Advanced modeling of pharmaceutical solubility in solvents using artificial intelligence techniques: assessment of drug candidate for nanonization processing

Turki Al Hagbani; Sameer Alshehri; Sami Bawazeer

doi:10.3389/fmed.2024.1435675

Advanced modeling of pharmaceutical solubility in solvents using artificial intelligence techniques: assessment of drug candidate for nanonization processing

Front Med (Lausanne). 2024 Jul 22:11:1435675. doi: 10.3389/fmed.2024.1435675. eCollection 2024.

Authors

Turki Al Hagbani¹, Sameer Alshehri², Sami Bawazeer³

Affiliations

¹ Department of Pharmaceutics, College of Pharmacy, University of Hail, Hail, Saudi Arabia.
² Department of Pharmaceutics and Industrial Pharmacy, College of Pharmacy, Taif University, Taif, Saudi Arabia.
³ Department of Pharmaceutical Sciences, Faculty of Pharmacy, Umm Al-Qura University, Makkah, Saudi Arabia.

Abstract

This research is an analysis of multiple regression models developed for predicting ketoprofen solubility in supercritical carbon dioxide under different levels of T(K) and P(bar) as input features. Solubility of the drug was correlated to pressure and temperature as major operational variables. Selected models for this study are Piecewise Polynomial Regression (PPR), Kernel Ridge Regression (KRR), and Tweedie Regression (TDR). In order to improve the performance of the models, hyperparameter tuning is executed utilizing the Water Cycle Algorithm (WCA). Among, the PPR model obtained the best performance, with an R² score of 0.97111, alongside an MSE of 1.6867E-09 and an MAE of 3.01040E-05. Following closely, the KRR model demonstrated a good performance with an R² score of 0.95044, an MSE of 2.5499E-09, and an MAE of 3.49707E-05. In contrast, the TDR model produces a lower R² score of 0.84413 together with an MSE of 7.4249E-09 and an MAE of 5.69159E-05.

Keywords: drug development; machine learning; modeling; optimization; solubility prediction.

Grants and funding

The author(s) declare that financial support was received for the research, authorship, and/or publication of this article. This research was funded by Taif University, Saudi Arabia, Project No. (TU-DSPP-2024-61).