Machine learning-based prediction and mathematical optimization of Capecitabine solubility through the supercritical CO2 system

This work represents development of computational intelligence models for assessment of estimating solubility/ bioavailability of orally-taken drugs in SCF (supercritical fluid). The SCF considered in this work is CO2. This approach can provide an appropriate opportunity for the development of sustainable and environmentally-friendly technology with valuable advantages in the drug delivery industry. We modeled the solubility of Capecitabine in CO2SCF using multiple machine learning models. The inputs are Pressure and Temperature in the used dataset and the single output (Y) is the solubility of the drug. Three well-known supervised learning models of KNN (K-Nearest Neighbor), MLP (Multilayer Perceptron), and SVR (Support Vector Regression) were selected for modeling in this research. The hyper-parameters of them were optimized using Cuckoo search algorithm (CS) and then final models assessed using multiple metrics. In terms of coefficient of determination, KNN, MLP, and SVR models have score values of 0.959, 0.996, and 0.952, respectively. Also, they have error rates of 13.78, 1.54, and 19.23 with MSE metric. These facts introduce MLP as the best model obviously and so we used this model for final analysis in this study due to its superior architecture.