Utilization of sequential model-based optimizer integrated machine learning models in correlation of famotidine solubility in supercritical carbon dioxide

We investigated solubility variations of a medication in supercritical carbon dioxide with an insight into preparation of nanomedicines with improved aqueous solubility. As the case study, the solubility of famotidine (FAM) medicine in sc-CO2 (supercritical carbon dioxide) was computed as a function of temperature and pressure, with a particular focus on modeling and predicting solubility and sc-CO2 density. Three regression models with machine learning behavior including Quadratic Polynomial Regression (QPR), Weighted Least Squares (WLS), and Orthogonal Matching Pursuit (OMP) were employed to analyze the data, and Sequential Model-Based Optimization (SMBO) was utilized for hyper-parameter tuning. Among these models, the best-performing model for predicting FAM solubility was the QPR model, with an impressive coefficient of determination (R2) of 0.95858 for all sets including training and validation. Additionally, QPR exhibited low MAPE of 1.64278E + 00, RMSE of 9.6833E-02, and a maximum error of 1.49480E-01, while exhibiting a higher maximum error of 18.99 kg/m³ for density predictions, indicating areas for potential improvement. These results highlight the accuracy and precision of the QPR model in predicting FAM solubility in sc-CO2. For the prediction of sc-CO2 density, QPR again proved to be the most effective model with a remarkable R2 score of 0.99733. This model achieved a low MAPE of 1.06004E-02, RMSE of 8.4072E + 00, and a maximum error of 1.89894E + 01. The QPR model demonstrates its exceptional capability in accurately predicting sc-CO2 density in terms of temperature and pressure.

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