Monte Carlo Simulation for the Pharmaceutical Industry Concepts, Algorithms, and Case Studies

Helping you become a creative, logical thinker and skillful "simulator," Monte Carlo Simulation for the Pharmaceutical Industry: Concepts, Algorithms, and Case Studies provides broad coverage of the entire drug development process, from drug discovery to preclinical and clinical trial aspects to commercialization. It presents the theories and methods needed to carry out computer simulations efficiently, covers both descriptive and pseudocode algorithms that provide the basis for implementation of the simulation methods, and illustrates real-world problems through case studies. The text first emphasizes the importance of analogy and simulation using examples from a variety of areas, before introducing general sampling methods and the different stages of drug development. It then focuses on simulation approaches based on game theory and the Markov decision process, simulations in classical and adaptive trials, and various challenges in clinical trial management and execution. The author goes on to cover prescription drug marketing strategies and brand planning, molecular design and simulation, computational systems biology and biological pathway simulation with Petri nets, and physiologically based pharmacokinetic modeling and pharmacodynamic models. The final chapter explores Monte Carlo computing techniques for statistical inference. This book offers a systematic treatment of computer simulation in drug development. It not only deals with the principles and methods of Monte Carlo simulation, but also the applications in drug development, such as statistical trial monitoring, prescription drug marketing, and molecular docking.

Simulation, Simulation Everywhere Modeling and SimulationIntroductory Monte Carlo ExamplesSimulations in Drug Development Virtual Sampling TechniquesUniform Random Number Generation General Sampling MethodsEfficiency Improvement in Virtual SamplingSampling Algorithms for Specific Distributions Overview of Drug DevelopmentIntroduction Drug DiscoveryPreclinical DevelopmentClinical Development Meta-Simulation for Pharmaceutical IndustryIntroductionGame Theory BasicsPharmaceutical GamesPrescription Drug Global Pricing Macro-Simulation for Pharmaceutical R & DSequential Decision-Making Markov Decision Process Pharmaceutical Decision Process Extension of Markov Decision Process Clinical Trial Simulation (CTS)Classical Trial SimulationAdaptive Trial Simulation Clinical Trial Management and ExecutionIntroduction Clinical Trial Management Patient Recruitment and Projection Randomization Dynamic and Adaptive Drug Supply Statistical Trial Monitoring Prescription Drug CommercializationDynamics of Prescription Drug Marketing Stock-Flow Dynamic Model for Brand Planning Competitive Drug Marketing Strategy Compulsory Licensing and Parallel Importation Molecular Design and SimulationWhy Molecular Design and Simulation Molecular Similarity Search Overview of Molecular Docking Small Molecule Confirmation AnalysisLigand-Receptor Interaction Docking Algorithms Scoring Functions Disease Modeling and Biological Pathway SimulationComputational System BiologyPetri NetsBiological Pathway Simulation Pharmacokinetic SimulationOverview of ADME Absorption Modeling Distribution Metabolism Modeling Excretion Modeling Physiologically Based PK Model Pharmacodynamic SimulationWay to Pharmacodynamics Enzyme Kinetics Pharmacodynamic Models Drug-Drug InteractionApplication of Pharmacodynamic Modeling Monte Carlo for Inference and BeyondSorting AlgorithmResampling MethodsGenetic Programming Appendix A: JavaScript ProgramsAppendix B: K-Stage Adaptive Design Stopping Boundaries Afterword Bibliography A Summary and Exercises appear at the end of each chapter.

Mark Chang is the executive director of biostatistics and data management at AMAG Pharmaceuticals in Lexington, Massachusetts. Dr. Chang is an elected fellow of the American Statistical Association. He is the author of the best-selling Adaptive Design Theory and Implementation Using SAS and R and co-author of the best-selling Adaptive Design Methods in Clinical Trials.