Attrition in the Pharmaceutical Industry

Alexander Alex, Dr. rer. nat., is director of Evenor Consulting and has over 20 years' experience as consultant and as director and research fellow in drug discovery in the pharmaceutical industry. C. John Harris, PhD, is the director of cjh Consultants and has a successful track record in drug discovery, research management, small company fund-raising and start-ups. Dennis A. Smith, PhD, is an independent consultant with a long track record in drug discovery and development with an emphasis on metabolism and safety. He has published four books, including Pharmacokinetics and Metabolism in Drug Design (1st and 2nd editions) and Reactive Drug Metabolites published by Wiley.

Contributors xiii

Introduction 1
Alexander Alex C. John Harris and Dennis A. Smith

References 4

1 Attrition in Drug Discovery and Development 5
Scott Boyer Clive Brealey and Andrew M. Davis

1.1 “The Graph” 5

1.2 The Sources of Attrition 7

1.3 Phase II Attrition 9

1.3.1 Target Engagement 11

1.3.2 Clinical Trial Design 11

1.4 Phase III Attrition 12

1.4.1 Safety Attrition in Phase III 14

1.5 Regulation and Attrition 17

1.6 Attrition in Phase IV 19

1.7 First in Class Best in Class and the Role of the Payer 32

1.8 Portfolio Attrition 34

1.9 “Avoiding” Attrition 36

1.9.1 Drug Combinations and New Formulations 36

1.9.2 Biologics versus Small Molecules 37

1.9.3 Small-Molecule Compound Quality 38

1.10 Good Attrition versus Bad Attrition 39

1.11 Summary 40

References 42

2 Compound Attrition at the Preclinical Phase 46
Cornelis E.C.A. Hop

2.1 Introduction: Attrition in Drug Discovery and Development 46

2.2 Target Identification HTS and Lead Optimization 50

2.3 Resurgence of Covalent Inhibitors 55

2.4 In Silico Models to Enhance Lead Optimization 56

2.5 Structure-Based and Property-Based Compound Design in Lead Optimization 59

2.5.1 Risks Associated with Operating in Nondrug-Like Space 62

2.6 Attrition Due to ADME Reasons 64

2.6.1 Metabolism Bioactivation and Attrition 68

2.6.2 PK/PD Modeling in Drug Discovery to Reduce Attrition 69

2.6.3 Human PK Prediction Uncertainties 70

2.7 Attrition Due to Toxicity Reasons 72

2.8 Corporate Culture and Nonscientific Reasons for Attrition 75

2.9 Summary 76

References 76

3 Attrition in Phase I 83
Dennis A. Smith and Thomas A. Baillie

3.1 Introduction 83

3.2 Attrition in Phase I Studies and Paucity of Published Information 84

3.3 Drug Attrition in not FIH Phase I Studies 85

3.4 Attrition in FIH Studies Due to PK 86

3.4.1 Attrition due to Pharmacogenetic Factors 88

3.5 Attenuation of PK failure 90

3.5.1 Preclinical Methods (In Vivo) 90

3.5.2 Preclinical Methods (In Vitro) 91

3.5.3 Phase 0 Microdose Studies in Humans 92

3.5.4 Responding to Unfavorable PK Characteristics 94

3.6 Phase I Oncology Studies 95

3.7 Toleration and Attrition in Phase I Studies 97

3.7.1 Improving the Hepatic Toleration of Compounds 98

3.7.2 Rare Severe Toxicity in Phase I Studies 98

3.8 Target Occupancy and Go/No]Go Decisions to Phase II Start 99

3.9 Conclusions 102

References 102

4 Compound Attrition in Phase II/III 106
Alexander Alex C. John Harris Wilma W. Keighley and Dennis A. Smith

4.1 Introduction 106

4.2 Attrition Rates: How Have they Changed? 107

4.3 Why do Drugs Fail in Phase II/III? Lack of Efficacy or Marginal Efficacy Leading to Likely Commercial
Failure 108

4.4 Toxicity 111

4.5 Organizational Culture 112

4.6 Case Studies for Phase II/III Attrition 112

4.6.1 Torcetrapib 112

4.6.2 Dalcetrapib 113

4.6.3 Onartuzumab 114

4.6.4 Bapineuzumab 115

4.6.5 Gantenerumab 115

4.6.6 Solanezumab 116

4.6.7 Pomaglumetad Methionil (LY]2140023) 116

4.6.8 Dimebon (Latrepirdine) 117

4.6.9 BMS]986094 117

4.6.10 TC]5214 (S]Mecamylamine) 118

4.6.11 Olaparib 118

4.6.12 Tenidap 119

4.6.13 NNC0109]0012 (RA) 120

4.6.14 Omapatrilat 120

4.6.15 Ximelagatran 121

4.7 Summary and Conclusions 122

References 123

5 Postmarketing Attrition 128
Dennis A. Smith

5.1 Introduction 128

5.2 On-Target Pharmacology-Flawed Mechanism 130

5.2.1 Alosetron 130

5.2.2 Cerivastatin 130

5.2.3 Tegaserod 133

5.3 Off-Target Pharmacology Known Receptor: An Issue of Selectivity 135

5.3.1 Fenfluramine and Dexfenfluramine 135

5.3.2 Rapacuronium 136

5.3.3 Astemizole Cisapride Grepafloxacin and Thioridazine 138

5.4 Off-Target Pharmacology Unknown Receptor: Idiosyncratic Toxicology 142

5.4.1 Benoxaprofen 142

5.4.2 Bromfenac 142

5.4.3 Nomifensine 143

5.4.4 Pemoline 144

5.4.5 Remoxipride 144

5.4.6 Temafloxacin 145

5.4.7 Tienilic acid 145

5.4.8 Troglitazone 146

5.4.9 Tolcapone 146

5.4.10 Trovafloxacin 147

5.4.11 Valdecoxib 148

5.4.12 Zomepirac 148

5.5 Conclusions 150

References 151

6 Influence of the Regulatory Environment on Attrition 158
Robert T. Clay

6.1 Introduction 158

6.1.1 How the Regulatory Environment has Changed Over the Last Two Decades 159

6.1.2 Past and Current Regulatory Attitude to Risk Analysis and Risk Management 161

6.2 Discussion 162

6.2.1 What Stops Market Approval? 162

6.2.2 Impact of Black Box Warnings 166

6.2.3 Importance and Impact of Pharmacovigilance 167

6.2.4 Prospects of Market Withdrawals for New Drugs 168

6.2.5 What are the Challenges for the Industry Given the Current Regulatory Environment? 173

6.2.6 Future Challenges for Both Regulators and the Pharmaceutical Industry 174

6.3 Conclusion 175

References 176

7 Experimental Screening Strategies to Reduce Attrition Risk 180
Marie-Claire Peakman Matthew Troutman Rosalia Gonzales and Anne Schmidt

7.1 Introduction 180

7.2 Screening Strategies in Hit Identification 183

7.2.1 Screening Strategies and Biology Space 183

7.2.2 Screening Strategies and Chemical Space 187

7.2.3 High-Throughput Screening Technologies 191

7.2.4 Future Directions for High-Throughput Screening 194

7.3 Screening Strategies in Hit Validation and Lead Optimization 194

7.4 Screening Strategies for Optimizing PK and Safety 197

7.4.1 High-Throughput Optimization of PK/ADME Profiles 198

7.4.2 Early Safety Profiling 202

7.4.3 Future Directions for ADME and Safety in Lead Optimization 204

7.5 Summary 205

References 206

8 Medicinal Chemistry Strategies to Prevent Compound Attrition 215
J. Richard Morphy

8.1 Introduction 215

8.2 Picking the Right Target 216

8.3 Finding Starting Compounds 216

8.4 Compound Optimization 218

8.4.1 Drug-Like Compounds 218

8.4.2 Structure-Based Drug Design 219

8.4.3 The Thermodynamics and Kinetics of Compound Optimization 220

8.4.4 PK 220

8.4.5 Toxicity 222

8.5 Summary 225

References 226

9 Influence of Phenotypic and Target]Based Screening Strategies on Compound Attrition and Project Choice 229
Andrew Bell Wolfgang Fecke and Christine Williams

9.1 Drug Discovery Approaches: A Historical Perspective 229

9.1.1 Phenotypic Screening 229

9.1.2 Target-Based Screening 230

9.1.3 Recent Changes in Drug Discovery Approaches 231

9.2 Current Phenotypic Screens 233

9.2.1 Definition of Phenotypic Screening 233

9.2.2 Recent Anti-infective Projects 233

9.2.3 Recent CNS Projects 235

9.3 Current Targeted Screening 237

9.3.1 Definition of Targeted Screening 237

9.3.2 Recent Anti-infective Projects 237

9.3.3 Recent CNS Projects 239

9.4 Potential Attrition Factors 241

9.4.1 Technical Doability and Hit Identification 241

9.4.2 Compound SAR and Properties 246

9.4.3 Safety 248

9.4.4 Translation to the Clinic 250

9.5 Summary and Future Directions 252

9.5.1 Summary of Impact of Current Approaches 252

9.5.2 Future Directions 254

9.5.3 Conclusion 255

References 255

10 In Silico Approaches to Address Compound Attrition 264
Peter Gedeck Christian Kramer and Richard Lewis

10.1 In Silico Models Help to Alleviate the Process of Finding Both Safe and Efficacious Drugs 264

10.2 Use of In Silico Approaches to Reduce Attrition Risk at the Discovery Stage 265

10.3 Ligand-Based and Structure-Based Models 265

10.4 Data Quality 268

10.5 Predicting Model Errors 270

10.6 Molecular Properties and their Impact on Attrition 272

10.7 Modeling of ADME Properties and their Impact of Reducing Attrition in the Last Two Decades 275

10.8 Approaches to Modeling of Tox 276

10.9 Modeling PK and PD and Dose Prediction 276

10.10 Novel In Silico Approaches to Reduce Attrition Risk 278

10.11 Conclusions 280

References 280

11 Current and Future Strategies for Improving Drug Discovery Efficiency 287
Peter Mbugua Njogu and Kelly Chibale

11.1 General Introduction 287

11.2 Scope 288

11.3 Neglected Diseases 289

11.3.1 Introduction 289

11.3.2 Control of NTDs 290

11.3.3 Drug Discovery Potential of Neglected Diseases 290

11.4 Precompetitive Drug Discovery 292

11.4.1 Introduction 292

11.4.2 Virtual Discovery Organizations 293

11.4.3 Collaborations with Academic Laboratories 295

11.4.4 CoE and Incubators 296

11.4.5 Screening Data and Compound File Sharing 297

11.5 Exploitation of Genomics 297

11.5.1 Introduction 297

11.5.2 Target Identification and Validation 298

11.5.3 Target-Based Drug Discovery 298

11.5.4 Phenotypic Whole-Cell Screening 301

11.5.5 Individualized Therapy and Therapies for Special Patient Populations 302

11.6 Outsourcing Strategies 304

11.6.1 Introduction 304

11.6.2 Research Contracting in Drug Discovery 305

11.7 Multitarget Drug Design and Discovery 305

11.7.1 Introduction 305

11.7.2 Rationale for Multitargeted Drugs 306

11.7.3 Designed Multitarget Compounds for Neglected Diseases 307

11.8 Drug Repositioning and Repurposing 315

11.8.1 Introduction 315

11.8.2 Cell Biology Approach 317

11.8.3 Exploitation of Genome Information 318

11.8.4 Compound Screening Studies 318

11.8.5 Exploitation of Coinfection Drug Efficacy 318

11.8.6 In Silico Computational Technologies 319

11.9 Future Outlook 319

References 319

12 Impact of Investment Strategies Organizational Structure and Corporate Environment on Attrition and Future Investment Strategies to Reduce Attrition 329
Geoff Lawton

12.1 Attrition 329

12.2 Costs 331

12.2.1 The Costs of Creating a New Medicine 331

12.2.2 The Costs of Not Creating a New Medicine 332

12.3 Investment Strategies 334

12.3.1 RoI 334

12.3.2 Investment in a Portfolio of R&D Projects 335

12.3.3 Asset-Centered Investment 335

12.3.4 Sources of Funds 336

12.4 Business Models 337

12.4.1 FIPCO 337

12.4.2 Fully Integrated Pharmaceutical Network (FIPNET) 338

12.4.3 Venture-Funded Biotech 339

12.4.4 Fee-for-Service CRO 339

12.4.5 Hybrids 339

12.4.6 Academic Institute 340

12.4.7 Social Enterprise 341

12.5 Portfolio Management 341

12.5.1 Portfolio Construction 341

12.5.2 Project Progression 343

12.5.3 The Risk Transition Point 343

12.6 People 344

12.6.1 Motivation 344

12.6.2 Culture and Leadership 344

12.6.3 Sustainability 344

12.7 Future 345

12.7.1 Business Structures 345

12.7.2 Skilled Practitioners 347

12.7.3 Partnerships 348

12.7.4 A Personal View of the Future 349

References 351

Index 353