Skip to main content

ORIGINAL RESEARCH article

Front. Med.
Sec. Translational Medicine
Volume 11 - 2024 | doi: 10.3389/fmed.2024.1435675
This article is part of the Research Topic Recent Advances in Pharmaceutical Analysis: Applications and New Challenges for the Quality of Medicines View all articles

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

Provisionally accepted
Sami Bawazeer Sami Bawazeer *
  • Umm al-Qura University, Mecca, Saudi Arabia

The final, formatted version of the article will be published soon.

    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, Solubility prediction, optimization, machine learning, modeling

    Received: 20 May 2024; Accepted: 26 Jun 2024.

    Copyright: © 2024 Bawazeer. This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) or licensor are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.

    * Correspondence: Sami Bawazeer, Umm al-Qura University, Mecca, Saudi Arabia

    Disclaimer: All claims expressed in this article are solely those of the authors and do not necessarily represent those of their affiliated organizations, or those of the publisher, the editors and the reviewers. Any product that may be evaluated in this article or claim that may be made by its manufacturer is not guaranteed or endorsed by the publisher.