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

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. METHODS The accuracy of the proposed prediction method was evaluated using collected experimental solubility data from the literature. The proposed method is: log Xm,T = fc log Xc,T + fw log Xw,T + fcfw[724.21/T + 485.17(fc-fw)/T + 194.41(fc-fw)2/T] Where Xm,T, Xc,T and Xw,T are the solute solubility at temperature (T) in mixed solvent and neat cosolvent and water, respectively, fc and fw 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 degrees 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.