Predicting physical stability of solid dispersions by machine learning techniques

J Control Release. 2019 Oct:311-312:16-25. doi: 10.1016/j.jconrel.2019.08.030. Epub 2019 Aug 26.


Amorphous solid dispersion (SD) is an effective solubilization technique for water-insoluble drugs. However, physical stability issue of solid dispersions still heavily hindered the development of this technique. Traditional stability experiments need to be tested at least three to six months, which is time-consuming and unpredictable. In this research, a novel prediction model for physical stability of solid dispersion formulations was developed by machine learning techniques. 646 stability data points were collected and described by over 20 molecular descriptors. All data was classified into the training set (60%), validation set (20%), and testing set (20%) by the improved maximum dissimilarity algorithm (MD-FIS). Eight machine learning approaches were compared and random forest (RF) model achieved the best prediction accuracy (82.5%). Moreover, the RF models revealed the contribution of each input parameter, which provided us the theoretical guidance for solid dispersion formulations. Furthermore, the prediction model was confirmed by physical stability experiments of 17β-estradiol (ED)-PVP solid dispersions and the molecular mechanism was investigated by molecular modeling technique. In conclusion, an intelligent model was developed for the prediction of physical stability of solid dispersions, which benefit the rational formulation design of this technique. The integrated experimental, theoretical, modeling and data-driven AI methodology is also able to be used for future formulation development of other dosage forms.

Keywords: Machine learning; Molecular modeling; Physical stability; Solid dispersion.

Publication types

  • Research Support, Non-U.S. Gov't

MeSH terms

  • Drug Stability*
  • Estradiol / chemistry
  • Machine Learning
  • Models, Molecular*
  • Povidone / chemistry


  • Estradiol
  • Povidone