An Interpretable PK-Informed Hybrid Model for Voriconazole Exposure Prediction: Roles of CYP2C19 Genotype and Inflammation.

Purpose: Voriconazole (VCZ) exhibits nonlinear pharmacokinetics, a narrow therapeutic window, and substantial interindividual variability. Inaccurate dosing may lead to underexposure or overexposure, causing treatment failure or toxicity. Existing population pharmacokinetic (PPK)-machine learning (ML) models either lack mechanistic interpretability or inadequately characterize VCZ exposure. Therefore, we propose a hybrid model embedding ML within a PPK framework to associate clinical covariates with VCZ exposure. Patients and Methods: A total of 489 inpatients receiving VCZ at the Third Affiliated Hospital of Soochow University between March 2020 and May 2024 were included. We identified candidate predictors of CL/F using dual-feature selection with Boruta and LASSO. Overlapping features were used to train four ML algorithms to estimate CL/F. The predicted CL/F values were incorporated into a steady-state PPK equation to back-calculate VCZ concentrations, followed by quadratic calibration to reduce bias. Causal mediation analysis assessed pathways from key covariates to VCZ concentration via CL/F, and Shapley Additive exPlanations (SHAP) values were used to quantify feature contributions. Results: Under the PK-informed hybrid strategy, XGBoost achieved the best concentration prediction Conclusion: The hybrid model improved VCZ concentration prediction versus direct ML modeling while preserving CL/F-centered mechanistic interpretability. It may help guide dose adjustment and reduce clinically relevant misdosing. This framework may be generalizable to other narrow-therapeutic-window drugs.