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

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Abstract

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 of interest. We gathered available solubility data sets from papers published from 2012 to 2016 (56 data sets, 3488 data points totally). The mean percentage deviations (MPD) used to check the accuracy of predictions was calculated by Eq. 10. Fifty-six 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 training 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). 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.

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Acknowledgements

This report is a part of the results of S. Dadmand’s Pharm D thesis submitted to the Faculty of Pharmacy, Tabriz University of Medical Sciences, Tabriz, Iran.

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Authors and Affiliations

  1. Drug Applied Research Center, Tabriz University of Medical Sciences, Tabriz, Iran

    Sina Dadmand

  2. Neurosciences Research Center (NSRC), Tabriz University of Medical Sciences, Tabriz, Iran

    Farzin Kamari

  3. Department of Chemistry, University of North Texas, Denton, Texas, 76203-5070, USA

    William E. Acree Jr.

  4. Pharmaceutical Analysis Research Center and Faculty of Pharmacy, Tabriz University of Medical Sciences, Tabriz, Iran

    Abolghasem Jouyban

  5. Kimia Idea Pardaz Azarbayjan (KIPA) Science Based Company, Tabriz University of Medical Sciences, Tabriz, 51664, Iran

    Abolghasem Jouyban

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  1. Sina Dadmand
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  2. Farzin Kamari
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  3. William E. Acree Jr.
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  4. Abolghasem Jouyban
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Correspondence to Abolghasem Jouyban.

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Dadmand, S., Kamari, F., Acree, W.E. et al. Solubility Prediction of Drugs in Binary Solvent Mixtures at Various Temperatures Using a Minimum Number of Experimental Data Points. AAPS PharmSciTech 20, 10 (2019). https://doi.org/10.1208/s12249-018-1244-4

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