Comparative Predictive Performance of Machine Learning and Population Pharmacokinetic Models for Valproic Acid Clearance and Trough Concentrations: An In-silico Simulation Study in Epilepsy Scenarios.

purposeWhile population pharmacokinetic (PopPK) models traditionally guide valproic acid (VPA) dosing, machine learning (ML) may better capture complex, nonlinear relationships. A direct comparison of their predictive performances remains poorly defined. This study compared the predictive performance of ML and PopPK models for VPA clearance and trough concentrations.

methodsPopPK and ML models were developed and validated using two independent simulated datasets. Trough concentration and clearance were each evaluated under a priori and a posteriori conditions and were compared against matched PopPK references.

resultsFor trough concentration, most ML models significantly outperformed PopPK population prediction (PRED) under a priori condition, while individual prediction (IPRED) significantly outperformed all ML models under a posteriori condition. For clearance, the best-performing ML model (kNN) significantly outperformed a population-typical prediction (CL PRED) under a priori condition, whereas the best-performing ML model (CatBoost) showed a small but statistically significant advantage over the empirical Bayes estimate for clearance (CL EBE) under a posteriori condition.

conclusionsRelative performance depended on the prediction target and available data. Under a priori condition, ML outperformed PopPK for both endpoints. Under a posteriori condition, PopPK remained superior for trough concentration, while ML performed comparably for clearance. Further real-world validation is needed.