Machine learning simulation of pharmaceutical solubility in supercritical carbon dioxide: Prediction and experimental validation for busulfan drug

Abstract

An artificial intelligence-based predictive model was developed using a support vector machine to investigate the solubility data of the drug Busulfan drug in supercritical carbon dioxide. The data for simulations were collected from literature. The model was trained and implemented in order to determine the correlation between the solubility values and the input parameters, namely, temperature and pressure. These parameters were used as the inputs as they are known to have a significant effect on the solubility of Busulfan in supercritical carbon dioxide. In the artificial intelligence model, a polynomial model with kernel function was applied to the data, and the model’s findings were compared with measured data for fitting. Good agreement was observed between the model’s outputs and the measured data with coefficient of determination greater than 0.99.