A Mechanism-Based Multi-Level Population PK/PD Model for Potassium-Competitive Acid Blockers.

Potassium-competitive acid blockers (PCABs) are emerging alternatives to proton pump inhibitors for the treatment of acid-related diseases. However, due to the complex, nonlinear interaction between drug exposure, food intake, and physiological rhythms, optimizing dosing strategies remains challenging. A multi-leveled population analysis was conducted using published pharmacokinetic and pharmacodynamic data on four representative PCABs: tegoprazan, YH4808, fexuprazan, and vonoprazan. A semi-mechanistic population PK/PD model was developed to account for food effects, circadian pH rhythms, and pH-dependent drug absorption. A multi-level nonlinear mixed-effects modeling framework was implemented to capture both inter-drug and inter-study variability. The model successfully described the time course of plasma concentration and intragastric pH for all four PCABs under various conditions. The model identified differences in pharmacokinetics and pharmacodynamic potency between drugs (with the relative in vitro potency ranked as vonoprazan > fexuprazan > YH4808 > tegoprazan), and simulations demonstrated that both pre- and post-meal administration enhanced pH control in early time period via potentially distinct mechanisms: the pre-meal effect may arise from temporally separated contributions of food- and drug-induced pH elevation, whereas the post-meal effect is likely driven by temporally overlapping, additive actions, particularly under low-dose or non-steady-state conditions. Predicted pH profiles and holding times above pH 4 closely matched reported clinical outcomes. The study demonstrates the application of a mechanistic, multi-level population approach for cross-drug PK/PD evaluation of PCABs. The findings support drug-specific dose optimization and highlight the clinical relevance of food-drug interactions. The modeling approach provides a model platform for pharmacotherapy or model-informed drug development (MIDD).