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Systems Biology in Drug Discovery and Development
Daniel L. Young (Author), Seth Michelson (Author)
9780470261231, Wiley
Hardback, published 18 November 2011
380 pages
24.4 x 16.3 x 2.5 cm, 0.699 kg
“I enjoyed reading this book as it should be essential reading for those involved in drug discovery and development for all others who share an interest in this field.” (International Society for the Study of Xenobiotics, 2012) "In short, this is an indispensable tool for both experienced and early stage investigators and others to understand the current and future impact of systems biology on the drug discovery and development process." (Doody's, 6 January 2012)
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.
Part I: Introduction to Systems Biology Approach. Chapter 1. Introduction to systems biology in drug discovery and development. Chapter 2. Methods for In Silico Biology: Model Construction and Analysis. Chapter 3. Methods in In Silico Biology: Modeling Feedback Dynamics in Pathways. Chapter 4. Simulation of Population Variability in Pharmacokinetics. Part II: Applications to Drug Discovery. Chapter 5. Applications of Systems Biology Approaches to Target Identification and Validation in Drug Discovery. 5.1 Introduction. Chapter 6. Lead Identification and Optimization. Chapter 7. The role of core biological motifs in dose-response modeling: an example with switch-like circuits. Chapter 8. Mechanism Based Pharmacokinetic-Pharmacodynamic Modeling During Discovery and Early Development. Part III: Applications to Drug Development. Chapter 9. Developing Oncology Drugs Using Virtual Patients of Vascular Tumor Diseases. Chapter 10. Systems Modeling Applied to Candidate Biomarker Identification. Chapter 11. Simulating Clinical Trials. Part IV: Synergies with other technologies. Chapter 12. Pathway Analysis in Drug Discovery. Chapter 13. Functional mapping for predicting drug response and enabling personalized medicine. Chapter 14. Future Outlook of Systems Biology.
1.1 Introduction.
2.1 Introduction.
2.2 Model building.
2.3 Parameter estimation.
2.4. Model analysis.
2.5 Conclusions.
3.1 Introduction.
3.2 Statistical modeling.
3.3 Mathematical modeling.
3.4 Feedback and feedforward.
3.5 Conclusions.
4.1 Introduction.
4.2 PBPK modeling.
4.3 Simulation of pharmacokinetic variability.
4.4 Conclusions and future directions.
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.
6.1 Introduction.
6.2 The systems biology toolkit.
6.3 Conclusions.
7.1 Introduction: systems perspective in drug discovery.
7.2 Systems biology and toxicology.
7.3 Mechanistic/computational concepts in a molecular/cellular context.
7.4 Response motifs in cell signaling and their role in dose response.
7.5 Discussion and conclusions.
8.1 Introduction.
8.2 Challenges in drug discovery and development: the need to bring together PK and PD.
8.3 Methodological aspects and concepts.
8.4 Application during lead optimization.
8.5 Application during clinical candidate selection.
8.6 Entry into human (EIH) preparation and translational PK/PD modeling.
8.7 PK/PD for 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.
9.1 Introduction.
9.2 Modeling angiogenesis.
9.3 Use of rigorous mathematical analysis for gaining insight on drug development.
9.4 Use of angiogenesis models in theranostics.
9.5 Use of angiogenesis models in drug salvage: the virtual patient technology.
9.6 Summary and conclusions.
10.1 Introduction.
10.2 Biomarker discovery approaches.
10.3 Examples of systems modeling approaches for identification of candidate biomarkers.
10.4 Conclusions.
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.
12.1 Introduction: pathway analysis, dynamic modeling, and network analysis.
12.2 Software systems for pathway analysis.
12.3 Pathway analysis in modern drug development pipeline.
12.4 Conclusions.
13.1 Introduction.
13.2 Functional mapping.
13.3 Predictive modeling.
13.4 Future directions.
14.1 Introduction.
14.2 Systems 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.
Subject Areas: Chemistry [PN]
