A Conceptual Framework for Advancements in Clinical Pharmacology: Integrating Pharmacokinetics, Pharmacodynamics, Personalized Medicine, and Artificial Intelligence with Bayesian Inference and Global Sensitivity Analysis

This paper presents a conceptual framework for advancing clinical pharmacology, emphasizing drug efficacy, safety, pharmacodynamics (PD), pharmacokinetics (PK), and the integration of artificial intelligence (AI). The framework combines mathematical derivations, computational simulations, global sensitivity analyses, hierarchical Bayesian inference, and uncertainty quantification to support the development of individualized therapeutic strategies. Simulations are implemented in reproducible Python code using parameters derived from the literature. This approach provides a resource for clinicians, researchers, and educators to explore the interactions between pharmacotherapeutics and biological systems, with potential applications in drug development and patient care.