Theoretical investigations on the manufacture of drug nanoparticles using green supercritical processing: Estimation and prediction of drug solubility in the solvent using advanced methods

Abstract

We studied the possibility of development of a novel green methodology for preparation of nanomedicine. The designed process does not use organic solvent in the manufacturing the drug and supercritical gas is used as solvent in the process. The method is efficient to enhance the drug efficacy for the patients. We used a dataset of solubility data in our study, which includes two inputs, pressure, and temperature, as well as one output, solubility, in order to carry out the study. In fact, the solubility of a drug namely Tolmetin has been predicted in supercritical carbon dioxide as the solvent using machine learning based models. As part of this research, a heap-based optimizer (HBO) was used on three selected machine learning models to obtain optimal estimators that can be used in the future. Machine learning models are multilayer perceptron (MLP), polynomial support vector regression (PSVR), and ridge regression. PSVR, Ridge, and MLP each have R2-scores of 0.976, 0.749, and 0.957, and MSEs of 1.81 × 10−8, 1.40 × 10−7, and 4.18 × 10−8, respectively. So, PSVR was selected as the most accurate model among all models assessed in this work for description of Tolmetin solubility in supercritical CO2. The results indicated that the developed models are robust and accurate enough for prediction of the pharmaceutical solubility in different solvents and operational ranges.