Kimko / Peck Clinical Trial Simulations

Applications and Trends
2011
ISBN: 978-1-4419-7415-0
Verlag: Springer
Format: PDF
Kopierschutz: 1 - PDF Watermark

E-Book, Englisch, Band 1, 540 Seiten

Reihe: AAPS Advances in the Pharmaceutical Sciences Series

This edition includes both updates and new uses and issues concerning CTS, along with case studies of how clinical trial simulations are being applied in various therapeutic and application areas. Importantly, the book expands on the utility of CTS for informing decisions during drug development and regulatory review. Each chapter author was selected on the basis of demonstrated expertise in state-of-the-art application of CTS. The target audience for this volume includes researchers and scientists who wish to consider use of simulations in the design, analysis, or regulatory review and guidance of clinical trials. This book does not embrace all aspects of trial design, nor is it intended as a complete recipe for using computers to design trials. Rather, it is an information source that enables the reader to gain understanding of essential background and knowledge for practical applications of simulation for clinical trial design and analysis. It is assumed that the reader has a working understanding of pharmacokinetics and pharmacodynamics, modeling, pharmacometric analyses, and/or the drug development and regulatory processes.

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.

Autoren/Hrsg.

Kimko, Holly H. C.

Peck, Carl C.

Inhaltsverzeichnis

1;Clinical Trial Simulations;3
1.1;Preface;5
1.2;Contents;7
1.3;Contributors;11
1.4;Chapter 1: Clinical Trial Simulation and Quantitative Pharmacology;17
1.4.1;1.1 Introduction;17
1.4.2;1.2 Encouragement by EMA and FDA;19
1.4.3;1.3 Clinical Trial Protocol Deviations and Adherence;21
1.4.4;1.4 CTS-Supported Strategic Decisions in Drug Development;23
1.4.5;1.5 Conclusion;24
1.4.6;References;25
1.5;Part I: Application of M&S in Regulatory Decisions;28
1.5.1;Chapter 2: Contribution of Modeling and Simulation Studies in the Regulatory Review: A European Regulatory Perspective1;29
1.5.1.1;2.1 Introduction;29
1.5.1.2;2.2 Regulatory Guidance;31
1.5.1.2.1;2.2.1 Available Guidelines;31
1.5.1.2.2;2.2.2 Would a Specific European Guideline on MandS Be of Value?;39
1.5.1.3;2.3 Regulatory Decisions: When and Impact;39
1.5.1.3.1;2.3.1 Pediatric Investigation Plan;40
1.5.1.3.2;2.3.2 Clinical Trial Application;40
1.5.1.3.3;2.3.3 Scientific Advice;41
1.5.1.3.4;2.3.4 Approval for Marketing Authorization;42
1.5.1.4;2.4 Examples of Contribution of MandS Documentation in the Regulatory Review;43
1.5.1.4.1;2.4.1 Keppra (levetiracetam);43
1.5.1.4.2;2.4.2 Celsentri (Maraviroc);45
1.5.1.4.3;2.4.3 Bridion (sugammadex);46
1.5.1.5;2.5 Future Perspectives and Summary;46
1.5.1.6;References;48
1.5.2;Chapter 3: Contribution of Modeling and Simulation in the Regulatory Review and Decision-Making: U.S. FDA Perspective;51
1.5.2.1;3.1 History of Pharmacometrics at FDA;51
1.5.2.2;3.2 Division of Pharmacometrics;52
1.5.2.2.1;3.2.1 Vision and Strategic Goals;52
1.5.2.2.2;3.2.2 Pharmacometric Reviews;52
1.5.2.2.2.1;3.2.2.1 NDA and BLA Submissions;53
1.5.2.2.2.2;3.2.2.2 QT Study Design and Analysis;53
1.5.2.2.2.3;3.2.2.3 Protocol Design;54
1.5.2.2.2.3.1;Pediatric Trials;54
1.5.2.2.2.3.2;End-of-Phase 2a Meetings;55
1.5.2.2.2.4;3.2.2.4 Knowledge Management;55
1.5.2.2.3;3.2.3 Research and Policy Development;56
1.5.2.3;3.3 Impact of Pharmacometric Analyses on Regulatory Decisions from 2000 to 2008;57
1.5.2.3.1;3.3.1 Summary of Regulatory Impact;57
1.5.2.3.2;3.3.2 Scope of Pharmacometric Reviews;58
1.5.2.3.2.1;3.3.2.1 Published Case Examples;58
1.5.2.3.2.2;3.3.2.2 Pediatric Dosing Regimen;62
1.5.2.3.2.3;3.3.2.3 Drugs with Approved Doses Not Directly Evaluated in Phase 3 Trials;64
1.5.2.3.2.4;3.3.2.4 Pharmacometric Analysis Used as Evidence of Effectiveness;66
1.5.2.3.2.4.1;Provide Confirmatory Evidence;66
1.5.2.3.2.4.2;Model-Based Primary Endpoint for Phase 3 Trials;66
1.5.2.4;3.4 Future Perspectives;68
1.5.2.5;References;69
1.6;Part II: Strategic Applications of M&S in Drug Development;72
1.6.1;Chapter 4: Decision-Making in Drug Development: Application of a Model Based Framework for Assessing Trial Performance;73
1.6.1.1;4.1 Introduction;73
1.6.1.2;4.2 Notation and Terminology;75
1.6.1.3;4.3 Illustrative Example Using Bivariate Normal Distributions;78
1.6.1.3.1;4.3.1 Introduction to the Example;78
1.6.1.3.2;4.3.2 Operating Characteristics for Decision Criteria Based on Point Estimates;79
1.6.1.3.3;4.3.3 Operating Characteristics for Decision Criteria Based on Interval Estimates;82
1.6.1.4;4.4 Applying Decision Criteria in a Dose-Response Example;85
1.6.1.4.1;4.4.1 Introduction to the Dose-Response Example;85
1.6.1.4.2;4.4.2 Operating Characteristics for Decisions Based on Relative Potency Alone;86
1.6.1.4.3;4.4.3 Operating Characteristics for Decisions Based on Relative Potency and Efficacy at the Top Dose;87
1.6.1.4.4;4.4.4 Operating Characteristics for Decisions Based on the Estimated Effect at Each Dose;89
1.6.1.5;4.5 Practicalities of Simulation in Model-Based Drug Development;90
1.6.1.6;4.6 Discussion;92
1.6.1.7;References;94
1.6.2;Chapter 5: Decision-Making in Drug Development: Application of a Clinical Utility IndexSM;96
1.6.2.1;5.1 Introduction;96
1.6.2.2;5.2 A CUI Overview;100
1.6.2.2.1;5.2.1 Setting the Decision Context Before CUI Creation;100
1.6.2.2.2;5.2.2 The Clinical Utility Index: ``Nuts and Bolts´´;101
1.6.2.2.3;5.2.3 Calculating Utility: A Quick Example;104
1.6.2.3;5.3 A Detailed Example of CUI;107
1.6.2.4;5.4 Related Publications on CUI;111
1.6.2.5;5.5 Conclusion: Putting the CUI into Practice;113
1.6.2.6;Appendix: History and Theory;114
1.6.2.6.1;Notes on Multiplicative Functions;114
1.6.2.6.2;Basic Elicitation Steps;116
1.6.2.6.3;Link to Conjoint Analysis;116
1.6.2.7;References;117
1.6.3;Chapter 6: Adaptive Trial Designs;119
1.6.3.1;6.1 Background: What are Adaptive Designs and Why Can They Be Useful?;119
1.6.3.2;6.2 Adaptive Designs in the Learn Phase of Development;121
1.6.3.2.1;6.2.1 Objectives: Finding an Adequate Dose and Learning About Dose-Response;121
1.6.3.2.2;6.2.2 Adaptive Dose-Ranging Approaches;123
1.6.3.2.2.1;6.2.2.1 General Adaptive Dose Allocation (GADA);123
1.6.3.2.2.2;6.2.2.2 Adaptive Multiple Comparison Procedure-Modeling (aMCP-Mod);124
1.6.3.2.2.3;6.2.2.3 Combined D- and C-Optimality (DcoD);124
1.6.3.2.2.4;6.2.2.4 Focus on Interesting Region of DR Profile (IntR);125
1.6.3.2.2.5;6.2.2.5 Multiple Objectives (MULTOB);125
1.6.3.2.2.6;6.2.2.6 t-Test Adaptation;125
1.6.3.2.3;6.2.3 Remarks on ADR Methods;126
1.6.3.3;6.3 Adaptive Designs for Confirmatory Studies;127
1.6.3.3.1;6.3.1 Group Sequential Designs;127
1.6.3.3.2;6.3.2 Adaptive Designs;128
1.6.3.3.3;6.3.3 Sample Size Re-Estimation;129
1.6.3.3.4;6.3.4 Applications: Treatment Selection and Enrichment Designs;130
1.6.3.3.5;6.3.5 Practical Considerations;132
1.6.3.4;6.4 Adaptive Designs and Trial Simulations;133
1.6.3.4.1;6.4.1 Operating Characteristics;133
1.6.3.4.2;6.4.2 An Illustration: Comparing ADR Approaches;134
1.6.3.5;6.5 Concluding Remarks and Further Thoughts on Adaptive Designs;137
1.6.3.6;References;138
1.6.4;Chapter 7: Keys of Collaboration to Enhance Efficiency and Impact of Modeling and Simulation;141
1.6.4.1;7.1 Introduction;141
1.6.4.2;7.2 Corifollitropin Alfa Development Program;144
1.6.4.3;7.3 Phase II Development: Design of a Dose-Response Study;146
1.6.4.4;7.4 Phase III Development: Dose Selection;150
1.6.4.5;7.5 Discussion;154
1.6.4.6;References;158
1.6.5;Chapter 8: Leveraging Pharmacometrics in Early Phase Anti-inflamatory Drug Development;159
1.6.5.1;8.1 Introduction;159
1.6.5.2;8.2 The Learn-Confirm-Learn Process;161
1.6.5.3;8.3 The Model-Based Approach to Drug Development and Pharmacometrics;163
1.6.5.3.1;8.3.1 Pharmacometric Knowledge Integration;163
1.6.5.4;8.4 Information, Knowledge, Understanding, and Wisdom Paradigm;164
1.6.5.5;8.5 Leveraging Pharmacometrics in Early Phase Drug Development;164
1.6.5.5.1;8.5.1 Example of Model-Based Early Development;166
1.6.5.5.1.1;8.5.1.1 Translation of Nonclinical Information into Knowledge;167
1.6.5.5.1.2;8.5.1.2 Pharmacometric Leveraging of Nonclinical Knowledge to Gain Insight into a Proposed FTIH Study;169
1.6.5.5.1.3;8.5.1.3 Wisdom for the Performance of FTIH Study;172
1.6.5.5.1.4;8.5.1.4 The First-Time in Human Study;172
1.6.5.5.1.4.1;Data;172
1.6.5.5.1.4.2;Population Pharmacokinetic Analysis;174
1.6.5.5.1.4.3;Exposure: Response Analysis;177
1.6.5.5.1.4.4;Comparison of Performance of the FTIH Study Outcome with FTIH Clinical Trial Simulation Outcome;179
1.6.5.5.1.5;8.5.1.5 Wisdom for the Design of Proof of Concept Study;181
1.6.5.6;8.6 Summary;181
1.6.5.7;References;182
1.7;Part III: Application of M&S in Selected Therapeutic Areas;183
1.7.1;Chapter 9: The Application of Drug-Disease Models in the Development of Anti-Hyperglycemic Agents;184
1.7.1.1;9.1 Diabetes Mellitus;184
1.7.1.1.1;9.1.1 Treatment Option and Drug Class;186
1.7.1.1.2;9.1.2 Biomarkers;187
1.7.1.1.3;9.1.3 Experimental Techniques;187
1.7.1.1.3.1;9.1.3.1 Glucose Tolerance Tests;188
1.7.1.1.3.2;9.1.3.2 Clamp Studies;188
1.7.1.2;9.2 Drug-Disease Models of Diabetes;189
1.7.1.2.1;9.2.1 Mechanistic Models of Glucose-Insulin Regulation;189
1.7.1.2.2;9.2.2 Time-Course Models;190
1.7.1.2.3;9.2.3 Indirect Response Models;192
1.7.1.2.4;9.2.4 Mechanistic Linked Model of FPG-HbA1c;193
1.7.1.2.5;9.2.5 Models Incorporating Disease Progression;194
1.7.1.2.6;9.2.6 Literature Data for Developing Drug-Disease Models;194
1.7.1.2.7;9.2.7 Biologically Based Mathematical Models;196
1.7.1.3;9.3 Applications in Drug Development;196
1.7.1.3.1;9.3.1 Discovery and Candidate Selection;198
1.7.1.3.2;9.3.2 Proof of Concept and Time-Course of Response;199
1.7.1.3.3;9.3.3 Dose Response of Efficacy and Safety Attributes;201
1.7.1.3.4;9.3.4 Dose Selection;202
1.7.1.4;9.4 Regulatory Considerations;203
1.7.1.5;9.5 Future Directions;204
1.7.1.6;References;205
1.7.2;Chapter 10: Modeling and Simulation in the Development of Cardiovascular Agents;208
1.7.2.1;10.1 Hypercholesterolemia;208
1.7.2.1.1;10.1.1 Overview of Hypercholesterolemia;208
1.7.2.1.2;10.1.2 Overview of Pharmacology of Statins;209
1.7.2.1.3;10.1.3 Model Based Evaluations of Cholesterol Lowering Agents;210
1.7.2.2;10.2 Antithrombus Therapy;213
1.7.2.2.1;10.2.1 Overview of Pathophysiology of Thrombus Formation;213
1.7.2.2.2;10.2.2 Pharmacology of Anticoagulant Agents;214
1.7.2.2.3;10.2.3 Modeling and Simulation for Dosing of Anticoagulants;216
1.7.2.3;10.3 Stroke;218
1.7.2.3.1;10.3.1 Overview of Stroke and Clinical Endpoints;218
1.7.2.3.2;10.3.2 Example Stroke Disease Progression Models;219
1.7.2.3.3;10.3.3 Longitudinal Model for Nonmonotonic Stroke Scale Data;223
1.7.2.4;10.4 Hypertension;225
1.7.2.4.1;10.4.1 Overview of Hypertension;225
1.7.2.4.2;10.4.2 Pharmacology of Antihypertensive Agents;225
1.7.2.4.3;10.4.3 Modeling and Simulation for Antihypertensive Agents;227
1.7.2.5;10.5 Adaptive Dosing Simulation Techniques: Focus on Cardiovascular Medicines;229
1.7.2.6;References;232
1.7.3;Chapter 11: Viral Dynamic Modeling and Simulations in HIV and Hepatitis C;236
1.7.3.1;11.1 Introduction;236
1.7.3.2;11.2 Basic Viral Dynamic Model;237
1.7.3.3;11.3 Viral Dynamic Modeling and Simulations in HIV;239
1.7.3.3.1;11.3.1 Basic PK-PD Principles of R0 and RMIC;243
1.7.3.3.1.1;11.3.1.1 For R0 and RMIC;243
1.7.3.3.1.2;11.3.1.2 For Binary Outcomes Analysis;243
1.7.3.3.2;11.3.2 Dose and Dosing Schedule;243
1.7.3.3.3;11.3.3 Estimation of Model Parameters;246
1.7.3.4;11.4 Viral Dynamic Modeling and Simulations in Hepatitis C;248
1.7.3.5;11.5 Conclusions;255
1.7.3.6;References;256
1.7.4;Chapter 12: A Model-Based PK/PD Antimicrobial Chemotherapy Drug Development Platform to Simultaneously Combat Infectious Diseasesand Drug Resistance;260
1.7.4.1;12.1 Introduction;260
1.7.4.2;12.2 Why Develop a Platform to Simultaneously Combat Infectious Diseases and Drug Resistance?;261
1.7.4.2.1;12.2.1 Classical Empirical Antibiotics vs. Synthetic Antibacterials;263
1.7.4.3;12.3 PK/PD-Driven Clinical Trial Design for Chemotherapeutic Antimicrobial Dose-Regimen Rationalization;263
1.7.4.3.1;12.3.1 Knowledge Generation for Design and Simulation of Antiinfective Clinical Trials;266
1.7.4.3.1.1;12.3.1.1 Microbiological Effect Concentration;266
1.7.4.3.1.2;12.3.1.2 Time-Kill Study Evaluation;267
1.7.4.3.1.3;12.3.1.3 Evaluating Effect (E)-vs.-Concentration [a] Curve Symmetry/Asymmetry;269
1.7.4.3.1.4;12.3.1.4 Relating the Hill Model to MICs;270
1.7.4.3.1.5;12.3.1.5 Antimicrobial Drug Combinations;271
1.7.4.3.1.6;12.3.1.6 In Vivo Thigh Model;273
1.7.4.3.1.7;12.3.1.7 Emergence of Resistance Submodel;274
1.7.4.3.1.8;12.3.1.8 Host Defense Submodel;274
1.7.4.4;12.4 Antimicrobial Chemotherapy Knowledge Integration, from Bench-to-Bedside;275
1.7.4.5;12.5 Clinical Application;277
1.7.4.6;12.6 Summary;282
1.7.4.7;References;282
1.7.5;Chapter 13: PKPD and Disease Modeling: Concepts and Applications to Oncology;289
1.7.5.1;13.1 General Concepts and History of Model-Based Research in Oncology;289
1.7.5.2;13.2 Modeling Tumor Growth and Disease Progression;292
1.7.5.3;13.3 Modeling Tumor Growth and DecayUnder Treatment;297
1.7.5.4;13.4 Modeling Biomarkers vs. Surrogate Endpoints;298
1.7.5.5;13.5 Translational Models;302
1.7.5.6;13.6 Modeling and Prediction of Adverse Events;306
1.7.5.7;13.7 Clinical Trial Simulations;309
1.7.5.8;13.8 Concluding Remarks;312
1.7.5.9;References;313
1.7.6;Chapter 14: Application of Pharmacokinetic-Pharmacodynamic Modeling and Simulation for Erythropoietic Stimulating Agents;319
1.7.6.1;14.1 Introduction;319
1.7.6.2;14.2 Extrapolating PK/PD from Preclinical to Clinical;320
1.7.6.3;14.3 Predicting the Outcome of an Extended Dosing Interval Regimen;323
1.7.6.4;14.4 Pediatric Study Design;328
1.7.6.5;References;333
1.7.7;Chapter 15: Model Based Development of an Agent for the Treatment of Generalized AnxietyDisorder;336
1.7.7.1;15.1 Introduction;336
1.7.7.2;15.2 Materials and Methods;339
1.7.7.2.1;15.2.1 Data;339
1.7.7.2.2;15.2.2 Dose Response Analysis;340
1.7.7.2.2.1;15.2.2.1 Efficacy;340
1.7.7.2.2.2;15.2.2.2 Tolerability;341
1.7.7.2.2.3;15.2.2.3 Model Selection;341
1.7.7.2.2.4;15.2.2.4 Model Uncertainty;341
1.7.7.2.3;15.2.3 Dose Selection for Phase 3 Studies;342
1.7.7.2.4;15.2.4 Model Validation;343
1.7.7.3;15.3 Results;343
1.7.7.3.1;15.3.1 Efficacy;343
1.7.7.3.2;15.3.2 Tolerability;345
1.7.7.3.3;15.3.3 Simulation Results;346
1.7.7.3.4;15.3.4 Comparison of Week 6 Phase 3 Outcome and Model Prediction;348
1.7.7.4;15.4 Discussion;348
1.7.7.5;References;350
1.7.8;Chapter 16: Balancing Efficacy and Safety in the Clinical Development of an Atypical Antipsychotic, Paliperidone Extended-Release;352
1.7.8.1;16.1 Introduction;352
1.7.8.2;16.2 Rationale;353
1.7.8.3;16.3 Methods and Data;354
1.7.8.3.1;16.3.1 D2-Receptor Occupancy as a Biomarker for Efficacy and Safety;354
1.7.8.3.1.1;16.3.1.1 D2-Receptor Occupancy Model;354
1.7.8.3.1.2;16.3.1.2 Population PK-Model for Paliperidone ER;355
1.7.8.3.1.3;16.3.1.3 Prediction of D2-Receptor Occupancy;355
1.7.8.3.2;16.3.2 Extrapyramidal Symptoms;356
1.7.8.3.2.1;16.3.2.1 Pharmacokinetic/Pharmacodynamic (PK/PD)-Model;356
1.7.8.3.2.2;16.3.2.2 PK/PD-Simulation;358
1.7.8.4;16.4 Results;360
1.7.8.4.1;16.4.1 Predicting Efficacy and Safety using D2-Receptor Occupancy as a Biomarker;360
1.7.8.4.2;16.4.2 Predicting EPS-Incidence;362
1.7.8.4.2.1;16.4.2.1 PK/PD Model for EPS-Incidence;362
1.7.8.4.2.2;16.4.2.2 PK/PD Simulation for EPS-Incidence;364
1.7.8.5;16.5 Discussion;365
1.7.8.6;References;367
1.8;Part IV: Expanded Applications of M&S;369
1.8.1;Chapter 17: Application of Modeling and Simulation in the Development of Protein Drugs;370
1.8.1.1;17.1 Introduction;370
1.8.1.2;17.2 PK-PD Models of Protein Drugs;372
1.8.1.2.1;17.2.1 Absorption and Bioavailability;372
1.8.1.2.2;17.2.2 Bimolecular Interaction of Drug with Target;374
1.8.1.2.3;17.2.3 Biodistribution;375
1.8.1.2.4;17.2.4 Clearance;377
1.8.1.2.4.1;17.2.4.1 Target-Mediated Drug Disposition;379
1.8.1.2.4.1.1;Target-Mediated Drug Disposition Model;380
1.8.1.2.4.1.2;Quasi-Steady-State (QSS) and Rapid Binding (RB) Approximations;382
1.8.1.2.4.1.3;Michaelis-Menten Approximation;382
1.8.1.2.5;17.2.5 Interactions of Drug and Soluble Target;384
1.8.1.2.6;17.2.6 Cytokinetics;384
1.8.1.2.6.1;17.2.6.1 Cell Lifespan;385
1.8.1.2.6.2;17.2.6.2 Cell Proliferation, Maturation, and Feedback Regulation of PK;387
1.8.1.3;17.3 Applied Modeling and Simulation from Discovery Through Clinical Development;387
1.8.1.3.1;17.3.1 Drug Discovery: Target Evaluation and Lead Drug Optimization and Selection;388
1.8.1.3.2;17.3.2 Preclinical Development;390
1.8.1.3.3;17.3.3 Clinical Development;391
1.8.1.3.3.1;17.3.3.1 Utility of PK-PD Models in a Clinical Setting;393
1.8.1.3.3.2;17.3.3.2 Selection of Phase II/III Doses;393
1.8.1.3.3.3;17.3.3.3 Characterization of the Dose-Concentration-Effect Relationship;394
1.8.1.3.3.4;17.3.3.4 Demonstration of the Similarity of PK-PD Relationship Between Different Patient Populations;395
1.8.1.3.3.5;17.3.3.5 Evaluation of Demographic or Disease Covariates to Determine the Need of Dose Modifications for Subpopulations;396
1.8.1.3.3.6;17.3.3.6 Evaluation of Drug-Drug Interactions;398
1.8.1.3.3.7;17.3.3.7 Comparison of Fixed Dosing vs. Body Size-Based Dosing;399
1.8.1.3.3.8;17.3.3.8 Provision of Support for Switching to Alternative Route of Administration, Dosage Form, or Dosing Regimen;400
1.8.1.3.3.9;17.3.3.9 Provision of Rationale for Clinical Observations in Phase III Trials in Regulatory Interactions for Approval;401
1.8.1.4;17.4 Conclusions;401
1.8.1.5;References;402
1.8.2;Chapter 18: Modeling and Simulation in Pediatric Research and Development;406
1.8.2.1;18.1 Introduction;406
1.8.2.2;18.2 Methods for Modeling and Simulation Applications;410
1.8.2.2.1;18.2.1 Common Trial Designs for Pediatrics;411
1.8.2.2.2;18.2.2 Converting the Developing Child into the In Silico Child;413
1.8.2.3;18.3 The Pediatric CTS Model;419
1.8.2.3.1;18.3.1 Priors for Pediatric CTS;421
1.8.2.3.2;18.3.2 Typical Workflow;424
1.8.2.4;18.4 Examples;425
1.8.2.4.1;18.4.1 Safety/PK Trial to Fulfill Regulatory Requirements - Low Molecular Weight Heparin;425
1.8.2.4.2;18.4.2 BPCA Trial: Written Request for Actinomycin-D and Vincristine in Children with Cancer;428
1.8.2.4.3;18.4.3 Exploratory PK/PD Trial: Topirimate Dose Finding in Post Surgical Neonates;430
1.8.2.5;18.5 Other Considerations;434
1.8.2.5.1;18.5.1 The Role for ``Bottom-Up´´ Approaches;434
1.8.2.5.2;18.5.2 Pediatric Outcomes;434
1.8.2.5.3;18.5.3 Pediatric Disease Progression;435
1.8.2.6;References;435
1.9;Part V: Evolving Methodologies in M&S;439
1.9.1;Chapter 19: Disease Progression Analysis: Towards Mechanism-Based Models;440
1.9.1.1;19.1 General Concepts;440
1.9.1.2;19.2 Overview of Disease Process and Disease Progression Models;443
1.9.1.2.1;19.2.1 Descriptive Models;443
1.9.1.2.2;19.2.2 Mechanism-Based Models;446
1.9.1.2.2.1;19.2.2.1 Turnover Models;447
1.9.1.2.2.2;19.2.2.2 Cascading Turnover Models;449
1.9.1.2.3;19.2.3 Systems Pharmacology;452
1.9.1.3;19.3 Practical Challenges and Implementations;454
1.9.1.4;19.4 Summary;458
1.9.1.5;References;459
1.9.2;Chapter 20: Using a Systems Biology Approach to Explore Hypotheses Underlying Clinical Diversity of the Renin Angiotensin System and theResponse to Antihypertensive Therapies;463
1.9.2.1;20.1 Introduction;463
1.9.2.2;20.2 Overview;465
1.9.2.2.1;20.2.1 Epidemiology and Pathophysiology of Hypertension;465
1.9.2.2.2;20.2.2 Role of the RAS Pathway in Modulating Arterial Pressure;465
1.9.2.2.3;20.2.3 Modeling Approaches to Long-Term Regulation of Blood Pressure;467
1.9.2.2.4;20.2.4 Creating a Model of Hypertension Incorporating the RAS Pathway;467
1.9.2.3;20.3 Model of Systemic RAS;468
1.9.2.3.1;20.3.1 Model Structure;468
1.9.2.3.2;20.3.2 Systemic RAS Model Assumptions;470
1.9.2.3.3;20.3.3 Parameterization of a Representative Normotensive Virtual Patient (VP);472
1.9.2.4;20.4 Model Validation;473
1.9.2.4.1;20.4.1 Angiotensin Peptide Infusion Experiments;473
1.9.2.4.2;20.4.2 Representation and Parameterization of Antihypertensive Therapies;474
1.9.2.4.3;20.4.3 Validation of Antihypertensive Therapies in the Model;475
1.9.2.4.4;20.4.4 Representation of Variability Across Different Clinical Populations;476
1.9.2.4.5;20.4.5 Insights from Model Simulations;477
1.9.2.5;20.5 Modeling RAS Within the Kidney;478
1.9.2.5.1;20.5.1 Introduction;478
1.9.2.5.2;20.5.2 Model Development;478
1.9.2.5.3;20.5.3 Parameterization of the Renal RAS Model;480
1.9.2.5.3.1;20.5.3.1 Renal Vascular Compartment;480
1.9.2.5.3.2;20.5.3.2 Renal Tissue Compartment;481
1.9.2.5.4;20.5.4 Hypothesis Testing and Other Applications of the Renal RAS Model;482
1.9.2.6;20.6 RAS Pathway Model Application in Drug Development;482
1.9.2.7;20.7 Conclusion;484
1.9.2.8;References;485
1.9.3;Chapter 21: Recent Developments in Physiologically Based Pharmacokinetic Modeling;489
1.9.3.1;21.1 Introduction;489
1.9.3.2;21.2 Input Parameters for PBPK Modeling;492
1.9.3.2.1;21.2.1 Prediction of Hepatic Drug Clearance;492
1.9.3.2.2;21.2.2 Prediction of In Vivo CL from In Vitro CL;493
1.9.3.2.3;21.2.3 Scaling of In Vitro CLint to In Vivo CLint;494
1.9.3.2.4;21.2.4 Factors Influencing Hepatic Clearance;494
1.9.3.3;21.3 Physiologically Based Predictions of Tissue Distribution;495
1.9.3.4;21.4 Prediction Models for Oral Absorption and Bioavailability;497
1.9.3.5;21.5 Applying Physiologically Based Approaches in Drug Development;498
1.9.3.6;21.6 Concluding Remarks;501
1.9.3.7;References;502
1.9.4;Chapter 22: Covariate Distribution Models in Simulation;506
1.9.4.1;22.1 Introduction;506
1.9.4.2;22.2 Covariate Distribution Models;507
1.9.4.2.1;22.2.1 Internal Databases;510
1.9.4.2.2;22.2.2 External Databases;511
1.9.4.2.2.1;22.2.2.1 The United States Census of Demographic Characteristics of Americans;511
1.9.4.2.2.2;22.2.2.2 National Institutes of Health Databases;512
1.9.4.2.2.3;22.2.2.3 The US National Health and Nutrition Examination Survey;513
1.9.4.2.2.3.1;Generating Covariate Probability Density Functions;514
1.9.4.2.2.3.2;Generating Laboratory Clinical Values;521
1.9.4.3;22.3 Future Perspectives;525
1.9.4.4;References;526
1.10;Index;528