Machine Learning-Based Prediction of Drug Solubility in Lipidic Environments: The Sol_ME Tool for Optimizing Lipid-Based Formulations with a Preliminary Apalutamide Case Study

Abstract

Lipid-based formulations are essential for enhancing drug solubility and bioavailability, yet selecting optimal lipid excipients for specific drugs remains challenging. This study introduces Sol_ME, a machine learning-based model designed to predict drug solubility in lipidic environments, thereby streamlining the formulation process. The Sol_ME model uses PubChem® fingerprints, focusing on solubility correlations with lipid excipients, minimizing reliance on traditional parameters like LogP and molecular weight. The model was trained on a dataset of 1,379 drug-solvent entries and applied to the formulation of Apalutamide, a BCS Class II drug. Experimental validation was performed with 35 drug-solvent combinations to assess the accuracy of predicted solubilities. Sol_ME achieved a high predictive accuracy with a correlation coefficient of 0.998. The model successfully identified Cinnamon oil as the optimal excipient for Apalutamide, further refining the formulation with Vanillin. This reduced formulation volume by 75%, enabling the development of a single-unit 240 mg soft gelatin capsule. Experimental validation showed 80% alignment between predicted and actual solubilities. The Sol_ME model demonstrates significant potential to optimize lipid-based drug formulation, offering a data-driven approach that enhances efficiency. The success of Apalutamide formulation highlights its practical utility. Future work will expand the dataset and extend the model to solid lipid systems, broadening its application in drug delivery technologies.