Abstract

AI-POWERED PHARMACOKINETICS: A COMPREHENSIVE REVIEW OF COMPUTATIONAL METHODS FOR ADME PREDICTION

Sahana V. M. Vats* and Sunil Kumar

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Abstract

Pharmacokinetics, the study of drug absorption, distribution, metabolism, and excretion (ADME), is pivotal in shaping the efficacy and safety of therapeutic agents. Traditionally driven by in vitro and in vivo experimentation, pharmacokinetic analysis is often resourceintensive, ethically challenging, and limited by species variability. The emergence of artificial intelligence (AI) and machine learning (ML) has ushered in a transformative era, offering predictive models that harness vast datasets to enhance accuracy, speed, and cost-efficiency. This review explores the current landscape of AI-driven computational methods for ADME prediction. It moves into a spectrum of approaches, from Random Forests and Support Vector Machines to Deep Learning and Graph Neural Networks, highlighting their application in predicting absorption rates, volume of distribution, metabolic sites, and excretory pathways. We also examine challenges such as data quality, model interpretability, and regulatory hurdles, and envision a future where AI synergizes with real-world patient data for personalized pharmacokinetics. This confluence of pharmacological tradition and digital innovation heralds a paradigm shift in drug development and individualized therapy.

Keywords: Pharmacokinetics, Artificial Intelligence, Machine Learning, Deep Learning, ADME Prediction, Drug Absorption, Volume of Distribution, Plasma Protein Binding, Blood-Brain Barrier Permeability, Metabolic Stability, Cytochrome P450, Drug Metabolism, Drug Exc