Year: 2021
Type: Article
Title: Optimization of self-emulsifying drug delivery system of cefuroxime axetil
Author: Trajanovska, Eleonora
Author: Simonoska Crcarevska, Maja
Author: Mirchev, Miroslav
Author: Jovanovikj, Frosina
Author: Atanasova, Ana
Author: Ugarkovic, Sonja
Author: Glavas Dodov, Marija
Abstract: Abstract Overcoming solubility problems is the greatest challenge during formulation of poorly soluble active pharmaceutical ingredients (API’s) into oral solid dosage forms. Different formulation approaches were used to surpass this problem and enhance their solubility in the gastrointestinal (GI) fluids, in order to achieve a faster dissolution and better absorption, which will directly influence their therapeutic effect. In this paper, an evaluation of the potential of a self-emulsifying drug delivery system (SEDDS) to improve the solubility of the active ingredient cefuroxime axetil (CA) was done. Screening of the solubility of the API in different excipients was done, and Tween 80, PEG 400, and Olive oil as a surfactant, co-solvent, and oil, respectively, were chosen as the most convenient system constituents. An optimal self-emulsification and solubilization ability of this system was assessed using mixture experimental design statistical tools based on the response surface methodology (RSM). The prepared CA-SEDDS were evaluated for droplet size (d10, d50, d90 in µm), droplet size distribution (Span factor), and absorbance. As a complementary approach, for better representation of the non-linear relationship between the formulation compositions and the observed dispersion characteristics an artificial neural network (ANN) was used. Optimal formulation that consists of 10% (w/w) Tween 80 as surfactant, 80% (w/w) PEG 400 as co-solvent and 10% (w/w) Olive oil, was obtained. Both, mixture experimental design and ANN were combined for a comprehensive evaluation of CA-SEDDS and the obtained results suggested that formulation of SEDDS is a useful approach for improving the solubility of the CA. Keywords: self-emulsifying drug delivery systems (SEDDS), cefuroxime axetil, design of experiment, artificial neural network (ANN)
Publisher: Macedonian Pharmaceutical Association
Relation: Macedonian Pharmaceutical Bulletin
Identifier: oai:repository.ukim.mk:20.500.12188/23074
Identifier: http://hdl.handle.net/20.500.12188/23074Identifier: 10.33320/maced.pharm.bull.2020.66.02.007
Identifier: http://bulletin.mfd.org.mk/volumes/Volume%2066_2/66_2_007.pdfIdentifier: 66
Identifier: 2
