Predictive analysis of solubility data with pressure and temperature in assessing nanomedicine preparation via supercritical carbon dioxide.

This work presents a comprehensive study on the prediction of phenytoin solubility at supercritical state using advanced techniques including machine learning analysis. The solubility of small-molecule pharmaceutical was analyzed and calculated to enhance its solubility and bioavailability as well. The models were employed to approximate the solubility at various pressures and temperatures. The dataset comprises temperature (T), pressure (P), and solubility (y) values, along with the corresponding solvent density measurements that were used in the models. Three models, namely Automatic Relevance Determination Regression (ARD), Gaussian process regression (GPR), and Linear Regression (LR) were designed and tuned to build predictive models. The ADABOOST ensemble technique was applied to strengthen the predictive capabilities of the models, while hyperparameter tuning was conducted using the Jellyfish Optimization (JO) algorithm. For phenytoin solubility prediction, the ADA-GPR model demonstrated outstanding accuracy, obtaining an R² of 0.99644. The ADA-LR model also produced competitive results, attaining an R² value of 0.93381, whereas the ADA-ARD model showed robust performance, yielding an R² of 0.95249. In terms of solvent density prediction, the ADA-GPR model once again outperformed the others, with an R² value of 0.9933.