Multi-species assessment of propranolol pharmacokinetics via minimal and whole-body PBPK modeling.

Propranolol is a widely-used β-adrenergic blocker with extensive pharmacokinetic (PK) data across multiple species, rendering it appropriate for comparing various interspecies scaling methodologies. The present study aimed to apply a quantitative interspecies PK modeling framework for propranolol using literature-derived datasets from seven mammalian species, namely, mouse, rat, rabbit, cat, dog, human, and horse. Conventional allometric analyses indicated body weight-dependent relationships for both clearance and steady-state volume of distribution. For the top-down minimal physiologically-based pharmacokinetic (mPBPK) modeling approach, the model incorporated two peripheral tissue compartments governed by shared distribution parameters across different species. The simultaneous fitting of intravenous (IV) propranolol PK data from seven species to the mPBPK model produced parameter estimates that reasonably captured the observed plasma PK data. Subsequent individual fitting of IV and oral propranolol PK data for each species adequately estimated additional absorption-related parameters. To improve physiological interpretability, top-down parameter estimates were aligned with bottom-up whole-body PBPK parameters via tissue-lumping principles. A unitless product-ratio plot further provided species-dependent kinetic distinctions on the biological half-lives of propranolol. Finally, translational performance was evaluated by scaling preclinical datasets, where the mPBPK model derived from three common preclinical species provided reasonable predictions of human IV PK profiles. Collectively, these findings provide a clearer physiological interpretation of propranolol disposition across species. Building upon the previous efforts using mPBPK modeling, the current study demonstrates a more mechanistically informed, refined workflow for interspecies scaling and translational human PK prediction applicable to drug candidates.