In silico prediction of drug solubility in water-ethanol mixtures using Jouyban-Acree model

Abstract

Purpose. A predictive method was proposed to predict solubility of drugs in water-ethanol mixtures at various temperatures based on the Jouyban-Acree model. The model requires the experimental solubility data of the drug in mono-solvent systems. Method. The accuracy of the proposed prediction method was evaluated using collected experimental solubility data from the literature. The proposed method is: log X-m,X-T = f(c) log X-c,X-T + f(w) log X-w,X-T + f(c)f(w)[724.21/T + 485.17(f(c) - f(w))/T + 194.41(f(c) - f(w))(2)/T] Where X-m,X-T, X-c,X-T and X-w,X-T are the solute solubility at temperature ( T) in mixed solvent and neat cosolvent and water, respectively, f(c) and f(w) denote the solute free fraction of cosolvent ( ethanol) and water. The average absolute error (AAE) of the experimental and the predicted solubilities was computed as an accuracy criterion and compared with that of a well-established log-linear model. Results. The AAE (+/- SD) of the Jouyban-Acree and log-linear models were 0.19 (+/- 0.13) and 0.48 (+/- 0.28), respectively. The mean difference of AAEs was statistically significant (p < 0.0005) revealing that the Jouyban-Acree model was provided more accurate predictions. Although the log-linear model was used to predict solubility at a fixed temperature ( 25 or 23 C), the results also showed that the model could be employed to predict the solubility in solvent mixtures at various temperatures. Conclusion. More accurate predictions were provided using the Jouyban-Acree model in comparison with a previously established log-linear model of Yalkowsky. The prediction methods were successfully extended to predict the solubility in water-ethanol mixtures at various temperatures.