Abstract
Supercritical solvent-based engineering integrated nanomedicine has been developed in this study for development of advanced pharmaceutical manufacturing. Analysis of machine learning was employed to estimate the solubility of drug in supercritical CO2. Indeed, bioavailability of solid-dosage oral formu-lations is of great importance in which the majority of newly invented drugs possess poor water solubility which make them inefficient for patients. In order to improve drugs bioavailability, their solubility need to be increased which can be done by nanonization of drugs. The method of computation is machine learning in which different algorithms are selected and tuned to best fit the solubility data. For this pur -pose, we employed a tiny data set with pressure and temperature as input features and solubility as an output. Three models, the Multilayer Perceptron (MLP), the Kernel Ridge Regression (KRR), and the Gaussian Process Regression (GPR), have been employed to examine and model the data. Finally, the tuned models were created by optimizing the three models' hyper-parameters with the help of the Bat optimization algorithm (BA). In the end, the models were assessed using multiple metrics. On the basis of the R2 metric, the GPR model was found to be the most effective. In addition, the MAPE criterion yields a final model error of 2.52 x 10-2, the RMSE criterion yields an error of 1.96 x 10-2, and the Max error value yields an error of 2.90 x 10-2.(c) 2023 Elsevier B.V. All rights reserved.