Navigating the Deep Eutectic Solvent Landscape: Experimental and Machine Learning Solubility Explorations of Syringic, p-Coumaric, and Caffeic Acids.

ABSTRACT: Efficiently identifying suitable solvents for active pharmaceutical ingredients (APIs) is critical in drug formulation, yet the vast number of possible solvent-solute combinations presents a significant experimental challenge. This study addresses this by developing a robust machine learning (ML) model for accurately predicting the solubility of three phenolic acids (syringic, p-coumaric, and caffeic) in various deep eutectic solvents (DESs), integrating both experimental and computational investigations. Measured solubility data showed that the choline chloride combined with triethylene glycol in a 1:2 molar ratio was the most efficient system for the dissolution of the studied APIs. Different ML models, utilizing nu-Support Vector Regression (nuSVR) as the core regressor and based on des

SUBMITTER: Cysewski P 

PROVIDER: S-EPMC12564265 | biostudies-literature | 2025 Oct

REPOSITORIES: biostudies-literature