Analysis of enhancing drug bioavailability via nanomedicine production approach using green chemistry route: Systematic assessment of drug candidacy

Abstract

Evaluation of drug candidacy for processing in nanonization is of great importance due to the importance of nanomedicine for enhanced bioavailability approach. Given that the majority of newly discovered drugs are poorly soluble in the body which makes also poor bioavailability of the drug substances, advanced techniques are required in order to enhance the drug efficacy and consequently its solubility. We employed three distinct machine learning models to make predictions in this work, including Multilayer Perceptron regressor (MLP), K-nearest Neighbors regressor (KNN), and Decision Tree Regressor (DT). The regression task has two input features: temperature (K) and pressure (MPa), and the only output is the solubility value of ANA (Anastrozole) drug in supercritical CO2 as the solvent. We tuned all three models using their important hyper-parameters and found the most appropriate combinations for each. Then, many standard measures are used to evaluate their performance. DT, KNN, and MLP models have R2-scores of 0.875, 0.992, and 0.9984, respectively, and have MSE errors of 1.5242, 1.0006 × 10−1, and 2.5688 × 10−2. Additionally, taking into account the MAE, 1.01, 2.62 × 10−1, and 1.17 × 10−1 values acquired. Finally, the multilayer perceptron (MLP) is selected as the most accurate model. Additionally, this model has a maximum error of 3.78 × 10−1 on the dataset.