Evaluating the Effectiveness of Reference Solvent Solubility Calculations for Binary Mixtures Based on Pure Solvent Solubility: The Case of Phenolic Acids.

ABSTRACT: Predicting the solubility of active pharmaceutical ingredients (APIs) in binary solvent mixtures is a major challenge in formulation science, as physics-based models often fail to capture complex, non-additive mixing effects. This study presents a robust machine learning (ML) framework to overcome this limitation, enabling accurate predictions from pure solvent data alone and molecular descriptors derived from COSMO-RS (computed with COSMOtherm). Firstly, our experimental knowledge of binary solvent mixtures solubility was expanded through newly measured data of caffeic and ferulic acids in aqueous mixtures of DMF, DMSO, and 4-formylmorpholine (4-FM). These new data, combined with values in the literature, formed a comprehensive dataset of 1636 points for ten phenolic and benzoic acids. To

SUBMITTER: Cysewski P 

PROVIDER: S-EPMC12655719 | biostudies-literature | 2025 Nov

REPOSITORIES: biostudies-literature