ABSTRACT

The discipline of pharmacokinetics delineates the absorption, distribution, metabolism, and excretion (ADME) of pharmaceutical agents within the corporeal framework, whereas pharmacodynamics elucidates the biological consequences and mechanisms of action of these substances. The integration of pharmacokinetic-pharmacodynamic (PK/PD) modelling has become increasingly pivotal in contemporary drug development, facilitating the prognostication of therapeutic outcomes across heterogeneous patient populations and pathological milieux. With the emergence of synthetic intellect and advanced hierarchical learning the capacity to analyse voluminous biomedical datasets has been augmented, enabling more robust structure-activity relationship modelling, target identification, virtual screening. These computational paradigms not only expedite the discovery process but also enhance the precision of dosage regimen design and toxicity prediction. However, the efficacious deployment of these methodologies necessitates scrupulous validation to mitigate the perils of bias and overestimation of predictive prowess. The present treatise underscores the significance of hybrid modelling approaches, which amalgamate linear and nonlinear techniques, and emphasizes the imperative of model interpretability and transparency in high-stakes pharmacological decision-making. Keywords: Artificial intelligence, Pharmacokinetics, Pharmacodynamics, Deep Learning, Personalized Medicine
Publication date: 01/07/2026

https://www.ijbpas.com/pdf/2026/July/MS_IJBPAS_2026_10306.pdf

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https://doi.org/10.31032/IJBPAS/2026/15.7.10306