Drug Discovery Toxicology

Yvonne Will, PhD, is a Senior Director and the Head of Science and Technology Strategy, Drug Safety Research and Development at Pfizer, Connecticut, USA. She co-edited the book Drug-Induced Mitochondrial Dysfunction, published by Wiley in 2008.J. Eric McDuffie, PhD, is the Director of the Discovery / Investigative Toxicology and Laboratory Animal Medicine groups at Janssen Research & Development, California, USA.Andrew J. Olaharski, PhD, is an Associate Director of Toxicology at Agios Pharmaceuticals, Massachusetts, USA.Brandon D. Jeffy, PhD, is a Senior Principal Scientist in the Exploratory Toxicology division of Nonclinical Development at Celgene Pharmaceuticals, California, USA.

• LIST OF CONTRIBUTORS xxiFOREWORD xxvPART I INTRODUCTION 11 Emerging Technologies and their Role in Regulatory Review 3Thomas J. Colatsky1.1 Introduction 31.2 safety assessment in Drug Development and Review 41.3 The Role of New Technologies in Regulatory Safety Assessment 61.4 Conclusions 8References 8PART II SAFETY LEAD OPTIMIZATION STRATEGIES 132 Small‐Molecule Safety Lead Optimization 15Donna M. Dambach2.1 Background and Objectives of Safety Lead Optimization Approaches 152.2 Target Safety Assessments: Evaluation of Undesired Pharmacology and Therapeutic Area Considerations 162.3 Implementing Lead Optimization Strategies for Small Molecules 162.4 Conclusions 23References 233 Safety Assessment Strategies and Predictive Safety of Biopharmaceuticals and Antibody Drug Conjugates 27Michelle J. Horner, Mary Jane Hinrichs and Nicholas Buss3.1 Background and Objectives 273.2 Target Safety Assessments: Strategies to Understand Target Biology and Associated Liabilities 283.3 Strategic Approaches for Biopharmaceuticals and ADCs 293.4 Predictive Safety Tools for Large Molecules 333.5 Strategies for Species Selection 343.6 Strategy for Dose‐Ranging Studies for Safety Evaluation of Biopharmaceuticals 353.7 Conclusions 35References 364 Discovery and Development Strategies for Small Interfering Rnas 39Scott A. Barros and Gregory Hinkle4.1 Background 394.2 Target Assessments 404.3 siRNA Design and Screening Strategies 414.4 Safety Lead Optimization of siRNA 454.5 Integration of Lead Optimization Data for Candidate Selection and Development 484.6 Conclusions 49References 49PART III BASIS FOR IN VITRO–IN VIVO PK TRANSLATION 535 Physicochemistry and the Off‐Target Effects of Drug Molecules 55Dennis A. Smith5.1 Lipohilicity, Polar Surface Area, and Lipoidal Permeability 555.2 Physicochemistry and Basic ADME Properties for High Lipoidal Permeability Drugs 565.3 Relationship between Volume of Distribution (Vd) and Target Access for Passively Distributed Drugs 585.4 Basicity, Lipophilicity, and Volume of Distribution as a Predictor of Toxicity (T): Adding The T to ADMET 595.5 Basicity and Lipophilicity as a Predictor of Toxicity (T): Separating the D from T in ADMET 605.6 Lipophilicity and PSA as a Predictor of Toxicity (T): Adding the T to ADMET 605.7 Metabolism and Physicochemical Properties 615.8 Concentration of Compounds by Transporters 615.9 Inhibition of Excretion Pumps 635.10 Conclusions 64References 656 The Need for Human Exposure Projection in the Interpretation of Preclinical In Vitro and In Vivo ADME Tox Data 67Patrick Poulin6.1 Introduction 676.2 Methodology Used for Human PK Projection in Drug Discovery 676.3 Summary of the Take‐Home Messages from the Pharmaceutical Research and Manufacturers of America Cpcdc Initiative on Predictive Models of Human PK from 2011 72Abbreviations 77References 777 A DME Properties Leading to Toxicity 82Katya Tsaioun7.1 Introduction 827.2 The Science of ADME 837.3 The ADME Optimization Strategy 837.4 Conclusions and Future Directions 89References 90PART IV Predicting Organ Toxicity 938 Liver 95J. Gerry Kenna, Mikael Persson, Scott Q. Siler, Ke Yu, Chuchu Hu, Minjun Chen, Joshua Xu, Weida Tong, Yvonne Will and Michael D. Aleo8.1 Introduction 958.2 DILI Mechanisms and Susceptibility 968.3 Common Mechanisms that Contribute to DILI 988.4 Models Systems Used to Study DILI 1088.5 In Silico Models 1148.6 Systems Pharmacology and DILI 1188.7 Summary 119References 1219 Cardiac 130David J. Gallacher, Gary Gintant, Najah Abi‐Gerges, Mark R. Davies, Hua Rong Lu, Kimberley M. Hoagland, Georg Rast, Brian D. Guth, Hugo M. Vargas and Robert L. Hamlin9.1 General Introduction 1309.2 Classical In Vitro/Ex Vivo Assessment of Cardiac Electrophysiologic Effects 1339.3 Cardiac Ion Channels and In Silico Prediction 1379.4 From Animal Ex Vivo/In Vitro Models to Human Stem Cell‐Derived Cms for Cardiac Safety Testing 1409.5 In Vivo Telemetry Capabilities and Preclinical Drug Development 1419.6 Assessment of Myocardial Contractility in Preclinical Models 1449.7 Assessment of Large Versus Small Molecules in CV SP 1479.8 Patients do not Necessarily Respond to Drugs and Devices as do Genetically Identical, Young Mature, Healthy Mice! 148References 15210 Predictive In Vitro Models for Assessment of Nephrotoxicity and Drug–Drug Interactions In Vitro 160Lawrence H. Lash10.1 Introduction 16010.2 Biological Processes and Toxic Responses of the Kidneys that are Normally Measured in Toxicology Research and Drug Development Studies 16310.3 Primary Cultures of hPT Cells 16410.4 Toxicology Studies in hPT Primary Cell Cultures 16610.5 Critical Studies for Drug Discovery in hpt Primary Cell Cultures 16810.6 S ummary and Conclusions 168References 17011 Predicting Organ Toxicity In Vitro: Bone Marrow 172Ivan Rich and Andrew J. Olaharski11.1 Introduction 17211.2 Biology of the Hematopoietic System 17211.3 Hemotoxicity 17311.4 Measuring Hemotoxicity 17311.5 The Next Generation of Assays 17511.6 Proliferation or Differentiation? 17511.7 Measuring and Predicting Hemotoxicity In Vitro 17611.8 Detecting Stem and Progenitor Cell Downstream Events 17711.9 Bone Marrow Toxicity Testing During Drug Development 17711.10 Paradigm for In Vitro Hemotoxicity Testing 17811.11 Predicting Starting Doses for Animal and Human Clinical Trials 17911.12 Future Trends 17911.13 Conclusions 180References 18012 Predicting Organ Toxicity In Vitro: Dermal Toxicity 182Patrick J. Hayden, Michael Bachelor, Mitchell Klausner and Helena Kandárová12.1 Introduction 18212.2 Overview of Drug‐Induced Adverse Cutaneous Reactions 18212.3 Overview of In Vitro Skin Models with Relevance to Preclinical Drug Development 18312.4 Specific Applications of In Vitro Skin Models and Predictive In Vitro Assays Relevant to Pharmaceutical Development 18412.5 Mechanism‐Based Cutaneous Adverse Effects 18712.6 Summary 188References 18913 In Vitro Methods in Immunotoxicity Assessment 193Xu Zhu and Ellen Evans13.1 Introduction and Perspectives on In Vitro Immunotoxicity Screening 19313.2 Overview of the Immune System 19413.3 Examples of In Vitro Approaches 19613.4 Conclusions 198References 19914 Strategies and Assays for Minimizing Risk of Ocular Toxicity during Early Development of Systemically Administered Drugs 201Chris J. Somps, Paul Butler, Jay H. Fortner, Keri E. Cannon and Wenhu Huang14.1 Introduction 20114.2 In Silico and In Vitro Tools and Strategies 20114.3 Higher‐Throughput In Vivo Tools and Strategies 20214.4 S trategies, Gaps, and Emerging Technologies 20814.5 Summary 210References 21015 Predicting Organ Toxicity In Vivo—Central Nervous System 214Greet Teuns and Alison Easter15.1 Introduction 21415.2 Models for Assessment of CNS ADRs 21415.3 S eizure Liability Testing 21615.4 Drug Abuse Liability Testing 21815.5 General Conclusions 22215.5.1 In Vitro 22215.5.2 In Vivo 223Abbreviations 223References 22416 Biomarkers, Cell Models, and In Vitro Assays for Gastrointestinal Toxicology 227Allison Vitsky and Gina M. Yanochko16.1 Introduction 22716.2 A natomic and Physiologic Considerations 22816.3 GI Biomarkers 22916.4 Cell Models of the GI Tract 23116.5 Cell‐Based In Vitro Assays for Screening and Mechanistic Investigations to Gi Toxicity 23516.6 Summary/Conclusions/Challenges 236References 23617 Preclinical Safety Assessment of Drug Candidate‐Induced Pancreatic Toxicity: From an Applied Perspective 242Karrie A. Brenneman, Shashi K. Ramaiah and Lauren M. Gauthier17.1 Drug‐Induced Pancreatic Toxicity 24217.2 Preclinical Evaluation of Pancreatic Toxicity 24517.3 Preclinical Pancreatic Toxicity Assessment: In Vivo 24717.4 Pancreatic Biomarkers 24917.5 Preclinical Pancreatic Toxicity Assessment: In Vitro 25317.6 Summary and Conclusions 257Acknowledgments 258References 258PART V A DDRESSING THE FALSE NEGATIVE SPACE—INCREASINGPREDICTIVITY 26118 Animal Models of Disease for Future Toxicity Predictions 263Sherry J. Morgan and Chandikumar S. Elangbam18.1 Introduction 26318.2 Hepatic Disease Models 26418.3 Cardiovascular Disease Models 26818.4 Nervous System Disease Models 27018.5 Gastrointestinal Injury Models 27318.6 Renal Injury Models 27918.7 Respiratory Disease Models 28218.8 Conclusion 285References 28719 The Use of Genetically Modified Animals in Discovery Toxicology 298Dolores Diaz and Jonathan M. Maher19.1 Introduction 29819.2 Large‐Scale Gene Targeting and Phenotyping Efforts 29919.3 Use of Genetically Modified Animal Models in Discovery Toxicology 30019.4 The Use of Genetically Modified Animals in Pharmacokinetic and Metabolism Studies 30319.5 Conclusions 309References 30920 Mouse Population-Based Toxicology for Personalized Medicine and Improved Safety Prediction 314Alison H. Harrill20.1 Introduction 31420.2 Pharmacogenetics and Population Variability 31420.3 Rodent Populations Enable a Population‐Based Approaches to Toxicology 31620.4 Applications for Pharmaceutical Safety Science 32020.5 Study Design Considerations for Genomic Mapping 32220.6 Summary 326References 326PART VI STEM CELLS IN TOXICOLOGY 33121 Application of Pluripotent Stem Cells in Drug‐Induced Liver Injury Safety Assessment 333Christopher S. Pridgeon, Fang Zhang, James A. Heslop, Charlotte M.L. Nugues, Neil R. Kitteringham, B. Kevin Park and Christopher E.P. Goldring21.1 The Liver, Hepatocytes, and Drug‐Induced Liver Injury 33321.2 Current Models of Dili 33421.3 Uses of iPSC HLCs 33821.4 Challenges of Using ipscs and New Directions for Improvement 33921.5 Alternate Uses of HLCs in Toxicity Assessment 341References 34222 Human Pluripotent Stem Cell‐Derived Cardiomyocytes: A New Paradigm in Predictive Pharmacology and Toxicology 346Praveen Shukla, Priyanka Garg and Joseph C. Wu22.1 Introduction 34622.2 A dvent of hPSCs: Reprogramming and Cardiac Differentiation 34722.3 iPSC‐Based Disease Modeling and Drug Testing 34922.4 Traditional Target‐Centric Drug Discovery Paradigm 35422.5 iPSC‐Based Drug Discovery Paradigm 35422.6 Limitations and Challenges 35822.7 Conclusions and Future Perspective 359Acknowledgments 360References 36023 Stem Cell‐Derived Renal Cells and Predictive Renal In Vitro Models 365Jacqueline Kai Chin Chuah, Yue Ning Lam, Peng Huang and Daniele Zink23.1 Introduction 36523.2 Protocols for the Differentiation of Pluripotent Stem Cells into Cells of the Renal Lineage 36723.3 Renal In Vitro Models for Drug Safety Screening 37623.4 Achievements and Future Directions 378Acknowledgments 379Notes 379References 379PART VII CURRENT STATUS OF PRECLINICAL IN VIVO TOXICITY BIOMARKERS 38524 Predictive Cardiac Hypertrophy Biomarkers in Nonclinical Studies 387Steven K. Engle24.1 Introduction to Biomarkers 38724.2 Cardiovascular Toxicity 38724.3 Cardiac Hypertrophy 38824.4 Diagnosis of Cardiac Hypertrophy 38924.5 Biomarkers of Cardiac Hypertrophy 38924.6 Case Studies 39224.7 Conclusion 392References 39325 Vascular Injury Biomarkers 397Tanja S. Zabka and Kaïdre Bendjama25.1 Historical Context of Drug‐Induced Vascular Injury and Drug Development 39725.2 Current State of Divi Biomarkers 39825.3 Current Status and Future of In Vitro Systems to Investigate Divi 40225.4 Incorporation of In Vitro and In Vivo Tools in Preclinical Drug Development 40325.5 Divi Case Study 403References 40326 Novel Translational Biomarkers of Skeletal Muscle Injury 407Peter M. Burch and Warren E. Glaab26.1 Introduction 40726.2 Overview of Drug‐Induced Skeletal Muscle Injury 40726.3 Novel Biomarkers of Drug‐Induced Skeletal Muscle Injury 40926.4 Regulatory Endorsement 41126.5 Gaps and Future Directions 41126.6 Conclusions 412References 41227 Translational Mechanistic Biomarkers and Models for Predicting Drug‐Induced Liver Injury : Clinical to In Vitro Perspectives 416Daniel J. Antoine27.1 Introduction 41627.2 Drug‐Induced Toxicity and the Liver 41727.3 Current Status of Biomarkers for the Assessment of DILI 41827.4 Novel Investigational Biomarkers for DILI 41927.5 In Vitro Models and the Prediction of Human Dili 42227.6 Conclusions and Future Perspectives 423References 424PART VIII Kidney Injury Biomarkers 42928 Assessing and Predicting Drug‐Induced Kidney Injury, Functional Change, and Safety in Preclinical Studies in Rats 431Yafei Chen28.1 Introduction 43128.2 Kidney Functional Biomarkers (Glomerular Filtration and Tubular Reabsorption) 43328.3 Novel Kidney Tissue Injury Biomarkers 43528.4 Novel Biomarkers of Kidney Tissue Stress Response 43628.5 Application of an Integrated Rat Platform (Automated Blood Sampling and Telemetry, Abst) for Kidney Function and Injury Assessment 437References 43929 Canine Kidney Safety Protein Biomarkers 443Manisha Sonee29.1 Introduction 44329.2 Novel Canine Renal Protein Biomarkers 44329.3 Evaluations of Novel Canine Renal Protein Biomarker Performance 44429.4 Conclusion 444References 44530 Traditional Kidney Safety Protein Biomarkers and Next‐Generation Drug‐Induced Kidney Injury Biomarkers in Nonhuman Primates 446Jean‐Charles Gautier and Xiaobing Zhou30.1 Introduction 44630.2 Evaluations of Novel Nhp Renal Protein Biomarker Performance 44730.3 New Horizons: Urinary MicroRNAs and Nephrotoxicity in Nhps 447References 44731 Rat Kidney MicroRNA Atlas 448Aaron T. Smith31.1 Introduction 44831.2 Key Findings 448References 44932 MicroRNAs as Next‐Generation Kidney Tubular Injury Biomarkers in Rats 450Heidrun Ellinger‐Ziegelbauer and Rounak Nassirpour32.1 Introduction 45032.2 Rat Tubular miRNAs 45032.3 Conclusions 451References 45133 MicroRNAs as Novel Glomerular Injury Biomarkers in Rats 452Rachel Church33.1 Introduction 45233.2 Rat Glomerular miRNAs 452References 45334 Integrating Novel Imaging Technologies to Investigate Drug‐Induced Kidney Toxicity 454Bettina Wilm and Neal C. Burton34.1 Introduction 45434.2 Overviews 45534.3 Summary 456References 45635 In Vitro to In Vivo Relationships with Respect to Kidney Safety Biomarkers 458Paul Jennings35.1 Renal Cell Lines as Tools for Toxicological Investigations 45835.2 Mechanistic Approaches and In Vitro to In Vivo Translation 45935.3 Closing Remarks 460References 46036 Case Study: Fully Automated Image Analysis of Podocyte Injury Biomarker Expression in Rats 462Jing Ying Ma36.1 Introduction 46236.2 Material and Methods 46236.3 Results 46336.4 Conclusions 465References 46537 Case Study: Novel Renal Biomarkers Translation to Humans 466Deborah A. Burt37.1 Introduction 46637.2 Implementation of Translational Renal Biomarkers in Drug Development 46637.3 Conclusion 467References 46738 Case Study: Microrn as as Novel Kidney Injury Biomarkers in Canines 468Craig Fisher, Erik Koenig and Patrick Kirby38.1 Introduction 46838.2 Material and Methods 46838.3 Results 46838.4 Conclusions 470References 47039 Novel Testicular Injury Biomarkers 471Hank Lin39.1 Introduction 47139.2 The Testis 47139.3 Potential Biomarkers for Testicular Toxicity 47239.4 Conclusions 473References 473PART IX Best Practices in Biomarker Evaluations 47540 Best Practices in Preclinical Biomarker Sample Collections 477Jaqueline Tarrant40.1 Considerations for Reducing Preanalytical Variability in Biomarker Testing 47740.2 Biological Sample Matrix Variables 47740.3 Collection Variables 48040.4 Sample Processing and Storage Variables 480References 48041 Best Practices in Novel Biomarker Assay Fit‐for‐Purpose Testing 481Karen M. Lynch41.1 Introduction 48141.2 Why Use a Fit‐for‐Purpose Assay? 48141.3 Overview of Fit‐for‐Purpose Assay Method Validations 48241.4 Assay Method Suitability in Preclinical Studies 48241.5 Best Practices for Analytical Methods Validation 48241.6 Species‐ and Gender‐Specific Reference Ranges 48641.7 Analyte Stability 48741.8 Additional Method Performance Evaluations 487References 48742 Best Practices in Evaluating Novel Biomarker Fit for Purpose and Translatability 489Amanda F. Baker42.1 Introduction 48942.2 Protocol Development 48942.3 Assembling an Operations Team 48942.4 Translatable Biomarker Use 49042.5 Assay Selection 49042.6 Biological Matrix Selection 49042.7 Documentation of Patient Factors 49142.8 Human Sample Collection Procedures 49142.9 Choice of Collection Device 49142.10 Schedule of Collections 49242.11 Human Sample Quality Assurance 49242.12 Logistics Plan 49342.13 Database Considerations 49342.14 Conclusive Remarks 493References 49343 Best Practices in Translational Biomarker Data Analysis 495Robin Mogg and Daniel Holder43.1 Introduction 49543.2 Statistical Considerations for Preclinical Studies of Safety Biomarkers 49643.3 Statistical Considerations for Exploratory Clinical Studies of Translational Safety Biomarkers 49743.4 Statistical Considerations for Confirmatory Clinical Studies of Translational Safety Biomarkers 49843.5 Summary 498References 49844 Translatable Biomarkers in Drug Development: Regulatory Acceptance and Qualification 500John‐Michael Sauer, Elizabeth G. Walker and Amy C. Porter44.1 Safety Biomarkers 50044.2 Qualification of Safety Biomarkers 50144.3 Letter of Support for Safety Biomarkers 50244.4 Critical Path Institute’s Predictive Safety Testing Consortium 50244.5 Predictive Safety Testing Consortium and its Key Collaborations 50444.6 Advancing the Qualification Process and Defining Evidentiary Standards 505References 506PART X Conclusions 50945 Toxicogenomics in Drug Discovery Toxicology: History, Methods, Case Studies, and Future Directions 511Brandon D. Jeffy, Joseph Milano and Richard J. Brennan45.1 A Brief History of Toxicogenomics 51145.2 Tools and Strategies for Analyzing Toxicogenomics Data 51345.3 Drug Discovery Toxicology Case Studies 519References 52546 Issue Investigation and Practices in Discovery Toxicology 530Dolores Diaz, Dylan P. Hartley and Raymond Kemper46.1 Introduction 53046.2 Overview of Issue Investigation in the Discovery Space 53046.3 Strategies to Address Toxicities in the Discovery Space 53246.4 Cross‐Functional Collaborative Model 53346.5 Case‐Studies of Issue Resolution in The Discovery Space 53646.6 Data Inclusion in Regulatory Filings 538References 538ABBREVIATIONS 540CONCLUDING REMARKS 542INDEX 543