemical and molecular properties of this receptor. The initial GABA receptor binding assay, which utilized [3H]GABA as a radioligand, labels primarily the GABAA receptor recognition site. Over the years, other GABAA recognition site agonist and antagonist radioligands have been developed, including [3H]muscimol, [3H]piperidine-4-sulfonic acid, [3H]THIP (a structural analog of muscimol), and [3H]bicuculline (Möhler and Okada, 1977; Beaumont et al., 1978; Krogsgaard-Larsen et al., 1981; Falch and Krogsgaard-Larsen, 1982). Of these, only [3H]muscimol and [3H]bicuculline are currently commercially available, and given its high affinity and selectivity for GABAA receptors, muscimol is generally preferred for binding assays. Ligand binding assays revealed other components of the GABAA receptor that could serve as targets for therapeutic agents, including the [3H]α-dihydropicrotoxinin binding site, a component of the GABAA receptor–associated chloride ion channel (Ticku et al., 1978). Binding assays suggest this may be the site of action of some sedative-hypnotic agents, such as the barbiturates (Olsen, 1981). A component of most GABAA receptors is labeled with benzodiazepines, such as [3H]flunitrazepam (Möhler et al., 1980). These drugs bind to a site on the GABAA receptor physically independent of, but associated with, the neurotransmitter recognition and ion channel binding sites. Recent molecular cloning studies revealed the GABAA receptor is a pentameric structure that forms a chloride ion channel spanning the plasma membrane (Möhler et al., 1997). Seventeen subunits (six α, four β, four γ, one σ, and two ρ) have been identified that, in various combinations, form physiologically active GABAA receptors. Although the potential number of molecularly distinct GABAA receptors is large given the number of subunits and the pentameric structure of the site, only a dozen or so are thought to be present in mammalian brain. The predominant forms of the GABAA receptor are those composed of α1β2γ2, α2,β3γ2, or α3β3γ2 subunits, although the precise stoichiometries of these sites are unknown.

Binding assays suggest that GABAA recognition site agonists, such as muscimol or GABA, attach to the β subunit of the receptor, whereas the benzodiazepine site is present only when selected α subunits are associated with certain γ subunits (Möhler et al., 1997). Given these considerations, [3H]GABA and [3H]muscimol are the ligands of choice for labeling the greatest number of GABAA receptors since, by definition, all must possess a neurotransmitter receptor recognition site. In contrast, radiolabeled benzodiazepines label only those GABAA sites that possess the correct combination of α and γ subunits. Indeed, the population of GABAA receptors labeled may vary somewhat among the benzodiazepines, since there is a variation of affinities within this class for different combinations of α and γ subunits (Möhler et al., 1997). These findings suggest it may be possible to develop specific radioligands for each of the GABAA receptor subtypes, facilitating the identification of more selective agonists and antagonists for these receptors. Ligand binding assays played a major role in initially identifying GABAB receptors (Bowery et al., 1985). While activated by GABA and baclofen, GABAB receptors are not inhibited by bicuculline or picrotoxin, nor are they influenced by benzodiazepines as are the GABAA sites. Biochemical and molecular cloning experiments indicate the GABAB site is a sevensubunit, membrane-spanning, G protein–coupled receptor (Kaupmann et al., 1997). It would seem likely that [3H]baclofen would serve as the radioligand of choice for labeling GABAB receptors, since it should be more selective for this site than [3H]GABA. However, the radiolabeled neurotransmitter itself is preferred, since for unknown reasons it yields more robust and consistent data than [3H]baclofen. The inclusion of calcium, as well as a GABAA receptor agonist, in the incubation medium helps ensure that specifically bound [3H]GABA attaches primarily to the GABAB site. Recently, highly potent and selective GABAB receptor antagonists have been used to study GABAB binding sites (Kaupmann et al., 1997). When, if ever, these radiolabeled substances become commercially available, they should supplant [3H]GABA and [3H]baclofen as ligands for this assay. Less is known about the GABAC receptor. For some time there has been evidence of bicuculline- and baclofen-insensitive [3H]GABA binding sites (Polenzani et al., 1991; Drew and Johnston, 1992). Pharmacological evidence suggested the GABAC receptor, which is most

1.7.16 Current Protocols in Pharmacology

abundant in retina and cerebellum, may be homomeric GABAA sites composed solely of ρ subunits (Cutting et al., 1991). While [3H]cis4-aminocrotonic acid has been used as a selective ligand for this site, its toxic properties, which endanger the experimenter, preclude widespread use (Drew and Johnston, 1992). Although information about the pharmacological properties of this receptor may be obtained using [3H]GABA to label the GABAC receptor, progress may be hindered until a more selective, and possibly more potent, radioligand is developed for this receptor site.

Critical Parameters and Troubleshooting Of the assays described in this unit, those for GABAA are the most reliable, with a specific/nonspecific (signal/noise) binding ratio of 50% to 90%. For this assay, specific binding of either [3H]GABA or [3H]muscimol is most enhanced if the tissue has been treated with Triton X-100. For both the GABAB and GABAC receptor binding assays, the specific/nonspecific ratio normally approximates 50% and is somewhat more variable than that observed with the GABAA receptor binding assay. The reasons for this difference are unknown, although they may be related to the fact that special conditions must be used to direct radioligands away from the GABAA sites, which are abundant, and toward the GABAB or GABAC receptors. The most common problem associated with these assays is a reduction in specific (displaceable) binding. This may occur as a result of a decrease in total binding, as a selective increase in nonspecific binding, or as a selective decrease in specific binding. In general, a decrease in the specific/nonspecific binding ratio to ≤40% signals a faulty assay. Detailed below are steps to be taken to address this issue (in order of priority): 1. Prepare a fresh batch of membranes. Most often, a decline in specific binding is due to a loss of receptors, which may be destroyed as a result of prolonged or inappropriate storage or mishandling of tissue during preparation. 2. Terminate reaction by centrifugation rather than filtration. The Kd values for these radioligands vary from low to mid-nanomolar. While this should be sufficient to allow for detection of specific binding with the thorough rinsing procedure associated with filtration, even a modest alteration in affinity could result in the dissociation of specifically bound ligand under these conditions. This can be rectified by

terminating the reaction by centrifugation and by more gentle rinsing of the tissue, as described in Alternate Protocol 1 and in Basic Protocols 2 and 3. Comparison of results using the centrifugation and filtration methods also helps detect whether a significant amount of radioligand adheres to the glass fiber filters used in the latter, which tends to increase nonspecific binding. In general, nonspecific binding is greater with the centrifugation assay since the tissue is rinsed less thoroughly than with filtration. 3. Prepare fresh buffer. Preparation of a fresh stock of Tris buffer on a weekly basis is advisable, even though it is usually stable for longer periods when kept refrigerated. Nonetheless, a significant change in the amount of specific binding could be due to an error in the preparation of the buffer, such as titration to an inappropriate pH, or to microbial contamination. For the GABAB assay, it is also important to ensure the buffer contains 2.5 mM CaCl2, since calcium is essential for maximal binding of either [3H]GABA or [3H]baclofen to the GABAB site. With both the GABAB and GABAC assays, the buffer must contain 40 µM isoguvacine to prevent attachment of [3H]GABA to the GABAA receptor site. It is best to add fresh isoguvacine on a daily basis rather than include it in the stock solution of buffer. 4. Assess purity of radioligand. All of the radioligands used in these protocols are chemically stable if stored under the conditions recommended by the manufacturer. Thus, destruction of radioligand is seldom a problem with these assays. Nonetheless, if the tissue preparation, method of assay termination, and buffer appear fine, it is conceivable an accumulation of radioactive breakdown products could account for a change in specific binding. A simple analysis using thin-layer chromatography can be employed to assess the purity of the radioligand. The sample should be purified, or a new supply of radioligand obtained, if the purity falls below 98%.

Anticipated Results Shown after each protocol are examples of binding site saturation data and the substrate selectivity for each site using the procedure described. Saturation data are typically analyzed using one of the programs available for this purpose, such as LIGAND (Munson and Rodbard, 1980). At a minimum, eight to ten, and possibly up to 24, different concentrations of the radioligand should be tested over at least a 1000-fold range to obtain reliable Kd and Bmax

Receptor Binding

1.7.17 Current Protocols in Pharmacology

values. The number of concentrations employed depends upon the number of binding sites, with 24 recommended if two sites are present. Approximate Kd and Bmax values and the IC50 data for the various assays are shown.

Time Considerations Excluding the time required to prepare the tissue, it should be possible to conduct assays with 200 to 300 tubes on a daily basis. For each of the assays, the actual incubation period is quite brief (5 to 30 min). Most of the time is needed for preparing solutions, numbering tubes, dissolving tissue samples, and centrifugations. Although centrifugation assays require more time than filtration, 200 to 300 incubation tubes is not an unreasonable figure for an 8-hr day. This does not include quantification of radioactivity, since it may be necessary to allow samples to sit overnight to maximize counting efficiency. Depending on whether the membranes used for assay are taken directly from a whole brain sample or a subcellular fraction, up to 3 hr may be required for preparing the samples. After preparation, the tissue samples can be divided into aliquots and stored frozen for later analysis.

Literature Cited

Characterization of GABA Receptors

Beaumont, K., Chilton, W.S., Yamamura, H.I., and Enna, S.J. 1978. Muscimol binding in rat brain: Association with synaptic GABA receptors. Brain Res. 148:153-162. Bowery, N.G., Hill, D.R., and Hudson, A.L. 1985. [3H](−)-Baclofen: An improved ligand for GABAB sites. Neuropharmacology 24:207-210. Cutting, G.R., Lu, L., O’Hara, B.F., Kasch, L.M., Montrose-Rafizadeh, C., Donovan, D.M., Shimada, S., Antonarakis, S.E., Guggino, W.B., Uhl, G.R., and Kazazian, H.H. 1991. Cloning of the γ-aminobutyric acid (GABA) ρ 1 cDNA: A GABA receptor subunit highly expressed in the retina. Proc. Natl. Acad. Sci. U.S.A. 88:26732677. Drew, C.A. and Johnston, G.A.R. 1992. Bicuculline- and baclofen-insensitive γ-aminobutyric acid binding to rat cerebellar membranes. J. Neurochem. 58:1087-1092. Enna, S.J. and Snyder, S.H. 1977. Influences of ion, enzymes and detergents on γ-aminobutyric acid receptor binding in synaptic membranes of rat brain. Mol. Pharmacol. 13:442-453. Enna, S.J. and Snyder, S.H. 1975. Properties of γ-aminobutyric acid (GABA) receptor binding in rat brain synaptic membrane fractions. Brain Res. 100:81-97. Falch, E. and Krogsgaard-Larsen, P. 1982. The binding of the GABA agonist [3H]THIP to rat brain synaptic membranes. J. Neurochem. 38:11231129.

Kaupmann, K., Huggel, K., Heid, J., Flor, P.J., Bischoff, S., Mickel, S.J., McMaster, G., Angst, C., Bittiger, H., Froestl, W., and Bettler, B. 1997. Expression cloning of GABAB receptors uncovers similarity to metabotropic glutamate receptors. Nature 386:239-246. Krogsgaard-Larsen, P., Snowman, A., Lummis, S.C., and Olsen, R.W. 1981. Characterization of the binding of the GABA agonist [3H]piperidine4-sulfonic acid (P4S) to bovine brain synaptic membranes. J. Neurochem. 37:401-409. Krogsgaard-Larsen, P., Jacobsen, P., and Falch, E. 1983. Structure-activity requirements of the GABA receptor. In The GABA Receptors (S.J. Enna, ed.) pp. 149-176. Humana Press, Totowa, N.J. Möhler, H. and Okada, T. 1977. Properties of γ-aminobutyric acid receptor binding with (+)[3H]bicuculline methiodide in rat cerebellum. Mol. Pharmacol. 14:256-265. Möhler, H., Benke, D., Benson, J., Lüscher, B., Rudolph, U., and Fritschy, J.M. 1997. Diversity in structure, pharmacology, and regulation of GABAA receptors. In The GABA Receptors, 2nd ed. (S.J. Enna and N.G. Bowery, eds.) pp. 11-36. Humana Press, Totowa, N.J. Möhler, H., Battersby, M.K., and Richards, J.G. 1980. Benzodiazepine receptor protein identified and visualized in brain tissue by a photoaffinity label. Proc. Natl. Acad. Sci. U.S.A. 77:1661-1670. Munson, P.J. and Rodbard, D. 1980. LIGAND: A versatile computerized approach for characterization of ligand-binding systems. Anal. Biochem. 107:220-239. Olsen, R.W. 1981. The GABA postsynaptic membrane receptor-ionophore complex: Site of action of convulsant and anticonvulsant drugs. Mol. Cell. Biochem. 39:261-279. Polenzani, L., Woodward, R.M., and Miledi, R. 1991. Expression of mammalian γ-aminobutyric acid receptors with distinct pharmacology in Xenopus oocytes. Proc. Natl. Acad. Sci. U.S.A. 88:4318-4322. Ticku, M.K., Ban, M., and Olsen, R.W. 1978. Binding of [3H]α-dihydropicrotoxinin, a γ-aminobutyric acid synaptic antagonist, to rat brain membranes. Mol. Pharmacol. 14:391-402.

Key References Enna and Snyder, 1975. See above. Provides detailed description and appropriate citations for preparation of crude P2 membrane preparation from rat brain tissue. Enna and Snyder, 1977. See above. Details the effect of detergents on GABA receptor binding.

Contributed by S.J. Enna and Kenneth E. McCarson University of Kansas Medical Center Kansas City, Kansas

1.7.18 Current Protocols in Pharmacology

Characterization of Neuronal Nicotinic Acetylcholine Receptors

UNIT 1.8

Neuronal nicotinic acetylcholine receptors (nAChRs) are members of the ligand-gated ion channel superfamily of receptors. The potential of nAChRs as targets of therapeutic intervention has been revealed, in part, by recent studies showing that a diversity of nAChR subunits exist in brain and other tissues, and that discrete subunit combinations may be involved in mediating specific neurophysiological functions and behaviors (Arneric et al., 1995). To date, eleven gene products (α2-α9; β2-β4) representing nAChRs have been identified (McGehee and Role, 1995). Historically, the two major classes of nAChR binding sites identified in brain had high affinity for (−)-nicotine or α-bungarotoxin (α-BgT; Marks et al., 1986). Studies with transfected cell lines indicate that the high-affinity (−)-nicotine binding site in brain corresponds to the α4β2 subunit combination (Gopalakrishnan et al., 1996), while the distribution of the α7 subunit combination coincides somewhat with the distribution of high-affinity [125I]α-BgT binding sites in rat brain (Clarke et al., 1985; Seguela et al., 1993; see Table 1.8.1). This unit examines the binding properties of the α4β2 receptor in rat brain (see Basic Protocol) or in transformed cell lines expressing the recombinant receptor (see Alternate Protocol). NOTE: All protocols using live animals must first be reviewed and approved by an Institutional Animal Care and Use Committee (IACUC) or must conform to governmental regulations regarding the care and use of laboratory animals. MEASUREMENT OF HIGH-AFFINITY NICOTINE BINDING SITES IN BRAIN MEMBRANE HOMOGENATES

BASIC PROTOCOL

[3H](−)-Cytisine is the radioligand of choice for labeling high-affinity nicotine binding sites in brain (Pabreza et al., 1991; Anderson and Arneric, 1994). Nicotine, the classical ligand for nAChRs, is not a useful radioligand for this purpose because of high nonspecific binding (see Background Information). The basic principles underlying the [3H](−)-cytisine binding assay are similar to those described for other neurotransmitter receptors. Brain membranes are prepared and incubated with [3H](−)-cytisine and various concentrations of unlabeled compound(s) for 75 min at 4°C. Bound and free radioligand are separated by filtration, with membrane-bound radioactivity quantified by liquid scintillation counting. Table 1.8.1

Characteristics of Cloned Human α4β2 and α7 nAChRsa

Receptorb

GenBank accession number (human clone)

α4β2

α4 L35901 β2 X53179

α7

L25827

Agonists

Antagonists

(−)-Nicotine, (−)-epibatidine, ABT-418, A-85380, SIB 1765F, methylcarbamylcholine, (+)-anatoxin (−)-Nicotine, (−)-epibatidine, DMAC

Mecamylamine, dihydro-β-erythroidine

α-Bungarotoxin, methyllycaconitine

aAbbreviations: ABT-418, (S)-3-methyl-5-(1-methyl-2-pyrrolidinyl)isoxazole; A-85380, 2-(S)-(azetidinylmethoxy)pyridine; SIB 1765F, (±)-5-ethynyl-3-(1-methyl-2-pyrrolidinyl)pyridine); DMAC, 3-(4)-dimethylaminocinnamylidine. bWhile a large number of other subunit combinations have been biophysically and/or pharmacologically characterized in

Xenopus oocytes or transfected cell lines (i.e., α2β2, α3β2, α3β4, α3α5β4, α3α5β2, and α9), binding assays using selective radioligand probes for these subunit combinations remain to be identified.

Receptor Binding Contributed by James P. Sullivan and David J. Anderson Current Protocols in Pharmacology (1998) 1.8.1-1.8.9 Copyright © 1998 by John Wiley & Sons, Inc.

1.8.1 Supplement 2

Materials Male Sprague-Dawley rats (1 to 3 months old, 250 to 400 g) 0.32 M sucrose, ice cold Brain homogenate assay buffer (see recipe), ice cold Nonradiolabeled ligands (see Table 1.8.2): [3H](−)-Cytisine (NEN Life Sciences) 0.05% polyethyleneimine (PEI; Sigma) in brain homogenate assay buffer, ice cold Polytron homogenizer (Brinkmann) Glass fiber filters (Whatman GF/B or Schleicher & Schuell no. 32) Filtration apparatus (e.g., Brandel, Skatron, or Packard) Cell scraper III (Costar) Prepare the tissue 1. Remove whole brains from male Sprague-Dawley rats and discard the cerebellum. Weigh the remainder of the brain and place in 20 vol (w/v) ice-cold 0.32 M sucrose. 2. Homogenize the tissue in a Polytron for 10 sec at setting 5. 3. Centrifuge the homogenate 10 min at 1000 × g, 4°C, and decant the supernatant. Discard the pellet. 4. Centrifuge supernatant 20 min at 20,000 × g, 4°C. Discard the supernatant. 5. Homogenize the pellet in 20 ml of ice-cold water in the Polytron for 10 sec at setting 7.

Characterization of Neuronal Nicotinic Acetylcholine Receptors

Table 1.8.2

Pharmacology of nAChR Ligands at the [3H](−)-Cytisine Binding Site

Compound

[3H](−)-Cytisine binding (Ki, nM)

Compound source

(±)-Epibatidine (−)-Epibatidine A-85380 (+)-Epibatidine (−)-Cytisine (−)-Nicotine (−)-Lobeline Methylcarbamylcholine ABT-418 SIB-1765F 1,1-Dimethyl-4-phenylpiperazanium (+)-Nicotine Dihydro-β-erythroidine (±)-Nornicotine GTS-21 Carbachol Tacrine Mecamylamine α-Bungarotoxin Methyllycaconitine

0.05 0.05 0.05 0.05 0.16 1.0 1.5 1.5 3 7.5 8 14 15 16 20 31 >10,000 >10,000 >10,000 >10,000

RBI RBI RBI RBI Sigma Sigma Sigma RBI Abbotta SIBIAa Sigma Sigma RBI Sigma Taishoa Sigma RBI Sigma Molecular Probes RBI

aNot commercially available.

1.8.2 Supplement 2

Current Protocols in Pharmacology

6. Centrifuge homogenate 20 min at 48,000 × g, 4°C. Discard the supernatant. 7. Repeat steps 5 and 6. 8. Resuspend pellets in 30 to 40 ml ice-cold assay buffer to yield ∼0.5 mg protein/ml (0.1 mg protein/assay tube). If desired, pellets can be frozen on dry ice prior to resuspension and stored at −80°C. Determine protein concentration using the method of Lowry et al. (1951) with BSA as a standard (see APPENDIX 3A).

Measure [3H](−)-cytisine binding 9. If frozen, thaw the membrane preparation at room temperature. 10. Resuspend the membranes in ice-cold assay buffer to a protein concentration 2.5× the desired final assay protein concentration. The final protein concentration should be on the linear portion of the protein concentration-binding curve and should be such that total radioactivity bound is 100,000

aValues representing K values for A and A i 1 2A receptors from Jarvis et al. (1989); values for A3 receptors from Patel et al. (1997). ND, not determined. bAbbreviations: APNEA, N-[2-(4-aminophenyl)ethyl]adenosine; CGS 21680, (2-[p-(2-carboxyethyl)phenethylamino]-5′-N-ethylcarboxamidoadenosine; CGS 15943, 9-chloro-2-(2-furyl)-[1,2,4]-triazolo[1,5-c]quinazolin-5-amine; CHA, N 6-cyclohexyladenosine; 2-CADO, 2-chloroadenosine; CPA, N 6-cyclopentyladenosine; CPT, 8-cyclopentyltheophylline; DPCPX, 1,3-dipropyl-8-cyclopentylxanthine; IB-MECA, iodobenzylmethylethylcarboxyamidoadenosine; NECA, 5′-N-ethylcarboxamidoadenosine; R-PIA, N 6-(R)phenylisopropyladenosine; S-PIA, N 6-(S)-phenylisopropyladenosine; XAC, xanthine amino acid congener. cZM 241385 is available from Tocris Cookson; all other compounds listed are available from RBI (see SUPPLIERS APPENDIX). dData from Jacobson et al. (1995). Saturation binding parameters (K and B d max) for A1 and A2A are for rat brain. The A3 binding parameters (Kd, Bmax, and Ki) are for the recombinant human A3 receptor.

5. Decant the supernatant and resuspend the resulting pellet to 20 mg/ml in 4°C assay buffer containing 2 IU/ml adenosine deaminase (ADA). Incubate 30 min at 37°C to remove endogenous ADO. This step is critical to remove endogenous ADO from the membrane preparation (Williams and Risley, 1980). The assay will not work without this step.

6. Centrifuge the membrane homogenate 10 min at 48,000 × g, 4°C. 7. Decant the supernatant and resuspend the membrane pellet in 4°C assay buffer to a concentration of 10 to 20 mg/ml. Divide into 10-ml aliquots and centrifuge aliquots 10 min at 48,000 × g, 4°C. Discard supernatant, quick-freeze pellets in an acetone/dry ice bath, and store at −70°C. This procedure will typically yield 30 to 35 aliquots (each 10 ml at 20 mg/ml original tissue weight) of cortical membranes. The final membrane pellets can be stored up to 3 months at −70°C. Protein concentrations can be determined by the method of Bradford (1976), described in APPENDIX 3A, using BSA as reference standard.

Receptor Binding

1.9.3 Current Protocols in Pharmacology

Perform binding assay 8. Prepare sufficient 12 × 75–mm polypropylene test tubes for triplicate assays. Alternatively, set up assays in 96-well plates, reducing all volumes to one-quarter those for test tube assays.

9a. For radioligand competition studies: Add 9 to 18 different concentrations of inhibitor (test or reference compound) ranging from 2 pM to 200 µM, 2 nM [3H]CHA, and 4°C assay buffer to 0.5 ml. In the final 1-ml incubation volume (after the membrane suspension is added in step 10), the [3H]CHA concentration will be 1 nM and inhibitor concentrations will range from 1 pM to 100 ìM. Adenosine agonists and antagonists at concentrations of 1 mM are generally soluble in 10% to 20% ethanol or DMSO. Final concentrations of ethanol or DMSO ≤1% do not significantly alter specific [3H]CHA binding. Some ADO antagonists (e.g., XAC and CGS 15943) are more readily soluble in weak bases. It should be noted that some ADO agonists, such as CGS 21680, are not stable in acidic solutions at high concentrations. When stored at −4°C, solutions of ADO agonists and antagonists are stable for at least 1 month.

9b. For saturation studies: Add 10 to 20 different concentrations of [3H]CHA, ranging from 0.02 to 200 nM, and 4°C assay buffer to 0.5 ml. In the final 1-ml incubation volume [3H]CHA concentrations will range from 0.01 to 100 nM. As with most agonist binding assays to G protein–coupled sites, [3H]CHA will label both high- and low-affinity states of the A1 receptor. Radioligand concentrations in saturation studies should be selected such that at least eight data points are used to define each binding component. These studies allow determination of the affinity of the receptor for [3H]CHA (Kd), and receptor density (Bmax).

9c. To determine nonspecific binding: Add 40 µM 2-CADO (a nonselective ADO agonist), 2 nM [3H]CHA, and 4°C assay buffer to 0.5 ml. In the final 1-ml incubation volume the 2-CADO concentration will be 20 ìM and the [3H]CHA concentration will be 1 nM.

10. Thaw brain membrane pellet (from step 7) and resuspend in assay buffer to 10 ml. Add 0.5 ml of this suspension (100 to 200 µg protein) to each tube. Incubate 2 hr at 23°C. [3H]CHA binding reaches equilibrium within 90 min and remains stable for at least 3 hr (Bruns et al., 1986). Total assay volumes may be reduced to 0.25 ml for adaptation to 96-well-plate formats. Care must be taken to insure clean filtration of each assay well during the separation step.

Separate bound and unbound radioligand 11. Terminate binding reactions by filtering through Whatman GF/B filters under reduced pressure (50 mm of vacuum) using a Brandel M-48 cell harvester. For 96-well-plate assays, filter all wells simultaneously using a Tomtec Harvester 96 (Tomtec) or equivalent filtration equipment. 12. Wash filters twice with 5 ml ice-cold assay buffer and place filters in 7-ml scintillation vials. For 96-well-plate assays, wash filter mats three to five times with ice-cold assay buffer. The volume of each wash should be at least 250 µl or based on the filtration equipment manufacturer’s recommendations. Characterization of P1 (Adenosine) Purinoceptors

13. Add 4 ml scintillation cocktail.

1.9.4 Current Protocols in Pharmacology

14. Determine total binding, nonspecific binding, and unbound radioactivity concentrations using a liquid scintillation spectroscopy counter at an efficiency of 40% to 50%. For 96-well assays, process filter mats according to methods dictated by the type of scintillation counter format used (i.e., 6-channel or micro-plate). Typically, 3 to 10 ml scintillation cocktail and the filter mat are added to a packet and then sealed. The sealed packet is placed into the appropriate scintillation counter loading tray. Typical counts per min (cpm) values are 1500 to 2000 cpm for total binding and 150 to 200 cpm for nonspecific binding tubes. Similar data (i.e., % specific binding) are obtained from 96-well-plate-format assays when binding reactions are terminated using the Tomtec Harvester 96 and bound radioactivity determined with a Beta-plate and/or MicroBeta scintillation counter (LKB, Wallac) or equivalent. However, due to the reduced counting efficiency of most high-capacity scintillation counters, the actual cpm values obtained can be reduced 2- to 3-fold.

Analyze data 15. Analyze binding data using a nonlinear regression curve-fitting program, using equations that describe the competitive interactions of a drug with two noninterconvertible recognition sites (Williams and Jarvis, 1989). A partial F-test (P < 0.01) may be used to determine estimates of whether a one- or two-component model best describes the binding data (see UNIT 1.3). DETERMINATION OF [3H]CGS 21680 BINDING TO A2A RECEPTORS Described in this protocol is a radiolabeled ligand-binding assay for the pharmacological characterization of ADO A2A receptors. [3H]CGS 21680 (2-[p-(2-carboxyethyl)phenethylamino]-5′-N-ethylcarboxamidoadenosine) is the ligand of choice in this assay because of its high affinity (Kd = 15 nM) and pharmacological selectivity for this receptor.

BASIC PROTOCOL 2

NOTE: Receptor binding assays can be conducted using a 96-well-plate format, with the total assay volume reduced to 0.25 ml and the volumes of ingredients (radioligand, test or reference compounds, and brain membranes) likewise reduced to one-fourth of those stated in the steps. Because microtiter plate–based scintillation counters differ in the efficiency with which they detect β emissions, the total number of counts obtained may be significantly reduced (as much as 2- to 3-fold) from those stated. However, the percent of specific binding should remain 80% to 95%. Under these assay conditions, radioligand binding to receptors can be reliably measured and the pharmacological profile of each receptor can be quantitated as described below. Materials Adult male Sprague-Dawley (TAC:SD) rats (or recombinant human A2A receptor expressed in HEK-293 cells; Research Biochemicals) 50 mM Tris⋅Cl, pH 7.4 at 23°C (APPENDIX 2A) Assay buffer: 50 mM Tris⋅Cl (pH 7.4 at 23°C)/10 mM MgCl2 Adenosine deaminase (Type III; Boehringer Mannheim) [3H]N6 -Cyclohexyladenosine ([3H]CGS 21680; 20 to 30 Ci/mmol; NEN Life Sciences) Receptor-selective ADO ligand for use as reference compound (see Tables 1.9.1 and 1.9.2; available from Research Biochemicals) 2-chloroadenosine (2-CADO; Research Biochemicals) Scintillation cocktail: e.g., Aquasol 2 (NEN Research Products) CO2 source Polytron homogenizer (Brinkmann) High-speed centrifuge (Sorvall RC-5B, NEN Life Sciences), 4°C

Receptor Binding

1.9.5 Current Protocols in Pharmacology

Acetone/dry ice bath 12 × 75–mm polypropylene test tubes or 96-well microtiter plates MK-48 cell harvester (Brandel) Whatman GF/B glass-fiber filters Liquid scintillation counter (e.g., Beckman) and 7-ml scintillation vials, or microtiter plate–based scintillation counter Nonlinear regression curve-fitting program: e.g., RS/1 (Bolt, Beranek, and Newman) or Prism (GraphPad Software) Additional reagents and equipment for protein concentration assay (APPENDIX 3A) Prepare receptor source 1. Sacrifice 200- to 250-g adult male Sprague-Dawley rats with excess CO2. Commercially available (Biosignal) recombinant rat A2A receptor expressed in HEK-239 cells may be substituted for cerebral cortical membranes.

2. Rapidly remove brains and dissect out the corpus striatum using the general methodology described by Emson and Koob (1978). Thirty Sprague-Dawley rat brains yield ∼2.5 g of corpus striatum tissue. Previously frozen rat brain tissue (e.g., from Pel-Freez) may also be used to characterize brain ADO receptors (Bruns et al., 1986). Significant interspecies and interstrain differences in both ADO receptor density and pharmacology have been reported. While there is generally a good correlation between radioligand binding data obtained from rat and human brain tissue, variations in the selectivity of compounds for different ADO receptors may be large and require careful interpretation.

3. Pool corpus striatum tissue from 30 rats and homogenize in 20 vol ice-cold 50 mM Tris⋅Cl (pH 7.4) using a Polytron homogenizer (20 sec at setting 6, 4°C). 4. Centrifuge the crude membrane homogenate 10 min at 48,000 × g, 4°C. 5. Decant the supernatant and resuspend the resulting pellet to 20 mg/ml in 4°C 50 mM Tris⋅Cl (pH 7.4) containing 2 IU/ml adenosine deaminase (ADA). Incubate 30 min at 37°C to remove endogenous ADO. This step is critical to remove endogenous ADO from the membrane preparation (Williams and Risley, 1980). The assay will not work without this step.

6. Centrifuge the membrane homogenate 10 min at 48,000 × g, 4°C. 7. Decant the supernatant and resuspend the membrane pellet in 4°C 50 mM Tris⋅Cl (pH 7.4) to a concentration of 10 to 20 mg/ml. Divide into 10-ml aliquots and centrifuge aliquots for 10 min at 48,000 × g, 4°C. Discard supernatant, quick-freeze pellets in an acetone/dry ice bath, and store at −70°C. This procedure will typically yield five aliquots (each 10 ml at 20 mg/ml original tissue weight) of striatal membranes. The final membrane pellets can be stored for up to 3 months at −70°C. Protein concentrations are determined by the method of Bradford (1976), described in APPENDIX 3A, using BSA as reference standard.

Perform binding assay 8. Prepare sufficient 12 × 75–mm polypropylene test tubes for triplicate assays. Characterization of P1 (Adenosine) Purinoceptors

Alternatively, set up assays in 96-well plates, reducing all volumes to one-quarter those for test tube assays.

1.9.6 Current Protocols in Pharmacology

9a. For radioligand competition studies: Add 9 to 18 different concentrations of inhibitor (test or reference compound) ranging from 2 pM to 200 µM, 10 nM [3H]CGS 21680, and 4°C assay buffer to 0.5 ml. In the final incubation volume (after the membrane suspension is added in step 10), the [3H]CGS 21680 concentration will be 5 nM and inhibitor concentrations will range from 1 pM to 100 ìM.

9b. For saturation studies: Add 10 to 20 different concentrations of [3H]CGS 21680, ranging from 0.2 to 600 nM, and 4°C assay buffer to 0.5 ml. In the final 1-ml incubation volume [3H]CGS 21680 concentrations will range from 0.1 to 300 nM. As with most agonist binding assays to G protein–coupled sites, [3H]CGS 21680 can label both high- and low-affinity states of the A2A receptor. Radioligand concentrations in saturation studies should be such that at least eight data points are used to define each binding component (Kd and Bmax). It should be noted that there are several reports of a lower-affinity binding site (Kd = 40 to 50 nM) for [3H]CGS 21680 observed in mammalian brain cerebral cortex and hippocampus (Cunha et al., 1996) that does not appear to represent an ADO receptor and for which a physiological function has yet to be defined.

9c. To determine nonspecific binding: Add 40 µM 2-CADO (a nonselective ADO agonist), 10 nM [3H]CGS 21680, and 4°C assay buffer to 0.5 ml. In the final 1-ml incubation volume the 2-CADO concentration will be 20 ìM and the [3H]CGS 21680 concentration will be 5 nM.

10. Thaw brain membrane pellet (from step 7) and resuspend in assay buffer to 10 ml. Add 0.5 ml of this suspension (100 to 200 µg protein) to each tube. Incubate 2 hr at 23°C. [3H]CGS 21680 binding reaches equilibrium within 90 min and remains stable for at least 3 hr (Jarvis et al., 1989). The final assay volume may be reduced to 0.25 ml for adaptation to 96-well plate formats. However, care must be taken to insure clean filtration of each assay well during the separation step.

Separate bound and unbound radioligand 11. Terminate binding reactions by filtering through Whatman GF/B filters under reduced pressure using an M-48 cell harvester. For 96-well-plate assays, filter all wells simultaneously using a Tomtec Harvester 96 (Tomtec) or equivalent filtration equipment. 12. Wash filters twice with 5 ml ice-cold assay buffer and place filters in 7-ml scintillation vials. For 96-well-plate assays, wash filter mats three to five times with ice-cold assay buffer. The volume of each wash should be at least 250 µl or based on the filtration equipment manufacturer’s recommendations. 13. Add 4 ml scintillation cocktail. 14. Determine total binding, nonspecific binding, and unbound radioactivity concentrations using a liquid scintillation spectroscopy counter at an efficiency of 40% to 50%. For 96-well assays, process filter mats according to methods dictated by the type of scintillation counter format used (i.e., 6-channel or micro-plate). Typically, 3 to 10 ml scintillation cocktail and the filter mat are added to a packet and then sealed. The sealed packet is placed into the appropriate scintillation counter loading tray. Typical counts per min (cpm) values are 1500 to 2000 cpm for total binding and 150 to 200 cpm for nonspecific binding tubes. Similar data (i.e., % specific binding) are obtained from 96-well-plate-format assays when binding reactions are terminated using the Tomtec Harvester 96 and bound radioactivity determined with a Beta-plate and/or MicroBeta

Receptor Binding

1.9.7 Current Protocols in Pharmacology

scintillation counter (LKB, Wallac) or equivalent. However, due to the reduced counting efficiency of most high-capacity scintillation counters, the actual cpm values obtained can be reduced 2- to 3-fold.

Analyze data 15. Analyze binding data using a nonlinear regression curve-fitting program, using equations that describe the competitive interactions of a drug with two non-interconvertible recognition sites (Williams and Jarvis, 1989). A partial F-test (P < 0.01) may be used to determine estimates of whether a one- or two-component model best describes the binding data. BASIC PROTOCOL 3

DETERMINATION OF [125I]AB-MECA BINDING TO A3 RECEPTORS Described in this protocol is a radioligand binding assay for the pharmacological characterization of ADO A3 receptors. While the A3 receptor is not as highly expressed in mammalian tissue as other ADO receptor subtypes, several mammalian A3 receptors (including human) have been cloned and stably expressed in various mammalian cell lines (Jacobson et al., 1995). No receptor-selective radioligands have yet been developed for the A3 receptor. However, the radioligands [125I]APNEA (N-[2-(4-aminophenyl) ethyl]adenosine) and [125I]AB-MECA [N6 -(4-amino-3-iodobenzyl)adenosine-5′-(Nmethyluronamide)] bind to the human A3 receptor with high affinity, although they also have significant affinity for the ADO A1 and A2A receptors (Sherman and Weaver, 1997). Thus, the use of a recombinant receptor expression system is necessary to delineate receptor specific pharmacology. It should be noted that there are significant interspecies differences in the sensitivity of human and rat A3 receptors to prototypical xanthine ADO antagonists, with the rat A3 receptor showing much less sensitivity to these antagonists than the human A3 receptor. Materials Recombinant human A3 receptor expressed in HEK-293 cells (Research Biochemicals or Receptor Biology) Assay buffer: 50 mM Tris⋅Cl (pH 8.25 at 4°C; APPENDIX 2A)/10 mM MgCl2/1 mM EDTA containing 2 IU/ml adenosine deaminase (Type III; Boehringer Mannheim), ice cold [125I]AB-MECA (2200 Ci/mmol; Amersham) Receptor-selective ADO ligand for use as reference compound (see Tables 1.9.1 and 1.9.2; available from Research Biochemicals) (R)-Phenylisopropyladenosine (R-PIA; RBI or Sigma) 12 × 75–mm polypropylene test tubes M-48 cell harvester (Brandel) Whatman GF/B glass-fiber filters Gamma detector (e.g., Cobra Auto-Gamma, Packard) or microtiter plate–based scintillation counter Nonlinear regression curve-fitting program: e.g., RS/1 (Bolt, Beranek, and Newman) or Prism (GraphPad Software) Additional reagents and equipment for protein concentration assay (APPENDIX 3A) 1. Dilute HEK-293 cell membranes expressing human recombinant A3 receptors according to manufacturer’s instructions (typically a 1:10 dilution with 4°C assay buffer; this may vary by lot of cells).

Characterization of P1 (Adenosine) Purinoceptors

Protein concentrations are determined by the method of Bradford (1976), described in APPENDIX 3A, using BSA as reference standard.

1.9.8 Current Protocols in Pharmacology

2. Prepare sufficient 12 × 75–mm polypropylene test tubes for triplicate assays or alternatively use 96-well microtiter plate. If using 96-well plates, reduce all volumes to one-quarter those for test tube assays.

3a. For radioligand competition studies: Add appropriate amounts of [125I]AB-MECA and of inhibitor (test or reference compound) to yield a final [125I]AB-MECA concentration of 0.1 nM and 9 to 18 different inhibitor concentrations ranging from 1 pM to 100 µM after the membrane suspension is added in step 4. Add 4°C assay buffer to 0.9 ml. 3b. For saturation studies: Add appropriate amounts of [125I]AB-MECA to yield final concentrations ranging from 0.01 to 2 nM after the membrane suspension is added in step 4. Add 4°C assay buffer to 0.9 ml. Saturation studies allow determination of ligand Kd and receptor density (Bmax).

3c. To determine nonspecific binding: Add appropriate amounts of R-PIA and [125I]ABMECA to yield a final R-PIA concentration of 100 µM and a final [125I]AB-MECA concentration of 0.1 nM after the membrane suspension is added in step 4. Add assay buffer to 0.9 ml. 4. Add 0.10 ml of cell membrane suspension (100 µg protein) to each tube. Incubate 1 hr at 4°C. 5. Terminate binding reactions by filtering through Whatman GF/B filters under reduced pressure using an M-48 cell harvester. For 96-well-plate assays, filter all wells simultaneously using a Tomtec Harvester 96 (Tomtec) or equivalent filtration equipment. The amount of nonspecific binding to GF/B filters can vary among different suppliers. Nonspecific binding of [125I]AB-MECA can be reduced by presoaking filters 1 hr in a solution of 0.5% polyethylenimine (Sigma).

6. Wash filters three times with 5 ml ice-cold assay buffer and place filters in test tubes. For 96-well-plate assays, wash filter mats three to five times with ice-cold assay buffer. The volume of each wash should be at least 250 ìl or based on the filtration equipment manufacturer’s recommendations.

7. Determine total binding, nonspecific binding, and unbound radioactivity concentrations using gamma detection. For 96-well assays, process filter mats according to methods dictated by the type of scintillation counter format used (i.e., 6-channel or microplate). Typical count per min (cpm) values are 1000 to 1500 cpm for total binding tubes and 150 to 250 cpm for nonspecific binding rubes. Microtiter plate–based scintillation counters can be used to reliably count emissions from 125I-labeled radioligands. However, the quality of the data can be assay dependent and should be carefully evaluated.

8. Analyze binding data using a nonlinear regression curve-fitting program, using equations that describe the competitive interactions of a drug with two noninterconvertible recognition sites (Williams and Jarvis, 1989). A partial F-test (P < 0.01) is used to determine estimates of whether a one- or two-component model best describes the binding data.

Receptor Binding

1.9.9 Current Protocols in Pharmacology

SUPPORT PROTOCOL

AUTORADIOGRAPHIC LOCALIZATION OF ADO RECEPTOR SUBTYPES Quantitative receptor autoradiography is a powerful tool for localizing cell surface receptors in mammalian tissues (see UNIT 8.1). This methodology is particularly useful in studying highly localized ADO receptor populations (e.g., A1 receptors in the CA-3 region of the hippocampus or A2A receptors in the corpus striatum) in different physiological manipulations or following chronic drug treatment. Materials Adult male Sprague-Dawley rats (TAC:SD), 200 to 250 g PBS/sucrose (see recipe) Isopentane, −10°C Gelatin-subbed slides (see recipe) Assay buffer: 50 mM Tris⋅Cl buffer, pH 7.4 at 23°C (APPENDIX 2A), 37°C, 23°C, and ice cold Adenosine deaminase (Type III; Boehringer Mannheim) Kodak D-19 developer (Eastman Kodak) Kodak fixer (Eastman Kodak) CO2 source Model OTF/AS/MR/EC cryostat (Bright/Hacker) Microscope slide mailing tubes (Lab-Tek) Tritium-sensitive film (Bromma) RAS-1000 video-based densitometer (Amersham) or equivalent Radioactive microscales (Amersham; optional) 1. Sacrifice adult male Sprague-Dawley rats with excess CO2. 2. Immediately perfuse rats transcardially by injecting 60 ml PBS/sucrose into the right atrium. 3. Remove brains rapidly and freeze in isopentane at −10°C. Brains can be routinely stored up to 1 month at −70°C.

4. Cut 20-µm sagittal brain sections with a cryostat and thaw-mount onto gelatin-subbed microscope slides (see UNIT 8.1 for details of thaw-mounting procedure). Tissue sections may be stored up to 1 week at −70°C.

Perform binding assay 5. To remove endogenous ADO, preincubate rat brain sections 30 min at 37°C in assay buffer containing 2 IU/ml adenosine deaminase. 6. Incubate tissue sections 2 hr at 23°C in assay buffer containing appropriate concentrations of radioligand and inhibitor. These incubations are best if conducted using microscope slide mailing tubes containing the buffer. An assay volume of 5 ml allows four or five slides to be incubated simultaneously.

7. Follow binding assay protocols for labeling adenosine A1, A2A, or A3 receptors (see Basic Protocols 1, steps 1 to 11; see Basic Protocol 2, steps 1 to 11, or see Basic Protocol 3, steps 1 to 4). 8. Terminate binding reactions on rat brain sections by washing sections in the mailing tubes for 5 min in ice-cold assay buffer, then rinse quickly (3 sec) with ice-cold distilled water to remove buffer salts. Characterization of P1 (Adenosine) Purinoceptors

9. Dry brain sections rapidly using a standard hair dryer.

1.9.10 Current Protocols in Pharmacology

10. Secure dried brain sections to a section of cardboard using double-sided tape and then appose slides to film. When using tritiated ligands, use tritium-sensitive film and expose 21 to 30 days; when using [125I]radioligands, the exposure time can be reduced to 12 to 24 hr. 11. Develop the film using standard photographic techniques (i.e., using the following series of washes): Kodak D-19: Kodak fixer: Water:

4 min at 18°C; 4 min at 18°C; 15 min at 23°C.

Analyze data 12. Perform quantitative analysis of the resulting autoradiograms using video-based densitometery. 13. Convert gray values obtained from each brain region to a measure of radioactivity (dpm/mg tissue) based upon internal brain-paste standards (Jarvis, 1988) or commercially supplied radioactive microscales. Internal brain paste standards are constructed by combining known amounts of radioactivity with homogenized brain tissue, freezing the homogenate, and sectioning as for the rat brain sections.

REAGENTS AND SOLUTIONS Use deionized, distilled water in all recipes and protocol steps. For common stock solutions, see APPENDIX 2A; for suppliers, see SUPPLIERS APPENDIX.

Gelatin-subbed slides Coat slides with 5% gelatin (Type III, Sigma)/5% chromium potassium sulfate in distilled water. Better tissue adhesion is obtained by applying two coats of the subbing solution.

Phosphate-buffered saline (PBS)/sucrose 3 mM sodium phosphate dibasic 15 mM sodium phosphate monobasic 140 mM sodium chloride 75 mM sodium nitrite 10% (w/v) sucrose Store up to 1 month at 4°C COMMENTARY Background Information There is now abundant evidence to indicate that the purine nucleoside ADO functions as a modulator of cellular function in mammalian physiology (Williams and Burnstock, 1997). In addition to its actions on neurotransmission, ADO exerts profound effects on cardiovascular, renal, respiratory, and immune system function (Williams, 1995). The fact that ADO potently inhibits neurotransmitter release in both the central and peripheral nervous systems lead to the hypothesis that it is an inhibitory buffer to excitatory neurotransmission. This hypothe-

sis is strengthened by a large body of evidence demonstrating that ADO antagonists (e.g., caffeine and theophylline) are psychomotor stimulants (Jarvis, 1997). The ability of ADO to modulate cellular activity is mediated primarily through an interaction with cell surface receptors. Historically, the pharmacological characterization of ADO receptor subtypes has been based on an analysis of radioligand binding data coupled with a functional response (e.g., adenylate cyclase activity). This lead to the general classification of two major subtypes of ADO receptors, A1 and

Receptor Binding

1.9.11 Current Protocols in Pharmacology

Characterization of P1 (Adenosine) Purinoceptors

A2. Both classes are coupled to G proteins such that the activation of the ADO A1 receptor subtype causes inhibition of cAMP (cyclic adenosine monophosphate) formation, while similar activation of the A2 receptor stimulates the formation of cAMP. In addition to these different functional effects, the agonist potency profiles of the two receptors, and their regional distribution in brain tissue, are also very different (Jarvis, 1988; Jacobson et al., 1992). More recently, advances in the understanding of the structure-activity relationships for these two receptor subtypes, as well as insights provided by molecular biology and computational chemistry, have provided evidence for the existence of additional ADO receptor subtypes in a variety of mammalian tissues (Jacobson et al., 1992). In addition to the cloning of an A1 receptor from rat brain, the structural identification of two subtypes of the A2 receptor has provided further support for the pharmacological differentiation (Bruns et al., 1986) of this receptor into a high-affinity A2A receptor that is selectively localized in the corpus striatum and a lower-affinity A2B receptor that appears to be distributed more ubiquitously. The A3 receptor was originally cloned from rat testes as an orphan putative G protein– coupled receptor that was found to have significant sequence identity with known ADO receptors. Functional characterization of an identical clone from rat brain resulted in the designation of this receptor as the A3 subtype (Jacobson et al., 1995). Pharmacological characterization of the A2B receptor has been hampered by a lack of highaffinity ligands for this site. The nonselective agonist NECA stimulates the formation of cAMP in cell lines containing the transfected cDNA for the A2B receptor. While [3H]NECA binds the A1, A2A, and A3 receptors with high affinity, its binding to recombinant A2B receptors occurs with low affinity such that it cannot be discriminated from its binding to the ubiquitous nonreceptor ADO (adenotin) binding protein (Jarvis and Saltzman, 1993), a binding site that has some structural homology to mammalian stress proteins (Hutchison et al., 1990). At present, pharmacological evaluation of this ADO receptor subtype is dependent upon a functional analysis of receptor activation in cell lines expressing the recombinant A2B receptor. Pharmacological characterization of the A3 receptor is also dependent on recombinant expression of the mammalian receptor, since there are no selective radioligands for this site (Sherman and Weaver, 1997). A number of ADO

agonists have been radiolabeled and proven useful for characterizing the recombinant A3 receptor. These include [125I]ABA ([125I]Naminobenzyladenosine), [125I]APNEA, and [125I]AB-MECA. However, all of these also have significant affinity for both A1 and A2 receptors (Sherman and Weaver, 1997), limiting their utility in characterizing native A3 sites. Pharmacological analysis of A3 receptors is complicated further by significant interspecies differences in the affinity of antagonists for this receptor subtype. Thus, it has been found that xanthine and nonxanthine ADO antagonists are significantly less active at the rat A3 receptor than at other ADO receptors (Jacobson et al., 1995). However, these antagonists do show significantly greater potency for the human A3 receptor than for the rat A3 receptor subtype. This point is illustrated for the nonxanthine antagonist CGS 15943, which is inactive (Ki >10 µM) at the rat A3 receptor, while binding with high affinity (Ki = 13 nM) at the human A3 receptor (Kim et al., 1996). Recently, the Semliki Forest Virus expression system has been shown to express high levels of human A3 receptor (Patel et al., 1997).

Critical Parameters and Troubleshooting A1 receptor (Basic Protocol 1) The A1 receptor binding assay is relatively straightforward. The stability of both the ligand and the receptor under these assay conditions increases the likelihood of success. [3H]CHA reaches equilibrium relatively quickly and binding is stable for up to 8 hr. The most critical factor in this assay is the preincubation step with adenosine deaminase (ADA). If the assay fails to yield the expected results, the likely culprit is inadequate removal of endogenous ADO. ADA has a limited shelf life (1 to 3 months), and care must be taken to maintain correct protein concentrations during ADA treatment. A2A receptor (Basic Protocol 2) The A2A receptor binding assay to rat brain corpus striatal membranes is straightforward, with the stability of both the ligand and the receptor under the assay conditions increasing the likelihood of obtaining the expected result. The high selectivity of [3H]CGS 21680 for the A2A receptor simplifies the technical requirements of the binding assay as compared to the use of the nonselective agonist [3H]NECA in the presence of 50 nM CPA to label A2A recep-

1.9.12 Current Protocols in Pharmacology

tors (Jarvis et al., 1989). [3H]CGS 21680 attains equilibrium relatively quickly and the binding is stable for at least 4 hr. The most critical parameter in this assay is the preincubation step involving the use of adenosine deaminase. If the assay fails to yield the expected results, the likely culprit is inadequate removal of endogenous ADO. Adenosine deaminase has a limited shelf life (1 to 3 months) and care must be taken to maintain correct protein concentrations during the adenosine deaminase treatment. A3 receptor (Basic Protocol 3) The A3 binding assay using human recombinant A3 receptors is a straightforward radioligand binding procedure. Use of a cell line that overexpresses the recombinant A3 receptor facilitates the pharmacological characterization of this receptor subtype and reduces the complexities of data interpretation associated with the use of nonselective radioligands. [125I]ABMECA attains equilibrium relatively quickly and the binding is stable for several hours. The methodology described herein was developed to label the human A3 receptor with a minimum of nonspecific binding, while also minimizing the use of radioactive material. As with the other ADO receptor assays, the preincubation step with adenosine deaminase is critical. Additionally, the stability of the recombinant human A3 receptor stored at −70°C is limited (≤2 months). If this assays fails to yield the expected results, the likely culprits are inadequate removal of endogenous ADO or receptor degradation. Adenosine deaminase also has a limited shelf life (1 to 3 months) and care must be taken to maintain correct protein concentrations during the adenosine deaminase treatment. Autoradiography (Support Protocol) As with the membrane-homogenate binding assays, ADO radioligands, such as [3H]CHA and [3H]CGS 21680, yield high levels of specific binding, to the extent that nonspecific binding typically does not appear on the film.

Anticipated Results A1 receptor binding (Basic Protocol 1) Specific binding of [3H]CHA to rat cerebral cortical membranes typically averages 85% to 90% of total binding. [3H]CHA labels both high-affinity (Kd = 1 nM) and low-affinity (Kd = 50 to 100 nM) states of the A1 receptor in rat brain provided that sufficiently high concentrations of free radioligand are included in the

saturation binding assay to define the lower-affinity component. The Bmax values for [3H]CHA binding to rat cerebral cortical membranes are ∼400 fmol/mg protein and ∼1 to 2 pmol/mg protein respectively for the high- and low-affinity states. Representative Ki values for compounds that compete for [3H]CHA binding to the recombinant human A3 receptor are shown in Table 1.9.2. Monophasic competition curves (with Hill slopes not significantly different from unity) are typically obtained for agonists competing for 1 nM [3H]CHA binding to rat brain tissue. A2A receptor binding (Basic Protocol 2) Specific binding of [3H]CGS 21680 to rat corpus striatum membranes averages 80% to 90% of the total ligand binding. [3H]CGS 21680 labels both high-affinity (Kd = 15 nM) and low-affinity (Kd = 50 to 100 nM) states of the A2A receptor provided that sufficiently high concentrations of free radioligand are used to define the low-affinity component. The Bmax values for [3H]CGS 21680 binding are ∼300 fmol/mg protein and ∼1 to 2 pmol/mg protein respectively for the high- and low-affinity states of the receptor. Representative Ki values for compounds that compete for [3H]CGS 21680 binding in rat brain corpus striatum are shown in Table 1.9.2. Monophasic competition curves (with Hill slopes not significantly different from unity) are normally observed with agonists competing for 1 nM [3H]CGS 21680 binding to rat brain tissue. A3 receptor binding (Basic Protocol 3) Specific binding of [125I]AB-MECA to recombinant human A3 receptors expressed on HEK 293 cells typically averages 80% of the total ligand binding. [125I]AB-MECA labels a single class of high-affinity recognition sites (Kd value = 1 nM). The apparent Bmax for [125I]AB-MECA binding to human recombinant A3 receptors can be as high as 500 fmol/mg protein using optimal expression systems (Patel et al., 1997). Representative Ki values for compounds that compete for [125I]AB-MECA binding in rat brain are shown in Table 1.9.2. Monophasic competition curves (with Hill slopes not significantly different from unity) are typically obtained for agonists competing for 0.1 nM [125I]AB-MECA binding to rat brain tissue. Autoradiography (Support Protocol) The use of radioligands selective for specific receptor subtypes typically provides excellent

Receptor Binding

1.9.13 Current Protocols in Pharmacology

A C CA1 CA3 CG DG

CP TH

CM

NA

OT

B

NA

OT

Figure 1.9.1 (A) Representative autoradiographic image of specific [3H]CHA (1 nM) binding to rat brain sagittal sections (Jarvis, 1988). (B) Representative autoradiographic image of specific [3H]CGS 21680 (5 nM) binding to ADO A2A receptors in an adjacent rat brain sagittal section. Specific binding is revealed by digital subtraction autoradiography, in which the image of specific binding is obtained through subtraction of the linearized nonspecific binding image from the total binding image. Dark areas represent regions of densely bound radioligand while lighter areas indicate little or no specific binding. Abbreviations: C, cerebral cortex; CP, caudate-putamen; NA, nucleus acumbens; OT, olfactory tubercle; TH, thalamus; CA1 and CA3, CA1 and CA3 regions of hippocampus; CG, cerebellum, granular layer; CM, cerebellum, molecular layer.

Characterization of P1 (Adenosine) Purinoceptors

autoradiograms, thus allowing a discrete and sensitive labeling of ADO receptor populations in brain tissue (Fig. 1.9.1). It should be noted, however, that the use of radioligands with poor selectivity for specific receptor subtypes can greatly complicate the interpretation of autoradiographic images (e.g., A3 radioligands; Sherman and Weaver, 1997).

Time Considerations Given the length of the assay incubation time, ∼3 hr are required to complete basic saturation and/or competition studies in Basic Protocols 1 and 2. Allow ∼2 hr to complete basic saturation and/or competition studies using Basic Protocol 3. Allow an extra 3 to 4 hr, plus appropriate developing time, to perform autoradiography using the Support Protocol.

1.9.14 Current Protocols in Pharmacology

Literature Cited Bradford, M.M. 1976. A rapid and sensitive method for the quantitation of microgram quantities of protein utilizing the principle of protein-dye binding. Anal. Biochem. 72:248-254. Bruns, R.F., Lu, G.H., and Pugsley, T.A. 1986. Characterization of the A2 adenosine receptor labeled by [3H]NECA in rat striatal membranes. Mol. Pharmacol. 29:331-346. Cunha, R.A., Johansson, B., Constantino, M.D., Sebastiao, A.M., and Fredholm, B.B. 1996. Evidence for high affinity binding sites for the adenosine agonist [3H]CGS 21680 in rat hippocampus and verebral cortex that are different from striatal A2a receptors. Naunyn-Schmiedeberg’s Arch. Pharmakol. 349:374-380. Emson, P.C. and Koob, G. 1978. The origin and distribution of dopamine-containing afferents to the rat frontal cortex. Brain Res. 142:249-267. Hutchison, K.A., Nevins, B., Perini, F., and Fox, I.H. 1990. Soluble and membrane associated low affinity adenosine binding proteins (adenotin): Properties and homology with mammalian and avian stress proteins. Biochemistry 29:51385144.

Kim, Y.C., Ji, X.D. and Jacobson, K.A. 1996. Derivatives of the triazoloquinazoline adenosine antagonist (CGS 15943) are selective for the human A3 receptor subtype. J. Med. Chem. 39:4142-4148. Patel, M., Harris, C., and Lundstrom, K. 1997. The binding of [125I]AB-MECA to the human cloned adenosine A3 receptor using the Semliki Forest Virus Expression system. Drug Dev. Res. 40 (in press). Paxinos, G. and Watson, C. 1986. The Rat Brain in Stereotaxic Coordinates. Academic Press, New York. Sherman, L.P. and Weaver, D.R. 1997. [125]4-Aminobenzyl-5′-N-methylcarboxamidoadenosine ([125I]AB-MECA) labels multiple adenosine receptor subtypes in rat brain. Brain Res. 745:1020. Williams, M. 1995. Purinoceptors in Central Nervous System Function: Targets for Therapeutic Intervention. In Psychopharmacology: The Fourth Generation of Progress (F.E. Bloom and D.J. Kupher, eds.) pp. 643-655. Raven Press, New York.

Jacobson, K., van Galen, P., and Williams, M. 1992. Adenosine receptors: Pharmacology, structureactivity relationships and therapeutic potential. J. Med. Chem. 35:407-422.

Williams, M. and Jarvis, M.F. 1989. Biochemical approaches to drug discovery and characterization. In Modern Drug Discovery Technologies (C.R. Clarke and W.H. Moos, eds.) pp. 129-166. VCH Publishers, New York.

Jacobson, K.A., Kim, H.O., Siddiqi, S.M., Olah, M.E., Stiles, G.l., and von Lubitz, D.K.J.E. 1995. A3-adenosine receptors: Design of selective ligands and therapeutic prospects. Drugs Future 20:689-699.

Williams, M. and Risley, E.A. 1980. Biochemical characterization of putative purinergic receptors by using 2-chloro-[3H]adenosine, a stable analog of adenosine. Proc. Natl. Acad. Sci. U.S.A. 77:6892-6896.

Jarvis, M.F. 1988. Autoradiographic localization and characterization of brain adenosine receptor subtypes. In Receptor Localization: Ligand Autoradiography (F. Leslie and C.A. Altar, eds.) pp. 95-113. Alan R. Liss, New York. Jarvis, M.F. 1997. Psychomotor aspects of adenosine receptor activation. In Purinergic Approaches in Experimental Therapeutics (K.A. Jacobson and M.F. Jarvis, eds.) pp. 405-421. Wiley-Liss, New York. Jarvis, M.F. and Saltzman, A. 1993. [3H]5′-N-Ethylcarboxamidoadenosine selectively labels the low affinity adenosine binding protein, adenotin, on intact Chinese hamster ovary cells. Drug. Dev. Res. 29:305-309. Jarvis, M.F., Schulz, R., Hutchinson, A., Do, U., Sills, M., and Williams, M. 1989. [3H]CGS 21680, a selective A2 adenosine receptor agonist directly labels A2 receptors in rat brain. J. Pharmacol. Exp. Ther. 251:888-893.

Key References Jarvis, 1988. See above. Jarvis et al., 1989. See above. Williams, M. and Burnstock, G. 1997. Purinergic neurotransmission and neuromodulation: An historical perspective. In Purinergic Approaches in Experimental Therapeutics (K.A. Jacobson and M.F. Jarvis, eds.) pp. 3-26. Wiley-Liss, New York.

Internet Resources http://mgddk1.niddk.nih.gov:8000/ Web site of the NIH Molecular Recognition Section.

Contributed by Michael F. Jarvis Abbott Laboratories Abbott Park, Illinois

Receptor Binding

1.9.15 Current Protocols in Pharmacology

Characterization of Angiotensin II Receptors

UNIT 1.10

Angiotensin II (Ang II) is the primary mediator of the renin angiotensin system (RAS; Timmermans et al., 1993; Griendling et al., 1996). Ang II acts primarily as a circulating hormone, but may also act as a paracrine or autocrine factor. The active octapeptide is synthesized primarily from angiotensinogen substrate (in the liver) by the sequential actions of renin (from renal juxtaglomedullary cells), which produces the biologically inactive angiotensin I, and angiotensin converting enzyme (ACE, on endothelial cells), which forms the biologically active Ang II. Ang II may also be synthesized in blood vessels, heart, kidney, and brain, although the importance of these sources remains controversial. Ang II binds specifically to G protein–coupled receptors. In most cells, Ang II binds to its specific receptor (AT1) and initiates a Gq-coupled activation of phospholipase C (PLC). PLC in turn generates inositol 1,4,5-trisphosphate and diacylglycerol, thereby effecting stimulation of intracellular Ca2+ release and protein kinase C activation (Catt et al., 1993; see UNIT 2.1). Ang II binding triggers a number of characteristic cellular responses, including vascular smooth muscle contraction and adrenal cellular aldosterone secretion. Following binding, the Ang II receptor complex undergoes rapid internalization and recycling. Ang II receptors are widely distributed in mammalian tissues, but vary significantly in density among and within organ systems. The biological activity of Ang II fragments such as desaspartyl–Ang II (Ang III), Ang 3-8 (Ang IV), and Ang 1-7 has also been reported. Ang III does not have a separate receptor, but rather binds to the Ang II site. In contrast, Ang IV has a unique receptor, at least in some species. No receptor has yet been identified for Ang 1-7. It should be noted that Ang II and its peptide fragments can be generated by a number of proteolytic enzymes. Although an awareness of these precursors or breakdown products must be kept in mind when studying Ang II receptors, the focus of this unit is the identification of Ang II binding sites and receptor subtypes. It is now clear that there are multiple receptor subtypes for Ang II (see Table 1.10.1); however, the distinction between “binding sites” versus “receptors” with their associated functional manifestations has been controversial (see Commentary). There are two principal human receptor subtypes, designated AT1 and AT2. These are defined by the selective affinity of the nonpeptide antagonists losartan and PD123177 (Smith and Timmermans, 1994). In rodents, additional isoforms of the Ang II receptor have been cloned (e.g., AT1A and AT1B), although their functional distinctions are undetermined.

Table 1.10.1

Characteristics of Cloned Angiotensin Receptors

Subtype

Species

GenBank no.a Agonistsa,b

Antagonistsa,b

AT1

Human

P30556

Ang II, Ang III

AT2

Human

P50552

AT1A AT1B

Rodent Rodent

NA NA

Ang II, CGP42112 (CG) NA NA

Losartan (EM), EXP3174 (EM), valsartan (CG), irbesartan (BMS), candesartan (A) PD123319 (RB), PD123177 NA NA

aNot available for rodent. bSuppliers: A, Astra; BMS, Bristol-Meyers Squibb; CG, Ciba-Geigy; EM, EM Science; RB, Research Biochemicals.

PD123177 is no longer commercially available.

Receptor Binding Contributed by Ronald D. Smith, Dale E. McCall, and Pieter B.M.W.M. Timmermans Current Protocols in Pharmacology (1998) 1.10.1-1.10.17 Copyright © 1998 by John Wiley & Sons, Inc.

1.10.1 Supplement 1

This unit describes radioligand competition binding assays that are used to determine the affinity of Ang II for receptors on adrenal cortical microsomes (see Basic Protocol), whole adrenal membranes (see Alternate Protocol 1), or CHO cells transfected with the AT1A receptor (see Alternate Protocol 2). NOTE: Ang is the recommended abbreviation of the hormone angiotensin. The amino acid sequence of human [Ile5] angiotensin-(1-10) decapeptide (Ang I) is the reference for all angiotensin peptides (i.e., human Ang II is [Ile5]angiotensin-(3-10) octapeptide, having the sequence Asp-Arg-Val-Tyr-Ile-His-Pro-Phe). The Ang receptor is abbreviated AT, and should be preceded by the species (i.e., human AT or rat AT). Receptor subtypes are identified by the subscripts 1 or 2, (i.e., AT1 or AT2). Additional isoforms of receptor subtypes are designated by the subscripts A or B (i.e., AT1A or AT1B; DeGasparo et al., 1995). BASIC PROTOCOL

MEASUREMENT OF ANGIOTENSIN II AFFINITY USING COMPETITION BINDING ASSAYS IN ADRENAL CORTICAL MICROSOMES The receptor affinity assay is performed by measuring radioligand binding as a function of the concentration of unlabeled Ang II (displacement method; UNIT 1.3). With this procedure, a fixed concentration of labeled ligand is displaced with increasing concentrations of unlabeled Ang II or peptide antagonist (Chiu et al., 1989b). To define the affinity for a specific receptor subtype, the experiments can be conducted in the presence of receptor subtype–specific agents (e.g., the portion of AT2 sites can be determined in the presence of AT1 receptor blockade with losartan, and the portion of AT1 sites can be determined in the presence of the AT2 antagonist PD123177; Chiu et al., 1989a). PD123177 is not commercially available, but another related AT2-selective compound, PD123319, can be purchased from Research Biochemicals, and has similar high specificity for the AT2 binding site. Materials Adult male rats (250 to 350 g) Sucrose buffer, pH 7.4 (see recipe), ice cold Tris buffer 1, pH 7.2 (see recipe), ice cold Bio-Rad Protein Assay Reagent (optional) 8% dimethylsulfoxide (DMSO; EM Science) 80 µM unlabeled Ang II working solution (see recipe) Unlabeled peptides and test compounds in 8% DMSO 1.2 nM [125I]Ang II working solution (see recipe) 2 mM 2-n-butyl-4-chloro-5-(hydroxymethyl)-1-{[2′-(1H-tetrazol-5-yl) biphenyl4-yl]methyl}-imidazole (losartan; mol. wt. 461; research samples for nonhuman studies can be obtained by qualified individuals free of charge from Merck Research Laboratories) 2 mM S-(+)-1-{[4-dimethylamino-3-methylphenyl]methyl}-5-(diphenylacetyl)4,5,6,7-tetrahydro-1H-imidazo[4,5,-c]pyridine-6-carboxylic acid ditrifluoroacetate (PD123319; Research Biochemicals) 0.2% (v/v) polyethylenimine (PEI; available as 50% aqueous solution; Sigma)

Characterization of Angiotensin II Receptors

Chilled aluminum block or equivalent cold dissection surface Ground-glass tissue grinder or tissue homogenizer with Teflon pestle and 0.004- to 0.006-in. clearance between pestle and tube (Ten Broeck or equivalent) Talboy model 1342 T-line stirrer 30-ml polycarbonate centrifuge tube with cap (Sorvall) Sorvall RT 6000 tabletop centrifuge Fine cheesecloth

1.10.2 Supplement 1

Current Protocols in Pharmacology

Sorvall OTD 65B ultracentrifuge with T865 rotor Ultrasonicator (Braun-sonic 1510 with 3/8-in. intermediate probe) Plate reader (Dynatech Labs) 96-well polypropylene microtiter plates (Costar) 934AH or GF/B glass fiber filters (Whatman) Cell harvester (Packard Micromate 196 or Brandel M-24 or M-48) 80°C oven Gamma counter (Packard TopCount or Packard Cobra II) 12 × 75–mm polystyrene test tubes Additional reagents and equipment for Bradford assay (APPENDIX 3A) with BSA standard Prepare adrenal cortical membranes 1. Remove adrenal glands from adult male rats. On a chilled aluminum block, remove the adventitial fat using microforceps and scissors. Place the adrenals in a beaker of fresh, ice-cold sucrose buffer to remove residual blood. 2. Separate the adrenal cortex from the medulla by squeezing the adrenal gland between thumb and forefinger, being certain to remove all of the medulla. Place the cortices and medullae in separate beakers, each containing 10 ml fresh, ice-cold sucrose buffer (maximum 20 adrenals/10 ml). 3. Homogenize tissues using ∼5 up-and-down passes in a chilled ground-glass tissue grinder turned by a T-line stirrer (torque 750 rpm maximum at setting 40). 4. Decant the suspension into a chilled 30-ml polycarbonate centrifuge tube. Rinse the grinding tube and pestle with 10 ml ice-cold sucrose buffer and add to the homogenate. Cap the centrifuge tube and mix gently. 5. Centrifuge the homogenate 10 min at 3000 × g, 4°C, in a Sorvall RT 6000 tabletop centrifuge. 6. Filter the supernatant through fine cheesecloth and centrifuge the filtrate 10 min at 12,000 × g, 4°C, in a Sorvall OTD 65B ultracentrifuge with a T865 rotor. 7. Discard the pellet and centrifuge the supernatant 60 min at 102,000 × g, 4°C (Sorvall OTD 65B). 8. Resuspend the resulting pellet by adding 1 ml ice-cold Tris buffer 1, and loosen material from the walls of the tube by placing tube in an ultrasonicator at 30 W, 10 sec maximum. 9. Transfer the crude pellet suspension with a pipettor to the tissue grinder, and rinse the tube and pipettor with sufficient ice-cold Tris buffer 1 to attain a volume of 10 ml. Repeat homogenization procedure as in step 3. Dilute the homogenate with ice-cold Tris buffer 1 to achieve one adrenal cortex (or medulla) for each 2 ml of buffer. No peptidase inhibitors are used in this protocol. If degradation of Ang II is a concern, several inhibitor cocktails have been used successfully (see Critical Parameters and Troubleshooting, discussion of assay conditions).

10. Assay the protein concentration of a 10-µl aliquot of the tissue homogenate using the Bradford method (APPENDIX 3A) and BSA as a standard protein. Alternatively, dilute 1 vol Bio-Rad Protein Assay Dye Reagent in 3 vol of distilled H2O, add 300 µl diluted reagent to 10 µl tissue homogenate, and read at 595 nm on a plate reader, again using BSA as a standard. Calculate the protein concentration from the standard protein curve. Adjust protein concentration of the tissue homogenate to 130 to 200 µg/ml with ice-cold Tris buffer 1.

Receptor Binding

1.10.3 Current Protocols in Pharmacology

Supplement 1

Prepare assay plate 11. Prepare a reference map for a 96-well polypropylene microtiter plate. Perform samples in duplicate, and designate two wells for determination of nonspecific binding and four wells for total binding. Nonspecific binding wells will contain 10 ìM unlabeled Ang II. Total binding wells will contain no unlabeled peptides or test compounds. All other wells will contain varying concentrations of unlabeled Ang II or test compounds. Quadruplicate total binding wells are used because total binding is more variable than nonspecific binding.

12. Add 25 µl of 8% DMSO to total binding wells, 25 µl of 80 µM unlabeled Ang II working solution to nonspecific binding wells (10 µM final), and 25 µl of the appropriate dilution of unlabeled peptides or test compounds to all remaining wells. 13. Add 25 µl of 1.2 nM [125I]Ang II working solution to each well (0.15 nM final assay concentration). [125I]Ang II is certified to be >99% radiochemically pure. Degradation can be assessed using thin layer chromatography, but is usually not necessary. Reorder radioligand if nonspecific binding increases over time.

14. Optional: Block specific receptor subtypes by diluting 2 mM losartan (to block AT1) or 2 mM PD123319 (to block AT2) in tissue homogenate at a final concentration of 1 µM. Perform binding assay 15. Add 150 µl tissue homogenate to each well (final assay volume 200 µl). There are abundant binding sites in adrenal tissue and therefore very good specific binding is observed using 150 ìl (containing 20 to 30 ìg protein) per well. The signal-to-noise ratio (i.e., total-to-nonspecific binding) should range from 9:1 to 10:1 (85% to 95% specific binding) for this tissue.

16. Incubate the microtiter plate 60 min at room temperature (∼22°C) on an orbital shaker set at a very slow speed. The incubation period of 60 min has been determined by separate experiments in which the total binding of radiolabeled Ang II was sampled at various times after addition of a single concentration of [125I]Ang II to the tissue homogenate/microsomal preparation. While maximum binding was achieved in less than 60 min, incubation for this period ensures equilibrium.

17. During the incubation, soak glass fiber filters 5 min in 0.2% PEI. 18. Terminate the incubation by aspirating the well contents onto the PEI-soaked filters with a cell harvester. Rinse the assay plate and filters seven times with 200 µl Tris buffer 1. After the last rinse, wait 30 sec, raise the filter head, and let the filters dry 1 min by vacuum. Remove the filter mat and dry 10 min in an 80°C oven. 19. Count filters 10 to 20 min on a Packard TopCount gamma counter. Alternatively, separate individual circular filters from the strip, place them into 12 × 75–mm polystyrene test tubes, and quantify radioactivity with a Packard Cobra II gamma counter. Analyze data 20. Calculate specific binding by subtracting nonspecific binding from total binding (see steps 11 and 12). Calculate the percent inhibition, IC50, and Ki values (UNIT 1.3). Characterization of Angiotensin II Receptors

Table 1.10.2 shows sample data from a ligand displacement experiment. Calculations were performed using the Excel spreadsheet function “Trend.” The IC50 value for Ang II is 3.5 nM. IC50 values can also be calculated using KaleidaGraph curve fit, where y = 100/[1 + (m1/m0)^m2] (Fig. 1.10.1). The IC50 value calculated by this method is 3.38 ± 0.12 nM.

1.10.4 Supplement 1

Current Protocols in Pharmacology

Table 1.10.2

Sample Data of [125I]Ang II Binding to Rat Adrenal Microsomes

Unlabeled test compound (nM)

Specific [125I]Ang II bindinga

[125I]Ang II binding Sample 1 (cpm) Sample 2 (cpm)

0 10,000d 100 30 10 3 1 0.3

9098 9244

9322 9827

1108 1572 2874 5058 7266 8697

722 1373 2921 5358 6910 7687

Mean (cpm) 8981 392 524 1083 2506 4817 6701 7801

% Totalc

ln(logit)b

100 0 2.8 12.2 27.9 53.6 74.6 86.9

−2.78 −1.99 −0.95 0.15 1.08 1.89

aSpecific binding equals mean of duplicate samples minus nonspecific binding. bln(logit) equals the natural logarithm of the logit numbers: x% bound/100 − x% bound. At 50% bound, ln(logit) = 0. c% Total equals percent of mean specific binding with no unlabeled compound. dNonspecific binding.

100 y = 100/ [1 + (m1/m0)^m2] value error m1 3.3793 0.11753 m2 –0.85763 0.024154 χ2 NA 7.3724 0.99912 NA R2

% Control

80

60

40

20

0 0.1

1

101

102

103

Angiotensin II (nM)

Figure 1.10.1 Displacement of [125I]Ang II by unlabeled Ang II in rat adrenal cortical microsomes plotted by KaleidaGraph curve fit (m0 = [Ang II]; m1 = IC50). NA, not applicable.

MEASUREMENT OF ANGIOTENSIN II AFFINITY FOR SPECIFIC RECEPTOR SUBTYPES IN WHOLE ADRENAL MEMBRANES The Basic Protocol can be modified as desired for alternate radioligands and/or alternate tissue (receptor) preparations. As an example, this protocol describes the use of labeled [Sar1Ile8]Ang II and whole adrenal membranes. AT1 and AT2 receptor subtypes are found in most tissues including the adrenal glands, but the total binding and the relative proportions of these sites varies greatly. Ang II and related peptide analogs such as [Sar1Ile8]Ang II have equal affinity for both receptor subtypes, whereas the nonpeptide antagonist losartan and PD123319 (or PD123177) are selective for AT1 and AT2 sites, respectively. Thus, the displacement experiment described in the Basic Protocol can be modified to characterize the relative population of each receptor present in a given tissue by adding either losartan or PD123319 to selectively block AT1 or AT2 sites, respectively.

ALTERNATE PROTOCOL 1

Receptor Binding

1.10.5 Current Protocols in Pharmacology

Supplement 1

Additional Materials (also see Basic Protocol) Tris buffer 2, pH 7.7 (see recipe), ice cold Resuspension buffer (see recipe), ice cold 52 µM unlabeled [Sar1Ile8]angiotensin II acetate salt ([Sar1Ile8]Ang II; mol. wt. 968.2; Sigma) in 8% DMSO 1.4 nM [125I][Sar1Ile8]Ang II working solution (see recipe) Binding assay buffer (see recipe) 0.1% (w/v) BSA (Sigma) Tris/NaCl buffer (see recipe), ice cold Polytron tissue homogenizer (Brinkmann) with ST-20 probe 37-ml polypropylene centrifuge tubes (Sorvall) Sorvall RC5B superspeed centrifuge and SS-34 rotor 96-deep-well polypropylene microtiter plates (Beckman) Prepare whole adrenal suspension 1. Isolate adrenal glands as described (see Basic Protocol, step 1). Weigh whole adrenals and homogenize 100 mg in 10 ml ice-cold Tris buffer 2, using a Brinkmann Polytron with ST-20 probe, 15 sec on setting 5. 2. Rinse Polytron with 5 ml ice-cold Tris buffer 2 and add it to the homogenate. Pour the homogenate into a ground-glass tissue homogenizer with Teflon pestle, bring the total volume to 35 ml with Tris buffer 2, and homogenize with five passes of the Teflon pestle. 3. Centrifuge the homogenate 15 min at 47,800 × g, 4°C, in 37-ml polypropylene centrifuge tubes with a Sorvall RC5B superspeed centrifuge and SS-34 rotor. 4. Discard the supernatant and resuspend the pellet in 20 ml ice-cold Tris buffer 2 with the Polytron (5 sec at setting 5). Rinse the Polytron with Tris buffer 2, add effluent to the homogenate, and bring to a total volume of 35 ml. 5. Repeat centrifugation and resuspension twice using ice-cold resuspension buffer. Resuspend the final pellet in ∼150 vol ice-cold resuspension buffer (e.g., 15 ml for four adrenals weighing 25 mg each) using the Polytron (5 sec at setting 5), and keep on ice until needed. 6. Determine protein concentration in a 100-µl aliquot (see Basic Protocol, step 10). Adjust to 1.1 to 1.6 mg protein/ml with ice-cold resuspension buffer. Prepare assay plates 7. Prepare a reference map for a 96-deep-well polypropylene microtiter plate. Designate two wells for determination of nonspecific binding and four wells for total binding. Nonspecific binding wells will contain 1 ìM [Sar1Ile8]Ang II. Total binding wells will contain no unlabeled Ang II or test compound. All remaining wells will contain unlabeled [Sar1Ile8]Ang II or test compounds. Additional total binding wells are used because total binding is more variable than nonspecific binding.

8. Add 10 µl of 8% DMSO to total binding wells, 10 µl of 52 µM unlabeled [Sar1Ile8]Ang II (1 µM final) to nonspecific binding wells, and 10 µl of appropriately diluted test compound to all remaining wells. 9. Add 10 µl of 1.4 nM [125I][Sar1Ile8]Ang II working solution to each well (∼0.025 nM final assay concentration). Characterization of Angiotensin II Receptors

10. Dilute 5.2 ml membrane suspension (step 6) with 47 ml binding assay buffer (1/10) immediately before use.

1.10.6 Supplement 1

Current Protocols in Pharmacology

% Specific [125I][Sar1Ile8]Ang II binding

100 Ang II

80

60 Saralasin 40

20

0 11

10

9

8

7

6

5

4

–log[inhibitor] (M)

Figure 1.10.2 Inhibition of [125I][Sar1Ile8]Ang II binding by unlabeled Ang II and saralasin in rat whole adrenal membranes pretreated with PD123319 to block AT2 sites. The IC50 values for Ang II and saralasin are 16 nM and 0.36 nM, respectively. The IC50 values were calculated by linear regression analysis of the logit transform of the parent bound data.

11. Add 26 µl of 2 mM losartan to 52 ml diluted membrane suspension (1 µM final) to block AT1 sites, or add 26 µl of 2 mM PD123319 (1 µM final) to block AT2 sites. Perform binding assay 12. Add 500 µl diluted, receptor-blocked membrane suspension to each well (final 52 to 78 µg protein per 520-µl assay volume) and mix briefly on orbital plate shaker. 13. Incubate the assay plate 90 min in a 37°C shaking water bath. 14. During the incubation, soak GF/B glass fiber filters 15 min in 0.1% BSA. 15. Terminate the incubation by adding 0.5 ml ice-cold Tris/NaCl buffer and immediately filtering with the Brandel M-48 cell harvester through the presoaked GF/B glass fiber filters. Rinse the assay plate and filters four times with 1 ml Tris/NaCl buffer. 16. Air dry filters 1 min with the harvester head open. Separate individual circular filters from the strip and place each in a 12 × 75–mm polystyrene test tube. Count the filters 1 min in a gamma counter (Packard Cobra II). 17. Calculate specific binding as total binding minus nonspecific binding (binding in the presence of 1 µM unlabeled [Sar1Ile8]Ang II). Calculate the IC50 for unlabeled compounds by plotting specific binding versus concentration of test compound (see UNIT 1.3). Figure 1.10.2 depicts an experiment comparing inhibition of [125I][Sar1Ile8]Ang II binding by unlabeled Ang II to inhibition by unlabeled saralasin ([Sar1Ala8]Ang II).

Receptor Binding

1.10.7 Current Protocols in Pharmacology

Supplement 1

ALTERNATE PROTOCOL 2

MEASUREMENT OF ANGIOTENSIN II AFFINITY FOR CLONED RECEPTORS ON TRANSFECTED CELLS The cloning of Ang II receptors has enabled the study of receptor subtypes in cells that do not normally express these sites. Stably transfected cell lines, such as CHO cells, can provide a ready supply of receptor for binding studies. Described in this protocol is a binding assay employing CHO cells stably transfected with a rat AT1A receptor clone using a transfectase lipofection procedure (Fukushige and Sauer, 1992). While most studies with transfected cells utilize membranes prepared from an homogenized cell pellet (Chiu et al., 1993), this is an Ang II binding assay using intact cloned cells. Additional Materials (also see Basic Protocol) AT1A-transfected Chinese hamster ovary (CHO) cells (NEN Life Sciences) CHO growth medium (see recipe) 0.25 nM [125I]Ang II working solution (see recipe) 50 µM unlabeled Ang II working solution (see recipe) HEPES buffer (see recipe), ice cold and room temperature 50× test compound (inhibitor) in HEPES buffer CHAPS buffer (see recipe) 24-well culture plates 1. Grow AT1A-transfected CHO cells in CHO growth medium in 24-well culture plates. Cells are harvested two passages from thaw and split on confluency, and at no time permitted to grow past confluency.

2. Prepare radiolabeled assay solutions by mixing 245 µl of 0.25 nM [125I]Ang II working solution with 5 µl of the following (one per solution): 50 µM unlabeled Ang II working solution, 0.25 nM [125I]Ang II working solution, HEPES buffer only, or 50× test compound. 3. Aspirate medium from 24-well plates using a Pasteur pipet attached to a vacuum source. Do not wash cells. 4. Add 250 µl radiolabeled assay solution from step 2 to each assay well and incubate plates 60 min at room temperature (∼22°C) on an orbital shaker at low speed. 5. Terminate the incubation by aspirating the radioactive solution with a 24-well Brandel cell harvester. Wash the wells three times with 0.5 ml ice-cold HEPES buffer using the cell harvester. 6. Add 250 µl CHAPS buffer to each well and shake several minutes on an orbital plate shaker. 7. Aspirate the solution from the wells with a micropipettor and transfer to 12 × 75–mm polystyrene test tubes. Analyze with a gamma counter (Packard, Cobra II). 8. Determine specific binding and calculate the IC50 values (UNIT 1.3). Figure 1.10.3 depicts the inhibition of Ang II receptor binding by various inhibitors.

Characterization of Angiotensin II Receptors

1.10.8 Supplement 1

Current Protocols in Pharmacology

% Specific [125I]Ang II binding

100

80

Ang II Iosartan PD123177 saralasin Ang I Ang III

60

40

20

0 10

9

8

7

6

5

4

–log[inhibitor] (M)

Figure 1.10.3 The inhibition of [125I]Ang II binding in intact Chinese hamster ovary cells expressing AT1A receptors. The IC50 values are 2.3 nM (Ang II), 22 nM (losartan), >1 µM (PD123177), 1.7 nM (saralasin), 87 nM (Ang I—reflects conversion to Ang II), and 6.4 nM (Ang III). IC50 calculations were performed as in Table 1.10.2 and Figure 1.10.1.

REAGENTS AND SOLUTIONS Use deionized, distilled water in all recipes and protocol steps. For common stock solutions, see APPENDIX 2A; for suppliers, see SUPPLIERS APPENDIX.

Angiotensin II (Ang II) working solution, unlabeled, 80 and 50 ìM Prepare a 1 mM stock solution of unlabeled human Ang II (Asp-Arg-Val-Tyr-IleHis-Pro-Phe, mol. wt. 1046; Peninsula) in water. Divide into 50-µl aliquots. Store up to 1 year at −20°C. Prepare 80 µM working solution by adding 575 µl of 8% dimethylsulfoxide (DMSO) to a 50-µl aliquot. Prepare 50 µM working solution by adding 950 µl HEPES buffer (see recipe) to a 50-µl aliquot. Prepare working solutions fresh before each use. [125I]Ang II working solution, 0.25 and 1.2 nM Reconstitute [Tyr-125I]Ang II (5-L-isoleucine; NEN Life Sciences; 2200 Ci/mmol) to 50 µCi/ml (∼23 nM) in distilled water. Divide into 150-µl aliquots in small polyethylene vials (Nalgene) and store up to 1 month at −70°C. Prepare a 1.2 nM working solution by adding 2.7 ml Tris buffer 1 (see recipe) to a 150-µl aliquot. Prepare a 0.25 nM working solution by adding 13.5 ml HEPES buffer (see recipe) to a 150-µl aliquot. Prepare working solutions fresh before each use. When diluted in the incubation wells, the final assay concentration is 0.15 nM. This amount is sufficient for one 96-well assay plate.

[125I][Sar1Ile8]Ang II working solution, 1.4 nM Reconstitute [Tyr-125I][Sar1Ile8]Ang II (NEN Life Sciences; 2200 Ci/mmol) at 50 µCi/ml (∼23 nM) in distilled water. Divide into 175-µl aliquots in small polyethylene vials (Nalgene) and store up to 1 month at −20°C. Make a 1.4 nM working solution fresh before each use by adding ∼2.7 ml binding assay buffer (see recipe) to a 175-µl aliquot. When diluted in the incubation wells, the final assay concentration is ∼0.025 nM (∼40,000 cpm per 10 ìl). This amount is sufficient for two 96-well assay plates.

Receptor Binding

1.10.9 Current Protocols in Pharmacology

Supplement 1

Binding assay buffer 100 ml resuspension buffer (see recipe) 20 mg soybean trypsin inhibitor (type II, Sigma) 14 mg bacitracin (Sigma) 1.98 mg phenanthroline (Sigma; dissolve in 100 µl methanol, then add slowly to solution) Make fresh daily CHAPS buffer 20 mM sodium acetate (2.72 g/liter) Adjust to pH 4.0 with NaOH Add 1% (w/v) 3-[(3-cholamidopropyl)dimethylammonio]-1-propanesulfonate (CHAPS; Sigma) Store up to 4 weeks at 22° to 25°C CHO growth medium Eagle’s minimum essential medium (EMEM) α medium containing: 10% FBS 200 mM L-glutamine 5 µg/ml gentamicin 200 µg/ml G418 Store up to 1 week at 4°C HEPES buffer 20 mM HEPES acid (Sigma; 4.77 g/liter) 130 mM NaCl (7.6 g/liter) 1.5 mM CaCl2 (0.167 g/liter) 5 mM KCl (0.373 g/liter) 1 mM MgCl2⋅6H2O (0.203 g/liter) Adjust pH to 7.2 with NaOH Store up to 4 weeks at 4°C Resuspension buffer 120 mM NaCl (6.96 g/liter) 10 mM Na2HPO4 (1.42 g/liter) 5 mM disodium ethylenediaminetetraacetic acid (Na2EDTA; Fisher; 1.86 g/liter) 0.1 mM phenylmethylsulfonyl fluoride (PMSF; Sigma; 17.4 mg/liter) Mix all but PMSF and adjust pH to 7.4 with 5 N NaOH. Store up to 1 week at 4°C. Before use, dissolve PMSF completely in 0.5 ml absolute ethanol, then add slowly (dropwise) to solution with stirring. Sucrose buffer 250 mM sucrose (85.6 g/liter) 10 mM Tris base (1.21 g/liter) 1 mM disodium ethylenediaminetetraacetic acid (Na2EDTA; Fisher; 0.372 g/liter) Prepare fresh and adjust pH to 7.4 with 1 N HCl Tris buffer 1, pH 7.2 50 mM Tris base (Sigma; 6.055 g/liter) 5 mM MgCl2⋅6H2O (EM Science; 1.017 g/liter) Adjust pH to 7.2 with 6 N HCl (∼7 ml) Store up to 4 weeks at 4°C Characterization of Angiotensin II Receptors

1.10.10 Supplement 1

Current Protocols in Pharmacology

Tris buffer 2, pH 7.7 50 mM Tris⋅Cl (7.38 g/liter) Adjust pH to 7.7 at 25°C, pH 8.26 at 5°C Store up to 4 weeks at 4°C Tris/NaCl buffer 50 mM Tris base (6.055 g/liter) 0.9% NaCl (9 g/liter) Adjust to pH 7.4 with 6 N HCl Store up to 4 weeks at 4°C COMMENTARY Background Information The most recent proposal for the nomenclature of angiotensin (Ang) receptors included only two major subgroups of mammalian Ang receptors, AT1 and AT2 (DeGasparo et al., 1995). Although the functional importance of the AT2 receptor remains controversial, it has been cloned and its gene has been localized to chromosome X. According to the current nomenclature, radiolabeled Ang II binding sites inhibited by losartan or related molecules (or by high concentrations of DTT) are designated AT1, whereas binding sites inhibited by PD123177 (and related molecules such as PD123319 and PD123198) and by CGP42112 are designated AT2. Both of these Ang II binding sites are inhibited by unlabeled Ang II and by peptide analog agonists (i.e., [Sar1Ile8]Ang II) and partial agonists (i.e., [Sar1Ala8]Ang II also known as saralasin). If an Ang II binding site is not inhibited by losartan or PD123177, but is inhibited by a peptide agonist/antagonist like saralasin, the binding site is designated as atypical. Such atypical sites have been described in neuro 2A neuroblastoma cells, bovine adrenal cortex, and microorganisms such as mycoplasma that may contaminate cell cultures (Timmermans and Smith, 1994). Ang II receptor radioligand competition binding assays have been used extensively to localize sites of Ang II action and to identify receptor antagonists (Table 1.10.3). Information generated from this type of experiment includes (1) the affinity of Ang II (usually expressed as Kd), (2) the number of Ang II binding sites (expressed as the Bmax in fmol or pmol/mg protein), (3) the ratio of AT1/AT2 receptor subtypes, and (4) the affinity of a test compound (expressed as IC50 or Ki). The basic assumptions of the assay are that the incubation of the membrane-bound receptor and ligand is sufficient to allow maximum association of the

ligand with the receptor; the concentration of the ligand is near the Kd (half-maximum binding); and the binding reaction proceeds to equilibrium. The results are determined by a number of key experimental parameters including (1) the source and preparation of the tissue containing Ang II receptors, (2) the radioligand used, and (3) the assay conditions.

Critical Parameters and Troubleshooting Source of receptors Ang II binding sites are widely distributed in mammalian tissues, although their expression varies considerably from species to species and from tissue to tissue within a species. In potentially important target tissues such as the kidney, heart, and brain, Ang II binding sites are localized on specific cell types. For example, in the kidney they are found on efferent and afferent arterioles, mesangial cells, sympathetic nerves, and proximal tubules. In the inferior olive of the rat brain, AT2 receptors are highly expressed, whereas AT1 receptors predominate in human brain. The AT2 site is highly expressed in fetal tissue, and may have a role in growth and development. Although an antiproliferative role has been reported in isolated spontaneously hypertensive rat coronary endothelial cells in culture, the AT2 knockout mouse appears to reproduce and develop normally. While specific actions of Ang II are unlikely in the absence of binding sites, the presence of such sites proves neither cause nor effect. In normal and failing heart, the total binding is low (i.e., 6 fmol/mg protein in heart versus 6 pmol/mg protein in adrenal glands). In human heart, the AT2 site appears to predominate, but the functional (inotropic and norepinephrinereleasing) effects of Ang II appear to be AT1mediated (Timmermans and Smith, 1994).

Receptor Binding

1.10.11 Current Protocols in Pharmacology

Supplement 1

Table 1.10.3

Ang II Affinity and Receptor Density in Different Tissues

Receptor source

Radioligand

Kd (nM)

Rat adrenal cortexa [3H]Ang II b Human renal artery [125I]Ang II c Human adrenal gland Human right atriumd Rabbit ovarye Human uterine artery (pregnant)f Human uterine artery (nonpregnant) Rat liverg PC/2W cellsh AT1A-transfected CHO cellsi

1.2 0.53 ± 0.02 0.32 ± 0.03 0.65 ± 0.03 [125I]Ang II 1.3 (range 0.6-2.0) 125 1 8 [ I][Sar Ile ]Ang II 0.27 ± 0.04 [125I]Ang II 0.8 ± 0.01 [125I]Ang II

0.9 ± 0.01

[125I][Sar1Ile8]Ang II 0.30 ± 0.07 [125I][Sar1Ile8]Ang II 0.25 [125I]Ang II 3.05 ± 0.27

Bmax (fmol/ng)

Protein assay Tempera- Time Receptor (µg/µl) ture (°C) (min) subtype

2.600 365 ± 135 771 ± 102 325 ± 23 294 (range 111-2,073) 44 ± 18 221 ± 36

20-30 10-35 10-35

25 25

60 60

5±3

25

50

AT1 > AT2 AT1 AT1 AT2 AT2 > AT1

20 37-133

22 18

60 90

AT1 AT2 > AT1

159 ± 27

36-110

20,100 853 ± 17 134 ± 26

50 20 68-90

AT2 > AT1 21-23 25 22

120 90 120

AT1 AT2 AT1

aChiu et al. (1989b). bWhitebread et al. (1989). cChiu et al. (1993); Chang et al. (1995). dRogg et al. (1996). eFeral et al. (1996). fCox et al. (1996). gGrove and Speth (1993). hMeffert et al. (1996). iChiu et al. (1993).

Characterization of Angiotensin II Receptors

Autoradiographic studies show that the Ang II binding sites are localized in interstitial areas. However, such studies cannot discern a low level of binding that is distributed diffusely throughout the many cell types in cardiac tissue. The use of radiolabeled Ang II binding assays to screen potential new nonpeptide receptor antagonists has proven quite useful with the discovery of AT1-selective antagonists (i.e., losartan, valsartan, candesartan, and eprosartan; Timmermans et al., 1993), AT2-selective antagonists (i.e., PD123177, EXP801, and L159,686) and compounds with high affinity for both AT1 and AT2 receptors (i.e., XR510 and L-163017; Wexler et al., 1996). All of these molecules were first tested for their ability to inhibit [125I]Ang II or [125I][Sar1Ile8]Ang II binding. Compounds with low nanomolar affinity for the Ang II receptors were then studied for their effects on the functional responses of Ang II in isolated tissue (i.e., the contractile response of rabbit aorta). There is a strong correlation between the IC50 to inhibit Ang I binding and the IC50 (pA2) to inhibit the functional response of Ang II in isolated tissue.

Further, there is an excellent correlation between the pA2 values in rabbit aorta and the blood pressure–lowering effects of a test compound. The ultimate effectiveness of losartan and the other nonpeptide AT1-selective receptor antagonists to block the effects of Ang II in normal volunteers, and to lower blood pressure in hypertensive patients, has validated the usefulness of binding assays in the drug discovery process. In the case of the PD123177 series, these compounds were identified because of the presence of DTT in the assay buffer, which blocked AT1 receptors and selected for AT2-selective compounds. The discovery of the AT2selective agonist/antagonist CGP42112 followed the use of tissue from the human uterus, which is high in Ang II AT2 binding sites. Ang II binding studies with cultured cells are an important way to study Ang II receptor subtypes and to evaluate potential new receptor agonists and antagonists. Now that the human AT1 and AT2 receptors have been cloned, human recombinant receptors have been expressed and stably transfected into cells not normally expressing these receptors (i.e., CHO

1.10.12 Supplement 1

Current Protocols in Pharmacology

cells; Chiu et al., 1993). These cells provide a ready source of receptor for drug screening. Likewise, mutant Ang II receptors have been prepared using yeast expression vectors in which specific amino acids have been deleted, or in which parts of the Xenopus sequence have been exchanged for the human sequence. In these studies, Ang II binding affinity data are used to identify critical amino acid binding sites. The affinities of Ang II and the nonpeptide receptor antagonists for mutated receptors have been shown to be quite different, suggesting that the amino acid binding sites are unique. The study of AT2 receptor function has also been facilitated by the use of cells that exclusively express the AT2 site, including rat PC12W pheochromocytoma cells and human 3T3 fibroblasts. The tissue or cell homogenates utilized in most studies (see examples in Table 1.10.3) are prepared in a similar fashion to those described in this unit. The total counts achieved with [125I]Ang II or [125I][Sar1Ile8]Ang II are high, with nonspecific counts responsible for only ∼10% of the total (Whitebread et al., 1989). However, in some tissues (e.g., human atria), it may be necessary to optimize the tissue grinding or centrifugation steps, or to try different filtration techniques to increase specific binding. BSA (0.1% to 0.2%) and/or bacitracin (0.1 mM) are often added to the homogenization solution and should be used if adequate counts are not achieved. Resuspension of membrane pellets can also be achieved in KCl (0.6 M) and histidine (30 mM) at pH 7.0 to solubilize actin and myosin filaments in cardiac tissue. In such studies, measurement of the plasma membrane marker 5′-nucleotidase is used to confirm the nature of the binding sites (Nozawa et al., 1996). Choice of radioligand The commercial availability of HPLC-purified, high–specific activity [125I]Ang II has largely eliminated the need to label peptides or to use [3H]Ang II. [125I][Sar1Ile8]Ang II is also widely used, presumably because it is less susceptible to proteoylsis. However, most binding studies are performed in the presence of multiple enzyme inhibitors, so this is not a valid reason to avoid the use of Ang II. There is some evidence that [125I]Ang II and [125I][Sar1Ile8]Ang II may yield different results. It has been reported, for example, that if [125I]Ang II is used, AT2 sites appear to predominate, whereas if [125I][Sar1Ile8]Ang II is

used, both AT1 and AT2 sites appear to be abundant in rat brain (Chang et al., 1990). Recently, a number of radiolabeled nonpeptide receptor antagonists have become commercially available, including [3H]losartan and [125I]EXP985 (NEN Life Sciences). Both have been evaluated in rat adrenal cortical microsomes and have been shown to bind to highaffinity sites. In the direct comparison of [3H]losartan and [3H]Ang II in adrenal cortical microsomes, the Kd values were 6.4 nM (Bmax 1.4 pmol/mg protein) and 2.4 nM (Bmax 2.1 pmol/mg protein), respectively (Chiu et al., 1990). In rat liver, however, [3H]losartan has been shown to have high affinity for non–Ang II sites, and unlabeled Ang II displaces less than one-third of its specific binding in rat liver, limiting its use in studying AT1 receptors (Grove and Speth, 1993). The non–Ang II binding of losartan likely reflects the high degree of protein binding (i.e., in plasma, losartan is >99% bound to plasma proteins; Christ, 1995). However, it should be noted that this does not imply that losartan is unable to completely displace all radiolabeled Ang II. Unlabeled losartan has been shown to inhibit Ang II binding in a wide variety of tissues and to completely block functional responses to Ang II in vitro and in vivo in all species tested, including man (Timmermans et al., 1993). A number of other radiolabeled nonpeptide AT1-selective receptor antagonists have been r ep or ted, including [3H]valsartan, 3 3 [ H]SKF108566, and [ H]LF70156. LF70156 appears to have less nonspecific binding than losartan, although both completely block Ang II (Nouet et al., 1994). SKF108566 appears to bind to different amino acid sites within the AT1 receptor than losartan (Schambye et al., 1995), so differences in binding of the radioligand may be expected. [125I]CGP42112 is a peptide molecule with selectivity for the AT2 site. Interpretation of results with this molecule are confounded by both agonist and antagonist actions and by its binding to non–Ang II sites (Johren et al., 1995). All of these compounds display significant protein binding, which must be taken into account when interpreting results. Assay Conditions The principal assay condition variables are (1) the use of peptidase inhibitors, (2) the incubation time and temperature, and (3) the amount of tissue (receptor protein) added. Additional factors include the addition of BSA, divalent cations, and other agents such as DTT that may affect binding.

Receptor Binding

1.10.13 Current Protocols in Pharmacology

Supplement 1

Ang II is highly sensitive to degradation by tissue peptidases, and this degradation is a potential source of error in estimating receptor affinity. Such degradation can affect determination of the maximum number of receptors, or can alter the linearity of binding as a function of receptor concentration. Either effect leads to an underestimation of Kd and Bmax. To minimize radioligand degradation, a number of protease inhibitors can be added to the assay medium, including benzamidine, phosphoramidon, pepstatin A, leupeptin, and amastatin at 1 µg/ml; bacitracin and antipain at 0.01% (w/v); and aminopeptidase M and bestatin at 50 to 200 µM (see references in Table 1.10.3).

A

Most Ang II binding assays are conducted at ambient temperature (18° to 25°C). Ang II receptor binding is stable at 0°C, has minimal loss at 22°C, and is unstable at 37°C (30% decrease in 1 hr). Ang II and its receptor can also undergo rapid internalization and recycling. This process is greatest at 37°C, minimal at 22°C, and absent at 0° to 4°C. At 4°C, however, the maximum binding is markedly reduced (McQueen and Semple, 1991). The incubation time is established by assessing binding at various times following addition of the radioligand to the assay. The incubation time should be long enough to achieve the maximum response (usually 50 to 120 min),

0.07 0.06

B/T

0.05 0.04 0.03 0.02 0.01

0

B

0.07

B/F

–9.7

0.035

–8.667 –7.633 log [Ang II] (M)

–6.6

0 0

Characterization of Angiotensin II Receptors

1.5 Bound (x 10 –10)

3

Figure 1.10.4 Displacement of [125I]Ang II by unlabeled Ang II in rat adrenal cortical microsomes. (A) Increasing concentrations of [125I]Ang II were added to a fixed amount of adrenal cortical membrane homogenate to create a ligand displacement curve (data from Table 1.10.2). Nonspecific binding was determined in the presence of 10 µM unlabeled Ang II. (B) The Scatchard plot produces a straight line and provides evidence for interaction of Ang II with a single site. The Kd for unlabeled Ang II is 3.934 nM, and the Bmax is 2.468 pmol/mg of protein. Data were analyzed using LIGAND. B, bound; F, free; T, unlabeled [Ang II] (M).

1.10.14 Supplement 1

Current Protocols in Pharmacology

when it is assumed that the ligand-receptor binding has reached equilibrium. If maximum binding is achieved in a shorter period of time, degradation of the ligand must be considered. The amount of receptor added is limited by the filtration process. In tissues such as adrenal glands and liver, and in transfected cell lines, the receptor number and the total counts are high. However, in tissues such as rabbit ovary, the Bmax is low, and higher amounts of protein may be desirable. If the increase in maximum counts is not linear from 10 to 100 µg protein, ligand breakdown in the assay might be suspected, whereas with higher amounts of protein, filter problems may be involved. Increasing assay volumes (i.e., from ∼150 to 500 µl) may also improve the assay when using tissues with low receptor density. Another component of the assay mixture is nonspecific protein (i.e., BSA), which prevents the absorption of Ang II to the incubation vessel. BSA is used in concentrations of 0.1% to 1%, and can markedly affect the binding of some nonpeptide receptor antagonists. Although the IC50 values for unlabeled Ang II are not affected by 0.25% BSA, the IC50 for DuP 532 (an AT1-selective antagonist related to losartan) decreases from 4.7 µM in the presence of BSA to 3 nM in its absence. The apparent binding affinity of other nonpeptide molecules such as EXP3174 (a metabolite of losartan) may be affected by BSA, while others, including losartan and L163017, are not (Chiu et al., 1991). Sodium and a number of divalent cations (including Ca2+, Mg2+, and Mn2+) increase equilibrium binding to membranes from a variety of tissues, including vascular smooth muscle, liver, and myocardium. Of these, NaCl (120 to 150 mM) and MgCl2 (5 to 10 mM) are most often included in the assay. Special note should be made of the use of DTT in the assay buffer. DTT at 0.1 to 0.5 mM increases Ang II binding, whereas higher concentrations may inhibit binding (McQueen and Semple, 1991). In one study, DTT inhibited binding to rat aorta smooth muscle cells (IC50 = 5 mM) while increasing binding in the human uterus (IC50 = 2.5 mM; Whitebread et al., 1989). Thus, caution must be exercised when adding solutes to the incubation medium, including the execution of appropriate control experiments.

Anticipated Results Figure 1.10.4 shows the results of a displacement experiment using rat adrenal cortical mi-

crosomes. Transformation of the raw data and a subsequent Scatchard plot yields a straight line (Fig. 1.10.4B), which is supportive of the hypothesis that Ang II interacts with a single site on the receptor (UNIT 1.3). An example of representative data for an AT receptor binding assay is shown in Table 1.10.2, and additional information on AT receptor binding in various tissues is listed in Table 1.10.3. Figures 1.10.1 and 1.10.2 show typical radioligand displacement curves using the protocols described in this unit.

Time Considerations The time required to complete the binding analysis of Ang II receptors is largely dependent on the source of receptors. The preparation of microsomes takes >3 hr, whereas the membrane preparation takes 300 >300 8.0

2.0 2.0 0.5 >300 >300 2.0 3.0 0.5 10 >300 >300

4.0 3.0 1.0 >300 >300 4.0 2.0 2.0 15 >300 >300

ah, human; p, porcine. Peptides can be purchased from Bachem California or

Peninsula Labs. BIBP 3226 can be obtained from Research Biochemicals. bAssayed in the presence of 200 nM unlabeled pNPY 13-36. cAssayed in the presence of 100 nM unlabeled p[Leu31Pro34]NPY.

Basic Protocol 1 and Alternate Protocol 1 can also be performed in a microtiter plate format. In this case the binding reaction is performed in round-bottomed 96-well plates, and the total assay volume is adjusted to 100 µl. The reaction is terminated using a Packard Filtermate 196 cell harvester and UniFilter 96 GF-C plates. Otherwise, the protocol is the same as described for the 48-tube filtration format. Materials Frozen rat cerebral cortex (Y1) or hippocampus (Y2), dissected fresh and immediately frozen, or purchased frozen from a commercial vendor (e.g., Pel-Freez) Homogenization buffer (see recipe), ice cold Tissue binding buffer (see recipe), ice cold Competing ligands: unlabeled peptide YY (PYY), reference peptides (e.g., Bachem California or Peninsula Labs; see Table 1.11.2), or other test compounds 25 nM [125I]PYY (Amersham; ∼2200 Ci/mmol; store aliquots at −20°C) 50 mM HEPES buffer, pH 7.4, ice cold Motor-driven, glass-fitted Teflon homogenizer, chilled High-speed refrigerated centrifuge (e.g., Beckman model J2-MI) Motorized tissue homogenizer (e.g., Brinkmann Polytron) 12 × 75–mm polypropylene tubes Whatman GF/C filters 0.1% (w/v) polyethyleneimine (PEI) Brandel 48-sample cell harvesting unit NOTE: Buffers should be kept cold. Always keep tissue homogenates and pellets on ice. Characterization of Neuropeptide Y (NPY) Receptors

1.11.2 Supplement 1

Current Protocols in Pharmacology

Prepare tissue 1. Weigh out frozen, dissected rat cerebral cortex (for Y1 receptors) or hippocampus (for Y2 receptors), and thaw tissue in 20 vol ice-cold (4°C) homogenization buffer in a chilled glass homogenizing tube fitted to a motor-driven, Teflon homogenizer. One gram of tissue is sufficient for 200 assay tubes.

2. Homogenize tissue using 10 strokes. Transfer the homogenate to a clean 50-ml centrifuge tube and centrifuge the homogenate 10 min at 1000 × g, 4°C, in a high-speed refrigerated centrifuge. Steps 8 to 11 (setting up assay tubes and ligands) can be performed as time allows during preparation of the tissue.

3. Carefully pour the supernatant into a clean, preweighed 50-ml centrifuge tube. Discard the pellet. Centrifuge the supernatant 10 min at 48,000 × g, 4°C. 4. Pour off and discard the supernatant. Resuspend the pellet in the original volume of ice-cold tissue binding buffer using a motorized tissue homogenizer. Centrifuge the homogenate 10 min at 48,000 × g, 4°C. 5. Repeat step 4. 6. Discard the supernatant. Dry the inside of the centrifuge tube with tissue paper, taking care not to disrupt the pellet. Dry the outside of the tube. Weigh the pellet and tube. Calculate the tissue wet weight by subtracting the weight of the tube from the combined weight of the tube and pellet. 7. Resuspend the pellet in tissue binding buffer at 2 mg wet weight/ml. Perform competition binding studies 8. Set up 12 × 75–mm polypropylene tubes for triplicate assays, as well as tubes for making dilutions of competing ligands. Also assign control tubes for determination of nonspecific binding. Alternatively, prepare plate map if using a microtiter plate format. Scale down reaction volumes to 100 µl (500-µl assay described here). Total binding is defined as the amount of [125I]PYY bound in the absence of any competing, unlabeled ligand. Nonspecific binding is defined as that portion of total binding that is not displaceable by an excess of unlabeled PYY (1 ìM final).

9. Prepare competing ligands at 20× final assay concentration in ice-cold tissue binding buffer, and add 25 µl to the appropriate assay tubes. Use several concentrations of competing ligand (generally eight or more) to determine receptor number and affinity. A range of final concentrations between 10 pM and 10 ìM is sufficient. Rat cerebral cortex contains a heterogeneous mixture of Y1 and Y2 receptors. To selectively study Y1 receptors, include a Y2-selective compound (e.g., NPY13-36, 100 nM final) when adding competing ligands to block [125I]PYY binding to Y2 receptors. Rat hippocampus is enriched in Y2 receptors; therefore, no masking agent is required. For further details, see Critical Parameters and Troubleshooting, discussion of receptor-ligand selectivity.

10. Dilute 25 nM [125I]PYY to 1 nM in ice-cold tissue binding buffer. Add 25 µl (∼80,000 cpm) to each assay tube (final 50 pM). 11. Add the appropriate amount of ice-cold tissue binding buffer to each tube to bring the volume to 100 µl. 12. Initiate the binding reaction by adding 400 µl tissue homogenate to each assay tube (final assay volume 500 µl). Incubate 120 min at room temperature. 13. During incubation, soak Whatman GF/C filters 2 hr in 0.1 % PEI.

Receptor Binding

1.11.3 Current Protocols in Pharmacology

Supplement 1

%[125I]PYY specific binding

A

Y1 (cortex) 110 100 90 80 70 60 50 40 30 20 10 0

hNPY hPYY hPP p[Leu31Pro34 ] NPY

hNPY3-36 pNPY13-36

%[125I]PYY specific binding

B

Y2 (cortex)

110 100 90 80 70 60 50 40 30 20 10 0

%[125I]PYY specific binding

C

Y2 (hippocampus)

110 100 90 80 70 60 50 40 30 20 10 0 –10

–9

–8 log[peptide] (M)

–7

–6

Figure 1.11.1 Specific [125I]PYY binding to Y1 (A) and Y2 (B) receptor subtypes in rat brain cerebral cortical membranes, and (C) to Y2 receptors in hippocampal membranes. The binding assay was performed as described in the text (see Basic Protocol 1). Each point represents the mean of at least three independent determinations.

Characterization of Neuropeptide Y (NPY) Receptors

1.11.4 Supplement 1

Current Protocols in Pharmacology

14. Terminate the reaction by rapid filtration over presoaked Whatman GF/C filters using a Brandel 48-sample cell harvester. Rinse filters twice with 2 ml ice-cold 50 mM HEPES buffer (pH 7.4). 15. Place individual filters into clean 12 × 75–mm polypropylene tubes and quantify the membrane-bound radioactivity using a gamma counter. 16. Determine specific binding by subtracting nonspecific binding from total binding. Plot specific binding versus concentration of inhibitor (Fig. 1.11.1). Determine binding parameters (i.e., Bmax, Kd, and Ki) as described in UNIT 1.3. SATURATION ANALYSIS TO DETERMINE Y1 AND Y2 RECEPTOR NUMBER AND AFFINITY IN RAT BRAIN TISSUES

ALTERNATE PROTOCOL 1

This protocol employs a range of radioligand concentrations to determine receptor number and affinity in rat brain membranes. Total and nonspecific binding are determined for each radioligand concentration. This protocol can also be modified for a microtiter plate format (see Basic Protocol 1 introduction). 1. Prepare tissue as described in Basic Protocol 1, steps 1 to 7. 2. Set up 12 × 75–mm polypropylene tubes for triplicate assays, as well as control tubes for determination of nonspecific binding. Alternatively, prepare plate map if using a microtiter plate format. Scale down reaction volumes to 100 µl (500-µl assay described here). 3. Prepare a range of 20× [125I]PYY radioligand concentrations necessary to achieve final assay concentrations ranging from 0.01 to 10 nM. The final radioligand concentration should range from one-tenth to ten times the predicted equilibrium dissociation constant (Kd) of the radioligand. The affinity of Y1 and Y2 receptors for [125I]PYY is between 0.1 and 1.0 nM.

4. Add 25 µl of 20× radioligand to the appropriate tubes (see Basic Protocol 1, step 10). 5. To determine nonspecific binding, add 25 µl of 20 µM unlabeled PYY (final 1 µM) to the appropriate tubes. 6. Follow steps 11 to 16 as described in Basic Protocol 1. COMPETITION ANALYSIS TO DETERMINE Y1 AND Y2 RECEPTOR NUMBER AND AFFINITY IN HUMAN NEUROBLASTOMA CELL LINES

BASIC PROTOCOL 2

This protocol describes the use of the human neuroblastoma cell lines SK-N-MC and SK-N-BE2 to study Y1 and Y2 receptors, respectively. KAN-TS human neuroblastoma cells, which are commercially available, can also be used to study Y2 receptors (also see Critical Parameters and Troubleshooting, discussion of receptor source). The binding assay is performed using whole cells, and the protocol is similar for all three cell lines. Materials Human neuroblastoma cell lines: SK-N-MC (American Type Culture Collection, ATCC, no. HTB 10), SK-N-BE2 (W. Karbon), or KAN-TS (Amersham Research Biochemicals) Growth medium (see recipe) Competing ligands: unlabeled peptide YY (PYY), reference peptides (e.g., Bachem California or Peninsula Labs; see Table 1.11.3), or other test compounds Cell binding buffer (see recipe) 25 nM [125I]PYY (Amersham; ∼2200 Ci/mmol; store aliquots at −20°C)

Receptor Binding

1.11.5 Current Protocols in Pharmacology

Supplement 6

Table 1.11.3 Pharmacological Characterization of [125I]PYY-Labeled Y1 and Y2 Receptors in Human Neuroblastoma Cells

Compounda hNPY hPYY p[Leu31Pro34]NPY h[Leu31Pro34]PYY pNPY2-36 hNPY3-36 hPYY3-36 pNPY13-36 hPP BIBP 3226

Ki (nM) Y1 (SK-N-MC) Y2 (SK-N-BE2) 3.0 2.0 3.0 5.0 22 110 80 300 >300 10

3.0 1.0 220 250 5.0 5.0 1.0 3.0 >300 >300

ah, human; p, porcine. Peptides can be purchased from Bachem Cali-

fornia or Peninsula Labs. BIBP 3226 can be obtained from Research Biochemicals.

Eagle’s minimal essential medium (EMEM; Life Technologies), ice cold 1 N NaOH Humidified 95% O2/5% CO2 incubator Fibronectin-coated 24-well tissue culture plates (Becton Dickinson Labware) 12 × 75–mm polypropylene tubes Additional reagents and equipment for counting cells (Phelan, 1998) 1. Maintain human neuroblastoma cells in growth medium at 37°C in a humidified 95% O2/5% CO2 incubator. Count cells with a hemacytometer or Coulter counter. 2. Plate cells at a density of 5 × 105 cells/well in 24-well, fibronectin-coated tissue culture plates. Set up sufficient wells to perform assays in triplicate. Return plates to the incubator and culture 24 hr. 3. On the day of the assay, prepare competing ligands prior to removal of growth medium. Dilute competing ligands to 10× final assay concentrations in room temperature cell binding buffer. Use several concentrations of competing ligand (generally six or more) to determine receptor number and affinity. A range of final concentrations between 10 pM and 10 ìM is sufficient.

4. After 24 hr culture, remove growth medium and replace with 400 µl cell binding buffer. 5. Add 50 µl of 10× competing ligand to the appropriate wells. Add 50 µl cell binding buffer instead of competing ligand to wells used to determine total binding. Total binding is defined as the amount of [125I]PYY bound in the absence of any competing, unlabeled ligand. Nonspecific binding is defined as that portion of total binding that is not displaceable by an excess of unlabeled PYY (1 ìM final concentration in assay).

Characterization of Neuropeptide Y (NPY) Receptors

6. Dilute 25 nM [125I]PYY to 0.5 nM in cell binding buffer. Initiate the binding reaction by adding 50 µl (∼80,000 cpm) to each well (final 50 pM [125I]PYY in 500 µl assay volume).

1.11.6 Supplement 6

Current Protocols in Pharmacology

7. Incubate 120 min at room temperature. Terminate the reaction by aspirating the cell binding buffer and washing the cells twice with 0.5 ml/well of ice-cold EMEM. 8. Aspirate EMEM and solubilize the cells by adding of 750 µl of 1 N NaOH to each well. Transfer the total volume of each well to 12 × 75–mm polypropylene tubes, and quantify radioactivity using a gamma counter. 9. Determine specific binding by subtracting nonspecific binding from total binding. Plot specific binding versus concentration of inhibitor (Fig. 1.11.1). Determine binding parameters (i.e., Bmax, Kd, and Ki) as described in UNIT 1.3. SATURATION ANALYSIS TO DETERMINE Y1 AND Y2 RECEPTOR NUMBER AND AFFINITY IN HUMAN NEUROBLASTOMA CELL LINES

ALTERNATE PROTOCOL 2

This assay employs a range of radioligand concentrations to determine receptor number and affinity in human neuroblastoma cells. Total and nonspecific binding are determined for each radioligand concentration. 1. Prepare and plate cells as described in Basic Protocol 2, steps 1 and 2. 2. Prepare a range of 10× [125I]PYY radioligand concentrations necessary to achieve final assay concentrations ranging from 0.01 to 10 nM. The final radioligand concentration should range from one-tenth to ten times the predicted equilibrium dissociation constant (Kd) of the radioligand. The affinity of Y1 and Y2 receptors for [125I]PYY is between 0.1 and 1.0 nM.

3. After 24 hr culture, remove culture medium, replace with 400 µl cell binding buffer. 4. Add 50 µl of 10 µM unlabeled PYY (1 µM final) to the appropriate wells to determine nonspecific binding. Add 50 µl cell binding buffer to those wells that are not exposed to unlabeled PYY (total binding wells). 5. Add 50 µl of 10× radioligand to all wells. 6. Perform steps 7 to 9 as described in Basic Protocol 2. REAGENTS AND SOLUTIONS Use deionized, distilled water in all recipes and protocol steps. For common stock solutions, see APPENDIX 2A; for suppliers, see SUPPLIERS APPENDIX.

Cell binding buffer Eagle’s minimal essential medium (EMEM; Life Technologies) containing: 25 mM N-2-hydroxyethylpiperazine-N′-2-ethanesulfonic acid (HEPES; Sigma) 1 mM CaCl2 (Sigma) 1 mM MgCl2 (Sigma) Adjust to pH 7.4 Store up to 1 month at 4°C On the day of the assay, add 1-ml aliquots of the following per 100 ml buffer: 100 mg/ml BSA (final 1 mg/ml; Sigma) 10 mg/ml soybean trypsin inhibitor (final 0.1 mg/ml; Boehringer Mannheim) 10 mg/ml 4-(2-aminoethyl)-benzenesulfonyl fluoride hydrochloride (AEBSF; final 0.1 mg/ml; Boehringer Mannhein) BSA and inhibitor stocks can be stored up to 6 months at −20°C.

Receptor Binding

1.11.7 Current Protocols in Pharmacology

Supplement 1

Growth medium 880 ml Eagle’s minimal essential medium (EMEM; Life Technologies) 100 ml heat-inactivated FBS (Irvine Scientific) 10 ml penicillin/streptomycin/glutamine (Life Technologies) 10 ml nonessential amino acids (Life Technologies) Adjust to pH 7.4 Store up to 1 month at 4°C Warm to room temperature prior to use Homogenization buffer 10 mM N-2-hydroxyethylpiperazine-N′-2-ethanesulfonic acid (HEPES; Sigma) 0.32 M sucrose (Sigma) Adjust to pH 7.4 Store up to 1 month at 4°C Tissue binding buffer 50 mM N-2-hydroxyethylpiperazine-N′-2-ethanesulfonic acid (HEPES; Sigma) 1 mM CaCl2 (Sigma) 1 mM MgCl2 (Sigma) Adjust to pH 7.4 Store up to 1 month at 4°C On the day of the assay, add 1-ml aliquots of the following per 100 ml buffer: 100 mg/ml BSA (final 1 mg/ml; Sigma) 10 mg/ml soybean trypsin inhibitor (final 0.1 mg/ml; Boehringer Mannheim) 10 mg/ml 4-(2-aminoethyl)-benzenesulfonyl fluoride hydrochloride (AEBSF; final 0.1 mg/ml; Boehringer Mannhein) BSA and inhibitor stocks can be stored up to 6 months at −20°C.

COMMENTARY Background Information

Characterization of Neuropeptide Y (NPY) Receptors

Neuropeptide Y receptors An important step in understanding the physiological role of neuropeptide Y (NPY) and related peptides lies in identifying the receptors with which they interact. The first suggestion of the existence of multiple NPY receptors came from studies on sympathetic neuroeffector junctions, which revealed that long C-terminal fragments of NPY (e.g., NPY13-36) exhibited a selective prejunctional effect, whereas the full-length peptide had both preand postjunctional actions (Wahlestedt et al., 1986). Accordingly, a nomenclature was introduced designating the receptor requiring the intact peptide as Y1, and the receptor activated by long C-terminal fragments as Y2 (Wahlestedt et al., 1987). The Y1 receptor was additionally defined as having high affinity for C-terminalsubstituted analogs such as [Pro34]NPY, a modification that is not well tolerated by Y2 receptors (Fuhlendorff et al., 1990). Four additional NPY receptors have been described and designated as Y3, Y4 (also known

as PP1), Y5, and y6 (PP2), each of which has a unique pharmacological profile. Y1 and Y2 receptors are activated by NPY and peptide YY (PYY), but not pancreatic polypeptide (PP; Wahlestedt et al., 1991; Krause et al., 1992; Rose et al., 1995). The Y3 receptor recognizes NPY, but not PYY or PP (Michel, 1991). Y4 receptors have high affinity for PP and much lower affinity for NPY and PYY (Bard et al., 1995; Yan et al., 1996), whereas Y5 receptors bind NPY, PYY, and human PP with high affinity (Gerald et al., 1996; Hu et al., 1996). The mouse y6 gene encodes a functional, full length receptor whose pharmacological properties vary depending upon the conditions used to study the receptor identified (Weinberg et al., 1996; Gregor et al., 1996), but an orthologous rat gene has not been identified. The y6 genes from human and all other primate species studied contain one or more mutations predicting a truncated, nonfunctional receptor (Gregor et al., 1996; Matsumoto et al., 1996), suggesting that the gene has been uniformly inactivated in primates. Of the six reported NPY receptor subtypes, only Y3 has not been cloned. Cell

1.11.8 Supplement 1

Current Protocols in Pharmacology

lines that functionally express NPY receptors have been developed (Krause et al., 1992; Rose et al., 1995; Gerald et al., 1996; Yan et al., 1996), including a commercially available cell line expressing human Y1 (BioSignal). Y1 and Y2 are the most well-characterized NPY receptor subtypes, in part due to the identification of tissues and cell lines in which they are expressed. Accordingly, this unit focuses on protocols for determining Y1 and Y2 receptor number and affinity. Competition versus saturation analysis Both competition and saturation studies are used to determine NPY receptor number and affinity. Saturation analysis is used to determine the Kd of the radioligand (e.g., [125I]PYY) and provides the most accurate estimate of receptor number. Determining the Kd of the radioligand is critical, because this value is necessary to determine Ki values for competing ligands in competition studies. Saturation analysis is also useful in distinguishing between competitive and noncompetitive receptor-drug interactions. In most cases, a competitive inhibitor will alter radioligand affinity, whereas a noncompetitive inhibitor will reduce receptor number with little or no effect on receptor affinity. In addition, the effects of in vivo drug administration on receptor number and affinity are best assessed using saturation analysis in membranes prepared from affected tissues. Competition analysis is best used to validate or determine the pharmacological identity of a given receptor by assessing the affinity of the receptor for a range of pharmacological agents, and for determining the potencies of novel compounds.

Critical Parameters and Troubleshooting Receptor source Using tissue preparations to study receptors offers an advantage over using immortalized cell lines in that the receptors are in a tissue that is of interest to the investigator. However, tissue receptor populations are often heterogeneous, making it difficult to study an individual receptor subtype. Therefore, it is important when using tissue preparations to carefully characterize receptor binding and to use masking agents to increase the selectivity of the assay (see discussion of receptor-ligand selectivity below). Clonal cell lines are advantageous because they express a homogeneous population of re-

ceptors, making them useful tools to characterize receptor pharmacology and signaling properties. SK-N-MC and HEL cells are most commonly used to study Y1 receptors, whereas SK-N-BE(2), SMS-KAN, and SMS-MSN express Y2 receptors (Wan and Lau, 1995). Cell lines are also easily manipulated, allowing for investigations of receptor regulation under in vitro conditions. However, clonal cell lines are transformed and often overexpress receptors, two factors that can influence receptor signaling and regulation. Receptor-ligand selectivity At least six NPY receptor subtypes are known, four of which have been cloned (see Background Information). Each has a distinct pharmacological profile as determined in binding studies on stably transfected cells, but most NPY-family peptides and their derivatives recognize more than one receptor subtype. Therefore, it is critical to rigorously ascertain the pharmacological identity of the binding sites being studied by determining the potencies of a variety of reference peptides before assigning a subtype designation. This is particularly important when performing binding studies in tissue preparations containing a heterogeneous population of NPY receptors (as is the case for most brain regions). Carefully analyze the displacement curves. Shallow displacement curves often suggest the existence of a mixed population of labeled receptors with different affinities for the competing ligand (see UNIT 1.3). Incomplete displacement typically indicates that the competing ligand recognizes a subpopulation of labeled sites. Most rat brain regions contain predominantly Y2 receptors, and [125I]PYY binds to both Y1 and Y2 receptors with high affinity. Accordingly, the Y1 protocol described in this unit incorporates the use of a Y2-selective compound (NPY13-36) to mask [125I]PYY binding to Y2 receptors in rat brain cortex. Frontoparietal cortex has been described as being enriched in Y1 receptors (Dumont et al., 1995), but the use of a masking agent is still required to selectively study Y1 receptor binding in this brain region. An alternative approach to the use of masking agents is to use a receptor subtype–selective radioligand. For example, Dumont et al. (1995) have described the use of [125I][Leu31Pro34]PYY to selectively label Y1 receptors in rat brain tissue. However, it has recently been shown that C-terminal-substituted analogs of NPY and PYY bind not only to Y1, but also to

Receptor Binding

1.11.9 Current Protocols in Pharmacology

Supplement 1

Y4 and Y5 receptors (Gerald et al., 1996; Yan et al., 1996). Therefore, caution must be exercised in choosing the appropriate radioligand. Because rat hippocampus and hypothalamus are enriched in Y2 receptors, a masking agent is typically not required to selectively label Y2 sites with [125I]PYY in these brain regions. Nonetheless, [125I]PYY3-36 might be used as an alternative to [125I]PYY, as the former has high affinity for Y2 and very low affinity for Y1 receptors (Dumont et al., 1995). However, it must be remembered that PYY3-36 also has a high affinity for Y5 receptors, and Y5 mRNA has been detected in both hippocampus and hypothalamus, suggesting the presence of Y5 receptors in these brain regions (Gerald et al., 1996). Specific binding The protocols described in this unit have been demonstrated to yield a high percentage of specific binding, typically in the range of 80% to 90%. A lower-than-expected level of specific binding might indicate that the binding reaction is not at equilibrium. A kinetic experiment should be performed to confirm that equilibrium binding is achieved when validating any new binding assay. A low level of specific binding could also result from radioligand degradation, which often occurs when radioligands are stored for extended periods of time. It is recommended that Kd and Bmax values be determined for each new batch of radioligand to ensure consistency in the data generated using different batches of radioligand. Radiolabeled NPY is not recommended as an alternative to [125I]PYY due to its low level of specific binding. PP cannot be used to label Y1 and Y2 receptors, because of its low affinity, but is a preferred radioligand for labeling Y4 receptors.

Characterization of Neuropeptide Y (NPY) Receptors

Receptor internalization In general, agonist radioligands are not ideal for use in whole-cell binding studies because of their tendency to induce receptor down-regulation, which can result in an inaccurate estimate of receptor number. However, this issue has not been specifically addressed for [125I]PYY or other NPY receptor radioligands, and there are no commercially available NPY receptor antagonist radioligands. Increasing the concentration of unlabeled agonist competitor might also lead to receptor internalization and an inaccurate estimate of agonist affinity. Again, this has not been studied in detail. Receptor internalization can be minimized by conducting the assay at room temperature in the

presence of a trace amount of radioligand, as described in this unit. In the event that a wholecell assay is not required, membranes can be prepared from clonal cell lines and studied in the same way as described for tissues. However, it should be noted that pharmacological differences are sometimes detected when comparing whole-cell and membrane binding, especially when agonist radioligands are used.

Anticipated Results Radioligand binding studies have revealed that NPY Y1 and Y2 receptors possess distinct pharmacological profiles (Fig. 1.11.1). As shown for rat brain cerebral cortical membranes (Fig. 1.11.1A,B and Table 1.11.2), Y1 receptors have high affinity for C-terminal-substituted analogs of NPY (e.g., [Leu31Pro34]NPY) and low affinity for long C-terminal fragments (e.g., NPY13-36), whereas Y2 receptors exhibit the opposite pattern of selectivity. Y2 receptors in the rat hippocampus share a similar pharmacological profile with cortical Y2 receptors (Fig. 1.11.1C and Table 1.11.2). When studied in human neuroblastoma cell lines, Y1 and Y2 receptors exhibit the same pharmacological preferences as do rat brain Y1 and Y2 receptors (Table 1.11.3).

Time Considerations

It typically takes ∼4 hr to complete an [125I]PYY binding assay. This includes tissue preparation and preparation of drug dilutions, pipetting the appropriate assay constituents into individual tubes or wells, incubation time, and assay termination. For assays performed on membranes prepared from tissues or cell lines, preparation and dispensing of test compounds and radioligand can be accomplished while the tissue is being pelleted.

Literature Cited Bard, J., Walker, M.W., Branchek, T.A., and Weinshank, R.L. 1995. Cloning and functional expression of a human Y4 subtype receptor for pancreatic polypeptide, neuropeptide Y and peptide YY. J. Biol. Chem. 270:26762-26765. Blomquist, A.G. and Herzog, H. 1997. Y-receptor subtypes–How many more? Trends Pharmacol. Sci. 20:294-298. Dumont, Y., Fournier, A., St.-Pierre, S., and Quirion, R. 1995. Characterization of neuropeptide Y binding sites in rat brain membrane preparations u sin g [125I][Leu31,Pro34] p ep tide YY an d [125I]peptide YY3-36 as selective Y1 and Y2 radioligands. J. Pharmacol. Exper. Ther. 272:673680.

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Current Protocols in Pharmacology

Fuhlendorff, J., Gether, U., Aakerlund, L., Langeland-Johansen, N., Thogersen, H., Melberg, S.G., Olsen, U.B., Thastrup, O., and Schwartz, T.W. 1990. [Leu31,Pro34]Neuropeptide Y—A specific Y1 receptor agonist. Proc. Natl. Acad. Sci. U.S.A. 187:182-186.

Rose, P.M., Fernandes, P., Lynch, J.S., Frazier, S.T., Fisher, S.M., Kodukula, K., Kienzle, B., and Seethala, R. 1995. Cloning and functional expression of a cDNA encoding a human type 2 neuropeptide Y receptor. J. Biol. Chem. 29:22661-22664.

Gerald, C., Walker, M.W., Criscione, L., Gustafson, E.L., Batzl-Hartmann, C., Smith, K.E., Vaysse, P., Durkin, M.M., Laz, T.M., Linemeyer, D.L., Schaffhauser, A.O., Whitebread, S., Hofbauer, K.G., Taber, R.I., Branchek, T.A., and Weinshank, R.L. 1996. A receptor subtype involved in neuropeptide Y-induced food intake. Nature 382:168-171.

Wahlestedt, C., Yanaihara, N., and Håkanson, R. 1986. Evidence for different pre- and postjunctional receptors for NPY and related peptides. Regul. Pept. 13:307-318.

Gregor, P., Feng, Y., DeCarr, L.B., Cornfield, L.J., and McCaleb, M.L. 1996. Molecular characterization of a second mouse pancreatic polypeptide receptor and its inactivated human homologue. J. Biol. Chem. 271:27776-27781. Hu, Y., Bloomquist, B.T., Cornfield, L.J., DeCarr, L.B., Flores-Riveros, J.R., Friedman, L., Jiang, P., Lewis-Higgins, L., Sadlowski, Y., Schaefer, J., Velazquez, N., and McCaleb, M.L. 1996. Identification of a novel hypothalamic neuropeptide Y receptor associated with feeding behavior. J. Biol. Chem. 42:26315-26319. Krause, J., Eva, C., Seeburg, P.H., and Sprengel, R. 1992. Neuropeptide Y1 subtype pharmacology of a recombinantly expressed neuropeptide receptor. Mol. Pharmacol. 41:817-821. Matsumoto, M., Nomura, T., Momose, K., Ikeda, Y., Kondou, Y., Akiho, H., Togami, J., Kimura, Y., Okada, M., and Yamaguchi, T. 1996. Inactivation of a novel neuropeptide Y/peptide YY receptor gene in primate species. J. Biol. Chem. 271:27217-27220. Michel, M.C. 1991. Receptors for neuropeptide Y: Multiple subtypes and multiple second messengers. Trends Pharmacol. Sci. 12:3789-3794. Phelan, M.C. 1998. Techniques for mammalian cell tissue culture. In Current Protocols in Molecular Biology (F.M. Ausubel, R. Brent, R.E. Kingston, D.D. Moore, J.G. Seidman, J.A. Smith, and K. Struhl, eds.) pp. A.3F.1-A.3F.14. John Wiley & Sons, New York.

Wahlestedt, C., Edvinsson, L., Ekblad, E., and Håkanson, R. 1987. Effects of neuropeptide Y at sympathetic neuroeffector junctions: Evidence of Y1 and Y2 receptors. In Neuronal Messengers in Vascular Function (A. Nobin, C. Owman, and B. Arneklo-Nobin, eds.) pp. 231-241. Elsevier/North-Holland, Amsterdam. Wahlestedt, C., Regunathan, S., and Reis, D.J. 1991. Identification of cultured cells selectively expressing Y1-, Y2-, or Y3-type receptors for neuropeptide Y/peptide YY. Life Sci. 50:PL-7– PL-12. Wan, C.P. and Lau, B.H.S. 1995. Neuropeptide Y receptor subtypes. Life Sci. 56:1055-1064. Weinberg, D.H., Sirinathsinghji, D.J.S., Tan, C.P., Shiao, L.-L., Morin, N., Rigby, M.R., Heavens, R.H., Rapoport, D.R., Bayne, M.L., Cascieri, M.A., Strader, C.D., Linemeyer, D.L., and MacNeil, D.J., 1996. Cloning and expression of a novel neuropeptide Y receptor. J. Biol. Chem. 271:16435-16438. Yan, H., Yang, J., Marasco, J., Yamaguchi, K., Brenner, S., Collins, F., and Karbon, W. 1996. Cloning and functional expression of cDNAs encoding human and rat pancreatic polypeptide receptors. Proc. Natl. Acad. Sci. U.S.A. 93:4661-4665.

Contributed by William Karbon, Julie Marasco, and Clarence Hale Amgen, Inc. Thousand Oaks, California

Receptor Binding

1.11.11 Current Protocols in Pharmacology

Supplement 1

Characterization of Cholecystokinin (CCK) Receptors

UNIT 1.12

Cholecystokinin (CCK) and gastrin are structurally related peptide hormones synthesized and secreted by endocrine cells in the gastric antrum, the proximal small intestine, and the central and enteric nervous systems. The varied molecular forms of these peptides share the same carboxyl-terminal pentapeptide-amide (Gly-Trp-Met-Asp-Phe-NH2), representing the minimal pharmacophore for recognition. The structures of gastrin and CCK are divergent beyond this shared domain. Both possess a particularly interesting and unusual tyrosine residue. In CCK, this tyrosine is seven residues from the carboxyl terminus and is always sulfated, while in gastrin it is six residues from the carboxyl terminus and may or may not be sulfated (Fig. 1.12.1). Standard nomenclature for CCK peptides is unusual, dating back to the original isolation of a 33–amino acid peptide from porcine duodenum. All molecular forms share the carboxyl-terminal tetrapeptide-amide of that peptide, with the length of the peptide (extending in the amino-terminal direction) noted—e.g., CCK-4, CCK-8, CCK-12, CCK-33, and CCK-58. A broad spectrum of cells express receptors and represent physiologic targets for these peptides. The major physiologic targets for gastrin are in the oxyntic gastric mucosa, including the parietal cells and select enterochromaffin cells. CCK has a broader range of targets, with most involved in nutrient assimilation and appetite control, and the regulation of anxiety. CCK1 (CCKA) receptors are present on pancreatic acinar cells, gallbladder muscularis smooth muscle cells, smooth muscle at various levels along the gut, and in cells of specific brain nuclei. CCK2 (CCKB) receptors predominate in the brain, are present on enteric smooth muscle, and are targets of gastrin as mentioned above. It now appears that the traditional gastrin receptor and the CCK2 receptor are the same molecule (Lee et al., 1993). While the existence of additional types of receptors has been postulated, particularly those responsible for the trophic effects of these hormones, none have yet been demonstrated definitively. CCK1 and CCK2 receptors are typical G protein–coupled receptors in the β-adrenergic receptor family. They have been cloned from several species and are highly homologous with each other (50% identical, 66% similar). On natural target cells, these receptors are present at low density (a few thousand molecules/cell), but demonstrate a high affinity for the ligand (low nanomolar range). While the CCK2 receptors bind all molecular forms of gastrin and CCK with approximately equal affinity, the CCK1 receptors are much more selective, requiring the carboxyl-terminal heptapeptide-amide of CCK, including the

A

Lys-Ala-Pro-Ser-Gly-Arg-Met-Ser-Ile-Val-Lys-Asn-Leu-Gln-Asn-Leu-Asp1 5 10 15 -Pro-Ser-His-Arg-Ile-Ser-Asp-Arg-Asp-Tyr-Met-Gly-Trp-Met-Asp-Phe-NH2 20 25 SO3H 30

B

pGlu-Gly-Pro-Trp-Leu-Glu-Glu-Glu-Glu-Glu-Ala-Tyr-Gly-Trp-Met-Asp-Phe-NH2 1 5 10 SO3H 15

Figure 1.12.1 Amino acid sequences of human (A) CCK and (B) gastrin. pGlu, pyroglutamic acid.

Receptor Binding

Contributed by Laurence J. Miller

1.12.1

Current Protocols in Pharmacology (1998) 1.12.1-1.12.10 Copyright © 1998 by John Wiley & Sons, Inc.

Supplement 1

Table 1.12.1

Human Cholecystokinin Receptor Subtypes

Receptor subtype

GenBank accession no.

CCK1 CCK2

Table 1.12.2

Agonists CCK-8 CCK-8-DS Gastrin CCK-4 Antagonists Devazepide Loxiglumide L-365,260 PD140376

Agonists

Antagonists

P32238

A 71623 CCK

Devazepide (MK 329)

P32239

CCK gastrin

YM 022 L 365,260 PD 140376 CI 988

CCK Receptor Binding Data (IC50, nM)

CCK1 receptor

CCK2

Suppliera

0.1-3 100-500 1,000 5,000-10,000

0.2-4 0.2-20 0.5-1 2-30

B, RP B B RP

0.1-1 300 200-300 —

30-100 9,000 2-5 0.2

MRL MRL MRL PD

aAbbreviations: B, Bachem; MRL, Merck Research Laboratories; PD, Parke-Davis; RP, Research Plus.

sulfated Tyr residue in its typical position. Gastrin, even its sulfated forms, is recognized by the CCK1 receptor with an affinity approximately four to five orders of magnitude lower than that of CCK for this site. Table 1.12.1 lists pharmacological agents for human CCK1 and CCK2 receptors. Binding data for these receptors is listed in Table 1.12.2. This unit describes the determination of receptor number (Bmax), ligand affinity (Kd), and inhibition constants (Ki) for CCK1 and CCK2 receptors (see Basic Protocol). Preparation of dispersed pancreatic acini (see Support Protocol 1) and enriched plasma membranes (see Support Protocol 2) from rat pancreas is also described for use as a source of CCK receptors in the Basic Protocol. For CCK2 receptors, gastric parietal cells are usually used as a source, although preparation of these cells is very difficult. It is also possible to use a recombinant receptor-bearing CHO cell line as a source of CCK2 receptors. NOTE: All protocols using live animals must first be reviewed and approved by an Institutional Animal Care and Use Committee (IACUC) or must conform to governmental regulations regarding the care and use of laboratory animals.

Characterization of Cholecystokinin (CCK) Receptors

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Current Protocols in Pharmacology

BINDING ASSAYS FOR CCK1 AND CCK2 RECEPTORS The following protocol may be used for studying the binding of radiolabeled ligands to cells and membranes expressing both CCK1 and CCK2 receptors. Variations are described with respect to sample and the method used to separate bound from free radioligand (depending on the available equipment). Cells or membranes (and thus bound radioligand) can be separated either by filtration with a Skatron cell harvester (or other cell harvesters) or by centrifugation. Typically, cells permit the closest correlation of binding with biological activity, while membranes provide the best opportunity for biochemical characterization of binding sites.

BASIC PROTOCOL

Materials Skatron buffers: 0.15 M NaCl and 0.2% (w/v) BSA Radioligand (select one or more, according to experimental design): [125I]Bolton-Hunter-CCK-8 (full agonist at CCK1 and CCK2 receptors; 2200 Ci/mmol, NEN Life Sciences; preferred radioligand for this procedure) [125I-Tyr12]human gastrin-I (high-affinity radiolabeled CCK2 receptor agonist with low affinity for the CCK1 receptor; 2200 Ci/mmol, NEN Life Sciences) [N-methyl-3H]L-364,718 (selective CCK1 receptor antagonist; 60 to 87 Ci/mmol, NEN Life Sciences) [3-methylphenyl-2,4,6-3H]L-365,260 (selective CCK2 receptor antagonist; 70-100 Ci/mmol, NEN Life Sciences) [3H]PD-140376 (selective CCK2 receptor antagonist; 40-60 Ci/mmol, Amersham) Krebs Ringer’s/HEPES (KRH) medium (see recipe), ice-cold Competing unlabeled receptor ligands (select one or more, according to experimental design; prepare all stock solutions at 0.05 mM): 0.05 mM CCK-8 stock solution (see recipe) CCK-8-desulfate (CCK-8-DS; Bachem) CCK-4 (Research Plus) Gastrin 17-I (Bachem) Gastrin 17-II, sulfated (Bachem) L-364,718 (Dr. Roger Freidinger; Merck Research Laboratories) L-365,260 (Merck Research Laboratories.) Target cells (e.g., a receptor-bearing cell population; see Support Protocol 1) or a membrane preparation from those cells (see Support Protocol 2) 12 × 75–mm polypropylene test tubes (Sarstedt) Cell harvester (Skatron) Receptor-binding filtermats (Skatron) Software for analysis of binding data (e.g., LIGAND; Munson and Rodbard, 1980 and UNIT 1.3) Set up experiment 1. Label appropriate tubes (12 × 75–mm polypropylene test tubes for Skatron experiments and for centrifugation experiments with cells, and 1.5-ml polypropylene microcentrifuge tubes for centrifugation experiments with membranes) in duplicate or triplicate and place them into a rack. Include tubes with no competing ligand, tubes containing competing ligand (concentrations spanning at least two orders of magnitude on either side of the expected IC50 value of ∼1 nM), and tubes containing 0.1 µM unlabeled ligand to establish nonspecific radioligand binding. The competing ligand concentrations typically range from 10 pM to 0.1 µM for CCK-8 and most CCKA and CCKB receptors. Twelve to fourteen different competing ligand concentrations are typically used.

Receptor Binding

1.12.3 Current Protocols in Pharmacology

Supplement 1

All assays contain a fixed amount of receptor-bearing cells or their membranes and a fixed concentration of radioligand.

2. Optional (for Skatron cell harvester): Prepare a Skatron cell harvester with Skatron buffers according to manufacturer’s instructions in a 4°C cold room. Fill and connect wash bottle 1 with 0.2% (w/v) BSA and wash bottle 2 with 0.15 M NaCl. If this apparatus is not available, proceed with steps 3 through 8, then use step 9b in place of step 9a.

3. Dilute radioligand in KRH medium to yield ∼20,000 cpm per 50 µl, and store on ice. 4. Prepare serial 10-fold dilutions of competing ligand in KRH medium. Working concentrations typically range from 10 µM to 1 nM. Store on ice. 5. Suspend cells in KRH medium at 0.5–1 × 106 cells per 100 µl, or suspend membrane preparation at 5 to 25 µg protein per 50 µl. Store on ice. Perform binding assay 6. Add KRH medium to each assay tube, anticipating other reagents (see step 7) to bring the final volume to 500 µl. 7. Add the following to each tube in the order indicated (final 500 µl): appropriate volume of competing ligand (from serial dilutions prepared in step 4) 50 µl diluted radioligand 100 µl diluted cells or 50 µl diluted membrane preparation 8. Suspend and mix contents of tubes by briefly vortexing membranes or gently, manually stroking tubes with cells. Allow the reaction to proceed for 1 hr at room temperature with periodic gentle shaking for membranes, and constant gentle shaking for cells. Separate bound and free radioligand 9a. For Skatron cell harvester: Separate bound and free radioligand with receptor-binding filtermats, according to manufacturer’s instructions. Set port 1 to “0” and port 2 to “9.” Advance the filter prior to processing each group of tubes. Punch out filter discs into counting vials. 9b. For centrifugation: Add 0.75 ml ice-cold KRH medium to each tube, and transfer the tubes to ice. Centrifuge cells 3 min at 800 × g, 4°C, or microcentrifuge membrane preparations 5 min at maximum speed, 4°C. Aspirate and discard the supernatants, resuspend the pellets in 1 ml fresh ice-cold KRH medium, and repeat centrifugation and aspiration. Analyze results 10. Quantify bound radioactivity in each condition using a gamma counter for the radioiodinated ligands, and scintillant and a beta counter for the tritiated ligands. When the bound and free ligands have been separated by centrifugation, the radioiodinated ligand may be counted in the same tube in which it was centrifuged. For the tritiated ligand, cut off the end of the tube containing the pellet and add it to a vial containing scintillant.

11. Analyze binding data using a standard program, such as LIGAND. See UNIT 1.3 for details on data analysis. Characterization of Cholecystokinin (CCK) Receptors

Specific binding is determined by subtracting the nonspecific binding from the total binding. Programs such as LIGAND will provide determinants for the number of classes of binding sites, with an affinity (Ki) and density (Bmax) for each.

1.12.4 Supplement 1

Current Protocols in Pharmacology

PREPARATION OF DISPERSED RAT PANCREATIC ACINI Preparations of rat pancreatic acinar cells, which express only the CCK1 receptor, are extensively utilized and well characterized. This preparation of dispersed acini (the functional unit of the exocrine pancreas) provides a fully coupled and functional receptor that is ideal for binding or functional assays.

SUPPORT PROTOCOL 1

Materials 100- to 125-g male Harlan/Sprague-Dawley rat Krebs Ringer’s/HEPES (KRH) medium (see recipe), ice cold 1 U/µl collagenase (Worthington Biochemical) in KRH medium Source of oxygen OMG dissecting instruments Paraffin-coated petri dish (fill with melted paraffin and allow to set) 6-ml syringe with 27-G needle Silanized 50-ml Erlenmeyer flask No. 200-mesh nylon Additional reagents and equipment for counting cells (Phelan, 1998) 1. Sacrifice a 100- to 125-g rat by a technique approved by Institutional Animal Care and Use Committee (IACUC). One satisfactory method is the intramuscular injection of pentobarbital (0.2 to 0.3 ml of a 50 mg/ml stock for rats of the recommended size).

2. Rapidly and gently dissect and excise the pancreas, and place it in 15 ml ice-cold KRH medium. Keep on ice. The pancreas is best identified in the hilum of the spleen, with the dissection following the greater curvature of the stomach and the duodenal sweep. Extraneous fat is most easily identified and trimmed after watching it float in the ice-cold KRH medium.

3. Pin out the pancreas with gentle stretching in a paraffin-coated petri dish that is placed on ice. 4. Dilute 400 µl of 1 U/µl collagenase in 6 ml ice-cold KRH medium. Using a 27-G needle and a 6-ml syringe, inject this solution into the parenchyma of the pancreas, following the visible vessels and ducts in an attempt to “balloon” the gland. Repeat this three times, using the solution that escapes from the pancreatic tissue into the petri dish. Ballooning of the pancreas helps provide adequate infiltration without causing tissue damage.

5. Mince the pancreas with sharp dissecting scissors and transfer it with the collagenasecontaining fluid into a silanized 50-ml Erlenmeyer flask. Flush the solution with oxygen for 15 min, cap, and incubate 5 min in a 37°C shaking water bath at 120 rpm. 6. Remove the flask from the water bath and shake vigorously by hand ∼10 min (until large clumps disappear). 7. Pipet the cell suspension over no. 200 mesh nylon, collecting the effluent into a conical tube on ice. 8. Centrifuge 3 min at 60 × g, 4°C, and aspirate off the medium. Resuspend the cell pellet in 10 ml fresh, ice-cold KRH medium and repeat the wash procedure. 9. Resuspend the washed cell pellet into 150 ml ice-cold KRH medium. Count cell density (Phelan, 1998) and store on ice until ready for use. This cell preparation remains healthy for ∼2 hr.

Receptor Binding

1.12.5 Current Protocols in Pharmacology

Supplement 1

SUPPORT PROTOCOL 2

PREPARATION OF ENRICHED PLASMA MEMBRANES FROM RAT PANCREAS This preparation is ideal for binding assays with the CCK1 receptor. It is quite stable and well characterized. Materials Twenty-five 125- to 150-g male Harlan/Sprague-Dawley rats 0.3 M and 2 M sucrose solutions (see recipe), ice cold Krebs Ringer’s/HEPES (KRH) medium (see recipe), ice cold Wheaton Dounce homogenizers: 40-ml equipped with “A” (tight) and “B” (loose) pestles, and 8-ml equipped with “A” pestle Beckman ultracentrifuge with SW28 rotor and 38-ml polyallomer ultracentrifuge tubes, plus Ti50.2 rotor and 26.3-ml polycarbonate bottles with caps (or equivalent) Cheesecloth, 2 layers Additional reagents and equipment for dissecting and excising pancreases (see Support Protocol 1) and for determining protein concentration (APPENDIX 3A) 1. Place the 8-ml and 40-ml Dounce homogenizers, eighteen polyallomer ultracentrifuge tubes and twelve polycarbonate bottles on ice or in a 4°C cold room. Equilibrate and maintain an ultracentrifuge at 4°C. 2. Sacrifice twenty five 125- to 150-g rats by a technique approved by Institutional Animal Care and Use Committee (IACUC). One satisfactory method is the intramuscular injection of pentobarbital (0.2 to 0.3 ml of a 50 mg/ml stock for rats of the recommended size).

3. Dissect, excise, and “balloon” pancreases (see Support Protocol 1, steps 2 to 4). Weigh tissue using a tared beaker. All of the pancreases may be kept in a single beaker containing ice-cold KRH medium.

4. Mince pancreases with sharp dissecting scissors. Place into 40-ml Wheaton Dounce homogenizer and suspend at 10% (w/v) in ice-cold 0.3 M sucrose. 5. Homogenize using four strokes with the “B” (loose) pestle, followed by four strokes with the “A” (tight) pestle. 6. Filter homogenate through two layers of cheesecloth. 7. Bring the sucrose concentration to 1.3 M by adding ice-cold 2.0 M sucrose (1.43 times the volume of the 0.3 M sucrose). 8. Dispense this into the eighteen polyallomer tubes. Gently overlay each with ice-cold 0.3 M sucrose to fill the tube. 9. Balance tubes, load into SW28 rotors, and ultracentrifuge 3 hr at 100,000 × g, 4°C. 10. Collect the membranes at the interface of the two sucrose layers and dilute to 300 ml with ice-cold water. 11. Dispense into the polycarbonate bottles, balance, and load into a Ti50.2 rotor. Centrifuge 50 min at 225,000 × g, 4°C. Characterization of Cholecystokinin (CCK) Receptors

12. Resuspend the pellets in 5 ml ice-cold KRH medium and homogenize in 8-ml Dounce homogenizer with four strokes of the “A” (tight) pestle.

1.12.6 Supplement 1

Current Protocols in Pharmacology

13. Determine protein concentration in the membranes by washing an aliquot of the pellet with water, resuspending it it water, and performing the bicinchoninic acid (BCA) assay (APPENDIX 3A) using bovine serum albumin as a protein standard. Store the membranes at −70°C until use. This membrane preparation maintains binding activity for months, even years, at −70°C.

REAGENTS AND SOLUTIONS Use deionized, distilled water in all recipes and protocol steps. For common stock solutions, see APPENDIX 2A; for suppliers, see SUPPLIERS APPENDIX.

CCK-8 stock solution, 0.05 mM Add 100 µl methanol to a 0.5-mg vial of CCK-8 (Bachem), and vortex. Add 100 µl of 0.1 M acetic acid, and vortex. Centrifuge 5 min at 800 × g, 4°C, to be certain that all peptide and salts are in solution. Add 4 ml water and 4.549 ml KRH medium (see recipe). Store in 0.5-ml aliquots at −80°C. CCK-8 is stable for at least 6 months under these conditions. Krebs Ringer’s/HEPES (KRH) medium Ca2+-free 10× stock solution 29.79 g HEPES (250 mM) 30.37 g NaCl (1.04 M) 1.86 g KCl (50 mM) 0.68 g KH2PO4 (10 mM) 1.48 g MgSO4 (12 mM) Water to 500 ml Store (without adjusting pH) up to 4 weeks at 4°C 1× working solution 25 ml 10× KRH stock 75 ml water 5 ml 100 mM CaCl2 (2 mM) Adjust pH to 7.4 with 1 N NaOH, and bring to 250 ml with water. Oxygenate by bubbling for at least 15 min (for incubations with live cells), and add 25 mg soybean trypsin inhibitor (final 0.01%), 0.5 g BSA (final 0.2%), and 1.25 ml of 200 mM PMSF stock solution (see recipe; final 1 mM). Prepare fresh daily. Phenylmethylsulfonyl fluoride (PMSF) stock solution, 200 mM Dissolve 34.8 mg phenylmethylsulfonyl fluoride per 1 ml dimethylsulfoxide. Prepare only enough for the experiments to be performed that day. This must be prepared daily, and is stable for a very limited time in aqueous solution. Sucrose solutions, 0.3 M and 2 M 0.3 M: Dissolve 51.36 g sucrose into final volume of 500 ml. Store up to 6 weeks at 4°C. On day of use, add 50 mg soybean trypsin inhibitor (0.01%) and 0.25 ml of 200 mM PMSF stock solution (see recipe; 0.1 mM). 2 M: Dissolve 205.2 g sucrose into final volume of 300 ml. Store up to 6 weeks at 4°C. On day of use, add 30 mg soybean trypsin inhibitor (0.01%) and 0.15 ml of 200 mM PMSF stock solution (see recipe; 0.1 mM).

Receptor Binding

1.12.7 Current Protocols in Pharmacology

Supplement 1

COMMENTARY Background Information The extensive repertoire of physiological actions of gastrin and cholecystokinin (CCK) appears to be mediated by two receptors, CCK1 and CCK2 receptors. These are present in a variety of target tissues, each with its distinctive pattern of sensitivity, desensitization characteristics, and indications and contraindications for the use of agonists and antagonists. Some actions of these hormones/neurotransmitters make these receptors attractive targets for drugs. The binding assays described in this unit are useful for screening compounds that may compete with the natural ligand for binding to these receptors. They can also be used to characterize and quantify the receptors for these peptides in normal or pathological tissues or cell lines. If the goal is to study receptor regulation, these binding assays may also be used to determine these binding sites before and after exposure to various treatments.

Critical Parameters

Characterization of Cholecystokinin (CCK) Receptors

While receptor binding assays have broad applicability, it is important to define the conditions of the assay. For example, an agonist radioligand may provide different information than an antagonist. Both agonists and antagonists are commercially available for both CCK receptors. Similarly, binding to intact cells and to cell membranes can yield different results (Gaisano et al., 1989). It is also important to remember that none of the currently available ligands for the CCK receptors are absolutely selective; all show some affinity for both binding sites. While many of the available reagents are quite selective, it is essential to perform concentration-response or affinity experiments to establish the degree of selectivity under the incubation conditions (Freidinger, 1989). In addition, the specificity of some ligands is species-dependent (Beinborn et al., 1993). The critical nature of the Met15 residue in gastrin and the analogous residue in CCK (Met31) are well recognized. This residue is sensitive to oxidation, which will destroy the biological activity of the peptide. For this reason, radioligand binding to gastrin receptors is optimized using 125I-labeled [Leu15]gastrin, which retains its high affinity and biological activity after oxidative radioiodination (Johnson, 1985). Unfortunately, this radioligand is not commercially available at this time. Instead, [125I]Bolton-Hunter-CCK-8 (Miller et al., 1981) can be used. This reagent binds to the

CCK2 receptor and possesses biological activity at that receptor that is indistinguishable from that of natural gastrin. [125I]human gastrin-I, which binds to the CCK2 receptor like gastrin but which loses its biological activity during the oxidative radioiodination, is also listed as a radioligand (see Basic Protocol). This ligand should probably only be used for CCK2 receptor characterization when CCK1 receptors are also present on the tissue, because the use of a radioligand that binds to both receptors with similar affinity will complicate interpretation of the data. For CCK receptor binding, some laboratories have utilized a CCK analogue containing Nle in place of Met, and which adds a site for oxidative radioiodination: D-Tyr-Gly[Nle28,31]CCK-26-33 (Powers et al., 1988). This also is not available commercially. Instead, the Bolton-Hunter reagent conjugate of the natural hormonal peptide CCK-8 is used. This is an ideal radioligand that retains all the binding and biological characteristics of the natural substance (Miller et al., 1981). Both of these radioligands have the additional advantage of having their amino-terminus blocked, making them resistant to aminopeptidase cleavage. This is a problem when dealing with proteolytically active tissues such as the exocrine pancreas. The peptides in the CCK family are quite difficult to dissolve and maintain in solution. For this reason, it is important to prepare a stock solution of CCK-8 (or other competing ligands) and work from this stock each day, making serial dilutions as necessary. Also, the high concentration of BSA in KRH medium is critical to quench potential hydrophobic binding sites on tubes and other surfaces. This helps maintain the peptide in solution. Polypropylene tubes are most useful in reducing such nonspecific binding, with polystyrene and borosilicate capable of binding larger amounts of CCK, even in the presence of BSA. When working with pancreas, which is a prime target of CCK, emphasis must be placed on inhibiting proteases. Radioligands, unlabeled ligands, and receptors are fair targets for destruction by these enzymes, which could lead to artifacts. Soybean trypsin inhibitor and phenylmethylsulfonyl fluoride are useful in this regard, but degradation also depends on the quality of the membrane preparation and the treatment of the cells. High quality membranes can be safely stored for many months (or even

1.12.8 Supplement 1

Current Protocols in Pharmacology

Table 1.12.3

Troubleshooting Guidelines for CCK Binding Assays

Problem

Possible cause

Solution

Low saturable binding Damaged radioligand

Repurify or replace with fresh radioligand

Damaged receptor

Prepare new cells or membranes and add fresh protease inhibitors to KRH medium; be more gentle when shaking cells

Suboptimal materials (e.g., tubes)

Use polypropylene or siliconized borosilicate glass

Binding curves shifted to right from expected results

Work from a more concentrated stock Less competing ligand in solution than solution; prepare fresh stock solutions; use polypropylene tubes for dilutions expected

Low yield of acinar cells

Animal too large

Use smaller animal (with less fibrotic pancreas)

Collagenase not adequately active

Increase amount of collagenase used or time of enzymatic digestion

Poor binding to acinar Damaged receptor cell membranes

Be more gentle during dissection of pancreas; prepare membranes from pancreas of smaller animal

years) at −70°C with no loss of binding activity. In contrast, a crude particulate fraction from pancreas rapidly loses CCK binding activity, even in the presence of protease inhibitors. The Skatron cell harvester, equipped with receptor-binding filtermats, greatly facilitates the CCK and gastrin binding assays. The glass fiber filtermats, even when coated, bind a significant amount of radioligand. This apparatus provides similar binding parameters to the centrifugation assay, but is more efficient. It is easy to study several hundred conditions with a Skatron binding assay, while the centrifugation assay is limited by the number of positions in the centrifuge rotor and by the time necessary

to aspirate supernatants (12 to 24 tubes per assay).

Troubleshooting Table 1.12.3 lists a number of common problems encountered with CCK binding assays along with suggestions on how to overcome them.

Anticipated Results Most naturally occurring receptors for gastrin and CCK have affinities in the low nanomolar range, with receptor densities in the low thousands of molecules per cell. The CCK2 receptor should recognize all gastrin and CCK-

CCK-8 CCK-8-DS gastrin CCK-4

Bound (% of max)

100 75 50 25 0

CCK1

CCK2

0 –12 –11 –10 –9 –8 –7 –6 –5

0 –12 –11 –10 –9 –8 –7 –6 –5

log[peptide] (M)

Figure 1.12.2 Typical displacement curves for saturable binding of a CCK-like radioligand to membrane preparations from Chinese hamster ovary cell lines expressing CCK1 and CCK2 receptors. Displacement by various unlabeled peptides in the CCK/gastrin family is shown.

Receptor Binding

1.12.9 Current Protocols in Pharmacology

Supplement 1

like molecules. For the CCK1 receptor, binding is most dependent upon the presence of the carboxyl-terminal heptapeptide of CCK, which includes sulfated tyrosine. Figure 1.12.2 shows typical displacement curves of both CCK1 and CCK2 receptors using a CCK-like radioligand ([125I]Bolton-Hunter-CCK-8) and various unlabeled competitors. Table 1.12.2 lists IC50 values for various agonists and antagonists at both CCK1 and CCK2 receptors.

Time Considerations The amount of time required to complete the binding assay is dependent on the time necessary to attain steady state. This typically involves a 1-hr incubation, with the entire assay easily performed in 4 hr. Preparation of dispersed pancreatic acini can be achieved in 2 hr. The preparation of pancreatic membranes from a large number of animals takes a half day, if solutions are prepared the day before.

Literature Cited Beinborn, M., Lee, Y.-M., McBride, E.W., Quinn, S.M., and Kopin, A.S. 1993. A single amino acid of the cholecystokinin-B/gastrin receptor determines specificity for non-peptide antagonists. Nature 362:348-350. Freidinger, R.M. 1989. Cholecystokinin and gastrin antagonists. Med. Res. Rev. 9:271-290. Gaisano, H.Y., Klueppelberg, U.G., Pinon, D.I., Pfenning, M.A., Powers, S.P., and Miller, L.J. 1989. Novel tool for the study of cholecystokinin-stimulated pancreatic enzyme secretion. J. Clin. Invest. 83:321-325. Johnson, L.R. 1985. Gastrin receptor assay. Methods Enzymol. 109:56-64. Lee, Y.-M., Beinborn, M., McBride, E.W., Lu, M., Kolakowski, L.F., and Kopin, A.S. 1993. The human brain cholecystokinin-B/gastrin receptor: Cloning and characterization. J. Biol. Chem. 268:8164-8169.

Miller, L.J., Rosenzweig, S.A., and Jamieson, J.D. 1981. Preparation and characterization of a probe for the cholecystokinin octapeptide receptor, Nα(125I-desaminotyrosyl)CCK-8, and its interactions with pancreatic acini. J. Biol. Chem. 256:12417-12423. Munson, P.J. and Rodbard, D. 1980. LIGAND: a versatile computerized approach for characterization of ligand-binding systems. Anal. Biochem. 107:220-239. Phelan, M.C. 1998. Techniques for mammalian cell tissue culture. In Current Protocols in Molecular Biology (F.M. Ausubel, R. Brent, R.E. Kingston, D.D. Moore, J.G. Seidman, J.A. Smith, and K. Struhl, eds.) pp. A.3F.1-A.3F.14. John Wiley & Sons, New York. Powers, S.P., Pinon, D.I., and Miller, L.J. 1988. Use of N,O-bis-Fmoc-D-Tyr-ONSu for introduction of an oxidative iodination site into cholecystokinin family peptides. Int. J. Pept. Protein Res. 31:429-434.

Key References Miller et al., 1981. See above. Detailed description of preparation of ideal agonist radioligand for both types of CCK receptors, and its application to characterize CCK1 receptors on rat pancreatic acinar cells. Gaisano et al., 1989. See above. Comparison of different types of radioligands and of use of intact cells versus membranes for binding assays. Freidinger, 1989. See above. Excellent review of antagonists for both type 1 and type 2 CCK receptors.

Contributed by Laurence J. Miller Mayo Clinic Center for Basic Research in Digestive Diseases Rochester, Minnesota

Characterization of Cholecystokinin (CCK) Receptors

1.12.10 Supplement 1

Current Protocols in Pharmacology

Characterization of Corticotropin-Releasing Factor (CRF) Receptors

UNIT 1.13

Corticotropin-releasing factor (CRF; Fig. 1.13.1) and its receptors play a major role in regulating response to physical, emotional, and environmental stress (De Souza and Grigoriadis, 1994). While the primary role of CRF is the regulation of adrenocorticotropin hormone (ACTH) secretion from the pituitary and modulation of the hypothalamic-pituitary adrenal axis, CRF is also widely distributed in the central nervous system where it produces a broad spectrum of autonomic, electrophysiological, and behavioral effects consistent with a role as a neurotransmitter or neuromodulator. Recently, the discovery of multiple receptor subtypes for CRF has increased the understanding of this complex system (see Background Information; Table 1.13.1). This unit describes methods for characterizing CRF receptors. The first four protocols utilize a well-characterized radioligand-receptor binding assay that yields quantitative information about the affinity (Table 1.13.2) and density of receptors in a variety of tissues. The technical aspects of kinetic assays (see Basic Protocol 1 and Alternate Protocol), saturation assays (see Basic Protocol 2), and competition assays (see Basic Protocol 3) are described in detail. Another procedure describes receptor autoradiography for the discrete anatomical localization of CRF receptors in brain tissues (see Basic Protocol 4; also see UNIT 8.1). The fundamental advantage of this approach is that the resulting receptor map yields enhanced sensitivity as compared to conventional radioligand binding to membrane fragments. Additionally, preparation of membranes from tissues and cells transfected with various CRF receptor genes is described (see Support Protocol). NOTE: All protocols using live animals must first be reviewed and approved by an Institutional Animal Care and Use Committee (IACUC) or must conform to governmental regulations regarding the care and use of laboratory animals.

A

human CRF SER GLU GLU PRO PRO ILE SER LEU ASP LEU THR PHE HIS LEU LEU 1 5 10 15 ARG GLU VAL LEU GLU MET ALA ARG ALA GLU GLN LEU ALA GLN GLN 20 25 30 ALA HIS SER ASN ARG LYS LEU MET GLU ILE ILE NH2 35

B

40

human urocortin ASP ASN PRO SER LEU SER ILE ASP LEU THR PHE HIS LEU LEU ARG 1 5 10 15 THR LEU LEU GLU LEU ALA ARG THR GLN SER GLN ARG GLU ARG ALA 20 25 30 GLU GLN ASN ARG ILE ILE PHE ASP SER VAL NH2 35

40

Figure 1.13.1 Peptide sequences of human (A) corticotropin-releasing factor (CRF) and (B) urocortin. Receptor Binding Contributed by Dimitri E. Grigoriadis, Marge T. Lorang, Nicola Duggan, and Errol B. De Souza Current Protocols in Pharmacology (1998) 1.13.1-1.13.18 Copyright © 1998 by John Wiley & Sons, Inc.

1.13.1 Supplement 2

Table 1.13.1

CRF Receptor Subtypes

Receptor

Human GenBank Agonistsa,b accession no.

Antagonistsb

CRF1

L23332

CRF Urocortin

CRF2(a)

U34587

CRF Urocortin CRF Urocortin CRF Urocortin

α-Helical oCRF(9-41) Astressin antalarmin CP154526 NBI27914 α-Helical oCRF(9-41) Astressin α-Helical oCRF(9-41) Astressin α-Helical oCRF(9-41)

CRF2(b) CRF2(c)

AF019381

aCRF and urocortin are nonselective activators of CRF receptors and are approximately equipotent. bAntalarmin, N-butyl-N-ethyl-(2,5,6-trimethyl)-7-[2,4,6-trimethylphenyl]-7H-pyrrolo[2,3-d]pyrimidin4-yl)amine (Webster et al., 1996); astressin, cyc30-33[D-Phe12Nle21,38Glu30Lys33]CRF(12-41) (Gulyas et al., 1995); CP154526, butylethyl-(2,5-dimethyl-7-[2,4,6-trimethylphenyl]-7H-pyrrolo[2,3-d]pyrimidin-4-yl)amine (Pfizer); CRF, corticotropin-releasing factor (Fig. 1.13.1; Peninsula Labs); α-helical oCRF(9-41) (Peninsula Labs); NBI27914, 2-methyl-4-(N-propyl-N-cyclopropanemethylamino)-5-chloro6-(2,4,6-trichloroanilino)pyrimidine (Neurocrine Biosciences); urocortin (Fig. 1.13.1; Peninsula Labs).

Table 1.13.2 Affinity Constants (Ki) of Reference Ligands at CRF Receptor Subtypes

Compound

CRF1

CRF2(a)

CRF2(b)

CRF-BPa

CRF Urocortin α-Helical oCRF(9-41) Astressin NBI27914 Antalarmin CP154526

2.4 0.64 105 19.5 2.0 1.6 2.7

209 5.3 336 51.6 >10,000 >10,000 >10,000

178 2.0 84 38 >10,000 >10,000 >10,000

0.37 2.5 0.2 ND >10,000 >10,000 ND

aCRF-BP, CRF binding protein (see Background Information). ND, not determined.

NOTE: When working with radioactivity, take appropriate precautions to avoid contamination of the experimenter and the surroundings. Conduct experiments and dispose of wastes in an appropriately designated area, following the guidelines provided by the local radiation safety officer. NOTE: This unit uses the IUPHAR nomenclature for CRF receptor subtypes as CRF2(a), CRF2(b), and CRF2(c). These designations correspond to the formerly used designations CRF2α, CRF2β, and CRF2γ, respectively. BASIC PROTOCOL 1

Characterization of CorticotropinReleasing Factor (CRF) Receptors

ASSOCIATION KINETIC ASSAY TO DETERMINE TIME COURSE FOR EQUILIBRIUM BINDING This protocol describes a radioligand binding assay for determining the kinetic and pharmacological profile of CRF receptors in animal tissues, or of cloned CRF receptors transfected and expressed in cell lines. Detailed analytical and quantitative methodologies and underlying theories are described elsewhere (UNITS 1.2 & 1.3). In this assay, centrifugation rather than filtration is used to separate bound from free ligand (see Critical Parameters). The procedure is used primarily for the determination of the time course for equilibrium binding of the labeled ligand to its receptor. In addition, the association and dissociation rate constants (k+1 and k−1) are calculated by generating both

1.13.2 Supplement 2

Current Protocols in Pharmacology

association and dissociation curves (see Alternate Protocol), which allows calculation of the dissociation constant (KD; also referred to as the affinity) of the labeled ligand for the specific receptor (UNIT 1.3). Materials Assay buffer (see recipe) 6 µM unlabeled rat/human corticotropin-releasing factor (r/hCRF; Peninsula Labs) in assay buffer Radioligand in assay buffer (select one): 900 pM [125I]ovine CRF ([125I]oCRF; NEN Life Sciences; for CRF1 receptor subtype) 900 pM [125I]sauvagine (NEN Life Sciences; for CRF1, CRF2(a) or CRF2(b) receptor subtypes) 900 pM [125I]r/hCRF (2000 to 2200 Ci/mmol; NEN Life Sciences; for CRF1 receptor subtype) 900 pM [125I]urocortin (2000 to 2200 Ci/mmol; Amersham; nonselective activator of CRF receptors) Membrane suspension (see Support Protocol) Wash buffer (see recipe), ice cold 1.5-ml polypropylene microcentrifuge tubes Tabletop microcentrifuge with 60-tube capacity and speed >10,000 × g (e.g., Beckman Microcentrifuge 12, Eppendorf model 5403) Vacuum aspirator: aspiration flask connected to tubing with Pasteur pipet/pipet tip (for radioactive waste) Microcentrifuge tube cutter (Fisher) or dog nail clippers (local pet supply) 12 × 75–mm gamma counter tubes Set up binding reaction 1. Prepare duplicate or triplicate 1.5-ml polypropylene microcentrifuge tubes for total and nonspecific binding at each time point. Add 50 µl assay buffer to each tube. 2. Add another 50 µl assay buffer to total binding tubes. 3. Add 50 µl of 6 µM unlabeled r/hCRF to all nonspecific binding tubes (1 µM final). This final concentration is 1000-fold greater than the affinity of the radioligand for the receptor.

4. Add 50 µl radioligand to each tube. Also add 50 µl radioligand to separate tubes to determine the exact concentration of radioligand added. These conditions have been determined to yield a final concentration of ∼150 pM radioligand in the final assay volume, and represent the approximate KD of these ligands at the respective receptors. [125I]Sauvagine can be used to assess all known CRF receptor subtypes expressed in transfected cells. [125I]oCRF is used to study the CRF1 receptor subtype only, due to its low affinity for the CRF2 receptor subtypes.

5. Add 150 µl membrane suspension to all tubes to initiate the reaction (total assay volume 300 µl). Incubate at room temperature (22°C). For transfected cells, 1.5 million cells/tube should yield an excellent signal using stably transfected cell lines. As individual expression levels vary from one clonal cell line to another, it is necessary to perform a preliminary determination of the optimal cell number per tube to yield a good signal-to-noise ratio.

Separate bound from free radioligand 6. At desired times following addition of the membrane suspension (e.g., 5, 10, 30, 60, and 120 min), centrifuge 10 min at 14,000 × g, 22°C, in a tabletop microcentrifuge to pellet the membranes and separate bound from free radioligand.

Receptor Binding

1.13.3 Current Protocols in Pharmacology

Supplement 2

Because this is a time course, the reaction must be stopped at different times and the bound radioligand separated from free radioligand by centrifugation. The maximum recommended incubation time is ∼4 hr, as longer times have been associated with the deterioration of the specific binding signal.

7. Remove supernatant with a vacuum aspirator, being careful not to disturb the pellet. 8. Add 1 ml ice-cold wash buffer, gently washing the walls of the tube and the surface of the pellet. Do not resuspend the pellet. Resuspending the pellet will cause the radiolabel to dissociate from the receptor.

9. Centrifuge 10 min at 14,000 × g, 22°C, and aspirate the supernatant.

A Specific binding (fmol/mg protein)

60 50 40 30 20 10 0

B

0

30 60

90 120 150 180 210 240 270 Time (min)

0

30

90 120 150 180

0

ln(B/B0)

– 0.1 – 0.2 – 0.3 – 0.4 – 0.5 60

Time (min)

Characterization of CorticotropinReleasing Factor (CRF) Receptors

Figure 1.13.2 Association (A) and dissociation (B) of [125I]sauvagine binding to human CRF2(a) receptors expressed in stable CHO cell lines. (A) For association experiments, cell membrane homogenates were incubated at 22°C with ∼100 to 200 pM [125I]sauvagine for various times. Nonspecific binding was defined in the presence of 1 µM D-PheCRF(12-41) at each time point. The association rate constant (k+1) was determined (assuming pseudo-first-order kinetics) by plotting ln[Be/(Be−B)] versus time, where Be is specific binding (fmol/mg protein) at equilibrium and B is specific binding at any given time point. An example of such a plot is found in UNIT 1.3 (Fig. 1.3.12). k+1 was calculated from the equation kob − k−1 = k+1 × CL, where kob is the slope of the association plot described above, k−1 is the dissociation rate constant, and CL is ligand concentration. In the experiment described above, k+1 was observed to be 0.415 min−1 nmol−1. (B) Following equilibrium, dissociation of [125I]sauvagine was initiated by the addition of 1 µM D-PheCRF(12-41), and the reaction was stopped at various times by centrifugation. Specific binding, B, was calculated for each time point, t, and the dissociation constant was determined from the equation ln(B/B0) = k−1 × t, where B0 is specific binding at equilibrium. In the experiment described above, k−1 was observed to be 0.0252 min−1.

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Collect and analyze data 10. Cut the bottom of the microcentrifuge tubes just above the pellet using a microcentrifuge tube cutter. Place them in 12 × 75–mm gamma counter tubes and quantify radioactivity in a gamma counter. 11. Determine specific binding at each time point by subtracting nonspecific binding from total binding. Proceed with the association kinetics analysis (UNIT 1.3 and Fig. 1.13.2) to determine k+1. Figure 1.13.2A is a plot of transformed data from an association time course experiment.

KINETIC ASSAY TO DETERMINE DISSOCIATION TIME COURSE For dissociation kinetics, the radioligand is allowed to reach equilibrium before a 1000-fold excess of unlabeled ligand is added to displace the bound radioligand from its receptor. The dissociation time course is determined as for association (see Basic Protocol 1) and the dissociation rate constant (k−1) is then calculated.

ALTERNATE PROTOCOL

Materials (also see Basic Protocol 1) 31 µM unlabeled rat/human corticotropin-releasing factor (r/hCRF; Peninsula Labs) in assay buffer (see recipe) 1. Set up and perform radioligand binding as described for the association kinetic assay (see Basic Protocol 1, steps 1 to 5), allowing the radioligand to achieve equilibrium (as determined by the association kinetics in Basic Protocol 1). 2. Add 10 µl of 31 µM unlabeled r/hCRF (1 µM final) to all tubes. 3. At various times following addition of unlabeled r/hCRF (e.g., 5, 10, 30, 60 and 120 min), centrifuge 10 min at 14,000 × g, 22°C, in a tabletop microcentrifuge to pellet the membranes and separate bound from free radioligand. Because this is a time course, the displacement must be stopped at different times and the bound radioligand separated from free radioligand by centrifugation. The maximum recommended incubation time is ∼4 hr, as longer times have been associated with deterioration of the specific binding signal.

4. Proceed with separation, data collection, and analysis as for association kinetic assay (see Basic Protocol 1, steps 7 to 11), and determine the dissociation time course. Determine k−1 from the binding curve (UNIT 1.3 and Fig. 1.13.2B). Figure 1.13.2B is a plot of transformed data from a dissociation time course. k−1 is the slope of the plot.

SATURATION (SCATCHARD) ASSAYS TO DETERMINE KD AND Bmax The saturation or Scatchard analysis requires that increasing concentrations of the radioligand be incubated with membranes under the equilibrium conditions determined from kinetic experiments (see Basic Protocol 1 and Alternate Protocol). In addition, a parallel set of tubes is incubated with radioligand, membranes, and a 1000-fold excess of unlabeled peptide to determine nonspecific binding. The difference in the amount of radioligand bound between these two sets of tubes defines the specific binding from which the affinity (KD) and receptor density (Bmax) are determined. Details of the analysis are described in UNIT 1.3.

BASIC PROTOCOL 2

Receptor Binding

1.13.5 Current Protocols in Pharmacology

Supplement 2

Materials Assay buffer (see recipe) 6 µM unlabeled rat/human corticotropin-releasing factor (r/hCRF; Peninsula Labs) or D-Phe r/hCRF(12-41) (Rivier et al., 1993) in assay buffer 6× radioligand solutions (12, 6, 3, 1.5, 0.75, 0.375, 0.186, and 0.09 nM) in assay buffer (select one): [125I]ovineCRF ([125I]oCRF; NEN Life Sciences; for CRF1 receptors) [125I]sauvagine (NEN Life Sciences; for CRF1 or CRF2 receptors) [125I]r/hCRF (2000 to 2200 Ci/mmol; NEN Life Sciences; for CRF1 receptors) [125I]urocortin (2000 to 2200 Ci/mmol; Amersham; nonselective activator of CRF receptors) Membrane suspension (see Support Protocol) Wash buffer (see recipe), ice cold 1.5-ml polypropylene microcentrifuge tubes Tabletop microcentrifuge with 60-tube capacity and speed >10,000 × g (e.g., Beckman Microcentrifuge 12, Eppendorf model 5403) Vacuum aspirator: aspiration flask connected to tubing with Pasteur pipet/pipet tip (for radioactive waste) Microcentrifuge tube cutter (Fisher) or dog nail clippers (local pet supply) 12 × 75–mm gamma counter tubes Set up binding reaction 1. Prepare duplicate or triplicate 1.5-ml polypropylene microcentrifuge tubes for total and nonspecific binding at each final concentration of radioligand to be tested. Add 50 µl assay buffer to each tube. Final radioligand concentrations will be 2, 1, 0.5, 0.25, 0.125, 0.063, 0.031, and 0.015 nM. These concentrations are four points above and four points below the expected KD (0.2 nM) of both [125I]oCRF for the CRF1 receptor and [125I]sauvagine for CRF1 and CRF2 receptors.

2. Add an additional 50 µl assay buffer to total binding tubes. 3. Add 50 µl of 6 µM unlabeled r/hCRF or D-Phe r/hCRF(12-41) (1 µM final) to nonspecific binding tubes. This final concentration is 1000-fold greater than the affinity of the radioligand for the receptor.

4. Add 50 µl of the appropriate 6× radioligand solution to each tube. Also add 50 µl of each 6× radioligand solution to separate tubes to determine the exact total concentration of radioligand added at each dilution. It is preferable not to use a serial dilution of the label, as pipetting errors multiply through the dilution. Instead, pipet each concentration from the original radioactive stock to prepare the 6× solutions.

5. Add 150 µl membrane suspension to all tubes to initiate the reaction (total assay volume 300 µl). For transfected cells, 1.5 million cells/tube should yield an excellent signal using stably transfected cell lines. As individual expression levels vary from one clonal cell line to another, it is necessary to perform a preliminary determination of the optimal cell number per tube to yield a good signal-to-noise ratio.

6. Incubate 2 hr at room temperature (22°C) to achieve equilibrium. Characterization of CorticotropinReleasing Factor (CRF) Receptors

The 2-hr incubation time is longer than required to reach equilibrium as determined from the kinetic experiments described in Basic Protocols 1 and 2. The long incubation is important because lower concentrations of radioligand will associate with the receptors

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more slowly than higher concentrations of radioligand. Because the binding of [125I]oCRF remains at equilibrium for up to 4 hr at 22°C, the 2-hr incubation is used to ensure that all concentrations reach equilibrium during the assay.

Separate bound from free radioligand 7. Centrifuge 10 min at 14,000 × g, 22°C, in a tabletop microcentrifuge to pellet the membranes and separate bound from free radioligand. 8. Remove supernatant with a vacuum aspirator, being careful not to disturb the pellet. 9. Add 1 ml ice-cold wash buffer, gently washing the walls of the tube and the surface of the pellet. Do not resuspend the pellet. Resuspending the pellet will cause the radiolabel to dissociate from the receptor.

10. Microcentrifuge the tubes 10 min at 14,000 × g, 22°C, and aspirate the supernatant. Collect and analyze data 11. Using a microcentrifuge tube cutter, cut the bottom of the microcentrifuge tubes just above the pellet, place them in 12 × 75–mm gamma counter tubes, and quantify radioactivity in a gamma counter.

A 200

total binding nonspecific binding specific binding

Binding (fmol/mg protein)

175 150 125 100 75 50 25 0 0

0.2 0.4 0.6 0.8

1

1.2 1.4

1.6 1.8

[125I]sauvagine concentration (nM)

B 0.1

B/T

0.08 0.06 0.04 0.02 0 0

50

100

150

200

Specific binding (fmol/mg protein)

Figure 1.13.3 Saturation (A) and Scatchard (B) analyses of [125I]sauvagine binding to human CRF2(a) receptors expressed in stable CHO cell lines. Human CRF2(a) receptor–transfected CHO cell membranes were incubated with 10 to 12 concentrations of [125I]sauvagine (1 pM to 2 nM) at 22°C as described (see Basic Protocol 2). Nonspecific binding was defined in the presence of 1 µM D-Phe r/hCRF(12-41) at each concentration. KD and Bmax values were estimated by nonlinear regression analysis using Prism software (GraphPad).

Receptor Binding

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12. Determine specific binding for each concentration by subtracting nonspecific binding from total binding. Proceed with saturation analysis (UNIT 1.3) to determine KD and Bmax. Figure 1.13.3 shows representative raw data from a radioligand binding assay along with a Scatchard plot of the transformed data. BASIC PROTOCOL 3

COMPETITION ASSAYS TO DETERMINE Ki VALUES OF COMPETING LIGANDS Competition assays require a single concentration of radioligand (usually around its KD for the receptor) and increasing concentrations of unlabeled compounds incubated with membranes under equilibrium conditions. This allows for the determination of a rank order of potency of the test agents. Because a full concentration-response curve is generated using high concentrations of the unlabeled substance, no independent measure of nonspecific binding is necessary. A 10- to 12-point concentration-response curve, beginning at 1 µM and proceeding with dilutions at every half-log step, is sufficient for determination of the Ki of the competing ligand. Analysis of competition curves can be complex, depending upon the number of apparent binding sites or states of a receptor that the unlabeled agent recognizes. These analyses are detailed in UNIT 1.3. Materials Assay buffer (see recipe) 6× competing ligand solutions (see recipe; e.g., 6 µM, 1.9 µM, 600 nM, 190 nM, 60 nM, 19 nM, 6 nM, 1.9 nM, 0.6 nM, and 0.19 nM): agonists: rat/human corticotropin-releasing (r/hCRF), ovine CRF (oCRF), sauvagine, urotensin I, urocortin antagonists: D-Phe r/hCRF(12-41), α-helical oCRF(9-41), astressin 1.2 nM radioligand in assay buffer: [125I]ovine CRF ([125I]oCRF; NEN Life Sciences; for CRF1 receptor subtype) or [125I]sauvagine (NEN Life Sciences; for CRF1 or CRF2 receptor subtypes) Membrane suspension (see Support Protocol) Wash buffer (see recipe), ice cold 1.5-ml polypropylene microcentrifuge tubes Tabletop microcentrifuge with 60-tube capacity and speed >10,000 × g (e.g., Beckman microcentrifuge 12, Eppendorf model 5403) Vacuum aspirator: aspiration flask connected to tubing with Pasteur pipet/pipet tip (for radioactive waste) Microcentrifuge tube cutter (Fisher) or dog nail clippers (local pet supply) 12 × 75–mm gamma counter tubes Set up binding reaction 1. Arrange duplicate or triplicate 1.5-ml polypropylene microcentrifuge tubes at each concentration of unlabeled peptide to be tested. Add 50 µl assay buffer to each tube. 2. Add 50 µl of the appropriate 6× competing ligand solution to each tube. These 6× stock solutions will yield a 10-point competition curve including the following 1× amounts, expressed in log concentration: −6, −6.5, −7, −7.5, −8, −8.5, −9, −9.5, −10, −11, and −12 M. A 10- to 12-point competition curve should be generated.

Characterization of CorticotropinReleasing Factor (CRF) Receptors

3. Add 50 µl of 1.2 nM radioligand to each tube (0.2 nM final). Add 50 µl to separate tubes to determine the exact concentration of radioligand added. This concentration is approximately the KD of [125I]oCRF at the CRF1 receptor subtype, and of [125I]sauvagine at CRF1 and CRF2 receptor subtypes.

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120

Sauvagine binding (% specific)

100

80

60 sauvagine 40

20

r/hCRF α-helCRF oCRF

0 10–12

10–11

10–10

10–9 10–8 [Peptide] (M)

10–7

10–6

10–5

Figure 1.13.4 Competition of CRF-related peptides for [125I]sauvagine binding to human CRF2(a) receptors stably expressed in CHO cell lines. CRF2(a)-expressing cell membranes were incubated with 200 pM [125I]sauvagine at 22°C along with varying concentrations of competing peptides, and were analyzed for the ability of competitors to inhibit binding. The rank order of potencies of these peptides defines the receptor subtype. That is, the rank order is identical for the CRF2(a) receptor subtype, regardless of the tissue or species from which it is derived. The rank order of potencies is sauvagine (3 nM), r/hCRF (20 nM), α-helCRF(9-41) (150 nM), and oCRF (300 nM). α-helCRF, α-helical oCRF(9-41).

4. Add 150 µl membrane suspension to each tube to initiate the reaction (total assay volume 300 µl). For transfected cells, 1.5 million cells/tube should yield an excellent signal using stably transfected cell lines. As individual expression levels vary from one clonal cell line to another, it is necessary to perform a preliminary determination of the optimal cell number per tube to yield a good signal-to-noise ratio.

5. Incubate 2 hr at room temperature (22°C) to achieve equilibrium. In this case, it is assumed that the 2-hr incubation period is sufficient to achieve equilibrium for the unlabeled agents at all the concentrations studied. While this is a necessary and reasonable assumption, independent kinetic curves with the equivalent radiolabeled substance would have to be performed to be certain.

Separate bound from free radioligand 6. Centrifuge 10 min at 14,000 × g, 22°C, in a tabletop microcentrifuge to pellet the membranes and separate bound from free radioligand. 7. Remove supernatant with a vacuum aspirator, being careful not to disturb the pellet. 8. Add 1 ml ice-cold wash buffer, gently washing the walls of the tube and the surface of the pellet. Do not resuspend the pellet. Resuspending the pellet will cause the radiolabel to dissociate from the receptor.

9. Microcentrifuge 10 min at 14,000 × g, 22°C, and aspirate the supernatant.

Receptor Binding

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Collect and analyze data 10. Using a microcentrifuge tube cutter, cut the bottom of the microcentrifuge tubes just above the pellet. Place them into 12 × 75–mm gamma counter tubes and quantify radioactivity in a gamma counter. 11. Plot data on a logarithmic x axis, and proceed with the competition analysis (UNIT 1.3) to determine the Ki values of the competing ligands. Figure 1.13.4 shows the monophasic concentration-response curves obtained using [125I]sauvagine for cloned CRF2(a) receptors expressed in CHO cell membranes. Increasing concentrations of four unlabeled CRF-related peptides were added to the binding assays, and the Ki values for each were determined from curve-fitting analyses. SUPPORT PROTOCOL

PREPARATION OF CORTICOTROPIN-RELEASING FACTOR (CRF) RECEPTORS FROM TISSUES OR CELLS The tissue preparation for radioligand binding studies is unique for the individual receptor system being studied; however, all have some common features. The conditions for binding (such as protein concentration, pH, temperature, and ion concentrations) all must be optimized before a detailed characterization can be performed. For the CRF system (CRF1, CRF2(a), and CRF2(b) receptors), these parameters have been well documented and can be found in numerous reviews and original publications (De Souza and Nemeroff, 1990; Owens and Nemeroff, 1991; Lovenberg et al., 1995; Grigoriadis et al., 1996). The preparation of membranes is identical for all the assays described in this unit, and can be performed using either brain tissue or cultured cells. The highest densities of CRF1 receptors in the rodent brain are found in the prefrontal cortex, parietal cortex, olfactory bulb, and cerebellum. The highest densities of the CRF2 receptors in the rodent brain are in the olfactory bulb, lateral septum, and ventromedial hypothalamus. Cell lines expressing CRF receptor subtypes have been described by Chen et al. (1993), Lovenberg et al. (1995), Liaw et al. (1996), and Sperle et al. (1997). Materials Fresh whole rodent brain or cells (e.g., mammalian, insect) transfected transiently or stably, and expressing the desired CRF receptor subtype Tissue buffer (see recipe) 50 mM Tris⋅Cl (APPENDIX 2A) or Dulbecco’s PBS (DPBS; Life Technologies), pH 7.0 at 22°C, containing 5 mM EDTA 5- to 15-ml round-bottom high-speed (40,000 × g) centrifuge tubes (e.g., Fisher) Polytron tissue homogenizer (Brinkmann) or Tissue Tearor (Fisher) Additional reagents and solutions for counting cells (Phelan, 1998) and for BCA protein determination (APPENDIX 3A) Suspend cells For brain tissue: 1a. Quickly remove whole brain or dissect brain regions (e.g., prefrontal cortex, olfactory bulb), and use immediately or quick-freeze on dry ice and store at −80°C until use. 2a. Thaw tissue if necessary, weigh, and place in 50 vol tissue buffer (5 ml for 100 mg original wet weight) in a round-bottom high-speed centrifuge tube.

Characterization of CorticotropinReleasing Factor (CRF) Receptors

For transfected cell lines (adherent or suspended): 1b. Harvest adherent cells into 50-ml conical centrifuge tubes using 50 mM Tris⋅Cl or DPBS with 5 mM EDTA, or add cell suspension to a 50-ml conical centrifuge tube.

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Current Protocols in Pharmacology

Count cells and pellet by centrifuging 5 min at 1000 × g, 4°C. Use cell pellets immediately, or quick-freeze on dry ice and store at −80°C until use. Tris⋅Cl and DPBS give equivalent results. If chemical cross-linking studies are to be performed, Tris-based buffers are contraindicated due to the interaction of the buffer with the chemical cross-linking reagent. The use of EDTA to remove adherent cells from culture flasks is of key importance as trypsin (typical for cell culturing) destroys cell-surface proteins, including the expressed receptors. For routine passage of cells, the use of trypsin is recommended.

2b. Thaw (if necessary) a known number of cells and resuspend in tissue buffer at 107/ml in a round-bottom high-speed centrifuge tube. Prepare membranes 3. Homogenize cell supension 15 sec on ice using a Polytron homogenizer at 25,000 rpm. 4. Centrifuge 10 min at 30,000 to 40,000 × g, 4°C. 5. Aspirate supernatant and resuspend pellet in the same volume of tissue buffer. 6. Repeat steps 3 and 4. 7. Aspirate supernatant and resuspend pellet in tissue buffer to a working concentration of ∼1.2 mg protein/ml or 3 × 106 cells/ml. Protein concentration can be measured using the BCA assay (APPENDIX 3A), with BSA (fraction V; Sigma) as a protein standard.

8. Store on ice until needed for the binding reaction. This membrane preparation is stable for 2 to 3 hr on ice. However, it is highly recommended that all assays be performed with minimum delay between the preparation of reagents and the assessment of receptors.

RECEPTOR AUTORADIOGRAPHY TO STUDY CORTICOTROPINRELEASING FACTOR (CRF) RECEPTORS

BASIC PROTOCOL 4

This protocol describes the use of receptor autoradiographic assays performed on slidemounted sections of whole tissues to determine the specific anatomical distribution of receptor subtypes in discrete regions of the central nervous system. The protocol is used for the elucidation and anatomical mapping of CRF1, CRF2(a), and CRF2(b) receptors utilizing [125I]sauvagine (which has equal affinity for these receptor subtypes), and for defining specific receptor subtypes by their localization and selectivity for r/hCRF and oCRF. If desired, autoradiograms can be analyzed using an imaging system. A variety of commercial digital imaging systems are available for quantifying radioreceptor autoradiograms and in situ hybridization autoradiograms. The key items are (1) a computer system with video frame-grabber card, including an external device (preferably removable) for storage of images (e.g., Bernoulli, Zip, Optical); (2) a high-resolution digital charge-coupled device (CCD) video camera, color or black and white; (3) a transilluminating light box (many models available) with constant and even illumination across the entire field; and (4) image analysis software. Some imaging systems come complete with proprietary software. Alternatively, the free image analysis software NIH Image can be used. The latest version is available from the NIH Image web site (http://rsb.info.nih. gov/nih-image/) or by anonymous FTP from zippy.nimh.nih.gov. Receptor Binding

1.13.11 Current Protocols in Pharmacology

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Materials Whole rodent brain (dissect fresh, freeze immediately, and store at −80°C) Tissue-Tek O.C.T. Compound embedding medium (VWR) 0.2 nM radioligand (2000 to 2200 Ci/mmol; NEN Life Sciences) in assay buffer: [125I]oCRF or r/hCRF (for CRF1 receptors) [125I]sauvagine (for CRF1 and CRF2 receptors) 100 µM competing ligand solution (see recipe): agonists: rat/human corticotropin-releasing factor (r/hCRF), ovine CRF (oCRF), sauvagine, urotensin I, urocortin antagonists: D-Phe r/hCRF(12-41), α-helical oCRF(9-41), astressin Assay buffer (see recipe), ice cold Wash buffer (see recipe), ice cold Refrigerated cryostat (e.g., Hacker, Leica) Superfrost/PLUS slides (Fisher) Glass Coplin jars (or equivalent staining dishes) and appropriate slide racks Blow dryer (Fisher) X-ray cassettes (Sigma) Radioactive standard microscales ([125I]microscales; Amersham) X-ray film (e.g., Kodak Biomax-MR) Automated X-ray film developer or manual X-ray film developing supplies Prepare slide-mounted sections 1. Mount a fresh-frozen whole rodent brain (cerebellum/brain stem down) onto the chuck of a refrigerated cryostat using Tissue-Tek O.C.T. Compound embedding medium in dry ice, making certain the brain is frozen firmly onto the chuck. 2. Place the chuck and brain into the cryostat, and allow it to equilibrate to the cryostat chamber temperature (20 to 30 min). Also place a box of Superfrost/PLUS slides in the cryostat so they attain the same temperature as the tissue and chamber. 3. Cut 15- to 20-µm sections and press a chilled slide onto each section on the knife. Place one or two sections per slide as desired. 4. Remove slide from chamber and thaw-mount the section onto the slide by holding a finger on the back of the slide. 5. Place on the bench and allow to dry completely. Store slides in slide boxes at −80°C until use. Perform radioligand binding 6. Place frozen slides on a bench and bring to room temperature (20 to 30 min). Labeling solutions can be made while slides are thawing.

7. Place ∼50 ml of 0.2 nM radioligand into two glass Coplin jars from a single radioligand solution. Add 0.5 ml of 100 µM competing ligand (1.5 µM final) to one jar for nonspecific binding. The KD of [125I]sauvagine for the CRF1 and CRF2 receptor subtypes is 0.2 nM, as are the KD values of [125I]oCRF and [125I]r/hCRF at CRF1 receptors. Because r/hCRF has higher nonspecific binding, oCRF is recommended for CRF1-selective studies. Characterization of CorticotropinReleasing Factor (CRF) Receptors

It is important to use a single batch of radioligand solution for both the total and nonspecific binding determinations because sections incubated in the presence or absence of competing ligands should be exposed to identical concentrations of radioligand. Two Coplin jars (or 100 ml of radioligand solution) are sufficient for 30 slides.

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8. Place slides into slide racks, immerse in the appropriate radioactive solutions, and incubate 2 hr at 22°C. Wash sections 9. During incubation, prepare four wash baths for each condition (total and nonspecific binding): one containing assay buffer, two containing wash buffer, and one containing deionized water. Keep all jars on ice and all solutions at 4°C to limit the dissociation of radioligand from sections during the wash procedure. 10. Following incubation, dip slides into the first wash jar (assay buffer) five times quickly to rinse off the bulk of the radioactivity from the slide. 11. Wash slides 5 min in each of the two jars containing wash buffer. These 5-min washes have been optimized for the CRF system using either [125I]sauvagine or [125I]oCRF by varying the time the slides are immersed in wash buffer. Timing may differ for other radioligands, and is a balance between reducing background (nonspecific labeling) and the dissociation rate of the radioactive substance (i.e., maximizing specific binding). These wash times must be derived empirically.

12. Dip slides 5 times quickly in deionized water and place them on an absorbent pad to remove most of the water.

[125I]oCRF

[125I]sauvagine

Figure 1.13.5 Localization of CRF1 and CRF2 receptor ligand binding sites by autoradiography using [125I]sauvagine and [125I]oCRF in rat brain. Horizontal slide-mounted rat brain sections were incubated with either radiolabeled oCRF or sauvagine and apposed to film as described (see Basic Protocol 4). [125I]oCRF (left) labels predominantly the CRF1 receptor subtype in the internal granular layer of the olfactory bulb, and in cortical and cerebellar regions, with virtually no labeling of the CRF2(a) subtype. [125I]Sauvagine (right), which has equal affinity for both the CRF1 and CRF2(a) receptor subtypes, labels all of the same CRF1 receptor regions as [125I]oCRF, and also labels regions high in CRF2(a) receptor density, such as the lateral septum, the choroid plexus, and the ependymal layer of the olfactory bulb. This confirms the apparent selectivity profile of the two radioligands, and is consistent with the rank order profile demonstrated in the competition studies.

Receptor Binding

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13. Place the slides under a blow dryer at a cool setting until they are completely dry. Develop autoradiogram 14. Arrange slides onto an X-ray cassette. If the resulting images are to be quantified, include a slide with [125I]microscales in the same cassette. 15. Under safelight conditions in a darkroom, appose Biomax-MR X-ray film directly onto the slides and store the cassette in the dark at room temperature for 2 days to 3 weeks. Exposure times depend largely on the amount of radioactivity bound to the section and on the receptor levels in the sections being studied. For high–specific activity 125I-labeled ligands and abundant receptor levels, 3 to 5 days are sufficient. For low abundance receptors in discrete brain regions, 3 to 4 weeks may be required for optimal visualization.

16. Remove the film from the cassette under safelight conditions and develop it using standard X-ray developing methods (either using an automated X-ray film developer, or manually using trays with standard developer and fixative solutions). Figure 1.13.5 shows localization of CRF1 and CRF2 receptors in a rat brain slice.

REAGENTS AND SOLUTIONS Use deionized, distilled water in all recipes and protocol steps. For common stock solutions, see APPENDIX 2A; for suppliers, see SUPPLIERS APPENDIX.

Assay buffer Tissue buffer (see recipe) containing: 0.15% (w/v) BSA, fraction V (Sigma) 0.15 mM bacitracin 1.5% (w/v) aprotinin Prepare fresh before use Competing ligand solutions, 100 ìM and 6× Prepare all peptides as 100 µM stock solutions in 10 mM acetic acid and 0.1% (w/v) BSA. Store up to 6 months at −80°C. Prepare 6× solutions by diluting 100 µM stock solutions directly in assay buffer (see recipe). Do not use serial dilutions. Prepare fresh immediately before use. Most peptides are available from Peninsula Labs. Astressin (Gulyas et al., 1995), antalarmin (Webster et al., 1996), and D-Phe r/hCRF(12-41) (Rivier et al., 1993) are not commercially available as of this writing.

Tissue buffer 50 mM Tris⋅Cl (APPENDIX 2A) or Dulbecco’s PBS (DPBS) without calcium or magnesium (Life Technologies) 10 mM MgCl2 2 mM ethylene glycol bis(β-aminoethylether)-N,N,N′,N′-tetraacetic acid (EGTA) Adjust pH to 7.0 at 22°C Store up to 2 weeks at 4°C

Characterization of CorticotropinReleasing Factor (CRF) Receptors

Wash buffer Ice-cold Dulbecco’s PBS (DPBS) without calcium or magnesium (Life Technologies) 0.01% (v/v) Triton X-100 Adjust pH to 7.0 at 4°C Store up to 4 weeks at 4°C

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Current Protocols in Pharmacology

COMMENTARY Background Information Stress, or disruptions of homeostasis, can result from physical, psychological, and immunological challenges. Corticotropin-releasing factor (CRF) is a key neurohormone for integrating the response to stress throughout the neuro-endocrine-immune axis. CRF has been widely reported to play a major role in coordinating the endocrine, autonomic, and behavioral responses to stress through actions in the brain and periphery. This hormone is produced and secreted primarily from parvocellular neurons of the paraventricular hypothalamic nuclei, and activates corticotrophs in the anterior pituitary. This, in turn, causes secretion of adrenocorticotropin hormone (ACTH), which stimulates adrenal glucocorticoid release (De Souza and Nemeroff, 1990; Owens and Nemeroff, 1991). The primary event in the action of CRF is the interaction with cell-surface receptor proteins. Two receptor subtypes, termed CRF1 and CRF2, have recently been identified and cloned. The CRF1 receptor was first cloned from a human Cushing’s corticotropic adenoma using expression cloning, and was characterized as a 415–amino acid protein (Chen et al., 1993). Independently, this receptor subtype was also identified in mouse (Vita et al., 1993) and rat (Chang et al., 1993; Perrin et al., 1993). In all three species, CRF1 receptor mRNAs encode proteins of 415 amino acids that are 98% identical to one another. Following the cloning of the CRF1 subtype, two forms of a second family member were discovered in the rat. These are termed CRF2(a) and CRF2(b). The rat CRF2(a) receptor (Lovenberg et al., 1995) is a 411–amino acid protein with ∼71% identity to the CRF1 receptor. The CRF2(b) receptor has been cloned from both rat (Lovenberg et al., 1995) and mouse (Kishimoto et al., 1995; Perrin et al., 1995), and is a 431– amino acid protein. The CRF2(a) and CRF2(b) subtypes differ in the N-terminal extracellular domain, where the first 34 amino acids of CRF2(a) are replaced by 54 different amino acids in CRF2(b). The genomic structure and corresponding cDNA of the human CRF2(a) and CRF2(b) receptor subtypes have recently been cloned and characterized (Kostich et al., 1996; Liaw et al., 1996). The pharmacological characteristics of these human isoforms are similar to those previously reported for rat receptors (Chalmers et al., 1996; Grigoriadis et al., 1996).

A third splice variant, the CRF2(c) receptor, has recently been identified in human brain (Sperle et al., 1997). This splice variant uses yet a different 5′ alternative exon for its amino terminus and replaces the first 34 amino acid sequence of the CRF2(a) receptor with a unique 20–amino acid sequence. Reverse transcription PCR (RT-PCR) analysis of human brain mRNA demonstrates expression in amygdala and hippocampus but Southern analysis of rat genomic DNA yields negative results, suggesting that this subtype does not exist in rat. As full characterization of the CRF2(c) subtype has not yet been elucidated in terms of native characteristics and function, this subtype is not discussed further. All splice variants of the CRF2 receptor have potential N-glycosylation and phosphorylation sites that are analogous to those found in CRF1 receptors. In addition to the CRF1 and CRF2 receptors, a 322–amino acid CRF binding protein (CRFBP) has been described that binds CRF and urocortin with high affinity and is hypothesized to inactivate them. This hypothesis has been supported by the fact that plasma levels of CRF are quite low under normal conditions, but are markedly elevated in late gestational stages of pregnancy, and this CRF is bioactive in releasing ACTH from cultured pituitary cells. In spite of the high levels of CRF in the maternal plasma, there is no evidence of markedly increased ACTH secretion or hypercortisolism in pregnant women. This paradoxical situation was addressed by the presence of a binding protein in the plasma of pregnant women that could specifically inhibit the biological actions of CRF. In fact, this hypothesis was recently validated by the isolation of a CRF-binding protein (CRF-BP) from human plasma and its subsequent cloning and expression. This protein has very high affinity for r/hCRF and urocortin but virtually no affinity for the ovine form of CRF, demonstrating a distinct and selective pharmacological profile from the receptors. Expression of this protein in the corticotrophs strongly suggests that the CRF-BP is involved in the regulation of neuroendocrine functions of CRF by limiting and/or affecting the interactions of CRF with its receptor, which is also known to reside on corticotrophs. However, the detailed role of the binding protein in various central nervous system disorders or in regulating pituitary-adrenal function remains to be elucidated (for review see Chadwick et al., 1993). Receptor Binding

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Recent clinical data have implicated CRF in the etiology and pathophysiology of various endocrine, psychiatric, neurologic, and inflammatory illnesses. Hypersecretion of CRF in brain may contribute to the symptomatology seen in neuropsychiatric disorders (such as depression), anxiety-related disorders, and anorexia nervosa. Deficits in brain CRF have been noted in neurodegenerative disorders such as Alzheimer’s disease, Parkinson’s disease, and Huntington’s disease. The recent discovery of novel receptor family members and novel alternative ligands serve not only to increase the understanding of the system, but also to provide a basis for selective and rational drug design for the treatment of disorders associated with aberrant levels of CRF (De Souza and Nemeroff, 1990; Dunn and Berridge, 1990; Owens and Nemeroff, 1991; Chadwick et al., 1993; De Souza and Grigoriadis, 1994).

Critical Parameters and Troubleshooting

Characterization of CorticotropinReleasing Factor (CRF) Receptors

Radioligand binding Nonspecific binding is defined as radioligand binding that remains in the presence of an excess of unlabeled drug known to interact with the receptor. Thus, it is measured in parallel with total binding by adding an excess (e.g., 100 nM to 10 mM) of an unlabeled drug that has a high specificity for the receptor of interest. In the case of CRF, nonspecific binding is defined as [125I]oCRF that remains bound in the presence of 1 µM unlabeled CRF. Alternatively, specific binding is defined as binding that can be inhibited by 1 µM unlabeled CRF. It is good general practice to use similar, but not identical, compounds to define nonspecific binding. For example, if [125I]oCRF is used to label the receptor binding site, a closely related analog, such as the rat/human form of CRF (r/hCRF), should be used to define nonspecific binding. Use of closely related analogs reduces the chance of inhibiting radioligand attachment to nonreceptor sites. The concentration of r/hCRF used (1 µM) was empirically determined from direct competition curves as that concentration which inhibits >90% the total binding of [125I]oCRF in membrane homogenates from tissues known to contain a high density of CRF receptors. The radiolabeled receptor in the membrane can be separated from the free (unbound) radioligand by a variety of techniques including filtration, centrifugation, and precipitation. For details on the determination of the optimal binding pa-

rameters, as well as all of the separation techniques, see Chapter 1 (especially UNIT 1.3). Because all the CRF receptor agonists and antagonists available are peptides, the use of BSA in the assay buffer is recommended to minimize the interaction of the peptides with the labware. Typically, the level of total binding should not exceed 10% of the total amount of radioactivity added in the assay. This is especially critical for the determination of kinetic parameters, where receptor-induced ligand depletion will affect estimates of these parameters. A protein concentration binding curve will serve to identify the optimum protein concentration for incubation. Receptor mapping and autoradiography Methods for the identification, localization, characterization, and quantification of receptors by autoradiography are documented in UNIT 8.1.

Anticipated Results Binding studies using [125I]oCRF or typically yield an 80% to 90% specific signal in membranes from cells expressing the CRF1, CRF2(a), or CRF2(b) receptor subtypes. If brain homogenates are used, the signal usually drops slightly to between 60% and 80% specific binding. There is a very discrete localization of CRF receptor subtypes in brain (Chalmers et al., 1995; Fig. 1.13.5). Using [125I]oCRF (which will not label the CRF2 receptor subtype under the conditions described), high levels of binding should be evident in the olfactory bulb, frontal and parietal cortex, and cerebellum. Very little, if any, binding of [125I]oCRF is apparent in subcortical regions. Using [125I]sauvagine (which has high affinity for all known CRF receptor subtypes), high levels of binding are observed in subcortical regions such as the lateral septum and ventromedial hypothalamus (primarily CRF2(a)), and choroid plexus (primarily CRF2(b)). In addition, the binding of [125I]sauvagine matches the pattern of distribution observed with [125I]oCRF. [125I]sauvagine

Time Considerations A typical radioligand binding assay for the study of CRF receptors can be performed in 6 hr from membrane preparation to analysis of data. This does not include the time required for preparation of reagents or for tissue culture and harvesting of cells if stable cell lines are used. Typically, cell culture and harvesting of cells is performed independently, and the cell

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pellets are frozen until required for the assay. Preparation for the assay (i.e., membrane preparation, peptide dilutions, setting up, and pipetting) can be performed in ∼2 hr. The incubation of the assay requires 2 hr. Separation of bound from free radioligand by centrifugation, counting of samples, and data analysis may take another 2 hr, depending on the number of tubes being processed. A typical autoradiographic experiment can also be performed in one day, but this does not include the sectioning of the tissue or exposure to film. Typically, brains are removed and sectioned so that a bank of slide-mounted tissue sections are readily available in the freezer for assay. The preparation of reagents requires ∼1 hr and the incubation time is 2 hr. The wash steps are performed in a couple of hours, depending on the number of slides being processed. It is not unreasonable to leave the slides on the bench overnight to dry completely before apposing them to film, as they should be completely dry before exposure. The exposure time of the film is highly dependent on the level of binding and the concentration of radioligand used, and must be derived empirically. It is not uncommon to have exposure times as short as 2 days or as long as a month or more.

Literature Cited Chadwick, D.J., Marsh, J., and Ackrill, K. (eds.). 1993. Corticotropin-releasing factor. In CIBA Foundation Symposium, Vol. 172. John Wiley & Sons, Chichester, England. Chalmers, D.T., Lovenberg, T.W., and De Souza, E.B. 1995. Localization of novel corticotropinreleasing factor receptor (CRF2) mRNA to specific sub-cortical nuclei in rat brain: Comparison with CRF1 receptor mRNA expression. J. Neurosci. 15:6340-6350. Chalmers, D.T., Lovenberg, T.W., Grigoriadis, D.E., Behan, D.P., and De Souza, E.B. 1996. Corticotropin-releasing factor receptors: From molecular biology to drug design. Trends Pharmacol. Sci. 17:166-172.

D.J. Kupfer, eds.) pp. 505-517. Raven Press, New York. De Souza, E.B. and Nemeroff, C.B. (eds.) 1990. Corticotropin-Releasing Factor: Basic and Clinical Studies of a Neuropeptide. CRC Press, Boca Raton, Fla. Dunn, A.J. and Berridge, C.W. 1990. Physiological and behavioral responses to corticotropin-releasing factor administration: Is CRF a mediator of anxiety of stress responses? Br. Res. Rev. 15:71100. Grigoriadis, D.E., Lovenberg, T.W., Chalmers, D.T., Liaw, C., and De Souza, E.B. 1996. Characterization of corticotropin-releasing factor receptor subtypes. In Neuropeptides: Basic and Clinical Advances (J.N. Crawley and S. McLean, eds.) pp. 60-80. The New York Academy of Sciences, New York. Gulyas, J., Rivier, C., Perrin, M., Koerber, S.C., Sutton, S., Corrigan, A., Lahrichi, S.L., Craig, A.G., Vale, W., and Rivier, J. 1995. Potent, structurally constrained agonists and competitive antagonists of corticotropin-releasing factor. Proc. Natl. Acad. Sci. U.S.A. 92:10575-10579. Kishimoto, T., Pearse, R.V. II, Lin, C.R., and Rosenfeld, M.G. 1995. A sauvagine/corticotropin-releasing factor receptor expressed in heart and skeletal muscle. Proc. Natl. Acad. Sci. U.S.A. 92:1108-1112. Kostich, W., Chen, A., Sperle, K., Horlick, R.A., Patterson, J., and Largent, B.L. 1996. Molecular cloning and expression analysis of human CRF receptor type 2α and β isoforms. Soc. Neurosci. Abstr.22:1545. Liaw, C.W., Lovenberg, T.W., Barry, G., Oltersdorf, T., Grigoriadis, D.E., and De Souza, E.B. 1996. Cloning and characterization of the human CRF2 recep to r gene and cDNA. Endocrinology 137:72-77. Lovenberg, T.W., Liaw, C.W., Grigoriadis, D.E., Clevenger, W., Chalmers, D.T., De Souza, E.B., and Oltersdorf, T. 1995. Cloning and characterization of a functionally distinct corticotropinreleasing factor receptor subtype from rat brain. Proc. Natl. Acad. Sci. U.S.A. 92:836-840. Owens, M.J. and Nemeroff, C.B. 1991. Physiology and pharmacology of corticotropin-releasing factor. Pharmacol. Rev. 43:425-473.

Chang, C.P., Pearse, R.I., O’Connell, S., and Rosenfeld, M.G. 1993. Identification of a seven transmembrane helix receptor for corticotropin-releasing factor and sauvagine in mammalian brain. Neuron 11:1187-1195.

Perrin, M.H., Donaldson, C.J., Chen, R., Lewis, K.A., and Vale, W.W. 1993. Cloning and functional expression of a rat brain corticotropin releasing factor (CRF) receptor. Endocrinology 133:3058-3061.

Chen, R., Lewis, K.A., Perrin, M.H., and Vale, W.W. 1993. Expression cloning of a human corticotropin-releasing-factor receptor. Proc. Natl. Acad. Sci. U.S.A. 90:8967-8971.

Perrin, M., Donaldson, C., Chen, R., Blount, A., Berggren, T., Bilezikjian, L., Sawchenko, P., and Vale, W. 1995. Identification of a second corticotropin-releasing factor receptor gene and characterization of a cDNA expressed in heart. Proc. Natl. Acad. Sci. U.S.A. 92:2969-2973.

De Souza, E.B. and Grigoriadis, D.E. 1994. Corticotropin-releasing factor: Physiology, pharmacology and role in central nervous system and immune disorders. In Psychopharmacology: The Fourth Generation of Progress (F.E. Bloom and

Phelan, M.C. 1998. Techniques for mammalian cell tissue culture. In Current Protocols in Molecular Biology (F.M. Ausubel, R. Brent, R.E. Kingston, D.D. Moore, J.G. Seidman, J.A. Smith, and K.

Receptor Binding

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Struhl, eds.) pp. A.3F.1-A.3F.14. John Wiley & Sons, New York. Rivier, J., Rivier, C., Galyean, R., Miranda, A., Miller, C., Craig, A.G., Yamamoto, G., Brown, M., and Vale, W. 1993. Single point D-substituted corticotropin-releasing factor (CRF) analogs: Effect on potency and physicochemical characteristics. J. Med. Chem. 36:2851-2859. Sperle, K., Chen, A., Kostich, W., and Largent, B.L. 1997. CRH2γ: A novel CRH2 receptor isoform found in human brain. Soc. Neurosci. Abstr. 23:689.14. Vita, N., Laurent, P., Lefort, S., Chalon, P., Lelias, J.M., Kaghad, M., Le, F.G., Caput, D., and Ferrara, P. 1993. Primary structure and functional expression of mouse pituitary and human brain corticotrophin releasing factor receptors. FEBS Lett. 335:1-5.

Webster, E.L., Lewis, D.B., Torpy, D.J., Zachman, E.K., Rice, K.C., and Chrousos, G.P. 1996. In vivo and in vitro characterization of antalarmin, a nonpeptide corticotropin-releasing hormone (CRH) receptor antagonist: Suppression of pituitary ACTH release and peripheral inflammation. Endocrinology. 137:5747-5750.

Contributed by Dimitri E. Grigoriadis, Marge T. Lorang, Nicola Duggan, and Errol B. De Souza Neurocrine Biosciences San Diego, California

Characterization of CorticotropinReleasing Factor (CRF) Receptors

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Characterization of Wild-Type Excitatory Amino Acid Ion Channel Receptors

UNIT 1.14

This unit describes receptor binding assays for studying ion channel–forming excitatory amino acid (EAA) receptors in the mammalian central nervous system. Not considered is the use of binding assays to study homologous or heterologous cloned and expressed receptor constructs (see Commentary), although the protocols could be applied to these situations. Discussion of these systems is omitted because the native structure of ionotropic (channel-forming) receptors, which consist of multiple potential combinations of heterologous subunits, remains to be determined. Since there are currently no commercially available radioligands for the study of G protein–linked EAA receptors, no protocols are presented for studying these sites either. The three major ionotropic EAA receptors are defined according to the classification of Watkins and Evans (1981). The original quisqualate receptor is now referred to as an AMPA receptor, for (RS)-α-amino-3-hydroxy-5-methyl-4-isoxazole propionic acid. The other two ionotropic receptors are classified on the basis of the original agonists identified for these sites: kainic acid (KA) and N-methyl-D-aspartic acid (NMDA). Thus, the three recognized ionotropic receptors are the AMPA, KA, and NMDA recognition sites. Like γ-aminobutyric acid (GABA) receptors (see UNIT 1.7), the AMPA and NMDA receptors have multiple regulatory sites. The basic protocol addresses measurement of binding for AMPA receptors, providing both centrifugation and filtration protocols for separating bound from free ligand. Two alternate protocols give modifications for KA and NMDA receptors. A support protocol is provided for the preparation of brain membranes to study wild-type EAA binding sites (Enna and Synder, 1976; also see UNIT 1.7). NOTE: All protocols using live animals must first be reviewed and approved by an Institutional Animal Care and Use Committee (IACUC) or must conform to governmental regulations regarding the care and use of laboratory animals. MEASUREMENT OF [3H]AMPA BINDING TO WILD-TYPE AMPA RECEPTORS This protocol details a procedure for evaluating the competitive interaction of [3H]AMPA to wild-type AMPA receptors in brain membranes using a fixed concentration of ligand (see UNIT 1.3 for a discussion of constant analyses). The assay may be performed using either centrifugation or filtration for separating bound from free ligand. The original method was described by Murphy et al. (1987). While the method described is for low-throughput analyses (1-ml reactions), it may be scaled down (typically to 100- to 200-µl reactions) for high-throughput screening (HTS) applications, in which case a 96-well microtiter plate format is used. However, if the assay is terminated using a 96-well format, it is recommended that it be performed in 1.5-ml polypropylene tubes and the incubates filtered onto a 96-well filter sheet.

BASIC PROTOCOL

NOTE: Incubation in the 96-well microtiter plate is not recommended as the reduction in the quantity of assay constituents decreases the precision and reliability of the procedure. Materials Tris/thiocyanate buffer: 0.05 M Tris⋅Cl supplemented with 0.1 M potassium thiocyanate, pH 7.1 at 4°C Buffy coat membrane suspension (see Support Protocol) Receptor Binding Contributed by John W. Ferkany Current Protocols in Pharmacology (1999) 1.14.1-1.14.12 Copyright © 1999 by John Wiley & Sons, Inc.

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L-Glutamate (Sigma; RBI) [3H](RS)-α-Amino-3-hydroxy-5-methyl-4-isoxazole propionic acid ([3H]AMPA; NEN Life Sciences) Test compounds Tissue solubilizer (e.g., Solvable; NEN Life Sciences; for centrifugation assays) Scintillation cocktail (aqueous compatible; e.g., NEN Life Sciences)

7-ml plastic scintillation vials Homogenizer (e.g., Polytron, Tekmar Tissumizer) Centrifuge (Beckman J2-21M with JA-20 and JA-20.1 rotors, or equivalent) 12 × 75–mm borosilicate glass tubes or 1.5-ml polypropylene tubes (for filtration assays) Vacuum filtration tissue harvester (Brandell or equivalent) with pump (for filtration assays) Glass fiber filters (Whatman GF/B or equivalent; for filtration assays) CAUTION: Do not confuse potassium thiocyanate with potassium cyanide or potassium cyanate. The latter are extremely hazardous if not handled properly. Prepare buffer and membranes 1. Prepare Tris/thiocyanate buffer on the day prior to the experiment, allowing it to chill to 4°C overnight. The buffer may be prepared by using ice-cold water at the time of preparation and adjusting the pH to 7.1. Alternatively, the buffer may be prepared at room temperature by adjusting the pH to 6.9. When chilled, the pH will be ∼7.1. Although the buffer may be made by mixing appropriate amounts of Tris⋅Cl and Tris base, a simpler approach is to adjust the pH of a Tris base solution using 10 N HCl.

2. On the day of assay, thaw the buffy coat membrane suspension (e.g., one rat forebrain equivalent weight for ∼50 assay tubes). Wash the tissue by homogenizing for 30 sec (Polytron setting 5 to 6) in 40 to 50 ml ice-cold Tris/thiocyanate buffer and then centrifuge the suspension for 10 min at 40,000 to 50,000 × g in a refrigerated (4°C) centrifuge. Decant the supernatant and repeat this wash step four times. The washes remove endogenous EAAs that may interfere with the binding of the ligand.

Prepare assay tubes and components 3. While the tissue is washing, assemble the assay tubes in triplicate. If the assay is to be terminated by centrifugation, all reaction components will be placed directly into 7-ml plastic scintillation vials, which double as reaction vessels. If the assay is to be terminated by filtration, use either 12 × 75–mm borosilicate tubes or 1.5-ml polypropylene tubes. Assays may be performed in duplicate in preliminary experiments, but should never be performed using only a single tube unless the experiment is being performed in an HTS environment. In this case, positive results should always be confirmed using more rigorous conditions. If the tissue washing procedure is completed before the assay tubes are prepared, place the tube(s) containing the final pellet in the refrigerator or in an ice-water bath. The membranes can be maintained this way for up to an hour prior to use.

Characterization of Wild-Type Excitatory Amino Acid Ion Channel Receptors

4. Prepare working solutions of 10 mM L-glutamate and of 100 nM [3H]AMPA, both in ice-cold Tris/thiocyanate buffer. L-Glutamate will take some time to dissolve in a neutral buffer like Tris⋅Cl. The process can be expedited by adding a small amount of 1 N KOH.

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While unlabeled AMPA may be substituted for L-glutamate, this is more expensive and, from a theoretical perspective (isotope dilution), less desirable. The L-glutamate is used to determine nonspecific binding (UNIT 1.3), e.g., the binding of the radiolabeled ligand to nonreceptor sites. During data analysis, nonspecific binding is subtracted from total binding to determine the amount of ligand bound to the receptor being studied.

5. Prepare working solutions of test compounds in ice-cold Tris/thiocyanate buffer at 10× the final concentration to be studied. Typically about seven concentrations are used, initially spanning a wide range. Additional experiments can be performed later to fine tune the appropriate concentration range. If the compounds are insoluble in water, dimethyl sulfoxide (DMSO) may be used, provided its final concentration in the assay does not exceed 1% (v/v). If DMSO is used, an equivalent amount should be incorporated into total and nonspecific tubes. Avoid the use of strong acids or bases to solubilize compounds as the solutions may change the pH of the assay and adversely influence the final data.

6. Set up reactions in each tube as shown in Table 1.14.1. 7. After all additions are made, place the tubes in an ice-water bath. It is important the tubes are jacketed by the ice water and not simply put on top of the ice.

Measure AMPA binding 8. Resuspend the washed tissue pellet in ice-cold Tris/thiocyanate buffer using the tissue homogenizer at a mid-level setting (e.g., Polytron 5 or 6). The equivalent of a single rat forebrain (or 1 g tissue) should be resuspended in 25 ml buffer.

9. Rapidly aliquot 0.5 ml tissue suspension into each assay tube and allow the reaction to proceed on ice for ≥60 min, but not more than 120 min. A repeating pipettor (e.g., Eppendorf, Rainin) is useful for the addition of tissue suspension. Remember to account for the time it will take to terminate the reaction and to include this amount of time between each group of 24, 48, or 96 assay tubes. Add tissue to one group of tubes at a time, waiting the necessary period between each group of tubes. In most cases, the reaction may proceed for >60 min since it will be at equilibrium. However, the timing is critical for association and dissociation experiments (UNIT 1.3).

Table 1.14.1 Composition of Tubes for EAA Binding Experiments (Total 500 µl)

Tube number

Radioliganda (µl)

Bufferb (µl)

1-3 4-6 7-9 10-12 13-15 16-18 19-21 22-24 25-27

100 100 100 100 100 100 100 100 100

400 300 300 300 300 300 300 300 300

10 mM L-glutamate

— 100 — — — — — — —

(µl)

Test compound (µl) — — 100 of conc. A 100 of conc. B 100 of conc. C 100 of conc. D 100 of conc. E 100 of conc. F 100 of conc. G

aFor example, [3H]AMPA for binding to AMPA receptors (see Basic Protocol) or [3H]KA for KA

receptors (see Alternate Protocol 1). bTris/thiocyanate buffer for AMPA receptors; Tris buffer for KA and NMDA receptors.

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Terminate the assay Centrifugation method: 10a. Place the reaction tubes in a prechilled (4°C) rotor, taking care not to splash the mixture from the tubes. If the rotor is not stored in a cold room or refrigerator, it can be chilled quickly by performing a low-speed (∼2000 × g) centrifugation for 10 min in a cooled 4°C centrifuge.

11a. Centrifuge the mixture 10 min at 40,000 to 50,000 × g at 4°C. 12a. Remove the tubes from the rotor and transfer them to a rack placed in an ice-water bath. 13a. Invert each tube individually to decant the contents. Carefully, but rapidly, wash the inside of each vial three times with 2 ml ice-cold Tris/thiocyanate buffer using a repeating syringe, and then place it upside down in a rack. Place the rack over a disposable absorbent material to trap residual radioactivity. This is the most difficult aspect of the assay since care must be taken not to dislodge the pellet from the vial while thoroughly rinsing the inside of the reaction vessel to remove residual radioactivity. The process should be practiced prior to attempting an assay since it is necessary to invert and rotate the tube while rinsing the sides and avoiding dislodging the pellet. Done properly, the entire process of three washes should be accomplished in 2 to 4 sec per tube.

14a. After completely washing one group of vials, swab the inside of each vial with an absorbent material to remove residual moisture. Take care not to dislodge the tissue pellet. This is best accomplished by clamping two or three tissues (e.g., Kimwipes) in a hemostat that will fit into the vial. Alternatively, cotton swabs can be used. It is not necessary to remove all the residual moisture, only the majority. Gently shaking the entire set of tubes prior to drying will facilitate removal of residual liquid.

15a. Add 0.5 ml tissue solubilizer to each vial and let stand in a 37°C water bath until the pellet is dissolved (∼1 hr). Dissolution occurs more rapidly at elevated temperatures. Do not allow the vials to stand overnight as the solubilizer may dry, ruining the experiment. Do not use NaOH to solubilize the pellet as it produces chemiluminescence in most scintillation cocktails.

16a. Add a compatible scintillation cocktail (typically 3 to 4 ml), shake the vials for ≥1 hr, and quantify radioactivity using a β scintillation counter. 17a. Plot either specific binding (dpm) or percent specific binding on the y axis versus the log[test compound] on the x axis to yield a sigmoidal binding curve. From this plot, use a curve-fitting program (e.g., Prism, Sigma Plot, LIGAND) to determine the IC50 value (the concentration of unlabeled competitor that inhibits specific binding by 50%), which is a measure of the potency of the compound interacting with the receptor. If the Kd value of the radioligand is known, then calculate the Ki value for the test compound using the Cheng-Prusoff equation (for more details on data analysis, see UNIT 1.3). Characterization of Wild-Type Excitatory Amino Acid Ion Channel Receptors

Filtration method: 10b. Pour the assay mixtures over GF/B glass fiber filters arrayed on a vacuum filtration manifold. Rapidly rinse the filters four or five times with 1 to 2 ml ice-cold Tris/thiocyanate buffer each time.

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When performing the filtration, the tissue should be harvested from the tubes prior to rinsing the tubes and filters with Tris/thiocyanate buffer. Do not add the buffer to the assay tube before filtering since this enhances the release of radioligand from the membranes, decreasing the amount of bound radioactivity.

11b. Place the washed filters in 7-ml scintillation vials, add 3 to 4 ml of an appropriate aqueous-compatible scintillation cocktail, and allow to equilibrate for ≥1 hr. Quantify radioactivity using scintillation spectroscopy. If a 96-position micro-filter is used, fit it to an appropriate plastic manifold and add scintillant to each well.

12b. Perform data analysis as described in step 17a. MEASUREMENT OF [3H]KA BINDING TO WILD-TYPE KA RECEPTORS This protocol details modifications of the Basic Protocol for evaluating the competitive interaction of [3H]kainic acid (KA) to wild-type KA receptors in brain membranes using a fixed concentration of ligand (see UNIT 1.3 for a discussion of constant analyses). The original method was developed by London and Coyle (1979). All of the precautions noted in the Basic Protocol, as well as the modifications for 96-well format, apply to this protocol.

ALTERNATE PROTOCOL 1

Additional Materials (also see Basic Protocol) Tris buffer: 0.05 M Tris⋅Cl, pH 7.6 at 4°C (APPENDIX 2A) [3H]Kainic acid ([3H]KA; NEN Life Sciences) Kainic acid (Sigma; RBI) 1. Prepare Tris buffer on the day prior to the experiment and allow it to chill overnight to 4°C. The buffer may be prepared by using ice-cold water at the time of preparation and adjusting the pH to 7.6. Alternatively, the buffer may be prepared at room temperature and the pH adjusted to 7.4. When chilled, the pH will be ∼7.6. The buffer may be made by mixing appropriate amounts of Tris⋅Cl and Tris base, but a simpler approach is to adjust the pH of a Tris base solution using 10 N HCl.

2. On the day of assay, wash membranes and set up assay tubes as described (see Basic Protocol, steps 2 and 3). 3. Prepare working solutions of 10 µM L-glutamate and 50 nM [3H]KA, both in ice-cold Tris buffer. Both will be diluted 10-fold in the final assay medium. See Basic Protocol, step 4, for notes concerning use of glutamate or other unlabeled ligands (in this case, KA) for nonspecific binding.

4. Perform binding reactions and terminate the assay as described (see Basic Protocol, steps 5 to 17a for centrifugation or 5 to 12b for filtration), but use [3H]KA in place of [3H]AMPA and use ice-cold Tris buffer in place of Tris/thiocyanate buffer. MEASUREMENT OF [3H]LIGAND BINDING TO WILD-TYPE NMDA RECEPTORS

ALTERNATE PROTOCOL 2

This protocol describes modifications of the Basic Protocol for evaluating the competitive interactions of ligands and wild-type NMDA receptors in brain membranes using a fixed concentration of ligand (see UNIT 1.3 for a discussion of constant analyses). All of the precautions noted in the Basic Protocol, as well as the modifications for 96-well format, apply to this protocol.

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A number of radioligands can be used for this purpose, including tritiated forms of CPP (Lehmann et al., 1987), CGS 19755 (Murphy et al., 1988), and CGP 37849 (Sills et al., 1991). Of these, CGP 37849 exhibits the highest affinity for the NMDA binding site. While all of these are derivatives of the prototypical NMDA antagonists 2-amino-5-phosphonopentanoic acid (AP5) or 2-amino-7-phosphonoheptanoic acid (AP7), they do not necessarily attach to the same sites (Kaplita and Ferkany, 1990). Use of [3H]AP5 is unwarranted because of its low affinity for the receptor. Likewise, use of [3H]AP7 is discouraged since the in vitro binding profile of this compound is not characteristic of NMDA receptors (Ferkany and Coyle, 1983). The most useful ligands for labeling this site are antagonists. While the agonist ligand L-glutamate is available to label the receptor, the assay is exceptionally complex and does not provide information beyond that which can be obtained using more practical approaches. [3H]NMDA is not an appropriate ligand because of its low in vitro affinity for the receptor complex. Additional Materials (also see Basic Protocol) Tris buffer: 0.05 M Tris⋅Cl, pH 7.6 at 4°C 3 H-labeled radioligand (NEN Life Sciences): e.g., [3H]D-(−)-3-(2-carboxypiperazine- 4-yl)propyl-1-phosphonic acid ([3H]CPP); [3H]-(±)-cis-4phosphono-methyl-2- piperidine carboxylic acid ([3H]CGS 19755); or [3H](±)-E-2-amino-4-methyl- 5-phosphono-3-pentanoic acid ([3H]CGP 37849) N-methyl-D-aspartate (NMDA), L-glutamate, or CGS 19755 (Sigma; RBI) 1. Prepare Tris buffer on the day prior to the experiment, allowing it to chill overnight to 4°C. The buffer may be prepared at room temperature, adjusting the pH to 7.4. When chilled, the pH will be ∼7.6, which is acceptable for washing membranes. Although the buffer may be made by mixing appropriate amounts of Tris⋅Cl and Tris base, a simpler approach is to adjust the pH of a Tris base solution using 10 N HCl.

2. On the day of assay, wash membranes and set up assay tubes as described (see Basic Protocol, steps 2 and 3). 3. Prepare working solutions of 10 mM L-glutamate and an appropriate 10× solution of radioligand (i.e., ∼500 nM for CPP, 100 nM for CGS 19755, and 30 nM for CPP 37849), each in Tris buffer. These will be diluted 10-fold in the final assay. See Basic Protocol, step 4, for notes concerning use of glutamate or other unlabeled ligands (e.g., NMDA or CGS 19755) for nonspecific binding.

4. Prepare working solutions of test compounds in Tris buffer at 10× the desired final concentration. Typically about seven concentrations are used, initially spanning a wide range. Additional experiments can be performed later to fine tune the appropriate concentration range. If the compounds are insoluble in water, dimethyl sulfoxide (DMSO) may be used, provided its final concentration in the assay does not exceed 1% (v/v). If DMSO is used, an equivalent amount should be incorporated into total and nonspecific tubes. Avoid the use of strong acids or bases to solubilize compounds as the solutions may change the pH of the assay and adversely influence the final data. Characterization of Wild-Type Excitatory Amino Acid Ion Channel Receptors

5. Set up reactions in each tube as shown in Table 1.14.1. 6. After all additions are made, place the tubes on the bench top, as this reaction is performed at room temperature.

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Current Protocols in Pharmacology

7. Resuspend the washed tissue pellet in Tris buffer using the tissue homogenizer at a mid-level setting (Polytron 5 or 6). Use 25 ml buffer for every 1 g wet weight tissue (or a single rat brain).

8. Rapidly aliquot 0.5 ml tissue suspension into each assay tube and allow the reaction to proceed for ≥30 min at ∼23°C. A repeating pipettor (e.g., Eppendorf, Rainin) is useful for the addition of tissue suspension. Remember to account for the time it will take to terminate the reaction and to include this amount of time between each group of 24, 48, or 96 assay tubes. Add tissue to one group of tubes at a time, waiting the necessary period between each group of tubes. In most cases, the reaction may proceed for >60 min since it will be at equilibrium. However, the timing is critical for association and dissociation experiments (UNIT 1.3).

9. Terminate the assay as described (see Basic Protocol, steps 10a to 17a for centrifugation or steps 10b and 12b for filtration), but use Tris buffer in place of Tris/thiocyanate buffer. PREPARATION OF MEMBRANES FOR BINDING ASSAYS Described below is a protocol for preparing brain membranes for studying heterologous populations of EAA receptor subtypes. The method was originally developed by Enna and Snyder (1976) and is widely applicable to the evaluation of neurotransmitter receptor binding in brain. While it does not yield a highly purified receptor population, it concentrates receptors and removes from the membrane suspension endogenous inhibitors that may interfere with binding. The approach requires the use of freshly harvested tissues. In most instances, the procedure is applicable to the bulk preparation of membrane samples. Since its introduction, a number of derivations have been introduced, although there is little evidence that these are superior to the original procedure.

SUPPORT PROTOCOL

Materials Experimental animals (e.g., rat, guinea pig, rabbit) 0.32 M sucrose, 4°C Small-animal decapitator (or CO2 chamber if required by IACUC) Surgical instruments to penetrate cranium and remove forebrain 20- to 40-ml glass homogenizer vessel with Teflon pestle (use only manufacturer-mated sets) Homogenizer controller (∼700 rpm) 40- to 50-ml polypropylene centrifuge tubes Beckman J2-21M centrifuge with JA-20 rotor (or equivalent) Homogenizer (e.g., Polytron, Tekmar Tissumizer) Homogenize tissue 1. Fill a beaker with chilled 0.32 M sucrose and place it on wet ice. Prepare the area for euthanizing animals. If tissues are obtained from a slaughterhouse, prepare a bucket of sucrose and transport the tissue in an appropriate insulated container. Approximately 50 ml of 0.32 M sucrose is required for each whole rat forebrain or 1 g of tissue. For batch preparations, it is recommended to process 50 g tissue or more at a time. This should allow sufficient tissue for ∼2,500 binding assays.

2. Euthanize animals according to IACUC-approved guidelines. Anesthesia with CO2 may be used prior to euthanasia. The most commonly used method of sacrifice for rodents is rapid decapitation. When using guinea pigs or rabbits, consult

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with the IACUC on appropriate methods. Euthanasia by barbiturate overdose is inappropriate since these drugs are centrally active compounds and may interfere with the binding assay. When collecting tissues from slaughterhouses, USDA-approved methods of euthanasia are appropriate.

3. Carefully remove the forebrain or fractions of brain and place the tissue in chilled 0.32 M sucrose. Discard the remaining tissue. 4. Place one or two rat forebrains or 1 to 2 g of brain tissue obtained from larger animals (providing this represents a mix of forebrain areas) into an ice-jacketed glass homogenizer vessel approximately half filled with 0.32 M sucrose. Homogenize the tissue using seven to nine complete strokes of the Teflon pestle at a setting of ∼700 rpm. It is essential that the homogenizer be jacketed with wet ice. Without this, friction will heat the unit and the homogenization process will become progressively more difficult. Additionally, the dimensions of the glass/Teflon interface change with heating, thereby changing the characteristics of each subsequent homogenization. A typical process will require 1 to 2 min per tube.

5. Transfer the suspension to a 40- to 50-ml polypropylene centrifuge tube and immerse in an ice-water bath. Fill the centrifuge tube to capacity using 0.32 M sucrose. Cap the tube with Parafilm and invert several times to ensure a homogeneous suspension. Repeat steps 4 and 5 for remaining tissue. It commonly requires 30 to 60 min to complete the homogenization process for all tissues. Keep each tube, as completed, on wet ice.

6. Centrifuge for 10 min at 2000 × g at 4°C. Carefully decant the supernatant into a fresh centrifuge tube. Since the tissue pellet is not firm, it is best to decant only ∼75% of the supernatant to the new tube to avoid retrieving contaminants from the pelleted nuclear fraction. If a white streak is observed in the supernatant, this represents the transfer of myelin and decantation should be halted.

7. Fill the tube to capacity using 0.32 M sucrose, cap with Parafilm, and invert to ensure a homogeneous suspension. 8. Centrifuge for 20 min at 12,500 × g at 4°C. Carefully decant and discard the supernatant and place the tube containing the pellet on wet ice. If subcellular fractionation studies are to be performed, retain the supernatant for preparation of the microsomal fraction.

Rupture and isolate membranes 9. Fill the tube to half capacity with distilled or deionized water. Using a Polytron (not a sonicator-type) homogenizer, disrupt the pellet for ∼30 sec at a setting of 5 or 6. Immediately store suspension on wet ice and adjust tube volume to capacity using distilled water. 10. Centrifuge for 20 min at 8000 × g at 4°C and then transfer the tubes to an ice-water bath. Using a vortex mixer, collect the buffy coat into the supernatant and pour it into a clean centrifuge tube. The tubes contain a dense core pellet surrounded by lighter material referred to as the buffy coat. Characterization of Wild-Type Excitatory Amino Acid Ion Channel Receptors

In the absence of a vortex mixer, vigorously shake the tubes after capping with Parafilm. The dense core pellet is quite resistant to disruption. However, if it does come free from the tube, repeat step 10.

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Current Protocols in Pharmacology

NOTE: Do not use new centrifuge tubes because the walls are too smooth and the pellet will not adhere. Score new centrifuge tubes before use by repeated washes with a cleaning brush. This applies for all remaining steps.

11. Centrifuge the suspension for 10 min at 40,000 to 50,000 × g at 4°C. Carefully decant the supernatant, add distilled or deionized water to the pellet, and resuspend using a Polytron homogenizer set at 5 or 6 for 20 to 30 sec. Adjust the volume of the tube to capacity using distilled or deionized water and repeat this step three additional times. The original procedure called for the use of distilled water to achieve the maximal hypotonic disruption of vesicles. While this is useful, experience suggests that the addition of a nominal amount (e.g., 2% v/v) of Tris⋅Cl buffer (pH 7.0 to 7.4; 4°C) substantially increases the adherence of the pellet to the centrifuge tube during the wash procedures. The hypotonicity of the media is not changed significantly.

12. Decant the final supernatant, cap the tube with Parafilm, and store pelleted membranes frozen (−20° to −80°C) until used. Under these conditions, membranes are stable for ≥2 weeks and up to 1 month depending upon the receptor under investigation. Storage conditions should be verified by experiment or literature reference. Some recommend that membranes be resuspended in the assay buffer and then frozen. While this is appropriate if buffers are common to assays, it is a significant disadvantage when thawing membranes for use in assays. There are no substantial comparative studies on the effect of different storage conditions on experimental results. Aluminum foil is an appropriate substitute for Parafilm if tissues are to be stored at −70° to −80°C. Parafilm becomes brittle at these temperatures.

COMMENTARY Background Information The excitatory actions of dicarboxylic acids, glutamate, and aspartate on central neurons were first described in the late 1950s. For many years it was argued that the widespread distribution of these amino acids, and their role in metabolic processes, precluded them from functioning as neurotransmitters. It is now recognized that this ubiquitous distribution results from the fact that excitatory amino acid (EAA)mediated neurotransmission is the most prevalent type of fast, chemically mediated communication in the brain and spinal cord. EAA-mediated neurotransmission is thought to participate in a host of physiological and pathological events including learning and memory; epilepsy; neurodegenerative conditions such as Alzheimer’s, Parkinson’s and Huntington’s diseases; and the neurodegeneration associated with stroke, head trauma, and neuropathic pain. Given their importance in neurotransmission and pathophysiology, it is not surprising that a number of EAA receptors have been identified. Like receptors for the inhibitory amino acid γ-aminobutyric acid (GABA; UNIT 1.7), EAA receptors are either channel forming (ionotropic) or G protein–linked (metabotropic).

Although an oversimplification, the initial classification of Watkins and Evans (1981) of the ionotropic receptors remains suitable as an introductory framework. Originally defined by agonists, the three main families are more or less selectively identified by the isoxazole AMPA (Krogsgaard-Larsen et al., 1996), the pyrrolidine kainic acid (London and Coyle, 1979), and the synthetic amino acid N-methylD-aspartate (NMDA). Like GABA receptors, the ionotropic EAA receptors are comprised of multiple heterologous subunits and are associated with a number of binding sites for modulatory substances that alter the response to the primary agonist. The metabotropic glutamate receptors, of which at least six have been identified and cloned, represent a unique family of G protein– coupled receptors dissimilar in structure to other known members of this family. While there is tremendous interest in this receptor system, there are currently no commercially available, high-affinity radioligands for EAA metabotropic sites. Because ionotropic EAA receptors are composed of multiple heterologous subunits, the possible number of receptor configurations is quite large. Through the use of in vitro expres-

Receptor Binding

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Table 1.14.2

Troubleshooting Amino Acid Binding Assays

Problem

Possible cause

Solution

No specific binding

Degraded radioligand

Check the radioligand date; verify purity by TLC or other method; obtain new radioligand

Specific binding has been washed Decrease rinse time or volume; make sure away in rinse step rinse buffer is chilled Failed to add reference agent for determination of specific binding Reference agent is degraded or solution was improperly prepared Presence of endogenous amino acids in preparation

Variable results among sets of tubes

Tissue concentration is too high Did not allow sufficient time for equilibration in scintillation cocktail Tissue degradation Batch to batch variation in levels of endogenous modulators

sion systems, different arrangements of subunits have been studied and a variety of combinations have been shown to be active. However, it remains unknown which, if any, of these combinations are the same as the wild-type receptor. Thus, the study of wild-type receptor populations remains useful, particularly in evaluating ontological, disease-induced, or experimentally induced changes in receptor populations, or for the discovery of new compounds that interact with these sites. That stated, the methods described in this unit are easily adapted to the study of EAA receptors in expression systems.

Critical Parameters

Characterization of Wild-Type Excitatory Amino Acid Ion Channel Receptors

Verify purity of reference agent or purchase new lot; check laboratory notebook calculations Perform more extensive washing of membrane preparation

Dislodged pellet during wash Practice wash procedure procedure Buffer contaminated with bacteria Make fresh buffer

Very high binding

Variable results among experiments

Check laboratory notebook calculations

The AMPA, KA, and NMDA binding assays are quite robust when performed using either filtration or centrifugation. As with any binding assay, it is important to validate the procedure within one’s own laboratory with regards to all critical parameters. This includes investigating items such as the time necessary to reach equilibrium (i.e., when no additional specific binding occurs), the amount of tissue that can be added while still maintaining linearity of binding (tissue linearity), and the various affinity

Decrease tissue concentration Increase time in scintillation cocktail before quantifying radioactivity Prepare new batch of membranes Apply a highly consistent approach to membrane preparation

constants (Kd, Bmax, Ki) for reference standards. Since all EAA binding sites exist in multiple forms, detailed analyses are required using a sufficient number of data points in order to satisfy the requirements of iterative, nonlinear curve-fitting data analysis packages (generally a minimum of four points per parameter being measured). Thus, if a radioligand binds to two sites with different affinities and Bmax, a minimum of sixteen points are required to accurately assess binding characteristics (see UNIT 1.3 for a more detailed explanation of each of these items). Since all of the EAA assays utilize Trisbased buffers, attention to proper pH is critical; in particular, both the incubation and wash buffers should maintain the same pH even when incubation and wash temperatures differ. When terminating reactions by centrifugation, dislodging of the pellet from the reaction vessel is a common cause of assay variability. This is easily detected by visual inspection of the assay tube. With filtration assays, slow filtration or washing the filters with large volumes of buffer can decrease binding by dissociating the ligand from the tissue trapped on the filter. Assay variability will occur in both centrifugation and

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Current Protocols in Pharmacology

filtration methods if insufficient time is allowed for the bound ligand to equilibrate in the scintillation cocktail (≥1 hr should be allowed in all cases). Because the concentration of native amino acids in brain is very high, the tissues must always be adequately washed to remove endogenous inhibitors of ligand binding. In the case of the NMDA assays, binding is modulated by glycine, an amino acid present not only in the tissue itself, but frequently in distilled water. Finally, while the buffy coat preparation is relatively stable when properly stored, it should be discarded and a new batch should be prepared if there is evidence of desiccation.

Troubleshooting As with any experimental procedure, binding assays occasionally fail for no apparent reason (see causes and solutions in Table 1.14.2). When this occurs, it is generally appropriate to first suspect degradation of the radioligand. In most cases, ligand degradation is reflected by an increase in nonspecific binding, a decrease in total binding, or both. If an increase in nonspecific binding is observed, this may be associated with degradation (or incorrect preparation of) the solutions used to define the nonspecific component. Marked increases

in binding can occur if buffers are old and have become contaminated with bacteria that either bind or incorporate the ligand. In this instance, the binding will not yield the appropriate pharmacological profile.

Anticipated Results In most instances, the binding of AMPA or KA to their receptors is robust with total-toblank ratios of at least 5 to 1. In the case of the NMDA receptor, binding is excellent with radiolabeled CGP 37849 and CGS 19755, and similar ratios are obtained with up to 80% of the total binding being specific binding. In the case of [3H]CPP, the total-to-blank ratios may be as low as 2 to 1. While high-affinity (100 0.5-1.5 0.06 >100 >100 >100 >100 >100 >100

0.06; 0.7 >100 0.05;0.5 0.002;0.02 0.02;0.2 >100 ND 5 >100 >100 >100 >100 >100 >100

0.15 >100 20 >100 >100 5 ND ND 0.1-0.3 ND ND ND 5.0-10 1.0-2.0

0.10 >100 >10 >100 >100 1.5 ND ND 0.1 0.1 0.02 0.2 0.5 1.5

0.05 >100 >100 >100 >100 3.5 ND ND 0.10 0.05 ND ND ND ND

aSources: L-Glutamate, AMPA, NBQX, CNQX, domoic acid, kainic acid, NMDA, CPP, CGS 19755, D-(−)-AP5, and DL-(±)-AP7 from Sigma Chemical or Research Biochemicals International; CGPC 37849 from Novartis Corporation; NPC 17422 from Guilford Pharmaceuticals. bFor KA some compounds label both high- and low-affinity binding components, thus there are two K values i listed. ND, not determined.

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fact, given the knowledge of EAA receptors obtained through molecular techniques, it is likely that heterologous tissue preparations contain a multitude of subtly distinct receptor populations within each group. This notwithstanding, use of brain tissue is valuable in studying experimentally induced alterations in receptor densities, in ontological studies, and in general drug discovery screening.

Time Considerations Binding assays consisting of 500 tubes can easily be completed in a single day by a skilled individual for any of these assays using centrifugation methods. Using low-throughput filtration procedures and a 48- or 96-well manifold, >2000 tubes can be filtered in one day, frequently making access to scintillation counters a limiting factor. When batch preparing tissues for assays according to the Support Protocol, and assuming two centrifuges are available, a minimal preparation would consist of 50 g of brain tissue in one day. A more desired expectation would be 100 g, which is sufficient to support lowthroughput assays for 5000 to 10,000 data points.

Literature Cited Enna, S.J. and Snyder, S. 1976. Influences of ions, enzymes and detergents on gamma-aminobutyric acid-receptor binding in synaptic membranes of rat brain. Mol. Pharmacol. 13:442-453. Ferkany, J.W. and Coyle, J.T. 1983. Specific binding of [3H](±)-2-amino-7-phosphono heptanoic acid to rat brain membranes in vitro. Life Sci. 33:1295-1305. Kaplita, P.V. and Ferkany, J.W. 1990. Evidence for direct interactions between the NMDA and glycine recognition sites in brain. Eur. J. Pharmacol. 188:175-179.

Krogsgaard-Larsen, P., Ebert, B., Lund, T.M., Brauner-Osborne, H., Slok, F.A., Johansen, T.N, Brehm, L., and Madsen, U. 1996. Design of excitatory amino acid receptor agonists, partial agonists and antagonists: Ibotenic acid as a key lead structure. Eur. J. Med. Chem. 31:515-537 Lehmann, J., Schneider, J., McPherson, S., Murphy, D.E., Bernard, P., Tsai, C., Bennett, D.A., Pastor, G., Steele, D.J., Boehm, C., Cheney, D.L., Liebmann, J.M., Williams, M., and Wood, P.L. 1987. CPP, a selective N-methyl-D-aspartate (NMDA)-type receptor antagonist: Characterization in vitro and in vivo. J. Pharmacol. Exp. Ther. 240:737-746. London, E.D. and Coyle, J.T. 1979. Specific binding of [3H]kainic acid to receptor sites in rat brain. Mol. Pharmacol. 15:492-505. Murphy, D.E., Snowhill, E.W., and Williams, M. 1987. Characterization of quisqualate recognition sites in rat brain tissue using DL-[3H]αamino-3-hydroxy-5-methylisoxazole-4-propio nic acid (AMPA) and a filtration assay. Neurochem. Res. 12:775-781. Murphy, D.E., Hutchison, A.J., Hurt, S.D., Williams, M., and Sills, M.A. 1988. Characterization of the binding of [3H]CGS-19755: A novel N-methyl-D-aspartate antagonist with nanomolar affinity in rat brain. Br. J. Pharmacol. 95:932-938. Sills, M.A., Fagg, G., Pozz, M., Angst, C., Brundish, D.E., Hurt, S.D., Wilusz, E.J., and Williams, M. 1991. [3H]CGP 39653: A new N-methyl-Daspartate antagonist radioligand with low nanomolar affinity in rat brain. Eur. J. Pharmacol. 192:19-24. Watkins, J.C. and Evans, R.H. 1981. Excitatory amino acid transmitters. Annu. Rev. Pharmacol. Toxicol. 21:165-204.

Contributed by John W. Ferkany Oread, Inc. Farmington, Connecticut

Characterization of Wild-Type Excitatory Amino Acid Ion Channel Receptors

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Current Protocols in Pharmacology

Characterization of Tachykinin Receptors

UNIT 1.15

The mammalian tachykinin peptides are a family of neuropeptides characterized by a common C-terminal amino acid sequence of the form Phe-X-Gly-Leu-Met-NH2, where X is either Phe or Val. The family includes substance P (SP), neurokinin A (NKA), neurokinin B (NKB), and N-terminally extended forms of NKA, neuropeptide K (NPK), and neuropeptide γ (NPγ). To date, three classes of tachykinin receptors have been identified by bioassay and radioligand binding, and have been cloned (Table 1.15.1). The NK1 receptor preferentially binds SP, the NK2 receptor binds NKA, and the NK3 receptor binds NKB. All natural tachykinin peptides are capable of acting as full agonists at these receptor types, though with different levels of affinity (Table 1.15.1). Tachykinin peptides have various biological activities, including excitation of both peripheral and central neurons, stimulation of smooth muscle contraction, stimulation of exocrine and endocrine gland secretion, and involvement in immune and inflammatory responses. Tachykinin receptors belong to a superfamily of G protein–coupled receptors, and ligand-receptor binding results in phospholipase C activation and the release of arachidonic acid. This unit presents basic methods for the characterization of tachykinin receptors by radioligand binding assay. The Basic Protocols detail methods for each of the three major classes of tachykinin receptors, reflecting the different properties of the radioligands. Support Protocols present techniques for the generation of high-specific-activity iodinated radioligands (see Support Protocol 1) and preparation of membranes containing tachykinin receptors (see Support Protocol 2). CAUTION: When working with radioactivity, take appropriate precautions to avoid contamination of the experimenter and the surroundings. Carry out the experiment and dispose of wastes in an appropriately designated area, following guidelines provided by the local radiation safety officer. COMPETITION BINDING ASSAY FOR NK1 OR NK2 RECEPTOR–BEARING CELLS AND TISSUES

BASIC PROTOCOL 1

This protocol describes the use of radiolabeled tachykinin peptides in a competitive receptor-binding assay for determining basic parameters of ligand-receptor interaction (receptor affinity, receptor number, IC50 of competitors) for the NK1 and NK2 tachykinin receptors. The protocol can be performed using whole cells or membrane preparations from tissues (see Support Protocol 2). Materials Cells or tissue membrane preparations to be assayed (see Support Protocol 2) Radioligand peptide: e.g., 2175 Ci/mmol [125I]Tyr−1-substance P ([125I]Tyr−1-SP for NK1), [125I]histidyl–neurokinin A ([125I]histidyl-NKA for NK2), or other 125 I-labeled tachykinin peptide (NEN Life Science, Amersham, or see Support Protocol 1) Unlabeled Tyr−1-SP, NKA, or other tachykinin peptide (Bachem) Test compound (competitor) 0.2% (v/v) polyethyleneimine (PEI; Sigma) NK1/NK2 cell binding buffer (see recipe), ice cold Membrane binding buffer (see recipe), ice cold PBS, ice cold: 50 mM sodium phosphate buffer, pH 7.4 (APPENDIX 2A), containing 120 mM NaCl Tris-buffered saline (TBS)/MnCl2, ice cold: 50 mM Tris⋅Cl, pH 7.4 (APPENDIX 2A), containing 120 mM NaCl and 3 mM MnCl2 Contributed by Vladimir V. Karpitskiy Current Protocols in Pharmacology (1999) 1.15.1-1.15.17 Copyright © 1999 by John Wiley & Sons, Inc.

Receptor Binding

1.15.1 Supplement 4

Table 1.15.1

Characteristics of Cloned Tachykinin Receptorsa

Receptor

GenBank accession Agonists number (human clone)

Rank order of affinity (Kd) Antagonists to ligands (nM)

NK1

P25103

NK2

P21452

NK3

P29371

SP > NKA > NKB (0.2 > 20 > 400) NKA = NPγ = NPK > NKB > SP (2 > 60 > 600) NKB > NKA > SP (5 > 600 > 10,000)

SP methylester, [Sar9,Met(O2)11]SP, [Pro9]SP NKA, NPγ, NPK

NKB, [MePhe7]NKB, senktide

SR140333, LY303870, CP99994, GR94800, GR159897, SR48968 SR142802, SB223412, PD157672

aAbbreviations: CP99994, dihydrochloride salt of (+)-(2S,3S)-3-(2-methoxybenzylamino)-2-phenylpiperidine; GR159897, 5-fluoryl-3-ylethyl(4[phenylsulphinylmethyl])piperidine; GR94800, N-α-benzoyl-Ala-Ala-D-Trp-Phe-D-Pro-Pro-Nle-NH2; LY303870, (R)-1-(N-[2-methoxybenzyl]acetyl-

amino)-3-(1H-indol-3yl)-2-(N-[2-{4-(piperidin-1-yl)piperidin-1-yl}acetyl]amino)propane; NKA, neurokinin A; NKB, neurokinin B; NPK, neuropeptide K; NPγ, neuropeptide γ; PD157672, Boc(S)Phe(R)αMePheNH(CH2)7NHCONH2; SB223412, (S)-(−)-N-(α-ethylbenzyl)-3-hydroxy-2phenylquinoline-4-carboxaminde; senktide, succinyl-Asp-Asp-Phe-MePhe-Gly-Leu-Met-NH2; SP, substance P; SR140333, 1-[2-[3-(3,4-dichlorophenyl)-1-(3-isopropoxyphenylacetyl)piperidin-3-yl]ethyl]-4-phenyl-1-azonia-bicyclo[2.2.2]octane chloride; SR142802, (S)-(N)-(1- [3-{1-benzoyl3-(3,4-dichlorophenyl)piperidine-3-yl}propyl]-4-phenylpiperidin-4-yl)-N-methylacetamide; SR48968, (S)-N-methyl-N-[4-(acetylamino-4-phenyl piperidino)-2-(3,4-dichlorophenyl)butyl]benzamide.

Tissue culture tubes 2.5-cm no. 32 glass filters (Schleicher & Schuell) 12 × 75–mm plastic and glass tubes Platform shaker at 4°C Vacuum filtration manifold or other type of membrane-harvesting apparatus γ scintillation counter Perform competition binding reactions 1a. For cells: Harvest the desired number of cells into a tissue culture tube. Centrifuge cells 10 min at 1000 × g, 4°C. Wash cells twice by resuspending in ice-cold PBS and repeating centrifugation. Resuspend cells in ice-cold PBS and store on ice. If the cells express ∼200,000 receptors per cell, then use 100,000 cells per reaction. Adjust accordingly if the cells have a higher or lower level of expression. A plastic cell scraper can be used to detach adherent cells. The cell suspension can be stored on ice for 2 to 3 hr. Prepare 400 ìl cell suspension per reaction, including duplicates of the desired number of competitor dilutions plus total and nonspecific binding controls. The number of cells used per binding reaction will vary (see step 7). If the cells express ∼200,000 receptors per cell, then use 100,000 cells per reaction. Adjust accordingly if the cells have a higher or lower level of expression.

1b. For tissue: Use membrane preparations directly from Support Protocol 2. 2. Make a 100× solution of radioligand in ice-cold NK1/NK2 cell binding buffer (for cells) or ice-cold membrane binding buffer (for tissue membranes) to give 5 to 10 µCi/ml (1.1 to 2.2 × 107 cpm/ml). Use [125I]Tyr−1-SP for NK1 receptors or [125I]histidyl-NKA for NK2 receptors. Alternatively, [125I]Tyr−1-NKA or [125I]histidyl-NPγ can also be used for NK2 receptors.

Characterization of Tachykinin Receptors

3. Mix 5 to 10 µl of this diluted radioligand solution with seven to nine serial dilutions (e.g., 1/3) of a test compound (competitor) in ice-cold cell or membrane binding buffer to yield a final volume of 100 µl per duplicate reaction. Add these mixtures to 12 × 75–mm plastic tubes in duplicate (100 µl/tube; final reaction volume is 500 µl) and store on ice.

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Current Protocols in Pharmacology

Table 1.15.2 Experimenta

Representative Data from a Competition Radioligand Binding

log[NKB] (M)

cpm

Average cpm

−6.000 −6.477 −6.954 −7.432 −7.910 −8.397 −8.863 None −5.000 of NKA

1118, 1055 1386, 1402 2309, 2517 4250, 4384 6096, 5493 6643, 6579 6982, 7144 7000, 7144 699, 743

1086 1394 2413 4317 5794 6611 7063 7075 721a

Specific binding (B)b 365 673 1692 3596 5073 5890 6342 6354 (B0)

B/B0

log [B/(B0 − B)]

0.058 0.106 0.266 0.566 0.798 0.927 0.998

−0.926 −0.440 0.115 0.598 1.104

aThe experiment was performed using [125I]Tyr−1-NKA, CHO cells expressing the cloned rat NK receptor 2 and NKB as unlabeled competitor. bSpecific binding (B) = average cpm − nonspecific binding (average cpm in presence of NKA). Total specific binding (B0) is specific binding in absence of NKB = 7075 − 721 = 6354 cpm.

The final concentration of the ligand for competition experiments should be 5- to 10-fold below its Kd. For Tyr−1-SP (Kd = 0.2 to 0.4 nM), this corresponds to a final concentration of 0.03 to 0.06 nM in the assay or 120,000 to 240,000 cpm in 100 ìl. The affinity of NK2 receptor ligands to NK2 receptors is 2 to 4 nM. The radioligand solution can be stored on ice for 2 to 4 hr.

4. For total binding: Add dilute radioligand solution (step 2) to ice-cold cell or membrane binding buffer without a test competitor to a new duplicate set of tubes (total 100 µl per tube). Store on ice. 5. For nonspecific binding: Add a 500- to 1000-fold molar excess of unlabeled Tyr−1-SP (for NK1) or histidyl-NKA (for NK2) to another duplicate set of tubes containing the radioligand solution (total 100 µl per tube). Store on ice. 6. Soak no. 32 glass filters (one per tube) in 0.2% PEI for ≥1 hr at room temperature. Store at room temperature until the binding reaction is terminated. 7. Initiate ligand binding reaction by adding 400 µl cell suspension (step 1) or tissue membrane preparation to each tube containing radioligand. Incubate for 2 hr at 4°C with gentle shaking on a platform shaker. The number of cells used per binding reaction depends on the number of receptors (either estimated or previously determined) present on the surface of the cells being examined. As a general rule for competition experiments, the specific binding should be ≥80% of the total binding. For tissue membrane preparations, 50 to 100 ìg protein per binding reaction tube is usually sufficient to obtain detectable specific binding.

Terminate and filter reactions 8. Place a PEI-soaked filter in a vacuum manifold with the vacuum off. 9. Working quickly, add 4.0 ml ice-cold PBS (for cells) or TBS/MnCl2 (for membranes) to one reaction tube, pour the tube contents into the vacuum manifold, turn the vacuum on, and allow the solution to pass through the filter. Two major factors in the filtering procedure may affect attainment of binding equilibrium: the increase of reaction volume with the PBS or TBS/MnCl2 wash and the time of filtration. Consequently, all actions should be performed in rapid succession within 15 to 20 sec per Receptor Binding

1.15.3 Current Protocols in Pharmacology

Supplement 4

tube. The level of vacuum should allow each filtration to be completed within 0.5 to 1.0 sec, and a repeating syringe or pipettor should be used for fast delivery of wash solutions.

10. Rinse the filter three times with 4 ml ice-cold PBS or TBS/MnCl2. 11. Remove the filter with forceps and put it in a 12 × 75–mm glass tube. 12. Repeat steps 8 to 11 for remaining tubes and quantify the radioactivity with a γ scintillation counter. Determine IC50 value and Hill coefficient 13. Calculate specific binding by subtracting the cpm of the tubes containing the excess unlabeled peptide (nonspecific binding) from the cpm of the tubes lacking the unlabeled ligand (total binding). 14. Subtract nonspecific binding cpm from the cpm of the tubes containing test competitor and perform a Hill transformation of the data obtained. For each concentration of test competitor, calculate log10[B/(B0 − B)], where B0 is total specific binding (cpm) and B is specific binding (cpm) in the presence of the test competitor. 15. Construct a Hill plot where the y axis is log10[B/(B0 − B)] and the x axis is log10 of the test competitor concentration. The slope of the Hill plot is the Hill coefficient and the abscissa value where log10[B/(B0 − B)] = 0 is the IC50 value. Table 1.15.2 shows results of a typical competition experiment using cells that express the cloned rat NK2 receptor, and Figure 1.15.1 is the resulting plot and Hill transformation. UNIT 1.3 provides additional detail on data analysis.

A

B

2

1.0 log[B/(B0 – B )]

B/B0

0.8 0.6 0.4

y = – 7.8133 – 1.0631x, R 2 = 1.000

1

0

0.2 0.0 – 9.0

– 8.0

–7.0

log[NKB] (M)

Characterization of Tachykinin Receptors

– 6.0

–1 – 9.0

–8.0

IC50–7.0

– 6.0

log[NKB] (M)

Figure 1.15.1 Results from a representative competition radioligand binding experiment using [125I]Tyr−1-NKA as the radioligand (220,000 cpm/tube or 0.045 nM) and NKB as the competitor (concentrations are indicated). CHO cells expressing cloned rat NK2 receptor (700,000 receptors/cell) were used as the receptor source. The binding assay was performed as described (see Basic Protocol 1). (A) Competition curve. (B) Hill transformation plot (see step 15, Basic Protocol 1). Solving the equation when y = 0 gives x = log[IC50] = −7.3495; therefore, the IC50 for NKB = 44.7 nM.

1.15.4 Supplement 4

Current Protocols in Pharmacology

COMPETITION BINDING ASSAY FOR NK3 RECEPTOR–BEARING CELLS AND TISSUES

ALTERNATE PROTOCOL 1

This protocol describes modifications of the competition assay that are specific for the NK3 receptor. The procedure is written for cells, but can be performed with tissue membranes simply by adding 3 mM MnCl2 to the TBS solutions as noted. Additional Materials (also see Basic Protocol 1) Tris-buffered saline (TBS), ice cold: 50 mM Tris⋅Cl, pH 7.4 (APPENDIX 2A), containing 120 mM NaCl 66 to 69 Ci/mmol [3H]senktide (NEN Life Science) radioligand solution NK3 cell binding buffer (see recipe) or membrane binding buffer (see recipe), ice cold Unlabeled senktide (Bachem) TBS/BSA/Tween 20: TBS containing 2% (w/v) BSA and 0.1% (w/v) Tween 20 (Sigma) TBS/0.01% (w/v) SDS Liquid scintillation solution (e.g., ScintiSafe 30%; Fisher) 5-ml liquid scintillation vials Liquid scintillation counter Perform competition binding reactions 1. Optional: Harvest cells as described for the NK1/NK2 procedure (see Basic Protocol 1, step 1), but resuspend the final pellet in ice-cold TBS instead of PBS. 2. Dilute [3H]senktide radioligand solution to 20× final concentration in NK3 cell binding buffer or membrane binding buffer, then add the diluted radioligand to seven to nine 3-fold serial dilutions of a test competitor in the corresponding ice-cold buffer. Add dilutions to duplicate 12 × 75–mm plastic tubes (total 20 µl/tube; final reaction volume is 100 µl) and store on ice. The final concentration of the ligand for competition experiments should be 5- to 10-fold below the Kd for the radioligand. For [3H]senktide, 10-fold below the Kd corresponds to a final concentration of 2 to 4 nM or 20,000 to 40,000 cpm in 20 ìl at a final reaction volume of 100 ìl. [125I][MePhe7]NKB can be used at 0.2 to 0.4 nM, due to its higher specific activity.

3. For total binding: If using a cell preparation, add the same amount of radioligand solution to ice-cold NK3 cell binding buffer (without test competitor) in a new duplicate set of tubes (total 20 µl/tube). If using a membrane preparation, add the radioligand to ice-cold membrane binding buffer (without test competitor). Store on ice. 4. For nonspecific binding: Add a 500- to 1000-fold molar excess of unlabeled senktide to another set of tubes containing the radioligand solution (total 20 µl/tube). Store on ice. 5. Presoak no. 32 glass filters (one per tube) in TBS/BSA/Tween 20 for ≥1 hr. Store at room temperature in the solution until the binding reaction is terminated. For use with tissue membrane preparations, add 3 mM MnCl2 to the soaking solution.

6. Initiate ligand binding reaction by adding 80 µl cell suspension from step 1 (or containing 50 to 100 µg protein, if using a tissue membrane preparation) to each tube containing radioligand. Incubate tubes for 2 hr at 4°C with gentle shaking on a platform shaker. Receptor Binding

1.15.5 Current Protocols in Pharmacology

Supplement 4

Terminate and filter reactions 7. Terminate the binding reaction and separate bound from free ligand (see Basic Protocol 1, steps 8 to 10), using the TBS/BSA/Tween 20–soaked filters and using TBS/0.01% SDS for dilution and washing. For use with tissue membrane preparations, add 3 mM MnCl2 to both solutions.

8. Remove the filter and use forceps to place it into a 5-ml scintillation vial, and repeat filtration for remaining tubes. 9. Add 4 ml ScintiSafe to each vial, cap, and leave overnight at room temperature with shaking to extract the radioactivity. 10. Quantify radioactivity in a liquid scintillation counter. Analyze data 11. Determine IC50 values and Hill coefficients as described above (see Basic Protocol 1, steps 13 to 15). BASIC PROTOCOL 2

SATURATION BINDING ASSAY FOR NK1 OR NK2 RECEPTOR–BEARING CELLS AND TISSUES This protocol describes the use of radiolabeled tachykinin peptides in a saturation receptor-binding assay for determining basic parameters of ligand-receptor interaction (receptor affinity, receptor number) for the NK1 and NK2 tachykinin receptors. As with Basic Protocol 1, this protocol can be performed using whole cells or membrane preparations from tissues. Materials Cells or tissue membrane preparations to be assayed (see Support Protocol 2) Radioligand peptide: e.g., 2175 Ci/mmol [125I]Tyr−1-substance P ([125I]Tyr−1-SP for NK1), [125I]histidyl–neurokinin A ([125I]histidyl-NKA for NK2), or other 125 I-labeled tachykinin peptide (NEN Life Science, Amersham, or see Support Protocol 1) Unlabeled Tyr−1-SP, NKA, or other tachykinin peptide (Bachem) 0.2% (v/v) polyethyleneimine (PEI; Sigma) For cells: NK1/NK2 cell binding buffer (see recipe), ice cold PBS, ice cold: 50 mM phosphate buffer, pH 7.4 (APPENDIX 2A), containing 120 mM NaCl For membranes: Membrane binding buffer (see recipe), ice cold Tris-buffered saline (TBS)/MnCl2, ice cold: 50 mM Tris⋅Cl, pH 7.4 (APPENDIX 2A), containing 120 mM NaCl and 3 mM MnCl2 Tissue culture tubes 2.5-cm no. 32 glass filters (Schleicher & Schuell) 12 × 75–mm plastic and glass tubes Platform shaker at 4°C Vacuum filtration manifold or other type of membrane-harvesting apparatus γ scintillation counter Computer program for analysis of binding data (e.g., LIGAND)

Characterization of Tachykinin Receptors

Perform saturation binding reactions 1. Dilute radioligand peptide in ice-cold NK1/NK2 cell binding buffer (for cells) or ice-cold membrane binding buffer (for tissue membranes) to give 5 to 10 µCi/ml (1.1

1.15.6 Supplement 4

Current Protocols in Pharmacology

to 2.2 × 107 cpm/ml). Use [125I]Tyr−1-SP for NK1 receptors or [125I]histidyl-NKA for NK2 receptors. Alternatively, [125I]Tyr−1-NKA or [125I]histidyl-NPγ can also be used for NK2 receptors.

2. Prepare ten or eleven 2-fold (1:1) serial dilutions of 5× radioligand solutions in 200 µl cell or membrane binding buffer. Start with 25 nM [125I]Tyr−1-SP (5 nM final in assay) or 100 nM [125I]histidyl-NKA (20 nM final). Add dilutions to 12 × 75–mm plastic tubes in duplicate (100 µl/tube; final reaction volume will be 500 µl) and store on ice. The concentration of the radioligand in saturation equilibrium experiments should range both 10-fold above and 10-fold below the Kd, which is 0.2 to 0.4 nM for the NK1 receptor. The affinity of NK2 receptor ligands to NK2 receptors is 2 to 4 nM (4 nM corresponds to 6.6 ìCi of 128I). High concentration points involve high levels radioactivity and two approaches can be used to circumvent this problem. The first is to decrease the specific activity of the radioligand 5- to 10-fold by adding unlabeled ligand (e.g., from 2175 Ci/mmol to 400 to 200 Ci/mmol). The second is to decrease the total volume of the binding reaction to 50 to 100 ìl by proportionally scaling down the volume of solutions.

3. To measure nonspecific binding, include a separate set of tubes containing the same concentration range of the radioligand and a 500- to 1000-fold molar excess of unlabeled [125I]Tyr−1-SP or NKA (total 100 µl/tube for each radioligand concentration). Store on ice. 4. Soak no. 32 glass filters (one per tube) in 0.2% PEI for ≥1 hr at room temperature. Store at room temperature until the binding reaction is terminated. 5. Prepare cells (see Basic Protocol 1, step 1) or membrane suspension (see Support Protocol 2). 6. Initiate the ligand binding reaction by adding 400 µl cell suspension or tissue membrane preparation to each tube containing radioligand. Incubate at 4°C with gentle shaking until binding equilibrium is attained. The number of cells used per binding reaction depends on the number of receptors (either estimated or previously determined) present on the surface of the cells being examined. As a general rule, the specific binding should be ≥80% of the total binding. For tissue membrane preparations, 50 to 100 ìg protein per binding reaction tube is usually sufficient to obtain detectable specific binding. The time required to reach equilibrium can be determined in a separate experiment. However, the equilibrium binding time for the NK1 receptor does not vary considerably and the binding of the iodinated SP to the NK1 receptor reaches saturation within 2 hr at a wide range of concentrations.

Terminate and filter reactions 7. Place a PEI-soaked filter in a vacuum manifold with the vacuum off. 8. Working quickly, add 4.0 ml ice-cold PBS (for cells) or TBS/MnCl2 (for membranes) to one reaction tube. Pour the tube contents into the vacuum manifold, turn the vacuum on, and allow the solution to pass through the filter. Two major factors in the filtering procedure may affect attainment of binding equilibrium: the increase of reaction volume with the PBS or TBS/MnCl2 wash and the time of filtration. Consequently, all actions should be performed in rapid succession within 15 to 20 sec per tube. The level of vacuum should allow each filtration to be completed within 0.5 to 1.0 sec, and a repeating syringe or pipettor should be used for fast delivery of wash solutions. Receptor Binding

1.15.7 Current Protocols in Pharmacology

Supplement 4

9. Rinse the filter three times with 4 ml ice-cold PBS or TBS/MnCl2. 10. Remove the filter with forceps and put it in a 12 × 75–mm glass tube. 11. Repeat steps 7 to 10 for remaining tubes and quantify the radioactivity with a γ scintillation counter. Analyze data 12. Analyze equilibrium binding data with a computer program (e.g., LIGAND) to determine Kd and Bmax values. ALTERNATE PROTOCOL 2

SATURATION BINDING ASSAY FOR NK3 RECEPTOR–BEARING CELLS AND TISSUES This protocol describes the saturation assay to be used for the NK3 receptor, and combines aspects of the saturation experiment (see Basic Protocol 2) with the NK3 modifications described above (see Alternate Protocol 1). As with these other protocols, the procedure can be performed using cells or tissue membranes. All materials are described in these two previous protocols.

5

4

Kd = 39.8 ± 3.3 nM

3 0.22

y = 0.21207 – (2.3485)– 2x, R 2 = 0.915

0.20

Bound/free

Bound (fmol/100,000 cells)

Bmax = 53,800 ± 12,300 sites/cell

2

1

0.18 0.16 0.14 0.12 0.10

0

3 1 2 4 Bound (fmol/100,000 cells)

5

0 0

20

40

60

80

[Senktide] (nM)

Characterization of Tachykinin Receptors

Figure 1.15.2 Example of data output from a representative saturation equilibrium radioligand binding experiment using [3H]senktide (concentrations are indicated). CHO cells expressing cloned human NK3 receptor were used as the receptor source. Nonspecific binding was defined with a 1000-fold excess of unlabeled senktide. The binding assay was performed as described (see Alternate Protocol 2). The inset is a Scatchard plot of the data. The values presented for Kd and Bmax are the averages from four independent experiments. The values were obtained by a nonlinear least-squares analysis using the LIGAND program. (Plotted data are from Krause et al., 1997.)

1.15.8 Supplement 4

Current Protocols in Pharmacology

Table 1.15.3

Representative Data from a Saturation Equilibrium Radioligand Binding Experimenta

Input (cpm) Input (nM) 570,960 353,273 247,119 167,393 101,399 72,456 46,188 30,971 20,174 14,202

Total binding (cpm)

62.196 38.483 26.919 18.235 11.046 7.893 5.031 3.374 2.198 1.574

1645 1350 1086 778 541 402 265 193 135 94

Nonspecific Specific binding binding (cpm) (cpm) 867 564 417 286 226 152 97 69 48 42

778 786 669 492 315 250 168 124 87 52

Bound (fmol/ 100,000 cells)

Free (nM)

Bound/Free

4.237 4.281 3.644 2.680 1.716 1.364 0.915 0.675 0.474 0.283

38.336 38.336 26.801 18.150 10.987 7.849 5.003 3.353 2.183 1.537

0.068 0.112 0.136 0.148 0.156 0.174 0.183 0.201 0.217 0.184

aThe experiment was performed using [3H]senktide and CHO cells expressing the cloned human NK receptor. 3

1. Make 2-fold serial dilutions of [3H]senktide (see Basic Protocol 2, step 2) using ice-cold NK3 cell binding buffer or membrane binding buffer, beginning with a stock concentration of 1 µM (200 nM in final 100-µl assay). Prepare duplicate 12 × 75–mm plastic tubes at 20 µl/tube. Also include tubes for nonspecific binding (see Basic Protocol 2, step 3) using ice-cold NK3 cell binding buffer or membrane binding buffer at a total of 20 µl/tube. 2. Prepare cells or membranes, soak glass filters, and initiate ligand binding reaction as described for the NK3 competition experiment (see Alternate Protocol 1, steps 1, 5, and 6). Incubate cells at 4°C with gentle shaking until binding equilibrium is attained. The time required to reach binding equilibrium can be determined in a separate experiment. However, the equilibrium binding time for NK3 receptors is normally attained within 2 hr at wide range of concentrations.

3. Terminate the binding reaction and separate bound from free ligand (see Alternate Protocol 1, steps 7 to 10). 4. Analyze equilibrium binding data with an appropriate computer program (e.g., LIGAND) to determine the Kd and Bmax values. Table 1.15.3 and Figure 1.15.2 depict results from a saturation equilibrium binding experiment using cells that express the cloned human NK3 receptor.

RADIOIODINATION OF THE TACHYKININ PEPTIDES This protocol describes a direct oxidative iodination methodology using sodium 125I and individual tachykinin peptides. A chromatographic procedure is used to obtain monoiodinated peptides with a maximum specific activity of 2175 Ci/mmol and a high level of 125 I incorporation (up to 70%). In the radioiodination procedure, sodium 125I is oxidized by chloramine-T in the presence of the peptide to be labeled, with subsequent incorporation of 125I into tyrosine or histidine residues of the peptide at high yield.

SUPPORT PROTOCOL 1

NOTE: Specific activity of the radioligand will decrease with the rate of radioactive decay of 125I (half-life 60 days). Receptor Binding

1.15.9 Current Protocols in Pharmacology

Supplement 4

Materials Tachykinin peptide (>95% purity by HPLC; Bachem): e.g., Tyr−1-substance P (Tyr−1-SP), neurokinin A (NKA), Tyr−1-NKA, neuropeptide γ (NPγ), or [MePhe7]NKB 0.1% (v/v) trifluoroacetic acid (TFA) 80% (v/v) acetonitrile in 0.1% TFA Chloramine-T (Sigma) Sodium metabisulfite (Sigma) Bovine serum albumin (BSA; Sigma) 0.2 M sodium phosphate buffer, pH 7.4 (APPENDIX 2A) and pH 8.5 1 mCi Na125I, carrier free (NENsure vial; NEN Life Science) HPLC solvent A: 0.1 M phosphoric acid (HPLC grade) containing 0.1 M NaH2PO4, pH 2.5 HPLC solvent B: 100% acetonitrile (HPLC grade) 2-Mercaptoethanol (2-ME; Sigma) Sep-Pak cartridge Classic (Waters Division of Millipore) 10-ml and 3-ml disposable syringes with Luer-lock tip 0.5-ml and 1.5-ml plastic, conical microcentrifuge tubes 12 × 75–mm plastic and glass tubes SpeedVac evaporator (Savant) Tabletop 65°C water bath Programmable HPLC system utilizing separate columns (e.g., 15-cm analytical Vydac C4) and injectors γ scintillation counter Iodinate peptide 1. Dissolve target tachykinin peptide in H2O at 1 mg/ml. Store in 50- to 100-µl aliquots at −70°C. 2. Condition a Sep-Pak cartridge with 10 ml of 80% acetonitrile in 0.1% TFA according to the manufacturer’s instructions. Flush the cartridge with 20 ml of 0.1% TFA. Do not allow the cartridge to dry out. 3. Dissolve chloramine-T, sodium metabisulfite, and BSA individually in H2O at 1 mg/ml. Always use freshly prepared solutions of these reagents.

4. Add 30 µl of 0.2 M sodium phosphate buffer directly to a vial containing 1 mCi of carrier-free 125I. Use the buffer at pH 7.4 for preferential iodination of tyrosine sidechains on peptides (Tyr−1-SP and Tyr−1-NKA). Use the buffer at pH 8.5 for preferential iodination of the imidazole ring of histidine (NKA, NPγ, [MePhe7]NKB). 5. In rapid succession, while continuously and gently agitating the vial, add 10 µl chloramine-T solution followed by 5 µl target peptide solution. Mix the final solution by gently pipetting in and out. It is strongly recommended that filtered pipet tips be used throughout the procedure and that several pipets be used with the solutions prefilled into the tips. This will speed up the procedure and minimize the risk of radiation contamination.

6. Terminate the iodination reaction by adding 20 µl sodium metabisulfite solution. Add 20 µl of BSA solution as a carrier. Characterization of Tachykinin Receptors

The BSA protects against nonspecific absorption of the peptide to surfaces.

1.15.10 Supplement 4

Current Protocols in Pharmacology

Separate labeled peptide from unreacted 125I and other reagents 7. Connect a 10-ml disposable syringe barrel to the preconditioned Sep-Pak cartridge. Fill the barrel with 10 ml of 0.1% TFA. Transfer the iodination reaction mixture into the barrel and mix well. 8. Insert the syringe plunger and flush the final solution through the cartridge at a slow flow rate of 1 to 2 ml/min (2 to 3 drops/sec). Discard the flowthrough according to radiation safety guidelines. 9. Connect the cartridge to another syringe filled with 10 ml of 0.1% TFA. Flush the cartridge at high flow rate (0.5 to 1.0 ml/sec). Repeat flushing with 10 ml of 0.1% TFA two more times. Disconnect the cartridge. Elute iodinated material 10. Fill a 3-ml syringe with 80% acetonitrile in 0.1% TFA and connect it to the cartridge. Flush the cartridge at a low flow rate (1 drop/sec) into a 12 × 75–mm plastic tube. 11. Dry the eluted material using a SpeedVac evaporator. 12. Resuspend dry iodinated material in 95 µl of 0.1% TFA with intensive vortexing. Add 5 µl of 2-ME, cap the tube or cover with Parafilm, and incubate 3 to 4 hr in a 65°C water bath. This reduction reaction will convert sulfoxide methionine to methionine.

13. Transfer the mixture to a 0.5-ml microcentrifuge tube. Centrifuge for 5 min at 14,000 × g at room temperature. Transfer supernatant to another 0.5-ml tube. At this stage, the iodinated material can either be loaded for immediate HPLC purification or stored at −20°C for several days prior to HPLC purification.

Purify iodinated peptide by reverse-phase HPLC 14. Prepare an elution gradient using HPLC solvents A and B (see Table 1.15.4 for recommended gradients).

Table 1.15.4 Peptidesa

Recommended Gradients for HPLC Purification of Iodinated Tachykinin

HPLC solvent/ elution time

Tyr−1-SP

Tyr−1-NKA

NKA

NPγ

[MePhe7]NKB

Initial loading conditions Time 0-2 min A 90% B 10%

0-2 min 90% 10%

0-2 min 90% 10%

0-10 min 89% 11%

0-2 min 85% 15%

Linear separation gradient Time 2-52 min A 90%-60% B 10%-40%

2-52 min 90%-64% 10%-36%

2-52 min 90%-64% 10%-36%

10-55 min 89%-87% 11%-13%

2-52 min 85%-60% 15%-40%

Elution of noniodinated peptide Time 32-33 min 30-31 min

30-31 min

33-34 min

41-42 min

Elution of iodinated peptide Time 37-38 min

34-35 min

37-38 minc

45-46 min

34-35 minb

aPerformed using solvents, HPLC system, and flow rate as described (see Support Protocol 1).

bElution time for Tyr−1-[125I]NKA. Additional iodination of histidine residue results in a longer elution time (37 to 38 min). cElution time for mono-[125I]NPγ. Di- and tri-[125I] forms have longer elution times (40 to 41 min and 45 to 46 min).

Receptor Binding

1.15.11 Current Protocols in Pharmacology

Supplement 4

15. Equilibrate a C4 analytical column in an HPLC system at initial conditions for 15 to 20 min. NOTE: The HPLC system should be dedicated for work with 125I.

16. Load 5 µg noniodinated peptide in 100 µl of 0.1% TFA and run the HPLC with the gradient to determine the elution time of cold peptide (see Table 1.15.4 for elution times of noniodinated reduced peptides). 17. Reequilibrate the column at initial conditions for 15 to 20 min. 18. Load the iodinated material and run the sample with the same gradient, collecting fractions at 1-min intervals into 12 × 75–mm glass tubes (see Table 1.15.4 for elution times of iodinated reduced peptides). Monitor elution of the peptide both by absorbance at 210 nm and by determination of radioactivity. A small degree of absorbance is observed to comigrate with the peak radioactivity.

19. Remove 1 µl from each fraction collected after the elution time of the noniodinated reduced peptide and eject the pipet tip containing the 1-µl aliquot into the bottom of a 12 × 75—mm glass test tube. Quantify the radioactivity using a γ scintillation counter. 20. Transfer the fraction with peak radioactivity to a 1.5-ml microcentrifuge tube and add 1 µl of 2-ME. Store the iodinated peptide at −20°C. Figure 1.15.3 shows an example of HPLC purification of radioiodinated Tyr−1-SP with calculations of the product output. The high salt concentration (0.2 M sodium phosphate) and low pH (2.5) of the ligand solution does not usually affect the parameters of the binding reaction due to high final dilution. However, if this is a problem (e.g., low level of 125I incorporation, inefficient reduction, split of radioactivity peak between neighboring collecting tubes) the iodinated peptide can be desalted as described above (steps 7 to 11) and resuspended in a small volume 0.1% TFA to obtain concentrated iodinated material. SUPPORT PROTOCOL 2

PREPARATION OF MEMBRANE FROM TISSUE BEARING TACHYKININ RECEPTORS This protocol describes a basic procedure for obtaining a crude membrane preparation from tissues expressing all types of tachykinin receptors. Materials Receptor-containing tissue Tris-buffered saline (TBS), ice cold: 50 mM Tris⋅Cl buffer, pH 7.4, containing 120 mM NaCl 50 mM Tris⋅Cl, pH 7.4 (APPENDIX 2A), ice cold 50 mM Tris⋅Cl, pH 7.4, containing 300 mM KCl and 10 mM EDTA, ice cold TBS containing 3 mM MnCl2, ice cold Polytron and Teflon/glass (Dounce) homogenizers Additional reagents and equipment for determining protein concentration (APPENDIX 3A) 1. Homogenize tissue in ice-cold TBS at a ratio of 1:10 (w/v) using a Polytron homogenizer for 20 sec at a setting of 5 or 6.

Characterization of Tachykinin Receptors

2. Centrifuge homogenate for 20 min at 25,000 × g, 4°C.

1.15.12 Supplement 4

Current Protocols in Pharmacology

35.02

1

2

Inject

3.11

29.85 31.64 33.41

27.92

Absorbance

53.80

A

3

4 1

Inject

3.14

Absorbance

B

Time (min)

Figure 1.15.3 Examples of HPLC separation of unlabeled and radioiodinated Tyr−1-SP species by reversed-phase HPLC. (A) HPLC separation of the radioiodinated material (see Support Protocol 1); (B) HPLC trace of intact (unlabeled) Tyr−1-SP. In both panels, the HPLC solvent gradient progression is illustrated by the horizontal line rising from left to right. Small black arrows 1 and 2 represent peaks of unlabeled SP species: peak 1 is the reduced peptide (intact form), elution time 35 min; peak 2 is the oxidized peptide (methylsulfoxide form), elution time 27 min. The other peaks represent material originating from ingredients of radioiodination reaction (e.g., BSA, 2-ME). Small black arrows 3 and 4 represent elution times of [125I]Tyr−1-SP species: arrow 3 indicates the fraction containing oxidized [125I]Tyr−1-SP, elution time 33 min, yield 104.3 µCi/ml; arrow 4 indicates the fraction containing reduced [125I]Tyr−1-SP, elution time 40 min, yield 603.8 µCi/ml or 60% of incorporation from total 1 mCi 125I used in the reaction. The total amount of iodinated SP = activity collected/specific activity of 125I (2175 Ci/mmol) = 603.8 µCi/ml = 277.6 pmol/ml = 2.175 µCi/pmol.

Receptor Binding

1.15.13 Current Protocols in Pharmacology

Supplement 4

3. Discard supernatant and resuspend pellet in ice-cold 50 mM Tris buffer, pH 7.4, containing 300 mM KCl and 10 mM EDTA using the Polytron homogenizer. 4. Incubate the homogenate for 15 min at 4°C with gentle shaking. Centrifuge again as described above. 5. Wash twice by resuspending pellet in ice-cold 50 mM Tris buffer using a Teflon-glass homogenizer at 4°C and centrifuging as described above. 6. Resuspend resulting pellet in ice-cold TBS containing 3 mM MnCl2 at the desired protein concentration at 4°C (see APPENDIX 3A for protein assays; use BSA as a standard). The presence of MnCl2 in the reaction media maintains G protein coupling to the receptor, yielding a high-affinity state for the agonist ligand. Generally, 50 to 100 ìg protein per binding reaction tube is sufficient to obtain detectable specific binding from membrane preparations.

7. Store suspension on ice if binding experiment is to be completed on the same day. Freeze aliquots of membrane preparation up to 6 months at −70°C for future experiments. REAGENTS AND SOLUTIONS Use deionized, distilled water in all recipes and protocol steps. For common stock solutions, see APPENDIX 2A; for suppliers, see SUPPLIERS APPENDIX.

Membrane binding buffer Tris-buffered saline (TBS; 50 mM Tris⋅Cl, pH 7.4, with 120 mM NaCl) containing: 3 mM MnCl2 1 mg/ml BSA (Sigma) 0.2 mg/ml bacitracin (Sigma) 20 µg/ml chymostatin (Sigma) 20 µg/ml leupeptin (Sigma) Prepare immediately before use and store on ice TBS can be stored up to 6 months at room temperature.

NK1/NK2 cell binding buffer PBS (50 mM phosphate buffer, pH 7.4, with 120 mM NaCl) containing: 1 mg/ml BSA (Sigma) 0.2 mg/ml bacitracin (Sigma) 20 µg/ml chymostatin (Sigma) 20 µg/ml leupeptin (Sigma) Prepare immediately before use and store on ice PBS can be stored up to 6 months at room temperature.

Characterization of Tachykinin Receptors

NK3 cell binding buffer Tris-buffered saline (TBS; 50 mM Tris⋅Cl, pH 7.4, with 120 mM NaCl) containing: 1 mg/ml BSA (Sigma) 0.2 mg/ml bacitracin (Sigma) 20 µg/ml chymostatin (Sigma) 20 µg/ml leupeptin (Sigma) Prepare immediately before use and store on ice TBS can be stored up to 6 months at room temperature.

1.15.14 Supplement 4

Current Protocols in Pharmacology

COMMENTARY Background Information Peptides are involved in signal transmission in the nervous system as either neurotransmitters or modulators of neurotransmission. The peptides are typically stored in large dense-core vesicles and are secreted by way of a calciumdependent mechanism. These vesicles are not concentrated at the synaptic release site, but are uniformly distributed throughout the synapses. Upon release, the peptides diffuse to their target, which may be adjacent to the release site or may be a few cell body diameters distant (Otsuka and Yoshioka, 1993). The binding of the various tachykinin peptides to their cognate receptors results in the G-protein-mediated activation of several second messenger signaling systems, elevation of intracellular calcium levels, stimulation of phosphoinositol turnover, mobilization of arachidonic acid, and accumulation of cAMP (Maggi et al., 1993; Krause et al., 1994). Receptors in the tachykinin peptide family, when coupled to G proteins, display a high affinity (subnanomolar to nanomolar) for the ligand, whereas uncoupled receptors have an affinity some 30- to 100-fold lower (high nanomolar or micromolar range). The most intensively studied tachykinin peptide is substance P. Binding studies providing evidence for a substance P receptor were published as early as 1980 (Hanley et al., 1980). The first evidence for multiple substance P receptors was published in 1982 (Lee et al., 1982). The availability of the nonmammalian peptides physalaemin and eledoisin allowed for the initial receptor differentiation. Subsequently this was confirmed by the discovery of multiple mammalian tachykinin peptides, which have been demonstrated by the molecular cloning of their polyprotein precursors from cDNA libraries (Nakanishi, 1991; Krause et al., 1994). Various agonist forms of these distinct peptides have been radiolabeled (125I or 3H) and used in radioligand binding assays. Several graphical approaches are used to analyze the binding of ligands to various tachykinin receptors. The preferred method to analyze saturation binding data is a computerized Scatchard plot analysis (Munson and Rodbard, 1980). To analyze competition binding data, the Hill plot is preferred. This provides information on both the IC50 value and the potential heterogeneity of sites. Moreover, if the radioligand concentration is substantially below the Kd value for the receptor, the IC50

value should be equivalent to the Ki value (Cheng and Prusoff, 1973).

Critical Parameters Radiolabeling tachykinin peptides The tachykinin peptides have either (1) amino groups (available for acylation using the Bolton-Hunter reagent) in addition to natural histidyl residues that can be oxidatively iodinated, or (2) tyrosine residues (artificially inserted into the sequences with little impact on biological activity) that can be oxidatively iodinated. Because oxidative iodination methodology may result in sulfoxidation of methionine residues, these must be reduced to the normal methionine form prior to purification. Synthetic peptides corresponding to each of the substrates for radioiodination are commercially available and relatively inexpensive. Substantial amounts of each radioligand can be prepared using straightforward procedures (see Support Protocol 1). Alternatively, each of these radioligands is commercially available. The iodinated radioligands are relatively stable, and can be stored for up to 1 month at −20°C in the presence of low concentrations (e.g., 1mM) of a reducing agent such as 2-mercaptoethanol. Radioligand binding procedures The major problems associated with the described binding procedures relate to issues of nonspecific binding. This is addressed by pretreatment of the glass fiber filter in appropriate blocking buffers. It is also crucial to ensure that binding equilibrium is attained in the assay. Moreover, the assay must be conducted with the number of cells or amount of membrane protein known to be within the linear range of binding. Time to equilibrium and tissue linearity should be independently verified for each radioligand and cell/tissue source.

Troubleshooting Table 1.15.5 describes some common problems encountered with radioligand binding assays using tachykinin receptors. Also discussed are potential explanations for the problems as well as suggested remedies.

Anticipated Results As discussed, the radioiodination conditions yield high levels of iodine incorporation into tyrosyl residues, and a lesser incorporation into

Receptor Binding

1.15.15 Current Protocols in Pharmacology

Supplement 4

Table 1.15.5

Troubleshooting Guide for Correcting Problems with Tachykinin Receptor Radioligand Binding Assays

Problem

Possible cause

Solution

No binding or binding lower than expected

Receptor is not expressed or expressed in low numbers Radioligand was degraded

Increase number of cells or concentration of membrane preparation Prepare fresh radioligand or try commercially available radioligand Add or increase concentration of protease inhibitors Make sure that all manipulations before initiating binding reaction are performed on ice Use dedicated HPLC columns for each tachykinin peptide to be iodinated Wash column with 50% acetonitrile/50% H2O after every purification Use more shallow gradient to increase the difference in elution time of the noniodinated and iodinated peptides Increase vacuum level to provide the time for single filtration step of 1 sec (the total time of filtration should not exceed 10 sec if affinity level is ∼10 nM) Check the concentration of MnCl2, which should be 3 mM to maintain G protein coupling (this salt is very hygroscopic) Decrease cell number or membrane concentration

Radioligand was contaminated with cold tachykinin peptides during HPLC purification

Specific binding was washed away during the filter rinse steps due to slow filtration Receptor in the membrane preparation is not G protein coupled Very high total binding (>10% of Too many cells or too high total radioactivity added) and high membrane concentration nonspecific binding (>15% of total binding) Receptor internalization

Binding is not linear with cell number or with the concentration of membrane preparation

Too many cells or too high membrane concentration

Control temperature throughout procedures: perform all steps on ice before initiating binding reaction and keep binding reaction strictly at 4°C Decrease cell number or membrane concentration

Binding equilibrium is not reached Perform time course experiment for the highest concentration of ligands and use this equilibrium time for binding reaction Ligand binding to the filters Strictly follow recommendations for filter presoaking given in this unit High nonspecific binding to the Try plastic tubes from different suppliers wall of reaction tubes Pretreat tubes with protein solution Increase BSA content in binding buffer Receptor internalization See above (high total binding) Ligand has aggregated or Add a small amount of a carefully tested precipitated solvent or detergent; DMSO is the first choice (up to 2% DMSO in binding reaction does not affect binding of tachykinin peptides and receptors) Heterogeneity of receptor Use highly selective nonpeptide tachykinin population (e.g., tissue expresses antagonists as radioligands or blocking agents both NK1 and NK2 receptors)

1.15.16 Supplement 4

Current Protocols in Pharmacology

histidyl residues. Modification of the HPLC gradients may be necessary as octadecylsilaneor C4-based HPLC columns may show variations. Nonradioactive iodinated peptides and oxidized peptides can be prepared by methods similar to those described above for use as standards for analytical purposes. The radioligand binding assays will yield different Bmax values based on the tissue or cell source. Typical values range from low fmol/mg protein in brain regions, up to 1 to 10 pmol/mg protein in recombinant expression systems. The high level of expression in recombinant expression systems is equivalent to 105 to 106 high affinity sites per cell. The affinity state of the receptor is dependent upon coupling to G proteins and may vary with the radioligand used. Finally, tissues may express more than one type of tachykinin receptor, which may yield results suggesting binding heterogeneity, depending upon the radioligand.

Time Considerations

For the radioiodination process, ∼1.5 days are required to complete both the synthesis and purification. Though the procedure is time-consuming, there is a considerable cost savings as up to 500 µCi radioligand can be prepared using this protocol. For the radioligand binding studies, ∼4 hr are required to conduct the binding assay using a 2-hr incubation time. In addition, the preparation of membranes or cells requires up to 4 hr. For tachykinin receptors, membrane preparations can be stored for up to 6 months at −80°C with little loss in specific binding.

Literature Cited Cheng, Y.-C. and Prusoff, W.H. 1973. Relationship between the inhibition constant (Ki) and the concentration of inhibitor which causes 50 percent inhibition (IC50) of an enzymatic reaction. Biochem. Pharm. 22:3099-3108.

Krause, J.E., Staveteig, P.T., Nave-Mentzer, J., Schmidt, S.K., Tucker, J.B., Brodbeck, R.M., Bu, J.-Y., and Karpitskiy, V.V. 1997. Functional expression of a novel human neurokinin-3 receptor homologue that binds [3H]senktide and [125IMePhe7]-NKB, and is responsive to tachykinin peptide agonist. Proc. Natl. Acad. Sci. U.S.A. 94:310-315. Lee, C.M., Iversen, L.L., Hanley, M.R., and Sandberg, B.E.B. 1982. The possible existence of multiple receptors for substance P. NaunynSchmiedeberg’s Arch. Pharmacol. 318:281-287. Maggi, C.A., Patacchine, R., Rovero, P., and Giachetti, A. 1993. Tachykinin receptors and tachykinin receptor antagonists. J. Autonom. Pharmacol.13:23-93. Munson, P.J and Rodbard, D. 1980. LIGAND: A versatile computerized approach for characterization of ligand binding systems. Anal. Biochem. 107:220-239. Nakanishi, S. 1991. Mammalian tachykinin receptors. Annu. Rev. Neurosci. 14:123-136. Otsuka, M. and Yoshioka, K. 1993. Neurotransmitter functions of mammalian tachykinins. Physiol. Rev. 73:229-308.

Key References Cheng and Prusoff, 1973. See above. Describes the appropriate level of substrate to use in competition binding experiments such that IC50 values are equal to Ki values. Limbird, L.E. 1986. Cell Surface Receptors: A Short Course of Theory and Methods. Martinus Nijhoff Publishing, Zoetermeer, The Netherlands. An excellent source of basic information about theoretical and practical aspects of ligand-receptor interactions. Munson and Rodbard, 1980. See above. Describes a computerized method to determine Kd and number of binding sites. Takeda, Y., Cremins, J.D., Takeda, J., and Krause, J.E. 1991. Analysis of tachykinin peptide family gene expression patterns by combined high-performance liquid chromatography-radioimmunoassay. Methods Neurosci. 6:119-130.

Hanley, M.R., Sandberg, B.E.B, Lee, C.M., Iversen, L.L., Brundish, D.E., and Wade, R. 1980. Specific binding of 3H-substance P to rat brain membranes. Nature 286:810-812.

Describes HPLC procedures and conditions for separating closely related peptides.

Krause, J.E., Blount, P., and Sachais, B.B. 1994. Molecular biology of receptors: Structures, expression and regulatory mechanisms. In The Tachykinin Receptors (S.H. Buck, ed.) pp. 165218. Humana Press, Totowa, N.J.

Contributed by Vladimir V. Karpitskiy Washington University School of Medicine St. Louis, Missouri

Receptor Binding

1.15.17 Current Protocols in Pharmacology

Supplement 4

Benzodiazepine Binding to GABAA Receptors

UNIT 1.16

Described in this unit are ligand-binding assays for the characterization of the benzodiazepine (BZ) site of GABAA receptors in the central nervous system. Compounds such as diazepam, acting at this site, are widely used therapeutically as tranquilizers, hypnotics, anticonvulsants, and muscle relaxants. Originally, this site was termed the BZ receptor. However, since it does not characterize a receptor in its own right, but rather a modulatory site of GABAA receptors (UNIT 1.7), the term BZ site is now frequently used. Differences in ligand binding to BZ sites are used to distinguish GABAA receptor subtypes. GABAA receptors are ligand-gated ion channels consisting of a pentameric subunit assembly. A repertoire of more than a dozen genes codes for GABAA receptor subunits (α1-6, β1-3, γ1-3, δ, ε; π, θ, ρ1-3; see Table 1.16.1 and UNIT 1.7). However, most GABAA receptors consist of an α and β subunit and the γ2 subunit variant (Fig. 1.16.1). Diazepam-sensitive GABAA receptors are characterized by the α variants α1, α2, α3, and α5 in combination with either the β1, β2, or β3 subunit and the γ2 subunit. In contrast, diazepam-insensitive GABAA receptors contain the α-variants α4 or α6, in combination with the β1, β2, or β3 subunit and the γ2 subunit (Fig. 1.16.1). The structural difference between the diazepam-sensitive and diazepam-insensitive types of α subunits is mainly due to a conserved histidine residue in the BZ site, which is replaced by an arginine residue in the diazepam-insensitive types of α subunits (Wieland et al., 1992; Benson et al., 1998). Although insensitive to diazepam, BZ sites of GABAA receptors containing the α4 or α6 subunits bind certain BZs, such as Ro 15-4513. These drugs are, however, not of clinical relevance. The Basic Protocol provides a procedure for analyzing benzodiazepine binding to GABAA receptors from brain. The experimental design of the method is suitable for all commercially available radioligands that interact with the BZ site of central GABAA receptors. By using the appropriate radioligand in combination with particular displacer compounds, it is possible to identify individual (or groups of) GABAA receptor subtypes (see Commentary). In addition, two radioligands directly permit an analysis of individual receptor subtypes. [3 H]Zolpidem binds with high affinity selectively to α1 subunitcontaining GABAA receptors and [3 H]L655,708 selectively to α5 subunit-containing receptors. Preparation of membrane suspensions for the radioligand displacement procedure is described in the Support Protocol. Five Alternate Protocols are described for the identification of diazepam-sensitive GABAA receptors (Alternate Protocol 1), the α1-subtype of diazepam-sensitive receptors (Alternate Protocol 2), the α5 subtype Table 1.16.1 Molecular Biology and Pharmacology of GABAA Receptors

Receptor subtype Benzodiazepine (BZ) site of GABAA receptor

GenBank accession number —b

Agonistsa

Antagonistsa

Inverse agonistsa

Diazepam Clonazepam Flunitrazepam Zolpidem

Ro 15-1788

Ro 15-4513 DMCM βCCM

a Available from, e.g., Tocris or Sigma-Aldrich. b GABA

A receptors are pentameric ion channels formed from different classes of subunits: α1-6, β1-3, γ1-3, δ, ε, π, θ, and ρ1-3.

Receptor Binding Contributed by Dietmar Benke and Hanns M¨ohler Current Protocols in Pharmacology (2006) 1.16.1-1.16.14 C 2006 by John Wiley & Sons, Inc. Copyright


1.16.1 Supplement 35

Figure 1.16.1 Classification by distinctive affinities of benzodiazepine site ligands of native GABAA receptor subtypes. The subunit combinations representing the respective receptor subtypes are indicated. αx or βx: subunit isoform has not been defined, can be any α(1-6) or β(1-3) subunit.

of diazepam-sensitive receptors (Alternate Protocol 3), both diazepam-sensitive and diazepam-insensitive receptors (Alternate Protocol 4), and diazepam-insensitive receptors exclusively (Alternate Protocol 5). Each protocol contains a brief description of the requirements for radioligand binding to these five types of BZ sites.

STRATEGIC PLANNING Various radioligands interacting with the BZ sites of GABAA receptors have been described in the literature. These include [3 H]diazepam, [3 H]flunitrazepam, [3 H]clonazepam, [3 H]zopiclone, [3 H]zolpidem, [3 H]CL 218,872, [3 H]Ro 15-1788, [3 H]Ro 15-4513, [3 H]CGS 8216, β-[3 H]CCM, and β-[3 H]CCE. More recently, two radioligands have been described in the literature that display preferential high affinity for receptors containing the α5 subunit ([3 H]L-655,708 and [3 H]RY80; Quirk et al., 1996; Skolnick et al., 1997). However, in this unit, consideration is given only to those radioligands that are commercially available (Table 1.16.2). [3 H]Diazepam: Full agonist (70 to 87 Ci/mmol; Perkin Elmer or BioTrend Chemikalien GmbH, http://www.biotrend.com). This was formerly the most commonly used radioligand, since it was the first BZ available with sufficiently high specific activity for analytical use. It binds to diazepam-sensitive BZ sites of GABAA receptors. [3 H]Flunitrazepam: Full agonist (70 to 87 Ci/mmol; Perkin Elmer, GE Healthcare, or BioTrend Chemikalien GmbH). This compound has higher affinity than diazepam, giving it the advantage of dissociating more slowly from its binding sites at low temperatures. This reduces possible loss during washing, which may give rise to errors in Bmax determinations. A further advantage of the higher affinity of [3 H]flunitrazepam is the lower nonspecific binding than [3 H]diazepam. As with diazepam, flunitrazepam binds to diazepam-sensitive BZ-sites of GABAA receptors.

BZ Binding to GABAA Receptors

[3 H]Ro 15-1788: Antagonist (70 to 87 Ci/mmol; Perkin Elmer or Bio Trend Chemikalien GmbH). This displays an even higher affinity than [3 H]flunitrazepam for diazepamsensitive BZ sites of GABAA receptors.

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Current Protocols in Pharmacology

Table 1.16.2 Radioligands for Benzodiazepine Binding Site of GABAA Receptors in the CNS

Ligand [3 H]Ro 15-1788 3

Affinity (KD ) 1-2 nM

Action

Receptor subtypesa

Antagonist

DZ-sensitive

[ H]Diazepam

5-18 nM

Full agonist

DZ-sensitive

[3 H]Flunitrazepam

2-5 nM

Full agonist

DZ-sensitive

[3 H]Zolpidem

6-9 nM

Full agonist

α1 subtype

[3 H]Ro 15-4513

5-12 nM

Partial inverse agonist

DZ-sensitive and DZ-insensitive

[3 H]L655,708

0.5-1 nM

Inverse agonist

α5 subtype

a DZ, diazepam.

[3 H]Ro 15-4513: Partial inverse agonist (20 to 40 Ci/mmol; Perkin Elmer). Binds to both diazepam-sensitive and diazepam-insensitive BZ sites of GABAA receptor subtypes, with only the small population of GABAA receptors containing the γ1 subunit not being labeled. A disadvantage is lower specific activity than the other radioligands. [3 H]Zolpidem: Full agonist (40 to 80 Ci/mmol; NEN Life Sciences). Interacts preferentially with diazepam-sensitive BZ sites of receptors containing the α1 subunit (Besnard et al., 1996). [3 H]L655,708: Inverse agonist (Bio Trend Chemikalien GmbH). Binds with high affinity selectively to diazepam-sensitive BZ sites of α5 subunit-containing receptors. Both [3 H]diazepam and [3 H]flunitrazepam also bind to the peripheral type of BZ binding sites, although with lower affinity. This has to be considered, and can be compensated for by the use of a displacing ligand highly selective for BZ sites of GABAA receptors, e.g., clonazepam or Ro 15-1788, for determination of nonspecific binding. This is particularly important at high radioligand concentrations, where both sites become labeled.

BENZODIAZEPINE BINDING IN BRAIN MEMBRANES This protocol describes an in vitro assay for radioligand binding to the BZ binding site of GABAA receptors in crude brain membrane preparations. The assay is valid for all the commercially available radioligands listed in Table 1.16.2. Unlabeled clonazepam is used as a displacer to define nonspecific (undisplaceable) binding, except with [3 H]Ro 15-4513, where unlabeled Ro 15-1788 is used as displacer. Specific radioligand binding is revealed by subtracting nonspecific binding from total binding. This protocol may be used to define the KD (affinity) of the radioligand in saturation binding experiments using a “hot” or “cold” method and to determine the IC50 and Ki values of an unlabeled test compound. For an accurate determination of the respective values, it is important to cover a sufficient range of concentrations for the radioligand or the unlabeled test compound. In general, the ligand concentration should span a range from at least 10-fold below to 10-fold above the KD for saturation experiments, and 100-fold both below and above the estimated IC50 value in the case of competition experiments. The final volume of the binding assays is 1.0 ml, which is a good compromise between the economical use of the expensive radioligands and accuracy in pipetting small volumes. Nevertheless, the final volume of the assays may be scaled down to as low as 100 to 200 µl (including membranes), which allows the use of microtiter plates (96- or even 384-well plates) required for high-throughput screening (HTS). Using the appropriate filtration and counting device (e.g., Packard, Skatron), samples on a single plate may

BASIC PROTOCOL

Receptor Binding

1.16.3 Current Protocols in Pharmacology

Supplement 35

be processed simultaneously. In addition, the protocols may be adapted for scintillation proximity assays (SPA, see UNIT 3.4), which would permit incubation and counting of samples in the same plate without separating bound from free radioligand. This is an attractive method that works well with the radioligands described here and is particularly applicable to HTS situations where time considerations are critical. However, the SPA technology yields considerable nonspecific binding (∼50%) and thus filtration of the samples is recommended whenever possible because of the superior signal-to-noise ratio (∼5% nonspecific binding).

Materials Frozen membrane preparation (see Support Protocol) 50 mM Tris-citrate buffer, pH 7.4 (see recipe), 4◦ C Radioligands (see Strategic Planning and Table 1.16.2) Clonazepam (Sigma-Aldrich) or Ro 15-1788 (Sigma-Aldrich) for determination of nonspecific binding Unlabeled ligand (same compound as radioligand) for “cold” saturation binding assay Tissue homogenizer (e.g., Polytron, Brinkmann) Refrigerated centrifuge with rotor and tubes 10 × 75–mm assay tubes Whatman GF/B or GF/C glass fiber filters Vacuum filtration device (e.g., Sartorius, Millipore) or cell harvester (e.g., Brandel, Scatron) 6-ml scintillation vials Additional reagents and equipment for protein determination (APPENDIX 3A) Prepare membranes for binding assay 1. Thaw the frozen membrane preparation and re-suspend in 20 to 50 vol ice-cold 50 mM Tris-citrate buffer, pH 7.4, using a tissue homogenizer. 2. Centrifuge 20 min at 48,000 × g, 4◦ C. Discard the supernatant and wash the pellet at least three times, each time by resuspending the pellet in 20 to 40 vol of cold 50 mM Tris-citrate buffer, pH 7.4, centrifuging again for 20 min at 48,000 × g, 4◦ C, then removing the supernatant. Extensive washing of the membranes is required for removal of endogenous GABA, which is present in high concentrations in brain tissue and may allosterically influence BZ binding.

3. Resuspend the final pellet in ice-cold 50 mM Tris-citrate buffer, pH 7.4, determine protein (APPENDIX 3A), and dilute concentration to yield a protein solution of 0.2 to 0.5 mg protein/ml. The final protein concentration required depends on the final volume of the assay. Each reaction mixture containing the radioligand, competitor, and buffer is diluted with an equal volume of the membrane suspension (see below). The final protein content in the 1-ml reaction mixture should be in the range of 100 to 250 µg. For determination of protein concentrations, fast and convenient assays such as Bradford (Bio-Rad) or BCA (Pierce) are recommended (see APPENDIX 3A). Bovine serum albumin (BSA) is commonly used as reference standard but should be replaced by γ -globulin when the Bradford assay is used, since the dye color development is significantly greater with BSA than most other proteins.

BZ Binding to GABAA Receptors

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Current Protocols in Pharmacology

Measure binding of [3 H]BZ ligand to GABAA receptors For competition assay 4a. Prepare 10 × 75–mm assay tubes in triplicate. Select one of the following radioligands to determine total binding and add the suggested concentration (final concentration for a 1-ml assay) in each tube. Keep total volume 60 Ci/mmol; NEN Life Sciences) in 50 mM Tris⋅Cl, pH 7.4 1 mM picrotoxinin (Sigma) in 50 mM Tris⋅Cl, pH 7.4 (see NOTE below) Scintillation cocktail

Characterization of the Picrotoxin Site of GABAA Receptors

25-ml polycarbonate centrifuge tubes (Beckman or equivalent) Tissue homogenizer (e.g., Brinkmann Polytron or Tekmar Tissumizer) 12 × 75–mm borosilicate glass culture tubes Whatman GF/B glass-fiber filters Vacuum filtration device (e.g., Brandel cell harvester) Filter forceps (Millipore) 6-ml scintillation vials

1.18.6 Supplement 8

Current Protocols in Pharmacology

Liquid scintillation counter Computer program (e.g., DeltaGraph; Delta Point) Additional reagents and equipment for protein assays (see APPENDIX 3A) NOTE: Picrotoxinin should first be dissolved to 100 mM in dimethylsulfoxide (DMSO), but the final concentration of DMSO should not exceed 0.1% (v/v) in the 1-ml assay. Prepare membrane suspension for binding assay 1. Prepare membranes as described (see Basic Protocol 1, steps 1 to 4). Generate association kinetics data 2. To measure total binding: Prepare triplicate 12 × 75–mm borosilicate glass culture tubes for total binding for each time interval by combining the following reagents: 100 µl 1.5 M KCl (150 mM final) 3.3 µl 1.2 µM [35S]TBPS (4 nM final) 50 mM Tris⋅Cl to 0.9 ml. For association kinetics, data are obtained in the absence (total binding) and presence (nonspecific binding) of a saturating concentration of displacer (100 ìM picrotoxinin) using multiple incubation times. Total and nonspecific binding are determined in parallel at each time interval using a single concentration of [35S]TBPS (4 nM final). It is important to add KCl (150 mM final), because [35S]TBPS binding is Cl- ion dependent. To investigate the association kinetics of a test compound, replace [35S]TBPS with a radiolabeled test compound. The optimum concentration should be determined using Basic Protocol 1.

3. To measure nonspecific binding: Prepare parallel triplicate tubes for nonspecific binding for each time interval. Combine reagents as described in step 2, but add 100 µl of 1 mM picrotoxinin (final 100 µM) and decrease the Tris⋅Cl to maintain a 0.9-ml volume. 4. To initiate the binding assay, add 100 µl membrane suspension (0.2 to 0.3 mg protein) to each assay tube (final 1.0 ml), gently vortex to mix the contents, and incubate corresponding nonspecific and total binding tubes for different periods of time (e.g., 10, 20, 40, 60, 90, 120, and 180 min) at room temperature. [35S]TBPS has negligible binding at 0°C (Squires et al., 1983). Execute the timing of the assay assembly in such a way that the termination of binding reaction in the assay tubes can be completed in a short time without any significant interruption in the filtration process. It usually takes ∼2 to 3 min to process one rack of 48 assay tubes using a vacuum filtration device with 48 slots (e.g., Brandel cell harvester model MB-48L). Therefore, a gap of 4 to 5 min should be maintained between different racks of assay tubes. The time course must include a 180-min time point so that Be (step 9) can be calculated.

5. Terminate the binding reaction by filtering the contents of the test tubes through Whatman GF/B glass-fiber filters maintained under reduced pressure in a vacuum filtration device. Wash each filter rapidly three times (3 to 5 sec each) with 2 ml ice-cold 50 mM Tris⋅Cl. The washing time should be kept constant and as short as possible so as to minimize dissociation of bound radioligand from the receptors. It is not necessary to soak the filter paper with polyethyleneimine (PEI). Receptor Binding

1.18.7 Current Protocols in Pharmacology

Supplement 8

6. Transfer each filter to a 6-ml scintillation vial using filter forceps, add 4 ml scintillation cocktail, and allow to sit for ∼12 hr at room temperature. Shaking the vials can reduce this period.

7. Quantify radioactivity using a liquid scintillation counter. Analyze binding data 8. Calculate the specific radioligand binding (dpm) by subtracting nonspecific binding from total binding.

A

Be–B t (%) Be

100 90 80 70 60 50

40 30 20

10 20

80

40 60 Time (min)

100

B 100

B t/Be (%)

50

10 5 6 mM PTZ 10 µM GABA 100 µM etazolate

1 0

10

20

30

40

50

60

70

80

90

100

Time (min)

Characterization of the Picrotoxin Site of GABAA Receptors

Figure 1.18.2 Analysis of association and dissociation kinetics of [35S]TBPS binding to rat cortical membranes. (A) Association of [35S]TBPS (4 nM) to rat cortical membranes at 25°C. The apparent association rate constant (kapp) and association rate constant (k1) of [35S]TBPS were found to be 0.0183 min−1 and 3.6 × 106 min−1 M−1, respectively. (B) Dissociation of [35S]TBPS (3 nM) binding from rat cortical membranes by pentylenetetrazole (PTZ; 6 mM), GABA (10 µM), and etazolate (100 µM) at 25°C. The respective half-lives of dissociation with these drugs were found to be 68 min, 1.3 min (first phase)/12 min (second phase), and 0.7 min (first phase)/20 min (second phase). Reprinted from Maksay and Ticku (1985) with permission from Raven Press.

1.18.8 Supplement 8

Current Protocols in Pharmacology

9. Plot ln[(Be − Bt)/Be] against time t, where Be and Bt are specific binding at equilibrium and at time t, respectively (see Fig. 1.18.2A). Calculate the apparent association rate constant (kapp) from the slope of the resulting line. 10. Determine the association rate constant (k1) using the equation kapp = k1[L] + k−1, where [L] is the molar concentration of radioligand and k−1 is the dissociation rate constant (see Basic Protocol 4). Any suitable computer program such as DeltaGraph can be used for analyzing the association kinetic data. However, caution should be exercised in setting the limits for various variables while analyzing the data using such computer programs because varied results may be obtained if unrealistic values are set.

DISSOCIATION KINETICS OF BINDING TO THE PICROTOXIN SITE OF GABAA RECEPTORS IN RAT BRAIN MEMBRANES

BASIC PROTOCOL 4

This protocol describes an in vitro assay for determining the dissociation kinetics of binding (dissociation rate constant and affinity constant) of various radioligands to the picrotoxin site of GABAA receptors in rat brain membranes. These studies are utilized to investigate whether GABAergic drugs bind to the picrotoxin site or modulate this site as a result of their binding to a distinct, but allosterically coupled, site on the GABAA receptor. Materials Frozen membrane preparation (see Support Protocol) 50 mM Tris⋅Cl, pH 7.4 (APPENDIX 2A), ice cold 1.5 M KCl in 50 mM Tris⋅Cl 1.2 µM [35S]t-butylbicyclophosphorothionate ([35S]TBPS; >60 Ci/mmol; NEN Life Sciences) in 50 mM Tris⋅Cl 1 mM picrotoxinin (Sigma) in 50 mM Tris⋅Cl, pH 7.4 (see NOTE below) Unlabeled test compound in 50 mM Tris⋅Cl, pH 7.4 (see NOTE below) Scintillation cocktail 25-ml polycarbonate centrifuge tubes (Beckman or equivalent) Tissue homogenizer (e.g., Brinkmann Polytron or Tekmar Tissumizer) 12 × 75–mm borosilicate glass culture tubes Whatman GF/B glass-fiber filters Vacuum filtration device (e.g., Brandel cell harvester) Filter forceps (Millipore) 6-ml scintillation vials Liquid scintillation counter Computer program (e.g., DeltaGraph; Delta Point) Additional reagents and equipment for protein assays (see APPENDIX 3A) NOTE: If unlabeled competitor is insoluble in 50 mM Tris⋅Cl, dimethylsulfoxide (DMSO) can be added. Picrotoxinin should first be dissolved to 100 mM in DMSO, but in all cases, the final concentration should not exceed 0.1% (v/v) in the final 1-ml assay volume. Prepare membrane suspension for binding assay 1. Prepare membranes as described (see Basic Protocol 1, steps 1 to 4). Generate dissociation kinetics data 2. To measure total binding: Prepare and label triplicate 12 × 75–mm borosilicate glass culture tubes for total binding and add the following reagents: Receptor Binding

1.18.9 Current Protocols in Pharmacology

Supplement 8

100 µl 1.5 M KCl (150 mM final) 3.3 µl 1.2 µM [35S]TBPS (4 nM final) 50 mM Tris⋅Cl to 0.9 ml. For dissociation kinetics, equilibrium data are obtained in the absence (total binding) and presence (nonspecific binding) of a saturating concentration of displacer (100 ìM picrotoxinin), and binding data are obtained for various time points in the presence of a saturating concentration of an unlabeled test compound (displacer) that causes dissociation of the bound radioligand. Total and nonspecific binding are determined in parallel using a single concentration of [35S]TBPS (4 nM final). The total assay volume will be 1 ml after the addition of membrane suspension (step 5). It is important to add KCl (150 mM final), because [35S]TBPS binding is Cl− ion dependent. To investigate the dissociation kinetics of a test compound, replace [35S]TBPS with a radiolabeled test compound. The optimum concentration should be determined using Basic Protocol 1.

3. To measure nonspecific binding: Prepare triplicate tubes for nonspecific binding. Combine reagents as described in step 2, but add 100 µl of 1 mM picrotoxinin (final 100 µM) and decrease the Tris⋅Cl to maintain the appropriate volume. 4. To measure dissociation kinetics: For each time interval (e.g., 1, 2, 3, 4, 5, 10, 20, 30, 40, 60, 80, and 100 min), prepare triplicate tubes as in step 2, but decrease Tris⋅Cl to allow for addition of unlabeled test compound (step 6). 5. Initiate the binding assay in all tubes by adding 100 µl membrane suspension (0.2 to 0.3 mg protein). Gently vortex to mix the contents, and begin to incubate at room temperature. [35S]TBPS has negligible binding at 0°C (Squires et al., 1983).

6. Initiate the dissociation of [35S]TBPS binding at the appropriate times by adding a saturating concentration of unlabeled test compound (displacer) to the tubes prepared in step 4. Vortex gently. Continue to incubate all tubes for a total of 180 min. Time the addition of displacer such that the membrane suspension is incubated for 180 min with [35S]TBPS and for a specified time with a displacer, and so that all the tubes, including those for total and nonspecific binding, are ready for the termination of the binding reaction without any significant interruption in the filtration process. For example, in order to obtain the data point representing the dissociation of the [35S]TBPS binding in 100 min, add membrane suspension to the tubes containing buffer, KCl, and [35S]TBPS (with and without picrotoxinin). Vortex the tubes gently. Wait for 80 min, and then add displacer. Vortex the tubes gently, incubate an additional 100 min, and then terminate the binding reaction.

7. Terminate the binding reaction by filtering the contents of the test tubes through Whatman GF/B glass-fiber filters maintained under reduced pressure in a vacuum filtration device. Wash each filter rapidly three times (3 to 5 sec each) with 2 ml ice-cold 50 mM Tris⋅Cl. The washing time should be kept constant and as short as possible so as to minimize additional dissociation of bound radioligand from the receptors. It is not necessary to soak the filter paper in polyethyleneimine (PEI).

Characterization of the Picrotoxin Site of GABAA Receptors

1.18.10 Supplement 8

Current Protocols in Pharmacology

8. Transfer each filter to a 6-ml scintillation vial using filter forceps, add 4 ml scintillation cocktail, and allow to sit for ∼12 hr at room temperature. Shaking the vials can reduce this period.

9. Quantify radioactivity using a liquid scintillation counter. Analyze binding data 10. Calculate the specific radioligand binding (dpm) by subtracting nonspecific binding from total binding (Be) and from each time interval (Bt). 11. Plot ln[Bt/Be] versus time t, where Bt and Be are the amount of specific binding at time t and at equilibrium, respectively. Calculate the rate constant of dissociation (k−1) from the slope of the resulting line (see Fig. 1.18.2B). Any suitable computer program such as DeltaGraph can be used for analyzing the dissociation kinetic data. However, caution should be exercised in setting the limits for various variables while analyzing the data using such computer programs because varied results may be obtained if unrealistic values are set.

12. Calculate the affinity constant (Kd) of the radioligand by dividing the dissociation rate constant (k−1) by the association rate constant (k1). CHARACTERIZATION OF THE PICROTOXIN SITE OF GABAA RECEPTORS USING [3H]TBOB [3H]t-Butylbicycloorthobenzoate ([3H]TBOB) and [35S]TBPS label the same picrotoxin site on the GABAA receptor (Lawrence et al., 1985). However, [3H]TBOB has a much longer radioactive half-life, and binding assays conducted at 0°C, 24°C, and 37°C yield similar Kd values, whereas [35S]TBPS has negligible binding at 0°C. Furthermore, a shorter incubation time is needed for [3H]TBOB binding (90 min versus 180 min at 24°C). In spite of these advantages, [35S]TBPS is more commonly used as a research tool due to its relatively lower Kd value (20 nM versus 60 nM) at 24°C, and because [3H]TBOB binding is considerably less sensitive than [35S]TBPS binding to inhibition by GABA, muscimol, and Ro 5-4864, due to differences in the interactions of these radioligands with the identical picrotoxin site of the GABAA receptor (Squires et al., 1983; Ticku and Ramanjaneyulu, 1984; Lawrence et al., 1985). However, [3H]TBOB is a superior radioligand for autoradiographic studies, due to better resolution. [3H]TBOB is available commercially (20 to 60 Ci/mmol; Amersham Pharmacia).

ALTERNATE PROTOCOL 1

For [3H]TBOB binding assays, follow Basic Protocols 1 to 4, replacing [35S]TBPS with [3H]TBOB. The assay buffer (50 mM Tris⋅Cl, pH 7.4) should be adjusted to contain 0.5 M NaCl at all stages. Incubation should be carried out for either 90 min at 24°C, 120 min at 0°C, or 60 min at 37°C. Nonspecific binding can be carried out using picrotoxinin (100 µM) or unlabeled TBOB (4 µM). Dimethylsulfoxide (DMSO) is used to dissolve picrotoxinin as well as TBOB, with a final concentration of DMSO not exceeding 0.1% (v/v) in the final 1-ml assay volume. CHARACTERIZATION OF THE PICROTOXIN SITE OF GABAA RECEPTORS USING [3H]EBOB 4′-Ethynyl-4-n-[2,3-3H2]propylbicycloorthobenzoate ([3H] EBOB), also called [3H]ethynylbicycloorthobenzoate, is structurally related to TBOB. It has high affinity (Kd ∼2 nM) for the picrotoxin site on the GABAA receptor and is available commercially (30 to 60 Ci/mmol; NEN Life Sciences). However, [35S]TBPS is still widely used because [3H]EBOB is a new radioligand and its binding has not been extensively characterized.

ALTERNATE PROTOCOL 2

Receptor Binding

1.18.11 Current Protocols in Pharmacology

Supplement 8

For [3H]EBOB binding assays, follow Basic Protocols 1 to 4, replacing [35S]TBPS by [3H]EBOB. The assay buffer (50 mM Tris⋅Cl, pH 7.4) should be adjusted to contain 0.3 M NaCl at all stages. Incubation should be carried out for 120 min at 24°C or 90 min at 37°C. Nonspecific binding can be carried out using picrotoxinin (100 µM), unlabeled EBOB (2 µM), or lindane (5 µM; Sigma). If DMSO is used to dissolve compounds, its final concentration should not exceed 0.1% (v/v) in the final 1-ml assay volume. ALTERNATE PROTOCOL 3

CHARACTERIZATION OF PICROTOXIN SITE OF GABAA RECEPTOR USING [3H]BIDN [3H]3,3-bis-Trifluoromethyl-bicyclo[2.2.1]heptane-2,2-dicarbonitrile ([3H]BIDN) is suitable for studies of GABA-gated chloride channels of vertebrates and insects. The cage convulsants (EBOB, TBPS, and TBOB) and picrotoxinin displace [3H]BIDN binding in rat membranes, but not in insect membranes, suggesting that the convulsant site of the GABAA receptor is somewhat different in insects as compared to rats. However, dieldrin displaces [3H]BIDN binding competitively in insect membranes. Unlike picrotoxin, which antagonizes a variety of ligand-gated chloride channels such as vertebrate glycinegated and invertebrate L-glutamategated chloride channels, BIDN blocks only GABAgated chloride channels in vertebrates and insects (Rauh et al., 1997). Thus, [3H]BIDN offers the advantage of being selective for GABA-gated chloride channels, and can be utilized to investigate the pharmacology of chloride channels in vertebrates as well as in insects (Rauh et al., 1997; Hamon et al., 1998). However, its low affinity (higher Kd value) for rat brain membranes limits its use as an experimental tool. Kd values for [3H]BIDN binding are 200 to 300 nM in rat brain membranes and 26 to 61 nM in insect membranes. [3H]BIDN is available commercially (40 to 60 Ci/mmol; NEN Life Sciences). For [3H]BIDN binding assays, follow Basic Protocols 1 to 4, replacing [35S]TBPS with [3H]BIDN.The assay buffer (50 mM Tris⋅Cl, pH 7.4) should be adjusted to contain 0.5 M NaCl at all stages. Incubation should be carrried out for 45 min at 37°C for rat brain membranes or for 45 min at 24°C for insect membranes. In general, higher incubation temperatures enhance the association kinetics but decrease the affinity (higher Kd value) of the radioligand. Thus, it is important to strike a balance between these two parameters by selecting appropriate incubation conditions to obtain an acceptable binding signal. The above incubation temperatures are recommended when using [3H]BIDN to obtain optimal binding signals. Nonspecific binding is carried out using unlabeled BIDN (50 µM). If DMSO is used to dissolve compounds, its final concentration should not exceed 0.1% (v/v) in the final 1-ml assay volume.

SUPPORT PROTOCOL

PREPARATION OF RAT BRAIN MEMBRANES Membranes can be prepared either from fresh brain sample or tissue samples that have been stored frozen at −80°C. Materials Brain tissue sample 0.32 M sucrose solution in 50 mM Tris⋅Cl, pH 7.4, ice cold 50 mM Tris⋅Cl, pH 7.4, ice cold

Characterization of the Picrotoxin Site of GABAA Receptors

Potter-Elvehjem glass homogenizer with Teflon pestle Tissue homogenizer (e.g., Brinkmann Polytron or Tekmar Tissumizer) 50-ml polypropylene and 25-ml polycarbonate centrifuge tubes (Beckman or equivalent)

1.18.12 Supplement 8

Current Protocols in Pharmacology

1. Place fresh/frozen brain tissue into 15 vol ice-cold 0.32 M sucrose solution (pH 7.4) in a Potter-Elvehjem glass homogenizer fitted with a Teflon pestle. Allow tissue to thaw if it is frozen. Homogenize tissue until a uniform mixture is obtained and transfer the contents to a 50-ml polypropylene centrifuge tube. Ten to twelve up-and-down strokes are usually sufficient.

2. Centrifuge homogenate 10 min at 1000 × g, 4°C. 3. Discard pellet and transfer the supernatant to a 25-ml polycarbonated centrifuge tubes. Centrifuge supernatant 30 min at 140,000 × g at 4°C to obtain a pellet containing the mitochondrial plus microsomal fraction (i.e., P2 + P3 fraction). 4. Disperse this fraction (pellet) in 15 vol ice-cold double-distilled deionized water. Homogenize in a tissue homogenizer (Polytron or Tissumizer) at midpoint setting for two 10-sec bursts, 10-sec apart. 5. Centrifuge 30 min at 140,000 × g, 4°C, and suspend the pellet in 15 vol ice-cold 50 mM Tris⋅Cl by homogenization. Multiple washings of the tissue are necessary to remove endogenous GABA that is present in high concentrations in brain tissue; otherwise, residual GABA in the membrane preparation will interfere with the binding assay.

6. Centrifuge and wash as in step 5, and freeze the suspension at −80°C overnight. 7. Thaw the suspension and wash three more times as in step 5. Following the final centrifugation, suspend the pellet in small volume (e.g., 1 ml/g) of ice-cold 50 mM Tris⋅Cl by homogenization and store frozen in 500-µl aliquots for up to 3 months at −80°C. A freeze-thaw cycle followed by homogenization and centrifugation helps in removing endogenous GABA.

COMMENTARY Background Information The GABAA receptor is a transmembrane hetero-oligomeric pentameric subunit assembly derived from various subunits such as α1 to α6, β1 to β3, γ1 to γ3, δ, ε, π and ρ1 to ρ3 and it is expressed in the peripheral and central nervous systems (Mehta and Ticku, 1999b). The β subunit of GABAA receptors has been suggested as a necessary requirement for the picrotoxin site (Slany et al., 1995; Zezula et al., 1996). Although the homo-oligomeric GABAA receptors derived from the β3-subunit exhibit a specific high-affinity binding for [35S]TBPS, the GABAA receptor assemblies consisting of α1β3γ2 or α1β3 subunits have higher affinity for [35S]TBPS over those consisting of β3γ2 or β3 subunits (Slany et al., 1995; Zezula et al., 1996). GABA produces both pre- and postsynaptic inhibition in a variety of preparations by increasing the conductance of chloride ions. One of the first compounds that was shown to block the GABA responses in crayfish was picrotoxin (Elliot and Florey, 1956; Takeuchi and

Takeuchi, 1969), which was known to have a convulsant effect as early as 1875. Later studies revealed that picrotoxin antagonizes GABA responses in vertebrates in a noncompetitive manner, suggesting the possibility of different sites of action for these drugs. Picrotoxin is made up of picrotoxinin and picrotin in an equimolar ratio; picrotoxinin is ∼50-fold more potent than picrotin. To investigate whether there is a distinct site for picrotoxin-like convulsants at the GABA synapse, [3H]α-dihydropicrotoxinin (DHP) was synthesized and its binding characterized (Ticku et al., 1978; Leeb-Lundberg et al., 1981). These studies revealed that the specific binding to brain membranes was only 10% to 15% of the total binding, and it was inhibited by GABAergic drugs, suggesting that the picrotoxin site at the GABA synapse is a target of drug action for several categories of centrally acting agents that affect GABAergic transmission (Ticku et al., 1978; Leeb-Lundberg et al., 1981).

Receptor Binding

1.18.13 Current Protocols in Pharmacology

Supplement 8

Subsequently, other radioligands, such as [35S]TBPS (or [35S]TBPT), [3H]TBOB, [3H]EBOB, and [3H]BIDN, were introduced to study the picrotoxin site of the GABAA receptor. The greatest advantage of [35S]TBPS over [3H]DHP is a far better signal-to-noise ratio. The binding of [35S]TBPS to brain membranes

is specific, saturable, chloride ion dependent, and modulated by GABAergic drugs (Squires et al., 1983; Ramanjaneyulu and Ticku, 1984a,b; Supavilai and Karobath, 1984; Mehta and Ticku, 1998, 1999a). Its binding to brain membranes is inhibited by picrotoxin, pentylenetetrazole, and other tetrazoles in a competi-

Table 1.18.1 IC50 and Ki Values of Various Ligands for [35S]TBPS Binding in Rat Brain Membranes, continued

Ligand Convulsants: Anisatin Bemegride 6-o-Chlorophenyltetrazole 1-Cyclohexyl-5-methyl-tetrazole 6, 6′-Dichloropentamethylenetetrazole Ethylbicyclophosphate Ethyl-β–carboline-3-carboxylate (βCCE) Heptamethylenetetrazole 1-Isobutyl-5-methylenetetrazole Isopropylbicyclophosphate l-Kynurenine sulfate N-Methyl-β-carboline-carboxamide (FG7142) 1-Methyl-5-cyclohexyltetrazole Methyl-6, 7-dimethoxy-4-ethyl-β-carboline-3′carboxylate (DMCM) 7-Methyl-9-isopropylpentamethylenetetrazole 7-Methyl-10-isopropylpentamethylenetetrazole 7-Methylpentamethylenetetrazole Pentylenetetrazole (PTZ) Picrotoxinin Quinolinic acid Ro 5-3663 Depressants, anticonvulsants, anxiolytics, and other drugs: Buspirone HCl γ-Butyrolactone Cannabidiol Cartazolate Dextronantradol HCl Diazepam (±)5-(1,3-dimethylbutyl)-5-ethyl barbituric acid (DMBB) Etazolate Ethanol (−)Etomidate (+)Etomidate sulfate Flurazepam Characterization of the Picrotoxin Site of GABAA Receptors

IC50 (µM)

Ki (µM)

0.10 200 310 40 10 1.2 >2000 230 480 0.16 >1000 >2000 500 >1000

0.09 185 287 37 9.3 1.1 >2000 213 444 0.15 >1000 >2000 463 >1000

200 145 160 700 0.40 120 22

185 134 148 648 0.37 111 20

>2000 >2000 40 0.52 50 >200 50

>2000 >2000 37 0.48 46 >200 46

5 500 100 9 >200

4.6 463 93 8.3 >200 continued

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Current Protocols in Pharmacology

Table 1.18.1 IC50 and Ki Values of Various Ligands for [35S]TBPS Binding in Rat Brain Membranes

Ligand

IC50 (µM)

Ki (µM)

GABA (−)Hexobarbital (+)Hexobarbital (±)Hexobarbital (−)Ketamine HCl (+)Ketamine HCl Levonantradol HCl (±)Mephobarbital Methaqualone Metharbital (−)N-Methyl-5-phenylpropyl barbituric acid (MPPB) (+)N-Methyl-5-phenylpropyl barbituric acid (MPPB) MK-801 (−)Pentobarbital (+)Pentobarbital (±)Pentobarbital Phenobarbital Ro 5-4864 Ro 11-6896 (−)Secobarbital (+)Secobarbital (±)Secobarbital Tofizopam

0.45 380 140 220 >2000 >2000 40 210 100 145 116

0.42 352 130 204 >2000 >2000 37 194 93 134 107

760

704

>400 70 125 90 480 10 16 40 70 20 160

>400 65 116 83 444 9.3 14.8 37 65 18.5 148

aDrugs that inhibit specific [35S]TBPS binding by 100,000

2.1

146b

S-α-methyl histamine

>100,000

>100,000

38

–

RBI

7.6

6100

1500

–

RBI RBI RBI

H1 Antagonists Chlorpheniramine Diphenhydramine

17

2500

>10,000

>10,000b

Promethazine

5.0

200

>10,000

–

Burimamide

>100,000

2,600

5.1

180b

Cimetidine

>10,000

320

>100,000

>10,000b

RBI RBI

H2 Antagonists Smith-Kline Beechama

Ranitidine

>10,000

77

21,000

>10,000b

Tiotidine

>100,000

27

30,000

–

Tocris Cookson

1200

41

1700

–

Tocris Cookson

45,000

80,000

0.75

1880

Bioprojeta

Clobenpropit

2900

5000

0.84

12.8b

RBI

Iodophenpropit

800

1100

0.72

–

Tocris Cookson Tocris Cookson

Zolantidine H3 Antagonists Ciproxifan

>100,000

>100,000

72.6

27b

A-331440

2940

14,400

3.2

>10,000

Sigma

ABT-239

1620

6760

0.45

>10,000

Abbott Labsa

72.6

27b

Tocris Cookson

>5000b

4.1b

Johnson & Johnson

Thioperamide

H4 Antagonists Thioperamide a

JNJ7777140

>100,000 >100,000 >10,000b

>1000b

a Not available commercially. b Liu et al., 2001a.

appropriate scintillation cocktail (e.g., Ready-Solv HP). Quantify radioactivity in an appropriate counting device. 20. Analyze data to determine Kd and Bmax in saturation experiments (see UNIT 1.3) or IC50 and/or Ki values of test compounds in competition experiments (see Support Protocol and UNIT 1.3).

Receptor Binding

1.19.7 Current Protocols in Pharmacology

Supplement 37

Figure 1.19.3 Saturation binding results for H1 receptors expressed in HEK-293 cells, based on the assay illustrated in Figure 1.19.1. Human H1 receptors were incubated with different concentrations of [3 H]mepyramine as described in Basic Protocol 1. Nonspecific binding (triangles) was subtracted from total binding (circles) to yield specific binding (squares). As can be seen, most of the binding is specific. Transforming the specific binding data according to the procedure of Scatchard (inset) provides an estimate of the affinity of the radioligand (Kd = 0.85 nM, derived from the inverse of the slope of the line) and the receptor density (Bmax = 2700 fmol/mg protein, derived from the x-intercept).

Figure 1.19.3 shows results for binding of [3 H]mepyramine to H1 receptors expressed in HEK-293 cells in a saturation assay. ALTERNATE PROTOCOL 1

MEASUREMENT OF [3 H]MEPYRAMINE BINDING TO NATIVE H1 RECEPTORS IN TISSUE MEMBRANE HOMOGENATES Protocols used to determine radioligand binding to native H1 receptors in guinea pig brain are similar to those used with cloned receptors (see Basic Protocol 1). Differences in these procedures are due to the lower density of native receptors in brain tissue as compared to the overexpression typically obtained with cloned receptors.

Additional Materials (also see Basic Protocol 1) Male Hartley strain guinea pigs, 6 to 8 months in age 0.9% NaCl solution in squeeze bottle Dissection instruments (e.g., Stoelting) Small animal decapitator Operating scissors Bone rongeurs Strong forceps Cold dissecting plate (or ice-filled Petri dish) Scalpel Dissecting knife Gas cylinder containing 60% CO2 /40% O2 connected to a suitable chamber for anesthetizing guinea pig

1.19.8

Prepare and freeze brain tissue 1. Place guinea pig into a chamber filled with a CO2 /O2 mixture until the anesthetic effect of CO2 has rendered the animal unconscious. Sacrifice guinea pig by decapitation.

Supplement 37

Current Protocols in Pharmacology

Characterization of Histaminergic Receptors

2. Cut through the cranium with scissors, removing bone with rongeurs and forceps. Remove brain to cold dissecting plate, rinsing liberally with 0.9% NaCl solution to remove blood and other debris. 3. Remove and discard the cerebellum and bisect the cerebrum at the midline, using a scalpel. Dissect the cerebral cortex from the rest of the brain using a dissecting knife. 4. Weigh the cerebral hemispheres, freeze on dry ice, and store up to 1 year at −70◦ C until time of membrane preparation. Alternatively, guinea pig brain can be purchased and shipped on dry ice from suppliers, e.g., Analytical Biological Service or animal supply houses.

Prepare membranes for H1 receptor binding 5. Weigh then homogenize the frozen cerebral tissue (∼4 grams tissue per guinea pig) in 30 volumes of ice-cold Na+ /K+ assay buffer using a tissue homogenizer at high speed (e.g., T 25 Ultra-Turrax at a setting of 20,000 rpm). Homogenize using two 10-sec bursts with a 10- to 20-sec cooling interval between homogenizations. 6. Centrifuge 10 min at 39,000 × g, 4◦ C. 7. Decant the supernatant and wash the pellet by resuspending in 30 volumes ice-cold Na+ /K+ assay buffer and centrifuging 10 min at 39,000 × g, 4◦ C. 8. Decant the supernatant and resuspend the final pellet in 6.25 volumes (per gram original wet weight) ice-cold Na+ /K+ assay buffer and divide into 4-ml aliquots. Flash freeze in liquid nitrogen and store up to 1 year at −80◦ C.

Measure [3 H]mepyramine binding 9. On the day of the assay, gradually thaw a 4-ml aliquot of frozen membranes to room temperature and dilute with an additional 12 ml Na+ /K+ assay buffer (16 ml total). Keep the membrane preparations cold while gently mixing on a stir plate. Save 10 to 100 µl of the suspension for subsequent protein analysis (i.e., Bradford, Lowry, or BCA protein assays; see APPENDIX 3A). This dilution will provide sufficient membrane material for ∼64 assay tubes. The tissue content is generally too great for use in 96-well filtermat formats, although 24-well filter mats can be used in some harvesters and Topcount counters (e.g., Brandel).

10. Perform the binding assay (see Basic Protocol 1, steps 11 to 20), adding 250 µl of tissue homogenate to the assay tubes or microplate wells in step 15.

MEASUREMENT OF [3 H]TIOTIDINE BINDING TO CLONED HUMAN H2 RECEPTORS

BASIC PROTOCOL 2

This protocol describes a radioligand binding assay that labels cloned human H2 receptors expressed in cultured cells. The radioligand of choice for this assay is [3 H]tiotidine, a histamine H2 receptor antagonist that displays high affinity (∼5 nM) and selectivity for this site when these receptors are highly expressed in cultured cells (∼1 pmol/mg protein).

Materials Na+ /K+ assay buffer (see recipe), ice-cold 1 mM cimetidine (Research Biochemicals) in 50 mM Na+ /K+ assay buffer, or other unlabeled ligand to measure nonspecific binding (Table 1.19.2) 0.75 nM [3 H]tiotidine (70 to 90 Ci/mmol; Perkin-Elmer Life Sciences; Table 1.19.3) in 50 mM Na+ /K+ assay buffer (see recipe)

Receptor Binding

1.19.9 Current Protocols in Pharmacology

Supplement 37

Test compounds (optional) in Na+ /K+ assay buffer (see recipe) 0.5% (v/v) polyethyleneimine (PEI) Rinse buffer, ice-cold: 50 mM Tris·Cl (APPENDIX 2A), pH 7.7 at 25◦ C, pH 7.4 at 0◦ C Scintillation fluid: Microscint 20 (Packard) or Ready-Solv HP (Beckman Coulter) Deep well 96-well microtiter plates (2.2-ml volume; e.g., Bioblocks, Brandel), 2-ml strip tubes, or 12×75–mm glass test tubes GF/B filters or Unifilter GF/B plates (Packard), or equivalent Cell harvester or vacuum filtration manifold (e.g., Packard, Brandel, or Skatron), optional 60◦ C oven, optional Additional reagents and equipment for preparing membranes (see Basic Protocol 1) and performing Bradford, Lowry, or BCA protein assays (APPENDIX 3A) Prepare membranes for H2 receptor binding 1. Prepare membranes from cell lines expressing H2 receptors at an appropriate receptor density (see Basic Protocol 1, steps 1 to 9). Measure [3 H]tiotidine binding 2. On the day of assay thaw the 1-ml frozen aliquot and mix with an additional 50 ml ice-cold Na+ /K+ assay buffer before use, to provide ∼7 µg protein per assay tube. Keep the membrane preparations cold while gently mixing on a stir plate. Save 10 to 100 µl of the suspension for subsequent protein analysis (i.e., Bradford, Lowry, or BCA protein assays; see APPENDIX 3A). This dilution typically provides ∼7 µg protein per assay tube. However, a lower receptor density may require more concentrated membrane preparations. Samples for protein determinations can be stored at room temperature for several weeks until assayed if diluted to a total volume of 1 ml with 1 N NaOH.

3. Prepare sufficient deep-well 96-well microtiter plates, 2-ml strip tubes, or 12×75– mm glass test tubes for the assay. See Figure 1.19.1 for the layout of a typical saturation experiment or Figure 1.19.2 for a typical competition experiment, both in 96-well format.

4. Add 50 µl of Na+ /K+ assay buffer or 50 µl of 1 mM cimetidine (100 µM final) to appropriate total and nonspecific binding tubes or wells, respectively. For saturation assays (Fig. 1.19.1), total and nonspecific binding are tested in parallel at each radioligand concentration. For competition assays (Fig. 1.19.2), total and nonspecific binding are tested as separate samples from those containing test compound. The nonspecific binding agent (i.e. cimetidine) and test compounds are diluted 10-fold in the final assay volume of 500 µl, so 10× stock solutions are generally prepared.

For saturation experiments: 5a. Dilute the [3 H]tiotidine into Na+ /K+ assay buffer to obtain ten to twelve concentrations that will result in final assay concentrations ranging from 0.5 to 50.0 nM, where half of the final concentrations are less than the Kd (5.0 nM) and the remainder are greater. The assay concentrations should generally span at least a 100-fold range.

6a. Add 200 µl of increasing concentrations of [3 H]tiotidine to triplicate tubes. Also add an appropriate amount of radioligand directly to several scintillation vials to quantify the amount of ligand added to each assay tube. Characterization of Histaminergic Receptors

1.19.10 Supplement 37

Current Protocols in Pharmacology

One can use one-tenth the amount of radiolabel used in the actual assay, applied to a filter paper in a vial and dried (see step 11), so that the scintillation cocktail does not need to be compatible with aqueous solutions.

For competition experiments: 5b. Add 50 µl various concentrations (generally six to eighteen) of test compounds to duplicate tubes. See Figure 1.19.2 for a scheme encompassing eleven concentrations, spanning six orders of magnitude, of four compounds in a 96-well format. Most compounds (e.g., cimetidine, ranitidine; Table 1.19.2) can be tested over a concentration range of 0.1 nM to 100 µM, particularly if assays for multiple receptor subtypes are run in parallel using the same concentration ranges of compounds in each assay.

6b. Add 200 µl of 0.75 nM [3 H]tiotidine (0.3 nM final) to all tubes. Also add an appropriate volume of radioligand directly to several scintillation vials to quantify the amount of ligand added to each assay tube. One can use one-tenth the amount of radiolabel used in the actual assay, applied to a filter paper in a vial and dried (see step 11), so that the scintillation cocktail does not need to be compatible with aqueous solutions.

7. Add 250 µl diluted membrane preparation (from step 2) to each tube or well. 8. Mix gently, but thoroughly, and incubate 45 min at 25◦ C, with shaking. 9. Separate free from receptor-bound [3 H]tiotidine by aspirating the incubation mixture with a cell harvester onto Unifilter GF/B plates presoaked for 3 min in 0.5% PEI. Alternatively, PEI-soaked GF/B filter sheets and cell harvesters can be used for tubebased assays. The radiolabeled ligand-receptor complexes are trapped on the GF/B filters.

10. Wash each filter four times with sufficient ice-cold rinse buffer to fill each assay well (∼2 ml). If using 12×75–mm tubes, wash four times with 4 ml of buffer each time. The filter size used in 96-well assays is much smaller than that of a tube-based assay; however, it is the repetitive nature of the washing that removes unbound ligand, so the volume of rinse buffer used in 96-well assays compared to 12 × 75–mm tube assays does not affect results.

11. Dry filters in Unifilter plates ∼1 hr at room temperature and then add 40 µl Microscint 20 scintillation fluid to each well. Alternatively, punch filter mats from Brandel-type harvesters into scintillation vials, dry 1 hr at 60◦ C, and add an appropriate scintillation cocktail (e.g., Ready-Solv HP). Quantify radioactivity in an appropriate counting device. 12. Analyze data to determine Kd and Bmax in saturation experiments (see UNIT 1.3) or IC50 and/or Ki values of test compounds in competition experiments (see Support Protocol and UNIT 1.3). Figure 1.19.4 shows results for binding of H2 receptors expressed in HEK-293 cells to [3 H]tiotidine in a saturation assay.

Receptor Binding

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Figure 1.19.4 Saturation binding results for human H2 receptors expressed in HEK-293 cells, based on an assay strategy similar to that illustrated in Figure 1.19.1. Human H2 receptors were incubated with different concentrations of [3 H]tiotidine as described in Basic Protocol 2. Nonspecific binding (triangles) was subtracted from total binding (circles) to yield specific binding (squares). As can be seen, most of the binding is specific binding. Transforming the specific binding data according to the procedure of Scatchard (inset) provides an estimate of the affinity of the radioligand (Kd = 6.6 nM) and the receptor density (Bmax = 23 pmol/mg protein).

ALTERNATE PROTOCOL 2

MEASUREMENT OF [125 I]AMINOPOTENTIDINE BINDING TO NATIVE H2 RECEPTORS IN TISSUE MEMBRANE HOMOGENATES Protocols used to determine radioligand binding to native H2 receptors in guinea pig brain are similar to those used with cloned receptors (see Basic Protocol 2). Differences in these procedures are due to the lower density of native receptors in brain tissue as compared to the overexpression levels typically obtained with cloned receptors and to the low affinity (Kd ∼ = 6 nM) of [3 H]tiotidine for these receptors, and the low specific activity of the radioligand. An alternate, high specific activity radioligand [125 I]aminopotentidine (Kd ∼ = 0.2 nM), a histamine H2 receptor antagonist, is recommended for measuring histamine H2 receptor binding in brain tissue. This radioligand could also be used for cloned H2 receptors as described in Basic Protocol 2, although it is available only by custom synthesis and is therefore costly.

Additional Materials (also see Basic Protocol 2) Male Hartley strain guinea pigs, 6 to 8 months in age 0.9% NaCl solution in squeeze bottle 500 µM cimetidine (Research Biochemicals) in 50 mM Na+ /K+ assay buffer, or other unlabeled ligand to measure nonspecific binding (Table 1.19.2) 60 pM [125 I]iodoaminopotentidine (2000 Ci/mmol; Amersham; Table 1.19.3) in 50 mM Na+ /K+ assay buffer

Characterization of Histaminergic Receptors

Dissection instruments (e.g., Stoelting) Small animal decapitator Operating scissors Bone rongeurs Strong forceps Cold dissecting plate (or ice-filled Petri dish) Scalpel

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Dissecting knife Gas cylinder containing 60% CO2 /40% O2 connected to a suitable chamber for anesthetizing guinea pig Additional reagents and equipment for preparing guinea pig cortical membranes (Alternate Protocol 1) 1. Prepare guinea pig cortical membranes (see Alternate Protocol 1, steps 1 to 8) and freeze membranes in 1-ml aliquots up to 1 year at −70◦ C (Alternate Protocol 1, step 8). 2. On the day of assay, gradually thaw a 1-ml aliquot to room temperature and dilute with an additional 5.25 ml of 50 mM Na+ /K+ assay buffer (6.25 ml final). Save 10 to 100 µl for subsequent protein determination (APPENDIX 3A). See Basic Protocol 2, step 2, for additional details. This dilution typically provides about 100 µg protein per assay tube. This yields sufficient membrane material for ∼60 assay tubes.

3. Perform 250-µl binding assays (see Basic Protocol 2, steps 3 to 12), with the following modifications: a. Add 50 µl of 500 µM cimetidine (100 µM final) to nonspecific binding tubes in step 4. b. For saturation assays, add 100 µl of increasing concentrations of [125 I]iodoaminopotentidine in step 6a. c. For competition assays, add 50 µl test compounds in step 5b plus 100 µl of 60 pM [125 I]iodoaminopotentidine (24 pM final) to appropriate tubes in step 6b. d. Add 100 µl diluted membrane preparation in step 7 (250 µl final). e. Incubate 120 min in step 8. The nonspecific binding agent and test compounds are diluted 5-fold in the final assay volume of 250 µl, so 5-fold concentrated stock solutions are generally prepared. The tissue content is generally too great for use in 96-well filtermat formats, although 24-well filter mats can be used in some harvesters and Topcount counters.

MEASUREMENT OF [3 H]N-α-METHYL HISTAMINE BINDING TO CLONED HUMAN H3 RECEPTORS IN MEMBRANES

BASIC PROTOCOL 3

The human H3 receptor was successfully cloned in 1999 (Lovenberg et al., 1999). The ligand-binding assays used for this site are based on procedures described by these authors, using the agonist radioligand, [3 H]N-α-methyl histamine (NAMH).

Materials Cell line (e.g., HEK-293 cells, ATCC #CRL-1573, or rat C6 glioma, ATCC #CCL-107) transfected (see Lovenberg et al., 1999) with human H3 receptors, grown to confluence TEP assay buffer (see recipe) Tris/EDTA assay buffer (without proteases; see recipe), ice-cold 100 µM histamine (Research Biochemicals) in Tris/EDTA assay buffer, or other unlabeled ligand (e.g., 30 µM thioperamide; Tocris Cookson) to measure nonspecific binding (Table 1.19.2) 1 nM [3 H]N-α-methyl histamine (NAMH; 45 to 90 Ci/mmol; Perkin-Elmer Life Sciences; Table 1.19.3) in Tris/EDTA assay buffer (see recipe) Test compounds (optional) in Tris/EDTA assay buffer (see recipe) 0.5% (v/v) polyethyleneimine (PEI)

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Rinse buffer, ice-cold: 50 mM Tris·Cl (APPENDIX 2A), pH 7.7 at 25◦ C, pH 7.4 at 0◦ C Scintillation fluid: Microscint 20 (Packard) or Ready-Solv HP (Beckman Coulter) Tissue homogenizer (e.g., T 25 Ultra-Turrax; IKA Works) Clinical tabletop centrifuge Deep-well 96-well microtiter plates (2.2-ml volume, e.g., Bioblocks, Brandel), 2-ml strip tubes, or 12 × 75–mm glass test tubes GF/B filters or Unifilter GF/B plates (Packard) Cell harvester or vacuum filtration manifold (e.g., Packard, Brandel, or Skatron) 60◦ C oven (optional) Additional reagents and equipment for preparing membranes (see Basic Protocol 1 and Alternate Protocol 1) and for Bradford, Lowry, or BCA protein assays (APPENDIX 3A) Prepare membranes for H3 receptor binding 1. Obtain cell pellets from rat C6 glioma cultured cells expressing the H3 receptor as described in steps 1 to 5 of Basic Protocol 1, but resuspend the cell pellets in 30 vol TEP assay buffer (with protease inhibitors). 2. Homogenize the cell pellets using a tissue homogenizer with two 10-sec bursts at 13,500 rpm and a 10- to 20-sec cooling interval between homogenizations. 3. Centrifuge 20 min at 41, 000 × g, 4◦ C. 4. Resuspend the final pellet in 6.25 vol Tris-EDTA assay buffer (without protease inhibitors). Membranes can be flash-frozen at this point in 1-ml aliquots in cryovials using liquid nitrogen and stored up to 1 year at −80◦ C.

5. Save 10 to 100 µl of the suspension for subsequent protein analysis (i.e., Bradford, Lowry, or BCA protein assays; see APPENDIX 3A). Samples for protein determinations can be stored at room temperature for several weeks until assayed if diluted to a total volume of 1 ml with 1 N NaOH.

Measure [3 H]R-α-methyl histamine binding 6. Prepare sufficient deep-well 96-well microtiter plates, 2-ml strip tubes, or 12×75– mm glass test tubes for the assay. See Figure 1.19.1 for the layout of a typical saturation experiment or Figure 1.19.2 for the layout of a typical competition experiment, both in 96-well format. 7. Add 50 µl Tris/EDTA assay buffer or 50 µl of 100 µM histamine (10 µM final) to the appropriate total and nonspecific binding tubes, respectively. For saturation assays (Fig. 1.19.1), total and nonspecific binding are tested in parallel at each radioligand concentration. For competition assays (Fig. 1.19.2), total and nonspecific binding are tested as separate samples from those containing test compound. The nonspecific binding agent and test compounds will be diluted 10-fold in the final assay volume of 500 µl, so 10-fold concentrated stock solutions are generally prepared.

For saturation experiments: 8a. Dilute the [3 H]NAMH into Tris/EDTA assay buffer to obtain twelve stock concentrations that will result in final assay concentrations ranging from 0.05 to 5.0 nM, where half of the final concentrations are less than the Kd (0.5 nM) and the remainder are greater. Characterization of Histaminergic Receptors

The assay concentrations should generally span more than a 100-fold range.

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9a. Add 200 µl of increasing concentrations of [3 H]NAMH to triplicate tubes. Also add an appropriate amount of radioligand directly to several scintillation vials to quantify the amount of ligand added to each assay tube. One can use one-tenth the amount of radiolabel used in the actual assay, applied to a filter paper and dried (see step 14), so the scintillation cocktail does not need to be compatible with aqueous solutions.

For competition experiments: 8b. Add 50 µl various concentrations (generally six to eighteen) of test compounds to appropriate duplicate tubes. See Figure 1.19.2 for a scheme encompassing eleven concentrations, spanning six orders of magnitude, of four compounds in 96-well format. Most compounds (e.g., histamine, thioperamide; Table 1.19.2) can be tested over a concentration range of 0.1 nM to 100 µM, particularly if assays for multiple receptor subtypes are run in parallel using the same concentration ranges of compounds in each assay. More potent compounds (e.g., clobenpropit, iodophenpropit) may require 10-fold lower concentrations in the H3 receptor assay.

9b. Add 200 µl of 1 nM [3 H]NAMH (0.4 nM final) to all tubes. Also add an appropriate amount of radioligand directly to several scintillation vials to quantify the amount of ligand added to each assay tube. One can use one-tenth the amount of radiolabel used in the actual assay, applied to a filter paper in a vial and dried (see step 14), so that the scintillation cocktail does not need to be compatible with aqueous solutions.

10. Add 250 µl membrane preparation from step 4 to each tube (final 500 µl). 11. Mix tubes gently, but thoroughly, and incubate 30 min at 25◦ C with shaking. 12. Separate free from receptor-bound [3 H]NAMH by aspirating the incubation mixture with a cell harvester onto Unifilter GF/B plates presoaked for 3 min in 0.5% PEI. Alternatively, PEI-soaked GF/B filter sheets and cell harvesters (e.g., Brandel) can be used for tube-based assays. The radiolabeled ligand-receptor complexes are trapped on the GF/B filters.

13. Wash each filter four times with sufficient ice-cold rinse buffer to fill each assay tube. If using 12×75–mm tubes, wash each filter four times with 4 ml buffer. The filter size used in 96-well assays is much smaller than that of a tube-based assay; however, it is the repetitive nature of the washing that removes unbound ligand, so the volume of rinse buffer used in 96-well assays compared to 12 × 75–mm tube assays does not affect results.

14. Dry filters in Unifilter plates ∼1 hr at room temperature and add 40 µl Microscint 20 scintillation fluid. Alternatively, punch filter mats from Brandel-type harvesters into scintillation vials, dry 1 hr in a 60◦ C oven, and add an appropriate scintillation cocktail (e.g., Ready-Solv). Determine radioactivity in an appropriate counting device. 15. Analyze data to determine Kd and Bmax in saturation experiments (UNIT 1.3), or IC50 and/or Ki values of test compounds in competition experiments (see Support Protocol and UNIT 1.3). Figure 1.19.5 shows data from a saturation binding assay using human H3 receptors expressed in HEK-293 cell membrane homogenates, and a range of concentrations of [3 H]NAMH. Receptor Binding

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Figure 1.19.5 Saturation binding results for human H3 receptors expressed in HEK-293 cell membrane homogenates, based on the assay illustrated in Figure 1.19.1. Human H3 receptors were incubated with different concentrations of [3 H]N-α-methyl histamine (NAMH) as described in Basic Protocol 3. Nonspecific binding (triangles) was subtracted from total binding (circles) to yield specific binding (squares). As can be seen, most of the binding is specific binding. Transforming the specific binding data according to the procedure of Scatchard (inset) provides an estimate of the affinity of the radioligand (Kd = 0.50 nM) and the receptor density (Bmax = 1220 fmol/mg protein).

ALTERNATE PROTOCOL 3

MEASUREMENT OF [3 H]N-α-METHYL HISTAMINE BINDING TO NATIVE H3 RECEPTORS Until the human H3 receptor was cloned (Lovenberg et al., 1999), only native H3 receptors in tissue homogenates were used for radioligand assays of these receptors. Additionally, only agonist radioligands were commercially available for labeling studies. The cloning of the H3 receptor from multiple species (including human and rat; Lovenberg et al., 2000) revealed pharmacological differences for known H3 antagonists, including lower affinity for thioperamide at the human H3 receptor. However, general procedures for H3 receptor binding assays are analogous to those described above for H1 or H2 receptors and can be performed in parallel.

Additional Materials (also see Basic Protocol 3) Male Sprague-Dawley rat (200 to 250 g) or male Hartley guinea pig (6 to 8 months in age) TEP assay buffer (see recipe), ice-cold Tris-EDTA assay buffer (see recipe) 30 µM thioperamide (Tocris Cookson) in TEP assay buffer, or other unlabeled ligand to measure nonspecific binding (Table 1.19.2) 1.5 nM [3 H]N-α-methyl histamine (NAMH; 45 to 90 Ci/mmol; Perkin-Elmer Life Sciences; Table 1.19.3) in TEP assay buffer Additional reagents and equipment for obtaining brain tissue (Alternate Protocol 1)

Characterization of Histaminergic Receptors

Prepare membranes for H3 receptor binding 1. Prepare frozen rat or guinea pig cerebral cortex (see Alternate Protocol 1, steps 1 to 4).

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2a. For guinea pig tissue: Homogenize frozen guinea pig cerebral cortex as in Alternate Protocol 1, step 5, but use ice-cold TEP assay buffer. 2b. For rat tissue: Homogenize ∼1g frozen cerebral cortex in 10 volumes of ice-cold TEP assay buffer using a tissue homogenizer at high speed. Dilute the homogenate to 40 vol with TEP buffer. 3. Centrifuge 20 min at 39,000 × g, 4◦ C. 4. Wash the pellet in 40 vol TEP assay buffer by decanting the supernatant, homogenizing to resuspend the pellet in buffer (as in step 2), and centrifuging 20 min at 39,000 × g, 4◦ C. 5. Resuspend the final pellet in 6.25 volumes (per gram original wet weight) TrisEDTA assay buffer and flash-freeze 4-ml aliquots in liquid nitrogen. Store up to 1 year at −80◦ C. 6. At the time of assay, gradually thaw 4 ml frozen membranes to room temperature and dilute in an additional 8.5 ml of Tris-EDTA assay buffer (12.5 ml final volume). 7. Homogenize using a tissue homogenizer using two 10-sec bursts at 8000 rpm with a 10- to 20-sec cooling interval between bursts. Dilute homogenate with Tris-EDTA assay buffer to a total volume of 150 ml. 8. Rehomogenize using a 10 sec-burst at 13,500 rpm. This process will provide sufficient membrane material for ∼600 assay tubes.

Measure [3 H]N-α-methyl histamine binding 9. Perform 500-µl binding assays (see Basic Protocol 3, steps 6 to 15), with the following modifications: a. Add 50 µl of 30 µM thioperamide (3 µM final) to nonspecific binding tubes in step 7. b. For competition assays, use 1.5 nM [3 H]NAMH (0.6 nM final) in step 9b. c. Add 250 µl diluted rat or guinea pig cortical membranes in step 10 (500 µl final). The tissue content is generally too great for use in 96-well filtermat formats, although 24-well filter mats can be used in some harvesters and Topcount counters. The nonspecific binding agent and test compounds will be diluted 10-fold in the final assay volume of 500 µl, so 10-fold concentrated stock solutions are generally prepared.

MEASUREMENT OF [3 H]HISTAMINE BINDING TO CLONED HUMAN H4 RECEPTORS IN MEMBRANES

BASIC PROTOCOL 4

The H4 receptor was the last member of the histamine receptor family to be cloned. It shares ∼35% homology at the amino acid level with the H3 receptor (Oda et al., 2000; Liu et al., 2001a). It is expressed predominantly in the cells of hematopoietic origin such as mast cells, dendritic cells, and eosinophils. The difficulty of detecting H4 receptors in native cells is compounded by the low affinity of H4 receptors for [3 H]-histamine, the only commercially available radioligand for labeling this site. For that reason, the radioligand binding assay for this receptor is normally performed with cloned human H4 receptor membranes. General procedures for H4 receptor binding assays (Liu et al., 2001b) are similar to those described above for H1 , H2 , and H3 receptors and can be performed in parallel. Receptor Binding

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Materials Cell lines (e.g., human HEK-293, ATCC# CRL-1573; or SK-N-MC, ATCC# HTB-10) transfected (Liu et al., 2001a) with human H4 receptors, grown to confluence TEP assay buffer (see recipe) Tris-EDTA assay buffer (see recipe) 200 µM thioperamide (Tocris Cookson) in Tris-EDTA assay buffer (see recipe) to define nonspecific binding [3 H]histamine (Perkin-Elmer) Test compounds Scintillation cocktail: e.g., Ready-Solv HP (Beckman Coulter) Tissue homogenizer (e.g., T 25 Ultra-Turrax; IKA Works) Deep-well 96-well microtiter plates (2.2 ml volume; e.g., Bioblocks, Brandel), 2-ml strip tubes, or 12×75–mm glass test tubes GF/B Unifilter plates (Packard), or equivalent Cell harvester or vacuum filtration manifold (e.g., Packard, Brandel, or Skatron), optional ◦ 60 C oven, optional Additional reagents and equipment for preparing H4 receptor membranes (see Basic Protocol 1) Prepare membranes 1. Obtain cell pellets from cultured cells expressing the H4 receptor (see Basic Protocol 1, steps 1 to 4). 2. Decant the supernatant, weigh the cell pellets, and resuspend the pellets in 30 vol TEP assay buffer. 3. Homogenize using a tissue homogenizer with two 10-sec bursts at 13,500 rpm and a 10- to 20-sec cooling interval between homogenizations. 4. Centrifuge 20 min at 41,000 × g, 4◦ C. 5. Wash the pellet in 30 vol TEP assay buffer, recentrifuge, and resuspend the final pellet in 6.25 volumes (per gram original wet weight) Tris-EDTA assay buffer (without protease inhibitors). 6. Flash freeze 4-ml aliquots in cryovials in liquid nitrogen until use. Frozen membrane preparations stored at –80◦ C are stable for up to 1 year.

Measure [3 H]histamine binding 7. At the time of assay, gradually thaw 8 ml of frozen membranes to room temperature and dilute with Tris-EDTA assay buffer (12.5 ml final). Save 10 to 100 µl of the suspension for subsequent protein analysis (i.e., Bradford, Lowry, or BCA protein assays; see APPENDIX 3A). 8. Prepare sufficient deep-well 96-well microtiter plates, 2-ml strip tubes, or 12×75– mm glass test tubes for the assay. See Figure 1.19.1 for the layout of a typical saturation experiment or Figure 1.19.2 for the layout of a typical competition experiment, both in 96-well format. 9. Add 25 µl Tris/EDTA assay buffer or 25 µl of 200 µM thioperamide (10 µM final) to the appropriate total and nonspecific binding tubes, respectively. Characterization of Histaminergic Receptors

For saturation assays (Fig. 1.19.1), total and nonspecific binding are tested in parallel at each radioligand concentration. For competition assays (Fig. 1.19.2), total and nonspecific binding are tested as separate samples from those containing test compound.

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The nonspecific binding agent or test compounds are diluted 20-fold in the final assay volume of 500 µl, so 20-fold more concentrated stock solutions are generally prepared.

For saturation experiments: 10a. Dilute the [3 H]histamine into Tris/EDTA assay buffer to obtain twelve stock concentrations that will result in final assay concentrations where half of the final concentrations are less than the and the remainder are greater. The assay concentrations should generally span more than a 100-fold range.

11a. Add 200 µl of increasing concentrations of [3 H]histamine to triplicate tubes. Also add an appropriate amount of radioligand directly to several scintillation vials to quantify the amount of ligand added to each assay tube. One can use one-tenth the amount of radiolabel used in the actual assay, applied to a filter paper and dried (see step 16), so the scintillation cocktail does not need to be compatible with aqueous solutions.

For competition experiments: 10b. Add 25 µl various concentrations (generally six to eighteen) of test compounds to the appropriate duplicate tubes. See Figure 1.19.2 for a scheme encompassing eleven concentrations, spanning six orders of magnitude, of four compounds in 96-well format. Most compounds, such as histamine and thioperamide, (Table 1.19.2) are tested over the concentration range of 0.1 nM to 100 µM, particularly if assays for multiple receptor subtypes are run in parallel.

11b. Add 225 µl of 44.4 nM [3 H]histamine (20 nM final) to all tubes. Also add an appropriate amount of radioligand directly to several scintillation vials to quantify the amount of ligand added to each assay tube. One can use one-tenth the amount of radiolabel used in the actual assay, applied to a filter paper in a vial and dried (see step 16), so that the scintillation cocktail does not need to be compatible with aqueous solutions.

12. Add 250 µl of the diluted membrane preparation from step 7 to each tube (final 500 µl). 13. Mix tubes gently, but thoroughly, and incubate 30 min at 25◦ C with shaking. 14. Separate free from receptor-bound [3 H]NAMH by aspirating the incubation mixture with a cell harvester onto Unifilter GF/B plates presoaked for 3 min in 0.5% PEI. Alternatively, PEI-soaked GF/B filter sheets and cell harvesters (e.g., Brandel) can be used for tube-based assays. The radiolabeled ligand-receptor complexes are trapped on the GF/B filters.

15. Wash each filter four times with sufficient ice-cold rinse buffer to fill each assay tube. If using 12×75–mm tubes, wash each filter four times with 4 ml buffer. The filter size used in 96-well assays is much smaller than that of a tube-based assay; however, it is the repetitive nature of the washing that removes unbound ligand, so the volume of rinse buffer used in 96-well assays compared to 12 × 75–mm tube assays does not affect results.

16. Dry filters in Unifilter plates ∼1 hr at room temperature and add 40 µl Microscint 20 scintillation fluid. Alternatively, punch filter mats from Brandel-type harvesters into scintillation vials, dry 1 hr in a 60◦ C oven, and add an appropriate scintillation cocktail (e.g., Ready-Solv). Determine radioactivity in an appropriate counting device.

Receptor Binding

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17. Analyze data to determine Kd and Bmax in saturation experiments (UNIT 1.3), or IC50 and/or Ki values of test compounds in competition experiments (see Support Protocol and UNIT 1.3). SUPPORT PROTOCOL

DATA ANALYSIS Specific binding is defined as total binding (dpm in the absence of any inhibitor of radioligand binding) minus that binding (dpm) determined in the presence of compound used to define nonspecific binding. Transformed data (percent specific binding, percent inhibition) or even untransformed data (dpm or moles of ligand bound) is analyzed by nonlinear regression using programs such as GraphPad Prism (GraphPad Software). Nonlinear regression is a rigorous method of data analysis and provides the advantage of determining statistical best fits to single- or multiple-site models of receptor binding. The use of nontransformed data is particularly advantageous in potentially heterogeneous receptor systems to avoid the assumptions that might be inherent in transforming data into a percent inhibition or percent specific binding format. Ki values can be determined from IC50 values by utilizing the Cheng-Prusoff equation (Cheng and Prusoff, 1973) where Ki = IC50 /(1 + L/Kd ) where L equals the concentration of the radioligand employed in the assay and Kd is the binding affinity of the radioligand.

REAGENTS AND SOLUTIONS Use deionized, distilled water in all recipes and protocol steps. For common stock solutions, see APPENDIX 2A; for suppliers, see SUPPLIERS APPENDIX.

Dulbecco’s phosphate-buffered saline (DPBS), Ca2+ - and Mg2+ -free 2.7 mM KCl (0.2 g/liter) 1.5 mM KH2 PO4 (0.2 g/liter) 137 mM NaCl (8.0 g/liter) 8.0 mM Na2 HPO4 ·7H2 O (2.16 g/liter) Store up to 3 months at 4◦ C This is also commercially available from Life Technologies.

Na+ /K+ assay buffer 12.2 mM KH2 PO4 (1.66 g/liter) 37.8 mM Na2 HPO4 ·7H2 O (10.133 g/liter) Adjust pH to 7.4 at 25◦ C Store up to 3 months at 4◦ C Tris/EDTA assay buffer 20 mM Tris base (2.423 g/liter) 0.5 mM EDTA (0.146 g/liter) Adjust pH to 7.4 at 25◦ C Store up to 3 months at 4◦ C TEP (Tris/EDTA/ protease inhibitors) assay buffer

Characterization of Histaminergic Receptors

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50 mM Tris base (6.057 g/liter) 5 mM EDTA (1.461 g/liter) Adjust pH to 7.4 at 25◦ C with 1.0 N HCl (pH will be 7.7 at 4◦ C). Store up to 3 months at 4◦ C Immediately before use add: 1 mM benzamidine (0.156 g/liter; Sigma) 2 mg/ml aprotinin (Roche Applied Science) 2.1 µM leupeptin (1 mg/liter; Roche Applied Science) 1.5 µM pepstatin (1 mg/liter; Roche Applied Science) Current Protocols in Pharmacology

COMMENTARY Background Information Other units in this chapter, particularly UNITS 1.3 to 1.6, detail many of the general concerns and practical aspects of radioligand binding assays, all of which apply to histaminergic receptors, as well. However, some particular aspects of binding to histaminergic receptors deserve additional commentary. There are currently no commercially available antagonist radioligands for the H3 and H4 receptors. Several publications describe imidazole-based H3 receptor antagonist radioligands for this site, but few are commercially available (Jansen et al., 1992; AlvesRodrigues et al., 1996; Brown et al., 1996;

Harper et al., 1997). While [125 I]iodoproxyfan may be purchased from Amersham, it does not appear to be particularly selective for the H3 site (Ligneau et al., 1994; Harper et al., 1997). The non-H3 receptor binding of this compound may be masked by inclusion of 100 µM metyrapone, a nonselective cytochrome p450 inhibitor, in the binding assays. This approach has also been found to be useful for studying [125 I]iodoproxyfan binding (Yao et al., 2006). Other imidazole-based H3 receptor antagonist radioligands often have low specific activity, low affinity, a high degree of nonspecific binding, or other limitations. Two non-imidazole

Table 1.19.3 Properties of Commercially Available Radioligands for Histamine Receptors.

Radioligand

Agonist or Kd for Specific Nonspecific antagonist receptor activity binding (nM) (Ci/mmol)

Signal-to- Source noise ratio

Comments

Antagonist 0.5 - 2.0 20-30

Low

High

Perkin-Elmer

Antagonist 100-500 10-30

High

Low

Amersham

[ H]Tiotidine

Antagonist 2-10

70-90

High

Low

Perkin-Elmer Good SNR for recombinant receptors

[125 I]Iodoaminopotentidine

Antagonist 0.1-0.5

2000

High

Low

Amersham

[3 H]Histamine

Agonist

2-10

25-30

High

Low

Perkin-Elmer Selectivity: H3 > H4 > H1 > H2

[3 H]R-α-methyl histamine

Agonist

0.2-0.8

20-50

Moderate

Moderate Amersham

[3 H]N-α-methyl histamine

Agonist

0.2-0.8

45-90

Low

High

Perkin-Elmer SNR in tissue-based assays > RαMH

2000

High

Low

Amersham

25-30

High

Low

Perkin-Elmer Selectivity: H3 > H4 > H1 > H2

H1 Receptor [3 H]Mepyramine H2 Receptor [3 H]Cimetidine 3

Cost-limiting for broad-based screening Custom synthesis required

H3 Receptor

[125 I]Iodo-proxyfan Antagonist 0.2-0.5

Binds to H3 R and sites displaced by metyrapone

H4 Receptor [3 H]Histamine

Agonist

50-200

Receptor Binding

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Characterization of Histaminergic Receptors

H3 receptor antagonist radioligands, [3 H]A317920 (Yao et al., 2006) and [3 H]A-349821 (Witte et al., 2006) have been described, and they exhibit high specificity and affinity for rat ([3 H]A-317920 and [3 H]A-349821) and human ([3 H]A-349821) H3 receptors. Thus, nonimidazole H3 receptor antagonist radioligands may prove to be more useful tools for defining H3 receptor pharmacology and localization because agonist radioligands generally label only a subset of H3 sites (Witte et al., 2006). No suitable agonist radioligands have been described for H1 or H2 receptors. As for H3 receptor agonists, radiolabeled N-α-methyl histamine yields a better signal-to-noise ratio than R-α-methyl histamine. [3 H]histamine is currently the only available radioligand for H4 receptors and, while useful for labeling human H4 receptors, it has lower affinity for rodent receptors (Liu et al., 2001b), limiting its utility for examining this site in natively expressed systems. See Table 1.19.3 for some points of comparison and technical aspects of commercially available radioligands. There are relatively few reports specifically characterizing radioligand binding at each of the histamine receptor subtypes (particularly for older H1 and H2 ligands such as diphenhydramine and cimetidine). Studies with H3 and H4 receptor antagonists tend to characterize their affinity for all four receptor subtypes, although functional bioassays are often used instead of receptor binding assays. Moreover, many H1 and H2 receptor antagonists have not been fully characterized for activity at H3 and H4 receptors. Because the amino acid sequences differ among the four receptor subtypes, it would be anticipated that pharmacological selectivity will vary as well. Indeed, this is indicated by the differences among the binding affinities for most of the compounds listed in Table 1.19.2. The H3 and H4 receptors are more similar to one another in terms of sequence homology (∼35%) than are other members of this receptor family, and it is not surprising there are few agents able to differentiate between these two sites. There are indications of receptor heterogeneity for the histamine H3 site (West et al., 1990, 1999). However, this finding has not been extensively verified either pharmacologically or through molecular biological techniques, nor is there evidence for heterogeneity of H1 , H2 , or H4 receptors. Multiple, alternatively spliced isoforms exist for the H3 receptor (Drutel et al., 2001; Coge et al., 2001). While they display differential expression and functional characteristics, the impact of this

heterogeneity on the pharmacological binding properties of these isoforms still remains to be elucidated (Hancock et al., 2003).

Critical Parameters and Troubleshooting Several units in Chapter 1 particularly UNITS to 1.6, describe many of the general concerns and practical aspects of radioligand binding assays, and all are applicable to the binding assays described in this unit. Issues pertaining to tissue preparation and general troubleshooting are included. 1.3

Literature Cited Alves-Rodrigues, A., Leurs, R., Wu, T.S., Prell, G.D., Foged, C., and Timmerman, H. 1996. [3 H]Thioperamide as a radioligand for the histamine H3 receptor in rat cerebral cortex. Br. J. Pharmacol. 118:2045-2052. Arrang, J.M., Garbarg, M., and Schwartz, J.C. 1983. Autoinhibition of brain histamine release by a novel class (H3 ) of histamine receptor. Nature 302:832-837. Bakker, R.A., Timmerman, H., and Leurs, R. Histamine receptors: Specific ligands, receptor biochemistry, and signal transduction. 2002. Clin. Allergy Immunol. 17:27-64. Barger, G., and Dale, J.J. 1910. Chemical structure and sympathomimetic action of amines. J. Physiol. 41:19-59. Black, J.W., Duncan, W.A.M., Durant, G.J., Ganellin, C.R., and Parsons, M.E. 1972. Definition and antagonism of histamine H2 receptors. Nature 236:385-390. Brown, J.D., O’Shaughnessy, C.T., Kilpatrick, G.J., Scopes, D.I.C., Beswick, P., Clitherow, J.W., and Barnes, J.C. 1996. Characterization of the specific binding of the histamine H3 receptor antagonist radioligand [3 H]GR168320. Eur. J. Pharmacol. 311:305-310. Cheng, Y.C. and Prusoff, W.H. 1973. Relationship between the inhibition constant (KI ) and the concentration of inhibitor which causes 50 per cent inhibition (I50 ) of an enzymatic reaction. Biochem. Pharmacol. 22:3099-3108. Coge, F., Guenin, S.P., Audinot, V., Renouard-Try, A., Beauverger, P., Macia, C., Ouvry, C., Nagel, N., Rique, H., Boutin, J.A., and Galizzi, J.P. 2001. Genomic organization and characterization of splice variants of the human histamine H3 receptor. Biochem. J. 355:279-288. De Backer, M.D., Gommeren, W., Moereels, H., Nobels, G., Van Gompel, P., Leysen, J.E., and Luyten, W.H. 1993. Genomic cloning, heterologous expression and pharmacological characterization of a human histamine H1 receptor. Biochem. Biophys. Res. Commun. 197:16011608. de Esch, I.J., Thurmond, R.L., Jongejan, A., and Leurs, R. 2005. The histamine H4 receptor as a new therapeutic target for inflammation. Trends Pharmacol. Sci. 26:462-469.

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Drutel, G., Peitsaro, N., Karlstedt, K., Wieland, K., Smit, M.J., Timmerman, H., Panula, P., and Leurs, R. 2001. Identification of rat H3 receptor isoforms with different brain expression and signaling properties. Mol. Pharmacol. 59: 1-8. Du Buske, L.M. 1996. Clinical comparison of histamine H1 -receptor antagonist drugs. J. Allergy Clin. Immunol. 98:S307-S318. Esbenshade, T.A., Fox, G.B., and Cowart, M.D. 2006. Histamine H3 receptor antagonists: Preclinical promise for treating obesity and cognitive disorders. Mol. Interv. 6:77-88. Fourneau, E. and Bovet, D. 1933. Recherches sur l’action sympathicolytique d’un nouveau d´eriv´e du dioxane. Arch. Int. Pharmacodyn. 46:179191. Gantz, I., Munzert, G., Tashiro, T., Schaffer, M., Wang, L., DelValle, J., and Yamada, T. 1991. Molecular cloning of the human histamine H2 receptor. Biochem. Biophys. Res. Commun. 178:1386-1392. Hancock, A.A., Esbenshade, T.A., Krueger, K.M., and Yao, B.B. 2003. Genetic and pharmacological aspects of histamine H3 receptor heterogeneity. J. Life Sci. 73:3043-3072. Harper, E.A., Shankley, N.P., and Black, J.W. 1997. Characterisation of the binding of the histamine H3 -receptor antagonist, [3 H]clobenpropit, to sites in guinea-pig cerebral cortex membranes. Br. J. Pharmacol. 122:432P. Hill, S.J., Ganellin, C.R., Timmerman, H., Schwartz, J.C., Shankley, N.P., Young, J.M., Schunack, W., Levi, R., and Haas, H.L. 1997. International Union of Pharmacology. XIII. Classification of histamine receptors. Pharmacol. Rev. 49:253-278. Jansen, F.P., Rademaker, B., Bast, A., and Timmerman, H. 1992. The first radiolabeled histamine H3 receptor antagonist, [125 I]iodophenpropit: Saturable and reversible binding to rat cortex membranes. Eur. J. Pharmacol. 217:203-205. Leurs, R., Smit, M.J., and Timmerman, H. 1995. Molecular pharmacological aspects of histamine receptors. Pharmacol. Ther. 66:413-463. Leurs, R., Bakker, R.A., Timmerman, H., and de Esch, I.J. 2005. The histamine H3 receptor: from gene cloning to H3 receptor drugs. Nat. Rev. Drug Discov. 4:107-120. Ligneau, X., Garbarg, M., Vizuete, L., Diaz, J., Purand, K., Stark, H., Schunack, W., and Schwartz, J-C. 1994. [125 I]Iodoproxyfan, a new antagonist to label and visualize cerebral histamine H2 receptors. J. Pharmacol. Exp. Ther. 271:452-459. Liu, C., Ma, X., Jiang, X., Wilson, S.J., Hofstra, C.L., Blevitt, J., Pyati, J., Li, X., Chai, W., Carruthers, N., and Lovenberg, T.W. 2001a. Cloning and pharmacological characterization of a fourth histamine receptor (H4 ) expressed in bone marrow. Mol. Pharmacol. 59:420-426. Liu, C., Wilson, S.J., Kuei, C., and Lovenberg, T.W. 2001b. Comparison of human, mouse, rat,

and guinea pig histamine H4 receptors reveals substantial pharmacological species variation. J. Pharmacol. Exp. Ther. 299:121-130. Lovenberg, T.W., Roland, B.L., Wilson, S.J., Jiang, X., Pyati, J., Huvar, A., Jackson, M.R., and Erlander, M.G. 1999. Cloning and functional expression of the human histamine H3 receptor. Mol. Pharmacol. 55:1101-1107. Lovenberg, T.W., Pyati, J., Chang, H., Wilson, S.J., and Erlander, M.G. 2000. Cloning of rat H3 receptor reveals distinct species pharmacological profiles. J. Pharmacol. Exp. Therap. 293:771778. Oda, T., Morikawa, N., Saito, Y., Masuho, Y., and Matsumoto, S.-I. 2000. Molecular cloning and characterization of a novel type of histamine receptor preferentially expressed in leukocytes. J. Biol. Chem. 275:36781-36786. Thurmond, R.L., Desai, P.J., Dunford, P.J., FungLeung, W.-P., Hofstra, C.L., Jiang, W., Nguyen, S., Riley, J.P., Sun, S., Williams, K.N., Edwards, J.P., and Karlsson, L. 2004. A potent and selective histamine H4 receptor sntagonist with snti-Inflammatory properties. J. Pharmacol. Exp. Ther. 309:404-413. West, R.E. Jr., Zwieg, A., Shih, N.Y., Siegel, M.I., Egan, R.W., and Clark, M.A. 1990. Identification of H3 histamine receptor subtypes. Mol. Pharmacol. 38:610-613. West, R.E. Jr., Wu, R-L., Billah, M.M., Egan, R.W., and Anthes, J.C. 1999. The profiles of human and primate [3 H]Nα -methyl histamine binding differ from that of rodents. Eur. J. Pharmacol. 177:233-239. Witte, D.G., Yao, B.B., Miller, T.R., Carr, T.L., Cassar, S., Sharma, R., Faghih, R., Surber, B.W., Esbenshade, T.A., Hancock, A.A., and Krueger, K.M. 2006. Detection of multiple H3 receptor affinity states utilizing [3 H]A-349821, a novel, selective, non-imidazole histamine H3 receptor inverse agonist radioligand. Br. J. Pharmacol. 148:657-670. Yao, B.B., Witte, D.G., Miller, T.R., Carr, T.L., Kang, C.H., Cassar, S., Faghih, R., Bennani, Y.L., Surber, B.W., Hancock, A.A., and Esbenshade, T.A. 2006. Use of an inverse agonist radioligand [3 H]A-317920 reveals distinct pharmacological profiles of the rat histamine H3 receptor. Neuropharmacology. 50:468-478.

Key References De Backer, et al., 1993. See above. Cloning of the human H1 receptor gene and initial pharmacological characterization. Gantz et al., 1991. See above. Cloning of the human H2 receptor gene and initial pharmacological characterization. Gajtkowski, G.A., Norris, D.B., Rising, T.J., and Wood, T.P. 1983. Specific binding of [3 H]tiotidine to histamine H2 receptors in guinea-pig cerebral cortex. Nature 304:65-67. First successful radioligand described for H2 receptors after a number of false starts.

Receptor Binding

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Hancock et al., 2003. See above. Comprehensive review of histamine H3 receptor isoform heterogeneity. Lovenberg et al., 1999. See above. Cloning of the human H3 receptor gene and initial pharmacological characterization. Oda et al., 2000. See above. Cloning of the human H4 receptor gene and initial pharmacological characterization. West et al., 1999. See above. Characterization of homogenate radioligand binding in rat, human and non-human primate brain indicating pharmacological differences may exist.

Contributed by Marina Strakhova and Timothy A. Esbenshade Abbott Laboratories Abbott Park, Illinois

Characterization of Histaminergic Receptors

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[3H](+)MK801 Radioligand Binding Assay at the N-Methyl-D-Aspartate Receptor

UNIT 1.20

The N-methyl-D-aspartate subtype of glutamate receptor (Tables 1.20.1 and 1.20.2) is unusual in that it requires two endogenous agonists for activation. Thus, in addition to glutamate, the amino acid glycine (or possibly D-serine) is an essential coagonist. This unit presents a radioligand binding protocol that detects ligand activity at the NMDA receptor-associated glycine site. This is a convenient approach that exploits the ability of NMDA receptor modulators to alter the kinetics of ligands that bind to the channel-blocking site of the NMDA receptor. This protocol takes advantage of one of the most potent and specific ligands that bind to this receptor, [3H](+)MK801. Rather than binding directly to the glycine site, (+)MK801 binds to and blocks the channel of the NMDA receptor complex in a use-dependent manner, making it an effective antagonist of the NMDA receptor complex. (+)MK801 is a use-dependent inhibitor of the NMDA receptor, which means that it can only gain access to its binding site when the receptor is activated by glutamate and glycine. This characteristic of the action of (+)MK801 is reflected in the binding properties of [3H](+)MK801. Binding is enhanced by agonists at both the glutamate and glycine agonist sites, and decreased by antagonists at either site. Although there are ligands that bind directly to the glycine site of the NMDA receptor, these ligands have much lower affinity than [3H](+)MK801 and are therefore more difficult to use effectively. In addition, the present protocol can detect and differentiate agonists and antagonists that bind to the glycine site, which is not the case for ligands that bind directly to the glycine site. Provided here is an [3H](+)MK801 binding assay for measuring glycine site activity (see Basic Protocol), along with support protocols that provide information to aid in the design of (1) assays of agonists and antagonists of the glycine site and (2) data analysis. MEASUREMENT OF NMDA RECEPTOR-ASSOCIATED GLYCINE SITE ACTIVITY WITH [3H](+)MK801 BINDING

BASIC PROTOCOL

This protocol uses non-equilibrium [3H](+)MK801 binding to rat brain membranes to detect activity at the NMDA receptor-associated glycine site.

Table 1.20.1 Characteristics of the N-methyl-DAspartate Receptor

Receptor subunita

GenBank number (Human clone)

NMDA-R 1 NMDA-R 2A NMDA-R 2B NMDA-R 2C NMDA-R 2D

NM_000832 NM_000833 NM_000834 NM_000835 NM_000836

aNote that the NMDA receptor is typically comprised of at least one

NMDA-R 1 subunit and at least one of the NMDA-R 2 subunits. The glycine site of the NMDA receptor complex has not been explicitly localized to any one of the subunits.

Receptor Binding Contributed by Ian J. Reynolds Current Protocols in Pharmacology (2000) 1.20.1-1.20.8 Copyright © 2000 by John Wiley & Sons, Inc.

1.20.1 Supplement 11

Materials Rat brains (may be fresh or frozen) 20 mM HEPES, pH 7.4, containing 1 mM tetrasodium EDTA, room temperature and 4°C 20 mM HEPES, pH 7.4, room temperature and 4°C 22 to 25 Ci/mmol [3H](+)MK801 (NEN Life Sciences) 1 mM glutamic acid in deionized water (stable for >1 month at −20°C) Test compounds Water compatible scintillation cocktail (e.g., ScintiSafe 30%, Fisher Scientific) 50-ml polycarbonate centrifuge tubes Polytron homogenizer (Brinkmann) Glass fiber filter strips (e.g., Whatman GF-B) compatible with cell harvester Scintillation vials Additional reagents and equipment for total protein assays (APPENDIX 3A) NOTE: HEPES buffer is stable up to 1 month at room temperature, but visually inspect the solution periodically to ensure that there is no growth of bacteria or fungi which may be a source of contaminating glycine. This is not typically a problem with deionized or distilled water sources. Prepare membranes for binding assay 1. Remove and discard brainstem and cerebellum from rat brain. 2. Add 10 vol of 20 mM HEPES buffer, pH 7.4, containing 1 mM EDTA (see Critical Parameters) at 4°C to the remaining brain tissue in a 50-ml polycarbonate centrifuge tube and homogenize using the Brinkmann Polytron for 10 to 15 sec on setting∼5 until the homogenate is essentially uniform. 3. Centrifuge homogenate for 10 min at ∼40,000 × g, 4°C. 4. Decant supernatant, add 10 vol of 20 mM HEPES buffer, pH 7.4, containing 1 mM EDTA, rehomogenize and recentrifuge as in steps 2 and 3. Repeat this procedure two additional times. The homogenate can be frozen at −20°C at this point for up to 2 months. If the homogenate is frozen, thaw and centrifuge before proceeding to the next step.

5. Resuspend the pellet in 10 vol 20 mM HEPES buffer, pH 7.4, without EDTA. Incubate suspension for 30 min at 37°C. Centrifuge homogenate for 10 min at ∼40,000 × g,

Table 1.20.2

Agonists Glycine D-Serine ACCb

Affinities of Some Glycine Site Ligands

EC50 (µM)a Antagonists 0.2 0.2 0.14

Kynurenate 7-Chlorokynurenate 5,7-Dichlorokynurenate L 689,560 MDL 105,519

IC50 (µM) 154 7 3 0.13 0.028

aValues are based on inhibition of [3H](+)MK801 or [3H]TCP binding, and are

[3H](+)MK801 Radioligand Binding Assay at the NMDA Receptor

taken from Leeson and Iversen (1994). bAbbreviations: ACC, aminocyclopropane carboxylic acid; L 689,560, trans-2carboxy-5,7-dichloro-4-phenylaminocarbonyl amino-1,2,3,4-tetrahydroquinoline; MDL 105,519, (E)-3-(2-phenyl-2-carboxyethenyl)-4,6-dichloro-1H-indole-2carboxylic acid.

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4°C. Repeat centrifugation and resuspension procedure four additional times in HEPES buffer without EDTA. A number of divalent cations can interfere with the [3H](+)MK801 binding assay. These include Ca2+ and Mg2+, which are present in the initial homogenate. The addition of EDTA will remove these cations. As some experimental designs may involve directly studying the effects of these cations, it is helpful to wash the chelator out of the membrane preparation during the preparative process. The 30-min incubation at the elevated temperature is a step that was empirically determined to help reduce the contamination of the membrane preparation by endogenous glutamate and glycine.

6. Resuspend the final pellet in the original starting volume (i.e., 10 vol of 20 mM HEPES buffer without EDTA, pH 7.4) and determine the protein concentration of the suspension (APPENDIX 3A). This suspension of membranes should be ∼4 mg protein/ml, and can be frozen at −20°C until use, which should be within ∼2 months of freezing. Aliquots of 10 ml are convenient for a typical binding assay, although this volume can be adjusted for additional convenience when the assay conditions have been established.

Perform the [3H](+)MK801 binding assay 7. Dilute the membrane preparation to 1.3× the final desired protein concentration in 20 mM HEPES buffer, pH 7.4, at room temperature. Perform all subsequent steps at room temperature. If using a frozen sample, thaw the membrane preparation and rehomogenize as in step 2 with the Polytron to yield a homogenous suspension.

8. Dilute [3H](+)MK801 to 20× the desired final concentration in 20 mM HEPES buffer, pH 7.4. Typically, ligand concentrations between 0.1 and 0.5 nM are used. Raising ligand concentrations above this level will accelerate the binding reaction and will shorten the time to equilibrium. As this assay is performed under nonequilibrium conditions, higher ligand concentrations will necessitate shorter incubation times (discussed in more detail in Critical Parameters).

9. Dilute the test compounds to 10× the desired final concentrations in 20 mM HEPES buffer, pH 7.4. 10a. To determine total binding: to each tube add 100 µl of 1 mM glutamate stock, 50 µl of [3H](+)MK801, and 100 µl of 20 mM HEPES buffer, pH 7.4. 10b. For the test compounds: add 100 µl of the test compound at the appropriate concentration, 100 µl of 1 mM glutamate stock, and 50 µl of [3H](+)MK801. 10c. To determine nonspecific binding: add 100 µl of the compound used to define nonspecific binding, 100 µl of 1 mM glutamate stock, and 50 µl of [3H](+)MK801. 11. Add 750 µl of the membrane suspension to initiate the binding reaction. Incubate for 1 hr at room temperature. The final reaction volume is 1 ml and the final protein concentration should be ∼200 ìg/ml. It is important to note that this assay is intended to be used under nonequilibrium conditions (see Critical Parameters).

12. Wet the glass fiber filters with 20 mM HEPES buffer, pH 7.4. Filter the assay mixture and wash each filter three times with 5 ml of 20 mM HEPES buffer each. The filtration process may be varied for optimization purposes. [3H](+)MK801 dissociates very slowly from its receptor sites so that much more extensive filtration is possible if high levels of nonspecific binding are obtained. Receptor Binding

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[3H](+)MK801 binding (cpm)

A

B 3000

2000

1000

0 –7

–6

–5

–4

Log [agonist]

–3 –8

–7

–6

–5

–4

Log [antagonist]

Figure 1.20.1 Typical curves for agonist and antagonist compounds. (A) These curves show the anticipated profile of a glycine site agonist added alone (open symbols) or in the presence (filled symbols) of the glycine site antagonist, 5,7-dichlorokynurenate (5 µM). Data are modeled based on the expected results from the protocol provided here. Note that the addition of the antagonist reduces basal binding. This results from the inhibition of the binding of the endogenous glycine in the membrane preparation. Note also that the EC50 value of the test compound will shift to the right in the presence of 5,7-dichlorokynurenate, consistent with a competitive interaction of the test compound with the glycine site. The EC50 value of the test compound in this case is 2 µM. (B) These curves show the anticipated profile of a glycine site antagonist added in the nominal absence (open symbols) or presence (filled symbols) of glycine (10 µM). The addition of glycine increases the amount of binding and also shifts the IC50 value to the right, again consistent with a competitive interaction with the glycine site. The IC50 value of the antagonist in this example is 0.1 µM. Total binding is shown in these graphs, and the nonspecific binding is 500 cpm.

13. Place washed filters in scintillation vials. Add water-compatible scintillation cocktail to the filters and determine radioactivity by scintillation counting. 14. Analyze data (see Support Protocol). Figure 1.20.1A shows the results of an [3H](+)MK801 binding experiment and the effects of the glycine site antagonist 5,7-dichlorokynurenate. ALTERNATE PROTOCOL

[3H](+)MK801 Radioligand Binding Assay at the NMDA Receptor

ASSAYING GLYCINE SITE AGONISTS AND ANTAGONISTS The modulation of [3H](+)MK801 binding by glycine site ligands reflects an allosteric interaction (see UNIT 1.21). The level of nonequilibrium [3H](+)MK801 binding achieved in the Basic Protocol depends on the state of activation of the NMDA receptor. This, in turn, depends on the occupancy of the glycine site by agonists (the NMDA recognition site is saturated by a full agonist, glutamate, under the assay conditions described here). This makes it possible to assay both agonists and antagonists of the glycine site with minor modifications of the Basic Protocol, and also to look for competitive interactions between glycine site agonists and antagonists. To assay antagonists (so that the antagonist is the test compound in the Basic Protocol), it is useful to add a concentration of glycine or other agonist sufficient to produce substantial occupancy of the glycine site. Typically, the affinity of glycine is determined to be ∼0.2 µM, so 3 µM is sufficient to produce >90% occupancy of the site. Addition of antagonists will then decrease [3H](+)MK801 binding to nonspecific levels. Increasing the glycine concentration in 10-fold increments will then result in a parallel rightward shift in the inhibition curve of the antagonist, if the interaction with glycine is indeed

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competitive. Note that this approach will require a minor modification of the composition of the solutions described in the Basic Protocol. In step 10, a 10× stock solution of desired glycine concentration can be incorporated with the glutamate stock. Otherwise a 20× glutamate stock can be used (50 µl per tube) along with a 20× stock of the desired glycine concentration (also 50 µl per tube). Agonists can similarly be assayed. Starting with a nominally glycine-free condition, concentration-response curves can be constructed. Adding increasing concentrations of a competitive antagonist, such as 5,7-dichlorokynurenate, will result in a parallel rightward shift in the concentration-response relationship for the agonist. Using concentrations between 0.1 and 100 µM should provide a family of curves for the agonist that shifts up to 1000-fold with the highest concentration of 5,7-dichlorokynurenate. Note that contamination of the membrane preparation with residual glycine is almost inevitable, so that the addition of the competitive antagonist will decrease ligand binding observed in the presence of glutamate alone to nonspecific levels if sufficient antagonist is used. Figure 1.20.1B shows [3H](+)MK801 binding data obtained both in the presence and absence of 10 µM glycine. DATA ANALYSIS The binding data generated by the Basic Protocol and Alternate Protocol will show concentration-dependent increases or decreases in [3H](+)MK801 binding, depending on whether agonists or antagonists are the test compounds. Some data analysis programs that are dedicated to binding analysis do not handle increases in binding effectively. However, more general software intended for pharmacological analysis, such as Prism (GraphPad Software), can effectively analyze both agonist and antagonist data. For example, concentration-response curves using glycine can be analyzed using the following equation: Bound  3 H  ( + ) MK801 =

(

( max − min )

1 + 10

(log EC50 −[L]n H )

)

SUPPORT PROTOCOL

+ min

where max is the maximal binding observed, min is the minimum binding observed, EC50 is the half-maximal concentration of the agonist, [L] is the agonist concentration, and nH is the Hill coefficient for the agonist. Thus, bound ligand (either as counts per minute, or after conversion to moles bound per unit of protein) is plotted on the y-axis, while the concentration of the test compound is plotted on a log scale on the x-axis (see Figure 1.20.1). Typically none of these parameters would be constrained so that the curve fitting program can determine the best fit of all of the parameters to the data. It is not unusual for agonists to produce Hill coefficients >1 in these assays, presumably reflecting the binding of multiple molecules of agonist to each receptor. However, Hill coefficients that are different from 1 can also indicate that the interaction between the test ligand and the receptor is not competitive (see UNITS 1.3 and 1.2). Allowing the software to determine the best value for the Hill coefficient can thus provide information about the mechanism of the interaction of the test ligand with the receptor. Values for maximal and minimal binding and EC50 will depend on assay conditions and the test compound. The maximal binding is determined by the amount of tissue added and the amount of radioligand used. Thus, if the protein concentration was doubled, the maximum level of binding should also be doubled. The minimum binding should be the same as the nonspecific binding determined as described in the Basic Protocol. The amount of nonspecific binding also depends on the ligand and tissue concentration. The EC50 value is a property of the compound being assayed. Receptor Binding

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COMMENTARY Background Information

[3H](+)MK801 Radioligand Binding Assay at the NMDA Receptor

The activity of the NMDA receptor may be modulated by a number of endogenous ligands, including glutamate, glycine, magnesium, zinc, polyamines, and reactive oxygen species (McBain and Mayer, 1994). Using electrophysiological approaches, Johnson and Ascher (1987) first described the glycine modulation of the NMDA receptor, while Kleckner and Dingledine (1988) demonstrated an absolute dependence on glycine for any receptor activity at all. Subsequent studies demonstrated that glycine sensitivity could be demonstrated using radioligand binding approaches (Reynolds et al., 1987), and a combination of the two approaches has facilitated the development of a rich pharmacological appreciation of the NMDA receptor-associated glycine site (reviewed by Kemp and Leeson, 1993; Leeson and Iversen, 1994). Rather less clear is the role of dynamic changes in extracellular glycine in synaptic transmission, because ambient glycine concentrations may be sufficient to support NMDA receptor activity. It has also been suggested recently that D-serine may be the endogenous ligand for this site (Wolosker et al., 1999). The [3H](+)MK801 binding assay in this unit exploits the use-dependent inhibition of NMDA receptors by channel-blocking drugs. Ligands that bind to the channel site only gain access to their binding site when the channel is activated. This is manifested as an alteration in the binding kinetics that can be observed either as a change in the association or dissociation rates of ligand binding, with no net change in ligand affinity (Bonhaus and McNamara, 1988; Kloog et al., 1988; Starmer et al., 1987). Assaying ligands under nonequilibrium conditions then results in an increase in ligand binding when the experiment is performed in the presence of agents that activate the receptor, because the binding rate is faster. Conversely, less binding is observed within a fixed time period in the presence of an antagonist because binding rates are slower. It should be noted that this assay can potentially detect ligands binding to almost all of the sites that have been associated with NMDA receptor modulation. Establishing that an interaction occurs at the level of the glycine site may require additional characterization, such as examining test compounds in the presence of increased concentrations of glycine and determining whether a competitive interaction oc-

curs. Several ligands are available that bind directly to the glycine site. These include [3H]glycine (Monahan et al., 1989), [3H]MDL 105,519 (Baron et al., 1996), and [3H]L 689,560 (Grimwood et al., 1992). However, these ligands do not differentiate glycine site agonists and antagonists readily, and are considerably more difficult to use on a routine basis due to their lower affinity compared to [3H](+)MK801.

Critical Parameters and Troubleshooting As already noted, the [3H](+)MK801 binding assay used under non-equilibrium conditions is sensitive to a number of different parameters. The problem most likely to be encountered is the failure of glycine (or similar agonist) to stimulate [3H](+)MK801 binding. This can be caused by several different problems. 1. Amino acid contamination. It is very difficult to remove all amino acids from membrane preparations, so even the extensive wash procedure described still results in significant residual glycine concentrations (Reynolds and Palmer, 1991). An additional source of glycine is the water supply; any kind of bacterial, fungal, or algae growth in the water supply will release glycine, and this glycine may not be effectively removed by filtration-type water purification devices. Bear in mind that glycine concentrations around 1 µM are sufficient to produce an almost maximal glycine effect. The best way to identify this problem is to either measure glycine directly using HPLC, or add a well-characterized glycine site antagonist to the preparation and determine whether it inhibits [3H](+)MK801 binding and at what concentration. For example, in the absence of glycine, 5 µM 5,7-dichlorokynurenate should result in 97% occupancy of the glycine site (based on a Ki of 0.15 µM; Reynolds and Rothermund, 1995), which should reduce [3H](+)MK801 binding to essentially nonspecific levels. Much less inhibition than this would be consistent with glycine contamination. This problem could be the result of inadequate washing of the membrane preparation, in which case a new membrane preparation could be prepared. Alternatively, the water supply could be contaminated, an issue that could be resolved by using a different water source, or by measuring the glycine content with HPLC.

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2. Cation contamination. A number of divalent cations, including calcium and magnesium, as well as organic cations such as polyamines, stimulate [3H](+)MK801 binding, at least partially by increasing the affinity of glycine (Reynolds and Rothermund, 1995). The inclusion of a chelator in the membrane preparation procedure should prevent divalent cation contamination, provided that cations are not reintroduced inadvertently later in the procedure. 3. Incubation time. The key principle in this app ro ach is the measurement of [3H](+)MK801 binding under non-equilibrium conditions. Typically, under the conditions described here and in the presence of maximal concentrations of both glutamate and glycine site agonists, the assay will reach equilibrium in 2 to 4 hr. However, this time will be decreased if the ligand concentration is increased, or other positive modulators (such as divalent cations or polyamines) are added. If the assay reaches equilibrium in the presence of relatively low glycine concentrations, then the effects of adding more glycine will not be detected. Under these circumstances, decreasing the assay time is recommended. 4. Solution pH. The NMDA receptor and the [3H](+)MK801 binding assay are pH sensitive. The mid-point of sensitivity is pH 7.4, so that small variations around the norm will substantially alter binding (Rajdev and Reynolds, 1993). Low pH will inhibit binding, while elevated pH will increase binding. Some of the glycine site antagonists, such as the kynurenic acid derivatives, are most soluble in basic solutions and it is important to ensure that the test compounds do not alter the pH of the assay buffer.

Anticipated Results Using the procedures described above, an assay that contains 0.2 mg of forebrain-derived protein, 0.5 nM [3H](+)MK801, a saturating concentration of glutamate and nominally glycine-free, should yield basal total counts of 1000 to 1500 cpm. Nonspecific binding should be about 500 cpm. Saturating the glycine site would increase total cpm to about 2500 to 3000. Thus, in general it is anticipated that going from a nominally glycine-free condition to a glycine-saturated condition in the presence of glutamate should produce a ∼2-fold increase in specific [3H](+)MK801 binding. Note that the difference between basal total cpm and glycinestimulated cpm will depend on the extent of glycine contamination under the nominally

glycine-free condition. The expected affinities of some glycine site ligands can be found in Table 1.20.2. As a guide, the agonists glycine and D-serine should half-maximally stimulate [3H](+)MK801 binding with an EC50 value of ∼0.2 µM. The potent and selective antagonist, 5,7-dichlorokynurenate is reported to have an IC50 value of 0.15 µM (Reynolds and Rothermund, 1995), although the affinity observed is very dependent on the extent of glycine contamination.

Time Considerations The membrane preparation procedure takes several hours due to the large number of centrifugation steps. The assay is straightforward, and the time required will be dictated by the number of samples to be assayed. Generating several hundred data points per day is readily accomplished using this assay.

Literature Cited Baron, B.M., Siegel, B.W., Harrison, B.L., Gross, R.S., Hawes, C., and Towers, P. 1996. [3H]MDL 105,519, a high-affinity radioligand for the Nmethyl-D-aspartate receptor-associated glycine recognition site. J. Pharmacol. Exp. Ther. 279:62-68. Bonhaus, D.W. and McNamara, J.O. 1988. Nmethyl-D-aspartate receptor regulation of uncompetitive antagonist binding in rat brain membranes: Kinetic analysis. Mol. Pharmacol. 34:250-255. Grimwood, S., Moseley, A.M., Carling, R.W., Leeson, P.D., and Foster, A.C. 1992. Characterization of the binding of [3H]L-689,560, an antagonist for the glycine site on the N-methylD-aspartate receptor, to rat brain membranes. Mol. Pharmacol. 41:923-930. Johnson, J.W. and Ascher, P. 1987. Glycine potentiates the NMDA response in cultured mouse brain neurons. Nature 325:529-531. Kemp, J.A. and Leeson, P.D. 1993. The glycine site of the NMDA receptor–Five years on. Trends Pharmacol. Sci. 14:20-25. Kleckner, N.W. and Dingledine, R. 1988. Requirement for glycine in activation of NMDA receptors expressed in Xenopus oocytes. Science 241:835-837. Kloog, Y., Nadler, V., and Sokolovsky, M. 1988. Mode of binding of [3H]dibenzocycloalkenimine (MK-801) to the N-methyl-Daspartate (NMDA) receptor and its therapeutic implication. FEBS Lett. 230:167-170. Leeson, P.D. and Iversen, L.L. 1994. The glycine site on the NMDA receptor: Structure-activity relationships and therapeutic potential. J. Med. Chem. 37:4053-4067. McBain, C.J. and Mayer, M.L. 1994. N-methyl-Daspartic acid receptor structure and function. Physiol. Rev. 74:723-760.

Receptor Binding

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Monahan, J.B., Corpus, V.M., Hood, W.F., Thomas, J.W., and Compton, R.P. 1989. Characterization of a [3H] glycine recognition site as a modulatory site of the N-methyl-D-aspartate receptor complex. J. Neurochem. 53:370-375. Rajdev, S. and Reynolds, I.J. 1993. Effects of pH on the actions of dizocilpine at the N-methyl-Daspartate receptor complex. Br. J. Pharmacol. 109:107-112. Reynolds, I.J., Murphy, S.N., and Miller, R.J. 1987. 3 H-labelled MK-801 binding to the excitatory amino acid receptor complex from rat brain is enhanced by glycine. Proc. Natl. Acad. Sci. U.S.A. 84:7744-7748. Reynolds, I.J. and Palmer, A.M. 1991. Regional variations in [3H]MK801 binding to rat brain NMDA receptors. J. Neurochem. 56:1731-1740. Reynolds, I.J. and Rothermund, K.D. 1995. Characterization of the effects of polyamines on the modulation of the N-methyl-D-aspartate receptor by glycine. Neuropharmacol. 34:1147-1157.

Starmer, C.F., Packer, D.L., and Grant, A.O. 1987. Ligand binding to transiently accessible sites: Mechanisms for varying apparent binding rates. J. Theoret. Biol. 124:335-341. Wolosker, H., Blackshaw, S., and Snyder, S.H. 1999. Serine racemase: A glial enzyme synthesizing D-serine to regulate glutamate-N-methyl-Daspartate neurotransmission. Proc. Natl. Acad. Sci. U.S.A. 96:13409-13414.

Key References Johnson, J.W. and Ascher, P. 1987, See above. First demonstration of glycine modulation of the NMDA receptor.

Contributed by Ian J. Reynolds University of Pittsburgh Pittsburgh, Pennsylvania

[3H](+)MK801 Radioligand Binding Assay at the NMDA Receptor

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Overview of Receptor Allosterism

UNIT 1.21

HISTORICAL PERSPECTIVE Most of the theoretical framework that has come to be associated with the study of ligand-receptor interactions was developed in the first half of the 20th century, when very little was known about the actual identity of receptors themselves. By borrowing heavily from studies in the field of enzyme kinetics, pharmacologists and physiologists adopted the law of mass action as a mechanistic descriptor of the interaction between a ligand and its receptor. Often, the simplest form of the mass action model, namely a reversible, saturable, one-to-one interaction between ligand and receptor, was deemed compatible with the experimental observations. Even today, where much has been accomplished in terms of identifying the proteinaceous nature and physicochemical properties of the major receptor families, the starting point for the qualitative or quantitative analysis of drug-receptor data remains the concept of the ligand interacting at a “primary” binding site recognized by agonists and competitive antagonists. However, studies of the behavior of many ligand-gated ion channels (LGICs) have long provided quite direct evidence that more than one molecule of ligand was able to bind to each receptor complex, a phenomenon termed “cooperativity.” The conclusion that some receptors possessed more than one binding site for ligands thus invoked another phenomenon that was originally described in the field of enzymology, that is, the concept of allosteric binding sites. The term “allosteric” (from the Greek meaning “other site”), was first used by Monod and Jacob (1961) and subsequently defined by Monod et al. (1963) in a paper describing the ability of enzymes to have their biological activity modified, either in a positive or negative fashion, by the binding of ligands to sites that were topographically distinct from the substrate-binding site. Monod et al. (1963) defined these accessory binding sites as allosteric sites, in contrast to the substrate-binding (active) site, which was defined as the isosteric site. Thus, allosteric interactions arise because the binding of a ligand to the allosteric site induces a conformational change in the protein that modulates the binding of the substrate to the isosteric site, and vice versa. The biological activity of the enzyme was assumed to arise from the subsequent (modified) properties of the substrate-binding site, and not through a direct effect of the allosteric modulator itself. Monod et al. (1963) referred to this conformational change in the enzyme as an allosteric transition, although that term has since come to encompass a slightly different concept (see below). With regard to receptor proteins, the primary binding site recognized by the endogenous agonist or hormone is conceptually equivalent to an enzyme’s isosteric site, and has been referred to as the orthosteric site (Proska and Tucek, 1994). Any binding site on the receptor protein that is able to modulate the binding properties of the orthosteric site by mediating a conformational change in the receptor may be classed as an allosteric site. Hence, many of the cooperative interactions that had been reported for ion channel-linked receptors in the literature in the past, such as the binding of two acetylcholine molecules to a single nicotinic acetylcholine receptor (Galzi et al., 1991), or the binding of two GABA molecules to a GABAA receptor (Sigel and Buhr, 1997), are also allosteric interactions because the binding of one equivalent of ligand actually alters the affinity of the subsequent binding of the next equivalent(s) of ligand. Ion channels and ion channel-linked receptors are known to exist as oligomers; that is, they are composed of multiple protein subunits, and with an increased complexity in macromolecular structure comes an increased probability of multiple ligand binding sites. Allosteric interactions at ion channel-linked receptors, therefore, have been well documented and studied for almost half a century now. In contrast, G protein–coupled receptors Receptor Binding Contributed by Arthur Christopoulos Current Protocols in Pharmacology (2000) 1.21.1-1.21.45 Copyright © 2000 by John Wiley & Sons, Inc.

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(GPCRs) have been traditionally identified as monomers, and, with one notable exception to be described below (see The Allosteric Ternary Complex Model, below), it is not surprising that relatively fewer allosteric interactions occurring at GPCRs have been identified relative to ion channel-linked receptors. Nevertheless, it is now apparent that orthosteric ligand binding at GPCRs can indeed be subject to allosteric modulation by other ligands or other proteins. THE MANY SHADES OF RECEPTOR ALLOSTERISM Before discussing allosteric mechanisms in greater detail, it is necessary to address some of the issues that have arisen in the past regarding the terminology applied to allosteric proteins. The term “allosteric” has itself been used by various authors in different ways and this has led to some confusion in the literature as to what it really means (e.g., see Colquhoun, 1998). Nowadays, it appears that a distinction is at least necessary between the terms “allosteric interaction” and “allosteric transition.” For the purposes of this overview, an allosteric interaction is defined as an interaction that occurs between two (or more) topographically distinct binding sites on the same receptor complex. The essential features of a simple allosteric interaction are as follows: a. The binding sites are not overlapping, that is, there is no mutual exclusivity in binding. b. The binding of one ligand to its site affects the binding of the second ligand at the other site and vice versa. Allosteric interactions are thus reciprocal in nature. c. The effect of an allosteric modulator can be either negative or positive with respect to the binding and/or function of an orthosteric ligand. Although Monod et al. (1963) initially defined the conformational change in protein structure associated with an allosteric interaction as an allosteric transition, they subsequently presented a formalized model of allosteric proteins that shifted the emphasis away from an interaction between sites to an interaction between conformational states (Monod et al., 1965), thus giving rise to a conceptually different picture. Allosteric proteins were then described as follows:

Overview of Receptor Allosterism

a. They are oligomeric in nature (i.e., composed of more than one subunit). b. Each subunit possesses one (equivalent) binding site for ligand, thus giving rise to cooperative interactions. c. They can exist as an equilibrium mixture of two or more states in the absence of ligand, with the transition between states now being defined as the “allosteric transition.” d. The transition between conformational states involves a conservation of molecular symmetry such that all subunits “flip” from one state to another in a concerted fashion. e. Ligands that prefer binding to one state over another will “select” the preferred state, and thus increase the proportion of proteins in that state. As a consequence, observed ligand affinity will alter depending on the type and amount of conformational state that predominates. It can be seen that this last definition of allosteric proteins is quite explicit. Its description of interactions between multiple subunits makes it immediately applicable to oligomeric proteins that display cooperative binding, e.g., ion channel–linked receptors, and it is no surprise that the model based on this definition was first applied to these types of receptors (Karlin, 1967; Colquhoun, 1973; Thron, 1973). However, this particular model of allosteric transitions also predicts another important property, namely, the ability of the receptor to display activity in the absence of ligand, purely as a consequence of its ability

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Table 1.21.1 A Glossary of Terms Commonly Used in the Description of Allosteric Effects

Term

Description

Allosteric site

An additional binding site on a receptor that is distinct from the primary (agonist) binding site. The agonist binding site on a receptor. An interaction between two topographically distinct binding sites on the same receptor complex. At equilibrium, these interactions are reciprocal in nature. The isomerization of a receptor protein between multiple conformational states. This term is commonly used to describe the binding of two or more molecules to the same receptor. However, its strict definition is the binding of more than one molecule of the same ligand to a receptor complex. Allosteric interactions between identical binding sites. Allosteric interactions between different binding sites.

Orthosteric site Allosteric interaction

Allosteric transition Cooperative

Homotropic Heterotropic

to isomerize between two or more conformational states (Thron, 1973). This model is now more commonly referred to as the “two-state” or “multi-state” model and has been generalized to describe certain properties of any receptor, including monomeric GPCRs that can exist in more than one conformation (e.g., Colquhoun, 1998; Kenakin, 1997; Lefkowitz et al., 1993). Obviously, the concept of receptor allosterism within the context of multiple conformational equilibria is somewhat removed from the concept of an interaction occurring between distinct binding sites on the one protein. For instance, multi-state models allow allosterism to arise simply as a consequence of the transition between one orthosteric conformation to another, without necessarily postulating the existence a second binding site in each conformational state. In contrast, the simple model of allosteric interaction between two sites does not explicitly consider the existence of multiple conformations of the protein on which the sites are situated. These two ideas are not mutually exclusive (see An Extended Model of Allosteric Interactions, below), but they do address different aspects of a protein’s ability to undergo conformational changes. The remainder of this overview will focus predominantly on allosteric effects that arise due to cross-interactions between distinct binding sites on a single GPCR protein or between a GPCR and other proteins. Receptor models based on receptor isomerization between conformational states are discussed elsewhere in this volume (UNIT 1.2). Table 1.21.1 contains a glossary of terms that are commonly used in the description of allosteric effects and allosteric proteins. THE ALLOSTERIC TERNARY COMPLEX MODEL The simplest model of an allosteric interaction at GPCRs involves the concomitant binding of two ligands, A and B, to the one receptor, R, to form a ternary complex, ARB (Equation 1.21.1).

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Ka B+R+A

B + AR

αKb

Kb

BR + A

ARB αKa Equation 1.21.1

Ligand A binds to the orthosteric site, while ligand B, the allosteric modulator, binds to the allosteric site. The constants Ka and Kb denote the equilibrium association constants for the binding of A and B, respectively, to their binding sites on the unoccupied receptor. In this regard, each of these bimolecular reactions is no different from the standard mass-action schemes applied to orthosteric binding. However, allosteric interactions are not only characterized by unconditional ligand affinity constants, but also by an additional parameter, the “cooperativity factor” denoted by the symbol, α (Weber, 1975; Ehlert, 1985, 1988). This latter parameter is a useful scaling factor that serves as a measure of the magnitude of the allosteric effect of the interaction between an orthosteric and an allosteric ligand at equilibrium. The reciprocity of allosteric interactions is thus embodied in the cooperativity factor, as it is simply the ratio of affinities of either ligand for the occupied receptor relative to the free receptor (Weber, 1972, 1975; Wyman, 1975). Values of α > 1 denote positive cooperativity, whereas α < 1 denotes negative cooperativity. Values of α approaching zero would be indistinguishable from competitive antagonism. In contrast, an α value equal to 1 denotes an allosteric interaction that results in unaltered

100

% Specific binding

0 GTP 0.1 µM 0.3 µM 1 µM 10 µM 100 µM

50

0 –9

–8

–7

–6

–5

–4

–3

Log [carbachol]

Overview of Receptor Allosterism

Figure 1.21.1 Allosterism between a receptor and its G protein: the effects of GTP on the competition between [3H]N-methylscopolamine and the agonist, carbachol, at the muscarinic acetylcholine M2 receptor in rat myocardial membranes. Competition is measured in the absence or presence of GTP at concentrations ranging from 0.1 µM to 100 µM. Data taken from Ehlert and Rathbun (1990).

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ligand affinity at equilibrium. Allosteric interactions can still be discerned under nonequilibrium conditions, and this is discussed later (see Assays of Radioligand Binding). It should be noted that an allosteric modulator does not necessarily have to be a drug or hormone. It can just as easily be another protein. In fact, the best-known allosteric modulators of ligand binding at GPCRs are G proteins themselves. In general, the interaction between agonist binding and G protein coupling is positively cooperative in nature (Ehlert, 1985). This is logical, given the mechanisms that are thought to underlie signaling via GPCRs. Agonist binding to the orthosteric site results in an alteration of receptor conformation that displays a higher affinity towards the G protein, thus favoring coupling. However, the binding of GTP to its site on the G protein results in a change of G protein structure that is transmitted to the receptor’s conformation as a negatively cooperative effect on agonist binding, thus promoting the uncoupling of the activated G protein from the receptor and allowing signaling to proceed. These negatively cooperative effects of GTP on agonist binding underlie the so-called “GTP shift” that has often been used as a biochemical measure of agonist efficacy (Kenakin, 1997; Christopoulos and El-Fakahany, 1999). Figure 1.21.1 shows an example of the GTP shift for the binding of the agonist, carbachol, to the M2 muscarinic acetylcholine receptor. The curves reflect the extent of the allosteric interaction that is possible between agonist binding and G protein coupling for this receptor. In addition to the well characterized allosteric effects between agonists and G proteins occurring at GPCRs, a growing number of studies are identifying additional allosteric sites located on specific GPCRs. The best-studied examples involve the muscarinic acetylcholine receptors, with allosteric interactions having been conclusively demonstrated at all five subtypes of these receptors (see Birdsall et al., 1996; Lee and El-Fakahany, 1991; Tucek and Proska, 1995; Ellis, 1997; Christopoulos et al., 1998; Holzgrabe and Mohr, 1998). However, allosteric interactions between various ligands have also been demonstrated at other GPCRs including α1 (Waugh et al., 1999; Leppik et al., 2000) and α2 adrenoceptors (Nunnari et al., 1987; Horstman et al., 1990; Leppik et al., 1998), adenosine A1 (Bruns and Fergus, 1990; Bhattacharya and Linden, 1995) and A2A receptors (Gao and Ijzerman, 2000), dopamine D1 and D2 receptors (Hoare and Strange, 1996; Schetz and Sibley, 1997), and serotonin 5-HT7 receptors (Thomas et al., 1997; Hedlund et al., 1999). Although this may appear as a rather diverse list of receptors, allosteric interactions share a number of common features that can allow them to be detected and possibly exploited in a therapeutic sense. In order to gain insight into these features, it is useful to examine the behavior of the allosteric ternary complex model in greater detail. Effects of Allosteric Modulators on Orthosteric Ligand Binding From the model described in Equation 1.21.1, the following equilibrium equations are derived:

Ka =

[AR] [A][R]

Kb =

[BR] [B][R]

αKa =

[ARB] [BR][A]

αK b =

[ARB] [AR][B]

Equation 1.21.2

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A

1.0 α = 0.1 0.8

ρA

0.6 0.4 0.2 0.0 –8

B

–7

–6

–5

–4

–8

–7

–6

–5

1.0 α = 10 0.8

ρA

0.6 0.4 0.2 0.0

C

–9 1.0

competitive

0.8

ρA

0.6 0.4 0.2 0.0 –8

–7

–6

–5

–4

–3

Log [A]

Figure 1.21.2 Effect of: (A) negative allosteric modulator, (B) positive allosteric modulator, or (C) competitive antagonist on orthosteric ligand-receptor occupancy (ρA). For all the simulations, pKA = 6 and pKB = 9. The modulator modifies orthosteric ligand affinity to a limit determined by the cooperativity factor (α) that characterizes the interaction between allosteric and orthosteric sites. In these examples, ligand affinity is either enhanced (A) or diminished (B) by a factor of 10. In contrast, simple competitive interactions (C) are characterized by mutually exclusive binding of the two ligands for the same site and thus allow for a theoretically limitless dextral shift of orthosteric ligand occupancy. Ligand concentration = [A]. Overview of Receptor Allosterism

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And the total receptor ([R]T) conservation equation is: [R]T = [R] + [AR] + [BR] + [ARB] Equation 1.21.3

Fractional receptor occupancy by the orthosteric ligand (ρA) i s equal to ([AR]+[ARB]/[R]T) and is expressed as: [AR] + [ARB] = ρA = [R]T

[A] (1 + [B]K b ) [A] + K a (1 + α[B]K b )

Equation 1.21.4

This equation can be recast in terms of ligand equilibrium dissociation constants by defining KA = 1/Ka and KB = 1/Kb, thus leading to the following expression: ρA =

[A]  [B]  K A 1 +  KB   [A] +  α[B]  1 +  KB   Equation 1.21.5

In the absence of an allosteric modulator, receptor occupancy of the orthosteric site is determined by the orthosteric ligand’s equilibrium dissociation constant, KA. However, when an allosteric ligand is present, the occupancy of the orthosteric site will now be determined by the following composite parameter, KApp:

K App

 [B]  K A 1 +  KB   =  α[B]  1 +  KB  

Equation 1.21.6

If the interaction between A and B is positively cooperative (α > 1), then KApp < KA and the binding curve of ligand A at the modulator-occupied receptor will be shifted to the left relative to the binding curve of A at the free receptor. In contrast, negative cooperativity between A and B (α < 1) will be manifested as a rightward displacement of the binding curve for A (i.e., KApp > KA). Figure 1.21.2 illustrates these relationships for the binding of an orthosteric ligand in the presence of increasing concentrations of an allosteric modulator with an α value of either 0.1 (negative cooperativity) or 10 (positive cooperativity). This figure also illustrates an important aspect of allosteric interactions, namely, that these types of interactions approach a limit, the extent of which is governed by the magnitude of α. The closer the value of α is to 1, the more easily the limit is approached with increasing concentrations of B. Receptor Binding

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Another way of visualizing the effects of allosteric modulators on orthosteric ligand binding is to monitor the effects of increasing concentrations of the modulator while maintaining a fixed concentration of the ligand. An examination of the ternary complex model can reveal some interesting properties of allosteric interactions that are observed under these conditions. Figure 1.21.3 illustrates the interaction between a radiolabeled orthosteric tracer, A*, in the presence of increasing concentrations of unlabeled modulator, B. Panel A of the figure shows the saturation binding properties of A* in the presence of modulator. The parameters for these simulations were set identical to those used in Figure 1.21.2. Also shown in the panel are three arbitrarily chosen levels of occupancy by A*. These levels are denoted as L, I, and H for the low, intermediate, and high degrees of occupancy, respectively, that they represent. It is quite informative to monitor the effects of the modulator on the apparent occupancy by A* under each of these three conditions. Panel B of Figure 1.21.3 depicts the effects of a negatively cooperative interaction between A* and B (α = 0.1). At the lowest level of radioligand occupancy, denoted by level L in the figure ([A*] < KA), the allosteric modulator is almost able to completely inhibit the binding of the orthosteric ligand. The naive observer may conclude that the interaction is competitive in nature. Increasing the initial occupancy of orthosteric ligand such that [A*] = KA (level I in the figure) readily reveals that the modulator cannot fully inhibit the binding of the orthosteric tracer, no matter how high the concentration of modulator used. This illustrates the limiting nature of allosteric interactions, and is even more pronounced when the effect on very high initial levels of orthosteric ligand occupancy (level H; [A*] > KA) is monitored. The smaller graph in panel B shows the same curves, but normalized as a percentage of the initial level of binding in each instance. It can be seen, therefore, that negative allosteric interactions are best revealed at high orthosteric ligand occupancies. Panel C in Figure 1.21.3 illustrates the effect of positive allosteric modulation under the same conditions. In contrast to negatively cooperative effects, the limiting nature of allosteric interactions dictates that positively cooperative equilibrium effects (α = 10 in this example) are best revealed at low levels of receptor occupancy by the orthosteric ligand. This is most pronounced when the occupancy curves for A* are normalized (compare binding at level L to level H). Effects of Allosteric Modulators on Orthosteric Ligand Function The ability of orthosteric ligands, once bound, to modify the signaling properties of receptors has been defined as a measure of orthosteric ligand efficacy (see UNIT 1.2). The very nature of efficacy is intertwined with the ability of the orthosteric ligand to promote a conformation of the receptor that either promotes signaling (as is seen with positive agonists) or attenuates constitutive receptor signaling (as is observed with inverse agonists). Because the binding of an allosteric modulator to a distinct accessory site on the receptor causes its own alteration of receptor conformation, it is conceivable that the resulting conformation may influence orthosteric ligand efficacy, in addition to the effects on orthosteric ligand affinity described in the preceding section. In order to consider the possible repertoire of allosteric effects on the observed cellular or tissue responses to agonists, it is necessary to define two general equations, one describing the effect of an allosteric modulator on the stimulus imparted by an agonist to the cell, and the other describing the effect of stimulus-response coupling on the final observed response.

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A

L

1.0

I

H

0.8

ρA*

0.6 α = 0.1

α = 10 0.4 0.2 0.0 –9

B

1.0

–8

–7

–6 –5 Log[A*]

–4

–3

H

0.8

ρA*

0.6

I

0.4 0.2

L

0.0 –11 –10

–9

–8 Log[B]

–7

–6

%B/B0

100 H

50 0 –11–10 – 9 – 8 –7 – 6 Log[B]

C

1.0

Figure 1.21.3 Effects of increasing concentrations of an allosteric modulator on the binding of a fixed concentration of radiolabeled orthosteric ligand ([A*]). For all the simulations, pKA = 6 and pKB = 9. (A) The saturation binding profile of ligand [A*] in the absence or presence of increasing concentrations of positive or negative modulator. The solid circles on the control saturation binding isotherm of [A*] indicate three arbitrarily chosen levels of occupancy—denoted L (low), I (intermediate), and H (high)— by the radioligand. The lower two panels illustrate the effect of a negative (panel B) or positive (panel C) allosteric modulator on the binding of [A*] at each of these three initial occupancy levels. The smaller graphs in each panel show the same data normalized as a percentage of the control level of binding.

I L

H

ρA*

0.8 0.6

I

0.4 0.2

L

0.0 –11

–10

–9 –8 Log[B]

%B/B0

600

–7

–6

L

300 I H

0 –11–10 –9 –8 –7 –6 Log[B]

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In accordance with the conventions of classic receptor theory (nm, 1966; Stephenson, 1956; UNIT 1.2), the stimulus (S) is defined as being proportional to the concentration of agonist-occupied receptors. In the presence of an allosteric modulator, the stimulus may be expressed as: S = e[AR] + eµ[ARB] Equation 1.21.7

In this equation, the parameter e denotes the efficacy of the orthosteric ligand as defined by Stephenson (1956) and includes receptor density, while the parameter µ is a modifying factor that describes the ability of an allosteric modulator to alter the signaling capacity of the [ARB] ternary complex. Values of µ < 1 thus denote an attenuation in signaling, a situation that may be classed as a form of noncompetitive antagonism of functional responses, values of µ = 1 denote no change in the signaling capacity of the receptor in the presence of modulator, and values of µ > 1 denote an increased capacity of the receptor to signal in the presence of modulator. Substituting the occupancy relationship described in Equation 1.21.4 into Equation 1.21.7 results in the following general form of the allosteric stimulus equation:  µα[B]  e[A] 1 +  KB   S=  α[B]   [B]  [A] 1 +  + K A 1 +  KB    KB  Equation 1.21.8

In order to derive the relationship between agonist concentration and final tissue response, an allowance must be made for the stimulus-response function of the tissue. Since most relationships between occupancy and response are nonlinear in nature, Equation 1.21.8 must be processed through a nonlinear function to derive an equation describing tissue response, and the simplest nonlinear function is the rectangular hyperbola. Thus, the tissue response (E) to an agonist as a fraction of the maximal response (Emax) may be defined as:

E S = Emax S + K E Equation 1.21.9

where KE is a parameter denoting the efficiency of tissue stimulus-response coupling. Substituting Equation 1.21.8 into Equation 1.21.9 gives the following equation: e[A] (µα[B] + K B ) E = Emax K E [A] (α[B] + K B ) + K A ([B] + K B )([B] + K B ) + e[A] (µα[B] + K B ) Overview of Receptor Allosterism

Equation 1.21.10

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This equation thus describes the hyperbolic concentration-response relationship for an agonist in the presence of an allosteric modulator. At saturating agonist concentrations, the maximal tissue response approaches the following limit: e (αµ[B] + K B ) E = Emax e (αµ[B] + K B ) + K E (α[B] + K B ) Equation 1.21.11

In the absence of any effect of the modulator on receptor function (µ = 1), Equation 1.21.11 simplifies to: E

=

Emax

e e + KE

Equation 1.21.12

which is the standard equation describing the maximal fractional response of a hyperbolic stimulus-response system (Christopoulos and El-Fakahany, 1999). In contrast, if the ternary complex is completely unable to signal (µ = 0), then the maximal fractional response of the tissue is given by the following equation: E eK B = Emax eK B + K E (α[B] + K B ) Equation 1.21.13

In this situation, the maximal tissue response will be depressed as [B] is increased. The EC50 value, that is, the concentration of agonist yielding 50% of the maximum observed response, may also change depending on the effect of the allosteric modulator on both binding and function. From Equation 1.21.10, the EC50 value can be derived as: EC50 =

K E K A ([B] + K B )

e (µα[B] + K B ) + K E (α[B] + K B ) Equation 1.21.14

µ=0

α

1

α=1

KA

KA

µ=1

K E ([B] + K B) eKB + K E (α[B] + K B)

K E ([B] + K B) e K

+ K E ([B] + B K

)

B

KA

KA

K E ([B] + K B) e+ K

E

(α[B] + KB)

KE e + KE

Figure 1.21.4 Equations describing the EC50 for a hyperbolic concentration-response curve according to the simple allosteric ternary complex model (Equations 1.21.10 and 1.21.14) for specific values of the cooperativity factor (α) and the signal modifying factor (µ).

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Figure 1.21.4 summarizes the equations for the EC50 value under the special conditions of µ being equal to 1 and/or zero and α being equal or not equal to 1. Some of the predictions of the ternary complex model for agonist concentration-response curves can give rise to an interesting array of curve profiles. In the simplest case, that is where the modulator only affects occupancy and not response (µ = 1), the resulting agonist concentration-response curves are simply shifted to the left (for a positive allosteric modulator) or to the right (for a negative allosteric modulator) with no change in curve shape, in a manner that is exactly analogous to the effects on agonist occupancy alone (Figure 1.21.5). A different picture emerges, however, if the modulator does indeed modify the capacity of the ternary complex to signal.

A

1.0 α = 10 0.8

E/Emax

0.6

0.4

0.2

0.0 –11

B

–10

–9

–8

–7

–6

–9

–8

–7

–6

–5

1.0 α = 0.1

E/Emax

0.8

0.6

0.4

0.2

0.0 –10

Log[A]

Overview of Receptor Allosterism

Figure 1.21.5 Effect of a positive (A) or negative (B) allosteric modulator that does not alter receptor signaling capacity on the hyperbolic concentration-response curve of an orthosteric ligand, A. Curves were simulated according to Equation 1.21.10 with pKA = 6, pKB = 9, e = 10, µ = 1 and KE = 0.1. Cooperativity factors (α) are indicated in each panel. Under these conditions, the effect of allosteric modulation on orthosteric ligand responses is identical to the effect on orthosteric ligand occupancy.

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α=1

A

B

α = 0.1

C

α = 10

10

Stimulus

8 6 4 2 0

D

1.0

E

F

E/Emax

0.8 0.6 0.4 0.2 0.0 –10 –9 –8 –7 –6 –5 –4 –3 –10 –9 –8 –7 –6 –5 –4 – 3 –10 –9 –8 –7 –6 –5 –4 –3 Log[A] Log[A] Log[A]

Figure 1.21.6 Effect of an allosteric modulator that completely suppresses receptor signaling capacity (µ = 0) on orthosteric ligand stimulus (A-C) or response (D-F). Concentration-stimulus curves were simulated using Equation 1.21.8 and concentration-response curves were simulated using Equation 1.21.10. The following parameters were used: pKA = 6, pKB = 9, e = 10, µ = 0, KE = 0.1 and the cooperativity factors (α) indicated in the figure. The simulations show the effects of neutral (A, D), negative (B, E), and positive (C, F) allosteric modulation when the [ARB] complex is unable to signal. Note that the left shift of the concentration-stimulus function for the positive modulator (α = 10) is offset by the reduction in signaling efficiency, leading to an overall right shift of the corresponding concentration-response curves.

Let us first consider the situation where µ = 0. Under these circumstances, the agonist found in the ternary complex ARB cannot impart a stimulus to the cell. In essence, the allosteric modulator is acting as a noncompetitive antagonist of functional responses, and this is irrespective of its effects on orthosteric ligand affinity. Figure 1.21.6 illustrates this phenomenon both from the point of view of the stimulus (panels A to C) or the observed response (panels D to F). In all cases, the effect of the modulator is to cause a progressive reduction in the stimulus and an accompanying collapse of the concentration-response curve maxima. Even for a positive allosteric modulator (α = 10), the left shift that is evident in the concentration-stimulus curve is opposed by the effect of the modulator on the efficiency of stimulus-response coupling, which leads to an overall right shift of the concentration-response curves. An even more interesting profile of concentration-response curves can be found in those situations where the allosteric modulator is able to enhance the actual efficiency of agonist signaling in the ternary complex (µ > 1) or where the modulator depresses signaling, but to an incomplete extent (0 < µ < 1). Panels A and D of Figure 1.21.7 illustrate the effect of a negative (Fig. 1.21.7A) or positive (Fig. 1.21.7D) allosteric modulator that is able to enhance agonist signaling (µ = 2) in a system that already demonstrates efficient stimulus-response coupling (KE = 0.1). The effect of the modulator on maximal system responsiveness is not discernible under these circumstances because the agonist is already generating a close to maximal response. Instead, the effect of the modulator on signaling is manifested in a much more subtle fashion, namely, in the shift of the curves (EC50

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A

µ=2 KE = 0.1

E/Emax

B

1.0 0.8

µ=2 KE = 1

α = 0.1

µ = 0.5 KE = 1

C

α = 0.1

α = 0.1

0.6 0.4 0.2 0.0 –10

D

1.0

E/Emax

0.8

–9

–8 –7 Log[A]

–6

–5 –9

–8

E

α = 10

–7 –6 –5 Log[A]

–4 –3 –9

–8

F

α = 10

–7 – 6 –5 – 4 –3 Log[A]

α = 10

0.6 0.4 0.2 0.0 –11 –10

–9 –8 Log[A]

–7

–6 –9

–8 –7 – 6 – 5 – 4 – 3 – 9 Log[A]

–8

–7 – 6 –5 Log[A]

–4 –3

Figure 1.21.7 Effect of an allosteric modulator that either enhances (µ > 1) or incompletely dampens (0 < µ < 1) orthosteric ligand signaling capacity. The curves were simulated according to Equation 1.21.10 using the following parameters: pKA = 6, pKB = 9, e = 10 and the values of µ, KE and α that are indicated in the panels. In a system demonstrating efficient stimulus-response coupling (A, D), an enhancement of signaling efficiency is manifested as an enhanced potency (compare these panels to Figure 1.21.5). In a system demonstrating reduced signaling efficiency (B, C, E, F), complex concentration-response curves are predicted due to the differential effects of the modulator on orthosteric ligand occupancy (determined by α) and signaling capacity (determined by µ).

parameter). When compared to the corresponding curves in Figure 1.21.5, the negative allosteric modulator that enhances agonist signaling capacity demonstrates a reduced propensity to right shift the agonist curve, whereas the positive allosteric modulator causes an exaggerated left shift. The most dramatic effects of modulators on agonist signaling, however, are observed with those agonists that are unable to generate the maximal tissue response, that is, partial agonists. Panels B and E in Figure 1.21.7 illustrate this effect. For a negative allosteric modulator, the right shift of the agonist concentration-response curve is accompanied by an increase in the observed maximal response. For the positive allosteric modulator, a similar effect is observed for the maximal response, but this accompanies a left shift of the agonist curve. Finally, an incomplete dampening of agonist signaling as a consequence of allosteric modulation can also yield a variety of curve profiles. Panels C and F in Figure 1.21.7 demonstrate the effects of an allosteric antagonist of function (µ = 0.5) coupled with a negative modulatory effect on occupancy (α = 0.1; Fig 1.21.7C) or a positive modulatory effect on occupancy (α = 10; Fig. 1.21.7E). Again, differential effects on curve location and maximal response may be observed that are most evident for partial agonists. AN EXTENDED MODEL OF ALLOSTERIC INTERACTIONS Overview of Receptor Allosterism

The preceding discussion has considered the consequences of the simple ternary complex model of allosteric interaction on orthosteric ligand binding and function in a general sense. However, the fact that allosteric modulators are able to alter receptor conformation

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in their own right brings them into the same realm as other potential modifiers of receptor properties, such as agonists, inverse agonists, and G proteins. At the molecular level, therefore, the simple ternary complex model of allosteric interactions is a subset of a more general, extended, model of receptor activity. In order to visualize such a model, we can begin with a general schematic of a receptor protein that contains separate binding sites for an orthosteric ligand, an allosteric modulator, and a G protein (Figure 1.21.8A). Thermodynamic considerations imply that the occupancy of any one of the binding sites on this receptor can alter its conformation such that the occupancy of any of the other sites on the protein is also altered. This cross-reciprocity can be quantified in terms of separate cooperativity factors for the interaction between orthosteric and allosteric sites (α), orthosteric and G protein sites (β), and allosteric and G protein sites (γ). Since efficacy at GPCRs is invariably related to the ability of the receptor to interact with its cognate G protein(s), then efficacy at the molecular level can be impacted upon not only by the value of β, but also by the value of γ. Theoretically, therefore, allosteric modulators may directly affect receptor function in the absence of orthosteric ligand, and can thus be subdivided into the following categories (Lutz and Kenakin, 1999): a. Allosteric enhancers (α > 1): These ligands exert their effects by enhancing the affinity of the orthosteric ligand for its site on the receptor. b. Allosteric agonists (γ > 1): These ligands exert their effects by promoting G protein coupling independently of any effects on orthosteric agonist binding. c. Allosteric antagonists (α < 1 and/or γ < 1): These ligands can exert their effects by one or a combination of mechanisms; they can decrease the affinity of the receptor for its orthosteric agonist and/or decrease the affinity of the receptor for its G protein(s).

A allosteric interaction α allo

ortho β

R

γ

G protein

B

receptor isomerization

(allosteric transition)

R

R*

Figure 1.21.8 Schematic diagrams depicting two different aspects of allosterism at G protein-coupled receptors. (A) Allosteric interactions arising between orthosteric ligand, allosteric ligand and G protein binding sites on the one receptor and governed by the cooperativity factors α, β, and γ. It is possible for the allosteric modulator to modify receptor coupling (γ) independent of its effects on orthosteric ligand occupancy (α). (B) Receptor isomerization between multiple conformational states (e.g., R and R*). This is also referred to as an “allosteric transition” and highlights the fact that multiple receptor states may be associated with their own specific complement of orthosteric and allosteric binding sites that differ in their ligand binding properties and cooperativity factors.

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To be thermodynamically complete, the extended model of allosteric interactions between multiple binding sites on the same protein must also take into account the ability of the receptor to isomerize between multiple conformational states (Figure 1.21.8B). At equilibrium, each conformational state is characterized by its own set of α, β, and γ factors. Even for the “simplest” case of two receptor conformations (R for inactive and R* for active), the resulting thermodynamic picture (Christopoulos et al., 1998) can become quite complicated (Figure 1.21.9), and the model quickly becomes overparameterized and loses its predictive capabilities. Nevertheless, the extended model of receptor allosterism does reflect the concept that allosteric modulators possess a rich repertoire of behaviors that can theoretically extend beyond simple changes on orthosteric ligand binding affinities. For instance, the allosteric modulator gallamine has been shown to inhibit the binding of the agonist, acetylcholine, at M2 muscarinic receptors (α < 1; Lazareno and Birdsall, 1995), but to activate the receptor in the absence of any other ligand (γ > 1; Jakubík et al., 1996). Similarly, the allosteric modulator, PD 81723, is able to enhance agonist binding to adenosine A1 receptors (α > 1; Cohen et al., 1994), decrease antagonist binding at these receptors (α < 1; Bruns and Fergus, 1990) and activate the receptors in its own right (γ > 1; Bruns and Fergus, 1990). DETECTING ALLOSTERIC INTERACTIONS Because allosteric interactions can be quite complex, there are a number of pharmacological approaches that are best utilized in tandem to detect and quantify allosterism at GPCRs. These interactions can be detected using both radioligand binding assays (see UNIT 1.22) and functional tissue or cellular assays. Many allosteric effects are often subtle and characterized by low degrees of cooperativity. Thus, the screening assay will need to be optimized for detecting these particular effects, and this may entail using different conditions than would normally be used for screening orthosteric ligands. Assays of Radioligand Binding Radioligand binding assays often provide the most direct means of visualizing allosteric behavior. For example, Figure 1.21.10A shows the effects of the negative allosteric

R*G RG

AR*G

ARG

R*

AR* AR*BG

BR*G BRG

R BR* BR

Overview of Receptor Allosterism

AR

ARBG AR*B

ARB

Figure 1.21.9 A thermodynamically complete, extended model of receptor allosterism at G protein-coupled receptors taking into account the concomitant binding of orthosteric ligand, A, allosteric ligand, B, and G protein, G, on a receptor that can exist in two conformational states (R and R*).

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A

120 0 oleamide 0.1 nM 10 nM

100

Specific binding (% max)

30 nM 100 nM 300 nM

80

1 µM

60

40

20

0 –10

–9

–9.5

–8

–8.5

– 7.5

–7

Log [5-HT]

B

3 2.5 2

Log (affinity shift –1)

1.5 1 0.5 0 –0.5

–1 –1.5

–1

–0.5

0

0.5

1

1.5

2

2.5

3

Log [oleamide]/KD

Figure 1.21.10 Allosteric modulation by oleamide of the saturation binding of [3H]5-HT in HeLa cell membranes transiently transfected with the 5-HT7 receptor. (A) Radioligand saturation binding curves obtained in the presence of up to 1 µM oleamide. (B) Effect of oleamide on the ratio of [3H]5-HT KD values (“affinity-shift”) determined in the presence or absence of the modulator. The dashed line shows the predicted behavior of a competitive antagonist. Data taken from Hedlund et al. (1999).

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modulator, oleamide, on the saturation binding properties of [3H]5-HT at the 5-HT7 receptor expressed in HeLa cells. Although oleamide is able to shift the radioligand binding curves to the right with no significant change in slope or maximal curve asymptote, the allosteric nature of the interaction is revealed as progressively higher concentrations of oleamide fail to cause significant rightward displacements of the [3H]5-HT curve, in direct contrast to what would be expected for a simple competitive interaction. Scatchard transformations of these data would reveal a reduction in the slope of the plots with no significant change in the estimate of Bmax (see Ehlert, 1988). This phenomenon can also be illustrated by determining the “affinity shift,” that is, the ratio

A 100

0.5 × K A 10 × K A

% Specific binding

80

60 pK B = 6.9 α = 0.067

40

20

0 –9

–8

–7 –6 Log[gallamine]

–5

–4

B

% Specific binding

800

600 pK B = 5.6 α = 10

400

200 0 –9

Overview of Receptor Allosterism

–8

–7 –6 Log[alcuronium]

–5

–4

Figure 1.21.11 Effect of the allosteric modulators gallamine and alcuronium on the binding of the orthosteric antagonist, [3H]N-methylscopolamine, at M2 muscarinic acetylcholine receptors in guinea pig atrial membranes. (A) Negative cooperativity between gallamine and two different concentrations of the radioligand: 0.1 nM (0.5 × KA) and 2 nM (10 × KA). (B) Positive cooperativity between alcuronium and 0.1 nM radioligand. Data were fitted to Equation 1.21.5 to derive the parameter estimates shown in the figure.

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of radioligand affinity in the presence (KApp) to that obtained in the absence (KA) of each concentration of modulator. A plot of log(affinity shift-1) versus log[inhibitor] should yield a straight line with a slope of 1 for a competitive interaction, but a curvilinear plot for an allosteric interaction. Figure 1.21.10B shows this plot for the interaction between oleamide and [3H]5-HT. Radioligand competition binding assays are more commonly used for the routine screening of novel chemical entities than saturation binding assays, so it is quite likely the first detection of an allosteric modulator may occur during this type of experiment. Of course, in this latter instance the interaction cannot be called “competitive,” but for allosteric modulators with high degrees of negative cooperativity, the interaction may be mistaken as competitive if low degrees of radioligand occupancy are investigated. Figure 1.21.11A shows the interaction between the muscarinic receptor antagonist, [3H]N-methylscopolamine, and the allosteric modulator gallamine, which is characterized by negative cooperativity. It can be seen that the use of a sub-KA concentration of radioligand (which is quite common for these types of screening assays) results in an apparently complete inhibition of specific radioligand binding. Increasing the concentration of the radioligand to 10 times its KA, however, unmasks the limited ability of the negative allosteric modulator to inhibit specific binding. In contrast, the interaction between the same radioligand and the modulator, alcuronium, at the same receptor, is characterized by a marked positive cooperativity, clearly deviating from the predictions of simple competition (Figure 1.21.11B). Findings such as these highlight another important aspect of allosteric interactions, that is, they are unique for each and every pair of interacting ligands involved. A positive allosteric modulator of one particular orthosteric ligand is not necessarily a positive modulator of another orthosteric ligand. Table 1.21.2 demonstrates this with a series of examples for the interaction between alcuronium and a variety of orthosteric ligands at the M2 muscarinic acetylcholine receptor. The above examples highlight two important considerations when screening for allosteric ligands. First, assays should generally use low concentrations of radioligand ( 1) while the interaction between B and A is negatively cooperative (α < 1). This yields a very steep inhibition curve (Hill slope ∼ 2), as was observed experimentally for the interaction between MIA and [3H]spiperone at the D2 receptor. In Figure 1.21.12D, the interaction between A and B is positively cooperative (α > 1), while the interaction between the two molecules of B is neutrally cooperative (β = 1), yielding the observed bell-shaped curve. It may of course be argued that the experimental observations were due to some complex reaction mechanisms other than mixed allosteric/competitive interactions; however the allosterism inherent in the examples shown in Figure 1.21.12, panels A and B, has been independently confirmed in another important type of radioligand binding assay, namely the dissociation kinetic assay. Indeed, the study of allosteric modulator effects on radioligand kinetic binding properties probably represents the most sensitive direct measurement of allosteric interactions at GPCRs. Receptor Binding

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Association and dissociation of a ligand from its binding site (orthosteric or allosteric) on a receptor are exponential processes. Importantly, the actual rate constants that govern the ligand association (kon) and dissociation (koff) can be determined experimentally from kinetic experiments that measure radioligand binding as a function of time, and are very sensitive indicators of the interaction of the ligand with a particular conformation of receptor. Hence, a change in receptor conformation induced by an allosteric agent would be expected to result in an alteration of orthosteric ligand association and/or dissociation characteristics. Indeed, it is this alteration in orthosteric ligand kinetics that underlies the effects of allosteric modulators on orthosteric ligand affinity at equilibrium. From the ternary complex model shown in Equation 1.21.1, the association constant, Ka, can be redefined according to its respective kinetic rate constants. That is, Ka = konA/koffA. In the simplest case (and thus far the most commonly observed experimental situation), the kinetics of the modulator are more rapid than those of the orthosteric ligand. Under these conditions, the rate of dissociation of an orthosteric ligand in the presence of an allosteric modulator may be derived as follows: ρ At = ρ A ⋅ e-k offobs ⋅t Equation 1.21.17

where

koffobs

α[B]k offAB + koffA KB = α[B] 1+ KB

Equation 1.21.18

In these two equations, ρAt denotes the receptor occupancy by [A] at time t, ρA denotes the receptor occupancy by [A] at equilibrium, koffobs denotes the experimentally observed dissociation rate constant for [A], and koffAB denotes the dissociation rate constant for [A] from the ternary complex [ARB]. The remaining parameters are as defined previously. The association of an orthosteric ligand under similar conditions is derived as:

(

ρAt = ρA ⋅ 1-e-k onobs ⋅t

)

Equation 1.21.19

where  [A]  konobs = koffobs 1 +   K App  Equation 1.21.20

Overview of Receptor Allosterism

The parameter, konobs, denotes the apparent association rate constant of orthosteric ligand in the presence of allosteric modulator. KApp is defined in Equation 1.21.6.

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Allosteric modulators may thus increase or decrease the association and/or dissociation characteristics of the orthosteric ligand at its binding site on the receptor. To date, an enhancement of association rate has not been conclusively demonstrated for allosteric modulators of GPCRs. Negative allosteric modulators may exert their kinetic effects via slowing orthosteric ligand association, but this is experimentally difficult to distinguish from simple competitive inhibition. In either instance, the observed orthosteric ligand association rate is effectively reduced. In contrast, dissociation kinetic experiments theoretically monitor only the disintegration characteristics of a preformed orthosteric ligand-receptor complex, and any changes in the observed dissociation rate are much more unambiguously attributed to allosteric effects. These latter types of experiments, therefore, represent the most common type of radioligand kinetic assay used to detect and quantify allosterism at GPCRs. Figure 1.21.13A shows the effects of the allosteric modulator, 5-(N-ethyl-N-isopropyl)amiloride (EPA), on the dissociation of [3H]yohimbine from the human α2A adrenoceptor. It can be seen that increasing the concentration of EPA results in a progressive increase in the dissociation of the orthosteric radioligand as the occupancy of the allosteric site by EPA becomes greater. This effect explains the reduction in [3H]yohimbine affinity by EPA observed in equilibrium binding assays. In contrast, the allosteric modulator, PD117,975 slows the dissociation rate of the agonist, [3H]CHA from adenosine A1 receptors (Fig. 1.21.13B), thus accounting for its positively cooperative effects on agonist radioligand affinity at equilibrium. An interesting situation can arise, however, with certain allosteric modulators. Figure 1.21.14 illustrates the effects of the modulator, tetra-W84, on both the apparent association and dissociation rates of the orthosteric antagonist, [3H]N-methylscopolamine, from the cardiac M2 muscarinic acetylcholine receptor. It can be seen that the concentration-effect curves showing the ability of the modulator to slow both association and dissociation of the radioligand are very close together. The consequence of this dual effect is seen in the curve of the interaction between tetra-W84 and [3H]Nmethylscopolamine, determined separately in an equilibrium binding assay (open circles). Under equilibrium binding conditions, it appears that tetra-W84 has no effect on binding. In fact, this is an example of a neutrally cooperative interaction (α = 1). Its allosteric nature is quite convincingly revealed in the radioligand kinetic assays, whereas it can be missed in equilibrium binding assays. The quite profound effects that allosteric modulators can exert on orthosteric ligand kinetics can also lead to pitfalls in data analysis and interpretation. The most insidious effect is seen in binding experiments that are ostensibly conducted under standard “equilibrium” conditions but which are, in fact, not at equilibrium due to the marked effects of the modulator on orthosteric ligand association and dissociation. This is most commonly observed with positive and neutrally cooperative ligands, because their effects on equilibrium binding occur over the same concentration ranges as their effects on the approach of the system to equilibrium. The consequences of this kinetic artifact can be modeled using Equation 1.21.19 and are shown in Figure 1.21.15. Even after 64 hours, a positive allosteric modulator that is able to completely inhibit the dissociation of an orthosteric ligand from the ARB complex (koffAB = 0) yields a bell-shaped binding curve. The effects of high concentrations of the modulator on the kinetics of the orthosteric ligand are so marked that equilibrium has not been achieved in the presence of the high modulator concentrations. Only after 2048 hours (∼85 days) is equilibrium achieved. Experimentally, the easiest way of circumventing this problem is to prelabel the receptors with orthosteric radioligand prior to exposure to the allosteric agent (see UNIT 1.22). Alternatively, the use of nonequilibrium kinetic assays to directly quantify the interaction (Equation 1.21.17, Equation 1.21.18, Equation 1.21.19, or Equation 1.21.20) may be preferred. Parenthetically, this kinetic effect is reminiscent of the binding profile that has

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been seen in equilibrium binding assays in some receptor systems (e.g., see Figure 1.21.12B). Although that binding profile may be due to mixed allosteric/competitive modes of interaction (Equation 1.21.15), the investigator must first rule out any kinetic artifacts of the allosteric modulator on the approach of the orthosteric radioligand to equilibrium. Assays of Receptor Function Although radioligand binding assays provide the most direct means for visualizing and quantifying allosteric interactions at GPCRs, functional assays of receptor activity can

A 1

B t /B 0

control

+ 0.03 mM EPA

0.1 + 3 mM EPA

0

+ 0.3 mM EPA

+ 1 mM EPA

10

20

+ 0.1 mM EPA

30

40

Time (min)

B 2,500

[3H]CHA binding (cpm)

2,000 1,500 10 µM PD 117,975

1,000 500

control nonspecific

0 0

30

60

90

120

150

180

Time (min)

Overview of Receptor Allosterism

Figure 1.21.13 Effects of allosteric modulators on orthosteric ligand dissociation kinetics. (A) Enhancement of the dissociation rate of [3H]yohimbine from the human α2A receptor expressed in CHO cell membranes by the modulator 5-(N-ethyl-N-isopropyl)-amiloride (EPA). Data taken from Leppik et al. (1998). (B) Slowing of the dissociation rate of [3H]CHA from the adenosine A1 receptor in rat brain membranes by the modulator PD117,975. Data taken from Bruns and Fergus (1990).

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100

equilbrium

dissociation

association

50

0 10 – 8

10 – 7 [tetra-W84] (M)

10 – 6

10 – 5

Figure 1.21.14 Neutral cooperativity between [3H]N-methylscopolamine and the modulator, Tetra-W84 at the M2 muscarinic acetylcholine receptor. Increasing concentrations of modulator are able to decrease the rate of radioligand association and dissociation, thus revealing the allosteric nature of the interaction (solid symbols). However, because the kinetics of the radioligand are influenced over similar concentration ranges and to similar extents, equilibrium binding studies show minimal effects on levels of radioligand binding (open circles). Data taken from Kostenis and Mohr (1996).

100 2048 hr 16

64

4

75

0.5

% [AR]/[R]T

0.2 50

25

0 –10

–9

–8

–7

–6

–5

–4

–3

Log[B]

Figure 1.21.15 Allosteric modulation under nonequilibrium conditions. Orthosteric radioligand binding was simulated for a positive allosteric modulator (α = 10) using Equation 1.21.18, Equation 1.21.19, and Equation 1.21.20, and the following parameters: pKA = pKB = 7, koffA = 0.5 min−1, koffAB = 0 min−1, Log[A] = −7. The curves represent the concentration-occupancy relationship for the interaction at the different times (hours) shown in the figure. It is evident that allosteric modulators may slow the kinetics of the system to such an extent that equilibrium is unachievable during the time course of the experiment, thus yielding complex binding curves.

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also be used. In fact, the earliest demonstrations of receptor allosterism relied on these types of assays. According to the simple ternary complex model (Equation 1.21.1), an allosteric modulator that affects orthosteric ligand affinity but not efficacy will displace the concentration-response curves of an orthosteric agonist in a parallel fashion, with no change in basal response, maximal tissue response or curve shape and slope (see Figure 1.21.5, for example). In the case of positive cooperativity, the ascription of an allosteric mechanism to the experimental data would be relatively straightforward, as the agonist curves would be displaced to the left of the control agonist curve. However, as is the case for radioligand binding assays, negative allosteric modulation may be misinterpreted as competitive antagonism, particularly for modulators with high degrees of negative cooperativity. An important key to the successful detection and quantification of negative allosteric modulation is to investigate the effects of as large a range of antagonist concentrations as is practicable. Competitive antagonists should theoretically shift the agonist concentration-response curve to the right according to a constant value of (1+[B]/KB), whereas allosteric antagonists will approach a limit defined by (1+[B]/KB)/(1+α[B]/KB). These relationships become very apparent when plotted in the form of Schild regressions. In essence, Schild analysis relies on the determination of the “concentration-ratio” (CR) or “dose-ratio,” that is, the ratio of equiactive agonist concentrations obtained in the absence and presence of antagonist for a wide range of antagonist concentrations (see UNIT 1.2). A subsequent plot of the log (CR-1) versus log[B] should yield a straight line with a slope of 1 for a competitive antagonist. In contrast, an allosteric antagonist would deviate from this linear relationship as the limit of cooperativity is approached, yielding a curvilinear Schild plot. Figure 1.21.16A shows the antagonism by gallamine of the negative inotropic effects of acetylcholine at M2 muscarinic receptors in the guinea pig electrically stimulated left atrium. It can be seen that as the concentration of modulator is increased, the dextral displacement of the acetylcholine curves approaches a limit. The Schild plot of the same data is shown in Figure 1.21.16B, where the deviation from a straight line is clearly evident. In fact, a linear regression through the data points yields an unsatisfactory fit with a slope factor of 0.65. The appropriate fit of the allosteric model to the data can be obtained with the following equation:  [B] (1 − α )    α[B] + K B 

Log(CR − 1) = Log 

Equation 1.21.21

As shown in Figure 1.21.16, Equation 1.21.21 allows an estimate to be obtained of the cooperativity factor and the dissociation constant of the modulator for the allosteric site.

Overview of Receptor Allosterism

Although applicable to those cases where allosteric modulators affect only orthosteric ligand affinity, the functional quantification of allosteric interactions using the ternary complex model is prone to the impact of possible allosteric effects on stimulus-response coupling. In the most obvious cases, this can be manifested as an observed response to the allosteric modulator in the absence of agonist. However, more subtle effects may not be detected. An example is illustrated using the theoretical functional data from Figures 1.21.5B and 1.21.7A. In each case, the negative allosteric modulator is characterized by an α value of 0.1, but in the latter instance the modulator has the additional ability to enhance agonist efficacy (µ = 2), while in the former instance it does not. Figure 1.21.17 illustrates the Schild plots for these two sets of data, where it can be seen that application of Equation 1.21.21 only gives the correct estimate of α for the dataset where the modulator does not affect the efficacy of the agonist.

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USEFULNESS OF ALLOSTERIC LIGANDS Allosteric modulators of receptor function offer a number of advantages over orthosteric ligands. The first advantage relates to the ability of modulators to selectively “tune” tissue responses in those organs where the endogenous agonist exerts its physiological effects. Because neurohumoral signaling involves the pulsatile release of hormones and variations in the activity of nerves that release neurotransmitters, an allosteric modulator would only be expected to exert its effects when endogenous agonist is present. If nerve activity is reduced, for instance, a modulator would have minimal effects, despite its continued presence in the receptor compartment. This is not possible with orthosteric agonists or antagonists, which will continuously modify receptor function as long as they are present.

A 100

Percent inhibition

80

60 0 gallamine 10 µM 30 µM 100 µM 300 µM 500 µM

40

20

0 –9

–8

–7

–6

–5

–4

Log [ACh] (M)

B

1.0

Log (CR –1)

2

0.65

α = 5.3 × 10 – 3 pK B = 6.03

1

0 –6

–5

–4

–3

Log [gallamine]

Figure 1.21.16 Effect of the allosteric modulator, gallamine, on the ability of acetylcholine to inhibit the electrically-evoked contractions of the guinea pig left atrium. (A) Effects of acetylcholine (ACh) in the absence (solid squares) or presence of up to 500 µM gallamine. All experiments were conducted in the presence of the cholinesterase inhibitor, diisopropylfluorophosphate. (B) Schild plot of the data shown in A. The solid line (slope = 1) denotes the behavior expected for a competitive antagonist, while the dashed line shows the best fit linear regression (and associated slope factor) through the points. The curve through the points and associated parameter estimates represent the fit of the allosteric model (Equation 1.21.21) to the data.

Receptor Binding

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1.5

Log (CR–1)

1.0

theoretical α = 0.1 pK B = 9

estimates α pK B 0.1 9

µ=1

0.2

µ=2

0.5

9

0.0

–10

–9

–8

–7

–6

–5

–4

Log [B]

Figure 1.21.17 Potential error in the pharmacological estimation of the cooperativity factor from a functional receptor assay. The figure shows the Schild plots based on simulated data using Equation 1.21.10 (pKA = 7, e = 10, KE = 0.1) and the parameter values shown in the figure. It can be seen that the correct estimate of α (0.1) is only obtained when the allosteric modulator does not change signaling capacity (µ = 1). Points were fitted to the allosteric model (Equation 1.21.21). The dashed line denotes the behavior expected for a competitive antagonist.

Thus, allosteric modulators maintain the temporal patterns of physiological signaling to a far greater extent than orthosteric ligands. Figure 1.21.18 shows a series of simulations that highlight this property. Panel A illustrates the theoretical response profile of 50 arbitrary brain regions to the release of the same neurotransmitter. Panel B illustrates the effect of depletion of the neurotransmitter as a consequence, perhaps, of some sort of neurodegenerative process (e.g., effects of Alzheimer’s disease on acetylcholine release). One potential approach to this problem is to introduce an orthosteric agonist in an effort to overcome the reduced responsiveness of the system. Panel C shows that, although a fixed concentration of exogenous agonist can indeed increase system responsiveness, the resulting profile of activity in each brain region (dark areas) often does not match the normal physiological profile of the system—i.e., some areas are overstimulated and others remain understimulated. In contrast, the addition of a positive allosteric modulator restores the neurotransmitter deficit and yields a profile that resembles the normal physiological response much more closely (panel D). A second advantage of allosteric modulators is related to the magnitude of the cooperativity between orthosteric and allosteric receptor sites. Specifically, the ability of a modulator to alter receptor function is limited by the extent of the conformational change it can induce in receptor structure and orthosteric occupancy. Thus, there is a “ceiling” to the effects of an allosteric modulator that is retained irrespective of the dose that is administered therapeutically. As a consequence, allosteric modulators are generally much safer in overdosage than orthosteric ligands, and they can be given in quite high doses if necessary to maintain adequate receptor concentrations without fear of overstimulating or overinhibiting receptor function.

Overview of Receptor Allosterism

Finally, allosteric ligands offer the possibility of an “absolute selectivity” in receptor action by one (or both) of two mechanisms. The first relates to the fact that allosteric sites are necessarily different from orthosteric sites, and it is thus quite conceivable that

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Neurotransmitter enhancement

0.5

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0.0 5

15

25

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Brain region

Figure 1.21.18 Point responses from 50 randomly constructed concentration-response curves given random concentrations of agonist. (A) Hypothetical normal pattern of brain response according to the texture of neurotransmitter release in different regions. (B) Uniform reduction in response in each brain region by a factor of four. (C) Effect of addition of a constant amount of agonist to the complete system. The dark regions represent response that either overshoots or undershoots the normal level in A. (D) Effect of an allosteric modulator that enhances receptor sensitivity by a factor of three. Reprinted with permission from Lutz and Kenakin (1999).

receptors may show a greater divergence in sequence homology in the domains that define the allosteric site in contrast to the orthosteric site. The likelihood of subtype selectivity is therefore enhanced if drug discovery programs target receptor allosteric sites. The second mechanism for selectivity is related to cooperativity rather than affinity. Because the affinity of a modulator for its binding site is not correlated with the degree of cooperativity that exists between orthosteric and allosteric sites, a modulator may display the same affinity for each subtype of a receptor but still exert a selective effect by having different degrees of cooperativity at each subtype. Absolute selectivity may thus be obtained where a modulator remains neutrally cooperative at all receptor subtypes except the one targeted for therapeutic purposes. Table 1.21.3 shows data obtained for the allosteric modulator, N-chloromethylbrucine (NCB), at each of the five subtypes of muscarinic acetylcholine receptor when tested against acetylcholine. Although the affinity for the allosteric site at each receptor subtype was within a 5-fold range of values for NCB, the cooperativity factors were quite different. This compound was positively cooperative with acetylcholine at the M3 receptor, negatively cooperative at the M1 and M2 receptors, and effectively neutrally cooperative at the M4 receptor. Thus, some degree of absolute selectivity was achieved. Receptor Binding

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Table 1.21.3 Affinity and Cooperativity Estimates for the Allosteric Modulator, N-Chloromethylbrucine, at the Five Subtypes of Muscarinic Acetylcholine Receptorsa

Subtype

Log[KA−1]

M1 M2 M3 M4 M5

4.38 4.22 3.66 4.32 3.66

α 0.45 0.094 3.26 1.03 0.055

aData from Lazareno et al. (1998).

RECEPTOR COMPLEXATION The very nature of GPCRs requires that they couple to other membrane components in order to transduce the stimulus imparted to the receptor by an agonist. The ability of receptors to complex with other proteins thus leads to the possibility of allosteric interactions occurring between the proteins, and the specific example of receptor- G protein coupling has already been described above in this context. In the case of the receptor, the orthosteric site is the agonist binding site, whereas for the G protein, the orthosteric site may be defined as the guanine nucleotide binding site on the Gα subunit. Although this description ignores the additional allosteric effect that can occur as a consequence of G protein βγ subunit binding (Onaran et al., 1993), it is nevertheless sufficient to illustrate the best studied example of GPCR complexation. Beyond the G protein paradigm, however, GPCRs have generally been considered to behave as monomeric proteins with respect to their interactions with orthosteric ligands. Even the examples of allosterism illustrated in the preceding sections are all instances of where more than one binding site is located on the receptor monomer, and allosteric behavior arises as a consequence of interactions between these sites. More recently, the paradigm of GPCRs as monomers has been scrutinized due to the realization that they can form complexes with proteins other than G proteins. The most compelling evidence comes from the increasing number of studies demonstrating the ability of GPCR monomers to combine and form dimers, or even higher-order oligomers, but studies are now expanding the list of “accessory proteins” that may act as partners with GPCRs in an array of signaling complexes. In all these instances, the possibility exists for allosterism as a consequence of protein-protein interactions. Receptor Dimerization In contrast to GPCRs, receptors from other superfamilies have long been known to form multimeric complexes in order to participate in cellular signaling. For example, members of the growth factor receptor family such as the EGF-R, PDGF-R, FGF-R and interferon γ receptors have been identified as structural and functional dimers (Hebert and Bouvier, 1998). Ion-channel linked receptors are also known to exist as hetero-oligomers—i.e., they are composed of multiple subunits of different protein types, thus leading to a diverse array of receptor subtypes. Each of these instances can lead to cooperative behavior if more than one molecule of the orthosteric ligand is able to bind to the multimeric receptor complex. Overview of Receptor Allosterism

Indirect evidence has also been available for cooperative binding of orthosteric ligands at GPCRs for quite some time. For instance, radioligand binding assays at the β2 adrenoceptor (Limbird et al., 1975), the muscarinic receptors (Chidiac et al., 1997; Henis et al.,

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1989; Lee et al., 1986; Mattera et al., 1985; Potter et al., 1988, 1991; Wregget and Wells, 1995), and the histamine receptor (Sinkins et al., 1993; Sinkins and Wells, 1993; Steinberg et al., 1985a,b,c) have often described orthosteric binding properties that could not be readily reconciled either with simple mass-action monomeric receptor behavior or within the framework of a simple ternary complex model between orthosteric ligand, receptor and G protein. For example, Figure 1.21.19 shows the binding of the orthosteric agonist, oxotremorine-M, against the orthosteric antagonist, [3H]AF-DX 384, at native M2 muscarinic receptors. In the presence of G protein coupling (circles), the competition curve is inhibitory, although it is characterized by a biphasicity that suggests multiple affinity states. Interestingly, when the nonhydrolyzable GTP analog, Gpp(NH)p, is included in the assay to uncouple receptor-G protein complexes, a distinctly bell-shaped binding curve (square data points) is obtained for the agonist-antagonist interaction, characterized by an initial element of positive cooperativity. Given that both ligands recognize the orthosteric site of the muscarinic receptor, this pattern cannot be reconciled with the simple ternary complex model of allosteric interaction described above, nor with the ternary complex model of orthosteric ligand-receptor-G protein. This behavior can be rationalized, however, if it is assumed that GPCRs can exist as dimers within the cell membrane. A simple model of receptor dimerization is illustrated in Equation 1.21.22.

ARB

βKb

αKa

Ka

βKa

RB

AR

Kb

A+R+B

αKa

Ka

Kb

γKb

BRB

RA

βKb

BR γKb

ARA

ARB βKa

Equation 1.21.22

where R represents a dimerized receptor (e.g., R-R), A and B represent orthosteric ligands that can bind to either or both orthosteric sites on the dimer, and Ka and Kb denote the equilibrium association constants for binding of either ligand to a vacant dimer. The symbol α denotes the cooperativity factor for the binding of a second equivalent of ligand A to a dimer that is already occupied by a molecule of A, the symbol β denotes the cooperativity factor for the binding of a molecule of ligand B to a dimer that is already occupied by a molecule of A, whereas the symbol γ denotes the cooperativity factor for the binding of a second equivalent of ligand B to a dimer that is already occupied by a molecule of B. The receptor conservation equation for this scheme is as follows: [R]T = [R] + 2[AR] + 2[BR] + 2[ARB] + [ARA] + [BRB] Equation 1.21.23 Receptor Binding

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The equilibrium occupancy by ligand A (ρA) in the presence of ligand B may then be derived as follows:

ρA =

2[AR] + [ARA] + 2[ARB] = [R]T

α[A] 2β[B]  [A]  + 2 +  KA  KA KB 

1+

α[A] 2β[B]  [B]  γ[B]  [A]  + 2 + + 2 +  KA  KA KB  KB  KB 

Equation 1.21.24

Figure 1.21.20 illustrates a series of binding curves simulated according to Equation 1.21.24. The only difference between the curves is the degree of cooperativity (β) between A and B on the receptor dimer, yet this is sufficient to accommodate a wide range of binding profiles, including multiple affinity states and bell-shaped curves. It is apparent that even a receptor dimer provides scope for a bewildering array of allosteric interactions occurring between orthosteric binding sites. More direct biochemical and/or structural evidence of GPCR dimerization is also available, having been obtained from photoaffinity labeling experiments, receptor cross-linking studies, mutagenesis experiments, and the construction of receptor chimeras (see Hebert and Bouvier, 1998, for references). The latter studies, in particular, have provided an impetus for much of the more recent work on GPCR dimerization. For instance, Wess and colleagues (Maggio et al., 1993a,b) constructed a series of α2-adrenoceptor/M3 muscarinic receptor chimeras that contained the first 5 transmembrane domains of one receptor type linked to the last two of the other type of receptor, and then studied their properties in a recombinant expression system. When transfected alone, neither chimera showed significant ligand binding activity. However, when they were coexpressed,

250

Total binding (pM)

200 150 100 50

0 –10 – 9

–8

–7

–6

–5

–4

–3

–2

Log [oxotremorine-M]

Overview of Receptor Allosterism

Figure 1.21.19 Interaction between the orthosteric agonist, oxotremorine-M, and the orthosteric antagonist, [3H]AF-DX 384, at the M2 muscarinic acetylcholine receptor copurified with G proteins from porcine sarcolemmal membranes. The data were obtained in the absence (circles) or presence (squares) of the nonhydrolyzable GTP analog, Gpp(NH)p. Curves through the data represent the best fit based on a model of receptor oligomerization. Data taken from Wregget and Wells (1995).

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significant numbers of both α2 and M3 binding sites were detected. Furthermore, this phenomenon was functionally relevant, as the cotransfected cells were able to respond to stimulation with a muscarinic receptor agonist. This functional “rescue” of receptor activity on coexpression of the two different chimeric constructs could only be explained by an intermolecular rearrangement of transmembrane domains between the two receptor chimeras, thus highlighting the possibility of GPCR-GPCR interactions. Further conclusive evidence of functionally relevant GPCR dimerization has been most recently provided by Bouvier and colleagues (Hebert et al., 1996, 1998), who used a strategy of differential epitope tagging to demonstrate that the β2 adrenoceptor responds to agonist binding by forming receptor homodimers. Importantly, a peptide derived from transmembrane (TM) domain VI of the β2 adrenoceptor was able to inhibit both dimer formation (Figure 1.21.21) and isoproterenol-mediated adenylyl cyclase activity. This finding provided structural evidence for the TM VI interface as being an important determinant of β2 adrenoceptor homodimerization, as well as suggesting a requisite role of the dimerization process in β2 receptor activation. Although originally identified in cellular membrane fragments, β2 adrenoceptor homodimerization has subsequently been demonstrated in vivo in intact cells (Angers et al., 2000). From these findings, it may be concluded that GPCR homodimerization could represent a generalized paradigm of receptor activation. However, the δ opioid receptor has been found to display quite a different dimerization profile in response to agonist stimulation (Cvejic and Devi, 1997; UNIT 1.4). Figure 1.21.22 shows the effects of agonist stimulation on the ratio of δ opioid receptor dimers to receptor monomers as a function of time. It can be seen that the effect of the agonist is to increase the formation of receptor monomers. This agonist-mediated monomerization precedes agonist-mediated internalization of the receptors, thus suggesting a role for δ opioid receptor dimers in the internalization process. Interestingly, studies of bradykinin B2 receptor dimers have found that dimer formation

0.7

β = 10

α = 10 γ = 0.001

0.6 5

0.5 0.4

ρA

3

0.3 0.2

1

0.1

0.03

0.0 –10

–9

–8

–7

–6

–5

–4

–3

Log[B]

Figure 1.21.20 Predicted behavior of a dimeric receptor system. Effects of orthosteric ligand B on the binding of orthosteric ligand A according to Equation 1.21.24 with the following parameter values pKA = 9, pKB = 8, Log[A]= −7. The cooperativity factors shown in the figure represent the interaction between two molecules of A (α), two molecules of B (γ) and a molecule each of A and B (β) on the receptor dimer.

Receptor Binding

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is required both for agonist-mediated receptor activation and desensitization (AbdAlla et al., 1999). Given the current paucity of detailed studies on the functional consequences of GPCR dimerization, it is quite likely that further studies will identify a number of roles for the dimerization process that will be dependent on both the nature of the dimerization mechanism and the cellular background in which this mechanism is operative. For example, the sensitivity of muscarinic M3 (Zeng and Wess, 1999) and κ opioid (Jordan and Devi, 1999) receptor homodimers to reducing agents suggests a role for the disulfide bonds of the extracellular receptor loops in the mechanism of receptor dimerization. In contrast, other GPCRs, including the bradykinin B2 receptor, the metabotropic glutamate receptor and the calcium-sensing receptor rely on their N-terminal regions to form homodimers (AbdAlla et al., 1999, and references therein). As described above, β2 adrenoceptor homodimers require the structural integrity of receptor TM region VI, while dopamine D2 homodimers rely on TM VI and VII (Ng et al., 1996). It is possible that these latter types of transmembrane interface interactions extend to other GPCRs, as the chimeric receptor studies of Wess and colleagues (outlined above) also suggested a role for intermolecular interactions between transmembrane domains of α2/M3 receptor chimeras. A general model to account for this latter type of interaction has been proposed by Gouldson et al. (1998), and termed “domain swapping”. This model postulates that GPCR homodimers can form by “swapping” TM regions V and VI (Figure 1.21.23). The advantages of dimer formation using this mechanism are that it is energetically favorable, using the same type of bonding forces that maintain the structure of a standard GPCR monomer, and that it can minimize the effects of loss-of-function mutations. A number of studies of “functional receptor rescue” have demonstrated how mutated receptors that

60 50

% Dimer

40 30 20 10 0

Overview of Receptor Allosterism

CON

ISO

TIM

TM VI TM VI Pep Pep + ISO

Figure 1.21.21 Effects of β2 adrenoceptor ligands on receptor dimerization. Graphs show the proportion of receptor dimer in Sf9 cell membranes, determined by densitometric analysis of immunoblot experiments, after 30 min treatment under the following conditions: vehicle (CON), 1 µM isoproterenol (ISO), 10 µM timolol (TIM), 0.15 µg/ml of peptide derived from transmembrane domain VI of the β2 adrenoceptor (TM VI Pep) or isoproterenol followed by TM VI peptide (TM VI Pep + ISO). Data taken from Hebert et al. (1996).

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cannot signal are able to do so when they undergo a dimerization with another equivalent of receptor (see Gouldston et al., 1998). GPCR dimerization does not necessarily have to be restricted to the formation of homodimers. Indeed, some receptors may need to form heterodimers in order to function properly. The first discovery of this phenomenon was in relation to the metabotropic GABAB receptor. Although cloning studies had identified two distinct monomeric receptor subtypes, termed the GABABR1 and GABABR2 receptors (see Marshall et al., 1999), appropriate functional responses corresponding to native receptor properties could only be obtained when these two subtypes were co-expressed in the same cell (Jones et al., 1998; Kaupmann et al., 1998; White et al., 1998). Subsequent studies have identified the GABAB heterodimer as a tightly associated “coiled-coil” structure (Figure 1.21.24) that is most likely preformed in the endoplasmic reticulum and therefore does not need to be induced by agonist binding (Marshall et al., 1999). Another recently identified example of GPCR heterodimerization involves the combination of δ and κ opioid receptors (Jordan and Devi, 1999). In contrast to δ opioid homodimers (Figure 1.21.22), κ-δ heterodimers display a minimal tendency to monomerize in the presence of agonist (Figure 1.21.25). This suggests a role for heterodimerization in modulating receptor function. Indeed, the κ-δ heterodimers also display profound differences in their ability to bind δ or κ-selective ligands. Table 1.21.4 shows some examples of the binding properties of selective opioid ligands to the δ, κ or κ-δ receptor complexes. What is most striking is the enhancement of apparent ligand affinity at the heterodimer when measured in the presence of another ligand, suggesting positive cooperativity in the mode of agonist binding to the heterodimer. As with the ion channel–linked receptors, therefore, it appears that heterodimerization of GPCRs represents an important mechanism for generating receptor subtypes with a pharmacological profile that is distinct from that of either monomer alone. Accessory Binding Proteins Advances in the fields of protein biochemistry and molecular biology have led to an unprecedented appreciation of the diversity of coupling modes utilized by GPCRs. The standard paradigm of receptor-G protein interaction is now recognized as an oversimpli-

% surface fluorescence

Dimer/monomer ratio

100 75

0.9 0.6

50

dimer/monomer ratio

0.3

25

0.0

% Surface fluorescence

1.2

0 0

20

40

60

80

Time (min)

Figure 1.21.22 Comparison of the time course of agonist-induced changes in the δ opioid receptor dimer-monomer ratio with the time course of agonist-induced internalization in CHO cells. Dimermonomer ratios were determined by Western analysis and internalization was determined using flow cytometry. In each instance, the agonist used was 100 nM DADLE. Data taken from Cvejic and Devi (1997).

Receptor Binding

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fication, being superimposed on additional receptor complexing phenomena that are only beginning to be unraveled. The previous discussion of GPCR homo- and heterodimerization had focused on receptor-receptor coupling, but there are other proteins that are known to complex with GPCRs and modulate their functionality as well. One of the earliest family of proteins identified as novel coupling partners to GPCRs are the arrestins. These proteins play a crucial role in the termination of GPCR signaling and/or the resensitization of these receptors (Bunemann et al., 1999). In addition, they are also emerging as key players in the recruitment of alternative signaling pathways that can be utilized by the same GPCR under different conditions (Lefkowitz, 1998). Arrestins show highest selectivity for GPCRs that have been phosphorylated by another family of proteins, the G protein–coupled receptor kinases (GRKs). By binding to GRK-phosphorylated receptors, the arrestins are able to disrupt any further coupling between receptor and G protein, thus participating in the process of homologous GPCR desensitization. Interestingly, the coupling of different types of arrestins to GPCRs can be influenced by the conformational changes in GPCR structure induced by agonist coupling, a phenomenon reminiscent of the allosteric interaction between receptors and G proteins. Figure 1.21.26 shows experiments where the binding of norepinephrine has been monitored at purified, phosphorylated β2 adrenoceptors. Compared to the binding curve obtained in the absence of arrestins, the norepinephrine competition curves seen in the presence of arrestins demonstrate higher affinity states. Importantly, studies using a number of different agonists have shown that the proportion of high-affinity agonist binding in the presence of arrestin is dependent on the efficacy of the agonist used (Gurevich et al., 1997). These findings are very similar to those obtained with studies of receptor coupling to G proteins themselves. An important difference, however, is that the agonist-receptor-

2

1

3 4 5

6

7

5 4

7

6

1

2

3 2

1

3

7

4

6

5

5

6

4 7 1

Overview of Receptor Allosterism

3 2

Figure 1.21.23 Proposed model of “domain-swapping” (Gouldson et al., 1998) as a mechanism for the formation of G protein-coupled receptor dimers. In this scheme, intermolecular interactions between two receptors result in a swap of TM regions at the interface of TM5 and 6, leading to a dimer composed of two antiparallel monomers and sharing domains from each receptor.

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arrestin ternary complex is extremely stable and not readily reversible compared to the agonist-receptor-G protein complex. Nevertheless, these findings suggest that conformational changes in the receptor related to agonist efficacy may extend to influence the coupling of receptors to other cellular proteins beyond the family of G proteins. More recently, one of the most exciting findings in cell biology has been the discovery of RAMPs (receptor activity modifying proteins). These are single transmembrane-spanning proteins that are able to modulate the expression and/or function of certain GPCRs (Sexton, 1999). Their discovery arose as a consequence of studies on the class B GPCRs related to the peptides calcitonin, calcitonin gene-related peptide (CGRP), and amylin. Initial attempts at cloning the receptor for CGRP led to the identification of a GPCR gene product termed the calcitonin receptor–like receptor (CRLR). However, expression of this GPCR on its own in recombinant systems gave variable results. It was only when the CRLR was coexpressed with another protein (subsequently termed RAMP-1), that a CGRP receptor phenotype was detected, possessing properties similar to the CGRP receptor found in native tissues (McLatchie et al., 1998). Coexpression of the CRLR with either RAMP-2 or RAMP-3, the other two RAMPs identified thus far, led to the expression of adrenomedullin receptor phenotypes. Here then was an example of a single GPCR that could associate with another protein to yield quite distinct receptor phenotypes. These findings have now been extended to other receptors of this family; for instance coexpres-

Table 1.21.4 Ligand-binding Properties of Selected Opioid Ligands for the κ-δ Heterodimera

KI (nM)

Ligand U69593 DPDPE U69593 (+ 10 µM DPDPE) DPDPE (+ 10 µM U69593)

κ

δ

κ-δ

14.4 > 1000 14.4 ND

> 1000 21.8 ND 24.8

> 1000 > 1000 9.2 20.0

aData from Jordan and Devi (1999). ND, Not determined.

GABAB R1 GABA

GABAB R2 GABA

coiled-coil

Figure 1.21.24 Model of the GABAB heterodimer showing the coiled-coil arrangement between the two intracellular C termini that leads to the formation of a strongly-associated dimer. The dimer is also able to bind two molecules of GABA.

Receptor Binding

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Surface fluorescence (% control)

120 100 80

*

60

*** 40 20 0

δ

κ

κ−δ

Figure 1.21.25 Internalization of δ and κ opioid receptor homo or heterodimers in a recombinant expression system. Cells were exposed to 1 µM etorphine for 60 min and receptor internalization quantified by flow cytometry. When expressed individually, δ opioid receptors (light bar; far left) undergo a significant internalization relative to κ receptors (dark bar; center). In contrast, cells coexpressing both κ and δ receptors (far right) show a marked attenuation in the ability of the δ receptor to undergo internalization, presumably because of its association with the κ receptor to form a heterodimer. Data taken from Jordan and Devi (1999).

100

% [125I]IPIN bound

80

60

40

20

0 –7

–6

–5

–4

Log [NE]

Overview of Receptor Allosterism

Figure 1.21.26 Effect of wild type arrestins on the competition between [125I]iodopindolol ([125I]PIN) and norepinephrine (NE) at the hamster phosphorylated β2 adrenoceptor. Competition curves are shown in the absence (circles) or presence of 1 µM β-arrestin (triangles) or 300 nM arrestin 3 (squares). Data taken from Gurevich et al. (1997).

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A glycosylation

RAMP ligand terminal modification

GPCR

B

C

Figure 1.21.27 Possible mechanisms for the modulation of receptor phenotype by receptor activity modifying proteins (RAMPs). (A) The RAMP is able to alter the terminal glycosilation status of the receptor, thus modifying ligand binding. (B) The RAMP is able to modify receptor conformation via an allosteric interaction. (C) The RAMP physically associates with the receptor and contributes to the formation of the orthosteric ligand binding site.

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sion of the calcitonin receptor with either RAMP-1 or RAMP-3 yields the amylin receptor phenotype (Christopoulos et al., 1999). The discovery of RAMPs has led to a reevaluation of what exactly constitutes a receptor phenotype. At the moment, the actual mechanisms involved in RAMP-GPCR interaction are far from elucidated. Figure 1.21.27 shows a schematic summarizing three possibilities that have been proposed (Sexton, 1999). The first is a RAMP-mediated alteration in receptor glycosylation status; the second is a true allosteric protein-protein interaction, while the third involves a physical contribution of the RAMP to the GPCR orthosteric site. What is known, however, is that the same agonist binding to the same GPCR gene product can exhibit quite markedly different pharmacology depending on the RAMP that is associated with the receptor (Fig. 1.21.28). Further issues, including the role of RAMPs on G protein coupling, remain to be addressed. Some of the most emergent additions to the list of GPCR coupling partners promise to quash the concept of the receptor-G protein signaling hierarchy altogether. These novel coupling partners may be loosely termed “targeting proteins,” and encompass an evergrowing array of proteins containing specific amino acid “modules” that allow them to bind to complementary modules in other proteins and thus lead to the assembly of multimeric signaling complexes. One important family of targeting proteins are the “PDZ domain-containing” proteins. These possess a GLGF sequence and a conserved arginine that can self-aggregate and/or interact with other proteins containing a –S/TxV motif. The PDZ proteins derive their name from the three cell-organizing proteins in which this association was first noted, the postsynaptic density (PSD-95) protein, the Drosophila disks large (dlg) protein, and the zona occludens (zo-1) protein. Although already known to play a crucial role in coupling to the NMDA ion channel–linked receptors and targeting them to post-synaptic densities in neurons, PDZ domain–containing proteins are now known to interact directly with GPCRs as well. For example, members of the metabotropic glutamate (mGluR2 and mGluR5) receptor family couple to a PDZ domain-containing

[125I]Rat amylin bound (CPM)

5,000

4,000

3,000

2,000

1,000

0 –12

+ RAMP-1 + RAMP-3 –11

–10

–9

–8

–7

–6

Log[CGRP]

Overview of Receptor Allosterism

Figure 1.21.28 Generation of two distinct amylin receptor phenotypes with different ligand binding properties by RAMP-1 and RAMP-3. Curves show the competition between [125I]rat amylin and CGRP in COS-7 cells cotransfected with the rat calcitonin receptor and either RAMP. Data taken from Christopoulos et al. (1999).

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protein known as “Homer” which then targets the receptors to their appropriate site of action (Bockaert and Pin, 1999; Neubig, 1998), and the somatostatin receptor subtype 2 is able be targeted in a similar fashion by coupling to another PDZ domain-containing protein called SSTRIP (Zitzer et al., 1999). The β2 adrenoceptor and the P2Y1 purinergic receptor also couple to a PDZ domain-containing protein known as NHERF (Na+/H+ exchange regulatory factor) and are able to regulate its function completely independently of their ability to couple to G proteins (Hall et al., 1998). Finally, GPCRs that contain polyproline-rich regions, such as those found in the third intracellular loop of the β1 adrenoceptor, are able to bind with other targeting proteins that contain Src homology (SH)3 domains. The “endophilins” (SH3p4/p8/p13) are one such group of proteins that are able to bind to the β1 adrenoceptor and may play a role in agonist-mediated internalization of that receptor (Tang et al., 1999). Findings such as these highlight the bewildering array of GPCR-accessory protein interactions, but it should be noted that many of these may prove to utilize allosteric mechanisms in subserving their physiological roles. SUMMARY Allosteric interactions come in many flavors, but all involve the transmission of a conformational change across a receptor surface such that the subsequent ability of that receptor to bind other ligands and/or proteins is modified. Thus, allosteric mechanisms allow for profound alterations in cellular homeostasis in response to subtle receptor-binding events. This overview has focused on the concept of allosterism within GPCRs as well as between GPCRs and other proteins. Although the manifestations and consequences of allosteric interactions involving these receptors may vary dramatically, the study and quantification of these phenomena often involve similar methodological approaches that can provide a remarkable insight into the communication machinery of the cell. Ultimately, the exploitation of allosteric phenomena may lead to novel therapeutic regimens that provide maximum benefit while causing minimal adverse effects. LITERATURE CITED AbdAlla, S., Zaki, E., Lother, H., and Quitterer, U. 1999. Involvement of the amino terminus of the B2 receptor in agonist-induced receptor dimerization. J. Biol. Chem. 274:26079-26084. Angers, S., Salahpour, A., Joly, E., Hilairet, S., Chelsky, D., Dennis, M., and Bouvier, M. 2000. Detection of β2-adrenergic receptor dimerization in living cells using bioluminescence resonance energy transfer (BRET). Proc. Natl. Acad. Sci. U.S.A. 97:3684-3689. Bhattacharya, S. and Linden, J. 1995. The allosteric enhancer, PD 81,723, stabilizes human A1 adenosine receptor coupling to G proteins. Biochim. Biophys. Acta 1265:15-21. Birdsall, N.J., Lazareno, S., and Matsui, H. 1996. Allosteric regulation of muscarinic receptors. Prog. Brain Res. 109:147-51. Bockaert, J. and Pin, J.P. 1999. Molecular tinkering of G protein-coupled receptors: An evolutionary success. EMBO J. 18:1723-1729. Bruns, R.F. and Fergus, J.H. 1990. Allosteric enhancement of adenosine A1 receptor binding and function by 2-amino-3-benzoylthiophenes. Mol. Pharmacol. 38:939-949. Bunemann, M., Lee, K.B., Pals-Rylaarsdam, R., Roseberry, A.G., and Hosey, M.M. 1999. Desensitization of G-protein-coupled receptors in the cardiovascular system. Annu. Rev. Physiol. 61:169-192. Chidiac, P., Green, M.A., Pawagi, A.B., and Wells, J.W. 1997. Cardiac muscarinc receptors: Cooperativity as the basis for multiple states of affinity. Biochemistry 36:7361-7379. Christopoulos, A. and El-Fakahany, E.E. 1999. Qualitative and quantitative assessment of relative agonist efficacy. Biochem. Pharmacol. 58:735-748. Christopoulos, A., Lanzafame, A., and Mitchelson, F. 1998. Allosteric interactions at muscarinic cholinoceptors. Clin. Exp. Pharmacol. Physiol. 25:184-194. Receptor Binding

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Christopoulos, G., Perry, K.J., Morfis, M., Tilakaratne, N., Gao, Y., Fraser, N.J., Main, M.J., Foord, S.M., and Sexton, P.M. 1999. Multiple amylin receptors arise from receptor activity-modifying protein interaction with the calcitonin receptor gene product. Mol. Pharmacol. 56:235-242. Cohen, F., Lazareno, S., and Birdsall, N.J.M. 1994. Allosteric interactions of PD 81,723 at the human adenosine A1 receptor. Br. J. Pharmacol. 113:51P. Colquhoun, D. 1973. The relation between classical and cooperative models for drug action. In Drug Receptors. (H.P. Rang, ed.) pp. 149-182. Macmillan Press, London. Colquhoun, D. 1998. Binding, gating, affinity and efficacy: The interpretation of structure-activity relationships for agonists and of the effects of mutating receptors. Br. J. Pharmacol. 125:924-947. Cvejic, S. and Devi, L.A. 1997. Dimerization of the δ opioid receptor: Implication for a role in receptor internalization. J. Biol. Chem. 272:26959-26964. Ehlert, F.J. 1985. The relationship between muscarinic receptor occupancy and adenylate cyclase inhibition in the rabbit myocardium. Mol. Pharmacol. 28:410-421. Ehlert, F.J. 1988. Estimation of the affinities of allosteric ligands using radioligand binding and pharmacological null methods. Mol. Pharmacol. 33:187-194. Ehlert, F.J. and Rathbun, B.E. 1990. Signaling through the muscarinic receptor-adenylate cyclase system of the heart is buffered against GTP over a range of concentrations. Mol. Pharmacol. 38:148-158. Ellis, J. 1997. Allosteric binding sites on muscarinic receptors. Drug Dev. Res. 40:193-204. Furchgott, R.F. 1966. The use of β-haloalkylamines in the differentiation of receptors and in the determination of dissociation constants of receptor-agonist complexes. Adv. Drug Res. 3:21-55. Galzi, J.-L., Revah, F., Bessis, A., and Changeux, J.P. 1991. Functional architecture of the nicotinic acetylcholine receptor: From electric organ to brain. Ann. Rev. Pharmacol. 31:37-72. Gao, Z.G. and Ijzerman, A.P. 2000. Allosteric modulation of A2A adenosine receptors by amiloride analogues and sodium ions. Biochem. Pharmacol. 60:669-676. Gouldson, P.R., Snell, C.R., Bywater, R.P., Higgs, C. and Reynolds, C.A. 1998. Domain swapping in G-protein coupled receptor dimers. Protein Eng. 11:1181-1193. Gurevich, V.V., Pals-Rylaarsdam, R., Benovic, J.L., Hosey, M.M., and Onorato, J.J. 1997. Agonist-receptorarrestin, an alternative ternary complex with high agonist affinity. J. Biol. Chem. 272:28849-28852. Hall, R.A., Ostedgaard, L.S., Premont, R.T., Blitzer, J.T., Rahman, N., Welsh, M.J., and Lefkowitz, R.J. 1998. A C-terminal motif found in the β2-adrenergic receptor, P2Y1 receptor and cystic fibrosis transmembrane conductance regulator determines binding to the Na+/H+ exchanger regulatory factor family of PDZ proteins. Proc. Natl. Acad. Sci. U.S.A. 95:8496-8501. Hebert, T.E. and Bouvier, M. 1998. Structural and functional aspects of G protein-coupled receptor oligomerization. Biochem. Cell Biol. 76:1-10. Hebert, T.E., Loisel, T.P., Adam, L., Ethier, N., St. Onge, S., and Bouvier, M. 1998. Functional rescue of a constitutively desensitized β2AR through receptor dimerization. Biochem. J. 330:287-293. Hebert, T.E., Moffett, S., Morello, J.-P., Loisel, T.P., Bichet, D.G., Barret, C., and Bouvier, M. 1996. A peptide derived from a β2-adrenergic receptor transmembrane domain inhibits both receptor dimerization and activation. J. Biol. Chem. 271:16384-16392. Hedlund, P.B., Carson, M.J., Sutcliffe, J.G., and Thomas, E.A. 1999. Allosteric regulation by oleamide of the binding properties of 5- hydroxytryptamine7 receptors. Biochem. Pharmacol. 58:1807-1813. Hejnova, L., Tucek, S., and El-Fakahany, E.E. 1995. Positive and negative allosteric interactions on muscarinic receptors. Eur. J. Pharmacol. 291:427-430. Henis, Y.I., Kloog, Y., and Sokolovsky, M. 1989. Allosteric interactions of muscarinic receptors and their regulation by other membrane proteins. In The Muscarinic Receptors. (J.H. Brown, ed.) pp. 377-418. Humana Press, Clifton, New Jersey. Hoare, S.R.J. and Strange, P.G. 1996. Regulation of D2 dopamine receptors by amiloride and amiloride analogs. Mol. Pharmacol. 50:1295-1308. Holzgrabe, U. and Mohr, K. 1998. Allosteric modulators of ligand binding to muscarinic acetylcholine receptors. Drug Disc. Today 3:214-222. Horstman, D.A., Brandon, S., Wilson, A.L., Guyer, C.A., Cragoe, E.J. Jr., and Limbird, L.E. 1990. An aspartate conserved among G-protein receptors confers allosteric regulation of α2-adrenergic receptors by sodium. J. Biol. Chem. 265:21590-21595. Hulme, E.C. and Birdsall, N.J.M. 1992. Strategy and tactics in receptor-binding studies. In Receptor-Ligand Interactions. A Practical Approach (E.C. Hulme, ed.) pp. 63-176. Oxford University Press, New York. Overview of Receptor Allosterism

Jakubík, J., Bacakova, L., Lisa, V., El-Fakahany, E.E., and Tucek, S. 1996. Activation of muscarinic acetylcholine receptors via their allosteric binding sites. Proc. Natl. Acad. Sci. U.S.A. 93:8705-8709.

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Jakubík, J., Bacakova, L., El-Fakahany, E.E., and Tucek, S. 1997. Positive cooperativity of acetylcholine and other agonists with allosteric ligands on muscarinic acetylcholine receptors. Mol. Pharmacol. 52:172-179. Jones, K.A., Borowsky, B., Tamm, J.A., Craig, D.A., Durkin, M.M., Dai, M., Yao, W.J., Johnson, M., Gunwaldsen, C., Huang, L.Y., Tang, C., Shen, Q., Salon, J.A., Morse, K., Laz, T., Smith, K.E., Nagarathnam, D., Noble, S.A., Branchek, T.A., and Gerald, C. 1998. GABAB receptors function as a heteromeric assembly of the subunits GABABR1 and GABABR2. Nature 396:674-679. Jordan, B.A. and Devi, L.A. 1999. G-protein-coupled receptor heterodimerization modulates receptor function. Nature 399:697-700. Karlin, A. 1967. On the application of “a plausible model” of allosteric proteins to the receptor for acetylcholine. J. Theor. Biol. 16:306-320. Kaupmann, K., Malitschek, B., Schuler, V., Heid, J., Froestl, W., Beck, P., Mosbacher, J., Bischoff, S., Kulik, A., Shigemoto, R., Karschin, A., and Bettler, B. 1998. GABA(B)-receptor subtypes assemble into functional heteromeric complexes. Nature 396:683-687. Kenakin, T.P. 1997. Pharmacologic Analysis of Drug-Receptor Interaction, 3rd ed. Lippincott-Raven, Philadelphia. Kostenis, E. and Mohr, K. 1996. Composite action of allosteric modulators on ligand binding. Trends Pharmacol. Sci. 17:443-444. Lazareno, S. and Birdsall, N.J.M. 1995. Detection, quantitation, and verification of allosteric interactions of agents with labeled and unlabeled ligands at G protein-coupled receptors: Interactions of strychnine and acetylcholine at muscarinic receptors. Mol. Pharmacol. 48:362-378. Lazareno, S., Gharagozloo, P., Kuonen, D., Popham, A., and Birdsall, N.J.M. 1998. Subtype-selective positive cooperative interactions between brucine analogues and acetylcholine at muscarinic receptors: Radioligand binding studies. Mol. Pharmacol. 53:573-589. Lee, N.H. and El-Fakahany, E.E. 1991. Allosteric antagonists of the muscarinic acetylcholine receptor. Biochem. Pharmacol. 42:199-205. Lee, T.W., Sole, M.J., and Wells, J.W. 1986. Assessment of a ternary model for the binding of agonists to neurohumoral receptors. Biochemistry 25:7009-7020. Lefkowitz, R.J. 1998. G protein-coupled receptors. III. New roles for receptor kinases and beta-arrestins in receptor signaling and desensitization. J. Biol. Chem. 273:18677-18680. Lefkowitz, R.J., Cotecchia, S., Samama, P., and Costa, T. 1993. Constitutive activity of receptors coupled to guanine nucleotide regulatory proteins. Trends Pharmacol. Sci. 14:303-307. Leppik, R.A., Lazareno, S., Mynett, A., and Birdsall, N.J.M. 1998. Characterization of the allosteric interactions between antagonists and amiloride analogues at the human α2A-adrenergic receptor. Mol. Pharmacol. 53:916-925. Leppik, R.A., Mynett, A., Lazareno, S., and Birdsall, N.J. 2000. Allosteric interactions between the antagonist prazosin and amiloride analogs at the human α1A-adrenergic receptor. Mol. Pharmacol. 57:436-445. Limbird, L.E., De Meyts, P., and Lefkowitz, R.J. 1975. β-adrenergic receptors: Evidence for negative cooperativity. Biochem. Biophys. Res. Commun. 64:1160-1168. Lutz, M. and Kenakin, T. 1999. Quantitative Molecular Pharmacology and Informatics in Drug Discovery, West Sussex, England, Wiley & Sons. Maggio, R., Vogel, Z., and Wess, J. 1993a. Coexpression studies with mutant muscarinic/adrenergic receptors provide evidence for intermolecular “cross-talk” between G-protein-linked receptors. Proc. Natl. Acad. Sci. U.S.A. 90:3103-3107. Maggio, R., Vogel, Z., and Wess, J. 1993b. Reconstitution of functional muscarinic receptors by co-expression of amino- and carboxyl-terminal receptor fragments. FEBS Lett. 319:195-200. Marshall, F.H., Jones, K.A., Kaupmann, K., and Bettler, B. 1999. GABAB receptors: The first 7TM heterodimers. Trends Pharmacol. Sci. 20:396-399. Mattera, R., Pitts, B.J., Entman, M.L., and Birnbaumer, L. 1985. Guanine nucleotide regulation of a mammalian myocardial muscarinic receptor system. Evidence for homo- and heterotropic cooperativity in ligand binding analyzed by computer-assisted curve fitting. J. Biol. Chem. 260:7410-7421. McLatchie, L.M., Fraser, N.J., Main, M.J., Wise, A., Brown, J., Thompson, N., Solari, R., Lee, M.G., and Foord, S.M. 1998. RAMPs regulate the transport and ligand specificity of the calcitonin- receptor-like receptor. Nature 393:333-339. Monod, J. and Jacob, F. 1961. General conclusions: Teleonomic mechanisms in cellular metabolism, growth, and differentiation. Cold Spring Harbor Symp. Quant. Biol. 26:389-401. Monod, J., Changeux, J.-P., and Jacob, F. 1963. Allosteric proteins and cellular control systems. J. Mol. Biol. 6:306-329. Monod, J., Wyman, J., and Changeux, J.-P. 1965. On the nature of allosteric transitions: A plausible model. J. Mol. Biol. 12:88-118.

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Neubig, R.R. 1998. Specificity of receptor-G protein coupling: Protein structure and cellular determinants. Sem. Neurosci. 9:189-197. Ng, G.Y., O’Dowd, B.F., Lee, S.P., Chung, H.T., Brann, M.R., Seeman, P., and George, S.R. 1996. Dopamine D2 receptor dimers and receptor-blocking peptides. Biochem. Biophys. Res. Commun. 227:200-4. Nunnari, J.M., Repaske, M.G., Brandon, S., Cragoe, E.J. Jr., and Limbird, L.E. 1987. Regulation of porcine brain α2-adrenergic receptors by Na+, H+ and inhibitors of Na+/H+ exchange. J. Biol. Chem. 262:1238712392. Onaran, H.O., Costa, T., and Rodbard, D. 1993. βγ subunits of guanine nucleotide-binding proteins and regulation of spontaneous receptor activity: Thermodynamic model for the interaction between receptors and guanine nucleotide-binding protein subunits. Mol. Pharmacol. 43:245-256. Potter, L.T., Ferrendelli, C.A., and Hanchett, H.E. 1988. Two affinity states of M1 muscarine receptors. Cell. Mol. Neurobiol. 8:181-191. Potter, L.T., Ballesteros, L.A., Bichajian, L.H., Ferrendelli, C.A., Fisher, A., Hanchett, H.E., and Zhang, R. 1991. Evidence for paired M2 muscarinic receptors. Mol. Pharmacol. 39:211-221. Proska, J. and Tucek, S. 1994. Mechanisms of steric and cooperative actions of alcuronium on cardiac muscarinic acetylcholine receptors. Mol. Pharmacol. 45:709-717. Schetz, J.A. and Sibley, D.R. 1997. Zinc allosterically modulates antagonist binding to cloned D1 and D2 dopamine receptors. J. Neurochem. 68:1990-1997. Sexton, P.M. 1999. Recent advances in our understanding of peptide hormone receptors and RAMPS. Curr. Opin. Drug Disc. Devel. 2:440-448. Sigel, E. and Buhr, A. 1997. The benzodiazepine binding site of GABAA receptors. Trends Pharmacol. Sci. 18:425-429. Sinkins, W.G. and Wells, J.W. 1993. Protein-linked receptors labeled by [3H]histamine in guinea pig cerebral cortex. II. Mechanistic basis for multiple states of affinity. Mol. Pharmacol. 43:583-594. Sinkins, W.G., Kandel, M., Kandel, S.I., Schunack, W., and Wells, J.W. 1993. Protein-linked receptors labeled by [3H]histamine in guinea pig cerebral cortex. I. Pharmacological characterization. Mol. Pharmacol. 43:569-582. Steinberg, G.H., Eppel, J.G., Kandel, M., Kandel, S.I., and Wells, J.W. 1985a. H2 histaminic receptors in rat cerebral cortex. 1. Binding of [3H]histamine. Biochemistry 24:6095-6107. Steinberg, G.H., Kandel, M., Kandel, S.I., and Wells, J.W. 1985b. H2 histaminic receptors in rat cerebral cortex. 2. Inhibition of [3H]histamine by H2 antagonists. Biochemistry 24:6107-6115. Steinberg, G.H., Kandel, M., Kandel, S.I., and Wells, J.W. 1985c. H2 histaminic receptors in rat cerebral cortex. 3. Inhibition of [3H]histamine by H2 agonists. Biochemistry 24:6115-6125. Stephenson, R.P. 1956. A modification of receptor theory. Br. J. Pharmacol. 11:379-393. Tang, Y., Hu, L.A., Miller, W.E., Ringstad, N., Hall, R.A., Pitcher, J.A., DeCamilli, P., and Lefkowitz, R.J. 1999. Identification of the endophilins (SH3p4/p8/p13) as novel binding partners for the β1-adrenergic receptor. Proc. Natl. Acad. Sci. U.S.A. 96:12559-12564. Thomas, E.A., Carson, M.J., Neal, M.J., and Sutcliffe, J.G. 1997. Unique allosteric regulation of 5-hydroxytryptamine receptor-mediated signal transduction by oleamide. Proc. Natl. Acad. Sci. U.S.A. 94:14115-14119. Thron, C.D. 1973. On the analysis of pharmacological experiments in terms of an allosteric receptor model. Mol. Pharmacol. 9:1-9. Tucek, S. and Proska, J. 1995. Allosteric modulation of muscarinic acetylcholine receptors. Trends Pharmacol. Sci. 16:205-212. Waugh, D.J., Gaivin, R.J., Damron, D.S., Murray, P.A., and Perez, D.M. 1999. Binding, partial agonism, and potentiation of α1-adrenergic receptor function by benzodiazepines: A potential site of allosteric modulation. J. Pharmacol. Exp. Ther. 291:1164-1171. Weber, G. 1972. Ligand binding and internal equilibria in proteins. Biochemistry 11:864-878. Weber, G. 1975. Energetics of ligand binding to proteins. Adv. Prot. Chem. 29:1-83. White, J.H., Wise, A., Main, M.J., Green, A., Fraser, N.J., Disney, G.H., Barnes, A.A., Emson, P., Foord, S.M., and Marshall, F.H. 1998. Heterodimerization is required for the formation of a functional GABAB receptor. Nature 396:679-682. Wregget, K.A. and Wells, J.W. 1995. Cooperativity manifest in the binding properties of purified cardiac muscarinic receptors. J. Biol. Chem. 270:22488-22499. Wyman, J. 1975. The turning wheel: A study in steady states. Proc. Natl. Acad. Sci. U.S.A. 72:3983-3987. Overview of Receptor Allosterism

Zeng, F.Y. and Wess, J. 1999. Identification and molecular characterization of m3 muscarinic receptor dimers. J. Biol. Chem. 274:19487-19497.

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Zitzer, H., Honck, H.H., Bachner, D., Richter, D., and Kreienkamp, H.J. 1999. Somatostatin receptor interacting protein defines a novel family of multidomain proteins present in human and rodent brain. J. Biol. Chem. 274:32997-33001.

Contributed by Arthur Christopoulos University of Melbourne Victoria, Australia

Receptor Binding

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Quantification of Allosteric Interactions at G Protein Coupled Receptors Using Radioligand Binding Assays

UNIT 1.22

Allosteric interactions are noncompetitive interactions that occur when the binding of a ligand to a secondary (allosteric or allotopic) site on the receptor alters the protein conformation such that the ability of the primary ligand (e.g., agonist, competitive antagonist) to bind to the classic (orthosteric) site on the receptor is modified or modulated (see also UNITS 1.2 & 1.21). This modulation is often manifested as a change in affinity, either an increase (positive cooperativity) or a decrease (negative cooperativity). Furthermore, whatever the allosteric modulator does to orthosteric ligand binding, the latter also does to the former i.e., allosteric interactions are reciprocal in nature. Although the scope of allosterism encompasses many biologically relevant phenomena (see UNIT 1.21), the current unit focuses on allosteric interactions between different ligands observed at G protein-coupled receptors (GPCRs). These interactions can be studied in both radioligand binding or functional assays, but the former represent the most direct means for examining allosteric mechanisms in detail and the protocols outlined in this unit are thus limited to the study of allosterism in equilibrium and non-equilibrium binding assays. The specific examples given in this unit are based on studies conducted on muscarinic acetylcholine receptors, but the protocols themselves are essentially applicable to any GPCR with minimal alterations. Indeed, it should be noted that all the protocols are modifications of the standard radioligand binding procedures outlined in other units of this chapter and they can be readily applied to different receptor families, simply by choosing the appropriate radioligand and any other orthosteric ligands specific to the receptor of interest. In general, the different approaches applied to the study of allosteric phenomena can be divided into binding assays conducted at equilibrium and binding assays conducted under non-equilibrium conditions. This unit presents three general groups of protocols that encompass both of these situations. The first group (see Basic Protocol 1 and Alternate Protocol 1) is based on “saturation”-type equilibrium binding assays that rely on more than one radioligand concentration; the second group is based on equilibrium (see Basic Protocol 2) and non-equilibrium (see Alternate Protocol 2) binding assays that monitor the effects of various concentrations of allosteric modulator on a fixed concentration of radioligand; the third group (see Basic Protocol 3 and Alternate Protocols 3 and 4) is based on experiments that monitor the rate of radioligand dissociation in the absence and presence of different concentrations of modulator over time. Of utmost importance to the quantification of allosteric interactions, however, is the appropriate mode of data analysis, which is often more complicated than that applied to the study of competitive interactions. Thus, this unit also contains a large Support Protocol outlining different methods applied to the analysis of allosteric interactions in radioligand binding assays. MEASUREMENT OF ALLOSTERIC MODULATION OF RADIOLIGAND BINDING: SATURATION EXPERIMENTS

BASIC PROTOCOL 1

This protocol describes the procedures for directly measuring the binding of a radioligand to a GPCR in the absence or presence of various concentrations of an allosteric modulator. These experiments can be used to assess the effects of the modulator on radioligand affinity (e.g., KDvalue) and the ability of the radioligand to maximally occupy the receptor population (e.g., Bmaxvalue). Being noncompetitive in nature, the interaction between an Receptor Binding Contributed by Arthur Christopoulos Current Protocols in Pharmacology (2000) 1.22.1-1.22.40 Copyright © 2000 by John Wiley & Sons, Inc.

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allosteric modulator and an orthosteric radioligand, as depicted in the ternary complex model (see Commentary), can result in a true alteration of KD. It should be noted, however, that the ternary complex model utilized in this unit to describe allosterism does not predict a reduction in Bmax. Materials Appropriate receptor preparation (e.g., see other units in Chapter 1 for detailed methods on preparing membranes containing various receptor populations) Assay buffer (e.g., HEPES or Tris-based buffers) Radioligand Test compound (allosteric modulator) Unlabeled (non-radioactive) competitive (orthosteric) ligand Wash buffer (usually the same as the assay buffer), ice-cold Scintillation cocktail (e.g., Packard Ultima Gold; Wallac HiSafe) 12 × 75–mm glass or polypropylene culture tubes Shaking water bath, 37°C Glass fiber filters (e.g., Whatman GF/B) Cell harvester (e.g., Brandell or Skatron) Scintillation counter and appropriate vials 1. Prepare a fresh receptor preparation or thaw a frozen one. Keep on ice until required, and vortex to ensure suspension of the pellet. Detailed methodology on the preparation of muscarinic receptor membrane homogenates may be found in Hulme and Buckley (1992). Methods specific to CHO cells expressing the human muscarinic receptor subtypes can be found in Christopoulos et al. (1999).

2. Dilute the receptor preparation with assay buffer to 10× the protein concentration desired in the final assay (see Critical Parameters and Troubleshooting). Ideally, the final receptor concentration should correspond to ∼0.1× the KD of the radioligand; however, this amount should be determined separately for each preparation of cells, as expression levels vary between passages, as well as between receptor subtypes. Also, receptor concentrations below 50 pM may often yield a level of counts for some tritiated ligands that is too low to be reliable. In any case, the final total protein concentration should be optimized such that specific binding increases linearly with increasing protein concentration, and no more than 10% of the total added radioligand is bound. This approach is empirical and is valid irrespective of whether the radioligand KD is known or not.

3. Prepare serial dilutions of the radioligand in assay buffer at concentrations 10× the final concentration desired. At least 8 or more concentrations of radioligand should be prepared that cover its KD value by at least an order of magnitude in either direction.

4. Prepare dilutions of the test compound (allosteric modulator) at 10× the final concentration desired. Due to solubility considerations, the most concentrated dilution of the test compound may need to be made in a diluent other than water (e.g., 5 mM HCl, ethanol, or dimethylsulfoxide), prior to further dilution in assay buffer. In these instances, parallel experiments should be conducted to assess the effects of the vehicle on radioligand binding. Quantification of Allosteric Interactions at G Protein Coupled Receptors

5. Prepare a stock solution of unlabeled competitive ligand at a concentration corresponding to 1,000× to 10,000× its KD value for the receptor. This agent will be used to define nonspecific binding and will undergo a 1:10 dilution in the assay tubes, yielding a final concentration of 100× to 1000× its KD value.

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Prepare reactions NOTE: Standard saturation binding assays monitor the binding of increasing radioligand concentrations to the receptor of interest. The protocol described here is more complex because it involves constructing a number of such saturation curves in the absence and presence of various concentrations of allosteric modulator. NOTE: Prepare all reactions in triplicate and add reagents in the order indicated. 6. To measure total binding in the absence of modulator, add 800 µl assay buffer to 12 × 75–mm culture tubes, followed by 100 µl radioligand. 7. To measure total binding in the presence of modulator, add 700 µl assay buffer to 12 × 75–mm culture tubes. Add 100 µl of allosteric modulator, followed by 100 µl of radioligand. 8. To measure nonspecific binding in the absence of modulator, add 700 µl assay buffer to 12 × 75–mm culture tubes. Add 100 µl of unlabeled orthosteric ligand stock solution, followed by 100 µl of radioligand. Table 1.22.1 Assaya

Example of a Multiple Curve Radioligand Saturation Binding

dpm boundc [3H]NMSb (nM) Log [3H]NMS

0.045 0.098 0.216 0.474 1.04 2.283 5.012 0.369 0.708 1.359 2.610 5.012 0.631 1.259 2.512 5.012 10.0 1.01 3.162 10.0

−10.35 −10.01 −9.67 −9.32 −8.98 −8.64 −8.30 −9.43 −9.15 −8.87 −8.58 −8.30 −9.20 −8.90 −8.60 −8.30 −8.00 −9.00 –8.50 −8.00

[gallamine] (µM) 0

1

85.5 138.1 283.5 418.0 468.8 560.3 567.4

57.7 108.9 236.9 326.9 450.9 483.2

3

10

100

97.9 147.6 209.3 360.7 412.4 33.0 133.1 217.4 323.4 364.2 76.9 172.1 285.0

aAssay was performed using the antagonist [3H]N-methylscopolamine ([3H]NMS) at cloned M 2

muscarinic acetylcholine receptors conducted in the absence or the presence of increasing concentrations of the allosteric modulator, gallamine. Source of the receptor was the human M2 clone, stably expressed in CHO-K1 cells and provided by Dr. Mark Brann (University of Vermont Medical School, Burlington). 50 µg protein was added to each tube in a total volume of 1 ml HEPES assay buffer (30 mM HEPES pH 7.4/0.5 mM EGTA/5 mM MgCl2). All dilutions of the modulator and radioligand were made in assay buffer. Nonspecific binding was determined in the presence of 1 µM atropine. bRadioligand concentrations represent the free [3H]NMS determined after subtracting total bound dpm from total added dpm for each concentration of radioligand utilized. Specific activity of the radioligand was 84.5 Ci/mmol. Data are also shown in Figure 1.22.2.

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9. To measure nonspecific binding in the presence of modulator, add 600 µl assay buffer to 12 × 75–mm culture tubes. Add 100 µl of unlabeled orthosteric ligand stock solution, followed by 100 µl of allosteric modulator and 100 µl of radioligand. 10. Add 100 µl receptor preparation to all reactions and vortex. 11. Incubate for 90 min in a 37°C shaking water bath at 55 rpm to attain binding equilibrium. Allosteric modulators can exert profound effects on the association and dissociation kinetics of orthosteric ligands. The effects of the highest concentrations of modulator on the attainment of equilibrium by the lowest concentrations of radioligand employed need to be determined separately, and should be used to determine the equilibration time for the saturation assay. If it is found that equilibrium is not attained during any of the incubation times tested, then this protocol cannot be utilized for the particular modulator-radioligand pair, and a nonequilibrium protocol should be used instead (see Basic Protocol 3 and Alternate Protocol 2).

12. Terminate the reaction by filtering the assay mixtures using rapid vacuum filtration over glass fiber filters (e.g., Whatman GF/B) positioned on a cell harvester. Wash the filter 3 times with 3-ml aliquots of ice-cold wash buffer. The rinse conditions and filter soaking conditions will need to be determined experimentally. If necessary, presoak the filters in 0.1% to 0.5% (w/v) polyethyleneimine (PEI), especially if lipophilic ligands are used.

13. Allow the filters to dry thoroughly. Place in 10-ml scintillation vials and add 5 ml scintillation cocktail. Shake the vials and let them sit for at least 6 hr to allow the filters to become uniformly translucent prior to counting in a scintillation counter. 14. Take triplicate aliquots of each working radioligand concentration. Place them in scintillation vials, add 5 ml scintillant and count in a scintillation counter. 15. Analyze the data as described in the Support Protocol. Table 1.22.1 shows an example of this type of experiment conducted on cloned human M2 muscarinic acetylcholine receptors in the absence or presence of different concentrations of the allosteric modulator gallamine. ALTERNATE PROTOCOL 1

DETERMINATION OF THE AFFINITY RATIO Saturation binding assays (see Basic Protocol 1), can be quite time-consuming and expensive. Often, a rapid screening process that does not necessitate an exhaustive quantification of the data may be required that, nevertheless, is still useful for detecting allosterism and providing information that may be analyzed at least in a semiquantitative fashion. The affinity ratio assay meets these criteria and also offers the advantages of being graphically versatile, as the effects of the modulator on the binding of any orthosteric ligand (labeled or unlabeled) can be visualized, and analyzed simply. The semiquantitative information can be calculated directly from the data rather than estimated by nonlinear regression analysis using dedicated computer software. Additional Materials (also see Basic Protocol 1) 10× stock solution of a different, unlabeled, orthosteric ligand (optional)

Quantification of Allosteric Interactions at G Protein Coupled Receptors

1. Prepare a fresh receptor preparation or thaw a previously-frozen one. Keep on ice until required, and vortex to ensure suspension of the pellet. 2. Dilute the receptor preparation with assay buffer to 10× the protein concentration desired in the final assay.

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Prepare two concentrations of radioligand NOTE: The affinity ratio assay requires the utilization of one high and one low radioligand concentration. The high concentration is only used to determine maximal specific binding of the radioligand in the absence of test agent (i.e., modulator). The lower concentration is used for the remainder of the assay where the effects of the test ligand are assessed. 3. Dilute radioligand in assay buffer to 10× the desired final assay concentration (i.e., diluted to 10× KD). For most of the tubes, a low final concentration (∼KD or less) of radioligand is required.

4. Make a small amount (approximately 1 to 2 ml) of a very concentrated solution of radioligand (yielding at least a final concentration of 10× KD) to saturate the receptors for determination of Bmax. For a final concentration of 10× KD, therefore, make a stock solution of radioligand at 100× K D.

5. Prepare a stock solution of an unlabeled competitive ligand at a concentration corresponding to 10,000× its KD value for the receptor. This agent will be used to define nonspecific binding.

6. Prepare serial dilutions of the test compound (allosteric modulator) in assay buffer at concentrations 10× the final concentration desired. Routinely, a minimum of three different concentrations of modulator should be utilized. These should cover three orders of magnitude (e.g., 0.1 mM, 1 mM, and 10 mM) and will provide some idea of the activity of the compound. If desired, more concentrations can be added to the assay, but this can defeat the purpose if the main goal of the assay is preliminary and rapid screening. If a more detailed investigation of the activity of the modulator is required, use a different protocol (see Basic Protocol 1 and 2).

Prepare reactions NOTE: Reagents must be added in the order indicated. Prepare reactions for each allosteric modulator concentration. 7. To measure total (maximal) binding in absence of modulator, add 800 µl assay buffer to 12 × 75–mm culture tubes, followed by 100 µl concentrated radioligand (100× KD stock; see step 4). Prepare tubes in quadruplicate.

8. To measure total binding of low radioligand concentration in the absence of modulator, add 800 µl assay buffer to 12 × 75–mm culture tubes. Add 100 µl of dilute radioligand (10× KD stock; see step 3). Prepare tubes in quadruplicate.

9. To measure total binding in the presence of modulator, add 700 µl assay buffer to 12 × 75–mm culture tubes. Add 100 µl of test compound, followed by 100 µl of dilute radioligand (10× KD stock; see step 3). Prepare tubes in triplicate or quadruplicate.

10. To measure nonspecific binding, add 700 µl assay buffer to eight 12 × 75–mm culture tubes, followed by 100 µl of unlabeled competitive ligand (10,000× KD stock; see step 5). Add 100 µl of dilute radioligand (see step 3) to four tubes and 100 µl of concentrated radioligand (100× KD stock; see step 4) to the other four. Receptor Binding

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11. For determination of the effects of an allosteric modulator on another unlabeled orthosteric ligand (optional): Add 600 µl assay buffer to a 12 × 75–mm culture tube. Add 100 µl of test compound (allosteric modulator), followed by 100 µl of a 10× stock solution of a different, unlabeled, orthosteric ligand and 100 µl of dilute radioligand (10× KD; see step 4). Prepare tubes in triplicate or quadruplicate per point. This step is particularly useful when information is required about the interaction between the modulator and an orthosteric ligand that is not available in a radiolabeled form. A reasonable concentration for any additional unlabeled orthosteric agent should be determined in separate competition assays between this agent and the radioligand (UNIT 1.3). This is desirable for two reasons. First, the equilibrium dissociation constant of the unlabeled orthosteric ligand (KI) is required for the affinity ratio calculations. Second, the chosen concentration of unlabeled ligand must not be too high, such that it causes a large (>50% to 60%) reduction in radioligand binding in its own right. Otherwise, if a negatively cooperative allosteric modulator were combined with the radioligand and an additional unlabeled competitor, further reductions in specific binding may yield counts that are too low to be reliable.

12. Add 100 µl membrane homogenate to all tubes and vortex. 13. Incubate for 90 min (or as appropriate) in a 37°C shaking water bath at 55 rpm to ensure binding equilibrium. The choice of incubation time should be sufficient to allow for equilibrium to be achieved in the assay, and needs to be determined in separate experiments. See Basic Protocol 1, step 11 for further discussion.

14. Terminate the reaction by filtering the assay mixtures using rapid vacuum filtration over glass fiber filters (e.g., Whatman GF/B) positioned on a cell harvester. Wash the filter 3 times with 3-ml aliquots of ice-cold wash buffer. 15. Allow the filters to dry thoroughly. Place in 10-ml scintillation vials and add 5 ml scintillation cocktail. Shake the vials and let them sit for at least 6 hr to allow the filters to become uniformly translucent prior to counting in a scintillation counter. 16. Take triplicate aliquots of the working radioligand concentrations (low and high), place in scintillation vials, add 5 ml scintillant and count in a scintillation counter. 17. Analyze the data as described in the Support Protocol. BASIC PROTOCOL 2

Quantification of Allosteric Interactions at G Protein Coupled Receptors

MEASUREMENT OF ALLOSTERIC MODULATION OF RADIOLIGAND BINDING: INHIBITION (OR POTENTIATION) EXPERIMENTS The measurement of the effects of increasing concentrations of an unlabeled test compound on the binding of a fixed concentration of radioligand represents the most common type of binding experiment. Most often, these assays are undertaken to measure binding of a test compound that interacts at the same site on the receptor as the radioligand (i.e., the interaction is competitive in nature; UNIT 1.2). Assuming this to be the case, the experiment is often referred to as a “competition binding experiment” or a “displacement experiment”. In each instance, the observed effect is a reduction in the specific binding of the radioligand as increasing concentrations of test ligand are utilized. The data may then be analyzed by a number of methods (UNIT 1.3) to determine the affinity of the test compound for the receptor labeled by the radioligand. The same type of experimental methodology may also be employed in the measurement and quantification of allosteric interactions (UNIT 1.21); however, because the observed

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effect of a modulator may be to either increase or decrease radioligand binding by a noncompetitive mechanism, it is inappropriate to refer to these types of experiments as “competition” or “displacement” assays. If the interaction is characterized by negative cooperativity (i.e., the modulator reduces the affinity of the radioligand for the receptor), then specific radioligand binding will be inhibited, whereas positive cooperativity between radioligand and modulator results in an enhancement or potentiation of radioligand binding. The following protocol describes the procedures for measuring binding of a fixed concentration of radioligand in the absence or presence of increasing concentrations of an allosteric modulator. This type of assay can be analyzed quantitatively to derive the pharmacological properties of the modulator (affinity for the allosteric site, degree of cooperativity between modulator, and radioligand). Materials Appropriate receptor preparation (e.g., see other units in Chapter 1 for detailed methods on preparing membranes containing various receptor populations) Assay buffer (e.g., HEPES or Tris-based buffers) Radioligand Unlabeled (non-radioactive) competitive (orthosteric) ligand Test compound (allosteric modulator) Wash buffer (usually the same as the assay buffer), ice-cold Scintillation cocktail (e.g., Packard Ultima Gold; Wallac HiSafe) 12 × 75–mm glass or polypropylene culture tubes Shaking water bath, 37°C Glass fiber filters (e.g., Whatman GF/B) Cell harvester (e.g., Brandell or Skatron) Scintillation counter and appropriate vials 1. Prepare a fresh receptor preparation or thaw a previously frozen one. Keep on ice until required, and vortex to ensure suspension of the pellet. 2. Dilute the receptor preparation in assay buffer to 10× the protein concentration desired in the final assay. 3. Dilute radioligand in assay buffer to 10× the desired final assay concentration (e.g., 10× KD). This will yield a final radioligand concentration equal to its KD value. These types of binding assays typically aim to keep the final radioligand concentration as small as possible, usually between 0.1–1.0 × KD. One reason why this is done is to minimize the impact of receptor occupancy by the radioligand on the derived pharmacological parameter estimates of the test compound, as these will be influenced by the affinity and concentration of radioligand employed (UNIT 1.3). Ideally, the minimal radioligand concentration that yields a reasonable signal-to-noise ratio is utilized; however, for very high affinity radioligands, the use of very small concentrations to label the receptor population can lead to the phenomenon of “ligand depletion”, whereby >10% of the radioligand in each tube is bound to the receptor. Under these circumstances, the standard analyses that are employed on the data yield invalid parameter estimates. To avoid ligand depletion, keep the amount of protein content per tube constant, but increase the incubation volume so as to reduce the concentration of receptors. Of course, this will also necessitate the use of more drug, radioligand, and test compound to maintain the same desired final ligand concentrations in each tube.

4. Prepare a stock solution of an unlabeled competitive ligand at 10,000× its KD value for the receptor. This agent will be used to define nonspecific binding.

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5. Prepare dilutions of the test compound (allosteric modulator) at 10× the final concentration desired. Table 1.22.2 presents an example equilibrium inhibition binding assay using the muscarinic receptor modulator, gallamine. A minimum of 10 concentrations of test compound should be utilized. Increasing concentrations at approximately 0.5 log unit intervals are often used. The number and range of concentrations will often be determined empirically, allowing for an equal number of concentrations above and below the IC50 value of the binding curve and a good definition of maximal and minimal curve asymptotes; however, smaller numbers of points (e.g., spanning decades) may be utilized for routine screening assays or pilot experiments.

Prepare reactions NOTE: Reagents must be added in the order indicated. Prepare reactions in duplicate or triplicate for each concentration of allosteric modulator. 6. To measure total binding in the absence of modulator, add 800 µl assay buffer to 12 × 75–mm culture tubes, followed by 100 µl radioligand. 7. To measure total binding in the presence of modulator, add 700 µl assay buffer to 12 × 75–mm culture tubes. Add 100 µl of test compound (allosteric modulator) followed by 100 µl of radioligand.

Table 1.22.2

Example of an Equilibrium Inhibition Binding Assaya

[gallamine] (stock)

[gallamine] (final)

Log [gallamine] dpm (final) (low)b

dpm (high)c

10 nM 30 nM 100 nM 200 nM 500 nM 1 µM 2 µM 5 µM 10 µM 20 µM 50 µM 100 µM 300 µM 1 mM

1 nM 3 nM 10 nM 20 nM 50 nM 100 nM 200 nM 500 nM 1 µM 2 µM 5 µM 10 µM 30 µM 100 µM

−9.00 −8.52 −8.00 −7.70 −7.30 −7.00 −6.70 −6.30 −6.00 −5.70 −5.30 −5.00 −4.52 −4.00

7056.0 7595.7 7120.3 7513.7 7396.3 7176.3 6568.7 6432.0 6155.0 5400.0 4152.3 3335.0 2298.7 2080.3

5279.0 5054.3 5613.7 5493.0 4839.3 4862.0 4786.0 3941.7 3335.7 2717.7 1788.0 1216.3 698.0 526.3

aAssay was performed using the antagonist, [3H]N-methylscopolamine ([3H]NMS) and

Quantification of Allosteric Interactions at G Protein Coupled Receptors

the allosteric modulator, gallamine, at cloned M2 muscarinic acetylcholine receptors. The source of the receptor was the human M2 clone, stably expressed in CHO-K1 cells and provided by Dr. Mark Brann (University of Vermont Medical School, Burlington). 50 µg of protein was added to each tube in a total volume of 1 ml HEPES assay buffer (30 mM HEPES, pH 7.4/0.5 mM EGTA/5 mM MgCl2). All dilutions of the modulator and radioligand were made in HEPES assay buffer. Nonspecific binding was determined in the presence of 1 µM atropine. bSpecific binding observed in the presence of 0.12 nM (final concentration) [3H]NMS. Total binding in the absence of inhibitor was determined as 5591 dpm and nonspecific binding as 59 dpm. cSpecific binding observed in the presence of 1 nM (final concentration) [3H]NMS. Total binding in the absence of inhibitor was 7995 dpm and nonspecific binding was 333 dpm. Specific activity of the radioligand was 84.5 Ci/mmol. Data are also shown in Fig. 1.22.5.

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8. To measure nonspecific binding, add 700 µl assay buffer to 12 × 75–mm culture tubes. Add 100 µl of unlabeled competitor (1,000× KD final) followed by 100 µl of radioligand. A rigorous exploration of the properties of the test compound will need to examine directly whether this agent affects nonspecific binding. In general, the allosteric modulator will also affect binding of the unlabeled competitive agent used to define nonspecific binding to the receptor; however, the concentration of the latter ligand is in vast excess such that it will still compete effectively and fully displace the radioligand from the specific binding sites, irrespective of the nature and extent to which its own specific binding is also modulated. In contrast, allosteric modulators are not generally known to affect nonspecific (nonreceptor) binding of radioligands. An internal check may simply be to set up an additional set of tubes that contain radioligand, unlabeled competitor, and the highest concentration of test compound utilized in the assay. Ideally, this value should not be significantly different from that determined in the tubes with (excess) unlabeled competitor, radioligand, and membrane alone. Alternatively, if the binding curve is well-defined by the concentration points utilized, then the computerized curve-fitting procedure will be able to extract the appropriate pharmacological model parameters from the data whether nonspecific binding is included in the data or not, provided that no significant ligand depletion occurs in any of the tubes.

9. Add 100 µl receptor preparation to all tubes and vortex. 10. Incubate for 90 min (or as appropriate) in a 37°C shaking water bath at 55 rpm to attain binding equilibrium. Because allosteric modulators can exert significant effects on the kinetics of the radioligand, different incubation times should be tested to ascertain that the binding curve indeed represents a true equilibrium situation (also see Alternate Protocol 2). As a rough guide, the reaction should be allowed to proceed for at least 5 radioligand dissociation half-lives, the latter determined in a dissociation kinetic assay (see Basic Protocol 3).

11. Terminate the reaction by filtering the assay mixtures using rapid vacuum filtration over glass fiber filters (e.g., Whatman GF/B) positioned on a cell harvester. Wash the filter 3 times with 3-ml aliquots of ice-cold wash buffer. 12. Allow the filters to dry thoroughly. Place them in 10-ml scintillation vials and add 5 ml scintillation cocktail. Shake the vials and let them sit for at least 6 hr to allow the filters to become uniformly translucent prior to counting in a scintillation counter. 13. Take triplicate aliquots of the working radioligand concentration, place in scintillation vials, add 5 ml scintillation cocktail and count in a scintillation counter. 14. If deemed necessary, repeat steps 1 through 10 with a higher (e.g., 10×) concentration of radioligand. The ternary complex model of allosteric interaction between two ligands occupying different sites on the same receptor (see UNITS 1.2 & 1.21; also see Commentary) predicts that the magnitude of the allosteric effect that one ligand exerts on the equilibrium dissociation constant of the other (and vice versa) trends to a limiting value, and this value is defined as the “cooperativity factor”. As a consequence, the observed binding can exhibit quite a different profile if multiple curves are constructed using a different, fixed, radioligand concentration for each curve. This phenomenon can be exploited for both diagnostic and quantitative purposes, especially for interactions characterized by negative cooperativity (positive cooperativity between modulator and radioligand is obvious; specific binding increases with increasing modulator concentrations). If the degree of negative cooperativity is high, then the modulator may be able to reduce radioligand affinity to such an extent that the specific binding of low radioligand concentrations (i.e., 0.1–1.0× KD) is completely inhibited, and the interaction may be misinterpreted as competitive; however, if the experiment was repeated utilizing a higher final radioligand concentration, such as 10× KD, then the allosteric nature of the interaction may be revealed by an inhibition

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binding curve that plateaus to a level of specific binding that is much higher than 0% (i.e., incomplete inhibition of radioligand binding is observed at maximal concentrations of modulator). Similar arguments apply for positively cooperative interactions (i.e., a limit to the enhancement of radioligand binding by modulator is more readily observed when higher concentrations of radioligand are employed).

15. Optional: Another extension of this protocol is to repeat the experiments in the presence of an additional, fixed concentration of unlabeled orthosteric ligand, as described for the affinity ratio assay in Alternate Protocol 1. This type of experiment allows for the quantification of the allosteric interaction between the modulator and the unlabeled orthosteric ligand, as well as the radioligand. If the unlabeled orthosteric ligand is an agonist, then 1 mM GTP, or an equivalent concentration of nonhydrolyzable GTP analog, should also be incorporated into the assay medium in order to minimize the impact of any agonist-promoted receptor-G protein coupling on the resulting binding curves. The success of this treatment should also be assessed in separate competition experiments between the radioligand and agonist alone.

16. Analyze the data as described in the Support Protocol. ALTERNATE PROTOCOL 2

MEASUREMENT OF ALLOSTERIC MODULATION OF RADIOLIGAND BINDING: INHIBITION (OR POTENTIATION) EXPERIMENTS UNDER NONEQUILIBRIUM CONDITIONS Binding assays such as those outlined above (see Basic Protocols 1 and 2) assume that the reaction between the radioligand, the receptor, and the allosteric modulator is allowed to proceed for a sufficient length of time such that equilibrium is attained; however, it is quite possible that an allosteric modulator can occupy the receptor and induce a conformational change that results in a slowing of the kinetics of association and dissociation of an orthosteric radioligand to such an extent that binding equilibrium is not achieved in the time course of the experiment (UNIT 1.21). This phenomenon is dependent on the concentration of modulator employed, its affinity for the allosteric site and the extent and nature of the cooperativity. For negatively cooperative ligands, this is not expected to be a great problem because the inhibitory effects of the modulator on equilibrium binding occur at lower concentrations than their effects on kinetics; however, in the case of positive or even “neutral” cooperativity (see Commentary), the kinetic effects and effects on radioligand equilibrium binding will occur over the same range of modulator concentrations. In these latter instances, it is possible that equilibrium is not attained, especially if the degree of positive cooperativity is very high. This protocol outlines a procedure that is designed to assess “equilibrium” binding data for kinetic artifacts. The protocol exploits the ability to attain equilibrium in two different ways. In the first, receptors are prelabeled with the radioligand alone and then different concentrations of modulator are added for an additional period of time. In the second, radioligand and modulator are added together to the receptor preparation and the interaction is followed over the same period of time. The data thus generated can be analyzed as outlined below (see Support Protocol) to obtain the relevant pharmacological parameters quantifying the equilibrium allosteric interaction, even if the experiment was not terminated at equilibrium. A schematic representation of this protocol is shown in Figure 1.22.1. For materials see Basic Protocol 1. 1. Prepare a fresh receptor preparation or thaw a previously frozen one. Keep on ice until required, and vortex to ensure suspension of the pellet.

Quantification of Allosteric Interactions at G Protein Coupled Receptors

2. Prepare “mixture A” by diluting the receptor preparation in assay buffer to 100× the protein concentration desired in the final assay.

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This is the concentrated receptor preparation.

3. Prepare “mixture B” by diluting the radioligand in assay buffer to 100× the concentration desired in the final assay. This is the concentrated radioligand preparation.

4. Assemble two different sets of 12 × 75–mm culture tubes, labeled “set A” and “set B”, each with a series of triplicate tubes containing the final concentrations of allosteric modulator to be tested in 1 ml assay buffer total. In addition, prepare at least four tubes containing 1 ml of assay buffer alone, and four tubes containing 1 ml

A

"set A" tubes

mixture A

mixture B

receptor (100 × final)

radioligand (100 × final)

etc.

tubes contain assay buffer +/- modulator (1 ml total volume)

"set B" tubes

etc.

B mixture A

half

half

mixture B

vortex; incubate 5 min

mixture C

receptor : radioligand (50 × final)

C

10 µl mixture C

20 µl

"set A" tubes

mixture A

etc.

"set B" tubes

10 µl mixture B

etc.

vortex tubes; incubate; filter

Figure 1.22.1 Schematic representation of the nonequilibrium binding assay methodology. Tubes in “set A” are exposed to prelabeled receptors, whereas tubes in “set B” are exposed to the receptor and radioligand at the same time.

Receptor Binding

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of assay buffer (total) with unlabeled competitor at a final concentration of 1000× its KD value (for determining nonspecific binding). 5. Prepare “mixture C” by mixing half of mixture A (the concentrated receptor preparation; step 2) and half of mixture B (the concentrated radioligand preparation; step 3) together. Allow to sit for 5 min at 37°C; in the meantime, add 10 µl of radioligand mixture A to each of the tubes in set B. The resulting preparation, mixture C, now contains radioligand and receptor at 50× their desired final concentrations. The point of this step is to allow for a very rapid equilibration of the receptor preparation with the radioligand. Given the very high concentrations of both mixtures, equilibration should be readily achieved by most radioligand-receptor pairs within 5 min, especially at 37°C. For some systems, it is possible that extremely slow binding kinetics or instability issues necessitate the use of longer incubation times or a lower temperature. This will need to be determined empirically for such systems.

6. After the 5 min have elapsed, add 20 µl of radioligand-receptor mixture C to each of the tubes in set A, and add 10 µl of receptor mixture B to the tubes in set B. 7. Vortex tubes and incubate at 37°C for a set period of time (e.g., 90 min). The purpose of this particular assay is to detect and/or exploit equilibrium artifacts. Ideally, the incubation time should be as long as possible, but this does not really matter for two reasons. First, equilibrium may never be attained with some ligands. Second, the analysis of the data obtained under these conditions takes the incubation time into account. Thus, 90 min was chosen arbitrarily. As a general rule of thumb, allow the incubation to proceed for at least 5 radioligand dissociation half lives.

8. Terminate the reaction by filtering the assay mixtures using rapid vacuum filtration over glass fiber filters (e.g., Whatman GF/B) positioned on a cell harvester. Wash the filter 3 times with 3-ml aliquots of ice-cold wash buffer. 9. Allow the filters to dry thoroughly and then place in 10-ml scintillation vials. Add 5 ml scintillation cocktail. Shake the vials and let them sit for at least 6 hr to allow the filters to become uniformly translucent prior to counting in a scintillation counter. 10. Take triplicate aliquots of the working radioligand concentration (i.e., 10 µl of the concentrated solution), place in scintillation vials, add 5 ml scintillant and count in a scintillation counter. 11. Analyze the data as described in the Support Protocol. BASIC PROTOCOL 3

Quantification of Allosteric Interactions at G Protein Coupled Receptors

MEASUREMENT OF ALLOSTERIC MODULATION OF RADIOLIGAND BINDING TO CLONED RECEPTORS IN MEMBRANES: DISSOCIATION KINETIC STUDIES USING ISOTOPIC DILUTION The very nature of an allosteric interaction involves a change in the conformation of the receptor. Thus, when an allosteric modulator binds to its site on the receptor protein, the resulting conformational change yields a protein with different “properties”, as far as the orthosteric ligand is concerned. These properties are readily manifested as a change in the association and/or dissociation characteristics of the orthosteric ligand to and from its binding site on the receptor. This is the mechanism behind the alteration in orthosteric ligand affinity that is induced by an allosteric modulator (and vice versa). It is of no surprise, therefore, that nonequilibrium kinetic assays represent the most common and reliable means of detecting an allosteric interaction in a radioligand binding assay. It should be noted, however, that an apparent reduction in radioligand association may also be observed in the case of competition between two orthosteric ligands, because the approach to equilibrium of the radioligand is delayed simply by the occupancy of the

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orthosteric binding site by a fixed concentration of competitor. Yet, experimentally this phenomenon can appear identical to the situation whereby a true alteration in radioligand association occurs due to allosteric modulation. In contrast, a change in the dissociation characteristics of an orthosteric ligand in the presence of a second agent can only occur as a consequence of a conformational change in the receptor (i.e., by an allosteric mechanism). Thus, the dissociation kinetic assay has become the standard experimental kinetic procedure for the detection and quantification of allosteric modulation. A common misconception in kinetic assays is that radioligand dissociation can be readily “induced” by some experimental manipulation. This is not true. Ligand dissociation is a random process, governed by physiochemical properties specific to the ligand, the receptor and their microenvironment. For instance, under the normal circumstances of competition of two ligands for the same binding site, one ligand does not “make” another ligand dissociate; competition occurs due to the fact that one ligand, once bound, prevents the association of the other. Thus, the success of dissociation kinetic assays relies on the ability of the experimenter to completely prevent reassociation of the radioligand once spontaneous dissociation of the ligand-receptor complex has occurred. Under these circumstances, the decline in radioligand specific binding with time is governed by an exponential process that is a function of time and the dissociation rate constant (or multiple constants for isomerization mechanisms) specific for the ligand-receptor pair. In order to physically force a change in the dissociation characteristics of a ligand from its binding site, the actual properties of the receptor that contribute to the intimate binding forces have to be altered, for example, by an allosteric conformational change. The prevention of radioligand reassociation may be achieved via two general approaches. This protocol will address the first approach, often referred to as “isotopic dilution”. The second approach, “infinite” dilution in buffer is discussed below (see Alternate Protocol 4). To maximize the analytical properties of this assay, this protocol should be repeated utilizing different concentrations of allosteric modulator. Alternatively, a two-point kinetic experimental design may be utilized as described below (see Alternate Protocol 3). Materials (also see Basic Protocol 1) Appropriate receptor preparation (e.g., see other units in Chapter 1 for detailed methods on preparing membranes containing various receptor populations) Assay buffer (e.g., HEPES or Tris-based buffers) Radioligand Test compound (allosteric modulator) Unlabeled (non-radioactive) competitive (orthosteric) ligand Wash buffer (usually the same as the assay buffer), ice-cold Scintillation cocktail (e.g., Packard Ultima Gold; Wallac HiSafe) 12 × 75–mm glass or polypropylene culture tubes Shaking water bath, 37°C Multipipettor (e.g., Eppendorf Multipette) Glass fiber filters (e.g., Whatman GF/B) Cell harvester (e.g., Brandell or Skatron) Scintillation counter and appropriate vials 1. Prepare a fresh receptor preparation or thaw a previously frozen one. Keep on ice until required, and vortex to ensure suspension of the pellet. Receptor Binding

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2. Dilute the receptor preparation in assay buffer to 10× the protein concentration desired in the final assay. 3. Dilute the radioligand in assay buffer to 10× the concentration desired in the final assay. 4. Prepare a dilution of the test compound (allosteric modulator) in assay buffer at 100× the final concentration desired. 5. Prepare a stock solution of an unlabeled competitive (orthosteric) ligand at 100,000× its KD value. This solution will be used to prevent the reassociation of radioligand, and will itself undergo a 100× dilution in each tube such that it remains in an excess of 1,000× its KD value.

6. For specific binding: As a starting point, prepare duplicate 12 × 75–mm tubes containing 800 µl of assay buffer for each of the following time points: 5 sec, 10 sec, 20 sec, 40 sec, 60 sec, 2 min, 5 min, 10 min, 20 min, 40 min, 60 min, 120 min, 240 min, 360 min, and 480 min. This assay follows the dissociation of a single concentration of radioligand over time, thus enough replicate tubes need to be prepared to define the residual binding of the radioligand to the receptor population over a reasonable number of time points. Because dissociation is an exponential process, it is best to utilize a geometric distribution of time intervals. The time points outlined above should give very good coverage and allow for the detection of any complicated kinetic effects, such as different rapid and slow phases of dissociation; however, some of the earliest time points are highly reliant on the rapidity of the filtration process and the affinity of the radioligand to remain bound throughout the filtration and washing steps. For some radioligands (e.g., those with dissociation constants Table 1.22.3

Quantification of Allosteric Interactions at G Protein Coupled Receptors

Reverse Time Protocola

Tube No.b

Add homogenate (min)c

Add 10 µl atropine (min)d

Time of dissociation (min)e

1, 2 3, 4 5, 6 7, 8 9, 10 11, 12 13, 14 15, 16 17, 18 19, 20, 21 Filter at 80 min

0 10 13 15 16 17 18 19 19.5 20

60 70 73 75 76 77 78 79 79.5 −

20 10 7 5 4 3 2 1 0.5 −

aAn outline for studying the dissociation of the antagonist, [3H]N-methylscopolamine from the M2 muscarinic acetylcholine receptor expressed in CHO cells. bAt the outset of the experiment, all tubes (except 22 to 24) contain 800 µl HEPES assay buffer (30 mM HEPES, pH 7.4/0.5 mM EGTA/5 mM MgCl2) and 100 µl [3H]N-methylscopolamine from a 2 nM stock. Tubes 19 to 21 are used to define binding at equilibrium, prior to dissociation. The entire experiment is repeated using tubes that also contain atropine at a final concentration of 1 µM, in order to determine the time course of nonspecific binding. cAt the indicated time, 100 µl of the homogenate preparation is added to the tubes. dAt the indicated time, 100 µM stock with or without 10 µl of allosteric modulator at 100× its desired final concentration, is added to the tubes. eThis is the total time that the dissociation of the radioligand will have proceeded prior to termination by filtration.

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>1 nM), the accurate determination of specific binding at these time points may not be possible.

7. For nonspecific binding: Prepare a separate set of duplicate tubes that also contain a final concentration of unlabeled competitive ligand at 1000× its KD value for each timepoint described above. These tubes will allow determination of how nonspecific binding changes with time. Table 1.22.3 outlines a protocol designed to assess the dissociation of [3H]N-methylscopolamine from the M2 muscarinic acetylcholine receptor, where fewer time points are utilized because dissociation from this receptor is quite rapid.

8. Add 100 µl of the radioligand to each tube. 9. Using a reverse time protocol (e.g., see Table 1.22.3), begin the assay by adding 100 µl of membrane preparation to each of the replicate sets of tubes in a staggered fashion such that each replicate is allowed to equilibrate with the radioligand and receptor for 60 min (or as long as necessary to ensure radioligand equilibrium binding; see Basic Protocol 1, step 11) in a 37°C shaking water bath at 55 rpm. Vortex the tubes. For the addition of the various drug and receptor preparations, a reverse time protocol is utilized whereby the longest time points are started first and the shortest time points last. In this manner, all tubes may be filtered at the same time on a cell harvester. It is also worth noting that the temperature can be lowered in order to better observe rapid components of dissociation. It is quite common, for instance, to see these types of experiments conducted at 25°C, 30°C, or even 4°C.

10. Once each set of receptor preparations has equilibrated with the radioligand, quickly add 10 µl of the concentrated stock solution of unlabeled competitive ligand with (modulator curve) or without (control curve) 10 µl of the stock solution of allosteric modulator to the tubes using a multipipettor. Quickly vortex the tubes and place them back in the 37°C water bath. Position the vortex and water bath in close proximity to one another to facilitate rapid transfer of tubes back and forth.

11. After the last time point has been determined, terminate the reaction by filtering the assay mixtures using rapid vacuum filtration over glass fiber filters (Whatman GF/B) positioned on a cell harvester, using a new filter for each reaction. Quickly wash the filter 3 times with 3-ml aliquots of ice-cold wash buffer. 12. Allow the filters to dry thoroughly and then place them in 10-ml scintillation vials and add 5 ml scintillation cocktail. Shake the vials and let them sit for at least 6 hr to allow the filters to become uniformly translucent prior to counting in a scintillation counter. 13. Analyze the data as described in the Support Protocol. “TWO-POINT” KINETIC EXPERIMENTS If the dissociation kinetic curve of a radioligand is monophasic in nature (i.e., it is adequately described in terms of a single rate constant of dissociation) and it remains monophasic in the presence of an allosteric modulator, then Basic Protocol 3 can be simplified to use fewer time points. Specifically, the amount of radioligand bound at the start of the procedure and the amount of residual radioligand bound at a fixed time interval afterwards are sufficient to define a monophasic exponential decay. This procedure is often employed to save time and reagents, and allows for multiple concentrations of modulator to be examined in the same experiment. For materials see Basic Protocol 3.

ALTERNATE PROTOCOL 3

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Table 1.22.4

Two-Point Kinetic Protocola

Add Tube No.b homogenate (min)c

Add atropine ± modulator (min)d

[Atropine] [Modulator] µMe µMe

1–4 6 5 0 6 0 7 0 8 0 9 0 10 0 11 0 12 0 Filter at 66 min

− 60 60 60 60 60 60 60 60

− 1 1 1 1 1 1 1 1

− − 0.1 0.3 1 3 10 30 100

aAn outline for studying the dissociation of the antagonist, [3H]N-methylscopolamine, from the M2 muscarinic acetylcholine receptor expressed in CHO cells. bAt the outset of the experiment, all tubes contain 800 µl HEPES assay buffer (30 mM HEPES, pH 7.4/0.5 mM EGTA/5 mM MgCl2) and 100 µl [3H]N-methylscopolamine (2 nM stock). Tubes 1 to 4 are used to define total binding at equilibrium, prior to dissociation. cAt the indicated time, 100 µl of the homogenate preparation is added to the tubes. dAt the indicated time, 10 µl of atropine (100 µM stock) with or without 10 µl of allosteric modulator at 100× its desired final concentration (the latter is shown in the table), is added to the tubes. The experiment is then repeated using a set of tubes that also contain 1 µM atropine (final concentration) to define nonspecific binding. eFinal concentrations of indicated drugs in the tubes.

1. Choose a concentration of allosteric modulator that represents the highest concentration to be tested in a dissociation assay. Perform a dissociation kinetic assay utilizing this concentration of modulator as outlined above (see Basic Protocol 3). This is a necessary preliminary experiment that will allow determination of whether the dissociation curve of the radioligand is monophasic in the absence and presence of the highest concentration of allosteric modulator. A sampling of multiple time points is therefore required. In addition, the control radioligand dissociation curve (i.e., observed in the absence of modulator) may be examined to determine which time point will be monitored in subsequent two-point assays. If the monophasic nature of the curve changes in the presence of modulator, then the interaction is characterized by complex kinetics and it is inappropriate to proceed further with a two-point experimental design.

2. Prepare receptor preparation and radioligand as described above (see Basic Protocol 3, steps 1 to 3). 3. Prepare a series of dilutions of the test compound (allosteric modulator) in assay buffer at 100× the desired final concentration. Concentrations spanning 0.5 or 1 log unit intervals up to the highest concentration of modulator to be tested (as determined in step 1) are appropriate.

4. Prepare a stock solution of an unlabeled competitive ligand at 100,000× its KD value for the receptor.

Quantification of Allosteric Interactions at G Protein Coupled Receptors

5. Prepare sufficient 12 × 75–mm tubes to define the residual binding of the radioligand to the receptor population at the beginning and at a fixed time interval after. This will entail a quadruplicate set of tubes for control binding at time t = 0, and a single tube for each concentration of modulator (including a tube for no modulator) to be assayed.

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For instance, the specific example shown in Table 1.22.4 tested 7 concentrations of modulator. The contents of the tubes are to be prepared as described in Basic Protocol 3, steps 6 (for specific binding) and 7 (for nonspecific binding). Because these experiments are designed to assess the effects of a number of modulator concentrations at the same time point, replicate tubes are probably best avoided as they may introduce too much of a lag time between drug addition and filtration. Instead, determine each observation in a single tube (except total equilibrium binding) and repeat the experiment a number of times.

6. Add 100 µl of the radioligand to each tube. 7. Using a reverse time protocol, begin the assay by adding 100 µl of membrane preparation to each of the replicate sets of tubes in a staggered fashion such that each replicate is allowed to equilibrate with the radioligand and membrane for 60 min in a shaking water bath (55 rpm) at 37°C (or as long as necessary to ensure radioligand equilibration). Vortex the tubes and return them to the water bath. 8. Once each set of membrane preparations has equilibrated with the radioligand, quickly add 10 µl unlabeled competitor stock solution, with or without 10 µl allosteric modulator stock solution to the tubes using a multipipettor. Vortex the tubes and place them back in the 37°C water bath. 9. After a fixed period of time (as determined in step 1), terminate the reaction by filtering the assay mixtures using rapid vacuum filtration over glass fiber filters (e.g., Whatman GF/B) positioned on a cell harvester. Quickly wash the filter 3 times with 3-ml aliquots of ice-cold wash buffer. 10. Allow the filters to dry thoroughly and then place in 10-ml scintillation vials and add 5 ml scintillation cocktail. Shake the vials and let them sit for at least 6 hr to allow the filters to become uniformly translucent prior to counting in a scintillation counter. 11. Repeat the experiment, using a set of tubes that also contains an excess of unlabeled competitor at a final concentration of 1000× its KD value for the receptor. These tubes are used to define nonspecific binding.

12. Analyze the data as described in the Support Protocol. MEASUREMENT OF ALLOSTERIC MODULATION OF RADIOLIGAND BINDING: DISSOCIATION KINETIC STUDIES USING “INFINITE DILUTION” IN BUFFER

ALTERNATE PROTOCOL 4

An alternative method for preventing radioligand reassociation is to dilute the equilibrated ligand-receptor preparation in a large volume (≥100×) of buffer, and then sample the mixture at various time intervals. Theoretically, the large dilution of the mixture minimizes the probability of any dissociated radioligand rebinding to receptor, and thus the decline in radioactivity over time is a reflection of the dissociation process of the ligand-receptor complex. An advantage of this approach is that it bypasses the need to utilize another ligand to prevent radioligand reassociation (as is required for isotopic dilution). This is particularly useful when it is not known whether the unlabeled ligand, utilized in excess to prevent radioligand reassociation, acts purely by an orthosteric mechanism without exerting any type of allosteric or cooperative effect in its own right. In addition, a comparison of results obtained from isotopic dilution with infinite dilution experiments should yield similar dissociation rate estimates for the radioligand if the underlying mechanism of interaction conforms to simple, one-to-one mass-action binding. Receptor Binding

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The infinite dilution approach suffers from some potential drawbacks. One is practical, in that this method can necessitate the use of large volumes of buffer and concentrated receptor preparations. The other pitfall is more insidious, in that the initial concentration (prior to dilution) of labeled receptor preparation may be so high that some radioligand rebinding does in fact occur, leading to an artifactual apparent slowing of the overall dissociation process. Additional Materials (also see Basic Protocol 1) 100- to 200-ml flasks for large volumes of buffer Magnetic stirrer/hotplate Sampling manifold (e.g., Millipore 1225) 1. Prepare a fresh receptor preparation or thaw a previously frozen one. Keep on ice until required, and vortex to ensure suspension of the pellet. 2. Dilute the receptor preparation in assay buffer to 100× the desired final concentration. One milliliter of this mixture is sufficient to cover a large number of time points.

3. Prepare a separate sample of membrane in the same manner, but include an unlabeled competitor at a final concentration of 1000× its KD value for the receptor. This sample will be treated in the same fashion as the control sample, but will be used to define nonspecific binding.

4. Prepare two separate 100- to 200-ml flasks containing assay buffer at 100× the volume of concentrated membrane sample. Place in a 37°C shaking water bath at 55 rpm or heat to the same temperature on a magnetic hot plate with stirring. 5. Prepare a small volume of the radioligand at 100× the desired final concentration. One milliliter of this radioligand solution will be sufficient for 100 tubes.

6. Add the radioligand stock to the concentrated membrane preparations at a volume of 10 µl radioligand for every 1 ml of membrane suspension. Vortex and allow to equilibrate for 60 min (or as long as is necessary to ensure radioligand equilibration; see Basic Protocol 1, step 11) in a 37°C water bath. Stagger the addition of radioligand between the two samples (total and nonspecific binding) by at least 10 to 15 min. Alternatively, the assay for one sample can be completed before proceeding to the next. The time between addition of radioligand to each of the two concentrated receptor preparations will need to be determined based on the sampling times for the dissociation experiment. Ideally, most of the rapid sampling will be in the early time points (0 to 15 min), so it is not a good idea to have to try and keep track of two different reactions over this initial period of time.

7. Transfer the first of the concentrated mixtures to a large flask containing buffer. Make sure that the resulting mixture is well-stirred and immediately begin sampling aliquots of the diluted mixture at various time points. Filter using the vacuum manifold. Repeat for the nonspecific binding samples. The sampling volume is equal to the volume of the initial concentrated receptor preparation. For example, if 1 ml total concentrated receptor preparation was utilized, then this will be diluted in 100 ml of assay buffer and 1 ml aliquots will subsequently be sampled. Quantification of Allosteric Interactions at G Protein Coupled Receptors

8. Prepare an additional two flasks in the same manner as for Step 3 above, but also containing the allosteric modulator at its desired final concentration. 9. Repeat steps 1 to 7.

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10. Remove the filters and allow them to dry thoroughly. Place in 10-ml scintillation vials and add 5 ml scintillation cocktail. Shake the vials and let them sit for at least 6 hr to allow the filters to become uniformly translucent prior to counting in a scintillation counter. 11. Analyze the data as described in the Support Protocol. DATA ANALYSIS This Support Protocol contains extensive details on the analysis of the different types of assays outlined in the preceding protocols. Two methods are first presented for the analysis of the saturation binding assays. The choice of method will depend on the properties of the software package available to the researcher. A method is then presented for the analysis of the affinity-ratio experiments that involves only simple calculations. Subsequent methods deal with the analysis of inhibition/potentiation binding assays under both equilibrium and non-equilibrium conditions, and the final methods describe the analysis of dissociation kinetic experiments.

SUPPORT PROTOCOL

From the allosteric ternary complex model (see Commentary), Equation 1.22.1 may be derived, which describes the specific binding of the radioligand (A) in the presence of a fixed concentration of allosteric modulator (B): [AR] + [ARB] =

[R]T ⋅ [A] B  1+ K B [A] + K A ⋅   1 + αB  KB 

     

Equation 1.22.1

Where [AR] denotes the concentration of binary radioligand-receptor complex, [ARB] denotes the concentration of ternary radioligand-modulator-receptor complex, [R]T denotes the total concentration of receptors (i.e., Bmax), KA and KB denote the equilibrium dissociation constants for the orthosteric and allosteric drugs, respectively, and α denotes the cooperativity factor, i.e., the magnitude by which the equilibrium dissociation constant of either ligand for its site on the receptor is modified by the concomitant presence of the other ligand. In the above formulation, values of α1 denote positive cooperativity, and values not significantly different from 1 denote neutral cooperativity, i.e., no change in the equilibrium binding properties of either ligand (although the kinetics of the interaction may still change). Equation 1.22.1 can be manipulated and utilized to analyze equilibrium binding data according to the allosteric ternary complex model. Equilibrium experiments were outlined above (see Basic Protocols 1 and 2 and Alternate Protocol 1). Analysis of Saturation Binding Experiments For saturation experiments (see Basic Protocol 1), two approaches can be utilized to analyze the binding data. The first (see Method 1) requires the use of programming that is able to simultaneously fit the data from all binding experiments, both in the absence and presence of modulator. The second (see Method 2) presents a multistep, nonlinear regression approach for computer programs that do not allow direct fitting of the allosteric model to the complete family of datasets. The major difference between the two is that parameters are shared across datasets in Method 1 (the preferred approach), whereas they

Receptor Binding

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are not shared in Method 2. The choice of method is based on the capabilities of the available software packages. Method 1. Simultaneously fit all datasets, obtained in the absence and presence of modulator, to Equation 1.22.1 to derive estimates of [RT], KA, KB, and α that describe the entire family of curves. This is the preferred approach, but requires a computer program that allows the user to share parameters across datasets. Programs such as SPSS SigmaPlot, Microcal Origin, Micromath Scientist, and Microsoft Excel can all be programmed to perform this procedure, but other popular packages such as GraphPad PRISM or Synergy Kaleidagraph cannot do so. If the data are to be analyzed according to this approach, Equation 1.22.1 should first be reparameterized to Equation 1.22.2 to allow for a meaningful statistical comparison of the parameter estimates if this is desired: Y=

[R]T ⋅ 10log A [B]  1 + log K  10 B 10logA + 10log KA ⋅   1 + 10logα ⋅ [B] 10log KB 

    

Equation 1.22.2

As can be seen above, Equation 1.22.2 has recast all dissociation constants, as well as the cooperativity factor, as logarithms. This is because the logarithms of these parameters are approximately normally distributed, whereas the nontransformed parameters are not (Christopoulos, 1998). Equation 1.22.1 also has two independent variables, namely the concentration of orthosteric radioligand, [A], and the concentration of modulator, [B]. The former has been recast as a logarithm in Equation 1.22.2 to facilitate the graphing of the experimental data in semilogarithmic space. In addition, if the program does not allow for more than one independent variable, then the concentration of modulator, [B], can be assigned as another model parameter, but its value fixed as a constant during the curve-fitting procedure. Outlined below is an example of a script, written for Microcal Origin, that allows for the analysis of the datasets shown in Figure 1.22.2 and Table 1.22.1: KB=10^LogKB; KA=10^LogKA; KApp=KA*(1+B/KB)/(1+((10^Logalpha)*B)/KB); Y=Rtotal*(10^X)/((10^X)+Kapp)

The equation has been written in a series of steps to make the programming less cluttered. Since most programs require the letter “X” to denote the independent variable, this has been used above in place of “LogA”. Also, “Rtotal” is used for [R]T. “KApp” has been used to describe the apparent dissociation constant of A observed in the presence of B:

K App Quantification of Allosteric Interactions at G Protein Coupled Receptors

[B]    1+ K  B  = KA ⋅  α [B] 1+    K B  

Equation 1.22.3

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Equation 1.22.3 shows that the true dissociation constant of the radioligand-receptor complex will be modified as a function of allosteric modulator affinity (1/KB), concentration ([B]) and the cooperativity (α) between the two ligands. If α>1, then KApp will be >1/KA (i.e., affinity will be enhanced). If α10×) relative to the radioligand, and thus monophasic dissociation curves would be observed in the absence or presence of modulator. This assumption may be checked experimentally in separate dissociation kinetic

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experiments as described above (see Basic Protocol 3). Biphasic dissociation curves in the presence of allosteric modulator but not in the absence indicate slow binding kinetics for the modulator and/or more complex reaction mechanisms than those expected for the simple allosteric model and thus further analysis under this circumstance is outside the scope of this unit. In the case of radioligand association, the apparent association rate constant (konobs) is defined according to Equation 1.22.16 below:  [A]  konobs = koffobs ⋅ 1 +   K App   Equation 1.22.16

Where [A] is the radioligand concentration and koffobs and KApp are as defined in Equations 1.22.15 and 1.22.3, respectively. A further assumption of Equation 1.22.16 is that the dissociation of the radioligand from the modulator-occupied receptor is so slow as to be negligible (i.e., koffB approaches zero). In this latter instance, Equation 1.22.15 simplifies to Equation 1.22.17: koffobs =

koff [B] 1+ K ( B / α)

Equation 1.22.17

Equation 1.22.12, describing the equilibrium binding of A in the presence of B, may be rewritten to Equation 1.22.18: [A] K App = [A] 1+ K App [R]T ⋅

BAB

Equation 1.22.18

Where BAB denotes bound radioligand to both the free and occupied receptor. Finally, Alternate Protocol 2 described a procedure whereby receptors are prelabeled (“set A” tubes) with a high concentration of radioligand in a mix yielding 50× the concentration utilized in the nonprelabeled set of tubes (“set B”). The equilibrium binding for the high radioligand concentration in the set A tubes is given by Equation 1.22.19: [R]T ⋅ [A] ⋅ 50 ⋅ 1 K A BHI = 1 + [A] ⋅ 50 ⋅ 1 K A Equation 1.22.19

Where BHI denotes the bound radioligand at the high concentration utilized. Receptor Binding

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The data obtained from Alternate Protocol 2 (i.e., set A containing prelabeled receptors and set B where radioligand and modulator are exposed to receptor at the same time) are simultaneously fitted to Equation 1.22.20: Bt = BAB ⋅ 1 − exp ( − t ⋅ konobs ) + BHI ⋅ exp ( − t ⋅ konobs ) Equation 1.22.20

Where t denotes incubation time, Bt denotes radioligand binding at time t and all other parameters are as defined above. For set A tubes, the data are fitted to the complete equation, whereas for set B (nonprelabeled) tubes, the data are fitted to the equation with BHI set to zero (i.e., the second term in the equation does not apply). A simultaneous curve fit of the data in this manner can yield estimates of [R]T (i.e., Bmax), KA, koff, koffB (which should be close to zero), KB, and α. The value of t is fixed to the time of the incubation. Of course, if the data cannot define the model well, all of these parameters may not be obtained; however, some of them can be determined in other types of experiments and then set as constants when fitting equation 1.22.20. Specifically: 1. [R]T and KA may be fixed to values determined previously from saturation experiments. Alternatively, KA can be fixed to a previously determined value and [R]T may be estimated from Equation 1.22.20 (this is preferable to fixing [R]T and estimating KA). 2. Interactions characterized by neutral cooperativity may necessitate fixing the value of koff (control radioligand dissociation rate) to that determined from separate kinetic experiments. This is because of the high degree of correlation between koff and KB in the curve fitting procedure. 3. Most likely, koffB will probably need to be fixed to a value of zero, as it is assumed to be negligible. Effects of fixing parameters on curve fitting may be assessed by performing an extra-sumof-squares test (F-test; UNIT 1.3). The above considerations are a guide to some of the more common problems associated with fitting Equation 1.22.20 to experimental data. Figure 1.22.7 shows an example of this analysis for the interaction between the modulator N-chloromethyl brucine and the radioligand, [3H]N-methylscopolamine at the cloned M1-M4 muscarinic acetylcholine receptors. It is also worth noting that Equation 1.22.20 (with BHI set to zero) can also be fitted to data that were obtained with the assumption of equilibrium, but where the resulting curve is characterized by a significant biphasic shape (e.g., bell-shaped). This is a strong indicator of a non-equilibrium reaction. Shown below is the programming for GraphPad PRISM of Equation 1.22.20 for analyzing data obtained under the latter circumstance. KApp=(10^LogKA)*(((10^LogKB)+(10^X))/((10^LogKB)+(10^X)*(10^Logalpha)) koffobs=(((10^X)*koffB)/((10^LogKB)*(10^Logalpha))+koff)/ (1+(10^X)/((10^LogKB)*(10^Logalpha))) konobs=koffobs*(1+(10^LogA)/KApp) Bab=(RT*(10^LogA)/KApp)/(1+(10^LogA)/KApp) Y=Bab*(1-exp(-1*t*konobs)) Quantification of Allosteric Interactions at G Protein Coupled Receptors

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10,000

7,500

M2

Specific dpm bound

M1 7,500 5,000 5,000 2,500 2,500

0 5,000

0 –5

–4

–3

–5

M3 Specific dpm bound

–4

–3

5,000

M4

4,000

4,000

3,000

3,000

2,000

2,000

1,000

1,000

0

0 –5

–4 –3 Log [CMB] (M)

–5 prelabeled not prelabeled

–4

–3

Log [CMB] (M)

Figure 1.22.7 Nonequilibrium assay of the effects of the allosteric modulator N-chloromethyl brucine (CMB) at the human M1-M4 muscarinic acetylcholine receptors expressed in CHO cells. Receptors were either unlabeled or prelabeled with [3H]N-methylscopoloamine, as indicated, before the incubation with CMB. The final radioligand concentrations after dilution were 0.142, 0.280, 0.142, and 0.151 nM, and the incubation times were 90, 30, 95, and 67 min at the M1-M4 receptors, respectively. The incubation temperature was 30°C. The lines represent the best fit, via constrained, simultaneous nonlinear regression, to Equation 1.22.20. Taken from Lazareno et al. (1998).

Analysis of Dissociation Kinetic Experiments As mentioned previously, if the binding kinetics of the modulator are rapid (>10×) relative to the radioligand, then monophasic dissociation curves will be observed in the absence or presence of modulator. If this is observed experimentally, then the apparent rate constant for the observed radioligand dissociation in the presence of modulator (koffobs) is as defined in Equation 1.22.15. Data obtained from dissociation kinetic experiments (see Basic Protocol 3 and Alternate Protocols 3 and 4) may be fitted in a number of ways. Two methods are presented here. There first is for computer programs allowing simultaneous curve-fitting and parameter sharing across datasets (Method 3). The second is used if constrained simultaneous nonlinear regression cannot be performed (Method 4). Receptor Binding

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Method 3: If the computer program allows simultaneous curve-fitting and parameter sharing across datasets, then the entire family of dissociation curves obtained in the absence and presence of modulator may be fitted to Equation 1.22.21: Bt = B0 .e− koffobs .t Equation 1.22.21

Where Bt denotes the binding of radioligand at time t after reassociation has been prevented and B0 represents radioligand binding at time t = 0 (i.e., equilibrium level of binding at the onset of the dissociation procedure). Equations 1.22.21 and 1.22.15 are fitted simultaneously to all datasets with koff, koffB, and (KB/α) constrained to be shared across datasets. Note that this analysis cannot differentiate α and KB from each another, but estimates the composite parameter, (KB/α), i.e., it determines the dissociation constant of the modulator for the radioligand-occupied receptor. Shown below is a programming script that has been used in the Microcal Origin program to fit such data. KBalpha=10^LogKBalpha; numerator=(B/KBalpha)*koffB+koff;denominator=1+B/KBalpha;koffobs=numerator/denominator;y=Bound*exp(-koffobs*x)

Sometimes, the fitting procedure may be unable to converge to a reasonable estimate of all the parameters, for example if the data do not allow the model to be well-defined. In this instance, it may be necessary to set the value of koff to that determined in the absence of modulator (i.e., fit Equation 1.22.21 to the control dissociation curve, in which case koffobs = koff) and then to fix this value as a constant for the analysis of the curves determined in the presence of modulator. In addition, if the modulator slows radioligand dissociation from the receptor to such an extent that the radioligand cannot dissociate from the modulator-occupied receptor, then koffB will approach zero and it may be necessary to fix the parameter koffB to a value of zero for the fit to converge. This approach can also be applied directly to two-point kinetic experiments, provided the data points are sufficient to define the model. This will almost always be the case if the control curve and at least one more curve in the presence of modulator are fully defined at various time points. An example of this type of experiment is shown in Figure 1.22.8, where it can be seen that the entire family of curves was fitted well by Equation 1.22.21. Method 4: If constrained simultaneous nonlinear regression cannot be performed, an alternative approach may be utilized that allows for the determination of (KB/α). This method is done in a series of steps: 1. Fit each individual curve to an equation describing a monoexponential dissociation (e.g., equation 1.22.21). Note the value for koffobs. Remember, the value of koffobs for the control curve (obtained in the absence of modulator) will be equal to the true radioligand koff.

2. For each modulator concentration employed, divide the value of koffobs by the corresponding value for the control curve (i.e., koffobs/koff). Quantification of Allosteric Interactions at G Protein Coupled Receptors

3. Plot the value of koffobs/koff against the logarithm of the concentration of modulator. 4. Fit the resulting sigmoidal curve to the logistic function, Equation 1.22.22:

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Y = start +

(end − start) ⋅10logB 10logB + 10log(KB / α )

Equation 1.22.22

Where “start” denotes the curve asymptote in the absence of modulator (this should approach a value of 1), “end” denotes the curve asymptote in the presence of a very large excess of modulator (multiplying this value by the control koff should yield an estimate of koffB), and (KB/α) is as defined above. Here is an example of Equation 1.22.22 that has been reparameterized and entered as a script in GraphPad PRISM: Y=Start + (End-Start)/(1+10^((LogKBalpha-X)))

NOTE: With programs like PRISM, it is even easier to analyze data according to Method 4 than outlined above, because the program already has built-in equations for analyzing monoexponential dissociation curves (e.g., Step 1) and three-parameter logistic equations (e.g., Step 4). The inset to Figure 1.22.8 shows an example of analyzing the same data as in the main figure but using Method 4.

1.0

koffobs /k off

8.8 0.6 0.4 0.2

B 0 = 1416 dpm koff = 1.04 min –1 koffB =0.009 min –1 log(K B /α) = – 5.66

0.0 –8

start = 0.99 end = 0.01 log(KB /α) =– 5.76

–7 –6 –5 Log10 [C7/3-phth] (M)

–4

Specific binding (dpm)

1500 [C7/3-phth] (µM) 0 0.1 0.3 1 3 10 30 100

1200 900 600 300 0 0

2

4

6

8

10

Time (min)

Figure 1.22.8 Effect of the allosteric modulator, heptane-1,7-bis-(dimethyl-3′-phthalimidopropyl)ammonium bromide (C7/3-phth), on the dissociation rate of [3H]N-methylscopolamine in CHO cell membranes expressing the cloned M2 muscarinic receptor. Membranes were incubated with 0.2 nM [3H]N-methylscopolamine for 60 min at 37°C before dissociation was monitored after the addition of 1 µM atropine alone or in combination with C7/3-phth at the indicated concentrations. Main figure, normalized curves represent the best fit via constrained, simultaneous nonlinear regression analysis, using Microcal Origin, according to Equations 1.22.15 and 1.22.21. Inset, nonlinear regression analysis of the same data set using GraphPad PRISM according to Equations 1.22.21 and 1.22.22.

Receptor Binding

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Extensions of the Allosteric Model Although not explicitly considered in this unit, it is quite possible to conceive of the experimental situation where the ability of two different allosteric modulators to act at a common allosteric site may need to be evaluated. Under these circumstances, the methodology is just an extension of all the protocols described in this unit (by simply adding an additional modulator to the assay tubes as required). For reference, the main equations describing the effects of two allosteric modulators (acting at a common site) on the equilibrium binding and the dissociation kinetics of an orthosteric radioligand are described below. Let the terms B, KB, and α denote the first modulator, its equilibrium dissociation constant and its cooperativity factor, respectively, and C, KC and γ denote the second modulator, its equilibrium dissociation constant and its cooperativity factor, respectively. Equilibrium binding The fractional binding of an orthosteric radioligand, A, in the presence of two allosteric modulators, B and C, is given by Equation 1.22.23: [AR] + [ARB] + [ARC] [A] = ′′ [R]T [A] + K App Equation 1.22.23

where Equation 1.22.24 is also true:

′′ K App

 [B] [C]  K A ⋅ 1 + +  KB KC   = α[B] γ[C] + 1+ KB KC Equation 1.22.24

Dissociation kinetics The observed dissociation of a radioligand over time has already been described in Equation 1.22.21; however, when this dissociation is monitored in the presence of two allosteric modulators acting at the same site, then the apparent radioligand dissociation rate constant, koffobs, is defined as demonstrated in Equation 1.22.25: koffB ⋅ [B] koffC ⋅ [C] + ( KB / α ) ( KC / γ ) [B] [Y] 1+ + ( KB / α ) ( KC / γ )

koff + koffobs =

Equation 1.22.25

Quantification of Allosteric Interactions at G Protein Coupled Receptors

where koffC denotes the dissociation rate constant of the radioligand for the receptor that is already occupied by modulator C, and all other parameters are as defined previously. When using this equation for the analysis of experimental data, the same assumptions used in the derivation of Equation 1.22.21 are applied.

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COMMENTARY Background Information The term “allosterism” was first introduced in the field of enzymology (Monod et al., 1963, 1965; Koshland et al., 1966), but was soon adopted in pharmacology and applied to specific types of drug-receptor interactions (Changeux, 1966; Karlin, 1967; Colquhoun, 1973; Thron, 1973). In essence, the concept of allosterism relates to the ability of proteins, such as enzymes or receptors, to undergo conformational changes that affect the subsequent interactive properties of these proteins with other substances, such as neurotransmitters, hormones, drugs or even other proteins. It is therefore obvious that allosteric (or “allotopic”; UNIT 1.2) interactions can be widespread in nature and play a role in many biological processes. For instance, it is quite common to encounter the term “allosteric transition” in discussions of “two-state” or “multi-state” receptor theory (UNIT 1.2), as these theories describe the isomerization of receptors between multiple conformational states. In addition, the well-known “ternary complex model” of agonist-receptor-G protein coupling (De Lean et al., 1980) is a mechanism that explicitly involves an allosteric interaction. In this model, the allosteric interaction is between the agonist binding to the extracellular region of the receptor and the G protein binding to the intracellular face of the receptor. Yet another form of allosteric interaction can occur when single (monomeric) receptors couple to one another to form dimers or higher order oligomers and influence each other’s binding properties (Wregget and Wells, 1995). A variant of this phenomenon is observed with many ligandgated ion channels, which consist of multiple protein subunits that can interact with each another in an allosteric fashion (Changeux, 1993). In fact, the best known allosteric modulators with a proven therapeutic track record, the benzodiazepines, exert their effects by binding to an allosteric site on the GABAA ion channel/receptor complex to enhance the binding of the neurotransmitter, GABA, to its binding site on the complex (Ehlert et al., 1983). In the past few decades, however, it has become apparent that many GPCRs also possess allosteric binding sites on the receptor protein that may be targeted by different chemical classes of agents. Examples of GPCRs that have been shown to possess allosteric binding sites include, but are not limited to, the muscarinic acetylcholine receptors (Tucek and

Proska, 1995; Christopoulos et al., 1998) the α2-adrenergic receptor (Leppik et al., 1998), the dopamine D1 and D2 receptors (Hoare and Strange, 1996; Schetz and Sibley, 1997), the adenosine A1 receptor (Bruns and Fergus, 1990) and the 5-HT2 and 5-HT7 receptors (Kaumann and Frenken, 1985; Thomas et al., 1997). The GPCR superfamily is still growing, with new proteins, splice variants, and “orphan” receptors being continually added to this list. The possibility of discovering more agents that exert their effects via an allosteric mechanism is also becoming greater. In general, the simplest mode of allosteric interaction at GPCRs is considered to occur when two ligands can concomitantly occupy the receptor protein, resulting in a conformational change that exerts reciprocal effects on the equilibrium binding constants of each ligand (Ehlert, 1988; Lazareno and Birdsall, 1995). This implies, by necessity, different binding domains on the same protein for allosteric agents versus orthosteric (classical competitive) agents. Figure 1.22.9 outlines the simplest mechanism that can describe this interaction. Ligand A (agonist or competitive antagonist) binds to the orthosteric site with an equilibrium dissociation constant KA, whereas ligand B (allosteric modulator) binds to its site with an equilibrium dissociation constant KB. When both ligands occupy the receptor to form the ternary complex, ARB, the principle of microscopic reversibility dictates that the respective dissociation constants governing the binding of either ligand to the occupied receptor must be modified in a reciprocal manner. This is encompassed in the “cooperativity factor”, a. In Figure 1.22.9, values of a1 result in a decrease of the equilibrium dissociation constant, (i.e., ligand affinity for the occupied receptor is increased; positive cooperativity). Interestingly, it is possible for the equilibrium dissociation constant of either ligand to remain unaltered at the occupied receptor; a is then equal to 1 and the interaction is characterized by “neutral cooperativity”. It is important to note that an allosteric interaction has still taken place, as the protein conformation has been altered. This will be detected as a change in the kinetic properties of either ligand; however, at equilibrium, the net effect is to observe no change in ligand affinity.

Receptor Binding

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“absolute selectivity” can be achieved at the latter subtype with no significant effect on the binding or signaling properties of all other subtypes (Lazareno et al., 1998). The area of allosteric modulators of GPCRs and therapeutic interventions has only recently begun to blossom. While there are currently no useful allosteric modulators of GPCRs commercially available, it is possible that some of the more recently discovered non-peptide blockers of peptide-binding GPCRs are allosteric in their mode of action. For example, certain forms of HIV infection are known to require the interaction of viral proteins and human chemokine receptors in a manner that involves multiple binding domains (Doms and Peiper, 1997). The ability of small non-peptide molecules to inhibit this multi-site interaction (Baba et al., 1999) suggests that they are able to induce an allosteric change in receptor protein structure that may prove useful in preventing HIV infection.

The main implications of the ternary complex model described in Fig. 1.22.9 are threefold. First, the presence of an additional binding site on the receptor protein opens up a new arena for quantitative structure-activity studies. This is particularly useful for receptor subtypes that display a high sequence homology in their orthosteric binding domains, thus making the discovery of selective orthosteric ligands particularly difficult. Second, successful quantification of allosteric interactions requires the ability to estimate not only ligand-receptor dissociation constants, but also the magnitude of the cooperativity factor characterizing the interaction between ligands. This means that a variety of protocols, as outlined in this unit, will often have to be employed in tandem in order to fully define the relevant parameters of the model for specific pairs of ligands. Third, allosteric modulation of receptor function offers a number of advantages that are not possible with orthosteric ligands. For instance, allosteric modulators have the potential to be much safer in overdose, as their effect is not to continually activate (or inhibit) the orthosteric binding site, but rather to “tune up” or “tune down” endogenous orthosteric signaling (Birdsall et al., 1996). In the absence of an orthosteric agent, allosteric modulators should, theoretically, exert a minimal effect irrespective of dose. In addition, structure-activity relationships can be manipulated to achieve selectivity via exploitation of the cooperativity factor, rather than the ligand dissociation constant (UNIT 1.21). It is possible that allosteric agents can be designed with similar dissociation constants for different receptor subtypes and neutral cooperativity at all but the desired subtype of interest. Thus,

B+R+A

Critical Parameters and Troubleshooting Because allosteric interactions are noncompetitive in nature, they can be manifested in a variety of ways and are usually first detected when the researcher notes a deviation of the experimental data from the expectations of simple mass-action kinetics; however, similar findings may also be due to other experimental artifacts, such as inappropriate equilibration times or a receptor preparation that is too concentrated. Thus, it is up to the investigator to first rule out other reasons for “anomalous” data before deciding to initiate studies aimed at examining potential allosteric properties of the

KA

B + AR

KB α

KB

ARB

BR + A KA α

Quantification of Allosteric Interactions at G Protein Coupled Receptors

Figure 1.22.9 Ternary complex model of allosteric interaction. R denotes the receptor, A and B denote the orthosteric and allosteric ligands, respectively, KA and KB denote the equilibrium dissociation constants of AR and BR, respectively. The symbol, α, denotes the cooperativity factor, and is a quantitative measure of the maximal, reciprocal alteration of the equilibrium dissociation constant of A and B for their respective binding sites, when both ligands bind concomitantly to form the ternary complex ARB.

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Ionic strength and pH Allosteric phenomena are extremely dependent on the ionic conditions of the assay medium. It is well established that ions themselves can exert allosteric effects on agonist and antagonist binding by interacting with distinct modulatory sites on the receptor protein (Limbird, 1996; Kenakin, 1997). Thus, it is not surprising that the ability of allosteric modulators to recognize secondary binding sites can also be affected by the ionic strength of the medium. Another common finding is that functional assays of allosterism, which often require the use of high ionic strength “physiological” buffers, tend to reveal allosteric interactions that are characterized by markedly different degrees of cooperativity. Often, negatively cooperative interactions may appear indistinguishable from simple competition in functional assays, unless very high concentrations of modulator are tested (Lanzafame et al., 1996). Thus, the investigator should not be too surprised if they find that a particular modulator

ligands under investigation. Figure 1.22.10 illustrates a flow-chart strategy for assessing potential allosteric modulators for artifactual properties prior to attempting quantification of allosterism. In addition, there are a number of general considerations that pertain to all types of radioligand binding assays that must first be considered (see below). General considerations As with all binding assays, care must be taken to ensure that membrane concentrations are maintained at a level whereby specific binding is linear with respect to protein content, that total binding for any radioligand concentration does not exceed 10% of the total added amount of radioligand, and that the filter washing procedure is sufficient to significantly remove nonspecific filter binding while allowing detectable specific binding. These issues are discussed in greater detail in other units of this chapter (e.g., UNITS 1.3 & 1.4).

suspected allosteric modulator

from a modulator drug discovery program?

no

from a functional assay?

yes

no

yes

yes

check for equilibrium artifacts; multiple mechanisms of drug action e.g., extraneuronal uptake, enzyme inhibition, ion channel effects assess and quantify allosteric properties; 1. effects on orthosteric radioligand dissociation kinetics 2. affinity ratio assay 3. full quantitative equilibrium or nonequilibrium binding assay

from an no equilibrium binding assay?

yes

check for equilibrium artifacts; vehicle effects; metabolism of ligand; receptor subtypes; ligand effects on membrane properties; definition of nonspecific binding

from a nonequilibrium kinetic assay yes

check for too high a receptor density; compare isotopic dilution to "infinite" dilution in buffer

still suspect allosterism? no end

Figure 1.22.10 Flow chart strategy for dealing with compounds identified as potential allosteric modulators.

Receptor Binding

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displays different characteristics in the assay when compared to previous findings with the same agent conducted in a different buffer. Temperature Temperature will affect the association and dissociation characteristics of the ligand-receptor interaction (Limbird, 1996). For allosteric phenomena, this is most relevant when dissociation kinetic studies are undertaken. Rapid dissociation of the radioligand from the receptor may necessitate lower incubation temperatures and the monitoring of dissociation over longer time points for improved accuracy. Choice of orthosteric ligand Allosteric interactions are characterized by cooperativity between the binding of an orthosteric ligand and an allosteric modulator (see Background Information). Thus, it is naïve to expect that the same modulator, examined under the same assay conditions and using the same receptor preparation, can exert similar allosteric effects when the choice of orthosteric ligand (radiolabeled or not) is changed. Although this is an additional complication particular to the study of allosteric interactions, it is also an important consideration when attempting to design modulators for specific therapeutic purposes. Assays quantifying the allosteric effects of a particular modulator for therapeutic purposes should always aim to include the effects of the modulator on, at least, the endogenous neurotransmitter/hormone that is known to interact with the receptor of interest.

Quantification of Allosteric Interactions at G Protein Coupled Receptors

Intact cells versus homogenates Although the protocols described in this unit are specific to membrane preparations, binding assays conducted on intact cells are also quite common. In general, the demonstration of allosteric modulation of receptor function in intact cells is a very good validation of the physiological significance of the phenomenon. Such a finding would imply that the allosteric effect involves recognition sites on the extracellular face of the receptor, rather than intracellular residues that have been exposed during the membrane disruption associated with homogenization; however, intact cell studies often involve assay media with a different ionic strength to that utilized for homogenate assays, so the magnitude of the allosteric effect can vary dramatically between experiments conducted under the two different sets of conditions.

Receptor concentration The concentration of membrane-bound receptors is obviously an important consideration in any binding assay. In general, the higher the receptor concentration the better, provided that the receptor density is not so high as to result in ligand depletion; however, very high receptor concentrations can also yield binding artifacts that can have a profound effect on dissociation kinetic assays. In turn, these artifacts can cause a significant misinterpretation of the results of kinetic assays that are so crucial in identifying and quantifying allosteric phenomena. As such, some discussion is warranted about the most significant kind of experimental artifact in kinetic assays that is related to receptor concentration, namely the phenomenon of binding at the collisional limit. When the density of receptors exceeds 5000 to 10,000 sites per cell, the probability of a dissociated molecule of ligand diffusing away into the bulk medium according to simple bimolecular mass-action kinetics is significantly decreased to the point that subsequent binding to adjacent receptors occurs. Under these circumstances, binding is considered to have reached the “collisional limit” (Abbott and Nelsestuen, 1988; Kenakin, 1997). As a consequence, the apparent dissociation rate constant of a ligand that is examined under conditions of collision-limited binding will appear much smaller than if the dissociation were monitored when receptor density is much lower. Most important for the study of allosteric phenomena, collision-limited dissociation will appear different if infinite dilution in buffer is used to monitor the reaction rather than isotopic dilution. In the former instance, the dissociation rate will appear slower than in the latter, because the presence of a vast excess of unlabeled orthosteric ligand used for isotopic dilution will minimize the collision-limited binding of radioligand to the receptor. Therefore, a change in the kinetic characteristics of the control radioligand dissociation curve when studied using both infinite dilution in buffer and isotopic dilution with excess orthosteric ligand may be mistaken as an example of negative cooperativity when it is simply a kinetic artifact due to a receptor concentration that is too high. Data analysis Choosing the appropriate mode of data analysis to study any drug-receptor interaction is important, and especially so for allosteric interactions. It should be appreciated that many of the analyses described in this unit rely heav-

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ily on computerized nonlinear regression. Linearizing data transformations should only really be used for presentation purposes (if at all).

Anticipated Results Examples of the results from different types of protocols, as outlined in this unit, are shown in Table 1.22.1 and Figure 1.22.2 (equilibrium saturation binding), Figure 1.22.3 (affinity ratio assay), Table 1.22.2 and Figures 1.22.5 and 1.22.6 (fixed radioligand concentration equilibrium binding assay), Figure 1.22.7 (nonequilibrium binding assay), and Figure 1.22.8 (dissociation kinetic assay). It is important to reiterate that the various types of assays yield quantitative or semiquantitative parameter estimates that reflect different aspects of the allosteric ternary complex model (e.g., affinity measures or rate constants for the ligand versus the non-liganded receptor, cooperativity factors), but that should still be complementary with one another. In addition, it is important to note that the experimental assay conditions will affect the absolute value of the parameter estimates, and this should be borne in mind if comparisons are to be made with other studies reported in the literature.

Time Considerations Experiments conducted at equilibrium will take the same time to complete, regardless of whether the protocol is a radioligand saturation assay or a single-point inhibition (or potentiation) assay. What is most crucial is that true equilibrium conditions are ascertained. For many allosteric interactions, especially those characterized by negative cooperativity, equilibrium is assured for most concentrations of modulator tested if the reaction is allowed to proceed for approximately five radioligand dissociation half-lives. For positively-cooperative interactions, equilibrium may require extraordinarily long incubation times (e.g., ≥6 hr at 30° to 37°C), in which case a nonequilibrium protocol (e.g., see Alternate Protocol 2) may need to be employed. This is definitely warranted if an “equilibrium” potentiation binding protocol yields bell-shaped binding curves (i.e., an increase in specific radioligand binding with low modulator concentrations and a decrease in specific binding at high modulator concentrations). Dissociation kinetic assays will generally not take longer than a few hours, and for many receptors will take less than 90 minutes at 37°C. Once the dissociation characteristics of the radioligand from the receptor of interest

are determined under control conditions (i.e., absence of modulator), the timing of the assay can be optimized. For the protocols described in this unit, the entire procedure should be completed within half a day after thawing the membrane preparation.

Literature Cited Abbott, A.J. and Nelsestuen, G.L. 1988. The collisional limit: An important consideration for membrane-associated enzymes and receptors. FASEB J. 2:2858-2866. Arunlakshana, O. and Schild, H.O. 1959. Some quantitative uses of drug antagonists. Br. J. Pharmac. 14:48-57. Baba, M., Nishimura, O., Kanzaki, N., Okamoto, M., Sawada, H., Iizawa, Y., Shiraishi, M., Aramaki, Y., Okonogi, K., Ogawa, Y., Meguro, K., and Fujino, M. 1999. A small-molecule, nonpeptide CCR5 antagonist with highly potent and selective anti-HIV-1 activity. Proc. Natl. Acad. Sci. U.S.A. 96:5698-5703. Birdsall, N.J., Lazareno, S., and Matsui, H. 1996. Allosteric regulation of muscarinic receptors. Prog. Brain Res. 109:147-51. Bruns, R.F. and Fergus, J.H. 1990. Allosteric enhancement of adenosine A1 receptor binding and function by 2-amino-3-benzoylthiophenes. Mol. Pharmacol. 38:939-949. Changeux, J.-P. 1966. Responses of acetylcholinesterase from Torpedo marmorata to salts and curarizing drugs. Mol. Pharmacol. 2:369-392. Changeux, J.-P. 1993. Allosteric proteins: From enzymes to receptors—personal recollections. Bioessays 15:625-634. Christopoulos, A. 1998. Assessing the distribution of parameters in models of ligand-receptor interaction: To log or not to log. Trends Pharmacol. Sci. 19:351-357. Christopoulos, A., Lanzafame, A., and Mitchelson, F. 1998. Allosteric interactions at muscarinic cholinoceptors. Clin. Exp. Pharmacol. Physiol. 25:184-194. Christopoulos, A., Sorman, J.L., Mitchelson, F. and El-Fakahany, E.E. 1999. Characterization of the subtype selectivity of the allosteric modulator heptane-1,7-bis-(dimethyl-3′-pthalimidopropy l) ammonium bromide (C7/3-phth) at cloned muscarinic acetylcholine receptors. Biochem. Pharmacol. 57:171-179. Colquhoun, D. 1973. The relation between classical and cooperative models for drug action. In Drug Receptors (H.P. Rang, ed.) pp. 149-182. Macmillan Press, London. De Lean, A., Stadel, J.M., and Lefkowitz, R.J. 1980. A ternary complex model explains the agonistspecific binding properties of the adenylate cyclase-coupled β-adrenergic receptor. J. Biol. Chem. 255:7108-7117. Doms, R.W. and Peiper, S.C. 1997. Unwelcome guests with master keys: How HIV uses chemokReceptor Binding

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ine receptors for cellular entry. Virol. 235:179190.

receptors: Radioligand binding studies. Mol. Pharmacol. 53:573-589.

Ehlert, F.J. 1988. Estimation of the affinities of allosteric ligands using radioligand binding and pharmacological null methods. Mol. Pharmacol. 33:187-194.

Leppik, R.A., Lazareno, S., Mynett, A., and Birdsall, N.J. 1998. Characterization of the allosteric interactions between antagonists and amiloride analogues at the human α2A-adrenergic receptor. Mol. Pharmacol. 53:916-925.

Ehlert, F.J., Roeske, W.R., Gee, K.W., and Yamamura, H.I. 1983. An allosteric model for benzodiazepine receptor function. Biochem. Pharmacol. 32:2375-2383.

Limbird, L.E. 1996. Cell Surface Receptors: A Short Course on Theory and Methods., 2nd Edition. Kluwer Academic Publishers, Boston.

Hoare, S.R.J. and Strange, P.G. 1996. Regulation of D2 dopamine receptors by amiloride and amiloride analogs. Mol. Pharmacol. 50:1295-1308.

Monod, J., Changeux, J.-P., and Jacob, F. 1963. Allosteric proteins and cellular control systems. J. Mol. Biol. 6:306-329.

Hulme, E.C. and Buckley, N.J. 1992. Receptor preparations for binding studies. In Receptor-Ligand Interactions. A Practical Approach. (E.C. Hulme, ed.) pp. 177-212. Oxford University Press, New York.

Monod, J., Wyman, J., and Changeux, J.-P. 1965. On the nature of allosteric transitions: A plausible model. J. Mol. Biol. 12:88-118.

Karlin, A. 1967. On the application of “a Plausible Model” of allosteric proteins to the receptor for acetylcholine. J. Theor. Biol. 16:306-320. Kaumann, A.J. and Frenken, M. 1985. A paradox: The 5-HT2-receptor antagonist ketanserin restores the 5-HT-induced contraction depressed by methysergide in large coronary arteries of calf. Allosteric regulation of 5-HT2-receptors. Naunyn-Schmied. Arch. Pharmacol. 328:295300.

Schetz, J.A. and Sibley, D.R. 1997. Zinc allosterically modulates antagonist binding to cloned D1 and D2 dopamine receptors. J. Neurochem. 68:1990-1997. Thomas, E.A., Carson, M.J., Neal, M.J., and Sutcliffe, J.G. 1997. Unique allosteric regulation of 5-hydroxytryptamine receptor-mediated signal transduction by oleamide. Proc. Natl. Acad. Sci. U.S.A. 94:14115-14119. Thron, C.D. 1973. On the analysis of pharmacological experiments in terms of an allosteric receptor model. Mol. Pharmacol. 9:1-9.

Kenakin, T.P. 1997. Pharmacologic Analysis of Drug-Receptor Interaction., 3rd Edition. Lippincott-Raven, Philadelphia.

Tucek, S. and Proska, J. 1995. Allosteric modulation of muscarinic acetylcholine receptors. Trends Pharmacol. Sci. 16:205-212.

Koshland, D.E., Némethy, G., and Filmer, D. 1966. Comparison of experimental binding data and theoretical models in proteins containing subunits. Biochem. 5:365-385.

Wregget, K.A. and Wells, J.W. 1995. Cooperativity manifest in the binding properties of purified cardiac muscarinic receptors. J. Biol. Chem. 270:22488-22499.

Lanzafame, A., Christopoulos, A., and Mitchelson, F. 1996. Interactions of agonists with an allosteric antagonist at muscarinic acetylcholine M2 receptors. Eur. J. Pharmacol. 316:27-32.

Key References

Lazareno, S. and Birdsall, N.J.M. 1995. Detection, quantitation, and verification of allosteric in0teractions of agents with labeled and unlabeled ligands at G protein-coupled receptors: Interactions of strychnine and acetylcholine at muscarinic receptors. Mol. Pharmacol. 48:362-378. Lazareno, S., Gharagozloo, P., Kuonen, D., Popham, A., and Birdsall, N.J.M. 1998. Subtype-selective positive cooperative interactions between brucine analogues and acetylcholine at muscarinic

Ehlert, F.J. 1988. See above. Lazareno, S. and Birdsall, N.J.M. 1995. See above. These citations cover the detection and quantification of allosteric interactions at GPCRs in great detail.

Contributed by Arthur Christopoulos University of Melbourne Victoria, Australia

Quantification of Allosteric Interactions at G Protein Coupled Receptors

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Characterization of 5-HT1A,B and 5-HT2A,C Serotonin Receptor Binding

UNIT 1.23

This unit describes assays for measuring the binding of radioligands to two major types of receptors for 5-hydroxytryptamine (5-HT or serotonin), 5-HT1 and 5-HT2 receptors, in homogenates of brain tissue or cloned into cells in culture. The specific receptor subtypes covered here are 5-HT1A, 5-HT1B, 5-HT2A, and 5-HT2C. In addition, methodology for using quantitative autoradiography to measure radioligand binding to serotonin receptors in brain slices is described. The nomenclature for serotonin receptors has undergone considerable evolution. Current classification schemes (Barnes and Sharp, 1999) list seven major types of receptors for serotonin (5-HT1-7) with a total of 14 subtypes in mammals (see Table 1.23.1). Except for the 5-HT3 receptor, a ligand-gated ion channel, serotonin receptors are G protein–coupled receptors (GPCRs). Agonist binding to GPCRs is complex as they have different affinities for “uncoupled” versus “coupled” receptors (UNIT 1.3). By contrast, antagonists do not differentiate well between such conformations. Originally, the radioligand used to bind Table 1.23.1 5-Hydroxytryptamine (Serotonin) Receptors (GPCR; 2.1.5HT.00.000.00.00)a,b

Receptor

GenBank/ SwissProt no. (human)

5-HT1A

Agonists

Antagonists

Signal transduction

P8908

8-OH-DPAT U 92106A dp-5-CT

WAY 100635 NAN 190 pMPPF

5-HT1B

P28222

L 694247 Sumatriptan

GR 55562 GR 127935 SB 216641

5-HT1D

P28221

BRL 15572

5-ht1e

P28566

Sumatriptan Almotriptan Zomitriptan L 694247 BRL 54443

5-ht1f

P30939

5-HT2A

P28223

Inhibition of cAMP formation Increase Kir activity PLC activation Inhibition of cAMP formation Increase Kir activity PLC activation Inhibition of cAMP formation Increase Kir activity PLC activation Inhibition of cAMP formation Inhibition of cAMP formation Increase in AA release, PI hydrolysis, and elevation of [Ca2+]i

5-HT2B

P41595

5-HT2C

P28221

5-HT4

Y09586

BRL 54443 LY 334370 DOB (+)-DOI m-CPP α-Me-5-HT BW 723C86 α-Me-5-HT m-CPP α-Me-5-HT (+)-DOI BIMU8 SC 53116 RS 67506

None None Ketanserin MDL 100907 AMI 193 SB 206553 SB 200646 SB 204741 Mesulergine SB 242084 RS 102221 GR 113808 RS 100235 SB 204070

Increase in AA release, PI hydrolysis, and elevation of [Ca2+]i Increase in AA release, PI hydrolosis, and elevation of [Ca2+]i Increase in cAMP formation

continued Receptor Binding Contributed by William P. Clarke, Kelly A. Berg, Georgianna Gould, and Alan Frazer Current Protocols in Pharmacology (2001) 1.23.1-1.23.33 Copyright © 2001 by John Wiley & Sons, Inc.

1.23.1 Supplement 12

Table 1.23.1 5-Hydroxytryptamine (Serotonin) Receptors (GPCR; 2.1.5HT.00.000.00.00)a,b, continued

Receptor

GenBank/ SwissProt no. (human)

Agonists

Antagonists

Signal transduction

5-ht5a 5-ht5b 5-ht6

P47898 P35365 P50406

LSD LSD LSD

Unknown Unknown Increase in cAMP formation

5-HT7

P50407

5-CT

None None Ro 046790 Ro 630563 SB 258719

Increase in cAMP formation

5-Hydroxytryptamine (Serotonin) Receptor (LGIC; 1.1.5HT.00.000.00.00)a,b 5-HT3 P46098 2Me5-HT Tropisetron Acts as ion channel to SR57227 Granisetron increase [Ca2+]i Ondansetron aChemical abbreviations:

Characterization of 5-HT1A,B and 5-HT2A,C Serotonin Receptor Binding

α-Me-5-HT: α-methyl-5-hydroxytryptamine AA: arachidonic acid AMI 193: 8-[3-(4-fluorophenoxy)propyl]-1-phenyl-1,3,8-triazaspiro[4.5]decan-4-one BIMU8: (endo-N-8-methyl-8-azabicyclo[3.2.1]oct-3-yl)-2,3-dihydro-3-isopropyl-2-oxo-1H-benzimidazol-13-carboxamide hydrochloride BRL 15572: 3-[4-(3-chlorophenyl)piperazin-1-yl]-1,1-diphenyl-2-propanol BRL 54443: 3-(1-methylpiperidin-4-yl)1H-indol-5-ol BW 723C86: 1-[5(2-thienylmethoxy)-1H-3-indolyl]propan-2-amine hydrochloride 5-CT: 5-carboxamidotryptamine DOB: (±)-2,5-dimethoxy-4-bromoamphetamine DOI: (±)-2,5-dimethoxy-4-iodoamphetamine dp-5-CT: dipropyl-5-carboxamidotryptamine GR 55562: 3-[3-(dimethylamino)propyl]-4-hydroxy-N-[4-(4-pyridinyl)phenyl]benzamide GR 113808: {1-2[(methylsulfonyl)amino]ethyl}-4-piperidinyl]methyl-1-methyl-1H-indole-3-carboxylate GR 127935: N-[methoxy-3-(4-methyl-1-piperazinyl)phenyl]-2′-methyl-4′-(5-methyl-1,2,4-oxadiazol-3-yl)[1,1biphenyl]-4-carboxamide Kir: inwardly rectifying potassium channel m-CPP: m-chlorophenylpiperazine MDL 100907: (±)-2,3-dimethoxyphenyl-1-[2-(4-piperidine)methanol] L 694247: 2-{5-[3-(4-methylsulfonylamino)benzyl-1,2,4-oxadiazol-5-yl]-1H-indol-3-yl}ethanamine LSD: lysergic acid diethylamide LY 334370: 4-fluoro-N-[3-(1-methyl-4-piperidinyl)-1H-indole-5-yl]benzamide 2Me5-HT: 2-methyl-5-hydroxytryptamine. NAN 190: 1,1-(2-methoxyphenyl)-4-[4-(2-phthalimido)butyl]piperazine 8-OH-DPAT: 8-hydroxy-dipropylaminotetralin PI: phosphoinositide PLC: phospholipase C pMPPF: methoxy(4-C21 methoxyphenyl)-1-C21-(N-211-pyridyl)-p-fluorobenzamido]-ethyl-piperazine Ro 046790: 4-amino-N-[2,6-bis(methylamino)pyrimidin-4-yl]benzenesulfonamide Ro 630563: 4-amino-N-[2,6-bis(methylamino)pyridin-4-yl]benzenesulfonamide RS 67506: 1-(4-amino-5-chloro-2-methoxyphenyl)-3-(1-n-butyl-4-piperidinyl)-1-propanone RS 100235: 1-(8-amino-7-chloro-1,4-benzodioxan-5-yl)-5-{[3-(3,4-dimethoxyphenyl)prop-1-yl]piperidin-4-yl}propan1-one RS 102221: 8-[5-(5-amino 2,4-dimethoxyphenyl)-5-oxopentyl]-1,3,8-triazaspiro[4.5]decane-2,4-dione SB 204070: 1-butyl-4-piperidinylmethyl-8-amino-7-chloro-1-4-benzodioxan-5-carboxylate SB 206553: 5-methyl-1-(3-pyridylcarbamoyl)-1,2,3,5-tetrahydropyrrolo[2,3-f]indole SB 216641: N-{3-[2-(dimethylamino)ethoxy]-4-methoxyphenyl}-2′-methyl-4′-(5-methyl-1,2,4-oxadiazol)-3-yl) [1,1′biphenyl]-4-carboxamide SB 242084: 6-chloro-5-methyl-1-[2-(2-methylpyridyl-3-oxy)-pyrid-5-ylcarbamoyl]indoline SB 258719: (R)-3-N-dimethyl-N-[1-methyl-3-(4-methylpiperidin-1-yl)propyl]benzene sulfonamide SC 53116: 4-amino-5-chloro-N-{[(1S,7aS)-hexahydro-1H-pyrrolizin-1-yl]methyl}-2-methoxy-benzamide SR 57227: 4-amino-(6-chloro-2-pyridyl)-1-piperidine hydrochloride WAY 100635: N-(2-(4-(2-methoxyphenyl)-1-piperazinyl)ethyl)-N-(2-pyridyl)-cyclohexanecarboxamide trichloride. bReference:Boess and Martin (1994).

1.23.2 Supplement 12

Current Protocols in Pharmacology

selectively to 5-HT1A receptors was the agonist [3H]8-hydroxy-2-(di-n-propylamino)tetralin ([3H]8-OH-DPAT). This radioligand is still extensively used for 5-HT1A receptor binding studies today. More recently, antagonist radioligands for this subtype have become available—e.g., [O-methyl 3H]-(N-[2-[4-(2-methoxyphenyl)-1-piperazinyl]ethyl]-N-2-pyridinyl-cyclohexanecarboxamide) (3H- WAY 100635) or [3Hmethyl](4-fluoro-N-[4-(2-methoxyphenyl)-1-piperazinyl]ethyl)-(N-2-pyridyl)-benzam ide dihydrochloride ([3H]-p-MPPF). Although [3H]WAY 100635 has perhaps become the more popular selective antagonist radioligand for 5-HT1A receptors (Gozlan et al, 1995; Khawaja, 1995; Khawaja et al., 1995), most published reports have used [3H]p-MPPF, as it was available earlier than [3H]WAY 100635. When used as described, [3H]p-MPPF appears equally as useful as [3H]WAY 100635, although nonspecific binding may be somewhat higher for [3H]p-MPPF. The choice of whether to use the agonist [3H]8-OHDPAT or one of the antagonists [3H]p-MPPF or [3H]WAY 100635 is based upon the goals of the experiment. It is usually preferable to use an antagonist for GPCR binding studies since antagonist binding is generally insensitive to the desensitization state of the receptor and to membrane-associated signal transduction components (UNIT 1.3). As a consequence, antagonists are believed to label the entire population of receptors in a preparation, unlike agonists, which at practical concentrations, label only a portion of the receptor population (high-affinity, G protein coupled); however, an agonist radioligand can be used to examine high affinity binding characteristics and to explore receptor-G protein coupling efficiency. In this unit, protocols are provided for characterization of both saturation binding (see Basic Protocol 1) and competition binding (see Basic Protocol 2) of radioligands to cloned 5-HT2A and 5-HT2C receptors. A procedure for measurement of 5-HT1A binding in tissue membrane homogenates is also provided (see Basic Protocol 3). Instructions for data analysis of these radioligand binding assays is also described (see Support Protocol 1). For the study of native receptors in tissues, detailed instructions are given for quantitative autoradiography of radioligand binding to 5-HT1A (see Basic Protocol 4) and 5-HT1B (see Basic Protocol 5) receptors in rat brain slices. In addition, methodology is provided for the quantification (image analysis) of radioligand binding in brain tissue sections to determine receptor density (see Support Protocol 3), preparation of rat brain sections for quantitative autoradiography (see Support Protocol 2) and thionin staining of thawmounted tissue sections to define certain brain regions (see Support Protocol 4). Analysis of binding data is, in general, common to all types of these receptors. Consequently, data analysis is described in detail in Support Protocol 1 for 5-HT2 receptors (see Basic Protocols 1 and 2) and is not repeated for the 5-HT1 protocols (see Basic Protocol 3). Similarly, quantitation of autoradiograms is similar, so this is described in detail for 5-HT1A receptors (see Basic Protocol 4) and is not repeated in the 5-HT1B protocol (see Basic Protocol 5). MEASUREMENT OF BINDING PROPERTIES TO CLONED 5-HT2A AND 5-HT2C RECEPTORS EXPRESSED IN CELLS—SATURATION BINDING

BASIC PROTOCOL 1

This protocol describes the procedures for measuring the binding properties of the radioligand [3H]ketanserin to cloned human 5-HT2A receptors or [3H]mesulergine to cloned human 5-HT2C receptors expressed in CHO cells. The assay is run in a total volume of 500 µl in 12 × 75-mm borosilicate test tubes. However, it should be possible to scale down the assay volume to 250 µl such that the assays can be run in 96-well microtiter plates (e.g., flat-bottom, GF/C glass fiber, 96-well filter plates from Millipore). To terminate the binding assay, the assay mixture is filtered with a vacuum filtration manifold (e.g., Millipore Multiscreen Vacuum Manifold) and the radioactivity bound to the membranes on the filter bottom of each well can be quantified Receptor Binding

1.23.3 Current Protocols in Pharmacology

Supplement 12

by either adding scintillation cocktail to each well and counting with a plate counter (e.g., Packard TopCount), or by transferring the filter from each well, using a sharp forceps, to a 7-ml scintillation vial, adding scintillation cocktail and counting in a standard liquid scintillation counter. Materials CHO cell line expressing 5-HT2A or 5-HT2C serotonin receptors in 15-cm plates Hanks Basic Salt Solution (HBSS; Life Technologies), ice-cold HEPES homogenization buffer, pH 7.4 (see recipe), 37° and 4°C HEPES assay buffer, pH 7.4, 4°C 60 to 90 Ci/mmol [3H]ketanserin (5-HT2A receptors; NEN Life Sciences) or 70 to 85 Ci/mmol [3H]mesulergine (5-HT2C receptors; Amersham) Masking ligand: methysergide (5-HT2A receptors) or mianserin (5-HT2C receptors) 0.5% (v/v) polyethyleneimine (Sigma) Scintillation fluid (e.g., Beckman Ready-Solv) Rubber cell scraper (Gilco) 1-ml disposable transfer pipets (Fisher) 50-ml round plastic centrifuge tubes (Nalgene) 2-ml cryotube Polytron homogenizer (Brinkmann Instruments) Refrigerated Sorvall RC-5 centrifuge and SA-600 rotor Potter-Elvehjem glass-Teflon homogenizer (VWR) or equivalent, prechilled 7-ml scintillation vials (Fisher) GF/C filter strips (Whatman) 24-channel cell harvester (Brandel) Scintillation counter (Beckman) Additional equipment and reagents for Lowry protein assay (APPENDIX 3A). Prepare membranes 1. Remove media from each 15-cm plate containing 1–1.2 × 107 cells expressing 5-HT2A or 5-HT2C serotonin receptors and rinse twice with 10 ml ice-cold Hanks Basic Salt Solution (HBSS). See Berg et al. (1994), Saltzman et al. (1991), and UNIT 6.3 for procedures on constructing receptor-containing cell lines. For cells with receptor expression levels of ∼200 fmol/mg protein, cells from five or six plates are combined to provide enough material for an assay using approximately 50 ìg protein per tube. Thus, at 200 fmol/mg protein, there will be 10 fmol receptor per tube. With a specific activity of 80 Ci/mmol and counter efficiency of 50%, one would expect ~880 cpm (including nonspecific) when all receptors are occupied by radioligand, or ∼88 cpm when 10% of receptors are occupied (1 Ci = 2.22 × 1012 dpm). Depending upon the level of receptor expression, the number of plates required will vary.

2a. For immediate use: Add 1 ml ice-cold HBSS to each 15-cm plate and scrape the bottom of the plates with a rubber cell scraper to detach cells. Using a 1-ml disposable transfer pipet, transfer the cells from each plate to a 50-ml round plastic centrifuge tube on ice. Alternatively, at this point, cells can be pelleted and frozen for storage. Characterization of 5-HT1A,B and 5-HT2A,C Serotonin Receptor Binding

2b. To store cells for later use: Detach cells as above and transfer to a 2-ml cryotube. Centrifuge in a tabletop centrifuge for 5 min at 500 × g at 4°C. Remove the supernatant, freeze the pellet in liquid nitrogen vapor, and store until assay. At the time of the assay, remove the cryotubes from liquid nitrogen and allow the pellets to

1.23.4 Supplement 12

Current Protocols in Pharmacology

thaw at room temperature for ~2 min. Add 1 ml ice-cold HEPES homogenization buffer, pH 7.4 and transfer the cells from the cryotube to a 50-ml centrifuge tube using a 1-ml disposable pipet. The frozen cell pellets can be stored indefinitely in liquid nitrogen.

3. Add 20 ml HEPES homogenization buffer, 4°C to the centrifuge tube 4. Homogenize on ice using a Polytron homogenizer at setting 7 using two 15-sec bursts separated by 15 sec. It is important to keep the centrifuge tube on ice during the homogenization procedure since the Polytron can generate a damaging amount of heat. It is also a good idea to chill the shaft of the Polytron using ice-cold HEPES homogenization buffer before homogenization.

5. Add 20 ml ice-cold HEPES homogenization buffer to the centrifuge tube The tube now contains 41 ml total volume.

6. Centrifuge 10 min at 39,000 × g (16,500 rpm using a Sorvall SA-600 rotor), 4°C. 7. Discard the supernatant. 8. Add ~10 ml ice-cold (4°C) HEPES homogenization buffer to the centrifuge tube. Vortex briefly to free the pellet from the bottom of the tube. Pour the pellet and the buffer into a Potter Elvehjem glass-Teflon homogenizer and vortex vigorously for 5 to 10 sec. With the homogenizer in ice, homogenize the pellet using 5 strokes of the pestle by hand, and then pour the homogenate back into the centrifuge tube. Rinse the homogenizer and the pestle with 30 ml ice-cold HEPES homogenization buffer and pour the rinse back into the centrifuge tube. 9. Repeat steps 6 through 8 three times. Centrifuge the homogenate a final time as in step 6, discard the supernatant, resuspend the pellet in HEPES assay buffer (e.g., 12 ml) and place the membrane preparation on ice. The volume needed for resuspension depends upon receptor expression levels and the volume required for the assay. Using CHO cells that express 200 fmol receptor/mg protein, at least 50 ìg protein per tube is required. As a very rough estimate, one 15-cm plate containing 1 × 107 CHO cells has approximately 0.5 mg protein. Making a membrane preparation from 5 plates would yield a total of ∼2.5 mg protein, which is enough for 50 tubes at 50 ìg per tube and is sufficient to run a typical saturation binding assay consisting of 38 tubes with some membrane preparation remaining for protein analysis. In this example, the membrane pellet after the last centrifugation would be resuspended in 12 ml assay buffer. If the level of receptor expression is unknown, the volume for resuspension is determined empirically by examining the magnitude of radioligand binding in preparations with different protein concentrations. As protein concentration in the assay increases, radioligand binding will also increase. The optimum protein concentration is one in which (1) there is sufficient detectable binding (at least 3 times background) when 10% of the receptors are occupied (i.e., at a radioligand concentration of 0.1 × KD ); (2) the amount of radioligand bound does not exceed 10% of the added radioactivity (i.e., the amount bound should not alter the amount of free radioligand); and (3) the signal-to-noise ratio (specific to nonspecific binding) is optimized. It is possible to freeze the membrane suspension at −80°C or in liquid nitrogen for subsequent analysis. Frozen membrane suspensions appear to remain stable in terms of binding for at least 3 months.

Perform the assay 10. Just before use, prepare solutions of 60 to 90 Ci/mmol [3H]ketanserin or 70 to 85 Ci/mmol [3H]mesulergine (for 5-HT2A or 5-HT2C receptor binding, respectively) at 10× the final assay concentrations in HEPES assay buffer at 4°C: 0.1, 0.4, 1, 2, 4, 6, 8, 10, 20, 40, 60, 100, and 200 nM (13 10× concentrations) in 12 × 75–mm borosilicate test tubes.

Receptor Binding

1.23.5 Current Protocols in Pharmacology

Supplement 12

Calculation of the radioligand concentration in the stock container from the vendor is performed as follows: Divide the isotope concentration (mCi/ml) by the specific activity (Ci/mmol) and then divide by 1000 (mCi/Ci) to obtain molarity (moles/liter). For example, with an isotope concentration of 1 mCi/ml and a specific activity of 80 Ci/mmol, the ligand concentration is 1.25 × 10−5 M.

11. Prepare a solution of masking ligand at 10× the final assay concentration in HEPES assay buffer just before use. 10× is 10 ìM methysergide for 5-HT2A or 10 ìM mianserin for 5-HT2C receptors.

12. Pipet appropriate amounts of assay buffer, radioligand and masking ligand into duplicate 12 × 75-mm disposable culture tubes (see Table 1.23.2). Keep tubes on ice. a. For total binding: Combine 200 µl assay buffer and 50 µl appropriate radioligand concentration. b. For nonspecific binding: Combine 150 µl assay buffer, 50 µl appropriate radioligand concentration, and 50 µl masking ligand. 13. Pipet 50 µl of each radioligand concentration directly into individual 7-ml scintillation vials.

Table 1.23.2

Tube no.b

Saturation Binding Assay Using [3H]Mesulergine to Label the 5-HT2C Receptora

[3H]mesulergine (nM) 10× conc.

Total binding 1&2 3&4 5&6 7&8 9 & 10 11 & 12 13 & 14 15 & 16 17 & 18 19 & 20 21 & 22 23 & 24 25 & 26

Characterization of 5-HT1A,B and 5-HT2A,C Serotonin Receptor Binding

0.1 0.4 1 2 4 6 8 10 20 40 60 100 200

Nonspecific Binding Tubes 27 & 28 0.1 29 & 30 1 31 & 32 4 33 & 34 10 35 & 36 40 37 & 38 100 39 & 40 200

50 µl

Mianserin

Assay buffer

Volume (µl) Volume (µl)

Membranes

Final mesulergine

250 µl

(nM)c

√ √ √ √ √ √ √ √ √ √ √ √ √

0 0 0 0 0 0 0 0 0 0 0 0 0

200 200 200 200 200 200 200 200 200 200 200 200 200

√ √ √ √ √ √ √ √ √ √ √ √ √

0.01 0.04 0.10 0.20 0.40 0.60 0.80 1.00 2.00 4.00 6.00 10.00 20.00

√ √ √ √ √ √ √

50 50 50 50 50 50 50

150 150 150 150 150 150 150

√ √ √ √ √ √ √

0.01 0.10 0.40 1.00 4.00 10.00 20.00

aThe K of ketanserin for 5-HT D 2A receptors is similar to the KD of mesulergine for 5-HT2C receptors, so the concentrations listed in this table can also be used to study 5-HT2A receptors with [3H]ketanserin. bFinal assay volume is 500 µl. cReactions are run in duplicate; therefore, there are two tube numbers per condition.

1.23.6 Supplement 12

Current Protocols in Pharmacology

After quantifying this radioactivity and taking into account the specific activity of the radioligand, the concentration of the radioligand in each condition can be accurately assessed.

14. Optional: If membranes have been frozen, thaw them at room temperature or by placing them in warm water (~45°C). Vortex well and maintain on ice. Prolonged exposure (>15 min) to temperatures >4°C can lead to reduced binding capacity, presumably as a result of receptor degradation. The use of protease inhibitors (e.g., 5 ìg/ml benzamidine, 1 mM phenylmethylsulfonyl fluoride, 5 ìg/ml leupeptin, 5 ìg/ml soybean trypsin inhibitor, 10 mM Na4P2O7, or a commercially available cocktail—e.g., Sigma) may help to reduce receptor degradation.

15. To initiate the binding assay, add 250 µl membrane preparation to each tube, vortex, and incubate the tubes for 1 hr at 37°C. Save remaining membrane preparation on ice or in the refrigerator for later protein assay. 16. While the assay is incubating, soak the GF/C filter strips in 0.5% polyethyleneimine for 60 min. 17. Rinse the Brandel cell harvester with distilled deionized water followed by ice-cold homogenization buffer, using separate 12 × 75–mm disposable culture tubes. This prechills the tubing in the harvester and serves to reduce dissociation of bound radioligand from the receptor, which can occur upon warming during separation of bound from free.

18. Insert the polyethyleneimine presoaked GF/C filters into the harvester. 19. Add ~3 ml ice-cold homogenization buffer to the assay tubes using the harvester. Filter the binding mixture through GF/C filters and then rinse the tubes and filters two times with 1.5 ml ice-cold HEPES homogenization buffer. 20. Place the filters into 7-ml scintillation vials, add 5 ml scintillation cocktail, cap, shake 10 sec to ensure that the filter is in the scintillation fluid, and quantify radioactivity in a liquid scintillation counter. 21. Determine the amount of protein added to each assay tube (i.e., in 250 µl membrane preparation) using a Lowry protein assay (APPENDIX 3A) with bovine serum albumin (BSA) as the standard. 22. Rinse the harvester with warm deionized distilled water to clean. 23. Proceed with data analysis (see Support Protocol 1). MEASUREMENT OF LIGAND AFFINITY TO CLONED 5-HT2A AND 5-HT2C RECEPTORS EXPRESSED IN CELLS—COMPETITION BINDING

BASIC PROTOCOL 2

This protocol describes the procedures for measuring the affinity of ligands for cloned human 5-HT2A or human 5-HT2C receptors expressed in CHO cells. The assay is run in a total volume of 500 µl in 12 × 75-mm borosilicate test tubes. However, it should be possible to scale down the assay volume to 250 µl such that the assays can be run in 96-well microtiter plates (e.g., flat-bottom, GF/C glass fiber, 96-well filter plates from Millipore). To terminate the binding assay, the assay mixture is filtered with a vacuum filtration manifold (e.g., Millipore Multiscreen Vacuum Manifold) and the radioactivity bound to the membranes on the filter bottom of each well can be quantified by either adding scintillation cocktail to each well and counting with a plate counter (e.g., Packard TopCount), or by transferring the filter from each well, using a sharp forceps, to Receptor Binding

1.23.7 Current Protocols in Pharmacology

Supplement 12

a 7-ml scintillation vial, adding scintillation cocktail and counting in a standard liquid scintillation counter. Materials Test (competitor) compounds HEPES assay buffer, pH 7.4 at 4°C (see recipe) 60 to 90 Ci/mmol [3H]ketanserin (NEN Life Sciences; for 5-HT2A receptors) or 70 to 85 Ci/mmol [3H]mesulergine (Amersham; for 5-HT2C receptors) Competitor ligand Additional reagents and equipment for preparing membranes (see Basic Protocol 1). 1. Prepare membranes as described above (see Basic Protocol 1, steps 1 to 9). 2. Just before use, prepare solutions of radioligand at 10× the final assay concentration in HEPES assay buffer, 4°C. 10× is 10 nM [3H]ketanserin or [3H]mesulergine for 5-HT2A or 5-HT2C receptor binding, respectively. Calculation of the radioligand concentration in the stock container from the vendor is performed as follows: Divide the isotope concentration (mCi/ml) by the specific activity (Ci/mmol) and then divide by 1000 (mCi/Ci) to obtain molarity (moles/liter). For example, with an isotope concentration of 1 mCi/ml and a specific activity of 80 Ci/mmol, the ligand concentration is 1.25 × 10−5 M. The volume of the radioligand solution needed depends upon the number of tubes in the assay. A typical assay includes at least 10 concentrations of the competitor in duplicate (20 tubes). Although the volume needed for 20 tubes is 1 ml (at 50 ìl radioligand per tube), it is recommended that 1.5 ml of the 10× concentration be prepared to ensure a sufficient supply.

3. Just before use, prepare solutions of the test (competitor) compounds at 10× final assay concentrations in HEPES, 4°C assay buffer. Use at least 5 concentrations at half-log units above and below the expected IC50 value.

Table 1.23.3 Receptor

Tube no.a

Characterization of 5-HT1A,B and 5-HT2A,C Serotonin Receptor Binding

1&2 3&4 5&6 7&8 9 & 10 11 & 12 13 & 14 15 & 16 17 & 18 19 & 20 21 & 22 23 & 24 25 & 26

Competition Binding Assay Using [3H]Mesulergine to Label the 5-HT2C

Competitor

[3H]mesulergine

Assay buffer

Membranes

Final competitor

10× conc. Volume (nM) (µl)

Volume (µl)

Volume (µl)

250 µl

(nM)b

50 50 50 50 50 50 50 50 50 50 50 50 50

200 150 150 150 150 150 150 150 150 150 150 150 150

√ √ √ √ √ √ √ √ √ √ √ √ √

0.1 0.3 1 3 10 30 100 300 1000 3000 10,000 30,000

0 1 3 10 30 100 3000 1000 3000 10,000 30,000 100,000 300,000

0 50 50 50 50 50 50 50 50 50 50 50 50

aReactions are run in duplicate; therefore, there are two tube numbers per condition. bFinal assay volume is 500 µl.

1.23.8 Supplement 12

Current Protocols in Pharmacology

4. Pipet the appropriate amounts of assay buffer, radioligand and competitor ligand into 12 × 75-mm disposable culture tubes (see Table 1.23.3). Keep tubes on ice. a. For total binding (i.e., zero competitor): Combine 200 µl assay buffer and 50 µl appropriate radioligand concentration. b. For tubes containing the competitor: Combine 150 µl assay buffer, 50 µl appropriate radioligand concentration, and 50 µl the appropriate competitor concentration. 5. Perform the rest of the assay as described (see Basic Protocol 1, steps 13 to 23). DATA ANALYSIS FOR SATURATION AND COMPETITION ASSAYS A convenient method of data analysis involves the combination of a spreadsheet (e.g., Microsoft Excel) and a graphing program with nonlinear regression analysis capability. There are many commercial packages available to analyze receptor binding data. One available for Macintosh OS and Windows is GraphPad Prism (GraphPad Software; http://www.graphpad.com). This general graphing and data analysis software comes with an excellent tutorial for receptor binding data analysis and for nonlinear curve fitting in general.

SUPPORT PROTOCOL 1

Materials Spreadsheet program (e.g., Microsoft Excel) Graphing program (e.g., GraphPad Prism, KaleidaGraph) Saturation assay NOTE: An example of data obtained from a saturation binding experiment using [3H]mesulergine binding to the 5-HT2C receptor, along with the analysis accomplished by the Excel spreadsheet, is shown in Table 1.23.4. 1a. Using a spreadsheet, enter the tube number (“Tube no.” in Table 1.23.4) and the planned radioligand concentration for that tube (column 2: “[3H]mesulergine (M)”). 2a. Enter the binding data (radioligand bound in cpm) from the scintillation counter (column 3: “Bound—cpm”). 3a. Calculate the amount of radioligand bound (fmol/mg protein) as follows (column 4: “Bound—fmol/mg”): radioligand bound (cpm; column 3) radioligand specific activity (cpm /fmol) × amount of protein added per tube (mg) The specific activity in cpm/fmol is derived from the specific activity in Ci/mmol provided by the supplier as follows: specific activity (Ci/mmol) multiplied by 2.2 × 1012 (dpm/Ci) multiplied by the efficiency of the liquid scintillation counter multiplied by 1 × 10−12 (fmol/mmol). If data obtained from the liquid scintillation counter are dpm, then the amount of radioligand bound (numerator) is entered as dpm and the specific activity of the radioligand (denominator) is entered as dpm/fmol. Radioligand specific activity in dpm/fmol is derived from the specific activity in Ci/mmol provided the supplier as follows: specific activity (Ci/mmol) multiplied by 2.2 × 1012 dpm/Ci, multiplied by 1 × 10-12 fmol/mmol. Similarly, in steps 4a and 6a below, dpm data should be entered in place of cpm data.

4a. Enter the cpm data obtained from (in this example) 50-µl aliquots of the radioligand solutions made for the assay (column 5: “Radioligand—cpm”; also see Basic Protocol 1, step 13).

Receptor Binding

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Table 1.23.4 Representative Saturation Binding Data Using [3H]Mesulergine to Label the 5-HT2C Receptora

Tube no.

[3H]mesulergine (M)

Bound

Radioligand

Actual concentrations (M)

cpm

fmol/mg

cpm

Mean

Added

Corrected

1.00E-11 1.00E-11 4.00E-11 4.00E-11 1.00E-10 1.00E-10 2.00E-10 2.00E-10 4.00E-10 4.00E-10 6.00E-10 6.00E-10 8.00E-10 8.00E-10 1.00E-09 1.00E-09 2.00E-09 2.00E-09 4.00E-09 4.00E-09 6.00E-09 6.00E-09 1.00E-08 1.00E-08 2.00E-08 2.00E-08

358 257 328 295 390 408 607 851 727 732 975 966 905 870 961 1018 1160 1179 1422 1363 1508 1731 2175 1983 2397 2264

80.18 57.56 73.46 66.07 87.34 91.37 135.94 190.59 162.82 163.94 218.36 216.34 202.68 194.84 215.22 227.99 259.79 264.05 318.47 305.25 337.73 387.67 487.11 444.11 536.83 507.04

239 246 743 752 1954 1959 3756 3749 7086 7054 10518 10440 13183 14092 19770 18964 35160 35237 66720 67390 100165 101790 169966 166572 368345 370764

243

1.28E-11 1.28E-11 3.95E-11 3.95E-11 1.03E-10 1.03E-10 1.98E-10 1.98E-10 3.74E-10 3.74E-10 5.54E-10 5.54E-10 7.21E-10 7.21E-10 1.02E-09 1.02E-09 1.86E-09 1.86E-09 3.54E-09 3.54E-09 5.34E-09 5.34E-09 8.89E-09 8.89E-09 1.95E-08 1.95E-08

3.36E-12 3.36E-12 3.08E-11 3.08E-11 9.31E-11 9.31E-11 1.82E-10 1.82E-10 3.54E-10 3.54E-10 5.28E-10 5.28E-10 6.97E-10 6.97E-10 9.98E-10 9.98E-10 1.83E-09 1.83E-09 3.51E-09 3.51E-09 5.30E-09 5.30E-09 8.84E-09 8.84E-09 1.95E-08 1.95E-08

Nonspecific bindingb 27 1.00E-11 28 1.00E-11 29 1.00E-10 30 1.00E-10 31 4.00E-10 32 4.00E-10 33 1.00E-09 34 1.00E-09 35 4.00E-09 36 4.00E-09 37 1.00E-08 38 1.00E-08 39 2.00E-08 40 2.00E-08

389 235 253 188 303 379 274 420 594 629 778 784 1300 1387

87.12 52.63 56.66 42.10 67.86 84.88 61.36 94.06 133.03 140.87 174.24 175.58 291.15 310.63

239 246 1954 1959 7086 7054 19770 18964 66720 67390 169966 166572 368345 370764

1.28E-11 1.28E-11 1.03E-10 1.03E-10 3.74E-10 3.74E-10 1.02E-09 1.02E-09 3.54E-09 3.54E-09 8.89E-09 8.89E-09 1.95E-08 1.95E-08

2.54E-12 6.61E-12 9.67E-11 9.84E-11 3.66E-10 3.64E-10 1.02E-09 1.01E-09 3.53E-09 3.53E-09 8.87E-09 8.87E-09 1.95E-08 1.95E-08

Total binding 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26

748 1957 3753 7070 10479 13638 19367 35199 67055 100978 168269 369555

243 1957 7070 19367 67055 168269 369555

aThis example is for a Microsoft Excel spreadsheet. bMianserin (1 µM final) was used as the masking ligand.

Characterization of 5-HT1A,B and 5-HT2A,C Serotonin Receptor Binding

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5a. Determine the mean values of the duplicate radioligand counts (column 6: “Radioligand—Mean”). 6a. Calculate the concentration of radioligand in the aliquot: mean radioligand count (cpm) fmol = µl specific activity (cpm /fmol) × volume of radioligand aliquot counted (µl) 7a. Calculate the total concentration of radioligand added to the assay tubes by converting the result from step 6 into molar units (column 7: “Actual concentrations—Added”): fmol 1 × 106 µl 1 × 10-15 fmol 1 mol × × × = µl liter mol 10 liter The last multiplication of 1⁄10 accounts for the 10-fold dilution of the radioligand in the assay.

8a. To determine the concentration of free radioligand in the assay tube (column 8), the concentration of radioligand in the assay tube as calculated in step 7a must be corrected by subtracting the amount of radioligand bound in the assay tube. First calculate the number of moles of radioligand added to the assay tube: Concentration radioligand added to the tube (mol/liter; column 7) × assay volume (2.5 × 10−4 liters) 9a. Next, calculate the number of moles of radioligand bound in the tube: Radioligand bound (fmol/mg protein; column 4) × milligrams protein × 10−15 mol/fmol 10a. Subtract the amount (mol) of radioligand bound (from step 9a) from the amount (mol) of radioligand added to the tube (from step 8a) to obtain the amount (mol) of radioligand free in solution. Divide by the assay volume to determine the concentration (M) of free radioligand. total radioligand added (mol; step 8a) - bound radioligand (mol; step 9a) 500 µl (assay volume) Note that the corrected radioligand concentration is slightly different for total binding tubes versus nonspecific binding tubes at the same planned/actual radioligand concentration (columns #2 and #7). This is because there is more radioligand bound in total tubes (specific plus nonspecific) than in nonspecific tubes, thus reducing free radioligand concentration in total tubes. Note also that this method is only an approximation of the actual free radioligand concentration for a couple of reasons: (1) the amount of radioligand bound includes some radioligand bound nonspecifically to the filters, which was free in solution during the assay (thus producing an underestimation of the free radioligand concentration) and (2) not all of the radioligand added will be free in solution since some may adhere to the tube (thus producing an overestimation of the free radioligand concentration). One additional note, for the equations used to determine the KD of the radioligand and the total receptor density (Bmax) to be valid, the amount of radioligand bound should not appreciably alter the free radioligand concentration (see Kenakin, 1997; also see UNIT 1.3). Consequently, it is advisable to check that the amount of radioligand bound in the assay tubes containing 0.25 × KD concentration of radioligand is 10% but 40%), or if the fitted curve does not go through the points, try the quadratic equation. Furthermore, if it is appropriate to use the quadratic equation, the fit of the standard equation will usually result in an IC50 value at least 5- to 10-fold larger than that derived from the quadratic equation, and the IC50 value will approximate [E] from the quadratic equation (within 2- to 3-fold). However, if the fit of the quadratic equation results in [E] 1) may suggest that the binding reaction is not at equilibrium at lower concentrations of agonist. Thus, the rate of association of an agonist with its receptor is concentration-dependent, so that the higher concentrations may be at equilibrium whereas the lower ones are not. Simply increasing incubation times will often correct this problem and yield rectangular hyperbolic (n = 1) curves. Flat curves (n < 1) may reflect the fact that more than one receptor is being activated. This is particularly important in the ileum assay, where it might suggest that

1.2

1

response

0.8

control

0.6

B C

B+C

0.4 B×C 0.2

0 0.1

Cholecystokinin (CCK) Assays

1

10 log [A]

100

1000

Figure 4.13.8 The effect of two antagonists (B and C) acting at the same receptor or at two independent sites. The antagonist DR values have been set with B = 8 and C = 11, so that for competition at the same site, the two antagonists added together (B + C) have a DR of 18, and when they are acting at independent sites (B × C) they have a DR of 88.

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setting A equal to the ED50 in Equation 4.13.2, the equation can be simplified to: DR = 1 +

[B]

m

KB

Equation 4.13.3

By plotting DR against [B], a straight line of slope m is obtained. This is often called a Schild plot (Arunlakshana and Schild, 1959). When m is equal to unity, then KB = [B] in the condition of DR = 0. Thus, KB is determined graphically from the intersection with the abscissa after extrapolation. Theory predicts that when two antagonists (B and C) act on the same receptor, then their interaction produces a DRB+C, which is basically an addition of the DRs of the individual antagonists. Thus, if B is the reference competitive antagonist: DR B + C = DR B + DR C − 1 Equation 4.13.4

If, however, antagonist C is not acting on the CCK2 receptor, but is truly noncompetitive, then DRB+C is equal to the product of the two individual DRs. Namely, DR B + C = DR B × DR C Equation 4.13.5

These concepts are exemplified theoretically in Figure 4.13.8, where the two extremes of either both antagonists acting at the same receptor or both acting through independent sites are shown. In practice, this might occur if one antagonist blocks acetylcholine receptors while the other is a competitive antagonist at the CCK2 site. In the case of hemiequilibrium, a fast-dissociating antagonist will not only reveal the underlying antagonism in terms of the combined DR, but will also protect against the reduction in the maximal response (Rmax). Where the test antagonist meets the requirement of being competitive, receptor concentration–ratio analysis also allows another estimate of the KB of the antagonist, as derived from the following equation: DR BC = 1 +

 

[B]

KB 1 +

 K 

[C]

Equation 4.13.6

C

where DRBC is the DR obtained with both antagonists, but measured from the curve produced in the presence of antagonist C alone. If B, C, and the KB of the reference compound are known, then the KB for C (KC) can be calculated. This is a particularly powerful method if antagonist C has both a mixture of competitive and noncompetitive antagonism, as it allows the true KB for the competitive component to be estimated. Decreasing baseline during equilibration This could indicate that the tissue is hypoxic. An increase in the oxygen supply should eliminate the problem. Increasing baseline This could be due to an exaggerated tension (>0.5 g for the LMMP or >1.0 g for the gall bladder) applied to the preparation. A reduction in tension should eliminate the problem. Abnormal contractions The authors define contractions as abnormal when the tissue spontaneously contracts, or when there is no correlation between the concentrations of the agonist used and the contractile effect produced. Moreover, for the experiments with cholecystokinin, abnormal contractions are defined as those in which a slow increase in the tissue tension is produced by the agonist, instead of a rapid contraction phase. Abnormal contractions may indicate that the preparation has not been carefully isolated from the tissue. Agonists fail to produce contractions This could mean that the tissue tension reached at equilibrium is insulin) and IGF-II (IGF-II > IGF-I > insulin) receptors on these neurons. Results are from an experiment performed in quadruplicate. (A) IC50 values of 0.1, 2.9, and 99.7 nM for IGF-II, IGF-II and insulin, respectively against [125I]IGF-I and (B) 0.1 and 20.5 nM for IGF-II and IGF-I, respectively against [125I]IGF-II (insulin being inactive). Adapted from Doré et al. (1997b), with permission.

[125I]IGF-I Silver grains/neuron

300

200

100

0 125

[125I]IGF-II Silver grains/neuron

100

75

50

25

0 total

+ IGF-II

+ IGF-I

+ insulin

Figure 8.2.5 Histograms showing the quantitative analysis of emulsion receptor autoradiography of [125I]IGF-I and [125I]IGF-II binding on primary rat hippocampal culturedneurons grown in serum free conditions. Quantification of the silver grains was performed at 100 magnification using a microscope attached to MCID image analysis system. Autoradiographic results from the quantitative analysis were expressed as number of silver grains/neuron, without implying that all neurons were positive.

CHAPTER 9 Drug Discovery Technologies INTRODUCTION he focus of drug discovery has historically been on using small-molecule drugs to restore the function of diseased tissues. These molecules can be discovered in a wide variety of chemical sources, as outlined by David Triggle in UNIT 9.1. Natural products, including fermentation extracts, microbial and plant sources, spiders, ants, frogs, and sea snails, which survive in habitats as diverse as the desert and rain forest, aerobic microbes from the drill plugs of deep-sea oil rigs, marine sources, and the still unexploited herbal pharmacopoeias of the traditional Chinese medicine and Ayurvedic systems, remain rich sources of new compounds. At a different level, the fertile mind of the medicinal chemist adds immeasurably to this diversity by modeling the naturally occurring neuroeffectors or neurohormones in the body (the syntopic approach that led Sir James Black and his colleagues to derive the H2 blocker, cimetidine, from histamine). Additional new avenues for drug development include the design of new compounds around privileged pharmacophores, with the chemical “decorating” of these molecules to refine key biological activities. Finally, the powerful tools of structural biology can be used to design new compounds on the basis of their computer-modeled interactions with molecular targets of known structure.

T

The ability to exploit the diversity of chemical sources has resulted from the evolution of screening technologies. From modest beginnings in 1984 at Merck when Ray Chang and his colleagues identified the nonpeptide CCK antagonist, asperlicin, from a fermentation extract, thus providing an alternative, reductionistic trend toward total in silico drug design, high-throughput screening (HTS) has come to occupy a central position in the drug discovery process. Thus, while in 1979 running 50 compounds a week in 2 or 3 assays was “state of the art,” by the beginning of the 21st century running millions of compounds a week in multiple assays has become de rigueur. One aspect of the cell-based approach to compound screening has been the development of fluoresence-based dye assays that measure membrane potential, changes in intracellular calcium concentration, and intracellular pH. The combination of these assays in cells transfected with receptor targets with high-throughput, real-time, microtiter plate-based optical detection devices like the fluorometric imaging plate reader (FLIPR) has revolutionized assays to measure ligand function (agonist/antagonist). While initially expensive, machines like the FLIPR can provide vast amounts of data, including concentration-response curves in minutes, providing computer-based readouts that visually depict compound activity and allowing the researcher to check assay viability and the robustness of compound response in a very limited time. While there are optical detection devices in addition to FLIPR (e.g., VIPR from Wallac), in UNIT 9.2, Whiteaker and colleagues describe protocols to measure changes in membrane potential and intracellular calcium changes on the FLIPR. Over the past decade, the powerful, albeit controversial (Hird, 2000), tools of combinatorial chemistry have provided the ability to generate large numbers of compounds (from hundreds to many thousands) to further enhance the diversity of chemical structures that represent potential leads for drug discovery. In UNIT 9.3, Crooks and Charles provide Drug Discovery Technologies Contributed by Mike Williams Current Protocols in Pharmacology (2006) 9.0.1-9.0.3 C 2006 by John Wiley & Sons, Inc. Copyright


9.0.1 Supplement 35

a comprehensive overview of combinatorial chemistry highlighting both the initial, historical approaches and the more conservative parallel synthesis techniques that are so pervasive (and successful) in nearly all medicinal chemistry laboratories. UNIT 9.4 by Entzeroth reviews, in great detail, logistical aspects of high-throughput screening (HTS) assays covering compound libraries, preparation and storage, bar-code-based tracking systems, assay formats and design, binding and reporter assays, robotics, the nebulous “hit-to-lead” process, data evaluation, and recent advances in screening technology platforms. UNIT 9.5 by Kenakin addresses screening in constitutively active, ternary receptor Gprotein coupled receptor (GPCR) systems. The concept that receptors could have activity in the absence of ligand has been controversial since first reported by Costa and Herz in 1989 and extended by Lefkowitz and coworkers in the early 1990s. Kenakin’s own seminal research and didactic writings in the area have provided a convincing rationale for the concept that has, in turn, been supported by the discovery of a variety of inverse agonists, a class of compound initially thought of exclusively as allosteric modulators of ion channels, but for which a wealth of data now exists in GPCR systems. UNIT 9.6 on microarray technologies by Waring describes the use of microtiter plate and chip technologies to measure mRNA expression in human and animal tissues. The former represent disease and matched “normal” controls, and the latter tissue from genetically modified or drug-treated animals. Microarray technology has the potential to provide important clues and corroboration for genotyping and identification of disease-associated genes in human studies. UNIT 9.7 by Salfeld et al. provides an overview on the use of therapeutic antibodies in drug discovery. While antibodies have represented a significant tool in the biomedical research toolbox, it is only with the ability to humanize antibodies, using phage display or traditional hybridoma technology with transgenic mice genetically engineered to contain the human IgGκ immunoglobulin loci, that it has become feasible to develop antibodybased drugs. Immunogenicity is a major concern and is addressed by either genetically introducing humanizing elements or eliminating immunogenic sequences. The concept that such large molecules might be used as an alternative to more traditional small molecules was considered unlikely 25 years ago, but is now a reality. Antibody-based drugs or therapeutic antibodies are routinely used in the treatment of rheumatoid arthritis, transplant rejection, infection, and oncology.

Introduction

UNIT 9.8 by Blaney provides a detailed, in-depth overview of molecular modeling approaches to 7-transmembrane helical receptors, also known as G protein-coupled receptors or GPCRs. This receptor family is the most successful drug target family to date, with major drug classes—antihypertensives, antipsychotics, antiulcerogenic and antiplatelet agents, and opioid pain relievers, to name but a few—producing their effects by specifically modulating 7TM receptor function. Because of its pharmacological importance, the 7TM receptor family has been of considerable interest in the area of molecular modeling. While these receptors have been extremely difficult to isolate in purified form, the seminal 7TM receptor, bacterial rhodopsin, has been used extensively as the model on which to base the modeling of other 7TM receptor families, with varying degrees of success. Such models, together with the ability to alter the native amino acids in the loops connecting transmembrane helices, have made it possible to identify key molecular features for ligand recognition, receptor activation, and signal transduction, as well as to identify allosteric sites. Such knowledge has been used to both understand ligand recognition characteristics and to design new molecules based on both the structure activity/recognition relationships of known ligand families (those that interact as well as those that do not) and the key binding and transduction sites in the receptor.

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UNIT 9.9 by Ator et al. provides an overview of the drug discovery process from target selection to the introduction of the drug to the marketplace and is thus the integrative unit for the entire chapter. Covered in this unit are target families, chemical library selection and synthesis, compound testing, in vitro and in vivo models, pharmacokinetics, the IND and NDA processes, and clinical trials.

The present chapter of Current Protocols in Pharmacology complements the remainder of the manual in providing overviews of emerging technologies that have the potential to provide chemical diversity paralelling the increasing knowledge of biological diversity provided by bacterial, viral, and mammalian genomics. Mike Williams

Drug Discovery Technologies

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Overview of Chemical Diversity Scale of dragon, tooth of wolf Witches’ mummy, maw and gulf Of the ravin’d salt-sea shark Root of hemlock digg’d i’ the dark, Macbeth, IV, 1. Contemporary medicines do not, perhaps, go to such lengths for their ingredients as did Shakespeare’s witches. However, the search for new compounds and the identification of compound sources is a substantial and expensive preoccupation of the pharmaceutical industry. In this search, natural products continue to play an extremely important role, principally because they represent a source, real and potential, of remarkable molecular diversity and one, moreover, that has been shaped under “biological pressures.” Humans have also been inveterate searchers for drugs for spiritual, offensive, and recreational purposes. That cocaine, nicotine, and hashish have been found in detectable amounts in Egyptian mummies dating from ∼1000 B.C. is testimony to the continuity of this search and use. Part of the impetus for Christopher Columbus’ voyage of discovery was the search for new medicines. Indeed, he took a pharmacist with him on his voyage to the New World to help identify plants. Among the plants he sought were rhubarb, described as a laxative in very early Chinese herbals, cloves, and black pepper. Columbus did find the hardwood tree called Guaiacum, which later became a popular cure for syphilis in Europe. Pharmacognosy, a discipline apparently once out of favor, is now regaining increased and deserved recognition. Although the advances in drug discovery in the 20th century have been significantly fueled and paralleled by advances in synthetic organic chemistry, structural biology, and computational chemistry, they continue to be linked to both natural product and biological chemistry. The dramatic progress in synthetic molecular diversity achieved through combinatorial chemistry is heavily influenced by our understanding of biologically driven molecular diversity. Additionally, through advances in genomics, synthetic chemistry is being linked to biochemical systems to create hybrid “natural-synthetic” products, “unnatural” peptides and “self-evolving” molecules. Thus, no single strategy of compound source identification is dominant or superior.

Contributed by David J. Triggle Current Protocols in Pharmacology (2000) 9.1.1-9.1.18 Copyright © 2000 by John Wiley & Sons, Inc.

UNIT 9.1

New chemical entities (NCEs) are discovered, or developed, from: 1. Natural products and the screening of biodiversity 2. Modifications of existing chemical structures 3. Identification and exploitation of pharmacophores 4. Rationally planned approaches 5. Combinatorial chemistry 6. Evolutionary chemistry 7. Orphan ligands and orphan receptors. This unit discusses specific instances where diversity is generated by exploitation of biological pathways. These strategies are intimately linked to processes of biological screening, and in particular to high-throughput screening.

HIGH-THROUGHPUT SCREENING (HTS) To identify compounds, it is imperative that a rapid, economical, and information-rich evaluation of biological activities be available. The term high-throughput screening describes a set of techniques designed to permit rapid and automated (robotic) analysis of a library of compounds in a battery of assays that generate specific receptor- or enzyme-based signals. These signals may be membrane-based (radioligand binding, enzyme) or cell-based (flux, fluorescence). The primary purpose of HTS is not to identify candidate drugs, but rather to identify lead structures, preferably containing novel chemical features, that may serve as a guide for more tailored iterative optimization. (There are exceptions to this generalization: cyclosporine was identified directly from a biological screen.) High-throughput screens should generate as few false-positive leads as possible since exploitation of leads is an expensive component of the drug discovery process. Presently, most high-throughput screens are designed to give information principally about potency, and a combination of screens may provide information about selectivity and specificity. The increasing use of cell-based assays provides additional information, including agonist/antagonist characteristics and a biological “read-out” under physiological or nearly physiological conditions. Additionally, cell-based assays can provide information about the cytotoxicity and bioavailability of

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molecules. The increasing use of “designed cells” with visual and fluorescent signal readouts will continue to facilitate the screening process and in the future will doubtless include measures of metabolism, toxicity, bioavailability, and other important pharmacokinetic parameters.

NATURAL PRODUCTS AND THE SCREENING OF BIODIVERSITY Approximately 50% of the active constituents in contemporary medications are natural product–based: this includes the antibiotics. Natural products have a long and distinguished history as medicines—the opiates, belladonna alkaloids, ephedrine, cocaine, digitalis, physostigmine, salicylic acid, curare alkaloids, reserpine, theophylline, quinine, and numerous other molecules either are still in use or have provided structural clues for synthetic analogs. It is estimated that medicinal and herbal extracts form the basis for the health care of ∼80% of the world’s population and that some 21,000 plant species are used world-wide. There is a continued search for new active principle compounds, even in relatively well-explored areas such as China (De-Zai, 1996). There is, however, an increasingly realized fear that the continued destruction of habitat and the accompanying loss of animal and plant species may impede further natural product–based drug discovery. These fears have led to cooperation between pharmaceutical companies and imperiled lands or peoples in an attempt to preserve species diversity. An ethnobotanical account of the search for new medicines has been provided by Mark Plotkin (1993).

General Considerations

Overview of Chemical Diversity

The general issues of designing an effective strategy for natural product–based drug discovery have been reviewed by Kingston (1996). These include: The acquisition and extraction of biomass. Scientific, political, and economic considerations are important. Political and economic considerations relate to the recognition and compensation of national and tribal rights and scientific considerations to the issues of random versus ethnobotanical plant collections. Screening methods. The choice of screening methodologies is increasingly a proprietary process, although the general principles are well understood. However, given the potential molecular value of the collected material, it is useful to have several discrete functional screens.

Isolation and structure determination of active principle compounds. This is likely to be the slowest and most time-consuming aspect of the process. The importance of minimizing false positives in the screening process becomes clear at this stage. Pharmacological evaluation. Despite the increasingly comprehensive information obtained from screening, it is still necessary for information to be obtained in a variety of pharmacological preparations to determine in vitro and in vivo activities, including toxicity. Large-scale availability. If activity of interest is observed, large amounts of material will be needed. This may require cultivation of plant or animal species and ultimately the development of synthetic or semisynthetic methodologies. Paclitaxel from Taxus is an instructive example: requirements for this plant threatened its natural availability and led to the rapid establishment of efficient synthetic methodologies.

Lost Opportunities While it is true that “The global diversity of living organisms remains virtually untouched as a resource for the molecular prototype of drugs” (de Vries and Hall, 1994), there are already disturbing examples of what may have been lost. Many interesting drugs and drug leads have vanished with the destruction of habitat or overharvesting. For example, the plant called silphion by the Greeks and sylphium by the Romans grew around the citystate of Cyrene in North Africa. It may have been an extremely effective anti-fertility drug in the ancient world. It has apparently been harvested to extinction (Riddle and Estes, 1992). It is likely that hundreds, and possibly thousands, of potential medicines have been similarly lost.

Biodiversity Quantitation of biodiversity is very uncertain (Bull et al., 1992). While we probably know close to 100% of the world’s mammals, as few as 1% to 5% of other species, notably bacteria, viruses, fungi, and most invertebrates, are well characterized. It has been estimated that only ∼0.00002% to 0.003% of the world’s estimated 3–500 × 106 species are used as a source of modern drugs (de Vries and Hall, 1994). Exploration of environments previously assumed to be hostile to life has revealed bacterial species living at extreme depths, at extraordinary temperatures, and in the presence of high concentrations of heavy metals. Exploitation of these species has already yielded novel

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biochemical systems, including obligately anaerobic carboxydotrophic bacteria producing carbon dioxide from volcanic gases, hyperthermophilic methanogens that live optimally at 100°C, and enzymes that operate under conditions far removed from the conventionally defined “physiological” parameters (Bull et al., 1992). Continued exploration will almost certainly continue to yield both novel chemistry and novel chemical leads. However, this will demand the availability of better taxonomic databases, including DNA sequence databases. Bull and coworkers (1992) have reviewed the issue of biodiversity from a microbial perspective and have outlined the following general principles for sampling the environment. These principles may, with appropriate modification, serve as a guide to the sampling of other environments: 1. Maintain awareness of both macro- and microhabitats 2. Collect multiple samples 3. Sample during different seasons 4. Analyze samples rapidly 5. Relate the objective of the search to the physicochemical environment of the site 6. Habitats with weak selective pressures are likely to have the greatest biodiversity 7. Pretreatment of environment may be an effective way of encouraging competitive growth 8. Both exotic and commonplace environments may contain novel species 9. Be prepared to think outside the boundaries of conventional wisdom.

Marine Biodiversity deVries and Hall (1994) note that the sea covers almost three-quarters of the earth’s surface and contains a broader genetic variation among species relative to the terrestrial environment. Although a number of important molecules have been derived from marine sources, including arabinosyl nucleotides, didemnin B, and bryostatin 1, there has overall been an inadequate focus on this potentially chemically productive biosphere. The number of chemical entities isolated up to 1994 was estimated as 5–10 × 104 (de Vries and Hall, 1994); given the paucity of exploration, the potential chemical diversity of the marine environment may be orders of magnitude greater than this.

New Drug Classes and Structures from Natural Sources Many examples of drugs and drug leads from natural sources are available, and are sim-

ply too numerous to be discussed in detail (Bull et al., 1992; de Vries and Hall, 1994; Kingston, 1996). However, some recent examples present interesting case studies. Garlic. The recorded history of garlic is at least 3000 years old and it is perhaps the most widely quoted medicinal herb (Agarawal, 1996). Several hundred compounds have been isolated from this plant and particular attention has been paid to the lipid-lowering, antithrombotic, and antitumor actions of garlic extracts. Allicin (di-allyl thiosulfinate) is a potent inhibitor of platelet aggregation at concentrations of ∼100 µM. More complex sulfur-containing compounds, including ajoene (4,5,9-trithdodeca-1,6,11-triene-9-oxide) and the glycoside (−)-N-(1′′-deoxy-1′-β-D-fructopyranosyl)-Sallyl-L-cysteinesulfoxide are also platelet inhibitory agents. Agarawal (1996) has reviewed the lipid-lowering and antitumor actions of garlic and garlic extracts and has concluded that these activities are demonstrable, but the underlying principles are not well understood. Leads for inhibitors of fatty acid synthesis, HMGCoA reductase, and peroxidative processes may also be present in garlic extracts. Green tea. Mitscher and his colleagues have reviewed the evidence that consumption of green tea, Camellia sinensis, has “a potentially significant prophylactic role in human medicine” against cancer (Mitscher et al., 1997). Green tea contains significant amounts of polyphenols, ∼400 mg per brewed cup, and these are likely the protective antioxidative agents. These polyphenols include epigallocatechin gallate, epigallocatechin, catechin, and epicatechin (Fig. 9.1.1). Epibatidine. The poison frogs of the Dendrobatidae family contain a wide variety of skin-localized poisonous alkaloids. Presumably, these agents are secreted for defensive purposes (Daly et al., 1997). Among the chemical structures present are the batrachotoxins, pumiliotoxins, histrionicotoxins, gephyrotoxins, and decahydroquinolines (Fig. 9.1.2). At least some of these are of dietary origin. These alkaloids are directed against both voltage- and ligand-gated ion channels. Of particular interest is the alkaloid epibatidine, exo-2-(6-chloro-3-pyridyl)-7-azabicyclo[2.2.1]heptane, a pyridylazabicyclo[2.2.1]heptane (Fig. 9.1.3) present as a trace entity in Epipedobates tricolor and shown to have powerful analgetic activities, being some 200 times more potent than morphine in a variety of tests (Badio et al., 1994). Epibatidine is a potent nicotinic agonist with considerable

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ognized as a powerful analgesic, it was not until 1992 that its structure was published. An additional complexity was that frogs raised in captivity did not secrete the toxin, or produced only very small amounts. The discovery of epibatidine as a novel and potent analgesic led to the

selectivity among nicotinic receptor subtypes, binding preferentially to the α3β2 subtype. The history of its discovery and structural elucidation is indicative of the difficulties of this work (Kellar, 1995). Although it was first isolated by John Daly and his colleagues in 1974 and rec-

OH OH

OH

H

HO

H

HO

O

O

OH

OH

OH

OH

H

OH H

OH

(–)-epicatechin

(–)-epigallocatechin

OH OH H

HO

O

OH

H

O OH

OH O (–)-epigallocatechin gallate

OH OH

Figure 9.1.1 Components of green tea.

H3C

H H3C N

O

NH

HO O O

R

NH

OH

O

HO batrachotoxin: R = CH3

histrionicotoxin

homobatrachotoxin: R = CH3CH2

CH3

R

CH3

OH OH

CH3

N

OH H3C

N CH3

pumiliotoxin A: R = H

cyclopentaquinolizidine 251 F

pumiliotoxin B: R = OH

Overview of Chemical Diversity

Figure 9.1.2 Alkaloids from South American frogs.

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brevis come the brevotoxins (Fig. 9.1.4), which interact at a receptor on the voltage-gated Na+ channel to produce persistent activation. These continued successes in isolating new compounds and structures from natural sources do not, by any means, negate the need to consider the virtues of organized processes for preserving biodiversity as “the library of life.”

identification of ABT-594 (Fig. 9.1.3), a cholinergic channel modulator with equivalent analgesic efficacy to epibatidine but with reduced side effect liabilities (Bannon et el., 1998). Drugs to come? New potential drug sources may come from likely and unlikely directions. Hypericum perforatum, St. John’s wort, is widely used as an herbal antidepressant, particularly in Germany. The active principle is unknown, but it may well be an amine uptake blocker (Linde et al., 1996). A new toxin, of currently unknown structure, is currently producing major fish kills in Chesapeake Bay. The toxin is produced by Pfiesteria piscidia, a dinoflagellate and a member of a family that is responsible for “red tides” and other outbreaks that affect both animal and human life. The hypothesis has been advanced that the first of the ten great Egyptian plagues of biblical times was caused by such an organism (Barker, 1996). From the dinoflagellate Ptychodiscus

MODIFICATION OF EXISTING STRUCTURES The modification of existing structures, both natural and synthetic, has long been a popular approach to the development of molecules that may have higher potency, greater selectivity, better efficacy, a more favorable pharmacokinetic profile, or improved formulation. Many examples may be cited and are discussed in most standard reference volumes of medicinal chemistry.

H O

N H

HN N

Cl

N

epibatidine

Cl

ABT-594

Figure 9.1.3 Epibatidine.

OH Me O Me Me

Me

O

O O

O

O

O

O

O

O

O

R

O

Me

Me

Me BvTX-X X

B

C

D

E

O R

O Cl

H CH2

Figure 9.1.4 Brevetoxins.

O

O

OH CH2

Me

CH2

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Digitalis analogs. The use of cardiac glycosides as positive inotropes has a long history, beginning with the description by Withering in 1785 of the use of the foxglove plant in the treatment of dropsy. However, because the safety profiles of these cardiac glycosides are not adequate, the search continues for nonsteroidal digitalis replacements (Repke, 1997). This search has not yet been successful, and the examination of digitalis analogs with enhanced safety profiles continues (Fig. 9.1.5). ACE inhibitors. The development of captopril (Fig. 9.1.6) as an inhibitor of angiotensinconverting enzyme (ACE) marked the introduction of a major new class of cardiovascular drugs and provides an example of rational drug design (Ondetti, 1994). The discovery of captopril led rapidly to the development and introduction of a number of analogs, including enalapril, cilazapril, and lisinopril (Fig. 9.1.6). Benzodiazepines. The discovery of the benzodiazepine Librium [7-chloro-2-(methylamino)-5-phenyl-3H-1,4-benzodiazepine-4oxide] is a remarkable chemical story (Sternbach, 1979). Initial chemical work to synthesize heptoxdiazines yielded only quinazoline3-oxides of little pharmacological interest. However, these quinazolines did yield a compound of unknown chemical structure with potent muscle relaxant and anticonvulsant activity. This was shown by classical chemical degradation techniques to be a 1,4-benzodiazepine. The successful introduction of Librium took only 2.5 years from the first pharmacologi-

A

O

O

cal testing. A large number of analogs have since been synthesized and introduced (Fig. 9.1.7). 1,4-Dihydropyridines. The Ca2+-channel antagonists have been a remarkably successful group of cardiovascular drugs, exhibiting antihypertensive, antianginal, and antiarrhythmic properties (Triggle, 1997). Nifedipine [2,6-dimethyl-3,5-dicarbomethoxy-4-(2-nitrophenyl) 1,4-dihydropyridine] was the first member of the dihydropyridine family, a structure that embraces both Ca2+ channel antagonists and activators (Goldmann and Stoltefuss, 199l). The introduction of nifedipine was followed by the synthesis of a number of analogs (Fig. 9.1.8) characterized generally by a more prolonged duration of action and enhanced vascular selectivity (Triggle, 1997).

RATIONALLY PLANNED APPROACHES Rationally planned approaches to structures are an increasingly important part of the drug discovery landscape, whether planned ab initio, derived from structural knowledge of a putative ligand-binding site on a biological macromolecule in the absence of prior chemical information, or derived by rational exploitation of an existing chemical lead (Greer et al., 1994; Whittle and Blondell, 1994). It is to be expected that the iterative ab initio approach, including automated combinatorial chemistry centered around the active site structure and automated structural database searching, will be an in-

B O

R

O

OH OH

O

β-D-digitoxose β-D-digitoxose β-D-digitoxose

O

β-D-glucose actodigin

digoxin: R = OH digitoxin: R = H

Overview of Chemical Diversity

Figure 9.1.5 Digitalis analogs. (A) Digoxin and digitoxin. (B) Actodigin.

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creasingly common approach to identifying compound sources. ACE inhibitors are an important class of cardiovascular drugs that block the conversion of angiotensin I, formed by the action of renin on substrate angiotensinogen, to angiotensin II

(a powerful pressor and growth factor agent). Until 1973, the only known inhibitors of this enzyme were peptides derived from an extensive analysis of the venom of the Brazilian viper Bothrops jaracara. From this, the nonapeptide teprotide (pyro-Glu-Trp-Pro-Arg-Pro-Glu-Ile-

N

EtOOC

N

N H O

N

N H

EtOOC

O

COOH

enalapril

COOH

cilazapril NH2

HOOC

N

N H O

COOH

lisinopril

Figure 9.1.6 Angiotensin-converting enzyme (ACE) inhibitors.

Me O

C

Cl

N

O

H N

N

C

O2N

N

Cl

diazepam Me C

clonazepam Me

N

C

CH

C

N

N

N Cl

N

N

F

C

Cl

N

Cl

midazolam

triazolam

Figure 9.1.7 Diazepam and second-generation benzodiazepines.

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Pro-Pro-OH) was characterized as an orally active and competitive inhibitor of ACE (Ondetti, 1994). However, the observation by Byers and Wolfenden (1973) that benzylsuccinic acid was a potent inhibitor of carboxypeptidase A led Ondetti and his colleagues to argue, in light of the structural and mechanistic similarities between ACE and carboxypeptidase A, that similar compounds should also be ACE inhibitors. This observation led to the synthesis of increasingly potent and selective ACE inhibitors assayed by their ability to block the contractile activity of angiotensin I and potentiate the contractile activity of bradykinin in isolated smooth muscle. N-succinyl-L-proline was selected as the lead compound with an IC50 of 330 µM, which was then optimized based on the known presence of a Zn2+ in the active site of ACE (and carboxypeptidase) and the likely presence of hydrophobic pockets. From this lead was derived the α-methyl analog, which was both more active and demonstrated stereoselectivity of interaction. The introduction of an SH group with which to coordinate Zn2+ was a further major advancement in potency, and the reintroduction of the α-methyl group led to the first clinically available ACE inhibitor, captopril (Fig. 9.1.9). There are interesting parallels between the development of ACE inhibitors and angiotensin II receptor antagonists. In both cases, potent peptide antagonists were known, but were unsuitable for clinical use. Saralasin (Sar1-Ala8Ang-II) is a potent antagonist, but has poor bioavailability and possesses both agonist and

antagonist properties at angiotensin receptors. The benzylimidazole losartan is the first introduced nonpeptide angiotensin II receptor antagonist, and it was derived chemically from structures first reported by Takeda in the patent literature in 1982. These imidazole-5-acetic acid derivatives were extremely weak, but selective, inhibitors of angiotensin II receptors (Fig. 9.1.10). Optimization of these structures was based on the assumption that they overlapped a solution conformation of angiotensin II. With this assumption, development of the molecule led to the enhancement of affinity, the generation of oral activity, and the synthesis of the first clinical AII antagonist, losartan (Wexler et al., 1996). The development histories of nonpeptide ACE inhibitors and angiotensin II antagonists share common features. In both cases, peptide ligands were available, and in both cases smallmolecule leads were identified which had activities that could be rationalized in terms of structural features of the receptor or peptide substrate. These discoveries led to rational molecular changes designed to enhance pharmacodynamic and pharmacokinetic properties, and in both instances the first developed nonpeptide drug became the base on which numerous second-generation analogs were modeled. However, the success of both approaches depended upon the empirical discovery of small-molecule leads, which were then rationally exploited. In the process referred to as “SAR by NMR” (structure-activity relationships by NMR), a pathway has been described by which small

nifedipine: R2 = R6 = Me; R3 = R5 = COOMe; X = 2-NO2 nitrendipine: R2 = R6 = Me; R3 = COOEt; R5 = COOMe; X = 3-NO2 felodipine: R2 = R6 = Me; R3 = COOEt; R5 = COOMe; X = 2,3-Cl2 amlodipine: R2 = CH2OCH2 CH2NH2; R6 = Me; R3 = COOEt; R5 = COOMe; X = 2-Cl nicardipine: R2 = R6 = Me; R3 = COOCH2 CH2NMeCH2 Ph; R5 = COOMe; X = 3-NO2 nimodipine: R2 = R6 = Me; R3 = COOCH2 CH2OMe; R5 = COOCHMe2 ; X = 3-NO2

X R5 R6 Overview of Chemical Diversity

R3 N H

R2

Figure 9.1.8 Nifedipine and second-generation 1,4-dihydropyridines.

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molecules that bind to protein subsites are identified, optimized, and linked together to generate high-potency ligands (Shuker et al., 1996; Kessler, 1997). The process combines random screening of low-molecular-weight compounds whose binding is measured by NMR shifts using two-dimensional techniques with 15N-enriched proteins. Using this technique, two small molecules were detected that bound to FK binding protein with micromolar and millimolar affinities; combination of these two molecules, determined by modeling techniques, led to molecules with nanomolar affinities (Fig. 9.1.11). This approach combines random screening with both combinatorial chemistry and rational design.

IDENTIFICATION AND EXPLOITATION OF BASIC PHARMACOPHORES It is increasingly apparent that a small number of common structures—“basic pharmacophores,” “templates,” or “scaffolds”—are associated with a multiplicity of diverse biological activities. Such structures are obvious starting points for combinatorial chemical approaches to ligand diversity. Wiley and Rich (1993) argued in their discussion of chemical approaches to peptidomimetics that the template design needs to possess certain molecular characteristics, notably the ability to resist internal hydrophobic collapse and the ability to function as H-bond donors or acceptors. Some of these structures are presented in Figure 9.1.12; they feature prominently in drugs of diverse classes

N

and they presumably mimic defined protein conformational features such as β-turns. The benzodiazepines. The role of the benzodiazepines as anxiolytics, hypnotics, and muscle relaxants is well established clinically. The benzodiazepine nucleus does, however, occur in natural products. Asperlicin is a naturally occurring ligand that is a weak, albeit selective, antagonist at cholecystokinin receptors and contains a benzodiazepine nucleus (Chang et al., 1985; Fig. 9.1.13). From this lead was derived a series of potent and selective benzodiazepine ligands active at CCK-A and CCK-B receptors (Evans et al., 1988; Fig. 9.1.13). The benzodiazepine nucleus also serves as a template for other receptors (Fig. 9.1.14), including the opioid (Romer et al., 1982a,b), somatostatin (Papaageorgiou and Borer, 1996), and glutamate receptors (Donevan et al., 1994), and inward-rectifying potassium channels (Johnson et al., 1993). The benzodiazepine template is readily incorporated into combinatorial chemistry (Balkenhohl et al., 1996) and will doubtless be found in many other compound libraries. The 1,4-dihydropyridines. The 1,4-dihydropyridine is a well-established chemical entity derived from the classic chemistry of Hantzsch (Stout and Meyers, 1982). Nifedipine and several related 1,4-dihydropyridines, including amlodipine, felodipine, nicardipine, nimodipine, and nisoldipine, are all well-established antihypertensive and vasodilating agents acting through voltage-gated L-type Ca2+ channels in the vasculature (Triggle, 1992). However, the

N

COOH

HO

COOH

HO O

O

O

O

N-succinylproline

Me α-methyl analog

N

N

HS

COOH O

HS

COOH O

Me

thiol derivative

Figure 9.1.9 The discovery pathway to captopril.

captopril Drug Discovery Technologies

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1,4-dihydropyridines also interact at other classes of Ca2+ channels, including N-type channels (Furukawa et al., 1997; Li et al., 1997), T-type channels (Cohen et al., 1992), and “leak” channels (Hopf et al., 1996)—albeit with lower activity than against L-type channels. Additionally, 1,4-dihydropyridines are described as blocking delayed rectifier K+ channels (Zhang et al., 1997), blocking transient outward K+ currents (Gotoh et al., 1991), and blocking cardiac Na+ channels (Miyawaki et al., 1991). These actions have been generally described for 1,4-dihydropyridines already in clinical use or selected from L-type Ca2+ channel screens. Predictably, their actions at these non-Ca2+ channels are not selective. However, 1,4-dihydropyridines exist that have substantial selectivity for non-L-type Ca2+ channels (Fig.

9.1.15). UK-74,505 is a potent and selective PAF receptor antagonist (Cooper et al., 1992), compound 28 is selective for adenosine A3 receptors over other adenosine receptors and L-type Ca2+ channels (vanRhee et al., 1996), ZM 244085 is a selective activator of K+ ATP channels (Trivedi et al., 1995), and derivatives of niguldipine, including SNAP 5089, demonstrate potent selectivity for the human α1A-adrenoceptor (Wetzel et al., 1995). The 1,4-dihydropyridine template is, like the benzodiazepine template, also well positioned for combinatorial methodologies.

COMBINATORIAL CHEMISTRY The advent of combinatorial chemistry has made possible a radical reevaluation of the term “compound library.” For the first time it is now

Cl N

Cl COOH

N

Bun

N

COOH

N

Bun

EXP 6155 IC50 ~10–6 M orally inactive

X Takeda lead

Cl

Cl N

N

OH N

Bun

X

COOMe

N

Bun

orally inactive

EXP 6803 IC50 ~10–7 M orally inactive

HOOC

Cl N Bun

NHCO

HOOC

Cl N

OH N

Bun

EXP 7711 IC50 ~10–7 M orally active HOOC Overview of Chemical Diversity

COOH

OH N

losartan

N N – N N

Figure 9.1.10 The discovery pathway to losartan.

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may secrete up to 100 toxins. Therefore, there are likely to be several tens of thousands of these toxins, representing a library of substantial structural and functional diversity. These peptides are first synthesized as larger precursors from which the mature peptide is cleaved (Olivera et al., 1994). In the mature peptide there is a constant N-terminus region and a hypervariable C-terminus region from which the biological diversity is derived (Fig. 9.1.16). These toxins have proven to be invaluable as molecular probes for a variety of ion channels and neuronal receptors and as templates for drug design. Similarly, the polyketides, a family of important natural products containing many important pharmaceutical agents, are synthesized through the multienzyme polyketide synthase, which can display substantial molecular diversity with respect to chain length, monomer incorporated, reduction of keto groups, and stereochemistry at chiral centers (Khosla and Zawada, 1996; Fig. 9.1.17). It is this variability, together with the existence of several discrete structural classes of polyketide synthase, that makes possible the generation of such impor-

possible to synthesize literally billions of molecules, clearly a major step forward in the exploration of “molecular space.” Since, however, it has been estimated that there are some 10200 possible organic molecules with molecular weight 450 nm) indicators such as Fluo-3, Fluo-4, and Calcium Green-1 are generally used in the FLIPR system (Kao et al., 1989). These dyes excite with the 488-nm excitation line of the argon laser and emit in the 500 to 560 nm range. Figure 9.2.4 depicts the general principles of the intracellular calcium change assay. Generally, cells are loaded with the acetoxymethyl (AM) ester version of the dye, which allows it to pass across the cell membrane. Once inside the cell, the AM moiety is cleaved by endogenous esterases and the dye remains trapped in the cell. As [Ca2+]i levels increase, the ion complexes with the dye resulting in an increased fluorescence response. Indicator dyes that are excited in the UV range, such as Indo-1, cannot be used because of poor excitation by the argon laser in this range and poor UV transmission through polystyrene microplates. Cells Both adherent and weakly adherent cells can be used, although assays using adherent cells are generally more common. Cellular Ca2+ dynamics in a wide variety of clonal cell lines expressing native or recombinant receptors and ion channels (e.g., Chinese hamster lung cells, CHO cells, HEK-293 cells, HeLa S3, IMR32 cells, A10 cells, 1321N1, IMCD-3 cells, and M-1 cells) and various primary dissociated cultures (e.g., cerebellar granule neurons, cortical neurons, vascular and nonvascular smooth muscle cells) have been studied in the FLIPR. For this assay, cells are seeded at a density that allows for a uniform, confluent monolayer to be obtained on the day of the experiment. Cells that are weakly adherent, such as the neuroblastoma IMR-32 (CCl-127), tend to detach from the plate during wash steps (Kuntzweiler et al., 1998). Hence, these types of cells should be grown on plates precoated with a suitable matrix such as poly-D-lysine, laminin, or polyethyleneimine (e.g., Biocoat, Becton Dickinson Labware; also see recipe in Reagents and Solutions).

Cell-Based Assays Using the FLIPR

In some cell types, such as CHO (CCL-61), PC12 (CRL-1271), and peritoneal macrophages, the indicator dye does not remain localized within the cytoplasmic matrix, because the anionic forms of the dye are extruded into the extracellular medium or sequestered within intracellular organelles or both (Di Virgilio et al., 1990; Simchowitz and Bibb, 1990). These processes are mediated by anion transport systems and can be blocked by anionic exchange protein inhibitors such as probenecid. Probenecid (2.5 mM) has been used to increase dye retention in many cell types such as CHO, THP-1, and NIH-3T3. Other inhibitors such as DIDS (4,4-diisothiocyanatostilbene-2,2-disulfonate) and sulfinpyrazone may also be used.

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extracellular

Ca2+ receptor

ion channel

excitation (488 nM)

IP3

Ca2+ Fluo-3AM

esterase

intracellular

ER

Fluo –3

Ca2+

fluorescence emission

Figure 9.2.4 Principle of Fluo-3 based Ca2+ flux assay in the FLIPR. Cells are initially incubated with the membrane-permeant Fluo-3-acetoxymethyl ester. The AM form of Fluo-3 is hydrolyzed by intracellular esterases and is trapped in cells as fluorescent free acid form. Elevation in intracellular calcium trigged by receptor or ion channel signaling increases the Fluo-3 fluorescence due to cellular Ca2+ binding to trapped Fluo-3 free acid, which is detected by the CCD camera.

Dye Loading and Cell Washing Growth media is aspirated and cells are loaded with phosphate buffered saline (D-PBS) containing dye. Cells are incubated at room temperature for 30 to 60 min. After dye loading, the cell monolayer is washed 3 to 6 times using D-PBS to remove extracellular dye. This can be done manually or by using a commercially available cell washer (e.g., Labsystems or Molecular Devices). Thorough washing is necessary to eliminate artifacts in fluorescence signals generated by the dilution of any extracellular residual dye. The following protocol describes measurement of changes in intracellular calcium in a human medulloblastoma cell line TE671 using the indicator Fluo-3. In this example, acetylcholine is used to evoke an increase in intracellular calcium and then atropine, a muscarinic receptor antagonist, is added to attenuate the effect. Materials Confluent monolayer of TE-671 cells (ATCC # CRL-8805) Dulbecco’s phosphate-buffered saline (D-PBS with Ca2+; Life Technologies) Calcium indicator dye: 1 mM Fluo-3 stock solution (see recipe for dye stock solutions) Agonist (acetylcholine) and antagonist (atropine) Plate washer (Labsystems or Molecular Devices) Additional reagents and equipment for cell culture (UNIT 12.1) and FLIPR analysis (see Basic Protocol 1) Drug Discovery Technologies

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Culture and seed cells in 96-well plates 1a. For adherent cells: Remove growth medium from a confluent 75-cm2 cell culture flask of TE671 cells, add ~3 ml trypsin/EDTA and let stand 30 to 45 sec. Remove trypsin/EDTA from flask and let stand until cells begin to detach. Add 10 ml growth medium and resuspend cells by repeated pipetting. Plate cells in 96-well clear-bottomed black tissue culture plates at a density of about 50,000 cells/well. Include sufficient wells for the test compounds, controls and reference compounds. Incubate until a confluent monolayer is formed (∼2 to 3 days). See Basic Protocol 1 for materials used here and additional details. Generally, one 75-cm2 flask would yield enough cells to plate four to five 96-well plates.

1b. For nonadherent (i.e., suspension cultures) or weakly adherent cells: Harvest cells and media from culture flasks and centrifuge at for 5 min 200 × g at room temperature. Remove media and resuspend cells in loading medium (see annotation) containing 4 µM dye (prepared from a 2 mM stock solution made in 20% (w/v) pluronic acid) at a density of 8 × 105 to 1.5 × 106 cells/ml in a sterile tube or Petri dish. Incubate at 37°C in 5% CO2 for 60 min. Remove the unincorporated dye by repeated centrifugation in ~12 ml of wash buffer (HBSS and 1 M HEPES) for 5 min at 200 × g at room temperature and resuspend the cells by gentle trituration. Seed cells into 96-well plates at a density of 8 × 106 to 3 × 107 cells/plate (10 to 12 ml of cell suspension per plate). Prior to use in the FLIPR, centrifuge the microtiter plate for 5 min at 200 × g, room temperature, so the cells settle to form an adherent monolayer (Groebe et al., 1999). Proceed with data acquisition starting with step 9, below. The loading medium can be either growth media with 10% or 1% fetal bovine serum (FBS) or Hank’s balanced salt solution containing 1% FBS or 1% bovine serum albumin. See UNIT 12.1 for cell culture techniques.

Prepare compound 2. Prepare compounds in D-PBS with Ca2+(or in an assay buffer of choice; see Basic Protocol 1, steps 8 and 9). Dilute the stock solutions to appropriate concentrations into an addition plate. For example, in the actual assay, a 50 µl addition of a 4× concentration of the test compound into a 96-well cell plate containing 150 µl of assay buffer yields the final desired concentration.

3. Design an appropriate layout of the compound dilutions and assay plates. A variety of assay plate configurations may be utilized for preparing the compound addition plates. Figure 9.2.3 illustrates two typical plate formats that may be used.

Load cells with dye 4. Prepare the loading medium by diluting the 1 mM Fluo-3 dye stock in buffer to attain a final concentration of 4 µM. Prepare ~10 ml loading medium, sufficient for loading one plate (100 µl/well); scale up as required. TE671 cells may be loaded using D-PBS with Ca2+ in the absence of serum. Probenecid is not required in this system. Other cell types may require a different incubation buffer or may be loaded with growth medium (with or without reduced serum). In cells where dye loading is poor since the dye does not remain localized within the cytoplasmic matrix, the loading buffer should include 2.5 mM probenecid to inhibit the anion transport systems.

5. Remove growth medium from cells by gentle aspiration. Transfer 100 µl of loading medium into the wells and incubate for 1 hr. Cell-Based Assays Using the FLIPR

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6. Aspirate loading media and wash cells gently with the assay buffer. Wash ~3 to 5 times to thoroughly remove any residual dye. The wash buffer is generally the same as the buffer used to dilute compounds. An automatic cell washer with adjustable dispensing, aspirating heights, and speed is generally preferable to remove unincorporated dye; this instrument can also be programmed to leave 100 to 150 µl of the buffer in the wells (see step 7). Alternatively, for small-scale operations, a multichannel aspirator may be employed for manual washing.

7. Leave an appropriate amount of buffer (150 µl in this example, or 100 µl if two additional steps are involved) in the wells following the final wash step. 8. During initial assay development, designate a set of wells with buffer containing no dye to assess the magnitude of dye loading into cells. Set up instrument and acquire data 9. Transfer the cell plate and the test compounds to appropriate compartments of the FLIPR chamber. Perform start-up procedures for the FLIPR (see Basic Protocol 1, steps 16 to 20). Assays are generally carried out at room temperature. A generalized format for protocol settings for intracellular calcium flux assay using Fluo-3 is shown in Table 9.2.4.

10. For this example, set the laser to 0.6 W and the CCD camera shutter speed to 0.4 sec. 11. Adjust the basal fluorescence to ∼5000 U by performing a signal test followed by adjustment of the camera exposure and shutter settings. 12. Add test compound(s) and acquire data (also see Basic Protocol 1, step 21). Also test effects of agonists and antagonists. a. Initiate the one-step addition experiment by adding 50 µl of buffer containing the agonist (acetylcholine) with or without varying concentrations of the antagonist (atropine). After the addition, sample the signals 60 times at 1-sec intervals followed by 180 samples at 5-sec intervals. b. Alternatively, as with membrane potential assays, execute a two step-addition protocol to evaluate both agonists and inhibitors. For example, the first addition is used to identify an agonist, and the second addition of a reference agonist to evaluate inhibitors. 13. Perform data analysis (see Basic Protocol 1, step 21). Shut down FLIPR (see Basic Protocol 1, step 23). REAGENTS AND SOLUTIONS Use deionized, distilled water in all recipes and protocol steps. For common stock solutions, see APPENDIX 2A; for suppliers, see SUPPLIERS APPENDIX.

Assay buffer 20 mM HEPES 120 mM NaCl 2 mM KCl 2 mM CaCl2 1 mM MgCl2 5 mM glucose (add on day of experiment) Adjust pH to 7.4 with 1 N NaOH Glucose-free buffer can be prepared in large volumes and stored at 4°C until use (< 2 months).

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Table 9.2.4

General Setup and Dispense Parameters for Intracellular Calcium Assays

Setup

Parameters

General

Exposure = 0.4 Filter selection = 1 Presoak tips = right tray Second sequence = Yes Third sequence = No

First dispense sequence

First interval: Sample interval = 1 sec Sample count = 60 Second interval: Sample interval = 5 sec Sample count = 180 Fluid addition: Active = Yes Fluid volume = 50 µl After sample = 10 Pipettor heighta = 180 µl Dispense speed = 60 Mix after addition = No Add from: right tray

Second Dispense Sequence

First interval: Sample interval = 1 sec Sample count = 60 Second interval: Sample interval = 5 sec Sample count = 180 Fluid addition: Active = Yes Fluid volume = 50 µl After sample = 10 Pipettor height = 240 µl Dispense speed = 60 Mix after addition = No Add from: left tray Mixing: No Mixing volume = not applicable Number of mix cycles = not applicable Tip positioning: Leave tips in well during data collection: No Fluid addition: Remove fluid after addition = Yes Camera aperture = 2 Laser = 0.6 W

Pipetting

Other aBased on height of liquid in 96-well plate.

Cell-Based Assays Using the FLIPR

Dye stock solutions DiBAC4(3): Purchase vials of 25 mg bis-(1,3-dibutylbarbituric acid) trimethine oxonol [DiBAC4(3)] from Molecular Probes. Reconstitute the dye in 4.84 ml of DMSO to obtain a final concentration of 10 mM. Aliquot samples and store at –20°C until use (< 4 months). Calcium-sensitive indicator dyes (Fluo-3, Fluo-4, or Calcium Green-1): Prepare a 1 mM stock solution of the dye on the day of the experiment. For example, for Fluo-3, dilute a 50-mg sample with 22 ml of DMSO and 22 ml of 20% w/v pluronic acid.

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Growth medium The following are examples of the growth media used for A10 and TE671 cells. Use the recommended medium for other cell types. All ingredients listed are available from Life Technologies or HyClone Laboratories. For A10 cell line: 445 ml Dulbecco’s modified Eagle medium (DMEM) with high glucose and Lglutamine 50 ml fetal bovine serum (FBS), heat inactivated 1 hr at 57°C (or purchased preinactivated) 5 ml 100× antibiotic-antimycotic solution (final concentrations: 100 U/ml penicillin, 100 µg/ml streptomycin sulfate, 0.25 µg/ml amphotericin B). For TE-671 cell line: 470 ml Dulbecco’s modified Eagle medium (DMEM) with high glucose and Lglutamine 25 ml fetal bovine serum (FBS), heat inactivated 5 ml 100× antibiotic-antimycotic solution For some cells, including primary cultures, it is important that the serum be heat inactivated to avoid complement-mediated cell lysis. For others, heat inactivation of the serum may not be necessary. Growth medium may be supplemented with additional L-glutamine (5 ml of a 200 mM stock).

Matrix coating Cells that adhere weakly may be disrupted during repeated rinse procedures. A simple visual inspection following the rinse procedure is often sufficient for a preliminary evaluation of cell adherence. Testing the uniformity of the fluorescence intensity after a signal test prior to any pipetting procedures may also help assess the integrity of the cell layer. The adherence of the cells can be improved by culturing them on plates precoated with a matrix such as poly-D-lysine, fibronectin, laminin, or polyethyleneimine. Alternatively, black, clear-bottomed 96-well plates precoated with poly-D-lysine are commercially available (e.g., Biocoat, Becton Dickinson Labware). Poly-D-lysine: Dissolve 5 mg poly-D-lysine (Sigma) in 500 ml of deionized water (10 µg/ml final) and filter sterilize through a 0.2-µm filter. Add ∼150 to 200 µl of the solution to each well of a multiwell plate and let sit at room temperature for ∼20 min. Aspirate the solution and rinse the wells twice with deionized water. Air dry the plates in the laminar flow hood. Polyethyleneimine (PEI, 10% w/v): Dissolve 1 g of PEI (Sigma) in deionized water to a final volume of 10 ml (this stock solution may be stored at 4°C for months). For coating plates, dilute the stock solution 1:10,000 in distilled water and transfer 100 to 150 µl to cover the bottom surface of each wells. Let plate sit 2 hr at room temperature, then decant PEI solution and wash plates three times with deionized water and dry in the laminar flow hood prior to use. Coated plates can be stored at 4°C and used for several weeks.

COMMENTARY Background Information The FLIPR system was initially developed to perform cell-based high throughput functional screening for a membrane potential assay by Kirk Schroeder and Brad Neagle in collaboration with Dr. Vince Groppi of PharmaciaUpjohn. Using a unique integration of optics,

fluidics, and temperature control, FLIPR has proven to be very useful for many fluorometric assays, including measurement of intracellular calcium, intracellular pH, and various luminescence-based assays. The essential advantage of FLIPR is that it simultaneously stimulates and optically reads all 96 wells of a microtiter plate

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Table 9.2.5

FLIPR Maintenance and Troubleshooting

Problem

Suggestion(s)

Variability in fluorescence intensity between wells

This might arise because of poor alignment of either the laser or the camera. Laser alignment: Use the yellow calibration plate in the sample plate position to run a signal test with the aperture at 2, the laser output at 300 mW, and the exposure length at 0.1 sec. Check the uniformity across the plate. A standard deviation of ∼5% to 8% is optimal. Adjust laser if needed. Camera alignment: Check the mechanical alignment of the camera and the bottom of the cell plate. Place a test plate in the sample plate position. Use the alignment option in the Set Up menu. An image of the bottom of the plate will appear on the screen. To align the system, two reference points must be specified. First, move the cursor to the uppermost left well, align the vertical cross hair in the center of the well and the horizontal cross hair one pixel into the well and click the left mouse button, making sure that the upper-left radio button is selected. Next select the lower-right radio button and repeat the procedure for the lower right well. The new coordinates will be updated when the update values button is pressed. To keep the previous values, press the Cancel button.

Laser shuts off without warning

Check the pressure of the water supply to the laser. If needed, adjust the water pressure to the desired range (2.6 to 3.0 gallons/min)

Temperature fluctuations

Ensure that the heating option is turned off at least 30 min prior to the run. Check the water in the bottle for the humidity chamber is at least one-third full. The bottle will normally need filling every couple of weeks.

Interlock warning appears on the FLIPR setup screen

Check all interlock junctions such as the sample drawer, pipet positions, and ensure that these are seated as desired. Ensure that the airflow supply is adequate for the pipettor function (80 psi). continued

Cell-Based Assays Using the FLIPR

in a rapid fashion (≤1 sec). The system works with both adherent and nonadherent cell lines. A typical laboratory layout of FLIPR is presented in Figure 9.2.1A. The essential components of the FLIPR system are as follows: 1. Optics: 6-W argon laser, cooled CCD camera. 2. Fluid handling: pipettor (96-well). 3. Incubation chamber with holders for microplates. 4. Computer (Windows NT) running FLIPR software. 5. Other system requirements: a. Water supply and drain capability of 2.5 gal/min for cooling laser. b. Clean air supply of 80 to 90 psi and 5 psi. With the FLIPR384 system, additional components include:

1. Interchangeable pipettor heads for 384or 96-well plates. 2. Integrated tip washing station. 3. Integrated plate stacker. 4. Heated stage. 5. Robot-friendly workstation. Optical measurements An argon laser is typically used to excite the fluorescent indicator dye and a cooled CCD camera detects the emitted light. Data points may be taken from each of the wells at intervals as often as every second. Additionally, the manner of exciting the sample at an angle and masking each well permits signal discrimination at the level of the cell monolayer, eliminating undesirable extracellular background fluorescence found in many conventional fluorescent assays.

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Table 9.2.5

FLIPR Maintenance and Troubleshooting, continued

Problem

Suggestion(s)

Poor humidity in the assay chamber

Check for proper airflow into the chamber. Pull out the drawer and check for airflow at the intake at the rear of the instrument. If the airline has condensation, it should be drained off.

Variability in dispersal volumes in the assay wells.

Maintain the pipettor accuracy by performing a calibration procedure (recommended every 3 to 4 months)

No response in the 96-well panel window or in the multiple well overlay window

Ensure that the pipet tips are loaded and seated. Ensure that the addition plates are in the appropriate chambers. For multiple well overlay windows, ensure that the software options are highlighted prior to the run.

Nonuniform fluorescence This might arise from optical artifacts or from poor cell quality. intensity between individual wells The mirrors may be dirty in some areas. Clean mirror surface with alcohol and a soft tissue. Mirrors should be cleaned gently as they can be easily scratched. Cleaning is restricted to accessible surfaces only. Ensure that the cells remain attached as a confluent monolayer by maintaining an appropriate seeding density in the multiwell plate and/or by plating cells in poly-D-lysine precoated plates so that the cells remain attached during washing and addition procedures Fluorescence quenching/other artifacts

Other

Certain compounds may cause quenching of fluorescence of the indicator dye [e.g., D(3)] resulting in changes in fluorescence response. This can be distinguished generally by the abruptness of the change in fluorescence signal. Proper tune up and maintenance of the pipettor, camera, and other mechanical components is necessary to prevent other instrumentation artifacts (although rare). Also, proper training on the routine use and maintenance of FLIPR is highly recommended.

Fluid addition The FLIPR utilizes an integrated 96-well (FLIPR) or 384-well (FLIPR384) pipettor. The pipettor is used to rapidly aspirate, dispense, and mix fluid from microplates containing test compounds into the microplate containing the cells. Two plates containing test compounds can be used in each data run—for example, one plate that contains test compounds and a second plate containing a known activator or antagonist—depending upon the type of assay. An alternate third position may be utilized for bulk dispensing. With the 96-well pipettor, disposable tips are generally employed to eliminate contamination from carryover between experiments. Humidity and temperature control The incubation chamber can be maintained at constant temperature and humidity, thereby permitting experiments to be conducted at the appropriate temperature conditions. This is particularly important for DiBAC4(3)-based assays.

Software The FLIPR system is controlled through a Windows-compatible software package. The FLIPR software controls the action of the pipettor, the shutter for the laser and cameras, the selection of the emission filters, and data sampling. The motion of the pipettor is controlled by stepping motors that control the vertical and horizontal movement of the pipettor as well as the aspiration and dispense functions. The experimental setup for the FLIPR run defines the camera exposure time, filter selection, tip presoak options, multiple dispense sequence options, speed of dispensing, and reagent mixing status. Software-controlled experimental parameters (listed in Tables 9.2.3 and 9.2.4) include: 1. Transfer volumes and dispensing speed. 2. Addition time, mixing intervals, and data sampling intervals. 3. Temperature and humidity. 4. Choice of emission filters (i.e., single or dual). Drug Discovery Technologies

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The 96-well pipettor of the FLIPR can be programmed to dispense compounds from addition plates. Other FLIPR software features include real-time display of fluorescence changes, data export functions, microplate viewing options, diagnostics for temperature, and interlock warnings for pipettor tip seating and drawer positions. In addition, data from selected wells can be displayed in an overlay mode in real time. These various options provided by the software allow FLIPR to be quickly adapted to various applications and assay protocols. Other applications Although not covered in this unit, the FLIPR system may be utilized for measurement of intracellular pH changes using appropriate dyes, as well as for various luminescence measurements. Luminescence Assays that utilize luminescence, either short-lived or long-lived, can be assayed using the FLIPR. For example, FLIPR can be used as a powerful tool to measure luminescence for detection of aequorin or luciferase activity in a high-throughput mode. The luciferase gene has become one of the most frequently used reporter genes (see UNIT 6.2) and is typically cloned downstream of a DNA promoter sequence. Activation of a cell surface receptor causes regulatory proteins in the cell to bind to the DNA promoter sequence, initiating luciferase expression. Following cell lysis, the expression level of the luciferase is determined by the addition of luciferin and ATP. Compounds that interfere with the activity of the target protein result in reduced luciferase levels. The FLIPR system has also been successfully used with a Dual-Luciferase Reporter assay (Sherf et al., 1996) integrating firefly and renilla luciferases expressed in a single population of transiently transfected CHO cells. Electrical stimulation A FLIPR-integrated electrostimulator has also been used to examine cytosolic calcium transients in cortical neurons (Arndts, 1999). Picrotoxin-induced bursting signals of intracellular Ca2+ changes in cultured cortical neuronal preparations have been used to assay activity of sodium channel ligands (Rock, 1999).

Cell-Based Assays Using the FLIPR

Intracellular pH FLIPR may be used to assess changes in intracellular pH using the pH-sensitive fluorescen t dye 2′,7′-bis(2-carboxyethyl)-5,6-car-

boxyfluorescein (BCECF). Cells are initially loaded with the ester derivative, BCECF-AM, which becomes hydrolyzed in the cell to liberate the free acid. Fluorescence excitation is obtained using the 488-nm line of the argon laser and 540-nm emission is monitored over time using the appropriate filters. Limitations Some of the present limitations of the FLIPR system include: 1. In the available configuration, FLIPR is suitable only for fluorescent dyes that are excitable by argon laser (e.g., 488 nm). 2. The resolution of the FLIPR system is ~1 sec, which limits analysis of fast and rapidly desensitizing responses (< 1sec) known to be associated with certain receptor/ion channels. 3. Use of the slow distribution of the membrane potential dye, DiBAC4(3), limits the utility of the membrane potential assay for measuring rapid changes. Clearly, fast responsive membrane potential probes are needed. More recently, voltage sensors that utilize paired flurophores to measure membrane potential in real time by fluorescence resonance energy transfer (FRET) have become available (González and Tsien, 1995; 1997). These voltage-sensitive dyes and high-throughput screening ion channel instrumentation, including the voltage ion probe reader (VIPR), may be licensed for use from Aurora Biosciences. 4. Lastly, the widespread utilization of FLIPR technology is somewhat limited by the cost of the unit, approximately $260,000 and $460,000 for the 96-well and 384-well formats, respectively.

Critical Parameters and Troubleshooting General troubleshooting guidelines for FLIPR-based assays are listed in Table 9.2.5. While developing an assay for the FLIPR, it is important to refine cell plating and growth conditions for optimal activity. With transfected cell lines, a number of clones need to be evaluated to determine the one with optimal levels of expression for functional studies. Various protocols should be tested using various available agonists and antagonists as reference compounds to define well-to-well reproducibility of the responses. Selection of the dye with appropriate loading efficiency is equally important, especially for intracellular calcium assays. Upon determination of pharmacological profiles, especially for a new target, comparisons should be made with other assays—biochemi-

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Figure 9.2.5 Screen capture showing sample output from a membrane potential change assay from the FLIPR. Shown in the 96-well panel windows are series of 8 concentration-response kinetic assays spanning five concentrations in duplicate (compound 1, A1 to A5 and B1 to B5; compound 2, C1 to C5 and D1 to D5; compound 3, E1 to E5 and F1 to F5; compound 4, G1 to G5 and H1 to H5; compound 5, A7 to A11 and B7 to B11; compound 6, C7 to C11 and D7 to D11; compound 7, E7 to E11 and F7 to F11; and compound 8 (G7 to G11 and H7 to H11). The highlighted rows (gray) showing negative and positive controls are displayed in the multiple well overlay window. The initial addition is made after a 5-min baseline period. At the 25-min time point, the antagonist is added.

cal or electrophysiological—to assess the validity of FLIPR measures. While assessing antagonists, it is possible to observe apparent noncompetitive inhibition by classically defined competitive antagonists (Miller et al., 1999b). This occurs as a result of nonequilibrium conditions due to slow dissociation of preequilibrated antagonists and the kinetic constraints of the rapid Ca2+ transients. In some instances, ligand-induced desensitization of the receptor-signaling pathways may reduce maximum response.

Anticipated Results In the membrane potential assay, a reference compound (depolarizing or hyperpolarizing) should evoke a change from the basal fluorescence in a magnitude ranging from 2000 to 8000 U, relative to the negative control. Fluorescence changes of >3000 U may be satisfactory for concentration-response analysis. With intracellular calcium assays, a magnitude of

change in fluorescence of at least 6000 U is desired to adequately define agonist or antagonist concentration-response profiles. Typically, an antagonist should be able to reverse or attenuate agonist-evoked fluorescence changes. Membrane potential assay A sample output of the FLIPR membranepotential change assay in A10 cells is depicted in Figure 9.2.5. In A10 vascular smooth muscle cells, compounds that modulate ATP-sensitive potassium channels, for example, potassium channel openers (KCOs) such as cromakalim and pinacidil (Research Biochemicals), can elicit membrane hyperpolarization, which translates into a decrease in fluorescence response. In the example illustrated in Figure 9.2.5, a series of potassium channel openers were evaluated in a concentration-dependent fashion. Five concentrations were tested, and the addition plate was set up as shown in Figure 9.2.3B. Baseline fluorescence data was col-

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A

B 120 100

–1000 –3000 –5000

–7000 –9000

glyburide (µM)

0

10

40

20 30 Time (min)

C

80 60 40 20 0

–9

–8 –7 –6 Log [compound] (M)

–5

D 1000 0 –1000 –2000 –3000 –4000 –5000 –6000 –7000

Fluorescence response (%10 µM)

Fluorescence (units)

Fluorescence response (%10 µM)

Fluorescence (units)

1000

0

5

10 15 Time (min)

20

25

100 80 60 40 20 0

–10

–8 –7 –6 –9 Log [compound] (M)

–5

Figure 9.2.6 Analysis of changes in DiBAC4(3) fluorescence responses in the A10 cell line. (A) Changes in fluorescence in response to addition of varying concentrations of a potassium channel opener (1, 10, and 100 nM; 1 and 3 µM). (B) Concentration dependency of fluorescence responses from panel A. Plotted are maximal fluorescence changes expressed as a percentage of the response elicited by 10 µM of a reference compound (% 10 µM). In this experiment, the EC50 value for the opener was calculated to be 90 nM. (C) Attenuation of potassium channel opener evoked responses by the inhibitor, glyburide. Shown are responses evoked by the potassium channel opener (3 µM) in the absence (control) and presence of varying concentrations (indicated in nM) of glyburide. Note attenuation of responses of the opener in the presence of increasing concentrations of the inhibitor. (D). Concentration-response curves of inhibition of potassium channel opener-evoked responses by glyburide. Data exported from FLIPR were analyzed using a spreadsheet and statistical analysis software package.

Cell-Based Assays Using the FLIPR

lected for 5 min, then compounds were added and data collected for an additional 25 min. Glyburide (glibenclamide), a potassium channel blocker, was then added and changes in fluorescence assessed for another 15 min. Figure 9.2.6 depicts analyzed fluorescence data for an ATP-sensitive potassium channel opener. Figure 9.2.6A shows the mean fluorescence change of duplicate wells. The maximum fluorescence change is normalized to the responses evoked by the reference compound (rows 6C to 6F in Fig. 9.2.5) and is plotted in a concentration-response fashion in Figure 9.2.6B. The calculated EC50 (90 nM) of this compound agrees well with data reported from other functional and radioligand binding assays. It should be pointed out that, generally, a

good correlation is observed between the potencies determined from the FLIPR-based membrane potential assays versus those assessed by other functional measures such as smooth muscle relaxation (Gopalakrishnan et al., 1999). Coincubation with glyburide inhibited the opener-evoked fluorescence response in a concentration-dependent manner with an IC50 value of 0.15 µM (Fig. 9.2.6C, D). Intracellular calcium assay The example shown in Figures 9.2.7 and 9.2.8 evaluates muscarinic receptor-evoked intracellular calcium responses in medulloblastoma TE671 cells. The functional properties of muscarinic cholinergic receptors have been well studied in this cell line using various bio-

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Figure 9.2.7 Screen capture showing sample output from an intracellular calcium influx assay. Shown in the 96-well panel windows are concentration-dependent changes in intracellular calcium responses to acetylcholine in the absence (columns A and B) and presence of three different concentrations of atropine (columns C and D, E and F, and G and H) in duplicate. A series of negative control and positive control traces (highlighted in dark gray in row 12) is displayed in the multiple well overlay window.

chemical measures (Bencherif and Lukas, 1991). Muscarinic stimulation causes large and quick elevations of cytosolic Ca2+ by activation of the M3 receptor subtype coupled to the phosphoinositide hydrolysis pathway via a choleratoxin sensitive mechanism. Figure 9.2.7 shows a typical output of data from the FLIPR following addition of test compounds. The analyzed data is depicted in Figure 9.2.8. The kinetic data are exported from the FLIPR software and transferred to an analysis spreadsheet for plotting the kinetic and concentration-response analysis. The peak of the Ca2+ transient is considered as the relevant signal and these values are used for analysis. Figure 9.2.8A shows the effects of varying concentrations of acetylcholine on the kinetics of [Ca2+]i release (and Fluo-3 fluorescence) in medulloblastoma TE671 cells. Acetylcholineevoked concentration-dependent effects were rapid, peaking within 30 sec, and declining to lower sustained levels of elevated fluorescence. The EC50 value for ACh for evoking [Ca2+]i

transient responses was determined to be 0.3 µM (Fig. 9.2.8B). Atropine inhibited AChevoked calcium responses in a concentrationdependent manner (Fig. 9.2.8C) and behaved as a competitive antagonist. Schild analysis of the inhibition data (see UNIT 4.1 ) gave a pA2 value of 8.16 and slope close to unity (0.90; Figure 9.2.8D).

Time Considerations Initial development of FLIPR-based assays may take from one to four weeks. This includes time for optimizing cell density growth conditions and confluence appropriate to tolerate washing and pipetting procedures. Once the assay has been developed, throughput can typically range from 30 to 50 plates/day for calcium-based assays, or 10 to 12 plates/day for membrane potential assays. Cell culture throughput, especially in assays using primary cultures, is also critical for cycle time reduction in the high throughput screening (HTS) mode.

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B 12000 10000 8000 6000 4000 2000 0 0.0 0.5 1.0 1.5 2.0 2.5 3.0 Time (min)

100 80 60 40

20 0 –9 –8 –7 –6 –5 –4 –3 –2 Log [acetylcholine] (M)

D

0.5 1.0 1.5 2.0 2.5 3.0 Time (min)

120 100 80 60 40

4 Log (Dose ratio– 1)

12000 10000 8000 6000 4000 2000 0 0.0

Fluorescence response (Percent 30 µM ACh)

Fluorescence (units)

C

Fluorescence response (Percent 30 µM ACh)

Fluorescence (units)

A

3 2 1

pA2 = 8.1 20 0 9 8 7 6 5 4 Log [Atropine] 0 –8 –7 –6 –5 – 4 – 3 – 2 Log [acetylcholine] (M)

Figure 9.2.8 Analysis of acetylcholine-stimulated intracellular calcium elevation in the human medulloblastoma TE671 cell line. (A) Changes in Fluo-3 fluorescence in response to addition of varying concentrations of acetylcholine (100 nM to 1 mM). For the sake of clarity, only representative curves are plotted. (B) Concentration dependency of acetylcholine-evoked responses. Plotted are maximal fluorescence changes expressed as a percentage of the maximal response elicited by 30 µM ACh. In this experiment, the EC50 value of ACh was calculated to be 0.27 µM. (C) Attenuation of acetylcholine-evoked intracellular calcium elevations by atropine. Shown is the response to 30 µM acetylcholine in the absence (solid line) and presence (dashed line) of atropine (10 µM). (D) Effect of competitive muscarinic receptor antagonist on acetylcholine-evoked intracellular calcium elevations. Concentration-response curves of acetylcholine were determined in the absence of atropine (filled boxes) or in the presence of atropine at the following concentrations: 1 nM (open boxes), 10 nM (filled circles), 100 nM (filled diamonds), 1 µM atropine (filled point-down trianges), and 10 µM (filled point-up triangles) The inset depicts Schild analysis of the inhibition of acetylcholine responses by atropine data (pA2 value = 8.16 and slope = 0.90). Data shown are the mean values from duplicate wells from a single experiment. A Microsoft Excel–based spreadsheet and statistical analysis software package were used to analyze the time sequence data exported from the FLIPR.

LITERATURE CITED

Arndts, D. 1999. Ca2+ kinetic registrations by FLIPR in electrically stimulated cell culture. Abstr. 3rd Int. Cell Analysis Products Users Meet. Monterey, Calif. Bencherif, M. and Lukas, R.J. 1991. Ligand binding and functional characterization of muscarinic acetylcholine receptors on the TE671/RD human cell line. J Pharmacol Exp Ther. 257:946-953. Di Virgilio F., Steinberg, T.H., and Silverstein, S.C. 1990. Inhibition of fura-2 sequestration and secretion with organic anion transport blockers. Cell Calcium. 11:57-62.

Dickinson, K.E., Baska, R.A., Cohen, R.B., Bryson, C.C., Smith, M.A., Schroeder, K., and Lodge, N.J. 1998. Identification of [3H]P1075 binding sites and P1075-activated K+ currents in ovine choroid plexus cells. Eur. J. Pharmacol. 345:97101. Epps, D.E., Wolfe, M. and Groppi, V. 1994. Characterization of the steady-state and dynamic fluorescence properties of the potential-sensitive dye bis-(1,3-dibutylbarbituric acid)trimethine oxonol (Dibac4(3)) in model systems and cells. Chem Phy Lipid. 69:137-150.

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Fadayel, G.M., Izzo, N.J., and Bahinski, A. 1999. High-throughput optical measurement of membrane potential: Expression and block of a voltage-dependent potassium channel. Abstr. 3rd Int. Cell Analysis Products Users Meet. Monterey, Calif. González, J.E. and Tsien, R.Y. 1995. Voltage sensing by fluorescence resonance energy transfer in single cells. Biophys. J. 69:1272-1280. González, J.E. and Tsien, R.Y. 1997. Improved indicators for cell membrane potential that use fluroscence resonance energy transfer. Chem Biol. 4:269-277. González, J.E., Oades, K., Leychkis, Y., Harootunian, A., and Negulescu, P.A. 1999. Cell-based assays and instrumentation for screening ionchannel targets. Drug Discov. Today. 4(9):431-439. Gopalakrishnan, M., Whiteaker, K.L, Molinari, E.J., Davis-Taber, R.A., Monteggia, L.M., Scott, V.E.S., Buckner, S., Milicic, I., Cain, J., Postl, S., Brioni, J.D., and Sullivan, J.P. 1999. Characterization of ATP-sensitive potassium channels (KATP) in guinea-pig bladder smooth muscle cells. J. Pharmacol. Exp. Ther., 289:551-558. Groebe, D., Gopalakrishnan, S.M., Hahn, H., Warrior, U., Traphagen, L., and Burns, D. 1999. Use of a Fluorometric Imaging Plate Reader in high throughput screening. SPIE Proc. 3603:277-306. Jorgensen, T.D. 1999. Monitoring Ca2+-activated K+ channels using FLIPR. Abstr. 3rd Int. Cell Analysis Products Users Meet. Monterey, Calif. Kao, J.P.Y., Harootunian, A.T., and Tsien, R.Y. 1989. Photochemically generated cytosolic calcium pulsed and their detection by Fluo-3. J. Biol. Chem. 264:8179. Kuntzweiler, T.A., Arneric, S.P., and DonnellyRoberts, D.L. 1998. Rapid assessment of ligand actions with nicotinic acetylcholine receptors using calcium dynamics and FLIPR. Drug Dev. Res. 44:14-20. Miller, T., Molinari, E., Anderson, K.L., Davis-Taber, R., Scott, V., Brioni, J.D., Sullivan J.P., and Gopalakrishnan, M. 1999a Pharmacological and molecular characterization of ATP-sensitive K+ channels in the TE671 human medulloblastoma cell line. Eur. J. Pharmacol. 370:179-185. Miller, T.R., Witte, D.G., Ireland, L.M., Kang, C.H., Roch, J. M., Masters, J.N., Esbenshade, T. A., and Hancock, A. A. 1999b. Analysis of apparent noncompetitive responses to competitive H1histamine receptor antagonists in Fluorescent Imaging Plate reader-based calcium assays. J. Biomol. Screen. 4:249-258. Rock, D. 1999. Using intracellular Ca2+ measurements to evaluate activity of Na+ channels in cultured neurons using the FLIPRTM system.

Abstr. 3rd Int. Cell Analysis Products Users Meet., Monterey, Calif. Schroeder K.S. and Neagle B.D. 1996. FLIPR: A new instrument for accurate, high throughput optical screening. J Biomol Screen. 1:75-81. Sherf, B.A., Navarro, S.L., Hannah, R.R., and Wood, K.V. 1996. Dual-Luciferase Reporter Assay, an advanced co-reporter technology integrating firefly and renilla luciferase assays. Promega Notes #57. Simchowitz, L. and Bibb, J.A. 1990 Functional analysis of the modes of anion transport in neutrophils and HL-60 cells. Annu Rev Physiol. 52:381-397. Sirotina-Meisher, A., Stauruch, M.J.O., and Boltz., R.C.D. 1999. Drug screening measurement of membrane potential in human T cells using the FLIPR system. Abstr. 3rd Int. Cell Analysis Products Users Meet. Monterey, Calif. Whiteaker, K.L., Davis-Taber, R., Molinari, E.J., Hoogenboom, L., Cain, J., Scott, V.E.S., Sullivan, J.P., Brioni, J.D., and Gopalakrishnan, M. 1999. Cardiac and vascular KATP channels: molecular and pharmacologic distinctions. FASEB J., 816.4.

KEY REFERENCES Annual International Cell Analysis Products Users Meeting (1990) (organized by Molecular Devices, Sunnyvale, Calif). Abstracts. Handbook of Fluorescent Probes and Research Chemicals, Molecular Probes, Eugene, OR. FLIPR (Fluorometric Imaging Plate Reader User Manual), Molecular Devices, Sunnyvale, CA.

INTERNET RESOURCES http://www.moldev.com Molecular Devices Web site. Information/updates of FLIPR, protocol notes on FLIPR-based applications; other cell analysis products. http://www.probes.com Molecular Probes Web site. An excellent site for information of various fluorescence applications and dyes. http://www.atcc.org Web site of American Type Culture Collection (ATCC). Comprehensive supplier of cell lines.

Contributed by Kristi L. Whiteaker, James P. Sullivan, and Murali Gopalakrishnan Abbott Laboratories Abbott Park, Illinois

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Overview of Combinatorial Chemistry Pharmaceutical drug discovery and development has the reputation of being very time and labor intensive. It takes on the order of 12 to 15 years to bring a new molecule from discovery to market, at a cost of over $500 million. Accordingly, modern competition to be “first to market” with drugs for new indications is driving pharmaceutical companies to find faster and more economical ways to discover and develop these agents. Because the rate of compound synthesis and testing has been one of the most time-consuming segments of the drug discovery process, companies have begun to move away from the “make one– screen one” approach to a discovery process known as combinatorial chemistry. Combinatorial chemistry is an approach for rapidly generating hundreds to hundreds of thousands of compounds in sets called libraries. By preparing large, diverse libraries of compounds, drug companies can more quickly and economically identify drug candidates. While non-pharmaceutical applications of combinatorial chemistry appear to be on the rise, this overview is concerned primarily with combinatorial chemistry as it applies to drug discovery.

WHAT IS COMBINATORIAL CHEMISTRY? The term “combinatorial” is used somewhat loosely among practitioners of the art. In a classical sense, it refers to combining sets of reagents together to form molecules such that every possible combination has been synthesized. Consider a very simple example: combining a solution of 3 primary amines with a solution of 3 acid chlorides produces all 9 possible combinations as amide products (Fig. 9.3.1). One can easily imagine that by combining 20 amines with 20 acid chlorides, 400 different amides would promptly be produced. This is one of the simplest types of combinatorial libraries. A powerful modification of the above mixture synthesis is the “mix-and-split” approach, which has been extensively employed in the synthesis of libraries of linear molecules such as peptides and oligonucleotides. Here, a given set of reagents are mixed in a single vessel (e.g., monomers X, Y, and Z; see Fig. 9.3.2). The mixture is then “split” into equal portions and distributed into an equal number of reaction vessels for reaction with each of the monomer units. The products from all the vessels are

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Figure 9.3.1 A simple 3 × 3 “combinatorial” library, synthesized as a mixture of all 9 possible combinations.

Contributed by Stephen L. Crooks and Leslie J. Charles Current Protocols in Pharmacology (2000) 9.3.1-9.3.16 Copyright © 2000 by John Wiley & Sons, Inc.

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Figure 9.3.2 A diagram of the mix-and-split technique to prepare all possible trimeric combinations of 3 building blocks. The darkened spheres represent a support material such as a polystyrene bead.

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Overview of Combinatorial Chemistry

again mixed and split, followed by another round of reactions with the separate monomers. In theory, what results from this application of the mix-and-split method is a set of equimolar mixtures of all the molecules prepared and every possible combination of the monomers used in the synthesis, although in fact the product distributions may vary depending on individual reaction rates. The library of compounds formed in mixand-split combinatorial libraries is made up of Rn molecules, where R is the number of reactants used and n is the number of reaction cycles performed (or the number of monomers in the chain). In the above example there are 3 reactants, and preparing all possible trimeric combinations gives a library of 27 compounds (i.e., 33 = 27). The numbers grow rapidly—a

Z

Y

mix-and-split library of all possible tetrameric combinations of these 3 reactants (R) would have 34 = 81 compounds. The power of mix-and-split synthesis is primarily the number of compounds that can be generated with relatively few reaction steps. The traditional synthesis of 1000 trimeric combinations of 10 monomers would require 1000 separate reactions (i.e., 10 × 10 × 10). By using the mix-and-split process described above, only 20 reactions would be needed. Drawbacks of mix-and-split libraries include the fact that the compounds may be present in quite small amounts and that some type of “encoding” strategy is generally needed in order to identify the structure of any active compounds, typically requiring additional synthetic steps (see

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Jacobs and Ni, 1998, for a nice discussion of encoding strategies). Another way the term “combinatorial” is used is in the sense of parallel synthesis. Parallel synthesis can be the reaction of reactants in an array (such as the 3 × 3 array in Fig. 9.3.1), where the reactions are performed in individual reaction vessels such that separate compounds are produced. More commonly, parallel synthesis is the reaction of a single starting molecule (i.e., a “template” molecule) with a variety of reactants, again to produce individual compounds. This generates a library in which the “template” is present in all molecules of the library. Thus, parallel synthesis is especially popular for projects where there are hints of the structural requirements for biological activity and where the purpose of the library is to optimize activity around a very specific lead candidate. While parallel synthesis requires more reaction steps to produce a library of compounds than the mix-and-split approach, advances in automation are making parallel synthesis a practical and desirable option, given the need to ultimately identify individual active compounds as potential new drugs. Many fine summaries of combinatorial chemistry exist (Gordon and Kerwin, 1998; Czarnik and DeWitt, 1997), including “parallel personal comments” by the authors of several classical papers in combinatorial chemistry (Lebl, 1999). The scope of this overview cannot be all-encompassing, but rather provides the reader with a basic understanding of the field, including: I. Library design A. Lead identification libraries B. Lead optimization libraries C. Structure-based drug design II. Library construction A. Strategies 1. Solid phase vs. solution phase 2. Solid phase chemistry a. Supports b. Linkers 3. Solution phase chemistry B. Resources 1. Labware 2. Automation 3. Informatics C. Analytical requirements D. Chemistry development III. Examples of libraries IV. Trends and future directions

LIBRARY DESIGN Compound libraries are designed with one of two primary objectives: (1) to discover new drug leads in high-throughput biological assays, or (2) to rapidly explore the structure-activity requirements of a lead molecule in order to optimize biological properties. The first approach is called Lead Identification, and the second, Lead Optimization.

Lead Identification Libraries In the pharmaceutical industry, drug discovery is often aimed at a particular disease or at least at a target biochemical mechanism that might have therapeutic relevance for that disease. When biological targets such as enzymes or receptor systems are discovered, there is often limited information about what types of molecules will interact effectively at those targets. One approach to identifying molecules that interact with a given biological target is to prepare a large collection of compounds that includes many different types of chemical structures, and then test them all against the target. Compounds that exhibit a desired activity (this can mean inhibition of an enzyme, antagonism or activation of a receptor, or simply binding to the target of interest) then serve as leads for preparing structurally similar compounds for optimization of activity. One specific example of such a screening library is the Optiverse library of over 70,000 individual compounds that was designed and synthesized in a collaborative effort between Tripos and Panlabs several years ago. This library was designed to be “optimally diverse” and may be expected to provide hits that can serve as starting points for further library design, synthesis, and testing, in order to optimize activity. However, questions remain on how to decide whether a given lead identification library is large enough, and how to measure the diversity of the library. Measuring the diversity of a given combinatorial library has been done by calculations of molecular characteristics such as partition coefficients, molecular weights, dipole moments, molecular dimensions, and 3-dimensional structures. Numerical descriptors that indicate the presence or absence of various arrangements of atoms can be mathematically grouped and compared to give a numerical representation of diversity in the library. For example, a list of structural motifs (e.g., C-O-C, C=O, C-N) can be associated with a string of 1s and 0s to indicate whether a particular motif is present or not, and these binary strings can

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be compared (subtracted) to give a measure of molecular similarity. Libraries can be synthesized to include or exclude compounds falling within certain parameters. Whatever the approach to library design, it is widely agreed that lead identification libraries are primarily intended to find starting points, and are not expected to produce molecules that are ready for preclinical development without further optimization.

Lead Optimization Libraries

Overview of Combinatorial Chemistry

Lead optimization denotes the presence of an initial lead compound that has shown some interesting activity of therapeutic potential. The purpose of a lead optimization library is to better understand the structure-activity relationship (SAR) of related compounds and make an initially poorly understood molecule better by changing it in a logical, consistent manner. An optimization library may serve to improve not only potency, but also solubility, ability to be formulated, or stability to physiological conditions. The term “better” will be very specific to individual project goals and the lead compound that was initially discovered. There are many factors to consider in the design of lead optimization libraries. Properties such as solubility, metabolic profile, and compatibility with a desired formulation matrix play major roles in guiding the design of optimization libraries, because compounds from lead identification libraries often lack potency or selectivity. This can be for several reasons including low purity or an incomplete understanding of structure and how it relates to activity. For example, sometimes compounds with high levels of potency in vitro fail to give useful levels of activity when dosed orally because they are poorly absorbed, metabolized quickly by the body, or unstable to physiological conditions. It is common practice to design lead optimization libraries that are structurally similar, yet diverse at certain points of the molecule, in order to explore SARs. Although this is what medicinal chemists have done for years, advances in instrumentation and information handling are making it possible to synthesize many individual compounds more quickly, accelerating SAR exploration. The size of lead optimization libraries can vary widely. The amount of optimization work required depends on how good the initial lead is and what improvements need to be made. Improving a compound’s solubility may be easier than changing its toxicity profile. This is

where a good understanding of SAR becomes very important. Many lead optimization libraries fall within the range of 500 to 1000 compounds. Smaller libraries may not show important SAR information, while larger libraries often become filled with superfluous data that do not add significant value to the information gained from the first 1000 compounds.

Structure-Based Drug Design Structure-based drug design is a technique arising from recent advances incorporating information about the active site of the biological target. A particularly powerful lead optimization strategy involves cocrystallization of a lead molecule with the biological target protein thought to be critical to a disease process, and subsequent examination of the X-ray crystal structure to provide a three-dimensional picture of the interaction. Additionally, advances in nuclear magnetic resonance (NMR) protein spectroscopy can also permit a visualization of this interaction. By noting key molecular interactions, it is possible to design analogs that have a good chance of enhancing those interactions, which presumably will increase the desired activity. Typically, several iterations of structure determination, analog synthesis, and biological testing are carried out to improve activity. There are caveats, such as the possible low correlation of solid-state crystal structures to real-life biological interactions that occur under physiological conditions. However, there are success stories, and it is instructive to examine how this approach has been successfully employed in the design of orally active agents, such as thrombin inhibitors. Thrombin is a key mediator in the blood coagulation system, and thrombin inhibitors are thus important as anticlotting agents. Recently, researchers have discovered several potent peptide-like thrombin inhibitors such as L-371,912 (Brady et al., 1998; Fig. 9.3.3A). These compounds inhibited thrombin in vitro at a concentration of 5 nM; however, they were not active when given to dogs orally. Researchers examined the X-ray crystal structures of L-371,912 (as well as several related compounds) bound to thrombin, and identified a binding region that might accommodate a variety of hydrophobic groups (which might be important for auxilliary binding to thrombin at the active site). From this information, a group of chemically diverse carboxylic acids was selected to prepare some 200 individual analogs of the lead structure. The compounds were

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A

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Figure 9.3.3 Peptide-like thrombin inhibitors. (A) L-371,912. In vitro activity concentration found to be KI = 4.9 nM, but has low oral activity. (B) L-372,460. In vitro activity concentration found to be KI = 1.5 nM and found to be orally active.

prepared in small groups of 25 to 40 compounds, followed by in vitro screening. After several rounds of optimization, diverse compounds with favorable oral IC50 values emerged. One of the early hits, L-372,460 (Fig. 9.3.3B), proved to have significant oral activity in dogs (i.e., 74% bioavailable, plasma levels lasting >6 hr). The success of this application of a single round of structure determination demonstrated that detailed structural information about critical biological interactions can aid in the optimization of lead compounds.

LIBRARY CONSTRUCTION Strategies Given a diverse set of starting materials, it is safe to assume that some will react better than others in a particular solvent, at a certain temperature, during an acceptable length of time, and in the presence of a number of other factors that can critically affect the success of a library synthesis. The combinatorial format magnifies the challenge since a library can be very large. Again, the library’s size is dependent on whether its purpose is for lead discovery or lead optimization. Regardless, maximizing the number of successful reactions requires thoughtful selection of reagents and reaction conditions. Solid phase chemistry Solid phase techniques were originally developed several decades ago to simplify the preparation and purification of biopolymeric structures such as peptides (Merrifield and Stewart, 1965) and oligonucleotides (Amarnath and Broom, 1977). More recently, however,

they have been used in the synthesis of small molecules (Leznoff, 1977; Hermkens et al., 1996 and 1997). Solid phase chemistry involves the attachment of a modifiable template to a solid support, where it remains throughout the synthesis. A template is the molecule that is built upon throughout a library synthesis and therefore common to all molecules in the library. Once attached to a support such as glass or polystyrene beads, the template is put through a series of transformations using soluble reagents, with purification at each stage of the synthesis being achieved by rinsing the support with an appropriate solvent such as methylene chloride, methanol, or dimethylformamide. Since the molecule being prepared is attached to this insoluble support, it is retained while excess reagents and byproducts are washed away. The synthesis continues this way until the desired molecule has been prepared, at which point it is detached from the substrate, characterized, and screened (Fig. 9.3.4). Since purification remains the single most time-consuming task in compound synthesis, rapid purification of molecules is essential. The primary benefit of solid phase synthesis is that it greatly simplifies the separation of intermediates from products by washing procedures that remove soluble byproducts. Although more rigorous purification is often performed after a molecule has been cleaved from the support, the process is considerably simplified because of earlier washings. Another benefit of solid phase techniques is that, due to the easy removal of reagents, lessefficient reactions can be driven to completion by using excess reagents. This can be especially important for iterative coupling sequences

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Figure 9.3.4 Solid phase synthesis, showing ability to wash away excess reagents and byproducts.

Overview of Combinatorial Chemistry

where incomplete reactions give rise to undesired byproducts and low yield of desired material. Additionally, complex mix-and-split techniques mentioned earlier are more used with solid supports as compared to solution phase chemistry. Moreover, solid-supported materials are easily packaged and manipulated manually. For example, polymer resins or glass beads, which are very small (≤100 µm), can be encapsulated in mesh bags or made into solid sheets of material, allowing the molecules attached to these supports to be more easily moved around, singled out, or washed by hand.

The mechanics of solid phase synthesis are not automation-intensive. A few flasks to stir the supported materials and a method for identifying where individual molecules reside are all that are needed. It is uncommon to perform hundreds of repetitive reagent additions during mix-and-split chemistry, so hand-held pipettors or graduated cylinders can adequately be used to dispense liquid materials. Supports Several supports used for solid phase synthesis are glass (Cohen et al., 1997), cellulose (Englebretsen and Harding, 1992), polystyrene

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copolymers, silica gel (Keana et al., 1986), and polyamide resin (Atherton and Sheppard, 1989). These substrates can take many forms such as beads, sheets, pins, and tubes, and due to their make-up, different supports exhibit very different physical properties, such as porosity, ability to swell, and hydrophobicity. The variety of solid supports enables the combinatorial chemist to select materials that best fit the chemistry for a particular library synthesis. Currently, the most commonly used supports in small-molecule synthesis are polystyrene-divinylbenzene copolymers in the form of resin beads. The topology of very fine, sandlike resin beads consists of a large, porous network with active functional groups interspersed throughout the inner and outer surfaces. These functional groups, such as hydroxyl or amino groups, serve as attachment points for linkers and templates of interest. Pins and tubes are also used as supports, although somewhat less often. “Pins” refer to Geysen Pins which made their entry into the literature in 1984 through the work of one of combinatorial chemistry’s most inspiring chemists, Mario Geysen (Geysen, 1984). This apparatus involves an array of many metal or plastic posts, each with a pinhead on top. The pins are formatted to be compatible with 96well microtiter plates, and each pinhead is functionalized with active sites, similar to polystyrene beads, where molecules can be attached. By inverting these pins into various reagent cocktails, a synthesis can be carried out and the identity of each molecule prepared tracked by its location in the matrix. Regardless of the support type, the functional groups are generally quite distant from each other, which provides attached molecules a high dilution environment. This important characteristic affects the chemistry in two basic ways. First, the resin beads will have a low loading level (typically 0.5 indicate excellent assays suitable for use in screening campaigns.

ROBOTIC VERSUS AUTOMATED WORKSTATIONS Automation in HTS may involve both fully automated robotic systems and dedicated workstations. There is an ongoing debate over

Overview of High-Throughput Screening

which is the most efficient and cost-effective automated equipment for HTS (Oldenburg et al., 1999; Widley et al., 1999). The choice between complete robotic systems and assayspecific workstations is dependent on factors such as budget, frequency in change of methodology, and available personnel. The laboratory workstations are based on liquid handling modules designed for particular steps in a protocol. Examples are the Beckman Biomek FX, the Packard PlateTrack, and the CyBio AG Jobiscreen. In some cases, it is difficult to introduce modifications in the workstation design when the requirements, such as the assay protocol or detection mode, are changed. Compared to robotic systems, the workstation is generally less expensive and more readily installed. Robotic systems, which are quite popular with many pharmaceutical firms, are often preferred because of their flexibility. Using open architecture software, robotic systems can be expanded and modified depending on the specific needs, and updated equipment can be added as it becomes available. Some system integrators even make it possible to run different types of screening assays in parallel. The providers of such systems generally supply the robots with equipment of the user’s choice installed (Fig. 9.4.10). Some technical aspects should be taken into account before or during the implementation of a robotic screening system since the integration of such devices into the laboratory environment

Figure 9.4.10 Robotic HTS system. Circular systems consisting of one or more robots provide high sample throughput.

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requires considerable planning. For automated robotic systems, continuous runs (24 hr/day, 7 days/week) should be anticipated to make full use of the equipment and to justify the costs. Dedicated error handling and recovery, as well as monitoring and remote access, enable compliance. Error handling and recovery enable the robotic system to self-recover from non-fatal errors, e.g., communication problems. Logfiles and video surveillance allow monitoring and reconstruction of events during or after a system run. One of the major bottlenecks in many existing robot systems is the compound distribution step. Low-volume pipetting in the 96- and 384-well formats is commercially available and is useful for accelerating assay performance and enabling volume reduction. Other difficulties can be avoided by creating redundancies, although, in many cases, the robot is the limiting factor and cannot be easily duplicated. Modern linear track systems redistribute assay steps into the periphery, i.e., autonomous workstations attached to the core system, to free the robot from unnecessary movements and to increase the system throughput. Zymark provides a system consisting of small workstations, one for each assay step, each equipped with a small, dedicated pickand-place robot that performs the plate transport. The use of these off-the-shelf technologies, when systematically included, provides a stateof-the-art robotic system with the capability of achieving a throughput of >100,000 compounds/day.

HIT-TO-LEAD PROCESS A hit found in HTS does not guarantee a lead suitable for further development since key questions about its suitability remain unanswered. From the point of view of a chemist, the compound should be “drug like” and lend itself to SAR studies. The biologist needs information on specificity for a target class, its selectivity within the class, and the mechanism of action. Side-effect-related criteria as well as physicochemical and pharmacodynamic properties are also of critical importance. During the hit-to-lead phase, an effort is made to resolve these issues and select the most appropriate candidate.

PHARMACOLOGICAL AND PHARMACEUTICAL PROFILING The results from HTS and uHTS yield a host of primary hits that require secondary testing. Because an HTS program of 100,000 samples

will typically generate between 100 and 500 primary hits, emphasis is placed on the automation of downstream secondary assays to shorten the hit-to-lead phase. While HTS yields compounds active in a target assay, there are several additional criteria that must be met to qualify as a potential drug candidate. For example, the ideal drug is a compound that displays high affinity at the target, works by the desired mechanism of action in functional tests, is highly selective, and possesses pharmaceutically desirable properties. While it is unrealistic to expect primary screening to provide compounds that meet all of these criteria, it is important to rapidly identify the positive attributes and the liabilities of each hit. To this end, the following criteria are generally assessed.

Affinity at the Primary Target Affinity is determined by performing simple concentration-response studies, generally using the screening assay. Measurement of potency is used as a way to initially rank hits from primary screens. Usually Ki or Kd values in the low-micromolar range, or less, are found to be acceptable at this stage.

Mechanism of Action While methods used to determine mechanism of action are target-specific, some generalizations can be made. If the primary screening assay measures enzyme inhibition, it is important to know whether the hit is acting in a competitive or noncompetitive manner. Likewise, for receptor binding assays, it is crucial to establish whether the hit is an agonist or antagonist at the sites, and whether it is acting in a competitive or noncompetitive (allosteric) manner. Such determinations are made through kinetic experiments or, alternatively, by using functional assays (see below). An additional and equally important function of such studies is to eliminate compounds that are active in the primary screening assay for purely trivial reasons. These could include inhibitors of assay endpoint determination, such as compounds with inherent fluorescence or quenching, or inhibitors of reporter enzymes (e.g., luciferase, peroxidase, CAT).

Functional Activity in a Whole Cell or Tissue Model Functional activity is determined by testing hits in whole cells, tissues, or in some cases, animal models. Generally, primary screening is conducted using assays optimized for throughput at the cost of some biological relevance.

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Functional assays are designed to provide a more relevant (even if more cumbersome) target assay and can often be configured to help define the mechanism of action (e.g., agonist versus antagonist).

Selectivity for the Target Molecule Selectivity is determined by testing hits on closely related sites, such as different subtypes of the target receptor (or ion channel) or different isozymes of the target enzyme. In most cases, it is desirable to have greater than a ten-fold selectivity for the intended target as compared with other sites (e.g., Doods et al., 1996). Selectivity is also evaluated relative to a broad array of unrelated targets. The primary purpose in this case is to identify and discard “promiscuous” compounds that interact with several unrelated targets or common ancillary proteins. Such interactions can be indicative of side effects or safety problems. For these reasons, selection of assays is often tailored to check for known undesirable interactions. Examples are given in Figure 9.4.11. Selectivity of the compound for particular target subtypes is important for successful development, as is the general pharmacological profile of the compound. The profiling of compounds against multiple targets allows a knowledge-based selection of the optimal candidate for further development.

ion channels

Pharmaceutical Properties: Solution Characteristics, In Vitro Assessments of Absorption, Metabolism, and Safety Solution properties include solubility, pKa, partition coefficient, and in some cases, the degree of plasma protein binding (UNIT 7.5). Thus, concentrations of the compound in doseresponse experiments may exceed solubility limits, a high partition coefficient may indicate that the hit will partition into cell membranes, and very high serum protein binding may indicate a low effective concentration at the target tissue in vivo. Traditionally these criteria have been assessed in a sequential manner, with a round of optimization through medicinal chemistry between each step. In addition, each step is used to rank a group of hits from primary screening. As this approach is not practical when attempting to assess a large group fo compounds, the lowest ranked are generally dropped at each step. As a consequence, many hits are found to have poor selectivity or inappropriate pharmaceutical properties only after investment of significant time and expense. At this time, it is becoming practical through application of HTS technologies to quickly and cost effectively generate a full data set of secondary assays to address these criteria. CEREP has pioneered this type of profiling (Entzeroth et al., 1999). The advantage of this parallel (as opposed to sequential) approach is that key decisions are made with complete knowledge

cell based receptor binding

enzymes

Overview of High-Throughput Screening

Compounds

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Figure 9.4.11 Results of the profiling of 15 compounds in 120 different biological models (i.e., 17 enzymatic, 11 cell-based, 83 receptor binding, and 9 ion channel assays) at one concentration (10 µM). The gray-scale coding represents the activity obtained in each test, typically expressed as percent activation or inhibition, depending on the target.

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of the positive and negative attributes of each compound or compound class.

DATA EVALUATION Efficient data management is essential for establishing HTS and secondary screening systems. With the large number of tests, manual data handling is no longer possible. The data analysis must assure the quality of the accumulated data and identify false positives (compounds that are not active against the target but identified as hits in the assay). Other than interferences generated by the optical properties of the test compounds (e.g., quench, autofluorescence, absorption), errors may be caused by a malfunction in the equipment. Proper controls (e.g., pre-reading of the plates to determine spectral properties, quench correction) in the primary assay prevent a drain in resources and time during the reconfirmation in a secondary test (Broach et al., 1996). Data mining procedures to rapidly score hits may be useful for monitoring quality and tracking outliers. Such mining methods can be based on a structureactivity relationship that assigns probabilities of being a hit to each compound and tracking inconsistencies between the calculated score and the actual biological activity (Engels et al., 2002). Visual tools in the data presentation facilitate the recognition of patterns on the microtiter plates generated by defective equipment. A variety of tools (e.g., MDL Screen, MDL Information Systems; ActivityBase, ID Buisiness Solutions) are available that can assist in the design of assays, in tracking and analyzing the screening data, in monitoring the performance of automated equipment, and in sharing key results with other researchers. These packages must be adapted to the user’s environment; off-the-shelf packages are difficult to use if fully automated data analysis is required. Many pharmaceutical companies have, therefore, developed their own exploration tools to fully automate data generation, processing, and storage.

IS IT WORTH THE EFFORT? The increasing number of compounds screened per campaign, the highly diversified automated equipment, and the need for even more sophisticated logistics have driven the costs for HTS into the multimillion dollar range at many of the larger pharmaceutical firms. However, it is hoped that the revenues anticipated from the rapid discovery of novel agents will justify these expenditures.

OUTLOOK AND FURTHER DIRECTIONS HTS has become crucial for the success of the drug discovery program. Miniaturization of biological assays is expected to continue as the number of samples increases. In parallel, computational approaches in lead discovery either based on mining of existing data sets or virtual screening using fingerprint (Wang et al., 1999; Cargill, 2000) and docking analyses (Walters et al., 1998; Baxter et al., 2000) will speed drug discovery and make it more cost-effective. Such computational approaches are gaining increasing attention, especially with respect to hit selection. The successful drug discovery program will utilize both approaches in a balanced manner and will achieve better, less expensive, and faster identification of novel leads and development candidates. The technological advances observed over the last two decades augurs well for the future in regard to the integration of newer technologies that will further accelerate and improve the drug discovery process. An interesting facet of these advances is the periodic change in mindset from amplification to retooling. In the early days of HTS, the way to enhance throughput was to amplify the existing technology platform. Thus, if 10 filtration units could perform x number of assays, then 20 could do 2x and so on. With the incorporation of systems engineering and robotic expertise, this highly simplistic approach was totally reengineered using state-ofthe-art technologies to incorporate aspects previously unknown to the biological laboratory (e.g., fuzzy logic, different platforms, etc.). Therefore, rather than the assay simply being amplified to achieve the required number of assays, the assay was reworked to achieve the same results. In essence, it was only with the incorporation of specialties outside biochemistry and pharmacology that the promise of HTS could be fully realized.

LITERATURE CITED Ackerman, M.J. and Clapham, D.E. 1997. Ion channels: Basic science and clinical disease. N. Engl. J. Med. 336:1575-1586. Albanese, C., Christin-Maitre, S., Sluss, P.M., Crowley, W.F., and Jameson, J.L. 1994. Development of a bioassay for FSH using a recombinant human FSH receptor and a cAMP responsive luciferase reporter gene. Mol. Cell Endocrinol. 101:211-209.

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Allen, S., Davies, J., Davies, M.C., Dawkes, A.C., Roberts, C.J., Tendler, S.J., and Williams, P.M. 1999. The influence of epitope availability on atomic-force microscope studies of antigen-antibody interactions. Biochem. J. 341:173-178.

Coward, P., Chan, S.D., Wada, H.G., Humphries, G.M., and Conklin, B.R. 1999. Chimeric G proteins allow a high-throughput signaling assay of Gi-coupled receptors. Anal. Biochem. 270:242248.

Allen, M., Reeves, J., and Mellor, G. 2000. High throughput fluorescence polarization: A homogeneous alternative to radioligand binding for cell surface receptors. J. Biomol. Screen. 5:6370.

Dafforn, A., Kirakossian, H., and Lao, K. 2000. Miniaturization of the luminescent oxygen channeling immunoassay (LOCI(TM)) for use in multiplex array formats and other biochips. Clin. Chem. 46:1495-1497.

Astle, T.W. 1998. Microplate standardization report. J. Biomol. Screen. 3:3-8.

Daly, J.W. 1995. The chemistry of poisons in amphibian skin. Proc. Natl. Acad. Sci. U.S.A. 92:913.

Ajay, A., Walters, W.P., and Murcko, M.A. 1998. Can we learn to distinguish between “drug-like” and “nondrug-like” molecules? J. Med. Chem. 41:3314-3324. Ajay, A., Bemis, G.W., and Murcko, M.A. 1999. Designing libraries with CNS activity. J. Med. Chem. 42:4942-4951.

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Overview of High-Throughput Screening

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Contributed by Michael Entzeroth S*BIO Pte Ltd. Singapore

The author wishes to acknowledge Drs. Mark Crawford and Thierry Jean for their assistance in the development of this work.

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Testing for Inverse Agonism with Constitutive Receptor Systems This unit discusses the use of constitutive 7-transmembrane/G protein-coupled receptor (7TM/GPCR) activity for screening new drug entities. Following an introduction to constitutive 7TM/GPCR activity, the unit centers on the three basic components of a constitutive screening system: the receptor, the receptor coupling components (G protein), and the response reporting system. The design of specific assays to detect inverse agonism and the application of such systems to drug screening is also discussed. Finally, the relative advantages and disadvantages of inverse agonists as therapeutic agents are considered.

GPCR CONSTITUTIVE ACTIVITY Introduction The first quantitative study of constitutive G protein-coupled receptor (GPCR) activity was conducted by Costa and Herz (1989) with an NG108-15 cell line expressing µ-opioid receptors. In these experiments it was shown that basal GTPase activation (an indicator of receptor/G protein interaction) could be elevated by receptor transfection. An opioid peptide, ICI 174864 ([N,N
-diallyl-Tyr1 , Aib2,3 ]Leu5 enkephalin), depressed basal GTPase activity, and this effect appeared to be a receptormediated phenomenon in that it was competitively antagonized by the opioid antagonist MR 2266. The depressant ligand was termed an inverse agonist. The observations of Costa and Herz (1989) led to the modification of the original ternary complex model of GPCRs (De Lean et al., 1980) to the extended ternary complex (ETC) model, which posits that receptors can spontaneously interact with G proteins in the absence of ligand (Samama et al., 1993). Constitutive activity was ultimately shown to be due to the spontaneous interaction of an active state conformation of the receptor with G proteins. The development of GPCR models, from the original proposed by De Lean et al. (1980), to those accounting for constitutive activity, such as the ETC and cubic ternary complex (CTC) models, are discussed in detail in UNIT 1.2. Only the behavior of GPCR constitutive systems, insofar as they relate to drug screening, is considered in the present unit.

UNIT 9.5

The thermodynamically complete model of GPCR systems (CTC model) suggests that both the active and inactive states of the receptor interact with G protein. With this model, response emanates only from the interaction of the G protein with the active state of the receptor (Fig. 9.5.1). There are three basic elements to constitutive GPCR systems: receptor, G protein, and a stimulus-response system capable of detecting the species producing the constitutive stimulus. These elements are considered separately below.

The Receptor in Constitutive GPCR Systems At equilibrium, the ratio of active-state to inactive-state receptor is described by the allosteric constant L ([Ra ]/[Ri ] = L). This constant is characteristic of the receptor, i.e., the energy barrier to the formation of active state receptor may be lower for some receptors than for others. Therefore, the first preferred property of a constitutive GPCR system is a receptor that naturally forms the active state (relatively high value of L). For example, whereas neuropeptide Y receptor types 2 and 4 (NPY2, NPY-4) readily produce constitutive activity, the NPY-1 subtype does not (Chen et al., 2000). Irrespective of the magnitude of L, the ability to spontaneously form active states suggests a practical method of constructing GPCR constitutive systems by overexpressing receptors in recombinant cellular systems. This is because an increase in the total number of receptors will necessarily increase the number of active-state receptors available for response coupling according to the allosteric constant. For example, for a receptor with L = 10−3 , only 1 receptor in 1000 is in the active state at any one time. In a system with a low receptor number, this may be an undetectable level of spontaneous activity. However, if the receptor level is increased to 100,000 there would be 100 active-state receptors in the system. The receptor number can be increased until the threshold for production of observable constitutive activity is reached. The dependence of constitutive activity on receptor number is defined by the CTC Drug Discovery Technologies

Contributed by Terry Kenakin Current Protocols in Pharmacology (2006) 9.5.1-9.5.13 C 2006 by John Wiley & Sons, Inc. Copyright 

9.5.1 Supplement 32

Figure 9.5.1 Basic components of a constitutively active GPCR screening system. The receptor should have the ability to spontaneously form the active state Ra from the inactive state Ri (favorable magnitude for L; 1), there should be G protein with favorable affinity βKG available for coupling to the active state (2), and there must be a means to detect activation of the G protein by the receptor (3).

model as:

Equation 9.5.1

Inverse Agonism in Constitutive Receptor Systems

where β is the differential affinity of the activestate over the inactive-state receptor for G protein, and KG is the equilibrium dissociation constant of the receptor/G protein complex. Thus, increases in [Ri ] lead to concomitant increases in constitutive activity. As shown in Figure 9.5.2, the relationship between receptor number and constitutive activity is a hyperbolic function. The location of the curve along the receptor concentration axis is defined by the magnitude of L, and the maximal constitutive activity is determined by the relative affinities of the G protein for the active and inactive states of the receptor (defined by β; see UNIT 1.2 for further details). Another method of producing a constitutively active system is to biochemically alter the magnitude of L. At present, there are only a limited number of ways to do this. One method applied to µ-opioid receptors (Costa and Herz, 1989) and α2 -adrenoceptors (Tian et al., 1994) is the removal of Na+ , a maneuver that dramatically increases constitutive activity. Since the ionic content of the aqueous environment alters the tertiary protein conformation, alteration of ion concentration could theoretically alter the equilibrium between the active and inactive conformations of receptors by desta-

bilization of the inactive form. Thus, as seen from the inset of Figure 9.5.2, an effectively undetectable level of constitutive activity of a receptor with L = 0.01 in membranes from cells transfected at a concentration of [R]/KG of 0.1 could produce 20% constitutive activity by a shift of L to 0.3. Finally, the magnitude of L can also be altered by point mutation, with slight changes in the amino acid composition of a receptor producing mutants with a level of constitutive activity far greater than that seen with the native host receptor (Kjelsberg et al., 1992; Samama et al., 1993; Coughlin, 1994; Spalding et al., 1995; Porter et al., 1996; Kudo et al., 1996; Schwartz and Rosenkilde, 1996; Spiegel, 1996). There is a rationale for this phenomenon supported by the observation that particular amino acid sequences in the intracellular loops of GPCRs bind to G proteins to produce G protein activation (Dohlman et al., 1991; Hedin et al., 1993; Strader et al., 1994). In fact, isolated amino acid sequences have been used in vitro to produce G protein activation (Konig et al., 1989; Munch et al., 1991). This suggests that the inactive state of the receptor protects these coupling sequences from exposure to G protein, i.e., the inactive form of the receptor is the special one, with its disruption leading to conformations that may relax into active states or copies thereof. It is known that certain single amino acid residues of GPCRs can be altered to form constitutively active receptors. For example, the substitution

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Figure 9.5.2 Constitutive receptor activity ([Ra G]) as a function of level of receptor transfected into a cellular system, expressed as Log([R]/KG ). Constitutive activity calculated with Equation 9.5.1 for (curve 1) a quiescent receptor (L = 0.01) and receptors with increasing allosteric constants (curve 2) L = 0.1 and (curve 3) L = 0.3. Graph shows the hyperbolic relationship between level of receptor transfection and constitutive activity. Inset shows how a subthreshold level of receptor transfection can produce constitutive activity upon increase in the allosteric constant.

of any of twenty amino acids into position 293 of the α1A -adrenoceptor produces a constitutively active site (Kjelsberg et al., 1992). Alteration of the tertiary structure of the receptor, through point mutation, could disrupt this protection and partially expose activating sequences, thereby allowing receptor/G protein coupling. This could, in turn, produce constitutive activity. While receptor mutation is a facile method of producing constitutive activity, there is the caveat that the results obtained with such a receptor may differ from those obtained with the physiologically relevant receptor. This must be taken into consideration when using mutant receptors for drug screening purposes.

The G protein(s) in Constitutive GPCR Systems The G protein is another required component in a constitutively active GPCR system. Increasing the relative quantity of G protein versus receptor can produce constitutive activity. A simplified version of the ternary complex model illustrates this. In this case, the equilibrium between inactive state [Ri ] and active state [Ra ] is shown along with the coupling of the active state to G protein in the equation

Equation 9.5.2

where the equilibrium between [Ra ] and [Ri ] is defined by L, while the equilibrium dissociation constant of the active-state receptor/G protein complex (Ra G) is defined by βKG . Since Ra G produces constitutive activity, and the amount of Ra G species is proportional to the amount of constitutive activity, the amount of Ra G species formed as a fraction of total receptor is given by the equation:

Equation 9.5.3

Accordingly, as shown in Figure 9.5.3, increasing [G] increases constitutive activity. This is because the amount of Ra at equilibrium, as defined by L, is altered by the external influence of binding to G. As Ra G is formed, the amount of Ra is depleted, with more receptor needing to be produced to replace it. Thus, the presence of G causes more activestate receptor to be produced than normally defined by L. Increased constitutive activity through in vitro addition of G protein has been observed with dopamine receptors (Senogles et al., 1990). In cellular systems, cotransfection of Gq -protein (which couples ubiquitously to most receptors) produces constitutive activity in combination with metabotropic glutamate receptors (Parmentier et al., 1998).

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Figure 9.5.3 The effect of G protein concentration on constitutive activity. Activity simulated with Equation 9.5.2 with β = 10 and L = 0.1. As G protein is added to the system, constitutive activity increases.

Response Reporting Systems

Inverse Agonism in Constitutive Receptor Systems

The final requirement of a constitutively active screening system is a method to monitor changes in Ra G. While GTPase activity from the endogenous receptor species Ra G can be monitored, this is less sensitive than utilizing a cellular system that couples the receptor to an amplified response mechanism. Such stimulus-response coupling mechanisms greatly enhance receptor signals, allowing maximal cellular responses from low levels of receptor stimulation (see UNIT 1.2 for further discussion). Mathematically, these stimulusresponse mechanisms can be described as hyperbolic functions of stimulus yielding a response. Some typical examples of such functions for β-adrenoceptor tissues are shown in Figure 9.5.2. Considering the relatively low values of L for most receptors, very high levels of receptor are needed to generate directly measurable levels of Ra G complex. However, if low levels of Ra G are coupled to a highly amplified stimulus-response mechanism, observable levels of constitutive activity can be achieved with small increases in Ra G. Figure 9.5.4 illustrates constitutive activity expressed in terms of Ra G complex produced. In this particular example, 1% production of Ra G (as a fraction of total R) produces 20% of the maximal response. By amplifying low-level changes in Ra G, a sensitive system of detection can be achieved. Another reason to utilize sensitive response reporter mechanisms is that there may be a

ceiling for the maximal amount of Ra G species formed for a given receptor. From Equation 9.5.1, it can be seen that as [R] → ∞, the amount of Ra G (as a fraction of [Rtotal ]) approaches a limiting value of βL/(1 + βL). Thus, the amplifying stimulus function used for Figure 9.5.4 is [Ra G]/([Ra G] + 0.1), where 0.1 represents the coupling efficiency of the cytosolic stimulus-response functions (see UNIT 1.2 for further details). For a receptor with a low allosteric constant (L = 0.01, β = 10), the maximal level of constitutively activated [Ra G] species would be 9%. However, for the stimulus-response system shown in Figure 9.5.4, that would translate to a constitutively active response level of 50% of the attainable maximal response; for receptors with a low allosteric constant the maximal amount of Ra G species that may be formed through receptor overexpression might be small. An amplification system for that small amount of Ra G would allow the detection of low-level changes in Ra G and result in a useful constitutive screening assay.

THE INFLUENCE OF LIGANDS ON CONSTITUTIVE GPCR SYSTEMS The basic mechanism for producing a ligand-generated signal in constitutively active GPCR systems is selective affinity for the various receptor protein species. This is illustrated with a simplified version of the ternary complex model, namely a two-state receptor system. Thus, a receptor protein can be viewed as existing in one of two conformations, Ri or

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Figure 9.5.4 Constitutive activity expressed as a fraction of the maximum response capability of the system. For the curve labeled ‘receptor,’ the maximal constitutive activity is given as [Ra G]/[Rtotal ]. For the curve labeled ‘response,’ the constitutive activity is expressed as a fraction of the maximal response described by the logistic stimulus-response function [Ra G]/([Ra G] + 0.1 (see UNIT 1.2 for further discussion of the use of this mathematical approximation of cellular stimulus-response functions). Through amplification of stimulus with this function it can be seen that a small (1%) increase in Ra G can yield a considerable increase in response (20%).

Ra , the ratio of which is defined by an allosteric constant L:

Equation 9.5.4

Ligand A binds to Ri with an equilibrium association constant Ka and to Ra with an equilibrium association constant αKa , where the factor α denotes the differential affinity of the agonist for Ra , i.e., if α = 10, then the ligand has a ten-fold greater affinity for the Ra form. The complete scheme with ligand involved is then:

species. This can be calculated by examining the amount of Ra species, both as Ra and ARa , present in the absence and presence of ligand. The equilibrium expression for ([Ra ] + [ARa ])/[Rtot ], where [Rtot ] is the total receptor concentration given by the conservation equation [Rtot ] = [Ri ] + [ARi ] + [Ra ] + [ARa ]), is:

Equation 9.5.6

where ρ is the fraction of total receptors, [A] is the concentration of ligand, L is the allosteric constant, KA is the equilibrium dissociation constant of the agonist-receptor complex (KA = 1/Ka ), and α is the differential affinity of the ligand for the Ra state. Thus, in the absence of agonist ([A] = 0), ρ0 = L/(1 + L), and in the presence of a ligand concentration that saturates the receptors ([A] → ∞), ρ∞ = [α (1 + L)]/(1 + αL). Therefore, the effect of a ligand on enriching the Ra state is given by the ratio ρ∞ /ρ0 . This ratio is given by:

Equation 9.5.5

It can be shown that the selective binding of the ligand to Ra (i.e., α > 1) enriches the Ra

Equation 9.5.7

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which predicts that if the ligand has an equal affinity for both the R and Ra states (α = 1), then ρ∞ /ρ0 equals unity and no enrichment of the Ra will result from maximal ligand binding. However, if α > 1, then the presence of the conformationally selective ligand will cause the ratio ρ∞ /ρ0 to be >1. For example, if the affinity of the ligand is ten-fold greater for the Ra than the R state, then in a system where 20% of the receptors are spontaneously in the Ra state (L = 0.2), the saturation of the receptors with this ligand will increase the amount of Ra by a factor of 4 (20% to 80%). Similarly, if α 10 to 100 nM) using phage or yeast display technologies (Hoogenboom and Chames, 2000; Wittrup, 2001). These procedures rely on extensive combinatorial diversity and powerful selection methods to identify rare events that contribute to increased affinity. A purely rationally designed antibody has yet to be generated for clinical testing, and these approaches remain largely academic. A structure-activity relationship (SAR) discovery effort requires cocrystallization of candidate antibodies or their fragments with the target antigen and identifying and modifying the contact residues, a process that often takes longer than random combinatorial approaches. The availability of thousands of antibody variable-region sequences in databases, three-dimensional structural information of antibodies, and improved CDR modeling algorithms and reliable antigen docking programs may in the future enable computerbased design predictions to yield optimized antibodies.

Constant Region Engineering Effector functions Antibodies function to eliminate the antigens to which they bind. Cellular or viral antigens bound or opsonized by specific antibodies are eliminated by leukocyte phagocytosis. To this end, the Fc receptors on phagocytes recognize and bind the Fc domains of the opsonizing immune complex and trigger cellular engulfment of the target. Another clearance mechanism for cellular or viral antigens is complement-dependent lysis by way of the classical complement pathway. A third mechanism is antibody-dependent cell-mediated cytotoxicity (ADCC), which involves antibody binding to targets, subsequent leukocyte recruitment through Fc receptor interactions, and activation of cell-killing mechanisms, such as Fas-ligand and TNF-triggered apoptosisdriven release of reactive oxygen intermediates. Depending on the particular target and disease, it may be desirable to increase Fcmediated effector functions which are the basis for the efficacy of therapeutic antibodies such as Trastuzumab (anti-her2/Neu; Cooley et al., 1999) and Rituximab (anti-CD20; Manches et al., 2003). Opportunities exist to engineer increased activity into the Fc region. For example, if more efficient lysis of tumor cells is desired, an antibody with increased affinity for Fc receptors may be more efficacious (Clynes et al., 2000; Shields et al., 2001). Structurefunction studies have identified the critical Fc residues that can be altered to improve Fc receptor activation (Duncan and Winter, 1988; Duncan et al., 1988). Fc-mediated effector functions may be undesirable for certain therapeutic antibodies. Orthoclone (OKT3), a mouse monoclonal antibody to the CD3 antigen on T cells, was one of the first antibodies approved by the FDA, in 1986, for prevention of acute rejection in renal transplantation (Ackermann et al., 1988). The clinical utility of OKT3 is limited because of its immunogenicity and adverse side effects: within hours after receiving the first dose of OKT3, many patients experience fever, chills, rigors, tachycardia, tachypnea, diarrhea, nausea, and vomiting. In some patients, severe adverse effects such as pulmonary edema or even death are associated with OKT3 administration. These adverse effects are due to release of systemic cytokines, including IL-2, IL-6, TNF, IFNγ, and GM-CSF (Abramowicz et al., 1989). Cytokine release is induced by engagement of Fc receptors on nontarget cells by OKT3-antigen complexes (Ceuppens et al.,

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1985; Smith et al., 1986). Various strategies for reducing Fc-mediated toxicities have been attempted. For example, enzymatic cleavage of OKT3 antibody to an F(ab)2 to eliminate the Fc receptor binding domains markedly reduces cytokine release in vitro (Woodle et al., 1991a). In addition, changing the isotype of the OKT3 antibody to an isotype that binds poorly to Fc receptors attenuates cytokine release (Woodle et al., 1991b). The immunoglobulin residues involved in Fc receptor binding have been mapped by mutational analysis to the lower hinge region of the antibody (see Fig. 9.7.1; Duncan and Winter, 1988). Mutating the Fc receptor binding residues in this region of the OKT3 antibody reduces in vitro activation of cytokine release. Mutated OKT3 antibody displays minimal first-dose adverse effects in the clinic (Woodle et al., 1999).

Immune complex clearance The clearance of antibody bound to soluble antigens, termed immune complex clearance, is initiated by the binding to Fc receptors on tissue-resident cells of the reticuloendothelial system, with subsequent internalization and lysosomal degradation. Some large immune complexes can find and activate complement components in solution with subsequent binding to complement receptors on red blood cells. These red blood cells then transport the complex to the liver and spleen, where the antigen-antibody complexes are taken up by reticulocytes. The biological events that result from an antibody binding to an antigen constitute the effector functions of an immunoglobulin. The sites on the antibody molecule responsible for these effector functions, which have been mapped to specific amino acids in the Fc domains, provide targets for altering the fate of antigen-antibody complexes (Duncan and Winter, 1988; Duncan et al.,1988).

Pharmacokinetic considerations Normal human immunoglobulins have a wide range of serum half-lives ranging from 5 to 23 days, depending on the isotype. Most therapeutic antibodies, which are of the IgG class, typically have serum half-lives of 10 to 23 days. Strategies for altering the halflife of an antibody include reducing the size to a smaller antigen-binding fragment, which can shorten half-life if the size is below the threshold for renal clearance (∼50 kDa), or increasing half-life by chemical modification with polyethylene glycol (PEG). Antibody fragments such as Fab or F(ab)2 generally

retain antigen-binding functions but have much shorter half-lives in vivo, in the range of a few hours. Several studies have explored the interactions of IgG with the neonatal Fc receptor (FcRn, also called Bramble receptor) expressed in endothelial cells. IgG-FcRn binding has been proposed as the critical regulator of the long IgG half-life (Ghetie et al., 1997). The critical Fc residues of human IgG that contribute to FcRn binding have been mapped to the interface of the CH 2-CH 3 domain (Kim et al., 1999), allowing structure-activity analyses in mice. In one study, mutations to these critical Fc residues that reduced the affinity for FcRn resulted in shorter serum half-lives (Medesan et al., 1997). However, in another study several versions of a human IgG1 antibody were engineered to have higher affinity for FcRn. When tested in mice, these mutated antibodies were found to have lower serum concentrations than the wild-type antibody. (Dall’Acqua et al., 2002). Therefore, the relationship between the affinity for FcRn binding and serum half-lives of antibodies remains complex. Further investigations are required before half-life optimization via FcRn binding modulation can be recommended. Although the full-length size of an immunoglobulin contributes to its long half-life, this desirable trait must be balanced against the slower distribution and tissue penetration rates for IgG. This balance between half-life and tissue penetration can potentially affect the clinical efficacy of an antibody. For some therapeutic and diagnostic applications an appropriate balance between two design goals needs to be found. First, the antibody should have a sufficiently long half-life in circulation to achieve high enough concentrations with reasonable administration regimens. This is required because only a fraction of that total available drug will enter the target tissue. Second, the antibody should be small enough to increase the fraction entering deep tissues so as to deliver an effective therapeutic or diagnostic dose.

IMMUNOGENICITY In patients, the induction of antibodies resulting from an immune response to a therapeutic drug has been observed with most biologics. Induced antibodies can have a variety of effects ranging from loss of efficacy of the drug to severe adverse effects, such as the recently reported cases of pure red cell aplasia in patients treated with some forms of human erythropoetin (Schellekens, 2002). Finding strategies for reducing the immune response towards therapeutic antibodies has been the

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Overview on the Use of Therapeutic Antibodies in Drug Discovery

focus of antibody engineering for the last 20 years. Before the advent of modern technologies to generate fully human antibodies for human therapy, mouse antibodies to human targets were utilized. The most critical parameter for the immunogenicity of a biologic is the degree of foreignness of the biologic to the counterpart human protein, and early mouse-derived antibodies were highly immunogenic in patients. The generation of chimeric antibodies comprised of mouse variable and human constant regions was a logical next step to reduce the immunogenicity of therapeutic antibodies. Although no direct comparison between chimeric and mouse antibodies has been done in humans, in a mouse model chimeric antibodies with human variable and mouse constant regions were less immunogenic than fully human antibodies (Bruggemann et al., 1989). The next improvement in reducing immunogenicity came from transferring murine CDR sequences into a human antibody framework (reshaping/CDR grafting/humanization), first accomplished for a therapeutic antibody with CAMPATH-1 (Riechmann et al., 1988), and subsequently with a large number of murine antibodies, some of which have become approved drugs. Over time, many refinements have been proposed to the basic CDR grafting approach. For example, because the germline sequences of human and rodent CDRs are different, an acceptor human immunoglobulin candidate was chosen based on similarities in the canonical structures of the CDRs. This method, termed superhumanization, was applied to a mouse anti-CD28 antibody 9.3. Humanized 9.3 and original mouse versions were expressed as Fab fragments and tested in vitro. Superhumanized 9.3 antibody was biologically active in a bioassay with a modest decrease in binding affinity for CD28 (Tan et al., 2002). The clinical consequences of this procedure are unknown. The newer, fully human antibodies are expected to have reduced immunogenicity compared with murine, chimeric, or humanized antibodies. Another approach to reduce the immunogenicity of therapeutic antibodies is the elimination of certain sequences that are predicted to be immunogenic. Identification of these specific sequences can take two directions. In one approach, after a first generation biologic has been tested in humans and found to be unacceptably immunogenic, the B cell epitopes can be mapped and then altered to avoid immune detection. Other methods

attempt to predict and remove potential T cell epitopes. Briefly, for any protein to elicit an immune response, it must be proteolytically processed within an antigen-presenting cell into small peptides, which are then bound to MHC molecules and presented to T cells. In silico and in vitro methods have been developed to scan and identify the peptide sequences of biologic therapeutics with the potential to bind to MHC proteins, and then substitute these residues with nonbinding residues that will not cause a loss of function. Further information can be obtained from the Web sites of the providers of these technologies (Biovation, http://www.biovation.com; Epimmune Inc., http://www.epimmune.com; Genecor International, http://www.genencor.com/). An antibody engineered by this approach against a prostate-specific antigen has been tested in 75 patients, apparently without detectable immune response (http://www.biovation.com). Although conceptually intriguing, clinical experience with antibodies engineered by epitope prediction algorithms is limited, and further investigations are warranted to assess their possible advantages. When considering all of the factors that can affect the immunogenicity of a therapeutic antibody, it is also important to look beyond the protein sequence. Physicochemical properties of the antibody, such as the aggregation or oxidation states, can also be determinants for immunogenicity. Aggregated proteins have been recognized as potent stimulators of the immune system, and their presence in therapeutic antibody preparations should be minimized. Purity of the drug substance has also been postulated as contributing to immunogenicity. Indeed, the increasing understanding of the role of DNA impurities in triggering receptors of the Toll receptor family provide a new basis for investigations in this direction. For example, microbial unmethylated deoxycytidyldeoxyguanosine dinucleotide (CpG) fragments activate the immune system through Toll-like receptor 9 (Uhlmann and Vollmer, 2003). Lastly, the dose of administered antibody could be an important determinant in the incidence of immunogenicity. Higher doses may evoke reduced immune responses. For a humanized anti-TNF antibody administered into healthy subjects, no anti-idiotypic response was observed in the 2 mg/kg dose group, whereas all the recipients of the 0.1 mg/kg dose group had measurable human anti–humanized antibody (HAHA) titers (Stephens et al., 1995).

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EXPRESSION TECHNOLOGIES

Target Class

Therapeutic antibodies are complex molecules. Native antibodies comprise a heterodimer of two light and two heavy chains with a single N-glycosylation site in the heavy chain constant region. Proper assembly and glycosylation are important for antibody function. Therefore, full-length antibodies should be expressed in mammalian cells, or other cells capable of appropriate glycosylation. Antibody fragments have been expressed successfully in E. coli and yeast. The choice of expression system should be made based on the complexity of the molecule, the specific biological activity achieved with a given system, and the appropriate technical, intellectual property, and regulatory considerations for manufacturing at the required scale. Expression of therapeutic antibodies in a variety of systems has been reviewed recently (Humphreys and Glover, 2001; Sanna, 2002). Methods include expression in transgenic animals (Houdebine, 2002), in plants (Kathiuria et al., 2002; Stoger et al., 2002), in the more standard myeloma systems (Yoo et al., 2002), or in CHO cells (Miescher et al., 2000). The recovery and purification of therapeutic antibodies has also been reviewed (Fahrner et al., 2001). For the reasons discussed above, when designing purification schemes, attention should be paid to reduction of aggregation and DNA content, especially if bacterial systems are involved, to reduce immunogenicity. In each case, a defined set of assays needs to be applied to the purified final product to ascertain the purity and specific activity of the expressed antibody. This is especially important if reconstitution and refolding is part of the process (Lee and Kwak, 2003).

Antibodies are preferred over small molecules for blocking protein-protein interactions, and they are, therefore, often the drug class of choice if an antagonistic or agonistic therapeutic that recognizes an extracellular molecule is desired. These extracellular proteins include cytokines, hormones, growth factors, adhesion molecules, receptor molecules, and pathogenic microorganisms, including bacteria and viruses. Small-molecule drugs, which are generally designed as ligands to inhibit enzymatic activity or directed as antagonists or agonists towards receptors, present the only option so far for intracellular targets. Antibodies will not work in this setting because they are poorly internalized and unstable in the reducing environment of the cytoplasm. Despite these barriers, some laboratories have made efforts to develop technologies that may open up this target class for antibody therapeutics. These attempts have taken two directions: introduction of antibodies into cells using mostly viral vector peptides, and intracellular expression of antibodies. Nonantibody protein molecules have been transduced into cells, achieving biologically active concentrations (Kabouridis et al., 2002). In contrast, studies with antibodies achieved only limited intracellular levels, even in model tissue culture experiments (Zhao et al., 2001). As an alternative strategy for intracellular antibody delivery, the chemical modification of antibody molecules by derivatization of surface carboxyl groups (Hong et al., 2000) has been explored. Neither approach will be commercially viable any time in the near future. For review, see Donaldson (2003) or Prossnitz (2004). The alternative strategy for targeting intracellular molecules with antibodies is based on expression of antibodies as “intrabodies” inside cells (Marasco, 2001; Cohen, 2002). However, the utility of this approach is limited because the antibody gene must be delivered into a cell by gene therapy techniques (Bai et al., 2003) or by ex vivo transduction into somatic cells and reintroduction into the body. Neither approach is expected to make a major contribution to therapeutic applications soon. Nevertheless, antibody delivery into cells and intracellular expression of intrabodies will be of interest to researchers for target validation in cellular or animal model systems.

HOW DO THERAPEUTIC ANTIBODIES COMPARE WITH TRADITIONAL DRUGS? Therapeutic antibodies are routinely used by physicians for the treatment of a variety of disorders, including cancer, rheumatoid arthritis, infections, and transplant rejection. Approximately 25% of drugs in development are based on antibodies (Glennie and van de Winkel, 2003; Brekke and Sandlie, 2003). As experience with this important class of drugs has increased over the last decade, it has become possible to distinguish between the respective advantages of antibodies and small molecules.

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Selectivity In contrast to traditional chemical drugs, antibodies have a significantly larger footprint on the target molecule. This increases the number of specific intermolecular interactions between drug and target for an antibody compared with a small molecule, resulting in better discrimination between possible antigens. Therefore, selection of highly specific antibodies is routine, whereas achieving similar specificity with small molecules remains a primary drug discovery challenge. This advantage for antibodies becomes especially important in cases where the target protein is a member of a closely related gene family. A very high degree of selectivity can be achieved with an antibody, whereas this is challenging or impossible with a small molecule. An immediate clinical advantage of the exquisite selectivity of antibodies is the minimal off-target toxicity seen with most antibodies.

Route of Administration As with all proteins, antibodies must be administered by subcutaneous or intravenous routes. Oral bioavailability is a major advantage for small molecules; improved delivery technologies will be required for proteins to achieve similar levels of bioavailability (Langer, 2001). These differences become especially important in chronic indications where the convenience of oral drug treatment and compliance of patients must be considered. Therefore, low-volume self-administration, and convenient dose and schedule are critical aspects in the design of antibodies. Appropriate pharmacokinetic/pharmacodynamic (PK/PD) studies may become a routine part of the evaluation process for late-stage antibody candidates.

Intellectual Property Issues

Overview on the Use of Therapeutic Antibodies in Drug Discovery

Much has been written about differences in intellectual property treatment between therapeutic antibodies and chemical drugs. As with any discovery project, a comprehensive patent assessment is recommended for both the desired target antigen and the selected antibody generation and expression technologies. Unlike the patent landscape for small molecule drugs, in which blanket claims are not granted prophetically, broad patents for antibodies with specificity for a defined target are customarily granted without the need for reduction to practice. A recent trend has been to include antibodies as part of even broader claims to functionally defined binding

molecules. Broadly granted antibody claims may, on one hand, appear to be a disadvantage, but a valid claim can also provide far-reaching exclusivity for an antibody-based therapeutic. The different technologies that are applied to generate and produce recombinant antibodies are covered by a large number of increasingly complex patents, with numerous companies and inventors owning various components thereof. Although this situation has augmented the difficulty for drug companies to manage intellectual property obligations, recently it has also forced the handful of key technology providers, who often require access to enabling third-party intellectual property as well, to enter into cross-licensing arrangements. This has created reasonably predictable discovery and development paths for their customers.

Differences in the Drug Discovery Approach The key difference between therapeutic antibodies and small-molecule drugs is that antibodies provide a more predictable, faster route to the clinic, and therefore may be the first choice to clinically validate a novel target. Small molecules can be fast followers with their advantage of oral bioavailability. The property profile of a clinical drug candidate includes criteria such as specificity, affinity, potency, pharmacokinetics, tissue distribution, solubility, metabolism, and nonmechanistic toxicity. In spite of recent advancements using in silico techniques, for small molecules these properties remain largely unpredictable—individually and more so as a composite. A low-molecular-weight (LMW) drug assembles in a relatively small structural space all pharmacophoric elements and other structural moieties, which typically are interdependent in determining the overall compound profile. Structural modifications to alter one property regularly impact other parameters as well, often with deleterious results. This usually results in the need to simultaneously pursue a multiparameter optimization process. In contrast, a larger antibody molecule has independent functional domains, and this domain structure is the basis for important advantages in the drug discovery process as outlined above. Like traditional drugs, the desired characteristics of the therapeutic antibody must be defined up front. For antibodies, these include affinity, potency, antigen/ligand on- and offrate, specificity, half-life, and immune effector functions. Importantly, because the structural correlates to these properties reside in different

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domains, they can be predictably manipulated and refined as relatively independent parameters within the overall antibody structure.

Lead identification Lead identification for therapeutic antibodies differs between the available antibody technologies. In the case of the in vivo approaches, the screening of initial antibody leads involves either ELISA techniques or functional assays (Green, 1999). In the case of in vitro approaches, lead selection from phage or yeast libraries involves affinity selection steps on the target antigen (Kretzchmar and von Ruden, 2002; O’Connell et al., 2002). Traditional chemical drug discovery identifies initial leads by experimental approaches, including high-throughput screening (Bajorath et al., 2003), cross-target analysis of combinatorial libraries designed around target class–specific privileged structural motifs (Klabunde and Hessler, 2002), and SAR by NMR (Shuker et al., 1996; Vogtherr and Fiebig, 2003). In addition, frequently utilized ligand-based design approaches include virtual screening (Jenkins et al., 2003), pharmacophore modeling (Hecker et al., 2002; Ghose et al., 2001), and quantitative structureactivity relationship screens (Winkler, 2002). Although these approaches utilize the target protein itself in experimental-based screening or the three-dimensional structure in virtual screening, the drug screening is typically not based on the direct selection and isolation of drug candidates by binding to the drug target. However, efforts toward affinity selection of compounds from combinatorial libraries are underway (Jiang and Lee, 2001).

Lead optimization Conceptually, the biggest difference between traditional chemical drugs and therapeutic antibodies lies in the area of lead optimization. In traditional drug discovery the stepwise and sometimes tedious elucidation of structure-activity relationships helps to correlate specific residues with effects on the test system. In addition, structure-aided drug discovery programs benefit from the identification of the interaction sites between a small molecule ligand and specific amino acid residues of the target protein. However, a given moiety of a LMW drug will potentially affect various properties, such as binding to its target, absorption, distribution, metabolism, and halflife of the molecule. Optimization of chemical entities is therefore always multidimensional, meaning, for example, that any improvement

in specificity may be accompanied by a concomitant decrease in other desirable properties such as absorption or half-life. Structural variation to address these new deficits are again likely to affect specificity. Because multiple iterations are required to optimize a small molecule, this process can prolong the traditional drug discovery approach, increasing costs, and carries the risk that a drug with the desired characteristics may never be discovered. Even if a compound meets a set of design criteria, it remains inherently impossible to screen against all possible off-target effects. Thus, the assessment of specificity is limited to the panel of the counter-screen assays that is available. Therefore, nonmechanistic toxicity is more frequently observed with small molecules than with antibodies. To address some of these complications, new methods for prediction of half-life and toxicity of smallmolecule drugs are being developed (Van De Waterbeemd and Gifford, 2003). In contrast to lead optimization of LMW drugs, antibodies have a well-understood domain structure with defined and separate functional regions, such as the variable region (Fv), that incorporates structures conferring specificity and affinity, and the Fc domain that is responsible for half-life and immune effector functions. This domain separation has important consequences for drug discovery because lead optimization can be done in one dimension. For example, introducing extensive variations in the CDR3 regions of heavy or light variable region chains to increase affinity will not affect the half-life or effector functions of the antibody, whereas designing changes to the effector functions by switching isotypes or mutating Fc residues will typically not affect the affinity. Clearly, this one-dimensional optimization is more straightforward and predictable than the multidimensional optimization of small-molecule lead compounds. A recent review (Reichert, 2001) has confirmed the shorter development time and lower development costs for therapeutic antibodies compared with traditional chemical drugs. And unlike small molecules, for which there is inherent uncertainty regarding nontarget interactions, antibodies are exquisitely specific and cannot cross cell membranes. Therefore, they are typically devoid of nonmechanistic toxicity and have significantly lower discovery and development risks. It should be apparent that, for a given project, there may be exceptions to this general experience. Therefore, at appropriate key milestones of the lead optimization process,

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any lead antibody candidate should be profiled for its relevant functional properties. Also, the general lack of nonmechanistic toxicity seen with antibody therapeutics does not remove the need for meaningful and appropriate safety testing. This testing is necessary in the development phase to assure the safety of any new therapeutic antibody (Cavagnaro, 2002).

SUMMARY Therapeutic antibodies are an exciting class of potential pharmaceuticals. Antibodies have been approved for the treatment of different cancers, infectious disease, inflammatory disease, and transplant rejection, thus validating the promise of antibody therapy in a variety of clinical settings. Project-specific requirements can be defined precisely, and new discovery, expression, and manufacturing technologies now enable many of these design concepts to be realized. In some cases, new combinations of existing techniques are required. Appropriate in vitro and, possibly, in vivo assays are required to determine success in each design phase. Antibody generation is also typically less complex and less time-consuming than traditional drug discovery with small molecules. Therefore, antibodies are often the preferred approach for initial preclinical and clinical validation of new therapeutic principles. Although in some cases antibodies may be replaced by orally active agents, for some targets and indications antibodies will remain the therapy of choice due to their specific safety and efficacy features.

Literature Cited Abramowicz, D., Schandene, L., Goldman, M., Crusiaux, A., Vereerstraeten, P., De Pauw, L., Wybran, J., Kinnaert, P., Dupont, E., and Toussaint, C. 1989. Release of tumor necrosis factor, interleukin-2, and gamma-interferon in serum after injection of OKT3 monoclonal antibody in kidney transplant recipients. Transplantation 47:606-608. Ackermann, J.R., LeFor, W.M., Kahana, L., Weinstein, S., Shires, D.L., Tardif, G., and Baxter, J. 1998. Four-year experience with exclusive use of cytomegalovirus antibody (CMV-Ab)-negative donors for CMV-Ab-negative kidney recipients. Transplant Proc. 20:469-471.

Overview on the Use of Therapeutic Antibodies in Drug Discovery

Adams, G.P., Schier, R., McCall, A.M., Simmons, H.H., Horak, E.M., Alpaugh, R.K., Marks, J.D., and Weiner, L.M. 2001. High affinity restricts the localization and tumor penetration of single-chain fv antibody molecules. Cancer Res. 61:4750-4755. Bai, J., Sui, J., Zhu, R.Y., Tallarico, A.S., Gennari, F., Zhang, D., and Marasco, W.A. 2003. Inhibition of Tat-mediated transactivation and HIV-1

replication by human anti-hCyclinT1 intrabodies. J. Biol. Chem. 278:1433-1442. Barbas, C.F. 3rd., Bain, J.D., Hoekstra, D.M., and Lerner, R.A. 1992. Semisynthetic combinatorial antibody libraries: A chemical solution to the diversity problem. Proc. Natl. Acad. Sci. U.S.A. 89:4457-4461. Boder, E.T., Midelfort, K.S., and Wittrup, K.D. Directed evolution of antibody fragments with monovalent femtomolar antigen-binding affinity. 2000. Proc. Natl. Acad. Sci. U.S.A. 97:10701-10705. Brekke, O.H. and Sandlie, I. 2003. Therapeutic antibodies for human diseases at the dawn of the twenty-first century. Nat. Rev. Drug Discov. 2:52-62. Bruggemann, M., Winter, G., Waldmann, H., and Neuberger, M.S. 1989. The immunogenicity of chimeric antibodies. J. Exp. Med. 170:21532157. Budinger, L. and Hertl, M. 2000. The skin as target for IgE-mediated allergic reactions: Immunologic mechanisms in hypersensitivity reactions to metal ions: An overview. Allergy (CPH) 55:108-115. Cavagnaro, J.A. 2002. Preclinical safety evaluation of biotechnology-derived pharmaceuticals. Nat. Rev. Drug Discov. 1:469-475. Ceuppens, J.L., Bloemmen, F.J., and van Wauwe, J.P. 1985. T cell unresponsiveness to the mitogenic activity of OKT3 antibody results from a deficiency of monocyte Fc gamma receptors for murine IgG2a and inability to cross-link the T3Ti complex. J. Immunol. 135:3882-3886. Cohen, P.A. 2002. Intrabodies. Targeting scFv expression to eukaryotic intracellular compartments. Methods Mol. Biol. 178:367-378. Cooley, S., Burns, L.J., Repka, T., and Miller, J.S. 1999. Natural killer cell cytotoxicity of breast cancer targets is enhanced by two distinct mechanisms of antibody-dependent cellular cytotoxicity against LFA-3 and HER2/neu. Exper. Hematol. 27:1533-1541. Clackson, T., Hoogenboom, H.R., Griffiths, A.D., and Winter, G. 1991. Making antibody fragments using phage display libraries. Nature 352:624-628. Clynes R.A., Towers T.L., Presta L.G., and Ravetch J.V.. 2000. Inhibitory Fc receptors modulate in vivo cytoxicity against tumor targets. Nat. Med. 6:443-446. Dall’Acqua, W.F., Woods, R.M., Ward, E.S., Palaszynski, S.R., Patel, N.K., Brewah, Y.A., Wu, H., Kiener, P.A., and Langermann, S. 2002. Increasing the affinity of a human IgG1 for the neonatal Fc receptor: biological consequences. J. Immunol, 169:5171-5180. de Wildt, R.M., Mundy, C.R., Gorick, B.D., and Tomlinson, I.M. 2000. Antibody arrays for highthroughput screening of antibody-antigen interactions. Nat. Biotechnol. 18:989-994. Donaldson, J.G. 2004. Multiple roles for Arf6: sorting, structuring, and signaling at the plasma membrane. J. Biol. Chem. 278:41573-41576.

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Houdebine, L.M. 2002. Antibody manufacture in transgenic animals and comparisons with other systems. Curr. Opin. Biotechnol. 13:625-629. Humphreys, D.P. and Glover, D.J. 2001. Therapeutic antibody production technologies: Molecules, applications, expression and purification. Curr. Opin. Drug Discov. Devel. 4:172-185. Jeffris R., Lund J., and Pound J.D., 1998. IgG-Fcmediated effector functions: molecular definition of interaction sites for effector ligands and the role of glycosylation. Immunol. Rev. 163:5976 Jenkins, J.L., Kao, R.Y., and Shapiro, R. 2003. Virtual screening to enrich hit lists from highthroughput screening: a case study on smallmolecule inhibitors of antiogenin. Proteins 50:81-93. Jespers, L.S., Roberts, A., Mahler, S.M., Winter, G., and Hoogenboom, H.R. 1994. Guiding the selection of human antibodies from phage display repertoires to a single epitope of an antigen. Biotechnology 12:899-903. Jiang, Y., and Lee, C.S. 2001 On-line coupling of hollow fiber membranes with electrospray ionization mass spectrometry for continuous affinity selection, concentration and identification of small-molecule libraries. J. of Mass Spectrometry 36:664-669. Kabouridis, P.S., Hasan, M., Newson, J., Gilroy, D.W., and Lawrence, T. 2002. Inhibition of NFkappa B activity by a membrane-transducting mutant of I kappa B alpha. J. Immunol. 169:2587-2593. Kim, J.K., Firan, M., Radu, C.G., Kim, C.H., Ghetie, V., and Ward, E.S. 1999. Mapping the site on human IgG for binding of the MHC class 1-related receptor, FcRn. Eur. J. Immunol. 29:2819-2825. Klabunde, T. and Hessler, G. 2002. Drug design strategies for targeting G-protein-coupled receptors. Chemobiochem. 3:928-944. Kobayashi, N., Shibahara, K., Ikegashira, K., Shibusawa, K., Goto, J. 2002. Single-chain Fv fragments derived from an anti-11-deoxycortisol antibody. Affinity, specificity, and idiotype analysis. Steroids 67:733-742. K¨ohler, G. and Milstein, C. 1975. Continuous cultures of fused cells secreting antibody of predefined specificity. Nature 256:495-497. Krebs, B., Rauchenberger, R., Reiffert, S., Rothe, C., Tesar, M., Thomassen, E., Cao, M., Dreier, T., Fischer, D., Hoss, A., Inge, L., Knappik, A., Marget, M., Pack, P., Meng, X.Q., Schier, R., Sohlemann, P., Winter, J., Wolle, J., and Kretzschmar, T. 2001. High-throughput generation and engineering of recombinant human antibodies. J. Immunol. Methods 254:6784. Kretzschmar, T. and von Ruden, T. 2002. Antibody discovery: phage display. Curr. Opin. Biotechnol. 13:598-602. Langer, R. 2001. Drug delivery. Drugs on target. Science 293:58-59.

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Lee, M.H. and Kwak, J.W. 2003. Expression and functional reconsstitution of a recombinant antibody (Fab’) specific for human apolipoprotein B-100. J. Biotechnol. 101:189-198. Manches, O., Lui, G., Chaperot, L., Gressin, R., Molens, J., Jacob, M., Sotto, J., Leroux, D., Bensa, J., Plumas, J. 2003. In vitro mechanisms of action of rituximab on primary non-Hodgkin lymphomas. Blood 101:949-954. Marasco, W.A. Intrabodies as antiviral agents. 2001. Curr. Top. Microbiol. Immunol. 260:247-270. Marks, J.D., Hoogenboom, H.R., Bonnert, T.P., McCafferty, J., Griffiths, A.D., and Winter, G. 1991. By-passing immunization. Human antibodies from V-gene libraries displayed on phage. J. Mol. Biol. 222:581-97. Mattheakis, L.C., Bhatt, R.R., and Dower, W.J. 1994. An in vitro polysome display system for identifying ligands from very large peptide libraries. Proc. Natl. Acad. Sci. U.S.A. 91:90229026. McCafferty, J., Griffiths, A.D., Winter, G., and Chiswell, D.J. 1990. Phage antibodies: filamentous phage displaying antibody variable domains. Nature 348:552-554. Medesan, C., Matesoi, D., Radu, C., Ghetie, V., and Ward, E.S. 1997. Delineation of the amino acid residues involved in transcytosis and catabolism of mouse IgG1. J. Immunol. 158:2211-2217. Mendez, M.J., Green, L.L., Corvalan, J.R., Jia, X.C., Maynard-Currie, C.E., Yang, X.D., Gallo, M.L., Louie, D.M., Lee, D.V., Erickson, K.L., Luna, J., Roy, C.M., Abderrahim, H., Kirschenbaum, F., Noguchi, M., Smith, D.H., Fukushima, A., Hales, J.F., Klapholz, S., Finer, M.H., Davis, C.G., Zsebo, K.M., and Jakobovits, A. 1997. Functional transplant of megabase human immunoglobulin loci recapitulates human antibody response in mice. Nat. Genet. 15:146-156. Miescher, S., Zahan-Zabal, M., DeJesus, M., Moudry, R., Fisch, I., Vogel, M., Kobr, M., Imboden, M.A., Kragten, E., Bichler, J., Mermod, N., Stadler, B.M., Amstutz, H., and Wurm, F. 2000. CHO expression of a novel human recombinant igG1 anti-RhD antibody isolated by phage display. Br. J. Haematol. 111:157-166. Morrison, S.L., Johnson, M.J., Herzenberg, LA., and Oi, V.T. 1984. Chimeric human antibody molecules: Mouse antigen-binding domains with human constant region domains. Proc. Natl. Acad. Sci. U.S.A. 81:6851-6855. O’Connell, D., Becerril, B., Roy-Burman, A., Dawas, M., and Marks, J.D. 2002. Phage versus phagemid libraries for generation of human monoclonal antibodies. J. Mol. Biol. 321:49-56. Orlandi, R., Gussow, D.H., Jones, P.T. and Winter, G. 1989. Cloning immunoglobulin variable domains for expression by the polymerase chain reaction. Proc. Natl. Acad. Sci. U.S.A. 86:38333837. Overview on the Use of Therapeutic Antibodies in Drug Discovery

Paul,W.E. (ed.) 1999. Fundamental Immunology, 4th ed. Lippincott-Raven, New York.

Prossnitz ER. 2004 Novel roles for arrestins in the post-endocytic trafficking of G protein-coupled receptors. Life Sci. 75:893-899. Reichert, J.M. 2001. Monoclonal antibodies in the clinic. Nat. Biotechnol. 19:819-822. Riechmann, L., Clark, M., Waldmann, H., and Winter, G. 1988. Reshaping human antibodies for therapy. Nature 332:323-327. Roberts, R.W. and Szostak, J.W., 1997. RNApeptide fusions for the in vitro selection of peptides and proteins. Proc. Natl. Acad. Sci. U.S.A. 94:12297-12302. Roguska, M.A., Pedersen, J.T., Keddy, C.A., Henry, A.H., Searle, S.J., Lambert, J.M., Goldmacher, V.S., Blattler, W.A., Rees, A.R., and Guild, B.C. 1994. Humanization of murine monoclonal antibodies through variable domain resurfacing. Proc. Natl. Acad. Sci. U.S.A. 91:969-973. Sanna, P.P. 2002. Expression of antibody Fab fragments and whole immunoglobulin in mammalian cells. Methods Mol. Biol. 178:389-395. Sblattero, D. and Bradbury, A. 1998. A definitive set of oligonucleotide primers for amplifying human V regions. Immunotechnology 3:271-278. Schellekens, H. 2002. Bioequivalence and the immunogenicity of biopharmaceuticals. Nat. Rev. Drug Discov. 1:457-462. Shields, R.L, Namenuk, A.K, Hong, K., Meng, Y.G., Rae, J., Briggs, J., Xie, D., Lai, J., Stadlen, A., Li, B., Fox, J.A., and Presta, L.G. 2001. High resolution mapping of the binding site on human IgG1 for Fc gamma RI, Fc gamma RII, Fc gamma RIII, and FcRn and design of IgG1 variants with improved binding to the Fc gamma R. J. Biol. Chem. 276:6591-6604. Shuker, S.B., Hajduk, P.J., Meadows, R.P., and Fesik, S.W. 1996. Discovering high-affinity ligands for proteins: SAR by NMR. Science 274:15311534. Skerra, A. and Pluckthun, A. 1988. Assembly of a functional immunoglobulin Fv fragment in Escherichia coli. Science 240:1038-1041. Smith, G.P. 1985. Filamentous fusion phage: novel expression vectors that display cloned antigens on the virion surface. Science 228:1315-137. Smith, K.G., Austyn, J.M., Hariri, G., Beverley, P.C., and Morris, P.J. 1986. T cell activation by anti-T3 antibodies: comparison of IgG1 and IgG2b switch variants and direct evidence for accessory function of macrophage Fc receptors. Eur. J. Immunol. 16:478-486. Stephens, S., Emtage, S., Vetterlein, O., Chaplin, L., Bebbington, C., Nesbitt, A., Sopwith, M., Athwal, D., Novak, C., and Bodmer, M. 1995. Comprehensive pharmacokinetics of a humanized antibody and analysis of residual anti-idiotypic responses. Immunology 85:668-674. Stoger, E., Sack, M., Fischer, R., and Christou, P. 2002. Planatibodies: applications, advantages and bottlenecks. Curr. Opin. Biotechnol. 13:161166.

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Tan, P., Mitchell, D.A., Buss, T.N., Holmes, M.A., Anasetti, C., and Foote, J. 2002. “Superhumanized” antibodies: Reduction of immunogenic potential by complementarity-determining region grafting with human germline sequences: application to an anti-CD28. J. Immunol. 169:11191125. Taylor, L.D., Carmack, C.E.., Schramm, S.R., Mashayekh, R., Higgins, K.M., Kuo, C.C., Woodhouse, C., Kay, R.M., and Lonberg, N. 1992. A transgenic mouse that expresses a diversity of human sequence heavy and light chain immunoglobulins. Nucleic Acids Res. 20:62876295. Uhlmann, E. and Vollmer, J. 2003. Recent advances in the development of immunostimulatory oligonucleotides. Curr. Opin. Drug Disc. & Devel. 6:204-217. Van De Waterbeemd, H. and Gifford, E. 2003. ADMET in silico modelling: Towards prediction paradise? Nat. Rev. Drug. Discov. 2:192-204. Vaughan, T.J., Williams, A.J., Pritchard, K., Osbourn, J.K., Pope, A.R., Earnshaw, J.C, McCafferty, J., Hodits, R.A., Wilton, J., and Johnson, K.S. 1996. Human antibodies with subnanomolar affinities isolated from a large nonimmunized phage display library. Nat. Biotechnol. 14:309-314. Vogtherr, M. and Fiebig, K. 2003. NMR-based screening methods for lead discovery. Exper. Suppl. 93:183-202. Winkler, D.A. 2002. The role of quantitative structure–activity relaltionships (QSAR) in biomolecular discovery. Brief Bioinform. 3:7386. Wittrup, K.D. 2001. Protein engineering by cellsurface display. Curr. Opin. Biotechnol. 12:395399. Woodle, E.S., Xu, D., Zivin, R.A., Auger, J., Charette, J., O’Laughlin, R., Peace, D., Jolliffe, L.K., Haverty, T., Bluestone, J.A., and Thistlethwaite, J.R. Jr. 1999. Phase I trial of a humanized, Fc receptor nonbinding OKT3 antibody, huOKT3gamma1 (Ala-Ala) in the treatment of acute renal allograft rejection. Transplantation 68:608-616. Woodle, E.S., Thistlethwaite, J.R., Ghobrial, I.A., Jolliffe, L.K., Stuart, F.P., and Bluestone, J.A. 1991. OKT3 F(ab’)2 fragments–retention of the immunosuppressive properties of whole antibody with marked reduction in T cell activation and lymphokine release. Transplantation 52:354-360. Woodle, E.S., Thistlethwaite, J.R. Jr., Jolliffe, L.K., Fucello, A.J., Stuart, F.P., and Bluestone, J.A. 1991 Anti-CD3 monoclonal antibody therapy: An approach toward optimization by in vitro analysis of new anti-CD3 antibodies. Transplantation 52:361-368. Xu, L., Aha, P., Gu, K., Kuimelis, R.G., Kurz, M., Lam, T., Lim, A.C., Liu, H., Lohse, P.A., Sun, L., Weng, S, Wagner, R.W., and Lipovsek, D.

2002. Directed evolution of high-affinity antibody mimics using mRNA display. Chem. Biol. 9:933-942. Yoo, E.M., Chintalacharuvu, K.R., Penichet, M.L, and Morrison, S.L. 2002. Myeloma expression systems. J. Immunol. Methods 261:1-20. Zhao, Y., Lou, D., Burkett, J., and Kohler, H. 2001. Chemical engineering of cell penetrating antibodies. J. Immunol. Methods 254:137-145.

Internet Resources http://www.uspto.gov/main/patents.htm U.S. Government patent office Web site. http://ep.espacenet.com/ European patent office Web site. http://www.abgenix.com Abgenix Web site. Developer of human antibody transgenic mice. http://www.medarex.com Medarex Web site. Developer of human antibody transgenic mice. http://www.tcmouse.com Kirin Web site. Developer of human antibody transgenic mice. http://www.cambridgeantibody.com Cambridge Antibody Technology Web site. Developer of antibody therapeutics using phage display technology. http://www.domantis.com Domantis Web site. Developer of antibody therapeutics using phage display technology. http://www.morphosys.com Morphosys AG Web site. Developer of antibody therapeutics using phage display technology. http://www.dyax.com Dyax Web site. Developer of antibody therapeutics using phage display technology. http://www.biosite.com Biosite Web site. Developer of antibody therapeutics using phage display technology. http://www.biovation.com Biovation Web site; technology provider. http://www.epimmune.com Epimmune Web site; technology provider. http://www.genencor.com Genencor Web site; technology provider.

Contributed by Michael Roguska, Zehra Kaymakcalan, and Jochen Salfeld Abbott Bioresearch Center Worcester, Massachusetts Drug Discovery Technologies

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Approaches to the Molecular Modeling of 7-Transmembrane Helical Receptors

UNIT 9.8

Background The G protein-coupled receptors (GPCRs) form a large subgroup of the general class of integral membrane-bound proteins, a class which also includes the ion channels and transporter proteins. By far the most important protein target class of interest to the pharmaceutical industry, GPCRs represent the sites of action of up to 50% of all currently marketed drugs. GPCRs consist of seven amphipathic α-helical transmembrane (TM) segments, three cytoplasmic and three extracellular loop regions, an extracellular N-terminal domain (NTD), and a cytoplasmic C-terminal region. Their TM helices are predicted to contain a minimum of 20 amino acid residues, although the third TM helix (TM3) of bovine rhodopsin is known to have 36 residues (Polczewski et al., 2000). Because the seven TM helices of GPCRs pack together to form a bundle, the common alternative name for this family is 7TM receptors. The bundle configuration led to the proposal of structural similarity between GPCRs and bacteriorhodopsin (BR). The structure of BR, which was solved by cryo-electron diffraction, contains a 7-helix bundle (Henderson et al., 1990). While BR is not a GPCR, the similarities in topography led to its widespread use as a template in early GPCR modeling work. Classification In the twenty years since the first GPCR sequences were published, the number of known receptors has grown rapidly and is now in excess of 1000. These receptors are triggered by light, a wide variety of small molecules (e.g., neurotransmitters, prostaglandins, nucleotides and nucleosides, fatty acids, amino acids), most peptide hormones, and even larger proteins such as glycoprotein hormones and chemokines. GPCRs mediate the senses of vision, taste, and smell. As a group, GPCRs represent the primary mechanism for transmitting signals to cells. The GPCRs derive their name from the fact that they are bound on the cytoplasmic side to a G-protein, a heterotrimeric assemblage. The Gα subunit of this trimer binds the nucleosides GDP or GTP, depending on its state. When the GPCR is stimulated, the Gα subunit becomes activated, exchanges GDP for GTP and breaks away from the βγ subunits to stimulate one of several secondary messenger systems in the cell. To complete the cycle, the Gα subunit hydrolyzes GTP back to GDP before recoupling with the βγ assembly and the GPCR. Although there is often a lack of sequence identity, the various GPCR domains contain one or more highly conserved residues. For example TM2 contains an aspartate, while TM6 and TM7 contain prolines (see Figure 9.8.3). Because these residues are also present in the opsins, the group is referred to as the rhodopsin family or family A receptors, and it is the largest subdivision of GPCRs. The importance of this classification will become more apparent later in this unit. A second group of receptors, commonly known as the secretin family or family B GPCRs, show high sequence identity to each other but possess few, if any, of the TM domain motifs of family A receptors. These receptors, which are stimulated by peptide hormones, are characterized by a larger NTD of 120 to 150 amino acids. The domain contains six cysteines that form three disulfide bonds. This family includes the secretin, glucagon, calcitonin, and parathyroid receptors. The peptide hormones acting at these Drug Discovery Technologies Contributed by Frank E. Blaney Current Protocols in Pharmacology (2006) 9.8.1-9.8.41 C 2006 by John Wiley & Sons, Inc. Copyright


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sites are thought to bind largely in the N-terminal domain, although it is likely that some component of the binding occurs in the 7TM bundle. Recently a solution NMR structure of a corticotropin releasing factor (CRF) receptor NTD has been solved (Grace et al., 2004). The family C receptors represent a third subgroup of therapeutically important GPCRs. The main members of this group are the various metabotropic glutamate sequences (eight in total), the GABA-B and calcium-sensing receptors. This group is characterized by a very large (900 to 1200 residues) N-terminal ligand-binding domain. The structure of one of these NTDs (rat MgluR-1) has been solved by crystallography (Kunishima et al., 2000) and shown to be, as originally predicted from modeling studies, structurally similar to the bacterial periplasmic binding proteins (O’Hara et al., 1993). This large N-terminal domain has a bilobal structure that remains open in the inactive state. When the agonist ligand binds, the structure closes, causing an allosteric conformational change in the TM domain. The 7TM bundle of family C receptors is generally more hydrophobic than those associated with families A and B. Besides these three, other distinctive GPCR families (e.g., the yeast pheromone and the slime mold cyclic AMP receptors) have also been described. These families are not, however, found in higher species. Other subfamilies gaining importance are the Frizzled, vomeronasal, and taste receptors. While the literature on GPCRs is vast, there is also an excellent Web site (GPCRDB at http://www.gpcr.org/7tm; Horn et al., 1998) which provides details about sequence, classification, and alignment, some three-dimensional models, site-directed mutagenesis (SDM) data, articles, and original literature references.

History of GPCR Modeling Early modeling attempts The first three-dimensional (3D) computational models of GPCRs appeared in the early 1990s (Findlay and Eliopoulos, 1990; Humblet and Mirzadegan, 1992). Two main schools of thought were behind these models. In one, the models were based on the assumed structural similarity with bacteriorhodopsin (BR). Although there is no sequence identity between BR and the GPCRs, and BR is not G protein-coupled, it is a membrane-bound 7TM helical bundle with an established structure. Those supporting this concept used “homology” modeling techniques to generate 3D structures of the TM bundle. While Findlay et al. were the first to describe a GPCR model of ovine rhodopsin (Findlay and Eliopoulos, 1990), the two pioneering papers from Hibert’s group (Trumpp-Kallmeyer et al., 1991, 1992) described models of the catecholamine neurotransmitter receptors with bound agonist ligands. These binding modes were particularly appealing because they were in agreement with the published SDM work of that time, which implicated the aspartate on TM3 and two serines on TM5 as key binding residues.

Molecular Modeling of 7TM Helical Receptors

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In the second school of thought, several groups used the lack of homology between BR and GPCRs as an argument for developing some de novo helix packing methodology. The TM domains, which are generally more conserved than the loops, were assigned by hydropathy plots and multiple sequence alignments. Fourier transform techniques based on the periodicity of helices were used to derive hydrophobic moments that indicate that the face of the helix is oriented towards the membrane. Using a conservation value applied to multiple sequence alignments, Donnelly et al. used a Fourier transform to define a moment describing the inward-facing side of the helix. While all these methods could determine the helices and their orientation in the plane of the bilayer (Donnelly et al., 1993), they could not deal with the inter-helical tilts that were usually generated from some subsequent molecular dynamics simulation stage. By studying the tilts in the structures of integral membrane proteins available at the time, Vriend (see http://www.gpcr. org/7tm/articles/model.html) concluded that the tilts in membrane bundles are similar to the tilts in globular proteins. Current Protocols in Pharmacology

These two theoretical approaches represented the first generations of GPCR models. ◦ The next breakthrough came in 1993 when Schertler et al. published a 9-A electron diffraction map of rhodopsin (Schertler et al., 1993). From these data it became obvious that there are differences in helix packing between rhodopsin and BR. This new information led many to re-evaluate their models. There are numerous examples of these third generation models (Bikker et al., 1998), and many of them were used with varying degrees of success in drug design. It was during this period that GPCR modeling was firmly established as an important laboratory technique in both industry and academia.

Fourth generation models Prior to the eventual publication of a crystal structure, the most important breakthrough in ◦ GPCR modeling came in 1997 when Schertler published a 6-A electron diffraction map of frog rhodopsin (Unger et al., 1997). The most important aspect of this publication was that ◦ the data were collected at 4-A slices through the plane of the membrane. This meant that the change in the density peaks could be followed through the membrane. Although these data were still not at atomic resolution, they gave direct information on the interhelical tilts of the TM bundle. Quickly following this, Baldwin described a C-alpha model for GPCRs based on the helical axes derived from Schertler’s density data (Baldwin et al., 1997). While the placement of TM6 on the data was fairly obvious because of its large proline-induced kink, the other helices were open to different interpretations. By using a large amount of published biophysical data along with the density-derived helical axes, a

Figure 9.8.1 The X-ray crystal structure of bovine rhodopsin. (Brookhaven PDB code 1F88). For the color version of this figure go to http://www.currentprotocols.com.

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number of GPCR receptor models have been generated and successfully used in various structure-based drug design programs (Bromidge et al., 2000, Blaney et al., 2001).

Fifth generation models Schertler’s pioneering cryo-electron diffraction work published in 1993 and 1997 laid the foundation for much of the GPCR modeling work of that decade. The breakthrough came in 2000 when Polczewski et al. (2000) published the first X-ray structure of a GPCR, bovine rhodopsin (see Fig. 9.8.1). An unexpected feature was the buried nature of the second extracellular loop which has two antiparallel beta strands forming a fourstranded β-sheet with the NTD. There is no evidence that this feature is conserved in other GPCRs. The seven TM helices are clearly defined, but a second unexpected feature was the presence of an eighth helix lying parallel to the plane of the membrane on the cytoplasmic side. Standard, well established homology modeling protocols could now be used to build 3D models of GPCRs. These are the basis for much of the remaining material in this unit.

THEORY BEHIND METHODS USED IN FIFTH GENERATION MODELS Hydropathic Analysis Profile analyses have been used for many years as an addition to secondary structure prediction tools (Rost, 2001) to help in the prediction of protein packing. The most common class of these is known as hydropathy profiles, where an attempt is made to calculate the average hydrophobicity or hydrophilicity over a continuous length of sequence. The outcome should give an indication as to which portions of the sequence are likely to be buried, exposed to solvent, or membrane-spanning. This was one of the first tools used in the prediction of the TM helical regions of GPCRs. Perhaps the best known method of this type is that of Kyte and Doolittle (1982). In this method the hydropathy of each amino acid type is ranked from Ile (+4.5) to Arg (−4.5). These values were derived from combinations of normalized values for the
G◦ of water-vapor transfer and from the fractions of residues buried in a set of twelve established protein structures. The values are summed over a sliding window of user-definable length (by default nine residues) with the resulting number being an indication of whether the sequence prefers to be exposed to solvent or predominantly buried. In the case of the TM regions of GPCRs, the usual window length used is about nineteen, because this is the minimum number of residues in an alpha helix that can span a typical lipid membrane bilayer. Other profile methods include those of Hopp and Woods (1983) and Goldman, Engelman, and Steitz (Engelman et al., 1986). These differ only in the derivation and actual values of the amino acid hydropathies used.

Molecular Modeling of 7TM Helical Receptors

Fourier-Transform Methods An alpha helix is the most efficient structural element for allowing a protein to satisfy all its backbone hydrogen bonds. Therefore, it is the most common element found in the membrane-spanning domain of any integral membrane-bound protein, including GPCRs. By its nature it is periodic and, if one were to look down along the axis of a perfect regular helix, it can be seen that it forms a projection of a wheel, with the side-chain Cα-Cβ bonds as the spokes (see Fig. 9.8.2B). Each spoke is separated from its neighbor by an angle of 100◦ . This periodicity is used to detect helicity in a sequence. For GPCRs the hydrophobic residues are often clustered on one face of each helix, which in turn faces the lipid environment. By assigning a value of the hydrophobicity to each amino acid and performing a Fourier transform, a moment is obtained that defines the direction of the hydrophobic face.

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Figure 9.8.2 (A) A multiple sequence alignment of various 5-HT receptors in TM7. (B) A HELANAL (Fourier transform) plot calculated from this alignment. The conservation moment (Donnelly moment) is represented by the orange arrow at the left of the picture, while the hydrophobic moment is the cyan arrow on the right. The dark blue arc estimates the exposure of this helix to the bilayer. For the color version of this figure go to http://www.currentprotocols.com.

The location of polar or charged residues is usually an indication of where the TM helix emerges from the lipid bilayer into the surrounding aqueous environment. Although there is a fair degree of variation in the residues facing the membrane, a much higher level of conservation is found among those residues facing the interior of the TM bundle, a feature which is often noted in globular proteins. Donnelly described a Fourier transform method for assigning a conservation value to each position in a multiple sequence alignment (Donnelly et al., 1993). This was then transformed to give a conservation moment that

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defined the inward face of the TM helix. Some corrections to the method are necessary for GPCRs. In particular, certain TM helices contain highly conserved prolines facing the lipid bilayer instead of the interior of the TM bundle. A special value is assigned to these prolines to take this into account. A full description of the theory behind these Fourier methods can be found in the original paper by Donnelly as well as the program PERSCAN that he developed to perform these calculations. This program is freely available (see Donnelly et al., 1993) and has been modified to calculate and display a hydrophobic arc around the helical wheel, giving an estimate of the degree to which the helix is exposed to the lipid (Blaney and Tennant, 1996). In the example shown in Figure 9.8.2, the arc around the 5-HT helix 7 is quite short, suggesting this helix is deeply buried in the TM bundle.

Energy-Based Calculation Methods The basis of structural refinement with most molecular systems is that the molecule prefers to remain in its lowest energy state (or conformation). Generally, the most accurate way of calculating the energy of a system is to take full account of the electronic motions of the molecule(s) using a quantum mechanical method. In the case of enzyme reactions, where high-energy intermediates are involved, this is the only way of dealing with the chemistry of the reaction. Because the computational cost of such calculations is prohibitive, many approximations are needed. Receptor binding, on the other hand, does not involve transition states or reactive intermediates, and the molecules can generally be thought of as under equilibrium conditions. In this case the energy of the molecule may be described as a system of balls and springs, subject to classical mechanical forces. This approximation is usually referred to as molecular mechanics. In this approximation, the total energy of a molecular system is defined by Equation 9.8.1.

∆E =

Aij

Cij

ij

ij

∑ kb (r − r0 )2 + ∑ kθ (θ − θ0 )2 + ∑ kφ [1 + cos(nφ − γ )] + ∑∑ r12 − r 6 i

j

+

qi q j rij

Equation 9.8.1

where r0 is the equilibrium bond length between the appropriate atom types, r is the actual bond length, and kb is the stretching force constant of that atom type pair. Similar interpretations apply to the θ and φ. To solve this equation it is necessary to assign atom types to each atom of the system. Modern molecular mechanics programs often have hundreds of different atom types, reflecting the different chemical environment of each element. Thus, hydrogen atoms may be polar, as in hydrogen bonding, or nonpolar, as in hydrocarbons. Carbons can be aliphatic, or aromatic, and if the latter, then different atom types may be assigned depending on the size of the ring or whether it is at a ring junction. Once all the types are assigned, the internal coordinates of the molecule are determined, i.e., the bonds, the angles and the dihedral (torsion) angles. In Equation 9.8.1, the first three terms represent the summations of all the bond stretching, the angle bending and bond rotation energies, respectively. For example, the bond between any two atom types has an ideal or equilibrium length (e.g., the length between two saturated aliphatic carbons ◦ is 1.54 A). To stretch or deform this bond it is necessary to apply a force. The energy Molecular Modeling of 7TM Helical Receptors

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required can be described by thinking of the bond as a spring which obeys Hooke’s law. Thus,
E = kb (r − r0 )2 In the second term of Equation 9.8.1 the same form of law is applied to bond angles, again with different equilibrium values and angle bending force constants for each possible triplet of atom types. The energy of rotation around a bond, defined by four atom types, obeys a simple periodic trigonometric function where n in Equation 9.8.1 is the periodicity. Because rotation around a single aliphatic carbon-carbon bond, for example, goes through three energy maxima and minima, it has a periodicity of 3. The remaining terms of Equation 9.8.1 represent the non-bonded terms and are summed over all atom pairs not involved in bonds, angles or dihedrals. The last is the electrostatic energy between two atoms i and j and is defined as the product of the point partial charges q on those atoms, divided by the distance, r, between them. Aij and Cij are the van der Waals repulsive and attractive nonbonded terms, respectively and, as can be seen from the distance dependence, fall off very rapidly with distance. Occasionally, other terms, such as a separate hydrogen bonding term, are added to the general molecular mechanics equation. All the appropriate atom types and their associated equilibrium values and force constants (usually called parameters) are known collectively as a force field. A great deal of effort has gone into establishing the parameters for amino acids in proteins. This has led to the development of popular programs such as CHARMm (Brooks et al., 1983; http://www.charmm.org/info/license.shtml), AMBER (Cornell et al., 1995; http://amber.scripps.edu), and GROMOS (Koehler et al., 1987; http://www.igc. ethz.ch/gromos). These differ slightly with regard to equations and parameters, but much more so in the input used for the programs themselves. While CHARMm is used in this unit as an example, the same general principles apply for the other programs as well. Given the internal coordinates (geometry) of the molecular system, and the appropriate force field, with Equation 9.8.1 the calculation of the energy is extremely fast, typically less than a second for several thousand atoms. Therefore, it is ideal for the problems associated with GPCR modeling, i.e., the final structure-refinement process following homology modeling, the optimization of loops, and the study of ligand-GPCR interactions. Optimization of the geometry is an important stage. It is generally assumed that the actual geometry of the structure will be close to the global energy minimum. Energy minimization is an essential part of any molecular mechanics program, with the refinement of the protein model usually depending on it. While numerous algorithms have been developed for this purpose, a full discussion of them is not provided here; see Leach (2000) or another of the definitive textbooks on molecular modeling. Another alternative and/or addition to the application of molecular mechanics, as used with modeling GPCRs, is molecular dynamics. As proteins are in constant motion they should move towards an energy-minimized conformation. At the same time, they will exist as an ensemble of low-energy states. Molecular dynamics assumes that the system obeys the laws of classical mechanics and is therefore governed by Newton’s laws of motion: 1. A body moves in a straight line at constant velocity unless a force acts against it. 2. The force equals the rate of change of momentum, or more commonly stated, equals mass times acceleration. 3. Every action has an equal and opposite reaction.

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Thus, the velocity and the displacement (movement) of any particle in a molecule can be determined by integrating Newton’s second law. Each atom has a kinetic energy associated with it and is under the influence of all the other atoms in the system. This gives rise to the potential energy of that atom. The forces governing this can be calculated from the force field described earlier. It is usual to provide some initial energy to the system by heating it to a specified temperature, such as 300K. This total energy should then always be a constant because energy cannot be created or destroyed, only transformed. Using molecular dynamics, it is possible to simulate the motion of the whole molecular ensemble, thus sampling the many conformations a molecule may adopt as it finds its way to a potential energy minimum. It is a very useful method for studying the possible conformations of loop regions in GPCRs because they are generally assumed to be more flexible than the TM helical bundle. Selected conformations can be minimized and assessed as to how well they represent potential candidates for good loop models. See Leach (2000) or McCammon and Harvey (1987) for further discussion of molecular dynamics. Monte Carlo simulations were originally used in the calculation of thermodynamic properties of bulk liquids and small molecules in solution. The Monte Carlo calculation uses the same force field as that used in molecular mechanics minimization and dynamics, but differs from the latter in that it ignores the kinetic energy term of the simulation. Instead, it assigns a random shift in the position of each atom of the system and recalculates the energy. This configuration, which must conform to a preset number of thermodynamic conditions such as temperature and volume, will then be accepted or rejected according to some probabilistic criteria. Because of the random nature of the moves, each configuration depends only on the one immediately preceding it, making it independent of any time course. The method produces a large number of acceptable configurations that can be used to generate a partition function from which the thermodynamic properties can be calculated. A full discussion of the theory of Monte Carlo calculations can be found in Leach (2000). This method works well for bulk liquids where the molecules are small and therefore the movement of the atoms is to a large extent independent of one another. This is not the case for proteins where the system is interconnected and random movements can cause large changes in energy due to internal coordinate deformation. This inevitably leads to a very high rejection rate. For this reason Monte Carlo has not traditionally been used in protein simulations. An interesting exception to this is the ICM program developed by Abagyan et al. (1994). This program performs Monte Carlo simulations mostly in torsional internal coordinates, which can be restricted to preferred ranges of values arising from the Ramanchandran backbone areas and rotamer libraries (see Refinement and Protein Checking).

Distance Geometry Because distance geometry is an alternative method of sampling conformational space, it is an excellent way of generating initial peptide conformations. It can be used with subsequent energy minimization or in conjunction with molecular dynamics to generate possible conformations of loop regions in GPCRs. In one published case it has even been used to generate a potential conformation of an N-terminal domain (Taylor et al., 2003).

Molecular Modeling of 7TM Helical Receptors

The conformation of a molecule is generally described in terms of its Cartesian or internal coordinates. However, it can also be uniquely defined by all the distances between pairs of atoms. For a molecule with n atoms this is conveniently written as an n × n matrix, although the diagonal elements of this will be zero, with the upper and lower triangles being mirror images of each other. By randomly assigning arbitrary

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distances to each of these matrix elements it is possible to generate an infinite number of conformations. While this in itself is not particularly useful, in practice it is possible to set upper and lower limits to the range of distances for each atom pair. For example, bonded pairs of atoms or the 1-3 distances of atoms involved in an angle have very narrow allowed distance ranges. Likewise, the 1-4 distances of 4 atoms defining a dihedral angle have lower (cis) and upper (trans) values. For those atom pairs not involved in bonds, angles or dihedrals, the lower limit can be set as the sum of their van der Waals radii because two atoms cannot approach closer than this. For peptide chains, the upper limit cannot exceed the distance of the two atoms’ fully extended conformation. In practice, experimental constraints can also be used. Distance ranges can be obtained from two-dimensional NOE NMR spectroscopy. This is how solution conformations of many small proteins have been solved. Distance constraints can also be obtained from hydrogen bonds in secondary structure elements such as α-helices or β-sheets. Once the n × n matrix is defined, the upper and lower distance bounds are added. Arbitrary values between these bounds are assigned for each atom pair. In the next stage the distance matrix is converted into a trial set of Cartesian coordinates and this trial conformation is refined, usually by energy minimization, and analyzed. A useful additional step with peptides or proteins is to apply a rotamer library (see Side-chain modeling) prior to the minimization step. The mathematics behind the conversion step is beyond the scope of this unit, but excellent accounts can be found in Leach (2000) or in the definitive book on distance geometry by Crippen and Havel (1988). DGEOM95 (program no. 590 from http://qcpe.chem. indiana.edu) is an excellent distance geometry program which accepts protein or peptide molecules in standard PDB format and generates most of the bounds automatically (Spellmeyer et al., 1997).

Homology Modeling The most accurate way to model an unknown protein from sequence information alone is by homology modeling, assuming the 3D structures of related proteins are known. The process can be divided into several stages: 1. Sequence alignment 2. Construction of the structurally conserved regions 3. Loop modeling 4. Side-chain modeling 5. Final model refinement 6. Checking the model for abnormal protein features. All of these steps are incorporated into standalone complete homology modeling programs. For example, Blundell et al. have written a knowledge-based suite of programs known as Comparer and Composer (see Sali et al., 1990). Probably the best known program of this type is Modeler (Sanchez and Sali, 1997). The program can be used alone, although interfaces to it are available through the programs Quanta and Insight (see Table 9.8.1). In addition, these latter two programs, and other commercial protein modeling packages (e.g., MOE, Prime, WHAT IF; see Table 9.8.1) have built-in homology modeling routines. However, it is best for the user to proceed through these steps manually. In particular, the first sequence alignment stage is a crucial one since errors occurring here are propagated throughout the remaining stages.

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Table 9.8.1 Commonly Available Protein Modeling Packages

Program

Supplier and/or URL

Quanta

Accelrys (http://www.accelrys.com)

Insight II

Accelrys (http://www.accelrys.com)

MOE

Chemical Computing Group (http://www.chemcomp.com)

Prime

Schrodinger (http://www.schrodinger.com)

SYBYL Biopolymer

Tripos (http://www.tripos.com)

WHAT IF

http://swift.cmbi.kun.nl/whatif

Sequence alignment methods Before modeling can begin, the unknown sequence must be aligned with one or more sequences for which structural information is known. One disadvantage of homology modeling is that it is assumed that the fold of the unknown sequence is related to the known fold, and once modeling has begun this cannot be altered in the way that could be done by a Monte Carlo protein folding simulation. This constraint places great importance on the initial alignment since an error early on may not be detected, and may survive into the final structure. Many alignment methods or programs have been developed, with the two most commonly used being ClustalW(X) (Higgins et al., 1994) and PsiBlast (Altschul et al., 1997; downloads for both available at http://www.ebi.ac.uk/FTP). An excellent description of the theory behind these is provided by Leach (2000). It is worth noting that some form of scoring is necessary to quantitatively assess similarity between two protein sequences. This is generally accomplished using a scoring matrix of some form. This consists of a 20 × 20 matrix with elements representing values for how often nature has allowed each amino acid to change to one of the nineteen others during evolution. Further rows/columns are added as a measure of inserting gaps into an alignment (a gap penalty) or extending that gap along that sequence. Dayhoff et al. (1972) derived a series of substitution tables by analyzing the frequency of substitution of various residues in multiple sequence alignments. These values were then collated into a series of scoring matrices known as PAM (point accepted mutation) matrices. Because they are based on actual multiple sequence alignments of related proteins, they are often thought of as reflecting evolutionary change in a protein. Other matrices reflect a direct comparison of physicochemical properties such as hydrophobicity, aromaticity, size, shape, and electronic features. The conservation score used in the Fourier transform method is another form of scoring matrix.

Molecular Modeling of 7TM Helical Receptors

Sequence alignments have been used extensively with GPCRs. It soon became apparent, initially with family A receptors, that certain positions in the TM bundle were completely conserved. Figure 9.8.3 displays alignments of the TM domains from a small diverse set of GPCRs, including bovine rhodopsin. As shown, each TM region contains at least one invariant motif: TM1 has an asparagine, usually preceded by a glycine and followed 3 residues later by a valine; TM2 has an aspartate; TM3 has the so-called DRY motif (a triplet amino acid sequence of aspartic acid, arginine, and tyrosine) at the intracellular side of the helix where the arginine is invariant, but the aspartate is frequently replaced by a glutamate and the tyrosine by a phenylalanine, histidine or even a cysteine; TM4 has a tryptophan; and TM5, TM6 and TM7 have conserved prolines. TM3 usually has a cysteine at the extracellular end, although this is not completely conserved, as in the case of the cannabinoid receptor. This cysteine forms a disulfide bond with another cysteine

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Figure 9.8.3 Alignment of the TM regions for a small diverse set of family A 7TM receptors (from top to bottom, serotonin 5-HT2A , interleukin IL8, chemokine CKR2, neurokinin NK1, vasopressin V1a, prostaglandin PER2, cannabinoid CB1, and bovine rhodopsin. Starting from the block in the upper left, blocks from left to right correspond to TM1 to TM7. For the color version of this figure go to http://www.currentprotocols.com.

in the second extracellular loop, the latter being an important residue on which to base alignments of this loop region. The proline in TM7 is usually preceded by an asparagine, although fairly often it is replaced by an aspartate. All these motifs have important structural and/or functional roles (Fig. 9.8.4). Prolines give rise to kinks in an α-helix and, particularly in TM6 where it is believed to be important for transferring, upon agonist binding, a conformational change via a levertype motion to the intracellular side of the TM bundle. The asparagines on TM1 and TM7 form a hydrogen bonding network with the TM2 aspartate, which is essential for proper functioning of the receptor. The acidic and basic residues of the DRY motif in TM3 form an internal salt bridge with each other, which is believed to break upon activation. The TM4 tryptophan is a large aromatic residue that packs against the adjacent TM3 helix reinforcing its steep packing angle in the helix bundle. Occasionally one of these motifs is not present. This is most common in TM5, as can be seen with the prostaglandin and cannabinoid receptors (Fig. 9.8.3). This makes it necessary to rely on other, secondary conserved features for alignments. For example, there is very often a tyrosine residue found two helical turns after the proline. Again, this is not present in the prostaglandin sequence, making it necessary to resort to the alignment tools described above, possibly combined with hydropathy or Fourier analysis.

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Figure 9.8.4 (A) A family A receptor showing important key residues. (B) Inset showing a close-up of the DRY region in TM3. (C) Inset showing the hydrogen bonding network between the asparagine (TM7 left), the aspartate (TM2) and the second asparagine (TM1). For the color version of this figure go to http://www. currentprotocols.com.

All the motifs are present in bovine rhodopsin (Fig. 9.8.3), the only sequence for which a 3D crystal structure is known. This makes alignment of the TM domains of an unknown family A GPCR with rhodopsin extremely easy. The next stage in the homology modeling process, building the structurally conserved regions, is usually straightforward.

Molecular Modeling of 7TM Helical Receptors

Structurally Conserved Regions Once the alignment has been obtained, the known structure or structures are used to build the structurally conserved regions (SCRs). These are topographically invariant regions, connected by conformationally unknown stretches of sequence, e.g., loops, which are conserved across the family. Typically, SCRs are large secondary structural elements, e.g., the α-helices of GPCRs, or the β-sheets of cytokine receptors. Whenever multiple known structures are available, it is sometimes useful to overlay them to define the conserved 3D backbone. All the commonly used protein modeling programs (e.g., Insight, SYBYL, and MOE; see Table 9.8.1) have routines for carrying out this step. They simply copy the

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backbone and conserved side-chain coordinates from the known structure to the unknown one and build the remaining mutated side-chain coordinates from an internal library of amino acids. In the case of GPCRs, the SCRs are obviously the TM helices, while the unknown regions are comprised of the loops and the N- and C-terminal domains. It is commonly believed, and indeed in many cases confirmed by site directed mutagenesis, that ligand binding occurs primarily in the TM bundle of family A receptors. Many published models, particularly those prior to 2000, did not routinely have the loop and terminal regions included. Since then it has become customary to build the extracellular loops, because there is often a component of ligand binding in these regions. This is especially true for peptide receptors. The intracellular domains are usually ignored because they are not involved in binding. The N-terminal region is often far too long for making any useful speculations on its conformation, and it is therefore generally ignored in modeling.

Loop searching The most difficult aspect of homology modeling is the construction of the unknown loop regions that span the SCR framework. While some advances have been made recently, much research is still necessary before this step becomes routine. For certain classes of loops (e.g., the four-residue β-turns; Wilmot and Thornton, 1988), the conformational preference of different amino acids at each position is well established, and these can be modeled with some accuracy. For other short loops, it is possible to use energy-based conformational searching (molecular dynamics or Monte Carlo) to generate an ensemble of configurations, which are further evaluated by energy. With longer loops, it is usual to resort to database loop searching. Standard databases containing the necessary information have been derived from the Brookhaven Protein Databank (PDB) and are available with the commonly used protein modeling programs. The database is typically searched for sequences of the same length as the required loop and with end coordinates fixed within a threshold value of the ends of the two neighboring SCRs. Loops can be ordered in suitability either by the RMS difference between these anchor points, or by sequence similarity between the loop and the database fragments. Most force field programs search conformational space using Cartesian coordinates. However, bond stretching, and even angle bending, require much greater changes in energy than simply rotating around single bonds. It is the latter that is responsible for conformational variation in loop regions, so the alternative is to keep the bond lengths and angles constant and to search in torsional space only. The ICM program (Abagyan et al., 1994) does exactly this, and has been used for generating loops of twenty or more residues. It is, however, still time consuming. Distance geometry represents an alternative technique to energy-based methods or database searching for generating conformations. It is the author’s method of choice for building the loop regions of GPCRs, especially when coupled with molecular dynamics refinement. The loops in GPCRs exist between transmembrane helices. Having defined these helices in the SCR phase of homology modeling, it is evident that all the interatomic distances between the residues in these helices are known because, at this stage, they are fixed in space. Typically, a linear peptide is generated containing the five N- and C-terminal helical residues at either end of the loop region. The intra- and inter-atomic distances are then generated for these helical residues. Additionally, if the loop is long enough, secondary structure prediction (SSP) methods (Rost, 2001) are used to estimate structural elements within the loop region itself. This prediction is considerably enhanced if several SSP algorithms arrive at a consensus prediction. The backbone interatomic distances and hydrogen bonding patterns of the common structural elements such as helices, β-strands,

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Figure 9.8.5 Construction of an extracellular loop region using distance constraints (reproduced from Blaney et al., 2006 with permission from Wiley-VCH).

and turns are well known and can thus give additional distance constraints. Previously modeled loop regions give distances that can be included in the sampling. Finally, the well documented disulfide bond in extracellular loop 2 can be used to give a constraint between the cysteine SG sulfurs in the loop and TM3 (Fig. 9.8.5). Initial conformational sampling is usually performed with 500 to 1000 random conformations being generated. Each loop structure is minimized by molecular mechanics and the conformations are analyzed by goodness of backbone phi-psi angles in Ramachandran space (see Refinement and Protein Checking) using the criteria described by Wilmot and Thornton (1988). They are then clustered into backbone dihedral families that can be ranked by energy. The best conformations are chosen for subsequent molecular dynamics analysis using Elber’s locally enhanced sampling (LES) algorithm (Elber and Karplus, 1990; Roitberg and Elber, 1991), where multiple copies are simulated simultaneously until some structural convergence is reached. This procedure is implemented in the CHARMm program which is used in the example below. The converged loop conformation is then further refined by energy minimization before inclusion in the final receptor model.

Side-chain modeling Once the backbone is generated, the side chains must be added to complete the structure. Many of the possible side-chain conformations are sterically forbidden since they would cause the side chain to pass too close to the backbone or cause side-chain bonds to become eclipsed. Allowed (or actually observed) values of amino acid side-chain torsional angles have been derived, either from statistical analysis of the Brookhaven PDB database or from theoretical energy calculations and formulated into tables known as rotamer libraries.

Molecular Modeling of 7TM Helical Receptors

Early rotamer libraries, defined by groups such as Ponder and Richards (1987) or Dunbrack and Karplus (1993), have been implemented in the standard protein modeling packages (e.g., Quanta; Table 9.8.1). These libraries usually describe preferred sidechain dihedrals in each of the standard α-helix, β-sheet, or coil backbone regions. An excellent review of the current state of the art with rotamer libraries is available (Dunbrack, 2002). This group has developed one of the most popular rotamer library programs, SCRWRL (Bower et al., 1997).

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These libraries are applied by setting the torsion angles of each amino acid stepwise to those defined by the library, and iteratively working through the structure, minimizing the number of side-chain clashes at each cycle until no further change is found. The analysis of the Brookhaven database is largely based on globular proteins. It has been found that there is no difference in the preference of rotameric states between membrane-bound and soluble globular proteins. Applying the rotamer library is usually the final step before further structure refinement with energy-based methods.

Refinement and Protein Checking Once a homology model has been built, the final refinement is performed using molecular mechanics minimization or molecular dynamics. Checks on the quality of a protein structure rely on goodness of backbone dihedrals. The backbone angles of a protein are commonly known as phi (φ; the dihedral angle between the Cα carbon and the nitrogen of the peptide bond), psi (ψ; the dihedral angle between the Cα carbon and the carbonyl carbon of the peptide bond), and omega (ω; the peptide bond itself which is usually close to 180◦ . An analysis of the backbone angles of proteins in the Brookhaven database (Ramachandran et al., 1963) showed that these take on fairly restricted values, with most φ angles being negative and ψ angles being positive in β-sheet regions and negative in α-helical structures. The reason for this is that, as one rotates around the φ angle, steric clashes occur between the backbone and the side chain. An exception is glycine, which has no side chain. Turn regions also often lie outside the normal range. Plots of allowed torsional regions are often known as Ramachandran plots (Fig. 9.8.6), and structures that have amino acids lying outside these regions are suspect. Another common feature that defines a good quality protein structure is an estimate of buried hydrophilic or exposed hydrophobic residues, neither of which are generally found in globular proteins. However, they are frequent in membrane TM bundles of GPCRs because hydrophobic residues prefer to face the hydrophobic membrane. Other features include bad side-chain conformations, cis-amides, wrong chirality, and the existence of

Drug Discovery Technologies Figure 9.8.6

A Ramachandran map showing the α, β, and left-hand (L) helix regions.

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unnecessary cavities. Programs such as PROCHECK (Laskowski et al., 1993), the Quanta Protein Health option, and QPACK (Gregoret and Cohen, 1990) are all readily available and can perform these analyses routinely. Another more recent and useful computational approach uses the 3D-Profiles program (see Bowie et al., 1991; available as part of the Insight II program from Accelrys at http://www.accelrys.com). This program categorizes each amino acid into one of eighteen different classes according to its secondary structural environment (helix, β-strand, or coil), exposure to the environment, and percentage polar surface area. Each amino acid has its own preferences and a cumulative score can be assigned to a given protein by analyzing the environment of all its amino acids. It should be noted that the method was developed for globular proteins and has not really been applied to GPCRs where amino acid environments are likely to be different.

BUILDING A 5-HT2A MODEL BASED ON THE BOVINE RHODOPSIN CRYSTAL STRUCTURE This section describes the various steps used in constructing a human serotonin 2a (5-HT2A ) receptor model. One step in particular, the energy refinement stage, varies considerably as a function of the computer program employed. In this example, the CHARMm program was utilized, with details about each line in the command script shown in italics to distinguish them from the script itself (see Appendix at the end of this unit). While the input for AMBER or GROMOS will differ from that used for CHARMm, the underlying principles are the same; the appropriate program manual should be consulted for more complete instruction.

Hydropathic analysis The first stage in constructing a GPCR model is generation of a hydropathy plot from the sequence. The TM helical regions appear as positive peaks in the graph of approximately 20 to 30 residues in width. This information is particularly important because anomalies may occur in the next sequence alignment step, making it essential to know the exact location of the TM helix. The hydropathy plot of the 5-HT2A receptor is shown in Figure 9.8.7. As displayed, there is large loop region of approximately 50 residues between TM5 and TM6, as evidenced by the prominent negative peak. This third intracellular loop, which generally contains

Molecular Modeling of 7TM Helical Receptors

Figure 9.8.7 A Kyte-Doolittle hydropathy plot of the 5-HT2A receptor sequence. Although this plot was generated from software written in-house, numerous programs are available for creating these plots, including the Web site http://wrpsun3.bioch.virginia.edu/fasta www2/ fasta www.cgi?rm=misc1.

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numerous basic residues, is important in binding the G-protein complex. For many, though not all, GPCRs it is larger than the other loops. The peaks of TM6 and TM7 are essentially merged together, with no return to baseline between the peaks, reflecting the fairly short extracellular loop between them.

Sequence alignment In cases where there is no known structure on which to base the model, it would be appropriate to use a Fourier transform method in a de novo helix packing study. However, because this sequence belongs to the family A receptors for which the structure of bovine rhodopsin is known, a sequence alignment between 5-HT2A and rhodopsin will be carried out. The result, using the ClustalW program, is shown in Figure 9.8.8. The first three helical regions are clearly aligned with asparagine in TM1, aspartate in TM2, and the

Figure 9.8.8 A ClustalW alignment of the 5-HT2A receptor and bovine rhodopsin, using the sequences from the SwissProt database. The consensus line below the alignment shows sequence homology indicated by an asterisk (*) for identical residues, a colon (:) for very similar residues, and a period (.) for less similar ones. The helical regions of rhodopsin are shown as stretches of ..hhhh.. under each consensus line. Conserved TM motifs are shown in bold. Drug Discovery Technologies

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cysteine and DRY motifs in TM3, all being in synch. Likewise, the commonly found FxxxWxP sequence of TM6 and the NP of TM7 show perfect alignments. However, problems arise with the TM4 and TM5 regions. Since there is a gap in TM4 in the alignment at the start of the helical region, the side chains of the 5-HT2A helix built on this would be displaced by one residue. This cannot happen in a helix because of the hydrogen bonding pattern, and it is necessary to move the first few residues of rhodopsin to the right by one residue, even though this now breaks the alignment of the RF pair. The situation is far worse for TM5 where the helix of rhodopsin is completely broken in the alignment. This is due, in part, to the alignment of the FFIP in 5-HT2A with the YYTP of rhodopsin, the general lack of sequence identity in this region, and the much longer third intracellular loop. This is where the previously calculated hydropathy plot becomes particularly useful. Figure 9.8.9 is a close-up of the hydropathy plot of the TM5 region with its associated residues. As illustrated, it does include the FFIP region, and it is this proline which is the conserved motif of TM5. Because the helix of the TM5 region of rhodopsin contains an FIIP stretch it is necessary to manually adjust the alignment so that the two helical regions overlap completely. There is a highly conserved disulfide bond between cysteines in TM3 and the second extracellular loop, and there is a cysteine in 5-HT2A at position 227, although this is not aligned with the correct residue of rhodopsin. A further manual adjustment is therefore necessary to align the relative cysteines. This portion of the revised final sequence alignment is shown in Figure 9.8.10.

Figure 9.8.9 A close-up of the hydropathy plot of the TM5 region of the 5-HT2A receptor sequence with associated residues shown below. Molecular Modeling of 7TM Helical Receptors

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Figure 9.8.10

Revised sequence alignment of the TM4-ECL2-TM5 region.

Generating the Structurally Conserved Regions The generation of an acceptable sequence alignment, the most important step in the construction of a homology model, is now completed. The next stage is building the initial starting coordinates for the SCR domains. Generally, this involves the use of a computer program that copies the backbone coordinates of the known crystal structure and uses template structures to generate new side-chain coordinates where these have been changed for the mutated amino acids. Many programs are available for this step. The Quanta Protein Design module, for example, makes it possible to select a range of sequences from the read-in alignment file and automatically generates the new coordinates. Energy Refinement of the 5-HT2A TM Bundle Alternatively, CHARMm will build the missing coordinates of a protein residue using the residue name and its backbone geometry. At this stage the user may wish to build a GPCR model of the TM bundle alone. A CHARMm script for performing this maneuver is show in CHARMm Script 1 in the Appendix at the end of the unit. It is important to monitor the energy when performing an energy minimization routine using a program such as CHARMm. If the energy level remains high as the minimization proceeds there is almost certainly something wrong with the starting structure. It is usually necessary to examine it using a protein modeling graphics program such as Quanta (see Table 9.8.1). The generation of the initial model using the homology modeling tools available does not necessarily result in a particularly good structure. For example, it may be discovered that a side chain is projecting through the aromatic ring of an adjacent residue. A failure in minimization is usually due to some atoms being too close together and in a conformation where the strain cannot be relieved by the usual algorithms in the program. It then becomes necessary to alter the relevant torsion angles manually and restart the program. While the preceding example built a model of the 7TM bundle, it is often desirable to include the extracellular loop (ECL) regions. As before, it is necessary to obtain a starting structure for these. This can come from loop library searches, distance geometry sampling, or from a variety of other conformation generation techniques. The author’s group has generated a library of loops of varying lengths using a distance geometry sampling followed by extensive refinement by molecular dynamics. These are all in the correct reference frame with respect to the rhodopsin structure; thus, the overall initial model is easy to generate. It is important to place the coordinates of the loop segments in the same order as they appear in the CHARMm script. That is, ECL1 is placed between the end of helix 2 (HLX2) and the start of HLX3; ECL2 between HLX4 and HLX5; and ECL3 between HLX6 and HLX7. ECL1 is generally 2 to 4 residues in length and is easy to model from the rhodopsin structure itself. Occasionally, as in the case of the chemokine receptors, a direct alignment

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with rhodopsin results in a negative loop length for ECL1. In this situation the ends of TM2 and TM3 must be unfolded to form the new loop. This is achieved by relieving the NOE constraints on the appropriate residues. While the ECL2 and ECL3 loops are generally longer and more variable, a similar situation arises with many aminergic receptors, including 5-HT2A , in that the region between the disulfide bonded cysteine in ECL2 and the top of TM5 is very short. In the case of 5-HT2A , it is only two residues that, when fully extended, are still not long enough to bridge the gap. This makes it necessary to unfold the top of TM5 to accommodate this situation. A modification to the earlier CHARMm script, which now includes the ECL2 region, is shown (see CHARMm Script 2 in the Appendix at the end of the unit. Similar insertions are made for the ECL1 and ECL3 loops, except that no unfolding of the helix ends is necessary. The dynamics trajectories produced with script 2 can be viewed on a graphics workstation and various analyses performed. For example, it is possible to monitor the changes in potential energy with time or measure how geometrical aspects such as certain atom-atom distances change during the simulation. All these can be addressed using CHARMm, AMBER, or similar programs. If the dynamics simulation is performed as above to sample the possible conformations of the loop, then it would be appropriate to extract the saved coordinates of the protein at each step, minimize, and then analyze them for properties such as energy and agreement with Ramachandran space. CHARMm Script 3 in the Appendix at the end of this unit can be used to extract each step from the production trajectory coordinate file (@YY PRODA.DCD). To run this script it is necessary only to read in the sequence and a starting coordinate file, then establish the internal coordinate tables. This is identical to Script 1 (see the Appendix at the end of this unit) up to the IC BUILD line.

SUMMARY AND FURTHER CONSIDERATIONS The example described above details the steps necessary for constructing a typical antagonist model of a family A GPCR. Alignment tools (e.g., ClustalW or PsiBlast), hydropathy prediction algorithms (e.g., Kyte-Doolittle), and the distance geometry program DGEOM95 are all publicly available. Many of the energy refinement programs are also available free or for a nominal charge. The availability of the CHARMm program makes it possible to build such a model of the TM bundle and some or all of the extracellular loops. While the command line structure of other molecular mechanics programs (e.g., AMBER) are different, the basic concepts are the same as for CHARMm. There are many aspects of GPCR modeling that are beyond the scope of this unit. One is the inclusion of the rest of the protein in the model. The main argument against building the intracellular loops (ICL) and the C-terminal domain is that these regions are assumed not to be involved in ligand binding, but rather are important for G protein activation. ICL1 and ICL3 are usually of reasonable length and can be modeled in the same way as the extracellular regions. Both the C-terminal domain and ICL3 vary considerably in length and in some cases exceed 100 residues, making it impossible to construct meaningful models. Although the N-terminus is no doubt more important for ligand binding, there is a wide variation in length and, unless the region is extremely short (as with adenosine receptors) or of a similar sequence length and similarity to rhodopsin itself, then models will be speculative at best. With the appropriate experience a user should be able to routinely implement modeling of additional features, if desired.

Molecular Modeling of 7TM Helical Receptors

The X-ray structure of rhodopsin represents a GPCR in an inactive (antagonist) state. There is great interest in GPCR agonists and a wealth of biophysical data showing that significant conformational changes occur in switching between antagonist and agonist conformations (Gouldson et al., 2004). While no generally accepted definitive agonist

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model of a GPCR exists, many have attempted to build structures using the data. The usual approach is to define a set of distance constraints from biophysical data which reflect a change in conformation in switching to an agonist state. These constraints are then applied to the antagonist model in a steered molecular dynamics simulation, where large force constants are applied to the particular distance so that the agonist conformation can be adopted (Gouldson et al., 2004). The modeling described thus far has been conducted without consideration of the environment (in vacuo). Of course, this results in a major approximation because the intraand extra- cellular domains are immersed in an aqueous media while the TM bundle sits in a hydrophobic lipid bilayer. The simulation of explicit water environments is well established in molecular dynamics studies, and has been applied to GPCRs. Because the time taken to perform such simulations rises dramatically with the number of water molecules involved, such simulations are not routinely performed. Likewise, because there are good models for lipid bilayers, attempts have been made to simulate TM bundles in such environments. Again, such full atom simulations are prohibitively expensive in terms of computer time, and the potential benefits of such an analysis must be balanced against the cost. The main use of such an approach is in studying the changes in antagonist-agonist states, rather than routine examination of ligand binding. A recent advance is in the derivation of an implicit membrane description using modifications to the generalized Born formalism developed for aqueous environment simulations (Spassov et al., 2002). The method has been implemented in the latest versions of CHARMm. While family B and family C receptors have 7TM bundles, there is no sequence identity between these proteins and the family A members, with the exception of the conserved disulfide bond between cysteines in TM3 and ECL2. The construction of TM bundle models of family B and C GPCRs presents a challenge similar to that for family A models prior to 1997. All of the methods described earlier (including hydropathy plots, sequence alignments and Fourier methods, hydrophobic energy, molecular mechanics/dynamics and constraints from SDM and other biophysical methods) are being used to construct such models (Donnelly, 1997; Frimurer and Bywater, 1999; Malherbe, 2003a,b; Miedlich et al., 2004). An important fact about these two subfamilies is that the primary binding of agonist occurs in the large extracellular NTDs. An X-ray crystal structure of the family C m-GluR1 receptor NTD has been published (Kunishima et al., 2000), as has an NMR structure of the family B CRF NTD (Grace et al., 2004). This has made it possible to build models of these domains using the homology modeling technique described earlier. Mention should be made of how these models are actually used. This is a totally different, often controversial, topic from that of model building. At one level these models have led to an increased knowledge of receptor mechanisms and the molecular basis for selectivity. However, their ultimate utility should be in increasing the understanding of how different ligands interact with the receptors, leading to hypotheses on binding sites based on ligand structure-activity relationships (SARs), followed by tests using SDM. Such data may yield a virtual library and result in structure-based design of novel ligands. There have been many excellent reports and reviews giving testimony to the utility of GPCR modeling (Rognan, 2006). Interest in the field continues to increase and will continue to gain support as more receptors are validated as therapeutic targets. It is expected that the increasing numbers of structures confirmed by experimental methods such as SDM will continue to validate the modeling conducted up to this time. Drug Discovery Technologies

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LITERATURE CITED Abagyan, R., Totrov, M., and Kuznetsov, D. 1994. ICM - a new method for protein modeling and design: Applications to docking and structure prediction from the distorted native conformation. J. Comp. Chem. 15:488-506. Altschul, S.F., Madden, T.L., Schaffer, A.A., Zhang, J., Zhang, Z., Miller, W., and Lipman, D.J. 1997. Gapped BLAST and PSI - BLAST: A new generation of protein database search programs. Nucleic Acids Res. 25:3389-3402. Baldwin, J.M., Schertler, G.F.X., and Unger, V.M. 1997. An alpha-carbon template for the transmembrane helixes in the rhodopsin family of G-protein-coupled receptors. J. Mol. Biol. 272:144-164. Bikker, J.A., Trumpp-Kallmeyer, S., and Humblet, C. 1998. G-Protein coupled receptors: Models, mutagenesis, and drug design. J. Med. Chem. 41:2911-2927. Blaney, F.E. and Tennant, M. 1996. Computational tools and results in the construction of G proteincoupled receptor models. In Membrane Protein Models (J.B.C. Findlay, ed.) pp. 161-176. Bios Scientific Publishers, Oxford. Blaney, F.E., Raveglia, L.F., Artico, M., Cavagnera, S., Dartois, C., Farina, C., Grugni, M., Gagliardi, S., Luttmann, M.A., Martinelli, M., Nadler, G.M., Parini, C., Petrillo, P., Sarau, H.M., Scheideler, M.A., Hay, D.W., and Giardina, G.A. 2001. Stepwise modulation of neurokinin-3 and neurokinin-2 receptor affinity and selectivity in quinoline tachykinin receptor antagonists. J. Med. Chem. 44:1675-1689. Blaney, F.E., Capelli, A.-M., and Tedesco, G. 2006. 7TM models in structure-based drug design. In Ligand Design for G Protein-coupled Receptors (D. Rognan, ed.) pp. 205-239. Wiley-VCH Verlag GmbH & Co. KG, Weinheim, Germany. Bower, M.J., Cohen, F.E., and Dunbrack, R.L. Jr. 1997. Prediction of protein side-chain rotamers from a backbone-dependent rotamer library: A new homology modeling tool. J. Mol. Biol. 167:1268-282. Bowie, J.U., Luthy, R., and Eisenberg, D. 1991. A method to identify protein sequences that fold into a known three-dimensional structure. Science 253:164-170. Bromidge, S.M., Dabbs, S., Davies, D.T., Davies, S., Duckworth, D.M., Forbes, I.T., Gaster, L.M., Ham, P., Jones, G.E., King, F.D., Mulholland, K.R., Saunders, D.V., Wyman, P.A., Blaney, F.E., Clarke, S.E., Blackburn, T.P., Holland, V., Kennett, G.A., Lightowler, S., Middlemiss, D.N., Trail, B., Riley, G.J., and Wood, M.D. 2000. Biarylcarbamoylindolines are novel and selective 5-HT2C receptor inverse agonists: identification of 5-methyl-1-[[2-[(2-methyl-3-pyridyl)oxy]-5-pyridyl]carbamoyl]6-trifluoromethylindoline (SB-243213) as a potential antidepressant/anxiolytic agent. J. Med. Chem. 43:1123-1134. Brooks, B.R., Bruccoleri, R.E., Olafson, B.D., States, D.J., Swaminathan, S., and Karplus, M. 1983. CHARMM: A program for macromolecular energy, minimization, and dynamics calculations. J. Comput. Chem. 4:187-217. Cornell, W.D., Cieplak, P., Bayly, C.I., Gould, I.R., Merz, K.M. Jr., Ferguson, D.M., Spellmeyer, D.C., Fox, T., Caldwell, J.W., and Kollman, P.A. 1995. A second generation force field for the simulation of proteins, nucleic acids, and organic molecules. J. Am. Chem. Soc. 117:5179-5197. Crippen, G.M. and Havel, T.F. 1988. Distance Geometry and Molecular Conformation. John Wiley & Sons, Hoboken. Dayhoff, M.O. 1972. Atlas of Protein Sequence and Structure, Vol. 5. National Biomedical Research Foundation, Silver Spring, Md. Donnelly, D. 1997. The arrangement of the transmembrane helixes in the secretin receptor family of G-protein-coupled receptors. FEBS Lett. 409:431-436. Donnelly, D., Overington, J.P., Ruffle, S.V., Nugent, J.H.A., and Blundell, T.L. 1993. Modeling a-helical transmembrane domains: The calculation and use of substitution tables for lipid-facing residues. Protein Sci. 2:55-70. Dunbrack, R.L. Jr. 2002. Rotamer libraries in the 21st century. Curr. Opin. Struct. Biol. 12:431-440. Dunbrack, R.L. Jr. and Karplus, M. 1993. Backbone-dependent rotamer library for proteins. Application to side-chain prediction. J. Mol. Biol. 230:543-574. Elber, R. and Karplus, M. 1990. Enhanced sampling in molecular dynamics: use of the time-dependent Hartree approximation for a simulation of carbon monoxide diffusion through myoglobin. J. Am. Chem. Soc. 112:9161-9175. Engelman, D.M., Steitz, T.A., and Goldman, A. 1986. Identifying nonpolar transbilayer helixes in amino acid sequences of membrane proteins. Annu. Rev. Biophys. Biophys. Chem. 15:321-353. Findlay, J. and Eliopoulos, E. 1990. Three-dimensional modeling of G protein-linked receptors. Trends Pharm. Sci. 11:492-499. Molecular Modeling of 7TM Helical Receptors

Frimurer, T.M. and Bywater, R.P. 1999. Structure of the integral membrane domain of the GLP1 receptor. Proteins 35:375-386.

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Gouldson, P.R., Kidley, N.J., Bywater, R.P., Psaroudakis, G., Brooks, H.D., Diaz, C., Shire, D., and Reynolds, C.A. 2004. Toward the active conformations of rhodopsin and the β 2-adrenergic receptor. Proteins 56:67-84. Grace, C.R., Perrin, M.H., DiGruccio, M.R., Miller, C.L., Rivier, J.E., Vale, W.W., and Riek, R. 2004. NMR structure and peptide hormone binding site of the first extracellular domain of a type B1 G protein-coupled receptor. Proc. Natl. Acad. Sci. U.S.A. 101:12836-12841. Gregoret, L.M. and Cohen, F.E. 1990. Novel method for the rapid evaluation of packing in protein structures. J. Mol. Biol. 211:959-974. Henderson, R., Baldwin, J.M., Ceska, T.A., Zemlin, F., Beckmann, E., and Downing, K.H. 1990. Model for the structure of bacteriorhodopsin based on high-resolution electron cryo-microscopy. J. Mol. Biol. 213:899-929. Higgins, D., Thompson, J., Gibson, T., Thompson, J.D., Higgins, D.G., and Gibson, T.J. 1994. CLUSTAL W: Improving the sensitivity of progressive multiple sequence alignment through sequence weighting,position-specific gap penalties and weight matrix choice. Nucleic Acids Res. 22:4673-4680. Hopp, T.P. and Woods, K.R. 1983. A computer program for predicting protein antigenic determinants. Mol. Immunol. 20:483-489. Horn, F., Weare, J., Beukers, M.W., H¨orsch, S., Bairoch, A., Chen, W., Edvardsen, O., Campagne, F., and Vriend, G. 1998. GPCRDB: An information system for G protein-coupled receptors. Nucleic Acids Res. 26:277-281. Humblet, C. and Mirzadegan, T. 1992. Three-dimensional models of G-protein coupled receptors. Ann. Rep. Med. Chem. 27:291-300. Koehler, J.E., Saenger, W., and van Gunsteren, W.F. 1987. A molecular dynamics simulation of crystalline alpha-cyclodextrin hexahydrate. Eur. Biophys. J. 15:197-210. Kunishima, N., Shimada, Y., Tsuji, Y., Sato, T., Yamamoto, M., Kumasaka, T., Nakanishi, S., Jingami, H., and Morikawa, K. 2000. Structural basis of glutamate recognition by a dimeric metabotropic glutamate receptor. Nature 407:971-977. Kyte, J. and Doolittle, R.F. 1982. A simple method for displaying the hydropathic character of a protein. J. Mol. Biol. 157:105-132. Laskowski, R.A., Macarthur, M.W., Moss, D.S., and Thornton, J.M. 1993. PROCHECK: A program to check the stereochemical quality of protein structures. J. Appl. Cryst. 26:31. Leach, A.R. 2000. Molecular Modelling: Principles and Applications. Prentice Hall, Upper Saddle, River, N.J. Malherbe, P., Kratochwil, N., Knoflach, F., Zenner, M.T., Kew, J.N., Kratzeisen, C., Maerki, H.P., Adam, G. and Mutel, V. 2003a. Mutational analysis and molecular modeling of the allosteric binding site of a novel, selective, noncompetitive antagonist of the metabotropic glutamate 1 receptor. J. Biol. Chem. 278:8340-8347. Malherbe, P., Kratochwil, N., Zenner, M.T., Piussi, J., Diener, C., Kratzeisen, C., Fischer, C., and Porter, R.H. 2003b. Mutational analysis and molecular modeling of the binding pocket of the metabotropic glutamate 5 receptor negative modulator 2-methyl-6-(phenylethynyl)pyridine. Mol. Pharmacol. 64:823-832. McCammon, J.A. and Harvey, S.C. 1987. Dynamics of Proteins and Nucleic Acids. Cambridge University Press, Melbourne, Australia. Miedlich, S.U., Gama, L., Seuwen, K., Wolf, R.M., and Breitwieser, G.E. 2004. Homology modeling of the transmembrane domain of the human calcium sensing receptor and localization of an allosteric binding site. J. Biol. Chem. 279:7254-7263. O’Hara, P.J., Sheppard, P.O., Thogersen, H., Venezia, D., Haldeman, B.A., McGrane, V., Houamed, K.M., Thomsen, C., Gilbert, T.L., and Mulvihill, E.R. 1993. The ligand-binding domain in metabotropic glutamate receptors is related to bacterial periplasmic binding proteins. Neuron 11:41-52. Polczewski, K., Kumasaka, T., Hori, T., Behnke, C.A., Motoshima, H., Fox, B.A., Le Trong, I., Teller, D.C., Okada, T., Stenkamp, R.E., Yamamoto, M., and Miyano, M. 2000. Crystal structure of rhodopsin: A G protein-coupled receptor. Science 289:739-745. Ponder, J.W. and Richards, F.M. 1987. Tertiary templates for proteins: Use of packing criteria in the enumeration of allowed sequences for different structural classes. J. Mol. Biol. 193:775-791. Ramachandran, G.N., Ramakrishnan, C., and Sasisekharan, V. 1963. Stereochemistry of polypeptide chain configurations. J. Mol. Biol. 7:95-99. Rognan, D. (ed). 2006. Ligand Design in G Protein–coupled Receptors, Vol. 30. John Wiley & Sons, Hoboken, N.J. Roitberg, A. and Elber, R. 1991. Modeling side chains in peptides and proteins: Application of the locally enhanced sampling and the simulated annealing methods to find minimum energy conformations. J. Chem. Phys. 95:9277-9287.

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Rost, B. 2001. Review: Protein secondary structure prediction continues to rise. J. Struct. Biol. 134:204218. Sali, A., Overington, J.P., Johnson, M.S., and Blundell, T.L. 1990. From comparisons of protein sequences and structures to protein modeling and design. Trends Biochem. Sci. 15:235. Sanchez, R. and Sali, A. 1997. Evaluation of comparative protein structure modeling by MODELLER-3. Proteins 1:50-58. Schertler, G.F.X., Villa, C., and Henderson, R. 1993. Projection structure of rhodopsin. Nature 362:770772. Spassov, V.Z., Yan, L., and Szalma, S. 2002. Introducing an implicit membrane in generalized Born/solvent accessibility continuum solvent models. J. Phys. Chem. 106:8726-8738. Spellmeyer, D.C., Wong, A.K., Bower, M.J., and Blaney, J.M. 1997. Conformational analysis using distance geometry methods. J. Mol. Graph. Model. 15:18-36. Taylor, W.R., Munro, R.E., Petersen, K., and Bywater, R.P. 2003. Ab initio modeling of the N-terminal domain of the secretin receptors. Comput. Biol. Chem. 27:103-114. Trumpp-Kallmeyer, S., Hibert, M.F., Bruinvels, A., and Hoklack, J. 1991. Three-dimensional models of neurotransmitter G-binding protein-coupled receptors. Mol. Pharmacol. 40:8-15. Trumpp-Kallmeyer, S., Hoklack, J., Bruinvels, A., and Hibert, M.F. 1992. Modeling of G-protein-coupled receptors: application to dopamine, adrenaline, serotonin, acetylcholine, and mammalian opsin receptors. J. Med. Chem. 35:3448-3462. Unger, V.M., Hargrave, P.A., Baldwin, J.M., and Schertler, G.F. 1997. Arrangement of rhodopsin transmembrane alpha-helices. Nature 389:203-206. Wilmot, C.M. and Thornton, J.M. 1988. Analysis and prediction of the different types of β-turn in proteins J. Mol. Biol. 203:221-232. Wilmot, C.M. and Thornton, J.M. 1990. β-Turns and their distortions: A proposed new nomenclature. Protein Eng. 3:479-493.

INTERNET RESOURCES http://www.gpcr.org/7tm/articles/model.html The full text of an article about molecular modeling of GPCRs by G. Vriend in 1995.

Contributed by Frank E. Blaney GlaxoSmithKline, NFSP (North) Harlow, Essex United Kingdom

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APPENDIX: CHARMm SCRIPTS CHARMm is one of the most popular force field programs for performing a wide variety of molecular mechanics and dynamics–related tasks. It uses a scripting language where each line is interpreted by the program sequentially. Three characters appearing in a CHARMm command line have a special meaning: 1. An asterisk (*) in the first character of the line means that this is a title line. These are often required by the program. 2. An exclamation mark (!) as a first character indicates a comment line. As with any programming language this helps other readers to follow what is going on in the script. 3. A hyphen (-) at the end of a line indicates a continuation, i.e., that the command is spread over two or more lines. IMPORTANT NOTE: The italicized text in the scripts is commentary and NOT part of the code.

CHARMm Script 1 This script will build the missing coordinates of a protein residue using the residue name and its backbone geometry. *TEMPORARY TITLE LINE FOR CHARMM * set YY 5ht2a human The SET command above means that a variable, YY, is being defined. If it appears anywhere in the script, preceded by a “@” character, it will be interpreted as 5ht2a human. Variables can either be text or numeric. BOMLEV -2 The BOMLEV command sets a level of error checking. If it is exceeded the program will terminate. The next stage is to read in a standard RTF (Residue Topology File). This file, AMINO.BIN, contains all the information about atom names, types and charges and all the definitions for the internal coordinates for each of the standard amino acids. It also contains the definitions of the PATCH files. These are special RTF files which allow modifications of amino acids, e.g., capping it with an N-terminal amide or generating a disulphide bond between two cysteines. Placing the filename in quotes means that the exact case of the path and file name is followed, as UNIX is case-sensitive. The “BIN” suffix means that it is a binary file and this is opened and read with the FILE command. If it were an ASCII file, the CARD command would be used. OPEN READ UNIT 51 FILE NAME "/usr/data/AMINO.BIN" READ RTF UNIT 51 FILE CLOSE UNIT. 51 Drug Discovery Technologies

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The file containing all the force field parameters is now read in. Note that this is an ASCII file. OPEN READ UNIT 12 CARD NAME "/usr/data/PARM.PRM" READ PARAMETERS UNIT 12 CARD CLOSE UNIT 12 The next step is to read in the sequence that is to be built. This will be divided into segments each with its own unique name. As the 7TM bundle is being built, they will be called HLX1, HLX2, . . . ,HLX7. The sequences are read in, in the order that they appear in the starting coordinate file. Each segment starts with a title, ending in a single line containing only a “*” as the first character. Then the number of residues in that segment is read followed by the actual residues in standard 3 letter nomenclature. The GENERATE command uses the information from the RTF file read at the beginning, to set up the internal coordinate lists for each segment. Finally the PATCH residues are applied. As the model is only of the TM bundle, amide patches are most appropriate, i.e., the NH3 + is exchanged for a CH3 CONH group and the COO- for a CONHCH3 . This is just an approximation for the fact that there are missing residues in the model which are not being built and it prevents unnecessary large electrostatic attractive forces between the helix ends during the minimization stage. READ SEQUENCE CARD *5h2a human receptor model * 33 HIS LEU GLN GLU LYS ASN TRP SER ALA LEU LEU THR ALA VAL VAL ILE ILE LEU THR ILE ALA GLY ASN ILE LEU VAL ILE MET ALA VAL SER LEU GLU ! Generate the PSF for helix 1 GENERATE HLX1 SETUP PATCH NTER HLX1 1 WARN SETUP PATCH NACT HLX1 1 WARN SETUP PATCH CMAM HLX1 33 WARN SETUP

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READ SEQUENCE CARD *5h2a human receptor model * 32 ASN ALA THR ASN TYR PHE LEU MET SER LEU ALA ILE ALA ASP MET LEU LEU GLY PHE LEU VAL MET PRO VAL SER MET LEU THR ILE LEU TYR GLY ! Generate the PSF for helix 2 GENERATE HLX2 SETUP PATCH NTER HLX2 1 WARN SETUP PATCH NACT HLX2 1 WARN SETUP PATCH CMAM HLX2 32 WARN SETUP READ SEQUENCE CARD *5h2a human receptor model * 36 LEU PRO SER LYS LEU CYS ALA VAL TRP ILE TYR LEU ASP VAL LEU PHE SER THR ALA SER ILE MET HIS LEU CYS ALA ILE SER LEU ASP ARG TYR VAL ALA ILE GLN ! Generate the PSF for helix 3 GENERATE HLX3 SETUP PATCH NTER HLX3 1 WARN SETUP PATCH NACT HLX3 1 WARN SETUP PATCH CMAM HLX3 36 WARN SETUP

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READ SEQUENCE CARD *5h2a human receptor model * 24 ARG THR LYS ALA PHE LEU LYS ILE ILE ALA VAL TRP THR ILE SER VAL GLY ILE SER MET PRO ILE PRO VAL ! Generate the PSF for helix 4 GENERATE HLX4 SETUP PATCH NTER HLX4 1 WARN SETUP PATCH NACT HLX4 1 WARN SETUP PATCH CMAM HLX4 24 WARN SETUP READ SEQUENCE CARD *5h2a human receptor model * 28 ALA ASP ASP ASN PHE VAL LEU ILE GLY SER PHE VAL SER PHE PHE ILE PRO LEU THR ILE MET VAL ILE THR TYR PHE LEU THR ! Generate the PSF for helix 5 GENERATE HLX5 SETUP PATCH NTER HLX5 1 WARN SETUP PATCH NACT HLX5 1 WARN SETUP PATCH CMAM HLX5 28 WARN SETUP

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READ SEQUENCE CARD *5h2a human receptor model * 33 ASN GLU GLN LYS ALA CYS LYS VAL LEU GLY ILE VAL PHE PHE LEU PHE VAL VAL MET TRP CYS PRO PHE PHE ILE THR ASN ILE MET ALA VAL ILE CYS ! Generate the PSF for helix 6 GENERATE HLX6 SETUP PATCH NTER HLX6 1 WARN SETUP PATCH NACT HLX6 1 WARN SETUP PATCH CMAM HLX6 33 WARN SETUP READ SEQUENCE CARD *5h2a human receptor model * 24 GLY ALA LEU LEU ASN VAL PHE VAL TRP ILE GLY TYR LEU SER SER ALA VAL ASN PRO LEU VAL TYR THR LEU ! Generate the PSF for helix 7 GENERATE HLX7 SETUP PATCH GLYP HLX7 1 WARN SETUP PATCH NACT HLX7 1 WARN SETUP PATCH CMAM HLX7 24 WARN SETUP The internal coordinate lists and other necessary information for the model are complete. It is now necessary to fill in the actual values for the various bond lengths, angles and dihedral. This comes from the starting model built during the earlier stages of the homology modelling process. The next stage therefore is to read in the Cartesian coordinates of this model. Note here the use of the variable YY, defined at the start of the script.

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OPEN READ UNIT 51 CARD NAME @YY start.crd or replace with the following after the rotamer library (see below) OPEN READ UNIT 51 CARD NAME @YY ROTAMER END.CRD READ COOR UNIT 51 CARD CLOSE UNIT 51 Now the internal coordinates are generated from a mixture of the Cartesian coordinates which have just been read and the standard values from the AMINO.BIN file. The next two lines perform this task. The IC BUILD command fills in Cartesian coordinates for those atoms which may be missing from the starting model. For example, the atoms of the PATCH residues are often not present, or hydrogens may be missing. Also CHARMm has the ability to mutate residues just by putting the correct name in the READ SEQUENCE command. Thus an entire starting homology model could be built manually without the use of another computer program, simply by providing a backbone coordinate file with the correct atom names. This of course would be time consuming to edit by hand. IC FILL PRESERVE IC PARAMETERS IC BUILD The full coordinates of the starting model have now been generated. It is at this stage that it would be appropriate to apply a rotamer library. CHARMm itself does not do this so it is necessary to write out the coordinates, then read them into an appropriate modeling package or a program such as SCWRL, and perform the rotamer search. The new coordinates are then read back into CHARMm. The previous READ statement needs to be changed with the new filename before performing the IC commands above. OPEN WRITE UNIT 51 CARD NAME @YY ROTAMER START.CRD WRITE COOR UNIT 51 CARD CLOSE UNIT 51 Having read in the coordinates after the rotamer library application, everything is now ready to perform the energy minimization. It is often useful to do this in stages. In the next few lines of the script, the coordinates of the backbone will be kept constant. The side-chain atoms of those residues which were previously mentioned as being important structurally or functionally (these are the key TM motifs used in the sequence alignment) will also be fixed. The CONS (constraint) command allows one to set a variety of constraint types. This is usually followed by a selection command (SELE . . . . . . END) which defines which atoms, residues, segments, etc., the constraint applies to. The wild card character, “*”, in, for example, HLX1 23 *, means all atoms in the 23rd residue of segment HLX1. Likewise HLX* * CA refers to all the C-alpha atoms of ALL residues in ALL seven segments starting with the letters, HLX. Note the use of the hyphen at the end of each line, indicating that this line is continued onto the next. Molecular Modeling of 7TM Helical Receptors

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cons fix sele atom HLX1 22 * .or. atom HLX1 23 * .or. atom HLX2 14 * .or. atom HLX3 6 * .or. atom HLX3 30 * .or. atom HLX3 31 * .or. atom HLX3 32 * .or. atom HLX4 12 * .or. atom HLX5 17 * .or. atom HLX6 16 * .or. atom HLX6 22 * .or. atom HLX7 18 * .or. atom HLX7 19 * .or. atom HLX* * CA .or. atom HLX* * C .or. atom HLX* * N .or. atom HLX* * H .or. atom HLX* * O end The following lines perform the actual energy minimization. The first two set the various parameters such as nonbonded cutoffs, dielectric constant, etc., for the calculation. Then the minimization is done using the Steepest Descent (SD) algorithm for 300 steps with the energy terms being printed out every 50 steps. This is a fast method which is good for relieving major problems in the structure. This is followed by 3000 steps with the more refined gradient-based Newton-Raphson algorithm (ABNR). Finally the intermediate geometry at this stage is written out to a file. NBONDED NBXMOD 5 ATOM CDIE EPS 7.0 SHIFT VATOM VSHIFT CUTNB 15.0 CTOFNB 14.0 E14FAC 0.5 WMIN 1.5 MINI SD NSTEP 300 NPRINT 50 MINI ABNR NSTEP 3000 NPRINT 50 OPEN WRITE UNIT 51 CARD NAME @YY INTERMED OPTIM.CRD WRITE COOR UNIT 51 CARD CLOSE UNIT 51 Typically the next stage of the modeling would be to relax the backbone constraints completely and reminimize the whole model. In practice it is useful to at least maintain the hydrogen bonding pattern of the helices by generating a set of distance constraints (NOE) between every ith backbone carbonyl and its corresponding i+4th NH. Rather than entering every constraint this can be carried out using what is known as a STREAM file. First the previous constraints are turned off and the key important amino acids are again fixed. The distance constraints list is then initialized and the stream file, helix noe.str, is applied to each helix in turn. cons fix sele none end This turns off all previous constraints.

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cons fix sele atom HLX1 22 * .or. atom HLX1 23 * .or. atom HLX2 14 * .or. atom HLX3 6 * .or. atom HLX3 30 * .or. atom HLX3 31 * .or. atom HLX3 32 * .or. atom HLX4 12 * .or. atom HLX5 17 * .or. atom HLX6 16 * .or. atom HLX6 22 * .or. atom HLX7 18 * .or. atom HLX7 19 * end noe reset end set A 1 set B 33 set G 100 set J 100 set K 100 set Z HLX1 open read unit 52 card name helix noe.str stream unit 52 close unit 52 set A 1 set B 32 set G 23 set J 100 set K 100 set Z HLX2 open read unit 52 card name helix noe.str stream unit 52 close unit 52

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set A 1 set B 36 set G 2 set J 100 set K 100 set Z HLX3 open read unit 52 card name helix noe.str stream unit 52 close unit 52 set A 1 set B 24 set G 21 set J 23 set K 100 set Z HLX4 open read unit 52 card name helix noe.str stream unit 52 close unit 52 set A 1 set B 28 set G 17 set J 100 set K 100 set Z HLX5 open read unit 52 card name helix noe.str stream unit 52 close unit 52

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set A 1 set B 33 set G 22 set J 100 set K 100 set Z HLX6 open read unit 52 card name helix noe.str stream unit 52 close unit 52 set A 1 set B 24 set G 19 set J 100 set K 100 set Z HLX7 open read unit 52 card name helix noe.str stream unit 52 close unit 52

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A stream file is essentially a routine which is continuously repeated until some condition is met. It is similar to the concept of a subroutine in computer programming. As can be seen from above six variables, A, B, G, J, K and Z are set before the helix noe.str file is run. Here A and B are the first and last residues of the helix. Proline residues do not contain a backbone NH group and therefore cannot form the normal hydrogen bond in a helix. They often therefore give rise to kinks in the helical axis. The variables G, J and K are the positions of prolines in the TM region, with a value of 100 meaning that there is no other proline present. Z is the name of the appropriate segment. The stream file itself is shown in Figure 9.8.11. In this stream file variable A starts with the value 1, and the first two lines set a new variable to have a value of A+4, i.e., 5. This is because in a regular alpha helix, the backbone hydrogen bond exists between the ith and i+4th residue. A label is now set which is a point in the routine to which it will repeatedly return. The routine now checks for prolines. If residue number D is a proline, it bypasses the distance constraint by going to the label SKIP. If it is not a proline, then the NOE command is used to maintain the distance between the ith backbone oxygen and the i+4th ◦ backbone amide hydrogen, between a normal hydrogen bond distance range of 1.8A to ◦ 2.35A. The routine then increments the variables A and D by 1, and checks to see if D has passed the last residue of the helix, B. If so, the routine will continue by returning to the main program. If D is still within the helix range, the routine goes back to the label START and repeats the cycle. Continuing with the CHARMm script, another round of energy minimization is carried out as before and the final 5-HT2A GPCR model of the

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Figure 9.8.11

The HELIX NOE.str stream file.

TM bundle is written out to a new file. This can be displayed and manipulated using one of the normal graphics programs. NBONDED NBXMOD 5 ATOM CDIE EPS 7.0 SHIFT VATOM VSHIFT CUTNB 15.0 CTOFNB 14.0 E14FAC 0.5 WMIN 1.5 MINI SD NSTEP 500 NPRINT 50 MINI ABNR NSTEP 5000 NPRINT 50 OPEN WRITE UNIT 51 CARD NAME @YY OPTIM.CRD WRITE COOR UNIT 51 CARD CLOSE UNIT 51 STOP

CHARMm Script 2 This modification of script 1 will unfold the top of TM5 to bridge the gap required to form a required loop structure. READ SEQUENCE CARD *5h2a human receptor model * Current Protocols in Pharmacology

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24 ARG THR LYS ALA PHE LEU LYS ILE ILE ALA VAL TRP THR ILE SER VAL GLY ILE SER MET PRO ILE PRO VAL ! Generate the PSF for helix 4 GENERATE HLX4 SETUP PATCH NTER HLX4 1 WARN SETUP PATCH NACT HLX4 1 WARN SETUP The segment, HLX4 is as before except that the C-terminal carboxamide patch is no longer applied. At this stage ECL2 is now built before starting with HLX5. READ SEQUENCE CARD *5h2a human receptor model * 17 PHE GLY LEU GLN ASP ASP SER LYS VAL PHE LYS GLU GLY SER CYS LEU LEU generate ECL2 setup patch LINK ECL2 1 HLX4 24 warn setup patch DISU HLX3 6 ECL2 15 warn setup Two different types of patch residues are now applied to this segment. The LINK patch tells the program to form a peptide bond between the first residue of ECL2 and the last (24th) residue of HLX4. The DISU patch instructs CHARMm to generate a disulfide bond between cysteine 15 in ECL2 and cystein 6 in HLX3. Now HLX5 is generated as before except that another peptide bond is formed between its first residue and the last (17th ) residue of ECL2. This bond will initially be too long because of the short loop length but it will be quickly optimized during the subsequent minimization. READ SEQUENCE CARD *5h2a human receptor model * Molecular Modeling of 7TM Helical Receptors

28 ALA ASP ASP ASN PHE VAL LEU ILE GLY SER

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PHE VAL SER PHE PHE ILE PRO LEU THR ILE MET VAL ILE THR TYR PHE LEU THR ! Generate the PSF for helix 5 GENERATE HLX5 SETUP PATCH NTER HLX5 1 WARN SETUP PATCH LINK HLX5 1 ECL2 17 WARN SETUP PATCH CMAM HLX5 28 WARN SETUP The rest of the command script will be as before until the NOE stream file for HLX5 is reached. To help relieve the strain, it is necessary to unfold the top of HLX5 slightly. This is easily achieved by starting the stream at residue 4, rather than 1, thus allowing the first 3 residues to fully relax. set A 4 set B 28 set G 17 set J 100 set K 100 set Z HLX5 open read unit 52 card name helix noe.str stream unit 52 close unit 52 However, because ECL2 is particularly strained in the starting geometry, it is desirable to optimize this region by itself before moving on to a full minimization. In addition, it is useful to allow the loop to further sample conformational space by using molecular dynamics, as the minimization procedure by itself often results in a loop structure which lies outside allowed Ramachandran space. The DEFINE command allows one to select a set of residues which can be treated separately as a single group. Rather than proceeding to the NOE constraints stage, the following list of commands can be inserted into the script after the IC BUILD command. define moving sele atom ECL2 * * .or. atom HLX5 1 * .or. atom HLX5 2 * .or. atom HLX5 3 * end cons fix sele .not. moving end

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This has defined a set of residues as all those in ECL2 together with the first three residues of HLX5, with the name “moving”. These can now be manipulated together as a group simply by using that name. In this case all atoms in the protein will be fixed EXCEPT for those defined in “moving”. The subsequent minimization and dynamics simulation will only affect these atoms. Some minimization is always necessary before performing a dynamics simulation. As only 20 residues in total are being optimized, it is appropriate to reduce the number of steps. NBONDED NBXMOD 5 ATOM RDIE SHIFT VATOM VSHIFT CUTNB 15.0 CTOFNB 14.0 E14FAC 0.5 WMIN 1.5 ! MINI SD NSTEP 200 NPRINT 50 MINI ABNR NSTEP 2000 NPRINT 50 open write unit 56 card name @YY LOOPOPTIM.CRD write COOR UNIT 56 CARD close unit 56 The molecular dynamics simulation is now started. This proceeds in three stages; (1) heating (HT), during which the protein is heated to the desired temperature, typically 300K; (2) equilibration (EQ), where various parameters such as temperature, total energy, pressure, atom velocities, etc. are monitored until they are deemed to be stable, and (3) production (PROD), where the actual motions of the molecule are simulated under the specified conditions, and the trajectory data is collected. For each of the three stages, the data is written to a series of files. These are (a) the coordinates at each step (DCD), (b) the atom velocities (DVL), (c) the total energy components, i.e., kinetic, potential, electrostatic, bonding, etc. (ENE). A restart (RST) file is also written which contains all the information necessary to start the calculation from any point at which it may have been stopped. Of the many options below, important ones worth mentioning here are the time step (TIME 0.001psec), the total number of steps at that stage (NSTEP), the starting and final temperatures, the temperature increment and the number of steps calculated at that increment (FIRSTT, FINALT, TEMINC and IHTFRQ respectively) and the interval at which the coordinates are saved to file (NSAVC). ! OPEN UNIT 30 WRITE FILE NAME @YY HT.DCD OPEN UNIT 31 WRITE CARD NAME @YY HT.RST OPEN UNIT 33 WRITE FILE NAME @YY HT.DVL

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OPEN UNIT 34 WRITE CARD NAME @YY HT.ENE SHAKE BONH DYNAMICS VERLET STRT TIME 0.001 NSTEP 100000 ISEED 314159 IPRFRQ 100 IHTFRQ 100 IEQFRQ 0 NTRFRQ 400 IUNREA -1 IUNCRD 30 IUNWRI 31 IUNVEL 33 KUNIT 34 NPRINT 50 NSAVC 200 NSAVV 200 INBFRQ 200 FIRSTT 0.0 FINALT 1000.0 TEMINC 1.0 IASORS 1 IASVEL 1 ISCVEL 0 ICHECW 1 TWINDH 10.0 TWINDL -10.0 CLOSE UNIT 30 CLOSE UNIT 31 CLOSE UNIT 33 CLOSE UNIT 34 ! ! now do the equilibration run ! OPEN UNIT 29 READ CARD NAME @YY HT.RST OPEN UNIT 30 WRITE FILE NAME @YY EQ.DCD OPEN UNIT 31 WRITE CARD NAME @YY EQ.RST OPEN UNIT 33 WRITE FILE NAME @YY EQ.DVL OPEN UNIT 34 WRITE CARD NAME @YY EQ.ENE SHAKE BONH DYNAMICS VERLET REST TIME 0.001 NSTEP 30000 ISEED 314159 IPRFRQ 100 IHTFRQ 0 IEQFRQ 200 NTRFRQ 400 IUNREA 29 IUNCRD 30 IUNWRI 31 IUNVEL 33 KUNIT 34 NPRINT 50 NSAVC 200 NSAVV 200 INBFRQ 200 FIRSTT 1000.0 FINALT 1000.0 IASORS 1 IASVEL 1 ISCVEL 0 ICHECW 1 TWINDH 10.0 TWINDL -10.0

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! CLOSE UNIT 29 CLOSE UNIT 30 CLOSE UNIT 31 CLOSE UNIT 33 CLOSE UNIT 34 ! label FRANK5 ! production run OPEN UNIT 29 READ CARD NAME @YY EQ.RST OPEN UNIT 30 WRITE FILE NAME @YY PROD.DCD OPEN UNIT 31 WRITE CARD NAME @YY PROD.RST OPEN UNIT 33 WRITE FILE NAME @YY PROD.DVL OPEN UNIT 34 WRITE CARD NAME @YY PROD.ENE ! SHAKE BONH DYNAMICS VERLET REST TIME 0.001 NSTEP 200000 ISEED 314159 IPRFRQ 100 IHTFRQ 0 IEQFRQ 0 NTRFRQ 400 IUNREA 29 IUNCRD 30 IUNWRI 31 IUNVEL 33 KUNIT 34 NPRINT 50 NSAVC 200 NSAVV 200 INBFRQ 200 FIRSTT 1000.0 FINALT 1000.0 IASORS 1 IASVEL 1 ISCVEL 0 ICHECW 1 TWINDH 10.0 TWINDL 10.0 ! CLOSE UNIT 29 CLOSE UNIT 30 CLOSE UNIT 31 CLOSE UNIT 33 CLOSE UNIT 34 Molecular Modeling of 7TM Helical Receptors

! stop

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CHARMm Script 3 This script can be used to extract each step from the production trajectory coordinate file. open unit 30 read file name @YY PRODA.DCD set 9 1 set CC 1 This opens the file containing the coordinates for each step. It defines two variables, 9 and CC, and sets their initial values to be 1. LABEL STEPER ! REWIND UNIT 30 READ COOR UNIT 30 FILE IFILE @9 Each step is stored sequentially as a complete coordinate set in the DCD file. Thus each set of coordinates can be read individually by defining the step number. In the first pass of the loop, the variable 9 is translated as 1, i.e., it opens and reads the coordinate set for step number 1. open unit 35 write card name @YY frame @CC.crd write coor card unit 35 close unit 35 The coordinates are written to an individual file with the variables YY and CC translated appropriately. incr CC by 1 incr 9 by 1 if 9 gt 1000 goto STEPEND goto STEPER label STEPEND ! STOP A total of 1000 steps were written out to the DCD file. The loop above therefore extracts each one and writes it out with an appropriate numbered suffix, then ends. Each of these conformations can be individually minimized as described earlier. Drug Discovery Technologies

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Overview of Drug Discovery and Development INTRODUCTION Over the past century, drugs produced by the pharmaceutical industry have increased life expectancy and the quality of life for many individuals in the developed world, and are beginning to make inroads with respect to improving health care in the Third World. Bacterial infections, polio, smallpox, tuberculosis, and other diseases that were often fatal at the beginning of the 20th century have, to a very great extent, become minor public health concerns. Drugs have also altered, albeit controversially, the fabric of society, from birth control pills and those that improve male sexual performance to those that increase life expectancy, shifting population demographics towards an increasingly elderly population. As a consequence of the abovementioned increase in life expectancy, cancer, as well as neurodegenerative diseases, degenerative diseases, traumatic diseases (e.g., stroke), and autoimmune diseases have increased in prevalence, resulting in a greater portion of the gross national product in the G7 countries being focused on the provision of health care. Thus, diseases related to aging that have resulted from the success of the pharmaceutical industry have become a major financial and social challenge to society. For individuals infected with AIDS, access to drug treatment has transformed a disease with a once-fatal prognosis into a chronic condition. Similarly, cancer is becoming viewed as a potentially chronic, rather than fatal, array of diseases with the advent of drugs like Gleevec (Novartis) and Herceptin (Genentech), as well as newer, noncytotoxic approaches, such as angiogenesis inhibitors. Drugs have also replaced costly surgical interventions, e.g., gastric ulceration is now routinely treated with histamine H2 receptor blockers and/or proton pump inhibitors in conjunction with antibiotics to eradicate the causal H. pylori bacterium involved in ulcer genesis, a controversial discovery that was the topic of the 2005 Nobel Prize in Medicine. In the 21st century there remains, however, a need for novel, innovative therapeutic agents, not only for the many diseases associated with the aging process for which there are generally no effective medications

UNIT 9.9

(e.g., Alzheimer’s disease, mild cognitive impairment, stroke), but also in areas that are historically well served, e.g., anti-infectives, where the development of bacterial resistance requires the generation of new classes of antibiotics with novel mechanisms of action ideally based on the bacterial genome (Binder et al., 1999). The process of drug discovery encompasses many complex processes and scientific disciplines that are focused on the identification, chemical and pharmaceutical optimization, and development of novel compounds to treat human disease states. These agents, known as new chemical entities (NCEs) in their exploratory phase and as drugs in the form approved for marketing by regulatory authorities, generally produce their beneficial effects either by restoring normal cellular homeostasis or by killing viral, fungal, and bacterial pathogens. Agonist ligands can be used either to replace the neurotransmitters/hormones/ modulators that have decreased in quantity/ availability as a result of the disease (or aging) process (e.g., dopamine in Parkinson’s disease) or to improve the efficacy of faulty signal transduction processes. Antagonist ligands produce their beneficial actions via their ability to block the effects of overactive cellular systems (e.g., β-adrenoceptors in hypertension), a phenomenon that may relate to aging being a product of constitutive receptor activity (Kenakin, 2003). As a distinct and emerging scientific discipline, drug discovery is focused on the practical application of novel findings in chemical and biomedical research to identify NCEs that will, subject to the optimization of their intrinsic physicochemical, pharmacodynamic (PD), and pharmacokinetic (PK) properties, become compounds that have sufficient drug-like properties to enter human clinical trials. The generic attributes of an NCE that make it drug-like are diverse and are frequently interdependent (Table 9.9.1). Modifying a molecule to improve its physiochemical attributes, e.g., bioavailability, half-life, or blood-brain barrier penetration, can reduce other, positive attributes, e.g., efficacy and/or target selectivity. The optimization process is Drug Discovery Technologies

Contributed by Mark A. Ator, John P. Mallamo, and Michael Williams Current Protocols in Pharmacology (2006) 9.9.1-9.9.26 C 2006 by John Wiley & Sons, Inc. Copyright


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thus iterative rather than linear—in constant change, depending on the area/property manipulated. A successful drug discovery project depends on: 1. Appropriate target selection and access to the target. 2. Identification of “hits,” i.e., NCEs that interact with high affinity with the target. 3. Optimization of these hits to lead compounds, which are more selective in their interactions with the target and have more drug-like properties. Optimization of these properties is accomplished through a hierarchically complex series of in vitro and in vivo assays that measure efficacy, selectivity, side-effect liability (off-target activity), absorption, distribution, metabolism and excretion (ADME) and nonclinical toxicology (Pritchard et al., 2003). In order to do this in an effective and integrated manner, it is essential both to establish clear criteria and time-related milestones for compound advancement and to effectively manage the process to set expectations (SamsDodd, 2005). It is also crucial to build upon the existing knowledge of how drugs effective against known targets have evolved, in order to understand the realistic potential for identifying, validating, and prioritizing new ones. The core disciplines in the drug discovery process are medicinal chemistry and pharmacology, the latter being an integrative discipline that utilizes all available technologies to address questions related to how compounds interact with their cellular targets and subsequently modify cell and tissue function (Williams, 2005).

PHARMACOLOGY

Overview of Drug Discovery and Development

Originating in the seminal work of German, French, and English physiologists, the discipline of pharmacology was formalized in the work of Ehrlich and Langley, who independently conceptualized the existence of “receptive substances,” or receptors, on cells. This led to the seminal “lock and key” hypothesis that now, more than a century after its publication, remains the unifying element for drug discovery irrespective of the target type (Kenakin, 2004). Pharmacology, while overshadowed for a period of nearly three decades at the end of the 20th century by an overtly reductionistic, biotechnology-driven focus on technology-based approaches, has now re-emerged at center stage as the driving force for successful drug discovery (In Vivo Pharmacology Training Group, 2002; Williams, 2005). Acting as a central integra-

tive discipline for the drug discovery process, pharmacology develops a hypothesis and then recruits the appropriate biological disciplines to gather information from in vitro, tissue, and whole-animal systems to support or refute the hypothesis. Molecular pharmacology, molecular biology, pharmacokinetics, recombinant protein expression, cloning, cell transfection, behavior, and animal disease models are all disciplines used in pharmacology. Pharmacology is based on three basic concepts: (1) the existence of specific molecular drug targets both on, and within, the cell; (2) receptor theory, based on the Law of Mass Action (LMA), defining the pharmacodynamic outcome of the ligand interaction with its cognate receptor(s)/target(s) as being doseand concentration-dependent, reversible, and selective; and (3) a null hypothesis–based integrative approach to experimentation (Kenakin, 2004). Pharmacology, unlike molecular biology, is a quantitative rather than qualitative science, with compound activity being characterized in terms of its IC50 , EC50 , Ki , or pA2 value(s) (Neubig et al., 2003). These values reflect both the pharmacokinetic (PK) and pharmacodynamic (PD) actions of the compound—the former representing the effect of the host environment on the NCE and the latter representing the effect of the NCE on the host environment.

MEDICINAL CHEMISTRY The role of medicinal chemistry in the drug discovery process is to design and make: (1) compounds as tools to validate biological systems; and (2) new chemical entities (NCEs) as leads for optimization to bona fide candidates with appropriate drug-like properties. Like pharmacology, medicinal chemistry comprises many subdisciplines and associated disciplines including organic, analytical, and computational chemistry, as well as parallel synthesis/hit-to-lead chemistry, discussed in detail below. The unifying theme of medicinal chemistry is to make drug-like molecules that have unique target recognition characteristics used to derive a structure-activity relationship (SAR) and that impart affinity and selectivity for the target of interest; the necessary intrinsic efficacy (or lack thereof) to modulate the deficit in cell function associated with the targeted disease state; bioavailability, usually so that the compound can be given orally; metabolic and chemical stability; chiral purity with a facile and cost-effective synthetic route; and the necessary novelty to be patentable and to recoup the research investment made in discovering it.

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Table 9.9.1 Drug Characteristics of an NCEa

Synthesis and chemical properties Facile synthetic route from readily available starting materials; < =8 steps with chiral and chemical purity >98%. Good yields (>40%) Patentable Aqueous solubility at physiological pH >10 µg/ml Appropriate solid-state properties (stable crystal polymorph, optimal salt form) Suitable physical properties (e.g., pKa, cLogP (100-fold selective in panel of >100 targets (receptors/enzymes) Defined SAR Similarly active in both recombinant and native cell systems Active against molecular target in preclinical efficacy model Efficacy Agonist or antagonist mechanism demonstrated in appropriate cellular systems (native and recombinant) Efficacy established in ex vivo system and animal model of disease state Plasma levels in pharmacology experiments associated with efficacy and side effects (PK/PD model) ADME properties Metabolically stable in liver S-9 preparation from multiple species, including human Caco-2 permeability; compound is permeable with little evidence for P-gp substrate activity Identification of primary metabolic pathways and routes of elimination Minimal potential for drug-drug interactions Absence of potent inhibition of CYP isozymes in human recombinant and microsomal systems Minimal induction of CYP3A4 in fresh human hepatocytes Minimal human P-gp interactions Protein binding (preclinical species and human); 20% Rodent PK dose escalation and tolerability; dose-proportional increases in exposure Rodent PK fed/fasted Rodent repeat-dose PK/tolerability; no decrease in plasma levels after repeat dosing, compound is tolerated Safety pharmacology Therapeutic index >30; optimally >100 hERG inactive >30 µM (if active, followed by patch clamp and telemeterized rat studies) CV safety (blood pressure, heart rate, dP/dT) CNS safety (core battery studies, Category A, ICH7A) General behavioral observations Spontaneous motor activity General anesthetic effects Potential synergism/antagonism with general anesthetics Effects on convulsions (proconvulsant activity and synergy with convulsive agents) Effects on body temperature Effects on GI motility and renal function Repeat-dose PK/tolerability (nonrodent) Genetic toxicity (e.g., Ames, in vitro micronucleus, chromosomal aberrations), negative a Adapted from Williams (2005).

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DRUG DISCOVERY

Overview of Drug Discovery and Development

Drug discovery remains an extremely costly process because of the inherent risk in applying novel science to produce products to treat human disease. While there may be compelling data from studies at the molecular level, more recently supported by human genetics, that have been recapitulated in animal models, for unknown reasons compounds that should be efficacious in humans often are not. A recent example of this involved several druglike antagonists of the neurokinin-1 (NK-1) or Substance P receptor, which produced robust activity in multiple animal models of pain yet singularly failed to show any evidence of analgesia in humans (Hill, 2000). The most recent estimate (DiMasi et al., 2003) of the preapproval cost of an NCE advancing to the market was $802 million (calculated on the basis of year-2000 dollars). This figure was based on a cohort of 68 selforiginated new drugs developed between 1983 and 2000 by ten major pharmaceutical companies. The factors contributing to this cost include: (1) the inherent complexity of the drug discovery process; and (2) increases in the attrition rate. The more that is learned about the properties of an NCE as it advances through the approval process, the more definable the problems become. The complexity of both the biological systems and the regulatory processes involved in moving a compound from in vitro studies to human testing make drug discovery a difficult endeavor, especially as many of the compounds being advanced are themselves providing the proof of concept. Thus, in areas of emerging novel science where there are few, if any, truly effective drugs (neurodegenerative diseases, stroke, septic shock, many cancers), new compounds (re)define the systems that are being studied, adding an additional challenge that increases the potential for failure. The probability of a new drug discovery program progressing from initiation to the approval of a NCE is thus low, with estimates of 1 in 100 to 1 in 5000 compounds reaching the clinic (Roberts, 2003). For new targets, only 3% of projects reach the preclinical development stage, as compared with 17% of those focused on established targets (Carney, 2005). For the purposes of this overview, a semi-parallel approach to drug discovery is described, originating with target identification through the NDA (New Drug Application) process (Fig. 9.9.1). Following identification/selection and confidence build-

ing/validation of a suitable molecular target (Williams, 2003; Kopec et al., 2005), an appropriate cell line or tissue source containing the target is prepared and used to develop highthroughput biochemical screening (HTS) assays. The Lead Discovery stage, a critical step in the overall drug discovery process, begins with an in vitro assay that is used to screen a variety of compound libraries and sometimes natural product sources for hits that can serve as the starting point for a medicinal chemistry optimization effort. Hits are NCEs that demonstrate an initial interaction with a target, often in the high nanomolar/low micromolar range. Once hits have been reconfirmed, a process that involves both assessing the biological activity in repeated assays and also reconfirming the structure, they are considered lead compounds. In the absence of tractable lead compounds, the drug discovery process cannot proceed. After leads are identified, their in vitro and in vivo activity, selectivity, and pharmacokinetic characteristics are optimized in an iterative, multifactorial process, leading to the selection of a development candidate. Following the preclinical development process and filing of an Investigational New Drug (IND) application, the compound enters clinical studies that assess its safety and efficacy (Pritchard et al., 2003). If clinical studies are positive and the NCE is safe, the process culminates in the filing of a New Drug Application (NDA) requesting clearance to market the compound.

DRUG TARGETS Receptors Of the approximately 500 known molecular targets through which currently available drugs act, 71% are receptors (Drews, 2000). Of these drugs, approximately 90% produce their effects by antagonizing the actions of endogenous agonists (Sneader, 1985). Enzyme inhibition has also been a fruitful arena for drug discovery, with 317 marketed drugs directed against 71 enzyme targets (Copeland, 2005; Robertson, 2005). The latter are also particularly well represented in the anti-infective area, where differences between the host and pathogen genomes can be readily exploited. With advances in the understanding of tissue complexity driven by draft maps of the human genome (Lander et al., 2001; Venter et al., 2001), the number of potential drug targets is in significant excess of the 500 to 700 at which current drugs act.

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Figure 9.9.1 The drug discovery process: key events and timelines. The preclinical stage involves the gamut of activities from target validation to the identification of an NCE for scale-up and safety evaluation in anticipation of an IND submission. The compound then proceeds through the various sequential stages of the clinical trials process, culminating in the NDA. Attrition rates indicate that 1 in 5000 compounds are required for an NDA and that, on average, only 10% of compounds that enter clinical trials make it to the NDA stage. For further details on attrition rates see Roberts (2003) and Kola and Landis (2004).

Receptors exist in four major classes: 1. Heptahelical, 7-transmembrane (7-TM) G-protein-coupled receptors (GPCRs). 2. Ion channels. 3. Transcription factor/intracellular receptors. 4. Enzyme-associated receptors. The 7-TM/GPCRs have been the most fertile class for drug discovery, and include receptors for the bioamine neurotransmitters (e.g., 5-HT, dopamine), peptide hormones, and lipid signaling molecules (Vassilatis et al., 2003). This has not, until recently, been by design, but because many drugs discovered before the advent of molecular biology were subsequently found to act at 7TM receptors (Drews, 2000). While many 7TM receptors elicit their physiological effects via G-protein signal transduction mechanisms, there are also instances where ion channels use G-proteins for signal transduction, where unique, non-7TM receptors act via G-proteins (Maghazachi, 2005), and where 7TM receptors do not use G-proteins for signal transduction. Hence, the term 7TM is more appropriately used than GPCR Current Protocols in Pharmacology

to describe this receptor class (Lefkowitz, 2004). Between 1000 and 2000 7TM receptors are present in the human genome, with over 1000 of these coding for odorant and pheromone receptors. These include the orphan 7TM receptors, over 100 of which remain to be deorphanized, lacking cognate ligands and ascribed function that in the absence of both ligand and function are viewed as having considerable potential as novel drug targets, an example of minimal knowledge provoking maximal interest. 7TM receptors are classified in four main families: Family 1: The majority of 7TM/GPCRs for peptide hormones, neurotransmitters, odorants and a large group of orphans. These can be divided by structural similarity into subfamilies: 1a, which includes rhodopsin, α-adrenoceptors, thrombin, and the adenosine A2A receptor, with a binding site localized within the 7TM motif; 1b, which includes receptors for peptides with the ligand-binding site in the extracellular loops, the N-terminal, and the superior regions of the TM motifs;

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and 1c, which includes glycoprotein receptors where ligand binding is primarily extracellular. Family 2 is morphologically, but not sequence related to the 1c family and includes four GPCRs activated by the hormones glucagon, secretin, and VIP-PACAP. Family 3 includes four metabotropic glutamate receptors and three GABAB receptors. Family 4 is a group of 7TM receptors that includes “frizzled” and “smoothened” and that are associated with the Wnt pathway involved in embryonic development and tumorogenesis (Caricasole et al., 2005). The majority of 7TM receptors signal through heterotrimeric G-proteins that exist as complexes formed from α, β and γ -type subunits, of which there are multiple subtypes. α subunits are most fully characterized and consist of four G-protein families that interact with 7TM receptors: (1) Gs - activating adenylyl cyclase; (2) Gi/o inhibiting adenylyl cyclase and regulating ion channel function and activation of cGMP phosphodiesterase; (3) Gq activating phospholipase C; and (4) G12 regulating Na+ /H+ exchange. β and γ subunits are less well characterized, being involved in ion channel and phospholipase C signaling, membrane trafficking, and receptor interactions. The cyclic nucleotide phosphodiesterases (PDEs) responsible for the hydrolytic degradation of cAMP and cGMP exist in more than 15 isoforms. The protein kinase family, which includes PKA and PKC, the GPCR kinases, GRK 1 to 6, and another 500+ kinases (Vieth et al., 2005) and the protein phosphatases, responsible for protein dephosphorylation (Gee and Mansuy, 2005) and potentially numbering in excess of 1300, significantly increase the complexity of 7TM/GPCR-associated signaling processes. Accessory proteins to these signaling proteins are the calmodulins, which mediate calcium modulation of receptor function, the β-arrestins, which are involved in receptor inactivation (Lefkowitz, 2004), RGS (regulators of G-protein signaling), and RAMPs (receptor activity-modifying proteins). With the number of 7TM receptors in the human genome, as well as multiple G-protein subtypes, phosphatases, and the 7TM/GPCR-associated signaling proteins, there is scope for considerable complexity in the cell signaling events associated with activation of the 7TM/GPCR family. 7TM receptors also form homomeric and heteromeric forms (e.g., GABAB , adenosine A1 and A2A , angiotensin, bradykinin, chemokine, dopamine, metabotropic gluta-

mate, muscarinic, opioid, serotonin, and somatostatin), further increasing the potential complexity of ligand-driven GPCR signaling processes (Dean et al., 2001). With knowledge of these interactions, novel approaches to drug discovery include: (1) antagonists that act via inhibition of dimer formation; (2) bivalent compounds/binary conjugates; and (3) compounds targeting the 7TM-Gprotein interface. Ion channels are divided into ligand-gated (LGIC), voltage-sensitive calcium (VSCC, Cav) and potassium (Kir), and ion/pHmodulated (acid sensing, ASIC) subtypes, all of which have similar, but distinct, multimeric structural motifs consisting of homomeric or heteromeric complexes numbering between three (P2X) and eight (Kirs) distinct subunits. The GABAA /benzodiazepine (BZ) receptor is an LGIC that is the target for clinically efficacious anxiolytic, anticonvulsant, muscle relaxant, and hypnotic drugs, the majority of which contain a benzodiazepine (BZ) structural motif. These compounds produce their therapeutic actions by allosterically enhancing the actions of the inhibitory neurotransmitter, GABA. This pentameric LGIC is formed from a family of six α, four β, one γ, and two δ, subunits, with the potential to form several thousand different pentamers, only 20 of which are physiologically functional. The functional receptor complex contains a GABAA receptor, a BZ recognition site, and, by virtue of its pentameric structure, a central chloride channel. In addition to the BZ binding site, allosteric recognition sites on this complex include those for ethanol, avermectin, barbiturates, picrotoxin, and neurosteroids like allopregnanolone (Johnston, 2005). The NMDA receptor is a part of the glutamate receptor superfamily, which mediates the effects of the major excitatory transmitter, glutamate (Millan, 2005). It is composed of an NMDA receptor, a central ion channel that binds magnesium, the dissociative anesthetics ketamine and phencyclidine (PCP), the noncompetitive NMDA antagonist dizocilpine (MK 801), glycine and polyamine binding sites, activation of which can markedly alter NMDA receptor function, and some 70 other ancillary proteins, the physiological function of which remains to be determined. The activation state of the receptor can define the effects of the allosteric modulators. Thus, some are termed “use-dependent,” reflecting modulatory actions only when the channel is opened by glutamate.

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The nicotinic cholinergic receptor, nAChR, is another pentameric LGIC comprised of distinct functional regions and subunits (Lloyd and Williams, 2001). The subunit composition of the receptor varies, imparting different functionality when the channel is activated by nicotine or by the endogenous ligand, acetylcholine (ACh). Allosteric modulators of neuronal nAChRs include dizocilpine, avermectin, steroids, barbiturates, and ancillary proteins. The P2X receptor is an LGIC responsive to ATP, which functions as a trimer formed from a family of seven subunits that can form both homomers and heteromers with at least seven distinct receptor subtypes (Vial et al., 2004). TRPV (transient receptor potential) ion channels are modulated by temperature and include the vanilloid receptors, which are a primary target in pain research. There is limited knowledge of the structural elements involved in the ligand pharmacology and function of LGICs, with signaling transduction pathways having limited characterization. For nAChRs, the recognition site for acetylcholine (ACh) and nicotine is formed between two subunits. Thus, multiple orthosteric sites for ACh are possible, depending on the types of subunit forming the receptor. The transcription factor/intracellular receptor superfamily includes nuclear hormone and steroid receptors—glucocorticoid receptors (GR), progesterone receptors (PR), mineralocorticoid receptors (MR), androgen receptors (AR), and nonsteroid receptors including thyroid hormone receptors (TRs), peroxisome proliferator–activated receptors (PPARs), retinoic acid receptors (RXR, RAR), and vitamin D3 receptors (VDR). Once bound to steroids, these receptors are translocated to the nucleus, where they bind to hormone responsive elements (HREs) on DNA promotor regions to alter gene expression. Steroids are highly effective anti-inflammatory agents; however, they have a plethora of side effects that limit their utility. The intracellular receptor family includes the cytochrome P450 (CYP) family, the SMAD family of tumor suppressors, the intracellular kinase and phosphatase signaling pathways, nitric oxide synthases, caspases, the retinoic acid receptor (RXR, RAR) superfamilies, receptor activated transcription factors (RAFTs), and signal transducers and activators of transcription (STATs) such as AP-1, NFkB, NF-AT, STAT-1, PPARs, various hormone responsive elements on DNA and RNA promoters, and ribozymes.

The enzyme-associated receptor superfamily is a family of single-subunit or multisubunit proteins containing a subunit with a single transmembrane domain. The largest groups within this superfamily are the single-subunit tyrosine kinase receptors, e.g., PDGF and EGF receptors, and the multimeric complexes that utilize kinases, e.g., JAK-type kinases, for signal transduction.

Neurotransmitter Transporters Neurotransmitter transporters (NTs) comprise the SCDNT (Na+ /Cl– dependent transporter) family and are responsible for terminating the effects of various neurotransmitters by removing them from the extracellular space (Masson et al., 1999). NTs are integral membrane proteins present on both the plasma membrane and on intracellular vesicles where they effect vesicular packaging of neurotransmitters. The activity of most NTs—e.g., dopamine transporters (DAT), norepinephrine transporters (NET), and serotonin transporters (SERT)—is dependent on the Na+ intracellular/extracellular gradient. Transporters for glutamate also require K+ (SKDGTs). NTs bind their cognate neurotransmitters with high affinity and transport them across the plasma membrane into the cell. GABA transporters comprise the GAT family, which consists of four members. NT inhibitors represent a major class of drugs producing their effects by blocking neurotransmitter uptake, thus potentiating the actions of endogenous neurotransmitters. As such, inhibitors of NTs can be considered as indirect receptor agonists. Inhibitors of SERT (selective serotonin reuptake inhibitors or SSRIs; e.g., fluoxetine) are a major class of antidepressants. Mixed SERT/NET inhibitors (SNRIs) are also antidepressants and have analgesic activity, while DAT inhibitors are well known stimulants, including cocaine and amphetamine. Inhibitors of GAT are anticonvulsants (e.g., tiagabine).

Drug Receptors Most drugs interact with receptors (or enzymes) for which the natural ligand (or substrate) is known. There are, however, a number of receptors, distinct from the evolving class of 7TM orphan receptors, for which a synthesized drug is the only known ligand. The best example of this is the central benzodiazepine (BZ) receptor present on the GABAA ion channel complex that was originally identified using radiolabeled diazepam. Because this is the site of action of the widely used BZ anxiolytic drug class, this is a bona fide receptor

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with clinical relevance. However, despite considerable efforts and a number of intriguing candidate compounds, no endogenous ligand has yet been identified that would act as the endogenous modulator of anxiety via this site. Examples of drug receptors for which synthetic ligands were identified before the endogenous agonists were identified are the cannabinoid receptors, CB1 and CB2 , through which the activity of δ9 -tetrahydrocannabinol, the active entity in marijuana, takes place; the vanilloid receptor, VR-1, the best known ligand for which is capsaicin, the ingredient in red pepper that evokes heat sensation; and the opioid receptor family, the site of action of morphine and other derivatives of the poppy. The endogenous mammalian ligand, anandamide, was later identified for the cannabinoids; similarly, for the vanilloid receptor the endovanilloid, N-arachidonyl-dopamine (NADA) was identified as an endogenous ligand, and for the opioid receptors the enkephalins, and orphanin/FQ were identified as endogenous ligands.

Target Identification Much has been written (Collins et al., 2003; Williams, 2006) regarding the potential for identifying new, disease-associated drug targets from the draft maps of the human genome (Lander et al., 2001; Venter et al. 2001). The information resulting from the technologies used for disease-related gene and drug target identification (e.g., microarrays) has resulted in both opportunities and a number of challenges including: (1) the effective handling of the bewildering flow of data arising from genome-, proteome-, interactome-and structure-based studies and HTS; and (2) the validation and prioritization of potential new targets for drug discovery. The first issue is being addressed via bioinformatics and chemoinformatics (Lutz and Kenakin, 1999). However, for target validation, the prioritization of a potential new target based on its cellular function and its relationship to the intended disease state, the process is less clear and, in reality, is more a process of confidence building than one of true validation (Kopec et al., 2005).

LEAD DISCOVERY

Overview of Drug Discovery and Development

Once a suitable drug discovery target has been identified and accessed for screening, the challenge becomes the identification of compounds that interact with the target and that can be used as the basis of a lead optimization program to identify potential drug candidates. Techniques involved at this stage of the drug

discovery process include chemical library assembly and access, high-throughput screening (HTS), and structure-based drug design (Davis et al., 2005).

COMPOUND SOURCES Many thousands of new chemical entities (NCEs) have been made since the origins of pharmaceutical industry in the late 19th century. In 2001, the Merck Index, a compendium of drugs and research tools, listed a total of 10,250 compounds. Thus, from a hypothetical million compounds, approximately 1% have proven to be of sustained interest as either therapeutic agents or research tools. Until the advent of the various techniques of combinatorial chemistry, chemical libraries at most pharmaceutical companies ranged between 50,000 and 800,000 compounds in size, including newly synthesized compounds and those from fermentation and natural product sources (UNIT 9.1). New chemical entities (NCEs) are discovered, or developed/optimized, from: (1) natural products and biodiversity screening; (2) exploitation of known pharmacophores; (3) synthetic diversity libraries; (4) rationally planned approaches, e.g., computerassisted molecular design; (5) combinatorial or focused library chemistry approaches; and (6) evolutionary chemistry.

Natural Product Sources Approximately 70% of the drugs currently in human use originate from natural sources, notably morphine, theophylline (Fig. 9.9.2A), cocaine, salicylic acid, reserpine, the taxols, and many antibiotics (Sneader, 1985). Natural product screening led to the discovery of the immunosuppressants cyclosporin, rapamycin, and FK 506. It has been estimated that only 0.00002% to 0.003% of the world’s estimated 3–500 × 106 species are used as a source of modern drugs (UNIT 7.1). Sea snails, “nature’s combinatorial chemistry factories,” produce a diverse array of conotoxins, typically 10 to 30 amino acid residues in length, active at mammalian drug targets (Olivera et al., 1995). A novel conopeptide NET inhibitor, XEN 2174 (Sharpe et al., 2002), has analgesic actions. Poison frogs of the Dendrobatidae family contain a wide variety of skin-localized poisonous alkaloids including the batrachotoxins, pumiliotoxins, histrionicotoxins, gephyrotoxins, and decahydroquinolines, which are presumably secreted for defensive purposes. These target both voltage- and ligandgated ion channels. The alkaloid epibatidine

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(Fig. 9.9.2A), present as a trace entity isolated from Epipedobates tricolor, was found in 1976 to be a powerful analgesic, being 200 times more potent than morphine, acting as potent neuronal nicotinic channel agonist selective for the α4β2 subtype (Daly et al., 2000).

Pharmacophore-Based Ligand Libraries The large chemical libraries in major pharmaceutical companies represent the cumulative synthetic efforts of medicinal chemists within a company over half-century time periods. Typically, the chemical diversity in these libraries is limited, as synthetic efforts have been focused on defined pharmacophores (Fig 9.9.2B) in lead series with the rational and systematic development of the SAR. Thus, Roche has focused efforts on the benzodiazepine pharmacophore, Merck on cyproheptadine, Johnson and Johnson on arylpiperidines, Pfizer on purines and azoles, and GlaxoSmithKline on β-lactams and nucleosides.

Privileged Pharmacophores A small number of common structures— “basic pharmacophores,” “templates,” “scaffolds,” or “stem molecules”—are associated with a multiplicity of diverse biological activities. These structures represent facile starting points for parallel synthesis efforts to develop ligands with functional diversity. The benzodiazepines (BZs) are well known as anxiolytics, hypnotics, and muscle relaxants, comprising, e.g., diazepam, clonazepam, midazolam, and triazolam (Fig 9.9.2C). The BZ nucleus is also found in natural products like asperlicin, which is a weak, albeit selective, antagonist at cholecystokinin receptors. This led to the potent and selective BZ ligand, active at CCK1 and CCK2 receptors, L-364,718 (Evans et al., 1987). Other BZs with activity at receptors distinct from the classical BZ receptor are tifluadom (opioid receptor), GYKI 52466 (glutamate receptor), and ligands active at Kir channels (Williams et al., 2005). The 1,4-dihydropyridine (DHP) pharmacophore is another well-established privileged structure that includes nifedipine (Fig. 9.9.2C), amlodipine, felodipine, nicardipine, nimodipine, and nisoldipine, which are widely used as antihypertensive/vasodilating agents that act through voltage-gated L-type Ca2+ channels. DHPs also interact with Ca2+ channels, including N- and T-type channels and “leak” channels, and can block delayed rectifier K+ channels and cardiac Na+ channels.

Other DHP analogs (Fig. 9.9.2C; UNIT 9.1) are active at PAF receptors (UK 74,505), adenosine A3 receptors (MRS 1191), K+ ATP channels (ZM 24405, A-278367), capacitative SOC channels (MRS 1845), and α1A -adrenoceptors (SNAP 5089).

Diversity-Based Ligand Libraries Chemical diversity is key to the identification of hits for novel targets, and libraries with diversity can be obtained from a variety of commercial sources. Using computerized cluster programs, the selection of compounds based on diverse structures can be considerably enhanced to provide maximum coverage of potential molecular space. Libraries of approximately 2000 compounds can be generated to rapidly identify potential leads for a target using the SAR generated in an HTS assay. Such libraries are assembled from compounds available in relatively large supply and are not always proprietary. Their value is in rapidly eliminating unlikely structures in a systematic manner for each screening target. Combinatorial chemistry, which has provided the means to synthesize literally billions of molecules, conceptually providing a major step forward in the exploration of “molecular space,” is a random or “brute-force” approach, and has been a major disappointment, with quality being sacrificed for quantity (Kubinyi, 2003). However, the combinatorial exploration around leads or pharmacophores to generate dedicated libraries (the parallel synthesis approach) coupled with HTS, provides an economical and efficient way to rapidly generate quality lead compounds (Gooding, 2004).

BIOLOGICALS, ANTISENSE, AND RNA INTERFERENCE The use of the natural hormones as drugs is far from new, as evidenced by the use of insulin and epinephrine. Molecular biology techniques allow the production of an increasing number of native and modified hormones and their soluble forms. Erythropoietin (EPO) is a classic example of a highly successful drug taken from the human body, which, in addition to its actions as a hematopoietic cytokine, can be modified to become a neuroprotective agent (Leist et al., 2004). Soluble receptors like etanercept (Enbrel; Wyeth-Ayerst) and humanized antibodies like trastuzumab (Herceptin; Genentech) and adalimumab (Humira; Abbott Laboratories) are additional examples of how cloning has altered the concept of rational drug design and

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Figure 9.9.2

Chemical structures of key compounds. (A) Natural products; (B) pharmacophores;

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Figure 9.9.2

(continued) (C) the BZ and dihydropyridine pharmacophores—privileged scaffolds.

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what is now considered a drug. Antisense oligonucleotides (Estibeiro and Godfray, 2001) and RNA interference (RNAi; Van Es and Arts, 2005) approaches may similarly provide novel, specific drugs via the suppression/modification of a specific gene product. The fundamental concept is that an oligonucleotide complementary to a diseasecausing gene will bind to the gene to prevent transcription. In a similar manner, RNAi approaches utilize the phenomena whereby double-stranded RNA is rapidly degraded and double-stranded RNA oligomers can trigger degradation of the genes encoded by them. While delivery issues remain a challenge, one topically active antisense drug, fomivirsen (Vitravene; Isis Pharmaceuticals), was approved for the treatment of CMV-induced retinitis.

of c-Abl (Nagar et al., 2002), VEGF-R2 kinase (Manley et al., 2004), p38 (Chakravarty and Dugar, 2002), and EGFR kinase (Noble et al., 2004). A close analog of STI-571 (imatinib, Gleevec; Novartis) was found to bind to an inactive conformation of the enzyme (c-Abl) where the activation loop appeared to orient in a natural autoinhibitory configuration blocking the binding of the peptide substrate (Schindler et al., 2000; Schiering et al., 2003). It was subsequently shown that imatinib itself binds to an inactive conformation of the c-Abl with the same autoinhibitory conformation of the activation loop. Atomic-level detail in the STI-571/c-Abl complex identified a single amino acid mutation (Thr315 → Ile) that contributed to Gleevec clinical resistance (Gorre et al., 2001).

COMPUTATIONAL CHEMISTRY

HIGH-THROUGHPUT SCREENING

Rational approaches to structure design are a key part of the drug discovery process, whether planned ab initio, derived from structural knowledge of a putative ligand-binding site on a target in the absence of ligand information, or derived by rational exploitation of an existing chemical lead. Failure to use contemporary computational techniques to facilitate an understanding of the recognition process between a compound and its target is a competitive disadvantage for a practicing medicinal chemist. The use of atom-level information in the design of biologically interesting compounds— whether computed, measured or modeled— permeates most aspects of contemporary drug discovery. However many targets of current interest lack detailed structural information. In these instances, homology modeling or smallmolecule-based QSAR (Quantitative SAR) approaches have been useful (Becker et al., 2004; Bultinck et al., 2004; Trabanino et al., 2004). For those targets where high-resolution information has been obtained via crystal structure solution, insights into compound design can be directly applied to the processes of drug discovery and development (Jorgensen, 2004). Structural information of the target provided key insights into the design of HIV protease inhibitors (Kempf et al., 1998), thrombin inhibitors (Costanzo et al., 2005), and caspase inhibitors (Wei et al., 2000). For selective kinase inhibitors, the fundamental understanding of enzyme structure— “closed” versus “open”—of inhibitor interactions with hinge-region amino acids and of “gate-keeper” residues has guided the synthesis leading to potent and selective inhibitors

To identify novel leads, a rapid, economical, and information-rich evaluation of biological activity has become necessary. The term high-throughput screening (HTS) describes a set of technologies designed to permit rapid and automated (robotic) analysis of a library of compounds in a range of assays that generate specific receptor- or enzyme-based signals (Janzen, 2002; Posner, 2005). These signals may be cell-free (radioligand binding in membranes, enzyme assays with isolated proteins) or cell-based (ion flux, fluorescence, luminescence). The primary purpose of HTS is not to identify candidate drugs, but rather to identify promising active chemical structures, preferably containing novel structural features that can serve as a start for an iterative “hit to lead” campaign to optimize the efficacy and drug-like properties of a hit. HTS should generate as few false-positive leads as possible, since lead exploitation is an expensive and time-consuming component of the drug discovery process. The majority of HTS efforts are designed to give information principally about potency, although a combination of screens may also provide information about selectivity and specificity. The increasing use of cell-based assays provides additional information, including functional characterization of agonists, antagonists, and inverse agonists, as well as a biological “read-out” under physiological or nearly physiological conditions. Cell-based assays also provide information on the cytotoxicity and permeability of NCEs. The increased use of “designer cells” with visual and fluorescent signal readouts will continue to facilitate the screening process and, in the future, will doubtless include measures of

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metabolism, toxicity, bioavailability, and other important pharmacokinetic parameters. Using a variety of assay techniques in 96-, 384-, or 1536-well microtiter plate array formats, many thousands of compounds can be screened through 10 to 20 targeted assays in less than 1 week, and complete libraries numbering in the millions can be assessed at single concentration points in less than 6 weeks. The key issues are the success rate, typically in the 0.1% range, and the ability to capture and store the data for subsequent retrieval (Lutz and Kenakin, 1999). Clearly, the quality and diversity of the compounds and the robustness of the assays play a key role in a successful screening program. In the early 1980s, with the as-yet-tomaterialize promise that compounds could be designed and tested in silico, random screening of large numbers of compounds against selected targets came to be viewed as irrational. Indeed, the head of research at one of the top ten pharmaceutical companies publicly informed the medicinal chemists at that company in the early 1980s that the demand for their skills was decreasing and would cease by the 1990s, a viewpoint somewhat akin to the apocryphal story of the head of the U.S. Patent Office in the early 1900s who recommended its closure because “everything that could be discovered had been discovered.” A drug company without chemists is, at best, a car without gasoline. The validation for a screening approach came in 1984 with the identification by Chang et al. (1985) at Merck of the CCK2 antagonist asperlicin and its subsequent use as a lead structure that was further elaborated to the clinical candidate, MK 329 (Fig. 9.9.2C; Evans et al., 1988). Considerable effort has been expended in the biotech pharmaceutical industry worldwide to capitalize on this approach, with significant successes that have enhanced the search for novel chemical entities and that have provided research tools to better understand receptor and enzyme function.

HIT TO LEAD The hit-to-lead (HtL) process is an integral part of the early-stage effort to apply more stringent criteria to the advancement of NCEs (Bleicher et al., 2003) by providing a broader “menu” of diverse NCEs from which to select lead compounds for subsequent optimization. The limited number of quality compounds advanced to lead optimization based on the linear approach (MacCoss and Baillie, 2004) is one factor contributing to the decline in productiv-

ity in drug discovery (Williams, 2005). The linear approach, because of low compound throughput and later-stage attrition, has not only limited choice for the primary series but has provided little in the way of mature backup compounds. With the multifactorial approach and advances in high-throughput compound characterization, it is now possible to advance both multiple compounds and multiple series with better characterization of both efficacy and drug-like properties. The HtL process begins with an broad assessment of: (1) reconfirmation of structure; (2) the drug-like properties of the hits from an HTS campaign, including the preliminary evaluation of any emerging SARs from related compounds; (3) the physicochemical and pharmacodynamic properties of the hit(s), including adherence to Lipinski’s database searches to find any existing information on properties of NCEs occupying similar chemical space and; (4) an assessment of any issues with the synthesis of the hits including potential toxic groups, adherence to Lipinski’s rule of 5, cost, and the ease of creating new libraries based on the hits. While there are many examples of successful HtL campaigns (Gillespie and Goodnow, 2004), there are also examples where there have been neither an interesting hit amenable to drugability nor a SAR. One example of the latter is the novel P2Y12 receptor antagonist, CT 50547, the stringent activity requirements of which precluded the advancement of this novel structure as a potential drug candidate (Scarborough et al., 2001).

LEAD OPTIMIZATION The identification of lead compounds is an important transition point in drug discovery. A compound or series discovered by HTS or structure-based approaches, while attractive from the perspective of activity against the chosen target, generally lacks many of the drug-like attributes required of a drug candidate. Thus, these leads must be converted into drug candidates via iterative efforts in medicinal chemistry and biology, a process known as lead optimization. Newly synthesized analogs of the lead series are then evaluated in a variety of assay systems (including in vitro activity against the molecular target in both cell-free and cellular systems, selectivity, in vivo efficacy, pharmacokinetics, and toxicity, among others). The information from these studies is then utilized to design the next generation of compounds, with the iterative process ultimately resulting in the identification of compounds suitable for advancement to

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development. The challenge in the optimization process is to identify compounds that have the unique recognition characteristics to impart affinity, selectivity, and efficacy for the target, and that are also bioavailable, stable, chirally pure, and facile and cost-effective to make. In the traditional drug discovery model, lead optimization follows a linear flow (Fig. 9.9.3; MacCoss and Baillie, 2004). Compounds proceed sequentially through a series of activities, with advancement dependent on achievement of predefined criteria in each assay. While this scenario effectively maximizes the efficiency of downstream assays by limiting the number of compounds tested, it is extremely slow and permits optimization of only a single parameter at any one time. This step-wise approach also overlooks the possibility that structural changes intended to enhance one aspect of the compound properties may be deleterious in other regards. Sequential lead optimization also suffers from the fact that some assays that represent potential go/no go decision points are performed very late in the process, identifying potentially fatal liabilities in a lead series after the point at which they could be addressed by medicinal chemistry. Lead optimization has recently evolved to a more parallel process due to advances in assay throughput and informatics capabilities. The activity of new representatives of a lead series can be evaluated almost simultaneously in assays for in vitro and in vivo biochemical and functional efficacy, selectivity, pharmacokinetics, and in vitro toxicology (MacCoss and Baillie, 2004). In this multifactorial lead optimization paradigm, the impacts of structural changes are simultaneously evaluated for multiple properties (Fig. 9.9.3). Iterative feedback loops established between biology and medicinal chemistry for these assays support the simultaneous optimization of multiple parameters in a parallel fashion. This strategy identifies potential liabilities early in the medicinal chemistry program, allowing these to be optimized in a timely manner.

STRUCTURE-ACTIVITY RELATIONSHIPS (SARs)

Overview of Drug Discovery and Development

The SAR of a compound series is an effective means to relate changes in chemical diversity to the biological activity of a compound in vitro and in vivo and to the pharmacokinetic (e.g., gut/blood brain barrier transit, liver metabolic stability, plasma protein binding) and toxicological properties of the molecule. SARs for different properties of a molecule are

often distinct, such that structural changes that improve one aspect can be detrimental for others, making compound optimization a highly iterative and dynamic process. With SAR characterization and the documentation of the effects of different pharmacophores and various substituents on biological activity, it is possible to theoretically model the way in which the ligand interacts with its target and thus derive a two- or threedimensional approximation of the active site of the receptor or enzyme. Computer-assisted molecular design (CAMD) techniques (Jacobson, 2004) can then be used to predict the key structural requirements for ligand binding, thus defining those regions of the receptor target that are necessary for ligand recognition and/or functional coupling to a second messenger system to permit the design of new pharmacophores. As noted, SARs can be further delineated in terms of the type of activity measured. In vitro, this can be enzyme inhibition, displacement of a radioligand from a receptor, receptor activation as measured in a functional assay, or blockade of receptor function by an antagonist ligand. The selectivity of a compound can determine its side-effect profile, given that the targeted mechanism itself does not produce untoward effects when stimulated beyond the therapeutic range. The development of a ligand-binding profile (Ator and Williams, 2005) for a ligand active at a new target is of considerable use in assessing its effects in more complex tissue systems.

DRUG METABOLISM AND PHARMACOKINETICS To be useful as potential drugs, NCEs need to reach their intended target. Hodgson (2001) noted that “a chemical cannot be a drug, no matter how active nor how specific its action, unless it is also taken appropriately into the body (absorption), distributed to the right parts of the body, metabolized in a way that does not instantly remove its activity, and eliminated in a suitable manner—a drug must get in, move about, hang around and then get out.” It is only in the past 15 years that evaluation of the pharmacokinetics of an NCE and its ability to reach its putative site of action has been an established priority early in the discovery phase of compound identification. For many years, it was naively assumed that there were generic approaches that would transform compounds with poor bioavailability into drug candidates. The realization that approximately 40% of lead

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Figure 9.9.3 (A) Serial or sequential systems cannot keep pace with the expectations for industry productivity, and fosters a “fail late” scenario. (B) Parallel creation of a “multifactorial” knowledge base informs compound design on overall performance criteria, and facilitates decisions based upon on aggregate data. See text for additional detail. Adapted from McCoss and Baillie (2004).

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compounds fail in the clinical development process due to poor pharmacokinetic characteristics (Prentis et al., 1988) demonstrated the fallacy of this assumption, although this specific situation has improved more recently with a greater emphasis on pharmacokinetics in compound selection (Kola and Landis, 2004). Thus, profiling of the pharmaceutical properties and intrinsic bioavailability of NCEs now occurs early in the drug discovery process, to avoid advancing compounds that have limited value as potential drugs (Roberts, 2003; Kerns and Di, 2005). Imparting drug-like properties to an NCE is perhaps the most challenging part of the drug discovery process, and, while there are emerging technologies to improve the pharmacokinetic properties, these are not universally applicable. To achieve acceptable oral bioavailability, a compound must be soluble and membrane permeable as well as metabolically and chemically stable. A combination of in silico, in vitro, and in vivo techniques are routinely employed to evaluate and enhance these metabolic and pharmacokinetic characteristics of an NCE series in parallel with optimization of its biological activity. Adaptation of the high-throughput methods used for screening of compound libraries has made feasible the assessment of the properties of large numbers of NCEs early in a discovery program. While it is possible to generate great amounts of in vitro data, it is essential that in vitro–in vivo correlations be established whenever possible, to ensure that the in vitro observations have some relevance to the living system. The molecular properties of marketed oral drugs and those in development have been evaluated to assess the degree to which various molecular properties affect the “drug-like” character of compounds (van de Waterbeemd et al., 2001; Wenlock et al., 2003; Vieth et al., 2004). While the properties associated with acceptable oral bioavailability still remain somewhat elusive, several consistent concepts have emerged. Lipinski et al. (1997) evaluated the molecular properties of orally administered drugs and drug candidates and elaborated a simple set of factors important for oral bioavailability known as Lipinski’s “rule of 5.” The physical properties determined as limiting bioavailability were: a molecular weight greater than 500; more than 5 hydrogen bond donors; more than 10 hydrogen bond acceptors; and a clogP value greater than 5. These properties were further proposed to be associated with aqueous solubility and intestinal

permeability, which are prerequisites for satisfactory oral bioavailability. Passive transcellular permeability across membrane barriers, e.g., intestinal absorption or brain penetration, requires that an NCE has an appropriate degree of polarity. The dynamic polar surface area (PSA), which defines the area of a compound occupied by nitrogen and oxygen atoms or the hydrogen atoms attached to those heteroatoms, correlated with absorbed fraction after oral administration for a small series of compounds that were predominantly absorbed by passive processes (Palm et al., 1997). The correlation of PSA with passive oral absorption was subsequently confirmed, and a linear relationship between PSA and brain penetration was demonstrated (Kelder et al., 1999). Significant brain penetration was observed for compounds with PSA values ≥60 ◦ to 70 A2 . A retrospective evaluation by Veber et al. (2002) of the oral bioavailability in rat of 1100 novel drug candidates from SmithKline Beecham’s drug discovery efforts further elaborated the impact of physical properties on oral bioavailability. Reduced molecular flexibility, as measured by the number of rotatable bonds, and low PSA or total hydrogen bond count (sum of acceptors and donors) were found to be important predictors of acceptable (>20% to 40%) oral bioavailability. In contrast to the finding of Lipinski et al. (1997), a molecular weight cutoff of 500 was not able to discriminate between compounds with acceptable and poor oral bioavailability. Molecular weight was instead suggested to represent a surrogate for rotatable bonds, polar surface area, and hydrogen bond count, which tend to increase with increasing molecular weight. Veber et al. (2002) proposed that compounds with 10 or fewer rotatable bonds and a po◦ lar surface area ≤140 A2 (representing 12 or fewer H-bond acceptors and donors) have a high probability of demonstrating satisfactory oral bioavailability in rats. A variety of experimental techniques are applied to the evaluation of solubility and permeability, the key factors related to absorption. Measurement of solubility, once restricted to low-throughput shake-flask techniques, can now be accomplished in the early stages of discovery in high-throughput methods using filtration and UV absorption (Avdeef, 2001) or light scattering (Bevan and Lloyd, 2000). While these methods are unable to measure the potentially distinct solubilities of different crystal polymorphs of an NCE, they have substantial value early in the discovery process.

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Permeability can be readily determined in artificial membrane or cellular monolayer systems. The parallel artificial membrane permeability assay (PAMPA, Kansy et al., 1998) provides a relatively rapid and cost-effective assessment of the ability of NCEs to traverse an artificial lipid membrane formed on a 96well filter plate. The rate of membrane diffusion corresponds to the passive permeability of an NCE, but does not reflect any active transport processes that may occur in vivo. Modulation of the composition of the lipid membrane can be used to predict passive diffusion through the blood-brain barrier (Di et al., 2003). Cell monolayer methods employ cell lines like Caco-2 (a human colon carcinoma cell line; UNIT 7.2), which, when grown to confluency in a Transwell apparatus, form tight junctions that limit paracellular diffusion of NCEs (Hidalgo et al., 1989; Bohets et al., 2001). The observed permeability is the sum of passive transcellular permeability, active transport of the compound across the monolayer, and active efflux mediated by transporters such as P-glycoprotein (Pgp). The complexity of the Caco-2 system more closely models the realities of intestinal absorption, and good correlations have been observed between permeability in the Caco-2 assay and human intestinal absorption (Bohets, 2001). To complement the Caco-2 assay, a number of less laborious test systems are used to assess active efflux of compounds due to Pgp, including cell monolayer studies in MDCK cells overexpressing Pgp and ATPase assays using Pgp-overexpressing membranes (Polli et al., 2001). A primary mechanism for drug clearance is hepatic extraction and metabolism. In vitro metabolic stability studies using liver subcellular fractions, liver slices, or isolated hepatocytes can identify NCEs that are highly susceptible to hepatic metabolism and therefore likely to demonstrate high in vivo clearance. Liver microsomes from the species of interest are used in these studies, although the S-9 fraction is sometimes employed since it also contains phase II metabolizing enzymes and other metabolically active soluble enzymes. The amount of parent compound remaining as a function of incubation time is determined by LC/MS techniques. The in vitro intrinsic clearance values calculated from these studies can be used to predict in vivo clearance (Obach et al., 1997) and are particularly useful for interspecies comparison and extrapolation to humans. The practicality of using primary human hepatocytes in metabolic stability studies

has increased greatly in recent years, primarily due to the development of methods for cryopreservation of metabolically competent cells (Li et al., 1999). Metabolic stability studies are typically followed by metabolite profiling for the identification of predominant sites of metabolism and metabolic routes (Yan and Caldwell, 2001; UNIT 7.10). Compounds are incubated with an in vitro hepatic metabolizing system (microsomes or hepatocytes) and the resulting metabolites identified by mass spectrometry. These studies can also be used for identification of the preclinical species whose metabolic profile most closely mirrors that observed in human in vitro systems. The cytochrome P450 isozymes responsible for predominant metabolic events can be determined through incubation with isolated recombinant CYP isozyme systems or in microsomal studies with isozyme-selective inhibitors. Evaluation of the potential for drug-drug interactions involves the analysis of both inhibition and induction of various CYP isozymes (Riley and Grime, 2004). If an NCE inhibits a particular CYP isozyme, it will also inhibit the metabolism of other drugs that are processed by the same isozyme. HTS methods for determining CYP inhibition use recombinant CYPs and fluorescent assay substrates (Crespi et al., 2002; Hutzler et al. 2005; UNIT 3.9). Multiple isozymes (generally 1A2, 2C9, 2C19, 2D6, and 3A4) are evaluated in parallel and compounds are grouped into categories of high (Ki < 1 µM), moderate (Ki = 1 to 10 µM), and low (Ki > 10 µM) inhibitory potential. More detailed confirmatory studies in the advanced candidate selection phase are usually performed using pooled human liver microsomes, quantifying products by chromatographic or LC/MS techniques. NCEs that induce drug-metabolizing enzymes have the potential of causing drug-drug interactions by enhancing the metabolism of other therapeutic agents (Luo et al., 2002). CYP induction can be evaluated in human hepatocyte systems by culturing cells in the presence of drug and measuring enzyme activity or levels of protein or mRNA. Primary human hepatocyte induction studies are tedious and difficult to perform due to the sporadic availability of suitable hepatocytes. Gene reporter cell lines, particularly directed toward PXR-mediated induction of CYP3A4, show promise as predictive, highthroughput methods (Heijne et al., 2005) and correlate well with primary human hepatocyte systems, but are more amenable to early drug discovery than hepatocyte studies.

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While a multitude of in vitro studies can be implemented to predict the PK characteristics of an NCE, in vivo PK studies are still required in preclinical species that involve i.v. or i.v./p.o. dosing in rodents, with quantitation of plasma levels of the compounds by LC/MS. A variety of strategies have been evaluated to increase the throughput of these studies, including multiple compound dosing (“cassette dosing”; White and Manitspitkul, 2001) and the pooling of plasma samples from individually dosed animals (Hop et al., 1998). Mechanistic studies designed to evaluate hepatic barriers to oral bioavailability, including intraduodenal dosing and portal vein sampling to assess firstpass extraction, can also be performed during the discovery phase. As NCEs advance, PK is also evaluated in dogs or nonhuman primates, although the degree to which these studies predict human PK is always in question. It is also advantageous to perform in vitro toxicology experiments that will identify fatal flaws in an NCE series early in the discovery process. The interaction of NCEs with the hERG potassium channel, which is involved with cardiac repolarization, is currently a high priority. Compounds that interfere with hERG channel function have the potential to induce prolongation of the heart QT interval, with a risk of sudden cardiac death due to a ventricular arrhythmia—torsade de pointes (Roden, 2004). Induction of QT prolongation has led to nine marketed drugs being withdrawn. Guidance from the International Council on Harmonization (ICH S7B Guideline, 2004) outlines a strategy for the preclinical evaluation of NCEs for hERG interactions and involve, sequentially, [3 H]astemizole binding (Chiu et al., 2004), hERG -dependent Rb+ efflux (Tang et al., 2001), and electrophysiological studies using patch-clamp techniques. The definitive test is, however, the telemeterized dog (Porsolt and Williams, 2005). While neither this nor electrophysiology are economically viable in the early stages of drug discovery, the high incidence of false positives has been viewed as contributing to excessive compound attrition (Schmid and Smith, 2005). A second important toxicological endpoint measured early in the discovery process is mutagenicity, which can be assessed in a scaleddown version of the well known Ames test, the mini-Ames assay (Flamand et al., 2001). By decreasing the number of bacterial strains tested and miniaturizing the assay, this test can be performed using the milligram quantities of NCEs typically available early in the discovery process. The desire to uncover toxicological

liabilities as early as possible has led to a high degree of interest in approaches such as the use of proteomics to derive potential toxicological profiles.

PRECLINICAL DEVELOPMENT Process Chemistry Process chemistry describes the activities involved in NCE scale-up in an industrial context (Repiˇc, 1998). As the drug development process progresses, it will require increasing amounts of drug. It is the responsibility of process chemistry to discover, design, and develop a chemical synthesis that can meet these everincreasing supply demands. Up to this point in the drug discovery process, the amounts of an NCE produced are probably a maximum of 10 to 20 g, typically produced by the initial synthetic route devised by the medicinal chemist. While modest improvements in synthetic yield and optimization in terms of the number of steps required to make the compound may have occurred with the synthesis of subsequent batches, scale-up of the synthetic route will not have been accomplished. This may require a different assembly strategy to identify the optimal synthetic method for the NCE on a larger scale that can cost-effectively manufacture multi-kilogram quantities of bulk drug for use in safety and early-stage clinical studies. Often this investigation reduces the number of synthetic steps by 50% or more, and specialized industrial-scale equipment can increase yields that may be in the 10% to 20% range at the bench level by four-fold. This larger-scale synthesis is conducted under the current Good Manufacturing Practices (cGMP) guidelines of the U.S. Food and Drug Administration (http://www.fda.gov) and requires significant attention to environmental considerations, i.e., “green chemistry.” Further optimization of the process is required to move the NCE to pilot plant–scale production (≥50 kg) or commercial production (in the range of metric tons or greater). There are many challenges in the process chemistry arena, e.g., when low temperature, high pressure, or exotic reagents are required to synthesize bulk drug or when chiral NCEs need to undergo asymmetric syntheses (Repiˇc, 1998).

Absorption, Distribution, Metabolism and Excretion (ADME) In assessing NCE activity in vivo, there should be good correlation between the efficacy of an NCE and the plasma concentration of either the parent NCE and/or its active

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metabolites, although the temporal aspects, given the ability of many NCEs to induce gene expression following a relatively brief interaction with their target, is not always clear. The complex interaction of ADME properties of an NCE on its plasma concentration requires assessment of different vehicles, doses, species, routes of administration, and dosing regimens. Establishing an efficacious dose in animal studies, recognizing the inherent limitations of animal models of human disease states (Williams, 2005), can provide a framework for dose selection for “first-time in human studies.” There are many instances where lack of efficacy of NCEs interacting with novel targets was found to be due to an absence of compound in the body rather than a proof of concept for the mechanism. The situation regarding compound availability is further complicated when the NCE needs to reach the brain and thus needs to cross the blood-brain barrier. ADME studies can be used to both design and monitor clinical trials, thus reducing much trial and error by using biological efficacy as the only readout.

Safety Pharmacology Safety pharmacology studies are defined by regulatory authorities as “studies that investigate the potential undesirable pharmacodynamic effects of a substance on physiological functions in relation to exposure in the therapeutic dose-range and above” (European Agency for the Evaluation of Medicinal Products, 2000). These are typically performed in an acute mode to determine the potential safety risk of NCEs by: (1) identifying undesirable pharmacodynamic properties; (2) evaluating adverse pharmacodynamic/ pathophysiological effects; and (3) establishing the mechanism of any adverse effects. The ICH (International Committee on Harmonization) has provided recent guidelines that focus on three vital organ systems referred to as the “core battery” for safety pharmacology evaluation. These are the CNS, cardiovascular (CV) system, and respiratory system. A description of this core battery is given in detail by Porsolt and Williams (2005) and in UNIT 10.1 of this manual.

Toxicological Safety Evaluation Once an NCE has evolved to advanced lead status, its initial safety is determined in a battery of safety pharmacology tests and a therapeutic index is determined, the latter being the dose ratio between efficacy and side effects.

An NCE is concurrently evaluated for in vitro mutagenicity in cell culture models that assess effects on bacterial and mammalian cell replication. The Ames test (bacterial reverse mutagenicity) and mouse micronucleus tests are usually the first toxicological test procedures carried out on an NCE. Further studies are conducted to assess the overt toxicity of a compound before human exposure. For a single-dose exposure in humans, regulatory guidelines (FDA, 2002) require a 2- to 4-week rising-dose study of GMP-grade NCE in rodents in the formulation that is intended for use in the clinic. For a 28-day trial in humans, a 3- to 6-month period of rodent exposure is required and would also include a second species. Two measures arise from these studies: the maximum recommended starting dose (MRSD) of an NCE that is used for the first time in humans and the no observed adverse effect level (NOAEL), the highest dose that does not produce adverse effects. The NOAEL is typically converted to a human equivalent dose, historically on the basis of body surface area but more recently in terms of plasma concentrations of the NCE. More chronic toxicity studies require 6 to 12 months of NCE exposure. An NCE in clinical trials will also be evaluated over a 2-year period for reproductive effects/genotoxicity, teratogenicity, and immunological and behavioral toxicity both in adult mammals and their offspring. Carcinogenicity studies expose mice and rats to an NCE for more than 2 years (Gad, 2002).

Clinical Development Clinical development is divided into four distinct phases, I to IV, preceded by the filing of a Notice of Claimed Investigational Exemption for a New Drug, which, in the U.S., corresponds to the Investigational New Drug (IND) application to the FDA. Similar submissions are used elsewhere in the world, including the Clinical Trial Certificate (CTC) which in the U.K. is submitted to the Medicines Control Agency (MCA) and in Europe to EMEA (European Agency for the Evaluation of Medicinal Products; http://www.emea.eu.int) when a centralized procedure process is initiated. Japan uses the IND procedure as part of the ICH initiative to standardize Good Clinical Practice (GCP) guidelines that were established in 1997 to avoid excessive duplication in the IND process. In China, the Department of Drug Registration and Drug Evaluation Division of the State Drug Administration are responsible for drug regulations (Chow and

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Liu, 2004). Contrary to many press releases, the FDA does not approve an IND but rather, under the U.S. Codes of Federal Regulation (CFR) for clinical trials (21 CFR 312), has 30 days to review an IND at the end of which the sponsoring drug company (commercial) or institution (noncommercial) can begin to initiate Phase I trials. The IND can run to many hundreds to thousands of pages, documenting the preclinical safety and efficacy studies performed on an NCE, its metabolic fate, and stability. The IND also includes the CMC (chemistry, manufacturing and control) section, which describes “the composition, manufacture and controls of the drug substance (API—Active Pharmaceutical Ingredient) and the drug product (the formulated API),” and the CDP (Clinical Development Plan), or Clinical Trial Protocol (CTP), which describes the clinical studies that are planned to establish the safety and efficacy of the NCE. Additional documentation includes the Investigator’s Brochure (IB), used by the IRB (Investigational Review Board) to review and approve the studies contained in the IND. An IRB is formally designated by a public or private institution clinical trial center with its composition and function being subject to stringent FDA guidelines to avoid conflict of interest and gender or discipline bias and to ensure that nonscientific interests related to the trial are represented. The IRB is charged by the FDA to evaluate the ethical acceptability and the scientific validity of a clinical trial, and also to monitor the trial so as to ensure that the participants are not exposed to unreasonable risk(s) during the course of the clinical studies (Petricciani, 1981). Each investigator in an IND is required to submit all clinical protocols to an IRB. Unlike the IND process, the IRB must formally grant permission for trials to be initiated. Following the clinical trials, the sponsor files a New Drug Application (NDA) or Biologics License Application (BLA), depending on whether proposed drug is a small molecule or a biologic, e.g., antibody, with the regulatory authority for approval. The four phases of clinical trials (Fig. 9.9.1) are: Phase I: The first time that an NCE is tested in humans to assess the safety of an NCE is referred to as Phase I. Phase I studies are typically performed on healthy volunteers numbering between 10 and 100 individuals, with a cost in the range of $8 million to $10 million. These are often “open label” (with no placebo group) or, when placebo-controlled, use a lim-

ited number of subjects in the placebo group. While both approaches are cost effective, they do not provide sufficient information when there are adverse events to definitively ascribe these to NCE administration, even when several doses are evaluated. Rising-dose studies with the NCE allow the determination of the maximum tolerated dose (MTD) via route of administration used. Pharmacokinetic and bioavailability studies are also carried out at this time and may include multiple dosing regimens in preparation for Phase II trials. Blood, urine, and feces are collected to assess ADME. Depending on the targeted disease and patient demographics, patients or targeted populations may also be included in the Phase I trials. For critically ill or terminal patients, the option can be given to enter Phase I trials depending on the assessed risk-benefit ratio. In the U.S., the sponsor meets with the FDA to review the results of the trial and agree on the CTD/CTP for Phase II. This is the End of Phase I meeting. Phase II: Phase II studies continue the evaluation of the safety of an NCE and also examine efficacy in the targeted disease group. They involve 50 to 500 individuals, with a cost in the range of $15 million to $20 million and involve discrete arms, e.g., patients assigned to discrete treatment groups. While placebo-controlled Phase II trials would appear the most logical, many Phase II trials are still run in open mode, e.g., with no control group. This may be due to the nature of the disease, e.g., oncology, as described above, or a preference of the sponsoring investigator. While useful information leading to initial claims of efficacy can be derived quickly and cost effectively in an open-label Phase II trial, more rigid trial conditions within the context of placebo, blinded regimens frequently fail to replicate the initial finding, especially in the CNS area. Blinding refers to the practice where the patient does not know whether he or she is receiving the NCE or placebo, to avoid bias. In double-blind studies, neither the patient nor those involved in providing health care have knowledge of whether a placebo, a vehicle formulation identical to that used for the active principle of the NCE but lacking the NCE, or the drug itself is being given. This can rule out psychological aspects of drug treatment, especially when evaluating CNS drugs. A typical Phase II trial will have 3 to 4 arms, a placebo control group, and 2 to 3 groups that are divided according to the dose of the NCE they receive. Ideally, an independent, active arm is included where patients receive a

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compound that is the standard of treatment for a given disease. An active arm allows the NCE to be compared for efficacy and safety against a known drug and can also be used to validate the trial. If the both the active and NCEs show no efficacy against placebo, then the trial can be considered a failure. End points in a Phase II trial can be disease related, e.g., a decrease in blood pressure in hypertension, or surrogate, e.g., a decrease in plasma cholesterol, and are statistically analyzed. Unlike the majority of preclinical studies, study design for clinical trials involves statisticians who “power” the trial with sufficient patients in order to derive statistics that define the probability of correctly detecting a clinically meaningful difference in the efficacy of an NCE. The more patients, the higher the cost, but the better the power and the ability to avoid unconvincing trends in efficacy. Mere patient numbers are not enough, however, to ensure the correct power if too many variables are introduced by the trial design. If a trial involves three arms plus placebo with 40 patients per arm, this requires a total of 160 patients. If the trial is run in one or two clinical research centers, there is a possibility that bias would be introduced; four to five centers would be more representative of the patient population as a whole. On the other hand, the more centers, the greater the opportunity for variability in patient diagnosis and NCErelated outcomes. In one memorable instance, a sponsor planned to run a 3-arm, placebocontrolled trial for an NCE in 60 centers, giving an average of 2.67 patients per center. The power of the trial was diluted by the number of centers and resulted in a highly predictable outcome—it failed. Phase II is colloquially divided into “a” and “b” stages. Phase IIa is a limited trial to ascertain some degree of efficacy, and is followed by Phase IIb, a broader trial involving a larger number of patients. In those instances where there is no known treatment for a disease, it is ethical, as laid down in the revised Declaration of Helsinki (http:// www.wma.net/e/ethicsunit/helsinki.htm), to have the control group remain untreated in order to prove efficacy. It has been widely documented that placebo therapy can appear to be effective in a large number of instances, especially where there is a psychological component. For instance, retrospective studies have shown that four out of six trials for antidepressants fail (Quitkin et al. 2000; Khan et al., 2003). The phrase “breaking the code” refers to the identification of which treatment

a patient has received. A Phase IIa “signal,” the first indication of efficacy in the targeted population, is a major go/no go milestone in the clinical development of an NCE. If discussions with the regulatory agency have led to the conclusion that the Phase IIa trial is a pivotal trial, it can be used together with a second successful trial as part of the New Drug Application (NDA) package. In the U.S., the sponsor meets with the FDA to review the results of Phase II trial(s) and to plan for Phase III. This is the End of Phase II (EOP2)/prePhase III meeting. Phase III: The Phase III trial encompasses pivotal clinical studies that evaluate full-scale evaluation of treatment in several different medical or regional centers. The design is to compare the NCE with known treatments and with placebo in controlled blinded trials that involve diverse patient groups demographically representative of the intended patient group for the marketed drug. The dosage and formulation of the NCE used in this stage is critical, as Phase III data are those upon which regulatory decisions are made and which support the labeling and subsequent marketing of the NCE as a drug. European regulations require that a minimum of 1000 patients be exposed to an efficacious dose of the NCE for at least a year. The number of patients involved in this portion of compound development can be several hundred to thousands, with costs in the $60 million to $120 million range and a 2- to 5-year time frame. Phase III studies also include an extensive assessment of drug interactions. Once data have been accumulated from pivotal trials, the sponsor will meet with the agency to prepare for the filing of the NDA (U.S.) or a Marketing Authorization Approval/Product License Application (PLA) in the European Union to request approval to market the NCE as a drug. The NDA/PLA is many thousands of pages in length, containing efficacy data as a basis for the claimed indications, as well as safety information and a summary of the risks and benefits for the NCE. Once filed, it can take 12 to 18 months for regulatory review and approval. Phase IV: In the Phase IV process, postmarketing surveillance is conducted after the drug is approved. This phase involves the monitoring of adverse effects and additional long term, large-scale studies of drug efficacy. In addition, Phase IV trials can be used to monitor additional indications for a new drug for a subsequent NDA and to gather information to assess the pharmacoeconomics for the

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introduction of a new treatment modality. Such information can be useful for healthcare payers in assessing the cost/benefit ratio for the use of a new drug, i.e., whether its use saves money or whether it offers significant benefit over existing therapy (surgery or other drugs) to the extent that there are savings in either the initial or long term as reflected in time for patient recovery and quality of life. As an example, the U.K.’s National Institute for Health and Clinical Excellence (NICE) recently recommended that the drugs currently approved for use in the treatment of Alzheimer’s disease (e.g., donezepil) no longer be prescribed, as their benefit to the patient does not justify their cost.

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pharmacology: Prime time for an iPharm concept?. Biochem Pharmacol. 70:1707-1716. Williams, M. 2006. The genome—5 years on Editorial. Curr. Opin. Invest. Drugs 7:1-3. Yan, Z. and Caldwell, G.W. 2001. Metabolism profiling, and cytochrome P450 inhibition and induction in drug discovery. Curr. Top. Med. Chem. 1:403-425.

Contributed by Mark A. Ator, John P. Mallamo, and Michael Williams Cephalon, Inc. West Chester, Pennsylvania

Overview of Drug Discovery and Development

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CHAPTER 10 Safety Pharmacology/Toxicology INTRODUCTION n recent years, several factors have converged that significantly affect the pharmaceutical drug discovery and development process. Foremost among these are the enormous costs associated with the introduction of new medicines to the marketplace, estimated to range from $357 million in 1991 to an anticipated $1 billion or more at the turn of the century. Secondly, the application of high-throughput screening technologies coupled with other synthetic methods (e.g., high-speed analoging), as well as rational computational modeling, frequently results in the identification of multiple lead candidates, making it difficult to decide which is most appropriate for further development. This plethora of potential riches creates a situation wherein considerations (e.g., bioavailability, pharmacokinetics, synthetic process development, pharmacoeconomics) that were formerly the province of the “development” portion of the pharmaceutical R & D process are now a component of the “research” stage. Key considerations in choosing among development candidates are the relative “safety” of one compound compared to others in its analog family, and the overall toxicological profile of the lead.

I

Safety pharmacology differs from efficacy pharmacology in that it focuses on describing the actions of compounds on organ systems other than those associated with the principal therapeutic target. It differs from toxicological testing in that safety assessments are performed at or near the efficacious dose to provide a therapeutic index, whereas toxicological testing evaluates the consequences of administering exaggerated (often heroic) doses of a compound. Safety testing is now a routine consideration in lead selection; toxicological testing retains its traditional stature as the gateway to proceed to “first in human” trials. Both disciplines are sufficiently developed that separate scientific societies are devoted to the further advancement of each. UNIT 10.1 of this chapter provides an in-depth overview, from a global regulatory perspective, of the role of safety pharmacology in the pharmaceutical development process. Included in this unit are not only a discussion of practical considerations (e.g., primary and secondary testing) but also a current update of the crucial and evolving role that safety testing plays within the industry. The unit is of value not only to industry scientists but also to academicians who may be involved in or considering technology transfers from the private to commercial sector. In contrast, UNIT 10.3 is an extensive and detailed overview of the role of toxicological testing in the drug discovery and development process. This unit can be compared with UNIT 10.1 to provide the reader an understanding of the distinction between safety pharmacology and toxicology testing.

presents an introductory discussion and protocol on gastrointestinal irritation models that lays the groundwork for understanding future units dealing with safety testing. The contributions by Lacroix (QT interval; UNIT 10.7) and Crum and Cavero (hERG; UNIT 10.8) provide an understanding of and methods for evaluating the potential side-effect liabilities of drugs to elicit adverse cardiovascular actions not routinely detected in more general evaluation of the cardiovascular profile of drug substances. Both reflect the growing momentum within regulatory agencies to include expanded in vitro safety data within the drug development and characterization dossiers. Similarly, both UNIT 10.9 (assessment UNIT 10.2

Contributed by John W. Ferkany Current Protocols in Pharmacology (2006) 10.0.1-10.0.2 C 2006 by John Wiley & Sons, Inc. Copyright


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of respiratory function) and UNIT 10.11 (head-out plethysmography) are directed toward methods to evaluate the effects of new drugs on pulmonary function. The remaining material provides protocols to assess potentially adverse responses elicited through central nervous system actions, i.e., UNIT 10.4 (place preference), UNIT 10.5 (abuse liability), and UNIT 10.6 (vigilance-controlled EEG), or general physiological/CNS mechanisms, namely UNIT 10.10 (Irwin testing). In aggregate these units provide the reader with the materials necessary to design and execute a safety assessment campaign for a drug substance in the three core safety areas of cardiovascular, pulmonary, and central nervous system pharmacology. UNIT 5.3 should also be considered in the safety battery in order to determine the actions of new drugs on gastrointestinal motility. John W. Ferkany

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Overview of Safety Pharmacology Safety pharmacology, a relatively recent concept, is situated between classical toxicology and pharmacology (Fossa, 1994). Toxicology examines chemical-induced changes as a result of drug action, usually after repeated treatment at supratherapeutic doses, whereas safety pharmacology entails the search for changes that occur after acute administration of a chemical agent at doses approximating those to be employed clinically. A first attempt at defining safety pharmacology was contained in the Japanese Guidelines (Japanese Ministry of Health and Welfare, 1995), which provides considerable details on the types of pharmacology studies considered essential before an agent is administered to humans. More recently, the European Agency for the Evaluation of Medicinal Products proposed a new set of guidelines contained within the International Conference on Harmonization (ICH) safety guidelines (http://www.ich. org/cache/compo/276-254-1.html). ICH S7A deals with core battery studies, whereas ICH S7B is concerned specifically with assessment of QT interval prolongation, an effect that can lead to cardiac arrhythmias (torsades de pointe) and death. The ICH S7A guidelines came into effect in Europe in June, 2001 and have since been adopted in both the United States (US Food and Drug Administration, 2001) and Japan. The ICH S7B guidelines have also been adopted in the United States (US Food and Drug Administration, 2005). Described in this unit are a number of important issues relating to safety pharmacology as it is currently defined by regulatory agencies.

TERMINOLOGY A source of confusion about safety pharmacology arises from the various terms used to categorize the kinds of studies covered by this discipline. Besides safety pharmacology, other terms used to describe such studies include general pharmacology, ancillary pharmacology, secondary pharmacology, high-dose pharmacology, and regulatory pharmacology. Moreover, experiments performed to define the mechanisms of adverse effects observed during toxicological studies are often considered part of safety pharmacology. In its strictest sense, the word safety implies the absence of untoward effects that might endanger the health of a patient. Thus, the

Contributed by Roger D. Porsolt Current Protocols in Pharmacology (2006) 10.1.1-10.1.6 C 2006 by John Wiley & Sons, Inc. Copyright


UNIT 10.1

term safety pharmacology could be applied to all pharmacological studies undertaken to ensure the absence of adverse effects when the drug candidate is administered in a manner, and over a dose range, that is clinically relevant. Only studies predictive of risk should be part of a safety pharmacology assessment. The primary aim is to demonstrate that, at doses thought to be appropriate for obtaining the therapeutic benefit, there are no other effects that could be considered risk factors. A further aim would be to determine the maximum dose that could be administered before encountering adverse events. Such studies should also be useful for establishing a bridge between therapeutic doses and those to be used in toxicological studies, and for determining the maximum doses that can be administered and still ensure safety during Phase I human studies. While the term high-dose pharmacology would appear to be relevant in this regard, it is too restrictive in not including the notion that the drug candidate might have adverse effects on other systems even at therapeutic doses. In contrast, the terms general or ancillary pharmacology encompass all studies undertaken to characterize the probable therapeutic responses to the new substance. The aim of general pharmacology studies is to determine the selectivity of the drug candidate for the intended indication. For example, a novel anticancer compound would not usually be expected to display psychotropic activity, although there might be a therapeutic advantage if the drug had antidepressant or anxiolytic effects. Studies aimed at exploring such issues would come under general pharmacology since proof of safety is not their main purpose.

SAFETY PHARMACOLOGY VERSUS TOXICOLOGY Traditionally, safety pharmacology studies were conducted in a toxicological context. Toxicology differs from pharmacology in that toxicologists investigate mainly structure whereas pharmacologists investigate mainly function (Sullivan and Kinter, 1995). Indeed, there is an entire area of study linking morphological with functional changes and in defining their relationship to adverse events. For example, vomiting, diarrhea, or a change in body weight are easily detected during the clinical evaluations performed as part of

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toxicological studies. Other potentially adverse responses, such changes in cardiac rhythmicity, might not be noted unless specific pharmacological studies are undertaken. Furthermore, changes in physiological function may occur in the absence of changes in organ structure and frequently occur at doses lower than those necessary to induce a structural change; and not all structural modifications are clearly associated with a detectable change in function. Although structural and functional changes are sometimes clearly related, it is not always possible to link them in terms of cause and effect. Thus, safety pharmacology and classical toxicology are complementary, with both providing information important for determining the safety of a new substance. While regulatory authorities may place more emphasis on formal toxicology studies, clinical pharmacologists may find safety pharmacology data more reassuring when designing clinical trials (Sullivan and Kinter, 1995).

COMPARISON OF JAPANESE AND ICH GUIDELINES

Overview of Safety Pharmacology

The Japanese Guidelines have their origins in a notification issued by the Japanese Ministry of Health and Welfare in September 1967 concerning “basic policies for approval to manufacture drugs.” The 1995 version of the Japanese Guidelines (Japanese Ministry of Health and Welfare, 1995) does not specifically mention safety pharmacology, describing instead general pharmacology as having the following aims: 1. to assess the overall profile of “general pharmacological effects” as compared to “principal pharmacological effects,” 2. to “obtain useful information on potential adverse effects,” and 3. to evaluate “effects of drugs on physiological functions not necessarily detectable in toxicological studies.” ICH S7A specifically mentions safety pharmacology and defines it as “those studies that investigate the potential undesirable pharmacodynamic effects of a substance on physiological functions in relation to exposure in the therapeutic dose-range and above.” The ICH definition has the considerable advantage in restricting safety pharmacology to the assessment of risk, thereby eliminating a wide range of studies that could be included if the definition were more general. Reducing the number of studies reduces drug development costs, which is an advantage to both the drug developer and the consumer.

The Japanese guidelines differ from ICH S7A in making more specific recommendations concerning the tests to be employed. Safety pharmacology studies are divided into Category A and Category B. Category A includes essential evaluations, whereas Category B studies are those to be carried out “when necessary.” For central nervous system (CNS) evaluations, Category A includes general behavioral observations, measures of spontaneous motor activity, general anesthetic effects (including assessment of potential synergism/antagonism with general anesthetics), proconvulsant effects, analgesia, and effects on body temperature. Category B includes effects on the electroencephalogram (EEG), the spinal reflex, conditioned avoidance response, and locomotor coordination. As far as the other body systems, the Japanese Category A includes studying effects on the cardiovascular and respiratory systems (e.g., respiration, blood pressure, blood flow, heart rate and electrocardiogram or ECG) the digestive system (including intestinal transit and gastric emptying) water and electrolyte metabolism (e.g., urinary volume, urinary concentrations of sodium, potassium and chloride ions) and “other important pharmacological effects.” The three vital systems the European guidelines (i.e., ICH guidelines) include in core battery studies are the CNS, cardiovascular (CV) system, and the respiratory system. Core battery CNS studies include motor activity, behavioral changes, coordination, sensory/motor reflex responses, and body temperature, with the remark that “the CNS should be assessed appropriately.” A similar remark is made about appropriate CV assessment, with specific mentions of blood pressure, heart rate and ECG, together with a suggestion that “in vivo, in vitro and/or ex vivo evaluations, including methods for repolarization and conductance abnormalities, should also be considered.” Comparison of the Japanese Guidelines and ICH S7A suggests a clear intent expressed in the ICH guidelines to free safety pharmacology from the constraints of a cookbook approach. On the other hand, their vagueness does not provide a clear idea of what evaluations could or should be performed. This is most apparent in the recommendations for follow-up studies. For the CNS, the ICH indicates that follow-up studies should include “behavioral pharmacology, learning and memory, ligand-specific binding, neurochemistry, visual, auditory and/or electrophysiology examinations, etc.” The subject of drug dependence/abuse, although a major concern for

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many substances with CNS effects, receives only a one-word mention in the section on “Other Organ Systems.” The ICH guidelines are much more explicit for the CV and respiratory systems. Indeed, all of ICH S7B is devoted to an analysis and recommendations for arrhythmogenic risk. ICH S7A specifically mentions renal/urinary, autonomic and gastrointestinal systems, although, surprisingly, no mention is made of nausea, despite the fact that it is a common adverse event.

CNS SAFETY PHARMACOLOGY ICH S7A guidelines recommend that core battery CNS studies include measures of drug-induced signs of CNS dysfunction as well as measures of spontaneous locomotion and motor coordination. Three other measurements, originally recommended by the Japanese Guidelines Category A, but dropped from the ICH S7A core battery, are the convulsive threshold, interaction with hypnotics, and effects on pain threshold (Porsolt et al., 2005). In spite of their exclusion from ICH S7A, such measures are useful in a core battery of CNS safety pharmacology procedures. Decreases in the convulsive threshold are an important component in the assessment of CNS safety. Several substances, including antipsychotics, such as clozapine, do not induce frank convulsions at any dose but clearly decrease the convulsive threshold. Even anticonvulsive activity, which in itself is not a risk factor, could be a predictor of cognition-impairing effects. Several anticonvulsants, such as benzodiazepines and glutamic acid NMDA receptor antagonists, are known to impair cognition. Thus, anticonvulsant activity could represent a useful first screen for potential cognitionimpairing effects. Likewise, sleep-inducing or sleep-attenuating activity could be unmasked by a barbiturate interaction procedure. While benzodiazepines, for example, do not by themselves induce sleep, their sleep-enhancing activity can be readily detected by studying their interaction with barbiturates. The same is true for psychostimulants, which may or may not induce signs of excitation in a primary observation procedure, but clearly block barbiturateinduced sleep. Finally, a drug-induced increase in pain sensitivity can be readily assessed using a simple nociception procedure (e.g., the hotplate; UNIT 5.7). Whereas analgesic activity is not in itself a risk factor, the presence of analgesic

activity could be a useful predictor of abuse liability.

CARDIOVASCULAR SAFETY PHARMACOLOGY ICH S7B guidelines, which deal exclusively with the evaluation of proarrhythmic risk, recommend that core battery cardiovascular studies include measures of drug-induced effects on arterial blood pressure, heart rate, and ECG, together with the suggestion that “in vivo, in vitro and/or ex vivo evaluations, including methods for repolarization and conductance abnormalities, should also be considered.” Recommended methodologies include measurements of ionic currents in isolated animal or human cardiac myocytes, and in cultured cell lines or heterologous expression systems with cloned human channels. Other procedures include measurement of action potential (AP) parameters in isolated cardiac preparations or analysis of specific electrophysiologic parameters indicative of AP duration in anesthetized animals, and measurement of ECG in conscious or anesthetized animals. While it is clear that proarrhythmic effects represent a major cardiovascular danger for new substances, the excessive focus on this risk by ICH S7B, together with the avalanche of reports dealing with methodological issues, have taken attention away from the many other types of drug-induced cardiovascular risk. A comprehensive systemic, cardiac, pulmonary, and renal hemodynamic evaluation in a large animal species like the dog is essential for an adequate evaluation of cardiovascular risk. Other risk factors, such as drug-induced depression of myocardial contractility or pulmonary hypertension, are critically important, even in the absence of other cardiac electrical disorders. In contrast to the telemetered conscious dog, the number of cardiovascular parameters that can be studied in an acute hemodynamic study in the anesthetized animal yields a wealth of information regarding the mechanisms responsible for effects on the cardiovascular system (Lacroix and Provost, 2000). Parameters that can be studied in the anesthetized animal include aortic blood pressure, heart rate, cardiac output, stroke volume, pulmonary artery blood pressure, peripheral vascular resistance, renal blood flow, and ECG. The hERG channel assay is now considered the model of choice for cardiac proarrhythmic risk assessment. Whereas hERG channel assays, using binding techniques and automated

Safety Pharmacology/ Toxicology

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Overview of Safety Pharmacology

technology, would seem appropriate as a first screen for QT prolongation in the early stages of safety evaluation, the use of the hERG channel patch clamp technique (UNIT 10.8) is recommended for the core battery of cardiovascular studies. Although more time-consuming, the patch clamp technique is an indicator of function, as opposed to receptor affinity, and lends itself more readily to compliance with good laboratory practice (GLP; see http://www.oecd.org), which is mandatory for ICH S7A-recommended core battery studies. On the other hand, the hERG channel assay cannot constitute a stand-alone in vitro test for evaluating proarrhythmic risk. Calcium agonists, for example, are known to lengthen the AP duration and favor the occurrence of early afterdepolarizations and/or delayed afterdepolarizations, either of which can lead to torsades de pointe. Cardiac risk related to this calciumdependent mechanism cannot be detected by the hERG channel assay. Furthermore, the hERG channel assay can frequently overestimate the cardiac risk for a new substance because partial inhibition of the potassium ion channel conductance (IKr ) may not result in AP prolongation in a Purkinje fiber preparation because of counteracting effects on other cardiac ion channels. For this reason the Purkinje fiber assay (UNIT 11.3) constitutes a necessary adjunct for investigating the effects of a test substance on the different parameters of the AP. Whichever in vitro assay is used, it cannot fully mimic the in vivo situation. All in vitro data must be considered in the context of plasma protein binding (UNIT 7.5), pharmacokinetic parameters (UNIT 7.1), and anticipated plasma concentrations of the test substance; thus, in vivo analyses in conscious animals monitored by telemetry remain an essential component in the assessment of proarrhythmic risk. Nonetheless, neither does telemetry in conscious animals constitute a stand-alone technique, since it provides little information regarding the mechanism responsible for any observed effect. Orthostatic hypotension is another common cardiovascular risk not covered by the ICH S7A core battery. As this constitutes a major adverse effect associated with many different classes of drugs, it is important to determine whether it is a property of a new chemical entity intended for human use. A simple animal model for orthostatic hypertension is the tilting test in the anesthetized rat (Hashimoto et al., 1999). With this assay, orthostatic hypotension is exacerbated by prazocine and

β-adrenoceptor antagonists. Inclusion of such a test in a cardiovascular core battery would not constitute a major expense but would provide a more complete assessment of cardiovascular risk.

RESPIRATORY SAFETY PHARMACOLOGY Drugs can cause changes in pulmonary function (UNIT 10.9) by direct actions on the respiratory system or as a consequence of central, metabolic (alterations in acid-base balance) or vascular (pulmonary hypertension) effects. ICH S7A guidelines include respiration as a vital function that must be assessed during safety evaluations. Whole-body plethysmography is a method of choice for examining whether a test substance affects airway function. Whole-body plethysmography is performed either in the unrestrained rat or guinea pig following oral or intravenous administration of the test agent. Modern systems allow a number of ventilatory parameters to be measured, including inspiratory and expiratory times, peak inspiratory and expiratory flows, tidal volume, respiratory rate, relaxation time, and pause and enhanced pause. This allows for the differentiation between effects on respiratory control and the mechanical properties of the lung (Murphy, 2002). As a general screen, the whole-body test is preferable to the head-out method because the animals can be studied over longer periods of time since they have freedom of movement. A weakness of whole-body plethysmography is that it is not sensitive to the respiratory-depressant effects of some drugs, such as barbiturates and opioids. Attempts to increase the sensitivity of this assay system by placing the animals in a CO2 -enriched environment (Van den Hoogen and Colpaert, 1986; Gengo et al., 2003) appear promising. Although the anesthetizeddog preparation does not lend itself to the evaluation of spontaneous lung function, it is well suited for evaluating the risk of pulmonary hypertension (as mentioned above). For this reason the anesthetized dog constitutes an important component in a comprehensive respiratory safety evaluation.

TIMING OF SAFETY PHARMACOLOGY STUDIES AND GOOD LABORATORY PRACTICE One of the issues not clearly addressed in either the Japanese or ICH guidelines is the timing of safety pharmacology studies. Whereas the Japanese guidelines imply that safety data are required for marketing

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approval, ICH S7A clearly states they are needed prior to initiating Phase 1 human trials. Both the Japanese and ICH guidelines require that Category A or core battery studies be performed according to Good Laboratory Practice (GLP). Exceeding these regulatory requirements, many pharmaceutical firms engage in safety pharmacology studies early in the drug discovery process, even at the very beginning of in vivo experiments (Sullivan and Kinter, 1995). A primary observation procedure, such as the Irwin test (UNIT 10.10), is frequently the first assessment in living animals for determining acute toxicity, the active dose-range, and the principal effects on behavior and physiological function. Substances are also frequently screened very early in the discovery process for potential proarrhythmic risk using hERG procedures (UNIT 10.8). Such early safety screening is rarely conducted according to GLP, and therefore falls outside the requirements of the regulatory agencies. Nonetheless, information gained from such tests is vital in directing the discovery program and the selection of clinical candidates. Safety studies are performed differently early in the discovery process than at pre-Phase 1. An early-stage Irwin test tends to use fewer animals, the mouse rather than the rat, and with doses selected sequentially on the basis of effects observed with previous doses, to determine the lethal dose as rapidly as possible. With later-stage Irwin tests, the dose range is fixed in advance, beginning with the lowest dose approximating the therapeutic dose, followed by multiples of 10 or 100, up to but not including a lethal dose. Similarly, early stage hERG tests employing a binding assay or a patch clamp procedure might examine just a single high concentration of several substances within the same chemical series rather than a range of concentrations, as would be the case in a later hERG evaluation. Indeed the experimental aims of the early and late tests are different. In early-stage work the objective is to detect the presence of risk as a guide in the selection of substances for development. Later-stage analysis is performed to confirm the absence of risk in the relevant dose-range for the selected compound.

possible and to minimize their suffering and discomfort. Since the goal of safety pharmacology is to assess the risk of side effects, the possibility of causing suffering in the experimental animals is higher than in other areas of pharmacology. The investigator must remain sensitive to these issues, not only in planning and designing the protocols, but also while performing the experiments. For example, procedures for terminating the experiment in the event of well-defined events, such as pain or death, must be established. Ethical issues must be considered within the context of the aims of the experiments, which, ultimately, are to minimize human risk. While reducing risk to humans is of paramount importance, it is still possible to devise scientifically valid experiments using a reduced number of laboratory animals. For example, it is now accepted that the traditional LD50 acute toxicity test, which requires the use of a large number of animals, yields only limited information. Considerably more information, requiring the use of fewer animals, is obtained with the Irwin procedure (UNIT 10.10).

STATISTICAL EVALUATION Since identification of risk is the chief aim of a safety pharmacology test, it is essential that positive results not be overlooked. In other words, the generation of false negatives should be kept to a minimum (Porsolt et al., 2005). False positives, or the erroneous detections of possible risk, although bothersome, are less serious and can usually be corrected with supplementary testing. Thus, the risk of false negatives should be decreased as much as possible, even if there is an increase in the risk of false positives. A test substance found not to have significant safety risks based on preclinical studies, even after the use of oversensitive statistics, is more likely to be truly devoid of risk. As a consequence, safety pharmacology, in contrast to efficacy pharmacology, should employ statistical procedures possessing maximal sensitivity for detecting possible effects on a dose-by-dose basis, at the acknowledged risk of increasing the number of false positives.

CONCLUSIONS ETHICAL AND ANIMAL WELFARE ISSUES As with all procedures involving living animals, there are important ethical considerations in the choice of methods. The guiding principles are to use as few animals as

Thanks in part to ICH S7A, safety pharmacology can now be considered an independent discipline situated between traditional toxicology and efficacy/discovery pharmacology. Safety pharmacology is, however, a pharmacological rather than a toxicological

Safety Pharmacology/ Toxicology

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Supplement 32

discipline since it concerns the study of drug actions on physiological function rather than on physical/anatomical structure. Although using identical methods, safety pharmacology differs from efficacy pharmacology in that the former evaluates the potentially adverse effects of test substances on normal function, whereas the latter is aimed at establishing therapeutic potential. Both provide information critical for drug discovery and development.

LITERATURE CITED Fossa, A.A. 1994. Inaugural conference on general pharmacology/safety pharmacology. Drug Dev. Res. 32:205-272. Gengo, P.J., Pettit, H.O., O’Neill, S.J., Su, Y.F., McNutt, R., and Chang, K.J. 2003. DPI-3290 [(+)-3-((α-R)-α-((2S,5R)-4-Allyl2,5-dimethyl-1-piperazinyl)-3-hydroxybenzyl)N-(3-fluorophenyl)-N-methylbenzamide]. II. A mixed opioid agonist with potent antinociceptive activity and limited effects on respiratory function. J Pharmacol. Exp. Ther. 307: 1227-1233. Hashimoto, Y., Ohashi, R., Minami, K., and Narita, H. 1999. Comparative study of TA-606, a novel angiotensin II receptor antagonist, with losartan in terms of species difference and orthostatic hypotension. Jpn. J. Pharmacol. 81:63-72. Japanese Ministry of Health and Welfare. 1995. Japanese guidelines for nonclinical studies of drugs manual. Pharmaceutical Affairs Bureau, Japanese Ministry of Health and Welfare, Yakugi Nippo, Japan. Lacroix, P. and Provost, D. 2000. Basic safety pharmacology: The cardiovascular system. Therapie 55:63-69. Murphy, D.J. 2002. Assessment of respiratory function in safety pharmacology. Fundam. Clin. Pharmacol., 16:183-196.

Sullivan, A.T. and Kinter, L.B. 1995. Status of safety pharmacology in the pharmaceutical industry. Drug Dev. Res. 35:166-172. US Food and Drug Administration. 2001. ICH guidance for industry: S7A safety pharmacology studies for human pharmaceuticals. US Food and Drug Administration, Rockville, Md. US Food and Drug Administration. 2005. ICH guidance for industry: S7B nonclinical evaluation of the potential for delayed ventricular repolarization (QT interval prolongation) by human pharmaceuticals. US Food and Drug Administration, Rockville, Md. Van den Hoogen, R.H. and Colpaert, F.C. 1986. Respiratory effects of morphine in awake unrestrained rats. J. Pharmacol. Exp. Ther. 237:252-259.

Internet Resources http://www.fda.gov/cber/gdlns/ichs7a071201.htm The FDA’s S7A safety guidelines for pharmacology studies of human pharmaceuticals. http://www.fda.gov/cber/gdlns/ichqt.htm The FDA’s S7B safety guidelines for pharmacology studies of human pharmaceuticals. http://www.ich.org/cache/compo/276-254-1.html Contains links to both the ICH S7A (Safety Pharmacology Studies for Human Pharmaceuticals) and S7B (The Nonclinical Evaluation of the Potential for Delayed Ventricular Repolarization (QT Interval Prolongation) By Human Pharmaceuticals) safety guidelines. http://www.oecd.org The OECD Website contains comprehensive information about GLPs.

Contributed by Roger D. Porsolt Porsolt & Partners Pharmacology Boulogne-Billancourt, France

Porsolt, R.D., Picard, S., and Lacroix, P. 2005. International safety pharmacology guidelines (ICH S7A and S7B): Where do we go from here? Drug Dev. Res. 64:83-89.

Overview of Safety Pharmacology

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Current Protocols in Pharmacology

Models of Inflammation: Measuring Gastrointestinal Ulceration in the Rat In rats, nonsteroidal anti-inflammatory drugs (NSAIDs) such as indomethacin induce gastrointestinal ulcerations. A single oral dose of indomethacin, administered to rats that have been deprived of food for the previous 18 to 24 hr, produces erosive lesions in the gastric mucosa within 4 to 6 hr. In rats that receive food and water ad libitum, administration of NSAID for several days will induce deep, erosive, and perforating ulcers of the small intestine. In humans, this action of NSAIDs can lead to hospitalization and in some instances, death. The present protocol describes a procedure to elicit and measure indomethacin-induced gastrointestinal lesions. This approach can be used to test for gastrointestinal side effects of potential anti-inflammatory and other agents.

UNIT 10.2

BASIC PROTOCOL

NOTE: All protocols using live animals must first be reviewed and approved by an Institutional Animal Care and Use Committee (IACUC) or must conform to governmental regulations regarding the care and use of laboratory animals. Materials Female Crl:CD(SD)BR Vaf+ rats weighing 110 to 120 g at the time of arrival (Charles River Labs) Indomethacin (see recipe) Vehicle (see recipe) Test compound 0.9% (w/v) NaCl (Irrigation USP, McGaw) CO2 supply 1% (w/v) Evan’s blue (Sigma) in 0.9% (w/v) NaCl Scales accurate to 0.1 g 1- and 3-ml syringes with Luer-Lok hubs (Becton Dickinson Labware) 16-G, 3-in. gavage needles (Popper & Sons) 25-G, 5⁄8-in. needles (Becton Dickinson Labware) Wire-bottom cages Sharp dissecting scissors 12-ml irrigation syringes with tapered curved tip (Monoject) Magnified light (VWR) Prepare the animals 1. Acclimate female Crl:CD(SD)BR Vaf+ rats weighing ∼110 g for ≥1 week in solidbottom cages with wood shavings. Animals are acclimated under standard lighting and temperature conditions to eliminate the effect of stress. Food and water are available ad libitum. Other strains may be used.

2. At the onset of the experiment, weigh each rat using a scale accurate to 0.1 g and identify each rat with tail markings. Divide the rats into groups of five, one group for each treatment. House the animals for each group together. Each rat should weigh ≥150 g at this time.

Administer the drugs 3. Using a 3-ml syringe with a 16-G, 3-in. gavage needle attached, administer indomethacin orally. Similarly administer test compound at three concentrations and Contributed by Phyllis E. Whiteley and Stacie A. Dalrymple Current Protocols in Pharmacology (1998) 10.2.1-10.2.4 Copyright © 1998 by John Wiley & Sons, Inc.

Safety Pharmacology/ Toxicology

10.2.1

vehicle control to appropriate animals. Check each animal twice daily for discomfort, diarrhea, lethargy, ruffled fur or other general signs of illness. Indomethacin (the reference compound) is given at a dose of 4.75 mg/kg in a volume of 1.0 ml/100 g body weight. It is prepared in vehicle (see recipe). Experimental design for testing a compound is determined based on the chemistry of the compound and known in vitro and pharmacokinetic data. Typically three to four doses of the test compound are used, enough doses to produce a linear response curve. Vehicle for the test compound is defined by the experimental protocol or is selected based on physiochemical properties of the test compound. If any animal dies or needs to be euthanized during the experiment, it should be necropsied to verify that the cause of death is due to perforation of the small intestine.

4. Administer each compound once a day, for 4 days. 5. Following administration of the final dose of indomethacin, vehicle, or test compound on day 4, transfer the rats to wire-bottom cages without food, but provide water ad libitum for ∼14 hr prior to euthanasia. Animals are housed in wire-bottom cages to prevent them from eating their feces during the period of starvation.

6. On day 5, 30 min prior to euthanasia, inject 1 ml of 1% Evan’s blue in saline intravenously into the tail vein, using a 1-ml syringe and 25-G, 5⁄8-in. needle. Evan’s blue dye is used to aid in identification and evaluation of lesions and ulcerations.

Examine animals for gastrointestinal ulcerations 7. Weigh the rat and euthanize it in a CO2 atmosphere. 8. Remove the stomach and small intestine (to the cecum), intact if possible. Lay the tissue out on 0.9% NaCl–moistened filter paper. With a pair of sharp dissecting scissors, open the stomach along the greater curvature and the small intestine lengthwise along the antimesenteric line. Using an irrigation syringe filled with saline, wash away residual material and mucus. Consult A Color Atlas of the Rat: Dissection Guide (Olds, 1979) for information to assist with the dissection.

9. Examine the tissues under magnification with obliquely reflected light (magnified light). Table 10.2.1

Score

Characteristics

0 1

No ulcerations, or mucosal damage. Up to 15 small mucosal ulcerations (4 mm in diameter.

3

Small and medium ulcerations and ≤5 ulcerations >4 mm in diameter; no intestinal adhesions. Predominantly medium and large ulcerations (>5 total); large ulcerations exhibit signs of perforations and adhesions which make it difficult to remove the intestine intact. Necropsy of deada or euthanized animals reveals evidence of massive peritonitis resulting from intestinal perforations.

4 Models of Inflammation: Measuring Gastrointestinal Ulceration in the Rat

10.2.2

Scoring System for Gastrointestinal Lesions in the Rat

5

aAll animals found dead should be necropsied to confirm that the most likely cause of death

was due to intestinal ulcerations. Current Protocols in Pharmacology

10. Score the stomach and small intestine separately using a five-point scale (see Table 10.2.1). 11. Calculate the total score (sum of scores for all of the rats in the group) and the mean score (total score divided by number of rats in the group). REAGENTS AND SOLUTIONS Use deionized, distilled water in all recipes and protocol steps. For common stock solutions, see APPENDIX 2A; for suppliers, see SUPPLIERS APPENDIX.

Indomethacin Homogenize 19 g indomethacin (Sigma; 0.475 mg/ml final) in 10 ml vehicle (see recipe) for 25 sec at 20 rpm using a Polytron with a homogenization probe (Brinkmann, VWR). Add another 10 ml of vehicle and homogenize again. Repeat until 40 ml of vehicle have been added. Sonicate 3 min in a bath sonicator (Branson) at room temperature. Store unused solution at 4°C. Each day before use, allow the solution to come to room temperature and sonicate for 1 min. This recipe is for preparation of 40 ml of a 0.475-mg/ml solution which is sufficient material for four daily doses for an experimental group of five animals. Administration of 1 ml/100 g of body weight delivers a dose of 4.75 mg/kg. For other doses, adjust the concentration of indomethacin appropriately.

Vehicle 987 ml water 9 ml benzyl alcohol (Aldrich; 0.9% final) 4 ml polysorbate 80 (Sigma: 0.4% final) 9 g NaCl (0.9% final) 5 g type 7L sodium carboxymethylcellulose (Aqualon; 0.5% final) Add each ingredient in order to a beaker with stirring. Stir for 2 to 3 hr. Transfer to Pyrex bottles and sterilize by autoclaving. Store up to 8 months at room temperature in the dark. COMMENTARY Background Information Nonsteroidal anti-inflammatory drugs (NSAIDs) exert a wide range of beneficial effects that are balanced to varying degrees by adverse effects on the gastrointestinal tract and kidney. The irritant and erosive actions of NSAIDs on the gastrointestinal tract represent the primary dose-limiting feature of these agents. All drugs of this structurally diverse class appear to have as their sole biochemical mechanism of action the ability to inhibit prostaglandin H synthetase (cyclooxygenase); both the therapeutic and deleterious effects are believed to result from inhibition of this enzyme. Recently, it has been determined that prostaglandin H synthetase exists in two distinct forms: a constitutive enzyme that is continually expressed in most cells (COX-1), and an inducible enzyme that is expressed in certain cells in response to appropriate stimulation and at sites of inflammation (COX-2; see UNIT 3.1).

Experiments in animal models and recent human data with selective COX-2 inhibitors have shown that inhibition of COX-1 produces the adverse effects of NSAIDs on the gastrointestinal tract and kidney, while inhibition of COX2 is responsible for the desirable therapeutic effects (Masferrer et al., 1994). Existing NSAIDs have been found to inhibit both forms of the enzyme with, for the majority of agents, a relatively greater effect on COX-1 (Jouzeau et al., 1997). In rats, the site of NSAID-induced gastrointestinal ulcerations depends on the experimental procedure employed. A single oral dose of indomethacin administered to rats deprived of food for the previous 18 to 24 hr will produce erosive lesions of the gastric mucosa within 4 to 6 hr. In contrast, rats that receive food and water ad libitum will not develop stomach lesions with a single dose of drug, but daily administration of the NSAID for several days

Safety Pharmacology/ Toxicology

10.2.3 Current Protocols in Pharmacology

Table 10.2.2

Evaluation of Gastrointestinal Ulceration in Small Intestine

Test compound

Daily dose (mg/kg)

Vehicle control Indomethacin Indomethacin Indomethacin Indomethacin

— 4.00 4.25 4.50 4.75

Clinical scores Rat 1 Rat 2 Rat 3 Rat 4 Rat 5 0 1 1 3 4

0 0 0 3 4

0 1 2 3 4

0 1 2 2 3

0 0 2 3 3

Total scorea

Mean score (± SEM)b

0 3 7 14 18

0±0 0.6 ± 0.54 1.4 ± 0.40 2.8 ± 0.22 3.6 ± 0.25

aSum total of all clinical scores. Maximum total score is 25 with five animals per treatment group. bMean score is defined as the total score divided by the number of rats per group (n = 5). SEM, standard error of the mean.

causes deep, erosive, and perforating ulcers of the small intestine in these animals (Kent et al., 1969). Although there is much debate as to the exact cellular mechanism of NSAID-induced gastrointestinal lesions, it is generally believed to be a consequence of inhibition of the prostanoids, PGI2 and PGE2 which are thought to play a homeostatic, beneficial role in the gut.

Critical Parameters Rats must be acclimated to their environment ≥1 week prior to initiation of the experiment to eliminate the effect of stress. Animals should be monitored twice daily and euthanized and necropsied if they appear critically ill.

Anticipated Results Rats administered 4.75 mg/kg of indomethacin will have a mean score between 3 and 4 (see Table 10.2.2). Stomach lesions are rarely detected under this protocol. Generally, all NSAIDs that inhibit both cyclooxygenase 1 and cyclooxygenase 2 cause gastrointestinal ulcerations in this assay, although the response occurs at different doses for different compounds.

Time Considerations Rats must be weighed and drug administered daily for 4 days. Depending on the number of animals studied, this procedure should take 1 to 2 hr. On the day of sacrifice and analysis (day 5), a significant amount of time is needed for preparation and scoring of the individual animals. The more groups that are

included, the more time it will take for dosing on day 1 and for analysis on day 5. Typically, no more than six groups are assessed, and it takes approximately all day for one person to score lesions.

Literature Cited Jouzeau, J.Y., Terlain, B., Abid, A., Nedelec, E., and Netter, P. 1997. Cyclooxygeanse isoenzymes. How recent findings affect thinking about nonsteroidal anti-inflammatory drugs. Drugs 53:563-582. Kent, T.H., Cardinelli, R.M., and Stamler, F.W. 1969. Small intestinal ulcers and intestinal flora in rats given indomethacin. Am. J. Pathol. 54:237-249. Masferrer, J.L., Zweifel, B.S., Manning, P.T., Hauser, S.D., Leahy, K.M., Smith, W.G., Isakson, P.C., and Seibert, K. 1994. Selective inhibition of inducible cyclooxygenase 2 in vivo is antiinflammatory and nonulcerogenic. Proc. Natl. Acad. Sci. U.S.A. 91:3228-3232. Olds, R.J. 1979. A Color Atlas of the Rat: Dissection Guide. John Wiley & Sons, New York.

Key Reference Young, J.M. and Yee, J.P. 1994. Ketorolac. In Nonsteroidal Anti-Inflammatory Drugs: Mechanisms and Clinical Uses. (A.J. Lewis and D.E. Furst, eds.) pp. 247-266. Marcel Dekker, New York. A good review on the NSAIDs and rat ulcerogenesis.

Contributed by Phyllis E. Whiteley and Stacie A. Dalrymple Roche Bioscience Palo Alto, California

Models of Inflammation: Measuring Gastrointestinal Ulceration in the Rat

10.2.4 Current Protocols in Pharmacology

Toxicology in the Drug Discovery and Development Process Therapeutic agents, or drugs, have been used throughout human history. Indeed, the eagerness and willingness to ingest therapeutic substances that would in other circumstances be regarded as poisonous is a distinguishing human trait. All chemical entities, however, are toxic at sufficiently high doses. The therapeutic index (TI) is defined as the ratio of the toxic dose to the therapeutic dose. The term has been used in defining “safety margins” in clinical studies. For nonclinical toxicology evaluations, designed to provide data supporting safety in a clinical trial, the safety term used most often is the margin of safety (MOS). The MOS relates the No Observed Adverse Effect Level (NOAEL; a dose that produces no relevant adverse effects) to the maximum targeted dose in a clinical trial or a therapeutically effective dose in a nonclinical model. In the discussion that follows, the term MOS will be used as an expression of safety. The information about the MOS must be viewed within the context of the nature of the disease to be treated, currently available therapies, and the overall risk/benefit relationship. It is well established that no therapeutic agent is without risk. The identification of the potential risk, and the appreciation of the benefit, are important aspects of the drug development process. A drug is defined by the World Health Organization Scientific Group as “any substance or product that is used or intended to be used to modify or explore physiological systems or pathological states for the benefit of the recipient.” The process of drug discovery is wide-ranging, high-risk, multifaceted, expensive, and rewarding. The ultimate goal of drug development is to discover new chemical entities (NCEs) or new biological entities (NBEs) that are safe and effective in treating the targeted condition. The potential toxicity of NCEs and NBEs must be sufficiently defined to allow initiation of clinical trials. Toxicology evaluations have three main purposes: determination of the toxicological spectrum over a broad range of doses in laboratory animals; extrapolation of responses to other species, with particular emphasis on the potential for undesirable effects in humans; and determination of safe levels of exposure. Toxicology studies must be conducted in accordance with regulatory guidelines. In the early 1990s,

an effort was initiated to harmonize drug development regulatory guidelines in the European Union (E.U.), Japan, and the United States (U.S.). The International Conference on Harmonization (ICH; see Regulatory Guidelines for Toxicology Profiles, below) has taken the responsibility for providing a set of mutually acceptable regulatory guidelines that will support global drug development. This effort has been largely successful, with studies conducted in the E.U., Japan, and U.S. being generally acceptable for submission in each of the other regions. Toxicology plays a major role in the drug discovery and development process (Gad and Chengelis, 1995; Diener, 1997; Dorato and Vodicnik, 2001).

NEW CHEMICAL ENTITIES (NCEs; SYNTHETIC ORGANIC CHEMICALS) The use of combinatorial chemistry has provided drug discovery scientists with a large number of leads for potential drug candidates. Once an NCE, or class of NCEs, is identified, the chemical must quickly move through early efficacy testing using in vitro and in vivo models. A comprehensive overview of the international pharmaceutical industry’s toxicology testing strategies in relation to clinical development is provided in The Pharmaceutical R&D Compendium (Findlay and Kermani, 2000). Indications of potential efficacy for a therapeutic target require the mobilization of additional resources. Defining potential toxicity and drug disposition issues early in the discovery process facilitates decisions on further development of that particular chemical entity. Early drug discovery and development efforts are relatively inexpensive, with the longer-term safety studies and clinical trials being much more capitalintensive. Elimination of an NCE as a potential drug candidate early in the process most efficiently utilizes resources. The new NCE first moves through an abbreviated nondefinitive toxicology profile (early investigative work, pilot studies), usually including studies of up to 2 weeks in rodents and, in some instances, nonrodents. Pharmacologic profiling is often valuable at this early stage to determine pharmacologic effects other than those intended for the therapeutic endpoint—e.g., undesirable

Contributed by Michael A. Dorato and Lorrene A. Buckley Current Protocols in Pharmacology (2006) 10.3.1-10.3.35 C 2006 by John Wiley & Sons, Inc. Copyright


UNIT 10.3

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10.3.1 Supplement 32

effects on blood pressure, cardiac activity, or respiration. Provided that the results of the efficacy studies, early investigative toxicology studies, and early definitive toxicology studies are positive, clinical studies of safety, pharmacokinetics, and pharmacodynamics may be initiated. As human clinical trials progress, the NCE will progress through definitive toxicology evaluations that last from 2 weeks to 1 year. Studies of potential reproductive toxicity, and eventually of carcinogenic potential, are conducted. Among the characteristics evaluated for NCEs identified as drug candidates are acute toxic effects, cumulative toxicity, absorption, elimination half-life (t1/2 ), accumulation in deep tissue compartments, milk excretion, teratogenicity, mutagenicity, sensitization and local irritation, and carcinogenicity. The risk of failure associated with one or more of these parameters has been reviewed (Beary, 1997; Findlay and Kermani, 2000). Approximately 0.01% to 0.02% of NCEs are ultimately marketed as drugs. Even fewer (∼0.002%) return a profit to support the development of new therapeutic agents (Fig. 10.3.1). It is estimated that the development

Toxicology in the Drug Discovery and Development Process

Figure 10.3.1 process.

of a new therapeutic NCE or NBE can take 6 to 12 years, and costs $0.6 to $1.8 billion. Most pharmaceutical companies are committed to reducing development time, with a target of ≤6 years, while maintaining a focus on product safety (Mullin, 2003). The information presented in Figure 10.3.1 is historical. The cost of drug development continues to increase, as shown in the data available through the Tufts Center for the Study of Drug Development (2005). The cost of innovation in drug development has been reviewed (DiMasi et al., 1991, 2002). The relative success rate in drug development, as shown in Figure 10.3.1, was confirmed by a recent review in Drug Discovery and Development (Koppal, 2004). By definition, a drug must modify a biological process. While this alteration can have therapeutic benefit, it also carries some degree of risk. The critical role of toxicology in early and late phases of drug development is to determine the level and acceptability of this risk. The initial focus of toxicology is to define the circumstances under which an NCE may produce potential harm and under which no

Attrition rate of new drug candidates in the drug discovery and development

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Figure 10.3.2 A typical toxicology profile. Early nondefinitive toxicology studies are designed to identify major safety issues. Timing of the developmental toxicology studies is determined by the inclusion of women of childbearing potential (WCBP) in early clinical trials. Currently the acceptable duration of nonrodent chronic toxicology studies is 9 months. However, if the data indicate a progression of toxicity, a 1-year study may be required by the FDA. Depending on the therapeutic indication, carcinogenicity studies may be conducted after approval (Phase IV) if there is no special cause for concern in that regard.

adverse effects are produced. A typical toxicology profile is shown in Figure 10.3.2. Toxicology plays an important role throughout the drug discovery and development process (Fig. 10.3.3). During the early discovery process, toxicologists employ rapid, quantitative screening methods, focusing on a limited number of end points. The goal is an early selection of drug candidates with the most acceptable safety profiles. These preliminary screens, including investigations of surrogate markers and in vitro evaluations, are, however, only a prelude to the required comprehensive safety assessments demanded by regulatory agencies. Regulatory requirements, termed Good Laboratory Practices (GLPs), dictate many aspects of study protocols, and must be followed closely for all toxicology studies used in support of a new drug application. The early toxicology studies, conducted during the discovery phase, are not required to be in full compliance with GLPs, although application of the scientific method is expected. Prior to the initiation of clinical trials, physicians need a toxicity evaluation of the NCE in relevant animal models. Prior to the first human dose (FHD), comparative information, i.e., of human and animal metabolism, Current Protocols in Pharmacology

is limited to in vitro evaluations using relevant tissue preparations. Such data provide an initial understanding of the relevance of the animal model. The clinician usually needs to know both the effect and the no-effect levels in nonclinical studies, signs and duration of toxic response, progression of the toxic response with duration of dosing, reversibility, target organ(s), and relevance of the nonclinical model to humans. The answers to these and other questions form the basis of the toxicology profile supporting initial and continued clinical trials. In addressing this, the toxicologist should not become embroiled in the political issues of toxicologic and pharmacologic effects. Some equate pharmacology with “good” and toxicology with “bad” effects. Pharmacologic activity can have very severe consequences, and may be considered a toxicity. For example, digitalis glycosides increase the force of cardiac contraction and slow electrical transmission, restoring cardiac rate and rhythm toward normal. The acute toxicity of digitalis glycosides represents extensions of these activities, with nausea, vomiting, slow heart rate, heart block, cardiac arrhythmia, and cardiac arrest. It is important to know whether the observed response is

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Figure 10.3.3 Duration of toxicology involvement in drug discovery and development showing the major milestones and study types by phase. The goal is to reduce development time while maintaining a focus on nonclinical and clinical safety assessment. Abbreviations: CE, candidate evaluation; CS, candidate selection; FHD, first human dose; LO, lead optimization; PD, product decision.

Toxicology in the Drug Discovery and Development Process

desirable or undesirable, and, if undesirable, to determine whether it is manageable. Simply classifying a response as expected pharmacology does not satisfy the safety evaluation obligation of the toxicologist. The major objectives of the toxicology profile change between the early and later discovery phases of the drug discovery and development process. Thus, during the early stages of drug development, the focus is on screening. Definitive toxicology studies are very timeconsuming and much more expensive than the nondefinitive screening procedures. Accordingly, the relatively inexpensive, short-term screening procedures are used to eliminate the most toxic compounds early in the development process. The initial screening approaches have a number of inherent limitations: the affected systems may not be fully evaluated, the assay procedures may be inadequate or improperly timed relative to the onset of the toxic response, target-organ exposure may be insufficient, functional evaluations may not be included, metabolic, anatomic, and physiologic differences between species may go unrecognized, and the animal model may not express the same responses as humans (Zbinden, 1989). In the broadest sense, nonclinical safety studies should adequately characterize the toxicity of a new drug candidate in several species, when appropriate, so the clinician can be alerted to potential adverse effects during the initial clinical trials.

There is concern about the adequacy of the definitive toxicology screening procedures and their ability to protect the public. Opinions vary on the ability of toxicology screening to affect the occurrence of drug toxicity in the human population (Cluff, 1980; Karch, 1980). The magnitude of adverse clinical toxicity seems small, with ∼1 per 10,000 patients reported to demonstrate adverse responses (Karch, 1980). A review of the number of NCEs and NBEs introduced in the United Kingdom, the U.S., and Spain from 1974 to 1993 indicates that ∼3% to 4% of all drugs introduced during that time were discontinued for safety reasons (Bakke et al., 1995). Although the number of safety withdrawals is low, U.S. companies, or their foreign subsidiaries, have been involved in the majority of cases (Bakke et al., 1995). The ability of the toxicology screening process to prevent adverse clinical events, however, is virtually impossible to evaluate. In some cases, toxicology screening has failed to provide adequate information on potential human risk, since drugs can cause unexpected clinical toxicity. Moreover, it is difficult to predict from animal studies subjective clinical responses such as nausea, dizziness, heartburn, or headache. Zbinden (1991) has provided a list of drug disasters resulting from the failure to conduct adequate toxicity evaluations. The list of drug disasters, however, is balanced by the even larger list of NCEs that would have caused

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serious adverse effects in humans had they not been detected and eliminated using appropriate animal experiments. For the most part, data supporting the ability of toxicology screening to prevent human toxicity are generally not reported and remain buried in company files, since data need not be submitted to regulatory agencies if the NCE is canceled prior to human testing. The available information, however, indicates that the majority of NCEs that pass animal toxicity screens are safe in clinical trials (Scales, 1990). In addition to the NCE, the drug-delivery system to be employed may require safety evaluation. Data must be accumulated indicating the delivery system is safe and effective. Today, an increasing number of new delivery systems such as inhalation devices, oral delivery (for proteins), ocular delivery, depot formulation, and transdermal delivery are under investigation (Gad and Chengelis, 1995; Wolff and Dorato, 1997). DeGeorge et al. (1997) have presented considerations for toxicity evaluations of respiratory drug products. The regulatory requirements for known and novel drug-delivery systems have been reviewed by Weissinger (1990). Updated information is available on the U.S. Food and Drug Administration (FDA) and European Medicines Agency (EMEA) Web sites.

NEW BIOLOGICAL ENTITIES (NBEs) Since 1982 there has been a dramatic increase in the rate of development of drugs produced by recombinant DNA (rDNA) technology. The introduction of modern rDNA technology has allowed large-scale production of proteins that would have been very difficult to produce using classical synthetic techniques. rDNA products are complex, highmolecular-weight substances that may require immunologic, biochemical, or bioassay techniques to quantify the material and assess activity. When recombinant human insulin was introduced in 1982, there were few regulatory guidelines for addressing the problems associated with testing products of this new technology (Zbinden, 1987). Indeed, the possibility that each biotechnology product might require a uniquely designed safety assessment has been given serious consideration (Stoll, 1987). The current harmonized regulatory guidelines for safety testing of recombinant proteins are reviewed below. It is important to demonstrate to regulatory authorities that the recombinant protein under development is, in fact, identical to the natCurrent Protocols in Pharmacology

urally occurring substance and is devoid of contaminants that could raise safety concerns (Galloway and Chance, 1984). This is especially important today with the reorganization of the FDA, placing NBEs and NCEs under the same reviewing organization. In the approval process for recombinant human insulin, for example, frequent meetings between regulatory and industrial scientists were held to review the manufacturing process, the molecular biology, the hormone purification process, and the clinical trial programs. Such cooperation was critical in facilitating the rapid regulatory approval of rDNA insulin. This illustrates the importance of involving the U.S. Food and Drug Administration (FDA) early in the nonclinical and clinical development plan to facilitate the approval process. The U.S. Biotechnology Policy (1992) has taken the position that rDNA products per se do not pose an unusual risk to human health or the environment. This is based, in part, on the assumption that the rDNA product is chemically identical to the naturally occurring protein, which is not always the case. Both the regulatory and industry representatives to the International Conference on Harmonization (ICH) of Technical Requirements for Registration of Pharmaceuticals for Human Use support the position that nonclinical toxicologic evaluations of rDNA products should be decided on a case-by-case basis (ICH Topic S6; see Internet Resources for information on accessing current ICH topics). The data generated are used to guide the clinical trial and allow judgments on appropriate workplace exposure levels. In this way, scientific judgment, not regulatory dogma, guides the safety assessment process. The role of the toxicologist, therefore, is less routine when dealing with rDNA products than with the more conventional synthetic organic chemicals, although the principal goals are the same when assessing the safety of either class of agents. These include detecting major toxicity, identifying minor toxicity, determining dose response, defining duration of response, evaluating the relevance of the test model, and investigating the mechanism(s) of toxicity. Toxicity evaluations of rDNA products have established a greater emphasis on the expected pharmacology of the materials. The three areas of concern are intrinsic toxicity, exaggerated pharmacodynamics (anticipated toxicity), and immunotoxicity (Zbinden, 1987). Intrinsic toxicity is defined as undesirable effects having no relationship to the pharmacodynamic properties of the agent. Pharmacodynamic toxicity is defined as an

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exaggerated pharmacologic response (e.g., hypoglycemic shock from insulin). Due to the potency of NBEs, the boundaries between pharmacology and toxicology are blurred, making it best to regard effects as desirable or undesirable. Immunotoxicity is related to hypersensitivity reactions, to cell transformations, and to production of neutralizing antibodies resulting in loss of biologic activity. There is presently no consensus on the relevance of animal models of immunotoxicity for rDNA products; some researchers feel the models do not adequately predict human responses, whereas others, such as Graham (1987), suggest this varies case by case. Factors to consider are the similarity to the natural protein, homology across species, immune response in animal models, and production of neutralizing antibodies in nonclinical and clinical studies. Each NBE must be carefully considered on an individual basis. The rapid regulatory approval of rDNA insulin may have created unrealistic expectations in the biotechnology industry. Two factors facilitate the regulatory approval of rDNA products: therapeutic importance, and the relationship of the rDNA product to an established therapeutic agent. The U.S. FDA has established a “fast-track” approval process for therapeutic agents of critical importance. Even though it is an international body, ICH has also agreed on a case-by-case, scientifically based approach to the approval of rDNA products, although it is likely that many of the requirements for NCEs will still have to be met. Regulatory and industrial scientists continue to ask questions about the existence of subtle changes in the chemical structure of rDNA products that may influence pharmacokinetics, pharmacodynamics, and mutagenicity. Recent information from the Tufts Center for the Study of Drug Development (2005) indicates that the total development times for NCEs and NBEs have been converging since the mid-1980s, although the biotechnology products seem to be enjoying a better approval success rate. For all potential therapeutic agents, it is appropriate to evaluate the safety of both the parent compound (drug) and known contaminants or residues resulting from the manufacturing and purification processes. The ICH has provided a process to address the discovery of new impurities in bulk drugs (drug substances before final formulation) and formulated drugs that have not been through the toxicology screen (ICH Topics Q3A(R) and Q3B(R); see Internet Resouces for information on accessing current ICH topics).

In addition to evaluating the patient’s response to exposure to a new drug, worker exposure and reaction to various end products and intermediates during the manufacturing process may be of concern. Thus, toxicologists must take into account issues related to the therapeutic use of the material, its manufacture, and the effect of the manufacturing process on the environment. In the clinical situation, there is usually a clear therapeutic benefit associated with the use of a new drug. In the workplace, exposure to the drug through the manufacturing process has no therapeutic benefit. Because the workers are not patients, exposure to the new drug substances must be considered on the basis of potential toxicity. In both the workplace and immediate environment, all responses to drug substances must be considered as potentially undesirable, even those that are clearly related to the beneficial pharmacologic effect in clinical situations. Accordingly, the toxicologist must be prepared to address risk perception, risk assessment, and risk management in the clinic, the workplace, and the environment.

MODELS OF DRUG EFFECT A meaningful safety assessment profile requires the selection of experimental models that best predict human toxicity. To this end, a key assumption of toxicology is that other organisms and biological systems can provide predictive models for effects in humans.

In Vivo Because of the limited, although growing, knowledge regarding the extensive and complex interactions between a host of cellular and biochemical systems, whole-animal models remain the standard for predicting toxicity in humans (Table 10.3.1). As discussed below, selection of the appropriate species is critical and is based on the following considerations (Wilson and Hayes, 1994): 1. Early studies of comparative metabolism (i.e., in vitro studies with animal and human liver microsomes) or of toxicity observed in animals for which some human information is known or response is expected. 2. Sensitivity to the drug (generally the most sensitive species should be used) and responsiveness of specific organs and tissues. 3. Availability of an adequate historical control database (especially growth parameters, clinical pathology, and histopathology).

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Table 10.3.1 Animal Models Traditionally Employed in the Safety Assessment of Pharmaceutical Agents

Assessment

Animal model

Comment

General toxicity

Rodent (rat, mouse); nonrodent (dog, monkey)

Rat and dog are preferred species; other species may be selected based on a closer similarity to humans

Ocular irritation

Rabbit

Draize model

Dermal toxicity/irritation

Rabbit, rat

Draize model

Dermal sensitization

Guinea pig

—

Phototoxicity

Guinea pig, mouse

—

Immunotoxicity

Primarily mouse but also rat

For antigenicity studies, rabbit and guinea pig are also used

Developmental toxicity

Rodent (rat); nonrodent (rabbit)

Mouse is often used as an alternative to rabbit in special cases where rabbit is inappropriate (i.e., antibacterials); dog has been used for neonatal studies

Carcinogenicity

Rodents (rat, mouse)

Rodents have been used because of the relative ease in maintaining a large number of animals over a lifetime (1.5 to 2 years)

Environmental toxicity

Lower organisms (earthworm, Daphnia, rainbow trout)

Effects on target species evaluated directly

4. Availability of healthy animals from a reputable supplier. 5. Ability of the facility and staff to provide adequate care and maintenance of animals. The relevance of experimental animal models in the assessment of risk to humans is an important contemporary issue in toxicology (Dorato and Vodicnik, 2001). Despite the increased use of pharmaceuticals, the incidence of major human toxicity is relatively low (Karch, 1980; Zbinden, 1980), supporting the reliability of nonclinical safety assessment. In a recent survey conducted by the International Life Sciences Institute (ILSI), the concordance of the toxicity of pharmaceuticals in humans and animals was evaluated (Olson et al., 2000; Greaves et al., 2004). Overall, the true positive concordance rate for human toxicities was ∼71%. In other words, 71% of human target organ toxicities were predicted by one or more animal species in the same organ system. Of these predicted toxicities, the nonrodent (primarily dog) predicted 21% of all human toxicities, with an additional 7% of human toxicities observed in rodents only (primarily rat), and an additional 36% of human toxicities detected in

both nonrodents and rodents, suggesting a considerable overlap in toxicities between species. There was no relationship between toxicities in laboratory animals and those observed in humans in the remaining 29% of human toxicities. Most of the serious differences observed between animal and human toxicity are related to differences in anatomy and physiology (i.e., metabolism or immune responsiveness) and to differences between the exposure of animals in nonclinical experiments and human clinical exposure (in the quantity, route, and duration of administration). For example: 1. High doses employed in animal testing may be so excessive as to distort the results and thus render the model inappropriate or insensitive for human safety assessment (Slikker et al., 2004a,b). 2. So-called idiosyncratic reactions are difficult to predict because only a small subgroup of subjects are uniquely susceptible, the mechanism is poorly understood or not definable, and/or a dose-response relationship is not apparent.

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3. Current pharmaceutical research aimed at developing drugs specific for human therapeutic targets complicate selection of an appropriate model of toxicity. Examples where animal responses are judged not relevant to human risk include D-limonene, kidney tumors due to male rat– specific α 2u -globulin binding protein, atrazine, mammary tumors associated with persistent secretion of estrogen and prolactin specifically in Sprague-Dawley rats, phenobarbital, and thyroid tumors in rats based on quantitative kinetic and dynamic differences from humans (Cohen et al., 2004). Rodent endocrine tumors appear to have little relevance to human cancer risk (Cohen, 2004). By necessity, human safety assessment is conservative and assumes that, unless proven otherwise, toxicity in animals is relevant to humans and, for purposes of risk assessment, humans can be more sensitive than the most sensitive animal species studied.

In Vitro In vitro alternatives to whole-animal studies have been developed largely in response to a growing need for rapid, inexpensive screening assays, public concern for the welfare and humane treatment of animals used in biomedical research, and biotechnology advances that support a stronger scientific basis for the toxicologic evaluation process. Apart from the hope that these models may one day provide more definitive insight into potential in vivo

toxicity, and thus be more useful in human safety assessment, alternative models have been useful as screens for early detection of adverse properties associated with compounds early in the drug discovery process. Alternative models offer the advantages of requiring small quantities of drug, reduced cost, and increased speed, all of which expedite the drug discovery and development process. In addition, a variety of in vitro systems have been developed as specific tools to probe and understand discrete mechanisms of toxicity. The final expression of toxicity in humans or animals is typically the integrated summation of extensive and complex cellular and biochemical interactions. Just as the dissection of a complex system into simpler pieces challenges the extrapolation of in vitro models to the intact, integrated organism, simple, well defined in vitro systems allow for the selective isolation, and thus evaluation, of a particular response, thereby aiding in the mechanistic studies of drug effects. In vitro systems range in structural and biologic complexity from isolated organs to subcellular preparations. A thorough knowledge of the strengths and weaknesses of a given model is critical for establishing the relevance of the results to humans (Table 10.3.2). Systems to evaluate ocular toxicity are the most developed because of concern over the inhumane aspects of the traditional in vivo Draize test. Some in vitro models of toxicity are shown in Table 10.3.3. In most cases, the

Table 10.3.2 Hierarchy of In Vitro Systems to Evaluate Toxicitya

Preparation

Some advantages and disadvantages

Tissue preparations Isolated, perfused organs

Morphologically identical to organ in vivo; monitoring of function and hemodynamics possible; only short-term use possible

Tissue slices

Tissue architecture and heterogeneity maintained; easily prepared; only short-term use possible

Cells

Toxicology in the Drug Discovery and Development Process

Primary cell cultures

Closely related to fresh tissue

Cultured cell lines

Easily obtained and subcultured; origin often ill defined; greater dedifferentiation present

Subcellular preparations

Metabolism absent; easily manipulated; heterogeneous in nature (may be contaminated with other cells)

a Adapted from Williams and Rush (1992).

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Table 10.3.3 Some In Vitro Models of Toxicity Employed in the Toxicologic Characterization of Pharmaceuticals

Endpoint

In vitro system

Specific observations

Comments

Lethality

Cultured cell systems (mouse lymphoma, hepatocytes)

Cell viability, membrane permeability, metabolic competence

Lack integrative functions of a larger, intact organism

Ocular irritation

Cell systems (>70 systems)

Altered morphology, cytotoxicity Many assays validated on (compromised cell adhesion and a limited scale proliferation, membrane integrity, or cell metabolism), release of inflammatory mediators

Dermal irritation

Skin organ cultures or cultured cells (i.e., human keratinocytes)

Altered morphology, cytotoxicity, May aid in understanding release of inflammatory factors, mechanisms of irritation altered function (i.e., membrane permeability)

Toxicity or Cultured rat skeletal muscle irritation caused by cells (L6) IV or IM administration

Developmental toxicity

Medium creatinine kinase levels

Erythrocytes

Hemolysis

Limulus amebocyte lysate (LAL) test

Pyrogenicity associated with bacterial endotoxins

Lower organisms (Drosophila, brine shrimp, Medaka)

Anatomical, functional, biochemical, and molecular alterations

No acceptable methodology to allow culture of a single mammalian conceptus throughout the entire development period

Cell or organ cultures Sub/mammalian embryos Target-organ toxicity

Isolated organ preparation

Morphologic, observational, functional parametersa

Tissue/organ culture Cultured cells Carcinogenicity

Bacteria, cultured cells

Genetic damage

Assess genotoxicity as a signal of potential carcinogenicity

Primary/early-passage cells, established cell lines

Neoplastic (morphologic) transformation

Assess promotional activity

Human tumor cell lines

Screen for anticancer activity

a Summarized by Gad (1993) for respiratory, nervous, renal, cardiovascular, hepatic, pancreatic, gastrointestinal, and reticuloendothelial systems.

combined results from a battery of assays, as opposed to a single in vitro test, is used to provide the weight of evidence needed to characterize toxicity. Finally, in vitro techniques have also been helpful in providing information about the comparative metabolism of the agent in humans and laboratory animals commonly employed in toxicity testing.

INCORPORATION OF TOXICOKINETICS INTO THE TOXICITY PROFILE Exposure Versus Dose Characterization of dose-response relationships for effects caused by exposure to xenobiotics represents a fundamental goal in the toxicologic assessment of human risk.

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Figure 10.3.4 Representation of the administered dose-response continuum. The biologically effective dose is that which is available to interact with a molecular target.

Toxicology in the Drug Discovery and Development Process

However, toxicity observed in animal testing is frequently not a linear function of administered dose. Prior to reaching the ultimate site of action, a drug is subject to many dispositional processes (Fig. 10.3.4); thus, characterization of properties relating to absorption, distribution, metabolism, and excretion (ADME) is necessary to maximize the effectiveness of study design and data interpretation. The primary objective of toxicokinetics is to describe the systemic exposure achieved in animals, its relationship to dose, and the time course of toxicity. Exposure is represented by pharmacokinetic parameters quantifying the local and systemic burden of the parent drug and its metabolites. Exposure is typically characterized by the toxicokinetic parameters Cmax (peak plasma concentration) and AUC(0→t) (area under the concentration curve from time zero to t). Toxicokinetic profiling, based on measurements of plasma drug levels, can provide evidence of absorption and exposure and reveal nonlinearity of ADME processes across doses. It can also aid in selection of dose, treatment regimen, and species for toxicity evaluation, and can be used to support extrapolations across dose and species. Toxicokinetics based on plasma concentrations should be used with caution in making safety assessments because plasma levels may not reflect the dose of drug contained in tissues or delivered to the site of action (Fig. 10.3.4). For example, some therapeutic agents preferentially accumulate in certain tissues during continued administration through tissuespecific binding and/or induction of new binding sites (Dorato and Vodicnik, 2001). Liposomal formulations can yield very high tissue concentrations, especially in the reticuloendothelial system, with long retention times

(Voisin et al., 1990). Finally, it is difficult to measure short-lived reactive metabolites in plasma. A basic goal of nonclinical safety assessment is the accurate extrapolation of data from laboratory animals to humans to make more accurate predictions of toxicity. Scaling factors represent a means for extrapolations across species, implicitly accounting for differences in pharmacokinetics and pharmacodynamics (http://www.fda.gov/cder/ guidance/5541fnl.pdf). Conventionally, dose adjustments across species are done on a body-weight basis, assuming an equivalency of dose expressed as mg of test article per kg body weight. However, many important metabolic functions that may be critical determinants of toxicokinetics or toxicodynamics are well correlated with body surface area, which is approximately proportional to (body weight)2/3 (Vocci and Farber, 1988; Table 10.3.4). For anticancer agents and antiviral nucleoside analogs, dosing based on body surface area yields better dose-response correlations across species than dosing based on body weight alone. The use of body surface area has important implications for safety assessment. Therapeutic indices based on body surface area are generally more conservative than those based on body weight (Table 10.3.4), and body surface area is the preferred measure of dose for estimation of therapeutic index in the E.U. Even so, there are limitations regarding the use of surface area for interspecies conversions. For example, the metabolic profiles of some drugs do not correlate with overall metabolic rate and therefore surface area (Voisin et al., 1990). Ultimately, interspecies comparisons are most reliable when pharmacokinetic data

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Table 10.3.4 Conversion of Dosage Based on Body Weight (mg/kg) to Dosage Based on Surface Area (mg/m2 )a

Species

Weight (kg)

Surface area (m2 )

Factorb

Dose (mg/kg)

Dose (mg/m2 )

Mouse

0.02

0.0066

3

100

300

Rat

0.15

0.0250

6

100

600

Monkey

3.00

0.2400

12

100

1200

Dog

8.00

0.4000

16

100

1600

Human

60.00

1.6000

37

100

3700

a Dose (mg/m2 ) = dose (mg/kg) x factor; from Freireich et al. (1966). b For a mouse no-observed-effect level (NOEL) of 10 mg/kg (30 mg/m2 ) and a human clinical trial dose of

0.5 mg/kg, the margin of safety (MOS) based on body weight is 20x; the MOS based on body surface area is 1.6x.

are available, assuming comparable bloodlevel-response relationships between species (Voisin et al., 1990). The use of conventional toxicokinetic analyses (plasma level Cmax or AUC) and the development of biologically based mathematical models, wherein determinants of disposition and dynamics are explicitly defined, represent far more accurate and informed approaches than body weight or surface area for extrapolations across species. Conventional toxicokinetic models are purely mathematical descriptions representing a best fit of the data. In contrast, physiologically based pharmacokinetic models are structural, quantitative descriptions of biologic systems. Rather than deriving values for compartments

and parameters by mathematically fitting the experimental data, real physiologic structures, such as tissues and organs, and parameters representing biologic processes, such as blood flow and breathing rates, and chemicalspecific properties, including tissue/blood partition coefficients and metabolic constants, are precisely defined (Fig. 10.3.5). A multicompartmental biological system can be described by connecting individual tissue compartments in parallel (Fig. 10.3.5). A set of mass-balance differential equations describing the rate of change of the amount of chemical in each compartment can be solved simultaneously to relate exposure concentrations to the amount of drug in blood and tissues.

Figure 10.3.5 Schematic presentation of a simple physiologically based pharmacokinetic model. C denotes concentration of the drug, Q denotes blood flow rate, P denotes tissue/blood partition coefficient, and V denotes volume. a, arterial; v, venous; l, liver; f, fat; r, richly perfused tissues; s, slowly perfused tissues. KM and Vmax are metabolic rate constants.

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Physiologically based pharmacokinetic modeling provides a powerful alternative to traditional methods for predictive extrapolations of dose, route, and species (Clewell and Andersen, 1986; Krishnan and Andersen, 1994). By changing relevant physiologic parameters, or adding appropriate equations to represent input functions for different routes of administration, the same model can be used to describe the dynamics of chemical transport and metabolism in different species or when using different exposure routes or scenarios. Physiologically based pharmacokinetic models have been developed for a number of drugs, including cefazolin, retinoids, nicotine, methotrexate, and thiopental (Dedrick et al., 1973; Tsuji et al., 1985; Mordenti and Chappell, 1989; Plowchalk et al., 1992; Clewell et al., 1997). The need for large amounts of data to fuel these models, however, represents a limitation in their use. Qualitative and quantitative differences in metabolite profiles are important when comparing exposure and safety of a drug in a nonclinical species relative to humans. If a major metabolite is formed, its exposureresponse characteristics may need to be evaluated. The definition of “major” is controversial and currently ranges from 10% to 25% of systemic exposure compared to the parent drug (Baillie et al., 2002; Hastings et al., 2003). At the heart of the issue is determining which human metabolite(s), major or minor, constitute a safety concern, and the extent of toxicologic testing that should be employed to assess that concern.

Maximum Tolerated Dose (MTD)

Toxicology in the Drug Discovery and Development Process

The premise that toxicologists can predict human risks using animal models is based on two main principles: (1) that there is a basic similarity in biologic structure and function across species, and (2) that exposure of animals to high doses is necessary and valid for identification of potential human toxicity. In carcinogenicity studies, the high dose has traditionally been a maximum tolerated dose (MTD). Experimentally, the MTD should cause no more than a 10% depression in body weight gain and should not elicit toxicity that would be predicted to shorten life span for reasons other than induction of neoplasm. The definition has been expanded to allow MTD selection on the basis of a broader range of biologic information (Bucher et al., 1996). While the MTD is designed to provide a level of toxicity indicative of sufficient chemical challenge to define toxicity, there are drawbacks in inter-

preting effects that occur only at the MTD and in their extrapolation to low-dose risk assessment for humans. One major complication is the potential for metabolic saturation leading to irrelevant metabolism or clearance. The use of measured kinetic parameters, such as Cmax or AUC versus dose, to set doses for carcinogenicity studies is encouraged to ensure an adequate margin between animal and intended human exposure, as discussed by Contrera et al. (1995) and ICH S1C (see Internet Resources); also see below, Regulatory Guidelines for Toxicology Profiles, Carcinogenicity Studies.

Species Specificity In the ILSI survey, the best concordance between animal model and human response was found for human hematological, gastrointestinal, and cardiovascular toxicities, with the least concordance observed for human cutaneous toxicity (Olson et al., 2000). Nonrodents tend to predict cardiovascular and gastrointestinal toxicity much better than rodents. For anticancer agents, the dog in particular is a strong predictor of gastrointestinal toxicity, whereas the monkey was resistant to vomiting, a common human adverse event. A robust correlation was found between cardiovascular findings in dog and human. The recent spate of drug withdrawals and updated label warnings for marketed drugs highlights areas of inter- and intra-species differences (FDA Web site; see Internet Resources), but did not in rats and monkeys, the main species used in the toxicity studies, which had much higher rates of drug clearance. Species variability in the expression of druginduced toxicity may be related to differences in drug disposition associated with bioavailability, protein binding, or formation of reactive metabolites. Species variability in toxicity may also be related to differences in responses associated with receptor number and distribution. Eason et al. (1990) have reviewed a number of instances where animal toxicity studies failed to predict human toxicity due to species differences in metabolism, pharmacokinetics, or receptor activities. FPL 52757, an orally active antiasthmatic drug candidate, caused hepatotoxicity in dog and humans, but did not in rats and monkeys, the main species used in the toxicity studies, which had much higher rates of drug clearance. In contrast, thrombocytopenia induced by amrinone, a cardiotonic drug, was not associated with a marked species difference in pharmacokinetics, but rather with a natural predisposition toward the production

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of larger platelets in humans and marmoset. The progestogen lynestrenol causes carcinomas in dogs, but not rats or mice, due to the exquisite sensitivity of canines to the tissueproliferative effect of progestogens. However, a very high MOS was predicted for humans at very low therapeutic doses based on comparison of pharmacokinetics, tissue receptor concentrations, and receptor binding. Ciprofibrate, a safe and effective hypolipidemic agent in humans, causes gastric and hepatic tumors in rodents. The gastric tumors are likely related to species differences in receptor response, whereas the hepatic tumors are thought to be associated with the unique susceptibility of small animals to drug-mediated generation of oxygen radicals (which is inversely related to body weight). Unfortunately, tienilic acid– induced hepatotoxicity appears to be related to a metabolism-dependent immune mechanism of toxicity. This resulted in several patient deaths even though there were no obvious effects in studies with rats and dogs. Disease state, age, and genetic anomalies and idiosyncrasies within the human population are responsible for many important differences in response to chemicals (Eason et al., 1990). Similarly, the importance of strain as a determining factor in the differential responsiveness of rats to certain chemicals has been reviewed by Kacew et al. (1995). Selection of the most relevant species for toxicity testing should be based on an understanding of ADME processes affecting the drug disposition, which can be derived from both in vitro tests, such as with liver microsomal preparations, and from in vivo tests. A sensitive and selective assay of the compound in plasma and urine is needed at an early stage of drug development to support absorption and bioavailability studies in animal models. Synthesis of radiolabeled compounds for whole-body autoradiography aids significantly in studies of absorption and distribution. The importance of dispositional characteristics in interspecies extrapolations is a primary reason for determining metabolic and toxicokinetic profiles of a drug for each animal model. The data are then compared with human data to understand the relevance of the nonclinical toxicology findings.

REGULATORY GUIDELINES FOR TOXICOLOGY PROFILES Toxicological assessment of NCEs and NBEs has been reviewed previously (Gad, 1994; Gad and Chengelis, 1995; Cavagnaro, 1997 Diener, 1997; Dorato and Vodicnik,

2001). Traditionally, regulatory requirements for pharmaceuticals have differed between countries. Recently, however, the regulatory guidelines have been harmonized under the auspices of the ICH. This unique undertaking brought together the regulatory authorities of Europe, Japan, and the U.S., along with pharmaceutical experts from academia and industry, to discuss scientific and technical aspects of product registration. The ICH has compiled a database of internationally acceptable guidelines for the safe and ethical development of pharmaceuticals (Table 10.3.5). The timing of nonclinical studies has been reviewed by Dorato and Vodicnik (2001). Flexibility has been built into the process through the acknowledgment that pharmaceuticals under development for life-threatening diseases such as AIDS-associated conditions and cancer, for which there are no current effective therapies, should be dealt with on a case-by-case basis (Tomaszewski and Smith, 1997; DeGeorge et al., 1998) whereby particular studies listed in the general requirements for registration of a pharmaceutical may be abbreviated, deferred, or omitted. The aim is to speed development of life-saving therapy while providing adequate assurances of safety. In keeping with facilitating the rate of drug development, the FDA has recently published a draft guidance on Exploratory IND Studies (http://www.fda.gov/ cder/guidance/7086fnl.pdf). This topic has been much discussed in Europe and the U.S. over the past decade (FDA, 1996; CHMP, 2004). The ICH process includes five approval steps, before a guideline is implemented, in the three principal geographic regions (Table 10.3.6). The ICH has defined the clinical phases of drug development that dictate the various levels of toxicology support. For example, human pharmacology studies (Phase I) correspond to the FHD, and are generally single-dose, dose-escalation, or shortterm repeated-dose studies in small numbers of healthy volunteers. Therapeutic exploratory studies (Phase II) are generally small-scale safety and efficacy studies in healthy volunteers and sometimes in patients. Therapeutic confirmatory studies (Phase III) are largescale, expensive safety and efficacy studies in patients. These definitions fit well with the drug development and approval process in the U.S. (Fig. 10.3.6).

Animal Welfare Considerations Animal welfare is a concern for all toxicologists. In addition to ethical considerations,

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Table 10.3.5 ICH Guidelines for the Conduct of Nonclinical Studies

Topic

Topic numbera

Title and contents

Toxicity testing

S4

Single Dose and Repeat Dose Toxicity Tests (Step 5) Recommendation to abandon LD50 determination; reduction in duration of longest-term dose toxicity study in rodents from 12 to 6 months

S4A

Repeat-Dose Toxicity Tests in Nonrodents (Step 5) Reduction of duration of repeat dose toxicity studies in nonrodents from 12 to 9 months

S1A

Need for Carcinogenicity Studies of Pharmaceuticals (Step 5) Definition of circumstances requiring carcinogenicity studies, taking into account known risks, indications, and duration of exposure

S1B

Testing for Carcinogenicity in Pharmaceuticals (Step 5) Need for studies in two species Alternatives to 2-year rodent bioassay

S1C

Dose Selection for Carcinogenicity Studies in Pharmaceuticals (Step 5) Criteria for selection of high dose

S1C(R)

Addendum to S1C: Addition of a Limit Dose and Related Notes (Step 5)

S2A

Genotoxicity: Specific Aspects of Regulatory Tests (Step 5) Specific guidance for in vitro and in vivo tests plus glossary of terms

S2B

Genotoxicity: Standard Battery Tests (Step 5) Identification of a standard set of assays Extent of confirmatory experimentation

S5A

Detection of Toxicity to Reproduction for Medicinal Products (Step 5) Specific guidance for testing reproductive toxicity

S5B(M)

Maintenance of the ICH Guideline on Toxicity to Male Fertility: An Addendum to the Guideline on Detection of Toxicity to Reproduction for Medicinal Products

Carcinogenicity studies

Genotoxicity studies

Reproductive toxicology

Toxicokinetics and S3A pharmacokinetics

Biotechnology products

Toxicokinetics: Guidance on the Assessment of Systemic Exposure in Toxicity Studies (Step 5) Integration of kinetic information into toxicity testing

S3B

Pharmacokinetics: Guidance for Repeat Dose Tissue Distribution Studies (Step 5) Need for tissue distribution studies, when appropriate data cannot be derived from other sources

S6

Preclinical Safety Evaluation of Biotechnology-Derived Pharmaceuticals (Step 5) Nonclinical safety studies, use of animal models of disease and other alternative methods, need for genotoxicity and carcinogenicity studies, impact of antibody formation continued

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Table 10.3.5 ICH Guidelines for the Conduct of Nonclinical Studies (continued )

Topic

Topic numbera

Title and contents

Joint M3(M) safety/efficacy studies (multidisciplinary)

Maintenance of the ICH Guideline on Nonclinical Safety Studies for the Conduct of Human Clinical Trials for Pharmaceuticals (Step 5) Principles for development of nonclinical testing strategies (addresses full range of studies to support clinical trials for NCEs)

Pharmacology studies

S7A

Safety Pharmacology Studies for Human Pharmaceuticals (Step 5)

S7B

The Nonclinical Evaluation of the Potential for Delayed Ventricular Repolarization (QT Interval Prolongation) by Human Pharmaceuticals (Step 3)

Immunotoxicology S8 studies

Immunotoxicology Studies for Human Pharmaceuticals (Step 3)

a The most recent information on ICH guidelines can be found on the ICH Web site (see Internet Resources).

Table 10.3.6 Steps in ICH Approval

Step

Action(s)

1

First draft of a “TOPIC” is prepared and reviewed by the Expert Working Group (EWG)

2

Draft is approved by the ICH Steering Committee (SC) and transmitted to the three regional regulatory agencies in the European Union (EU), Japan, and the United States (USA) for formal consultation

3

Comments are collected and exchanged between regulatory authorities; the Step 2 draft is amended and approved by the EWG

4

The final draft is reviewed within the SC and recommended for adoption to the three regulatory bodies of the EU, Japan, and USA

5

Full recommendations are incorporated into domestic regulations according to national and regional procedures

the quality of research depends on the quality of the experimental animal models employed. Strict compliance with federal regulations, which reflect public concerns regarding the use and treatment of laboratory animals in biomedical research, is absolutely necessary. There are specific regulations and guidelines available to aid scientists in providing adequate animal care. The U.S. Animal Welfare Act (AWA), which is administered by the U.S. Department of Agriculture (USDA), applies to all animals (excluding rodents) used for research purposes and consists of three parts: definitions, regulations, and standards. The AWA includes specific guidelines for the humane handling, care, treatment, and transportation of animals used in research and educational programs, and explicitly defines the

minimum requirements for exercising dogs and assuring the psychological well-being of primates. USDA inspectors from the Animal and Plant Health Inspection Service (APHIS) conduct unannounced visits at least annually to inspect physical facilities and to evaluate the training of animal care personnel and the overall care of animals. The National Institutes of Health (NIH) has published the Guide for the Care and Use of Laboratory Animals (1996), the primary reference guide for animal care and use in the U.S. The Public Health Service (PHS) policy on the humane care and use of laboratory animals lists areas of concern beyond those given in the AWA, which must be satisfied by institutions receiving federal support for research or training involving laboratory animals.

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Table 10.3.7 Major Professional Organizations Providing Guidance for the Care and Use of Laboratory Animalsa

Organization

Abbreviation

Function

American Association AAALAC for Accreditation of Laboratory Animal Care

Provides a voluntary accreditation program for which institutions may apply. AAALAC accreditation assures compliance with the AWA and PHS policy

American Association for Laboratory Animal Science

AALAS

Provides training materials and programs and offers certification for laboratory animal technical staff

Animal Veterinary Medical Association

AVMA

Major national organization of veterinarians

American College of Laboratory Animal Medicine

ACLAM

Specialty board to encourage education, training, and research in laboratory animal medicine

a Abbreviations: AWA, The U.S. Animal Welfare Act; PHS, U.S. Public Health Service.

Included in the AWA regulations and the PHS policy is the requirement that each facility “...operate a program with clear lines of authority and responsibility for self monitoring the care and welfare of such laboratory animals.” For this purpose, each institution must establish an Institutional Animal Care and Use Committee (IACUC) that provides oversight regarding animal welfare issues similar to that provided by Institutional Review Boards for clinical trials. Committee members (five) must include a chairperson, a scientist conducting laboratory animal research, an experienced veterinarian, one nonscientist, and a person not affiliated with the facility or institution. The IACUC meets at regular intervals to: 1. Ensure compliance with the Guide for the Care and Use of Laboratory Animals. 2. Inspect the facility every 6 months and provide written reports of the inspections. 3. Review all protocols and procedures for the use of each species. 4. Review or investigate concerns regarding animal care and handling, especially those associated with procedures that may involve pain and distress, such as prolonged restraint or multiple or invasive surgeries. 5. Ensure that adequate veterinary care exists.

Toxicology in the Drug Discovery and Development Process

6. Verify that staff who care for and use laboratory animals are qualified and trained and that evidence of such training is documented. The FDA GLP recommendations also contain provisions for the care and use of lab-

oratory animals, including requirements for proper training of personnel (with documentation of that training), animal housing, and separation of species. In addition, various professional organizations provide information and guidance regarding the humane care and treatment of laboratory animals in research (Table 10.3.7). Several professional societies, including the Society of Toxicology, have developed and published position statements on the use of animals in experimentation. In addition to obtaining study approval from an IACUC, it is the responsibility of each practicing toxicologist to evaluate the necessity of any research performed with laboratory animals and the number of animals necessary to answer a particular question. Furthermore, animals should not be subject to undue pain or distress. U.S., European, and Japanese testing guidelines also recommend that toxicologists design studies to obtain the maximum amount of relevant information from the smallest number of animals (ICH Topic S4; see Internet Resources). For example, determination of an accurate LD50 is unnecessary. In addition, doses known to cause marked pain and distress due to corrosive or severely irritant actions need not be administered, even when no mortality has been observed at tolerated doses. The Code of Ethics of the Society of Toxicology (SOT) states that each member “shall observe the spirit as well as the letter of the laws, regulations, and ethical standards with regard to welfare of humans and animals involved in any experimental procedure” (SOT, 1999). In addition, the Society is committed to what has been termed the principle of the “3 R’s” of animal use in toxicologic testing:

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Reduction of the numbers of animals used, when scientifically valid and appropriate; Replacement of animals, when possible, for testing; and Refinement of research protocols to allow for the use of less painful or stressful procedures and to improve animals’ care (see The Interagency Coordinating Committee on the Validation of Alternative Methods; http:// iccvam.niehs.nih.gov/home.htm).

Guidelines for NCEs The ICH has developed a comprehensive document on the scope and duration of nonclinical safety studies to support the conduct of clinical trials worldwide [ICH Topics M3(M) and S6; see Internet Resources; also see Table 10.3.5]. The specific studies included in an NCE toxicology profile depend on a number of factors such as duration of treatment, route of administration, pharmacologic mechanism of action, proposed patient population, and experience with other agents in the same therapeutic class. Animal toxicity testing is conducted in three, or possibly four, phases (Fig. 10.3.6). The FHD is generally supported by

short-term (≤1 month) studies. As clinical trials progress, longer-term (up to 6-month) toxicology studies are conducted. The duration of chronic toxicology studies in nonrodents has received a great deal of attention (Contrera et al., 1993). The international consensus supports a 9-month nonrodent toxicology study as the acceptable standard (DeGeorge et al., 1999). However, should the toxicology profile indicate a progression of severity of toxicity or the development of new toxicity with increasing duration, the FDA may require a 1year nonrodent toxicology study. The majority of toxicologic evaluations occur prior to drug registration. However, based on the therapeutic indication and compassionate-use concerns, some toxicology studies may be conducted after drug approval. For example, the 2-year carcinogenicity studies for Pulmozyme, an inhaled pharmaceutical for cystic fibrosis, were conducted after it was launched (Green, 1994). Following widespread use of a new therapeutic agent, additional toxicology studies may be necessary to examine potential mechanisms of action for unanticipated side effects observed

Figure 10.3.6 Schematic of the drug development and approval process in the United States. Similar processes are employed in worldwide pharmaceutical testing and approval. Adapted from Gordon and Wierenga, 1992, and Beary, 1997.

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as the patient population increases. The influence of genetic differences, environmental factors, age, patient history, and drug interactions may not have been completely evaluated in the patient population studied before registration. Today, however, the patient population in registration trials is increasing, with regulatory agencies requiring additional testing if new formulations are developed, for new indications, or for the inclusion of patient populations, such as pediatric, that were not covered by the original registration. As previously indicated, initial clinical trials focus on pharmacokinetics and safety, usually in healthy volunteers, and typically include only one or a few doses. The clinical trials are conducted in a dose-escalation fashion until an acceptable multiple of the anticipated efficacious dose, or toxicity, is achieved. Drug candidates with known, serious toxic potential, such as oncolytics, are initially tested in patient populations. A major consideration in designing animal studies to support clinical trials is the margin of safety (MOS) between the no-effect (or minimal-effect) level in animals, and the maximum anticipated exposure in clinical trials or nonclinical models (Fig. 10.3.7). Doses selected for animal studies should provide exposure that exceeds the highest anticipated human exposure. It is no longer acceptable to base the MOS on a comparison of administered dose (e.g., mg/kg), as this does not provide adequate information on potential species differences in absorption, distribution, and metabolism (see Toxicokinetic Studies, below). There is no guideline on an acceptable MOS. However, a lower MOS is tolerated for

Toxicology in the Drug Discovery and Development Process

compounds intended to treat life-threatening diseases, especially if they are expected to offer a distinct advantage over current therapies.

No-Observed-Adverse-Effect Level (NOAEL) The NOAEL is an important concept in development of pharmaceuticals (Calabrese and Baldwin, 1994; Lewis et al., 2002; Dorato and Engelhardt, 2005). It may be considered to be the highest dose/exposure that does not cause biologically important increases in the frequency or severity of adverse effects between the exposed population and the appropriate control. While minimal toxic effects may be observed at the NOAEL, they are not thought to endanger human health or be precursors of serious adverse events. Lewis et al. (2002) have presented a procedure for determination of an adverse event. This includes applying the following factors: 1. Differentiation of a chance difference from control from a treatment-related effect. 2. Differentiation of a nonadverse effect of treatment from an adverse effect. This approach fits very well with the position expressed at the first ICH conference that the effect to be determined is the toxicologically relevant effect, i.e., the effect that may endanger human health (Hess, 1991). The evaluation of adverse events leads to a careful evaluation of the toxicologically relevant effects (Fig. 10.3.8). The ICH has taken major steps to eliminate wide variations in regulatory requirements for the duration of toxicity studies to support clinical trials (Table 10.3.9). It is possible to

Figure 10.3.7 Relationship of margin of safety (MOS) to toxicity profile. NOAEL, No-ObservedAdverse-Effect Level (NOAEL).

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Figure 10.3.8 Approach to classifying toxicology study results as adverse or non-adverse, showing the considerable gray area (modified from Lewis et al., 2002).

discuss the duration of toxicology studies with regulators and, depending on the particulars of the test substance in question, modify these recommendations.

Safety Pharmacology The ICH Topic M3(M) (see Internet Resources) recognized safety pharmacology as an important facet of the toxicology profile. These studies assess effects on vital functions such as the cardiovascular, central nervous, respiratory, and renal systems, and should be conducted prior to human exposure (UNIT 10.1). Evaluations may be conducted as additions to planned toxicology studies, or separately. There are geographical differences in the extent of the safety pharmacology tests, which are generally performed prior to initiation of clinical trials. Properly performed safety pharmacology studies provide valuable information that complements more traditional toxicological evaluations. The knowledge gained from these studies adds mechanistic information and functional evaluations to the toxicology profile. Safety pharmacology provides crucial information for the selection of NCEs during the early discovery process, the design of toxicology studies, and the design of safety monitoring in clinical trials (Lumley, 1994; Proakis, 1994). The ICH process now includes select pharmacology guidance in the series of safety guidelines (Table 10.3.5). ICH Topic S7A (see Internet Resources) addresses the definition, objectives, and scope of safety pharmacol-

ogy studies, as well as studies needed prior to Phase I clinical trials and for marketing approval. ICH Topic S7B(R) (see Internet Resources) provides recommendations for nonclinical studies to address the potential for QT interval prolongation and guidance on an integrated risk assessment. ICH Topic S8 (see Internet Resources) provides general guidance and recommendations primarily for nonclinical studies of immunosuppression induced by low-molecular-weight drugs. While the guidance is titled “Immunotoxicity,” it deals primarily with immunosuppression.

Genotoxicity Genotoxicity is defined as the ability of an agent to damage DNA or alter DNA sequence in such a way as to cause mutation. The most serious effects of these mutations are neoplasms, inheritable neoplasms, or birth defects. In vitro tests for the evaluation of mutations and chromosomal damage should generally be conducted prior to the first human dose (Table 10.3.8), and the entire battery of tests should be completed prior to Phase II. Genotoxicity analysis entails in vitro and in vivo tests designed to detect compounds that induce direct or indirect genetic damage by various mechanisms. The suspicion that a compound may induce heritable effects is considered to be just as serious as the possibility that it may induce cancer. The standard battery of tests recommended by ICH consists of a gene mutation assay in bacteria, an in vitro test of chromosomal damage, or an in vitro mouse

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Table 10.3.8 Standard Genetic Toxicology Test Batterya

Genetic toxicology test

Purpose

Ames bacterial mutation assay

Gene mutation in bacteria

Mouse lymphoma assay (MLA) Chinese hamster ovary (CHO) chromosomal aberration assay

In vitro evaluation of chromosomal damage

Micronucleus test (MNT)

Evaluation of in vivo chromosomal damage in bone marrow polychromatic erythrocytes

a ICH Topics S2A and S2B (see Internet Resources).

lymphoma thymidine kinase (TK) assay, and an in vivo test of chromosomal damage using rodent hematopoietic cells. Additional evaluations may be necessary to confirm a negative result. The conduct of genotoxicity studies on biotechnology products has been an area of much discussion, with the case-by-case approach being generally accepted. Gocke et al. (1999) presented a flow scheme for conducting genotoxicity studies for compounds that interfere with DNA synthesis, that interfere with growth regulation, that have been modified with use of reactive agents, or that are produced by unusual methods.

the approach with the appropriate regulatory agency before initiating the studies. Acute toxicology testing generally entails single-dose studies with a 14-day observation period, whereas subchronic tests are multipledose studies usually lasting from 2 weeks to 6 months. Chronic toxicology involves multipledose studies of ≥6 months. A list of the parameters commonly evaluated in toxicology studies has been presented by Dorato and Vodicnik (2001). In general, a toxicology profile represents a series of building blocks with the knowledge from previous studies, or the knowledge from other agents in the same therapeutic class or of similar chemical structure, resulting in the addition or deletion of parameters from a study protocol. The duration of nonclinical studies in support of clinical trials of various duration are shown in Table 10.3.9. Acute toxicology studies are generally conducted in two species, both of which can be rodents, prior to the first human dose (ICH Topic S4; see Internet Resources). This is generally

Acute, Subchronic, and Chronic Toxicology The toxicity evaluation of most pharmaceuticals includes tests in each of these three categories. While the nature and duration of the clinical trial generally dictates the nature of the toxicity evaluation, it is advisable to discuss

Table 10.3.9 International Guidelines for the Duration of Animal Toxicology Studies Necessary to Support Clinical Trials of Various Duration [from ICH Topic M3(M); see Internet Resources]a

Toxicology in the Drug Discovery and Development Process

Clinical trial duration

Toxicology duration to support Toxicology duration to support Phase I, II (E.U.), and Phase I, II, Phase III (E.U.) and marketing III (U.S. and Japan) (all regions) Rodents

Nonrodents

Rodents

Nonrodents

Single dose

2 weeks

2 weeks

—

—

≤2 weeks

2 weeks

2 weeks

1 month

1 month

≤1 month

1 month

1 month

3 months

3 months

≤3 months

3 months

3 months

6 months

3 months

>3 months

—

—

6 months

chronic

≤6 months

6 months

6 months

—

—

>6 months

6 months

chronic

—

—

a Assessment of reversibility may be necessary in 3- or 6-month toxicology studies. Carcinogenicity studies are

not required prior to clinical trials, and may be conducted post-approval for some indications unless there is a cause for concern.

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accomplished by conducting single-dose studies and observing the animals for 14 days. A well designed dose-escalation study in two rodent species, or one rodent and one nonrodent species, is also acceptable. Requirements for single-dose studies and the elimination of the classic LD50 determination were harmonized at the first ICH (D’Arcy and Harron, 1992). For single-dose toxicity studies, assessments of both the intended clinical route and a parenteral route are required, unless the only intended clinical route is parenteral. The FDA has published revised guidelines on single-dose toxicity testing as part of the implementation of the ICH Safety Working Group consensus (FDA, 1996). This represents an area of continued regional difference, since the FDA guide applies only to the U.S. Recently, the Committee for Medicinal Products for Human Use has published a position paper on nonclinical safety studies to support a single microdose in a clinical trial (CHMP, 2004). This position paper is similar to the FDA approach, but slightly more restrictive. The FDA allows the use of a single-dose toxicity study to support a single-dose Investigational New Drug (IND) application for screening drug development candidates in clinical trials. The FDA Screening IND approach is designed to quickly identify drug development candidates in a clinical setting. Clinical trials are supported by toxicology studies of ≤2 weeks, and perhaps single-dose toxicology studies. Additional support is provided by a limited genetic toxicology package such as the Ames assay to test for bacterial mutagenic potential, an assay for clastogenesis, a limited safety pharmacology package, and close interaction with the FDA reviewer. This approach requires a clear clinical plan with established decision points. Another advantage to this approach is that multiple compounds can be tested under the same IND. The first human dose is a critical event in the development of a new therapeutic agent. The single-dose acute nonclinical studies in support of the screening IND are designed to assess dose response, pharmacokinetics, tolerability, and bioavailability (Choudary et al., 1996). They also include clinical pathology and histopathology evaluations both at an early time and at the end of the study for the identification of maximum effect and recovery. Because the clinician is interested in disabling and potentially life-threatening acute responses, the singledose toxicity approach is more useful if the studies focus on functional changes. The more traditional toxicology approach to acute stud-

ies (less interim evaluation, or focus on function) is recommended if the aim is to progress smoothly and rapidly into multiple-dose clinical trials (Choudary et al., 1996). One option is to use the acute toxicology studies to screen compounds quickly in the clinic, whereas another is to plan for success and screen compounds in nonclinical studies, using more traditional approaches to move rapidly from the first human dose to multiple-dose clinical trials. The FDA has published a draft guidance on Exploratory IND Studies (http://www.fda.gov/ cder/guidance/7086fnl.pdf), which evaluates possibilities for rapid entry to limited clinical trials. Repeated-dose toxicity studies are generally conducted in two mammalian species, rodent and nonrodent. The duration of these studies is typically equal to, or greater than, the duration of the clinical trial, up to the maximum duration recommended (ICH Topic S4A; see Internet Resources). In some cases, clinical trials may extend beyond the duration supported by the repeated-dose toxicity studies. This is true primarily when there is a significant therapeutic advantage to the test substance and a lack of adverse effects observed clinically. Strong regional differences still exist in the recommendations for conducting nonrodent toxicology studies. The E.U. and Japan are satisfied with 6 months as the longest duration for nonrodent toxicology studies, whereas the FDA takes the position that 6-month studies are not sufficient to address potential adverse effects (Contrera et al., 1993). Therefore, there is an international consensus that rodent studies of 6 months and nonrodent studies of 9 months are acceptable for a tripartite development plan (ICH Topic S4A; see Internet Resources). Local tolerance studies should be conducted prior to the first human dose (FHD; ICH Topic M3(M); see Internet Resources). Assessment of local tolerance should be conducted using the clinically relevant route of administration and may be evaluated in the context of other toxicology studies.

Toxicokinetic Studies The importance of characterizing systemic exposure when designing studies and when interpreting and understanding the clinical relevance of nonclinical data cannot be overstated. Currently, there are no U.S. regulations that define the scope and extent of toxicokinetic studies needed to support nonclinical safety studies. Individual experiments are performed based on scientific merit, and are decided on

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an individual basis. The ICH has published a guideline to aid in understanding the application of kinetics to toxicity studies (ICH Topic S3A; see Internet Resources). Toxicokinetic studies may be an integral part of nonclinical toxicity studies or may be conducted as separate, supportive studies. In general, toxicokinetic studies should be performed according to GLP regulations in conjunction with drug safety studies. As discussed earlier, the primary objective of toxicokinetics studies is to define systemic exposure in animals along with the relationship of such exposure to the dose level and time course of the toxicity study. Secondarily, kinetic analyses relate exposure to toxicology findings and contribute to the assessment of the relevance of these findings to clinical safety. They also support the choice of species and treatment regimen in nonclinical toxicity studies and provide information needed to design subsequent studies. In toxicokinetic studies, the matrix of choice (e.g., blood, plasma, excreta, or tissues) should be sampled frequently enough to permit estimation of the exposure without interfering with normal conduct of the study or causing undue physiologic stress to the animals. The doses chosen for toxicokinetic evaluations should be based on those used in the single- and multiple-dose toxicology studies. Typically, samples are collected from animals in all dose groups, but those from controls are discarded without analysis. However, a draft guidance recently issued by EMEA recommends assaying levels of test substance in samples from control animals as well to assess the impact of potential contamination (EMEA, 2000). At some point, kinetics should be characterized in each sex, using the minimum number of animals needed for definitive data. Although toxicokinetic analyses focus on measurement of the parent drug, knowledge of metabolite concentrations is especially important when the test substance is a prodrug, when it is biotransformed to active metabolite(s), or when it is extensively metabolized such that measurement of metabolite is the only practical way of establishing exposure. Species differences in protein binding, tissue accumulation, receptor properties, and metabolic profiles, as well as in the antigenicity of biotechnology products, should also be considered when interpreting exposure data. The toxicokinetic strategy to support alternate routes of exposure should be based on the pharmacokinetic properties of the substance

when it is administered by the intended route. If exposure is not substantially greater or different by the new route, additional toxicology studies may focus on local toxicity. The ICH guideline (ICH Topics S3A and S3B; see Internet Resources) also provides specific direction on developing kinetic strategies for single- and repeated-dose toxicity studies, genotoxicity studies (demonstration of systemic exposure may be appropriate for negative in vivo studies), carcinogenicity studies (dietary administration should have confirmation of exposure), and reproductive toxicity studies (assessment of pharmacokinetics in pregnant or lactating animals, analysis of concentration in milk, and analysis of fetal exposure). Single-dose tissue distribution studies are required in regulatory submissions worldwide and are generally considered to provide sufficient information to support a preclinical safety assessment program. However, under some circumstances repeated-dose distribution studies should be considered (ICH Topic S3B; see Internet Resources). These circumstances include the following: when the estimated half-life of elimination in tissues significantly exceeds that in plasma and is greater than twice the dosing interval; when steadystate levels determined in repeated-dose studies are not as predicted from single-dose kinetics; when histopathologic changes occur that would not have been predicted from shortterm toxicity or single-dose distribution studies; and when the drug is being developed for site-specific, targeted delivery. Study duration from 1 to 3 weeks is generally adequate for repeated-dose drug disposition studies. Analysis of parent drug and/or metabolites in the target tissue should be considered, especially in cases of extensive tissue accumulation or targeted delivery. Overall, the timing and design of repeated-dose tissue distribution studies should be determined with each agent individually.

Reproductive Toxicology Studies of potential adverse effects on fertility, fetal development and behavior, and fetal toxicity should be conducted to support the populations chosen for a clinical trial. Sutherland (1996) reviewed guidelines for reproductive toxicology studies. The FDA published a draft guidance that describes an integrative approach to assessment of concerns about human reproductive and developmental toxicities (see Integration of Study Results to Assess Concerns About Human Reproductive

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Table 10.3.10 Regional Differences in the Timing of Reproduction Toxicity Studies to Support the Inclusion of Women of Child-Bearing Potential (WCBP) in Clinical Trials

Region

Requirements

Japan

Female fertility and embryo-fetal development must be completed before inclusion of WCBP using birth control in clinical trials

E.U.

Embryo-fetal development must be completed prior to Phase I Female fertility must be completed prior to Phase III

U.S.

WCBP may be included in early, carefully controlled clinical trials prior to the conduct of reproduction toxicity studies, provided adequate precautions are taken Female fertility and embryo-fetal development must be completed prior to Phase III

and Developmental Toxicities; http://www. fda.gov/cder/guidance/4625dft.pdf). A variety of nonclinical information, such as general and reproductive toxicity, toxicokinetics and metabolism, and clinical information are systematically considered to evaluate the potential to increase the risk of an adverse developmental or reproductive outcome in humans. In the U.S., there is an interest in the early inclusion of women in clinical trials, particularly for new therapies targeted at the treatment of life-threatening diseases. The FDA guideline for the study of gender differences has effectively lifted the previous ban on the inclusion of women of childbearing potential (WCBP) in early clinical trials. The new FDA guideline allows the inclusion of WCBP in early clinical trials prior to the conduct of fertility and teratology studies. Despite protection provided by the informed consent process, the pharmaceutical industry is concerned with legal liability should a pregnancy occur during a Phase I clinical trial. Therefore, the conduct of developmental toxicity studies may be moved to a much earlier point in the drug development process. The view of 41 pharmaceutical companies representing the E.U., Japan, and the U.S. on the ideal approach to the timing of reproduction and developmental toxicity studies has been published by Parkinson et al. (1997). The ICH guideline on reproductive toxicity (ICH Topic S5A and 5B; see Internet Resources) does not address the inclusion of WCBP in early clinical trials. Men may be included in Phase I and II clinical trials before any male fertility studies. Histologic evaluation of male reproductive organs in repeateddose toxicity studies provides an assessment of potential effects on male fertility. In Japan, unlike the U.S. and E.U., male fertility studies have been performed prior to inclusion of men

in clinical trials. Histopathologic evaluation of male reproductive organs in 1-month repeateddose studies is now recommended in Japan. In the U.S. and E.U., a 2-week repeated-dose study is sufficient for this purpose. Male fertility studies must be completed before Phase III clinical trials. Women not of childbearing potential may be included in clinical trials without an evaluation of reproductive effects, provided that a careful histopathologic evaluation of female reproductive organs was conducted in repeated-dose toxicity studies. There are currently regional differences in the timing of reproductive toxicity studies to support the inclusion of WCBP in clinical trials (Table 10.3.10). In all geographic regions, however, female reproduction studies and a full genotoxicity battery should be completed before including in clinical trials WCBP not using birth control or whose pregnancy status is unknown. There is general agreement that before including pregnant women in clinical trials, all reproduction toxicity studies and a complete genotoxicity battery should be conducted. Safety data from previous human exposure is also required.

Carcinogenicity Studies Carcinogenicity studies are not generally required in advance of clinical trials unless there is concern about a class effect relevant to humans, evidence of preneoplastic lesions in repeated-dose toxicity studies, long-term tissue retention resulting in local reactions, or structural features suggesting carcinogenic risk (ICH Topic S1A; see Internet Resources). Carcinogenicity studies may be completed after NCE approval if the test substance is under evaluation for serious life-threatening diseases. The current FDA

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Supplement 32

Toxicology in the Drug Discovery and Development Process

position on peroxisome proliferation– activated receptors (PPARs) requires carcinogenicity studies, in some cases before entering efficacy clinical trials. Carcinogenicity studies should be conducted for any NCE with an expected clinical use of at least 6 months (ICH Topic S1A; see Internet Resources). They should also be conducted for NCEs intended to be used frequently but intermittently in the treatment of a chronic or recurrent disease, such as anxiety or allergy. While the FDA has traditionally required a 3-month time frame for carcinogenicity studies, 6 months has generally been required in the E.U. and Japan. Carcinogenicity studies are usually not required for oncolytic agents intended for treatment of advanced cancers. As a result of questions about their utility in identifying therapeutic agents that pose a carcinogenic risk to humans, the use of 2year rodent carcinogenicity studies is being reevaluated. ICH discussions indicate that the rat would be the preferable species for carcinogenicity studies in the absence of any evidence favoring the mouse (ICH Topic S1B; see Internet Resources). Alternatives to the 2-year study have been proposed, such as initiationpromotion assays or assays using transgenic or neonatal rats. The choice of an alternative method should be based on the degree to which the information is of value in assessing risk. Given the debate on this issue, it is advisable to discuss the selection of alternative methods with the appropriate regulatory agency prior to initiating the study. Important issues in dose selection for oncogenicity studies have been addressed by ICH [ICH Topics S1C and S1C(R); see Internet Resources]. Selection of the maximum dose in carcinogenicity studies is based on one of the following: maximum tolerated dose (MTD), the dose expected to produce minimal toxicity over the course of the carcinogenicity study (namely a 100

aEffects are expressed as IC values (i.e., concentration blocking the current carried by the studied channel by 50%). 50

Compounds were chosen based on reports they prolong the QT interval in human. bHaloperidol is available from Sigma Chemicals. The other compounds can be obtained from the manufacturers.

10.8.16 Supplement 20

Current Protocols in Pharmacology

therapeutic effect (Ekins et al., 2002; Table 10.8.5).

Time Considerations Once a stable control signal is obtained, it may take ∼2 to 5 min to achieve new steadystate responses following the exposure to a hydrophilic drug. Complete washout may take 5 to 20 min. With hydrophobic drugs, the time for attaining steady state responses may be longer and washout may be impossible since the responses are likely to undergo rundown after a prolonged period of patching. These time considerations are also dependent on the tissue bath volume, drug concentration, as well as the perfusion rate. The construction of a full concentration-response-curve may take 2◦ C Intensity 3: decrease > 3◦ C Presence (+), with 3 levels based on mean temperature measured in treated and control animals: Intensity 1: increase > 1◦ C Intensity 2: increase > 2◦ C Intensity 3: increase > 3◦ C Presence (+), with 3 intensities based on mean pupil diameter measured in treated and control animals: Intensity 1: decrease > 10 units Intensity 2: decrease > 20 units Intensity 3: decrease > 30 units Presence (+), with 3 intensities based on mean pupil diameter measured in treated and control animals: Intensity 1: increase > 10 units Intensity 2: increase > 20 units Intensity 3: increase > 30 units

Ptosis Exophthalmos Loss of grasping

Akinesia

Catalepsy

Loss of traction

Loss of corneal reflex

Analgesia

Defecation Salivation Lacrimation Hypothermia

Hyperthermia

Myosisb

Mydriasisb

Primary Observation (Irwin) Test in Rodents

a Evaluation of symptoms preceded by an asterisk (∗) does not require handling of the animal; these symptoms are also

evaluated from 0 to 15 min after administration. b For mice, 1 mm = 45 units; for rats, 1 mm = 30 units.

10.10.18 Supplement 27

Current Protocols in Pharmacology

Table 10.10.2 Effects of Diazepam in the Primary Observation (Irwin) Test in the Rata, b

Doses (mg/kg i.p.) 1

2

4

8

16

32

↓ Muscle tone (3/3) 15 min → 60 min

Abnormal gait (rolling) (3/3) at 15 min and 30 min ↑ Reactivity to touch (3/3) at 15 min and 30 min ↓ Muscle tone (3/3) at 15 min and 30 min Loss of traction (1/3) at 15 min

Sedation + (3/3) at 15 min, 120 min and 180 min ++ (3/3) at 30 min and 60 min Abnormal gait (rolling) (3/3) → 60 min Motor incoordination (1/3) → 15 min ↓ Fear (3/3) at 60 min ↑ Reactivity to touch (3/3) at 15 min ↓ Reactivity to touch (3/3) at 30 min and 60 min ↓ Muscle tone (3/3) 15 min → 180 min Akinesia (1/3) at 15 min Hypothermia + at 30 min and 60 min

Sedation + (3/3) at 15 min and 60 min ++ (3/3) at 30 min Abnormal gait (rolling) (3/3) → 60 min Motor incoordination (1/3) → 15 min Loss of balance (1/3) → 15 min and at 30 min ↓ Fear (3/3) at 30 min ↑ Reactivity to touch (3/3) at 15 min ↓ Reactivity to touch (3/3) at 30 min ↓ Muscle tone (3/3) 15 min → 120 min Loss of traction (3/3) at 15 min (2/3) at 30 min Defecation (1/3) at 60 min Myosis + at 30 min

Sedation ++ (3/3) at 15 min, 30 min and 120 min ++ (1/3) at 60 min +++ (2/3) at 60 min + (3/3) at 180 min Abnormal gait (rolling) (3/3) → 15 min, at 15 min and 120 min Motor incoordination (3/3) →30 min (1/3) at 60 min Loss of balance (3/3) at 15 min ↓ Fear (3/3) 15 min → 60 min ↓ Reactivity to touch (3/3) 15 min → 60 min ↓ Muscle tone (3/3) 15 min → 180 min Akinesia (3/3) at 15 min (2/3) at 30 min (1/3) at 60 min Loss of traction (3/3) at 15 min (2/3) at 30 min and 60 min Myosis + at 120 min Hypothermia + at 15 min and 120 min ++ at 30 min and 60 min

Sedation +++ (3/3) 15 min → 30 min ++ (3/3) at 60 min ++ (2/3) at 120 min and 180 min + (1/3) at 120 min and 180 min Abnormal gait (rolling) (3/3) → 15 min and 120 min → 180 min Motor incoordination (3/3) → 15 min and at 60 min Loss of balance (3/3) at 60 min and 120 min ↓ Respiration (3/3) → 60 min ↓ Fear (3/3) → 180 min ↓ Reactivity to touch (3/3) 15 min → 60 min ↓ Muscle tone (3/3) 15 min → 180 min (2/3) at 24h Loss of righting reflex (3/3) at 15 min and 30 min Loss of grasping (3/3) at 15 min and 30 min Akinesia (2/3) at 60 min and 120 min Catalepsy (3/3) 15 min → 60 min (2/3) at 120 min (1/3) at 180 min Loss of traction (3/3) 15 min → 60 min (1/3) at 120 min and 180 min Myosis + at 15 min and 30 min Hypothermia ++ 15 min → 60 min

a Diazepam was administered at 1, 2, 4, 8, 16 and 32 mg/kg i.p. Three rats were used per group. The doses are expressed in mg/kg of base. Data are

presented as the number of animals showing the symptoms during the test, with an indication of the intensity for sedation (+ = slight; ++ = moderate; +++ = marked). (X/N) indicates the number of rats showing the symptoms. Hypothermia (+ = slight; ++ = moderate) and myosis (+ = slight) were evaluated by comparison of the mean scores obtained in treated and control animals. Observations were performed at 15, 30, 60, 120, 180 min and 24 hr after administration. The symptoms that did not necessitate handling were also observed up to 15 min immediately following administration. b Up and down arrows indicate increase or decrease, respectively, in the particular sign or behavior.

Safety Pharmacology/ Toxicology

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Supplement 27

Table 10.10.3 Effects of Haloperidol in the Primary Observation (Irwin) Test in the Rata, b

Doses (mg/kg i.p.) 0.5

1

No Sedation change + (1/3) at 30 min ↓ Muscle tone (2/3) at 30 min

2

4

8

16

32

Sedation + (3/3) 30 min → 120 min ↓ Fear (2/3) at 30 min ↓ Reactivity to touch (1/3) at 30 min (2/3) at 60 min ↓ Muscle tone (3/3) at 30 min (2/3) at 60 min

Sedation + (1/3) at 15 min + (3/3) 30 min → 180 min ↓ Fear (3/3) 30 min → 60 min ↓ Reactivity to touch (3/3) 30 min → 60 min ↓ Muscle tone (3/3) 30 min → 60 min (2/3) at 120 min Myosis + 30 min → 60 min

Sedation + (1/3) at 15 min + (3/3) 30 min → 180 min ↓ Fear (3/3) 30 min → 60 min ↓ Reactivity to touch (3/3) 30 min → 60 min ↓ Muscle tone (3/3) 30 min → 60 min (2/3) at 120 min Myosis + 30 min → 60 min

Sedation + at 15 min and 120 min → 180 min ++ 30 min → 60 min Abnormal gait (rolling) (3/3) 15 min → 180 min ↓ Fear (3/3) 15 min → 60 min ↓ Reactivity to touch (3/3) 15 min → 180 min ↓ Muscle tone (3/3) 15 min → 180 min Catalepsy (3/3) 60 min → 180 min Myosis + at 15 min and 60 min → 120 min ++ at 30 min

Sedation + (3/3) at 15 min ++ (3/3) at 30 min; (1/3) at 60 min and (2/3) 120 min → 180 min +++ (1/3) 120 min → 180 min Abnormal gait (rolling) (3/3) 15 min → 30 min (1/3) at 60 min and (2/3) 120 min → 180 min ↓ Fear (3/3) 15 min → 180 min ↓ Reactivity to touch (3/3) 15 min → 180 min ↓ Muscle tone (3/3) 15 min → 180 min Catalepsy (3/3) 30 min → 180 min Loss of traction (3/3) 30 min → 60 min Myosis + 30 min → 180 min

a Three rats were used per group. Data are presented as the number of animals showing the symptoms during the test, with an indication of the intensity for sedation (+ = slight; ++ = moderate; +++ = marked). (X/N) indicates the number of rats showing the symptoms. Observations were performed at 15, 30, 60, 120, 180 min and 24 hr after administration. The symptoms that did not necessitate handling were also observed up to 15 min immediately following administration. Myosis was evaluated by comparison of the mean scores obtained in treated and control animals. b Up and down arrows indicate increase or decrease, respectively, in the particular sign or behavior.

Primary Observation (Irwin) Test in Rodents

10.10.20 Supplement 27

Current Protocols in Pharmacology

Table 10.10.4 Effects of Amphetamine in the Primary Observation (Irwin) Test in the Rata, b

Dose (mg/kg i.p.) 0.5

1

2

4

8

16

32

Stereotypies (sniffing) (3/3) at 60 min Mydriasis + at 15 min

Excitation + (3/3) at 30 min Stereotypies (sniffing) (3/3) → 30 min ↑ Fear (1/3) at 30 min Mydriasis + 15 min → 60 min

Excitation + (3/3) 30 min → 60 min Stereotypies (sniffing) (3/3) → 120 min Mydriasis + at 60 min Hyperthermia + at 60 min

Excitation ++ (3/3) 15 min → 30 min + (3/3) 60 min → 120 min Abnormal gait (rolling) (1/3) → 15 min Stereotypies (sniffing) (3/3) → 120 min ↑ Fear (3/3) 15 min → 30 min ↑ Reactivity to touch (3/3) 15 min → 180 min

Tremor (1/3) at 30 min Excitation ++ (3/3) 15 min → 30 min + (3/3) 60 min → 120 min Abnormal gait (rolling) (3/3) → 15 min Stereotypies (sniffing) (3/3) → 180 min ↑ Fear (3/3) 15 min → 180 min ↑ Reactivity to touch (3/3) at 15 min Mydriasis + 15 min → 120 min Hyperthermia + 15 min → 60 min

Tremor (3/3) → 30 min Straub (1/3) → 15 min Excitation +++ (3/3) 15 min → 60 min ++ (3/3) at 120 min + (3/3) at 180 min Abnormal gait (rolling) (3/3) → 15 min Abnormal gait (tip-toe) (3/3) at 15 min and 30 min Fore-paw treading (3/3) 15 min → 120 min Piloerection (3/3) at 15 min and 30 min Stereotypies (sniffing) (3/3) → 15 min and at 180 min Stereotypies (head movements) (3/3) → 120 min ↑ Fear (3/3) 15 min → 120 min ↑ Reactivity to touch (3/3) 15 min → 180 min

Lethality (1/3) at 24 hr Tremor (3/3) → 30 min Straub (2/3) → 15 min Excitation +++ (3/3) 15 min → 30 min ++ (3/3) 60 min → 180 min + (2/2) at 24h Abnormal gait (rolling) (3/3) → 15 min Abnormal gait (tip-toe) (3/3) at 15 min and 30 min Motor incoordination (3/3) 60 min → 180 min Loss of balance (1/3) at 60 min (3/3) at 120 min and 180 min Fore-paw treading (2/3) → 15 min (3/3) 15 min → 60 min Piloerection (2/3) → 15 min (3/3) at 15 min and 30 min Stereotypies (sniffing) (1/3) → 15 min (2/2) at 24 hr

continued

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Table 10.10.4 Effects of Amphetamine in the Primary Observation (Irwin) Test in the Rata, b, continued

Dose (mg/kg i.p.) 0.5

1

2

4

8

16

32

Exophtalmos (1/3) → 15 min and at 60 min Loss of traction (3/3) 15 min → 120 min (1/3) at 180 min Salivation (3/3) 15 min → 60 min Lacrimation (2/3) at 15 min and 30 min Hyperthermia ++ 15 min → 60 min + at 120 min

Stereotypies (head movements) (2/3) → 15 min (3/3) 15 min → 60 min (2/2) at 24 hr ↑ Fear (3/3) 15 min → 180 min ↑Reactivity to touch (3/3) 15 min → 180 min (2/2) at 24h Exophtalmos (1/3) 15 min → 120 min Loss of traction (3/3) 15 min → 180 min Salivation (3/3) 15 min → 60 min Lacrimation (3/3) at 15 min and 30 min Hyperthermia + at 15 min and 24 hr ++ at 30 min +++ at 60 min

a D-amphetamine sulfate was administered at 0.5, 1, 2, 4, 8, 16 and 32 mg/kg i.p. Three rats were used per group. The doses are expressed in mg/kg of

salt. Data are presented as the number of animals showing the symptoms during the test, with an indication of the intensity for excitation (+ = slight; ++ = moderate; +++ = marked). (X/N) indicates the number of rats showing the symptoms. Hyperthermia (+ = slight; ++ = moderate; +++ = marked) and mydriasis (+ = slight) were evaluated by comparison of the mean scores obtained in treated and control animals. Observations were performed at 15, 30, 60, 120, 180 min and 24 hr after administration. The symptoms that did not necessitate handling were also observed up to 15 min immediately following administration. b Up and down arrows indicate increase or decrease, respectively, in the particular sign or behavior.

Primary Observation (Irwin) Test in Rodents

10.10.22 Supplement 27

Current Protocols in Pharmacology

Table 10.10.5 Effects of Morphine in the Primary Observation (Irwin) Test in the Rata, b

Doses (mg/kg i.p.) 1

2

4

8

16

32

No change

Abnormal gait (rolling) (3/3) at 30 min ↑ Reactivity to touch (3/3) at 60 min

Excitation + (3/3) at 30 min and 120 min Abnormal gait (rolling) (3/3) 15 min → 30 min ↑ Reactivity to touch (3/3) at 60 min Exophthalmos (1/3) at 15 min

Straub (1/3) at 30 min Sedation + (3/3) at 15 min Excitation + (3/3) at 30 min and 120 min Abnormal gait (tip-toe) (3/3) at 15 min and 30 min ↑ Reactivity to touch (3/3) at 60 min Analgesia (2/3) at 30 min Hyperthermia + 60 min → 120 min

Straub (2/3) at 30 min (3/3) 60 min → 120 min Sedation + (3/3) 30 min → 60 min Excitation + (3/3) 120 min → 180 min Abnormal gait (tip-toe) (3/3) → 120 min ↑ Fear (3/3) at 30 min ↑ Reactivity to touch (3/3) 15 min → 30 min and 120 min → 180 min ↑ Muscle tone (3/3) 60 min → 120 min Exophthalmos (3/3) 30 min → 120 min Analgesia (1/3) at 30 min and 120 min (2/3) at 60 min Hyperthermia + at 120 min

Lethality (2/3) at 60 min Tremor (3/3) at 30 min (1/1) 60 min → 180 min Straub (3/3) → 30 min (1/1) 60 min → 180 min Sedation + (3/3) 15 min → 30 min (1/1) 60 min → 120 min Excitation + (1/1) at 180 min Abnormal gait (tip-toe) (3/3) → 30 min (1/3) 60 min → 120 min Jumps (1/3) at 120 min ↑ Fear (3/3) 15 min → 30 min ↑ Reactivity to touch (3/3) 15 min → 30 min (1/1) 60 min → 180 min ↑ Muscle tone (3/3) at 30 min (1/1) 60 min → 180 min Exophthalmos (3/3) 15 min → 30 min (1/1) 60 min → 120 min Loss of traction (1/1) 60 min → 120 min Analgesia (3/3) 15 min → 30 min (1/1) 60 min → 120 min Lacrimation (1/3) 60 min → 120 min Hyperthermia + at 60 min Mydriasis + at 15 min

a Morphine hydrochloride was administered at 1, 2, 4, 8, 16 and 32 mg/kg i.p. Three rats were used per group. The doses are expressed in mg/kg of

salt. Data are presented as the number of animals showing the symptoms during the test, with i an indication of the intensity for sedation or excitation (+ = slight; ++ = moderate; +++ = marked). (X/N) indicates the number of rats showing the symptoms. Hyperthermia (+ = slight) and mydriasis (+ = slight) were evaluated by comparison of the mean scores obtained in treated and control animals. Observations were performed at 15, 30, 60, 120, 180 min and 24 hr after administration. The symptoms which did not necessitate handling were also observed up to 15 min immediately following administration. b Up and down arrows indicate increase or decrease, respectively, in the particular sign or behavior.

Safety Pharmacology/ Toxicology

10.10.23 Current Protocols in Pharmacology

Supplement 27

Head-Out Plethysmography in Safety Pharmacology Assessment The importance of evaluating the potential for compounds to produce unwanted or adverse effects on the respiratory system has been underscored by the International Conference on Harmonization (see U.S. Food and Drug Administration, 2001), representing regulatory agencies from the United States, the European Union, and Japan. This document, released in July of 2001, defines the respiratory system as a vital organ system and states that the effects of test substances on respiratory function should be evaluated prior to first-time-in-human administration. Evaluation of respiratory function should include, at a minimum, evaluation of ventilatory parameters, including rate and volume measurements and overall pulmonary ventilation (i.e., minute volume). A comprehensive overview of the assessment of respiratory function in safety pharmacology is provided by Murphy (2002).

UNIT 10.11

BASIC PROTOCOL

Because the respiratory system is considered to be a vital organ system and the evaluation of respiratory function is a critical component of the core battery in the safety pharmacology package, the ability to accurately and reliably evaluate respiratory function in animals has become increasingly important. The choice of the individual techniques utilized to assess respiratory function will have a direct effect on the ability to generate accurate results and thus generate an accurate profile of the compound being tested. This unit describes the use of head-out plethysmography to assess respiratory function in conscious rats. It also includes a technique previously described by Murphy et al. (1998) for telemetrically measuring pleural pressure, also in conscious rats. The combined techniques allow the simultaneous measurement of both ventilatory parameters (e.g., tidal volume, respiratory rate, and minute volume) and total pulmonary resistance (as a measure of lung function). NOTE: All protocols using live animals must first be reviewed and approved by an Institutional Animal Care and Use Committee (IACUC) or must conform to governmental regulations regarding the care and use of laboratory animals.

Materials Sealant (e.g., silicone), optional Male or Female Sprague-Dawley rats, 250 to 500 g (Charles River Laboratories) Anesthesia: isoflurane (1% to 4% in 100% O2 ) by inhalation Topical disinfectant (e.g., Betadine) 70% ethanol Medical grade adhesive (Vetbond or equivalent) and a cellulose patch Test compounds in appropriate vehicles for oral or parenteral administration Plethysmograph device (manufactured locally; see Fig. 10.11.1) consisting of: flow-measurement chamber head-out animal-holding chamber, 3-liter internal volume with pneumotach port (six layers of 325 mesh stainless steel wire cloth; Small Parts) and rubber collar (1/8-in. to 3/16-in. thick, e.g., Neoprene; McMaster-Carr) Antivibrational table (e.g., marble) Differential pressure transducer (Validyne Engineering MP-45-14; http://www.validyne.com) 1/8-in. copper threaded fitting, ∼3 cm Flexible tubing (0.48-cm o.d., 0.16-cm i.d.)

Contributed by Jonathan P. Renninger Current Protocols in Pharmacology (2006) 10.11.1-10.11.18 C 2006 by John Wiley & Sons, Inc. Copyright 

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10.11.1 Supplement 33

Ambient reference chamber, ∼2-liter internal volume (VWR Scientific Products) Amplifier: differential pressure demodulator/amplifier/signal conditioner specifically designed for the Validyne 1/2 bridge transducer and recommended for low-pressure applications (LDS Life Science VP-800; http://www.lds-group.com) C12V analog voltage-to-frequency converter (DSI, http://www.datasci.com) Data interface system and cable: Dataquest Advanced Research Technology (A.R.T.) or Dataquest OpenART system (DSI, http://www.datasci.com) Telemetry receiver unit (DSI RLA1020 or RPC-1; http://www.datasci.com) Data acquisition system (pulmonary compliance and resistance analysis module of the PONEMAH Physiology Platform; http://www.ponemah.com) Telemetry transmitter unit (DSI TA11PA-C40 or TL11M2-C50-PXT; http://www.datasci.com) 60-ml plastic syringe 3-way stopcock LCD digital pressure indicator (e.g., Heise Model PPM2; http://www.heise.com) Surgical clippers Surgical equipment 2 × 2–in. Versalon sponges, sterile (or equivalent): moisten with sterile 0.9% NaCl 22-G needle, 1 in. long with ∼90◦ bend 5 1/8-in. cannulation forceps (Roboz Surgical Instruments) Nonabsorbable and absorbable sutures or surgical wound clips Polycarbonate box with bedding, clean Animal balance accurate to 1 g (e.g., Mettler PE2000) Continuous airflow source (e.g., house air or tank of breathable air) Digital flow meter with minimum range of 1 to 1000 ml/min (e.g., J & W Scientific ADM 2000) Plethysmograph calibration chamber: head-out animal-holding chamber, sealed and with no hole for the animal’s head (manufactured locally) Calibrated voltage generator with output of 0 to 1 V (e.g., Bio-System Calibrator, Coulbourn Instruments) Automated data analysis program (e.g., SAS, http://www.sas.com; or custom written programs, e.g., in Microsoft Excel, http://www.microsoft.com) NOTE: Connect and test all equipment prior to conducting the first measurements (see Fig. 10.11.1 for a schematic of the complete system).

Prepare test equipment 1. Place the flow-measurement chamber of the plethysmograph device on an antivibrational table. Connect the differential pressure transducer (serving as a pneumotach) to the port on the back of the animal holding chamber using an 1/8-in. copper threaded fitting ∼3 cm in length. This connection needs to form an airtight seal with the chamber. (Use an appropriate sealant such as silicone, if necessary.)

2. Connect the opposite end of the differential pressure transducer to the ambient reference chamber using flexible tubing (see Fig. 10.11.1). Connect the electrical connection from the differential pressure transducer to the appropriate input on the amplifier. Head-Out Plethysmography in Safety Pharmacology Assessment

The demodulator/amplifier/signal conditioner provides an interface from the transducer to the data acquisition system by converting sensor input into a standard signal for input into the A/D board.

10.11.2 Supplement 33

Current Protocols in Pharmacology

Figure 10.11.1 Components needed to evaluate ventilatory and lung function in conscious, restrained rats using head-out plethysmography. The system components include: (A) head-out animal-holding chamber, (B) flow-measurement chamber, (C) telemetry receiver, (D) antivibrational table, (E) ambient reference chamber, (F) differential pressure transducer (pneumotach), (G) amplifier, (H) C12V analog voltage-to-frequency converter, and (I) data interface and acquisition systems.

3. Connect the output from the amplifier (BNC connector) to the C12V voltage-tofrequency converter, and then connect the output from the C12V to channel one of the input data exchange matrix of the DSI A.R.T. system using a data interface cable. If using the Dataquest OpenART system, refer to the manufacturer’s Web site for a detailed description for calibration and setup (http://www.datasci.com).

4. Place the telemetry receiver unit in proximity to the plethysmograph chamber, and connect the telemetry receiver to channel two of the input data exchange matrix of the DSI A.R.T system. 5. Within the A.R.T. system software, set up channel one for flow and channel two for pressure (see the manufacturer’s manual). 6. Adjust the settings in the analog output for flow and pressure to correspond to the calibration values being used for the acquisition system (see steps 27 and 28 below). 7. Connect channel one (flow) and channel two (pressure) of the data exchange analog output to the appropriate inputs on the acquisition system, e.g., the pulmonary compliance and resistance (PCR) analysis module of the PONEMAH Physiology Platform P3 acquisition system according to the manufacturer’s manual.

Calibrate telemetry transmitter unit 8. Remove the telemetry transmitter unit from the factory package and place inside a standard 60-ml plastic syringe. Be careful to slowly re-insert the plunger of the syringe to avoid exposing the transmitter unit to excessive pressures.

Safety Pharmacology/ Toxicology

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Supplement 33

Table 10.11.1 Calibration of TL11M2-C50-PXT Telemetry Transmitter Unit

Pressure (mmHg)

Frequency (Hz)

755

634.477

785

691.653

725

577.301

9. Connect the end of the syringe to a 3-way stopcock and connect one of the other ends of the stopcock to an LCD digital pressure indicator. 10. Perform a three-point calibration as follows: a. Record the actual ambient pressure and the corresponding frequency of the transmitter unit from the computer (refer to the manufacturer’s manual for the A.R.T. system). b. Gently press on the plunger of the syringe and apply 30-mm Hg positive pressure (as measured by the LCD digital pressure indicator) to the transmitter unit. Record the corresponding frequency value from the computer along with the pressure value (ambient + 30 mmHg). c. Gently pull back on the plunger of the syringe and apply 30 mmHg negative pressure (as measured by the LCD digital pressure indicator) to the transmitter unit. Record the corresponding frequency value from the computer along with the pressure value (ambient – 30 mmHg). 11. Record the calibration values obtained in step 10 for future reference. Plot the pressure and frequency values to ensure that the transmitter unit has a linear response. See Table 10.11.1 for an example of calibration values for a typical TL11M2-C50-PXT transmitter unit.

Prepare the animal 12. Anesthetize a Sprague-Dawley rat by isoflurane (1% to 4% in 100% O2 ) inhalation. Prepare the surgical area by shaving with surgical clippers and scrubbing with a topical disinfectant, followed by a 70% ethanol wash. 13. Make an abdominal incision (∼4 to 5 cm in length) along the linea alba. Retract the lobes of the liver within the abdominal cavity to expose the esophagus and gently pack the lobes against the abdominal wall using moist 2 × 2–in. Versalon (or equivalent) squares. 14. Isolate the esophagus ∼2 cm below the hiatus esophagus (junction with the diaphragm), insert a 22-G needle (1 in. long with ∼90◦ bend) into the esophagus between the serosa and muscularis layers, and tunnel the needle into the pleural space (Fig. 10.11.2). It is very important to keep the esophagus completely straight while the needle is being inserted; otherwise, the needle may re-emerge through the serosa layer and directly enter the pleural space. This may lead to fibrosis around the catheter tip and result in a loss of the pressure signal. Head-Out Plethysmography in Safety Pharmacology Assessment

Also, take care not to advance the needle too far up the esophagus (generally no more than 1.5 cm). If the needle is advanced too far, there is a risk of the needle coming into contact with the heart.

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Figure 10.11.2 Drawing of a rat showing placement of the catheter and body of the telemetry pressure transmitter unit. The enlargement shows a cross-section of the esophagus detailing the position of the catheter between the serosal and muscularis layers. Reproduced with permission from Murphy et al., 1998.

15. Once the needle is advanced to a point ∼1 cm beyond the diaphragm junction, remove the needle and thread the catheter from the telemetry transmitter unit up the channel. IMPORTANT NOTE: During this step, do not apply pressure to the catheter for risk of damaging the fluid-filled catheter and telemetry unit. A pair of vessel cannulation forceps can be used to successfully advance the catheter without damaging the unit.

16. Once the catheter is advanced to the end-point of the channel created by the needle, pause to view the pressure signal from the transmitter unit. The pressure signal may be viewed using the A.R.T. system. Follow the user manual to set up the system for viewing data signals. Maximal and acceptable pleural pressure signal is determined by the maximum value of the pressure signal and the morphology of the waveform. Acceptable pleural pressures during isoflurane-induced anesthesia are generally between 8 and 20 cmH2 O.

17. Obtain a maximal and acceptable pleural pressure signal between 8 and 20 cmH2 O by slowly moving the catheter up and down the channel in the esophagus. 18. Secure the catheter in place using medical grade tissue adhesive (Vetbond or equivalent) and a cellulose patch. Secure the body of the transmitter unit to the abdominal wall with nonabsorbable sutures during the closure of the abdominal musculature (Fig. 10.11.2). 19. Close the skin layer with absorbable sutures and/or surgical wound clips. 20. Place the rat in a clean polycarbonate box with soft bedding for ∼7 to 10 days before removing sutures and/or surgical wound clips. Observe the rat daily for any signs of distress and obtain body weights on days 2, 5, and 7 post surgery.

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Table 10.11.2 Plethysmograph Collar Sizing Chart

Body weight range (g)

Diameter of hole in collar

< 300

1 in.

301–400

1 1/8 in.

> 400

1 1/4 in.

A rat may initially lose 10% of its body weight after surgery but should be gaining weight by day 5 postsurgery.

Acclimate the animal 21. Weigh each rat prior to placement in a plethysmograph chamber. Place the appropriate size rubber collar, based on the weight of each rat (see Table 10.11.2), onto the plethysmograph animal-holding chamber. 22. Place the rat into the back of the plethysmograph chamber and insert the plunger behind the animal. Gently advance the plunger until the animal’s head extends through the hole in the collar. Make sure that the tail of the animal is enclosed within the chamber and secure the plunger in place. 23. Gently adjust the animal’s head to ensure that the entire head is protruding through the hole in the collar and that there is an adequate seal around the neck region. If using a neoprene collar, no additional lubricant or sealant is generally needed to ensure a tight seal around the neck of the animal.

24. Allow the animal to acclimate to the chamber for ∼5 to 15 min. Continuously observe the animal for signs of stress (excessive struggling, labored breathing) during the acclimation period. On occasion, an animal may display signs of chromodacryorrhea (reddish staining around the eyes and nose) during the first acclimation session. This may be considered to be a normal response to acute stress in rats (Harkness and Ridgway, 1980). If this condition persists for more than two acclimation periods, the animal should not be used in experimentation. Some animals may attempt to exit the chamber through the collar hole. This is a very dangerous situation because an animal can get stuck in the collar hole with subsequent obstruction of its airflow. It is important to never leave an animal unattended while confined to a plethysmograph chamber. If an animal can maneuver a forelimb through the collar hole, the collar should immediately be replaced with one that has a smaller diameter hole (see Table 10.11.2).

25. Repeat the acclimation procedure for a minimum of three times (once daily for three consecutive days) for each rat. Keep an acclimation log for each rat as a means of removing animals from a potential experiment due to poor behavior. In addition, perform a pretest screen of ventilatory parameters on each animal to ensure normal breathing patterns (Fig. 10.11.3). Animals are generally excluded from use in an experiment due to excessive struggling or sniffing while in the chamber. Head-Out Plethysmography in Safety Pharmacology Assessment

By the third acclimation session, rats should be relatively calm in the chamber for extended periods of time. Well acclimated rats generally have respiratory rates between 120 and 180 breaths/min.

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Figure 10.11.3 A typical flow, pressure, and waveform tracing from a rat in a head-out plethysmograph chamber. Peak inspiratory and expiratory flows in a normal, untreated rat are ∼15 ml/sec, maximum pleural pressure is approximately −12 cm H2 O, and tidal volume is ∼1.5 ml. Definitions:
F, change in air flow rate;
P, change in pleural pressure at zero flow points;
P1 , change in pleural pressure at 70% isovolumetric point (indicated by the unlabeled, horizontal, dotted line in the volume panel);
V, change in volume at zero flow points.

Set up the transmitter configuration file 26. Set up a configuration file within the A.R.T. software system with the appropriate calibrations for the specific telemetry transmitter unit being used (see steps 8 to 11) and for the flow input from the C12V analog voltage-to-frequency converter, according to the A.R.T. system manufacturer’s manual. Make sure that all of the equipment is set up according to steps 1 to 7. This protocol utilizes the A.R.T. analog system for receiving the pleural pressure signal from the surgically implanted transmitter unit and the Life Science Suite PONEMAH Physiology Platform P3 system for acquisition and analysis of flow and pressure data. See Figure 10.11.1 for an overview of the complete system setup. Refer to the A.R.T. analog system manufacturer’s manual for a complete description of setting up a configuration file within the software system.

27. For the flow signal from the C12V voltage-to-frequency converter, select the “gauge pressure” signal type option and enter the following values for a 1:1 input:ouput ratio: calibration 1 at 0 mmHg: 1500 Hz calibration 2 at 100 mmHg: 1450 Hz. Refer to the DSI A.R.T. manufacturer’s manual to set up the analog output from the flow and pressure sources.

28. For the pleural pressure signal, select the “pressure” signal type option and enter the following value: slope: −0.135951 V/unit. The value for the slope corresponds to the calibration value that will be used for the acquisition system. The negative sign will invert the pressure signal so that it is displayed as a positive signal in the acquisition system. For the acquisition system, 1 V will equal 10 cmH2 O (see steps 37 to 40 below). Therefore, it is important to calculate a slope value for the output from the pressure signal from the A.R.T. system to equal this calibration.

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In the example above, one unit is equal to 1 mmHg and 0.135951 V is equal to 1 mmHg. Accordingly, 1 V is equal to 7.356 mmHg and since 1 mmHg is equal to 1.35951 cmH2 O, 7.356 mmHg is equal to 10 cmH2 O, which is equal to 1 V.

29. Refer to the manufacturer’s manual for setting up a protocol file using the PONEMAH Physiology Platform P3 acquisition system. Select the appropriate inputs for channels one (flow from A.R.T. system) and two (pressure from A.R.T. system). 30. For evaluating respiratory function, including total pulmonary resistance, select the pulmonary compliance and resistance (PCR) analysis module for channel one. For channel two, select PCRP (this is the channel for the pleural pressure signal).

Calibrate flow 31. Set the gain on channel one of the amplifier to “off ” or the zero setting, and adjust the baseline flow to zero. 32. Attach tubing connected to a continuous airflow source and a digital flow meter to the plethysmograph calibration chamber. The calibration chamber is identical to the animal chamber except that it has no hole for the animal’s head and is sealed.

33. Turn the amplifier to the desired gain setting and apply 0 ml/sec of airflow to the chamber and zero the baseline flow by adjusting the zero knob, or equivalent, on the amplifier. The acquisition system should display a flow value of 0 ml/sec.

34. Apply continuous airflow of 10 ml/sec to the calibration chamber. Be sure to verify the flow with the digital flow meter. Adjust the voltage on the acquisition system to the desired voltage (in this case 1 V), using the gain screw, or equivalent. 35. Repeat step 34 using a higher continuous rate of airflow to the chamber. For example, apply 30 ml/sec of continuous flow and verify the voltage on the amplifier/acquisition system to be equal to 3 V. 36. Perform a two-point calibration, minimum. Calibrations should include flow values at or greater than those to be measured during a procedure. In addition, a calibration should be high enough to allow for an adequate increase and/or decrease in airflow during a procedure. For example, if an acquisition system has a maximum voltage input of ±5 V on the A/D board, adjusting the gain on the amplifier so that a high calibration of 10 ml/sec is equal to 2.5 V means that accurate acquisition of flow values during a procedure are limited to maximum values of 20 ml/sec.

Calibrate pressure 37. Attach a calibrated voltage generator to the acquisition system corresponding to the channel for measuring pleural pressure. 38. Set the gain on channel two of the amplifier to “off ” or zero setting and adjust the baseline pressure to zero. 39. Set the amplifier to an appropriate gain setting and apply continuous DC voltage to the pleural pressure input.

Head-Out Plethysmography in Safety Pharmacology Assessment

40. Adjust the gain knob, or equivalent, on the amplifier until the voltage displayed by the acquisition system is equivalent to that displayed by the calibrated voltage generator (in this case, both should be equal to 1 V). The pressure signal from the telemetry transmitter unit will ultimately be converted to an analog signal for input into the acquisition system; therefore, it is important to verify that the acquisition system is properly calibrated in regards to an analog voltage current.

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Acquire ventilatory parameters 41. Place the rat in the plethysmograph chamber as in steps 22 and 23. 42. Begin collecting data using the appropriate acquisition system. Although other data acquisition systems are available (e.g., from Buxco or Notocord), the author recommends the Life Science Suite PONEMAH Physiology Platform P3 system (see step 29). The total collection time may vary depending on the study design (see Table 10.11.3).

43. Once the specified period of time has elapsed, stop data acquisition, remove the animal from the chamber and return it to its cage. 44. Repeat the same procedure (steps 41 to 43) for the specified number of time points as determined by the study design. 45. Perform the same procedure using rats treated with test compounds. Experimental design and time frames for testing are discussed in the Commentary section (see Background Information, Parameters and study design). See Diehl et al. (2001) for information about oral or parenteral administration of test compounds.

Analyze data 46. Collect data on a breath-by-breath basis for a predefined time period, generally 5 to 15 min. 47. Evaluate a continuous tracing of the breathing waveform until a minimum of 60 acceptable breaths are identified from each data file. Each data file should be analyzed separately either by a trained individual who can recognize artifacts in the breathing waveforms or using an automated exclusion criteria. Possible reasons for excluding individual breaths may include but are not limited to the following: sniffing episode, animal movement artifact, apparent upper airway obstruction, augmented breath, disrupted flow signal, a breath influenced by obstruction or augmented breath, disruption in telemetry signal, and possible electrical interference. An automated program, such as SAS, can also be used to quickly edit data files using a predefined acceptance criteria. Based on this criteria, the program will evaluate the individual data for each breath and either accept or reject the breath based on the defined criteria. A listing of typical acceptance criteria are presented in Table 10.11.4. Table 10.11.3 Typical Data Collection Times for Various Routes of Test Compound Administration

Route of administration

Data collection time (min)

Number of collection intervalsa

5

4-7

60

1-3

oral (p.o.)b i.v./s.c./i.p./i.m.

c

a Data should be acquired prior to dosing, around the known or estimated times of C

max

and

at one or two times after Cmax to evaluate recovery or delayed effects. b Data acquired on a breath-by-breath basis. c Data acquired continuously as 1- or 5-min mean values.

Table 10.11.4 Typical Acceptance Criteria for Individual Breaths

Parameter

Acceptable values

Tidal volume (ml)

0.2 ≤ ml ≤ 5.0

Respiratory rate (breaths/min)

10 ≤ breaths/min ≤ 200

Pleural pressure (cmH2 O)

3 ≤ cmH2 O ≤ 30

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48. Calculate a mean value from acceptable values for each parameter for each file at each time point. Data is generally evaluated as an absolute change from the value obtained at the predose measurement interval.

COMMENTARY Background Information The technique of plethysmography has been used for nearly 50 years to assess basic respiratory function (Chapin, 1954; Drorbaugh and Fenn, 1955; Bargeton and Barres, 1968). A plethysmograph chamber is a rigidwalled device that encloses the test animal and allows the measurement of respiratory flows and volumes. There are four basic types of plethysmograph chambers. Volume displacement (flow box, as described in this protocol) and constant volume (pressure box) plethysmograph chambers allow the test animal to breathe outside of the chamber. The animal’s neck or nose extends through a sealed opening in the chamber and a pneumotachograph is used to measure respiratory airflows. A barometric plethysmograph chamber (Fenn box) encloses the test animal entirely within the chamber (Drorbaugh and Fenn, 1955). As the animal breathes, volumes are detected by small pressure changes due to heating and humidification of inspired gas. Volumes are calculated by measuring the difference in pressure at end-inspiration from a baseline composed of successive end-expiratory points. Finally, a dual-type plethysmograph (Pennock box) contains the body or thorax of the test animal in one chamber and the head of the animal in an-

Head-Out Plethysmography in Safety Pharmacology Assessment

other separate chamber (Pennock et al., 1979). In this design, flows and/or volumes are determined from a time delay or restriction of airflow between the nose and the thorax of the animal in their respective chambers. Although there is much debate on this particular subject, the volume displacement, head-out, plethysmograph chamber is widely considered the most accurate device for measuring respiratory flows and volumes in small animals (Chapin, 1954; Murphy, 2002). In relation to the other types of plethysmograph chambers, the volume displacement chamber is the least sensitive to leaks, variations in the size of the test animal in relation to the size of the chamber, and frequency dependence (Sinnett et al., 1981). The whole-body plethysmograph (Fenn box) is limited because there is a very minimal change in pressure (i.e., requiring a high gain system) and it is subject to artifacts such as changes in temperature and humidity. The method proposed by Drorbaugh and Fenn (1955) was disputed by DuBois et al. (1956) who contended that the Fenn box could be used for determination of alveolar pressure and not tidal volume, unless airflows were also measured. The concept proposed by Dubois is now widely accepted (Peslin et al., 1995;

Figure 10.11.4 A head-out plethysmograph chamber used for evaluating respiratory function in the conscious dog.

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Figure 10.11.5 scious monkey.

A plethysmograph helmet used for evaluating respiratory function in the con-

Stocks et al., 1996; Enhorning et al., 1998). The dual-chamber plethysmograph is limited in that it only provides an indirect measure of airway resistance. Regardless of the type of plethysmograph chamber used, it is important to note that an accurate evaluation of ventilatory function should include both volume and rate measurements. This is important because tidal volume and respiratory rate are independently controlled and various test substances have been shown to selectively alter respiratory rate, tidal volume and/or the inspiratory and expiratory phases of breathing (Boggs, 1992; Murphy, 2002). In addition, it is important to obtain an accurate measurement of resistance to airflow as a predictor of airway constriction/obstruction. This is important because mild to moderate increases in resistance to airflow (up to 150%) are not sufficient to alter ventilatory patterns in conscious rats (D. Murphy, pers. comm.). The method described in this unit has been applied in both the dog (D. Murphy, pers. comm.) and nonhuman primate (Murphy et al., 2001). The technique for the dog is similar to the rat in using a head-out, volume displacement plethysmograph chamber with a pneumotach to measure flow and volume. Lung function is evaluated by surgically implanting a telemetry transmitter unit (e.g., Data Current Protocols in Pharmacology

Sciences International TL11M3-D70-PCP) to monitor pleural pressure while the dog is in the chamber (Fig. 10.11.4). This technique can be modified slightly to evaluate respiratory function in the monkey. A modified helmet, with a continuous bias flow and an attached pnuemotach is used to measure flow and volume (Fig. 10.11.5). Pleural pressure is monitored using a surgically implanted telemetry transmitter unit (e.g., Data Sciences International Model TL11M3-D70-PCP). Parameters and study design As mentioned previously, both respiratory rate and tidal volume need to be measured to accurately assess ventilatory function. Tidal volume is defined as the volume change in the lung that occurs with each breath, and respiratory rate is the number of breaths that occur in one minute. The duration of each breath is defined as the time between the onsets of inspirations. The product of these parameters, i.e., minute volume, is a measure of the total pulmonary ventilation of the test subject or the volume of gas that moves in and out of the lung each minute. In addition, a measure of airway resistance (i.e., total pulmonary resistance) should be obtained to effectively evaluate lung function. Total pulmonary resistance is a measure of the pressure required to generate a defined rate of gas flow into

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Table 10.11.5 Treatments (A to D) Ordered in a Typical 4×4 Latin Square Design Matrix

Treatment period Subject number

1

2

3

4

1

A

B

D

C

2

B

C

A

D

3

C

D

B

A

4

D

A

C

B

Table 10.11.6 Troubleshooting Guide for Head-Out Plethysmography

Problem

Possible cause

Solution

All derived parameters reported as zero within acquisition software

Minimum flow set too high for the specified signal

Reduce minimum flow value

Resistance value equal to zero or very high

Pressure trigger channel not Verify the pressure channel is assigned properly assigned a valid pulmonary pressure algorithm

Tidal volume incorrect

Pressure value too high

Verify calibration and setup of pressure signal

Flow values too low

Ensure the plethysmograph chamber is properly sealed with no leaks

Flow signal drifting above or below the zero

Enable AC coupling in the acquisition system

Inspiration cycle not a positive value

Verify the inspiratory phase of the breath is in the positive direction

Wrong units of flow selected Enter setup in acquisition system and select proper units (generally ml/sec) Verify calibrations were performed correctly Respiratory rate (BPM) doubled, halved, or off by some other factor

Algorithm triggers upon Change minimum flow value in the noise or other artifact in the attributes to a higher or lower value flow waveform Excessive baseline noise in flow waveform

Pressure signal does not Offset waveform baseline appear on graphic screen

Set raw data filter to a higher value Change high and low values on the y axis to locate the pressure waveform Adjust zero offset on preamplifier to increase or decrease the pressure baseline value

Incorrect output pressure Verify presence of waveform from the signal from Dataquest ART Dataquest A.R.T. pressure channel, system using an oscilloscope; if the baseline is offset, adjust by entering an appropriate value in the offset or slope field during A.R.T. setup Head-Out Plethysmography in Safety Pharmacology Assessment

continued

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Table 10.11.6 Troubleshooting Guide for Head-Out Plethysmography, continued

Problem

Possible cause

Solution

Positive instead of negative pressure signal (or if inverting the signal in the acquisition system, negative instead of positive)

Pleural pressure catheter not properly located in the serosal layer of the esophagus within the pleural space; catheter is most likely in the abdominal cavity

Pleural pressure data cannot be collected when the catheter is located in the abdominal cavity (only ventilatory data); remove animal from study and replace with a new animal if collecting resistance data

Animal moving excessively during measurements

Animal not acclimated to the chamber

Provide more training for the animal; if no improvement is noted, remove animal from study

Animal too loose in the chamber

Remove animal from chamber, reinsert, and tighten plunger

the lung and is calculated by the formula:
PT /
F, where
PT and
F are the changes in transpulmonary pressure (cmH2 O) and airflow rate (ml/sec), respectively, that occur at isovolumetric points during the inspiratory and expiratory phases of each breath (see Fig. 10.11.3). Transpulmonary pressure is the difference between pleural pressure and the pressure at the airway opening, and is a measure of the force required to inflate the lungs.

Critical Parameters The number of animals tested should be identical for each treatment group. Five or six animals per treatment group is a generally accepted number of animals for statistical comparisons. As an alternative to this design and to minimize the number of animals used for a particular experiment, a Latin square crossover design may be used. This type of design is ideal for a repeated measures study. A 4 × 4 Latin square design is used to balance the order of treatments and eliminate the effect of carryover from one treatment to another (Neter et al., 1996). Carryover effects are eliminated by choosing a design in which every treatment follows every other treatment an equal number of times (Table 10.11.5). The total number of measurement intervals may vary depending on the kinetics of the compound being tested. A measurement should always be performed just prior to dosing and at a minimum of one interval at or near the times of maximal plasma drug concentration. Measurements should also be performed at times outside of maximal drug plasma concentration (Cmax ) to properly evaluate reversibility or delayed effects. For compounds with longer half lives, for example 24 hr, a measurement should

be performed at 6 or 7 days postdose to effectively evaluate reversibility. The author’s laboratory generally uses six or seven measurement intervals: predose, three to four around the times of Cmax , and two to three later time intervals to evaluate reversibility or delayed effects.

Troubleshooting Table 10.11.6 describes some problems commonly encountered when making headout plethysmography measurements, along with explanations of possible causes and suggestions for overcoming or avoiding these problems.

Anticipated Results Data is collected on a breath-by-breath basis and generally reported as a single mean value for each measurement interval. Change from predose values is calculated at each measurement interval, and data is expressed as group means for both change from predose and absolute values. The results displayed in Figures 10.11.6 to 10.11.9 indicate no drug-related changes in ventilatory parameters when expressed as either change from predose or absolute values. However, a drug-related effect on total pulmonary resistance, as a measure of airway constriction, is shown in Figures 10.11.7 and 10.11.9. The increase in resistance is observed at the times of maximal drug plasma concentration, ∼4 and 8 hr postdose, and also at the extended intervals of 24 and 48 hr after dosing. The change in resistance is no longer evident 168 hr after dosing, indicating that the effect is reversible. This increase in resistance, calculated to be ∼60%, would be considered to be mild since there were no

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Figure 10.11.6 Typical results from four rats receiving vehicle and three treatment levels via a 4×4 Latin square crossover dosing paradigm. Ventilatory parameters tidal volume, respiratory rate, and minute volume are presented as change from predose at various intervals after dosing (1, 4, 8, 24, 48, and 168 hr postdose).

associated changes in breathing patterns, as would be expected with moderate-to-severe increases in total pulmonary resistance. Changes in total pulmonary resistance produced by the known bronchoconstrictive agent methacholine have shown that increases of up to ∼100% are not sufficient to produce significant changes in the breathing pattern of conscious rats (Figs. 10.11.10 and 10.11.11).

Time Considerations Head-Out Plethysmography in Safety Pharmacology Assessment

Time requirements for various phases of the protocol are highly variable, depending upon the experience of the investigator(s). However, once the major components of the test equipment are set up, the following time esti-

mates should be feasible for an experienced researcher: calibration of the telemetry transmitter units (∼5 to 10 min per implant), surgery to implant transmitters (∼20 to 30 min per animal), animal recovery (∼7 to 10 days), animal acclimation (∼7 to 10 days), configuration of files and calibration of flow and pressure (∼30 min), and acquisition of data (∼5 min per rat per time point. For orally administered compounds, the usual testing routine includes four time points per day times four rats (i.e., 16 time points). For intravenously administered compounds, data collection may involve continuous measurements for 15 min before dosing and up to 4 hr after dosing for each animal.

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Figure 10.11.7 Typical results from four rats receiving vehicle and three treatment levels via a 4×4 Latin square crossover dosing paradigm. The lung function parameter, total pulmonary resistance, is presented as change from predose at various intervals after dosing (1, 4, 8, 24, 48, and 168 hr postdose).

Figure 10.11.8 Typical results from four rats receiving vehicle and three treatment levels via a 4×4 Latin square crossover dosing paradigm. Ventilatory parameters tidal volume, respiratory rate, and minute volume are presented as absolute values at various intervals after dosing (1, 4, 8, 24, 48, and 168 hr postdose).

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10.11.15

Figure 10.11.9 Typical results from four rats receiving vehicle and three treatment levels via a 4×4 Latin square crossover dosing paradigm. The lung function parameter, total pulmonary resistance, is presented as absolute values at various intervals after dosing (1, 4, 8, 24, 48, and 168 hr postdose).

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Figure 10.11.10 Effect of methacholine on ventilatory parameters. Vehicle (0.9% NaCl) and methacholine were administered to conscious rats at time zero as a 15-min i.v. infusion. Each value represents the mean of four rats, and error bars are ±SEM. Change is measured from time zero, which is the mean of the 5-min interval immediately prior to dosing. Current Protocols in Pharmacology

Figure 10.11.11 Effect of methacholine on total pulmonary resistance. Vehicle (0.9% NaCl) and methacholine were administered to conscious rats at time zero as a 15-min i.v. infusion. Each value represents the mean of four rats and error bars are ±SEM. Change is measured from time zero, which is the mean of the 5-min interval immediately prior to dosing.

Literature Cited Bargeton, D. and Barres, G. 1968. Time characteristics and frequency response of body plethysmograph. In Progress in Respiration Research, Body Plethysmography (A.B. DuBois, K.P. Van de Woestijne, and H. Herzog, eds.) pp. 2-23. S. Karger, New York. Boggs, D.F. 1992. Comparative control of respiration. In Comparative Biology of the Normal Lung, Vol. 1 (R.A. Parent, ed.) pp. 309-350. CRC Press, Boca Raton, Fla. Chapin, J.L. 1954. Ventilatory response of the unrestrained un-anesthetized hamster to CO2 . Am. J. Physiol. 179:146-148. Diehl, K.-H., Hull, R., Morton, D., Pfister, R., Rabemampianina, Y., Smith, D., Vidal, J.M., van de Vorstenbosch, C., European Federation of Pharmaceutical Industries Association, and European Centre for the Validation of Alternative Methods. 2001. A good practice guide to the administration of substances and removal of blood, including routes and volumes. J. Appl. Toxicol. 21:15-23.

norvegicus): Etiologic considerations. Lab. Anim. Sci. 30:841-844. Murphy, D.J. 2002. Assessment of respiratory function in safety pharmacology. Fundam. Clin. Pharmacol. 16:183-196. Murphy, D.J., Renninger, J.P., and Gossett, K.A. 1998. A novel method for chronic measurement of pleural pressure in conscious rats. J. Pharmacol. Toxicol. Methods 39:137-141. Murphy, D.J., Renninger, J.P., and Coatney, R.W. 2001. A novel method for chronic measurement of respiratory function in the conscious monkey. J. Pharmacol. Toxicol. Methods 46:13-20. Neter, J., Kutner, M.H., Nachtsheim, C.J., and Wasserman, W. 1996. Latin square and related designs In Applied Linear Statistical Models, Fourth Edition, pp. 1207-1233. McGraw Hill, Boston, Mass. Pennock, B.E., Cox, C.P., Rogers, R.M., Cain, W.A., and Wells, J.H. 1979. A noninvasive technique for measurement of changes in specific airway resistance. J. Appl. Physiol. 46:399-406.

Drorbaugh, J.E. and Fenn, W.O. 1955. A barometric method for measuring ventilation in newborn infants. Pediatrics 16:81-87.

Peslin, R., Duvivier, C., Vassiliou, M., and Gallina, C. 1995. Thermal artifacts in plethysmographic airway resistance measurements. J. Appl. Physiol. 79:1958-1965.

Dubois, A.B., Botelho, S.Y., and Comroe, J.H. Jr. 1956. A new method for measuring airway resistance in man using a body plethysmograph: Values in normal subjects and in patients with respiratory disease. J. Clin. Invest. 35:327-335.

Sinnett, E.E., Jackson, A.C., Leith, D.E., and Butler, J.P. 1981. Fast integrated flow plethysmograph for small animals. J. Appl. Physiol. 50:11041110.

Enhorning, G., Schaik, S., Lundgren, C., and Vargas, I. 1998. Whole-body plethysmography, does it measure tidal volume of small animals? Can. J. Physiol. Pharmacol. 76:945-951. Harkness, J.F., and Ridgway, M.D. 1980. Chromodacryorrhea in laboratory rats (Rattus

Stocks, J., Marshal, F., Kraemer, R., Gutkowski, P., Yishay, E.B., and Godfrey, S. 1996. Plethysmographic assessment of functional residual capacity and airway resistance. In Infant Respiratory Function Testing (J. Stocks, P.D. Sly, R.S. Teeper, and W.J. Morgan, eds.) pp. 190-240. Wiley-Liss, New York.

Safety Pharmacology/ Toxicology

10.11.17 Current Protocols in Pharmacology

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U.S. Food and Drug Administration. 2001. ICH guidance for industry: S7A safety pharmacology studies for human pharmaceuticals. U.S. Food and Drug Administration, Rockville, Md.

Contributed by Jonathan P. Renninger GlaxoSmithKline Pharmaceuticals King of Prussia, Pennsylvania

Head-Out Plethysmography in Safety Pharmacology Assessment

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Current Protocols in Pharmacology

CHAPTER 11 Electrophysiological Techniques INTRODUCTION he study of electrical activity in nerve cells dates back to the 18th century with the pioneering studies of Luigi Galvani on what was initially termed “animal electricity” in frog nerves and muscle. Electrophysiology is thus a well established discipline, involving both the study of endogenous electrical currents in cells and the effects of applied electrical currents on these cells. Several Nobel prizes have been awarded for work in electrophysiology, including those to Eccles, Hodgkin, and Huxley in 1963, Neher and Sakmann in 1991, and MacKinnon in 2003.

T

The lipid nature of the surface membrane of nerve cells results in a physical barrier that is resistant to ion flow. Accordingly, electrical activity in nerve cells is controlled by the movement of ions, including sodium (Na+ ), potassium (K+ ), chloride (Cl– ), and calcium (Ca2+ ), through specific pores in the surface membrane. These are composed of membrane-delimited protein subunits arranged into multimers known as ion channels. Some of these pores are selective for a single ion, e.g., the K+ -channel family, while others, e.g., ATP-gated P2X receptors, are nonselective. Factors that control ion flow through ion channels include the electrical potential and corresponding ion gradients that exist across a given membrane, pH, and activation of receptors that are part of, or associated with, ion channels that directly or indirectly, via various second-messenger systems, modulate ion channel function. Much of the current thinking regarding ion channels is based on two families of ion channel. The first is the nicotinic receptor, isolated, in those days in the mid-20th century when molecular biology was known as biochemistry, from the Torpedo electroplax. As a result of their abundance, nicotinic receptors were intensively studied for several decades, until breakthroughs in receptor cloning (Noda et al., 1982; LeNovere and Changeux, 1995) and medicinal (Arneric et al., 2007) and natural product (Daly et al., 2000) chemistry made this family the focus of drug-discovery efforts in the areas of pain, depression, schizophrenia, substance abuse, including nicotine itself and alcohol, neurodegenerative disorders including Alzheimer’s and Parkinson’s diseases, and gut and bladder dysfunction (Lloyd and Williams, 2000). The second family of ion channels that has influenced thinking in the area is the GABAA receptor family (Barnard et al., 1998). The importance of this family of ion channels was highlighted when they were found to be the target site of action of the benzodiazepines (Braestrup and Squires, 1978; Mohler and Okada, 1978). This latter class of drugs included diazepam and clonazepam, with anxiolytic, hypnotic, and muscle relaxant activity, and had been in widespread human use for nearly 20 years before their target site of action was identified. Providing a site against which a structure-activity relationship could be developed led to both improved benzodiazepines and structurally distinct compounds that interacted with the same site as the benzodiazepines, termed “non-benzodiazepine benzodiazepines.” Of additional importance was the finding that the benzodiazepines were allosteric modulators of the GABAA receptor ion channel, a finding that, while slow to develop, has led to a highly novel approach to the development of new drugs (Christopoulos, 2002). Many other classes of ion channel have been identified, yet their study has been hindered by the inherent complexity of ion channel structure, including the existence of

Electrophysiological Techniques

Current Protocols in Pharmacology 11.0.1-11.0.3, December 2007 Published online December 2007 in Wiley Interscience (www.interscience.wiley.com). DOI: 10.1002/0471141755.ph1100s39 C 2007 John Wiley & Sons, Inc. Copyright


11.0.1 Supplement 39

heteromeric forms of the subunit building blocks and the presence of ancillary associated proteins (Williams and Raddatz, 2007); other obstacles have been the time-intensive methods in electrophysiology that were historically used to study channel function and a lack of selective molecules to pharmacologically alter channel function. For many years, the only tools available to study channels were complex natural products that included bungarotoxin, tetrodotoxin, and methyllycaconitine, but these have been used as probes to identify more traditional synthetic small molecules (Breining et al., 2005; Daly et al., 2000). Dysfunction of ion channel function leading to several diseases generally referred to as channelopathies (Ashcroft, 2000) has led to an increased focus on the understanding of ion channel structure, function, and pharmacology. Similarly, the interaction of several highly successful drugs with an inwardly rectifying channel in the heart known as HERG (human ether–a go go), which regulates cardiac conduction, has led to the withdrawal of these drugs because of their potential to increase the cardiac QT wave, potentially leading to the phenomenon of Torsades de Pointe and the possibility of associated fatalities. Electrophysiological analysis of compound interactions with HERG and other channels involved in cardiac conduction is now routinely required for compound advancement (Fermini and Fossa, 2003), and while initially viewed as a binary unacceptable risk factor in newly identified compounds, has been modified for assessing the therapeutic index in cases where there is considerable unmet medical need in diseases with a predictably fatal endpoint, e.g., cancer. In the past 30 years, several advances have facilitated study of cell and ion channel function using electrophysiological techniques. These include patch clamping, oocyte transfection, and the development of automated screening systems which have moved cellular recording into the quasi-high-throughput mode, a far cry from the 1970s when recording from 1 to 2 cells a day was the norm. The structure of the first ion channel, the potassium channel, was determined (Doyle et al., 1998), leading to a 2003 Nobel Prize in Chemistry for Rod MacKinnon. Chapter 11 of Current Protocols in Pharmacology provides units by several of the leading experts in the world of electrophysiology. UNIT 11.1, by Bertrand, is an overview of basic electrophysiological concepts and the techniques used to study ligand- and voltage-gated ion channels and G-protein coupled receptors (GPCRs), including patch-clamp and single-channel recording. UNIT 11.2, by Proctor and Dunwiddie, outlines electrophysiological approaches to the characterization of GPCRs. These two units complement and extend radioligand binding (Chapter 1) and second messenger–based (Chapter 2) methodologies for studying channel and GPCR function. UNIT 11.3, by Rou et al., describes three protocols for studying action potentials in isolated Purkinje fibers from rabbit heart for the assessment of the effects of compounds on HERG and related channel function. UNIT 11.4, by Thomas and Smart, focuses on patch clamp–based strategies for recording currents from whole cells using native and recombinant ligand-gated ion channel preparations.

Introduction

UNIT 11.5, by Castle et al., documents the electrophysiological analysis of heterologously expressed potassium channels, focusing on the voltage-dependent Kv and voltageindependent, calcium-activated SK/IK channels that are part of the diverse family of potassium channels.

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UNIT 11.6,

by Shieh and Gopalakrishnan, extends the focus on potassium channels to protocols for the study of the hetero-octameric ATP-sensitive potassium (KATP ) channels in mammalian cells and Xenopus oocytes. These include various combinations of inwardly rectifying K+ channels (Kirs) with sulfonylurea (SUR) regulatory proteins. UNIT 11.7, by Bertrand and Bertrand, provides an overview of electrophysiological techniques used to characterize neuronal nicotinic acetylcholine receptors (nAChRs). UNIT 11.8, by Dong and Priestley, outlines techniques to analyze the function of the tetrodotoxin-resistant sodium channel (NaV 1) family. UNIT 11.9, by Jarvis and Niforatos, focuses on the diverse P2X receptor family that currently

constitute seven members plus heteromeric forms. UNIT 11.10, by Myers, describes protocols used in studying the electrophysiology of airway

nerves. The increased availability of automated recording systems to study ion channel function, the continued study of new classes and new class members of these channels, and the increasing efforts in compound synthesis focused on ion channel modulators, agonists, and antagonists emphasize the importance of these targets for novel drug discovery.

LITERATURE CITED Arneric, S.P., Holladay, M.W. and Williams, M. 2007. Neuronal nicotinic receptors: A perspective on two decades of drug discovery research. Biochem. Pharmacol. 74:1092-1101. Ashcroft, F.M. 2000. Ion Channels and Disease: Channelopathies. Academic Press, San Diego. Barnard, E.A., Skolnick, P., Olsen, R.W., Mohler, H., Siegart, W., Biggio, G., Braestrup, C., Bateson, A.N., and Langer, S.Z. 1998. International Union of Pharmacology XV: Subtypes of γ-aminobutyric acidA receptors. Pharmacol. Rev. 50:291-314. Braestrup, C. and Squires, R.F. 1978. Brain specific benzodiazepine receptors. Br. J. Psychiatr. 133:249260. Breining, S.R., Mazrov, A.A., and Miller, C.H. 2005. Neuronal nicotinic acetylcholine receptor modulators: Recent advances and therapeutic potential. Ann. Rep. Med. Chem. 40:3-16. Christopoulos, A. 2002. Allosteric binding sites on cell-surface receptors: Novel targets for drug discovery. Nature Rev. Drug Discov. 1:198-210. Daly, J.W., Garraffo, H.M., Spander, T.F., Decker, M.W., Sullivan, J.P., and Williams, M. 2000. Alkaloids from frog skin: The discovery of epibatidine and the potential for developing novel non-opioid analgesics. Natural Prod. Rep. 17:131-135. Doyle, D.A., Cabral, J.M., Pfuetzner, R.A., Kuo, A., Gulbis, J.M., Cohen, S.L., Cahit, B.T., and MacKinnon, R. 1998. The structure of the potassium channel: Molecular basis of K+ conduction and selectivity. Science 280:69-77. Fermini, B. and Fossa, A.A. 2003. The impact of drug-induced QT interval prolongation on drug discovery and development. Nature Rev. Drug Discov. 2:439–447. Le Nov`ere, N. and Changeux, J.P. 1995. Molecular evolution of the nicotinic acetylcholine receptor: An example of multigene family in excitable cells. J. Mol. Evol. 40:155–172. Lloyd, G.K. and Williams, M. 2000. Perspective in pharmacology: Neuronal nicotinic receptors as novel drug targets. J. Pharmacol. Exp. Ther. 292:461–467. M¨ohler, H. and Okada, T. 1978. The benzodiazepine receptor in normal and pathological human brain. Br. J. Psychiatr. 133:261–268. Noda, M., Takahashi, H., Tanabe, T., Toyosato, M., Furutani, M., Hirose, T., Asai, M., Inayama, S., Miyata, T., and Numa, S. 1982. Primary structure of a subunit precursor of Torpedo californica acetylcholine receptor deduced from cDNA sequence. Nature 299:793–797. Williams, M., and Raddatz, R. 2007. Receptors as drug targets. In Short Protocols in Pharmacology and Drug Discovery. (S.J. Enna, M. Williams, J.W. Ferkany, T. Kenakin and R.D. Porsolt, eds.) pp. 2-1–2-20. John Wiley & Sons, Hoboken, N.J.

Mike Williams

Electrophysiological Techniques

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Overview of Electrophysiology Electrophysiology is defined as the study of endogenous electrical currents in cells and of the effects of applied electrical currents on cell function. The basic properties of a cell are determined by the membrane that separates the intracellular and extracellular milieux (which differ in their ionic compositions). The cell membrane is composed of a lipid bilayer in which various proteins are embedded (Fig. 11.1.1A). From an electrical perspective, the lipid bilayer is an extremely high-resistance medium that separates two conductors and that can be described in terms of a resistance and a capacitance (the passive components). Depending upon their properties and conformations, the membrane proteins function as a variable resistance placed in parallel with the passive components (Fig. 11.1.1B) The density

UNIT 11.1

of intrinsic membrane proteins can be very high. For instance, the number of sodium channels at the node of Ranvier can be in the range of 3000/cm2. Any perturbation of the electrical circuit of the membrane, such as a modification to the properties of the integral membrane protein or a change in the composition of the extracellular or intracellular milieu, results in a net current flow across the membrane. In addition, a current flow will also circulate in the extracellular medium to close the current loop. From this simple concept, it follows that: 1. Any variation in the properties of the cell membrane will result in a net current flow; 2. Alteration of the extracellular or intracellular environments can alter the transmembrane potential of the cell; and

external

A

50 Å

internal

channels external

B gK

g Na Cm

+ –

gL

– +

EK

E Na

internal

Figure 11.1.1 The cell membrane and its electrical equivalence. (A) A fraction of the lipid bilayer forming the cell membrane, with integral membrane proteins. (B) The electrical circuit corresponding to the cell membrane including two selective conductances for sodium and potassium. Cm, cell membrane capacitance; gL, equivalent leak resistance; gK, potassium conductance; EK, potassium equilibrium potential (approximately −80 mV); gNa, sodium conductance; ENa, sodium equilibrium potential (+30 mV).

Electrophysiological Techniques

Contributed by Daniel Bertrand

11.1.1

Current Protocols in Pharmacology (1998) 11.1.1-11.1.11 Copyright © 1998 by John Wiley & Sons, Inc.

Supplement 3

mental condition the voltage is maintained constant by the electronic feedback circuit that supplies, at any instant, a current of equal value but opposite sign to that flowing through the membrane. It is therefore important to note that the current measured corresponds to the mirror image of the current that flows through the membrane. By convention, injection of a negative charge by the electrode into the cell is termed inward current, while a positive charge is an outward current. An inward current depolarizes a cell, whereas an outward current causes hyperpolarization. Studies with the squid giant axon demonstrated that the regenerative electrical activity of a nerve cell is dependent upon sodium and potassium voltagegated currents (Hodgkin and Huxley, 1952). Although the current-clamp and voltageclamp techniques have been crucial for determining the electrical properties of cells, their use is restricted to cells large enough to withstand penetration by the electrode tip without damage to its overall physical properties (typically cells >10 µm in diameter). In contrast, patch clamping (Hamill et al., 1981) allows recordings to be made in almost any type of cell or portion of a cell. This technique relies on the establishment of a very tight mechanical and electrical seal (gigaseal) between the cell membrane and the glass of the electrode using clean, fire-polished capillary tubes (Fig. 11.1.3) and allows measurements of currents in the picoampere (pA) range. The patch-clamp technique can be used in four configurations (letters refer to the figure): (a) the cell-attached patch, (b) whole-cell recording, (c) the outside-out patch, and (d) the inside-out patch. Given the very high electrical resistance of the seal, the pA sensitivity of this method is

3. A current flowing through the membrane can be recorded either across the membrane or in the extracellular fluid.

METHODS Although many electrophysiological techniques, such as the electrocardiogram and electroencephalogram, rely on measurement of the extracellular potential, only those that measure potential changes at the transmembrane and membrane levels are discussed in this unit. Transmembrane recordings are made by placing an electrode inside the cell, being careful to avoid disrupting the integrity of the cell membrane. Until recently, this was generally accomplished using the fine-tip electrode approach whereby a small glass capillary filled with a saturating concentration (3 M) of KCl is inserted into the cell. The glass provides insulation, while the KCl constitutes the “wire” through which the potential is measured. The transmembrane potential is recorded by measuring the difference in potential between the tip of the electrode and a reference electrode placed in the bath (Fig. 11.1.2A). This method is referred to as current-clamp recording. An alternative method is the voltage-clamp procedure developed by Cole and Curtis (1939). This involves placing a second electrode in the cell through which a current is applied with an external feedback electronic device, maintaining a constant potential at the cell membrane (Fig. 11.1.2B). With this procedure, the current that must be applied by the amplifier to maintain the cell at a steady potential is measured. By evaluating the electrical properties of the cell at a fixed potential, voltage- and time-dependent properties, such as those associated with voltage-gated channels (Hille, 1992), can be assessed. In this experi-

A

B

V-command

+ –

+ – V

Overview of Electrophysiology

+ –

I

V

Figure 11.1.2 Single-cell electrophysiological recording. (A) Electrical diagram of the currentclamp recording with a fine-tip electrode. (B) Typical voltage-clamp circuit using two electrodes. V-command is the voltage at which the cell should be maintained; V and I are the voltage (in mV) and current (nA), respectively.

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sufficient to detect the current flowing across a single membrane component, allowing singlechannel measurements. In addition, because of the low access resistance of the pipet, voltageclamp investigations of cell properties can be performed with a single microelectrode. This has allowed investigations of excitable as well as nonexcitable cells. A number of voltage-dependent, voltage-independent, second messenger–activated, and ligand-gated currents have also been studied in this manner (Hille, 1992). The electrophysiological techniques used to determine the physiological and/or pharmacological properties of membrane ion channels at the single-cell level are described in the following sections.

PASSIVE MEMBRANE PROPERTIES The capacitance and resistance of cellular membranes are independent of the cell studied. A typical value for capacitance is 1 µF/cm2, while the membrane resistance is ∼103 Ωcm2. These properties of the membrane determine the speed at which the transmembrane potential is modified. In some instances, however, these values are used to define cell surface characteristics. For example, measurement of cellular capacitance is used to assess changes in membrane surface properties that occur during exocytosis or endocytosis (Gillis, 1995). Currently, it is possible to detect changes in membrane capacitance on the order of femtofarads (fF),

which roughly correspond to a variation in the cell surface of ∼200 nm. This is equivalent to a change brought about by the liberation of a single secretory granule. Since the temporal resolution is in the millisecond range, it can be employed to monitor the synaptic release process (Breckenridge and Almers, 1987; Haller et al., 1998).

IONIC SELECTIVITY Cell membrane channels are very small pores that have an opening of only a few angstroms in diameter. Activation (or opening) of a channel in the cell membrane produces a current flow that depends upon the channel resistance, the gradient of the ions permeating through the pore, and the membrane potential. A single channel permits the diffusion of ∼106 ions/sec. In considering an ideal channel that is permeable to a single type of ion, x, the current (Ix) that flows through the open channel can be represented as: I x = gx × ( E − Ex ) Equation 11.1.1

where x is the ion, gx is the channel conductance, E is the transmembrane potential, and Ex is the equilibrium potential for the ion. Ex is the result of the difference in ion concentration between the extracellular and intracellular

A

B

D

C

Figure 11.1.3 Patch-clamp recording configurations. (A) formation of a gigaseal between the electrode and the cell, or cell-attached configuration. (B) opening of the membrane patch allows contact between the cytoplasm and electrode recording medium or whole-cell configuration. (C) outside-out patch formed after withdrawing from the whole-cell configuration. (D) inside-out patch obtained by withdrawing the electrode (from A) without breaking the membrane patch and then rupturing the remaining vesicle.

Electrophysiological Techniques

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compartment, as defined by the Nernst equation: Ex =

RT zF

× ln

Xo Xi

Equation 11.1.2

where R is the gas constant, T is the absolute temperature (Kelvin scale), z is the charge of the ion, F is the Faraday constant, and Xo and Xi are the respective extracellular and intracellular concentrations of the ion, x. For a monovalent ion at room temperature, this equation can be approximated to: Ex = 58 log10

Xo Xi

Equation 11.1.3

Because of the opposite driving forces for Na+ and K+, activation of a voltage-gated Na+ channel produces an inward current (cell depolarization), whereas activation of a voltagegated K+ channel causes an outward current (cell hyperpolarization). In many cases, however, the channel selectivity is rather limited, with several ions of the same polarity permeating through an open pore. As a consequence, Equation 11.1.1 does not apply, but must be extended to take into account the summation of the different ionic fluxes. Assuming that each of the ions flows independently of the others, the ionic current carried by one ion is expressed in the Goldman-Hodgkin-Katz (GHK) equation: Is = Ps zs2 ×

EF2 RT

×

Si − So e ( 1− e

− zs FE / RT )

− zs FE / RT

Equation 11.1.4

Overview of Electrophysiology

where Ps is the relative ion selectivity and the other terms have already been defined. Using this equation, a reversal potential corresponding to the sum of the different ionic currents can be determined. Excitatory and inhibitory receptors utilize ion selectivity to modify cellular responses (UNIT 1.1). Functionally these receptors represent the binding site for a specific neurotransmitter and possess an ion channel. Collectively, they are known as ligand-gated ion channels (LGICs). In vertebrates, excitatory LGICs, such as glutamate, nicotinic acetylcholine (nAChR; UNIT 1.8), and serotonin-3 (5-HT3) receptors, that are permeable to cations produce an inward

current when activated. Inhibitory LGICs such as GABAA (UNIT 1.7) and glycine receptors are permeable to anions, with their activation resulting in an outward current. For instance, the nAChR, which is permeable to both mono- and divalent cations, displays a reversal potential near −10 mV, and its activation produces an inward current that depolarizes the cell (Bertrand et al., 1993). The current polarity and ion selectivity of a given channel are important considerations for classifying a system. Equation 11.1.1 reveals that zero current will flow through a given channel when the membrane potential equals the ionic equilibrium. This value is termed the reversal potential. By determining the reversal potential of a current under different ionic conditions, it is possible to measure both the charge of the ions flowing through the channels and their respective permeability ratios (Bertrand et al., 1993). Voltage-clamp and/or patch-clamp recording methods are ideally suited for this analysis.

TIME RESOLUTION A major advantage of electrophysiological measurements over other functional assay techniques is the ability to make measurements with a specific time resolution (in the micro- to millisecond scale)—a value that can be delineated in terms of the frequency bandwidth of the measurement, which in turn is defined by the passive properties of the cell and the electrode and the characteristics of the amplifier. For large cells, such as Xenopus laevis oocytes, the time resolution is in the millisecond range, which is limited by the time required to charge the cell capacitance (for smaller cells, this time is in the range of tens of microseconds and is not a limiting factor). Whenever possible both whole-cell and single-channel measurements should be made. Voltage clamping initially allows determination of the macroscopic properties of a particular current, while patch clamping allows resolution of cellular kinetics at the single-channel, microscopic level. Further analysis of cellular kinetics at the single-channel level can be made during activation or closure of a given channel.

PHYSIOLOGICAL AND PHARMACOLOGICAL STUDIES Ligand-Gated Ion Channels (LGICs) The neuronal nAChR is a typical LGIC whose response to a test compound can be studied using both voltage-clamp and single-

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Current Protocols in Pharmacology

channel recordings. It is important to remember that in voltage clamp the current (I) flowing through open channels is measured and is defined as: I=γ×N Equation 11.1.5

where γ is the mean conductance of a channel and N is the number of open channels. Since both measures depend on agonist concentration, the measured current represents the degree of activation of the LGIC and is therefore a measure of the apparent affinity, or EC50, of the compound studied. However, since activation of an LGIC is a complex process, this value is distinct from the binding affinity (Kd) of the receptor for the ligand. The properties of neuronal nAChRs can be studied in native, wild-type receptors from isolated neurons maintained in culture, or in transfected receptors (UNIT 6.3). Approaches for obtaining the latter include transient expression of nAChR cDNA in Xenopus oocytes (Bertrand et al., 1991a) and stable transfection into a “null” cell line (Gopalakrishnan et al., 1995). Technical information regarding the prepara-

tions and recording methods can be found elsewhere (Bertrand et al., 1991a; Buisson et al., 1996). Both the oocyte and stable cell line reconstitution methods rely on the fact that exogenous DNA can be expressed by any given cell provided an appropriate promoter is present. When activated, the promoter provokes DNA transcription and production of the corresponding mRNA. Protein synthesis is then initiated and, providing that the resulting product is processed by the cell, the protein is targeted to its natural location within either the intracellular or cytoplasmic membrane compartment. It is important to note that these systems can be successfully employed to express either intracellular or integral membrane proteins. When reconstituted in Xenopus oocytes, integral membrane proteins, such as LGICs, can be readily investigated using voltage-clamp measurements and their sensitivity to either natural or exogenous ligands determined. In the absence of ligand, voltage-clamp studies on nAChRs expressed in oocytes reveal passive properties that roughly correspond to the resistance and capacitance components of the oocyte membrane (Fig. 11.1.1). Thus, the current required to hold the cell at any potential

0.5

5 sec

250 nA

Normalized response

1

0 0.01

1

100

10,000

[ACh] (µM)

Figure 11.1.4 Acetylcholine (ACh) dose-response curve of the human α4β2 receptor reconstituted in Xenopus laevis oocytes. Data obtained from four cells were normalized to the maximal evoked current and plotted as a function of agonist concentration. The continuous line corresponds to the empirical Hill equation with an EC50 value of 5.5 µM and an nH value of 0.7. Inset: Currents generated in a single cell in the presence of several ACh concentrations (0.1, 0.3, 1, 10, 100, and 1000 µM) are superimposed. ACh application is symbolized by the bar. Cells were held at −100 mV throughout the experiment.

Electrophysiological Techniques

11.1.5 Current Protocols in Pharmacology

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other than its resting membrane potential (−30 mV) reflects the cell input resistance. A current ejection of approximately −30 nA is generally sufficient to maintain the cell at −100 mV. This current, which compensates for the passive properties of the cell, is usually referred to as the leak current. Larger values of the leak current (greater than −100 nA) usually suggest that the cellular electrode penetration is unsatisfactory; oocytes displaying such values should not be used. Application of ACh induces a sustained inward current with an amplitude and time course dependent upon the amount of neurotransmitter applied. Measurement of the peak evoked current as a function of the agonist concentration makes it possible to establish a dose-response curve that is described by the Hill equation (Fig. 11.1.4): 1 y= n EC 50 H 1+ x

FH

IK

Equation 11.1.6

where y is the amount of receptor activated, x is the concentration of agonist, EC50 the half activation, and nH the Hill coefficient. A determining factor in studying LGICs is the speed of application of the neurotransmitter and how closely it mimics the true time course of its spread in the synaptic cleft. Oocytes are constrained in this regard by their large size,

which limits the speed at which compounds can be applied. Although it might be anticipated that this would limit dose-response studies, dose-response curves generated in oocytes and cell lines with the same transfected neuronal nAChRs yield identical apparent affinities and Hill coefficients (Buisson et al., 1996). Generation of dose-response curves is not limited to whole cells but can also be determined from single-channel patch-clamp experiments. In this case, the probability of opening (Po) is analyzed and plotted as a function of agonist concentration. A problem that often occurs with the patch-clamp technique is a progressive decline in channel activity, or “rundown.” For neuronal nAChRs, run-down is such that only the single-channel amplitude can be reliably measured. Electrophysiological measurements can also be used to characterize receptor antagonists. Thus, competitive antagonists induce a rightward shift of the dose-response curve to the agonist without diminishing the total evoked current, whereas noncompetitive antagonists diminish the evoked current response. An example of the effects of a competitive inhibitor on the dose-response curve is shown in Figure 11.1.5. Noncompetitive antagonists are further classified in regard to their mode of action into two groups: the open-channel blockers

Normalized response

100

50

0 0.01

+ 0.3 µM MLA EC50 = 1.4 µM nH = 1 control EC50 = 0.4 µM nH = 1

1

100

[ACh] (µM)

Overview of Electrophysiology

Figure 11.1.5 Competitive inhibition of the neuronal nAChR. Dose-response curve to acetylcholine (ACh) in oocytes (n = 3) expressing the chick α4β2 receptor were measured in the absence and then in the presence of 0.3 µM of the antagonist methyllycaconitine (MLA). Continuous lines correspond to best fits obtained with the Hill equation.

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Current Protocols in Pharmacology

B

A

OCB

–150

–100

–50

0

50 Mv

+ hexamethonium –500

–1000

control

–1500

Figure 11.1.6 The effect of hexamethonium, an open channel blocker (OCB), on human α4β2 nAChR (nicotinic acetylcholine receptor) responses. (A) Schematic representation of the neuronal nAChR and the putative localization of an open channel blocker molecule. (B) Current-voltage relationship of a HEK-293 cell expressing the human α4β2 nAChR determined in response to ACh in control conditions and in presence of 30 µM hexamethonium. Continuous lines correspond to mathematical models that take into account both the rectification and/or the open channel blockade.

(OCBs), and compounds that act outside the channel domain. OCBs that act by sterically hindering the ion pore (Fig. 11.1.6A) are often identified on the basis of a voltage-dependent blockade. Thus, a typical experiment involves determining the percent inhibition caused by a fixed concentration of the antagonist at different voltages (Fig. 11.1.6B). It is important to note that the current-voltage relationship measured under control conditions for the neuronal nAChR is nonlinear and that the current decreases almost to zero near −20 mV. Because of this current reduction, these receptors are termed inward rectifiers, and it is necessary to record evoked currents of neuronal nAChRs at a fixed potential. Otherwise, the depolarization caused by the opening of the nAChR will progressively drive the cell potential toward zero and, consequently, the evoked current will be abolished. A physiological consequence of this effect is that activation of neuronal nAChRs can produce a depolarization of a neuron only when its membrane potential is below −40 mV. In this respect, neuronal nAChRs are concomitant detectors that are active only when another condition is met. From this observation it follows that ion flux measurements made at a variable voltage, as in the current-clamp approach, must be affected by the cell transmembrane potential.

Voltage-Dependent Channels Properties of voltage-dependent and/or voltage-activated channels are examined using approaches similar to those described for LGICs. Moreover, information regarding the mechanism of action of a compound can be deduced using identical experimental paradigms independently of the type of ion channel. An example of the application of singlechannel recording is the use of this method to study voltage-dependent channels and the effect of an open-channel blocker at these sites. The voltage-dependent channel chosen for this illustration is the fast-activating potassium current (IA) endogenously expressed by the human cell line TE-671. Single-channel currents are initially recorded in the outside-out configuration in control conditions. A trace obtained under these conditions shows that up to three channels are activated at the same time at the onset of the voltage jump (Fig 11.1.7A). Current inactivation leads to a progressive reduction in the number of open channels and, as a consequence, only a single level of opening is seen at the end of the pulse. A more precise method to determine the number of channels in a patch, and their probability of opening, is by computing an amplitude histogram (Fig. 11.1.7B,C). In this case, current values are determined at each sampling interval and cumulated into a corresponding bin amplitude. The graph is generated by plotting the bin

Electrophysiological Techniques

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control

A

o o o c +3 mM 4-AP

5 pA

B

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Overview of Electrophysiology

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Figure 11.1.7 Effects of an open channel blocker on voltage-dependent potassium channels. (A) Superposition of single-channel recordings of an outside-out patch obtained from a TE-671 cell (upper traces). Currents evoked by a voltage jump (lower trace). Closed and open states are indicated by the dashed line and letters on the right. Addition of 3 mM 4-aminopyridine (4-AP) to the external medium induces a fast blockade of the channels. (B,C) All-points amplitude histograms obtained from several traces recorded in control conditions and during 4-AP exposure. Continuous curves are the sum of five Gaussian curves with equal variance.

11.1.8 Supplement 3

Current Protocols in Pharmacology

values as a function of the current amplitude and is commonly referred to as an all-points amplitude histogram. The data obtained from these computations are fitted to a sum of Gaussian curves, with peak positions corresponding to the current passing through the channels. The first Gaussian curve corresponds to the noise recorded when all channels are closed, while the others reflect the various open-channel states. The surface ratios of the different peaks of the histogram are indicative of the probability of the channel being in a given state. When the same histogram is determined for traces recorded in the presence of 3 mM 4-aminopyridine (4-AP), a different picture is observed. The amplitudes of the peaks corresponding to the opening of one or more channels are greatly reduced, whereas a larger number of data points are obtained for the closed condition. The identical peak positions indicate, however, that 4-AP alters the opening probability of the channel but not its conductance properties. Single-channel analysis of patches containing more than one channel often preclude more detailed studies, however.

CHANNEL MODULATION AND INTERACTIONS The properties of integral membrane proteins can be modified by many factors, such as phosphorylation, binding of extracellular or intracellular molecules, or alterations in the lipid environment. The resultant modifications in the physiological properties of the protein can be revealed by electrophysiological recordings, as has been illustrated for glycine modulation of NMDA receptors (Johnson and Ascher, 1987), potentiation of nAChR function by extracellular calcium (Léna and Changeux, 1993), the effects of nitric oxide (NO) etc., and the products of arachidonic acid. In these instances, receptor properties are modified by the presence of another molecule that affects channel opening, but does not alter ligand binding and can therefore only be detected by measuring the functionality of the receptor and the current flow across the membrane. Quantification of the current flowing through an open pore also permits investigation of any allosteric modulations of membrane proteins, such as that observed in steroid inhibition of the neuronal nAChR (Bertrand et al., 1991b; Valera et al., 1992). Typically, allosteric modulations can be determined in four steps: (1) measure the receptor sensitivity and the IC50 (negative allosteric effector) or EC50 (positive allosteric effector); (2) determine the agonist dose-re-

sponse profile in the absence or presence of the effector; (3) measure I-V (current-voltage) relationships in control conditions or during exposure to the effector; and (4) quantify, at the single-channel level, the current amplitude, mean open time, and probability of opening. In the case of a negative allosteric effector, such as progesterone, half inhibition of the α4β2 receptor is observed at ∼10 µM. Determination of the ACh dose-response relationship reveals that steroid inhibition neither shifts the ACh EC50 nor modifies the I-V relationship. In addition, no significant modification in the single-channel current is observed, which indicates that progesterone does not mediate its effect by steric hindrance.

INVESTIGATION OF SEVENTRANSMEMBRANE-DOMAIN RECEPTORS The seven-transmembrane-domain receptors constitute a broad family of proteins that are known to trigger many crucial events in the cell (Selbie and Hill, 1998). Most seven-transmembrane-domain proteins mediate their action through activation of a second messenger that interacts with one or many other molecules (Fig. 11.1.8A). Phototransduction in vertebrate receptors is one of the best-characterized examples, in which absorption of a photon by a rhodopsin molecule in the intracellular disk membrane triggers a modification of the cGMP level through the G protein cascade and results in the closure of sodium channels inserted in the cytoplasmic membrane of the rod (reviewed in Yau and Baylor, 1989). In view of this mechanism, it is clear that measurement of ionic current constitutes an indirect method to detect the level of second messengers, and subsequently the activation of the seven-transmembrane-domain protein. A typical example of indirect detection of activation of a seven-transmembrane-domain protein using electrophysiology is shown in Figure 11.1.8B. Oocytes injected with cDNA encoding the substance K receptor, which belongs to the seven-transmembrane-domain superfamily, were assessed using voltage clamp after 2 days of incubation. When held at −100 mV, cells expressing the substance K receptor displayed a large inward current when exposed to substance K. Both latency and amplitude of the evoked current are variable between cells. Although no correlation could be deduced between the current amplitude and the agonist concentration, a significant reduction of the

Electrophysiological Techniques

11.1.9 Current Protocols in Pharmacology

Supplement 3

A

B

substance K receptor N

substance K

IP3 C Ca2+ CI –

Current (µA)

0 –0.8 –1.6 – 2.4

0

5

10 15 Time (sec)

20

25

Figure 11.1.8 Expression of a substance K receptor in Xenopus laevis oocytes. (A) Schematic diagram of the second messenger events triggered by substance K activation. (B) Current evoked in a oocyte in response to substance K application.

latency between the application and the onset of the current was observed. Measurement of the reversal potential of the evoked current confirmed that the current is carried by chloride ions. This is in agreement with the hypothesis that activation of the substance K receptor provokes an increase in inositol 1,3,4-trisphosphate (IP3). This cascade then leads to an increase in free calcium that triggers the activation of calcium-dependent chloride channels natively expressed by oocytes (Fig. 11.1.8). Clearly, electrical measurements indicate the functionality of a given receptor by indirectly reflecting its degree of activation.

SUMMARY In summary, electrophysiological techniques offer unique advantages for studying signals mediated by the activation of integral membrane proteins. The main advantages are (1) the ability to investigate the primary signal triggered by the activation of plasma membrane proteins and (2) the speed of measurements. At present the drawbacks remain that considerable technical skill is needed for proper cellular investigations and that the techniques cannot be extended to large-scale investigations or high-throughput approaches.

LITERATURE CITED Bertrand, D., Cooper, E., Valera, S., Rungger, D., and Ballivet, M. 1991a. Electrophysiology of neuronal nicotinic acetylcholine receptors expressed in Xenopus oocytes following nuclear injection of genes or cDNA. Methods Neurosci. 4:174-193. Overview of Electrophysiology

Bertrand, D., Valera, S., Bertrand, S., Ballivet, M., and Rungger, D. 1991b. Steroids inhibit nicotinic

acetylcholine receptors. NeuroReport 2:277280. Bertrand, D., Galzi, J.L., Devillers-Thiéry, A., Bertrand, S., and Changeux, J.P. 1993. Mutations at two distinct sites within the channel domain M2 alter calcium permeability of neuronal α7 nicotinic receptor. Proc. Natl. Acad. Sci. U.S.A. 90:6971-6975. Breckenridge, L.J. and Almers, W. 1987. Currents through the fusion pore that forms during exocytosis of a secretory vesicle. Nature 328:814-817. Buisson, B., Gopalakrishnan, M., Arneric, S.P., Sullivan, J.P., and Bertrand, D. 1996. Human α4β2 neuronal nicotinic acetylcholine receptor in HEK-293 cells: A patch-clamp study. J. Neurosci. 16:7880-7891. Cole, K.S. and Curtis, H.J. 1939. Electrical impedance of the squid giant axon during activity. J. Gen. Physiol. 22:649-670. Gillis, K.D. 1995. Techniques for membrane capacitance measurements. In Single-Channel Recording (B. Sakmann and E. Neher, eds.) pp. 155198. Plenum, New York. Gopalakrishnan, M., Buisson, B., Touma, E., Giordano, T., Campbell, J.E., Hu, I.C., DonnellyRoberts, D., Arneric, S.P., Bertrand, D., and Sullivan, J.P. 1995. Stable expression and pharmacological properties of the human α7 nicotinic acetylcholine receptor. Eur. J. Mol. Pharmacol. 290:237-246. Haller, M., Heinemann, C., Chow, R.H., Heidelberger, R., and Neher, E. 1998. Comparison of secretory responses as measured by membrane capacitance and by amperometry. Biophys. J. 74:2100-2113. Hamill, O., Marty, A., Neher, E., Sakmann, B., and Sigworth, F. 1981. Improved patch clamp techniques for high-resolution current recording from cells and cell-free membrane patches. Pflugers Arch. 391:85-100.

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Current Protocols in Pharmacology

Hille, B. 1992. Ionic Channels of Excitable Membranes. Sinauer Associates, Sunderland, Mass. Hodgkin, A.L. and Huxley, A.F. 1952. Currents carried by sodium and potassium ions through the membrane of the giant axon of Loligo. J. Physiol. (Lond.) 6:449-472. Johnson, J.W. and Ascher, P. 1987. Glycine potentiates the NMDA response in cultured mouse brain. Nature 325:529-531. Léna, C. and Changeux, J.P. 1993. Allosteric modulations of the nicotinic acetylcholine receptor. Trends Neurosci. 16:181-186.

Valera, S., Ballivet, M., and Bertrand, D. 1992. Progesterone modulates a neuronal nicotinic acetylcholine receptor. Proc. Natl. Acad. Sci. U.S.A. 89:9949-9953. Yau, K.W. and Baylor, D. 1989. Cyclic GMP–activated conductance of retinal photoreceptor cells. Annu. Rev. Neurosci. 12:289-327.

Contributed by Daniel Bertrand University of Geneva Geneva, Switzerland

Selbie, L.A. and Hill, S.J. 1998. G protein-coupledreceptor cross-talk: The fine tuning of multiple receptor-signaling pathways. Trends Pharm. Sci. 19:87-93.

Electrophysiological Techniques

11.1.11 Current Protocols in Pharmacology

Supplement 3

Electrophysiological Analysis of G Protein– Coupled Receptors in Mammalian Neurons

UNIT 11.2

G protein–coupled receptors (GPCRs; see UNIT 1.1) can be investigated using electrophysiological analysis in a variety of biological systems: cultured cells, acutely dissociated cells, organotypic slice cultures, expression systems (such as oocytes or cultured cells previously injected with specific mRNAs or cDNAs that code for particular GPCRs), brain slices, and intact brain. Outlined in this unit are specific methods for studying GPCR-gated ion channels using electrophysiological techniques for single-cell recording. Three basic transmitter/GPCR activation procedures are presented: bath application of neurotransmitters/neuromodulators to the preparation (Basic Protocol), receptor activation by local brief drug application directly to the cell surface (Alternate Protocol 1), and transmitter release evoked by synaptic stimulation (Alternate Protocol 2). The focus of this unit is the measurement of changes in membrane potential (or holding current in the case of voltage-clamp experiments) within individual neurons in brain slices due to activation by transmitter molecules of GPCRs that modulate ion-channel activity. These protocols can also be used with minimal adaptations for similar experiments utilizing other kinds of acutely dissociated or cultured cells. The opening or closing of an ion channel can be detected by changes in the membrane potential of the cell as the ion flow into or out of the cell increases or decreases. In addition to the procedures for measuring changes in membrane potential following activation/inhibition of specific GPRC-gated ion channels (Basic Protocol, Alternate Protocols 1 and 2), this unit includes descriptions of the preparation of brain slices (Support Protocols 5 and 6), data analysis (Support Protocol 7), and the fabrication and use of necessary equipment—including a tissue storage system (Support Protocol 1), sharp-point electrodes (Support Protocol 4), whole-cell patch electrodes and drug pipets (Support Protocol 3), and a media/drug perfusion manifold (see Support Protocol 2). NOTE: All protocols using live animals must first be reviewed and approved by an Institutional Animal Care and Use Committee (IACUC) or must conform to governmental regulations regarding the care and use of laboratory animals. NOTE: High-purity water–filtered, distilled, de-ionized, or reverse osmosis treated– should be used throughout. STRATEGIC PLANNING Procedures for basic electrophysiological recording are described in The Axon Guide (Axon Instruments, 1993) and in two detailed protocols: UNIT 11.1 of this manual and Stuart (1999). Following is a general outline of the basic equipment required for perfusing and recording from individual isolated or cultured cells or from cells in brain slices. Electrophysiological Setup The equipment utilized is specific for intracellular or whole-cell recordings. The setup must include the necessary electrophysiological system to measure voltage or current responses, including an appropriate electrometer (e.g., AxoClamp 2B, Axon Instruments), amplifier (e.g., Model 440, BrownLee Precision Instruments), oscilloscope (e.g., digital 2-channel, Tektronix), event/time generator (Master-8, AMPI), storage device (tape and/or computer), and data analysis and graphing software (pClamp, Axon Instruments; Excel, Microsoft; or Origin, Microcal). Contributed by William R. Proctor and Thomas V. Dunwiddie Current Protocols in Pharmacology (1999) 11.2.1-11.2.22 Copyright © 1999 by John Wiley & Sons, Inc.

Electrophysiological Techniques

11.2.1 Supplement 7

Media Control System Continuous superfusion of the preparation is achieved with artificial cerebrospinal fluid (aCSF) saturated with 95% oxygen (for brain slices) and 5% carbon dioxide (for bicarbonate-buffered media). The system must have a means of temperature control for both the medium and preparation. Room temperature to 33°C is commonly used for recording most responses. Also, a suitable recording chamber must be available (Warner Instrument). Optical System A dissection microscope (generally suitable for “blind” recordings from brain slices, e.g., Olympus SZ-III), an inverted microscope (for cultured cells or isolated cells, Nikon or Zeiss), and an upright differential interference contrast (DIC)–equipped microscope for visualized slice recordings (E600-FN, Nikon; BX50WI, Olympus; or Axioskop-2-FS, Zeiss) are utilized. Mechanical Systems An antivibration table (Technical Manufacturing Corporation or Newport Corporation) and micromanipulators (e.g., Soma, Sutter, Narishige, Burleigh) are used throughout the procedures. BASIC PROTOCOL

MEASUREMENT OF RESPONSES FROM DRUG-ACTIVATED GPCRs: BATH APPLICATION Responses to bath application of neurotransmitters or neuromodulators that activate GPCR-gated ion channels are readily observed using standard electrophysiological recording methods on individual neurons. A response or change in the membrane potential of the cell, when the appropriate drug is applied, indicates activation of GPCRs. Bath application works well for GPCRs that show little or no desensitization (loss of receptor response), and where the time course of the response does not need to be determined with a high degree of resolution. Drugs in this category include neurotransmitters (e.g., γ-amino-n-butyric acid, GABA, an agonist for both GABAA and GABAB receptors) and neuromodulators (e.g., adenosine, when activating specific adenosine A1 receptors) that activate specific GPCRs that “gate” or open/close specific ion channels (e.g., K+ channels). Other drugs (synthetic or endogenous) can also modulate ion channels (e.g., baclofen, which acts on a GPCR that opens K+ channels via a GABAB receptor). The amount of time required to observe a maximal drug response in a brain slice typically ranges from 1 to 5 min, but will vary depending on the rate of perfusion of aCSF, the recording chamber volume, the tissue thickness and the depth of the cell in the slice, and the physical properties of the drug.

Electrophysiological Analysis of GPCRs in Mammalian Neurons

Superfusing cells or slices with GABA or adenosine will cause the drug molecules to interact with GPCRs that open K+ channels. For the measurement of K+ currents, cells are usually held near the resting membrane potential (approximately −60 mV), which is positive to the K+-ion equilibrium potential (EK). Under these conditions, K+ ions flow out of the cell when these GPCR-gated K+ channels open, and a hyperpolarization of the membrane potential of the cell (or outward current when recording in voltage-clamp mode) is observed. If the cell membrane potential is held more negative than the EK, activation of K+ channels will result in a depolarization of the membrane potential (or an inward current in voltage-clamp conditions). Under some circumstances, these currents can be considerably larger than the outward currents recorded near the resting potential, which are limited by inward rectification, and these currents can also be enhanced by increasing the K+ concentration in the extracellular medium (Sodickson and Bean, 1996).

11.2.2 Supplement 7

Current Protocols in Pharmacology

Support Protocol 7 describes data collection and analysis considerations that should be incorporated into experimental designs prior to execution of the actual electrophysiological procedures. Materials Internal electrode solution (see recipe; Table 11.2.1) aCSF (see recipe) Sample tissue: cultured or isolated cells, or brain slices (see Support Protocols 5 and 6) in a brain-slice storage chamber (see Support Protocol 1) 95%/5% (v/v) O2/CO2 2 mM γ-amino-n-butyric acid (GABA; Sigma) in high-purity water 3 mM baclofen (Sigma) in high-purity water 10 mM adenosine (Sigma) in high-purity water Incubator for maintenance and storage of cell cultures and acutely dissociated cells Electrophysiological setup (see Strategic Planning; UNIT 11.1; Stuart, 1998) Adapter port (or manifold, Warner Instrument; or see Support Protocol 2) with calibrated syringe pump Recording microelectrodes (see Support Protocols 3 and 4) Recording chamber, e.g.: small chamber to hold 12-mm glass cover slips for cultured or isolated cells (Warner Instrument) chamber appropriate for “visualized slice” cell recording in brain slices (Warner Instrument) using a differential interference contrast (DIC)–equipped microscope with water-immersion optics larger recording chamber for use with brain slices utilizing a dissecting microscope (“blind recording” setup for cell recording; H & H Woodworking) Computer with analog-to-digital (A/D) board (e.g., Digidata 1320, Axon Instruments; ISC-16, RC Electronics) and software (see Support Protocol 7) 1. Prepare an electrophysiological setup, an aCSF perfusion system with an adapter port or manifold for a calibrated syringe pump, a recording chamber, and the appropriate recording microelectrodes. 2. Place and anchor sample tissue in the recording chamber (see Support Protocols 5 and 6). 3. Aerate aCSF with 95% O2/5% CO2 and superfuse the cells or slices at ∼2 ml/min to allow for sufficient exchange of fresh medium. 4. Secure and position a recording microelectrode over the tissue (see Support Protocols 3 and 4). 5. Obtain stable baseline measurements (5 to 20 min) from an individual neuron (see Support Protocols 3 and 4) as a control. 6. Activate the syringe pump to add a compound that activates a specific GPCR—e.g., 20 µM GABA, 30 µM baclofen, or 100 µM adenosine (final bath concentrations). Monitor and record results. A change in resting membrane potential (or holding current under voltage-clamp conditions) should be observed. It may take 1 to 5 min to achieve a new steady-state condition. Electrophysiological Techniques

11.2.3 Current Protocols in Pharmacology

Supplement 7

150

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Figure 11.2.1 Continuous monitoring of the holding current in a CA1 hippocampal pyramidal neuron during bath applications of adenosine and then baclofen. The cell was voltage-clamped at −60 mV. Both drugs cause an outward current resulting from activation of GPCR-gated K+ channels.

7. Turn off the pump to wash out the drug and monitor the return of the response to baseline control values. With slices, this may take 5 to 60 min, depending on the compound used (Fig. 11.2.1).

8. Collect and analyze data for 10 to 20 cells as described below (see Support Protocol 7). SUPPORT PROTOCOL 1

FABRICATION OF BRAIN-SLICE STORAGE CHAMBER Keeping brain slices viable and healthy for up to 10 hr is necessary for obtaining stable recordings from several cells during a daily recording session. The storage chamber described in this protocol works well for various rodent brain tissues for animals 2 to 8 weeks of age (it is very difficult to prepare good, viable brain slices from older animals). The basic principle is to have a vigorous, continuous flow of freshly oxygenated aCSF passing around the slices to maintain adequate oxygenation of the tissue. Materials aCSF (see recipe) 95%/5% (v/v) O2/CO2

Electrophysiological Analysis of GPCRs in Mammalian Neurons

400-ml glass beaker Fluorescent light fixture diffuser, with 0.5-in. (1.3 cm) square compartments with 0.5-in.-high walls (hardware or building supply store) Cyanoacrylate glue Polyethylene mesh (526-µm mesh opening, 540-µm thickness; e.g., Spectra/Mesh, Spectrum) 10-ml plastic centrifuge tube Sintered-glass gas diffuser (gas dispersion tube with fritted cylinder; Fisher Scientific)

11.2.4 Supplement 7

Current Protocols in Pharmacology

Plastic pipet tip Parafilm 21° to 35°C water bath 1. Cut a piece of fluorescent light fixture diffuser to fit horizontally into a 400-ml beaker (Fig. 11.2.2). 2. Use cyanoacrylate to glue a piece of polyethylene mesh onto the bottom surface of the diffuser. When the glue cures, trim the mesh around the perimeter of the diffuser a little larger than the outside diameter of the beaker so the mesh will fit snugly against inside walls of beaker. 3. Cut off the bottom of a 10-ml plastic centrifuge tube leaving approximately a 7- to 8-cm length of tube. Place the tube into one corner of the diffuser and slide the diffuser-tube assembly into the beaker so that the tube is ∼2 mm from bottom of the beaker and ∼2 cm above the top of the diffuser. The diffuser unit should rest about a third of the way into the beaker.

4. Place a sintered-glass gas diffuser into the plastic tube and suspend it ∼5 mm from the bottom of the beaker using a piece of plastic pipet tip. 5. Fill the beaker with aCSF to just over the top of the plastic tube and bubble with 95% O2/5% CO2 gas. Bubbling should be strong enough so that bubbles cover the entire surface of the aCSF.

6. Lay a piece of Parafilm over the beaker to reduce loss of O2 and CO2.

O2/CO2

aCSF plastic diffuser plastic mesh plastic tube

glass bubbler truncated pipet tip

aCSF

Figure 11.2.2 Slice storage apparatus. Brain slices are placed into the individual compartments onto the plastic mesh. Bubbling with 95% O2/5% CO2 gas causes the aCSF to circulate downward past the slices. The small piece of pipet tip holds the gas bubbler off the bottom surface of the beaker so the aerated aCSF exits from the top of the plastic tube.

Electrophysiological Techniques

11.2.5 Current Protocols in Pharmacology

Supplement 7

7. Place the slice storage unit in a water bath maintained at the desired temperature (usually 23° or 35°C). SUPPORT PROTOCOL 2

CONSTRUCTION OF A MANIFOLD FOR ADDITION OF COMPOUNDS The manifold is a convenient way to bath apply one or more compounds to the aCSF perfusion medium. It consists of a small mixing chamber through which the aCSF flows by way of a large-diameter needle inserted into the mixing chamber. Small-diameter needles affixed to syringe tubing are then pushed into this mixing chamber so that the compound is mixed with the aCSF before it enters the recording chamber. Materials Concentrated stock solution of drug: 100 to 1000× desired final bath concentration dissolved in high-purity water or freshly oxygenated artificial cerebrospinal fluid (aCSF; see recipe) Gum-rubber septum (Millipore) 18- and 22-G syringe needles 24-G wire (single or multi-stranded copper) PE-50 and PE-160 polyethylene tubing (Intramedic/Clay Adams, Becton Dickinson) Recording chamber (see Basic Protocol 1, Materials) 1-liter glass media bottle 10- to 12-ml plastic syringe Calibrated syringe pumps (Razel Scientific Instruments) 1. Stretch a gum-rubber septum over the plastic end of an 18-G syringe needle and secure with a tight wire wrap. 2. Connect the output end of the needle to a recording chamber with polyethylene tubing (PE-160). The length of tubing should be kept to a minimum (1000 nM

UCL-1848

—

—

—

—

>1000 nM —

1 nM

0.1 nM

2.1 nM

>1000 nM

Apamin

—

—

>1000 nM —

—

—

3 nM

0.08 nM

1 nM

>1000 nM

Scyllatoxin

—

—

>1000 nM —

—

—

80 nM

0.3 nM

8 nM

>1000 nM

Maurotoxin

>300 nM 0.1 nM

>300 nM

—

—

—

>1000 nM >1000 nM >1000 nM 1 nM

Charybdotoxin

—

5 nM

0.7 nM

—

—

—

>1000 nM >1000 nM >1000 nM 5 nM

Margatoxin

—

—

0.05 nM

—

>200 nM 5 nM

—

—

—

∼500 nM

Tityustoxin Kα >100 nM 0.2 nM

—

—

—

—

—

—

—

—

δ Dendrotoxin

—

—

—

—

—

—

—

—

0.03 nM 350 nM

aMost of the above agents can be acquired from Sigma-Aldrich or Alomone Labs.

cell current. In these cases, the inside-out configuration of the patch clamp can be useful for assessing the effect of intracellularly applied compounds, or, in the case of SK/IK channels, the calcium dependency of channel gating, and its effects on such parameters as compound potency and efficacy. It is possible to use the current-clamp configuration of the patch clamp technique to examine the effects of channel modulation on membrane potential in cells expressing heterologous channels. However, it is important to remember that the actual role that channels play in controlling membrane potential depends on many factors, including the ionic conditions and the presence of other ion channels. Since the complement and level of expression of ion channels in a heterologous system will differ from those in native cells, care must be taken not to overinterpret the consequences of observed changes in membrane potential. Another alternative to patch clamp studies of Kv1.x and SK/IK channels is to use the two-electrode voltage clamp of Xenopus oocytes heterologously expressing the channels of interest (see UNIT 11.1). While Kv1.x and SK/IK channels have been successfully studied in Xenopus oocytes (Hopkins, 1998; Grunnet et al., 2001), care should be taken in interpreting

pharmacological studies, as these can be complicated by onset and washout kinetics and shifts in apparent potency, particularly for agents that interact with the membrane of the oocyte or with the intracellular face of the channel being studied.

Critical Parameters and Troubleshooting The following checklist for validating electrophysiological experiments examining Kv1.x and/or, SK/IK channels provides a summary of criteria that need to be satisfactorily addressed during the acquisition or analysis of data. 1. Is the amplitude of the current within a range that can be satisfactorily clamped by the patch clamp amplifier? When possible, cells with maximum current amplitudes of 80%. 2. Is the current amplitude stable in control conditions? Current amplitude kinetic parame-

Electrophysiological Techniques

11.5.25 Current Protocols in Pharmacology

Supplement 20

ters (activation/inactivation) must be stable before applying test compounds. 3. For Kv1.x channels, is the voltage step of sufficient duration to allow time-dependent inhibitors to reach steady state by the end of the stimulating pulse? The duration should be long enough to allow the lowest concentration to be tested, to be able to reach steady state. 4. For Kv1.x channels, is the frequency of application of voltage steps low enough to allow for unbinding of the drug (i.e. removal of block) during the period between voltage steps? This parameter is important if the time course of block during a voltage step is to be measured accurately. If the voltage steps are applied too frequently, an apparent frequency-dependent (or use-dependent) block may be observed. This is seen as a progressive reduction in peak current amplitude with each successive voltage step, resulting in an apparent loss of time dependence. Note this phenomenon is not usually an issue for state-independent modulators like peptide toxin inhibitors.

Anticipated Results The expected results from electrophysiological experiments examining Kv1.x and SK/IK potassium channels are summarized in the figures and in the annotations accompanying the protocols described in this unit. The actual potassium current waveforms observed will depend on the particular channels being examined. Similarly, the effect of compounds on the current waveforms can be equally diverse, as illustrated by three closely related local anesthetic agents on Kv1.1 in Figure 11.5.4.

Time Considerations

Electrophysiological Analysis of Kv and SK/IK Potassium Channels

The time that is required to perform electrophysiological evaluation of Kv1.x or SK/IK potassium channels will depend on the type of experiment being performed and the time required for test compounds to reach equilibrium. However, most experiments will require stable recordings of minimally 20 to 30 min to allow control responses, drug effects, and washout to be monitored. Because the seal between the patch micropipet and the cell can fail spontaneously, it is important to have test solutions and preplanned protocols ready before establishing the whole-cell configuration. If compound effects reverse completely upon washout, it is possible to test multiple compounds on a given cell, thereby extending the length of an experiment. However, consideration should be given as to whether it is more prudent to wait

for washout before testing another compound on the same cell, or commence another experiment on a new cell. It is also worth noting that data analysis will require as much time, if not more, than the acquisition component of the experiment.

Literature Cited Axon Instruments. 1993. The Axon Guide. Axon Instruments, Union City, Calif. Beeton, C., Wulff, H., Barbaria, J., Clot-Faybesse, O., Pennington, M., Bernard, D., Cahalan, M.D., Chandy, K.G., and Beraud, E. 2001. Selective blockade of T lymphocyte K+ channels ameliorates experimental autoimmune encephalomyelitis, a model for multiple sclerosis. Proc. Natl. Acad. Sci. U.S.A. 98:13942. Cahalan, M.D. and Chandy, K.G. 1997. Ion channels in the immune system as targets for immunosuppression. Curr. Opin. Biotechnol. 8:749756. Castle, N.A. 1999. Recent advances in the biology of small conductance calcium-activated potassium channels. Perspect. Drug Discov. Des. 15/16:131-154. Castle, N.A., Fadous, S., Logothetis, D.E., and Wang, G.K. 1994. Aminopyridine block of Kv1.1 potassium channels expressed in mammalian cells and Xenopus oocytes. Mol Pharmacol. 46:1175-1181. Chen, J.Q., Galanakis, D., Ganellin, C.R., Dunn, P.M., and Jenkinson, D.H. 2000. bis-Quinolinium cyclophanes: 8,14-diaza-1,7(1, 4)-diquinolinacyclotetradecaphane (UCL 1848), a highly potent and selective, nonpeptidic blocker of the apamin-sensitive Ca2+-activated K+ channel. J. Med. Chem. 43:3478-3481. Coleman, S.K., Newcombe, J., Pryke, J., and Dolly, J.O. 1999. Subunit composition of Kv1 channels in human CNS. J. Neurochem. 73:849-858. Courtemanche, M., Ramirez, R.J., and Nattel, S. 1999. Ionic targets for drug therapy and atrial fibrillation-induced electrical remodeling: Insights from a mathematical model. Cardiovasc. Res. 42:477-489. Devor, D.C., Singh, A.K., Gerlach, A.C., Frizzell, R.A., and Bridges, R.J. 1997. Inhibition of intestinal Cl– secretion by clotrimazole: Direct effect on basolateral membrane K+ channels. Am. J. Physiol. 273:C531-40. Grissmer, S., Nguyen, A.N., Aiyar, J., Hanson, D.C., Mather, R.J., Gutman, G.A., Karmilowicz, M.J., Auperin, D.D., and Chandy, K.G. 1994. Pharmacological characterization of five cloned voltagegated K+ channels, types Kv1.1, 1.2, 1.3, 1.5, and 3.1, stably expressed in mammalian cell lines. Mol. Pharmacol. 45:1227-1234 Grunnet, M., Jensen, B.S., Olesen, S.P., and Klaerke, D.A. 2001. Apamin interacts with all subtypes of cloned small-conductance Ca2+-activated K+ channels. Pflugers Arch. 441:544-550

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Current Protocols in Pharmacology

Haugland, R.P. (ed.) 2002. Handbook of Fluorescent Probes and Research Products, 9th ed. Molecular Probes, Eugene, Oreg. Heinemann, S.H., Rettig, J., Graack, H.R., and Pongs, O. 1996. Functional characterization of Kv channel beta-subunits from rat brain. J. Physiol. 493:625-633 Hopkins, W.F. 1998. Toxin and subunit specificity of blocking affinity of three peptide toxins for heteromultimeric, voltage-gated potassium channels expressed in Xenopus oocytes. J. Pharmacol. Exp. Ther. 285:1051-1060. Ishii, T.M., Silvia, C., Hirschberg, B., Bond, C.T., Adelman, J.P., and Maylie, J. 1997. A human intermediate conductance calcium-activated potassium channel. Proc. Natl. Acad. Sci. U.S.A. 94:11651-11656. Jensen, B.S., Strobaek, D., Olesen, S.P., and Christophersen, P. 2001. The Ca2+-activated K+ channel of intermediate conductance: A molecular target for novel treatments? Curr. Drug Targets 2:401-422. Khanna, R., Chang, M.C., Joiner, W.J., Kaczmarek, L.K., and Schlichter, L.C. 1999. hSK4/hIK1, a calmodulin-binding KCa channel in human T lymphocytes: Roles in proliferation and volume regulation. J. Biol. Chem. 274:14838-14849. Koren, G., Liman, E.R., Logothetis, D.E., Nadal-Ginard, B., and Hess, P. 1990. Gating mechanism of a cloned potassium channel expressed in frog oocytes and mammalian cells. Neuron 4:39-51 Nashmi, R. and Fehlings, M.G. 2001. Mechanisms of axonal dysfunction after spinal cord injury: With an emphasis on the role of voltage-gated potassium channels. Brain Res. Brain Res. Rev. 38:165-191. Rasband, M.N. and Trimmer, J.S. 2001. Subunit composition and novel localization of K+ channels in spinal cord. J. Comp. Neurol. 429:166176. Snyders, D.J., Tamkun, M.M., and Bennett, P.B. 1993. A rapidly activating and slowly inactivating potassium channel cloned from human heart: Functional analysis after stable mammalian cell culture expression. J. Gen. Physiol. 101:513543. Strobaek, D., Jorgensen, T.D., Christophersen, P., Ahring, P.K., and Olesen, S.P. 2000. Pharmacological characterization of small-conductance Ca2+-activated K+ channels stably expressed in HEK 293 cells. Br. J. Pharmacol. 129:991-999. Syme, C.A, Gerlach, A.C., Singh, A.K., and Devor, D.C. 2000. Pharmacological activation of cloned intermediate- and small-conductance Ca2+-activated K+ channels. Am. J. Physiol. Cell Physiol. 278:570-581.

Wang Z, Fermini B, and Nattel S. 1993. Sustained depolarization-induced outward current in human atrial myocytes: Evidence for a novel delayed rectifier K+ current similar to Kv1.5 cloned channel currents. Circ. Res. 73:1061-1076. Werkman, T.R., Gustafson, T.A., Rogowski, R.S., Blaustein, M.P., and Rogawski, M.A. 1993. Tityustoxin-K alpha, a structurally novel and highly potent K+ channel peptide toxin, interacts with the alpha-dendrotoxin binding site on the cloned Kv1.2 K+ channel. Mol. Pharmacol. 44:430-436.

Internet Resources http://www.axon.com http://www.axon.com/MR_Axon_Guide.html The Axon Instruments Web site provides information on their product line of electrophysiology equipment as well as a comprehensive guide for conducting electrophysiological experiments. Additional information about patch clamp electrophysiological protocols can be found at the following Web sites http://usa.biologists.com/Micro/index.html Web site of the Microelectrode Techniques Handbook. Contains additional information about patch clamp electrophysiological protocols. http://web.ukonline.co.uk/a.hughes/patchwork/ patchwork.htm “Patch Works” Web site containing the online booklet: Patch Clamping, A Guide. Contains additional information about patch clamp electrophysiological protocols. http://www.atcc.org/home.cfm Web site of American Type Culture Collection (ATCC). Comprehensive supplier of cell lines. http://www.stanford.edu/∼cpatton/webmaxc2.htm Web site of online calculator for estimating free calcium concentration in solutions containing calcium chelators. http://www.gene.ucl.ac.uk/nomenclature/genefamily/KCN.shtml HUGO Gene Nomenclature Committee Web site provides information/updates on the gene families and nomenclature for potassium channels.

Contributed by Neil A. Castle, Alan D. Wickenden, and Anruo Zou Icagen Durham, North Carolina

Electrophysiological Techniques

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Supplement 20

Electrophysiological Analysis of ATP-Sensitive Potassium Channels in Mammalian Cells and Xenopus Oocytes

UNIT 11.6

ATP-sensitive K+ channels (KATP) couple cellular metabolism to membrane excitability (Ashcroft and Rorsman, 1990). Since the first KATP channel was identified from heart tissue (Noma, 1983), there have been numerous studies demonstrating the existence of KATP channels in a variety of tissues that control vital functions such as insulin secretion, neuronal excitability, heartbeat, and smooth muscle contraction. ATP-sensitive K+ channels are the molecular targets for diabetes and antihypertensive compounds, and modulators of these channels are currently being investigated for the treatment of cardiac and nonvascular smooth muscle disorders (Coghlan et al., 2000; Shieh et al., 2000). Recent molecular biology efforts have identified genes encoding pancreatic or neuronal, smooth muscle, and cardiac types of KATP channels. KATP channels are hetero-octameric complexes comprising four inward-rectifying K+ channel subunits belonging to KIR 6.1 or KIR

A

pore (P-loop)

A

SU R

B C

B

A SUR1, SUR2(A,B)

R

intracellular

N

SU

C

M2

SU R K .n ir6 r6 .n Ki

M1

K .n ir6 r6 .n Ki

R

TM2

TM1

SU

extracellular TM0

N

B

Kir6.1 Kir6.2

C Subunits

Tissue

Function

SUR1-Kir6.2

Pancreas Hypothalamus

Insulin Leptin signaling

SUR2A-Kir6.2

Heart

Action potential

SUR1-Kir6.1(?)

Heart

Cardioprotection

SUR2B + Kir6.2 (and/or) Kir6.1

Smooth muscle

Relaxation

Figure 11.6.1 The KATP channel complex: organization and diversity. (A) Membrane topology of sulfonylurea receptor (SUR) and the inward-rectifying K+ channel (Kir). SUR is thought to have three transmembrane domains, TM0, TM1, and TM2, which consist of five, six, and six transmembrane segments, respectively. The nucleotide binding motifs are located in the intracellular loop between TM1 and TM2 and in the C-terminal region, respectively. Locations of the Walker A motif (A) and Walker B motif (B) are shown. N and C indicate the N and C terminus, respectively. Kir consists of two transmembrane segments (M1, M2) and the K+ ion pore-forming region (P-loop). (B) Heterooctameric organization of the KATP channel depicting assembly of four inward-rectifying K+ channels and four sulfonylurea receptors. (C) Subtypes of KATP channels and their function in diverse tissues. For example, SUR1-Kir6.2 is the channel combination in pancreatic β-cells that is critical to insulin release and is the target for the antidiabetic sulfonylurea compounds such as glyburide and tolbutamide. Contributed by Char-Chang Shieh and Murali Gopalakrishnan Current Protocols in Pharmacology (2003) 11.6.1-11.6.25 Copyright © 2003 by John Wiley & Sons, Inc.

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6.2 and four sulfonylurea receptor (SUR) regulatory proteins (Fig. 11.6.1; Aguilar-Bryan et al., 1999; Seino, 1999). The KIR subunits are responsible for ion permeation and contain the primary site for ATP inhibition of KATP channel activity, whereas the SURs are relatively larger proteins that belong to the ATP-binding cassette superfamily and host binding sites for sulfonylurea compounds, channel openers, and nucleotides. Functional analysis of combinations of the various SUR isoforms, SUR1 and the SUR2 splice variants (SUR2A and SUR2B) with KIR subunits (Kir6.1, Kir6.2, or Kir6.1-6.2 tandem subunits) has revealed KATP channels with distinct biophysical and pharmacological properties. SUR1-Kir6.2 is expressed in pancreatic tissues, whereas SUR2A-Kir6.2 is the proposed subunit combination of plasmalemmal KATP channels in the heart. On the other hand, the SUR2B isoform in conjunction with Kir6.1, Kir6.2, or the Kir6.1-6.2 combination is thought to constitute the diverse smooth muscle type KATP channels. Analysis of KATP channels is performed with both native tissues and clonal cell lines or oocytes expressing recombinant KATP channels. Transfection of the genes for KATP channel subunits (SUR and Kir) into mammalian cell lines or injection of cRNA into oocytes results in expression of functional KATP channels (Inagaki et al., 1995; Gribble et al., 1997; Babenko et al., 1998; Gopalakrishnan et al., 2000). This unit describes specific methods for studying KATP channels using electrophysiological techniques including patch-clamp single-channel recording (Basic Protocol 1), whole-cell current recording (Alternate Protocol 2), inside-out macropatch recording (Alternate Protocol 1), and perforated-patch recording (Alternate Protocol 3) from dissociated mammalian cells or clonal cell lines. Along with the procedures used to achieve these various recording configurations, a method for studying the effects of test compound application on KATP channels is included (Basic Protocol 3) as well as two methods for generating a current-voltage relationship curve (I-V curve; Alternate Protocols 4 and 5). Finally, a two-electrode voltage-clamp technique for analyzing KATP channels expressed in Xenopus oocytes (Basic Protocol 2) and a discussion of data analyses (Support Protocol) are described. These protocols were designed around the use of the Axopatch 200B amplifier (Axon Instruments) and the GeneClamp 500B (Axon Instruments). If other equipment is used, the manufacturer’s instructions should be followed to modify the protocols accordingly. STRATEGIC PLANNING Changes in ATP-sensitive K+ currents due to the effects of channel modulators (openers or blockers) can be evaluated by patch-clamp single-channel or whole-cell current recordings from native tissue, such as neurons, single smooth muscle cells, islet β-cells, or cardiac myocytes. Stable clonal cell lines (Gopalakrishnan et al., 2000) or Xenopus oocytes (Gribble et al., 1997) expressing subtypes of KATP channels allow identification of selective KATP channel modulators in a convenient and robust manner. Channel openers or blockers can be applied at a single concentration initially, and changes in channel open probability or current amplitudes in the presence of test compounds can be measured. A minimum of three replicate determinations should be made to confirm the activities of compounds. For compounds of interest, several (e.g., five to seven) concentrations may be tested to generate a concentration-response curve to derive values of efficacy and potency.

Analysis of ATP-Sensitive Potassium Channels

A comprehensive review of the biophysical properties of ion channels is presented by Hille (2001). Readers should consult Sakmann and Neher (1995) for a useful review of patch-clamp recording techniques. Basic concepts of electrophysiology for ligand- and voltage-gated ion channels are outlined in UNIT 11.1.

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Electrophysiological Setup A variety of equipment is available to assemble a patch-clamp setup. The equipment described in this unit is only presented as an example. The equipment utilized for patch-clamp recording includes: Axopatch 200B patch-clamp amplifier (Axon Instruments) Data acquisition and stimulation hardware (Digidata 1200B; Axon Instruments) Oscilloscope (Tektronix) Fast solution perfusion system (DAD-12 Superfusion system; ALA Scientific Instruments) Inverted microscope (Eclipse TE300; Nikon) Micromanipulator (MP-285; Sutter Instruments) Vibration isolation table (Technical Manufacturing) Computer to execute commands and for data storage and analysis. The equipment used for two-electrode voltage-clamp recording in oocytes includes: GeneClamp 500B (Axon Instruments), which includes an electrode holder Recording chamber (Warner Instrument) Microscope with magnification sufficient to observe oocytes and microelectrodes. Other instruments commonly used in the electrophysiology lab include: Micropipet puller (P-87; Sutter Instruments) Microforge for fire polishing patch pipet. Software used for data analysis and graphing includes: pClamp (Axon Instruments) Excel (Microsoft) Origin (MicroCal) Prism (GraphPad Software) PATCH-CLAMP SINGLE-CHANNEL RECORDING FOR KATP CHANNELS Single-channel recording reveals the activity of a single channel protein in response to the application of modulators or to changes in membrane potential under voltage-clamp conditions. A low-noise recording technique is required to observe small ionic currents mediated through a single channel in the picoampere (pA) range. This can be achieved by tightly sealing a glass pipet onto the plasma membrane of an intact cell. Thus, channel activities within the membrane patch can be measured through a glass pipet connected to the patch-clamp amplifier. Based on the configuration of the patch pipet and cell membrane, single-channel recording can have cell-attached, inside-out, or outside-out recording configurations (Fig. 11.1.3), each of which is discussed below. In cell-attached recording, the pipet attaches to the outside surface of the membrane. Because the membrane patch is still attached to the cell, the channels remain in their physiological environment. This type of configuration allows recording of ion channel activities under normal physiological conditions, which would otherwise be lost upon patch excision. Because the ion channel is confined within the patch pipet under tight seal conditions and the patch membrane remains intact with the cell, effective exchange of pipet solution and cytosolic solution is difficult. Furthermore, under the cell-attached configuration, lack of knowledge about the resting membrane potential of the cell limits

BASIC PROTOCOL 1

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the accuracy of the desired voltage clamp. One way to compensate for this is to bathe the cell in a high concentration of K+ (e.g., 100 mM) to depolarize the membrane potential to 0 mV. In the inside-out recording configuration, the membrane patch is excised from the cell so the surface that was originally the inside surface of the membrane is exposed to the bath solution. In this way, inside-out patch-clamp recording enables the exchange of cytosolic solution during recording because the patched membrane is excised after the tight-seal cell-attached formation is achieved. This configuration is widely used to study the effects of ATP, ADP, nucleotide diphosphates, and modulators on gating of KATP channels at single-channel or macropatch (see Alternate Protocol 1) recording levels (Inagaki et al., 1995; Isomoto et al., 1996; Gribble et al., 1997; Shyng et al., 1997; Babenko et al., 1998). This configuration is also widely used to study the modulation of ion channel activity by cytosolic signaling molecules. However, in the inside-out recording mode, the potential loss of key cytosolic factors after membrane excision may affect channel activities. Furthermore, in some instances, the formation of a vesicle rather than a planar patch can occur, thus limiting test compound application to the cytoplasmic site. In the outside-out configuration, the patch membrane is excised from the cell such that the external side of the membrane remains exposed to the bath solution. Outside-out patch recording is commonly used to study ligand-gated ion channels because this configuration allows exchange of solution at the extracellular side of the patch. Similar to cell-attached recording, the ability to change the ionic composition of the intracellular side of the patch is limited in the outside-out recording mode. Furthermore, stable outside-out patch recording is more difficult because of the complexity of the procedures. Materials (also see Strategic Planning) Sylgard (184 silicone elastomer; Dow Corning) Single-channel pipet solution (see recipe) Single-channel bath solution (see recipe) HEK293 cells expressing channel of interest, (for example, Gopalakrishnan et al. (2000), for human SUR1-Kir6.2 Test compound(s) appropriate for channel of interest Corning 7056 glass capillary tubing: outer diameter (o.d.), 1.65 mm; inner diameter (i.d.), 1.1 mm; 100 mm long (Warner Instrument) Prepare patch pipets 1. Use a micropipet puller to fabricate patch pipets with a diameter of 1 to 2 µm from Corning 7056 glass capillary tubing. To reduce pipet noise and capacitance, coat the pipet wall with a thin layer of Sylgard (avoid contact of the tip of pipet with Sylgard). Fire polish pipets with a microforge. The patch pipet should have a diameter of 1 to 2 ìm. The diameter of the patch pipet can be calibrated under the microscope.

2. Fill pipet with appropriate single-channel pipet solution. Obtain gigaohm seal 3. Set the external command switch of an Axopatch 200B patch-clamp amplifier to SEAL TEST. Set METER to VTRACK and MODE to TRACK. Analysis of ATP-Sensitive Potassium Channels

4. Lower pipet into appropriate single-channel bath solution. To offset the difference in voltage between bath electrode and patch electrode, adjust the PIPET OFFSET potentiometer until VTRACK is zero.

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Current Protocols in Pharmacology

For inside-out recording (step 8a), the bath solution should have an ionic composition similar to the intracellular milieu. Because the amplifier generates a positive 5-mV rectangular pulse, a square pulse of current whose amplitude depends on the pipet resistance should appear in the oscilloscope (or computer) monitor (Fig. 11.6.2A). The patch pipet should have a pipet resistance of 8 to 10 MΩ.

5. Press pipet onto the membrane of an HEK293 cell expressing the channel of interest. When the pipet touches the membrane, a decrease in the size of the current pulse occurs because of an increase in resistance (Fig. 11.6.2B).

6. Gently apply negative pressure (by applying suction to the patch pipet) and monitor the decreases in the current pulse on the oscilloscope. The rectangular current pulse will eventually disappear and be replaced by capacitance transients when a gigaohm seal is established (Fig. 11.6.2C).

A

D pipet in bath

B

capacitance cancellation

E pipet against cell

C

whole-cell configuration

F seal formation

whole-cell capacitance cancellation

500 pA 10 msec

Figure 11.6.2 Illustration of patch-clamp recording: seal formation and whole-cell recording. (A) When the patch pipet is brought into the bath solution, a square pulse of current can be generated in response to a 5-mV positive-going rectangular pulse through the Axopatch 200B patch-clamp amplifier in SEAL TEST mode. (B) When the pipet touches the cell, a reduction in the size of current pulse occurs because of increased resistance. (C) By a gentle suction, a stable seal formation with resistance >1 GΩ is obtained. The transient currents reflect the capacitance of the pipet and the solution on the pipet wall. (D) After a suitable adjustment of Pipet Capacitance Compensation as suggested by the manufacturer of the amplifier, the transient capacitance currents can be canceled. A cell-attached recording configuration is achieved. (E) By a brief and strong suction or using the ZAP function supplied with the amplifier, the membrane can be ruptured to obtain a whole-cell patch-clamp recording. The increases in transient capacitance currents result from the capacitance of the cell membrane. (F) When the Whole-cell Capacitance switch (supplied with the amplifier) is turned on, the transient capacitance currents can be minimized by adjusting the whole-cell Capacitance and Series Resistance simultaneously.

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7. Adjust the fast capacitance compensation on the amplifier to cancel the transient currents caused by the capacitance of the pipet and the solution on the pipet wall (Fig. 11.6.2D). For cell-attached recording, continue with step 9. Once a stable gigaohm seal is obtained, the configuration of cell-attached recording is achieved, making it possible to detect single-channel currents if channels are present in the patch.

8a. For inside-out recording: Excise the patch membrane by lifting the patch pipet away from the cell (∼1 to 2 mm) until the pipet detaches from cell. A successful inside-out configuration should retain gigaohm seal resistance.

8b. For outside-out recording: Apply negative pressure by gentle suction to disrupt the membrane. Slowly retract the patch pipet away from the cell until membrane reseals across the tip to form the outside-out configuration. At the point at which the membrane is disrupted, increases in capacitance current can be observed. A successful outside-out patch should retain gigaohm seal resistance.

Record single-channel activities 9. Switch the amplifier to voltage-clamp mode. Program the voltage protocols as desired and begin to record single-channel activities (see Basic Protocol 3 and see Alternate Protocols 4 and 5). 10. Apply solutions containing varying concentrations of test compounds and examine the effects on channel activities. For data analysis, see Support Protocol. Single-channel recording reveals the opening probability and amplitude of a single KATP channel. Thus, the effects of test compounds on KATP channels can be evaluated by measuring changes in opening probability or current amplitude. These effects can be evaluated at a single concentration or in a concentration-dependent manner. The inside-out single channel recording configuration is commonly used to study effects of modulators (openers or inhibitors) on KATP channels because of the ability to measure changes in intracellular nucleotide concentrations that modulate KATP channel functions. ALTERNATE PROTOCOL 1

INSIDE-OUT MACROPATCH RECORDING FOR KATP CHANNELS The macropatch recording configuration (Hilgemann, 1995) is similar to that used for single-channel recording, except that the ionic currents are monitored from a population of channels in the small area of membrane (patch), and not from a single channel. In macropatch recording, the patch pipet has a diameter of 8 to 30 µm. Although the ability to form a tight seal with gigaohm resistance becomes increasingly difficult with the larger pipet tip, it is achievable for most cells. Inside-out macropatch recording is commonly used for studying KATP channels because the configuration allows efficient exchange of intracellular ATP or ADP. Materials (also see Basic Protocol 1) Inside-out pipet solution (see recipe) Inside-out bath solution (see recipe) 1. Use a micropipet puller to fabricate patch pipets with a diameter of 8 to 30 µm from Corning 7056 glass capillary tubing. Fire polish the pipets with a microforge.

Analysis of ATP-Sensitive Potassium Channels

2. Fill pipet with appropriate inside-out pipet solution. The patch pipet should typically have a pipet resistance of ∼0.2 to 0.4 MΩ when the patch pipet is in the bath (internal) solution.

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Current Protocols in Pharmacology

3. Obtain a gigaohm seal as described (see Basic Protocol 1, steps 3 to 7), using inside-out bath solution. 4. Excise the patch by retracting the pipet from the cell. 5. Switch the amplifier to voltage-clamp mode. Program the voltage protocols and begin recording channel activities (see Basic Protocol 3 and see Alternate Protocols 4 and 5). 6. Apply solutions containing test compounds and examine their effects on the KATP channel activities. For data analysis, see Support Protocol. In inside-out macropatch recording, the currents measured are the summation of multiple single KATP channels located in the excised patch membrane. This protocol provides a method to evaluate compounds that modulate KATP channel activity potentially through the cytoplasmic side. The effects of the compounds on KATP channels can be evaluated by measuring changes in current amplitude at a single concentration or in a concentrationdependent fashion.

WHOLE-CELL PATCH CLAMP RECORDING OF KATP CURRENTS Whole-cell patch clamp recording (Hamill et al., 1981) is a standard method for recording KATP currents from the entire cell. This technique is also suitable for studying exocytotic activity in secretory cells by measuring changes in capacitance. In this configuration, the patch membrane is disrupted so that the patch pipet solution can be exchanged with the cytosolic fluid. It is not possible, however, to modify the cytosolic solution during the recording period.

ALTERNATE PROTOCOL 2

Materials (also see Basic Protocol 1) Whole-cell pipet solution (see recipe) Whole-cell bath solution (see recipe) 1. Use a micropipet puller to fabricate patch-clamp pipets with a diameter of 3 to 5 µm from Corning 7056 glass capillary tubing. Fire polish the pipets with a microforge. 2. Fill patch-clamp pipet with appropriate whole-cell pipet solution containing a low concentration of Ca2+. The patch pipet should have a pipet resistance of ∼2 to 5 MΩ when the patch pipet is in the bath solution.

3. Obtain a gigaohm seal as described (see Basic Protocol 1, steps 3 to 7), using whole-cell bath solution. 4. Apply a pulse of suction via the pipet until the size of the capacitive transient current suddenly increases (Fig. 11.6.2E). The increase in transient capacitance current occurs when the cell membrane is ruptured. An alternative method to break the patch membrane is to apply the “zap” technique, a feature available on most amplifiers. This technique is used to rupture the patch by applying a brief voltage pulse to cause dielectric breakdown of the membrane.

5. Minimize transient capacitance currents by adjusting the whole-cell capacitance function supplied with the patch-clamp amplifier. Also set the series resistance compensation to 90% to 95% by adjusting the series resistance compensation function supplied with the amplifier.

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The series resistance compensation will minimize voltage-clamp errors resulting from pipet resistance and other access resistances to the cell.

6. Switch the amplifier to voltage-clamp mode. Program the voltage protocols and begin recording whole-cell currents (see Basic Protocol 3 and see Alternate Protocols 4 and 5). 7. Apply solutions containing test compounds and examine their effects on KATP channel activities. For data analysis, see Support Protocol. In the whole-cell patch clamp mode, the currents measured represent the summation of the KATP channels in the cell. Thus, the effects of compounds on KATP channels can be evaluated by measuring changes in whole-cell current amplitude upon application of a test compound. The effects of compounds can be evaluated at a single concentration or multiple concentrations to generate a concentration-response curve. ALTERNATE PROTOCOL 3

PERFORATED-PATCH RECORDING OF KATP CHANNELS Perforated patch-clamp recording is a variant of the whole-cell patch-clamp recording technique whereby the patch membrane is permeabilized rather than disrupted (Horn and Korn, 1992). This technique prevents dialysis or washout of the cytoplasmic components through the patch-pipet solution and therefore prevents channel rundown, a common problem in whole-cell recording. Channel-forming reagents such as nystatin (Horn and Marty, 1988) and amphotericin B (Rae et al., 1991) are commonly included in the patch-pipet solution for perforated-patch recording. Some considerations in using this technique include the relatively lengthy time required for obtaining a low-resistance perforated patch, the greater noise associated with the recordings, and the inability to exchange the intracellular solution during the recording period. Materials (also see Basic Protocol 1) 20 mg/ml amphotericin B (Sigma) in DMSO, prepared fresh on day of experiment Perforated-patch pipet solution (see recipe) Perforated-patch bath solution (see recipe) Ultrasonicator (e.g., Bransonic Ultrasonic Cleaner 1510; Branson Ultrasonics) 1. Use a micropipet puller to fabricate patch-clamp pipets with a diameter of 3 to 5 µm from Corning 7056 glass capillary tubing. Fire polish the pipets with a microforge. 2. Add 20 mg/ml amphotericin B in DMSO to perforated-patch pipet solution so that final concentration of amphotericin B is 300 µg/ml. Sonicate pipet solution in an ultrasonicator just before use to improve the solubility of amphotericin B. Nystatin (Sigma) can be used instead of amphotericin B at a final concentration of 800 to 1000 ìg/ml.

3. Fill the patch-clamp pipet tip by aspiration with amphotericin-free pipet solution. Backfill pipet with pipet solution containing 300 µg/ml amphotericin B. The patch pipet should typically have a pipet resistance of ∼2 to 5 MΩ when the patch pipet is in the bath solution.

4. Obtain a gigaohm seal as described (see Basic Protocol 1, steps 3 to 7), using perforated-patch bath solution. Analysis of ATP-Sensitive Potassium Channels

5. Adjust the fast capacitance compensation on the amplifier to cancel the transient currents caused by the capacitance of the pipet and the solution on the pipet wall. 6. Wait 5 to 20 min to allow perforation to reach a steady-state level.

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Current Protocols in Pharmacology

Increases in capacitance current can be observed during the process of perforation. When perforation reaches the steady-state level, the peak capacitance current should remain constant. The amount of time needed will vary depending on cell type, pipet resistance, and other factors.

7. Adjust the whole-cell capacitance and series resistance compensation as described (see Alternate Protocol 2, step 5). 8. Switch the amplifier to voltage-clamp mode. Program the voltage protocols and begin recording whole-cell currents (see Basic Protocol 3 and see Alternate Protocols 4 and 5). 9. Apply solutions containing test compounds and examine their effects on KATP channel activities. For data analysis, see Support Protocol. Similar to whole-cell patch clamp recording, perforated-patch recording allows measurement of whole-cell currents from a population of KATP channels except that no solution exchange occurs between the pipet and cytoplasm. Thus, the effects of compounds on KATP channels can be evaluated by measuring changes in current amplitudes.

TWO-ELECTRODE VOLTAGE CLAMP IN XENOPUS OOCYTES Since the original observation that injection of foreign mRNA (extracted from rat brain or cat muscle) into Xenopus oocytes incorporates functional voltage-gated K+ and Na+ channels into the membrane (Gundersen et al., 1983), the oocyte expression system has become a widely used model to study the function of ion channels and receptors. It is convenient to obtain hundreds of oocytes at a time because they can survive for up to 2 weeks in vitro. Furthermore, recent advances in molecular biology have resulted in the cloning of a variety of ion channels, including KATP channels. Thus, the cDNAs or cRNAs encoding ion channels are routinely available for expression studies in oocytes. To prepare cRNA, DNA constructs are initially linearized at their 3′ ends by digestion with a unique restriction enzyme. In vitro transcription with RNA polymerase (e.g., T7, T3, or SP6 depending on the expression vector) can be performed using the mMessage mMachine kit (Ambion).

BASIC PROTOCOL 2

Although oocytes are widely used as an expression system, there are some disadvantages with this approach. For example, the large size of oocytes often hinders the accuracy of voltage clamping. In some instances, the endogenous currents can interfere with current measurements, especially if the channel has low expression levels. In some cases, oocytes exhibit seasonal variations, thus reducing channel expression and the ability to obtain consistent recordings. Two-electrode voltage-clamp recording is commonly used to measure ionic currents expressed in oocytes. The scheme of a two-microelectrode voltage clamp and its principles are shown in Figure 11.1.2. Recent advances in automation and microfluidics technology have facilitated conventional two-electrode voltage-clamp measurements in high-throughput electrophysiological recording systems. These include Roboocytes (Multi Channel Systems), Parallel Oocytes Electrophysiological Tester station (POETs; Abbott Laboratories), and OpusXpress (Axon Instruments). For a discussion of these systems, see Background Information. NOTE: All protocols using live animals must first be reviewed and approved by an Institutional Animal Care and Use Committee (IACUC) and must conform to governmental regulations regarding the care and use of laboratory animals.

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Materials (also see Strategic Planning) Female Xenopus laevis frogs (NASCO) 1-liter beaker containing 500 ml water and 1.4 g tricane (3-aminobenzoic acid ethyl ester; Sigma) Ca2+-free Barth’s solution (see recipe) 15-ml centrifuge tube containing 10 ml of 2 mg/ml type 1A collagenase (Sigma) in Ca2+-free Barth’s solution (see recipe) 2 mg/ml type 1A collagenase (Sigma) in Ca2+-free Barth’s solution (see recipe) Barth’s solution (see recipe) 0.01 µg/µl cRNA encoding Kir6.2 or Kir6.1 0.05 µg/µl cRNA encoding SUR1, SUR2A, or SUR2B 3 M KCl Two-electrode bath solution (see recipe) Test compound(s) appropriate for channel of interest Dissection board, ice cold Sterile dissection instruments, including scissors and forceps 35-mm and 100 × 15–mm petri dishes Rocking platform (e.g., model no. 100; VWR Scientific) Dissection microscope Borosilicate glass capillary tubes: outer diameter (o.d.), 1.0 mm; inner diameter (i.d.), 0.5 mm (Sutter Instruments), for cRNA injection Fine forceps Drummond Nanoject injector (Drummond Scientific) attached to micromanipulator Incubator, 14° to 19°C Borosilicate glass capillary tubes with filaments: o.d., 1.5 mm; i.d., 1.1 mm (Sutter Instruments), for two-electrode whole-cell recordings Prepare oocytes 1. Place a female X. laevis frog in a 1-liter beaker containing 500 ml water and 1.4 g tricane, and anesthetize frog 15 to 30 min. Anesthetization is complete when frog does not respond to any touch.

2. Remove frog from the anesthetic, rinse frog with water, and place on an ice-cold dissection board. 3. Make an ∼0.5-inch incision through the skin and peritoneum of the frog using sterile dissection instruments. 4. Gently tease the ovarian lobes out through the incision. Remove ovarian lobes to a 100 × 15–mm petri dish filled with Ca2+-free Barth’s solution. 5. Cut the ovarian lobes apart and use a pair of forceps to gently open each lobe. Defolliculate oocytes 6. Place ovarian lobes in 10 ml of 2 mg/ml collagenase in Ca2+-free Barth’s solution in a 15-ml centrifuge tube. 7. Place the tube on a rocking platform and gently shake (at a low speed) for 1 hr. 8. Replace solution with 10 ml of fresh 2 mg/ml collagenase in Ca2+-free Barth’s solution. Continue to rotate until defolliculation is complete in ∼80% of the oocytes (≤1 hr). Analysis of ATP-Sensitive Potassium Channels

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Defolliculation can be visualized under the microscope by the disappearance of the thin, shiny follicular layer that surrounds the oocyte. Oocytes should be incubated in enzyme solution for no longer than necessary to achieve 80% defolliculation.

9. Wash oocytes in a conical tube with 50 ml Ca2+-free Barth’s solution five times to remove residual enzymes. Replace solution with normal Barth’s solution and wash another five times. 10. Place oocytes in a 100 × 15–mm petri dish filled with Barth’s solution. Using a dissection microscope, select healthy and defolliculated stage V or VI oocytes. Allow oocytes to settle for ≥1 hr and discard any damaged oocytes. Stage V and VI oocytes have a diameter of 1000 to 1200 ìm and have clearly delineated animal and vegetal hemispheres (Shih et al., 1998).

Inject oocytes with cRNAs 11. Use a micropipet puller to fabricate microinjection pipets from borosilicate glass capillary tubes (o.d., 1.0 mm; i.d., 0.5 mm) and break the tips to a diameter of 5 to 10 µm using a pair of fine forceps under a microscope. 12. Fill a 35-mm petri dish with ice and place Parafilm on top of the petri dish lid. The ice reduces evaporation of the liquid containing cRNAs or cDNAs from the Parafilm surface.

13. Mix 1 µl of 0.01 µg/µl cRNA encoding Kir6.2 (or Kir6.1) with 4 µl of 0.05 µg/µl cRNA encoding either SUR1, SUR2A, or SUR2B. 14. Make a dent in the Parafilm and place a drop of cRNA mixture in the center of the dent area. 15. Calibrate a Drummond Nanojet injector attached to a micromanipulator so as to inject 50 nl cRNA from the microinjection pipet each time. 16. Aspirate the cRNA mixture into the injection pipet using the Drummond Nanoject injector. 17. Place ∼100 oocytes in a 100 × 15–mm petri dish filled with Barth’s solution. Place the dish on the stage of the dissection microscope. 18. While viewing the oocytes through the microscope, gently lower the microinjection pipet and align it with an oocyte. Gradually lower the pipet until it pierces the oocyte. Inject 50 nl cRNA mixture per oocyte. In general, any region of the oocyte can be impaled; however, it is a good idea to pierce the oocyte through the animal pole because the dark color in this region provides good contrast for viewing the pipet tip. A slight increase in cell size can be observed when 50 nl cRNA is injected into the oocyte.

19. Place injected oocytes in a 35-mm petri dish filled with Barth’s solution and store in an incubator at 14° to 19°C for 1 to 4 days. The incubation time, which should correspond to the time needed for optimal expression, should be determined empirically for each subunit combination.

Establish two-electrode voltage clamp 20. Use a micropipet puller to fabricate electrodes with a diameter of ∼3 µm from borosilicate glass capillary tubes with filaments (o.d., 1.5 mm; i.d., 1.1 mm) and fill with 3 M KCl.

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21. Mount one electrode each into voltage and current electrode holders that are attached to the headstages and connected to a GeneClamp 500B amplifier. Set the amplifier to the Setup mode. 22. Place an injected oocyte (step 19) into a recording chamber containing two-electrode bath solution. In experimental conditions requiring a high K+ concentration, NaOH in the bath solution can be substituted with equimolar KOH.

23. Introduce the electrodes into the bath solution. Offset the electrode potentials to 0 mV. The microelectrodes should have resistance of 5 min to evoke steady-state currents. Although diazoxide (Sigma) can be used to evoke SUR1/Kir6.2 KATP currents, its use is somewhat limited by its low potency and

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solubility. For SUR2/Kir6.2, KATP currents are evoked by K+ channel openers (KCOs) such as cromakalim (Toronto Research Chemicals) and pinacidil (Sigma). The evoked KATP currents should be sensitive to blockade by sulfonylurea analogs such as glyburide (Sigma) or tolbutamide (Sigma; Table 11.6.1). This protocol is commonly used in characterizing KATP channels by whole-cell patch-clamp recording in native tissues such as smooth muscle cells where other types of ion channels may also be present. Materials Test compound(s) appropriate for channel of interest, such as KATP channel openers (KCOs) and KATP channel blockers Electrophysiology setup, including cells or oocytes expressing channel of interest, for inside-out macropatch recording, whole-cell patch clamp, or two-electrode voltage clamp (see Strategic Analysis, see Basic Protocol 2, and see Alternate Protocols 1 to 2) pClamp software (Axon Instruments), including Clampex 1. In Clampex, a program in pClamp, set the voltage clamp at a holding potential of –80 (or –70) mV. Because the current is recorded in a high extracellular K+ concentration of 40 or 60 mM, a holding potential of −80 mV (for 40 mM) or −70 or −80 mV (for 60 mM) is suitable for revealing inward currents.

2. After establishing appropriate recording configuration (patch-clamp single channel, whole-cell voltage clamp, or two-electrode voltage clamp) for an electrophysiology setup, clamp the cell or macropatch at –80 mV (or –70 mV). 3. To study the effects of KCOs: Record stable basal currents at the holding potential for ∼1 min, and then apply desired concentrations of test compounds (single or multiple

Table 11.6.1 Single-Channel Conductances and Pharmacological Properties of Native and Recombinant KATP Channels

Property

β-cells/neurons

Smooth muscle (e.g., Cardiac/skeletal muscle vascular, bladder, gastrointestinal)

Subunits Single-channel conductance (pS) ATP (IC50, µM)

SUR1-Kir6.2 76

SUR2A-Kir6.2 76-80

SUR2B-Kir6.2 7-80.3

10-28

20-100

17-68

∼1.2 1120.8

0.1 351

No effect >30 2 NA Inagaki et al., 1996; Babenko et al., 1998; Shindo et al., 1998

37 0.6 2-10 0.2 Bonev and Nelson, 1993; Isomoto et al., 1996; Shindo et al., 1998; Shieh et al., 2000

Blockers (IC50, ìM): Glyburide 0.0018 Tolbutamide 32 Openers (EC50, ìM): Diazoxide Cromakalim Pinacidil P1075a References

60 >80 >100 NA Inagaki et al., 1995; Gribble et al., 1997; Inagaki et al., 1995; Gopalakrishnan et al., 2000

aAbbreviation: NA, not applicable.

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concentrations). Record increases in inward KATP currents in the presence of test compounds (Fig. 11.6.3). A minimum of three recordings from three different cells for each compound is typically generated. Known KCOs include 30 to 100 ìM diazoxide for SUR1-Kir6.2 channels and 10 ìM cromakalim, 10 ìM pinacidil, or 10 ìM P1075 for SUR2-Kir6.2 (Table 11.6.1)

4. To study the effects of KATP channel blockers: Evoke KATP currents using known KCOs. When the evoked KATP currents attain a steady state, apply the desired concentrations of blockers (single or multiple concentrations) and record inhibition of KATP currents. Table 11.6.1 lists approximate concentrations required for inhibition of various KATP channel subtypes. In cases where the use of a KCO to activate KATP currents is not feasible (e.g., when only weak openers are available), KATP currents should be evoked by perfusing with a solution containing metabolic inhibitors such as ∼3 mM azide, ∼1 mM cyanide, or ∼1 ìM of the proton inhibitor FCCP, and the resulting current increases recorded. Another way to deplete the intracellular ATP is to dialyze intracellular medium with a patch pipet containing a solution lacking ATP. Because of the high resistance of the patch pipet, however, the dialysis may not be efficient and may require a longer duration (5 to 10 min) for currents to attain steady-state levels. For data analysis, see Support Protocol.

KCO (10 µM)

glyburide (5 µ M)

−40 pA

50 pA 5 min

Analysis of ATP-Sensitive Potassium Channels

Figure 11.6.3 Activation of glyburide-sensitive KATP currents from a single guinea pig bladder smooth muscle cell by a KATP channel opener (KCO). After the whole-cell current recording configuration was established, a single smooth muscle cell was voltage clamped at –80 mV (a constant-voltage protocol) and membrane currents were recorded for 2 min to obtain a baseline. Upon addition of a KCO (10 µM cromakalim), increases in membrane currents were evoked, which reached a steady state after 10 min of application. Upon addition of 5 µM glyburide in the presence of cromakalim, the membrane currents reverted to the control level. The intracellular pipet solution contained 107 mM KCl, 1.2 mM MgCl2, 1 mM CaCl2, 10 mM EGTA, 5 mM HEPES, and 0.1 mM ATP, pH 7.2, adjusted with KOH (total K+ of ~140 mM). The bath solution contained 60 mM KCl, 80 mM NaCl, 2.6 mM CaCl2, 1.2 mM MgCl2, and 5 mM HEPES, pH 7.4, adjusted with NaOH.

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B

A

current-voltage curve (I-V curve)

glyburide (100 nM)

control (fully activated)

3

nA

2 mV

1

−100−80−60−40−20 −1

1 nA 100 msec

20 40 60 80 100

−2

control

−3

glyburide

C voltage-clamp protocol 100 mV −30 mV −100 mV

Figure 11.6.4 Activation of glyburide-sensitive KATP currents from RINm5F clonal cells. (A) The cell was voltage clamped at a holding potential of –30 mV after whole-cell current recording configuration was established. The membrane currents were evoked when the step voltage pulse was elicited from –100 mV to +100 mV for 400 msec with a 10-mV increment in each step using a patch pipet containing ATP-free solution. Addition of glyburide (100 nM) substantially diminished evoked membrane currents. (B) Currentvoltage (I-V) relationship curves of evoked KATP currents (filled squares) and currents in the presence of glyburide (open circles) were generated by plotting steady-state currents as a function of step changes in membrane potential. The evoked KATP currents had a reversal potential of approximately −30 mV. (C) The step voltage pulse protocol. The bath solution contained 40 mM KCl, 100 mM NaCl, 2.6 mM CaCl2, 1.2 mM MgCl2, and 5 mM HEPES, pH 7.4, adjusted with NaOH. The intracellular pipet solution contained 107 mM KCl, 1.2 mM MgCl2, 1 mM CaCl2, 10 mM EGTA, 5 mM HEPES, pH 7.2, adjusted with KOH (total K+ of ~140 mM).

STEP VOLTAGE PULSE PROTOCOL FOR KATP CURRENT RECORDING This voltage-clamp protocol is suitable for characterizing heterologous expression of recombinant KATP channels in Xenopus oocytes or clonal cell lines where endogenous ionic currents are negligible (Fig. 11.6.4). KATP currents are recorded at each test potential, and the current amplitude is plotted as a function of test potential to generate a currentvoltage relationship curve (I-V curve; Fig. 11.6.4B). Within a wide and appropriate range of testing potentials, the I-V curve can provide information on some biophysical properties of KATP channels such as their weak inward rectification and reversal potential.

ALTERNATE PROTOCOL 4

Materials Electrophysiology setup, including cell or oocyte expressing channel of interest (see Basic Protocol 2, and see Alternate Protocols 1 and 2) Test compound(s) appropriate for channel of interest, such as KATP channel openers (KCOs) and sulfonylurea KATP channel blockers glyburide and tolbutamide 1. Voltage clamp the single cell in an electrophysiology setup at a holding potential of –80 mV. Apply a test pulse from −100 mV to +50 mV (10 mV per each step) for 400 msec. Record the changes in membrane currents evoked by each test pulse.

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2. Carry out experiments with appropriate test compounds as described (see Basic Protocol 3, steps 3 and 4). 3. Apply sulfonylurea blockers such as glyburide or tolbutamide (Table 11.6.1) at the end of the experiment to record endogenous currents (non-KATP currents) that can be evoked by voltage stimulation. 4. Subtract endogenous currents from the total currents for accurate evaluation of compound effects on KATP currents. For data analysis, see Support Protocol. ALTERNATE PROTOCOL 5

RAMP PROTOCOLS FOR KATP CURRENT RECORDING This protocol is similar to the step pulse protocol (see Alternate Protocol 4), except that voltage clamp by ramp allows recording of KATP currents across a wide range of membrane potentials. Because the ramp protocol can accommodate a wide range of potentials within a few hundred milliseconds, it provides a quick and convenient way to generate a current

A

B KATP current

KCO (10 µM) glyburide (5 µM) + KCO

1000 pA

500 pA 250

500 mV

mV

control

−100 −80 −60−40 −20 -500

−100−80−60−40 −20 −250

20 40 60

20 40 60

−500

-1000

C ramp protocol

+60 mV

Time (msec) 0

100

−80 mV

200

300

400 −80 mV

−100 mV

Analysis of ATP-Sensitive Potassium Channels

Figure 11.6.5 Activation of KATP currents using the ramp protocol. A single bladder smooth muscle cell from pig was voltage clamped at a holding potential of –80 mV in a whole-cell patch-clamp configuration. (A) The whole-cell membrane currents (control, dotted line) were evoked by voltage clamp using a ramp protocol from –100 mV to +60 mV within 400 msec. Upon addition of 10 µM cromakalim, a KATP channel opener (KCO), increases in membrane currents were recorded under the same protocol (solid line). The increases in currents were reduced to near control levels when 5 µM glyburide was added in the presence of the KCO (dash-dotted line). The KCO-evoked and glyburide-sensitive KATP current was obtained by subtracting the membrane current recorded in the presence of KCO from that recorded in the presence of KCO and glyburide. (B) This current had a reversal potential of –20 mV. (C) The ramp protocol used to measure KATP current. The intracellular pipet and bath solutions contained ion compositions that are identical to those described in Figure 11.6.3.

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A

B glyburide (5 µM)

KCO (µM) 0.05

0.1

0.5

1

100

10

% control

−10 pA

80 60 40 EC 50 = 0.61 ± 0.13 µM

20 30 pA

0 −8

−7

2 min

−6 KCO log (M)

−5

Figure 11.6.6 Concentration-dependent increases in membrane currents evoked by a KATP channel opener (KCO). A single bladder smooth muscle cell from guinea pig was voltage clamped at –80 mV in a whole-cell current recording configuration. (A) The KCO (cromakalim) activated concentration-dependent increases in membrane currents that were reversed following addition of 5 µM glyburide. (B) The current responses were normalized to the control value obtained in the absence of the KCO. The concentration-response data were fitted using the logistic Hill equation (see Support Protocol). The KCO elicited increases in membrane current with an EC50 value of 0.61 ± 0.13 µM.

voltage relationship curve (I-V curve) for KATP currents (Fig. 11.6.5). The materials needed are the same as those for Basic Protocol 3. 1. In Clampex, a program in pClamp, select RAMP as the voltage-clamp protocol. 2. Determine the holding potential (e.g., −80 mV), the range of the voltage ramp (e.g., from −100 to +100 mV), and time needed to run through the ramp (e.g., 400 msec). The range of the voltage ramp should be chosen by the operator. In this case (−100 to +100 mV), the I-V curve can be seen over a wide voltage range.

3. Establish appropriate recording configuration (whole-cell patch clamp or two-electrode voltage clamp) for an electrophysiology setup. In whole-cell current recording configuration, the KATP currents can be recorded in the presence of metabolic inhibitors as described (see Basic Protocol 3).

4. Evoke KATP currents by repetitive voltage ramp (one trial per 3 sec) until the current amplitudes attain a steady state. 5. Apply desired concentration of test compounds (single or multiple concentrations) and examine effects (inhibition or activation) on KATP currents. For data analysis, see Support Protocol.

ANALYSIS OF DATA FROM KATP CHANNEL RECORDINGS Test Compound Effects (Concentration-Response Curve) The effects of compounds on KATP currents can be evaluated by normalizing the current amplitude obtained in the presence of the test compound to the amplitude measured in its absence (control value). The concentration-response data can be fitted to the logistic Hill equation, I = Imax/[1+(EC50/X)nH], where I is the current response, Imax is the maximal current, X is the concentration of the test compound, EC50 represents the concentration for a half-maximal effect, and nH represents the Hill coefficient (Fig. 11.6.6).

SUPPORT PROTOCOL

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A

B

3 nA

mV

−2 −3

20 40 60 80 100 [K+]

o

Rev (mV)

1 −100−80−60−40 −20 −1

PNa /P K = 0.017 ± 0.004

0

2

−20 −40 −60

(mM)

−80

100

−100

50 10

1

10 [K+] o(mM)

100

Figure 11.6.7 Ionic selectivity of KATP channels expressed in HEK-293 cells. (A) Membrane currents from HEK-293 cells stably transfected with SUR1-Kir6.2 were evoked by whole-cell patch-clamp recording using a patch pipet containing ATP-free solution. Changes in reversal potentials were observed when whole-cell currents were measured at varying extracellular K+ concentrations (−8.6, −23, and –60 mV at a [K+]o of 100, 50, and 10 mM, respectively). (B) The reversal potential is plotted as a function of extracellular K+ concentration and the curve was fitted to a modified Goldman equation (see Support Protocol). The SUR1-Kir6.2 KATP currents have a PNa/PK value of 0.017 ± 0.004.

K+ Selectivity A unique feature of KATP currents is that the evoked ionic current is selective for K+ ions. This information is critical especially when a novel current needs to be characterized in native tissues or in cell lines. This biophysical property can be studied by evoking KATP currents at varying extracellular K+ concentrations (2 to 100 mM) and by measuring the changes in reversal potential. Different extracellular K+ concentrations can be prepared by equimolar substitution with NaCl in the bath solution. The measured reversal potential is then plotted as a function of K+ concentration and the data are fitted to a modified Goldman equation: Erev = 59 × [log(Ko + αNao)/Ki], where Erev is the reversal potential; α is the apparent selectivity ratio, PNa/PK (where PNa and PK are the permeability of Na+ and K+, respectively); Ko and Nao are the extracellular concentrations of K+ and Na+, respectively; and Ki is the intracellular K+ concentration (Fig. 11.6.7). ATP Inhibition The inhibition of KATP currents by intracellular ATP can be obtained by measuring the evoked KATP currents at various concentrations of ATP. The modulation of KATP channels by ATP is different from that of purinergic receptors such as P2X3, in which ATP serves as an agonist to evoke nonselective cationic currents. Inside-out single-channel or macropatch recording is commonly used for this measurement because these methods allow intracellular ATP concentrations to be varied efficiently. In inside-out single-channel recording, the single-channel opening probability in the presence of ATP is normalized to the control value in the absence of ATP (Babenko et al., 1998). In inside-out macropatch recording, the evoked currents in the presence of ATP are normalized to the control value (Gribble et al., 1997). The concentration-response curve can be fitted to the Hill equation Y = 1/[1+([ATP]/Ki)nH], where [ATP] is the intracellular ATP concentration, Y is the normalized response, Ki is the ATP concentration at which inhibition is half-maximal, and nH is the Hill coefficient. Analysis of ATP-Sensitive Potassium Channels

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REAGENTS AND SOLUTIONS Use deionized, distilled water in all recipes and protocol steps. To remove the particles that might interfere with seal formation between pipet and cell membrane, filter sterilize all pipet and bath solutions with a 0.2-ìm filter. All solutions can be stored for 4 to 6 weeks at 4°C, unless otherwise specified. For common stock solutions, see APPENDIX 2A; for suppliers, see SUPPLIERS APPENDIX.

NOTE: Because the specific pipet and bath solution components will depend on the cell type and channel of interest, these recipes should be considered as guidelines. Previously published reports are a good source for specific solution recipes. Barth’s solution Prepare a solution containing 90 mM NaCl, 1 mM KCl, 0.66 mM NaNO3, 2.4 mM NaHCO3, 0.7 mM CaCl2, 0.82 mM MgCl2, 10 mM HEPES, and 2.5 mM pyruvate, pH 7.5, adjusted with 5 M NaOH. Immediately before use, supplement with 5 ml/liter penicillin-streptomycin solution (Sigma). Ca2+-free Barth’s solution Prepare Barth’s solution (see recipe) using 1.52 mM MgCl2 and no CaCl2. Inside-out bath solution Prepare a physiological solution containing 140 mM KCl, 1 mM MgCl2, 10 mM EGTA, and 5 mM HEPES, pH 7.4, adjusted with 5 M KOH. The bath solution should contain ionic components similar to the physiological internal solution.

Inside-out pipet solution The macropatch pipet solution used for recording KATP currents will have ionic components similar to that of the single-channel pipet solution (see recipe). In some cases, a K+-enriched external solution may also be used. For example, for recording of KATP currents from Xenopus oocytes co-injected with SUR1 and Kir6.2, prepare a physiological solution containing 140 mM KCl, 1.2 mM MgCl2, 2.6 mM CaCl2, and 10 mM HEPES, pH 7.4, adjusted with 5 M KOH (Gribble et al., 1997). Perforated-patch bath solution Prepare a physiological solution containing 140 mM NaCl, 3 mM KCl, 2.5 mM CaCl2, 1.2 mM MgCl2, 7.7 mM glucose, and 10 mM HEPES-NaOH, pH 7.2, adjusted with 5 M NaOH. For a K+-enriched bath solution, replace an equimolar concentration of NaCl with KCl to obtain the desired concentration of K+ and adjust the pH with 5 M KOH (Hogg and Adams, 2001). Perforated-patch pipet solution Prepare a physiological solution containing an ionic composition similar to intracellular solution. For example, the patch-pipet solution used to record KATP currents in neurons from intracardiac ganglia contains 75 mM K2SO4, 55 mM KCl, 5 mM MgSO4, and 10 mM HEPES, pH 7.2, adjusted with 5 M KOH (Hogg and Adams, 2001). Single-channel bath solution For cell-attached or outside-out patch-clamp recording: Prepare a physiological solution containing 140 mM NaCl, 5 mM KCl, 1 mM MgCl2, 1 mM CaCl2, and 10 mM HEPES, pH 7.4, adjusted with 5 M NaOH. In some experiments where a K+-enriched solution is needed, prepare the bath solution with 145 mM KCl, 1 mM MgCl2, 1 mM CaCl2, and 10 mM HEPES, pH 7.4, adjusted with 5 M KOH. The bath solution contains ionic components similar to the physiological external solution.

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For inside-out patch clamp recording: Prepare a physiological solution containing 140 mM KCl, 1 mM MgCl2, 10 mM EGTA, and 5 mM HEPES, pH7.4, adjusted with 5 M KOH. The bath solution contains ionic components similar to the physiological internal solution.

Single-channel pipet solution Prepare a physiological solution containing 140 mM NaCl, 5 mM KCl, 1 mM MgCl2, 1 mM CaCl2, and 10 mM HEPES, pH 7.4, adjusted with 5 M NaOH (Babenko et al., 1998). Depending on the experimental conditions, the pipet solution can be enriched for K+.

For K+-enriched pipet solution for cells: Prepare a solution containing 145 mM KCl, 1 mM MgCl2, 1 mM CaCl2, and 10 mM HEPES, pH 7.4, adjusted with 5 M KOH (Babenko et al., 1998). For K+-enriched pipet solution for oocytes: Prepare a solution containing 140 mM KCl, 1.2 mM MgCl2, 2.6 mM CaCl2, and 10 mM HEPES, pH 7.4, adjusted with 5 M KOH (Gribble et al., 1997). Two-electrode bath solution Prepare a physiological solution containing 87.5 mM NaCl, 2.5 mM KCl, 1.8 mM CaCl2, 1 mM MgCl2, and 5 mM HEPES, pH 7.4, adjusted with 5 M NaOH. For a K+-enriched bath solution, replace an equimolar concentration of NaCl with KCl to obtain the desired concentration of K+ and adjust the pH with 5 M KOH (Gribble et al., 1997). Whole-cell bath solution Prepare a physiological solution containing 135 mM NaCl, 5 mM KCl, 2.6 mM CaCl2, 1.2 mM MgCl2, and 5 mM HEPES, pH 7.4, adjusted with 5 M NaOH. For a K+-enriched bath solution, replace an equimolar concentration of NaCl with KCl to obtain the desired concentration of K+ and adjust the pH with 5 M KOH (Shieh et al., 2001). The bath solution should have an ionic composition similar to normal physiological solution.

Whole-cell pipet solution Prepare a physiological solution containing ionic components resembling those of cytoplasmic fluid (e.g., low Ca2+). For example, to record KATP currents from bladder smooth muscle cells, prepare a solution with 107 mM KCl, 1.2 mM MgCl2, 1 mM CaCl2, 10 mM EGTA, 5 mM HEPES, and 0.1 mM ATP, pH 7.2, adjusted with 5 M KOH. Prepare fresh. The total K+ concentration is ∼140 mM (Shieh et al., 2001). For longer storage (up to 4 to 6 weeks at 4°C), the pipet solution should be prepared without ATP. A fresh preparation of ATP is then added to a final concentration of 0.1 mM before each experiment.

COMMENTARY Background Information

Analysis of ATP-Sensitive Potassium Channels

ATP-sensitive K+ channels are a family of weak inward rectifiers expressed in diverse cell types where they couple cellular energy metabolism to membrane electrical activity. Their activity is inhibited by increases in intracellular ATP and is enhanced by increases in MgADP (Quayle et al., 1997). As with other K+ channels, KATP channels regulate resting membrane

potential such that the opening of KATP channels brings the membrane potential toward the K+ equilibrium potential (termed hyperpolarization, −80 mV under normal physiological conditions) because of the efflux of intracellular K+. On the other hand, inhibition of KATP channels causes cellular depolarization. Modulation of KATP channels is known to be a useful approach for the treatment of human

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disease. Blocking KATP channels in pancreatic β-cells by sulfonylureas such as glyburide or tolbutamide stimulates release of insulin, making them useful for the treatment of type II diabetes (Ashcroft and Rorsman, 1990). Opening KATP channels in smooth muscle cells causes membrane hyperpolarization, thereby relaxing smooth muscle previously stimulated by neurotransmitters and receptor agonists (Bonev and Nelson, 1993; Nuttle and Farley, 1997; Shieh et al., 2001). Thus, KATP channel openers (KCOs) can, in principle, be used for the treatment of disorders associated with overactive smooth muscle contraction such as hypertension, asthma, and idiopathic bladder instability. Activation of cardiac KATP channels (possibly mediated through mitochondrial KATP channels) protects against ischemic or reperfusion injury to the myocardium (Grover and Garlid, 2000). Furthermore, KATP channels in hypothalamic neurons are regulated by the obese gene product, leptin, suggesting that leptin modulation of KATP channels may be integral for maintaining homeostasis of body weight and energy balance (Spanswick et al., 1999). During the past few years, several candidate genes encoding KATP channels have been cloned. Reconstitution studies suggest that the native KATP channel exists as an octameric heteromultimer composed of four subunits of sulfonylurea receptors (SUR) and inward rectifier K+ channels belonging to either Kir6.1 or Kir6.2 (Fig. 11.6.1). SUR1 and the two SUR2 splice variants, SUR2A and SUR2B, have been co-expressed with either Kir6.1 or Kir6.2 to generate diverse KATP channels. These heterologous expression studies have shown that SUR1-Kir6.2 is the predominant subunit combination of KATP channels in the pancreas and neurons (Inagaki et al., 1995). Although the subunits forming mitochondrial KATP channels remain to be conclusively elucidated, the subunit composition of the cardiac sarcolemmal KATP channels is thought to be SUR2AKir6.2 (Inagaki et al., 1996; Babenko et al., 1997). SUR2B, in conjunction with Kir6.1 or Kir6.2, might form diverse smooth muscle– type KATP channels as revealed by biophysical and pharmacological criteria (Isomoto et al., 1996; Yamada et al., 1997; Gopalakrishnan et al., 1999).

General methods for studying KATP channels and screening for channel modulators Electrophysiology. Patch clamp can be used to measure properties of ionic currents from diverse single cells. The protocols described in this unit provide methods to characterize the biophysical properties of KATP channels and to permit analysis of direct ligand-channel interactions. The voltage-clamp protocol can be used to observe effects of channel modulators (openers or inhibitors) on K+ currents mediated through KATP channels without interference from other ion channels. Electrophysiology provides an accurate measurement of ion channel activities and of the effects of test compounds, although the assay throughput utilizing classical approaches is relatively slow. High-throughput electrophysiology systems. Electrophysiology remains the standard for measuring test compound interactions with ligand-gated and voltage-gated ion channels. To overcome the limitation of low throughput typically encountered by classical approaches, automated and robust oocyte and patch-clamp recordings have begun to emerge. Roboocyte (Multi Channel Systems) is a fully automated oocyte recording system that screens each oocyte sequentially in a 96-well format (Schnizler et al., 2003). Parallel Oocyte Electrophysiology Test station (POETs; Abbott Laboratories) is a robust system with six workstations that allow recording from oocytes expressing voltage- or ligand-gated ion channels in a parallel fashion with a throughput of ~1000 to 2000 compounds a day (Trumbull et al., 2003). OpusXpress (Axon Instruments) is another automated voltage-clamp workstation for studying ion channels in oocytes. Patch-on-a-chip is an emerging technology that converts conventional patch-clamp recording using a glass patch pipet into a 96-patch array format. In the patch array, the pipet is replaced with a planar array of recording interfaces miniaturized on the surface of silicon, silicone elastomer (PDMS; Klemic et al., 2002), or glass substrate that allows the formation of a high-quality tight seal (~1 GΩ) with the cell membrane with a high rate of success. Cells expressing ion channels are loaded into the array to form a tight seal and can subsequently be ruptured to obtain whole-cell recording in an automated manner. This technology is currently under development and the high-throughput patch array recording station for ion channels, including KATP channels, could be forthcoming. Some of the emerging

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Table 11.6.2

Troubleshooting Guide for KATP Channel Recording

Problem

Possible cause

Solution

Cell surface is not clean or poor cell quality Bath solution is not filtered Patch pipet is not clean Poor cell quality

Try a new cell

Cannot obtain stable recording

Patch pipet is too large Cell is not viable Vibration occurs during recording

No KATP current is evoked

Flow rate of bath solution is not stable Cell is not viable Cell does not express currents Compound is not active

Try a new patch pipet with smaller tip Try a new cell Check and adjust vibration table to prevent vibration during recording Check and adjust flow of bath solution

Patch clamp: Cannot make tight seal

Leak current increases after making whole-cell recording

Try a new cell Try a new cell Confirm with a known KATP channel opener

Two-electrode voltage clamp (oocytes): Leak current increases after oocyte is Poor oocyte quality impaled Grounding problem No expression of KATP currents

Voltage clamp is inappropriate

Analysis of ATP-Sensitive Potassium Channels

Try a new oocyte

Oocyte may not have been injected with cRNA or cDNA cRNA or cDNA is degrading Poor oocyte quality Electrode resistance is too high

instrumentation for automatic patch-clamp recording includes Ionworks (Molecular Devices; Kiss et al., 2003) and PatchExpress (Axon Instruments). In addition, another automation method for patch clamp is currently being developed (Asmild et al., 2003). Fluorescence-based assays. An alternative and indirect method to study the effects of modulators on KATP channels utilizes a fluorescence-based technique, such as the Fluorometric Imaging Plate Reader (FLIPR; UNIT 9.2). As mentioned above, opening KATP channels can lead to membrane hyperpolarization, whereas blockade of KATP channels can cause membrane depolarization. Thus, by loading cells that express KATP channels with membrane potential–sensitive fluorescent dyes, such as DiBAC4(3) or FLIPR membrane potential dye, effects can be monitored by changes in fluorescence (Whiteaker et al., 2001) in a highthroughput (96- or 384-well) format. FLIPR is

Filter bath solution Try a new pipet Try a new cell

Check electrical circuits and make sure ground wire is coated with chloride Try a new oocyte Make new batch of cRNA or cDNA Try a new oocyte Replace a new electrode with low resistance

currently one of the most widely used high throughput screening tools for KATP channel modulators. False readouts can arise because of changes in membrane potential by modulating other non-KATP channels or because of fluorescent or quenching compounds. Radioligand binding assays. Radioligand b ind ing assays using [3H]glyburide or [3H]P1075 have been used to identify compounds that exhibit high affinity for KATP channels (Yagupolskii et al., 1999; Gopalakrishnan et al., 2000). More recently, radioligands with an improved profile such as [125I]A-312110 have emerged, and these could facilitate screening for KATP channel openers and blockers in a high-throughput manner (Gopalakrishnan et al., 2002). Radioligand binding assays provide a medium- to high-throughput screen for KATP channel modulators. However, a lack of functional information limits the utility of these assays.

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Cationic flux assays. Radioactive isotopes have been used to trace the cellular influx or efflux of specific ions that permeate through K+ channels. The rubidium-86 (86Rb+) cationic efflux assay is commonly used for measuring K+ channel function, including KATP channels (Gopalakrishnan et al., 2000). Cells expressing KATP channels are loaded with media containing 86Rb+ for 4 to 5 hr. After the 86Rb+-containing medium is washed out, cells are incubated with modulators and the effects on KATP channels are monitored by quantifying the amount of effluxed radioactivity. Although this is a high-throughput method, it provides relatively little kinetic information compared with the voltage-clamp method. It also suffers from the inconvenience of handling radioactive materials. Although it has been demonstrated that a nonradioactive cell-based Rb+ efflux assay can be used to evaluate K+ channel function (Terstappen, 1999), the lack of kinetic resolution and limited functional readout continue to be limiting factors.

Critical Parameters and Troubleshooting Some of the critical parameters for analyzing KATP currents by patch clamp include the quality of the cell, patch electrode, and seal. During cell dissociation, the duration of enzyme digestion is critical to prevent cells from overdigestion. During cell dissociation, which was not covered in this unit, the duration of enzyme digestion is critical to prevent cells from overdigestion. This is also true for the defolliculation of Xenopus embryos (see Basic Protocol 2). In either case, overdigestion will damage the membrane, and the cells or oocytes will become leaky and fragile, which will make electrophysiological recording very difficult. In transfected clonal cell lines or oocytes, the expression levels depend on transfection efficiency, quality of cDNA or cRNA, and cell culture conditions. Table 11.6.2 describes general problems encountered during the electrophysiological recording of KATP channels.

Anticipated Results The protocols described in this unit can be used to measure KATP currents from a variety of native single cells such as neurons, pancreatic β-cells, cardiac myocytes, endothelial cells, and smooth muscle cells (Fig. 11.6.1). These protocols are also suitable for characterizing KATP currents from cells transfected with recombinant subunits. The amplitude of evoked KATP currents ranges from a few pi-

coamperes (pA; 10−12 amperes) to nanoamperes (nA; 10−9 amperes), depending on the cell type, the quality of the patch-clamp recording, and the quality of the cell. The evoked KATP currents should exhibit the following features: (1) sensitivity to blockade by sulfonylureas (Fig. 11.6.3), (2) sensitivity to inhibition by intracellular ATP, (3) K+ ion selectivity of evoked currents (Fig. 11.6.7), and (4) sensitivity to activation by KCOs (Table 11.6.1). This unit also provides a practical guide for examining the effects of modulators (openers or blockers) on KATP currents in a concentrationdependent manner (Fig. 11.6.6).

Time Considerations Although not covered in this unit, tissue dissection and single-cell dissociation for patch clamp recording of native cell types typically requires ~2 to 4 hr. For acute dissociation of single smooth muscle cells, the cells can be stored overnight in a refrigerator at 4°C before patch-clamp studies (Shieh et al., 2001). After single smooth muscle cells are transferred to a coverslip coated with poly-L-lysine in the recording chamber, cells should be allowed to attach to the coverslip for ≥5 to 10 min. For dissociated neurons or myocytes, after cells are plated in the cover slip in the culture medium, allow at least ~1 to 4 hr for cells to recover from the dissociation process. Time required to prepare oocytes for injection is ∼3 to 4 hr. It takes ∼30 to 40 min to inject cRNA into 100 oocytes. An automated cDNA or cRNA injection system (e.g., Roboocyte) might reduce the time required for injecting (Schnizler et al., 2003). Fabrication of patch pipets and preparation of test compound solutions may be carried out during the cell or oocyte recovery time. Making a whole-cell current recording can take ∼5 to 10 min. A considerable amount of time (5 to 10 min) is first required, however, to identify good-quality cells; make a good seal; and adjust the capacitance, series resistance compensation, and recording parameters. The perforated-patch technique requires ∼20 to 30 min for successful perforation. When studying the effects of modulators on KATP currents, ~5 to 7 min is needed to test a single concentration (Fig. 11.6.3) and ∼12 to 16 min is needed to generate a concentrationresponse curve (Fig. 11.6.6). The time required includes recording baseline, measuring KATP currents in the presence of compounds, and assessing the sensitivity of evoked KATP currents to sulfonylurea inhibition.

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Literature Cited Aguilar-Bryan, L., Clement, J.P. IV, Gonzalez, G., Kunjilwar, K., Babenko, A., and Bryan, J. 1998. Toward understanding the assembly and structure of KATP channels. Physiol. Rev. 78:227-245. Ashcroft, F.M. and Rorsman, P. 1990. ATP-sensitive K+ channels: A link between β-cell metabolism and insulin secretion. Biochem. Soc. Trans. 18:109-111. Asmild, M., Oswald, N., Korsgaard, M.P.G., Krzywkowski, K.M., Friis, S., Jacobsen, R.B., Reuter, D., Taboryski, R., Kutchinsky, J., Vestergaard, R.K., Schroder, R.L., Sorensen, C.B., Bech, M., and Willumsen, N.J. 2003. Upscaling and automation of electrophysiology: Toward high throughput screening in ion channels drug discovery. Receptors Channels 9:49-58. Babenko, A.P., Gonzalez, G., Aguilar-Bryan, L., and Bryan, J. 1998. Reconstituted human cardiac KATP channels: Functional identity with the native channels from the sarcolemma of human ventricular cells. Circ. Res. 83:1132-1143. Bonev, A.D. and Nelson, M.T. 1993. ATP-sensitive potassium channels in smooth muscle cells from guinea pig urinary bladder. Am. J. Physiol. 264:C1190-C1200. Coghlan, M., Carroll, W.A., and Gopalakrishnan, M. 2001. Recent developments in the biology and medicinal chemistry of potassium channel modulators: Update from a decade of progress. J. Med. Chem. 44:1627-1653. Gopalakrishnan, M., Whiteaker, K.L., Molinari, E.J., Davis-Taber, R., Scott, V.E., Shieh, C.C., Buckner, S.A., Milicic, I., Cain, J.C., Postl, S., Sullivan, J.P., and Brioni, J.D. 1999. Characterization of the ATP-sensitive potassium channels (KATP) expressed in guinea pig bladder smooth muscle cells. J. Pharmacol. Exp. Ther. 289:551-558. Gopalakrishnan, M., Molinari, E.J., Shieh, C.C., Monteggia, L.M., Roch, J.M., Whiteaker, K.L., Scott, V.E., Sullivan, J.P., and Brioni, J.D. 2000. Pharmacology of human sulphonylurea receptor SUR1 and inward rectifier K+ channel Kir6.2 combination expressed in HEK-293 cells. Br. J. Pharmacol. 129:1323-1332. Gopalakrishnan, M., Davis-Taber, R., Molinari, E.J., Whiteaker, K.L., Buckner, S.A., Shieh, C.C., Scott, V.E.S., Rotert, G., Altenbach, R., Coghlan, M., and Carroll, W.A. 2003. [125I]A312110: A novel high affinity 1,4-dihydropyridine ATP-sensitive K+ channel opener: Characterization and pharmacology of binding. Mol. Pharm. (in press). Gribble, F.M., Ashfield, R., Ammala, C., and Ashcroft, F.M. 1997. Properties of cloned ATP-sensitive K+ currents expressed in Xenopus oocytes. J. Physiol. 498:87-98.

Analysis of ATP-Sensitive Potassium Channels

Grover, G.J. and Garlid, K.D. 2000. ATP-sensitive potassium channels: A review of their cardioprotective pharmacology. J. Mol. Cell. Cardiol. 32:677-695.

Gundersen, C.B., Miledi, R., and Parker, I. 1983. Voltage-operated channels induced by foreign messenger RNA in Xenopus oocytes. Proc. R. Soc. Lond. B 220:131-140. Hamill, O.P., Marty, A., Neher, E., Sakmann, B., and Sigworth, F.J. 1981. Improved patch-clamp techniques for high-resolution current recording from cells and cell-free membrane patches. Pflugers Arch. 391:85-100. Hilgemann, D.W. 1995. The giant membrane patch. In Single-Channel Recording, 2nd ed. (B. Sakmann and E. Neher, eds.) pp. 307-327. Plenum Press, New York. Hille, B. 2001. Ion Channels of Excitable Membranes, 3rd ed. Sinauer Associates, Sunderland, Mass. Hogg, R.C. and Adams, D.J. 2001. An ATP-sensitive K+ conductance in dissociated neurons from adult rat intracardiac ganglia. J. Physiol. 534:713-720. Horn, R. and Korn, S.J. 1992. Prevention of rundown in electrophysiological recording. Methods Enzymol. 207:149-155. Horn, R. and Marty, A. 1988. Muscarinic activation of ionic currents measured by a new whole-cell recording method. J. Gen. Physiol. 92:145-159. Inagaki, N., Gonoi, T., Clement, J.P. IV, Namba, N., Inazawa, J., Gonzalez, G., Aguilar-Bryan, L., Seino, S., and Bryan, J. 1995. Reconstitution of IKATP: An inward rectifier subunit plus the sulfonylurea receptor. Science 270:1166-1170. Inagaki, N., Gonoi, T., Clement, J.P., Wang, C.Z., Aguilar-Bryan, L., Bryan, J., and Seino, S. 1996. A family of sulfonylurea receptors determines the pharmacological properties of ATP-sensitive K+ channels. Neuron 16:1011-1017. Isomoto, S., Kondo, C., Yamada, M., Matsumoto, S., Higashiguchi, O., Horio, Y., Matsuzawa, Y., and Kurachi, Y. 1996. A novel sulfonylurea receptor forms with BIR (Kir6.2) a smooth muscle type ATP-sensitive K+ channel. J. Biol. Chem. 271:24321-24324. Kiss, L., Bennett, P.B., Uebele, V.N., Koblan, K.S., Kane, S.A., Neagle, B., and Schroeder, K. 2003. High throughput ion-channel pharmacology: Planar-array-based voltage clamp. Assay Drug Dev. Technol. 1:127-135. Klemic, K.G., Klemic, J.F., Reed, M.A., and Sigworth, F.J. 2002. Micromolded PDMS planar electrode allows patch clamp electrical recordings from cells. Biosens. Bioelectron. 17:597604. Noma, A. 1983. ATP-regulated K+ channels in cardiac muscle. Nature 305:147-148. Nuttle, L.C. and Farley, J.M. 1997. Muscarinic receptors inhibit ATP-sensitive K+ channels in swine tracheal smooth muscle. Am. J. Physiol. 273:L478-L484. Quayle, J.M., Nelson, M.T., and Standen, N.B. 1997. ATP-sensitive and inwardly rectifying potassium channels in smooth muscle. Physiol. Rev. 77:1165-1232.

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Rae, J., Cooper, K., Gates, P., and Watsky, M. 1991. Low access resistance perforated patch recordings using amphotericin B. J. Neurosci. Methods 37:15-26.

Spanswick, D., Smith, M.A., Groppi, V.E., Logan, S.D., and Ashford, M.L. 1997. Leptin inhibits hypothalamic neurons by activation of ATP-sensitive potassium channels. Nature 390:521-525.

Sakmann, B. and Neher, E. 1995. Single-Channel Recording, 2nd ed. Plenum Press, New York.

Terstappen, G.C. 1999. Functional analysis of native and recombinant ion channels using a high-capacity nonradioactive rubidium efflux assay. Anal. Biochem. 272:149-155.

Schnizler, K., Fejtl, M., Kuster, M., and Methfessel, C . 2003. The Roboocyte: Automated cDNA/mRNA injection and subsequent TEVC recording on Xenopus oocytes in 96-well microtiter plates. Receptors Channels 9:41-48. Seino, S. 1999. ATP-sensitive potassium channels: A model of heteromultimeric potassium channel/receptor assemblies. Annu. Rev. Physiol. 1:337-362. Shieh, C.C., Coghlan, M., Sullivan, J.P., and Gopalakrishnan, M. 2000. Potassium channels: Molecular defects, diseases, and therapeutic opportunities. Pharmacol. Rev. 52:557-594. Shieh, C.C., Feng, J., Buckner, S.A., Brioni, J.D., Coghlan, M.J., Sullivan, J.P., and Gopalakrishnan, M. 2001. Functional implication of spare ATP-sensitive K+ channels in bladder smooth muscle cells. J. Pharmacol. Exp. Ther. 296:669675. Shih, T.M., Smith, R.D., Toro, L., and Goldin, A.L. 1998. High-level expression and detection of ion channels in Xenopus oocytes. Methods Enzymol. 293:529-556. Shindo, T., Yamada, M., Isomoto, S., Horio, Y., and Kurachi, Y. 1998. SUR2 subtype (A and B)-dependent differential activation of the cloned ATP-sensitive K+ channels by pinacidil and nicorandil. Br. J. Pharmacol. 124:985-991. Shyng, S., Ferrigni, T., and Nichols, C.G. 1997. Control of rectification and gating of cloned KATP channels by the Kir6.2 subunit. J. Gen. Physiol. 110:141-153.

Trumbull, J.D., Bertrand, D., Maslana, E.S., McKenna, D.G., Nemcek, T.A., Niforatos, W., Pan, J.Y., Parihar, A.S., Shieh, C.C., Wilkins, J.A., and Briggs, C.A. 2003. High throughput electrophysiology using a fully automated, multiplexed recording system. Receptors Channels 9:19-28. Whiteaker, K.L., Gopalakrishnan, S.M., Groebe, D., Shieh, C.C., Warrior, U., Burns, D.J., Coghlan, M.J., Scott, V.E., and Gopalakrishnan, M. 2001. Validation of FLIPR membrane potential dye for high throughput screening of potassium channel modulators. J. Biomol. Screen. 6:305-312. Yagupolskii, L.M., Antepohl, W., Artunc, F., Handrock, R., Klebanov, B.M., Maletina, I.I., Marxen, B., Petko, K.I., Quast, U., Vogt, A., Weiss, C., Zibold, J., and Herzig, S. 1999. Vasorelaxation by new hybrid compounds containing dihydropyridine and pinacidil-like moieties. J. Med. Chem. 42:5266-5271. Yamada, M., Isomoto, S., Matsumoto, S., Kondo, C., Shindo, T., Horio, Y., and Kurachi, Y. 1997. Sulphonylurea receptor 2B and Kir6.1 form a sulphonylurea-sensitive but ATP-insensitive K+ channel. J. Physiol. 499:715-720.

Contributed by Char-Chang Shieh and Murali Gopalakrishnan Abbott Laboratories Abbott Park, Illinois

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Overview of Electrophysiological Characterization of Neuronal Nicotinic Acetylcholine Receptors INTRODUCTION When studying receptors such as the neuronal nicotinic acetylcholine receptors (nAChRs), it is often necessary to recall the mechanisms underlying the fundamental processes of synaptic transmission. Briefly, the nAChRs belong to the large family of ligandgated channels (LGICs). These integral membrane proteins share the structural characteristic of forming the ligand-binding site and ionic pore at the same time. Binding of the neurotransmitter stabilizes the receptor in the active (open) configuration, allowing ions to flow through the pore. The time constant of receptor activation is typically in the microsecond time scale. Prolonged exposure to the ligand causes a gradual desensitization of the receptor that is evidenced by a reduction of the ionic flux through the channel. Removal of the agonist from the synaptic cleft quickly results in channel closure and termination of the action of the neurotransmitter. To properly study nAChRs, it is therefore necessary either to examine their properties at a functional synapse or to recreate, in vitro, conditions that mimic those found in vivo. These requirements necessitate establishing stringent agonist application and cellular conditions. Electrophysiological investigations, with their microsecond time resolution, provide numerous avenues to characterize ligand-gated receptors such as nAChRs. Basic receptor properties, including sensitivity to agonists, antagonists, or desensitization, can be readily and accurately determined. In addition, electrophysiology allows measurement of the ionic selectivity of the channels as well as determination of effects of allosteric effectors. It is important to recall that electrophysiology is the only technique that allows investigation of the effects of cholinergic transmission in brain slices or ganglia preparation at the cellular level. This overview presents a step-by-step discussion of the requirements and methods for investigation of neuronal nicotinic acetylcholine receptors. The representative results presented herein can be obtained with commercially available materials; therefore, the focus

UNIT 11.7

of the unit is on the biological questions rather than technical issues.

DRUG APPLICATION METHODS AND CONSIDERATIONS Drug application is one of the critical points in electrophysiological characterization of fast responding ligand-gated channels such as nAChRs. It must be kept in mind that, in the synaptic cleft, the concentration of the neurotransmitter increases extremely rapidly and can reach up to 1 mM in Ca2+

Na+ = K+ > Ca2+

Na+ = K+ > Ca2+ ; N.S. pore

Slow

Slow

Slow

Slow

Slow

Ligandsc

KN-62 (40 nM)

Physical properties

Ion permeability

Na+ = K+ > Ca2+

Kineticsd

Fast Fast (10–100 ms) (15 min to recover completely from desensitization. Control responses are considered stable usually when two to three consecutive recordings are achieved that do not differ by >10% in amplitude.

13a. After achieving stable controls, carry out experiments, always maintaining a 2- to 4-min interval between applications. For example, apply different doses of agonist to generate a concentration-response curve, test antagonists to generate inhibition curves, or apply agonist while varying membrane potential to generate current-voltage (I/V) relationships. Steps 14a to 18a are used to characterize an ion channel or test compound of interest.

14a. For agonist and antagonist dose-response data, measure peak current amplitudes using pCLAMP software by calculating the difference between peak current amplitude achieved during agonist application and the holding current amplitude before agonist application for each recording. 15a. Express dose-response data as a percentage of control amplitude and curve fit using graphics software to generate an EC50 or IC50 . 16a. For current-voltage relationships, measure peak current amplitudes as stated above (step 15a) and plot amplitude of each response against the membrane potential. Properties such as inward rectification (i.e., P2X3 ) can be seen from these plots.

17a. Measure rise times of recorded inward currents using pCLAMP software between 10% and 90% peak. 18a. For desensitizing currents, estimate exponential time constants of current desensitization (τ ) by pCLAMP software using a Chebyshev curve-fitting algorithm. Typically, the data are best fitted by two exponential functions (see Burgard et. al., 1999).

To record agonist-evoked nondesensitizing currents (i.e., P2X2 , or P2X2/3 ) 11b. Use pCLAMP software to trigger agonist application from the push/pull applicator and then synchronize it with data acquisition. Unlike desensitizing receptors, agonists can be bath applied to nondesensitizing receptors, but for ease in synchronizing data capturing with agonist application or co-applying and preapplying antagonists, use of the push/pull applicator or piezo-electric device is preferred.

12b. Apply agonist in 30-sec to 1-min intervals to establish control responses. Since these receptors do not desensitize, intervals can be of much shorter duration than for the desensitizing receptors. Control responses are considered stable usually when two to three recordings are obtained which do not differ by >10% in amplitude.

13b. Record and save these current values before proceeding with the experiment.

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14b. After achieving stable controls, carry out a number of experiments as described in steps 13a to 17a. SUPPORT PROTOCOL 1

OOCYTE HARVESTING, DEFOLLICULATION, AND INJECTION In this protocol, oocytes are collected, follicular cells are removed, and the oocytes are injected with mRNA or cDNA for receptor expression.

Materials 2.5-year-old female Xenopus laevis frogs (e.g., Xenopus 1, NASCO) 0.28% (w/v) 3-aminobenzoic acid ethyl ester methanesulfonate salt (tricaine): dissolve 1.4 g tricane in 500 ml H2 O (prepare fresh) Low-Ca2+ Barth’s solution (see recipe) 2 mg/ml collagenase: 20 mg collagenase type 1A (Sigma) in 10 ml low-Ca2+ Barth’s solution (prepare fresh) Normal Barth’s solution (see recipe), 16◦ to 19◦ C 1 µg/µl P2X receptor cDNA or mRNA Mineral oil 100 × 20–mm glass petri dishes Surgical instruments (e.g., scissors, forceps, 18-G needle) Synthetic, absorbable, 4-0 suture Nonabsorbable, surgical, 4-0 suture (i.e., black monofilament nylon) 50-ml conical tubes Dissecting microscope (e.g., Nikon SMZ-2B stereomicroscope with 0.5× objective and 20× eyepiece) 16◦ to 19◦ C incubator (e.g., Sheldon Manufacturing) Micropipet puller (e.g., Model P-80/PC Flaming/Brown, Sutter Instruments) Injection pipet: 0.5-mm-i.d. × 1.0-mm-o.d. borosilicate glass (Sutter Instruments, cat. no. B100-50-10) Three-dimensional micromanipulator (Narishige) Fine forceps 60 × 15–mm plastic petri dish 100 × 20–mm glass petri dish with polypropylene mesh glued to the bottom (Warner Instruments) PV830 Pneumatic PicoPump (World Precision Instruments) with either a standard floor vacuum pump or house vacuum attached, and compressed N2 NOTE: All protocols using live animals must first be reviewed and approved by an Institutional Animal Care and Use Committee (IACUC) and must follow officially approved procedures for the care and use of laboratory animals. NOTE: All chemicals are purchased from Sigma unless otherwise noted. All laboratory supplies are purchased from VWR unless otherwise noted.

Anesthetize frog 1. Anesthetize a 2.5-year-old female Xenopus laevis frog by placing it in a 500-ml 0.28% (w/v) tricaine bath for 10 to 20 min. An adequate plane of anesthesia is obtained when the frog exhibits no response to a deep pinch to the hind limb. Characterization of Recombinant and Native P2X Receptors

2. While the frog is being anesthetized, prepare a bed of ice on which to lay it during removal of oocytes. Set up two 100 × 20–mm glass petri dishes containing lowCa2+ Barth’s solution, 16◦ to 19◦ C, to receive the removed ovarian lobes (see step 4).

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Figure 11.9.1 Protocol 1).

The removal of Xenopus laevis frog oocytes as described (see Support

3. When the frog is fully anesthetized, place it on its back on the bed of ice and rinse its belly with distilled water.

Remove ovarian lobes 4. Using sterile surgical instruments, make a small diagonal incision ∼1 cm in length midlaterally on the abdomen (see Fig. 11.9.1). The skin is difficult to pierce with scissors; therefore, a sharp needle can be used to start the incision.

5. Cut through the skin as well as the underlying fascia to expose the lobes of the ovary containing the oocytes. 6. Using forceps, pull out as many lobes as are needed (usually two or three are sufficient). Cut the lobes with scissors and place them in the petri dish containing low Ca2+ Barth’s solution (step 2). Rinse and place in the second petri dish containing low-Ca2+ Barth’s solution. Replace the remaining lobes back into the frog’s abdomen.

Dress frog and allow to recover 7. Close the incision by stitching together the underlying fascia using sterile synthetic, absorbable, 4-0 suture. Close the skin incision using sterile nonabsorbable, surgical 4-0 suture. Usually two to three stitches for each layer are sufficient depending on the size of the incision.

8. Rinse the frog’s belly with water and transfer the animal to a small container of water, 23◦ C, for recovery (30 to 60 min). Once recovered, place the frog back in the colony. Frogs can be used multiple times, allowing time for healing and always alternating sides of the abdomen for incision. After multiple surgeries, oocyte deterioration is often observed.

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Defolliculate oocyctes 9. Use forceps to open the freshly removed oocyte lobes and thus better expose oocytes to the collagenase treatment (step 10). Opened lobes can also be cut into smaller pieces.

10. Place the opened lobes into a 50-ml conical tube containing 10 ml of 2 mg/ml collagenase. Incubate 1 hr at room temperature by taping the 50-ml conical tube to a rocking platform set for gentle agitation. 11. Aspirate the collagenase and rinse ten times with 10 ml (each) fresh low-Ca2+ Barth’s solution. Transfer oocytes to a new 50-ml conical tube and add 10 ml fresh 2 mg/ml collagenase. 12. Continue gentle agitation (see step 10) at room temperature. Check every 15 min to determine if oocytes are free of the follicular layer. Stop collagenase treatment (i.e., proceed to step 13) before all oocytes are fully defolliculated (∼1 hr), by rinsing with fresh low-Ca2+ Barth’s solution. If collagenase is left on too long, oocyte membranes will start to deteriorate and the rinse solution will become increasingly cloudy, after which, the oocytes become largely unusable.

Select healthy oocytes 13. Rinse ten times with 10 ml (each) fresh low-Ca2+ Barth’s solution. Repeat this washing step (i.e., ten 10-ml washes) again in normal Barth’s solution. 14. Transfer rinsed oocytes to a sterile glass petri dish. Use a dissecting microscope to select healthy stage V or VI oocytes. Healthy stage V to stage VI oocytes have clearly delineated animal and vegetal hemispheres and are at least 1 mm in diameter.

15. Maintain the selected oocytes in normal Barth’s solution in a 16◦ to 19◦ C incubator. Oocytes are usually maintained 2 to 24 hr before injection. This allows for the removal of additional oocytes that may not have survived the collagenase treatment.

Prepare for injection 16. Vortex 1 µg/µl P2X receptor cDNA or mRNA. Microcentrifuge in a 1-ml microcentrifuge tube ∼60 sec at 10,000 rpm to pellet any insoluble material that may be present in the sample. 17. Program a micropipet puller to pull an injection pipet to an extremely fine tip (≤30 µm), following manufacturer’s instructions. 18. Load the injection pipet into the holder mounted on a three-dimensional micromanipulator. While looking through the microscope, break the tip back to ∼30-µm o.d. using fine forceps.

Load injection pipet 19. Color the inside of the top cover of a 60 × 15–mm plastic petri dish with a black lab marker in order to create a dark background to better see the drop of DNA or RNA. Cover the entire dish with Parafilm. Using forceps, draw a small box with an “X” marked inside directly on the Parafilm carrying the petri dish.

Characterization of Recombinant and Native P2X Receptors

20. Pipet the desired amount (∼2 to 5 µl of 1 µg/µl cDNA or 5 µl of 1 µg/µl mRNA) of sample to be injected onto the marked “X” with a sterile pipet tip. Both concentration and volume of injected genetic material can be altered to get the desired level of receptor expression.

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21. Lower the injection pipet tip into the sample and turn the vacuum switch of the PicoPump to the On position in order to draw the sample up into the injection pipet.

Calibrate injection pipet 22. Place a 200-µl drop of mineral oil onto the Parafilm. 23. Inject the sample into the mineral oil and measure the resulting sphere diameter using the microscope reticule. Calculate the volume of the sphere (V = 4π r3 /3). Vary the injection time (typically 0.2 to 2 sec) to determine the appropriate volume. Due to the oil/water interface, the sample of RNA or DNA that is injected into the oil makes a clearly delineated sphere. Twelve nanoliters has a surface diameter of ∼270 µm, which is an appropriate amount for cDNA injection. An appropriate volume for mRNA injections is 54 nl, which has a surface diameter of ∼450 µm.

Inject oocyte 24. Fill the polypropylene mesh glued to the bottom of a 100 × 20–mm glass petri dish with normal Barth’s solution. After the injection pipet is calibrated, place oocytes (step 15) on the mesh. 25a. For RNA (cytoplasmic): Insert the injection pipet into the oocyte on the animal pole (dark hemisphere) and, using a PV830 Pneumatic PicoPump, deliver the calibrated amount of mRNA (∼50 nl). Using the gauge on the PicoPump, the vacuum pressure is typically set to ∼25 in. Hg. Compressed nitrogen also feeds into the PicoPump and is usually set to ∼35 psi using the gauge on the PicoPump.

25b. For DNA injection (nuclear): Inject on the following manner: a. Use fine forceps to tilt the oocyte so that its animal pole is perpendicular to the injection pipet. b. Insert the injection pipet into the oocyte slightly further than for RNA injections targeting the center of the oocyte in order to get the DNA into the nucleus. c. Pump in the appropriate quantity (∼12 nl).

Characterize expression 26. Store injected oocytes in a fresh dish containing normal Barth’s solution in a 16◦ to 19◦ C incubator up to 7 to 10 days. Change medium daily (normal Barth’s) and discard dead or deteriorating oocytes. 27. Check for expression as early as 24 hr after injection using two-electrode voltage clamp recording techniques (see Basic Protocol 1). Use expressing oocytes for measurements.

CONSTRUCTION OF A PUSH/PULL APPLICATOR A push-pull applicator is used to apply agonist to oocytes (see Basic Protocol 1). Construction of this applicator should be completed and put in position before oocyte recording. This equipment can be reused until damaged or excessively dirty. When this occurs replace only dirty/damaged parts.

SUPPORT PROTOCOL 2

Materials 1.44-mm-o.d. × 1.2-mm-i.d.glass capillary (Warner Instruments) Silicone tubing, sizes 13 and 14 (Masterflex, Cole Palmer Instrument) T-fittings that can accommodate size 13 silicone tubing

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Figure 11.9.2

Schematic diagram of the push/pull applicator (see Basic Protocol 1 and Support Protocol 2).

PE50 polyethylene tubing (Becton-Dickinson) Microscope Peristaltic pump, three-channel (Masterflex Model 7523-30, Cole Palmer Instruments) Micromanipulator (i.e., Narishige) Oocyte chamber (Warner Instruments) Computer-driven solenoid valve with valve driver (Series 1, General Valve) PC computer with analog-to-digital (A/D) board (i.e., Digidata 1200) and pCLAMP software (Axon Instruments) Ring stand Antivibration table (Technical Manufacturing) 10-ml plastic syringes with Luer-Loks Rubber stoppers, size 1 18-G × 2-in. stainless steel Luer-Lok hypodermic needles Female Luer fittings NOTE: See Figure 11.9.2 for a schematic of this apparatus.

Characterization of Recombinant and Native P2X Receptors

Construct tubing for the applicator 1. To construct the outer tubing of the applicator, heat one end of a 1.44-mm-o.d. × 1.2-mm-i.d. glass capillary using a Bunsen burner until it can be bent gently with forceps. Bend to a 45◦ angle ∼1 cm from the end of the capillary. Fire-polish both ends.

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The bent end of the capillary will be positioned close to the oocyte for direct agonist application.

2. Put small pieces of silicone tubing, size 13, on each straight arm of a T-fitting. Connect the straight end of the glass capillary to one of the straight arms. 3. For the inner tubing of the applicator, thread PE50 polyethylene tubing through the straight arm of the T-fitting into the glass capillary all the way through the bent end. Cut the end of the polyethylene tubing with a razor blade. 4. Using a microscope, pull the inner tubing back into the glass capillary until it is 1.0 mm from the end.

Set up peristaltic pump 5. Set up a three-channel peristaltic pump with appropriate pump head sizes to hold two different sizes of silicone tubing: one size 13 and two size 14. The device will be used in order to pump solution in and out of the applicator as well as into the chamber itself.

6. Set the peristaltic pump to perfuse at a rate of 1 ml/min using size 13 tubing.

Connect applicator to the pump 7. Using size 13 silicone tubing, connect one end to the size 13 pump head. Connect the other end to the PE50 tubing that is threaded through the glass capillary and up through the straight-arm of the T-fitting (step 3). Using the peristaltic pump, this pump head pumps agonist through the PE50 tubing threaded through the glass capillary at a rate of 1.0 ml/min.

8. Using size 14 silicone tubing, connect one end of the tubing to the size 14 pump head so that it pulls in the reverse direction from the size 13 pump head (step 7). Connect the other end to the remaining side arm of the T-fitting. Allow suctioned liquid to drain to a waste container. This pump head, because it is connected in the reverse direction, pulls agonist out from the end of the glass capillary with bathing solution at a rate of 3.5 ml/min due to its larger size (14) silicone tubing. Agonist-containing solution does not leak out the end of the capillary tubing unless triggered, for two reasons. First, the agonist-containing tube flows at a rate of 1.0 ml/min but is being pulled out the side arm at a rate of 3.5 ml/min. Second, the inner tubing is positioned far enough from the end of the capillary (1.0 mm) to prevent leakage while close enough to minimize the dead volume. Bath perfusion is also accomplished using larger tubing (size 14) fit into the appropriate pump head so that the perfusion rate is also 3.5 ml/min (see below).

9. Connect size 14 silicone tubing to the second size 14 pump head. Connect the other end of the size 14 silicone tubing to the oocyte chamber. This feeds the bath at a rate of 3.5 ml/min.

10. Using a micromanipulator, mount the applicator so that it can be lowered into the oocyte chamber, positioned to within 200 to 400 µm of the oocyte, when ready to record agonist-induced current.

Set up valve for agonist application 11. Set up a computer-driven solenoid valve so that when the valve driver is triggered, the suction will be shut off from the applicator. Insert the solenoid valve in the size 14 silicone tubing that is pumping agonist in reverse from the end of the glass capillary (step 9; also see Fig. 11.9.2). This setup allows agonist to flow out the end of the glass capillary and reach the oocyte for the specified time.

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12. Automate control of agonist application and synchronization with recording of data by connecting the valve driver to the digital output of a Digidata 1200 A/D converter board mounted in a PC computer running pCLAMP software.

Connect in-line drippers 13. Set up a ring stand on an antivibration table to hold three 10-ml plastic syringes. Fit each 10-ml syringe tightly with a size 1 rubber stopper and insert an 18-G × 2-in. needle through the center of each rubber stopper so that ∼0.75 in. of the needle hangs down into the syringe. The 10-ml syringes act as in-line drippers to absorb pressure pulses and reduce electrical noise.

14. Fit a female Luer fitting on the bottom end of each 10-ml syringe. 15. Cut the size 13 silicone tubing pumping agonist into the PE50 tubing of the glass capillary (step 8) and connect one cut end to the 18-G needle fitted into the rubber stopper. Connect the other cut end of the silicone tubing to the Luer fitting on the bottom of the 10-ml syringe. 16. Cut the size 14 silicone tubing that pumps agonist in reverse from the agonist applicator (step 9) and connect it to a 10-ml syringe in the same manner as described above. Repeat for the size 14 silicone tubing that perfuses the oocyte bath (step 9). BASIC PROTOCOL 2

MEASUREMENT OF ATP-EVOKED RESPONSES FROM ACUTELY DISSOCIATED DORSAL ROOT GANGLION NEURONS USING WHOLE-CELL PATCH CLAMP A variety of ATP-evoked responses are seen in dorsal root ganglion (DRG) neurons whose diameters range from 15 to >50 µm. In particular, studies show that on small-diameter neurons (≤25 µm) labeled with the FITC-labeled plant lectin BSI-B4 (Vulchanova et al., 1998), ATP-evoked responses involve the P2X3 receptor or the P2X2/3 heteromeric receptor (Burgard et al., 1999). This protocol describes basic recording procedures to acquire ATP-evoked responses from acutely dissociated rat DRG neurons

Materials

Characterization of Recombinant and Native P2X Receptors

Internal electrode solution (see recipe) Cultured rat DRG neurons (see Support Protocol 3) Extracellular recording solution (see recipe) Test compounds (agonist, antagonist; Table 11.9.1) Micropipet puller (Sutter Instruments) Recording microelectrodes: borosilicate glass with 1.5-mm filament (World Precision Instruments) Pipet filler: 1-ml syringe with 34-G microfiller attached (World Precision Instruments) Electrode holder with pressure port (Axon Instruments) Micromanipulator attached to microscope stage to hold headstage with electrode holder (Burliegh Instruments) PE-160 tubing 10-ml plastic syringe Piezo-driven rapid application system (see Support Protocol 4) Cyberamp (Axon Instruments) pCLAMP software for data acquisition and analysis (Axon Instruments)

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Graphics software (e.g., Prism, Graphpad Software) Recording chamber (Warner Instruments) NOTE: An electrophysiological setup as described in UNITS 11.3 & 11.4 must include the necessary equipment to record whole-cell currents from rat neurons. Procedures for basic two-electrode voltage clamp electrophysiological recording are described in The Axon Guide (Axon Instruments, 1993). Make whole-cell recordings using an amplifier (e.g., Axoclamp 200B, Axon Instruments) and monitor with a two-channel digital oscilloscope (e.g., Tektronix). Capture the data with a Digidata 1322A A-D converter (Axon Instruments) using pCLAMP software (Axon Instruments). Make a ground connection with a silver pellet. Continuously perfuse a small recording chamber which holds 12-mm round coverslips (Warner Instruments) with extracellular recording solution. The chamber is mounted on the stage of an inverted microscope (e.g., Olympus) and the microscope is placed on an antivibration table (Technical Manufacturer) with Faraday cage. A headstage (e.g., Axon Instruments) is used to hold the microelectrode mounted on a micromanipulator (e.g., Burleigh).

Prepare recording electrode 1. Program a micropipet puller to pull recording microelectrodes. Fire polish the tips to an o.d. of ∼0.8 to 1.2 µm, which should result in resistances of 2 to 10 M when filled with internal electrode solution. 2. Remove the 34-G microfiller from a pipet filler, draw internal electrode solution into a 1-ml syringe, and reattach the microfiller syringe. Fill the electrode ∼20% full with solution. 3. Place the recording electrode into an electrode holder with pressure port so that the electrode wire touches the solution inside the pipet. Attach the electrode holder to the headstage on the micromanipulator.

Prepare electrode for recording 4. Attach a piece of PE-160 tubing to the pressure port of the electrode holder. Attach a 10-ml plastic syringe to the free end of the PE tubing. 5. Place cultured rat DRG neurons in the recording chamber on the microscope stage and constantly bath perfuse with extracellular recording solution at a rate of ∼0.5 ml/min. 6. Using the 10-ml syringe attached to the pressure port of the electrode, apply positive pressure to the electrode so that internal solution comes out the tip.

Form whole-cell configuration 7. Choose a round phase-bright neuron ∼25 µm in diameter with no visible processes from which to record. 8. Advance the electrode carefully using the micromanipulator until the electrode tip touches the cell. An increase in resistance will be observed on contact.

9. Carefully pull back on the 10-ml syringe attached to the pressure port of the electrode holder until a 1-G seal is formed. Remove the syringe. 10. Using slight mouth suction to the free end of the PE tubing from which the 10-ml syringe was just removed, achieve whole-cell configuration by rupturing the cell membrane.

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At this time low resistance access is achieved and there will be an increase in the capacitance transients.

Prepare for agonist delivery 11. Set up the piezo-driven rapid application system (see Support Protocol 4). This should be set up before whole-cell recording.

12. When ready to test the cell with agonist, switch to agonist (e.g., ATP) in the agonist barrel of the theta tube. Movement of the piezo is often jarring and can lead to loss of the whole-cell configuration. The Cyberamp helps filter the movement of the piezo to make it a smooth event (see Support Protocol 4).

Record currents To record agonist-evoked desensitizing currents (i.e., P2X3 ) 13a. Use pCLAMP software to trigger movement of the piezo-electric device (i.e., apply agonist) and then synchronize it with data acquisition. 14a. Apply agonist in set intervals of 2 to 4 min to establish stable control responses. Record and save these current values before proceeding with the experiment. Responses at these timed intervals will not be as large in amplitude as the first response, but keeping the timed interval constant will ensure reproducible responses and considerably shorten experiments. Receptors such as P2X3 can take >15 min to recover completely from desensitization. Control responses are considered stable usually when two to three consecutive recordings are achieved that do not differ by >10% in amplitude.

15a. After achieving stable controls, carry out experiments, always maintaining a 2- to 4-min interval between applications. For example, apply different doses of agonist to generate a concentration-response curve, test antagonists to generate inhibition curves, or apply agonist while varying membrane potential to generate current-voltage (I/V) relationships. Steps 16a to 20a are used to characterize an ion channels or test compound of interest.

16a. For agonist and antagonist dose-response data, measure peak current amplitudes using pCLAMP software by calculating the difference between peak current amplitude achieved during agonist application and the holding current amplitude before agonist application for each recording. 17a. Express dose-response data as a percentage of control amplitude and curve fit using graphics software to generate an EC50 or IC50 . 18a. For current-voltage relationships, measure peak current amplitudes as stated above (step 17a) and plot amplitude of each response against the membrane potential. Properties such as inward rectification (i.e., P2X3 ) can be seen from these plots.

19a. Measure rise times of recorded inward currents using pCLAMP software between 10% and 90% peak. 20a. For desensitizing currents, estimate exponential time constants of current desensitization (τ ) by pCLAMP software using a Chebyshev curve-fitting algorithm typically best fitted by two exponential functions (see Burgard et. al., 1999). Characterization of Recombinant and Native P2X Receptors

To record agonist-evoked nondesensitizing currents (i.e., P2X2 , or P2X2/3 ) 13b. Use pCLAMP software to trigger movement of the piezo-electric device (i.e., apply agonist) and then synchronize it with data acquisition.

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Unlike desensitizing receptors, agonists can be bath applied to nondesensitizing receptors, but for ease in synchronizing data capturing with agonist application, or co- and pre-application of antagonists, use of the piezo-electric device is preferred.

14b. Apply agonist in 30-sec to 1-min intervals to establish control responses. Since these receptors do not desensitize, intervals can be of much shorter duration than for the desensitizing receptors. Control responses are considered stable usually when two to three recordings are obtained which do not differ more than 10% in amplitude.

15b. Record and save these current values before proceeding with experiment. 16b. After achieving stable controls, carry out experiments, always maintaining a 2- to 4-min interval between applications. For example, apply different doses of agonist to generate a concentration-response curve, test antagonists to generate inhibition curves, or apply agonist while varying membrane potential to generate current-voltage (I/V) relationships. Steps 17b to 20b are used to characterize an ion channel or test compound of interest.

17b. For agonist and antagonist dose-response data, measure peak current amplitudes using pCLAMP software by calculating the difference between peak current amplitude achieved during agonist application and the holding current amplitude before agonist application for each recording. 18b. Express dose-response data as percent of control amplitude and curve fit using graphics software to generate an EC50 or IC50 . For current-voltage relationships, measure peak current amplitudes as stated above (step 18b) and plot amplitude of each response against the membrane potential. Properties such as inward rectification (i.e., P2X3 ) can be seen from these plots.

19b. Measure rise times of recorded inward currents using pCLAMP software between 10% and 90% peak. 20b. For current-voltage relationships, measure peak current amplitudes as stated above (step 16a) and plot amplitude of each response against the membrane potential. Properties such as inward rectification (i.e., P2X3 ) can be seen from these plots.

Apply antagonist 21. To apply antagonist, preapply through the external solution barrel of the theta tube for a specified time and then test by co-applying with agonist (ATP) through the agonist barrel of the theta tube. PREPARATION OF ACUTELY DISSOCIATED DORSAL ROOT GANGLION NEURONS FROM THE RAT

SUPPORT PROTOCOL 3

This protocol describes the methods used to obtain cultured rat dorsal root ganglion neurons suitable for whole-cell patch clamp (see Basic Protocol 2). In these cultures, ATP-evoked responses are found on over 50% of small- to medium-sized diameter (20 to 35 µm) cells, the majority involving homomeric P2X3 or heteromeric P2X2/3 receptors (Burgard et al., 1999). These cultures are typically used within 24 hr, after which clamping the neuron becomes difficult due to growth of neuronal processes.

Materials Dissection solution: Dulbecco’s modified Eagle’s medium (DMEM) containing 100 U/ml penicillin and 100 mg/ml streptomycin (store up to 1 week at 4◦ C) Male Sprague-Dawley rats, 7- to 8-weeks old or 250 to 300 g (Charles River) 80% CO2 /20% O2 Ethanol

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0.3% collagenase solution (see recipe) Trypsin solution (see recipe) Dulbecco’s modified Eagle’s medium (DMEM; Invitrogen) Dulbecco’s phosphate-buffered saline (DPBS) without Ca2+ or Mg2+ (Invitrogen) DRG incubation medium (see recipe) 0.5-, 0.75-, and 1.0-mm-i.d. Pasteur pipets Surgical instruments (e.g., scissors, fine forceps, microspring scissors; Fine Science Tools) Dissecting microscope 60 × 15–mm plastic petri dishes Rodent nose-cone Rodent guillotine PEI-coated coverslips (see recipe) 37◦ C, 5% CO2 incubator NOTE: All protocols using live animals must first be reviewed and approved by an Institutional Animal Care and Use Committee (IACUC) and must follow officially approved procedures for the care and use of laboratory animals.

Set up for experiment 1. Using the flame of a Bunsen burner, fire-polish a number of Pasteur pipets with the following bores: Large (∼1-mm opening) Medium (∼0.75-mm opening) Small (∼0.5-mm opening). Autoclave and save for future use. Large bore Pasteur pipets should be fire-polished so that just the edge of the glass is smooth.

2. Set up a surgical area with instruments and a dissecting microscope. 3. Pour dissection solution into a 60 × 15–mm plastic petri dish.

Remove vertebral column from rat 4. Sacrifice a 7- to 8-week-old or 250- to 300-g male Sprague-Dawley rat by deeply anesthetizing with 80% CO2 /20% O2 followed by decapitation. 5. Swab the dorsal surface of the rat with ethanol and make a single dorsal incision with scissors through skin. Remove the skin to expose all of dorsal/dorsolateral surface. 6. Using scissors, remove the entire vertebral column by cutting down each side of the vertebral column through the rib cage and then all the way to the tail.

Bisect vertebral column 7. Place the vertebral column in a large petri dish and rinse in ice-cold DPBS without Ca2+ or Mg2+ several times, washing away blood and hair. 8. Using scissors, remove muscle and connective tissue from dorsal surface of column in order to expose bones of the vertebral column (see Fig. 11.9.3). Repeat the same procedure for the ventral side of the vertebral column. Characterization of Recombinant and Native P2X Receptors

9. While holding the neck portion with forceps, use scissors to cut a midline longitudinal incision through the bones of the vertebral column as well as the spinal cord from neck to tail.

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Figure 11.9.3

Schematic diagram of dorsal root ganglia (DRG) along dissected vertebral column.

10. Keeping the spinal cord in place as well as possible, flip the vertebral column and cut another longitudinal incision along the ventral portion of the vertebral column from neck to tail. The vertebral column has now been completely bisected.

Collect and clean ganglia 11. Under a dissecting microscope collect the ganglia from each half of the column from the region of interest only and transfer to dissection solution on ice (step 3). Ganglia are pronounced swellings of each nerve trunk and are more transparent than the opaque white nerve trunks (See Fig. 11.9.3).

12. Using microspring scissors and fine forceps, cut away and remove nerve roots and connective tissue from the ganglia.

Digest connective tissue 13. Transfer the ganglia to 0.3% collagenase solution and incubate 50 to 60 min at 37◦ C. 14. After incubation, centrifuge the ganglia 5 min at 65 × g, 23◦ C. 15. Remove supernatant and discard. Replace with 3 ml trypsin solution and incubate 20 to 30 min at 37◦ C. Repeat step 14.

Triturate 16. Remove supernatant and replace with 3 ml DMEM. Triturate with large-bore firepolished Pasteur pipet until tissue dissociates. Repeat step 14. 17. Remove supernatant and replace with 3 ml DMEM. Triturate again with a mediumand then small-bore fire-polished Pasteur pipet for about five repetitions each to dissociate many cells without too much damage. Repeat step 14. 18. Remove supernatant and resuspend pellet in DRG incubation medium at ∼0.5 to 1 DRG/ml. Electrophysiological Techniques

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Plate cells on coverslips 19. Plate cells at ∼0.5 to 1 ml/well on PEI-coated coverslips in a 24-well plate.. 20. Incubate at 37◦ C, 5% CO2 for sufficient time to allow the neurons to attach well to the coated coverslip so that during agonist application they are not dislodged (∼4 to 24 hr). ALTERNATE PROTOCOL

MEASUREMENT OF ATP-EVOKED RESPONSES FROM STABLY TRANSFECTED CELL LINES EXPRESSING P2X RECEPTORS Whole-cell patch-clamp recordings of cells stably expressing various P2X receptors can be accomplished using techniques identical to those described above for rat DRG neurons (see Basic Protocol 2). Interpretation of the data generated from such experiments is greatly simplified by using a parental cell line that is devoid of the receptors to be studied. In this case, human astrocytoma 1321N1 cells are useful as they lack endogenous ATPevoked electrophysiological responses (Burgard et al., 1999). Individual P2X receptors are cloned by RT-PCR and cDNAs are introduced into 1321N1 cells using standard RT-PCR and lipid transfection techniques (Lynch et al., 1999).

Additional Materials (also see Basic Protocol 2 and Support Protocol 3) Cells (e.g., 1321N1 cells; Communi and Boeynaems, 1997) transfected with receptor of interest (e.g., human astrocytoma cells transfected with P2X receptor) Incubation medium (see recipe) 1. Plate cells transfected with the receptor of interest onto 12-mm round PEI-coated coverslips at a density ranging from 20–50 × 103 cells per coverslip in the same 24-well plate format as described for DRG neurons, using 1 ml incubation medium (see Support Protocol 3, step 19). 2. Store stable 1321N1 cells in a 37◦ C, 5% CO2 incubator anywhere from 1 to 3 days or until the culture becomes too confluent to find single cells. Allow sufficient time for the cells to recover from the plating procedure to ensure reliable recording (i.e., 2 to 24 hr). Typically, cells are plated the evening before recording.

3. Perform the experiment as described for DRG neurons, except use coverslips plated with transfected cells (see Basic Protocol 2). Typically, chose cells for recording that are slightly elongated or bipolar in shape without extensive processes and not touching another cell. SUPPORT PROTOCOL 4

Characterization of Recombinant and Native P2X Receptors

PIEZO-DRIVEN RAPID APPLICATION SYSTEM FOR RECORDING WHOLE-CELL CURRENTS FROM RAT DRG NEURONS OR STABLY TRANSFECTED CLONAL CELLS Rapid application is necessary to study agonist/antagonist interactions at fast desensitizing receptors, such as P2X1 and P2X3 . This setup allows for the rapid movement of agonist across the cell, as well as restricting the time of agonist exposure. Cells are continually perfused with bath solution in the chamber at a rate of 0.5 ml/min. The theta tube glass is positioned on the piezo-electric rapid application system so that the theta tube is ∼100 µm away from the cell. External solution perfuses one barrel of the glass theta tube. The theta tube is positioned so that only this barrel containing external solution perfuses the cell until the applicator is activated. Agonist solution perfuses the other barrel of the theta tube and is applied across the cell by an approximately +100-V command to the piezo, so that the agonist-perfusing barrel of the theta tube moves across the cell to completely

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bathe the cell in agonist for the specified time. This movement is controlled by pCLAMP software through an analog-to-digital (A/D) converter. Agonist applications are typically 400 to 800 msec in duration. A Cyberamp is used to filter (fine adjustment) the movement of the theta tube. Without filtering the movement of the theta tube, the movement is too jarring and often causes loss of the whole-cell recording configuration. Follow manufacturer’s instruction to make proper connections of the piezo device and the Cyberamp. Construction of this applicator should be completed and put in position before whole-cell recordings commence. This equipment can be reused until the theta tube glass becomes damaged or excessively dirty. When this occurs, replace only dirty/damaged parts.

Materials Extracellular recording solution (see recipe) Micropipet puller (Sutter Instruments) Glass theta tube (FHC) Fine forceps Syringe needles Silicone rubber adhesive sealant or equivalent (e.g., epoxy) PE-tubing connected to multichannel fittings 20-ml syringe barrels Ring stand Piezo-electric rapid application system (Burliegh Instruments) attached to microscope stage 1. Using a micropipet puller, pull a glass theta tube and carefully break the pulled tip with fine forceps so that each barrel has an i.d. of ∼50 to 100 µm. Try to make the break as square across the pipet as possible to maintain a linear interface of flow between the two barrels. A theta tube is basically a double-barreled glass pipet allowing two different solutions to flow side by side separated by a linear interface. The linear interface is important to prevent mixing of the two solutions.

2. Insert a syringe needle into each barrel of the theta tube and seal with silicone rubber adhesive. 3. Affix PE-tubing connected to multichannel fittings to the back of each of two syringe needles using silicone sealant or equivalent. Attach the other ends of the PE-tubing to 20-ml syringe barrels set up on a ring stand. 4. Position the theta tube in the piezo-electric rapid applicator arm attached to the microscope stage. The theta tube should be positioned 100 µm away from the intended cell and aligned so that only the barrel with external solution will bathe the cell. Positioning of the theta tube for proper agonist/antagonist application can be tricky. For this reason it is a good idea to check/record the pipet open tip solution exchange time using a 90% external solution (10% water) in place of agonist in the agonist barrel and sweep it across the open pipet tip. Also, by using the 90% external solution, a visible interface (midline) can be seen between the two barrels of the theta tube, aiding in proper positioning for good agonist application. By fitting a single exponential function to the resulting junction current transient, a time constant (τ ) between 1 and 2 msec is typically achieved. By contrast, whole-cell exchange times are typically slower on the order of approximately τ = 20 msec. Previously measured activation and deactivation rates for α,β-meATP-induced currents have

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Figure 11.9.4 Using a piezoelectric-driven glass theta tube, solution exchange times were determined by whole cell or pipet recording in control solution (90% saline, open bar) and rapidly switching into test solution (closed bar, 55 mM potassium ± 10 µM α,β-meATP). A single exponential function was fitted to each response and the resulting time constant (τ) is shown. All currents have been scaled for visual comparison and the whole-cell current response has been inverted to maintain polarity. Data taken from Burgard et al. (2000), with permission.

been shown to be close to this whole-cell exchange rate (Burgard et al., 2000; see Fig. 11.9.4).

5. Run external solution through both barrels of the theta tube.

REAGENTS AND SOLUTIONS Use deionized, distilled water in all recipes and protocol steps. For common stock solutions, see APPENDIX 2A; for suppliers, see SUPPLIERS APPENDIX.

Barth’s solution, low-Ca2+ Prepare 1 liter of the following 10× stock: 87.5 mM NaCl 2.5 mM KCl 10 mM sodium HEPES buffer Store up to 1 month at 4◦ C On day of oocyte preparation, add 1 M MgCl2 (prepare 50 ml and store up to 2 to 3 months at 4◦ C) to 1 mM (final) and 10 ml of 100 U/ml penicillin/100 µg/ml streptomycin to 100 ml of 10× low-Ca2+ Barth’s stock. Adjust volume to 1 liter with water. Adjust pH to 7.55 with 5 N NaOH. Pass through a 0.22-µm filter to sterilize.

Characterization of Recombinant and Native P2X Receptors

Barth’s solution, normal Prepare 1 liter of the following 10× stock: 90 mM NaCl 1 mM KCl 0.66 mM NaNO3 2.4 mM NaHCO3 2.5 mM sodium pyruvate 10 mM sodium HEPES buffer Store up to 1 month at 4◦ C On day of oocyte recording, add 1 M MgCl2 (prepare 50 ml and store up to 2 to 3 months at 4◦ C) to 0.82 mM (final) and 1 M CaCl2 (prepare 50 ml and store up to continued

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2 to 3 months at 4◦ C) to 0.74 mM (final) in 100 ml of 10× normal Barth’s stock. Add 10 ml of 100 U/ml penicillin/100 µg/ml streptomycin and adjust volume to 1 liter with water. Adjust pH to 7.55 with 5 N NaOH and pass through a 0.22-µm filter to sterilize.

Collagenase solution, 0.3% Dissolve 100 mg collagenase/dispase (Roche) in 1 ml of Dulbecco’s phosphatebuffered saline (DPBS), Ca2+ and Mg2+ free (Invitrogen), and prepare 50-µl aliquots. Dissolve 100 mg collagenase B (Roche) in 1 ml DPBS, Ca2+ and Mg2+ free, and prepare 50-µl aliquots. On the day of rat DRG dissociation, thaw and combine one aliquot each of collagenase/dispase and collagenase B and 3.2 ml Dulbecco’s modified Eagle’s medium (DMEM) with high glucose and L-glutamine (Invitrogen) in a 15-ml conical tube. Store aliquots of collagenase/dispase and collagenase B up to 6 months at −20◦ C. DRG incubation medium Prepare DMEM containing the following: 10% (v/v) FBS (JRH Biosciences) 50 µg/µl nerve growth factor (NGF) 2.5S, grade II (Boehringer-Mannheim) 100 U/ml penicillin (Sigma) 100 µg/ml streptomycin (Sigma) Store up to 1 week at 4◦ C Extracellular recording solution Prepare the following 10× stock at 1 liter with high purity H2 O: 155 mM NaCl 5 mM KCl 2 mM CaCl2 1 mM MgCl2 10 mM HEPES 12 mM glucose Store up to 3 to 4 weeks at 4◦ C On day of experiment, dilute the concentrate 10-fold with water to make ≥1 liter of 1× solution. Adjust to pH 7.4 with 5 N NaOH. Adjust volume with H2 O to give an osmolarity between 320 and 330 mOsm. Pass through a 0.22-µm filter to sterilize. Incubation medium Prepare the following in DMEM containing 4.5 mg/ml glucose and 4 mM L-glutamine (Invitrogen): 300 µg/ml G418 (for P2X3 only) 100 µg/ml hygromycin B (for P2X3 only) 150 µg/ml G418 and 75 µg/ml hygromycin B (for P2X2/3 ) Store up to 2 to 3 weeks at 4◦ C Internal electrode solution 140 mM potassium aspartate 20 mM NaCl 10 mM EGTA 5 mM HEPES Adjust volume to 50 ml with H2 O continued

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Adjust pH to 7.3 by slowly adding 1 M KOH Add sufficient water to give an osmolarity between 290 and 300 mOsm Pass through a 0.22-µm filter to sterilize Store in 1-ml aliquots in culture tubes up to 2 to 3 months at −20◦ C PEI-coated coverslips Prepare a 10% (w/v) stock solution of PEI (Sigma). Store up to 12 months at 4◦ C. Dilute 10% (w/v) PEI 1:10,000 in water (e.g., 50 µl in 500 ml) and pass through a 0.22-µm filter to sterilize. Working in a biological hood, place the coverslips in sterile plastic petri dishes and incubate in the diluted PEI solution at least 2 hr or up to overnight at room temperature. Aspirate the diluted PEI solution and wash the coverslips three times in sterile water, leaving them soaking in the third wash for 30 min. Aspirate the third wash and let the coverslips dry in the hood. When dry, use autoclaved forceps to place them in 24-well plates and store covered (i.e., dust free) up to 12 months at room temperature. Recording solution Prepare 1 to 2 liters of the following 10× stock: 96 mM NaCl 2 mM KCl 5 mM sodium pyruvate 5 mM sodium HEPES buffer Store up to 1 month at 4◦ C On the day of oocyte recording, add 1 M CaCl2 or BaCl2 (prepare 50 ml and store up to 2 to 3 months at 4◦ C) to 1.8 mM (final) and 1 M MgCl2 (prepare 50 ml and store up to 2 to 3 months at 4◦ C) to 1 mM (final) to 100 to 200 ml of 10× recording solution stock. Adjust volume to 1 to 2 liters with water, respectively. Adjust pH to 7.55 with 5 N NaOH. Pass through a 0.22-µm filter to sterilize. Trypsin solution On the day of the experiment, add 1 ml of 2.5% trypsin (Invitrogen) to 9 ml DPBS without Ca2+ /Mg2+ in a 15-ml conical centrifuge tube. Discard unused solution. COMMENTARY Background Information

Characterization of Recombinant and Native P2X Receptors

When expressed alone, the P2X3 subunit forms functional channels that exhibit ATP responses similar to those of native sensory dorsal root ganglion neurons (Chen et al., 1995; Rae et al., 1998; Burgard et al., 1999). P2X3 receptors also form functional heteromultimeric channels with the P2X2 receptor (P2X2/3 ), which exhibit properties similar to ATP-sensitive nondesensitizing currents found in native neurons (Rae et al., 1998; Burgard et al., 1999). Consequently, three types of ATPstimulated currents can be characterized in small diameter (95%.

REAGENTS AND SOLUTIONS All buffers and solutions should be prepared from analytical grade reagents. Use deionized, distilled water in all recipes and protocol steps. For common stock solutions, see APPENDIX 2A; for suppliers, see SUPPLIERS APPENDIX.

Boyden chemotaxis medium To RPMI 1640 (Life Technologies) medium add: 0.5% (w/v) BSA 25 mM HEPES 2 mM L-glutamine 10% heat-inactivated FBS (APPENDIX 2A) Store up to 4 weeks at 4°C ECV 304 tissue culture media In medium 199 (Life Technologies) combine heat-inactivated FBS (APPENDIX 2A) to 10% (w/v) and glutamine to 2 mM. Add penicillin-streptomycin (Life Technologies) to 100 U/ml penicillin and 100 mg/ml streptomycin. Store up to 1 month at 4°C. Fluorometric imaging plate reader (FLIPR) assay buffer 10 mM HEPES 145 mM NaCl 5 mM KCl 2 mM CaCl2 1 mM MgCl2 10 mM glucose 1 µM cyclosporin A (Calbiochem) 1 µM probenicid (Sigma) Store up to 1 month at 4°C Krebs-Ringer Buffer Stock solutions KRB solution 1 0.6 M NaCl (136 mM) 9.02 mM KCl (1.8 mM) 5.9 mM KH2PO4 (1.2 mM) 5.9 mM MgSO4 (1.2 mM) Store up to 1 month at 4°C continued

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KRB solution 2 31.24 mM NaHCO3 (5.0 mM) 91.4 mM NaCl (136 mM) 1.23 mM EGTA (add only on the day of the experiment; 0.2 mM) Store up to 1 month at 4°C Working solution The day of the experiment, combine: 200 ml KRB solution 1 160 ml KRB solution 2 20 ml 1 M HEPES, pH 7.2 (20 mM) 100 ml 12 mM CaCl2 (1.2 mM) Adjust pH to 7.4 with 1 M NaOH and the volume to 1 liter with deionized water. Add 1 g D-glucose (5.5 mM), 1 g BSA (1 mg/ml), and 1 ml of 1 M fura-2-AM (2 µl/ml or 2 mM; Molecular Probes) that has been sonicated for 5 min in an ultrasonic water bath. Protect the solution from light with aluminum foil and use immediately to resuspend the cells. Concentrations given in parentheses reflect those of the final working solution. Phosphate buffered saline (PBS), 1× 0.14 M NaCl 2.7 mM KCl 1.5 mM KH2PO4 10.1 mM Na2HPO4 Adjust the pH to 7.2 with 1 M NaOH Store up to 1 month at 4°C Transendothelial chemotaxis medium 1 vol medium 199 (Life Technologies) 1 vol RPMI 1640 (Life Technologies) 1% FBS (APPENDIX 2A) ECV 304 tissue culture media (see recipe) 10% heat-inactivated FBS (APPENDIX 2A) 2 mM L-glutamine 50 U/ml of penicillin and 50 µg/ml of streptomycin Store up to 4 weeks at 4°C COMMENTARY Background Information

Cellular Assays of Chemokine Receptor Activation

Chemokines are a large subgroup of the cytokine family whose name is derived from their ability to recruit leukocytes (i.e., chemoattractant cytokines). In addition to this common activity, they share two features that distinguish them from other cytokines. First, they are all small (8 to 10 kDa) proteins sharing a conserved 4-Cys structural motif, which allows their classification into two principal subclasses, the CXC (α-) and the CC (β-) chemokines. Two other subclasses have been also been identified, each with a single member to date, the C subclass which has only two of the four Cys, and the CX3C subclass in which the first two cysteines are separated by three amino

acids. The second shared feature of chemokines is that they are the only members of the cytokine family that activate seven transmembrane (7TM)–spanning, G protein–coupled receptors. For more detailed information, the reader is referred to several recent reviews of the chemokine family (Rollins, 1997; Luster, 1998; Wells et al., 1998; Proudfoot et al., 1999a). The study of chemokines in the immune system is a relatively new area of research. Chemokines were initially isolated from tissue extracts or from cell culture supernatants, and later by cDNA cloning using conserved sequences to allow the identification of new members of the chemokine and chemokine receptor families by degenerate oligonucleotide-based

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PCR cloning methods (Chapter 6). The focus on chemokines intensified when two chemokine receptors, CXCR4 and CCR5, were identified as being the coreceptors along with CD4 for HIV cell entry (Proudfoot et al., 1999a). Recently, the number of new chemokines has augmented rapidly due to their identification from expressed sequence tag (EST) databases (Wells and Peitsch, 1997). Now the challenge is to identify their corresponding receptors. The methods of analysis of chemo-kine activation described in this unit, namely calcium mobilization and chemotaxis, have been instrumental in the identification of many receptor-ligand pairs. When the first chemokines were discovered, it was envisaged that distinct leukocyte types would have a specific receptor allowing selective recruitment by its ligand; however, this turned out to be an oversimplification since most chemokines identified to date interact with multiple chemokine receptors. The corollary is that most chemokine receptors are activated by several ligands. Taken together with the finding that most leukocytes express several types of chemokine receptor, it appeared that the chemokine system is highly redundant; however, it is now clear that the recruitment of leukocytes is very finely tuned by a complex chemokine system. Chemokine and chemokine receptor expression is spatially and temporally regulated in vivo during development and cell differentiation, and is also influenced by numerous growth factors and cytokines in the extracellular milieu. Together these factors describe highly selective patterns of leukocyte recruitment. A good example of this complexity is demonstrated in T lymphocytes. Naïve T cells express principally CXCR4, while most activated T cells express predominantly CXCR3 receptors. On polarization to the TH1 phenotype, T cells express high levels of CCR5, whereas CCR4, and to a lesser extent CCR8 and CCR3, are markers of TH2 T cells. Distinct chemokine receptor expression profiles also appear to be important in tissue-specific lymphocyte homing—e.g., CCR4 to skin, CCR9 to mucosal sites, and CCR7 to secondary lymphoid tissues (Campbell et al., 1999; Cyster, 1999; Kunkel et al., 2000). Chemokine receptor expression also defines the differentiation state of monocyte/macrophages—i.e., while CCR1 and CCR2 are the principal receptors expressed on monocytes, as the cell acquires the adherent macrophage phenotype, the level of CCR2 decreases concomitant with a dramatic increase

in expression of CCR5. Switches in the chemokine receptor expression profile are also known to occur during the maturation of dendritic cells (DCs). Immature bone marrow-derived DCs express predominantly CCR6, whereas on maturation the expression of CCR6 declines and is replaced by CCR7 (Desaintvis et al., 1998). The regulation of both ligands and receptors, at least for those whose expression is modulated during inflammation, is under the control of proinflammatory cytokines. For example, all CXCR3 ligands identified to date— i.e., interferon inducible protein 10 (IP-10), monokine induced by interferon γ (MIG), and IFN-inducible T-cell α chemoattractant (ITAC)—were originally identified as interferon γ–inducible proteins. Another example is the acquisition of the response to CCR1 and CCR3 ligands by neutrophils in the presence of IFN-γ (Bonecchi et al., 1999). Interestingly, the responsiveness of certain cell types to certain ligands in calcium and chemotaxis assays can also vary (Proudfoot et al., 1999b). Recently, a new level of control of the chemokine system has become apparent—i.e., receptor trafficking. The receptor down-modulation and recycling bioassays described in this unit have been used for these analyses. Chemokine receptors appear to have significant differences in their endocytotic pathways. The IL-8 receptors CXCR1 and CXCR2 behave differently upon ligand activation. CXCR1 is rapidly down-modulated, and then recycled, while CXCR2 is down-modulated slowly and is then degraded in the lysosomal compartment (Mueller et al., 1997). Recently, it has become apparent that the CC chemokine receptors also behave differently (Fig.12.4.9). CCR5 recycles after exposure to RANTES, although the recycling can be inhibited by an amino terminallymodified (AOP-)RANTES (Mack et al., 1998). CCR3 does not recycle completely and a portion of the receptor population is directed to the lysosomal compartment and subsequently degraded (Zimmerman et al., 1999). CCR1 behaves differently again and none of the receptors recycle after activation with RANTES (Elsner et al., 2000). The redundancy of the chemokine system could raise the question to those in the pharmaceutical industry as to whether this system will provide sufficiently specific targets for therapeutic intervention. In fact, studies with chemokine receptor antagonists in animal models of inflammation have indicated that inhibition of this receptor family abrogates inflam-

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mation. Thus proteins such as Met-RANTES and a truncated form of MCP-1 are able to alleviate the symptoms of arthritis in murine models of this disease (Gong et al., 1997; Plater-Zyberk et al., 1997). Met-RANTES has in fact been shown to be active in several other inflammation models (Lloyd et al., 1997; Gonzalo et al., 1998; Grone et al., 1999). Another RANTES analogue, AOP-RANTES, has been shown to be particularly effective in inhibiting R5-mediated HIV-1 infection (Simmons et al., 1997), while the CXCR4 ligand, SDF-1, inhibits X4-mediated HIV-1 infection (Proudfoot et al., 1999a). Studies such as these, as well as many other approaches, have supported screening programs for novel antiinflammatory therapeutics, as well as a therapy for AIDS. The assays described in this unit, in particular chemotaxis (see Basic Protocol 1 and Alternate Protocols 1, 2, and 3) and calcium mobilization (see Basic Protocol 2 and Alternate Protocol 4), have been used as robust secondary assays following primary screenings using the binding assays described in UNIT 1.24, which has had an immeasurable impact on chemokine biology. In addition, the third assay described (see Basic Protocol 3) allows a secondary assay which is not G protein dependent. Chemokine receptors are easier targets to block than other cytokine receptors in that they are single polypeptide chain 7TM receptors, which are well known as cornerstones of the pharmaceutical industry. Screening efforts are beginning to pay off with the number of patent applications growing exponentially (Ponath, 1998; Schwarz and Wells, 1999) and the scientific community can now look forward to the results of the clinical trials that should soon commence with novel therapies for diseases ranging from inflammatory disorders to HIV infection.

Critical Parameters

Cellular Assays of Chemokine Receptor Activation

Chemokine preparation Lyophilized chemokines should be dissolved in distilled water prior to dilution into buffer or medium. Chemokines are soluble in water at concentrations up to 10 mg/ml, which allows flexibility for concentrations required for biological assays. In general, the last step of chemokine preparation is chromatographic purification by reversed-phase HPLC using a gradient of acetonitrile in trifluoroacetic acid (TFA). This applies to both synthetic chemokines (e.g., Dictagene and Gryphon) and chemokines recombinantly expressed in bacte-

rial hosts such as E. coli (e.g., PeproTech or R&D Systems). HPLC is appropriate for small scale preparations, while for large quantities (10 to 100 mg), the purified chemokine is dialyzed against 0.1% TFA prior to lyophilization. The protein forms a trifluoroacetic salt on lyophilization that is acidic when solubilized in water, and in certain cases the direct addition of a physiological buffer causes the protein to precipitate. When working with small quantities, such as 10-µg aliquots in plastic microcentrifuge tubes, it is not possible to visualize if the protein is solubilized; however, solubilization in water results in a solution of pH 3.4, which is perfectly amenable to pH adjustments to physiological pH values and the required ionic conditions. Fura-2-AM solutions in DMSO should be prepared in aliquots which are stored at −20°C in the dark. They should not be subjected to repeated cycles of freeze-thawing.

Troubleshooting In the micro-Boyden chemotaxis assay (see Basic Protocol 1), caution should be used in counting the filters since adherent cells not scraped off the top surface of the filters may be mistaken for migrant cells. By focusing on the pores, adherent cells can be easily distinguished from migrating cells. The migrating cells frequently adhere to one another and have the appearance of grape clusters. These can be mistakenly counted as one cell resulting in an underestimate of responses (unless one is able to assess the exact number of cells within such a clump). When using the 96-well chemotaxis assay (see Alternate Protocol 1), it is imperative to optimize the migration time and concentration of cells in the upper chamber for each chemokine used and for a particular cell type. This is particularly important when comparing the chemotaxis behavior of several chemokines interacting at the same receptor. In the receptor down-regulation assay (see Basic Protocol 3), if no down-modulation of receptors is achieved at 37°C, the activity of the chemokines should be evaluated further (e.g., by calcium flux or migration assays). Alternatively, certain stably transfected cells lines might not have the appropriate intracellular machinery for receptor internalization and therefore are unable to internalize chemokine receptors. In this case, one should perform the experiments with primary cells. Moreover, certain chemokine receptors such as CCR7 may not be internalized efficiently, although the

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cells appear to respond to the same chemokines in other receptor activation assays.

Anticipated Results The assays described in this unit measure the activation of chemokine receptors, and are essential tools for understanding chemokine biology. These assays should be carried out in parallel with the receptor binding assays described in UNIT 1.24. It should be noted, however, that the affinities obtained in binding assays do not always correlate with those obtained in these activation assays. This is due to the fact that chemotaxis involves a relay of signaling events that employ multiple processes. A similar situation exists for Ca2+ mobilization, and, even though receptor down-modulation is not G protein coupled, multiple signaling events are involved in this process as well. Thus it is a general observation that receptor affinity is higher than the EC50 value for these in vitro assays. It should also be noted that certain receptors will bind ligands in certain cell lines but there may be no signaling. This is attributed to incorrect coupling in the host cell or to the use of a heterologous cell line. Furthermore, rank order potency of ligands with respect to a given receptor vary considerably in the literature. This is again due to the use of different cell lines, and is attributed to differential coupling of signal pathways; therefore, primary cells should be used whenever possible, although chemokine responsiveness can also be affected by the method of isolation and degree of activation. Transfected cell lines are certainly useful as long as the caveats described above are considered.

Time Considerations The assays described in this unit can be performed in one day, but some require the analysis to be carried out the following day; however, assay optimization is almost always required, especially for the alternate chemotaxis assays described (see Alternate Protocols 1 to 3), which may require extensive investigation to determine optimal conditions. When designing experiments using cultured cells, it is important to know the doubling time so that adherent cultures are at the optimal level of confluence or cell density on the day of the experiment. Cells should be passaged appropriately to obtain the correct number for the required experimental day. If the experiments are to be performed on isolated leukocytes, it should be noted that certain cells like neutro-

phils can only be maintained for a very short time (i.e., 4 hr), whereas monocytes can be kept in culture medium overnight at 4°C and T cells can be maintained in culture for up to 3 weeks.

Literature Cited Bonecchi, R., Polentarutti, N., Luini, W., Borsatti, A., Bernasconi, S., Locati, M., Power, C.A., Proudfoot, A.E.I., Wells, T.N.C., Mackay, C., Mantovani, A., and Sozzani, S. 1999. Up-regulation of CCR1 and CCR3 and induction of chemotaxis to CC chemokines by IFN-gamma in human neutrophils. J. Immunol. 162:474-479. Campbell, J.J., Haraldsen, G., Pan, J., Rottman, J., Qin, S., Ponath, P., Andrew, D.P., Warnke, R., Ruffing, N., Kassam, N., Wu, L., and Butcher, E.C. 1999. The chemokine receptor CCR4 in vascular recognition by cutaneous but not intestinal memory T cells. Nature 400:776-780. Cyster, J.G. 1999. Chemokines and cell migration in secondar y lymphoid organs. Science 286:2098-2102. Desaintvis, B., Fugiervivier, I., Massacrier, C., Gaillard, C., Vanbervliet, B.A., Banchereau, J., Liu, Y.J., Lebecque, S., and Caux, C. 1998. The cytokine profile expressed by human dendritic cells is dependent on cell subtype and mode of activation. J. Immunol. 160:1666-1676. Elsner, J., Mack, M., Brühl, H., Dulkies, Y., Kimmig, D., Simmons, G., Clapham P.R., Schlondorff, D., Kapp, A., Wells, T.N.C., and Proudfoot, A.E.I. 2000. Differential activation of CC chemokine receptors by AOP-RANTES. J. Biol. Chem. 275:7787-7794. Falk, W., Goodwin, R.H. Jr., and Leonard, E.J. 1980. A 48-well micro chemotaxis assembly for rapid and accurate measurement of leukocyte migration. J. Immunol. Methods 33:239-247. Gong, J.H., Ratkay, L.G., Waterfield, J.D., and Clark, L.I. 1997. An antagonist of monocyte chemoattractant protein 1 (MCP-1) inhibits arthritis in the MRL-lpr mouse model. J. Exp. Med 186:131-137. Gonzalo, J.A., Lloyd, C.M., Albar, J.P., Wen, D., Wells, T.N.C., Proudfoot, A.E.I., Martinez-A, C., Bjerke, T., Coyle, A.J., and Gutierrez-Ramos, J.C. 1998. The coordinated action of CC chemokines in the lung orchestrates allergic inflammation and airway hyperresponsiveness. J. Exp. Med. 188:157-167. Grone, H.J., Weber, C., Weber, K.C., Grone, E.F., Rabelink, T., Klier, C.M., Wells, T.C., Proudfoot, A.E., Schlondorff, D., and Nelson, P.J. 1999. Met-RANTES reduces vascular and tubular damage during acute renal transplant rejection: Blocking monocyte arrest and recruitment. FASEB J. 13:1371-1383. Grynkiewicz, G., Poenie, M., and Tsien, R.Y. 1985. A new generation of Ca2+ indicators with greatly improved fluorescence properties. J. Biol. Chem. 260:3440-3450. Kunkel, E.J., Campbell, J.J., Haraldsen, G., Pan, J., Boisvert, J., Roberts, A.I., Ebert, E.C., Vierra,

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M.A., Goodman, S.B., Genovese, M.C., Wardlaw, A.J., Greenberg, H.B., Parker, C.M., Butcher, E.C., Andrew, D.P., and Agace. W.W. 2000. Lymphocyte CC chemokine receptor 9 and epithelial thymus-expressed chemokine (TECK) expression distinguish the small intestinal immune compartment: Epithelial expression of tissue-specific chemokines as an organizing principle in regional immunity. J. Exp. Med. 192:761-768.

Proudfoot, A.E., Wells, T.N., and Clapham, P.R. 1999a. Chemokine receptors—future therapeutic targets for HIV? Biochem. Pharmacol. 57:451-463.

Lloyd, C.M., Dorf, M.E., Proudfoot, A.E.I., Salant, D.J., and Gutierrez-Ramos, J.C. 1997. Role of MCP-1 and RANTES in inflammation and progression to fibrosis during murine crescentic nephritis. J. Leukoc. Biol. 62:676-680.

Rollins, B.J. 1997. Chemokines. Blood 90:909-928.

Luster, A.D. 1998. Chemokines—chemotactic cytokines that mediate inflammation. N. Engl. J. Med. 338:436-445.

Simmons, G., Clapham, P.R., Picard, L., Offord, R.E., Rosenkilde, M.M., Schwartz, T.W., Buser, R., Wells, T.N.C., and Proudfoot, A.E.I. 1997. Potent inhibition of HIV-1 infectivity in macrophages and lymphocytes by a novel CCR5 antagonist. Science 276:276-279.

Mack, M., Luckow, B., Nelson, P.J., Cihak, J., Simmons, G., Clapham, P.R., Signoret, N., Marsh, M., Stangassinger, M., Borlat, F., Wells, T.N., Schlondorff, D., and Proudfoot, A.E. 1998. Aminooxypentane-RANTES induces CCR5 internalization but inhibits recycling: A novel inhibitory mechanism of HIV infectivity. J. Exp. Med. 187:1215-1224. Mueller, S.G., White, J.R., Schraw, W.P., Lam, V., and Richmond, A. 1997. Ligand-induced desensitization of the human CXC chemokine receptor-2 is modulated by multiple serine residues in the carboxyl-terminal domain of the receptor. J. Biol. Chem. 272:8207-8214. Plater-Zyberk, C., Hoogewerf, A.J., Proudfoot, A.E.I., Power, C.A., and Wells, T.N.C. 1997. Effect of a CC chemokine receptor antagonist on collagen induced arthritis in DBA/1 mice. Immunol. Lett. 57:117-120. Ponath, P.D. 1998. Chemokine receptor antagonists: Novel therapeutics for inflammation and AIDS. Exp. Opin. Invest. Drugs 7:1-18. Power, C.A. and Meyer, A. 2000. Generation of stable cell lines expressing chemokine receptors. In Chemokine Protocols Vol. 138 (A.E.I. Proudfoot, T.N.C. Wells, and C.A. Power, eds.) pp. 99-104. Humana Press, Totowa, N.J. Power, C.A., Church, D.J., Meyer, A., Alouani, S., Proudfoot, A.E., Clark-Lewis, I., Sozzani, S., Mantovani, A., and Wells, T.N. 1997. Cloning and characterization of a specific receptor for the novel CC chemokine MIP-3alpha from lung dendritic cells. J. Exp. Med. 186:825-835.

Proudfoot, A.E., Buser, R., Borlat, F., Alouani, S., Soler, D., Offord, R.E., Schroder, J.M., Power, C.A., and Wells, T.N. 1999b. Amino-terminally modified RANTES analogues demonstrate differential effects on RANTES receptors. J. Biol. Chem. 274:32478-32485. Schwarz, M.K. and Wells, T.C. 1999. Recent developments in modulating chemokine networks. Exp. Opin. Ther. Pat. 9:1471-1490.

Wells, T.N.C. and Peitsch, M.C. 1997. The chemokine information source: Identification and characterization of novel chemokines using the WorldWideWeb and expressed sequence tag databases. J. Leukoc. Biol 61:545-550. Wells, T.N.C., Power, C.A., and Proudfoot, A.E.I. 1998. Definition, function and pathophysiological significance of chemokine receptors. Trends Pharmacol. Sci. 19:376-380. Wood, J.C.S. 1998. Principles of Gating. In Current Protocols in Cytometry (J.P. Robinson, Z. Darzynkiewicz, P.N. Dean, A. Orfao, P.S. Rabinovitch, H.J. Tanke, and L.L. Wheeless, eds.) pp. 1.8.1-1.8.12. John Wiley & Sons, N.Y. Zimmermann, N., Conkright, J.J., and Rothenberg, M.E. 1999. CC chemokine receptor-3 undergoes prolonged ligand-induced internalization. J. Biol. Chem. 274:12611-12618.

Contributed by Amanda E.I. Proudfoot, Christine A. Power, and Dennis J. Church Serono Pharmaceutical Research Institute Plan-les-Ouates, Switzerland Dulce Soler Millennium Pharmaceuticals Cambridge, Massachusetts Matthias Mack Ludwig-Maximilians-University of Munich Munich, Germany

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Production and Use of HIV-1 Luciferase Reporter Viruses

UNIT 12.5

Presented in this unit are protocols for the preparation and use of HIV-1-based luciferase reporter viruses. These viruses are competent for only a single cycle of infection and thus serve as useful tools for accurate quantitation of the early steps in the virus replication cycle, starting from entry into the cell, up to transcription of the integrated provirus. Measurement of reporter gene activity is much easier and more quantitative than classical supernatant p24 ELISA. In addition, because the reporter viruses can be pseudotyped (prepared with heterologous envelope glycoproteins), findings can be definitively attributed to envelope glycoprotein function. The first procedure describes a method for the production of reporter virus stocks (see Basic Protocol 1). This is followed by the basic method for infecting cells with the HIV-1 luciferase reporter virus and measuring the resulting signal (see Basic Protocol 2). Two versions of the protocol are presented, one for infection of adherent cells and the other for nonadherent cells. The third protocol describes how to evaluate test compounds that inhibit entry of the virus into the cell (see Basic Protocol 3). The protocol can be adapted with slight modifications for evaluating neutralizing antibodies that target the viral glycoprotein or molecules that act postentry. For the first two protocols, amounts of virus, numbers of cells, and other parameters can be varied and should be tested for particular applications. Signal intensities may vary for different viruses and different cell type combinations. For higher throughput applications, the assay can be scaled for 384-well format. CAUTION: The mutation in env greatly reduces the biohazard of working with reporter virus as compared to live virus; however, single-cycle viruses are not considered harmless because the stocks contain low levels of replication-competent virus that result from recombination during transfection or reversion. The viruses must, therefore, be used under conditions similar to those used for live HIV-1. Use containment procedures as for live HIV—ie., Biosafety level 3 (BSL3). Transfections can be prepared with BSL2 procedures and then moved into the BSL3 area. All materials are then to be handled with gloves and lab coat, and protective safety glasses worn. Aspirate discarded liquids into bleach and autoclave plasticware before disposal. NOTE: All solutions and equipment coming into contact with cells must be sterile, and proper sterile technique should be used accordingly. NOTE: All culture incubations should be performed in a humidified 37°C, 5% CO2 incubator unless otherwise specified. NOTE: pNL-Luc reporter virus plasmids can be obtained from the NIH Aids Research and Reference Reagent Program. Similiar vectors are also available from several other laboratories. pNL-Luc4 reporter virus is now available in which the luciferase gene has been replaced with the codon-optimized luciferase gene. This plasmid yields viruses that result in higher luciferase activity upon infection of primary cells. Envelope glycoprotein vectors are available from many laboratories and from the NIH AIDS Research and Reference Reagent Program, HEK293 and -293T cells (see Critical Parameters) are available from the American Type Culture Collection, and cell lines expressing individual chemokine receptors are available through the NIH AIDS Research and Reference Reagent Program (see Internet Resources). NOTE: The Gag p24 ELISA is not described in this unit, but can be performed with home-made reagents or with a commercially available kit (e.g., Abbott, Coulter, DuPont Alliance/PerkinElmer). Contributed by Carsten Münk and Nathaniel R. Landau Current Protocols in Pharmacology (2003) 12.5.1-12.5.12 Copyright © 2003 by John Wiley & Sons, Inc.

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BASIC PROTOCOL 1

PRODUCTION OF HIV-1 LUCIFERASE REPORTER VIRUS Reporter virus is generated by cotransfecting HEK293 cells with reporter virus plasmid DNA and an equal amount of envelope glycoprotein expression vector. The transfection protocol is based on the calcium phosphate method (Graham and van der Eb, 1973), which results in >80% of cells expressing a cotransfected enhanced green fluorescence protein (EGFP) expression plasmid. Reporter viruses can also be produced by lipofection which yields virus stocks of similar titer and is somewhat easier, but considerably more expensive to perform due to the cost of the lipid. While the protocol presented is for transfection of a single 10-cm plate, it may be scaled up by using multiple dishes of HEK293 cells or by doubling the DNA and HBS cocktail and using 15-cm culture dishes. A single transfected dish will yield 10 ml virus stock. For virus at 100 ng p24/ml, this will be sufficient for 1000 infections in a 96-well format. To serve as a negative control, a transfection should be performed in the absence of glycoprotein expression vector. In this case, virus will be produced, but will be noninfectious. Materials Confluent plate of HEK293 (ATCC# CRL-1573) or HEK293T (ATCC# CRL-11268) cells PBS (Life Technologies) Trypsin-Versene (Biowhittaker) DMEM-10 (Biowhittaker, Life Technologies; also see UNIT 7.2) Plasmid mixture: equal mixture of pNL-Luc reporter (available from authors) and envelope expression plasmids (NIH AIDS Research and References Reagent Program; see Internet Resources) 2 M CaCl2 (see recipe) 2× HBS (see recipe) Gag p24 ELISA kit (e.g., Dupont) 10-cm plates 2054 tubes, sterile (Becton-Dickinson) 0.45-µm syringe filter Prepare reporter virus by transfection of HEK293 cells 1. Trypsinize a confluent plate of HEK293 or HEK293T cells by aspirating the culture medium, adding 5 ml PBS, aspirating the PBS, and then adding 1 to 2 ml trypsin versene. Incubate 2 to 5 min at room temperature or 37°C until the cells detach from the surface. See Critical Parameters for information on choice of cell type.

2. Resuspend the cells in 5 ml DMEM-10. Count cells and transfer 2.0 × 106 cells into a 10-cm plate in 10 ml medium. Incubate overnight. The cells will be ~25% confluent the next day.

3. The following day, add 20 µg plasmid mixture (10 µg reporter virus and 10 µg envelope glycoprotein plasmid) in a total volume of 450 µl, into a 1.5-ml microcentrifuge tube. Add 62 µl of 2 M CaCl2 and vortex.

Production and Use of HIV-1 Luciferase Reporter Viruses

These steps are performed under sterile conditions in a laminar flow hood using sterile pipets and pipet tips. Although the plasmid DNA is not sterile, it should be kept as clean as possible. For pseudotyping with the VSV-G (see Background Information), 6.0 ìg VSV-G expression plasmid is sufficient.

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4. Add 500 µl 2× HBS into a sterile 2054 tube. Add the CaCl2/DNA solution dropwise from a Pasteur pipet at a rate of about 2 drops/sec without mixing. Immediately after completing the addition, flick several times to mix. Incubate 20 min on ice. A fine precipitate should form that is visible only at high power magnification. The precipitate will not be easily visible until several hours after transfection.

5. Add the precipitate drop-wise to the 10-cm plate of cells (step 2). Incubate overnight and change the medium the next day. 6. After 48 hours post-transfection, harvest the supernatant (which contains the virions) and centrifuge 5 min at 300 × g (1200 rpm in most table-top centrifuges), 4°C. Filter the supernatant through a 0.45-µm syringe filter and freeze in 0.5- to 1.0-ml aliquots at −80°C. Quantitate the virus using a Gag p24 ELISA kit. Virus can be stored at –80°C for several years without affecting activity. A successful prep will yield 50 to 300 ng p24/ml.

INFECT CELLS WITH HIV-1 LUCIFERASE REPORTER VIRUS − −

The NL-Luc-R E reporter virus (Chen et al., 1994; Connor et al., 1995) used in this protocol and produced as described (see Basic Protocol 1) contains the firefly luciferase gene (Photinus pyralis), which is detected by flash or glow reagents. The glow reagents are preferable because they produce light at a nearly constant level for several hours, allowing for flexibility in the timing of the experiment. The NL-R-Luc-R−E− reporter virus contains the Renilla (Renilla reniformis) luciferase gene (Mariani et al., 2000). The two luciferase genes can be detected individually in the same culture using Stop and Glow reagents (Promega), making it possible to use both viruses together and yet quantify them separately. This strategy has been used in some applications for normalization purposes (Mariani et al., 2000).

BASIC PROTOCOL 2

See Critical Parameters for a description of important controls. Materials Cells (NIH AIDS Research and Reference Reagent Program; see Internet Resources) Reporter virus, frozen (see Basic Protocol 1) DMEM-10 (UNIT 7.2) Luc-Lite luciferase assay reagent (Packard) 96-well culture dishes Microtiter plate luminometer Hemacytometer 96-well black microtiter plates Transparent microplate adhesion sealing film (Packard) Infect adherent cells 1a. Plate cells the day before infection in 96-well culture dishes at 2 × 103 cells/well. Incubate overnight. Alternatively, 24-well plates can be used with 8 × 103 cells/well.

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2a. The next day, thaw a frozen aliquot of reporter virus in a 37°C water bath (do not vortex). Transfer the thawed sample tube to an ice bucket. Adjust the virus to 20 ng p24/ml in DMEM-10. While vortexing reduces the infectivity of the virus, keeping it cold during preparation will preserve its infectivity.

3a. Remove the medium from the plate of cells and add 50 µl fresh medium. Add 50 µl diluted virus to each well. Assay three replicates per infection. 4a. Measure luciferase activity 3 days post-infection by adding 100 µl Luc Lite assay reagent, incubating the plate 30 min at room temperature, and then reading in a microplate luminometer. Infect suspension (nonadherent) cells 1b. The day of infection, pellet 1.0 × 107 cells by centrifuging 5 min at 300 × g (1200 rpm in most tabletop centrifuges), room temperature 2b. Remove supernatant, resuspend the cells in 2.0 ml DMEM-10, and determine the number of cells per milliliter using a hemacytometer. Adjust cell density to 5.0 × 105/ml with medium and distribute 50 µl into the wells (2.5 × 104 cells/well). Nearly any type of infectable transformed T cell line is suitable, including Jurkat, CEM, CEMx174, SupT1, Hut78, and PM-1. For primary activated lymphocytes, increase the number of cells/well 5-fold.

3b. Thaw a frozen aliquot of reporter virus in a 37°C water bath (do not vortex). Transfer the thawed sample tube to an ice bucket. Adjust the virus to 20 ng p24/ml medium. Add 50 µl diluted virus to each well. Incubate 3 days. While vortexing reduces the infectivity of the virus, keeping it cold during preparation will preserve its infectivity.

4b. Add 100 µl Luc Lite luciferase assay reagent to each well. Transfer 100 µl of each lysate to the corresponding wells of a black 96-well microtiter plate. Cover the plate with transparent microplate adhesion sealing film and read the luciferase activity in a microplate luminometer. For primary cells, assay 5 days postinfection. BASIC PROTOCOL 3

EVALUATION OF CELL ENTRY INHIBITORS WITH HIV-1 LUCIFERASE REPORTER VIRUS This protocol is for the evaluation of small molecules targeted to CCR5 (e.g., TAK-779, Baba et al., 1999; SCH-C, Striziki et al., 2001), but can easily be adapted to testing CXCR4 inhibitors (Schols et al., 1997), testing peptide inhibitors of gp41 (Munoz-Barroso et al., 1998), or titering neutralizing antibodies. A nonadherent transformed T cell line is used as the target in this example, although activated primary lymphocytes can be substituted. Adherent cells can be used with the modifications described above (see Basic Protocol 2).

Production and Use of HIV-1 Luciferase Reporter Viruses

Several different reporter viruses can be used in a single assay, each pseudotyped by a different envelope glycoprotein. VSV-G (Naldini et al., 1996; Reiser et al., 1996; Sharma et al., 1996) or A-MuLV Env pseudotypes (Page et al., 1990) should always be included to measure nonspecific and post-entry effects of inhibitors (see Commentary). See Critical Parameters for a discussion of important controls.

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Materials Cells (NIH AIDS Research and Reference Reagent Program; see Internet Resources) Medium (e.g., RPMI-10; InVitrogen, Biowhittaker, Life Technologies; also see UNIT 7.2) Viral entry inhibitor in DMSO DMSO Reporter virus, frozen (see Basic Protocol 1) Luc-Lite luciferase assay reagent (Packard) Hemacytometer 96-well culture dishes Multichannel micropipettor (optional) 96-well black microtiter plates Transparent microplate adhesion sealing film Microtiter plate luminometer 1. Pellet 1 × 107 cells by centrifuging 5 min at 300 × g, room temperature. Remove supernatant, resuspend the cells in 2 ml medium, and determine the number of cells per milliliter with a hemacytometer. Adjust the cell density to 6.25 × 105/ml and distribute 40 µl into each well of a 96-well culture dish (2.5 × 104 cells/well). If activated primary T cells are used, then the cell number per well should be increased 5-fold.

2. In medium, make a 5-fold serial dilution of the viral entry inhibitor in DMSO from 10−1 to 10−9 as follows. Add 40 µl medium into nine sterile microcentrifuge tubes. Add 10 µl inhibitor into the first tube, vortex, and serially transfer 10 µl into each tube. Because the DMSO will cause a small increase in infection, generate a parallel serial dilution of DMSO and use it as a control for its effects on virus entry. If the inhibitor stock is 1 mM in DMSO, this dilution scheme will test a range of inhibitor concentration from 20 ìM to 0.05 nM, which is sufficient to cover the range of known antiviral compounds. The assay can also be used to titer neutralizing antibodies. Prepare a 5-fold dilution series of antibody or inactivated serum in DMEM-10. Add 100 ìl of each dilution to a 96-well culture dish. Add 20 ìl diluted reporter virus to each well and incubate 1 hr at 37°C. Transfer 60 ìl of each well to the cells prepared in step 1. Incubate 6 hr or overnight. Continue at step 5.

3. Transfer 10 µl of each dilution to the cells in triplicate (step 1). Leave three wells with no test compound. To these add 10 µl medium. Incubate for 30 min at 37°C. 4. Thaw an aliquot of reporter virus in a 37°C water bath (do not vortex). Transfer the thawed sample tube to an ice bucket and adjust the virus to 20 ng p24/ml medium. Add 50 µl diluted virus to each well. Incubate 6 hr or overnight. While vortexing reduces the infectivity of the virus by partially detaching the HIV envelope from the virion, keeping it cold during preparation preserves its infectivity.

5. The next day, centrifuge the plate 5 min at 300 × g, room temperature. Using a 200-µl micropipettor or multichannel micropipettor, remove as much of the medium as possible without disturbing the cells. Add 100 µl medium to each well. 6. Harvest the cultures 3 days postinfection by first adding 100 µl Luc Lite luciferase assay reagent and then transferring 100 µl of each lysate to the corresponding wells of a 96-well black microtiter plate. 7. Cover the plate with transparent microplate adhesion sealing film and read the luciferase activity in a microplate luminometer

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REAGENTS AND SOLUTIONS Use deionized, distilled water in all recipes and protocol steps. For common stock solutions, see APPENDIX 2A; for suppliers, see SUPPLIERS APPENDIX.

CaCl2, 2 M Add 29.40 g CaCl2⋅2H2O to water and adjust volume to 100 ml. Pass through a 0.45-µm filter to sterilze. Store in aliquots up to 2 to 3 years at −20°C; after thawing, store up to 6 months at 4°C. HBS, 2× 1.0 g HEPES 1.6 g NaCl 0.074 g KCl 0.025 g Na2HPO4 (for 7 H2O use 0.047 g) Adjust pH to 7.05 to 7.15 Adjust volume to 100 ml with H2O Pass through a 0.45-µm filter to sterilize Store in aliquots up to 2 to 3 years at −20°C; after thawing, store up to 6 months at 4°C Accurate pH and the correct amount of phosphate are critical for achieving high transfection efficiency.

ATTACHMENT/FUSION

UNCOATING

co-receptor antagonists CD4 lgG monoclonal antibodies peptide inhibitors

REVERSE TRANSCRIPTON nucleoside or non-nucleoside RT inhibitor MATURATION protease inhibitor

DNA

RNA

protein

ASSEMBLY

INTEGRATION TRANSLATION TRANSCRIPTION

Production and Use of HIV-1 Luciferase Reporter Viruses

Figure 12.5.1 HIV-1 life cycle. Inhibitors are listed that act at various stages of viral replication. Entry inhibitors interfere with receptor interaction or membrane fusion. Inhibitors of reverse transcription are active postentry, but before integration. Protease inhibitors target the viral protease and inhibit maturation after virus release by the infected cell.

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COMMENTARY Mariani et al., 2000), enhanced green fluorescent protein (EGFP; Page et al., 1997), alkaline phosphatase (He and Landau, 1995), and chloramphenicol acetyltransferase (CAT; Parolin et al., 1996). Viruses containing drug resistance markers such as gptr, neor, or hygror have also been useful (Page et al., 1990). The protocols presented in this unit use pNL-LucR−E−, a virus that contains the firefly luciferase gene in Nef, and has frameshifts engineered in env and vpr (viral protein R). Inactivation of Vpr eliminates potential negative effects caused by the apoptotic effects of Vpr. Inactivation of env restricts the virus to a single round of replication. Because it is replication-defective, the virus must be produced by transfection and harvested as a culture supernatant. The virus is quantitated and frozen in aliquots for use as needed. Because they are env−, an envelope glycoprotein expression vector plasmid DNA is included in the transfection and the viruses are released from the transfected

Background Information Reporter viruses are an important research tool for the analysis of the cellular and viral components in HIV-1 replication. They allow for rapid and quantitative measurement of virus infection and can be used to great advantage for the screening and evaluation of antivirals. Most reporter viruses consist of infectious HIV-1 proviral DNA that has been engineered to contain a reporter gene either in nef (a gene whose function is dispensible for single-round infection in vitro) or env. Expression of the reporter requires only the early steps of the virus replication cycle (entry into the cell, reverse transcription, integration, and provirus transcription). Subsequent steps in virus replication (virus assembly and budding) are not measured and do not affect reporter gene activity (see Fig. 12.5.1). Reporter viruses have been constructed that contain genes for firefly luciferase (Photinus pyralis), Renilla luciferase (Renilla reniformis;

A Luciferase activity (cps)

106 105 104 103 102 101 100

no virus

VSV-G

ADA

JR.FL

no virus

VSV-G

JC2

SF33

B Luciferase activity (cps)

106 105 104 103 102 101 100

Pseudotype

Figure 12.5.2 Infection of (A) HOS.CD4.CCR5 and (B) HOS.CD4.CXCR4 with NL-LucR−E−. Cells were infected with 1 ng p24, pseudotyped by CCR5-tropic (ADA, JR.FL) or CXCR4-tropic (JC2, SF33) glycoproteins, or with VSV-G. Luciferase activity was measured 3 days postinfection and plotted as counts per second (cps). Infection of 4 × 103 cells per 96-well dish with 1 ng HIV-1 luciferase reporter virus resulted in 5 × 104 to 2 × 105 cps. Cells not infected showed a background activity of 10 to 20 cps.

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Production and Use of HIV-1 Luciferase Reporter Viruses

cells as pseudotypes bearing the transfected glycoprotein. A panel of pseudotypes can be generated, each with a tropism and entry mechanism dictated by the incorporated glycoprotein. Because retroviruses are fairly promiscuous with respect to glycoprotein incorporation, a wide range of infectious pseudotypes can be generated (Landau et al., 1991). Figure 12.5.2 shows the infection of HOS.CD4.CCR5 and HOS.CD4.CXCR4 cells with NL-Luc virus pseudotyped either with CCR5-tropic HIV1 envelope (strains ADA and JR.FL), CXCR4tropic HIV-1 envelope (strains JC2 and SF33), or VSV-G glycoprotein. Because of the promiscuous nature of HIV-1 envelope glycoprotein incorporation, reporter viruses can readily be produced as pseudotypes in which the glycoprotein is derived from heterologous viruses (Landau et al., 1991). HIV-1 glycoproteins vary with respect to coreceptor usage (either CCR5 or CXCR4), CD4 dependence, and clade. Nevertheless, any HIV-1 glycoprotein is expected to efficiently pseudotype the NL-Luc-R−E− core to form infectious virus. A-MuLV env and VSV-G pseudotypes enter cells independent of CD4 and coreceptor. Thus, they provide a means for definitively determining whether an inhibitor targets viral entry into cells or whether postentry or nonspecific cellular toxicities are at work (see Fig. 12.5.1). For example, an inhibitor that reduces infection of JR.FL-pseudotyped reporter virus, but does not inhibit VSV-G pseudotypes, acts at entry; however, whether the inhibitor targets the virus or the cell is not clear and distinguishing these two possibilities may not be straightforward. One means to test this is to add the inhibitor to cells, remove it following a brief incubation, and then add the reporter virus. If infection is blocked, the inhibitor most likely acts on the cell (either on CD4, CCR5, or CXCR4). If no effect is observed, no conclusion can be drawn. The converse, adding the inhibitor to the virus, is less practical, because removing the inhibitor by centrifugation reduces infectivity and takes too long. Pharmacologic inhibitors of HIV-1 replication can be characterized by virus growth kinetics in culture using replication-competent virus. Inhibitors are added to activated primary CD4+ lymphocytes or to an infectable transformed T cell line and virus replication is quantitated by measuring supernatant over a 2-week period by p24 ELISA or a reverse transcriptase assay. This method measures the effect of the inhibitor through multiple rounds of virus replication. However, generating growth curves is

labor-intensive, requiring frequent sampling and passaging of the infected cultures. Growth curves are not readily suited for high throughput formats, are expensive, and do not provide information about which step in the virus replication cycle is being affected. Input virus may be carried over and mistaken for virus production at early points in the growth curve. In addition, there is the biohazard associated with working with live virus. Single-cycle reporter viruses offer a useful alternative for characterizing HIV-1 inhibitors, particularly those that target early steps in virus replication. The measurement is sensitive (5 min in the absence of inhibitor. It is often easiest to add the compounds to the plate before adding cells.

15. Incubate the plate 1 hr in a humidified, 37°C incubator. Wash plates 16. Prepare a basin or container in which to rinse the cells. 17. Working with one plate at a time, quickly invert and flick the plate to transfer the liquid from the wells to the basin. CAUTION: This material contains cells and should be disposed of in accordance with standard biological safety practices.

18. Using a 12-channel pipettor, quickly but gently add 200 µl binding buffer down the sides of each well of all of the plates. 19. Invert the plate as described in step 17. Repeat this wash two more times. Some plate washers can be programmed to perform this washing automatically; however, it is important that the washer used is capable of washing gently and evenly (see Critical Parameters).

Lyse cells 20. After the final rinse, add 50 µl lysis buffer to each of the wells. Incubate the plates 10 min at room temperature with agitation. This lysis step makes the fluorescence in the wells more homogeneous. The lysed plates can be refrigerated overnight as long as they are kept in the dark.

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21. Read the plates on a fluorescence plate reader using an excitation wavelength of 480 nm and an emission wavelength of 520 nm. Calcein AM is a fluorescent dye which is retained by the RAMOS cells until the cells are lysed. The amount of fluorescence is proportional to the number of RAMOS cells present at the time of lysis.

14

no inhibitor

Fluorescence

12 10

50% inhibition

8 6 4 2 0

no substrate 10−2

cyclic peptide

10−1 100 Cyclic peptide (µ M)

101

Figure 12.7.1 Standard RAMOS binding assay to CS-1 peptide and inhibition by cyclic peptide. The histogram on the left illustrates the binding of RAMOS cells to the Neutravidin-CS-1 complex in the absence of an inhibitor or cyclic peptide (hatched bar) or to Neutravidin alone (solid bar). All values are five replicates ±SEM

12

Fluorescence

10 8

no compound

6

50% inhibition

4 2 0

Measurement of VLA-4/CS-1 and VLA-4/VCAM Adhesion Inhibition

cyclic peptide

no TNF- α 10−3

10−2

10−1 100 Cyclic peptide (µ M)

101

Figure 12.7.2 Standard expected results using RAMOS cells binding to TNF-α-stimulated HUVEC cells and inhibition by cyclic peptide. The histogram on the left illustrates the binding of RAMOS cells to TNFα-stimulated HUVEC cells (solid bar) and to HUVEC cells which were not stimulated with TNFα (hatched bar). All values are five replicates ±SEM.

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Analyze data 22. Determine IC50 values (UNIT 1.3) for inhibitors using nonlinear regression software. The dynamic range of the assay is best illustrated by the fluorescence of the cells from wells with and without CS-1 peptide in the absence of test compounds (see Figs. 12.7.1 and 12.7.2). These controls can be shown as a histogram adjacent to the concentration-response curves for each compound. Note that there is some background binding of the cells to neutravidin. This complicates calculation of IC50 values as some compounds, such as the cyclic peptide, block the background binding and thus lower the IC50 values below background levels. To ensure the IC50 value reflects the intended interaction alone, it is best to set the lower limit of the nonlinear regression analysis to the no-peptide control. This is easily accomplished with software such as Prism (GraphPad).

MEASUREMENT OF VLA-4/VCAM ADHESION INHIBITION This assay differs from the VLA-4/CS-1 assay (see Basic Protocol) only in the use of activated HUVEC cells rather than CS-1 peptide to bind the RAMOS cells. In all other respects, the protocols are identical. The VCAM/VLA-4 interaction is a central event in diapedesis and is responsible for initiating the adhesion and spreading of cells prior to extravasation. For this reason, inhibition of this interaction is a promising route to the treatment of asthma (Yusuf-Makagiansar, 2002).

ALTERNATE PROTOCOL

Additional Materials (also see Basic Protocol) HUVEC cells (e.g., Clonetics) HUVEC medium (e.g., EGM from Clonetics) TNF-α (e.g., Calbiochem) 96-well tissue culture plates Prepare HUVEC Plates 1. Evenly seed HUVEC cells in 96-well tissue culture plates at ∼20,000 cells per well in HUVEC medium 2 to 3 days before performing the experiment. The cells should be confluent but not peeling up from the bottom of the plates when the experiment is initiated.

2. Add TNF-α to the cells at a final concentration of 10 U/ml the night before the experiment. To define the background adhesiveness of the cells, add medium without TNF-α to some of the wells. Cells should be exposed to TNF-α for 16 to 20 hr before performing adhesion inhibition experiments.

3. Prepare plates containing 10× test compounds as described (see Basic Protocol, steps 7 and 8). 4. Prepare RAMOS cells as described (see Basic Protocol, steps 9 to 13). 5. Immediately before beginning the experiment, discard the TNF-α media and replace it with 90 µl binding buffer per well. Substitute these plates for Neutravidin/CS-1 plates and proceed with the assay as described (see Basic Protocol, steps 9 to 22). The wells do not require additional blocking and it is not necessary to keep TNF-α present in the assay. The cyclic peptide has an IC50 value of ∼5 ìM in this assay.

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REAGENTS AND SOLUTIONS Use deionized, distilled water in all recipes and protocol steps. For common stock solutions, see APPENDIX 2A; for suppliers, see SUPPLIERS APPENDIX.

Binding buffer 50 mM Tris⋅Cl, pH 7.5 (50 ml 1 M Tris⋅Cl, pH 7.5; APPENDIX 2A) 150 mM NaCl (30 ml 5 M NaCl) 1 mM CaCl2 (1 ml 1 M CaCl2) 1 mM MgCl2 (1 ml 1 M MgCl2) 1 mM MnCl2 (1 ml 1 M MnCl2) Store up to 1 week at 4°C The volumes given in parentheses are for a 1 liter solution.

Lysis buffer 1% Nonidet P-40 (1 ml Nonidet P-40) 50 mM Tris⋅Cl, pH 7.5 (5 ml 1 M Tris⋅Cl, pH 7.5; APPENDIX 2A) 5 mM EDTA (1 ml 0.5 M EDTA) Store up to 1 week at 4°C The volumes given in parentheses are for a 100 ml solution.

COMMENTARY Background Information

Measurement of VLA-4/CS-1 and VLA-4/VCAM Adhesion Inhibition

The role of cell adhesion in inflammation is well established (Lobb, 1999; Yusef-Makagiansar, 2002). In response to chemotactic signals, leukocytes migrate from the bloodstream to the source of inflammation via a sequential series of adhesive events: rolling, firm adhesion, and extravasation. The integrins are heterodimeric adhesion proteins on the surface of leukocytes and endothelial cells with α and β subunits that recombine to confer different binding specificities (van der Fler, 2001). The VLA-4 examined in this unit, also known as the α4β1 integrin, is thought to have an important role in asthma (Lobb, 1996; Lin, 1999), although there have been some disappointing clinical trials involving VLA-4 antagonists. Both of the protocols described in this unit (see Basic Protocol and Alternate Protocol) use a VLA-4-expressing (RAMOS) cell line. The cells are fluorescently labeled and allowed to bind either to a CS-1 fibronectin fragment or VCAM expressed by HUVEC cells. Nonadherent RAMOS cells are removed by gentle washing and the remaining cells are quantified by their fluorescence. Many of these interactions have been assayed in the past using cell-free systems and Fc/integrin fusion constructs in modified ELISAs. Inhibitors typically appear more potent in this type of assay, probably because of the multivalency of the cellular interactions. The modified ELISA approach has the advantage

of being insensitive to toxic compounds, although it may overestimate the potency of inhibitors. For more information on the stimulation of HUVEC cells, see Swerlick (1992).

Critical Parameters Reagents Manganese serves to activate VLA-4 and is essential for obtaining a strong signal (Masumoto and Hemler, 1993). Strepavdin and avidin are not acceptable substitutes for neutravidin, as they yield a very high background. Avidin is a glycoprotein which can be bound by lectins on the surface of RAMOS cells and streptavadin contains an RQD sequence which may mimic the RGD of VCAM-1. Neutravidin is free from these background-enhancing issues. False inhibition signals due to lysis Compounds that lyse RAMOS cells will appear as though they are inhibiting binding. It is possible to control for this by including 5 µM ethidium homodimer (Molecular Probes) in the assay media. The fluorescence of the ethidium homodimer is read prior to rinsing the plates. There is a dramatic increase in this fluorescence when the cells are lysed. The wavelengths for ethidium homodimer excitation (528 nm) and emission (617 nm) are sufficiently far from those of calcein to allow the assays to be run simultaneously. However, the brief exposure of RAMOS cells to the test compound makes

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compound-induced lysis a relatively rare problem, so ethidium homodimer would be an unnecessary complication in a standard assay. Saponin effectively lyses RAMOS cells at 0.1%, but care must be taken not to contaminate adjacent wells with it. False inhibition signals due to nonadherence Compounds that cause HUVEC cells to lift off the plate will also appear to be inhibitors of VLA-4/VCAM; therefore, the VLA-4/CS-1 assay is less subject to false positives. When using the VLA-4/VCAM assay, it is recommended to check plates containing promising inhibitors by microscopy to ensure that the monolayers remain intact. Controls The lack of commercially available, smallmolecule antagonists for use as controls in these assays can be overcome to some extent by using cyclic peptide, although its potency in the VCAM/VLA-4 assay is fairly low (∼5 µM). The difference in binding between the background (no peptide or no TNF-α) and the noinhibitor wells provides confidence that the observed binding is not artifactual. Antibodies can also be used to demonstrate the specific adhesion molecules involved (Vanderslice, 1997). In general, the results obtained in one assay system for VLA-4 binding will be difficult to compare to those obtained in other systems unless the same controls are employed (Yang, 2003). HTS considerations This assay is fairly easy to automate with the most difficult step being the washing of the plates; most ELISA washers default to a wash cycle that is much too vigorous. When this washing is slowed, uneven washing of the plate can become a problem due to occlusion of the washing pins with salts. Ensure that plate washers are on their most gentle settings and that the washing pins have been cleaned. Alternatively, plates can be washed with a series of dispense/aspirate cycles on a multichannel pipettor.

Troubleshooting High variability This is typically due to nonuniform washing of plates. Extra caution should be used to ensure that every well gets the same treatment. Vari-

ability in the form of plate-to-plate drift can also occur if one attempts to process a large number of plates simultaneously. Six plates are essentially the maximum that a single person can wash by hand. Clumping of RAMOS cells can also cause variability in the results. A recommended test to control for variability in cell addition is to measure the fluorescence of the plates prior to washing. Low binding RAMOS cells are viable for 20 to 30 passages from those received from the ATCC; older cells can lose their adhesiveness. Ensure that low binding is not due to poor dye uptake. This is achieved by determining if RAMOS cells are brightly fluorescent when pelleted following staining. Also, if the HUVEC cells are over- or under-confluent, this can result in low binding. High background Inadequate blocking is usually to blame for high background in the CS-1 assay. Ensure that the BSA concentration is 3%. HUVEC cells will bind more tenaciously if more TNF-α is provided or if the media is contaminated with mitogens such as lipopolysaccharide (LPS). It is advisable to titer new sources of TNF-α in this assay system.

Anticipated Results The background (i.e., CS-1 wells that received no biotinylated peptide or VCAM wells with HUVECs that did not receive TNF-α) should have about one-tenth the fluorescence of the control wells that received these elements but no inhibitors. Wells containing inhibitors should produce values between these two extremes, depending upon the potency and concentration of the compounds.

Time Considerations VLA-4/CS-1 assay It is typically most convenient to block the CS-1 plates overnight. The assay itself can then be run in 2 to 3 hr the next morning. VLA-4/VCAM Assay HUVEC cells require about 2 days to become stable confluent monolayers. After this, the assay can be run in 2 or 3 hr. Both assays A single technician performing all washes and liquid transfers by hand can conveniently

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process four 96-well plates. This is sufficient for an eight-point dose-response assay of ten compounds in quadruplicate, including controls. Alternately, 160 compounds could be screened in duplicate at a single concentration. Of course, automation can allow for much greater throughput depending on the equipment used.

Literature Cited Hocking, D.C. 2002. Fibronectin matrix deposition and cell contractility. Chest 122:275S-278S. Lin, K., Ateeq, H.S., Hsiung, S.H., Chong, L.T., Zimmerman, C.N., Castro, A., Lee, W.C., Hammond, C.E., Kalkunte, S., Chen, L.L., Pepinsky, R.B., Leone, D.R., Sprague, A.G., Abraham, W.M., Gill, A., Lobb, R.R., Adams, S.P. 1999. Selective, tight-binding inhibitors of integrin 41 that inhibit allergic airway responses. J. Med. Chem. 42(5):920-34. Lobb, R.R. and Adams, S.P. 1999. Small molecule antagonists of α4 integrins: Novel drugs for asthma. Exp. Opin. Invest. Drugs. 8(7):935-945.

Swerlick, R.A., Lee, K.H., Li, L., Sepp, N.T., Caughman, S.W. and Lawley, T.J. 1992. Regulation of vascular cell adhesion molecule 1 on human dermal microvascular endothelial cells. J. Immunol. 149:698-705. van der Flier, A. and Sonnenberg, A. 2001. Function and interactions of integrins. Cell Tissue Res. 305:285-298. Vanderslice, P. Ren, K., Revelle, J.K., Kim, D.C., Scott, D., Bjercke, R.J., Yeh, E.T.H., Beck, P.J., and Kogan, T.P. 1997. A cyclic hexapeptide is a potent antagonist of α4 integrins. J. Immunol. 158:1710-1718. Yang, G.X. and Hagman, W.K. 2003. VLA-4 antagonists: Potent inhibitors of lymphocyte migration. Med. Res. Rev. 23:369-392. Yusef-Makagiansar, H., Anderson, M.E., Yakovleva, T.V., Murray, J.S., and Siahaan, T.J. 2002. Inhibition of LFA-1/ICAM-1 and VLA-4/VCAM-1 as a therapeutic approach to inflammation and autoimmune disease. Med. Res. Rev. 22(2):14667.

Key Reference

Lobb, R.R., Abraham, W.M., Burkly, L.C., Gill, A., Ma, W., Knight, J.A., Leone, D.R., Antognetti, G., and Pepinsky, R.B. 1996. Pathophysiologic role of alpha4 integrins in the lung. Ann. N.Y. Acad. Sci. 796:113-123.

Yusef-Makagiansar et al., 2002. See above.

Masumoto, A. and Hemler, M.E. 1993. Multiple activation states of VLA-4. J. Biol. Chem. 268:228-234.

This review focuses on compounds developed as VLA-4 inhibitors.

Phelan, M.C. 1997. Techniques for mammalian cell tissue culture. In Current Protocols in Protein Science (J.E. Coligan, B.M. Dunn, D.W. Speicher, P.T. Wingfield, eds.) pp. A.3C.1A.3C.14. John Wiley & Sons, Hoboken, N.J.

This is a broad review covering cell adhesion as a therapeutic target with specific reference to VLA-4. Yang et al., 2003. See above.

Contributed by Christopher Mehlin University of Washington Seattle, Washington

Measurement of VLA-4/CS-1 and VLA-4/VCAM Adhesion Inhibition

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Measurement of Cell Death in Mammalian Cells

UNIT 12.8

Methods for assessing mammalian cell death are presented in this unit. The unit is divided into the following five sections: (1) a brief overview of cytotoxicity and pathways of cell death, (2) an improved method to measure cell death using lactate dehydrogenase (LDH) release as a marker of membrane integrity (see Basic Protocol 1), (3) a flow cytometry method that simultaneously measures two types of cell death, oncosis and apoptosis (see Basic Protocol 2), (4) use of nuclear morphology to assess apoptosis and oncosis, (see Basic Protocol 3), and (5) a discussion of the use of cytotoxicity assays for determining the mechanisms of cell death. NOTE: All protocols using cells isolated from live animals must first be reviewed and approved by an Institutional Animal Care and Use Committee (IACUC) and must conform to government regulations concerning the care and use of laboratory animals.

Cytotoxicity: Oncosis versus Apoptosis Cytotoxicity is caused by the adverse actions of chemicals and physical agents on cells. Methods used to assess cytotoxicity typically compare cellular function and/or integrity in control cells to those exposed to a toxicant or stress. The cellular function being measured depends on the cell type, the injurious agent used, and the type of cell death being studied. Cell death typically occurs by oncosis or apoptosis (Figure 12.8.1, Table 12.8.1). Oncosis, which is ATP-independent, is characterized by cell and organelle swelling, pyknosis, loss of ion gradients, increased permeability with loss of cell membrane integrity, and the release of intracellular contents to the extracellular milieu (Cummings and Schnellmann, 2001). In contrast, apoptosis is ATP-dependent and characterized by cell shrinkage, maintenance of plasma membrane integrity, chromatin condensation, nuclear fragmentation, and activation of a family of cysteine-containing, aspartate-directed proteases called caspases (Salvesen and Dixit 1997; Lemasters 1999; Cummings et al., 2000). In vivo, oncotic cell death typically induces inflammation while apoptotic cell death does not. Both oncosis and apoptosis lead to necrosis, which is the pathological term used to describe the appearance of dead cells in histological sections, regardless of the pathway by which the cells died (apoptosis or oncosis; Levin et al., 1999). The modifiers “apoptotic” and “oncotic” cell death specify the predominant pathways of cell death (Levin et al., 1999). Depending on the cell model, and the chemical or physical agent used, several features of apoptosis and oncosis may be shared (Figure 12.8.1, Table 12.8.1). For example, the mitochondrial membrane permeability transition (MPT), which is the result of the opening of a high-conductance pore in the mitochondrial inner membrane, can be altered in apoptosis and oncosis (Lemasters et al., 1999). If cellular ATP levels are maintained during MPT, then apoptosis will ensue. In contrast, if ATP is severely depleted, then oncosis will result. Other features of cell death present in both apoptotic and oncotic cells are DNA degradation and nuclear condensation. Protocols designed to differentiate between oncosis or apoptosis should assess at least two exclusive markers for oncosis or apoptosis (see Fig 12.8.1 and Table 12.8.1). Historically, caspase activation and activity were thought to be necessary for apoptosis and were, therefore, used as markers for this type of cell death. However, numerous investigators have observed caspase-independent apoptosis (Kitanaka and Kuchino, 1999; Lankiewicz et al., 2000; Cummings and Schnellmann, 2002; Wu et al., 2002). Caspaseindependent apoptosis is similar to classical apoptosis in terms of cellular and nuclear In Vitro Cellular Assays Contributed by Brian S. Cummings and Rick G. Schnellmann Current Protocols in Pharmacology (2004) 12.8.1-12.8.22 C 2004 by John Wiley & Sons, Inc. Copyright 

12.8.1 Supplement 25

Table 12.8.1 Morphological and Biochemical Features of Apoptosis and Oncosis

Apoptosis

Oncosis

Externalization of phosphatidylserine

Phosphatidylserine degraded, internalized, or released

Activation of caspases

Caspase are not activated

Maintenance of membrane integrity

Loss of membrane integrity

Decrease in cellular volume (cell shrinkage)

Increase in cellular volume (cell swelling)

No release of intracellular contents

Release of intracellular contents

No inflammation (in vivo)

Inflammation (in vivo)

Formation of cellular buds and fragments

Formation of cellular blebs

Chromatin condensation

No chromatin condensation

ATP concentrations are slightly decreased or maintained Organelles retain integrity

Loss of ATP concentrations

Plasma membrane Ca

2+

Organelle swelling and loss of integrity

gradients maintained Loss of Ca2+ gradients Shared features Membrane permeability transition DNA degradation Nuclear condensation

morphology and maintenance of membrane integrity. However, this type of apoptosis proceeds in the absence of caspase activity. Caspase-independent apoptosis can be identified by the demonstration of cell and nuclear apoptotic morphology in the presence of either a broad-spectrum caspase inhibitor, such as ZVAD-fmk, or in cells devoid of caspase activity using antisense or knockout technologies. Both techniques require that the absence of caspase activity be confirmed by quantification of caspases using immunoblot analysis or by caspase activity (see Table 12.8.2). However, it is difficult to prove that all caspase activity is completely inhibited or that inhibition of one caspase isoform does not result in the activation of another. Furthermore, it is difficult to account for unknown caspases. Despite these problems, several studies have provided evidence of caspase-independent apoptosis induced by anticancer agents (Cande et al., 2002; Cummings and Schnellmann 2002), serum deprivation, and oxidants (Cande et al., 2002).

Measurement of Cell Death in Mammalian Cells

The existence of multiple pathways for the induction of cell death necessitates the development of multiple protocols and markers for their measurement. Many of these methods are listed in Table 12.8.2, along with references to studies in which they have been utilized. Cell death has been evaluated using the measurement of mitochondrial dehydrogenase activities (Fanning et al., 1990; Smith et al., 1992), cellular respiration (Schnellmann, 1994), and mitochondrial membrane potential using the fluorometric dyes 5,5 6,6 -tetrachloro-1,1 ,3,3 -tetraethylbenzimidazocarbocyanine iodide (JC-1) and tetramethylrhodamine methylester, both available from Molecular Probes (Reers et al., 1995; Heiskanen et al., 1999; Lemasters et al., 1999). Cell toxicity has also been assessed by studying protein synthesis (Sorger and Germinario, 1983; Nony and Schnellmann, 2001), unscheduled DNA synthesis (Brambilla et al., 1979), DNA damage (Sorger and Germinario, 1983; Shen et al., 1991; Sasaki et al., 2000; Yan et al., 2000), and cytosolic free Ca2+ levels (Ogden et al., 1995). One of the best markers for cell death is membrane integrity. Plasma membrane permeability is assessed using propidium iodide or lactate dehydrogenase (see below), and lysosomal membrane integrity is determined using neutral red (Monks et al., 1988; Mertens et al., 1995). The activities of proteases such as

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Figure 12.8.1 Pathways and markers of cell death. Cells typically die by one of two pathways, oncosis or apoptosis, both of which proceed in distinct phases. Oncosis and apoptosis are cellular processes that ultimately lead to necrosis. Necrosis is the term used to describe dead cells or tissue, regardless of the pathway or process involved. Cells dying by oncosis have distinct morphology compared to those dying by apoptosis. Further, cells dying by oncosis and apoptosis can be identified by markers of each pathway. However, depending on the type of cell being studied and the toxicant used, some markers or events occur in both oncosis and apoptosis. Adapted from Majno and Joris (1995).

calpains and caspases have also been utilized to assess cell toxicity using zymography, immunoblot analysis, or analysis of cleavage of fluorometric-labeled peptide substrates (Liu et al., 2001; Cummings and Schnellmann, 2002; Liu et al., 2002). The list of markers of cytotoxicity shown in Table 12.8.2 is not comprehensive. Any cellular function can serve as a marker of cytotoxicity. This unit describes three protocols that can be used either separately or in tandem to measure cell viability, oncosis, and apoptosis. The first describes methods used to assess the release of the cytosolic enzyme lactate dehydrogenase (LDH; see Basic Protocol 1). Release of this protein to the extracellular milieu is a standard marker for cell viability, loss of membrane integrity, and the presence of oncosis. The second and third protocols describe the use of flow cytometry (see Basic Protocol 2) and fluorescence microscopy (see Basic Protocol 3) for the simultaneous determination of apoptotic and oncotic cell death. Both use propidium iodide (PI) as a marker for membrane integrity and oncosis. For apoptosis, the flow cytometry protocol uses fluoresceinisothiocyanate (FITC)-conjugated annexin V as an indicator. Annexin V binds to phosphatidylserine, a phospholipid that is externalized relatively early during apoptosis. In contrast to annexin V, the microscopy method uses nuclear morphology to identify apoptosis, which is visualized using the nucleic acid stain 4 ,6 -diamidino2-phenylindole (DAPI). These three protocols are applicable for both freshly isolated cell suspensions and cells isolated from cultures. These protocols represent accurate and

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Table 12.8.2 Markers and Methods Used to Assess Cytotoxicity

Morphological or biochemical event Externalization of phosphatidylserine

Marker

Reference

Annexin V binding

Schutte et al., 1998; Cummings and Schnellmann, 2002

Activation of caspases

Fluorometric substrate cleavage Expression of specific caspases Maintenance of plasma and PI staining lysosomal membrane integrity Neutral red Cellular volume Cell size

Bortner and Cidlowski, 2001

Release of intracellular contents

LDH

Mertens et al., 1995; Moran and Schnellmann, 1996

Inflammation

Inflammatory cell infiltration or expression of markers of inflammation Cell morphology

Jaeschke et al., 1996; Licht et al., 1999

Formation of cellular buds, fragments, or blebs

Thornberry et al., 1997; Saraste and Pulkki, 2000

Ferlini et al., 1996; Singh, 2000

Lemasters et al., 1987; Zhang et al., 1998; Zhang et al., 1999

Chromatin condensation

DAPI or Hoechst staining

Lieberthal et al. 1996; Weber et al., 1997

ATP levels

HPLC analysis

Groves and Schnellmann, 1996

DNA fragmentation

DAPI or Hoechst staining Agarose gel electrophoresis DNA hypoploidy

Mongelard et al., 1999; Singh, 2000; Cummings and Schnellmann, 2002

Ca2+ gradients

FURA-2

Schnellmann, 1991; Lemasters,1999; Lemasters et al., 1999

Mitochondrial function and integrity

MTT JC-1 Tetramethylrhodamine

Reers et al., 1995; Lemasters, 1999; Lemasters et al., 1999; Cummings and Schnellmann, 2002

Cellular and mitochondrial respiration

O2 consumption

Schnellmann, 1994

relatively straightforward methods for the initial determination of the mechanisms of cell death.

BASIC PROTOCOL 1

Measurement of Cell Death in Mammalian Cells

MEASUREMENT OF PLASMA MEMBRANE INTEGRITY AND VIABILITY USING LDH RELEASE During oncosis, cell viability is lost through the breakdown of the cellular membrane, resulting in the release of intracellular constituents, including enzymes such as lactate dehydrogenase (LDH). Traditionally, LDH release has been measured using an NADH-linked UV-visible spectrophotometric method (Schnellmann and Mandel, 1986). However, this method has several limitations, one being that samples are typically run serially, resulting in a time-intensive protocol. The method described below is an NADH-linked LDH assay using a fluorescence plate reader (Moran and Schnellmann, 1996). This method has a high correlation (r2 = 0.95) with the traditional UV-visible spectrophotometric method and allows for the parallel processing of multiple samples of smaller volumes, thereby decreasing analysis time and costs.

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The LDH assay can be used with freshly isolated cells or cells isolated from adherent cultures. Typically, results are expressed as the percentage LDH activity released from the cells into the media compared to the total LDH activity (LDH activity in cells plus that present in the media or cell-free buffer). If adherent cultures are used, LDH activity should be determined in a cell-free sample of the media and compared to the total activity found in media isolated from treated cells, detached cells, and in attached cells.

Materials Cellular material (∼1 mg protein/ml) Toxicants 2:1 (v/v) n-butyl phthalate/dioctyl phthalate (store at room temperature for up to 1 month) Cell medium or buffer Liquid nitrogen 16 mM pyruvic acid in LDH-PO4 buffer (prepare fresh daily) LDH-PO4 buffer: 50 mM K2 HPO4 and 9 mM KH2 PO4 , pH 7.4 (prepare fresh weekly) 0.3 mM β-NADH in LDH-PO4 buffer (prepare fresh daily) 1.5-ml microcentrifuge tubes 48-well transparent plate Fluorometric plate reader with a 360-nm excitation filter (40 nm bandwidth) and a 460-nm emission filter (BMG Laboratories, Molecular Devises, and Bio-Tek) 1. Treat cells with desired concentrations of toxicants or solvent controls. 2. Prepare three 1.5-ml microcentrifuge tubes for sample collection for each treatment group. Place 0.4 ml of 2:1 n-butyl phthalate/dioctyl phthalate solution into the first microcentrifuge tube and 0.5 ml of medium, or buffer, into the second microcentrifuge tube. The ratio of n-butyl phthalate to dioctyl phthalate may be altered depending on the density of cells being utilized. The proper ratio is that which results in no cellular material being present in the supernatant after centrifugation. If cellular material is present, then the ratio should be increased. The presence of cellular material in the supernatant can be determined visually using microscopy. The purpose of the phthalate solution is to separate cells from the extracellular buffer or media. The cell-free medium will be transferred to the third microcentrifuge tube.

3. After incubation for a predetermined time, remove a 1-ml aliquot of cells, layer it on the top of the 2:1 n-butyl phthalate/dioctyl phthalate solution in the first microcentrifuge tube, and centrifuge 2 min at 10,000 × g, room temperature. Transfer the supernatant, which contains the LDH activity released into the medium (i.e., the cell-free medium), into the third microcentrifuge tube. 4. Remove a 0.5-ml aliquot from the cell suspension and place it into the second microcentrifuge tube. This mixture represents the total LDH activity in the cells and medium.

5. If working with adherent cells grown in culture, scrape the cells in the plate with medium and transfer a 0.5-ml aliquot to the second microcentrifuge tube. This tube will be used to determine the “total” LDH activity.

6. To obtain cell-free medium when working with adherent cells grown in culture, transfer an aliquot of the medium to the first microcentrifuge tube, centrifuge 2

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min at 10,000 × g, room temperature, and transfer the supernatant to the third microcentrifuge tube. At this point all samples may either be analyzed immediately or stored frozen for at least 2 weeks at −80◦ C.

7. At the time of analysis, subject all samples to three freeze/thaw cycles using liquid nitrogen to lyse the cells. Previous studies have shown that the freeze-thaw process does not decrease LDH activity (Moran and Schnellmann, 1996). An alternate method to lyse the cells is to treat media and “total” samples with 1% (v/v) Triton X-100.

8. Add 100 µl of the cell-free medium sample (supernatants from step 3 or 6) and 25 µl of the corresponding “total” sample (samples from step 4 or 5) to adjacent wells in a 48-well transparent plate. Leave 4 wells empty for blanks. 9. Add 50 µl of 16 mM pyruvic acid (the LDH substrate) to each sample and to the four blank wells. 10. Swirl the plate gently to mix contents. 11. Adjust the volume in the wells to 450 µl using LDH-PO4 buffer (25◦ C) and gently swirl the plate to mix contents. 12. Immediately prior to scanning on the fluorometric plate reader, add 50 µl of the β-NADH solution to each sample and gently swirl the plate to mix contents. 13. Scan the entire plate using 360 nm excitation (40-nm band width) and 460 nm emission for five cycles of 24 sec each. Calculate the change in fluorescence (or F) as the change in value from the first to the fifth cycle. The F value should decrease as NADH is consumed by LDH.

14. Subtract the average F in the blank wells from the average F in the cell-free medium and total samples. Determine percent LDH release (Equation 12.8.1) by dividing the corrected F value of the cell-free medium sample by that of the total sample and adjust for the dilution factor (8 in the above scenario).

Equation 12.8.1

Retention of cellular LDH activity, as opposed to percent LDH release, is an alternative quantity that can be used for the assessment of cell viability.

Measurement of Cell Death in Mammalian Cells

Cell viability can also be measured using a number of different fluorescent or spectrophotometric vital dyes such as trypan blue, neutral red, MTT exclusion, or DNA staining using propidium iodide. Often, these markers can be used in tandem in a single model to assess viability. For example, Figure 12.8.2 demonstrates the time-dependent toxicity of GSH-conjugated hydroquinones to a renal proximal tubular cell line as assessed using LDH activity and MTT and neutral red staining (Mertens et al., 1995). Decreases in LDH activity correlated to decreases in MTT and neutral red exclusion at both 2 and 3 hr of treatment. Figure 12.8.2 illustrates the use of multiple vital dyes and LDH to determine cell viability in a single model.

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Figure 12.8.2 Differences in cytotoxicity of (A) 2-Br-(diglutathione-S-yl)hydroquinone and (B) 2-Br-6-(glutathione-S-yl)hydroquinone to renal tubular epithelial cells (LLC-PK1 ) as assessed by neutral red accumulation, MTT reduction, or intracellular LDH activity (as indicated in panels A and B). Cells were exposed to 0.5 mM 2-Br-(diglutathione-S-yl)hydroquinone and 2-Br-6(glutathione-S-yl)hydroquinone for 2 hr. After removal of toxicants, cells were incubated for 1 hr in neutral red solution, MTT solution, or Earle’s balanced salt solution, and LDH activity was determined. ∗ , Earliest time at which a significant decrease in viability was observed with each assay. For 2-Br-(diglutathione-S-yl)hydroquinone, p < 0.05 for LDH at 2 hr. For 2-Br-6-(glutathione-Syl)hydroquinone, p 4, the combination is antagonistic; FIC A + B = FIC A or FIC B, the combination is indifferent. Table 13A.3.3 shows a representative microtiter checkerboard experiment using Haemophilus influenzae with a range of concentrations of azithromycin and compound X in µg/ml. Synergistic behavior is noted at specific concentrations of the two compounds.

REAGENTS AND SOLUTIONS Use deionized, distilled water in all recipes and protocol steps. For common stock solutions, see APPENDIX 2A; for suppliers, see SUPPLIERS APPENDIX.

Antibiotic and test compound stock solutions Before weighing, let all compounds come to room temperature (∼25◦ C). Prepare 10 mg/ml stock solutions of the antibiotics or new chemical entities to be tested. Ideally, drug stock solutions should be prepared in sterile water. If MIC testing is required for an antimicrobial that is not water-soluble, then the compound can be prepared in DMSO and diluted in the medium. For most organisms, the final concentration of DMSO in the test medium should not exceed 5%. This concentration of DMSO will solubilize most compounds but at the same time is non-toxic to the growth of test microorganisms. When DMSO is used, a drug-free growth control should be included with each test strain to assure that DMSO has no effect on the test organisms. Use the following CLSI formulae to calculate the amount of diluent or powder needed for a standard solution. volume (ml) = [weight (mg) × potency (µg/mg)]/[concentration (µg/ml)] Or weight (mg) = [volume (ml) × concentration (µg/ml)]/[potency (µg/mg)]

McFarland barium sulfate standard A McFarland standard is prepared by adding barium chloride to aqueous sulfuric acid purchased as barium sulfate standards from Remel. Store for up to 1 year at room temperature. The density of the resulting barium sulfate precipitate can be used to approximate the colony count of a prepared suspension; e.g., 0.5 McFarland is the equivalent of ∼1 × 108 CFU/ml. Medium for fastidious organisms 70 ml brain heart infusion broth (BD Biosciences), trypticase soy, or MuellerHinton broth 100 ml horse serum (BD Biosciences) 30 ml glycerol (Fisher) Sterilize by filtration through a 0.45-µm cellulose acetate filter Store up to 3 months at 4◦ C COMMENTARY Background Information The basic microbiological techniques described in this unit can be applied to antibiotic discovery programs in which new chemical entities are screened for antimicrobial activity. The methods are also applied to situations where the potency and antimicrobial spectrum

of a compound need to be determined in anticipation of its development as a new antibiotic. These techniques are generally easy to perform with equipment commonly found in most microbiological laboratories. The materials needed for these assays (e.g., media) are welldefined and readily obtained from a number

Anti-Infectives

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Table 13A.3.4 Troubleshooting Guide for Basic Microbiological Techniques Used in Antibacterial Drug Discovery

Problem

Possible cause

Solution

More than one colony type on agar plate (colony counts)

Contamination

Check media for contamination; Check culture dilution tubes for contamination; Check initial culture for purity; Check for environmental contamination.

No growth on agar plates after 24 hr (pour-plate method)

Slow growing organism

Continue incubation for an additional 24 hr

Culture added while agar too hot

Repeat, making sure agar is adequately cooled

Wrong medium used or required supplement not added

Repeat, using correct medium

Fastidious organism

Requires 5% CO2 for growth

No growth in growth control well (microtiter assay)

Did not use fresh initial culture; culture not viable in test antibiotic solvent (if not water soluble)

Confirm selection of medium and proper incubation conditions

Growth in sterility well (microtiter assay)

Contamination

Check medium

Plate inoculated incorrectly

Check orientation of microtiter plate

Initial inoculum not 105 CFU/ml

Check colony counts of initial inoculum

Wrong medium used

Check medium

Wrong QC strain used

Check strain

Wrong antibiotic concentration

Check antibiotic starting concentration

Antibiotic not soluble in test medium

Check solubility

Inactivation of test compound

Check compound stability. If tested in medium that contains blood/serum, check for excessive serum protein binding by comparing its MICs in the presence and absence of serum. The MIC will be much higher in the serum-containing medium if excessive serum protein binding is an issue.

Test culture develops resistance to test antibiotic

Check test culture for elevated MIC

Not all culture exposed to antibiotic; broth re-inoculation

When preparing kill-curve tubes, make sure no culture is on sides of tubes

Contamination

Check for contaminant

Wrong MIC used

Check MIC

Skipped wells in microtiter plate

Well not inoculated properly or culture in well did not grow

If one skipped well, record MIC as higher value. If more than one skipped well, repeat test.

“Trailing” growth (microtiter assay)

Antagonizing substance in medium (e.g., thymidine versus trimethoprim)

Record MIC-80

Control antibiotic values for QC strain not in range—too high/too low

Regrowth after 24 hr (kill-curves)

continued Basic Microbiological Techniques For Antibacterial Drug Discovery

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Table 13A.3.4 Troubleshooting Guide for Basic Microbiological Techniques Used in Antibacterial Drug Discovery, Continued

Problem

Possible cause

Solution

Morphology of culture changes after 24 hr incubation in presence of antibiotic

Contamination

Confirm identification of culture

Culture under stress

Note this under experimental results

Solubility problem

Check antimicrobial solubility

Paradoxical effect

See Basic Protocol 3

Plates drying out in incubator

Make sure lids of plates are on securely. If necessary, incubate plates wrapped in plastic bags.

Growth at higher concentrations of antibiotic, no growth at lower concentrations (microtiter assay) Decreased volume in microtiter wells Agar plates drying out

of reputable sources. The materials used in microbiological testing are comparatively inexpensive. The procedures described in this unit are widely accepted because they are easily standardized to assure reproducibility from laboratory to laboratory. Standard conditions for microbiological testing have been detailed by the CLSI and CAP. The conditions under which the assays are performed are controllable and easily manipulated. Experimental results can be obtained within 24 to 72 hr, depending on the growth characteristics of the test organisms. Accuracy and reproducibility of results can be assured with the proper quality control measures. Because the in vitro evaluation of an antimicrobial is conducted in agar or broth under artificial conditions, the results obtained are only suggestive of in vivo activity since the test agent is not subjected to many of the complex biological functions of the living host.

Critical Parameters When designing experiments, consider the following: What is the general purpose of the study? Is the purpose of the study to merely determine antimicrobial activity or to compare activity of an unknown to that of a known antimicrobial, which should be included and tested under the similar conditions?

Make sure that interior of incubator is adequately hydrated. If necessary, wrap plates in plastic bags for incubation. What are some of the chemical properties of the compound being evaluated (solubility, stability at elevated temperatures, binding to proteins, pH effect)? Proper storage of antimicrobial compounds is essential to assure their stability. Most antimicrobials should be stored at 4◦ C as powders and prepared immediately before testing. Alternatively, frozen stock solutions maintained at −80◦ C can be used. Ideally, test compounds should be water-soluble. If this is not the case, they should be prepared in a solvent, such as DMSO, that will not affect the growth of the test organism. If a test compound is affected by interfering substances in a standard test medium, it is necessary to define conditions under which the compound can be evaluated (e.g., alternative medium, incubation conditions, etc.). These deviations from the norm should be defined as part of the experimental results. What are the growth needs/characteristics of the test organism(s)? This information is required for proper media selection, media supplementation, incubation conditions, and aeration. Whenever possible, fresh bacterial subcultures should be used for testing. Ideally, test culture(s) should grow well enough in the selected medium so that positive/negative results are easily determined by visual inspection after 18 to 24 hr of incubation. Test cultures should grow well without CO2 . When incubated under Anti-Infectives

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Figure 13A.3.4

Skipped wells in a microtiter MIC.

CO2 , there are pH changes in media that lead to the inactivation of certain classes of antimicrobials. Therefore, it is not recommended that plates used for antimicrobial susceptibility testing be incubated under CO2 . What quality control organisms and compounds should be used to assure reliable results? A list of recommended QC cultures obtained through the American Type Culture Collection (ATCC) and reference values for known antibiotics can be found in the CLSI Guidelines (http://www.clsi.org/). There are limitations to the determination of MIC and MBC by microdilution (see Basic Protocol 3). Occasionally there are skipped wells in a microbroth MIC assay in which growth did not happen in one or more of the series of wells (see Fig. 13A.3.4). In the case of one or two skipped wells, read the MIC as the higher value. However, if there are more than two skipped wells, repeat the assay.

Troubleshooting Table 13A.3.4 describes some common problems that may be encountered with the protocols in this unit, along with possible causes and suggestions for addressing these problems.

Anticipated Results See individual protocols for representative results.

Time Considerations

Basic Microbiological Techniques For Antibacterial Drug Discovery

When inoculated into appropriate nutrient broth or agar, most non-fastidious grampositive and gram-negative bacteria grow well within 18 to 24 hr when incubated at 35◦ C. When inoculated into a broth medium, many bacteria will experience an initial lag phase in their growth, where the initial cell count may actually decline. This lag period generally lasts 1 to 2 hr, after which growth quickly rebounds. The growth of bacterial cultures in broth medium can be accelerated with

the aeration that occurs when cultures are grown in a shaking incubator. To re-isolate an organism on appropriate medium requires 24 to 48 hr. It requires 3 to 4 days to determine the MIC results for, say, seven to eight compounds (if well-organized, MICs can be determined in 72 hr). A kill-curve with a 24-hr endpoint requires 96 hr (4 days).

Key References Clinical and Laboratory Standards Institute. Performance Standards for Antimicrobial Susceptibility Testing; Fifteenth Informational Supplement. M100-S15. CLSI 2005. Provides up-to-date information for drug selection, interpretation, and quality control. Lorian,V. ed. Antimicrobial combinations. In Antibiotics in Laboratory Medicine, 4th ed. pp. 330338. Williams & Wilkins, Baltimore, Maryland. Provides detailed in vitro and in vivo methods for analyzing antimicrobial compounds. National Committee for Clinical Laboratory Standards, 2000. Methods for Dilution Antimicrobial Susceptibility Tests for Bacteria that Grow Aerobically; Approved Standard 5th ed. M7A5. National Committee for Clinical Laboratory Standards, Wayne, Pa. Describes performance, applications, and limitations of antimicrobial susceptibility testing techniques, and includes a series of procedures to standardize the way the tests are done. National Committee for Clinical Laboratory Standards. 1999. Methods for Determining Bactericidal Activity of Antimicrobial Agents; Approved Guideline M26-A. National Committee for Clinical Laboratory Standards, Wayne, Pa. Detailed procedures for kill-curves and MBCs.

Contributed by Mary Motyl, Karen Dorso, John Barrett, and Robert Giacobbe Merck and Co., Inc. Rahway, New Jersey

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Current Protocols in Pharmacology

Primary Rodent Infection Models for Testing Antibacterial Compound Efficacy In Vivo

UNIT 13A.4

There are many reasons for employing animal models of infection. These include the ability to establish a range of infections caused by a variety of pathogens at different host sites, to understand how in vitro antibiotic sensitivity or resistance correlates to therapy failure in vivo, to control the virulence of the infecting organism as well as the timing and route of infection, and to study drug pharmacokinetics (Bergeron, 1978; Andes and Craig, 2002; Dagan, 2003; Jacobs, 2003). The challenge to the scientist is to utilize or develop an animal infection model that closely approximates human disease and that can reliably predict clinical efficacy. The goal is to compare the effectiveness of a novel drug with an established agent by demonstrating either increased survival due to protection from a lethal pathogen, or reduction of bacterial numbers at a specific site in the host. There is a complex relationship between the drug, the host, and the pathogen that can only be addressed in the context of a whole animal. As such, it is imperative that the scientist understands, or is able to gain information on, the virulence of the pathogen, the pharmacokinetics of the new chemical entity (NCE) and its possible metabolites, any potential toxicity associated with the test agent, and host defenses, in order to make these studies ethically justifiable as well as scientifically robust. Some investigators have found it useful to divide infection models into four classes: basic, or primary screening models; ex vivo models; mono-parametric models; and discriminative models (Bergeron, 1978; Zak and O’Reilly, 1991). The basic screening models are most useful for obtaining a rough approximation of the efficacy of a potential new drug, while optimizing the route of administration and dosing regimen and identifying associated toxicity (Zak and O’Reilly, 1991). These are generally single-step, simple infections, with a short duration and easily interpretable results, usually lethality. The dose of test compound that protects 50% of infected animals is the protective dose, or PD50 . This value is useful for directly comparing the efficacy of agents against a pathogenic organism. This unit describes three primary infection models routinely used to evaluate antibacterial efficacy. All three share the features of being simple to perform, utilize outbred mice, are compound-sparing, reproducible, and have easily interpretable outcomes (Zak and O’Reilly, 1991). In two of the models, the efficacy of potential antibacterial compounds can be evaluated either by monitoring survival or by enumerating bacterial numbers at the site of infection. Probably the most commonly used infection model is the murine acute systemic infection model, which is also called murine peritonitis or septicemia (see Basic Protocol 1; Zak and Sande, 1999). This model is most useful for demonstrating the relationship between the in vitro potency and the in vivo activity of a compound (Zak and Sande, 1999), and is important for the early demonstration of potential anti-bacterial compounds to eradicate infection. The murine pulmonary infection, also called the respiratory tract infection or RTI (see Basic Protocol 2), is especially important for the evaluation of compounds against pneumococcal pneumonia (Zak and Sande, 1999). The seriousness and high incidence of this infection worldwide, coupled with the rising rates of bacterial resistance, make it a critical target of antibacterial drug discovery. The thigh lesion infection (see Basic Protocol 3) is a non-lethal infection model that is generally self-limiting and results in a localized infection. It has been used to evaluate the efficacy Anti-Infectives Contributed by Andrea Marra and Dennis Girard Current Protocols in Pharmacology (2005) 13A.4.1-13A.4.13 C 2005 by John Wiley & Sons, Inc. Copyright


13A.4.1 Supplement 31

of potential drugs, the role of host defense responses, interactions between drugs and pathogens, and the kinetics of drugs in infected hosts (Acred, 1986). It is important to note that there is neither a single method of preparing bacterial inocula for these studies nor a defined number of organisms that should be used. For each model described in this unit, the experimenter must empirically determine the appropriate bacterial number based on the age and strain of the mice, the immune status of the host, the organism, the timing of infection and therapy, and the method of determining efficacy. The inoculum that is lethal to 100% of infected animals must be determined beforehand. NOTE: All protocols using live animals must first be reviewed and approved by an Institutional Animal Care and Use Committee (IACUC) or must conform to governmental regulations regarding the care and use of laboratory animals.

BASIC PROTOCOL 1

MOUSE PERITONITIS PRIMARY INFECTION MODEL TO EVALUATE ANTIBACTERIAL EFFICACY Use of this model provides a good estimate of the oral and parenteral efficacy of a test agent, as well as its tolerability and toxicity (Zak and O’Reilly, 1990). It has the advantages of being simple, with short duration and easily-evaluated endpoints (Zak and Sande, 1999). The key parameter to keep constant in this model is the number of bacteria used for infection, as this can have a major effect on the results (Zak and O’Reilly, 1990). Animals are infected by inoculating bacteria directly into the peritoneal cavity. Very shortly thereafter, activation of the host immune response results in a rapid increase in the numbers of local macrophages and neutrophils. As such, there is no infectious focus and bacteria are quickly transported by way of the lymphatic system to the bloodstream, with bacteremia occurring within 30 min as bacterial numbers in the bloodstream rapidly approach those in the peritoneal cavity. In this regard, the model differs from human infection since organs do not become infected in such a rapid fashion (Zak and O’Reilly, 1990). The systemic infection model diverges from the human scenario in that human infections can often involve multiple pathogens, both aerobic and anaerobic. In a murine infection, bacterial growth kinetics mimic what is seen in in vitro broth culture, and, unlike with human infection, antibiotic efficacy can be directly correlated to the size of the inoculum (Zak and Sande, 1999). Test agents are typically administered by any of a number of routes shortly after infection, likening this model to prophylactic therapy rather than preventive treatment (Zak and O’Reilly, 1990; Zak and Sande, 1999). The most significant differences between this model and humans pertain to pharmacokinetics and metabolism. Likewise, the age, gender and strain of mice used can affect the response to infection and therapy. Compound efficacy is evaluated by either monitoring survival or by quantifying bacterial counts in the peritoneum and/or blood. As in humans, death of the host can result from overwhelming infection as well as from the production of bacterial toxins (Zak and Sande, 1999; Freise et al., 2001). Because human pathogens are used to establish an infection in rodents, these organisms may not have the same level of virulence in mice. For some organisms to be pathogenic in this model, it may be necessary to perform several, usually two or three, passages of intraperitoneal inoculation followed by bloodstream or peritoneal recovery. That is, infect mice by the intraperitoneal route and, after 4 to 24 hr, recover organisms from the bloodstream or by swabbing the peritoneal cavity and incubate overnight on solid laboratory medium. The recovered organisms are then either passaged again or used to establish an infection until the desired virulence is attained.

Rodent Infection Models for Testing Antibacterials

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Current Protocols in Pharmacology

Materials Microorganism Sterile Dulbecco’s phosphate-buffered saline (1× liquid, PBS) or brain-heart infusion broth (Difco; Becton-Dickenson) Outbred male or female CF-1, CD-1, ICR, DBA mice (3 to 5 weeks old; 18 to 30 g; Charles River Laboratories) Skin disinfectant (e.g., betadine or 70% ethanol) Mouse food Test agents in appropriate vehicle 1- or 5-ml syringes, 20- or 26-G needles Mouse cages UV spectrometer GraphPad Prism 4.0 software or equivalent 1. For each microorganism, perform an initial pilot LD100 study to determine the optimum inoculum that will result in lethality to 100% of untreated (control) animals, but not so high as to result in death prior to the onset of therapy. Record subsequent timing of death. Depending on the organism, establish cultures in appropriate liquid or solid medium and harvest either by centrifuging 15 min at 6000 × g, room temperature, or by washing a plate of overnight growth into brain-heart infusion broth or PBS. Because growth phase and the use of either solid or liquid medium can affect the virulence of some organisms, a pilot study should include samples of each for comparison. A wide range of bacterial numbers should be examined in the pilot study, as described in step 2.

2. Before performing any animal infections, prepare ten-fold serial dilutions of the microorganism (in the range of 100 to 109 bacteria/ml) in PBS and, using a UV spectrometer, determine the absorbance at 450 nm. Determine the bacterial numbers in each dilution by further titrating each dilution. Again, prepare ten-fold serial dilutions of each in PBS (may be done in microtiter plates) and spread 0.1-ml aliquots on appropriate solid medium. Incubate plates overnight under appropriate conditions and determine colony forming units (CFU). To calculate CFU per milliliter in each dilution, multiply CFU count on each titration plate by the dilution factor and by 10 (because 0.1 ml was plated). This concentration of bacteria corresponds to the absorbance read at 450 nm. Once the actual number of bacteria for each A450 reading is determined, in subsequent infection experiments, the A450 can be adjusted to obtain an approximation of the desired inoculum. It is important to quantify bacterial inocula for each experiment to obtain an accurate count, in addition to having an absorbance reading. The inoculum may be delivered in brain-heart infusion broth, with 3% Brewer’s yeast, or 5% to 10% hog gastric mucin. Brewer’s yeast can be compared with mucin in a pilot infection study to determine which yields the more robust infection.

3. Hold a mouse by the tail between the thumb and forefinger of one hand. Fold the tail around the little finger to provide traction and quickly grab the mouse by the scruff of the neck with the thumb and forefinger to immobilize the head. By pronating the hand, the mouse is held flat with its abdomen exposed.

4. For an intraperitoneal injection, wipe the area of injection with a skin disinfectant and direct the needle toward either of the lower quadrants of the abdomen so as to avoid the liver and spleen. Insert the needle at a shallow angle ∼1 cm into the peritoneum, taking care not to inject into the intestines, because this can result in animal survival with even a lethal inoculum, resulting in experimental variability. Use volumes of 0.1 to 0.5 ml, and inject five to ten mice for each of the following groups:

Anti-Infectives

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a. Non-infected, untreated (vehicle); b. Positive (known antibiotic, preferably in the same class as the test compound, known to be active against the infecting organism); c. Negative (treatment with a known antibiotic, preferably in the same class as the test compound, for which the infecting organism has demonstrated non-susceptibility). d. Test compound(s). It is also informative to add an uninfected control group that is treated with the highest dose of the drug to look for signs of potential toxicity. It is, however, difficult to determine a correlation between toxicity in animals and that in humans as it is likely that most drugs showing toxicity in animal models are not usually tested in humans (Greaves et al., 2004).

5. Return the mice to their home cage and allow them access to food and water ad libitum, with mice being housed ten to a cage in microisolator units. Maintain a worksheet in the animal room for tracking and scoring events. In cases where mice are seen to have ruffled fur or signs of lethargy, mark the tail with a felt-tipped pen, monitor the progression of these signs at later, more frequent, time points, and euthanize if necessary. Common symptoms of systemic problems include ruffled hair, weight loss, ocular discharge, lethargy, hunched back, ataxia, tremor, hypothermia, and cyanosis. Animals exhibiting a combination of ruffled fur, ocular discharge, and lethargy require very careful monitoring and should be euthanized if these conditions show signs of progression. Animals exhibiting hunching and ataxia, tremor, hypothermia, or cyanosis should be humanely killed immediately. Also, animals with an impaired ability to gain access to food and water due to circling, paralysis, repeated seizures, or other causes, should be euthanized immediately. As a general rule, animals used in bacterial infection models should be observed at least two times daily. Observation frequency should be tailored to the virulence of the pathogen and model being employed to ensure that animals do not remain in a moribund state for long periods of time (NRC, 1996; Olfert and Godson, 2000; Fallon, 2002). Monitoring of body temperature in rodents has been suggested as an alternate endpoint, with a decrease in temperature beyond a certain point being highly correlated with death in several infection models. The predictive temperature appears to be pathogen-dependent, with monitoring being accomplished using laser-directed infrared scanners or implanted thermistor microchips (Morton, 1999; Olfert and Godson, 2000).

6. Prepare the test agents in an appropriate vehicle. Prepare a range of doses for each compound based on mg/kg body weight. While control and experimental compounds may be prepared in different diluents, it is best to use a single vehicle for all compounds in a particular study. To cover a broad range of doses and still obtain meaningful data, a typical study will span 1.56 to 100 mg/kg/dose or 3.125 to 200 mg/kg/dose in four-fold increments.

7. Administer test compounds to groups of mice in a range of doses by i.p., i.v., s.c., i.m., or p.o. routes. Administer two doses of compounds, with the first given shortly after infection, and the second a few hours later (e.g., at 30 min and 4 hr post-infection, or at 1 and 5 hr post-infection, or at 0 and 3 hr post-infection). The route of administration is determined by the expected route of administration in the clinic and/or compound solubility. By day 5, all of the mice in the untreated control group should have succumbed to the infection.

Rodent Infection Models for Testing Antibacterials

8. Monitor animals two times daily for signs of morbidity. If they are unable to eat or drink, have labored breathing, ruffled fur, and/or are lethargic, euthanize by carbon dioxide asphyxiation or cervical dislocation and note the date. Score these animals as lethalities. Record all lethalities daily.

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Table 13A.4.1 Estimation of PD50 Values for Commercial Antibiotics Evaluated in a Mouse Peritonitis Infection Model with S. pyogenes (ATCC no. 12384)

Dose (mg/kg)

Survivorship on day 5 (survivors/group size)a Amoxicillin

Linezolid

Telithromycin

50

n.d.

10/10

10/10

12.5

n.d.

9/10

10/10

3.12

10/10

7/10

9/10

0.78

10/10

0/10

0/10

0.2

10/10

n.d.

n.d.

0.05

5/10

n.d.

n.d.

0.0

0/10

0/10

0/10

PD50 (mg/kg)

0.05

2.6

1.9

(0.05–0.05)

(2.0–3.1)

(1.7–2.1)

95% confidence interval a n.d., not determined.

9. After 5 days, calculate the PD50 as follows: fit the ratio of the survival data (survivors/group size) versus dose to the unconstrained non-linear regression function (four-parameter logistic equation) using GraphPad Prism 4.0. In the event that the data do not converge, use a parameter-constrained model by setting the upper bound to 1 and the lower bound to 0. Enter dose as the x values and ratio of survivors/group size (e.g., 9/10 = 0.9) as the y values. Analyze the data using the log transform of the x values. Then, analyze the transformed data using the nonlinear regression curve fit and select the sigmoidal dose response with variable slope. Should the analysis not converge or output is inconsistent with biological outcome, then constrain the model by setting top to 1 and bottom to 0. The PD50 is presented as ED50 and the 95% confidence interval of the ED50 is also calculated in the output table. The utility estimates both the ED50 (PD50 ) and 95% confidence interval (CI). Sample outcome data for amoxicillin, linezolid, and telithromycin in a peritonitis model induced by S. pyogenes (ATCC no. 12384) are summarized on Table 13A.4.1. Oral administration commenced 0.5 hr after infection, with the second dose given 4 hr after infection. On day 5, the study was terminated and the survival data summarized. Comparison of the activities between compounds can be made using the 95% CI. That is, if the CI values do not overlap between compounds, then the PD50 values for the two compounds are significantly different with a p value proximal tibia Proximal humerus

Histologic high grade (microscopic criteria for aggression)

95%

85%–90%

Withrow et al. (1991); Vail and MacEwen (2000)

Chemotherapy na¨ıve metastatic rate

90% before 12 months

80% before 24 months

Withrow et al. (1991); Vail and MacEwen (2000)

Metastatic sites

Lung > bone > soft tissue

Lung > bone > soft tissue Withrow et al. (1991); Vail and MacEwen (2000)

Chemotherapy impact

Significant

Significant

Withrow et al. (1991); Vail and MacEwen (2000)

Her2/Neu expression

Yes

Yes

Zhou et al. (2003); Flint et al. (2004)

CXCR4 expression

Yes (cell lines) a

Yes

Laverdiere and Gorlick (2006); Oda et al. (2006)

IGF-I receptor expression Yes

Yes

Khanna et al. (2002); Mansky et al. (2002)

c-Met expression

Yes

Yes

MacEwen et al. (2003)

RANKL expression

Yes

Yes

Grimaud et al. (2003); Barger et al. (2007)

TGFβRI and TGFβRII expression

Yes (cell lines) a

Yes

Matsuyama et al. (2003)

ETA and ETB receptor expression

Yes (cell lines)a

Yes

Felx et al. (2006)

a T.M. Fan, unpub. observ.

in plasma vascular endothelial growth factor concentrations and circulating endothelial precursors for the evaluation of novel antiangiogenic therapies or (2) reductions in biochemical surrogate markers of bone resorption for the investigation of potent cancer antiresorptive agents. Specifically for the K7M2 orthotopic, syngeneic OSA tumor model, the effectiveness of experimental therapies is assessed by comparing the number of surface pulmonary metastatic nodules or overall survival times

in mice treated with various therapies. Given that untreated mice implanted with K7M2 tumor fragments develop a relatively low number of pulmonary metastatic lesions 40 days post-amputation (5.4 spontaneous pulmonary nodules), the evaluation of marginally active compounds may require larger sample sizes to identify a significant and measurable therapeutic effect. If overall survival time is the clinical endpoint, investigators should be aware that the median survival time of untreated K7M2 implanted recipient mice is approximately 76

Cellular and Animal Models in Oncology and Tumor Biology

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days and, therefore, effective treatment strategies may preclude median survival times as a measurable clinical endpoint (Khanna et al., 2000). Similar to the K7M2 murine OSA model, investigators wishing to utilize dogs with spontaneously arising appendicular OSA as a comparative tumor model should be aware of the expected longevity of untreated canine patients. Typically, 1 year post-limb resection, with the cause of death most commonly being pulmonary metastatic disease. In order for the dog to be used successfully as a comparative tumor model, it is imperative that investigators network and collaborate with boarded veterinary oncologists who are interested in clinically relevant research. A list of all board-certified veterinary oncologists is available at http://www.acvim.org.

Anticipated Results

Models of Appendicular Osteosarcoma

For both the murine K7M2 and canine spontaneous OSA tumor models, expected results will be dictated by the biologic endpoints to be assessed. Typically, for the murine K7M2 model, effective therapies should reduce the number of pulmonary metastatic lesions at pre-defined time points in comparison to untreated controls. Additionally, biologically active compounds would extend overall survival times in K7M2-implanted mice receiving experimental therapies in comparison to sham-treated controls. In dogs with spontaneously arising OSA, assessment of response could pertain to (1) controlling the local primary tumor and its associated malignant osteolysis, or (2) in amputated dogs, the extension of disease-free intervals and overall survival times. In OSA-bearing dogs not treated with amputation, assessing the effectiveness of novel anticancer and antiresorptive compounds may utilize the quantification of surrogate indices of bone metabolism, including both bone resorptive and bone formation markers within the urine and serum of study subjects. Additionally, the effect of investigational drugs on malignant osteolysis may be determined through serially quantifying changes in relative bone mineral density at the level of the primary tumor via dual energy X-ray absorptiometry (Berruti et al., 2000; Fan et al., 2005). Anticipated results for OSA-bearing dogs treated with biologically effective agents that minimize or reverse focal malignant osteolysis would be reductions in circulating bone resorption markers and increases in relative bone mineral density at the

level of the primary tumor (Fan et al., 2005; Lacoste et al., 2006). In cases where the primary tumor in dogs is controlled through amputation of the affected limb, the effect of novel therapies can be assessed by comparing disease-free intervals and overall survival times in treated and untreated (control) animals.

Time Considerations The time required for utilizing the K7M2 orthotopic, syngeneic OSA tumor system as a tool for investigating OSA biology and therapy is approximately 90 to 110 days. This includes generating tumor fragments from a donor mouse, implantation and growth of K7M2 fragments into recipient mice, amputation of affected limbs, and enumeration of pulmonary metastatic nodules 40-days post amputation. Although spontaneously arising OSA in the dog serves as an excellent comparative model, one of its potential drawbacks in comparison to the K7M2 model is the requirement for case accrual of client-owned companion animals. For a typical double-blinded, placebocontrolled clinical trial utilizing OSA-bearing dogs (n = 50) conducted at a single academic institution, a typical time frame for study initiation to completion with a minimum follow up period of 6 to 9 months in all study entrants will approach 3 to 4 years. Collaborative or multi-institutional studies would allow for greater and faster case accrual, potentially allowing study completion in 2 to 3 years.

Literature Cited Barger, A.M., Fan, T.M., de Lorimier, L.P., Sprandel, I.T., and Anderson, K.O. 2007. Expression of receptor activator of nuclear factor κ-B ligand (RANKL) in neoplasms of dogs and cats. J. Vet. Intern. Med. 21:133-140. Bergman, P.J., McKnight, J., Novosad, A., Charney, S., Farrelly, J., Craft, D., Wulderk, M., Jeffers, Y., Sadelain, M., Hohenhaus, A.E., Segal, N., Gregor, P., Engelhorn, M., Riviere, I., Houghton, A.N., and Wolchok, J.D. 2003. Long-term survival of dogs with advanced malignant melanoma after DNA vaccination with xenogeneic human tyrosinase: A phase I trial. Clin. Cancer Res. 9:1284-1290. Berlin, O., Samid, D., Donthineni-Rao, R., Akeson, W., Amiel, D., and Woods, V.L. Jr. 1993. Development of a novel spontaneous metastasis model of human osteosarcoma transplanted orthotopically into bone of athymic mice. Cancer Res. 53:4890-4895. Berruti, A., Dogliotti, L., Osella, G., Cerutti, S., Reimondo, G., Martino, A., Gorzegno, G., Catolla, R., and Angeli, A. 2000. Evaluation by dual energy X-ray absorptiometry of changed bone density in metastatic bone sites as a

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consequence of systemic treatment. Oncol. Rep. 7:777-781. Chun, R. and de Lorimier, L.P. 2003. Update on the biology and management of canine osteosarcoma. Vet. Clin. North Am. Small Anim. Pract. 33:491-516. Crnalic, S., Hakansson, I., Boquist, L., Lofvenberg, R., and Brostrom, L.A. 1997. A novel spontaneous metastasis model of human osteosarcoma developed using orthotopic transplantation of intact tumor tissue into tibia of nude mice. Clin. Exp. Metastasis 15:164-172. Dow, S., Elmslie, R., Kurzman, I., MacEwen, G., Pericle, F., and Liggitt, D. 2005. Phase I study of liposome-DNA complexes encoding the interleukin-2 gene in dogs with osteosarcoma lung metastases. Hum. Gene Ther. 16:937946. Fan, T.M., de Lorimier, L.P., Charney, S.C., and Hintermeister, J.G. 2005. Evaluation of intravenous pamidronate administration in 33 cancer-bearing dogs with primary or secondary bone involvement. J. Vet. Intern. Med. 19:7480. Felx, M., Guyot, M.C., Isler, M., Turcotte, R.E., Doyon, J., Khatib, A.M., Leclerc, S., Moreau, A., and Moldovan, F. 2006. Endothelin-1 (ET1) promotes MMP-2 and MMP-9 induction involving the transcription factor NF-kappaB in human osteosarcoma. Clin. Sci. 110:645-654. Flint, A.F., U’Ren, L., Legare, M.E., Withrow, S.J., Dernell, W., and Hanneman, W.H. 2004. Overexpression of the erbB-2 proto-oncogene in canine osteosarcoma cell lines and tumors. Vet. Pathol. 41:291-296. Goblirsch, M.J., Zwolak, P., and Clohisy, D.R. 2005. Advances in understanding bone cancer pain. J. Cell. Biochem. 96:682-688. Gorlick, R., Anderson, P., Andrulis, I., Arndt, C., Beardsley, G.P., Bernstein, M., Bridge, J., Cheung, N.K., Dome, J.S., Ebb, D., Gardner, T., Gebhardt, M., Grier, H., Hansen, M., Healey, J., Helman, L., Hock, J., Houghton, J., Houghton, P., Huvos, A., Khanna, C., Kieran, M., Kleinerman, E., Ladanyi, M., Lau, C., Malkin, D., Marina, N., Meltzer, P., Meyers, P., Schofield, D., Schwartz, C., Smith, M.A., Toretsky, J., Tsokos, M., Wexler, L., Wigginton, J., Withrow, S., Schoenfeldt, M., and Anderson, B. 2003. Biology of childhood osteogenic sarcoma and potential targets for therapeutic development: Meeting summary. Clin. Cancer Res. 9:5442-5453. Grimaud, E., Soubigou, L., Couillaud, S., Coipeau, P., Moreau, A., Passuti, N., Gouin, F., Redini, F., and Heymann, D. 2003. Receptor activator of nuclear factor kappaB ligand (RANKL)/osteoprotegerin (OPG) ratio is increased in severe osteolysis. Am. J. Pathol. 163:2021-2031. Hansen, K. and Khanna, C. 2004. Spontaneous and genetically engineered animal models: Use in preclinical cancer drug development. Eur. J. Cancer 40:858-880.

Henry, C.J., Buss, M.S., Hellstrom, I., Hellstrom, K.E., Brewer, W.G., Bryan, J.N., and Siegall, C.B. 2005. Clinical evaluation of BR96 sFvPE40 immunotoxin therapy in canine models of spontaneously occurring invasive carcinoma. Clin. Cancer Res. 11:751-755. Jia, S.F., Worth, L.L., and Kleinerman, E.S. 1999. A nude mouse model of human osteosarcoma lung metastases for evaluating new therapeutic strategies. Clin. Exp. Metastasis 17:501-506. Khanna, C., Anderson, P.M., Hasz, D.E., Katsanis, E., Neville, M., and Klausner, J.S. 1997. Interleukin-2 liposome inhalation therapy is safe and effective for dogs with spontaneous pulmonary metastases. Cancer 79:1409-1421. Khanna, C., Prehn, J., Yeung, C., Caylor, J., Tsokos, M., and Helman, L. 2000. An orthotopic model of murine osteosarcoma with clonally related variants differing in pulmonary metastatic potential. Clin. Exp. Metastasis 18:261-271. Khanna, C., Prehn, J., Hayden, D., Cassaday, R.D., Caylor, J., Jacob, S., Bose, S.M., Hong, S.H., Hewitt, S.M., and Helman, L.J. 2002. A randomized controlled trial of octreotide pamoate longacting release and carboplatin versus carboplatin alone in dogs with naturally occurring osteosarcoma: Evaluation of insulin-like growth factor suppression and chemotherapy. Clin. Cancer Res. 8:2406-2412. Khanna, C., Lindblad-Toh, K., Vail, D., London, C., Bergman, P., Barber, L., Breen. M., Kitchell, B., McNeil, E., Modiano, J.F., Niemi, S., Comstock, K.E., Ostrander, E., Westmoreland, S., and Withrow, S. 2006. The dog as a cancer model. Nat. Biotechnol. 24:1065-1066. Lacoste, H., Fan, T.M., de Lorimier, L.P., and Charney, S.C. 2006. Urine N-telopeptide excretion in dogs with appendicular osteosarcoma. J. Vet. Intern. Med. 20:335-341. Laverdiere, C. and Gorlick, R. 2006. CXCR4 expression in osteosarcoma cell lines and tumor samples: Evidence for expression by tumor cells. Clin. Cancer Res. 12:5254. London, C.A., Hannah, A.L., Zadovoskaya, R., Chien, M.B., Kollias-Baker, C., Rosenberg, M., Downing, S., Post, G., Boucher, J., Shenoy, N., Mendel, D.B., McMahon, G., and Cherrington, J.M. 2003. Phase I dose-escalating study of SU11654, a small molecule receptor tyrosine kinase inhibitor, in dogs with spontaneous malignancies. Clin. Cancer Res. 9:27552768. MacEwen, E.G., Kutzke, J., Carew, J., Pastor, J., Schmidt, J.A., Tsan, R., Thamm, D.H., and Radinsky, R. 2003. c-Met tyrosine kinase receptor expression and function in human and canine osteosarcoma cells. Clin. Exp. Metastasis 20:421-430. Mack, G.S. 2005. Cancer researchers usher in dog days of medicine. Nat. Med. 11:1018. Mack, G.S. 2006. Clinical trials going to the dogs: Canine program to study tumor treatment, biology. J. Natl. Cancer Inst. 98:161-162.

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Mansky, P.J., Liewehr, D.J., Steinberg, S.M., Chrousos, G.P., Avila, N.A., Long, L., Bernstein, D., Mackall, C.L., Hawkins, D. S., and Helman, L.J. 2002. Treatment of metastatic osteosarcoma with the somatostatin analog OncoLar: Significant reduction of insulin-like growth factor-1 serum levels. J. Pediatr. Hematol. Oncol. 24:440-446. Matsuyama, S., Iwadate, M., Kondo, M., Saitoh, M., Hanyu, A., Shimizu, K., Aburatani, H., Mishima, H.K., Imamura, T., Miyazono, K., and Miyazawa, K. 2003. SB-431542 and Gleevec inhibit transforming growth factor-beta-induced proliferation of human osteosarcoma cells. Cancer Res. 63:7791-7798. Oda, Y., Yamamoto, H., Tamiya, S., Matsuda, S., Tanaka, K., Yokoyama, R., Iwamoto, Y., and Tsuneyoshi, M. 2006. CXCR4 and VEGF expression in the primary site and the metastatic site of human osteosarcoma: Analysis within a group of patients, all of whom developed lung metastasis. Mod. Pathol. 19:738-745. Porrello, A., Cardelli, P., and Spugnini, E.P. 2004. Pet models in cancer research: General principles. J. Exp. Clin. Cancer Res. 23:181-193. Porrello, A., Cardelli, P., and Spugnini, E.P. 2006. Oncology of companion animals as a model for humans. An overview of tumor histotypes. J. Exp. Clin. Cancer Res. 25:97-105. Rusk, A., McKeegan, E., Haviv, F., Majest, S., Henkin, J., and Khanna, C. 2006. Preclinical evaluation of antiangiogenic thrombospondin-1 peptide mimetics, ABT-526 and ABT-510, in companion dogs with naturally occurring cancers. Clin. Cancer Res. 12:7444-7455. Schmidt, J., Strauss, G.P., Schon, A., Luz, A., Murray, A.B., Melchiori, A., Aresu, O., and Erfle, V. 1988. Establishment and characterization of osteogenic cell lines from a spontaneous murine osteosarcoma. Differentiation 39:15160.

Thamm, D.H., Kurzman, I.D., King, I., Li, Z., Sznol, M., Dubielzig, R.R., Vail, D.M., and MacEwen, E.G. 2005. Systemic administration of an attenuated, tumor-targeting Salmonella typhimurium to dogs with spontaneous neoplasia: Phase I evaluation. Clin. Cancer Res. 11:48274834. Vail, D.M. and MacEwen, E.G. 2000. Spontaneously occurring tumors of companion animals as models for human cancer. Cancer Invest. 18:781-792. Wan, X., Mendoza, A., Khanna, C., and Helman, L.J. 2005. Rapamycin inhibits ezrin-mediated metastatic behavior in a murine model of osteosarcoma. Cancer Res. 65:2406-2411. Withrow, S.J., Powers, B.E., Straw, R.C., and Wilkins, R.M. 1991. Comparative aspects of osteosarcoma. Dog versus man. Clin. Orthop. Relat. Res. 270:159-168. Zhou, H., Randall, R.L., Brothman, A.R., Maxwell, T., Coffin, C.M., and Goldsby, R.E. 2003. Her-2/ neu expression in osteosarcoma increases risk of lung metastasis and can be associated with gene amplification. J. Pediatr. Hematol. Oncol. 25:27-32.

Internet Resource http://ccr.cancer.gov/resources/cop/COTC.asp Provides information about clinical oncology trials using dogs with spontaneously arising tumors as comparative models of human disease.

Contributed by Timothy M. Fan University of Illinois at Urbana-Champaign Urbana, Illinois

Models of Appendicular Osteosarcoma

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Carcinogen-Induced Animal Models of Head and Neck Squamous Cell Carcinoma

UNIT 14.2

The heavily keratinized head and neck epithelia of experimental small animals are markedly different from the nonkeratinizing epithelium that covers most of the human upper aerodigestive tract. Nevertheless, the orthokeratinized hamster cheek pouch epithelium (see Basic Protocol 1) is frequently used as a target organ for experimental oral carcinogenesis. In the last decade, a rat tongue carcinogenesis model (see Basic Protocol 2), that displays remarkable similarities to its human counterpart, has also been used to test tobacco/snuff-related products or compounds, and in chemoprevention studies. Although these models have been used to study head and neck carcinogenesis and chemoprevention, they have not been proposed as standard carcinogenesis bioassay systems. Rather, the cutaneous chemical carcinogenesis bioassay (see Basic Protocol 3) is used for this purpose and is proposed as an alternative surrogate model for head and neck squamous carcinogenesis. The two-stage carcinogenesis model is based on application of a subcarcinogenic dose of a known chemical carcinogen followed by repetitive doses of a noncarcinogenic compound called a promotor (usually a phorbol ester), i.e., an agent that increases epithelial cell proliferation without being a mutagen. The popularity of this protocol is driven in part by its utility for defining in a temporal context several of the steps in the process of tumor development. This allows determination of when to apply putative modulators or inhibitors of carcinogenesis. The Alternate Protocol is a variant of Basic Protocol 3, but consists of repetitive applications of a single complete carcinogen, an agent possessing both mutagenic and promotor activities. This protocol is particularly relevant to human tobacco-related cancer because it is based on the application of the human carcinogen benzo(a)pyrene, a well-known component of tobacco smoke and other pyrolytic processes. By their nature, these carcinogen-induced tumor models do not lend themselves as rapid screens for drug discovery. Rather, the biological utility of these models lies in their similarities to human squamous cell carcinomas (SCCs) of the upper aerodigestive tract and in their utility as a means for studying the molecular pathology of the multi-step progression of human SCC. Nevertheless, these protocols are used frequently to confirm the chemopreventive effects of test compounds (Mitsunaga et al., 1997; Gupta and Mukhtar, 2002; Chen et al., 2006; Yang et al., 2006). NOTE: All protocols using live animals must first be reviewed and approved by an Institutional Animal Care and Use Committee (IACUC) or must conform to governmental regulations regarding the care and use of laboratory animals.

THE HAMSTER CHEEK POUCH CARCINOGENESIS MODEL The Syrian golden hamster cheek pouch carcinogenesis model is a classical animal model that has been used since the 1950s (Salley, 1954). Its value lies in the fact that it closely recapitulates events and lesions that occur during human head and neck (HN) squamous carcinogenesis (Gimenez-Conti, 1993). Although it is one of the best-characterized models for HN squamous cell carcinomas (SCCs), its popularity has declined in recent decades because of the relative paucity of genetic tools available for use in hamsters as compared with murine models. Moreover, investigators prefer to use experimental animals that are more docile than the sometimes aggressive hamsters. Nevertheless, the well-known sequence of oral pre-invasive and invasive SCCs induced by a host of cigarette smoke– associated carcinogens (polycyclic aromatic hydrocarbons (PAHs), nitrosamines, etc.)

Contributed by Daniel E. Bassi and A.J.P. Klein-Szanto Current Protocols in Pharmacology (2007) 14.2.1-14.2.19 C 2007 by John Wiley & Sons, Inc. Copyright


BASIC PROTOCOL 1

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makes it a useful model for evaluating therapeutic and preventive drugs (Shklar and Schwartz, 1993; Trickler et al., 1993; Yang et al., 2006). This has included testing the chemopreventive effect of diverse groups of compounds such as genistein and nitric oxide synthase inhibitors (Chandra Mohan et al., 2005, 2006; Yang et al., 2006).

Materials Male Syrian golden hamsters (6 weeks old; 80 to 180 g; Charles River or Harlan) Hamster food: AIN-93M diet (Research Diets) 7,12-dimethylbenz(a)anthracene (DMBA solution I; see recipe) Mineral oil (Sigma) Test compound Disposable cages (Fisher Scientific) Micropipettor with disposable plastic tips or paint brush (e.g., camel hair brush no. 4) CAUTION: DMBA is a potent carcinogen and should be handled with care. Weigh and handle DMBA in a chemical fume hood (e.g., Multihazard Glove Boxes) dedicated for carcinogens, and use a spatula reserved for this specific purpose only. DMBA may be weighed into a glass scintillation vial, which is then capped immediately after use.

Prepare animals and housing 1. House three to four 6-week-old male Syrian golden hamsters weighing 80 to 180 g per cage in a room with controlled temperature and humidity with 12-hr light/dark cycles. Feed animals AIN-93M diet and water ad libitum. Males are preferred over females since females are dominant in hamsters and are more aggressive. Increased irritation caused by the administration of carcinogen may result in increased aggressiveness between females. Although this behavior can also be observed in males, it is definitely less frequent and intense. Handling animals for a few minutes each day (light cycle) during the 4 to 7 days preceding treatment reduces the anxiety level of the animal during the experimental period.

Prepare carcinogen solution 2. After preparing a concentrated 5% DMBA solution I, dissolve an aliquot of the concentrated solution in mineral oil to obtain a 0.5% working solution, prior to each application. Topical applications require 100 µl of solution per animal. Since typically 30 hamsters are typically used in an experimental group, at least 3 ml of the 0.5% working solution should be prepared for each timepoint. It is not unusual to prepare the working solution once a week, i.e., at least 3 ml × 3 timepoints/week to yield at least 9 ml working solution. The solution should be kept at 4◦ C during this period. If the experiment involves the use of an additional group for evaluation of a test compound, twice as much working solution should be prepared for the additional 30 hamsters involved in compound testing. Inactivate the unused DMBA by vigorous oxidation by adding at least an equal volume of concentrated bleach.

Apply topical carcinogen 3. After 4 to 7 days of acclimation, treat one cheek pouch of each animal topically with 0.5% DMBA solution I in 100 µl mineral oil using a paintbrush, three times per week for 6 weeks.

CarcinogenInduced Models of Squamous Cell Carcinoma

Topical applications can be conveniently performed by first opening the pouches with a cheek retractor instrument (e.g., Dynaflex) or manually and then applying the carcinogen solution with a micropipettor and spreading it throughout the entire pouch mucosa using a camel hair brush no. 4.

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Handling of hamsters requires some practice. Males are easier to handle than females and, after practicing with anesthetized animals (halothane inhalation), eversion and cleaning of the cheek pouches (food is often present) before treatment can be done without the use of anesthesia. Only one cheek pouch should be treated with carcinogen. It is not recommended to use the untreated pouch as control because small amounts of carcinogen can reach the opposite pouch (usually through transfer of food). Treating both pouches is also problematic. The hamsters tend to stop eating after both cheek pouches develop lesions.

4. Transfer hamsters to disposable cages and leave in a dedicated fume hood for carcinogen treatment for the duration of the experiment. Most studies are terminated between 12 and 24 weeks after initial treatment. Regularly inspect the cheek pouch mucosa and other oral areas such as tongue, gingival, and buccal mucosae for inflammation, or irritation. In case of hypersensitivity, as evidenced by substantial redness, scars, or early erosion/ulcer, subsequent topical treatment should be delayed for 1 or more weeks.

Administer test compound 5a. For cancer chemoprevention experiments: begin administration of test compound on the same day as the initial application of carcinogen and repeat treatments two or more times per week for the duration of the carcinogenesis protocol. The route of administration will depend on the physico-chemical properties of the compound and the goals of the experiment. Chemopreventive drugs are dissolved in mineral oil (concentrations between 0.5% and 0.05%) and applied topically (50 to 100 µl) (Li et al., 2005; Sun et al., 2006). These experiments should be accompanied by a shamtreated control group treated with mineral oil alone without the test compound. Oral administration, accomplished by adding the compound to the diet or by gavage, has also been used (Chandra Mohan et al., 2005; Zhou et al., 2006).

5b. For chemotherapeutic evaluation of test compounds: administer test compounds (same concentrations as in step 5a) after tumors are macroscopically detected (after ∼9 weeks) and continue for at least 10 weeks if the animals’ health conditions permit. Deteriorating health is evidenced by excessive vocalization, loss of >15% body weight, excessive tumor burden or ulcerations, or severe infection. This model has not been commonly used for this evaluation of chemotherapeutic compounds.

Acquire measurements and analyze data 6. Record the number and size of lesions that appear a few (6 to 15) weeks following DMBA application. Some investigators measure the tumors directly with a caliper after euthanizing the animals and/or at different time points. An alternative is to photograph the tumors and calculate tumor dimensions from the pictures using a reference scale. Imaging methods could also be applied in vivo (MRI, PET, etc.). Maintain accurate records of the number and size of grossly visible lesions for each animal. Although most precursor lesions are difficult to see, early patches of hyperkeratinization and erythema may be observed in 2 to 6 weeks. Exophytic lesions (lesions that grow outward from the epithelial surface) may appear after 9 weeks, possibly indicating the presence of malignant papillary carcinomas. Such changes must be noted. In addition, it is useful to take photographs of the tumors using a digital camera to follow their evolution.

7. At the end of the experiment, cull hamsters using an approved method such as CO2 asphyxiation or cervical dislocation (preceded by anesthesia with halothane) and carefully dissect the tumors. Calculate carcinoma volume using the following equation: V = [(L1 + L2)/2] × L1 × L2 × 0.526 where L1 and L2 are the length and width of the tumors, respectively, and 0.526 is a correction factor to approximate tumor volume to that of an ellipsoid. Current Protocols in Pharmacology

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Plot percent of tumor-bearing animals, tumor volume, and tumors per animal versus time (weeks after initial DMBA treatment). Tumor graphs should be constructed during the experimental period to follow the evolution of carcinomas (growth, possible regression, fusion of two or more of these tumors). Typically, these carcinogenesis experiments last 20 to 25 weeks after the first DMBA application. However, experiments should be terminated whenever the animals display signs of discomfort as assessed by excessive vocalization, loss of >15% body weight, excessive tumor burden or ulcerations, and severe infections. Large carcinomas appear after 15 weeks, although early, usually fast-growing, carcinomas may be observed as early as 9 weeks. Carcinomas arise either from apparently normal mucosa or from precursor lesions.

8. Collect carcinomas and mucosa samples for further analysis. It is recommended to freeze aliquots of these samples for future assays or treat the sample according to the type of studies that are to be performed. For histological analysis, samples can be kept frozen at −80◦ C in OCT (for frozen sections) or fixed in 10% phosphate buffered formaldehyde (for a sample aliquot not previously frozen) for subsequent embedding in paraffin. BASIC PROTOCOL 2

RAT TONGUE CANCER MODEL The rat tongue cancer model was developed in the 1980s (Tanaka et al., 1986) and popularized as an assay for chemoprevention of experimental oral cancer (Tanaka et al., 1993; Mori et al., 1997). It has the advantage of using an established carcinogen, a watersoluble quinoline derivative that rapidly produces a series of hyperplastic, dysplastic, and neoplastic lesions of the oral mucosa, especially of the tongue. Although this carcinogen can be applied topically, it is easier to administer in drinking water, a route commonly used to test new cancer-preventive agents. While most investigators use rats for this assay, mice can be used, as well (Steidler and Reade, 1984; Tang et al., 2004).

Materials Male Fisher 344 rats (Charles River), 3 to 4 weeks old, 50 to 75 g body weight Rat food 4-nitroquinoline 1-oxide (4-NQO solution; see recipe) Test compound Disposable cages and bottles (Fisher Scientific) CAUTION: 4-NQO is a potent carcinogen and should be handled with care. It should be weighed in a dedicated closed hood equipped with multihazard glove boxes and a spatula used for this specific purpose only. 4-NQO may be weighed into a glass scintillation vial, which is then capped immediately after use. As with the other carcinogens described in this unit, use safety glasses, gloves, and good ventilation when handling 4-NQO. 1. Assign rats to experimental groups of 15 to 30 by age- and weight-matching (3 to 4 weeks of age and 50 to 75 g weight). Provide food and water ad libitum. Thus, a prototype experiment would consist of 20 animals given 4-NQO, 20 rats not exposed to carcinogen, and 20 rats given 4-NQO plus a test compound.

2. Provide rats in the experimental groups daily with distilled water containing 20 ppm (0.002%) 4-NQO, and provide animals in the control group with distilled water only.

CarcinogenInduced Models of Squamous Cell Carcinoma

The duration of 4-NQO administration varies according to the experiment. Rats are typically exposed to the carcinogen for 8 to 15 weeks (Tanaka et al., 1986, 1993; Yamamoto et al., 2003; Yoshida et al., 2005).

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Some investigators gradually increase the concentration of 4-NQO in the drinking water, i.e., 5 ppm for the first 6 weeks and 15 ppm for an additional 9 weeks (Fong et al., 2005). Average 4-NQO consumption can be calculated by measuring the daily intake of water for each cage divided by the exposure period and expressed as mg/day/kg body weight. Water containing 4-NQO-containing should be replaced once a week since the compound degrades over time. CAUTION: Any excess or unused drinking water should be treated and disposed of as biohazard chemical fluid.

3. For cancer chemoprevention experiments: Begin administration of test compound on the same day as the initial dose of carcinogen. The route of administration will depend on the compound and the goals of the experiments. In this type of experiment, the compounds to be evaluated are usually administered either in the drinking water or in the diet after preliminary studies have determined the solubility, absorption, distribution, and toxicity of the chemical. In the classical chemopreventive assay with this model, 4-NQO is administered for 8 weeks and a chemopreventive agent dissolved in drinking water at concentrations of 100 to 2000 ppm is given from week 9 to week 25 (Tanaka et al., 1993). A similar approach can be taken by treating with 4-NQO for 8 weeks, always in the drinking water, followed by the test compound mixed in the solid diet at 100 to 1000 ppm (Yoshida et al., 2005). The use of this model for the study of cancer chemotherapy, i.e., once tumors have been recognized macroscopically after week 20, is not as common (Srinivasan et al., 2006).

4. During the last 15 weeks of the experiment, visually observe the animal’s tongue to macroscopically evaluate lesions that develop during treatment. It is not recommended to anesthetize the animals for this purpose. Manual restraint should suffice. Typically, these carcinogenesis experiments last 20 to 30 weeks. However, terminate experiments whenever animals feel discomfort as evidenced by excessive vocalization, loss of >15% body weight, excessive tumor burden or ulcerations, and severe infections. In these cases, euthanize as described in step 6. Although tumors on the dorsal tongue, which may appear early, are often preceded by dysplastic in situ lesions, most investigators describe tumor incidence (50% to 100%) and multiplicity (0.5 to 1.2 tumor/rat) at ∼30 weeks. SCCs may also appear in the esophagus. This can be accompanied by rapid weight loss and distress. Euthanize animals when this is observed.

5. Collect carcinomas and mucosa samples at 24 to 30 weeks for further analysis. Portions of these samples should be frozen for future analyses or treated according to the type of studies that are to be performed. For histological analysis, samples can be stored at −80◦ C in OCT (for frozen sections) or fixed in 10% phosphate buffered formaldehyde (for a sample aliquot not previously frozen) for subsequent embedding in paraffin.

6. Euthanize rats at the end of the study using an approved method such as CO2 asphyxiation or cervical dislocation. After culling the animals, record the number of lesions on the tongue and esophagus of each animal.

MURINE SURROGATE HEAD AND NECK CARCINOGENESIS MODEL: TWO-STAGE CHEMICAL CARCINOGENESIS OF THE SKIN The paucity of mouse models for head and neck SCC (HNSCC) has resulted in the use of a chemical skin carcinogenesis model as a surrogate test. Because of its easy accessibility and similarities with human head and neck epithelia, mouse skin/epidermal carcinogenesis has frequently been used as a model for tobacco-associated SCC (DiGiovanni, 1992; Ruggeri et al., 1993; Yuspa, 1994; Mitsunaga et al., 1997). The obvious differences notwithstanding, it is an acceptable system for studying the etiopathogenesis, biology, and prevention of HNSCC.

BASIC PROTOCOL 3

Cellular and Animal Models in Oncology and Tumor Biology

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In this protocol, a single initiating dose of 7,12-dimethylbenz(a)anthracene (DMBA), a potent carcinogen, is topically applied to the shaved back of a mouse. Thereafter, twice weekly applications of 12-O-tetradecanoylphorbol-13-acetate (TPA) serve to promote tumor formation and growth. TPA, lacking carcinogenic activity itself, drives a potent hyperproliferative response, increasing the total number of epithelial cells. Since the initiating dose of DMBA is sub-carcinogenic, repetitive TPA treatments are responsible for expanding the subpopulation of initiated cells to yield neoplastic lesions. This protocol follows the evolution of tumors from hyperplastic skin and papillomas and finally to carcinomas. On the other hand, repetitive applications of DMBA or another complete carcinogen such as B(a)P results in multiple mutagenic events, with increased chances of producing more malignant tumors (higher mutation rate, and higher number of cells mutated) with the consequent decrease in less aggressive or benign lesions (see Alternate Protocol 1). This protocol describes an example chemoprevention experiment (step 11), whereas the example provided in Alternate Protocol 2 describes a novel type of chemotherapy study in which test agents are administered to transgenic mice that overexpress the enzyme ornithine decarboxylase (ODC) following tumor initiation by topical DMBA. ODC overexpression causes these mice to be highly susceptible to chemical carcinogenesis. These mice constitute an ideal model for the evaluation of therapeutic drugs targeting ODC or related metabolic pathways controlled directly by this particular enzyme. It is recommended to construct graphs of tumor volume and number of tumors per animal versus time simultaneously with the experiment to follow the evolution of SCCs (growth, possible regression, fusion of two or more of these tumors). Other parameters to evaluate are the tumor incidence (percent of tumor-bearing animals), tumor volume, and survival time (see step 16).

Materials Female mice (6 to 8 weeks old; any of several lines and strains may be used, e.g., SENCAR and FVB × FVB (N); Taconic) Mouse food Bleach DMBA solution II (see recipe) Acetone (analytical grade, Fisher Scientific) 12-O-tetradecanoylphorbol-13-acetate (TPA solution; see recipe) Test compound (optional) Shaver and blades (e.g., Olsten Golden A5, equipped with an A5 clipper, size 40) Disposable cages (Fisher Scientific) 25-ml vials Digital camera Caliper

CarcinogenInduced Models of Squamous Cell Carcinoma

CAUTION: DMBA is a potent carcinogen and should be handled with care. DMBA should be weighed in a dedicated closed hood equipped with multihazard glove boxes and with a spatula used for this specific purpose only. DMBA may be weighed into glass scintillation vials that are capped immediately after use. Concentrated (10×) solutions of DMBA can be prepared in a biosafety level 2 hood by dissolving DMBA in acetone. Mix by pipetting and/or with a rotating plate. Pipets that are used to handle carcinogens should not be used for other purposes and should be kept in the hood at all times. Pipet tips and excess DMBA solutions should be immediately decontaminated by the addition of three to five parts of bleach and disposed of as biohazardous material.

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Prepare animal and housing 1. House animals in cages with sterilized bedding and filter cage covers, and provide autoclaved solid feed and water. In addition, routinely inspect animal health. Acclimate mice in ventilated cage racks for at least 1 week before DMBA application. Typical experiments consist of groups of 30 mice treated with the carcinogen alone, 30 mice treated with the carcinogen plus test compound, and a similar number of shamtreated controls (usually treated with acetone alone).

2. Shave the dorsal skin of the animals the day before the first topical application of carcinogen. Use animals in the resting phase of the hair cycle (∼6 weeks of age), housing five animals per cage. Do not damage the skin as this may cause wound healing–related hyperproliferation and allow direct contact of carcinogen and/or promoter with the systemic circulation. Depilatory creams are not recommended in order to avoid possible chemical interactions.

Prepare carcinogen solution 3. Before handling DMBA, prepare a 25-ml vial containing bleach for DMBA inactivation. Weigh the required amount of DMBA into a glass vial in the hood and dissolve it in acetone to obtain a concentrated solution (usually 10×). Inactivate any unused DMBA solution II with concentrated bleach. Also submerge in bleach any tip or tube that has been in contact with the carcinogen solution. CAUTION: DMBA is a potent human carcinogen and must be handled with extreme care.

4. Dilute an aliquot of the concentrated DMBA solution II in acetone (dilute 1/10) to prepare 200 µl of 1× working solution per animal immediately prior to each application. Keep the DMBA solution in a closed glass container at 4 mm in diameter are evaluated on the dorsal skin using a caliper. Metastasis evaluations require complete autopsies with special emphasis on the dissection of lymph nodes and lung lobules.

Acquire and analyze data 13. Record the number and size of papillomas for each mouse (they will appear ∼8 weeks after DMBA application). Use a digital camera to photograph individual tumors to follow their evolution (using a reference scale or ruler). Papillomas may be measured with a caliper. Nevertheless, many experienced investigators estimate the sizes of tumors in groups of 4 mm. The authors recommend always using calipers for tumors >4 mm. CarcinogenInduced Models of Squamous Cell Carcinoma

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Although most of papillomas present in the dorsal skin are characterized by the presence of a stalk and a cauliflower-like tumor mass (usually benign), other types of lesion may be observed such as flat growth, ulcers, skin surface changes (redness or erythema), or papillomas lacking a stalk (sessile). It is essential to maintain a record of these observations as they may indicate the presence of SCCs. Current Protocols in Pharmacology

14. Count and use a caliper to measure the dimensions of squamous cell carcinomas. SCCs will appear at ∼20 weeks, although early carcinomas, usually fast-growing, may be observed as early as 10 weeks. SCCs arise either de novo (from apparently normal epithelia or from microscopic precursor lesions) or by conversion from papillomas. In either case, they should be counted and measured with a caliper to calculate their volume.

15. Calculate SCC volume using the following equation: V = [(L1 + L2)/2] × L1 × L2 × 0.526 where L1 and L2 are the length and width of the tumors, respectively, and 0.526 is a correction factor to approximate tumor volume to that of an ellipsoid. 16. Plot tumor volume and number of tumors per animal versus time (weeks after DMBA treatment). Also record the percent of tumor-bearing animals (incidence) and survival time. Construct the graphs simultaneously with the experiment to follow the evolution of SCCs (growth, possible regression, fusion of two or more of these tumors). Typical two-stage carcinogenesis experiments that include a complete follow-up of SCCs (not only papilloma induction) end 30 to 35 weeks after the first DMBA application. However, individual animals should be culled whenever they are sick, i.e., as evidenced by significant weight loss, excessive tumor burden, large ulcerations, and severe infections.

Perform tissue analysis 17. Euthanize mice using any approved method such as CO2 asphyxiation or cervical dislocation after anesthetizing the animals with methoxyflurane or halothane. Cervical dislocation is preferred since it is more humane and prevents CO2 -induced over-excitation and distress.

18. Collect carcinoma, papilloma, and skin samples for further analysis. Samples should be collected after euthanasia, either at the end of the experiments or after intermediate time points. Tumors should not be excised from live animals under any circumstance, since tumor presentation and future development (aggressiveness, proliferation, metastatic potential) is affected by this operation. All tumors are usually portioned into aliquots for (1) fixation in 10% phosphate buffered formaldehyde and further histological processing, (2) freezing in OCT compound for frozen sectioning, or (3) snap-freezing for biochemical/molecular analysis.

COMPLETE CARCINOGENESIS USING BENZO(A)PYRENE—“ONE-STAGE” CARCINOGENESIS In the complete carcinogenesis protocol, a single chemical possessing both tumor- initiating and promoting effects is used. The ubiquitous human carcinogen benzo(a)pyrene [B(a)P] is used here as a complete carcinogen to produce skin SCC (Ruggeri et al., 1993; Mitsunaga et al., 1997). The application of this protocol results in very few or no papillomas. The precursor lesions are flat in situ dysplastic epidermal lesions, frequently carcinomas in situ, which precede the formation of invasive SCCs. The complete carcinogenesis protocol has many procedural similarities with the two-stage protocol (see Basic Protocol 3), however, the repetitive use of B(a)P for up to 50 weeks requires extra safety considerations. Animals must be kept in the carcinogen hood for the entire experiment, and disposable cages should be used at all times. Used, disposable cages with bedding should be discarded in a biohazard bag clearly labeled to indicate the nature of its content (i.e., B(a)P-carcinogen). It is not necessary to discard filter covers, water bottles, or solid food pellets in this manner.

ALTERNATE PROTOCOL 1

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Materials Female FVB × FVB (N) mice (6 to 8 weeks old; Taconic) Benzo(a)pyrene (B(a)P; see recipe) Acetone (analytical grade; Fisher Scientific) Bleach Test compound Disposable animal cages (Fisher Scientific) Shaver with blades 1. House animals and assign them to groups as described in Basic Protocol 3, step 1. 2. Shave the dorsal skin of the animals the day before topical application of carcinogen. Use animals in the resting phase of the hair cycle (∼6 weeks of age), housing five animals per cage. Special care should be taken when shaving the animal as any damage to the integrity of the skin may facilitate introduction of the carcinogen into the systemic circulation. This is more critical than in the two-step carcinogenesis procedure (see Basic Protocol 3) where TPA (a tumor promoter, but not a carcinogen itself) is used after the weekly shavings. Typical experiments consist of 30 carcinogen-treated animals, 30 carcinogen plus test compound-treated mice, and a similar number of sham-treated controls (animals treated with acetone alone).

3. Weigh B(a)P in a carcinogen-dedicated hood (e.g., Multihazard Glove Boxes). The concentrations commonly used range from 200 to 400 nmol per animal, applied once a week. These amounts are sufficient for the development of carcinoma within 40 weeks in most of the moderately sensitive strains. As with DMBA, the concentration should be determined empirically for mice with an altered genetic background. Weigh the amount of B(a)P required for the entire experiment. Depending on the number of animals to be treated, milligram-scale portions may be weighed and stored in a 25-ml glass scintillation vial separately in a dedicated box. One vial per treatment should be prepared. Thus, for a 30-week experiment, prepare 30 vials. Each vial should contain enough carcinogen for treating the entire group. If the experimental group consists of 30 mice, each vial should have at least sufficient carcinogen for 30 applications: 30 × 200 to 400 nmol. The amount needed for each treatment timepoint may be stored at room temperature, protected from light and oxidizers.

4. In a biosafety hood, dissolve one aliquot of the pre-weighed B(a)P in acetone to obtain a working solution for all experimental animals at each time point. Topical applications require 200 µl of solution per animal. Keep the B(a)P solution in a closed dark glass vial at 15% body weight or develop ascites or jaundice (as evidenced by a yellow or orange tint to the skin), or at the end of 12-week post-implantation period. 25. Upon necropsy, perform gross assessment of tumor dissemination throughout the peritoneal cavity and to various organ sites. In those mice where tumor spread is clearly due to distant and discrete metastases (tumor nodules in the liver, for example, that are clearly not adjacent to the implanted tumor), passage the primary tumor tissue into additional mice as described in steps 3 to 21, above. One large primary tumor will typically provide 20 to 30 fragments of 2-mm3 volume for implantation. For a large study of 60 to 100 mice, 3 to 5 donor mice will be needed to supply sufficient tumor tissue for implantation. Implantation is performed as described above in steps 7 to 20. Three serial passages of tissue into recipient mice are typically required before a robust and sustained metastatic phenotype is obtained. Once metastases occur, passage the primary pancreatic tumor tissue into 3 to 5 additional mice in order to maintain the metastatic pattern. Alternatively, a cell line may be developed from the tissue from mice with the appropriate metastatic phenotype (see Alternate Protocol). The metastatic phenotype that develops is one of diffuse peritoneal, lymphatic, and hepatic metastatic spread of pancreatic origin. Typically >90% of untreated mice will develop metastases, with 10% having scores of II, indicating spread to the lymph nodes and 90% having scores of III or IV, indicating metastases to the liver and lymph nodes, as well as more diffuse peritoneal spread involving other organs, such as kidney, spleen or diaphragm (see Jones-Bolin et al., 2002; Dobrzanski et al., 2004 and Basic Protocol 2 for metastases scoring criteria).

USE OF THE ORTHOTOPIC PDAC MODEL IN PHARMACOLOGICAL EFFICACY STUDIES Once the metastatic phenotype has been developed and reproduced in recipient mice, studies may be initiated to evaluate advanced lead compounds against standard-of-care therapies, as well as other treatments of interest. As stated in the unit introduction, this model is too time consuming and labor intensive to use as a screen for NCEs. This model has been used to evaluate standard-of-care therapies such as gemcitabine, either alone or in combination with pan-trk kinase or pan-VEGF-R kinase inhibitors. The model may also be used to study tumor biology, including tumor growth production and metastases. Specifically, analysis of intratumoral microvessel density (Factor VIII and CD34 immunostaining) and apoptosis (TUNEL labeling), as well as analysis of tumor tissue via immunoprecipitation for targets of interest, have been performed using primary and metastatic tumor tissue from this model. Moreover, this model is used to test treatments directed towards alleviating or preventing the muscle wasting and weight loss typically associated with cachexia in humans. Some treatments evaluated in this way include anti-TNF-α antibodies (Remicade, a registered trademark of Centocor), eicosapentanoic acid, and the proteasome inhibitor Velcade (a registered trademark of Janssen-Pharmaceutica).

BASIC PROTOCOL 2

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Results from a study evaluating lestaurtinib, a pan-Trks/Jak2/Flt3-ITD kinase inhibitor (see Miknyoczki et al., 1999; Ruggeri et al., 1999) and a separate study evaluating CEP7055, a pan-VEGF-R kinase inhibitor (Ruggeri et al., 2003; Jones-Bolin et al., 2005) are described in Anticipated Results, to illustrate the utility of the model. Also described in Anticipated Results are data that demonstrate the development of cancer cachexia in mice bearing orthotopically implanted human PDAC tissue.

Materials Mice with surgically implanted xenograft of primary pancreatic tissue (Basic Protocol 1, steps 1 to 21) Monitor and score mice 1. At a time point 7 days following surgical implantation of primary pancreatic tissue from donor mice into recipient mice, randomize mice into treatment groups (typically eight to ten mice per group to provide sufficient statistical power, although this will vary according to the study design). Monitor behavior daily and weigh twice weekly. See Critical Parameters for criteria to use as justification for sacrificing animals in a survival study: Typical groups would include two different therapies, alone and in combination, plus vehicle-treated controls. As the tumor grows, the weight of the mouse will change. If the mouse started at 25 g, an increase of 2 to 3 g over a few weeks is an indication of the increase in tumor weight or associated tumor burden.

2. Optional: Obtain plasma samples at the end of the study for compound analyses of assessment of circulating cytokines, trophic factors of interest, or other parameters, depending upon the purpose of the study.

Evaluate necropsy of mice 3. Upon completion of the study, perform a necropsy of each mouse. Include a thorough examination of both the abdominal and thoracic cavities to determine the extent of gross metastatic spread of the orthotopically implanted human PDAC tumor. Use the following criteria for metastatic scoring (Dobrzanski et al., 2004): a. A score of I is given if the mouse had a primary mass along with 0 to 10 enlarged mesenteric lymph nodes and no other visible signs of peritoneal or thoracic cavity spread. b. A score of II is given if the mouse had a primary mass along with 10 to 100 enlarged mesenteric lymph nodes and no other visible signs of peritoneal or thoracic cavity spread. c. A score of III is given if the mouse had a primary mass along with too-numerousto-count (TNTC) enlarged mesenteric lymph nodes and visible plaques on the diaphragm, the presence of gross liver nodules, minimal-to-moderate degree of ascites, and no thoracic cavity spread. d. A score of IV is assigned if the mouse had a primary mass along with TNTC enlarged mesenteric lymph nodes and visible plaques on the diaphragm, the presence of gross liver nodules, and moderate-to-severe ascites. A score of IV is given to any mouse that developed jaundice.

Orthotopic Model of Pancreatic Ductal Adenocarcinoma

Prepare tissues of interest for immunohistochemistry 4. Record weights of the primary tumor with attached spleen, liver, and other involved organs for each mouse.

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Figure 14.3.2 Hemotoxylin and eosin histological sections of a liver metastasis revealing (A) very little normal liver present in this section, as the pancreatic metastatic tissue has displaced the normal liver tissue; (B) primary pancreatic ductal carcinoma (arrows mark islands of tumor cells among normal glandular tissue); (C) peripancreatic (invasive) metastasis (arrows mark islands of tumor cells); and (D) mesenteric lymph node metastasis (arrows mark islands of tumor cells) from orthotopically-grafted COLO-357 human pancreatic carcinoma tissue. Immunostaining for the pancreas-selective antigen CA19-9 may be performed (Friess et al., 1997) to confirm the pancreatic origin of metastatic lesions. For the color version of this figure go to http://www.currentprotocols.com.

5. Optional: Fix all grossly involved tissues in formalin for histopathological analyses (Fig.14.3.2) by placing the tissue in 10% formalin for at least 24 hr. After fixation, tissue can be processed within a few weeks to a few months. The fixed tissue can be stored at room temperature.

USE OF THE ORTHOTOPIC PDAC MODEL IN CANCER CACHEXIA STUDIES Mice bearing orthotopically implanted tumor tissue develop the muscle wasting and weight loss typically associated with cancer cachexia in humans. Treatments directed at alleviating or preventing the development of cancer cachexia that have been evaluated in this model include anti-TNF-α antibodies (Remicade), eicosapentenoic acid (EPA), and the proteasome inhibitor, bortezomib (Velcade). In order to confirm that mice orthotopically implanted with human pancreatic ductal adenocarcinoma (PDAC) manifest a cancer cachexic phenotype, mice are evaluated 8 weeks following surgical implantation of PDAC tissue. Sham-implanted mice, which undergo surgery but do not receive a tumor implant, are also evaluated as a procedural control for surgical orthotopic implantation of human PDAC tissue fragments as detailed in Basic Protocol 1. Five mice each from sham- and tumor-implanted group are euthanized, and primary tumor weights, metastatic scores, and muscle weights (gastrocnemius and soleus muscles from the right hind leg) are obtained for each mouse at 8 weeks following tumor implantation. Food weights are also recorded for each cage of mice. Usually, food consumption is comparable in orthotopic PDAC-implanted mice and sham-implanted mice, indicating that the body weight loss and severe skeletal muscle loss characteristic of cachexia are not the result of

BASIC PROTOCOL 3

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Figure 14.3.3 (A) Body weights of athymic nude mice implanted with human PDAC tumor tissue and body weights of sham-implanted mice. Mice implanted with human PDAC tumor tissue (indicated by asterisk over the bar) exhibit significant body weight loss as compared to shamimplanted mice (p < 0.05) despite comparable food consumption. (B) Right hind-leg gastrocnemius and soleus muscle weights of athymic nude mice implanted with human PDAC tumor tissue or sham-implanted mice. Mice implanted with human PDAC tumor tissue (indicated by asterisk over the bar) exhibit significant gastrconemius and soleus muscle mass loss as compared to shamimplanted mice (p < 0.05). All mice including sham-implanted, vehicle-treated, and NCE-treated mice are euthanized on day 60 of dosing, and their right hind-leg muscles evaluated.

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reduced food consumption by tumor-bearing animals (data not shown). Statistical analyses comparing primary and metastatic tumor weights, mouse body weights, and mean and median survival are performed using the Mann-Whitney Rank Sum test. Survival data from tumor-bearing mice are analyzed by the Kaplan-Meier method as required for data sets using SAS (SAS 8.2, SAS Institute, Inc. http://www.sas.com). Results for body weights and muscle weights at 8 weeks post-implantation are shown in Figures 14.3.3A and B.

ESTABLISHING CELL LINES FROM PDAC TUMOR TISSUE Alternatively or in addition to the tumor passaging described in Basic Protocol 1, a cell line may be developed from tumor tissue that has been obtained from mice with the appropriate metastatic phenotype. The development of a cell line provides a backup for the in-life passage animals generated in Basic Protocol 1, in the event that the metastatic phenotype fails to develop after years of passage, or in the event that the maintenance of passage animals is not possible due to space limitations or other constraints.

ALTERNATE PROTOCOL

Additional Materials (see Basic Protocol 1) Mice bearing PDAC tumors (from step 23 of Basic Protocol 1) Growth medium (see recipe) 10% (v/v) DMSO/90% (v/v) FBS 70-µm nylon cell strainers 50-ml conical centrifuge tubes Plunger from 1-ml syringe Refrigerated centrifuge 75-cm2 tissue culture flasks 1.5-ml cryotubes (e.g., Nunc) Nalgene Cryo 1◦ C Freezing Container Syringe with 30-G needle Additional reagents and equipment for euthanasia of mice (Donovan and Brown, 2006), implanting pancreatic tumor cells in mice (Basic Protocol 1), and evaluating PDAC model (Basic Protocols 2 and 3) NOTE: All culture incubations are performed in a humidified 37◦ C, 5% CO2 incubator unless otherwise specified. NOTE: All solutions and equipment coming into contact with living cells must be sterile, and aseptic technique should be used accordingly. 1. Euthanize mice using CO2 (Donovan and Brown, 2006) and place in a sterile petri dish in a laminar flow hood or biological safety cabinet. 2. Open the abdominal cavity using sterile scissors and tissue forceps. Use new sterile scissors and dressing forceps to cut the primary pancreatic tumor tissue from the mouse (see Fig. 14.3.1 and relevant steps of Basic Protocol 1). 3. Use sterile dressing forceps to place the excised tumor tissue into a clean, sterile petri dish for dissection. Wash the tissue with sterile PBS to remove any blood from the outside of the tissue. Remove and discard grayish white necrotic or visibly hemorrhagic areas of the tumor. 4. Using a sterile scalpel and dressing forceps, cut the non-necrotic tumor tissue into 2-mm3 pieces. Place these tissue fragments on ice and then in a 70-µm nylon cell strainer over a 50-ml conical centrifuge tube.

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5. Homogenize the tissue by pressing it through the cell strainer using the back of a plunger from a 1-ml syringe. Wash the tissue homogenate with 5 ml ice-cold growth medium. Repeat the tissue homogenization and washing steps a second time. There should be about 10 ml of filtered material collected from the tumor tissue. Maintain the solution on ice.

6. Centrifuge the primary tumor cells 6 min at 100 to 130 × g, 4◦ C, and discard the supernatant either by pipetting or carefully decanting to avoid cell loss. Resuspend the cell pellet in growth medium at the desired volume (10 to 15 ml). Transfer the cell suspension into a 75-cm2 tissue culture flask begin incubation. Monitor cells, replacing the medium every 2 to 3 days initially and splitting the cells when confluency is reached. 7. Once a cell line is established and growing well in culture, expand the number of flasks so that several vials can be frozen for future use. Freeze cells as follows: a. Resuspend cell pellet in a mixture of 10% DMSO and 90% FBS at 5 ml per 5 × 106 cells. b. Divide into 1-ml aliquots in 1.5-ml cryotubes and place cryotubes in a Nalgene Cryo 1◦ C Freezing Container. This is a container that is lined with isopropyl alcohol in order to ensure a reproducible rate of sample freezing.

c. Place cell freezing container in a −80◦ C freezer overnight, then store frozen cryotubes long-term in a liquid nitrogen freezer. These cells may be used to implant into mice as described in Basic Protocol 1.

8. Proceed as described in Basic Protocol 1, but, instead of implanting tumor pieces, inject 1×106 cells in 50 µl of sterile PBS subcapsularly (into the tissue parenchyma) into a portion of the pancreas just beneath the spleen using a 30-G needle to form a small fluid bleb without leakage into the intraperitoneal space (Bruns et al., 1999). Monitor mice as described in Basic Protocol 1 for development of metastatic phenotype and passaging. The metastatic phenotype that develops is one of diffuse peritoneal, lymphatic, and hepatic metastatic spread of pancreatic origin. Typically, >90% of untreated mice will develop metastases, with 10% having scores of II, indicating spread to the lymph nodes, and 90% having scores of III or IV (on a scale of I to IV), indicating metastases to the liver and lymph nodes, as well as more diffuse peritoneal spread involving other organs, such as kidney, spleen or diaphragm. See Basic Protocol 2 for metastasis-scoring criteria.

REAGENTS AND SOLUTIONS Use deionized, distilled water in all recipes and protocol steps. For common stock solutions, see APPENDIX 2A; for suppliers, see SUPPLIERS APPENDIX.

Growth medium Minimal Essential Medium (MEM with glucose, L-glutamine, and sodium bicarbonate; e.g., Invitrogen or ATCC) 10% (v/v) fetal bovine serum (FBS; Hyclone or ATCC; also see APPENDIX 2A) 1× penicillin/streptomycin (e.g., Invitrogen) Store at 4◦ C until the expiration date on the container, barring any obvious contamination. Orthotopic Model of Pancreatic Ductal Adenocarcinoma

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Ketamine/xylazine mixture Prepare a mixture of 55 mg/kg animal body weight ketamine hydrochloride (e.g., from 100 mg/ml stock; Hanna Pharmaceutical Supply Company) and 10 mg/kg animal body weight xylazine (e.g., from 20 mg/ml stock; J.A. Webster) in sterile saline (0.9% w/v NaCl). Ketamine is a CIII controlled substance and will require a DEA license to order.

COMMENTARY Background Information Pancreatic ductal adenocarcinoma (PDAC) represents the fourth leading cause of cancerrelated deaths in the United States, with a 5year survival rate of only 2% to 10% (Sener et al., 1999; Jemal et al., 2004; Eckel et al., 2006). PDAC is an aggressive tumor and has often metastasized to distant sites (liver, lung, and adjacent intestines) by the time of diagnosis. Treatment options for PDAC remain limited and the disease carries a grave prognosis for most patients (Sener et al., 1999; Eckel et al., 2006). The treatment options available to patients with PDAC (5-fluorouracil, mitomycin, external beam radiation therapy) have proven largely ineffective, with many offering only palliative relief with no significant increase in survival time (Gunzburg et al., 2002; Eckel et al., 2006). Gemcitabine, the standardof-care agent for this disease, in combination with other chemotherapeutic agents, has been shown to improve patient quality of life and survival time (Gunzburg et al., 2002; Eckel et al., 2006). However, even with combination chemotherapy regimens, the survival rates for aggressive, nonresectable pancreatic cancer remain poor. Consequently, novel therapies directed at inhibiting primary tumor growth and, more importantly, the local and distant metastatic dissemination of tumors, is of substantial clinical importance.

Critical Parameters Aseptic surgical technique is very important in the procedures detailed in this unit, so that infection does not develop. The surgical technique will require some practice to master, with the implantation of tissue onto the pancreas and proper closure of the peritoneal cavity and skin being of critical importance. Anesthesia delivery and monitoring is also critical to ensure that the mouse survives the surgery. Post-surgical care and monitoring for development of tumors and metastatic spread is also of great importance in the development of the metastatic phenotype. The mice need to be monitored daily and weighed twice

weekly with any one of the following criteria providing justification for sacrificing animals in a survival study: (1) weight loss (>15% of body weight); (2) lethargy; (3) hunching for more than 1 or 2 days as a result of surgery or treatment regimens being evaluated; (4) ascites or body weight gain of ≥2 grams over a few days period of time; (5) debilitation that impairs animals’ ability to reach and/or consume food and water; (6) vocalization indicative of severe pain or distress; (7) dehydration; or (8) open, bleeding or infected wound or tumor.

Troubleshooting Table 14.3.1 describes some problems commonly encountered with the PDAC model, along with recommended measures to take to avoid or overcome these difficulties.

Anticipated Results Untreated mice bearing orthotopically implanted tumor tissue will become moribund ∼60 days after surgery. The orthotopic model described in this unit yields a reproducible metastatic phenotype analogous to the human clinical course, with widely disseminated peritoneal, hepatic, and mesenteric lymph node metastases of human pancreatic carcinoma origin. Ascites is common, and some mice also develop jaundice. The percentage of animals developing primary tumors is >90% when the implant is performed properly and, once established, the metastatic phenotype is reproducible from passage to passage, displaying similar peritoneal spread with large primary tumor infiltrating into surrounding tissue along with distant spread to mesenteric lymph nodes and liver. This model is extremely useful in the evaluation of primary tumor biology and in defining the mechanisms of disease pathogenesis, local and metastatic tumor spread, survival characteristics, and cancer cachexia. Three studies that have been performed using the PDAC model are described below. The first study uses a pan-Trks/Jak2/Flt3-ITD

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Table 14.3.1 Troubleshooting the Murine PDAC Model

Problem

Recommended action

Tumor does not develop

Make certain that tumor tissues are well coated in Matrigel and that they are properly attached to the pancreas at two sites. Use only the outermost layer of the tumor tissue for passage, as this is the most viable.

Metastasis does not occur following first implantation

Continue to passage primary tumor from donor mice into three to five recipient mice, using three to five different donor mice. It may require more than three passages for the metastatic phenotype to develop. Once the metastatic pattern develops, only passage tissue from mice with this pattern into recipient mice.

Mice develop infection

Make certain that instruments are sterilized before use and change instruments after incising the skin in order to avoid contamination. Also change instruments between cages of mice (five mice per cage is average) and make certain to follow aseptic surgical techniques.

MAP testing of cells is positive

Obtain new cells

Mice die during surgery

Check the level of isoflurane and decrease the flow rate or amount as needed. Monitor the respirations of the mouse every 1-2 min during the surgery; if breathing becomes erratic, remove the isoflurane cone for 1-2 min until breathing is more rhythmic.

inhibitor, lestaurtinib (CEP-701), alone or in combination with gemcitabine (Gemzar), to evaluate the effects of these compounds on survival and primary tumor growth and spread of the orthotopically implanted human PDAC tumor tissue in nude mice. The second study describes the effects of a pan-VEGF-R kinase inhibitor, CEP-7055, on primary tumor growth and metastatic score of the orthotopically implanted human PDAC tumor tissue in nude mice. The third example describes the effects of therapeutic interventions on preventing or alleviating the cachexia that develops in mice orthotopically implanted with human pancreatic ductal adenocarcinoma.

Orthotopic Model of Pancreatic Ductal Adenocarcinoma

Example 1 The effects of chronic administration of lestaurtinib and gemcitabine, alone and in combination, on the survival of nude mice bearing orthotopically implanted human pancreatic ductal adenocarcinoma (PDAC) xenografts: Lestaurtinib is a pan-Trks inhibitor (IC50 = 3 nM) with Jak2/FLT3-ITD kinase inhibitory activity (Ruggeri et al., 1999). Previous studies have demonstrated that lestaurtinib significantly inhibits the in vivo growth and/or in vivo invasiveness of five of six human PDAC-derived cell lines using two different xenograft model systems (Miknyoczki et al., 1999).

The objective of this study was to evaluate the effects of chronic administration of lestaurtinib and a clinically based dosing regimen of gemcitabine, separately and in combination, on the survival and metastatic profile of nude mice bearing orthotopically implanted PDAC, and to determine whether the combination of lestaurtinib and gemcitabine confers a significant survival benefit relative to that achieved with gemcitabine monotherapy in this tumor model. At a time point 7 days following surgical implantation of primary pancreatic tissue from donor mice into recipient mice, mice were randomized into treatment groups of 10 mice per group. The effects of lestaurtinib administration (10 mg/kg, subcutaneously, twice daily), alone and in combination with a clinically based dosing regimen of gemcitabine (Gemzar, 100 mg/kg, intraperitoneally, twice weekly), on the survival and metastatic burden of mice orthotopically implanted with human PDAC tissue fragments are shown in Table 14.3.2 and in Figure 14.3.4. The treatments were well tolerated, with no significant weight loss or mortality clearly attributable to monotherapies or the combination of lestaurtinib and gemcitabine. Administration of lestaurtinib alone improved mean survival time of tumor-bearing animals relative to vehicle-treated and untreated

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Table 14.3.2 Summary of the Effects of Lestaurtinib and Gemcitabine Administration, Separately and in Combination, on Survival and Metastatic Profile of Orthotopically-implanted Human Pancreatic Ductal Carcinoma Tissues in Nude Mice

Therapya

Mean survival in daysb

Metastatic parameters (% incidence) Liver metastases

Lymph node netastases

Tumor-associated ascites

Vehicle

54

90%

100%

80%

Untreated

58

100%

100%

80%

Lestaurtinib

63

80%

80%

60%

126**

100%

100%

50%

97*

80%

100%

80%

Lestaurtinib plus gemcitabine Gemcitabine

a Treatment regimens initiated 7 days post-implantation of human PDAC tissues orthotopically onto the splenic pancreatic

lobe of nude mice as detailed in Basic Protocol 1. b Single asterisk (*) signifies mean survival significant to vehicle- and lestaurtinib monotherapy-treated mice (p < 0.001)

Double asterisk (**) signifies mean survival significant to vehicle- and lestaurtinib monotherapy-treated mice, as well as gemcitabine-treated mice (p < 0.001 and p = 0.04, respectively).

Figure 14.3.4 The effects of chronic administration of lestaurtinib and GMZ, separately and in combination, on the survival of nude mice orthotopically implanted with human PDAC tissue. Kaplan-Meier survival curves showing effects of treatment regimens on survival of tumor-bearing mice were analyzed by the Kaplan-Meier method; Mann-Whitney Rank Sum test analyses were used to compare mean and median survival times between treatment groups. Single asterisks signify mean survival significant to vehicle- and lestaurtinib monotherapy–treated mice (p < 0.001). Double asterisks signify mean survival significant to vehicle and lestaurtinib monotherapy–treated mice as well as gemcitabine-treated mice (p < 0.001 and p = 0.04, respectively).

animals, although this difference was not significant (63 days versus 54 days and 58 days, respectively). The mean survival time (Fig. 14.3.4) of the animals receiving gemcitabine monotherapy (97 days) was longer than the survival time recorded for the vehicle-treated and untreated controls (54 days, 58 days, respectively; p < 0.001) and

the lestaurtinib monotherapy group of mice (63 days; p < 0.001). There were no significant differences observed among groups in the incidence of hepatic and lymph node metastases and ascites production. The combination of lestaurtinib and gemcitabine was well tolerated and resulted in a significant improvement in the mean survival time (126 days)

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Table 14.3.3 Effects of CEP-7055 on the Metastatic Tumor Burden of Nude Mice Orthotopically Grafted with Human Pancreatic Ductal Carcinoma Xenograft Tissue

Pancreatic tumors

Metastatic scorea

Gross metastatic incidence

Therapy

Take rate

Tumor weight (g)b,c

Incidence in liver

Liver weight (g)b,d

Incidence in lymph nodes

Incidence in ascites

Vehicle

100%

2.88 ± 0.24a

100%

1.66 ± 0.14b

100%

100%

I (0); II (0); III (20%); IV (80%)

CEP7055

100%

2.03 ± 0.23

30%

1.33 ± 0.10

60%

30%

I (50%); II (20%); III (20%); IV (10%)

a Metastatic scores further defined in Figure 14.3.5 and Basic Protocol 2. b Median ± SEM. c Primary pancreatic tumor weights are significantly lower in CEP-7055-treated mice compared to vehicle-treated mice (p < 0.02). d Liver weights in CEP-7055-treated mice compared to vehicle-treated mice show a trend toward lower liver weights in the CEP-7055-treated

mice (p < 0.07).

of tumor-bearing mice relative to vehicletreated and untreated mice (54 days and 58 days, respectively; p < 0.001), and the lestaurtinib monotherapy group (63 days; p < 0.001; Fig. 14.3.4). Moreover, the combination of lestaurtinib and gemcitabine had a positive impact on mean survival time relative to the gemcitabine monotherapy group (126 days versus 97 days; p = 0.04), although there were no consistent and significant reductions in the incidence of metastatic lesions and ascites between the lestaurtinib and gemcitabine combination group and the gemcitabine monotherapy group.

Orthotopic Model of Pancreatic Ductal Adenocarcinoma

Example 2 The effects of chronic oral administration of the pan-VEGF-R kinase inhibitor, CEP-7055, on primary tumor growth and metastatic profile of nude mice bearing orthotopically implanted human pancreatic ductal adenocarcinoma (PDAC) xenografts: CEP-7055 is the prodrug of CEP-5214, a potent pan-VEGF-R kinase inhibitor that has shown potent antiangiogenic activity in multiple in vitro, ex vivo, and in vivo models, and significant oral antitumor efficacy against a variety of aggressive rodent and human tumor xenograft models in athymic nude mice, including subcutaneous xenograft models of human pancreatic carcinoma (Ruggeri et al., 2003; Underiner et al., 2004). The ester, CEP-7055, was prepared to provide an increase in aqueous solubility and improved oral bioavailability compared to CEP-5214. Oral administration of CEP-7055

has also demonstrated significant dose-related inhibition of subcutaneous pancreatic carcinoma xenograft growth comparable to, or better than, that achieved by direct oral administration of CEP-5214 (Ruggeri et al., 2003). The objective of this study was to evaluate the effects of chronic oral administration of CEP-7055 on primary tumor growth and the metastatic profile of nude mice bearing orthotopically implanted PDAC. At a time point 7 days following surgical implantation of primary pancreatic tissue from donor mice into recipient mice, mice were randomized into treatment groups of 10 mice per group. The mice tolerated the treatment regimens well, with no significant weight loss or mortality clearly attributable to the therapy. The effects of CEP-7055 administration (23.8 mg/kg, orally, twice daily) on primary tumor growth and metastatic burden of mice orthotopically implanted with human PDAC tissue fragments are shown in Table 14.3.3 and Figure 14.3.5. The study was terminated on day 60, when vehicle-treated mice began to die or were sacrificed due to tumor burden. Upon necropsy, a thorough examination of both the abdominal and thoracic cavities was performed to determine the extent of gross metastatic spread of the orthotopically implanted PDAC tumor. A metastatic score of I, II, III, or IV was given as defined in Basic Protocol 2. Oral administration of CEP-7055 significantly inhibited the local and metastatic spread of the orthotopically implanted PDAC tumors as compared

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Figure 14.3.5 Metastatic score of mice orthotopically implanted with human PDAC and treated with CEP-7055 or vehicle. A score of I indicates minimal spread beyond the primary mass while a score of IV indicates widespread peritoneal metastasis. See Basic Protocol 2 for further definition of metastatic scoring criteria.

to vehicle-treated mice. Comparison of the mean primary pancreatic tumor weights in the vehicle-treated group compared to the CEP-7055-treated group revealed a 30% reduction in primary pancreatic tumor weight (p < 0.02) in the CEP-7055-treated mice (Table 14.3.3). In contrast, although the gross number and appearance of tumor nodules was reduced in the CEP-7055-treated group of mice (30% incidence) compared to the vehicle-treated mice (100% incidence), there was no significant difference in liver weights between the two groups. A strong trend toward lower liver weights was observed, however, in the CEP-7055-treated mice (p < 0.07; Table 14.3.3). In addition, mice administered CEP-7055 had a significant reduction in the incidence of peritoneal lymph node metastases (60% incidence) and of ascites (30% incidence) compared to the 100% incidence of these lesions in the corresponding vehicletreated mice (p < 0.05). These effects of CEP7055 administration resulted in a significant reduction in the severity of the metastatic score assigned to these tumor-bearing mice: 70% of the mice in the CEP-7055 treatment group had scores of I and II, with 20% having a score of III and 10% a score of IV (Fig. 14.3.5). In summary, administration of CEP-7055 resulted in a significant reduction in primary pancreatic tumor mass (p < 0.02), incidence of ascites, and magnitude and extent of hepatic and peritoneal metastases (metastatic score; p < 0.05) relative to vehicle-treated mice.

Example 3 Applications of orthotopic PDAC model for the study of cancer cachexia: Cancer cachexia (CC) is a multifactorial paraneoplastic syndrome characterized by anorexia, body weight loss, and loss of adipose tissue and skeletal muscle, and accounts for at least 20% of deaths in cancer patients. Muscle wasting is the hallmark feature of CC and the principal cause of functional impairment, fatigue, and respiratory complications, mainly related to a hyperactivation of muscle proteolytic pathways. Most therapeutic strategies for CC have proven to be only partially effective. The pathogenic mechanisms of cachexia and anorexia are multifactorial, but cytokines such as TNF-α and IL-6, as well as tumorderived factors, play a significant role, suggesting they may be a possible therapeutic target (Inui, 1999; Ramos et al., 2004). Remicade is an antibody that is directed at human TNFα, and, although not commonly used for treatment of cancer cachexia, it has been proposed as a treatment for this condition given the role of TNF-α in its development. Eicosapentanoic acid (EPA) has been shown to alleviate cancer cachexia in mice (Smith et al., 2004). Activation of the ubiquitin-proteasome pathway is also associated with altered expression of specific genes known as atrogenes, which control induction of muscle-specific ubiquitin ligases (Lazarus et al., 1999; Lecker et al., 2004). This complex pathway provides additional targets for therapeutic intervention in

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cancer-related cachexia, including proteasome inhibitors (Lazarus et al., 1999). Bortezomib (Velcade) is a proteasome inhibitor with proposed efficacy in a variety of tumor types including human PDAC. The purpose of these studies was to determine if EPA, infliximab (Remicade), or bortezomib had efficacy in preventing or alleviating the cachexia that develops in mice orthotopically implanted with human pancreatic ductal adenocarcinoma (PDAC). Athymic nude mice were surgically implanted with serially passaged tumor tissue derived from donor mice implanted with human PDAC carcinoma xenograft tissue, as described in Basic Protocol 1. At a time point 7 days following surgical implantation of the primary pancreatic tissue from donor mice into recipient mice, the animals were randomized into treatment groups: sham (n = 4); vehicle (n = 10, saline); EPA (n = 10, 2 g/kg, orally, 6 days during week 5); infliximab (n = 9, 10 mg/kg, intraperitoneally, once weekly); and bortezomib (n = 10, 1.0 mg/ kg, intravenously, twice weekly for 8 doses). Sham-treated mice received surgery without implantation of tissue. At the end of the study

Orthotopic Model of Pancreatic Ductal Adenocarcinoma

(day 60), when tumor-implanted mice began to exhibit mortality from metastatic PDAC, all mice were euthanized and primary tumor weights, metastatic (liver) tumor weights, and muscle weights (gastrocnemius and soleus muscles from the right hind leg) were recorded for each mouse. Food weights for each cage of mice demonstrated that the PDAC-implanted mice were eating similar amounts of food to that eaten by sham-implanted mice (data not shown). The effect of the different therapies on muscle weights for gastrocnemius and soleus muscles are shown in Figure 14.3.6. Bortezomib resulted in significant increases in gastrocnemius (**p = 0.003) and soleus (**p= < 0.001) muscle weights compared to vehicle-treated and sham-treated mice. EPA similarly resulted in significant increases in gastrocnemius (*p = 0.004) and soleus (*p = 0.018) muscle weights compared to vehicle-treated mice, whereas Remicade administration had no significant effect on muscle weights compared to vehicle-treated mice. The effect of the different therapies on metastatic scores is shown in Figure 14.3.7. Velcade therapy resulted in fewer mice (40%, 4 of 10) with

Figure 14.3.6 Effects of EPA, infliximab, and bortezomib on muscle weights in an orthotopic model of human PDAC in nude mice. The y axis shows right hind-leg gastrocnemius and soleus muscle weights of athymic nude mice implanted with human PDAC tumor tissue or sham-implanted mice. Bortezomib therapy caused significant increases in gastrocnemius (**p = 0.003) and soleus (**p =< 0.001) muscle weights compared to vehicle-treated mice. EPA therapy also increased gastrocnemius (*p = 0.004) and soleus (*p = 0.018) muscle weights compared to vehicle-treated mice. Infliximab therapy had no effect on muscle weights compared to vehicle-treated mice.

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Figure 14.3.7 Effects of EPA, infliximab, and bortezomib on metastatic scores in an orthotopic model of human PDAC in nude mice, where a score of I indicates minimal spread beyond the primary tumor mass while a score of IV indicates extensive spread. Bortezomib-treated mice had a lower incidence (40%, 4 of 10) of metastatic scores of III or IV compared to vehicle-treated mice (90%, or 9 of 10). Neither EPA nor infliximab therapy had an effect on metastatic scores, with 80% (8 of 10) of the mice having scores of III or IV.

Figure 14.3.8 Effects of EPA, infliximab, and bortezomib on primary tumor mass weights in an orthotopic model of human pancreatic ductal adenocarcinoma (PDAC) in nude mice. Bortezomib therapy had a significant (*p = 0.024) effect on primary tumor weight compared to vehicle-treated mice. Neither EPA nor infliximab therapies provided any effect on primary tumor weights in this model. Sham tumor weights were not included on the graph because the weights of the normal pancreas plus spleen are very low (∼0.2 to 0.3 g).

metastatic scores of III or IV compared to vehicle-treated (90%, 9 of 10). As shown in Figure 14.3.8, bortezomib therapy resulted in a significant (*p = 0.024) reduction in primary tumor weight compared to vehicletreated mice. Neither EPA nor infliximab ther-

apies had an effect on reducing primary tumor weights or metastatic scores in this tumor model, with 80% (8 of 10) of the mice exhibiting scores of III or IV similar to vehicletreated mice. Sham tumor weights, the weights of the normal pancreas plus spleen, are very

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low (∼0.2 to 0.3 g) and, therefore, are not shown on the figure. In summary, bortezomib therapy was effective at reducing both primary tumor weights and metastatic scores, and in mitigating the severity of cancer cachexia as compared to vehicle-treated mice. Both EPA and bortezomib therapies were effective at increasing gastrocnemius and soleus muscle weights as compared to vehicle-treated mice, consistent with their known anticachexic activity. Infliximab had no effect on muscle weights as compared to vehicle-treated mice. Not surprisingly, neither EPA nor infliximab had any effect on reducing primary tumor mass or metastatic scores as compared to vehicletreated mice; EPA and infliximab are not antitumor agents and were not expected to provide antitumor efficacy. The presence of the tumor did not significantly alter food intake in any of the treatment groups as compared to vehicle-treated mice (data not shown), a result also seen in previous studies using this tumor model.

Time Considerations

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Surgery time will vary, but initially it will likely take about 30 min per mouse. This time will decrease with experience. It will only take about 15 min per mouse if another person is able to assist with injection of the anesthetic agents, suture the abdominal wall, apply the skin wound clips, and mark the mouse for later identification. With assistance, a study requiring 60 to 100 mice can be accomplished over a 2-day period. Sham-treated mice should receive the surgical procedure without tissue implantation at the same time as the tumorimplanted mice, so that the weights and other characteristics of these mice can be compared to the tumor-bearing animals at the end of the study. It is recommended that the surgical implantations for a study be performed on a Monday and Tuesday so that on the following Monday (7 days following surgical implantation), the mice can be randomized into study groups and dosing can begin that day or the next day. For a large study, a staggered start may be necessary where surgical implantation occurs a week apart. If a staggered start is necessary, then it is recommended that the number of mice be similar so that each treatment group is equally affected. In other words, for a group of 10 mice in each group, 5 mice from each treatment group would be implanted and subsequently begin treatment a week apart throughout the study. At the end of the study, the data may then be compared based on total

number of days in life. A survival study may be conducted for more than 120 days, requiring dosing and monitoring during that time. Otherwise, the study should conclude when vehicletreated mice become moribund or die, usually about day 60 after surgical implantation.

Literature Cited Bruns, C.J., Harbison, M.T., Kuniyasu, H., Eue, I., and Fidler, I.J. 1999. In vivo selection and characterization of metastatic variants from human pancreatic adenocarcinoma by using orthotopic implantation in nude mice. Neoplasia 1:5062. Bruns, C.J., Solorzano, C.C., Harbison, M.T., Ozawa, S., Tsan, R., Fan, D., Abbruzzese, J., Traxler, P., Buchdunger, E., Radinsky, R., and Fidler, I.J. 2000. Blockade of the epidermal growth factor receptor signaling by a novel tyrosine kinase inhibitor leads to apoptosis of endothelial cells and therapy of human pancreatic carcinoma. Cancer Res. 60:2926-2935. Capella, G., Farre, L., Villanueva, A., Reyes, G., Garcia, C., Tarafa, G., and Lluis, F. 1999. Orthotopic models of human pancreatic cancer. Ann. N.Y. Acad. Sci. 880:103-109. Dobrzanski, P., Hunter, K., Jones-Bolin, S., Chang, H., Robinson, C., Pritchard, S., Zhao, H., and Ruggeri, B. 2004. Antiangiogenic and antitumor efficacy of EphA2 receptor antagonist. Cancer Res. 64:910-919. Donovan, J. and Brown, P. 2006. Euthanasia. Curr. Protoc. Immunol. 73:1.8.1-1.8.4. Eckel, F., Schneider, G., and Schmid, R.M. 2006. Pancreatic cancer: A review of recent advances. Expert Opin. Investig. Drugs. 15:1395-1410. Friess, H., Kleeff, J., Gumbs, A., and Buchler, M.W. 1997. Molecular versus conventional markers in pancreatic cancer. Digestion 58:557-563. Fu, X., Guadagni, F., and Hoffman, R.M. 1992. A metastatic nude-mouse model of human pancreatic cancer constructed orthotopically with histologically intact patient specimens. Proc. Natl. Acad. Sci. U.S.A. 89:5645-5649. Grippo, P.J. and Sandgren, E.P. 2005. Modeling pancreatic cancer in animals to address specific hypotheses. Methods Mol. Med. 103:217-243. Gunzburg, W.H., Lohr, M., and Salmons, B. 2002. Novel treatments and therapies in development for pancreatic cancer. Expert Opin. Investig. Drugs. 11:769-786. Hahn, S.A., Seymour, A.B., Hoque, A.T.M.S., Schutte, M., da Costa, L.T., Redson, M.S., Caldas, C., Weinstein, C.L., Fischer, A., Yeo, C.J., Hruban, R.H., and Kern, S.E. 1995. Allelotype of pancreatic adenocarcinoma using xenograft enrichment. Cancer Res. 55:46704675. Inui, A. 1999. Cancer anorexia-cachexia syndrome: Are neuropeptides the key? Cancer Res. 59:4493-4501. Jemal, A., Tiwari, R.C., Murray, T., Ghafoor, A., Samuels, A., Ward, E., Feuer, E.J., Thun, M.J.,

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and the American Cancer Society. 2004. Cancer statistics, 2004. CA Cancer J. Clin. 54:8-29. Jones-Bolin, S., Hunter, K., Zhao, H., and Ruggeri, B. 2002. The effects of orally active VEGFR kinase inhibitor, CEP-7055, on primary tumor growth and metastatic profile in orthotopic models of human pancreatic ductal carcinoma and murine renal carcinoma (RENCA) in mice. Proc. Am. Assoc. Cancer Res. 43:2601. Jones-Bolin, S., Hunter, K., Zhao, H., Klein-Szanto, A., and Ruggeri, B. 2005. Trk inhibitors provide better efficacy than a VEGF-R kinase inhibitor in combination with gemcitabine on improving survival in an orthotopic model of human PDAC. Proc. Am. Assoc. Cancer Res. 46:3026. Lazarus, D.D., Destree, A.T., Mazzola, L.M., McCormack, T.A., Dick, L.R., Xu, B., Huang, J.Q., Pierce, J.W., Read, M.A., Coggins, M.B., Solomon, V., Goldberg, A.L., Brand, S.J., and Elliott, P.J. 1999. A new model of cancer cachexia: Contribution of the ubiquitinproteasome pathway. Am. J. Physiol. 277:E332E341. Lecker, S.H., Jagoe, R.T., Gilbert, A., Gomes, M., Baracos, V., Bailey, J., Price, S.R., Mitch, W.E., and Goldberg, A.L. 2004. Multiple types of skeletal muscle atrophy involve a common program of changes in gene expression. FASEB J. 18:39-51. Miknyoczki, S.J., Chang, H., Klein-Szanto, A., Dionne, C.A., and Ruggeri, B.A. 1999. The Trk tyrosine kinase inhibitor CEP-701 (KT-5555) exhibits significant antitumor efficacy in preclinical xenograft models of human pancreatic ductal adenocarcinoma. Clin. Cancer Res. 5:22052212. Ramos, E.J., Suzuki, S., Marks, D., Inui, A., Asakawa, A., and Meguid, M.M. 2004. Cancer anorexia-cachexia syndrome: Cytokines and neuropeptides. Curr. Opin. Clin. Nutr. Metab. Care 7:427-434. Reyes, G., Villanueva, A., Garcia, C., Sancho, F.J., Piulats, J., Liuis, F., and Capella, G. 1996. Orthotopic xenografts of human pancreatic carcinomas acquire genetic aberrations during dissemination in nude mice. Cancer Res. 56:57135719. Ruggeri, B.A., Miknyoczki, S.J., Singh, J., and Hudkins, R.L. 1999. Role of neurotrophin-trk interactions in oncology: The anti-tumor effi-

cacy of potent and selective trk tyrosine kinase inhibitors in pre-clinical tumor models. Curr. Med. Chem. 6:845-857. Ruggeri, B. Singh, J., Gingrich, D., Angeles, T., Albom, M., Yang, S., Chang, H., Robinson, C., Hunter, K., Dobrzanski, P., Jones-Bolin, S., Pritchard, S., Aimone, L., Klein-Szanto, A., Herbert, J.M., Bono, F., Schaeffer, P., Casellas, P., Bourie, B., Pili, R., Isaacs, J., Ator, M., Hudkins, R., Vaught, J., Mallamo, J., and Dion, C. 2003. CEP-7055: A novel, orally active pan inhibitor of vascular endothelial growth factor receptor tyrosine kinases with potent antiangiogenic activity and anti-tumor efficacy in pre-clinical models. Cancer Res. 63:5978-5991. Sener, S.F., Fremgen, A., Menck, H.R., and Winchester, D.P. 1999. Pancreatic cancer: A report of treatment and survival trends for 100,313 patients diagnosed from 1985-1995, using the National Cancer Database. J. Am. Coll. Surg. 189:1-7. Smith, H.J., Greenberg, N.A., and Tisdale, M.J. 2004. Effect of eicosapentaenoic acid, protein and amino acids on protein synthesis and degradation in skeletal muscle of cachectic mice. Br. J. Cancer 91:408-412. Tuveson, D.A. and Hingorani, S.R. 2005. Ductal pancreatic cancer in humans and mice. Cold Spring Harb. Symp. Quant. Biol. 70:65-72. Underiner, T.L., Ruggeri, B., and Gingrich, D.E. 2004. Development of vascular endothelial growth factor receptor (VEGFR) kinase inhibitors as anti-angiogenesis agents in cancer therapy. Curr. Med. Chem. 11:729-743. Uomo, G., Gallucci, F., and Rabitti, P.G. 2006. Anorexia-cachexia syndrome in pancreatic cancer: Recent development in research and management. JOP 7:157-162. Vezeridis, M.P., Doremus, C.M., Tibbetts, L.M., Tzanakakis, G., and Jackson, B.T. 1989. Invasion and metastasis following orthotopic transplantation of human pancreatic cancer in the nude mouse. J. Surg. Oncol. 40:261-265.

Contributed by Susan Jones-Bolin and Bruce Ruggeri Cephalon, Inc. West Chester, Pennsylvania

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Orthotopic Models of Human Gastric Carcinoma in Nude Mice: Applications for Study of Tumor Growth and Progression

UNIT 14.4

Animal models of human gastric carcinoma (GC) include orthotopic and subcutaneous xenografts, transgenic and knockout, carcinogen-induced, and Helicobacter models. These models have been used to evaluate oncologic therapeutic agents as well as to understand the pathophysiology and molecular pathology of the disease (Pritchard and Prezemeck, 2004). Animal models combining carcinogen exposure (polycyclic hydrocarbons or N-nitroso compounds) and Helicobacter infection have been used to evaluate various tumor-promoting and inhibitory factors and the role of genetic susceptibility in the pathogenesis of gastric carcinoma (Hahm et al., 2003; Tatematsu et al., 2003; Ushijima and Sasako, 2004). Transgenic and knockout mouse models which target genes (car9; CEA424-Tag) involved in ion transport (Gut et al., 2002; Ushijima et al., 2004), signal transduction (Gut et al., 2002; Ushijima et al., 2004), transcriptional regulation (Gut et al., 2002; Ushijima et al., 2004), and cell adhesion (Gut et al., 2002; Ushijima et al., 2004; Nockel et al., 2006 for the CEA424-Tag) are available that closely mimic the human course of the disease. Transgenic models have contributed greatly to basic research at the molecular level and have proven to be powerful tools for elucidating complex biological processes linked to dysregulation of specific molecular or genetic targets. However, these models are expensive and require a great deal of effort to develop and establish, making them less readily available. Subcutaneous and orthotopic models in athymic nude mice have been both useful and practical in the study of GC growth and metastatic progression in vivo, and for drug discovery, with subcutaneous xenograft models of particular value in early screening of new chemical entities (NCEs; Teicher et al., 2001; Yamaguchi et al., 2001; Illert et al., 2003). However, metastatic rates from subcutaneous or intramuscular tumor xenografts in immunocompromised murine hosts have been low or nonexistent. Previous reports suggest that the implantation of human tumor cells, including human GC cells, orthotopically in the corresponding organ of nude mice resulted in much higher metastatic rates and a phenotype more closely resembling the human clinical course (Teicher et al., 2001; Yamaguchi et al., 2001; Illert et al., 2003). The protocol below details the development and characterization of an orthotopic model of human GC in nude mice. This model presents with peritoneal, lymphatic, and hepatic metastases of gastric origin. Details are also provided on the scoring of local and metastatic spread of the primary tumor during necropsy. This protocol utilizes the GTL16 tumor cell line, a highly c-Met-dependent model that expresses constitutively activated c-Met due to receptor overexpression subsequent to c-Met gene amplification (Morotti et al., 2002). This model enables the evaluation of: 1. Primary gastric carcinoma tumor biology and understanding the mechanisms of tumor pathogenesis. 2. Evaluation of c-Met-dependent pathways and downstream effector molecules such as STAT3, Akt, p38, Gab-1, and survivin. 3. Local and metastatic tumor spread and gastric carcinoma progression, including the development of cancer cachexia (a syndrome of progressive weight loss, anorexia, and persistent erosion of host body cell mass in response to a malignant growth).

Contributed by Susan Jones-Bolin and Bruce Ruggeri Current Protocols in Pharmacology (2007) 14.4.1-14.4.16 C 2007 by John Wiley & Sons, Inc. Copyright


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4. Effects of therapeutic interventions on primary and metastatic gastric tumor burden, median and overall survival, and the severity of cancer cachexia. The complexity, time considerations, and labor-intensiveness of establishing the orthotopic gastric carcinoma model described in this unit limits its utility for routine screening of NCEs in oncology drug discovery. Rather, the clinicopathological similarities of this model to the metastatic progression of human gastric carcinoma make this model particularly useful to characterize the efficacy profile of advanced lead oncology therapeutic agents alone or in combination with standard-of-care chemotherapeutic agents for gastric cancer on primary and metastatic burden, the development of cancer cachexia, and effects on median survival. Results presented from a pharmacological efficacy study using this model (see Anticipated Results) provide an example as to how this model may be utilized to facilitate the discovery and characterization of small molecule kinase inhibitors. The study describes the use of a c-Met inhibitor, PHA665752 (Christensen et al., 2003; Ma et al., 2005), and 5-fluorouracil (5-FU) standard-of-care chemotherapy on primary GC growth and metastases. Together with surgery and radiation, combination chemotherapy using 5-fluorouracil with other agents is the most common adjuvant-combined modality therapy for patients with advanced GC that offers improved survival time (Macdonald et al., 2001; Ajani et al., 2006; Van Cutsem et al., 2006). NOTE: All protocols using live animals must first be reviewed and approved by an Institutional Animal Care and Use Committee (IACUC) and must follow officially approved procedures for the care and use of laboratory animals. Aseptic surgical techniques must be employed for all rodent surgical preparations. NOTE: All solutions and equipment coming into contact with living cells must be sterile, and proper aseptic technique should be used accordingly. BASIC PROTOCOL 1

ORTHOTOPIC MODEL OF HUMAN GASTRIC CARCINOMA IN ATHYMIC NUDE MICE The surgical procedures described in this protocol require some practice to master with regard to abdominal incision and exteriorization of the stomach, suturing of tissue fragments to the dorsal side of the stomach without entering the lumen of the stomach, and closure of the abdominal wall and skin layers to prevent infection. Small-animal surgical experience and familiarity with aseptic surgical techniques and anesthesia are needed for excising the tumor tissue and preparing it for successful orthotopic implantation into athymic nude mice.

Materials GTL-16 human gastric carcinoma cell line or human gastric carcinoma cell lines with comparable phenotype (such as NCI-N87, ATCC) Gastric carcinoma growth medium (see recipe) 1× trypsin/EDTA (Mediatech) 1× PBS Nude mice, female, 6- to 8-weeks-old (20 to 25 g; athymic nu/nu or comparable strain; Charles River Labs) Matrigel synthetic basement membrane (Collaborative Research) Ketamine/xylazine mixture Isoflurane with nose cone apparatus Orthotopic Mouse Models of Gastric Carcinoma

Tissue culture flasks 1-ml syringes and 27-G needles

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50-ml conical centrifuge tubes Hemacytometer or a commercial cell counter 70-mm (or larger) petri dish, sterile Surgical instruments including (two sets of instruments recommended): 4.5-in. iris scissors, straight, sharp ends Disposable scalpels 4.75-in. Adson forceps or delicate dressing forceps, serrated 4.5-in. tissue forceps, 1 × 2 teeth 5.5-in. Mayo-Hegar or similar needle holder Suture: prolene 6/0, taper needle, for suturing tissue to stomach; one pack per 5 to 10 mice (Webster Veterinary Supply) Vicryl 5/0 or 6/0 –or- PDS II 5/0, for closing peritoneal layer; one pack per 5 to 10 mice (Webster Veterinary Supply) 9-mm wound clips, with applier and remover (VWR) Autoclave for sterilizing instruments, gauze sponges, and wound clips Circulating water heating pad: Gaymar T-pump or similar Sterile towels Distilled water to fill pump for heating pads Sterile alcohol prep pads, medium Sterile gauze sponges (2-in. × 2-in.) Sharpie marker, ear punch, or tattooing device to identify individual mice Additional reagents and equipment for euthanizing using CO2 (Donovan and Brown, 2006) Isolate and prepare GC tissue for orthotopic implantation Tumor tissue must be generated to provide samples for the primary implantation. The following steps provide detailed information on how to generate xenograft tissue for implantation onto the stomach wall of athymic nude mice. For a typical study of 60 to 100 mice, three to five donor mice will be needed to supply sufficient tumor tissue for implantation. 1. Grow human GTL-16 human gastric carcinoma tumor cells to subconfluency in several tissue culture flasks containing gastric carcinoma growth medium. Several tissue culture flasks will be needed to generate a sufficient number of cells to inject mice for subcutaneous tumor formation.

2. Add 1 to 2 ml of trypsin/EDTA to the flask and allow cells to make contact for 2 to 3 min. Next, pipet 3 to 5 ml of growth medium into the flask and collect the cells in a 50-ml conical-shaped centrifuge tube. Centrifuge 6 min, 100 to 130 × g, 4◦ C to form a pellet. Remove supernatant. Several tissue culture flasks will be needed to generate enough cells to inject mice for subcutaneous tumor formation.

3. Resuspend the pellet in 1 ml of sterile 1× PBS and use a hemacytometer or a commercial cell counter to determine cells/ml. Resuspend cells in sterile 1× PBS to yield the desired concentration (1 × 107 cells per 100 µl or 1 × 108 cells/ml). Have cells tested for mouse antibody production (MAP) and mycoplasma by a commercial laboratory (e.g., Bio Reliance; http://bioreliance.com) prior to use. Standard IACUC regulations require that the cells must be free of mouse pathogens (which the MAP screen detects). If cells test positive for these pathogens they should not be used or the immune-compromised mouse would be exposed to a pathogen that could cause morbidity or mortality. New cells must be obtained. Mycoplasma contamination can be tested using a PCR kit (EZ-PCR Mycoplasma Test Kit; MD Biosciences, http://www.mdbiosciences.com/home.shml) or be sent for mycoplasma testing (ATCC). If cells are infected they will not grow well, if at all, in culture. Again new cells would be

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required. MAP testing is done once before animal studies are initiated and as often as required by the IACUC. Mycoplasma may be tested as often as needed, whenever cultured cells appear to be contaminated or are not growing well in culture.

4. Generate human gastric carcinoma (GC) xenograft tumors by injecting 100 µl containing 1 × 107 GTL-16 human GC cells in 1× PBS subcutaneously, using a 1-ml syringe and a 27-G needle, in the left flanks of 6- to 8-week-old female athymic nu/nu mice. 5. Once subcutaneous tumors of 200- to 300-mm3 volume are palpable (typically 10 to 14 days after injection), euthanize the mouse using CO2 (Donovan and Brown, 2006) and place in a sterile 70-mm or larger petri dish in a Class II biological safety cabinet or laminar-flow hood. This should be done ∼60 min prior to implantation (Hahn et al., 1995) of tumor tissue into the nude mouse host.

6. Aseptically remove the subcutaneous tumor tissues (300 ± 50-mm3 size) from the flank area of the mouse using sterile tissue forceps and scissors to remove the overlying skin and any surrounding tissue. 7. Use fresh sterile dressing forceps to place the excised tumor tissue into a clean, sterile petri dish for dissection. Using sterile tissue forceps and disposable scalpels, remove and discard necrotic (grayish-white) areas of tumor. Next, using sterile disposable scalpel and dressing forceps, cut the non-necrotic tumor tissue into 2-mm3 fragments (Vezeridis et al., 1989; Illert et al., 2003). 8. Place the tissue fragments (two pieces per mouse to be implanted) in a small, sterile, covered petri dish on ice and add a sufficient volume of sterile, undiluted Matrigel synthetic basement membrane to cover the tissue fragments completely. Two to three subcutaneous tumors will provide 10 to 15 tumor fragments for orthotopic implantation into 5 recipient mice (2 or 3 fragments each). The fragments must be used within a few hours or they will not be viable for implantation.

Orthotopically implant GC tissues into nude mice Orthotopic human GC implantation in nude mice is performed similar to that described previously (Vezeridis et al., 1989; Illert et al., 2003). 9. Activate and preheat the water heating pad to 107◦ F ∼30 min prior to initiating the surgical procedure. The heating pad may be preheated to a range of temperatures, but the authors use the manufacturer’s recommended setting of 107◦ F.

10. Create a sterile field for surgery using a sterile towel or towels in the hood on top of the circulating water heating pad. 11. Anesthetize female nude mice (nu/nu) by intramuscular injection of 100 µl ketamine/ xylazine mixture, then maintain anesthesia with isoflurane in a nose cone apparatus (1% to 2% isoflurane in 0.2 liters/min oxygen). Nude mice are typically manipulated under a laminar-flow hood or similar device to prevent infection.

Orthotopic Mouse Models of Gastric Carcinoma

12. Verify that a surgical plane of anesthesia has been reached (typically within a few minutes) by monitoring for lack of pedal or corneal reflex. Place the mouse on a sterile gauze-lined petri dish with the back of the mouse against the dish surface and the tail pointing toward the surgeon. Use 1 × 2 teeth tissue forceps to pinch the paw or toe of the mouse to check pedal reflex. Use a moistened Q-tip or gauze sponge to gently brush the eye area of the mouse to check

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Figure 14.4.1

(A) Site of incision for GC model. (B) Position of retracted stomach relative to incision.

for corneal (blink) reflex. Preferably, apply a small amount of protective eye ointment (Ophthalmic Base Ointment, Webster Veterinary Supply) to the eyes of the mouse at this time and this will also serve to check for corneal (blink) reflex. There should be no pedal or blink reflex response if the animal is under an adequate plane of anesthesia. Monitor the mouse frequently to ensure it is breathing regularly, kept warm, and maintained under an adequate plane of anesthesia.

13. Using sterile technique, swab the abdominal area of the mouse’s upper left quadrant with a sterile surgical alcohol prep pad, wiping the area just to the left of midline and under the rib cage. 14. Visually locate the dark, horizontal-lying spleen under the skin of the nude mouse. Make a small, 0.5-cm horizontal incision in the skin over the left lateral abdominal area, just above the spleen, using sterile operating scissors and tissue forceps (Fig. 14.4.1A). Avoid cutting the area near the rib cage so as not to damage the lungs, diaphragm, and rib cage.

15. Visually locate the inner peritoneal lining and cut it in a similar manner to the skin, using the forceps to pull the layer upward before incising with the scissors to prevent damage to underlying and surrounding organs and tissues. 16. Once the abdominal cavity is exposed, use sterile dressing forceps to gently move the stomach—which lies rostral or anterior (towards the head) and dorsal to the spleen and pancreas—to the incision area using gentle traction (Fig. 14.4.1B). Place sterile

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saline-soaked gauze sponges near the incision to moisten the exteriorized stomach to prevent drying of the tissue. While it is not possible to exteriorize the stomach, its dorsal surface should be manipulated gently so that it lies at the opening of the incision.

17. Once the underside of the stomach is exposed, use sterile dressing forceps to retrieve two roughly 2 × 2–mm3 tumor xenograft fragments embedded in sterile, undiluted Matrigel synthetic basement membrane from the petri dish. Using the tapered needle of the 6/0 prolene suture and dressing forceps, pierce one of the tumor pieces with the tapered needle and gently glide it onto the prolene suture material. Anchor the tissue fragment onto the suture and loosely sew it to the dorsal side of the stomach in the mid-section, using two or three surgical knots. Care must be taken not to cut the lumen of the stomach with the suture needle; only the superficial layers (serosa) of the stomach should be incised using the tapered 6/0 prolene suture needle.

18. Attach the second tumor fragment in a similar manner to the same (dorsal) side of the stomach again, making sure not to disrupt or damage the lumen of the stomach. Attachment of the tumor tissue to the dorsal side of the stomach prevents the direct extension of tumor growth into the overlying liver tissue, ensuring that any tumors in the liver are true metastases and not the result of spread incurred during surgical manipulation of the abdominal field. One suture packet should provide enough suture for five to ten mice, depending on the length of the suture.

19. Apply a small amount (0.5 to 1.0 ml) of sterile saline to the stomach with attached tumor tissue fragments, and then use sterile tissue forceps to raise the abdominal wall and gently guide the stomach back in place within the abdominal cavity. Moisten the skin and abdominal layers with saline for easier surgical closure. 20. Close the abdominal incision with 6-0 Vicryl suture, using two to four surgical knots, depending on the length of the incision. One suture packet should provide enough suture for 5 to 10 mice, depending on the length of the suture.

21. Close the skin with 2 or 3 skin wound clips. 22. Immediately following surgery, mark individual mice for later identification by, for example, using a colored Sharpie marker on the tail, ear punch, or tattoo. 23. Place mice on the heated circulating water pad until they achieve sternal recumbency and are able to move around. Mice may also be placed in their cage which has been placed on top of the water circulating heated pad until they have recovered.

Administer post-surgical care and monitor for metastatic GC phenotype 24. Observe mice daily, and following removal of the skin staples at 10 days post-surgery, for signs of infection (redness, swelling, or discharge) along the incision line. 25. Following removal of the wound clips, perform gentle abdominal palpation once or twice weekly until a 2-cm diameter tumor mass is palpable (∼35 days postimplantation) in the abdominal cavity. Also, weigh mice twice weekly. 26. Sacrifice mice bearing palpable tumors when they lose >15% body weight, develop ascites, or at the end of 8 weeks post-implantation period. Orthotopic Mouse Models of Gastric Carcinoma

27. Upon necropsy, perform gross assessment of tumor dissemination throughout the peritoneal cavity and to various organ sites. In mice where tumor spread is clearly

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due to distant and discrete metastases (e.g., tumor nodules in the liver that are clearly not adjacent to the implanted tumor), serially passage the tumor tissue into additional mice as detailed below. Serial passage of tumor tissue in mice is the only way to select for a metastatic gastric carcinoma phenotype. a. Euthanize tumor-bearing mice using CO2 (Donovan and Brown, 2006) and transfer the mouse to a sterile petri dish in a laminar-flow hood or biological safety cabinet. b. Open the abdominal cavity using sterile scissors and tissue forceps as detailed in steps 14 to 16. Use new sterile scissors and dressing forceps to excise the primary stomach tumor tissue. c. Use sterile dressing forceps to place the excised tumor tissue into a clean, sterile petri dish for dissection. Remove and discard necrotic areas of tumor. Necrotic areas are typically in the center of the tumor and appear white or gray in color.

d. Using a sterile scalpel and dressing forceps, cut the non-necrotic tumor tissue into 2-mm3 pieces. e. Place these tissue fragments (two pieces per mouse to be implanted) in a small, sterile, covered petri dish on ice and add sterile, undiluted Matrigel synthetic basement membrane to cover the tissue pieces for 60 min prior to implantation (Hahn et al., 1995) in the nude mouse host as described above. One large primary tumor will typically provide 20 to 30 fragments of 2-mm3 volume for implantation. For a large study of 60 to 100 mice, 3 to 5 donor mice will be required to supply sufficient tumor tissue for implantation.

f. Perform implantation and animal identification as described in steps 17 to 23. It typically requires three serial passages of gastric carcinoma tissue into recipient mice before a robust and sustained gastric carcinoma metastatic phenotype is achieved.

28. Once metastases occur, passage the primary stomach tumor tissue into three to five additional mice to maintain the metastatic pattern. The metastatic phenotype that develops consists of peritoneal, lymphatic, and hepatic metastases of gastric origin. Typically 90% of untreated mice will develop metastases, with 40% having scores of II, indicating spread to the liver or lymph nodes, and 60% having scores of III or IV, indicating metastases to the liver and lymph nodes or more diffuse peritoneal spread involving other organs, such as kidney, spleen, or diaphragm, along with the development of ascites. Metastatic score is defined in greater detail in Basic Protocol 2. Alternatively or in addition, a cell line may be developed from the tissue from mice with the appropriate metastatic phenotype (see Alternate Protocol), and these cells are injected instead of implanting tumor tissue.

PHARMACOLOGICAL EFFICACY STUDIES USING THE ATHYMIC NUDE MOUSE ORTHOTOPIC GASTRIC CARCINOMA MODEL Once the metastatic phenotype has developed and has been reproduced in recipient mice (in Basic Protocol 1), studies can be initiated to evaluate therapeutic compounds or treatments of interest for gastric carcinoma. The complexity, time considerations, and labor intensiveness of establishing the orthotopic GC model described in Basic Protocol 1 limits its utility for routine screening of new chemical entities (NCEs) in oncology drug discovery. Rather, the clinicopathological similarities of this model to the metastatic progression of human gastric carcinoma make it particularly useful for characterizing the efficacy profile of advanced lead oncology therapeutic agents alone or in combination with standard-of-care chemotherapeutic agents for gastric cancer on primary and metastatic burden, the development of cancer cachexia, and effects on median survival.

BASIC PROTOCOL 2

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This model has been used to evaluate standard-of-care therapies such as 5-FU and experimental c-Met kinase inhibitors. The model may also be used to study gastric tumor biology, including tumor growth, localized tumor spread, and metastases. Specifically, intratumoral microvessel density (Factor VIII and CD34 immunostaining), apoptosis (TUNEL labeling), and analysis of tumor tissue by immunoblotting for proteins of interest may be performed using primary and metastatic tumor tissue from this model. Results from one study evaluating the c-Met inhibitor, PHA665752, and the chemotherapeutic agent, 5-FU, on the growth of GTL-16 human gastric carcinoma orthotopic xenografts in nude mice are provided to illustrate the utility of the model.

Materials Nude mice exhibiting appropriate metastatic phenotype (Basic Protocol 1) Additional reagents and equipment for nude mice exhibiting appropriate metastatic phenotype (Basic Protocol 1) 1. Randomize mice into treatment groups 7 days following surgical implantation of primary stomach tissue from donor mice into recipient mice. Monitor the mice daily and weigh twice weekly. For criteria to use as a basis for sacrificing animals in a survival study see Critical Parameters.

2. Optional: Obtain plasma samples at the end of the study for compound analyses of assessment of circulating cytokines, trophic factors of interest, or other parameters, depending upon the purpose of the study. 3. Upon completion of the study perform a necropsy of each mouse. The necropsy should include a thorough examination of both the abdominal and thoracic cavities to determine the extent of gross metastatic spread of the orthotopically implanted human GC tumor. Use the following criteria for metastatic scoring: a. A score of I is given if the mouse has a primary mass with no other visible organ or peritoneal or thoracic cavity spread. b. A score of II is given if the mouse has a primary mass with metastatic spread to one other visible organ (usually liver or lymph node). c. A score of III is given if the mouse has a primary mass with metastatic spread to two organ sites (usually liver and lymph node). d. A score of IV is assigned if a mouse has a primary mass with a diffuse peritoneal metastatic spread involving three or more organ sites or if the mouse has any degree of ascites. 4. Obtain weights of the primary tumor, liver, and other involved organs for each mouse. All grossly involved tissues may be formalin-fixed for histopathological analyses by placing the tissue in 10% formalin for at least 24 hr. The fixed tissue should be stored at room temperature and then processed within a few weeks to a few months.

5. Compare primary and metastatic tumor weights, and mouse body weights using the Mann-Whitney Rank Sum test (Robakiewicz and Ryder, 2000). For survival studies, analyze mean or median survival data from tumor-bearing mice using the Kaplan-Meier method as required using SAS (SAS 8.2, SAS Institute). Hematoxylin and eosin (H & E) stained sections of the primary tumor and metastases to liver and lymph node are shown in Figure 14.4.2. Orthotopic Mouse Models of Gastric Carcinoma

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Figure 14.4.2 Histopathological analysis of tumor tissues from nude mice orthotopically implanted with human GC tumor tissue. Primary: Hematoxylin and eosin histological sections of primary poorly differentiated gastric carcinoma tumor composed of small and medium size carcinoma cells with a few areas of necrosis (arrow). Liver Met: Metastatic nodule in liver parenchyma showing a central necrotic area (arrow) surrounded by poorly differentiated carcinoma cells. LN Met: The lymph node tissue has been totally replaced by carcinoma cells. A necrotic focus is seen (arrow). For color version of this figure see http://www.currentprotocols.com.

ESTABLISHING CELL LINES FROM GASTRIC CARCINOMA TUMOR TISSUE

ALTERNATE PROTOCOL

Alternatively or in addition to the tumor passaging described in Basic Protocol 1, a cell line may be developed from the primary or metastatic gastric tissue from mice with the appropriate metastatic phenotype. The development of a cell line provides a backup for the in-life passage animals generated in Basic Protocol 1, in the event that the metastatic phenotype fails to develop after years of passage or in the event that the maintenance of passage animals is not possible due to space limitations or other constraints. These cells may be used to implant into mice instead of implanting passaged tumor pieces in steps 17 to 23 or step 27f of Basic Protocol 1.

Additional Materials (see Basic Protocol 1) Mice with tumors (from Basic Protocol 1) DMEM containing 10% (v/v) FBS, ice-cold Gastric carcinoma growth medium (see recipe) 10% (v/v) DMSO/90% (v/v) FBS 70-µm nylon cell strainer (BD Biosciences) Plunger from 1-ml syringe 75-cm2 tissue culture flask Humidified 5% CO2 , 37◦ C incubator 1.5-ml cryotubes (e.g., Nunc) Nalgene Cryo 1◦ C Freezing Container (Nalge Nunc International) −80◦ C freezer Liquid nitrogen freezer 30-G needle Additional reagents and equipment for developing mice with tumors (Basic Protocol 1) 1. Euthanize tumor-bearing mice using CO2 (Donovan and Brown, 2006) and place in a sterile petri dish in a laminar-flow hood or biological safety cabinet. 2. Open the abdominal cavity using sterile scissors and tissue forceps. Use new sterile scissors and dressing forceps to dissect away the primary stomach tumor tissue from the mouse. The tumor tissue should be visibly obvious.

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3. Use sterile dressing forceps to place the excised tumor tissue into a clean, sterile petri dish for dissection. Wash the tissue with sterile 1× PBS to remove any blood from the outside of the tissue. Remove and discard grayish-white necrotic or visibly hemorrhagic areas of the tumor. 4. Using a sterile scalpel and dressing forceps, cut the non-necrotic tumor tissue into 2-mm3 pieces. Place these tissue fragments on ice and then in a 70-µm nylon cell strainer over a 50-ml conical centrifuge tube. 5. Homogenize the tissue by pressing it through the cell strainer using the back of a plunger from a 1-ml syringe. Wash the tissue homogenate with 5 ml ice-cold DMEM containing 10% FBS. Repeat the tissue homogenization and washing steps a second time. There should be about 10 ml of filtered material collected from the tumor tissue. Maintain the filtered material on ice.

6. Centrifuge the primary tumor cells 6 min at 100 to 130 × g, 4◦ C, and discard the supernatant either by pipetting or carefully decanting to avoid cell loss. Resuspend the cell pellet in gastric carcinoma growth medium at the desired volume (10 to 15 ml). Transfer the cell suspension into a 75-cm2 tissue culture flask and place in a humidified 5% CO2 , 37◦ C incubator. Monitor cells, replacing the medium or split cells as needed. Replace medium every 2 to 3 days initially and split the cells when confluency is reached. 7. Once a cell line is established and growing well in culture, expand the number of flasks so that several vials can be frozen for future use. Freeze as follows: a. Resuspend cell pellet in a mixture of 10% DMSO and 90% FBS. If the total number of cells is 5 × 106 , add 5 ml DMSO/FBS to cell pellet. This will provide enough cells for five 1-ml aliquots for freezing (see below).

b. Pipet 1 ml into 1.5-ml cryotubes and place cryotubes in a Nalgene Cryo 1◦ C Freezing Container, This is a container that is lined with isopropyl alcohol.

c. Place cell freezing container in a −80◦ C freezer overnight, then store frozen cryovials long term in a liquid nitrogen freezer. 8. Instead of implanting tumor pieces as described in steps 17 to 23 or step 27f of Basic Protocol 1, inject 1×107 cells in 50 µl of sterile 1× PBS under the serosal membrane in the greater curvature of the antrum (mid-portion of the stomach) using a 30-G needle to form a small bleb without penetrating the lumen of the stomach (Yamaguchi et al., 2001). Monitor mice as described in steps 24 to 27 of Basic Protocol 1 for development of metastatic phenotype and serial passaging. The metastatic phenotype is characterized by peritoneal, lymphatic, and hepatic metastases of gastric origin as described in Basic Protocol 1. Typically 90% of untreated mice develop metastases, with 40% having metastatic scores of II, indicating spread to the liver or lymph nodes, and 60% having metastatic scores of III or IV, indicating metastases to the liver and lymph nodes or more diffuse peritoneal spread involving other organs, such as kidney, spleen, or diaphragm. There may also be development of ascites. Metastatic score is defined in greater detail in Basic Protocol 2.

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REAGENTS AND SOLUTIONS Use deionized, distilled water in all recipes and protocol steps. For common stock solutions, see APPENDIX 2A; for suppliers, see SUPPLIERS APPENDIX.

Gastric carcinoma growth medium Dulbecco’s modified Eagle medium (DMEM; e.g., Invitrogen) 10% (v/v) fetal bovine serum (FBS; Hyclone or ATCC; also see APPENDIX 2A) 1× penicillin/streptomycin (e.g., Invitrogen) Store at 4◦ C until the expiration date on the container, barring any obvious contamination. For long-term storage, keep in liquid nitrogen freezer.

Ketamine/xylazine mixture Prepare a mixture of 55 mg/kg animal body weight ketamine hydrochloride (e.g., from 100 mg/ml stock; Hanna Pharmaceutical Supply Company) and 10 mg/kg animal body weight xylazine (e.g., from 20 mg/ml stock; J.A. Webster) in sterile saline (0.9% w/v NaCl). Ketamine is a CIII controlled substance and will require a DEA license to order.

COMMENTARY Background Information Gastric carcinoma (GC) represents the second most common cause of cancer-related deaths worldwide and is 14th in incidence among the major types of cancer malignancies in the United States (American Cancer Society, 2006). Given the regional variations in GC between the Far East and the United States, genetic factors appear to play an important role in addition to the more common epigenetic factors such as diet (salted fish and meat) and life style (Lin et al., 2000; Jemal, et al., 2004; Lynch et al., 2005). An estimated 5% of the total cases of GC, projected as 22,710 new cases in 2004 (13,640 males; 9070 females), have a contributing hereditary factor. Included among the many genetic factors is the activation of oncogenes such as c-Met, K-ras, and erbB-2 (Lin et al., 2000; Wang et al., 2004). Greater than 50% of patients can be cured of localized distal gastric cancer. However, earlystage disease accounts for only 10% to 20% of all cases diagnosed in the United States. The remaining patients present with metastatic disease in either regional or distant sites (e.g., lymph nodes, liver, and colon). The overall 5-year survival rate of these patients ranges from almost zero for those with disseminated disease to ∼50% for patients with localized distal gastric cancers confined to resectable regional disease. Patients with gastric carcinoma of the proximal portion of the stomach are typically diagnosed in advanced stages and surgical resection of this area of the stomach is much more difficult and involved. Even with

apparent localized disease, the 5-year survival rate of patients with proximal gastric cancer is only 10% to 15%. Although the treatment of patients with disseminated gastric cancer may result in palliation of symptoms and some prolongation of survival, long remissions are uncommon (Macdonald et al., 2001). Treatment options available to GC patients include radical surgery, which is the standard form of therapy having curative intent (Macdonald et al., 2001), and adjuvant combined modality therapy offering improved survival time. The adjuvant combined modality therapy in the form of preoperative and post-operative 5fluorouracil and leucovorin, as well as other fluorouracil chemotherapy combinations, plus concurrent external beam radiation therapy offer improved survival outcomes in patients with advanced gastric carcinoma (Macdonald et al., 2001; Ajani et al., 2006; Van Cutsem et al., 2006). However, even with combination modality therapy the survival rates for aggressive, nonresectable gastric cancer remain poor (Macdonald, 2004). Consequently, novel therapies directed at inhibiting primary tumor growth and, more importantly, the local and distant metastatic dissemination of tumors is of substantial clinical importance. Several targeted therapies have been evaluated in patients with gastric carcinoma, including inhibitors directed at the epidermal growth factor receptor (EGFR), vascular endothelial growth factor (VEGF), and matrix metalloproteinase (MMP). Gefitinib (Iressa, ZD1839) an EGFR inhibitor, bevacizumab (Avastin) a humanized

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monoclonal antibody directed to VEGF, and marimastat (BB 2516, TA 2516) an orally active MMP inhibitor, have been evaluated in phase II/III trials in patients with advanced GC and have demonstrated some objective responses involving partial remissions, stable disease, and a modest improvement in survival time (Tabernero et al., 2005).

Critical Parameters Aseptic surgical technique is essential in the procedures detailed in this unit to prevent infection. The surgical technique requires some practice, with the critical steps being implantation of tissue onto the stomach serosa without penetrating the lumen of the stomach and the proper closure of the peritoneal cavity and skin. Anesthesia delivery and monitoring is also critical to ensure the mouse survives the surgery. Post-surgical care and monitoring of mice for development of tumors and metastatic spread is also of great importance in the development of the metastatic phenotype. Mice need to be monitored daily and weighed twice weekly, with any one of the following criteria used to decide if an animal should be sacrificed during a survival study: (1) weight loss >15% of body weight, (2) lethargy, (3) hunching for more than 1 or 2 days as a result of surgery or treatment regimens being evaluated, (4) ascites in which there is a weight gain of ≥2 grams

over a few days, (5) debilitation that impairs the animal’s ability to reach and/or consume food and water, (6) vocalization indicative of severe pain or distress, (7) dehydration, or (8) open, bleeding or infected wound or tumor.

Troubleshooting Table 14.4.1 describes some common problems that may be encountered with the described orthotopic GC nude mouse model, along with recommendations for overcoming or avoiding these difficulties.

Anticipated Results Untreated mice bearing orthotopically implanted GC tissue will become moribund ∼56 days after surgery. The resulting orthotopic GC model generates a reproducible metastatic phenotype analogous to the human clinical course, with peritoneal, hepatic, and mesenteric lymph node metastases of human gastric carcinoma origin. Ascites development occurs in some mice. When the protocol is properly performed, the percentage of animals developing primary tumors is >90%. Once established, the metastatic phenotype is usually reproducible from passage to passage in vivo, displaying similar peritoneal spread with large primary tumor infiltrating into surrounding tissue along with distant spread to mesenteric lymph nodes and liver. The model is extremely

Table 14.4.1 Troubleshooting the Nude Mouse Orthotopic Gastric Carcinoma Model

Orthotopic Mouse Models of Gastric Carcinoma

Problem

Recommended action

Tumor does not develop

Make certain that tumor tissues are well coated in Matrigel and that they are properly attached to the stomach at two sites. Use only the outermost layer of the tumor tissue for passage as this is the most viable.

Metastasis does not occur following first implantation

Continue to passage primary tumor from donor mice into 3 to 5 recipient mice, using 3 to 5 different donor mice. It may require more than three passages for the metastatic phenotype to develop. Once the metastatic pattern develops, only passage tissue from mice with this pattern into recipient mice.

Mice develop infection

Make certain instruments are sterilized before use and change instruments after incising the skin in order to avoid contamination. Also, change instruments between each cage of mice (5 mice per cage is average) and make certain to follow aseptic surgical techniques.

MAP testing of cells is positive

Obtain new cells

Mice die during surgery

Check the level of isoflurane and decrease the flow rate or amount as needed. Monitor the respirations of the mouse every 1 or 2 min during surgery. If breathing becomes erratic, remove the isoflurane cone for a minute or two until breathing is more rhythmic.

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useful in the evaluation of primary gastric tumor biology, understanding the mechanisms of disease pathogenesis, local and metastatic tumor spread, and assessment of therapeutic approaches to inhibit tumor growth and improve survival. Example study This section describes a study performed using the abovementioned model. The study uses a c-Met inhibitor, PHA665752 (Christensen et al., 2003; Ma et al., 2005) and 5-FU (Macdonald et al., 2001; Ajani et al., 2006; Van Cutsem et al., 2006) to evaluate the effects of these compounds on primary tumor growth and metastatic spread of the orthotopically implanted human GC tumor tissue in nude mice. c-Met is a high-affinity receptor for hepatocyte growth factor (HGF) and plays a crucial role in embryonic development and tissue repair. It is implicated in proliferation, survival, motility and invasiveness, angiogenesis, and metastatic progression in a variety of human malignancies (Peruzzi and Bottaro, 2006). Overexpression and amplification of c-Met are often observed in various cancer tissues, including gastric carcinoma (Lin et al., 2000; Heideman et al., 2001; Wang et al., 2004). The overexpression of c-Met in tumor tissues from patients with gastric carcinoma is related to tumor size, invasion, lymph node metastases, and tumor stage (Amemiya et al., 2002; Wang et al., 2004). A correlation of elevated c-Met expression in tumors of patients with liver metastases in association with stage IV gastric cancer has been observed, suggesting that c-Met expression might be a useful indicator of liver metastases in patients with gastric carcinoma (Amemiya et al., 2002; Wang et al., 2004). The purpose of this study was to evaluate the effect of the c-Met inhibitor, PHA665752 (Christensen et al., 2003; Ma et al., 2005) and the chemotherapeutic agent 5-FU on the growth of GTL-16 human gastric carcinoma orthotopic xenografts in nude mice. This xenograft is a highly c-Met-dependent model that expresses constitutively activated c-Met due to receptor overexpression subsequent to c-Met gene amplification (Morotti et al., 2002). Briefly, human GC xenograft tissue serially passaged in nude mice to select for a phenotype distinguished by peritoneal, lymphatic, and hepatic metastases of gastric origin was surgically implanted onto the midportion of the stomach of nude mice as described in Basic Protocol 1. Seven days postimplantation, mice were randomized into three

treatment groups (n=10) and administered vehicle (3% DMSO, 10% solutol, 87% PBS), PHA665752 (30 mg/kg/dose, s.c., three times per week), or 5-FU (100 mg/kg of 5-FU, 100 µl i.p., once every 14 days for 3 treatments). The effects of PHA665752 and 5-FU administration on primary tumor growth and metastatic burden of mice orthotopically implanted with human GC tissue fragments are shown in Figures 14.4.3 and 14.4.4. The study was terminated when vehicle-treated mice began to die or were sacrificed due to their tumor burden (no later than day 48 of dosing). Administration of 5-FU and PHA665752 resulted in a significant decrease in primary tumor mass (47%, p≤0.05; and 24%, p≤0.05, respectively) as compared to vehicle-treated mice (Fig. 14.4.3). However, administration of 5-FU was significantly more effective than PHA665752 in reducing primary tumor mass (31%, p≤0.01; Fig. 14.4.3). The percent decrease was determined by taking the mean primary tumor weights from the treated group and dividing by the mean primary tumor weight of the vehicle-treated mice, and multiplying by 100. In addition, metastatic scoring of tumor-bearing mice (Fig. 14.4.4) revealed that 5-FU treatment resulted in scores of I (primary mass with no other visible organ or peritoneal or thoracic cavity spread; 40%) and II (a primary mass with metastatic spread to one other visible organ; 40%), with only 10% having a metastatic score of IV (primary mass with metastatic spread to three or more organs or presence of ascites). Administration of PHA665752 resulted in metastatic scores of II (80%) and III (primary mass with metastatic spread to two organ sites; 10%). The dosing regimens (s.c. vehicle, PHA665752, or 5-FU) were well tolerated, with no significant weight loss or mortality clearly attributable to therapy. The c-Met inhibitor, PHA665752 has been shown to inhibit c-Met phosphorylation in tumor xenografts for up to 12 hr following a single i.v. dose of 25 mg/kg. (Christensen et al., 2003). The inhibition of c-Met phosphorylation was associated with dose-dependent tumor growth inhibition/growth delay of GTL-16 xenografts over a repeated administration schedule at well-tolerated doses (Christensen et al., 2003). The data generated in the orthotopic GC model described here revealed that PHA665752 was able to inhibit primary tumor growth and metastases in this aggressive model using published doses, albeit not as effectively as the standard-ofcare chemotherapeutic agent, 5-FU. In summary, while both compounds significantly

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Figure 14.4.3 Effects of PHA665752 or 5-FU on primary tumor weights of human GC orthotopically implanted into nude mice. Athymic nude mice bearing GTL-16 implanted orthotopic xenografts were treated with vehicle (3% DMSO, 10% solutol, 87% PBS), PHA665752, 30 mg/kg/dose, s.c., three times per week, or 5-FU 100 mg/kg 100 µl i.p., once every 14 days for 3 treatments. *p≤0.05: vehicle as compared to 5-FU and vehicle as compared to PHA665752; **p ≤ 0.01: 5-FU as compared to PHA665752 by Mann-Whitney Rank Sum test.

Figure 14.4.4 Metastatic score of nude mice orthotopically implanted with human GC and treated with PHA665752 or 5-FU compared to vehicle-treated mice. A score of I indicates minimal spread beyond the primary mass while a score of IV indicates widespread peritoneal metastasis. Basic Protocol 2 provides more detail on metastatic scoring.

inhibited the growth and metastasis of GTL16 orthotopic xenografts as compared to vehicle, 5-FU treatment appears to be significantly more effective than PHA665752 at the doses administered. Orthotopic Mouse Models of Gastric Carcinoma

Time Considerations Surgery time will vary, but initially it will likely take ∼30 min per mouse. This time will

decrease with experience. Surgery will only take ∼15 min per mouse if another person is able to assist with injection of the anesthetic agents, suture the abdominal wall, apply the skin wound clips, and mark the mouse. With assistance, a study requiring 60 to 100 mice can be accomplished over a 2-day period. Sham-treated mice should receive the surgical procedure without tissue implantation at the

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same time as the tumor-implanted mice, so that the weights and other characteristics of these mice may be compared to the tumor-bearing animals at the end of the study. It is recommended that the surgical implantations for a study be performed on a Monday and Tuesday so that the following Monday (7 days following surgical implantation), the mice can be randomized into study groups, and treatment regimens initiated that day or the next day. For a large study, a staggered start may be necessary where surgical implantation occurs a week apart. If a staggered start is necessary, the number of mice should be similar so that each treatment group is equally affected. In other words, for a group of 10 mice in each group, 5 mice from each treatment group would be implanted and subsequently begin treatment a week apart throughout the study. At the end of the study, the data may then be compared based on total number of days in life. A survival study may be conducted for more than 120 days and will require dosing and monitoring for the full duration of the study. Otherwise, the study should conclude when vehicletreated mice become moribund or die, usually about 50 days after surgical implantation.

Literature Cited Ajani, J.A., Winter, K., Okawara, G.S., Donohue, J.H., Pisters, P.W., Crane, C.H., Greskovich, J.F., Anne, P.R., Bradley, J.D., Willett, C., and Rich, T.A. 2006. Phase II trial of preoperative chemoradiation in patients with localized gastric adenocarcinoma (RTOG 9904): Quality of combined modality therapy and pathologic response. J. Clin. Oncol. 24:3953-3958. Amemiya, H., Kono, K., Itakura, J., Tang, R.F., Takahashi, A., An, F.Q., Kamei, S., Iizuka, H., Fujii, H., and Matsumoto, Y. 2002. c-Met expression in gastric cancer with liver metastasis. Oncology 63:286-296. American Cancer Society. 2006. Cancer Facts and Figures. American Cancer Society, Atlanta, Georgia. Christensen, J.G., Schreck, R., Burrows, J., Kuruganti, P., Chan, E., Le, P., Chen, J., Wang, X., Ruslim, L., Blake, R., Lipson, K.E., Ramphal, J., Do, S., Cui, J.J., Cherrington, J.M., and Mendel, B. 2003. A selective small molecule inhibitor of c-Met kinase inhibits c-Met-dependent phenotypes in vitro and exhibits cytoreductive antitumor activity in vivo. Cancer Res. 63:7345-7355. Donovan, J.D. and Brown, P. 2006. Euthanasia. Curr. Protoc. Immunol. 73:1.8.1-1.8.4. Gut, M.O., Parkkila, S., Vernerova, Z., Rohde, E., Zavada, J., Hocker, M., Pastorek, J., Karttunen, T., Gibadulinova, A., Zavadova, Z., Knobeloch, K.P., Wiedenmann, B., Svoboda, J., Horak, I.,

and Pastorekova, S. 2002. Gastric hyperplasia in mice with targeted disruption of the carbonic anhydrase gene Car9. Gastroenterol. 123:18891903. Hahm, K.B., Song, Y.J., Oh, T.Y., Lee, J.S., Surh, Y.J., Kim, Y.B., Yoo, B.M., Kim, J.H., Han, S.U., Nahm, K.T., Kim, M.W., Kim, D.Y., and Cho, S.W. 2003. Chemoprevention of Helicobacter pylori-associated gastric carcinogenesis in a mouse model: Is it possible? J. Biochem. Mol. Biol. 36:82-94. Hahn, S.A., Seymour, A.B., Hoque, A.T.M.S., Schutte, M., da Costa, L.T., Redson, M.S., Caldas, C., Weinstein, C.L., Fischer, A., Yeo, C.J., Hruban, R.H., and Kern, S.E. 1995. Allelotype of pancreatic adenocarcinoma using xenograft enrichment. Cancer Res. 55:46704675. Heideman, D.A., Snijders, P.J., Bloemena, E., Meijer, C.J., Offerhaus, G.J., Meuwissen, S.G., Gerritsen, W.R., and Craanen, M.E. 2001. Absence of tpr-met and expression of c-met in human gastric mucosa and carcinoma. J. Pathol. 194:428-435. Illert, B., Otto, C., Braendlein, S., Thiede, A., and Timmermann, W. 2003. Optimization of a metastasizing human gastric cancer model in nude mice. Microsurgery 23:508-512. Jemal, A., Tiwari, R.C., Murray, T., Ghafoor, A., Samuels, A., Ward, E., Feuer, E.J., Thun, M.J., and the American Cancer Society. 2004. Cancer statistics, 2004. CA Cancer J. Clin. 54:8-29. Lin, W., Kao, H.W., Robinson, D., Kung, H.J., Wu, C.W., and Chen, H.C. 2000. Tyrosine kinases and gastric cancer. Oncogene 19:5680-5689. Lynch, H.T., Grady, W., Suriano, G., and Huntsman, D. 2005. Gastric cancer: New genetic developments. J. Surg. Oncol. 90:114-133. Ma, P.C., Schaefer, E., Christensen, J.G., and Salgia, R. 2005. A selective small molecule c-MET Inhibitor, PHA665752, cooperates with rapamycin. Clin. Cancer Res. 11:2312-2319. Macdonald, J.S., Smalley, S.R., Benedetti, J., Hundahl, S.A., Estes, N.C., Stemmermann, G.N., Haller, D.G., Ajani, J.A., Gunderson, L.L., Jessup, J.M., and Martenson, J.A. 2001. Chemoradiotherapy after surgery compared with surgery alone for adenocarcinoma of the stomach or gastroesophageal junction. N. Engl. J. Med. 345:725-730. Macdonald, J.S. 2004. Clinical overview: Adjuvant therapy of gastrointestinal cancer. Cancer Chemother. Pharmacol. 1:S4-S11. Morotti, A., Mila, S., Accornero, P., Tagliabue, E., and Ponzetto, C. 2002. K252a inhibits the oncogenic properties of Met, the HGF receptor. Oncogene 21:4885-4893. Nockel, J., van den Engel, N.K., Winter, H., Hatz, R.A., Zimmermann, W., and Kammerer, R. 2006. Characterization of gastric adenocarcinoma cell lines established from CEA424/SV40 T antigen-transgenic mice with or without a human CEA transgene. BMC Cancer 6:57.

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Peruzzi, B. and Bottaro, D.P. 2006. Targeting the c-Met signaling pathway in cancer. Clin. Cancer Res. 12:3657-3660. Pritchard, D.M. and Przemeck, S.M. 2004. Review article: How useful are the rodent animal models of gastric adenocarcinoma? Aliment Pharmacol. Ther. 19:841-859. Robakiewicz, P. and Ryder, E.F. 2000. Statistics: Detecting differences among groups. Curr. Protoc. Protein Sci. 21:A.3G.1-A.3G.22. Tabernero, J., Macarulla, T., Ramos, F.J., and Baselga, J. 2005. Novel targeted therapies in the treatment of gastric and esophageal cancer. Ann. Oncology 16:1740-1748. Tatematsu, M., Nozaki, K., and Tsukamoto, T. 2003. Helicobacter pylori infection and gastric carcinogenesis in animal models. Gastric Cancer 6:1-7. Teicher, B.A., Menon, K., Alvarez, E., Liu, P., Shih, C., and Faul, M.M. 2001. Antiangiogenic and antitumor effects of a protein kinase C beta inhibitor in human hepatocellular and gastric cancer xenografts. In Vivo 15:185-193. Ushijima, T. and Sasako, M. 2004. Focus on gastric cancer. Cancer Cell 5:121-125. Van Cutsem, E., Moiseyenko, V.M., Tjulandin, S., Majlis, A., Constenla, M., Boni, C., Rodrigues, A., Fodor, M., Chao, Y., Voznyi, E., Risse, M.L.,

Ajani, J.A., and the V325 Study Group. 2006. Phase III study of docetaxel and cisplatin plus fluorouracil compared with cisplatin and fluorouracil as first-line therapy for advanced gastric cancer: A report of the V325 Study Group. J. Clin. Oncol. 24:4991-4997. Vezeridis, M.P., Doremus, C.M., Tibbetts, L.M., Tzanakakis, G., and Jackson, B.T. 1989. Invasion and metastasis following orthotopic transplantation of human pancreatic cancer in the nude mouse. J. Surg. Oncol. 40:261-265. Wang, J.Y., Hsieh, J.S., Chen, C.C., Tzou, W.S., Cheng, T.L., Chen, F.M., Huang, T.J., Huang, Y.S., Huang, S.Y., Yang, T., and Lin, S.R. 2004. Alterations of APC, c-met, and p53 genes in tumor tissue and serum of patients with gastric cancers. J. Surg. Res. 120:242-248. Yamaguchi, K., Ura, H., Yasoshima, T., Shishido, T., Denno, R., and Hirata, K. 2001. Liver metastatic model for human gastric cancer established by orthotopic tumor cell implantation. World J. Surg. 25:131-137.

Contributed by Susan Jones-Bolin and Bruce Ruggeri Cephalon, Inc. West Chester, Pennsylvania

Orthotopic Mouse Models of Gastric Carcinoma

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Metastatic Model of Colon Carcinoma in Mice: Utility in the Study of Tumor Growth and Progression

UNIT 14.5

Susan Jones-Bolin1 and Bruce Ruggeri1 1

Cephalon, Inc., West Chester, Pennsylvania

ABSTRACT Colorectal cancer is the second leading cause of cancer deaths in the United States with an estimated 150,000 diagnosed cases and over 56,000 fatalities annually (Jemal et al., 2006). Approximately one-third to one-half of cases are localized to the colon and rectum and have a favorable prognosis, while one-third to one-half present with regional lymph node metastases at diagnosis and generally are refractory to various chemotherapeutic regimens. Treatment options (surgery, radiation, and chemotherapy) are limited and the disease carries a grave prognosis for many patients. An orthotopic model of colon carcinoma in mice provides a way to evaluate the pathogenesis of tumor growth and metastasis as an aid in developing effective therapies and to better understand the underlying biology of colon tumor growth and metastasis. The protocol described in this unit details the development and characterization of an orthotopic model of murine colon carcinoma in BALB/c mice with diffuse lymphatic and hepatic metastatic spread, closely mimicking the course of the human disease. Curr. Protoc. Pharmacol. 38:14.5.1C 2007 by John Wiley & Sons, Inc. 14.5.13.
Keywords: colon carcinoma r athymic nude r BALB/c mice r orthotopic r metastasis Transgenic and knockout mice, carcinogen-induced tumors, subcutaneous xenografts, and orthotopic animal models have all been used to evaluate oncologic therapeutic agents for the treatment of colon carcinoma and to define the pathophysiology and molecular pathology of the disease (Kobaek-Larsen et al., 2000; Corpet and Pierre, 2005). Transgenic and knockout mice models targeting genes such as APCmin , Cdx2, Smad3, IL-2, IL-10, and Muc2, closely mimic the human course of the disease (Kobaek-Larsen et al., 2000; Rogers and Fox, 2004). The combination of microbial-induced inflammation and targeted mutations of genes involved in immune signaling such as Smad3, IL-2, IL-10, and Muc2, induce the development of inflammatory bowel disease (IBD)-like disorders and lower bowel tumors (Rogers and Fox, 2004). Transgenic models (germ-line and conditional-regulated) have contributed greatly to an understanding of the complex biological processes linked to dysregulation of specific molecular or genetic targets (Becher and Holland, 2006; Singh and Johnson, 2006). Although histologically and genetically similar to human cancer, these models are often heterogeneous with respect to tumor frequency and latency. Additionally, these models are expensive and a great deal of effort is required to develop, establish, and maintain these genetically engineered animals. Subcutaneous and orthotopic models in syngeneic and athymic nude mice have proven to be both useful and practical in the study of colon carcinoma growth and metastatic progression in vivo and as aids in drug discovery. The subcutaneous xenograft models are particularly useful in early screening of new chemical entities (NCEs) because of their reproducibility, cost effectiveness, and simplicity (Ruggeri et al., 2003; Yokoi et al., 2005; Jones-Bolin et al., 2006). However, metastatic rates from subcutaneous or intramuscular tumor xenografts in murine hosts are low to nonexistent. Previous reports revealed that the implantation of colon carcinoma cells or tissue explants, including

BASIC PROTOCOL

Current Protocols in Pharmacology 14.5.1-14.5.13, September 2007 Published online September 2007 in Wiley Interscience (www.interscience.wiley.com). DOI: 10.1002/0471141755.ph1405s38 C 2007 John Wiley & Sons, Inc. Copyright


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murine and human colon carcinoma cells, orthotopically in the spleen or cecum of mice yields much higher metastatic rates and a phenotype more closely resembling the human clinical course (Kubota, 1994; Morikawa et al., 1988; Yorozuya et al., 2005). Detailed in this unit is the development and characterization of a mouse orthotopic model of colon carcinoma metastases that presents with peritoneal, lymphatic, and hepatic metastases of colonic origin. The CT-26 cell line used in the protocol is an undifferentiated murine colorectal adenocarcinoma induced in a BALB/c mouse by chemical carcinogens (Anzai et al., 1992). The metastasis model described in the Basic Protocol is a modification of one described previously (Schackert and Fidler, 1989; Wilmanns et al., 1992; Bruns et al., 2000; Yokoi et al., 2005). This model enables evaluation of: 1. Primary colon carcinoma tumor biology and pathogenesis. 2. Local and metastatic tumor spread and colon carcinoma progression. 3. Effects of therapeutic interventions on primary and metastatic colon tumor burden. Results of a pharmacological study using this model are presented (see Anticipated Results) to demonstrate how it may be used to facilitate the discovery and characterization of small molecule kinase inhibitors. The study describes the use of a pan-vascular endothelial growth factor receptor (VEGF-R) kinase inhibitor, CEP-7055 (Ruggeri et al., 2003; Jones-Bolin et al., 2006), administered alone or in combination with irinotecan or oxaliplatin, standard therapies for the treatment of primary colon carcinoma growth and metastases. For the past 30 years, the major therapeutic option for patients with colorectal carcinoma has been 5-fluorouracil (5-FU) alone or in combination with leucovorin (LV). The addition of irinotecan or oxaliplatin to the 5-FU/LV regimen (IFL or FOLFOX, respectively) has had a significant impact on response rates and on progression-free survival in clinical trials (Guichard et al., 2001; Raymond et al., 2002; Andre et al., 2004; Braun et al., 2004). This protocol provides detailed information on how to generate cells for implantation into the spleen or cecal wall of BALB/c mice. The surgical procedures associated with this protocol require some practice to master, particularly with regard to abdominal incision and exteriorization of the spleen or cecum, injection of cells into these organs, and closure of the abdominal wall and skin layers to prevent infection. Small-animal surgical experience and familiarity with aseptic surgical techniques and anesthesia are needed for successful orthotopic implantation into mice. Once the mice have been injected, studies can be initiated to evaluate NCEs or treatments of interest for colon carcinoma. The complexity, time considerations and laborintensiveness of establishing this orthotopic colon carcinoma model limits its utility as a routine screen for NCEs in oncology drug discovery. Rather, the clinicopathological similarities of this model to the metastatic progression of human colon carcinoma make it particularly useful for characterizing the efficacy profile of advanced lead compounds alone or in combination with conventional therapies used for treating colon cancer. This model has been used to evaluate standard-of-care therapies such as irinotecan and oxaliplatin and experimental VEGF-R kinase inhibitors (Yokoi et al., 2005; Cussack et al., 2006). It has also been employed to study colon tumor biology, including tumor growth, localized tumor spread, and metastases. Metastatic Model of Colon Carcinoma in Mice

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NOTE: All protocols using live animals must first be reviewed and approved by an Institutional Animal Care and Use Committee (IACUC) and must follow officially approved procedures for the care and use of laboratory animals. Aseptic surgical techniques must be employed for all rodent surgical preparations. Current Protocols in Pharmacology

Materials CT-26 murine colon carcinoma cell line obtained under a Materials Transfer Agreement (MTA) from Dr. James L. Abbruzzese, University of Texas (M. D. Anderson Cancer Center) or human colon carcinoma cell lines (such as HT-29 or HCT-116) from ATCC Media for cell growth. This includes RPMI 1640 for the CT-26 cell line, and MEM or McCoys 5a for the HT-29 or HC-116 cell lines (Gibco BRL) 10% fetal bovine serum (FBS; Hyclone or ATCC) Penicillin/streptomycin Trypsin 1× PBS 6- to 8-week-old female (20 to 25 g) BALB/c mice (Charles River Labs) for the CT-26 murine colon tumor cell line 6- to 8-week-old female (20 to 25 g) athymic nude mice (Charles River Labs) for the human colon carcinoma cell lines (HT-29 or HCT-116) Ketamine (100 mg/ml, 10 ml vial; Hanna Pharmaceutical Supply) Xylazine (20 mg/ml, 20 ml vial; Webster Veterinary Supply) Isoflurane Surgical scrub (e.g., Nolvassan or betadine) Saline: 0.9% (w/v) NaCl (sterile; VWR) 5% CO2 incubator 50-ml conical-shaped centrifuge tube Circulating water heating pad. (e.g., Gaymar T-pump or similar product). Sterile towels Nose cone apparatus Petri dishes, 70-mm or larger diameter Sterile gauze Cordless rechargeable electric clippers (e.g., Wahl, Webster Veterinary Supply) Sterile alcohol prep pads, medium (Nice Pak or Curity; VWR or Fisher Scientific) Autoclave for sterilizing instruments, gauze sponges, wound clips Two sets of surgical instruments including (VWR or Fine Science Tools): 4.5-inch Iris scissors, straight, sharp ends, sterile 4.5-inch tissue forceps, 1 × 2 teeth 4.75-inch Adson forceps or delicate dressing forceps, serrated Suture: Vicryl 5/0 or 6/0 or PDS II 5/0, for closing peritoneal layer; one pack per five to ten mice (Webster Veterinary Supply) 9-mm wound clips, with applier and remover (VWR) Disposable scalpels 5.5-inch Mayo-Hegar or similar needle holder Sterile gauze sponges (2 × 2 in.) 27-G needle Ear punch or tattooing device to identify individual mice Additional reagents and equipment for using a hemacytometer for cell counting (Phelan, 2006) and euthanasia by CO2 asphyxiation (Donovan and Brown, 2006) NOTE: Ketamine is a CIII controlled substance, so a DEA license is needed to order it.

Prepare for orthotopic implantation of colon carcinoma cells 1. Grow murine CT-26 colon carcinoma tumor cells to subconfluency in 75-cm2 flasks containing RPMI 1640 supplemented with 10% fetal bovine serum and 1% penicillin/streptomycin in a 5% CO2 incubator. The CT-26 model is presented here because it uses immune competent BALB/c mice and the duration of the studies is shorter. Also, it is possible to determine the efficacy of the therapy in a shorter amount of time.

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Several tissue culture flasks are needed to generate enough cells to inject mice for subcutaneous tumor formation.

2. Pipet 1 to 2 ml of trypsin into the flask and allow the cells to make contact with the trypsin for 2 to 3 min. Following contact, pipet 3 to 5 ml of cell growth medium into the flask and collect the cells into a 50-ml conical-shaped centrifuge tube. Centrifuge the cells 6 min at 100 to 130 × g, 4◦ C to form a pellet. 3. Decant the supernatant and resuspend the pellet by pipetting up and down in 1 ml sterile 1× PBS. Use a hemacytometer or a commercial cell counter to determine cells per milliliter (Phelan, 2006). 4. Resuspend cells in sterile 1× PBS to yield the desired concentration (1 × 104 per 50 µl or 2 × 105 per ml for CT-26 cells; 1 × 106 per 50 µl or 2 × 107 per ml for HT-29 or HCT-116 cells). Prior to use, have the cells tested for mouse antibody production (MAP) and mycoplasma by a commercial laboratory (Bio Reliance). Standard IACUC instructions dictate that the cells must be free of mouse pathogens and, if cells test positive, they should not be used in the immune-compromised mouse because of exposure to a pathogen that could cause morbidity/mortality. New cells would have to be generated. Mycoplasma contamination, which can be identified using a PCR kit, will prevent the cells from growing well in culture. If contamination is found, new cells would have to be generated. MAP testing is always performed once before animal studies are initiated, with some IACUCs requiring retesting later on. Mycoplasma may be tested as needed, such as whenever cultured cells appear contaminated or are not thriving in culture.

5. Preheat the water heating pad to the desired temperature ∼30 min prior to initiating the surgical procedure. The water heating pad should be preheated, generally at the manufacturer’s recommended setting of 107◦ F before beginning surgery. Use distilled water to fill pump for heating pads.

6. Create a sterile field using a sterile towel(s) in a laminar flow/tissue culture hood on top of the circulating water heating pad. 7. Anesthetize female mice by i.m. injection of a mixture of ketamine hydrochloride and xylazine (100 µl of a mixture of 55 mg/kg ketamine and 10 mg/kg xylazine in sterile saline) and maintain anesthesia with isoflurane using a nose cone apparatus (1% to 2% isoflurane in 0.2 liters/min oxygen). 8. Once a surgical plane of anesthesia is attained, as verified by lack of pedal or corneal reflex, place the mouse on a sterile gauze-lined petri dish with the back of the animal against the dish surface and the tail pointing toward the investigator. Use 1 × 2 teeth tissue forceps to pinch the paw or toe of the mouse to check pedal reflex. Use a moistened Q-tip or gauze sponge to gently brush the eye area of the mouse to check for corneal (blink) reflex. Preferably, apply a small amount of protective eye ointment (Ophthalmic Base Ointment, Webster Veterinary Supply) to the eyes of the mouse at this time and this will also serve to check for corneal (blink) reflex. There should be no pedal or blink reflex response if the animal is under an adequate plane of anesthesia. If a response is seen, allow more time for anesthesia to take effect and check reflexes again until an adequate plane of anesthesia is reached before making the incision. The mouse should be monitored frequently throughout the procedure to ensure it is breathing regularly, kept warm, and maintained under an adequate plane of anesthesia. Metastatic Model of Colon Carcinoma in Mice

9. With haired mice, shave the upper left quadrant of the animal’s abdominal area using an electric clipper, scrub the shaved area with a surgical scrub such as Nolvassan or

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Figure 14.5.1 Site of incision for colon carcinoma model. (B) Position of retracted spleen and ventral aspect of cecum. The cecum will be under some intestines.

betadine, and then swab the area with a sterile surgical alcohol prep pad, wiping the area just to the left of midline and under the rib cage (Fig 14.5.1A). The dark horizontal-lying spleen should be visible under the skin of the mouse.

10. Make a small, 0.5-cm vertical incision in the skin over the left lateral abdominal area, just to the left (towards midline) of the spleen, using sterile operating scissors and tissue forceps. Care must be taken to avoid lacerating the area near the rib cage to avoid damaging the lungs and diaphragm. The inner peritoneal lining should now be visible.

11. Cut the inner peritoneal lining in a similar manner to the skin, using forceps to pull the layer upward before dissecting with the scissors to prevent damage to underlying and surrounding organs and tissues. 12. Once the abdominal cavity is exposed, use sterile dressing forceps to gently exteriorize the spleen and pancreas–or the cecum which lies caudal (toward the tail) to the spleen–to the incision area using gentle traction (Fig. 14.5.1B). Use sterile saline-soaked gauze sponges to moisten the exteriorized spleen or cecum to prevent drying of the tissue. If performing a study to test a compound of interest or chemotherapeutic agent, mice that would be for a sham group would not have any injection performed but would proceed to step 15. Mice for treated groups, including vehicle control, would receive injection of cells as described in step 13 and the following steps.

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13. Once a few millimeters of spleen or cecum have been exposed, gently hold the organs using sterile dressing forceps. Inject 50 µl of cells (prepared in step 4) into the spleen parenchyma or, alternatively, into the submuscosal area of the cecum, using a tuberculin syringe with a 27-G needle. A successful subcapsular intrapancreatic injection of tumor cells into the spleen or cecum is identified by the appearance of a small discolored area at the site of the injection without intraperitoneal leakage. To prevent leakage into the peritoneal cavity, a cotton swab may be held for 1 min over the site of injection.

14. Apply a small amount (0.5 to 1.0 ml) of sterile saline to the cell-injected spleen or cecum, and then gently return it to the abdominal cavity using sterile tissue forceps to raise the abdominal wall and gently guide the tissue back into place. Moisten the skin and abdominal layers with saline to ease surgical closure. 15. Close the abdominal incision with 6-0 Vicryl using surgical knots (usually 2 to 4 sutures are required to close completely, depending on the length of the incision). One suture packet should provide enough material for 5 to 10 mice, depending on the length of the incisions.

16. Close the skin with wound clips. Typically, two or three clips are required for this purpose.

17. Identify each mouse using a colored Sharpie marker on the tail, an ear punch, or a tattoo. Mice are typically marked for identification immediately following surgery.

18. Place mice on the heated circulating water pad until they have recovered from anesthesia and are able to move around. Alternatively, allow the mice to recover in their cage if it has been placed on top of the heated circulating water pad.

Post-surgical care and in vivo selection of metastatic colon carcinoma phenotype 19. Observe mice daily for signs of infection, which may include redness, swelling, or discharge along the incision line and following removal of the skin staples. Weigh animals twice weekly, with any one of the following indicating the subject should be sacrificed: (1) loss of >15% body weight), (2) lethargy, (3) hunching for >1 or 2 days as a result of surgery or treatment with experimental or therapeutic compounds, (4) ascites or change in body weight in which there is a gain of 2 or more grams over a few days, (5) debilitation that impairs access to, or consumption of, food and water, (6) vocalization indicative of severe pain or distress, (7) dehydration, and (8) open, bleeding, or infected wound or tumor. 20. Two days following surgical injection of CT-26 colon carcinoma cells, or 7 days after the injection of HT-29 or HCT-116 cells, randomize mice into treatment groups. There are usually 8 to 10 animals per treatment group; however, this number will vary based on experimental design.

21. Remove the wound clips 7 to 10 days after surgery if the study lasts more than 21 days.

Metastatic Model of Colon Carcinoma in Mice

In the case of the CT-26 model, the wound clips are usually left in place since the study period is 15% body weight, (2) lethargy, (3) hunching for more than 1 or 2 days as a result of surgery or treatment regimens, (4) ascites in which there is a weight gain of 2 g or more over several days, (5) debilitation that impairs access to, or consumption of, food

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and water, (6) vocalization indicative of severe pain or distress, (7) dehydration, or (8) open, bleeding or infected wound or tumor.

Troubleshooting Table 14.5.1 describes some problems that may be encountered in generating the described orthotopic model of murine colon carcinoma in BALB/c mice, along with some recommendations on how to overcome or avoid these procedural difficulties.

Anticipated Results Untreated mice bearing orthotopically implanted colon carcinoma tissue will become moribund ∼20 days after surgery for the CT-26 injected mice and ∼5 weeks after surgery for the HT-29 or HCT-116 injected animals. The resulting orthotopic colon carcinoma model is a reproducible metastatic phenotype analogous to the human clinical course, with peritoneal, hepatic, and mesenteric lymph node metastases of colonic carcinoma origin. Ascites development also occurs in some subjects. Greater than 90% of animals develop primary tumors when the model is performed properly, and the metastatic phenotype has been reproducible from study to study, dis-

playing similar peritoneal spread with large primary tumor infiltrating into surrounding tissue along with distant spread to mesenteric lymph nodes and liver. This model has proven to be useful in the evaluation of primary colon tumor biology, in understanding the mechanisms of disease pathogenesis, local and metastatic tumor spread, and for assessment of therapeutic approaches to inhibit tumor growth and improve survival. Described below is an example study evaluating the effectiveness of the pan-VEGF-R kinase inhibitor, CEP-7055, alone and in combination with irinotecan or oxaliplatin, in inhibiting primary colon carcinoma growth and metastases. Example study The effects of oral administration of CEP7055, alone or in combination with irinotecan or oxaliplatin on primary tumor growth and metastatic profile in a metastasis model of colon carcinoma in BALB/c mice: Based on the highly vascularized nature of colon tumors, novel therapeutic strategies are being considered that employ anti-angiogenic agents in combination with traditional cytotoxic agents. The use of bevacizumab in patients with

Table 14.5.1 Troubleshooting the Orthotopic Model of Murine Colon Carcinoma in BALB/c Mice

Problem:

Recommended action

Tumor does not develop

Make certain that cell viability is not an issuea and also ensure that cells are not confluent as this may cause poor growth rate in vivo. May need to increase the number of cells injected.b

Metastasis does not occur

Make certain that cell viability is not an issuea and also ensure that cells are not confluent as this may cause poor growth rate in vivo. May need to increase the number of cells injected.b

Mice develop infection

Make certain instruments are sterilized before use and change instruments after incising the skin to avoid contamination. Also, change instruments between each cage of mice (5 mice per cage is average) and be sure to use aseptic surgical techniques.

MAP testing of cells is positive

Generate new cells

Mice die during surgery

Check the level of isoflurane and decrease the flow rate or amount as needed. Monitor the respirations of the mouse every 1-2 min during the surgery. If breathing becomes erratic, remove the isoflurane cone for a minute or two until breathing is more rhythmic.

a A number of commercial assays are available for determining both the viability and proliferation rate of a given cell

Metastatic Model of Colon Carcinoma in Mice

population, such as the intake of [3 H]thymidine for cell proliferation or trypan blue exclusion assay for determining cell viability, as well as others. Suppliers such as Promega provide kits for this purpose and anyone performing cell culture work should have an assay to measure this in place as a standard practice. b If the primary tumor is slower to develop and metastases are not developing within 10 to 14 days of injection, then it may be worthwhile injecting more cells to improve the take rate or shorten the time to disease progression. The authors typically adjust the cell number by increments of 500,000 (0.5 × 106 ) as a starting point.

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colorectal cancer in combination with irinotecan, 5-fluorouracil and leucovorin (IFL) resulted in clinical benefit by increasing survival, objective response, and time-to-progression of disease (Caprioni and Fornarini, 2007). This increase in the survival rate, as well as the improvement of other biomarkers indicating a clinical benefit, provided clinical proof-ofconcept for the use of anti-angiogenesis inhibitors targeting the VEGF-VEGF-R axis in combination with standard chemotherapy as a treatment for this condition (Bruns et al., 2000; Ruggeri et al., 2003; Jones-Bolin et al., 2006). This study was conducted to test whether a pan VEGF-R kinase inhibitor, CEP-7055 (Ruggeri et al., 2003; Jones-Bolin et al., 2006), when administered alone or in combination with irinotecan or oxaliplatin, could inhibit primary tumor growth and metastatic spread of the injected CT-26 murine colon carcinoma tumor cells in BALB/c mice. CEP-7055, is a potent pan-VEGF-R kinase inhibitor that has completed Phase I clinical trials in cancer patients (CEP-7055 can be obtained by an investigator only through a Materials Transfer Agreement.). Briefly, murine CT-26 colon carcinoma cells were injected into the splenic parenchyma of BALB/c mice as described

above in the Basic Protocol. Two days after surgery, the mice were randomized into the following treatment groups (n = 10): 1% aqueous acetic acid vehicle; CEP-7055 monotherapy (23.8 mg/kg/dose, p.o., BID); irinotecan (20 mg/kg/dose, i.p., once daily for 5 days in 0.9% sterile saline); CEP-7055 in combination with irinotecan; oxaliplatin (10 mg/kg/dose i.v. in 5% dextrose); and CEP-7055 in combination with oxaliplatin. For in vivo studies, CEP-7055 was formulated in 1% aqueous acetic acid (Ruggeri et al., 2003), whereas irinotecan (Pfizer Oncology) was resuspended in 0.9% sterile saline and oxaliplatin (SanofiAventis) was resuspended in 5% dextrose immediately prior to administration (Guichard et al., 2001). The dosing regimens for irinotecan and oxaliplatin, conventional therapies for colorectal cancer, have been described previously (Jansen et al., 1997; Guichard et al., 2001; Raymond et al., 2002; Miknyoczki et al., 2003). The effects of CEP-7055 administration, alone and in combination with irinotecan or oxaliplatin, on primary splenic tumor and liver metastatic burden were evaluated using the murine CT-26 metastatic colon carcinoma model in BALB/c mice (Figs 14.5.2

Figure 14.5.2 Effects of CEP-7055 alone or in combination with irinotecan or oxaliplatin on primary splenic tumor weights of CT-26 murine colon carcinoma tumor-bearing BALB/c mice following 18 days of treatments. Statistical analyses were performed using the Mann-Whitney Rank Sum test. *p < 0.05, **p < 0.001 relative to vehicle-treated controls. (A) p < 0.01 for the combination of CEP-7055 (23.8 mg/kg/dose p.o. bid) and irinotecan (20 mg/kg/dose, i.p., qd × 5 days) treatment relative to irinotecan monotherapy; (B) No statistically significant difference between the combination of CEP-7055 (23.8 mg/kg/dose p.o. bid) and oxaliplatin (10 mg/kg/dose i.v.) treatment relative to oxaliplatin monotherapy.

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Figure 14.5.3 Effects of CEP-7055 alone or in combination with irinotecan or oxaliplatin on liver metastatic tumor weights of CT-26 murine colon carcinoma tumor-bearing BALB/c mice following 18 days of treatments. Statistical analyses of metastatic burden were performed using the MannWhitney Rank Sum test. *p < 0.05, **p < 0.001 relative to vehicle-treated controls. (A) p < 0.01 for the combination of CEP-7055 (23.8 mg/kg/dose p.o. bid) and irinotecan (20 mg/kg/dose, i.p., qd × 5 days) treatment relative to irinotecan monotherapy; (B) p = 0.08 for the combination of CEP-7055 (23.8 mg/kg/dose p.o. bid) and oxaliplatin (10 mg/kg/dose, i.v.) treatment relative to oxaliplatin monotherapy.

and 14.5.3). Although administration of CEP7055 alone to CT-26 colon tumor-bearing mice had no effect on primary colon carcinoma mass relative to control mice, irinotecan monotherapy (20 mg/kg/dose i.p. × 5 days) and oxaliplatin monotherapy (10 mg/kg/dose i.v.) resulted in 71% (p < 0.001) and 53% (p < 0.05) reductions in primary tumor mass, respectively, relative to vehicle controls. The combination of irinotecan and CEP-7055 treatment resulted in an 86% reduction in primary tumor mass relative to vehicle control mice (p < 0.001) and a 52% reduction in primary tumor mass relative to that achieved with irinotecan monotherapy (p < 0.01; Fig 14.5.2). In contrast, although the combination of oxaliplatin and CEP-7055 resulted in a significant reduction in primary tumor mass relative to vehicle controls (p < 0.05), there was no significant additional benefit of CEP-7055 and oxaliplatin administered in combination relative to oxaliplatin monotherapy. A slight reduction in antitumor effect relative to that achieved with oxaliplatin monotherapy was observed (Fig. 14.5.2).

Irinotecan monotherapy resulted in a 44% reduction in liver metastatic mass relative to vehicle controls (p < 0.001), while neither oxaliplatin monotherapy nor CEP-7055 monotherapy had significant effects on liver metastatic burden compared to vehicle-treated mice (Fig 14.5.3). The combination of CEP7055 and irinotecan resulted in a 58% reduction in liver metastatic burden relative to vehicle controls (p < 0.001), and a 52% reduction in liver metastatic burden relative to that achieved with irinotecan monotherapy (p < 0.01). The administration of CEP-7055 and oxaliplatin resulted in a 43% reduction in liver metastatic burden relative to controls (p < 0.05) and demonstrated a trend for an improvement relative to oxaliplatin monotherapy (p=0.08). Based on the results obtained with this animal model, the results suggest that combination therapies of CEP-7055 and irinotecan, and to a lesser extent oxaliplatin, demonstrate significant reductions in primary colon carcinoma and liver metastatic burden relative to those achieved by monotherapy with these agents. Moreover, the combination

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therapy of oxaliplatin plus CEP-7055 was well tolerated, with no signs of morbidity, and the combination of irinotecan plus CEP7055 resulted in a transient body weight loss which achieved significance (p≤0.01) on day 8 relative to irinotecan monotherapy, although the animals regained weight by day 12 (Fig 14.5.4).

Time Considerations While the amount of time necessary to perform the surgery will vary, initially it will likely take ∼30 min per mouse and this time will decrease with experience. It will only take ∼15 min per mouse if another person assists with injection of the anesthetic agents, suturing of the abdominal wall, applying the skin wound clips, and marking the mice. With assistance, a study requiring 60 to 100 mice can be completed in 2 days. Sham-treated mice should receive the surgical procedure without tissue implantation at the same time as the tumor-implanted mice. This ensures that the body and tissue weights of these mice may be compared to the tumor-bearing animals at the end of the study. [It is recommended that

the surgical implantations be performed on a Monday and Tuesday so that the following Monday [day 3 (CT-26) or day 7 (HT-29 or HCT-116) following surgical implantation], the mice may be randomized into study groups and treatments begun that day or the next day. For a large study, a staggered start may be necessary where surgical implantation occurs a week apart. If a staggered start is necessary, it is recommended that the number of mice be similar so that each treatment group is equally affected. In other words, for a study of 10 mice in each group, 5 mice from each treatment group would be implanted and subsequently begin treatment a week apart throughout the study. At the end of the study, the data may then be compared based on the total number of days in life. A survival study may be in life for more than 40 days, with compound administration and monitoring for that entire period of time. Otherwise, the study should conclude when vehicle-treated mice become moribund or die, usually at about post-surgical day 20 for CT-26 injected mice, and 5 to 6 weeks postsurgery for the HT-29 or HCT-116 injected mice.

Figure 14.5.4 Body weights of CT-26 murine colon carcinoma tumor-bearing BALB/c mice treated with CEP-7055 alone or in combination with irinotecan or oxaliplatin. Error bars represent standard error of the mean. Statistical analyses were performed using the Mann-Whitney Rank Sum test. **p < 0.001 relative to vehicle-treated controls.

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Literature Cited Andre, T., Boni, C., Mounedji-Boudiaf, L., Navarro, M., Tabernero, J., Hickish, T., Topham, C., Zaninelli, M., Clingan, P., Bridgewater, J., Tabah-Fisch, I., and de Gramont, A. 2004. Multicenter International Study of Oxaliplatin/ 5-Fluorouracil/Leucovorin in the Adjuvant Treatment of Colon Cancer (MOSAIC) Investigators. Oxaliplatin, fluorouracil, and leucovorin as adjuvant treatment for colon cancer. N. Engl. J. Med. 350:2343-2351. Anzai, H., Frost, P., and Abbruzzese, J.L. 1992. Synergistic cytotoxicity with 2 -deoxy-5azacytidine and topotecan in vitro and in vivo. Cancer Res. 52:2180-2185. Becher, O.J. and Holland, E.C. 2006. Genetically engineered models have advantages over xenografts for preclinical studies. Cancer Res. 66:3355-3359. Braun, A.H., Achterrath, W., Hansjochen, W., Vanhoefer, U., Harstrick, A., and Preusser, P. 2004. New systemic frontline treatment for metastatic colorectal carcinoma. Cancer 100:1558-1577. Bruns, C.J., Liu, W., Davis, D.W., Shaheen, R.M., McConkey, D.J., Wilson, M.R., Bucana, C.D., Hicklin, D.J., and Ellis, L.M. 2000. Vascular endothelial growth factor is an in vivo survival factor for tumor endothelium in a murine model of colorectal carcinoma liver metastases. Cancer 89:488-490. Caprioni, F. and Fornarini, G. 2007. Bevacizumab in the treatment of metastatic colorectal cancer. Future Oncol. 3:141-148. Corpet, D.E. and Pierre, F. 2005. How good are rodent models of carcinogenesis in predicting efficacy in humans? A systematic review and meta-analysis of colon chemoprevention in rats, mice and men. Eur. J. Cancer 41:1911-1922. Cusack, J.C. Jr., Liu, R., Xia, L., Chao, T.H., Pien, C., Niu, W., Palombella, V.J., Neuteboom, S.T., and Palladino, M.A. 2006. NPI-0052 enhances tumoricidal response to conventional cancer therapy in a colon cancer model. Clin. Cancer Res. 12:6758-6764. Donovan, J. and Brown, P. 2006. Euthanasia. Curr. Protoc. Immunol. 73:1.8.1-1.8.4. Guichard, S., Arnould, S., Hennebelle, I., Bugart, R., and Canal, P. 2001. Combination of oxaliplatin and irinotecan on human colon cancer cell lines: Activity in vitro and in vivo. Anti-Cancer Drugs 12:741-751. Jansen, W.J., Zwart, B., Hulscher, S.T., Giaccone, G., Pinedo, H.M., and Boven, E. 1997. CPT-11 in human colon-cancer cell lines and xenografts: Characterization of cellular sensitivity determinants. Int. J. Cancer 70:335-340.

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Jemal, A., Siegel, R., Ward, E., Murray, T., Xu, J., Smigal, C., and Thun, M.J., 2006. Cancer statistics, 2006. CA Cancer J. Clin. 56:106130. Jones-Bolin, S., Zhao, H., Hunter, K., Klein-Szanto, A., and Ruggeri, B. 2006. The effects of the oral, pan-VEGF-R kinase inhibitor CEP-7055 and

chemotherapy in orthotopic models of glioblastoma and colon carcinoma in mice. Mol. Cancer Ther. 5:1744-1753. Kobaek-Larsen, M., Thorup, I., Diederichsen, A., Fenger, C., and Hoitinga, M.R. 2000. Review of colorectal cancer and its metastases in rodent models: Comparative aspects with those in humans. Comp. Med. 50:16-26. Kubota, T. 1994. Metastatic models of human cancer xenografted in the nude mouse: The importance of orthotopic transplantation. J. Cell. Biochem. 56:4-8. Miknyoczki, S.J., Jones-Bolin, S., Pritchard, S., Hunter, K., Zhao, H., Wan, W., Ator, M., Bihovsky, R., Hudkins, R., Chatterjee, S., Klein-Szanto, A., Dionne, C., and Ruggeri, B. 2003. Chemo-potentiation of temozolomide, irinotecan, and cisplatin activity by CEP-6800a poly (ADP-ribose) polymerase (PARP) inhibitor. Mol. Cancer Therap. 2:371-382. Morikawa, K., Walker, S.M., Jessup, J.M., and Fidler, I.J. 1988. In vivo selection of highly metastatic cells from surgical specimens of different primary human colon carcinomas implanted into nude mice. Cancer Res. 48:19431948. Raymond, E., Faivre, S., Chaney, S., Woynarowski, J., and Cvitkovic, E. 2002. Cellular and molecular pharmacology of oxaliplatin. Mol. Cancer Ther. 1:227-235. Rogers, A.B. and Fox, J.G. 2004. Inflammation and cancer. I. Rodent models of infectious gastrointestinal and liver cancer. Am. J. Physiol. Gastrointest. Liver Physiol. 286:G361G366. Ruggeri, B.A., Chang, H., Hunter, K., Robinson, C., and the Cephalon-Sanofi-Synthelabo Joint Project Development Team. 2003. CEP-7055: A novel, orally active pan inhibitor of vascular endothelial growth factor receptor tyrosine kinases with potent anti-angiogenic activity and anti-tumor efficacy in pre-clinical models. Cancer Res. 63:5978-5991. Schackert, H.K. and Fidler, I.J. 1989. Development of an animal model to study the biology of recurrent colorectal cancer originating from mesenteric lymph system metastases. Int. J. Cancer 44:177-181. Singh, M. and Johnson, L. 2006. Using genetically engineered mouse models of cancer to aid drug development: An industry perspective. Clin. Cancer Res. 12:5312-5328. Wilmanns, C., Fan, D., O’Brian, C.A., Bucana, C.D., and Fidler, I.J. 1992. Orthotopic and extopic organ environments differentially influence the sensitivity of murine colon carcinoma cells to doxorubicin and 5-fluorouracil. Int. J. Cancer 52:98-104. Wong, S.F. 2005. Cetuximab: An epidermal growth factor receptor monoclonal antibody for the treatment of colorectal cancer. Clin. Ther. 27:684-694. Yokoi, K., Thaker, P.H., Yazici, S., Rebhun, R.R., Nam, D.H., He, J., Kim, S.J., Abbruzzese, J.L.,

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Hamilton, S.R., and Fidler, I.J. 2005. Dual inhibition of epidermal growth factor receptor and vascular endothelial growth factor receptor phosphorylation by AEE788 reduces growth and metastasis of human colon carcinoma in an orthotopic nude mouse model. Cancer Res. 65:3716-3725. Yorozuya, K., Kubota, T., Watanabe, M., Hasegawa, H., Ozawa, S., Kitajima, M., Chikahisa, L.M., and Yamada, Y. 2005. TSU-68 (SU6668) inhibits local tumor growth and liver metastasis of human colon cancer xenografts via antiangiogenesis. Oncol. Rep. 14:677-682.

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Models of Melanoma Metastasis: Using a Transient siRNA-Based Protein Inhibition Strategy in Mice to Validate the Functional Relevance of Pharmacological Agents

UNIT 14.6

Arati Sharma1 and Gavin P. Robertson1,2,3 1

The Pennsylvania State University College of Medicine, 500 University Drive, Hershey, Pennsylvania 2 The Foreman Foundation for Melanoma Research, 500 University Drive, Hershey, Pennsylvania 3 PSU Melanoma Therapeutics Program, 500 University Drive, Hershey, Pennsylvania

ABSTRACT While a pharmacological agent may inhibit the activity of a protein in cultured cells by triggering a particular biological process, it may function differently in intact animals. Thus, an assay is needed to rapidly assess whether a drug candidate displays the same mechanism of action in vivo as in vitro. The experimental approach described in this unit utilizes synthetic siRNA in a transient animal assay to define the action of a drug candidate when inhibiting the activity of a particular gene. Commercially available synthetic siRNA is introduced into cancer cells by nucleofection to reduce protein expression. Cells are then introduced into animals and the mechanism responsible for tumor inhibition assessed. The action of a compound identified in vitro is then compared to that noted in vivo following siRNA-mediated inhibition to determine whether it reduces tumor development in the same manner in both systems. Curr. Protoc. Pharmacol. 38:14.6.1C 2007 by John Wiley & Sons, Inc. 14.6.15.
Keywords: melanoma r metastases r siRNA r pharmacological agents r GFP-tagged cells r cancer r protein inhibition

Melanoma is the most deadly form of skin cancer due to its high potential to metastasize to other organs in the body (Sharma et al., 2006). Many genes are deregulated to promote metastasis. While melanoma cells can invade any organ, the lung is a primary site for metastasis (Sharma et al., 2006). To develop new therapies for this condition, assays are needed to rapidly validate the involvement of candidate proteins (e.g., targets) in the metastatic process and to demonstrate that a drug candidate inhibits the targeted gene or associated signaling cascade function in the same manner as when the gene is inhibited using small interfering RNAs (siRNAs). The use of siRNAs has been proven to be an effective targeting approach to inhibit expression (activity) of proteins against which it is designed (Sharma et al., 2006). Typically, this process involves creating cells in which protein expression is stably knocked down using vectors that produce siRNA and subsequently measuring the effect of gene silencing on metastasis development (Hingorani et al., 2003). However, this technique is labor intensive and time consuming. To circumvent these limitations, the experimental approach described in this unit utilizes synthetic siRNA in a transient animal assay to demonstrate the function a pharmacological agent needs to perform when inhibiting the activity of a particular gene (Sharma et al., 2006). siRNA-mediated

Current Protocols in Pharmacology 14.6.1-14.6.15, September 2007 Published online September 2007 in Wiley Interscience (www.interscience.wiley.com). DOI: 10.1002/0471141755.ph1406s38 C 2007 John Wiley & Sons, Inc. Copyright


BASIC PROTOCOL

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Figure 14.6.1 Diagrammatic outline of the transient siRNA-protein knockdown assay and test agent inhibition assay to validate the effectiveness of the test substance.

inhibition of cultured cells is relatively easy to accomplish. Thus, described in this protocol is an in vivo procedure using commercially available synthetic siRNAs to rapidly test whether a pharmacological agent inhibits melanoma lung metastasis in the same manner as when the gene is knocked down using siRNA (Sharma et al., 2006). This procedure saves time and effort compared to vector-based in vivo approaches. Using the procedure outlined in Figure 14.6.1, a compound can be demonstrated as having the same effect as siRNA-mediated inhibition of expression of a specific gene. This assay can also be used to identify the protein target that should be manipulated to most effectively inhibit the pathway. NOTE: All animal experimentation and protocols need approval by an Institutional Animal Care and Use Committee (IACUC) and must conform to governmental regulations regarding the care and use of laboratory animals.

Materials

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GFP-tagged metastatic human melanoma cell lines: 1205 Lu (can be obtained from Dr. Meenhard Herlyn, Wistar Institute) C8161.Cl9 (can be obtained from Dr. Danny R. Welch, University of Alabama) UACC 903 M (can be obtained from Dr. Gavin Robertson, Penn State University College of Medicine) DMEM (Invitrogen) Fetal bovine serum (Hyclone) Stealth siRNA for targeted genes and controls (Invitrogen); scrambled siRNA controls as well as siRNA to a non-involved gene should be included (see recipe) DEPC-treated water (Invitrogen) Fluorescently tagged siRNA (Alexa Fluor 546 labeled nonsilencing duplex siRNA; Qiagen, no. P1027098) 4 ,6-diamidino-2-phenylindole (DAPI) Phosphate-buffered saline (PBS) 4% paraformaldehyde Mounting medium with DAPI (Vectashield, Vector Laboratories)

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Protein lysis buffer (see recipe) BCA Protein Assay (Pierce) Primary antibodies: Anti–pErk and anti–pMek (Cell Signaling Technologies) Antibodies to B-Raf, C-Raf, Erk 2, Cyclin D1, p27 and α-enolase (Santa Cruz Biotechnology) Horseradish peroxidase–conjugated secondary antibodies (Santa Cruz Biotechnology) Blocking solution: TBS-T (see recipe) containing 5% (w/v) nonfat dry milk TBS-T (see recipe) Trypsin Hanks’ balanced salt solution without calcium, magnesium, and phenol red (Mediatech) Nude mice: Female, age between 3- and 4-weeks-old upon arrival (Harlan) UO126 (see recipe) or BAY 43-9006 (see recipe), a nonspecific Raf kinase inhibitor 4% paraformaldehyde in PBS (see recipe) 0.5 M EDTA (Mediatech) 70% ethanol 0.01 M citrate buffer 3% hydrogen peroxide (H2 O2 ) 1% bovine serum albumin (BSA) Biotinylated anti–rabbit IgG (Vector Labs) Peroxidase-labeled streptavidin (BD Pharmingen) AEC substrate kit (Zymed laboratories) Hematoxylin 37◦ C humidified incubator, 5% CO2 Nucleofector (Amaxa) Nucleofection kits (Amaxa); e.g., Cell Line Nucleofector Kit R (with Solution R) and program K-17 for 1205 Lu, C8161.Cl9 and UACC 903 M cell lines Cell culture dishes 6-well plates Glass coverslips Clear nail polish Cell scraper (Sarstedt, no. 83.1830) 200-µl pipet NuPage gel (Life Technologies) Polyvinylidene difluoride (PVDF) membrane (Pall Corporation) Novex western transfer apparatus (Invitrogen, no. EI0001 or no. EI9051) Enhanced chemiluminescence (ECL) detection system (Amersham Pharmacia Biotech) 120-W bulb Mouse restrainer 1-ml syringes (Becton Dickinson) 27-G, 1/2-in. needles (Becton Dickinson) Alcohol prep pads (isopropyl alcohol, 70% v/v; NovaPlus) Standard dissecting instruments, sterile Microscopes Nikon Eclipse E600 microscope with attached Cool SNAP Digital camera Nikon SMZ 1500 dissecting microscope with a Plan Apo 1.6× objective and fluorescence detection capabilities ImagePro analysis software (Scanalytics) 1/2-ml insulin syringes with 27-G, 1/2-in. needles (Becton Dickinson) Slides (e.g., Fisherbrand Superfrost/plus microscope slides or similar product, Fisher Scientific)

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95◦ C water bath Additional reagents and equipment for quantitation of total protein (APPENDIX 3A), counting cells using a hemacytometer (Phelan, 2006), euthanizing animals using CO2 asphyxiation (Donovan and Brown, 2006), electrophoresis (APPENDIX 3B) and embedding and sectioning tissue (APPENDIX 3D) NOTE: Duplicate experiments consisting of 8 to 10 animals per group should be performed.

Prepare cells 1. Grow cell lines in DMEM supplemented with 10% FBS at 37◦ C in a humidified atmosphere and 5% CO2 . GFP-tagged metastatic human melanoma cell lines 1205 Lu, C8161.Cl9, or UACC 903 M can be used for this protocol. Other metastatic melanoma cell lines could also be used; however, they must be metastatic to the lungs following i.v. administration. Cells should be grown in culture for 1 to 2 splits after thawing and used when in the exponential phase of cell growth.

Introduce siRNA into metastatic cells 2. Resuspend 20 nmol duplexed Stealth siRNA in 1 ml of DEPC-treated water to yield a 20 µM solution. This solution can be divided into several tubes in 25- to 50-µl aliquots to prevent degradation of siRNA by repeated freezing and thawing cycles. Aliquots can be stored at −20◦ C for 6 to 12 months. Stealth siRNA is chemically modified through a proprietary procedure to decrease degradation and minimize off-target effects. It has prolonged knockdown efficiency in animals as compared to nonmodified siRNA (Stahl et al., 2004; Sharma et al., 2005, 2006).

3. Introduce 100 pmol siRNA into 1×106 1205 Lu, C8161.Cl9, or UACC 903 M cells via nucleofection with an Amaxa Nucleofector. Use Solution R/program K-17 for these cell lines. Additional information regarding the Amaxa nucleofector, protocols, and nucleofection reagents can be found at http://www.amaxa.com/. Any alternative transfection procedure may be used provided >90% of cells can be transfected with fluorescently tagged siRNA (as detailed in step 6).

4. As a control, transfect the same type of cells with fluorescently tagged siRNA (Alexa Fluor 546 labeled nonsilencing duplex siRNA) to measure transfection efficiency following nucleofection or other transfection approach. This step employs a randomly scrambled siRNA sequence (usually 40% to 50% GC content) that does not alter gene expression.

5. Counterstain cell nuclei with DAPI to identify cells that did not take up the fluorescent siRNA:

Models of Melanoma Metastasis

a. Plate 1 × 104 1205 Lu cells transfected with fluorescently tagged siRNA in each well of 6-well plates containing a glass coverslip: b. Allow cells to attach for 4 hr, add 2 ml additional medium, and incubate for 24 hr (DMEM containing 10% fetal bovine serum). c. Rinse coverslips with phosphate-buffered saline and fix adherent cells in 4% paraformaldehyde solution. d. Aspirate the 4% paraformaldehyde and wash coverslips three times in PBS, 5 min each wash.

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Figure 14.6.2 Uptake of fluorescently tagged siRNA by melanoma cells. Control red fluorescently tagged siRNA (Alexa Fluor 546; 100 pmol) was nucleofected into 1205 Lu cells. Twentyfour hr later, cells were fixed in 4% paraformaldehyde followed by staining with DAPI. The left image shows the cell nuclei stained blue and the middle image shows red siRNA taken into cells. The rightmost image shows merged nuclei and siRNA, magnification 200×. Greater than 97% of cells took up the fluorescently tagged red siRNA. For color version of this figure see http://www.currentprotocols.com.

e. Remove coverslips from 6-well plates; invert each coverslip onto a glass slide containing 10 to 20 µl Vectashield mounting medium containing DAPI, and seal edges with clear nail polish. 6. Count 200 to 300 cells and determine the number of cells that have taken up siRNA (red fluorescence). Express transfection efficiency as the percentage of siRNA uptake. Percentage of siRNA uptake = [number of red cells/total number of cells (DAPIstained blue nuclei) in each field] × 100. Optimal transfection efficiency should be >90%. A representative example of optimal transfection efficiency is shown in Figure 14.6.2.

7. Following nucleofection, replate cells in DMEM medium supplemented with 10% FBS for a 36-hr recovery period. At this point, if siRNA specificity has already been established, one may either proceed with the protocol and perform immunoblotting (steps 8 through 15) or proceed directly to mouse injection (step 16).

Measure protein knockdown prior to introduction of cells into mice Immunoblot analysis is performed to confirm decreased expression of proteins corresponding to the particular siRNA targeting that protein. 8. After the 36-hr recovery period, add 75 to 100 µl protein lysis buffer dropwise to cells in cell culture dishes. Keep plates on ice and scrape cells from surface of plate with a cell scraper. Use a 200-µl pipet to transfer lysed cells to a 1.5- or 2.0-ml microcentrifuge tube. Pipet up and down several times before transferring to break up clumps and cellular debris. Incubate on ice for 30 min with occasional mixing. 9. Centrifuge whole-cell lysates 10 min at ≥10,000 × g, 4◦ C to remove cell debris. 10. Quantify protein concentration in the supernatant using the BCA Protein Assay from Pierce or use the method described in APPENDIX 3A. 11. Load 30 µg of protein lysate per lane onto a NuPage Gel and electrophorese (APPENDIX 3B). 12. Following electrophoresis, transfer samples to PVDF membrane by electroblotting using the Novex western transfer apparatus (XCell SureLock Electrophoresis Cell and XCell Blot module)]. Any established protein transfer procedure will probably work equally well.

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13. Probe blots with primary antibodies according to the supplier’s recommendations: anti–pErk and anti–pMek; antibodies to B-Raf, C-Raf, Erk 2, Cyclin D1, p27, and α-enolase. Incubate membrane in blocking solution (5% w/v nonfat dry milk in TBS-T) for 1 hr at room temperature. 14. Remove primary antibody solution and wash membrane three times for 5 to 15 min in TBS-T. Incubate blots with horseradish peroxidase-conjugated secondary antibodies directed against the species in which the primary antibody was raised (e.g., rabbit, mouse, or goat). Remove secondary antibody solution and wash membrane three times for 5 to 15 min in TBS-T. 15. Develop immunoblots using the enhanced chemiluminescence (ECL) detection system. Protein knockdown of >70% is ideal. An example of protein knockdown following siRNAmediated inhibition is shown in Figure 14.6.3 (Sharma et al., 2006). The duration of siRNA knockdown in vitro can be measured over a number of days from cells replated in media and harvested 0, 2, 4, 6, and 8 days following nucleofection with siRNA and subjected to immunoblotting analysis. Examples of efficient protein knockdown of different gene targets using siRNA designed to specific genes are shown in Figure 14.6.4 (Sharma et al., 2006).

Transient metastasis assays with cells nucleofected with siRNA 16. At a time point 36 hr after nucleofection and replating of cells into cell culture plates, aspirate existing medium from culture dishes. Rinse cells with 2 ml PBS and remove rinse. Add 2 ml 1× trypsin and leave on cells for 1 to 2 min at room temperature. Watch for cells to round up and then add 5 to 6 ml fresh medium to neutralize the trypsin and disperse cells by pipetting up and down. Count cells on a hemacytometer (Phelan, 2006). Suspend 1×106 1205 Lu or C8161.Cl9 cells or 0.5×106 UACC 903 M cells in 0.2 ml of Hanks’ balanced salt solution without calcium, magnesium, and phenol red. 17. Prepare nude mice for injection by warming the tail of the animal using a 120-W bulb to dilate the tail vein before placing the animal in the restraint device.

Models of Melanoma Metastasis

Figure 14.6.3 Duration of siRNA-mediated protein knockdown in cultured melanoma cells (Sharma et al., 2006). siRNA-mediated reduction of mutant V600E B-Raf protein expression persists for 8 to 10 days in 1205 Lu melanoma cells. siRNA-mediated knockdown of B-Raf reduces protein expression in cells for 8 to 10 days following nucleofection compared to controls nucleofected with buffer or scrambled siRNA. Knockdown of at least 50% protein expression is required, with >70% being ideal. Erk2 and α-enolase were used as controls for protein loading. The MuA or A siRNA, referred to as (A), reduces expression of wild-type and mutant protein, whereas the Com4 or 4 siRNA, referred to as (4), only lowers expression of mutant protein (Hingorani et al., 2003).

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Figure 14.6.4 Efficient protein knockdown of multiple proteins of the Map kinase pathway in melanoma cells (Sharma et al., 2006). siRNA-mediated reduction of mutant V600E B-Raf, Mek, Erk, and Cyclin D1 reduced expression of each respective protein 72 hr after nucleofection into 1205 Lu melanoma cells. Shown are immunoblots probed with antibodies against each respective protein to indicate specificity of siRNA-mediated knockdown. α-enolase served as a control for protein loading. The MuA or A siRNA, referred to as (A), reduces expression of wild-type and mutant protein, whereas the Com4 or 4 siRNA, referred to as (4), only lowers expression of mutant protein (Hingorani et al., 2003).

18. Place the animal in a mouse restrainer with the tail extending from the device. This could be a commercially manufactured mouse restrainer device or an ethanolsterilized plastic bottle with one end removed and a hole cut in the opposite end, through which the tail can be passed.

19. Fill a 1-ml syringe with 0.25 ml cell suspension before attaching the 27-G needle to prevent the cells from shearing. Wipe the tail with an alcohol prep pad and then insert the needle into the tail vein and slowly inject 0.2 ml cells. Return injected animal to normal housing conditions. Following removal of the needle, a few drops of blood may be observed, which should be wiped off with the alcohol pad. If injected correctly into vein, no resistance should be felt, nor should the injected area turn white. Rather, with application of slight pressure the solution should pass easily into the vein. While only 0.2 ml of solution is injected the syringe should be filled to 0.25 ml. The excess is used to fill the void volume within the syringe.

20. At a time point 17 days after i.v. injection of tumor cells sacrifice the animals by CO2 inhalation (Donovan and Brown, 2006) followed by cervical dislocation. This involves placing the mouse in a closed container in which dry-ice (solid CO2 pellets) is added to a vessel containing warm tap water. CO2 gas released euthanizes the mouse in 500 mm3 have substantial areas of hypoxia as determined by oxygen tension readings (see Fig. 14.7.7), but not all tumor models develop hypoxia at this size. Also, the lack of oxygen tension in large areas of tumor may indicate necrosis, but could indicate severely hypoxic areas as well. Therefore, development of necrotic areas should be visualized by H and E staining of histology slides of different tumor sizes to determine when a particular tumor develops hypoxia.

3. Pre-cool 1× PBS containing 0.1% gentamicin by placing on ice. 4. Sacrifice the animal by cervical dislocation. 5. Rinse the tumor area with 70% ethanol. 6. Under a laminar flow or biosafety hood, place animal on sterile pad. Excise the tumor by making an incision around the tumor where the skin and tumor connect. Pull the skin off the tumor, leaving the tumor attached. Carefully cut beneath the tumor, removing it from the leg. Place the tumor into a dry Petri dish. Pads are used to cover a work surface large enough to place a mouse and sterile instruments.

Radiotherapy and Chemotherapy in Pre-Clinical Tumor Models

Process tumor cells for growth 7. Remove non-tumor or necrotic tissue from the excised tissue. Necrotic tissue is usually visually distinguishable from viable tissue. Necrotic tissue appears white or red with a gelatinous consistency as it is infiltrated with lymphocytes and red blood cells from pooled blood.

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8. Place the excised tumor into a Petri dish containing ice-cold 1× PBS containing 0.1% gentamicin. Cut the tumor into small pieces using a cross-scalpel motion. If preparing a single-cell suspension, continue to cut the tumor into the smallest pieces possible and proceed to step 9. For repassaging the tumor into a recipient animal, use a measuring screen to cut the tumor into ∼2 × 2 × 2–mm pieces and subcutaneously re-implant two or three of these pieces into the donor mouse implantation site (not for growth studies) using a 25-G trocar. Although the tumor pieces need not be sutured into place, close the trocar wound with a staple and remove 1 week later. Repeat the tumor harvest and implantation process (steps 2 to 8) two additional times before preparing a single-cell suspension (see Basic Protocol 2). 9. Place tumor pieces into a 50-ml centrifuge tube on ice. 10. Place a cell strainer on top of another 50-ml centrifuge tube. 11. Use a 25-ml pipet to transfer the PBS solution containing tumor pieces onto the strainer. Force the pieces through the strainer with a rubber cell scraper. 12. Pass the suspension through a 17-G blunt-end syringe three times. 13. Centrifuge the cell solution 6 min at 400 × g, 6◦ C.

Grow tumor cells 14. Discard the supernatant and resuspend the pellet in an equal volume of ice-cold medium containing 10% FBS/0.1% gentamicin solution. Close the lid of the centrifuge tube and vortex the contents to homogeneity. 15. Pipet the homogeneous solution into a 175-cm2 flask containing 50 ml of ice-cold medium containing 10% FBS/0.1% gentamicin solution. Swirl the contents so the cells are well dispersed in the flask. 16. Place flask for 24 to 48 hr in a 37◦ C, 5% CO2 humidified incubator. 17. Decant medium at the end of the incubation period. 18. Replace half of the medium every 2 days to maintain growth factors until a normal growth rate has been established. 19. Passage cells as normal by changing medium and passaging as established prior to implantation.

PREPARATION OF CELLS FOR TUMOR INOCULATION USING CELLSTRIPPER

BASIC PROTOCOL 2

The steps described in this protocol are designed to gently remove cells from culture flasks to generate a single cell suspension for injection into mice. The gentle removal of cells from culture flasks is accomplished using Cellstripper (Mediatech), a non-enzymatic chelator solution. Cells can be exposed to Cellstripper for greater lengths of time than with protein digestive enzymes without damage. The viability of cells prepared for tumor inoculation should be >90%.

Materials Serum-free growth medium 1× PBS without calcium and magnesium Tumor cells grown as a monolayer (80% confluent) in a 175-cm2 flask (see Basic Protocol 1) Cellstripper solution (Mediatech), 37◦ C 0.4% trypan blue 37◦ C incubator 50-ml centrifuge tubes Current Protocols in Pharmacology

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14.7.7 Supplement 38

Hemacytometer 17-G blunt-end needle 1. Place medium and PBS without calcium and magnesium in a wet ice bucket. 2. Aspirate medium from tumor cells grown in a 175-cm2 culture flask. 3. Rinse cells with 10 ml ice-cold 1× PBS without calcium and magnesium, and aspirate. 4. Add 5 ml of 37◦ C Cellstripper into each flask and swirl. 5. Incubate cells 10 min in a 37◦ C incubator. 6. Remove flasks from the incubator and agitate them to loosen cells. 7. Add 5 ml serum-free growth medium to each flask. 8. Combine cells into a 50-ml centrifuge tube, add medium to final volume of 40 ml, and centrifuge 6 min at 400 × g, 6◦ C. 9. Decant supernatant and add 5 ml of serum-free growth medium per 0.2 ml of pellet. 10. Resuspend to produce a single-cell suspension by passing three times through a blunt-end needle and repeat steps 8 and 9. 11. Count a portion of the cell suspension using 0.4% trypan blue and a hemacytometer. If cell viability is not >90%, repeat steps 8 and 9 to remove dead cells in the supernatant. 12. Centrifuge 6 min at 400 × g, 6◦ C. 13. Resuspend the cells in serum-free growth medium at 1 × 107 viable cells/ml and place on ice. Cells can be stored in this manner for up to 4 hr. It is recommended that a viability test be performed if the cells are left for 85% leukemic blasts as verified by morphologic and immunophenotype analysis. Cryopreserved buffy coats from bone marrow biopsies are not recommended, due to their poor success rate for engraftment.

7. Transfer the cell suspension into a 15-ml centrifuge tube. Add 10 ml of prewarmed complete medium (add the first 5 ml dropwise for best viability). 8. Centrifuge the cells 5 min at 200 × g, 20◦ C, then discard the supernatant and resuspend the cells in 10 ml of complete medium. 9. Perform a cell count and estimate the concentration of viable cells using trypan blue and a hemacytometer. Centrifuge as in step 8, resuspending the cells in a volume of sterile CMF-PBS to obtain 2–10 × 106 cells per 100 µl per mouse. Place the suspension on ice for up to 2 hr until transplantation. For successful primary engraftments, at least 5 × 106 cells per mouse should be used for inoculation. Robust engraftment can be achieved at subsequent passages using as few as 1 × 106 cells.

Transplant leukemia cells into NOD/SCID mice 10. Using a 1-ml insulin syringe with a 27-G, 1/2-in. needle, draw up the cell suspension and eliminate any air bubbles from the syringe.

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11. Place the box of mice 15 to 20 cm in front of a heat lamp. Monitor continuously to ensure that the mice do not suffer from heat stress, as indicated by excessive condensation in the cage, sweating, and inactivity of the mice. Warming mice assists vasodilation, improving the success of cell inoculation.

12. Place the mouse restrainer in a biological safety cabinet. 13. Place a mouse in the restrainer and, holding the tail with the lateral vein exposed, gently insert the needle into the tail vein to a depth up to 1 cm. Inject 100 µl of the cell suspension. If the first attempt at inoculation is unsuccessful, the next attempt should be made using the alternate tail vein to avoid local clotting of the blood. Making further attempts is difficult and not recommended.

14. Remove the needle and compress the site using a sterile tissue until bleeding has ceased. 15. Return the mouse to its box and examine daily for general well being.

Monitor leukemia engraftment: blood collection Blood samples are taken weekly to monitor engraftment. 16. Prepare the mice as in steps 11 and 12. 17. While holding the mouse in the restrainer, pierce the lateral tail vein using a 23-G, 1–1/4-in. needle. Blood sampling of the mice is achieved with the aid of a needle. Other methods, such as creating a nick in the tail with a scalpel blade, result in increased incidence of tail necrosis, which is unsustainable for long-term experiments. Multiple needle perforations in the vein, more than three times in any one day, can also cause necrosis, making future blood collections difficult.

18. Collect 50 to 75 µl of blood into a blood collection tube. 19. Compress the puncture site using a sterile tissue until bleeding has ceased. 20. Return the mouse to its cage and examine the animal daily for general well being. Blood samples can be kept at room temperature overnight prior to analysis.

Monitor leukemia engraftment: flow cytometric analysis 21. Transfer 50 to 75 µl of blood from each tube into a flow cytometry tube. 22. To each tube add 94 µl of flow cytometry buffer, 5 µl of APC-conjugated anti-human CD45, CD19, or CD7 antibody, and 1 µl of FITC-conjugated anti-mouse CD45. Mix by brief vortexing. Each blood analysis should include one sample stained with equivalent amounts of relevant IgG isotype control antibodies to enable accurate gating of the flow cytometry data. Minimize light exposure, as fluorochrome-conjugated antibodies are light sensitive.

23. Incubate the samples at room temperature for 30 min. 24. To each tube, add 8 vol (400 to 600 µl) of RBC lysis solution. Incubate the tubes for 15 min at 37◦ C to lyse the red blood cells.

Preclinical Models of Common Childhood Cancers

25. Add 3 ml of flow cytometry buffer to each tube to neutralize the RBC lysis solution and centrifuge for 5 min at 200 × g, room temperature. 26. Discard the supernatant and resuspend the pellet in 100 µl flow cytometry buffer.

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27. Analyze the samples by dual-color flow cytometry with appropriate compensation settings for FITC and APC, acquiring at least 10,000 events per sample. 28. Calculate the proportion of human versus murine CD45+ cells to determine the extent of dissemination of leukemia into the peripheral blood. This procedure can reliably detect >0.1% human CD45+ cells in murine peripheral blood.

Harvest mouse organs 29. When the proportion of human CD45+ cells in the peripheral blood exceeds 50%, or at the first indication of morbidity (≥20% weight loss, lethargy, ruffled fur), sacrifice the mouse by cervical dislocation or CO2 inhalation. The 2000 Report of the American Veterinary Medical Association Panel on Euthanasia (2000) recommends both cervical dislocation and CO2 inhalation as humane techniques for mouse euthanasia.

30. Collect blood by cardiac puncture and transfer it to a blood collection tube. 31. Using sterile scissors and forceps, collect the spleen, liver, lungs, kidney, and brain, and place them into harvest tubes containing 1 ml complete medium and process as described below to assess the extent of leukemic infilitration. See Figure 7.1.1 for schematic layout of organs.

32. Collect the bone marrow by flushing the femurs with 5 ml of complete medium using a 27-G, 1/2-in. needle. Bone marrow and spleens of animals that exhibit >50% human CD45+ cells in the peripheral blood are routinely >90% human CD45+ and can readily be used for subsequent retransplantation (Lock et al., 2002).

33. For tissues that are collected for flow cytometric analysis only, macerate tissues with the plunger of a 1-ml syringe, using 50 µl of the suspension for analysis. For tissues to be cryopreserved, use 50 µl of the suspension from step 36. 34. Add 5 µl of normal mouse serum to the cell suspension to block binding of the antibody to nonspecific epitopes, then incubate for 5 min at room temperature. Stain cells as described in steps 22 to 28, omitting step 24.

Cryopreserve xenograft cells 35. Prepare cell suspensions of spleen or other organs (step 31) by placing them into a sterile tea strainer and homogenizing them with complete medium using the plunger of a 10-ml syringe to mince the tissues. 36. Homogenize the tissue further by filtering the suspension through 40-µm cell strainers into 50-ml tubes. Expected yields of human cells from highly engrafted mice are 2–4 × 107 for bone marrow and 2.5 × 108 to 2 × 109 cells for spleen.

37. Purify the mononuclear cells from spleens and bone marrow by density gradient centrifugation as follows: a. Place 30 ml of the cell suspension in complete medium in a 50-ml centrifuge tube. b. Put 15 ml of lymphocyte isolation medium in a 20-ml syringe with a mixing cannula attached and put the tip of the cannula in the cell suspension so that the tip is on the bottom of the tube. c. Slowly eject the isolation medium into the centrifuge tube to underlay the cell suspension.

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Receptor Nomenclature Guidelines A receptor is present either on the cell surface or inside the cell and is classically defined as the recognition site for a physiologically or pharmacologically active compound. Activation of the receptor produces a functional response that in normal situations contributes to cellular and tissue homeostasis (see UNIT 1.1). The classification of receptors has been a major challenge in recent years, given the cloning and expression of a multitude of proteins that are members of the various receptor families. Many of these proteins are classified on the basis of their structural homology with known receptors and have also been pharmacologically characterized using selective agonist and antagonist ligands, while others have a known structural homology but lack pharmacological evidence confirming function. Members of the latter group are known as orphan receptors and encompass both G protein–coupled (Wilson et al., 1998) and nuclear receptors (Kliewer et al., 1999). Receptors exist in four distinct structural motifs: (1) the 7 transmembrane (7TM), heptahelical G protein–coupled receptors (GPCRs) such as the β-adrenoceptor; (2) ion channels that are gated by ligands (LGICs)— such as the nicotinic acetylcholine receptor (nAChR), glutamate receptor, γ-aminobutyric acid (GABAA) receptor, and ATP (P2X) receptor—as well as channels sensitive to cations (e.g., calcium-sensing receptors, CaSRs), pH (acid-sensing ion channels, ASICs), and volume (volume-regulated anion channels, VRACs); (3) the retinoid receptors (RXR, RAR); and (4) catalytic receptors (e.g., neurotrophin receptors). The individual subunits of the various ion channels show marked variations in their structural motifs, varying from two transmembrane units in P2X receptor subunits to thirteen transmembrane units in inwardly rectified potassium channels (Kirs). Receptors are typically characterized via two distinct approaches. 1. Combined pharmacological and molecular approach. This approach involves identification and pharmacological characterization of a receptor-mediated response using classical pharmacological and/or radioligand approaches in tissues and animal models with selective agonist and antagonist ligands. Additionally, it involves the cloning and expression of proteins with structural homology to known

Contributed by Michael Williams Current Protocols in Pharmacology (1999) A.1B.1-A.1B.64 Copyright © 1999 by John Wiley & Sons, Inc.

receptors, the function of which is subsequently established by studying the structure-activity relationship (SAR) of receptor-mediated responses. 2. An additional means to characterize receptors proceeded and evolved with the structural approach, namely classification in terms of signal transduction mechanisms. This is a logical process in terms of G protein systems coupled with native receptors, but is fraught with problems when signal transduction mechanisms are derived in transfected cell systems, due to the potential for promiscuous coupling (Kenakin, 1996), or when receptor mutations are used to investigate ligand binding affinity and agonist efficacy (Colquhoun, 1998). With the tremendous amount of research ongoing in the identification and characterization of receptors, it is inevitable that occasionally two or more proteins with the same identified function will be designated in different ways by different research groups, leading to considerable confusion (Williams, 1987; Green, 1987). In an attempt to address this issue, the International Union of Pharmacology (IUPHAR) created guidelines (Vanhoutte et al., 1996) and selected working groups for each receptor family to establish a common nomenclature system. Reports from those groups that have reached some degree of consensus have been summarized in various issues of Pharmacological Reviews and in The IUPHAR Compendium of Receptor Characterization and Classification (IUPHAR, 1998). Additional sources of information are the annual TiPS Receptor and Ion Channel Nomenclature Supplement (Alexander and Peters, 1999) and the RBI Handbook of Receptor Classification and Signal Transduction (Watling, 1998). In addition, a working party report on receptor nomenclature has been published by the British Journal of Pharmacology (Nomenclature Working Party, 1998). Given historical precedents and the copious amount of disparate literature, the work of the IUPHAR groups has been challenging and, in some instances, reports have not been issued. Such is the case for the various LGICs, which offer a significant challenge because of their diverse and potentially heterogeneous structure (Green et al., 1998). Nonetheless, although interim reports have been issued, historical and

APPENDIX 1B

Nomenclature and Useful Data

A.1B.1 Supplement 6

sometimes conflicting nomenclatures remain in the literature. Nomenclature guidelines vary from receptor family to receptor family. As a rule, receptors are named on the basis of a recognized abbreviation for the endogenous ligand (e.g., 5-HT) or a pharmacological agent that selectively interacts with the receptor (e.g., muscarine and nicotine for the muscarinic and nicotinic families of the acetylcholine receptor; benzodiazepines for the GABAA receptor complex). Receptor subtypes for those families for which structural, pharmacological, and functional data exist are then designated by a combination of arabic numerals and uppercase letters (e.g., adenosine A2A). Receptors for which only structural information is available are designated in lower case (e.g., p2y3), while those uncloned receptors for which pharmacological evidence exists are shown in italics. The situation is an order of magnitude more complex for ligand-gated ion channels that exist as trimers, tetramers, and pentamers, and where functional homomeric and heteromeric complexes can be formed. Based on cloning, a large number of different subunits and associated proteins have been identified that predict the existence of thousands of potential channel constructs. For instance, in mammals, six α, four β, three γ, one δ, three ρ, one ε, and one π subunits have been reported for the GABAA receptor. Alternatively spliced versions of several of these subunits have also been identified. While α1β2γ2, α2β3γ2, α3β3γ2, and α5βxγ2 channel constructs have been identified in vivo, it is not known which, if any, of the remaining subunit permutations are present in nature. A similar situation exists for the P2 and nicotinic receptor superfamilies. In these instances, receptor characterization is being driven by the identification of selective ligands and the creation of knockouts of the individual subunits. Yet another layer of unexpected complexity has arisen from the recent finding that GPCRs like the GABAB receptor, the adenosine A1 receptor, and members of the calcitonin gene– related peptide (CGRP) receptor family exist as dimers, which could make it difficult to

classify these sites solely on the basis of the structure of one subunit or the pharmacological responses to a single agent. More recently, Humphrey and Barnard (1998) proposed an alphanumeric classification system for receptors, where a unique receptor code (RC) is given to each receptor. As noted by the authors, “An RC will be reserved for the polypeptide product(s) of a single orthologous gene. Species variants will share a common RC, but will be differentiated by a three-letter species code”. Thus, the human P2X1 receptor is proposed to have an RC designation 1.4.NUCT.01.HAS.00.00.S. The first number, 1, designates the structural class of the receptor, in this instance an ion channel receptor. The second number, 4, assigns it to a class “related to epithelial Na+ channels, non-peptide gated.” NUCT is the family code that assigns the receptor to the family “adenosine and uridine triphosphates.” The designation 01 refers to the trivial nomenclature already established, e.g., P2X1. HAS is the species code (Homo sapiens). The remaining numbers allow for the designation of splice variants, and the final letter, S, refers to the subunit. For the 5-HT1A GPCR, the RC is 2.1.5-HT.01A.RNO.00.00, where 2.1 designates a GPCR related to rhodopsin, 5-HT is for serotonin, 01A is the trivial name (5HT1A), RNO is for Rattus norwegius (rat), and the remaining numbers are splice variants. While this is a very comprehensive and accurate nomenclature system, its utility and vernacular acceptability will not be known for some time. Tables A.1B.1 to A.1B.62 provide synopses of current pharmacological, structural, and functional data relating to a large number of receptors. These tables also provide RC numbers and references. This appendix is very much a working document, reflecting new discoveries and the complexity of the LGICs. Thus, the author would appreciate receiving corrections and new information so that this appendix can be updated on a regular basis to ensure accuracy and utility. The author can be contacted at [email protected].

Receptor Nomenclature Guidelines

A.1B.2 Supplement 6

Current Protocols in Pharmacology

Tables A.1B.1 to A.1B.62 are listed below. Table A.1B.1 Table A.1B.2 Table A.1B.3 Table A.1B.4 Table A.1B.5 Table A.1B.6 Table A.1B.7 Table A.1B.8 Table A.1B.9 Table A.1B.10 Table A.1B.11 Table A.1B.12 Table A.1B.13 Table A.1B.14 Table A.1B.15 Table A.1B.16 Table A.1B.17 Table A.1B.18 Table A.1B.19 Table A.1B.20 Table A.1B.21 Table A.1B.22 Table A.1B.23 Table A.1B.24 Table A.1B.25 Table A.1B.26 Table A.1B.27 Table A.1B.28 Table A.1B.29 Table A.1B.30 Table A.1B.31 Table A.1B.32 Table A.1B.33 Table A.1B.34 Table A.1B.35 Table A.1B.36 Table A.1B.37 Table A.1B.38 Table A.1B.39 Table A.1B.40 Table A.1B.41 Table A.1B.42 Table A.1B.43

Acetylcholine Receptors: Muscarinic Acetylcholine Receptors: Nicotinic Adenosine Receptors (P1 purinergic receptors) α1-Adrenoceptors α2-Adrenoceptors β-Adrenoceptors Angiotensin Receptors ATP Receptors: P2X (P2 purinergic receptors) ATP Receptors: P2Y (P2 purinergic receptors) Atrial Natriuretic Peptide Receptors Benzodiazepine Receptors Bombesin Receptors Bradykinin Receptors Calcitonin Gene-Related Peptide Receptors Calcium Channels Cannabinoid Receptors Chemokine Receptors: CC Chemokine Receptors: CXC Chloride Channels Cholecystokinin and Gastrin Receptors Corticotropin-Releasing Factor Receptors Cytokine Receptors: Hemopoeitin Receptor Family Cytokine Receptors: Interleukin-1 Receptor Family Cytokine Receptors: Tumor Necrosis Factor Family Dopamine Receptors Endothelin Receptors GABAA Receptor GABAB Receptors Galanin Receptors Glutamate Receptors: Ionotropic Glutamate Receptors: Metabotropic Glycine Receptors Glycoprotein Hormone Receptors Histamine Receptors 5-Hydroxytryptamine (Serotonin) Receptors: GPCR 5-Hydroxytryptamine (Serotonin) Receptor: LGIC Imidazoline Binding Sites Inositol Trisphosphate (IP3) Receptors Leukotriene Receptors Lysophospholipid Receptors Melanocortin Receptors Melatonin Receptors Neuropeptide Y Receptors continued

Nomenclature and Useful Data

A.1B.3 Current Protocols in Pharmacology

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Receptor Nomenclature Tables, continued Table A.1B.44 Table A.1B.45 Table A.1B.46 Table A.1B.47 Table A.1B.48 Table A.1B.49 Table A.1B.50 Table A.1B.51 Table A.1B.52 Table A.1B.53 Table A.1B.54 Table A.1B.55 Table A.1B.56 Table A.1B.57 Table A.1B.58 Table A.1B.59 Table A.1B.60 Table A.1B.61 Table A.1B.62

Neurotensin Receptors Neurotrophin Receptors Opioid Receptors Oxytocin Receptor Pituitary Adenylate Cyclase-Activating Polypeptide Receptor Platelet-Activating Factor Receptor Potassium Channels Prostanoid Receptors Protease-Activated Receptors Receptor Tyrosine Kinases Ryanodine Receptors Sodium Channels Somatostatin Receptors Steroid/Retinoid Receptors Tachykinin Receptors Thyrotropin-Releasing Hormone Receptor Vanilloid (Capsaicin) Receptors Vasoactive Intestinal Peptide Receptors Vasopressin Receptors

Receptor Nomenclature Guidelines

A.1B.4 Supplement 6

Current Protocols in Pharmacology

Table A.1B.1 Acetylcholine Receptors: Muscarinic (GPCR; 2.1.ACH.00.000.00)a,b

Receptor

GenBank/ SwissProt no. (human)

M1

Agonists

Antagonists

Signal transduction

P11229

Acetylcholine CDD 0097

PLC: IP3 and Ca2+ M-current inhibition Adenylate cyclase activation

M2

P08172

Acetylcholine Bethanecol

M3

P20309

Acetylcholine L 689660

M4

P08173

Acetylcholine

M5

P08912

Acetylcholine

4-DAMP MT7 Pirenzepine Telenzepine Tripitramine Methoctramine AFDX 116 4-DAMP Hexahydro-siladifenidol Darifenacin Tropicamide MT3 AFDX 384 None

Kir activation Adenylate cyclase inhibition PLC: IP3 and Ca2+ M-current inhibition Adenylate cyclase activation Inhibition of voltage-gated Ca2+ channels PLC: IP3 and Ca2+ M-current inhibition Adenylate cyclase activation

aChemical abbreviations:

AFDX 116: 11-(2-{2-[(diethylamino)methyl]-1-piperidinyl}acetyl)-5,11-dihydro-6H-pyrido[2,3-b](1,4)benzodiazepin-6-one AFDX 384: (±)-5,11-dihydro-11-{[(2-{2-[(dipropylamino)methyl]-1-piperidinyl}ethyl)amino]carbonyl}-6H-pyrido[2,3-b](1,4) benzodiazepin-6-one CDD 0097: 5-propargyloxycarbonyl-1,4,5,6-tetrahydropyrimidine 4-DAMP: 4-diphenylacetoxy-N-methylpiperidine methiodide IP3: inositol trisphosphate Kir: inwardly rectifying potassium channel L 689660: 1-azabicyclo[2.2.2]octane-3-(6-chlorophyraziyl)maleate MT3, MT7: metallothionein 3, 7. PLC: phospholipase C. bReferences: Eglen and Watson (1996); Wess (1996).

Nomenclature and Useful Data

A.1B.5 Current Protocols in Pharmacology

Supplement 6

Table A.1B.2 Acetylcholine Receptors: Nicotinic (LGIC; 1.1.ACH 00.00.00)a,b

Receptor

Subunit composition

Neuronal (α-bungarotoxin insensitive)

α4β2 α3β4 α4β4

Neuronal (α-bungarotoxin sensitive)

α7 α8 α9

Ganglionic

α3α5β4 α7 α3α5β2β4 α3α7x α1β1δγ(ε)

Skeletal muscle (α-bungarotoxin sensitive)

Agonists

Antagonists

Signal transduction

Cytisine RJR 2403 ABT 418 A 85380 Anatoxin-a DMAC GTS 21 AR-R 17779 DMPP SIB 1553

Mecamylamine Dihydro-β-erythroidine Erysodine

Cation conductance

Methyllycacontine α-Bungarotoxin α-Conotoxin IMS

Cation Conductance

κ-Bungarotoxin

Cation Conductance

TMA

α-Bungarotoxin

Cation conductance

aChemical abbreviations:

A 85380: 3-(2-s-azetidinylmethoxy)pyridine hydrochloride ABT 418: S-3-methyl-5-(1-methyl-2-pyrrolidinyl)isoxazole AR-R 17779: spiro(1-azabicyclo[2.2.2]octane-3,5′-oxazolidin)-2′-one DMAC: 3,4-dimethylaminocinnamylidine DMPP: 1,2-dimethyl-4-phenylpiperazinium iodide GTS 21: 3-(2,4-dimethoxybenzylidene)-anabaseine RJR 2403: N-methyl-4-(3-pyridinyl)-3-buten-1-amine SIB 1553: 4-{[2-(1-methyl-2-pyrrolidinyl)ethyl]thio}-phenol hydrochloride TMA: N,N,N-trimethylmethanaminium. bReference: Holladay et al. (1997).

Receptor Nomenclature Guidelines

A.1B.6 Supplement 6

Current Protocols in Pharmacology

Table A.1B.3 Adenosine Receptors (P1 Purinergic Receptors; GPCR; 2.1.ADO.00.00.00)a,b

Receptor

GenBank/ SwissProt Agonists no. (human)

A1

P30542

A2A

P29274

A2B A3

P29275 P33765

CPA CCPA CHA DPMA CGS 21680 HE-NECA NECA Cl-IB-MECA

Antagonists

Signal transduction

DPCPX N 0840

Adenylate cyclase inhibition PLC activation Ca2+ channel blockade Adenylate cyclase activation

SCH 58261 ZM 241385 CSC KF 17837 Enprofylline MRS 1220 L 268605

Adenylate cyclase activation Adenylate cyclase inhibition

aChemical abbreviations: CCPA: 2-chloro-N6-cyclopentyladenosine

CGS 21680: 2-[4-(2-carboxyethyl)phenethylamino]adenosine-5′-N-ethyluronamide CHA: N6-cyclohexyladenosine Cl-IB-MECA: 2-chloro-N6-(3-iodobenzyl)adenosine-5′-N-methyluronamide CPA: N6-cyclopentyladenosine CSC: 8-chlorostyrylcaffeine DPCPX: 8-cyclopentyl-1,3-dipropylxanthine DPMA: N6-[2-(3,5-dimethoxyphenyl)-2-(2-methylphenyl)ethyl]adenosine HE-NECA: 2-[(E)-1-hexenyl]adenosine-5′-N-ethyluronamide KF 17837: (E)-8-(3,4-dimethoxystyryl)-1,3-dipropyl-7-methylxanthine L 268605: 3-(4-methoxyphenyl)-5-amino-7-oxo-thiazolo[3,2]pyrimidine MRS 1220: 9-chloro-2-(2-furyl)-5-phenylacetylamino[1,2,4]triazolo[1,5-c]quinazoline N 0840: N6-cyclopentyl-9-methyladenine PLC: phospholipase C SCH 58261: 5-amino-2-(2-furyl)-7-phenylethyl-pyrazol[4,3-e]-1,2,4-triazolo[1,5-c]pyrimidine ZM 241385: 4-{2-[7-amino-2-(2-furyl)[1,2,4]triazolo[2,3-a][1,3,5]triazin-5-yl amino]ethyl}phenol. bReference: Ralevic and Burnstock (1998).

Nomenclature and Useful Data

A.1B.7 Current Protocols in Pharmacology

Supplement 6

Table A.1B.4 α1-Adrenoceptors (GPCR; 2.1.ADR.A1.00.00)a,b

Receptor

GenBank/ SwissProt no. (human)

α1A

α1B α1D

Agonists

Antagonists

Signal transduction

P35348

A 61603 Oxymetazoline SKF 89748

Increase in PI turnover

P35368 P25100

NE = E NE = E

(+)-Niguldipine SNAP 5089 KMD 3213 A 131701 5-Methyluradapil Indoramin RS 17053 AH 11110A BMY 7378 SKF 105854

Increase in PI turnover Increase in PI turnover

aChemical abbreviations:

A 131701: 3-{2-[(3aR,9bR)-cis-6-methoxy-2,3,3a,4,5,9b-hexahydro-1H-benze[e]isoindol-2-yl]ethyl}pyrido[3′,4′:4,5]thieno[3,3-d]pyrimidine-2,4(1H,3H)-dione A 61603: N-[5-(4,5-dihydro-1H-imidazol-2-yl)-2-hydroxy-5,6,7,8-tetrahydronaphthalen-1-yl]methanesulfonamide hydrobromide AH 11110A: 1-(biphenyl-2-yloxy)-4-imino-4-piperidin-1-yl-butan-2-ol BMY 7378: 8-{2-[4-(2-methoxyphenyl)-1-piperazin]ethyl}-8-azaspiro[4.5]decane-7,9-dione dihydrochloride E: epinephrine KMD 3213: (–)-(R)-1-(3-hydroxypropyl)-5-(2-{2-[2-(2,2,2-trifluoroethoxy)phenoxyl]ethylamino}propyl)indoline7-carboxamide NE: norepinephrine PI: phosphatidyl inosital RS 17053: N-[2-(2-cyclopropylmethoxyphenoxy)ethyl]-5-chloro-α,α-dimethyl-1H-indole-3-ethanamide SKF 89748: 1,2,3,4-tetrahydro-8-methoxy-5-methylthio-2-naphthalenamine SKF 105854: 2-bromo-7-chloro-3,4,5,6-tentrahydro-4-methyl-furo[4,3,2-ef][3]benzazepine SNAP 5089: 2,6-dimethyl-4-(4-nitrophenyl)-1,4-dihydropyridine-3,5-dicarboxylate-N-[3-(4,4-diphenylpiperidin-1yl)propyl]amide methyl ester. bReference: Heible et al. (1995).

Receptor Nomenclature Guidelines

A.1B.8 Supplement 6

Current Protocols in Pharmacology

Table A.1B.5 α2-Adrenoceptors (GPCR; 2.1.ADR.A2.00.00)a,b

Receptor

GenBank/ SwissProt no. (human)

α2A

Agonists

Antagonists

Signal transduction

P08913

E > NE Oxymetazoline

BRL 44408

α2B

P18089

Clonidine UK 14304 Dexmedetomidine

ARC 239 Prazosin

α2C

P18825

Clonidine

BAM 1303

Inhibition of adenylate cyclase Inhibition of voltage-gated ion channels Inhibition of adenylate cyclase Inhibition of voltage-gated ion channels Inhibition of adenylate cyclase Inhibition of voltage-gated ion channels

aChemical abbreviations:

ARC 239: 2-[2,4-(O-methoxylphenyl)-piperazin]-1-yl BAM 1303: 2-bromo-6-methyl-8-[(2-phenyl-1H-imidazol-1-yl)methyl]-8β-ergoline BRL 44408: 2-[(4,5-dihydro-1H-imidazol-2-yl)methyl]-2,3-dihydro-1-methyl-1H-isoindole E: epinephrine NE: norepinephrine UK 14304: 5-bromo-6-(2-imidazolin-2-ylamino)quinoxaline. bReference: Heible et al. (1995).

Nomenclature and Useful Data

A.1B.9 Current Protocols in Pharmacology

Supplement 6

Table A.1B.6

β-Adrenoceptors (GPCR; 2.1.ADR.B0.00.00)a,b

Receptor

GenBank/ SwissProt no. (human)

β1

Agonists

Antagonists

Signal transduction

P08588

Denopamine Xamoterol

Stimulation of adenylate cyclase

β2

P07550

β3

P13945

Terbutaline Procaterol Zinterol BRL 37344 CGP 12177A Carazolol CL 316243 CL 316243 CGP 12177A > iodocyanopindolol

CGP 20712A Betaxolol Atenolol ICI 118551 Butoxamine SR 59230A Bupranolol

Stimulation/inhibition of adenylate cyclase

None

Stimulation of adenylate cyclase

β4

Stimulation/inhibition of adenylate cyclase

aChemical abbreviations:

BRL 37344: sodium 4-{2-[2-hydroxy-(3-chlorophenyl)ethylamino]propyl}phenoxyacetate CGP 12177A: (–)-4-(3-tert-butylamino-2-hydroxypropoxy)-benzimidazol-2-one CGP 20712A: 2-hydroxy-5-[2-({2-hydroxy-3-[4-(1-methyl-4-trifluoromethyl-2imidazolyl)phenoxy]propyl}amino)ethoxy]benzamide CL 316243: disodium (R,R)-5-(2-{[2-(chlorophenyl)-2-hydroxyethyl]-amino}propyl)-1,3-benzodioxole-2,2dicarboxylate ICI 118551: (±)-1-[2,3-(dihydro-7-methyl-1H-inden-4-yl)oxy]-3-[(1-methylethyl)amino]-2-butanol SR 59230A: 3-(2-ethylphenoxy)-1-[(1S)-1,2,3,4-tetrahydronaphth-1-ylamino]-(2S)-propanol oxalate. bReference: Heible et al. (1995).

Receptor Nomenclature Guidelines

A.1B.10 Supplement 6

Current Protocols in Pharmacology

Table A.1B.7 Angiotensin Receptors (GPCR; 2.1.ANG.00.00.00)a,b

Receptor

GenBank/ SwissProt no. (human)

AT1

AT2

Agonists

Antagonists

Signal transduction

P30556

L-162313 L-163491

Valsartan > losartan EXP 3174

P50052

CGP 42112A

ICI 118551 PD 123319 L-161638

Inhibition of adenylate cyclase Increase in IP3/DAG Stimulation/inhibition of adenylate cyclase Increase in phosphatase activity

aChemical abbreviations

CGP 42112A: nicotinic acid-Tyr-(N-benzoylcarbonyl-Arg)Lys-His-Pro-Ile-OH DAG: diacylglycerol EXP 3174: n-butyl-4-chloro-1-{[2′-(1H-tetrazol-5-yl)biphenyl-4-yl]methyl}imidazole-5-carboxylate ICI 118551: (±)-1-[2,3-(dihydro-7-methyl-1H-inden-4-yl)oxy]-3-[(1-methylethyl)amino]-2-butanol IP3: inositol trisphosphate L-161638: 6-(N-benzyl-2-thienylcarboxamido)-2-ethyl-3-[2′-(1H-tetrazol-5-yl)biphenyl-4ylmethyl]quinazolin4(3H)-one L-162313: 5,7-dimethyl-2-ethyl-3-({4-[2-(n-butyloxycarbonylsulfonamido)-5-isobutyl-3-thienyl]phenyl}methyl) imidazo[4,5-b]pyridine L-163491: 5,7-dimethyl-2-ethyl-3-[(2′-butyloxycarbonyl)amino-sulfonyl]-5′-(3-methoxybenzyl)-[(1,1′-biphenyl4-yl)methyl]-3H-imidazo[4,5-b]pyridine PD 123319: (S)-1-(4-[dimethylamino]-3-methylphenyl)methyl-5-(diphenylacetyl)-4,5,6,7-tetrahydro-1Himidazo[4,5-c]pyridine-6-carboxylate. bReference: Greindling et al. (1996).

Nomenclature and Useful Data

A.1B.11 Current Protocols in Pharmacology

Supplement 6

Table A.1B.8 ATP Receptors: P2X (LGIC; 1.4.NUCT.00.000.00.00)a,b

Receptor

Subunit compositionc

Agonists

Antagonists

Signal transductione

P2X1

P2X1

α,β-MeATP

Cation channel (pCa2+/pNa2+ ∼4)

P2X2

P2X2/P2X3

2-MeSATP ATPγS

P2X3

P2X2/P2X3

α,β-MeATP

P2X4 P2X5

2-MeSATP 2-MeSATP

P2X6

P2X4 P2X5 P2X1/P2X5 P2X6

DIDS Suramin MRS 2220 TNP-ATP Suramin Pyndoxal 5 Phospate Suramin PPADS TNP-ATP None Suramin PPADS None

P2X7

P2X7

2-MeSATP ATPγS ATP4− Bz ATPd

KN 62 HMA Tenidap o-ATP

Cation channel (pCa2+/pNa2+ ∼4) Cation channel (pCa2+/pNa2+ ∼4) Cation channel (pCa2+/pNa2+ ∼4) Cation channel Cation channel Pore-forming cation channel

aChemical abbreviations:

ATP: adenosine triphosphate DIDS: 4,4-diisothiocyanostilbene-2,2′-disulfonic acid HMA: hexamethylene amiloride KN 62: (S)-5-isoquinoline sulfonic acid or 4-[2-(5-isoquinolinylsulfonyl)methylamino]-3-oxo-3-(4-phenyl-1piperazinylpropyl)phenylester α,β-MeATP: α1β-methylene ATP 2-MeSATP: 2-methylthio-adenosine-5′-triphosphate MRS 2220: cyclic pyridoxine-α4,5-monophosphate-2′,5′-disulfonic acid PPADS: pyridoxalphosphate-6-azophenyl-2′,4′-disulfonate TNP-ATP: trinitrophenyl-ATP. bReference: Ralevic and Burnstock (1998). cThe stochiometry of P2X receptors has yet to be conclusively determined with evidence for tri-, tetra-, and pentameric forms. Heteromers are likely to be the rule rather than the exception. dBz ATP has been used as a selective ligand at the P2X receptor, but is far more potent at other members of the P2X 7 family. epCa2+/pNa2+ is defined as the conductance ratio.

Receptor Nomenclature Guidelines

A.1B.12 Supplement 6

Current Protocols in Pharmacology

Table A.1B.9 ATP Receptors: P2Y (GPCR; 2.1.NUCT.00.000.00.00)a,b

Receptor

GenBank/ SwissProt no. (human)

Agonists

Antagonists

Signal transduction

P2Y1 P2Y2 P2Y4 P2Y6 P2Y11

P47900 P41231 P51582 Q15077 AF030335

2-HexylthioADP UTPγS UTP UDP 2-MeSATP

MRS 2179 Suramin PPADS None None

2-MeSATP

AR C69931MX ARL 67058

Increase in IP3/DAG Increase in IP3/DAG Increase in IP3/DAG Increase in IP3/DAG Increase in IP3/DAG Increase in adenylate cyclase activity Inhibition of adenylate cyclase Increase in IP3/DAG

P2YADP/P2T

aChemical abbreviations: AR C69931MX: N6-(2-methylthioethyl)-2-(3,3,3-trifluoropropylthio)-5′-adenylic acid dichloromethylene

bis(phosphonic acid) ARL 67058: 2-propylthio-5′-adenylic acid dichloromethylene bis(phosphonic acid) DAG: diacylglycerol IP3: inositol trisphosphate 2-MeSATP: 2-methylthio-adenosine-5′-triphosphate MRS 2179: N6-methyl-2′-deoxyadenosine-3′,5′-bisphosphate PPADS: pyridoxalphosphate-6-azophenyl-2′,4′-disulfonate. bReference: Ralevic and Burnstock (1998).

Nomenclature and Useful Data

A.1B.13 Current Protocols in Pharmacology

Supplement 6

Table A.1B.10 Atrial Natriuretic Peptide Receptors (2.1.ANP.00.00.00)a,b

Receptor

GenBank/ SwissProt no. (human)

Agonists

Antagonists

Signal transduction

ANPA

P16066

ANP > BNP >> CNP

[Asu7,23]β-ANP7-28

ANPB

P20594

CNP >> ANP = BNP

None

Activation of guanylate cyclase Activation of guanylate cyclase

aChemical abbreviations:

ANP: α-atrial natriuretic peptide BNP: brain natriuretic peptide CNP: type-C natriuretic peptide. bReference: Anand-Srivastava and Trachte (1993).

Table A.1B.11 Benzodiazepine Receptors (LGIC)a

Receptor

Subunit

Agonists

Antagonists

Signal transduction

Central BZ

GABAA receptor

Flumazenil ZK 93426

See GABAA receptora

Peripheral BZ

Unknown

Diazepam Zolpidem Abecarnil None

PK 11195

Unknown

aChemical abbreviations:

GABA: γ-aminobutyric acid (see Table A.1B.27) PK 11195: 1-(2-chlorophenyl)-N-methyl-N-(1-methylpropyl)-3-isoquinoline carboxamide ZK 93426: 5-isopropyl-4-methyl-β-carboline-3-carboxylate ethyl ester.

Receptor Nomenclature Guidelines

A.1B.14 Supplement 6

Current Protocols in Pharmacology

Table A.1B.12 Bombesin Receptors (2.1.BB.00.000.00)a,b

Receptor

GenBank/ SwissProt no. (human)

BB1

Agonists

Antagonists

Signal transduction

P28336

Neuromedin B Bombesin

PD 165929

BB2

P30550

GRP

bb3

P32247

[D-Tyr6,bAla11,Phe13,Nle14] Bombesin6-14 > bombesin

Kuwanon H 1-Napthoyl[D-Ala25,D-Pro26, y26-27]GRP20-27 BW 1023U90

Inhibition of adenylate cyclase Increase in IP3/DAG Inhibition of adenylate cyclase Increase in IP3/DAG Inhibition of adenylate cyclase Increase in IP3/DAG

aChemical abbreviations:

BW 1023U90: N-[3-(4-hydroxyphenyl)-1-oxopropyl]-L-histidyl-L-tryptophyl-L-alanyl-L-valyl-N-{(1S)-2-[(2R)-2-({[(1S)-2-amino-2-oxo-1(phenylmethyl)ethyl]amino}methyl)-1-pyrrolidinyl]-1-(1H-imidazol-4ylmethyl)-2-oxoethyl}-D-alaninamide DAG: diacylglycerol IP3: inositol trisphosphate GRP: gastrin-releasing peptide PD 165929: α-[({[2,6-bis(1-methylethyl)phenyl]amino}carbonyl)amino]-α-methyl-N-{[1-(2-pyridinyl)cyclohexyl]methyl}-(αS)-1H-indole-3propanamide. bReference: Kroog et al. (1995).

Nomenclature and Useful Data

A.1B.15 Current Protocols in Pharmacology

Supplement 6

Table A.1B.13 Bradykinin Receptors (2.1.BK.00.000.00.00)a,b

Receptor

GenBank/ SwissProt no. (human)

B1

P46663

B2

P30411

Agonists

Antagonists

Signal transduction

Lys-[des-Arg9]BK BK1-8 [Tyr(Me)8]BK FR190997

B9958 R715 HOE 140 FR 173657 NPC 17331

PLC, PLA2 PLC, PLA2

aChemical abbreviations: B9958: Lys-Lys[Hyp3,Cpg5,D-Tic7,Cpg8][des-Arg9]BK

BK: bradykinin HOE 140: D-Arg[Hyp3,Thi5,D-Tic7,Oic8]BK FR 173657: (E)-3-(6-acetamido-3-pyridyl)-N-(N-{2,4-dichloro-3[(2-methyl-8-quinolinyl)oxymethyl]phenyl}-Nmethylaminocarbonylmethyl)acrylamide FR190997: 8-(2,6,-dichloro-3-{N-[(E)-4-(N-methylcarbamyl)cinnamidoacetyl]-N-methylamino}benzyloxy)-2methyl-4-(2-pyridylmethoxy)quinoline NPC 17331: D-Arg[Hyp3,D-HypE(transpropyl)7,Oic8]BK PLA2: phospholipase A2 PLC: phospholipase C R715: AcLys[D-βNal7,Ile8][des-Arg9]BK. bReference: Regoli et al. (1998).

Receptor Nomenclature Guidelines

A.1B.16 Supplement 6

Current Protocols in Pharmacology

Table A.1B.14 Calcitonin Gene–Related Peptide, Amylin, and Adrenomedullin Receptors (2.2.GGRP.00.00.00)a,b,c

Receptor

GenBank/ SwissProt no. (human)

CGRP1

P30988

CGRP2

U17473

Agonists

Antagonists

Signal transduction

hGCRPα Salmon calcitonin [CYS(ACM)2,7]hCGRPα

CGRP(8-37α) None

Amylin

Amylin

AC 187

Adrenomedullin

Adrenomedullin

hAdrenomedullin(22-52)

Increase in adenylate cyclase activity Increase in adenylate cyclase activity Increase in adenylate cyclase activity Increase in adenylate cyclase activity

aNovel forms of the CGRP family occur when these are co-expressed with a series of receptor activity–modifying proteins (RAMPs). bChemical abbreviations: AC 187: acetyl-[Asn30,Tyr32]salmon calcitonin [CYS(ACM)2,7]hCGRPα: [acetamidomethyl-Cys2,7]human CGRP. cReferences: Poyner (1992); Foord and Marshall (1999).

Nomenclature and Useful Data

A.1B.17 Current Protocols in Pharmacology

Supplement 6

Table A.1B.15

Receptor

Calcium Channels (LGIC)a,b

Subunit Composition

Signal Transduction

Agonists

Antagonists

SZ(+)-(S) 202791 ARL 64176 (–)-(S)-BayK 8644

Nifedipine Diltizaem Verapamil Calciseptine

IL 25 pS

L

α1S,α2δ,β,γ α1C,α2δ,β α1D, α2δ,β α1F8,9

N

α1B, α2δ,β

ω-Conotoxin GVIA

IN 12 -20 pS

P

α1A, α2δ,β

ω-Agatoxin IVA ω-Agatoxin IVB ω-Conotoxin MVIIC

IP 9 -19 pS

Q

α1A, α2δ,β

R T

α1E, α2δ,β α1G α1H α1I

ω-Agatoxin IVA ω-Agatoxin IVB ω-Conotoxin MVIIC

IQ 16 pS

Ni2+ Mibefradil, Flunarizine, Kurtoxin

IR Cation channel pore forming

aChemical abbreviations:

ARL 64176: 2,5-dimethyl-4 [2-(phenylmethyl)benzoyl]-H-pyrrole-3-carboxylate (–)-(S)-BayK 8644: (–)-(S)-methyl-1,4-dihydro-2,6-dimethyl-3-nitro-4-(2-trifluromethylphenyl)-pyridine5-carboxylate SZ(+)-(S) 202791: Isopropyl-4-(2,1,3-benzoxadiazol-4-yl)-1,4-dihydro-2,6-dimethyl-5-nitro-pyridine carboxylate bReference: Walker and DeWaard (1998).

Receptor Nomenclature Guidelines

A.1B.18 Supplement 6

Current Protocols in Pharmacology

Table A.1B.16 Cannabinoid Receptors (2.1.CBD.00.000.00.00)a,b

Receptor

GenBank/ SwissProt no. (human)

Agonists

Antagonists

Signal transduction

CB1

P21554

CP 55940

SR 141716A LY 320135

CB2

P34972

WIN 55212 JWH 015

SR 144528

Inhibition of adenylate cyclase Inhibition of N- and P/Q-type Ca2+ currents K+ channel activity Inhibition of adenylate cyclase

aChemical abbreviations:

CP 55940: (–)-3-[2-hydroxy-4-(1,1-dimethylheptyl)phenyl]-4-(3-hydroxypropyl)cyclohexan-1-ol JWH 015: 1-propyl-2-methyl-3-(1-naphthoyl)indole LY 320135: [6-methoxy-2-(4-methoxyphenyl)benzo[b]thien-3-yl]-4-cyanophenyl-methanone SR 141716A: N-(piperidin-1-yl)-5-(4-chlorophenyl)-4-methyl-1H-pyrazole-3-carboxamide hydrochloride SR 144528: N-[(1S)-endo-1,3,3-trimethylbicyclo[2.2.1]heptan-2-yl]-5-(4-chloro-3-methylphenyl)-1-(4-methylbenzyl) pyrazole-3-carboxamide WIN 55212: (R)-(+)-[2,3-dihydro-5-methyl-3-[(4-morpholino)methyl]pyrrolo-[1,2,3-de]-1,4-benzoxazin-6-yl-(1naphthyl)methanone. bReference: Pertwee (1997).

Nomenclature and Useful Data

A.1B.19 Current Protocols in Pharmacology

Supplement 6

Table A.1B.17 Chemokine Receptors: CC (β Family; 2.1.CC.00.000.00.00)a,b

Receptor

GenBank/ SwissProt no. (human)

Agonists

Antagonists

Signal transduction

CCR1

P32446

MIP-1α ≈ MCP-3 > RANTES

None

CCR2

P41597

MCP-1

None

CCR3

P51677

mAb7B11

CCR4

P51679

CCR5

P51681

Eotaxin RANTES TARC MDC MIP-1β

Increase in [Ca2+] Inhibition of adenylate cyclase Increase in [Ca2+] Inhibition of adenylate cyclase Increase in [Ca2+] Inhibition of adenylate cyclase Unknown

CCR6 CCR7

P51684 P32248

None None

CCR8 CCR9

P51685 Y12815

LARC (a.k.a. MIP-3α) MIP-3b ELC SLC TCA-4 Exodus-2 I 309 CC chemokines MCP-1 HCC-1 CX3C chemokines (fractalkine, neurotactin) GRO-α IL-8 IL-8 GRO-α NAP-2 MIP-1α MIP-1β IL-8 NAP-2

Increase in [Ca2+] Inhibition of adenylate cyclase Increase in [Ca2+] Unknown

None None

Inhibition of adenylate cyclase Inhibition of adenylate cyclase

None

Inhibition of adenylate cyclase

None

Unknown

None

Unknown

None

Unknown

None

Inhibition of adenylate cyclase activity Increase in IP3/DAG

CX3CR1 DARC ECRF3

US28 KSHV

None mAb2D7

aChemical abbreviations:

DAG: diacylglycerol DARC: Duffy antigen receptors for chemokines ELC: Epstein-Barr virus-induced receptor ligand chemokine IL-8: interleukin 8 IP3: inositol trisphosphate KSHV: Kaposi’s sarcoma-associated herpes virus LARC: liver and activation-related chemokines MCP: monocyte chemoattractant protein MDC: macrophage-derived chemokine MIP: macrophage inflammatory protein NAP-2: neutrophil-attractant peptide 2 RANTES: regulated on activation, normal T cell expressed and secreted SLC: second lymphoid tissue chemokine TARC: T-cell and activation-related chemokine. bReference: Baggioloni et al., (1997).

Receptor Nomenclature Guidelines

A.1B.20 Supplement 6

Current Protocols in Pharmacology

Table A.1B.18 Chemokine Receptors: CXC (α Family; 2.1.CXC.00.000.00.00)a,b

Receptor

GenBank/ SwissProt no. (human)

Agonists

Antagonists

Signal transduction

CXCR1

P25024

IL-8 > GCP-2

mAb9H1

CXCR2

P25025

mAb10H2

CXCR3

P49682

CXCR4

P30991

IL-8 GRO-α ENA-78 Mig γ-IP-10 SDF-1α (PBSF) SDF-1β

Increase in [Ca2+] Inhibition of adenylate cyclase Increase in [Ca2+] Inhibition of adenylate cyclase Unknown

CXCR5

P32302

BCA-1

None mAb12G5 ALX40 4C AMD 3100 T22 None

Increase in [Ca2+] Inhibition of adenylate cyclase Unknown

aChemical abbreviations:

BCA-1: B cell chemoattractant 1 ENA-78: epithelial cell–derived neutrophil-activating factor GCP-2: granulocyte chemotactic protein 2 IL-8: interleukin-8 SDF: stromal cell–derived factor T22: [Tyr5,12,Lys7]polyphemusin II. bReferences: Baggiolini et al. (1997); Murphy et al. (1998).

Nomenclature and Useful Data

A.1B.21 Current Protocols in Pharmacology

Supplement 6

Table A.1B.19 Chloride Channels: Voltage-Sensitive, cAMP-Regulated, Ca2+-Activated, Maxi Cl−, and Volume-Regulateda,b

Channel family

Conductance (pS)

Inhibitors

Function

ClC-1

1

ClC-2 ClC-3 ClC-4 ClC-5 ClC-6 ClC-7 ClC-Ka ClC-Kb CTFR CaCC

3-5 Unknown Unknown Unknown Unknown Unknown Unknown Unknown 6-10 0.5-5

Cell volume regulation Membrane potential regulation Unknown Unknown Unknown Unknown Unknown Unknown Unknown Unknown Transepithelial transport Unknown

VRAC

20-50

Maxi Cl−

250-430

Unknown

1-10

SITS DIDS NPPB None None None None None None None None Glibenclamide Niflumeic acid Mibefradil Tamoxifen NPPB 9-AC Tamoxifen Gossypol Mibefradil Nucleotides SITS DIDS NPPB DPC SITS DIDS NPPB

Unknown

Unknown

Neurotransmitter-regulated smooth muscle contraction

aChemical abbreviations:

9-AC: anthracene-9-carboxylic acid CaCC: Ca2+-activated Cl− channel CTFR: cystic fibrosis transmembrane conductance regulator DIDS: 4,4′-diisothiocyanostilbene-2,2′-disulfonic acid DPC: diphenylamine carboxylic acid NPPB: 5-nitro-2-(3-phenylpropylamino)benzoic acid SITS: 4′-isothiocyanostilbene-2,2′-disulfonic acid VRAC: volume-regulated anion channel. bReferences: Jentsch and Gunther (1997).

Receptor Nomenclature Guidelines

A.1B.22 Supplement 6

Current Protocols in Pharmacology

Table A.1B.20 Cholecystokinin And Gastrin Receptors (2.1.CCK.00.000.00.00)a,b

Receptor

GenBank/ SwissProt no. (human)

CCK1

P32238

CCK2

P32239

Agonists

Antagonists

Signal transduction

A 71623 JMV 180 AR-R 15849 BC 264 CCK-8 desulfated

Devazepide SR 27897 PD 140548 YM 022 CI 988 RP 73870 RP 69758

Stimulation of adenylate cyclase PLC activation PLC activation Arachidonic acid release

aChemical abbreviation:

A 71623: t-butyloxycarbonyl-Trp-Lys-(O-toluylaminocarbonyl)-Asp-(N-methyl-Phe)-NH2 AR-R 15849: (Hpa(SO3H)-Nle-Gly-Trp-Nle-N-(methyl)-Asp-Phe-NH2) BC 264: Tyr(SO3H)-gNle-mGly-Trp-(N-methyl-Nle)-Asp-Phe-NH2 CI 988: [R-(R*,R*)]-4-[2-({3-(1H-indol-3-yl)-2-methyl-1-oxo-2-[(tricyclo[3.3.1.13.7]dec-2yloxy)carbonyl]amino}propyl)amino]-1-phenylethylamino-4-oxobutamoate-1-deoxy-1-(methylamino)-D-glucitol JMV 180: t-butyloxycarbonyl-Tyr(SO3H)-Nle-Gyl-Trp-N6-Asp-2-phenylethylester PD 140548: N-{α-methyl-N-[(tricyclo[3.3.1.13,7]dec-2-yloxy)carbonyl]-L-tryptophyl}-D-3-phenylmethyl-β-alanine PLC: phospholipase C RP 69758: 3-{3-[N-(N-methyl-N-phenyl-carbamoylmethyl)-N-phenyl-carbamoylmethyl]ureido}phenylacetic acid RP 73870: ({[(N-methoxy-3-phenyl)-N-(N-methyl-N-phenyl-carbamoylmethyl)carbamoylmethyl]-3-ureido}-3phenyl)-2-ethylsulfonate-(RS) SR 27897: 1-({2-[4-(2-chlorophenyl)thiazole-2-yl]aminocarbonyl}indolyl)acetic acid YM 022: (R)-1-[2,3-dihydro-1-(2′-methylphenacyl)-2-oxo-5-phenyl-1H-1,4-benzodiazepin-3-yl]-3-(3methylphenyl)urea. bReferences: Wank (1995); Roques and Noble (1998).

Nomenclature and Useful Data

A.1B.23 Current Protocols in Pharmacology

Supplement 6

Table A.1B.21 Corticotropin-Releasing Factor Receptors (2.2.CRF.00.000.00.00)a,b

Receptor

GenBank/ SwissProt no (human)

CRF1

Agonists

Antagonists

Signal transduction

P34998

CRF Urocortin Sauvagine

Stimulation of adenylate cyclase

CRF2(a)

Q13324

CRF2(b)

P32239

CRF Urocortin CRF Urocortin None

CP 154526 Astressin NBI 27914 Antalarmin α-Helical oCRF(9-41) α-Helical oCRF(9-41) None

CRF2(c)

Stimulation of adenylate cyclase Stimulation of adenylate cyclase Stimulation of adenylate cyclase

aChemical abbreviations:

CP 154526: butyl-ethyl-[2,5-dimethyl-7-(2,4,6-trimethylphenyl)-7H-pyrrolo[2,3-d]pyrimidin-4-yl]amine NBI 27914: 2-methyl-4-(N-propyl-N-cyclopropanemethylamino)-5-chloro-6-(2,4,6-trichloroanilino)pyrimidine oCRF: ovine CRF. bReference: De Souza et al. (1998).

Receptor Nomenclature Guidelines

A.1B.24 Supplement 6

Current Protocols in Pharmacology

Table A.1B.22 Cytokine Receptors: Hematopoeitin Receptor Family (3.2.IL.00.000.00)a,b,c

Receptor

Agonists

IL-2

IL-2

IL-3 IL-4

IL-3 IL-4

IL-5 IL-6 IL-7 IL-9 IL-10 IL-11 IL-12 IL-13 IL-15 GM-CSF G-CSF EPO

IL-5 IL-6 IL-7 IL-9 IL-10 IL-11, OSM IL-12 IL-13 IL-15 GMCSF GCSF EPO SB LIF OSM OSM CNTF TPO Unknown

LIF OSM OSM Type II CNTF TPO Leptin

Antagonists

Signal transduction Ick, lyn, JAK/STAT NFκB JAK/STAT JAK/STAT IRS-1, IRS-2/4PS JAK/STAT lyn, JAK/STAT JAK/STAT JAK/STAT STAT JAK/STAT JAK/STAT JAK/STAT JAK/STAT JAK/STAT JAK/STAT JAK/STAT JAK/STAT JAK/STAT JAK/STAT JAK/STAT JAK/STAT JAK/STAT

aThere are no known antagonists of the hematopoeitin cytokine receptors. bChemical abbreviations:

CNTF: ciliary neurotropic factor EPO: erythropoetin G-CSF: granulocyte colony-stimulating factor GM-CSF: granulocyte/macrophage colony-stimulating factor Ick: Lystra kianse IL: interleukin JAK: Janus kinase LIF: leukemia inhibitory factor OSM: oncostatin M STAT: signal transducer and activator of transcription TPO: thrombopoeitin. cGiven the complexity of the area and the many acronyms given to the same factors by different laboratories and based on species differences, the reader is referred to the Cytokines Online Pathfinder Encyclopaedia (COPE) Web site at http://www.copewithcytokines.de/cope.cgi.

Nomenclature and Useful Data

A.1B.25 Current Protocols in Pharmacology

Supplement 6

Table A.1B.23 Cytokine Receptors: Interleukin-1 Receptor Family (3.2.IL-1.00.000.00)a,b

Receptor

Agonists

Antagonists

Signal transduction

IL-1R1

IL-1α IL-1β None IGIF IGIF None None None None None

IL-1Ra

SAPK/NFκB

None None None None None None None None

NFκB NFκB NFκB NFκB Unknown Unknown Unknown Unknown

ST-2 IL-1rrp-1 IL-1rrp-2 TLR-1 TLR-2 TLR-3 TLR-4 TLR-5

aChemical abbreviations:

IL: interleukin IL-1Ra: IL-1 receptor antagonist rrp: receptor-related protein TLR: Toll-like receptor. bGiven the complexity of the area and the many acronyms given to the same factors by different laboratories and based on species differences, the reader is referred to the Cytokines Online Pathfinder Encyclopaedia (COPE) Web site at http://www.copewithcytokines.de/cope.cgi.

Receptor Nomenclature Guidelines

A.1B.26 Supplement 6

Current Protocols in Pharmacology

Table A.1B.24 Cytokine Receptors: Tumor Necrosis Factor Family (3.2.TNF.00.000.00)a-d

Receptor

Agonists

TNF-I (p55)

TNF3 LTα TNF3 LTα LTα1β2 LTα2β1 NGF LIGHT Fas ligand CD40 ligand CD30 ligand

TNF-II (p75) TNF-III (LTβR) NGF LIGHT Fas/CD95/TNF-SF6 CD40 CD30 CD27

CD27 ligand CD70

OX40/CD134 DR-1 DR-2 wsl-1/DR-3/TR-3 Osteoprotegerin

CD134 TRAIL TRAIL TRAIL ODF RANKL TNF RSF11A

Antagonist

Signal transduction TRAF/SAPK TRAF/SAPK TRAF/SAPK Trk kinase TRAF/SAPK Apoptosis Src kinase TRAF Apoptosis Apoptosis NFκB SAPK Unknown Apoptosis Apoptosis Apotosis Apoptosis

aThe IL-8 receptor is a member of the CXC chemokine family (see Table A.1B.18). bThere are no known antagonists of the TNF cytokine receptors. cChemical abbreviations:

DR: death receptor Ick: Lystra kinase JAK: Janus kinase LIGHT: inducible lymphotoxin on T cells that binds to herpes virus entry protein NGF: nerve growth factor ODF: osteolclast differentiation factor RANKL: receptor activator of NFkB ligand SAPK: stress-activated protein kinase STAT: signal transducer and activator of transcription TR: TNF receptor like TRAF: TNF receptor–associated factor TRAIL: TNF-related apoptosis-inducing ligand. dGiven the complexity of the area and the many acronyms given to the same factors by different laboratories and based on species differences, the reader is referred to the Cytokines Online Pathfinder Encyclopaedia (COPE) Web site at http://www.copewithcytokines.de/cope.cgi.

Nomenclature and Useful Data

A.1B.27 Current Protocols in Pharmacology

Supplement 6

Table A.1B.25 Dopamine Receptors (2.1.DA.00.000.00.00)a,b

Receptor

GenBank/ SwissProt no. (human)

D1

P21728

D2

P14416

D3

P35462

D4

P21917

D5

P21918

Agonists

Antagonists

Signal transduction

(R)-(+)-SKF 81297 A 77636 ABT 431 (+)-PHNO U 91356PS Lisuride Bromocriptine PD 128907 Pramipexole Quinelorane PD 168077

SCH 23390 SCH 39166

Stimulation of adenylate cyclase

Nemonapride Raclopride Domperidone L 741626 Nafadotride U 99194A

Inhibition of adenylate cyclase

ABT 431 SKF 38393

RBI 257 L 745870 U 101387 SCH 23390

Inhibition of adenylate cyclase MAP kinase activation Inhibition of adenylate cyclase Stimulation of adenylate cyclase

aChemical abbreviations:

A 77636: (−)-(1R,1S)-3-adamantyl-1-aminomethyl-2,4-dihydro-5,6-dihydroxy-1H-2-benzopyrzn hydrochloride ABT 431: (−)-trans-9,10-acetoxy-2-propyl-4,5,5a,6,7,11b-hexahydro-3-thia-5-azacyclopent-1-ena[c]phenanthrycene hydrochloride L 741626: 4-(4-chlorophenyl)-1-(1H-indol-3-ylmethyl)-4-piperidinol L 745870: 3-{[4-(4-chlorophenyl)piperazin-1-yl]methyl}-1H-pyrrolo[2,3-b]pyridine MAP kinase: mitogen-activated protein kinase PD 128907: (R)-(+)-trans-3,4,4a,10b-tetrahydro-4-propyl-2H,5H-[1]benzopyranol[4,3-b]-1,4-oxazine-9-ol (+)-PHNO: 9-hydroxy-4-propyl-naphthoxazine RBI 257: 1-(4-iodobenzyl)-4-[C2-(3-isopropoxy)pyridyl]methylaminopiperidine maleate SCH 23390: 7-chloro-8-hydroxy-3-methyl-5-phenyl-2,3,4,5-tetrahydro-1H-3-benzazepine SCH 39166: (–)-trans-6,7,7a,8,9,13b-hexahydro-3-chloro-2-hydroxy-N-ethyl-5H-benzo[d]naphtho[2,b]azepine SKF 38393: 7,8-dihydroxy-1-phenyl-2,3,4,5-tetrahydro-1H-3-benzazepine (R)-(+)-SKF 81297: (R)-(+)-6-chloro-7,8-dihydroxy-1-phenyl-2,3,4,5-tetrahydro-1H-benzazepine U 101387: (S)-(–)-{4-[4-(isochroman-1-yl)ethyl]piperazin-1-yl}benzenesulfonamide U 91356PS: [(R)-5,6-dihydro-5-(propylamino)-4H-imidazo[4,5,1-1j]quinolin-2-(1H)-one monohydrochloride U 99194A: 2,3-dihydro-5,6-dimethoxy-N,N-dipropyl-1H-inden-2-amine hydrochloride. bReference: Missale et al. (1998).

Receptor Nomenclature Guidelines

A.1B.28 Supplement 6

Current Protocols in Pharmacology

Table A.1B.26 Endothelin Receptors (2.1.ET.00.000.00.00)a,b

Receptor

GenBank/ SwissProt no. (human)

Agonists

Antagonists

Signal transduction

ETA

P25101

ET-1 = ET-2 > ET-3

Increase in PI turnover: elevation of [Ca2+]i

ETB

P24530

ET-1 = ET-2 = ET-3 IRL 1620 BQ 3020

A 127722 ABT 627 BQ 123 PD 151242 BQ 788 IRL 2500 PD 142893 Ro 468443

Increase in PI turnover: elevation of [Ca2+]i

aChemical abbreviations:

A 127722: trans-trans-2-(4-methoxyphenyl)-4-(1,3-benzodioxol-5-yl)-1-[(N,Ndibutylamino)carbonylmethyl]pyrrolidine-3-carboxylate ABT 627: (2R,3R,4S)-2-(4-methoxyphenyl)-4-(1,3-benzodioxol-5-yl)-1-{[(N,N-dibutylamino)carbonyl]methyl}pyrrolidine-3carboxylic acid BQ 123: cyclo-(D-Trp-D-Asp-Pro-D-Val-Leu) BQ 788: N-cis-2,6-dimethylpiperidinocarbonyl-L-γ-methylleucyl-D-1-methoxycarbonyl-D-norleucine BQ 3020: N-acetyl-Leu-Met-Asp-Lys-Glu-Ala-Val-Tyr-Phe-Ala-His-Leu-Asp-Ile-Ile-Trp IRL 1620: Suc[Glu9,Ala11,15]ET-1-10-21 IRL 2500: N-(3,5-dimethylbenzoyl)-N-methyl-D-(4-phenylphenyl)-Ala-Trp PD 142893: N-acetyl-β-phenyl-D-Phe-L-Leu-L-α-Asp-L-Ile-L-Ile-L-Trp disodium salt PD 151242: {N-[(hexahydro-1-azepinyl)carbonyl]}-Leu(1-Me)-D-Trp-D-Tyr PI: phosphoinositide Ro 468443: (R)-4-tert-butyl-N-[6-(2,3-dihydroxypropoxy)-5-(2-methoxyphenoxy)-2-(4-methoxyphenyl)pyrimidin-4yl]benzenesulfonamide. bReference: Davenport and Masaski (1998).

Nomenclature and Useful Data

A.1B.29 Current Protocols in Pharmacology

Supplement 6

Table A.1B.27 GABAA Receptor (LGIC; 1.1.GABA.00.000.00)a,b,c

Receptor

Agonists

Antagonists

Signal transduction

GABAA

THIP Muscimol

Bicuculline SR 95531

Cl− influx

aChemical abbreviations:

GABA: γ-aminobutyric acid SR 95531: 2-(3′-carboxy-2′-propyl)-3-amino-6-p-methoxyphenylpyridazinium bromide THIP: 4,5,6,7-tetrahydroisoxazolo[5,4-c]pyridinyl-3-ol. bThe GABA receptor is a subtype of the GABA receptor. C A cReference: Costa (1998).

Table A.1B.28 GABAB Receptors (GPCR; 2.1.GABA.00.000.00)a,b

Receptor

Genbank/SwissProt Agonists no. (human)

GABABR1

AJO12185

GABABR2

AJO12188

(R)-Baclofen CGP 35024

Antagonists

Signal Transduction

Phaclofen SCH 50911 CGP 35348

Inhibition of adenylate cyclase; Modulation of Kir activity

aChemical abbreviations:

CPG 35348: p-(3-aminopropyl)-P-diethoxymethylphosphinic acid Kir: inwardly rectified potassium channel SCH 50911: (+)-(2S)-5,5-dimethyl-2-morpholineacetic acid. bFunctional GABA receptors require dimer formation between two subunits, GABA R and GABA R . GABA R B B 1 B 2 B 1 can bind antagonists, but is non-functional. cReference: Bowery (1993); White et al. (1998).

Receptor Nomenclature Guidelines

A.1B.30 Supplement 6

Current Protocols in Pharmacology

Table A.1B.29 Galanin Receptors (2.1.GAL.00.000.00)a,b

Receptor

GenBank/ SwissProt no. (human)

Agonists

Antagonists

Signal transduction

GAL1

P42711

Galanin 1-16

None

GAL2 GAL3

AF040630 AF073749

Galanin 2-16 Galanin 2-29

None None

Inhibition of adenylate cyclase activation Increase IP3/DAG Unknown

aChemical abbreviations:

DAG: diacylglycerol IP3: inositol trisphosphate. bReference: Bartfai (1995).

Nomenclature and Useful Data

A.1B.31 Current Protocols in Pharmacology

Supplement 6

Table A.1B.30 Glutamate Receptors: Ionotropic (LGIC; 1.2.GLU.00.000.00)a,b

Receptor

Subunit composition

NMDA (Glu site)

NR1/NR2(A-D) NMDA Quinolinic acid

NMDA (Gly site)

Agonists

Glycine D-Serine

NMDA (NR2B)

None

AMPA

GLUR 1-4

Kainate

KA1/2 GLUR 5-7

AMPA (S)-5-Fluorowillardine Quisqualic acid Kainic acid Domoic acid 4-Methylglutamate

Antagonists

Signal transduction

CGS 19755 D-CPPene MK 801c Ketaminec L 689560 7-U-Kynurenic acid MNQX GV 19677A Ro 256981 CP 101606 Ifenprodil CNQX YM90K

Ion flux (Na+/K+/Ca2+)

CNQX DNQX LY 294486

Ion flux (Na+/K+/Ca2+)

Unknown

Unknown

Ion flux (Na+/K+/Ca2+)

aChemical abbreviations:

AMPA: (RS)-2-amino-3-(3-hydroxy-5-methyl-4-isoxazolyl)proprionic acid CGS 19755: 4-phosphonomethyl-2-piperidinecarboxylic acid CNQX: 6-cyano-7-nitroquinoxaline-2,3-dione CP 101606: (1S,2S)-1-(4-hydroxyphenyl)-2-(4-hydroxy-4-phenylpiperidino)-1-propanol D-CPPene: (±)-3-(2-carboxypiperizin-4-yl)propyl-1-phosphonic acid DNQX: 6,7-dinitroquinoxaline-2,3-dione GLUR: glutamate receptor GV 19677A: (E)-4,6-dichloro-3-(2-oxo-1-phenylpyrrolidin-3-ylidenemethyl)-1H-indole-2-carboxylic acid L 689560: trans-2-carboxy-5,7-dichloro-4-phenylaminocarbonylamino-1,2,3,4-tetrahydroquinoline LY 294486: (3SR,4αSR,6SR,8SR)-6-({[(1H-tetrazol-5-yl)methyl]oxy}methyl)-1,2,3,4α,5,6,7,8,8αdecahydroisoquinolone-3-carboxylic acid MK 801: (5R,10S)-(+)-5-methyl-10,11-dihydro-5H-dibenzo[a,d]cyclohepte-5,10-imine MNQX: 6,8-dinitroquinoxaline-2,3-dione NMDA: N-methyl-D-aspartate Ro 256981: α-(4-hydroxyphenyl)-β-methyl-4-phenylmethyl-(αR,αS)-1-piperidinepropanol YM90K: [6-(1H-imidazol-1-yl)-7-nitro-2,3(1H,4H)]quinoxalinedione monohydrochloride. bReference: Hollman and Heinemann (1994). cChannel blocker.

Receptor Nomenclature Guidelines

A.1B.32 Supplement 6

Current Protocols in Pharmacology

Table A.1B.31 Glutamate Receptors: Metabotropic (GPCR; 2.1.GLU.00.000.00)a,b

Receptor

GenBank/ SwissProt no. (human)

Agonists

Antagonists

Signal transduction

mglu1

Q13255

DHPG

Increase in IP3/DAG

mglu2

Q14416

mglu3

Q14832

(2R,4R)-APDC LY 354740 DCG-IV NAAG

AIDA (S)-(+)-CBPG LY 341495

LY 341495

mglu4

Q14833

L-AP4 L-SOP

MAP4

mglu5 mglu6

P41594 O15303

DHPG L-AP4

4CPG MAP4

mglu7

Q14831

L-AP4

MAP4

mglu8

O00222

L-AP4

MPPG

Inhibition of adenylate cyclase Inhibition of adenylate cyclase Increase in IP3/DAG Inhibition of adenylate cyclase Inhibition of adenylate cyclase Inhibition of adenylate cyclase

Inhibition of adenylate cyclase

aChemical abbreviations:

AIDA: 1-aminoindan-1,5(RS)-dicarboxylic acid L-AP4: S-2-amino-4-phosphonobutyrate (2R,4R)-APDC: aminopyrrolidine-2R,4R-dicarboxylate (S)-(+)-CBPG: (S)-(+)-2-(3′-carboxybicyclo[1.1.1]phenyl)glycine 4CPG: 4-carboxyphenylglycine DAG: diacylglycerol DCG-IV: (2S,1′R,2′R,3′R)-2-(2,3-dicarboxycyclopropyl)glycine DHPG: S-3,5-dihydroxyphenylglycine IP3: inositol trisphosphate NAAG: N-acetylaspartylglutamate L-SOP: L-serine-O-phosphate. bReference: Conn and Pin (1998).

Nomenclature and Useful Data

A.1B.33 Current Protocols in Pharmacology

Supplement 6

Table A.1B.32 Glycine Receptors (1.1.GLY.00.000.00)a,b

Receptor

Agonists

Antagonists

Signal transduction

Glycine

Glycine β-Alanine Taurine

Strychnine

Cl− influx

PMBA

Unknown

Glycine (strychnineinsensitive)

aChemical abbreviation:

PMBA: 3-[2-phosphonomethyl-(1,1-biphenyl)-3-yl]alanine. bReference: Betz (1991).

Table A.1B.33 Glycoprotein Hormone Receptors (GPCR; 2.1.SH.00.000.00)a

Receptor

GenBank/ SwissProt no. (human)

SH

P23945

LSH

P22888

Follicle-stimulating hormone Luteinizing hormone

TSH

P16473

Thyrotropin hormone

Agonists

Antagonists

Signal transduction Activation of adenylate cyclase Activation/inhibition of adenylate cyclase Activation/inhibition of adenylate cyclase

aReference: Simoni and Nieschlag (1997).

Receptor Nomenclature Guidelines

A.1B.34 Supplement 6

Current Protocols in Pharmacology

Table A.1B.34 Histamine Receptors (GPCR; 2.1.HIS.00.000.00)a,b

Receptor

GenBank/ SwissProt no. (human)

H1

P35367

H2

P20521

H3

AF140538

Agonists

Antagonists

Signal transduction

2-[3-(Trifluromethyl)] phenylhistamine Dimaprit Amthamine Imetit (R)-α-Methylhistamine

(+)-Chlorpheniramine Triprolidine Cimetidine Ranitidine Ciproxifan Thioperamide GT 2016

Increase in IP3/DAG Increase in adenylate cyclase activity Inhibition of adenylate cyclase activity

aChemical abbreviations:

DAG: diacylglycerol GT 2016: 1-(5-cyclohexyl-1-oxopentyl)-4-(1H-imidazol-4-yl)piperidine IP3: inositol trisphosphate. bReferences: Leurs et al. (1995); Lovenberg et al. (1999).

Nomenclature and Useful Data

A.1B.35 Current Protocols in Pharmacology

Supplement 6

Table A.1B.35 5-Hydroxytryptamine (Serotonin) Receptors (GPCR; 2.1.5HT.00.000.00.00)a,b

Receptor

GenBank/ SwissProt no. (human)

5-HT1A

Agonists

Antagonists

Signal transduction

P8908

8-OH-DPAT U 92106A

WAY 100635 NAN 190

5-HT1B

P28222

L 694247 Sumatriptan

GR 55562 GR 127935 SB 216641

5-HT1D

P28221

BRL 15572

5-ht1e

P28566

Sumotriptan Almotriptan Zomitriptan L 694247 BRL 54443

5-ht1f

P30939

5-HT2A

P28223

Inhibition of cAMP formation Increase Kir activity PLC activation Inhibition of cAMP formation Increase Kir activity PLC activation Inhibition of cAMP formation Increase Kir activity PLC activation Inhibition of cAMP formation Inhibition of cAMP formation Increase in PI hydrolysis and elevation of [Ca2+]i

5-HT2B

P41595

BW 723C86

5-HT2C

P28221

m-CPP

5-HT4

Y09586

5-ht5a 5-ht5b 5-ht6

P47898 P35365 P50406

BIMU8 SC 53116 RS 67506 LSD LSD LSD

5-HT7

P50407

5-CT

BRL 54443 LY 334370 DOB (+)-DOI

None None Ketantenin MDL 100907 AMI 193 SB 206553 SB 242084 RS 102221 GR 113808 RS 100235 SB 204070 None None Ro 046790 Ro 630563 SB 258719

Increase in PI hydrolysis and elevation of [Ca2+]i Increase in PI hydrolosis and elevation of [Ca2+]i Increase in cAMP formation

Unknown Unknown Increase in cAMP formation Increase in cAMP formation

aChemical abbreviations:

Receptor Nomenclature Guidelines

AMI 193: 8-[3-(4-fluorophenoxy)propyl]-1-phenyl-1,3,8-triazaspiro[4.5]decan-4-one BIMU8: (endo-N-8-methyl-8-azabicyclo[3.2.1]oct-3-yl)-2,3-dihydro-3-isopropyl-2-oxo-1H-benzimidazol-13carboxamide hydrochloride BRL 15572: 3-[4-(3-chlorophenyl)piperazin-1-yl]-1,1-diphenyl-2-propanol BRL 54443: 3-(1-methylpiperidin-4-yl)1H-indol-5-ol BW 723C86: 1-[5(2-thienylmethoxy)-1H-3-indolyl]propan-2-amine hydrochloride 5-CT: 5-carboxamidotryptamine DOB: (±)-2,5-dimethoxy-4-bromoamphetamine DOI: (±)-2,5-dimethoxy-4-iodoamphetamine GR 55562: 3-[3-(dimethylamino)propyl]-4-hydroxy-N-[4-(4-pyridinyl)phenyl]benzamide GR 113808: {1-2[(methylsulfonyl)amino]ethyl}-4-piperidinyl]methyl-1-methyl-1H-indole-3-carboxylate GR 127935: N-[methoxy-3-(4-methyl-1-piperazinyl)phenyl]-2′-methyl-4′-(5-methyl-1,2,4-oxadiazol-3-yl)[1,1-biphenyl]-4carboxamide Kir: inwardly rectifying potassium channel m-CPP: m-chlorophenylpiperazine MDL 100907: (±)-2,3-dimethoxyphenyl-1-[2-(4-piperidine)methanol] L 694247: 2-{5-[3-(4-methylsulfonylamino)benzyl-1,2,4-oxadiazol-5-yl]-1H-indol-3-yl}ethanamine LSD: lysergic acid diethylamide LY 334370: 4-fluoro-N-[3-(1-methyl-4-piperidinyl)-1H-indole-5-yl]benzamide NAN 190: 1,1-(2-methoxyphenyl)-4-[4-(2-phthalimido)butyl]piperazine 8-OH-DPAT: 8-hydroxy-dipropylaminotetralin

continued

A.1B.36 Supplement 6

Current Protocols in Pharmacology

Table A.1B.35 5-Hydroxytryptamine (Serotonin) Receptors (GPCR; 2.1.5HT.00.000.00.00)a,b, continued aChemical abbreviations: (continued)

PI: phosphoinositide PLC: phospholipase C Ro 046790: 4-amino-N-[2,6-bis(methylamino)pyrimidin-4-yl]benzenesulfonamide Ro 630563: 4-amino-N-[2,6-bis(methylamino)pyridin-4-yl]benzenesulfonamide RS 67506: 1-(4-amino-5-chloro-2-methoxyphenyl)-3-(1-n-butyl-4-piperidinyl)-1-propanone RS 100235: 1-(8-amino-7-chloro-1,4-benzodioxan-5-yl)-5-{[3-(3,4-dimethoxyphenyl)prop-1-yl]piperidin-4yl}propan-1-one RS 102221: 8-[5-(5-amino 2,4-dimethoxyphenyl)-5-oxopentyl]-1,3,8-triazaspiro[4.5]decane-2,4-dione SB 204070: 1-butyl-4-piperidinylmethyl-8-amino-7-chloro-1-4-benzodioxan-5-carboxylate SB 206553: 5-methyl-1-(3-pyridylcarbamoyl)-1,2,3,5-tetrahydropyrrolo[2,3-f]indole SB 216641: N-{3-[2-(dimethylamino)ethoxy]-4-methoxyphenyl}-2′-methyl-4′-(5-methyl-1,2,4-oxadiazol)-3-yl) [1,1′-biphenyl]-4-carboxamide SB 242084: 6-chloro-5-methyl-1-[2-(2-methylpyridyl-3-oxy)-pyrid-5-ylcarbamoyl]indoline SB 258719: (R)-3-N-dimethyl-N-[1-methyl-3-(4-methylpiperidin-1-yl)propyl]benzene sulfonamide SC 53116: 4-amino-5-chloro-N-{[(1S,7aS)-hexahydro-1H-pyrrolizin-1-yl]methyl}-2-methoxy-benzamide WAY 100635: N-(2-(4-(2-methoxyphenyl)-1-piperazinyl)ethyl)-N-(2-pyridyl)-cyclohexanecarboxamide trichloride. bReference:Boess and Martin (1994).

Nomenclature and Useful Data

A.1B.37 Current Protocols in Pharmacology

Supplement 6

Table A.1B.36 5-Hydroxytryptamine (Serotonin) Receptor (LGIC; 1.1.5HT.00.000.00.00)a,b

Receptor

GenBank/ SwissProt no. (human)

5-HT3

P46098

Agonists

Antagonists

Signal transduction

2Me5-HT SR57227

Tropisetron Granisetron Ondansetron

Acts as ion channel to increase [Ca2+]i

aChemical abbreviations:

2Me5-HT: 2-methyl-5-hydroxytryptamine. SR 57227: 4-amino-(6-chloro-2-pyridyl)-1-piperidine hydrochloride bReference: Boess and Martin (1994).

Table A.1B.37 Imidazoline Binding Sitesa,b,c

Receptor

GenBank/SwissProt Agonists no. (human)

I1 I2

Cirazoline BU 224 RX801077

Antagonists

Signal Transduction Unknown Monoamine oxidase?

aThere are no known antagonists for imidazoline binding sites. bChemical abbreviations:

BU 224: 2-(2-benzofuranyl)-2-imidazoline RX 801077: 1H-imidazole-2-(2-benzofuranyl)-4,5-dihydro. cReference: Regunathan and Reis (1996).

Receptor Nomenclature Guidelines

A.1B.38 Supplement 6

Current Protocols in Pharmacology

Table A.1B.38

Inositol Trisphosphate (IP3) Receptorsa

Receptor

Subunit Agonists Composition

IP31

Tetramer

IP32

Tetramer

IP33

Tetramer

Ins(1,4,5)P3 Ins(2,4,5)P3 Adenophostin A Ins(1,4,5)P3 Ins(2,4,5)P3 Adenophostin A Ins(1,4,5)P3

Antagonists

Signal Transduction

Xestospongin C Heparin Caffeine Heparin

Ca2+ single channel conductance ∼70 pS

Ins(1,4,5)P3

Ca2+ single channel conductance ∼70 pS/390 pS Ca2+ single channel conductance ∼88 pS

aReference: Joseph (1996)

Nomenclature and Useful Data

A.1B.39 Current Protocols in Pharmacology

Supplement 6

Table A.1B.39 Leukotriene Receptors (2.1.LT.00.000.00.00)a,b

Receptor

GenBank/ SwissProt no. (human)

Agonists

Antagonists

Signal transduction

BLT

Q15722

LTB4

Increase in IP3/DAG

CysLT1

AF119711

LTD4

CysLT2

NAc

LTC4

RG 14893 SB 209247 Montelukast Pranlukast ZD 3523 Zafirlukast SKF 104353 BAYx 7195 BAYu 9773

Increase in IP3/DAG

Increase in IP3/DAG

aChemical abbreviations:

BAYu 9773: (6R)-(4′-carboxyphenylthio)-(5S)-hydroxy-(7ZE,11Z,14Z)-eicosatetraenoic acid BAYx 7195: (4S)-(4-carboxyphenylthio)-7-[4-(4-phenoxybutoxy)phenyl]hept-5(Z)-enoic acid DAG: diacylglycerol IP3: inositol trisphosphate LTB4, LTC4, and LTD4: leukotrienes B4, C4, and D4 RG 14893: 4-{2-[methyl(2-phenylethyl)amino]-2-oxoethyl}-8-phenylmethoxy-2-naphthalenecarboxylic acid SB 209247: (E)-3-(6-{[(2,6-dichlorophenyl)thio]methyl}-3-[2-phenylethoxy]-2-pyridinyl)-2-propenoic acid SKF 104353: (2S)-hydroxy-(3R)-(2-carboxyethylthio)-3-[2-(8-phenyloctyl)phenyl]propanoate ZD 3523: 4-[(5-{[(2R)-2-methyl-4,4,4-trifluorobutyl]carbamoyl}-1-methylindol-3-yl)methyl]-3-methoxy-N-[(2methylphenyl)sulfonyl]benzamide. bReferences: Brooks and Summers (1996); Metters (1995). cNA, no sequence information available.

Table A.1B.40

Lysophospholipid Receptorsa,b

Receptor

GenBank/ SwissProt no. (human)

Agonists

edg1

P21453

SPP > SPC > LPA

edg2

Q92633

LPA

edg3 edg4

Q99500 AFO11466

SPP > SPC LPA

edg5c

SPP > SP

Antagonists

Signal Transduction Inhibition of adenylate cyclase Inhibition of adenylate cyclase Gq Inhibition of adenylate cyclase Gq

aChemical abbreviations:

LPA: lysophosphatidic acid SPC: sphingosylphosphorylcholine SPP: sphingosine-1-phosphate bReference: Goetzl and An (1998). cRat homolog has been cloned.

Receptor Nomenclature Guidelines

A.1B.40 Supplement 6

Current Protocols in Pharmacology

Table A.1B.41 Melanocortin Receptors (GPCR; 2.1.MC.00.000.00.00)a,b

Receptor

GenBank/SwissProt no. (human)

Agonists

Antagonists

Signal transduction

MC1

Q01726

SHU 9119

Agouti+

MC2

Q01718

ACTH

None

MC3

P41968

g2MSH

MC4

P32245

None

AGRP SHU 9119 SHU 9119

MC5

P33032

SHU 9119

None

None

None

Increase in adenylate cyclase activity Increase in adenylate cyclase activity Increase in adenylate cyclase activity Increase in adenylate cyclase activity Increase in adenylate cyclase activity Unknown

MC6 aChemical abbreviations:

ACTH: adrenocorticotropic hormone Agouti: 131–amino acid peptide made by dermal follicular cells AGRP: Agouti-related peptide MSH: melanocyte-stimulating hormone SHU 9119: acetyl-Nle4-c[Asp4,D-Phe7,Lys10]-α-(MSH4-10)-NH2. bReference: Cone et al. (1996).

Nomenclature and Useful Data

A.1B.41 Current Protocols in Pharmacology

Supplement 6

Table A.1B.42 Melatonin Receptors (GPCR; 2.1.MLT.00.000.00.00)a,b

Receptor

GenBank/ SwissProt no. (human)

mt1

P48039

MT2

P49286

MT3

Agonists

Antagonists

Signal transduction

Melatonin GR 196429 S 20098 Melatonin

Luzindole

Inhibition of adenylate cyclase activity

4P-PDOT

GR 135531

Prazosin

Inhibition of adenylate cyclase activity Increase in IP3/DAG

aChemical abbreviations:

DAG: diacylglycerol GR 135531: {2-[2-(acetylamino)ethyl]-1H-indol-5-yl}carbamic acid methyl ester GR 196429: N-[2-(2,3,7,8-tetrahydro-1H-furo[2,3-g]indol-1-yl)ethyl]acetamide IP3: inositol trisphosphate 4P-PDOT: 4-phenyl-2-propionamidotetraline S 20098: N-[2-(7-methoxy-1-naphthalenyl)ethyl]acetamide. bReference: Reppert et al. (1996).

Receptor Nomenclature Guidelines

A.1B.42 Supplement 6

Current Protocols in Pharmacology

Table A.1B.43 Neuropeptide Y Receptors (GPCR; 2.1.NPY.00.000.00.00)a,b

Receptor

GenBank/ SwissProt no. (human)

Agonists

Antagonists

Signal transduction

Y1

P25929

NPY ≥ PYY >PP

Inhibition of adenylate cyclase activity

Y2

P49146

Y4

P50391

Y5

U56079

Y6

Y59431

NPY3-36 PYY3-36 PP PYY NPY NPY3-30 NPY PYY PP

BIBP 3226 SR 120819A GR 231118 T4-(NPY33-36)4 None None None

Inhibition of adenylate cyclase activity Inhibition of adenylate cyclase activity Inhibition of adenylate cyclase activity Inhibition of adenylate cyclase activity

aChemical abbreviations: BIBP 3226: (R)-N2-diphenylacetyl-N-(4-hydroxyphenyl)methyl-argininamide

GR 231118: homodimeric Ile-Glu-Pro-Dpr-Tyr-Arg-Leu-Arg-Tyr-CONH2 (also known as 1229U91 or GW1229) NPY3-36: NPY fragment consisting of amino acids 3 to 36 PP: pancreatic polypeptide PYY: peptide YY SR 120819A: (R,R)-1-[2-(2-naphthylsulfamoyl)-3-phenylprionamido]-3-{4-[N-(4-dimethylaminomethyl)-cis(cyclohexylmethyl)amidino]phenyl}proprionylpyrolidine. bReference: Blomquist and Herzog (1997).

Nomenclature and Useful Data

A.1B.43 Current Protocols in Pharmacology

Supplement 6

Table A.1B.44 Neurotensin Receptors (2.1.NTSN.00.000.00.00)a,b

Receptor

GenBank/ SwissProt no. (human)

NTS1

nts2

Agonists

Antagonists

Signal transduction

P30989

Neurotensin JMV 449

SR 48692

P70310

Neurotensin

None

Increase in IP3/DAG Inhibition of adenylate cyclase Unknown

aChemical abbreviations:

DAG: diacylglycerol IP3: inositol trisphosphate JMV 449: H-Lysψ(CH2NH)-Lys-Pro-Tyr-Ile-Leu-OH SR 48692: 2-{[1-(7-chloro-4-quinolinyl)-5-(2,6-dimethoxyphenyl)pyrazol-3yl]carboxylamino}tricyclo[3.3.1.13.7]decan-2-carboxylic acid. bReference: Rostene and Alexander (1997).

Receptor Nomenclature Guidelines

A.1B.44 Supplement 6

Current Protocols in Pharmacology

Table A.1B.45 Neurotrophin Receptors (3.1.NT.00.000.00.00)a,b

Receptor

GenBank/ SwissProt no. (human)

trkA trkB

P04629 Q16620

trkC p75/NGFR

Q16288 P08138

Agonists

Antagonists

Signal transduction

NGF > NT3 BDNF > NT4/5 > NT3 NT4, NT3 NGF = BDNF = NT4/5 = NT3

None None

Tyrosine kinase Tyrosine kinase

None None

Tyrosine kinase Tyrosine kinase

aChemical abbreviations:

BDNF: brain-derived neurotrophic factor NGF: nerve growth factor NGFR: nerve growth factor receptor NT: neurotrophin p75: low-affinity neurotrophin receptor trk: neurotrophin tyrosine receptor kinase. bReference: Kaplan and Miller (1997).

Nomenclature and Useful Data

A.1B.45 Current Protocols in Pharmacology

Supplement 6

Table A.1B.46 Opioid Receptors (GPCR; 2.1.OP.00.000.00.00)a,b

GenBank/ Receptorc SwissProt no. (human)

Agonists

Antagonists

Signal transduction

δ1 (OP1A) P41143

DPDPE DADLE

BNTX ICI 174864

δ2 (OP1B)

DSLET

Naltriben

Inhibition of adenylate cyclase Opening of K+ channels Shutting of Ca2+ channel Inhibition of adenylate cyclase Opening of K+ channels Shutting of Ca2+ channel Inhibition of adenylate cyclase Opening of K+ channels Shutting of Ca2+ channel Inhibition of adenylate cyclase Opening of K+ channels Shutting of Ca2+ channel Inhibition of adenylate cyclase Opening of K+ channels Shutting of Ca2+ channel

κ (OP2)

P41145

U 69593 U 50488 CI 977

Nor-BNI

µ (OP3)

P35372

DAMGO Sufentanil

β-FNA CTOP

ORL1

P41146

Nociceptin

[Phe1{ψ(CH2NH)-Gly2] Nociceptin1-13NH2

aChemical abbreviations:

β-FNA: β-funaltrexamaine BNTX: (E)-7-benzylidenenaltrexone CI 977: (5R)-5α,7α,8β-(–)-N-methyl-N-[7-(1-pyrrolidinyl)-1-oxaspiro[4.5]dec-8-yl]-4-benzofuranacetamide monohydrochloride CTOP: D-Phe-Cys-Tyr-D-Trp-Lys-Thr-Pen-The-NH2 DADLE: [D-Ala2,D-Leu5]enkephalin DAMGO: Tyr-D-Ala-Gly-(N-methyl-Phe)-NH(CH2)2-OH DPDPE: cyclic[D-Pen2,D-Pen5]enkephalin DSLET: [D -Ser2,Leu5,Thr6]enkephalin ICI 174864: N,N-diallyl-Tyr-Aib-Aib-Phe-Leu Nor-BNI: Nor-binaltrophimine U 50488: trans-(+)-3,4-dichloro-N-methyl-N-[2-(1-pyrrolidinyl)cyclohexyl]benzeneacetamide methanesulfonate U 69593: 5α,7α,8β-(–)-N-methyl-N-[7-(1-pyrrolidinyl)-1-oxaspiro[4.5]dec-8-yl]benzene acetamide. bReferences: Satoh and Minami (1995); Dhawan et al. (1998). cOpioid receptors were renamed OP by the IUPHAR Nomenclature subcommittee. This recommendation has not been widely accepted in the field.

Receptor Nomenclature Guidelines

A.1B.46 Supplement 6

Current Protocols in Pharmacology

Table A.1B.47 Oxytocin Receptor (GPCR; 2.1.OXY.00.000.00.00)a,b

Receptor

GenBank/ SwissProt no. (human)

OT

P30559

Agonists

Antagonists

Signal transduction

Oxytocin [Thr4,Gly7]OT

L 372662

Increase in IP3/DAG

aChemical abbreviations:

DAG: diacylglycerol IP3: inositol trisphosphate L 372662: 1-(1-{4-[1-(2-methyl-1-oxidopyridin-3-ylmethyl)piperidin-4-yloxyl]-2-methoxybenzoyl}-piperidin-4-yl)1,4-dihydrobenz[d][1,3]oxazin-2-one OT: oxytocin. bReference: Barberis et al. (1998).

Table A.1B.48 Pituitary Adenylate Cyclase–Activating Polypeptide Receptor (GPCR; 2.2.PACAP.00.000.00.00)a,b

Receptor

GenBank no. (human)

PACAP

P41586

Agonists

Antagonists

Signal transduction

Maxadilan PACAP1-27

PACAP6-38

Increase in IP3/DAG

aChemical abbreviations:

DAG: diacylglycerol IP3: inositol trisphosphate. bReference: Harmar et al. (1998).

Nomenclature and Useful Data

A.1B.47 Current Protocols in Pharmacology

Supplement 6

Table A.1B.49 Platelet-Activating Factor Receptor (GPCR; 2.1.PAF.00.000.00.00)a,b

Receptor

GenBank/ SwissProt no. (human)

Agonists

Antagonists

Signal transduction

PAF

P25105

PAF

Apafant Lexipafant

Increase in IP3/DAG

aChemical abbreviations:

DAG: diacylglycerol IP3: inositol trisphosphate. bReference: Chao and Olsen (1993).

Receptor Nomenclature Guidelines

A.1B.48 Supplement 6

Current Protocols in Pharmacology

Table A.1B.50 Potassium Channels (1.6.NUCT.00.000.00.00)a,b

Channel class

Subunits

Voltage-gated: Shaker Kv1.1-Kv1.7

Examples

Opener

Blocker

Lymphocyte (Kv1.3), neuronal, heart IKur (Kv1.5)

None

Dendrotoxin Charybdotoxin Margatoxin Hongotoxin 4-Aminopyridine

None None None

Quinine TEA Barium Clofilium Sotalol Sematilide Hanatoxins None None

None

None

None None

None None

None

None None

Dofetilide E4031 Cisapride Terfenadine Sertindole Chromanol 293B None Barium

None None

Barium Barium

None Cromakalim Pinacidil Diazoxide Minoxidil RP 49356 None None None None NS 1619, DHS-1

None Glibenclamide Glipizide Ciclazindol

Voltage-gated: Shab Voltage-gated: Shaw Voltage-gated: Shal

Kv2.1 Kv3.1-Kv3.4 Kv4.1-Kv4.3

Voltage-gated

Voltage-gated

Kv5.1, Kv6.1, Kv8.1 Kv9.1-9.3 Kv b1, b2, b3 Regulatory KchAP HERG Heart (IKr)

Voltage-gated: KQT

KvLQT1

L 364373

Voltage-gated: IsK Inward rectifier

minK Kir 1.1-1.4

Inward rectifier Inward rectifier

Inward rectifier Inward rectifier

Kir 2.1-2.3 Kir 3.1-3.4 (G proteincoupled) Kir 5.1 Kir 6.1, 6.2

Inward rectifier Two-pore Two-pore Two-pore Calcium-activated: large

Kir 7.1 Epithelial TWIK-1 TREK-1 TASK, TASK2 slo-a Calcium-activated BKca

Voltage-gated Voltage-gated

Heart ITO (Kv4.2/4.3)

Heart (KvLQT1 + minK = IKs) Kidney (Kir 1.1 + CFTR) Heart IKACh (Kir 3.1 + 3.4), brain (Kir 3.1 + Kir3.2) Pancreatic KATP (Kir 6.2 + SUR1) Cardiovascular (Kir 6.2 + SUR2)

None None None None Iberiotoxin continued

Nomenclature and Useful Data

A.1B.49 Current Protocols in Pharmacology

Supplement 6

Table A.1B.50 Potassium Channels (1.6.NUCT.00.000.00.00)a,b, continued

Channel class Calcium-activated: intermediate/small

Subunits hSK1 hSK2 hSK3 hSK4

Examples Calcium-activated

Opener None

Blocker Apamin UCL 1684

aChemical abbreviations:

CFTR: cystic fibrosis transmembrane conductance regulator DHS-1: dehydrosoyasaponin-1 HERG: human ether-a-go-go-related gene hSK: human small-conductance calcium-activated K+ channel (apamin-sensitive types) IKr: rapid component of delayed rectifier current IKs: slow component of delayed rectifier current IKur: ultrarapid component of delayed rectifier current IsK: combines with KvLQT1 to form the cardiac IKs ITO: transient outward current KchAP: K+ channel–associated protein Kir: inwardly rectified potassium channel KQT: K+ channel family comprised of KCNQ1 and KvLQT1 (LQT mutations), and KCNQ2 and KCNQ3 (epileptic disorder mutatoins) Kv: voltage-gated potassium channel L 364373: (3R)-1,3-dihydro-5-(2-fluorophenyl)-3-(1H-indol-3-ylmethyl)-1-methyl-2H-1,4-benzodiazepin-2-one LQT: long-QT syndrome minK: also known as miniK or IsK (see above) NS 1619: 1-(2′-hydroxy-5′-trifluoromethylphenyl)-5-trifluoromethyl-2(3H)-benzimidazolone RP 49356: N-methyl-2-(3-pyridyl)-tetrahydrothiopyran-2-carbothioamide-1-oxide TASK: tandem acid-sensitive K+ channel (pH-sensitive background channel) TEA: tetraethylammonium TREK: TWIK-related K+ channel TWIK: tandem weak inward rectifier K+ channel UCL 1684: 6,10-diaza-3-(1,3)-8-(1,4)-dibenzena-1,5-(1,4)-diquinolinacyclodecaphane. bReference: Edwards and Weston (1997).

Receptor Nomenclature Guidelines

A.1B.50 Supplement 6

Current Protocols in Pharmacology

Table A.1B.51 Prostanoid Receptors (GPCR; 2.1.PR.00.000.00.00)a,b,c

Receptor

GenBank/ SwissProt no. (human)

DP

Q13258

EP1

P34995

EP2

P43116

EP3

P43115

EP4

P35408

FP

P43088

IP

P43119

TP

P217321

Agonists

Antagonists

Signal transduction

PGD2 BW 245C ZK 110841 RS 93520 Iloprost 17Ph-ω-PGE2 Butraprost AH 13205 Enprostil GR 63799

BWA 868C AH 6809

Increase in adenylate cyclase activity

SC 51089 AH 6809 AH 6809

Increase in IP3/DAG

PGE2 > PGI2α > PGF2α Cloprostenol Fluprostenol Cicaprost PGI2 BMY 45778 AGN 192093 U 46619

None

AH 22921 None

Increase in adenylate cyclase activity Increase in IP3/DAG Decrease in adenylate cyclase activity Increase in adenylate cyclase activity Increase in IP3/DAG

None

Increase in IP3/DAG Increase in adenylate cyclase

SQ 29548 ONO 3708 L 655240 BMS 180291

Increase in IP3/DAG

aReceptors sensitive to prostaglandins D, E, F, I, and T and denoted DP and so on. bChemical abbreviations:

AGN 192093: (Z)-7-{(1a,5a,6a,7b)-7-[(1E,3S)-3-hydroxy-1-octenyl]-3-oxo-2,4-dioxobicyclo[3.2.1]oct-6-yl}-5heptenol AH 6809: 6-isopropoxy-9-oxoxanthen-2-carboxylic acid AH 13205: trans-2-[4-(1-hydroxyhexyl)phenyl]-5-oxocyclopentaneheptanoate AH 22921: 7-{(1R,2R,5S)-5-[(1,1′-biphenyl)-4-ylmethoxy]-2-(4-morpholino)-3-oxocyclopentyl}-(5Z)-rel-5heptenoic acid BMS 180291: (1S-1α,2α,3α,4α)-2-[(3-{4-[(pentylamino)carbonyl]-2-oxazolyl}-7-oxabicyclo[2.2.1]hept-2yl)methyl]benzenepropanoic acid BMY 45778: 3-[4-(4,5-diphenyl-2-oxazolyl)-5-oxazolyl]phenoxy acetate BW 245C: 5-(6-carboxyhexyl)-1-(3-cyclohexyl-3-hydroxypropyl)hydantoin BWA 868C: 3-benzyl-5-(6-carboxyhexyl)-1-(2-cyclohexyl-2-hydroxyethylamino)hydantoin DAG: diacylglycerol GR 63799: {1R-[1α(Z),2β(R*),3α]}-4-(benzoylamino)phenyl-7-(3-hydroxy-3-phenoxypropoxy]-5-oxycyclopentyl)4-heptanoate IP3: inositol trisphosphate L 655240: 3-[1-(4-chlorobenzyl)-5-fluoro-3-methyl-indoyl-2-yl]-2,2-dimethylpropanoic acid ONO 3708: (9,11)-(11,12)-dideoxa-9α,11α-dimethylmethano-11,12-methano-13,14-dihydro-13-aza-14-oxo-15cyclopentyl-16,17,18,19,20-pentanor-15-epi-thromoboxane A2 PGD2, PGE2, PGF2α: prostaglandins D2, E2, F2α PGI2: prostacyclin I2 RS 93520: (Z)-4-[(C3′S,1R,2R,3S,6R)-2C3′-cyclohexyl-3′-hydroxyprop-1-ynyl)-3-hydroxybicyclo[4.2.0]oct-7-ylidene butyrate SC 51089: 8-chlorodibenz[b,f][1,4]oxazepine-10(11H)-carboxylic acid, 2-[1-oxo-3-(3-pyridinyl)propyl]hydrazide SQ 29548: {1S-[1α,2β(5Z),3β,4α]}-7-(3-{[2-(phenylamino)carbonyl]hydrazino}methyl)-7-oxabicyclo[2.2.1]hept-2yl-(5Z)-heptenoate U 46619: 11α,9α-epoxymethano-PGH2 ZK 110841: 9β-chloro-11α,15β-dihydroxy-16,20-methanoprost-(5Z,13E)-dienoate. cReference: Coleman et al. (1994).

Nomenclature and Useful Data

A.1B.51 Current Protocols in Pharmacology

Supplement 6

Table A.1B.52 Protease-Activated Receptors (Thrombin Receptors; 2.1.THR.00.000.00.00)a,b

Receptor

GenBank/ SwissProt no. (human)

PAR1

P25116

PAR2

U34038

PAR3

U92971

PAR4

AF080214

Agonists

Antagonists

Signal transduction

SFLLRN TFRIFD Thrombin Trypsin Trypsin Tryplase SLIGRC-NH2 Thrombin Factor Xa Thrombin Trypsin GYPGq/11

TFRIFD-NH2 TFLLR-NH2 SFLLFD-NH2

Increase in IP3/DAG

SLIGR-NH2

Unknown

None

Unknown

None

Unknown

aChemical abbreviations:

DAG: diacylglycerol IP3: inositol trisphosphate GYPG, SFLLFD, SFLLRN, SLIGR, SLIGRC, TFLLR, and TFRIFD are peptide sequences. bReferences: Hollenberg (1996); Brass and Molino (1997).

Receptor Nomenclature Guidelines

A.1B.52 Supplement 6

Current Protocols in Pharmacology

Table A.1B.53 Receptor Tyrosine Kinase (3.1.GFR.00.000.00.00)a,b

Receptor

Agonists

Antagonists

Signal transduction

EGFR

EGF TGF-a

Dimerization followed by trans-autophosphorylation

PDGFR

PDGF

IGF1-R

IGF-1

FGFR

FGF

Tyrophostin AG 1478 PD 153035 Tyrophorstin AG 1295 AG 1024 AG 533 SU 5402

Dimerization followed by trans-autophosphorylation Dimerization followed by trans-autophosphorylation Dimerization followed by trans-autophosphorylation

aChemical abbreviations:

AG 1024: {[3-bromo-5-(1,1-dimethylethyl)-4-hydroxyphenyl]methylene}propanedinitrile AG 1295: 2-phenyl-6,7-dimethylquinoxaline; 6,7-dimethyl-2-phenylquinoxaline AG 1478: N-(3-chlorophenyl)-6,7-dimethoxy-4-quinazolinamine EGF: epidermal growth factor FGF: fibroblast growth factor IGF-1: insulin-like growth factor 1 PD 153035: N-(3-bromophenyl)-6,7-dimethoxy-4-quinazolinamine PDGF: platelet-derived growth factor SU 5402: 5-[(1,2-dihydro-2-oxo-3H-indol-3-ylidene)methyl]-4-methyl-1H-pyrrole-3-propanoic acid TGF-α: transforming growth factor α. bReference: Levitzki and Gazit (1995).

Nomenclature and Useful Data

A.1B.53 Current Protocols in Pharmacology

Supplement 6

Table A.1B.54 Ryanodine Receptors (Conductance Channels; 1.3.RYR.00.000.00.00)a

Receptor

Conduction (pS) Agonists

Antagonists

Signal transduction

RY1

100-150

Unknown

RY2

100-150

RY3

100-150

Ruthenium red Ryanodine Procaine Ruthenium red Ryanodine Procaine Ruthenium red Ryanodine

Ryanodine Caffeine Heparin Ryanodine Caffeine Heparin Ryanodine Caffeine Heparin

Unknown

Unknown

aReferences: Meissner (1994); Sutko and Airey (1996).

Receptor Nomenclature Guidelines

A.1B.54 Supplement 6

Current Protocols in Pharmacology

Table A.1B.55 Sodium Channelsa,b

Channel subtype

Conductance (pS)

Localization

Inhibitors

Modulators

I

2.5-25

Brain

TTX STX Phenytoin Local anesthetics

II

2.5-25

Young brain

TTX STX Phenytoin Local anesthetics

IIA

2.5-25

Brain

TTX STX Phenytoin Local anesthetics

III

2.5-25

Brain

TTX STX Phenytoin Local anesthetics

µ1

2.5-25

Skeletal muscle

TTX STX Local anesthetics µ-Conotoxin

PN1

6.3-10.7

Sympathetic ganglia

TTX STX Phenytoin Local anesthetics

V1

Unknown

Brain

TTX

h1

2.5-25

Heart

Cd2+ Zn2+ Phenytoin Cd2+ Pb2+

Batrachotoxin Veratridine Aconitine Brevetoxins STX Batrachotoxin Veratridine Aconitine Brevetoxins STX Batrachotoxin Veratridine Aconitine Brevetoxins STX Batrachotoxin Veratridine Aconitine Brevetoxins STX Batrachotoxin Veratridine Aconitine Brevetoxins STX Batrachotoxin Veratridine Aconitine Brevetoxins STX Batrachotoxin Veratridine Aconitine Brevetoxins STX None

PN3/SNS 3.4-6.3

Dorsal root ganglion

None

aChemical abbreviations:

STX: saxitoxin TTX: tetrodotoxin. bReferences: Catterall (1995).

Nomenclature and Useful Data

A.1B.55 Current Protocols in Pharmacology

Supplement 6

Table A.1B.56 Somatostatin Receptors (2.1.SRIF.00.000.00.00)a,b

Receptor

GenBank/ SwissProt no. (human)

Agonists

Antagonists

Signal transduction

sst1

P30872

CH 275

None

sst2

P30874

Seglitide MK 678 BIM 23027

CL 154806

sst3

P32745

None

None

sst4

P31391

NNC 269100

None

sst5

P35346

L 362855

BIM 23056

Inhibition of adenylate cyclase activity Inhibition of adenylate cyclase activity Stimulation of PI metabolism Inhibition of adenylate cyclase activity Stimulation of PI metabolism Inhibition of adenylate cyclase activity Inhibition of adenylate cyclase activity

aChemical abbreviations:

BIM 23027: c[N-methyl-Ala-Tyr-D-Trp-Lys-Abu-Phe] BIM 23056: D-Phe-Phe-Tyr-D-Trp-Lys-Val-Phe-D-Nal-NH2 CH 275: des-AA1,2,5(D -Trp8,Lamp9)-somatostatin CL 154806: acetyl-4-NO2-Phe-c[D -Cys-Tyr-D-Trp-Lys-Thr-Cys]D-Tyr-NH2 L 362855: c[Aha-Phe-Trp-D-Trp-Lys-Thr-Phe] MK 678: c[N-methyl-Ala-Tyr-D-Trp-Lys-Vla-Phe] NNC 269100: 1-{3-[N-(5-bromopyridin-2-yl)-N-(3,4-dichlorobenzyl)aminopropyl]-3-(3-1H-imidazol-1yl)propyl}thiourea PI: phosphoinositide. bReference: Schindler et al. (1996).

Receptor Nomenclature Guidelines

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Current Protocols in Pharmacology

Table A.1B.57 Steroid/Retinoid Receptors (4.2STR.00.00.000.00.00 and 4.1.RAR/RXR.00.000.00.00)a,b

Receptor

Agonists

Antagonists

Signal transduction

ERα ERβ

17β-estradiol Estrone Droloxifen CP 336156 Dexamethasone Triamicinolone Progesterone R 5020 Norethynodiol Testosterone Stanalone

4DH-Tamoxifen Keoxifene

Transcription factor modulation

RU 486 ZK 98299 RU 486 ZK 98299

Transcription factor modulation Transcription factor modulation

Casodex Nilutramide 20H-Flutamide Spironolactone

Transcription factor modulation

GR PR

AR

MR RAR

RXR PPARγ TR VDR LXRα LXRβ FXR

Aldosterone 9-sdCIS LGD 1069 TTNPB MX 33511 LGD 1069 Triiodothyronine Thyroxine Dehydroxyvitamin D3 RS 980400 None None None None

SR 11335 Ro 415253

Transcription factor modulation Unknown

LG 100754 None

Unknown Unknown

None

Unknown

None None None None

Unknown Unknown Unknown Unknown

aChemical abbreviations:

AR: androgen receptor CP 336156: 5,6,7,8-tetrahydro-6-phenyl-5-{5-[4-(1-pyrrolidinyl)ethoxy]phenyl}-(5R,6S)-2naphthalenol-(2S,3S)-2,3-dihydroxybutanedioate (1:1 salt) ER: estrogen receptor FXR: farnesoid X-activated receptor GR: glucocorticoid receptor LG 100754: 3-methyl-7-(5,6,7,8-tetrahydro-5,5,8,8-tetramethyl-3-propoxy-2-naphthalenyl-[2E, 4E, 6Z]-2,4,6-octatrienoic acid) LGD 1069: 9-cis-retinoic acid LXR: liver X receptor MR: mineralocorticoid receptor PPAR: peroxisome proliferator-activated receptor PR: progesterone receptor R 5020: 17-methyl-17-(l-oxopropyl)-17β-4,9-diene-3-one RAR: retinoic acid receptor RU 486: mifepristone RXR: retinoid X receptor TR: thyroid hormone receptor TTNPB: 4-[(1E)-2-(5,6,7,8-tetrahydro-5,5,8,8-tetramethyl-2-naphthalenyl)-1-propenyl] benzoate VDR: vitamin D receptor ZK 98299: 11β-(4-dimethylaminophenyl)-17α-hydroxy-17β-(3-hydroxypropyl)-13α-methyl-4,9gonadien3-one. bReference: Tsai and O’Malley (1994).

Nomenclature and Useful Data

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Supplement 6

Table A.1B.58 Tachykinin Receptors (GPCR; 2.1.NK.00.000.00.00)a,b

Receptor

GenBank/ SwissProt no. (human)

Agonists

Antagonists

Signal transduction

NK1

P25103

[Sar9Met(O2)11]SP

Increase in IP3/DAG

NK2

P21452

Neurokinin A GR 64349

NK3

P29371

Senktide Neurokinin B

GR 82334 MK 869 RP 67580 L 703606 L 659877 MEN 11420 SR 48968 GR 159897 SR 142801 SB 223412 PD 157672

Increase in IP3/DAG

Increase in IP3/DAG

aChemical abbreviations:

DAG: diacylglycerol GR 64349: Lys-Asp-Ser-Phe-Val-Gly-[(R)-γ-lactam] GR 82334: 9-deglycine-10-[(5S)-6-oxo-L-α-(2-methylpropyl)-1,7-diazaspiro[4.4]nonane-7-acetic acid]-11-Ltryptophanamide-physalemin GR 159897: 5-fluoryl-3-ylethyl-[4-(phenylsulfinylmethyl)]piperidine IP3: inositol trisphosphate L 659877: cyclo-[L-glutaminyl-L-tryptophyl-L-phenylalanylglycyl-L-leucyl-L-methionyl] L 703606: cis-2(diphenylmethyl)-N-[(2-iodophenyl)methyl]-1-azabicyclo[2.2.2]octan-3-amide MEN 11420: [Asn(2-AcNH-β-D-Glc)-Asp-Trp-Phe-Dap-Leu]c(2β-5β) NK: neurokinin PD 157672: t-butyloxycarbonyl-(S)-Phe-(R)-[α-methyl-Phe]-NH(CH2)7NHCONH2 RP 67580: [1-imino-2-(2-methoxyphenyl)ethyl]-7,7-diphenyl-4-perhydroisoindolone-(3αR,7αR) SB 223412: (S)-(–)-N-(α-ethylbenzyl)-3-hydroxy-2-phenylquinoline-4-carboxamide SR 48968: (S)-N-methyl-N-(4-acetylamino-4-phenylpiperidino)-2-(3,4-dichlorophenyl)butylbenzamide SR 142801: (S)-N-(1-{3-[1-benzoyl-3-(3,4-dichlorophenyl)piperidin-3-yl]propyl}-4-phenylpiperidin-4-yl)-Nmethylacetamide. bReferences: Buck (1994).

Receptor Nomenclature Guidelines

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Table A.1B.59 Thyrotropin-Releasing Hormone Receptor (GPCR; 2.1.TRH.00.000.00.00)a

Receptor

GenBank/ SwissProt no. (human)

TRH

P34981

Agonists

Antagonists

TRH Midazolam (pGlu-His-Pro-NH2) Chlordiazepoxide Diazepam

Signal transduction Unknown

aReference: Gershengorn and Osman (1998).

Table A.1B.60 Vanilloid (Capsaicin) Receptors (LGIC; 1.X.VR.00.000.00.00)a,b

Receptor

GenBank/ SwissProt no. (human)

VR1

VR-L

AF129112

Agonists

Antagonists

Signal transduction

pH Heat Capsaicin RFX pH Heat

Capsazepine

Nonselective cation channel

None

Nonselective cation channel

aChemical abbreviations:

RFX: resiniferatoxin VR-L: vanilloid receptor–like. bReferences: Szallasi and Blumberg (1996); Caterina et al. (1997, 1999).

Nomenclature and Useful Data

A.1B.59 Current Protocols in Pharmacology

Supplement 6

Table A.1B.61 Vasoactive Intestinal Peptide Receptors (GPCR; 2.2.VIP.00.000.00.00)a,b

Receptor

GenBank no. Agonists (human)

VPAC1

P32241

VIP Helodermin

[AcHis1,D-Phe2,Lys15,Arg16] Increase in adenylate VIP3-7GRF8-27NH2 cyclase activity

VPAC2

P41587

None

Increase in adenylate cyclase activity

PAC1

P41586

VIP Ro 251553 PACAP1-27 Maxadilan

PACAP6-38

Increase in adenylate cyclase activity

Antagonists

Signal transduction

aChemical abbreviations:

GRF: growth hormone–releasing factor PACAP: pituitary adenylate cyclase–activating polypeptide Ro 251553: acetyl-His1[Glu8,Lys12,Nle17,Ala19,Asp25,Leu26,Lys27,28,Gly29,30,Thr31]-NH2 vasoactive intestinal polypeptide (cyclo21-25). bReference: Harmar et al. (1998).

Receptor Nomenclature Guidelines

A.1B.60 Supplement 6

Current Protocols in Pharmacology

Table A.1B.62 Vasopressin Receptors (GPCR; 2.1.VASO.00.000.00.00)a,b

Receptor

GenBank/ SwissProt no. (human)

V1A

P37288

V1B

P47901

V2 OT

Agonists

Antagonists

Signal transduction

OPC 21268 SR 49059 αP[Tyr(Me2)]AVP

Increase in IP3/DAG Increase in IP3/DAG

P30518

Vasopressin [Phe2,Orn8]VP Vasopressin Deamino-[D-3′(pyridyl)Ala2]AVP Vasopressin

P30559

[Thr4,Gly7]OT

OPC 31260 SR 121463A L 372662

Increase in adenylate cyclase activity Increase in IP3/DAG

aChemical abbreviations:

AVP: arginine vasopressin DAG: diacylglycerol IP3: inositol trisphosphate L 372662: 1-(1-{4-[1-(2-methyl-1-oxidopyridin-3-ylmethl)piperidin-4-yloxyl]-2-methoxybenzoyl}piperidin-4-yl)-14-dihydrobenz[d][1,3]oxazin-2-one OPC 21268: N-[3-(4-{[4-(3,4-dihydro-2-oxo-1(2H)-quniolinyl)-1piperidinyl]carbonyl}phenoxy)propyl]acetamide OPC 31260: 5-dimethylamino-1-[4-(2-methylbenzoylamino)benzoyl]-2,3,4,5-tetrahydro-(1H)-benzazepine SR 49059: (2S)-1-{(2R,3S)-[5-chloro-3-(chlorophenyl)-1-(3,4-dimethoxysulfonyl)]-3-hydroxy-2,3-dihydro-1Hindole-2-carbonyl-pyrrolidine-2-carboxamide} SR 121463A: 1-[4-(N-tert-butylcarbamoyl)-2-methoxybenzene-sulfonyl]-5-ethoxy-3-spiro-[4-(2morpholinoethoxy)cyclohexane]indol-2-one, fumarate; equatorial isomer VP: vasopressin. bReference: Barberis et al. (1998).

Nomenclature and Useful Data

A.1B.61 Current Protocols in Pharmacology

Supplement 6

LITERATURE CITED Alexander, S.P.H. and Peters, J.A. 1999. TiPS Receptor and Ion Channel Nomenclature Supplement, 10th ed. Elsevier Trends Journals, Cambridge, U.K. Anand-Srivastava, M.B. and Trachte, G.J. 1993. Atrial naturetic factor receptors and signal transduction mechanisms. Pharmacol. Rev. 45:455-497. Baggiolini, M., Dewald, B., and Moser, B. 1997. Human chemokines: An update. Ann. Rev. Immunol. 15:675-705. Barberis C., Mouillac, B., and Durroux, T. 1998. Structrual base o f vasopressin/oxytocin receptor function. J. Endocrinol. 156:223-229. Bartfai, T. 1995. Galanin: A neuropeptide with important central nervous system actions. In Psychopharmacology: Fourth Generation of Progress (F.E. Bloom and D.E. Kupfer, eds.) pp. 563-571. Raven Press, New York.

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Betz, H. 1991. Glycine receptors: Heterogeneous and widespread distribution in the mammalian brain. Trends Neurosci. 14:458-461.

De Souza, E.B., Grigoriadis, D.E., and Vale, W.W. 1998. Corticotropin Releasing Factor Receptors. In The IUPHAR Compendium of Receptor Characterization and Classification (IUPHAR, ed.) pp. 134-140. IUPHAR Media.

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Bowery, N.G. 1993. GABAB receptor pharmacology. Ann. Rev. Pharmacol. Toxicol. 33:109-147.

Eglen, R.M. and Watson, N. 1996. Selective muscarinic receptor agonists and antagonists. Pharmacol. Toxicol. 78:59-68.

Blomquist, A.F. and Herzog, H. 1997. Y-receptor subtypes—How many more? Trends Neurosci. 20:294-298. Brass, L.F. and Molino, M. 1997. Protease-activated G protein coupled receptors on human platelets and endothelial cells. Thromb. Hemost. 78:234241. Brooks, C.D.W. and Summers, J.B. 1996. Modulators of leukotriene biosynthesis and receptor activation. J. Med. Chem. 39:2629-2654.

Foord, S.M. and Marshall, F.H. 1999. RAMPs: Accessory proteins for seven transmembrane domain receptors. Trends Pharmacol. Sci. 20:184187. Gershengorn, M.C. and Osman, R. 1998. Molecular and cellular biology of thyrotropin-releasing hormone (TRH) receptors. Physiol. Rev. 76:175191.

Buck, S.H. (ed.) 1994. The Tachykinin Receptors. Humana Press, Totowa, N.J.

Goetzl, E.J. and An. S., 1998. Diversity of cellular receptors and functions for the lysophospholipid growth factors lysophosphatidic acid and sphingosine 1-phosphate. FASEB J. 12: 1589-1598.

Caterina, M.J., Schumacher, M.A., Tominaga, M., Rosen, T.A., Levin, J., and Julius, D. 1997. The capsaicin receptor: A heat-activated ion channel in the pain pathway. Nature 389:816-824.

Green, J.P. 1987. Pharmacological receptors: The need for a compendium of classification, nomenclature and structure. Trends Pharmacol. Sci. 8:90-94.

Caterina, M.J., Rosen, T.A., Tominaga, M., Brake, A.J., and Julius, D. 1999. A capsaicin-receptor homologue with a high threshold for noxious heat. Nature 398:436-441.

Green, T., Heinemann, S.F., and Gusella, J.F. 1998. Molecular neurobiology and genetics: Investigation of neural function and dysfunction. Neuron 20:427-444.

Catterall, W.A. 1995. Structure and function of voltage gated ion channels. Ann. Rev. Biochem. 64:493-531.

Greindling, K.K., Lassegue, B., and Alexander, W. 1996. Angiotensin receptors and their therapeutic implications. Ann. Rev. Pharmacol. Toxicol. 36:281-306.

Chao, W. and Olsen, M.S. 1993. Platelet activating factor: Receptors and signal transduction. Biochem. J. 292:617-629. Coleman, R.A., Smith, W.L., and Narumiya, S. 1994. Classification of prostanoid receptors. Pharmacol. Rev. 46:205-229. Receptor Nomenclature Guidelines

Cone, R.D., Lu, D., and Chen, W. 1996. The melanocortin receptors: Agonists, antagonists, and the hormonal control of pigmentation. Recent Prog. Horm. Res. 51:287-318.

Colquhoun, D. 1998. Binding, gating, affinity and efficacy: The interpretation of structure-activity relationships for agonists and the effects of mutating receptors. Br. J. Pharmacol. 125:923-947.

Harmar, A.J., Arimura, A., Gozes, I., Journot, L., Laburthe, M., Pisegna, J.R., Rawlings, S.R., Robberecht, P., Said, S.I., Sreedharan, S.P., et al. 1998. VIP and PACAP receptors. In The IUPHAR Compendium of Receptor Characterization and Classification (IUPHAR, ed.) pp. 256-265. IUPHAR Media.

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Heible, J.P., Bondinelli, W.E., and Ruffolo, R.R., Jr. 1995. Alpha and beta adrenoceptors: From the gene to the clinic. Part I. J. Med. Chem. 38:34153444. Holladay, M.W., Dart, M.J., and Lynch, J.K. 1997. Neuronal nicotinic cholinergic receptors as targets for drug discovery. J. Med. Chem. 40:41694194. Hollenberg, M.D. 1996. Proteinase-mediated signalling: New paradigms for cell regulation and drug development. Trends Pharmacol. Sci. 17:37. Hollman, M. and Heinemann, S. 1994. Cloned glutamate receptors. Ann. Rev. Neurosci. 17:31-108. Humphrey, P.P.A. and Barnard, E.A. 1998. International Union of Pharmacology. XIX. The IUPHAR receptor code: A proposal for an alphanumeric classification system. Pharmacol. Rev. 50:271-277. International Union of Pharmacology (IUPHAR; ed.). 1998. The IUPHAR Compendium of Receptor Characterization and Classification, pp. 21-30. IUPHAR Media. Jentsch, T.J. and Gunther, W. 1997. Chloride channels: An emerging molecular picture. BioEssays 19:117-126. Joseph, S.K. 1990. The inositol trisphosphate receptor family. Cell Signal 8:1-7. Kaplan, D.R. and Miller, F.D. 1997. Signal transduction by the neurotrophin receptors. Curr. Opin. Cell Biol. 9:213-221. Kenakin, T. 1996. The classification of seven transmembrane receptors in recombinant expression systems. Pharmacol. Rev. 48:413-465. Kliewer, S.A, Lehmann, J.M., and Willson, T.M. 1999. Orphan nuclear receptors: Shifting endocrinology into reverse. Science 285:757-760. Kroog, G.S., Jensen, R.T., and Battey, J.F. 1995. Mammalian bombesin receptors. Med. Res. Rev. 15:389-417. Leurs, R., Smit, M.J., and Timmerman, H. 1995. Molecular pharmacological aspects of histamine receptors. Pharmacol. Ther. 66:413-463. Levitzki, A. and Gazit, A. 1995. Tyrosine kinase inhibition: An approach to drug development. Science 257:1782-1788. Lovenberg, T.W., Roland, B.L., Wilson, S.J., Jian, X., Pyati, J., Huvar, A., Jackson, M.R., and Erlander, M.G. 1999. Cloning and functional expression of the human histamine H3 receptor. Mol. Pharmacol. 55:1101-1107.

Murphy, P.M., Charo, I.F., Hebert C.A., Horuk, R., Miller, L.H., Power C.A., and Oppenheim, J.J. 1998. Chemokine Receptors. In The IUPHAR Compendium of Receptor Characterization and Classification (IUPHAR, ed.) pp. 103-126. IUPHAR Media. Nomenclature Working Party. 1998. Nomenclature Guidelines for Authors. Br. J. Pharmacol. 123:11-14. Perez-Reyes, E. and Schnieder, T. 1994. Calcium channels structure, function and classification. Drug Dev. Res. 33:295-318. Pertwee, R.G. 1997. Pharmacology of cannabinoid CB1 a n d CB2 receptors. Pharmacol. Ther. 74:129-180. Poyner, D.R. 1992. Pharmacological of receptors for calcitonin gene related peptide and amylin. Pharmacol. Ther. 56:23-51. Ralevic, V. and Burnstock, G. 1998. Receptors for purines and pyrimidines. Pharmacol. Rev. 50:413–492. Regoli, D., Geppetti, P., Hess, J.F., Marceau, F., Muler- Esterl, W., and Schoelkens, B.A. 1998. Bradykinin Receptors. In The IUPHAR Compendium of Receptor Characterization and Classification (IUPHAR, ed.) pp. 87-93. IUPHAR Media. Regunathan, S. and Reis, D.J. 1996. Imidazoline receptors and their endogenous ligands. Ann. Rev. Pharmacol. Toxicol. 36:511-544. Reppert, S.M., Weaver D.R., and Godson, C. 1996. Melatonin receptors step into the light: Cloning and classification of subtypes. Trends Pharmacol. Sci. 17:100-102. Roques, B.P. and Noble, F. 1998. Cholecystokinin Receptors. In The IUPHAR Compendium of Receptor Characterization and Classification (IUPHAR, ed.) pp. 127-133. IUPHAR Media. Rostene, W. and Alexander, M.J. 1997. Neurotensin and neuroendocrine regulation. Frontiers Neuroendocrinol. 18:115-173. Satoh, M. and Minami, M. 1995. Molecular pharmacology of opioid receptors. Pharmacol. Ther. 68:343-364. Schindler, M., Humphrey, P.P.A., and Emson, P.C. 1996. Somatostatin receptors in the central nervous system. Prog. Neurobiol. 50:9-47. Simoni, M.J.G. and Nieschlag, E. 1997. The follicle stimulating hormone receptor: Biochemistry, molecular biology, physiology and pathophysiology. Endocrine Rev. 18:739-773.

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Vanhoutte, P., Humphrey, P.P.A., and Spedding, M. 1996. International Union of Pharmacology. X. Pharmacology recommendations for nomenclature of new receptor subtypes. Pharmacol. Rev. 48:1-2. Walker, D. and DeWaard, M. 1998. Subunit interaction sites in voltage-dependent calcium channels. Trends Neurosci. 21:148-154. Wank, S.A. 1995. Cholecystokinin receptors. Am. J. Physiol. 269:G628-G646. Watling, K.J. 1998. The RBI Handbook of Receptor Classification and Signal Transduction, 3rd ed. RBI, Natick, Mass. Wess, J. 1996. Molecular biology of muscarinic acetylcholine receptors. Crit. Rev. Neurobiol. 10:69-99.

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Contributed by Michael Williams Abbott Laboratories Abbott Park, Illinois

Receptor Nomenclature Guidelines

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STOCK SOLUTIONS, EQUIPMENT, AND LABORATORY GUIDELINES

APPENDIX 2

Common Stock Solutions, Buffers, and Media

APPENDIX 2A

This collection describes the preparation of buffers and reagents used in this manual for cell culture, manipulation of neural tissue, molecular biological methods, and neurophysiological/neurochemical measurements. When preparing solutions, use deionized, distilled water and reagents of the highest available grade. Sterilization—by filtration through a 0.22-µm filter or by autoclaving—is recommended for most applications. Acid, concentrated stock solutions See Table A.2A.1. Ammonium hydroxide, concentrated stock solution See Table A.2A.1. Table A.2A.1

Molarities and Specific Gravities of Concentrated Acids and Basesa

Molecular weight

Acid/base Acetic acid (glacial) Ammonium hydroxide Formic acid Hydrochloric acid Nitric acid Perchloric acid Phosphoric acid Sulfuric acid aCAUTION:

% by weight

Molarity (approx.)

99.6 28 90 98 36 70 60 72 85 98

17.4 14.8 23.6 25.9 11.6 15.7 9.2 12.2 14.7 18.3

60.05 35.0 46.03 36.46 63.01 100.46 98.00 98.07

1 M solution Specific (ml/liter) gravity 57.5 67.6 42.4 38.5 85.9 63.7 108.8 82.1 67.8 54.5

1.05 0.90 1.205 1.22 1.18 1.42 1.54 1.70 1.70 1.835

Handle strong acids and bases carefully.

EDTA (ethylenediaminetetraacetic acid), 0.5 M (pH 8.0) Dissolve 186.1 g disodium EDTA dihydrate in 700 ml water. Adjust pH to 8.0 with 10 M NaOH (∼50 ml; add slowly). Add water to 1 liter and filter sterilize. Store up to 6 months at room temperature. Begin titrating before the sample is completely dissolved. EDTA, even in the disodium salt form, is difficult to dissolve at this concentration unless the pH is increased to between 7 and 8.

Ethidium bromide staining solution Concentrated stock (10 mg/ml): Dissolve 0.2 g ethidium bromide in 20 ml H2O. Mix well and store at 4°C in dark or in a foil-wrapped bottle. Do not sterilize. Working solution: Dilute stock to 0.5 µg/ml or other desired concentration in electrophoresis buffer (e.g., 1× TBE or TAE) or in H2O. Ethidium bromide working solution is used to stain agarose gels to permit visualization of nucleic acids under UV light. Gels should be placed in a glass dish containing sufficient working solution to cover them and shaken gently or allowed to stand for 10 to 30 min. If necessary, gels can be destained by shaking in electrophoresis buffer or H2O for an equal continued Current Protocols in Pharmacology (1998) A.2A.1-A.2A.3 Copyright © 1998 by John Wiley & Sons, Inc.

Stock Solutions, Equipment, and Laboratory Guidelines

A.2A.1

length of time to reduce background fluorescence and facilitate visualization of small quantities of DNA. Alternatively, a gel can be run directly in ethidium bromide by using working solution (made with electrophoresis buffer) as the solvent and running buffer for the gel. CAUTION: Ethidium bromide is a mutagen and must be handled carefully.

Fetal bovine serum (FBS) Thaw purchased fetal bovine serum (shipped on dry ice and kept frozen until needed). Store 3 to 4 weeks at 4°C. If FBS is not to be used within this time, aseptically divide into smaller aliquots and refreeze until used. Store ≤1 year at −20°C. To inactivate FBS, heat serum 30 min to 1 hr in a 56°C water bath. Repeated thawing and refreezing should be avoided as it may cause denaturation of the serum. Inactivated FBS (FBS that has been treated with heat to inactivate complement protein and thus prevent an immunological reaction against cultured cells) is useful for a variety of purposes. It can be purchased commercially or made in the lab as described above.

Gel loading buffer, 6× 0.25% (w/v) bromphenol blue 0.25% (w/v) xylene cyanol FF 40% (w/v) sucrose or 15% (w/v) Ficoll 400 or 30% (v/v) glycerol Store at 4°C (room temperature if Ficoll is used) This buffer does not need to be sterilized. Sucrose, Ficoll 400, and glycerol are essentially interchangeable in this recipe. Other concentrations (e.g., 10×) can be prepared if more convenient.

LB medium (Luria broth) and LB plates Per liter add: 10 g tryptone 5 g yeast extract 5 g NaCl 1 ml 1 M NaOH 15 g agar or agarose (for plates only) Autoclave solution. Add filter-sterilized additives (see below) after the solution has cooled to 55°C. For LB plates, pour agar-containing solution into sterile petri dishes in a tissue culture hood. Store autoclaved medium up to 1 month at room temperature. Store antibiotic-containing plates in the dark up to 2 weeks at 4°C. Contamination is easily identified by the clouding of solutions or the presence of growth on plates.

Additives: Antibiotics (if required): Ampicillin to 50 µg/ml Tetracycline to 12 µg/ml

Galactosides (if required): 5-bromo-4-chloro-3-indolyl-β-Dgalactoside (Xgal) to 20 µg/ml Isopropyl-1-thio-β-D-galactoside (IPTG) to 0.1 mM

Potassium phosphate buffer, 0.1 M Solution A: 27.2 g KH2PO4 per liter (0.2 M final) Solution B: 34.8 g K2HPO4 per liter (0.2 M final) Referring to Table A.2A.2 for desired pH, mix the indicated volumes of solutions A and B, then dilute with water to 200 ml. Filter sterilize if necessary. Store up to 3 months at room temperature. Common Stock Solutions, Buffers and Media

This buffer may be made as a 5- or 10-fold concentrate simply by scaling up the amount of potassium phosphate in the same final volume. Phosphate buffers show concentration-decontinued

A.2A.2 Current Protocols in Pharmacology

pendent changes in pH, so check the pH of the concentrate by diluting an aliquot to the final concentration.

Sodium phosphate buffer, 0.1 M Solution A: 27.6 g NaH2PO4⋅H2O per liter (0.2 M final) Solution B: 53.65 g Na2HPO4⋅7H2O per liter (0.2 M) Referring to Table A.2A.2 for desired pH, mix the indicated volumes of solutions A and B, then dilute with water to 200 ml. Filter sterilize if necessary. Store up to 3 months at room temperature. This buffer may be made as a 5- or 10-fold concentrate simply by scaling up the amount of potassium phosphate in the same final volume. Phosphate buffers show concentration-dependent changes in pH, so check the pH of the concentrate by diluting an aliquot to the final concentration. Table A.2A.2 Preparation of 0.1 M Sodium and Potassium Phosphate Buffersa

Desired pH

Solution A (ml)

Solution B (ml)

Desired pH

Solution A (ml)

Solution B (ml)

5.7 5.8 5.9 6.0 6.1 6.2 6.3 6.4 6.5 6.6 6.7 6.8

93.5 92.0 90.0 87.7 85.0 81.5 77.5 73.5 68.5 62.5 56.5 51.0

6.5 8.0 10.0 12.3 15.0 18.5 22.5 26.5 31.5 37.5 43.5 49.0

6.9 7.0 7.1 7.2 7.3 7.4 7.5 7.6 7.7 7.8 7.9 8.0

45.0 39.0 33.0 28.0 23.0 19.0 16.0 13.0 10.5 8.5 7.0 5.3

55.0 61.0 67.0 72.0 77.0 81.0 84.0 87.0 90.5 91.5 93.0 94.7

aAdapted by

permission from CRC (1975).

TE (Tris/EDTA) buffer 10 mM Tris⋅Cl, pH 7.4, 7.5, or 8.0 (or other pH; see recipe below) 1 mM EDTA, pH 8.0 (see recipe above) Store up to 6 months at room temperature Tris⋅Cl, 1 M Dissolve 121 g Tris base in 800 ml H2O Adjust to desired pH with concentrated HCl Adjust volume to 1 liter with H2O Filter sterilize if necessary Store up to 6 months at 4°C or room temperature Approximately 70 ml HCl is needed to achieve a pH 7.4 solution, and ∼42 ml for a solution that is pH 8.0. IMPORTANT NOTE: The pH of Tris buffers changes significantly with temperature, decreasing approximately 0.028 pH units per 1°C. Tris-buffered solutions should be adjusted to the desired pH at the temperature at which they will be used. Because the pKa of Tris is 8.08, Tris should not be used as a buffer below pH ∼7.2 or above pH ∼9.0.

LITERATURE CITED Chemical Rubber Company. 1975. CRC Handbook of Biochemistry and Molecular Biology, Physical and Chemical Data, 3d ed., Vol. 1. CRC Press, Boca Raton, Fla.

Stock Solutions, Equipment, and Laboratory Guidelines

A.2A.3 Current Protocols in Pharmacology

Standard Laboratory Equipment and Supplies

APPENDIX 2B

Special equipment is also itemized in the materials list of each protocol. In the individual materials lists we have not attempted to list all items required for each procedure, but rather have noted those items that might not be readily available in the laboratory or that require special preparation. Listed below are standard pieces of equipment in the modern pharmacology laboratory—i.e., items used extensively in this manual and thus not usually included in the individual materials lists. See SUPPLIERS APPENDIX for contact information for commercial vendors of laboratory equipment. Applicators, cotton-tipped and wooden

Dry ice

Autoclave

Filtration apparatus, for collecting acid precipitates on nitrocellulose filters or membranes

Balances, analytical and preparative Beakers Bench protectors, plastic-backed (including “blue pads”)

Flasks, glass (e.g., Erlenmeyer, beveled shaker) Forceps

Biohazard disposal containers and bags Bottles, glass and plastic

Fraction collector Freezers, −20° and −80°C

Bunsen burners Cell harvester, for determining radioactivity uptake in 96-well microtiter plates

Geiger counter

Centrifuges, low-speed (6,000 rpm) and highspeed (20,000 rpm) refrigerated centrifuges and an ultracentrifuge (20,000 to 80,000 rpm) are required for many procedures. At least one microcentrifuge that holds standard 0.5- and 1.5-ml microcentrifuge tubes is essential. It is also useful to have a tabletop swinging-bucket centrifuge with adapters for spinning 96-well microtiter plates. NOTE: Centrifuge speeds are provided as × g or as rpm (with example rotor models) throughout the manual Clamps Cold room or cold box, 4°C Computer (IBM-compatible or Macintosh) and printer Containers, assortment of plastic and glass dishes for gel and membrane washes

Gel dryer Gel electrophoresis equipment, at least one full-size horizontal apparatus and one horizontal minigel apparatus, one vertical full-size and minigel apparatus for polyacrylamide protein gels, and specialized equipment for two-dimensional protein gels Gloves, plastic and latex, disposable and asbestos Graduated cylinders Heating blocks, variable temperature up to 100°C; these thermostat-controlled metal heating blocks that hold test tubes and/or microcentrifuge tubes are very convenient for carrying out enzymatic reactions Heat-sealable plastic bags and sealing apparatus Hemacytometer Hoods, chemical (fume), microbiological safety, and laminar flow (tissue culture)

Coplin jars or staining dishes, glass, for 75 × 25–mm slides

Hot plates, with or without magnetic stirrer

Cryovials, sterile—e.g., Nunc Cuvettes, plastic disposable, glass, and quartz

Ice maker

Darkroom and developing tank, or X-Omat automatic X-ray film developer (Kodak) Desiccators (including vacuum desiccators) and desiccant

Current Protocols in Pharmacology (1998) A.2B.1-A.2B.2 Copyright © 1998 by John Wiley & Sons, Inc.

Ice buckets Incubators, 37°C for bacteria and humidified 37°C, 5% CO2 for tissue culture Kimwipes, or equivalent lint-free tissues Lab coats

Stock Solutions, Equipment, and Laboratory Guidelines

A.2B.1 Supplement 2

Light box, for viewing gels and autoradiograms

Refrigerator, 4°C

Liquid nitrogen and Dewar flask

Rotator, end-over-end

Lyophilizer

Rubber bands

Magnetic stirrers, (with heater is useful)

Rubber policemen

Markers, including indelible markers and china-marking pencils

Rubber stoppers

Microcentrifuge, Eppendorf-type, maximum speed 12,000 to 14,000 rpm

Scalpels and blades

Microcentrifuge tubes, 1.5-ml and 0.5-ml Microscope, standard optical model (optionally with epifluorescence or phase-contrast illumination) and inverted microscope for tissue culture

Ring stands and rings

Safety glasses Scintillation counter Scissors Sectioning equipment, cryostat microtome, sliding microtome (with stage and knife), Vibratome

Paper cutter, large size, for 46 × 57–cm Whatman paper sheets

Shakers, orbital and platform, room temperature or 37°C. An enclosed shaker (e.g., New Brunswick Controlled Environment Incubator Shaker) that can spin 4-liter flasks is essential for growing 1-liter E. coli cultures. A rotary shaking water bath (New Brunswick R76) is useful for growing smaller cultures in flasks.

Paper towels

Spectrophotometer, UV and visible

Parafilm

Speedvac evaporator (Savant)

pH meter

Stir-bars, assorted sizes

pH paper

Surgical equipment, scale for weighing animals, electric razor, syringes, hypodermic needles, dissection instruments (scissors, scalpels, forceps, hemostats, retractor, bone drill, sutures), operating microscope (optional), heating pad, sterile gauze

Microscope slides and coverslips Mortar and pestle Ovens, drying, hybridization, vacuum, and microwave

pH standard solutions Pipet bulbs, or battery-operated pipetting devices—e.g., Pipet-Aid (Drummond Scientific) Pipets, Pasteur and graduated, glass and plastic, serological (1-ml to 25-ml) Pipettors, adjustable delivery, volume ranges 0.5 to 10 µl, 10 to 200 µl, and 200 to 1000 µl. It is best to have one set of these three sizes for each full-time researcher and sets dedicated for radioactive and PCR experiments.

Tape, masking and electrician’s Thermometers Timer UV cross-linker (e.g., Stratalinker from Stratagene)

Plastic wrap, UV-transparent (e.g., Saran Wrap)

UV light sources, long- and short-wave, stationary or hand-held

Pliers, needle nose

UV transilluminator

Polaroid camera

Vacuum aspirator

Power supplies, 300-V power supplies are sufficient for polyacrylamide gels; 2000- to 3000V is needed for some applications

Vacuum line

Racks, for test tubes and microcentrifuge tubes Radiation shield, Lucite or Plexiglas Radioactive waste containers, for liquid and solid waste

Vortex mixers Wash bottles, plastic and glass Water baths, variable temperature up to 80°C Water purification equipment, e.g., Milli-Q system (Millipore) or equivalent X-ray film cassettes and intensifying screens

Razor blades Standard Laboratory Equipment and Supplies

A.2B.2 Supplement 2

Current Protocols in Pharmacology

Assays for Determination of Protein Concentration

APPENDIX 3A

Bradley J.S.C. Olson1 and John Markwell2 1 2

Michigan State University, East Lansing, Michigan University of Nebraska, Lincoln, Nebraska

ABSTRACT Biochemical analysis of proteins relies on accurate quantitation of protein concentration. This appendix describes how to perform commonly used protein assays, e.g., Lowry, Bradford, BCA, and UV spectroscopic protein assays. The primary focus of the appendix is assay selection, emphasizing sample and buffer compatibility. Protein assay standard curves and data processing fundamentals are discussed in detail. This appendix also details high-throughput adaptations of the commonly used protein assays, and also contains a protocol for BCA assay of total protein in SDS-PAGE sample buffer that is used for equal loading of SDS-PAGE gels, which is reliable, inexpensive, and quick. Curr. Protoc. C 2007 by John Wiley & Sons, Inc. Pharmacol. 38:A.3A.1-A.3A.29.
Keywords: protein assay r spectrophotometry r SDS-PAGE

INTRODUCTION Accurate measurement of protein concentration is critical since the results are used in other calculations, such as determination of enzyme activity. Errors in protein concentration determination tend to amplify overall errors in these calculations. Furthermore, protein assays are often purchased as kits from commercial suppliers, which result in a poor understanding of the underlying chemistry. This unit presents a survey of the common protein assay methods and highlights their usefulness and limitations. Additionally, the chemistry underlying each assay is explained to aid in troubleshooting and assay selection. There is no single protein assay method that yields absolutely accurate results. Each method has different advantages and limitations. The primary intention of this unit is to inform users how to select the most appropriate protein assay for a specific application. The Kjeldahl method (Ballentine, 1957) and the acid digestion–ninhydrin method (Lovrien and Matulis, 1995) are no longer in general use and are not included here. This appendix discusses the following protein assay methods that are commonly used in biochemical laboratories: the Lowry assay (see Basic Protocol 1), the Bradford assay (see Basic Protocol 2 and Alternate Protocol 1), the BCA assay (see Basic Protocol 3 and Alternate Protocol 2), and UV spectroscopy to determine protein concentration (see Basic Protocol 4). Support Protocol 1 discusses standard curves and data processing in detail and is a good place for a novice to start before beginning any experimentation. Also included are two precipitation strategies for dealing with buffer incompatibility. Finally, many applications of SDS-PAGE require equal loading of samples based on total protein. Typically, this requires laborious extraction in a buffer compatible with a protein assay, before adding SDS-PAGE loading buffer. Alternate Protocol 3 details a method for alkylation of excess reducing agent in SDS-PAGE loading buffer for direct analysis in the BCA assay. The protocols in this appendix should produce valid results for most applications. There are also kits available for protein determination from Sigma, Bio-Rad, and Pierce. Prior to

Current Protocols in Pharmacology A.3A.1-A.3A.29, September 2007 Published online September 2007 in Wiley Interscience (www.interscience.wiley.com). DOI: 10.1002/0471141755.pha03as38 C 2007 John Wiley & Sons, Inc. Copyright


Standard Techniques

A.3A.1 Supplement 38

employing an assay kit or protocol, the user is advised to consult the Strategic Planning section because it describes how to select the most appropriate assay for a particular task. The assays have different strengths and weaknesses, particularly regarding buffer compatibility. Since some investigators may have limited experience with biochemical techniques, the Commentary presents a discussion of the critical parameters for protein assays, focusing on the standard curve and a brief review of spectrophotometry. Finally, a Support Protocol provides suggestions for processing data using a spreadsheet.

STRATEGIC PLANNING Assay choice Two common applications of the protein assay are enzyme activity assays and equal protein loading of SDS-PAGE gels. The strategies and considerations for these applications are considerably different. In enzyme activity calculations, the primary concern is the accuracy of the results, whereas for equal loading of SDS-PAGE gels, precision is more important. There are many other applications for protein assays, e.g., in nutrition

Figure A.3A.1 Flow chart for selecting a protein assay. To use the chart, begin with step A to find the protein assays that are most compatible with the sample. Assays for Determination of Protein Concentration

A.3A.2 Supplement 38

Current Protocols in Pharmacology

Figure A.3A.1 (continued) Next, proceed to step B to obtain the list of assays compatible with the buffer system. Compare the results from each step to find the most compatible assay for the sample. Refer to the text for the assay and Table A.3A.1 to confirm assay compatibility before using a protein assay strategy. Abbreviations: 2-ME, 2-mercaptoethanol; DTT, dithiothreitol; SDS, sodium dodecyl sulfate. Standard Techniques

A.3A.3 Current Protocols in Pharmacology

Supplement 38

and pharmacology, and it is strongly recommend that the literature of the particular field be consulted before employing one of the assays presented in this unit to ensure that these assays are applicable in the field. It is particularly important to realize that these assays are estimation procedures unless the individual assay has been standardized to total amino acid analysis. One of the most difficult aspects of assaying protein concentration is the selection of an assay compatible with the sample. All of the assays presented in this appendix detect specific properties of a protein, not the entire protein itself; thus protein and buffer composition will be the primary determinants of the appropriate assay. Figure A.3A.1 presents a flow chart of the assay selection process. If selection of a routine estimation procedure for relatively pure protein samples is the goal, highest accuracy will be achieved by standardizing the assay to a total amino acid analysis of the unknown. All of the presented methods have different sensitivities to different proteins such as bovine serum albumin and gamma-globulin (Stoscheck, 1990).

Sample composition Sample composition is critical when choosing a protein assay. For example, a protein rich in arginine residues will produce an artificially high result using a Bradford assay, whereas the same protein will most likely produce a more accurate result using either the Lowry or BCA assay. Conversely, a protein high in cysteine would produce an artificially high result using the BCA assay, but would likely produce better results with the Lowry or Bradford assay. In general, the BCA and Lowry assays perform better with complex protein mixtures. Buffer composition Buffer composition is an important consideration when selecting a protein assay. The Lowry assay is very sensitive for determining protein concentration; however, common buffer components such as ethylenediaminetetraacetic acid (EDTA) interfere with chromophore production. The BCA assay is compatible with a wide range of detergents, including sodium dodecylsulfate (SDS), but does not tolerate reducing agents such as dithiothreitol (DTT). The Bradford assay will not tolerate high concentrations of detergents but will work in the presence of reducing agents, such as DTT or 2-ME. See Table A.3A.1 for a comprehensive table listing interfering compounds and the limiting concentration of each for the different assay methods. If it is not possible to find an assay that is compatible with the buffer system, then the best strategy is to employ a precipitation step (see Support Protocols 2 and 3). After precipitation, the sample is resuspended in a buffer compatible with one of the protein assays.

Assays for Determination of Protein Concentration

High-throughput adaptations The Bradford and BCA assays are easily adapted for high-throughput analysis using a microtiter plate format, as presented in Alternate Protocols 1 and 2, respectively. These protocols are carried out in flat-bottom 96-well microtiter plates and are the methods of choice when processing a large number of unknowns. If microtiter plate–based assays are performed often, it is recommended to use a multichannel repeating pipettor to increase accuracy and decrease user fatigue as samples are dispensed. A convenient approach is to set up a master plate with pre-diluted standards and unknowns that can then be dispensed onto multiple replicate plates. Another significant advantage of this format is that a larger number of standard curve points can be processed, thus increasing the overall accuracy of the results.

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Current Protocols in Pharmacology

Selection of microtiter plate and plate reader To adapt protein assays to high-throughput analyses, it is important to select a microtiter plate reader that will accommodate the rigors of analysis. An instrument that has two features: a tungsten and mercury or deuterium lamp as a light source, permitting assays at both UV and visible wavelengths is recommended. The instrument should also utilize interchangeable filter wheels or a diffraction grating to permit tuning of the light source. If a filter wheel is used, it is important to purchase a filter wheel with a fairly narrow bandpass (typically, a bandpass range of 10 to 20 nm; a wider bandpass may result in overlap with unbound Coomassie brilliant blue G-250 dye (CBBG) in the Bradford assay or the copper reagent in the BCA assay). Interference filters are more expensive than simple glass filters, but have a narrower bandpass. For the BCA assay, it is desirable to have a short time between each sample reading. Ideally, all 96 wells in the plate should be read in ≤2 min. Any polystyrene untreated flat-bottomed microplate should be acceptable. Suppliers include BD Falcon, Corning Costar, and Whatman. THE LOWRY ASSAY The Lowry method (Lowry et al., 1951) relies on two different reactions. The first reaction is the formation of a copper ion complex with amide bonds, forming reduced copper in alkaline solutions. This is called a Biuret chromophore and is commonly stabilized by the addition of tartrate (Gornall et al., 1949). The second reaction is reduction of the Folin-Ciocalteu reagent (phosphomolybdate and phosphotungstate), primarily by the reduced copper-amide bond complex as well as by tyrosine and tryptophan residues. The reduced Folin-Ciocalteu reagent is blue and thus detectable with a spectrophotometer in the range of 500 to 750 nm. The Biuret reaction itself is not very sensitive. Using the Folin-Ciocalteu reagent to detect reduced copper makes the Lowry assay nearly 100 times more sensitive than the Biuret reaction alone. Several useful modifications of the original Lowry assay have been developed to increase the dynamic range of the assay over a wider protein concentration (Hartree, 1972), to make the assay less sensitive to interference by detergents (Dulley and Grieve, 1975), and to first precipitate the proteins to remove interfering contaminants (Bensadoun and Weinstein, 1976).

BASIC PROTOCOL 1

The Lowry assay is relatively sensitive, but requires more time than other assays and is susceptible to many interfering compounds (Table A.3A.1). The following substances are known to interfere with the Lowry assay: detergents, carbohydrates, glycerol, Tricine, EDTA, Tris, potassium compounds, sulfhydryl compounds, disulfide compounds, most phenols, uric acid, guanine, xanthine, magnesium, and calcium. Many of these interfering substances are commonly used in buffers for preparing proteins or in cell extracts. This is one of the major limitations of the assay. The Lowry assay is also sensitive to variations in the content of tyrosine and tryptophan residues, a trait shared with the ultraviolet assay at 280 nm (see Basic Protool 4). The assay is linear over the range of 1 to 100 µg protein (Fig. A.3A.2). The absorbance can be read in the region of 500 to 750 nm, with 660 nm being the most commonly employed. Other wavelengths can also be used, however, and may reduce the effects of contamination (e.g., chlorophyll in plant samples interferes at 660 nm, but not at 750 nm). Also, if the A660 values are low, sensitivity can be increased by rereading the samples at 750 nm. A typical Lowry assay standard curve is depicted in Figure A.3A.2 and a typical assay spreadsheet is listed in Table A.3A.2.

Standard Techniques

A.3A.5 Current Protocols in Pharmacology

Supplement 38

Table A.3A.1 Concentration Limits of Chemicals in Protein Assaysa

Substanceb

Concentration limits Enhanced copperc

BCAd

Dyee

UVf 280 nm

205 nm

Acids and bases HCl

0.1 M

0.1 M

>1 M

0.5 M

NaOH

0.1 M

0.1 M

>1 M

25 mM

PCA