An Integrated Population Pharmacokinetic and Machine Learning Model for Predicting Tacrolimus Exposure in Adult...

Tacrolimus (TAC) is a calcineurin inhibitor widely used for immunosuppressive therapy. It has a narrow therapeutic window and substantial interindividual variability, making therapeutic drug monitoring (TDM) essential. Traditional population pharmacokinetic (PPK) models have limited capacity to capture complex nonlinear relationships and multifactorial interactions. This study aimed to integrate PPK and machine learning (ML) to predict TAC plasma concentration.

A total of 182 consecutive patients with nephrotic syndrome (NS) who received oral TAC therapy and underwent TDM and CYP3A5 genotyping at the First Affiliated Hospital of Xinjiang Medical University between January 2018 and December 2023 were enrolled. Individual PK parameters derived from the PPK model were incorporated as new features into ML models. Feature selection was performed using recursive feature elimination with cross-validation (RFECV), and the union of features selected by the three models with the highest R² was used to construct the final feature set. Nine ML models were then developed, and the three best-performing models were combined into a weighted ensemble. Model performance was assessed by R², mean absolute error (MAE), and root mean square error (RMSE). SHAP (SHapley Additive exPlanations) was used to interpret feature contributions.

Incorporating PK parameters resulted in a consistent improvement in predictive performance compared with the model without PK parameters (R²: 0.633 vs. 0.602). The weighted ensemble of CatBoost, AdaBoost, and GraBoost (5:3:2) achieved the best performance (R² = 0.633, MAE = 1.081, RMSE = 1.377), whereas the optimal weighting without PK parameters was 3:6:1 (R² = 0.602, MAE = 1.139, RMSE = 1.432). SHAP analysis indicated that PK parameters was the most influential feature, exceeding conventional biochemical indicators.

Incorporating PK parameters enhanced model predictive performance, providing quantitative support for individualized TAC dosing and facilitating safer and more precise clinical applications.