Harnessing machine learning for predictive pharmacokinetics: revolutionizing drug development and personalized medicine

Abstract

Despite extensive existing literature on the role of machine learning (ML) in pharmacokinetics and drug development, there remains a gap in understanding its real-world implementation challenges, especially across diverse populations. This review aims to bridge this gap by focusing on specific case studies that illustrate the practical impact of ML on addressing the limitations of traditional pharmacokinetic (PK) methods. By leveraging large datasets and sophisticated algorithms, ML techniques provide improved predictions of absorption, distribution, metabolism, and excretion (ADME) processes, offering an individualized approach to patient care. Unlike traditional PK modeling, ML allows for the handling of large-scale, multidimensional data, improving the prediction accuracy for diverse patient populations. This review delves into recent advancements in ML applications for PK, emphasizing their impact on early-stage drug discovery, dose optimization, and tailoring personalized treatment plans. Specific case studies illustrate the advantages of ML over conventional approaches, particularly in addressing the variability in drug responses among patients. The challenges and opportunities of using ML in PK modeling are discussed, highlighting the potential of these techniques to revolutionize pharmaceutical sciences.