Original scientific paper
https://doi.org/10.15255/KUI.2021.008
Cumulative Drug Release Modelling of PCL-PVP Encapsulated Tramadol by DA-SVM, MLR, PLS, and OLS Regression Techniques
Ahmed Chabane
; Laboratoire des Matériaux Organiques (LMO), Département de Génie des Procédés, Faculté de Technologie, Université de Bejaia, 06000 Bejaia, Alžir
Fatiha Bouchal
; Laboratoire des Matériaux Organiques (LMO), Département de Génie des Procédés, Faculté de Technologie, Université de Bejaia, 06000 Bejaia, Alžir
Mohamed Hentabli
orcid.org/0000-0002-6693-0708
; Laboratory of Biomaterials and Transport Phenomena (LBMPT), Faculty of Technology, University Yahia Fares of Médéa 26 000, Médéa, Alžir
Farouk Rezgui
; Laboratoire des Matériaux Organiques (LMO), Département de Génie des Procédés, Faculté de Technologie, Université de Bejaia, 06000 Bejaia, Alžir
Houssam Eddine Slama
orcid.org/0000-0002-3052-883X
; Quality Control Laboratory, SAIDAL-Médéa 26 000, Médéa, Alžir
Abstract
This work aimed to model the kinetics of cumulative drug release from formulations based on encapsulation by biodegradable polycaprolactone and polyvinylpyrrolidone polymers. Different ratios of the polymerswere prepared by a solvent evaporation method using Span 20 and Span 80 as surfactants. The cumulative drug release was estimated depending on the formulation component and time. Four models: hybrid model of support vector machine and dragonfly algorithm (DA-SVM), partial least squares (PLS) model, multiple linear regression (MLR) model, and ordinary least squared (OLS) model, were developed and compared. The statistical analysis proved there were no issues in variable inputs. The results showed that the DA-SVM model gave a better result where a determination coefficient was close to one and RMSE error close to zero. A graphical interface was built to calculate the cumulative drug release.
This work is licensed under a Creative Commons Attribution 4.0 International License.
Keywords
Dragonfly algorithm; support vector machine; Tramadol; cumulative drug releases; modelling, biopolymer; least squares
Hrčak ID:
270988
URI
Publication date:
17.1.2022.
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