Mathematical optimization and prediction of Febuxostat xanthine oxidase inhibitor solubility through supercritical CO2 system using machine-learning approach

Abstract

Scientific research towards the synthesis and discovery of novel therapeutic agents with optimal safety, great biological efficiency and acceptable toxicity profile is known as the driving force of pharmaceutical industry in the recent decades. This research study intends to mathematically predict the solubility of Febuxostat (FBX) as a prevalent type of xanthine oxidase inhibitor in the supercritical CO2 (SCCO2) system. For this purpose, Quantile Regression (QR), Multi Linear Regression (MLR), and Multilayer perceptron (MLP) were used to establish models for the prediction of solubility of FBX drug based on temperature and pressure features. The settings of the models were optimized based on their important hyper-parameters and then evaluated and compared. In the final evaluation, models MLP, MLR, and QR have R2 scores of 0.999, 0.941, and 0.854. Also, the RMSE error rate for model MLP is equal to 5.97E-02, for model MLR is equal to 4.77E-01, and for model QR is equal to 6.73E-01. Therefore, MLP can be recognized as the best model among these three methods. The MAPE error metric for this model is 8.26E-02.