Constructing an Intelligent Model Based on Support Vector Regression to Simulate the Solubility of Drugs in Polymeric Media

Authors

Sait Senceroglu
Mohamed Arselene Ayari
Tahereh Rezaei
Fardad Faress, The University of Texas Rio Grande Valley
Amith Khandakar
Muhammad E. H. Chowdhury
Zanko Hassan Jawhar

Document Type

Article

Publication Date

11-14-2022

Abstract

This study constructs a machine learning method to simultaneously analyze the thermodynamic behavior of many polymer–drug systems. The solubility temperature of Acetaminophen, Celecoxib, Chloramphenicol, D-Mannitol, Felodipine, Ibuprofen, Ibuprofen Sodium, Indomethacin, Itraconazole, Naproxen, Nifedipine, Paracetamol, Sulfadiazine, Sulfadimidine, Sulfamerazine, and Sulfathiazole in 1,3-bis[2-pyrrolidone-1-yl] butane, Polyvinyl Acetate, Polyvinylpyrrolidone (PVP), PVP K12, PVP K15, PVP K17, PVP K25, PVP/VA, PVP/VA 335, PVP/VA 535, PVP/VA 635, PVP/VA 735, Soluplus analyzes from a modeling perspective. The least-squares support vector regression (LS-SVR) designs to approximate the solubility temperature of drugs in polymers from polymer and drug types and drug loading in polymers. The structure of this machine learning model is well-tuned by conducting trial and error on the kernel type (i.e., Gaussian, polynomial, and linear) and methods used for adjusting the LS-SVR coefficients (i.e., leave-one-out and 10-fold cross-validation scenarios). Results of the sensitivity analysis showed that the Gaussian kernel and 10-fold cross-validation is the best candidate for developing an LS-SVR for the given task. The built model yielded results consistent with 278 experimental samples reported in the literature. Indeed, the mean absolute relative deviation percent of 8.35 and 7.25 is achieved in the training and testing stages, respectively. The performance on the largest available dataset confirms its applicability. Such a reliable tool is essential for monitoring polymer–drug systems’ stability and deliverability, especially for poorly soluble drugs in polymers, which can be further validated by adopting it to an actual implementation in the future.

Comments

Copyright: © 2022 by the authors. Licensee MDPI, Basel, Switzerland

Recommended Citation

Senceroglu, S.; Ayari, M.A.; Rezaei, T.; Faress, F.; Khandakar, A.; Chowdhury, M.E.H.; Jawhar, Z.H. Constructing an Intelligent Model Based on Support Vector Regression to Simulate the Solubility of Drugs in Polymeric Media. Pharmaceuticals 2022, 15, 1405. https://doi.org/ 10.3390/ph15111405

Creative Commons License

This work is licensed under a Creative Commons Attribution 4.0 International License.

Publication Title

Pharmaceuticals

DOI

10.3390/ph15111405