Systems Biology in Drug Discovery and Development E-Book

Daniel L. Young

• Herausgeber: John Wiley & Sons
• Kategorie: Wissenschaft und neue Technologien
• Serie: Wiley Series on Technologies for the Pharmaceutical
• Sprache: Englisch

Seitenzahl: 661

Veröffentlichungsjahr: 2011

Beschreibung

The first book to focus on comprehensive systems biology as applied to drug discovery and development Drawing on real-life examples, Systems Biology in Drug Discovery and Development presents practical applications of systems biology to the multiple phases of drug discovery and development. This book explains how the integration of knowledge from multiple sources, and the models that best represent that integration, inform the drug research processes that are most relevant to the pharmaceutical and biotechnology industries. The first book to focus on comprehensive systems biology and its applications in drug discovery and development, it offers comprehensive and multidisciplinary coverage of all phases of discovery and design, including target identification and validation, lead identification and optimization, and clinical trial design and execution, as well as the complementary systems approaches that make these processes more efficient. It also provides models for applying systems biology to pharmacokinetics, pharmacodynamics, and candidate biomarker identification. Introducing and explaining key methods and technical approaches to the use of comprehensive systems biology on drug development, the book addresses the challenges currently facing the pharmaceutical industry. As a result, it is essential reading for pharmaceutical and biotech scientists, pharmacologists, computational modelers, bioinformaticians, and graduate students in systems biology, pharmaceutical science, and other related fields.

Leseprobe

Table of Contents

Cover

Series page

Title page

Copyright page

PREFACE

CONTRIBUTORS

PART I: INTRODUCTION TO SYSTEMS BIOLOGY IN APPROACH

CHAPTER 1 Introduction to Systems Biology in Drug Discovery and Development

SYSTEMS BIOLOGY IN PHARMACOLOGY

CHAPTER 2 Methods for In Silico Biology: Model Construction and Analysis

2.1. INTRODUCTION

2.2. MODEL BUILDING

2.3. PARAMETER ESTIMATION

2.4. MODEL ANALYSIS

2.5. CONCLUSIONS

CHAPTER 3 Methods in In Silico Biology: Modeling Feedback Dynamics in Pathways

3.1. INTRODUCTION

3.2. STATISTICAL MODELING

3.3. MATHEMATICAL MODELING

3.4. FEEDBACK AND FEEDFORWARD

3.5. CONCLUSIONS

CHAPTER 4 Simulation of Population Variability in Pharmacokinetics

4.1. INTRODUCTION

4.2. PBPK MODELING

4.3. SIMULATION OF PHARMACOKINETIC VARIABILITY

4.4. CONCLUSIONS AND FUTURE DIRECTIONS

PART II: APPLICATIONS TO DRUG DISCOVERY

CHAPTER 5 Applications of Systems Biology Approaches to Target Identification and Validation in Drug Discovery

5.1. INTRODUCTION

5.2. TYPICAL DRUG DISCOVERY PARADIGM

5.3. INTEGRATED DRUG DISCOVERY

5.4. DRIVERS OF THE DISEASE PHENOTYPE: CLINICAL ENDPOINTS AND HYPOTHESES

5.5. EXTRACELLULAR DISEASE DRIVERS: MECHANISTIC BIOTHERAPEUTIC MODELS

5.6. RELEVANT CELL MODELS FOR CLINICAL ENDPOINTS

5.7. INTRACELLULAR DISEASE DRIVERS: SIGNALING PATHWAY QUANTIFICATION

5.8. TARGET SELECTION: DYNAMIC PATHWAY MODELING

5.9. CONCLUSIONS

CHAPTER 6 Lead Identification and Optimization

6.1. INTRODUCTION

6.2. THE SYSTEMS BIOLOGY TOOL KIT

6.3. CONCLUSIONS

CHAPTER 7 Role of Core Biological Motifs in Dose–Response Modeling: An Example with Switchlike Circuits

7.1. INTRODUCTION: SYSTEMS PERSPECTIVES IN DRUG DISCOVERY

7.2. SYSTEMS BIOLOGY AND TOXICOLOGY

7.3. MECHANISTIC AND COMPUTATIONAL CONCEPTS IN A MOLECULAR OR CELLULAR CONTEXT

7.4. RESPONSE MOTIFS IN CELL SIGNALING AND THEIR ROLE IN DOSE RESPONSE

7.5. DISCUSSION AND CONCLUSIONS

CHAPTER 8 Mechanism-Based Pharmacokinetic–Pharmacodynamic Modeling During Discovery and Early Development

8.1. INTRODUCTION

8.2. CHALLENGES IN DRUG DISCOVERY AND DEVELOPMENT

8.3. METHODOLOGICAL ASPECTS AND CONCEPTS

8.4. USE OF PK–PD MODELS IN LEAD OPTIMIZATION

8.5. USE OF PK–PD MODELS IN CLINICAL CANDIDATE SELECTION

8.6. ENTRY-INTO-HUMAN PREPARATION AND TRANSLATIONAL PK–PD MODELING

8.7. USE OF PK–PD MODELS IN TOXICOLOGY STUDY DESIGN AND EVALUATION

8.8. JUSTIFICATION OF STARTING DOSE, CALCULATION OF SAFETY MARGINS, AND SUPPORT OF PHASE I DESIGN

8.9. PHASE I AND BEYOND

8.10. SUPPORT OF EARLY FORMULATION DEVELOPMENT

8.11. OUTLOOK AND CONCLUSIONS

PART III: APPLICATIONS TO DRUG DEVELOPMENT

CHAPTER 9 Developing Oncology Drugs Using Virtual Patients of Vascular Tumor Diseases

9.1. INTRODUCTION

9.2. MODELING ANGIOGENESIS

9.3. USE OF RIGOROUS MATHEMATICAL ANALYSIS TO GAIN INSIGHT INTO DRUG DEVELOPMENT

9.4. USE OF ANGIOGENESIS MODELS IN THERANOSTICS

9.5. USE OF ANGIOGENESIS MODELS IN DRUG SALVAGE

9.6. SUMMARY AND CONCLUSIONS

CHAPTER 10 Systems Modeling Applied to Candidate Biomarker Identification

10.1. INTRODUCTION

10.2. BIOMARKER DISCOVERY APPROACHES

10.3. EXAMPLES OF SYSTEMS MODELING APPROACHES FOR IDENTIFICATION OF CANDIDATE BIOMARKERS

10.4. CONCLUSIONS

CHAPTER 11 Simulating Clinical Trials

11.1. INTRODUCTION

11.2. TYPES OF MODELS USED IN CLINICAL TRIAL DESIGN

11.3. SOURCES OF PRIOR INFORMATION FOR DESIGNING CLINICAL TRIALS

11.4. ASPECTS OF A TRIAL TO BE DESIGNED AND OPTIMIZED

11.5. TRIAL SIMULATION

11.6. OPTIMIZING DESIGNS

11.7. REAL-WORLD EXAMPLES

11.8. CONCLUSIONS

PART IV: SYNERGIES WITH OTHER TECHNOLOGIES

CHAPTER 12 Pathway Analysis in Drug Discovery

12.1. INTRODUCTION: PATHWAY ANALYSIS, DYNAMIC MODELING, AND NETWORK ANALYSIS

12.2. SOFTWARE SYSTEMS FOR PATHWAY ANALYSIS

12.3. PATHWAY ANALYSIS IN THE MODERN DRUG DEVELOPMENT PIPELINE

12.4. CONCLUSIONS

CHAPTER 13 Functional Mapping for Predicting Drug Response and Enabling Personalized Medicine

13.1. INTRODUCTION

13.2. FUNCTIONAL MAPPING

13.3. PREDICTIVE MODEL

13.4. FUTURE DIRECTIONS

CHAPTER 14 Future Outlook for Systems Biology

14.1. INTRODUCTION

14.2. SYSTEM COMPLEXITY IN BIOLOGICAL SYSTEMS

14.3. MODELS FOR QUANTITATIVE INTEGRATION OF DATA

14.4. CHANGING REQUIREMENTS FOR SYSTEMS APPROACHES DURING DRUG DISCOVERY AND DEVELOPMENT

14.5. BETTER MODELS FOR BETTER DECISIONS

14.6. ADVANCING PERSONALIZED MEDICINE

14.7. IMPROVING CLINICAL TRIALS AND ENABLING MORE COMPLEX TREATMENT APPROACHES

14.8. COLLABORATION AND TRAINING FOR SYSTEMS BIOLOGISTS

14.9. CONCLUSIONS

Index

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Wiley Series on Technologies for the Pharmaceutical Industry

Sean Ekins, Series Editor

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Edited by Sean Ekins

Pharmaceutical Applications of Raman Spectroscopy

Edited by Slobodan Sasic

Pathway Analysis for Drug Discovery: Computational Infrastructure and Applications

Edited by Anton Yuryev

Drug Efficacy, Safety, and Biologics Discovery: Emerging Technologies and Tools

Edited by Sean Ekins and Jinghai J. Xu

The Engines of Hippocrates: From the Dawn of Medicine to Medical and Pharmaceutical Informatics

Barry Robson and O. K. Baek

Pharmaceutical Data Mining: Approaches and Applications for Drug Discovery

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The Agile Approach to Adaptive Research: Optimizing Efficiency in Clinical Development

Michael J. Rosenberg

Pharmaceutical and Biomedical Project Management in a Changing Global Environment

Edited by Scott D. Babler

Systems Biology in Drug Discovery and Development

Edited by Daniel L. Young and Seth Michelson

Editorial Advisory Board

Dr. Renee Arnold (ACT LLC, USA)

Dr. David D. Christ (SNC Partners LLC, USA)

Dr. Michael J. Curtis (Rayne Institute, St Thomas’ Hospital, UK)

Dr. James H. Harwood (Delphi BioMedical Consultants, USA)

Dr. Maggie A.Z. Hupcey (PA Consulting, USA)

Dr. Dale Johnson (Emiliem, USA)

Prof. Tsuguchika Kaminuma (Tokyo Medical and Dental University, Japan)

Dr. Mark Murcko (Vertex, USA)

Dr. Peter W. Swaan (University of Maryland, USA)

Dr. Ana Szarfman (Food and Drug Administration, USA)

Dr. David Wild (Indiana University, USA)

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Library of Congress Cataloging-in-Publication Data:

Systems biology in drug discovery and development / edited by Daniel L. Young, Seth, Michelson.

p. ; cm.

 Includes bibliographical references.

 ISBN 978-0-470-26123-1 (cloth)

 1. Drug development. 2. Systems biology. I. Young, Daniel L., editor. II. Michelson, Seth., editor.

 [DNLM: 1. Drug Discovery–methods. 2. Models, Biological. 3. Pharmacokinetics. 4. Pharmacological Processes. 5. Systems Biology–methods. QV 744]

 RM301.25.S97 2011

 615'19–dc22

2010043475

oBook ISBN: 978-1-118-01643-5

ePDF ISBN: 978-1-118-01641-1

ePub ISBN: 978-1-118-01642-8

PREFACE

Despite the wealth of data describing mechanisms underlying health and disease in living systems, health care costs continue to rise, and there is a growing need for improved and more affordable treatments. Efficient drug discovery and development requires methods for integrating preclinical data with patient data into a unified framework to project both efficacy and safety outcomes for new compounds and treatment approaches.

In this book we present the foundations of systems biology, a growing multidisciplinary field, applied specifically to drug discovery and development. Systems biology formally integrates knowledge and information from multiple biological sources into a coherent whole by employing proven engineering and mathematical modeling approaches. The integrated system allows rapid analysis and simulation that can inform and optimize the drug research and development processes, by formalizing, and testing, the set of acceptable hypotheses in silico, thereby reducing development time and costs and ultimately improving the efficacy of novel treatments.

This book is the first systems biology text to focus on how systems biology can be specifically applied to enhance drug discovery and development, with particular emphasis on real-world examples. Other texts on systems biology to date have focused on particular subdisciplines of systems biology (such as cellular networks) and have not specifically addressed drug discovery and development. This book introduces key methodologies and technical approaches for helping to solve many of the current challenges facing the pharmaceutical and biotechnology industries.

The target audience for the book includes those training or currently involved in all phases of drug discovery and development. Specific examples include life scientists, pharmacologists, computational and systems biology modelers, bioinformaticians, clinicians, and pharmaceutical/biotech management. The methods and case studies presented here will help researchers understand the diverse applications of the systems approach and integrate these technologies into their drug discovery and development programs. Those who incorporate these approaches successfully should increase their organization’s competitiveness to address unmet market needs as well as more complex diseases and therapies.

The book is divided into four complementary parts. Providing a foundation for the techniques of systems biology, Part I provides an introduction to engineering and mathematical methods employed to characterize biological systems. In particular, Chapter 2 overviews model construction and analysis, focusing on model building, parameter estimation, model validation, and sensitivity analysis. Chapter 3 presents general statistical modeling approaches as well as methods for representing and analyzing nonlinear dynamical biochemical networks, of which feedback and feedforward loops are central players. In addition to modeling fundamental biological interactions and dynamics, an essential element of the systems biology approach is the study and simulation of population-level variability. To this end, Chapter 4 presents how drug pharmacokinetics is affected by variations in drug absorption, distribution, metabolism, and excretion, illustrating methods for predicting interindividual variability essential for rationale compound evaluation.

Part II highlights systems biology techniques aimed at enhancing the drug discovery process. An essential component of drug discovery is target identification and validation. To tackle many of the challenges inherent in these processes, Chapter 5 introduces a variety of complementary systems approaches, including text-mining, disease and therapeutics modeling, large multicontext data sets, regression modeling, and network and dynamic pathway modeling. In Chapter 6, systems biology approaches are applied to lead identification and optimization disciplines. In particular, systems approaches are shown to enable building bridges between compounds’ chemical and biological activities. In this way, lead identification and optimization are enhanced by the systematic quantification of the optimal pharmacokinetic and pharmacodynamic compound profiles, defined potentially for specific patient populations. Chapter 7 addresses drug safety by exploring the role of biological motifs, in particular switchlike circuits, critical for dose–response models. Such models help uncover complex emergent behaviors and reveal factors driving variable patient responses to drugs that could limit efficacy or even lead to low-incidence adverse responses. Finally, Chapter 8 presents the use of mechanistic systems models for the study of pharmacokinetics and pharmacodynamics during discovery and early development. These models integrate a mechanistic understanding of biology and disease processes into a framework to aid in the selection of lead compounds, evaluation of dosing regimens, and support of optimal study design for specific patient populations.

Part III addresses particular applications of systems biology to drug development. Illustrating practical drug development challenges, Chapter 9 details the development and validation of a multiscale mathematical model for angiogenesis, integrating molecular and tissue-level processes. Here the exemplary model is applied for treatment personalization, and results suggest that an arrested drug candidate can be efficacious if applied in combination with current standards of care. Chapter 10 presents methods for applying systems biology to candidate biomarker identification. In particular, the chapter highlights the biomarker discovery process, its application to drug development, and the utility of mechanistic systems modeling to biomarker development in cardiovascular disease and rheumatoid arthritis. Finally, to aid in the design and execution of costly clinical programs, essential aspects of clinical trial simulations are presented in Chapter 11, where both clinical efficacy and safety are essential considerations.

In the final section of the book, Part IV, we address how systems biology technologies can synergize with other approaches. To this end, Chapter 12 presents how biological pathway analysis can be integrated into drug discovery systems approaches. Chapter 13 addresses aspects of personalized medicine and how functional mapping aimed at understanding genes and genetic networks can be used to help predict drug responses in patients. The book concludes in Chapter 14 with a broad overview of opportunities and challenges in systems biology that should ultimately help to extend both its reach and its acceptance, thereby further enhancing pharmaceutical productivity and the success of drug discovery and development for the benefit of patients.

In addition to the contributing authors of this book, we would like to thank our collaborators and colleagues throughout the years who have helped develop and apply systems biology approaches to drug discovery and development. We look forward to future advances and successes in the coming years as these approaches are applied and extended by dedicated researchers for enhanced drug discovery and development and ultimately, better care for patients.

Daniel L. Young

Palo Alto, California

Seth Michelson

Redwood City, California

CONTRIBUTORS

Zvia Agur, Institute for Medical Biomathematics, Bene-Ataroth, Israel

Kwangmi Ahn, Department of Public Health Sciences, Pennsylvania State College of Medicine, Hershey, Pennsylvania

Melvin E. Andersen, Division of Computational Biology, The Hamner Institutes for Health Sciences, Research Triangle Park, North Carolina

Sudin Bhattacharya, Division of Computational Biology, The Hamner Institutes for Health Sciences, Research Triangle Park, North Carolina

Naamah Bloch, Optimata Ltd., Ramat-Gan, Israel

Peter Chang, Chemical Engineering Department, University of California, Santa Barbara, California

Pascale David-Pierson, Modeling and Simulation Group, Drug Metabolism and Pharmacokinetics Department, F. Hoffmann–La Roche Ltd., Basel, Switzerland

Francis J. Doyle III, Chemical Engineering Department, University of California, Santa Barbara, California

Kapil Gadkar, Theranos, Inc., Palo Alto, California

Kalyan Gayen, Chemical Engineering Department, University of California, Santa Barbara, California

Boris Gorelik, Optimata Ltd., Ramat-Gan, Israel

Hans Peter Grimm, Modeling and Simulation Group, Drug Metabolism and Pharmacokinetics Department, F. Hoffmann–La Roche Ltd., Basel, Switzerland

Bart S. Hendriks, Merrimack Pharmaceuticals, Cambridge, Massachusetts

Wei Hou, Department of Epidemiology and Health Policy Research, University of Florida, Gainesville, Florida

Ananth Kadambi, Entelos Inc., Foster City, California

Marina Kleiman, Optimata Ltd., Ramat-Gan, Israel

Yuri Kogan, Institute for Medical Biomathematics, Bene-Ataroth, Israel

Eric Kwei, Chemical Engineering Department, University of California, Santa Barbara, California

Thierry Lavé, Modeling and Simulation Group, Drug Metabolism and Pharmacokinetics Department, F. Hoffmann–La Roche Ltd., Basel, Switzerland

Yao Li, Quantitative Genetic Epidemiology, Fred Hutchinson Cancer Research Center, Seattle, Washington

Seth Michelson, Genomic Health Inc., Redwood City, California

Henry Mirsky, Chemical Engineering Department, University of California, Santa Barbara, California

Ying Ou, Modeling and Simulation Group, Drug Metabolism and Pharmacokinetics Department, Roche Palo Alto LLC, Palo Alto, California

Tom Parke, Tessella plc, Oxfordshire, UK

Micaela Reddy, Modeling and Simulation Group, Drug Metabolism and Pharmacokinetics Department, Roche Palo Alto LLC, Palo Alto, California

Yael Ronen, Optimata Ltd., Ramat-Gan, Israel

Yael Sagi, Optimata Ltd., Ramat-Gan, Israel

D. Sidransky, The Johns Hopkins University School of Medicine, Baltimore, Maryland

Peter Wellstead, The Hamilton Institute, NUIM, Maynooth, Republic of Ireland

Olaf Wolkenhauer, Systems Biology and Bioinformatics, University of Rostock, Rostock, Germany

Rongling Wu, Center for Statistical Genetics, Pennsylvania State University, Hershey, Pennsylvania

Jiansong Yang, Simcyp Ltd., Sheffield, UK

Daniel L. Young, Theranos Inc., Palo Alto, California

Theresa Yuraszeck, Chemical Engineering Department, University of California, Santa Barbara, California

Anton Yuryev, Ariadne Genomics Inc, Rockville, Maryland

Qiang Zhang, Division of Computational Biology, The Hamner Institutes for Health Sciences, Research Triangle Park, North Carolina

Wei Zhao, Department of Biostatistics, St. Jude Children’s Research Hospital, Memphis, Tennessee

PART I: INTRODUCTION TO SYSTEMS BIOLOGY IN APPROACH

CHAPTER 1

Introduction to Systems Biology in Drug Discovery and Development

SETH MICHELSON

Genomic Health Inc., Redwood City, California

DANIEL L. YOUNG

Theranos Inc., Palo Alto, California

Summary

Over the last several decades, medical and biological research has opened vast windows into the mechanisms underlying health and disease in living systems. Integrating this knowledge into a unified framework to enhance understanding and decision making is a significant challenge for the research community. Efficient drug discovery and development requires methods for bridging preclinical data with patient data to project both efficacy and safety outcomes for new compounds and treatment approaches. In this book we present the foundations of systems biology, a growing multidisciplinary field applied specifically to drug discovery and development. These methods promise to accelerate time lines, to reduce costs, to decrease portfolio failure rates, and most significantly, to improve treatment by enhancing the workflow, and thus the competitiveness, of pharmaceutical and biotechnology organizations. Ultimately, these improvements will improve overall health care and its delivery.

SYSTEMS BIOLOGY IN PHARMACOLOGY

Discovering a new medicine is a multistep process that requires one to:

Identify a biochemically based cause–effect pathway (or pathways) inherent in a disease and its pathophysiologyIdentify those cells and molecular entities (e.g., receptors, cytokines, genes) involved in the control of those pathways (typically termed targets)Identify an exogenous entity that can manipulate a molecular target to therapeutic advantage (typically termed a drug)Identify, with some level of specificity, how manipulation modulates the disease effects (termed the mechanism of action of the drug)Identify that segment of the patient population most likely to respond to manipulation (typically through the use of appropriate surrogates termed biomarkers)

Given these challenges, pharmaceutical drug discovery and development is an extremely complex and risky endeavor. Despite growing industry investment in research and development, only one in every 5000 new drug candidates is likely to be approved for therapeutic use in the United States (PhRMA, 2006). In fact, approximately 53% of compounds that progress to phase II trials are likely to fail, resulting in amortized costs of between $800 million and $1.7 billion per approved drug (DiMasi et al., 2003; Gilbert et al., 2003; Pharmaceutical Research and Manufacturers of America, 2006). Clearly, the crux of the problem is the failure rate of compounds, especially those in late-stage clinical development. To solve this problem, one must clearly identify the most appropriate compound for the most appropriate target in the most appropriate subpopulation of patients, and then dose those patients as optimally as possible. This philosophy forms the cornerstone of the “learn and confirm” model of drug development suggested by Sheiner in 1997.

For example, to address these three issues specifically, the Center for Drug Development Science at the University of California–San Francisco has developed a set of guidelines for applying one particular in silico technology, biosimulation, to the drug development process (Holford et al., 1999).

These guidelines define a three-step process. During step 1, the most relevant underlying biology describing the pathophysiology of the disease is characterized, as are the pharmacokinetics of any candidate compound aimed at its treatment. In step 2, the various clinical subpopulations expected to receive the compound are identified and characterized, including measures of interpatient variability in drug absorption, distribution, metabolism, and excretion, and compound-specific pharmacodynamics are established. Once steps 1 and 2 are complete, this information is used in step 3 to simulate and thus design the most efficient clinical trial possible.

We believe that the general principles outlined above should not be restricted to only one methodology (i.e., biosimulation) but should be extended to the entire spectrum of in silico technologies that make up the generic discipline called systems biology. Systems biology is a rapidly developing suite of technologies that captures the complexity and dynamics of disease progression and response to therapy within the context of in silico models. Whether these models and their incumbent analytical methodologies represent explicit physiological models and dynamics, statistical associations, or a mix thereof, en suite they provide the pharmaceutical researcher with access to the most pertinent information available. By definition, that information must be composed of those data that best characterize the disease and its pathophysiology, the compound and its mechanism of action, and the patient populations in which the compound is most likely to work. With the advance of newer and faster assay technologies, the gathering of those data is no longer the rate-limiting process it once was. Rather, technologies capable of sampling the highly complex spaces underlying biological phenomena have made the interpretation of those data in the most medically and biologically reasonable context the next great hurdle in pharmaceutical drug discovery and development.

To address these challenges adequately, the pharmaceutical or clinical researcher must be able to understand and characterize the effects of diverse chemical entities on the pathways of interest in the context of the biology they are meant to affect. To accomplish that, research scientists and clinicians must have at their disposal the means to acquire the most pertinent and predictive information possible. We believe that systems biology is a particularly attractive solution to this problem. It formally integrates knowledge and information from multiple biological sources into a coherent whole by subjecting them to proven engineering, mathematical, and statistical methodologies. The integrated nature of the systems biology approach allows for rapid analysis, simulation, and interpretation of the data at hand. Thus, it informs and optimizes the pharmaceutical discovery and development processes, by formalizing, and testing, the most biologically relevant family of acceptable hypotheses in silico, thereby enabling one to reduce development time and costs and improve the efficacy of novel treatments.

REFERENCES

DiMasi, J.A., Hansen, R.W., and Grabowski, H.G. (2003). The price of innovation: new estimates of drug development costs. J Health Econ 22, 151–185.

Gilbert, J., Henske, P., and Singh, A. (2003). Rebuilding big pharma’s business model. In Vivo 21, 1–10.

Holford, N.H.G., Hale, M., Ko, H.C., Steimer, J.-L., Sheiner, L.B., and Peck, C.C. (1999). Simulation in drug development: good practices. http://bts.ucsf.edu/cdds/research/sddgpreport.php.

PhRMA (2006). Pharmaceutical Industry Profile 2006. Pharmaceutical Research and Manufacturers of America, Washington, DC.

Sheiner, L.B. (1997). Learning versus confirming in clinical drug development. Clin Pharmacol Ther 61, 275–291.