Role of Systems Modeling in Regulatory Drug Approval -- Quantitative Systems Pharmacology: Applications and Adoption in Drug Development -- Systems Pharmacology: An Overview -- Discrete dynamic modeling: a network approach for systems pharmacology -- Kinetic Models of Biochemical Signaling Networks -- Mechanistic Models of Physiological Control Systems -- Foundations of Pharmacodynamic Systems Analysis -- Direct, Indirect, and Signal Transduction Response Modeling -- Irreversible Pharmacodynamics -- Feedback Control Indirect Response Models -- Nonlinear Mixed Effects Modeling in Systems Pharmacology -- Detecting Pharmacokinetic and Pharmacodynamic Covariates From High-Dimensional Data -- Multi-Scale Modeling of Drug Action in the Nervous System -- Mechanistic Modeling of Inflammation -- Systems Pharmacology of Tyrosine Kinase Inhibitor-Associated Toxicities -- Translational Modeling of Antibacterial Agents -- Viral Dynamic Modeling of Hepatitis C Virus Infection: Past Successes and Future Challenges -- Using Systems Pharmacology to Advance Oncology Drug Development -- Systems Pharmacology Modeling in Type 2 Diabetes Mellitus

While systems biology and pharmacodynamics have evolved in parallel, there are significant interrelationships that can enhance drug discovery and enable optimized therapy for each patient. Systems pharmacology is the relatively new discipline that is the interface between these two methods. This book is the first to cover the expertise from systems biology and pharmacodynamics researchers, describing how systems pharmacology may be developed and refined further to show practical applications in drug development. There is a growing awareness that pharmaceutical companies should reduce the high attrition in the pipeline due to insufficient efficacy or toxicity found in proof-of-concept and/or Phase II studies. Systems Pharmacology and Pharmacodynamics discusses the framework for integrating information obtained from understanding physiological/pathological pathways (normal body function system vs. perturbed system due to disease) and pharmacological targets in order to predict clinical efficacy and adverse events through iterations between mathematical modeling and experimentation