Modeling and validation of drug release kinetics using hybrid method for prediction of drug efficiency and novel formulations.

Autor: Alshahrani SM; Department of Pharmaceutics, College of Pharmacy, Prince Sattam Bin Abdulaziz University, Al-Kharj, Saudi Arabia., Alotaibi HF; Department of Pharmaceutical Sciences, College of Pharmacy, Princess Nourah Bint AbdulRahman University, Riyadh, Saudi Arabia., Alqarni M; Department of Pharmaceutical chemistry, College of Pharmacy, Taif University, Taif, Saudi Arabia.
Jazyk: angličtina
Zdroj: Frontiers in chemistry [Front Chem] 2024 Jun 21; Vol. 12, pp. 1395359. Date of Electronic Publication: 2024 Jun 21 (Print Publication: 2024).
DOI: 10.3389/fchem.2024.1395359
Abstrakt: This paper presents a thorough examination for drug release from a polymeric matrix to improve understanding of drug release behavior for tissue regeneration. A comprehensive model was developed utilizing mass transfer and machine learning (ML). In the machine learning section, three distinct regression models, namely, Decision Tree Regression (DTR), Passive Aggressive Regression (PAR), and Quadratic Polynomial Regression (QPR) applied to a comprehensive dataset of drug release. The dataset includes r (m) and z (m) inputs, with corresponding concentration of solute in the matrix (C) as response. The primary objective is to assess and compare the predictive performance of these models in finding the correlation between input parameters and chemical concentrations. The hyper-parameter optimization process is executed using Sequential Model-Based Optimization (SMBO), ensuring the robustness of the models in handling the complexity of the controlled drug release. The Decision Tree Regression model exhibits outstanding predictive accuracy, with an R 2 score of 0.99887, RMSE of 9.0092E-06, MAE of 3.51486E-06, and a Max Error of 6.87000E-05. This exceptional performance underscores the model's capability to discern intricate patterns within the drug release dataset. The Passive Aggressive Regression model, while displaying a slightly lower R 2 score of 0.94652, demonstrates commendable predictive capabilities with an RMSE of 6.0438E-05, MAE of 4.82782E-05, and a Max Error of 2.36600E-04. The model's effectiveness in capturing non-linear relationships within the dataset is evident. The Quadratic Polynomial Regression model, designed to accommodate quadratic relationships, yields a noteworthy R 2 score of 0.95382, along with an RMSE of 5.6655E-05, MAE of 4.49198E-05, and a Max Error of 1.86375E-04. These results affirm the model's proficiency in capturing the inherent complexities of the drug release system.
Competing Interests: The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.
(Copyright © 2024 Alshahrani, Alotaibi and Alqarni.)
Databáze: MEDLINE
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