Modeling and computational study on prediction of pharmaceutical solubility in supercritical CO₂ for manufacture of nanomedicine for enhanced bioavailability

Enhancement of drug bioavailability for poorly water-soluble drugs is of great importance for pharmaceutical industry. In this work, a computational task was carried out in order to predict the solubility of a drug model, namely salsalate in supercritical carbon dioxide as the solvent. The experimental data was collected for the solubility at different values of temperature and pressure to understand the effect of operational parameters on the solubility of salsalate. Several machine learning data was employed to predict the solubility data as function of input parameters, i.e., temperature (T) and pressure (P). On the provided data, we employed Linear SVR (Support Vector Regression), Nu SVR, and Bayesian Ridge Regression (BRR) models with two inputs, including X1 = P(bar) and X2 = T(K), and only one output which is the drug solubility (mole fraction unit). The simulation results indicated that linear SVR, Nu SVR, and BRR have R-squared scores of 0.784, 0.998, and 0. 876, respectively. In addition, they exhibit MAE error rates of 3.71 × 10⁻⁴, 8.41 × 10⁻⁵, and 3.04 × 10⁻⁴. Another statistic which was considered in the predictions is RMSE, which indicated error rates of 4.21 × 10⁻⁴, 1.39 × 10⁻⁴, and 3.60 × 10⁻⁴ for linear SVR, Nu SVR, and BRR, respectively. Indeed, the model of Nu SVR was chosen as the best model based on these statistical metrics and some visual examination, and it was utilized to discover optimal values that can be summarized as a vector: (X1 = 400, X2 = 338, Y = 0.00387). The results revealed that the developed models are capable pf predicting the solubility of salsalate for a wide range, and can be also employed for development of nanomedicine manufacturing based on supercritical-based processing.