ABSTRACT Physiologically Based Pharmacokinetic (PBPK) modeling is a powerful tool in drug development that integrates drug‐specific information with physiological parameters to predict drug concentrations. However, parameter estimation in PBPK models presents significant challenges due to the large number of parameters involved and limited observed data. This tutorial introduces a novel approach coupling whole‐body PBPK (WB‐PBPK) models with population estimation methods (popWB‐PBPK) to leverage individual data and estimate inter‐individual variability on physiologically relevant parameters. The framework employs an optimized Stochastic Approximation Expectation–Maximization (SAEM) algorithm, reducing the estimation runtime through an adaptive parameter grid optimization and linear interpolation techniques. Using theophylline as a case study, we illustrate how this approach can accurately estimate drug‐specific parameters (CYP1A2 clearance and lipophilicity) while incorporating covariate effects (smoking status). The optimized algorithm significantly reduces computational time compared to the standard SAEM algorithm. Our implementation in the saemixPBPK R package provides an accessible framework for parameter estimation in PBPK models, enabling more robust predictions of pharmacokinetic behavior leveraging individual data. This approach represents an important advancement in mechanistic modeling, allowing simultaneous estimation of population parameters, variability, and uncertainty while maintaining the physiological relevance of PBPK models.
Teutonico et al. (Thu,) studied this question.