Abstract

Abstract This article explores the application of artificial intelligence (AI) and machine learning (ML) in pharmacokinetic modeling and drug development. Pharmacokinetics, encompassing absorption, distribution, metabolism, and excretion (ADME) processes, plays a crucial role in determining the efficacy and safety of drugs. Traditional pharmacokinetic modeling approaches face challenges due to the complexity of physiological processes and interindividual variability. AI and ML offer data-driven solutions to overcome these challenges by analyzing vast datasets and developing predictive models. Advancements in AI-driven pharmacokinetic modeling enable personalized dosing regimens and facilitate drug discovery processes. However, challenges such as data quality, interpretability, and regulatory considerations must be addressed to realize the full potential of AI and ML in drug development. Collaborative efforts between academia, industry, and regulatory agencies are essential to establish standards and frameworks for responsible and ethical AI adoption in pharmacokinetics and drug development