Takuto Niino

Objective: The objective of this study was to develop a Physiologically Based Biopharmaceutics Modeling (PBBM) framework that can predict PK profiles in humans based on data generated from the BE Checker. Methods: Metoprolol and dipyridamole were selected as model drugs. A mathematical model was developed to describe drug dissolution, membrane permeation, and dynamic changes in pH and fluid volume within the BE Checker system. Using data generated under various experimental conditions, dissolution rate constants were estimated. For dipyridamole, the precipitation rate constant was also estimated, assuming simultaneous dissolution and precipitation processes. The estimated parameters were subsequently incorporated into the human PBBM to simulate PK profiles. Finally, the predictive accuracy of PK parameters such as Cmax and AUC was assessed. Results: For metoprolol, the PK profiles using the paddle revolution rates of 100 and 200 rpm closely matched the observed human data, particularly for Cmax and AUC, a key indicator of BE. In the case of dipyridamole, accurate predictions of the mean human PK profile were achieved when using BE Checker data obtained under high paddle speed (200 rpm) and longer pre-FaSSIF infusion times (20–30 min). Conversely, simulations based on lower paddle speed (50 rpm) and shorter pre-FaSSIF infusion time (10 min) underestimated plasma concentrations in humans. Conclusions: These findings suggest that the combination of BE Checker data acquired under high agitation conditions and the in silico mathematical model developed in this study enables accurate prediction of average human PK profiles.