Optimization of Drug Release from Compressed Multi Unit Particle System (MUPS) Using Generalized Regression Neural Network (GRNN)
Abstract
The purpose of this study was development of diclofenac sodium extended release compressed matrix pellets and optimization using Generalized Regression Neural Network (GRNN). According to Central Composite Design (CCD), ten formulations of diclofenac sodium matrix tablets were prepared. Extended release of diclofenac sodium was acomplished using Carbopol (R) 71G as matrix substance. The process of direct pelletisation and subsequently compression of the pellets into MUPS tablets was applied in order to investigate a different approach in formulation of matrix systems and to achieve more control of the process factors over the principal response - the release of the drug. The investigated factors were X(1) -the percentage of polymer Carbopol (R) 71 G and X(2)- crushing strength of the MUPS tablet. In vitro dissolution time profiles at 5 different sampling times were chosen as responses. Results of drug release studies indicate that drug release rates vary between different formulations,... with a range of 1 hour to 8 hours of dissolution. The most important impact on the drug release has factor X(1) -the percentage of polymer Carbopol (R) 71 G. The purpose of the applied GRNN was to model the effects of these two causal factors on the in vitro release profile of the diclofenac sodium from compressed matrix pellets. The aim of the study was to optimize drug release in manner wich enables following in vitro release of diclofenac sodium during 8 hours in phosphate buffer: 1 h: 15-40%, 2 h: 25-60%, 4 h: 35-75%, 8 h: >70%.
Keywords:
Matrix pellets / Multi unit particle system (MUPS) / Direct pelletization / Diclofenac sodium / Carbopol (R) 71G / Extended releaseSource:
Archives of Pharmacal Research, 2010, 33, 1, 103-113Publisher:
- Pharmaceutical Soc Korea, Seoul
DOI: 10.1007/s12272-010-2232-8
ISSN: 0253-6269
PubMed: 20191351
WoS: 000274052000014
Scopus: 2-s2.0-77949897807
Collections
Institution/Community
PharmacyTY - JOUR AU - Ivić, Branka AU - Ibrić, Svetlana AU - Betz, Gabriele AU - Đurić, Zorica PY - 2010 UR - https://farfar.pharmacy.bg.ac.rs/handle/123456789/1391 AB - The purpose of this study was development of diclofenac sodium extended release compressed matrix pellets and optimization using Generalized Regression Neural Network (GRNN). According to Central Composite Design (CCD), ten formulations of diclofenac sodium matrix tablets were prepared. Extended release of diclofenac sodium was acomplished using Carbopol (R) 71G as matrix substance. The process of direct pelletisation and subsequently compression of the pellets into MUPS tablets was applied in order to investigate a different approach in formulation of matrix systems and to achieve more control of the process factors over the principal response - the release of the drug. The investigated factors were X(1) -the percentage of polymer Carbopol (R) 71 G and X(2)- crushing strength of the MUPS tablet. In vitro dissolution time profiles at 5 different sampling times were chosen as responses. Results of drug release studies indicate that drug release rates vary between different formulations, with a range of 1 hour to 8 hours of dissolution. The most important impact on the drug release has factor X(1) -the percentage of polymer Carbopol (R) 71 G. The purpose of the applied GRNN was to model the effects of these two causal factors on the in vitro release profile of the diclofenac sodium from compressed matrix pellets. The aim of the study was to optimize drug release in manner wich enables following in vitro release of diclofenac sodium during 8 hours in phosphate buffer: 1 h: 15-40%, 2 h: 25-60%, 4 h: 35-75%, 8 h: >70%. PB - Pharmaceutical Soc Korea, Seoul T2 - Archives of Pharmacal Research T1 - Optimization of Drug Release from Compressed Multi Unit Particle System (MUPS) Using Generalized Regression Neural Network (GRNN) VL - 33 IS - 1 SP - 103 EP - 113 DO - 10.1007/s12272-010-2232-8 ER -
@article{ author = "Ivić, Branka and Ibrić, Svetlana and Betz, Gabriele and Đurić, Zorica", year = "2010", abstract = "The purpose of this study was development of diclofenac sodium extended release compressed matrix pellets and optimization using Generalized Regression Neural Network (GRNN). According to Central Composite Design (CCD), ten formulations of diclofenac sodium matrix tablets were prepared. Extended release of diclofenac sodium was acomplished using Carbopol (R) 71G as matrix substance. The process of direct pelletisation and subsequently compression of the pellets into MUPS tablets was applied in order to investigate a different approach in formulation of matrix systems and to achieve more control of the process factors over the principal response - the release of the drug. The investigated factors were X(1) -the percentage of polymer Carbopol (R) 71 G and X(2)- crushing strength of the MUPS tablet. In vitro dissolution time profiles at 5 different sampling times were chosen as responses. Results of drug release studies indicate that drug release rates vary between different formulations, with a range of 1 hour to 8 hours of dissolution. The most important impact on the drug release has factor X(1) -the percentage of polymer Carbopol (R) 71 G. The purpose of the applied GRNN was to model the effects of these two causal factors on the in vitro release profile of the diclofenac sodium from compressed matrix pellets. The aim of the study was to optimize drug release in manner wich enables following in vitro release of diclofenac sodium during 8 hours in phosphate buffer: 1 h: 15-40%, 2 h: 25-60%, 4 h: 35-75%, 8 h: >70%.", publisher = "Pharmaceutical Soc Korea, Seoul", journal = "Archives of Pharmacal Research", title = "Optimization of Drug Release from Compressed Multi Unit Particle System (MUPS) Using Generalized Regression Neural Network (GRNN)", volume = "33", number = "1", pages = "103-113", doi = "10.1007/s12272-010-2232-8" }
Ivić, B., Ibrić, S., Betz, G.,& Đurić, Z.. (2010). Optimization of Drug Release from Compressed Multi Unit Particle System (MUPS) Using Generalized Regression Neural Network (GRNN). in Archives of Pharmacal Research Pharmaceutical Soc Korea, Seoul., 33(1), 103-113. https://doi.org/10.1007/s12272-010-2232-8
Ivić B, Ibrić S, Betz G, Đurić Z. Optimization of Drug Release from Compressed Multi Unit Particle System (MUPS) Using Generalized Regression Neural Network (GRNN). in Archives of Pharmacal Research. 2010;33(1):103-113. doi:10.1007/s12272-010-2232-8 .
Ivić, Branka, Ibrić, Svetlana, Betz, Gabriele, Đurić, Zorica, "Optimization of Drug Release from Compressed Multi Unit Particle System (MUPS) Using Generalized Regression Neural Network (GRNN)" in Archives of Pharmacal Research, 33, no. 1 (2010):103-113, https://doi.org/10.1007/s12272-010-2232-8 . .