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

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.

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.

Under the PK-informed hybrid strategy, XGBoost achieved the best concentration prediction (R² = 0.739, MAE = 0.357, RMSE = 0.526, MAPE = 7.78%), outperforming a direct ML approach treating PK-related variables as inputs (CatBoost: R² = 0.459). The temporal external validation performance of the hybrid model remained stable (R² = 0.661, MAE = 0.473, RMSE = 0.651, MAPE = 14.71%). Mediation analysis demonstrated that CRP affected VCZ exposure primarily through CL/F, whereas albumin and age acted as modifiers. A web-based calculator was developed for real-time individualized prediction and assistance with clinical-dose adjustment.

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.