Prediction of drug solubility in ethylene glycol plus water mixtures using generally trained cosolvency models

This work reports the results obtained from the generally trained versions of Yalkowsky and Jouyban-Acree models for prediction of drugs' solubility in binary mixtures of ethylene glycol+ water at different temperatures. To achieve a full predictive model, the Abraham solvation solute parameters are combined with the Jouyban-Acree and Jouyban-Acree-van't Hoff models. In this respect, the solubility data sets of 14 drugs with various polarities and structural features in aqueous ethylene glycol mixtures are collected from literature and correlated with the above-mentioned models, the overall mean relative deviation values for the back-calculated drug's solubility indicating the generally trained models have an acceptable performance in solubility prediction and can be useful in pharmaceutical industry. (C) 2021 Elsevier B.V. All rights reserved.

Automated summary

This study successfully predicted the solubility of drugs in binary mixtures of ethylene glycol+ water using the Yalkowsky and Jouyban-Acree models combined with Abraham solvation solute parameters. The results indicate that these models have a promising application in the pharmaceutical industry.