Data-driven prediction of lipophilic drug solubility in supercritical CO2 using an adaptive ensemble learning architecture

Introduction Predicting how lipophilic pharmaceuticals dissolve in supercritical carbon dioxide (SC-CO 2 ) remains a central challenge for process development, largely because experimental measurements are time-consuming and traditional modeling approaches often fail to generalize across chemically diverse compounds. Methods This study examines the SC-CO 2 solubility of four representative drugs Sirolimus, Tacrolimus, Rifampin, and Teriflunomide and introduces an adaptive computational framework designed to improve predictive power across varied molecular structures. The approach combines multiple boosting-based regressors within an ensemble scheme that incorporates a DST-inspired aggregation step and a weighted integration mechanism. Model hyperparameters are optimized using a bio- inspired Electric Eel Foraging strategy. Reliability of the final model was evaluated through repeated cross-validation, comparative statistical tests, and uncertainty estimation. Results and discussion The resulting ensemble achieved high predictive accuracy ( R 2 = 0.99, RMSE = 0.089) with consistent performance across the investigated compounds. By integrating molecular descriptors, thermodynamic features, and process conditions within a unified ensemble framework, the approach provides a practical computational tool for supporting solubility estimation in SC-CO 2 systems.