Correlation of rivaroxaban solubility in mixed solvents for optimization of solubility using machine learning analysis and validation - Muteb Alanazi; Jowaher Alanazi; Tareq Nafea Alharby; Bader Huwaimel (Scientific Reports)
Abstract In this study, the solubility of rivaroxaban, a poorly water-soluble drug, was investigated in mixed solvent systems to address challenges in pharmaceutical formulation and bioavailability enhancement. Solubility optimization is essential for the effective delivery and therapeutic performance of rivaroxaban, as its low aqueous solubility limits oral bioavailability and necessitates innovative approaches for drug formulation. The study explored the role of primary alcohols combined with dichloromethane in improving solubility, emphasizing their industrial relevance in crystallization, purification, and drug manufacturing processes. To complement experimental insights, machine learning models were employed to predict rivaroxaban solubility based on temperature, solvent type, and mass fraction of dichloromethane. Three models—AdaBoost Gaussian process regression (ADAGPR), AdaBoost multilayer perceptron (ADAMLP), and AdaBoost LASSO regression (ADALASSO)—were evaluated using $$\:R^{2}$$ , RMSE, and MAPE metrics. Among these, ADAGPR demonstrated superior performance with an R² score of $$\:0.96485$$ , outperforming ADAMLP $$\:(R^{2}\:=\:0.96022)$$ and $$\:ADALASSO\:(R^{2}=\:0.887121)$$ . It also achieved the lowest total RMSE $$\:(1.2949×\:{10}^{-4})$$ and MAPE $$\:(5.60309×\:{10}^{-1})$$ , confirming its predictive precision and reliability. Optimal solubility conditions were identified at $$\:308.15\:K$$ with a mass fraction of 0.8190 in a dichloromethane-methanol mixture, yielding a predicted solubility of $$\:4.232736×\:{10}^{-3}$$ . These findings highlight the potential of combining chemical engineering principles with advanced predictive modeling to optimize solubility in complex solvent systems, offering significant value to pharmaceutical development and process optimization.