Computational intelligence modeling using Artificial Intelligence and optimization of processing of small-molecule API solubility in supercritical solvent

Abstract

Preparation of small-molecule API (Active Pharmaceutical Ingredient) at submicron size would be of great benefit for pharmaceutical engineering, as the drug particles at submicron size possess higher solubility in water. Indeed, the drug bioavailability can be enhanced when the nanomedicine is prepared. In this study, the solubility of the drug desoxycorticosterone acetate (DA) is being examined to assess its viability of nanonization using supercritical operation. Two inputs are temperature and pressure which were considered for machine learning modeling in this study. The drug's solubility is the only output to be estimated by the optimized models. This dataset has 45 rows of data that were gathered at 5 different pressure and temperature levels. Support vector machine (SVM) is used as the core of the models built in this research. Epsilon-SVR and nu-SVR are models based on this concept, which together with two different polynomial and RBF kernels form the four models built in this research for estimation of DA drug solubility. The models are also optimized with the help of a new TLCO method. All four final models have an R2 score higher than 0.9, and among them, the Epsilon-SVR model with RBF kernel has the best performance with 0.967.

Keywords

• Pharmaceutics
• Solubility
• Machine learning
• RBF kernel
• Polynomial kernel