Application of Machine Learning and Mechanistic Modeling to Predict Intravenous Pharmacokinetic Profiles in Humans

Accurate prediction of new compounds' pharmacokinetic (PK) profile in humans is crucial for drug discovery. Traditional methods, including allometric scaling and mechanistic modeling, rely on parameters from <i>in vitro</i> or <i>in vivo</i> testing, which are labor-intensive and involve ethical concerns. This study leverages machine learning (ML) to overcome these limitations by developing data-driven models. We compiled a large data set of small molecules' physicochemical and PK properties from public sources and digitized human plasma concentration-time profiles for approximately 800 compounds from the literature. We introduced a hybrid modeling framework that combines ML with physiologically based pharmacokinetic modeling and a hierarchical ML framework that employs two steps of learning to directly estimate PK profiles. Tested on 106 drugs, these frameworks demonstrated prediction accuracies within a 2-fold and 5-fold error for 40-60% and 80%-90% of compounds, respectively, in both AUC and <i>C</i>_max. Proposed approaches could enhance early molecular screening and design, advancing drug discovery capabilities.