Clinical Trial Simulations: Applications and Trends

Clinical Trial Simulations: Applications and Trends (AAPS Advances in the Pharmaceutical Sciences Series, 1)......Page 1
Front-matter......Page 2
Title page......Page 4
Copyright......Page 5
Preface......Page 6
Contents......Page 8
Contributors......Page 12
1.1 Introduction......Page 18
1.2 Encouragement by EMA and FDA......Page 20
1.3 Clinical Trial Protocol Deviations and Adherence......Page 22
1.4 CTS-Supported Strategic Decisions in Drug Development......Page 24
1.5 Conclusion......Page 25
References......Page 26
Part I: Application of M & S in Regulatory Decisions......Page 30
2.1 Introduction......Page 32
2.2.1 Available Guidelines......Page 34
2.3 Regulatory Decisions: When and Impact......Page 42
2.3.2 Clinical Trial Application......Page 43
2.3.3 Scientific Advice......Page 44
2.3.4 Approval for Marketing Authorization......Page 45
2.4.1 Keppra (levetiracetam)......Page 46
2.4.2 Celsentri (Maraviroc)......Page 48
2.5 Future Perspectives and Summary......Page 49
References......Page 51
3.1 History of Pharmacometrics at FDA......Page 54
3.2.2 Pharmacometric Reviews......Page 55
3.2.2.2 QT Study Design and Analysis......Page 56
Pediatric Trials......Page 57
3.2.2.4 Knowledge Management......Page 58
3.2.3 Research and Policy Development......Page 59
3.3.1 Summary of Regulatory Impact......Page 60
3.3.2.1 Published Case Examples......Page 61
3.3.2.2 Pediatric Dosing Regimen......Page 65
3.3.2.3 Drugs with Approved Doses Not Directly Evaluated in Phase 3 Trials......Page 67
Model-Based Primary Endpoint for Phase 3 Trials......Page 69
3.4 Future Perspectives......Page 71
References......Page 72
Part II: Strategic Applications of M&S in Drug Development......Page 75
4.1 Introduction......Page 78
4.2 Notation and Terminology......Page 80
4.3.1 Introduction to the Example......Page 83
4.3.2 Operating Characteristics for Decision Criteria Based on Point Estimates......Page 84
4.3.3 Operating Characteristics for Decision Criteria Based on Interval Estimates......Page 87
4.4.1 Introduction to the Dose-Response Example......Page 90
4.4.2 Operating Characteristics for Decisions Based on Relative Potency Alone......Page 91
4.4.3 Operating Characteristics for Decisions Based on Relative Potency and Efficacy at the Top Dose......Page 92
4.4.4 Operating Characteristics for Decisions Based on the Estimated Effect at Each Dose......Page 94
4.5 Practicalities of Simulation in Model-Based Drug Development......Page 95
4.6 Discussion......Page 97
References......Page 99
5.1 Introduction......Page 102
5.2.1 Setting the Decision Context Before CUI Creation......Page 106
5.2.2 The Clinical Utility Index: ``Nuts and Bolts´´......Page 107
5.2.3 Calculating Utility: A Quick Example......Page 110
5.3 A Detailed Example of CUI......Page 113
5.4 Related Publications on CUI......Page 117
5.5 Conclusion: Putting the CUI into Practice......Page 119
Notes on Multiplicative Functions......Page 120
Link to Conjoint Analysis......Page 122
References......Page 123
6.1 Background: What are Adaptive Designs and Why Can They Be Useful?......Page 126
6.2.1 Objectives: Finding an Adequate Dose and Learning About Dose-Response......Page 128
6.2.2.1 General Adaptive Dose Allocation (GADA)......Page 130
6.2.2.3 Combined D- and C-Optimality (DcoD)......Page 131
6.2.2.6 t-Test Adaptation......Page 132
6.2.3 Remarks on ADR Methods......Page 133
6.3.1 Group Sequential Designs......Page 134
6.3.2 Adaptive Designs......Page 135
6.3.3 Sample Size Re-Estimation......Page 136
6.3.4 Applications: Treatment Selection and Enrichment Designs......Page 137
6.3.5 Practical Considerations......Page 139
6.4.1 Operating Characteristics......Page 140
6.4.2 An Illustration: Comparing ADR Approaches......Page 141
6.5 Concluding Remarks and Further Thoughts on Adaptive Designs......Page 144
References......Page 145
7.1 Introduction......Page 148
7.2 Corifollitropin Alfa Development Program......Page 151
7.3 Phase II Development: Design of a Dose-Response Study......Page 153
7.4 Phase III Development: Dose Selection......Page 157
7.5 Discussion......Page 161
References......Page 165
8.1 Introduction......Page 166
8.2 The Learn-Confirm-Learn Process......Page 168
8.3.1 Pharmacometric Knowledge Integration......Page 170
8.5 Leveraging Pharmacometrics in Early Phase Drug Development......Page 171
8.5.1 Example of Model-Based Early Development......Page 173
8.5.1.1 Translation of Nonclinical Information into Knowledge......Page 174
8.5.1.2 Pharmacometric Leveraging of Nonclinical Knowledge to Gain Insight into a Proposed FTIH Study......Page 176
Data......Page 179
Population Pharmacokinetic Analysis......Page 181
Exposure: Response Analysis......Page 184
Comparison of Performance of the FTIH Study Outcome with FTIH Clinical Trial Simulation Outcome......Page 186
8.6 Summary......Page 188
References......Page 189
Part III: Application of M&S in Selected Therapeutic Areas......Page 190
9.1 Diabetes Mellitus......Page 192
9.1.1 Treatment Option and Drug Class......Page 194
9.1.3 Experimental Techniques......Page 195
9.1.3.2 Clamp Studies......Page 196
9.2.1 Mechanistic Models of Glucose-Insulin Regulation......Page 197
9.2.2 Time-Course Models......Page 198
9.2.3 Indirect Response Models......Page 200
9.2.4 Mechanistic Linked Model of FPG-HbA1c......Page 201
9.2.6 Literature Data for Developing Drug-Disease Models......Page 202
9.3 Applications in Drug Development......Page 204
9.3.1 Discovery and Candidate Selection......Page 206
9.3.2 Proof of Concept and Time-Course of Response......Page 207
9.3.3 Dose Response of Efficacy and Safety Attributes......Page 209
9.3.4 Dose Selection......Page 210
9.4 Regulatory Considerations......Page 211
9.5 Future Directions......Page 212
References......Page 213
10.1.1 Overview of Hypercholesterolemia......Page 216
10.1.2 Overview of Pharmacology of Statins......Page 217
10.1.3 Model Based Evaluations of Cholesterol Lowering Agents......Page 218
10.2.1 Overview of Pathophysiology of Thrombus Formation......Page 221
10.2.2 Pharmacology of Anticoagulant Agents......Page 222
10.2.3 Modeling and Simulation for Dosing of Anticoagulants......Page 224
10.3.1 Overview of Stroke and Clinical Endpoints......Page 226
10.3.2 Example Stroke Disease Progression Models......Page 227
10.3.3 Longitudinal Model for Nonmonotonic Stroke Scale Data......Page 231
10.4.2 Pharmacology of Antihypertensive Agents......Page 233
10.4.3 Modeling and Simulation for Antihypertensive Agents......Page 235
10.5 Adaptive Dosing Simulation Techniques: Focus on Cardiovascular Medicines......Page 237
References......Page 240
11.1 Introduction......Page 244
11.2 Basic Viral Dynamic Model......Page 245
11.3 Viral Dynamic Modeling and Simulations in HIV......Page 247
11.3.2 Dose and Dosing Schedule......Page 251
11.3.3 Estimation of Model Parameters......Page 254
11.4 Viral Dynamic Modeling and Simulations in Hepatitis C......Page 256
11.5 Conclusions......Page 263
References......Page 264
12.1 Introduction......Page 268
12.2 Why Develop a Platform to Simultaneously Combat Infectious Diseases and Drug Resistance?......Page 269
12.3 PK/PD-Driven Clinical Trial Design for Chemotherapeutic Antimicrobial Dose-Regimen Rationalization......Page 271
12.3.1.1 Microbiological Effect Concentration......Page 274
12.3.1.2 Time-Kill Study Evaluation......Page 275
12.3.1.3 Evaluating Effect (E)-vs.-Concentration [a] Curve Symmetry/Asymmetry......Page 277
12.3.1.4 Relating the Hill Model to MICs......Page 278
12.3.1.5 Antimicrobial Drug Combinations......Page 279
12.3.1.6 In Vivo Thigh Model......Page 281
12.3.1.8 Host Defense Submodel......Page 282
12.4 Antimicrobial Chemotherapy Knowledge Integration, from Bench-to-Bedside......Page 283
12.5 Clinical Application......Page 285
References......Page 290
13.1 General Concepts and History of Model-Based Research in Oncology......Page 298
13.2 Modeling Tumor Growth and Disease Progression......Page 301
13.3 Modeling Tumor Growth and DecayUnder Treatment......Page 306
13.4 Modeling Biomarkers vs. Surrogate Endpoints......Page 307
13.5 Translational Models......Page 311
13.6 Modeling and Prediction of Adverse Events......Page 315
13.7 Clinical Trial Simulations......Page 318
13.8 Concluding Remarks......Page 321
References......Page 322
14.1 Introduction......Page 328
14.2 Extrapolating PK/PD from Preclinical to Clinical......Page 329
14.3 Predicting the Outcome of an Extended Dosing Interval Regimen......Page 332
14.4 Pediatric Study Design......Page 337
References......Page 342
15.1 Introduction......Page 346
15.2.1 Data......Page 349
15.2.2.1 Efficacy......Page 350
15.2.2.4 Model Uncertainty......Page 351
15.2.3 Dose Selection for Phase 3 Studies......Page 352
15.3.1 Efficacy......Page 353
15.3.2 Tolerability......Page 355
15.3.3 Simulation Results......Page 356
15.4 Discussion......Page 358
References......Page 360
16.1 Introduction......Page 362
16.2 Rationale......Page 363
16.3.1.1 D2-Receptor Occupancy Model......Page 364
16.3.1.3 Prediction of D2-Receptor Occupancy......Page 365
16.3.2.1 Pharmacokinetic/Pharmacodynamic (PK/PD)-Model......Page 366
16.3.2.2 PK/PD-Simulation......Page 368
16.4.1 Predicting Efficacy and Safety using D2-Receptor Occupancy as a Biomarker......Page 370
16.4.2.1 PK/PD Model for EPS-Incidence......Page 372
16.4.2.2 PK/PD Simulation for EPS-Incidence......Page 374
16.5 Discussion......Page 375
References......Page 377
Part IV: Expanded Applications of M&S......Page 380
17.1 Introduction......Page 382
17.2.1 Absorption and Bioavailability......Page 384
17.2.2 Bimolecular Interaction of Drug with Target......Page 386
17.2.3 Biodistribution......Page 387
17.2.4 Clearance......Page 389
17.2.4.1 Target-Mediated Drug Disposition......Page 391
Target-Mediated Drug Disposition Model......Page 392
Michaelis-Menten Approximation......Page 394
17.2.6 Cytokinetics......Page 396
17.2.6.1 Cell Lifespan......Page 397
17.3 Applied Modeling and Simulation from Discovery Through Clinical Development......Page 399
17.3.1 Drug Discovery: Target Evaluation and Lead Drug Optimization and Selection......Page 400
17.3.2 Preclinical Development......Page 402
17.3.3 Clinical Development......Page 403
17.3.3.2 Selection of Phase II/III Doses......Page 405
17.3.3.3 Characterization of the Dose-Concentration-Effect Relationship......Page 406
17.3.3.4 Demonstration of the Similarity of PK-PD Relationship Between Different Patient Populations......Page 407
17.3.3.5 Evaluation of Demographic or Disease Covariates to Determine the Need of Dose Modifications for Subpopulations......Page 408
17.3.3.6 Evaluation of Drug-Drug Interactions......Page 410
17.3.3.7 Comparison of Fixed Dosing vs. Body Size-Based Dosing......Page 411
17.3.3.8 Provision of Support for Switching to Alternative Route of Administration, Dosage Form, or Dosing Regimen......Page 412
17.4 Conclusions......Page 413
References......Page 414
18.1 Introduction......Page 418
18.2 Methods for Modeling and Simulation Applications......Page 422
18.2.1 Common Trial Designs for Pediatrics......Page 423
18.2.2 Converting the Developing Child into the In Silico Child......Page 425
18.3 The Pediatric CTS Model......Page 431
18.3.1 Priors for Pediatric CTS......Page 433
18.3.2 Typical Workflow......Page 436
18.4.1 Safety/PK Trial to Fulfill Regulatory Requirements - Low Molecular Weight Heparin......Page 437
18.4.2 BPCA Trial: Written Request for Actinomycin-D and Vincristine in Children with Cancer......Page 440
18.4.3 Exploratory PK/PD Trial: Topirimate Dose Finding in Post Surgical Neonates......Page 442
18.5.2 Pediatric Outcomes......Page 446
References......Page 447
Part V: Evolving Methodologies in M&S......Page 452
19.1 General Concepts......Page 454
19.2.1 Descriptive Models......Page 457
19.2.2 Mechanism-Based Models......Page 460
19.2.2.1 Turnover Models......Page 461
19.2.2.2 Cascading Turnover Models......Page 463
19.2.3 Systems Pharmacology......Page 466
19.3 Practical Challenges and Implementations......Page 468
19.4 Summary......Page 472
References......Page 473
20.1 Introduction......Page 478
20.2.2 Role of the RAS Pathway in Modulating Arterial Pressure......Page 480
20.2.4 Creating a Model of Hypertension Incorporating the RAS Pathway......Page 482
20.3.1 Model Structure......Page 483
20.3.2 Systemic RAS Model Assumptions......Page 485
20.3.3 Parameterization of a Representative Normotensive Virtual Patient (VP)......Page 487
20.4.1 Angiotensin Peptide Infusion Experiments......Page 488
20.4.2 Representation and Parameterization of Antihypertensive Therapies......Page 489
20.4.3 Validation of Antihypertensive Therapies in the Model......Page 490
20.4.4 Representation of Variability Across Different Clinical Populations......Page 491
20.4.5 Insights from Model Simulations......Page 492
20.5.2 Model Development......Page 493
20.5.3.1 Renal Vascular Compartment......Page 495
20.5.3.2 Renal Tissue Compartment......Page 496
20.6 RAS Pathway Model Application in Drug Development......Page 497
20.7 Conclusion......Page 499
References......Page 500
21.1 Introduction......Page 504
21.2.1 Prediction of Hepatic Drug Clearance......Page 507
21.2.2 Prediction of In Vivo CL from In Vitro CL......Page 508
21.2.4 Factors Influencing Hepatic Clearance......Page 509
21.3 Physiologically Based Predictions of Tissue Distribution......Page 510
21.4 Prediction Models for Oral Absorption and Bioavailability......Page 512
21.5 Applying Physiologically Based Approaches in Drug Development......Page 513
21.6 Concluding Remarks......Page 516
References......Page 517
22.1 Introduction......Page 522
22.2 Covariate Distribution Models......Page 523
22.2.1 Internal Databases......Page 526
22.2.2.1 The United States Census of Demographic Characteristics of Americans......Page 527
22.2.2.2 National Institutes of Health Databases......Page 528
22.2.2.3 The US National Health and Nutrition Examination Survey......Page 529
Generating Covariate Probability Density Functions......Page 530
Generating Laboratory Clinical Values......Page 537
22.3 Future Perspectives......Page 541
References......Page 542
Index......Page 544