Systems biology in 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.

Author Notes

Daniel L. Young, Phd, is the Director of Computational Biosciences at Theranos Inc., where he leads the development of systems biology approaches to advances and entrance drug discovery and development and the optimal delivery of healthcare. He has written over twenty publications in the field of systems biology.
Seth Michelson, PhD, is the Director of Nonclinical Biostatistics at Genomic Health, Inc. inventor or co-inventor for fourteen patient applications and one issued patient, and has contributed to over severity publications.

Seth Michelson and Daniel L. YoungTheresa Yuraszeck and Peter Chang and Kalyan Gayen and Eric Kwei and Henry Mirsky and Francis J. Doyle IIIPeter Wellstead and Olaf WolkenhauerJiansong YangBart S. HendriksSeth MichelsonSudin Bhattacharya and Qiang Zhang and Melvin E. AndersenHans Peter Grimm and Ying Ou and Micaela Reddy and Pascale David-Pierson and Thierry LaveZvia Agur and Naamah Bloch and Boris Gorelik and Marina Kleiman and Yuri Kogan and Yael Sagi and D. Sidransky and Yael RonenAnanth Kadambi and Daniel L. Young and Kapil GadkarTom ParkeAnton YuryevYao Li and Wei Hou and Wei Zhao and Kwangmi Ahn and Rongling WuDaniel L. Young and Seth Michelson

Preface	p. xi
Contributors	p. xv
Part I Introduction to Systems Biology in Approach
1 Introduction to Systems Biology in Drug Discovery and Development	p. 3
Systems Biology in Pharmacology	p. 3
References	p. 5
2 Methods for In Silico Biology: Model Construction and Analysis	p. 7
2.1 Introduction	p. 7
2.2 Model Building	p. 8
2.3 Parameter Estimation	p. 21
2.4 Model Analysis	p. 28
2.5 Conclusions	p. 32 References
3 Methods in In Silico Biology: Modeling Feedback Dynamics in Pathways	p. 37
3.1 Introduction	p. 37
3.2 Statistical Modeling	p. 39
3.3 Mathematical Modeling	p. 46
3.4 Feedback and Feedforward	p. 49
3.5 Conclusions	p. 56
References	p. 56
4 Simulation of Population Variability in Pharmacokinetics	p. 59
4.1 Introduction	p. 59
4.2 PBPK Modeling	p. 60
4.3 Simulation of Pharmacokinetic Variability	p. 61
4.4 Conclusions and Future Directions	p. 79
References	p. 80
Part II Applications to Drug Discovery
5 Applications of Systems Biology Approaches to Target Identification and Validation in Drug Discovery	p. 95
5.1 Introduction	p. 95
5.2 Typical Drug Discovery Paradigm	p. 97
5.3 Integrated Drug Discovery	p. 99
5.4 Drivers of the Disease Phenotype: Clinical Endpoints and Hypotheses	p. 100
5.5 Extracellular Disease Drivers: Mechanistic Biotherapeutic Models	p. 106
5.6 Relevant Cell Models for Clinical Endpoints	p. 109
5.7 Intracellular Disease Drivers: Signaling Pathway Quantification	p. 110
5.8 Target Selection: Dynamic Pathway Modeling	p. 117
5.9 Conclusions	p. 123
References	p. 125
6 Lead Identification and Optimization	p. 135
6.1 Introduction	p. 135
6.2 The Systems Biology Tool Kit	p. 139
6.3 Conclusions	p. 142
References	p. 143
7 Role of Core Biological Motifs in Dose-Response Modeling: An Example with Switchlike Circuits	p. 147
7.1 Introduction: Systems Perspectives in Drug Discovery	p. 147
7.2 Systems Biology and Toxicology	p. 148
7.3 Mechanistic and Computational Concepts in a Molecular or Cellular Context	p. 151
7.4 Response Motifs in Cell Signaling and Their Role in Dose Response	p. 152
7.5 Discussion and Conclusions	p. 165
References	p. 169
8 Mechanism-Based Pharmacokinetic-Pharmacodynamic Modeling During Discovery and Early Development	p. 175
8.1 Introduction	p. 175
8.2 Challenges in Drug Discovery and Development	p. 176
8.3 Methodological Aspects and Concepts	p. 179
8.4 Use of PK-PD Models in Lead Optimization	p. 183
8.5 Use of PK-PD Models in Clinical Candidate Selection	p. 188
8.6 Entry-into-Human Preparation and Translational PK-PD Modeling	p. 189
8.7 Use of PK-PD Models in Toxicology Study Design and Evaluation	p. 189
8.8 Justification of Starting Dose, Calculation of Safety Margins, and Support of Phase I Design	p. 191
8.9 Phase I and Beyond	p. 193
8.10 Support of Early Formulation Development	p. 195
8.11 Outlook and Conclusions	p. 196
References	p. 197
Part III Applications to Drug Development
9 Developing Oncology Drugs Using Virtual Patients of Vascular Tumor Diseases	p. 203
9.1 Introduction	p. 203
9.2 Modeling Angiogenesis	p. 205
9.3 Use of Rigorous Mathematical Analysis to Gain Insight into Drug Development	p. 213
9.4 Use of Angiogenesis Models in Theranostics	p. 220
9.5 Use of Angiogenesis Models in Drug Salvage	p. 226
9.6 Summary and Conclusions	p. 230
References	p. 231
10 Systems Modeling Applied to Candidate Biomarker Identification	p. 239
10.1 Introduction	p. 239
10.2 Biomarker Discovery Approaches	p. 245
10.3 Examples of Systems Modeling Approaches for Identification of Candidate Biomarkers	p. 252
10.4 Conclusions	p. 260
References	p. 260
11 Simulating Clinical Trials	p. 265
11.1 Introduction	p. 265
11.2 Types of Models Used in Clinical Trial Design	p. 272
11.3 Sources of Prior Information for Designing Clinical Trials	p. 276
11.4 Aspects of a Trial to Be Designed and Optimized	p. 277
11.5 Trial Simulation	p. 279
11.6 Optimizing Designs	p. 281
11.7 Real-World Examples	p. 283
11.8 Conclusions	p. 284
References	p. 284
Part IV Synergies with other Technologies
12 Pathway Analysis in Drug Discovery	p. 289
12.1 Introduction: Pathway Analysis, Dynamic Modeling, and Network Analysis	p. 289
12.2 Software Systems for Pathway Analysis	p. 292
12.3 Pathway Analysis in the Modern Drug Development Pipeline	p. 293
12.4 Conclusions	p. 298
References	p. 299
13 Functional Mapping for Predicting Drug Response and Enabling Personalized Medicine	p. 303
13.1 Introduction	p. 304
13.2 Functional Mapping	p. 306
13.3 Predictive Model	p. 311
13.4 Future Directions	p. 315
References	p. 318
14 Future Outlook for Systems Biology	p. 323
14.1 Introduction	p. 323
14.2 System Complexity in Biological Systems	p. 324
14.3 Models for Quantitative Integration of Data	p. 325
14.4 Changing Requirements for Systems Approaches During Drug Discovery and Development	p. 328
14.5 Better Models for Better Decisions	p. 330
14.6 Advancing Personalized Medicine	p. 334
14.7 Improving Clinical Trials and Enabling More Complex Treatment Approaches	p. 337
14.8 Collaboration and Training for Systems Biologists	p. 340
14.9 Conclusions	p. 342
References	p. 343
Index	p. 349

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Table of Contents