Exploring Tafamidis Effects Through PBPK-QSP Modelling

Background/Objectives: Tafamidis, a transthyretin kinetic stabilizer, increases circulating transthyretin levels in treated patients. While this effect is well documented, its underlying mechanism remains incompletely understood. This study aimed to evaluate the performance of a Physiologically Based Pharmacokinetic (PBPK) model performance and to calibrate a hypothesis-consistent Quantitative Systems Pharmacology (QSP) model of tafamidis and transthyretin dynamics, to explore mechanistic hypotheses underlying the clinically observed increase in circulating transthyretin and the associated dose–response relationship. Methods: A PBPK–QSP model was constructed in Simcyp (V23) using LUA-based modules. The PBPK part was parameterized from literature and validated against data from therapeutic single-dose, therapeutic multiple-dose, and supratherapeutic dose clinical studies. The QSP part of the model describes tafamidis–TTR binding kinetics, stabilization, and clearance of bound complexes. Simulations were performed in thirty virtual healthy male subjects aged 30–40 years, incorporating physiological variability in baseline TTR concentrations. Results: Mean predicted versus observed ratios of tafamidis AUC and C_max values were within a 1.3-fold range across validation studies. The integrated model reproduced the clinically reported 33% increase in TTR concentration through a calibrated clearance-scaling factor. It supports the hypothesis that reduced clearance of tafamidis-bound TTR may explain the observed effect without modifying TTR synthesis. Dose-sensitivity simulations indicated that patients with low baseline TTR may achieve adequate stabilization at reduced doses, while those with higher baseline TTR concentration may require higher doses. Conclusions: The developed PBPK–QSP model does not only reproduce tafamidis pharmacokinetics and TTR responses but also proposes a plausible mechanistic hypothesis implicating clearance modulation of stabilized TTR as a key driver of the clinical effect. This may potentially support rational dose individualization and inform future experimental research.