Development of machine learning model and analysis study of drug solubility in supercritical solvent for green technology development

Abstract

The current research is focused on development of machine learning model for estimation of pharmaceutical solubility in supercritical CO2 as the green solvent. The main aim is to assess the suitability of supercritical processing for preparation of nanomedicine. Oxaprozin was taken as model drug for the solubility measurements, and its solubility was determined at different operational conditions by variation of temperature and pressure of the process. Artificial Neural Network (ANN) model was implemented for simulation of the drug solubility, and the best model was obtained with R2 greater than 0.99 for the training and validation as well. The tested model was then exploited to understand the process, and it turned out that both pressure and temperature had major and considerable influence on the solubility of Oxaprozin in supercritical carbon dioxide as solvent. However, the effect of pressure was shown to be more significant on the solubility compared to the effect of pressure, which was attributed to the effect of pressure on the density of the supercritical solvent. The developed ANN model was indicated to be robust in estimating the values of drug solubility in wide range of conditions which can save time and cost of the measurements.