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 standard noncompartmental analysis (NCA)-based approach (prediction of CL and V_ss), a pure ML approach (non-mechanistic PK description), a compartmental modeling approach, and a physiologically based pharmacokinetic (PBPK) approach. Our study based on internal preclinical data shows that the latter three approaches yield PK profile predictions of comparable accuracy across a large data set (evaluated as geometric mean fold errors for each profile of over 1000 small molecules). In summary, we demonstrate the improved ability to prioritize drug candidates with desirable PK properties prior to synthesis with ML predictions.