Download e-book for kindle: Monte Carlo Simulation for the Pharmaceutical Industry: by Mark Chang

By Mark Chang

ISBN-10: 1439835926

ISBN-13: 9781439835920

Helping you turn into an inventive, logical philosopher and skillful "simulator," Monte Carlo Simulation for the Pharmaceutical undefined: innovations, Algorithms, and Case Studies presents vast insurance of the complete drug improvement technique, from drug discovery to preclinical and scientific trial elements to commercialization. It provides the theories and strategies had to perform machine simulations successfully, covers either descriptive and pseudocode algorithms that supply the root for implementation of the simulation equipment, and illustrates real-world difficulties via case studies.



The textual content first emphasizes the significance of analogy and simulation utilizing examples from numerous components, prior to introducing basic sampling equipment and different levels of drug improvement. It then makes a speciality of simulation techniques in accordance with video game thought and the Markov selection strategy, simulations in classical and adaptive trials, and numerous demanding situations in scientific trial administration and execution. the writer is going directly to conceal prescription drug advertising and marketing recommendations and model making plans, molecular layout and simulation, computational structures biology and organic pathway simulation with Petri nets, and physiologically dependent pharmacokinetic modeling and pharmacodynamic versions. the ultimate bankruptcy explores Monte Carlo computing thoughts for statistical inference.



This e-book bargains a scientific remedy of computing device simulation in drug improvement. It not just offers with the foundations and strategies of Monte Carlo simulation, but in addition the functions in drug improvement, resembling statistical trial tracking, prescription drug advertising, and molecular docking.

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Monte Carlo Simulation for the Pharmaceutical Industry: Concepts, Algorithms, and Case Studies (Chapman & Hall/CRC Biostatistics Series) by Mark Chang


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