PBPK Modeling of CES1-Activated Prodrugs Across Different Age Groups: Simultaneous Prodrug–Metabolite Prediction for Oseltamivir, Enalapril, and Cilazapril

Background/Objectives: Carboxylesterase 1 (CES1) is a key enzyme involved in the bioactivation of ester prodrugs. This study aimed to construct a mechanistic, whole-body, physiologically based pharmacokinetic (PBPK) model to simultaneously predict the pharmacokinetics of CES1-activated prodrugs and their metabolites across different age groups. Methods: A comprehensive whole-body prodrug–metabolite PBPK framework was constructed by integrating age-specific physiological parameters. Oseltamivir and enalapril were evaluated across adult, pediatric, and geriatric populations, whereas cilazapril validation was focused on adult and geriatric cohorts based on clinical data availability. The model was developed and validated in adults, and then extrapolated to pediatric and geriatric populations. Model performance was evaluated by comparing predicted and observed pharmacokinetic parameters, specifically the area under the curve (AUC0−t) and maximum plasma concentration (Cmax), with an acceptance criterion of 0.5- to 2.0-fold error. Results: The majority of observed plasma concentrations fell within the 5th–95th percentiles of predicted profiles. Additionally, for most simulations, the AUC0−t and Cmax were within 0.5- to 2-fold of the observed values. Sensitivity analyses revealed that the exposure of their active metabolites was highly related to the plasma unbound fraction, glomerular filtration rate, and hepatic CES1-mediated bioactivation. Conclusions: This whole-body PBPK framework provides a mechanistic basis for predicting CES1-mediated prodrug disposition across age groups. The framework achieved clinical validation across pediatric, adult, and geriatric populations for oseltamivir and enalapril, and provided proof-of-concept support for extending the approach to cilazapril. This platform may serve as a basis for future pharmacokinetic/pharmacodynamic integration and model-informed dose optimization.