Predicting Pharmacokinetics in Rats Using Machine Learning: A Comparative Study Between Empirical, Compartmental, and PBPK-Based Approaches.

ABSTRACT: A successful drug needs to combine several properties including high potency and good pharmacokinetic (PK) properties to sustain efficacious plasma concentration over time. To estimate required doses for preclinical animal efficacy models or for the clinics, in vivo PK studies need to be conducted. Although the prediction of ADME properties of compounds using machine learning (ML) models based on chemical structures is well established in drug discovery, the prediction of complete plasma concentration-time profiles has only recently gained attention. In this study, we systematically compare various approaches that integrate ML models with empiric or mechanistic PK models to predict PK profiles in rats after intravenous administration prior to synthesis. More specifically, we compare a stan

SUBMITTER: Walter M 

PROVIDER: S-EPMC11911599 | biostudies-literature | 2025 Mar

REPOSITORIES: biostudies-literature