Abstract
•Developing advanced models for estimation of pharmaceutical solubility.•Understanding the role of pressure and temperature on the drug solubility.•Validation of the models using measured data.
Preparation of nanodrug has been a subject of great interest in pharmaceutical manufacturing due to the inherent high solubility of drug nanoparticles in aqueous media. For the solid dosage oral formulations, the size of drug particles plays fundamental role in the solubility values of drug in aqueous media due to the enhanced surface energy associated with the nanoparticles. To develop the drug production at nanoscale, the solubility of drugs in the solvents must be determined prior to the operational processing. In this study, the solubility of Tolmetin (an anti-inflammatory drug) in supercritical carbon dioxide (SC-CO2) is modeled and analyzed using tree-based models. Temperature and pressure are the two features of the input, and the solubility of the Tolmetin is the target output of this modeling.The studied models are CART (Regression Tree), Extra Tree (ET), and Gradient Tree Boosting (GBRT). Their hyper-parameters were optimized based on multiple statistical criteria and three final models was obtained. CART, ET, and GBRT models had error values of 9.79E-08, 5.53E-08, and 1.24E-08 based on MSE parameter. Based on this fact and other metrics, we finally introduced the Gradient Tree Boosting (GBRT) model as the strongest and most accurate model developed in this research compared to other two models. The developed machine learning models for the solubility prediction, indicated that these models are robust enough to be considered for prediction of drug solubility in supercritical solvents.