Clinical Trial Simulations (eBook)

Holly H.C. Kimko, PhD is a senior pharmacometrics leader (Research Fellow) at the Department of Advanced Modeling & Simulation in Johnson & Johnson Pharmaceutical Research & Development, LLC, New Jersey, and Adjunct Professor in the faculty of the Pharmacy School of Rutgers University, New Jersey.  She was previously Assistant Professor in the Center for Drug Development Science in Georgetown University Medical School, Washington DC. Trained in biochemistry and pharmacy, Dr. Kimko earned her Ph.D. degree in Pharmaceutical Science from the State University of New York, Buffalo.  She has published key papers on indirect response modeling and applications of CTS, and co-edited Simulation for Designing Clinical Trials. Carl C. Peck, MD is Adjunct Professor, Center for Drug Development Science in the Department of Bioengineering and Therapeutic Sciences, Schools of Pharmacy and Medicine, University of California San Francisco, California.  He was previously Director of the FDA Center for Drug Evaluation and Research, Assistant U.S. Surgeon General, and President of the American Society for Clinical Pharmacology and Therapeutics.  Dr. Peck has also held professorial appointments in the faculties of UCSF, USUHS, and Georgetown University.  He is an author of more than 150 original research papers, chapters and books concerning advanced concepts and techniques of quantitative pharmacology, trial designs, and pharmaco-statistical modeling and simulation.

Clinical Trial Simulations 3
Preface 5
Contents 7
Contributors 11
Chapter 1: Clinical Trial Simulation and Quantitative Pharmacology 17
1.1 Introduction 17
1.2 Encouragement by EMA and FDA 19
1.3 Clinical Trial Protocol Deviations and Adherence 21
1.4 CTS-Supported Strategic Decisions in Drug Development 23
1.5 Conclusion 24
References 25
Part I: Application of M& S in Regulatory Decisions
Chapter 2: Contribution of Modeling and Simulation Studies in the Regulatory Review: A European Regulatory Perspective1 29
2.1 Introduction 29
2.2 Regulatory Guidance 31
2.2.1 Available Guidelines 31
2.2.2 Would a Specific European Guideline on MandS Be of Value? 39
2.3 Regulatory Decisions: When and Impact 39
2.3.1 Pediatric Investigation Plan 40
2.3.2 Clinical Trial Application 40
2.3.3 Scientific Advice 41
2.3.4 Approval for Marketing Authorization 42
2.4 Examples of Contribution of MandS Documentation in the Regulatory Review 43
2.4.1 Keppra (levetiracetam) 43
2.4.2 Celsentri (Maraviroc) 45
2.4.3 Bridion (sugammadex) 46
2.5 Future Perspectives and Summary 46
References 48
Chapter 3: Contribution of Modeling and Simulation in the Regulatory Review and Decision-Making: U.S. FDA Perspective 51
3.1 History of Pharmacometrics at FDA 51
3.2 Division of Pharmacometrics 52
3.2.1 Vision and Strategic Goals 52
3.2.2 Pharmacometric Reviews 52
3.2.2.1 NDA and BLA Submissions 53
3.2.2.2 QT Study Design and Analysis 53
3.2.2.3 Protocol Design 54
Pediatric Trials 54
End-of-Phase 2a Meetings 55
3.2.2.4 Knowledge Management 55
3.2.3 Research and Policy Development 56
3.3 Impact of Pharmacometric Analyses on Regulatory Decisions from 2000 to 2008 57
3.3.1 Summary of Regulatory Impact 57
3.3.2 Scope of Pharmacometric Reviews 58
3.3.2.1 Published Case Examples 58
3.3.2.2 Pediatric Dosing Regimen 62
3.3.2.3 Drugs with Approved Doses Not Directly Evaluated in Phase 3 Trials 64
3.3.2.4 Pharmacometric Analysis Used as Evidence of Effectiveness 66
Provide Confirmatory Evidence 66
Model-Based Primary Endpoint for Phase 3 Trials 66
3.4 Future Perspectives 68
References 69
Part II: Strategic Applications of M& S in Drug Development
Chapter 4: Decision-Making in Drug Development: Application of a Model Based Framework for Assessing Trial Performance 73
4.1 Introduction 73
4.2 Notation and Terminology 75
4.3 Illustrative Example Using Bivariate Normal Distributions 78
4.3.1 Introduction to the Example 78
4.3.2 Operating Characteristics for Decision Criteria Based on Point Estimates 79
4.3.3 Operating Characteristics for Decision Criteria Based on Interval Estimates 82
4.4 Applying Decision Criteria in a Dose-Response Example 85
4.4.1 Introduction to the Dose-Response Example 85
4.4.2 Operating Characteristics for Decisions Based on Relative Potency Alone 86
4.4.3 Operating Characteristics for Decisions Based on Relative Potency and Efficacy at the Top Dose 87
4.4.4 Operating Characteristics for Decisions Based on the Estimated Effect at Each Dose 89
4.5 Practicalities of Simulation in Model-Based Drug Development 90
4.6 Discussion 92
References 94
Chapter 5: Decision-Making in Drug Development: Application of a Clinical Utility IndexSM 96
5.1 Introduction 96
5.2 A CUI Overview 100
5.2.1 Setting the Decision Context Before CUI Creation 100
5.2.2 The Clinical Utility Index: ``Nuts and Bolts´´ 101
5.2.3 Calculating Utility: A Quick Example 104
5.3 A Detailed Example of CUI 107
5.4 Related Publications on CUI 111
5.5 Conclusion: Putting the CUI into Practice 113
Appendix: History and Theory 114
Notes on Multiplicative Functions 114
Basic Elicitation Steps 116
Link to Conjoint Analysis 116
References 117
Chapter 6: Adaptive Trial Designs 119
6.1 Background: What are Adaptive Designs and Why Can They Be Useful? 119
6.2 Adaptive Designs in the Learn Phase of Development 121
6.2.1 Objectives: Finding an Adequate Dose and Learning About Dose-Response 121
6.2.2 Adaptive Dose-Ranging Approaches 123
6.2.2.1 General Adaptive Dose Allocation (GADA) 123
6.2.2.2 Adaptive Multiple Comparison Procedure-Modeling (aMCP-Mod) 124
6.2.2.3 Combined D- and C-Optimality (DcoD) 124
6.2.2.4 Focus on Interesting Region of DR Profile (IntR) 125
6.2.2.5 Multiple Objectives (MULTOB) 125
6.2.2.6 t-Test Adaptation 125
6.2.3 Remarks on ADR Methods 126
6.3 Adaptive Designs for Confirmatory Studies 127
6.3.1 Group Sequential Designs 127
6.3.2 Adaptive Designs 128
6.3.3 Sample Size Re-Estimation 129
6.3.4 Applications: Treatment Selection and Enrichment Designs 130
6.3.5 Practical Considerations 132
6.4 Adaptive Designs and Trial Simulations 133
6.4.1 Operating Characteristics 133
6.4.2 An Illustration: Comparing ADR Approaches 134
6.5 Concluding Remarks and Further Thoughts on Adaptive Designs 137
References 138
Chapter 7: Keys of Collaboration to Enhance Efficiency and Impact of Modeling and Simulation 141
7.1 Introduction 141
7.2 Corifollitropin Alfa Development Program 144
7.3 Phase II Development: Design of a Dose-Response Study 146
7.4 Phase III Development: Dose Selection 150
7.5 Discussion 154
References 158
Chapter 8: Leveraging Pharmacometrics in Early Phase Anti-inflamatory Drug Development 159
8.1 Introduction 159
8.2 The Learn-Confirm-Learn Process 161
8.3 The Model-Based Approach to Drug Development and Pharmacometrics 163
8.3.1 Pharmacometric Knowledge Integration 163
8.4 Information, Knowledge, Understanding, and Wisdom Paradigm 164
8.5 Leveraging Pharmacometrics in Early Phase Drug Development 164
8.5.1 Example of Model-Based Early Development 166
8.5.1.1 Translation of Nonclinical Information into Knowledge 167
8.5.1.2 Pharmacometric Leveraging of Nonclinical Knowledge to Gain Insight into a Proposed FTIH Study 169
8.5.1.3 Wisdom for the Performance of FTIH Study 172
8.5.1.4 The First-Time in Human Study 172
Data 172
Population Pharmacokinetic Analysis 174
Exposure: Response Analysis 177
Comparison of Performance of the FTIH Study Outcome with FTIH Clinical Trial Simulation Outcome 179
8.5.1.5 Wisdom for the Design of Proof of Concept Study 181
8.6 Summary 181
References 182
Part III: Application of M& S in Selected Therapeutic Areas
Chapter 9: The Application of Drug-Disease Models in the Development of Anti-Hyperglycemic Agents 184
9.1 Diabetes Mellitus 184
9.1.1 Treatment Option and Drug Class 186
9.1.2 Biomarkers 187
9.1.3 Experimental Techniques 187
9.1.3.1 Glucose Tolerance Tests 188
9.1.3.2 Clamp Studies 188
9.2 Drug-Disease Models of Diabetes 189
9.2.1 Mechanistic Models of Glucose-Insulin Regulation 189
9.2.2 Time-Course Models 190
9.2.3 Indirect Response Models 192
9.2.4 Mechanistic Linked Model of FPG-HbA1c 193
9.2.5 Models Incorporating Disease Progression 194
9.2.6 Literature Data for Developing Drug-Disease Models 194
9.2.7 Biologically Based Mathematical Models 196
9.3 Applications in Drug Development 196
9.3.1 Discovery and Candidate Selection 198
9.3.2 Proof of Concept and Time-Course of Response 199
9.3.3 Dose Response of Efficacy and Safety Attributes 201
9.3.4 Dose Selection 202
9.4 Regulatory Considerations 203
9.5 Future Directions 204
References 205
Chapter 10: Modeling and Simulation in the Development of Cardiovascular Agents 208
10.1 Hypercholesterolemia 208
10.1.1 Overview of Hypercholesterolemia 208
10.1.2 Overview of Pharmacology of Statins 209
10.1.3 Model Based Evaluations of Cholesterol Lowering Agents 210
10.2 Antithrombus Therapy 213
10.2.1 Overview of Pathophysiology of Thrombus Formation 213
10.2.2 Pharmacology of Anticoagulant Agents 214
10.2.3 Modeling and Simulation for Dosing of Anticoagulants 216
10.3 Stroke 218
10.3.1 Overview of Stroke and Clinical Endpoints 218
10.3.2 Example Stroke Disease Progression Models 219
10.3.3 Longitudinal Model for Nonmonotonic Stroke Scale Data 223
10.4 Hypertension 225
10.4.1 Overview of Hypertension 225
10.4.2 Pharmacology of Antihypertensive Agents 225
10.4.3 Modeling and Simulation for Antihypertensive Agents 227
10.5 Adaptive Dosing Simulation Techniques: Focus on Cardiovascular Medicines 229
References 232
Chapter 11: Viral Dynamic Modeling and Simulations in HIV and Hepatitis C 236
11.1 Introduction 236
11.2 Basic Viral Dynamic Model 237
11.3 Viral Dynamic Modeling and Simulations in HIV 239
11.3.1 Basic PK-PD Principles of R0 and RMIC 243
11.3.1.1 For R0 and RMIC 243
11.3.1.2 For Binary Outcomes Analysis 243
11.3.2 Dose and Dosing Schedule 243
11.3.3 Estimation of Model Parameters 246
11.4 Viral Dynamic Modeling and Simulations in Hepatitis C 248
11.5 Conclusions 255
References 256
Chapter 12: A Model-Based PK/PD Antimicrobial Chemotherapy Drug Development Platform to Simultaneously Combat Infectious Diseasesand Drug Resistance 260
12.1 Introduction 260
12.2 Why Develop a Platform to Simultaneously Combat Infectious Diseases and Drug Resistance? 261
12.2.1 Classical Empirical Antibiotics vs. Synthetic Antibacterials 263
12.3 PK/PD-Driven Clinical Trial Design for Chemotherapeutic Antimicrobial Dose-Regimen Rationalization 263
12.3.1 Knowledge Generation for Design and Simulation of Antiinfective Clinical Trials 266
12.3.1.1 Microbiological Effect Concentration 266
12.3.1.2 Time-Kill Study Evaluation 267
12.3.1.3 Evaluating Effect (E)-vs.-Concentration [a] Curve Symmetry/Asymmetry 269
12.3.1.4 Relating the Hill Model to MICs 270
12.3.1.5 Antimicrobial Drug Combinations 271
12.3.1.6 In Vivo Thigh Model 273
12.3.1.7 Emergence of Resistance Submodel 274
12.3.1.8 Host Defense Submodel 274
12.4 Antimicrobial Chemotherapy Knowledge Integration, from Bench-to-Bedside 275
12.5 Clinical Application 277
12.6 Summary 282
References 282
Chapter 13: PKPD and Disease Modeling: Concepts and Applications to Oncology 289
13.1 General Concepts and History of Model-Based Research in Oncology 289
13.2 Modeling Tumor Growth and Disease Progression 292
13.3 Modeling Tumor Growth and DecayUnder Treatment 297
13.4 Modeling Biomarkers vs. Surrogate Endpoints 298
13.5 Translational Models 302
13.6 Modeling and Prediction of Adverse Events 306
13.7 Clinical Trial Simulations 309
13.8 Concluding Remarks 312
References 313
Chapter 14: Application of Pharmacokinetic-Pharmacodynamic Modeling and Simulation for Erythropoietic Stimulating Agents 319
14.1 Introduction 319
14.2 Extrapolating PK/PD from Preclinical to Clinical 320
14.3 Predicting the Outcome of an Extended Dosing Interval Regimen 323
14.4 Pediatric Study Design 328
References 333
Chapter 15: Model Based Development of an Agent for the Treatment of Generalized AnxietyDisorder 336
15.1 Introduction 336
15.2 Materials and Methods 339
15.2.1 Data 339
15.2.2 Dose Response Analysis 340
15.2.2.1 Efficacy 340
15.2.2.2 Tolerability 341
15.2.2.3 Model Selection 341
15.2.2.4 Model Uncertainty 341
15.2.3 Dose Selection for Phase 3 Studies 342
15.2.4 Model Validation 343
15.3 Results 343
15.3.1 Efficacy 343
15.3.2 Tolerability 345
15.3.3 Simulation Results 346
15.3.4 Comparison of Week 6 Phase 3 Outcome and Model Prediction 348
15.4 Discussion 348
References 350
Chapter 16: Balancing Efficacy and Safety in the Clinical Development of an Atypical Antipsychotic, Paliperidone Extended-Release 352
16.1 Introduction 352
16.2 Rationale 353
16.3 Methods and Data 354
16.3.1 D2-Receptor Occupancy as a Biomarker for Efficacy and Safety 354
16.3.1.1 D2-Receptor Occupancy Model 354
16.3.1.2 Population PK-Model for Paliperidone ER 355
16.3.1.3 Prediction of D2-Receptor Occupancy 355
16.3.2 Extrapyramidal Symptoms 356
16.3.2.1 Pharmacokinetic/Pharmacodynamic (PK/PD)-Model 356
16.3.2.2 PK/PD-Simulation 358
16.4 Results 360
16.4.1 Predicting Efficacy and Safety using D2-Receptor Occupancy as a Biomarker 360
16.4.2 Predicting EPS-Incidence 362
16.4.2.1 PK/PD Model for EPS-Incidence 362
16.4.2.2 PK/PD Simulation for EPS-Incidence 364
16.5 Discussion 365
References 367
Part IV: Expanded Applications of M& S
Chapter 17: Application of Modeling and Simulation in the Development of Protein Drugs 370
17.1 Introduction 370
17.2 PK-PD Models of Protein Drugs 372
17.2.1 Absorption and Bioavailability 372
17.2.2 Bimolecular Interaction of Drug with Target 374
17.2.3 Biodistribution 375
17.2.4 Clearance 377
17.2.4.1 Target-Mediated Drug Disposition 379
Target-Mediated Drug Disposition Model 380
Quasi-Steady-State (QSS) and Rapid Binding (RB) Approximations 382
Michaelis-Menten Approximation 382
17.2.5 Interactions of Drug and Soluble Target 384
17.2.6 Cytokinetics 384
17.2.6.1 Cell Lifespan 385
17.2.6.2 Cell Proliferation, Maturation, and Feedback Regulation of PK 387
17.3 Applied Modeling and Simulation from Discovery Through Clinical Development 387
17.3.1 Drug Discovery: Target Evaluation and Lead Drug Optimization and Selection 388
17.3.2 Preclinical Development 390
17.3.3 Clinical Development 391
17.3.3.1 Utility of PK-PD Models in a Clinical Setting 393
17.3.3.2 Selection of Phase II/III Doses 393
17.3.3.3 Characterization of the Dose-Concentration-Effect Relationship 394
17.3.3.4 Demonstration of the Similarity of PK-PD Relationship Between Different Patient Populations 395
17.3.3.5 Evaluation of Demographic or Disease Covariates to Determine the Need of Dose Modifications for Subpopulations 396
17.3.3.6 Evaluation of Drug-Drug Interactions 398
17.3.3.7 Comparison of Fixed Dosing vs. Body Size-Based Dosing 399
17.3.3.8 Provision of Support for Switching to Alternative Route of Administration, Dosage Form, or Dosing Regimen 400
17.3.3.9 Provision of Rationale for Clinical Observations in Phase III Trials in Regulatory Interactions for Approval 401
17.4 Conclusions 401
References 402
Chapter 18: Modeling and Simulation in Pediatric Research and Development 406
18.1 Introduction 406
18.2 Methods for Modeling and Simulation Applications 410
18.2.1 Common Trial Designs for Pediatrics 411
18.2.2 Converting the Developing Child into the In Silico Child 413
18.3 The Pediatric CTS Model 419
18.3.1 Priors for Pediatric CTS 421
18.3.2 Typical Workflow 424
18.4 Examples 425
18.4.1 Safety/PK Trial to Fulfill Regulatory Requirements - Low Molecular Weight Heparin 425
18.4.2 BPCA Trial: Written Request for Actinomycin-D and Vincristine in Children with Cancer 428
18.4.3 Exploratory PK/PD Trial: Topirimate Dose Finding in Post Surgical Neonates 430
18.5 Other Considerations 434
18.5.1 The Role for ``Bottom-Up´´ Approaches 434
18.5.2 Pediatric Outcomes 434
18.5.3 Pediatric Disease Progression 435
References 435
Part V: Evolving Methodologies in M& S
Chapter 19: Disease Progression Analysis: Towards Mechanism-Based Models 440
19.1 General Concepts 440
19.2 Overview of Disease Process and Disease Progression Models 443
19.2.1 Descriptive Models 443
19.2.2 Mechanism-Based Models 446
19.2.2.1 Turnover Models 447
19.2.2.2 Cascading Turnover Models 449
19.2.3 Systems Pharmacology 452
19.3 Practical Challenges and Implementations 454
19.4 Summary 458
References 459
Chapter 20: Using a Systems Biology Approach to Explore Hypotheses Underlying Clinical Diversity of the Renin Angiotensin System and theResponse to Antihypertensive Therapies 463
20.1 Introduction 463
20.2 Overview 465
20.2.1 Epidemiology and Pathophysiology of Hypertension 465
20.2.2 Role of the RAS Pathway in Modulating Arterial Pressure 465
20.2.3 Modeling Approaches to Long-Term Regulation of Blood Pressure 467
20.2.4 Creating a Model of Hypertension Incorporating the RAS Pathway 467
20.3 Model of Systemic RAS 468
20.3.1 Model Structure 468
20.3.2 Systemic RAS Model Assumptions 470
20.3.3 Parameterization of a Representative Normotensive Virtual Patient (VP) 472
20.4 Model Validation 473
20.4.1 Angiotensin Peptide Infusion Experiments 473
20.4.2 Representation and Parameterization of Antihypertensive Therapies 474
20.4.3 Validation of Antihypertensive Therapies in the Model 475
20.4.4 Representation of Variability Across Different Clinical Populations 476
20.4.5 Insights from Model Simulations 477
20.5 Modeling RAS Within the Kidney 478
20.5.1 Introduction 478
20.5.2 Model Development 478
20.5.3 Parameterization of the Renal RAS Model 480
20.5.3.1 Renal Vascular Compartment 480
20.5.3.2 Renal Tissue Compartment 481
20.5.4 Hypothesis Testing and Other Applications of the Renal RAS Model 482
20.6 RAS Pathway Model Application in Drug Development 482
20.7 Conclusion 484
References 485
Chapter 21: Recent Developments in Physiologically Based Pharmacokinetic Modeling 489
21.1 Introduction 489
21.2 Input Parameters for PBPK Modeling 492
21.2.1 Prediction of Hepatic Drug Clearance 492
21.2.2 Prediction of In Vivo CL from In Vitro CL 493
21.2.3 Scaling of In Vitro CLint to In Vivo CLint 494
21.2.4 Factors Influencing Hepatic Clearance 494
21.3 Physiologically Based Predictions of Tissue Distribution 495
21.4 Prediction Models for Oral Absorption and Bioavailability 497
21.5 Applying Physiologically Based Approaches in Drug Development 498
21.6 Concluding Remarks 501
References 502
Chapter 22: Covariate Distribution Models in Simulation 506
22.1 Introduction 506
22.2 Covariate Distribution Models 507
22.2.1 Internal Databases 510
22.2.2 External Databases 511
22.2.2.1 The United States Census of Demographic Characteristics of Americans 511
22.2.2.2 National Institutes of Health Databases 512
22.2.2.3 The US National Health and Nutrition Examination Survey 513
Generating Covariate Probability Density Functions 514
Generating Laboratory Clinical Values 521
22.3 Future Perspectives 525
References 526
Index 528