# Exploring Tafamidis Effects Through PBPK–QSP Modelling

## TL;DR

This study uses a combined PBPK–QSP model to explore how tafamidis increases transthyretin levels in patients and how this effect varies with dose.

## Contribution

The novel contribution is the development of a calibrated PBPK–QSP model to test mechanistic hypotheses of tafamidis action on transthyretin.

## Key findings

- The model accurately predicted tafamidis AUC and Cmax within 1.3-fold of observed values.
- A 33% increase in TTR concentration was simulated through reduced clearance of tafamidis-bound TTR.
- Dose-sensitivity simulations suggest lower baseline TTR patients may need smaller tafamidis doses.

## Abstract

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 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. The PBPK model constitutes the primary framework, while the coupled QSP component illustrates how tafamidis exposure predictions can be used to evaluate mechanistic hypotheses of TTR turnover. Methods: A PBPK–QSP model was constructed in Simcyp (V23) using LUA-based modules. The PBPK part was parameterized from the 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 Cmax 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 not only reproduces tafamidis pharmacokinetics and TTR responses but also proposes a plausible mechanistic hypothesis consistent with clearance modulation of stabilized TTR contributing to the clinical effect.