Investigating the Possibility of Solubility Prediction of Drugs in Binary Solvent Mixtures at Various Temperatures Using a Minimum Number of Experimental Data Points

Abstract

Solubility of a solute is the simplest phenomenon in chemical/pharmaceutical investigations, and the solubility data is required in many applications in industry. The experimental determination of drug’s solubility is still the most reliable method for obtaining accurate and valid data, however, it is time-consuming and costly. To address this point, a number of mathematical models were proposed. Aims: This study aimed to provide a rational experimental design to collect a minimum number of experimental data points for a drug dissolved in a given binary solvent mixture at various temperatures and to describe a computational procedure to predict the solubility of the drugs in any solvent composition and temperature. Methods: In this study, we gathered available solubility datasets from papers published from 2012-2016 (56 data sets, 3488 data points totally). The mean percentage deviations (MPD) used to check the accuracy of our predictions. Results: 56 datasets were analyzed using 8 training data points which the overall MPD was calculated to be 15.5% ± 15.1%, and for 52 datasets after excluding 5 outlier sets was 12.1% ± 8.9%. The paired t-test was conducted to compare the MPD values obtained from the models trained by 7 and 8 data points and the reduction in prediction overall MPD (from 17.7% to 15.5%) was statistically significant (p<0.04). To further reduction in MPD values, the computations were also conducted using 9 training data points, which did not reveal any significant difference comparing to the predictions using 8 training data points (p>0.88). Conclusion: This observation revealed that the model adequately trained using 8 data points and could be used as a practical strategy for predicting the solubility of drugs in binary solvent mixtures at various temperatures with acceptable prediction error and using minimum experimental efforts. These sorts of predictions are highly in demand in the pharmaceutical industry.