Development of a Population Pharmacokinetic Model for Aqueous Progesterone in Chinese Postmenopausal Women: Integrating Machine Learning for Covariate Screening

This study developed the first population pharmacokinetic (popPK) model of subcutaneous aqueous progesterone in Chinese healthy postmenopausal women, integrating machine learning (ML) for covariate screening. Data were obtained from an open-label, single-dose, two-period crossover bioequivalence study in 50 subjects. A two-compartment model with first-order absorption (including a lag time) and first-order elimination best described the concentration–time profiles. Prior to conventional stepwise covariate modeling, ML algorithms—including adaptive least absolute shrinkage and selection operator (LASSO), elastic net, and random forest—were employed to identify physiological predictors of pharmacokinetic (PK) variability. The ML ensemble demonstrated high predictive performance and highlighted height and weight as potential covariates. However, subsequent popPK evaluation retained the effects of height on clearance (CL) and body weight on peripheral volume ( V 2 ) and intercompartmental clearance (CL 2 ) in the final model. While these relationships were statistically identifiable, their clinical significance was considered limited. The final model was robust and used to simulate steady-state exposure following a once-daily 25 mg regimen. The simulated mean area under the concentration–time curve at steady-state (AUC ss ) was 324 ± 45.9 μg h/L, and the average concentration ( C avg,ss ) was 13.3 μg/L. Notably, 98.9% of the simulated population achieved a C avg,ss ≥ 10 μg/L, the threshold for adequate luteal support. This analysis provides the first popPK characterization of aqueous progesterone, supporting the 25 mg once-daily regimen for luteal phase support, without need for dose adjustment based on the evaluated intrinsic factors. Chinese Clinical Trial Registry ( CTR20221233, registration date: 30 May 2022; prospectively registered).