Toward Generalizable Data-Driven Pharmacokinetics with Interpretable Neural ODEs

Accurate modeling of drug concentration-time (C-t) profiles is central to pharmacokinetics (PK) and plays a critical role in both early-stage compound selection and late-stage individualized dosing. Traditional PK models offer mechanistic interpretability but often rely on rigid assumptions and extensive parametrization, limiting their scalability across structurally diverse compounds and heterogeneous patient populations. In this work, we propose Uni-PK, a unified neural framework that combines molecular representations with neural ordinary differential equations (NODEs) embedded within a mechanistically grounded PK structure. Rather than solely predicting PK parameters, Uni-PK directly models the dynamic trajectory of drug concentrations from molecular and individual inputs, enabling end-to-end learning under data-scarce and noisy conditions. To account for interindividual variability, we incorporated auxiliary covariates─such as species and dosing regimen─via a flexible context encoder, supporting personalized preclinical and clinical settings. Evaluated on rat and human data sets spanning multiple administration routes and physiological states, Uni-PK showed robust performance while remaining consistent with established pharmacokinetic principles. By integration of chemical structure and individual-specific information within a dynamic modeling framework, Uni-PK offers a scalable, interpretable, and animal-sparing solution for next-generation PK modeling and precision therapeutics.