Translational Prediction of Hemodialysis Clearance Integrating Molecular Structure and Dialysis Conditions

Quantitative prediction of drug removal by hemodialysis is essential for rational dose design in patients with renal failure. We aimed to develop a machine learning-based quantitative structure-pharmacokinetic relationship (QSPKR) model to predict hemodialysis clearance (CL_HD) using molecular descriptors and dialysis conditions. CL_HD data were compiled from published clinical studies, yielding a dataset comprising 46 compounds; 457 measurements; 7 dialysis membrane types; and dialysis parameters, including blood flow rate (Q_B) and dialysate flow rate (Q_D). Recursive feature elimination revealed six key predictors, which were used to construct and compare five models: lasso regression, random forest, support vector machine, averaged neural network, and extreme gradient boosting (XGBoost) models. Feature contributions were interpreted using Shapley additive explanations (SHAP) analysis. Among the models, XGBoost demonstrated the highest predictive performance (root mean square error = 51.71 mL/min; R² = 0.614). The SHAP analysis revealed that lipophilicity was the most influential predictor. The effects of Q_B and Q_D on CL_HD were nonlinear and saturable, consistent with the established dialysis clearance theory. CL_HD can be predicted from molecular structure and dialysis parameters using a QSPKR-based machine learning approach, providing a practical framework for estimating drug clearance and assisting in the preliminary evaluation of drug dialyzability when empirical data are limited.