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Optimization of Carboxymethyl-Xyloglucan-Based Tramadol Matrix Tablets Using Simplex Centroid Mixture Design
The aim was to determine the release-modifying effect of carboxymethyl xyloglucan for oral drug delivery. Sustained release matrix tablets of tramadol HCl were prepared by wet granulation method using carboxymethyl xyloglucan as matrix forming polymer. HPMC K100M was used in a small amount to contro...
Autores principales: | , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
Hindawi Publishing Corporation
2013
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC4595968/ https://www.ncbi.nlm.nih.gov/pubmed/26555977 http://dx.doi.org/10.1155/2013/396468 |
Sumario: | The aim was to determine the release-modifying effect of carboxymethyl xyloglucan for oral drug delivery. Sustained release matrix tablets of tramadol HCl were prepared by wet granulation method using carboxymethyl xyloglucan as matrix forming polymer. HPMC K100M was used in a small amount to control the burst effect which is most commonly seen with natural hydrophilic polymers. A simplex centroid design with three independent variables and two dependent variables was employed to systematically optimize drug release profile. Carboxymethyl xyloglucan (X (1)), HPMC K100M (X (2)), and dicalcium phosphate (X (3)) were taken as independent variables. The dependent variables selected were percent of drug release at 2nd hour (Y (1)) and at 8th hour (Y (2)). Response surface plots were developed, and optimum formulations were selected on the basis of desirability. The formulated tablets showed anomalous release mechanism and followed matrix drug release kinetics, resulting in regulated and complete release from the tablets within 8 to 10 hours. The polymer carboxymethyl xyloglucan and HPMC K100M had significant effect on drug release from the tablet (P > 0.05). Polynomial mathematical models, generated for various response variables using multiple regression analysis, were found to be statistically significant (P > 0.05). The statistical models developed for optimization were found to be valid. |
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