This authoritative resource distills the core principles of computational pharmacokinetics and pharmacodynamics into an accessible, yet rigorous compendium that will empower students, researchers, and professionals alike. Grounded in established theory and supported by comprehensive, fully annotated R code, this volume navigates the intricacies of single- and multi-compartment modeling, parameter estimation, and dose optimization with remarkable clarity.
Leverage the text's hands-on approach to accelerate your mastery of mathematical modeling techniques. Notably, you will find:
A detailed walk-through of numerical optimization strategies for parameter estimation coupled with example scripts that illustrate step-by-step coding in R.In-depth coverage of population-based approaches, including Random Effects models, shedding light on inter-individual variability for more realistic simulations.Clear explanations of absorptive and distributive processes, from basic one-compartment kinetics to physiologically based models that capture real-world complexities.Practical tips on applying Bayesian methods to PK/PD problems, enabling you to incorporate prior data effortlessly into your model-building pipeline.Readers will benefit from advanced plotting functions, code snippets that seamlessly integrate with popular data sciences packages, and robust exercises to deepen comprehension. Whether you are setting dosage regimens, simulating therapeutic outcomes, or refining population models, this volume offers the invaluable computational tools and foundational knowledge needed to excel in modern pharmacological research.