Heparin : Properties, Uses and Side Effects [1 ed.] 9781621004912, 9781621004318

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Copyright © 2012. Nova Science Publishers, Incorporated. All rights reserved. Heparin : Properties, Uses and Side Effects, Nova Science Publishers, Incorporated, 2012. ProQuest Ebook Central,

Copyright © 2012. Nova Science Publishers, Incorporated. All rights reserved. Heparin : Properties, Uses and Side Effects, Nova Science Publishers, Incorporated, 2012. ProQuest Ebook Central,

PHARMACOLOGY - RESEARCH, SAFETY TESTING AND REGULATION

HEPARIN

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PROPERTIES, USES AND SIDE EFFECTS

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Heparin : Properties, Uses and Side Effects, Nova Science Publishers, Incorporated, 2012. ProQuest Ebook Central,

PHARMACOLOGY - RESEARCH, SAFETY TESTING AND REGULATION

HEPARIN PROPERTIES, USES AND SIDE EFFECTS

DAVID E. PIYATHILAKE AND

RHONG LIANG Copyright © 2012. Nova Science Publishers, Incorporated. All rights reserved.

EDITORS

Nova Science Publishers, Inc. New York Heparin : Properties, Uses and Side Effects, Nova Science Publishers, Incorporated, 2012. ProQuest Ebook Central,

Copyright © 2012 by Nova Science Publishers, Inc. All rights reserved. No part of this book may be reproduced, stored in a retrieval system or transmitted in any form or by any means: electronic, electrostatic, magnetic, tape, mechanical photocopying, recording or otherwise without the written permission of the Publisher. For permission to use material from this book please contact us: Telephone 631-231-7269; Fax 631-231-8175 Web Site: http://www.novapublishers.com NOTICE TO THE READER The Publisher has taken reasonable care in the preparation of this book, but makes no expressed or implied warranty of any kind and assumes no responsibility for any errors or omissions. No liability is assumed for incidental or consequential damages in connection with or arising out of information contained in this book. The Publisher shall not be liable for any special, consequential, or exemplary damages resulting, in whole or in part, from the readers‘ use of, or reliance upon, this material. Any parts of this book based on government reports are so indicated and copyright is claimed for those parts to the extent applicable to compilations of such works.

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Independent verification should be sought for any data, advice or recommendations contained in this book. In addition, no responsibility is assumed by the publisher for any injury and/or damage to persons or property arising from any methods, products, instructions, ideas or otherwise contained in this publication. This publication is designed to provide accurate and authoritative information with regard to the subject matter covered herein. It is sold with the clear understanding that the Publisher is not engaged in rendering legal or any other professional services. If legal or any other expert assistance is required, the services of a competent person should be sought. FROM A DECLARATION OF PARTICIPANTS JOINTLY ADOPTED BY A COMMITTEE OF THE AMERICAN BAR ASSOCIATION AND A COMMITTEE OF PUBLISHERS. Additional color graphics may be available in the e-book version of this book. LIBRARY OF CONGRESS CATALOGING-IN-PUBLICATION DATA Heparin : properties, uses, and side effects / editors, David E. Piyathilake and Rhong Liang. p. ; cm. Includes bibliographical references and index. ISBN:  (eBook) I. Piyathilake, David E. II. Liang, Rhong. [DNLM: 1. Heparin. QV 193] LC classification not assigned 615.7'18--dc23 2011034014

Published by Nova Science Publishers, Inc.  New York

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Contents

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Preface

vii

Chapter I

The Pharmacology of Oral Heparins Linda Hiebert

Chapter II

Suppression of Membrane Vesiculation: A Possible Anticoagulant, Antimetastatic and Anti-inflammatory Effect of Heparin Veronika Kralj-Iglič, Vid Šuštar, Henry Hägerstrand, Mojca Frank, Rado Janša

1

27

Chapter III

Beyond Anticoagulation: Roles for Heparin in the Vasculature Joshua B. Slee, Raymond Pugh, Linda J. Lowe-Krentz

59

Chapter IV

Heparin Monitoring: From Blood Tube to Microfluidic Device Leanne F. Harris and Anthony J. Killard

83

Chapter V

Advances in the Analysis of the Heparins J. Timothy King and Umesh R. Desai

109

Chapter VI

Searching for Heparin Binding Partners Ester Boix,, Marc Torrent, M. Victòria Nogués, Vivian A. Salazar

133

Chapter VII

Heparin-related Nanomaterials Zi Gu, Barbara E. Rolfe, Anita C. Thomas, Julie H. Campbell, G. Q. (Max) Lu, and Zhi Ping Xu

159

Chapter VIII

Pharmacokinetic Differences among Unfractionated Heparin and Low Molecular Weight Heparins: Impact in Patients with Renal Impairment Shaker A. Mousa, Muhammed Manna

Chapter IX

Comprehensive and Updated Study on the Analysis Techniques of Heparin for Human Use Valeria Tripodi, Silvia Lucangioli

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179

197

vi Chapter X

Chapter XI

Chapter XII

Contents The Elaboration of New Blood Plasma Anticoagulant Complexes of High-molecular-weight Heparin with Glyproline Peptides Main Amino Acids on the Base of Mathematical Simulation of pH- metry Data L. S. Nikolaeva, A. N. Semenov Application of Chemometric Techniques to Heparin Purity Analysis and Quality Control Using Proton NMR Spectral Data Qingda Zang , David A. Keire , Lucinda F. Buhse , Richard D. Wood , Christine M. V. Moore , Moheb Nasr, Ali Al-Hakim , Michael L. Trehy and William J. Welsh Heparin: The Side Effect of Heparin, Particularly, Heparin Induced Thrombocytopenia Ryotaro Wake and Minoru Yoshiyama

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Index

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217

233

305 311

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Preface Heparin has been widely used in the medical community as an anti-coagulant. More recently, heparin has been shown to possess other biologically and medically relevant characteristics such as modulation of cell proliferation, inflammation, and cytokine production. In this book, the authors present current research in the study of the properties, uses and side effects of heparin, including the pharmacology of oral heparin; the possible antimetastatic, anti-inflammatory, and suppression of membrane vesiculation effect of heparin; heparin monitoring; searching for heparin binding patterns and heparin-related nanomaterials. Chapter I - As an important family of therapeutic drugs, heparins have been administered by intravenous and subcutaneous routes for the treatment and prevention of thrombosis. It is generally assumed that heparins are not absorbed or effective when administered orally and considerable funds and effort have been spent to create an oral heparin. Results from our own laboratory and others, however, have provided evidence that heparins are absorbed following oral administration. In rat studies, following single dose oral administration of cold or radiolabelled heparins and related polyanions, there is evidence of considerable recovery of the administered compounds from endothelium although little is found in plasma. Limited human studies show evidence of minor changes in anticoagulant activity but also recovery from urine following oral unfractionated heparin administration. More importantly, orally administered heparins, both unfractionated and low molecular weight heparins (LMWH), reduce thrombosis in rat and pig models. LMWHs are effective at lower single oral doses than unfractionated heparins suggesting a faster rate of absorption for LMWH. Effective single oral doses that reduced thrombosis by 50% were 7.5 mg/kg for unfractionated heparins and 0.1 mg/kg for the LMWH tinzaparin. In repeated dose studies, when the optimal intervals for antithrombotic activity in a venous model between three repeated doses were studied, 48 h intervals were most effective for unfractionated heparin while 12 h intervals were most effective for tinzaparin. Sub acute studies in rats, for up to 30 days, indicated that antithrombotic effects were similar when heparins were given orally or subcutaneously. There was a correlation between antithrombotic effects and amounts of heparin found on endothelium but not anticoagulant activity. In addition to antithrombotic activity the polyanionic nature of heparin results in additional interactions with the vascular wall, the formed elements and other molecules in the circulation. Studies suggest that heparins have potential uses as anti-inflammatory agents, as adjuncts to cancer therapy, as anti-viral agents,

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David E. Piyathilake and Rhong Liang

and prevention of microangiopathy associated with diabetes. The ability to administer heparin orally in repetitive doses would allow long term, pain free use of heparin for these additional applications.Further studies are needed in both basic and clinical science to understand the potential of oral heparin and its suitability in the clinical setting. Chapter II - Heparin is widely used as an anticoagulant in prophylaxis and treatment. Also it was found that it slows down tumor progression in some types of cancer and that it has an anti – inflammatory effect. On the other hand, it was found that concentration of microvesicles (membrane enclosed cell fragments smaller than a micrometer) in blood isolates is increased in patients with cancer, inflammation, infection and autoimmune diseases, and in patients with thromboembolic disorders, which may be secondary to these diseases. Microvesicles are created in the final stage of the budding of cell membranes. We hypothesized that the observed multiple effects of heparin may be due to suppression of microvesiculation. Experiments performed on giant phospholipid vesicles showed that attractive interaction between membranous structures mediated by molecules in the solution can cause the bud to adhere to the mother membrane, prevent pinching off the bud from the membrane and thereby suppress vesiculation. Budding and vesiculation of membranes and mediated interaction between membranous structures are basic physical mechanisms that importantly influence cells and organisms. Heparin enhances attractive interaction mediated by blood plasma which indicates a possibility for its suppressive role in microvesiculation. We show the effect of heparin in mediating interaction between membranes and its effect on the plasma of healthy subjects and patients with rheumatoid arthritis. Attractive interaction between membranous structures mediated by heparin therefore provides an explanation for anticoagulant, anti – metastatic and anti – inflammatory effects of heparin. Chapter III - Heparin, a naturally occurring glycosaminoglycan best known for its anticoagulant properties, has an emerging identity as an anti-inflammatory molecule that also modulates proliferation signaling in the vascular system. Heparin and low anticoagulant heparin inhibit the function, expression, and/or synthesis of adhesion molecules, proinflammatory cytokines, angiogenic factors and complement. In cells which respond to heparin, such as the vascular endothelium, heparin treatment inhibits activation of NFκB, one of the major transcription factors involved in the expression of pro-inflammatory cytokines. Heparin and heparin mimics which suppress inflammation by antagonizing TNFα-induced NFκB activation have also been developed. Heparin has been shown to exert its antiproliferative effects in vascular smooth muscle cells (VSMCs) via at least two mechanisms. These mechanisms include the regulation of mitogen-activated protein kinase (MAPK) cascade intermediates, Raf and Erk, involved in cell growth and proliferation and by imposing a cell cycle block at the G1 phase. Heparin regulates MAPK cascade intermediates by up-regulating MAPK Phosphatase-1 (MKP-1), which inactivates Erk by removing its two activating phosphate groups, thereby decreasing cell proliferation. Recent data from our laboratory indicate that heparin treatment results in decreased Raf activity, consistent with evidence in the literature suggesting that heparin treatment decreases MAPK activity. Heparin also controls cell proliferation through regulation of cell cycle proteins, such as cyclins, cyclin-dependent kinases (CDKs), and cyclin-dependent kinase inhibitors (CDKIs). Heparin strongly down-regulates the cyclins and CDKs involved in the progression of G1 to S phase and up-regulates CDKIs involved in preventing S phase entry. Perlecan and syndecan-1, both heparan sulfate proteoglycans (HSPGs) play inhibitory roles in VSMC proliferation. Along with decreasing cell proliferation, heparin and heparan sulfates promote cell proliferation

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Preface

ix

through interactions with various growth factors and their receptors in vascular endothelial cells. Recent evidence suggests that heparin enhances VEGF165-induced phosphorylation of VEGFR-2 and that depletion of cell surface heparan sulfate by heparinase results the reduced phosphorylation of VEGFR-2. Heparan sulfate appears to directly associate with VEGFR-2 and is involved in VEGF-stimulated endothelial cell proliferation in vitro. This chapter will focus on recent discoveries related to the effects of heparin in the vasculature aside from its role as an anticoagulant, highlighting its anti-inflammatory effects and its role in modulating proliferation signaling. Chapter IV - Heparin anticoagulant therapy has been pivotal in both the treatment and prophylaxis of thrombotic disease for many decades. It remains standard practice to monitor unfractionated heparin (UFH) therapy due to its unpredictable pharmacokinetics. The advent of low molecular weight heparins (LMWHs) reduced the need for continuous laboratory monitoring due to the improved dose-response relationships and pharmacokinetics of these drugs. However, special patient cohorts exist where monitoring becomes essential irrespective of the drug being administered. The standard assays used for heparin (UFH and LMWH) monitoring include the activated partial thromboplastin time (aPTT), activated clotting time (ACT), thrombin time (TT), and the anti-Xa assay. Clot-based assays such as the aPTT, ACT, and TT, comprise some of the more traditional assays that are employed in the haemostasis laboratory. The anti-Xa assay is a chromogenic assay more commonly used for monitoring patients on LMWH therapy. Over the last few years, significant efforts have been made towards point of care testing (POCT) which offers greater ease of use, convenience, efficiency, and faster turnaround times than laboratory-based tests. POCT, as its name suggests describes testing that can be performed near or beside the patient, be it in a primary care facility such as a doctor‘s surgery, the operating theatre, the emergency room or even in the home. While many point of care (POC) coagulation assays are available on the market, there is a certain degree of reticence among the medical community in their uptake, as these technologies compete with conventional laboratory testing, accompanied by reports of poor correlations between the two systems. The popularity of these devices remains controversial as they can face major challenges in the areas of regulatory compliance, quality control, and financial cost. Many POC technologies are commercially available for coagulation tests. For heparin monitoring in particular, the devices available, e.g., Hemochron® and i-STAT®, can perform tests such as aPTT, ACT, and TT. While current POC devices for measuring heparin are suitable for use in the hospital setting rather than in the home, the POC technologies of the future will need to encompass all patient settings. The future of coagulation testing could see a move away from the more traditional clot-based tests towards more modern analytical technologies, with a knock-on effect of improved assay variability, precision, and reliability. Such developments can only improve medical outcomes associated with heparin testing. Chapter V - Heparin, a heterogeneous, and highly sulfated glycosaminoglycan (GAG) harvested from pig intestinal and lung mucosa, is one of the longest serving drugs used in the clinic. Unfractionated heparin (UFH) has been used as an anticoagulant since the 1930s, while low molecular weight heparins (LMWH), derived from the depolymerization of UFH, were introduced in the mid-90s. A growing portfolio of biological activities suggest that these phenomenally complex mixtures may have applications far beyond the prevention and management of thrombosis. Yet our knowledge of the structure of these GAGs is limited. The variation of chain length, uronic acid conformation, and sulfation introduces astronomical complexity in these GAGs, which challenges their fingerprinting and characterization. This

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David E. Piyathilake and Rhong Liang

chapter reviews the analytical techniques used to characterization UFH and LMWH, which has undergone significant advances since the events of 2008 that resulted from contamination of UFH with oversulfated chondroitin sulfate, a related GAG. The FDA has since mandated high performance liquid chromatography and nuclear magnetic resonance protocols to detect these lethal contaminants. They will be discussed at length alongside other developments in these areas. Additionally, advances in electrophoretic techniques including capillary electrophoresis and polyacrylamide gel electrophoresis, liquid chromatography methods, principle component analysis, and mass spectroscopic protocols will be discussed. The merits and shortcomings of these approaches will be addressed with special emphasis on the development of novel protocols and their potential usefulness compared to the current FDA mandated requirements with reference to length of time, specificity, and sensitivity. Chapter VI - Heparin modulates diverse key physiological processes, from coagulation to angiogenesis, also contributing to the host –pathogen recognition process. New potential roles are still emerging, such as the inhibition of amyloidogenesis. To fully understand heparin function, and eventually modulate its action, we must first find out who are the travel companions. The chapter presents an overview on the heparin binding proteins known up to date. Following, the review offers useful tools to assist the discovering of new binding molecules, and identify the structural determinants involved in the interaction. Heparin complexes can be studied by experimental and in silico approaches, including docking and molecular dynamics simulations. Next, protein-ligand interfaces can be further analyzed by chemical-based computational tools to redesign even discontinuous binding epitopes in order to develop new active drugs. New in silico developed leads can be efficiently synthesized by high-throughput and combinatorial chemical synthesis allowing the screening of thousands of compounds. Thenceforth, they can be tested by experimental screening analysis to refine the leads designed, which once validated can undergo first clinical trials. Chapter VII - This chapter reviews heparin as a component of drug delivery system and as a drug to be delivered by nano-carriers for its therapeutic improvement. Heparin is a biocompatible polysaccharide administrated mainly as an anti-coagulant. Due to its outstanding biological and pharmacological activities, heparin has been used to produce delivery systems for cancer therapy, tissue and bone engineering. Heparin has been used to surface-functionalize a variety of nanoparticles, including liposomes, polymers, metal nanoparticles and carbon nanotubes. The virtues of heparin coating include avoiding phagocyte elimination, facilitating internalization by tumor cells and improving blood compatibility. Like many other drugs, heparin has therapeutic limitations, such as short halflife, systemic side-effects, low oral absorption and poor patient compliance, which can be overcome by using nano-carriers. These nano-carriers include liposomes, polymers and inorganic layered double hydroxides. Conjugation of heparin to bio-functional molecules, such as deoxycholic acid and antibody, has also been studied for efficient delivery of heparin. Finally, this chapter has also provided the perspectives for future development of heparinrelated nanomaterials. Chapter VIII - Heparins are often thought of as a class of drugs used for the prevention or treatment of different thrombotic diseases. However, such a simplistic view is not realistic and every heparin should be considered as a specific drug with specific pharmacokinetic and pharmacodynamic properties. This review will concentrate on how differences in molecular weight and pharmacokinetic distribution will affect the drug‘s tendency to accumulate in renal impairment and result in bleeding events.

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Preface

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Chapter IX - Heparin (Hep) is a linear, highly charged, sulfated polysaccharide that belongs to the glycosaminoglycan family (GAG). Hep has clinically been used for many years in the prevention and initial treatment of thrombosis. Hep is a natural product extracted from animal tissues, most commonly from porcine intestine. As a result of incomplete purification of Hep, other sulfated linear polysaccharides as dermatan sulfate (DS) and chondroitin sulfate (CS) are present in the manufactured product as impurities. The potency of Hep as pharmaceutical agent is defined based on units of biological activity using plasma clotting assays, rather than on the basis of physicochemical properties. However, considerable attention has been focused on analysis of Hep due to a health crisis in 2008 resulting from the contamination of the lots of pharmaceutical Hep with chemically modified chondroitin sulfated (oversulfated condroitin sulfate, OSCS) which causes angioedema, hypertension, swelling of the larynx, and in some cases, death. Therefore, different analytical methods have been introduced for government and academic laboratories to assure the quality and safety of pharmaceutical Hep. The elucidation of structure of GAGs has been a challenging task for the analytical laboratories due to their anionic complex structures with high molecular weight and the absence of the strong chromophore groups. Thus, common analytical methodologies often are not suitable for the analysis of Hep impurities and contaminants. Hence, in recent years, different analytical approaches for the analysis of Hep active pharmaceutical ingredient (API) and finished products have been developed by chromatographic techniques as anion exchange HPLC and capillary electrophoresis with varying degrees of success. On the other hand, it has also been developed spectroscopic methods based on analysis by NMR, infrared, Raman and fluorescence as well as bioassays to characterize and identify contaminants of Hep. This chapter describes a comprehensive and updated study on the development techniques for the analysis of Hep content and purity test in pharmaceuticals for human administration. Chapter X - In this chapter our viewpoint on the problem of goal-directed search for new nontoxic ligands-anticoagulants that is very actual for therapy of diseases with function disturbance of the blood coagulation is formulated as the computational chemistry problem of searching for agents, formating stable complexes with calcium ion in blood plasma and decreasing equilibrium concentration of biologically active calcium ion, which takes part in blood coagulation reactions. The solution of this problem is based on computer simulation of the multicomponent biochemical equilibria method by the experimental data in physiological solutions and the universal software for calculation of equilibria AUTOEQUIL.By the analysis of calculation results complex Ca2+ ion with ligand, predominating over the pH range of plasma 6.80≤pH≤7.40, is determined. Anticoagulant activity of this ligand is estimated by the decrease of equilibrium concentration of calcium ion Ca2+ over the pH range of plasma caused by the formation of complex of Ca2+ with the ligand. The search for new nontoxic blood anticoagulant based on complexes of clinically demanded high-molecular-weight heparin with endogenous blood plasma ligands, wich strengthen heparin anticoagulant activity, is of interest for the normalization of hemostasis in thrombosis. This investigation is completion of our earlier published works on complexation of heparin with each from two main the glyproline peptides amino acids Glycine (HGly) and Arginine (HArg) In those works on the base of mathematical simulation of chemical equilibria in systems Na4hep–HL– H2O–NaCl and МCl2–Na4hep–HL–H2O–NaCl (M = Ca2+, Mg2+,L=Gly-,Arg- ) by pH- metry data in a dilute physiological solution the anticoagulant activities of two heparin complexes with Glycine and Arginine have been estimated.

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Chapter XI - Heparin is a highly effective anticoagulant that can contain varying amounts of galactosamine impurities (mostly dermatan sulfate or DS). Currently, the United States Pharmacopeia (USP) monograph for heparin purity dictates that the weight percent of galactosamine in total hexosamine (%Gal) may not exceed 1%. In 2007 and 2008, heparin contaminated with oversulfated chondroitin sulfate (OSCS) was associated with adverse clinical effects, primarily a rapid and acute onset of a potentially fatal anaphylactoid-type reaction. To develop efficient and reliable screening methods for detecting and identifying contaminants in lots of heparin, chemometric techniques were applied to heparin proton nuclear magnetic resonance (1H NMR) spectral data to establish multivariate statistical models for discrimination between pure heparin samples and those deemed unacceptable based on their levels of DS and/or OSCS. The first major component of this research was the development of quantitative regression models to predict the %Gal in various heparin samples from NMR spectral data. Multivariate analyses including multiple linear regression (MLR), Ridge regression (RR), partial least squares regression (PLSR), and support vector regression (SVR) were employed to prediction models, with variables selected by genetic algorithms (GA) and stepwise methods. The second major component was the differentiation of pharmaceutical-grade heparin samples from those containing unacceptable levels of impurities and/or contaminants using pattern recognition and classification approaches, including principal components analysis (PCA), partial least squares discriminant analysis (PLS-DA), linear discriminant analysis (LDA), k-nearest-neighbor (kNN), classification and regression tree (CART), artificial neural networks (ANN) and support vector machine (SVM). Also employed were the class modeling techniques, soft-independent modeling of class analogy (SIMCA) and unequal dispersed classes (UNEQ). Overall, the results from this study demonstrate that NMR spectroscopy coupled with multivariate chemometric techniques show promise as a valuable strategy for evaluating the quality of heparin sodium active pharmaceutical ingredients (APIs). Beyond the present example, the combination of analytical methods and chemometric approaches may demonstrate utility in monitoring purity of other complex pharmaceutical products from high information content data. Chapter XII - Heparin is the useful anticoagulant, when rapid anticoagulation is required for thrombosis. Thrombosis which is a clot of the blood can occur in the arterial and venous circulation and sometimes leads to serious cardiovascular events. The pathology of arterial thrombosis differs from that of venous thrombosis as reflected by the different ways in which they are treated. Generally, arterial thrombosis is treated with drugs that target platelets. Venous thrombosis is treated with drugs that target proteins of the coagulation cascade.

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Chapter I

The Pharmacology of Oral Heparins Linda Hiebert Department of Veterinary Biomedical Sciences, University of Saskatchewan, Canada

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Abstract As an important family of therapeutic drugs, heparins have been administered by intravenous and subcutaneous routes for the treatment and prevention of thrombosis. It is generally assumed that heparins are not absorbed or effective when administered orally and considerable funds and effort have been spent to create an oral heparin. Results from our own laboratory and others, however, have provided evidence that heparins are absorbed following oral administration. In rat studies, following single dose oral administration of cold or radiolabelled heparins and related polyanions, there is evidence of considerable recovery of the administered compounds from endothelium although little is found in plasma. Limited human studies show evidence of minor changes in anticoagulant activity but also recovery from urine following oral unfractionated heparin administration. More importantly, orally administered heparins, both unfractionated and low molecular weight heparins (LMWH), reduce thrombosis in rat and pig models. LMWHs are effective at lower single oral doses than unfractionated heparins suggesting a faster rate of absorption for LMWH. Effective single oral doses that reduced thrombosis by 50% were 7.5 mg/kg for unfractionated heparins and 0.1 mg/kg for the LMWH tinzaparin. In repeated dose studies, when the optimal intervals for antithrombotic activity in a venous model between three repeated doses were studied, 48 h intervals were most effective for unfractionated heparin while 12 h intervals were most effective for tinzaparin. Sub acute studies in rats, for up to 30 days, indicated that antithrombotic effects were similar when heparins were given orally or subcutaneously. There was a correlation between antithrombotic effects and amounts of heparin found on endothelium but not anticoagulant activity. In addition to antithrombotic activity the polyanionic nature of heparin results in additional interactions with the vascular wall, the formed elements and other molecules in the circulation. Studies suggest that heparins have potential uses as anti-inflammatory agents, as adjuncts to cancer therapy, as anti-viral agents, and prevention of microangiopathy associated with diabetes. The ability to administer heparin orally in repetitive doses would allow long term, pain free use of heparin for these additional

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Linda Hiebert applications.Further studies are needed in both basic and clinical science to understand the potential of oral heparin and its suitability in the clinical setting.

Introduction Heparins, administered by intravenous and subcutaneous routes, have been used since the 1930‘s for the treatment and prevention of thrombosis and has[1]. Despite the use of these drugs for more than 80 years there is a great deal about heparin and related compounds that is not understood. The endogenous role of heparin is still under investigation.Questions remain about the complex pharmacokinetics and pharmacodynamics of heparin including its transport across cell membranes, distribution, metabolism and possible absorption. Only a few studies describe the molecular interactions of heparins with cell membranes and cell constituents. Thus the understanding of the pharmacology of heparin and related compounds is limited. Although heparins have been administered by parenteral routes there have been many attempts to modify heparins so that they could be administered by the oral route. However our own studies and those of others provide evidence that heparin, without modification, is effective following oral administration. This article reviews the studies on the oral administration of unmodified heparin,and without absorption enhancers, and discusses the pharmacology of heparins related to this phenomenon.

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What Are Heparins? Heparins belong to a family of endogenous glycosaminoglycans (GAGs) which include heparan, dermatan, chondroitin and keratan sulfate. Heparin has the highest charge density of the GAGs. Heparin is produced in the mast cell where it is a GAG found on the serglycin proteoglycan [2]. The structure and function of heparin is similar to heparan sulfate, a GAG found on proteoglycans in the extracellular matrix and on most cells including endothelial cells [3]. As a drug, heparin is prepared from animal tissue, the current source being primarily porcine mucosal tissue [4]. Chemically, unfractionated heparin (UFH) is a negatively charged polyanion with a core disaccharide structure of →4)-α-D-glucosamine-2,6-disulphate (1→4)α-iduronic acid-2-sulphate (1→. Variation exists in the disaccharide units, glucuronic acid may replace iduronic acid, –acetyl may replace –sulfate groups, and O-sulfate groups may be absent. There is also variation in the degree of polymerization where in a single preparation of UFH, chain lengths can vary from 10 to 80 disaccharide units [5] with the total molecular weight ranging from 2-40 kDa[6]. Derivatives of UFH, the low molecular weight heparins (LMWHs) of 3-7 kDa, have been developed using a variety of methods including nitrous acid degradation, benzylation-alkaline hydrolysis, peroxidative cleavage or fractionation using the enzyme heparinase[6]. Newer LMWHs have a lower average molecular weight and polydispersity and a more precisely defined composition [7-9]. Although each LMWH is chemically unique they are, in general, considered to have less protein binding and increased bioavailability or presence in the plasma on administration compared to UFH [10]. LMWHs when given subcutaneously are reported to have a more predictable dose response and a

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The Pharmacology of Oral Heparins

3

longer pharmacodynamic half-life as judged by plasma anticoagulant activity than UFH which is commonly given intravenously [10-12]. It has also been shown that LMWHs given subcutaneously are similar in safety andeffectiveness to UFH given intravenously [13].Recently, a third generation of chemically modified heparins have been produced including a synthetic pentasaccharide, the shortest heparin sequence that is known to interact with the plasma protein antithrombin resulting in anticoagulant activity [14]. Heparins are similar in structure and share properties with a variety of synthesized or modified biological compounds. These include dextran sulfate, pentosanpolysulfate and sucrose octasulfate. Dextran sulfate, the sulfated polymer of anhydroglucose has both anticoagulant [15] and antiviral activity [16]. Dextran sulfate is used chemically to precipitate lipoproteins and in molecular biology techniques where it promotes hybridization. It has been studied extensively for its potential in the treatment of HIV infection [17]. Pentosanpolysulfate, is a semi-synthetic drug manufactured from beech-wood hemicellulose that is modified by sulfate esterification of the xylopyranose hydroxyl groups [18]. It is used to treat interstitial cystitis [19], and to manage and treat osteoarthritis in veterinary medicine [20]. Its potential use to treat osteoarthritis [18], thrombosis [21], and antitumour activity [22], has been studied in humans. Sucrose octasulfate is a highly charged low molecular weight carbohydrate. Its aluminum salt is used to prevent ulcers in humans [23].

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The Binding of Heparin to Proteins Because of their polyanionic nature, heparins interact with a wide variety of proteins; the most studied being those responsible for anticoagulant activity. The binding of heparins to the plasma protein antithrombin (ATIII) greatly accelerates the inhibition of the serine protease coagulation factors thrombin (IIa) and factors Xa, IXa, XIa and XIIa[24]. About one-third of UFH chains have the specific pentasaccharidesequence which has high affinity for antithrombin, with only one or two pentasaccharide groups per chain making up about 5% of the total UFH preparation [25,26].Heparins also combine with heparin cofactor II to inhibit thrombin; however heparin chains of less than 24 monosaccharide units are not able to activate heparin cofactor II, thus theiranticoagulant effect is primarily due to antithrombin activation [27]. LMWHs therefore have a higher anti-factor Xa/IIa ratio than UFH. The activated partial thromboplastin time (APTT), which has been used to monitor UFH, is not very effective in measuring LMWH and therefore anti-factor Xa activity is used [28]. Correlation between APTT or anti-Xa activity and in vivo antithrombotic activity remains questionable [29-33]. Because of their high negative charge and polydispersity, heparins interact with other plasma proteins in the blood in addition to those involved with anticoagulant activity [34-37] including hormones [38,39], growth factors [40,41], and enzymes [42].

The Interaction of Heparin with Endothelium Heparins also interact with the formed elements and the vascular wall. The interaction of heparin with the endothelial surface causes the release of many compounds into the

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Linda Hiebert

circulation including lipoprotein lipase (LPL) [43], diamine oxidase[44], extracellular superoxide dismutase [45], platelet factor 4 [46], and tissue factor pathway inhibitor (TFPI)[34]. Release of TFPI from endothelium by heparin likely contributes to heparin‘s anticoagulant activity in vivo [47,48]. In addition to interaction and binding with the endothelial surface UFHs are avidly taken up by endothelial cells [49-58]. Internalization by endothelium with possible incorporation into lysosomes, evidence of degradation and release into cell medium has been observed [5961].LMWHs are also bound and internalized by endothelium [12,53,62] although with less affinity than UFH [35]. Although the interaction of heparins with anticoagulant serine protease inhibitors has received the most attention its interaction with the endothelium has many far reaching consequences many of which are unexplored. Firstly, binding to endothelium influences distribution and it is likely that the endothelial pool must be adequately saturated before heparin can be detected in plasma in any appreciable amounts. Thus when anticoagulant activity is measured in plasma, a considerable amount of heparin is likely attached to the endothelium. Binding to endothelium increases antithrombotic activity such that heparin need not be detected in plasma to be an effective antithrombotic agent [63]. It has also been shown that the interaction of heparin with endothelial cells increases the synthesis of heparan sulfate on the endothelial cell surface which may contribute to the antithrombotic effect of heparin [64,65]. Heparin binds to endothelial cell growth factors and modulates angiogenesis mediated by other agents [66,67].Evidence also suggests that heparins are protective of endothelium and prevent damage from reactive oxygen species (ROS) [68,69], and high glucose injury [70,71]. Heparin suppresses production of the vasoconstrictor endothelin[72].Both an increase and decrease in the vasodilator nitric oxide by heparin have been reported [73-75]. Heparins have anti-inflammatory activity and interfere with recruitment of leukocytes by the vascular wall. Heparins block the adhesion molecules L- and P-selectins[76], and bind to Mac-1 thus inhibiting neutrophil adhesion to endothelial cells [77].

Current and Potential Uses of Heparin Heparins are used primarily for the treatment and prevention of thrombosis where they are administered by intravenous and subcutaneous routes. Despite the development of other antithrombotic drugs, such as inhibitors of thrombin, factor Xa or platelet activation, heparins continue to be used for the prevention of venous thromboembolism including deep vein thrombosis, embolism associated with atrial fibrillation, prosthetic heart valves, in vascular and orthopedic surgery, for the prevention of stroke and myocardial infarction [78-80], and in dialysis [81]. They are also used for ―bridge therapy‖ when long term warfarin therapy needs to be interrupted for invasive procedures [82,83]. Heparins are also effective in preventing fetal loss in women with thrombophilia [84]. Further, many studies are underway regarding the use of heparin as an adjunct for cancer therapy [85,86].In addition, heparins and related polyanions have a variety of potential uses, supported by experimental data, including prevention of atherosclerosis [87,88]; as anti-inflammatory agents [89-91], for control of

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The Pharmacology of Oral Heparins

5

3

Concentration (g/cm )

fibromyalgia [92], asthma [93,94] and Crohn‘s disease [95]; as anti-viral agents [96]; to control of angiogenesis [66]; and treat hypertension [97]. 1200

A

1000

UFH

800 600 400 200

P=0.003 r=0.9540

0 0

10

20

30

40

50

60

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3

Concentration (g/cm )

Single dose (m g/kg) 3500

B

3000

Tinzaparin

2500 2000

1 0

1500

0

0.05 0.1

1000 500

P=0.007 r=0.8538

0 0

5

10

15

Single dose (m g/kg) Figure 1.The concentration of heparin recovered from aortic endothelium following oral administration is dose-dependent. Heparins were given by gastric lavage to rats. Four hours later endothelium was harvested from the aorta by application of cellulose acetate paper to the endothelial surface. The endothelium on the paper was extracted by acetone and the sample was run on agarose gel electrophoresis. Heparin was identified by migrationdistance and concentrations were determined by densitometry. Amount recovered was divided by the surface area of the vessel and expressed as µg/cm2 that was then converted to µg/cm3 bydividing by 2 x 105 , the average thickness of the endothelial surface. A. Bovine lung heparin. B. Tinzaparin. A significant correlation between dose administered and mean amount recovered from endothelium at each dose for both drugs is shown by P CrO4- > SO42- > OH- > F- > Cl- > Br- > NO3- > I-) [79, 80]. As a drug/gene delivery system, MgAl-LDH has many advantages over other nanomaterials [81, 82]. Firstly, preparation is easy, low-cost and versatile, which makes scale-up production highly feasible. Secondly, MgAl-LDH has a relatively stable, stacked layered structure that can provide full protection for the intercalated drug/gene. Thirdly, MgAl-LDH nanoparticles provide sustained/controlled release of loaded drugs/genes and facilitate cellular uptake/transfection. Finally, these inorganic nanoparticles possess low toxicity and good biocompatibility. With the aim of improving the therapeutic efficacy of LMWH and thus finding a better solution for prevention of restenosis (arterial reblocking after angioplasty), we have prepared LMWH-loaded LDH nanoparticles via co-precipitation [83] and studied their biological effects on rat vascular smooth muscle cells (SMCs). The LDH nanoparticles before and after intercalation of LMWH (i.e. Cl-LDH and LMWH-LDH) had a hexagonal plate-like shape, with a size of 100-200 nm (Figures 3 a and b) [83]. The intercalation of LMWH was confirmed by observing the enlargement of LDH interlayers using transmission electron microscopy (TEM) (Figures 3 c and d) as well as X-ray diffraction (XRD) (Figure 3 e)[83]. Release studies conducted under physiological conditions revealed a biphasic, sustained release of LMWH from LMWH-LDH nanocomplexes [83]. We propose that this sustained release of LMWH may benefit its clinical performance as an anti-restenotic drug because intermittent administration of LMWH exacerbates neointimal hyperplasia and cellular proliferative activity after agnioplasty [47]. LDHs show low toxicity to many mammalian cells [81]. Our cell culture studies have demonstrated that the LDH carrier was non-toxic to SMCs at concentrations below 50 µg/ml [84]. We also showed that cellular uptake of LMWH was increased by intercalation into LDH nanoparticles [84], and proposed that cellular uptake of LMWH-LDH underwent via a unique ‗modified endocytic‘ pathway, whereby LMWH-LDH nanoparticles were internalized by SMCs in early endosomes, sorted in late endosomes, and quickly released from late endosomes/lysosomes, avoiding degradation [85]. Moreover, LDH delivery enhanced the ability of LMWH to inhibit SMC proliferation and migration (two key stages in restenosis), which were attributed to the increased cellular uptake of LMWH-LDH nanohybrids and the sustained release of LMWH from the carrier [84]. LMWH-LDH nanoparticles may prove a useful anti-restenotic therapy. In order to target deliver LMWH-LDH nanoparticles to the site of arterial injury, we have conjugated them with an antibody to cross-linked fibrin (XLF) [86]. The efficacy of this antibody-LMWHLDH complex has been recently examined in a rat model of vascular injury (paper in preparation) and the preliminary results suggest the potential of this technique for clinical application.

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Figure 3. Transmission electron microscopic images of (a) and (c) Cl-LDH, (b) and (d) LMWH-LDH. a and b show the lateral dimension of particles; c and d show the layered structure of particles. The enlargement of interlayer spacing shown by (c) and (d) is confirmed by the X-ray diffration result interpreted in (e) schematic representation of LDH structure before and after LMWH intercalation.

4. Conjugation of Heparin with Bio-functional Molecules Apart from the forementioned nanoparticle-based heparin delivery systems, a variety of bio-functional molecules have been conjugated to heparin for improved oral administration and clinical device development (Figure 4) [87, 88]. Three examples of these bio-functional molecules are DOCA, sodium N-[8-(2-hydroxybenzoyl) amino]caprylate (SNAC), and

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sodium N-[10-(2-hydroxybenzoyl)amino]decanoate (SNAD) [87, 88]. To construct bloodcontacting medical devices, heparin was modified with DOCA and then homogeneously dispersed in a polyurethane film in the form of nanoparticles [89]. DOCA-conjugated heparin (DOCA-heparin) is formed by reacting the amine group of heparin with the carboxyl group of DOCA to result in a stable amide bond [87]. DOCA-heparin is slightly hydrophobic, and shows good solubility in the co-solvents of acetone and water [87]. In contrast to the noncoated film, the DOCA-heparin-deposited film prevented the fibrin clot formation and platelet adhesion [89]. When orally administered to mice, DOCA-LMWH had increased plasma antifactor Xa activity, compared with unmodified LMWH [90]. SNAC and SNAD are amino acids specifically designed to deliver heparin. Unlike the conjugation of DOCA to heparin, SNAC/SNAD was coupled to heparin via a non-covalent bond, allowing for ready dissociation and thus minimizing the damage to heparin molecules [91]. In rat models, heparin formulated with SNAC/SNAD was found effective for prevention and treatment of deep venous thrombosis as evidenced by increased anti-factor Xa activity and clotting time, as well as reduced thrombus weight [91-93]. Targeted delivery of heparin has also been achieved by using an antibody to XLF to prevent restenosis [86, 94]. Having shown that XLF was deposited onto the rat and rabbit artery luminal surface within 10 min after injury and remained for at least 24 weeks [95], Thomas and Campbell conjugated an antibody to XLF to the anti-restenotic drugs (heparin and LMWH), which were then administered to rabbits immediately after balloon catheter injury of the carotid artery [86]. They found that animals receiving conjugated drugs had reduced neointimal development with fewer neointimal cells than those given control drugs, thus confirming the suitability of using an antibody to XLF to effectively site-deliver heparin or LMWH to the site of arterial injury [86].

Figure 4. Conjugation of heparin with bio-functional molecules and the interactions with the targeted site.

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5. Summary and Perspectives This chapter reviews the recent advances in heparin-related nanomaterials. The multiple biological functions and unique physicochemical properties of heparin have sparked interest in fabrication of a myriad of nanoparticles using heparin either as the component or coating surface. These nanoparticles are synthesized by various methods that are categorized in this chapter as self-assembly, core-shell, and polyelectrolytes/layer-by-layer assembly. On the other hand, heparin is a drug whose limitations can be overcome by incorporation with nanoparticle-based drug delivery systems including liposomes, polymers, LDHs, antibody and chemical molecules. Heparin emerges as a novel component of nanoparticle-based delivery systems and its incorporation has many advantages. A biocompatible and biodegradable natural polysaccharide, heparin is thus better tolerated by mammalian cells than many synthetic molecules (e.g. polymers and lipids etc). Heparin-containing nanocarriers can better retain the structural integrity of the loaded drug and are more stable than amphiphilic block copolymer micelles [3]. Heparin also possesses many biological activities that most nanocarriers do not have, including anticoagulant activity [96], tumor inhibition [97] and suppression of arterial smooth muscle cell growth [98]. Heparin not only has the ability to bind and deliver growth factors, but it also has active functional groups (such as –COOH and –OH), which allow chemical conjugation/modification with targeting ligands (e.g. folic acid [99]), candidate drugs (e.g. retinoic acid [99]) and the hydrophobic molecules (e.g. DOCA [12]). Surface modification of nanoparticles with heparin increases their half-life in the blood, thus suggesting a mechanism for stabilization of nanoparticles [19]. Heparin can also act as the parent drug, delivered by a variety of nanoparticles. However, toxicity of these nanoparticles and affinity of heparin to its carrier are important factors which need to be considered. Factors that determine toxicity of liposomes include single/double-tail, the cationic nature, and the linker bond [100]. In relation to carrier affinity, the loaded drug should be delivered without dissociating from the carrier, and then be released at the site of interest. Although covalent interaction ensures a firm coupling between drug and carrier, it may influence proper function due to modifications to the drug. Alternatively, non-covalent binding to carrier may result in early dissociation of the drug before it is delivered to the target site. Heparin dissociation from its carrier is an issue with liposome-mediated oral delivery [54, 55]. LDH nanoparticles may represent an alternative carrier in this situation, because (1) the electrostatic interaction allows easy loading and release by ion-exchange; and (2) LDH layers provide protection from the physiological environment. Therefore, LDH may represent a novel candidate to improve the delivery and therapeutic activity of heparin. There are as yet few clinical trials of heparin-containing drug delivery systems underway, but this area is particularly promising, both for prevention and treatment of disease. Questions still to be answered are how to (1) take the most advantage of heparin as a delivery system; and (2) optimize heparin-based nanoparticle drug delivery systems for clinical application.

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[67] Hoffart V, Lamprecht A, Maincent P, Lecompte T, Vigneron C, Ubrich N. Oral bioavailability of a low molecular weight heparin using a polymeric delivery system. J Control Release 2006;113(1):38-42. [68] Bai SH, Thomas C, Ahsan F. Dendrimers as a carrier for pulmonary delivery of enoxaparin, a low-molecular weight heparin. J Pharm Sci 2007;96(8):2090-106. [69] Feng XY, Cheng YY, Yang K, Zhang JH, Wu QL, Xu TW. Host-Guest Chemistry of Dendrimer-Drug Complexes. 5. Insights into the Design of Formulations for Noninvasive Delivery of Heparin Revealed by Isothermal Titration Calorimetry and NMR SI.udies. J Phys Chem B 2010;114(34):11017-26. [70] Bai SH, Ahsan F. Synthesis and Evaluation of Pegylated Dendrimeric Nanocarrier for Pulmonary Delivery of Low Molecular Weight Heparin. Pharm Res 2009;26(3):53948. [71] Oh JM, Park M, Kim ST, Jung JY, Kang YG, Choy JH. Efficient delivery of anticancer drug MTX through MTX-LDH nanohybrid system. J Phys Chem Solids 2006;67(56):1024-7. [72] Kriven WM, Kwak SY, Wallig MA, Choy JH. Bio-resorbable nanoceramics for gene and drug delivery. MRS Bull 2004;29(1):33-7. [73] Ambrogi V, Fardella G, Grandolini G, Perioli L, Tiralti MC. Intercalation compounds of hydrotalcite-like anionic clays with anti-inflammatory agents, II: uptake of diclofenac for a controlled release formulation. AAPS PharmSciTech [serial on the Internet]. 2002; 3(3). [74] Ambrogi V, Fardella G, Grandolini G, Perioli L. Intercalation compounds of hydrotalcite-like anionic clays with antiinflammatory agents - I. Intercalation and in vitro release of ibuprofen. Int J Pharm 2001;220(1-2):23-32. [75] Zhang H, Zou K, Guo SH, Duan X. Nanostructural drug-inorganic clay composites: Structure, thermal property and in vitro release of captopril-intercalated Mg-Al-layered double hydroxides. J Solid State Chem 2006;179(6):1792-801. [76] Zhang H, Pan DK, Duan X. Synthesis, Characterization, and Magnetically Controlled Release Behavior of Novel Core-Shell Structural Magnetic Ibuprofen-Intercalated LDH Nanohybrids. J Phys Chem C 2009;113(28):12140-8. [77] Tyner KM, Schiffman SR, Giannelis EP. Nanobiohybrids as delivery vehicles for camptothecin. J Control Release 2004;95(3):501-14. [78] Ferruccio Trifiro AV. Hydrotalcite-like anionic clays. In: Jerry L. Atwood JEDD, David D. Macnicol, Fritz Vogtle, editor. Comprehensive supramolecular chemistry New York: Pergamon Press; 1996. p. 251-91. [79] Braterman PS, Xu ZP, Yarberry F. Layered Double Hydroxides (LDHs). In: Auerbach SM, Carrado KA, Dutta PK, editors. Handbook of Layered Materials. New York: Marcel Dekker; 2004. p. 373-474. [80] Miyata S. Anion-exchange properties of hydrotalcite-like compounds. Clay Clay Min 1983;31(4):305-11. [81] Xu ZP, Zeng QH, Lu GQ, Yu AB. Inorganic nanoparticles as carriers for efficient cellular delivery. Chem Eng Sci 2006;61(3):1027-40. [82] Xu ZP, Lu GQ. Layered double hydroxide nanomaterials as potential cellular drug delivery agents. Pure Appl Chem 2006;78(9):1771-9. [83] Gu Z, Thomas AC, Xu ZP, Campbell JH, Lu GQ. In vitro sustained release of LMWH from MgAl-layered double hydroxide nanohybrids. Chem Mat 2008;20(11):3715-22.

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[84] Gu Z, Rolfe BE, Xu ZP, Thomas AC, Campbell JH, Lu GQM. Enhanced effects of low molecular weight heparin intercalated with layered double hydroxide nanoparticles on rat vascular smooth muscle cells. Biomaterials 2010;31(20):5455-62. [85] Gu Z, Rolfe BE, Thomas AC, Campbell JH, Lu GQ, Xu ZP. Cellular trafficking of low molecular weight heparin incorporated in layered double hydroxide nanoparticles in rat vascular smooth muscle cells. Biomaterials 2011;32(29):7234-40. [86] Thomas AC, Campbell JH. Conjugation of an antibody to cross-linked fibrin for targeted delivery of anti-restenotic drugs. J Control Release 2004;100(3):357-77. [87] Lee YK, Moon HT, Byun Y. Preparation of slightly hydrophobic heparin derivatives which can be used for solvent casting in polymeric formulation. Thromb Res 1998;92(4):149-56. [88] Ross BP, Toth I. Gastrointestinal absorption of heparin by lipidization or coadministration with penetration enhancers. Curr Drug Deliv 2005;2(3):277-87. [89] Moon HT, Lee YK, Han JK, Byun Y. A novel formulation for controlled release of heparin-DOCA conjugate dispersed as nanoparticles in polyurethane film. Biomaterials 2001;22(3):281-9. [90] Kim SK, Vaishali B, Lee E, Lee S, Lee YK, Kumar TS, et al. Oral delivery of chemical conjugates of heparin and deoxycholic acid in aqueous formulation. Thromb Res 2006;117(4):419-27. [91] Salartash K, Gonze MD, Leone-Bay A, Baughman R, Sternbergh WC, Money SR. Oral low-molecular weight heparin and delivery agent prevents jugular venous thrombosis in the rat. J Vasc Surg 1999;30(3):526-31. [92] Gonze MD, Manord JD, Leone-Bay A, Baughman RA, Garrard CL, Sternbergh WC, et al. Orally administered heparin for preventing deep venous thrombosis. Am J Surg 1998;176(2):176-8. [93] Gonze MD, Salartash K, Sternbergh WC, Baughman RA, Leone-Bay A, Money SR. Orally administered unfractionated heparin with carrier agent is therapeutic for deep venous thrombosis. Circulation 2000;101(22):2658-61. [94] Thomas AC, Campbell JH. Targeted delivery of heparin and LMWH using a fibrin antibody prevents restenosis. Atherosclerosis 2004;176(1):73-81. [95] Thomas AC, Campbell JH. Timecourse of fibrin deposition and removal after arterial injury. Thromb Res 2003;109(1):65-9. [96] Linhardt RJ. 2003 Claude S. Hudson Award address in carbohydrate chemistry. Heparin: Structure and activity. J Med Chem 2003;46(13):2551-64. [97] Smorenburg SM, Van Noorden CJF. The complex effects of heparins on cancer progression and metastasis in experimental studies. Pharmacol Rev 2001;53(1):93-105. [98] Clowes AW, Clowes MM. Kinetics of cellular proliferation after arterial injury. II. Inhibition of smooth muscle growth by heparin. Lab Invest 1985;52(6):611-6. [99] Kim YJ, Park IK, Kim JS, Hwang J, Chung ES, Huh KM, et al., editors. Anticancer Drug Delivery for Tumor Targeting and Therapy. 2009 4th IEEE International Conference on Nano/Micro Engineered and Molecular Systems, Vols 1 and 2; 2009; New York: IEEE. [100] Lv HT, Zhang SB, Wang B, Cui SH, Yan J. Toxicity of cationic lipids and cationic polymers in gene delivery. J Control Release 2006;114(1):100-9.

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In: Heparin: Properties, Uses and Side Effects Editors: D. E. Piyathilake, Rh. Liang, pp. 179-195

ISBN: 978-1-62100-431-8 © 2012 Nova Science Publishers, Inc.

Chapter VIII

Pharmacokinetic Differences among Unfractionated Heparin and Low Molecular Weight Heparins: Impact in Patients with Renal Impairment Shaker A. Mousa,* Muhammed Manna The Pharmaceutical Research Institute, Albany College of Pharmacy and Health Sciences, Albany, NY, USA

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Abstract Background Heparins are often thought of as a class of drugs used for the prevention or treatment of different thrombotic diseases. However, such a simplistic view is not realistic and every heparin should be considered as a specific drug with specific pharmacokinetic and pharmacodynamic properties. This review will concentrate on how differences in molecular weight and pharmacokinetic distribution will affect the drug‘s tendency to accumulate in renal impairment and result in bleeding events.

Methods A literature search for original research articles and clinical trial reports was done. An evaluation of drug accumulation, pharmacokinetics differences at prophylaxis and treatment doses, and differing dosing strategies are presented in the context of varying degrees of renal impairment.

*

Corresponding author: Shaker A. Mousa, PhD, MBA, FACC, FACB; [email protected]; Tel: +1 518 694 7397; Fax: +1 518 694 7567.

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Results/discussion Unfractionated heparin (UFH) and low molecular weight heparins (LMWH) with a large molecular weight exhibit varying degrees of biodistribution into different compartments, which could have an impact on the elimination kinetics in patients with renal impairment. In renal impairment, and in particular severe renal impairment, monitoring of anti-Xa levels is recommended to avoid accumulation and bleeding risk. Patients should always have their renal function assessed when admitted for treatment because the population as a whole is getting older, and renal dysfunction is becoming more prevalent. Tailoring a dosing regimen to be patient-specific, rather than populationbased, and to take into account renal impairment and other factors such as body weight, seems to be the best option.

Conclusion In patients with renal impairment, the use of unfractionated heparin should be considered over relatively small molecular weight LMWHs and the pentasaccharide fondaparinux. Additionally, evidence suggests that LMWHs of larger molecular weight along with special chemical features such as tinzaparin, have less accumulation to no accumulation as compared to smaller molecular weight LMWHs such as enoxaparin and fondaparinux in patients with mild to severe renal failure.

Keywords: Renal Impairment, Low Molecular Weight Heparin, Unfractionated Heparin, Tinzaparin, Deltaparin, Enoxaparin, Fondaparinux

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Abbreviations LMWH UFH IV BID QD SC CKD AT III

Low Molecular Weight Heparin; Unfractionated Heparin; Intravenously; twice a day; once every day; subcutaneously; chronic kidney disease; Antithrombin III

Introduction Unfractionated heparin (UFH) and low molecular weight heparin (LMWH) are well established anticoagulants used in the prophylaxis and treatment of various thrombotic diseases, including deep vein thrombosis and acute coronary syndrome. Heparin now also has a role in cancer treatment because cancer cells and chemotherapy or radiotherapy have an effect on the coagulation cascade and can thus cause a hyper-coaguable state [1].

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LMWHs are the biological products of UFH after cleavage of the long chains of UFH, resulting in different heparin compounds of different molecular weight ranging from 1,700 6,500 Dalton [2]. The resulting chains usually contain 2-40 sugar units that can be further modified, leaving differences among the LMWHs themselves [2]. Thus, LMWHs are a complex of different biological products, with individual pharmacokinetic and clinical characteristics [2]. All anticoagulants have similar mechanisms of action by binding to antithrombin (AT) III and inhibiting factors in the clotting cascade. LMWHs differ in their affinity and binding to AT III. Both UFH and LMWH inhibit clotting factor Xa and thrombin (factor IIa), although to different degrees. UFH‘s larger molecular weight (average 15 kDa) allows it to inhibit factor Xa and thrombin in a 1:1 ratio [3]. LMWHs usually inhibit factor Xa to a larger degree than thrombin because of their lower molecular weight and shorter chains compared to UFH [3]. Fondaparinux, a synthetic 5-chain polysaccharide (commonly referred to as a ―pentasaccharide‖) also binds to AT III to carry out its pharmacological effect, but it mainly inhibits factor Xa and not thrombin [4]. LMWHs are replacing UFH in various indications due to numerous advantages observed with these agents. LMWHs in general have more predictable pharmacokinetics due to low plasma protein binding, owing to more bioavailability. Heparin resistance, a potential obstacle in heparin therapy, can be partially explained by its binding to plasma proteins [5], and UFH requires monitoring for dosing accuracy while LMWH does not [6]. LMWHs have the convenience of being administered in both inpatient and outpatient settings, while UFH must be administrated in the hospital via continuous IV infusion. Because of its better predictability, LMWH is generally considered safer and more convenient to use than UFH [7]. However, in special populations where the pharmacokinetics is less predictable, the benefits of LMWHs may not be enough to validate their use instead of UFH. In patients with renal insufficiency, the benefits are less clear and may favor the use of UFH. Also, among the various LMWHs there is differing pharmacokinetics that could favor choosing one LMWH over another.

Prevalence of Renal Failure Aging Population The necessity to understand the differences among the various heparins that are available stems from the increased prevalence of renal failure. The older population is expected to more than double by the year 2050 in the US, with 1 in every 5 Americans being 65 years or older [8]. Also, the population has seen an increased prevalence in obesity, diabetes, and hypertension, all risk factors for developing chronic kidney disease (CKD) [9].

Increased Prevalence of Chronic Kidney Disease There is an increased incidence of kidney failure, including dialysis patients, and a high prevalence of earlier stages of CKD [10]. Patients with CKD are defined as those patients

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having a glomerular filtration rate (GFR) < 60 ml/min/1.73 m2 and/or having kidney damage for 3 or more months. The prevalence of CKD among those age 70 years or older is estimated at 46.8%, compared to 6.71% in those aged between 40 and59 years old [10]. Cardiovascular risk factors such as obesity, diabetes, and hypertension are prevalent in CKD patients and lead to albuminuria and decreased GFR. Patients above 70 years old are the most at-risk population associated with co-morbid conditions and also are the most likely to be taking medications that can lead to acute kidney injury. Patients who develop acute kidney injury are less likely to recover kidney function once it is lost. A study by Coresh et al. assessed the prevalence of CKD in the general population over 2 different time periods. The study compared CKD in 15,488 patients during the years 1988 1994 with CKD in 13,233 patients during 1998 – 2004 [9]. The percentage of people with GFR categories of mildly reduced GFR (60-89 ml/min/1.73 m2), moderately reduced GFR (30-59 ml/min/1.73 m2), or severely reduced GFR (15-29 ml/min/1.73 m2) increased in every category in patients in the 1998 – 2004 group compared to the 1988 – 1994 group. With the population living longer, diseases such as CKD should only become more prevalent. Thus, a better understanding of which drugs should be used with caution in these patients is imperative. For all the available types of heparins there is a risk for bleeding, but understanding which heparins accumulate the most in patients with renal impairment necessitates an understanding of the differences in the pharmacokinetic profiles of the different heparin-derived compounds.

Bleeding Risk and Pharmacokinetic Profile

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The bleeding risk and pharmacokinetic profiles of UFH and the LMWHs tinzaparin, deltaparin, enoxaparin, and fondaparinux and their distribution properties in the body are compared in Table 1.

Unfractionated Heparin (UFH) The pharmacokinetics of UFH differs from those of LMWH. Because UFH has a much larger molecular weight than the LMWHs it has different binding availability in the body [11]. UFH binds to plasma proteins to a much larger degree than LMWHs do, which accounts for the less predictable bioavailability of UFH compared to LMWH. UFH also binds to the tissue factor pathway inhibitor (TFPI) of the endothelium and is thus distributed into the vascular system [12]. Over time TFPI can become depleted from the endothelium, increasing UFH‘s unpredictability, and thus there is a need for close dose monitoring [13]. Taking into account the distribution into plasma, there are 3 major compartments of distribution for UFH including plasmatic, vascular, and plasma protein binding compartments. UFH is being replaced in the clinic by LMWHs, such as enoxaparin, due to more convenient usage and more predicable pharmacokinetics. A study by Merli et al. showed enoxaparin to be as effective and safe as UFH [14]; the study showed comparable efficacy and safety of enoxaparin to that of UFH because the bleeding events were not statistically different. The results of another study comparing enoxaparin therapy to UFH therapy were that the use of

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enoxaparin was associated with statistically significant fewer cardiac events and similar rates of bleeding [15]. However, in a situation in which a patient has impaired renal function, the distribution of the heparin derivatives into more compartments should lessen accumulation and as a result should decrease the incidence of adverse bleeding events.

Tinzaparin

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Differences in molecular weight among the LMWHs are important to consider with regard to their pharmacokinetic distribution in the body. Tinzaparin‘s average molecular weight is 6.5 kDa and it has a high degree of sulfation, which allows for tight binding to endothelium leading to stimulation of the release of TPFI from the endothelium [16-17]. A study performed by Mousa et al. compared tinzaparin at a mean molecular weight of 6.5 kDa with LMWH of lesser molecular weight ranging from 1.7-4.5 kDa [18]. Results showed that TFPI levels were elevated for several hours in the 6.5 KDa tinzaparin formulation [18]. A study by Mahe et al. compared the bio-accumulation of tinzaparin to that of enoxaparin in patients with renal impairment [19]. Patients whose creatinine clearance (CrCl) was between 20 ml/min and 50 ml/min were examined for 8 days [19]. Mean anti-Xa activity significantly increased over the course of therapy in the enoxaparin group (1.55 vs. 0.62 IU/ml, p < 0.001), but not in the tinzaparin group (0.44 vs. 0.46 IU/ml, p = 0.296) [19]. Patients over 70 years old and renal impairment are important factors for enoxaparin bioaccumulation in these patients, but the less bioaccumulation seen with tinzaparin over the course of the 8 days suggests that the explanation can be due to differing pharmacokinetic distribution compared to that of enoxaparin.

Deltaparin Deltaparin has a relatively large vascular compartment of biodistribution, which matches its distribution in free plasma. The molecular weight of deltaparin is approximately 5.5 kDa, similar in size to tinzaparin, and thus like tinzaparin causes the release of TFPI and distributes in the vasculature. Stobe et al. conducted a study on 8 healthy volunteers and 8 patients with either moderate or severe renal impairment [20]. They found that baseline mean free TFPI plasma levels were significantly increased (p < 0.01) when compared to healthy volunteers [20]. Douketis et al. defined bioaccumulation in patients with severe renal impairment (CrCl < 30 ml/min) as having a trough anti-Xa level higher than 0.4 IU/ml [21]. Of 427 trough antiXa levels measured in 120 patients, no patient had bioaccumulation. Schmid et al. conducted a study using 3 different patient populations of renal impairment: [22]. Patients in group A had GFR ≥ 60 ml/min/1.73 m2, group B patients had GFR 30-59 ml/min/1.73 m2, and group C patients had GFR < 30 ml/min/1.73 m2. They found adjusted anti-Xa on day 10 was significantly higher in group B than in group A (p = 0.012), however there was no statistically significant difference between groups A and C or groups B and C. These studies show that indeed bioaccumulation of deltaparin may be less than that of enoxaparin or fondaparinux. The different compartments of distribution of biodistribution help to prevent significant bioaccumulation of deltaparin in patients with renal impairment.

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Enoxaparin Enoxaparin is the lowest molecular weight of the LMWHs. Enoxaparin has many advantages over UFH, but its use in renal impaired patients could be limited. Current guidelines recommend that a 50% dose reduction is necessary when used instead of UFH and other LMWHs in renally impairment individuals [3]. A study by Chow et al. supports reducing the dose of enoxaparin in patients with severe renal impairment [23]. They found that patients with CrCl ≥ 31 ml/min had lower anti-Xa levels than patients with CrCl ≤ 30 ml/min (0.91 IU/ml vs. 1.34 IU/ml, respectively, p < 0.05) [23]. However, the guidelines for dose reduction of enoxaparin cannot be applied as a class-wide recommendation with other LMWHs or for UFH. Significant evidence shows that LMWHs are not interchangeable because they have differing pharmacokinetics in the body [24]. Enoxaparin has the lower vascular distribution when compared to tinzaparin and deltaparin [25]. When assessing the vascular vs. plasma distribution of enoxaparin, the free plasma component of distribution heavily outweighs its distribution into the vasculature. Its low molecular weight (4.5 kDa) causes only minimal release of TPFI from the endothelium and thus only minimal binding occurs, which leaves a large distribution into free plasma. There are many studies that have shown enoxaparin accumulates in renal impaired patients. One particular study showed that enoxaparin given at 1 mg/kg twice a day (BID) reaches supra-therapeutic (greater than 1.2 IU/ml) anti-Xa and accumulates in the body [26]. Another study by Cadroy et al. was a pharmacokinetic study on enoxaparin in patients with severe renal impairment (CrCl 5-21 ml/min) [27]. The results were that the clearance of enoxaparin was cut by half and its halflife was almost doubled [27].

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Fondaparinux Fondaparinux is a pentasaccharide that functions by directly inhibiting factor Xa by binding to AT III. Fondaparinux is the smallest of the aforementioned heparins with a mean molecular weight of only 1.7 kDa. Fondaparinux is completely available in free plasma (100% of its pharmacokinetic distribution) and thus does not bind to TFPI or to plasma proteins [28]. Given as a subcutaneous (SC) injection, fondaparinux has nearly complete bioavailability and patients with renal impairment should be monitored for its bioaccumulation [29]. In patients with renal impairment who have a CrCl less than 30 ml/min, the use of fondaparinux is contraindicated according to current guidelines [3]. Recent studies have looked into the possibility of giving fondaparinux at a lower dose in renally impaired individuals to assess its bioaccumulation and risk of bleeding in these patients. Fondaparinux is usually dosed at 2.5 mg SC every day (QD) for most indications; however, studies have recently looked into dosing fondaparinux at a 1.5 mg SC QD. Delavenne et al. performed a study in patient populations treated with prophylactic doses of fondaparinux after major orthopedic surgery [30]. A total of 809 patients were included in the study and received 2.5 mg SC QD of fondaparinux [30]. The patients in the study were either elderly (75 or older) or nonelderly, had low body weight (less than 50 kg) or normal body weight, and had renal insufficiency or no renal insufficiency. Also, patients either had moderate or severe renal insufficiency as described by a CrCl less that 50 ml/min [30]. The

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simulations revealed that drug exposure increased significantly by 34% in patients with renal impairment, 41% in those with low body weight and by 9% in the elderly [30]. The results of the study suggest that renal clearance, old age, and low body weight are key contributors to bioaccumulation of fondaparinux. Table 1. Relative distribution of UFH and its low molecular weight derivatives tinzaparin, deltaparin, enoxaparin, and fondaparinux [11, 16-20, 25, 28, 33]

Unfractionated Heparin Tinzaparin Deltaparin Enoxaparin Fondaparinux

Average Molecular Weight (kDa) 15

Free Plasma

Vascular

Plasma Proteins

+

+

++++

6.5 5.5 4.5 1.7

+ ++ +++++ ++++++

+++++ ++ + ______

_____ ______ ______ ______

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(-) = Not significant; + = Low level of biodistribution; ++ = Medium level of biodistribution; ++++ or greater = High levels of biodistribution.

Turpie et al. did a study using pharmacokinetic simulations that compared fondaparinux to either enoxaparin or placebo [31]. The primary efficacy outcome of all the studies in the simulation was venous thromboembolism at the end of the study treatment period [31]. The pharmacokinetic simulations divided patients according to dose and renal function [31]. One group of patients with moderate renal impairment received 1.5 mg QD fondaparinux, another group with moderate renal impairment received 2.5 mg QD fondaparinux (normal dose), and another group with normal renal function received 2.5 mg QD fondaparinux [31]. The different trials with fondaparinux found that predicted exposure measured in terms of predicted area under the curve (AUC) (0-24), Cmax, and Cmin, was comparable between the groups receiving 1.5 mg QD fondaparinux and the group with normal renal function receiving 2.5 mg QD fondaparinux [31]. Predicted AUC (0-24), Cmax, and Cmin were 2-fold higher in patients with moderate renal impairment who received 2.5 mg than patients with normal renal impairment. By receiving a lower dose (1.5 mg SC QD), less accumulation is seen when compared to 2.5 mg SC QD in patients with renal impairment. A phase IV study was conducted in France in 2008 on patients with renal impairment (CrCl between 20-50 ml/min) undergoing venous thromboembolic event prevention after major orthopedic surgery [32]. The primary endpoint in the study was to assess major bleeding for 10 days in patients taking 1.5 mg SC QD [32]. With limited data and weak evidence on over-dosing and under-dosing of fondaparinux in renal insufficiency, the use of fondaparinux is discouraged in renal impaired patients until stronger evidence can prove its safety.

Dose Reduction versus Therapeutic Outcome The largest amount of data and trials advocating a reduction in dosage is for enoxaparin. Evidence strongly points to accumulation of enoxaparin in renally impaired patients [23]. For

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these patients dose reductions are needed to avoid bioaccumulation and ultimately bleeding. But reducing the dose of LMWH to the point that therapeutic concentrations are not met could potentially make this protective measure a risk factor of cardiac events and ultimately death, depending on the indication for treatment. Current recommendations from the ACCP guidelines recommend that patients with severe renal impairment (CrCl less than 30 ml/min) should use UFH as the first choice of treatment with the second choice being enoxaparin with a 50% dose reduction [3].

Conventional versus Individualized Dosing Conventional dosing of enoxaparin is 1 mg/kg BID or 1.5 mg/kg QD based on total body weight. Patients with renal impairment who have a CrCl clearance less than 30 ml/min are given enoxaparin 1 mg/kg daily according to guidelines. Individualized dosing strategies in most studies usually consist of a loading dose to maintain the patient within the recommended therapeutic range early in treatment, followed by a dose based on severity of renal impairment and body weight.

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Individualized Dosing Maintains Patients within Therapeutic Range A study compared patients receiving conventional dose adjustments for renal impairment with patients receiving individualized doing [35]. Individualized dosing consisted of 4 loading doses followed by a specific individualized dose based on how severe the patient is renally impaired and their total body weight. The individualized dosing arm achieved a significantly greater length of time in the therapeutic range compared with patients receiving the conventional dosing, 69.9% vs. 42.6 %, respectively (p = 0.02). The patients in the individualized dosing arm also had a much smaller length of time in the supra-therapeutic range compared to the conventional dosing arm (p = 0.02). Consequently, the study showed that patients receiving a dose tailored to their specific renal impairment and weight achieved the therapeutic range (0.5-1 IU/ml) more often than achieving sub-therapeutic or supratherapeutic range. A study by Green et al. also looked at individualized dosing vs. conventional dosing [36]. It showed that as the degree of renal impairment increased so did the anti-Xa levels in the body when patients all received the same dose of 1 mg/kg. This study also used loading doses (every 12 hours) followed by a specific dose according to renal function. Data showed that without the loading doses, the individualized dosing regimens would fall short of achieving the 0.5 IU/ml minimum of being within the therapeutic range for therapy. However, with the loading doses, therapeutic levels were reached much faster. This type of dosing scheme may be the key for how to dose renally impaired patients with LMWHs. Kruse et al. assessed different degrees of renal impairment using adjusted doses of enoxaparin [37]. Similar to previous studies, all patients in this study received a loading dose (1 mg/kg) of enoxaparin followed by a dose based on renal function. Following the loading dose, patients received 0.5 mg/kg SC every 12 hours if CrCl ≤ 30 ml/min, or 0.75 mg/kg SC

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every 12 hours if CrCl was 30-60 ml/min. The results were that anti-Xa levels fell within the therapeutic range 60% of the time for patients with severe renal impairment and 80% of the time for patients with moderate renal impairment. More patients with severe renal impairment had sub-therapeutic anti-Xa levels. Guidelines don‘t exist for adjusting non-therapeutic antiXa levels and consequently practitioners must estimate how to best adjust the dose if the dose given is not within the therapeutic range. A study by Barras in 2007 showed that among patients on an individual dosing regimen, only 1 patient experienced a bleeding event compared to 9 patients on the conventional dosing regimen (p = 0.03) [38]. The individual dosing regimen in this study was based on both renal impairment and body weight. Another study found that patients using individual dosing with a loading dose remained within the therapeutic range for longer periods of time and avoided accumulation better than patients receiving the standard dose (non-adjusted 1 mg/kg every 12 hours) [39]. The study suggested that, using a dose reduction decreasing in accordance with renal function while at the same time starting with a loading dose, will allow patients to remain in the therapeutic range longer without accumulation. A study by Lachish et al. assessed patients with stage 4 or 5 CKD on adjusted doses of enoxaparin (1 mg/kg QD) [40]. They found that the mean peak anti-factor Xa level after the first dose of enoxaparin was 0.6 U/ml with only slight increases with subsequent doses [40]. A summary of these studies can be found in Table 2. These studies show that when dosing enoxaparin in renally impaired individuals it is imperative to consider the body weight and renal impairment of the patient. In these patients an increase in body weight does not equal an increase in muscle mass and creatinine. Studies have shown that lean body weight is a more accurate descriptor of CrCl than total body weight [38]. Total body weight assumes that the body‘s ability to clear the drug increases with body weight, but lean body weight does not make this assumption [38]. This is important to consider when dosing renally impaired patients because of the potential to under-dose patients and thus leave patients at a sub-therapeutic level. Based on the evidence, when dosing patients with renal impairment it is better to individualize a dosing regimen for specific patients based on weight and renal clearance. Because individual LMWHs do not exhibit the same pharmacokinetic properties, it is impossible to recommend a dosing scheme that fits patients on a population basis. Adjusting the dose of a LMWH should be done on a patient-to-patient basis depending on the LMWH used and the severity of renal impairment. When adjusting the dose to prevent bioaccumulation and bleeding due to these anticoagulants, it is important to monitor the dosage to ensure that a therapeutic range is met. LMWHs are usually not monitored in standard practice, but knowing that the risk for bleeding is possible with renal impairment, and knowing that the risk of a fatal outcome is possible with under-dosing, it is necessary to monitor LMWH levels in the body to ensure they are within a safe yet effective range. Further studies need to be conducted on tinzaparin and deltaparin using individualized dosing compared to conventional dosing to be able to better assess their effect in renal dysfunction.

Prophylactic versus Therapeutic Treatment Depending on the indication being treated and the drug being used, there are different dosing strategies used for heparin. Generally therapeutic doses require more aggressive dosing than prophylactically given treatment. Pharmacokinetic and pharmacodynamic

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differences between the available heparins should become more apparent at higher therapeutic doses [41].

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Treatment Doses Fox et al. assessed renal function of over 19,000 patients treated with doses of either fondaparinux (2.5 mg QD) or enoxaparin (l mg/kg BID) [42]. Patients who were known to have severe renal impairment (CrCl < 30 ml/min) received a lower dose of enoxaparin according to guidelines (1 mg/kg QD). The results from the study showed that as renal function declined and the duration of therapy increased, the percentage of patients who experienced a bleeding event increased. The results also showed a statistically significant lower risk of bleeding for patients receiving treatment with fondaparinux compared to those receiving treatment with enoxaparin [42]. However, it should be noted that the patients in the study were being treated for non-ST segment elevation ACS, an indication not approved for fondaparinux use in the U.S. Another study found that when enoxaparin is given as a single 30 mg intravenous bolus followed by 1 or 1.25 mg/kg SC BID, it led to increased risk of major hemorrhagic episodes [43]; 51 patients with moderate renal impairment and 4 patients with severe renal impairment had an approximately 21% decrease in enoxaparin clearance. Thorevska et al. studied the therapeutic doses of both UFH and enoxaparin in patients with varying levels of renal impairment [44]. Patients either had mild, moderate, or severe renal impairment. Of 620 patients assessed, 331 patients received anticoagulation therapy with UFH and 250 patients received anticoagulation therapy with enoxaparin [44]. In all 3 groups the incidence of bleeding increased proportionally to a decrease in renal function and to an increase in duration of treatment. Treatment with enoxaparin was found to show a significantly higher rate of minor bleeding when compared to UFH [44]. This is not surprising when considering the pharmacokinetics of both drugs. Pautas et al. studied giving tinzaparin at a treatment dose to patients 70 years of age or older [45]. For the majority of patients in the study, the dose given was the standard 175 IU/kg QD, but some patients received doses higher than this recommended treatment dose, while others received lower doses. Anti-Xa levels were monitored in all patients and according to these levels dose adjustments were made for patients in whom accumulation was detected. The mean CrCl of the patients in the study was 51.2 ml/min and the mean duration of treatment was 19.1 days. Of 200 patients who took part in the study, only 3 developed major bleeding and only 2 of the 3 bleeding events were found to be related to tinzaparin therapy. The reason for such a low rate of bleeding of tinzaparin at a treatment dose in patients over 70 years of age could be due to patients being excluded if their CrCl was less than 20 ml/min, or the fact that dose adjustments were made depending on anti-Xa levels. Nevertheless, the pharmacokinetic effect of tinzaparin‘s large molecular weight could also be a factor in the low rate of accumulation. Also, the study further supports the fact that better efficacy and safety is observed with anti-Xa monitoring of LMWH in patients with renal impairment.

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Table 2. Summary of studies assessing individualized vs. conventional dosing in maintaining patients within therapeutic range (0.5-1.2 U/ml) Study

CrCl (ml/min) < 50

Dosing strategy

Lachish et al [40]

Stage 4 or 5 CKD < 30

1 mg/kg every 24 hours

19

Barras et al (2008) [38]

< 50

122

Hulot et al [39]

Ranged from > 80 to < 30

Kruse et al [37]

Moderate 3050 and severe < 30 RI

Green et al [36]

Median GFR 32 ml/min/1.73 m2

CD: l mg/kg (< 100 kg) or 1.5 mg/kg (>100 kg) ID: Conventional for first 48 hrs, then linear reduction based on CrCl LD: 1 mg/kg Moderate renal impairment: 0.8 mg/kg SC BID Severe renal impairment: 0.6 mg/kg SC BID LD: 1 mg/kg CrCl ≤ 30 ml/min: 0.5 mg/kg SC every 12 hours CrCl 30-60 ml/min: 0.75 mg/kg SC every 12 hours LD: 1 mg/kg every 12 hours for 3, 4, or 5 doses ID: GFR 10-19 ml/min/m2 has dose of 0.3 mg/kg BID. Every 0.1 ml/min/m2 increase in GFR = 0.1 mg/kg increase in dose.

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Barras et al (2010) [35]

CD: l mg/kg (< 100 kg) or 1.5 mg/kg (> 100 kg) ID: Same as conventional for first 48 hrs. Then linear reduction based on CrCl.

No. of patients 122

532

170

38

Results Fewer bleeding events (p = 0.03) and fewer composite bleeding and bruising events (p = 0.003) in doseindividualized patients than in conventionally dosed patients. Mean peak anti-factor Xa was 0.6 U/ml after the first dose. No value exceeded 1 U/ml. ID vs. CD patients reaching therapeutic range (0.5-1 U/ml) was 69.9% vs. 42.6%, respectively (p = 0.02). Enoxaparin clearance was decreased by 31% in moderate RI and 44% in severe RI. Therapeutic anti-Xa levels meet 60% if severe RI and 80% of the time for patients with moderate RI. Peak anti –Xa concentrations ranged from 4.8 to 1.13 U/ml. Four patients experienced minor bleeding and 1 experienced major bleeding.

CD: Conventional dose; ID: Individualized dose; LD: Loading dose; BID: twice a day; RI: renal impairment.

Other studies have also compared LMWH to UFH using standard treatment dosing. One study found no statistical differences in major or any hemorrhage when comparing UFH and enoxaparin in patients with renal impairment (p = 0.56 and p = 0.93, respectively) [46]. Another study found that LMWH alone was associated with significantly fewer in-hospital major bleeds than UFH alone (2.1 vs. 3.3%, p = 0.0006) [47]. The reason for better results

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using LMWH however could be due to using different LMWHs with different pharmacokinetics during the trial. LMWHs should not be considered as a group when assessing their effect because considerable evidence shows that they are not interchangeable medications.

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Prophylactic Doses Mahe et al. assessed prophylactic dosages of enoxaparin in older patients with decreased renal function. Patients had a mean age of 87.5 years, a mean bodyweight of 56.4 kg, and a mean CrCl of 39.8 ml/min [47]. Patients received treatment with enoxaparin 4000 IU SC QD for thromboprophylaxis. Thirty-nine patients had a CrCl of ≤ 30 ml/min. To measure accumulation, anti-Xa maximum concentration was compared among the patients with varying degrees of renal impairment. The results of the study showed negative correlations between CrCl and anti-Xa maximum concentrations over the 10 days of treatment (p = 0.0118) and also negative correlations between body weight and anti-Xa maximum concentrations. Overall, the results suggest that with mild or moderate renal impairment, patients will have a significant increase in anti-Xa maximum concentrations. However, it should be taken into consideration that older patients have a lowered ability to clear drugs from the body so that the clinical significance in terms of assessing drug accumulation in these patients with renal impairment is limited. Another study assessing prophylaxis dosing of enoxaparin showed a significant decrease in drug clearance in patients with severe renal impairment when compared to healthy patients (p = 0.0001) [48]. A similar study assessing the safety of deltaparin for the prophylaxis of venous thromboembolism was performed on patients 65 years of age or older with varying degrees of renal impairment [49]. Patients in the study either had mild, moderate, or severe renal impairment, and had their renal function assessed by measuring anti-Xa levels over at least 6 days. Patients at a high risk of developing a thromboembolism were given 5000 IU/ml SC QD and patients at a lower risk were given 2500 IU/ml SC QD. The study reported no major bleeding events among the 115 patients enrolled, but 3 (2.7%) patients developed minor hemorrhages (95% confidence intervals 0.6 to 6.7%). The 3 patients with the minor bleeding events were receiving the higher dose for prophylaxis, all had moderate renal impairment, and all were at least 88 years old. The results of this study suggest that using deltaparin at a prophylaxis dose is safe in older patients with renal impairment. As mentioned before in this review, because of dalteparin‘s large molecular weight compared to enoxaparin and fondaparinux, it seems that bioaccumulation in renal impairment is less when compared to these drugs. Also, the fact that a lower prophylaxis dose did not accumulate at all in all the patients further suggests that a prophylaxis dosage may be less inclined to accumulate and cause bleeding. Deltaparin was assessed in a more general population (18 years of age or older) for the prophylaxis of venous thromboembolism in patients with varying degrees of renal impairment [50]. Although there were only 19 patients enrolled in the study, no patient in the study had bioaccumulation or an event of bleeding recorded throughout the entire study. These studies further point out the differing pharmacokinetic properties among the available LMWHs.

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Discussion Each heparin molecule is chemically distinct and has different pharmacokinetic properties. Each molecule should be considered a separate drug with unique actions within the body, and with a different efficacy and safety profile. There is increasing knowledge of the risk for accumulation associated with the use of heparin in general, with greater risk seen in patients with renal impairment and in particular severe renal impairment. There are numerous warnings in drug monographs and guidelines about the risk associated with bleeding in renally impaired individuals [3]. Enoxaparin in particular has been researched extensively in patients with renal impairment; from the results of many clinical trials it is apparent that enoxaparin has a high risk of accumulation in patients with renal impairment. While studies on tinzaparin and deltaparin are limited in number, the results of studies discussed in this review suggest that bioaccumulation does occur with these drugs in renal impairment, but compared to enoxaparin the bioaccumulation does not seem to be as pronounced. The number of studies with fondaparinux is also limited, but results show significant accumulation in patients with renal impairment. Among the different LMWHs and fondaparinux, accumulation and bleeding is positively associated with the degree of renal impairment. However, tinzaparin and deltaparin do not seem to show such a prominent amount of accumulation as measured by anti-Xa levels as compared to enoxaparin and fondaparinux. The explanation for such results might be the different molecular weights and biodistribution properties of these drugs. Due to the many different compartments of biodistribution of UFH, it was predicted that accumulation would be significantly less when compared to relatively small molecular weight LMWHs and fondaparinux. However, some clinical trials have found the rate of bleeding of LMWHs and UFH to be comparable [46]. And yet other studies have found that the rate of minor bleeding to be increased more with the use of LMWH than with UFH [44]. Further studies comparing the safety of the different heparin molecules in renal impairment should be conducted to establish a more definitive relationship between renal impairment and bleeding. Considerable evidence from clinical trials has compared conventional vs. individualized dosing. The evidence from those trials showed that individualized dosing based on the patient‘s degree of renal impairment and body weight are better ways of dosing patients than conventional dosing. This is because any dosing recommendation needs to consider both the risk of bleeding over time and the risk of not achieving therapeutic concentrations. For example, guidelines recommend a 50% dose reduction when CrCl is below 30 ml/min, but it is not logical to halve the dose of a drug at some arbitrary number. Also, data from clinical trials show trends of higher accumulation with treatment doses rather than prophylaxis doses in patients with renal impairment. This would be expected because treatment doses are usually higher and thus have a higher risk of bleeding. Understanding the different pharmacokinetics of the available heparins is imperative to understanding how they act in the body. Only then can a clinician make the best decision in choosing which drug will be best for each patient, and at what starting dose.

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Conclusion UFH and LMWHs are well established anticoagulants that are used for a variety of different indications. In general practice, LMWHs are considered to be safer with more predictable pharmacokinetics when compared to UFH. However, the implication of having varying molecular weights among the different heparins has a significant effect on clearance in patients who are renally impaired. Since UFH has a larger molecular weight and distributes into different and more compartments, the evidence suggest that accumulation is less significant in compromised renal function when compared to the relatively smaller LMWHs. Any other anticoagulant that is mainly eliminated by the kidney would require a careful assessment of the pharmacokinetics and its compartment of distribution in order to define the need for dose adjustment. Depending on the degree of renal impairment, close monitoring of anti-Xa levels when a LMWH is administered in this population is recommended. Future research and studies should be conducted to gather more information to further differentiate the risk of accumulation between the different heparin molecules.

References

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[1]

Mousa, S.A. and L.J. Petersen, Anti-cancer properties of low-molecular-weight heparin: preclinical evidence. Thromb Haemost, 2009. 102(2): p. 258-67. [2] Fareed, J. and J.M. Walenga, Why differentiate low molecular weight heparins for venous thromboembolism? Thromb J, 2007. 5: p. 8. [3] Hirsh, J., et al., Parenteral anticoagulants: American College of Chest Physicians Evidence-Based Clinical Practice Guidelines (8th Edition). Chest, 2008. 133(6 Suppl): p. 141S-159S. [4] Merli, G.J., and B.J. Groce, Pharmacological and clinical differences between lowmolecular-weight heparins: implications for prescribing practice and therapeutic interchange. P & T, 2010. 35(2): p. 95-105. [5] Schmid, P., A.G. Fischer, and W.A. Wuillemin, Low-molecular-weight heparin in patients with renal insufficiency. Swiss Med Wkly, 2009. 139(31-32): p. 438-52. [6] Alsayegh, F., et al., Heparin anticoagulation responsiveness in a coronary care unit: a prospective observational study. Cardiovasc Ther, 2009. 27(2): p. 77-82. [7] Mousa, S.A., Comparative efficacy of different low-molecular-weight heparins (LMWHs) and drug interactions with LMWH: implications for management of vascular disorders. Semin Thromb Hemost, 2000. 26 Suppl 1: p. 39-46. [8] Sixty-five plus in the US. 1995 [cited 201 July 20]; Available from: http://www.census.gov/population/socdemo/statbriefs/agebrief.html. [9] Coresh, J., et al., Prevalence of chronic kidney disease in the United States. JAMA, 2007. 298(17): p. 2038-47. [10] Stevens, L.A., G. Viswanathan, and D.E. Weiner, Chronic kidney disease and endstage renal disease in the elderly population: current prevalence, future projections, and clinical significance. Adv Chronic Kidney Dis, 2010. 17(4): p. 293-301. [11] Mousa, S.A., Are low molecular weight heparins the same? Methods Mol Med, 2004. 93: p. 49-59.

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[12] Mousa, S.A., et al., Pharmacokinetics and pharmacodynamics of oral heparin solid dosage form in healthy human subjects. J Clin Pharmacol, 2007. 47(12): p. 1508-20. [13] Naumnik, B., J. Borawski, and M. Mysliwiec, Different effects of enoxaparin and unfractionated heparin on extrinsic blood coagulation during haemodialysis: a prospective study. Nephrol Dial Transplant, 2003. 18(7): p. 1376-82. [14] Merli, G., et al., Subcutaneous enoxaparin once or twice daily compared with intravenous unfractionated heparin for treatment of venous thromboembolic disease. Ann Intern Med, 2001. 134(3): p. 191-202. [15] Baird, S.H., et al., Randomized comparison of enoxaparin with unfractionated heparin following fibrinolytic therapy for acute myocardial infarction. Eur Heart J, 2002. 23(8): p. 627-32. [16] Mousa, S.A., Heparin, low molecular weight heparin, and derivatives in thrombosis, angiogenesis, and inflammation: emerging links. Semin Thromb Hemost, 2007. 33(5): p. 524-33. [17] Mousa, S.A., J. Bozarth, J. Hainer et al., Pharmacodynamics of tinzaparin 175 IU/kg SC administration in healthy volunteers on plasma TFPI. Thromb Haemost, 2001: p. 2299. [18] Mousa, S.A., J. Bozarth, and J.S. Barrett, Pharmacodynamic properties of the low molecular weight heparin, tinzaparin: effect of molecular weight distribution on plasma tissue factor pathway inhibitor in healthy human subjects. J Clin Pharmacol, 2003. 43(7): p. 727-34. [19] Mahe, I., et al., Tinzaparin and enoxaparin given at prophylactic dose for eight days in medical elderly patients with impaired renal function: a comparative pharmacokinetic study. Thromb Haemost, 2007. 97(4): p. 581-6. [20] Stobe, J., et al., Evaluation of the pharmacokinetics of dalteparin in patients with renal insufficiency. Int J Clin Pharmacol Ther, 2006. 44(10): p. 455-65. [21] Douketis, J., et al., Prophylaxis against deep vein thrombosis in critically ill patients with severe renal insufficiency with the low-molecular-weight heparin dalteparin: an assessment of safety and pharmacodynamics: the DIRECT study. Arch Intern Med, 2008. 168(16): p. 1805-12. [22] Schmid, P., et al., Study of bioaccumulation of dalteparin at a prophylactic dose in patients with various degrees of impaired renal function. J Thromb Haemost, 2009. 7(4): p. 552-8. [23] Chow, S.L., et al., Correlation of antifactor Xa concentrations with renal function in patients on enoxaparin. J Clin Pharmacol, 2003. 43(6): p. 586-90. [24] Sonawane, S., N. Kasbekar, and J.S. Berns, The safety of heparins in end-stage renal disease. Semin Dial, 2006. 19(4): p. 305-10. [25] Fareed, J., et al., Are the available low-molecular-weight heparin preparations the same? Semin Thromb Hemost, 1996. 22 Suppl 1: p. 77-91. [26] Bazinet A., K. Almanric, C. Brunet, I. Turcotte, J. Martineau, S. Caron, N. Blais, and L. Lalonde, Dosage of enoxaparin among obese and renal impairment patients. Thrombosis Research, 2004. 116(1): p. 41-50. [27] Cadroy, Y., et al., Delayed elimination of enoxaparin in patients with chronic renal insufficiency. Thromb Res, 1991. 63(3): p. 385-90. [28] Weitz, J.I., New anticoagulants for treatment of venous thromboembolism. Circulation, 2004. 110(9 Suppl 1): p. I19-26.

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[29] Donat, F., et al., The pharmacokinetics of fondaparinux sodium in healthy volunteers. Clin Pharmacokinet, 2002. 41 Suppl 2: p. 1-9. [30] Delavenne, X., et al., Population pharmacokinetics of fondaparinux administered at prophylactic doses after major orthopaedic surgery in everyday practice. Thromb Haemost, 2010. 104(2). [31] Turpie, A.G., et al., Pharmacokinetic and clinical data supporting the use of fondaparinux 1.5 mg once daily in the prevention of venous thromboembolism in renally impaired patients. Blood Coagul Fibrinolysis, 2009. 20(2): p. 114-21. [32] Prospective, Multicentre, Open-Label Study Evaluating 1.5 mg/Day of Fondaparinux. (PROPICE) Study. 2008. [33] Mousa, S.A., The low molecular weight heparin, tinzaparin, in thrombosis and beyond. Cardiovasc Drug Rev, 2002. 20(3): p. 199-216. [34] Ostadal, P., et al., Anti-Xa activity of enoxaparin and nadroparin in patients with acute coronary syndrome. Exp Clin Cardiol, 2008. 13(4): p. 175-8. [35] Barras, M.A., et al., Individualized Dosing of Enoxaparin for Subjects With Renal Impairment Is Superior to Conventional Dosing at Achieving Therapeutic Concentrations. Ther Drug Monit, 2010. 32(4) p. 482-8. [36] Green, B., et al., Dosing strategy for enoxaparin in patients with renal impairment presenting with acute coronary syndromes. Br J Clin Pharmacol, 2005. 59(3): p. 28190. [37] Kruse, M.W. and J.J. Lee, Retrospective evaluation of a pharmacokinetic program for adjusting enoxaparin in renal impairment. Am Heart J, 2004. 148(4): p. 582-9. [38] Barras, M.A., et al., Individualized compared with conventional dosing of enoxaparin. Clin Pharmacol Ther, 2008. 83(6): p. 882-8. [39] Hulot, J.S., et al., Dosing strategy in patients with renal failure receiving enoxaparin for the treatment of non-ST-segment elevation acute coronary syndrome. Clin Pharmacol Ther, 2005. 77(6): p. 542-52. [40] Lachish, T., et al., Enoxaparin dosage adjustment in patients with severe renal failure: antifactor xa concentrations and safety. Pharmacotherapy, 2007. 27(10): p. 1347-52. [41] Fareed, J., et al., Biochemical and pharmacologic heterogeneity in low molecular weight heparins. Impact on the therapeutic profile. Curr Pharm Des, 2004. 10(9): p. 983-99. [42] Fox, K.A., et al., Influence of renal function on the efficacy and safety of fondaparinux relative to enoxaparin in non ST-segment elevation acute coronary syndromes. Ann Intern Med, 2007. 147(5): p. 304-10. [43] Bruno, R., et al., Population pharmacokinetics and pharmacodynamics of enoxaparin in unstable angina and non-ST-segment elevation myocardial infarction. Br J Clin Pharmacol, 2003. 56(4): p. 407-14. [44] Thorevska, N., et al., Anticoagulation in hospitalized patients with renal insufficiency: a comparison of bleeding rates with unfractionated heparin vs enoxaparin. Chest, 2004. 125(3): p. 856-63. [45] Pautas, E., et al., Safety profile of tinzaparin administered once daily at a standard curative dose in two hundred very elderly patients. Drug Saf, 2002. 25(10): p. 725-33. [46] Spinler, S.A., et al., Safety and efficacy of unfractionated heparin versus enoxaparin in patients who are obese and patients with severe renal impairment: analysis from the ESSENCE and TIMI 11B studies. Am Heart J, 2003. 146(1): p. 33-41.

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[47] Mahe, I., et al., Elderly medical patients treated with prophylactic dosages of enoxaparin: influence of renal function on anti-Xa activity level. Drugs Aging, 2007. 24(1): p. 63-71. [48] Sanderink, G.J., et al., Pharmacokinetics and pharmacodynamics of the prophylactic dose of enoxaparin once daily over 4 days in patients with renal impairment. Thromb Res, 2002. 105(3): p. 225-31. [49] Tincani, E., et al., Safety of dalteparin for the prophylaxis of venous thromboembolism in elderly medical patients with renal insufficiency: a pilot study. Haematologica, 2006. 91(7): p. 976-9. [50] Rabbat, C.G., et al., Dalteparin thromboprophylaxis for critically ill medical-surgical patients with renal insufficiency. J Crit Care, 2005. 20(4): p. 357-63.

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Copyright © 2012. Nova Science Publishers, Incorporated. All rights reserved. Heparin : Properties, Uses and Side Effects, Nova Science Publishers, Incorporated, 2012. ProQuest Ebook Central,

In: Heparin: Properties, Uses and Side Effects Editors: D. E. Piyathilake, Rh. Liang, pp. 197-215

ISBN: 978-1-62100-431-8 © 2012 Nova Science Publishers, Inc.

Chapter IX

Comprehensive and Updated Study on the Analysis Techniques of Heparin for Human Use Valeria Tripodi1,3, Silvia Lucangioli2,3,* 1

2

Analytical Chemistry and Physicochemistry Department, Pharmaceutical Technology Department, Faculty of Pharmacy and Biochemistry. University of Buenos Aires, Argentina 3 CONICET

Abstract Copyright © 2012. Nova Science Publishers, Incorporated. All rights reserved.

Heparin (Hep) is a linear, highly charged, sulfated polysaccharide that belongs to the glycosaminoglycan family (GAG). Hep has clinically been used for many years in the prevention and initial treatment of thrombosis. Hep is a natural product extracted from animal tissues, most commonly from porcine intestine. As a result of incomplete purification of Hep, other sulfated linear polysaccharides as dermatan sulfate (DS) and chondroitin sulfate (CS) are present in the manufactured product as impurities. The potency of Hep as pharmaceutical agent is defined based on units of biological activity using plasma clotting assays, rather than on the basis of physicochemical properties. However, considerable attention has been focused on analysis of Hep due to a health crisis in 2008 resulting from the contamination of the lots of pharmaceutical Hep with chemically modified chondroitin sulfated (oversulfated condroitin sulfate, OSCS) which causes angioedema, hypertension, swelling of the larynx, and in some cases, death. Therefore, different analytical methods have been introduced for government and academic laboratories to assure the quality and safety of pharmaceutical Hep. The elucidation of structure of GAGs has been a challenging task for the analytical laboratories due to their anionic complex structures with high molecular weight and the *

Correspondence to Prof. Dr. Silvia Lucangioli, Pharmaceutical Technology Department. Faculty of Pharmacy and Biochemistry. University of Buenos Aires. Junin 956 (1113), Buenos Aires, Argentina. Tel: +54 11 4964 8262, Fax: +54 11 4964 8263; E-mail: [email protected].

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Valeria Tripodi and Silvia Lucangioli absence of the strong chromophore groups. Thus, common analytical methodologies often are not suitable for the analysis of Hep impurities and contaminants. Hence, in recent years, different analytical approaches for the analysis of Hep active pharmaceutical ingredient (API) and finished products have been developed by chromatographic techniques as anion exchange HPLC and capillary electrophoresis with varying degrees of success. On the other hand, it has also been developed spectroscopic methods based on analysis by NMR, infrared, Raman and fluorescence as well as bioassays to characterize and identify contaminants of Hep. This chapter describes a comprehensive and updated study on the development techniques for the analysis of Hep content and purity test in pharmaceuticals for human administration.

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Abbrevations API Active pharmaceutical ingredient aPTT Activated partial thromboplastine time ATR-IR Attenuated total reflection-infrared aXa Anti-factor Xa CE Capillary electrophoresis CS Chondroitin sulfate CZE Capillary zone electrophoresis DS Dermatan sulfate EIA Enzyme immunoassay EKC Electrokinetic Chromatography EP European Pharmacopoeia FDA Food and Drug Administration GAG Glycosaminoglycan HEP Heparin HPLC High Performance Liquid Chromatography IR Infrared spectroscopy LOD Limit of detection LOQ Limit of quantitation NaCl Sodium chloride NMR Nuclear magnetic resonance OSCS Oversulfated chondroitin sulfate PT Prothombine time SAX-HPLC strong-anion-exchange-high performance liquid chromatography SEC second USA United States of America USP United States Pharmacopoeia

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1. Introduction Heparin (Hep), a polymer consisting of major trisulfated disaccaride units with different molecular weights, belongs to the glycosaminoglycan (GAG) family. Hep is mostly obtained from suitable tissues of domestic animals used for human consumption [1-2]. Hep is one of the oldest drugs still use in clinical treatment of thromboembolic diseases due to its antithrombotic and anticoagulant propriety [3]. Hep is involved in the coagulation process due to its interaction with blood soluble proteins, blood vessel and associated cells [4]. Hep is parenterally administered due to its degradation when it is taken orally and in same cases it has to be injected at high doses [5]. Thus it is mandatory for pharmaceutical companies to be able to control its potency and purity by reliable analytical methods according to international guidelines.

1.1. Structure Heparin is a linear, highly sulfated, polydisperse, polysaccharide consisting of 1 to 4 linked pyranosyluronic acid (uronic acid) and 2-amino-2 deoxyglycopyranose (Dglucosamine) repeating units belongs a family of glycosaminoglycans (GAGs) [6] (Figure 1). Hep, a polydisperse polymer with a molecular weight ranging from 5 to 40 KDa with an average of 12 Kda, has a high content of sulfo and carboxyl groups. It possesses the highest negative charge density (about – 75) of all the biological macromolecules known [7].

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1.2. Biosynthesis Natural heparin is obtained by an extraction process from animal tissues such as porcine and bovine intestines and bovine lungs [1].

Figure 1. Chemical structure of heparin.

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The biosynthesis of Hep, as a proteoglycan, takes place in virtually all types of animal tissues [8] but it occurs intracellular instead of other GAGs that founded extracellulary. Hep is principally found in mast cells and granulated cells that are found in organs like liver, intestine, and lung [9]. Different structures are observed depending on the type of tissue and species of origin taken by industries. Porcine intestine is the organ most widely used as source for the production of pharmaceutical heparins [10]. However, the animals from which Hep is extracted must meet health requirements for human consumption. If Hep is administered for human use it must be produced by methods of manufacture designed to reduce or eliminate undesirable substances like those lowering blood pressure [11].

Figure 2. Chemical structure of chondroitin sulfate and dermatan sulfate.

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2. Pharmaceutical Heparin Pharmaceutical heparin is a purified polydisperse mixture with an average weight of 12 KDa having significant heterogeneity in its sequence. The Hep chains contain three sulfo groups per disaccharide unit [7]. The anticoagulant action of pharmaceutical Hep is the mostly known therapeutical activity [12]. Sodium heparin is defined in pharmacopoeias as the sodium salt of sulfated glycoaminoglycans present as a mixture of heterogeneous molecules with different molecular weights and it presents activity against different factors of the blood clotting cascade. The sourcing of Hep must be specified in compliance with regulatory requirements [11, 13]. The anticoagulation activity mainly occurs through the formation of a complex between Hep and the plasma proteins antithrombin and heparin cofactor II to potencies the inactivation of thrombin (factor lla). Other coagulation proteases in the clotting sequence are inhibited such as activated factor X (factor Xa). The ration of anti-factor Xa activity to antifactor lla potency is between 0.9 and 1.1 [13]. The potency of sodium heparin used as therapeutic agent is 180 USP unit per mg on dry basis [13]. For EP pharmacopoeia 180 IU dried of sodium heparin per mg is specified [11].

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3. Analysis of Pharmaceutical Heparin The heparin used as active pharmaceutical ingredient (API) is manufactured by extraction of mammalian tissues and as a result of its incomplete purification other sulfated linear polysaccharides like dermatan sulfate (DS) and chondroitin sulfate (CS) are present [14]. Pharmaceutical heparins are basically characterized by the determination of the biological potency and the identification and quantification of the related substances of Hep. The biological activity is determined by different biological methods described below. Instead, identification and quantification of impurities and contaminants are carried out by physicochemical methods such as spectroscopic, chromatographic and electrophoretic techniques as well as bioassay methods. Due to the fact that Hep and its related polysaccharides are complex structures with high molecular weights and without chromophore groups, its characterization is a great challenge for analysts.

3.1. Potency Assays

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According to the EP monographs, the potency of heparin is determined by activated partial thromboplastine time [11], whereas in USP monographs this method has been replaced by the more heparin-specific chromogenic anti-Factor II assay, also called antithrombin assay [13]. 3.1.1. Activated Partial Thromboplastine Time (aPTT) The anticoagulant activity of heparin could be determined comparing its ability to delay the clotting of recalcified citrated sheep plasma with that of a reference heparin. The aPTT assay measures the ability of blood to clot, specifically the intrinsic pathway (involving the factor IX and cofactors) and the common pathway (factors X and II, and cofactors) of coagulation. The action of Hep is focused on a specific step in the clotting process. In addition to clotting abnormalities, the aPTT is also used to monitor the effect of treatment with heparin used in conjunction with the prothrombin time (PT), which measures the extrinsic pathway (involving the factor VII and tissue factor). [15]. 3.1.2. Antithrombin Assay In clinical practice, this test is carried out to measure the amount of antithrombin III in blood with the purpose to evaluate and treat blood clotting disorders. In addition, it is used to evaluate potency of heparin samples in an assay using a chromogenic thrombin substrate and to determine the ability of heparin to inhibit factor IIa (thrombin). This assay is based on the fact that the binding of heparin to antithrombin III accelerates thrombin inhibition. It was observed that the absence of heparin, the thrombin half-life is 40 sec while in the presence of antithrombin III and heparin is 2 sec. The test calculates the regression of the curve of absorbance or change in absorbance/min against log concentration of the standard solution and the sample solution of different heparin dilutions. Thus, the potency of heparin, in USP

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units/mL is calculated using statistical methods and expressing the potency of heparin sodium per mg of sample on dried basis [13].

3.2. Related Substances

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Organic impurities are codified in the USP official method and in the EP official method. A 1% limit is set for the galactosamine impurities expressed as dermatan sulfate and other galactosamines along with the absence of oversulfated chondroitin sulfate (OSCS) a heparin contaminant for USP monograph [13]. In the case of EP monograph, the limit is 2% for the sum of DS and CS without other components [11]. 3.2.1. Oversulfated Chondroitin Sulfate as Heparin Contaminant In 2008, appeared contaminated heparin products in much of the pharmaceutical market, especially Europe and U.S.A [16]. The FDA published a warning about the severe adverse effects presented in patients who received sodium heparin injectable and recommended recalls of the contaminated heparin lots [17]. The contaminated Hep had been produced in China, the main producer of porcine Hep in the world [18]. The FDA has identified 12 chinese companies that supplied contaminated Hep products to 11 countries: Australia, Canada, China, Denmark, France, Germany, Italy, Japan, The Netherlands, New Zeland and the United States of America (USA). In Germany and USA severe side effects of Hep products have been reported [19]. The affected Hep contained up to 35% of a contaminant that was identified as oversulfated chondroitin sulfate (OSCS), a semi-synthetic derivate of chondroitin sulfate. OSCS can be obtained by sulfonation of native chondroitin sulfate with pyridine-sulfonic complex in dimethylformamide [18, 20]. Patients treated with OSCS-contaminated Hep experienced angioedema, hypertension, swelling of the larynx and in some cases, death [21-22]. Surprisingly, the batches of contaminated Hep passed the quality control tests codified in the pharmacopoeias and its identification and quantification needed collaborative studies between pharmaceutical industry, government authorities and academic research [18-19]. Different analytical methods have been reported for the detection and determination of impurities of Hep mainly expressed as dermatan sulfate and contaminat as OSCS. In this sense, spectroscopic methods such as NMR [23-24], attenuated total reflectioninfrared spectroscopy (ATR-IR) [19,26] and Raman [19] spectroscopy have been reported. On the other hand, chromatographic methods such as strong anionic-exchange-HPLC (SAXHPLC) [11,13,24,26], electrophoretic methods based on plate-electrophoresis [27] and capillary electrophoresis [28-31] have also been presented as well as other techniques as protombine time (PT) [32], enzyme immunoassay (EIA) [33,34], fluorescence and chromogenic assay [25, 35-36].

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3.2.2. Spectroscopic Methods

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3.2.2.1. NMR Spectroscopy 1 H NMR spectroscopic methods are codified in different official pharmacopoeias to identify OSCS as well as other impurities expressed as dermatan sulfate [11,13]. Moreover, different authors have reported the detection and quantification of impurities and contaminants of Hep by NMR spectroscopy [18,23-24]. The NMR spectrum allows to discriminate Hep, DS and OSCS by N-acetyl signal in the frecuency between 2.0 ppm and 2.2 ppm) (Figure 3). Slight variation in the position of the frequency (± 0.01 ppm) can be observed due to the anisotropy produced by sulfonation. Due to the polysaccharides as Hep and its impurities are complex mixtures, differing in their chain length, sulfonation pattern, and repeating disaccharide units, the quantification by NMR spectroscopy is carried out by measurement of the heights in the N-acetyl signal. However, the standard addition method is suggested to obtain more accurate results. [18]. The limit of detection (LOD) for OSCS is 0.1 % w/w and 0.5% w/w for DS and the limit of quantification (LOQ) for OSCS is 0.3 % w/w using a concentration of Hep sample of 35 mg/mL. Finally, even when NMR spectroscopic methods are the test of choice specially for the official pharmacopoeias, certain variables such as the shift of the signal as well as the expertise of the operator are critical points to consider.

Figure 3. A plot of the 1.90-2.30 ppm region of the 500 MHz 1H NMR spectra of crude heparin superposed on the spectra of crude containing 1.0%, 5.0%, or 10.0% OSCS. Arrows designate the N-acetyl proton resonances for OSCS at 2.16 ppm, dermatan sulfate at 2.08 ppm, heparin at 2.046 ppm, and chondroitin sulfate A at 2.02 ppm. From reference 24.

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Figure 4. IR spectra of a non-contaminated heparin sample (1), contaminated heparin sample (2), dermatansulphate (3), and OSCS (4). From reference 19.

3.2.2.2. Attenuated Total Reflection-infrared (ATR-IR) Spectroscopy and Raman Spectroscopy ATR-IR spectrum of Hep, DS and OSCS shows only shoulders and peak broadenings (Figure 4). Therefore, this spectroscopy technique is not suitable for a simple visual inspection of the spectra but it is adequate for the identification of impurities if a multivariate data analysis of the spectral data is carried out [18-19]. In the same case, for analysis of those compounds using Raman spectroscopy the application of the multivariate data analysis is mandatory. Although, the LOD value for OSCS is 1% (w/w), no sample pre-treatment is required and a great number of samples can be processed in a short time. IR spectroscopy technique can be applied as screening tests on batch-to-batch of Hep API. However, it is necessary to confirm the results by another analytical methodology such as NMR or chromatographic or electrophoretic techniques.

Figure 5. Standard containing heparin at approximately 20 mg/g of solution and approximately 0.8 mg of dermatan sulfate/g and 0.2 mg of OSCS/g of solution. Retention times are 16.0 min for dermatan sulfate, 19,2 min for heparin, and 22 min for OSCS. From reference 26.

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3.2.3. Chromatographic Methods Some strong-anion-exchange-high performance liquid chromatography (SAX-HPLC) methods have been developed to identification and quantitation of impurities and contaminant of Hep APIs [11,13,24,26]. These reported methods are based on the use of polymer-based strong exchange chromatography. Acceptable resolution of Hep from OSCS and DS was obtained. In all cases, the LOD for OSCS was 0.02%-0.03% w/w and LOQ was 0.07-0.1% w/w using Hep sample concentrations of 20 to 100 mg/mL (Figure 5). To improve sensibility, the samples can be dissolved in water instead of 2.5 M NaCl solution, requiring 20 mg/mL instead of 100 mg/mL of Hep sample. The USP official method codified a HPLC system to identify impurities and contaminants of Hep using a reduced diameter SAX-column, a gradient elution and UVdetection set at 202 nm. On the other hand, the same monograph codifies a HPLC method to quantify organic impurities as a measurement of DS and other galactosamines of Hep using an amino acid trap column coupled to an amperometric detector [13].

Figure 6. CE electropherograms of GlcN and GalN derivatized with anthranilic acid and obtained after the hydrolysis procedure of a mixture of OSCS (1%) in Hep (99%). D-ribose (Rib) is used as internel standard. From reference 21.

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Since 2011 EP has incorporated a HPLC method for identification of related substances of Hep using an anion exchange column but the acceptance criterion is that the sum of Dermatan sulfate and chondroitin sulfate must be less than 2% and absence of other peaks of the impurities [11]. 3.2.4. Electrophoretic Methods

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3.2.4.1. Capillary Electrophoresis Methods Capillary electrophoresis (CE) is a high-efficiency analytical technique that separates compounds by applying high voltage across buffer filled capillaries. The CE advantages with respect to other analytical techniques are very high resolution in short time of analysis, versatility, small volume of sample and low cost. This techniques has proved to be adequate for the analysis of numerous compounds like biological macromolecules, chiral drugs, inorganic ionics, organic acids, DNA fragments and even whole cells and virus particles [3739]. CE has been applied in different modes such as capillary zone electrophoresis (CZE) using only a buffer solution as electrolyte and electrokinetic chromatography (EKC) with the use of nanoparticules as pseudo-stationary phase. The nanoparticules can be micelles, vesicles, polymers or microdroplets, in an aqueous buffer [38].

Figure 7. Chemical structure of polymeric β-cyclodextryn. From reference 30.

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Figure 8. Chemical structure of poloxamine Tetronic® 1107. From reference 30.

Figure 9. Electropherograms of Hep (2) (0.1 mg/mL), OSCS (1) (10 µg/mL) and Der (3) (10 µg/mL) (A) BGE: 400 mM Tris-phosphate at pH 3.5, (B) BGE with 0.5 % polymeric--cyclodextrin (C) BGE with 0.5 % w/v polymeric--cyclodextrin and 0.4 % w/v Tetronic® 1107. From reference 30.

Different CZE methods have been reported for the analysis of Hep samples employing phosphate buffer due to its properties such as optical transparency in the low ultraviolet region, good buffer capacity at low pH and the possibility of using various counterions such as sodium, lithium, ammonium, and amines. In this regard, Linhardt and co-workers have developed an analytical method based on a sample pre-treatment by acidic hydrolysis of polysaccharides, and the hexosamines obtained were determined by CE after derivatization. As a result of this method, OSCS as contaminant could be detected and quantified at 1 % level in Hep samples [21] (Figure 6).

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Figure 10. Electropherogram of Hep (2) (0.1 mg/mL) and OSCS (1) and Der (3) (0.5 µg/mL) of each one, equivalent to 0.5 % w/w of Hep). From reference 30.

Wielgos et al have reported a CZE method based on the use of 600 mM sodium phosphate buffer at pH 3.5 and the comparison with 600 mM lithium phosphate at pH 2.8 to improve speed and resolution. The LOD obtained for OSCS was 0.1 % (30 µg/mL) using 30 mg/mL of Hep sample concentration [28]. Moreover, Somsen et al have developed a CE method using 850 mM tris-phosphate at pH 3.0 as background electrolyte using high Hep concentrations (up to 50 mg/mL) and large injection sample volumes. The mentionated method has been included in the Hep EP monograph of a previous edition [31]. LOD of 0.05 % w/w (19 µg/mL) for OSCS was reported [29]. Higher concentrations of buffer improved resolution, though a very high current was generated. To solve this problem, different authors worked with smaller diameter capillaries (25 µm). Consequently, higher concentrations of Hep samples (50-60 mg/mL) were necessary for the analysis [28-29] making impossible to test the finished product in which Hep concentration is usually down to 10 mg/mL. Another CE method has been developed based on a mixed-polymeric EKC system [30] using a 400 mM Tris-phosphate buffer at pH of 3.5 with 0.5 % of polymeric - cyclodextrin (Figure 7) and 0.4 % of a poloxamine copolymer (Tetronic® 1107) (Figure 8). Addition of polymeric β-cyclodextrin as pseudostationary phase in EKC allowed the formation of an additive- polysaccharide complex increasing their UV response to the UV detector allowing the quantification of impurities using as low as 0.1 mg/mL of Hep in the sample. In addition, the use of an amphiphilic block copolymer as poloxamine (Tetronic® 1107) composed of hydrophilic and hydrophobic chains with the presence of two charged ternary amine groups in the centre of the molecule (Figure 8) used as pseudostationary phase, helped in the resolution of the Hep peak from its impurities. This effect is probably attributed to the overlap of positive amino charge with negative sulfate groups of polysaccharides (Figure 9). Electropherogram of Hep (0.1 mg/mL) and OSCS and Der (0.5 µg/mL of each one,

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equivalent to 0.5 % w/w of Hep) are shown in figure 10. The highly sensitive method developed showed low values of LOD, 0.07 % w/w (0.07 µg/mL) (OSCS) and 0.1 % w/w (0.1 µg/mL) (Der), and values of LOQ 0.2 % w/w (0.2 µg/mL) (OSCS) and 0.3 % w/w (0.3 µg/mL) (Der) with a concentration level of heparin sample as low as 0.1 mg/mL. In brief, a very high sensitive mixed-polymeric EKC system was found to be suitable for the analysis of impurities and contaminant in Hep API and Hep finished products according to pharmacopoeias requirements.

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3.2.4.2. Plate-electrophoresis Additional analytical method applied to identify and quantify impurities and contaminant of Hep is a one-dimensional cellulose acetate electrophoretic method using 0.1 M barium acetate at pH value of 5.0 with isopropanol and 130 V is applied during 75 min [27]. Typically, impurities of Hep as DS and OSCS can be separated, detected and quantified by means of calibrated optical densitometry using Hep sample concentration of 30 mg/ mL (Figure 11). The LOD and LOQ values for DS are 0.4 % w/w and 0.5 % w/w, respectively. Moreover, the LOD and LOQ values for OSCS is less than 2 % w/w

Figure 11. Exemplary electrophoretic separation for demonstration of specificity of the analytical method. (1) Control heparin sample with known dermatan sulfate content of 2.0%); (2) Dermatan sulfate standard 0.5%; (3) Dermatan sulfate standard 2.5%; (4) Dermatan sulfate standard 4.5%; (5) Chondroitin sulfate A&C 2.0%;(6) Heparin sample containing 10.0% OSCS; (8) Mixture: heparin sample with OSCS 2.0% + dermatan sulfate 2.0% + condroitin sulfate A&C 2.0%. From reference 27

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Figure 12. Utility of heparin EIA test kit for detection of OSCS in heparin final containers. The relative responses of various heparin samples in the heparin EIA are shown. Heparin lots 037060 and 037081 are uncontaminated final product. Heparin final product lots 107064 and 107066 were found to be contaminated with 35 and 20 wt% OSCS, respectively, via NMR. The synthesized fully sulfated chondroitin sulfate is shown as OSCS-A. From reference 33.

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This reported electrophoretic method requires inexpensive equipment in contrast spectroscopic or chromatographic techniques. Moreover, one-dimensional cellulose acetate electrophoretic method is simple and reliable technique that can be used in quality control laboratory as a routine assay. 3.2.5. Biological Methods 3.2.5.1. Prothrombin Time (PT) Prothrombin is a protein produced by the liver that acts on the process of blood clotting. The production of this protein depends on the adequate intake of vitamin K and its absorption. During the process of coagulation, prothrombin is converted into thrombin. In patients with liver problems, blood prothrombin content decreases and PT assay could be determined. The clinical usefulness of this determination is the monitoring of treatment with coumarin or heparinoids, evaluation of liver function screening in suspected disorders of the factors II, VII, X, V, fibrinogen or dysfibrinogenemia and also in preoperative screening to detect a possible haemostatic disorder [18]. PT can also be used to screen the presence of heparin contaminants. Highly sulfated polysaccharides such as OSCS are known to act as anticoagulants, but they also induce contact activation promoting in vitro coagulation. One of the methods proposed to detect OSCS contaminants in both unfractionated heparin and low molecular weight heparins is the use of PT. Heparin contaminated with OSCS slightly prolonged the PT at plasma concentrations greater than 5 µg/mL of unfractioned heparin and 10 µg/mL of unfractionated heparin contaminated with 17.4% OSCS. At concentrations less than 5 µg/mL,

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contaminated unfractionated heparin, but not unfractionated heparin, reduced the PT. However, it is essential to check that PT reagent does not contain any heparin-neutralizing compound that antagonize the effects of OSCS. Using aPTT tests in which the coagulation is initiated by contact activators, OSCS procoagulant effect cannot be recognized. However, PT is quiet insensitive to heparins, so that any procoagulant effect of OSCS is not obscured. In conclusion, PT could be used in a validated form as a screening method for the quality control of heparins.[32]. However, though it has an excellent positive predictive value, because of its LOD of 3% it is only a rough method for initial screening [18]. 3.2.5.2. Heparin Enzyme Immunoassay This method is a simple enzyme immunoassay that can discriminate OSCS from normal heparin at levels below amounts that produce an adverse response in animal models. This is a competitive enzyme immunoassay that uses heparin-coated plates in conjunction with a recombinant detection protein. Contaminated heparin displayed distinctly different behavior compared with uncontaminated heparin. In addition, synthetic OSCS had a response greater than contaminated lots of heparin tested. This result demonstrate that the detector conjugate used in this test displays a higher affinity for the OSCS that it does for native heparin and shifts the dose-response curve to lower dilution levels by at least three orders of magnitude (Figure 12) [33]. It has recently developed a competitive microplate assay for oversulfated chondroitin sulfate in heparin, based on a water-soluble cationic polythiophene polymer (3-(2-(N-(N0methylimidazole))ethoxy)-4-methylthiophene (LPTP)) and heparinase digestion of heparin [34]. The assay takes advantage of several unique properties of heparin, OSCS, and LPTP, including OSCS inhibition of heparinase I and II activity, the molecular weight dependence of heparin-LPTP spectral shifts, and the distinct association of heparin fragments and OSCS to LPTP. It was utilized the unique properties of a cationic polythiophene polymer developed by Leclerc‘s laboratory that had been shown to change color in the presence of heparin. It was found that LPTP associates with OSCS, intact heparin and small heparin fragments to form solutions with distinct colors; yellow, orange, and red, respectively. In addition, OSCS and other oversulfated GAGs are known to inhibit heparinases. Thus, in the absence of OSCS, heparin is digested and a distinct color change from intact heparin is observed (i.e., orange to red). If OSCS is present, the enzymes are inhibited and heparin, heparin digest fragments and OSCS compete for association with LPTP, blocking the color change (i.e., the solution remains orange). The combination of these properties forms the basis for a very sensitive pass/fail colorimetric test for OSCS contamination in heparin. The best detection limits for the LPTP-heparinase assay (0.003% w/w) require the use of a plate reader. However, the color change inhibition compared to the control can be observed visually in heparin sodium APIs down to at least the 0.1% OSCS levels. On the basis of the results presented here, the heparin digest-LPTP pass/fail test is more sensitive than the USP SAXHPLC or other published tests for OSCS contamination, robust to common heparin impurities, and amenable to high-throughput screening with widely available laboratory equipment.

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3.2.5.3. Two-step Fluorescence Assay It was recently developed a direct quantitation methods to determine heparin and OSCS with high sensitivity, speed and simplicity [25,35]. This method was based on Polymer-H flurorescent labeled ( ex 320 mn, em 510 nm) which is a copolymer with a high affinity to heparin. The fluorescence intensity of Polymer-H was concentration-dependent amplified by heparins. On the other hand, the assay does not selectively detect heparin, but can be used to quantify all types of sulfated carbohydrates including OSCS. However, mixtures of heparin with other glycosaminoglycans or OSCS cannot be recognized, thus at the present time it is not qualified to test the purity control of heparin. To resolve this limitation a simple enzymatic degradation as a pre-treatment of the heparin sample using heparinase I, was introduced. [25,35]. This method is simple, rapid but has much higher sensitivity that the PT (LOD 0.5% w/w and LOQ of 0.6% w/w for OSCS). The unexpected high sensitivity was proved to result from the inhibitory effect of OSCS on heparinase I. That means that fluorescence increment observed with heparin samples containing small amounts of OSCS mainly results from residual non-degraded heparin. Moreover, in this method expensive equipment or much expertise of the analyst is not required in contrast to spectroscopic or chromatographic methods. Furthermore, only a few microlitres of a solution of Hep (0.1 mg/mL) is required and up to 40 samples can be measured in one assay run. This method is useful in clinical practise, to evaluate the efficacy of Hep preparation administered in patients. 3.2.5.4. Two-step Anti-factor Xa Assay Aditional method has been developed based on a chromogenic aXa assay (two-step aXa assay) [36]. In this method, indirect quantification of OSCS after heparinase I incubation is produced by of the anti-factor Xa (aXa) activity of the remaining undegrated Hep. Contaminant of Hep can be sensitively detected (LOD < 0.5% w/w). Two-step aXa assay is sensitive, rapid and simple method as well fluorescence assay. This method could even be performed in clinical laboratories to control patient who possess severe adverse reactions.

4. Conclusion In this chapter, it is presented a comprehensive and updated study on the analysis of Hep potency and purity test in pharmaceutical samples. Different analytical methods have been reported for the determination of the potency as well as the purity test. The variety of the presented assays offers also the option to implement additional or alternative purity test which are simple, robust and feasible in any laboratories. Methods of analysis with different OSCS limits of detection and quantification as well as a requirement of Hep sample are presented in table 1. Moreover, the finding of the contaminated Hep has promoted the revision of pharmacopoeial monographs as well as the development of analytical methods not only more reliable and sensitive, but also applicable and useful in quality control laboratories.

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Table 1 Comparison of analytical methods for detection and quantification of OSCS

NMR ATR-IR SAX-HPLC CE hydrolisis CE (CZE) CE (EKC) Plate-electrophoresis PT EIA aXa Fluorescence FA

OSCS (LOD % w/w)

OSCS (LOQ % w/w)

0.1 1.0 0.02-0.03 1% 0.05-0.1 0.07 1% 0.15-0.30 0.20 0 (i = 1,..., n), which represent the distance of sample xi from the margin of the pertaining class. Given the sum of the allowed deviations ∑ξi, the optimization requires simultaneously maximizing the margin 1/2||w||2 and minimizing the number of misclassifications. Accordingly, the objective function that is designed to balance the classification error with complexity of the model can be expressed as follows [68]: n 1 2 w  Ci 2 i 1

(26)

A soft margin that can separate the hyperplane is constructed by minimizing the dual form of the above expression, where the regularization parameter C is used to control a tradeoff between maximizing the margin and minimizing the model complexity. A small value of C allows great deviations ξi and, hence, the emphasis will be placed on margin maximization. Thus a large number of samples are retained as support vectors, leading to overfitting of the training data. In contrast, when C is too large, the second term dominates, allowing smaller deviations ξi and minimizing the training error, leading to a less complex boundary and smaller margin. The results of the SVM approach depend heavily on the choice of the kernel function that decides the sample distribution in the mapping space and may influence the performance of the final model. The most commonly used kernel function in SVM is radial basis function (RBF) or Gaussian function, and is formulated as [95]:

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2

K ( xi , x j )  exp(  xi  x j )

(27)

where xi and xj are two independent variables; γ is a tuning parameter that controls the amplitude of the kernel function and, therefore, controls the generalization performance of the SVM. A very large γ value can produce models with over-fitting because most of the training objects are used as the support vectors, while a very small γ value can lead to poor predictive ability as all data points are regarded as one object. 2.3.8. SIMCA Analysis Soft Independent Modeling of Class Analogy (SIMCA) is a popular class modeling technique in chemometrics. SIMCA uses principal component analysis (PCA) to develop a statistical model which describes the similarities among the samples of a category [61, 96]. The class model for each category is derived separately in the training set based on the computation of the principal components (PCs). The number of significant components, which determines the dimensionality of the inner space for each category and can differ for different categories, is evaluated by a cross validation procedure. Depending on the number of PCs or the variance retained in each data class, classes can be modeled by one of a series of linear structures, such as a point, a line, a plane, and so on [48]. The class boundaries around these linear structures can be built on the basis of the distribution of Euclidean distance between the data points of training samples and the fitted class model. A critical distance s crit is computed based on an F-test at a certain limit of confidence level, and a 95% confidence level was set in the present study on heparin to define each class. After the model has been developed on the training set, a new sample can be

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tested for its membership in the defined classes by the orthogonal projection distance s test between the new sample and the PC model of each class. It is then compared with the class confidence limit s crit . The new sample is assigned to one or more classes if it lies within the statistical limits, i.e., s test < s crit , and it is considered to be an outlier if the distance is larger. Therefore, a sample can be a member of a single class, more than one class, or none of the defined classes. The model generated by SIMCA for each category can be evaluated in terms of sensitivity (SENS) and specificity (SPEC), which are associated with the number of false positive and false negative errors for each class. The SENS of a class is the proportion of samples belonging to that class and correctly identified by the model, while SPEC corresponds to the proportion of samples outside the class and correctly rejected by the model. When more than two classes are present, SPEC can be calculated individually for each class. SENS and SPEC are closely associated with the concepts of type I (α) errors which refer to the probability of erroneously rejecting a member of the class as a non-member (false negative), and type II (β) errors which refer to the probability of erroneously accepting a nonmember of the class as a member (false positive). Assume n A and  n A  are the number of samples belonging to category A and the number of samples accepted by the model, respectively, while n A and  n A  are the number of samples not belonging to category A

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and the number of samples rejected by the model, respectively. Given these definitions, the two relationships follow:

SENS 

 nA   100%  100   nA

(28)

SPEC 

 nA   100%  100   nA

(29)

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indicating that SENS and SPEC are the complementary percent measure of type I and II errors, respectively. 2.3.9. UNEQ Analysis Unequal class modeling (UNEQ) is a class modeling technique equivalent to quadratic discriminant analysis (QDA) and is based on the assumption of multivariate normal distribution of the measured or transformed variables for each class population [61, 75]. In a specific class, the category space or the distance of each sample from the barycenter or centroid is calculated according to various measures that follow a chi-squared distribution. Usually, the Mahalanobis distance is applied, which is measured on the basis of correlations between variables and is a useful way for determining similarity of an unknown sample set to a known one. The Mahalanobis distance is different from Euclidean distance in that it accounts for the covariance structure, i.e., it considers the distribution of the sample points in the variable space and is independent of the scale of measurements (scale-invariant). Thus, for UNEQ class modeling, three parameters, i.e., the centroid, the matrix of covariance, and the Mahalanobis distance of each sample to the centroid, need to be estimated. As in SIMCA, a confidence interval that represents the class boundary is defined, and the membership of new samples is tested based on whether they fall within the defined class boundary. The class space is constructed as the confidence limit of hyper-ellipsoids around each centroid, which determines the 95% probability of the multivariate normal distribution.

3. Data and Methods Over 200 heparin sodium API samples were obtained from various manufacturers and suppliers, and analyzed by FDA laboratories in 2008-2009. These samples contained substantial amounts of DS (up to 19% of the DS impurity, w/w percent) and OSCS (in an amount from 1.0% and OSCS = 0%; and (c) OSCS: contaminated heparin with OSCS > 0% and any content of DS. An additional fourth class, namely [DS + OSCS], was included to characterize samples that contained DS > 1.0%, OSCS > 0%, or both. In order to obtain a model with validation capabilities, the entire data set was divided into two subsets: a training set employed to build the model, and a validation set employed to test the predictive ability of the model using data excluded from the training set. The original data set of 178 heparin samples was split 2:1 into 118 samples for training (54 Heparin, 33 DS, and 31 OSCS) and 60 samples for external validation and testing (28 Heparin, 17 DS, and 15 OSCS). Multivariate statistical modeling was conducted separately on the entire region (1.95-5.70 ppm) and two local regions (1.95-2.20 and 3.10-5.70 ppm), which correspond to 74, 9 and 65 variables, respectively. By applying multivariate statistical methods and pattern recognition techniques, the dimensionality of the data can be reduced to facilitate visualization, the inherent patterns among the sets of spectral measurements can be revealed, and classification models can be built. As one of the fundamental methodologies in chemometrics, the purpose of classification is to find a mathematical model to recognize the class membership of new objects by assigning a proper class. In this study, the NMR data were analyzed by both the unsupervised (PCA and HCA) and the supervised (PLS-DA, LDA, KNN, CART, ANN, SVM, SIMCA and UNEQ) approaches to distinguish the pure and contaminated heparin samples.

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4.3.1. Principal Components Analysis In order to obtain a general picture of the classification of heparin samples into groups, principal components analysis (PCA) was employed to provide an overview of the spectral data. Since PCA preserves most of the variance in just a few numbers of principal components (PCs), this information can be readily displayed in a graph of reduced dimensions and data can be visualized by using the scores plots that differentiate samples from various sources based on the measured properties. The most common way is to project the data into the subspace of PC1 versus PC2 with PC1 along the x-axis and PC2 along the yaxis, where the sample distribution on this graph may reveal patterns, clusters and other features that might be related to the general characteristics of the samples. The PCA scores plots obtained from analysis of the 1H NMR spectra for representative heparin samples are shown in Figures 6A, 6C and 6E. Each point on the plot represents one spectrum of an individual sample, and points of the same color indicate samples of the same origin, such as pure heparin (green), heparin with the impurity DS (magenta), or heparin with the contaminant OSCS (blue). The spectra with similar characteristics form a cluster and the variations along the PC axes maximize the differences between the spectra. The Heparin and DS samples were not well separated using this approach (Figure 6A). The Heparin class is located on the upper side while the DS class is distributed on the lower side. As the content of DS approached 1.0%, the overlapping of the two classes increased. With respect to the Heparin vs OSCS samples together, the scores plot of PC1 versus PC2 showed that the samples were separated into two distinct clusters (Figure 6C). Heparin samples were situated on the left side and formed a tighter cluster than the OSCS group. By contrast, the contaminant samples were distributed from left to right side as the content of OSCS increased. For the Heparin vs DS vs OSCS samples together, the PC1 scores were dominated by OSCS while the variations of Heparin, DS and OSCS were explain primarily by PC2 (Figure 6E). The three types of samples were separated by the PC1 with some sample overlap. OSCS clustered in a range lying toward the positive side of PC1, whereas the scores near zero or on the negative side of PC1 corresponded to Heparin, and the DS samples were mostly centered on the PC1 axis with some samples dispersed on the positive side of the axis. To achieve better separation and classification of the samples, we performed supervised analysis. 4.3.2. Partial Least Squares Discriminant Analysis To optimize separation between heparin and impure or contaminated samples and to build predictive models for class identification, partial least squares discriminant analysis (PLS-DA) was performed using the class membership of Heparin, DS or OSCS as the response variables. The scores plots of the first and the second latent variables are displayed in Figures 6B, 6D and 6F. With PLS-DA, nearly all of the samples were in distinct classes, and a clear discrimination of Heparin samples from the DS impurity and OSCS contaminant was observed. Here, the Heparin samples appeared in a more compact grouping, while the contaminated samples exhibited a distribution similar to that in the PCA model. Applying PLS-DA, the correct differentiation of these samples in three different groups was obtained as shown in Figure 6F, where Heparin and DS were located in the upper- and lower-left zones, respectively, while OSCS was distributed toward the right side. This supervised clustering approach gave much improved separation compared with the PCA model, and excellent class discrimination was achieved between the different types of heparin samples.

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A

B

C

D

E

F

271

Figure 6. Scores plots. (A) PCA, Heparin vs DS; (B) PLS-DA, Heparin vs DS; (C) PCA, Heparin vs OSCS; (D) PLS-DA, Heparin vs OSCS; (E) PCA, Heparin vs DS vs OSCS; (F) PLS-DA, Heparin vs DS vs OSCS.

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A

B

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C

D

Figure 7. Misclassification rate as a function of the number of PLS components for the PLS-DA model. (A) Heparin vs DS; (B) Heparin vs OSCS; (C) Heparin vs [DS + OSCS]; (D) Heparin vs DS vs OSCS.

Table 6. Number and Type of Misclassifications (Errors) by PLS-DA Classification Model for Test Sets Using Different Number of Components Components Model Heparin vs DS Heparin errors / 28 samples DS errors / 17 samples Heparin vs OSCS Heparin errors / 28 samples OSCS errors / 15 samples Heparin vs [DS + OSCS] errors / 28 samples [DS + OSCS] errors / 32 samples Heparin vs DS vs OSCS Heparin errors / 28 samples DS errors / 17 samples OSCS errors / 15 samples

1

2

4

6

8

10

12

14 16

18

20

4 5

2 5

1 6

1 6

2 6

4 6

4 7

5 7

5 7

5 8

6 8

0 3

0 2

0 2

0 1

0 1

0 0

0 0

1 0

1 1

1 1

1 1

3 9

4 6

2 7

1 6

2 5

2 5

3 5

3 5

4 5

5 6

8 7

31 7 7 6 5

1 8 4

1 8 2

2 8 1

3 7 1

3 7 1

4 7 6

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3 7 1

4 8 2

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Table 7. Wilks’ Lambda (  v ) and F-to-enter (F) of Variables (V) for Various Models Order

Heparin vs DS V (ppm) F 

1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20

2.08 3.61 5.34 2.17 2.14 4.61 2.11 3.95 5.67 4.04 5.43 3.70 4.46 3.76 3.73 5.40 3.67 4.01 5.19 5.31

103.0 15.8 8.9 1.7 2.3 1.5 1.1 2.1 1.2 1.9 1.6 1.1 1.7 1.7 1.5 1.7 1.0 1.1 1.6 1.5

v

0.54 0.48 0.45 0.44 0.43 0.42 0.42 0.41 0.41 0.40 0.40 0.39 0.39 0.39 0.38 0.38 0.37 0.37 0.37 0.36

Heparin vs OSCS Heparin vs [DS + OSCS] Hearin vs DS vs OSCS V (ppm) F V (ppm) F  V (ppm) F   v

2.17 2.08 4.49 4.16 4.04 3.55 4.52 3.64 5.61 5.67 4.37 5.25 3.73 5.03 2.14 5.49 3.67 4.10 5.28 5.19

14.0 15.1 8.1 6.7 5.5 2.5 5.2 4.2 8.0 4.0 1.9 4.4 3.1 3.9 2.4 3.5 2.7 3.9 4.0 2.3

0.36 0.33 0.29 0.26 0.24 0.22 0.21 0.20 0.19 0.18 0.18 0.17 0.16 0.15 0.15 0.14 0.14 0.13 0.13 0.12

v

2.08 4.49 2.14 4.16 4.46 5.16 5.10 5.61 4.28 3.55 4.94 5.49 4.97 4.61 4.22 5.19 5.43 4.34 5.58 5.25

97.1 23.3 3.6 5.3 3.3 2.7 2.6 2.8 3.9 4.1 2.2 3.8 1.9 2.2 1.0 2.2 1.9 1.1 1.4 1.6

0.63 0.55 0.52 0.50 0.49 0.47 0.46 0.46 0.45 0.44 0.43 0.42 0.41 0.40 0.40 0.40 0.39 0.39 0.39 0.38

v

2.11 3.86 3.52 4.49 5.16 3.58 2.14 3.95 4.46 5.00 4.43 3.70 5.13 5.03 5.46 4.64 4.13 4.16 4.28 4.22

134.3 30.9 9.2 7.1 10.1 6.5 4.1 4.4 3.5 3.5 3.0 5.3 2.1 2.5 1.5 2.1 1.8 1.5 1.9 1.8

0.38 0.28 0.25 0.23 0.20 0.19 0.18 0.17 0.16 0.15 0.15 0.14 0.14 0.13 0.13 0.13 0.12 0.12 0.12 0.11

PLS-DA classification models were built and tested while increasing the number of PLS components starting at 1. The number of correct classifications in both the training and test sets was taken as a measure of performance. Figure 7 illustrates the evolution of the misclassification rates as a function of the number of PLS components in the model. As expected for the training set, the number of correct classifications increased with the number of dimensions. For any model, the misclassification rates were small even with few PLS components and reached a plateau at which all the rates approached zero after 20 to 40 components. Leave-one-out cross-validation (LOO-CV) was employed to select the model with the optimal number of PLS components that minimize the misclassifications. Classification rates of 85, 97 and 82% were obtained for Heparin vs DS, Heparin vs OSCS, and Heparin vs [DS + OSCS] models, respectively. In addition, a 75% success rate was attained for the threefold Heparin vs DS vs OSCS model. The true test of the model depends on its performance when applied to an external set of samples that were not employed for building the model. Consequently, the model was validated using a test set of 60 samples. The results, plotted in Figure 7, point to the same conclusions as described above for the LOO-CV. By increasing the number of PLS components incrementally, we observed that the classification rates were optimal for the Heparin vs DS (84%), Heparin vs OSCS (100%), and Heparin vs [DS + OSCS] (88%) models when the number of components equaled 2 to 6, 10 to 12, and 6 to 10, respectively. Even for the threefold Heparin vs DS vs OSCS model, the classification rate was 85% using 16 components. The results for the corresponding test sets are presented in Table 6. For the Heparin vs DS model using 4-6 components, misclassification of Heparin as DS occurred

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only once and DS as Heparin six times. In nearly all of these cases the DS content was 1.061.20%, i.e., near the 1.0% boundary that defines the two classes. With respect to the Heparin vs OSCS model, misclassification of Heparin as OSCS was zero and OSCS as Heparin varied from 0 to 3 using 1 to 12 components. The number of misclassifications was zero (100% success rate) for the Heparin vs OSCS model using 10 to 12 components. For the Heparin vs [DS + OSCS] model, only two Heparin samples and five samples in the [DS + OSCS] group were misclassified using 8 to 10 components. As noted for the Heparin vs DS model, in most cases these misclassifications occurred when the DS content was close to the 1.0% DS boundary defining the Heparin and DS classes. The same interpretation applies to the threefold Heparin vs DS vs OSCS model, where most of the misclassifications involved samples near the 1.0% DS borderline between Heparin and DS. 4.3.3. Linear Discriminant Analysis As an alternative approach, linear discriminant analysis (LDA) was employed to classify the Heparin, DS and OSCS samples based on the predefined classes. For LDA analysis, the data matrix of variances-covariances needs to be inverted, which would be impossible if the number of samples is less than that of the variables, i.e., the data matrix for each class must present a high ratio between the number of training samples and the number of variables [61, 74]. In order to select a subset of the original variables that affords the maximum improvement of the discriminating ability between classes, stepwise LDA (SLDA) was performed before LDA analysis. Preliminary variable reduction using SLDA led to the selection of 20 variables (Table 7). LDA analysis was conducted using the squared Mahalanobis distance from the centers of gravity of each group for assigning the class affiliation of each sample. The results show that class discrimination improved markedly by employing appropriate variables (Table 8). For the training set, the success rates approached 100% with increasing the number of variables. The Heparin vs OSCS model required very few variables to achieve 100% success rates due to the clear distinction in spectral features between heparin and OSCS. Cross validation and external validation studies indicated that model performance reached a maximum using an intermediate number of variables. Optimal success rates, varying from 89% to 100%, for the Heparin vs DS, Heparin vs OSCS, Heparin vs [DS + OSCS] models were achieved using 614 variables depending on the specific model and testing procedure (Table 8). In the same way, the threefold Heparin vs DS vs OSCS model achieved an optimal success rate of 89% using 10-12 variables. Once again, the majority of misclassifications are attributed to Heparin and DS samples in which the DS content was near the 1.0% boundary between the two classes. 4.3.4. K-Nearest-Neighbor The k-nearest-neighbor method, a nonparametric technique, was also implemented to evaluate its performance for classification. When all the variables were used to build the classification model, the results obtained were inferior for kNN compared with LDA and PLS-DA. For example, the success rates for the Heparin vs DS, Heparin vs OSCS, Heparin vs [DS + OSCS], and Heparin vs DS vs OSCS models using k = 3 were respectively 69, 91, 82 and 68% for the test set. To achieve better classification rates, the PCA scores were employed as inputs to build the kNN models. Various combinations of PCs and k values were investigated, and the results are summarized in Table 9. Unlike the PLS-DA and LDA models

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where the misclassification rates for the training set decreased monotonically to 0% as the number of components or variables increased, the rates of the kNN models for the training set fluctuated within a range of values. The optimal performance was achieved using 15-25 PCs depending on the individual models.

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Table 8. Performance of LDA Classification Models under Different Variables Number of variables Model Heparin vs DS Training set Errors / 87 samples Success rates (%) CV set Errors / 87 samples Success rates (%) Test set Errors / 45 samples Success rates (%) Heparin vs OSCS Training set Errors / 85 samples Success rates (%) CV set Errors / 85 samples Success rates (%) Test set Errors / 43 samples Success rates (%) Heparin vs [DS + OSCS] Training set Errors / 118 samples Success rates (%) CV set Errors / 118 samples Success rates (%) Test set Errors / 60 samples Success rates (%) Heparin vs DS vs OSCS Training set Errors / 118 samples Success rates (%) CV set Errors / 118 samples Success rates (%) Test set Errors / 60 samples Success rates

2

4

6

8

10

12

14

16 18

20

14 84 15 83 7 84

12 86 13 85 6 87

10 89 12 86 5 89

10 89 12 86 5 89

9 90 10 89 6 87

9 90 10 89 6 87

8 91 12 86 7 84

6 93 13 85 8 82

5 94 14 84 8 82

3 97 14 84 10 78

6 93 6 93 2 95

4 95 5 94 1 98

4 95 4 95 1 98

2 98 4 95 1 98

1 99 2 98 0 100

1 99 0 100 0 100

0 100 1 99 1 98

0 100 2 98 2 95

0 100 3 97 2 95

0 100 5 94 3 93

17 86 19 84 7 88

15 87 18 85 6 90

14 88 18 85 5 92

14 88 16 86 5 92

13 89 14 88 4 93

13 89 11 91 5 92

12 90 10 92 6 90

10 92 12 90 6 90

9 93 15 87 6 90

9 93 17 86

26 78 28 76 12 80

24 80 27 77 11 82

21 82 25 79 10 83

19 84 19 84 9 85

16 86 15 87 6 90

14 88 13 89 6 90

12 90 16 86 8 87

12 90 18 85 8 87

10 92 19 84 10 83

8 93 21 82 10 83

87

The misclassification rates of k nearest neighbors for k = 1 to 25 are plotted in Figure 8. The black dots and the vertical bars represent the means as well as mean ±1 standard error for the misclassification rates using LOO-CV. The smallest LOO-CV error is depicted by a dotted horizontal line corresponding to the position of the mean plus one standard error. For the training sets, the misclassification rate was always zero for k = 1 and increased with larger k values for all four models. The test sets showed a similar pattern, i.e., the misclassification rates varied within a tight range, except the Heparin vs OSCS model for which the rates rose continuously after k > 4. For the Heparin vs DS, Heparin vs OSCS, Heparin vs [DS + OSCS], and Heparin vs DS vs OSCS models, the optimal k values were 2, 4, 3, and 3, respectively. The predictive ability evaluated for the external test set based on the optimal set of PCs and k values was 78, 93, 83 and 75%, respectively, for the four models (Table 9). For the

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Heparin vs DS model, one heparin sample was misclassified as DS but nine out of the seventeen DS test samples were misclassified as Heparin. Unlike PLS-DA and LDA, kNN was unable to completely discriminate Heparin and OSCS. For the Heparin vs [DS + OSCS] model, three Heparin samples were misclassified as [DS + OSCS] while six DS samples and one OSCS sample were misclassified as Heparin. Similarly, for the threefold Heparin vs DS vs OSCS model, kNN produced a total of fifteen misclassifications.

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Table 9. Performance of PCA-kNN Classification Models under Different PCs PCs Model Heparin vs DS (k = 2) Training set Errors / 87 samples Success rates (%) CV set Errors / 87 samples Success rates (%) Test set Errors / 45 samples Success rates (%) Heparin vs OSCS (k = 4) Training set Errors / 85 samples Success rates (%) CV set Errors / 85 samples Success rates (%) Test set Errors / 43 samples Success rates (%) Heparin vs [DS + OSCS] (k = 3) Training set Errors / 118 samples Success rates (%) CV set Errors / 118 samples Success rates (%) Test set Errors / 60 samples Success rates (%) Heparin vs DS vs OSCS (k = 3) Training set Errors / 118 samples Success rates (%) CV set Errors / 118 samples Success rates (%) Test set Errors / 60 samples Success rates

5

10

15

20 25

30 35 40 45 50 55 60

13 85 25 71 12 73

11 87 20 77 15 67

7 92 17 80 16 64

5 94 20 77 12 73

12 86 25 71 10 78

8 91 25 71 14 69

10 89 27 69 12 73

12 86 22 75 15 67

10 89 29 67 15 67

13 85 34 61 12 73

15 83 31 64 16 64

14 84 33 62 19 58

6 93 10 88 37 86

3 96 13 85 38 88

5 94 11 87 40 93

5 94 10 88 39 91

9 89 14 84 39 91

8 91 18 79 37 86

8 91 19 78 30 70

11 87 25 71 33 77

11 87 22 74 33 77

16 81 24 72 31 72

13 85 25 71 30 70

19 78 26 69 33 77

17 86 23 81 13 78

10 92 30 75 13 78

13 89 26 78 12 80

17 86 34 71 9 85

19 84 33 72 17 72

11 91 39 67 15 75

16 86 31 74 17 72

14 88 28 76 19 68

18 85 34 71 23 62

17 86 36 69 22 63

19 84 34 71 22 63

25 79 43 64 21 65

18 85 30 75 21 65

13 89 39 67 19 68

19 84 32 73 18 70

23 81 42 64 15 75

22 81 42 64 20 67

17 86 40 66 23 62

21 82 43 64 23 62

21 82 43 64 23 62

23 81 47 60 25 58

23 81 41 65 24 60

25 78 46 61 27 55

32 73 52 56 27 55

4.3.5. Classification and Regression Tree Classification trees were built using the three datasets composed of 9, 65 and 74 variables corresponding to the chemical shift regions of 1.95-2.20, 3.10-5.70 and 1.95-5.70 ppm, respectively. The trees were grown and pruned using the Gini index as a splitting criterion and the optimal size of the tree was determined using 10-fold cross validation (CV). For the model Heparin vs DS vs OSCS in the region of 1.95-2.20 ppm, the division of the samples by

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the nodes of the classification tree is shown in Figures 9A and 9B. The data were split according to 2.08 and 2.15 ppm, the characteristic chemical shifts of DS and OSCS, respectively. The first split is defined by variable 2.15 ppm that split the samples into two groups: (Heparin + DS) and OSCS, and then variable 2.08 ppm divided the (Heparin + DS) samples into two separate classes: Heparin and DS, leading to a classification tree with a complexity of three nodes. Each terminal node represents the majority of the samples in a specified class. The OSCS terminal node is called a pure node in that it contains only samples of the OSCS class, i.e., all of the 31 OSCS samples are correctly classified and no Heparin or DS samples are located in this terminal. The (Heparin + DS) group was split into the DS and Heparin classes solely by the chemical shift 2.08 ppm. Both of these terminal nodes contain misclassifications. The DS node contains two Heparin samples, while the Heparin node contains six DS samples. The classification rates, summarized in Table 10, were 93.2% (110/118) for the training set and 90.0% (54/60) for the test set.

A

B

C

D

Figure 8. kNN classification over the range k = 1 to 25. (A) Heparin vs DS (PCs = 25); (B) Heparin vs OSCS (PCs = 15); (C) Heparin vs [DS+OSCS] (PCs = 20); (D) Heparin vs DS vs OSCS (PCs = 20). Heparin : Properties, Uses and Side Effects, Nova Science Publishers, Incorporated, 2012. ProQuest Ebook Central,

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A

C

B

D

Figure 9. Classification trees, and corresponding plots of the 10-fold cross-validated relative error versus the Complexity Parameter (CP, bottom axis) and Tree Size (top axis) for the Heparin vs DS vs OSCS model. (A) and (B): for the 1.95-2.20 ppm region; (C) and (D): for the 3.10-5.70 ppm region. The vertical bar for each point in (B) and (D) represents the standard error.

When modeling the dataset of the 3.10-5.70 ppm region, the resulting tree was more complex, consisting of five terminal nodes (Figure 9C). The variables splitting the data are 3.53, 3.95, 4.48 and 5.67 ppm. Variable 4.48 ppm split off class OSCS from Heparin and DS, and then variable 3.53, 3.95 and 5.67 ppm sequentially divided the samples on the left side into two separate classes: Heparin and DS. Figure 9D shows the evolution of the relative error (RE, vertical axis) and complexity parameter (CP, horizontal axis) with the tree size. The RE decreases as the number of terminal nodes increases, having its lowest value for a tree with five terminal nodes. In order to select a simpler tree than the one with the minimum CV error, the rule of one standard deviation error (1-SE) is applied, for which the optimal tree is selected as the simplest one among those that have a CV error within (1-SE) of the minimal CV error. As shown in Figure 9D, the tree with the lowest error appeared at the size of 5

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whereas the tree with a size of 4 and a CP of 0.058 represents a simpler one within (1-SE) of the tree of size 5. The pruned tree is more appropriate for prediction purposes with a classification rate of 85.0% for test set. With respect to Heparin vs DS, the corresponding model has two terminal nodes by splitting the data using 2.08 ppm for both 1.95-2.20 and 1.95-5.70 ppm regions. The success rates of 90.8% (79/87) and 88.9% (40/50) were achieved for training and test sets, respectively. For the region of 3.10-5.70 ppm, chemical shifts 3.53 and 3.86 ppm were selected to divide the data, leading to a success rate of 83.9% (73/87) for the training set and 80.0% (36/45) for the test set. These trees have no pure nodes, meaning that absolute discrimination between Heparin and DS was not achieved by CART. For the Heparin vs OSCS model, the classification tree presents two terminal nodes by splitting the data of 1.952.20 or 1.95-5.70 according to 2.15 ppm. As a result, both Heparin and OSCS samples were classified on their respective terminal nodes on the classification tree, giving a perfect separation of the two groups (100% discrimination). In contrast, the accuracies for the region of 3.10-5.70 ppm are 97.6% (83/85) and 97.7% (42/43) corresponding to the training and test sets by selecting 4.48 ppm as a splitting variable. For Heparin vs [DS + OSCS], a model with complexity or tree size = 3 is built by splitting 2.08 and 2.15 ppm as with Heparin vs DS vs OSCS for both 1.95-2.20 and 1.95-5.70 ppm. The predictive ability of this model was 91.5% (108/118) for the training set and 90.0% (54/60) for the test set. For the 3.10-5.70 ppm region, a classification tree with five terminal nodes was obtained for the discrimination of Heparin from [DS + OSCS] by selecting four variables 3.53, 3.95, 4.48, and 5.67 ppm to divide the data, resulting in a tree very similar to that of Heparin vs DS vs OSCS, and the test set of 60 samples was predicted with 83.3% (50/60) accuracy. Table 10. Model Parameters and Classification Rates for CART, ANN and SVM

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Model

Region (ppm)

1.95 - 5.70 1.95 - 2.20 3.10 - 5.70 1.95 - 5.70 Heparin vs OSCS 1.95 - 2.20 3.10 - 5.70 Heparin vs [DS + OSCS] 1.95 - 5.70 1.95 - 2.20 3.10 - 5.70 Heparin vs DS vs OSCS 1.95 - 5.70 1.95 - 2.20 3.10 - 5.70 Heparin vs DS

CART ANN SVM Training Test (%) Training Test (%) Training Test (%) (%) (%) (%) 90.8 88.9 95.4 86.7 97.7 91.1 90.8 88.9 97.7 88.9 96.6 93.3 83.9 80.0 96.6 84.4 97.7 93.3 100 100 100 100 100 100 100 100 100 100 100 100 97.6 97.7 100 100 100 100 91.5 90.0 94.9 91.7 98.3 95.0 91.5 90.0 99.2 91.7 97.5 95.0 88.1 83.3 99.2 90.0 98.3 95.0 93.2 90.0 95.8 88.3 97.5 95.0 93.2 90.0 96.6 91.7 99.2 95.0 86.4 85.0 93.2 86.7 98.3 95.0

4.3.6. Artificial Neural Networks A three-layer feed-forward network trained with a back propagation algorithm was implemented to optimize separation between pure, impure and contaminated heparin samples, and to build predictive models for class identification. The input layer contained as many neurons as the independent variables of the dataset, which are the chemical shifts with

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numbers of 9, 65, and 74 for the regions of 1.95-2.20, 3.10-5.70, and 1.95-5.70 ppm, respectively, and the output corresponded to the four classes Heparin, DS, OSCS and [DS + OSCS]. The number of neurons in the hidden layer was varied to assess its influence on network performance. The sigmoid transfer function was exclusively employed for activation in both hidden and output layers. The output from the ANN is a prediction of the class membership in the samples of each class, consisting of a matrix Ŷ with the same dimensions as the dependent variable Y that contains the binary values of 1 or 0 for each class and comprises as many columns as there are classes. The numeric value of element ŷij in Ŷ is in an interval between 0 and 1, which can be regarded as an estimate of the probability for assigning the ith sample to the jth class. If the output value is close to 1, then the test sample is ascribed to the modeled class while the sample is assigned to the other classes if the value is close to 0. For ANN, a commonly used error function is the cross entropy or deviance defined in Equation 37 [48]: Minimize: 

n

k

 yˆ i 1 j 1

ij

log yˆ ij

(37)

Since ANN is very sensitive to overfitting, a regularization term, called weight decay, is introduced. The modified criterion is given by Equation 38: Minimize: 

n

k

 yˆ

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i 1 j 1

ij

log yˆ ij    ( parameters) 2

A

(38)

B

Figure 10. The variations of misclassification errors from ANN with the hidden units and weight decay for the model Heparin vs DS vs OSCS for the data set in the 1.95-5.70 ppm range. (A) Fixing weight decay with λ = 0.1; (B) Fixing the number of hidden units at 9.

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where ―parameters‖ represents the values of all parameters that are used in the ANN training. The magnitude of the adjusting parameter λ controls how much the constraint of shrinking the parameters should be addressed. When the value of λ is zero (i.e., no weight decay) or small, the boundary or edge between classes is rough or non-smooth, leading to overfitting of the model, while a smoother boundary is yielded as the weight decay increases. For ANN classification, the number of hidden units and the weight decay require optimization, which was achieved through cross validation. Figure 10 shows the relationship among the misclassification rate, the decay weight and the number of neurons for the training set, test set and 10-fold CV process for Heparin vs DS vs OSCS in the region of 1.95-5.70 ppm. In order to investigate the influence of the size of neurons in the hidden layer on the prediction accuracy, ANNs with neuron numbers ranging from 3 to 30 were developed with the weight decay fixed at 0.1. The prediction results are plotted as a function of the size of hidden units in Figure 10A that shows that 9 neurons in the hidden layer are optimal. The dependency of the error rate on the weight decay λ for 9 hidden units is depicted in Figure 10B.

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For Heparin vs DS vs OSCS, ANN showed a classification rate of 96.6% (114/118) and a prediction accuracy of 91.7% (55/60) for the 1.95-2.20 range. The accuracy for the training set and test set corresponded to 95.8% (113/118) and 88.3% (53/60) for the 1.95-5.70 range and, similarly, with 93.2% (110/118) and 86.7% (52/60) for the 3.10-5.70 ppm range (Table 10). For Heparin vs OSCS, the ANN model classified all members of the training and tests sets correctly with 100% prediction accuracy. The success rates for the Heparin vs DS model for the three regions are very close with 95.4-97.7% for the training set and 84.4-88.9% for the test set. For the Heparin vs [DS + OSCS] model, the prediction rates of the various networks were quite similar at 90.0-91.7% for the three regions as summarized in Table 10. In general, the performance of the models was slightly better for those built from the 1.95-2.20 ppm region than from either the 3.10-5.70 or 1.95-5.70 ppm regions. 4.3.7. Support Vector Machine Using the same training and test sets as for CART and ANN, the SVM algorithm with the non-linear soft margin was employed to build classification models. For SVM classification with the RBF kernel, the optimization requires to specify two parameters, i.e., the width of the kernel function γ and the regularization parameter C. Their combination determines the boundary complexity and thus the classification performance, i.e., prediction ability. The parameters C and γ are optimized by performing an exhaustive grid search with 10-fold CV on the training set using their various combinations. After all the combinations have been searched, a contour plot is created in decimal logarithmic scales, which indicates the prediction accuracy or classification error. Figure 11 presents the optimization grids in terms of cross validation classification rate for the models Heparin vs DS vs OSCS and Heparin vs OSCS. The two coarse grid plots of γ and C values delineate regions where the optimal parameter settings might be located.

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A

B

Figure 11. Contour plots in decimal logarithmic scales obtained from 9×9 grid search of the optimal values of γ and C for the SVM model. (A) Heparin vs DS vs OSCS for the 1.95-5.70 ppm region; (B) Heparin vs OSCS for the 1.95-5.70 ppm region.

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The set of C and γ values giving the highest percentage accuracy or the lowest error rate is selected for building the SVM model. The prediction accuracy is > 90% in all cases for all data sets as summarized in Table 10. SVM achieved nearly identical results for both the 1.952.20 ppm and 3.10-5.70 ppm regions, giving credence to its ability to differentiate even subtle structural differences between pure, impure, and contaminated heparin. In contrast, visual inspection of the Heparin, DS, and OSCS spectra (Figure 1) clearly reveals distinctions in the 1.95-2.20 ppm region but not in the 3.10-5.70 ppm region. 4.3.8. Analysis of Misclassifications As shown in Table 10, the predictive abilities of the classification models built from CART, ANN and SVM were outstanding in differentiating Heparin from DS and OSCS with few errors. In particular, higher predictive accuracies or fewer misclassifications were attained for the Heparin vs OSCS model than for the other models. While all three pattern recognition approaches were able to completely discriminate Heparin and OSCS with success rates of 100% under optimal conditions, it can be seen for the other models that using the same input variables, the model generated from the SVM algorithm consistently outperformed ANN, which in turn marginally outperformed CART. When the entire chemical shift region was divided into two subsets (1.95-2.20 and 3.10-5.70 ppm), better results were achieved for the former than the latter region. The sole exception was SVM, which achieved nearly identical results from both regions. SVM performed better in every aspect, as can be appreciated by comparing the misclassified rates in Table 10. Tables 11-13 summarize the results of the classification matrices evaluated by means of both training and test sets in the region of 1.95-5.70 ppm. All of the misclassified samples were between Heparin and DS: several samples belonging to Heparin were predicted as DS, while some DS samples were predicted as Heparin. Using SVM, only one Heparin sample was misclassified as DS and three DS samples were misclassified as Heparin for the Heparin vs DS model in the test set (Table 11). Misclassification of Heparin as DS occurred only once

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and DS as Heparin twice for the Heparin vs [DS + OSCS] model (Table 12). The same result occurred for the threefold Heparin vs DS vs OSCS model, that is, SVM produced a total of three misclassifications (Table 13). Table 11. Classification Matrices for the Heparin vs DS Model in 1.95-5.70 ppm Region

CART Heparin DS ANN Heparin DS SVM Heparin DS

Training set Heparin DS 52 6 2 27 52 2 2 31 53 1 1 32

All samples Test set Heparin DS 25 2 3 15 27 5 1 12 27 3 1 14

After removing borderline samples Training set Test set Heparin DS Heparin DS 48 3 23 0 2 23 2 13 50 0 25 2 0 26 0 11 50 0 25 1 0 26 0 12

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Table 12. Classification Matrices for the Heparin vs [DS + OSCS] Model in the 1.95-5.70 ppm Region All samples Training set Heparin [DS + OSCS] 5 CART Heparin 49 [DS + 5 59 OSCS] 4 ANN Heparin 52 [DS + 2 60 OSCS] 1 SVM Heparin 52 [DS + 2 63 OSCS]

Test set Heparin [DS + OSCS] 24 2 4 30

After removing borderline samples Training set Test set Heparin [DS + Heparin [DS + OSCS] OSCS] 46 3 23 0 4 54 2 28

27 1

4 28

50 0

0 57

24 1

2 26

27 1

2 30

50 0

0 57

25 0

1 27

Table 13. Classification Matrices for the Heparin vs DS vs OSCS Model in the 1.95-5.70 ppm Region All samples Training set Heparin DS 6 CART Heparin 52 DS 2 27 OSCS 0 0 3 ANN Heparin 52 DS 2 30 OSCS 0 0 0 SVM Heparin 53 DS 1 33 OSCS 0 0

Test set OSCS Heparin 0 25 0 3 31 0 0 25 0 3 31 0 0 27 0 1 31 0

DS 2 14 1 5 12 0 2 15 0

After removing borderline samples Training set Test set OSCS Heparin DS OSCS Heparin DS OSCS 0 48 3 0 23 1 0 0 2 23 0 2 12 0 15 0 0 31 0 0 15 0 50 0 0 25 2 0 0 0 26 0 0 11 0 15 0 0 31 0 0 15 0 50 1 0 25 1 0 0 0 25 0 0 12 0 15 0 0 31 0 0 15

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When examining the misclassified samples, in most cases, these occurred when the DS content of the sample ranged from 0.90% to 1.20%, i.e., they were close to the DS = 1.0% impurity limit defining the Heparin and DS classes. When removing these borderline samples from the dataset, the overall performance of the model improved dramatically with very few misclassifications. This improvement was particularly evident for the SVM model, where only one sample was misclassified in the test set (Tables 11-13).

4.4. Class Modeling for Discriminating Heparin Samples

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Previously we explored the ability of pure classification methods, i.e., PCA, PLS-DA, LDA, kNN, CART, ANN and SVM, to distinguish between pure, impure and contaminated heparin samples based on evaluation of their 1H NMR spectral data. Class modeling techniques represent a substantially different modeling strategy. Whereas pure discriminating methods focus on the dissimilarity between classes, class modeling approaches emphasize the similarity within each class. Soft independent modeling of class analogy (SIMCA) and unequal class modeling (UNEQ) were applied to differentiate heparin samples that contain varying amounts of DS impurities and OSCS contaminants. The two methods enable the construction of individual models for each class and the determination of the modeling ability of each variable in a class. 4.4.1. SIMCA Analysis In SIMCA, each class is modeled separately using PCA analysis, and class boundaries which define the range of acceptable samples at a selected confidence level are built around the PC model that encloses the internal space. SIMCA is able to indicate the discriminant power and modeling power for each variable when defining the similarity among the members of a class of samples. The SIMCA model was developed in the present study using a set of 1H NMR spectral data with 168 samples corresponding to 72 heparin samples, 50 DS samples and 46 OSCS samples with 74 variables. For each class, only components with eigenvalues greater than unity were employed to build the model. The numbers of PCs used for the class models were twelve for the class Heparin, and nine each for the DS, OSCS and [DS + OSCS] classes, accounting for 98.4, 99.3, 99.4, and 98.7% of the total variance, respectively. The results of SIMCA modeling after separate category autoscaling and column centering are reported in Table 14. It was observed that 16 of the 72 Heparin, 13 of the 50 DS, 7 of the 46 OSCS and 20 of the 96 [DS + OSCS] samples were erroneously rejected by their specific category models at the 95% confidence level, resulting in a sensitivity (SENS) of 77.8, 74.0, 84.8 and 79.2% for the four classes, respectively. The class models built using SIMCA exhibited high specificity (SPEC) particularly for the OSCS class model. Both Heparin and DS rejected all samples in OSCS, leading to a SPEC of 100%. OSCS also rejected all samples in Heparin and accepted only one sample in DS. In addition, the Heparin class model accepted the same five DS samples from both DS and [DS + OSCS] classes; hence, the SPEC values of Heparin for DS and of Heparin for [DS + OSCS] were 90.0% (45/50) and 94.8% (91/96), respectively. The DS content in these five samples was in the range 1.06% to 1.20%, i.e., they were nearby the borderline of the 1.0% acceptance criterion. On the other hand, the DS and [DS + OSCS] class models accepted 13 and 32 Heparin samples, corresponding to SPEC values of 81.9%

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and 55.6%, respectively. The low SPEC value of the [DS + OSCS] class model was due to its difficulty in discriminating Heparin samples from DS samples in cases where the DS content was close to the 1.0% acceptance criterion for DS. The results of class modeling can be displayed by means of Coomans plots, a useful tool for visualizing the groupings [74, 76]. In a Coomans plot, two classes are drawn against one another, and each category is plotted as a rectangle whose boundary corresponds to the confidence limit defined by the class space. The distance of each sample from both categories is measured by the coordinates in the axes. The samples accepted by only one model fall in either of two areas of the plot: one is the left upper rectangle and another is the right bottom rectangle. Samples located in the lower-left corner area, where the two categories overlap, are accepted by both of the two classes. Samples whose distance is beyond the critical limit for the class model are rejected as outliers for that specific class, and consequently they are plotted outside the area defining the class model. Samples rejected by both models are plotted in the upper-right square. The Coomans plots for different pairs of classes are displayed in Figure 12 where each sample is represented by its category index and the distribution of the distances from these models at the critical distance for 95% confidence is shown. Most of the samples were correctly accepted by their respective classes with only few samples plotted beyond their critical limits. Figure 12A shows the Coomans plot for the Heparin and OSCS classes, which are located in the upper left quadrant and lower right quadrant of the plot, respectively. All the OSCS samples are clustered at the right side forming a tight group, and all are far from the lower left corner, and are completely separated from the Heparin class without any overlap between the two classes, indicating 100% successful discrimination. The Coomans plot for the Heparin and DS classes is displayed in Figure 12B. The upper left and bottom right zones correspond to the samples accepted by the Heparin and DS class models, respectively. Heparin samples with low DS content are far from the bottom box, i.e., the DS class model, while samples with DS content close to 1.0% are located near or within the lower left square. For DS class, samples with high DS content are located on the right side while samples with low DS content are very close to the Heparin model. The samples situated in the lower left square of the diagram are accepted by both models. The Heparin class model accepted five DS samples while the DS class model accepted 13 Heparin samples as indicated in the left bottom square. Table 14. Sensitivity and Specificity from SIMCA Modeling for Heparin, DS, and OSCS Model (%) Heparin

Number of PCs Explained variance SENS (%) SPEC (%) 12 98.4 77.8 (56/72) 90.0 (45/50) for DS; 100 (46/46) for OSCS; 94.8 (91/96) for [DS + OSCS]. DS 9 99.3 74.0 (37/50) 81.9 (59/72) for Heparin; 100 (46/46) for OSCS. OSCS 9 99.4 84.8 (39/46) 100 (72/72) for Heparin; 98.0 (49/50) for DS. [DS + OSCS] 9 98.7 79.2 (76/96) 55.6 (40/72) for Heparin.

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A

B

C

D

Figure 12. Coomans plots for SIMCA class modeling. (A) Heparin vs OSCS; (B) Heparin vs DS; (C) Heparin vs [DS + OSCS]; (D) DS vs OSCS.

Figure 12C shows the Coomans plot for the Heparin and [DS + OSCS] classes. Similar to Figures 12A and 12B, this plot demonstrates that all OSCS samples are located on the right side and five DS samples are in the lower left square. Of the 72 samples belonging to the Heparin class, 32 are plotted in the lower left quadrant belonging to both classes, revealing the low degree of specificity of the [DS + OSCS] class model for the Heparin class. In the Coomans plot for the DS and OSCS classes (Figure 12D), all of the OSCS samples were significantly distant from the region of the left rectangle corresponding to the DS class model and far from the critical distance of the DS class model. No OSCS samples fell in the region for the DS model, thus the specificity of DS with respect to OSCS was 100%. Likewise, the OSCS model accepted only 1 of the 50 DS samples corresponding to 98%

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SPEC. Overall, excellent separation was achieved between the Heparin and OSCS classes and between the DS and OSCS classes. As a highly informative multivariate analysis technique, SIMCA allows the discrimination between those variables which make great contributions to distinguishing between classes and those which provide little useful information. The discriminant power (DP) of the variables indicates the importance of each variable in discriminating the samples into different class models [76]. DP is defined as the ratio of the residual standard deviation of samples in one class when fitted to the other class to the residual standard deviation of the samples when fitted to their own class, and it implies the ability for each variable to contribute to the discrimination between classes. A large value suggests a great contribution to the differentiation while a value of unity indicates no discrimination power at all.

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Table 15. Discriminant Power (DP) of the Variables (V) for Various Models Order Heparin vs DS Heparin vs OSCS Heparin vs [DS + DS vs OSCS OSCS] V (ppm) DP V (ppm) DP V (ppm) DP V (ppm) DP 1 2.08 8.29 2.14 61.88 2.14 38.63 2.14 41.41 2 3.56 3.46 4.07 16.81 2.17 15.64 4.31 12.80 3 4.46 3.05 2.20 15.19 4.07 15.10 2.08 12.32 4 4.04 3.04 2.17 15.06 3.80 13.95 5.01 11.85 5 2.11 3.00 5.01 12.02 5.04 12.46 4.49 10.58 6 3.92 2.89 5.04 11.71 3.95 12.34 2.17 9.45 7 4.01 2.82 4.22 10.92 5.34 12.16 5.16 8.37 8 3.53 2.80 4.37 10.02 5.01 11.13 5.19 7.12 9 3.71 2.68 2.08 9.65 4.01 9.25 4.98 7.02 10 3.95 2.51 3.80 9.56 4.43 9.13 4.07 7.01 11 4.31 2.48 5.43 9.44 5.61 8.76 3.89 6.95 12 3.86 2.47 5.37 9.23 5.43 8.68 2.20 6.85 13 3.59 2.39 4.25 9.18 3.89 8.17 3.98 6.71 14 5.37 2.34 4.58 9.13 4.22 7.86 5.10 6.55 15 3.89 2.25 4.10 9.03 5.25 7.81 4.64 6.40 16 3.50 2.23 4.55 8.71 5.31 7.76 2.11 6.05 17 4.25 2.22 4.61 8.69 3.92 7.73 3.74 5.88 18 3.80 2.19 5.19 8.52 3.50 7.68 4.61 5.77 19 5.34 2.05 4.49 8.41 3.53 7.66 2.02 5.59 20 3.74 2.00 4.98 8.31 2.08 7.47 3.53 5.54

Heparin vs DS vs OSCS V (ppm) DP 2.14 34.86 2.08 10.03 2.17 8.67 4.07 8.63 5.01 8.44 2.20 7.68 4.49 6.80 4.31 6.64 5.04 6.14 5.19 5.76 4.98 5.57 3.95 5.34 4.61 5.24 2.11 5.22 4.22 5.21 4.10 5.06 4.04 4.98 3.80 4.91 5.43 4.90 4.19 4.84

The importance of the individual variables and their DP for various class pairs were examined, and the variables that made the greatest contribution to the class discrimination are listed in Table 15. When analyzing the discriminating ability of the different variables, 2.08 ppm (DP = 8.29) was found to be the chemical shift with the highest discriminating power, being most effective in discriminating between the Heparin and DS classes. Significant

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discriminating ability was also shown by 3.56 ppm, 4.46 ppm, 4.04 ppm and 2.11 ppm. The highest DP value in the Heparin vs OSCS, DS vs OSCS, Heparin vs [DS + OSCS] and Heparin vs DS vs OSCS models was at 2.14 ppm, corresponding to 61.88, 41.41, 38.63 and 34.86, respectively. The same chemical shift contributed substantially to discriminating OSCS from all of the other classes. Other variables showing a significant discriminating power were 4.07 ppm, 2.20 ppm, 2.17 ppm, 5.01 ppm and 5.04 ppm for Heparin vs OSCS; 2.17 ppm, 4.07 ppm, 3.80 ppm, 5.04 ppm, 3.95 ppm, 5.34 ppm and 5.01 ppm for Heparin vs [DS + OSCS]; and 4.31 ppm, 2.08 ppm, 5.01 ppm and 4.49 ppm for DS vs OSCS. For Heparin vs DS vs OSCS, the results in Table 15 show that the variables with the greatest discriminating power are 2.14 ppm (DP = 34.86) and 2.08 ppm (DP = 10.03), which are the characteristic chemical shifts of OSCS and DS, respectively.

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A

C

B

D

Figure 13. Coomans plots for UNEQ class modeling. (A) Heparin vs OSCS; (B) Heparin vs DS; (C) Heparin vs [DS + OSCS]; (D) DS vs OSCS.

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4.4.2. UNEQ Analysis For UNEQ modeling, the number of samples is required to be at least three times greater than that of variables, and hence the variable selection was performed by stepwise linear discriminant analysis (SLDA) (Table 7). Results from UNEQ modeling using the selected subsets of variables as inputs are summarized in Table 16, which shows the SENS for each of the four categories Heparin, DS, OSCS and [DS + OSCS] together with the SPEC of each model between each pair of categories. For different systems, the subsets of selected variables were different so that the values of SENS and SPEC varied within a certain range. The values of SENS for Heparin, DS, OSCS and [DS + OSCS] were 84.7-87.5% (61-63/72), 80.0-90.0% (40-45/50), 87.0-93.5% (40-43/46) and 84.4-90.6% (81-87/96), respectively. In all cases, the SENS obtained using UNEQ was better than that evaluated with SIMCA for which the values for Heparin, DS, OSCS and [DS + OSCS] were 77.8% (56/72), 74.0% (37/50), 84.8% (39/46) and 79.2% (76/96), respectively. Compared with SIMCA, UNEQ accepted 7 more Heparin, 8 more DS, 4 more OSCS, and 11 more [DS + OSCS] samples by their specific category models under optimal conditions. In the modeling of the category Heparin, 63 of the 72 samples were accepted by the category model built using UNEQ for Heparin vs DS, while 56 of them were accepted by the SIMCA class model. The differences between the two methods were far more marked when the modeling of the other classes was considered. Of the 50 DS samples, 45 were correctly accepted by the UNEQ class model compared with 37 by the SIMCA model. In addition, 42 out of the 46 OSCS samples and 81 out of the 96 [DS + OSCS] samples were correctly recognized by the UNEQ class models compared with 39 out of 46 OSCS samples and 76 out of 96 [DS + OSCS] samples by the SIMCA models. Importantly, even though the SENS was greatly improved for the UNEQ model, a corresponding decrease was observed in the SPEC of UNEQ compared to SIMCA. This is most evident by comparing the models for the Heparin and DS classes as reported in Table 16. The SPEC of the individual class models was rather poor, most of the values being lower than 50%. The classes DS and [DS + OSCS] accepted a large number of Heparin samples (52/72 and 57/72, respectively), leading to significantly lower specificities (27.8% and 20.8%) than the corresponding SIMCA values 81.9% (59/72) and 55.6% (40/72). The specificity of Heparin with respect to DS remarkably decreased to 72.0% (36/50) from 90.0% (45/50), and that of Heparin to [DS + OSCS] decreased to 82.3% (79/96) from 94.8% (91/96). Furthermore, the UNEQ model showed a much poorer SPEC for OSCS to Heparin and DS. The values of the SPEC for OSCS with respect to Heparin and DS samples considerably decreased to 26.4% (19/72) and 26.0% (13/50) in UNEQ compared with 100% (72/72) and 98.0% (49/50) in SIMCA. A major exception was that of Heparin for OSCS, which remained perfect 100% (46/46). The SPEC of DS for OSCS was also high at 97.8% (45/46) for UNEQ compared with 100% (46/46) for SIMCA. The UNEQ class modeling results can be graphically visualized on the Coomans plots as displayed in Figure 13. Compared with those corresponding to the SIMCA models, the Cooman‘s plots produced from the UNEQ models revealed a large number of samples occupying the lower left quadrant belonging to both classes. This outcome is a consequence of the low SPEC of the UNEQ class models.

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Table 16. Sensitivity and Specificity from UNEQ Class Modeling for Heparin, DS and OSCS Model Heparin vs DS

Heparin DS Heparin Heparin vs OSCS OSCS Heparin vs [DS + OSCS] Heparin [DS + OSCS] DS DS vs OSCS OSCS Heparin vs DS vs OSCS Heparin DS OSCS

SENS (%) 87.5 (63/72) 80.0 (40/50) 84.7 (61/72) 87.0 (40/46) 86.1 (62/72) 84.4 (81/96) 90.0 (45/50) 91.3 (42/46) 84.7 (61/72) 86.0 (43/50)

SPEC (%) 72.0 (36/50) for DS 27.8 (20/72) for Heparin 100 (46/46) for OSCS 26.4 (19/72) for Heparin 82.3 (79/96) for [DS + OSCS] 20.8 (15/72) for Heparin 97.8 (45/46) for OSCS 26.0 (13/50) for DS 54.0 (27/50) for DS; 100 (46/46) for OSCS 23.6 (17/72) for Heparin; 89.1 (41/46) for OSCS 87.0 (40/46) 15.3 (11/72) for Heparin; 14.0 (7/50) for DS

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4.5. Analysis of Heparin Samples Spiked with Other GAGs Heparin APIs may contain GAG impurities other than dermatan sulfate (DS), such as chondroitin sulfate A (CSA) and heparan sulfate (HS), and other possible synthetic oversulfated contaminants that can mimic the functions of heparin could be used to adulterate heparin in the future. In order to assess the capability of the developed models to discriminate and detect a wide range of potential GAG-like impurities and contaminants previously unseen in the heparin samples, a series of blends was prepared by spiking heparin APIs with native impurities CSA, DS and HS, as well as their partially- or fully-oversulfated (OS) versions OS-CSA (i.e., OSCS), OS-DS, OS-HS and OS-heparin at the 1.0%, 5.0% and 10.0% weight percent levels [23]. The blend samples are highly diverse in composition when compared to the clearly defined Heparin, DS and OSCS classes, since they contain multiple components with varying degrees of sulfation and concentration from 1% to 10% as shown in Table 17. For exploratory purposes, agglomerative hierarchical cluster analysis (HCA) was performed on the 30 blend samples. As an unsupervised technique, HCA describes the nearness between objects, identifies specific differences, finds natural groupings of the data set, and allows the visualization of the relationships between objects in the form of a dendrogram [84, 86]. The procedure starts by setting each object in its own cluster, and then two objects closest together are joined, followed by the next step in which either a third object joins the just formed cluster, or two clusters join together into a new cluster. Each step yields clusters with a number less than the previous step. The iterative procedure repeats until all objects are merged into a single cluster. HCA analysis was implemented using the Euclidean distance for measuring the similarity among blend samples with average linkage for merging the clusters. Figure 14 depicts the hierarchical clustering of the blend samples in the 1.95-5.70 ppm region. From this dendrogram, two distinct clusters can be observed, which were formed according to the content of GAGs. The cluster on the left-side included samples with the low content of GAGs (1%), while the high content of GAGs (i.e., 5% and 10%) comprised the right-side cluster which consists of two sub-clusters, one is a cluster of the native GAGs, i.e.,

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CSA (B1 and B2), DS (B4 and B5) and HS (B7 and B8), and another is made of oversulfated GAGs, where the samples with the same GAG composition lay close to each other and clustered in pair.

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Figure 14. Dendrogram on the series of blends of heparin spiked with other GAGs, generated based on their Euclidean distances and average linkage.

The resulting multivariate statistical models from CART, ANN and SVM were used to test the class assignations of the blend samples for the Heparin vs DS vs OSCS model, and the test results are listed in Table 17. Blend samples B28-30 (blank or control samples), B4-6 (DS) and B10-12 (OS-CSA) correspond to the classes Heparin, DS and OSCS, respectively. As expected, all of them were correctly classified into their respective classes. By nature of their varied compositions, none of the other blends belong to any of the designated classes. Nevertheless, they must be assigned to a class. As can be seen, some blends containing low levels (1%) of GAGs were assigned to Heparin, most of the native impurities (CSA and HS) were classified as DS, while the blends with oversulfated synthetic compounds were assigned to OSCS except for several samples with low content (1%). SIMCA class modeling allowed us to investigate the capability of the models (e.g., Heparin, DS, and OSCS) to accept or reject the blend samples, thereby enabling detection of fraudulent or contaminated products. The test results from class modeling are summarized in Table 17. The blend samples can be assigned to one or more classes if they are situated within the statistical limits, and they can be considered to be outliers if the distance is beyond the limits. Thus, a test sample can be assigned to a single class, more than one class, or none of the above defined classes. Samples B28, B29 and B30 are blank ones, that is, they are pure heparin samples. Therefore, they are all accepted by the Heparin class. The Heparin class accepts some samples with low content (1%) of GAGs, such as B9 (1% HS), B18 (1% OSHS), B21 (1% OS-Hep), B24 (1% PS-CSA#1) and B27 (1% PS-CSA#2). The Heparin class rejects all blend samples with high content of GAGs (5% and 10%) as well as four low

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content samples, which are B3 (1% CSA), B6 (1% DS), B12 (1% FS-CSA) and B15 (1% FSDS). Blends B4, B5 and B6 are heparin samples spiked with 10%, 5% and 1% DS, respectively. They are all correctly accepted by the DS class. Samples B13, B14 and B15 correspond to fully-sulfated DS with content of 10%, 5% and 1%, respectively. The DS class only accepts the low content of sample (B15), and rejects B13 and B14. As with the Heparin class, samples B9 (1% HS) and B18 (1%OS-HS) are also accepted into the DS class. The OSCS class model accepts five blend samples, viz., B10, B11, B12, B22, and B25. B10, B11 and B12 are heparin samples spiked with 10%, 5% and 1% fully-sulfated CSA, i.e., OSCS. Consequently, they absolutely belong to the OSCS class. Samples B22 and B25 contain 10% partially-sulfated CSA and they present very similar structure to OSCS. Overall, the models can distinguish between pure heparin and unacceptable samples.

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Table 17. Compositions of the Series of Blends of Heparin Spiked with other GAGs and Test Results for Classification from SVM, CART and ANN in the 1.95-5.70 ppm Region

ID

GAGs

Content (%)

B1 B2 B3 B4 B5 B6 B7 B8 B9 B10 B11 B12 B13 B14 B15 B16 B17 B18 B19 B20 B21 B22 B23 B24 B25 B26 B27 B28 B29 B30

CSA CSA CSA DS DS DS HS HS HS FS-CSA FS-CSA FS-CSA FS-DS FS-DS FS-DS OS-HS OS-HS OS-HS OS-Hep OS-Hep OS-Hep PS-CSA#1 PS-CSA#1 PS-CSA#1 PS-CSA#2 PS-CSA#2 PS-CSA#2 Blank Blank Blank

10 5 1 10 5 1 10 5 1 10 5 1 10 5 1 10 5 1 10 5 1 10 5 1 10 5 1 -

Classification Class Modeling Classified as Heparin (H), DS (D) or Accepted (A) or Rejected (R) by OSCS (O) the classes SVM CART ANN Heparin DS OSCS D D D R R R D D D R R R D D D R R R D D D R A R D D D R A R D D D R A R D D D R R R D D D R R R H H H A A R O O O R R A O O O R R A O D D R R A O O O R R R O O O R R R D D D R A R O O O R R R O O O R R R H H H A A R O O O R R R O O O R R R H H H A R R O O O R R A O O D R R R D H H A R R O O O R R A O O D R R R D H H A R R H H H A R R H H H A R R H H H A R R

CSA: chondroitin sulfate A; DS: dermatan sulfate; HS: heparan sulfate; FS: fully sulfated; OS: oversulfated; PS: partially sulfated; Blank: control (pure heparin sample).

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Conclusions In order to differentiate heparin samples with varying amount of dermatan sulfate (DS) impurities and oversulfated chondroitin sulfate (OSCS) contaminants, proton NMR spectral data for heparin sodium active pharmaceutical ingredient (API) samples from different manufacturers were analyzed by multivariate chemometric methods for qualitative and quantitative evaluation. The following conclusions were drawn based on multivariate regression and pattern recognition separately. 1. Regression analysis. The content of galactosamine (%Gal) in heparin (primarily originating from the impurity dermatan sulfate, DS) was predicted from 1H NMR spectral data by means of four multivariate analysis approaches, i.e., multiple linear regression (MLR), Ridge regression (RR), partial least squares regression (PLSR), and support vector regression (SVR). Two datasets were extracted from the NMR data: Dataset A and B contained between 0-10% and 0-2% galactosamine, respectively. Variable selection was performed by genetic algorithms (GAs) or the stepwise method in order to build robust and reliable models. In all cases, the MVR models obtained using variable selection outperformed those obtained when all the variables were considered. Using GAs for variable selection produced the optimal MVR models in terms of model simplicity (fewest independent variables) and predictive ability (percent accuracy) when compared with the stepwise selection method. The four regression techniques were comparable in performance for Dataset A with high coefficients of determination and low prediction errors under optimal conditions, whereas SVR was clearly superior to the other three regression approaches for Dataset B. The present study offers guidance in selecting the appropriate MVR approach to predict the %Gal in heparin based on analysis of 1D 1H-NMR spectral data. 2. Classification analysis. To develop robust classification models for rapid screening of heparin samples with varying amounts of DS impurities and OSCS contaminants, six multivariate statistical approaches, i.e., PCA, PLS-DA, LDA, kNN, CART, ANN and SVM, were evaluated, and their performance was compared. We show that these chemometric methods are useful tools for the exploration and visualization of heparin NMR spectral data and for the generation of classification models with outstanding performance attributes. Data dimension reduction and variable selection by retention of only the most significant PCA components, implemented to avoid over-fitting the training set data, markedly improved the performance of the PLS-DA, LDA and kNN classification models. Three datasets corresponding to different chemical shift regions (1.95-2.20, 3.10-5.70, and 1.95-5.70 ppm) were analyzed by CART, ANN and SVM. While all three multivariate statistical approaches were able to effectively model the data from the 1.95-2.20 ppm region, SVM was found to substantially outperform CART and ANN in the 3.10-5.70 ppm region with respect to classification success rate. The degree of success of the classification models in discriminating the samples of pure heparin from those containing the impurity DS and the contaminant OSCS depended on the specific chemometric procedures for choosing the appropriate variables. Under optimum conditions, a 100% prediction rate was frequently achieved for discrimination between Heparin and OSCS samples on external test sets. The majority of classification errors between Heparin and DS involved cases where the DS content was close to the 1.0% DS boundary between the two classes. When removing the borderline samples, nearly perfect classification results can be attained. Among the

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chemometric methods evaluated in this study, it was found that SVM outperformed all other approaches for discrimination of the Heparin and DS samples, and gave the best classification results in all cases (Figure 15).

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Heparin vs DS

Heparin vs [DS + OSCS]

Heparin vs OSCS

Heparin vs DS vs OSCS

Figure 15. Comparison of the classification performance of the six approaches.

3. Class modeling analysis. Two chemometric class modeling techniques, SIMCA and UNEQ, were employed to assess the quality of the heparin samples and to perform pattern recognition among the various classes (pure heparin, impurities and contaminants). Compared to pure classification techniques, class modeling approaches focus more on the analogies among the samples from the same class than on the differences among the different classes; hence, class modeling approaches allow us to explore the fundamental details and individual characteristics of the classes. One of the advantages of class modeling is that a sample can be recognized to be a member of one or more classes, or none of the classes. SIMCA can work on a small set of samples (as low as 10) per class and does not apply any restriction on the

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number of measurement variables. This is especially important because the number of variables is usually greater than that of the analytical measurements (i.e. 1H NMR) of each sample. In contrast, UNEQ requires variable reduction since the ratio of the number of samples per class must be at least three-fold the number of variables in the model. Significant performance differences were observed between SIMCA and UNEQ analysis. When comparing the modeling results from these two approaches, it was found that UNEQ exhibited greater sensitivity (fewer false positives) while SIMCA exhibited greater specificity (fewer false negatives). The SIMCA models produced excellent class separation between the Heparin and OSCS classes and between the DS and OSCS classes, achieving nearly 100% specificity (SPEC). On the contrary, the UNEQ models produced excellent sensitivity but poor specificity. Although the Heparin and DS classes rejected most of the OSCS samples in UNEQ analysis, the OSCS class accepted a large number of Heparin and DS samples, leading to extremely poor specificity. However, when the computations were completed by UNEQ, the obtained models were significantly better in terms of sensitivity (SENS) and prediction ability. 4. Analysis of heparin blends spiked with other GAGs. The validated models of classification and class modeling were challenged for discrimination of blends of heparin spiked with non-, partially-, or fully oversulfated chondroitin sulfate A (CSA), dermatan sulfate (DS) and heparan sulfate (HS) at the 1.0%, 5.0% and 10.0% weight percent levels. Overall, the results obtained from classification assignation of CART, ANN and SVM models on the blends were excellent. However, as the three multivariate pattern recognition approaches are not class modeling techniques, any object - even a clear outlier - can be assigned to only one class. For SIMCA class modeling, the Heparin class accepted pure heparin samples as well as some blends with low content (1%) of GAGs, while the DS and OSCS classes accepted their respective GAG blends. Importantly, some blends, such as OSHS and OS-Hep, were rejected by all the three class models. We conclude that all of the samples containing partially or fully oversulfated components, and the potential GAG impurities, were readily distinguished from USP grade heparin by the SIMCA class models. Methods that remove the expert operator and subjective decision making from the PASS/FAIL decision process can make information rich analytical methods such as NMR more accessible for quality control type testing. SAX-HPLC and 1D-1H-NMR have been designated as gatekeeper assays by the FDA for heparin products. If these spectral and chromatographic data are routinely available for these samples tested by the USP specifications, then the models developed here can be used to check product quality at the heparin sodium API step prior to drug formulation. Such models and tests would help prevent poor quality heparin lots from reaching the market and putting consumers at risk. The present study reveals that 1H NMR spectroscopy, already a USP requirement for screening of contaminants in heparin, in combination with multivariate chemometric methods, provides a rapid and efficient way to quantitatively determine the galactosamine content in heparin and represents an effective strategy for fast and reliable identification of impurities (DS) and contaminants (OSCS) in heparin API samples. The pattern recognition approach applied here may be useful in monitoring purity of other complex naturally derived compounds.

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Appendix: Abbreviations ANN: artificial neural network APIs: active pharmaceutical ingredients BIC: Bayes information criterion CART: classification and regression tree CE: capillary electrophoresis CSA: chondroitin sulfate A CSB: chondroitin sulfate B CSC: chondroitin sulfate C CV: cross validation DP: discriminant power DPA: Division of Pharmaceutical Analysis DS: dermatan sulfate DSS: 4, 4-dimethyl-4-silapentane-1-sulfonic acid FDA: US Food and Drug Administration GAG: glycosaminoglycan GAs: genetic algorithms GCV: generalized cross-validation HA: hyaluronic acid HCA: hierarchical cluster analysis HPLC: high-performance liquid chromatography HS: heparan sulfate kNN: k-nearest neighbors LDA: linear discriminant analysis LOO-CV: leave-one-out cross-validation MLR: multiple linear regression MSEP: mean squared error for prediction MVR: multivariate regression NIR: near infrared NMR: nuclear magnetic resonance OSCS: oversulfated chondroitin sulfate PC: principal components PCA: principal component analysis PE: processing elements PLS-DA: partial least squares discriminant analysis PLSR: partial least squares regression PRESS: predictive error sum of squares QDA: quadratic discriminant analysis RBF: radial basis function RMSE: root mean squared error RR: Ridge regression RSD: relative standard deviation RSE: relative standard error RSS: residual sum of squares

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SAX-HPLC: strong-anion-exchange high-performance liquid chromatography SEP: standard error of prediction SIMCA: soft-independent modeling of class analogy SLDA: stepwise linear discriminant analysis SVM: support vector machine SVR: support vector regression TNs: terminal nodes UNEQ: unequal dispersed classes USP: United States Pharmacopeia

Acknowledgment The authors wish to acknowledge the journals Analytical Chemistry, Analytical and Bioanalytical Chemistry, and the Journal of Pharmaceutical and Biomedical Analysis for granting approval to reproduce certain tables and figures appearing in the present chapter. The specific tables and figures are: Table 2, Table 3, Table 4, Table 5, Figure 2, Figure 3, Figure 4, and Figure 5 from Ref. #25; Table 4, Table 5, Table 6, Figure 2, Figure 3, Figure 4, and Figure 5 from Ref. #107; Table 1, Table 3, Table 5, Figure 2, Figure 3, and Figure 4 from Ref. #106; and Table 1, Table S-2, Table 3, Figure 1, Figure 2, and Figure 3 from Ref. #96.

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[98] R: software, a language and environment for statistical computing. R Development Core Team, Foundation for Statistical Computing, www.r-project. [99] Maindonald J, Braun J. Data analysis and graphics using R. Cambridge (UK): Cambridge University Press; 2003. [100] Forina M, Lanteri S, Armanino C, Casolino C, Casale M. V-Parvus. 2007. http://www.parvus.unige.it. [101] Zhang Z, Li B, Suwan J, Zhang F, Wang Z, Liu H, Mulloy B, Linhardt RJ. Analysis of Pharmaceutical Heparins and Potential Contaminants Using 1H-NMR and PAGE. Journal of Pharmaceutical Sciences. 2009, 98(11):4017-4026. [102] Rudd TR, Gaudesi D, Lima MA, Skidmore MA, Mulloy B, Torri G, Nader HB, Guerrini M, Yates EA. High-sensitivity visualisation of contaminants in heparin samples by spectral filtering of 1H NMR spectra. Analyst. 2011, 136:1390-1398. [103] Rudd TR, Gaudesi D, Skidmore MA, Ferro M, Guerrini M, Mulloy B, Torri G, Yates EA. Construction and use of a library of bona fide heparins employing 1H NMR and multivariate analysis. Analyst. 2011, 136:1380-1389. [104] Üstün B, Sanders KB, Dani P, Kellenbach ER. Quantification of chondroitin sulfate and dermatan sulfate in danaparoid sodium by 1H NMR spectroscopy and PLS regression. Analytical and Bioanalytical Chemistry. 2011, 399:629-634. [105] McEwen I, Mulloy B, Hellwig E, Kozerski L, Beyer T, Holzgrabe U, Rodomonte A, Wanko R, Spieser JM. Determination of oversulphated chondroitin sulphate and dermatan sulphate in unfractioned heparin by 1H NMR. Pharmeuropa Bio/ the Biological Standardisation Programme. 2008, 1:31-39. [106] Zang Q, Keire DA, Wood RD, Buhse LF, Moore CMV, Nasr M, Al-Hakim A, Trehy ML, Welsh WJ. Combining 1H NMR spectroscopy and chemometrics to identify heparin samples that may possess dermatan dulfate (DS) impurities or oversulfated chondroitin sulfate (OSCS) contaminants. Journal of Pharmaceutical and Biomedical Analysis. 2011, 54(5):1020-1029. [107] Zang Q, Keire DA, Buhse LF, Wood RD, Mital DP, Haque S, Srinivasan S, Moore CMV, Nasr M, Al-Hakim A, Trehy ML, Welsh WJ. Identification of heparin samples that contain impurities or contaminants by chemometric pattern recognition analysis of proton NMR spectral data. Analytical and Bioanalytical Chemistry. 2011, 401(3), 939955.

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In: Heparin: Properties, Uses and Side Effects Editors: D. E. Piyathilake, Rh. Liang, pp. 305-310

ISBN: 978-1-62100-431-8 © 2012 Nova Science Publishers, Inc.

Chapter XII

Heparin: The Side Effect of Heparin, Particularly, Heparin Induced Thrombocytopenia Ryotaro Wake* and Minoru Yoshiyama Osaka City University Graduate School of Medicine, Osaka, Japan

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1. Introduction Heparin is the useful anticoagulant, when rapid anticoagulation is required for thrombosis. Thrombosis which is a clot of the blood can occur in the arterial and venous circulation and sometimes leads to serious cardiovascular events. The pathology of arterial thrombosis differs from that of venous thrombosis as reflected by the different ways in which they are treated. Generally, arterial thrombosis is treated with drugs that target platelets. Venous thrombosis is treated with drugs that target proteins of the coagulation cascade. Heparin is a sulfated polysaccharide, and isolated from mammalian tissues rich in mast cells. Most commercial heparin is derived from porcine intestinal mucosa and is a polymer of alternating D-glucuronic acid and N-acetyl-D-glucosamine residues [1]. Heparin exerts its anticoagulant effect by interacting with antithrombin. In the absence of heparin, antithrombin binds to and neutralizes thrombin, slowly. However, heparin bound antithrombin undergoes a conformational change that accelerates its ability to bind to and neutralize these factors. The major complication of heparin is bleeding, other complications include thrombocytopenia, osteoporosis and elevated levels of transaminases. Protamine sulfate can be used in the situation with serious bleeding. Protamine almost neutralizes heparin intravenously. A mixture of basic polypeptides isolated from salmon sperm, portamine sulfate

*

Corresponding to Ryotaro Wake MD, Tel: +81-6-6645-3801, FAX: +81-6-6646-6808, E-mail: [email protected], Department of Internal Medicine and Cardiology, Graduate School of Medicine, Osaka City University, Osaka, Japan. 1-4-3 Asahi-machi, Abeno-ku, Osaka 545-8585, Japan.

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binds heparin with high affinity to form protamine-heparin complexes that undergo renal clearance. Typically,1 mg of intravenous protamine sulfate neutralizes 100 units of heparin. Anaphylactoid reactions to protamine sulfate can occur, but administration by slow intravenous infusion reduces the risk of these problems [1]. The other complication is thrombocytopenia which is called heparin induced thrombocytopenia (HIT). Heparin induced thrombocytopenia causes thrombosis in the both of artery and vein. It sometimes causes life-threatening thrombosis. The mechanism is the interaction of antibody with a complex of heparin and platelet factor 4 (PF4) on the surfaces of platelets. The resulting platelet activation is associated with increased thrombin generation. However, heparin can cause severe thrombocytopenia even in the absence of thrombosis. Heparin induced thrombosis can actually occur with normal platelet count. We describe the management of the side effect of heparin, particularly, heparin induced thrombocytopenia in this chapter.

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2. The Diagnosis of HIT Typically, the platelet count fall begins 5 to 10 days after starting heparin, but can manifest earlier if the patient has received heparin within the 3 months (Table 1). Table 1 shows that the 4Ts assessment point system for patients with suspected HIT. Points assigned in each of four categories are totaled, and the pretest probability of HIT determined by the total points is as follows: high: 6 to 8 points, intermediate: 4 to 5 points: and low: 0 to 3 points [2]. It is rare for the platelet count to fall below 100,000/μL in patients with HIT, and even a 50% decrease in the platelet count from the pretreatment value should raise the suspicion of HIT in those receiving heparin. HIT is more common in surgical patients than medical patients and, like many autoimmune disorders, occurs more frquently in women than in men [3]. Table 1. The 4Ts Assessment Point System for Patients With Suspected HIT

Points assigned in each of four categories are totaled, and the pretest probability of HIT determined by the total points is as follows: high: 6 to 8 points, intermediate: 4 to 5 points, and low: 0 to 3 points.

HIT can be associated with arterial or venous thrombosis. Venous thrombosis , which manifests as deep vein thrombosis and/or pulmonary embolism, is more common than arterial Heparin : Properties, Uses and Side Effects, Nova Science Publishers, Incorporated, 2012. ProQuest Ebook Central,

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thrombosis. Arterial thrombosis can manifest as ischemic stroke or acute myocardial infarction. Rarely, platelet-rich thrombi in the distal aorta or iliac arteries can cause critical limb ischemia [4]. The diagnosis of HIT is established with enzyme-linked assays to detect antibodies against heparin-PF4 complexes or with platelet activation assays. Enzyme-linked assays are sensitive, but can be positive in the absece of any clinical evidence of HIT [5].

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3. The Treatment of the HIT Patients All heparin including low-molecular-weight heparin should be stopped in patients with suspected or documented HIT, and an alternative anticoagulant such as argatroban or fondaparinux should be administered to prevent or treat thrombosis [6]. The other managements of HIT are the followings. (1) Platelet transfusions should not be given. (2) warfarin should not be given until the platelet count returns to baseline levels, if warfarin was administred, vitamin K should be given to restore INR to normal. (3) The thrombosis, particularly deep vein thrombosis should be evaluated. The agents most often used for this indication are parenteral direct thrombin inhibitors, such as argatroban, or factor Ⅹa inhibitors, such as fondaparinux. Patients with HIT, particularly those with associated thrombosis, often have evidence of increased thrombin generation that can lead to consumption of protein C. If these patients received warfarin without a concomitant parenteral anticagulant, the further decrease in protein C levels induced by the vitamin K antagonist can trigger skin necrosis [7]. To avoid this problem, patients with HIT require treatment with a direct thrombin inhibitor or fondaparinux until the platelet count returns to normal levels. At this point, low-dose warfarin therapy can be introduced and the thrombin inhibitor can be discontinued when the anticoagulant response to warfarin has been therapeutic for at least 2 days [3-5,7].

4. The Clinical Management for the HIT Patients 4.1. Patients with a History of HIT Patients with a history of HIT may not invariably have recurrent HIT on heparin reexposure [8]. In addition, heparin has been tolerated for a brief period, such as during cardiac surgery, in patients in whom heparin-PF4 antibodies have fully waned [8]. However, until the risk of recurrent HIT in patients with a history of HIT is better defined, and because the consequences of recurrent HIT may be devastating, it is generally considered prudent to use an alternative anticoagulant to avoid reexposing these risk patients to heparin, when possible [8-10]. In special circumstances in which planned heparin exposure may occur, such as cardiac surgery when HIT antibodies are undetectable, an alternative agent should be used for preoperative and postoperative anticoagulation to limit heparin exposure [10,11]. The tube coated with heparin should not be used, when the intravenous drip. In a prospective study of acute alternative anticoagulation in patients with a history of HIT, argatroban 2 μg/kg/ minute, adjusted to aPTTs of 1.5 to 3.0 times baseline, provided

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adequate anticoagulation for venous or arterial thrombosis in 36 patients, without major bleeding or thrombotic complications [12].

4.2. Cardiovascular Surgery

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In patients with HIT or with a history of HIT and lingering HIT antibodies, cardiovascular surgery should be delayed until HIT is fully resolved and antibodies are undetectable by a sensitive assay. If delay is impossible or the urgency of the situation precludes assessment of HIT antibody status in a patient with a history of HIT, alternative anticoagulation should preferably be used during the surgery [10]. Limited experience exists with lepirudin, argatroban, bivalirudin, and danaparoid, sometimes together with antiplatelet agents, in this setting [11,13]. It should be emphasized that safe, effective doses of alternative anticoagulants during cardiovascular surgery have not been established in clinical trials, and hence, potential concerns about bleeding or thrombotic complications remain. Other concerns include the lack of specific antidotes for these alternative anticoagulants as well as the potential need for monitoring by assays often not readily available, such as the anti–factor Xa level. Prospective studies are now ongoing in the United States to evaluate bivalirudin anticoagulation in HIT patients undergoing on- or off-pump cardiac surgery. In the special circumstance of patients with a history of HIT who lack detectable HIT antibodies and require cardiac surgery, heparin is currently recommended over alternative anticoagulants owing to the limited experience with the latter agents in cardiovascular surgery and their universal lack of an antidote [10]. Again, care must be taken to minimize heparin exposure, using it only during surgery and administering alternative anticoagulation, when needed, before and after surgery.

4.3. Percutaneous Coronary Intervention Argatroban is the only alternative anticoagulant approved in the United States for use in patients with or at risk for HIT who are undergoing PCI. The safety and efficacy of argatroban in this setting was evaluated in 3 similarly designed, multicenter, prospective studies, and the combined-study data are reported. One hundred one patients with clinically significant hepatic dysfunction were excluded. Overall, 91 patients with HIT or a history of HIT underwent 112 PCIs while receiving intravenous argatroban 25 μg/kg/min (350- μg/kg, initial bolus), adjusted to achieve ACTs of 300 to 450 seconds. Among the 91 patients undergoing their first PCI on argatroban, subjective assessments of the satisfactory outcome of the procedure and adequate anticoagulation during PCI occurred in 94.5% and 97.8%, respectively; 7 (7.7%) patients experienced the composite of death (no patient), myocardial infarction (4 patients), or revascularization (4 patients) within 24 hours of PCI, and 1 (1.1%) patient had periprocedural major bleeding. No unsatisfactory outcomes occurred in 21 patients who underwent repeated PCI on argatroban at a mean of 150 days later. Findings from a multicenter, prospective study evaluating argatroban and glycoprotein IIb/IIIa inhibition therapy in patients undergoing PCI, 78 while not conducted specifically in HIT patients, suggest that a reduced dose of argatroban (perhaps a 300μg/kg bolus, followed by a

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15μg/kg/min infusion) provides adequate anticoagulation in combination with glycoprotein IIb/IIIa inhibition during PCI. Bivalirudin, lepirudin and danaparoid instead of heparin are also administered for the patients with HIT undergoing PCI in the United States of America [14-18]. In Japan, argatroban is administerd by one third of American use, according to Japanese Circulation Society guideline. The patients with HIT or a history of HIT undergoing PCI while receiving intravenous argatroban 100 μg/kg in 3-5 minutes bolus followed by a 6 μg/kg/min until 4 hours after PCI, adjusted to achieve ACTs of 250 to 450 seconds. Then, 0.7μg/kg/min argatroban is administered, adjusted to achieve aPTT of 1.5 to 3.0 times baseline. Physiologic saline with argatroban (1-5 mg argatroban / 100 ml physiological saline) is used instead of physiologic saline with heparin. In the HIT patients with hepatic dysfunction, about one forth argatroban of basal use is administered.

5. Conclusion Heparin is useful anticoagulant agent, because it is low cost and can be neutralized with protamin now. HIT is a well-recognized serious side effect associated with heparin use. Until the nonheparin anticoagulants which are useful equal to heparin are developed, HIT may not disappear. Until then, HIT is required to prompt the diagnosis, treatment and management as soon as possible.

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Hirsh, J. et al. (2008) Parenteral anticoagulants: American College of Chest Physicians Evidence-Based Clinical Practice Guidelines (8th Edition). Chest 133 (6 Suppl), 141S159S. Warkentin, T.E. and Heddle, N.M. (2003) Laboratory diagnosis of immune heparininduced thrombocytopenia. Curr Hematol Rep 2 (2), 148-157. Greinacher, A. (2009) Heparin-induced thrombocytopenia. J Thromb Haemost 7 Suppl 1, 9-12. Shantsila, E. et al. (2009) Heparin-induced thrombocytopenia. A contemporary clinical approach to diagnosis and management. Chest 135 (6), 1651-1664. Warkentin, T.E. and Linkins, L.A. (2009) Immunoassays are not created equal. J Thromb Haemost 7 (8), 1256-1259. Hirsh, J. et al. (2004) Treatment of heparin-induced thrombocytopenia: a critical review. Arch Intern Med 164 (4), 361-369. Srinivasan, A.F. et al. (2004) Warfarin-induced skin necrosis and venous limb gangrene in the setting of heparin-induced thrombocytopenia. Arch Intern Med 164 (1), 66-70. Warkentin, T.E. and Kelton, J.G. (2001) Temporal aspects of heparin-induced thrombocytopenia. N Engl J Med 344 (17), 1286-1292. Maloney, J.P. (2002) Lessening the punch of heparin-induced thrombocytopenia. Chest 122 (1), 5-6.

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[10] Warkentin, T.E. and Greinacher, A. (2004) Heparin-induced thrombocytopenia: recognition, treatment, and prevention: the Seventh ACCP Conference on Antithrombotic and Thrombolytic Therapy. Chest 126 (3 Suppl), 311S-337S. [11] Warkentin, T.E. and Greinacher, A. (2003) Heparin-induced thrombocytopenia and cardiac surgery. Ann Thorac Surg 76 (2), 638-648. [12] Matthai, W.H., Jr. et al. (2005) Argatroban anticoagulation in patients with a history of heparin-induced thrombocytopenia. Thromb Res 116 (2), 121-126. [13] Wake, R. et al. (2007) The effect of the gravitation of the moon on acute myocardial infarction. Am J Emerg Med 25 (2), 256-258. [14] Bittl, J.A. et al. (2001) Bivalirudin versus heparin during coronary angioplasty for unstable or postinfarction angina: Final report reanalysis of the Bivalirudin Angioplasty Study. Am Heart J 142 (6), 952-959. [15] Campbell, K.R. et al. (2000) Bivalirudin in patients with heparin-induced thrombocytopenia undergoing percutaneous coronary intervention. J Invasive Cardiol 12 Suppl F, 14F-19. [16] Cantor, W.J. et al. (1999) Combined use of Orgaran and Reopro during coronary angioplasty in patients unable to receive heparin. Catheter Cardiovasc Interv 46 (3), 352-355. [17] Mahaffey, K.W. et al. (2003) The anticoagulant therapy with bivalirudin to assist in the performance of percutaneous coronary intervention in patients with heparin-induced thrombocytopenia (ATBAT) study: main results. J Invasive Cardiol 15 (11), 611-616 [18] Manfredi, J.A. et al. (2001) Lepirudin as a safe alternative for effective anticoagulation in patients with known heparin-induced thrombocytopenia undergoing percutaneous coronary intervention: case reports. Catheter Cardiovasc Interv 52 (4), 468-472.

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Index

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A Abraham, 27, 180 accelerator, 207 access, 122 accounting, 136, 167, 335 acetone, 6, 202 acetonitrile, 143, 144 acidic, 7, 19, 88, 138, 142, 143, 151, 157, 180, 196, 245, 280 acidity, 31 ACL, 105, 107, 109 active compound, 199 active pharmaceutical ingredient (API), xiv, 234, 237, 280, 345 active site, 167, 180, 185 activity level, 231 AD, 91, 92, 208, 356 adaptation, 110, 122 adaptive immunity, 90 additives, 154, 352 adenocarcinoma, 66 adenosine, 130, 259 adenosine triphosphate, 259 adhesion, x, 5, 44, 46, 49, 50, 52, 53, 54, 55, 65, 68, 69, 70, 80, 82, 83, 85, 91, 129, 175, 176, 177, 183, 195, 202 adjustment, 227, 230 ADP, 180 adsorption, 192 adverse effects, 239

adverse event, 281, 282, 283, 353 age, 216, 223, 225, 226 agglutination, 108, 121 aggregation, 178, 259 AIDS, 30 air temperature, 303 airway epithelial cells, 57 albuminuria, 18, 29, 216 algorithm, 172, 262, 263, 266, 270, 286, 287, 311, 331, 332, 333, 354, 355 alkaline hydrolysis, 3 alters, 77 alveolar macrophage, 83 amine, 202, 246, 280 amine group, 202, 246 amines, 245 amino, xv, 27, 49, 54, 66, 107, 137, 165, 170, 178, 180, 202, 235, 242, 247, 258, 259, 262, 266, 272, 273, 306 amino acid, xv, 54, 107, 165, 170, 180, 202, 242, 258, 259, 262, 266, 272, 273 amino acids, xv, 54, 170, 180, 202, 258, 259, 272, 273 amino groups, 273, 306 ammonium, 144, 245 amplitude, 300, 318 amyloidosis, 178 anaphylaxis, 282 anchoring, 167 angina, 367 angioedema, xiv, 234, 239

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312 angiogenesis, xiii, 5, 25, 39, 59, 64, 77, 78, 79, 80, 92, 93, 155, 195, 206, 228 angiogenic process, 89 angioplasty, 199 anisotropy, 240 anti-angiogenic agents, 179 antibody, xiii, 27, 36, 53, 54, 67, 108, 122, 133, 188, 200, 202, 203, 210, 211, 362, 364 anti-cancer, 189, 190, 199 anticancer drug, 205, 209 anticardiolipin, 53 anticoagulation, xvi, 26, 65, 103, 113, 122, 124, 147, 154, 177, 223, 228, 237, 350, 352, 361, 364, 365, 366, 367 antigen, 23, 44, 64, 66, 108 anti-inflammatory agents, x, 2, 5, 210 antiphospholipid antibodies, 36, 43, 44, 50, 54, 67, 68 antiphospholipid syndrome, 37, 42, 43, 44, 53, 54, 55, 59, 61, 63, 68 antisense, 80 antitumor, 193, 205, 206 anti-viral agents, x, 2, 5 aorta, 6, 363 APA, 67 apoptosis, 36, 49, 50, 56, 58, 62, 79 aqueous solutions, 260, 261, 265, 266, 269 Argentina, 233, 252 arginine, 54, 170, 180, 273 arithmetic, 296 arrest, 83, 95 arterial hypertension, 63 arteries, 74, 363 arteriosclerosis, 82, 98 artery, 9, 10, 14, 37, 74, 90, 92, 98, 202, 362 arthritis, 29, 37 Artificial Neural Networks, 331 artificial neural networks (ANN), xvi, 278 ascites, 36 aspartate, 191

Index assessment, 52, 103, 149, 150, 205, 209, 227, 229, 279, 286, 293, 353, 354, 362, 364 asthma, 5, 49, 66, 67, 180, 198, 209 asymmetry, 36 atherosclerosis, 5, 27, 61, 82, 84, 90 atherosclerotic plaque, 74, 90 atherosclerotic vascular disease, 59 atomic force, 58 atopic asthma, 66 atrial fibrillation, 5 attachment, 79, 175, 191, 193 Austria, 197 authentication, 356 authorities, 239 autoimmune disease, x, 33, 36 autoimmune diseases, x, 33, 36 B background noise, 287 bacteria, 164, 181, 196 barium, 247, 280 base, xv, 258, 262, 264, 265, 266 BD, 117, 125 beef, 151, 351 behaviors, 85 beneficial effect, 195 benefits, 87, 109, 110, 215 bias, 291, 298 bile, 12 bile duct, 12 bioaccumulation, 218, 219, 220, 222, 225, 226, 229 bioassay, 237 bioavailability, 3, 100, 198, 199, 208, 209, 215, 217, 219 biochemical processes, 52 biochemistry, 125 biocompatibility, 192, 198, 199 biological activities, xii, 57, 127, 203 biological activity, xiv, 92, 146, 148, 199, 233, 237, 281 biological processes, 157, 176

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Index biological roles, 175 biological systems, 71 biologically active compounds, 259 biomarkers, 121 biomaterials, 86, 87 biomedical applications, 205 biomolecules, 121 biopolymer, 207 biosensors, 121, 125 biosynthesis, 20, 70, 129, 146, 147, 157, 184, 236, 280 biotin, 178 bleeding, xiv, 7, 15, 22, 98, 99, 101, 102, 131, 132, 158, 213, 214, 216, 217, 219, 220, 222, 223, 224, 225, 226, 227, 230, 362, 364, 365 bleeding time, 101, 102 blends, 342, 343, 348 blood clot, 19, 36, 38, 39, 44, 49, 112, 194, 237, 238, 249 blood flow, 101 blood plasma, x, xiv, 34, 44, 257, 258, 259, 274 blood pressure, 17, 27, 29, 195, 236, 281, 283 blood vessels, 36, 38, 80, 194, 196 bloodstream, 192 body fluid, 35, 36, 58 body weight, 214, 219, 221, 222, 225, 227 bonds, 54, 167, 198, 279 bone, xiii, 13, 49, 187, 189, 195, 207 bone form, 195 bone marrow, 13, 49 bradykinin, 134, 283 brain, 12, 59, 80, 93, 102 Brazil, 133 breakdown, 133 breast cancer, 57, 92, 205 bronchial asthma, 23 bronchoconstriction, 49, 66 budding, x, 33, 35, 39, 40, 44, 46, 47, 48, 52 building blocks, 135 by-products, 141

313 C

Ca2+, xv, 57, 137, 151, 175, 258, 259, 260, 261, 264, 267, 269, 270, 271, 272, 273, 353 calcium, xiv, 29, 30, 154, 182, 257, 259, 260, 262, 263, 264, 270, 272, 273, 280 calibration, 106, 110, 262, 286, 289, 291, 304, 310, 312, 355 cancer, x, xiii, 2, 5, 33, 36, 37, 38, 39, 49, 51, 52, 58, 59, 62, 63, 64, 65, 66, 77, 177, 187, 191, 198, 206, 211, 215, 227 cancer cells, 206, 215 cancer progression, 211 cancer therapy, x, xiii, 2, 5, 187, 198 candidates, 171 capillary, xii, xiv, 80, 110, 128, 136, 138, 140, 141, 142, 150, 151, 152, 153, 234, 239, 244, 253, 282, 283, 349, 350, 351, 352, 354 carbohydrate, 3, 39, 70, 174, 211, 279 carbohydrates, 129, 250, 279 carbon, xiii, 121, 187, 192, 193, 206, 209 carbon nanotubes, xiii, 121, 187, 192, 193, 206 carboxyl, 54, 157, 165, 202, 235 carcinoma, 37, 38, 63, 64, 66 cardiac surgery, 125, 364, 365, 366 cardiopulmonary bypass, 103 cardiovascular disease, 98, 122 cardiovascular system, 94 carotid arteries, 75, 79 cartilage, 21, 30 cascades, 79, 81, 83 case study, 355 caspases, 49 casting, 211 catalytic activity, 167 category a, 289, 294, 300, 336 cathepsin G, 82, 84 catheter, 75, 79, 202 cation, 151, 175, 351 CD8+, 84 CDC, 282

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314 cDNA, 80 CEC, 142, 151 cell culture, 59, 200 cell cycle, xi, 69, 71, 72, 73, 76, 94 cell death, 66, 191, 205 cell differentiation, 20 cell invasion, 79 cell line, 66, 79, 191, 194, 195 cell lines, 79 cell membranes, x, 2, 19, 33, 34, 39, 48, 52, 55 cell signaling, 176 cell surface, xi, 5, 58, 70, 78, 90, 167, 176, 177, 183, 184, 185 cellular growth factors, 181 cellulose, 6, 12, 141, 152, 170, 196, 247, 249, 354 central nervous system, 94 challenges, xii, 20, 98, 110, 127, 179 chaperones, 81 charge density, 2, 139, 198, 235, 280 chemical reactions, 263 chemiluminescence, 121 chemokine receptor, 88 chemokines, 50, 84, 85, 93, 129, 170, 181, 182 chemometric techniques, xv, 137, 278, 286, 306, 351, 354, 356 chemometrics, 150, 279, 286, 288, 291, 294, 300, 304, 319, 354, 355, 358 chemotherapy, 215 children, 100 China, 101, 239 chirality, 357 chitosan, 195, 196, 198, 206, 207, 208 Chitosan, 207, 209 chlorine, 173 cholesterol, 42, 57, 65 chondrocyte, 195, 207 chondroitin sulfate (CS), xiv, 233, 237 chromatographic technique, xiv, 145, 234, 249

Index chromatography, xii, 54, 128, 134, 138, 142, 143, 144, 145, 150, 153, 154, 168, 169, 170, 242, 244, 280, 284, 354 chromosome, 288, 310 chronic kidney XE "kidney" disease, 214, 216, 228 chronic kidney XE "kidney" disease (CKD), 216 chymotrypsin, 171 circulation, x, xvi, 2, 4, 7, 36, 38, 49, 191, 193, 198, 361 City, 97, 361 CKD, 214, 216, 222, 224 classes, xvi, 278, 285, 289, 293, 294, 295, 297, 299, 300, 301, 319, 320, 321, 324, 325, 328, 330, 331, 335, 336, 337, 338, 339, 340, 341, 342, 343, 344, 346, 347, 348, 350, 357 classification, iv, xv, 278, 285, 286, 287, 291, 293, 294, 295, 296, 297, 298, 299, 304, 319, 320, 321, 325, 328, 330, 331, 332, 333, 335, 345, 347, 348, 349, 357, 358 classification and regression tree (CART), xvi, 278, 285 Classification and Regression Tree (CART), 296 cleavage, 3, 50, 107, 215 clinical application, 87, 196, 200, 204 clinical presentation, 26 clinical syndrome, 283 clinical trials, xiii, 100, 134, 156, 158, 179, 204, 226, 364 cloning, 94 cluster analysis, 172, 294, 342, 349, 357 clustering, 293, 320, 342 clusters, 157, 173, 295, 319, 342 coagulation process, 235 coagulopathy, 59, 63, 64 coding, 20, 129 coherence, 128, 135 colitis, 27 collagen, 194, 206, 259 collateral, 206

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Index colon, 13, 63 color, iv, 250, 319 colorimetric test, 104, 250 coma, 37 commercial, 99, 106, 361 communication, 35 community, ix, xii, 65, 70, 81, 98, 121 comparative analysis, 167, 258, 259, 274 compatibility, xiii, 86, 91, 187, 193, 198 competition, 168, 170 competitors, 177 complement, x, 50, 56, 67, 69, 82, 85, 95, 176, 184, 192, 194 complex carbohydrates, 157 complex interactions, 171 complexity, xii, 74, 127, 137, 138, 146, 287, 290, 291, 292, 294, 297, 299, 305, 312, 328, 330, 332 compliance, xii, xiii, 98, 109, 187, 188, 196, 237 complications, 36, 50, 59, 131, 158, 191, 362, 364 composites, 90, 210, 357 composition, 3, 35, 52, 102, 121, 123, 131, 133, 144, 152, 153, 157, 199, 259, 260, 263, 273, 274, 283, 284, 287, 302, 306, 342, 351 compounds, ix, xiii, 1, 2, 3, 4, 7, 8, 19, 27, 66, 67, 137, 146, 151, 156, 210, 215, 216, 241, 243, 260, 261, 343, 349 compression, 142 computation, 172, 300 computer, xv, 257, 258, 262 computing, 358 conductance, 121 conductivity, 141 configuration, 45, 112 CONGRESS, iv conjugation, 194, 202, 204 consensus, 23, 165 constituents, 2, 44, 47, 53, 65 construction, 266, 270, 297, 305, 335 consumers, 281, 286, 348 consumption, 235, 236, 280, 363

315

containers, 249 contaminant, 135, 136, 148, 151, 153, 238, 239, 242, 245, 247, 253, 279, 281, 282, 283, 285, 302, 319, 320, 346, 353 contamination, xii, xiv, 127, 134, 135, 140, 145, 150, 233, 250, 281, 283, 286, 289, 306, 351, 352 contour, 333 controversial, xii, 98, 110 convergence, 173, 310 convergence criteria, 310 COOH, 204 copolymer, 203, 246, 250 copper, 138, 152 copyright, iv Copyright, iv coronary angioplasty, 367 coronary arteries, 85 coronary artery bypass graft, 84, 85 coronary artery disease, 60 coronary heart disease, 98 correlation, x, 1, 6, 22, 103, 124, 128, 129, 135, 136, 290, 312 correlations, xii, 98, 225, 301 corticosteroids, 49 cost, xii, 98, 111, 118, 122, 134, 172, 199, 243, 284, 292, 297, 366 cost-benefit analysis, 122 cotton, 11, 17 covalent bond, 202 CPB, 103, 111 CPU, 173 creatinine, 217, 222 cross-validation, 287, 305, 315, 320, 349 crystal structure, 157, 174, 177, 180, 184, 185 crystalline, 185 CSA, 279, 282, 302, 306, 307, 342, 343, 344, 345, 348, 349 CT, 67, 105, 353 culture, 24, 79, 88 culture media, 79 CV, 89, 291, 297, 305, 315, 317, 320, 321, 326, 328, 330, 331, 332, 349

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316

Index

CXC, 91 CXC chemokines, 92 cyclin-dependent kinases (CDKs), xi, 70 cyclins, xi, 70 cytokines, x, 27, 39, 50, 69, 84, 90 cytoplasm, 49, 82, 83 cytoplasmic tail, 80 cytoskeleton, 34 cytotoxicity, 192, 199

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D damages, iv data center, 296 data processing, 106 data reduction methods, 357 data set, 136, 137, 286, 294, 305, 319, 332, 333, 342 database, 65, 160 deaths, 281, 282, 283 decay, 331, 332 decision trees, 296 deep venous thrombosis, 28, 61, 202, 208, 211, 279 Deer, 277 defence, 181 deficiencies, 104, 123 deficiency, 26, 63, 191 deficit, 104 degradation, 3, 4, 7, 24, 30, 82, 91, 191, 194, 197, 200, 235, 251 dehydration, 193 Delta, 106 dendrogram, 342 Denmark, 239 deoxyribose, 184 dependent variable, 298, 331 depolymerization, xii, 127, 132, 138, 141, 143, 153, 184 deposition, 177, 178, 193, 211 deposits, 177 depression, 89 derivatives, 74, 82, 131, 178, 183, 198, 207, 208, 211, 217, 219, 228, 351

dermis, 197 desorption, 171 detectable, 12, 365 detection techniques, 113 deviation, 133, 172, 290, 292, 339 diabetes, x, 2, 216 diabetic patients, 18, 110 diacylglycerol, 57 diagnostic markers, 57, 60 dialysis, 5, 216 diamonds, 51 diapedesis, 84 diaphoresis, 281 diarrhea, 281 dielectric constant, 173 diffusion, 128, 136, 150, 354 digestion, 132, 133, 135, 171, 250, 280, 284 dilation, 23 diluent, 28 dimensionality, 300, 319 dimerization, 94, 174, 185 dimethylformamide, 239 disaccaride, 235 discomfort, 7, 21 discrete variable, 306 discriminant analysis, xvi, 278, 285, 287, 288, 295, 301, 304, 320, 324, 340, 349, 350, 356 discrimination, xv, 278, 284, 293, 320, 324, 330, 336, 338, 339, 346, 348, 355 disease activity, 61 diseases, x, xiii, xiv, 33, 36, 82, 100, 121, 213, 215, 216, 235, 257 disorder, 37, 249 dispersion, 107 displacement, 18 dissociation, 169, 170, 171, 194, 202, 204 distilled water, 263 distribution, xiv, 2, 4, 7, 8, 10, 12, 13, 28, 30, 65, 177, 206, 213, 217, 218, 219, 227, 265, 266, 267, 269, 270, 271, 272, 281, 289, 296, 300, 301, 319, 320, 336

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Index diversity, 91, 129, 133, 143, 147, 159, 174, 176, 280 DNA, 56, 75, 82, 83, 89, 129, 151, 167, 181, 192, 243 dogs, 21 dominance, 90 donors, 52 dosage, 100, 123, 124, 220, 222, 226, 228, 230 dose-response relationship, xi, 97, 100 dosing, 13, 98, 99, 123, 214, 215, 219, 220, 221, 222, 224, 225, 226, 230 down-regulation, 73, 80 drawing, 103 drinking water, 17 drug carriers, 193 drug delivery, xiii, 187, 188, 189, 191, 196, 203, 204, 209, 210 drug discovery, 129 drug interaction, 228 drug metabolism, 23 drug release, 191, 205 drug treatment, 109 drugs, ix, xi, xii, xiii, xvi, 1, 2, 5, 6, 10, 71, 97, 99, 127, 155, 158, 177, 179, 183, 187, 188, 189, 190, 199, 202, 204, 210, 213, 216, 223, 225, 226, 235, 243, 253, 255, 279, 285, 286, 357, 361 duodenum, 7, 8, 9, 19 E ECM, 74, 75, 79, 86, 92, 176 edema, 134 editors, iv, 65, 112, 115, 117, 122, 124, 125, 207, 210, 211 education, 109 elderly population, 228 electric charge, 65 electric current, 116 electric field, 45, 47, 64, 138 electrochemistry, 117, 125 electrolyte, 244, 246, 258, 263 electron, 34, 35, 47, 57, 58, 117, 201, 208

317

electrophoresis, xii, xiv, 6, 12, 128, 129, 134, 137, 140, 141, 150, 151, 152, 153, 234, 239, 243, 244, 247, 252, 253, 282, 283, 284, 349, 350, 351, 352, 354 electrophoretic separation, 248 electrospinning, 196, 207 ELISA, 44, 50, 85, 121 elucidation, xiv, 135, 148, 149, 234, 280 e-mail, 127 embolism, 5 embossing, 111 emergency, xii, 98, 101, 109 enantiomers, 137 encapsulation, 195, 198 encoding, 286, 288 endocarditis, 38 endothelial cells, xi, 2, 4, 5, 13, 23, 24, 25, 36, 37, 50, 58, 70, 71, 78, 79, 80, 81, 82, 88, 89, 90, 92, 93, 99, 100, 176, 192 endothelium, ix, x, 1, 4, 6, 7, 8, 10, 12, 13, 15, 16, 18, 19, 23, 24, 25, 28, 37, 39, 49, 63, 69, 70, 74, 83, 88, 206, 217, 219 end-stage renal disease, 228, 229 energy, 120, 172, 173 engineering, xiii, 121, 185, 187, 189, 195 England, 97 enlargement, 200, 201 entropy, 173, 331 environment, 7, 135, 204, 358 environments, 137, 191 enzyme, 3, 84, 117, 161, 239, 249, 363 enzyme immunoassay, 239, 249 enzymes, 4, 23, 66, 129, 133, 250 eosinophils, 167 epithelial ovarian cancer, 205 epitopes, xiii, 155, 169, 170, 183 equilibrium, xiv, 170, 174, 257, 258, 260, 262, 263, 270, 272, 273 equipment, 105, 122, 249, 250, 251, 284 erythrocytes, 34, 36, 57 ESI, 143, 145 ester, 49, 66, 155, 280 ethanol, 303 ethylene, 87, 189

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318

Index

ethylene glycol, 87, 189 Europe, 239 evidence, ix, xi, 1, 2, 4, 7, 15, 18, 19, 56, 59, 69, 71, 74, 83, 84, 87, 147, 180, 182, 214, 218, 220, 222, 225, 226, 227, 363 evolution, 98, 121, 287, 288, 320, 330 exclusion, 144 excretion, 14, 99 execution, 105, 122, 258, 274 exercise, 66 experimental condition, 173 expertise, 240, 251 exploitation, 121 exposure, 195, 219, 220, 282, 364, 365 external validation, 287, 319, 324 extracellular matrix, 2, 50, 70, 74, 86, 157, 176, 194 extraction, 133, 236, 237 extracts, 355

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F fabrication, 203 fainting, 282 false negative, 300, 348 false positive, 300, 348 families, 172 family members, 74 fat, 81 FDA, xii, 127, 128, 133, 134, 136, 141, 145, 148, 234, 239, 253, 278, 281, 282, 302, 318, 348, 349, 353 fear, 7 feature selection, 357 feces, 12 fibers, 196, 207 fibrillation, 37 fibrin, 39, 49, 64, 103, 105, 107, 114, 119, 189, 194, 195, 200, 202, 207, 210, 211 fibrinogen, 103, 118, 119, 249 fibrinolysis, 49 fibrinolytic, 26, 49, 66, 228, 259

fibroblast growth factor, 23, 25, 28, 67, 88, 89, 90, 92, 93, 129, 149, 163, 173, 175, 180, 182, 184, 185, 194, 206 fibroblasts, 79 fibromyalgia, 5, 27 film thickness, 120 films, 86, 206 filtration, 143, 170, 216, 280 financial, xii, 98 fine tuning, 130 fingerprints, 143, 144, 279, 285, 306 Finland, 33 fitness, 122, 288, 310 flexibility, 105, 172, 173, 174 flight, 144, 153 fluctuations, 44, 173 fluid, 34, 36, 56, 105 fluorescence, xiv, 64, 119, 138, 143, 152, 168, 170, 188, 234, 239, 250, 251, 254 fluorophores, 137 folate, 191, 205 folic acid, 204 food, 128, 354 Food and Drug Administration, 70, 234, 278, 281, 349 force, 138, 172, 181, 299 Ford, 29 formation, xv, 9, 10, 14, 36, 38, 39, 42, 44, 49, 58, 76, 86, 99, 101, 103, 104, 105, 106, 107, 111, 113, 114, 115, 118, 119, 120, 138, 177, 178, 192, 194, 198, 202, 237, 246, 258, 259, 266, 270, 273 formula, 198, 199, 208, 298 fragments, x, 24, 28, 33, 35, 58, 141, 171, 174, 243, 250 France, 108, 220, 239 fraud, 286 free energy, 40, 45, 47, 173 freedom, 172, 289 functionalization, 206 funds, ix, 1

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G gangrene, 366 gastric lavage, 6, 7, 9, 11, 12, 14, 17, 19 gastric mucosa, 19, 31 gastric ulcer, 7, 28 gastrointestinal tract, 14, 19, 31, 196 gel, xii, 6, 12, 128, 129, 137, 141, 151, 195, 280 gene expression, 77 genes, 20, 72, 73, 82, 199 Germany, 197, 239 glucosaminoglycans, 279 glucose, 5, 25, 110, 111, 122 glutathione, 191 glycans, 38, 52, 181, 182 glycine, 260, 261, 273 glycoproteins, 129, 198 glycosaminoglycan (GAG), xii, 70, 127, 235, 279 glycosaminoglycans, 2, 24, 50, 83, 88, 89, 93, 99, 129, 130, 146, 149, 152, 158, 159, 179, 180, 181, 182, 183, 235, 251, 283, 351 glycosylation, 67, 183 gold nanoparticles, 119, 120, 126 GPA, 206 grants, 146 granules, 48, 131 graph, 319 gravitation, 367 gravity, 296, 324 grids, 333 grouping, 294, 295, 320 growth, xi, 4, 5, 23, 37, 49, 62, 63, 69, 72, 74, 75, 77, 79, 80, 81, 84, 88, 89, 90, 91, 92, 93, 94, 95, 122, 164, 174, 175, 176, 178, 194, 195, 203, 206, 207, 211 growth factor, xi, 4, 5, 49, 70, 72, 74, 75, 77, 79, 80, 81, 84, 88, 90, 91, 92, 93, 94, 164, 174, 175, 176, 178, 194, 195, 203, 206, 207 growth hormone, 23 guanine, 25

319

guidance, 345 guidelines, 147, 218, 219, 220, 221, 223, 226, 227, 235 H haemopoiesis, 94 haemostasis, xi, 97, 102, 104, 105, 106, 109, 111, 118, 120, 121, 125 hair, 13 half-life, xiii, 3, 99, 100, 179, 187, 188, 194, 196, 197, 199, 204, 219, 238 handheld devices, 110 HE, 67 healing, 7, 28, 49, 50 health, xiv, 109, 233, 236, 283 heart valves, 5, 86 helical conformation, 157 hemicellulose, 3 hemocompatibility, 91 hemodialysis, 26, 100, 350 hemoglobin, 15, 191, 205 hemolytic uremic syndrome, 59 hemophilia, 123 hemophilia a, 123 hemorrhage, 71, 208, 225 hemostasis, xv, 9, 12, 52, 123, 124, 125, 131, 258 heparan sulfate proteoglycans (HSPGs), xi, 70 hepatocytes, 175 herpes, 129, 184 herpes simplex, 184 heterogeneity, 137, 157, 174, 230, 237, 280 histamine, 131 histogram, 311 history, 36, 364, 365, 366 HIV, 3, 18, 27, 30, 129, 175, 177, 183 HIV-1, 27, 175, 183 HM, 91, 205, 207, 209, 350 homocysteine, 17 homogeneity, 135 hormone, 23

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Index

hormones, 4 horses, 30 host, xiii, 35, 129, 155, 161, 167, 176, 181 human, ix, xiv, 1, 18, 21, 22, 23, 24, 27, 30, 34, 43, 53, 54, 57, 59, 64, 66, 67, 68, 71, 78, 79, 80, 82, 83, 87, 88, 90, 92, 94, 95, 126, 131, 134, 181, 182, 183, 184, 185, 191, 195, 207, 208, 228, 229, 234, 235, 236 human body, 87 human immunodeficiency virus, 21, 30 human skin, 208 human subjects, 18, 22, 30, 228, 229 hybridization, 3 hydrogels, 86, 88, 92, 207 hydrogen, 165, 167, 172 hydrogen atoms, 172 hydrogen bonds, 165, 167 hydrolysis, 143, 243, 245 hydrophilicity, 196 hydroxide, 196, 210 hydroxyl, 3, 165, 177, 282 hydroxyl groups, 3, 165, 177, 282 hyperactivity, 66 hyperplasia, 75, 86, 90, 94, 196, 200 hypersensitivity, 71, 134, 353 hypertension, xiv, 5, 38, 63, 216, 234, 239 hypertonic saline, 57 hypertriglyceridemia, 63 hypotension, 134, 281, 283 hypothermia, 103 hypothesis, 15, 50, 52, 53, 56 hypoxia, 39, 95 I ibuprofen, 210 ID, 224, 303, 344 ideal, 258, 262 identification, 64, 136, 143, 145, 153, 154, 237, 239, 241, 242, 243, 263, 284, 285, 320, 331, 348 identity, x, 69, 133, 148 IFN, 82, 84, 85, 90, 175

IL-8, 175, 181 image, 357 image analysis, 357 images, 201 immersion, 86 immobilization, 91, 171 immune response, 50, 82 immune system, 50 immunobiology, 88 immunogenicity, 133, 147 immunohistochemistry, 83 immunoprecipitation, 73 immunoreactivity, 195 improvements, 109, 121 in vitro, xi, 19, 31, 54, 56, 61, 66, 67, 70, 71, 82, 83, 91, 134, 148, 177, 194, 197, 208, 210, 249, 274 in vivo, 4, 20, 22, 50, 67, 71, 75, 76, 80, 82, 95, 134, 176, 177, 181, 192, 197, 207, 209, 274 incidence, 9, 11, 15, 16, 17, 98, 100, 216, 217, 223 incubator, 103 independent variable, 291, 298, 300, 305, 312, 318, 331, 345 India, 133 indirect measure, 119 individual character, 347 individual characteristics, 347 individuality, 147 individuals, 30, 172, 218, 219, 222, 226, 288 inducible protein, 84 industries, 236 industry, 239, 281 INF, 85 infancy, 85 infarction, 79 infection, x, 3, 30, 33, 36, 48, 52, 56, 177, 183 inferior vena cava, 9 inflammation, ix, x, xi, 27, 33, 48, 50, 56, 57, 69, 70, 82, 83, 86, 87, 88, 93, 148, 174, 176, 228

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Index inflammatory bowel disease, 67 inflammatory cells, 82, 83 inflammatory disease, 70 inflammatory responses, 26, 67, 88 infrared spectroscopy, 239 ingredients, xvi, 278, 349 inhibition, xiii, 4, 22, 54, 63, 73, 76, 78, 83, 85, 89, 90, 91, 92, 95, 99, 101, 102, 155, 176, 178, 193, 203, 238, 250, 365 inhibitor, 4, 22, 23, 74, 82, 84, 90, 91, 158, 160, 165, 175, 177, 178, 180, 183, 217, 229, 363 initiation, 9, 10, 134 injections, 136 injury, iv, 5, 21, 25, 74, 75, 76, 79, 89, 90, 91, 94, 178, 196, 200, 202, 206, 211, 216 innovator, 134 insulin, 25, 110 integration, 122, 173, 303 integrin, 26 integrity, 203, 284 intensive care unit, 109 intercellular adhesion molecule, 93 interface, 106, 110 interferon, 82, 93, 182 interferon gamma, 182 interferon-γ, 82, 93 internalization, xiii, 24, 85, 89, 167, 187, 194, 206 international standards, 106 interstitial cystitis, 3, 18, 21 intervention, 110, 367 intestine, xiv, 19, 31, 233, 236 intravenously, 3, 99, 100, 199, 362 invertebrates, 129, 146 ion-exchange, 204 ionization, 153 ions, 65, 121, 173, 258, 259, 260, 261, 262, 264, 270, 272, 273 IP-10, 82, 84, 85, 93 IR spectra, 241 IR spectroscopy, 241 Ireland, 97, 105, 106, 109, 122

321

iron, 113 ischemia, 26, 89, 363 ischemia XE "ischemia" -reperfusion injury, 25 isolation, 48, 157, 280 isotherms, 170 issues, 86, 110, 131 Italy, 239 J Japan, 239, 361, 365 jaundice, 123 joints, 30, 36 K K+, 137 keratinocyte, 88 kidney, 12, 14, 18, 30, 214, 216, 227, 228, 279, 281 kidney dialysis, 279, 281 kidney failure, 216 kill, 196 kinetic parameters, 171 kinetics, 171, 214 k-nearest-neighbor (kNN), xvi, 278 L lactoferrin, 167, 181 Lagrange multipliers, 292 larynx, xiv, 234, 239, 281 laws, 173 layered double hydroxides, xiii, 187, 188, 210 LC-MS, 128, 143, 144 LDL, 27 lead, 72, 87, 98, 165, 216, 280, 283, 289, 296, 300, 312, 315, 318, 363 learning, 298 LED, 113 lesions, 60, 76, 84 leukemia, 58

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322 leukocytes, 5, 49, 61, 64, 84, 88 life sciences, 357 ligand, xiii, xv, 26, 60, 62, 80, 83, 155, 160, 165, 166, 167, 170, 171, 172, 173, 174, 182, 189, 258, 259, 260, 261, 270, 273, 274 light, 42, 47, 107, 108, 112, 113, 121, 128, 137, 144, 151 light beam, 107 light scattering, 107, 108, 121, 128, 144 light transmission, 113 linear discriminant analysis (LDA), xvi, 278, 285, 324 linear function, 291, 295 linear model, 289, 318 linear polymers, 165 lipases, 176 lipid metabolism, 176 lipids, 52, 203, 211 lipolysis, 176 lipoproteins, 3, 60, 71, 95 liposomes, xiii, 187, 188, 192, 193, 196, 197, 203, 204, 205, 206, 208 liquid chromatography, xii, 127, 128, 129, 153, 154, 235, 242, 282, 284, 349, 350 Liquid Chromatography Mass Spectrometry, 353 liquid phase, 115 liquids, 207 lithium, 141, 142, 245, 246 liver, 12, 23, 157, 236, 249 liver cells, 157 localization, 78, 83 longevity, 131 low molecular weight heparins, ix, xi, xii, 1, 3, 20, 97, 127, 132, 146, 150, 152, 214, 227, 228, 230, 249, 354 low molecular weight heparins (LMWH), ix, xii, 1, 127, 214 lumen, 13 lung cancer, 59, 205 lupus, 37, 58 lupus anticoagulant, 58 lying, 320

Index lymphocytes, 18, 95 lysine, 54, 170, 180, 195, 207 lysozyme, 181 M machine learning, 291 macromolecules, 235, 243, 280 macrophages, 99, 194, 209 magnesium, 259, 260, 262, 263, 264, 270, 273 magnet, 113, 114 magnetic field, 113 magnetic resonance, 128, 135, 148, 149, 168, 234, 279 magnetic resonance spectroscopy, 128, 149 magnetic sensor, 105 magnetism, 188 magnets, 114 magnitude, 250, 270, 273, 292, 331 majority, 223, 296, 325, 328, 346 malaria, 56 malignancy, 65 mammalian cells, 200, 203 mammalian tissues, 237, 361 mammals, 34, 129 management, xii, 26, 105, 109, 120, 122, 124, 127, 228, 362, 366 manufacturing, 111, 133 MAPK/ERK, 93 mapping, 293, 300 marketplace, 284 mass, xii, 120, 128, 134, 138, 153, 154, 171, 258, 262, 266, 355 mass spectrometry, 134, 153, 154, 171, 355 mast cells, 131, 157, 236, 361 materials, 102, 121, 199 mathematical methods, 262 matrix, 27, 178, 189, 194, 195, 263, 290, 291, 293, 295, 296, 301, 303, 308, 324, 331 matrix metalloproteinase, 27

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Index matter, iv, 171, 286 MB, 56 MCP, 170, 171, 181 MCP-1, 170, 171, 181 measurement, 7, 19, 104, 106, 107, 108, 109, 110, 111, 115, 118, 119, 121, 240, 242, 284, 290, 347 measurements, 15, 113, 263, 266, 273, 301, 319, 348, 357 media, 79, 143 medical, ix, xii, 26, 65, 70, 98, 109, 110, 121, 125, 147, 157, 193, 196, 202, 229, 231, 280, 282, 284, 362 medicine, 3, 63, 110, 124, 125, 259 MEK, 73 melanoma, 58 mellitus, 60 membership, 294, 295, 300, 301, 319, 320, 331 membranes, x, 2, 7, 34, 39, 45, 47, 48, 52, 55, 64, 65, 68 memory, 82 messengers, 73 meta-analysis, 132, 147 Metabolic, 37, 356 metabolic syndrome, 60, 61 metabolism, 2, 13, 24, 50, 175 metabolites, 25, 355 metabolized, 12, 13 metal ion, 175, 262 metal ions, 262 metal nanoparticles, xiii, 121, 187, 189, 192, 193, 194 metastasis, 48, 49, 59, 63, 64, 65, 66, 176, 211 meter, 14 methanol, 9, 11, 17, 303 methodology, 174, 175, 241, 296, 357 methyl group, 306 methyl groups, 306 methylation, 198 Mg2+, xv, 137, 258, 259, 260, 261, 264, 267, 270, 271, 273

323

mice, 19, 23, 27, 37, 43, 63, 64, 75, 76, 94, 95, 176, 202 microfabrication, 111 micrometer, x, 33 microparticles, 36, 56, 57, 58, 59, 60, 61, 62, 63 microscope, 34, 39, 40, 41, 58, 119, 208 microscopy, 64 Microsoft, 303 microspheres, 207 migration, 77, 80, 81, 82, 85, 93, 200 military, 109 miscarriage, 62 Missouri, 124 mitogen, xi, 69, 78, 91 mitogens, 75 MMA, 119 model system, 80 modelling, 179 modifications, 157, 204 molar ratios, 260, 270, 273 molecular biology, 3 molecular dynamics, xiii, 155, 172 molecular mass, 48, 281 molecular weight distribution, 22, 134, 229 monosaccharide, 4, 171 Moon, 184, 208, 209, 211 morbidity, 98 mortality, 98 Moscow, 257, 263, 275 mother cell, 35, 47, 65 motif, 165, 167 moulding, 111 MR, 133 mRNA, 25, 56, 73, 74, 79, 93 mRNAs, 79 mucin, 38, 64 mucosa, xii, 19, 127, 158, 280, 361 mucous membrane, 197 mucus, 63, 198 multilayer films, 207 multiple linear regression (MLR), xv, 278, 285, 291, 308, 345

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324

Index

multiple regression, 287 multiple sclerosis, 61 multivariate analysis, 150, 304, 310, 338, 345, 356, 358 multivariate calibration, 308, 351 multivariate data analysis, 241, 357 multiwalled carbon nanotubes, 193 muscle mass, 222 mutagenesis, 167, 184 mutant, 81, 162, 163 mutation, 171, 172, 288, 310 mutation rate, 310 mutations, 54, 288 myocardial infarction, 5, 26, 79, 91, 100, 101, 228, 230, 363, 365, 367

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N Na+, 137 NaCl, xv, 136, 169, 234, 242, 258, 260, 262, 263, 264, 265, 266, 267, 269, 270, 271, 272, 273 nanodots, 121 nanomaterials, ix, xiii, 121, 188, 189, 199, 203, 210 nanometer, 52 nanoparticles, xiii, 65, 120, 121, 187, 188, 189, 190, 191, 192, 194, 195, 196, 198, 199, 200, 202, 203, 204, 205, 206, 207, 209, 210, 211 nanorods, 194, 206 nanostructures, 121 Nanostructures, 121 nanotechnologies, 120, 121 nanowires, 121 National Academy of Sciences, 20, 28, 352 National Institutes of Health, 146 natural science, 357 natural sciences, 357 natural selection, 287 nausea, 281 near infrared spectroscopy, 354 necrosis, 363, 366

neovascularization, 59, 64 Netherlands, 239 neural network, xvi, 278, 285, 298, 304, 349, 354, 356, 357 neural networks, xvi, 278, 354, 356, 357 neurons, 298, 331 neutral, 43, 44, 53, 139, 165, 197 neutrophils, 50 New England, 21, 354 next generation, 172, 288 NIR, 349, 358 nitric oxide, 5, 25, 26, 28 nitric oxide synthase, 25 nodes, 297, 328, 330, 350 non-polar, 172 normal distribution, 290, 301 Norway, 114, 116 nuclear magnetic resonance, xii, xv, 127, 128, 134, 149, 150, 209, 278, 282, 283, 349, 351, 353, 354 Nuclear Magnetic Resonance, 135, 136 nuclei, 303 nucleic acid, 165, 280 nucleotides, 165 nucleus, 34, 49, 82, 83 null, 76 nursing, 122 nursing home, 122 O obesity, 100, 216 occlusion, 10, 14, 15, 100 oedema, 23 OH, 199, 204, 207 oil, 8 old age, 219 oligomerization, 94 oligomers, 157, 174 oligosaccharide, 137, 138, 143, 145, 160, 172, 177, 178, 184, 185, 281 olive oil, 356, 357 oncogenes, 39 one dimension, 137

Heparin : Properties, Uses and Side Effects, Nova Science Publishers, Incorporated, 2012. ProQuest Ebook Central,

Index operations, 288 optical density, 106 optimal performance, 294, 326 optimization, 172, 286, 287, 292, 299, 310, 331, 332, 355, 358 optimization method, 310 organ, 24, 38, 49, 62, 101, 236, 281 organelles, 56 organic compounds, 356 organism, 35 organize, 39 organs, 236 original training, 297 osteoarthritis, 3, 18, 21 osteoporosis, 71, 100, 131, 132, 362 outpatient, 110, 132, 215 outpatients, 198 ovarian cancer, 60 overlap, 247, 294, 320, 336 overlay, 307 oversulfated chondroitin sulfate (OSCS), xv, 150, 238, 278, 279, 282, 345, 359 oxygen, 25, 191, 205

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P paclitaxel, 190, 191 pain, x, 2, 18, 21, 196 pairing, 129, 143, 144, 153, 280 parallel, 110, 111, 318 Partial Least Squares, 291, 295, 316, 320 partial least squares discriminant analysis (PLS-DA), xvi, 278, 285, 295, 320 partial least squares regression, xv, 278, 285, 304, 308, 345, 349, 355 partial least squares regression (PLSR), xv, 278, 285, 308, 345 partial thromboplastin time, xi, 4, 97, 102, 119, 123, 126, 194 pass/fail, 250 pathogenesis, 90, 176 pathogens, 167, 177 pathology, xvi, 361 pathways, 79, 81, 83, 157, 176, 283

325

pattern recognition, xv, 278, 284, 285, 294, 295, 298, 299, 308, 319, 333, 345, 347, 348, 349, 354, 355, 359 PCA, xvi, 136, 278, 285, 294, 295, 300, 319, 320, 322, 325, 326, 335, 346, 349 peptide, 25, 104, 107, 165, 171, 174, 178, 194, 258, 259, 273 peptides, xv, 177, 181, 258, 259, 262, 272 peripheral blood, 36, 37, 58 permeability, 179 permeation, 208 permission, iv, 140, 142, 144, 145 PET, 196, 206 pH, viii, xv, 19, 28, 31, 138, 141, 142, 144, 145, 198, 245, 246, 247, 257, 258, 259, 260, 262, 263, 264, 265, 266, 267, 269, 270, 272, 273, 274 phagocyte, xiii, 187 phagocytic cells, 191, 205 phagocytosis, 192, 197 pharmaceutical, xiv, xv, 67, 149, 151, 152, 179, 233, 234, 235, 236, 237, 239, 251, 255, 278, 279, 280, 283, 308, 345, 349, 352, 354, 355, 356, 357 pharmaceuticals, xiv, 129, 234, 279, 293 pharmacokinetics, xi, 2, 20, 24, 97, 100, 123, 131, 132, 208, 213, 215, 217, 218, 223, 225, 227, 229, 230 pharmacology, ix, 2, 18, 20 phenotype, 86, 88, 207 phenotypes, 56 phenylalanine, 170 phosphate, xi, 41, 44, 46, 47, 53, 69, 86, 141, 142, 152, 165, 170, 195, 245, 246, 351 phosphatidylcholine, 55, 68 phosphatidylserine, 37, 44, 54, 63 phospholipid vesicles, x, 33, 37, 39, 40, 41, 55, 64, 65 phospholipids, 44, 50, 67, 102, 113, 208 phosphorylation, xi, 70, 72, 73, 81, 85, 86 photolithography, 111 physical mechanisms, x, 34 physicians, 147

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326 physicochemical methods, 237 physicochemical properties, xiv, 188, 203, 233 Physiological, 91 physiology, 176, 183 pilot study, 231 PISA, 165 placebo, 21, 220 placenta, 67, 92 plasma levels, 8, 22, 79, 84, 218 plasma membrane, 58 plasma proteins, 4, 7, 12, 16, 22, 23, 86, 99, 132, 205, 215, 217, 219, 237 plasticity, 172 platelet aggregation, 259 platelet count, 131, 362, 363, 364 platelets, xvi, 7, 36, 39, 49, 56, 57, 59, 62, 63, 100, 103, 122, 132, 361, 362 platform, 119, 194 playing, 75, 78 PLS, xvi, 136, 278, 285, 295, 296, 319, 320, 321, 322, 323, 325, 327, 335, 346, 349, 356, 357, 358 PM, 207 polar, 165, 167 polarity, 138, 139, 140, 142, 152 policy, 278 poly(ethylene terephthalate), 196 polyacrylamide, xii, 128, 129, 137, 151 polyamines, 139 polydispersity, 3, 4 polyelectrolyte complex, 195, 198, 207 polymer, 3, 71, 130, 142, 145, 146, 193, 198, 199, 235, 242, 250, 361 polymer materials, 193 polymerase, 353 polymerization, 3, 133, 138 polymers, xiii, 129, 142, 187, 189, 191, 192, 193, 194, 195, 196, 198, 199, 203, 211, 244 polypeptides, 362 polysaccharide, xiii, xiv, 48, 129, 130, 137, 157, 187, 188, 195, 198, 203, 206, 215, 233, 235, 246, 262, 279, 280, 361

Index polysaccharide chains, 280 polysaccharides, xiv, 21, 130, 142, 153, 157, 177, 233, 237, 240, 245, 247, 249, 252, 280 polystyrene, 195 polyurethane, 202, 206, 211 population, 122, 214, 216, 222, 226, 227, 228, 288, 301, 310 population size, 310 portfolio, xii, 127 precipitation, 199 predictability, 100, 215 prediction models, xv, 278 predictive accuracy, 297 preeclampsia, 62, 63 pregnancy, 26, 27, 36, 50, 54, 62, 63, 67, 100, 110, 111, 122 preoperative screening, 249 preparation, iv, 3, 4, 64, 87, 139, 196, 199, 200, 251, 263, 274, 279, 280, 284, 353 prevention, ix, xii, xiii, xiv, 1, 2, 5, 16, 21, 65, 100, 122, 123, 127, 132, 147, 177, 183, 199, 202, 204, 213, 220, 229, 233, 366 priming, 63 principal component analysis, 150, 285, 300, 304, 349, 356 Principal Components Analysis, 294, 319 principal components analysis (PCA), xvi, 278, 294, 319 principles, 102 prions, 35, 56 prisons, 122 probability, 48, 288, 301, 310, 331, 362, 363 probe, 118, 119, 135, 165, 173, 303 professionals, 121 prognosis, 205 pro-inflammatory, x, 69, 83, 84 project, 160, 319, 358 proliferation, ix, x, 69, 70, 72, 73, 74, 75, 76, 77, 78, 80, 81, 89, 91, 94, 95, 191, 195, 200, 207, 211 proline, 258, 262, 264

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Index promoter, 134 propagation, 298, 331 prophylactic, 100, 219, 225, 229, 231 prophylaxis, x, xi, 26, 27, 33, 49, 97, 99, 132, 208, 213, 215, 225, 226, 227, 231 protease inhibitors, 4, 7 protection, 197, 199, 204 protein structure, 160 protein synthesis, 72, 77 protein-ligand interfaces, xiii, 155 proteins, xi, xiii, xvi, 4, 23, 25, 26, 44, 49, 54, 65, 67, 70, 71, 72, 102, 104, 121, 129, 131, 132, 133, 146, 155, 160, 165, 168, 171, 172, 174, 175, 177, 178, 179, 181, 194, 195, 235, 252, 280, 361 proteoglycans, xi, 2, 70, 77, 81, 89, 92, 93, 94, 157, 176, 183 proteolytic enzyme, 196 prothrombin, 102, 121, 160, 238, 249 protons, 137, 306 pruning, 297, 354 PTT, 4, 97, 102 pulmonary embolism, 26, 62, 100, 197, 363 pulmonary hypertension, 95 pure water, 64 purification, xiv, 133, 141, 233, 237, 280, 283 purity, xiv, xv, 149, 154, 234, 235, 251, 277, 278, 279, 281, 284, 302, 349, 352 purpura, 59 pyrolysis, 355 Q quality assurance, 148, 352 quality control, xii, 98, 106, 239, 249, 251, 279, 293, 308, 348, 352 quality of life, 109 quantification, 122, 153, 237, 239, 240, 246, 251, 307 quartz, 120, 126 quaternary ammonium, 280 Queensland, 187

327 R

radical polymerization, 194 radiotherapy, 215 radius, 173, 291, 318 Ramadan, 356 Raman spectroscopy, 241, 353 RANTES, 162, 184 raw materials, 281 RE, 67, 88, 95, 209, 330, 352, 353 reactions, xiv, 21, 49, 134, 148, 157, 251, 252, 257, 281, 362 reactive oxygen, 5 reagents, 102, 105, 109, 110, 111, 113, 119, 124, 263, 280, 303 real time, 136 reality, 110 recall, 282 receptors, xi, 35, 50, 70, 77, 78, 81, 89, 90, 94, 137, 146, 151, 177 recognition, xiii, 151, 155, 164, 174, 180, 181, 183, 184, 185, 205, 293, 366 recommendations, iv, 204, 220 recovery, ix, 1, 8, 21, 30 red blood cells, 15, 61 redistribution, 12, 119 refractive index, 171 regeneration, 195, 207 regression, xv, 238, 278, 285, 286, 287, 289, 290, 291, 292, 293, 295, 296, 298, 299, 304, 308, 309, 312, 315, 316, 317, 345, 349, 350, 352, 355, 356, 357, 358 regression analysis, 285, 287, 289, 312, 352 regression method, 286, 291, 318 regression model, xv, 278, 289, 291, 292, 293, 295, 308, 312, 316, 317 regulatory requirements, 237 rehydration, 193 relaxation, 303 relevance, 89 reliability, xii, 98, 121, 122, 123, 286 relief, 21 renal dysfunction, 214, 222

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328 renal failure, 62, 214, 216, 230 repair, 78, 79, 176 repetitions, 317 repression, 95 requirements, xiii, 121, 128, 132, 165, 236, 247 RES, 172 researchers, 78, 86, 87, 104, 195, 196, 281, 282 residual matrix, 295 residuals, 290 residues, 54, 68, 74, 90, 130, 131, 133, 135, 159, 165, 167, 168, 169, 170, 171, 179, 279, 361 resistance, 71, 215 resolution, 136, 138, 140, 141, 142, 143, 145, 149, 165, 168, 169, 171, 174, 180, 185, 242, 243, 246, 247, 282, 354 resonator, 121 resources, 173 response, 3, 22, 37, 49, 61, 66, 67, 75, 76, 78, 79, 81, 82, 83, 84, 89, 99, 100, 113, 161, 176, 206, 246, 250, 282, 287, 289, 290, 291, 293, 297, 298, 302, 312, 320, 364 responsiveness, 88, 102, 228 restenosis, 199, 200, 202, 208, 211 restoration, 75 RH, 57, 68, 92 rheumatoid arthritis, x, 17, 18, 29, 34, 51, 52, 61 ribose, 243 Ridge regression (RR), xv, 278, 285, 290, 308, 345 rights, iv rings, 172 risk, 22, 36, 48, 60, 99, 100, 132, 191, 214, 216, 217, 219, 220, 222, 223, 225, 226, 227, 281, 348, 362, 364, 365 risk factors, 22, 216 RNA, 56, 80, 167 room temperature, 44, 47, 207 root, 172, 297, 304, 350 rotations, 172

Index routes, ix, 1, 2, 5, 16, 197 rules, 288 Russia, 257, 263 ruthenium, 119, 126 S safety, xiv, 3, 123, 133, 217, 220, 223, 225, 226, 229, 230, 234, 353, 365 saliva, 36, 58 salmon, 362 salts, 280 saturation, 13, 15 scaling, 303, 355 scatter, 107 scattering, 107 school, 122 science, x, 2, 87, 110 sclerosis, 37 scope, 122 scrotum, 12 second generation, 20 secrete, 60, 86 secretion, 84 segregation, 44 selectivity, 121 self-assembly, 198, 203 self-monitoring, 110, 122 senses, 113 sensing, 115 sensitivity, xiii, 103, 119, 121, 124, 128, 135, 136, 141, 142, 145, 250, 251, 300, 306, 336, 348, 356, 358 sensors, 115, 120 sepsis, 37, 62 serine, 4, 7 serum, 43, 53, 65, 71, 72, 73, 75, 76, 95, 119, 179 services, iv shape, 34, 39, 40, 57, 64, 65, 200 shear, 193 sheep, 238, 283 shock, 281 shortness of breath, 134, 283

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Index showing, 8, 31, 40, 75, 83, 119, 340 shrimp, 129 sickle cell, 62 side effects, iv, ix, 71, 87, 132, 133, 158, 167, 239 signal transduction, 83 signaling pathway, 74 signals, 136, 284, 285, 303, 306, 308 signal-to-noise ratio, 299 significance level, 308 signs, 14 silanol groups, 138 silica, 102, 113, 138, 139, 152 silicon, 121 silver, 196, 264 simulation, xv, 65, 168, 173, 220, 257, 258, 259, 273 simulations, xiii, 155, 170, 173, 219, 220 siRNA, 80 skeleton, 34 skin, 13, 17, 18, 29, 66, 101, 196, 197, 363, 366 small intestine, 19 smooth muscle, xi, 69, 71, 73, 74, 76, 82, 83, 86, 88, 89, 90, 91, 92, 94, 95, 148, 200, 203, 210, 211 smooth muscle cells, xi, 69, 71, 73, 74, 86, 88, 89, 90, 92, 94, 95, 200, 210 SO42-, 199 sodium, xvi, 21, 27, 28, 129, 137, 138, 141, 147, 154, 173, 201, 229, 237, 238, 239, 245, 246, 250, 263, 264, 278, 280, 281, 282, 283, 284, 285, 302, 307, 308, 345, 348, 353, 358 sodium hydroxide, 264 softener, 197 soft-independent modeling of class analogy (SIMCA), xvi, 278, 285 software, xv, 165, 173, 257, 262, 303, 304, 358 solid tumors, 64, 191 solubility, 202 solution, x, xv, 34, 40, 42, 44, 45, 52, 53, 78, 119, 149, 157, 171, 179, 182, 197,

329

198, 199, 238, 242, 244, 250, 251, 257, 258, 259, 262, 263, 264, 265, 266, 267, 269, 270, 272, 273, 279, 282, 302 solvents, 202, 303 somatic mutations, 68 SP, 57, 117, 122, 125, 208 space shuttle, 109 Spain, 155 species, 5, 129, 136, 138, 139, 143, 145, 150, 167, 236, 263, 266, 267, 269, 270, 271, 272, 354 specifications, 348 spectroscopy, xvi, 128, 129, 135, 136, 149, 150, 151, 168, 182, 209, 234, 239, 240, 241, 278, 279, 282, 283, 284, 285, 302, 306, 348, 351, 353, 354, 355, 358 sperm, 362 sphincter, 19 spleen, 12 spontaneous abortion, 36 Spring, 183, 277 sprouting, 80, 89 squamous cell, 38 squamous cell carcinoma, 38 SS, 63, 94, 123 stability, 28, 64, 121, 179, 198, 272, 317, 355 stabilization, 204 stable complexes, xiv, 257, 259 standard deviation, 303, 304, 330, 339, 350 standard error, 316, 317, 326, 329, 350 standardization, 150, 304 stasis, 9, 16 state, 61, 62, 74, 86, 174, 176, 182, 183, 185, 215, 288 statistics, 165, 263 steel, 105 stem cells, 49 steroids, 49 stimulation, 75, 80, 217 stimulus, 90, 289 stoichiometry, 259, 270, 272, 273, 274 stomach, 7, 12, 19, 31

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330 storage, 105, 106, 110 stress, 82, 91 stress response, 82 stroke, 5, 37, 60, 98, 363 structural protein, 259 structural variation, 306 structure formation, 80 substitutes, 25 substitution, 151, 177, 179, 351 substitutions, 280 substrate, 73, 104, 107, 116, 118, 148, 167, 171, 180, 184, 238 substrates, 84, 90, 104, 107 success rate, 320, 321, 324, 325, 330, 332, 333, 346 sucrose, 3, 18, 28, 30 Sun, 27, 151, 207, 208, 209, 254 suppliers, 302, 308 supply chain, 283, 286 support vector machine (SVM), xvi, 278, 285, 291 support vector regression (SVR), xv, 278, 285 suppression, ix, x, 33, 48, 52, 53, 191, 203 surface area, 6, 7, 188 surface modification, 194, 206 surfactant, 138 surveillance, 286 survival, 17, 18, 29, 65, 82, 288 susceptibility, 100 suspensions, 34 Sweden, 118 swelling, xiv, 234, 239, 281 symptoms, 18, 49, 282, 283 syndrome, 27, 36, 37, 38, 39, 42, 53, 55, 59, 60, 61, 62, 63, 64, 65, 67, 207, 215, 230 synovial fluid, 36 synthesis, x, xiii, 5, 25, 57, 69, 72, 75, 88, 89, 94, 104, 129, 131, 146, 155, 179, 253 systemic lupus erythematosus, 61 systolic blood pressure, 17

Index T T cell, 57, 58, 82, 84, 85, 93, 94 T cell XE "T cell" s, 57, 58, 82, 84, 85, 93 T lymphocytes, 62 tachycardia, 281 target, xvi, 24, 85, 121, 148, 200, 204, 205, 292, 361 technological advances, 111, 121 technologies, xii, 87, 98, 105, 109, 110, 111, 112, 113, 118, 121 technology, 104, 105, 106, 109, 110, 112, 113, 114, 116, 117, 119 TEM, 200 temperature, 39, 47, 145, 173, 263 test data, 294, 296, 312, 315 testing, xii, 49, 97, 98, 104, 105, 109, 110, 111, 113, 122, 124, 125, 126, 136, 284, 296, 319, 325, 348 TGF, 176 theatre, xii, 98, 109 therapeutic effects, 50, 188 therapeutic interventions, 87 therapeutics, 71, 191 therapy, x, xi, xiii, xiv, 2, 5, 22, 26, 27, 30, 67, 97, 100, 102, 103, 104, 107, 118, 120, 122, 123, 124, 126, 177, 183, 187, 192, 198, 200, 207, 208, 215, 217, 221, 223, 228, 257, 364, 365, 367 thermodynamics, 173 thrombin, xi, 4, 5, 39, 76, 79, 81, 82, 93, 94, 97, 99, 102, 103, 104, 116, 117, 120, 121, 125, 126, 131, 132, 158, 161, 174, 177, 182, 183, 215, 237, 238, 249, 350, 362, 363 thrombocytopenia, 27, 71, 93, 100, 128, 131, 362, 366, 367 thrombocytopenic purpura, 37, 59 thrombophlebitis, 38 thrombus, 9, 10, 11, 14, 15, 16, 17, 86, 192, 197, 202 thymus, 12 time periods, 216 time series, 173

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Index tinzaparin, ix, 1, 8, 9, 10, 14, 15, 22, 133, 144, 145, 214, 217, 218, 219, 222, 223, 226, 229, 230 tissue, xiii, 2, 4, 7, 8, 9, 13, 22, 36, 38, 39, 49, 58, 59, 61, 62, 64, 75, 77, 87, 92, 102, 151, 176, 177, 178, 179, 187, 189, 205, 217, 229, 236, 238, 280, 283 tissue engineering, 87, 92 titanium, 86, 91 topology, 298 toxicity, 30, 199, 200, 204, 283, 357 toxin, 164 trachea, 12 trade, 133, 292, 299, 318 trade-off, 292, 299, 318 trafficking, 26, 210 training, 286, 289, 291, 292, 294, 295, 296, 297, 298, 299, 300, 305, 308, 310, 312, 314, 315, 317, 318, 319, 320, 324, 326, 327, 328, 330, 331, 332, 335, 346 trajectory, 173 transaminases, 362 transcription, xi, 49, 69, 82 transcription factors, xi, 69 transducer, 120 transduction, 88 transfection, 194, 199 transformation, 39, 57, 71 transformations, 40 transfusion, 57 translocation, 82, 83, 85 transmission, 57, 183, 200 transmission electron microscopy, 200 transparency, 245 transport, 2, 7, 19, 35, 39, 175, 198 transportation, 110 trauma, 49 trial, 21, 30, 147, 198, 213, 225 triggers, 91, 113 triglycerides, 18 trypsin, 84, 171 tryptophan, 170

331

tumor, x, xiii, 33, 36, 38, 39, 49, 56, 58, 59, 60, 63, 64, 65, 82, 176, 178, 187, 191, 192, 193, 203, 205, 206 tumor cells, xiii, 36, 187 tumor development, 176 tumor growth, 63, 176, 193 tumor metastasis, 38, 64 tumor necrosis factor, 82 tumor progression, x, 33, 49, 56, 59, 64, 65 tumors, 39, 192, 193 tumours, 36 Turkey, 253 twist, 87 type 2 diabetes, 60, 61 tyrosine, 79, 91, 170 Tyrosine, 92 U UK, 97, 117, 122, 125, 358 ulcer, 7 ulcerative colitis, 49, 67 ultrasound, 23 ultrastructure, 123 unequal dispersed classes (UNEQ), xvi, 278, 285 Unfractionated heparin (UFH), xii, 98, 127, 130, 214, 215 United, xv, 20, 70, 129, 148, 187, 228, 235, 239, 253, 277, 281, 282, 283, 350, 364, 365 United Kingdom, 187 United States, xv, 20, 70, 129, 148, 228, 235, 239, 253, 277, 281, 282, 283, 350, 364, 365 unstable angina, 100, 230 ureters, 13 urine, ix, 1, 12, 18, 30, 36, 59, 110 urticaria, 281 USA, 56, 69, 105, 106, 107, 108, 109, 112, 113, 114, 115, 116, 122, 124, 125, 127, 213, 235, 239, 253, 254, 277, 280

Heparin : Properties, Uses and Side Effects, Nova Science Publishers, Incorporated, 2012. ProQuest Ebook Central,

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Index

UV, 129, 142, 143, 144, 150, 242, 246, 260, 261 UV absorption spectra, 260, 261

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V vacuum, 151 validation, 286, 291, 297, 300, 315, 316, 318, 319, 324, 328, 331, 333, 349 variations, 295, 319, 332 vascular diseases, 70, 82 vascular endothelial growth factor (VEGF), 195 vascular system, x, 69, 71, 73, 80, 217 vascular wall, x, 1, 4, 5, 16 vasculature, xi, 70, 79, 85, 86, 87, 93, 218 vasculitides, 58 vasodilation, 281 vasodilator, 5, 134 vector, xv, 194, 206, 278, 285, 290, 291, 293, 296, 299, 304, 308, 318, 345, 350, 355, 356, 357, 358 VEGFR, xi, 70 vehicles, 196, 210 vein, 5, 9, 10, 11, 15, 16, 17, 18, 19, 22, 78, 100, 132, 197, 199, 215, 229, 362, 363 venipuncture, 101, 103 versatility, 121, 243 vesicle, 41, 44, 46, 47, 193 vessels, 13 viruses, 56, 280 viscosity, 120 visualization, 319, 342, 346 vitamin K, 102, 249, 363 vote, 296

VSMC proliferation, xi, 70, 74, 75, 76 W Washington, 29, 112, 124, 125, 303 waste, 141 water, 8, 39, 144, 172, 173, 202, 207, 242, 250, 258, 262, 263, 302, 303 wavelet, 356 wealth, 71 web, 172 wells, 84, 85 Western blot, 73, 85 white blood cells, 36 wild type, 169 windows, 303 wood, 3 workers, 195, 245 workflow, 121 workplace, 109 worldwide, 98, 99 wound healing, 77, 79, 88, 195, 207 X X-ray diffraction, 200 X-ray diffraction (XRD), 200 Y yield, 47, 70, 87, 129, 132, 133, 135, 136, 170, 289, 298 Z zinc, 182

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Heparin : Properties, Uses and Side Effects, Nova Science Publishers, Incorporated, 2012. ProQuest Ebook Central,