Translational Biotechnology : A Journey from Laboratory to Clinics. [1 ed.] 9780128219737, 0128219734

Table of contents :
Front Cover --
Translational Biotechnology --
Copyright Page --
Contents --
List of contributors --
Preface --
1 Introduction to translational biotechnology --
1 Translational biotechnology: A transition from basic biology to evidence-based research --
1.1 Introduction --
1.1.1 Background and emergence of the field --
1.2 The phases of translational research --
1.3 Challenges to solutions --
1.4 Applications --
1.4.1 Drug development --
1.4.1.1 Protein drugs --
1.4.1.2 Hormones --
1.4.1.3 Monoclonal antibodies --
1.4.1.4 Cytokines --
1.4.1.5 Vaccines --
1.4.2 Nanomedicine 1.4.3 Gene therapy --
1.4.4 Precision medicine and biomarker development --
1.4.5 Microbial engineering for bio-therapeutics --
1.4.6 Application of big data and translational bioinformatics --
1.5 Conclusion and future directions --
1.6 Highlights --
Acknowledgment --
Conflict of interest --
References --
2 Biotherapeutics --
2 Biotechnology-based therapeutics --
2.1 Introduction --
2.2 Human gene therapy --
2.2.1 Somatic cell gene therapy --
2.2.2 Germline gene therapy --
2.2.3 Gene transfer system --
2.2.3.1 Nonbiological delivery system --
2.2.3.1.1 Physical method --
Sonoporation Electroporation --
Magnetofection --
Hydroporation --
Gene gun --
2.2.3.1.2 Chemical method --
Liposomes --
Polymers --
Heat shock --
2.2.3.1.3 Biological method --
Bacterial vector --
Viral vector --
Retroviral vectors --
Adenoviral vectors --
Adeno-associated vectors --
Herpes simplex virus --
2.2.4 Gene-editing technology --
2.2.4.1 Zinc-finger nucleases --
2.2.4.2 Transcription activator-like effector nucleases --
2.2.4.3 Clustered regularly interspaced short palindromic repeat-Cas-associated nucleases --
2.2.5 Ethical issue --
2.3 Stem cell therapy --
2.3.1 Sources of stem cells 2.3.1.1 Pluripotent stem cells --
2.3.1.2 Multipotent stem cells --
2.3.2 Benefits of stem cell therapy in various disorder --
2.3.2.1 Retinal diseases --
2.3.2.2 Heart diseases --
2.3.2.3 Neural disease --
2.3.2.4 Lung disorder --
2.3.2.5 Liver disease --
2.3.3 Challenges and problems --
2.4 Nanomedicine --
2.4.1 Nano therapeutic applications --
2.4.1.1 Nano drug delivery --
2.4.1.1.1 Hydrogel --
2.4.1.1.2 Micelle --
2.4.1.1.3 Dendrimers --
2.4.1.1.4 Polymers --
2.4.1.1.5 Liposomes --
2.4.1.2 Nanosensor --
2.4.2 Tissue engineering --
2.4.3 Nanoimaging --
2.5 Drug designing and delivery 2.5.1 Rational drug design --
2.5.2 Computer-aided drug design --
2.5.2.1 In silico drug design --
2.5.2.2 Machine learning in drug design --
2.5.2.2.1 Artificial intelligence in drug design --
2.5.2.2.2 Artificial neural network in drug design --
2.5.3 Drug delivery --
2.6 Recombinant therapeutic proteins and vaccines --
2.6.1 Recombinant protein --
2.6.2 Expression system --
2.6.2.1 Bacteria --
2.6.2.2 Yeast --
2.6.2.3 Mammals --
2.6.3 Recombinant protein as a treatment --
2.6.3.1 Anemia --
2.6.3.2 Diabetes --
2.6.3.3 Human growth hormone --
2.6.3.4 Hepatitis B

Citation preview

TRANSLATIONAL BIOTECHNOLOGY

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TRANSLATIONAL BIOTECHNOLOGY A Journey from Laboratory to Clinics Edited by

YASHA HASIJA Department of Biotechnology, Delhi Technological University, Delhi, India

Academic Press is an imprint of Elsevier 125 London Wall, London EC2Y 5AS, United Kingdom 525 B Street, Suite 1650, San Diego, CA 92101, United States 50 Hampshire Street, 5th Floor, Cambridge, MA 02139, United States The Boulevard, Langford Lane, Kidlington, Oxford OX5 1GB, United Kingdom Copyright © 2021 Elsevier Inc. All rights reserved. No part of this publication may be reproduced or transmitted in any form or by any means, electronic or mechanical, including photocopying, recording, or any information storage and retrieval system, without permission in writing from the publisher. Details on how to seek permission, further information about the Publisher’s permissions policies and our arrangements with organizations such as the Copyright Clearance Center and the Copyright Licensing Agency, can be found at our website: www.elsevier.com/permissions. This book and the individual contributions contained in it are protected under copyright by the Publisher (other than as may be noted herein). Notices Knowledge and best practice in this field are constantly changing. As new research and experience broaden our understanding, changes in research methods, professional practices, or medical treatment may become necessary. Practitioners and researchers must always rely on their own experience and knowledge in evaluating and using any information, methods, compounds, or experiments described herein. In using such information or methods they should be mindful of their own safety and the safety of others, including parties for whom they have a professional responsibility. To the fullest extent of the law, neither the Publisher nor the authors, contributors, or editors, assume any liability for any injury and/or damage to persons or property as a matter of products liability, negligence or otherwise, or from any use or operation of any methods, products, instructions, or ideas contained in the material herein. British Library Cataloguing-in-Publication Data A catalogue record for this book is available from the British Library Library of Congress Cataloging-in-Publication Data A catalog record for this book is available from the Library of Congress ISBN: 978-0-12-821972-0 For Information on all Academic Press publications visit our website at https://www.elsevier.com/books-and-journals

Publisher: Stacy Masucci Senior Acquisitions Editor: Rafael E. Teixeira Editorial Project Manager: Mona Zahir Production Project Manager: Niranjan Bhaskaran Cover Designer: Victoria Pearson Typeset by MPS Limited, Chennai, India

Contents 2

List of contributors xi Preface xiii

Biotherapeutics 2 Biotechnology-based therapeutics 27

1

Ravichandran Vijaya Abinaya and Pragasam Viswanathan

Introduction to translational biotechnology

2.1 Introduction 28 2.2 Human gene therapy 29 2.2.1 Somatic cell gene therapy 30 2.2.2 Germline gene therapy 30 2.2.3 Gene transfer system 30 2.2.4 Gene-editing technology 33 2.2.5 Ethical issue 34 2.3 Stem cell therapy 34 2.3.1 Sources of stem cells 35 2.3.2 Benefits of stem cell therapy in various disorder 36 2.3.3 Challenges and problems 37 2.4 Nanomedicine 37 2.4.1 Nano therapeutic applications 37 2.4.2 Tissue engineering 39 2.4.3 Nanoimaging 40 2.5 Drug designing and delivery 40 2.5.1 Rational drug design 41 2.5.2 Computer-aided drug design 41 2.5.3 Drug delivery 44 2.6 Recombinant therapeutic proteins and vaccines 44 2.6.1 Recombinant protein 44 2.6.2 Expression system 44 2.6.3 Recombinant protein as a treatment 46 2.6.4 Recombinant vaccine 47 2.7 Conclusion and future applications 48 Acknowledgments 48 Conflicts of interest 48 Author’s contribution 48 References 49

1 Translational biotechnology: A transition from basic biology to evidence-based research 3 Debleena Guin, Sarita Thakran, Pooja Singh, S. Ramachandran, Yasha Hasija and Ritushree Kukreti

1.1 Introduction 4 1.1.1 Background and emergence of the field 4 1.2 The phases of translational research 5 1.3 Challenges to solutions 6 1.4 Applications 9 1.4.1 Drug development 12 1.4.2 Nanomedicine 16 1.4.3 Gene therapy 17 1.4.4 Precision medicine and biomarker development 19 1.4.5 Microbial engineering for bio-therapeutics 19 1.4.6 Application of big data and translational bioinformatics 19 1.5 Conclusion and future directions 21 1.6 Highlights 21 Acknowledgment 22 Conflict of interest 22 References 22

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3 Advanced biotechnology-based therapeutics 53 Srividhya Ravichandran and Gaurav Verma

3.1 Introduction 54 3.2 Technologies that lead to the discovery of therapy 55 3.2.1 Genome editing technologies 55 3.2.2 Role of nanomedicine in drug discovery approaches 56 3.2.3 Antibody
drug conjugates 58 3.3 Molecular diagnostics 60 3.3.1 Translational bioinformatics 62 3.3.2 Organoids—tools for disease models 63 3.4 Cell-based therapy 65 3.5 Nanotechnology and its uses in biomedicine 67 3.6 Genome-scale metabolic modeling 68 3.7 Critical processes in the flow from basic science to practical application in the clinic via clinical trials and translational studies 69 3.8 Major pitfalls in translational research 70 3.9 Advancement in devices, biologics, and vaccines as an introduction to biotechnology products that are being used in therapy 72 3.10 Conclusion and summary 74 References 74

3 Pathway and target discovery 4 Human in vitro disease models to aid pathway and target discovery for neurological disorders 81 Bhavana Muralidharan

4.1 Introduction 82 4.2 Generation of human disease models using iPSCs/patient fibroblasts 83 4.2.1 Directed differentiation into neural cells 84 4.2.2 Direct differentiation into neurons/ glia 86 4.2.3 Direct lineage reprogramming/ transdifferentiation into neurons 88 4.3 Modeling neurodevelopmental disorders 88 3.1 Rett syndrome 88 4.3.2 Fragile X syndrome 89

4.3.3 Autism spectrum disorders 89 4.3.4 Schizophrenia 90 4.4 Modeling neurodegenerative diseases 91 4.4.1 Amyotrophic lateral sclerosis 91 4.4.2 Alzheimer’s disease 92 4.4.3 Parkinson’s disease 93 4.5 Cerebral organoids and the future of human in vitro disease modeling 93 4.6 From bench to bedside—identification of pathways and drug targets for designing therapies 95 4.7 Future perspectives 97 Keyword definitions 97 Acknowledgments 98 References 98

5 Importance of targeted therapies in acute myeloid leukemia 107 Ajit Kumar Rai and Neeraj Kumar Satija

5.1 Introduction 107 5.1.1 Conventional therapy for acute myeloid leukemia 108 5.1.2 Significance of target discovery 108 5.2 Approaches in target discovery 109 5.2.1 Systems approach 110 5.2.2 Molecular approach 111 5.3 Acute myeloid leukemia
targeted therapies in clinics 117 5.3.1 BCL-2 inhibitors 117 5.3.2 Isocitrate dehydrogenase inhibitors 117 5.3.3 PML-RARα targeted therapy 118 5.3.4 Targeting FLT3-mutated acute myeloid leukemia: from bench to bedside (a case study) 119 5.4 Hurdles and emerging targeted therapies 120 5.5 Conclusion 125 Acknowledgments 125 References 126

4 Novel therapeutic modalities 6 Biological therapeutic modalities 137 Munish Chhabra

6.1 Introduction to biological therapeutic modalities 137 6.2 History of classical modalities 139

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6.3 New modalities 140 6.3.1 Small molecules 140 6.3.2 Nucleic acid therapeutics 142 6.3.3 Therapeutic proteins 143 6.3.4 Antibodies 145 6.3.5 Cell-based immunotherapies 148 6.3.6 Stem cells 150 6.3.7 Phage therapies 151 6.3.8 Microbiome-based therapeutics 153 6.4 Future of biological therapeutics 154 6.5 Case study—bio-therapeutic modalities in COVID-19 treatment 155 6.6 Conclusion 156 References 160

7 The journey of noncoding RNA from bench to clinic 165 Ravindresh Chhabra

7.1 Introduction 165 7.1.1 Noncoding RNAs and their classification 165 7.1.2 In silico ncRNA prediction tools 166 7.1.3 Screening and characterization of ncRNAs 167 7.1.4 Small noncoding RNAs (miRNAs and siRNAs) 167 7.1.5 Long noncoding RNAs 181 7.2 Patent landscape of noncoding RNA 187 7.3 Bottlenecks in the use of noncoding RNAs as biomarkers/therapeutics 189 7.4 Conclusions and future perspectives 191 References 192

8.4 Conclusion, limitations, and future directions 221 References 223

9 Bispecific antibodies: A promising entrant in cancer immunotherapy 233 Samvedna Saini and Yatender Kumar

9.1 Introduction 234 9.2 Evolution of bispecific antibodies 234 9.2.1 Different formats of bispecific antibodies 236 9.2.2 Mechanism of action 238 9.3 Production of bispecific antibodies 243 9.3.1 Hybrid hybridoma (quadroma technology) 243 9.3.2 Knob-into-hole approach 243 9.3.3 CrossMab approach 244 9.3.4 Chemical conjugation 244 9.4 Biomarkers in immunotherapy at a glance 246 9.4.1 Biomarkers for breast cancer 246 9.4.2 Biomarkers for prostate cancer 247 9.4.3 Biomarkers for checkpoint blockade immunotherapy 248 9.5 Engineering of therapeutic protein 248 9.5.1 Binding affinity enhancement 249 9.5.2 Immunogenicity minimization 249 9.5.3 Stability enhancement and half-life extension 250 9.6 Market analysis: past, present and future 250 9.7 Future challenges and opportunities 254 9.8 Conclusion 255 References 255

8 Peptide-based hydrogels for biomedical applications 203

10 Emerging therapeutic modalities against malaria 267

Debika Datta and Nitin Chaudhary

Suresh Kumar Chalapareddy, Andaleeb Sajid, Mritunjay Saxena, Kriti Arora, Rajan Guha and Gunjan Arora

8.1 Introduction 203 8.2 Peptide-based hydrogelators 204 8.2.1 β-Sheet forming peptides 204 8.2.2 α-Helical peptides 214 8.3 Biomedical applications 215 8.3.1 Therapeutic delivery 216 8.3.2 Scaffold for regenerative medicine 218 8.3.3 Wound dressing 219 8.3.4 Antimicrobial agents 220

10.1 Introduction 267 10.2 Heme-detoxification drugs 268 10.3 Drugs targeting DNA or protein synthesis 270 10.4 Drugs targeting membrane transporters 271 10.5 Natural products 272 10.6 Protein-based malaria vaccines 273 10.7 Nucleic acid vaccines for the new era 273 10.7.1 DNA-based vaccines 274

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10.7.2 RNA-based vaccines 10.8 Biological therapeutics 277 10.9 Conclusion 278 References 279

277

5 Healthcare bioinformatics 11 Translational bioinformatics: An introduction 289 Richa Nayak and Yasha Hasija

11.1 Introduction 289 11.2 The era of omics and big data: data mining and biomedical data integration 292 11.2.1 Data acquisition and warehousing 292 11.2.2 Data integration 293 11.2.3 Data mining 294 11.3 TBI in biomarker discovery 297 11.4 Computer-aided drug discovery 299 11.5 Artificial intelligence-based approach in TBI 300 11.5.1 Complex disease analysis using ML 301 11.5.2 Illustrious examples of ML in translational research 302 11.6 The implication of TBI in precision medicine 304 11.6.1 Data-driven precision medicine initiatives 305 11.6.2 Future prospects of transitional bioinformatics in personalized medicine 305 11.7 Conclusion 306 References 307

12 Pharmacodynamic biomarker for Hepatocellular carcinoma C: Model-based evaluation for pharmacokinetic
pharmacodynamic responses of drug 311 Nitu Dogra, Savita Mishra, Ruchi Jakhmola Mani, Vidhu Aeri and Deepshikha Pande Katare

12.1 Hepatocellular carcinoma 312 12.1.1 Possible risk factors of hepatocellular carcinoma 312

12.1.2 Stages of hepatocellular carcinoma 314 12.1.3 Challenges in therapeutic and medicinal drug treatment for hepatocellular carcinoma 316 12.2 Pharmacokinetic and pharmacodynamic profiles (PK
PD) 316 12.2.1 Pharmacokinetic profile (PK) 316 12.2.2 Pharmacodynamics (PD) 316 12.3 Pharmacokinetic and pharmacodynamic models 317 12.3.1 Compartmental models 317 12.3.2 Direct pharmacokinetic and pharmacodynamic models 318 12.3.3 Indirect pharmacokinetic and pharmacodynamic models 319 12.4 Advantages of pharmacokinetic and pharmacodynamic modeling 319 12.5 Development of pharmacodynamic (PD) biomarker in hepatocellular carcinoma 320 12.5.1 Proteomic approach for identification of pharmacodynamic biomarkers 321 12.5.2 Therapeutic outcome using PD biomarker 322 12.6 Pharmacokinetic and pharmacodynamic drug responses 323 12.7 Conclusions 323 References 324

6 Biological systems engineering 13 System biology and synthetic biology 329 Richa Nayak, Rajkumar Chakraborty and Yasha Hasija

13.1 Introduction 329 13.2 System biology 331 13.2.1 Central principles of scientific approaches to biology systems 332 13.2.2 Fields in therapeutic applications system biology 333 13.3 Synthetic biology 336 13.3.1 Role of synthetic biology in understanding disease mechanisms 337 13.3.2 Synthetic biology in drug discovery, development, and delivery 339

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13.3.3 Role of synthetic biology in personalized medicine 340 13.3.4 Regulation and ethical considerations of synthetic biology 340 13.4 Conclusion 341 References 342

7 Drug discovery and personalized medicine 14 Translational research in drug discovery: Tiny steps before the giant leap 347 Sindhuri Upadrasta and Vikas Yadav*

14.1 Introduction 348 14.2 Tools involved in translation drug discovery 349 14.3 Recent successful advances in translation drug discovery 351 14.3.1 Cancer 352 14.3.2 Diabetes 355 14.3.3 Acquired immunodeficiency syndrome 355 14.3.4 Autoimmune disorders 356 14.3.5 Neurological disorder 357 14.3.6 Cardiovascular disease (CVD) 357 14.4 Opportunities in translation drug discovery 358 14.5 Challenges in translation drug discovery 359 14.6 Approaches to boost translational drug discovery 360 14.7 Conclusion 364 14.8 Future perspective 364 References 365

15 FLAGSHIP: A novel drug discovery platform originating from the “dark matter of the genome” 371 Neeraj Verma, Siddharth Manvati and Pawan Dhar

15.1 Introduction 371 15.2 Designing novel therapeutic peptides from dark matter of the genome 373 15.2.1 Antimicrobial peptides 373

15.2.2 Antimalarial peptides 374 15.2.3 Anti-Alzheimer peptides 374 15.2.4 Drawbacks of peptides therapeutics 375 15.2.5 Future applications 375 15.3 Pseudogenes: a potential biotherapeutic target 376 15.3.1 Pseudogene-directed gene regulation 377 References 377

8 Socio-economic impact of translational biotechnology 16 Role of shared research facilities/core facilities in translational research 383 Vidhu Sharma

16.1 Introduction: socioeconomic impact of translational research 384 16.1.1 Challenges faced in translational research 385 16.2 Core facility: shared research
shared cost 386 16.2.1 Core facilities of prime significance in translational research 388 16.3 Research and development supporting mechanism: environmental scan (the United States and Canada) 389 16.3.1 Supporting translational research through core facilities in the United States—from past to present 390 16.3.2 Canada’s ecosystem of translational research and funding mechanism 392 16.3.3 Highlights around the world 394 16.3.4 Glimpses of global research and development expenditure 396 16.4 Efficiencies and lean practices in research management 399 16.4.1 Core facilities business model 399 16.4.2 Governance model for core facility 402 16.4.3 Core facilities and research outcome 402

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16.5 Final notes: learnings for future 403 16.5.1 Integration of core facilities within the institutional strategic plan 403 16.5.2 Comprehensive availability of infrastructure inventory 403 16.5.3 Impact measurement 404 Acknowledgments 404 References 404

17 A new TOPSIS-based approach to evaluate the economic indicators in the healthcare system and the impact of biotechnology 407 Priyanka Majumder and Apu Kumar Saha

17.1 Introduction 408 17.2 Technique for order of preference by similarity to ideal solution approach 410 17.2.1 Metric space 410 17.2.2 New technique for order of preference by similarity to ideal solution approach 411

17.3 Methodology 412 17.3.1 Selection of criteria 413 17.3.2 Selection of indicators 414 17.3.3 Application of new technique for order of preference by similarity to ideal solution approach 414 17.3.4 Analysis of sensitivity 416 17.4 Result and discussion 416 17.4.1 Result from technique for order of preference by similarity to ideal solution 1 416 17.4.2 Result from technique for order of preference by similarity to ideal solution 417 17.4.3 Result from sensitivity analysis 418 17.5 Conclusion 418 References 419

Glossary 421 Index 425

List of contributors Debleena Guin Genomics and Molecular Medicine Unit, Institute of Genomics and Integrative Biology (IGIB), Council of Scientific and Industrial Research (CSIR), Delhi, India; G N Ramachandran Knowledge Centre, Council of Scientific and Industrial Research (CSIR)—Institute of Genomics and Integrative Biology (IGIB), Delhi, India

Ravichandran Vijaya Abinaya Renal Research Lab, School of Biosciences and Technology, Centre for Biomedical Research, Vellore Institute of Technology, Vellore, India Vidhu Aeri Department of Pharmacognosy & Phytochemistry, SPER, Jamia Hamdard, New Delhi, India Gunjan Arora Yale University, New Haven, CT, United States Kriti Arora Proteus Digital Health, Redwood City, CA, United States

Yasha Hasija Department of Biotechnology, Delhi Technological University, Delhi, India; Department of Bioinformatics, Delhi Technological University, Shahbad Daulatpur, Main Bawana Road, Delhi, India

Inc.,

Rajkumar Chakraborty Department of Biotechnology, Delhi Technological University, Delhi, India

Deepshikha Pande Katare Proteomics and Translational Research Lab, Centre for Medical Biotechnology, Amity Institute of Biotechnology, Amity University, Noida, India

Suresh Kumar Chalapareddy National Institutes of Health., Bethesda, MD, United States Nitin Chaudhary Department of Biosciences and Bioengineering, Indian Institute of Technology Guwahati, Guwahati, India

Ritushree Kukreti Genomics and Molecular Medicine Unit, Institute of Genomics and Integrative Biology (IGIB), Council of Scientific and Industrial Research (CSIR), Delhi, India; Academy of Scientific and Innovative Research (AcSIR), Ghaziabad, India

Munish Chhabra Molecular Assemblies, San Diego, CA, United States Ravindresh Chhabra Department of Biochemistry, Central University of Punjab, Bathinda, Punjab, India

Yatender Kumar Netaji Subhas University of Technology, Delhi, India

Debika Datta Department of Biosciences and Bioengineering, Indian Institute of Technology Guwahati, Guwahati, India

Priyanka Majumder Department of Basic Science and Humanities (Mathematics), Techno College of Engineering Agartala, Maheshkhola, Agartala, Tripura, India

Pawan Dhar School of Biotechnology, Jawaharlal Nehru University, New Delhi India

Ruchi Jakhmola Mani Proteomics and Translational Research Lab, Centre for Medical Biotechnology, Amity Institute of Biotechnology, Amity University, Noida, India

Nitu Dogra Proteomics and Translational Research Lab, Centre for Medical Biotechnology, Amity Institute of Biotechnology, Amity University, Noida, India

Siddharth Manvati School of Biotechnology, Jawaharlal Nehru University, New Delhi India

Rajan Guha National Institutes of Health., Bethesda, MD, United States

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List of contributors

Savita Mishra Proteomics and Translational Research Lab, Centre for Medical Biotechnology, Amity Institute of Biotechnology, Amity University, Noida, India Bhavana Muralidharan Brain Development and Disease Mechanisms, inStem - Institute for Stem Cell Science and Regenerative Medicine, Bangalore, India Richa Nayak Department of Biotechnology, Delhi Technological University, Delhi, India Ajit Kumar Rai Systems Toxicology and Health Risk Assessment Group, CSIR-Indian Institute of Toxicology Research (CSIR-IITR), Vishvigyan Bhawan, Lucknow, India; Academy of Scientific and Innovative Research (AcSIR), Ghaziabad, India S.

Ramachandran G N Ramachandran Knowledge Centre, Council of Scientific and Industrial Research (CSIR)—Institute of Genomics and Integrative Biology (IGIB), Delhi, India; Academy of Scientific and Innovative Research (AcSIR), Ghaziabad, India

Academy of Scientific and Innovative Research (AcSIR), Ghaziabad, India Mritunjay Saxena ICMR-National Institute of Malaria Research, Delhi, India Vidhu Sharma BCCHR Core Technologies and Services, BC Children’s Hospital Research Institute, Vancouver, BC, Canada Pooja Singh Genomics and Molecular Medicine Unit, Institute of Genomics and Integrative Biology (IGIB), Council of Scientific and Industrial Research (CSIR), Delhi, India; Academy of Scientific and Innovative Research (AcSIR), Ghaziabad, India Sarita Thakran Genomics and Molecular Medicine Unit, Institute of Genomics and Integrative Biology (IGIB), Council of Scientific and Industrial Research (CSIR), Delhi, India; Academy of Scientific and Innovative Research (AcSIR), Ghaziabad, India Sindhuri Upadrasta CSIR-National Chemical Laboratory, Pune, India

Srividhya Ravichandran Department of Biotechnology, Indian Institute of Technology IIT, Chennai, India

Gaurav Verma Clinical Research Center, Lund University of Diabetic Center, Lunds University, Lund, Sweden

Apu Kumar Saha Department of Mathematics, National Institute of Technology Agartala, Barjala, Jirania, Tripura, India

Neeraj Verma School of Biotechnology, Jawaharlal Nehru University, New Delhi India

Samvedna Saini Netaji Subhas University of Technology, Delhi, India Andaleeb Sajid Yale University, New Haven, CT, United States Neeraj Kumar Satija Systems Toxicology and Health Risk Assessment Group, CSIR-Indian Institute of Toxicology Research (CSIR-IITR), Vishvigyan Bhawan, Lucknow, India;

Pragasam Viswanathan Renal Research Lab, School of Biosciences and Technology, Centre for Biomedical Research, Vellore Institute of Technology, Vellore, India Vikas Yadav Interdisciplinary Cluster for Applied Genoproteomics, University of Lie`ge, Lie`ge, Belgium; Present Address: Clinical Research Centre, Lund University, Malmo¨, Sweden

Preface The advancements in the field of biotechnology have been monumental and the pace thereof, exponential. However, the same is not paralleled in the clinical setting. The lack of adequate translation of basic biomedical research into clinical applications has been a matter of huge concern for the entire clinical scientific community. Therefore, there is a need to achieve congruence in both, so that the work done in research laboratories gets percolated into the real-time medical practice. This void has led to the emergence of an exciting field of “bench to bedside” translational research, so that the last man in the queue, that is, the patient, gets benefitted. This book “Translational Biotechnology: A journey from laboratory to the clinics” is a sincere effort toward understanding the steps involved in translating path-breaking innovative research and emerging scientific insights to reaching the patients, and in the process, creating new therapies, preventing, diagnosing and treating diseases, and improving health and living. The book, inter alia, is aimed at transferring fundamental biological discoveries and technologies from the research laboratories into patient care, in the quest for effective healthcare. It attempts to traverse the long journey from bench work to healthcare reforms and also tries to address the obstacles, low success rates, failures, and challenges in the complex voyage. In this book, we string together contributions from internationally acclaimed authors from various domains of biotechnology to offer unique insights in their

respective fields of expertise. It will take the readers through several facets of translational research in biotechnology with illustrative examples. The introductory sections of the book introduce advanced biotechnology principles and processes in disease studies. This section emphasizes technologies that lead to or assist in the discovery of better clinical outcomes. It is hoped that it will shape the understanding of critical processes in the flow from basic sciences to practical applications in the clinical setting, via translational studies and clinical trials. The book also discusses the advancements in devices, biologics, vaccines, and several biological modalities, as an introduction to biotechnology products that are being used in therapy. The subsequent sections deal with translational approaches in newer disciplines of biotechnology, like bioinformatics, systems biology, and synthetic biology. It discusses practical approaches in the development of personalized medicine, clinical systems, and translational medicine. It also outlines future research prospects of the bench to bedside approach. The conclusion section is one of its kind that gives the readers a birds-eye view of the socioeconomic aspects associated with translational biotechnology. The goal is to make the readers aware of the feasibility of carrying out translational research, its availability to the public, and the impact caused by discoveries made in the laboratory. It discusses the technological and monetary challenges faced in developing and underdeveloped countries in carrying

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out translational research, and ways to overcome them. It also deals with the legal and ethical aspects of translational biotechnology. The goal of the book is to provide in a lucid form, the research in the field of biotechnology that is translational in nature, is cost-effective, and readily available for use. I sincerely hope that the readers will benefit from this comprehensive book, which will further inspire and encourage them to adopt such practices in their research work, oriented toward clinical applications.

Every book is an embodiment of collective effort. I express my gratitude to all the contributors for delivering such insightful compilations of their respective areas of research, and the valuable input provided by the reviewers. I am indebted to the entire team at Elsevier for being in close collaboration at various stages for bringing out this book and ensuring a smooth sailing publication process. Yasha Hasija

S E C T I O N

1

Introduction to translational biotechnology

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1 Translational biotechnology: A transition from basic biology to evidence-based research Debleena Guin1,2, Sarita Thakran1,3, Pooja Singh1,3, S. Ramachandran2,3, Yasha Hasija4 and Ritushree Kukreti1,3 O U T L I N E 1.1 Introduction 4 1.1.1 Background and emergence of the field 4 1.2 The phases of translational research

5

1.3 Challenges to solutions

6

1.4 Applications 1.4.1 Drug development 1.4.2 Nanomedicine 1.4.3 Gene therapy 1.4.4 Precision medicine and biomarker development

1

2

3 4

1.4.5 Microbial engineering for bio-therapeutics 1.4.6 Application of big data and translational bioinformatics

9 12 16 17 19

19 19

1.5 Conclusion and future directions

21

1.6 Highlights

21

Acknowledgment

22

Conflict of Interest

22

References

22

Genomics and Molecular Medicine Unit, Institute of Genomics and Integrative Biology (IGIB), Council of Scientific and Industrial Research (CSIR), Delhi, India G N Ramachandran Knowledge Centre, Council of Scientific and Industrial Research (CSIR)—Institute of Genomics and Integrative Biology (IGIB), Delhi, India Academy of Scientific and Innovative Research (AcSIR), Ghaziabad, India Department of Bioinformatics, Delhi Technological University, Shahbad Daulatpur, Main Bawana Road, Delhi, India

Translational Biotechnology DOI: https://doi.org/10.1016/B978-0-12-821972-0.00006-X

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© 2021 Elsevier Inc. All rights reserved.

Section 1: Introduction to translational biotechnology

C H A P T E R

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1. Translational biotechnology: A transition from basic biology to evidence-based research

Section 1: Introduction to translational biotechnology

1.1 Introduction 1.1.1 Background and emergence of the field Biotechnology is an advanced field of biology that exploits technology to make and disseminate biological discoveries for human benefit. Biotechnology has a broad spectrum of applications, spanning from industry to agriculture, manufacturing of food, chemicals, probiotics, pharmaceuticals, and the list goes on. One of the primary focuses of biotechnology has been improving healthcare, and we have come a long way in that endeavor. Like, for instance, from microbial bioconversions for therapeutic use to vaccine development, drug discovery and development, clinical trial design, community medicine, personalized therapy, clinical informatics, etc. Through the course of time and with the accumulation of substantial biological information, biotechnology has progressed toward specific application-based research, which is the need of the decade. It has made a gateway for a new approach to research called “translational research.” The word “translation” as defined by the National Institute of Health funded National Center for Advancing Translational Sciences (NCATS), is “the process of turning observations in the laboratory, clinic, and community into interventions that improve the health of individuals and the public—from diagnostics and therapeutics to medical procedures and behavioral changes” (US Department of Health & Human Service, 2020). Moreover, the emerging field of “translational science” is focused on investigating the scientific and operational principles underlying each step of the translational process. Through the basic knowledge of human physiology in disease and how the intervention (say, a drug or a therapy) is affecting the diseases will expedite the translational process toward a focused science-driven, predictive, and effective drug development for the prevention and treatment of all diseases. With the availability of unlimited human biological data, this new subsidiary field of “translational biotechnology” is focused, distinct, and unique in operation, application, and implementation. While unique challenges in different territories hover, this field also provides us with unprecedented opportunities to widen the spectrum of biomedical enterprise. Comprising many disciplines of science and operations, including biology, chemistry, informatics, pharmaceutical, engineering, medicine, and public health management, translational biotechnology defines the scientific and operational relationships among these fields, builds bridges, and creates a transdisciplinary network for active development and deployment of interventions that benefit public health (Westfall, Mold, & Lyle, 2007). In this chapter, we introduce the key threads of translational research. We, first, provide a conceptual overview of the understanding in this field, the different stages in the broad spectrum of the translational research pipeline, followed by the numerous scientific and regulatory roadblocks in this field and their potential solution to speed up the process. Finally, summing up the varied applications of translational biotechnology in clinical sciences and public health. From the translational application in drug discovery and development, precision medicine and biomarker discovery, gene therapy, bio-therapeutic, and application of artificial intelligence (AI) in disease diagnosis and other clinical research. With an effective integrated network of robust multidisciplinary effort between patients, researchers, and healthcare providers within a system, can yield well-rounded and

1.2 The phases of translational research

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1.2 The phases of translational research The broad spectrum of translational research has been categorized into different phases for a convenient transition from basic research finding to its application mode for clinical implementation. This integrative research effort has been designated into four steps: T1, T2, T3, and T4 (Lorenzi, 2011). The steps in outcome-based clinical research follow: from basic scientific laboratory work based on understanding the human physiology and application of medicinal chemistry to preclinical studies to validate the clinical finding in vitro/in vivo model systems followed by clinical trial studies for implication for practice and its overall effect. Ultimately, the research findings are used for implications in community-based health benefits in clinical use. The final crucial step in clinical research is the delivery of recommended care to the right patient at the right time, resulting in improved patient health. It may be with improved prognosis, diagnostic tests, therapy or therapy adherence, and treatment outcome for use in clinical practice. At each of the transitions between each stage, there is a step for translational output. The spectrum of translational research is not linear or unidirectional; each stage builds upon each other and moves upward in the pyramid with a larger impact and greater population coverage. At all stages of the spectrum, according to NCATS, new approaches are developed, demonstrating their usefulness and disseminating the findings. Every stage of the translational research spectrum is based on research findings that derive from basic sciences. At T1, insights gained from the fundamental scientific discovery are accumulated that gives a “proof of concept” study with controlled experimental conditions and, therefore, their outcome. T2 provides clinical insights from study findings. From the previous stage, where researchers develop models to test interventions to understand the pathophysiology of the disease and, ultimately, its treatment. Such testing/data is used to elaborate clinical/physiological insights. Such testing is performed using in vitro models (animal tissue-specific cell lines) or in vivo model organisms like mouse, rat, drosophila, zebrafish, etc. and in silico-based computer-assisted simulation models of drug, device, or diagnostic interactions within living systems. This step includes preclinical validation studies, early phase I, and II trials. Subsequently, practice-based research includes testing interventions for human safety and effectiveness in patients with or without the disease, behavioral, and observational studies and outcomes and health services research at the T3 phase. The confirmed evidence from human subjects is introduced and disseminated into a patient care setting for implementation and deployment of clinical discovery. This step is called T4, and it is the final achievable goal, where the developed intervention is used for improving public health (Choi, Tubbs, & Oskouian, 2018). In this stage of translation, researchers validate the clinical outcome findings in an entire population to determine the burden of the diseases and clinical efforts to prevent it, diagnose, and treat them (US Department of Health & Human Service, 2020). These steps have been

Section 1: Introduction to translational biotechnology

competent teamwork who can ultimately commit to improved community health and healthcare costs.

1. Translational biotechnology: A transition from basic biology to evidence-based research

Section 1: Introduction to translational biotechnology

6

FIGURE 1.1 Phases of translational research.

elaborated in Fig. 1.1. Detailed phases and their translational steps in the pipeline for biomarker development for precision medicine in warfarin dosing have been described as an example in Table 1.1.

1.3 Challenges to solutions Translation of basic scientific discoveries to clinical applications, and finally to improvements of public health, has emerged as an important objective in biomedical research, and its essential role has been emphasized for more than three decades. During this period, much advancement was made, but despite these efforts, we are unable to create the balance between the advances made in biomedical sciences and its translatability into tangible health benefits. Basic scientific discoveries made in laboratory settings and its clinical implementation are like a bank of a vast river, a swimmer had to swim all the way through it to reach the other side facing many hurdles. To bridge this gap, there is a need

7

1.3 Challenges to solutions

Phases

Hypothesis

Finding

Basic scientific discovery

What causes the interindividual variability in drug response?

Genetic variants involved in PK/PD can cause a varied response to drugs in patients

T1

Translation of basic scientific finding for human health

The concept of pharmacogenomics

Clinical findings (proposed human application)

Which genetic variant is associated with drug response, and how?

There is a vast interindividual variability in warfarin dose required to achieve the target therapeutic effect. This variability is mostly caused by genetic variants in VKORC1, CYP2C9, CYP4F2 genes (Dean, 2012)

T2

Translation to clinical implication

PGx biomarkers for altered drug response to warfarin doses. For example, patients with CYP2C9*2 and CYP2C9*3 variants, require lower doses of warfarin, and take a longer time to reach target INR on starting warfarin therapy and are at a higher risk of bleeding complications (Johnson, Caudle, Gong, & Whirl-Carrillo, 2017)

The implication for practice (replication in larger population)

Is the genetic variant associated with poor metabolism of warfarin in all populations (Pavani, Naushad, & Rupasree, 2012)?

The effect of genetic association of VKORC1 and CYP2C9 in African and Asian are concordant with those in European ancestry, but the frequency distribution of allelic variants vary between major populations (Fung et al., 2012). Thirty percentage variability in warfarin dose is due to CYP2C9 and VKORC1 risk variants among European Americans and 10% among African Americans (Limdi et al., 2008)

T3

Translation to practice

Defining warfarin dosing algorithm for patients carrying specific genetic variant allele/ genotype (Pirmohamed et al., 2013)

Clinical practice guidelines

What dose/drug to be administered to which patients? Alternatively, patients are at risk of adverse effects.

Genotype-based drug prescription algorithms also better predict warfarin dose than the US FDA-approved warfarin label table (Finkelman, Gage, Johnson, Brensinger, & Kimmel, 2011)

T4

Evidence-based research

Population-specific clinical utility (Pirmohamed et al., 2013)

Clinical utility for public health benefit

Market dissemination and practice of PGx-based marker for drug administration

FDA approval for drug labeling based on pharmacogenomic biomarkers (US FDA, 2018)

CYP2C9, Cytochrome P450 family two subfamily C member 9; CYP4F2, cytochrome P450 family four subfamily F member 2; INR, international normalized ratio; PGx, pharmacogenomics; PK/PD, pharmacokinetic/pharmacodynamic; US FDA, the United States Food and Drug Administration; VKORC1, vitamin K epoxide reductase complex subunit 1.

Section 1: Introduction to translational biotechnology

TABLE 1.1 The translational research continuum: taking an example of precision medicine and drug response biomarker—from discovery to application in drug labeling by the US FDA.

Section 1: Introduction to translational biotechnology

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1. Translational biotechnology: A transition from basic biology to evidence-based research

FIGURE 1.2 A glance at translational research on cancer in the past 5 years.

to understand the issues in translational research better and to look for potential solutions (Harvard Catalyst Clinical Research Center, 2020) (Fig. 1.2). The vast majority of studies that give exciting and impressive results in preclinical studies usually fail at its nascent stage of rigorous target validation and do not reach up to clinical development. Issues and challenges that might account for differences between marked success in deciphering the mechanism, pathogenesis, and treatment of diseases in preclinical studies, and limiting success rate for translating most of these discoveries from bench to bedside include lack of proper validation of published findings, reproducibility problem, and lack of predictive efficacy and safety (Yoichi, 2019). The main causes of these issues include fewer studies conducted to support any research finding for application and lack of replicability of data between different studies due to different study designs. Other factors like sampling criteria, genetically diverse subjects included in studies, controlled experimental conditions, and recruitment of samples with specific phenotypic characteristics, pitfalls in animal experimentation, different statistical methods used, and overinterpretation of data (Collins & Tabak, 2014). Generally, academic clinical trial units conduct innumerable clinical studies on human subjects but report only the most promising results from studies that have favorable outcomes, and this leads to a lack of reproducibility of results. To enhance reproducibility and transparency of studies conducted, there is a need to discuss such challenges plaguing preclinical research across the globe in different populations to provide constructive guidance for therapy and recommend reporting standard clinical design. Potential solutions to tackle this problem include calls for random assignment of animals, blinding of preclinical treatment groups, more rigorous sample and effect size calculations, and formal rules for the handling of data involving outliers, prespecified primary and secondary endpoints, and replication of key experimental findings, development of novel methods to predict safety, conduction of Phase 0, and investigational exploratory trials to confirm the mode of action, validate biomarkers (Landis et al., 2012).

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The major non-scientific challenges that need to be addressed are to minimize the gaps between scientific discovery and their application in the clinical setting, which should be prioritized in advance by funders/research organizations. Other non-scientific factors include cultural differences between clinicians and basic scientists as scientists involved in basic science research have less exposure to the clinical environment and the lack of laboratory research experience in clinicians. Additionally, communication gap, the difference in work attitude and reward system of both groups, like academic researchers involved in translational research generally do not have proper incentives to channelize the movement of science, as their career trajectory is dependent on high-impact publications, funding, and patents, also add to the problem. This gap needs to be filled by coupling of hospitals and research-oriented scientific institutes, to define a separate field of translational research with a multidisciplinary viewpoint, and institutions should ensure and make new guidelines to properly evaluate and recognize the contributions made by scientists in translational research (Homer-Vanniasinkam & Tsui, 2012). The financing gap for translational research is also widening. Traditional investors involved in translational research are becoming increasingly risk-averse in the face of escalating challenges in the early stages of the drug development process. To counteract this trend, the medical research field needs to increase the field of promising research ventures that also attract investment opportunities by modifying both the research management process as well as current financing methods (MIL Report, 2012). Besides this lack of resources and scattered infrastructure, lack of skilled investigators with specific expertise and well-aware participants for the study, time taking, and costly phases of translational research, lack of collaborations and partnerships between clinics, researchers, and industries, conflict of interest, ethical, and various regulatory issues, right to privacy and incompatible databases adds to the challenges which slow down the pace of bench to bedside research. Potential solutions for these challenges include building national-level clinical and translational research capability, collaborative efforts, like a coherent partnership between academic and industrial research, where academia delivers skilled and trained researchers and industry exploits those human resource for the upliftment of translational activities for mankind. Cost reduction in clinical investigations, proper scrutiny of ethical and social issues should be looked into prior to the study as per the clinical set-up, and study design. An overview of obstacles and their potential solutions relevant to translational research are presented in Table 1.2.

1.4 Applications Regardless of many gaps in translating every research avenue to clinical importance, there have been several successful attempts. Biotechnology has a significant hand in translating clinical research. Initially, biotechnology was thought of being limited to the development of recombinant DNA (rDNA) technology and the production of recombinant proteins such as insulin. With advancements in technology, this field has evolved from the synthesis of proteins to biopharmaceutical discovery. Now, biotechnology has contributed to the development of gene therapy, immunotherapy, and personalized medicine. It includes synthesis of monoclonal antibodies (mAb), antisense strands, enzymes, cancer

Section 1: Introduction to translational biotechnology

1.4 Applications

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1. Translational biotechnology: A transition from basic biology to evidence-based research

Section 1: Introduction to translational biotechnology

TABLE 1.2 Issues, obstacles, and potential solution of translational research. Issues

Perceived obstacles

Potential solutions

1

Predictive efficacy and safety

The problem in the extrapolation of results derived from animal models to humans. Difficulty in obtaining samples from patients and their families with defined clinical characteristics for accurate correlation of genomic/proteomic data.

More systematic validation of published findings, organoids can be used in place of animal models where appropriate as there can variability in drug responses, in animal model and human. Potential solutions to access human disease samples as early as possible to validate hypotheses include community outreach and engagement projects and community advisory boards. Phase 0 or other exploratory trials to confirm the mode of action, validate biomarkers—development of novel methods to predict safety. For example, CAHSS, a state-funded system of care, facilitated translational research by making available to researchers a population of patients.

2

Reproducibility problem

Non-replication of data between different studies due to different experiment designs, genetic/environmental variability, sampling criteria and sample size, pitfalls in animal experimentation, different statistical methods used, and bias interpretation of data (Collins & Tabak, 2014). Majority of the negative results are ignored in and represent only the most promising results that worked best and yielded mechanisms consistent with the prevalent hypothesis. This yields bias (Collins & Tabak, 2014). Less number of studies conducted to support any research finding for application.

Solutions include larger and homogenous sample size, robust effect size calculation for true association calculation and blinded preclinical/ clinical trial designs, calls for random assignment of animals, proper handling of outlier data, and replication of key findings (Landis et al., 2012). Efforts to improve the reporting of negative results. For example, alltrials.net is an initiative to improve comprehensive reporting (Drucker, 2016).

3

Lack of population epidemiology information

Need for proper estimates of true prevalence and incidence of diseases, especially for complex and chronic diseases (Bhopal, 2009). No established system to track epidemiologic trends.

Scientists should encourage involvement in epidemiological studies, and data related to this should be made easily accessible (Bhopal, 2009).

4

Cultural differences between basic scientists and clinicians: different education and training

Less exposure of basic scientists to the clinical environment and lack of laboratory research experience in clinicians (Homer-Vanniasinkam & Tsui, 2012). Lack of scientists having an understanding of both basic research and experimental medicine who can act as a bridge. Clinician scientists have less time to research due to demanding clinical roles.

Collaboration between hospitals to researchoriented scientific institutes. We need to define a discipline of translational medicine with a multidisciplinary viewpoint and development of specific translational medicine training centers. For example, multidisciplinary institutions like MCGS and BMCTR had incorporated it into their curriculums. (Continued)

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1.4 Applications

TABLE 1.2 (Continued) Perceived obstacles

Potential solutions

5

Career In the present scenario, current progression progression and includes high-impact publications, bigger promotion grants, and scientists involved in translational research have comparatively fewer publications as it is time taking (Homer-Vanniasinkam & Tsui, 2012).

Institutions should ensure and make new guidelines to properly evaluate and recognize the contributions made by scientists in Translational research (HomerVanniasinkam & Tsui, 2012). More emphasis should be given to provide better training to translational scientists to improve their careers.

6

Time and cost

Translation often requires a large length of time and is costly as it requires many phases of research earlier in the lab then in clinics. The plethora of tools and techniques required for translational biotechnology enhances the cost, and also the human trial phase adds to it (Morris, Steven, & Jonathan, 2011).

Actionable policy interventions should be made that could speed up the translation process, where appropriate, and thus increase the return on research investment.

7

Lack of resources

Lack of interdisciplinary training, integrated infrastructure, skill, and technology to execute and interpret in vitro and in vivo preclinical studies required to demonstrate efficacy fully.

NCATS was established by NIH in 2012 to execute the translational science process (Mandal, Ponnambath, & Parija, 2017). Globally other infrastructures were also developed to promote translational research like THSTI in India. Build national clinical and translational research capability.

8

Funding

Lack of financial support for carrying out translational research as it is a long process and costly at the same time, and there is no guarantee of success, especially at the clinical phase (Editorial, 2017). Translational research processes having a higher chance of success and commercialization tend to have more fund allocations than those with a higher impact on public and community health but difficult to conduct and has less chance of success. Funding often gets reduced during the costly human trial phase.

To fill this financial gap in translational research, there is a need for research that will also attract investment opportunities by modifying both the research management process as well as current financing methods. The urgent need for targeted approaches for fast-track drug development pipeline that can be better at managing risk, lower capital investment, and improve research effectiveness in lesser time and access new capital sources (MIL Report, 2012).

9

Collaborations and partnerships

Lack of collaborations and partnerships between clinics and researchers.

There is a need to take initiatives that encourage partnerships and enhance consortium-wide collaborations. For example, NCATS is developed to encourage collaborative partnerships between clinicians and scientists as well as pharmaceutical industries.

Many ethical issues are involved in human research such as informed consent, irreversible consequences in human subjects on the application, social injustice and risk analysis, ethics regarding animal cruelty for

Clear, informed consent is very crucial in translational research to avoid therapeutic misconception. Drawn outcome of applications and subjects’ consent regarding the same.

10 Ethical issues

(Continued)

Section 1: Introduction to translational biotechnology

Issues

12

1. Translational biotechnology: A transition from basic biology to evidence-based research

TABLE 1.2 (Continued)

Section 1: Introduction to translational biotechnology

Issues

11 Data protection/ confidentiality

Perceived obstacles

Potential solutions

drug testing/ development (Mandal et al., 2017). Regulatory issues like FDA and MHRA approvals, toxicology, and manufacturing regulations (Homer-Vanniasinkam & Tsui, 2012). With invent of newer fields of cell and gene therapies and tissue engineering in Translational research, various new ethical and social issues are now being highlighted like germline gene therapy is controversial and the government of many countries does not allow federal funds to be used for research on germline gene therapy in people.

The risk-benefit analysis is a must for better translational output, thereby minimizing potential harm at the research subject and population-level against the benefit of researchers’ curiosity (Sofaer & Eyal, 2010). One integrated system should stand tall, which captures research information at different levels of approval, application, and review bodies like R&D offices, research ethics committees, MHRA, GTAC, and the NIGB. For example, IRAS developed by NIHR in 2008 (HomerVanniasinkam & Tsui, 2012).

Strict law should be there for the Lack of confidentiality and failure in confidentiality of data, and scientists are protecting the data generated during encouraged to follow these. translational research can lead to untoward adverse effects. Sharing of incomplete data or reporting conclusions at inappropriate stages of research could be dangerous/inconclusive for early unauthorized implementation leading to dangerous consequences such as bioterrorism (Hostiuc et al., 2016).

BMCTR, Brown’s Masters in Clinical and Translational Research; CAHSS, Canadian Academic Health Science Systems; GTAC, Gene Therapy Advisory Committee; IRAS, Integrated Research Application System; MCGS, Mayo Clinic Graduate School; MHRA, Medicines and Healthcare Regulatory Agency; NIGB, the National Information Governance Board; NIH, National Institute of Health; NIHR, National Institute of Health Research; THSTI, Translational Health Science and Technology Institute.

vaccines, novel drug discovery, biochips, microarray, and so on (Avidor, Mabjeesh, & Matzkin, 2003). With the emergence of these biotechnological products, there has been a rapid pace of development in healthcare. The Food and drug administration (FDA) has approved many of these drugs and therapies for diagnostic purposes. Products yielded from research in this field have been successful in meeting the demands of the pharmaceutical industry and healthcare like products based on ligand
receptor interaction, signal transduction, and cell signaling in healthy and diseased states. Hence, research in biotechnology leads to the development of therapeutics and a better understanding of genomics, proteomics, or rather the broader multiomics landscape for physiological advancements. Glimpses of applications of translational biotechnology are in varied fields as represented in Fig. 1.3 and are detailed below.

1.4.1 Drug development Drug development is one of the foremost and most extensive applications of translational biotechnology, which requires three important steps. First is, identification of

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1.4 Applications

FIGURE 1.3

protein target, followed by the generation of compounds that react with the target site in the desired manner, and last is the innovative delivery mechanism of a drug into target tissue or cell. It is a strenuous and time-consuming process. In order to escalate the process of drug discovery, in silco approach has been used. Various software and tools are used for identification of a target, selection, and optimization of a lead molecule and to analyze the pharmacokinetic and pharmacodynamics properties like absorption, distribution, metabolism, excretion, and toxicity of a lead molecule (Goyal, Jamal, Grover, & Shanker, 2018). For example, to study the dissolution and disintegration rate of drug, DDDPlus (Dose Disintegration and Dissolution Plus) software is used, and to simulate the pharmacokinetics and pharmacodynamics properties in humans and animals, GastroPlus software is used. For the prediction of ligand interaction and molecular dynamics, Autodock, Schrodinger, and GOLD software are used. For building molecular models and predicting structural activity relationships, Maestro, Sanjeevini, and ArgusLab software are used. Discovery Studio Visualizer, and QSARPro software are used for viewing and analyzing protein data. MARS (Multimodal Animal Rotation System) software is used for tracking nanoparticle, delivery, and enzyme activity study. Hence, these softwares are used to assist the drug designing and development process (Jamkhande, Ghante, & Ajgunde, 2017). Biotechnology advancement has been going on all of these fronts, and new genes and new protein targets are being investigated for therapeutic purposes. This field has flourished more after the completion of the human genome project. However, the human genome project provided insights on genetic makeup and can be used for understanding protein architecture for identifying novel drug targets leading to the generation of new protein target intervention. In the past, mostly peptides were used as drugs that could block specific pathways underlying a disease. Nowadays, different classes of drugs have

Section 1: Introduction to translational biotechnology

Translational biotechnology in human healthcare.

Section 1: Introduction to translational biotechnology

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1. Translational biotechnology: A transition from basic biology to evidence-based research

emerged in addition to protein drugs, like therapeutic enzymes, hormones, mAb, cytokines, gene therapy, and antisense drugs that are discussed in detailed in the following sections. 1.4.1.1 Protein drugs Protein drugs are classified based on the mode of action. Some of the most common proteins are recombinant human proteins (for instance, insulin, growth hormone, and erythropoietin), and several others are mAb [for instance, Remicade (infliximab; Johnson & Johnson, Kenilworth, NJ, United States), Rituxan (rituximab; Genentech; S. San Francisco, United States), and Erbitux (cetuximab; ImClone, New York, United States)]. Many others that are primarily manufactured are viral or bacterial proteins used as vaccines to elicit a specific immune response (Tomlinson, 2004). 1.4.1.2 Hormones rDNA technology has been used for the production of various biopharmaceutical products; hormones are one of them. Before the advent of biotechnology, porcine insulin and bovine insulin were used for the treatment of diabetes, which also caused allergic reactions in the human body. In 1978, rDNA technology was used for insulin synthesis using Escherichia coli. (Humulin, Novolin, Velosulin). In 1982, the FDA-approved recombinant human insulin for the treatment of diabetic patients. Now, recombinant human insulin is manufactured in different doses for therapeutic action (insulin lispro, insulin aspart, insulin glargine—with very fast, fast, long-acting, respectively) and for different modes of administration (intramuscular, subcutaneous, etc.). Other than insulin, recombinant growth hormone is used to treat growth disorders. Somatropin, a widely used recombinant human growth hormone, is marketed under various brand names such as Saizen, Nutropin, etc. Recombinant follicle-stimulating hormone and recombinant luteinizing hormone were made successful in enhancing ovulation and pregnancy. Hence, assisted reproduction treatment through stimulating follicular development is an achievement of rDNA technology (Khan et al., 2016). 1.4.1.3 Monoclonal antibodies Since the inception of hybridoma technology in 1975, significant advances have been made for the production of mAb and its derivatives to recognize an individual molecular target for their application in research, immunological investigations, and personal healthcare. mAb enables the antigenic profiling and visualization of macromolecular surfaces when used in combination with epitope mapping and molecular modeling techniques. They also play a keystone role in a vast array of clinical laboratory diagnostic tests in detecting and identifying cell markers and serum analytes. Apart from these applications, mAb are also used in various bio-techniques such as magnetic cell sorting, flow cytometry, immunoassays, and also for therapeutic purposes. Up to a certain level, mAb have replaced small molecules in pharmaceutical companies due to their exquisite target selectivity and less toxicity. To date, the United States Food and drug administration (US FDA) has approved mAb therapeutics for 33 targets in more than

37 distinct diseases, most commonly in cancer (27 approvals; Shepard, Phillips, Thanos, & Feldmann, 2017). New applications of mAbs are being tested with approval in hypercholesterolemia and bone metabolism. Now efforts for designing mAb, which simultaneously targets more than one antigen, is underway. The table given below is a comprehensive list of approved antibody used in therapeutics in 2019
20 with their indications and brand names (Table 1.3). 1.4.1.4 Cytokines Cytokines are potent chemicals that play a pivotal role in regulating lymphocytes, macrophages, monocytes, etc. in immune response and inflammation. It encompasses lymphokines, monokines, interleukins (IL), colony-stimulating factors (CSFs), interferons (IFNs), tumor necrosis factor, and chemokines. Currently, a number of cytokines TABLE 1.3 Monoclonal antibodies therapeutics approved by the US Food and Drug Administration in 2019
20 (Kaplon & Reichert, 2019). S. No. Product

Brand name

Target; format

Therapeutic indications

1

Teprotumumab

Tepezza

IGF-1R; human IgG1

Thyroid eye disease

2

Isatuximab

Sarclisa

CD38; chimeric IgG1

Multiple myeloma

3

Eptinezumab

Vyepti

CGRP; humanized IgG1

Migraine prevention

4

[fam]-Trastuzumab Enhertu deruxtecan

HER2; humanized IgG1 ADC

HER2 1 breast cancer

5

Enfortumab vedotin Padcev

Nectin-4; human IgG1 ADC

Urothelial cancer

6

Crizanlizumab

Adakveo

P-selectin; humanized IgG2

Sickle cell disease

7

Brolucizumab

BEOVU

VEGF-A; humanized scFv

Macular degeneration

8

Polatuzumab vedotin

Polivy

CD79b; humanized IgG1 ADC

Diffuse large B-cell lymphoma

9

Risankizumab

Skyrizi

IL-23p19; humanized IgG1

Plaque psoriasis

10

Romosozumab

Evenity

Sclerostin; humanized IgG2

Osteoporosis in postmenopausal women at risk of fracture

11

Caplacizumab

Cablivi

Von Willebrand factor; nonhumanized nanobody

Acquired thrombotic thrombocytopenic purpura

12

Ravulizumab

Ultomiris

C5; humanized IgG2/4

Paroxysmal nocturnal hemoglobinuria

13

Cemiplimab

Libtayo

PD-1; human mAb

Cutaneous squamous cell carcinoma

14

Fremanezumab

Ajovy

CGRP; human IgG2

Migraine prevention

15

Ibalizumab

Trogarzo

CD4; humanized IgG4

HIV infection

ADC, Antibody
drug conjugate; CD, cluster of differentiation; CGRP, calcitonin gene-related peptide; C5, complement component 5; HER2, human epidermal growth factor receptor-2; IGF-1R, insulin-like growth factor-1 receptor; IgG1, immunoglobulin G; IL-23p19, interleukin-23 p19 peptide; PD-1, programmed cell death 1 protein; scFv, single-chain fragment variable; VEGF-A, vascular endothelial growth factor-A.

Section 1: Introduction to translational biotechnology

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1.4 Applications

Section 1: Introduction to translational biotechnology

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1. Translational biotechnology: A transition from basic biology to evidence-based research

are used for therapeutics, and some have entered clinical trials for patients with cancer such as Granulocyte-Macrophage Colony-Stimulating Factor (GM-CSF) and common IL like IL-7, IL-12, IL-15, IL-18, and IL-21. Recombinant IL-2 for metastatic melanoma and renal cell carcinoma and IFN-α for the adjuvant therapy of stage III melanoma and recombinant IFN for patients with human immunodeficiency virus (HIV)-related Kaposi’s sarcoma, genital warts, hairy cell leukemia, and hepatitis B and C have already achieved FDA approval (Lee & Margolin, 2011). Recombinant versions of CSF, including GM-CSF, granulocyte CSF, and erythropoietin, have revolutionized the ability to treat myelosuppression. 1.4.1.5 Vaccines The emerging knowledge in molecular biology and immunology resulted in a revolution in vaccine development. These upcoming technologies in vaccine development, like reverse vaccinology, structural vaccinology, and synthetic vaccines, have revolutionized this field, resulting in marked progress in the vaccine development. Currently, DNA vaccine and mRNA vaccines are also emerging, but no commercial production of these vaccines are available for human use. Nowadays, vaccines are also used for noninfectious diseases such as cancer, Alzheimer’s disease, cardiovascular disease, and allergic reactions. Sipuleucel-T is the first therapeutic cancer vaccine approved by the FDA in 2010 (Kesik-Brodacka, 2018). Only because of vaccines, diseases like smallpox have been wholly eradicated globally, whereas polio, measles, and tetanus have been significantly controlled. Despite rapid development in this field, vaccines for infections like HIV, hepatitis C virus, severe acute respiratory syndrome, middle east respiratory syndrome and Zika virus, and Coronavirus disease are under research, and there are no effective vaccines available yet (Chen, Cheng, Yang, & Yeh, 2017) (Table 1.4).

1.4.2 Nanomedicine Nanomedicine is defined as the development of nanoscale (1
100 nm) or nanostructured objects/nano-robots/skin patches and their use in medicine for diagnostic and TABLE 1.4 Vaccine based on recombinant DNA technology (Chen et al., 2017). S. No.

Preventive infection

Therapeutic indications

Vaccine type

Administration

1

HBV

Hepatitis B

Subunit vaccine

Intramuscular injection

2

HPV

Cervical cancer

Subunit vaccine

Intramuscular injection

3

Neisseria meningitides group B strain

Meningitis

Subunit vaccine

Intramuscular injection

4

Dengue virus

Dengue

Live-attenuated vaccine

Intramuscular injection

5

Rotavirus

Gastroenteritis

Live-attenuated vaccine

Oral

HBV, Hepatitis B virus; HPV, human papilloma virus.

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therapeutic purposes based on the use of their structure, which has unique medical effects. These nanostructured particles have revolutionized the medical field in drug delivery, diagnostic devices, imaging technology and agents, in tissue engineering, regenerative medicine, and also in implants by improving the electrode charge transfer at the electrode
tissue interface in retina implant. Different applications of nanoscale particles in medicine are: (1) drug therapy and delivery: Nanoscale particles/molecules differ from traditional small molecules having unique medical effects such as fullerenes and dendrimers (nanomaterial) based drugs. Nanomaterials are developed to improve pharmacodynamics and pharmacokinetics of drugs because their small size, large surface area to mass ratio, can carry a high dose of therapeutic load resulting in more devastating effects at the target tumor site. These nanoparticles encapsulate drugs and modify their surface, which overcomes solubility and stability issues of drugs and enables more precise targeting with a controlled release. Nanoparticles are used to deliver drugs through the blood
brain barrier for targeting brain tumors, which is one of the positive outcomes of nanomedicine. Nanoparticle-based different drug delivery platforms such as liposomes, gold, and silver nanoparticles target cancer are used. Nano spheres target asthma and polymeric nanoparticles are used in HIV. Thus nanoparticle overcomes the drug delivery limitation, which includes a short plasma halflife, poor stability, and potential immunogenicity (Wang & Mullett, 2013). (2) In vivo imaging: Nanoparticle (iron oxide) contrast agents provide improved contrast and favorable bio-distribution and are used for magnetic resonance imaging (MRI) and ultrasound. The nanoparticle is coated with a peptide that binds to a cancer tumor, after binding to the tumor, nanoparticle magnetic property enhances the images of the MRI scan. Magnetic particle imaging is used for cell imaging for the spatial distribution, visualization, and quantification of magnetic nanoparticles. It is highly sensitive, provides good spatial and temporal resolution, and is free from the background noise of the surrounding tissue (Hendrik et al., 2020). (3) In vitro diagnostics: Nanoparticlebased diagnostics provide rapid and earlier stage detection of the disease. Novel sensor concepts based on nanotubes, nano-pore, nanowires, nano-flares cantilevers, or atomic force microscopy are applied to diagnostic devices/sensors (Hawk’s Perch Technical Writing, LLC, 2013). For example, the Nano mix (Emeryville, CA, United States) develops carbon nanotube-based sensors for monitoring respiratory functions, and Nanopore with AI can identify a single virus particle. For the detection of whole viruses for early diagnosis of viral infections, Bioforce’s Virichip (Ames, IA, United States) are used, which are based on atomic force microscopy (Wagner, Bock, & Zweck, 2006).

1.4.3 Gene therapy Gene therapy is the incorporation of genetic material in an organism either directly or by using vectors (such as viruses) or by using an ex vivo approach to inhibit or initiate the cellular processes by means of correcting the altered gene or by modification of specific site that can act as a therapeutic target for the treatment that underlies disease. Gene therapy is used in treating patients with enzyme deficiencies, classic Mendelian

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1. Translational biotechnology: A transition from basic biology to evidence-based research

gene disorders (cystic fibrosis, hemophilia, muscular dystrophy, and sickle cell anemia), cancer, and certain viral infections such as AIDS. Various strategies are used to perform gene therapy, such as rDNA technology, genome editing. For performing gene therapy, specific cell types have identified that need to be treated. Based on the target cell, gene therapy is divided into two types: germline therapy in which a functional gene is integrated into stem cells, that is, sperm or egg cells genome, which is hereditary. In somatic gene therapy, therapeutic genes are transferred in somatic cells, and their effect is restricted to a patient only and is not pass on to the future generations. A few gene therapies using the rDNA approach have been published and approved for clinical use. Various techniques are being used for successful gene editing like zinc finger nucleases, transcription activator-like effector nucleases, and gene targeting. These technologies have proven efficient in the treatment of childhood cancer through the engineering of immune cells, inactivation of the gene that encodes HIV coreceptor C
C chemokine receptor type 5 and also alleviates the HBB gene mutation in hematopoietic stem cells. Genome editing using clustered regularly interspaced short palindromic repeats (CRISPR) Cas-9: With the advancement in biotechnology, CRISPR
Cas-9 is setting a remarkable development due to its ability to modify the genome rapidly with high specificity and in an efficient way. CRISPR technique utilizes three molecules: one nuclease (generally Cas-9 of Streptococcus pyogenes) is a protein that assembles with the single guided RNA and then binds and makes a cut at 20-base-pair DNA sequence complementary to the guide RNA, single guide RNA, and target DNA. Recognition sites in DNA must be adjacent to the proto-spacer adjacent motif or PAM that triggers Cas-9 to make a double-stranded DNA break-in target sequence. Genetic editing is performed by knockout of the gene and integration of exogenous sequences and allele substitution. CRISPR-Cas has application in relevance to human health for the treatment of genetic disorders. Crygc gene mutation that is responsible for cataract was corrected in mice. CFTR locus responsible for cystic fibrosis is corrected by homologous recombination in primary adult intestinal stem cells derived from cystic fibrosis patients (Doudna & Charpentier, 2014). Sickle cell anemia and Duchenne muscular dystrophy (DMD) are two genetic disorders that could possibly be cured by CRISPR-Cas genome editing technology. In sickle cell anemia, single point mutation occurs in the HBB gene that encodes β-globin protein. This mutation could be cured by the genome editing technique in hematopoietic progenitor cells of the patient. This can be achieved by correcting the mutation or by activating the expression of γ-globin, a fetal form of hemoglobin that could substitute for defective β-globin, and then transplanting edited blood stem cells back into the patient’s body. DMD occurs due to a mutation in the dystrophin-coding gene (Doudna, 2020). More than 3000 different mutations can cause DMD. Genome editing offers the possibility of permanent restoration of the missing dystrophin protein and thus will provide therapeutic value. Leber’s congenital amaurosis 10 (LCA10), a rare blindness disease, occurs in children due to mutation in the gene CEP290. Currently, no treatment is available for treating LCA10. Various treatments based on antisense therapy such as sepofarsen can improve vision LCA10, and conventional gene therapy use virus for insertion of a healthy copy of this gene, and viral genome could not incorporate the large size of CEP290. Thus all efforts

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remained ineffective. Now, CRISPR
Cas-9 treatment is done for the first time in the human body for treating this disease, which is a significant jump for treating cells from a dish (Ledford, 2020; Leonova & Gainetdinov, 2020). To test the gene-editing capability of CRISPR
Cas-9, it is a landmark clinical trial. CRISPR
Cas-9 has potential applications in engineering, biomedicine, and synthetic biology.

1.4.4 Precision medicine and biomarker development Although “Precision medicine” is a relatively new term, the concept has been a part of healthcare for many years, and translational research had immensely contributed to this field. Basic research on disease genetics, population genetics, pharmacogenomics, etc. had very well translated for the improvement of clinical or nonclinical methods, including biomarker development, for precise diagnosis of disease, development of targeted therapies, or drug dose optimization for each patient (Marquet et al., 2015). Over the past decade, with the increasing availability and affordability of genomic sequencing technology, the discovery of genomic markers of efficacy and dosing of therapeutics have been increased and translated for clinical use when there are enough pieces of supporting evidence. For example, there are reports that carriers of the HLA-B*5701 genotype should avoid the HIV drug abacavir entirely to eliminate a severe adverse event (Ginsburg & Phillips, 2018). Another example as previously mentioned PGx biomarkers for altered drug response to warfarin doses is developed as there is evidence that individuals with the CYP2C9*2 and CYP2C9*3 variants are more likely to need lower doses of warfarin and take a longer time to reach target INR on starting warfarin therapy and have an increased risk of bleeding complications (Johnson et al., 2017).

1.4.5 Microbial engineering for bio-therapeutics Microbes are not just pathogens. They play a protective role in healthcare. A slight perturbation in gut microbiota can raise a disease condition. Presently, research is focusing more on engineering individual microbiota that has potential applications in live therapeutics. There is much possible use of engineered microbes as therapeutics to cure metabolic disorders, inflammation, infectious disease, immunomodulation, cancer, etc. Researchers have developed IL-17A secreting recombinant Lactococcus lactis that prevented the formation of tumors in the mouse model and IL-10 secreting recombinant Bifidobacterium bifidum ˇ that were effective in decreasing intestinal inflammation in mice (Berlec & Strukelj, 2019). Researchers also engineered a gut bacterium to sense signals that are produced by pathogenic bacteria that helped to clear the infection in mice and worms (Ainsworth, 2020). Now, the day is not far away from the first FDA-approved bio-therapeutic.

1.4.6 Application of big data and translational bioinformatics Big data is the new buzzword. It is generated exorbitantly in every field of our daily life; every year in clinics and different omics fields like genomics, transcriptomics, proteomics, pharmacogenomics, and drug discovery by using high-throughput technologies

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(Oliveira, 2019). Now biotechnology development is to maximize the use and integration of available data. AI and machine learning (ML) are two new advancements in biotechnology, having the capacity to find the pattern in big data to fulfill this goal. ML is an application of AI very widely used in almost every sphere of our lives today. It is used for the development of algorithms for pattern recognition, classification, and prediction using an existing data set and then applying it on the test data set. The applications of ML in clinical research can be broadly categorized into six topics: patient identification, risk prediction, diagnosis, disease subtype classification, disease progression and outcome, and monitoring and management. Patient prognoses are most frequently applied by pattern recognition using ML, and this approach is being used for all autoimmune diseases. It is also used for distinguishing celiac disease individual groups from at-risk groups for the diagnostic purpose (Stafford et al., 2020). Other than these applications, ML is used in genetics and genomics to identify genes, biomarkers for a disease, transcription start site, splice site, transcription factor binding site, genes, etc. using DNA sequence data. It is also used for predicting gene function, disease prognosis, disease phenotype, and gene expression prediction using DNA sequence and gene expression data as input. AI is used in various medical fields like radiology, dermatology, ophthalmology, and pathology for imaging analysis for diagnostic purposes. It is used to find a complex pattern from CT, MRI, tissue slide images, and also find minute differences in tissue densities. In some cases, it is challenging to identify patterns by a trained eye, where AI has been doing excel. In the medical field, it is also integrated as an assisting tool for physicians. Thus AI and ML have a significant role in healthcare development by increasing accuracy and decreasing time and energy effort in disease diagnosis (Hosny, Parmar, Quackenbush, Schwartz, & Aerts, 2018). Currently, AI is used for diagnosing patients for imaging, scans, and patient data analysis. Other than this now, AI is incorporated in drug development and discovery. AI has made a breakthrough in drug discovery by using an algorithm to invent the world’s first AI-based drug molecule DSP-1181 in just 12 months, which is long-acting, potent serotonin 5-HT1A receptor agonist could be used for the treatment of the obsessive-compulsive disorder. Standard research time for synthesizing a drug and preparing it for clinical trials in 5 years, which is cut down to 1 year by using AI technology. This drug is entering phase-I human clinical trial. By using a smart algorithm, in silico medicine has been used to develop a drug in 46 days, which is now being tested on mice. AI-based technologies are transforming the drug development process by reducing both cost and time to market (Lalan, 2020). “Digital Health Innovation Action Plan” issued by FDA in 2017, support the use of AI and ML in drug development (Davies, 2020). Summing up altogether, the developing approaches from different data helped in the advancements in translational biotechnology. This facilitates in unveiling the disease mechanism, better diagnostics, precision medicine, etc. One significant achievement of translational biotechnology is in the development of various types of drugs like proteins, mAb, hormones, enzymes, blood factors, growth factors, etc. which has been approved by the FDA. Human insulin was the first biotechnological product approved by the FDA in 1982. After that, biotechnology research products trickled change into a flood. Currently, a large number of drugs are widely used for the treatment of various chronic and rare diseases for which conventional therapies and therapeutics are ineffective. For example, antihemophilic factor A and factor B are produced using rDNA technology, which proved as

21

a boon for hemophilic patients. mAb due to exquisite target selectivity than small molecules initiated the personalized drug discovery and treatment in oncology. Other than this, researchers are now looking for bio-therapeutics. Recent advances like AI, ML, and genome editing via CRISPR-Cas-9 stands to bring benefit to combating treatment of untreatable disease, assisting physicians in disease diagnosis at a very early stage and will be very beneficial for the patients. Genome editing and therapy will soon be in clinics for treating human genetic disorders like Leber’s congenital amaurosis, sickle cell anemia, muscular dystrophy, and beta-thalassemia. In short, we can say that biotechnology is revolutionizing and reshaping our healthcare system.

1.5 Conclusion and future directions Translational research is the translation of basic research discoveries to the clinical and community level, that is, from “bench to bedside,” and its principal aim is to speed up scientific findings into patient and community advantage. Despite numerous advancements being made in biomedical sciences its translatability into tangible health benefit still face many challenges, the search for solutions is still on. The implementation of discoveries in biotechnology for clinical benefit from prognosis, diagnostic tests, treatment, or medicine has never seen a brighter side, and we envisage rapid progress in this area. However, the routine use of these new technologies is far-fetched in most settings for clinical implementation and reaching out to clinics/hospitals/healthcare centers. Dissemination of such an outcome should be carefully governed, determining a sublime balance between overregulation and premature conclusions. This may expose the public to scientific “inconsistencies.” In this way, scientists, clinicians, and healthcare providers can better translate biomedical research into ethically appropriate and approved clinical practice to improve valid scientific findings and better global health. The advantage of pilot phase research for small scale application of the new frontiers and further upgrading it to population-scale development is one of the key aspects of this kind of research, which makes it a scale-up program from both clinical application, to broader impact and regulatory and economic benefit.

1.6 Highlights • “Translation” by NCATS is defined as the process of turning observations in the laboratory, clinic, and community into interventions that improve the health of individuals and the public—from diagnostics and therapeutics to medical procedures and behavioral changes. • Translational research has often been described in four phases of translation, or “T-phases” (T1, T2, T3, and T4). These phases cover the transition between basic scientific discovery to preclinical studies to finding clinical insights through clinical trials and finally reaching the achievable goal of population-wise clinical benefits. • Several challenges pose a hindrance to most translational research ranging from scientific data replication, data/population variability to regulatory and financial issues.

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1.6 Highlights

Section 1: Introduction to translational biotechnology

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1. Translational biotechnology: A transition from basic biology to evidence-based research

The central challenge to translation is the gap in achievable goals between researchers and clinical practitioners. • Translational biotechnology has a wide range of applications, ranging from drug discovery and development to biomarker discovery for precision medicine, development of bio-therapeutics, nanomedicine, gene therapy to use of AI for better diagnosis and treatment outcome. • Translational research has emerged as an essential objective in biomedical research, and it uses an integrated team of experts (from scientists to clinicians to health policy bodies/investors) to focus on translating useful information from laboratories to clinicians/healthcare providers to bridge the gap for a “bench to bedside” application.

Acknowledgment We would like to acknowledge the Director, CSIR-IGIB, Dr. Anurag Agrawal, for his vision and support. This work has been supported by the Department of Biotechnology (DBT), Government of India (No.BT/PR5402/BID/7/408/ 2012). Funding support from CSIR and ICMR projects MLP1804 and GAP0136 are duly acknowledged. DG acknowledges ICMR for her fellowship. ST and PS acknowledge CSIR, Government of India, for their fellowship.

Conflict of interest None declared.

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3.

S E C T I O N

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Biotherapeutics

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C H A P T E R

Biotechnology-based therapeutics Ravichandran Vijaya Abinaya and Pragasam Viswanathan O U T L I N E 2.1 Introduction

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2.2 Human gene therapy 2.2.1 Somatic cell gene therapy 2.2.2 Germline gene therapy 2.2.3 Gene transfer system 2.2.4 Gene-editing technology 2.2.5 Ethical issue

29 30 30 30 33 34

2.3 Stem cell therapy 2.3.1 Sources of stem cells 2.3.2 Benefits of stem cell therapy in various disorder 2.3.3 Challenges and problems

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2.4 Nanomedicine 2.4.1 Nano therapeutic applications 2.4.2 Tissue engineering 2.4.3 Nanoimaging

37 37 39 40

2.5 Drug designing and delivery 2.5.1 Rational drug design

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2.5.2 Computer-aided drug design 2.5.3 Drug delivery 2.6 Recombinant therapeutic proteins and vaccines 2.6.1 Recombinant protein 2.6.2 Expression system 2.6.3 Recombinant protein as a treatment 2.6.4 Recombinant vaccine

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41 44 44 44 44 46 47

2.7 Conclusion and future applications

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Acknowledgments

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Conflicts of interest

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Author’s contribution

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References

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Renal Research Lab, School of Biosciences and Technology, Centre for Biomedical Research, Vellore Institute of Technology, Vellore, India

Translational Biotechnology DOI: https://doi.org/10.1016/B978-0-12-821972-0.00019-8

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© 2021 Elsevier Inc. All rights reserved.

Section 2: Biotherapeutics

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2. Biotechnology-based therapeutics

Section 2: Biotherapeutics

2.1 Introduction The field of translational biotechnology is defined as the use of biological materials and employing molecular engineering techniques to form, synthesize, or produce pharmaceutical products that can have a direct impact on improving healthcare (Evens & Kaitin, 2015). In the last three decades, the biotech industry has made astonishing advancements in both the laboratory and industry, especially in therapeutics. Numerous innovative technologies have been developed by researchers, leading to the identification of various new products in biotech industries. More than 4000 novel human therapeutic drugs have been approved, covering several aspects. Table 2.1 lists therapeutic biomolecules available in the market. TABLE 2.1 Therapeutic biomolecules on the market and their therapeutic indication. Drug

Therapeutic indication

Mechanism of action

Herceptin

Act against the breast cancer

Inhibits the overexpression of human epidermal growth factor receptor 2 (HER2) gene

Humira

Treats rheumatoid arthritis, psoriac arthritis, and Crohn’s disease

Inhibits the action of tumor necrosis factor (TNF)

Stelera

Treats psoriasis

Act as an antagonist for interleukin-12 (IL-12) and interleukin-23 (IL-23) antagonist

Avastin

Against the colon, breast, and lung cancer

Inhibits the action of vascular endothelial growth factor A (VEGF-A)

Synagis

Act against the infection caused by human respiratory syncytical virus (RSV)

Recognizes and inhibits the RSV protein and prevent from the infection

Zenapax

Declines the rejection level of acute kidney transplantation

Blocks specifically interleukin-2 (IL-2) containing cluster of differentiation-25 (CD25) subunit which is expressed on the lymphocytes

Tysabri

Act against the multiple sclerosis

Act as an antagonist for cell adhesion molecule α4-integrin

Cimzia

Treats rheumatoid arthritis

Inhibits the action of TNF

Erbitux

Treats metastatic colorectal cancer

Human epidermal growth factor receptor (EGFR) inhibitor

Zevalin

Treats nonhodgkin lymphoma

Antibody binds to CD20 antigen and cause cell death via that its eliminates B-cells from human body and regenerates healthy B-cells

Lantus

Treats type I and type II diabetes

Replace asparagine with glycine

Avonex

Treats early stage of multiple sclerosis

Interferon β downregulates the inflammatory protein in the brain by increasing the antiinflammatory protein

Panitumumab Act against colorectal carcinoma Prevnar

Treats infection of Streptococcus pneumoniae

Targets EGFR in human Conjugate vaccine for both infants and adults

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These lead to the opening of several thousands of biotech companies worldwide, and the global market value of medical biotechnology was $314 billion in 2018 and estimated to increase by $727 billion in 2025. During 20th and 21st centuries, biotechnology has emerged rapidly by including various new scientific fields, such as molecular engineering, recombinant DNA technology, applied immunology, and pharmaceutical biotechnology for the development of biological therapies and diagnostic tests for prevention and curing of various health disorders (Evens & Kaitin, 2015). Monoclonal antibodies, recombinant vaccines, recombinant proteins, stem cell and genetic therapies, nano-based drugs, and biomaterials are the major biotechnology-based therapeutic products in the market. These products help millions of people in treating and preventing various health diseases, which are even untreatable (Ponce & Gilbert, 2009). These therapeutic products mainly used to treat and prevent several disorders such as genetic diseases, autoimmune diseases, neuroinflammatory complications, various types of cancers, metabolic disorders, and other diseases (Mizan, 2013). Biotechnology-based products involve several stages during the development process such as (i) most fundamental and primary stage to understand the molecular mechanism behind the particular disease, (ii) the second stage to identify and understand the molecular function of the target biomolecules for the specific condition, (iii) third stage to synthesize, manufacture, and purification of the biomolecules, (iv) fourth stage involve to determine the shelf life, stability, toxicity, and immunogenicity of the products, and (v) final stage to find drug delivery method and to carry out animal testing and clinical trials (Mallela, 2010). In this chapter, we will discuss the novel and innovative biotechnological methods and their products for health management in the field of science. This chapter outlines how the medical biotechnology field was vastly improved using innovative molecular techniques and how their innovations help to save and improve the value of life for people suffering from various illnesses.

2.2 Human gene therapy Gene therapy is a novel technology for treating and curing human genetic disorders. In this technique, functioning genes will be introduced into the human cells to heal the genetic diseases or to replace the defective gene with regular counterparts (Mhashilkar, Chada, Roth, & Ramesh, 2001). The first authorized gene therapy was granted in the year of 1988 by the Recombinant DNA Advisory Committee of the National Institutes of Health. Over these past 30 years, nearly 3000 trials have been conducted from phases I to III clinical trials to treat various genetic ailments such as cystic fibrosis, vascular disease, rheumatoid arthritis neurodegenerative disorders, multiple types of cancers, and viral infections [hepatitis B and C viruses (HBV and HCV) and human immunodeficiency virus (HIV)]. The first Food and Drug Administration (FDA) approved commercially available gene therapies to enter the market in 2017 was luxturna [to treat retinal pigment epithelium 65 (RPE65) mutation-induced blindness] and kymriah (to treat chimeric antigen receptor T cell therapy) (Cucchiarini, 2016) (Fig. 2.1). The two approaches in gene therapy are 1. Somatic cell gene therapy 2. Germline gene therapy

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2.2 Human gene therapy

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FIGURE 2.1 Gene therapy: The therapeutic gene is inserted directly into a carrier called a vector. A vector is genetically engineered to deliver the gene into the cell. Genetically altered therapeutic genes will replace or remove the defective gene in the patient. After the replacement or removal of the defective gene, cell function is restored.

2.2.1 Somatic cell gene therapy Somatic cell gene therapy transfer or express human genes into a patient target cells (except cells that do not give rise to the eggs and sperm). This technique aims to treat a disorder only in the diseased person, not in their descendants. Somatic gene transfer is carried out by three methods, such as in vivo, in situ, and ex vivo. In vivo is the insertion of functioning genes directly to the human body through the bloodstream. To cure genetic disorder in ex vivo gene transfer, defective cells will be removed from a patient, and then, they are redesigned and replaced into the patient to recover its specific function. In in situ method a specific organ for gene transfer will be targeted, for example, eye. Nearly 600 clinical trials are carry out using this technique, so far, a very low number has shown success (Ledley, 1996).

2.2.2 Germline gene therapy In germline gene therapy, DNA is inserted into the reproductive cells (eggs or sperm) in the human body. Germline gene therapy will correct the genetic variants of the reproductive cells of an individual, and this would be passed down to future generations. This therapy removes a hereditary disorder from a family line forever. Hereditary disorders occur at human’s are possibly inherited from the germline cells. However, curing these diseases is possible only through modifying their nuclear and mitochondrial DNA mutations in preimplantation embryos, which is commonly known as germline gene therapy (Wolf, Mitalipov, & Mitalipov, 2019).

2.2.3 Gene transfer system Successful gene therapy requires an efficient gene transfer vector to deliver and express the foreign genetic material into a host cell in an effective manner. To be an ideal delivery

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2.2.3.1 Nonbiological delivery system 2.2.3.1.1 Physical method Sonoporation In the year 1990 the sonoporation technique was first developed; it uses ultrasound to generate pores in the cell membrane to permit genetic materials into cells. Sonoporation efficacy in gene delivery depends upon the pulse intensity, frequency, and duration. They are already established in the cornea, kidney, muscle, brain, and heart (Kamimura, Suda, Zhang, & Liu, 2011). Electroporation Genes are delivered in specific cells by creating pores in the cell membranes through an electric field at 10
20 kV/cm. They work on cells in both suspension and solid form. They have been undergone a clinical trial for DNA-based vaccination and in a different type of cancer (melanoma and prostate cancer) (Nayerossadat, Ali, & Maedeh, 2012). Magnetofection They use the magnetic field to carry out gene delivery. Concentrated magnetic nanoparticles coated with cationic lipids or polymers and made of iron oxide that target the specific cells by applying an external magnetic field. Then, the DNA transfer is achieved by endocytosis and pinocytosis method (Kamimura et al., 2011). Hydroporation Hydroporation utilizes a hydrodynamic capillary effect to permeabilize the cell membrane for the insertion of DNA. Hydroporation has been tested in rodents through injecting a large volume of DNA solution into the tail vein, which results in gene transfer. This technique mostly used in gene therapy studies in animal models (Nayerossadat et al., 2012). Gene gun The genes are transferred into human cells in both in vitro and in vivo. The DNA-coated gold particles with applied mechanical force deliver the DNA into the cells. This technique is applied to the skin for vaccination and immune therapy (Kamimura et al., 2011). 2.2.3.1.2 Chemical method Liposomes Cationic liposomes combine with the negatively charged DNA to form a complex known as lipoplexes, and then, cellular uptake of DNA will take place immediately. They are relatively cheap than other techniques, and they can also carry a large part of DNA (Allen & Cullis, 2013).

Section 2: Biotherapeutics

vehicle, gene delivery vector requirements include biocompatibility, accommodate foreign genes of even larger size, biodegradability, accomplish the gene expression to correct the defect successfully, low immunogenicity, targeting specific cell type, and safe. Gene delivery systems can be classified into three major categories, such as nonbiological (delivering a transgenic gene to specific cells by physical and chemical method) or biological (viruses and bacteria) and nonviral gene delivery systems (involves the genes carried on plasmid DNA) (McGarrity & Chiang, 1993).

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Polymers These cationic polymers work similar to liposomes, in which polymers condense the gene of interest and form a complex known as polyplexes and insert the gene of interest to the specific cells. Examples for polymers used in gene delivery: polyethyleneimine, polyallylamine, chitosan, and peptides (Sung & Kim, 2019).

Section 2: Biotherapeutics

Heat shock Cells are incubated in a calcium chloride solution at cold conditions, leading to cell disruption, and then, heat shock is applied, which makes a thermal imbalance in the cell membranes and forces the DNA to transfer into the cell through cell pores. 2.2.3.1.3 Biological method Bacterial vector The technique of transferring genetic material into eukaryotes via bacteria is known as bactofection. In this method, the bacterium acts as a “vector” and delivers the plasmid-based gene to the host cells through cell lysis method. Salmonella spp., Escherichia coli, and Listeria monocytogenes are used as bactofection vectors. They have been widely tested in cancer treatments and pyelonephritis. They are also inexpensive techniques due to rapid amplification. This technique is applied to both phagocytic and nonphagocytic cells (Mali, 2013). Viral vector Currently, the most successful gene therapy techniques available are viral vectors. They include retrovirus, adeno and adeno-associated virus, and herpes virus. By modifying or deleting some parts of viral genomes in the viral vector, makes them safe to deliver genes to the host cell. Although they have disadvantages such as mutation, immunogenicity, and toxin production, a few viral vectors have been designed for safe delivery of genes, and they are in clinical trials (Nayerossadat et al., 2012). Retroviral vectors The commonly used and most employed vector in gene therapy is the retroviral vector. They are used in both somatic and germline gene therapy. They contain three genomes such as gag for encoding viral proteins, pol for entering target cells, and env for enveloping the protein of the virus, which identify the receptors in the host cell and enable the viral entry. Retroviruses have an ability to cross the nuclear pores of mitotic cells, and this makes them useful in situ treatment. They are used in ex vivo treatment, as they can linearly integrate into the host cell genome. They have been demonstrated in familial hyperlipidemia gene therapy and tumor vaccination (Mali, 2013). Adenoviral vectors Adenovirus has proven its efficiency as a vector in gene therapy in both in vitro and in vivo in several tissues and cells. They also differ from retroviral vectors because they do not integrate their genes into the target cells. They also can transfer large sizes of DNA particles up to 38 kb. They are used in the clinical application of cystic fibrosis and tumor therapy. Adenoviral vectors rarely lead to the death of patients due to their low specificity. Recently, for the safety of these vectors, some essential genes have been deleted to make the viral replication under control and to prevent side effects (Nayerossadat et al., 2012). Adeno-associated vectors Adeno-associated virus single-stranded DNA virus. It has two genes, such as cap (encodes viral capsid) and rep (encodes for viral replication and

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Herpes simplex virus The herpes simplex virus is a recently emerged vector for gene transfer in the nondividing cells; through that, it becomes a novel vector for gene transfer in the nervous system. They can transfer DNA particles up to 150 kb due to their neuronotropic property. Their main side effects are cytotoxicity and specific gene expression. This vector consists of glycoprotein H defective mutant, and this genome propagates into complement cells, generates viral particles in subsequent cells, and replicates their own genome but they do not release any infectious particles in the host cell (Kamimura et al., 2011).

2.2.4 Gene-editing technology Genome editing (also called gene editing) technology has the ability to modify a living organism’s DNA. In this technique, genetic material can be added, deleted, or modified at specific locations in the genomes. In general, there are two categories, such as gene addition and gene correction. In gene correction a defective gene in the native chromosomal location will be replaced by a normal DNA sequence. This process can be achieved through homologous recombination, in which DNA sequences are excised precisely and substituted by other homologous pieces of DNA. Gene addition has the benefit of inserting genes to specific cells more efficiently compare to homologous recombination. They mostly used to recover genetic disorders or to add to new functions to the host. Several approaches have been developed for gene-editing technology for clinical approaches such as transcription activator-like effector nucleases, zinc-finger nucleases, and clustered regularly interspaced short palindromic repeat
Cas-associated nucleases (Gaj, Sirk, Shui, & Liu, 2016). 2.2.4.1 Zinc-finger nucleases Zinc-finger nucleases were discovered in 1985 from Xenopus oocytes; they have nonsequence-specific cleavage domain to a site-specific DNA-binding domain that is loaded on the zinc finger. Zinc-finger nucleases usually operate by involving two DNA binding proteins, which contain 3
6 zinc fingers and FokI endonuclease to dimerize to cleave the double-strand DNA. These two proteins identified two DNA sequences and connected the two zinc-finger proteins to specific sequences. The three major criteria for zinc-finger nucleases in sequence recognition and specificity are (1) amino acid sequence of fingers, (2) the numbers of finger, and (3) the interaction of the nuclease domain (Li et al., 2020). 2.2.4.2 Transcription activator-like effector nucleases In gene-editing technology, transcription activator-like effector nucleases have shed light on new opportunities. A catalytic domain from Flavobacterium okeanokoites (FokI) has been identified and termed transcription activator-like effectors. Termed transcription

Section 2: Biotherapeutics

integration). The adeno-associated virus requires helper virus such as adenovirus or herpes simplex virus for providing extra genes to replicate. Adeno-associated vectors are safer than adenoviral vectors, and they can transfer up to 4.8 kb of DNA particle. They have used studied in diseases such as cystic fibrosis and hemophilia B, and with the help of rep gene, they also integrate preferentially into a specific site on chromosome 19 (Kamimura et al., 2011).

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activator-like effectors can cut any gene sequence in higher frequency. However, the key challenges are to design intricate molecular cloning for each new target and its low efficacy of genome screening in a specific cell (Li et al., 2020).

Section 2: Biotherapeutics

2.2.4.3 Clustered regularly interspaced short palindromic repeat
Cas-associated nucleases Clustered regularly interspaced short palindromic repeat
Cas-associated nucleases is a newly identified gene-editing technology resulting from a bacterium. The bacteria recognize the DNA from invading viruses and create DNA segments known as clustered regularly interspaced short palindromic repeat arrays. This clustered regularly interspaced short palindromic repeat arrays allow the bacteria to produce RNA segments to target the virus DNA. Then, the bacteria use Cas9 enzyme to cut the DNA part of the virus, to restricts the infection. This technology has been employed to alter the genes of eukaryotic cells through RNA-guided DNA cleavage. Since 2013, clustered regularly interspaced short palindromic repeat/Cas9 technology has been rapidly increasing, because of their use gene correction or alteration in the epigenetics field (Li et al., 2020; Zhang, Sastre, & Wang, 2018).

2.2.5 Ethical issue As gene therapy causes changes to the body’s nature, it leads to the rise of many ethical concerns. That includes (1) whether the expense of gene therapy makes it accessible only to the rich? (2) whether this makes society accept people who are different? (3) whether gene therapy changes the common human traits such as height, intelligence, or athletic ability? Still, germline gene therapy is controversial as it changes the hereditary character of offspring form parents. These ethical concerns made the US Government restrict federal funds to research germline gene therapy in humans (Rabino, 2003).

2.3 Stem cell therapy Stem cell therapy is a newly evolving technology, which regenerates the human cells and tissues. Stem cell therapy is likely to reform the whole medicinal practices. Stem cells are the unspecialized cells that have an extraordinary ability to differentiating into one or more specialized cell types; therefore they play a vital role in homeostasis and tissue repair (Daley & Scadden, 2008). Stem cells are two types based on their origins such as embryonic stem cells and adult stem cells. Embryonic stem cells are obtained from blastocysts of human embryos. However, the therapeutic benefit of embryonic stem cells is still in controversy and raising ethical concerns due to the belief that the process of extraction of stem cells from an embryo destroys nature’s process. Adult stem cells are multipotent, and they can differentiate into more than one cell type but not all. They are classified into two types, such as hematopoietic stem cells (originates from peripheral blood) and mesenchymal stem cells (originates from the mesoderm layer of the fetus). Adult stem cells are demonstrated in various clinical applications successfully, such as dentistry, neurological disorder, etc. (Biehl & Russell, 2009).

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FIGURE 2.2 Stem cell therapy: Stem cells derived from bone marrow are separated from blood and transferred to the culture medium for cell expansion. Cells are centrifuged, trypsinized, and cryopreservatives are added for storage. Stored are cells are thawed and injected into patients.

Steps involved in stem cell therapy (Fig. 2.2) Cells are obtained from bone marrow or peripheral blood and transferred to the culture medium. 1. Then, they were centrifuged, trypsinized, and stored in the master cell bank. 2. In the master cell bank, they were further passaged with growth factors to yield more colonies and to differentiate into specific cell types. 3. Then, they were injected (intravenous) or cell encapsulation is followed in the patient for cell delivery (Nadig, 2009).

2.3.1 Sources of stem cells 2.3.1.1 Pluripotent stem cells Pluripotent stem cells are obtained from embryonic stem cells, and they have an ability to differentiate into any cell types in the human body. They give rise to the multipotent stem cells, which differentiate and give rise to specific cell types/tissues of the body. Pluripotent stem cells shed light in the therapeutic field of regenerative medicine. As they can differentiate and rise to every other cell type in the body (such as neurons, heart, pancreatic, and liver cells) (Biehl & Russell, 2009; Nadig, 2009; Romito & Cobellis, 2016). 2.3.1.2 Multipotent stem cells Multipotent stem cells are the best option for clinical applications. These have the ability to differentiate into multiple lineages and self-renewing. They play a vital role in tissue healing and defense. These stem cells have been demonstrated to differentiate into different tissue such as bone, muscle, cartilage, and fat. Multipotent stem cells have also applied in the treatment of various diseases such as autoimmune disorder bone fracture, spinal

Section 2: Biotherapeutics

2.3 Stem cell therapy

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cord injury, and rheumatoid arthritis. Recent progress about multipotent stem cells is isolation of these cells from the adult and fetal brain tissues, which is differentiated into new nerve cells. An antiinflammatory and immunomodulatory is another hallmark benefit of multipotent stem cells (Biehl & Russell, 2009; Sobhani et al., 2017).

Section 2: Biotherapeutics

2.3.2 Benefits of stem cell therapy in various disorder 2.3.2.1 Retinal diseases In retinal diseases such as age-related macular degeneration, retinitis pigmentosa, and the cells such as retinal ganglion cells, retinal photoreceptors, and RPE cells, die and lost their function. Stem cells are the best source of retinal cell therapy, as they have the ability to self-renew and differentiate. In addition, stem cells also possess functions, such as immunoregulation, antiapoptosis of neurons, and neurotrophin secretion and with the success of phase I/II clinical trial. The stem cell therapy is a promising way to restore visual ¨ ner, 2018). function in retinal disease patients (O 2.3.2.2 Heart diseases The heart is considered as an organ without self-renewal ability. However, recently, it has been identified that the heart can differentiate into endothelial cells, cardiomyocytes, and smooth muscle cells, which support in the regeneration of injured heart tissue. In past years, numerous different kinds of cardiac stem cells and cardiac progenitors have been discovered. After that, in vivo experiments have been conducted with cardiac stem cells to recover myocardial injury. In the year 2011, in phase I clinical trial of cardiac stem cell confirmed no mortality or adverse events after intracoronary infusion of cardiac stem cells in patients with cardiomyopathy. Although, several animal studies and clinical trials showed that cardiac stem cells are lost in the circulation, leaked, or squeezed out of the injection site. Even in patients with retained cells in the infarcted tissue, most of them die within the first few weeks. Overall, current stem cell therapy in cardiac repair still needs to improve (Li, Tamama, Xie, & Guan, 2016; Mu¨ller, Lemcke, & David, 2018). 2.3.2.3 Neural disease Stem cells are emerging as a new area for the therapeutics of the neural disorder. A neural stem cell is derived in both embryonic and adult neural stem cells. Several studies have been carried out in rodents and the adult human hippocampus, revealing selfrenewal of neural stem cells. In patients with Parkinson’s disease, transplant of embryonic neural stem cell resulted in survival and dopaminergic differentiation, and even numbers were low, some success was observed. In cerebral ischemia, neural stem cell also has shown some promise, when neural stem cell was transplanted, rodent models of ischemia, amelioration of cognitive deficits have been observed. In Alzheimer’s disease, neural stem cell therapy showed a promising effect, when they were grafting into the diseased brain, they migrate into diseased brain and differentiate into the necessary type of cells that are lacking in the brain (Lindvall & Kokaia, 2006; Ul Hassan, Hassan, & Rasool, 2009).

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2.3.2.4 Lung disorder

2.3.2.5 Liver disease Numerous human trial shows the efficiency of stem cell therapy in patients with endstage liver disease, including liver cirrhosis, liver failure, and liver tumors. A rodent model of acute liver failure also showed the promising role of stem cell therapies by enhancing liver function, downregulating apoptosis in the hepatocyte, and it also differentiates the new hepatocytes in animal models of acute liver failure (Wu, Chen, & Tang, 2018).

2.3.3 Challenges and problems 1. The first challenge the scientist used to face is to understand the mechanism via which stem cells works in the disease using animal models, and then, how to interpret the results of these studies to humans. 2. The second challenge is to find and isolate stem cells from humans and inducing the stem cells for differentiation into the specific cells or tissue types. 3. The third one is to avoid immune rejection after stem cell grafting. However, stem cell therapy is still in the infancy stages and is debated with ethical concerns. Mainly ethical debates are surrounding human embryonic stem cells, for example, to obtain human embryonic stem cells, require the destruction of the embryo. To reduce the ethical concern, researchers should enhance their prospect of the ethical issues associated with human embryonic stem cells (Ikehara, 2013; King & Perrin, 2014).

2.4 Nanomedicine Nanotechnology is a rapidly emerging field with revolutionary impact on various fields, such as medicine, electronics, etc. Nanomedicine is a subdivision in nanotechnology, which uses nanosize tools such as nanosensors, nanorobots, biocompatible nanoparticles to prevent and treat diseases. Development of new medicines in pharmaceutical industries by using nanotechnology, presently at a primary stage; however, it is expected to be an innovative medical solution to address unmet medical needs (Soares, Sousa, Pais, & Vitorino, 2018; Ventola, 2012).

2.4.1 Nano therapeutic applications With the advancement in the nanoparticle, made the researcher gain more attention on the nanotherapeutics in the modern world to meet the unmet medical needs. Mainly with

Section 2: Biotherapeutics

Chronic obstructive pulmonary disease is a major respiratory problem that has the worldwide impact. Stem cell therapy has been reported to show promising effects in chronic obstructive pulmonary disease animal models by improving the function of the injured respiratory system. However, in the clinical studies with moderate-to-severe chronic obstructive pulmonary disease, patients did not lead to clear respiratory functional improvements. The current stem cell approaches for chronic obstructive pulmonary disease should be improved (Balkissoon, 2018; Sun et al., 2018).

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Section 2: Biotherapeutics

increasing health disorders, the expectation of a nano-based drug delivery system grew rapidly. Below, we can see some of the nano-based therapeutic applications. 2.4.1.1 Nano drug delivery Nanoparticles can be used in targeted drug delivery at the site of disease to improve the uptake of poorly soluble drugs. They can be delivered in two ways, such as passive and self-delivery. In the passive method, drugs are delivered at the particular sites in the cellular structure through the hydrophobic effect. In the self-delivery method, the drugs are directly attached to the nanocarrier for their release into the target site (Patra et al., 2018). Drug targeting of drugs is another crucial aspect of drug delivery; they are classified into two types, such as active and passive. Active targeting is more specific, where antibodies and peptides are attached with nanocarrier to release them in the specific receptor site of the target. In passive targeting, the drug travels in the bloodstream to reach its target site by binding to the large molecule. This is known as the enhanced permeability and retention effect, which allows the drug carrier system to be transported exactly to the tumor cells. There are several nano-based drug delivery systems, such as hydrogel, micelle, dendrimers, polymers, and liposomes (Badar, Pachera, As, & Nk, 2019). 2.4.1.1.1 Hydrogel

These nanoparticles are dependent upon the hydrophobic polysaccharides for encapsulation and drug delivery. The main advantages of this system in drug delivery is their high degree of hydrophilicity, flexibility in size, easy handling, and high biocompatibility (Badar et al., 2019). 2.4.1.1.2 Micelle

They are ,100 nm in size. These nanoparticles have a strong potential for hydrophobic drug delivery as their core structure integrates into the drugs resulting in increased stability and bioavailability (Tyrrell, Shen, & Radosz, 2010). 2.4.1.1.3 Dendrimers

They are synthetic polymers, consist of synthetic or natural amino acid, nucleic acids, and carbohydrates. Drugs are attached to the dendrimers through electrostatic interaction and/or hydrogen bonds. The globular shape of this particle makes them an excellent system for drug delivery (Badar et al., 2019). 2.4.1.1.4 Polymers

Their size ranges from 10 to 100 nm. Drugs are incorporated into the polymers by either covalent bonding or electrostatically (Badar et al., 2019). 2.4.1.1.5 Liposomes

They are small spherical vesicles. They have the ability to reduce systemic toxicity and to prevent early degradation of the encapsulated drug after administration (Malam, Loizidou, & Seifalian, 2009).

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2.4.1.2 Nanosensor

• Cadmium selenide quantum dots used as a sensor to detect tumors in the body. • Silicon wires as nanosensors used to identify changes in organ health. For example, kidney failure is detected with nanosensors. • Nanosensors also use to trace the contamination in organ implants. • Nanosensors can also be used for identifying any changes in DNA. However, nanosensors in medicine is still a burgeoning field. Their expensiveness also made it difficult to manufacture nanosensors and commercialize the products. To overcome these drawbacks in the future, made researchers investigate the cost-effective materials in manufacturing nanosensors (Mousavi, Hashemi, Zarei, Amani, & Babapoor, 2018).

2.4.2 Tissue engineering Tissue engineering is a rapidly emerging medicinal field, which replaces or repair tissues, cells, organs using biomaterials, which exactly mimics native body tissue (Fig. 2.3).

FIGURE 2.3 Tissue engineering: Biopsy is performed in humans to collect tissues. Cells are isolated from tissues and cultured in the required media for the expansion of cells. Cells are cultured again on a 3D scaffold. Grafts are generated and implanted into the human body.

Section 2: Biotherapeutics

The sensor is a device that detects and measures changes in the physical stimuli and converts them into electrical signals. A sensor that is constructed on nanometer measurements is known as nanosensor. Nanosensors have various applications in biomedical fields such as,

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Section 2: Biotherapeutics

Over the past decade, several nanoparticles applied in the various applications of tissue engineering field such as (Shi, Votruba, Farokhzad, & Langer, 2010) • Gold nanoparticle is the most used nanoparticle in bone and cardiac tissue regeneration because of its high compatibility and surface modification ability (Shi et al., 2010). • Magnetic nanoparticles are mainly used in gene delivery, to construct complex 3D tissues, to control cell patterns, and to study cell mechanotransduction (Shi et al., 2010). • Titanium dioxide (TiO2) nanoparticles is used to increase the proliferation of cells in bone and cardiac tissue regeneration (Danie Kingsley, Ranjan, Dasgupta, & Saha, 2013). • Silver nanoparticles are used in healing wounds because of their antimicrobial property (Hasan et al., 2018). • Carbon nanotubes which possess high conductivity used for maintaining electrical signals between neural cells and researchers also observed neurites growth on carbon nanotube (Hasan et al., 2018).

2.4.3 Nanoimaging Medical imaging is used as a diagnostics tool for diagnosing the pathogenesis of various diseases. A recent development in the field of imaging technology and nanotechnology created more advantages in diagnosis and monitoring diseases (Shi et al., 2010). In imaging techniques like MRI and ultrasound, nanoparticle is employed for better imaging compare to conventional methods. The nanoparticles loaded in fluorescent dye are showing efficient results in the intravenous imaging system (Prasad et al., 2018). The use of quantum dots in imaging is more efficient and promising compare to other nanoparticles in imaging techniques. They are expressed in the form of light. They are easily adjusted to any size, and they easily penetrate the cell membrane by attaching to cell wall proteins. They are also used to detect and remove the tumor in surgery. Their images are better in resolution and identifying diseases than the other chemical compounds (Wang, Li, Li, & Liu, 2013). The polymeric nanoparticles, like poly(lactic-co-glycolic acid) with high biocompatible and biodegradable, made them the safest technique for imaging and drug delivery. They are also approved by the FDA for the safe delivery of drugs (Bolhassani et al., 2014). These are various applications of nanotechnology in the therapeutics field. In the near future, we can also expect major developments in the field of nanomedicine by identifying newer nanoparticles with cost-effective and safe in disease diagnosis and drug delivery.

2.5 Drug designing and delivery Drug discovery is the process, which involves the combo of computational and experimental approaches to find a new drug candidate. Despite advancements in biological systems and sophisticated software and hardware, still, drug discovery and designing are expensive, time-consuming, and challenging processes (Zhou & Zhong, 2017).

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2.5.1 Rational drug design

• Developing a new drug candidate with an already known target. • Developing a new drug candidate with an unknown target. For the known target, several steps are followed for designing the drug. They are (1) evaluation of molecular functions, protein, and cellular functions, their molecular pathways, and processes, (2) 3D structure of drug binding site by X-ray crystallography or NMR, (3) screening by virtual or high-throughput screening, (4) drug discovery, (5) evaluation of biological properties such as evaluating the binding scores, biodegradation ability, toxicity nature, absorption, distribution, metabolism, and excretion according to Lipinski’s rule, quantitative structure
activity relationship and quantitative structure
property relationship (QSPR). Then, they will undergo both preclinical and clinical studies for further approval of the drug (Mandal, Moudgil, & Mandal, 2009). The steps involved in the unknown targets are (1) screening of biological molecules via analyzing gene globally using bioinformatic tools and target identification by docking, (2) validating the targets using the known and unknown 3D structure, (3) quality evaluation by docking, (4) biological evaluation includes as same as to the first type such as assessing the binding scores, biodegradation profiles, toxicity, examination according to Lipinski’s rule, quantitative structure
activity relationship, and QSPR. Finally, the experimental and clinical evaluation will be done (Mandal et al., 2009; Todd et al., 2009).

2.5.2 Computer-aided drug design Using a computational approach for drug designing is known as computer-aided drug design (Yu & Mackerell, 2017). They are divided into two types, structure-based drug design and ligand-based drug design. • In structure-based drug design, they evaluate the 3D structure of proteins or RNA by X-ray crystallography or NMR or homology modeling to identify the target site for interactions and binding. Then, they use this information for designing the drugs that can inhibit or block the biological target in patients (Sliwoski, Kothiwale, Meiler, & Lowe, 2014; Yu & Mackerell, 2017). • In ligand-based drug design, 3D structure analysis is absent. Instead, a structure
activity relationship will be established using antibiotics ligands, and physiochemical properties of targets, from that information drugs, will be designed. Then, both types will undergo a biological evaluation, and finally, experimental and clinical studies will be done (Richards, 1994; Yu & Mackerell, 2017).

Section 2: Biotherapeutics

Drug design or rational drug design has emerged as an interdisciplinary field of identifying new drugs based on their biological target. Drugs will be designed as a small molecule, and this molecule will interact and bind to the biological target and hinder their reaction in the patients, via that they provide therapeutic benefit to disease (Todd, Anderson, & Groundwater, 2009). Rational drug design is classified into two types:

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2.5.2.1 In silico drug design

Section 2: Biotherapeutics

In silico drug design plays a vital role in target identification and designing novel drugs in the field of biotechnology. They mainly used to inspect the expression of genes, sequence analysis, molecular modeling, and their 3D structure (Wadood et al., 2013). Methods involved in in silico drug design: 1. Homology modeling: Homology modeling or comparative modeling uses the existing homologous protein structure to analyze the unknown atomic structure of the target protein from a related amino acid sequence. Steps involved in homology modeling: (1) using BLAST search, the template should be identified of a target sequence, (2) amino acid sequence alignment should be done for the target, (3) alignment corrections, loop, and side-chain modeling using MODELLER softwares, (4) optimize the energy minimization, and (5) final validation by the Ramachandran plot. 2. Molecular docking: Molecular modeling uses the interaction of two or more ligands to form the stable target molecule. It analyzes the 3D structure of any stable complex by the binding properties of ligand and target. Molecular modeling mainly uses the scoring functions to find the affinity of ligands to bind to the target site. Softwares used in the molecular docking tools are, AutoDock, ArgusDock, FRED, eHITS, and FTDock (Dar & Mir, 2017). 3. Virtual high-throughput screening: Virtual high-throughput screening identifies the specific sites on target molecules from an extensive collection of compound libraries. The success of virtual high-throughput screening depends on the careful execution of each and every step of computational screening from the target preparation to hit identification (Wadood et al., 2013). 4. Quantitative structure
activity relationship: Quantitative structure
activity relationship methods play an important role in predicting the biological properties of target compounds mainly based on their mathematical and statistical relationship. They mainly used to assess the relationship of structural properties of compounds with the help of their biological properties. They also predict the physiochemical properties depending on the molecular features of other molecules. This approach mainly carried out in research laboratories due to less cost and labor (Patel et al., 2014). 5. 3D pharmacophore mapping: The 3D pharmacophore search is a vital and simple method to identify the lead compound with the desired target. Pharmacophore is defined as a 3D arrangement of the functional molecule, which is crucial to attach or bind to an active site of an enzyme or molecule. As pharmacophore is recognized, with the use of 3D database search tools, innovative compounds are identified that are suitable for the pharmacophore model (Wadood et al., 2013). 2.5.2.2 Machine learning in drug design 2.5.2.2.1 Artificial intelligence in drug design

Artificial intelligence has given an enormous rise to various applications in drug discovery and design. In machine learning technology, artificial intelligence is a well-recognized technique to learn and predict the properties of novel and innovative compounds. In the field of pharmaceutical biotechnology, artificial intelligence technology has established extensive attention due to their more significant results in property prediction (Lo, Ren, Honda, &

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Davis, 2019). The vital key to the success of artificial intelligence in property prediction is their access to large datasets (Mak & Pichika, 2019). The most and widely used techniques in the healthcare system in artificial intelligence is artificial neural networks.

Artificial neural networks are used in the drug discovery process because of their ability to resolve nonlinear relationships faster and accurate in pattern analysis compare to other statistical techniques. They also utilized in mapping the variables in chemical structures and also applied in modeling the different physicochemical properties. Artificial neural networks impersonate the human brain in their functions and shape. As a brain, artificial neural networks program, analyze, and resolve large datasets. Therefore they are used in the drug discovery process. Features of artificial neural networks are (1) work based on the reference dataset, self-correcting the errors, (2) create and organize datasets for fast retrieval, (3) store information, and (4) perform parallel computing (Mandlik, Bejugam, & Singh, 2016; Xu, Yao, & Lin, 2018). Types of artificial neural network: 1. Backpropagation networks: They are the most used neural network. They work based on the backpropagation algorithm (Mandlik et al., 2016). 2. Counter propagation networks: They work based on Kohenen’s training algorithm. In this distance the calculation is done by using only the input layer, but in the adaptation steps, both the input and the output layers are taken into account (Mandlik et al., 2016). 3. Bayesian neural networks: They work based on the principle of the Bayesian viewpoint. They are used in pattern recognition and data analysis processes statistically (Mandlik et al., 2016; Xu et al., 2018). Applications of artificial intelligence techniques in the healthcare system: • In the process of developing the drug, artificial intelligence techniques have been used to identify pathways to treat diseases via genetic information, biochemical aspects, and target identification by a computational application known as Open Targets (Mak & Pichika, 2019). • Artificial intelligence techniques are also used to find the hit or lead compounds, which act as a primary stage for identifying innovative and high-grade molecules to treat disease. • Artificial intelligence techniques also used to predict the mode of drug delivery in humans, and also, there is in vivo safety profile of drugs before they are synthesized (Lo et al., 2019). • The artificial intelligence predictive modeling tool Aicure is specially designed and used for the selection of a patient for clinical trials by the patient’s history and their gene target for a particular disease. This strategy will raise the success ratio in clinical trials (Mak & Pichika, 2019). • Artificial intelligence in the polypharmacology field where they target multiple targets for particular one disease due to their more in-depth understanding of pathological conditions (Lo et al., 2019).

Section 2: Biotherapeutics

2.5.2.2.2 Artificial neural network in drug design

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2.5.3 Drug delivery Drug delivery is the process that involves methods, formulations, and technologies of administering a drug via several routes to achieve a therapeutic effect in patients. The amount of drug, duration of the drug, and site targeting in the body are the factors considered mainly in drug delivery, and this process involves systemic pharmacokinetics for assist (Tiwari et al., 2012). Routes of administration are gaining more importance, and there are multiple routes nasal, oral, topical, transmucosal, pulmonary, inhalation, or intravenous injection routes. In recent days, site-targeted delivery is on more focus, the drug is delivered on a specific target, and they are also active only in that specific area of the body (Allen & Cullis, 2004). Several drug delivery systems have been formulated, such as liposomes, microspheres, gels, prodrugs, nanoparticles, and polymers. To achieve site-targeted delivery, the drug delivery system should omit the host’s defense mechanisms and circulate to its expected site of action (Tiwari et al., 2012). Other methods in drug delivery are selfmicro emulsifying drug delivery systems, in that they use a microemulsion for drug delivery, in thin-film drug delivery use dissolving film or oral drug strip to deliver drugs. Even though each drug delivery system has its own advantages and disadvantages, every medication to each patient needs to be taken care of its own specific manner.

2.6 Recombinant therapeutic proteins and vaccines 2.6.1 Recombinant protein The distinguished and major innovation in the field of biotechnology is recombinant proteins. Recombinant proteins are more valuable medicine for treating patients because of their safety, time-consuming process, and site target treatment (Fig. 2.4). In the year 1982, first recombinant human insulin was discovered (Ohtake & Arakawa, 2013). After that, nearly 170 recombinant proteins are produced, and they are in practice for medicinal use. The recombinant protein industry has rapidly grown, with the expansion of market size, the pharma companies earning more. For recombinant protein production, the use of an expression system is essential. The recombinant expression system is based on the characteristics and application of the recombinant protein and their requirement of quantities needed (Andersen & Krummen, 2002). There is various recombinant expression system, such as bacteria, yeast, virus, and mammals.

2.6.2 Expression system 2.6.2.1 Bacteria Recombinant protein expression in bacteria requires the insertion of a DNA fragment into an expression vector, and then, they are transferred into bacteria through the transformation process. Then, the host cells are amplified to induce the expression of the desired protein. Finally, host cells are harvested by centrifugation, and recombinant proteins are purified and characterized. Bacteria is extensively used to produce recombinant proteins

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FIGURE 2.4

Recombinant DNA technology: Insertion of the gene of interest into a bacterial plasmid with the help of enzymes (restriction enzyme for cutting and ligase enzyme for joining). Transferring the recombinant vector into the host organism through the transformation process. The host organism is amplified and expanded using the required nutrients and growth factors. DNA isolation is performed on recombinant genes, and biological properties are tested.

because genetically, they are easily manipulated and require only inexpensive components only needed for cultivation (Langlais & Korn, 2006). The most widely used recombinant expression in bacteria is E. coli and Bacillus subtilis. Among all bacteria, E. coli is the most used recombinant system for foreign protein expression because of their doubling time is 20 minutes, and the use of inexpensive media and the transformation of E. coli can be achieved in 5 minutes. Escherichia coli also has certain disadvantages as a recombinant protein system, such as they easily reject the foreign DNA, accumulation of lipopolysaccharide (LPS) (pyrogenic to humans), and protein inactivity (Rosano & Ceccarelli, 2014). The major advantage of B. subtilis is complete absence of LPS production. They are also nonpathogenic and generally regarded as safe microorganisms for the recombination process. They also certain limitations, such as nonexpression of protein interest and instability of plasmid (Luan et al., 2014). 2.6.2.2 Yeast Yeast has a high market value in the recombinant expression system. Saccharomyces cerevisiae is the most extensively used in the expression system. They are in use for the past two decades in the market as an expression system due to their rapid growth, easy maintenance, and scale up with low-cost media, biosafety, economically cheap, and ability to perform posttranslational modifications in proteins (Xie, Han, & Miao, 2018). Yeast is used in the making the hepatitis B vaccine, subunit vaccine, and hantavirus vaccine.

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Saccharomyces cerevisiae, Hansenulla polymorpha, and Pichia pastoris are the most commonly used yeast in the recombinant system, due to their easy adaptation to large-scale production. Saccharomyces cerevisiae alone used in the production of 11 permitted vaccines against the HBV and one against human papillomavirus (Bill, 2015). Besides S. cerevisiae, in recent years, the use of P. pastoris as a yeast system is increased for their heterologous protein expression (Ahmad, Hirz, Pichler, & Schwab, 2014). In the meantime, several yeasts such as Arxula adeninivorans, Yarrowia lipolytica, and Kluyveromyces lactis are well established as expression systems in recombinant technology. 2.6.2.3 Mammals Mammalian expression systems have a wide range of applications in recombinant DNA technology due to their accurate protein folding, protein processing, signal synthesis, product assembly, posttranslational modifications, and process and can secrete glycosylate proteins, especially eukaryotic proteins. Several mammalian cell lines have been used as an expression system, in that most commonly used are Vero cells HEK 293 (human embryonic kidney) baby hamster kidney (BHK) cells, mouse L-cells, CHO (Chinese hamster ovary) and myeloma cell lines. Although mammalian expression system is expensive, complex process, and the possibility of high contamination in large-scale production, they are most commonly used expression system for various heterologous proteins such as viral protein, therapeutic proteins, and bioactive peptides due to their specific functional analysis and their ability to glycosylating the protein at exact sites (Khan, 2013).

2.6.3 Recombinant protein as a treatment 2.6.3.1 Anemia Anemia is defined as a lower concentration of hemoglobin than a normal level. They are mostly found in cancer patients, chronic renal failure, and other health disorders too. Epoetin beta is a well-recognized glycoprotein produced by recombinant DNA technology for the treatment of anemia. The European Union approves them for the treatment of cancer and chronic renal failure (Macpherson, Lindsay, & Reed, 2009). 2.6.3.2 Diabetes Several human insulins are in practice for the treatment of insulin-dependent diabetes patients. Among that, Lispro is most effective in human insulin. Escherichia coli and S. cerevisiae are mostly used recombinant expression systems in the production of human insulin (Ladisch & Kohlmann, 1992; Lomedico, 1982). 2.6.3.3 Human growth hormone Recombinant human growth hormone was found to treat in children and adults whose pituitary glands are not enough to support normal growth and development (Von Fange, McDiarmid, Mackler, & Zolotor, 2008).

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2.6.3.4 Hepatitis B

2.6.3.5 Ovulation and pregnancy Human follicle-stimulating hormone and luteinizing hormone is produced with the help of recombinant DNA technology. The mammalian cell expression system is used for human follicle-stimulating hormone and luteinizing hormone. Human follicle-stimulating hormone and luteinizing hormone interestingly used in the enhancement of ovulation and pregnancy in patients suffering from polycystic ovary syndrome (Khan et al., 2016). 2.6.3.6 Gene therapy In gene therapy, recombinant DNA technology plays a vital role in prevention and cure against various health disorders. In gene therapy the use of recombinant DNA technology in various ailments mainly in cancer (brain, breast, lung, and prostate cancer) is still under investigation (Khan et al., 2016). Some other diseases, such as renal disease, Gaucher disease, and Alport syndrome, are also under investigation.

2.6.4 Recombinant vaccine 2.6.4.1 Live-attenuated vaccine The use of a weakened live virus as a vaccine is known as the live-attenuated vaccine. These vaccines do not cause any severe threat in adults. Measles, mumps, rubella vaccine, and chickenpox vaccines are an example of a live-attenuated vaccine. Recently, Ebola virus and Marburg virus also treated by the live-attenuated recombinant vaccine. The advantages of live-attenuated vaccines are long-lasting immune responses, low cost, easily administered via the oral route, and does not need adjuvants and reduced use of booster vaccines (Jones et al., 2005). 2.6.4.2 Subunit vaccine Subunit vaccines consist of a small amount of virus particles, which induces protective immunity in patients. A subunit vaccine is an effective and inexpensive method for the prevention of health disorders. They have fewer side effects compared to live-attenuated vaccines, but they need adjuvants to enhance the effect. An example of a subunit vaccine is a lipoprotein, OspA from Borrelia burgdorferi, known as Braun’s lipoproteins have been approved by the United States as a human vaccine against Lyme disease. They are mostly expressed in the yeast vector system (Schulze & Zu¨ckert, 2006; Wang, Jiang, & Wang, 2016). 2.6.4.3 Vector vaccine A vector vaccine uses an immunologically weakened virus to transfer parts of the pathogen to induce an immune response. They use antigen coding surface proteins from the pathogens, and these parts of genes are inserted into the nonpathogenic organism, and

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For the treatment of hepatitis B infection, the recombinant hepatitis B vaccine is administered to patients. The hepatitis B vaccine is mostly produced from yeast expression systems (McAleer et al., 1984).

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then, they are expressed on the host surface and induce an immune response. Examples of vector vaccines are the hepatitis B vaccine and human papilloma vaccine, which are produced from yeast recombinant expression system (Bull, Nuismer, & Antia, 2019).

Section 2: Biotherapeutics

2.7 Conclusion and future applications Human health is a foremost concern across the globe due to the increase in infectious diseases. Quality of life and health of humans have been increased globally with the influence of biotechnology and by their innovating and promising technologies in every aspect of human health. However rapid and abundant expansion in biotechnology and biotech industries, it always raises a significant concern in ethics, to decrease the moral concern, the scientist must always enhance their prospect of the ethical issues associated with biotechnology-based therapeutic products. In past years, with the innovation of recombinant DNA technology and other molecular biological techniques, biotechnology modernized the field of medicine. However, the progress and advancement shown by the biotechnology field are considered only as a budding stage by several scientists. In the future, with the continuous development of biotech industries and innovations in biotechnology, research will develop novel medicines to treat untreatable diseases and help millions of more people worldwide. In upcoming days, the global pandemic also can be controlled by researchers using biotechnology techniques by synthesizing vaccines rapidly by identifying the molecular targets as soon as possible after the outbreak. In the next twenty years, scientists also predict personalized targeted therapies for each individual and invention of novel technologies leading to a new age in disease diagnosis, treatment, prevention, and cure.

Acknowledgments Authors acknowledge the Vellore Institute of Technology, India, for their financial support.

Conflicts of interest The authors have no conflict of interest.

Author’s contribution Ravichandran Vijaya Abinaya performed the literature search and drafted the book chapter. Ravichandran Vijaya Abinaya and Pragasam Viswanathan provided a critical review of the book chapter. Pragasam Viswanathan gave the final approval of the publication of this book chapter. All authors read and approved the final manuscript.

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1102. Available from: https://doi.org/10.1080/17460441.2018.1547278. Yu, W., & Mackerell, A. D. (2017). Computer-aided drug design methods. Methods in Molecular Biology. Humana Press. Available from: https://doi.org/10.1007/978-1-4939-6634-9_5. Zhang, Y., Sastre, D., & Wang, F. (2018). CRISPR/Cas9 genome editing: A promising tool for therapeutic applications of induced pluripotent stem cells. Current Stem Cell Research & Therapy, 13(4). Available from: https:// doi.org/10.2174/1574888x13666180214124800. Zhou, S. F., & Zhong, W. Z. (2017). Drug design and discovery: Principles and applications. Molecules, 22(2), 279. Available from: https://doi.org/10.3390/molecules22020279.

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3 Section 2: Biotherapeutics

Advanced biotechnology-based therapeutics Srividhya Ravichandran1 and Gaurav Verma2 O U T L I N E 3.1 Introduction 3.2 Technologies that lead to the discovery of therapy 3.2.1 Genome editing technologies 3.2.2 Role of nanomedicine in drug discovery approaches 3.2.3 Antibody
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3.6 Genome-scale metabolic modeling

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3.7 Critical processes in the flow from basic science to practical application in the clinic via clinical trials and translational studies 69

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3.9 Advancement in devices, biologics, and vaccines as an introduction to biotechnology products that are being used in therapy 72

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3.10 Conclusion and summary

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References

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Department of Biotechnology, Indian Institute of Technology IIT, Chennai, India Clinical Research Center, Lund University of Diabetic Center, Lunds University, Lund, Sweden

Translational Biotechnology DOI: https://doi.org/10.1016/B978-0-12-821972-0.00009-5

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© 2021 Elsevier Inc. All rights reserved.

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3.1 Introduction The scope of this chapter is to provide insights into the effective translation of the advanced principles of biotechnology into effective medical treatment strategies. According to Hopkins, Martin, Nightingale, Kraft, and Mahdi (2007), the evidence for a biotechnology revolution rapidly diminishes and the technology increasingly follows a well-established historical pattern of slow and incremental change. Translational research helps to bridge the gap that exists between basic and clinical research and fine-tunes the process of discovery (Butt, Mir, Tariq, & Arshad, 2018). The process starts with the application of a product that has a definite goal to be applied to a series of experimental models such as laboratory animals and groups of people in a laboratory setting and finally to a community. All the phases of a drug getting translated to a clinical stage are very important. Hence, the whole process of translation involves advances in bio-science, which are costly and also time-consuming. However, beyond the limits, biotechnology has made its imprints in revolutionizing mankind since its existence. The discovery of new genes and proteins and their functional interplays in several molecular pathways has led to the development of novel drugs since these genomic and proteomic data interprets the relative levels of these candidates in normal physiological and disease states and also during the administration of any novel drug and its impact on the metabolism of the cell. Data which are generated at the cellular and subcellular level have revolutionized the process of drug discovery. Microarrays and biochips have revamped the field of biomedicine and also fostered up the process of targeted drug discovery, validation, and related areas (Avidor, Mabjeesh, & Matzkin, 2003). At this juncture, the role of biotechnology in its contribution towards the production of effective diagnostics, prevention, and treatment measures, including the production of novel drugs and recombinant vaccines, becomes noteworthy (Henderson, 2005). The human genome project has sown the seeds of decoding and identifying the candidate genes linked to a particular state or a condition. Classification of disease states, especially in the cases of deadly diseases like cancer, has become a simpler process with the usage of techniques like DNA microarray technology. Biotechnology its imprints in several fields, including translational medicine. High-throughput screening, DNA microarrays, and next-generation sequencing (NGS) have helped the researchers to widen their knowledge on the genome and transcriptome (Qadir & Anwer, 2019). Using microarrays, transcriptome, and proteome analysis, even the stages of metastasis have been signified according to the expression of proteins in the several stages of cancer and are useful for the classification of the diseases at various levels (Wick & Hardiman, 2005). Thus the contribution of microarray towards the diagnosis of diseases has become a really useful molecular tool in drug discovery. The drug discovery process is reaching its maximum heights by the biotechnological advancements that happen on a day-to-day basis. Many different classes of therapeutic agents are produced via biotechnology, such as antibiotics, enzymes, vaccines, and monoclonal antibodies. Ever since the introduction of the process of translational biotechnology, the monoclonal antibody family of biologics is considered to be the low-molecular-weight medicines that led their way to the path of success in translational medicine. Several monoclonal antibodies have been produced using recombinant DNA technology and have been put to

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3.2 Technologies that lead to the discovery of therapy The drug discovery process is imposing a lot of transformational changes to the area of pharma research. The existing environment is not suitable to sustain with the basic research, and hence, advanced methodologies should be adopted for meeting the demands of the drug discovery (Speck-Planche & Cordeiro, 2015). In silico computational methods are highly helpful in the process of i-drug discovery and also help in identifying the lead compounds in a better manner. Several bioinformatic tools are available at present. Molecular docking studies are useful in finding out the binding properties and ligand specificity of a drug molecule with the target proteins or cells (Waszkowycz, Clark, & Gancia, 2011). High-throughput RNAsequencing-based transcriptomics, mass spectrometry-based proteomics, and nuclear magnetic resonance spectroscopy-mass spectra
based metabolomics are the most common popular statistical approaches that can be used for biomarker detection of deadly diseases namely cancer, cardiovascular disease, and other chronic diseases and also to find out the composition of the material that the cells obtained from their microenvironment. The strategies involved in the molecular characterization technologies like transcriptomics, epigenetics, and immunophenotyping, and to the evaluation of drug combinations, beyond monotherapy approaches have widespread applications in terms of clinical utility and personalized medicine. Some of them are listed under separate headings in the upcoming sections.

3.2.1 Genome editing technologies Genetic testing and editing became a plausible event in the history of translational science. The seeds were sown during 2003 by the Human Genome Project, which has laid the foundations of deciphering the human genome, especially the genes which are responsible for every disorder and the mutations associated with them. Gene sequencing was initially achieved by Sanger sequencing, which was later replaced by the NGS, which has led to several strategies like the whole-exome sequencing and whole-genome sequencing. These sequencing technologies have identified the genomic signatures, which have led to tailor-made therapies for diseases like cancer (Morganti et al., 2020). Genome editing technologies have played a tremendous role in revolutionizing the process of translational medicine. The paradigm shift towards the manipulation of the human genome targetting towards a productive repair of DNA by several technologies that would bring about changes at the genome level is highly remarkable and a generation-shift outcome in translational research. These gene-editing technologies employ nucleases that target the double-stranded DNA sequences and would create double-stranded nicks to stimulate the repair machinery inside the cell. Such “Gene

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use in the clinical arena. Genetic engineering has revolutionized our era by extending its approaches in the transfer of genetic traits to target organisms through various technologies. The role of recombinant DNA technology in the development of new vaccines and drugs is appreciable to a worthy extent. Developments in the novel ways of diagnostic methods, monitoring strategies, and therapeutic approaches have multiplied in leaps and bounds with the advent of genetic engineering technology (Khan et al., 2016).

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correction” strategies at the level of DNA using nucleases targets the genome well and provides an in situ application. Homology-directed repair (HDR) and nonhomologous end joining (NHEJ) are the two basic mechanisms in gene editing. Genome editing nucleases are of various types, namely TALENs (transcription activator-like effectors), ZFNs (zinc-finger nucleases), and CRISPR/Cas9 (clustered regularly interspaced palindromic repeat-associated Cas9). HDR helps us to reconstruct the cleaved DNA using exogenous DNA template analog to the break site sequence in order to introduce specific mutations targeted into cells directly, which aids in delivering an appropriately designed repair template. This sitespecific insertion or correction aids in a mutation correction or new sequence generation (Verma & Greenberg, 2016). On the other hand, NHEJ-mediated repair tends to result in errors which lead to deletion or insertion of a sequence targeted to a site and leads to gene inactivation (Chang, Pannunzio, Adachi, & Lieber, 2017). Similar to RNAi, if this NHEJmediated gene editing is applied to immortalized cell lines, the deletions can be permanent, leading to the complete knock out of a particular gene. In the beginning, ZFNs and TALENs were considered to be powerful gene editing tools. But CRISPR has proved its potential in modulating gene expression, which can lead to epigenetic and transcriptional modifications more easily. For a basic understanding of the mechanism of action of ZFNs, TALENs, and CRISPR, the readers are requested to refer to Fig. 3.1.

3.2.2 Role of nanomedicine in drug discovery approaches Nanoparticles comprise materials that are designed at the atomic or molecular level, which are usually small-sized nanospheres (Rudramurthy, Swamy, Sinniah, & Ghasemzadeh, 2016). Nanomedicine utilizes nanomaterials like nanotubes, rods, and nano biosensors. These particles exhibit physical, structural, chemical, magnetic, mechanical, electrical, and biological properties, so that they move freely in the human body, similar to biomolecules (Patra, Das, & Fraceto, 2018). Nanomedicine is the subject that deals with the application of nanotechnology for medical purposes and is also an area that exploits nanomaterials for diagnosis, monitoring, control, prevention, and treatment of diseases (Tinkle et al., 2014). Nanomedicine helps in early diagnosis and treatment of many diseases since the element of nanomedicine includes a nanostructure, which is a known delivery agent that could be used as drug encapsulating molecules or an adapter molecule attached to a drug of interest. Drug designing is another area of improvement rendered by the nanotechnological approaches. Apart from the drugs exhibiting their pharmacological properties, personalized drug responses and delivery systems have been greatly improved by the implications of nanomedicine. There were several issues pertaining to the physicochemical properties of the nano-formulation that could lead to the alteration of pharmacokinetic properties of a drug, namely the absorption, distribution, elimination, and metabolism, their potential to cross biological barriers, toxic properties and their persistence in the environment and human body. (Bleeker et al., 2013). However, nanoparticles could also be designed to exhibit their drug characteristics effectively as well as to avoid host immune responses. Drug delivery systems that employ lipid and polymer-based nanoparticles could serve as effective drugs for various diseases. Improved drug delivery systems and effective release of drugs have been achieved by tagging the drugs with nanoparticles, which

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FIGURE 3.1 Gene editing tools TALENs, ZFNs, and CRISPR/Cas9. TALENs, transcription activator-like effectors; ZFNs, zinc-finger nucleases; CRISPR/Cas9, clustered regularly interspaced palindromic repeat-associated Cas9. Source: Adapted from: Li, H., Yang, Y., Hong, W, Huang, M., Wu, M., &Zhao, X. (2020). Applications of genome editing technology in the targeted therapy of human diseases: Mechanisms, advances, and prospects. Signal Transduction and Targeted Therapy, 5, 1. (Li et al., 2020).

themselves aid in the transport of small drug molecules to the target sites. Drugs or drug delivery systems are crucial when they are based out of peptides since they exhibit low bioavailability owing to the action of physiologically active proteases (Bo¨ttger, Hoffmann, & Knappe, 2017). Drugs could be microencapsulated for controlled release into the physiological stream (Ragini and Anand, 2020). These encapsulation methods would prevent the drug from the action of active proteases and also to effectively target them towards the site of action of these drugs. Nanoparticles are used to deliver cancer drugs with high efficiency and also to enhance the process. Hydrophobic drugs can be easily delivered intravenously by the usage of nanoparticle delivery systems. In some instances, there could be some side effects of the drugs with effective drug targeting with the application of nanoparticles. The new drug delivery systems for targeting drugs to specific organs or body parts would solve critical issues like in vivo stability, poor bioavailability, poor solubility or absorption in the body, and also probable adverse side effects (Jahangirian, Lemraski, Webster, Rafiee-Moghaddam, & Abdollahi, 2017).

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FIGURE 3.2 Applications of nanotechnology in medicine. Nanoparticles are exploited for their basic properties as nanostructures and are used for several purposes, including bio-imaging, targeted drug delivery, tissue remodeling, and engineering and also in therapy and diagnostics. Source: Figure courtesy: Patra, J.K., Das, G., Fraceto, L. F., Ramos Campos, E.V., del Pilar RodriguezTorres, M., Susana Acosta-Torres, L., . . ., Shin, H.-S. (2018). Nano based drug delivery systems: Recent developments and future prospects. Journal of Nanobiotechnology, 16, 71.

In the stream of diagnosis, several nanoparticles have been used as molecular tags and used for several applications of biotechnology, including stem cell research and genesequencing approaches. These landmarks have entirely revolutionized the process of drug discovery and treatment approaches. Cell-based therapeutics is an advanced approach in treatment strategies. Nanotechnology has been an indispensable tool in the area of cellular imaging and translational medicine. Many nanoparticles act as biopharmaceuticals with therapeutic properties. The treatment strategies for the neurodegenerative disorders have found a benchmark with the employment of nanomaterials in the target delivery to Central Nervous System (CNS) wherein, the basic requirement of crossing the blood
brain barrier is met. Many nanoparticles as biopharmaceuticals in the form of gels, emulsions, etc., get carried in the bloodstream and reach the target effectively. Efforts have been taken in the development of technologies with micro/nanofluidics platform in order to analyze single circulating tumor cells from liquid biopsies to find out the efficiency of these nanomedicines in extracellular vesicles and exosomes prepared from these biopsies (Sarioglu et al., 2015). Nanotechnology and nanomedicine have played a crucial role in the improvement of advancement in medicine or drug formulations and controlled drug release processes in a successful manner. The role of nanotechnology and its constituent nanostructures in medicine is explained pictorially in Fig. 3.2.

3.2.3 Antibody
drug conjugates Antibody
drug conjugates (ADCs) contribute to the class of bio-therapeutic agents that involve a novel way of therapeutic strategy wherein a specific monoclonal antibody targeted to a particular epitope is linked to a highly effective cytotoxic agent. These are a class of cancer drugs with large molecular weight (around 150 kDa), which contain extremely small drug molecules, which are often called as payload, conjugated to a humanized antibody via a chemical linker (Chari, Miller, & Widdison, 2014). Usually,

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monoclonal antibodies are used for the targeted killing of cancer cells. Normal cells would not be affected by these antibodies. These antibodies are transported to the cancer cells through microvessel walls and get diffused in the tumor tissue before they bind to their target antigens expressed on cancer cells. These antigen
antibody complexes are internalized within the cancer cells (generally by the lysosomal action). It causes the release of the cytotoxic cancer drug into the neighboring cytosolic milieu, which would result in the lysis of the neighboring cancer cells (called as bystander effect). Such ADCs confer a highly selective tumorkilling efficiency, which would not possible using normal pharmacological agents. In some cases, the linkers are attached to the cytotoxic agents that directly target the DNA. These cytotoxic agents are internalized within the cells wherein the action of lysosomal enzymes cleave the linkers and release the drugs into the cells. The drugs are either targeted to the microsomal disruption or directly to bind the major groove of the DNA. These processes selectively kill tumor cells by affecting their proliferation, which is otherwise impossible. Currently, five ADCs are approved and are used for several diseases like metastatic breast cancer, lymphoma, and acute myeloid leukemia. Several ADCs are in the process of preclinical and clinical trials. The role of ADCs in cytotoxicity is well elucidated,

FIGURE 3.3 Basic structure of an antibody
drug conjugate. Source: Figure courtesy: Perez, H.L., Cardarelli, P.M., Deshpande, S., et al. (2014). Antibody-drug conjugates: Current status and future directions. Drug Discovery Today. 19(7), 869
881.

FIGURE 3.4 Modified antibody
drug conjugates for therapeutic applications. Antibody
drug conjugates have been used for cancer therapy with good therapeutic potential. The early generation antibodies were designed to utilize the mouse antibodies conjugated to cytotoxic agents to target the tumor cells, whereas the new generation antibodies use a humanized antibody conjugated to a cytotoxic agent to target tumor cells with high specificity. Source: Figure courtesy: Thomas, A., Teicher, B.A., & Hassan, R. (2016). Antibody-drug conjugates for cancer therapy. Lancet Oncology, 17(6), e254
e262.

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FIGURE 3.5 The plausible mechanisms which are accounted for the targeted killing of tumor cells includes the following processes which are indicated by numbers in the figure. 1. Specific monoclonal antibodies targeted to a particular cell which binds to a cell surface antigen is conjugated to a cytotoxic agent that targets the microtubule assembly or the DNA directly and internalization of the conjugated antibodies into the cells. 2. Lysosomal enzymes cleave the linker and release the cytotoxic agents. 3. Early and late endosomes mediate the process. 4. The released cytotoxic agents disrupt either the microtubule assembly or the replication by binding to the major groove of the DNA, and 5. the released cytotoxic agents would kill the neighboring cancer cells using bystander effect. Source: Figure Courtesy: Kovtun, Y.V., & Goldmacher, V.S. (2007). Cell killing by antibody-drug conjugates. Cancer Letters, 255(2), 232
240.

and for their detailed structure and mechanism of action, the readers could refer to Figs. 3.3
3.5.

3.3 Molecular diagnostics The role of biotechnology in the diagnosis of various diseases has seen a face-lift in the recent past. Many of the conventional methods which were employed to unravel the underlying cause of the disease or the symptoms of any disorder have proven to be cumbersome without the advent of biotechnology. Molecular medicine seems to be a forerunner of many diseases, and many techniques like gene therapy and cell therapy have found their appropriate place in the field of molecular therapy (Herzog, 2020). Many of these modern tools are very accurate, less time-consuming, and also costeffective (Afzal, Zahid, Ali, Sarwar, & Shakoor, 2016). The use of monoclonal antibodies, conventional and real-time assays, microarrays, and NGS have completely revolutionized the molecular diagnosis to be a forerunner in the present scenario. The contribution of recombinant DNA technology towards the development of novel diagnostic tools and vaccines needs a mention. Many diagnostic kits are available at effective prices. Molecular diagnostic tools aid in the field of precision medicine by combining advanced technologies like NGS and other profiling technologies, and also by the availability of new therapeutic agents (Malone, Oliva, & Sabatini, 2020).

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The area of diagnostics has improved tremendously in the recent past. As a conventional approach, blood tests would help us find out a disease status or used as a marker for a disease but were less reliable. To elucidate the levels of circulating cell-free tumor DNA would be a potent biomarker for cancer diagnostics (Perez, 2020). The expenditure of tumor biopsies for multiple times would incur more cost-effective and painful for the patient also. Hence a technology that helps us to use the blood sample to detect any mutation in the tumor which combines the data from several platforms like molecular alterations such as protein overexpression and gene mutations in regulatory elements, DNA would be a powerful tool in clinical practice. Conventional methods like immunohistochemistry and fluorescence in situ hybridization techniques have initiated the process of molecular therapeutic in the first place as a forerunner (Dietel et al., 2015). But translational biotechnology has widened its approach and extended the ways of elucidating several biomarkers for various diseases, especially tumors. Thus translational research bridges the gap in the lacunae existing in the prognosis and development of a treatment regimen for a given disease at the forefront. The tools that are required for molecular diagnostics should be very powerful and reliable. For instance, studies using transcriptomic data that quantifies relative mRNA levels in various tissues and cells would be most useful. Several transcriptomic studies like RT-PCR (reverse transcriptase quantitative polymerase chain reaction), qPCR (quantitative polymerase chain reaction) array, microarrays, and RNA sequencing have been used in recent years for generating useful data in the molecular diagnostic forum. RT-qPCR evaluates the relative gene expression of control and test samples accurately. Similarly, the qPCR array signifies the expression of several genes in a single go. Microarrays employ hybridization techniques to quantify gene expression levels. RNA sequencing utilizes sequencing methodologies to quantify and compare the transcriptomes. Since all these techniques work at the level of mRNA rather than DNA, the actual genes which are expressed in a physiological or a disease condition can be accurately found out. Many complex diseases like neurodegenerative, autoimmune, cardiomyopathies, and autism have been in the list of priority for companies who have developed transcriptome-based diagnostic tests. Diagnostic tests for these major diseases include the relative quantification of RNA levels in various instances. For example, the diagnostic test for Alzheimer’s disease employs RNA sequencing techniques to analyze the gene expression of 10 micro RNAs as molecular markers for Alzheimer’s. (Keller, Stahler, & Meese, 2014). The technique becomes noninvasive if the samples used for analysis are serum or blood and not biopsies. A transcriptome profile would contain 10
100s or 1000s of miRNAs levels that exemplify the expression pattern of disease-specific genes. According to Burska et al. (2014), the expression signature which is linked to particular diseases could be elucidated if the studies are harmonious to the clinical conditions and are aimed at producing new biomarkers for use in clinical practice. There are several challenges that should be addressed during any transcriptomic studies for molecular diagnosis. This also includes the redundancy of application and the clinical trial process and the concurrency of results during several trials. Multi dataset comparison while using voluminous transcriptomic data arises as a major challenge. The plethora of personalized medicine lies in the validation and compilation of multiple datasets during clinical studies.

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3.3.1 Translational bioinformatics Translational bioinformatics is one of the recent areas of development in translational medicine. It involves the development and use of computational methods that could handle the data generated by life sciences/biotechnology, which are accumulated, assimilated, and analyzed for creating new tools for medicine. There exists a huge difference between bioinformatics and translational bioinformatics. The biological discoveries emerging out of this subject would be directly translated to medicine or future medicine and are related to human health and disease (Butte, 2008). Translational bioinformatics is an emerging field of translational research that combines biomedical data sciences and informatics, which includes the development of technologies that efficiently translate basic molecular, genetic, cellular, and clinical data into clinical products or health implications (Ritchie, Moore, & Kim, 2020). The role of translational bioinformatics flows into precision medicine that underpins genomic, environmental, and clinical profiles of individuals that would allow the output of genomic data into personalized medicine. The scientific and statistical challenges imposed by the genomic data is curiously handled by the people working in this area. Several areas of translational bioinformatics include clinical genomics, genomic medicine, pharmacogenomics, and genetic epidemiology. In the aspect of translational biotechnology, precision medicine has its implications both in clinical medicine and also in therapeutic aspects, as in for the discovery of new drugs. The process of implementing new drugs would involve crucial rounds of clinical trials and also multiplex coordination of several people, including clinical staff, clinicians, laboratory staff, biostatisticians, and bioinformaticians (Li et al., 2019a). Clinical genomics aids in the development of new molecular biomarkers that are related to a disease condition or state and are validated by clinically relevant genetic tests (Pagon, Tarczy-Hornoch, & Baskin, 2002). Genomic medicine or pharmacogenomics deals more with the area of personalized medicine. The correlations between the genotype and the phenotype are taken care of by this area. Pharmacogenomics might also worry about the genomic/clinical phenotype relationships with the pharmacologically active substances (Thorn, Klein, & Altman, 2005). Genetic epidemiology deals with the aggregation of genome-based data in comparison to the public health and environmental registries (Little & Hawken, 2010). The integration of various disciplines like genomics, computer science, computational biology, and statistics have benefited the scope of translational medicine, which would evoke the process of conversion from basic research to a clinical setting. The theoretical understanding and predictive notions of therapy in basic science due to the complexity of the human physiology and the heterogeneity of the human population would be a limiting factor for the actual transformation of the same into the clinical practice but they would create some inputs to further medical innovations (Ali & Gittelman, 2016; Mittra, 2009). A descriptive picture of translational bioinformatics is given in Fig. 3.6 for an easy understanding. Several databases have created resources for the widespread existence and operation of translational bioinformatics. Genotype and phenotype variations and studies related to them are accessed from the clinical repositories from the eMERGE network, CLIPMERGE program, and Informatics for Integrating Biology and the Bedside program and Informatics for Integrating Biology and the Bedside (Fu et al., 2017). Multiomic data that

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FIGURE 3.6 Areas of translational bioinformatics. Translational bioinformatics is a multidisciplinary subject that encompasses several domains of biotechnology, including computational biology, clinical genomics, genomic medicine, pharmacogenomics, and genetic epidemiology. These areas are of immense importance in the actual translation of genomic data towards the clinical setting. Source: Figure courtesy: Sarkar, I.N., Butte, A.J., Lussier, Y.A., TarczyHornoch, P., &Ohno-Machado, L. (2011). Translational bioinformatics: Linking knowledge across biological and clinica realms. Journal of the American Medical Informatics Association: JAMIA, 18(4), 354
357 (Sarkar et al., 2011).

incorporates genomics, transcriptomic and proteomic data and their molecular profiling and the datasets obtained would be integrated with the existing or predicted clinical paradigms to generate some useful information for the characterization of any physiological or a pharmacological condition or the improvement of a treatment strategy.

3.3.2 Organoids—tools for disease models Tissue engineering is found as a frontier area of biotechnology that is considered to be a problem-solving tool for the management of organ shortages in the area of transplantation medicine. The therapeutic arena is moving towards 3D model cell cultures, and human tissue, organoids, and organ on a chip approach for diagnosis and treatment of diseases. For a better understanding of the physiological functions and etiology of a particular disorder, such organs and tissues are becoming more important. The idea of reconstructing tissues from cells by appropriate growth factors and other environmental factors necessary for the formation of a tissue is the main concept of tissue engineering. The reconstructed tissue or an organ is transplanted to patients as a measure of clinical therapy. The origin of the cells for tissue engineering is the cells with a rejuvenating potential isolated from the patient (Yan-Yan, You-Hui, Fang, Jin-Song, & Xiao-Gang, 2018; Langer & Vacanti, 1993). Regenerative medicine and tissue regeneration therapies have been the most achievable techniques in the recent past. Stem cells have the prominence to create a microenvironment by releasing certain trophic factors conducive to the repair of damaged tissues (Fierabracci et al., 2015; Fu et al., 2017). The trophic factors, mitochondria, mRNA, and miRNAs establish an intercellular communication thereby opening up system in these

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stem cells to repair the tissue and organ dysfunction (Matthay, Pati, &Lee, 2017) thereby opening up new frontiers in the treatment of wounds, cancer, neurological disorders, infection-related lung injuries, and inflammatory diseases (Li et al., 2019b). Animal experimental models have been the preferred models for understanding of any pathophysiology in the past. But as advances in stem cell medicine took over, in vitro 3D organoid cultures have proven to act as the powerful tools of translational research. Organoids refer to the cultured organ mimics that could replicate the human organ systems inside the body. These organoids have been produced from the tissue stem cells, which mimics the “stem cell niche,” which characterizes the physiological environment of the stem cells well capable of self-renewal and repair. It is reported that human models are limited in the case of kidney disorders. The integration of 3D models of tumors into the preclinical workflow would promote the process of therapeutics and clinical trials and would substantially reduce the usage of animals for in vivo studies also (Mapanao, Santi, & Voliani, 2020). Henceforth, the development of alternative approaches would be a promising candidate for the enrichment of the existing therapeutic strategies. 3D bio-printing technologies have revolutionized the frontiers of regenerative medicine. Tissue with bio-links is attached using a 3D inkjet bio-printers. The regenerated tissue must possess an excellent regenerative potential and also recoup the system after implantation. Several cases have been clinically treated for cardiac failures, and the success rates have been wonderful with the engineered tissue (Menasche´ et al., 2015). The fabricated scaffolds are in line with the required tissue dimensions, and this could be aided by a

FIGURE 3.7 The process of 3D bio-printing and its applications in clinical medicine. 3D bio-printing has become an evolving field in translational biotechnology wherein several biomaterials in the form of scaffolds are engineered with tissue models to bring out an alternative for tissue and organ replacement protocols. These bioprinted tissues have numerous applications in diagnosis and treatment. Source: Adapted from: Mandrycky, C., Wang, Z., Kim, K., & Kim, D.-H. (2016). 3D bio-printing for engineering complex tissues. Biotechnology Advances, 34 (4), 422
434, ISSN 0734
9750.

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computer-aided system that allows the adaptation of the bio-printed shape in accordance with the defective tissue or organ of a patient. For instance, bio-printing processes could yield a blood vessel-like vascular network in order to replace a defective heart tissue. For a better understanding, a detailed schematic representation of the bio-printing process and its applications are shown in Fig. 3.7. The clinically implanted heart tissue would be compatible with the tissue requirements like the pore size of the blood vessel, elasticity, and other biophysical properties. The applications of bio-printing are very much and are used to treat the congenital defects also (Kim, Lee, Kim, & Mao, 2010). A recent technology that encompasses the human organs-on-a chip is well known for its micro-engineered, fluidic systems that could simulate the physiological microenvironment of human cells in order to demonstrate their organ-level function. Bio-engineering approaches are followed here wherein the technology allows the control and fine-tuning of cellular functions, including differentiation and gene expression. Several niche areas like intercellular communications, spatiotemporal gradients, vascular perfusions, and mechanical forces are also brought about by this technology and are being developed for almost all the organs, including the brain, heart, liver, kidneys, and skin (Workman et al., 2018). The fluidic nature of the systems allows the interlinking of organs by multiple chips to build more complex systems in a single platform (Novak et al., 2019).

3.4 Cell-based therapy Cell based therapies have become an indispensable tool in translational biotechnology. During the process of clinical trial, therapeutic accountability of these cellular therapies require a detailed process of approval and the clinical trial protocols for the same are cumbersome. They integrate the methods of production and characterization as a part of the process. Prenatal transplantation of hematopoietic stem cells has found a little success during clinical runs. For instance, clinical trials for alpha-thalassemia major include the cells derived from the bone marrow of the mother infused into the umbilical vein during the early gestational age, which forms the transplantation in utero and hence does not need any mode of immune suppression of the fetal system (http://clinicaltrials. gov ID:NCT02986698). Stem cell transplantation involves a lot of ethical considerations in general. These products come under the logo of Advanced therapy medicinal products (ATMPs), which requires a lot of quality standards and Good Manufacturing practice (GMP) regulations (Ekblad-Nordberg, Walther-Jallow, Westgren, & Go¨therstro¨m, 2020). The stem cells have been used as disease models so far. They could generate ex vivo models for various disease conditions. The several applications of stem cells are depicted in Fig. 3.8, given below. Several challenges that have to be faced during the implementation of therapy include the manufacture, regulatory, testing, and delivery. Since the clinical trials involve patient data, safety, and consistency of the therapy in various fronts dictates the saga of cellular therapies per se. The commercialization of molecules that have proven clinical effects would fail to meet the demands owing to the huge cost of manufacturing. Also, a robust methodology of drug manufacturing or therapy could fail because of the failure in the clinical trials. The process of commercialization of a

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FIGURE 3.8 Stem cell-based therapeutic attributes available in translational medicine. The pluripotency of stem cells could be exploited at various levels like the manufacture of diagnostic elements like biosensors, engineering of tissues, and molecular and diagnostic devices at nano and microscale. Source: Adapted from: Argentati, C.; Tortorella, I.; Bazzucchi, M.; Morena, F.; Martino, S. (2020). Harnessing the potential of stem cells for disease modeling: Progress and promises. Journal of Personalized Medicine, 10, 8. (The article is an open-source article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium provided the original work is cited.)

drug through a process of translational biotechnology is the need of the hour. The scope and applications of systems biology and its relationship with translational medicine are listed in Fig. 3.9. The process of drug discovery has been divided into a number of stages from the bench side to bedside. Optimization of the methods for the manufacture of the drug and its efficacy studies consumes 3
6 years, whereas the clinical trial regimens spare 5
9 years. Henceforth, for a drug to come for use as a marketed drug, it takes about 8
15 years on an average. The major reasons for the failure of a drug include its lack of clinical efficacy (30%), toxicology (20%), and commercialization (20%) (Kola & Landis, 2004). There could be lacunae in the translation of

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FIGURE 3.9 Several challenges encountered in systems biology and translational science. Systems biology helps in the drug discovery process and also an up-gradation of an existing drug database. A matching of proteomic data available in the translational databases across several platforms helps us to match the drug
molecule interaction and paves the way for personalized medicine. Source: Adapted from: Fernandez-Moure, J.S. Lost in translation: The gap in scientific advancements, and Cyranoski, D. (2016). CRISPR gene-editing tested in a person for the first time. Nature, 539(7630), 479.

preclinical findings from animal models to clinical subjects and henceforth becomes the major drawback in the therapeutic front (Markou, Chiamulera, Geyer, Tricklebank, & Steckler, 2009).

3.5 Nanotechnology and its uses in biomedicine Nanotechnology has extended its applications to almost all fields of life, especially to biomedicine. In the therapeutic and diagnostic fronts, nanotechnology is doing quite well. Nanoparticles are used as tags or labels, which makes an assay quite simpler and effective. Gold nanoparticles are used for gene-sequencing purposes. Some gold nanoparticles are also used for the diagnosis and treatment of cancer by targeting cell surface receptors present on the cancer cells. Thus the specific killing of cancer cells is useful in the treatment of cancer. Nanoparticles are also used for tissue repair and regeneration processes. Nano biosensors are used for many diagnostic purposes, especially in cancer. Nanoparticle-based magnetic beads are useful for the sorting and separation of stem cells. A group of nanoparticles called quantum dots are some nano-devices are useful for the tracking and imaging of stem cells. Nanomedicines are also used for the drug delivery systems and also used for in vivo imaging purposes. Nanomedicines which are used for therapeutic purposes are very much advantageous over the conventional drugs wherein, they have more circulation time are also less susceptible to degradation by the liver. The site-specific delivery of these

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drugs is also an appreciable factor that paves the way for increasing the therapeutic index of the drugs (Duan & Li, 2013). These nanoparticles improve the efficiency of drug delivery as these are based out of natural and synthetic polymers and liposomes. The physicochemical properties of nanoparticles enhance the bioavailability of therapeutic agents after both systemic and local administration (Jo, Kim, Lee, & Kim, 2015), on the other hand, it can affect cellular uptake, biological distribution, penetration into biological barriers, and resultant therapeutic effects (Duan & Li, 2013). Nanoparticles have been tested for their properties as enzymes since 2007. This led to the concept of nanozymes where nanoparticles possess enzyme properties or, in other words, nanomaterials that act as enzyme mimics (Wu et al., 2019). These nanozymes are of nanoscale size, which can catalyze specific enzymatic reactions. At present, these nanozymes are used for industrial purposes for environmental treatments, as antimicrobial agents, medical treatments (Yan-Yan, You-Hui, Fang, Jin-Song, & Xiao-Gang, 2018), and also as biosensors (Qiu, Pu, Ran, Liu, & Ren, 2018) and for bioassays (Dudley, Deshpande, & Butte, 2011). These nanozymes are cost-effective, easy to manufacture, and possess stability and specificity.

3.6 Genome-scale metabolic modeling Prediction of “omics” data would bring about many discoveries in biomedicine. Translational biotechnology has driven the whole process of drug discovery and personalized medicine by its technological advancements in prediction and analysis of omics data and has created a vision for future aspects of biomedicine. Metabolic modeling methodology represents metabolism as a network and thus creates a high-throughput screening of omics data in a different dimension altogether. The metabolism of any organism at any point of time in a wide genome-scale would be an interesting point to ponder on. In silico metabolic modeling provides a generous platform to create an array of cellular metabolic phenotypes and tries to address all possible questions in translational research, thus aiding in the drug discovery processes. Genome-scale metabolic models (GEMs) are used as a strategy-driven approach for studying the metabolism of patients in a clinically relevant approach. This had led to the discovery of novel biomarkers and also added value to the target identification in a particular disease state of a patient. The effect of a drug or its side effect would also be validated using this approach. GEMs serve as computational signatures for gene
protein interactions for the metabolic genes of an organism. This is a systems biology approach to trace the metabolic status of an organism. GEMs are available for several organisms, including bacteria, S. cerevisiae, C. elegans, Arabidopsis, and humans. The systems biology approach to create metabolic models started long back, but the availability of human GEMs revolutionized drug discovery and a better understanding of the disease pathologies in humans. An automated process governs the reconstruction of GEM, which includes the annotation of a genome sequence, prediction of reaction kinetics using thermodynamic approaches, and enzyme localization. These GEM data from various organisms are useful in many areas, for example, to metabolically engineer organisms for the production of secondary metabolites or gene-knockout studies. By integrating of GEM data with genomic or transcriptomic or physiology data, novel drug targets could be identified, which would result in the improvement of therapeutic regimens, which are

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indispensable at this point. GEMs could also be used for the determination of genes involved in the metabolism of deadly diseases like cancer, wherein there would be several interplays of biological pathways and metabolic fluxes (Burchum & Rosenthal, 2019). GEMs were identified to be promising tools for biomedical and pharmaceutical applications hitherto.

Translational research involves the integrated application of innovative technologies that encompass multiple disciplines, including physiology, pathophysiology, the natural history of the disease, genetics, and proof-of-concept studies of drugs and devices (Zerhouni, 2005). Various stages involve the conversion of a new drug or a molecule from basic research to clinical research and then to clinical practice and then to the community (Westfall, Mold, & Fagnan, 2007). The stages of a drug that evolves from a laboratory to the patient have many phases, including the preclinical and clinical phases (I
IV). The preclinical phases are those which include animal studies that eliminate the toxicity and elucidates the biological activity and pharmacokinetic activities of the drug. Phase I clinical trials determine the efficacy and safety of the drug in a smaller group of healthy individuals. Several parameters, like pharmacokinetic activities and dosage determination, are carried out in Phase I. In Phase II, the drug is tested on a large number of patients who have the disease corresponding to the drug. This phase helps to determine the appropriateness and safety dosage of the drug in patients. Phase III trials are elaborate and extensive, wherein the study is randomized and double-blinded. From these results, one should be in a position to find out the long term use of the drug in an effective way. Following Phase III, the drug would enter the application and approval process from the concerned agencies like the FDA. Once the drug is approved, the drug is released into the market in Phase IV (Burchum & Rosenthal, 2019). Advanced technologies, such as genomics, proteomics, bioinformatics, and humanized animal models, are powerful new tools that can assist in advancing the efficacy and efficiency of translational research (Butte, 2008). The contribution of science and technology has led to the recent advances in sequencing technologies, molecular analysis and studies on the immune pathways that have improved the translation of scientific methods and discoveries into diagnostic and therapeutic engines of today in the field of cancer biology (Ezelle et al., 2020). Translational medicine combines disciplines, resources, expertise, and techniques to take the basic research from the bench side to the clinic and finally to the community in order to enhance prevention, diagnosis, and therapeutic areas of medicine (Shahzad, 2015). Basic research, preclinical studies, clinical trials, and implementation of research findings are all various parts of translational medicine (Chen, Zhao, Jin, & Shi, 2012). The role of clinical scientists to bridge the gap between laboratory research and clinical practice becomes indispensable in the first place. These “translators” help in the transformation of a molecule/drug to be put into practice into clinics and more to the patients who are in need of it (Anckaert, Cassiman, Cassiman, 2020). The process of translation might seem to be easy, but the crucial processes involved in it are cumbersome. Littman et al (2007) state that the success of translational research depends not only on overcoming

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scientific difficulties such as interspecies differences but also on surpassing financial, ethical, regulatory, legislative, and operational obstacles. Recent advancements in phenotypic personalized medicine encompass the combinatorial inputs of artificial intelligence and personalize combination therapy to improve the efficacy and safety of the drug combinations and their optimal levels (Lim, Goh, Abdul Rashid, Kai-Hua Chow, 2019). Pilot trials have been initiated for some diseases, and technologies help in the monitor of glucose, blood pressure, and neurological parameters using electronic devices, which are helpful in personalized diagnostics and analytics. Such a vast area of clinical translation which encompasses diagnostics and therapeutics parts governed by artificial intelligence-based personalized medicine is termed as “theranostics” (Li et al., 2019b). Personalized medicine using translational biotechnology encounters several lacunae at this juncture, for any drug to enter into clinical trials after the regulatory approval process takes a lot of time and manpower. Clinical trial protocols have strict regimens, and the approval process has several phases.

3.8 Major pitfalls in translational research In the context of a new drug or a technology getting into clinical practice, a case-to-case discussion is the need of the hour. A therapy or a clinical practice that could be introduced into a clinic must be successful in all phases of trials. For instance, a biomarker, which is either prognostic or predictive, the former which marks the onset or occurrence of the disease and the latter being a more conclusive way of the disease recovery status, has to guide the clinician to understand the potency of the biomarker in his practice. Often huge challenges are imposed on biostatisticians who work on a multiparametric approach to solve many paradigms in a clinical setting. According to Perez (2020), the working strategies for a biomarker-driven drug development encompass time, cost, and risk as good guiding principles. The time duration to enter the clinical Phase I has become the major pitfall in translational research. Some molecules which could withstand the tight timelines can be easily adapted into the clinical setting. The major pitfalls in the translational research are identified as follows: • There is a lack of complete knowledge of translational research, • limited manpower for clinical investigations in the inter and multidisciplinary approaches, • lack of organizational structures that govern the clinical trials, • inefficiency and inflexibility in clinical trial management, and • issues of regulatory bodies. The evolution process of a new drug involves a lot of challenges to face. Effective molecules that serve as good as drug candidates in animal models would not prove efficient in clinical settings and vice versa. The readers are requested to refer to Fig. 3.10 for a pictorial representation of the above topic. The immunogenic and virulence potential of novel biologics elicits a setback in the quick adoption of these products into a clinical arena. Viruses and cells are enlisted at this juncture. Some biologics need extensive processes for production and purification.

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3.8 Major pitfalls in translational research

FIGURE 3.10 Major pitfalls and successes in the process of drug discovery in translation. Certain molecules have proven its safety and application standards in animal experiments have not been effectively translated to the clinical setting, whereas some molecules that have been safe enough in human beings have not proven worthy in animal models. The evolution of a drug from the basic science experiments has to cross several milestones. Source: Figure courtesy: Buchan, N.S., Rajpal, D.K., Webster, Y., Alatorre, C., Gudivada, R.C., Zheng, C., . . . Koehler, J. (2011). The role of translational bioinformatics in drug discovery. Drug Discovery Today. 16(9-10), 426
434 (Buchan et al., 2011).

TABLE 3.1 The process of drug translation and its participants. Goals Bench to bedside

Support and expertise

Lacunae

Clinical and preclinical studies Clinicians, clinical support staff, of a new drug candidate patients, and organizations

Patient driven/market needs

Fundamental researchhypothesis driven

Academia and industry

Research funding/patents/licensing

Goals Clinical to Community side

Large patient input, clinical and toxicology experts

Failure of disease models, inefficiency of a drug during translation at a large scale

Commercialization

Regulatory affairs expert, pharmacology, and large-scale manufacturing

Failures in regulatory support, patenting, and ineffective industry
academia partnerships

The process of drug translation has major goals that focus on the improvisation of the existing drugs, the discovery of a new drug candidate, and its approval and licensing. Several domains of expertise are crucial for the translation process, which includes the participation of academics, industry, hospitals, basic research laboratories, and the regulatory bodies. The synchronization of all the process becomes the major aspect of the translation of a new drug from the bench side to the patient.

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In order to counteract these lacunae in clinical translational of a new drug, there must be collaborations among institutions so that the whole process becomes smooth and leads to an effective pathway of translational research. The need of the hour is the industry
academia partnership which could help bridge the gap existing in the real process of translating novel diagnostic or therapeutic biomarkers from bench side to community. Technology transfer organizations, patient organizations, and clinical trial support systems are increasingly in demand to help in the market approval and enhancement of the clinical trial accessibility. Such platforms ensure proper governance in the improvement of clinical trial processes involved in drug translation. The following Table 3.1 depicts the industry
academia partnership and each of their roles and responsibilities in translational medicine.

3.9 Advancement in devices, biologics, and vaccines as an introduction to biotechnology products that are being used in therapy Biotechnology-based advanced therapy medicinal products are divided into many groups. Gene therapy products, cell-based products, and tissue-engineered products, together called ATMPs or cell and gene therapy products, represent a heterogeneous group of innovative biologics, which can be classified in many different ways. ATMPs are viable cells, tissue, or genetic material. Many products of biotechnology, such as vaccines, biologics, cells, tissues, and recombinant therapeutic proteins, have been used in clinical applications as on date. Many gene therapy products are approved and used as on date. The first on this list being Gendicine, which is a recombinant adenoviral vector expressing recombinant p53 and used for the treatment of head and neck squamous cell carcinoma treatment (Zhang, Li, & Li, 2018). The CRISPR/Cas system has been recognized as the most powerful tool for editing the genome. The nucleases which are targeting a specific genome are guided by a guide RNA that is specific to that particular sequence. Several CRISPR products have been into clinical trials those of which including programmed cell death protein-1 that targets tumor-induced silencing of immune cells (Cyranoski, 2016) and also for inherited childhood blindness (Sheridan, 2018). The term “biologics” refers to any of the following molecules, like sugars, proteins, or nucleic acids, or a combination of these entities or even cells and tissues. Biologics can be naturally available from plant and animal sources or synthesized using a platform. Recombinant therapeutic proteins obtained from bacteria, yeasts, plants, and mammals have been used extensively. The development of biotechnology has always been a forerunner in the therapeutic regimens available in the market to date. Production of useful proteins like interferons, insulin, and other growth hormones and factors, recombinant blood products, monoclonal antibodies, gene therapy products, molecular pharming agents, and engineered tissue products have been in use since the advent of genetic engineering, and recombinant DNA technology was introduced (Afzal et al., 2016). Xenografts, bone grafts, collagen, and heart valves have been successfully engineered (Afzal et al., 2016). Lipid-based formulations and colloidal drug delivery systems have been tested and successfully used in therapeutics (Keller et al., 2014). Several techniques that include molecules like aptamers synthesized using a method called as

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3.9 Advancement in devices, biologics, and vaccines as an introduction

FIGURE 3.11

The mechanism of drug repositioning includes approaches like drug-based and disease-based. The drug-based approach depends on the biological effects and physiochemical properties of the drugs, whereas the disease-based approach depends on the etiology and the symptoms of the disease in general. Molecular modeling, targeting, and docking/simulation protocols help in the identification of new targets or modification of the existing targets for a disease.

SELEX (systemic evolution of ligands by exponential enrichment) by which the nucleotide sequences could be used to identify the hydrogen bonds present in DNA and RNA at molecular, cellular and tissue levels as the specificity of these oligos are utilized for target detection in diagnostics and therapeutics (Pang et al., 2018). Immune-based treatments employ T cells to be infused for the treatment of cancers. These T cells would be manipulated in vitro using interleukin 2 to activate these T cells. These cells are tested well for clinical applications. Several immunotherapies based on effector T cells, which are expanded ex vivo, have shown an increased antitumor efficacy when compared to free interleukins. Clinical trials have been conducted for several melanomas and primary tumors, and these cells have shown their efficiency towards these tumors by reducing metastasis (Li et al., 2019b). Gene silencing is now becoming a promising option for cancer therapy. In this, sequence-specific inhibition of oncogene expression, either interfering at mRNA or translation level, is achieved by the delivery of antagomirs to cancer cells. This method is highly specific with high efficiency and specificity when compared to traditional cancer therapy methods (Chen et al., 2018). The usage of drugs can be increased by knowing the spectrum of these drugs in a wider form. A recent technology called drug repositioning or repurposing helps us to extract

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new applications of an existing drug and could also provide solutions to the pharmaceutical companies that are facing problems. Conventional methods like testing of the drugs using cell lines, animal models have been now replaced effectively by computational methods, which are less time-consuming, less labor-intensive, noninvasive, and also costeffective. To find out, the multiple targets of a single-drug molecule provides huge data related to the drug. By modifying the functional groups and finding out the effective derivative of an existing drug has revolutionized the pharmaceutical industry to a huge extent. This mechanism of repositioning also helps us to elucidate the mechanism of action of a drug by finding out the target molecule in an easier way. A detailed picture of molecular modeling in drug discovery is given in Fig. 3.11.

3.10 Conclusion and summary Translational biotechnology helps to scale up the existing branches of basic research into the clinical setting and to the bench and community scale. Several areas of biotechnology, like bioinformatics, ADCs, genome-wide scaling, molecular remodeling, and drug repositioning areas, have been the most important topics pertaining to translational medicine. Many powerful tools of biotechnology help in the effective process of translation of any active drug candidate to the bedside. Some of these tools have been discussed in this chapter. Identification of several of these domains might help the future biotechnologists and clinicians to attain the goal of developing novel therapeutic approaches and diagnostic devices. The discovery and scope of the subject depend upon the drug target/process identification, large-scale production, and the commercialization of the same. The industry
academia partnership helps in the wide-scale interpretation and implementation of several of the recent technologies into a clinical platform.

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S E C T I O N

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C H A P T E R

Human in vitro disease models to aid pathway and target discovery for neurological disorders Bhavana Muralidharan O U T L I N E 4.1 Introduction 4.2 Generation of human disease models using iPSCs/patient fibroblasts 4.2.1 Directed differentiation into neural cells 4.2.2 Direct differentiation into neurons/glia 4.2.3 Direct lineage reprogramming/ transdifferentiation into neurons 4.3 Modeling neurodevelopmental disorders 4.3.1 Rett syndrome 4.3.2 Fragile X syndrome 4.3.3 Autism spectrum disorders 4.3.4 Schizophrenia

4.4 Modeling neurodegenerative diseases 4.4.1 Amyotrophic lateral sclerosis 4.4.2 Alzheimer’s disease 4.4.3 Parkinson’s disease

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91 91 92 93

4.5 Cerebral organoids and the future of human in vitro disease modeling 93

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4.6 From bench to bedside—identification of pathways and drug targets for designing therapies 95

88 88 88 89 89 90

4.7 Future perspectives

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Keyword definitions

97

Acknowledgments

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References

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Brain Development and Disease Mechanisms, inStem - Institute for Stem Cell Science and Regenerative Medicine, Bangalore, India Translational Biotechnology DOI: https://doi.org/10.1016/B978-0-12-821972-0.00013-7

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© 2021 Elsevier Inc. All rights reserved.

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4.1 Introduction Affecting millions of individuals, neurological disorders and neuropsychiatric diseases pose a substantial medical and socioeconomic burden on patients globally (Collins et al., 2011; World Health Organization (2008)). In India, too, they are among the leading causes of diseases that do not lead to death (India State-Level Disease Burden Initiative Mental Disorders Collaborators, 2020). Yet a comprehensive and effective cure for such diseases is not available. One of the biggest challenges currently in the field of medicine and particularly neurology would be to devise therapeutic strategies for brain disorders. There are no cures or treatments for most nervous system disorders, including autism, Alzheimer’s disease (AD), and Parkinson’s disease (PD). Most of our current understanding of the neurological disorders comes from postmortem patient brain samples, which represent the disease outcome. It is particularly challenging in medicine to study the initiation or progression of brain disorders, due to the fundamental inaccessibility of neural cell types and tissues affected in the disease, thereby preventing their isolation for further cellular and molecular analyses. Unquestionably, animal disease models have been beneficial tools to study neuropathological mechanisms in detail and have played important roles in the stages of lead drug discovery as well as for testing drug efficacy as part of preclinical development before conducting clinical trials (Ehrnhoefer, Butland, Pouladi, & Hayden, 2009; Perdomini, Hick, Puccio, & Pook, 2013). In addition, positive results from preclinical trials in animal models are not very reproducible at clinical trials in humans (Kola & Landis, 2004; Rubin, 2008). Mouse models are useful for studying monogenic disorders, which represent only a fraction of the diseases. Even for well-understood monogenic diseases such as Huntington’s disease (HD), spinal muscular atrophy (SMA), and fragile X syndrome (FXS), the mechanisms linking putative risk variants to clinical symptoms manifested by patients are poorly understood (Bebee, Dominguez, & Chandler, 2012; Crook & Housman, 2011; Dahlhaus, 2018; Dragunow, 2008). The importance of genetically manipulated animals in translational research is indisputable, but several species-specific differences between rodents and humans could result in misrepresentation or inadequate representation of human diseases when mice are used for neuronal disease modeling (Clowry, Molnar, & Rakic, 2010). Large animal models, which could possibly mimic better, come with their own set of problems such as massive costs, longer experimental timelines, and ethical considerations. Besides, larger animal models too suffer from the problem of being dissimilar to humans (Eaton & Wishart, 2017). The rodent and human brains differ markedly. Fundamentally, the rodent brain is inherently different from the human brain in terms of the size, structure, and organization of the cerebral cortex in particular. The human brain is gyrencephalic, whereas the mouse brain is lissencephalic (Seto & Eiraku, 2019). The cell-type composition varies—humans have a large upper cortical layer (Clowry et al., 2010) and highly developed frontal and temporal cortex involved in higher order brain functions such as cognition (Dolmetsch & Geschwind, 2011; Hansen, Rubenstein, & Kriegstein, 2011). Brain development changes include gene expression changes, progenitor cell-type changes, and the prolonged

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duration of gestation (Lui et al., 2014; Suzuki et al., 2018). The developing human cortex consists of an additional pool of progenitors called the outer subventricular zone progenitors or outer radial glia, which is absent in mice (Fietz et al., 2010; Hansen, Lui, Parker, & Kriegstein, 2010). The cells that sense serotonin in mice and human are different, thereby suggesting that mice are not good models for mood disorders that involve serotonin, such as depression (Setola & Roth, 2003). The gene behavior of microglia is also different, and microglia is implicated in neurodegenerative disorders such as AD and PD (Smith & Dragunow, 2014). The above genetic and anatomical variations have often led to inaccurate modeling of human disease phenotypes in mice models. Indeed, there is a need for alternative human disease models for identifying pathways and drug discovery. These models should preferably be based on actual patient genotypes, given that many human disorders are polygenic in nature and are also sporadic. These models could be used in concert with animal models to mimic the disorders better and may result in improved therapeutic outcomes for patients. Most neural cells and tissues can never be accessed from patients exception being some peripheral nerves and muscles or from the rare brain or spinal cord biopsies taken during diagnosis, and in such cases, the tissue is almost always rate-limiting precluding any mechanistic study of the disease. Recent advances in induced Pluripotent Stem Cells (iPSC) technology offer a new route to generating the diverse human neural types and in considerable amounts for studying neurological disorders (Marchetto, Winner, & Gage, 2010; Mattis & Svendsen, 2011). Human iPSCs can be differentiated into specific human neuronal subtypes such as cortical neurons, striatal interneurons, dopaminergic neurons, serotonergic neurons, or motor neurons to study the different neurological disorders (Marchetto, Brennand, Boyer, & Gage, 2011; Shi, Inoue, Wu, & Yamanaka, 2017). In this chapter, I reviewed how iPSC technology has changed our study of human neurological disorders and has paved the way for better disease and drug modeling in vitro and that the future looks promising with “brains in a dish” model to mimic threedimensional (3D) aspects of the human brain (Fig. 4.1).

4.2 Generation of human disease models using iPSCs/patient fibroblasts Overexpressing Oct4, Sox2, Klf4, and c-Myc converts somatic cells into pluripotent stem cells (Takahashi & Yamanaka, 2006; Takahashi et al., 2007). Thus began the revolutionary technology of iPSCs, which has brought in a new era for using patient genetic background
derived cells in the study of disease mechanisms, drug screening, or future personalized medicine. iPSCs can be differentiated into the different kinds of neurons, glial, and other differentiated cell types in the brain, thus enabling the study of different central nervous system (CNS) cell lineages. These iPSCs and the CNS cell types derived from them contain patient genetic information like mutations or disease-causing variants (Mertens, Marchetto, Bardy, & Gage, 2016).

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4.2 Generation of human disease models using iPSCs/patient fibroblasts

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FIGURE 4.1 Application of iPSCs to aid drug discovery. Schematic showing the application of iPSCs to help drug discovery for neurological disorders. Patient cells bearing the genetic mutation(s) are used for deriving iPSCs, which are then differentiated to 2D and 3D neural cell types of the brain. In vitro disease modeling using these neural cell types gives insights into human brain development and disease biology. Drug screening can be performed in the human in vitro disease model. They could also be gene edited for use in transplantation therapy. The underlying human genes could be expressed in mice models to make humanized mice models for in vivo studies and drug validations, thereby facilitating enabling new drugs to reach the patients. 3D, Three dimensional.

Currently, there are at least three different principal methods to derive neural cell types from somatic cells. Each methodology is unique and has potentials and limitations depending on the functional studies and likely downstream applications it may be used for. Table 4.1 summarizes the various methods currently available in the field and delineates the specific features of each protocol.

4.2.1 Directed differentiation into neural cells Somatic cells such as skin fibroblasts or blood cells can be reprogrammed to iPSC and then differentiated into CNS cell types. Directed differentiation is a step-by-step protocol that involves sequential use of combinations of signaling inhibitors and morphogens to specify the brain cell types by mimicking in vivo neural developmental cues (Fig. 4.2).

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4.2 Generation of human disease models using iPSCs/patient fibroblasts

TABLE 4.1 Modeling neurodevelopmental disorders with iPSCs. iPSC model

Phenotypic changes

Rett MeCP2 syndrome

Neurons

Deficit in KCC2 expression and Restoring KCC2 delayed GABA functional switch from function in Rett excitation to inhibition neurons

MeCP2

NSCs, neurons

Defective neural migration and maturation (reduced neurite outgrowth with fewer synapses)




Zhang et al. (2016)

ND

NSCs

Aberrant neurodevelopmental growth dynamics of maturing neurons, altered chromatin accessibility at NSC stage

Glutamatergic medication, for example, memantine

Schafer et al. (2019)

16p11.2 deletion

Neurons

Increased soma size, dendritic length, and reduced synaptic density




Deshpande et al. (2017)

ND

NSCs

Aberrant migration of NSCs, abnormal gene expressions and protein levels related to cytoskeletal remodeling




Brennand et al. (2015)

22q11.2 deletion

NSCs, neurons

Reduced neurosphere size, neural differentiation efficiency, neurite outgrowth, and cellular migration

Toyoshima et al. (2016)

Heterozygous CNTNAP2 deletion

NSCs

Aberrant migration of NSCs

Lee et al. (2015)

ND

iPSCs

Reduced capacity for cortical neuron differentiation, neurite outgrowth, decreased peak frequency (Ca21 signals)

Grunwald et al. (2019)

ND

Interneurons Dysregulated protocadherin-pathway activity in iPSC-derived cortical interneurons

ASD

SZ

Causative mutation

Possible therapeutic target, if any

Reference

(Tang et al., 2016)

Shao et al. (2019)

Fragile X FMR1 syndrome

Neurons

Impaired homeostatic synaptic plasticity in neurons mediated via retinoic acid signaling

mGlu5 negative allosteric modulators

Zhang et al. (2018)

BPD

ND

NPCs

Phenotypic differences at the level of neurogenesis and gene expression critical for neuroplasticity, for example, WNT pathway components

Pharmacological rescue using GSK3 inhibitor

Madison et al. (2015)

ND

DG neurons

Mitochondrial defects and hyperexcitability phenotype of young neurons

Lithium treatment

Mertens et al., 2015

ND

Neurons

Elevated pCRMP2:CRMP2 ratio in iPSC-derived neurons from lithiumresistant BPD patients

Tobe et al. (2017)

ASD, Autism spectrum disorder; BPD, bipolar disorder; DG, dentate granule; iPSC, induced Pluripotent Stem Cell; ND, not determined; NPC, neural precursor cells; NSC, neural stem cell; SZ, schizophrenia.

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Disease

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FIGURE 4.2 Different ways of converting somatic cells into CNS cell types. Somatic cells can be reprogrammed first to iPSCs using Yamanaka factors and then in a directed way by the use of growth factors, inhibitors, and morphogens to make a heterogeneous culture of neural cell types. iPSCs can be also be directed to differentiate by forced expression of transcription factors to make specific subclasses of neurons or glia. Somatic cells can be directly transdifferentiated into induced neuron or glia. Somatic cells can also be directly reprogrammed to NSCs and then differentiated into neurons. CNS, Central nervous system; NSC, neural stem cell.

Nervous system development progresses with the concerted effort of several different molecular programs, both intrinsic and extrinsic, to generate distinct neuronal and glial populations in specific regions of the brain. Utilizing this method, one can produce with the right combination of mitogens and morphogens, cortical neurons, spinal cord motor neurons, dopaminergic neurons, serotonergic neurons, and hippocampal neurons. Table 4.2 summarizes the salient features of some of these protocols.

4.2.2 Direct differentiation into neurons/glia Direct differentiation is a quick and nearly direct conversion of iPSCs to specific cell types using lineage specifying transcription factors that function during in vivo neurogenesis. Several studies have shown that iPSCs to direct neuron conversion is comparatively easy as it requires only one pro-neurogenic factor—Ascl1 (Chanda et al., 2014) or Ngn2 (Zhang et al., 2013). This protocol gives two distinct advantages over the directed neural

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4.2 Generation of human disease models using iPSCs/patient fibroblasts

TABLE 4.2 Different ways of generating central nervous system neural cell types. Mitogens

Cell type made

iPSCs

Small molecules

Glutamatergic cortical neurons 100 ( 1 astrocytes)

Shi, Kirwan, and Livesey (2012)

hESC

Small molecules

Dopaminergic neurons

Kriks et al. (2011)

28

B92

Yang et al. (2017)

iPSCs/ESCs NeuroG1
3, NeuroD1
3

Motor neurons

14

B94

Goparaju et al. (2017)

iPSC/ESC

Serotonergic neurons

28

B50

Lu et al. (2016)

Cortical layers 2/3 excitatory neurons

21

B100

Zhang et al. (2013)

Shh, Fgf4

iPSCs/ESCs DKK1, Hippocampal CA3 Noggin, Wnt3a, small molecules PBMCs

Oct3/4, Sox2, Klf4, c-Myc, small molecules

Fibroblasts/ Brn2, Klf4, skin cells Sox2, Zic3, small molecules Direct differentiation from somatic cells

25

Reference

iPSCs/ESCs Ascl1, Myt1l, Interneurons Dlx2

iPSCs/ESCs NeuroG2

Generation of iNSCs

Time in Efficiency culture of (days) conversion (%)

B56

iNSCs-dopaminergic neurons 56

Induced neuralplate border stem cells which can then be made into radial glia like stem cells, neural crest cells

Sarkar et al. (2018)

B88

Yuan et al. (2018)

48

Thier et al. (2019)

Adult fibroblasts

Brn2, Ascl1, Induced neuronal (iN) cells Myt1l (BAM)

B12
18 19.5

Vierbuchen et al. (2010)

Skin fibroblasts

Brn2, Myt1l, Fezf2, small molecules

iCtx cells

26
37

Miskinyte et al. (2017)

Mouse embryonic fibroblasts

shRNA against PTB protein

Neuronal cells

B14

Mouse astrocytes (in vivo)

NeuroD1

Glutamatergic cortical neurons 28

16

Xue et al. (2013) B80

Guo et al. (2014)

Small molecules: noggin, SB431542, dorsomorphin, SB431542, cyclopamine, ascorbic acid, cAMP, BDNF, CHIR99021, LDN193189. BDNF, Brain-derived neurotrophic factor; cAMP, cyclic adenosine mono phosphate; ESC, embryonic stem cell; hESC, human embryonic stem cell, iCtx, induced cortical; iNSC, induced neural stem cell; PBMC, peripheral blood mononuclear cell; PTB, polypyrimidine-tract binding.

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Directed differentiation of iPSC

Source

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differentiation—shorter culture times and a nearly homogeneous population of cells. The directed neural differentiation, as it mimics in vivo development, requires longer culture time and produces a heterogeneous mix of cultures of progenitors, mature and immature neurons.

4.2.3 Direct lineage reprogramming/transdifferentiation into neurons This method utilizes the overexpression of a combination of specific transcription factors to directly transdifferentiate somatic cells to a particular cell identity by bypassing several developmental stages. There is no pluripotent stage. The neurons thus produced are called induced neurons (iNs) (Kim, 2011). The advantages of these neurons are that iNs retain donor aging signatures, while iPSCs are rejuvenated and thus could prove a very valuable model system to study neurodegenerative diseases that require aging. As there is no pluripotent stage, the potential risk of tumor formation is reduced with this method, and thus iNs may prove very attractive for use in cell transplantation therapies. One disadvantage though is that the conversion efficiency with the existing methodologies is very low and limits the final number of cells obtained, but with newer advances and potential advantages for cell transplantation studies, it looks quite promising. Depending on the affected cell type in the different neurological disorders, researchers have used one of the above protocols to model neurological diseases such as early ageonset disorders (neurodevelopmental disorders) or late age-onset disorders such as neurodegenerative disorders.

4.3 Modeling neurodevelopmental disorders Neurodevelopmental disorders are characterized by the impairment of neural function during fetal and/or postnatal brain development. These include monogenic disorders such as Rett syndrome, FXS, Angelman syndrome, and Timothy syndrome and polygenic disorders such as autism spectrum disorders (ASDs), schizophrenia (SZ), and bipolar disorder (BPD). The following section showcases how iPSC technology is useful for identifying underlying cellular and molecular defects in patients with these disorders and to establish novel therapeutics in the future.

4.3.1 Rett syndrome One of the most extensively studied neurological disorders from patient-derived iPSCs is Rett syndrome. It is an X-linked pediatric neural development disorder that affects females (Amir et al., 1999). Spontaneous mutations in MECP2 cause most cases. MECP2 binds to methylated DNA, and several of its target genes involved in synaptogenesis are abnormally expressed in its absence (Amir & Zoghbi, 2000). The mouse model of Rett syndrome does not recapitulate the full spectrum of the pathogenesis (Van den Veyver & Zoghbi, 2002). iPSCs from four patients with MECP2 mutation showed neuronal defects in culture. They had less number of synapses and spines and showed less connectivity. Their

4.3 Modeling neurodevelopmental disorders

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4.3.2 Fragile X syndrome FXS is the leading monogenic cause of ASD. It is due to the trinucleotide CCG repeat in the 50 UTR of the FMR1 gene leading to loss of FMR1 expression. FMR1 is required for normal neuronal development, and its absence leads to neural dysfunction (Bassell & Warren, 2008; O’Donnell & Warren, 2002; Warren & Ashley, 1995). To model FXS, human iPSCs from multiple patients with the CGG repeats have been converted to postmitotic neurons and glia. The FXS iPSC models show aberrant neuronal differentiation and methylation of the FMR1 gene leading to the loss of FMR1 expression (Sheridan et al., 2011; Urbach, Bar-Nur, Daley, & Benvenisty, 2010). These studies show the role of FMR1 in early development and show potential for modeling FXS using iPSCs.

4.3.3 Autism spectrum disorders ASDs are a genetically heritable group of neurodevelopmental disorders characterized by abnormalities in brain function and morphology, resulting in impaired social behavior, cognitive capacity, repetitive patterns of behavior (Baio et al., 2018). Diagnosed children display a broad spectrum of social, emotional, and cognitive impairment (Ben-Itzchak, Ben-Shachar, & Zachor, 2013). ASDs have a complex genetic basis, as multiple pieces of evidence indicate that genetic, epigenetic, and environmental factors play a role in the disease onset (de la Torre-Ubieta, Won, Stein, & Geschwind, 2016; Yuen et al., 2016). Several hundreds of candidate genes have been identified as possible causatives for the spectrum of ASD clinical features and cellular phenotypes (Simons Foundation Autism Research Initiative; Basu, Kollu, & Banerjee-Basu, 2009; Abrahams, Arking, & Campbell, 2013). To model such a polygenic ASD is a challenge both in vivo and in vitro. iPSC technology that bears the patient genetic signature is highly useful to mimic nonsyndromic ASDs where one can study the different developmental timepoints and cell types in which the disorder can manifest contributing to the pathophysiology. Some of the cellular phenotypes captured in vitro from ASD patient
derived neural cells show abnormalities in vital developmental stages/cell types, thereby for the very first time shedding light into the modes of disease progression from nonsyndromic ASD as opposed to studying final stage disease phenotypes from postmortem samples (Brito, Russo, Muotri, & Beltrao-Braga, 2018). The balance between excitation and inhibition is considered a key point during neurodevelopment. iPSCs derived from ASD patients showed increased production of GABAergic neurons in the dish and transcriptional, and gene network analyses revealed increased expression

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electrophysiological phenotype was also altered as they had lower activity-dependent calcium signaling and decreased frequency of spontaneous postsynaptic currents. This work suggests that neurons of these patients established altered neuronal networks (Marchetto, Carromeu, et al., 2010).

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4. Human in vitro disease models to aid pathway and target discovery for neurological disorders

of FoxG1 contributing to the altered network dynamics (Mariani et al., 2015). A recent study of neurons derived from nonsyndromic ASDs showed a significant decrease in glutamate, synaptic gene expression, and spontaneous firing rate, thus revealing impaired activity of neurons (Russo et al., 2018). The most abundant cell type in the brain—the glial cells—is often ignored than their firing counterpart—the neurons. Yet they play essential functions by providing support and protection for neurons (Kettenmann & Verkhratsky, 2008; Zhang, 2001; Zheng et al., 2018). However, glial cells play a very important role in several brain disorders, including ASDs (Zheng et al., 2018). A neuron-astrocyte coculture study revealed that ASDs-derived astrocytes interfered with the development of the neurons. The neuronal morphology improved upon coculture with control astrocytes. This effect was seen to be because of increased secretion of IL-6 from ASDs-derived astrocytes, and by blocking IL-6, the authors were able to increase synaptogenesis (Russo et al., 2018). IL-6 is a key neuroimmune factor, the levels of which increase under pathological conditions, and could perhaps serve as a diagnostic marker in the future, and this study has shown the mechanism of its increased secretion from defective astrocytes. The above studies have demonstrated how particular cell types could contribute toward autism, thereby generating novel insights into disease biology and a basis for drug discovery and potential therapies.

4.3.4 Schizophrenia SZ is a neuropsychiatric disorder with symptoms ranging from aberrant perceptions, thought, and impaired social connectivity (Brennand et al., 2011). Large-scale genomic and transcriptomic approaches have implicated several genetic loci and gene expression changes in SZ (Gandal et al., 2018; Parikshak, Gandal, & Geschwind, 2015). Yet, a mechanistic understanding of what this gene discovery means is lagging behind. A few iPSC studies as delineated below are beginning to unravel the molecular underpinnings of disease-causing mechanisms in SZ. Mutations in DISC1 lead to aberrant synapse formation and vesicle release deficits in SZ patient
derived neurons (Wen et al., 2014). In another gene expression and proteomics study, cytoskeletal remodeling and oxidative stress were found to be the culprits in SZ patient
derived NPCs (Brennand et al., 2015). Some of the defects observed in SZ-iPSCs-derived dopaminergic neurons were reduced dopamine release and decreased neurite count (Robicsek et al., 2013). The defective neurons also showed perturbed mitochondria function suggestive that mitochondrial defects could play a key role in the pathogenesis of SZ (Mertens et al., 2015). Treatment with dopaminergic antagonist loxapine did increase functional connectivity in SZ-iPSCderived neurons (Soliman, Aboharb, Zeltner, & Studer, 2017). There are several studies from schizophrenic populations in Caucasians (Rajarajan et al., 2018; Willsey et al., 2018), and a recent study had emerged from the African populations (Gulsuner et al., 2020). There is yet no study emerging from Indian SZ patients to understand the cellular and molecular basis of these diseases.

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Building on these developments, inStem (Institute for Stem Cell Science and Regenerative Medicine), in collaboration with NCBS (National Centre for Biological Sciences) and NIMHANS (National Institute of Mental Health and Neurosciences), has put in place an interdisciplinary program of work, the Accelerator program for Discovery in Brain disorders using Stem cells (ADBS) (https://www.ncbs.res.in/adbs/about-adbs-program), to obtain mechanistic insights into the cellular basis of neuropsychiatric illness (Viswanath et al., 2018). This unique study has assembled a cohort of 250 families that are clinically dense for mental illness (SZ, BPD, obsessive
compulsive disorder, dementia, and substance use disorders). The selected individuals have a strong family history with at least one another first-degree relative with the disease, a healthy control who has crossed the age of risk, and another yet asymptomatic control below the median age of risk. Such families become a part of a single experimental unit of analysis. Besides, age-matched healthy controls have also been recruited to the study. Given the well-known heritability of mental illness, and the high degree of homozygosity in Indian genomes (Nakatsuka et al., 2017), these families will be a valuable resource for discovering genetic factors contributing to mental illness. Further, these individuals will undergo detailed clinical analysis at multiple scales of brain development and are also being subjected to genomic analysis. Finally, iPSC lines from individuals in such families are being generated and banked in a repository, thus providing an opportunity to generate in vitro models of human brain development from patients and controls that are genetically closely related. Thus the ADBS program offers a rich resource to explore the role of mechanisms of diseases such as SZ and BPD.

4.4 Modeling neurodegenerative diseases Neurodegenerative diseases are incurable and debilitating disorders in which there is a progressive degeneration of specific neurons in patients. AD, PD, and amyotrophic lateral sclerosis (ALS) are three of the major neurodegenerative disease. With each therapy for these disorders costing over $100,000 and with roughly about 50 million people suffering from dementia worldwide, the economic burden on the society is enormous. iPSC lines have been generated from several patients with neurodegenerative diseases and are a useful model to study disease mechanisms and pathways.

4.4.1 Amyotrophic lateral sclerosis ALS is a fast and progressive degeneration of motors neurons in the brain and spinal cord, which is characterized by muscular atrophy. The majority of ALS cases are sporadic (90%) with no known single cause for the disease, and the rest are familial/inherited (de Carvalho & Swash, 2011; Kiernan et al., 2011). More than 10 different genes have been implicated in ALS with no effectual treatment so far (Zinman & Cudkowicz, 2011). Several iPSC models have recently been reported that exhibited specific pathophysiology of ALS. Many of the observed disease-causing pathological phenotypes look promising to exploit as possible targets for drug therapy.

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C9orf72 mutation causing ALS is the most well studied and prevalent form of genetic ALS (Balendra & Isaacs, 2018). The disease is because of a large expansion of hexanucleotide repeats in the genome, which leads to the production of both toxic RNA and peptides in the cells leading to the degeneration of motor neurons. Animal/drosophila models are only as much useful because vast stretches of RNA repeats are rarely achieved in animal or other model systems, and thus key pathological mechanism that arises from .1000 repeats of RNA can be studied only from patient samples. There is no overt loss of spinal cord motor neurons in the dish from C9orf72 mutant patient
derived iPSCs-motor neurons. Several cellular phenotypes have been observed, which include the presence of toxic RNA foci that can be reduced using antisense oligonucleotides (ASOs) targeting the repeat expansions—a potential therapy (Dafinca et al., 2016; Sareen et al., 2013). Some other characteristic phenotypes uncovered include nuclear cytoplasmic protein shuttling defects, vulnerability to endoplasmic reticulum (ER) stress, and toxic stress (Dafinca et al., 2016; Freibaum et al., 2015). A group recently showed that mutant SOD1 binds to neurofilament mRNA, leading to swelling of neurites and degeneration, and that the isogenic correction successfully rescued the disease (Bhinge, Namboori, Zhang, VanDongen, & Stanton, 2017). iPSCs-derived neurons from sporadic cases display a toxic cytoplasmic aggregation of TDP-43 (Qian et al., 2017), and in another study, gene expression profiling suggested a deficiency in mitochondrial function (Alves et al., 2015). Altered neuronal excitability seems to be a common mechanism found in many cases of ALS—sporadic or genetic—and thus plays a common role in ALS disease progression. The hyperexcitability phenotype seen in ALS patient
derived neurons was shown to be effectively diminished by the small molecule retigabine, which activates specific voltagegated potassium channels (Wainger et al., 2014). Efforts are constantly on to identify druggable targets and/or drugs to increase the survival of motor neurons and to, therefore, use it as a therapy. In a repurposed phenotypic drug screen, Bosutinib, which inhibits c-Abl in the Src/cAbl signaling pathway, induced autophagy in motor neurons derived from ALS patients with the SOD1 mutation. Further, the drug decreased the amount of misfolded SOD1 protein and reduced the expression of genes in the mitochondria, namely, tricarboxylic acid cycle genes and genes of the respiratory electron transport chain. This drug was effective in the case of neurons derived from patients with sporadic and other familial forms of ALS as well (Imamura et al., 2017).

4.4.2 Alzheimer’s disease AD is the most common neurodegenerative disorder resulting in dementia and characterized by loss of memory and cognitive impairment. The two major hallmarks of the disease are extracellular accumulation of amyloid-beta (Aβ) plaques and intracellular aggregation of the microtubule-associated protein, Tau (Correa-Velloso et al., 2018). One study involved the testing of a compound R-33 a small molecular chaperone that stabilizes the retromer leading to reduction in Tau phosphorylation in a human iPSCs
derived neurons from sporadic and familial AD (Young et al., 2018).

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4.4.3 Parkinson’s disease PD is the second most common neurodegenerative disorder characterized by the loss of dopaminergic neurons in the substantia nigra. Several reviews have amply covered the use of iPSC technology in studying this disease in a dish (Ke, Chong, & Su, 2019; Stoddard-Bennett & Reijo Pera, 2019; Xiao, Ng, Takahashi, & Tan, 2016). I will mention one very recent paper that highlights the importance of studying the disease from a human patient sample. Using patient iPSC
derived human dopaminergic neurons, Burbulla et al. (2017) have demonstrated the relationship between alpha-synuclein aggregation, mitochondrial and lysosomal dysfunction, and dopamine toxicity. They uncovered a pathogenic cascade from mitochondria to lysosome leading to alpha-synuclein accumulation. Interestingly, this cascade was only evident in human but not mouse. The authors attributed the speciesdifference to differences in dopamine metabolism. This study highlights the need to harness human patient cell
derived neurons to understand the pathology of the disease and explains why perhaps drugs from mouse models are routinely a failure at clinical trials in human patients for neurological disorders. While modeling neurodegenerative disease, it will be useful to bear in mind that while iPSC technology has been very valuable for recapitulating disease phenotypes of development and early onset disorders, more caution needs to be exercised with their use for understanding age-related pathologies such as neurodegenerative syndromes. Hallmarks of biological age like epigenetic and transcriptional signatures are lost during reprogramming of somatic cells to pluripotent cells. Pluripotent cells and the neural cells derived from them are fetal in nature and may not show pathological symptoms that appear with age. This constraint can be overcome by using ways to accelerate cellular aging in vitro. Some of these methods for in vitro aging include the use of toxic stressors such as reactive oxygen species (ROS) which induce biochemical aging or age-inducing genetic factors such as progerin which trigger age-dependent degenerative phenotypes (Cornacchia & Studer, 2017). With little to no manipulations to mimic aging the iPSC-derived neural cells rarely show characteristic aspects of late-onset neurological diseases. Future studies should include such measures to capture the progression of aging neurological disorders accurately.

4.5 Cerebral organoids and the future of human in vitro disease modeling The brain is a 3D structure. 2D in vitro neuronal cultures though useful sometimes do not recapitulate all aspects of the human brain structure and function. With advances in

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In another iPSCs-derived in vitro neuron model of AD the plant polyphenol apigenin, which has antiinflammatory properties, conferred neuroprotective effects such as reduced hyperexcitability and protection against apoptosis (Balez et al., 2016). In recent work, human cholinergic neurons differentiated from AD patient-iPSCs transplanted in AD transgenic mice restored spatial memory impairment (Fujiwara et al., 2013).

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tissue engineering, 3D brain organoids generated from iPSCs are the more sophisticated models of the human brain and its development as these produce the various discrete and interdependent regions in the brain (Quadrato, Brown, & Arlotta, 2016). Brain organoids are being extensively used to model disease conditions and for use in gene and drug screening which has the potential to transform personalized medicine (Fig. 4.3). A brain organoid is derived from pluripotent stem cells that can self-organize into dynamic structures, containing different neural cell types. Neurons and other cell types in a brain organoid can connect and organize simplified neural networks and recapitulate steps in human neurodevelopment, and if kept in the dish long enough make mature functional connections (Giandomenico et al., 2019). Such structures can be used to study neurodevelopment and model neurological disorders in vitro. Given the potential of organoids, their usage in biological research is exploding (Arlotta, 2018; Lancaster & Huch, 2019; Pasca, 2019). Currently, varieties of neurological disorders are being modeled and will be continued to do so in the future using brain organoids. The different strategies and downstream

FIGURE 4.3 Cerebral organoids—“brain in a dish” modeling of human neurological disorders. To model the 3D cytoarchitecture of the brain, specific regions can be modeled by making region-specific organoids and subsequently affected neurodevelopmental disorders are being studied or can be studied. 3D, Three dimensional.

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analyses allow for the different regions of the brain to be modeled, and thus diseases can be studied in a region-specific manner (Fig. 4.3). FoxG1 was identified as a molecular signature in nonsyndromic ASD, which when modeled as brain organoids showed increased production of GABAergic inhibitory neurons (Mariani et al., 2015). Zika virus infection leads to congenital microcephaly in newborns. Zika virus infection, modeled using 3D brain organoids have shown that the virus infects cortical neural progenitors and affects their proliferative capacity leading to a decreased organoid size mimicking microcephaly (Dang et al., 2016; Ming, Tang, & Song, 2016; Qian, Nguyen, Jacob, Song, & Ming, 2017; Qian et al., 2016). Organoids derived from SZ patients with DISC1 mutation have helped uncover mechanistic details of how DISC1 interacts with its binding partner Nde1 and regulated cell cycle progression, disruption of which leads to impaired mitosis in cortical progenitors thereby shedding light on how genetic factors affect neuropsychiatric disorders (Ye et al., 2017). One of the promising applications of organoids is their use to study the effects of environmental toxins affecting neurodevelopment, thus enabling the assessment of how the environment in combination with genetic factors modulates disease outcomes in neuropsychiatric diseases. Understanding how substance abuse in expectant mothers affects brain development in offsprings could help us in devising strategies to potentially ameliorate the harmful effects on neurodevelopment. Nicotine causes long-term behavioral deficits in children and causes premature neuronal differentiation (Smith, Dwoskin, & Pauly, 2010; Swan & Lessov-Schlaggar, 2007). This was modeled in brain organoids which when exposed to nicotine showed premature neurogenesis followed by defects in brain regionalization and development (Wang, Wang, Zhu, & Qin, 2018). Cocaine exposure leads to signaling changes in the developing neocortex (Lee et al., 2008; Lee, Chen, Worden, & Freed, 2011). In neocortical organoids, cocaine exposure inhibited proliferation of cortical progenitors and led to premature neuronal differentiation. These effects were reversed by the knockdown of CYP3A5 as it induces the generation of ROS (Lee et al., 2017). Thus CYP3A5 seems like a good therapeutic target to alleviate the symptoms of cocaine exposure in fetal brain development. These two examples demonstrate that brain organoids could be valuable to model neurodevelopmental disorders induced by environmental factors.

4.6 From bench to bedside—identification of pathways and drug targets for designing therapies With advances on several fronts to understand neurological disorders—large-scale omics to identify underlying genetic causes, in vitro disease modeling to study mechanistic details of pathology and simultaneous correlation with clinical symptoms and imaging studies from patients—scientists have begun to put all of this together to accelerate discoveries from bench to the clinic.

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4.6 From bench to bedside—identification of pathways and drug targets for designing therapies

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Most medications for neuropsychiatric diseases have been repurposed rather than having emerged from an understanding of the pathophysiology. We still do not fully understand the mechanisms of action of current psychotropic drugs such as lithium, chlorpromazine, and monoamine oxidase inhibitors. Cellular models of the disorders point to specific regulatory or signaling pathways. Once the pathomechanisms are understood, our treatment measures could be directed toward the resolution of symptoms than simply ameliorating them. A few notable examples of mechanistic understanding of pathology have been summarized below, which pave the way for targeted drug discoveries. In a study for the treatment of FXS the authors performed an FMR reactivation screening using various epigenetic modulators to reactivate the FMR1 gene in iPSC-derived neural stem cell (NSC) culture. They hit upon a combination of chromatin remodeling agents to effectively increase the reactivation of FMR1 and showed proof of principle concept for FMR1 reactivation (Vershkov et al., 2019). Gene editing with clustered regularly interspaced short palindromic repeats (CRISPR)
CRISPR associated protein (CAS) as possible therapy tested in iPSC-derived neural cultures is showing promising results. In another elegant solution the authors utilized CRISPR
CAS strategy to accurately target and demethylate the FMR1 locus to activate the expression of FMR1. Gene editing was performed in iPSCs-derived NSCs where Tet1 that demethylates DNA was fused to an inactive form of Cas9, which was directed toward the CGG repeats by sequence-specific guide RNAs. Neurons derived from the edited NSCs restored FMR1 expression and showed normal neuronal physiology, integration into the mouse brain, and firing (Liu et al., 2018). In the case of Rett syndrome, patient-derived iPSC
derived neurons provided evidence that addition of IGF1 could rescue synaptic defects in neurons (Marchetto, Carromeu, et al., 2010). Based on these results from iPSC modeling, IGF1 treatment is undergoing clinical trials at present (Khwaja et al., 2014; Kolevzon et al., 2014). Maternal immune activation affects fetal development. In an iPSC model of neural aggregates, IL-6 leads to increased inflammation in astrocytes and a reduced number of neurons. Treatment with luteolin, a compound with antiinflammatory properties, reversed the effects of IL-6 (Zuiki et al., 2017). Luteolin has been further trialed with ASD patients leading to reduced serum levels of IL-6 (Tsilioni, Taliou, Francis, & Theoharides, 2015). The hexanucleotide repeat RNA from the C9orf72 gene locus folds itself into a G-quadruplex structure. In a small molecule screen, three structurally similar compounds were identified which stabilized the G-quadruplex structure and reduced the burden of toxic RNA foci and toxic repeat peptides in C9orf72 mutation patient
derived cortical and motor neuron. This provides proof of principle that targeting secondary structures has therapeutic potential (Simone et al., 2018). ASOs showed immense promise as therapies for two highly debilitating neurological disorders: SMA and HD. Successful clinical trials (Chiriboga et al., 2016; Tabrizi et al., 2019) have led to the ASOs being commercialized or in the process of commercialization as FDA-approved therapies (USFDA, 2016). iPSCs-derived neural cell types have huge potential to test ASOs before clinical trials. Promising results have already been seen in Spinocerebellar ataxia (Matsuzono et al., 2017) and C9orf72-ALS (Sareen et al., 2013) using iPSC-derived neurons.

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AD patient
derived cortical neurons secreted a specific form of Tau against which BMS-986168 was developed as an antibody (Bright et al., 2015). This antibody was then licensed by Biogen and entered Phase II for AD. In another study, retigabine, an approved drug for epilepsy, could rescue the hyperexcitability phenotype observed in ALS patient
derived motor neurons (Wainger et al., 2014). GSK is currently performing Phase II clinical trials with retigabine with 192 ALS patients. Another application of iPSCs is for use in cell-replacement therapy in neurodegenerative disease. NSCs derived from iPSCs migrated to various regions upon transplantation and also differentiated into glia and neurons including dopamine neurons in rats and primate models of PD (Kikuchi et al., 2017; Wernig et al., 2008). For this to become a therapy realistically, future studies must take into account the safety and efficacy of the transplanted cells. Newer advances are being generated to aid the process of drug discovery. Some of these include the development of high-content single-cell screening of key CNS and immune functional ligand
receptor interactions and downstream signaling mechanisms to rapidly screen efficacious drugs for neuropsychiatric disorders (Lago et al., 2019). CRISPRi (CRISPR interference) genetic screen performed in iPSCs-derived neurons revealed neuron-specific genes important for neuronal survival and morphology. Such a genetic screen can be used specifically in iPSC-derived neurons to reveal disease outcomes and potential pathways for drug targets (Tian et al., 2019).

4.7 Future perspectives iPSC-derived neural cell types and organoids open a whole new avenue of research which is constantly innovating. The field is burgeoning with newer methods which are robust, reproducible, more efficient and with shorter culture times to aid human in vitro disease modeling and drug discovery. The latest in this regard is the organ-on-a-chip, which is an emerging technology. It is a biomimetic system, which has a physiological organ-on-a-microfluidic-chip to mimic the environment of a physiological organ (Wu et al., 2020). Blood
brain barrier (BBB)-on-a-chip has been created to study the neurotoxic effects of drugs and the role of BBB in maintaining the physiology of the brain in health and disease (Griep et al., 2013). BBB-on-a-chip has been developed to study AD to mimic the continuous flow of Aβ in the vicinity of the cortical neurons (Park et al., 2015). The brain-on-a-chip was very useful to mimic the 3D cytoarchitecture of the brain microenvironment, and they observed neurotoxic effects of Aβ in this paradigm. Thus BBB-on-a-chip could be a low-cost replacement model system instead of animal models to test for drugs in the not-so-distant future.

Keyword definitions Cerebral cortex It is the outermost region of the forebrain and region where all higher order functions like thinking, memory, learning, etc. are processed. Gyrencephalic Brains like humans, which have a convoluted cerebral cortex. Lissencephalic Brains like rodents, which have a smooth cerebral cortex.

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Keyword definitions

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Monogenic disorder Changes in single gene lead to the disease. Neocortex Evolutionarily the most advanced part of the brain and center for processing higher order functions like thinking, language decision-making, and consciousness. It is six layered and is hugely expanded in primates and humans. Nonsyndromic disorders Disorders which have complex inheritance in which genetic and environmental factors may play a role in the disease pathology. Polygenic disorder Combined action of multiple genes cause the disease. Syndromic disorders Disorders which have known genetic cause. Spinocerebellar ataxia Genetic disorder characterized by progressive problems with movement due to damage to cerebellum and sometimes spinal cord. Spinal muscular atrophy Genetic disorders characterized by weakness and wasting of muscle involved in movement caused by loss of motor neurons.

Acknowledgments I thank Masood Ahmad Wani for preparing Fig. 4.1 and Table 4.1, Asha S Channakkar for preparing Figs. 4.2 and 4.3 and Leora D’Souza for preparing Table 4.2 and for assistance with the references. Work in BM lab is supported by the Department of Biotechnology/Wellcome Trust—India Alliance Intermediate Career Fellowship (grant number: IA/I/19/1/504288) and DST-SERB: Start up research grant (SRG)(Grant number: SRG/2020/ 001612 awarded to Bhavana Muralidharan and by intramural funds from the Department of Biotechnology to inStem.

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C H A P T E R

Importance of targeted therapies in acute myeloid leukemia Ajit Kumar Rai1,2 and Neeraj Kumar Satija1,2 O U T L I N E 5.1 Introduction 5.1.1 Conventional therapy for acute myeloid leukemia 5.1.2 Significance of target discovery

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5.2 Approaches in target discovery 5.2.1 Systems approach 5.2.2 Molecular approach

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5.3 Acute myeloid leukemia
targeted therapies in clinics 5.3.1 BCL-2 inhibitors

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5.3.2 Isocitrate dehydrogenase inhibitors 117 5.3.3 PML-RARα targeted therapy 118 5.3.4 Targeting FLT3-mutated acute myeloid leukemia: from bench to bedside (a case study) 119

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5.4 Hurdles and emerging targeted therapies

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5.1 Introduction Acute myeloid leukemia (AML) constitutes a group of disorders harboring diverse genetic abnormalities and mutations that lead to uncontrolled proliferation and differentiation arrest of myeloid precursors in the bone marrow (Gurnari, Voso, Maciejewski, & Visconte, 2020; Papaemmanuil et al., 2016). Globally children and adults account for B20% and B80% of AML cases, respectively (Lagunas-Rangel, Chavez-Valencia, Gomez-Guijosa, & Cortes-Penagos, 2017). 1

2

Systems Toxicology and Health Risk Assessment Group, CSIR-Indian Institute of Toxicology Research (CSIR-IITR), Vishvigyan Bhawan, Lucknow, India Academy of Scientific and Innovative Research (AcSIR), Ghaziabad, India

Translational Biotechnology DOI: https://doi.org/10.1016/B978-0-12-821972-0.00017-4

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© 2021 Elsevier Inc. All rights reserved.

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Majority of the AML patients present leukocytosis, anemia, thrombocytopenia associated with fatigue, weight loss, and anorexia (De Kouchkovsky & Abdul-Hay, 2016). AML patients have 20% or more blasts in bone marrow or blood except for patients having frequent genetic abnormalities such as t(16;16), t(15;17), inv(16), or t(8;21) (Dohner et al., 2010). Further diagnosis is made by immunophenotyping, presence of Auer rods, and myeloperoxidase activity. In AML patients, more than 3% of blast cells are myeloperoxidase positive (Kim et al., 2012). Based on these criteria and the occurrence of genetic rearrangements and mutations, AML patients are subdivided into various groups (Dohner et al., 2017). In 1976 the French
American
British classification of AML into eight groups (M0
M7) was established based on the differentiation stage of AML (Bennett et al., 1976). However, the classification system established by the World Health Organization (WHO), which is based on genetic, morphological, and immunophenotype, is widely used (Arber et al., 2016; Vardiman et al., 2009). AML can also be classified into three types: secondary AML (like myeloid proliferative disorder or myelodysplastic syndrome), therapy-related AML, and de novo AML (Lindsley et al., 2015). Based on the information on cytogenetic and molecular features, patients are stratified into three risk groups (good/favorable, intermediate, and poor/adverse) to predict their responsiveness to standard therapy (Roloff & Griffiths, 2018). This chapter discusses the importance of targeted therapies in AML.

5.1.1 Conventional therapy for acute myeloid leukemia The current therapeutic regimen being used for AML treatment has been in clinical practice since 1973 (Lichtman, 2013). For younger AML patients (below 60 years), cytarabine, a pyrimidine analog, and daunorubicin or idarubicin, anthracycline antibiotics, drugs have been used for induction therapy as “7 1 3” regimen. The patient is continuously infused with cytarabine intravenously for 7 days, and concurrently either daunorubicin or idarubicin is given to the patient for the first 3 days. Complete remission is considered to be achieved when ,5% blasts remain in the bone marrow aspirate upon a count of B200 nucleated cells (with no Auer rods), and neutrophil and platelet counts of more than 1000/μL and 100,000/μL, respectively, are attained (Cheson et al., 2003). With conventional induction chemotherapy, 65%
73% of patients achieve complete remission (Estey & Dohner, 2006; Lowenberg et al., 2009). To avoid the reoccurrence of AML, consolidation therapy consisting of high-dose cytarabine with or without hematopoietic stem cell transplantation is given to the patient after achieving remission (Robak & Wierzbowska, 2009). In older patients (above 60 years) the same induction therapy has been used, as for younger AML patients, with 40%
50% complete remission. Lowering the dose of the cytarabine in combination with decitabine and clofarabine has also been used for the treatment of older AML patients to reduce drug toxicity (Kantarjian, 2016). The median overall survival after 5 years in younger adults (below 60 years) is approximately 40%, and less than 10% in older patients (above 60 years) (Schlenk & Dohner, 2013).

5.1.2 Significance of target discovery Conventional AML therapy relies on the use of cytotoxic drugs, cytarabine, and daunorubicin/idarubicin, which kills normal dividing cells as well, resulting in side effects. The harmful effects of therapy can be minimized either by targeted delivery of

drugs to bone marrow or by developing drugs against molecules specifically present in AML cells. Another problem with AML is the prevalence of different mutations in AML subtypes. This necessitates the identification of novel, specific, and exclusive biomolecules present in AML cells, which can serve as a potential target for drug delivery or therapy, emphasizing the need for target discovery/identification. Therefore, targeted therapy is becoming one of the leading modalities for AML treatment. The primary goal of target therapy is to tackle three obstacles in AML treatment: reduce offtarget toxicity, improve treatment efficiency, and provide precise treatment for specific AML subtype.

5.2 Approaches in target discovery Myriad biotechnological strategies have been used for the identification and validation of biological targets, which can be grouped into two categories: the “molecular” and “systems” approach (Lindsay, 2003). The molecular approach uses in vitro models, proteomics, genomics, genetic association, and reverse genetics for target discovery. On the other hand, the systems approach uses in vivo models to identify potential targets. The steps involved in target discovery and development are summarized in Fig. 5.1.

FIGURE 5.1 An outline of target identification approach.

and

development

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5.2.1 Systems approach The systems approach involves the identification of targets through the investigation of a disorder in the whole organism. The systems approach gathers epidemiological, physiological, and pathological information from in vivo studies. Traditionally, systems approach has been the main approach for the target identification strategy and is still used for many diseases such as obesity, atherosclerosis, heart failure, behavioral disorder, and hypertension in which the phenotype of disease is significant at the organism level. Understanding of biological pathway and mechanism involved in the development and progression of the disease is necessary to develop a treatment or diagnosis. Animal models provide an alternative to understanding the human biological system. Particularly for AML, animal models offer an important tool to understand different AML subtypes and to decipher the important role of novel and known genetic abnormalities in disease progression. Many animal species have been used to understand disease prognosis, system biology, and genetic mutations. Mice, rat, drosophila, and zebrafish have been exploited to develop different models to study AML (reviewed in Skayneh et al., 2019). Select AML models that have led to the identification of new targets are discussed in the following subsections. 5.2.1.1 Zebrafish (Danio rerio) Zebrafish share a similar biological mechanism of hematopoiesis with humans (Howe et al., 2013). A short fecundity period with a high rate of fertility and low cost makes this model a better alternative for the study of the human system. A number of AML models have been generated which are used in understanding disease mechanism, to screen chemical libraries for identification of new therapeutics, evaluate the efficacy of new targeted therapy, and toxicity assessment (Rasighaemi, Basheer, Liongue, & Ward, 2015; Zizioli et al., 2019). For example, a chemical suppressor screen using transgenic zebrafish line, hsp70: AML1-ETO, led to the identification of nimesulide as an antagonist of AML1-ETO effects and involvement of COX-2 and β-catenin in AML1-ETO-mediated differentiation (Yeh et al., 2009). FLT3/ITD (internal tandem duplication) mutation is very common in AML patients (discussed under Section 5.3.4). FLT3/ITD AML phenotype is generated by injection of human FLT3/ITD plasmid into zebrafish embryos (He et al., 2014). Using this model, He et al. identified embryonic morphogen follistatin (FST) as a novel therapeutic target that was upregulated. Increased expression was also observed in FLT3/ITD knock-in mice and human FLT3/ITD AML. Leukemic cell growth was increased by FST, while its downregulation reduced FLT3/ITD AML cell growth (He et al., 2020). Fusion protein NUP98-HOXA9 (NHA9), a result of chromosomal translocation t(7;11) (p15, p15), is associated with poor prognosis in AML (Rio-Machin et al., 2017). Using a transgenic zebrafish line expressing human NHA9, DNMT1 (DNA methyltransferase 1) was identified as one of the most upregulated genes by microarray. Knocking down DNMT1 using morpholinos in transgenic NHA9 zebrafish blocked myeloproliferation. Further, the use of DNMT inhibitors, decitabine, or zebularine restored hematopoiesis by blocking NHA9 signaling. This study established the first link between NHA9 and epigenetic regulation via DNMT1, and the significance of epigenetic therapy in restoring normal hematopoiesis in NUP98-HOXA9-induced myeloid leukemia (Deveau et al., 2015).

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Completely sequenced genome, ability to manipulate the genome, small size, short reproductive cycle, availability of research tools, and relatively inexpensive maintenance make mice a preferred organism for cancer research. A number of AML models have been generated over the past years using either a chemical compound, irradiation, murine leukemia virus (MuLV) infection, by genetic manipulation (transgenic), a>/>nd injection of AML cell lines or patient-derived blasts into humanized mice such as severe combined immunodeficiency (SCID), non obese diabetic (NOD)/SCID, and NSG (NOD/SCID/ gamma) mice (xenografts) (Kohnken, Porcu, & Mishra, 2017; McCormack, Bruserud, & Gjertsen, 2005; Skayneh et al., 2019). Recombinant inbred strains of mice such as BXH-2 and AKXD-23, which are virusinduced AML models, have led to the identification of oncogenes such as MEIS1 and EVI-1, respectively (Moskow, Bullrich, Huebner, Daar, & Buchberg, 1995; Mucenski et al., 1988). These models have not only been instrumental in understanding the functions of these genes but led to the identification of more than 90 candidate genes by analyzing proviral integration sites by the inverse PCR method (Li et al., 1999). Mixed lineage leukemia (MLL)-AF9 fusion oncoprotein is associated with poor prognosis in AML (Stavropoulou, Peters, & Schwaller, 2018). To elucidate epigenetic pathways playing a role in AML, and in vivo screening of short hairpin RNA (shRNA) library targeting chromatin regulators was performed in MLL-AF9/NrasG12D AML mouse model. This led to the identification of bromodomain-containing 4 (BRD4) being necessary for AML, and a potential therapeutic target since BRD4 suppression by small molecule inhibitor JQ1 led to terminal myeloid differentiation in mouse and primary patient samples (Zuber et al., 2011). RNA-binding proteins (RBPs) have been shown to regulate gene expression in cancer. Musashi2 (MSI2), a RBP, is required for self-renewal of leukemic stem cells (LSCs) (Park et al., 2015). To identify MSI2 interactome and novel regulators of leukemia, Vu et al. performed proteomic analysis to identify MSI2 binding partners, and functionally validated them using an in vivo shRNA screen. This led to the identification of 24 genes necessary for MLL-AF9 AML, and SYNCRIP as a novel RBP controlling LSC fate since SYNCRIP deletion promotes apoptosis and differentiation (Vu et al., 2017). The tumor microenvironment provides support to leukemic cells in vivo (Zhou, Carter, & Andreeff, 2016). Therefore it is important to identify molecules that are involved in these support interactions as they can act as potential therapeutic targets. Miller et al. performed pooled in vivo shRNA screening on MLL-AF9 AML model identifying integrin beta 3 (ITGB3) and integrin alpha V (ITGAV) essential for leukemic cells. ITGB3 loss did not affect normal hematopoiesis but induced differentiation in leukemic cells (Miller et al., 2013).

5.2.2 Molecular approach Recently, there has been a considerable shift toward the molecular approach for the identification of new targets based on the fundamental understanding of cellular pathways, including disease genotype and phenotype. The emergence of new biotechnological advancements has helped researchers to identify molecules that regulate the course of a

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5.2.1.2 Mouse (Mus musculus)

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particular disease and correlate the change in genomic and proteomic profiles to a disease state. This approach is strongly dependent on access to clinical samples and requires supporting medical data. The molecular approach is used in the field of drug discovery, including toxicology and for the identification of biological markers. 5.2.2.1 Proteomic technologies Proteomics is the examination of all proteins in a defined nexus within a cell, a cellular organelle, an organ, or extracellular matrix. This approach employs techniques that identify proteins based on mass or using antibody. Thus this set of biotechniques could be the ideal method in the course of target identification. 5.2.2.1.1 Antibody-based approaches

The use of antibodies to assess hundreds to thousands of proteins in a sample provides a validated readout because of the known specificity of antibodies. Such approaches are sensitive, rapid, and detect even less expressed proteins. 5.2.2.1.1.1 Immunophenotyping Immunophenotyping is the analysis of cells in a heterogeneous population to differentiate cells of interest. Antibodies have been used for this purpose, which specifically recognizes antigen (known as a marker) present on the cells. These markers are generally cell surface proteins that are involved in cell functions such as adhesion, signaling, and cell
cell communication. Antibody array (Belov, de la Vega, dos Remedios, Mulligan, & Christopherson, 2001) or flow cytometry (Gedye et al., 2014) can be used for immunophenotyping. The identification of cell-specific surface protein by immunophenotyping can aid in antibody-mediated targeted drug delivery, or itself can be a therapeutic target. 5.2.2.1.1.2 Multiparameter flow cytometry Analysis of single cells or particles as they flow in a narrow stream intercepted by laser beams is known as flow cytometry. Every cell is interrogated for size and granularity by light scatter (forward scatter and side scatter) and protein expression by fluorescence from fluorochrome-conjugated antibodies. Flow cytometry has been used for the study of leukemia and its classification through biomarkers, diagnosis, prognosis, and chemotherapeutic response in patients. In 2004 Irish et al. applied multiparameter flow cytometry to analyze the phosphoprotein-driven signaling networks at the single-cell level in AML. By using this method, they were able to establish a correlation between the genetic features and clinical outcomes of a patient (Irish et al., 2004). Using flow cytometry, STAT5 signaling was identified in a subset of juvenile myelomonocytic leukemia patients, which can be a potential target (Kotecha et al., 2008). The advantage of using multiparameter flow cytometry is that it requires a little amount of sample and time, which makes this technique suitable for the analysis of patient samples. Despite all applications, it has few limitations in methodology, such as limited detection channels, overlapping of spectra from the used fluorophore, and requires a single-cell suspension. However, a combination of flow cytometry with mass spectrophotometry (mass cytometry) has overcome a few of the limitations (Tanner et al., 2008).

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5.2.2.1.1.3 Mass cytometry In mass cytometry, antibodies are labeled with transition elements instead of fluorochrome, which enables the cytometer to measure up to 120 parameters within a cell with minimal overlapping of signals. Furthermore, transition elements are rare and not found in cells, allowing minimum background noise from cells (Zeng, Konopleva, & Andreeff, 2017). Multiple signaling pathways can be monitored at a single-cell level to study differences between AML and normal samples, leading to the identification of pathways against which therapeutics can be developed. Using this approach, mTOR targets p-4EBP1 and p-S6 were identified as novel targets in FLT3-ITD AML cells (Zeng et al., 2017). 5.2.2.1.1.4 Antibody arrays An antibody array is a slide or membrane having immobilized antibodies that enables simultaneous analysis of hundreds to thousands of proteins. Different kinds of analysis can be performed such as changes in protein expression among samples, posttranslational modifications, identify protein
protein interactions, and profiling of cell surface markers. These studies can help in identifying signaling pathways and proteins for targeting as well as biomarkers for diagnosis (Kopf & Zharhary, 2007; Yuan et al., 2017). 5.2.2.1.2 Mass spectrometry
based approaches

Mass spectrometry (MS) is an investigative technique that computes the mass of particle-based on its mass-to-charge ratio (m/z). Different MS-based approaches have been developed for deciphering proteomes to study protein
protein interactions, quantify protein levels, and assess posttranslational modifications. 5.2.2.1.2.1 Two-dimensional difference gel electrophoresis Two-dimensional (2D) electrophoresis has been used to separate protein based on charge and size. 2D difference gel electrophoresis (2D-DIGE) is an advanced version of 2D gel electrophoresis, which allowed comparing two or three protein samples simultaneously on the same gel. In 2D-DIGE, protein samples are covalently conjugated with the different color fluorescent dye, which does not have an effect on the movement of protein in the electromagnetic field. The differential protein spots are identified by MS. In a study of leukemia with t(4,11) translocation, proteomics screening with 2D-DIGE and MALDI-MS/MS using MV4
11 and RS4:11 cells led to the identification of HSP90alpha and NM23 (nucleoside diphosphatase kinase) as a potential target and a treatment efficacy biomarker, respectively (Yocum, Busch, Felix, & Blair, 2006). A combination of 2D-electrophoresis with MS protein identification has also been used for target identification. This approach was used to identify target proteins of AML1-ETO fusion protein, one target being differentiation inhibitor factor NM23, which is upregulated by AML1-ETO (Singh et al., 2010). Although 2D-DIGE has a limitation with respect to the representation of protein analysis, still it permits the scientist to narrow down the potential protein targets for further study. 5.2.2.1.2.2 Stable isotope labeling with amino acids in cell culture Stable isotope labeling with amino acids in cell culture (SILAC) is an MS-based technology that was developed with the idea to measure the quantitative difference in the level of protein between two or more samples (Ong et al., 2002). SILAC was originally developed for quantitative proteomics studies in cell lines only (Zanivan, Krueger, & Mann, 2012). However, the development of SILAC mouse in 2012 has enabled quantitative assessment of proteomic changes

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taking place specific cells/tissue in vivo (Kruger et al., 2008; Zanivan et al., 2012). This method is often used in mechanistic studies of alterations induced by drugs, which also includes AML. For instance, SILAC has been used to determine the underlying mechanism of treatment as well as toxicity induced by arsenic trioxide for acute promyelocytic leukemia (APL) (Xiong & Wang, 2010). Researchers have used SILAC in combination with phosphoproteomics to examine the effects of different tyrosine kinase inhibitors on leukemia (Pan, Olsen, Daub, & Mann, 2009). This association of biotechnology has also been used to measure the change in phosphorylation of BCR-ABL kinase after imatinib treatment in human leukemic cells (Liang et al., 2006). Using SILAC, Chong et al. (2014) found LEO1, a component of RNA polymerase II association factor complex, a new target that regulates PRL-3 oncogenic activity in AML. Thus the sensitivity of SILAC method allows the study of novel signal transduction pathways and molecules, which will help in the identification of pharmacological targets and biomarkers for AML. 5.2.2.1.2.3 Isotope-coded affinity tags Isotope-coded affinity tag (ICAT) is an in vitro labeling method for proteins containing cysteine residues. ICAT reagents are cysteine binding tags with different molecular weights. Two protein samples are labeled with ICAT reagents and mixed and subjected to protease digestion. Further, these peptides are identified and quantified by MS/MS (Shiio & Aebersold, 2006). This method has been used to study proteomic changes in microsomal fraction HL-60 cells, an AML cell line, induced to undergo differentiation (Han, Eng, Zhou, & Aebersold, 2001). 5.2.2.1.2.4 Isobaric tags for relative and absolute quantification Isobaric tags for relative and absolute quantification (iTRAQ) involves labeling of samples with different isobaric tags of the same mass, which upon fragmentation in MS produce different unique ions. Thus the same peptide from different samples labeled with isobaric tags can be analyzed under the same MS experiment (Ow et al., 2008). AML cells with wild-type FLT3, FLT3 mutation via ITD and pointmutated in FLT3, have been analyzed by iTRAQ to identify and quantify phosphorylation. This analysis has shown different phosphorylation pattern in JAK2 protein, transcription activator protein STAT5a, and SH2-containing protein phosphate (SHP1) which further conclude FLT-ITD and FLT3 point mutation cause different signaling response in AML patients (Zhang et al., 2010). 5.2.2.1.2.5 Multiple reaction monitoring mass spectrometry Multiple reaction monitoring MS (MRM-MS) is a highly sensitive quantitative protein detection technology that allows the detection of low abundant proteins in a complex mixture (Kirkpatrick, Gerber, & Gygi, 2005). In particular, this technique specifically detects standard internal peptide in particular protein targets, which have incorporated with heavy isotope amino acid. Therefore this technology is more sensitive than other MS-based technologies. The activity of BCR-ABL kinase in chronic myeloid leukemia (CML) patients has been analyzed by MRM-MS (Yang, Eissler, Hall, & Parker, 2013).

5.2.2.2 Genomic technologies For the last 40 years, AML genomics has been extensively studied. Genomic study of AML is usually performed with three basic objectives: to decipher the molecular pathogenesis of AML, to identify a genetic marker for diagnosis, and to identify targets having therapeutic potential.

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5.2.2.2.1 Next-generation sequencing

5.2.2.2.1.1 Whole-genome sequencing Whole-genome sequencing (WGS) involves the complete sequencing of an organism’s genome. The entire DNA sequencing also includes mitochondrial DNA. This is very useful as it helps in identifying polymorphism and mutations in cancer genome. Some of the mutations identified in AML patients by NGS include DNMT3A (Ley et al., 2010), IDH1 (isocitrate dehydrogenase 1), and IDH2 (Mardis et al., 2009). Various molecules have been developed targeting these mutant proteins for treating AML (discussed ahead under Section 5.3). 5.2.2.2.1.2 Exome sequencing Human genome only has 1% of coding sequence (exome). Exome sequencing enables a more in-depth coverage of the coding regions. This is a more targeted approach that has helped in identifying somatically acquired mutations from normal genetic variation by comparison of normal and tumor samples. This technique has helped in identifying a number of recurrent mutations in genes such as FLT3, ATXN3, CEBPA, RUNX1 in AML (Heo et al., 2017; Opatz et al., 2013). 5.2.2.2.1.3 Transcriptome sequencing All the coding (mRNA) and noncoding [microRNA (miRNA), linc RNA, small RNA] RNAs constitute the transcriptome. Sequencing of the transcriptome provides information with regard to transcripts expressed, level of expression, identification of splice variants, investigation of untranslated region, and profiling of noncoding RNAs (Braggio et al., 2013). The information generated can be used for AML classification, risk assessment, and targeted therapy (Arindrarto et al., 2020). Transcriptome sequencing also helps in the identification of novel fusion genes such as CHD1-RUNX1 (Yao et al., 2015), KMT2A-USP2 (Ikeda et al., 2019).

5.2.2.2.2 Microarray

Microarray is a high-throughput technique that allows simultaneous measurement of expression of thousands of genes. This information has helped in understanding cellular signaling in disease, identification of novel gene targets for drug development, and drug responsiveness in disease (Barar, Saei, & Omidi, 2011). Microarray analysis of U937 cells expressing PML-RARα, PLZF-RARα, and AML1-ETO identified overexpression of MNK1, which negatively regulates myeloid differentiation and plays a role in AML fusion protein-induced differentiation block (Worch et al., 2004). Further, deregulation of Wnt/ β-catenin signaling was also found in these three U937 cell lines expressing AML translocation products (Muller-Tidow et al., 2004). Gene expression profiling of LSCs and hematopoietic stem cells led to the identification of cyclin-A1 as a candidate gene, minimally expressed in other tissues, to serve as a target for developing T cell
based therapy (Ochsenreither et al., 2012).

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Next-generation sequencing (NGS) has revolutionized the ability to investigate cancer. This high-throughput sequencing enables rapid and cheap sequencing compared to traditional Sanger sequencing. Today, NGS is employed to study cancer genome, transcriptome, and epigenome (Braggio, Egan, Fonseca, & Stewart, 2013).

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5.2.2.2.3 RNA interference

RNA interference (RNAi) or posttranscription gene silencing is a conserved biological mechanism activated by dsRNA in cells, which results in the downregulation of gene expression either by the degradation of mRNA or translational repression (Agrawal et al., 2003). Small-interfering RNA, shRNA, and miRNA are RNA molecules that are capable of activating RNAi. Since whole organisms have been sequenced and genes identified, RNAi libraries targeting all known genes or predefined subsets of genes are available to screen and identify potential target genes playing a role in the cellular process or disease progression. For example, RNAi has been used to target the entire tyrosine kinase family in AML cell lines for a better understanding of tyrosine kinase signaling in AML. In this study, JAK1 and FAK were identified to be crucial for cancer cell survival, and along with mutated JAK3 and c-KIT in CMK and HMC1.1 cell lines, respectively (Tyner et al., 2008). In another study, RNAi screening in primary AML cells led to the identification of ROCK1 as a therapeutic target whose knockdown or inhibition by fasudil decreased AML cell survival (Wermke et al., 2015). RNAi screening has also been used in vivo, which has been discussed earlier under the systems approach (Section 5.2.1). Although RNAi has been widely used in research, it often results in partial silencing of gene expression and also exhibits off-target effects. Due to these reasons, there is shifting toward more precise genome-editing technologies. 5.2.2.2.4 Genome-editing technologies

Genome editing is making the desired change in the genome of an organism. A number of approaches have emerged recently for genome editings such as zinc-finger nucleases (ZFNs), transcription activator-like effector nucleases (TALENs), and CRISPRCas9 (clustered regularly interspaced short palindromic repeats
CRISPR-associated protein 9). These technologies have been used as a screening tool to knock out all known genes or sets of genes and, thus, playing an important role in target discovery, which helps in disease diagnosis, prevention, and targeted therapy (Li et al., 2020). The application of these technologies is not limited to in vitro models but is also used in xenograft models as well. 5.2.2.2.4.1 Zinc-finger nucleases and transcription activator-like effector nucleases ZFNs comprise a DNA-binding domain, a C2H2 type zinc finger, fused to a DNA cleavage domain derived from restriction enzyme FokI (Carroll, 2011). While TALENs consist of a nonspecific endonuclease domain of FokI fused to a customizable DNA-binding domain containing bacterial TALE protein repeats (Joung & Sander, 2013). These chimeric endonucleases enable genome engineering by creating DNA double-strand breaks, which stimulates nonhomologous end-joining or homology-directed repair at cleavage sites. These two technologies achieve complete knockout, unlike RNAi, but they are very expensive due to specialized protein design and time-consuming (Gaj, Gersbach, & Barbas, 2013). 5.2.2.2.4.2 CRISPR/Cas system CRISPR/Cas system is an adaptation of bacteria defense mechanism that enables precise editing of the genome. It comprises a nuclease (Cas9)

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and a guide RNA (gRNA). gRNA specifically binds to the target sequence present in genomic DNA and directs Cas9 to a target site for cleavage, resulting in a doublestrand break (Gupta & Musunuru, 2014). This system has the advantage of multiplexing, that is, targeting multiple sites and ease of targeting over ZFN and TALEN (Gupta & Musunuru, 2014). All these genome-editing technologies are being used to study cancer by generating cell and animal models, identifying essential genes and signaling pathways, and correcting mutations. These systems play an important role in target discovery as well. For instance, CRISPR/Cas9 screens have led to the identification of XPO7 (a putative nuclear/cytoplasmic transporter) (Semba et al., 2019), DCPS (mRNA decapping enzyme scavenger) (Yamauchi et al., 2018), and miR-155 (Wallace et al., 2016) as potential therapeutic targets in AML cells.

5.3 Acute myeloid leukemia
targeted therapies in clinics Many targets for treating different subtypes of AML based on mutations have been identified. Different approaches to target these intracellular or cell surface molecules are being developed, which result in inhibition of proliferation, cell differentiation, or cell death (Fig. 5.2). Few targeted therapies that have been approved for use in clinics are summarized later and in Table 5.1. In addition, a case study on FLT3 targeted therapy is presented.

5.3.1 BCL-2 inhibitors BCL-2 is a proto-oncogene expressed in the mitochondria, which regulates cell apoptosis. Elevated expression of BCL-2 has been reported in AML patients, especially in M4/5 AML subtypes, and a high level is associated with chemotherapeutic resistance (Campos et al., 1993). A high-affinity inhibitor of the BH3 domain of BCL-2 was developed called ABT-199 (Venetoclax). ABT-199 is a BH3 mimetic having a minimal affinity for BCL-XL (Souers et al., 2013). It was approved for treating relapsed/refractory chronic lymphoid leukemia patients in 2016 by the Food and Drug Administration (FDA) (Juarez-Salcedo, Desai, & Dalia, 2019). However, FDA gave approval for its use in combination therapy with azacytidine or low-dose cytarabine for the treatment of AML patients of age 75 years or more, and patients have comorbidities, and no intensive chemotherapy indicated (Bohl, Bullinger, & Rucker, 2019).

5.3.2 Isocitrate dehydrogenase inhibitors IDH is an NADP-dependent enzyme with an important role in cellular aerobic respiration. Approximately 15%
20% of AML patients have IDH (IDH1 and IDH2) mutation, which causes abnormal maturation of myeloid cells (Stone, 2017). Several studies have been conducted for targeting IDH, and two inhibitors, ivosidenib and enasidenib, which block IDH1 and IDH2, respectively, have been approved by FDA (Norsworthy,

Section 3: Pathway and target discovery

5.3 Acute myeloid leukemia
targeted therapies in clinics

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5. Importance of targeted therapies in acute myeloid leukemia

FIGURE 5.2 Overview of AML targeted therapy approaches. Molecules already in clinical use are written in green; those in red are under clinical trials. AML, Acute myeloid leukemia. MoAb, monoclonal antibody.

Luo, et al., 2019; Reed et al., 2019). Enasidenib suppresses 2-hydroxyglutarate production and induces cell differentiation (Yen et al., 2017). However, IDH inhibitor treatment causes leukemic cells to release inflammatory cytokines, which further causes differentiation syndrome. This is a series of complications that include hypoxemia, hypotension, respiratory distress, and hepatic dysfunction (Birendra & DiNardo, 2016).

5.3.3 PML-RARα targeted therapy Chromosomal translocation, t(15,17), is the leading genetic mutation in APL and produces PML-RARα fusion protein, which has a central role in APL. Initially, APL was treated with conventional chemotherapy, but after the discovery of PML-RARα fusion protein, several studies were undertaken to improve APL patient survival. Alltrans retinoic acid (ATRA), in combination with arsenic trioxide (ATO), is able to achieve approximately 80%
90% survival rate in APL patients (Winer & Stone, 2019). This is an excellent example of targeted therapy. ATO induces differentiation in leukemic cells through SUMOylation of PML-RARα fusion protein, which further undergoes degradation (Geoffroy, Jaffray, Walker, & Hay, 2010). ATRA induces differentiation in APL cells toward granulocytes and mature granulocyte-like cells (Wang & Chen, 2008).

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5.3 Acute myeloid leukemia
targeted therapies in clinics

Target

Drug

Target patients

Approval date Reference

BCL-2

Venetoclax

AML diagnosed adult (75 years or above) patients, who have comorbidities that preclude intensive induction chemotherapy

November 21, 2018

Knight, Edwards, Taub, and Ge (2019)

CD33

Gemtuzumab ozogamicin (antibody
drug conjugate)

Adult and pediatric (2 years or more) patients with CD33 1 AML

September 1, 2017

Jen et al. (2018)

FLT3

Midostaurin

FLT3 1 AML

April 28, 2017

Abbas, Alfayez, Kadia, RavandiKashani, and Daver (2019)

FLT3-AXL receptor tyrosine kinase

Gilteritinib

Relapse or refectory FLT3 1 AML patients

November 28, 2018

Dhillon (2019)

IDH1

Ivosidenib

Adult patients with IDH1 1 AML

July 20, 2018

Norsworthy, Luo, et al. (2019)

IDH2

Enasidenib

IDH2 1 AML

August 1, 2017 Reed, Elsarrag, Morris, and Keng (2019)

SMO

Glasdegib

AML diagnosed adult (75 years or above) patients, who have comorbidities that preclude intensive induction chemotherapy

November 21, 2018

Norsworthy, By, et al. (2019)

5.3.4 Targeting FLT3-mutated acute myeloid leukemia: from bench to bedside (a case study) FLT3 belongs to the family of receptor tyrosine kinases (RTKs). It plays a role in the maintenance and proliferation of hematopoietic stem cells (Kiyoi, Kawashima, & Ishikawa, 2020). FLT3 gene shares strong relation with FMS and KIT proto-oncogenes, which also belong to the same receptor tyrosine kinase family and are highly expressed in AML patients (Rambaldi et al., 1988; Wang, Curtis, Geissler, McCulloch, & Minden, 1989). Therefore, Birg et al. (1992) looked for FLT3 transcript in AML patients and observed high levels in 92% of human AML samples, irrespective of AML subtype, using Northern blotting. Subsequent studies on FLT3 mRNA expression in AML patients revealed the presence of longer FLT3 transcripts in a few patients. The sequencing of these RT-PCR products led to the identification of ITD of FLT3 gene (Nakao et al., 1996). A mutation in the tyrosine kinase domain (TKD) at D835 (aspartic acid 835) was also identified in 2001 using PCR and sequencing

Section 3: Pathway and target discovery

TABLE 5.1 Food and Drug Administration (FDA)-approved acute myeloid leukemia (AML) targeted therapies.

Section 3: Pathway and target discovery

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5. Importance of targeted therapies in acute myeloid leukemia

(Abu-Duhier et al., 2001; Yamamoto et al., 2001). Both these mutations result in constitutive activation of FLT3, which encourages AML cell survival and proliferation, and inhibit differentiation (Sexauer et al., 2012). Patients suffering from AML with FLT3 mutations have a poor prognosis (Sexauer et al., 2012; Stubbs & Armstrong, 2007). Soon after the discovery of FLT3 role in AML, a series of follow-up studies started for the development of FLT3 inhibitors to selectively target leukemic cells for AML therapy. The first studies found that AG1296, an inhibitor of PDGF and KIT receptors, also inhibited FLT3 (Levis, Tse, Smith, Garrett, & Small, 2001; Tse, Novelli, Civin, Bohmer, & Small, 2001). Subsequently, semaxinib and sunitinib were designed, which inhibited RTKs such as FLT3, C-KIT, and VEGFR (Mendel et al., 2000; O’Farrell et al., 2003; Smolich et al., 2001; Yee et al., 2002). But due to nonspecific nature and poor tolerability in AML patients, these drugs did not get approved. Several other multitarget first-generation RTK inhibitors such as midostaurin, sorafenib, and lestaurtinib have been tested for the treatment of FLT3-mutated AML patients (Fiedler et al., 2015; Knapper et al., 2017; Stone et al., 2005; Wander, Levis, & Fathi, 2014). These inhibitors showed limited effectiveness when used alone but display mixed responses when used in combination with chemotherapeutic drugs (Levis et al., 2011; Rollig et al., 2015). Among these RTK inhibitors, the use of midostaurin along with conventional chemotherapy resulted in increased event-free and overall survival of AML patients having FLT3-ITD/TKD mutations in phase 3 trial (RATIFY) (Stone et al., 2017). The results of this phase 3 trial led to midostaurin being the first US FDA
approved FLT3 inhibitor for the treatment of FLT3-mutated AML patients (Gurnari et al., 2020). To minimize toxicity and off-target effects of nonspecific RTK inhibitors, FLT3-specific inhibitors have been developed, which include quizartinib, crenolanib, and gilteritinib (Daver, Schlenk, Russell, & Levis, 2019). Target-specific RTK inhibitors are basically of two types: type I inhibitor, which interacts with both active and inactive conformation of FLT3 receptor like crenolanib and gilteritinib, and type II inhibitor, which only targets inactive conformation of FLT3 like quizartinib. Gilteritinib alone has been shown to enhance survival and remission compared to conventional chemotherapy in a phase 3 trial (ADMIRAL) in relapsed or refractory FLT3-mutated AML patients (Perl et al., 2019). This led to gilteritinib being granted FDA approval in 2018 (Gurnari et al., 2020).

5.4 Hurdles and emerging targeted therapies Biotechnology has contributed to the discovery and development of pharmaceutical targetbased drugs, which have shown significant improvement in AML therapies. A number of new targets have been identified for different AML subtypes and are under clinical trial (Table 5.2), which include checkpoint inhibitors, chimeric antigen receptor (CAR) T-cell therapy, bispecific antibodies, and vaccine therapy. FLT3 inhibitors and PML-RARα are the best example of targetbased therapy in AML. But, unfortunately, targeted therapy still shows off-target effects, for example, IDH inhibitor causes differentiation syndrome (Birendra & DiNardo, 2016), and vincristine can cause sensory and motor neurotoxicity. These limitations have led to the development of targeted drug delivery, which includes liposomes, polymeric conjugates, polymeric micelles, nanoparticles, and antibodies. Liposomes-based drug delivery is the most widely used vehicle in clinical practice. Vincristine encapsulated in sphingomyelin or cholesterol envelope,

TABLE 5.2 Acute myeloid leukemia (AML) targeted therapies under clinical trials. Target

Drugs and therapy

Target patients

Trials/last updated

Clinical trial identifier

IDH1 and IDH2

Enasidenib or ivosidenib with induction or consolidation therapy

AML with IDH1 and IDH2

Phase 1/March 26, 2020

NCT02632708

Enasidenib or ivosidenib 1 AG-120/ azacytidine

IDH1/IDH2 1 AML patients who cannot able to receive intensive induction chemotherapy

NCT02677922

Enasidenib versus conventional therapy

IDH2 1 AML patients (60 years and above)

Phase 1/December 20, 2019 (with AG-120) Phase 1/December 20, 2019 (with azacytidine) Phase 3/February 26, 2020

FT-2102 1 azacytidine/olutasidenib (FT-2102)

IDH1 1 AML/MDS

NCT02719574

BAY1436032

IDH1 1 AML

Phase 1/December 5, 2019 (with FT-2102) Phase 2/December 5, 2019 (with azacytidine) Phase 1/May 14, 2019

Venetoclax 1 azacytidine or low-dose cytarabine

AML patients who cannot able to receive standard induction chemotherapy

BCL-2 antagonists

FLT3

NCT02577406

NCT03127735

Phase 3/February 28, 2020

NCT02993523 NCT03069352

Venetoclax 1 idasanutlin or cobimetinib Relapsed or refectory AML patient who unable to receive standard chemotherapy

Phase 1/April 13, 2020

NCT02670044

Quizartinib 1 standard chemotherapy

FLT3-ITD 1 AML

Phase 3/November 25, 2019 NCT02668653

Gilteritinib 1 induction and consolidation chemotherapy

FLT3 1 AML

Phase 1/March 16, 2020

NCT02236013

Crenolanib 1 standard chemotherapy

FLT3 1 AML

Phase 2/April 22, 2019

NCT02283177

Midostaurin

FLT3-ITD 1 AML to prevent relapse

Phase 2/April 23, 2019

NCT01883362

Gilteritinib 1 azacitidine

FLT3 1 AML

Phase 3/March 5, 2020

NCT02752035

Quizartinib 1 azacitidine/cytarabine

Relapsed or refectory AML/MDS

NCT01892371

Quizartinib

FLT3-ITD 1 AML

Phase 1/January 2, 2020 (with azacitidine) Phase 2/January 2, 2020 (with cytarabine) Phase 3/February 10, 2020

NCT02039726

Sorafenib 1 azacitidine/sorafenib

FLT3-ITD 1 AML/MDS

Phase 2/January 14, 2020

NCT02196857 (Continued)

Section 3: Pathway and target discovery

Section 3: Pathway and target discovery

TABLE 5.2 (Continued) Target

Drugs and therapy

Target patients

Trials/last updated

Clinical trial identifier

Gilteritinib

FLT3/ITD 1 AML after complete remission

Phase 2/April 10, 2020

NCT02927262

Gilteritinib

FLT3/ITD 1 AML after allogenic transplantation

Phase 3/April 9, 2020

NCT02997202

Gilteritinib

Relapsed or refectory FLT3 1 AML

Phase 3/April 7, 2020

NCT02421939

Crenolanib 1 standard chemotherapy

Relapsed or refectory FLT3 1 AML

Phase 3/December 19, 2018

NCT02298166

E6201

FLT3 1 AML/MDS

NCT02418000

Splicing modulators

H3B-8800

AML/MDS/CML

Phase 1/March 20, 2019 (for AML) Phase 2/March 20, 2019 (for MDS) Phase 1/December 10, 2019

NCT02841540

Bromodomain and extra-terminal motif (BET) inhibitors

FT-1101

AML/MDS/non-Hodgkin lymphoma

Phase 1/June 26, 2019

NCT02543879

CPI-0610

AML/MDS

Phase 1/October 30, 2019

NCT02158858

GSK525762

AML/MM/non-Hodgkin lymphoma

Phase 2/March 24, 2020

NCT01943851

MK-8628

AML/MDS

Phase 1/January 29, 2020

NCT02698189

ABBV-075 1 venetoclax

AML/MM/breast cancer/lung cancer/ prostate cancer

Phase 1/November 29, 2019 NCT02391480

GSK2879552 1 ATRA

AML

Phase 1/June 28, 2019

NCT02177812

INCB059872

AML/MDS, SCLC, myelofibrosis, Ewing sarcoma

Phase 1/August 29, 2019

NCT02712905

IMG-7289

AML/MDS

Phase 1/February 26, 2019

NCT02842827

Pracinostat 1 azacitidine

AML/APL

Phase 3/October 25, 2019

NCT03151408

Entinostat 1 azacitidine

Old AML patients (60 years or above)

Phase 2/December 20, 2019

NCT01305499

LSD1 inhibitors

HDAC inhibitors

Panobinostat 1 fludarabine 1 cytarabine AML/MDS patients (up to 24 years)

Phase 1/November 14, 2018 NCT02676323

CD123

SL-401 1 azacytidine

AML/MDS

SL-401 (diphtheria toxin domains fused BPDCN and AML to IL3)

Phase 1/April 7, 2020

NCT03113643

Phase 1/April 7, 2020 (for BPDCN) Phase 2/April 7, 2020 (for AML) Phases 1 and 2/April 7, 2020

NCT02113982

SL-401

AML after Complete remission

NCT02270463

UCART 123 (CAR T-cell)

Relapsed or refractory AML

Phase 1/December 6, 2019

NCT03190278

CD123CAR-CD28-CD3zeta-EGFRtexpressing T lymphocytes

Relapsed or refractory AML, AML

Phase 1/January 23, 2020

NCT02159495

MB-102 (CAR T-cell)

AML, BPDCN, and high risk MDS

Phases 1 and 2/May 30, 2020

NCT04109482

IMGN632 (antibody conjugated with drug)

AML, ALL, BPDCN, and CD123 1 hematopoietic malignancies

Phases 1 and 2/December 9, NCT03386513 2019

E-selectin inhibitor

GMI-1271 (uproleselan 1 chemotherapy)

Relapsed or refractory AML

Phase 3/March 18, 2020

NCT03616470

Histone methyltransferase inhibitor

Pinometostat 1 azacitidine

Relapsed or refractory AML and AML with 11q13 rearrangement

Phases 1 and 2/March 3, 2020

NCT03701295

Menin-MLL inhibitor

SNDX-5613

Relapsed or refractory AML and AML with 11q13 rearrangement

Phases 1 and 2/April 13, 2020

NCT04065399

KO-539

Relapsed or refractory AML

Phase 1/February 17, 2020

NCT04067336

MEK inhibitor

Trametinib

Relapsed or refractory juvenile myelomonocytic leukemia

Phase 2/April 15, 2020

NCT03190915

MDM2 inhibitor

Idasanutlin 1 cytarabine

Relapsed or refractory AML

Phase 3/March 5, 2020

NCT02545283

ALRN-6924

Leukemia, brain cancer, solid cancer, and lymphoma

Phase 1/November 1, 2019

NCT03654716

PRIMA-1 analog

APR-246

MDS

Phase 3/February 17, 2020

NCT03745716

CD3 and CD33

AMG330 (bispecific antibody)

Relapsed or refractory AML

Phase 1/November 25, 2019 NCT02520427

Flotetuzumab (bispecific antibody)

Relapsed or refractory AML

Phase 1/March 19, 2020

NCT04158739 (Continued)

Section 3: Pathway and target discovery

Section 3: Pathway and target discovery

TABLE 5.2 (Continued) Target

Drugs and therapy

Target patients

Trials/last updated

Clinical trial identifier

NKG2D

THINK

Colorectal, ovarian, bladder, triple-negative breast and pancreatic cancers, acute myeloid leukemia, and multiple myeloma

Phase 1/September 19, 2019 NCT03018405

CTLA-4

Ipilimumab (antibody) 1 decitabine

Relapsed or refractory AML, secondary AML

Phase 1/March 24, 2020

NCT02890329

PD-1

Nivolumab (monoclonal antibody)

AML in remission, AML arising from previous MDS

Phase 2/February 13, 2020

NCT02532231

Nivolumab 1 azacitidine with or without ipilimumab

Relapsed or refractory AML, newly diagnosed AML

Phase 2/February 5, 2020

NCT02397720

Nivoluman 1 idarubicin 1 cytarabine

AML

Phases 1 and 2/March 18, 2020

NCT02464657

CDX-1401 (Dec-205/Ny-ESO-1 fusion protein) 1 poly ICLC 1 decitabine 1 nivolumab

AML

Phase 1/March 19, 2020

NCT03358719

Pembrolizumab (antibody)

Relapsed or refractory AML

Phase 2/September 4, 2019

NCT02768792

Pembrolizumab 1 azacitidine

AML

Phase 2/November 29, 2019 NCT02845297

BPDCN, Blastic plasmacytoid dendritic cell neoplasm; CAR, chimeric antigen receptor; HDAC, histone deacetylase; IDH, isocitrate dehydrogenase; MDS, myelodysplastic syndrome; MLL, Mixed lineage leukemia; MM, multiple myeloma; SCLC, small cell lung cancer; THINK, THerapeutic Immunotherapy with NKR-2 cells (CAR T-cell). www.clinicaltrials.gov.

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known as liposomal vincristine, is designed to deliver the drug to the target tissue and decrease neurotoxicity by reducing free drug percentage in the blood (Pathak, Hess, & Weiss, 2014). Vincristine sulfate liposome is also under investigation in phase 2 trial for AML therapy (NCT02337478). However, CPX-351, cytarabine, and daunorubicin encapsulated in liposomes have been granted FDA approval for treating newly diagnosed adult secondary AML patients (Lancet et al., 2018). In addition, monoclonal antibodies are also under examination for the targeted delivery of drugs. For example, CLT030, a CLL1 antibody
drug conjugate, specifically targets AML blasts as well as LSCs (Jiang et al., 2018). Therefore, target-based drug delivery may lower off-target effects with increased efficiency. Another hurdle associated with target discovery technology is that the identified biological targets ultimately have to be tested in humans before approval. Drug development has a high failure rate, even those targets that have undergone extensive validation before introducing in clinical trials, expose huge complexity of the biological system. Thus the most significant problem associated with target discovery, in particular to target validation, is the selection of models that are truly anticipating diseases.

5.5 Conclusion As conventional therapy for AML has significant adverse effects on normal cells, tissue, and organ, a more specific therapy is needed for the treatment of AML. Thus every particular AML subtype is being thoroughly investigated for target identification. The molecular approach, along with the systems approach, is contributing to the field of target identification. The systems approach is a valuable asset for target discovery and drug validation and is likely to be advancing through large-scale study of gene editing in zebrafish and mice. The molecular approach is contributing via proteomic and genomic studies, which hold opportunities for finding new biomarkers and targets. Current proteomic approaches such as MS-based technology and flow cytometry are high-throughput and less timeconsuming compared to earlier techniques. Furthermore, genomics has improved after the invention of NGS, and genome-editing technologies, which have made AML WGS possible for target identification. However, the target-based approach also has off-target effects such as differentiation syndrome, which require a target-based carrier to transfer drugs to a specific location. Thus targeted therapy development entails the identification of an exclusive therapeutic target, development of molecule acting on the target, and a suitable targeted delivery system to minimize side effects and obtain maximum efficacy.

Acknowledgments Ajit Kumar Rai is a recipient of Senior Research Fellowship from University Grants Commission, New Delhi, India. The CSIR-IITR manuscript communication number is 3644.

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5.5 Conclusion

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Section 3: Pathway and target discovery

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6.1 Introduction to biological therapeutic modalities In Every Walks With Nature One Receives Far More Than He Seeks
John Muir

Biological therapeutics, also called bio-therapeutics or biopharmaceutics or biologics, are the biological substances derived from the biotic elements of our ecosystem like living plants and animal tissues used for therapies and disease treatment purposes. The biological products can be different cellular components like small molecules, chemical messengers like cytokines, interferons, nucleic acids like DNA or RNA, proteins, and even the whole cell or cells present in the microbiome/tissue organization. Scientists and different pharmaceutical/biotech Molecular Assemblies, San Diego, CA, United States

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companies use or engineer the biological products to be served as therapeutic drugs. In some cases, these biological modalities are combined to formulate a better version of medicine, showing improved characteristics like more specific targeting to the desired tumor region in the body and have limited toxicity, like antibody
drug conjugates that are used in cancer treatment. Research in the development of bio-therapeutic medicines has provided a tremendous amount of success in fighting against chronic diseases like cancer, rheumatoid arthritis, and diabetes. Commercially, the biopharmaceuticals business has been the fastest-growing part of the pharmaceutical industry. Top 20 biopharma companies have generated revenues of 13.4
81.5 billion USD in the year 2018 (PharmaShots, 2018). Pharma companies realizing the potential of biological therapeutics shifted the focus from chemical synthesis much earlier from the year 2014 (Otto, Santagostino, & Schrader, 2014). The current value of the biopharmaceutical market is 237
275 billion USD and estimated to grow at an annual rate of 12%
13% (Morrow & Langer, 2019). It is essential to understand the different biological therapeutic modalities used in the drug development process and appreciate this paradigmatic shift from chemical therapy to biological-based therapeutics adopted by the pharma/biotech companies. The chief advantages of bio-therapeutic products are high efficacy, potency, specificity, and have lower side effects, unlike the conventional chemically synthesized drugs. However, it comes with certain disadvantages and challenges that demand innovation and research for growth and development. One of the major disadvantages is that novel biopharmaceuticals are very expensive to be used by the patients. As an example, the current price of FDAapproved CAR-T-cell therapy drugs Kymriah (Novartis) and Yescarta (Gilead Sciences) is 475,000 and 373,000 USD, respectively; these drugs are used for the treatment of relapsed or refractory B-cell acute lymphoblastic leukemia (ALL) and B-cell non-Hodgkin’s lymphomas. The total cost can increase up to 1
1.5 million USD for administering the drugs to the patients kept in hospital-care (Hitchcock, 2019; Maziarz, 2019). Manufacturing and bioprocessing the drugs at a commercial scale require specialized expertise in the technology leading to its high price. The other disadvantage being some bio-therapeutic drugs or biologics are a complex macromolecule of protein/protein assembly derived from living organisms that it is very difficult to create an identical copy of the reference product. The biologics produced inside the cell undergo various chemical changes like posttranslational and posttranscriptional modifications. Their structural and functional stabilities are affected by differences in the buffer composition in which they are stored. Thus biological medicine derived products are also called biosimilars. They are not identical but are similar to the original reference drug differing in the minor parts of the structure (Lu & Jacob, 2019). In contrast to the generic drugs, they can be manufactured and produced identical to the reference product by a series of standardized chemical synthetic reactions. Biosimilars, before launching into the market, as per the FDA requirements, need to be tested for its safety, efficacy, and toxicity. The results should be within the permissible differences from the original biologics reference product. Manufacturers of biosimilars need to conduct clinical studies to demonstrate that their drug possesses the same pharmacokinetics and pharmacodynamics properties as the reference biologics product (AltaSciences, 2019; PhRMA, 2020). They are generally made for biologics whose patents have expired and are made available to patients at a reduced cost (AltaSciences, 2019; PhRMA, 2020).

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The practice of using biological products for disease treatment is very ancient. Shreds of evidence can be found in earlier human Egyptian civilizations’ medical records of pharmacopeia in Ebers Papyrus, dated 1550 BCE as shown in the historic timeline in Fig. 6.1. It contains extensive information about the medical herbal remedies and the cures against various illnesses encountered by Egyptians. The drugs were derived from a wide range of biological sources like plant materials containing herbs, leaves, shrubs, and vegetables and animal products like honey, milk, meat, animal tissues, and internal organs like the liver (Nunn, 2002). Medical records in Indian Vedic scripts comprise various Ayurvedic therapies and medicines that are majorly plant-origin biologics. Indian Ayurveda medicine system can be traced back to 5000 BCE, in the pre
Vedic period, with documentation records in Charaka Samhita and Susruta Samhita dated in early 1000 BCE (Dikshith, 2008). The earliest medical book of China, Shennong Ben Cao Jing, written by medicine practitioners of the Han and Qin Dynasty (200
250 BCE), contains information about therapeutic medicinal plants used by early Chinese people (Unschuld, Unschuld, & Lehmann, 1986). The earliest form of biological medicine used in the 18th century was vaccines for the treatment against infectious diseases. In 1796 inoculating cowpox virus to develop immunity against smallpox disease led to the development of early biological vaccines by Edward Jenner. Since then, different generations and forms of vaccines were developed comprising a wide variety of biological modalities such as microbial pathogen or their surface proteins or chemical toxins produced by them to fight against infectious diseases like chickenpox, polio, measles, mumps, rubella, and diphtheria.

FIGURE 6.1 Historic timeline for classical bio-therapeutic modalities. Source: Author’s creation.

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The first biologics approved for therapeutic use and commercialization was human insulin, a protein hormone in 1982 by Eli Lilly and Company. Medicine is used to regulate glucose levels in the blood for diabetic patients. Advancement in understanding the human immune system, antibody immunoglobulin proteins (Igs), and their interaction with antigen in the late 1890s and early 20th century led to the development of other types of biologics-monoclonal antibodies. The first monoclonal antibody approved by FDA was muromonab in 1985 used in kidney organ transplants by suppressing the activity of the T cells of the host binding to the cell surface receptor CD3 (Smith, 1996). This was followed by several other chimeric human-mouse monoclonal antibodies like Rituximab (brand name Rituxan) approved by FDA in 1997 for the treatment of non-Hodgkin’s lymphoma targeting against cancerous CD20 B-cell surface receptor (Pierpont, Limper, & Richards, 2018; Scott, 1998) and infliximab (brand name Remicade) put in medical use from 1998 in the United States and 1999 in Europe for the treatment of various autoimmune diseases like Crohn’s disease, ulcerative colitis, rheumatoid arthritis, psoriasis and ankylosing spondylitis (Cornillie, 2009). Adalimumab (brand name Humira) was the first human monoclonal antibody used for the treatment of rheumatoid arthritis, approved by the FDA in December 2002 (Glasure, 2018). Both infliximab and adalimumab target against the chemical messenger cytokine TNF-α and prevent their binding to the cell surface receptor (Cornillie, 2009; Glasure, 2018).

6.3 New modalities 6.3.1 Small molecules Small-molecule drugs are chemical compounds with a molecular weight in the range of 0.1
1 kDa. They are smaller than biologics or bio-therapeutic modalities, which are generally more than 1 kDa in molecular size, as shown in Fig. 6.2. Owing to the small size, they possess an advantage over biologics to target not only the extracellular components like cell surface receptors or protein domains attached to the cell membranes like glycoproteins but also the intracellular proteins like different kinases, as they can easily cross the outer plasma membrane of the cell. They are easy to synthesize by chemical reactions and are cheaper than biologics (Buvailo, 2018). They are mostly taken orally by the patients and are designed to be metabolized from an inactive prodrug to an active compound. The small-molecule drugs are developed to follow Lipinski’s rule of five to be made bioavailable to the patient and be cleared from the body after its action. The Lipinski’s rule of fiveADME governs that small-molecule drug has properties to be adsorbed (A) by the human body, be easily distributed (D) inside the human body, metabolized (M) to an active drug, and then later excreted (E) out form the system (Lipinski, 2004). Most of the therapeutic drugs (B90%) generated by pharma industries are still small molecules and cannot wholly be replaced by biologics in future (Buvailo, 2018; Cohen, 2015). Aspirin (chemically acetylsalicylic acid) is the oldest and the most popular example of small-molecule drugs commonly used for pain, fever, and inflammation (Cohen, 2015). In some cancer treatment, small molecular inhibitors specifically targeting to rapidly growing cancerous cells are considered as better options than traditional chemotherapy and radiotherapy given to the patient which can kill both normal and tumor cells of the body

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FIGURE 6.2 Different biological modalities from small molecules to microbiome-based biologics. Source: Redrawn and adapted from Revers, L., & Furczon, E. (2010). An introduction to biologics and biosimilars. Part I: Biologics: What are they and where do they come from? Canadian Pharmacists Journal/Revue des Pharmaciens du Canada, 143(3), 134
139. https://doi.org/10.3821/1913-701x-143.3.134 (Revers & Furczon, 2010).

leading to complications (Lavanya, Mohamed Adil, Ahmed, Rishi, & Jamal, 2014). Most of the small-molecule inhibitors approved by the FDA for cancer treatment that target tyrosine kinase cell surface receptors or intracellular serine/threonine kinases involved in the cellular signaling PI3K/Akt/mTORC1 signaling pathways (Lavanya et al., 2014). They are also designed to inhibit the interaction of apoptotic proteins, epigenetic regulators like bromodomains, and BCL family proteins to deregulate the cancer cell growth (Arkin, Tang, & Wells, 2014). There have been growing trend to design small molecules to target different RNA folds and secondary structures formed by them helping in diseases like Huntington’s, spinal muscular atrophy (SMA), and invasion by viral pathogen like HIV (Connelly, Moon, & Schneekloth, 2016; Di Giorgio & Duca, 2019). However, small molecules can bind to off-molecular targets leading to more side effects and toxicity than biologics. Some of them have a shorter half-life span in the body and need to be taken more regularly, unlike biologics, with longer life spans (Lavanya et al., 2014). Some chemically designed small molecules are not robust in their functional activity inside the human body. They are difficult to be made flexible and respond to the feedback pathways and systems operating inside our body (Gurevich & Gurevich, 2014). Currently, pharma industry and many biotech companies are using artificial intelligence (AI) based machine-learning technology and high throughput screening assays to identify a hit in small-molecule drug discovery programs. They virtually screen and explore through various databases of chemical compounds like GDB-17, ZINC, REAL, and

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FDB-17, containing 166 billion, 750 million, 650 million, and 10 million molecules, respectively. Small molecules are being developed by the companies to target not only proteins but also different folds and structures made by oligonucleotides like DNA/RNA.

6.3.2 Nucleic acid therapeutics A biological cell stores and retrieves its genetic information with the help of DNA and RNA cellular components. As per the essential central dogma process occurring inside every living cell, DNA is first transcribed into RNA by the cellular process of transcription, and RNA transcripts are then used to make polypeptide proteins by the process of translation. Nucleic acid therapeutics utilizes oligonucleotides made of DNA/RNA containing the complementary sequence to the intracellular target region of interest to which it anneals and regulates gene expression by halting the downstream cellular translation process. In the eukaryotic system the precursor messengers RNA (pre-mRNA) transcripts formed after transcription undergoes certain posttranscriptional modifications like splicing and capping of its 30 -terminus to form mature mRNA. The mature mRNA encodes for the polypeptide protein and interacts with ribosomes and other translational assembly components to form proteins. There are several noncoding RNAs (ncRNAs) produced inside the cell by transcription that do not undergo translation process and regulate the protein production translation process by annealing to the mature mRNA having its complementary antisense sequence. In antisense oligonucleotide (ASO) therapy, DNA/RNA oligonucleotides containing the complementary sequence of the targeted gene are delivered to the cell to turn off the gene expression or terminate the translation of the mRNA produced from the gene. ASOs are single-stranded chemically modified DNA/RNA molecules of 13
30 nucleotides in length (DeWeerdt, 2019). The first 21 mer ASO approved by FDA was Fomivirsen (Vitravene), chemically modified by phosphorothioate linkages, in 1998 used for the treatment of cytomegalovirus (CMV) retinitis in immunocompromised patients. It is an antiviral drug that binds to the mRNA produced by CMV gene and block its translation, abolishing the proliferation of CMV (Yu, Jian, Yu, & Tu, 2019). Another popular example of FDA-approved ASO in 2017 is Spinraza (nusinersen) used for the treatment of SMA. SMA is a genetic disorder where a mutated survival motor neuron gene (SMN2) encodes a truncated, unstable, dysfunctional SMN protein. Nusinersen, an 18 mer ASO, targets the precursor mRNA molecule transcribed from the mutated SMN2 gene to ensure that it undergoes proper splicing and a particular exon-7 is included to produce the functional SMN protein (Bajan & Hutvagner, 2020). Many other ASOs have been approved by FDA or are in clinical trials helping in treating a variety of diseases like Huntington’s disease, amyotrophic lateral sclerosis, acute nonarteritic anterior ischemic optic neuropathy, and Duchenne muscular dystrophy (Bajan & Hutvagner, 2020; Gopinath, 2019). Another bio-therapeutic example involving the use of double-stranded RNA oligonucleotides is RNA interference (RNAi) therapy. RNAi therapy harnesses the cellular RNAi pathway of gene silencing or downregulates its expression. Andrew Fire and Craig C. Mello received the 2006 Nobel Prize in Physiology or Medicine for discovering the RNAi process occurring in the nematode worm Caenorhabditis elegans. Small interfering RNAs (siRNAs) and microRNAs (miRNAs) are the two types of ncRNA involved in the RNAi pathway. miRNAs derived from RNA transcript folds onto itself to form a short hairpin that is then processed to form

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single-stranded functional miRNA. Single-stranded functional miRNA then interacts with RNAinduced silencing complex (RISC) to target the mRNA to be silenced. siRNA is similar to miRNA but longer (21
23 nucleotide) and originates from an exogenous double-stranded RNA (DeWeerdt, 2019; Gopinath, 2019). siRNA also undergoes processing and later complex with RISC to bind with the targeted mRNA and repress its translation. siRNA being longer in length and having a more complementary sequence binds more specifically to the target mRNA than miRNAs (Gopinath, 2019). Thus siRNA is a better bio-therapeutic tool than miRNAs. A breakthrough event in the field of RNAi therapy is getting an FDA approval of the first RNAi-based siRNA drug, Onpattro (patisiran) (Alnylam Pharmaceuticals) in 2018 for the clinical use of treating hereditary transthyretin-mediated amyloidosis (hATTR) in the EU and US market (Weng, Xiao, Zhang, Liang, & Huang, 2019). hATTR amyloidosis is a genetic caused by mutations in the transthyretin (TTR) gene encoding for misfolded protein that aggregates to form amyloid fibrils. Onpattro siRNA binds to the mutated mRNA transcript of the mutated TTR gene and prevents its translation to the misfolded deleterious protein (Bajan & Hutvagner, 2020). Many biotech and pharma companies are developing RNAibased drugs and biologics to find treatment for other diseases and are currently in preclinical or phase-I clinical trial studies. With the advent of another breakthrough discovery of CRISPR-Cas9 gene-editing technology using guide RNAs molecules, RNA therapeutics has a bright future with broader applications in the field of gene therapy. The limitations of nucleic acid therapeutics are the inefficient delivery of the large RNA molecules into the intracellular target mRNA and degraded by the ribonucleases present inside the cell. The foreign RNA molecules, when injected into the human body, induce an immunogenic response and could lead to uncontrolled autoimmune complications (Bajan & Hutvagner, 2020; Gopinath, 2019). The advancements made in the field of RNA therapeutics to develop next-generation RNA biologics with the increased stability is by chemically modifying the oligonucleotide, conjugating with N-acetylgalactosamine (GalNAc) group or delivering inside the cell by coating them in lipid nanoparticles (Kaczmarek, Kowalski, & Anderson, 2017).

6.3.3 Therapeutic proteins Proteins are the cellular building blocks. They are present either in primary or secondary form as fragments of peptides in α helices/β sheets/flexible loops or folded into globular tertiary/quaternary structures. Therapeutic protein biological modality could encompass the primary/secondary therapeutic peptides, or tertiary structure proteins or in complex with other macromolecules like RNA or proteins present in a quaternary structure form. The most prevalent examples of therapeutic proteins at each structural level are recombinant insulin at the primary/secondary level. Antibodies, enzymes, antibody conjugated to small-molecule drugs or peptides, and proteins fused to antibodies are some of the examples at a tertiary and a quaternary level. We will learn about antibodies biologic in detail in Section 6.3.4. In the following subsections, therapeutic peptides and enzymes have been discussed. 6.3.3.1 Therapeutic peptides Peptides are a polymer of amino acids linked to each other by a peptidic bond. They are placed in between small molecules chemical compounds and large protein biologics in terms of

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molecular size to be served as a bio-therapeutic modality as shown in Fig. 6.2. They offer advantages of low toxicity and more specificity as compared to small-molecule drugs and are still smaller than protein biologics to be efficiently delivered to the intracellular molecular targets (Fig. 6.2). However, peptides have very low oral bioavailability as they can be cleaved into shorter fragments when acted upon by various proteases and peptidases present in the human gut and gastrointestinal tract. Next-generation stapled therapeutic peptides are being developed by bridging different monomers using chemical linkage like disulfide bridge, lactam bridge, and ring-closing olefins to make it resistant from peptidase action thereby improving its bioavailability and other pharmacodynamics/pharmacokinetic properties (Ali, Atmaj, Van Oosterwijk, Groves, & Do¨mling, 2019; Atangcho, Navaratna, & Thurber, 2019). Certain cellpenetrating peptide fragments are conjugated with small-molecule drugs/nanoparticle to be used as a carrier for drug delivery purposes (Morales-Cruz et al., 2019). In oncology, they are used to target the tumor-infiltrating immunosuppressive cells (Fisher, Pavlenko, Vlasov, & Ramenskaya, 2019). Around 68 therapeutic peptides have been approved by the FDA from the 1950s to 2017 (Lau & Dunn, 2018). First one was corticotropin in 1952, a hormone produced by the pituitary gland also called adrenocorticotropic hormone used in multiple conditions like multiple sclerosis, rheumatoid arthritis, lupus, or allergic reactions (Lau & Dunn, 2018). In cancer therapeutics, many synthetic peptides like degarelix, histrelin, buserelin, leuprorelin, triptorelin, and goserelin are used for prostate cancer and breast cancer treatment targeting the gonadotropin-releasing hormone receptor (Lau & Dunn, 2018). Carfilzomib, bortezomib, and ixazomib peptides are used in multiple myeloma targeting the proteasome assembly of cancer cells (Sherman & Li, 2020). Epigenetically modulating therapeutic peptides like romidepsin, a bicyclic depsipeptide, is used in cutaneous T-cell and other peripheral T-cell lymphomas (Albericio & Kruger, 2012; Janssens, Wynendaele, Vanden Berghe, & De Spiegeleer, 2019). Antimicrobial peptides (AMPs) are another class of biologic compounds produced by the innate immune response to kill microbes. There are seven FDA-approved AMPs to date—vancomycin, gramicidin, daptomycin, telavancin, colistin, oritavancin, and dalbavancin—targeting against Gram-positive or negative bacterial infections (Lei et al., 2019). All exhibit bactericidal activity either by lysing the cell membrane or making pores or inhibiting the cell wall synthesis (Lei et al., 2019). 6.3.3.2 Therapeutic enzymes Enzymes are biocatalysts that accelerate the rate of different cellular processes occurring in vivo. There a multitude of diverse functions carried out by enzymes. Enzymes interact with a specific type of reactants or substrates to form a complex. Enzyme-substrate complex undergoes chemical reactions in its active site to form a product which is then released away from the enzyme upon the completion of the chemical reaction. Enzymes, if functionally active, interact with another substrate to form products in another cycle of reaction. Enzyme replacement therapy (ERT) is supplementing the patient’s body with those functional enzymes that are either present in a very low amount or not at all produced by the system due to genetic abnormalities. Lysosomal storage diseases, a heterogeneous group of disorders resulting from the deficiency of enzymes present in the lysosome cellular component that help to break-down and catalyze different cellular substrates, are treated by ERT. They are genetically inherited rare disorders like Gaucher disease, Fabry disease, and Pompe disease (Safary, Akbarzadeh Khiavi, Mousavi, Barar, & Rafi, 2018). Recombinant

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enzymes are administered to the patients intravenously in this therapy. Another strategy employed for treating such disorders is reducing the accumulation of the substrate to be identified by the enzymes that the patient is unable to synthesize called the substrate reduction therapy. Recently, FDA approved injections of Pegvaliase (Palynziq) novel enzyme substitution therapy for people suffering from the rare genetic disorder of phenylketonuria, which occurs due to the deficiency of an enzyme phenylalanine hydroxylase leading to accumulation of phenylalanine substrates in the blood (Hydery & Coppenrath, 2019). Besides rare genetic disease treatment, therapeutic enzymes are also used in digesting different food substrates and metabolites by lactose-intolerant patients or people allergic to gluten. They are used in lysing the blood clots by thrombolytic and anticoagulating enzymes to metabolize different coagulating proteins assembly. They can also be used as antimicrobial medicines and in cancer therapeutics directly or indirectly targeting the processes of tumor cell proliferation. The most popular example of therapeutic enzymes in cancer treatment is L-asparaginase, which is used to treat patients with blood cancer of ALL, acute myeloid leukemia, or non-Hodgkin’s lymphoma. L-Asparaginase enzyme converts L-asparagine amino acid substrate to aspartic acid and ammonia, depriving the tumor cells of the L-asparagine amino acid, which is required for their proliferation and growth (Broome, 1981; Fernandes, Silva Teixeira, Fernandes, Ramos, & Cerqueira, 2017). Therapeutic enzymes are being designed to conjugate with different nanocarriers and PEG (polyethylene glycol)-lipids for efficient drug delivery to the target (Dean, Turner, Medintz, & Walper, 2017). Advancements in protein engineering and machine-learning approaches have led the research of therapeutic enzymes to develop different nextgeneration products with improved pharmacokinetic and pharmacodynamics properties.

6.3.4 Antibodies Antibodies are the proteins secreted by the plasma B cells of the immune system in response to fight against the foreign invaders like viruses, microbial pathogens, or cancer cells present in our human body. The foreign substance that elicits the immune response of antibody production is called an antigen. Antibodies interact with the specific region of the antigen called the epitopes. The antigenic determinant-epitope is generally one to six monosaccharide, or five to eight amino acids present on the antigen surface to be identified by the antibodies. Structurally, antibodies are Y-shaped proteins containing four polypeptide chains, two identical heavy, and two identical light chains. Heavy and light chains interact with each other using disulfide bridges and noncovalent bonds (Fig. 6.3A). The region of antibody interacting with the epitope of an antigen is present in the N-terminal region of the heavy and light chain, also called the antigen-binding fragment (Fab) or paratope. This region is variable in sequence to interact with different types of antigens. The C terminus region of the heavy and light chain present in the tail of the antibody has a constant sequence and is called the Fc region (fragment region that is able to crystalize) (Fig. 6.3A). Fc region interacts with other immune cascade components and cell surface receptors. There are different Fc regions leading to five different isotypes of antibodies: IgG, IgM, IgE, IgA, and IgD. Each isotope is activated in different types of immune response. Antibodies are engineered to have Fab region from one organism like murine and Fc region from another human called chimeric antibodies. Chimeric antibodies are

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FIGURE 6.3 Schematic representation of the (A) antibody structure and (B) some different types of bi-specific antibody derivatives. Source: Redrawn and adapted from (A) Kindt, T. J., Goldsby, R. A., Osborne, B. A., & Kuby, J. (2007). Kuby immunology (6th ed.). W. H. Freeman (Kindt, Goldsby, Osborne, & Kuby, 2007) and (B) Labrijn, A. F., Janmaat, M. L., Reichert, J. M., & Parren, P. W. H. I. (2019). Bispecific antibodies: A mechanistic review of the pipeline. Nature Reviews Drug Discovery, 18(8), 585
608. https://doi.org/10.1038/s41573-019-0028-1.

designed in such a way so that they are accepted by the human body and not considered as foreign to induce any nonspecific immunogenic response. Simultaneously, the antigenic determinant Fab region being produced inside the murine organism is able to identify and interact with the antigen with higher affinity. Earlier, polyclonal antibodies are made by introducing the antigen to the rabbit/mouse organism to activate their immune system, which leads to the production of different cocktail mixtures of antibodies in their blood serum. These heterogeneous mixtures of polyclonal antibodies can recognize different epitopes of an antigen and, thus, have very high affinity. They are quick, inexpensive, and easy to produce. However, they are highly variable when extracted from one batch of animals to the other and possess high chances of cross-reactivity due to the recognition of multiple epitopes. Polyclonal antibodies are produced for diagnostic purposes, to detect the presence of an unknown antigen analytically and in immunoassays like ELISA to make secondary antibodies. Polyclonal antibodies are not used for therapeutic purposes due to a lack of specificity and a high degree of crossreactivity. It will be difficult to produce them on a commercial scale is highly variable. 6.3.4.1 Monoclonal antibodies In contrast to the polyclonal antibodies, monoclonal antibodies specifically target one epitope of an antigen. They are produced from the identical clones of parent B cell and thus are not variable as polyclonal antibodies. Ce´sar Milstein and Georges J. F. Ko¨hler, in 1975, developed hybridoma technique to produce monoclonal antibodies for which they got the Nobel Prize for Medicine and

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Physiology in 1984. In hybridoma technology, mouse is challenged with the antigen, and immune response is induced, leading to the production of B cells generating antibodies against the antigen. The B cells producing antibodies are extracted from the mouse and fused with cancerous myeloma cells ex vivo to make a hybrid cell line called hybridoma. Hybridomas are then cultured to grow genetically identical clones of cells producing one type of monoclonal antibodies. Hybridoma technology helped in the development of murine monoclonal antibodies and later with genetic engineering advancements in the development of chimeric murine-human antibodies. However, the technique has been replaced with an antibody phage display library preparation methods to generate completely humanized antibodies. In this technology, bacteriophage M13’s plasmid called phagemids is genetically engineered to contain a repertoire of different libraries of Fab antigenic determinant region of antibodies extracted from B cells. M13 phages express the Fab region and display or present it on their phage coats surface fused with the phage surface protein. The phage display antibody library is then enriched using a selection method with the ones that can specifically bind to the antigen in vitro with higher affinity (Frenzel et al., 2017). The antigens are immobilized to the surface via direct coating or biotinstreptavidin linkage (Ledsgaard, Kilstrup, Karatt-Vellatt, McCafferty, & Laustsen, 2018). Monoclonal antibodies have been one of the most successful and widely used biotherapeutic modalities. The global monoclonal antibody market is expected to generate revenue of 300 billion USD by the end of 2025 (Lu et al., 2020). Up until December 2019, 79 therapeutic monoclonal antibodies have been approved by FDA and commercialized in the market (Lu et al., 2020). The highest revenue-generating mAb is adalimumab (Humira), the first completely humanized monoclonal antibody generated by the antibody phage display method. Other popular ones used for different types of cancer treatment are nivolumab (Opdivo), ipilimumab (Yervoy), and pembrolizumab (Keytruda) (Lu et al., 2020). Pembrolizumab and ipilimumab drugs are developed from the advancements in cancer immunotherapy and had a profound impact on antibody-based cancer treatments (Clift, 2019). Pembrolizumab or nivolumab is an anti-PD-1, and ipilimumab is an anti-CTALA4 immune checkpoint inhibitor. The immune response of T cells is downregulated allowing cancer cells to proliferate when its cell surface receptors PD-1 or CTLA-4 bind to PD-L1 receptors present in cancer cells or CD80/CD86 receptors present in the antigen presenting cells like dendricitic cells and macrophages respectively. The drugs pemrolizumab and ipilimumab block the interaction of PD-1 and CTLA-4, respectively, and activate the immune system of T cells to identify and attack cancer cells. James P. Allison, PhD, and Tasuku Honjo, MD, PhD, were awarded the Nobel Prize in Physiology and Medicine in 2018 for the discovery of checkpoint inhibitors CTLA-4 and interaction of PD-1/PD-L1, respectively. MAbs targeting different viruses like Ebola virus, HIV, and SARS-Cov-2 coronavirus are currently in clinical trials. Currently, all major pharma companies are in the race to generate a human monoclonal antibody to fight against the coronavirus global pandemic of 2020. The spike glycoproteins present on the surface of SARS and MERS-Corona virus (SARS-CoV and MERS-CoV) interact with the angiotensin-converting enzyme-2 (ACE-2) cell and dipeptidyl peptidase 4 (DPP4) cell surface receptors to enter into the host cell upon viral infection. Human monoclonal antibodies are being designed to either interact with the spike protein epitope or to the cell surface receptors to block their interactions (Shanmugaraj, Siriwattananon, Wangkanont, & Phoolcharoen, 2020).

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6.3.4.2 Engineered multispecific antibodies Most of the earlier antibodies were designed to bind to a single antigen; the next-generation of antibodies being developed are bi-specific and tri-specific that can bind to more than one antigens at a time, 2 for bi-specific and 3 for tri-specific. Bi-specific antibodies can come in various possible shapes and formats either having a combination of fragments of different variable regions of heavy and light chains without Fc region like F(ab)2 is the fusion of Fab region of two different mAbs (Fig. 6.3B) and in scFv variable region of one light chain is combined with other heavy chains of a different mAb. Other combinatorial possibilities are fusing Fab region with Fc region in different symmetries like DVD-IgG where variable domains are fused into Fc region of another antibody-forming trifunctional or tri-specific antibodies, knob in a hole where one heavy chain has a knob to fit the other heavy chain with a hole or quadroma which is a combination of two heavy and light chains of two different mAbs, etc. (Labrijn, Janmaat, Reichert, & Parren, 2019; Runcie, Budman, John, & Seetharamu, 2018). Multispecific antibodies are produced by different methods like genetic engineering to express different antibody fragments linked with each other, or each component is expressed and purified separately in bacteria and then chemically linked or conjugated in vitro. Quadromas are produced by the somatic fusion of two hybridomas producing different mAbs. There are 57 bi-specific antibodies currently in clinical trials, 2 of them have been approved by FDA—blinatumomab (Blincyto) used for relapsed or refractory ALL and emicizumab (Hemlibra) for the treatment of hemophilia A (Labrijn et al., 2019; Runcie et al., 2018; Suurs, Lub-de Hooge, de Vries, & de Groot, 2019). Blinatumomab recruits immune T cells to malignant B cells by interacting with CD3 T-cell receptors (TCRs) with one region and the other with CD19 of B cells. Most of the other bi-specific antibodies in cancer treatment are designed to bring immune cells close to the tumor cells to elicit the immune response. Emicizumab antibody binds to coagulation factors IX and X and activates factor X. In cancer treatment, tri-specific antibodies are generated to recruit immune cells with cancerous cells along with other accessory cells like NK cells or antigen-presenting cells to elicit a targeted immune response. Catumaxomab trifunctional antibody was designed to bind to the CD3 TCR, tumor cells expressing epithelial cell adhesion molecule on their surface, and the Fc region was attached to the NK cells or antigen-presenting dendritic cells. The drug was approved by the EU in 2009 but was withdrawn from the market due to commercial reasons (Labrijn et al., 2019). Recently, tri-specific antibody has been designed against CD3, CD28, and CD38 cell receptors to target T cells to myeloma cells and efficiently stimulate them against cancer (Wu et al., 2020). The design is inspired by multispecific recombinant antibodies being generated to target different epitopes of HIV pathogen when it enters the host (Padte, Yu, Huang, & Ho, 2018). Bi-specific antibodies like blinatumomab have certain disadvantages of shorter life span and low bioavailability (Azvolinsky, 2019). Multispecific antibodies with Fc region can induce higher immunogenic cytokine release and complement cascade response (Fan, Wang, Hao, & Li, 2015).

6.3.5 Cell-based immunotherapies In earlier bio-therapeutic modalities, different cellular components like small molecules, peptides, proteins, enzymes, and antibodies were used to form medicines. In Section 6.3.5 on

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cell-based immunotherapies, Section 6.3.6 on stem cells, Section 6.3.7 on phages, and Section 6.3.8 on microbiome-based therapies, we will discuss how a whole biological cell or their collection in a tissue/microbiome organization could serve as biological drugs (Fig. 6.2). Cell-based immunotherapy harnesses the potential of the immune system in our human body to fight against foreign invaders like viruses, microbial pathogens, and cancerous cells. Our immune system can distinguish between normal self-cells and tumors or foreign nonself-cells based on the receptors presented by them on their cell surface. In the innate immune response, immune cells like dendritic cells, natural killer (NK) cells, and macrophages recognize the tumor or foreign microbial cells through certain receptors presented on the surface. Immune cells then act on them and kill them by phagocytosis method and present their small phagocytosed cellular debris like small peptides on their cell surface receptor called major histocompatbility complex (MHC) class-I receptors to naı¨ve T cells. Naı¨ve T cells upon identifying the MHC class-I presented components are activated to form clones of cytotoxic T-cell lymphocytes (CTLs), which produce T cell receptors (TCRs) on their surface specific to the MHC class-I presented antigen. These CTLs then act later when a human body is exposed to the foreign antigen, helping the body to adapt to fight against such infection by the adaptive immune response (Fischbach, Bluestone, & Lim, 2013; The Blue Matter Team, 2019). However, tumor cells often are able to bypass the adaptive immune response by suppressing the T-cell activation response by binding to immune checkpoint inhibitors like PD-1 and CTLA-4, as discussed earlier, or not presenting the antigen on their cell surface receptors to be identified by other immune cells. Tumor cells take advantage of the immunosuppressive environment to grow and proliferate inside the body. In cell-based immunotherapy the adoptive cell transfer method is employed in which the immune cells, majorly T cells, are taken from the patients and cultured or grown in larger amounts ex vivo and then later administered back to the body to induce a stronger immune response against the tumor. It is basically upgrading the immune system of the body with more advanced armaments or weapons to fight against the tumor. In the T-cell culture process, either certain endogenous T cells that are able to infiltrate the solid tumor called tumor-infiltrating lymphocytes (TILs) are grown in larger amounts, or they are genetically engineered to produce more endogenous TCRs or engineered TCR like chimeric antigen receptor-T cell (CAR-T) (Fig. 6.4). They are genetically engineered either by transduction using lentivirus, gammavirus, or adeno-associated virus or nonviral transfection methods like CRISPR-Cas9 (Clustered regularly interspaced short palindromic repeats) or transposon/transposase system. Nonviral means is better than viral methods as it gives lower immunogenic response and poses lower chances of insertional mutagenesis. New strategies are being discussed to supplement T-cell culture with costimulatory signals like cytokines to develop improved adoptive T-cell therapy (Greenberg; Redeker & Arens, 2016; The Blue Matter Team, 2019). Currently, there are two FDA-approved CAR-T-cell therapies for the treatment of leukemia and lymphoma, Axicabtagene ciloleucel (Yescarta), and Tisagenlecleucel (Kyrmriah) produced by Gilead Sciences and Novartis. They are CAR-T cells targeting CD19 receptors produced by cancer cells. Pharma companies and researchers are now trying to use CAR-T-cell therapy to find treatment for other cancers like multiple myeloma, prostate cancer, and breast cancer on observing the success with this technology in clinical trials with patients. Research is also being carried out to develop other immune cells
based therapy like NK cells and dendritic cells vaccines (Fan et al., 2018; Hu, Tian, & Zhang, 2019; Huber, Dammeijer, Aerts, & Vroman, 2018; Karmakar, 2014; Kriegsmann et al., 2019).

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FIGURE 6.4 Schematic representation of adoptive cell transfer CAR-T-cell therapy. Source: Redrawn and adapted from Designua. (2020). CAR T-cell therapy. Artificial leukocyte receptors are proteins that have been engineered for cancer immunotherapy (killing of tumor cells). Genetically engineered. Vector diagram. Retrieved from https://www.shutterstock. com/image-vector/car-tcell-therapy-artificial-leukocyte-receptors-1344538721 (Designua, 2020).

However, immunotherapy-based cell biologics are currently very expensive to be used by the patients. As the therapy is improving the immune system of the body, it can pose the risk of heightened autoimmune response by cytokines activation to the patients, which can lead to serious complications of the treatment being toxic and generating many side effects (Greenberg). Patients undergoing this therapy need to be kept in-constant medical supervision to monitor any inflammation development or decreased blood level count or any organ failure like heart and kidneys.

6.3.6 Stem cells Stem cells can differentiate and be directed in vitro to grow to form different cell types that make up the different body parts like heart, kidney, liver, lungs, spinal cord, and stomach. Stem cell therapy is like a regenerative medicine using stem cells to replace or recreate human tissues or organs to establish normal functions. Stem cells are pluripotent, meaning they have the potential to form the three germ layers of embryos, ectoderm, mesoderm, and endoderm that can later form different body parts. Ectoderm cells later form epidermal tissues and nervous system, mesoderm differentiates to muscle, bone, blood, and urogenital tissues and organs,

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endoderm proliferates to form the inner lining of stomach, gastrointestinal tract, and lungs. Embryonic stem cells (ESCs) cannot form extra-embryonic tissue called the placenta. Adult somatic cells that are nonpluripotent can be genetically reprogrammed to form induced pluripotent stem cells (iPSCs). Shinya Yamanaka and John Gurdon got the Nobel Prize in Physiology or Medicine 2012 for developing this genetic reprogramming technique to form iPSCs. The genetic reprogramming technique involved introducing a set of genes—Myc, Oct3/4, Sox2, and Klf4 in the somatic cells of mouse fibroblasts that encodes for different transcription factors making them pluripotent stem cells like ESCs. Pluripotent ESCs are derived from the mesoderm differentiate into multipotent hematopoietic stem cells (HSCs) and mesenchymal stem cells (MSCs) that have taken up the path to forming different blood cellular components and connective tissues like bones, cartilage, muscles, and adipose tissues, respectively. MSCs are distinct from HSCs and cannot be differentiated to form blood cells. MSCs and HSCs have extensive biomedical and clinical applications in stem cell regenerative therapy (Rizvanov, Persson, S¸ ahin, Bellusci, & Oliveira, 2016). However, stem cell therapy has raised many ethical and safety issues and concerns by scientists and researchers to be used on the patients (Volarevic et al., 2018). Thus, so far, there has been only one FDAapproved stem cell therapy
based treatment for blood cancer like multiple myeloma and leukemia, that is, HSC transplant. In this procedure, HSCs are taken from bone marrow, umbilical cord, or peripheral blood and intravenously administered to patients with the damaged or defective immune system. The donor of HSCs can either be from the same patient receiving the transplant called autologous transplant, or from another person called an allogeneic donor. Allogenic transplants increase the risk of graft versus host disease, that is, HSCs from the donor can be considered as foreign to induce an autoimmune response by the host. Stem cell therapy raises safety concerns of the stem cells being turned into cancerous cell lines as they have the potency to multiply and grow. When used for regenerative therapeutic purposes, it can be considered as foreign by the body to elicit autoimmune response leading to serious complications. One of the moral
ethical issues on dealing with human ESCs is destroying the human embryo limiting the research to clinical translation (Volarevic et al., 2018).

6.3.7 Phage therapies Infections by antibiotic-resistant microbial pathogens like methicillin-resistant Staphylococcus aureus, vancomycin-resistant Enterococcus, and multidrug-resistant (MDR) Mycobacterium tuberculosis have been one of the serious concerns faced by medical doctors, nurses, and hospitals. Scientists and researchers have shifted the focus from using antibiotics to bacteriophages-viruses that infect and kill bacteria as a therapeutic tool for the treatment of antibacterial infections (Koulenti et al., 2020; Lin, Koskella, & Lin, 2017). Bacteria colonize to forms biofilms to not allow antibiotic drugs to reach into the cell targets or have membrane-bound efflux pumps to actively transport out small-molecule drugs from the cell, gaining resistance to different antibiotics. Bacteriophage virus infects a specific bacterium, disrupts its biofilm formation, and reduces its growth, making it sensitive to antibiotics. In phage therapy, engineered phages along with the antibiotics are recommended to use for improved action (Abedon, 2019; Gordillo Altamirano & Barr, 2019; Tagliaferri, Jansen, & Horz, 2019).

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Phages structurally have a viral coat protein head that stores the genetic information of the virus in the form of DNA/RNA. The head is attached to a long cylindrical tail with tentacles at the end, which helps them to attach to the bacterial cell membrane (Fig. 6.5). Once attached to the bacterial cell surface, bacteriophages inject their genetic material into the cell. Bacteriophage DNA once inside the cell can be present as a separate plasmid DNA that encodes for making other phages, which lead to lytic infection, or they are integrated into the genome as a prophage and inherited along with other dividing bacteria (Fig. 6.5). The prophage DNA is activated in response to external stimuli to start making more phages undergoing lysogenic infection. The bacterial cell is ruptured and lysed as phages are made inside the cell (Fig. 6.5). In phage therapy, using lysogenic phages for clinical purposes can increase the risk of bacteria to acquire immunity or resistance against them due to the horizontal gene transfer. Thus majorly lytic phages are used in the therapy that can quickly infect and kill the bacterial host cell (Gordillo Altamirano & Barr, 2019). However, as in phage therapy, phage viruses are administered to the human body, many safety measures and regulatory concerns need to be considered. These phages are genetically engineered to be not toxic, less immunogenic, and be not virulent to the body. Phage therapy was first put in clinical trials in the 1920s where it was met with shortlived enthusiasm as antibiotics were introduced around this time, which took much of the fame (Abedon, 2019). Recently, FDA gave the green light to a clinical-stage biotechnology company Adaptive Phage Therapeutics to set up clinical trials of phage therapy treatment on humans to fight against infection by MDR bacterial pathogen (Taylor, 2020). AB-SA01 is another formulation of phage cocktail of three highly characterized bioengineered phages used against MRSA infection designed by Amrata Pharmaceuticals, which is currently in phase-I/II clinical trials (Koulenti et al., 2020). The therapy is still in its nascent form and has many regulatory hurdles and challenges with policy for its clinical use and implementation in the market (Furfaro, Payne, & Chang, 2018). It has been seen as a promising future modality for wider applications like wound healing (Pinto, Cerqueira, Ban˜obre-Lo´pes, Pastrana, & Sillankorva, 2020).

FIGURE 6.5 Lytic and lysogenic cycle of bacteriophage infection. Source: Redrawn and adapted from Feenex. (2010). Viruses used for good: Gene therapy. Scientific Scribbles, the University of Melbourne, Blog. Retrieved from https:// blogs.unimelb.edu.au/sciencecommunication/2010/11/07/viruses-used-for-good-gene-therapy/. https://blogs.unimelb.edu.au/ sciencecommunication/2010/11/07/viruses-used-for-good-gene-therapy/ (Feenex, 2010).

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In previously discussed, cell-based biologics like cell-based immunotherapy and phage therapy, one to three cell types were used to formulate a drug. In microbiome-based therapeutics the whole microbial communities comprising several heterogeneous types of microbes are used to design a biologic medicine. Stem cells and microbiome-based biotherapeutics can be placed at a level above cell-based immunotherapy and phage therapy biologics based on the cellular organizations (Fig. 6.2). The human body is teeming with different microbes since birth, estimated to have microbial cells at almost 1:1 ratio of trillions of human cells, and the ratio varies with each individual (Sharma, Das, Buschmann, & Gilbert, 2019). The microbial ecosystem is found in different human body parts like skin, gastrointestinal tract, oral cavities, eyes, lungs, urinary tract, placenta, and vagina. The microbes are present in either commensal or mutualistic symbiotic relationship with the humans. There has been extensive direct and indirect correlation seen between human health and the microbiome (Bamforth, 2019; Wong & Levy, 2019). Advancements in metagenomics, shotgun sequencing, metabolomics, proteomics, and transcriptomics have made it possible to characterize the communication between the microbial communities and the host (Zhang, Li, Butcher, Stintzi, & Figeys, 2019). The study of microbiome helps us to understand how individuals responded to different drugs and led to the development of pharmaco-microbiomics field (Sharma et al., 2019). It brings us a step closer to the future of the development of personalized medicines. Each individual, having a different set of gut microbiota, responds differently to the drug, exhibiting different pharmacokinetics and pharmacodynamics properties. Microbial communities have a direct impact on the drug response based on their capabilities to metabolize the drug (Sharma et al., 2019). The microbiome is a great therapeutic tool to be harnessed for development of next generation of biologics. Several pharma companies and venture capital investors are realizing this potential which have led to the development of around 200 public and private biotech startup companies working on microbiome-based therapeutic research in 2019 (Bamforth, 2019). The microbiome market is estimated to be valued at 1.7 billion USD in 2027, growing at a compound annual growth rate of 20%
40% (Bamforth, 2019). Current microbiome-based therapeutics is using different types of diet interventions like prebiotic fibers and probiotics. Prebiotic fibers are food materials that cannot be digested by the body and are metabolized by the healthy microbial communities present in the gut favoring their growth. Probiotics are a collection of beneficial, healthy microbes supplemented to the stomach. Prebiotics fiber food material is used by probiotics and can be taken together. Another microbiome-based therapeutics used for the treatment of recurrent Clostridium difficile infections is fecal microbiota transplant (FMT). The gut microbiome, when infected with C. difficile bacterial pathogen, is impaired as it produces harmful chemical toxins killing other microbes deranging the balance of different microbial communities called dysbiosis. The bacterium is resistant to antibiotic treatment. Patients with such infection are transplanted with microbial communities of healthy donors through FMT therapy. The therapy has shown success in treating digestion-related diseases like irritable bowel syndrome, ulcerative colitis, and Crohn’s disease. Many biotech companies formulate a cocktail of different healthy microbes as biologic drugs that are in phase II/III clinical trials used for the disease treatment (Guthrie & Kelly, 2019).

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FMT therapy poses a risk of inducing autoimmune response as the host can consider the transplant as foreign and reject it leading to serious complications. The response is highly dependent upon the genetic compatibility of the donor and the host. Recently, the FDA issued a safety alert and warning against investigational FMT as an immunocompromised patient receiving FMT therapy in clinical trials died (Sharma et al., 2019). Research in microbiome-based therapeutics has opened up avenues and opportunities to find novel treatment for different diseases but also brought in some challenges that need to be addressed for efficient drug development. It is still very difficult to understand the system biology of the complex microbial interactions and population dynamics taking place in the ecosystem to design a robust synthetic microbiome. Adopting advanced high throughput computational technologies like AI and neural network-based machine learning on the microbiome ecosystem can help us develop novel microbiome-based biologics.

6.4 Future of biological therapeutics The future of biological-based therapeutics is bright. As per the current market research report, the global biologics market predicts to grow at a compound annual growth rate of 7.6% from 2019 valued at 255.2 billion USD to 2027 (Shah, 2020). Bio-therapeutics modalities have shown success in treating a wide variety of diseases and disorders. It is a breakthrough technology in the pharmaceutical field of medicine development and poses to be the future of drugs used for disease treatment. As compared to conventional drugs, it offers advantages of higher specificity, efficacy, and lower toxicity. However, it brings certain disadvantages. It is expensive to produce and biomanufacture on a commercial scale. Different bio-therapeutic modalities with increasing molecular weight are more complex to handle (Fig. 6.2). Some of the biological modalities being bigger than small molecules are unable to penetrate through the cell membrane and target intracellular components. Currently, the focus of research and development is to work over the disadvantages and improve the characteristics of biologics so that it can be used as a future tool for human healthcare and disease treatment. Different biotech companies have come up with technologies like designer cell line development and micro-bioreactors, to customize the development of different cell lines that effectively produce biologics with higher yield and are easier to purify, thereby reducing the cost of manufacturing and bioprocessing (DePalma, 2017; Tanabe, 2019). As an example, a biotech company Lonza developed a proprietary glutamine synthetase gene expression system, GS Gene Expression System, used in bioprocessing and manufacturing of biologics in mammalian cell culture, CHO cell lines at a commercial scale (Fan, Frye, & Racher, 2013). Another biotech startup, ATUM, utilizes their Leap-In Transposase expression technology to effectively integrate the gene of interest in the transcriptionally active area of the chromatin and develop cost-effective cell lines to express biologics in a larger scale (Balasubramanian et al., 2019). Derivatives of biologics like biosimilars are developed for the medicines whose patents have expired and are expensive to be used for human patients. Nonpermeable biological modalities are conjugated with small-molecule drugs to effectively transport them to the intracellular target like antibody
drug conjugates, serving as carriers for drugs. Innovative

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approaches are taken to design and engineer different biological modalities, enhancing its capabilities to be used as a future drug. Different biotech startup companies are mushrooming working on the research and development of different bio-therapeutic modalities. It has become an attractive target for different investors and venture capitalists to invest in the moonshot long-term projects of making next-generation and personalized bio-therapeutic drugs.

6.5 Case study—bio-therapeutic modalities in COVID-19 treatment Certain bio-therapeutic modalities are used to find the treatment for Coronavirus global pandemic 2020 infection. Coronavirus delivers its genetic information present in the form of RNA into the human host through the interaction between their cell surface spiked S protein with ACE-2, aminopeptidase N, and other DPP4 receptors of human cells (Fehr & Perlman, 2015). SARS-Cov-2 virus, the causative agent for the 2020 global pandemic, interacts with the human ACE-2 receptor (Lan et al., 2020; Yan et al., 2020). Upon entry into the human host cell, the virus uses the host translational machinery and cellular mechanisms to synthesize nonstructural proteins (NSPs) that assemble into RNA-dependent RNA polymerase (RdRp) and other accessory proteins aiding in their replication and proliferation (Fehr & Perlman, 2015). The first antiviral drug developed to reach phase III clinical trial was Remdesivir by Gilead Sciences, a modified nucleotide analog when taken by the coronavirus RNA template dependent RNA polymerase (RdRp) inhibits RNA polymerase to elongate and thereby halt virus replication mechanism to grow inside the host (Gordon et al., 2020). Another treatment strategy employed is to inhibit the initial virus S protein interaction with the host cell surface receptor and thereby abolish its entry into the cell. Neutralizing antibodies are developed that competitively interact with a higher affinity to the viral receptor-binding domain of the S protein, blocking the interaction between the virus and the host cell surface receptors (Ju et al., 2020; Wu et al., 2020). Therapeutic peptide inhibitors are developed by in silico modeling and structural studies to target the viral protease 3CLpro protein which helps in assembling the viral NSPs to form RdRp machinery (Chen, Yiu, & Wong, 2020; Jin et al., 2020; Zhang et al., 2020). Different types of mRNA- and DNA-based vaccines are developed that encode to express the viral cell surface proteins which when recognized by the human immune system elicits immunogenic response and acquire memory-based adaptive immunity to fight against the infection (Mukherjee, 2020; Wang, Kream, & Stefano, 2020). The most popular ones are the mRNA-1273 vaccine developed by Moderna Therapeutics that showed promising increased immunogenic response with an increase in dosage in the phase-I clinical trials and DNA-based recombinant adenovirus type-5 (Ad5) vaccine (Moderna Inc., 2020; Zhu et al., 2020). Other forms of vaccines, like proteinbased subunits and whole inactivated virus or viral vectors, are also currently in development by different pharma and biotech companies for the COVID-19 treatment (Callaway, 2020). In another indirect approach, antibodies produced in the blood plasma or serum from the immune system of the patients who had coronavirus infection are used as a biotherapeutic modality. However, transfusions of convalescent plasma or serum pose the risk of transmitting blood-borne pathogens, antibodies from the donor can damage pulmonary blood vessels or the host is not compatible with the added blood volume (Kupferschmidt, 2020). The risks associated with the treatment are assessed in the randomized clinical trial

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studies being conducted (Kupferschmidt, 2020). Different pharma and biotech companies are in the race to develop an effective treatment for COVID-19. They are using one or a combination of different bio-therapeutic modalities discussed earlier in the chapter.

6.6 Conclusion Bio-therapeutic modalities are the medicines developed by humans for various disease treatments comprising different biological elements. The biological elements of bio-therapeutics widely range from small-molecule chemical messengers like cytokines to oligonucleotides like DNA/RNA and therapeutic peptides to proteins in complex and later the whole cell or whole biome organization. This chapter attempted to discuss most of the biological modalities, and we observed that with increasing the size of biologics, there is an increase in the complexity and difficulty to handle, summarized in Table 6.1. However, bio-therapeutic modalities have proven to be effective drugs and TABLE 6.1 Tabulated summary of diffetent biological modalities. Biological modalities

Advantages

Limitations

Some prevalent examples

Future advancements

Small molecules

1. Smaller size and permeable to cell membrane 2. Easy to adsorb, distribute, and excrete 3. Easier to synthesize 4. Cheaper in price

1. Multiple off-target binding and less specificity 2. More toxicity 3. Shorter half-life span 4. Less robust and limited scope of flexibility in mode of action

Aspirin for pain and inflammation.

Adoption of high throughput techniques, AI, and machine-learning computational technology for hit identification.

Nucleic acid therapeutics

1. Specific targeting to the gene of interest 2. Easier to chemically synthesize than other biologics

1. Unstable, prone to degradation by nucleases 2. Needs to be chemically modified or coated in lipid nanoparticle for efficient delivery into the cell 3. Capable to induce imunogenic response and pose risk for autoimmune complications

Fomivirsen (Vitravene) for cytomegalovirus retinitis infection, Spinraza (nusinersen) for SMA, Onpattro (patisiran) for treating hATTR, mRNA-1273, and Ad5 vector vaccines for COVID-19 treatment (currently in clinical trials).

Using CRISPR-Cas9 gene-editing technology for wider application in gene therapy, conjugating nucleotides with N-acetylgalactosamine (GalNAc) group to increase the stability.

(Continued)

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6.6 Conclusion

Biological modalities

Advantages

Limitations

Some prevalent examples

Future advancements

Therapeutic peptides

1. Low toxicity and more specificity compared to small molecule 2. Smaller than other biologics to be easily delivered inside the cell

1. Very low oral bioavailability, susceptible to proteases present in the stomach and gastrointestinal tract

Insulin hormone for regulating glucose metabolism in diabetic patients. Carflizomib and bortezomib peptides used in multiple myeloma treatment. Romidepsin for CTCL and other PTCL. AMPs— vancomycin, gramicidin, daptomycin, telavancin, colistin, oritavancin, and dalbavancin for bactericidal activity.

Development of nextgeneration therapeutic stapled peptides resistant to protease action and have more stability.

Therapeutic enzymes

1. Direct treatment of 1. Difficult to disorders/diseases synthesize at a due to enzyme commercial scale compared to deficiencies smaller biologics 2. Needs optimal storage buffer conditions to not lose the functional activity and stability of the enzyme.

Pegvaliase (Palynziq) used for phenylketonuria. L-Asparaginase to treat blood cancer such as ALL, AML, or non-Hodgkin’s lymphoma.

Enzymes conjugated with nanocarriers for effective delivery, adoption of proteinengineering technologies to develop enzymes with improved pharmaceutical properties.

Monoclonal antibodies

1. Highly specific, targeting one epitope of an antigen 2. Less toxic and less chances of multiple off-target binding

1. Expensive and difficult to biomanufacture and bioprocess 2. Like enzymes and proteins, needs optimal storage buffer and environmental conditions to retain its functional activity and structural stability 3. Lower shelf life

Adalimumab (Humira) for multiple conditions nivolumab (Opdivo), ipilimumab (Yervoy), and pembrolizumab (Keytruda) used for different cancer treatment.

MAbs targeting Ebola virus, HIV, and SARSCov-2 coronavirus are under clinical trials, development of antibody proteinengineering techniques to design antibodies with enhanced pharmaceutical properties. Conjugating antibodies with other biologics like smallmolecule drugs to form antibody
drug conjugates for better efficacy and delivering to the target. (Continued)

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TABLE 6.1 (Continued)

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Section 4: Novel therapeutic modalities

TABLE 6.1 (Continued) Biological modalities

Advantages

Limitations

Multispecific antibodies

1. Target multiple 1. Shorter life span antigen at one time 2. Low bioavailability 2. Engineered next3. Capable to generation produce high monoclonal immunogenic antibodies response posing risk of cytokine release and other autoimmune disorders

Cell-based 1. Harness the 1. Very high risk of immunotherapies potential of body’s autoimmune own immune complications and system to target high risk of organs different tumor failure cells 2. Expensive treatment 3. Increase in complexity to monitor/fine-tune the interactions between the target and the drug

Some prevalent examples

Future advancements

Blinatumomab (Blincyto) used for blood cancer acute lymphoblastic leukemia and emicizumab (Hemlibra) for the treatment of hemophilia A.

Using protein antibody engineering to design humanized multispecific antibodies that pose less risk of autoimmune disorders and its other pharmaceutical properties are not compromised.

CAR-T-cell therapy— axicabtagene ciloleucel (Yescarta) and tisagenlecleucel (Kyrmriah) for leukemia and lymphoma blood cancer treatment.

Use other components of immune system or genetically engineer the activated immune cells to customize the response action against the tumor cells, development of personalized immunotherapy by understanding its interaction with other systems like the microbiome.

Stem cells

1. Regenerative medicine to differentiate into the impaired body tissues/organs

1. Poses risk of autoimmune response and rejection of foreign organ grown in vitro by the host leading to GvHD 2. Possibility of stem cells converting into cancerous cells 3. More complexity 4. Ethical issues dealing with ESCs

Hematopoietic bone marrow stem cell transplant for blood cancer treatment.

Development of stem cell therapy for treatment of other diseases and techniques to grow organs or organ systems in vitro in lab on a chip.

Phage therapies

1. Phage viruses used 1. Eliminate in the therapy pose multidrug a risk of turning resistance or into lysogenic antibiotic mode of action to resistance problem the bacteria acquired by the making them bacterial immune to the pathogens viral invasion

AB-SA01 for MRSA treatment (in phase-I/II clinical trials).

Requires more research and development in the novel therapy. Designing the therapy for treatment of other diseases like wound healing.

(Continued)

159

6.6 Conclusion

Biological modalities

Advantages

Limitations

Some prevalent examples

Future advancements

Pro- and prebiotic supplements used in diet intervention, fecal microbiota transplantation for C. difficile bacterial associated diarrhea infection.

Personalized medicine development and emergence of the field of pharmacomicrobiome to understand the various drug response. Understanding the gut microbiome interaction with other organs like brain/ system like the immune system of the body to develop therapeutic tools treating a wider variety of diseases and disorders.

2. Specific targeting 2. Poses risk of to a bacterium and horizontal gene can penetrate the transfer biofilm formed by 3. Currently, therapy the bacterial is in its nascent colony form and need to overcome many regulatory and ethical hurdles Microbiomebased therapeutics

1. Risk of 1. Helps in treating autoimmune Clostridium difficile response in fecal associated microbiota bacterial gut transplantation infection resistant to many antibiotics 2. Very complex to understand and and drugs fine-tune/ 2. Opened up customize the avenues for the interaction and development of system biology other biobetween the therapeutic drugs difference with improved commensals and pharmaceutical symbionts properties microbes residing in the body 3. Currently, therapy is in its nascent form and needs more research and development

Ad5, Adenovirus type-5; AI, artificial intelligence; ALL, acute lymphoblastic leukemia; AML, acute myeloid leukemia; AMPs, Antimicorbial peptides; CTCL, cutaneous T-cell lymphoma; ESCs, embryonic stem cells; GvHD, graft versus host disease; hATTR, hereditary transthyretin-mediated amyloidosis; MRSA, methicillin-resistant Staphylococcus aureus; PTCL, peripheral T-cell lymphoma; SMA, spinal muscular atrophy.

shown success in treating a wide variety of diseases and disorders, unlike conventional chemically synthesized drugs. They exhibit higher specificity, low toxicity, and thereby show more efficacy than small-molecule drugs. The advantages offered by biologics outweigh the disadvantages. Thus pharma and biotech companies realize the potential of biologics or bio-therapeutic modalities and are focusing on the research and development of next-generation biologics with enhanced characteristics and improved pharmaceutical properties, ensuring bright future and market for bio-therapeutic modalities.

Section 4: Novel therapeutic modalities

TABLE 6.1 (Continued)

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Section 4: Novel therapeutic modalities

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C H A P T E R

The journey of noncoding RNA from bench to clinic Ravindresh Chhabra O U T L I N E 7.1 Introduction 7.1.1 Noncoding RNAs and their classification 7.1.2 In silico ncRNA prediction tools 7.1.3 Screening and characterization of ncRNAs 7.1.4 Small noncoding RNAs (miRNAs and siRNAs) 7.1.5 Long noncoding RNAs

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7.2 Patent landscape of noncoding RNA187 7.3 Bottlenecks in the use of noncoding RNAs as biomarkers/therapeutics 189 7.4 Conclusions and future perspectives 191

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7.1 Introduction 7.1.1 Noncoding RNAs and their classification The transcriptome of an organism can be broadly divided into two classes—coding and noncoding. For many decades, the noncoding RNA (ncRNA) was considered inconsequential for an organism. It is only in the 90s that scientists begin to understand the importance of ncRNA. Since then, the ncRNAs have been shown to be of utmost importance in almost all biological processes. The dysregulation of ncRNAs is responsible for a plethora of diseases, which makes it imperative to study them. Most of the ncRNAs exhibit tissue-specific expression too, which makes them attractive targets for biomarker and therapeutic development. Department of Biochemistry, Central University of Punjab, Bathinda, Punjab, India

Translational Biotechnology DOI: https://doi.org/10.1016/B978-0-12-821972-0.00016-2

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The ncRNAs have been subdivided into different classes. The first subdivision is based on their size with the threshold of 200 nucleotides—the ncRNA bigger than that are called long noncoding RNA (lncRNA), and smaller than that are grouped under small noncoding RNA. The small noncoding RNAs, on the basis of their biogenesis and biological function, are further classified as microRNAs (miRNAs), small interfering RNAs (siRNAs), piwiinteracting RNA (piRNAs), and small nucleolar RNAs (snoRNAs). The snoRNAs are also of two different kinds—one contains the box C (RUGAUGA) and D (CUGA) motifs, and the other contains the box H (ANANNA) and ACA elements (Kiss, 2002). There are staggering numbers of ncRNA, which have been reported, and their number continues to increase. The current release of the miRNA registry (miRBase 22) contains 1917 entries for human precursor miRNAs (pre-miRNAs), which express 2654 mature miRNAs (Griffiths-Jones, 2004; Kozomara, Birgaoanu, & Griffiths-Jones, 2019). The number of human lncRNAs transcripts reported so far is 127,802 (Volders et al., 2019). The number of human piRNAs is 35,356 as per piRNA Bank (Sai Lakshmi & Agrawal, 2008), but the number is close to 8 million as per piRbase v2.0 (Wang, Zhang, Lu, Li, & Zheng, 2019). There are 402 human snoRNAs reported in the literature (Lestrade & Weber, 2006). The number of endogenous siRNAs remains unknown. This chapter will discuss the clinical relevance of three major classes of ncRNA: miRNAs, siRNAs, and lncRNAs. This is because a significant number of research projects are focused on their clinical applications, and a lot of data are available for these classes of ncRNA in the clinical realm. However, this in no way implies that piRNAs and snoRNAs do not have clinical relevance. In fact, there are a few reports in the literature which highlight the potential of piRNAs (Iliev et al., 2016; Liu et al., 2019; Mai et al., 2018; Vychytilova-Faltejskova et al., 2018) and snoRNAs (Baraniskin et al., 2013; Okugawa et al., 2017; Steinbusch et al., 2017; Yoshinaga et al., 2015; Yoshida et al., 2017; Zhang et al., 2012) as biomarkers and therapeutic molecules. Most of the reports in the area of piRNA and snoRNA are recent, and it will take some time for these research endeavors to translate into clinical applications. It is worth mentioning that in the past few years, a new class of ncRNA, referred to as circular RNAs (circRNAs), has been discovered (Kristensen et al., 2019). The size of circRNAs ranges from a hundred to a few thousand nucleotides and as such cannot be assigned under small ncRNA or lncRNA. Since very little is known about them, they are not discussed in this chapter.

7.1.2 In silico ncRNA prediction tools The high-throughput sequencing technologies have helped uncover a large number of ncRNAs, but it is undoubtedly a challenge to know if the novel sequence of RNA is indeed noncoding. The traditional lab experimentation would involve either cloning the gene sequence of the ncRNA under a strong promoter or in vitro transcription and translation reactions of the gene sequence followed by detecting the formation of any peptide sequence. An easy alternative is provided by the computational analysis, which can predict the coding/noncoding potential of an RNA based on its sequence. The in silico ncRNA prediction tools are usually the first step in the annotation of a novel ncRNA. This section highlights the commonly used tools to predict the RNA sequences as miRNAs and lncRNA. There are different rules for in silico prediction of miRNAs and lncRNAs. The lncRNAs can be differentiated from the protein-coding genes by Coding
Noncoding Index (Sun et al., 2013),

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Coding Potential Assessment Tool (Wang et al., 2013), Coding potential calculator (Kang et al., 2017), and LncFinder (Han et al., 2019). The miRNAs can be predicted by miRNAFold (Tav, Tempel, Poligny, & Tahi, 2016) and RNAmicro (Hertel & Stadler, 2006). Recently, another miRNA prediction tool was developed that utilized mRNA sequences to predict the potential miRNA sequences, which can target that mRNA (Chakraborty & Hasija, 2020). This tool not only identifies the known miRNA targets for an mRNA but also predicts the novel miRNA sequences. There is a dearth of information on endogenous siRNAs, and no prediction tools have been developed for them. For miRNAs, in silico tools are also available for predicting its mRNA targets. This is owing to the fact that miRNAs (with a few exceptions) follow a certain set of rules to target mRNAs (Brennecke, Stark, Russell, & Cohen, 2005). One of the most important rules is the binding of miRNAs to 30 UTR of mRNA at the seed region (2
8 bases of mature miRNA). The target prediction tools (including the most common ones, TargetScan and PicTar) have already been reviewed in great detail in the literature (Hammell, 2010) and hence are not included in this chapter. The lncRNAs do not follow any such rules which explain the lack of any prediction tools to identify their targets.

7.1.3 Screening and characterization of ncRNAs The development of ncRNA-based therapeutics and biomarkers involves screening and characterization of ncRNAs. The huge numbers of ncRNAs make it imperative to employ high-throughput techniques to screen them in diseased conditions, followed by their individual characterization. Next-generation sequencing, including RNA sequencing for lncRNAs and small RNA sequencing for miRNAs along with RNA microarrays, are the most common methods to screen for ncRNAs. These methods reveal differentially expressed ncRNAs in the diseased conditions. The bigger challenge is to sift through the differentially expressed ncRNAs to identify the ones which can function as a biomarker or can be developed as a therapeutic target. For biomarker applications, the ncRNA must exhibit significant differences in its expression in healthy and patient samples, and its expression profile must not overlap with other diseased or physiological conditions. For certain conditions, a small subset of ncRNAs has been developed as a biomarker rather than a single ncRNA. For instance, the osteomir kit developed for fracture risk assessment examines the expression profile of 19 miRNAs (www.tamirna.com) and the survival of patients with esophageal squamous cell carcinoma can be predicted with the expression profile of three lncRNAs (Li et al., 2014). For therapeutic applications, in addition to the information about differential expression, it is essential to understand the functional relevance of the ncRNA in the disease. Moreover, the ncRNA being explored as a therapeutic should demonstrate high specificity and minimal off-target effects.

7.1.4 Small noncoding RNAs (miRNAs and siRNAs) 7.1.4.1 Biogenesis of miRNAs and siRNAs The biogenesis of miRNAs is quite elaborate when compared with other ncRNAs (Winter, Jung, Keller, Gregory, & Diederichs, 2009). The miRNAs can be transcribed from

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the intronic as well as the intergenic regions by RNA polymerase II (Pol II) in the nucleus to give rise to primary transcripts (pri-transcript) of a few thousand nucleotides. There are, however, a few exceptions where RNA polymerase III (Pol III) has also been shown to transcribe miRNA genes (Borchert, Lanier, & Davidson, 2006). The pri-transcript, in the nucleus, undergoes cleavage by Drosha/DGCR8 complex to form precursor miRNA (pre-miRNA), which is about a hundred nucleotides long (Fig. 7.1). The Ran-GTP/Exportin-5 complex, then, exports the pre-miRNA to the cytoplasm where it undergoes further cleavage by Dicer/TRBP complex to give rise to functional mature miRNA of B22 nucleotides. In addition, miRNAs can also originate from lncRNAs (Augoff, McCue, Plow, & Sossey-Alaoui, 2012;

FIGURE 7.1 Schematic representation of biogenesis and working mechanism of noncoding RNA. The noncoding RNAs are transcribed by either RNA polymerase II (Pol II) or RNA polymerase III (Pol III). There is very little known about the endogenous siRNA. siRNAs after being transcribed in the nucleus are cleaved by Dicer2 in the nucleus giving rise to small double-stranded siRNAs. The siRNA, along with Ago2 protein, is loaded onto the RISC assembly and cleaves its target mRNA. miRNA biogenesis begins in the nucleus with the formation of primary miRNA transcript (Pri-miRNA), which is a few thousand bases long. The pri-miRNA is cleaved in the nucleus by Drosha
DGCR8 complex to give rise to precursor miRNA (pre-miRNA), which is a few hundred bases long. The pre-miRNA gets exported to the cytoplasm by Exportin-5/Ran-GTP and undergoes the second cleavage by Dicer/TRBP complex to generate a mature miRNA of about 22 nucleotides. The mature miRNA binds to the 30 UTR of its target mRNA and either degrades mRNA or represses translation. The lncRNAs are localized in the nucleus or the cytoplasm or both nucleus and cytoplasm. The lncRNAs perform diverse functions in the cells, including histone methylation and chromatin remodeling, to enhance or inhibit transcription. They can also enhance mRNA stability and act as a precursor of miRNA.

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Dhir, Dhir, Proudfoot, & Jopling, 2015). The miRNA biogenesis is slightly different in plants where miRNAs also undergo methylation during miRNA biogenesis. The methylation of miRNAs helps protect them from uridylation, which degrades mRNA after miRNA-mRNA binding, thereby enabling efficient recycling of miRNAs (Chhabra, 2017). siRNAs, like miRNAs, are about 21
25 nucleotides in lengths. They originate as double-stranded transcripts from transposons, sense
antisense, or stem-loop transcripts in the nucleus. The functional siRNAs are formed in the cytoplasm after cleavage by Dicer. siRNAs interact with argonaute protein and bring about RNA degradation, thereby silencing the gene expression (Kim, Han, & Siomi, 2009). While miRNAs can cause either mRNA degradation or translational repression for gene silencing, the siRNAs induce gene silencing only through mRNA degradation. Also unlike miRNAs, there are a very few endogenous siRNAs that have been demonstrated (Chen, Dahlstrom, Lee, & Rangasamy, 2012; Yang & Kazazian, 2006). Most of the siRNAs that have been used to silence genes in research and are now being tested in the clinical trials have all been chemically synthesized siRNAs that utilize the cellular machinery to perform their function. 7.1.4.2 Working mechanism of miRNAs and siRNAs miRNAs generally follow a set of rules while targeting mRNA. Majority of them inhibit gene expression by binding to 30 UTR region of mRNAs and cause mRNA degradation or translational repression. Surprisingly, the binding of miRNA and its target mRNA is not throughout the length of miRNA. It is only the 2
8 nucleotides of the miRNA, referred to as the seed sequence, which binds the mRNA target. It is, perhaps, because of this reason that a single miRNA can target multiple mRNAs and a single mRNA can be the target for many miRNAs. There are, however, exceptions to this rule. A landmark paper published in 2009 proved that it is not necessary for miRNA to follow these rules (Lal et al., 2009). The authors showed that miR-24 inhibits the expression of E2F2, MYC, and other genes by binding to their 30 UTR without the seed sequence match. They observed that in addition to miR-24 targeting mRNAs with perfect complementarity to its seed sequence, it could also target mRNAs, which showed no complementarity to its seed sequence. A few miRNAs have also been reported to enhance the expression of mRNAs by binding to their 50 UTR regions (Ørom, Nielsen, & Lund, 2008; Qu et al., 2016). Also, miR-103a-3p was shown to have two target sites in the 50 UTR of GPRC5A where it can bind to represse its expression (Zhou & Rigoutsos, 2014). Duursma and coworkers in 2008 had shown that miRNAs could also target the coding region of their mRNA target (Duursma, Kedde, Schrier, Le Sage, & Agami, 2008). They observed that miR-148 regulates the expression of DNMT-3b by binding to its coding region. miR-148 repressed the expression of only one of the splice variants of DNMT3b, which carried the miR-148 binding site in the coding region. The other splice variant, which did not have this binding site, remained unaffected by miR-148. siRNAs do not follow this complex set of rules in deciding their RNA targets. They do have some preferences, though, for instance, they show enhanced efficacy and reduced off-target effects if the regions they target have low GC content, lack inverted repeats, and there is optimum thermodynamic stability of the duplex formed between siRNA and target mRNA (Naito & Ui-Tei, 2012; Reynolds et al., 2004). But primarily they bind to the complementary mRNAs based on the Watson
Crick base pairing and cause their degradation. Unlike miRNAs, they use their full-lengths to target mRNA sequences.

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7.1.4.3 Expression profile of miRNAs in disease pathology There are very few reports on endogenous siRNAs, and as such, their importance in pathophysiology remains largely unknown. miRNAs, on the other hand, seems to be significantly deregulated in almost every human disease. This is not surprising considering they regulate diverse pathways ranging from proliferation to apoptosis, pluripotency to differentiation, and gene imprinting to epigenetics. Because of the vast amount of information available about the role of miRNAs in diseases, it is impossible to cover them in a single chapter. This section briefly discusses a few of the miRNAs with an implicit role in cancers, cardiovascular diseases, neurodegenerative diseases, and ocular diseases. The number of studies undertaken for miRNAs in cancer far outnumbers the rest of miRNA studies in other diseases. The vast amount of miRNA differential expression data in cancers generated by high-throughput methods have been compiled in the freely available database, database of Differentially Expressed MiRNAs in human Cancers (Yang et al., 2017). Many miRNAs have already been established as the causal factor for a number of cancers (Chhabra & Saini, 2014). miRNAs can function as oncogenes as well as tumor suppressor genes. Some of the oncogenic miRNAs include miR-17-92 cluster, miR-21, -24, 25, -31,- 125, and -221 (Frixa, Donzelli, & Blandino, 2015) and the tumor suppressor miRNAs include let-7, miR-1, -34, -129-1, -152, and -876 (Ito et al., 2017; Liu, Zhang, Wang, & Zhang, 2017; Ursu et al., 2019; Zhang, Pan, Cobb, & Anderson, 2007). To further add to the complexity of the role of miRNAs in cancer, some miRNAs function as an oncogene in one cancer and as tumor suppressor gene in another cancer, for instance, miR-155 is oncogenic in breast cancer and a tumor suppressor in ovarian cancer and acute myeloid leukemia (Svoronos, Engelman, & Slack, 2016). This could be attributed to the fact that miRNAs have hundreds of targets, and the role of miRNA in tissue may also be dictated by the repertoire of the mRNA targets expressed in that tissue. In a way, therefore, it is misleading to classify miRNAs as either oncogenes or tumor suppressor genes. The cancer stem cell hypothesis states that the origin of cancer is attributed to a small subset of tumor population referred to as cancer stem cells, and miRNAs have been known to specifically target cancer stem cells too (Chhabra, 2018; Li, Wang, Wang, Song, & Liu, 2017; Liu et al., 2017; Mukohyama et al., 2019). This implies that the origin of cancer could itself be because of the deregulation of miRNAs. The importance of miRNAs in cancer has been reviewed in great detail recently (Peng & Croce, 2016; Slack & Chinnaiyan, 2019). Myocardial infarction (MI) (or heart failure) is the death of heart muscle cells because of a lack of blood supply to these cells. Multiple miRNAs have been known to have a significant role in MI. Among the many miRNAs demonstrated for their role in MI, miR-133a has been shown to have a lot of significance in multiple independent studies. In MI patients with ventricular fibrillation, miR-133a/b is downregulated. miR-133a protects against cardiac fibrosis (Matkovich et al., 2010) and augments the protective capacity of cardiac progenitor cells after MI (Izarra et al., 2014). miRNAs are implicated in the pathogenesis of cardiac hypertrophy too. While miR-212 and miR-132 (Ucar et al., 2012) were identified to drive the cardiac hypertrophy, miR-497 (Xiao, Zhang, Fan, Cui, & Shen, 2016), miR-1 (Ikeda et al., 2009), and miR-19a/b-3p (Liu et al., 2018) have been shown to prevent it. There is a strong evidence of change in circulatory miRNAs during the onset of

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cardiovascular diseases, which makes them excellent candidates to be developed as diagnostic markers for cardiovascular diseases (Vickers, Rye, & Tabet, 2014). miRNA dysregulation is seen in all neurodegenerative diseases, including Alzheimer’s disease, Parkinson’s disease (PD), and Huntington disease. Alzheimer’s disease (AD) is characterized by the enhanced expression of APP and BACE1 protein. The miRNAs which target APP and BACE1 can be a potential therapeutic, and quite a few of these miRNAs have already been discovered. The expression of miR-29 family (miR-29a, -29b-1, 29c) is markedly reduced in the patients with AD, and it has been demonstrated that the ectopic expression of miR-29 reduces the BACE1 levels in vitro (He´bert et al., 2008; Lei, Lei, Zhang, Zhang, & Cheng, 2015). Similarly, miR-106a and -520c reduce the expression of APP (Patel et al., 2008). An interesting association between the lncRNA, BACE1-AS, and miR-485 has been described in the regulation of BACE1 expression. The lncRNA BACE1-AS competitively binds to BACE1 and prevents miR-485-5p to target BACE1, thereby upregulating the expression of BACE1 in Alzheimer’s patients (Faghihi et al., 2010). In PD, there is a negative correlation between miR-205 and LRRK2 protein expression in the brains of PD patients. It was established that miR-205 inhibited LRRK2 expression in the cell lines and primary neuron cultures and prevented the neurite outgrowth defects in the neurons expressing a PD-related LRRK2 R1441G mutant (Cho et al., 2013). Additionally, miR-22 was shown to be neuroprotective in patients with PD (Jovicic, Zaldivar Jolissaint, Moser, Silva Santos, & Luthi-Carter, 2013). miR-22, along with miR-124 also slows down the progression of Huntington disease by promoting neuronal differentiation and survival (Jovicic et al., 2013; Liu, Im, Mook-Jung, & Kim, 2015). Glaucoma and age-related cataract are the leading cause of blindness worldwide. Glaucoma, often, remains undetected until the loss of vision becomes irreversible, which necessitates the need for good biomarkers. Glaucoma treatments mainly focus on lowering intraocular pressure (IOP). As a biomarker, however, IOP can vary and, therefore is inaccurate to predict disease progression. A study on glaucoma patients published recently indicated that circulating miRNAs could become potential diagnostic markers for glaucoma. A combination of three miRNAs (miR-637, -1306-5p, -3159) demonstrated the best ability to identify glaucoma patients with high sensitivity (85%) and specificity (87.5%) (Hindle et al., 2019). For glaucoma treatment, Li, Zhao, Xin, Li, & Luna (2017) had shown that the deletion of miR-143/145 in mice causes a significant decrease in IOP and can, therefore be looked at as a potential therapeutic. Cataract is a result of the clouding of the eye lens, which leads to loss of vision. Fibrosis is one of the reasons for cataracts. The overexpression of miR-26a and -26b has been shown to prevent lens fibrosis and cataract by inhibiting Jagged-1/Notch signaling pathway (Chen et al., 2017). In a separate study on cataract patients, an increase in let-7b was demonstrated to be the risk factor for the formation of age-related cataracts, thereby implying its potential as a biomarker (Peng et al., 2012). miRNAs have also been implicated in dry eye disease (Rassi et al., 2017) as well as retinal eye diseases (Zuzic, Rojo Arias, Wohl, & Busskamp, 2019). 7.1.4.4 miRNAs and siRNAs—from bench to clinic The miRNA therapeutics and miRNA biomarkers in different stages of their journey from bench to clinic have been compiled in Tables 7.1 and 7.2, respectively. Some of them are discussed in detail below.

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TABLE 7.1 miRNA therapeutics at different stages of clinical development. Trade name

miRNA

Function

Disease targeted

Cobomarsen (MRG-106)

miR-155 inhibitor

Inhibits cell proliferation Cutaneous T-cell and induces apoptosis lymphoma Adult T-cell lymphoma/ leukemia

Remlarsen (MRG-201) MRG-229

MRG-110

Developmental stage

Company

Reference

Phase 2

MiRagen Therapeutics

miragentherapeutics. com

Phase 1

miR-29 mimic Decreases the expression of collagen and proteins involved in scar formation Secondgeneration miR-29 mimic

Cutaneous fibrosis Phase 2

miR-92 inhibitor

Induces angiogenesis and wound healing

Ocular fibrosis

Preclinical

Idiopathic pulmonary fibrosis

Preclinical

Heart failure

Phase 1

Wound healing

Phase 1

MRX34

miR-34 mimic Acts as a tumor suppressor in many cancers

Multiple cancer types

Phase 1 study closed after adverse immune effects were observed in patients in 2016

miRNA Therapeutics (Now merged with Synlogic Therapeutics)

mirnatherapeutics. com

RG-012

miR-21 inhibitor

Reduces the progress of renal fibrosis

Alport syndrome

Phase 2 (Orphan Drug status)a

Regulus Therapeutics

regulusrx.com

RGLS4326

miR-17 inhibitor

Reduces kidney cyst formation and protects kidney function

Autosomal dominant polycystic kidney disease

Phase 1 (Partial clinical hold)

RG-101

miR-122 inhibitor

Prevents viral replication

Hepatitis C

Phase 2, abandoned because of the incidence of jaundice in patients

RGLS5040

miR-27 inhibitor

Prevents liver injury

Cholestasis

Preclinical, discontinued because of competition

RGLS5579

miR-10b inhibitor

TargomiRs

Glioblastoma multiforme

Preclinical

miR-16 Shows antitumor packaged in activity and inhibits cell EDV nanocell growth platform

Mesothelioma

Phase 1/Phase 2 to commence in 2020

EnGeneIC

engeneic.com

Miravirsen (SPC3649)

miR-122 inhibitor

Prevents viral replication

Hepatitis C

Phase 2

Roche/Santaris Pharma

roche.com

ABX464

Induces the expression of miR-124

Antiinflammatory

Ulcerative colitisRheumatoid arthritisCrohn’s disease

Phase 2

Abivax

abivax.com

Decreases viral load

HIV

Phase 2

Abivax

abivax.com

Induces apoptosis and prevents invasion

Hepatocellular carcinoma

Preclinical

InteRna technologies interna-technologies. com

INT1B3

miR-193a-3p mimic

Increases survival of animal models

a

Orphan drug status is an incentive for companies researching cures for rare diseases. They get a 7-year window of tax reductions and the exclusive right to develop a cure for that specific disease.

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TABLE 7.2 miRNA diagnostics at different stages of clinical development. Trade name

miRNA

Role

Disease targeted

Developmental stage

Company

Reference

OsteomiR

Set of 19 miRNAs (let-7b-5p, miR-127-3p, miR-133b, miR-141-3p, miR-143-3p, miR-144-5p, miR-152-3p, miR17-5p, miR-188-5p, miR-19b-3p, miR-203a, miR-214-3p, miR-29b-3p, miR-31-5p, miR-320a, miR-335-5p, miR-375, miR-550a-3p, miR-582-5p)

Fracture risk assessment

Osteoporosis

Research use only

TAmiRNA

tamirna.com

ThrombomiR Set of 11 miRNAs (miR-126-3p, miR-223-3p, miR-197-3p, miR-191-5p, miR-24-3p, miR-21-5p, miR-28-3p, miR320a, miR-150-5p, miR-27b-3p, miR-122-5p)

In vivo measure of platelet function

Cardiovascular disease

Research use only

ThyraMIR

Set of 10 miRNAs (miR-29b-1-5p, miR-31-5p, miR-138-13p, miR-139-5p, miR-146b-5p, miR-155, miR-204-5p, miR-222-3p, miR-375, miR-551b-3p)

Rules out thyroid cancer

Thyroid cancer

In vitro diagnostics

Interpace Biosciences

interpace.com

CogniMIR

Undisclosed

Early detection and prediction of Alzheimer’s disease progression

Alzheimer’s disease

Clinical trial assay development

DiamiR

diamirbio.com

Undisclosed

Undisclosed

Differentiates a cancer patient from a healthy individual with a simple blood test

NSCLC

Clinical trials

Hummingbird Diagnostics

hummingbirddiagnostics.com

LiverAce

miR-122

Enhanced level of miR-122 indicates liver injury or toxicity

Liver injury

Clinical validation complete

Destina Genomics/ Quanterix

destinagenomics. com quanterix.com

miRview meso

Three miRNAs (miR-193-3p, miR-200c, miR-192)

Differentiates mesothelioma from nonmesothelioma

Mesothelioma

Commercially available

Benjamin et al. (2010)

miRview Mets

miRNA library

Detects carcinoma of unknown primary origin

42 cancer types

Commercially available

Rosetta Genomics (filed for bankruptcy in 2018)

Meiri et al. (2012)

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miR-122 was first discovered in 2002. It was found to be abundantly expressed in mouse liver tissue with miR-122 and its variants, accounting for 72% of all miRNAs in the liver tissue (Lagos-Quintana et al., 2002). In 2005 it was found to be highly conserved in all vertebrates from zebrafish to humans (Wienholds et al., 2005). In a seminal study, Jopling, Yi, Lancaster, Lemon, and Sarnow (2005) discovered the importance of miR-122 in Hepatitis C virus (HCV) infection. The authors discovered that the interaction of miR-122 to the 50 noncoding region of HCV is essential for HCV replication. This is a remarkable observation considering the traditional role of miRNAs is to bind to the 30 UTR of their mRNA targets and inhibiting gene expression. Herein, however, the virus utilizes the host miRNA to enhance its replication. Inhibition of miR-122 reduced the HCV RNA replicon by 80% in the liver cell line, Huh7. The successful inhibition of miR-122 in mouse liver was shown using 30 cholesterol-conjugated, 20 -O-Me oligonucleotides (also referred to as antagomir) (Kru¨tzfeldt et al., 2005) or by unconjugated 20 -O-methoxyethyl
modified phosphorothioate oligonucleotide (Esau et al., 2006) or by using locked nucleic acid (LNA)modified antimiR oligonucleotide (Elme´n, Lindow, Silahtaroglu, Bak, & Christensen, 2008). The LNA-modified oligonucleotides had higher thermal stability and enhanced binding affinity, making them ideal for use as a therapeutic (Braasch & Corey, 2001). This prompted Elme´n, Lindow, Schu¨tz, Lawrence, and Petri (2008) to develop different types of LNA antimiR oligos against miR-122, out of which a 15 base LNA-modified antimiR, SPC3649, showed the strongest inhibition of miR-122 in the liver cell line, Huh7 as well as mice models at comparatively lower concentration. Since mice do not have HCV infection, the efficacy of miR-122 inhibition was evaluated by the lowering of plasma cholesterol levels. The SPC3649 was later renamed as miravirsen. The efficacy of miravirsen was confirmed in HCV-infected chimpanzees where it caused a significant decrease in HCV viral titer (Lanford et al., 2010). No side effects or viral resistance developed during the treatment cycle of 12 weeks (Lindow & Kauppinen, 2012). The structure of miravirsen ensures that it naturally accumulates in liver without the need of specialized delivery. The success of animal trials made miravirsen enter the human clinical trials in 2009 and was shown to have no toxicity in human subjects. The phase 2 clinical trials were taken over by Santaris Pharma (bought by Roche in 2014) in 2010 and they observed that miravirsen significantly reduced HCV RNA levels and in four out of nine patients receiving a higher dose of miravirsen had undetectable HCV RNA (Reesink et al., 2012). Miravirsen is currently in phase 2 clinical trials. As per ClinicalTrials database of NLM, there are eight listed clinical trials associated with miravirsen or SPC3649 out of which six are completed. The journey of miravirsen from bench to clinic is highlighted in Fig. 7.2. A similar approach was also adopted by Regulus Therapeutics in association with GSK and Ionis Pharmaceuticals when they developed an antagomir against miR-122 (RG-101) for HCV infections. RG-101 was GalNac modified, which allows it to be taken up exclusively by the liver cells, thereby aiding in its in vivo delivery. It also showed promising results like undetectable HCV virus titers in patients receiving this antagomir, but it caused hyperbilirubinemia (severe jaundice) in a number of patients prompting the FDA to halt the clinical trials in 2016 (Baek, Kang, & Min, 2014; Bonneau, Neveu, Kostantin, Tsongalis, & De Guire, 2019).

Section 4: Novel therapeutic modalities

7.1.4.4.1 Miravirsen for the treatment of Hepatitis C

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FIGURE 7.2 The timeline of miravirsen’s journey from bench to clinic. 7.1.4.4.2 MRX34 as cancer therapeutic

In the area of cancer, the miRNA-34a was among the most promising miRNA which entered clinical trials. miR-34a has been implicated in a number of cancers, including breast cancer, colorectal cancer, glioblastoma, and lung cancer (Bader, 2012). It acts as a tumor suppressor gene and is activated by p53 (Hermeking, 2012). It is also implicated in regulating epithelial
mesenchymal transition (EMT). EMT is an important biological process highly active during early development and is exploited by tumor cells to invade other tissues and metastasize. miR-34 was shown to inhibit invasion of bladder carcinoma cells (Sun, Tian, Xian, Xie, & Yang, 2015) and induce the expression of E-cadherin while reducing vimentin (EMT markers) in colon cancer cells (Siemens et al., 2011). miR-34a represses the expression of a number of oncogenes, including BCL2, CD44, CDK4/6, MEK1, MET, NOTCH1, PDGFR-α, and WNT 1/3 (Hong et al., 2016). Moreover, it is known to regulate cancer stem cells, as well (Chhabra & Saini, 2014). In almost all cancers, miR-34a is downregulated, which makes it an attractive therapeutic target. The importance of miR-34a in a large number of human cancers, as highlighted above, prompted the company, Mirna Therapeutics, to develop MRX34, a liposome-formulated miR-34a mimic. The liposome used ionic lipids, which helped in the efficient uptake of miR-34a by the tumor cells and prevented the unwanted effects of miR-34a on the healthy cells. This delivery technology, referred to as NOV-340 or SMARTICLES, was developed by Mirna Therapeutics (Bouchie, 2013). The delivery of MRX34 in mice models was effective in reducing liver tumors as well as nonsmall cell lung cancer (Zhang, Liao, & Tang, 2019). The clinical trial in 2013 with 47 patients of 11 different cancer types showed antitumor activity in all cancer types (Beg et al., 2017). However, in clinical trial NCT02862145, five immune-related serious adverse events were reported in the patients resulting in the death of three patients. This made Mirna therapeutics discontinue all research and development on MRX34, and the company was eventually merged with Synlogic in 2017. Nevertheless, the significance of miR-34a in cancer is unquestionable, and maybe with an alternative delivery mechanism and change in other parameters, it could one day be translated as an anticancer therapy. 7.1.4.4.3 OsteomiR and ThrombomiR as diagnostic markers

OsteomiR and ThrombomiR are the two diagnostic kits based on circulatory miRNA profile in serum/plasma developed by an Austrian company, TAmiRNA. OsteomiR provides the risk assessment for fracture in osteoporosis patients based on the expression

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profile of 19 miRNAs (Table 7.2) (Heilmeier et al., 2016; Kocijan et al., 2016). The expression data of 19 miRNAs are converted into individual fracture risk score based on a diagnostic algorithm developed by TAmiRNA and SimplicityBio. The diagnostic feature of OsteomiR can thus be utilized to initiate preventive therapy for patients with a high risk of fracture. ThrombomiR provides the measure of the platelet function based on the expression profile of 11 miRNAs (Table 7.2). These miRNAs are secreted from platelets during their activation, and miRNAs are protected from degradation as they are encapsulated in vesicles. The selection of 11 miRNAs is based on the study of patients with acute coronary syndrome, antiplatelet therapy, and type 2 diabetes (www.tamirna.com). The analysis of the miRNA expression data of these 11 miRNAs by TAmiRNA’s software makes it easy to assess the predisposition of patients to develop type 2 diabetes and other cardiovascular diseases. Both these tests are, however, only available for research use and not in a clinical set up. 7.1.4.4.4 miRView Meso and miRView mets as diagnostic markers

Rosetta Genomics filed for bankruptcy in 2018. But before that, they came up with two miRNA-based diagnostics that are available for clinical use. miRView meso is a reliable test to differentiate mesothelioma (cancer caused by exposure to asbestos) from other types of lung cancer. Mesothelioma usually affects the outer lining of the lungs, whereas lung cancer develops within the lung tissue. The symptoms of both types of cancer are similar, and that underlines the importance of miRView meso test. The test for any sample gives two separate scores, which helps determine if the cancer sample has mesothelioma or not (Benjamin et al., 2010). The expression level of three miRNAs (miR-193-3p, miR-200c, miR-192) is used for this test. miR-200c is expressed in epithelial cancers, including lung cancer but not in mesothelioma. miR-192 and 193a-3p are overexpressed in mesothelioma but not in lung cancer. miR-192 is also expressed in renal cell carcinoma. Hence, the combination of three miRNAs can distinguish between mesothelioma and nonmesothelioma samples. The identification of the tissue of origin of cancer is a challenging clinical problem because the optimal treatment cannot be decided without knowing the primary origin of cancer. miRView mets is a clinical test to determine the tissue of origin based on the expression level of 64 miRNAs. The first-generation miRview mets could differentiate among 25 tumor types on the basis of the expression level of 48 miRNAs (Rosenfeld et al., 2008). The second-generation test enhanced the differentiation ability to 42 tumor types based on the expression level of 64 miRNAs (Meiri et al., 2012). In comparison to miRNAs and lncRNAs, siRNA therapeutics has made the most significant progress in adapting to the clinical setup. This is also highlighted in the number of patent applications for siRNAs (Fig. 7.3). Please refer to Section 7.2 of this chapter for details. Some of the promising siRNA therapeutics at different stages of clinical trials are tabulated in Table 7.3, and a few of them are described in detail below. 7.1.4.4.5 Patisiran (or ONPATTRO) for the treatment of hereditary TTR-mediated amyloidosis

Hereditary TTR-mediated amyloidosis (hATTR) is a rare, inheritable, and fatal disease that results because of mutations in the transthyretin (TTR) gene (Hoy, 2018). The mutation in the TTR gene causes misfolding of TTR protein, which accumulates in multiple sites, including the peripheral nervous system, heart, eyes, kidney, and gastrointestinal tract. hATTR is

Section 4: Novel therapeutic modalities

7.1 Introduction

Section 4: Novel therapeutic modalities

TABLE 7.3 Some of the siRNA therapeutics at different stages of development. Trade name

Target

Function

Disease targeted

Developmental stage

ApoB SNALP (PRO040201)

APOB

Inhibits apolipoprotein B gene

Hypercholesterolemia Phase I clinical trial concluded, terminated as one of the two subjects treated with the highest dose experienced flulike symptoms

AB-729

Undisclosed

Inhibits HBV replication and antigen production

Hepatitis B

Phase 1a/1b clinical trial

ONPATTRO (Patisiran)

TTR

Reduces TTR protein in serum and TTR protein deposits in tissues

Hereditary ATTR amyloidosis

Commercialized, FDA approved in 2018

GIVLAARI (Givosiran)

ALAS1

Reduces levels of neurotoxins aminolevulinic acid and porphobilinogen

Acute hepatic porphyria

Commercialized, FDA approved in 2019

Lumasiran

HAO1

Inhibits oxalate production

Primary hyperoxaluria type 1

Orphan drug designation, awaiting FDA approval

Inclisiran

PCSK9

Reduces low-density Hypercholesterolemia Phase 3 completed lipoprotein (LDL) cholesterol—referred to as bad cholesterol

ARO-AAT

AAT

Inhibits the production of AAT protein

Liver disease associated with alpha-1 antitrypsin deficiency

ARO-APOC3 APOC3

Reduces circulating triglycerides

Hypertriglyceridemia Phase 2

ARO-ANG3

ANGPTL3

Reduces LDL-C

Dyslipidemia

Phase 2

JNJ-3989 (formerly ARO-HBV)

HBV genes

Silences all genes of HBV

Hepatitis B

Phase 2b

Phase 3, FDA fast track designation

Company

Reference

Arbutus Biopharma (formerly Tekmira Pharmaceuticals)

arbutusbio.com

Alnylam Pharmaceuticals

alnylam.com

Arrowhead pharmaceuticals

arrowheadpharma. com

Janssen Pharmaceuticals/ Arrowhead pharmaceuticals

AMG 890 (formerly ARO-LPA)

Lipoprotein (a)

Reduces production of apolipoprotein A, which is associated with increased risk of cardiovascular diseases

QPI-1007

Caspase-2

QPI-1002

Phase 2

Amgen/ Arrowhead pharmaceuticals

Inhibits apoptosis of Nonarteritic anterior retinal ganglion cells in ischemic optic nonarteritic anterior neuropathy ischemic optic neuropathy

Phase 3, orphan drug designation by FDA

Quark pharmaceuticals

quarkpharma.com

p53

Reduces apoptosis, and Prevents delayed allows the cells and tissue graft function in to repair the damage deceased donor kidney transplant patients

Phase 3, orphan drug designation and Fast Track designation by FDAa

PF-655

RTP801

Reduces blood vessel leakage

Macular degeneration, diabetic macular edema

Phase 2

Tivanisiran (SYL1001)

TRPV1

Blocks the perception of ocular pain

Dry eye disease

Phase 2/3

Sylentis

sylentis.com

Prevents oxalate overproduction

Primary hyperoxaluria

Phase 3, FDA granted orphan drug designation and breakthrough therapy designationa

Dicerna Pharmaceuticals

dicerna.com

Nedosiran LDHA (DCR-PHXC)

Cardiovascular disease

a Fast track designation and breakthrough therapy designation are both meant to expedite the development of drugs for serious diseases. There are subtle differences between the two. While fast track designation is given if the new therapy has the potential to address an unmet clinical need and breakthrough therapy designation is given if the new therapy shows significant improvement over existing therapies.

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characterized by polyneuropathy and cardiomyopathy. The traditional therapy for hATTR involved either the symptomatic relief medications or the orthotopic liver transplant, both of which had severe limitations. The medications could sometime exacerbate the disease pathology, and with the liver transplant, there are always issues of availability of organs and the risks associated with the transplant surgery (Kristen et al., 2019). Patisiran is a siRNA therapeutic bound by a lipid nanoparticle (LNP) designed to target TTR mRNA in the liver cells. Patisiran results in degradation of TTR mRNA, leading to a reduction in tissue deposits of TTR. The LNP for patisiran is composed of four lipid moieties, (1) Cholesterol, (2) DLin-MC3DMA [(6Z,9Z,28Z,31Z)-heptatriaconta-6,9,28,31-tetraen-19-yl-4-(dimethylamino)butanoate], (3) DSPC [1,2-distearoyl-sn-glycero-3-phosphocholine], and (4) PEG2000-C-DMG [α-(30 -{[1,2-di (myristyloxy)proponoxy]carbonylamino}propyl)-ω-methoxy, polyoxyethylene] (Zhang, Goel, & Robbie, 2019). The LNP protects the siRNA from the nucleases and helps in its targeted delivery to hepatocytes. The clinical trials with patisiran showed a significant reduction of serum TTR levels in a dose-dependent manner and a significant improvement in the polyneuropathy and cardiac structure and function (Kristen et al., 2019). There were no serious adverse reactions to patisiran, but to mitigate the infusion-related reactions, it was recommended to premedicate the patients with oral acetaminophen and an intravenous corticosteroid, histamine H1 receptor antagonist, and histamine H2 receptor antagonist (Hoy, 2018; Kristen et al., 2019). Patisiran (ONPATTRO) was approved by the FDA for the treatment of the polyneuropathy of hATTR in August 2018. The details about ONPATTRO and its functions are available on the website https://www.onpattro.com/. 7.1.4.4.6 Givosiran (or GIVLAARI) for the treatment of acute hepatic porphyria

Acute hepatic porphyria (AHP) is a genetic disorder of heme biosynthesis, which causes a buildup of neurotoxic precursors δ-aminolevulinic acid and porphobilinogen in the liver. ALAS1 (50 -aminolevulinate synthase 1) is the enzyme that catalyzes the rate-limiting step in heme synthesis. Hyperinduction of ALAS1 is responsible for the accumulation of neurotoxic precursors. The symptoms include stomach pain, neuropathy, and psychiatric symptoms (Bissell, Anderson, & Bonkovsky, 2017). The only known treatment in the clinical domain is the intravenous administration of hemin. However, it has a lot of toxicity issues (Wang, Rudnick, Cengia, & Bonkovsky, 2019) Givosiran is a siRNA therapeutic directed against ALAS1 to prevent AHP phenotype in hepatocytes. The siRNA is bound with N-acetylgalactosamine (GalNAc) residues that specifically bind with asialoglycoprotein receptors on hepatocytes (Scott, 2020). In preclinical trials in mice models and subsequent clinical trials on human subjects, givosiran showed dose-dependent reductions in ALAS1 mRNA as well as the neurotoxic precursors δ-aminolevulinic acid and porphobilinogen. The few adverse reactions of givosiran treatment included nausea, fatigue, injection site reactions, chronic kidney disease, and immune response. The FDA approved Givosiran for AHP in November 2019. The details about GIVLAARI and its functions are available on the website https://www.givlaari.com/. 7.1.4.4.7 QPI1007 for the treatment of nonarteritic anterior ischemic optic neuropathy

Nonarteritic anterior ischemic optic neuropathy (NAION), which leads to blindness or a visual impairment, is characterized by the loss of retinal ganglion cells (RGCs). Caspase-2 is a key protein that is upregulated in RGCs during NAION. The inhibition of caspase-2

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can prevent the apoptosis of RGC (Vigneswara, Berry, Logan, & Ahmed, 2012). Ahmed et al. (2011) had shown that injection of a chemically modified siRNA against caspase-2 in the eye prevented RGC apoptosis and enhanced RGC survival over a period of 30 days. The RGC survival could be extended further up to 12 weeks if the siRNA against caspase-2 was injected every 8 days (Vigneswara & Ahmed, 2016). The chemical modifications incorporated in the siRNA against caspase-2 provided resistance against nucleases in the vitreous humor for at least 24 hours and prevented the siRNA from inducing any innate immune response. This chemically modified siRNA against caspase-2 was later renamed as QPI1007. The preclinical studies in animal models revealed that QPI-1007 is chemically stable in the vitreous humor and is eliminated slowly from there with limited metabolism followed by a rapid clearance from the blood via renal filtration. There was no systemic or genetic toxicity induced by QPI-1007 (Solano et al., 2014). Phase 1 human clinical trials on QPI-1007, initiated in 2010, found it to be well tolerated in patients with NAION. Additionally, there were no serious side effects and no dose-limiting toxicities. Quark Pharmaceuticals was granted the US patent for QPI-1007 in 2016 (Patent No. US9382542B2). Currently, QPI1007 is in Phase 2/3 clinical trials.

7.1.5 Long noncoding RNAs 7.1.5.1 Biogenesis of lncRNAs The lncRNA is a rather vague name when compared with the names of other ncRNAs. It only implies that the ncRNA of this class has sequences greater than 200 bp. Their size varies from a few hundred to tens of thousands of bases. The name has to capture the essence of all types of RNA included in the class, and it cannot be based on their function like siRNA because they do varied functions, and one name cannot encapsulate that. It cannot be based on the specific type of protein it interacts with, like piRNA, because there is no specific protein that all lncRNAs interact with. Some lncRNAs function in the chromatin region, some in the nucleoplasm, and others in the cytoplasm. The name thus seems justified considering that this class of ncRNAs cannot be boxed under a label. The lncRNAs are remarkably different from the small ncRNAs. Unlike small ncRNA, they do not depend on any specific enzymes for their final maturation. The transcription of lncRNAs is quite similar to the protein-coding genes. Most of the lncRNAs are transcribed by Pol II complex, but a few have also been reported to be transcribed by Pol III complex (Khandelwal, Bacolla, Vasquez, & Jain, 2015). They are further subdivided into different classes based on their genomic loci and the direction of their transcription (Khandelwal et al., 2015; Kowalczyk, Higgs, & Gingeras, 2012): (1) intronic lncRNAs (transcribed from the intronic region of the coding genes), (2) intergenic lncRNAs (transcribed from the genomic region between coding genes), (3) sense lncRNAs (transcribed from the exonic region of the coding genes), (4) antisense lncRNAs (transcribed from the same genomic loci as the coding gene but from the opposite strand), (5) promoter lncRNAs (transcribed from promoter sequences), (6) pseudogene lncRNAs (transcribed from pseudogenes), and (7) enhancer lncRNAs (transcribed from enhancer sequences) (Chhabra, 2017). This classification does not ascribe any specific functionality to them. In fact, lncRNAs from different classes can have similar functions.

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Section 4: Novel therapeutic modalities

7.1.5.2 Working mechanisms of lncRNAs The central dogma of life explains the flow of genetic information from DNA to mRNA to protein. The lncRNA can regulate gene expression at all levels of this process, including chromatin remodeling, histone modification, DNA methylation, RNA stabilization, and protein interaction. There are cis-acting lncRNAs that regulate genes in the immediate vicinity of its genetic loci or trans-acting lncRNAs, which regulate genes far from their genetic loci. A few of these functions are highlighted in Fig. 7.1. Some of the lncRNAs alter histone methylation, thereby enhancing or inhibiting the transcription of a specific gene. The lincRNA, HOTAIR, was shown to inhibit the transcription of a number of genes by histone modification (Tsai et al., 2010). Tsai and coworkers had shown that 50 and 30 domains of HOTAIR bind to PRC2 (methylase) and LSD1 (demethylase), respectively. The coordinated targeting of PRC2 and LSD1 brings about the methylation of H3K27 and demethylation of H3K4, both marks of inhibiting gene transcription. HOTAIR was suggested to regulate hundreds of genes, including HOXD genes, through this mechanism (Tsai et al., 2010). The lncRNA, APOA1-AS, was demonstrated to inhibit the transcription of APOA1 through the recruitment of histone-modifying enzymes (Halley et al., 2014). APOA1-AS recruits LSD1 to the region encompassing APOA1 and removes the methylation marks on H3K4. In fact, APOA1-AS was shown to repress the transcription of three genes located in this locus, APOA1, APOA4, and APOC3. Surprisingly, APOA1-AS had no effect on APOA5, a gene lying 28 kb downstream of the affected genes, thereby implying the specificity of APOA1-AS (Halley et al., 2014). Interestingly, APOA4-AS has a role diametrically opposite to APOA1-AS. While APOA1-AS inhibits transcription by histone modification, APOA4-AS stabilizes APOA4 mRNA by interacting with mRNA stabilizing protein HuR (Qin et al., 2016). The expression of both APOA4 and APOA4-AS is enhanced in patients with fatty liver disease. lncRNAs can modulate gene expression by methylating DNA sequences as a result of their interaction with DNA methyltransferases (DNMTs). There are three DNMTs in mammals (DNMT1, DNMT3a, and DNMT3b), which add methyl groups to specific sites in a DNA sequence. The lncRNA, ecCEBPA, was shown to bind with DNMT1 and prevent the methylation of the promoter region of CEBPA (Di Ruscio et al., 2013). This, in turn, induces the expression of CEBPA. DACOR1, another lncRNA identified in 2015, enhances DNA methylation by recruiting DNMT1 to multiple genomic sites (Merry et al., 2015). lncRNAs are also known to modulate gene expression by hybridizing directly with DNA and RNA on the basis of Watson
Crick base pairing. Mondal et al. (2015) had observed that MEG3 regulates TGFβ pathway genes by forming an RNA
DNA triple helical structure with them. Similarly, ANRASSF1 binds the genomic DNA, forms the RNA
DNA triple helix, and regulates the expression of RASSF1 (Beckedorff et al., 2013). RNA
RNA binding is also one of the ways for a few lncRNAs to carry out their function, for instance, PTENP1-AS lncRNA binds the RNA of PTENP1 to enhance its stability and export to the cytoplasm (Johnsson et al., 2013). A few lncRNAs show an interesting liaison with miRNAs. MALAT1 acts as a sponge against miR-145 (Lu et al., 2016) and miR-124 (Liu, Song, Zeng, & Zhang, 2016) to enhance the radioresistance and promote cell growth and cell invasion of high-risk human papillomavirus cervical cancer, respectively. The lncRNAs with this function are often referred to as competing

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endogenous RNA (ceRNA). lncRNAs could also act as a precursor for the generation of miRNAs for instance, H19 is a precursor for miR-675 (Dey, Pfeifer, & Dutta, 2014; Keniry et al., 2012) and the lncRNA, LOC554202 is a precursor for miR-31 (Augoff et al., 2012).

The diverse functions of lncRNAs make it pretty evident that any deregulation in their expression would lead to a pathological condition. lncRNAs have been implicated in numerous diseases. Many of these studies are relatively recent, and different studies talk about different lncRNAs in the same disease pathology. Hence, it is still some time before we get a clear picture of the differential lncRNAs and their importance in the pathophysiology of diseases. This section briefly discusses some of the lncRNAs that have been described in cancers, cardiovascular diseases, neurodegenerative diseases, and ocular diseases. In cancers, the significance of lncRNAs is undeniable (Gutschner & Diederichs, 2012). They play oncogenic as well as tumor suppressor functions in carcinogenesis. MALAT1, a nuclear-localized lncRNA acts as an oncogene across many cancers. Its expression can be used as a marker for predicting metastasis in lung cancer. Inhibiting the function of MALAT1 prevents lung cancer metastasis (Gutschner et al., 2013). The metastatic function of MALAT1 was also seen in other cancers including pancreatic cancer (Jiao et al., 2014), esophageal squamous cell carcinoma (Hu et al., 2015), osteosarcoma (Dong et al., 2014), and melanoma (Tian, Zhang, Hao, Fang, & He, 2014). The tumor suppressor roles of lncRNAs have been described in relation to p53. The binding of MEG3 with p53 is responsible for activating the p53 pathway (Zhou et al., 2007). And in turn, p53 has been shown to induce the expression of multiple lncRNAs, including PINT, lincRNA-p21, and TUG1 (Huarte et al., 2010; Marı´n-Be´jar et al., 2013; Zhang et al., 2014). The studies on the role of lncRNA in cancer are comparatively much more than studies of lncRNA in any other disease. Therefore, in order to make sense of a large amount of data, there was a need to create a resource of lncRNAs with validated roles in cancer. Recently, the Cancer LncRNA Census (CLC) has been created as part of the ICGC/TCGA Pan-Cancer Analysis of Whole Genomes. CLC is a compilation of 122 GENCODE lncRNAs, which are validated to have causal roles in cancer (Carlevaro-Fita et al., 2020). The cardiovascular diseases have been on the rise even after years of intensive research. The role of lncRNAs in cardiovascular diseases has given hope for novel diagnostics and therapeutics. The lncRNAs, CHAST (Viereck et al., 2016), and CHAER (Wang et al., 2016) were found to be responsible for cardiac hypertrophy in mice. Knockdown of either of these lncRNAs was able to prevent hypertrophy and restore normal cardiac function. The lncRNAs, CARL and MDRL were found to be downregulated after MI (Wang, Long, Zhou, Liu, & Zhou, 2014; Wang, Sun, Li, Wang, & Wang, 2014). Overexpression of either CARL or MDRL resulted in enhanced apoptosis by inhibiting miR-539 or -361, respectively, and consequently smaller infarct size in vivo. In contrast, the expression of lncRNA, MIAT was found to be significantly enhanced following MI in vivo. MIAT was found to be instrumental in promoting cardiac fibrosis by inhibiting miR-24, -39, -30, and -133 (Qu et al., 2017). In AD, the upregulation of BACE1 is responsible for the accumulation of amyloid-42 protein aggregates, the hallmark of AD. In healthy individuals, miR-485-5p keeps the

Section 4: Novel therapeutic modalities

7.1.5.3 Expression profile of lncRNAs in disease pathology

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expression of BACE1 in check, but in Alzheimer’s patients, miR-485-5p is unable to bind to BACE1 because of the enhanced expression of the lncRNA, BACE1-AS. BACE1-AS acts as a ceRNA to miR-485-5p, binds to BACE1 mRNA in the binding sites of miR-485-5p, stabilizes it and upregulates its expression (Faghihi et al., 2010). In addition, the lncRNAs, 51A, 17A, NDM29, and BC200 are also implicated in AD (Luo & Chen, 2016). An exhaustive study by Kraus et al. in 2016 on human brain tissues revealed five differentially expressed lncRNAs in PD (lincRNA-p21, Malat1, SNHG1, TncRNA are upregulated, and H19 is downregulated). It was also revealed that the changes in these lncRNAs could serve as a prognostic marker for the disease (Kraus et al., 2017). In a separate study on the circulating leukocytes in Parkinson’s patients, four lncRNAs were found to be upregulated. Out of these, HOTAIRM1 and AC131056.3-001 were hypothesized to promote PD phenotype as both these lncRNAs induced apoptosis of SH-SY5Y, the cell line similar to dopaminergic neurons (Fan et al., 2019). In Huntington disease, the lncRNAs, NEAT1, and MEG3 were found to have a major role in its pathogenesis (Chanda et al., 2018). Both NEAT1 and MEG3 were increased in Huntington disease, and their knockdown was capable of decreasing the huntingtin aggregates, the characteristic feature of Huntington disease. Ocular diseases include a spectrum of eye diseases including glaucoma, corneal disease, proliferative and diabetic retinopathy, and retinoblastoma. In age-related cataract disease, the lncRNA, MIAT, is upregulated in the blood plasma and aqueous humor. The knockdown of MIAT can reduce the incidence of posterior capsule opacification (a complication of cataract surgery) by preventing the proliferation and migration of human lens epithelial cells (Shen et al., 2016). In the case of Glaucoma, MALAT1 was shown to have a protective role as it prevents the apoptosis of RGCs by promoting the PI3K/AKT pathway (Li et al., 2018). Gas5 was also shown to be antiproliferative and antiapoptotic in RGCs and, therefore may have a role in the pathogenesis of glaucoma (Xu & Xing, 2018). In other ocular diseases too, the lncRNAs have been implicated, for instance, NEAT1 in corneal neovascularization (Bai, Lv, Wang, Sun, & Zhang, 2018), MALAT1 and Sox2OT in diabetic retinopathy (Li, Wang, et al., 2017; Liu et al., 2014), and BANCR in retinoblastoma (Su et al., 2015). The lncRNAs in ocular diseases have recently been reviewed in detail (Zhang, Dong, Wang, Gao, & Lv, 2019) 7.1.5.4 lncRNAs—from bench to clinic lncRNAs do not follow the rules that are generally ascribed to miRNAs and siRNAs. Their sequence length varies from a few hundred to many thousands. They maybe localized in the nucleus or the cytoplasm and some cases in both. They can show cis effects by directly modulating the adjacent gene or trans effect by regulating a distant gene. With such varied characteristics and functions, it has taken a long time to understand the mechanisms of lncRNAs. As a result of which, they have lagged behind small ncRNAs in transitioning to the clinical setup. However, with many studies focusing on lncRNA, their improved understanding and technological innovations, lncRNAs have started their journey from the bench to clinics. Many lncRNAs have been identified as biomarkers, particularly in cardiovascular diseases and cancers. The two fundamental requirements for a biomarker are that their expression changes during the course of a disease and that their expression is under spatiotemporal regulation. Since lncRNAs follow both these rules, they are extremely attractive as biomarkers. The lncRNA

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7.1 Introduction

therapeutics and lncRNA biomarkers in different stages of their journey from bench to clinic have been compiled in Tables 7.4 and 7.5, respectively. A few of these lncRNAs are discussed in this section.

The lncRNA, H19, is a paternally imprinted gene expressed in a majority of the bladder tumors but suppressed in the healthy bladder. H19 is required for tumor initiation, progression, and metastasis (Raveh, Matouk, Gilon, & Hochberg, 2015). The Inodiftagene vixteplasmid therapy or BC-819 therapy has nothing to do with the function of H19 as such TABLE 7.4 Potential lncRNA therapeutics currently being experimentally explored. lncRNA

Function

Disease targeted

Developmental stage

Reference

H19 overexpression Trade name Inodiftagene vixteplasmid therapy (formerly BC-819)

Induces the expression of diphtheria toxin gene in the cells expressing H19 leading to cell death specifically in those cells

BCGunresponsive nonmuscleinvasive bladder cancer

Fast track designation from the FDA, discontinued after Phase 3 because of low response in patients

Anchiano Therapeutics (anchiano. com)

TUG1 inhibition

TUG1 promotes cancer stem cell formation by sponging miR-145. Inhibiting TUG1 reduces tumor growth

Glioma

Reduced tumor growth in xenograft mice models

Katsushima et al. (2016)

SMN-AS1 inhibition

SMN deficiency causes SMA. Inhibition of SMN-AS1 enhances SMN expression

Spinal Improved survival of severe muscular SMA mice atrophy (SMA)

d’Ydewalle et al. (2017)

CHAST inhibition

CHAST negatively regulates Plekhm1 (located on the opposite strand of CHAST), impedes cardiomyocyte autophagy, and drives hypertrophy. Inhibiting CHAST reverses the phenotype

Cardiac hypertrophy

Prevented disease in mice models and attenuated established cardiac hypertrophy with improved heart function

Viereck et al. (2016)

SCN1ANAT Trade name OPK88001 (CUR-1916) inhibition

SCN1ANAT is an endogenous repressor of SCN1A, inhibition of which increases the expression of SCN1A and thereby curing Dravet syndrome

Dravet syndrome

Clinical trial yet to begin, Orphan Drug Designation by FDA

OPKO Health Hsiao et al. (2016)

SAMMSON inhibition

SAMMSON inhibition sensitizes melanoma to MAPK-targeting therapeutics in vitro as well as in patientderived xenograft models

Melanoma

Reduced melanoma growth in mice models

Leucci et al. (2016)

Section 4: Novel therapeutic modalities

7.1.5.4.1 Inodiftagene vixteplasmid therapy (BC-819) for bladder cancer

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TABLE 7.5 Potential lncRNA biomarkers currently being experimentally explored. Target

Role

Disease targeted

Developmental stage

Reference

3 lncRNAs ENST00000435885.1, XLOC_013014 and ENST00000547963.1

Predicts the survival of patients with OSCC

esophageal squamous cell carcinoma (OSCC)

Prognostic marker

Li et al. (2014)

Prostate cancer antigen 3 (PCA3)

Detects prostate cancer and reduces the need for prostate biopsies

Prostate cancer

FDA approved in 2012

Ferreira et al. (2012), Groskopf et al. (2006)

LIPCAR

Predicts future death in patients with heart failure

Cardiac remodeling

Prognostic marker

Kumarswamy et al. (2014)

but exploits the tumor-specific expression of H19 (Ohana et al., 2002). BC-819 is an expression vector of 4.5 kb carrying a gene for the A fragment of diphtheria toxin (DT-A) under the control of the 832 bp H19 regulatory sequence. As a result, DT-A will only be expressed in the cells where the transcription factor for the H19 promoter exists, and those cells will express the toxic gene leading to cell death. The in vivo injections of BC-819 in the mice model of bladder cancer resulted in a significant decrease in the tumor size with no side effects (Ohana et al., 2002). US FDA granted the fast track designation to Anchiano Therapeutics (previously BioCanCell) for BC-819 in 2015. After a few successful clinical trials, in 2019 Anchiano Therapeutics discontinued the Phase 2 study evaluating BC-819 for BCG-unresponsive nonmuscle-invasive bladder cancer because of low response in patients. The company believed that it would not be able to secure regulatory approval based on the results. 7.1.5.4.2 OPK88001 (CUR-1916) for Dravet syndrome

Dravet syndrome is a genetic disorder characterized by severe seizures as it affects the cell’s ability to generate and transmit electrical signals. This is a manifestation of the heterozygous loss of function mutation in the SCN1A gene. There is currently no treatment for this condition. Since there are two alleles of a gene and mutation in only one of the alleles causes Dravet syndrome, the only therapeutic strategy is to enhance the protein production from the healthy allele of SCN1A. This would help bring the protein levels within the normal range and thus help patients overcome this condition. OPKO Health, in collaboration with other researchers, identified the lncRNA, SCN1ANAT, which represses the expression of SCN1A gene. They used an inhibitor of SCN1ANAT, referred to as CUR-1916, to remove the lncRNA repression of SCN1A, thereby, resulting in enhanced expression of SCN1A (Hsiao et al., 2016). The efficacy of CUR-1916 was tested in mice models as well as the African green monkey model, and significant improvements in the seizure phenotype, as well as neuronal electrophysiology, was observed (Hsiao et al., 2016). CUR-1916 has been given orphan drug designation by the FDA. The clinical trials in humans have not been initiated as yet.

7.2 Patent landscape of noncoding RNA

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The first line of diagnostics for prostate cancer patients is the prostate-specific antigen (PSA) test carried out in blood, followed by a biopsy to confirm the disease. PSA protein is produced by the normal prostate gland, but the level of PSA is elevated in prostate cancer patients. Elevated PSA can also result from other conditions, and thus the PSA test often gives a lot of false positives resulting in a lot of unnecessary biopsies. Bussemakers et al. (1999) discovered that lncRNA, PCA3 (DD3), is significantly overexpressed in prostate tumor samples. The expression of PCA3 was found to be highly specific for prostate cancer, as its expression was not detected in any other tumor sample or human tissue. However, there seems to be no correlation between the tumor grade and expression of PCA3 (de Kok et al., 2002). PCA3 is responsible for prostate cancer cell growth and viability as knockdown of PCA3 reduced cell growth and induced apoptosis (Ferreira et al., 2012). PCA3 is a standard urine test and has been shown to have higher specificity than PSA. However, it is less sensitive than PSA, and therefore it is only used in combination with the PSA test (Lu & Madu, 2014). After the clinical trials, PCA3 was approved by the FDA in 2012 to determine the need for repeat prostate biopsies in patients who have a previous negative biopsy (Cui et al., 2016).

7.2 Patent landscape of noncoding RNA A patent is a type of intellectual property, by virtue of which the government gives the patent holder exclusive rights to commercialize the product for a stipulated period of time, which is usually 20 years. The three key requirements for obtaining the patent for a product are that it should be novel, has an inventive step, and has industrial applicability. The patents granted in any given field are a measure of innovation in that field. Many of the scientific techniques and innovations, including therapeutic antibodies and recombinant products, have been patented (Picanco-Castro, Freitas, Bomfim, & Russo, 2014; Storz, 2015). The patents usually help to transfer the technological innovations in the academic sector to the industrial set up. The patenting safeguards the financial interests of the companies who spend a huge amount of money to bring a product into the market. In a way, patenting promotes scientific innovation. Over the past few years, there has been a significant increase in the number of patents granted in the area of ncRNA. A simple search using the terms “miRNA,” “siRNA,” and “lncRNA” in USPTO (http://patft.uspto.gov/ netahtml/PTO/search-bool.html) pegs the total number at 8992 for miRNA, 22,632 for siRNA, and 276 for lncRNA. The same search in EPO (http://www.epo.org/searchingfor-patents.html) gives 27,082 for miRNA, 54,088 for siRNA, and 2093 for lncRNA while WIPO patent search (https://patentscope.wipo.int/search/en/search.jsf) pegs the number at 7011 for miRNA, 9106 for siRNA, and 421 for lncRNA (Fig. 7.3A). The number of patents indicates the progress made in different classes of ncRNAs. It is rather remarkable to see that the majority of the patents for ncRNAs pertain to the area of cancer. A stringent search was done using USPTO, where a class of ncRNA and a specific disease or a biological term was used with an added condition that both the search terms have to be present in the patent abstract. This analysis revealed that 141 siRNA patents out of 914, 100

Section 4: Novel therapeutic modalities

7.1.5.4.3 PCA3 as a diagnostic marker for prostate cancer

Section 4: Novel therapeutic modalities

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miRNA patents out of 525, and 5 lncRNA patents out of 8 are all associated with some form of cancer (Fig. 7.3B). It is evident that the stringent search significantly reduced the number of patents for each class but highlighted the relation between ncRNAs and cancer. For the previous search in USPTO, the search term could be in any of the fields, including title, abstract, or main body of the patent. One of the earliest patents to be granted in the area of RNAi was filed by Fire, Mello, and their coworkers at Carnegie Institution. The patent (US6506559B1) was called “Genetic inhibition by double-stranded RNA,” and it expired in 2018. Usually, the patents in ncRNA fields have claims over the product, the oligonucleotide structure that increases

FIGURE 7.3 Patent profile of noncoding RNA. Fig. 7.3A The USPTO, EPO, and WIPO patent search was used with keywords “miRNA,” “siRNA,” and “lncRNA” to retrieve the total number of patents in each area. The number of patents in the siRNA field is the highest followed closely by miRNA. In comparison, the number of lncRNA patents is quite less, which is understandable considering that we have only just begun to understand the significance of lncRNAs. It is important to note that different search terms would give different data in the aforementioned patent databases for instances while miRNA search shows 7011 results, miRNA search shows 4191 results and the query miRNA or miRNA shows 10,020 results in WIPO patent search. Fig. 7.3B A stringent search was done using USPTO where a class of ncRNA and a specific disease or a biological term was used with an added condition that both the search terms have to be present in the patent abstract. These search parameters significantly reduce the number of patents that are shown in Fig. 7.3A but the purpose, here, was to highlight that majority of the patents in ncRNAs are in the area of cancer.

189

the efficacy of ncRNA, or the delivery mechanism of ncRNA. Thomas Tuschl and coworkers had filed for a patent titled “RNA interference mediating small RNA molecules” (US7056704B2), which lay claims on siRNA duplexes with 30 overhangs showing efficient cleavage of the target RNA. This patent has 29 April 2021 as its expiration date. It was initially believed that such broad patents granted in the early days of ncRNA might hamper the innovation and the growth of ncRNA therapeutics. However, it has taken about two decades for the first ncRNA drug to come in the market, and by the time other ncRNA drugs hit the market, most of these patents will be nearing their expiration date. Many of the patents protect the companies’ innovative delivery mechanisms of ncRNAs. Alnylam Pharmaceuticals has patents covering stable nucleic acid-lipid particles (SNALPs) and GalNAc-siRNA technology. SNALP is a LNP-based siRNA drug that comprises a special type of lipids like polyethylene glycol (PEG), cholesterol and is suitable to deliver siRNA in many cells and tissues. GalNAc-siRNA is a novel mechanism that exploits the hepatocyte receptor for siRNA delivery. The siRNA is conjugated with GalNAc via a triantennary, which binds with high affinity to the asialoglycoprotein receptor on the hepatocytes and is taken up by the cells through endocytosis. Silence Therapeutics has come up with its own delivery mechanisms, referred to as AtuPLEX and DACC. AtuPLEX is a lipid-based siRNA conjugate that combines siRNA with three-lipid liposomes and helps deliver siRNA to endothelial cells of different organs. DACC is also a lipid-based siRNA conjugate but has different biopharmaceutical properties and delivers siRNA to the pulmonary vascular endothelium (Esmond & Chung, 2014). Likewise, many companies have patented their innovative delivery mechanisms of ncRNAs. In 2016 Quark Pharmaceuticals was granted the US patent for QPI-1007 (Patent No. US9382542B2). It is a therapeutic siRNA for the treatment of NAION. The apoptosis of RGCs is the characteristic feature of NAION. QPI-1007 prevents the apoptosis of RGCs by inhibiting the expression of proapoptotic protein, caspase-2. Today, many of the companies working in the area of miRNA and siRNA therapeutics have a huge patent portfolio. Regulus Therapeutics, alone, claims to have more than 250 patents and patent applications related to miRNA drug products, drug design, delivery technologies, and therapeutics. In addition to protecting their innovations using patents, some of the companies engaged in ncRNA therapeutics also opt for trade secrets to protect their innovations. Patents give the innovator exclusive rights to commercialize their product for a stipulated tenure, but the details of the innovation are publically available, whereas, in case of the trade secret, the innovation is commercialized without anyone in public knowing the details. It is up to the company to decide whether to apply for a patent or keep it as a trade secret. Usually, the simpler drug molecules are protected by patents, and the more complex drug molecules are protected through trade secrets. The more detailed information on the patent profile of ncRNAs is available in the previously published literature (Chakraborty, Sharma, Sharma, Doss, & Lee, 2017; Esmond & Chung, 2014).

7.3 Bottlenecks in the use of noncoding RNAs as biomarkers/therapeutics The discovery of ncRNAs in the last two decades has been one of the major scientific achievements. While the detailed molecular mechanism of many of the ncRNAs is still

Section 4: Novel therapeutic modalities

7.3 Bottlenecks in the use of noncoding RNAs as biomarkers/therapeutics

Section 4: Novel therapeutic modalities

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being explored, it has, nevertheless, provided the scientists with an excellent alternative to modulate gene expression. The inhibition of RNAs, the silencing of genes, the modification of chromatin, and many more functions of ncRNAs have enhanced our understanding of basic biological processes by leaps and bounds. The implication of ncRNAs in disease pathways makes it inevitable that the ncRNAs would transition from bench to the clinics. A few of them have already reached the clinical stage, and many of them are quickly gearing up to enter the pharmaceutical market (Tables 7.1
7.5). One of the major bottlenecks in the use of ncRNAs as biomarkers, especially where the circulating ncRNA is concerned, is their lack of stability. These ncRNAs have low copy numbers and are more prone to degradation, thus impacting their overall use as diagnostic and prognostic markers. The technological innovations, which make it possible to accurately detect low copy number RNA species, could help in overcoming this limitation. Over the last few years, the ncRNAs have been shown to be associated with exosomes, which enhance their stability and prevent their degradation (Cheng, Sharples, Scicluna, & Hill, 2014; Ge et al., 2014; Li et al., 2015; Liu, Zhang, Wang, & Zhang, 2017). There are many studies in the literature that shows both miRNA and lncRNA are present in the exosomes (Bhome et al., 2018; Sun, Shi, Yang, Liu, & Zhou, 2018; Sun, Yang, Zhou, Wang, & Song, 2018; Yousefi et al., 2020). Therefore developing the ncRNA biomarkers, which are associated with exosomes, could help harness the full potential of ncRNAs as biomarkers. One of the issues with miRNAs therapeutics is that a single miRNA can have hundreds of mRNA targets, and even though it may have great potential as a therapeutic, it may also have some unwarranted effects. In order to effectively employ miRNA therapeutics, it is important to choose tissue-specific miRNA. With lncRNAs, the size of lncRNA could be a huge limitation in some cases. The use of ncRNAs is also limited by their delivery mechanisms. Some of the companies have devised innovative ways to deliver them to the target tissues while making sure that they are not degraded in the process. One of the ways is to use naked siRNAs (without any carriers). These naked oligos are usually more prone to degradation but have been shown to be effective in specific organs such as the brain, eyes, ear, skin, and so on. QPI-1007 and Tivanisiran are two such therapies that use the naked siRNA approach (Morrison, 2018). Miravirsen, the miRNA therapy targeting HCV infection, also used naked oligonucleotide (Burnett & Rossi, 2012). However, miravirsen uses modified LNA and phosphorothioate to enhance its stability and efficacy. Liposome-formulated oligos is another way of delivering therapeutic ncRNA into the cells. The structure of liposome-formulated siRNA consists of an inner aqueous layer surrounded by the outer lipid bilayer, which works by fusing with the cell membrane and delivering the oligo via endocytosis. Many pharmaceutical companies have developed additional technologies to modify the oligo in a way so as to enhance its efficacy or stability. Arbutus Biopharma developed SNALPs where the lipid bilayer is coated with PEG. The SNALPs protect the ncRNA oligos from nucleases when released into the cytoplasm by endocytosis. PRO-040201 (ApoB SNALP) is an example of this mechanism. Another interesting way is to conjugate these oligos with a ligand that recognizes the receptor of the target cells. GalNAc is one such ligand that recognizes the receptor on hepatocytes. Alnylam Pharmaceuticals came up with the strategy of using siRNA-GalNac conjugates and have effectively used it in many of their siRNA therapies, including ONPATTRO (Patisiran) and GIVLAARI (Givosiran).

7.4 Conclusions and future perspectives

191

7.4 Conclusions and future perspectives One of the major pursuits of scientific research is to safeguard human health by early detection of pathological conditions and the development of therapeutics to cure the diseases. Ever since Fire and Mello won the Nobel Prize in 2006 for RNAi, there have been huge expectations to see the quick adaptation of ncRNAs in the clinic. In retrospect, this was unwarranted as opening up a new class of pharmacological drugs is never easy. It takes extensive research, a lot of time and money to discover the ideal drug candidate, develop its delivery mechanism, do the in vivo assays, and proceed to the clinical trials. This initial optimism faltered soon as the expectations were not met. But with time, the understanding of ncRNAs has improved, and considerable success has been achieved in transitioning the ncRNAs to the clinics. The timeline of important events in the journey of ncRNAs is depicted in Fig. 7.4. Many companies have built a strong patent portfolio on ncRNAs, and there are quite a few therapeutics and biomarkers based on ncRNAs already approved for clinical use. This number is bound to go up in the near future, considering the number of companies working in this area. The tissue-specific expression and the differential expression of ncRNAs in disease pathologies have made it comparatively easier to develop them as diagnostic and prognostic markers. For instance, miRview meso, miRview mets, and PCA3 are already approved as biomarkers. The biomarker field of ncRNAs is dominated by miRNAs and, to some extent lncRNA since there is a significant gap in the knowledge of endogenous siRNAs. But the therapeutics field of ncRNA is mostly filled with siRNAs and, to a smaller extent miRNAs. The functions of lncRNA were uncovered much later, which justifies the slow adaptation of lncRNA in therapeutics in comparison to miRNAs

FIGURE 7.4 The timeline of landmark events in the journey of ncRNAs. ncRNAs, Noncoding RNA.

Section 4: Novel therapeutic modalities

It is important to note that each delivery mechanism has its own challenges of efficacy, toxicity, stability, specificity, and cost. The best delivery mechanism needs to be determined on a case by case basis.

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and siRNAs. With increased understanding, the other ncRNAs, including piRNAs, snoRNAs, and circRNAs will soon be entering the landscape of biomarkers and therapeutics. The world of ncRNAs is growing at a rapid pace, and with it grows the optimism to find definitive disease biomarkers and therapeutics.

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C H A P T E R

Peptide-based hydrogels for biomedical applications Debika Datta and Nitin Chaudhary O U T L I N E 8.1 Introduction

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8.2 Peptide-based hydrogelators 8.2.1 β-Sheet forming peptides 8.2.2 α-Helical peptides

204 204 214

8.3 Biomedical applications 8.3.1 Therapeutic delivery

215 216

8.3.2 Scaffold for regenerative medicine 218 8.3.3 Wound dressing 219 8.3.4 Antimicrobial agents 220 8.4 Conclusion, limitations, and future directions 221 References

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8.1 Introduction Self-assembly of molecules through noncovalent interactions is pervasive. Assembly of amphiphiles into micelles and vesicles, the formation of molecular crystals and colloids, the folding of proteins and nucleic acids, and the quaternary structures of proteins are some of the examples of self-assembly. Self-assembly of molecules into ordered structures is central to life. The very reason for the existence of living organisms is the self-assembly of molecules into ordered superstructures that can interact with each other. Taking inspiration from a large number of examples of molecular self-assembly in living organisms, material scientists are getting increasingly interested in the bottom-up approach of nanofabrication. As far as self-assembly of biological molecules is concerned, proteins and peptides are perhaps the most versatile molecules. The amino acids that make proteins have very Department of Biosciences and Bioengineering, Indian Institute of Technology Guwahati, Guwahati, India

Translational Biotechnology DOI: https://doi.org/10.1016/B978-0-12-821972-0.00003-4

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© 2021 Elsevier Inc. All rights reserved.

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diverse chemistry. The chemical diversity is manifested in the structures that proteins can fold into and functions they can perform. Proteins form some of the most complex machines known in the universe. The principles of protein folding are reasonably well known and allow designing molecules that can fold and function in predictable ways. Peptides, the shorter chains of amino acids, are particularly attractive. They can easily be synthesized using well-established methods. The chemical synthesis also permits expanding the chemical and structural diversity through the incorporation of nonnatural amino acids. Besides, they can readily be tagged with nonpeptidic moieties. Peptides have indeed been shown to self-assemble into a variety of ordered self-assembled structures that include vesicles, tubes, rods, fibers, ribbons, and sheets (Gazit, 2007; Hauser & Zhang, 2010; Zhao & Zhang, 2007) (Mandal, Nasrolahi Shirazi, & Parang, 2014). The assembly is mediated via noncovalent interactions such as hydrophobic interactions, aromatic stacking interactions, H-bonding, and electrostatic interactions. Peptide-based superstructures have displayed interesting material properties and are promising candidates for applications in photonics (Apter, Lapshina, Handelman, Fainberg, & Rosenman, 2018; Handelman, Apter, Lapshina, & Rosenman, 2018), nanoelectronics (Ardona & Tovar, 2015; Tao, Makam, Aizen, & Gazit, 2017), biosensors (Khalilzadeh et al., 2015; Puiu & Bala, 2018), and catalysis (Paul, Basu, Das, & Banerjee, 2018; Zozulia, Dolan, & Korendovych, 2018). Among the various emergent properties of the self-assembled peptides, the dispersing solvent’s gelation is particularly interesting as far as biomedical applications are concerned. Peptides have been shown to cause gelation of aqueous solutions (hydrogelation) as well as that of organic solvents (organogelation). As most organic solvents are incompatible with living systems, water-based gels (hydrogels) have been explored in reasonably great detail for biomedical applications (Dasgupta, Mondal, & Das, 2013). Hydrogels can act as a matrix for growing cells in three dimensions, thereby finding applications in tissue engineering and regenerative medicine (Matson & Stupp, 2012). They can trap small molecules, thus serving as drug-delivery vehicles (Li, Wang, & Cui, 2016). This chapter reviews the various classes of hydrogelating peptides and their biomedical applications (Table 8.1).

8.2 Peptide-based hydrogelators 8.2.1 β-Sheet forming peptides A staggering majority of peptide hydrogels are composed of β-sheet rich fibrillar structures. The peptide molecules self-assemble into fibrillar superstructures that entangle to form a three-dimensional (3D) network wherein water molecules get fixed. The following subsections discuss the major classes of β-sheet peptide hydrogelators. It is important to mention here that the classification is rather loose, and several features are shared by the peptides listed in different classes. 8.2.1.1 Peptides end-capped with aromatic moieties Aromatic interactions play a critical role in the self-assembly of small aromatic molecules. Reported by Brenzinger in 1892, N,N-dibenzoyl-L-cystine (1, Fig. 8.1) is probably one of the first well-documented examples of hydrogelation based on an aromatic amino acid

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8.2 Peptide-based hydrogelators

Peptide class

Subclass

Examples

References

β-Sheet peptides

Peptides with aromatic N-terminal cap

Fmoc-protected peptides Naphthyl-protected peptides

Loic (2017)

Designed peptides without aromatic caps

K24, P11-I, P11-II, DVFF, DFFV

Aggeli et al. (1997), (2001) Marchesan, Easton, Kushkaki, Waddington, and Hartley (2012)

Amyloidogenic proteins, peptides, and their analogs

α-Synuclein, lysozyme, hIAPP, Aβ16
20, other Aβ-derived peptides, and their analogs

Bhak, Lee, Park, Cho, and Paik (2010), Datta, Kumar, Kumar, Nagaraj, and Chaudhary (2019a, 2019b), Jean, Lee, Hodder, Hawkins, and Vaux (2016), Krysmann et al. (2008), Yang, Li, Yao, Yu, and Ma (2019)

Amphiphilic peptides

CH3CO-(AEAEAKAK)2-NH2 CH3CO-(RARADADA)2-NH2 NH2-FEFEFKFK-OH and NH2-FEFKFEFK-OH

Mohammed, Miller, and Saiani (2007), Zhang et al. (1995), Zhang, Holmes, Lockshin, and Rich (1993)

Peptide amphiphiles

Alkylated peptides

Hendricks, Sato, Palmer, and Stupp (2017)

Designed β-hairpin peptides

MAX family of peptides (Fig. 8.4)

Haines et al. (2005), Pochan et al. (2003)

PEGylated peptides

PEGylated tetra-phenylalanine

Tzokova et al. (2009)

Block peptides such as KxLy, ExLy, KxLyKx, KxLyKzLyKx, and coiled-coil peptides such as SAF-p1 (16), SAF-p2 (17)

Breedveld et al. (2004), Deming (2005), Li and Deming (2010), Nowak et al. (2002), Nowak, Sato, Breedveld, and Deming (2006), Pandya et al. (2000)

PEGylated coiled-coil peptides

Jing, Rudra, Herr, and Collier (2008)

α-Helical peptides

PEGylated peptides hIAPP, Human islet amyloid polypeptide.

(Brenzinger, 1892). Hydrogelation by N,N-dibenzoyl-L-cystine was subsequently reported by Gortner and Hoffman as well in 1921 (Gortner & Hoffman, 1921). The molecule is not a peptide per se, but the amino groups of cystine are indeed protected with benzoyl moiety through an amide linkage. The N,N-dibenzoyl-L-cystine gel was characterized almost a century later and aromatic π
π interactions were found to play a critical role in the selfassembly and gelation. Janmey and coworkers, in 1995, reported hydrogelation by the Nterminal Fmoc-protected dipeptides, namely, Fmoc-Leu-Asp, Fmoc-Ala-Asp, and FmocIle-Asp (Vegners, Shestakova, Kalvinsh, Ezzell, & Janmey, 1995). Janmey’s study reinforced the role of aromatic stacking interactions in the hydrogelation by small aromatic molecules. Bing Xu and coworkers subsequently reported other Fmoc-protected dipeptide hydrogelators (Zhang, Gu, Yang, & Xu, 2003). Interestingly, the Fmoc-DAla-DAla hydrogel could be converted to the sol phase by vancomycin, an example of ligand-receptor interaction. Coassembly of the antiinflammatory drug Fmoc-L-Leu with Nε-Fmoc-L-lysine was also

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TABLE 8.1 The major classes of hydrogelating peptides.

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FIGURE 8.1 The structures of N,N-dibenzoyl-L-cystine (1), the Fmoc-protected amino acids (2, 3, 5, 6), and the Fmoc-Phe-Phe dipeptide (4) hydrogelators.

shown to form distinct hydrogels (Yang, Gu, Zhang, Wang, & Xu, 2004). The amino acids that can participate in π
π interactions, understandably, gained considerable attention in the context of hydrogelation. All three naturally occurring amino acids, however, are nongelators at reasonable concentrations. Xu and coworkers, however, found that Fmoc-L-Tyr (2, Fig. 8.1) is a potent hydrogelator (Yang et al., 2004). They reported the enzymetriggered gelation of Fmoc-phosphotyrosine. Fmoc-phosphotyrosine is not a gelator; hydrolysis of the phosphoester bond by alkaline phosphatase generates unphosphorylated Fmoc-Tyr that self-assembles into fibrils resulting in a distinct hydrogel. The 0.2 wt% Fmoc-Tyr hydrogel is reported to have a storage modulus of around 1000 Pa. Triggerinduced hydrogels are particularly interesting as the hydrogelator molecules can be retained in the sol phase as long as the trigger for gelation is not given. Spectroscopic characterization of the hydrogels suggested the Fmoc-L-Tyr molecules to be arranged in an antiparallel orientation with an extended conformation. Adams and coworkers subsequently reported the hydrogel formed by Fmoc-Phe (3, Fig. 8.1) through gradual acidification (Sutton et al., 2009). The Fmoc-Phe hydrogel, however, showed at least 2
3 orders of magnitude lower storage modulus compared to the Fmoc-Tyr hydrogel prepared under identical conditions. Both Fmoc-Phe and Fmoc-Tyr gels could trap the model dyes such as Naphthol Yellow and Direct Red that showed sustained release in water. Ulijn and coworkers reported Fmoc-dipeptide gels that could serve as matrices for 3D cell culture (Jayawarna et al., 2006). Reches and Gazit (2003) reported the formation of highly ordered nanotubes by di-phenylalanine. The tubes could be used to cast metal nanowires but did not form hydrogels. Gazit and coworkers subsequently investigated the self-assembly of Fmoc-Phe-Phe (4, Fig. 8.1). The Fmoc-protected peptide, unlike Phe-Phe, self-assembled into fibers that resulted in optically transparent hydrogels with an impressive storage

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modulus of about 10,000 Pa for 0.5 wt.% gel (Mahler, Reches, Rechter, Cohen, & Gazit, 2006). Using spectroscopic and X-ray diffraction (XRD) data, Ulijn and coworkers proposed antiparallel β-sheet arrangement for the Fmoc-Phe-Phe molecules with dangling Fmoc moieties. Intersheet stacking of Fmoc moieties is proposed to be responsible for fibril formation (Smith et al., 2008). A large number of Phe-containing Fmoc-protected hydrogelating peptides are reported in the literature (Tao, Levin, Adler-Abramovich, & Gazit, 2016). Fmoc-Phe (3) and Fmoc-Phe-Phe (4), therefore, are attractive starting scaffolds to design hydrogels with desired properties (Diaferia, Morelli, & Accardo, 2019). Nilsson and coworkers investigated the hydrogelation propensity of Fmoc-protected pentafluorophenylalanine (Fmoc-Phe-F5) (5, Fig. 8.1) and found the Fmoc-Phe-F5 hydrogel to be of higher storage modulus compared to the Fmoc-Tyr gels (Ryan, Anderson, Senguen, Youngman, & Nilsson, 2010). Unlike Fmoc-Tyr, the hydrogels formed by Fmoc-Phe-F5 are proposed to assemble in the parallel orientation. Using Fmoc-Phe-F5 and Fmoc-Phe-3F (6, Fig. 8.1) as the model peptides, Nilsson and coworkers investigated the role of C-terminus in hydrogelation (Ryan, Doran, Anderson, & Nilsson, 2011). The C-terminal acids displayed pH-dependent gelation; rigid hydrogels were obtained at acidic pH, whereas weaker gels were formed at neutral pH. The C-terminal methyl esters self-assembled slowly without causing gelation. The C-terminal amides assembled rapidly at both acidic and neutral pH but resulted in rather unstable gels. Nanda and Banerjee (2012) prepared proteolytically stable hydrogel from β-amino acid
containing Fmoc-dipeptides, namely, Fmoc-βAla-Val and Fmoc-βAla-Phe. The 1.5 wt.% hydrogels displayed storage moduli of .30,000 Pa and are promising drug carriers. Das and coworkers synthesized designed Fmoc-peptide cationic amphiphiles by tagging pyridinium moiety at the C-terminus of Fmoc-amino acid and Fmoc-dipeptides (Debnath, Shome, Das, & Das, 2010). The peptides caused hydrogelation as well as displayed activity against both Gram-positive and Gram-negative bacteria. Ulijn and coworkers proposed an interesting reversed-hydrolysis triggered selfassembly and hydrogelation (Toledano, Williams, Jayawarna, & Ulijn, 2006). Coupling of an Fmoc-amino acid to the otherwise nongelating dipeptides by thermolysin from Thermoproteolyticus rokko resulted in hydrogelating tripeptides (Fig. 8.2). Compared to hydrolysis, a reverse hydrolysis is an interesting approach as no byproducts are produced. The hydrogelation by Fmoc-peptides prompted the researchers to explore the hydrogelinducing propensities of other bulky aromatic moieties. Xu and coworkers investigated the

FIGURE 8.2 Synthesis of hydrogelating peptides using reverse hydrolysis.

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FIGURE 8.3 The precursor molecule designed by

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Xu and coworkers that form enzyme-trigged photoresponsive hydrogel (Li et al., 2010).

naphthyl and its derivatives (Yang, Liang, Ma, Gao, & Xu, 2007). Nap-Gly-Ala was reported to form hydrogels under acidic conditions at a concentration as low as 0.07 wt.%. Subsequently, the same group reported a naphthyl-peptide system harboring phosphotyrosine and azobenzene moieties (7, Fig. 8.3) (Li, Gao, Kuang, & Xu, 2010). Azobenzene is a photochromic compound; the ratio of cis to trans conformation can be changed using the light of suitable wavelengths. Enzymatic hydrolysis of phosphoester bond of 7 rendered the peptide hydrogelating when the azobenzyl moiety was in trans configuration. A reversible gel
sol transition could then be achieved using the light of a suitable wavelength. Ulijn and coworkers subsequently used the azobenzene as the Nterminal aromatic moiety to design peptides that form enzyme/light-triggered hydrogels (Sahoo, Mohan Nalluri, Javid, Webb, & Ulijn, 2014). Responsive hydrogels are particularly useful materials, and many photoresponsive, pH-responsive, salt-responsive, and thermoresponsive hydrogels are reported in the literature and reviewed elsewhere (Dasgupta et al., 2013; Loic, 2017; Ryan & Nilsson, 2012). Various other aromatic moieties, other than Fmoc and Nap, have been used in the design of short hydrogelating peptides and have recently been reviewed by Martin and Thordarson (2020). 8.2.1.2 Amyloid peptides Amyloid fibrils were discovered as the insoluble protein deposits associated with Alzheimer’s disease. Subsequently, similar deposits were identified in other neurodegenerative and nonneurodegenerative diseases. Irrespective of the protein/peptide and the disease that they might be associated with, all amyloid fibrils are characterized by a common crossβ-sheet structure. The cross-β sheet was, therefore, recognized as a disease-associated molecular architecture until the end of the 20th century. Studies in the past two decades identified native proteins having amyloid-like architecture, thereby establishing the cross-β as a native protein fold (Chapman et al., 2002; Fowler et al., 2005; Herva´s et al., 2016; Maury, 2009). The cytotoxicity observed in the amyloid diseases resides, largely, in the prefibrillar oligomers.

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The increasing evidence supporting the noncytotoxic nature of amyloid fibrils opened up the possibility of using them for biomedical applications. Hamley and coworkers reported the gelation of phosphate-buffered saline (PBS) by the short amyloidogenic stretch of the β-amyloid peptide, NH2-KLVFF-OH (Krysmann et al., 2008). The 3 wt.% hydrogels of the peptide displayed a storage modulus of B30,000 Pa. Paik and coworkers reported hydrogelation caused by the curli α-synuclein fibrils (Bhak et al., 2010). The enzyme immobilization potential of the hydrogel was demonstrated by entrapping the horseradish peroxidase. The entrapment protected the enzyme from loss of activity due to multiple catalytic reactions. Maji and coworkers investigated the hydrogelation propensity of Fmoc-protected di- and tripeptides wherein the peptide sequences were selected from the β-amyloid peptide (Jacob et al., 2015). Four out of the eight peptides studied formed hydrogels that could support cell growth and stem cell differentiation. These peptides very much belong to Section 8.2.1 as well. Banerjee and coworkers reported pH-responsive and thermoreversible hydrogel by NH2-GAIL-OH, a short (tetrapeptide) amyloidogenic fragment of human islet amyloid polypeptide (hIAPP) and its Ala-Phe analog (Naskar, Palui, & Banerjee, 2009). Vaux and coworkers subsequently reported hydrogelation by the full-length hIAPP (Jean et al., 2016). Ma and coworkers reported injectable hydrogel from hydrolyzed hen egg-white lysozyme (HEWL) (Yang et al., 2019). The protein was hydrolyzed using HCl (pH 2) at 65 C and lyophilized. The lyophilized material was subsequently allowed to form Mg21-catalyzed amyloid fibrils at pH 2 and again lyophilized. The lyophilized material, when dissolved in phosphate buffer pH 7.4, gave a clear colloidal solution. Incubation at 37 C caused rapid gelation, wherein a 3 wt.% gel displayed the storage modulus of B1000 Pa. The hydrogel holds the promise to be used as an injectable drug-delivery vehicle. Miller and coworkers reported hydrogelation by intact HEWL in the presence of dithiothreitol. A 3 mM HEWL solution containing 20 mM dithiothreitol was heated to 85 C; cooling of the solution resulted in the hydrogel with a storage modulus of around 1000 Pa. The gel turned out to be thermoreversible and supported cell attachment and proliferation (Yan, Saiani, Gough, & Miller, 2006). Kumari and Ahmad reported HEWL hydrogelation at concentrations as low as 0.3 mM when incubated with a fourfold molar excess of tris(2-carboxyethyl)phosphine (TCEP). Heating of the TCEP/HEWL solution to 90 C followed by cooling to 25 C caused gelation (Kumari & Ahmad, 2019). Mezzenga and coworkers reported the hydrogelation of preformed lysozyme fibrils upon the addition of polyphenols such as epigallocatechin gallate. The hydrogels were reversible, stable up to 90 C, and displayed antibacterial activity (Hu et al., 2018). While investigating the aromatic analogs of the wellstudied β-amyloid fragment Aβ16
22 (CH3CO-KLVFFAE-NH2), we found that the aromatic analog CH3CO-KLVFYAE-NH2 forms optically transparent hydrogel at concentrations $0.2 wt.%. The native peptide, on the other hand, failed to cause hydrogelation up to 10fold higher concentration under identical conditions (Datta et al., 2019a). The gel shows promise to be used as a drug-delivery vehicle and scaffold for 3D cell culture. 8.2.1.3 Designed peptides without aromatic end-caps Inspiration from the self-assembling peptides present in biological systems, including the amyloidogenic peptides, led researchers to design, de novo, or through modifications of known self-assembling peptides, the novel hydrogelators. A large number of hydrogelating peptides wherein the termini are either free or capped with nonaromatic moieties have been

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reported in the literature (Dasgupta et al., 2013; Loic, 2017). As an in-depth discussion on the various approaches in designing and improving such peptides is beyond the scope of this chapter, a few selected examples have been presented in this section. In 1997 Boden and coworkers reported hydrogel formed by a 24-residue peptide, named K24 (NH2-KLEALYVLGFFGFFTLGIMLSYIR-OH), related to the transmembrane domain of the IsK protein (Aggeli et al., 1997). The same group subsequently designed two 11-residue glutamine-rich peptides. The peptides, known as P11-I (CH3CO-QQRQQQQQEQQ-NH2) and P11-II (CH3CO-QQRFQWQFEQQ-NH2), self-assemble to form tapes and fibrils that subsequently cause hydrogelation for samples having .4 mM peptide concentrations (Aggeli et al., 2001). In a subsequent study, hydrogels formed through coassembly of oppositely charged 11residue peptides were reported (Aggeli, Bell, Boden, Carrick, & Strong, 2003). Banerjee and coworkers investigated the hydrogelation by Nα-Boc-protected tri-phenylalanines (Basu et al., 2016). Permutations of L- and D-phenylalanine resulted in eight different peptides. Six out of eight peptides turned out to be gelators. Chauhan and coworkers reported hydrogels by α,β-dehydrophenylalanine (ΔPhe)-containing peptides having free termini (Panda, Mishra, Basu, & Chauhan, 2008; Thota, Yadav, & Chauhan, 2016). The 1 wt.% Phe-ΔPhe displayed an excellent storage modulus of B2 3 105 Pa. The gel was pH and salt-responsive that could be used for tunable drug release (Panda et al., 2008). The Leu-ΔPhe hydrogel also displayed a high storage modulus of 105 Pa and was injectable. The mitoxantrone-containing gel, when injected in the tumor containing mice, caused a reduction in tumor volume (Thota et al., 2016). Marchesan et al. (2012) investigated the tripeptides VFF and FFV for hydrogelation propensity. Both the peptides turned out to be nongelating. However, the analogs wherein the first amino acid was substituted with their D-counterpart turned out to be efficient gelators with storage moduli B10,000 Pa for 6.6 wt.% gels. 8.2.1.4 β-Turn-containing peptides In a seminal work, Schneider and coworkers designed peptides that display pH-dependent folding, self-assembly, and hydrogelation (Schneider et al., 2002). The peptide, named MAX1 (8, Fig. 8.4), contains two stretches made up of alternate Val and Lys residues separated by a FIGURE 8.4 The MAX family of β-hairpin peptides: (A) the scaffold of the β-hairpin peptide, (B) selected members of the MAX family (8
14).

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β-turn inducing tetrapeptide stretch Val-DPro-LPro-Thr. At acidic pH, due to intra- and intermolecular electrostatic repulsion, the peptide remains unstructured and monomeric. At alkaline pH, the peptide folds to take an amphiphilic β-hairpin conformation. The amphiphilic peptide molecules self-assemble causing hydrogelation. Further investigations into the behavior of MAX1 revealed that salt could be used as a trigger for gelation, and the rheological properties could be tuned by salt (Ozbas, Kretsinger, Rajagopal, Schneider, & Pochan, 2004). The MAX1 hydrogels were found to be cytocompatible (Kretsinger, Haines, Ozbas, Pochan, & Schneider, 2005) as well as inherently antibacterial (Salick, Kretsinger, Pochan, & Schneider, 2007). The same group subsequently reported, through modifications in MAX1, a series of MAX family peptides. One of the MAX family peptides, named MAX3 (9, Fig. 8.4), forms completely thermoreversible hydrogels (Pochan et al., 2003). An iterative design, using MAX1 peptide as the template, Schneider and coworkers afforded a cysteine analog, termed MAX7 (10, Fig. 8.4). Protection of the cysteine side chain with α-carboxy-2-nitrobenzyl (CNB) moiety afforded the photocaged peptide MAX7CNB (11, Fig. 8.4). Photocleavage of the CNB moiety causes peptide to fold and self-assemble to cause hydrogelation (Haines et al., 2005). Another member of the MAX family, named MAX8 (12, Fig. 8.4), was obtained through the substitution of Lys15 with Glu (Haines-Butterick et al., 2007). The peptide causes gelation of DMEM and has been shown to encapsulate stem cells. Besides, the MAX8 hydrogels were shown to exhibit a month-long release of the anticancer drug vincristine (Sun et al., 2016). The potential of the MAX family of peptides as delivery vehicles has been reviewed by Pochan and coworkers (Worthington, Langhans, & Pochan, 2017). Lu and coworkers designed a MAX1 analog by substituting the four middle Lys residues with His (Wang, Sun, Wang, Xu, & Lu, 2015). The peptide, named CBHH, displayed Cu21-mediated folding and hydrogelation. Intramolecular metal ion-coordination was proposed to cause peptide folding. Brimble and coworkers subsequently designed a peptidebased on MAX peptide architecture wherein Val residues were replaced by Ile, and four Lys residues toward termini were substituted by His (De Leon-Rodriguez, Hemar, Mitra, & Brimble, 2017). The peptide self-assembles, causing hydrogelation in the presence of Zn21 ions. Unlike CBHH, the self-assembly is mediated via intermolecular metal ion chelation. We recently reported the hydrogels formed by the tandem repeats of Aβ16
22 connected via β-turn-supporting motifs, namely, Asn-Gly, Aib-DPro, and DPro-Gly (Datta et al., 2019b). Even though Aβ16
22 failed to form hydrogels up to 20 mM concentrations, the β-turn-motif-harboring peptides readily formed transparent hydrogels at concentrations $2 mM. The hydrogels were found to be mechanoresponsive, showed sustained drug release, and could be used as cell culture scaffolds (Datta et al., 2019b). 8.2.1.5 Peptide amphiphiles and amphiphilic peptides Amphiphilicity is perhaps the most well-known molecular attribute that drives selfassembly. Amphiphilic molecules tend to self-assemble in water by shielding their nonpolar moieties from the polar water molecules. Self-assembly of detergent molecules into micelles and that of phospholipids into bilayers are two well-known examples of amphiphile selfassembly. Peptides can be conferred with amphipathicity through spatial segregation of polar and nonpolar residues within the sequence or through the intramolecular folding, as is the case with the MAX family of peptides (Worthington et al., 2017). Such peptides are referred to as amphiphilic peptides. Alternatively, amphipathicity can be imparted by

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attaching a hydrophobic nonpeptidic moiety to a peptide. An alkyl or a lipid moiety is often attached to a peptide chain affording a peptide amphiphile (Hendricks et al., 2017).

Section 4: Novel therapeutic modalities

8.2.1.5.1 Peptide amphiphiles

Stupp and coworkers designed a peptide amphiphile intending to achieve hydroxyapatite mineralization (Hartgerink, Beniash, & Stupp, 2001). The peptide amphiphile was obtained through the N-terminal palmitoylation of the peptide NH2-CCCCGGG-(phospho-S)-RGD-OH (15, Fig. 8.5). The peptide wherein Cys residues were reduced readily dissolved in water. Acidification of the peptide solution with a concentration higher than 0.25 wt.% resulted in a hydrogel. The underlying fibers were shown to form hydroxyapatite crystals when treated with CaCl2 and Na2HPO4 solutions. A careful look at the peptide amphiphile suggests a somewhat conical geometry (15, Fig. 8.5), and the fibers were proposed to be the cylindrical micelles. The same group subsequently reported analogs of the peptide amphiphile with varying properties (Hartgerink, Beniash, & Stupp, 2002). Stupp and coworkers subsequently designed branched peptide amphiphiles. Unlike the previously reported amphiphiles, the alkyl chain was attached to a C-terminal lysine. This afforded a free amino terminus that could be used for tagging the peptide with the epitopes (Guler et al., 2006). The presentation of various RGDS cell adhesion motifs on the fibers was demonstrated. In a subsequent study, the histidine-decorated branched peptide amphiphile fibers were reported to possess esterase activity (Guler & Stupp, 2007). Stupp and coworkers further reported tunable gels formed by negatively charged peptide amphiphiles, wherein a charged head group is composed of three glutamic acid residues (Greenfield, Hoffman, De La Cruz, & Stupp, 2010). Gels formed through acidification by HCl were found to be less stiff compared to those formed in the presence of Ca21 ions. Ca21 ions can cause intra- and interfibrillar

FIGURE 8.5 The peptide amphiphile (15) designed by Stupp and coworkers for hydroxyapatite mineralization (Hartgerink et al., 2001).

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cross-linking, thereby increasing the stiffness. The gel obtained through acidification, however, displayed better reversibility compared to the Ca21-induced gels. Building upon the similar designs, Stupp and coworkers have since then reported many peptide amphiphile hydrogelators (Hendricks et al., 2017; Matson & Stupp, 2012). Tirrell and coworkers reported pH-responsive injectable hydrogels, wherein the alkyl moiety is right in the middle of the peptide, attached to the lysine side chain (Lin et al., 2012). The gels were biocompatible and supported cell proliferation. Mata and coworkers recently reported hydrogels using complementary host
guest peptide amphiphiles (Redondo-Go´mez, Abdouni, Becer, & Mata, 2019). The isostructural peptide amphiphile lacking guest and host molecules formed hydrogels, the rheological properties of which could be modulated through coassembly with host and guest peptide amphiphiles without losing biocompatibility. Castelletto and coworkers designed a peptide amphiphile C16KTTβAH. The peptide amphiphile harbors the “KTT” tripeptide from the “Matrixyl” lipopeptide, a lipopeptide used in cosmetics. The “βAH” carnosine is a natural, free radical scavenger and antioxidant. The peptide amphiphile was shown to form hydrogels in PBS with a very high storage modulus of B104
105 Pa. In addition, the peptide amphiphile was shown to possess anticancer activity (Castelletto et al., 2019). 8.2.1.5.2 Amphiphilic peptides

Zhang et al. (1993, 1995) reported ionic self-complementary peptides CH3CO-(AEAEAK AK)2-NH2 (EAK16) and CH3CO-(RARADADA)2-NH2 (RADA16-II) that adopt a β-sheet conformation in water. The addition of either peptide into a salt solution, a physiological buffer, or the cell culture medium resulted in membranous matrices. Both the matrices allowed cell attachment and proliferation. The shuffling of amino acids afforded another gelator CH3CO-(RADARADA)2-NH2 (RADA16-I). The hydrogels obtained from these peptides were shown to support neurite growth and synapse formation in PC12 rat pheochromocytoma cells (Holmes et al., 2000). On a similar line, Saiani and coworkers reported NH2-FEFEFKFK-OH and NH2-FEFKFEFK-OH as short peptide hydrogelators (Mohammed et al., 2007). Zhao and coworkers reported a 9-residue peptide NH2-PSFCFKFEP-OH that self-assembles to form fishnet like structures (Ruan et al., 2009). The nanofibers were extensively branched and formed self-healing hydrogels. The hydrogel was shown to entrap and slowly release the model drug pyrene. The self-healing property of the hydrogel was shown to quickly stop the hemorrhage. Deming and coworkers reported hydrogelation by diblock copolypeptide amphiphiles (Nowak et al., 2002). The peptides contained blocks of hydrophobic (Val or Leu) and charged (Lys or Glu) amino acids. Many of these peptides formed hydrogels at 0.25
2 wt.% concentrations. The KxVy series of peptides formed hydrogels wherein the valine block takes up a β-sheet conformation. The KxLy and ExLy peptides, on the other hand, formed hydrogels wherein Leu block folds into α-helical conformation. Various other amphiphilic peptides and peptide amphiphiles are extensively reviewed elsewhere (Bowerman & Nilsson, 2012; Hamley, 2011; Zhao et al., 2010). 8.2.1.5.3 PEGylated peptides

Polyethylene glycol (PEG) is a water-soluble polyether that finds various applications in medicine that include drug delivery and regenerative medicine. PEGylation of proteins and

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peptides imparts them better solubility and lower clearance by kidneys (Canalle, Lo¨wik, & Van Hest, 2010). Lynn and coworkers reported the self-assembly of PEGylated-Aβ10
35. Aβ10
35 is an amyloidogenic stretch from the β-amyloid peptide (Burkoth et al., 1998). The PEG3000 was attached to the C-terminus of the peptide and its self-assembly investigated. The PEG moiety was found to impart reversibility to the Aβ10
35 aggregation. The peptide formed aggregates at pH 7 that could be reversed by lowering the pH to 3.6. Lo¨wik and coworkers reported peptide conjugates wherein alkyl groups were attached to the amino terminus of a short amyloidogenic peptide, while the peptide’s C-terminus was PEGylated (Meijer, Henckens, Minten, Lo¨wik, & Van Hest, 2007). Peptides with a C6 or longer alkyl chain formed distinct fibrils. The peptide, wherein the alkyl group was introduced via a photolabile nitrobenzyl linker; the fibrils could be disassembled by UV illumination. Adams and coworkers reported nanotube formation by PEGconjugated tetra-phenylalanine ethyl ether (Tzokova et al., 2009). Entanglement of nanotubes ultimately caused hydrogelation. Guler and coworkers designed a strategy for making peptide amphiphile-PEG conjugate hydrogels with tunable mechanical property and bioactivity. The strategy involved the coassembly of negatively charged peptide amphiphiles with positively charged amphiphiles (Goktas et al., 2015). Subsequent light-triggered cross-linking via PEG dimethylacrylate was used for tuning the hydrogel properties.

8.2.2 α-Helical peptides Alpha-helix is another major secondary structure in proteins. Like β-strands, α-helices can also interact with each other, forming higher order structures such as coiled coils. Even though the β-sheet-forming hydrogelating peptides comfortably outnumber the α-helical peptide gelators reported in the literature, the α-helical peptide gelators constitute an important class of peptide hydrogelators. Deming and coworkers reported the hydrogelation by diblock copolypeptides KxLy and ExLy (Deming, 2005; Nowak et al., 2002). The selfassembly is mediated by the α-helical oligoleucine motif. Subsequently, the triblock KxLyKx and the pentablock KxLyKzLyKx peptides were investigated and found to form hydrogels (Breedveld, Nowak, Sato, Deming, & Pine, 2004; Li & Deming, 2010; Nowak et al., 2006). Motivated by the coiled-coil motifs, Woolfson and coworkers designed two 28-residue peptides, termed as self-assembling fiber peptides SAF-p1 (16, Fig. 8.6) and SAF-p2 (17, Fig. 8.6) that harbor coiled-coil motifs along with the sticky ends (Pandya et al., 2000). The peptides self-assembled to form long, straight, and unbranched fibers. The peptides were subsequently engineered to modulate the assembly, morphology, and stability of the fibers (Papapostolou et al., 2007; Ryadnov & Woolfson, 2003). Woolfson and coworkers subsequently engineered these coiled-coil peptides to impart hydrogelation propensity (Banwell et al., 2009). The hydrogels were found to be biocompatible, supporting cell growth and differentiation. The hydrogels decorated with the integrinbinding motif RGD were found to be excellent biomaterials for tissue engineering (Mehrban et al., 2014, 2015). The gels supported the growth and differentiation of murine adrenal pheochromocytoma cells and embryonic neural stem cells. Dexter and coworkers designed a 21residue peptide AFD19 (CH3CO-LKELAKVLHELAKLVSEALHA-NH2) that forms a pHresponsive hydrogel. The peptide harbors three lysine, three glutamic acid, and two histidine residues. The peptide gives free-flowing solutions at pH 3.0 and 11.5. At pH 6.0 and 10.7, the

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FIGURE 8.6

peptide forms optically transparent hydrogels composed of the coiled-coil fibrils (Fletcher, Lockett, & Dexter, 2011). At neutral pH, however, the peptide precipitates out. Careful analysis revealed that overall charge around 21 or 1 1 was critical for gelation. Precipitation of the peptide around neutral pH made it unsuitable for biomedical applications. Dexter et al. (2017) designed an AFD19 analog by substituting the sole Ser residue with a lysine. The analog, named AFD36, had a higher isoelectric point compared to that of AFD19. In fact, the 1 1 charged state could be achieved near physiological pH, affording hydrogelation. Pechar et al. (2007) demonstrated that the coiled-coil peptides could be PEGylated without compromising their self-association propensity. Klok and coworkers subsequently designed PEG-peptide diblock conjugate that showed reversible self-assembly, mediated via 4-stranded α-helical coiled-coil motif (Vandermeulen, Tziatzios, & Klok, 2003). PEGylation of α-helical peptides in the context of hydrogelation has also been investigated. Collier and coworkers engineered a 37-residue peptide stretch from human fibrin (Jing et al., 2008). The peptide self-assembled to form coiled-coil dimers and tetramers. Triblock peptide-PEG-peptide conjugate obtained using the engineered peptide formed hydrogel. Many other PEGylated coiled-coil peptide hydrogelators have been reviewed by Kopeˇcek & Yang, 2009, 2012.

8.3 Biomedical applications The chemical diversity of the amino acids and the reasonably well-understood principles underlying protein folding allows the properties of the self-assembled structures to be controlled and modulated. A large number of peptide-based hydrogelators with very diverse physical and biological properties have indeed been reported, some of which have been described in the previous sections. The current interest is to design hydrogels that are inexpensive, biocompatible, injectable, responsive, have tunable rheological properties, and can respond to biological cues. The applications of such materials are manifold in translational medicine. Some of the biomedical applications with selected examples are discussed below.

Section 4: Novel therapeutic modalities

The self-assembling coiled-coil peptides (16 and 17) designed by Woolfson and coworkers (Pandya et al., 2000).

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Section 4: Novel therapeutic modalities

8.3.1 Therapeutic delivery Most peptide-based hydrogels are made up of entangled fibrillar aggregates having voids filled with water. Hydrogels, therefore, happen to be attractive candidates for sustained drug release. The water-soluble drug molecules may be taken up by the preformed hydrogels. Alternatively, the drug molecules may be trapped during gelation, provided that they do not adversely affect the gelation and the desired hydrogel properties. The entrapment and delivery of various classes of drug molecules are discussed. 8.3.1.1 Small molecules The average mesh size of the hydrogels is often too large to contain small drug molecules. One can argue that the mesh size of the hydrogels can be decreased by preparing gels with higher gelator concentration. The concentration, however, affects the properties of the hydrogel. Higher stiffness at higher gelator concentration, for example, may not be suitable for the desired purpose. Interestingly, however, the small drug molecules often interact with the hydrogels, thereby diminishing their diffusion resulting in slow release. Alternatively, the hydrogelators can be suitably modified to allow drug binding; a charged drug, for example, can bind to oppositely charged gels, while hydrophobic drugs can bind to the hydrophobic stretches in the gels. Slow degradation of the gel can afford slow drug release. Zhang and coworkers trapped 5-fluorouracil in an RGD motif-harboring Fmoc-peptide hydrogel. When administered in the filtering surgery of the rabbit eyes, the scleral flab fibrosis was inhibited (Xu et al., 2010). The peptide hydrogel displayed a gradual release for a week. A notable advantage of this strategy is little toxicity to the surrounding tissues compared to conventional 5-fluorouracil exposure. This hydrogel/5fluorouracil system, therefore, is a promising strategy for inhibiting postoperative scarring. Pochan and coworkers demonstrated sustained release of curcumin from injectable MAX8 hydrogels (Altunbas, Lee, Rajasekaran, Schneider, & Pochan, 2011). Curcumin, an antiinflammatory and anticancer drug, is poorly soluble in water. Curcumin stock solution was prepared in dimethyl sulfoxide (DMSO). This afforded efficient in situ incorporation of curcumin in the MAX8 (12, Fig. 8.4) hydrogels. Zhao and coworkers prepared paclitaxelloaded RADA16-I hydrogels (Liu, Zhang, Yang, & Zhao, 2011). Paclitaxel interacted with the RADA16-I, forming a colloidal suspension. The addition of PBS caused hydrogelation, wherein the peptide/drug colloids got trapped. The hydrogel displayed a slow release of paclitaxel for around 5 days. The paclitaxel-loaded hydrogels were shown to inhibit the growth of breast cancer cell line MDA-MB-435S efficiently. Li and coworkers designed a peptide amphiphile based on a Sup35 amyloidogenic fragment. The peptide, C16-GNNQQNYKD-OH, formed long fibers that caused hydrogelation (Hu et al., 2017). Losartan, an angiotensin II receptor blocker, was encapsulated, in situ, in the hydrogel. When injected into mice 4T1 tumors, the hydrogels were retained for over 9 days and inhibited cancer-associated fibroblasts and collagen synthesis. When used in conjunction with PEGylated doxorubicin containing liposomes, the hydrogel enhanced the intratumoral accumulation of the formulation, thereby retarding tumor growth and metastasis. Ding and coworkers designed an interesting drug-peptide conjugate system that acts as a progelator (18, Fig. 8.7) (Xu et al., 2018). The reduction of the conjugate system releases the hydrogelator molecule (19, Fig. 8.7). The hydrogel thus formed was found to be an

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FIGURE 8.7 The progelator molecule (18) designed by Ding and coworkers (Xu et al., 2018). The reduction by glutathione generated 19 that formed hydrogel.

exceptional radiosensitizer of colorectal cancers to ionizing radiation. We recently reported the fatty-acylated dipeptides that form water/alcohol gels (Datta, Nagaraj, & Chaudhary, 2020). Though not hydrogels, but the bigels, the gels could entrap the anticancer drug docetaxel that displayed a slow release for up to 5 days. 8.3.1.2 Vaccine adjuvant and macromolecule delivery Peptide hydrogels have also been successfully used to deliver large molecules, including proteins. Many bioactive proteins have delicate structures that are well-protected in a hydrogel. Janmey and coworkers in their seminal work demonstrated that the Fmoc-dipeptides could form hydrogels. In the same study, using Fmoc-Leu-Asp as the representative hydrogel, they went on to demonstrate that peptide-based gel could be used as adjuvants (Vegners et al., 1995). Fmoc-Leu-Asp hydrogels, entrapped with low molecular weight nonantigenic adamantanamine derivatives, could be used to produce specific antibodies without the need of additional adjuvants. Collier and coworkers explored the adjuvant potential of an 11-residue self-assembling peptide (CH3CO-QQKFQFQFEQQ-NH2) (Rudra, Tian, Jung, & Collier, 2010). The peptide was tagged with the 17-residue long antigenic peptide from chicken egg ovalbumin. The tagging did not interfere with the self-assembly, affording fibrils with surface-exposed epitopes. The system could raise IgG1, IgG2a, and IgG3 antibodies to the levels comparable to those raised in the complete Freund’s adjuvant. Sun and coworkers designed a hydrogelating peptide by combining a short sequence from the elastic segment of spider silk with a transmembrane segment of the human muscle Ca21 channel. The peptide, termed h9e (NH2-FLIVIGSIIGPGGDGPGGD-OH), formed hydrogels at pH 4.0 (Huang et al., 2011). In the presence of Ca21 ions, the peptide formed hydrogels at pH 7.0
9.0. The hydrogel was mechanoresponsive and turned out to be a better adjuvant for the H1N1 influenza vaccine compared to the commercial oil-based adjuvant. Yang and coworkers designed a naphthyl-protected peptide Nap-GFFY-NMe that forms a biocompatible hydrogel. The peptide self-assembles to form fibers that can condense DNA molecules, protecting them from

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degradation (Tian et al., 2014). Using HIV Env DNA, they demonstrated the hydrogels to be efficient vectors for DNA vaccines. The hydrogel/Env DNA elicited a strong humoral and cellular immune response in mice. In a subsequent study, the same group tested the adjuvant efficiency of the D-amino acid
containing peptide Nap-GDFDFDY-OH (Luo et al., 2017). The peptide forms thixotropic hydrogels that allow easy incorporation of antigens. The ovalbumin-containing hydrogel was shown to elicit a strong CD81 T-cell response. Other than these, many self-assembling and hydrogelating peptides that serve as adjuvants are reported in the literature (Eskandari, Guerin, Toth, & Stephenson, 2017). Liu and coworkers designed cationic amphiphilic peptides that form heparin-binding hydrogels. Hepatocyte growth factor (HGF) is a heparin-binding antiinflammatory protein. The HGF-loaded peptide/heparin hydrogel promoted the pancreatic β-cell survival and insulin secretion by modulating the inflammatory response (Liu, Zhang, Cheng, Lu, & Liu, 2016). The same group has recently developed a dual-delivery system platform for enhanced tissue repair after ischemiareperfusion injury. Alongside HGF, the TNF-α neutralizing antibody (anti-TNF-α) was also loaded in the peptide/heparin hydrogel. The anti-TNF-α is released fast, while HGF displayed a sustained release (Liu et al., 2020). In vivo studies showed that the combination of anti-TNF-α/HGF exhibited better renal protective potential than either anti-TNF-α or HGF alone. The study showcases the potential of self-assembling peptide hydrogels to deliver multiple drugs sequentially and is thus a promising delivery platform. 8.3.1.3 Therapeutic secretions from encapsulated cells Another strategy of delivering therapeutic molecules is to trap the therapeuticproducing cells in the hydrogels. Nonpeptide hydrogels have indeed been used for several decades for encapsulating the cells that secrete therapeutic molecules (Lim & Sun, 1980; Schmidt, Rowley, & Kong, 2008). Li and coworkers used Fmoc-KCRGDL-OH peptide hydrogel to demonstrate a personalized cancer vaccine for postsurgical immunotherapy (Wang et al., 2018). The vaccine was formulated by encapsulating JQ1, indocyanine green, and tumor cells. Activation of the formulation by 808 nm laser irradiation triggers antigen release and JQ1-mediated PD-L1 checkpoint blockade, thereby inhibiting tumor relapse.

8.3.2 Scaffold for regenerative medicine Most peptide-based hydrogels are made up of long fibrous structures and have high water content. The hydrogels are generally biocompatible and happen to be excellent extracellular matrix mimics. For these reasons, peptide hydrogels have been looked at with great expectations as material for tissue engineering and regenerative medicine. In fact, peptide-based matrices are found to be better than those made from synthetic materials or animal-derived materials (Koutsopoulos, 2016). Xu and coworkers used RADA16-I hydrogels for repairing bone defects. The demineralized goat allograft cancellous bone matrix containing RADA16-I hydrogel and mesenchymal stem cells was shown to repair femur defects in goats (Li et al., 2014). Due to the poor regenerative capacity of the neural tissue, brain injury is perhaps the most difficult to repair. Laminin-1 is a prolific stimulator of neurite extension in neuronal cells (Sanes, 1989). The pentapeptide IKVAV from α1 chain of laminin-1 is also known to promote neuronal differentiation (Sahab Negah, Khooei, Samini, & Gorji, 2018; Tashiro et al., 1989). Stupp and coworkers

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synthesized IKVAV-tagged peptide amphiphile. The peptide amphiphile self-assembled to form fibers decorated with the IKVAV peptide (Silva et al., 2004). The fibers subsequently resulted in a hydrogel that could be used as a scaffold for 3D cell culture. Compared to laminin or IKVAV peptide alone, the neuronal progenitor cells encapsulated in the hydrogels showed very rapid and selective differentiation into neurons, possibly due to a dense epitope presentation on the fibers. Using the same laminin-1 epitope, Wang and coworkers explored the potential of RADA16-I peptide for brain tissue engineering. The RADA16-I, having IKVAV peptide tagged to the C-terminus, self-assembled to form hydrogels with stiffness comparable to that of brain tissue (Cheng, Chen, Chang, Huang, & Wang, 2013). The rat neural stem cells were encapsulated in the hydrogel and injected into the brain injuries inflicted in the cortex. The cells differentiated into neurons, thereby healing the injury. Wang and coworkers designed a naphthylprotected pentapeptide bearing a thiol group at C-terminus (Huang et al., 2019). The peptide is linked through a disulfide linkage to a thiol group-bearing diglutamic acid, making it a progelator. The reduction of the thiol group releases the peptide, triggering its self-assembly and hydrogelation. The hydrogel enhanced the viability of encapsulated mesenchymal stem cells and improved the blood perfusion in murine ischemic hindlimb model. Various peptide-based hydrogels that serve as cell culture matrices have been reviewed elsewhere (Ravichandran, Griffith, & Phopase, 2014).

8.3.3 Wound dressing Wound healing is a highly complex process that gets triggered at the site of injury. Small injuries often get healed by themselves. Bigger injuries, however, may not get healed up by themselves and need appropriate dressing. Various polymers of biological origin have been used to make these dressings, and peptide-based hydrogels are one of the latest additions to the list. Owing to their tunable physical and chemical properties, their high water content, and their ability to encapsulate molecules and even cells, hydrogels are proving to be excellent wound-dressing material. Paladini et al. (2013) demonstrated the silver-impregnated Fmoc-Phe-Phe hydrogel to be a promising dressing material. Saito and coworkers found RADA16-I hydrogel to be compatible with the rat periodontal ligament cells. When applied to the surgically made periodontal defects, the hydrogel caused significantly enhanced healing compared to controls (Takeuchi et al., 2016). Hauser and coworkers investigated the wound-healing potential of simple hydrogelating peptides CH3CO-ILVAGK-NH2 and CH3CO-LIVAGK-NH2. The peptides form nonimmunogenic biocompatible hydrogels. The hydrogels caused faster healing of partial-thickness burn wounds in rat models compared to standard silicone-coated polyamide net (Loo et al., 2014). The same group subsequently extended the peptide CH3CO-LIVAGK-NH2 by a Cys residue at C-terminus. The peptide CH3CO-LIVAGKC-NH2 form hydrogels that display higher stiffness and shape fidelity upon Cys oxidation (Seow, Salgado, Lane, & Hauser, 2016). The gel was shown to accelerate the healing of full-thickness wounds in mice. Chronic wounds are known to develop in approximately 15% of diabetic patients. In addition, wound closure is known to take longer than usual in diabetic patients. Hartgerink and coworkers employed a multidomain peptide hydrogel for healing the fullthickness burn wounds in diabetic mice. The peptide NH2-K2(SL)6K2-OH formed

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injectable hydrogel that caused significantly faster healing of the surgically inflicted wounds compared to a clinically used hydrogel (Carrejo et al., 2018). Tekinay and coworkers reported a heparin-mimetic peptide amphiphile that forms nanofibers displaying sulfonate, carboxylate, and hydroxyl groups. Compared to the peptide amphiphile lacking the heparin-mimetic stretch, the hydrogel formed by the heparin-mimetic peptide amphiphile exhibited enhanced angiogenesis, re-epithelization, skin appendage formation, and granular tissue organization in surgically inflicted full-thickness wounds (Uzunalli et al., 2017). In a subsequent study, the same group demonstrated the healing of full-thickness burn injury (Yergoz et al., 2017). Liang and coworkers showed that Fmoc-FFGGRGD-OH hydrogel, when loaded with the antiinflammatory molecule resveratrol, caused wound healing by suppressing inflammation, thereby preventing scar formation (Zhao et al., 2020). These examples highlight the promise that peptide-based hydrogels have as wound-dressing material.

8.3.4 Antimicrobial agents As discussed in Section 8.3.1, peptide hydrogels can be used as carriers of drug molecules that include antimicrobial agents as well (Marchesan et al., 2013). Chauhan and coworkers, for example, demonstrated the encapsulation and slow release of several antibiotics in dipeptide hydrogel (Thota et al., 2016). Ko and coworkers prepared Nap-FFC peptide/silver nanoparticle composite hydrogel that displayed strong antibacterial activity (Simon, Wu, Liang, Cheng, & Ko, 2016). Dong and coworkers prepared antimicrobial peptide-loaded RADA16-I hydrogel. The antimicrobial peptide Tet213 was encapsulated, in situ, i.e. during gelation (Yang et al., 2018). The release of Tet213 was observed for up to 4 weeks. The hydrogel was found to promote bone mesenchymal stem cell proliferation while inhibiting bacterial growth. Interestingly, the peptide hydrogels can have intrinsic antimicrobial activity. Xu and coworkers designed a short precursor hydrogelator molecule (20, Fig. 8.8) that is taken up by the mammalian cells. Hydrolysis of the ester bond by intracellular esterases gives 21 that forms intracellular hydrogel causing cell death (Yang, Xu, Guo, Guo, & Xu, FIGURE 8.8 The progelator molecule (20) designed by Xu and coworkers (Yang, Xu, Guo, Guo, Xu, 2007). Ester hydrolysis by intracellular esterases released 21 that caused gelation.

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2007). This strategy is further extended to show bacterial killing through intracellular hydrogelation (Yang, Liang, Guo, Guo, & Xu, 2007). The MAX1 peptide designed by Schneider and coworkers, other than serving as a scaffold for 3D cell culture, was found to be inherently antibacterial as well. The hydrogel displayed antibacterial activity against both Gram-positive and Gram-negative bacteria (Salick et al., 2007). Replacing two lysine residues with arginine residues afforded another hydrogelating peptide termed MARG1 (13, Fig. 8.4). Both MAX1 and MARG1 exhibited activity against methicillin-resistant Staphylococcus aureus with MARG1 exhibiting far better activity than MAX1 (Salick, Pochan, & Schneider, 2009). The MARG1 coated tissue culture-treated plates showed complete inhibition of MRSA growth even at a very high bacterial density of 2 3 106 cells/cm2. The result inspired the group to explore more arginine mutants of MAX1 (Veiga et al., 2012). An analog, named PEP6R (14, Fig. 8.4), wherein six Lys residues were substituted with Arg, was identified as an optimal hydrogelating peptide. The hydrogel displayed activity against Gram-positive as well as Gram-negative bacteria, including multidrug-resistant Pseudomonas aeruginosa. Zhao and coworkers designed β-hairpin peptide by connecting the antimicrobial peptide (KIGAKI)3-NH2, via β-turn-supporting Thr-DPro-LPro-Gly tetrapeptide motif, to it’s retropeptide (Liu, Yang, Wang, & Zhao, 2013). At concentrations higher than 0.5 wt.%, the peptide formed transparent hydrogels at pH $8.8 that inhibited Escherichia coli growth (Liu et al., 2013). Hao and coworkers designed a naphthyl-capped peptide that exhibited zinc-triggered hydrogelation. The hydrogel exhibited Zn21 ion-mediated bacterial growth inhibition (Xu, Cai, Ren, Gao, & Hao, 2015). Banerjee and coworkers designed an Nα-Boc-protected dipeptide containing a long chain nonnatural amino acid, 11-aminoundecanoic acid. The peptide formed proteolytically stable injectable hydrogels at physiological pH. The hydrogel exhibited activity against both Gram-positive and Gram-negative bacteria (Baral et al., 2016). Xu and coworkers designed a surfactant-like peptide A9K2. The peptide formed hydrogel in the presence of fetal bovine serum or plasma amine oxidase. The hydrogel displayed excellent selectivity by favoring the adherence of mammalian cells while killing the bacteria in a coculture assay (Bai et al., 2016). Laverty and coworkers reported the antifungal properties of the Nap-FFKK-OH. The peptide forms hydrogels at $1 wt.% and inhibited Aspergillus niger and some Candida species (Albadr, Coulter, Porter, Thakur, & Laverty, 2018). The group subsequently used Galleria mellonella infection model to investigate the in vivo antimicrobial activity of Nap-FFKK-OH hydrogel (McCloskey et al., 2019). The #2 wt.% hydrogels did not show any observable toxicity in the G. mellonella larvae. The larvae were then inoculated with the maximum nonlethal doses of bacteria and treated with hydrogels. The hydrogel treatment inhibited Gram-positive as well as Gramnegative bacteria. Intrinsic antimicrobial activity is a valuable attribute of hydrogels that are to be used for biomedical applications.

8.4 Conclusion, limitations, and future directions Hydrogels are not new to medicine (Lim & Sun, 1980; Wichterle & Lı´m, 1960). They have been used as contact lenses, wound dressings, drug carriers, and matrices for tissue engineering. Peptide hydrogels, however, is a new class of biocompatible hydrogelators. The advent of peptide-based hydrogels has opened up a plethora of possibilities in medicine (Fig. 8.9). Unlike

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FIGURE 8.9 The summary of the chapter depicting the self-assembly of peptide-based molecules to form superstructures that cause hydrogelation. The potential biomedical applications are also indicated.

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large polymers, peptides can easily be synthesized with the desired sequence and physicochemical properties. The chemical diversity of the amino acids and the ease of incorporating nonnatural groups make them highly versatile materials. Several peptide-based hydrogels have been commercialized as matrices for 3D cell culture and tissue engineering (Loic, 2017). As far as the clinical translation of the peptide hydrogels is concerned, the bulk of the work remains preclinical. This can be attributed to the various factors, some of which are listed as follows: 1. The principles underlying hydrogelation are somewhat obscure. Given a peptide, it is very difficult to predict its hydrogelation propensity, the hydrogelation conditions, and the emergent mechanical properties. A subtle modification can impart or abolish the hydrogelation propensity under given conditions. This is a severe limitation in designing the hydrogelating peptides with desired emergent properties. 2. A vast majority of hydrogels are based on the β-sheet rich fibrillar aggregates, much like amyloid fibrils. As prefibrillar amyloid-aggregates are cytotoxic, it is necessary to have tight control over the self-assembly, and the terminal structures should be defectfree. This is one of the biggest hurdles in the clinical applications of peptide hydrogels. 3. The hydrogels to be used in regenerative medicine need to very closely mimic the extracellular matrix. 4. High production cost of peptides: The cost associated with peptide synthesis does not increase linearly with the peptide length. In general, the yield and the purity of the longer peptides are low. The design of short peptides without compromising the emergent hydrogel properties, therefore, could diminish the production cost. 5. The dissolution of peptides in organic solvents is another shortcoming that adversely affects their applications in medicine. Many peptide hydrogelators reported in the literature need to be dissolved in organic solvents. Subsequent dilution in water or an aqueous buffer causes gelation. It is, therefore, imperative to design water-soluble peptides that can self-assemble in response to a trigger. 6. The peptide hydrogels usually have low mechanical strength. Mechanical strength can be improved through chemical cross-linking; the crosslinker, however, should not exhibit toxicity. The challenge, therefore, lies in designing cost-effective peptides that form biocompatible and responsive hydrogels with little or no toxicity. It is important to note that the peptide hydrogels are a relatively newer class of biocompatible hydrogelators. Despite this newness, the peptide hydrogels, however, are making significant strides toward clinical translation, and this progress has been well-reviewed by Collier and coworkers (Hainline, Fries, & Collier, 2018).

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C H A P T E R

Bispecific antibodies: A promising entrant in cancer immunotherapy Samvedna Saini and Yatender Kumar O U T L I N E 9.1 Introduction

234

9.2 Evolution of bispecific antibodies 9.2.1 Different formats of bispecific antibodies 9.2.2 Mechanism of action

234 236 238

9.3 Production of bispecific antibodies 243 9.3.1 Hybrid hybridoma (quadroma technology) 243 9.3.2 Knob-into-hole approach 243 9.3.3 CrossMab approach 244 9.3.4 Chemical conjugation 244 9.4 Biomarkers in immunotherapy at a glance 9.4.1 Biomarkers for breast cancer 9.4.2 Biomarkers for prostate cancer

246 246 247

9.4.3 Biomarkers for checkpoint blockade immunotherapy 248 9.5 Engineering of therapeutic protein 248 9.5.1 Binding affinity enhancement 249 9.5.2 Immunogenicity minimization 249 9.5.3 Stability enhancement and half-life extension 250 9.6 Market analysis: past, present and future

250

9.7 Future challenges and opportunities 254 9.8 Conclusion

255

References

255

Netaji Subhas University of Technology, Delhi, India

Translational Biotechnology DOI: https://doi.org/10.1016/B978-0-12-821972-0.00014-9

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© 2021 Elsevier Inc. All rights reserved.

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9

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Section 4: Novel therapeutic modalities

9.1 Introduction Immunotherapy, in oncology, has gained ground in recent years due to the rise of a better understanding of underlying mechanisms and pathways of tumor biology and immunology. Subsequently, this has improved understanding of immune cell interactions in the tumor environment, which has incited the group of novel therapeutic agents such as immune checkpoint inhibitors (CPIs), T-cell transfer therapy, monoclonal antibodies, and treatment vaccines (Stanley & Mohamed S, 2017). Immunotherapy, also called biological therapy, is often projected as the latest tool against neoplastic malignancies. However, they can be traced back in the 1890s when it was illustrated by two German physicians, Fehleisen and Busch, who had worked independently on erysipelas infection (Dobosz & Dziecia˛tkowski, 2019). In 1981, William Bradley Coley, also regarded as the father of immunotherapy, first attempted to harness immune cells to treat cancer (Vernon, 2018). Immunology-based manipulations to circumvent the disease can further classify into active and passive categories. Active immunotherapy involves evoking an immune response in a cancer patient. However, passive immunotherapy entails immune-based reagents, such as cells, serum, or cell products, which, after being administered to the tumor-bearing host, will neutralize the tumor cells in the body (Rosenberg & Terry, 1977). Antibodies are being used in a plethora of studies as a conveyor for drugs, radioactive materials, toxins, and enzymes. Later on, a significant fraction of these immunology-based studies has been performed in experimental animals (Rosenberg & Terry, 1977). Immunotherapy for cancer has established a niche in the field of cancer oncology. Major pillars behind fueled growth of immune-driven therapy are CPIs and chimeric antigen receptors T-cells (Kruger et al., 2019). For immune CPIs, the current research domain focuses on new tumor entities and combinational therapies. Recently, the Nobel prize for physiology or medicine 2018 awarded to James P. Allison for the discovery of cytotoxic T-lymphocyte-associated protein (CTLA-4) blockade therapy (Peggs, Quezada, Chambers, Korman, & Allison, 2009) and Tasuku Honjo for programmed cell death protein 1/ programmed cell death protein ligand 1 (PD-1/PD-L1) immune blockade therapy for antitumor activity (Altmann, 2018). An anti-CTLA-4 antibody named ipilimumab has significantly increased the survival rate for metastatic melanoma when being compared with previous data (Hodi et al., 2010; Lipson & Drake, 2011; Schadendorf et al., 2015). However, despite the substantial enhancement in immunotherapy, the majority of patients receiving CPIs do not acquire significant benefits. Therefore there survives the need to identify and develop anticipated biomarkers of CPIs, both to sanction a precision therapeutic approach in cancer immunotherapy and to have an insightful approach to understand and overcome mechanisms of resistance (Fig. 9.1).

9.2 Evolution of bispecific antibodies As the research and technology progress into a new era of customized immunotherapy, the demand for bispecific or multispecific immunoglobulins to treat a wide array of diseases has increased at a humongous rate. Over the last two decades, bispecific antibodies (bsAbs)

235

FIGURE 9.1 Applications of convention IgG. IgG, Immunoglobulin G.

have grown expeditiously and further envisioned as a substantial therapeutic tool. bsAbs are a class of artificial antibodies that can engage with two different epitopes present on the same or different antigens simultaneously (Fan, Wang, Hao, & Li, 2015). The domain of immunology has gained interest in 1945, right after the discovery of interferons (Isaacs & Lindenmann, 1957), which further accelerated after the first cancer vaccine by Ruth and John Grahams (Graham & Graham, 1959). Nobel prize-winning report by Rodney Porter on the basic structure of the immunoglobulin molecule has fueled the discovery of bsAbs (The Nobel Prize in Physiology or Medicine 1972, 1972). The concept of an artificial antibodybased molecule constituting two binding sites was long back explained by Nisonoff and Rivers (1961) and paved the way toward antibody architecture (Riethmu¨ller, 2012). Nisonoff had used pepsin instead of Porter’s papain to produce univalent Fab fragments. Describing his findings, he speculated about the future experiments, “to attempt to prepare antibody of mixed specificity” (Nisonoff, Wissler, & Lipman, 1960). After the demonstration of the first bsAb concept in the 1960s, Ko¨hler & Milstein (1975) had introduced hybridoma technology to the world by producing monoclonal antibodies (Fig. 9.2). The concept of hybrid hybridomas or quadroma (1983) has opened a new horizon of research when two myeloma cells could produce hybrids that are capable of expressing the antibody genes of both parents (Milstein & Cuello, 1983). However, this random chain assembly by hybridomas can lead to arrays of combinations, for example, 16 combinations (Labrijn, Janmaat, Reichert, & Parren, 2019). This chain association issue was solved by using the knob-into-hole approach in the CH3 domain of an antibody for heavy-chain

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9.2 Evolution of bispecific antibodies

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9. Bispecific antibodies: A promising entrant in cancer immunotherapy

FIGURE 9.2 Evolution timeline of bsAbs. bsAbs, Bispecific antibodies.

dimerization (Klein et al., 2012; Ridgway, Presta, & Carter, 1996). Shortly, right after the discovery of quadroma technology in 1985, it has been demonstrated that distinct targeting of cytotoxic T-cells by anti-CD3 antibodies could elucidate T-cell responses such as cytotoxicity, mitogenesis, and lymphokine production (Perez, Hoffman, Shaw, Bluestone, & Segal, 1985; Staerz, Kanagawa, & Bevan, 1985). Subsequently, in 1999, natural immunoglobulin G (IgG) (subclass 4) antibody has reportedly shown to exist in bispecific form naturally (Schuurman et al., 1999). On the contrary, experimentally, IgG4 antibodies engage poorly with Fc-receptors when being compared to other counterparts such as IgG1 immunoglobulins and do not effectively participate in complement activation (Bru¨ggemann et al., 1987). In general, IgG4 antibodies are considered to be less effective in elucidating inflammatory responses. They may even inhibit the inflammatory effects of other antibodies (Hussain, Poindexter, & Ottesen, 1992; van der Zee, van Swieten, & Aalberse, 1986). Later on, in 2017, catumaxomab (EpCAM 3 CD3) was approved by the European Union (EU) for malignant ascites, however, terminated citing to various commercial reasons. After 2 years, the method for efficient generation of IgG1 antibody by Fab arm exchange had been designed (Labrijn et al., 2013; Strop et al., 2012). Afterward, blinatumomab approved in the United States in 2014, followed by approval in the EU in 2015 (Fig. 9.3).

9.2.1 Different formats of bispecific antibodies Based on the structural framework and modalities, bsAbs are broadly classified into two categories: one with the Fc (fragment crystallizable) trunk region, namely IgG, like bsAbs and others without the Fc region. The latter molecule being small in size brings deeper penetration in tissues, whereas the former is more stable with extended serum life and

237

FIGURE 9.3 Potential advantages of bsAbs over conventional antibody. bsAbs, Bispecific antibodies.

with ease in the process of purification (Brinkmann & Kontermann, 2017; Mertens et al., 2004). Different structure modality of bsAbs brings many challenges while framing the optimum structure for dual specificity binding. A typical IgG molecule constitutes two identical light and heavy chains bridged by disulfide bonds. In general, IgG is a monomer with a molecular weight of 146
160 kDa. A conventional antibody molecule is monospecific and bivalent. Valency is one of the significant characteristics, which can be manipulated for effective and desired target entities. Other than bivalent antibodies, trivalent and tetravalent bsAbs prevail in the market with efficient target biomarkers (Coloma & Morrison, 1997; Sedykh, Prinz, Buneva, & Nevinsky, 2018). Similarly, based on their mechanism of action, bsAb can be further classified, such as Bispecific T-cell Engager (BiTE) and Trimab. Blinatumomab/MT103, a BiTE (CD3 3 CD19) representative produced by Amgen, is approved for the use in the United States (Sedykh et al., 2018). The application of blinatumomab as a therapeutic drug against B-cell was first tested in 2008 in 38 patients with refractory non-Hodgkin’s lymphoma (Bargou et al., 2008), and the results of other preclinical and clinical trials have been published in various studies (Topp et al., 2011). bsAbs have been framed in different formats, such as the inclusion of linkers and dimers or antibody fragments (Litvak-Greenfeld, Vaks, Dror, Nahary, & Benhar, 2019). A conventional antibody constitutes of Fab (antigen-binding-fragment) encompassing variables and constant domain of heavy and light chain with Fc region comprising the constant region of heavy chains. Three basic fragments versions can be inferred, such as ScFv (single-chain variable fragment), Fab, and third-generation (3G) antibodies consisting of minior nanoantibodies (Nelson, 2010). Classically antigen-binding fragments are the earliest section of monoclonal antibody, tabulated by studies that all eight therapeutics entered in the clinical phase before 1995 were Fab-based (Nelson & Reichert, 2009). In 1994, Food and Drug Administration (FDA) approved Abciximab (Reo Pro, Chimeric 7E3 Fab), a Fab-based immunoglobulin specific to platelet glycoprotein IIb/IIIa, acting as an adjunct to prevent thrombotic

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9.2 Evolution of bispecific antibodies

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9. Bispecific antibodies: A promising entrant in cancer immunotherapy

activity in case of myocardial infarction (Tam, Sassoli, Jordan, & Nakada, 1998). Furthermore, in 2006, Ranibizumab manufactured by Lucentis, Genentech a humanized fragment antibody targeting vascular endothelial growth factor A as the treatment for age-related neovascular (wet) macular degeneration got approved. However, in spite of many successful projects, the idea for Fab-based immunoglobulins has not lifted off well. ScFvs are recombinant antibodies consisting of a variable region of heavy and light chains compressed in a single peptide by a flexible linker sequence (Huston et al., 1988). As of now, a series of variants have been designed for enhanced binding efficiency and stability (Bird et al., 1988). Approximately, among all, ScFv accounts for nearly 40% of clinically tested immunoglobulins (Nelson & Reichert, 2009). An anti-TNFα (tumour necrosis factor alpha) ScFv (ESBA-105) was developed by ESBATech for ophthalmic indications (Ottiger, Thiel, Feige, Lichtlen, & Urech, 2009). Similarly, Efungumab, manufactured by Mycograb, targets the heat shock protein of Candida albicans, was reported to be in Phase II development by NeuTec, in case of invasive candidiasis (Karwa & Wargo, 2009). Another proposition is a development of third-generation antibodies (nano-antibodies) by miniaturizing already existing IgG, that is, removing nonessential domains of an antibody. One of the examples for mab minions are small modular immunopharmaceuticals manufactured (SMIPs) from Trubion Pharmaceuticals comprise ScFv domain conjugated with a modified flexible hinge to Fc arm (Wood, 2011). Pharmaceuticals like Trubion and Facet Biotechnology collaborated for the production of TRU-016 (SMIP), targeting anti-CD37 for therapy of chronic lymphocytic leukemia. Similarly, Wyeth had successfully licensed the anti-CD20 SMIP SBI-087 for the medication of autoimmune diseases, including rheumatoid arthritis, systemic lupus erythematosus and possibly multiple sclerosis (Cohen et al., 2016). Biotecnol had also developed a “miniaturized” mAb, named CAB051, which is a “compacted” 100 kDa of about two-thirds of conventional immunoglobulin as an anti-HER2 antibody in preclinical research (Table 9.1). Presently, single-domain antibodies are being developed against several viral diseases such as human immunodeficiency virus-1 (HIV-1), influenza viruses, hepatitis C virus, and respiratory syncytial virus (Wu, Jiang, & Ying, 2017) (Fig. 9.4).

9.2.2 Mechanism of action The BsAb has always been described to target two different targets simultaneously present on either the same or different antigen, unlike, monospecific antibodies which are bivalent but monospecific. Out of 38 immune cells engaging bsAbs going under clinical trial, 36bsAbs are recruiting T-cell for subsequent and effective killing of tumor cells. Eighteen explicitly targeting hematological malignancies and the rest 16 for the therapeutics of solid tumors (Suurs, Lub-de Hooge, de Vries, & de Groot, 2019). Based on the structural framework, bsAbs possessing IgG format (Fc domain) manifest effector functions such as antibody-dependent cellular toxicity (ADCC) and complement-dependent cytotoxicity (CDC). While the smaller one (without Fc domain) counts back entirely on the antigen-binding domain, the application of bsAbs has been explored by more than three decades (Songsivilai & Lachmann, 2008; Staerz et al., 1985). In a cytolytic synapse, when T-cell and tumor cell engages, cytotoxic enzymes such as perforin and granzyme-B mediates tumor cell killing, as proven experimentally and further confirmed by confocal microscopy (Haas et al., 2009; Offner, Hofmeister, Romaniuk, Kufer, & Baeuerle, 2006).

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9.2 Evolution of bispecific antibodies

S. no

Antibody

Format

1.

RMab

Human IgG1

2.

BelimuMab

IgG1λ

3.

BlinatumoMab

BiTE

4.

TrastuzuMab

IgG1

5.

CetuxiMab

IgG1

6.

PertuzuMab

IgG1κ

7.

RituxiMab

IgG1

8.

BevacizuMab

IgG1

9.

NecitumuMab

IgG1

10.

AtezolizuMab

IgG1

11.

CatumaxoMab

IgG2 (Trimab)

12.

OfatumuMab

IgG1κ

13.

InotuzuMab

Humanized IgG4, κ-chain

14.

IpilimuMab

Human, derived IgG1, κ-chain

15.

NivoluMab

Humanized IgG4, κ-chain

16.

TocilizuMab

IgG1

17.

DaratumuMab

Transgenic mouse-derived IgG

18.

PembrolizuMab

IgG

19.

CemipliMab-RWLC

IgG1

20.

AtezolizuMab

IgG1

21.

IbritumoMab tiuxetan

Antibody-chelator conjugate

22.

TositumoMab

Mouse IgG2a, λ-chain

23.

PanitumuMab

Human, transgenic mouse-derived IgG2

IgG, Immunoglobulin G; BiTE, Bispecific T-cell Engager.

9.2.2.1 Bispecific T-cell Engager BiTE is a small ScFv joined by a flexible linker. They are non-IgG type antibody with a size roughly about one-third of conventional antibody. This miniature version of bsAb is effectively known to have better efficacy even at lower T-cells to target ratios (Wolf, Hofmeister, Kufer, Schlereth, & Baeuerle, 2005), stable structural modalities, and escalated tumor lysis. As one domain of ScFv will target CD3 for T-cell activation, others will be targeting tumor biomarker cancer or other inflammatory disorders. As indicated previously, the lytic synapse by engagement of T-cell with BiTE is identically similar to immune lytic synapse in vivo (Offner et al.,

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TABLE 9.1 List of immune therapeutics and their respective structural format.

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9. Bispecific antibodies: A promising entrant in cancer immunotherapy

FIGURE 9.4 Different formats of bsAbs, constituting asymmetrical and symmetrical formats. bsAbs, Bispecific antibodies.

2006). Kufer and colleagues designed a BiTE in 1995, recognizing epithelial 17-1A antigen (EpCAM) and CD3 in the CHO cell line (Mack, Riethmuller, & Kufer, 1995). Later on, in 2000, Lo¨ffler et al. (2003) designed a BiTE antibody by fusing a murine anti-CD19 fragment and a murine anti-CD3 fragment. BiTE antibody AMG420 /BI 836909 designed by Boehringer Ingelheim, Amgen (Micromet) to target B-cell maturation antigen, and CD3 is currently in phase I (Topp et al., 2019). Additionally, another BiTE AMG330 targeting CD33 and CD3 manufactured by Amgen (Micromet) is in phase I of a clinical trial (Friedrich et al., 2014). The infamous, blinatumomab, is another example of BiTE for T-cell recruitment. It targets CD33 and CD3 as a second inline treatment for relapsed or refractory acute lymphoblastic leukemia (ALL). In 2014, blinatumomab successfully achieved an accelerated FDA approval, where its indication was sprawled further to patients bearing Philadelphia chromosome
positive ALL (Pulte et al., 2018; Stein et al., 2019). 9.2.2.2 Immune payloads bsAbs, when combined with drug conjugates, can effectively eradicate tumor cells. Often termed as delivering vehicles for payloads, in vivo, bsAbs are capable of engaging with tumor comprising both positive and negative antigen cells (Kovtun et al., 2006). On that account, bsAbs can be complexed with cytotoxic molecules with limited selectivity to ensure prime specificity (Metz et al., 2011). Immunotoxin, a trastuzumab-DM1 conjugate known to treat metastatic malignancy in the breast, showed promising results in clinical trials (Krop et al., 2010). Antibody-drug pretargeting and targeting conjugates also applicable in radiotherapy and imaging applications, where localization of tumor-bearing cells is attained when subsequently administered with radionuclides (Goldenberg, Chatal, Barbet, Boerman, & Sharkey, 2007). Earlier, in the United

241

States, two monoclonal antibodies were approved for radioimmunotherapy. One is murinebased anti-CD20 labeled with 131I/90Y linked with either tositumomab or rituximab(Barbet, Goldenberg, Sharkey, Paganelli, & Chatal, 2006). However, the challenges and opportunities in radioimmunotherapy have been extensively studied and reviewed (Casadevall, 1998; Davis et al., 2004; Jhanwar & Divgi, 2005; Waldmann, 2003). According to Charlotte F. McDonagh and colleagues, a CD30 IgG1(cCA10) antibody conjugated with eight subunits of monomethyl auristatin E (MMAE), reported encompassing antitumor activity (McDonagh et al., 2006; Van De Donk & Dhimolea, 2012). Since MMAE is an antimitotic agent by disrupting the cell division (Payload of Antibody-drug Conjugates (ADCs)- MMAE and MMAF Brief Introduction
Creative Biolabs ADC Blog, n.d.). From the study by Rearden et al., conjugates with bsAbs will function double by targeting a specific antigen and by acting as a radiolabelled chelating agent (Gan, Van Den Bent, Lassman, Reardon, & Scott, 2017) (Fig. 9.5). Antibodies and payloads linked by a reducible disulfide bond be likely to exhibit bystander effect while nonreducible thioester bond was not. The prolonged exposure to bystander (healthy cells) by toxic entities complexed with bsAb can alleviate baneful and adverse effects (Boerman, van Schaijk, Oyen, & Corstens, 2003). Thus the nature of the linker between the antibody and the payload be governing the target-cell mediated killing of bystander cells (Kovtun et al., 2006).

FIGURE 9.5 Antibody conjugated with (ADC) CD30-MMAE mechanism of action (Siddiqi et al., 2014).

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9.2 Evolution of bispecific antibodies

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9.2.2.3 Immune checkpoint blockade inhibitors Another mode of action for bsAbs is by recruiting immune CPIs. The continuance of immune therapies, focusing on improving the potent killing of tumor cells, brings immune CPIs in the limelight. Targeting biomarkers in combination therapy, which seems to evade immune cells by attenuating T-cell activation, is a popular approach. Previous immune therapies focused on either T-cell recruitment or delivering toxic payloads. However, this one anchor on expanding phenotypically inactive group of T-cells (Korman, Peggs, & Allison, 2006). Commercial antibody ipilimumab, a mab, targets CPI CTLA-4 for inhibiting T-cell inactivation. In general, CTLA-4 prevents unwanted autoimmunity and provide self-antigen tolerance by a homeostatic feedback loop (Korman et al., 2006). Ipilimumab has been successful in showing promising results in phase III of clinical trials against melanoma and other solid tumors (Tarhini, Lo, & Minor, 2010). In combinational immune therapy, ATOR-1015, an IgG1-based bsAb designed against CTLA-4 3 OX40 for not only blocking T-cell attenuation but also by insinuating T-cell activation (Kvarnhammar et al., 2019). OX40 agonist arm of bsAb will alleviate T-cell cytotoxicity by inducing proliferation of CTL (cytotoxic T lymphocytes) (Peng et al., 2019). Furthermore, OX40, when linked with GSK2636771, a PI3Kb selective inhibitor, extends the survival rate and retards tumor growth. In the case of osteosarcoma, research studies have explained the role of PD-L1 in supplementing antitumor activity against GD2(1) and HER2(1) solid tumors (Park, Guo, Xu, & Cheung, 2019). PD-1 combined with T-cell engaging GD2-bispecific antibody (GD2), or HER2-bispecific antibodies (HER2), has explored options to circumvent ex vivo armed T-cells therapy with high efficiency in preclinical models. Therefore this approach by improving the T-cell activation and specific targeting to boost immune cells remains a popular option (Ellmark, Mangsbo, Furebring, Norle´n, & To¨tterman, 2017) (Table 9.2). TABLE 9.2 List of antibodies briefly discussed above in mechanism of action: BiTE, ADC, and immune payloads. S. no

Antibody

Mechanism of action

Target biomarker

References

1.

Solitomab

BiTE

EpCAM 3 CD3

Linke, Klein, and Seimetz (2010)

2.

Blinatumomab BiTE

CD19 3 CD3

Stein et al. (2019)

3.

AMG420

BiTE

BCMA 3 CD3

Einsele et al. (2019)

4.

AMG330

BiTE

CD33 3 CD3

Ravandi et al. (2018)

5.

Brentuximab

Antibody-drug conjugate

CD30

Francisco et al. (2003)

6.

Trastuzumab

Antibody-drug conjugate

HER-2

Peddi and Hurvitz (2013)

7.

Tositumomab

Antibody-drug conjugate

CD20

Friedberg and Fisher (2004)

8.

RituximabvcMMAE

Antibody-drug conjugate

CD20

Abdollahpour-Alitappeh, Hashemi Karouei, Lotfinia, Amanzadeh, and Habibi-Anbouhi (2018)

9.

Ipilimumab

Immune blockade inhibitors

CTLA-4

Tarhini et al. (2010)

10.

ATOR-1015

Bispecific immune blockade inhibitors

CTLA 3 OX40

Kvarnhammar et al. (2019)

BiTE, Bispecific T-cell Engager.

9.3 Production of bispecific antibodies

243

Bispecific therapeutics can be generated by ligation of heavy and light chains corresponding to desired targets, single domain antibodies, single- ScFvs, or other modified recombinant modalities with additional binding domains present on either N- or Cterminal of monoclonal antibodies (Spiess, Zhai, & Carter, 2015).

9.3.1 Hybrid hybridoma (quadroma technology) First documented work in 1961 about the generation of a hybridized BsAb comprising simultaneously two different antigen-binding domains of monoclonal antibodies. Before time, the generation of bsAbs was performed by quadroma technology (a somatic amalgamation of hybridomas cell lines) (Suresh, Cuello, & Milstein, 1986). Every hybridoma cell expresses monoclonal antibody bearing single specificity, which, when fused, results in quadroma cells that express both heavy and light chains of both origins (Suresh et al., 1986). After various studies, quadroma technology has few setbacks, such as low productivity and chain association issues (product heterogeneity) (Liu, Saxena, Sidhu, & Wu, 2017). These unspecific combinations can lead to 10 different combinations, out of which only 1 will lead to the desired combination of heavy and light chains (Brinkmann & Kontermann, 2017). Eventually, this issue is tackled by thye fusion of rat and murine hybridoma cell line (Ivana Spasevska, n.d.), resulting in chimeric bsAbs of mouse/rat, which will eliminate nonspecific association of heavy and light chains due to preferential species restricted pairing of H
L chains (Krishnamurthy & Jimeno, 2018; Lindhofer, Mocikat, Steipe, & Thierfelder, 1995). Above mentioned rat/mouse hybridomas are not only efficient in heterologous heavy-chain interspecies association but also proven to be a triumphant advantage in the purification of desired bsAbs by a discrete biological affinity for protein A and ion-exchange chromatography. Currently, protein G affinity is also being used for the purification of desired bsAbs. Thus this principle of purification has helped in scaling up for the production of desired bsAbs with finer purification (Lindhofer et al., 1995). With the advance development in quadroma technology, catumaxomab (anti-EpCAM 3 anti-CD3) and IgG-like bsAb were approved in Europe for malignant ascites (Linke et al., 2010).

9.3.2 Knob-into-hole approach The advancement in immunoglobulin engineering has led to the solution of nonspecific heavy-chain combinations that is knob-into-hole approach. This Fc dimerization approach involves the asymmetric changes in the CH3 domain of a heavy chain. In the context of knob-into-hole model, Francis crick was first in packaging amino acids in the alpha-helix domain of proteins (Crick, 1953). Subsequently, the knob-into-hole method was put on use to develop novel Fc domain engineering, further empowering them for heavy-chain heterodimerization. This Fc dimerization approach is similar to a lock and key model, where one heavy chain will resemble a lock (single amino acid substitution) and another as a key (Dall’Acqua, Simon, Mulkerrin, & Carter, 1998). The Fc dimerization approach is hinged on the transfection of recombinant genes encoding for bsAbs. Applying this approach, the combination will provide four possible combinations: one nonfunctional modality, one

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9. Bispecific antibodies: A promising entrant in cancer immunotherapy

functional bsAb, and two monospecific antibodies (Brinkmann & Kontermann, 2017). According to Carter (2001), one of the heavy chains mentioned as a “knob” variant, a small amino acid, is replaced with a large amino acid (T366Y) in the Fc domain. Similarly, in the other heavy-chain variant, a large amino acid is replaced with a smaller amino acid (Y407T). Since the light chain has no preferential binding for any of the heavy chain, this problem can be tackled by using common light chains (Jackman et al., 2010).

9.3.3 CrossMab approach Purposeful modifications of the CH3 domain for correct and functional heavy-chain heterodimerization but with two different light chains result in low productivity yield of bsAbs (four combinations; Wang et al., 2019). However, this problem was somewhat treated with using common light chains often combined with modified Fc domains. This CrossMab approach has ensured the correct light-chain association in bispecific biologics. In CrossMab technology, one Fab domain remains untouched, whereas the other being modified. Three proposed modalities for the CrossMab approach are (1) modifications in whole Fab region (CrossMabFab), (2) swapping of VH of fab domain with corresponding VL (CrossMab VH
VL), and (3) crossover of CH1 and CL domain of an arm of an antibody. However, the above approach can lead to Bence-Jones-like by-products, such as a combination of VL
CL chain with VL
CH1, where two light-chain domains are assembled (Wang et al., 2019). These combinations could be avoided theoretically by conjugating the CH1
CL domain with an electrosteering effect (Klein, Schaefer, & Regula, 2016). Based on CrossMab technology, bsAbs named vanucizumab (CrossMab CL
CH1) specific to vascular endothelial growth factor and angiopoietin-A manifesting anti-tumoral and antiangiogenic effects targeting neoplastic malignancies (Klein et al., 2012; Schaefer et al., 2011).

9.3.4 Chemical conjugation Production of high-end hybridoma cell lines for the production of bsAbs demands high-end efforts and resources. Although conventional Ig domain fusion by recombinant engineering is widely used, a sharp rise in the generation of chemically cross-linked bsAbs can be observed (Ellerman & Scheer, 2011). Starting with different structural domains of an antibody, in various combinations by chemical conjugation, gives a large assortment of bsAbs in terms of valency, specificity, and symmetry. Widely used chemical linkers are succinimidyl-3(2-pyridylthiol)propionate (SPDP) Raut’s reagent, sulpho-[succinimidyl-4(N-maleimidomethyl)-4-cyclohexane-1-carboxylate], o-phenylenedimaleimide (Ellerman & Scheer, 2011). Chemical linker SPDP randomly coheres to the ε-amino group present on lysine residues, forming disulfide bonds between antibody fragments (Segal & Bast, 1995). According to the protocol by Segal and Bast, SPDP dissolved in 100% ethanol with a final concentration of 2 mg/mL, link two Fab fragments, two conventional affinity-purified IgG, or two purified IgG mab (10 mg/mL). However, in chemical conjugation with SPDP, disulfide bond instability is a major concern. An alternative linkage by nonreducible thioether bonds, anchoring of maleimide-containing compound with amino acid residues present on one of antibody being conjugated, followed with incubation of second antibody

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9.3 Production of bispecific antibodies

S. no

Antibody

Method of production

References

1.

Removab

Quadroma technology (rat/mouse hybrid TrioMab)

Linke et al. (2010)

2.

BCD 121

Quadroma technology (ScFv added IgG)

JCS BIOCAD Russia (2017)

3.

LY3164530

Quadroma technology (ScFv IgG4)

Patnaik et al. (2018)

4.

ACE910

Quadroma technology (humanized IgG like)

Uchida et al. (2016)

5.

Bispecific (Fab)2 using G 1 fragment

Chemical conjugation

Brennan, Davison, and Paulus (1985)

6.

Bispecific (Fab gamma)2 thioester linked

Chemical conjugation (thioester linked)

Glennie, McBride, Worth, and Stevenson (1987)

7.

Anti-CD3/CD4-IgG

Knob-in-hole

Ridgway et al. (1996)

8.

ScFv-knobs-into-holes

Knob-in-hole

Xu et al. (2015)

IgG, Immunoglobulin G; ScFv, single-chain variable fragment.

already having reduced SPDP group, thus forming nonreducible thioether bonds between the two antibodies (Zhang, Schenauer, McCarter, & Flynn, 2013). Additional reagents used in this protocol are succinimidyl maleimidophenyl butyrate (SMPB, Pierce) dimethylsulfoxide, where, SMPB is an amine-to-sulfhydryl crosslinker that contains NHS-ester and maleimide. Consequently, creating a niche of chemical conjugation in the domain of bsAb production (Table 9.3). 9.3.4.1 Case study: blinatumomab/MT103 Blinatumomab, commercially known as (Blincyto), is the only FDA approved BiTE against a cluster of differentiation 3 (CD3) present in the market. Blinatumomab is a bispecific molecule directed against CD19 and CD3 advisable for the medication of adults and children having B-cell precursor ALL or in relapsed or refractory B-cell precursor ALL. 9.3.4.2 Molecular design BiTE is a small ScFv joined by a flexible linker. They are non-IgG-type antibody with a size roughly about one-third of conventional antibody. Blinatumomab is 504 amino acid long and possessing a weight of 54 kDa (Blincytos (blinatumomab) Product Information AUSTRALIAN PRODUCT INFORMATION-BLINCYTOs (BLINATUMOMAB) WARNING, n.d.). This miniature version of bsAb is effectively known to have better efficacy even at lower T-cells to target ratios (Wolf et al., 2005), stable structural modalities, and escalated tumor lysis. Thus the structural framework of blinatumomab constitutes of bivalent bispecific variable domain linked together by synthetic linker consisting of glycine and serine residues. In the present scenario, one variable domain will direct against

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TABLE 9.3 List of approved antibodies in the market based on their method of production.

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9. Bispecific antibodies: A promising entrant in cancer immunotherapy

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FIGURE 9.6 Molecular design of blinatumomab.

CD3 present on T-cell, whereas the other end will bind to CD19 present on the surface of B-cells (Fig. 9.6) 9.3.4.3 Manufacturing Manufacturing process commences with inoculation of Chinese hamster ovary cells from a vial of working cell bank into (flaks, bag bioreactors, and roller bottles) before inoculation in the main fermenter. The harvest of protein can be achieved by centrifugation, followed by the step of filtration to remove nonessential cells and debris. 9.3.4.4 Characterization For characterization, (1) a multiple cell-based cytotoxicity assay, (2) a CD3 binding assay, and (3) a CD19 competitive binding assay were used in the biological characterization of blinatumomab. Variations in blinatumomab active substances are collocated as product-related substances. The product-related species ranked as impurities are low and routinely controlled by the manufacturing process (CHMP, 2015). 9.3.4.5 Purification of blinatumomab Purification of blinatumomab includes three steps of chromatography, two concentration/defiltration steps followed by two viral inactivation/removal steps. The active molecules are thus retained by filtration and subsequently stored at 2 C
8 C. The C-terminal of blinatumomab contains an engineered hexahistidine residue (6X-His) for purification with zinc-immobilized metal affinity chromatography. Blinatumomab does not contain the N-linked glycosylation sequences and is aglycosylated (CHMP, 2015).

9.4 Biomarkers in immunotherapy at a glance 9.4.1 Biomarkers for breast cancer As per the report by the World Health Organization, breast cancer is known to be the primary cause of death, with about 2.09 million deaths reported in 2018 (Cancer -World Health

247

Organization, 2018). In breast cancer, biomarker analysis has originally arisen with analysis for hormone receptor expression to guide tamoxifen therapy (Colomer et al., 2018). In breast cancer, biomarkers such as estrogen receptor (ER), progesterone receptor (PR), and human epidermal growth factor receptor 2 (HER-2) are considered primary prognostic tools for optimum tailored therapy (Harris et al., 2007). In 1987, it was demonstrated that overexpression of HER-2 is associated with the occurrence of breast cancer (Slamon et al., 1987) since this gene is amplified in human breast cancer cell lines. A confirmation of breast cancer for HER-2 positive can be confirmed by determining protein expression based on immunohistochemistry or by fluorescence in situ hybridization gene amplification (Vidal et al., 2009; Wolff et al., 2013). Expression of ER-alpha is present on 70% of primary breast cancer cells (Ulaner et al., 2016). The expression profile for this hormone receptor makes it not only a desirable prognostic candidate (Harris et al., 2007) but also a high-yielding measure for the success of endocrine therapy (Abe et al., 2005). The expression of PR is strongly linked with the expression of ER-alpha. As with ER-alpha, the expression of PRs is 60%
70% in invasive ductal carcinoma. However, recent research shows that low expression of PRs is reporting false prognostic implications (Colomer et al., 2018).

9.4.2 Biomarkers for prostate cancer Prostate cancer is not only the most common nonskin cancer but also a major cause of fatality in men (Kohaar, Petrovics, & Srivastava, 2019). In general, the early detection of prostate cancer is deduced by a prostate-specific antigen test followed by biopsy for a conclusive diagnosis. Biomarkers for prostate cancer could be tissue-based gene signatures such as genome prostate score by assessing 22-gene RNA expression signature in formalin-fixed paraffin-embedded biopsy specimen, and NF-kB-activated recurrence predictor 21 by 21-gene signature in radical prostatectomy (Locke & Black, 2016). Another widely used biomarker for prostate cancer is prostate-specific membrane antigen (PSMA), a cell membrane-bound glycoprotein distinctively expressed in high amounts in the vasculature of prostate tumor cells. The higher expression of PSMA marker in cancer tissues especially in prostate cancer bearing cells is one of the prominent reasons to be used as a biomarker for cancer therapy (Chang, Reuter, Heston, & Gaudin, 2001; Horoszewicz, Kawinski, & Murphy, 1987; Kawakami & Nakayama, 1997; Lopes, Davis, Rosenstraus, Uveges, & Gilman, 1990; Silver, Pellicer, Fair, Heston, & Cordon-Cardo, 1997; Trover, Beckett, & Wright, 1995). As per recent research, PSMA as a target is not only bound to prostate cancer but can also use in other neoplastic malignancies (Chang, 2004). Currently, monoclonal antibody J591 has proven to be a successful targeted therapy for prostate cancer (Fung et al., 2016) along with mab Capromab which is approved by the FDA (Kahn et al., 1994). Nevertheless, it only recognizes the intracellular domain of PSMA and hence is not useful on viable cells. Hepsin is another biomarker found to be overexpressed in prostate cancer bearing cells. According to a study by Zhang et al. (2015), take action translational machinery to suppress CDK11p58, a protein in charge of pro-Apoptotic signaling in prostate cancer. Furthermore, the upregulation of epithelium cells of mice bearing prostate results in disruption of the basement membrane (Klezovitch et al., 2004).

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9.4 Biomarkers in immunotherapy at a glance

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9. Bispecific antibodies: A promising entrant in cancer immunotherapy

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9.4.3 Biomarkers for checkpoint blockade immunotherapy The paradigm for the treatment of cancer immunotherapy has evolved significantly over the last few years. Immune checkpoint blockade (ICB) therapy is at the vanguard of available immune therapies against cancer. Immune checkpoint molecule retards or suppresses the T-cell activation, which led to the evasion of tumor cells from the immune cells. In general, for T-cell activation, co-stimulatory interaction between CD28 on T-cells with B7-1 & B7-2 on antigen-presenting cells (APCs) is a prerequisite (Sigal, Reiser, & Rock, 1998; Tray, Weber, & Adams, 2018). In the cytotoxic T-lymphocyte-associated antigen (CTLA-4) pathway, CTLA-4 usually presents on CD41 T-cells (activated/exhausted) and Tregs (T-regulatory cells). CTLA-4 engages with CD80/CD86 ligands present the APCs led to T-cell anergy (Seidel, Otsuka, & Kabashima, 2018). The prime role of anti-CTLA antibodies perceived to block the engagement of CTLA-4 with B7-1 or CD80 and B7-2 or CD86 (Linsley et al., 1994). A commercial antibody named ipilimumab is the first ICB therapeutics to undergone a successful trial followed by approval of cancer immunotherapy (Hodi et al., 2003; Phan et al., 2003). CTLA-4 is majorly discovered in intracellular vesicles and expressed when activated in the immunological synapse before being internalized by cells (Leung, Bradshaw, Cleaveland, & Linsley, 1995). The role of CTLA-4 is not well explained. However, a crucial role is suggested by a conserved amino acid sequence present in the cytoplasmic tail of all studied species up to date (Leung et al., 1995). Programmed cell-death-1 (PD-1) is part of the CD28 family which conveys negative signals immediately after engagement with its two ligands programmed death-ligand-1(PDL-1) or (PDL-2) and thus its ligand plays a significant role in the activation and attenuation of T-cell compared with other CD28 family members (Jin, Ahmed, & Okazaki, 2010). Receptors for PD-1 are present on CD41 T-cells (follicular/activated/ exhausted) CD81 T-cells, B-cells, dendritic cells, mast cells, monocytes, and Langerhans cells. PD-1 was first recognized in 1992 as a molecule whose expression plays a crucial role in apoptosis stimulation (Ishida, Agata, Shibahara, & Honjo, 1992). The surface receptor PD-1 (CD 279) is a transmembrane protein consisting of a single Ig variable (V) domain in the extracellular region. Nevertheless, high expression of PD-1 cDNA transfectants cell lines is unsuccessful in inducing programmed cell death (Agata et al., 1996), thus making the function of PD-1 untouchable for many years. The immune-checkpoint receptor found on tumor-infiltrating lymphocytes and circulating tumor-specific T-cells, where it leads to attenuation of T-cell activation (Ahmadzadeh et al., 2009; Baitsch et al., 2011; Curran, Montalvo, Yagita, & Allison, 2010; Saito, Kuroda, Matsunaga, Osaki, & Ikeguchi, 2013).

9.5 Engineering of therapeutic protein Since the discovery and development of antibody therapeutics, the scale-up for production of immunoglobulins is accelerated by many folds. Generation of recombinant immunoglobulins is a boon not only for neoplastic malignancies but also for inflammatory and autoimmune diseases. However, these clinical recombinant antibodies have various complexities and restrictions (Saeed, Wang, Ling, & Wang, 2017). Engineering and modifications in immunoglobulins can combat the difficulties with efficient binding and high structural stability. The behest of antibody engineering in need of

249

novel protein therapeutics furnished with improved immune-protective abilities, such as effective tumor penetration, active engagement of immune effector functions, and capable binding affinity antibodies specific to novel targets. Modifications in the antibody domain could be a tedious or uncertain task consequential to a lack of information related to the dynamics of antibody engineering. However, due to the current advancement, antibody engineering aids in therapeutics and diagnostics fields of immunology. Over the last few years, the generation of polyclonal and monoclonal antibody through the medium of laboratory animals has given a wide perspective for the cure of contagious and infectious diseases (Marasco & Sui, 2007). Structural domains of an immunoglobulin consist of two identical heavy chain (variable domain
VH, joining region
JH, and constant region
HC) and two light chains with (variable domain
VL, joining region
JL, and constant region
LC), linked by noncovalent and disulfide bridges (Hamers-Casterman et al., 1993).

9.5.1 Binding affinity enhancement Complementarity determining regions (CDRs), framework regions, and residues present in the variable domain play a significant role in binding efficiency and/or specificity of antigen
antibody interaction. Applications such as bioinformatics aided tool alongside with plethora of structural databases have helped in assessing the active residues majorly taking part in the interaction interface. Improvised binding efficiency led to valid binding of an immunoglobulin while attaining structural stability and specificity. However, increased affinity could hinder solid tumor penetration by a phenomenon called “binding site barrier” (Juweid et al., 1992). Under natural conditions, affinity maturation of an immunoglobulin was achieved by somatic hypermutation with subsequent clonal selection (Doria-Rose & Joyce, 2015). IgG binding efficiency augmentation executed by (1) polymerase chain reaction (Unkauf, Hust, & Frenzel, 2018) and (2) incorporating codon degeneracy to instigate mutations within variable regions (Tiller et al., 2017). Additionally, site-directed mutagenesis can also be performed at desired regions, thereby increasing the binding efficiency. According to Kiyoshi et al. (2014), hypervariable loop 3 (CDR-3) is an essential location for mutations as it falls in the middle of the antigen-binding site, thereby increasing binding efficiency. Besides, CDRH3 reported having significant fluctuation of the variable domain region with different conformational entities. (Dondelinger et al., 2018; Morea, Tramontano, Rustici, Chothia, & Lesk, 1998). The latest development in next-generation sequencing has enabled the reconstruction of clonal antibody lineage, which was not available earlier in affinity maturation studies (Mishra & Mariuzza, 2018). Over the last few years, an emanation of the latest sequencing techniques in a combination of 3D crystal structures of an antibody in complex with their target antigen can provide insights of antibody evolution at the structural level, and antigen or virus evasion from immune cells (Mishra & Mariuzza, 2018).

9.5.2 Immunogenicity minimization The majority of the present antibodies have been found immunogenic at one point or another in their entire course of existence. Antibody immunogenicity can lead to the development of auto-antibodies or antidrug antibodies, ending up in the neutralization of immune cells (Arlotta & Owen, 2019). The speculation behind this immunogenic response could be

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9.5 Engineering of therapeutic protein

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9. Bispecific antibodies: A promising entrant in cancer immunotherapy

aggregation while improper folding and the presence of xenobiotic differences (Sathish et al., 2013). The most tabulated source of immunogenicity to therapeutic antibodies is the immune response toward IgG scaffolds from other species (van Schie, Wolbink, & Rispens, 2015). Muromonab-CD3 (OKT3), an immunosuppressant drug used in case of acute rejection in patients with organ transplants, is reported to cause immunogenic responses (Chatenoud, Jonker, Villemain, Goldstein, & Bach, 1986). In a measure to tackle this problem as mentioned earlier, grafting of constant human regions to generate chimeric antibodies has been developed (Jones, Dear, Foote, Neuberger, & Winter, 1986; Kim & Hong, 2012). Even so, human originated or humanized antibodies can generate an immune response (Harding, Stickler, Razo, & DuBridge, 2010). Computer-aided tools are also being implemented to recognize T-cell epitope sequences to minimize immune response against antibodies (Suurs et al., 2019). Furthermore, methods for the recognition of amino acid sequences that often leads to unfavorable isoelectric point or degradation are available (Kumar, Singh, Wang, Rup, & Gill, 2011).

9.5.3 Stability enhancement and half-life extension A naturally occurring immunoglobulin with Fc trunk is known to encompass a plethora of effector functions such as degranulation, phagocytosis, opsonization, and FcRn-mediated recycling. Fc region also facilitates ADCC and CDC. In general, FcγR requires glycosylation for binding the Fc domain of an antibody. However, FcRn does not necessarily require glycosylation as it equally binds to both glycosylated and aglycosylated antibodies (Jefferis, 2005). Neonatal Fc receptor is found to be accountable for the prevention of IgG degradation, by binding and recycling, and circulating them back after subsequent cellular uptake (Kuo & Aveson, 2011). FcRn-mediated recycling results in the extension of serum half-life. Research studies have shown that the binding of FcRn will revamp the circulation half-life at pH of 6. However, Grevys et al. (2015) have illustrated that antibody engineering to increase FcRn binding has reduced the binding efficiency for other FcγR, which are responsible for proinflammatory and antiinflammatory responses. Furthermore, Robbie et al. (2013) demonstrated that antibody Motavizumab with three-point mutations (M-252-Y, S-254-T, and T-256-E) is accountable for increased serum half-life by approximately fourfold.

9.6 Market analysis: past, present and future About three decades ago, first monoclonal antibody OKT3 (muromonab) commercialized in 1986, facilitating T-cell suppression for the prevention of kidney transplant rejection (Liu, 2014). Later on, chimeric antibodies were produced for overcoming mousebased mabs in 1990 (Weiner, 2015). By December 2016, approximately 64 monoclonal antibodies have been approved worldwide for the treatment of various infectious diseases (Lai et al., 2018). Since then immunotherapy arena has never looked back. Currently, immunotherapeutic proteins such as monospecific, bispecific, and multispecifics immunoglobulins are being used for the treatment of neoplastic malignancies and

251

other inflammatory and infectious diseases. Presently, there are about 50 approved monoclonal therapeutics in the market (Kaplon & Reichert, 2018). Additionally, 570 antibodies are in the robust pipeline, wherein 62 of them are in the late phase of clinical trials. As a result, the market for next-generation antibodies/bsAbs is going to multiply further by many folds (Igawa, 2017). By the statistical data of firestone, the global sales of monoclonal antibodies have increased to US$98 billion in 2015, which is about an approximately sevenfold increase in sales since 2005. Interestingly, in 2016, five mabs were nominated in the list of top 10 selling drugs. Different bsAbs modalities have been developed so far. The redirection of cytotoxic immune cells to cancerous cells through bsAbs has not only improved CDC and ADCC but also ensured precise specificity and sensitivity. Along with bsAbs, antibody-drug conjugates have advanced drastically. The market for bsAbs proclaimed to have significant fractions in North America and Europe, making them a global leader in therapeutic engineering. Four antibodies, such as sacituzumab govitecan, ravulizumab, risankizumab, romosozumab being considered for approval by the EU or United States by November 2018 (Kaplon & Reichert, 2019). Consequently, with the exponential advancement of research and technology, the Middle East and the Asia Pacific have escalated the necessity to generate dual targeting antibodies. Asian markets are quite promising with their rapidly growing healthcare infrastructure backed up by various financial schemes, including India. Global demand for immune therapeutics has escalated with an exponential rate during the forecast period due to the successful venture of breast cancer antibodies, resulting in market expansion in emerging economies such as Japan, Australia, India, South Korea, and China (Global Monoclonal Antibodies Market, 2018
2022 | Latest Developments | Technavio | Business Wire, 2018). The government initiative is also establishing a significant uplift for the production of bispecific therapeutics. An annual growth rate of 5.9% forecasted for the next 5
6 years for the market of bsAbs. The market of therapeutics includes recombinant IgG type antibodies, recombinant non-IgG type bispecific, or tri-specific antibodies. Several factors that are responsible for governing the market dynamics are (1) disease prevalence in significant geographies, (2) demand forecast in coming years, and (3) demographic factors. These biologics not only comprise recombinant modified proteins that target cancer but also include antibodies that target several inflammatory and autoimmune diseases with high prevalence. As the latest trend in the market and by data from the clinical trials, it can be depicted that the majority of recombinant antibodies are IgG-based format. At the same time, very few are non-IgG-based like ScFv or BiTE. However, the oncology segment is likely to dominate other categories of disorders in the present market. As of now, the market of bsAbs consists of both symmetric and asymmetric modalities, ranging from nanobodies like dual affinity re-targeting and asymmetric antibodies like Diabody (Holliger, Prospero, & Winter, 1993) and CrossMAbs. Similarly, tetravalent bsAbs like dual variable domain (Wu et al., 2009) and decay accelerating factor are among other proposed structural modalities soon to enter the market. Besides, nanobodies, which are also nonconventional antibodies, are produced by recombinant engineering of naturally occurring single-domain antibodies. Single-domain antibodies are new entrants in the field of antibodies. These antibodies are naturally occurring in some isotopes of camelids and sharks (Conrath, Lauwereys, Wyns, & Muyldermans, 2001; Greenberg et al., 1995). Single-domain antibodies against specific antigens are isolated from unique heavy chain immunized antibodies of camelids and sharks.

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9.6 Market analysis: past, present and future

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9. Bispecific antibodies: A promising entrant in cancer immunotherapy

The single-domain antibody is micro in size with a robust and efficient binding affinity (Conrath et al., 2001). Manufacturers who are likely to play a crucial role in the market of recombinant therapeutics are Amgen, Bayer AG, Dow Pharmaceutical Solutions, ImmunGene, Immunocore Limited, Merck & Co., Novartis AG, Pfizer, F. Hoffmann-La Roche AG, Janssen Biotech, OncoMed Pharmaceuticals, Inc., Paktis Antibody Services, and Fabion Pharmaceuticals among others (Global Bispecific Antibody Market to 2025: Drug Sales & Clinical Pipeline Insights with an $8 Billion Market Opportunity, n.d.). In late November 2018, out of 33 novel antibodies candidate in late clinical trials, the majority fractions were targeting solid tumors, whereas 20% of them are for hematological malignancies (Lai et al., 2018) (Table 9.4). TABLE 9.4 List of Therapeutic antibodies in the market targeting cancer, inflammatory, and infectious diseases. Antibodies

Generic name

Manufacturing pharmaceuticals

Target

Antibody format (isotype)

References

RaxibacuMab

AbThrax

GSK

PA (protective antigen) of Bacillus anthrax

IgG1

Tsai and Morris (2015); Mazumdar (2009)

MotavizuMab

NUMAX

MedImmune

Respiratory syncytial virus

IgG1

Cingoz (2009)

BezlotoxuMab

ZINPLAVA

Merck & Co., Bristol-Myers Squibb, University of Massachusetts

Clostridium difficile toxin B

Human, mousederived IgG1, κ-chain

Wilcox et al. (2017)

OblitoxaxiMab

ANTHIM

Elusys Therapeutics

PA of B. anthrax

IgG1κ

Yamamoto et al. (2016)

Human IgG1

Sparrow et al. (2018)

Antibodies for oncology RMab

RABISHIELD Serum Institute of India, MassBiologics

Rabies virus G glycoprotein

BelimuMab

BENLYSTA

GSK

B-lymphocyte IgG1λ Stimulator (BLyS)

Dubey et al. (2011)

BlinatumuMab

BLINCYTO

Amgen

CD19, CD3

(BiTE)

Brown (2018)

TrastuzuMab

HERCEPTIN

Genentech

HER-2

IgG1

Boekhout, Beijnen, and Schellens (2011)

CetuxiMab

ERBITUX

ImClone

EGFR

IgG1

Snyder, Astsaturov, and Weiner (2005)

PertuzuMab

PERJETA

Genentech

HER-2

IgG1κ

Jhaveri and Esteva (2014)

RituxiMab

RITUXAN

Genentech

CD20

IgG1

Sharma, Koller, Barclay, and Liddle (2007)

(Continued)

253

9.6 Market analysis: past, present and future

Antibodies

Generic name

Manufacturing pharmaceuticals

Target

Antibody format (isotype)

References

BevacizuMab

AVASTIN

Genentech

VEGF-A

IgG1

Kazazi-Hyseni, Beijnen, and Schellens (2010)

NecitumuMab

PORTRAZZA Eli Lilly

EGFR

IgG1

di Noia et al. (2018)

AtezolizuMab

TECENTRIQ

Genentech

PD-L1

IgG1

Colevas et al. (2018)

CatumaxoMab

REMOVAB

Fresenius Biotech GmbH & TRiOn Pharma

(EpCAM 3 CD3)

IgG2

Linke et al. (2010)

OfatumuMab

ARZERRA

GSK

CD20

IgG1κ

Zhang (2009)

InotuzuMab

BESPONSA

Pfizer

CD22

Humanized IgG4, κ-chain

Uy, Nadeau, Stahl, and Zeidan (2018)

IpilimuMab

YERVOY

Bristol
Myers CTLA4 Squibb Human, transgenic mousederived IgG1, κ-chain

Human-derived IgG1, κ-chain

Lipson and Drake (2011)

NivoluMab

OPDIVO

Bristol
Myers Squibb, Ono Pharmaceutical

PD1

Humanized IgG4, κ-chain

Guo, Zhang, and Chen (2017)

TocilizuMab

ACTEMRA

Genentech

IL-6R

IgG1

(Mihara, Nishimoto, and Ohsugi, 2005)

DaratumuMab

DARZALEX

Johnson & Johnson

CD38

Transgenic mousederived IgG

Plesner and Krejcik (2018)

PembrolizuMab KEYTRUDA

Merck

PD-1

IgG

Khoja, Butler, Kang, Ebbinghaus, and Joshua (2015)

CemipliMabRWLC

LIBTAYO

Regeneron

PD-1

IgG1

Ahmed, Petersen, Patel, and Migden (2019)

IbritumoMab Tiuxetan

ZEVALIN

Spectrum Pharmaceuticals

CD20

Antibody-chelator conjugate

Mondello, Cuzzocrea, Navarra, and Mian (2016)

TositumoMab

BEXXAR

Novartis

CD20

Mouse IgG2a, λ-chain

Srinivasan and Mukherji (2011)

PanitumuMab

VECTIBIX

Amgen, Takeda

EGFR

Human, transgenic mouse-derived IgG2

Keating (2010)

Amgen

TNF-α

IgG1

Kivitz and Segurado (2007), Sator (2018), Mease (2007)

Antibodies for inflammation AdalimuMab

AMJEVITA (USA)/ SOLYMBIC (EU)

(Continued)

Section 4: Novel therapeutic modalities

TABLE 9.4 (Continued)

254

9. Bispecific antibodies: A promising entrant in cancer immunotherapy

Section 4: Novel therapeutic modalities

TABLE 9.4 (Continued) Antibodies

Generic name

Manufacturing pharmaceuticals

Target

Antibody format (isotype)

References

GolimuMab

SIMPONI

Johnson & Johnson, Merck & Co.

TNF

Human, mousederived IgG1,

Frampton (2017)

ItolizuMab

ALZUMAB

Biocon, CIMAB, Probiotec

CD6

Humanized IgG1

Srivastava (2017)

OmalizuMab

XOLAIR

Roche, Novartis, Gentech

IgE

Humanized IgG1

Godse, Mehta, Patil, Gautam, and Nadkarni (2015)

MepolizuMab

NUCALA

GlaxoSmithKline

IL-5

Humanized IgG1

Abonia and Putnam (2011)

ReslizuMab

CINQAIR

Merck & Co., Teva, UCB

IL-5

Humanized IgG4

Hom and Pisano (2017)

BrodaluMab

SILIQ

Valeant Pharmaceuticals, Amgen, AstraZeneca

IL17RA

Human, mousederived IgG2,

Beck and Koo (2019)

9.7 Future challenges and opportunities Formation of bsAbs fragments via diversified assortments by researchers worldwide through utilizing the naturally existing modalities enabled the growth of immunotherapy not only for malignant disorders but also for several inflammatory and infectious diseases (Reichert & Valge-Archer, 2007). Anticipated functions of bispecific or multispecific antibodies in vivo are yet to unleash. According to the research by Nieva, Kerwin, Wentworth, Lerner, and Wentworth (2006), the antibody catalyzed the production of oxidants (utilizing riboflavin) by activating the antibody-catalyzed water oxidation pathway. This reaction might insinuate chemical cascade, which can complement the biological function of bsAbs. Molecular therapeutics is flourishing with bi/multispecific antibodies, antibody-drug conjugates (immuno toxin), radioactive antibody payloads, and a single-domain antibody. Thus the need for selection of a high-affinity antibody is highly stipulated (Chames & Baty, 2000). Relapse in monoclonal therapy and, negligible signal background and efficient specificity and high sensitivity, gives a preferential advantage to bsAb over monospecific antibodies (McBride et al., 2006). Challenges need to be conquered in bsAbs generation include (1) modification in the design of the structural framework and simplification in production strategies, (2) devising strategies to minimize adverse effect, and (3) recognition of biomarkers for the desired outcome (Thakur, Huang, & Lum, 2018). Owing to the rise in the poor translation of antibody candidates in clinical trials, it needs room for the improvement in optimizing the efficient model for the generation of fragment engineering, improvising efficacy, and cost-effective measures in downstream processing (Sewell et al., 2017). Thus some of the ways to circumvent the problems, as mentioned above, are innovations

255

in the future currently being researched to escalate the productiveness in clinical use (Liu, 2014). As evidenced by the approval of many bispecific or multispecific therapeutics by the FDA or EU, it is eliciting acclaimed interest in the field of immunotherapy. However, the adverse effects or toxic syndrome associated with immunotherapy in vivo is a domain that needs to be explored earnestly. In the combat against neoplastic malignancies and hematological malignancies, immune CPIs are among one of the established biological therapy with established indications (Alatrash, Daver, & Mittendorf, 2016; De Felice et al., 2015; Postow et al., 2015; Vansteenkiste, Craps, De Brucker, & Wauters, 2015). Yet, the challenges for checkpoint combinations therapy are multifarious. A comprehensive study of pharmacokinetics and pharmacodynamics is among various challenges. In the future, bsAbs might be devised as a next-generation or future drug as diagnostic and therapeutic devices. bsAbs, in the coming years, have an enormous number of formats in the pipeline for clinical trials (Thakur et al., 2018).

9.8 Conclusion Owing to the setbacks of chemotherapy and mabs, the generation of new methods to design bsAbs accelerated. The present novel designs vary from conventional IgG format, whereas majority of them are non-IgG-based scaffolds. bsAbs are playing a crucial role not only in T-cell recruitment (therapeutics) but also in targeting and pretargeting of malignant cells solid tumors. Thus making tumor-directed therapy/in situ vaccination currently being assessed in preclinical trials (Ellmark et al., 2017). New challenges and future opportunities while designing bispecific or multispecific antibodies include improvising pharmacodynamics, tissue penetration ability, efficacy, structural stability, serum half-life, size, valency, and the presence of the Fc domain (Sedykh et al., 2018). As per the research and its trend, soon, by combinations of therapies introduced earlier, a different range of bispecific proteins might be devised to target a wide range of antigens (biomarker). Further, with progression in biological drugs, there may come the time where already developed techniques will not be sufficient to treat malignant disorders and completely novel products have to be developed. In summary, devising new biological therapeutics provide (1) high product yield, (2) ease in purification process, (3) format and stability, and (4) serum half-life. Therefore bsAbs might be considered as the future drug in the conquest against evolving malignant disorders.

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C H A P T E R

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10 Emerging therapeutic modalities against malaria Suresh Kumar Chalapareddy1, Andaleeb Sajid2, Mritunjay Saxena3, Kriti Arora4, Rajan Guha1 and Gunjan Arora2 O U T L I N E 10.1 Introduction

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10.4 Drugs targeting membrane transporters

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10.5 Natural products

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10.6 Protein-based malaria vaccines

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10.7 Nucleic acid vaccines for the new era 10.7.1 DNA-based vaccines 10.7.2 RNA-based vaccines

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References

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10.1 Introduction Malaria is transmitted by the bite of female Anopheles mosquito, which releases Plasmodium sporozoites into the host (Beier, 1998). The parasite life cycle comprises an 1 2 3 4

National Institutes of Health., Bethesda, MD, United States Yale University, New Haven, CT, United States ICMR-National Institute of Malaria Research, Delhi, India Proteus Digital Health, Inc., Redwood City, CA, United States

Translational Biotechnology DOI: https://doi.org/10.1016/B978-0-12-821972-0.00018-6

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asexual and a sexual stage in humans and a sexual stage in mosquito. Sporozoites eventually migrate to the liver and, in 48 hours, develop into merozoites (Aly, Vaughan, & Kappe, 2009). The released merozoites invade red blood cells (RBCs) and start asexual development in the host that leads to symptomatic malaria. Plasmodium undergoes development to multiple stages inside the RBCs, viz. ring, trophozoite, and schizont stages and rapidly increases its numbers in the blood (Alaganan, Singh, & Chitnis, 2017; Cowman & Crabb, 2006; Crutcher & Hoffman, 1996). The World Health Organization (WHO) 2018 malaria report estimated an average of 228 million cases all over the world, which resulted in 405,000 deaths. This was a reduction of 2.8 million from the previous year (WHO, 2018). Since 2010, the fatality rate was reduced by about 15%, but recent reports suggest that the decrease is now plateauing. Among the five species of Plasmodium capable of causing malaria, the majority of the disease occurrences are caused by Plasmodium falciparum and Plasmodium vivax infection. P. falciparum is most prevalent in Africa, whereas P. vivax is more prevalent in many parts of Asia and South America (Howes et al., 2016). Major studies on malaria started after Sir Ronald Ross’s discovery of malarial transmission by mosquito bites (Hagan & Chauhan, 1997). However, the emergence of resistance to antimalarial drugs threatens the progress made and is driving research efforts for the discovery of antimalarial compounds with new mechanisms of action (MoA). We will briefly discuss all antimalarial compounds from the beginning of malaria chemotherapy to the next-generation antimalarial compounds (Table 10.1).

10.2 Heme-detoxification drugs Chloroquine: Chloroquine (CQ, 4-aminoquinoline) has been used as an antimalarial drug from the late 1940s. Earlier evidence suggests that the primary target for CQ is heme detoxification pathway in the digestive vacuole of the parasite (Egan, 2008). Within the parasite, hemoglobin degradation occurs in the digestive vacuole, and toxic heme monomers polymerize to form hemozoin (Fig. 10.1). CQ is a weak base and remains uncharged at neutral pH of the blood. Uncharged CQ can freely diffuse across the membranes. When CQ encounters acidic pH in the digestive vacuoles, it gets protonated and is unable to transfer across the membrane (Martin et al., 2009). As CQ accumulates in the digestive vacuole, it binds to hematin, which is a heme dimer (Fitch, 2004). Binding of CQ to hematin prevents its polymerization and hence results in accumulation of free heme moieties. Permeabilization of toxic free heme monomers into the cytosol eventually leads to the death of the parasite (Zhang, Krugliak, & Ginsburg, 1999). Mefloquine and halofantrine: Mefloquine (MFQ) is a 4-methanolquinoline, which is structurally similar to quinoline drugs like quinine, mepacrine, and CQ. During the blood stage of the parasite, MFQ disrupts the digestion of hemoglobin (Fig. 10.1) (Foley & Tilley, 1997). It was used in CQ resistant patients as a prophylactic and curative drug. In 1986, MFQ resistance was reported, and later it was found to be toxic to the central nervous system (Nevin & Croft, 2016). Halofantrine was developed in the early 1970s, which was widely used to treat all forms of the Plasmodium parasite (Cosgriff et al., 1982). Subsequently, the usage of halofantrine was reduced due to its undesirable side effects on the heart. So, it was used as a curative drug only in patients without heart diseases (Croft, 2007).

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TABLE 10.1

Antimalarial compounds, their targets, and mode of action.

Antimalarial compound

Chemical group

Halflife

Target

Mode of action

Quinine

Aryl amino alcohol

3
36 h

No specific target (NST)

Heme detoxification

Chloroquine

4-Aminoquinoline

5
12 days

NST

Heme detoxification

Mefloquine

4-Methanolquinoline

8
15 days

NST

Heme detoxification

Lumefantrine

Aryl amino alcohol

2
11 days

NST

Heme detoxification

Piperaquine

Bis-4-aminoquinolin

13
28 days

NST

Heme detoxification

Amodiaquine

4-Aminoquinoline

3
12 h

NST

Heme detoxification

Drugs targeting DNA or protein synthesis Proguanil

Biguanide

8
18 h

Dihydrofolate reductase (DHFR)

Pyrimidine synthesis

Atovaquone

Hydroxynaphthoquinone

1
6 days

Cytochrome bc1 complex

Electron transport chain

Pyrimethamine

Diaminopyrimidine derivative

2
19 days

DHFR

Pyrimidine synthesis

Sulfadoxine

Sulfonamide

4
11 days

Dihydropteroate synthetase

Folate biosynthesis

4
6 days

Plasmodium falciparum dihydroorotate dehydrogenase

Nucleotide biosynthesis

10 h

P. falciparum eukaryotic elongation factor 2

Protein translation

DSM265 M5717

Quinoline-4-carboxamide

Drugs targeting membrane transporters Artemisinin and its derivatives

Sesquiterpene lactone endoperoxide

0.5
2 h

Promote generation of reactive oxygen species

SJ733

Tetrahydroisoquinoline carboxanilide

8h

MMV253

Triaminopyrimidine

14
16 h P. falciparum

Impairs intracellular pH

5.3 h

Phosphatidylinositol kinase 4 pathway

P. falciparum Na1-ATPase

Alters intracellular sodium concentration

V-type H1-ATPase

UCT943

2-Aminopyrazine

Phosphatidylinositol 4-kinase

Amodiaquine, piperaquine, pyronaridine, lumefantrine, and tafenoquine: Most of these drugs were discovered and developed as part of Chinese National Malaria Elimination Program and Chinese antimalarial research efforts (Chen, Qu, & Zhou, 1982; Cui & Su, 2009; Tse, Korsik, & Todd, 2019). The MoA of these compounds is not clearly understood. Many studies suggested that these are all either directly or indirectly involved in binding with heme moieties leading to defects in generation of hemozoin in the parasite digestive vacuole (Fig. 10.1) (Combrinck et al., 2013; Croft et al., 2012; Vennerstrom et al., 1992). All of

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Heme-detoxification drugs

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Red blood cell Parasite cytosol

Section 4: Novel therapeutic modalities

PI4-K

UCT943

Golgi complex

DHFR

Digesve vacuole PYR Proguanil

Hb

Mitochondria ATO

Cytochrome bc1

DHOD

Heme

AMQ,CQ LMF, MFQ

Hemozoin

DSM265

FIGURE 10.1 Erythrocytic stage of Plasmodium infection: Plasmodium parasite infection in red blood cell is shown. Multiple drugs act at this stage of infection, targeting different essential enzymes of the parasite. Abbreviations of P. falciparum enzymes and proteins: DHFR, dihydrofolate reductase; DHOD, dihydroorotate dehydrogenase; Hb, hemoglobin; ATPase, P-type ATPase 4; PI4-K, phosphatidylinositol 4-kinase. Abbreviations of drugs: PYR, Pyrimethamine; ATO, atovaquone; CQ, chloroquine; AMQ, amodiaquine; LMF, lumefantine; MFQ, mefloquine.

these compounds are effectively used in the combination therapy with artemisinin and its derivatives (Eastman & Fidock, 2009; Tse et al., 2019). Quinine: Quinine is aryl amino alcohol, which was first isolated in 1820 from the bark of Cinchona tree and has been effective for the treatment of malaria (Achan et al., 2011). The mode of action is not clear. The mechanism is thought to be inhibition of hemozoin crystallization in the heme detoxification pathway (Foley & Tilley, 1997). It may also target the purine nucleoside phosphorylase enzyme (Dziekan et al., 2019). In 1980, resistance was reported against quinine (Bunnag et al., 1996), which eventually led to the discontinuation of its usage.

10.3 Drugs targeting DNA or protein synthesis Proguanil and atovaquone: Proguanil was the first antifolate antimalarial drug (Curd, Davey, & Rose, 1945), whereas atovaquone (ATO) was reported for the treatment of protozoan infection (Baggish & Hill, 2002). These two compounds have different MoA. ATO inhibits cytochrome bc1 complex, which in turn blocks the electron transport chain, whereas proguanil inhibits dihydrofolate reductase (DHFR), which disrupts deoxythymidylate

271

synthesis (Fry & Pudney, 1992). The synergistic effect of these two compounds in the treatment of malaria has been shown, and since the early 2000s, these two are effective in combination for the treatment (Fig. 10.1) (Srivastava & Vaidya, 1999). Pyrimethamine and sulfadoxine: Pyrimethamine (PYR) and sulfadoxine were developed in the 1950s and 1960s, respectively (Falco, Goodwin, Hitchings, Rollo, & Russell, 1951; Laing, 1965). Both the drugs target folate biosynthesis pathway in the parasite, PYR inhibits DHFR (same as proguanil), and sulfadoxine inhibits dihydropteroate synthetase (Lumb et al., 2011). DSM265: DSM265 is the new compound against malaria, which targets dihydroorotate dehydrogenase (Fig. 10.1), which plays an important role during the synthesis of essential metabolites for parasite replication at blood stage (Phillips et al., 2015). M5717: This is also another new antimalarial compound derived from quinoline-4carboxamide (Baragana et al., 2016). This drug has greater potency than other current drugs (mefloquine, artemisinin, and dihydroartemisinin). It has a new mechanism of inhibiting parasite protein translation by targeting PfeEF2 (Plasmodium falciparum eukaryotic elongation factor 2) (Baragana et al., 2015). This drug is effective for all stages of the parasite and shows equal potency against most drug-resistant parasite strains at nanomolar range. M5717 was also shown to have the potential to block transmission of the parasite and can be used as a prophylactic agent (Tse et al., 2019).

10.4 Drugs targeting membrane transporters Artemisinin and its derivatives: Commonly used in Chinese traditional medicine, artemisinin was extracted from the herb Artemisia annua in 1971 (Miller & Su, 2011). Artemisinin had an extraordinary impact on combating malaria for which Tu Youyou was awarded the Nobel Prize in 2015. Artemisinin and its derivatives, artemether, artesunate, arteether, and dihydroartemisinin are extensively used in combination therapy (Eastman & Fidock, 2009). The mechanism of artemisinin action on parasite clearance has been debated (O’Neill, Barton, & Ward, 2010). The widely accepted theory regarding the artemisinin MoA is that it is involved in the heme-mediated free radical generation, which in turn affects the proteins required by Plasmodium in the host (Tilley, Straimer, Gnadig, Ralph, & Fidock, 2016; Wang et al., 2015). There is other evidence for the artemisinin-based parasite clearance, for example, inhibition of PfATP6 (Ca12 transporter) and PfPI3K (phosphatidylinositol 3-kinase) (Mbengue et al., 2015; Shandilya, Chacko, Jayaram, & Ghosh, 2013). In 2015, a population transcriptomics of human malaria parasite study showed that artemisinin might be associated with the upregulation of the unfolded protein response pathway and subsequently delaying parasite development (Mok et al., 2015). Currently, with all approved antimalarial molecules, Medicines for Malaria Venture (MMV) are progressing with nine drugs for malaria treatment based on different combinations or formulations (Tse et al., 2019; WHO, 2019). Like other drugs, resistance is being generated against artemisinin also, indicating a need to discover new antimalarial drugs with a novel MoA. Many novel compounds have been shortlisted by high-throughput screening analyses that have a potential to be developed into highly promising antimalarial candidates. Here we

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discuss some examples of the next-generation antimalarial compounds that have cleared phase I trials on human volunteers. SJ733: SJ733 belongs to the tetrahydroisoquinoline carboxanilide class, which has excellent antimalarial activity (Guiguemde et al., 2010). SJ733 is more potent in vivo compared to artesunate, chloroquine, and pyrimethamine (Jimenez-Diaz et al., 2014). This molecule seems to inhibit P-type Na1-efflux pump, which helps the parasite to survive in high extracellular sodium concentration (Fig. 10.1) (Jimenez-Diaz et al., 2014). Another new compound, KAE609 (Cipargamin), is also thought to target Na1-ATPase. MMV253: MMV253 is triaminopyrimidine, another new antimalarial compound with a novel MoA. MMV253 inhibits V-type H1-ATPase, which helps in maintaining the intracellular pH of the parasite (Hameed et al., 2015). UCT943: UCT943 is a derivative of 2-aminopyrazine with single-dose curative potential (Le Manach et al., 2016). This compound targets phosphatidylinositol 4-kinase (PI4-K), which helps in the development of parasite at the schizont stage (McNamara et al., 2013). These are the most effective antimalarial drugs with novel MoA and target different proteins of the parasite. Along with these compounds, there are many other compounds also under development with the potential to be effective next-generation antimalarial drugs.

10.5 Natural products Besides artemisinin, several natural compounds are being tested for their antimalarial activity. A stilbene glycoside compound PBG [piceid-(1-6)-β-D-glucopyranoside] extracted from Parthenocissus tricuspidata (Vitaceae) has shown activity against blood-stage infection of Plasmodium berghei and in overall survival (Park, Lee, & Moon, 2008). Plasmodium tricuspidata extracts can inhibit P. falciparum with inhibitory concentration (IC50) of 5.3 μM (Son, Chung, Lee, & Moon, 2007). The leaf extract of Vernonia amygdalina, a natural plant popular in the southern region of Nigeria, which has shown activity against Plasmodium is used in the treatment of malaria infection (Bihonegn, Giday, Yimer, Animut, & Sisay, 2019; Iwalokun, 2008; Njan et al., 2008; Omoregie & Pal, 2016). Ethanol extract of the V. amygdalina leaf inhibited P. berghei growth in vivo in a dose-dependent manner. The leaf extracts also decreased nitric oxide, lipid peroxidation levels, and proinflammatory cytokines (TNF-α and IFN-γ), indicating immunomodulatory effects (Omoregie & Pal, 2016). Anti-plasmodial compounds have also been tested from different plants such as—Murraya koenigii, Quassia amara L. (Simaroubaceae), Calotropis gigantea (L.), Pongamia pinnata (L) Pierre, Zea mays L. (Poacae), Alchornea laxiflora, Toddalia asiatica (L) Lam. (Rutaceae), and Triclisia gilletii— against P. falciparum and P. berghei (Bertani et al., 2006; Cachet et al., 2009; Houel et al., 2009; Kamaraj et al., 2014; Kikueta et al., 2013; Okokon, Antia, Mohanakrishnan, & Sahal, 2017; Okokon, Augustine, & Mohanakrishnan, 2017; Orwa, Ngeny, Mwikwabe, Ondicho, & Jondiko, 2013; Satish & Sunita, 2017; Satish, Kumari, & Sunita, 2017). To find a solution for any disease, drugs and vaccines are two methodologies that can be explored. Whereas malaria drugs have been used for the treatment of infection historically, vaccines efforts have only come about over the last few decades. We discuss here

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the research and development of different malaria vaccine plans, including vaccines based on protein antigens and nucleic acid.

Malaria vaccines can significantly reduce the overall disease burden and incidence. Mosquirix or RTS,S/AS01 is the only approved vaccine against malaria (Laurens, 2020). Irradiated or attenuated Plasmodium sporozoites have been used to immunize mice and humans, targeting the pre-erythrocytic stage (Clyde, 1975; Hill, 2011; Nussenzweig, Vanderberg, Most, & Orton, 1967). The identification of different blood-stage antigens also initiated trials for blood-stage vaccines. The emergence of a peptide-based candidate vaccine corresponding to the fragments of the P. falciparum merozoite-specific proteins provided protection to the new world monkeys and humans (Patarroyo et al., 1988). However, the blood-stage vaccine did not show similar efficacy in clinical trials, and the focus again shifted to the pre-erythrocytic stage. The advancement in gene sequencing technologies, vaccinology and immunobiology potentiated the development of a new vaccine formulation called RTS,S, which was based on the fusion of the circumsporozoite protein (CSP) and hepatitis B surface antigen (HBsAg) plus a potent adjuvant (Ballou et al., 1987; Stoute et al., 1997). The fusion protein containing CSP central repeats, T-cell epitopes present in CSP, HBsAg is expressed in yeast cells. The fusion protein added to HbsAg, and adjuvant AS01 was developed as the final vaccine. In the last 20 years, two candidates—the RTS,S vaccine and whole irradiated sporozoites—have led the way, showing efficacy in different clinical trials (Ballou & Cahill, 2007; Hickey et al., 2016; Ishizuka et al., 2016; Jongo et al., 2018). Plasmodium transmission requires a sexual stage parasite to reach mosquito vector during a blood meal, failure of which will block the parasite spread to other individuals. Transmission can be blocked by developing a vaccine against sexual stage antigens such as Pfs48/45, Pvs230, and Pfs25H (Sagara et al., 2018; Tentokam et al., 2019; Theisen, Jore, & Sauerwein, 2017). In another approach, VAR2CSA fragments that bind to chondroitin sulfate A (CSA) and sequester the parasite-infected RBCs in the placenta are also being examined as placental malaria vaccine (Doritchamou et al., 2019; Fried & Duffy, 2015). There is emerging interest in nucleic acid vaccines due to their adaptability for multivalent vaccine approaches as well as being cost-effective.

10.7 Nucleic acid vaccines for the new era Nucleic acid vaccines use recombinant DNA- and RNA-based genetic engineering and in vivo production of antigens rather than using the proteins themselves (Donnelly, Ulmer, Shiver, & Liu, 1997; Francis, 2018). Primarily used for gene therapy, the development of DNA or RNA mammalian expression vectors led to the development of nucleic acid vaccines (Vogel & Sarver, 1995; Wolff et al., 1990). The nucleic acid vaccine candidate approaches are shown to be effective against different parasitic diseases

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(Da’ dara et al., 2008; Das et al., 2014; Guha et al., 2013; Gurunathan et al., 1997; Gurunathan, Klinman, & Seder, 2000).

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10.7.1 DNA-based vaccines The genes of pathogens cloned into a mammalian expression vector are used as DNA vaccines, avoiding the tedious preparation of recombinant proteins, or an attenuated version of the parasite itself. The desired antigen is primarily produced by host muscle cells that take up DNA plasmid (Sunita, Sajid, Singh, & Shukla, 2020; Vogel & Sarver, 1995; Wolff et al., 1990). In mice, intramuscular (IM) injection of nonreplicative/noninsertional DNA expression vector results in the expression of the protein in muscle cells for up to 19 months (Wolff et al., 1990). For DNA vaccination, the plasmid or the vector is constructed without eukaryotic origin of replication to restrict its division upon transfecting mammalian cells. The first effective DNA vaccine showed protection from heterologous Influenza A virus in mice, developing humoral and cytotoxic T-lymphocytes (CTL) response (Ulmer et al., 1993). Development of major histocompatibility complex (MHC) class I-restricted CTL responses indicate that DNA vaccination is superior to peptides and subunit-based vaccine approaches (Ulmer et al., 1993). In one of the first examples of a DNA vaccine in malaria, IM injection of DNA plasmid encoding Plasmodium yoelii CSP in mice induced antibody production and CTLs compared to immunization with irradiated sporozoites (Hoffman, Sedegah, & Hedstrom, 1994). Further, adjuvant-free IM injection of CSP-plasmid in mice showed reduced parasite burden in the liver stage after challenge with sporozoites (5 3 105). DNA vaccination provided 68% protection against malaria when challenged with a lower dose of sporozoites (1 3 102), and this protection depends on CTLs (CD8 1 ) (Sedegah, Hedstrom, Hobart, & Hoffman, 1994). This response of DNA vaccines can be further enhanced by boosting the dendritic cell-mediated antigen presentation of the PyCSP. In one study, plasmid encoding granulocyte-macrophage colony stimulating factor (GM-CSF) was shown to provide adjuvant effect, enhance IL-2 production, and CD4 1 T-cell activation. The addition of GMCSF not only increased in CSP-specific antibodies but also boosted overall CD8 1 T-cell function (Weiss et al., 1998). In another example of a DNA vaccine, a predicted antigenic determinant region of sporozoite asparagine-rich protein 1 (SAP1) was used. The vaccination led to an increase in cytokine and IgG levels and provided partial protection against P. yoelii 17XL infection. The SAP1 vaccine inhibited parasitemia and prolonged the survival of mice after infection (Zhao et al., 2013). DNA vaccination is a good method to screen the protective effect of complex antigens. For example, studies on another P. berghei antigen, glycosylphosphatidylinositolanchored micronemal antigen, showed good humoral response but only a slight reduction in the liver burden of BALB/c mice. Further, it had no effect on the blood-stage parasite infection or any transmission-blocking effect (Du, Wang, Zhao, Cao, & Luo, 2016). In another study, a fragment of merozoite surface protein-1 (PvMSP-1) from P. vivax was tested for immunogenicity by DNA vaccination in BALB/c mice. The study confirmed the immunogenicity of the recombinant vaccine plasmid, which was lower compared to the recombinant PvMSP-1 antigen. Interestingly, in animals injected with DNA vaccine, prime boosting with a recombinant protein

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leads to a significant elevation of antibody and the cytokines responses but overall remains lower than immunization with recombinant protein (Sheikh, Kaushal, Chandra, & Kaushal, 2016). This study suggests, although generally DNA vaccine provides a higher humoral response, its overall impact may differ according to antigens. DNA vaccines show no organ pathology or systemic toxicity, and there were no adverse effects observed by clinical chemistry in mice. The studies on CSP-based DNA vaccines clearly showed that they did not lead to dsDNA or antinuclear antibodies. The vaccination did not induce any autoimmune-mediated pathology in mice and rabbits (Parker et al., 1999). The studies also confirmed that the DNA vaccine remained IM at the site of injection, approximately 1:100 ratio of plasmid to genome and overall showed minimal integration at the level lower than spontaneous mutation (Martin et al., 1999). These studies confirm the safety and validity of DNA vaccination in animal models. There are also many other studies showing the safety of DNA vaccines in humans (Fig. 10.2). In the first trial, DNA vaccine monovalent plasmid encoding P. falciparum CSP (PfCSP) was well tolerated by humans and showed no significant biochemical or hematologic changes when immunized by either needle or needle-free jet challenge at the IM or intradermal (ID) sites. Needle-free jet injections allow a small amount of liquids to be injected with high pressure (Jones & Dean, 2016). Interestingly, CSP vaccine-induced CTLs restricted by six human leukocyte antigen (HLA) class-I alleles but did not induce antibodies when tested by ELISA or immunofluorescence assay (Epstein et al., 2002; Le et al., 2000; Wang et al., 1998). In another study, PfCSP-encoding DNA vaccine was administered FIGURE 10.2 Liverstage malaria and schematic of CSP-DNA vaccine: After mosquito bite, parasites are released in the host and reach liver. These parasites express CSP on their surface. Plasmid containing CSP gene is injected intramuscularly, from where cells uptake the DNA, reach the nucleus, and express CSP within the host. This indigenous CSP antigen helps in generating prominent immune response. CSP, Circumsporozoite protein.

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by a similar method of needle IM or needleless jet injection (IM or IM/ID) to investigate immunogenicity in human volunteers. All the volunteers exhibited antigen-specific IFN-γ responses, CTL responses were detected in the majority of subjects, however, IM jet injection provided a higher response (Wang et al., 2001). Immunization of mice with heterologous prime/boost immunization schedule constituting a DNA vaccine and recombinant virus encoding Plasmodium antigens have also been tested against malaria. In one of the first examples of heterologous prime/boost vaccination regimen of DNA plasmid and boosting with recombinant modified Vaccinia virus Ankara, provided partial protection and induced 5- to 10-fold higher antigen-specific T-cell responses in human volunteers to thrombospondin-related adhesion protein (TRAP), present on the surface of sporozoite (McConkey et al., 2003). In another study, a DNA prime, poxvirus (canarypox or recombinant attenuated vaccinia, COPAK), boost vaccination strategy was adopted comprising two sporozoite proteins [CSP and sporozoite surface protein 2 (SSP2)] and two antigens expressed during the blood stage (apical membrane antigen 1 and merozoite surface protein 1) of Plasmodium knowlesi in non-human primates (Rogers et al., 2001, 2002; Weiss et al., 2007). This strategy showed promising results with Rhesus Macaque showing a high level of protection when challenged with 100 P. knowlesi sporozoites (Rogers et al., 2001, 2002; Weiss et al., 2007). The study showed PkCSP vaccines provide a protective effect, though higher IFN-γ response to the PkCSP does not correlate with protection (Weiss et al., 2007). Interestingly, immunization with a vaccine containing CSP and SSP2 did not provide protection but delayed the onset of parasitemia. These studies showed that P. knowlesi protection in Rhesus Macaques is achieved by combining pre-erythrocytic and blood-stage parasite antigens. Also, there was no association between the inhibitory activity of antibodies in vitro and protection (Hamid et al., 2011). One of the most significant advantages that DNA vaccine has over others is to test a large number of candidates for those that act on Plasmodium at multiple stages of the life cycle in the host. This approach was tested by multi-stage DNA vaccine operation, 5 genes (MuStDO5), by using multiple plasmids that encode different Plasmodium antigens—CSP, TRAP, exported protein-1 (Exp1), liver-stage antigen-1 (LSA1), and liver stage antigen-3 (PfLSA3), that can act on the pre-erythrocytic stage of P. falciparum infection (Wang et al., 2005). The response was compared in the presence or absence of plasmid encoding the gene for human granulocyte-macrophage colony stimulating factor (hGM-CSF), which can further boost the immunogenicity of DNA vaccines. T-cell responses to PfCSP were comparable in this multivalent vaccine to PfCSP alone. However, contrary to expectations, class I-restricted IFN-γ responses were inhibited in the presence of hGM-CSF (Wang et al., 2005). This trial also showed the safety of the pentavalent DNA vaccine, the adjuvant property of hGM-CSF plasmids, and modest T-cell immune responses, paving the way for future vaccine trials in humans (Richie et al., 2012). DNA vaccine is a leap in progress to develop vaccines for malaria. The current vaccine candidates such as RTS,S (Mosquirix) lack broader protection against different Plasmodium strains. Further, other vaccine candidates are expressed in heterologous expression systems and lack native conformation. They also have altered posttranslational modifications that are required to generate effective humoral and cellular immune response. DNA vaccine offers a solution to these challenges and also provides long-lived immune responses,

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which is a major challenge for RTS,S, and as such natural immunity to malaria. The current focus of the malaria vaccine is to identify candidate antigens that can block the parasite growth in different stages of development in the mosquito as well as in human hosts. Traditional approaches have problems in giving a cocktail of antibodies, whereas different antigens can be combined in a single preparation of DNA cocktail to generate an effective immune response against Plasmodium.

10.7.2 RNA-based vaccines RNA vaccines entail an mRNA strand, once inside the host, cells synthesize the antigen which is recognized by immune cells. The most relevant and fastest-growing vaccination is in vitro transcribed mRNA platform (Versteeg, Almutairi, Hotez, & Pollet, 2019). As discussed for DNA vaccines, mRNA vaccines can also be combined in multivalent vaccines permitting screening of multiple vaccine candidates within a shorter timeframe. The major obstacles in the development of mRNA-based therapeutics are the instability of RNA molecules and mode of delivery to the cells. These have been overcome by synthesizing modified mRNA molecules having altered bases that are not prone to degradation. Recently, most of the mRNA vaccines have been designed as enclosed by lipidnanoparticles (mRNA-LNPs), which help in protecting the mRNA, their targeting to the choice of cells and slow-release (Geall et al., 2012; Pardi, Hogan, Porter, & Weissman, 2018; Versteeg et al., 2019). These vaccines can also be modified to boost humoral response and therefore are attractive for the development of future vaccines (Iavarone, O’Hagan D, Yu, Delahaye, & Ulmer, 2017; Versteeg et al., 2019). One of the major advantages of this platform is the activation of the innate immune response and CTL (CD8 1 ) response. Since mRNA products are well defined compared to many other complex platforms, they are most acceptable from a regulatory standpoint. Therefore several mRNA vaccine candidates, such as the one for COVID-19, are currently in advanced clinical trials. Recently, RNAbased vaccine was used for Plasmodium macrophage migration inhibitory factor (PMIF), a parasite antigen that attenuates T-cell responses of the host during pre-erythrocytic or erythrocytic stages of infection. Mice immunized with PMIF expressing self-amplifying mRNA “replicon” elicited robust cellular and humoral responses. Further, the RNA vaccine immunized group showed a slow increase in the parasite growth, an overall increase in survival time, and complete protection from reinfection (Baeza Garcia et al., 2018; Hekele et al., 2013).

10.8 Biological therapeutics Biological therapeutics are the next-generation therapeutics that use either direct or indirect substances obtained from living organisms or living organisms themselves to cure the disease. The most common example is of biological therapeutics, which act on the host immune system, commonly known as “immunotherapy.” The other routine example is antibody-based therapeutics. There are many biological treatments approved by the US

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Food and Drug Administration (FDA) for several diseases (FDA, 2020), but their use in infectious diseases, primarily malaria, remains limited. In a recent approach, monoclonal antibodies were isolated from the blood of a human subject who had received an investigational vaccine made from whole, weakened malaria parasites. One of the monoclonal antibody (CIS43) was highly effective in inhibiting P. falciparum infection in mice (Julien & Wardemann, 2019; Kisalu et al., 2018). Further modifications in the monoclonal antibody CIS43 yielded CIS43LS, which is more stable. To consider the safety and effectiveness of a monoclonal antibody (mAb CIS43LS) in humans who have not been previously exposed to malaria, recently, a phase 1 clinical trial was initiated by the National Institute of Allergy and Infectious Diseases. A single dose of this monoclonal antibody might be able to prevent malaria infections for several months and, therefore, could be provided to high-risk individuals (https://clinicaltrials.gov/ct2/ show/NCT04206332).

10.9 Conclusion The research on vector-borne diseases has helped in understanding the biology of diseases like malaria in far more details than ever. WHO in 2017 launched “Global Vector Control Response (GVCR) 2017
2030” to help the affected countries fight against these diseases, make strategic plans to control the vectors, which can prevent the disease outbreak (Wilson et al., 2020). As such, the spread of malaria disease in the human host has been studied exclusively. Still, new parameters are being discovered that elaborate the disease prognosis at various levels including site and stage of infection site, roles of innate and cellular immunity, host response, coinfection with other diseases, and so on (Arora et al., 2018; Dantzler et al., 2019; Kurtovic et al., 2020; Moebius et al., 2020; Portugal et al., 2011; Tran et al., 2019). In this chapter, we discussed malaria prevention as well as treatment strategies being followed all over the world. With these strategies, a significant progress is being observed, especially for reducing mortalities in endemic regions like tropical and subtropical countries (Tizifa et al., 2018). But there is still a room for improvement of these efforts, both for vaccine development and drug discoveries. We have discussed several drugs that have been discovered in the past to help control disease symptoms and to treat the disease. But the emergence of drug-resistant forms has been a setback and demands that more drugs be discovered with novel MoA. Besides chemical approaches, many biologically originated treatments (neutralizing-antibodies, immunotherapy) are being studied to overcome this caveat (Acharya, Garg, Kumar, Munjal, & Raja, 2017; Alanine et al., 2019; Karunarathne et al., 2016). The major advantage of using a non-chemical-based treatment is that there is little or no possibility of developing resistant forms of parasite against these treatments, and it provides a long-term solution. The disadvantage is the lack of availability for a large population and the expense of treatment. Generation of vaccines is another approach, which was motivated by the recent approval of RTS,S vaccine. Despite the low success rate, the vaccine was approved for human use due to the absence of any other treatment in endemic areas. With the ongoing research strategies and efforts, hopefully, there would be control over malaria in the coming years.

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S E C T I O N

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Healthcare bioinformatics

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C H A P T E R

Translational bioinformatics: An introduction Richa Nayak and Yasha Hasija O U T L I N E 11.1 Introduction

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11.2 The era of omics and big data: data mining and biomedical data integration 292 11.2.1 Data acquisition and warehousing 292 11.2.2 Data integration 293 11.2.3 Data mining 294 11.3 TBI in biomarker discovery

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11.4 Computer-aided drug discovery

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11.5 Artificial intelligence-based approach in TBI 300

11.5.1 Complex disease analysis using ML 11.5.2 Illustrious examples of ML in translational research

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11.6 The implication of TBI in precision medicine 304 11.6.1 Data-driven precision medicine initiatives 305 11.6.2 Future prospects of transitional bioinformatics in personalized medicine 305 11.7 Conclusion

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11.1 Introduction Translational bioinformatics (TBI) is a paradigm that links the enormous amount of data on molecular entities collected from laboratory experiments such as information on genomics, proteomics, transcriptomics, to clinical aspects such as disease prognosis, drugs, and patient response. The American Medical Informatics Association (AMIA) defines TBI Department of Biotechnology, Delhi Technological University, Delhi, India

Translational Biotechnology DOI: https://doi.org/10.1016/B978-0-12-821972-0.00004-6

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© 2021 Elsevier Inc. All rights reserved.

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as “the development of storage-related, analytic, and interpretive methods to optimize the transformation of increasingly voluminous biomedical data, and genomic data in particular, into proactive, predictive, preventive, and participatory health.” We are familiar with the term “translational research” that involves the development of novel techniques in biomedical sciences that would reflect directly in health care. Translational research in “bioinformatics” focuses on the development of data-driven techniques to amalgamate information collected from molecular and clinical research. Such integration of data would provide us with combined bioinformatics and clinical informatics platform that would make an interpretation of medical data more holistic, leading to better patient care. This approach benefits scientists working in the field of biomedical sciences by providing them with directives to carry out further research that benefits health care; helps clinicians decide a well-suited treatment regime; aids biotechnologists or engineers to develop advanced tools; and lastly, helps patients to understand their health care routine. This integrative approach makes the health care system very transparent, and any issue relating to the information base can be addressed through a clinical-informatics research domain. Databases are being created based on requirements and managed at regular intervals. Novel methods for analysis and interpretation are being developed to promote early diagnostics, disease risk prediction, monitoring severity range, pharmacological effects, and overall disease progression. In light of the recent pandemic caused by severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) (commonly known as the novel coronavirus 2019-nCoV) that has claimed millions of lives across the globe while the scientists have been grappling to find a cure has made even the common public aware of how time-consuming and laborious the process of drug discovery can be. At times like these, the importance of translational research is felt at all levels, from researchers, medical professionals to common people are suffering from the disease. During the pandemic, we have also witnessed how a lack of resources can be a significant setback in finding a cure, which has further brought our attention to bioinformatics and its potential to drastically decrease the time and steps in the process of de novo drug discovery. TBI is a relatively new area of research, and its extents are being explored. The dawn of the era of big data began with the development of high throughput technologies, which paved the way for new innovations in the field of biomedical sciences. The accumulation of enormous amounts of data posed various new challenges that demanded an exclusive field of research that dealt with data, and this is how bioinformatics came into existence. Since the beginning of the 21st century, both biomedical (experimental) research and clinical research gained momentum as a direct consequence of the availability of vast reservoirs of data. Computerized collection of data began in the early 1960s, demanding techniques for management and analysis of data. In the following decades, relational databases emerged, followed by structured query languages (SQL) that enabled analysis and interpretation of complex data collected from various research works. Advancement in technology led to an explosion of new modalities to be used in fundamental biomedical research and its clinical application, and these included high throughput sequencing technologies, advanced imaging modalities, efficient data storage techniques, rapid data analysis algorithms, etc. This has revolutionized health care to an extent, but many challenges remain to be addressed.

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TBI that combines computational biology and clinical informatics arose as a result of a multidisciplinary approach that combined medical sciences, experimental biology, system biology, mathematics, and computer sciences, devoted to improving health care by transforming current clinical practices as shown in Fig. 11.1. For making efficient data-driven decisions, various subfields of bioinformatics such as data warehousing, data mining, data analysis, machine learning, computer vision, etc. are blooming, with new technologies coming up every day. As we are dealing with an enormous amount of data that can be anything from images to texts to numbers, it is essential to develop analytical techniques that can interpret patterns, networks or associations in a meaningful manner. Data not only can provide us with information on existent relationships but also can predict future trends and possible associations that are not yet established. This property of big data makes it crucial for biomedical research and may uncover new aspects of clinical care. Before we delve into the details, we need to know the source of data being generated in bioinformatics and clinical informatics. In life sciences and molecular research, omics data are being generated in enormous amounts from next-generation sequencing techniques, microarrays, high-performance liquid chromatography, mass spectrometry, etc. An accumulation of genomics, proteomics, transcriptomics, and metabolomics data has led to an understanding of gene
protein functions and pathways involved.

FIGURE 11.1 Graphical representational of TBI in terms of translational research. TBI spans the data to clinic spectrum by analyzing data and converting biomedical big data into knowledge that further translates to various applications in health care.

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Further, advanced imaging modalities like electron microscopy, confocal microscopy, magnetic resonance imaging, and advanced image processing algorithms have led to an accumulation of image data that can be correlated with omics data to provide for visual evidence for molecular mechanisms. In clinical sciences, data is being attained from electronic health records (EHRs), Internet of Things (IoT)-based technologies, and advanced diagnostic modalities. The field of TBI is expected to meet the demand for analysis and interpretation of these data. Even with the history of only a few decades, TBI plays an indispensable role in health care and is a subfield of translational medicine that is aimed at improving health care based on predictability and outcomes with implications in both biomedical and clinical sciences (Day, Rutkowski, & Feuerstein, 2009). Bioinformatics as a subject gained importance once the Human Genome Project was completed as it warranted a platform to store and interpret the findings. The AMIA, in its annual symposium titled “Biomedical Informatics: One Discipline,” held in 2002, highlighted the importance of this discipline. The term “TBI” was coined by Atul Butte and Rong Chen at another one of the AMIA conference in a paper titled “Finding disease-related genomic experiments within an international repository: first steps in TBI” (Butte & Chen, 2006; Tenenbaum, 2016). The following years saw widespread recognition for TBI. In this chapter, we will discuss how TBI in revolutionizing health care, the role “big data” plays in it, the emergence of personalized medicine, and incorporation of machine learning approaches.

11.2 The era of omics and big data: data mining and biomedical data integration The dawn of the era of “big data” transpired in the field of biomedical and clinical sciences as an upshot of technological advancement and development of high throughput techniques for generation of “omics” data, artificial intelligence (AI)-based technologies, and IoT. The present-day scenario suggests that we have successfully developed efficient data acquisition and storage techniques. As a result of effective collaboration between data science and biomedical researchers, data analysis methods are ameliorating with each passing day. It has led to a change in the previously used reductionist approach to studying disease biology. The massive biological data repositories are aiding in clinical care, and its concatenation with EHRs is enabling personalized health care. Through investigation of molecular and clinical data followed by structural analysis using advanced bioinformatics tools and techniques would lead to knowledge discovery that will further propagate prompt clinical services. In this section, we discuss the nuances of data mining and interpretation.

11.2.1 Data acquisition and warehousing Data acquisition or procurement of data is the initial step and forms the foundation for subsequent steps. The reliability of the data collected depends on the quality control during data collection. By and large, the whole logical and analytical workflow, starting from

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experimental protocol to execution, including handling of samples to dealing with bioanalytical investigations and information collected during and post experimentation. Also, combined with computer-aided technologies for information integration, data mining and interpretation requires controlled techniques and institutionalized administrative regulations on each step, guarantee a high level of consistency and reproducibility of information and results. To ensure controlled data acquisition, guidelines have been set aside by the Food and Drug Authority (FDA) and other regulatory bodies. For experimental techniques, standard operating procedures have been designed that outline control measures to be used in protocols while performing certain experiments. Through processes like barcoding, data is gathered efficiently and can be transmitted directly to a central server for storage and analysis. Data warehouses are a storage unit for the massive amount of biomedical and clinical data being generated. As in warehouses, different products from different sources are stored in one place until they are needed to be distributed. Data warehousing is based on a similar analogy. Data storage in warehouses requires the format of the data to be uniform, and since primarily data are of different types, they need to be converted into a standard format. This forms the basis of all subsequent analyses. A data warehouse typically consists of a front unit and a back unit that is appropriately partitioned. The back unit is basically for importing data from its sources in source format, to a repository (Hernandez & Kambhampati, 2004). Storage in the data warehouse is for an indefinite period of time, and the retrieval of data is query-based. A front unit is a unit of tools and algorithms that serve as a window through which query data can be accessed for retrieval.

11.2.2 Data integration Data warehousing is followed by data integration, either subsequently or consecutively. It is defined as “combining the data residing at different sources, and providing the user with a unified view of the data” (Calı`, Calvanese, De Giacomo, & Lenzerini, 2001, 2002). Handling biomedical and clinical data poses a unique set of challenges. The data type can vary from experimental, omics, statistical, image-based, EHRs, etc. The data collected is not only diverse but also contains a varying degree of complexity. There is an exponential demand for new concepts and algorithms for managing biological databases and data heterogeneity arising from varying data formats, sources, structures, and study designs. The biomedical and clinical data can be broadly categorized into structured data, unstructured data, or semi-structured data, that can be managed using SQL, HTML, Extensible Markup Language (XML), etc., respectively. It is understandable from what we have learned so far that data alone is of no use until it is in an interpretable format. Thus the integration of this data is essential, in a manner so that it can be analyzed and extrapolated to find patterns and associations. All the bioinformatics and clinical informatics data should be managed in an interoperable manner so that association and interpretation that are of biological significance can be established, such as genotype
phenotype correlation, signaling pathways, disease progression, and drug action can be correlated amongst themselves. This integration would give rise to a knowledge base that could further lead to a more individualized treatment regime and an efficient health care system (Fig. 11.2).

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FIGURE 11.2

11. Translational bioinformatics: An introduction

A generalized workflow of data mining to knowledge discovery.

Data integration can be done using various techniques. Apart from warehousing, data integration can be carried about by mediation-based approaches. Unlike the warehouse, the mediation approach does not have a centralized storage unit, and instead, the data is accessed directly from source distributors based on a query (Grethe et al., 2009). Since biomedical data suffers from the “curse of heterogeneity,” a more targeted approach is used, called Semantic Web-based technologies, where structured metainformation is provided with the data (Cheung, Smith, Yip, Baker, & Gerstein, 2007; Pasquier, 2008). As the name indicates, this technique uses semantics by ontologies to attend to complexities arising due to heterogeneity, like differentiating between synonyms and homonyms. Annotation of metadata is important for data integration in semantic web approach, and XML is often used for the exchange of such information.

11.2.3 Data mining Data mining follows suit that forms the basis of knowledge discovery. Knowledge discovery is defined as a “nontrivial process of identifying valid, novel, potentially useful and ultimately understandable patterns in data,” as given by Fayyad (Data Mining and Knowledge Discovery in Real Life Applications, 2012; Fayyad, Piatetsky-Shapiro, & Smyth, 1996). In other words, knowledge discovery means deconstructing and reconstructing given data in such a manner that novel and relevant information can be interpreted from it. Efficient data mining plays a vital role in knowledge discovery. Data mining involves algorithms that can fish out patterns in a dataset and is often guided by a set of threshold specifications. Other methods like machine learning, AI, and statistical technique can also be used for the same purpose. For the data to be coherent to the machine, that is, in a machine-readable format, it requires preprocessing.

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Preprocessing involves normalization or rescaling of data to remove various outliers from the data. The use of machine learning algorithms for data mining approaches has brought about a significant boost in TBI. Data mining, in this field, is used to locate genes, find protein motifs, identify protein ligands, the search of biomarkers, determine pharmacogenomics properties from data available from omics, clinical trials, EHRs data, etc. Data in the field of biomedical sciences is available in a spectrum of granularity, for example, in a disease (X) study, a section of data may contain information on disease symptoms, patient response, and drug response. In contrast, another dataset may contain information on the causative agent, pathways involved, and genetic correlation. When data from all these sources are integrated using advanced computational techniques, prediction and decisions can be made based on the whole layout of the disease, since now we have new drug targets, and we can predict patient response by correlating it to available data. Nevertheless, this is only one example; several things can be done with efficient data mining techniques. These approaches can either be carried out using supervised learning methods or unsupervised learning methods. Data mining techniques commonly used in biomedical research (as shown in Fig. 11.3) are classification, regression, cluster analysis, and association analysis. Classification algorithms are popular in biomedical sciences since classifying clinical data is of primary importance like classifying diseases, benign and malignant tumors, biomarkers, and many others. Regression approaches are well suited for risk analysis studies, for grouping prognostically relevant symptoms and risk factors.

FIGURE 11.3 Data mining approaches used in biomedical research with examples.

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Cluster analysis techniques are opted when data needs to be grouped based on distance or similarity, such as while studying gene expression data or analyzing biomedical images. Predictive models are built that are sensitive and take into account real disease incidence rates for estimation. Cross-validation is performed at each step to ensure that the data interpreted is in agreement with actual observations. The potential of knowledge mining approaches is not limited to linking biomedical research and clinical applications; it can also assess public health characteristics such as the epidemiologic and public health consequences. Table 11.1 lists initiatives taken in TABLE 11.1 List of initiatives taken in TBI. TBI initiatives

Description

Web URL

Personal Genome Project

Open access public genome, trait and health data

http://www.personalgenomes.org/

The Human Variome Project

Genetic variations and its health repercussions

http://www.humanvariomeproject. org/

NCI Center for Biomedical Informatics and Information Technology

Knowledge base for cancer research contains semantic cancer resources and advanced computing solutions for cancer

https://datascience.cancer.gov/

ClinicalTrials

A record of clinical trial

http://clinicaltrials.gov/

MURDOCK

Biorepository that classifies or reclassifies diseases based on biomarkers

http://www.murdock-study.com

VectorMap

Disease distribution models and maps for vector transmitted diseases

http://vectormap.si.edu/

The eMERGE Consortium

A network that integrates genomic repositories and EHRs for the development of precision/genomic drugs

https://emerge-network.org/

Arena3D

Software for visualizing network and monitoring time-dependent phenotypic changes

http://arena3d.org/

FDA Adverse Event Reporting System

A record of error in medication and side effects

https://www.fda.gov/drugs/ surveillance/fda-adverse-eventreporting-system-faers

EUCLIS

Circadian system biology

https://www.euclock.org/

NHANES

Correlates nutrition and effects on health

https://www.cdc.gov/nchs/ nhanes/index.htm

Allen Brain Atlas

Spatiotemporal maps of CNS

http://portal.brain-map.org/

DICOM (Digital Imaging and Communications in Medicine)

Repository of medical imaging data

https://www.dicomstandard.org/

Google Baseline

Collects a varied range of molecular and clinical data to define a healthy individual

-

CNS, Central nervous system; EHRs, Electronic health records.

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bioinformatics that can translate into learning-based clinical practice. This has led to a transient shift in the health care industry from evidence-based practice for clinical care decisions wherein decisions were based on the current best evidence that involved experimental data or randomized clinical trials, to practice-based evidence wherein the decisions are based on big data collected from real instances (Embi & Payne, 2013; Tenenbaum, 2016). This meta-interdisciplinary approach of knowledge discovery in clinical sciences, based on bioinformatics, big data collection, data mining, and data integration technologies, is revolutionizing effective health care practice. However, bioinformatics is more than just biomedical data management; it has more important roles to play in the biomedical research enterprise. We will be discussing how bioinformatics is used in biomarker discovery and drug designing.

11.3 TBI in biomarker discovery The portmanteau of “biological markers” led the way to the neologism of the term “biomarker” (Strimbu & Tavel, 2010). The initial definition of a biomarker was given by the National Institute of Health Biomarkers Definitions Working Group as “a characteristic that is objectively measured and evaluated as an indicator of normal biological processes, pathogenic processes, or pharmacologic responses to a therapeutic intervention” (Atkinson et al., 2001). The World Health Organization (WHO) gave a more compact and broader definition of biomarkers that says “almost any measurement reflecting an interaction between a biological system and a potential hazard, which may be chemical, physical, or biological; the measured response may be functional and physiological, biochemical at the cellular level, or molecular interaction.” (Strimbu & Tavel, 2010; World Health Organization, 2001). From the definition, it is clear that the discovery of biomarkers is momentous to both basic and clinical research. The identification and discovery of effective biomarkers is a major challenge in translational biomedical research. Bioinformaticians have been facilitating biomarker discovery in recent times. Also, large-scale discovery of predictive and prognostic biomarkers is enabling the adoption of the personalized medicine-based health care system. Understanding and discovering measurable biological processes and evaluating their impact on clinical outcomes would help us better determine effective drug targets and suitable treatment regime for diseases. However, this cannot be achieved without a proper understanding of normal physiological and biological processes. A biomarker should be a well-assessed indicator of normal physiological processes, biological processes, pathological conditions, clinical outcomes, etc. that can be objectively measured (Yan, 2011). Historically, biomarkers were usually “tell-tale” signs or externally visible indicators of physiological processes that could indicate a diseased condition. As the mysteries of physiology unfolded on cellular and molecular levels, scientists were able to trace biomarkers to genes, proteins, metabolites, regulatory RNAs etc. With the advent of high throughput technologies, the identification of biomarkers accelerated and now biomarkers can be classified into various subtypes based on their features (Fig. 11.4). Biomarkers can be

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FIGURE 11.4 Types of biomarkers and their purpose they serve in studying disease biology.

indicative of disease progression (prognostic biomarkers), disease risk (antecedent biomarker), disease severity (staging biomarker), therapeutic efficacy (predictive biomarkers), disease diagnosis (diagnostic biomarkers), drug resistance (resistance biomarkers), drug effect (pharmacodynamic biomarkers) etc. Biomarker discovery is a complex process that involves overlapping steps. The initial step is the discovery of biomarkers that requires data from biological samples. Then one needs to ensure whether it satisfies the criteria to be designated as a biomarker that involves assay validations; the final step is clinical validation. Conventional approaches in biomarker discovery have been time-consuming, expensive, and inefficient. They often failed to take into account multifactorial factors that influence disease pathology, heterogeneity, and complex interactions. Bioinformatics and clinical informatics have specially made the “biomarker qualification” step of the biomarker discovery process, really convenient. With the presence of both biological and clinical data, one can now map the two endpoints, which is a prospective biomarker to a physiological or pathological state. A biomarker discovery tool was created by Mickael Leclercq and Benjamin Vittrant et al. named BioDiscML uses machine-learning models for feature selection to efficiently predict the best combination of biomarkers based on omics molecular profiling data (Leclercq et al., 2019). Bioinformatics has mostly facilitated biomarker discovery in the field of cancer research. Many of these have been approved by the FDA for use in diagnostic purposes. Pioneering work was done by Zhang Z. and Chan D.W. when they used ANN (artificial neural networks) to map biomarker for ovarian cancer, and this gave rise to Ova1 “the first-ever in vitro diagnostic multivariate index assay (IVDMIA) of proteomic biomarkers,” which surpassed the diagnostic ability of existing biomarker for classification of benign lesions from ovarian cancer.

11.4 Computer-aided drug discovery

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11.4 Computer-aided drug discovery The first thing that comes into our mind when we say health care is “drug.” Drugs have been the primary arsenal in the treatment of diseases since time immemorial. Our approach to dealing with diseases has always been for finding a cure, and in earlier times, the lookout for a drug was no less than a wild goose chase. Many times, drugs have been discovered accidentally, and in other instances, we have looked for potential drug molecules in nature such as plants, microorganisms, insects, animals, etc. Theses hit and trial methods were time-consuming, expensive, inefficient, and could barely meet the increasing demands for new drugs by the health care industry. Soon, we were faced with a multitude of new diseases, and chemistry jumped in for the rescue. However, even chemistry could not keep up with the growing demands. Patients continued to suffer due to ineffective drugs, side effects, and growing resistance to available drugs. Drug pipelines were running dry, and the demand for a faster and efficient drug discovery process was de rigueur. With available technology of high throughput sequencing, microarray analysis, protein structure determination techniques, and high-performance computational devices, in silico drug development came into being. Data mining is crucial to in silico drug designing. The massive amount of data available on protein structures, our understanding of Protein-protein interaction (PPI), and the ever-evolving techniques of structure determination have made it possible for us to design drugs in a simulation-based environment. Bioinformaticians have developed molecular docking software that is capable of carrying out structure-based or ligand-based drug designing when the structure of either the receptor or the ligand is known, respectively. Other allied databases and tools have been developed to aid in this process like QSAR (quantitative structure
activity relationship), pharmacophore modeling, PubChem, etc. In silico drug designing has accelerated the process of drug discovery and development, the best candidate from computationally determined drug molecule is then developed, and experiments followed by clinical trials are carried out. Nowadays, it is possible to even determine dosage and concentration for a drug based on simulation and pharmacodynamic studies. TBI encompasses the whole spectrum of drug discovery in today’s date, form target identification, docking, modeling, QSAR analysis, and clinical implication. The drug discovery process is now colossally, dependent on bioinformatics tools and techniques. Not only for discovery, but screening for drug molecules and drug repurposing has also been facilitated by bioinformatics. Using computational drug discovery, a lot of key signaling pathways in cancer have been targeted for drug discovery such as NFKB, apoptotic pathway, HRas, etc. Computational drug discovery has successfully led to the development of anticancer molecules like imatinib that are being used clinically for the treatment of chronic myelogenous leukemia

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The success stories of biomarker discovery using bioinformatics and its tremendous clinical contribution are endless. In the last decade, due to an upsurge of omic technologies, a massive amount of relatively quantitative datasets of genes, metabolites, differentially expressed mRNAs, regulatory RNAs, and proteins are available for analysis. Bioinformatics-based approaches are being used for building statistical models, metanalysis, and managing large quantitative datasets.

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(Singh et al., 2017). In a recent study by Beina Zhand et al. screened for PPARγ (peroxisome proliferator-activated receptor-gamma) ligands using CADD and synthesized thiazolidinedione derivatives with the antidiabetic property since it is a critical target in insulin resistance and is considered to be a safer drug option (Zhang et al., 2018). Sharma and Thelma (2019), in their research, have developed a pharmacophore model of Bruton’s tyrosine kinase inhibitor that is an ideal target in case autoimmune diseases and B cell malignancies; this could potentially give rise to better drug candidates than the ones commercially available. The average drug discovery process takes approximately 14 years to come from the laboratory to the clinics (On, 2018). This makes the drug discovery process cumbersome, expensive, and extremely low. Meanwhile, the lives of patients are lost in the long wait for a drug that could cure their ailment. The appalling reality of the situation was felt across the globe during the COVID-19 pandemic. Drug repurposing was the first to the rescue where broad-spectrum antivirals already in use were tired and tested against the SARS-CoV-2 virus (Zhou et al., 2020). High throughput screening of molecular databases to find molecules that could target the virus was also a popular strategy. A recent study published potential drug targets by analyzing therapeutic targets for SARS-CoV-2 using computational methods (Wu et al., 2020). As we move forward rapidly in the field of bioinformatics, we can hope that new drug targets and leads will be discovered at a faster pace and a reduced cost.

11.5 Artificial intelligence-based approach in TBI Machine learning has become indispensable to the field of bioinformatics for solving complex problems. Since there is a massive outflow of omics data due to the evolution of high throughput technologies, which outmatches the pace of development of techniques that can make sense of all the available data, machine learning emerges as a savior. Like any other field of science and technology, life science has embraced the AI-based approach for dealing with the overflowing data that is being generated. A catch-22 situation often encountered in life sciences is while studying complex or multifactorial diseases. Complex diseases are influenced by the effects of multiple loci, epigenetic factors, lifestyle, environmental factors, etc., and machine learning algorithms can help dramatically improve prognosis and provide a better understanding of the disease pathway. Research in the field of medical sciences and genome informatics has led us to believe that all conditions and diseases have a genetic component associated with it. The advancement in high throughput technologies for DNA, RNA, and protein analysis has evinced this association in the case of many diseases. While some diseases have a direct correlation to a gene or protein product, for example, monogenic disorders, many diseases are caused by many contributing factors. The latter type of disease is called multifactorial disorders or complex disorders. Diseases like diabetes, obesity, cancer, etc. do not have a single genetic cause; instead, they are likely to be influenced by multiple genes(polygenic) and their products, in combination with a variety of factors such as environmental and lifestyle factors. These diseases are being extensively studied by researchers around the globe; however, deciphering the underlying pathogenesis has been an arduous task. In recent times, there has been a surge in technological advancement that has led to the rapid development of high throughput

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genomic and proteomic technologies and big data accumulation. An enormous amount of heterogeneous data comprising patients’ genomics, proteomics, EHRs, DNA methylation, eQTLs data can now be collected relatively cost-effectively. A comprehensive characterization of all complex disorders owing to the data collected is necessary, as it would shed some light on the pathogenesis and prognosis of complex disorders. The challenge, however, is that, while studying complex disorders, one encounters several variables that often cannot be classified. The existing methods for understanding the genetic mechanism of complex disorders often neglect the heterogeneity phenomenon. Exhaustive analysis of disease association to multiple loci is prone to human error and suffers from a lack of reproducibility. Thus a more modern and efficient method of analysis for complex disorders is de rigueur. Datadriven computational approaches are a pragmatic option as they employ mechanistic models to make explicit hypotheses for analysis in a multitiered fashion. They are capable of making predictions from high-dimensional data, solving the “curse of dimensionality” that is often associated with genomics data of complex disorders. Data integration approaches are being used to foster a more accurate analysis of structured datasets and their integration across studies. Such data integration techniques for analysis of homogenous datasets are termed as meta-analysis. Meta-analysis has added momentum to novel discoveries in the clinical setting by integrating genotypic data [genome-wide association studies (GWAS) (Zeggini, Scott, Saxena, & Voight, 2008)] and phenotypic data for facilitating diagnosis and establish routes for treatment. The potency of AI is not limited to complex diseases. Infectious disease research equally benefits from an AI-based approach, as was seen in the case of COVID-19. In the era of AI, we often rely on computers to do routine and monotonous tasks for us. Machine learning is a part of AI that is being used extensively to make sense of big data that is being generated by the systems. The machine is trained to perform tasks on its own by learning and improving with experience, without being explicitly programmed, much like humans. The approach of a machine learning algorithm is similar to that of a researcher who first learns from the rules of data and, with every following experiment, gains experience to analyze similar data with more accuracy. Similarly, machine learning algorithms learn from a training dataset that it is provided with, to familiarize the system to the data. Then it sifts through a vast number of variables and, with each progression, learns patterns and combinations that will help it to predict outcomes reliably (Fig. 11.5).

11.5.1 Complex disease analysis using ML To understand the underlying pathogenesis of complex diseases, it is crucial for us to know the determining factors for a disease to be classified as a complex disease. The term complex disorder is ambiguous, a disease can be called a complex disorder if it shows clear heritability but is also influenced (severity, age of onset, etc.) by environmental factors, or when genetic liability does not always suffice to predict the emergence of the disease, that is, there is some probabilistic element as a stochastic factor along with the genetic component that determines the onset like in the case of inheritable cancers. Genomic studies indicate that the number of variants associated with complex diseases is

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FIGURE 11.5

Steps involved in training a machine

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learning model.

vast, so there is an increased complexity of heterogeneity along with missing heritability. Since a number of variants are associated, there can be several pathways associated with disease pathology. Risk assessment is also a significant challenge as a disease is seen to aggregate in families, but the segregation ways are not consistent with Mendelian inheritance. Thus the permutation and combination of all factors associated with a complex disease make disease prognosis difficult. The availability of a vast array of sequencing data (DNA, RNA, and protein), GWAS, PPI data has added momentum to the study of complex traits and disorders. In silico analysis of identified genes, proteins, interactions, and previous knowledge repositories can give significant insights into the pathogenesis of these diseases. These studies may include gene enrichment analysis, pathway, network analysis and may further be extended for predicting the risk associated with the disease based on shared properties and the use of machine learning tools. The apparent complexity of complex diseases arises from our inability to differentiate between the various underlying pathways and the multitude of factors that affect the prognosis of such diseases. The availability of big data, especially omics data, will transform the way we see complex diseases. Pathway analysis, interaction studies, and deciphering pathogenesis with the help of AI-based technologies will lead to better prognosis and pave the way to precision medicine.

11.5.2 Illustrious examples of ML in translational research Various machine learning algorithms are being adopted for the analysis of complex disorders and have shown an increasing degree of precision, matching the momentum of advancement in the field. This shift in paradigm is already having an impact on the clinical and diagnosis scenario. Support vector machine (SVM), a supervised learning-based

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ML algorithm, popularly used for classification is being used for building disease prediction and prognosis models. SVM classifier index clusters given data based on the number of features tested by identifying lines, planes, and hyperplanes that maximally separates two clusters in the training dataset. The distance between the support vectors (closest points of the data to the hyperplane) is ideally maximized, and in case of nonlinear classification, a “kernel” is used to transform the data into a high-dimensional space that further enhances the success rate of a classifier. In the recent past, SVMs have been used to refine treatment regimens and clinical counseling by incorporating data from biomarker discoveries, clinical studies, disease risk analysis, epidemiological data, etc. Wei et al. showed how SVM and L2-regularized (ridge) logistic regression enabled the construction of a highly predictive risk model for type-1 diabetes using less than 500 variants that passed a relatively stringent prefiltering threshold on a case
control dataset (Okser et al., 2014; Wei et al., 2009). Unnikrishnan et al. (2016) developed an SVM-based risk assessment/health parameter model using Framingham health parameters for risk assessment of cardiovascular diseases. Sanghani, Ang, King, and Ren (2018) applied machine learning to features derived from magnetic resonance images of glioblastoma multiforme, SVM was used for feature selection and prediction of overall survival prediction of patients. SVM algorithms were used for determining a prognostic model and prediction method for dehydrationassociated neurologic deterioration with data collected from patients hospitalized with acute ischemic stroke (Lin et al., 2018). One recent remarkable example of the use of SVM with radial-bias function kernel in the prediction of Alzheimer’s disease conversion in patients with mild and premild cognitive impairment was carried out by Grassi et al. (2018) that has promising clinical applications. Finkelstein and Jeong (2017) applied Bayesian networks and SVM to information collected from patient telemonitoring system to predict asthma exacerbations prior to their occurrence (). Machine learning algorithms like random forest algorithm (based on decision trees) that were developed more than a decade ago have been successfully used to build a risk assessment model for cardiovascular disease (Hsich, Gorodeski, Blackstone, Ishwaran, & Lauer, 2011). Similarly, for the prediction of prostate cancer, extreme learning machine and artificial neural network were used by a team of researchers with a prediction accuˇ racy of .90% (Jovi´c, Miljkovi´c, Ivanovi´c, Saranovi´ c, & Arsi´c, 2017). Deep learning has been used for heterogeneity analysis and diagnosis of complex disorders by Xiong Li et al. by using a K-mean clustering algorithm, which is very effective for diagnosis where epistasis and heterogeneity exist together (Xing, Xie, & Yang, 2016). A major example would be that of the recent COVID-19 pandemic and the use of machine learning approaches in its drug discovery process. IEEE Spectrum enlisted five companies that adopted a machine learning-based approach to finding drugs that could work on COVID-19. Various drugs identified by these companies were put into perspective by researchers and clinicians. Deargen, a South Korean company, used a deep learning-based approach to evaluate drug
target interaction and called it Molecule Transformer-Drug Target Interaction (Beck, Shin, Choi, Park, & Kang, 2020). It facilitated the identification of commercially available drugs that could be used against SARS-CoV-2 like atazanavir (HIV medication), antiviral remdesivir (currently under testing). Benevolent AI, a British company, used an ML-based approach to identify existing drugs that could block viral replication. They identified six potential targets that interfere with

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the viral entry process by blocking certain cellular pathways (Stebbing et al., 2020). They also predicted how these combined antiviral and antiinflammatory molecules would fare in the treatment of severe cases of COVID-19. Evidently, machine learning fares well than conventional statistical models while handling heterogeneous and complex data. ML methods are robust methods for handling non-linear systems but are not immune to limitations, and thus careful modeling and validation are required for drawing correct inferences. A common constraint encountered is when data on which features are applied is too large or too sparse, often spurious results are obtained. An ideal sample to feature ratio us 5
10:1 (Somorjai, Dolenko, & Baumgartner, 2003), but sometimes due to data sparsity, misleading interpretations are derived. Caution should be maintained while training datasets in ML to avoid “overfitting” issues by taking a sufficiently large dataset. With each passing day, researchers are coming up with methods to overcome the limitations and mapping out better ways to understand complex interactions and complexities of disease pathobiology. For the vast amount of information being generated to be of any translational impact, optimal ML algorithms and more competent analysis strategies need to be established.

11.6 The implication of TBI in precision medicine As previously discussed in this chapter, advancements in sequencing technology are revolutionizing the way we look at genome. Major research initiatives like human genome sequencing and GWAS have provided an insight into the genetic architecture that makes every individual one of a kind. Such techniques have revealed unique genotype
phenotype relationships and how polymorphisms in the genes can alter the way a particular gene function. Extensive analysis of the human genome has helped us lay out a framework to find out genetic variants that can be attributed to a particular disease. Further research in pharmacogenomics indicates that the genetic architecture of an individual also influences the way they respond to therapy. This understanding has led to the era of genomic or personalized medicine. Genomic medicine can be defined as “the use of information from genomes (from humans and other organisms) and their derivatives (RNA, proteins, and metabolites) to guide medical decision-making” (Integrating Large-Scale Genomic Information Into Clinical Practice, 2015). The term “Personalized medicine” is also used as a synonym for Genomic medicine. It is defined as a “predictive, participatory, personalized, and preventive (‘P4 medicine’) model of healthcare” (Overby & Tarczy-Hornoch, 2013; Snyderman, 2012), which in turn provides a venue for adopting genomic medicine as an application of modern-day genomics (Burnette, Simmons, & Snyderman, 2012). It is, however, important to note that personalized medicine may include nongenomic screening and diagnostic approaches (Overby & Tarczy-Hornoch, 2013). This heterogeneity in patient response to treatment and medication led to the culmination of precision or personalized medicine. The ultimate goal is that a patient receives the highest order of treatment possible for any disease. In the case of cancer, due to differences at the genetic level, some patients respond well to chemotherapy, whereas others receive immunotherapy well, some may not respond well to either. So, to categorize which treatment regime is suitable for an individual, bioinformatics methods have proven to be very useful.

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Stanford University manages a publicly accessible online knowledge base, PharmgKB (https://www.pharmgkb.org/) for curation, integration, storage of information regarding genetic variations and their impact on drug response. Such a platform provides for knowledge sharing between researchers and clinicians is a suitable example of TBI. For personalized medicine to be adopted wholly to the clinical routine or “to bring the right interventions to the right people with the right dosages and intensities at the right time,” (Yan, 2014) computational methods like complex adaptive systems are being adopted. With bioinformatic methods promoting systems approach to studying dynamic diseases, precision medicine is just around the horizon. National Center for Biotechnology Information, a branch of National Institutes of Health, contains the vast majority of data relating to genes, proteins, SNPs, GWAS, microarray, and various other handles where information on pharmacogenomics, drugs, etc. can be accessed by biologists and clinicians alike. The National Human Genome Research Institute built a consortium of five institutions and created an electronics Medical Records and Genomics (eMERGE) Network for integrating medical records with genomic repositories to explore the potential of data-driven research in routine health care (McCarty et al., 2011). The eMERGE network also attempts to address the ethical, legal, and social issues that might arise. Under the aegis of eMERGE, GWAS has been carried out on samples collected from people who have dementia, type-2 diabetes, hypertension, cardiovascular disease, etc. Another similar endeavor is of the Pharmacogenomics Research Network, which is a knowledge repository that lets the user access data and tools relating to genomic variations in various populations and studies that give an account on its effect on therapy, which has been a major contributing factor in precision medicine.

11.6.2 Future prospects of transitional bioinformatics in personalized medicine Translational bioinformaticians have made it possible for high throughput molecular data to be publicly accessible that facilitates the interpretation of its clinical outcomes and helps devise methods for more personalized treatment options for complex diseases. Complex diseases like cancer, metabolic disorders, cardiovascular disorders, and neurological disorders are hard to treat since they have a broad spectrum of pathophysiological manifestation in each individual and often treatment options fail in the majority of the cases. Such disorders require a unique cocktail of therapy that is tailor-made for each individual suffering from it, based on their genome. With technology advancing at an unmatchable pace, the sequencing of genomes will become a routine task in the health care sector. Individual genomes shall be sequenced at a faster pace and a lower cost. Perhaps in the near future, metabolomics, transcriptomics, and proteomics for induvial will be studied in-depth as well. New modalities need to be developed that focus on correlating genomic data and medical records of patients. With a faster turnover of personal genome data, new and efficient methods would be needed to move from disease-centered health care to patient-centered health care. Despite a number of initiatives to integrate biomedical and clinical data, a lot of the information is lost due to the manual method of data collection in the majority of cases. To decrease instances of misleading interpretation of

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data, data collection techniques need to be streamlined. Thus researchers working in the biomedical field, clinicians, and health care providers need to come together to deal with the caveats of this ambitious crossover. In the following chapters, we will discuss in detail about systems biology and precision medicine.

11.7 Conclusion We have entered a world driven by data that demands that the health care industry swiftly adopts and incorporates the data-driven culture into routine clinical practices. Although regulation policies and hospitals worldwide need to catch up with the technological advancements, bioinformatics research has set the pace for data-driven health care. Cutting-edge research in TBI promises a utopia of personalized patient care and management. We are now headed toward preventive and personalized medicine, as the popular idiom goes, “prevention is better than cure.” With technology advancing at light speed, the costs of high throughput techniques are reducing, and hospitals are getting equipped with state of the diagnostic tools, AI-based technology is shaping the foundation of automated diagnosis, high-quality data is being generated, EHRs are being maintained efficiently with IoT and the demand for experts who can put this jigsaw together is higher than ever. An omics analysis is pivotal to TBI. Present-day technology allows fetuses to be screened for genetic defects using sequencing and other methods. The day is not far when the genome sequencing of all newborns will be carried out, and the records will be maintained to be used in the future if and when an individual encounters a health issue. Biomarker discovery in genomics, proteomics, and metabolomics will pave the way for a better prognosis. Bioinformatics has steered disease studies from a reductionist approach that is mostly hypothesis-driven and focused on a singular feature, to a more holistic systems approach that is data/evidence-driven and attempts to study the biological system as a whole, so that now we can look at the “bigger picture” where we can understand the physiology and pathophysiology. The systems approach has helped us better understand metabolic pathways, signaling pathways, elucidate molecular pathways, study pathway perturbations, and its effects on human biology rather than comparing it to what is seen in model organisms. Complex disorders like cardiovascular disorders, neurological disorders, and comorbidities can now be studied in detail, more precise and targeted therapies can be developed. Bioinformatics research is fueling other dynamic fields of research like metagenomics and microbiome analysis. Recent studies indicate the importance of microbiota on human physical and mental health. The voluminous amount of data has been collected from the natural microbiota of human bodies like skin microflora and gut microflora. Analysis of these data helps us understand how microorganisms affect human metabolic pathways and develop synergistic ways to tackle problems like obesity, anxiety, depression, gastrointestinal disorders, metabolic disorders, and other complex diseases. Extensive research in this field is propelled by bioinformatics and is crucial to determining the appropriate therapeutic routine. Research in cancer biology and neurobiology has also gained momentum owing to bioinformatics-based biomarker discovery. This has led to better diagnosis and

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understanding of disease prognosis. Identification of quantifiable biomarkers that can help in the diagnosis of conditions like depression, anxiety, and other disorders relating to the mental health that are very hard to diagnose and classify using conventional methods. New modalities, like organ-on-chip technology, single-cell sequencing, clinical epigenetics, are being developed that will facilitate even faster clinical trial results, and new data will be generated in huge amounts. The demand for new and efficient data mining techniques and analytical biomedical informatics will always be on the rise. The importance of TBI has dawned on us at an even larger scale owing to the recent COVID-19 pandemic. Efficient translational research is essential in the mitigation of diseases of such a global impact. The field of TBI is new and emerging, and demands for an efficiently trained workforce to tackle the upcoming burden of a data surge challenges data integration and knowledge discovery. Tackling this necessitates a multidisciplinary approach and collaboration between researchers from various fields like life science, computer science, mathematics, physics, statistics, and clinicians. Also, research in the laboratory and clinics need to be in sync. From the examples mentioned in the chapter, one might be under the impression that we have come a long way in translational biotechnology, but in reality, we have only explored a fraction of the possibilities. We need to establish techniques for improving data standards, reduce redundancy in data, develop better data integration and management techniques. The Health care sector requires more efficient ways of data collection. In summary, TBI is an ambitious endeavor that is believed to transform health care. However, we still have a long way to go. For cross-disciplinary biomedical research to manifest its clinical application, it is imperative to develop new coordinating systems between all the consecutive steps from cellular-molecular to whole system dynamics, from laboratory to bedside.

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C H A P T E R

Pharmacodynamic biomarker for Hepatocellular carcinoma C: Model-based evaluation for pharmacokinetic
pharmacodynamic responses of drug Nitu Dogra1, Savita Mishra1, Ruchi Jakhmola Mani1, Vidhu Aeri2 and Deepshikha Pande Katare1 O U T L I N E 12.1 Hepatocellular carcinoma 12.1.1 Possible risk factors of hepatocellular carcinoma 12.1.2 Stages of hepatocellular carcinoma 12.1.3 Challenges in therapeutic and medicinal drug treatment for hepatocellular carcinoma

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12.2 Pharmacokinetic and pharmacodynamic profiles (PK
PD) 316 1

2

12.2.1 Pharmacokinetic profile (PK) 12.2.2 Pharmacodynamics (PD)

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12.3 Pharmacokinetic and pharmacodynamic models 317 12.3.1 Compartmental models 317 12.3.2 Direct pharmacokinetic and pharmacodynamic models 318 12.3.3 Indirect pharmacokinetic and pharmacodynamic models 319

Proteomics and Translational Research Lab, Centre for Medical Biotechnology, Amity Institute of Biotechnology, Amity University, Noida, India Department of Pharmacognosy & Phytochemistry, SPER, Jamia Hamdard, New Delhi, India

Translational Biotechnology DOI: https://doi.org/10.1016/B978-0-12-821972-0.00007-1

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© 2021 Elsevier Inc. All rights reserved.

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12.4 Advantages of pharmacokinetic and pharmacodynamic modeling 319

12.5.2 Therapeutic outcome using PD biomarker 322

12.5 Development of pharmacodynamic (PD) biomarker in hepatocellular carcinoma 320 12.5.1 Proteomic approach for identification of pharmacodynamic biomarkers 321

12.6 Pharmacokinetic and pharmacodynamic drug responses 323 12.7 Conclusions

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References

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12.1 Hepatocellular carcinoma Hepatocellular carcinoma (HCC) is the liver malignancy caused by chronic infection of hepatitis B and C viruses (HBV and HCV). The occurrence of HCC is mainly associated with three different stages—nonalcoholic fatty acid (NAFLD), nonalcoholic steatohepatitis (NASH), and cirrhosis. Nonalcoholic fatty liver disease (NAFLD) is a chronic metabolic condition induced by excessive fat accumulation or deposition of fat droplets within the hepatocyte. NAFLD can further lead to inflammation resulting in NASH, which recently has been predicted as the main reason for liver fibrosis and cirrhosis. The major symptoms for HCC are cirrhosis with long-term jaundice, bloating due to accumulation of fluids in the peritoneal cavity, blood clotting, loss of appetite, abdominal pain, weight loss, fatigue, and nausea. Certain health conditions including high blood cholesterol levels, obesity, metabolic syndrome, autoimmune diseases, type-2 diabetes, overweight, increased amounts of iron in the body, and medications like steroids are more likely to develop NAFLD and NASH. These health conditions may further lead to severe liver dysfunction and abnormalities such as fibrosis, cirrhosis eventually progressing to HCC (Fig. 12.1). It has been categorized as the second lethal and the sixth most frequent cancer, due to highly metastatic and angiogenic feature, estimating 840,000 new cases of liver cancer (including intrahepatic bile duct cancers) diagnosed recently in 2018, and there will be an estimated 1,762,450 new cancer cases diagnosed and 606,880 cancer deaths in the United States in 2019 (American Cancer Society, 2019). It is estimated that till 2030 new cases of HCC might rise by almost 50% of the current cases, which could be over 1.2 million (Kennedy et al., 2017). Hepatitis (B or C) endemic regions such as Eastern and Southeastern Asia are denoted as the highest prevalent region, and China alone has almost 50% of the cases worldwide. Liver cirrhosis is a major reason for HCC that mostly occurs in African and Asian countries as compared to other parts of the globe. However, the incidence of HCC in Western and European countries is on the rise, due to alcohol-associated diseases and hepatitis C infection (Fig. 12.2).

12.1.1 Possible risk factors of hepatocellular carcinoma According to the American Cancer Statistics, risk factors that may increase the occurrence of HCC include:

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12.1 Hepatocellular carcinoma

FIGURE 12.2

Liver cancer statistics since

2015
19.

1. Gender: Males are more prone to HCC than females due to their lifestyle and smoking habits (American Cancer Society, 2019). 2. Chronic viral infection: People infected with a chronic viral infection of HBV and HCV are at higher risk of developing cirrhosis, and eventually HCC (El-Serag, 2012).

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FIGURE 12.1 Progression of HCC, where HCV is the hepatitis C virus and HBV the hepatitis B virus (Jilkova, Kurma, & Decaens, HCC, Hepatocellular 2019). carcinoma.

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FIGURE 12.3 Causes of HCC. HCC, Hepatocellular carcinoma.

3. Alcohol and tobacco use: According to a study reported, the most common cause for cirrhosis is alcohol abuse and smoking, which in turn is associated with a higher rate of occurrence of HCC (Tang, Hallouch, Chernyak, Kamaya, & Sirlin, 2018). 4. Obesity: Obesity, which is also associated with cirrhosis and fatty liver, has been predicted as the major and independent risk factor in hepatocarcinogenesis (Pennisi, Celsa, Giammanco, Spatola, & Petta, 2019). 5. Diabetes: People with a metabolic disorder like type-2 diabetes has been linked with HCC because of the tendency of being obese, which causes the underlying liver problem and leads to HCC development (Reeves, Zaki, & Day, 2016). 6. Aflatoxins and certain medicaments: Aflatoxins are chemical toxins that are obtained from a fungal species, Aspergillus. It has been seen that AFB1 cause-specific mutation in the p53 tumor suppressor gene, which leads to liver cancer (Pfliegler, ˝ & Pusztahelyi, 2019). Some medicines such as steroids aspirin are Po´ csi, Gy ori, more likely to develop fatty liver and cirrhosis, resulting in increased chances of HCC (Fig. 12.3).

12.1.2 Stages of hepatocellular carcinoma Three different stages play a crucial role in the progression of HCC—nonalcoholic fatty liver (NAFLD), NASH, leading to fibrosis, and scarring of the liver tissues, that is, cirrhosis. 12.1.2.1 NAFLD Nonalcoholic fatty liver disease (NAFLD) is a chronic metabolic condition induced by excessive fat accumulation or deposition of fat droplets within the hepatocyte (Kutlu, Kaleli, & Ozer, 2018). An individual with NAFLD is also at higher risk with other metabolic syndromes, type-2 diabetes, and obesity. Major causes of NAFLD are cytokines and adipokines, which are developed due to the gradual accumulation of triglyceride, leading to steatohepatitis and, ultimately, fibrosis (Campo, Eiseler, Apfel, & Pyrsopoulos, 2019).

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NAFLD prevalence in the Indian population is estimated at 9%
32%, mostly among obese and diabetic patients (Kalra et al., 2013). Studies have shown that high saturated fatty acids are more detrimental for liver function and hepatocytes (Cleveland, Bandy, & VanWagner, 2018). Unhealthy diets have also shown deleterious effects on the liver, eventually promoting liver injury or dysfunction. Dysregulation in lipid metabolism induces lipogenesis in the hepatocytes causing fibrosis and inflammation contributing to hepatic steatosis (Cleveland et al., 2018). Lower adiponectin levels and increased inflammatory cytokines have found to be the main culprits for lipo-toxicity, ultimately triggering the accumulation of free fatty acids and steatosis (Polyzos, Kountouras, & Mantzoros, 2019). 12.1.2.2 Nonalcoholic steatohepatitis/fibrosis Liver fibrosis results from the amassing of extracellular matrix (ECM) proteins predominantly due to chronic HCV infections, alcohol abuse, and NASH. The ECM protein accumulation leads to contort hepatic architecture comprising the fibrous scar and nodule formation resulting in Cirrhosis. In recent years, NASH has been predicted as a major cause of liver fibrosis that increases incidences of liver scarring, leads to cirrhosis, and finally develops HCC. Metabolic syndrome such as obesity, dyslipidemia, T2DM with insulin resistance is the foremost reason for NASH development. A higher incidence of obesity leads to a rise in the prevalence of NASH (Del Campo, Gallego, & Grande, 2018). 12.1.2.3 Cirrhosis Cirrhosis is irreversible scarring in the liver due to several chronic infections. It increases hepatocyte dysfunction and obstruction in hepatic blood flow, resulting in hepatic insufficiency and portal hypertension. The scarring of the tissues, that is, cirrhosis, culminates in neoplasia in hepatocyte. The prevalence of cirrhosis has been increased almost twofold in the last decade, which leads to a higher incidence of HCC worldwide. The most common cause of cirrhosis is hepatitis viral infections and alcohol abuse. According to the World Health Organization, chances of developing cirrhosis or liver cancer in hepatitis B patients are only 20%
30%, whereas NIH (National Institute of Health) has reported that it takes almost two to three decades to develop HCC only in 5%
20% of hepatitis C patients (Grinspan, & Verna, 2018) (Fig. 12.4). FIGURE 12.4 Stages of HCC. HCC, Hepatocellular carcinoma.

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12.1.3 Challenges in therapeutic and medicinal drug treatment for hepatocellular carcinoma Sorafenib, the oral multikinase inhibitor, is the only first-line chemotherapeutic agent approved by the FDA available for HCC treatment. The long-term use of sorafenib has been associated with side effects such as high blood pressure, fatigue, diarrhea or constipation, and severe toxicity such as gastrointestinal perforation, hemorrhage, dermatological toxicity, and cardiac complications (Van Hootegem, Verslype, & Van Steenbergen, 2011). Apart from toxicity, patients have experienced a recurrence of HCC within the 5 months posttreatment with sorafenib. In patients with HCC characterized by vascular invasion, sorafenib is considered a treatment of choice. However, sorafenib-induced induced mild liver abnormalities to severe acute hepatitis are already reported. There is evidence that describes death from liver failure, having prolonged usage of sorafenib (Van Hootegem et al., 2011). Another study also showed that HCC patients with lung cancer complications cause severe hepatitis and a high fever that leads to liver coma, ultimately death. However, some patients have received sorafenib for 2 weeks (Van Hootegem et al., 2011). Other than synthetic compounds, there are different natural compounds obtained from herbal plants such as Silybum marianum, Andrographis paniculata, Tecomella undulata, Betula utilis, Podophyllum hexandrum, Camellia sinensis, and many others containing phytoconstituents having potent hepatoprotective effects. Sorafenib, being the only treatment for HCC its effect, is still unidentified for the early and advanced stages of HCC. There are some herbal remedies such as nutraceuticals or ayurvedic preparation “Rohitakarishta” available in the market which may protect the liver from inflammation or viral infections such as hepatitis and jaundice, but their effectiveness is unknown in the case of HCC. Our group has also reported a synergistic combination of sorafenib with herbal compounds’ efficacy in chemoprevention and HCC management (Mishra & Katare, 2017). Currently, there is no possible therapeutic cure for HCC. Due to this, there is an immediate urgency of improving the detection or diagnosis of the HCC patients and finding an alternative approach for an effective treatment targeting both early and advanced stages of HCC. Therefore, to prevail over this major challenge, we require the development of novel pharmacodynamic biomarkers which may monitor the dose
response of such chemotherapeutic drugs to achieve successful treatment and management of HCC.

12.2 Pharmacokinetic and pharmacodynamic profiles (PK
PD) 12.2.1 Pharmacokinetic profile (PK) PK is the study of different parameters of the drug inside the body as a state of function of time, in view of its absorption, bioavailability, metabolism, distribution, and excretion. It refers to the body’s response to the drug, its movement into and out of the body within the due course of time. Drug PK defines the onset, interval, and intensity of the effects of a drug.

12.2.2 Pharmacodynamics (PD) PD deals with the study of the physiological and biochemical effects of the drugs and their mechanism of action and the correlation amid drug dose and effects. It explains the

12.3 Pharmacokinetic and pharmacodynamic models

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12.3 Pharmacokinetic and pharmacodynamic models 12.3.1 Compartmental models Interpretation of the body’s responses is essential to predict the behavior of the drug. These interpretations can be made easily by applying mathematical principles to numerous responses. An elementary model used in the study of PK is a compartmental model. Its classification is based on the number of compartments required to describe the behavior of a drug in the body. Here, compartment represents a group of comparable fluids or tissues, which helps in predicting the drug concentration during the course of time. Compartmental models are also known as deterministic since the perceived drug concentration supports in determining the type of compartmental model requisite in describing the nature of PK of the drug. Compartmental model is usually classified into one and two-way compartmental models. Construction of these is based on the simplification of the body’s structure and organs. Organs or tissues representing a similar type of drug distribution are clubbed into one compartment. For instance, drug distribution into renal tissue varies from that of adipose tissue. Hence, these tissues fall in separate compartments. The perfused organs such as the kidney, heart, and liver often follow the similar distribution patterns of the drug; therefore these organs come under the same compartment. The compartment involving blood (plasma), kidney, lungs, heart, and liver are commonly denoted as central compartments. Other areas such as muscle tissue, cerebral spinal fluid, and adipose tissue are referred to as peripheral compartments (Bassingthwaighte, Butterworth, Jardine, & Raymond, 2012). The mathematical value of any model is based on how efficiently predicts the concentration of drugs in tissues and fluids. If a simplified compartment model is able to determine the plasma drug concentration, then an intricate model is not required. However, two or more compartment models are needed for determining tissue drug concentrations. One-compartment model is often used in clinical practice (Fig. 12.5). Moreover, it is presumed that after drug-dose administration, it instantaneously reaches to different regions of the body. Few drugs do not dispense instantly to different areas of the body, even though administered via the intravenous route. Distribution of drugs in

FIGURE 12.5 One-compartment model, where Y0 is the drug dose, Y1 the drug concentration in the body, and K the elimination rate constant.

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level of drug concentration required by the body, involving receptor sensitivity in binding, post binding effects, and all the concerned chemical interactions. PD, along with PK (response to drug dose), demonstrates the link between drug dose and associated response. Pharmacological responses are based on the binding of the drug with its corresponding target. The drug concentration at the receptor site controls its effect—factors such as aging, disorders, or other drugs influence the drug PD.

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FIGURE 12.6 Complex-compartment model, where Y0 is the drug dose, Y1 and Y2 are the drug concentration in different regions of body, and K12 and K21 the rate constant.

blood, perfused organs such as kidney, heart, liver, and lungs to other body tissues are signified by a two-compartmental model (Fig. 12.6). Drug concentration in the compartment is described as the definite amount of drug (mg/L) available in a specified volume. It relates the amount of drug to be distributed into body tissues and fluids. Volume of distribution ðLÞ 5 Drug concentration 5

Amount of drug in the body ðmgÞ Plasma concentration of drug ðmg=LÞ

Amount of drug administered in the body ðXÞ Volume ðVÞ

where X represents the content of available drugs inside the body compared to the known concentrations in the blood plasma (C). Volume (V) accounts for the entire drug within the body if the concentration of drugs in all the tissues is the same as that of plasma concentration. For instance, if 220 mg of the drug X is intravenously administered and the measured plasma concentration is 2 mg/L immediately after the dose is administered, then Volume of drug distribution 5

Drug dose 220 mg 5 5 110 L Concentration 2 mg=L

12.3.2 Direct pharmacokinetic and pharmacodynamic models This model deals with the situation where the effect of the drug is direct so that that drug concentration can be tracked directly by PD responses. For a few drugs the response is a characteristic function of time and concentration, where the effect is directly proportional to the concentration (C) and time (t) (C 3 t). In PK
PD terms, the effect is directly proportional to area under the curve (AUC); s 5 Cn 3 t, s represents the sensitivity of a drug for a specific system, and n denotes PD exponent determines the drug effect with respect to concentration and time. When n . 1, concentration is the determinant of PD effect (e.g., bolus administration of the drug is effective than infusion); n , 1, time is the determinant of PD effect. The s value is retrieved from experimental statistics via two-step process; where IC50 values are determined for a particular range of exposure of time, IC50 measurements (representing C values) fits into Millen Baugh equation: Cf 5 m

ðs=tÞm 1 Cm ; n

where f represents the fraction affected, m denotes the Hill coefficient (Jackson, 2012).

12.4 Advantages of pharmacokinetic and pharmacodynamic modeling

Examples of pharmacokinetic and pharmacodynamic models.

PK
PD modeling

Drugs/cell lines

PD biomarkers

Plasma PD biomarkers (Jackson, 2003)

Anticancer drugs (Thymitaq, AG337)

Circulation of deoxyuridine

Cytokinetic PD biomarkers (Basse et al., 2004)

Melanoma cell lines (NZMI3)

To study the effect of paclitaxel on cell cytokinetics, involving DNA degradation induced by drug and its effects on mitosis

Anticancer agents; inhibitors of To study the measurement of the extent Protein phosphorylation PD Mitogen-activated protein kinase kinase of phosphorylation induced by these biomarkers (Iadevaia, Lu, Morales, Mills, & Ram (2010)) (MEK), Bcr-abl, phosphoinositide phosphoproteins 3-kinase (PI-3 kinase), Vascular endothelial growth factor (VEGF-R2), and C-Met kinase Spindle checkpoint PD biomarkers (Kamei, Jackson, Zheleva, & Davidson, 2010)

Anticancer drugs (aneuploidy or polyploidy); Serine/threonine-protein kinase (NEK2), aurora kinase A, aurora kinase B, selectivity of anticancer drug depends on the frequency of defective mitotic spindle assembly checkpoint

Toxicity PD biomarkers (Greystoke et al., 2011)

Anticancer agent (plasma concentration For the selectivity and efficacy of the of VEGFD and VEGF-6) drug; blood pressure: toxicity biomarker

Apoptosis PD biomarkers (Linder, Olofsson, Herrmann, & Ulukaya, 2010)

Apoptosis and colorectal cancer

Kinetic model helps in determining the action site of aurora kinase B by utilizing Histone (H3) phosphorylation as biomarker

For the selectivity and efficacy of the drug; CK18, PLT3, IGF, IGFBP-2 and -3: PD biomarkers

12.3.3 Indirect pharmacokinetic and pharmacodynamic models There are four different types of indirect PD associations. If a PD response is mediated by the aggregation of specific ligand or metabolite, and the drug response enhances or prevents the aggregation, then the drug will be considered to have agonistic and antagonistic effects, respectively, in that particular system. Similarly, if the PD response is mediated by the reduction of metabolites and the drug effect speeds up or depletes the reduction, then again, the drug will have agonistic and antagonistic effects (Jackson, 2012) (Table 12.1).

12.4 Advantages of pharmacokinetic and pharmacodynamic modeling • Helps in determining a definite dose of the drug and optimal routes of drug administration. It can predict the plasma/tissue concentrations, where selectivity of the drug is difficult, as, in the case of oncology, PK
PD modeling will be more useful.

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TABLE 12.1

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• Optimizing the multiple doses scheduling where there is a misbalance between the drug concentration and its effects. Optimum scheduling of drugs is usually based on PK and time duration of PD effect. • Helps relate the efficacy of PD biomarkers with the clinical outcomes and determines the comparative details of substitute available biomarkers in the estimation of clinical efficacy. Preclinical data is also valuable in determining the rate of PD biomarker responses to the antitumor response. • Determining the influence of the specific rate of drug resistance clinically and can also be useful in developing treatment strategies where partial drug resistance has been developed. • Helps relate the toxicity PD biomarkers’ outcomes to tolerability. If the toxicity and efficacy biomarkers are available, the proportional selectivity of varied regimens may also be determined. • Drug having multiple action sites, PK
PD modeling can be proved useful in exploring the significant relationships between the altered mechanisms to toxicity and efficacy. • PK
PD modeling helps in predicting the aspects of drug combinations like synergism, additives, and antagonism and useful in designing protocols for drugs having cytokinetic or metabolic interactions. • Drug-dose toxicity cut-off can be predicted. By utilizing the population PK
PD data, the proportion of group receiving the treatment, showing a particular response can be predicted. • PK
PD models can be used in developing the virtual clinical trial software that can be used in comparing several probable trial designs in silico (Maharao, Antontsev, Wright, & Varshney, 2020). • Sample strategy can be developed through PK
PD models, which can be used to determine how and when the sampling of plasma and other tissues to be done to obtain maximum information.

12.5 Development of pharmacodynamic (PD) biomarker in hepatocellular carcinoma PD biomarkers are biomolecules that exhibit the effect of therapeutic intervention on the affected organ. PD biomarkers are used for the analysis of drug-target interaction and their biological response (Heikinheimo et al., 2015). The effect of cytotoxic drugs after treatment at the molecular level is the fundamental idea in pharmacodynamic biomarker discovery (Topalian, Taube, Anders, & Pardoll, 2016). Several imaging techniques such as dynamic contrast
enhanced magnetic resonance imaging, ultrasonography, and computed tomography have been used for screening of pharmacodynamic biomarkers (PD) (Bex, et al., 2014). Identification and characterization of protein-based PD biomarker is an emerging area of therapeutic research. The report on unresectable HCC using sunitinib showed a significant decline in circulatingproteins (serum vascular endothelial growth factors—A/C/2/3) and could be identified as pharmacodynamic biomarkers (Jain, 2017). Numerous high throughput

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technologies such as mass spectroscopy, LC
MS MALDI
TOF
MS have the potential to identify, characterize, and validate the target-specific protein signatures that can be used as PD biomarker (Ciocan-Cartita et al., 2019).

Proteomics is an emerging approach for a biological system, which comprises qualitative and quantitative analysis of proteins in the cell (Fig. 12.7). Overall proteomics can be signified as protein expression analysis and protein interaction at a specific phase of the cell cycle (Rizzetto, Priami, & Csika´sz-Nagy, 2015). Dysregulation in protein expression/ differentially expressed proteins can be used as a tool for the identification of biomarkers. Proteomics has opened a new dimension for the identification of novel biomarker/signatures of disease-associated proteins that can be used for diagnosis, prognosis, and pharmacodynamic as well as potential targets for HCC treatment. Protein-based discoveries have modernized the prospect of biomarker discovery and drug development (Panis, 2016). Crosstalk among significant oncogenes provides the platform for biomarker discovery. Biomarkers are biological molecules that exhibit physiologic conditions with disease progression. They are the indicators of early onset of disease progression, which helps in disease management and therapeutic interventions. The widely used serum-based markers; Alpha-fetoprotein and Des-gamma-carboxyprothrombin have low sensitivity and specificity in HCC patients. However, the combination of these two markers has enhanced sensitivity (Carden, et al., 2010). Therefore identification of a protein-based biomarker for a complete understanding of disease progression as well as the efficacy of therapeutic interventions is an emerging area of research (Fig. 12.7).

FIGURE 12.7 Scheme diagram of proteomic approach for identification of pharmacodynamic biomarker.

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12.5.1 Proteomic approach for identification of pharmacodynamic biomarkers

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12.5.2 Therapeutic outcome using PD biomarker As clinical trials are designed for increasingly smaller, molecularly defined patient populations, PD biomarkers are likely to grow in importance. In particular, PD studies may provide insights into optimal biologic dosing, toxicity, and proof of mechanism for targeted therapies. A few putative drug targets have been identified with respect to treatment. For example, the expression of several plasma proteins related to angiogenesis is PD biomarker for the response of several antiangiogenic drugs such as sorafenib, lenvatinib, sunitinib (Parchment, and Doroshow, 2016). It has been reported lately that predictive biomarker serum fibroblast growth factor and hepatocyte growth factor showed good performance in redacting the response to sorafenib and survival in patients with advanced HCC (Kim et al., 2018). Recently some chemotherapeutic drugs like cabozantinib, nivolumab, and ipilimumab administered to the patients who were previously treated with sorafenib have shown an increased response against HCC in phase II clinical trials (Liu et al., 2019). Therefore to understand the efficacy of these chemotherapeutic drugs such as sorafenib or other drugs that are under clinical trials using PD biomarkers is an unmet clinical need in patients with early as well as advanced-stage HCC.

FIGURE 12.8 Relationship between PK and PD in drug therapy; pharmacokinetic (dose regime): based on parameters like absorption, distribution, metabolism, and excretion; pharmacodynamic (dose concentrationeffect): target site concentration approach to rational dosing.

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The drug molecule’s interaction with the receptor leads to the initiation of cascading molecular events, resulting in various pharmacologic responses. It includes all the physiological and molecular changes, which are directly or indirectly associated with the drug and its receptor. Pharmacological research illustrates that the PD response produced by any drug depends mainly on the chemical structure of the drug and its binding affinity at the site of action (Salahudeen and Nishtala, 2017). It is essential to analyze responses of effected protein (PD biomarker) with respect to the PKs of the drug molecule. The interaction between PKs of drugs and PDs basically is time-dependent as well as nonlinear (van den Brink et al., 2019) (Fig. 12.8). The success of any drug development depends upon the selection of the correct drug-target site and its adequate concentration within plasma (estimated by PK studies) as well as target site responses, which can be estimated by PD responses (Rizk, Zou, Savic, & Dooley, 2017). Efficient PK
PD analysis and its interpretation give a clear idea to understand the mode of action of the drug, as well as identify pharmacokinetics characteristics of the drug for further improvement in optimal drug design. In addition, PK
PD modeling also increases the chances of in vitro drug potency to the in vivo condition that reduces fewer animal studies and improves translation of findings from preclinical studies into the clinical studies (Tuntland et al., 2014). Although current scenario biomarker discovery is basically based on pharmacological responses, molecular fingerprinting, which involves systems biology (genomics, proteomics, and transcriptomics), is not limited to these pathways. Molecular analysis has revealed numerous new methods in relation to drug responses and gives a new dimension to drug discoveries (van den Brink et al., 2019).

12.7 Conclusions Pharmacokinetics (PK) and pharmacodynamics (PD) are interrelated phenomena, and their relationship plays an emerging role in novel drug discoveries. A comprehensive molecular study with respect to drug concentration is an essential part of successful drug development. Efficient and sustained drug delivery depends on the selection of correct drug, target site, and also the adequate drug concentration at tumor site as well as in plasma. This drug-dose concentration PK versus dose efficacy on target cell PD is a vital tool in drug designing. Quantification of PK parameters of the exposed drug on-target site, and PD analysis like drug effects on target endpoints, involved pathway, and downstream biological processes are extremely important. This book chapter summarizes the importance of PK
PD relationship in optimal drug designing and highlights the therapeutic value of PD biomarkers in HCC treatment. Implementation of PD biomarkers in early clinical trials assists in the identification of the most appropriate patients and provides evidence for target modulation. PD biomarker can also help in the rational selection of dose and schedule as well as gives clarify or predict clinical outcomes. Efficient PK
PD interpretation and analysis give clarity to elucidate the mode of action for drug and its effective relationship with the target molecule for optimal drug designing. In addition to this

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PK
PD modeling helps in translating the in vitro drug molecule potency to in vivo settings and thereby helps in reducing the animal studies and improves the translation of drug findings from preclinical to clinical analysis.

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10. Available from https://doi.org/10.1186/s12885-018-4211-2. Kutlu, O., Kaleli, H. N., & Ozer, E. (2018). Molecular pathogenesis of nonalcoholic steatohepatitis- (NASH-) related hepatocellular carcinoma. Canadian Journal of Gastroenterology and Hepatology, 2018. Linder, S., Olofsson, M. H., Herrmann, R., & Ulukaya, E. (2010). Utilization of cytokeratin-based biomarkers for pharmacodynamic studies. Expert Review of Molecular Diagnostics, 10(3), 353
359. Liu, Z., Lin, Y., Zhang, J., Zhang, Y., Li, Y., Liu, Z., . . . Ye, J. (2019). Molecular targeted and immune checkpoint therapy for advanced hepatocellular carcinoma. Journal of Experimental & Clinical Cancer Research, 38(1), 447. Maharao, N., Antontsev, V., Wright, M., & Varshney, J. (2020). Entering the era of computationally driven drug development. Drug Metabolism Reviews, 52, 1
16. Mishra, S., & Katare, D. P. (2017). Synergistic combination for chemoprevention of hepatocellular carcinoma: an in silico and in vitro approach. Basic and Clinical Pharmacology and Toxicology, 120(6), 532
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525. Pennisi, G., Celsa, C., Giammanco, A., Spatola, F., & Petta, S. (2019). The burden of hepatocellular carcinoma in non-alcoholic fatty liver disease: screening issue and future perspectives. International Journal of Molecular Sciences, 20(22). Available from https://doi.org/10.3390/ijms20225613. Pfliegler, W. P., Po´csi, I., Gy˝ori, Z., & Pusztahelyi, T. (2019). The Aspergilli and their mycotoxins: Metabolic interactions with plants and the soil biota. Frontiers in Microbiology, 10. Polyzos, S. A., Kountouras, J., & Mantzoros, C. S. (2019). Obesity and nonalcoholic fatty liver disease: From pathophysiology to therapeutics. Metabolism: Clinical and Experimental, 92, 82
97. Reeves, H. L., Zaki, M. Y., & Day, C. P. (2016). Hepatocellular carcinoma in obesity, type 2 diabetes, and NAFLD. Digestive Diseases and Sciences, 61(5), 1234
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142. Rizzetto, S., Priami, C., & Csika´sz-Nagy, A. (2015). Qualitative and quantitative protein complex prediction through proteome-wide simulations. PLoS Computational Biology, 11(10). Salahudeen, M. S., & Nishtala, P. S. (2017). An overview of pharmacodynamic modelling, ligand-binding approach and its application in clinical practice. Saudi Pharmaceutical Journal, 25(2), 165
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117. Available from https://doi.org/10.1002/psp4.12370. Van Hootegem, A., Verslype, C., & Van Steenbergen, W. (2011). Sorafenib-induced liver failure: A case report and review of the literature. Case Reports in Hepatology, 2011.

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13 System biology and synthetic biology Richa Nayak1, Rajkumar Chakraborty1 and Yasha Hasija O U T L I N E 13.1 Introduction

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13.3.2 Synthetic biology in drug discovery, development, and delivery 339 13.3.3 Role of synthetic biology in personalized medicine 340 13.3.4 Regulation and ethical considerations of synthetic biology 340

13.2 System biology 331 13.2.1 Central principles of scientific approaches to biology systems 332 13.2.2 Fields in therapeutic applications system biology 333 13.3 Synthetic biology 13.3.1 Role of synthetic biology in understanding disease mechanisms

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13.1 Introduction System biology and synthetic biology are extending the frontiers of biological sciences. Both system biology and synthetic biology emerge from the interdisciplinary study of biological sciences combined with principles of mathematics, engineering, physics, chemistry, and computer sciences. System biology is the study of complex mechanisms of biological systems by considering each component, that is, genes, proteins, organs, biochemical, and physiological pathways, as a component of the whole, as opposed to the reductionist approach earlier used to study organisms (Kitano, 2002). Synthetic biology is a field that enables us to redesign these systems and manipulate them to have the desired qualities to fulfill various applications (Church, 2005). It has the ability to reengineer current systems, Department of Biotechnology, Delhi Technological University, Delhi, India 1 Richa Nayak and Rajkumar Chakraborty are equally contributing to the chapter.

Translational Biotechnology DOI: https://doi.org/10.1016/B978-0-12-821972-0.00012-5

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engineer novel artificial biological components, devices, and even whole organisms. System biology and synthetic biology are often studied in tandem where system biology encompasses the understanding of a system; synthetic biology focuses on engineering those systems. These fields are not limited to a single level of organization or focus on a particular area; rather the emphasis on an integrative approach that has led to significant discoveries in biomedical sciences. The study of systems biology emerged from the shortcomings of the reductionist approach of studying biology. Often the broader perspective was lost when molecular and cellular pathways were studied in isolation. Decades of molecular biology research using the reductionist approach had led to detailed studies of various biological pathways and molecular processes. Systems approach necessitated mathematical modeling and computational frameworks to understand better the dynamics of the elucidated biological pathways and mechanisms. Earlier strategies for molecular biology research were not all in vain. Instead, it provided the much-needed resolution into biological mechanisms and pointed us toward in the direction that all biological processes are interdependent and can be perceived as elaborate networks. Thus network approaches that make use of mathematical and computational models are used to study these previous findings in the context of more extensive integrated networks. Traditionally, system biology simply meant an integration of systems approach with biological findings (Mesarovi´c, 1968) but rapid advancements in high-throughput technologies for genomics, transcriptomics, proteomics, metabolomics, etc. have added dimensions to the systems approach. However, it is no simple task to illustrate the organizational structure of biological systems as a whole, and thus network approach simplifies the visualization and study of complex properties of biological systems in the form of interconnected nodes and edges. Network biology encompasses graph theory, mathematical modeling, computational analysis, and statistical theories. Biological networks can be of different resolutions such as metabolic networks, cellsignaling networks, gene-regulatory networks, protein
protein interaction networks, disease
gene interaction networks, drug interaction interwork, or an interactome that is an overlap between various network. In a network, the nodes depict biological components, and the edges indicate the interaction between them (Baraba´si & Oltvai, 2004). The understanding of network topology influences the inference of complex biological interactions. The study of biological networks has been of immense significance in the field of biomedical sciences. It has led to the emergence of a field called network medicine (Pawson & Linding, 2008). This field has proved ideal for the study of complex diseases like cardiovascular disorder, diabetes, cancer, and neurological disorders. It is used for building disease prediction models, identifying robust network biomarkers for diagnosis, understand pharmacological properties of drugs, and identifying new drug targets. There are still many challenges that need to be addressed in the field of systems biology; nevertheless, it is an essential driver of translational biotechnology. When discussing the drivers of translational biotechnology, it would be incomplete without the mention of synthetic biology. The term “synthetic biology” was used first used by physician and biologist Stephen Leduc in his book La Biologie Synthe´tique, as early as 1912. The official usage of the term synthetic biology began in the 1970s. Milestone discoveries like gene-regulatory networks, the discovery of endonucleases, de novo DNA synthesis technology, advancement in molecular biology techniques, and their

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automation fueled rapid development in the field of synthetic biology. This field is application-orientated and has direct implications in the biomedical field. Initially, synthetic biology was used to construct biological systems like genetic circuits and toggle switches, to elucidate the working mechanisms of such systems. However, recent efforts have been made to engineer biomaterials, synthetic vaccines, tissues, genome, organ, diagnostic materials, and personalized treatment regimes. Numerous potential applications of these sister disciplines are relevant to the challenges associated with the detection, diagnosis, drugs, and responses to emerging and reemerging infectious diseases like the one encountered recently that claimed many lives worldwide, the pandemic caused by SARS-CoV-2. These fields together have the ability to push the boundaries of biological research and understand the extent to which interdisciplinary research and computational approaches can influence the future of translational biotechnology. This chapter elaborates on the potential of systems biology, synthetic biology, and their integration for advancements in translational biotechnology.

13.2 System biology Over the last 30 years, the rapid growth of advanced science and medical research analytical technologies has led us to a point where almost all major primary molecular determinants considered to influence biological phenomena and diseases can be studied in detail. Advancements in molecular biology techniques such as next-generation sequencing, mass-spectrometry, and quantitative polymerase chain reaction (QPCR), researchers, and clinicians may now begin to examine any individual dysregulation occurring at genomic, transcriptomic, miRnomic, proteomic, and metabolic rates. All of these techniques are capable of extracting information from complex datasets to develop disease models for biological or clinical research. The interaction between bench laboratory practices (wet) and computational practices (dry) with relevant clinical knowledge is not just a major current component of translational medicine but is also allowed by device medicine. In this brave new era, however, there are drawbacks in that, in most experimental research, only different molecular rates are individually tested for their effect on any specific health condition. In an ideal world, any type of biomedical research with the potential to eliminate dysregulated molecular pathway interactions should focus on the holistic aspects of complex and multifactorial medical condition/s (Ayers & Day, 2015). These include a thorough and methodological analysis of all concomitant molecular interactions (e.g., genomics, transcriptomics). Such “big” research insights contribute to a greater understanding of complex and multifactorial disease conditions and, ultimately, “fast” recognition and clinical diagnosis of specific pathogenic molecular pathway dysregulations, along with the collective discovery of novel drug targets for the development of successful therapeutic regimens. Therefore the urgent need to resolve these research deficiencies has been recognized through the advent of a new area of system biology science in the last decade (Bruggeman & Westerhoff, 2007; Mesarovic, Sreenath, & Keene, 2004) (Table 13.1).

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TABLE 13.1 Translating systems biology
systems medicine. Systems Approach

Rationale

Model-based clinical design

Disease models built with the help of systems approach for improved selection of biomarkers, drug targets, and drug efficacy. Disease prediction models and models for prediction of predisease state with the help of dynamical network theory.

Computational algorithms and tools

In systems medicine, the extraction of knowledge remains a major challenge. Advanced computational algorithms and machine learning-based technologies for data extraction and integration will facilitate knowledge discovery.

Clinical data integration

Relevant clinical data should be monitored and stored. The integration of clinical data with biological insights can lead to the development of better therapeutic modules.

Annotation of clinical data Clinical data is often sparse and not annotated, and thus the elucidating the pathophysiological relevance from it in correspondence to biological networks proves challenging. Therefore the annotation of clinical data is important. Validation

The disease models and clinical models need to be experimentally validated for them to be used for therapeutic purposes.

13.2.1 Central principles of scientific approaches to biology systems Essentially, system biology is based on the premise that the phenotype of a living organism is the product of a simultaneous multiplicity of molecular interactions at all times combined at different levels to produce a phenotype holistically. As a result, contrasting to the traditional concept of simplicity approach under which the deregulation of an independent molecular marker is studied. Then, the data on the deregulation of several major molecular players at different levels of cellular organizations are combined and thoroughly analyzed in order to identify distinct differences in intermolecular patterns about the phenotype (Friboulet & Thomas, 2005; Tillmann et al., 2015). The methods used as the primary research tool differ in terms of the resolution required, the molecular level under investigation, and the amount of data generated. The most autonomous system biology research groups are now made up of researchers with discernible expertise in most molecular-techniques, specialists, or both. Subsequently, system biology is a multidisciplinary field of study involving technological structures and human research expertise from a wide range of medical science niches (Tillmann et al., 2015). However, the quantification of all the molecular components of an organ, or the biomedical technique, is far from faultless, and great efforts are being made to improve the sensitivity of the application and quantitatively the output data by implementing field standards (Cardinal-Ferna´ndez, Nin, Ruı´z-Cabello, & Lorente, 2014). Boolean methods help to create first-generation biological models of structures due to current constraints (Kaushik & Sahi, 2015). Another significant difference between system biology and system medicine is that the former assumes that the data are reliable and available so that the expertise in wet-laboratory data processing is therefore not the primary objective. Systems medicine seeks to provide exquisite data packages of medical and biochemical know-how to develop pathway or disease prediction models and therapies and hopefully contribute to stratified medical treatment, directly (Brazma, 2009; Huggett et al., 2013).

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It is an interdisciplinary research field that integrates bioinformatics, computational modeling, and mathematical modeling modules to respond to specific research questions. This use of computer technology can be duplicated in system biology, in particular by applying a mathematical “top-down” or “bottom-up” observational data strategy. The downstream, data-driven approach encourages the compilation of bulky datasets derived from a variety of omics-based approaches, followed by detailed mathematical modeling studies to integrate linkages amongst key molecular players from diverse omics findings (Wu et al., 2019). Network architecture is one of the key methodologies used by the bottom-up system biology analytical paradigm. A typical biological network consists of several nodes that communicate across borders, classifying nodes as independent major molecular players on all omics scales (such as genes, noncoding RNA families, and proteins) and edges representing experimentally confirmed biochemical interactions (Quaife-Ryan et al., 2017). There could be possible variation between nodes and edges within a particular biological circuit with respect to their nature and detail. However, very active nodes around the network are known as hubs. The hubs can be divided into further subgroups such as group hubs and relative hubs. Group hubs are nodes that interact with many other molecular partners simultaneously, whereas relative hubs are considerably more complex as they interact with other molecular partners over different spatiotemporal space. Top-down hypotheses are heavily dependent on statistical modeling for studies of tiny molecular interactions of a particular biological condition or phenotype as opposed to bottom-up observational techniques. For this purpose, dynamic modeling includes transformation to mathematical formats defined for molecular pathway associations in the organism studied, such as ordinary differential equations and partial differential equations, which can be analyzed and evaluated in a computational or stimulated environment (Eftimie, Bramson, & Earn, 2011). This method can be used because most intermolecular events occur with complex kinetics, which can be imitated by valid mathematical derivations (e.g., Michaelis-Menten kinetics). Dynamic modeling of biomolecular interactions, like the selection of reaction rate kinetics within the biomolecular interactions studied, can only be accurate (Karaca & Bonvin, 2013). In summary, four main phases of dynamic modeling are created, first being model designs to detect the basis of intermolecular activity, followed by model construction of these molecular interactions in representative differential equations, then comes model calibrations to identify and modulate nonkinetic parameters of individual biomolecular components to fine-tune the model.

13.2.2 Fields in therapeutic applications system biology 13.2.2.1 Systems medicine Systems medicine derives from the foundations of system biology and processes pharmacology, applying and adapting more fundamental methods to applied clinical studies and practices. Systems medicine encompasses the application of theoretical strategies through iterative and reciprocal exchanges of inputs between physicians, biologists, pharmacologists, bioinformaticians, and mathematicians in the fields of medical concepts,

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study, and practice (Benson, 2016). System medicine is therefore not disassociating from other methods to construct, incorporating multidimensional knowledge sources into its very core reciprocal interaction with computational modeling. The driving force behind the eventual improvement of patient outcomes is, therefore, the recurrent interaction between bedside examinations, experimental models, and statistical analyses (Cascante et al., 2014). The ultimate goal is a tangible change in patient safety through program-focused strategies and implementation. One example of improving the quality of care by system medicine is the latest definition of pediatric allergic diseases over a systematic translational approach involving detection, diagnosis, prevention, and therapy (Bousquet et al., 2016). Another international initiative culminating in positive improvements in clinical research included chronic pulmonary disorders, which can now be predicted more reliably and managed using a multifaceted system approach (Faner, Cruz, Lo´pez-Giraldo, & Agustı´, 2014). In addition, system-based modeling also resulted from heterogeneous and daunting conditions such as irritable bowel disease (Mayer, Labus, Tillisch, Cole, & Baldi, 2015). However, the cancer domain is the fastest-growing body of system-driven data that will eventually contribute to better care and is a paradigmatic illustration of the heterogeneous and emerging pathology, morbidity, mortality, and retention levels of which are most harmful to modern medicine (Siegel, Miller, & Jemal, 2018). In addition to this detailed system biology and pharmacology approach applied to hospitals, system medicine includes “anticipatory” and specific aspects of medicine. This kind of swiftly evolving and expanding approach goes beyond simple single biomarker stratification to the N 5 1 study concept, where intervention is fully adapted to an individual patient, disease, and time-to-time clinical evolution. Multiple data sources are needed to achieve this degree of accuracy and “precision,” the model-based integration of which provides a systematic and sophisticated clinical image, ultimately contributing to the identification of optimum advantage and safety interventions (Liang & Kelemen, 2017). {{{Fig. 13.1}}} 13.2.2.2 Systems pharmacology Variants unique to patients and diseases can have a significant effect on the efficacy of any medical strategy. Control of cancer, for example, is complicated by broad interpatient variables in profiles of molecular and complex cell and tissue and controlled drug reactions. Therefore the configuration of the treatment is vital to ensure effective therapy. Experiments on cell cultures, laboratory animals, and clinical trials involving cohorts (patients and population), multitype, and multiscale datasets are generated. Such massive volumes of data, thus produced across communities, necessitates dedicated approaches for accurate analysis of each dataset, addressing the nature of various data types and measurements, and eventually translating the results into effective therapy. Systems pharmacology also involves molecular, clinical, mathematical, and analytical approaches for drug production and pharmacotherapy personalization. Host physiology and molecular disease aspects are usually based on system pharmacology approaches. Therefore these studies are based on the notion that genes, proteins, cells, and organs interact with each other in complex ways that differ over time, and with the environment, they do not breakdown organisms into individual components. However,

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FIGURE 13.1 Overview of systems medicine.

the mathematical models attempt to predict the toxicity and efficacy of drugs eventually determined by pharmacokinetic (PK) and pharmacodynamic (PD) gene and protein networks in specific cell populations, whether healthy or diseased, in different organs. Therefore theoretical drug models and regulatory pathways of the whole body, PK, and PD properties of drugs are a sound physiological basis for treatment optimization. Furthermore, these detailed molecular and dynamic mathematical models enable patients and diseased tissues to participate directly in the decision on the treatment (Ortiz-Tudela, Mteyrek, Ballesta, Innominato, & Levi, 2013). Nevertheless, this complex biology and temporal structure are unlikely to be thoroughly investigated in individual patients due to the invasive and potentially unethical nature of the clinical steps needed. The power of these physiological models is the capacity to create human models using multiscale pipelines. Therefore mathematical models focus on molecular biology, and its parameters and variables have a physical significance retained in the scales considered. Therefore structures and parameter values for patients or individual patients can be tested and further integrated into in vitro and preclinical trials. Multiscale approaches to pharmacology usually integrate experimental findings into cell culture, animal, and clinical trials with the ultimate aim of developing formulations and schedules for humans or patients.

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SymCyp Ltd (Certara, United Kingdom) has presented a new rationale for using pharmacological approaches to track the effect of the patient’s genotype on rosuvastatin PK
PD, a lower cholesterol drug. The physiologically based drug model PK
PD integrates all drug PD into the liver as the core drug action site. The research focused on organically transported polypeptide 1B1 (OATP1B1) in polymorphic patients needing cellular uptake of rosuvastatin. Clinical evidence suggests that while in patients with some genotypes, the plasma concentration
time curve region has increased by more than 60%, the response to cholesterol synthesis remains unchanged in these same patients (Ste´phanou, Fanchon, Innominato, & Ballesta, 2018). The PK
PD model offered molecular insights into the whole body’s drug fate, which quantitatively explained the observed relation between PK and PD datasets (Rose et al., 2014; Ste´phanou et al., 2018).

13.3 Synthetic biology Synthetic biology is not a new idea in the field of biotechnology. However, its meaning has evolved through the years with rapid advancements in molecular biology, engineering sciences, and systems biology. Although the term was first used in 1912, followed by its mention by Waclaw Szybalski in 1974 (Matsoukas, 2013), the official mention of the term that correlates to the modern-day synthetic biology was in 2004 at a conference held in MIT (Bensaude Vincent, 2013). The modern synthetic biology approach can be defined as the application of engineering technology to construct novel biological entities such as genes, proteins, molecules, etc., by manipulating existing biological systems or, in some cases from scratch. The complexity of this approach lies in the dynamics of biological systems and thus requires a multidisciplinary approach. What sets synthetic biology apart from genetic engineering is similar to what sets systems biology apart from reductionist biology, that is, it follows a systems-oriented approach instead of dealing with biological entities in an isolated manner (O’Malley, Powell, Davies, & Calvert, 2008). Rapid advancements in high-throughput sequencing technologies led to the generation of big omics data. These developments simultaneously led to systematic development in systems and synthetic biology. Fig. 13.2 depicts an overview of how multidisciplinary approaches contribute to synthetic biology. The inception of synthetic biology is based on four abstractions. The first being the idea that it is possible to reconstruct a system from its essential framework. Second, biological molecules are essentially made up of chemical components, and thus synthetic biology could possibly be an extension of synthetic chemistry. Third is the notion that natural living systems evolve in order to exist and can be reconstituted with the help of engineering technology to stimulate our current understanding and build optimized systems for better comprehension. The fourth notion is that biological systems can be used as a technology (biotechnology) to produce novel and desirable products like enzymes, biopolymers, energy, and other biomaterials with enhanced properties (“What is Synthetic/Engineering Biology? | EBRC,” 2020). Thus synthetic biology can be used for basic biological understanding as well as for manipulation and creation of important biological structures. Synthetic biology has a wide range of applications, ranging from food, agriculture, pharmaceuticals, and other industrial products. One significant application is in the field of biomedical and clinical sciences. Biomedical sciences pose a unique set of challenges; diagnosis is critical, drug discovery and development are time-intensive, and drugs have

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FIGURE 13.2 Diagrammatic representation of synthetic biology and its components.

side effects and often encounter resistance. As a result, there is an increasing demand for synthetic biology approaches to engineer high-precision diagnostic tools, devices, biomaterials, novel drug development, and delivery mechanism that can accelerate clinical applications (Weber & Fussenegger, 2012). Fig. 13.3 shows a timeline of synthetic biology research that have biomedical implications. The following section elaborates on the application of synthetic biology in improving healthcare.

13.3.1 Role of synthetic biology in understanding disease mechanisms Synthetic biology has contributed to infectious disease research in more than one way. Advancements in sequencing technology and de novo DNA synthesis methods have facilitated the studies of host
pathogen interactions and the underlying mechanism of infection. Synthetic biology has provided mechanistic insight into such interactions by reconstructing or synthesizing viral or bacterial proteins in genomes. In 2003 the first synthetic virus was created (Smith, Hutchison, Pfannkoch, & Venter, 2003), followed by reconstruction of the 1918 H1N1 Spanish influenza pandemic virus (Tumpey et al., 2005).

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FIGURE 13.3

Timeline of synthetic biology depicting major milestones toward translational research.2

Similar research on chimeric viruses helps scientists characterize the virus, identify key virulent factors, predict the pandemic potential of certain virulent strains, study disease mechanisms, and investigate potential therapies. One such example is in the case of SARS 2

Fig. 13.3 is made by icons by genetic by Eucalyp from the Noun Project, Sheep by Nook Fulloption from the Noun Project, cell by Maurizio Fusillo from the Noun Project, DNA cutting by Berkah Icon from the Noun Project, Chemistry by Creative Art from the Noun Project, human genome by dDara from the Noun Project, gene therapy by dDara from the Noun Project, and DNA by dDara from the Noun Project.

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coronavirus (Becker et al., 2008), a chimeric SARS-like bat coronavirus was used for characterization as it was difficult to propagate the natural virus under laboratory conditions. The in vivo experiments on this recombinant coronavirus revealed the infection was enhancing the potential of spike proteins and its role in tropism switching (Weber & Fussenegger, 2012). Also, DNA synthesis served as a tool for reconstituting antigens for the production of high-throughput antigen arrays that could be used for diagnostics purposes (Burbelo, Ching, Bush, Han, & Iadarola, 2010). Synthetic biology also opened new frontiers for vaccine development; considering the advancements on genome synthesis and assembly front, attenuated vaccines could be developed with much ease, and live vaccine for poliovirus is one such example (Coleman et al., 2008). Synthetic biology has also been used for vector control in case of mosquitoes where with the help of a synthetic homing endonuclease; male mosquitoes were engineered in a way where they harbored a female-specific (since the carrier is female) lethal-gene that rendered the female mosquitoes flightless resulting in vector control (Windbichler et al., 2011). Synthetic biology has also proved instrumental in understanding mechanisms of immunebased disorders. Reconstituting B cell receptors revealed the true activators behind the signaling cascade responsible for antibody production (Yang & Reth, 2010). Further investigations have helped understand the onset of humoral response and mechanisms of autoimmunity. Furthermore, it has led to significant development in the field of immunotherapy, for example, Chimeric Antigen Receptor T cells that are being used for the treatment of several cancers (June, O’Connor, Kawalekar, Ghassemi, & Milone, 2018; Zhang, Liu, Zhong, & Zhang, 2017). With the help of synthetic biology tools, biosensors (tissue-based, DNA-based, and proteinbased) have been developed for the detection of the desired analyte. These biosensors can be used for early and rapid detection of diseases (Mehrotra, 2016).

13.3.2 Synthetic biology in drug discovery, development, and delivery Synthetic biology is not limited to understanding disease mechanisms alone. It has great potentials in the field of drug discovery and development owing to engineering-based capacities that ensure flexibility as compared to traditional screening for natural drug molecules. Since it follows a systems-based approach, the drugs have better potential, and since they are either based on or derived from biological entities, it ensures that they have lesser side effects than chemically synthesized drugs. Semisynthetic artemisinin was one of the first synthetic drugs to be marketed (Ro et al., 2006). Synthetic biology tools have enabled the elucidation of genetic circuits leading to better drug target identification and lead optimization. They have also paved the way for gene-based therapies. Gene knockouts models are ideal for studying disease mechanisms, target identification, and effect of drugs. Protein engineering and metabolic engineering have allowed for shuffling biosynthetic modules and modifying enzymes that can be used as drugs. It has led to the development of target therapies that can be regulated, for example, tumorinvading bacteria can target tumor cells specifically and have been engineered in a way that drug expression is triggered only after entering the tumor cells (Ganai, Arenas, & Forbes, 2009). Synthetic biology methods have helped in engineering inducible biomaterials that can be used for drug delivery. Hydrogels are one such example that is constructed use protein

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or DNA-polyacrylamide monomers dissolve when encountered by specific ligands (Ehrbar, Schoenmakers, Christen, Fussenegger, & Weber, 2008; Ka¨mpf et al., 2010). Nowadays, a wide range of inducible dose-dependent, drug sensing, and drug-releasing hydrogels are available. Synthetic biology also enables the construction of synthetic cells or systems for screening of drugs, thereby reducing the amount of time the drug will spend in clinical trials. The development of organ-on-chips (microscale recapitulation of complex tissue and organ systems) technology is a breakthrough that can have a significant impact on the clinical front (Bhatia & Ingber, 2014). It is an excellent preclinical screening model for lead molecules, toxicity, and drug delivery systems.

13.3.3 Role of synthetic biology in personalized medicine Synthetic biology for personalized medicine is still in its infancy. But since it is an engineered approach, it is not difficult to imagine the usage of synthetic biology for engineering a custom-made/personalized treatment regime. The potential hybrid resulting from a combination of synthetic biology and personalized medicine is called constructive personalized medicine (A. O’Malley, 2012). Synthetic biology has contributed to personalized medicine in many ways. It has facilitated rapid and effective pharmacogenomic studies, which is an essential part of personalized medicine. Synthetically generated genenetworks and prototype therapeutic circuits have the potential for developing personalized cell-based and gene-based therapies for complex diseases (Jain, 2013). One of the remarkable discoveries in the field was that of CRISPER-Cas9 for genome editing (Ran et al., 2013). It is known for its high specificity and precision and has excellent potential to be used in personalized therapy (Xing & Meng, 2019). Synthetic biology is a key player in stem cell research, and stem cell is integral to personalized medicine. It has been used to reprogram somatic cells to confer totipotency in the early 1960s by John Gurdon. Following this, high-efficiency reprogramming of differentiated human cells to confer pluripotency has been achieved using synthetic mRNA (Warren et al., 2010). Stem cells modified with synthetic biology can be used for personalized therapies like in organ or tissue replacement, drug screening, for introducing desirable properties, etc.

13.3.4 Regulation and ethical considerations of synthetic biology Synthetic biology is a widely accepted technology in the field of life sciences/biotechnology and has been adopted by several industries for its applications. However, since synthetic biology messes with the natural order, it is often a topic of controversy. Therefore it is essential to have regulatory boards set boundaries to ensure that research is carried out well within its limits. In 1974 the National Institute of Health forms an advisory committee called the Recombinant DNA Advisory Committee to address public worries concerning genetic manipulation using recombinant DNA technologies and its possible threats. To further regulate the possible use of biological weapons in warfare, the United Nations forms a

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Biological Weapons Convention. Guidelines were laid out by NIH to ensure that these experiments are carried well within ethics (Kubica, 2014). In 1980 the United States Supreme Court ruled in favor of human-made microorganisms for a patent (“Diamond v. Chakrabarty:: 447 U.S. 303 (1980):: Justia US Supreme Court Center,” 2020). Although the use of GMOs was permitted for specific purposes, several regulations were put in place in order to ensure that unethical practices are not followed, and potential threats are kept at bay. In 2002 Public Health Security and Bioterrorism Preparedness Response Act was signed was prevention and preparedness for bioterrorism or public health emergencies (Kubica, 2014). Despite several regulations, some research activities were permitted that could be of dual-use. Dual-use research of concern is those researches that can be used for both good and bad purposes. National Science Advisory Board for Biosecurity proposed a framework for oversight of dual-use research in the field of life sciences. “Ethics of Synthetic Biology” was published in 2009 that elaborated on the ethical, social, and legal implications of synthetic biology research (Kubica, 2014). Despite several attempts to minimize public health threat, a few incidents like the anthrax attacks in the United States had the regulatory boards reassess the control measures. Synthetic biology research must be carried well within ethical limitations. It has great potential to be used in biomedical sciences, but it can pose an even more significant threat if it falls into the wrong hands.

13.4 Conclusion Systems biology is changing the way we used to look at disease biology. The multilevel understanding offered by systems approach that integrates biological research and clinical research is revolutionizing the way we used to look at healthcare. Claude Bernard had already realized the necessity and enormous potential of the approach to the system in the 19th century. Denis Noble—after Bernard’s insightful advice (Noble 2008)—was the first to apply a multilevel approach to heart comprehension and to prove its usefulness and performance. Since then, this has helped define appropriate therapy for a wide range of pathologies. Several clinical applications opt for system approaches, and one striking case is the model built for HIV AIDS. All systems studies have a series of experimental and theoretical measures that act as the basis of incorporation of the components of a complex system into their respective time and space scales. These methodologies extend the frontiers of biological and clinical applications of systems biology. More precise disease prediction models can be built, and tailored therapeutic regimens can be developed by combining multilevel datasets from molecular biology experiments, pathological data, clinical monitoring data, symptoms, imaging results, EHR, etc. However, in the context of positive success stories, system science has yet to be fully applied to drug development, clinical trial design, or routine clinical practice. There is still a long way to go to achieve the full potential of biomedical applications of systems biology. Validation of system models is crucial before clinical application. Synthetic biology is still in a very nascent stage as far as its therapeutic applications are considered. Given the strict ethical regulations surrounding this area of research, its application potential has not yet fully been realized. However, current advances in synthetic

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13.4 Conclusion

Section 6: Biological systems engineering

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biology have provided us with an extended genetic code combined with de novo DNA synthesis has resulted in a plethora of new opportunities for manipulation of the synthetic genome for therapeutic purposes. Its applications in drug discovery, development, and delivery make it suitable for tailored, personalized medicine. A system-based approach also ensures that the treatment regimens are more effective and precise than traditional medicine. It also has the potential to decrease the amount of time involved in drug discovery and may even be at a reduced cost. Years of research in the field of biomedical sciences have led us to believe that biological systems are incredibly complex and dynamic. Traditional medicine fails to deal with such a system appropriately, and thus there is increasing drug resistance, failure of medication, and side effects to treatments. Integration of systems and synthetic medicine will ensure that the limitations of traditional medicine are overcome. The future of these sister disciplines is bright. As a higher number of researchers and students are trained in the interdisciplinary field of systems and synthetic biology, several new ideas will emerge. There are several challenges at both analysis and application level for both the fields that need to be addressed but these are beyond the scope of this chapter.

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14 Translational research in drug discovery: Tiny steps before the giant leap Sindhuri Upadrasta1 and Vikas Yadav2,3 O U T L I N E 14.1 Introduction

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14.2 Tools involved in translation drug discovery 349 14.3 Recent successful advances in translation drug discovery 351 14.3.1 Cancer 352 14.3.2 Diabetes 355 14.3.3 Acquired immunodeficiency syndrome 355 14.3.4 Autoimmune disorders 356 14.3.5 Neurological disorder 357 14.3.6 Cardiovascular disease (CVD) 357

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14.6 Approaches to boost translational drug discovery 360 14.7 Conclusion

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CSIR-National Chemical Laboratory, Pune, India Interdisciplinary Cluster for Applied Genoproteomics, University of Lie`ge, Lie`ge, Belgium Present Address: Clinical Research Centre, Lund University, Malmo¨, Sweden

Translational Biotechnology DOI: https://doi.org/10.1016/B978-0-12-821972-0.00010-1

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C H A P T E R

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Section 7: Drug discovery and personalized medicine

14.1 Introduction In the present scenario of the ever-growing COVID-19 pandemic, the world is facing a challenge of inconceivable loss of lives amalgamated with economic instability. Presently, the primary concern of the researchers and pharmaceutical industries worldwide is to develop innovative and clinically effective therapeutic options to combat this tough time (Fu et al., 2020). In such vulnerable situations, the concept of the Traditional drug discovery (TrDD) process is delusional due to the cost and time involved. On average, it takes around 12
15 years and more than $3 billion to introduce a fresh drug into the market. On the contrary, the concept of translational drug discovery (TDD) seems to be the ideal savior. TDD is a bidirectional process, wherein, a disease condition in a clinical setting becomes the driving force to identify drugs that can combat it and going back to the clinical setting to see the efficacy of the drug. In this way, the research is carried out by going back and forth until an effective drug molecule has been identified (Fishburn, 2013). TDD is pillared on the concept of “translational research” which is applicably synonymous to “translational medicine” or “translational science.” As shown in Fig. 14.1, the often-used phrase

FIGURE 14.1 Translational drug discovery involves a feedback loop between bench-bedside-bench. Illustration was drafted through an online tool (smart.servier.com).

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“bench to bedside,” aptly describes this process, as its main aim is to translate laboratorybased discoveries into clinical therapies, which will ultimately benefit human society. TrDD seeks to interpret the basic cellular mechanism and applies these understandings to design new therapies. Conversely, TDD identifies clinically relevant concerns and attempts to understand the underlying mechanism (Woolf, 2008). TDD covers an integrated array of steps ranging from target identification, interpretation of efficacy, and safety of drugs used in clinical interventions. Translational research also focuses on the identification of novel biomarkers for diseases. These biomarkers help in the prediction of disease progression, pharmacological response, and the clinical outcome in patients. Another key area of focus is the development and refinement of existing animal models for checking of drug efficacy, safety, and prediction of outcomes in patients, which will help in drug development and critical decision making (Arora, Maurya, & Kacker, 2017). Integration of data generated from laboratory-based molecular investigations, clinical research, and observations made by health care professionals gives rise to a plethora of information. Hence, for the administration and integration of clinical and molecular data, TDD requires coordinated orchestration between academia, clinical, and pharmacological research. Translational research has led to the closing of the gap between academia, pharmaceutical industries, and clinical setups. In TrDD mode, these groups functioned individually, that is, the academia would discover the molecule which would be developed by the pharmaceutical industry and tested by the clinics. TDD has reduced this chasm and given rise to collaborations between them, resulting in acceleration of the drug development process (Goldblatt & Lee, 2010). Gardasil-9 (peptide-based vaccine) developed for cervical cancer is a typical example of TDD, which was developed in close collaboration between academia and a biotech company called Merck (McNeil, 2006). Khoury et al., for the first time, described the four phases of translational research as “T-phase” (T0
T4), which was later adopted by the Institute of Translational Health Science as a universal concept (Khoury, Gwinn, Burke, Bowen, & Zimmern, 2007). T0 refers to the basic discovery phase, which includes laboratory-based studies. T1 refers to the processes involved in the transfer of concepts from basic discoveries to preclinical verification in humans. T2 constitutes domiciliation of effectiveness in humans according to clinical guidelines. T3 centralize on the application and circulation of the research finding. The T1, T2, and T3 stages coincide with the clinical trial stages. T4 is the culmination of all steps. It involves the evaluation of the outcomes and effectiveness in a population. With so much of precision and contoured approach, TDD possesses the potential of yielding effective and safer drugs at a faster rate than TrDD in a defined patient population (Plenge, 2016).

14.2 Tools involved in translation drug discovery In recent years, the development of high-throughput techniques in combination with the complete human genome sequence has provided insights into the molecular pathology of diseases. Concomitantly, advances in analytical methods have provided

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techniques to validate findings at the proteomics level. Translational research revolves around the use of several tools and techniques for target identification, validation, pathway elucidation, biomarker identification, etc. For target identification at the genomic level, DNA microarray or RNA-sequencing technologies have become fundamental and affordable tools for transcriptome-wide analysis of differentially expressed genes in a specific disease state, which could lead to the identification of new molecular targets. RNA-sequencing technologies possess an added advantage of identifying an unannotated gene signature that was missing in DNA microarray (Stark, Grzelak, & Hadfield, 2019). In equivalence to DNA-microarray, there is the availability of protein-array, which permits the detection of protein
protein interaction, protein
DNA, protein
small molecule interaction, and antigen
antibody interactions. Alternatively, these interactions can also be studied in silico through various softwares by translational researchers. Uzoma and Zhu (2013) have extensively reviewed the concept of interactome mapping (fusion of protein microarray and in silico approach) with key examples of their usage in TDD. Since the inception of CRISPR
Cas9 to genome editing, it has become an integral part of the investigation of the relationship between genome and phenotype, generate disease model of animals and cells. So far, its application is restricted to basic research, but its use for translational research is on the way (Chen, You, & Lu, 2018). It is preemptive to mention how basic techniques like MALDI-TOF (mass-spectrometry), cloning, yeast twohybrid assay, HPLC are still supporting present-day high-throughput techniques. Knockout animals are considered as an appropriate choice for phenotypic studies of gene-based therapy. However, substantial numbers of genes are of critical importance in the embryonic stage, which imposes limitations to this approach. To bypass the limitations of the constitutive knockout model, cre/lox system was introduced to study the time-restricted expression of genes. Similarly, the tetracycline controlled transcription (TET) ON/OFF system is also in practice (Kim et al., 2018). Target identification and validation are followed by toxicology testing, which is monitored by pharmacodynamics (PD) studies and is correlated with the pharmacokinetic (PK) profile of the drug molecule. Canuel, Rance, Avillach, Degoulet, and Burgun (2015) have extensively reviewed the in silico research platforms, which involves the integration of clinical and omics data. Computer programs, namely, Simcyp, COPASI, CellDesigner, etc., facilitate translational research program (Knight-Schrijver, Chelliah, Cucurull-Sanchez, & Le Nove`re, 2016). Schubert (2010) has extensively described how PK
PD analysis has successfully decreased the attrition rate in translational drug development. These analyses are performed to evaluate the efficacy or side effects of the drug, along with suggesting possible reasons for drug failure. A vast amount of data is generated throughout the life cycle of translational research such as demographics, medications, diagnostics, specimen collection, etc. The advent of modern techniques like artificial intelligence (AI), machine learning (ML), and next-generation sequencing generates terabytes of data related to omics, which produces huge challenges (Chan, Shan, Dahoun, Vogel, & Yuan, 2019). Thanks to the inception of translational bioinformatics in translational medicine, it provides a solution package for the management of such huge data. Ever-growing understanding and incorporation of molecular tools in TDD will transform the panorama of health care and life sciences in the near future (Fig. 14.2).

14.3 Recent successful advances in translation drug discovery

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14.3 Recent successful advances in translation drug discovery The discovery of Penicillin in 1928 led to the expansion of biological understanding and its widespread use in 1942, which led to the emergence of separate domains of basic scientific research and clinical practice. As science progressed, it brought technological advancements, and along with it, the process of drug discovery also changed. Studies in recent decades have shown that big pharmaceutical companies alone are not serving as fertile grounds for innovations. To boost their drug discovery pipeline, they are always looking for small venture companies or academic institutes. In this scenario, TrDD seems to be synonymous with TDD, as both are working to enhance human life expectancy (Florian et al., 2020). The present century is witnessing tremendous evolution in the field of biomedical science, so much so that 50% of the new drugs reported are based on academic publications. This highlights the influence of academic research on the modern-day drug discovery process (Patridge, Gareiss, Kinch, & Hoyer, 2015). Challenges are constantly being thrown-in the form of deadly infectious diseases such as severe acute respiratory syndrome (SARS), middle east respiratory syndrome (MERS), and presently the emergence of Covid-19. Within just a few weeks of identification of a previously unknown virus in Wuhan, China, the entire genome has been sequenced and published (Lu et al., 2020). All these developments have helped in the identification of genes and proteins against which investigators are exploring to develop vaccines and possible curative drugs. It is also an excellent example of translational research where basic discoveries are being carried out hand in hand with drug development against an unmet clinical need. The paradigm shift in the drug discovery process from traditional to translational methods has resulted in the development of drugs against diseases using a much rational approach. As shown in Fig. 14.3, we have attempted to provide a brief overview of the recent TDD mediated successful

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FIGURE 14.2 Schematic representation about the tools involved in translational drug discovery.

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FIGURE 14.3 Description of the application of translational drug discovery in several diseases. Illustration was drafted through an online tool (smart.servier.com).

advancements in diseases like cancer, diabetes, AIDS, neurological diseases, autoimmune disorders, muscle disorders, and cardiovascular disorders (CVD), etc. (Table 14.1, Fig. 14.3).

14.3.1 Cancer Cancer has been a leading cause of financial, emotional, and physical strain being exerted on the entire society. Constant efforts of investigators worldwide have led to the evolution of targeted therapies in cancer. Imatinib, a targeted therapy presently being used for chronic myelogenous leukemia (CML), acute lymphoblastic leukemia, and the number of other cancer types is a classic example of translational research. Imatinib (2-phenyl amino pyrimidine) specifically and strongly inhibits the tyrosine kinase domain of the ABL (a proto-oncogene), BCR-ABL, PDGFRA, and c-KIT all of which are involved in various biological processes like growth and development. Nicholas Lyndon, a biochemist by profession at the Novartis, first formulated the Imatinib. Later on, Brian Druker, an oncologist, postulated it for the treatment of CML. This translational research work was appreciated with numerous awards crediting both the scientists and is also included by the World Health Organization (WHO) in the list of essential medicines to fight against cancer (Iqbal & Iqbal, 2014). Trastuzumab is another such drug developed as a part of targeted therapy against the HER2-positive breast cancer. Of note, 15%
22% of breast cancer patients are known to possess overexpression in HER2 gene levels. Michael Shepard, Dennis J. Slamon, and Axel Ullrich

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14.3 Recent successful advances in translation drug discovery

Brief (not exhaustive) list of molecules discovered by translational drug discovery.

Drug name (Trade name)

Implication

FDA approval/ Clinical Trial

Manufacturing company

Target/pathway involved

References

1.

Imatinib (Gleevec)

CML, ALL, GIST

Approved in 2001

Novartis

Inhibits Bcr-Abl tyrsone kinase

Iqbal and Iqbal (2014)

2.

Bevacizumab (Avastin)

Glioblastoma, colon cancer, Approved in 2006 lung cancer, renal cell carcinoma

Genentech Inc.

Inhibits VEGF-A

Goodman (2004)

3.

Trastuzumab (Herceptin)

Breast and stomach cancer

Approved in 1998

Genentech Inc.

Inhibits Neureglin-1

Shepard (2019)

4.

Foxy5

Prostrate and breast cancer

Under Phase II Trial

WNT Research AB

WNT5A mimicking peptide

Prasad, Manchanda, Mohapatra, and Andersson (2018)

5.

AR231453

Antidiabetic

Phase I/Phase II trial

Arena Pharmaceuticals Inc.

GPR119 agonist

Han et al. (2018)

6.

GSK1292263

Antidiabetic

Phase II trial completed

GlaxoSmithKline GPR119 agonist

Han et al. (2018)

7.

MBX2982

Antidiabetic

Phase II trial completed

Metabolex

GPR119 agonist

Han et al. (2018)

8.

BMS903452

Antidiabetic

Phase I trial completed

Bristol-Myers Squibb

GPR119 agonist

Han et al. (2018)

9.

Semaglutide (Ozempic); Liraglutide (Victoza)

Antiobesity and antidiabetic

Ozempic approved in Novo Nordisk 2016; Victoza approved in 2010

GLP-1 mimicking peptide

Knudsen and Lau (2019)

10. Pramlintide (Symlin)

Antidiabetic

Approved in 2005

AstraZeneca

Peptide of amylin hormone

Ravussin et al. (2009)

11. Metreleptin (Myalept)

Antidiabetic

Approved in 2014

Amylin Pharmaceutical

Peptide of leptin hormone

Banaszczyk (2019)

12. Simeprevir (Olysio)

Hepatitis C

Approved in 2013

Medivir AB & Janssen Pharmaceutical

NS3/4A protease inhibitor

You and Pockros (2013)

13. Enfuvirtide (Fuzeon)

HIV/AIDS

Approved in 2003

Roche

HIV fusion inhibitor Woollard and Kanmogne (2015)

14. Leronlimab (PRO 140)

HIV/AIDS

Under phase III trial

CytoDyn Inc.

Antibody against CCR5 receptor on the CD4 cells.

Biswas et al. (2007)

15. Ibalizumab (Trogarzo)

HIV/AIDS

Approved in 2018

TiaMed Biologics USA

Humanized monoclonal antibody against CD4 receptors

Markham (2018)

16. Elipovimab (GS-9722)

HIV/AIDS

Under phase I trial

Gilead

Broadly neutralizing antibody (bNAb) against HIV envelope

Gilead.com

(Continued)

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TABLE 14.1

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TABLE 14.1 (Continued) Drug name (Trade name)

Implication

FDA approval/ Clinical Trial

Manufacturing company

Target/pathway involved

References

17. Etanercept (Enbrel)

Rheumatoid arthritis, plaque psoriasis (PP), Ankylosing spondylitis (AS)

Approved in 1998 for RA, 2004 for PP & 2003 for AS

Immunex, now acquired by Amgen

Binds to TNF-α

Hassett et al. (2018)

18. Infliximab (Remicab); Adalimumab

RA

Infliximab approved in 1998; Adalimumab approved in 2002

Infliximab by Centacor Inc.; Adalimumab by Abb Vie inc.

Binds to TNF-α

Stevenson et al. (2016)

19. Ustekinumab (Stelara)

Psoriasis, psoriatic arthritis, Approved in 2009 Crohn’s disease

Centocor Ortho Biotech Inc.

Binds and inhibits IL-12 and IL-23

Deepak and Loftus (2016)

20. Dupilumab

Atopic dermatitis, asthma

Approved for Atopic dermatitis in 2017, approved for Asthma in 2018

Regeneron Pharmaceuticals and Sanofi Genzyme

Binds and inhibits IL-4Rα

Florian et al. (2020)

21. Alirocumab (Praluent)

Anticholesterol

Approved in 2015

Sanofi Aventsis

Inhibitor of PCSK9

Jaworski et al. (2017)

22. Evolocumab (Repatha)

Anticholesterol

Approved in 2015

Amgen Inc

Inhibitor of PCSK9

Kasichayanula et al. (2018)

23. Guselkumab (Tremfya)

PP

Approved in 2017

Janssen Global services

Binds and inhibits IL-23

Nogueira and Torres (2019)

24. Risankizumab (Skyrizi)

PP

Approved in 2019

AbbVie & Boehringer Ingelheim

Binds and inhibits IL-23

Singh et al. (2015)

25. Tildrakizumab (Ilumya)

PP

Approved in 2018

Sun Pharma

Binds and inhibits IL-23

Banaszczyk (2019)

26. Exondys51 (Eteplirsen)

Duchenne muscular dystrophy

Approved in 2016

Sarepta Therapeutics

Gene therapy

Cowling and Thielemans (2019)

27. Sprinraza (Nusinersen)

Spinal mascular atrophy

Approved in 2016

Ionis Pharmaceuticals

AAV mediated SMN gene replacement therapy

Foust et al. (2010)

AAV, Adeno-associated virus; ALL, acute lymphocytic leukemia; CML, chronic myelogenous leukemia; FDA, Food and Drug Administration GIST, gastrointestinal stromal tumors; PCSK9, proprotein convertase subtilisin/kexin type 9; VEGF-A, vascular endothelial growth factor-A.

together did the ground research to invent the first humanized monoclonal antibody called Trastuzumab, which received the Food and Drug Administration (FDA) approval in 1998 for breast cancer. Trastuzumab development was equally supported by the clinical partner in the form of a Genentech company (Shepard, 2019). Another addition to the armory list to fight against cancer is Bevacizumab. It is a mouse monoclonal antibody that is humanized against vascular endothelial growth factor (VEGF) and was approved by the FDA in 2004 for cancer treatment. Folkman in 1971 suggested that antiangiogenesis might serve as an effective strategy to deal with cancer patients. In 1989, Ferrara from Genetech company identified VEGF-A as a modulator of angiogenesis. Ferrara and colleagues pursued this protein as a possible cure for cancer and showed that a mouse

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antibody against the VEGF leads to inhibition of tumor growth in the mice (Goodman, 2004). The present list of TDD products includes other VEGF targeting monoclonal antibodies like pazopanib, sunitinib, and everolimus, to name a few.

14.3.2 Diabetes Diabetes is a chronic metabolic disease affecting B422 million people worldwide. The most common type-2 diabetes mellitus (T2DM) is characterized by low insulin levels or insulin resistance. For people suffering from diabetes, access to treatment becomes critical for their survival. Diabetes is in the focus of several investigators, who are trying to identify suitable molecular targets to combat this disease. GPR119 (G-protein coupled receptor-119) is one such target that is predominantly expressed on the pancreas of humans and rodents. Stimulation of GPR119 enhances insulin secretion from the pancreatic β-cells and simultaneously enhances the release of the gut peptides GLP-1 (glucagon-like peptide 1), glucosedependent insulinotropic peptide, and polypeptide YY. As a result, blood glucose level decreases, causing a reduction in food intake and ultimately reducing body weight (Zhang, Li, & Xie, 2014). Experimental proofs provided by the first GPR119 agonist, AR231453, showed that indeed GPR119 agonists could function as potent antidiabetic drugs. Post this, several agonists were developed by leading pharmaceutical companies and are in different phases of clinical trials. A few examples are GSK1292263 (phase II, GSK), PSN821 (phase II, Astellas), MBX2982 (phase II, Metabolex), APD597/ADP668 (phase I, Arena), BMS903452 (phase I, BMS), and PSN821 (phase I, Prosidion/OSI) (Han, Lee, Park, Lee, & Choi, 2018). The FDA approvals of Liraglutide and Semaglutide for treatment of type-II diabetes are an example of the translational research group established within a big pharmaceutical giant called Novo Nordisk. Liraglutide and Semaglutide are two GLP-1 analogs that possess enhanced half-lives compared to the parent GLP-1 incretin hormone. Both these peptides fall under the category of pathway-based TDD (Knudsen & Lau, 2019). On similar grounds, Pramlintide and Metreleptin were developed by AstraZeneca and Amylin Pharmaceuticals, respectively. Both of these received approval from the FDA for use in the treatment of T2DM but failed in clinical trials conducted to test their potential as obesity controlling drugs. Pramlintide and Metreleptin are synthetic analogs of glucoregulatory hormones, amylin, and leptin, respectively. Both are synthesized by the β-cells of the pancreas and act concomitantly to improve insulin sensitivity (Chou & Perry, 2013; Ravussin, Smith, Mitchell, Shringarpure, & Shan, 2009).

14.3.3 Acquired immunodeficiency syndrome Identified in 1981, it was responsible for causing an epidemic that resulted in an estimated 75 million people being infected, and 32 million people died from acquired immunodeficiency syndrome (AIDS) at the end of 2018 (WHO health report 2018; www.who. int/gho/hiv/en/). There is always a need for a potent drug against the HIV, which is a causative organism for this deadly disease. HIV infection leads to a gradual decrease in body’s immune system culminating to AIDS. The development of antiretroviral drugs was boosted owing to the significant advancements made in the understanding of the

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replication cycle and pathogenesis of the viruses. The key molecular events include virus entry, nuclear import, reverse transcription, genomic integration, and viral maturation which serve as potential molecular targets to inhibit the replication of the virus (Desai, Field, Grant, & McCormack, 2017). Enfuvirtide, the FDA approved drug developed by Roche is known to block the fusion of the HIV with the host cell. It is a synthetic peptide that resembles a heptad region, HR2, of the viral glycoprotein GP41. It competitively binds to the other heptad region HR1 and thus prevents their interaction (Woollard & Kanmogne, 2015). Leronlimab and Ibalizzumab are two humanized monoclonal antibodies developed that prevent the entry of HIV-1 in human host cells. Leronlimab awaiting the FDA approval, binds to CCR5 receptor and was developed by CytoDyn. Ibalizumab (TiaMed Biologics Inc.) received the FDA approval in 2018 and blocks HIV entry by binding to CD4 cells. Ibalizumab is considered to be the pioneer humanized monoclonal antibody-based therapeutics involved in the treatment of AIDS. It is the first CD4-directed post-attachment HIV-1 inhibitor (Biswas, Tambussi, & Lazzarin, 2007; Markham, 2018).

14.3.4 Autoimmune disorders The use of recombinant DNA technology and monoclonal antibody generation found its way into the development of drugs against diseases involving cytokines. Constant efforts by researchers identified tumor necrosis factor (TNF) as the modulator of various proinflammatory cytokines that are involved in various autoimmune diseases (Maini, 2010). Discovery of Etanercept set an example for the use of translational research in autoimmune diseases. Etanercept is a fusion protein of the extracellular domain of the human TNF receptor and the Fc portion of the human IgG1. It was the first TNF inhibitors to get the FDA approval to be used as therapeutics against rheumatoid arthritis. Besides this, it is also used against several autoimmune diseases like plaque psoriasis, psoriatic arthritis, and ankylosing spondylitis, to name a few (Hassett et al., 2018). Several other drugs like Infliximab (Centacor Inc.), Adalimumab (AbbVie Inc.), Golimumab (Janssen Biotech Inc.), and Certolizumab Pegol (UCB, Inc.) were developed as recombinant monoclonal antibodies against the TNF and approved for use against rheumatoid arthritis (Stevenson et al., 2016). Similarly, Ustekinumab, a humanized immunoglobulin G1 (IgG1) kappa monoclonal antibody, has been developed to inhibit the function of the p40 subunit of the interleukins IL-12 and IL-23. This has been particularly used for the treatment of the autoimmune disorder called psoriasis. It is postulated to be effective in dealing with other conditions like Crohn’s disease (Deepak & Loftus, 2016). Mirikizumab (Eli Lilly and Company), Risankizumab (Boehringer Ingelheim and AbbVie), Tildrakizumab (Sun Pharma), Brazikumab (AstraZeneca), and Guselkumab (Janssen Biotech Inc.) are other drugs which target IL-23 and received the FDA approval for the treatment of psoriasis. To treat atopic dermatitis, Dupilumab (monoclonal antibody), which tethers and perturbs the IL4 receptor alpha, was developed as a joint effort between Regeneron Pharmaceuticals and Sanofi (Florian et al., 2020). The success of translational medicine in the treatment of autoimmune disorders has led to an increase in collaborations between academia and pharmaceutical industries.

14.3 Recent successful advances in translation drug discovery

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Neurology is a division of medical science that copes with the biology of nerves, nervous tissue, and brain function. Treatment of brain-related disorder continues to be a major health impediment, despite research advancements over several years. Unfortunately, late-stage clinical trial failure is proportionately higher for neurodegenerative disease compared to other diseases. Owing to the poor understanding of brain disorder, identification of promising molecular targets and its validation is difficult through TrDD. However, translational research done in the field of neuroscience, termed as translational neuroscience have opened up new avenues in treating neurological disorders. Few such examples of translational neuroscience include mechanical prosthetic limbs, functional magnetic resonance imaging, built-in insulin pumps, prosthetic heart valves, etc. (Stoyanov, 2017). Gerlach and Krajewski (2010) have extensively reviewed how translational neuroscience has identified a few second-line of anti-epileptic drugs such as Brivaracetam (SV2A target), Pregabalin (α2δ-subunit of voltage-gated calcium channels) and a novel drug called ICA-105665 which targets KCNQ2/3 potassium channels. Similarly, Stuve et al. (2010) explained how, after struggling for several decades, translational neuroscience identified Glatiramer Acetate and Natalizumab as potent drugs for multiple sclerosis. Both drugs were developed using the experimental autoimmune encephalomyelitis model and are approved by the FDA. Demuth et al. (2017) have extensively reviewed the ongoing research therapies that are due to be translated as promising therapeutic options for neurovascular and dementia in the future. Simultaneously they have also discussed in detail the mice model usage for specific neurological disease. It is worthwhile mentioning how the use of AI has improved the health and lives of people around the globe.

14.3.6 Cardiovascular disease (CVD) CVD accounts for the leading casualties in developed countries. Hypercholesterolemia is known to be the indisputable risk factor for CVD, and it is generally characterized by an increase in the presence of low-density lipoproteins (LDLs). TrDD-based discovery of Statins has shown improvement, but still, 50%
80% patient population does not exhibit recommended cholesterol levels (Kotseva et al., 2016). The PCSK9 (pro-protein convertase subtilisin/kexin type 9) gene was initially identified to be a regulator of cholesterol homeostasis. It is expressed in the liver and interacts with LDL receptors (LDLRs) of liver cells and promotes its degradation. Given that LDLRs are needed to clear the circulating LDL, a decrease in LDLR by the PCSK9 results in higher levels of LDL in the blood. Research efforts were made to establish Alirocumab (Sanofi Aventis) and Evolocumab (Amgen Inc.) as two cholesterol-lowering drugs that consist of monoclonal antibodies against the PCSK9. Alirocumab and Evolocumab act by inhibiting the crosstalk between PCSK9 and LDLRs, rendering them available to clear LDL (Jaworski, Jankowski, & Kosior, 2017). Lauer and Skarlatos (2010) have extensively reviewed several other translational medicine options that are under clinical trials for CVD.

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14.4 Opportunities in translation drug discovery The concept of translational research is familiar to the scientific community for over 30 years, but in the past 10 years, it has become an integral part of several ongoing research projects. Recently accomplished research breakthroughs like the Human Genome Project, technological advancements in high-throughput screening, AI-based advances in information technology have opened the horizon of opportunities for individual investigators or academic institutions to foothold their work foundation (Tellez, Ferrone, & Granada, 2019). The foremost opportunity meted out to the individual investigators is that they can take part in an intellectually stimulating process and visualize the transformation of their discoveries into solutions that would relieve people of their health issues. Taking part in such translational work not only helps them improve their knowledge and understanding of the various aspects of the disease but also gives them a chance to appreciate the relevance of their study in real-world problems. It also provides the investigators with several opportunities to learn new methods that can be applied to their existing projects, for getting new projects, and to publish their work faster. Translational research also promotes collaboration between basic science researchers and clinical investigators, where each one could share their interests and benefit from the association. Such collaborations can lead to the generation of ideas that are often fruitful (Fudge et al., 2016). Besides benefitting the researcher, participation in TDD is beneficial to the institute in terms of its growth and prosperity. It leads to the improvement of existing infrastructure, facilities, and instrumentation. Such developments benefit the researchers by providing them access to the latest cutting-edge technology and resources and expediting the translation process. The benefits are not just limited to researchers taking part in translational projects but also to other researchers who can utilize the resources in the institute like expensive instruments and equipment to conduct their work. There is a creation of a conducive environment that motivates young talent to enter the field of biomedical research. Given the buzz around translational research, many students like trainees, undergraduates, graduates, and post-doctoral fellows show their interest in joining such institutions. It leads to the generation of a talent pool that is committed to working on real-world problems (Rubio, Schoenbaum, Lee, Schteingart, & Marantz, 2010). Besides benefitting the researchers, participation and facilitating translational research helps any institute to build and work on its mission of promoting science and technology, which would improve the quality of human life. When an institute focuses on promoting translational research, the success gained by developing drugs, devices, techniques, or procedures tends to attract numerous collaborators and funding sources with shared interests in promoting work that would be beneficial for the people. An institute which performs in such a way will also be promoted by their national governments. These days, pharmaceutical companies are coming forward to form successful collaborations with academia and work toward the development of drugs for unmet clinical needs. In light of this, an institute with an environment conducive for carrying out translational research will attract not just the top researchers in the field but also pharmaceutical industries, government and philanthropic organizations, and start-ups that want to collaborate and develop useful medical interventions (Hobin, Deschamps, Bockman, Cohen, & Dechow, 2012).

14.5 Challenges in translation drug discovery

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The quote, “With great power comes great responsibility,” is apt for the process of TDD. Along with plentiful scope, translational research also encompasses considerable hurdles that make this journey more challenging (Fig. 14.4). The success of drug development depends on the use of the right model system while predicting the efficacy and potency of the drug. The choice of the right model system determines the path of the drug development process. An incorrect choice might not just slow down the process; in fact, it could lead to misleading results. The challenge pertains to the selection of a model system that would best represent the biological phenomena, and manipulation of this model with the molecule under study would lead to the same effects as expected in human patients. This is particularly applicable when using in vitro cultured cell lines as model systems. Given that these are grown in laboratory conditions, the results observed while working on these cell lines might not extend to in vivo conditions like tissues, organs, or an organism. At times, promising results were seen in in vivo models like mice fall short in humans. So, choosing the right model system influences the results while addressing a biological phenomenon. Apart from the laboratory settings, the infrastructure needed to promote translational work and take it from laboratory testing to clinical trials and then through production post-approval, also is a hurdle. At the laboratory scale, scarcity of perks and rewards for the researchers demotivates their innovative thinking in research. Inadequate funding and resources limit the main project investigator to develop and execute a research plan (Homer-Vanniasinkam & Tsui, 2012).

FIGURE 14.4 Some of the major challenges faced by translational drug discovery. Illustration was drafted through an online tool (smart.servier.com).

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The entire process of drug development includes several aspects other than research like getting approval from the institutional review board, the ethical committee and privacy board, the establishment of animal care protocols, reporting to the data safety monitoring boards, ensuring data integrity, registration for clinical trials, recruitment of patients, technology transfer, filing for intellectual property rights, as well as looking into other commercial aspects of the project. All investigators involved in the translational process need not have the required experience to handle these duties, and their respective institutions might not have the expertise and infrastructure to support them. The presence of a support system is essential, which would allow the investigator to focus on science and smoothly translate the discovery into therapeutic intervention. So, the second major challenge is in the form of getting approvals from the institutional and state-level regulatory authorities, including the USFDA, the United States Department of Human & Health Services, and the Medicine and Healthcare products Regulatory Agency. There are certain strict common rules laid by these authorities to which clinical research associates are bound to follow, which makes it more complex. Apart from regulatory authorities, patent laws impose a huge barrier to translational research, specifically in the pharmaceutical industry (Dale, 2011). The third major hurdle is rather more complex as it is based on human behavior. The TDD process involves people trained in understanding the molecular mechanisms of disease to work with those who have a clinical understanding of the disease. The establishment of sustainable collaborations between these two parties often tends to be difficult due to varied reasons. The primary reason can be due to the general nature of basic scientists who prefer working in their comfort zones, which is set apart from those of a clinical investigator. Geographical distances between the collaborators as well as lack of working knowledge of each other’s domain make it difficult to collaborate productively. While industry partners are generally looked upon as mere funding sources and not scientific collaborators, basic scientists are seen as a workforce to carry out basic research and incapable of commercializing their work. This mistrust in each other’s potentials can have a debilitating effect on fruitful collaborations. Integration of all the partners involved in the development of drug-like academia, pharmaceutical industries, clinical centers, and the public is highly challenging (Horig, Marincola, & Marincola, 2005). The translational approach leads to coming together of investigators from varying disciplines to share ideas and excitement with other unlike-minded individuals. This might slow down or, at times, stagnate the development process. This is known as the Silo problem. This problem is being overcome by the establishment of departments and centers exclusively for carrying out translational research. This helps in creating an environment conducive for bringing together multidisciplinary investigators (Ostergren, Hammer, Dingel, Koenig, & McCormick, 2014). However, all these challenges seem to slow down the pace of translational research, but they cannot completely dismay the mission and accomplishments of TDD. There are several federations worldwide that continuously watch and recommend steps at regular intervals to overcome these challenges.

14.6 Approaches to boost translational drug discovery Researchers all around the globe are endeavoring to find a cure for the ever-increasing number of unconquered medical implications. Many times, there is an outbreak of new

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diseases which spread epidemically, such as in the case of SARS, MERS, and COVID-19. In the outbreak of such a pandemic, it is almost impossible for an individual institution or company to find a cure. A well-orchestrated translational research approach can provide urgent hope to curb such susceptible circumstances. Therefore the individual institutions and companies need to prioritize their respective objectives and thrust area. Many centers around the globe are dedicated to specific diseases for which they have drafted a clear roadmap. Such committed centers like MRC in the United Kingdom, CDRD in Canada, NCATS in the United States, and TIA in Australia have now become a model for others to follow (Gilliland et al., 2016). Collaboration is an intrinsic part of any drug discovery program. In TrDD, collaboration is a matter of brand value or name, but in TDD, it is the soul of the whole project. For instance, we know that basic scientists understand the fundamentals of a disease and device methods or drugs to curb the same; they fall short of skills needed to apply their discoveries directly for patient management. In the same way, clinical researchers understand the mechanisms of a disease, but a lack of understanding of the basic biology fails them to recognize the application of certain scientific discoveries to benefit patient management. This arises due to narrowly focused training received by the basic researchers and clinical students. The training received by basic science graduate does not prepare them to understand the relevance of their scientific discoveries in a clinical context. On the other hand, clinical students are not advised on how to be part of the discovery process. This obstacle can be overcome by providing proper training and mentorship to students aided by good collaborations between academia and clinical organizations (Portilla & Rohrbaugh, 2014). Technology upgradation is another important aspect that should be looked upon to boost TDD projects. The inclusion of the latest technology maintains the speed and accuracy of high-end results generated in preclinical studies. In recent times, the concept of “Drug Repositioning (DR)” is gaining popularity owing to its direct pertinence to the market amalgamated with low financial risk. DR is the process of exploring unknown implications of the already existing drugs or orphaned drugs. DR was serendipitously identified and practiced in earlier times, but presently it has evolved as a big boost for translational research purpose as the molecule of interest has mostly crossed through the regulatory hurdle or phase-I trials. DR is conceptualized on the basis of two theories: first, a drug targeting a particular protein is manifested in multiple diseases, and second, drug targets multiple pathway proteins. Rise in the revenue-based popularity of duloxetine, dapoxetine, minoxidil, thalidomide, and sildanefil fascinates the big pharmaceutical giants to adopt the DR approach along with their TrDD plan (Pushpakom et al., 2018; Yadav & Talwar, 2019). It all started with the serendipitous identification of unknown side effects of minoxidil and sildanefil during clinical trials. Both of the drugs were explored for their DR capability before hitting the market. In the 1980s, Pfizer was testing its newly discovered drug sildanefil (Viagra) for coronary heart disease, which was developed on the course of its efficacy in targeting PDE5, an enzyme involved in regulating smooth muscle contraction and relaxation. In a few healthy subjects, the clinical team observed strong and enduring erections. Pfizer researchers were amused with such an observation, and they immediately set up clinical trials on men facing the problem of erectile dysfunction. Based on the success and

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revenue collections of sildanefil, Eli Lilly and ICOS repositioned tadalafil for erectile dysfunction (Ashburn & Thor, 2004). Correspondingly, minoxidil, which was developed to be used for ulcers, turned to be effective as an antihypertensive vasodilator. During clinical trials, its side effect of unexpected hair growth emerges out to be additionally effective for treating alopecia. Minoxidil is presently FDA approved for both the implications (Simsek, Meijer, van Bodegraven, de Boer, & Mulder, 2018). Unlike that of minoxidil and sildanefil, thalidomide is an example where its repositioned implication was approved long after its main implication. Thalidomide was approved for treating sleeping sickness in pregnant women in 1957, and after 50 years of its inception, it was approved in 2006 for multiple myeloma (Mercurio et al., 2017). Similarly, duloxetine was approved for two different implications within 1 year. Duloxetine was approved by the FDA in 2004 as an antidepressant and later on approved for diabetic peripheral neuropathy pain and osteoarthritis. Same ADMET profile and regulatory issues were considered while granting the repositioned implication of duloxetine, which makes its approval faster for the repositioned implication (Muscatello et al., 2019). Successful examples of DR candidate molecules are listed in Table 14.2. TABLE 14.2 Brief (not exhaustive) list of the FDA approved candidates based on drug repositioning strategy. Drug name (trade name)

FDA approved previous implication (target)

FDA approved present implication (target)

Approach

References

1.

Bimatoprost (Lumigan)

Ocular hypertension and glaucoma (imitates prostaglandinF2α)

Hypotrichosis Simplex (Imitates ProstaglandinF2α)

Experimental

Curran (2009)

2.

Budesonide

Asthma (antiinflammatory)

Ulcerative colitis, Crohn’s disease (Glucocorticoid receptor binding)

Bioinformatic, experimental

Iborra, AlvarezSotomayor, and Nos (2014)

3.

Bupropion

Depression (norepinephrine/ dopamine reuptake inhibitor)

Smoking cessation, seasonal affective disorder, obesity (norepinephrine/ dopamine reuptake inhibitor)

Experimental

Huecker, Smiley, and Saadabadi (2020)

4.

Celecoxib

Osteoarthritis (COX-2 inhibitor)

FEP, breast and colon cancer (COX-2 inhibitor)

Bionformatics and experimental

Ashburn and Thor (2004)

5.

Doxepin

Antidepressant (Histamine H1 receptor blocker)

Insomnia, itchy skin (uncharacterized)

Experimental

Everitt et al. (2018)

6.

Duloxetine (Cymbalta)

Depression (inhibitor of neuronal serotonin and norepinephrine reuptake)

Osteoarthritis, diabetic peripheral Experimental neuropathy (inhibitor of neuronal serotonin and norepinephrine reuptake)

Muscatello et al. (2019)

7.

Eflornithine

Anti-infective in sleeping sickness (ornithine decarboxylase inhibitor)

Reduces unwanted hair growth in women (ornithine decarboxylase inhibitor)

Experimental

Ashburn and Thor (2004)

8.

Finasteride

Benign prostatic hyperplasia (5-α reductase inhibition)

Hair loss (5-α reductase inhibition)

Experimental

Ashburn and Thor (2004)

(Continued)

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14.6 Approaches to boost translational drug discovery

(Continued)

Drug name (trade name)

FDA approved previous implication (target)

FDA approved present implication (target)

Approach

References

Fluoxetine

Antidepressant (SSRI)

Premenstrual dysphoric disorder (SSRI)

Bioinformatic and experimental

Steinberg, Cardoso, Martinez, Rubinow, and Schmidt (2012)

10. Hydroxychloroquine Antiparasitic (hemozoin biocrystallization inhibition)

Anti-SLE, Covid-19 (TLR signaling inhibition)

Experimental

Colson, Rolain, Lagier, Brouqui, and Raoult (2020)

11. Lidocaine

Local anesthesia (sodium channel blockage)

Corticosteroid-dependent asthma (sodium channel blockage)

Experimental

Ashburn and Thor (2004)

12. Methotrexate

Cancer (DHFR inhibition)

Rheumatoid arthritis (inhibition of IL1β, purine metabolism enzymes)

Clinical and experimental

Malaviya (2016)

13. Milnacipran

Depression (SNR inhibitor)

Fibromyalgia (SNR inhibitor)

Bioinformatic, experimental

Ormseth, Eyler, Hammonds, and Boomershine (2010)

14. Minoxidil

Antihypertensive vasodialator (potassium channel opener)

Male-pattern hair loss (potassium channel opener)

Experimental

Simsek et al. (2018)

15. Naltrexone

Opioid addiction (opioid receptor antagonist)

Alcohol withdrawal (unknown)

Experimental

Li and Steven (2012)

16. Raloxifene

Osteoporosis (SER modulator)

Breast cancer (SER modulator)

Experimental

Jordan (2007)

17. Retinoic Acid

Acne vulgaris (unknown)

Acute promyelocytic leukemia (retinoic acid receptor binding)

Experimental

Su et al. (2015)

18. Ropinirole

Parkinson’s disease (dopamine receptor agonist)

Restless leg syndrome (dopamine receptor agonist)

Experimental

Ashburn and Thor (2004)

19. Sildenafil

Pulmonary hypertension (PDE5 inhibitor)

Erectile dysfunction, (PDE5 inhibitor)

Experimental

Ashburn and Thor (2004)

20. Tadalafil

Cardiovascular disease (PDE5 inhibitor)

Erectile dysfunction, benign prostatic hypertrophy (PDE5 inhibitor)

Bioinformatic, clinical, experimental

Doumas, Lazaridis, Katsiki, and Athyros (2015)

21. Thalidomide

Sedation, nausea, insomnia (TNF-α inhibition)

Leprosy, multiple myeloma (TNF-α inhibition)

Experimental

Mercurio et al. (2017)

22. Topiramate

Epilepsy (modulate GABA & glutamate receptors)

Migraine, weight management

Experimental

Ashburn and Thor (2004)

23. Wortmannin

Antifungal (uncharacterized)

Leukemia and breast cancer (PI3K inhibitor)

Experimental

May 2019 Approved; FDA.gov

24. Zoledronic Acid

Hypercalcemia (FPP inhibition)

Multiple myeloma, bone metastasis from solid tumors (FPP inhibition)

Clinical and experimental

Cle´zardin (2013)

9.

Drugs under clinical trial or unapproved by FDA for both the implications are excluded from the list. COX-2, Cyclooxygenase-2; DHFR, dihydrofolate reductase; FDA, Food and Drug Administration; FEP, familial adenomatous polyposis; FPP, farnesyl pyrophosphate; PDE5, phosphodiestaerase5; SER, selective estrogen receptor; SLE, systemic lupus erythematosus; SSRI, selective serotonin receptor inhibitor; TLR, toll-like receptor.

Section 7: Drug discovery and personalized medicine

TABLE 14.2

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14. Translational research in drug discovery: Tiny steps before the giant leap

“Retrometabolic Drug Design (RMDD)” is another approach to introduce orphan toxic drugs back to the business. There are huge numbers of drugs that are failed in clinical trials owing to their toxic metabolite production capability; as such, they are very effective for their known therapeutic implications. RMDD represents a systematic methodology combining structure
activity relationships and structure
metabolic relationships to develop a safe and effective drug. The new drugs designed through this rational approach possess a masked toxic effect and enhanced target specificity (Buchwald & Bodor, 2014). Bhardwaj, Pareek, Jain, and Kishore (2014) have extensively reviewed the successful examples of drugs that are redesigned through this approach. Majority of small biotech companies have been constituted based on drug repositioning and RMDD strategies, which are always looking for a suitable partner in the form of academic institutes working on translational research to boost their drug discovery pipeline.

14.7 Conclusion Before the concept of “Translational Research,” pharmaceutical industries were considered to be “product-driven” centers and academic institutions as “knowledge-driven” centers, with a partial or no connection between both of them. However, in recent years, academia, biotech, and pharmaceutical companies are cradling for translational research to meet up the market pace. Translational research has received enormous attention from researchers worldwide, which is evident from the number of published research articles on PubMed. Presently, there are over 100,000 articles with the keywords “Translational Medicine,”Translational Research,” and “Translational Science” (Wagner & Kroetz, 2016). Considering the need as well as the success rate of translational research, governments of many countries around the world have opened up new nonprofit centers whose primary focus is TDD. However, this integrated drug discovery nexus is still in inception; its success or failure will depend on how these institutions will run and execute their plans. Advent of high-end technologies like ML, AI, organ-onchip will give extra wings for its flourishment. Strategies such as DR, retro-metabolic drug design, preinvestigational drug meetings are becoming highly beneficial in exploring the potential of orphan drugs. Every new step into the chapter of TDD seems to be tiny, but we can expect a giant leap in the form of improved and potent therapies for the betterment of mankind in the coming decades.

14.8 Future perspective The rigorous efforts and scientific spirit of investigators around the world have generated a plethora of information that can be applied for human health benefit. Technological advancements like the completion of the Human Genome Project have provided hints on the link between genes and complex diseases like cancer, rare diseases, neurodegenerative diseases, etc. This has led to the development of personalized medicine, which we believe is the culmination of translational research. Owing to the unprecedented outbreak of new diseases and increasing resistance toward existing drugs, investigators are trying to use a

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translational research-based approach to identify drugs. This has led to a paradigm shift in the drug discovery process, where the emphasis has shifted from random exploration of drugs to a more targeted approach identifying a disease that needs urgent attention and then identifying its molecular physiology. The emergence of Covid-19 as a pandemic has ushered in the use of a TDD approach like never before. Researchers all over the world are trying to identify drugs and vaccines which can help curb the situation. What we are observing is a perfect example of how TDD can be used for immediate drug intervention. Using the available information on the genetic architecture of the virus, investigators are trying to identify drugs/ vaccines that will interject the virus during various stages of its infection. We are also witnessing the repositioning of drugs like the Remdesivir (Gilead Sciences), which was used for treating the Ebola virus and the Marburg virus. Presently, this drug is being considered as a promising drug against the Covid-19 disease. Other drugs that have been suggested are Hydroxychloroquine, Azithromycin, and Lopinavir, to name a few (Serapin et al., 2020). TDD is an amalgamation of fields like clinical practice, molecular biology, cellular biology, chemistry, and bioinformatics, which are being used for finding drugs that will serve the patient better. It has given rise to several collaborations between academia and pharma and biotech companies. In the future, it shall lead to more such collaborative relations, which will help in the generation of information that will be critical for effective patient management and better outcomes. TDD is a promising field that is patient-centric and focused on optimizing drug intervention. Besides an increase in collaborations between different sectors, it shall also lead to improved funding by public and private companies, the establishment of various government schemes. To achieve success, the most important prerequisite is to reform the existing academic system. With academic and clinical students receiving proper early on exposure to clinical research and designing of curriculum that gives a glimpse into interdisciplinary work will help them cultivate ideas and opportunities likewise. Apart from this, there is also a need for having a supportive and efficient institutional framework that works dedicatedly towards helping their investigators by providing them with state-of-the-art facilities and administrative help, which would, in turn, allow the researchers to focus on their research. Finally, career-related evaluations, promotions, and sufficient funding opportunities, which will take into consideration the challenges associated with the overall field of translational research, are needed for the growth and progress of the individual investigator, the field of translational research, and the entire society.

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15 FLAGSHIP: A novel drug discovery platform originating from the “dark matter of the genome” Neeraj Verma, Siddharth Manvati and Pawan Dhar O U T L I N E 15.1 Introduction

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15.2 Designing novel therapeutic peptides from dark matter of the genome 373 15.2.1 Antimicrobial peptides 373 15.2.2 Antimalarial peptides 374 15.2.3 Anti-Alzheimer peptides 374 15.2.4 Drawbacks of peptides therapeutics 375

15.2.5 Future applications 15.3 Pseudogenes: a potential biotherapeutic target 15.3.1 Pseudogene-directed gene regulation References

375 376 377 377

15.1 Introduction Although the biological contribution of noncoding DNA is largely unknown and considered as an insignificant part of the genome, it is accepted that a huge part of the eukaryotic genomic DNA used to be known as “junk” DNA. That so-called junk DNA ends up playing a significant job in controlling genes regulation and proteins action (Ohno, 1972). Most purported “junk” DNA probably will not be junk anymore, and these puzzling components of the genome may have some important biological functions, regardless of whether they do not undergo protein expression. School of Biotechnology, Jawaharlal Nehru University, New Delhi India

Translational Biotechnology DOI: https://doi.org/10.1016/B978-0-12-821972-0.00011-3

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© 2021 Elsevier Inc. All rights reserved.

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C H A P T E R

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15. FLAGSHIP: A novel drug discovery platform originating from the “dark matter of the genome”

The identification of the intronic region and their relation with the split genes in precursor-mRNA was a breakthrough discovery that won the Nobel prize in 1993 in the area of physiology and medicine that shows an important consequence to noncoding DNA (Berget, Moore, & Sharp, 2000; Chow, Gelinas, Broker, & Roberts, 2000). When cells are under stress or in malignant condition, some stretch of the noncoding DNA can be transcriptionally converted into noncoding RNA and further activate the “molecular alert” to protect the cell (Forsdyke, 2011). “Junk” DNA has been documented to transcribe long noncoding RNAs (lncRNAs) is a huge and diversified group of long RNA molecules typically with the length of greater than 200 residues that do not translate into proteins. lncRNAs are identified as a regulator of genes expression which involved in chromatin remodeling, lncRNA also acts as a posttranscriptional gene regulator, and as a precursor molecules for small interfering RNA “siRNAs” (Mercer & Mattick, 2013). The nucleotide repeats of noncoding DNA appear to be insignificant and look like evolutionarily conserved clamor. Although studies have demonstrated that the repeats of noncoding DNA like satellites region of the genome and interspersed DNA elements might have a job to protect and shield important DNA molecules and proteins from unwanted rearrangements (Giunta & Funabiki, 2017) and also have important functional regulation which shows its biological significance (Gong & Maquat, 2011). Researchers have recently examined the basic genetics of noncoding DNA to prevent cancer (Stojic et al., 2016). Now it is well understood that those cis-acting regulatory DNA, which controls the expression of a gene, lie in the noncoding region of DNA. The cis-acting regulatory elements are noncoding DNA that controls the transcription of a nearly located gene, whereas the trans-acting elements are also noncoding DNAs that regulate the transcription of a distantly located gene. Both cis-acting and transacting noncoding DNA sequences are essential components for the proper functioning of gene expression. Expanding proofs are clearly demonstrated that if these stretches of noncoding DNA go in a wrong way may cause diseases such as tumor formation, genetic abnormalities, diabetes mellitus, and neurological disorders. The increased comprehension of biological properties and molecular mechanisms of noncoding DNA will encourage us to design targeted therapeutics and diagnosis of diseases. Additionally, the cross-communication between epigenetic factors and noncoding DNA has been seen to play a significant function in genetic mechanisms. Noncoding DNA may play a crucial function to affect epigenetic activities (Cao, 2014). Furthermore, epigenetic changes can prompt genetic modifications, which may be caused by noncoding DNA (Skinner, 2015). So the question is why does the cell need this extra junk DNA? Why did nature not discard these pieces of junk during the course of evolution? Clearly, there is a much more significant role of noncoding DNAs that need to be identified. It has also been proposed that noncoding DNA has a role in the development of cancer. A major part of mutation occurs in the noncoding region of the human genome is associated with cancer (Zhang et al., 2018), yet it is not clear how these mutations in the noncoding region will lead to tumor growth and development. Thus with the current knowledge, it is convincing enough to prove the significance of noncoding DNA. However, one of the strategies to address this issue and moreover, to make junk useful, deliberate conversion of noncoding DNA either in vivo or cell-free systems is required to make nonnatural proteins. Scientists were able to successfully synthesize nonnatural proteins from the noncoding genomic region of E. coli (Dhar et al., 2009). Encouraged by this work, researchers explored the possibility of

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making pseudogenes-derived stable and functional proteins. In silico analysis of the sequence and structural evidence has been suggested that some pseudogenes may act as potential candidates for artificial protein synthesis (Shidhi et al., 2015). A comprehensive study would be needed in the quest to understand the existence of noncoding DNA and moreover making functional nonnatural proteins that might later have an application in therapeutics or diagnostics.

15.2 Designing novel therapeutic peptides from dark matter of the genome Biomedical research is incomprehensibly improving and making progress because of the utilization of synthetic peptides. The emergence of synthetic therapeutic peptides demonstrates an exceptionally splendid future in disease diagnostics and medication. From little chemical modification or fusion with different agents, these peptides are generally simple for synthesis with high bioavailability. They are likewise extremely functional and can be promptly characterized. When they are functionalized or synthetically modified, they can be used as powerful therapeutic molecules with respect to certain mutated cells or tissues. In the medicinal and pharmaceutical field, synthetic peptides have discovered their valuable places in biochemistry, immunology, microbiology, and so on. Naturally found peptides are not considered as a suitable drug because of their hemolytic activity. Therefore the investigation of new peptide design and understanding of their mode of action are a great area of interest.

15.2.1 Antimicrobial peptides Synthetic peptides have great capability to combat various pathogens. They are very promising agents toward antimicrobial drug discovery. Antimicrobial peptides (AMPs) show a significant role in our body’s defense system. A few goals must be achieved to create AMPs into therapeutics agents. Considering the majority of this, peptide scientific experts and so forth have to perform bioinformatics and wet-lab experimental strategies to concentrate on peptide conformational security and structural stability. An AMP must show high activity against targeted microbes or pathogens and also have low toxicity at a minimal dose (i.e., the therapeutic index should be high). This opens an approach to deliver novel AMPs molecules from the intergenic DNA of the Drosophila melanogaster genome. Since the synthesis of peptides from the intergenic DNA has just been demonstrated (Dhar et al., 2009), there is a decent scope for the proposed synthesis of novel AMPs from noncoding DNA. The fundamental discoveries from the investigation open the route for a broad examination in recommencing on the noncoding DNA of D. melanogaster genome. Scientists have applied the combination of different strategies for examining the interaction of an artificial novel AMP to the plasma membrane of microorganisms (Yu et al., 2009). In addition, it would be advantageous to reduce hydrophobicity to increase peptide solubility (Yu, 2005). This approach can also utilize AMPs for food safety, and they have additionally discovered their place as an advantageous application in the food processing and food preservation. AMPs can be used in materials that have been

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utilized for antimicrobial packaging (Appendini & Hotchkiss, 2002). This kind of bundling serves to continue sanitation and quality by lessening bacterial development on item surfaces (Soares et al., 2009).

15.2.2 Antimalarial peptides Malaria diseases are one of the most pervasive in tropical regions all through the world. In people, such a significant number of potential medications against plasmodium are assessed. Thus immunization with suitable parasitic epitopes has not yet been especially fruitful. Synthetic peptides have been broadly assessed as potent antimalarial agents (Bell, 2011). and the idea of preventing erythrocyte invasion using synthetic peptides
based drugs. The peptide drugs against the hot spot region of merozoite’s surface have been investigated in many research articles (Bianchin et al., 2015). Naturally found or the synthetic one, these peptides mostly act on the cell membrane to disrupt its integrity, and few are selectively at parasite membranes. At the same time, others are supposed to act on specific targets inside the cell. The actual molecular mechanisms of active antimalarial peptides are largely unknown or uncertain (Bell, 2011). In the quest to design the potential peptide drug, which shows the antimalarial activity or can act as an active inhibitory molecule against plasmodium needs to develop. The in silico approach is used to predict the potential proteins or peptides from the noncoding genomic region of D. melanogaster. The genome analysis of noncoding DNA was able to create potential novel peptides from intergenic sequences of D. melanogaster. Total of 145 intergenic regions
derived peptides were predicted as conformationally stable if expressed inside the cell in different conditions. The computation functional prediction and characterization of these novel synthetic peptides show interesting profiles and characteristics like Histidine-rich region, Cysteine-rich region, Leucine zipper pattern, and so on (Deepthi et al., 2016). These DNA binding peptides might show promising outcomes if investigated to check the role in transcription regulation toward novel drug discovery. The experimental study would be needed to validate the reliability of the proposed models.

15.2.3 Anti-Alzheimer peptides Peptides sporadically found as natural by-products in a disease condition or sometimes itself cause disease. Many times changes in the key protein conformation are also a root cause of neurological disorders. Alzheimer’s disease (AD) is linked with the amyloid-beta peptides. The secondary structure conformational changes from Helix to β-sheets promote the formation of fibrils that are associated with AD progression. Interestingly, a diseased mice model with amyloid-beta deposition showed reduced deposition when treated with β-sheet breaking peptide, iAb5p (Permanne et al., 2002). This iAb5p peptide successfully crosses the blood
brain barrier and damages amyloid fibrils via binding to the beta-amyloid protein and disrupts β-sheet formation (Permanne et al., 2002; Soto et al., 1998). The inhibition of β-sheet formation might not be enough to guarantee therapeutic outcomes.

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Moving forward to identify the most potent therapeutic peptide against AD, the intergenic sequences of E. coli genome that do not code for protein are screened. Leading to discovering the inhibitory peptides against AD, a database of synthetic peptides was created from 2500 E. coli-derived intergenic sequences. Secondary and tertiary protein structure predictions studies have been done. These E. coli intergenic sequence
derived synthetic peptides were screened against a key therapeutic target protein BACE1 (β-site APP Cleaving Enzyme 1) is the rate-limiting step in the production of amyloid-beta. The BACE1 enzyme is evaluated as a crucial therapeutic target molecule, and its inhibitory drugs might be the potential agents for AD treatment. The molecular docking analysis was carried out using synthetic peptides from our database as a ligand and BACE1 protein as the target receptor. A total of 233 peptides were identified to interact with the BACE1 target protein. Based on the least global energy and the interacting amino acid residues, finally, five peptides were proposed as the potential target drug molecules against AD progression (Raj, 2015). Experimental validations of peptide leads and appropriate biomolecular and physicochemical assays are needed to establish the therapeutic potency of these peptides. The discoveries of these novel anti-BACE 1 peptides are the paradigm shift in the current research of Alzheimer’s drug discovery platform.

15.2.4 Drawbacks of peptides therapeutics Despite the extensive research in peptide sedates, the quantity of affirmed peptides in the antiinfection and antiviral area has not risen at a similar rate as resistance has developed (Chen, 2012). Indeed, the ongoing quest for a new drug is mostly the chemical modification of existing drugs available in the market, and this methodology is not an indication of advancements for defeating the resistance. Patients consistency is one of the important factors in changing a drug experiencing clinical preliminaries into a popularized effective medication. Peptides’ low bioavailability, restricted by their debasement and low epithelial retention, is the main trouble in the remedial utilization of peptides (Hamman, 2005).

15.2.5 Future applications Although few peptides entered in the clinical trials until now, considerable endeavors are being coordinated toward the improvement of peptides with inventive structures and functions. Every one of these molecules offers chances to build the usefulness of peptides and grow the scope of druggable targets thought to be reasonable for medication improvement. The idea of focusing on cytotoxins or other agents to cells is stabilized and has effectively utilized antibodies as focused molecules (Reichert, 2011). As we recently noticed, the first lead peptide made an entry into the clinical examination in mid-2013. Aileron Therapeutics is moreover assessing stapled peptides as potential medicines for a malignant growth in preclinical investigations. Results for the stapled peptide ATSP7041, a dual inhibitor of MDM2 and MDMX, in xenograft disease models were introduced in the 2012 EORTC-NCI-AACR Symposium on atomic targets and malignant growth therapeutics (Chang, 2012). Even though advances in peptide discovery have been moderate in coming due to a general absence of understanding of actually how peptides enter cells, learning picked up from investigation of the cell-penetrating peptides (CPPs) have not been applied in the

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FIGURE 15.1

Representation of noncoding-derived peptides against therapeutic applications.

structure of typically CPP that have appropriate medicine-like properties. A better comprehension of the fundamental biology associated with the active and passive transport of molecular drugs over cell membranes and advanced research on the structure of peptides may make it possible to target intracellular procedures with these peptide drugs. Model proteins and synthetic peptides have also been utilized to analyze the structure
function relationship. The majority of this work has engaged scientists in present-day research of peptide applications, including biotech organizations who have found new peptides that hold significant biomedical properties. The standard synthesis of substantial polypeptides or little proteins of 30
100 amino acids has improved peptide applications (Fig. 15.1).

15.3 Pseudogenes: a potential biotherapeutic target Pseudogenes are previously considered not a very significant part of the genome. Usually, these are gene duplicates that have lost their protein-coding capability either due to mutations in the coding part of genes or loss in the regulatory regions. Some pseudogenes

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like ACYL3 have been eliminated from the human race but are still held as a pseudogene (Zhu et al., 2007). Similarly, different pseudogenes have been restored as lncRNA genes that embrace new functions. For example, XIST is the noncoding RNA that initiates the inactivation of the X chromosome (Duret, Chureau, Samain, Weissenbach, & Avner, 2006). In this section, we discuss human diseases and their relation with pseudogenes, additionally, the therapeutic potential of those pseudogenes which have acquired the advanced functions during the course of evolution.

15.3.1 Pseudogene-directed gene regulation Although many pseudogenes are evolved by a mutation in their promoter sequence or changes in the regulatory elements, which leads to the loss of transcriptional capabilities of a gene, on the other hand, some may hold their regulatory elements intact or be embedded near the active promoter sequence. This type of pseudogene-derived active transcript knows as lncRNA and also called as pseudo-mRNA or ѰmRNAs (Zheng, Frankish, & Baertsch, 2007; Frith, Wilming, & Forrest, 2006; Harrison, Zheng, Zhang, Carriero, & Gerstein, 2005). These lncRNAs transcripts worked via the formation of secondary RNA structure motifs, which may act as the proteins binding domain or another DNA/RNA. These pseudogenes-derived transcripts can regulate the expression of their parent gene if the transcript shows homology or complementarity with it. The overexpression of GMGA1 pseudogene mRNA is potential therapeutic molecule type 2 diabetes. GMGA1 pseudogene mRNA competes with cytoplasmic RNA stabilizing factor (ɑCP1) to contribute to insulin resistance (Chiefari, Iiritano, & Paonessa, 2010). Similarly, neuronal nitric oxide synthase (nNOS) gene expression is regulated by its pseudogene posttranscriptionally. The nNOS gene expression regulation is identified in the CNS of the snail Lymnaea stagnalis (Korneev, Park, & O’Shea, 1999). It would be a potential regulatory gene candidate in the nervous system.

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362. Bianchin, A., Bell, A., Chubb, A. J., Doolan, N., Leneghan, D., Stavropoulos, I., . . . Mooney, C. (2015). Design and evaluation of antimalarial peptides derived from prediction of short linear motifs in proteins related to erythrocyte invasion. PLoS One. Cao, J. (2014). The functional role of long non-coding RNAs and epigenetics. Biological Procedures Online, 16, 11. Chang, Y. (2012). ATSP-7041, a dual MDM2 and MDMX targeting stapled a-helical peptide exhibits potent in vitro and in vivo efficacy in xenograft models of human cancer. 24th EORTC-NCI-AACR Symposium on Molecular Targets and Cancer Therapeutics Abstr. 226. Chen, Y., Cao, L., Zhong, M., Zhang, Y., Han, C., . . . Yan, H. (2012). Anti-HIV-1 Activity of a New Scorpion Venom Peptide Derivative Kn2-7. PLoS One. 7(4), e34947. Available from https://doi.org/10.1371/journal. pone.0034947.

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1655. Frith, M. C., Wilming, L. G., Forrest, A., Kawaji, Hideya, Tan, S. L., Wahlestedt, C., . . . Huminiecki, L. (2006). Pseudo-messenger RNA: Phantoms of the transcriptome. PLoS Genetics, 2(4), e23. Forsdyke, D. R. (2011). Evolutionary bioinformatics (2nd ed., p. 1509)New York: Springer. Giunta, S., & Funabiki, H. (2017). Integrity of the human centromere DNA repeats is protected by CENP-A, CENP-C, and CENP-T. Proceedings of the National Academy of Sciences of the United States of America, 114(8), 1928
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39. Soto, C., Sigurdsson, E. M., Morelli, L., Kumar, R. A., Castan˜o, E. M., & Frangione, B. (1998). β-sheet breaker peptides inhibit fibrillogenesis in a rat brain model of amyloidosis: Implications for Alzheimer’s therapy. Nature Medicine, 4, 822
826. Stojic, L., Niemczyk, M., Orjalo, A., Ito, Y., Ruijter, A. E., Uribe-Lewis, S., & Murrell, A. (2016). Transcriptional silencing of long non-coding RNA GNG12-AS1 uncouples its transcriptional and product-related functions. Nature Communications, 7, 10406. Yu, L, Ding, J. L., Ho, B., & Wohland, T. (2005). Investigation of a novel artificial antimicrobial peptide by fluorescence correlation spectroscopy: An amphipathic cationic pattern is sufficient for selective binding to bacterial type membranes and antimicrobial activity. Biochimica et Biophysica Acta - Biomembranes, 1716(1), 29
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344. Zhang, W., Bojorquez-Gomez, A., Velez, D. O., Xu, G., Sanchez, K. S., Shen, J. P., . . . Ideker, T. (2018). A global transcriptional network connecting non-coding mutations to changes in tumor gene expression. Nature Genetics, 50(4), 613
620. Zheng, D., Frankish, A., Baertsch, R., Kapranov, P., Reymond, A., Choo, S. W., . . . Gerstein, M. B. (2007). Pseudogenes in the ENCODE regions: Consensus annotation, analysis of transcription, and evolution. Genome Research, 17(6), 839
851. Zhu, J., Sanborn, J. Z., Diekhans, M., Lowe, C. B., Pringle, T. H., & Haussler, D. (2007). Comparative genomics search for losses of long-established genes on the human lineage. PLoS Computational Biology, 3(12), e247.

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16 Role of shared research facilities/core facilities in translational research Vidhu Sharma O U T L I N E 16.1 Introduction: socioeconomic impact of translational research 384 16.1.1 Challenges faced in translational research 385 16.2 Core facility: shared research
shared cost 386 16.2.1 Core facilities of prime significance in translational research 388 16.3 Research and development supporting mechanism: environmental scan (the United States and Canada) 389 16.3.1 Supporting translational research through core facilities in the United States—from past to present 390 16.3.2 Canada’s ecosystem of translational research and funding mechanism 392 16.3.3 Highlights around the world 394

16.3.4 Glimpses of global research and development expenditure 396 16.4 Efficiencies and lean practices in research management 399 16.4.1 Core facilities business model 399 16.4.2 Governance model for core facility 402 16.4.3 Core facilities and research outcome 402 16.5 Final notes: learnings for future 403 16.5.1 Integration of core facilities within the institutional strategic plan 403 16.5.2 Comprehensive availability of infrastructure inventory 403 16.5.3 Impact measurement 404 Acknowledgments

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BCCHR Core Technologies and Services, BC Children’s Hospital Research Institute, Vancouver, BC, Canada

Translational Biotechnology DOI: https://doi.org/10.1016/B978-0-12-821972-0.00008-3

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C H A P T E R

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16.1 Introduction: socioeconomic impact of translational research Science, technology, and innovation are important elements in national strategic priorities playing a crucial role in a country’s overall growth. In an earlier study by Bernardes and Albuquerque (de Souza, Souza, Marinho, Brum, & Morel, 2012), they classified countries into three groups based on the numbers of papers and patents as markers of scientific and technological advancement Group I, being the least developed countries where the scientific and technological advancements are immature; while Group II, the developing countries; and Group III, the industrialized countries where scientific output has significantly contributed to their development and growth potential of new health innovations (de Souza et al., 2012). The funding and engagement from the government are critical for achieving the goals set out for agriculture, education, infectious disease response, infrastructure development, and national defense. Usually, the investments that bring in immediate returns are on the higher forefront, taking the priority. Research by the nature of it does not bring such immediate returns and thus falls on much lower priority with regards to the funding allocation. The bigger corporates are looking for opportunities to invest in the utilization of created knowledge without having to invest in the initial resources to produce it. Research is also a slow and gradual process of knowledge creation with uncertain prospects for success, requires long-term funding commitments for personnel and infrastructure. With the recent global pandemic around coronavirus disease (COVID-19), there had been a directed effort to invest in biomedical research for various fast track programs toward the development of suitable vaccines and other therapeutic reagents, and diagnostics. There has been a realization to focus on investment in research and development (R&D) activities during this pressing time. R&D efforts are crucial for the long-term benefits of society by creating new knowledge about less common diseases as well as to find solutions for predominant diseases. Federal funding for research is absolutely essential and is of huge societal benefit because virtually all the discoveries that advance clinical medicine today are made in the basic research laboratory. Robert J. Lefkowitz, Recipient of the 2012 Nobel Peace Prize in Chemistry, Investigator, Howard Hughes Medical Institute, Professor of Medicine and Professor of Biochemistry and Chemistry, Duke University, Durham, NC (Websource, n.d.) (https://www.researchamerica.org/advocacy-action/research/reasons-research)

There is greater than ever need to connect the basic researchers with clinical researchers to bring the best outcome in the most efficient way. Translational research involves the integrated application of innovative technologies across multiple disciplines, including physiology, pathology, genetics, and advances in drug research (Homer-Vanniasinkam & Tsui, 2012; Zerhouni, 2005). Over the last few decades the revolutionary advances in biomedical sciences in gene therapy, regenerative medicine using stem cell research, the -omics, etc. are bringing new forefronts in research and discoveries. Translational research bridges the gap between these advances to the impact they can bring on patient care in an expedited way as well as improving the quality of life for the larger community. The research output is conventionally measured by quantitative measures of scientific productivity, such as publications, patents, technology development, funding success, and

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citations. These metrics are usually tied to the success of an investigator and for continuing to receive funding for future research. These metrics, unfortunately, overlook broader benefits to the well-being of society, for example, improved diagnostic methods developed, new cures, lives saved, improvements to health, and impact on community health quality. These beneficial impacts are more evident with translational research as its emphasis is to bring research to the forefront of health care practice and solve real-world problems. This measure is essential to give a snapshot of the outcome of scientific research for better understanding and making more informed decisions for federal budget and investment in scientific enterprises. Translational research is a continuum of research to develop novel treatments, cures, and diagnoses from the knowledge created by basic science discoveries (Fig. 16.1). The primary focus of translational research is to integrate scientific discoveries into clinical applications. In addition, clinical outcomes are used to build proposals for knowledge gaps in basic science to explore further.

16.1.1 Challenges faced in translational research The goal of translational research in health science is to accelerate discoveries for societal benefit. A scientific discovery takes almost two decades to be translated into clinical practice (Brownson, Kreuter, Arrington, & True, 2006; Morris, Wooding, & Grant, 2011). This huge gap to bring in new diagnostic tests, therapies, and technologies to actual use for the benefit of public health results from a variety of roadblocks FIGURE 16.1 Translational research continuum: the research outcome and actions mediate the progression of knowledge from the bench (discovery/knowledge) to the bedside (clinical outcome).

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between research and its implementation. The goal of translational research is to bridge the gap of scientific knowledge to community benefit. Among the various challenges, limited funding has been identified as one of the major barriers to accelerate translational research in addition to lack of interdisciplinary collaborations, inadequate training, regulatory guidelines, incentives to do translational research, need of experts from various disciplines, etc. (Brownson et al., 2006; Homer-Vanniasinkam & Tsui, 2012; Wolf, 1974). The return of investment in scientific research takes time. Therefore, even though the importance and benefits of research are clear to most, making long-term investment decisions into something with unpredicted return makes it lesser attractive. It is important to invest strategically in translational research and resolve the potential roadblocks leading to huge benefits to society. With ever-evolving new technological platforms the cost of infrastructure is only getting higher, making the possibility of bringing breakthroughs even more far-reaching for an early stage investigator. These new technologies are not only expensive to acquire but they also require high-level expertise as well as huge maintenance cost. Here, we discuss common approaches used by various academic institutes to organize their research infrastructure to facilitate translational research. The three prototypes in increasing order of organization and investment are multiuser equipment, core facilities, and technological platforms that are the building blocks of more complex and higher level arrangements such as technological consortium and innovation networks (de Souza et al., 2012). In the next section, we will explore the most common format, that is, core facility and its role in advancing research.

16.2 Core facility: shared research
shared cost Sharing infrastructure via core facility or shared facilities makes research more affordable and has been popular for certain technologies like gene/protein sequencing for the last many years. Based on a very extensive analysis early in 2012 (de Souza et al., 2012), they studied the top 200 universities to understand the organization of their translational research facilities. It was found that more than 80% identified their research facilities as core facilities. Through their analysis, they further proposed three basic evolutionary units of infrastructure arrangements—the simplest being a multiuser expensive equipment shared between two and three labs. The core facility is the next level that serves a larger scientific community like an institution, university, and even industrial clients and operates on a fee-for-service model. This is more organized and usually set up as a result of coordinated funding from federal, institutional, and sometimes provincial sources. There are designated staff providing expert consultation, imparting training to new users, and access to advanced technologies in core facilities. Usually, a portion of the operational cost of the core facilities is recovered through user fees for services. The next level is a technological platform or network/consortium, which is tasked to serve a much larger and even sometimes nation-wide client base. Looking more closely, we can appreciate that with the increasing level of complexity from multiuser equipment to big technological platforms, there is more investment from higher level organizations and even the government to strategically invest and accelerate

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translational scientific research. This shared usage is not just beneficial for sharing the research cost, but it also has various other obvious advantages like access to expertise, collaborations, etc., catalyzing discoveries and brings benefit to the community faster. To explain this with an example, let us imagine a basic researcher conducting research about the identification of the potential targets for drug development. To take scientific discovery to the next level, productive collaborations with molecular modeling, chemists, animal facility for toxicology studies, immunologist, etc. would be really beneficial (Fig. 16.2). The core facilities can help connect all such dots and reduce the transition time for each step significantly with various experts available for consultation and also various resources and equipment available to accelerate the outcome. The added advantage is that all this happens in a cost-effective and time-sensitive way as the researcher is essentially just paying a nominal fee for the usage and service instead of setting up each of such facility or getting trained for each such expertise. Core facilities are crucial in facilitating research output for both basic and translational research by providing open access and expertise to advanced scientific equipment. Core facilities are vital to catalyze modern research (Murray, 2009). They operate as a business unit for service models offering access to specialized technological services or equipment to a wide range of researchers. The range of expertise is diverse ranging from DNA or protein sequencing facilities, nuclear magnetic resonance spectroscopy, magnetic resonance imaging facility, mass spectroscopy, X-ray crystallography, etc. These specialized research facilities not just need very expensive infrastructure but also an advanced level of expertise. Over the last many years, universities, research institutes, hospitals, etc. are adopting the core facility model to strategically invest in infrastructure and encouraging shared usage of equipment in addition to leveraging the expertise to accelerate the research

FIGURE 16.2 The accessibility of multiple expertise and resources via shared facilities can help with the rapid progression of translational research.

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16.2 Core facility: shared research
shared cost

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outcome. The advantages of core facilities are not just limited to the academic sector, but it also helps private biotech or translational research industries to take advantage of affordable cost and expertise. This also opens up new opportunities for collaborations and partnerships between the private and public sectors that can further expedite the translational research outcome. Biology remains the same, but scientific progress reaches new milestones with technological advances. Whole-genome sequencing, personalized medicine, regenerative stem cell therapies are the result of new technological infrastructure and collaborative efforts of interdisciplinary experts from various fields of biology, physics, computer scientists, etc. These new technological advances are taking place very rapidly, and it is impossible for one researcher to master them all. In addition, these state-of-the-art infrastructures are fairly expensive to be affordable by a single lab. Therefore shared research facilities or core facilities fit in perfectly to meet the needs of the current research ecosystem to provide platform and expertise in an affordable manner (Meder et al., 2016). These facilities are also a great place for new career investigators to leverage the preexisting expertise and infrastructure and rapidly take part in new discoveries transforming the health outcome. That is the reason well-running core facilities are seen as an important strategic investment by the institutes and universities to attract talent and an incentive for early-career faculty to deliver research at a rapid pace.

16.2.1 Core facilities of prime significance in translational research Translational research can especially benefit from various technological advances working in a coordinated and collaborative way. The cancer research community often comes across the challenge of translating laboratory findings and clinical research data into actual clinical outcomes to benefit the diagnosis, treatment, and prevention of cancer (McCabe, 1997). The McGill Centre Translational Research in Cancer (MCTRC) offers expertise covering a large spectrum, from biochemistry and drug design to immunotherapies and translational proteomics and artificial intelligence, and computer science applied to diagnostic methods (https://www.mcgill.ca/translational-research-cancer/core-facilities). This collaborative and open-access research environment makes MCTRC uniquely positioned to rapidly translate scientific discoveries to patients by leveraging expertise and creating new partnerships with critical stakeholders in the government, biotech, and pharma sector. The Human Genome Project is considered one of the landmark discoveries initiating a whole new era of genomics, giving the world a wealth of detailed information about the structure, organization, and function of the complete set of human genes (Watson & CookDeegan, 1991). This led to further understanding of human biology and the advancement of various molecular techniques like proteomics, metabolomics, and microbiomics to understand and synthesize knowledge holistically. This multipronged approach leads to the development of potential novel diagnostic or drug discovery targets. While research in proteomics defines the structure and function of proteins encoded by the genome of an organism, metabolomics uses the systematic analysis of the chemical fingerprints left behind by cellular processes (Daviss, 2005; Oliver, Winson, Kell, & Baganz, 1998; Wilkins, Williams, Appel, & Hochstrasser, 1997). Microbiomics, on the other hand, is the

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characterization of the human microbiota to learn its impacts on human health and disease (Rajendhran & Gunasekaran, 2010). Further, metagenomics, a relatively newer field in this realm, involves the characterization of the microbiome’s genomes, as well as their corresponding mRNA, protein, and metabolites [Gill et al., 2006; National Research Council (US) Committee on Metagenomics, 2007]. Together, these “omics” comprise an extensive toolkit in systems biology at the forefront of translational research. These highly advanced technological platforms that can bring a significant impact in propelling translational research are the result of highly specialized interdisciplinary fields and require highly complex, advanced, and unique infrastructure. The accessibility and affordability of these technologies within a research lab serving the interest of an individual researcher would be a huge waste of investment. The post
genomic era saw rapid adoption of core facilities by many academic institutions primarily because of the huge cost associated with these technologies. In addition, it also needed a trained individual to run samples, and to continue to be updated on new sequencing technologies (Meder et al., 2016). These core facilities are open access to internal and external users and operate on a fee-for-service basis and essentially make research more affordable. The creation of these technology hubs that are shared across an institute/university, region, or even nationwide removes the roadblocks in a translational research outcome by reducing the overall cost of establishing these infrastructures and increased accessibility. With a shared facility/core facility model, the research propels faster, leading to the final goal of providing better health solutions or discoveries. In an extensive scan of technologies and resources catalyzing translational and clinical research, 52 generic areas were identified (Rosenblum, 2012). This comprised various traditionally relevant biomedical research cores like microscopy, flow cytometry, histopathology, high throughput screening, vaccine core, synthetic chemistry, biobank repositories, etc., while nanotechnology, in vitro fertilization, next-generation sequencing, single-cell sequencing, gene therapy, etc. are rapidly evolving platforms gaining significance in translational research (Rosenblum, 2012). A strategic and collaborative plan to connect the resources and expertise would bring immense returns to translational research advances and faster delivery of the desired outcome to public health.

16.3 Research and development supporting mechanism: environmental scan (the United States and Canada) There are about 700 core labs and facilities across the United States (https://www.genohm.com/2018/03/13/core-lab-means-business/). Access to core facilities is deemed essential to conduct clinical and translational research, especially in the biomedical field, for more than two decades in many developed countries, especially in North America. These facilities not only provide state-of-the-art equipment critical for research, highly qualified personnel operating the instruments but also significantly help in data interpretation and consultation. The research environment in developing countries would have obvious advantages by adopting a core facility model to support critical research infrastructure and to drive innovation in a cost-effective manner.

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16.3.1 Supporting translational research through core facilities in the United States—from past to present The recent evolution in advanced technologies like next-generation sequencing, computer modeling, high throughput screening techniques has resulted in creating innovative and rapid solutions to develop more advanced diagnostics and therapeutics (Fontana, Alexander, & Salvatore, 2012). Core facilities provide an effective and efficient way to rapidly translate knowledge generated through fundamental research into clinically applicable solutions by integrating interdisciplinary expertise, open access of data and information, and removal of financial and intellectual roadblocks. The National Institutes of Health (NIH), a part of the US Department of Health and Human Services, is the nation’s medical research agency making important scientific discoveries to improve health and save lives of millions of people worldwide (https://www. nih.gov/). NIH invests approximately $41.7 billion annually to support medical research for Americans (https://www.nih.gov/about-nih/what-we-do/budget#note). It is the major funding agency supporting health-related and biomedical research, including translational research. In an earlier editorial piece published in Nature Medicine, it was acknowledged that several NIH institutes describe translational research as “the process of applying ideas, insights, and discoveries generated through basic scientific inquiry to the treatment or prevention of human disease” (Editorial, 2004). There have been various iterations of this definition in the last many years. Translational research means different things to different people, but it seems important to almost everyone. Steven Woolf (Committee to Review the Clinical and Translational Science Awards Program at the National Center for Advancing Translational Sciences et al., 2013; Woolf, 2008)

The establishment of NIH’s Clinical and Translational Science Awards Program (CTSA) in 2006 brought a refreshed approach to accelerate basic research to the next stages of preclinical and clinical research and highlighted the importance of translational research (Fontana et al., 2012). This award represents one of the largest funding programs of the National Center of Research Resources (NCRR), supporting over 60 medical research institutions in 30 states across the United States (https://ncats.nih.gov/ctsa_2011/preface.html Progress report 2009
12). The purpose of this award was to expedite the translation of research from the lab to the community by providing resources and infrastructure. In addition, it also supported access to regulatory knowledge, epidemiological data, ethics, biostatistics, and community engagement (Rosenblum, 2012). The common vision of the CTSA Program was to provide each participating academic health center, a functional hub, to support clinical and translational scientists with all the resources needed to bring the translational research to the forefront of patient care. During this announcement of CTSA application, the NIH offered the following definition for translational research: Translational research includes two areas of translation. One is the process of applying discoveries generated during research in the laboratory, and in preclinical studies, to the development of trials and studies in humans. The second area of translation concerns research aimed at enhancing the adoption of best practices in the community. Cost-effectiveness of prevention and treatment strategies is also an important part of translational science. Rubio et al. (2010)

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Core facilities fit well to provide such a cost-effective approach to translational research. NIH product development scheme identified core facilities as a key player in accelerating research from target identification for a disease to various stages of clinical trials (Rosenblum, 2012). NIH defines core facility as “centralized shared research resources that provide access to instruments, technologies, services, as well as expert consultation and other services to scientific and clinical investigators. The typical core facility is a discrete unit within an institution and may have dedicated personnel, equipment, and space for operations, recovering their cost, or a portion of their cost, of providing service in the form of user fees that are charged to an investigator’s funds, often to NIH or other federal grants” [National Institutes of Health (NIH), n.d.a]. The centralized research support services or core facilities improve efficiency and accelerate research for biomedical and other investigators who otherwise may not have access to such resources (Rosenblum, 2012). The CTSA Program created a central website to provide information about the shared resources across the United States. This facilitated rapid engagement and fostering of collaboration between investigators involved in basic, clinical, and translational research. In March 2011 NIH launched a new program “National Center for Advancing Translational Sciences” (NCATS), which assumed the role of CTSA and dissolved the NCRR redistributing its other programs throughout other institutes and centers (IC) (Fontana et al., 2012). The aim of this new program was to further remove the bottlenecks and expedite translational research by exploring innovative methods and to bring a paradigm shift in conventional ways where basic science discoveries took more than 10 years to be translated into clinical discoveries. NCATS’s mission was defined to catalyze the generation of innovative methods and technologies enhancing the development; testing; and implementation of diagnostics, therapeutics, and devices across a wide range of human diseases and conditions (Collins, 2011; Fontana et al., 2012; Committee to Review the Clinical and Translational Science Awards Program at the National Center for Advancing Translational Sciences et al., 2013). There are various different federal funding opportunities to support research across the United States. Under NIH only, there are 27 IC, each hosting multiple types of funding competitions every year within a specific area of research or disease (https://grants.nih. gov/grants/oer.htm). This translates into enormous funding support for research activities. The NIH funding types are identified primarily as independent investigator-driven (parent announcement) or team applications for specific research programs [Program application (PA) and Request for Application (RFA)] (NIH, n.d.b). PA is usually the most common type of funding announcement relevant for multiuser research facilities through its S10 instrumentation programs {NIH
DPCPSI [Office of Research Infrastructure Programs (ORIP)], n.d.}. The specific kind of funding opportunity falls within various activity codes, reflecting the type of grant awards. Core facilities are usually assigned under the P30 activity code (https://grants.nih.gov/grants/funding/funding_program. htm), and the S10 instrumentation program facilitates the shared usage of high-end instrumentation needed for cutting-edge research. In addition to NIH, the National Science Foundation (NSF), an independent funding agency of the US government, supports scientific research activities through various funding mechanisms. For 2020 alone, NSF has $8.3 billion federal funding available to supporting fundamental scientific discoveries. NSF funds basic biomedical research and

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other nonmedical research disciplines, including mathematics, computer sciences, astronomy, and geosciences in contrast to NIH that funds primarily health sciences research. There are various funding programs run by NSF that can be utilized strategically to support nonhealth-related translational research, including agricultural biotechnology, etc. The Major Research Instrumentation Program (MRI) of NSF provides funding opportunities to research institutes to acquire and support state-of-the-art instrumentation and technologies to further their research capability (https://www.nsf.gov/od/oia/programs/ mri/). Researchers can apply for this funding for multiuser facilities as well as acquiring infrastructure that drives innovative basic and translational research. NIH and NSF together account for approx. 29% of the US federal budget for R&D activities (Congressional Research Service, n.d.). This accounts for about 10% of the total R&D budget for the United States.

16.3.2 Canada’s ecosystem of translational research and funding mechanism Canada has a strong tradition of research and discovery and announced $11.7 billion federal budget to support next-generation researchers and build research excellence (Statistics Canada, n.d.). There are various funding mechanisms at both provincial and federal levels, to support basic and applied scientific research, mainly through the following three agencies, also known as tri-agency with approx. $1.5 billion federal funding in the present budget year: • Canadian Institutes of Health Research (CIHR)—for research-related biomedical, clinical, population health, and health system services; • Natural Sciences and Engineering Research Council (NSERC)—for basic fundamental research usually nonhealth-related research streams (e.g., life sciences research that is nonhealth related, zoology, and geology); and • Social Sciences and Humanities Research Council (SSHRC)—supporting humanities and social science research. Among these funding agencies, CIHR is similar to NIH and promotes basic, translational, and clinical research in biomedicine. NSERC, in addition to funding basic and fundamental science, also runs funding competitions for supporting small-scale research infrastructure that may be required to carry out the research. The largest funding source for Canadian institutes to acquire state-of-the-art infrastructure and build networks and partnerships useful for accelerating innovation for both basic and translational research is through Canada’s Foundation of Innovation (CFI). CFI is the major federal organization with an annual disbursement of about $380 million in funding support to the Canadian research ecosystem (Statistics Canada, n.d.). It serves an important goal of attracting talent to Canadian universities by not just providing the start-up funds for new investigators but also by providing foundational research infrastructure required to become leaders and experts in their field. It provides funding for the research activities and also for the development of research infrastructure and its maintenance through infrastructure operational funds.

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CFI is also actively engaging researchers and bringing awareness about the available state-of-the-art infrastructure, facilities, and services across Canada through their online open-access platform, research navigator (https://navigator.innovation.ca/) that was started in 2013. This navigator is an online directory of research facilities all across Canada from various universities, institutes, hospitals, etc. helping researchers to connect, collaborate, and access the technologies required to expedite their research. Navigator features more than 675 research facilities across Canada covering 28 sectors of application, including aerospace, ocean industries, environmental technologies, advanced manufacturing, and life sciences. Another major initiative launched by CFI in 2011 was Major Science Initiatives (MSI) fund. The focus of this fund is to enable large-scale, multiuser national science facilities to operate at an optimal level and fully exploit their scientific and technical capabilities (https://www.innovation.ca/awards/major-science-initiatives-fund). Through this fund, the CFI supported 12 facilities until 2017, with a total of $211 million, representing a total of 35% of their operational budget. For such large-scale national facilities (NF) the CFI is also involved in providing guidance on oversight for governance and management policies and practices to ensure responsible stewardship of public investments and optimal performance in addition to financial contributions to stabilize operations. “The Centre for Phenogenomics” in Toronto, Ontario, funded via MSI in partnership with province and university is one such example of a national research facility for translational research with various technological expertise and collaboration under one roof. The various services offered by this facility, like genetically engineered animal models, clinical phenotyping, pathology, drug discovery, and evaluation, have been helping researchers all over Canada and other countries with advances in translational research (http://phenogenomics.ca/index2.html?v 5 8). There are various other funding bodies supporting research partnerships with industries, promoting science and technology collaborations and networks. Genome Canada and its regional organization is a not-for-profit organization, funded by the Government of Canada. It catalyzes the development and application of genomics-based technologies to benefit Canadians. They partner with various provincial funding bodies, industries, hospitals, foundations to invest in translational research leveraging the genomics platform (https://www.genomecanada.ca/en/about/technology-platforms). Since its inception in 2000, Genome Canada has catalyzed 10 genomics technology platforms providing researchers across Canada and internationally with access to cutting-edge state-of-the-art technologies used in genomics, proteomics, bioinformatics, metabolomics, etc. (https:// www.genomecanada.ca/en/about/technology-platforms). This, in turn, has been helping translate research breakthroughs across diverse sectors including health, environment, agriculture, fisheries, energy, and mining. Genome Canada harnesses the power of genomics and the vast information it can code to seize opportunities for growth of Canada’s bioeconomy. Furthermore, provincial government all across Canada also contributes to various funding programs by investing in research infrastructure and expanding the capabilities to transform the research to next level and bringing it closer to the benefit of public health, for example, B.C. Knowledge development fund, Alberta Innovates, and Ontario research fund. Together, in partnership with national, not-for-profit organization,

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industry partners, there are various mechanisms supporting innovation and translational research in Canada.

16.3.3 Highlights around the world 16.3.3.1 Funding mechanism for research and innovation Access to the state-of-the-art research infrastructure is considered as one of the crucial but rate-limiting steps globally. Thus similar to the United States and Canada, many economically developed countries invest strategically to build their research capacity and accelerate the impact. The mechanism by which a nation supports scientific R&D is varied and strikingly different between developed, developing, and economically underdeveloped nations. There seems to be a general consensus that national growth and development correlates with its scientific capacity. The countries that are consistently investing in science, technology, and innovation are taking leaps to improve community health, better response to health crisis, and overall leading toward a better quality of life. For example, China and the United States being the top two countries contributing to global R&D expenses are building the overall research capacity faster than many other countries. China, still being a developing country, is progressing much faster in terms of scientific advancement due to its stronger alignment with STI (science, technology, and innovation) initiatives. In a recent review on an environmental scan of research productivity in Nigeria, the authors described a lack of adequate funding, collaboration, and support to research activities that can translate to socioeconomic benefits of citizens (Odeyemi, 2019). In November 2011 the Science, Technology, and Innovation Policy (STIP) review of Ghana (https:// unctad.org/en/docs/dtlstict20098_en.pdf) also discussed the opportunities to develop strategic plan to enhance investment in science and technology to further the nation’s socioeconomic health. The nations with low-income economies can significantly benefit from more strategic administrative and infrastructure management plan, for example, implementation of shared research facilities. This will help them to get a higher return of investment at the same time it will help in catalyzing scientific discoveries, encouraging innovations, and fostering collaborations. The concept of core facility/shared infrastructure facilities is not very common in developing countries, for example, just a simple search of core facilities in the United States or Canada returns an endless number of results highlighting such facilities while they are very uncommon in many of these developing countries. South Africa adopted a series of policies to build its scientific capacity and drive innovation, as documented in a White paper released in 1996 (https://www.gov.za/sites/ default/files/gcis_document/201409/sciencetechnologywhitepaper.pdf). One of the striking observations in this report was the special attention given to the establishment of research labs at the national level rather than the institutional level due to higher cost. These NF were designed to cater to the need of big and innovative science to researchers all over the country by providing open access. The growth of South Africa in its scientific innovation over the last two decades may be attributed to a certain extent to such a wellthought strategy and scientific policy. The shared scientific infrastructure management at

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the national level, if executed well, can lead to phenomenal savings in asset acquisition and maintenance. This also reduces the redundancy and duplication of expensive infrastructure and maximizes the utilization over its useful shelf life. National Equipment Program of the South African National Research Foundation (NRF) is one such example. It hosts a database of equipment purchased through this program accessible at http://eqdb. nrf.ac.za/. Similarly, Australian Research Council provides funding support through its linkage program. Linkage infrastructure, equipment, and facilities scheme is specially designed to support the acquisition of high-end state-of-the-art research infrastructure to help basic, translational, and clinical researchers get the support they need (https://www.arc.gov.au/ grants/linkage-program/linkage-infrastructure-equipment-and-facilities). India has a very strong history of supporting scientific technologies, innovation, and discoveries. Department of Science and Technology, Government of India, launched a special program Fund for Improvement of Science and Technology Infrastructure (FIST) in 2001 to support state-of-the-art facilities and instrumentation and make scientific advances. Since its inception, FIST has funded approx. $373 million to fund B2817 science and technology departments in India (https://dst.gov.in/sites/default/files/DST%20Annual%20Report%20201819_English_F.pdf) specifically to improve scientific infrastructure. In recent years, India has developed its scientific leadership tremendously in the world and continues to adopt more efficient methods to continuously build and enhance its research capacity. Limited use of expensive research infrastructure leads to inefficient use of resources. It has been long realized that just acquiring high-quality expensive equipment is not sufficient, but new and innovative approaches need to be adapted to make the best use of them and making them more accessible to a larger scientific community. According to a study by the National Science and Technology Management Information System, DST India, it was found that a large number of equipment are not shared and/or are underutilized (http://www.nstmis-dst.org/Index.aspx). Sharing of scientific equipment has been proposed as an optimal solution to this problem that would build a culture of collaboration/sharing between researchers and institutions, resulting in better maintenance of the equipment. It would also help eliminate duplication and redundancy while the purchase of expensive infrastructure while maximizing the use of available resources. This is imperative in the context of developing countries where investment in science and technology is a very low priority. With this objective, the Indian Government has adopted Scientific Research Infrastructure Management and Networks (SRIMAN) Policy to provide a framework for universal access to scientific research infrastructure to a wide range of researchers, avoiding duplication, thus putting the best use of taxpayer’s money https://dst.gov.in/sites/default/files/latest-02-July-2018-SRIMAN-PolicyDocument.pdf. 16.3.3.2 Awareness of networking and engagement One of the important things to consider with all the resources available is the awareness and knowledge of the researchers/scientific community about their availability. This brings in the communication piece that is critical for the actual utilization of funding and infrastructure to advance translational and basic research. Most of the universities and funding organizations have been spending efforts to create awareness about the easily

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accessible inventories of equipment and databases of available resources. This has a twofold advantage for the researcher-one being the awareness and other being the collaboration and connection with expertise. In our earlier discussion about bottlenecks in the path of translational research, access to resources to essential technologies and necessary expertise are two of the common hurdles. Association of Biomolecular Research Facilities (ABRF) is one of the earliest professional organizations established in 1989 in the United States with more than 700 members representing more than 340 labs and members from various sectors like administration, government, academia, and industry (https://abrf. org/). They host a range of professional development activities, networking opportunities, and trainings/workshops to keep researchers up to date on technological advances and resources available to get access to such expertise. Their free searchable core marketplace database stores information about shared scientific resources all over the country and even some of the international facilities. Science Exchange is another very popular web-based marketplace platform connecting researchers and service providers and, at the same time, avenue to promote scientific collaboration to improve the quality and efficiency of research (https://www.scienceexchange.com/). While ABRF and Science exchange have marketplace, there are various funding organizations creating tools to raise awareness and facilitate access of their funded infrastructures to a bigger research community. For example, CFI Research Facility navigator has about 675 facilities covering more than 25 sectors of applications. Similarly, CatRIS is a recently launched portal funded under the H2020 program where European research infrastructure is listed (https://www.portal.catris.eu/search) by research institutes, core facilities, etc. Canadian Network of Scientific Platforms (CNSP) is a pan-Canadian network of professional/staff working in any aspect of scientific research platforms that works toward raising awareness, promoting the utility of shared scientific platforms and engaging with granting and governmental agencies, academic institutions, and other relevant stakeholders and affect the funding of these shared platforms (http://cnsp-rcps.ca/). In the last few years a very extensive nation-wide equipment management database was launched in India named I-STEM. I-STEM is a web-based portal for users to locate the specific facility(ties) they need for their R&D work (https://www.istem.gov.in/). Under the Government of India directive, all the institutions with R&D facilities funded by any of the government agencies are required to list their facility/equipment on I-STEM, which made this system very extensive. The user also has the ability to see the availability of equipment and can book instantly on the same portal. It is a very user-friendly, app-based interface fully aligning with digital India (Fig. 16.3).

16.3.4 Glimpses of global research and development expenditure Strong R&D leads to better social and economic growth of a country. NIH’s investment in the Human Genome Project has provided 141-fold return on investment by producing US$796 billion in economic growth within a decade (Dembe, 2014). North America and Western Europe are the world’s highest spenders on R&D activities, that is, B2.4% of their

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16.3 Research and development supporting mechanism: environmental scan (the United States and Canada)

FIGURE 16.3 I-STEM web-based portal is an easy to use interface to locate and book the resource instantly making it easier than ever. Source: https://www.istem.gov.in/.

GDP (Source: UNESCO Institute of Statistics data, http://uis.unesco.org/apps/visualisations/research-and-development-spending/). East Asia and Pacific region primarily comprising China, Japan, and Korea Republic, Australia are the next in line contributing B2% of GDP in R&D activities and also have a significant share of world researchers B38.5% which is very close to North America and Western Europe holding 39.5% of researchers (Source: UNESCO Institute of Statistics data, http://uis.unesco.org/apps/visualisations/ research-and-development-spending/). India ranks seventh amongst the top 15 countries in the world spending on research activities in terms of dollars spent with $48 billion (Source: UNESCO Institute of Statistics data, http://uis.unesco.org/apps/visualisations/research-and-development-spending/) even though its contribution in relation to its GDP is 0.8% (Fig. 16.4). The high investment in R&D activities by these countries has translated into a higher volume of knowledge generation

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FIGURE 16.4 Global distribution of R&D expenditure expressed as percent of national GDP. R&D, Research and development. Source: http://uis. unesco.org/apps/visualisations/research-and-development-spending/.

measured by the number of research publications. By 2018, China tops the chart with .528k publications and .1 million patent applications followed by the United States with about 422k research publications and B285k patents (Source: Worldbank data; https://data.worldbank.org/indicator/IP.JRN.ARTC.SC?contextual 5 similar&locations 5 CN). Looking closer, these two countries have been the highest investors in R&D activities with about .50% world share of total R&D expenditure totaling to B$848 billion (Source: UNESCO Institute of Statistics data, http://uis.unesco.org/apps/visualisations/research-and-development-spending/). The impact of research takes time, and there is no common goal that can be used to measure the outcome of their investment. But investing in research, technology, and innovation is one of the strategic risks that a country needs to take to reap the long-term benefits for health and society. Economically lesser developed countries usually have a much lower investment in R&D due to limited resources and infrastructure. The countries that have been making a conscious effort to invest in STI are steadily progressing toward increasing their research output. Some emerging countries like China, India, Turkey, Brazil showed a sharp increase in their global standing joining developed nations in terms of their scientific output, like increase in a number of scientific publications, patents, increased investment and/or increase in a number of scientists/engineers (Allard, 2015). Growth of scientific competence and investment in biomedical/translational research as national strategic priority determines the nation’s response to health crisis situations. An impressive example is the establishment of Africa Centres for Disease Control in 2016 by the African Union after the Ebola outbreak in 2013 in West Africa (Bockarie, 2020). This significantly improved region’s research capacity to address public health initiatives especially related to viral outbreaks. As a result, within a week of first COVID-19 infected case confirmation, the SARS-COV-2 genome was sequenced by local scientists in Nigeria (Bockarie, 2020). Scientific discoveries and knowledge generation need talent, skill, and resources. While talent can be available, but unless it is tapped and put to use by rightly investing in

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needed resources and providing adequate training to develop needed skills, the benefit cannot be reaped. The scientific infrastructure needed to translate discoveries into community benefit is expensive and should be thoughtfully planned. Shared research facilities facilitate to overcome this barrier and are thus gaining global attention. The role of these facilities in translational research can be phenomenal, especially in economically lessdeveloped nations where there are various unmet and urgent needs due to higher infectious disease burden. Sharing the research infrastructure through the core facility model is becoming a global strategic approach to enhance the benefits of investment in R&D. It is important to ensure that these core facilities deliver the goal of making scientific infrastructure and expertise readily and easily accessible ways while staying sustainable financially. These core facilities are shared expertise hubs that would be critical in advancing the basic and translational research in the limited funding situation by reducing duplication and redundancy while building the research capacity. We will look in more closely now to learn about the operation model of the core facility and see where the efficiencies can be further improved.

16.4 Efficiencies and lean practices in research management In the larger scheme of things, the strategic plan for R&D defines the direction of all research activities. It can be seen both as a “top-down” or “bottom-up” approach. Health and societal challenges that can be tackled by science and technology are identified (bottom-up), and they become part of the national strategic priority that provide guidance to the federal, provincial funding organizations to set their focus areas (top-down). These goals, if delivered with effective resource management, lead to additional benefits by reducing wastage and making the long-term plan sustainable. Thus developing tools to maximize resource usage improves efficiency and productivity. Using core facilities to share the scientific resources with good management practices is one such approach to maximize investment into productive solutions.

16.4.1 Core facilities business model Core facilities or shared research facilities are essentially not-for-profit business units within a university or a research organization serving academic customers on a nominal fee-for-service basis. This business does not have a big revenue as its target but essentially works on the cost recovery model while meeting the bigger goals set out in institutional/ university and government’s strategic plan to help advance and improve scientific discoveries. There is an obvious strategic advantage of acquiring the research infrastructure for shared facilities rather than singe lab use by allowing access to multiple users yielding greater benefits. As with any business, it is important to identify the key elements to understand this business model and have an operational plan to follow. These business units are expensive to set up and maintain initially but are crucial for the success of larger organizational goals of expediting translational research and bringing

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positive outcomes to the society. In addition, unlike a research lab that runs on one research program with limited personnel, core facilities serve larger and diverse research programs, multiple users, variety of technological expertise with highly qualified personnel along with strong business oversight for administration and sustainability planning. Conventionally, technological expertise is tagged along with a researcher, and usually, the administrative partner and oversight are often overlooked when determining the successful outcome of a core facility. The efficient management of core facility serves its purpose by maximizing the investment in research infrastructure and translating research into its expected outcome by building a strong framework of good management practices and solid foundation. It is important to understand that the overall operational cost of running a core facility is to build efficiencies and work toward its long-term sustainability. The operational cost of a core facility primarily comprises the following: human resources, equipment acquisition, upgrade, and its maintenance plan, operational cost, administrative overhead, and consumable cost. Learning about each of these will help in developing strategies to best utilize these resources, remove duplications, and efficient management practices. Here, we describe a generic business model for a shared research facility that captures key elements that play important role in its operation. Table 16.1 outlines the business model for a standard core facility, and we will discuss in detail three critical elements and how it can impact this business (Osterwalder, 2010). It is important to pay attention to all the key elements and see how they impact each other and possibly the larger goal of this business. The first step to initiate a shared facility model is the “customer segment.” This defines the client base that is being served. Usually, the researchers who would be accessing the facility locally are primarily the ones that are the biggest focus. When we discuss this in the context of larger institutes or universities or nation-wide facilities, the client group TABLE 16.1 Key elements of business model for core facility. Key elements

In context of core facility

Customer segment

• • • • • • • • • • • • • • • •

Value proposition

Resources

Partners Channel Impact

Local research community-subsidized service fee External academic clients—may be higher or same as internal clients Industrial clients—premium service fee Funding partners (federal, provincial, philanthropist): research outcome, societal benefits Funding partners (institute university): build research capacity, attract talent Researcher: access to resources, expertise, training, advancing research goals Infrastructure Expertise Funds Collaborators (neighboring core facilities): sharing expertise Vendors, service providers: service contracts, maintenance of infrastructure Awareness Networking Metrics of success: publications, patents, discoveries, research outcome Customer satisfaction survey Trained highly qualified personnel

Adapted from Osterwalder, A., & Pigneur, Y. (2013). Business model generation. Hoboken, NJ: Wiley.

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obviously changes. If core facility customer segment is limited to only certain groups of researchers as described earlier in multiuser facility, the investment and maintenance cost are all lesser, whereas the facility that has a mandate of serving nation-wide research community obviously needs larger investment. In many core facilities, it is standard to have a tiered system of pricing for internal, external, and industrial clients. Industrial clients are an important client base as they provide that additional revenue source that can be used to keep the cost for academic clients subsidized. Thus the customer segment directly had an impact on “cost recovery” element. “Value proposition” is the central element as it defines the value being delivered or committed to the stakeholders of this business. Stakeholders for the core facility are all those who have vested interest in this model and are impacted by its functioning. The various stakeholders and the proposed value for each of them are listed as follows: 1. Funding partners: There are various funding partners coming together to invest, for example, the federal funding bodies, provincial/state funding agencies, institution, university, foundation, and philanthropists. Value: For federal, provincial funding partners and philanthropists, delivering good quality research outcomes leading to societal benefit; institution and university will benefit by building research capacity, attracting talent, and increase the pool of highly qualified personnel. The higher retention and training of highly qualified personnel contribute to the human development index, which plays an important role in the nation’s economic status. Thus this value also contributes toward the national growth plan. 2. Researchers: They are impacted the most by the functioning of core facilities. Value: Access to state-of-the-art facilities, services, resources, expertise, training opportunities, able to pass through the hurdles to translating their research into impact. The translation of research to the community contributes to the nation’s socioeconomic goals and also helps better preparedness to health crisis. “Key resources” define everything needed to have these facilities ready, including the funding, expertise, and infrastructure. This is directly impacted by the customer segments and value proposition to the stakeholders. If there is an expert on-site that can benefit the larger research community and if that cost-sharing will reap bigger benefits to the society, creating such specialty hubs become part of bigger strategic plans. Infrastructure is usually acquired through large-scale federally and/or regional, institutional team grants. Identifying “key partners” is another important element that helps in leveraging and fostering collaborations to maximize the resource utilization, for example, a shared technician for common expertise can be used between multiple research facilities for consultation, vacation coverage, etc. Communication “channels” to create awareness among the research community locally and wider audiences about available resources are very important. We saw earlier how various funding agencies, federal agencies, and core marketplace actively work on creating this awareness, for example, I-STEM (Government of India) and Research Facility Navigator (CFI). The stakeholders making huge investments are challenged with finding the most efficient ways to manage core facilities and developing lean practices. These days there are new technological solutions to perform management of core facilities and add some

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16.4 Efficiencies and lean practices in research management

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automation to these processes like invoicing, billing, and booking resources, for example, Calpendo (http://www.exprodo.com/calpendo), and iLabs. These systems are usually on an annual license basis. Once configured, the automation in capturing the usage data and billing helps significantly. One of the important criteria to determine the success of the core facility is the “Impact.” There are various metrics used to measure this success like number of scientific breakthroughs, publications, patents, and clinical outcomes. The number of users served by the core facility, customer satisfaction surveys, accessibility to services/resources, number of new collaborations made, new scientists trained, etc. are the other useful metrics used to determine the utility and impact of core facility for research benefit (Turpen, 2016). These also help in determining the continuity of funding if the research outcome is giving societal benefits. The successful core facility is a result of a solid partnership between a technological expert with a business planner. The technological partner can steer the scientific direction of the core, while the business partner provides the financial and administrative oversight.

16.4.2 Governance model for core facility Most core facilities when they start are managed by the scientific investigators themselves, and it is usually limited to a few users. At this point, mostly investigators themselves manage the facility with some help from their technicians, trainees, or students. Once the facility takes the shape of a shared facility used by multiple users, the investigator takes the role of scientific lead steering the technological direction, while the business portfolio is managed by central institutional administration. This shared model of management is beneficial for investigators as well as to other stakeholders to share the load of running such shared facilities.

16.4.3 Core facilities and research outcome We learned earlier about various challenges in the path of research, especially in the tight funding situation. The core facility model is an evolving process to address several of those issues. The cost-sharing, collaboration building, training, education, shared expertise, and, most of all, progressing faster to the next steps in research are all obvious advantages. Core facilities enable scientists to design their studies using several advanced technologies that may not be affordable on their own. These programs are especially beneficial for a new faculty to help progress faster in their research career without worrying about funding to get all the required infrastructure. It is usually one of the key determinants for many investigators to accept a faculty position in an institute or university with a wellfunctioning core facility where they can access state-of-the-art technologies and expertise. Core facilities are integral for various institutes building their research capacity across US universities, and to date, there have been more than 700 such shared research facilities. Canada also continues to embrace shared infrastructure usage and has been working toward policies to create a more coordinated effort to invest in core facilities. Many other countries have developed policies and made concerted efforts by adopting this model.

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For example, the EMBL model for core facilities and “core for life” has gained a lot of recognition since it was established in 2001 in the European life sciences community (https://www.embl.de/services/core_facilities/). It has gradually led to significant expansion, with diverse sets of expertise and multiple state-of-the-art technologies. Similar shared facilities or technology hubs have contributed to some of the exemplary institutes in India. Translational Health Science and Technology Institute (THSTI) in India has a mandate of finding solutions to public health problems. They have grown rapidly in their research capacity within the last 8 years with various interdisciplinary research and partnerships. They have an organized core facility structure, which is not very common in many research institutes in India yet, and that may, in essence, be one of the reasons for expediting research (https://www.thsti.res.in/). Indian Institute of Sciences, Bangalore, one of the highly reputable research institute, has a well-equipped core facility housing various state-of-the-art equipment for flow cytometry, bioimaging, and many other advanced technologies serving both basic and translational research. With a high rate of success with these facilities and more awareness and integration of the core facility model within the national framework, these shared facilities will continue to deliver the benefits for both basic and translational researchers. The advantages of translational research are more obvious in delivering the outcomes faster.

16.5 Final notes: learnings for future The basic concept of shared resource usage is very simple and straightforward and seems to be flawless. It is also fairly easy to understand the benefits of adopting this model for a successful transition to research outcome. However, the development of core facilities historically has started without much planning. Core facilities have evolved over time, and there are various crucial things that should be planned carefully to maximize the return of investment.

16.5.1 Integration of core facilities within the institutional strategic plan The institutional strategic plan for research provides the roadmap, guidance, and priorities in research activities. It derives its vision and goals from the university’s strategic plan. During the planning stage, there should be a rigorous consultation and learning from various levels of stakeholders ranging from the institutional research community, federal, provincial, and university leadership to build a cohesive and coordinated plan to invest in the prioritized area of research. This could mean defining a collection of technological platforms that would be required to deliver the research output. This process can help in identifying the gaps in technologies and develop strategies to attract talent pool.

16.5.2 Comprehensive availability of infrastructure inventory This has been usually a very big challenge, as this information is not easily available. Sometimes, funding agencies have records of acquired infrastructure. It is important that

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each funding agency carries these records easy to access to build a robust inventory. There need to be some strict policies in place to report infrastructure actively by each institute as it recently started in India (https://www.istem.gov.in/) to manage the equipment database. In some facilities, when it is left to voluntarily add their infrastructure, it usually is not very effective.

16.5.3 Impact measurement There should be a proactive reporting method to capture any research breakthrough made through the use of the shared or individual facility. This will help in determining the actual difference these shared resources are making to the research ecosystem. In some core facilities, their database management is able to pull such info from the public sources. But it should be an active process of periodic reporting by the core management team to measure the impact of core facilities on research outcome. One of the easy-to-implement methods is to acknowledge the core facility and its services in research publications. Many institutes have started to make it mandatory for researchers to report the core facility used for carrying out the research. If it is left voluntary with the researcher, it usually gets overlooked, and this very important research output metric is lost. There are various examples of translational research success stories utilizing various shared research platforms like biobanks, stem cells, and genomics. These stories continue to inspire us to deliver good research in a responsible, cost-effective, and time-sensitive way, which is possible with a well-planned and managed core facility.

Acknowledgments I would like to acknowledge some great discussions with Dr. Claire Brown from McGill University, President of CNSP, to learn about Canadian core facilities and shared resources and Dr. Jay Shankar for providing critical insights and helping with revisions.

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17 A new TOPSIS-based approach to evaluate the economic indicators in the healthcare system and the impact of biotechnology Priyanka Majumder1 and Apu Kumar Saha2 O U T L I N E 17.1 Introduction

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17.2 Technique for order of preference by similarity to ideal solution approach 410 17.2.1 Metric space 410 17.2.2 New technique for order of preference by similarity to ideal solution approach 411 17.3 Methodology 412 17.3.1 Selection of criteria 413 17.3.2 Selection of indicators 414 17.3.3 Application of new technique for order of preference by similarity to ideal solution approach 414

1

2

17.3.4 Analysis of sensitivity

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17.4 Result and discussion 416 17.4.1 Result from technique for order of preference by similarity to ideal solution 1 416 17.4.2 Result from technique for order of preference by similarity to ideal solution 417 17.4.3 Result from sensitivity analysis 418 17.5 Conclusion

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References

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Department of Basic Science and Humanities (Mathematics), Techno College of Engineering Agartala, Maheshkhola, Agartala, Tripura, India Department of Mathematics, National Institute of Technology Agartala, Barjala, Jirania, Tripura, India

Translational Biotechnology DOI: https://doi.org/10.1016/B978-0-12-821972-0.00001-0

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© 2021 Elsevier Inc. All rights reserved.

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C H A P T E R

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17. A new TOPSIS-based approach to evaluate the economic indicators in the healthcare system

17.1 Introduction A robust healthcare system ensures the social and economic well-being of a community. The structure of the medical service system is different for each country and is influenced by parameters unique to the country. While the cost of the healthcare system is often under the spotlight, the economic impact and its influencing factors are poorly understood. In a recent Indian Healthcare Industry Report, it was predicted that the healthcare market has the potential to increase threefold to Rs 8.6 trillion (US$ 133.44 billion) by 2022 (IBEF, n.d.). It is one of the largest sectors in terms of service, employment, and revenue. The components of the healthcare system range from biomedical research, hospitals, medical devices, technology, training of medical professionals, insurance, and the socioeconomic status of patients. The health resource indicators are the number of medical institutions, the number of health professionals, the number of beds in medical institutions, and the number of family health services. It also depends on emergency hospital admissions, number of family planning surgical cases, emergency cases death rate, critically ill patients’ survival rate, and maternal mortality rate. Economic level indicators are general hospital outpatients and discharged patient’s medical costs per capita, gross domestic product (GDP) per capita, percentage of government revenue for expenditure, and Engel coefficient. Lastly, social and environmental factors such as birth rate, death rate, infectious disease death rate, prevalent disorders, rural and urban accessibility to resources, also play a role. There is a requirement for a model that will facilitate the assessment of economic indicators in healthcare, which takes into account new developments in the field, such as the integration of biotechnology. Such a model will help influence investment decisions, budget allocation, better planning, and economic development, which will, in turn, boost translational research opportunities. The multiattribute nature of the problem makes it a challenging task. Also, the inclusion of the human life factor that comes into the picture in healthcare decision making causes a conflict of interest and makes the decision-making process even more complicated (Fraza˜o, Camilo, Cabral, & Souza, 2018). Multicriteria decision making (MCDM) is a popular analysis method in Operations research, is used to structure and solve decision-making problems that involve multiple criteria by using computational and mathematical tools (Lootsma, 1999). It has been adopted in a wide range of fields by researches for evaluating, assessing, and ranking alternatives. Among a wide array of MCDM methods to solve real-life decisionmaking problems, TOPSIS (technique for order of preference by similarity to ideal solution) has been widely adopted in the area of health, safety, and environment management, with satisfactory results (Behzadian, Khanmohammadi Otaghsara, Yazdani, & Ignatius, 2012; Majumder, Majumder, Saha, Sarkar, & Nath, 2019; Majumder, Saha, & Majumder, 2018). MCDM is a multidisciplinary approach comprising mathematics, statistics, and computer science that makes use of both qualitative and quantitative factors for choosing an optimal solution (Mardani et al., 2015). MCDM has been actively adopted for decision making in healthcare, for example, for prioritizing diseases for R&D, evaluation of service quality, decisions on licensing treatments, hospital information system, prioritizing

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patients, and clinical routines (Hansen & Devlin, 2019). MCDM methods used in healthcare are mostly weighted sum-based methods or ranking-based methods. In 1981 Hwang and Yoon developed a ranking-based MCDM method called TOPSIS. The principle of this method is to find an ideal solution and an antiideal solution by comparing a set of alternatives, based on geometric distance (Euclidean) (Papathanasiou & Ploskas, 2018). The ideal solution should have the shortest Euclidean distance from the most ideal solution and the longest Euclidean distance from the negative-ideal solution. The logic behind the computational TOPSIS procedure is straightforward. In the standard TOPSIS method, the initial step is to locate the minimum gap from the best-performing arrangement (the best case) and the maximum distance from every one of the poor-performing arrangements (the worst case) (Jahanshahloo, Lotfi, & Izadikhah, 2006). TOPSIS takes advantage of all the attribute information and provides a cardinal ranking of the alternatives by identifying weights and normalizing scores for each criterion, and further determining the geometric (Euclidean) distance between the alternatives to find the optimal solution (Behzadian et al., 2012). The existing TOPSIS tool depends on the idea that the best indicators should have a maximum and minimum metric (Opricovic & Tzeng, 2004). The minimum metric between indicators is calculated in TOPSIS by the Euclidean metric (Hwang & Yoon, 1981). In general, Euclidean distance does not give the minimum distance between two coordinates in the same dimension. Theorem 1 shows that distance measure by supremum metric is always less than equal to distance measure by Euclidean metric. q ffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffi  Pn 0 00 2 and Theorem 1: Let ρN ; ρ: ℝn 3 ℝn -R 2 R2 define by ρðx; yÞ 5 i51 ðxi 2xi Þ ρN ðx; yÞ 5 maxfjx0i 2 x00i j: x0i ; x00i Aℝg then ρN ðx; yÞ # ρðx; yÞ for every element x 5 ðx01 ; x02 ; . . . ; x0n Þ and y 5 ðx001 ; x002 ; . . . ; x00n Þ belongs to ℝn . Proof: Let x 5 ðx01 ; x02 ; . . . ; x0n Þ and y 5 ðx100 ; x200 ; . . . ; xn00 Þ be two members of ℝn . qffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffi Then jxi0 2 xi0 0 j 5 ðxi0 2xi0 0 Þ2 for every x0i ; x00i Aℝ q ffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffi  Pn 0 0 0 2 for every x ; y Aℝ This gives jx0i 2 xi00 j # i i i51 ðxi 2xi Þ qffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffi ffi Pn 0 00 2 So maxfjx0i 2 xi00 j: i 5 1; 2; . . . ; ng # i51 ðxi 2xi Þ Hence, we can conclude that ρN ðx; yÞ # ρðx; yÞ for every x; yAℝn . From Theorem 1, it is clear that supremum distance always gives the minimum distance from Euclidean distance between the same dimensional two coordinates. In the existing TOPSIS approach, to find an ideal solution, the concept of shortest distance is used, which is determined using the Euclidean metric (Papathanasiou & Ploskas, 2018). So in this chapter, we propose a study in which we interchange the Euclidean metric by supremum metric to evaluate the ideal solution. However, for a negative-ideal solution, we use the concept of greatest distance. Since the Euclidean metric gives the greatest distance to the supremum metric, so the Euclidean metric is used to evaluate the nonideal solution. We apply this novel TOPSIS-based decision-making method that makes use of the supremum metric to evaluate the most important economic indicator in the healthcare system. The second part of the study performs a comparative analysis between the existing TOPSIS approach and the newly devised TOPSIS approach. This new approach, to our

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17.1 Introduction

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17. A new TOPSIS-based approach to evaluate the economic indicators in the healthcare system

knowledge, has been applied to the analysis of the healthcare service system for the first time. This study will supplement our understanding of the contributing factors of healthcare to the economy. The outcome of the study may further aid in the assessment of the impact of translational biotechnology on the economy. This knowledge will, in turn, draw more support and funding for translational research in biotechnology.

17.2 Technique for order of preference by similarity to ideal solution approach According to the literature, the existing TOPSIS approach uses the Euclidean metric for positive and negative-ideal solutions. However, in a positive ideal solution, we use the concept of the shortest distance. But the supremum metric always gives the shortest distance compare to the Euclidean metric. So, in this study, we use a new approach of TOPSIS. In the new TOPSIS method, we use the supremum metric in place of the Euclidean metric to find the positive ideal solution.

17.2.1 Metric space Let M 6¼ [ be set and a transformation ρ: M 3 M-R 2 R2 . This transformation ρ is called distance (or metric) on M if ρ satisfies following relation: 1. Nonnegative relation: Distance or gap of any two points of M satisfy nonnegative condition. that is; for each pair ðx; yÞAM 3 Mρðx; yÞAR 2 R2 2. Identification relation: Gap between two points of M is zero if and only if those two points are in the same place.     Mathematically; we can write ρ x; y 5 0 iff x 2 y 5 0; if x; y AM 3 M: 3. Symmetry relation: Any two points x; yAM; then the gap between x and y is equal to the gap between y and x.     that is; for each x; yAM then ρ x; y 5 ρ y; x 4. Triangular inequality relation: Triangular inequality satisfy under the transformation ρ of any three coordinates of M.     that is; for each x; yAM; ρðx; zÞ # ρ x; y 1 ρ y; z Then, the pair ðM; ρÞ is called metric space. Let ρ:ℝn 3 ℝn -R 2 R2 be a transformation defined by the formula q ffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffi  Pn 0 0 0 00 00 00 0 00 2 where x 5 ðx1 ; x2 ; . . . ; xn Þ and y 5 ðx1 ; x2 ; . . . ; xn Þ are a ρðx; yÞ 5 i51 ðxi 2xi Þ member of ℝn . Then ρ forms a metric space on ℝn . This distance is known as the Euclidean metric.

411

 0  00 0 00 define by ρN ðx; yÞ 5 max xi 2 xi : xi ; xi Aℝ

Also another mapping ρN : ℝn 3 ℝn -R 2 R2 0 0 0 00 00 00 where x 5 ðx1 ; x2 ; . . . ; xn Þ and y 5 ðx1 ; x2 ; . . . ; xn Þ are any two points of ℝn . Then ρN also n form a metric space in ℝ . This metric ρN is known as supremum distance.

17.2.2 New technique for order of preference by similarity to ideal solution approach The new TOPSIS approach consists of six steps. The difference between new and existing TOPSIS method is that we interchange the distance formula in the existing TOPSIS method to find the positive ideal solution. Fig. 17.1 represents the computational procedure of the new TOPSIS Approach. All the steps of the new TOPSIS approach are discussed in the following: 1. Let the number of alternatives be k. If N represents the decision matrix in a normalized form then N 5 ½nij k 3 k where: yij nij 5 qffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffi ; Pk 2 i51 yij

for all i; j

2. After calculating the normalized decision matrix, then we calculated another decision matrix, namely, the weighted normalized decision matrix. If sij denotes the weighted normalized value and wj ðCj Þ is the weights or priority value (PV) of the jth criteria then: sij 5 wj ðCj Þ 3 nij for all i; j

n X

wj 5 1

i51

3. In the next step, we calculated the positive- and negative-ideal solution and are denoted by I 1 and I 2 , respectively.   1 1 1 I 1 5 n1 1 ; n2 ; n3 ; . . . ; nm 5 fðmax nij jjACb Þ; ðmin nij jjACc Þg i

i

FIGURE

17.1 Flowchart depicting steps involved in the new TOPSIS approach method. TOPSIS, Technique for order of preference by similarity to ideal solution.

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17.2 Technique for order of preference by similarity to ideal solution approach

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17. A new TOPSIS-based approach to evaluate the economic indicators in the healthcare system

  2 2 2 I 2 5 n2 1 ; n2 ; n3 ; . . . ; nm 5 fðmin nij jjACb Þ; ðmax nij jjACc Þg i

i

where Cb and Cc are the benefit and cost criteria, respectively. 4. In the next step, we calculated the measure of separation, using k dimensional distance formula. In this new approach, positive- and negative-ideal solution calculated by supremum and Euclidean metric, respectively. Ti1 and Ti2 represent the positive- and negative-ideal solution, respectively. Ti1 5 maxfjuij 2 u1 j j: j 5 1; 2; . . . ; kg; ’i vffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffi uX 2 u k  ; ’i uij 2u2 Ti2 5 t j j51

5. In the following step, we evaluated the relative closeness of the ideal solution: the relative closeness of the Indicators or alternative Ii with respect to I 2 is denoted by RCi defined as: RCi 5

Ti2 ; ’i Ti2 1 Ti1

6. From the previous step, it is clear that the value RCi lies between 0 and 1 for all i. The height value of the index (RCi ) means the closer to the ideal solution for indicators.

17.3 Methodology The main objective of this present investigation is to identify the most significant economic indicator of healthcare system. Let EL denote the economic level: EL 5 f ðF; PÞ:

(17.1)

where F denotes the set of all factors of EL; and P denotes the set of weights of corresponding each indicator of F. F 5 fg: g represents all selected indicatorsg and P 5 fp: p represents weights all selected indicatorsg where g and p denote the selected indicator and its weights, respectively. In this present investigation the methodology is divided into four sections, namely, criteria selection, indicators selection, application of new TOPSIS approach, and analysis of sensitivity. We discuss the levels briefly in the following sections.

17.3.1 Selection of criteria For the selection of criteria, we conduct a literature survey from bibliographic databases using relevant keywords. We also conduct an expert survey and opinion survey amongst medical practitioners and patients. The selection of parameters for economic indicators of the healthcare system is based on 100 curated research papers. Based on these parameters, further standards were set, and options were selected. Since only literature survey does not provide a practical insight into the functioning of the healthcare system, thus we consult experts such as doctors, professors, and subject experts, from reputed medical colleges. Based on twelve expert opinions, we select alternatives for the study. We also consult consumers and patients to assess the economic criteria further. Let h is an indicator that is identified by using relations (17.2) and (17.3):

 q (17.2) h 5 select; if nðhÞ . 2

 q h 5 not select; if nðhÞ # : (17.3) 2 where nðhÞ and q denote the number of literature and source, respectively, also, ½x # x.

TABLE 17.1

List of alternatives and corresponding opinion as gathered by the survey.

Name of alternatives

Discussion

Unit

General hospital outpatients and discharged patient’s medical costs per capita (A1)

In lower- and middle-class family facing many economic problems with increasing cost of healthcare.

Yuan/ NB person

GDP per capita (A2)

GDP per capita is an evaluation parameter of a country’s economic output that accounts for its number of people. GDP calculated divides the country’s GDP by its total population.

Yuan/ B person

Percentage of government revenue to expenditure (A3)

If GDP is increased, then the percentage of government revenue to expenditure is increased.

%

B

Engel coefficient (A4)

Engel’s Law is an economic theory introduced in 1857 by Ernst Engel, a German statistician, stating that the percentage of income allocated for food purchases decreases as income rises. As a household’s income increases, the percentage of income spent on food decreases, while the proportion spent on other goods (such as luxury goods) increases.

%

B

GDP, Gross domestic product.

Beneficial/ nonbeneficial

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17. A new TOPSIS-based approach to evaluate the economic indicators in the healthcare system

17.3.2 Selection of indicators In this study, all the indicators are selected after a thorough literature review, a survey of experts’ opinions, and patient/relative of patients’ opinions that are denoted by C1, C2, and C3, respectively. The selected alternatives are discussed in Table 17.1.

17.3.3 Application of new technique for order of preference by similarity to ideal solution approach We present a new TOPSIS approach to evaluate the weights or PV of each indicator. The PV of criteria determine by using analytical hierarchy process (AHP). Using this PV of criteria, calculate the PV of indicators by the New TOPSIS approach. In this present study, we use five-point scales. Table 17.2 shows five-point scale. In this investigation, the “relative score” of each indicator with respect to each criterion is selected by literature survey TABLE 17.2 Five-point scale. Name

Low (L)

Below average (BA)

Average (A)

Good (G)

Excellent (E)

Score

1

2

3

4

5

TABLE 17.3 Score table of each indicator with the help of each criterion. Indicators

Criteria C1

C2

C3

A1

A

G

G

A2

G

G

A

A3

BA

A

BA

A4

BA

BA

L

TABLE 17.4 Numerical score values of Table 17.3 with the help of Table 17.2. Indicators

Criteria C1

C2

C3

A1

y11 5 3

y12 5 4

y13 5 4

A2

y21 5 4

y22 5 4

y23 5 3

A3

y31 5 2

y32 5 3

y33 5 2

A4 qffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffi P4 2ffi i51 yij

y41 5 2

y42 5 2

y43 5 1

5.744563

6.708204

5.477226

(LS), expert survey (ES), and relative of peasant opinions (RPO) survey. Table 17.2 shows the “relative score” of each indicator by five-point scale. Then we impose the new TOPSIS approach algorithm. With the help of five-point scale, all the entries of Table 17.3 are converted into the numerical score value table, which is shown in Table 17.4. After the conversion of the numerical score value of the decision matrix, we calculated the normalized decision matrix. Using Eq. (17.4) we converted the decision matrix into a normalized decision matrix. In this study, PV of each criterion AHP and all PV of each criterion shown in the second column of Table 17.5. yij nij 5 qffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffi P4 2 ; i51 yij

’ i 5 1; 2; 3; 4; j 5 1; 2; 3:

(17.4)

Next, we calculated the weighted normalized decision matrix using formula (17.5) from Table 17.5. That weighted normalized decision matrix represents in Table 17.5. Also, in Table 17.5 we calculated positive (I1) and negative (I2) ideal solution. The value of I1 and I2 is calculated by using Eqs. (17.6) and (17.7), respectively. The value of I1 and I2 is given in rows 7 and 8 of Table 17.6. Sij 5 wj ðCj Þ 3 nij ; TABLE 17.5

’i 5 1; 2; 3; 4; j 5 1; 2; 3:

(17.5)

Decision matrix in normalized form.

Indicators

Priority value of each criterion w1 ðC1 Þ 5 0:540 C1

w2 ðC2 Þ 5 0:297 C2

w3 ðC3 Þ 5 0:163 C3

A1

n11 5 0:52223297

n12 5 0:59628479

n13 5 0:7303

A2

n21 5 0:69631062

n22 5 0:59628479

n23 5 0:54772

A3

n31 5 0:34815531

n32 5 0:4472136

n33 5 0:36515

A4

n41 5 0:34815531

n42 5 0:2981424

n43 5 0:18257

TABLE 17.6

Weighted decision matrix in normalized form.

Indicator

Criteria C1(B)

C2(B)

C3(B)

A1

n11 5 0:2820058

n12 5 0:17709658

n13 5 0:11903837

A2

n21 5 0:37600774

n22 5 0:17709658

n23 5 0:08927878

A3

n31 5 0:18800387

n32 5 0:13282244

n33 5 0:05951918

A4

n41 5 0:18800387

n42 5 0:08854829

n43 5 0:02975959

I

n1 1

n1 2

5 0:17709658

n1 3 5 0:11903837

I2

n2 1 5 0:18800387

n2 2 5 0:08854829

n2 3 5 0:02975959

1

5 0:37600774

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17. A new TOPSIS-based approach to evaluate the economic indicators in the healthcare system

  1 1 1 I 1 5 n1 1 ; n2 ; n3 ; . . . ; nm 5 fðmax nij jjACb Þg:

(17.6)

  2 2 2 I 2 5 n2 1 ; n2 ; n3 ; . . . ; vm 5 fðmin nij jjACb Þg:

(17.7)

i

i

17.3.4 Analysis of sensitivity An investigation of sensitivity is a numerical recipe that is utilized in a monetary demonstration to ascertain whether different factors, called input factors, impact an objective variable. This examination utilized a sensitivity investigation to validate the model. The affectability examination was performed with the assistance of different info, one yield, tornado technique that was created by SensIt Limited. The extents for the info factors differed somewhere in the range of 0 and 1. The effect of each information was then acquired on the yields watched, and the outcomes were contrasted, and loads of the factors were found from the new MCDM approach.

17.4 Result and discussion In the present study, the results and discussion are divided into three parts: result from TOPSIS 1, result from TOPSIS, and result from sensitivity analysis, which are discussed in the following sections.

17.4.1 Result from technique for order of preference by similarity to ideal solution 1 2 In new TOPSIS approach, S1 i and Si are calculated supremum distance from Table 17.5 using the formula according to (17.8) and (17.9). Final aggregation is calculating from RCi using formula (17.10). Table 17.7 shows all the results of Ti1 , Ti2 , and RCi . According to the results, it was found that GDP per capita is the most important economic indicator in the healthcare system and is the Engel coefficient is the least important indicator.

Ti1 5 maxfjuij 2 u1 j j: j 5 1; 2; 3g;

’i 5 1; 2; 3; 4:

(17.8)

TABLE 17.7 RC* value for new technique for order of preference by similarity to ideal solution approach. Indicator

Ti1

Ti2

RCi

A1

0.094002

0.156996

0.625488

A2

0.02976

0.216168

0.878991

A3

0.188004

0.053346

0.221033

A4

0.188004

0

0

vffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffi u 3  2 uX 2 Ti 5 t ; vij 2u2 j

’i 5 1; 2; 3; 4:

(17.9)

j51

RCi 5

Ti2 ; Ti2 1 Ti1

’i 5 1; 2; 3; 4

(17.10)

17.4.2 Result from technique for order of preference by similarity to ideal solution We compared our new TOPSIS approach with the existing TOPSIS approach. So we calculated Ti1 and Ti2 from Table 17.6 using the Euclidean metric. RC* value gives the final ranking of each indicator. Using Eq. (17.10), evaluate the final aggregation of each indicator. The ranking of each indicator is found from existing TOPSIS, and the new TOPSIS approach is the same. The value of Ti1 , Ti2 , and RCi are shown in Table 17.8. Also, in this study, we find absolute and relative error between the RCi values of two TOPSIS methods (Table 17.9). In this investigation the RCi value of existing TOPSIS approach is considered as the true value. Since new TOPSIS approach is new MCDM, so the RCi value considers as an approximate value (Table 17.8).

TABLE 17.8 method.

RC* value of the existing technique for order of preference by similarity to ideal solution

Indicator

Ti1

Ti2

RCi

A1

0.09400193

0.15699638

0.62548779

A2

0.02975959

0.21616842

0.87899064

A3

0.20210935

0.05334635

0.2088282

A4

0.22617903

0

0

TABLE 17.9 Error estimation between RC* value between existing and new technique for order of preference by similarity to ideal solution approach. Indicator

Absolute error

Relative error

A1

0

0

A2

0

0

A3

0.012205

0.058444

A4

0

Does not exist

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17.4 Result and discussion

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17. A new TOPSIS-based approach to evaluate the economic indicators in the healthcare system

A2

0

1

A1

0

1

A3

0

1

A4

10 0.2

FIGURE 17.2

0.3

0.4

0.5

0.6

0.7

0.8 Output

0.9

1

1.1

1.2

1.3

1.4

Result of sensitivity analysis.

17.4.3 Result from sensitivity analysis The investigation of sensitivity was led to get to the sensitivity of the model as for its info pointers. In the current investigation, if the need estimation of the markers certifies the sensitivity of the pointers utilized as the contribution to the model, then the approval of the outcomes will be set up. The results indicate that GDP per capita (A1) was found to be more sensitive compared to the other indicators. The second most sensitive parameter was found to be equal to the general hospital outpatient’s and discharged patient’s medical costs per capita (A1), while the least sensitive parameter was found to be Engel coefficient (A4), displaying a swing2 value of 24.20% and 0.00%, respectively. Both the top indicators were found to have similar sensitivity and PV, which supports the selection of the alternatives. From the MCDM techniques, it was also found that the PV of the most sensitive parameter is maximum, and that of the least sensitive parameter is minimum. Fig. 17.2 depicts the analysis of the results.

17.5 Conclusion The current study uses a new approach of TOPSIS, a decision-making algorithm by replacing the Euclidean distance method with supremum distance to evaluate the economic indicators of the healthcare system. According to the result of the new TOPSIS approach, it was found that GDP per capita is the most important indicator, and the Engel coefficient is the least important parameter. The suggested model can be improved by taking into account more influencing factors, with the availability of more data on healthcare and research. This approach for evaluation opens avenues for the use of mathematical, computational, and statistical methods for evaluation of socioeconomic parameters relating to research and healthcare. This method may also indicate the impact of advancements in translational research on healthcare and the overall economy.

419

References Behzadian, M., Khanmohammadi Otaghsara, S., Yazdani, M., & Ignatius, J. (2012). A state-of the-art survey of TOPSIS applications. Expert Systems with Applications, 39. Available from https://doi.org/10.1016/j. eswa.2012.05.056. Fraza˜o, T. D. C., Camilo, D. G. G., Cabral, E. L. S., & Souza, R. P. (2018). ). Multicriteria decision analysis (MCDA) in health care: A systematic review of the main characteristics and methodological steps. BMC Medical Informatics and Decision Making, 18, 90. Available from https://doi.org/10.1186/s12911-018-0663-1. Hansen, P., & Devlin, N. (2019). Multi-criteria decision analysis (MCDA) in healthcare decision-making. Available from https://doi.org/10.1093/ACREFORE/9780190625979.013.98 Hwang, C.-L., & Yoon, K. (1981). Methods for multiple attribute decision making. Available from https://doi.org/ 10.1007/978-3-642-48318-9_3. IBEF. (n.d.). Healthcare industry in India, Indian healthcare sector, services. Retrieved from https://www.ibef.org/ industry/healthcare-india.aspx. (Accessed 18 July 2020). Jahanshahloo, G. R., Lotfi, F. H., & Izadikhah, M. (2006). An algorithmic method to extend TOPSIS for decisionmaking problems with interval data. Applied Mathematics and Computation, 175. Available from https://doi. org/10.1016/j.amc.2005.08.048. Lootsma, F. A. (1999). Multi-criteria decision analysis via ratio and difference judgement applied optimization. Springer, Applied optimization. Majumder, P., Majumder, M., Saha, A. K., Sarkar, K., & Nath, S. (2019). Real time reliability monitoring of hydropower plant by combined cognitive decision-making technique. International Journal of Energy Research, 43(9), 4912
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30). Springer. Available from https://doi.org/10.1007/978-3-319-91648-4_1.

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References

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Glossary Amphiphilic peptides Peptide-based molecules that have the tendency to self-assemble into high-aspectratio nanostructures under certain conditions of pH, temperature, and ionic strength. Amyotrophic lateral sclerosis A progressive nervous system disease that affects nerve cells in the brain and spinal cord, causing loss of muscle control. Ankylosing spondylitis A rare type of arthritis that causes pain and stiffness in your spine. Antagomirs Known as anti-miRs or blockmirs are a class of chemically engineered oligonucleotides that prevent other molecules from binding to a desired site on an mRNA molecule. Artificial Intelligence It is a branch of computer science concerned with building machines that are capable of performing complex tasks, usually associated with intelligent beings. Asialoglycoprotein Any glycoprotein from which the sialic acid residues have been removed. Cardiomyocytes Cells that make up the heart muscle/cardiac muscle. Cardiomyopathy An acquired or hereditary disease of heart muscle, this condition makes it hard for the heart to deliver blood to the body, and can lead to heart failure. Cardiomyopathy A disease of the heart muscle that makes it harder for your heart to pump blood to the rest of your body. Cerebral cortex It is the outermost region of the forebrain and region where all higher-order functions like thinking, memory, learning, etc. are processed. Cheminformatics A relatively new field of information technology that focuses on the collection, storage, analysis, and manipulation of chemical data. Clinical phenotyping Making observations and records of a set of clinical features for a particular disease in a patient and their family. Colistin An antibiotic used as a last-resort for multidrug-resistant Gram-negative infections including pneumonia. Corneal neovascularization The in-growth of new blood vessels from the pericorneal plexus into avascular corneal tissue as a result of oxygen deprivation. Cytomegalovirus retinitis An inflammation of the retina of the eye that can lead to blindness. Dalbavancin A novel second-generation lipoglycopeptide antibiotic. Daptomycin A cyclic lipopeptide antibiotic derived from the organism Streptomyces roseosporus. Diabetic retinopathy A diabetes complication that affects eyes. It is caused by damage to the blood vessels of the light-sensitive tissue at the back of the eye (retina). Dopaminergic neurons They are thought to control processes as diverse as movement and drug addiction. Drug repurposing It is an approach of exploring novel therapeutic possibilities of approved drugs that lie beyond the scope of the original medical indication. Duchenne muscular dystrophy A genetic disorder characterized by progressive muscle degeneration and weakness due to the alterations of a protein called dystrophin that helps keep muscle cells intact. eMERGE network A consortium of American medical institutions dedicated to advancing the use of electronic medical records for genomics research. Endocytosis The taking in of matter by a living cell by invagination of its membrane to form a vacuole. Epigenetics The study of heritable phenotype changes that do not involve alterations in the DNA sequence.

421

422

Glossary

Euclidean distance Distance between two points in either the plane or three-dimensional space measures the length of a segment connecting the two points. Fabry disease A rare genetic disorder that prevents the body from making an enzyme called alphagalactosidase A. Fomivirsen An antisense antiviral drug that was used in the treatment of cytomegalovirus retinitis (CMV) in immunocompromised patients, including those with AIDS. Gaucher disease A genetic disorder where fat-laden Gaucher cells build up in areas like the spleen, liver, and bone marrow. Glial cells Nonneuronal cells in the central nervous system (brain and spinal cord) and the peripheral nervous system that do not produce electrical impulses. Gramicidin A heterogeneous mixture of six antibiotic peptides obtained from the soil bacterium Bacillus brevis. Gyrencephalic Brains like humans, which have a convoluted cerebral cortex. Gyrencephalic Denoting brains, such as that of humans, in which the cerebral cortex has convolutions, in contrast to the lissencephalic (smooth) brains of small mammals. Hematopoiesis The formation of blood cellular components. All cellular blood components are derived from hematopoietic stem cells. Hydrogelation A network of polymer chains that are hydrophilic, sometimes found as a colloidal gel in which water is the dispersion medium. Hypercholesterolemia Also called high cholesterol is the presence of high levels of cholesterol in the blood. Hypoxemia Refers to the low level of oxygen in blood. Immunogenicity The ability of a foreign substance, such as an antigen, to provoke an immune response in the body of a human or other animal. Immunohistochemistry A technique that uses antibodies conjugated to enzymes that catalyze reactions to form detectable compounds to visualize and localize specific antigens in a tissue sample. Immunomodulation Encompasses all therapeutic interventions aimed at modifying the immune response. Immunomodulatory A chemical agent (as methotrexate or azathioprine) that modifies the immune response or the functioning of the immune system (as by the stimulation of antibody formation or the inhibition of white blood cell activity). Immunophenotyping The analysis of heterogeneous populations of cells for the purpose of identifying the presence and proportions of the various populations of interest. Ischemia-reperfusion injury The tissue damage caused when blood supply returns to tissue (re- 1 perfusion) after a period of ischemia or lack of oxygen (anoxia or hypoxia). Lissencephalic Brains like rodents, which have a smooth cerebral cortex. Lissencephalic Brain of human, lacking surface convolutions (Gyrification). Specialty. Medical genetics, neurology. Lymphoblastic A cancer of the lymphoid line of blood cells characterized by the development of large numbers of immature lymphocytes. Mechanotransduction The processes through which cells sense and respond to mechanical stimuli by converting them to biochemical signals that elicit specific cellular responses. Mesenchymal stem cells Multipotent stem cells found in bone marrow that are important for making and repairing skeletal tissues, such as cartilage, bone, and the fat found in bone marrow. Mesothelioma A malignant tumor that is caused by inhaled asbestos fibers and forms in the lining of the lungs, abdomen, or heart. Micro emulsion A clear, thermodynamically stable, isotropic liquid mixtures of oil, water, and surfactant, frequently in combination with a surfactant. Microarrays A laboratory tool used to detect the expression of thousands of genes at the same time.

Glossary

423

Microcephaly A condition where the head (circumference) is smaller than normal. Monoclonal antibodies Antibodies that are made by identical immune cells that are all clones of a unique parent cell. Monogenic disorder Changes in the single gene lead to the disease. Myelodysplastic syndrome A group of cancers in which immature blood cells in the bone marrow do not mature, so do not become healthy blood cells. Myeloproliferation Diseases of the bone marrow and blood. Neocortex Evolutionarily, the most advanced part of the brain and center for processing higher-order functions like thinking, language decision-making, and consciousness. It is six-layered and is hugely expanded in primates and humans. Neuroinflammatory The activation of the brain’s innate immune system in response to an inflammatory challenge and is characterized by a host of cellular and molecular changes within the brain. Neuropsychiatric A branch of medicine that deals with mental disorders attributable to diseases of the nervous system. Neurotropic (of a virus, toxin, or chemical) tending to attack or affect the nervous system preferentially. Nonsyndromic disorders Disorders that have a complex inheritance in which genetic and environmental factors may play a role in the disease pathology. Organogelation A class of gel composed of a liquid organic phase within a three-dimensional, crosslinked network. Organogenesis The phase of embryonic development that starts at the end of gastrulation and continues until birth. Oritavancin A novel semisynthetic glycopeptide antibiotic for the treatment of serious Gram-positive bacterial infections. Osteoporosis A bone disease that occurs when the body loses too much bone, makes too little bone, or both. Osteosarcoma A type of cancer that produces immature bone. Pathophysiology The disordered physiological processes associated with disease or injury. Perfusions The passage of fluid through the circulatory system or lymphatic system to an organ or a tissue, usually referring to the delivery of blood to a capillary bed in tissue. Pharmacodynamics The branch of pharmacology concerned with the effects of drugs and the mechanism of their action. Pharmacogenomics The branch of genetics concerned with determining the likely response of an individual to therapeutic drugs. Pharmacokinetic A branch of pharmacology dedicated to determine the fate of substances administered to a living organism. Phenogenomics Study of functional genomics using a combination of molecular phenotyping strategies and high throughput genomics technologies. Phenotype All the observable characteristics of an organism that result from the interaction of its genotype (total genetic inheritance) with the environment. Pheochromocytomas A rare adrenal tumors arising from chromaffin cells of the adrenal medulla. Phosphoproteomics A branch of proteomics that identifies, catalogs, and characterizes proteins containing a phosphate group as a posttranslational modification. Photochromic Undergoing a reversible change in color or shade when exposed to light of a particular frequency or intensity. Pinocytosis The ingestion of liquid into a cell by the budding of small vesicles from the cell membrane. Polygenic disorder Combined action of multiple genes causes the disease. Polyneuropathy A condition in which a person’s peripheral nerves are damaged. Pompe disease An autosomal recessive metabolic disorder which damages muscle and nerve cells throughout the body.

424

Glossary

Porphobilinogen An organic compound that occurs in living organisms as an intermediate in the biosynthesis of porphyrins, which include critical substances like hemoglobin and chlorophyll. Promyelocytic leukemia An aggressive type of acute myeloid leukemia in which there are too many immature blood-forming cells (promyelocytes) in the blood and bone marrow. Pseudogenes Regions of the genome that resemble genes but are no longer functional. These regions may have earlier been alleles of normal genes that have lost their function due to accumulation of mutations. Pyelonephritis The inflammation of the kidney is due to a specific type of urinary tract infection (UTI). Retinitis pigmentosa A group of rare, genetic disorders that involve a breakdown and loss of cells in the retina—which is the light-sensitive tissue that lines the back of the eye. Retinoblastoma An eye cancer that begins in the retina—the sensitive lining on the inside of your eye. Serotonergic neurons Located in the raphe nucleus are the unique resource of the neurotransmitter serotonin, which plays a pivotal role in the regulation of brain development and functions. Spinal muscular atrophy Genetic disorders characterized by weakness and wasting of muscle involved in movement caused by loss of motor neurons. Spinocerebellar ataxia Genetic disorder characterized by progressive problems with movement due to damage to cerebellum and sometimes spinal cord. Spinocerebellar ataxia One of a group of genetic disorders characterized by slowly progressive incoordination of gait and is often associated with poor coordination of hands, speech, and eye movements. SUMOylation A posttranslational modification process. Synaptogenesis The formation of synapses between neurons in the nervous system. Syndromic disorders Disorders that have known genetic cause. Telavancin A semisynthetic derivative of vancomycin is a bactericidal, lipoglycopeptide antibiotic approved for use in complicated skin and skin structure infections caused by susceptible Grampositive organisms. Thixotropy The property of becoming less viscous when subjected to an applied stress, shown for example by some gels which become temporarily fluid when shaken or stirred. Thrombocytopenia A condition characterized by abnormally low levels of thrombocytes, also known as platelets, in the blood. Transcriptome The sum total of all the messenger RNA molecules expressed from the genes of an organism. Translational science An effort to build on basic scientific research to create new therapies, medical procedures, or diagnostics. Uridylation The addition of one or more uridine moieties. Vancomycin An antibiotic used to treat a number of bacterial infections. Xenografts A tissue graft or organ transplant from a donor of a different species from the recipient.

Index Note: Page numbers followed by “f” and “t” refer to figures and tables, respectively.

A Abciximab, 237
238 ABT-199, 117 Academy of Scientific and Innovative Research (AcSIR), 107
108 Accelerator program for Discovery in Brain disorders using Stem cells (ADBS), 91 Acquired immunodeficiency syndrome (AIDS), 355
356 Acute hepatic porphyria (AHP), 180 Acute lymphoblastic leukemia (ALL), 138, 240 Acute myeloid leukemia (AML), 107
108, 121t acute myeloid leukemia
targeted therapies in clinics, 117
120 approaches in target discovery, 109
117 conventional therapy for, 108 hurdles and emerging targeted therapies, 120
125 significance of target discovery, 108
109 Acute promyelocytic leukemia (APL), 113
114 ACYL3 gene, 376
377 Adalimumab, 140 Adeno-associated vectors, 32
33 Adenoviral vectors, 32 Adenovirus, 32 Adenovirus type-5 (Ad5), 155
156 Adult stem cells, 34 Advanced biotechnology-based therapeutics advancement in devices, biologics, and vaccines, 72
74 cell-based therapy, 65
67 critical processes in flow from basic science to practical application, 69
70 GEMs, 68
69 molecular diagnostics, 60
65 nanotechnology and uses in biomedicine, 67
68 pitfalls in translational research, 70
72 technologies, 55
60 Advanced therapy medicinal products (ATMPs), 65, 72 Aflatoxins, 314 Age-related cataract, 171 AKXD-23 strain, 111 Alchornea laxiflora, 272 Alirocumab, 357

All-trans retinoic acid (ATRA), 118 α-carboxy-2-nitrobenzyl (CNB), 210
211 α,β-dehydrophenylalanine (ΔPhe), 209
210 α-helical peptides, 214
215 Alzheimer’s disease (AD), 82, 92
93, 171, 374 American Medical Informatics Association (AMIA), 289
290 Amino acids, 205
207 50 -Aminolevulinate synthase 1 (ALAS1), 180 Amodiaquine, 269
270, 269t Amphipathicity, 211
212 Amphiphilic peptides, 211
213 Amrata Pharmaceuticals, 152 Amyloid peptides, 208
209 Amyloid-beta plaques (Aβ plaques), 92 Amyotrophic lateral sclerosis (ALS), 91
92 Andrographis paniculata, 316 Anemia, 46 Angiotensin-converting enzyme-2 (ACE-2), 147 Animal disease models, 82 Animal experimental models, 64 Anopheles mosquito, 267
268 ANRASSF1, 182 Antagomir, 175 Anti-Alzheimer peptides, 374
375 Antibody-dependent cellular toxicity (ADCC), 238 Antibody/antibodies, 38, 113, 145
148, 234 antibody-based approaches, 112
113 arrays, 113 engineered multispecific antibodies, 148 engineering, 248
249 monoclonal antibodies, 146
147 Antibody
drug conjugates (ADCs), 58
60, 240
241 Antigen-binding fragment (Fab), 145
146 Antigen-presenting cells (APCs), 248 Antimalarial peptides, 374 Antimicrobial agents, 220
221 Antimicrobial peptides (AMPs), 144, 373
374 Antisense lncRNAs, 181 Antisense oligonucleotides (ASOs), 92, 142 Apigenin, 93 APOA1-AS, 182 Arsenic trioxide (ATO), 118

425

426

Index

Artemisia annua, 271
272 Artemisinin and derivatives, 269t, 271
272 Artificial antibody-based molecule, 234
235 Artificial intelligence (AI), 4
5, 19
20, 141
142, 292, 350 artificial intelligence-based approach in TBI, 300
304 in drug design, 42
43 Artificial neural network in drug design, 43 Arxula adeninivorans, 45
46 Ascl1, 86
88 Aspirin, 140
141, 314 Association of Biomolecular Research Facilities (ABRF), 395
396 ATOR-1015, 242 Atovaquone (ATO), 269t, 270
271 ATSP7041 peptide, 375 AtuPLEX, 189 Autism spectrum disorders (ASDs), 88
90 Autoimmune disorders, 356 Autologous transplant, 151 Awareness of networking and engagement, 395
396 Axicabtagene ciloleucel, 149 Azobenzene, 207
208

B B cells, 146
147 Baby hamster kidney cells (BHK cells), 46 Bacillus subtilis, 45 Backpropagation networks, 43 Bacteria, 44
45 Bacterial vector, 32 Bactofection, 32 Bayesian neural networks, 43 BC-819, 185
186 BCL-2 inhibitors, 117 Benevolent AI, 303
304 “βAH” carnosine, 213 β-sheet forming peptides, 204
214 β-turn-containing peptides, 210
211 amyloid peptides, 208
209 designed peptides without aromatic end-caps, 209
210 peptide amphiphiles and amphiphilic peptides, 211
214 peptides end-capped with aromatic moieties, 204
208 β-site APP Cleaving Enzyme 1 (BACE1), 171, 375 β-turn-containing peptides, 210
211 Betula utilis, 316 Bevacizumab, 352
355 BHK cells. See Baby hamster kidney cells (BHK cells) Bifidobacterium bifidum, 19 Big data, 19
21, 292
297

Binding affinity enhancement, 249 Binding site barrier, 249 Bio-science, 54 Bio-therapeutics. See Biological therapeutics BioDiscML, 298 Bioforce’s Virichip, 16
17 Bioinformatics, 292. See also Translational bioinformatics (TBI) Biological networks, 330, 333 Biological therapeutics, 72, 137
138, 277
278 future, 154
155 history of classical modalities, 139
140, 139f modalities, 137
138 in COVID-19 treatment, 155
156 new modalities, 140
154 Biological therapy. See Immunotherapy Biologics. See Biological therapeutics Biomarker(s), 70 development, 19 in immunotherapy, 246
248 TBI in biomarker discovery, 297
299 Biomedical data integration, 292
297 Biomimetic system, 97 Biopharmaceutics. See Biological therapeutics Biosimilars, 138 Biotechnology, 4, 54, 120
125, 238, 408 Biotechnology-based therapeutics drug designing and delivery, 40
44 human gene therapy, 29
34 nanomedicine, 37
40 recombinant therapeutic proteins and vaccines, 44
48 stem cell therapy, 34
37 therapeutic biomolecules on market and therapeutic indication, 28t Bipolar disorder (BPD), 88 Bispecific antibodies (bsAbs), 148, 234
235 biomarkers in immunotherapy, 246
248 engineering of therapeutic protein, 248
250 evolution, 234
242, 236f formats, 236
238 future challenges and opportunities, 254
255 immune therapeutics and respective structural format, 239t market analysis, 250
253 mechanism of action, 238
242, 242t production, 243
246, 245t therapeutic antibodies, 252t Bispecific T-cell Engager (BiTE), 237
240 Blinatumomab, 148, 235
238 Blinatumomab/MT103, 245 purification, 246 Blincyto. See Blinatumomab

Index

Blood
brain barrier (BBB), 97 BMS-986168, 97 Boolean methods, 332 Bortezomib, 144 Bosutinib, 92 Brain organoids, 94 Braun’s lipoproteins, 47 Breast cancer, biomarkers for, 246
247 Brivaracetam, 357 Bromodomain-containing 4 (BRD4), 111 Buserelin, 144 BXH-2 strain, 111 Bystander effect, 58
59

C C9orf72 gene, 92, 96 CAB051 gene, 238 Calotropis gigantea (L.), 272 Camellia sinensis, 316 Canada’s ecosystem of translational research and funding mechanism, 392
394 Canada’s Foundation of Innovation (CFI), 392
393 Canadian Institutes of Health Research (CIHR), 392 Canadian Network of Scientific Platforms (CNSP), 396 Canarypox or recombinant attenuated vaccinia (COPAK), 276 Cancer, 352
355 immunotherapy, 234 Cancer LncRNA Census (CLC), 183 Capromab, 247 Carbon nanotubes, 40 Cardiovascular disease (CVDs), 351
352, 357 Carfilzomib, 144 Caspase-2, 180
181 Cataract, 171 Cationic liposomes, 31 Catumaxomab trifunctional antibody, 148 CBHH peptide, 211 Cell-based immunotherapies, 148
150 Cell-based therapeutics, 58 Cell-based therapy, 65
67 Cell-penetrating peptides (CPPs), 375
376 Central nervous system (CNS), 58, 83 CEP290 gene, 18
19 Cerebral organoids and future of human in vitro disease modeling, 93
95, 94f Checkpoint blockade immunotherapy, biomarkers for, 248 Checkpoint inhibitors (CPIs), 234 Chemical conjugation, 244
246 blinatumomab/MT103, 245 characterization, 246 manufacturing, 246

427

molecular design, 245
246, 246f purification of blinatumomab, 246 Chimeric antibodies, 145
146 Chimeric antigen receptor-T cell (CAR-T), 149 Chinese hamster ovary cells (CHO cells), 46 Chloroquine (CQ), 268, 269t Chondroitin sulfate A (CSA), 273 Chromosomal translocation, 118 Chronic myelogenous leukemia (CML), 352 Chronic obstructive pulmonary disease, 37 Circular RNAs (circRNAs), 166 Circumsporozoite protein (CSP), 273 Cirrhosis, 312, 315 Cis-acting regulatory DNA, 372 CIS43LS, 278 Classification algorithms, 295
296 Clinical and Translational Science Awards Program (CTSA), 390
391 Clostridium difficile infections, 153 Cluster analysis techniques, 295
296 Cluster of differentiation 3 (CD3), 245 Clustered regularly interspaced short palindromic repeats
CRISPR-associated protein 9 (CRISPR-Cas9), 17
18, 34, 55
56, 116
117 Cocaine exposure, 95 Coiled-coil motifs, 214 Collaboration, drug discovery, 361 Colony-stimulating factors (CSFs), 15
16 Compartmental models, 317
318 Competing endogenous RNA (ceRNA), 182
183 Complement-dependent cytotoxicity (CDC), 238 Complementarity determining regions (CDRs), 249 Complex disorders, 300
302 Computational analysis, 166 Computer-aided drug design, 41
43 machine learning in drug design, 42
43 in silico drug design, 42 Computer-aided drug discovery, 299
300 Constructive personalized medicine, 340 Conventional therapy for acute myeloid leukemia, 108 Core facility, 386
389 business model, 399
402, 400t of prime significance in translational research, 388
389 and research outcome, 402
403 Coronavirus disease (COVID-19), 384 Counter propagation networks, 43 Cross-validation, 295
296 CrossMab approach, 244 CSIR-Indian Institute of Toxicology Research (CSIR-IITR), 107
108 CUR-1916, 186 Curse of dimensionality, 301

428

Index

Customer segment, 400
401 CYP2C9*2 variants, 19 CYP2C9*3 variants, 19 Cytarabine, 108
109 Cytochrome bc1 complex, 270
271 Cytokines, 15
16 Cytomegalovirus (CMV), 142 Cytoplasmic RNA stabilizing factor (ɑCP1), 377 Cytotoxic T-lymphocyte-associated protein (CTLA-4), 234, 242, 248 Cytotoxic T-lymphocytes (CTLs), 149, 274

D DACC, 189 Danio rerio. See Zebrafish (Danio rerio) Dark matter of genome designing novel therapeutic peptides from, 373
376 AMPs, 373
374 anti-Alzheimer peptides, 374
375 antimalarial peptides, 374 drawbacks of peptides therapeutics, 375 future applications, 375
376 pseudogenes, 376
377 Data acquisition and warehousing, 292
293 data-driven precision medicine initiatives, 305 integration, 293
294 mining, 292
297 Daunorubicin, 108
109 Deargen, 303
304 Deep learning, 303 Degarelix, 144 Dendrimers, 38 Designed peptides without aromatic end-caps, 209
210 Diabetes, 14, 46, 355 Dihydrofolate reductase (DHFR), 270
271 Dipeptidyl peptidase 4 (DPP4), 147 Direct lineage reprogramming/transdifferentiation into neurons, 88 Direct pharmacokinetic and pharmacodynamic models, 318 Directed differentiation into neural cells, 84
86 Discovery Studio Visualizer, 12
13 DNA DNA-microarray, 349
350 drugs targeting, 270
271 vaccines, 16, 274
277 DNA methyltransferases (DNMTs), 182 DNMT1, 110 Dose Disintegration and Dissolution Plus software (DDDPlus software), 12
13 Dravet syndrome, 186

Drosophila melanogaster, 373
374 Drug repositioning (DR), 73
74, 361
362 Drug(s) designing, 56 computer-aided drug design, 41
43 and delivery, 40
44 drug delivery, 44 rational drug design, 41 development, 12
16 cytokines, 15
16 hormones, 14 monoclonal antibodies, 14
15 protein drugs, 14 vaccines, 16 discovery, 40 platform, 375 process, 55 targeting DNA, 270
271 targeting membrane transporters, 271
272 DSM265, 269t, 271 Dual targeting, 251 Duchenne muscular dystrophy (DMD), 18 Duloxetine, 362 Dupilumab, 356 Dynamic modeling, 333

E Ebola virus, 147 Economic development, 408 Efungumab, 237
238 Electronic health records (EHRs), 292 Electronics Medical Records and Genomics network (eMERGE network), 305 Electroporation, 31 Embryonic stem cells (ESCs), 34, 150
151 Emicizumab, 148 Enfuvirtide, 355
356 Engineered multispecific antibodies, 148 Engineering of therapeutic protein, 248
250 binding affinity enhancement, 249 immunogenicity minimization, 249
250 stability enhancement and half-life extension, 250 Enhancer lncRNAs, 181 Env for enveloping protein of virus, 32 Enzyme replacement therapy (ERT), 144
145 Enzymes, therapeutic, 144
145 Epithelial
mesenchymal transition (EMT), 176 Escherichia coli, 32, 45 Estrogen receptor (ER), 247 Etanercept, 356 Euclidean distance, 409 Euclidean metric, 410 European Union (EU), 235
236

Index

Evolocumab, 357 Exome sequencing, 115 Exported protein-1 (Exp1), 276 Extensible Markup Language (XML), 293 Extracellular matrix proteins (ECM proteins), 315

F Fecal microbiota transplant (FMT), 153 Fibrosis, 171 Flavobacterium okeanokoites (FokI), 33
34 Flow cytometry, 112 FLT3 FLT3/ITD mutation, 110 targeting FLT3-mutated acute myeloid leukemia, 119
120 5-Fluorouracil, 216 Fmoc-DAla-DAla hydrogel, 205
207 Fmoc-Tyr hydrogel, 205
207 FMR1 gene, 89 Follistatin (FST), 110 FoxG1, 95 Fragile X syndrome (FXS), 82, 89 Fragment crystalize region (Fc region), 145
146 Fund for Improvement of Science and Technology Infrastructure (FIST), 395 Funding mechanism for research and innovation, 394
395 partners, 401

G G-protein coupled receptor-119 (GPR-119), 355 Gag for encoding viral proteins, 32 GalNAc. See N-acetylgalactosamine (GalNAc) GalNAc-siRNA technology, 188
189 Gardasil-9, 349 Gendicine, 72 Gene correction strategies, 55
56 gene-editing technology, 33
34 clustered regularly interspaced short palindromic repeat
Cas-associated nucleases, 34 transcription activator-like effector nucleases, 33
34 zinc-finger nucleases, 33 gun, 31 sequencing, 55
56 therapy, 17
19, 29, 47 TRANSFER system, 30
33 nonbiological delivery system, 31
33 Genetic epidemiology, 62 Genetic modifications, 372 Genome Canada, 393

429

Genome editing technologies, 55
56, 116
117 Genome-scale metabolic modeling (GEMs), 68
69 Genome-wide association studies (GWAS), 301 Genomic medicine, 304 Genomic technologies, 114
117 Germline gene therapy, 17
18, 30 Givosiran, 180, 190 Glatiramer Acetate, 357 Glaucoma, 171 Glial cells, 90 Global research and development expenditure, 396
399 Glucagon-like peptide 1 (GLP-1), 355 GMGA1 pseudogene mRNA, 377 Gold nanoparticles, 40, 67 Good Manufacturing practice (GMP), 65 Goserelin, 144 Governance model for core facility, 402 Granulocyte-Macrophage Colony-Stimulating Factor (GM-CSF), 15
16, 274 Granzyme-B, 238 Gross domestic product (GDP), 408 Group hubs, 333 Guide RNA (gRNA), 116
117

H H19, 182
183, 185
186 Half-life extension, 250 Halofantrine, 268 Hansenulla polymorpha, 45
46 HBB gene mutation, 17
18 Heart diseases, 36 Heat shock, 32 Hematin, 268 Hematopoietic stem cells (HSCs), 34, 151 Heme-detoxification drugs, 268
270 Hemlibra. See Emicizumab Hen egg-white lysozyme (HEWL), 208
209 Hepatitis B surface antigen (HBsAg), 273 Hepatitis B viruses (HBV), 29, 47
48, 312 Hepatitis C viruses (HCV), 29, 175, 312 Hepatocellular carcinoma (HCC), 312 causes, 314f challenges in therapeutic and medicinal drug treatment for, 316 liver cancer statistics, 313f pharmacokinetic and pharmacodynamic models, 316
319 progression, 313f risk factors, 312
314 stages, 314
315, 315f Hepatocyte growth factor (HGF), 217
218 Hepsin, 247

430

Index

Hereditary transthyretin-mediated amyloidosis (hATTR), 143, 177
180 Herpes simplex virus, 33 Histrelin, 144 HLA-B*5701 genotype, 19 Homology modeling, 42 Homology-directed repair (HDR), 55
56 Hormones, 14 HOTAIR, 182 Hubs, 333 Human epidermal growth factor receptor 2 (HER-2), 247 Human gene therapy, 29
34. See also Stem cell therapy ethical issue, 34 gene transfer system, 30
33 gene-editing technology, 33
34 germline gene therapy, 30 somatic cell gene therapy, 30 Human Genome Project, 55
56, 388 Human granulocyte-macrophage colony stimulating factor (hGM-CSF), 276 Human growth hormone, 46 Human immunodeficiency virus (HIV), 15
16, 29, 147 Human in vitro disease models cerebral organoids and future of human in vitro disease modeling, 93
95, 94f future perspectives, 97 identification of pathways and drug targets for designing therapies, 95
97 using iPSCs/patient fibroblasts, 83
88 neurodegenerative diseases, 91
93 neurodevelopmental disorders, 88
91 Human islet amyloid polypeptide (hIAPP), 208
209 Human papilloma vaccine, 47
48 Huntington’s disease (HD), 82 Hybrid hybridomas, 235
236, 243 Hybridoma technology, 14
15, 146
147 Hydrogels, 38, 203
204, 339
340 Hydrophobic drugs, 56
57 Hydroporation, 31 Hypercholesterolemia, 357

I i-drug discovery, 55 I-STEM, 396 iAb5p peptide, 374 ICA-105665, 357 Idarubicin, 108
109 Identification relation, 410 Imatinib, 352 Immune checkpoint blockade therapy (ICB therapy), 242, 248 Immune CPIs, 234

Immune payloads, 240
241 Immunogenicity minimization, 249
250 Immunoglobulin (Igs), 140 IgG, 235
237 IgG1, 356 Immunophenotyping, 112 Immunotherapy, 234, 277
278 immunotherapy-based cell biologics, 150 Immunotoxin, 240
241 In silico computational methods, 55 drug design, 42 ncRNA prediction tools, 166
167 research platforms, 350 In vitro diagnostic multivariate index assay (IVDMIA), 298 Indian Ayurveda medicine system, 139 Indirect pharmacokinetic and pharmacodynamic models, 319 Induced neurons (iNs), 88 Induced pluripotent stem cells (iPSCs), 150
151 human disease models using iPSCs/patient fibroblasts, 83
88 direct differentiation into neurons/glia, 86
88 direct lineage reprogramming/transdifferentiation into neurons, 88 directed differentiation into neural cells, 84
86 Infliximab, 140, 356 Inodiftagene vixteplasmid therapy for bladder cancer, 185
186 Institute for Stem Cell Science and Regenerative Medicine (inStem), 91 Institutes and centers (IC), 391 Insulin, 14 Integrin alpha V (ITGAV), 111 Integrin beta 3 (ITGB3), 111 Interactome, 330 Interferons (IFNs), 15
16 Intergenic lncRNAs, 181 Intergenic sequences, 374
375 Interleukins (IL), 15
16 IL-6, 90 Internal tandem duplication (ITD), 110 Intradermal sites (ID sites), 275
276 Intramuscular injection (IM injection), 274 Intraocular pressure (IOP), 171 Intronic lncRNAs, 181 Ipilimumab, 147, 234 Isobaric tags for relative and absolute quantification (iTRAQ), 114 Isocitrate dehydrogenase inhibitors (IDH), 117
118 Isotope-coded affinity tag (ICAT), 114 Ixazomib peptides, 144

Index

J Junk DNA, 371
372

K K-mean clustering algorithm, 303 KAE609, 272 Key partners, 401 Key resources, 401 Keytruda. See Pembrolizumab Kluyveromyces lactis, 45
46 Knob-into-hole approach, 243
244 Kyrmriah. See Tisagenlecleucel

L L-asparaginase,

145 amino acid, 145 Lactococcus lactis, 19 Laminin-1, 218
219 LDL receptors (LDLRs), 357 Leber’s congenital amaurosis 10 (LCA10), 18
19 Leukemic stem cells (LSCs), 111 Leuprorelin, 144 Lipid nanoparticle (LNP), 177
180 Lipinski’s rule of five, 140 Lipoplexes, 31 Liposomal vincristine design, 120
125 Liposomes, 31, 38 Liraglutide, 355 Listeria monocytogenes, 32 Live-attenuated vaccine, 47 Liver disease, 37 Liver-stage antigen-1 (LSA1), 276 Locked nucleic acid (LNA), 175 Long noncoding RNAs (lncRNAs), 166, 181
187, 372. See also Noncoding RNA (ncRNA) biogenesis, 181 biomarkers, 186t expression profile, 183
184 therapeutics, 185t working mechanisms, 182
183 Low-density lipoproteins (LDLs), 357 Lumefantrine, 269
270, 269t Lung disorder, 37 Lymnaea stagnalis, 377 L-asparagine

M M5717, 269t, 271 Machine learning (ML), 19
20, 301, 350 complex disease analysis using, 301
302 in drug design, 42
43 artificial intelligence in drug design, 42
43 artificial neural network in drug design, 43 examples, 302
304

431

Magnetic nanoparticles, 40 Magnetic resonance imaging (MRI), 16
17 Magnetofection, 31 Major histocompatbility complex (MHC) class-I receptors, 149 Major Research Instrumentation Program (MRI), 392 Major Science Initiatives (MSI), 393 Malaria, 267
268 antimalarial compounds, targets, and mode of action, 269t biological therapeutics, 277
278 diseases, 374 drugs targeting DNA or protein synthesis, 270
271 drugs targeting membrane transporters, 271
272 heme-detoxification drugs, 268
270 natural products, 272
273 nucleic acid vaccines, 273
277 protein-based malaria vaccines, 273 MALAT1, 182
183 Mammals, 46 MARG1 peptide, 221 Market analysis, 250
253 Mass cytometry, 113 Mass spectrometry
based approaches (MS
based approaches), 113
114 Mass-to-charge ratio (m/z ratio), 113 MAX1 peptide, 210
211, 221 McGill Centre Translational Research in Cancer (MCTRC), 388 Mechanisms of action (MoA), 268 Medical imaging, 40 Medical service system, 408 Medicines for Malaria Venture (MMV), 271
272 Mefloquine (MFQ), 268, 269t Membrane transporters, drugs targeting, 271
272 Merozoite surface protein-1 (PvMSP-1), 274
275 MERS-Corona virus, 147 Mesenchymal stem cells (MSCs), 34, 151 Meta-analysis, 301 Metagenomics, 388
389 Methicillin-resistant Staphylococcus aureus, 151 Metreleptin, 355 Metric space, 410
411 Micelle, 38 Microarray, 115 Microbial engineering for bio-therapeutics, 19 Microbiome-based therapeutics, 153
154 Microbiomics, 388
389 MicroRNAs (miRNAs), 115, 142
143, 166
167, 171
181 biogenesis, 167
169, 168f diagnostics, 174t expression profile, 170
171

432

Index

MicroRNAs (miRNAs) (Continued) miR-192, 177 miR-34a, 176 therapeutics, 172t working mechanism, 169 Middle east respiratory syndrome (MERS), 351
352 Millen Baugh equation, 318 Minoxidil, 361
362 Miravirsen for treatment of Hepatitis C, 175 miRView Meso, 177 miRView mets, 177 Mixed lineage leukemia (MLL), 111 MMV253, 269t, 272 MNK1, 115 Molecular approach, 109, 111
117. See also Systems approach 2D-DIGE, 113 antibody arrays, 113 antibody-based approaches, 112
113 CRISPR/Cas system, 116
117 exome sequencing, 115 genome-editing technologies, 116
117 genomic technologies, 114
117 ICAT, 114 immunophenotyping, 112 iTRAQ, 114 mass cytometry, 113 microarray, 115 MRM-MS, 114 MS
based approaches, 113
114 multiparameter flow cytometry, 112 NGS, 115 proteomic technologies, 112
114 RNAi, 116 SILAC, 113
114 TALENs, 116 transcriptome sequencing, 115 WGS, 115 ZFNs, 116 Molecular diagnostics, 60
65 organoids, 63
65 translational bioinformatics, 62
63 Molecular docking, 42 Monoclonal antibodies (mAb), 9
12, 14
15, 146
147, 278 therapeutics, 10t Monogenic disorders, 82 Monomethyl auristatin E (MMAE), 240
241 Mouse (Mus musculus), 111 Mouse models, 82 mRNA vaccines, 16 MRX34, 176 Multi-stage DNA vaccine operation, 5 genes (MuStDO5), 276

Multicriteria decision making (MCDM), 408 Multidrug-resistant (MDR), 151 Multifactorial disorders, 300
301 Multimodal Animal Rotation System software (MARS software), 12
13 Multiparameter flow cytometry, 112 Multiple reaction monitoring MS (MRM-MS), 114 Multipotent stem cells, 35
36 Murine leukemia virus (MuLV), 111 Muromonab-CD3, 249
250 Murraya koenigii, 272 Mus musculus. See Mouse (Mus musculus) Mycobacterium tuberculosis, 151 Myocardial infarction (MI), 170
171

N N,N-dibenzoyl-L-cystine, 204
205, 206f N-acetylgalactosamine (GalNAc), 143 Naı¨ve T cells, 149 Nano drug delivery, 38 Nano mix, 16
17 Nano therapeutic applications, 37
39 nano drug delivery, 38 nanosensor, 39 Nano-based drugs, 29 Nanoimaging, 40 Nanomaterials, 16
17 Nanomedicine, 16
17, 37
40, 67
68. See also Precision medicine in drug discovery approaches, 56
58 nano therapeutic applications, 37
39 nanoimaging, 40 tissue engineering, 39
40 Nanoparticles, 16
17, 56, 68 Nanosensor, 39 Nanotechnology, 37, 58 and uses in biomedicine, 67
68 Nanozymes, 68 Natalizumab, 357 National Center for Advancing Translational Sciences (NCATS), 4, 391 National Center of Research Resources (NCRR), 390
391 National Centre for Biological Sciences (NCBS), 91 National facilities (NF), 393 National Institute of Health (NIH), 315, 390
391 National Institute of Mental Health and Neurosciences (NIMHANS), 91 National Research Foundation (NRF), 394
395 National Science and Technology Management Information System, 395 National Science Foundation (NSF), 391
392 Natural killer cells (NK cells), 149

Index

Natural products, 272
273 Natural Sciences and Engineering Research Council (NSERC), 392 Network architecture, 333 Network medicine, 330 Neural cells, directed differentiation into, 84
86 Neural disease, 36 Neural stem cell (NSC), 96 Neurodegenerative diseases, 91
93 AD, 92
93 ALS, 91
92 PD, 93 Neurodevelopmental disorders, 88
91 ASDs, 89
90 FXS, 89 Rett syndrome, 88
89 SZ, 90
91 Neurological disorder, 357 Neurology, 357 Neuron-astrocyte coculture study, 90 Neuronal nitric oxide synthase (nNOS), 377 Neurons/glia, direct differentiation into, 86
88 Next-generation sequencing (NGS), 54
56, 115, 350 Ngn2, 86
88 Nicotine, 95 Nivolumab, 147 NM23, 113 Nonalcoholic fatty liver disease (NAFLD), 312, 314
315 Nonalcoholic steatohepatitis (NASH), 312, 315 Nonarteritic anterior ischemic optic neuropathy (NAION), 180
181 Nonbiological delivery system, 31
33 biological method, 32
33 chemical method, 31
32 physical method, 31 Noncoding DNA, 372 Noncoding RNA (ncRNA), 142, 165. See also Long noncoding RNAs (lncRNAs) as biomarkers/therapeutics, 189
191 and classification, 165
166 patent landscape of, 187
189 screening and characterization, 167 Nonhomologous end joining (NHEJ), 55
56 Nonnegative relation, 410 Nonstructural proteins (NSPs), 155
156 Novel coronavirus 2019 (nCoV), 289
290 Novo Nordisk, 355 Nucleic acid therapeutics, 142
143 vaccines, 273
277 DNA vaccines, 274
277 RNA vaccines, 277 NUP98-HOXA9 (NHA9), 110

433

O Office of Research Infrastructure Programs (ORIP), 391 Omics, 292
297 Onpattro, 143 Opdivo. See Nivolumab OPK88001 for Dravet syndrome, 186 Organically transported polypeptide 1B1 (OATP1B1), 336 Organoids, 63
65, 94 Organs-on-chips, 65, 97 OsteomiR, 176
177 Outer radial glia, 82 Outer subventricular zone progenitors, 82
83 Ovulation, 47 OX40, 242

P p53, 183 Paclitaxel, 216 Paratope, 145
146 Parkinson’s disease (PD), 82, 93, 171 Parthenocissus tricuspidata, 272 Patent landscape of noncoding RNA, 187
189 Patisiran, 177
180, 190 Payload, 58
59 PCA3 as diagnostic marker for prostate cancer, 187 PCSK9 gene. See Pro-protein convertase subtilisin/kexin type 9 gene (PCSK9 gene) Pegvaliase, 144
145 PEGylated peptides, 213
214 Pembrolizumab, 147 Penicillin, 351
352 Peptide(s), 143
144 amphiphiles, 211
214 drawbacks of peptides therapeutics, 375 end-capped with aromatic moieties, 204
208 peptide-based hydrogels, 204
215 β-sheet forming peptides, 204
214 biomedical applications, 215
221 classes of hydrogelating peptides, 205t Perforin, 238 Peripheral compartments, 317 Personalized medicine, synthetic biology in, 340 Phage therapies, 151
152 Phagemids, 146
147 Pharmaceutical target-based drugs, 120
125 Pharmacodynamics (PD), 316
317 biomarker in hepatocellular carcinoma, 320
322 gene, 334
335 studies, 350 Pharmacogenomics, 62 Pharmacogenomics Research Network, 305

434 Pharmacokinetic and pharmacodynamic models, 316
319, 319t advantages, 319
320 compartmental models, 317
318 direct pharmacokinetic and pharmacodynamic models, 318 drug responses, 323 indirect pharmacokinetic and pharmacodynamic models, 319 Pharmacokinetic profile (PK profile), 316, 334
335, 350 Pharmacophore, 42 Phenylketonuria, 144
145 Phosphate-buffered saline (PBS), 208
209 Phosphatidylinositol 3-kinase (PfPI3K), 271
272 Phosphatidylinositol 4-kinase (PI4-K), 272 Piceid-(1
6)-β-D-glucopyranoside (PBG), 272 Pichia pastoris, 45
46 Piperaquine, 269
270, 269t Piwi-interacting RNA (piRNAs), 166 Placenta, 150
151 Plasmodium, 271
272 antigens, 276 P. berghei, 272 P. falciparum, 268 P. knowlesi, 276 P. sporozoites, 267
268 P. tricuspidata, 272 P. vivax, 268 P. yoelii CSP, 274 Plasmodium falciparum CSP (PfCSP), 275
276 Plasmodium falciparum eukaryotic elongation factor 2 (PfeEF2), 271 Plasmodium falciparum liver stage antigen-3 (PfLSA3), 276 Plasmodium macrophage migration inhibitory factor (PMIF), 277 Pluripotent ESCs, 151 Pluripotent stem cells, 35 PML-RARα targeted therapy, 118 Podophyllum hexandrum, 316 Pol for entering target cells, 32 Polyclonal antibodies, 146 Polyethylene glycol (PEG), 188
189, 213
214 Polymers, 32, 38 Polyplexes, 32 Pongamia pinnata (L), 272 Pramlintide, 355 Prebiotics, 153 Precision medicine, 19. See also Nanomedicine implication of TBI in, 304
306 data-driven precision medicine initiatives, 305 future prospects, 305
306

Index

Precursor messengers RNA (pre-mRNA), 142 Precursor miRNAs (premiRNAs), 166
169 Predictive, participatory, personalized, and preventive medicine (‘P4 medicine’), 304 Predictive models, 295
296 Pregabalin, 357 Pregnancy, 47 Preprocessing, 295 Primary miRNA transcript (Pri-miRNA), 168f Pro-protein convertase subtilisin/kexin type 9 gene (PCSK9 gene), 357 Probiotics, 153 Progesterone receptor (PR), 247 Program application (PA), 391 Programmed cell death protein 1/programmed cell death protein ligand 1 (PD-1/PD-L1), 234 Programmed cell-death-1 (PD-1), 248 Proguanil, 269t, 270
271 Promoter lncRNAs, 181 “Proof of concept” study, 5
6 Prophage DNA, 152 Prostate cancer, biomarkers for, 247 Prostate-specific antigen (PSA), 187 Prostate-specific membrane antigen (PSMA), 247 Protein(s), 203
204 drugs, 14 protein-based malaria vaccines, 273 synthesis, 270
271 Proteomic approach for identification of pharmacodynamic biomarkers, 321 Proteomic technologies, 112
114 Pseudo-mRNA (ѰmRNAs), 377 Pseudogene lncRNAs, 181 Pseudogenes, 376
377 pseudogene-directed gene regulation, 377 Psoriasis, 356 Pyrimethamine (PYR), 269t, 271 Pyronaridine, 269
270

Q QPI-1007, 180
181, 189 QSARPro software, 12
13 Quadroma. See Hybrid hybridomas Quantitative polymerase chain reaction (qPCR), 61, 331 Quantitative structure
activity relationship (QSAR), 42, 299
300 Quantitative structure
property relationship (QSPR), 41 Quantum dots, 67 Quark Pharmaceuticals, 189 Quassia amara L., 272 Quinine, 269t, 270

Index

435

R

S

R-33 (chaperone), 92 RADA16-I hydrogels, 218
219 Random forest algorithm, 303 Ranibizumab, 237
238, 251 Reactive oxygen species (ROS), 93 Receptor tyrosine kinases (RTKs), 119
120 Recombinant DNA (rDNA), 9
12, 14 Recombinant DNA Advisory Committee, 340
341 Recombinant therapeutic proteins and vaccines, 44
48 expression system, 44
46 bacteria, 44
45 mammals, 46 yeast, 45
46 recombinant protein, 44 as treatment, 46
47 recombinant vaccine, 47
48 Red blood cells (RBCs), 267
268 Regression approaches, 295
296 Relative hubs, 333 Request for Application (RFA), 391 Research and development (R&D), 384 supporting mechanism, 389
399 Canada’s ecosystem of translational research and funding mechanism, 392
394 highlights, 394
396 supporting translational research through core facilities in United States, 390
392 Researchers, 401 Responsive hydrogels, 207
208 Retigabine, 97 Retinal diseases, 36 Retinal ganglion cells (RGCs), 180
181 Retinal pigment epithelium 65 (RPE65), 29 Retrometabolic drug design (RMDD), 364 Retroviral vectors, 32 Retroviruses, 32 Rett syndrome, 88
89 Reverse transcriptase quantitative polymerase chain reaction (RT-PCR), 61 Risankizumab, 251 RNA sequencing, 61 vaccines, 277 RNA-binding proteins (RBPs), 111 RNA-induced silencing complex (RISC), 142
143 RNA interference therapy (RNAi therapy), 116, 142
143 RNA template dependent RNA polymerase (RdRp), 155
156 RNAi therapy. See RNA interference therapy (RNAi therapy) Romosozumab, 251 RTS, S, 273

Saccharomyces cerevisiae, 45
46 Sacituzumab govitecan, 251 Salmonella spp., 32 Sanger sequencing, 55
56 Scaffold for regenerative medicine, 218
219 Schizophrenia (SZ), 88, 90
91 Science, technology, and innovation (STI), 394 Science, Technology, and Innovation Policy (STIP), 394 Science Exchange, 396 Scientific platforms, 396 Scientific Research Infrastructure Management and Networks (SRIMAN), 395 SCN1ANAT, 186 Seed sequence, 169 Self-assembly of molecules, 203 Semaglutide, 355 Semaxinib, 120 Sense lncRNAs, 181 Sepofarsen, 18
19 Severe acute respiratory syndrome (SARS), 351
352 Severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2), 147, 289
290 SH2-containing protein phosphate (SHP1), 114 Shared research facilities, 388 Short hairpin RNA (shRNA), 111 Shortest distance, 409 Sickle cell anemia, 18 Silence Therapeutics, 189 Silver nanoparticles, 40 Silybum marianum, 316 Sipuleucel-T, 16 SJ733, 269t, 272 Small interfering RNAs (siRNAs), 142
143, 166, 171
181 biogenesis, 167
169, 168f therapeutics, 178t working mechanism, 169 Small modular immunopharmaceuticals (SMIPs), 238 Small-molecule drugs, 140
142, 216
217 Small noncoding RNAs, 167
181 Small nucleolar RNAs (snoRNAs), 166 Social Sciences and Humanities Research Council (SSHRC), 392 Somatic cell gene therapy, 30 Somatic gene therapy, 17
18 Somatropin, 14 Sonoporation, 31 Sorafenib, 316 Spinal muscular atrophy (SMA), 82, 140
141 Sporozoite asparagine-rich protein 1 (SAP1), 274
275 Stability enhancement, 250

436

Index

Stable isotope labeling with amino acids in cell culture (SILAC), 113
114 Stable nucleic acid-lipid particles (SNALPs), 188
189 Stem cell therapy, 34
37. See also Human gene therapy benefits, 36
37 challenges and problems, 37 sources, 35
36 Stem cell(s), 150
151 niche, 64 Streptococcus pyogenes, 17
18 Structured query languages (SQL), 290 Substrate reduction therapy, 144
145 Subunit vaccines, 47 Succinimidyl-3(2-pyridylthiol)propionate (SPDP), 244
245 Sulfadoxine, 269t, 271 Sunitinib, 120 Support vector machine (SVM), 302
303 Supremum distance, 411 Survival motor neuron gene (SMN2 gene), 142 SymCyp Ltd, 336 Symmetry relation, 410 Synthetic biology, 329
331, 336
341 and components, 337f disease mechanisms, 337
339 in drug discovery, development, and delivery, 339
340 in personalized medicine, 340 regulation and ethical considerations, 340
341 Synthetic peptides, 373
374 System biology, 329
336 central principles of scientific approaches to biology systems, 332
333 fields in therapeutic applications system biology, 333
336 translating systems biology
systems medicine, 332t Systemic evolution of ligands by exponential enrichment (SELEX), 72
73 Systems approach, 109
111. See also Molecular approach mouse, 111 zebrafish, 110 Systems medicine, 333
334, 335f Systems pharmacology, 334
336

T T-cell receptors (TCRs), 148 T cells, 73 T-phase, 349 Tafenoquine, 269
270 Target discovery approaches in, 109
117 significance of, 108
109

Target identification and validation, 350 Targeted drug delivery, 120
125 Targeted therapy, 108
109 Technique for order of preference by similarity to ideal solution (TOPSIS), 408
412 methodology, 412
416 analysis of sensitivity, 416 application, 414
416 selection of criteria, 413 selection of indicators, 414 metric space, 410
411 new TOPSIS approach, 411
412 result from, 416
417 sensitivity analysis, 418 Tecomella undulata, 316 Tet213 peptide, 220
221 Tetracycline controlled transcription ON/OFF system (TET ON/OFF system), 349
350 Tetravalent bsAbs, 251 Thalidomide, 361
362 Theranostics, 70 Therapeutic delivery, of peptide-based hydrogels, 216
218 small molecules, 216
217 therapeutic secretions from encapsulated cells, 218 vaccine adjuvant and macromolecule delivery, 217
218 Therapeutic enzymes, 144
145 Therapeutic outcome using PD biomarker, 322 Therapeutic peptides, 143
144 Therapeutic proteins, 143
145 Thermoproteolyticus rokko, 205
207 Third-generation antibodies (3G antibodies), 237
238 Three dimension (3D) aspects, 83 bio-printing technologies, 64
65 network, 204 pharmacophore mapping, 42 ThrombomiR, 176
177 Thrombospondin-related adhesion protein (TRAP), 276 Tisagenlecleucel, 149 Tissue engineering, 39
40, 63
64 stem cells, 64 Titanium dioxide nanoparticles (TiO2 nanoparticles), 40 TNF-α neutralizing antibody (anti-TNF-α), 217
218 Toddalia asiatica (L), 272 Top-down hypotheses, 333 Transcription activator-like effector nucleases (TALENs), 33
34, 55
56, 116 Transcriptome sequencing, 115

Index

Translational bioinformatics (TBI), 19
21, 62
63, 289
290 artificial intelligence-based approach in TBI, 300
304 in biomarker discovery, 297
299 computer-aided drug discovery, 299
300 graphical representational, 291f implication of TBI in precision medicine, 304
306 initiatives, 296t omics and big data, 292
297 Translational biotechnology, 4, 28, 54
55 applications, 9
21 background and emergence of field, 4
5 challenges to solutions, 6
9 in human healthcare, 8f Translational drug discovery (TDD), 348, 348f approaches to, 360
364 challenges in, 359
360 FDA approved drug repositioning candidates, 362t future perspective, 364
365 list of drugs, 353t opportunities in, 358 successful advances in, 351
357 tools involved in, 349
350, 351f Translational Health Science and Technology Institute (THSTI), 402
403 Translational neuroscience, 357 Translational research, 4, 6f, 54, 289
290, 348
349, 385, 385f challenges, 385
386 continuum, 16t core facility, 386
389 efficiencies and lean practices in research management, 399
403 issues, obstacles, and potential solution of, 7t learnings for future, 403
404 comprehensive availability of infrastructure inventory, 403
404 impact measurement, 404 integration of core facilities within institutional strategic plan, 403 phases, 5
6, 13f research and development supporting mechanism, 389
399 socioeconomic impact, 384
386 Trastuzumab, 352
355 Tri-agency, 392 Triangular inequality relation, 410 Triclisia gilletii, 272 Trimab, 237
238 Triptorelin, 144 Tris(2-carboxyethyl)phosphine (TCEP), 208
209 Tumor cells, 149 environment, 234

437

Tumor necrosis factor (TNF), 356 Tumor-infiltrating lymphocytes (TILs), 149 Two-dimensional difference gel electrophoresis (2D-DIGE), 113 Two-dimensional electrophoresis (2D electrophoresis), 113 Type-2 diabetes mellitus (T2DM), 355

U UCT943, 269t, 272 United States Food and drug administration (US FDA), 9
12, 14
15, 29, 117, 237
238, 277
278, 292
293, 352
355 FDA-approved AML targeted therapies, 119t Ustekinumab, 356

V Vaccines, 15t, 16 adjuvant and macromolecule delivery, 217
218 Value proposition, 401 Vancomycin-resistant Enterococcus, 151 Vanucizumab, 244 VAR2CSA fragments, 273 Vascular endothelial growth factor (VEGF), 352
355 Vector vaccine, 47
48 Vernonia amygdalina, 272 Vincristine sulfate liposome, 120
125 Viral vector, 32 Virtual high-throughput screening, 42

W Whole-genome sequencing (WGS), 115 World Health Organization (WHO), 107
108, 268, 297, 352 Wound dressing, 219
220

X XIST, 376
377 XML. See Extensible Markup Language (XML)

Y Yarrowia lipolytica, 45
46 Yeast, 45
46 Yervoy. See Ipilimumab Yescarta. See Axicabtagene ciloleucel

Z Zea mays L., 272 Zebrafish (Danio rerio), 110 Zika virus infection, 95 Zinc-finger nucleases (ZFNs), 33, 55
56, 116