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 in vitro or in vivo 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 Cmax. Proposed approaches
could enhance early molecular screening and design, advancing drug
discovery capabilities.