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The application of generalized regression neural network in the modeling and optimization of aspirin extended release tablets with Eudragit (R) RS PO as matrix substance

Authorized Users Only
2002
Authors
Ibrić, Svetlana
Jovanović, M
Đurić, Zorica
Parojčić, Jelena
Solomun, Ljiljana
Article (Published version)
Metadata
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Abstract
The objective of this work is to use a generalized regression neural network (GRNN) in the design of extended-release aspirin tablets. As model formulations, 10 kinds of aspirin matrix tablets were prepared. Eudragit((R)) RS PO was used as matrix substance. The amount of Eudragit((R)) RS PO and compression pressure were selected as causal factors. In-vitro dissolution-time profiles at four different sampling times, as well as coefficients n (release order) and log k (release constant) from the Peppas equation were estimated as release parameters. A set of release parameters and causal factors were used as tutorial data for the GRNN and analyzing using a computer. A GRNN model was constructed. The optimized GRNN model was used for prediction of formulation with desired in vitro drug release. For two tested formulations there was very good agreement between the GRNN predicted and observed in vitro profiles and estimated coefficients. Calculated difference (f(1)) and similarity (f(2)) fac...tors indicate that there is no difference between predicted and experimental observed drug release profiles. This work illustrates the potential for an artificial neural network, GRNN, to assist in development of extended-release dosage forms. This method can be employed to achieve a desired in vitro dissolution profile.

Source:
Journal of Controlled Release, 2002, 82, 2-3, 213-222
Publisher:
  • Elsevier Science BV, Amsterdam

DOI: 10.1016/S0168-3659(02)00044-5

ISSN: 0168-3659

PubMed: 12175738

WoS: 000177945700003

Scopus: 2-s2.0-0037151316
[ Google Scholar ]
78
59
URI
https://farfar.pharmacy.bg.ac.rs/handle/123456789/321
Collections
  • Radovi istraživača / Researchers’ publications
Institution/Community
Pharmacy
TY  - JOUR
AU  - Ibrić, Svetlana
AU  - Jovanović, M
AU  - Đurić, Zorica
AU  - Parojčić, Jelena
AU  - Solomun, Ljiljana
PY  - 2002
UR  - https://farfar.pharmacy.bg.ac.rs/handle/123456789/321
AB  - The objective of this work is to use a generalized regression neural network (GRNN) in the design of extended-release aspirin tablets. As model formulations, 10 kinds of aspirin matrix tablets were prepared. Eudragit((R)) RS PO was used as matrix substance. The amount of Eudragit((R)) RS PO and compression pressure were selected as causal factors. In-vitro dissolution-time profiles at four different sampling times, as well as coefficients n (release order) and log k (release constant) from the Peppas equation were estimated as release parameters. A set of release parameters and causal factors were used as tutorial data for the GRNN and analyzing using a computer. A GRNN model was constructed. The optimized GRNN model was used for prediction of formulation with desired in vitro drug release. For two tested formulations there was very good agreement between the GRNN predicted and observed in vitro profiles and estimated coefficients. Calculated difference (f(1)) and similarity (f(2)) factors indicate that there is no difference between predicted and experimental observed drug release profiles. This work illustrates the potential for an artificial neural network, GRNN, to assist in development of extended-release dosage forms. This method can be employed to achieve a desired in vitro dissolution profile.
PB  - Elsevier Science BV, Amsterdam
T2  - Journal of Controlled Release
T1  - The application of generalized regression neural network in the modeling and optimization of aspirin extended release tablets with Eudragit (R) RS PO as matrix substance
VL  - 82
IS  - 2-3
SP  - 213
EP  - 222
DO  - 10.1016/S0168-3659(02)00044-5
ER  - 
@article{
author = "Ibrić, Svetlana and Jovanović, M and Đurić, Zorica and Parojčić, Jelena and Solomun, Ljiljana",
year = "2002",
abstract = "The objective of this work is to use a generalized regression neural network (GRNN) in the design of extended-release aspirin tablets. As model formulations, 10 kinds of aspirin matrix tablets were prepared. Eudragit((R)) RS PO was used as matrix substance. The amount of Eudragit((R)) RS PO and compression pressure were selected as causal factors. In-vitro dissolution-time profiles at four different sampling times, as well as coefficients n (release order) and log k (release constant) from the Peppas equation were estimated as release parameters. A set of release parameters and causal factors were used as tutorial data for the GRNN and analyzing using a computer. A GRNN model was constructed. The optimized GRNN model was used for prediction of formulation with desired in vitro drug release. For two tested formulations there was very good agreement between the GRNN predicted and observed in vitro profiles and estimated coefficients. Calculated difference (f(1)) and similarity (f(2)) factors indicate that there is no difference between predicted and experimental observed drug release profiles. This work illustrates the potential for an artificial neural network, GRNN, to assist in development of extended-release dosage forms. This method can be employed to achieve a desired in vitro dissolution profile.",
publisher = "Elsevier Science BV, Amsterdam",
journal = "Journal of Controlled Release",
title = "The application of generalized regression neural network in the modeling and optimization of aspirin extended release tablets with Eudragit (R) RS PO as matrix substance",
volume = "82",
number = "2-3",
pages = "213-222",
doi = "10.1016/S0168-3659(02)00044-5"
}
Ibrić, S., Jovanović, M., Đurić, Z., Parojčić, J.,& Solomun, L.. (2002). The application of generalized regression neural network in the modeling and optimization of aspirin extended release tablets with Eudragit (R) RS PO as matrix substance. in Journal of Controlled Release
Elsevier Science BV, Amsterdam., 82(2-3), 213-222.
https://doi.org/10.1016/S0168-3659(02)00044-5
Ibrić S, Jovanović M, Đurić Z, Parojčić J, Solomun L. The application of generalized regression neural network in the modeling and optimization of aspirin extended release tablets with Eudragit (R) RS PO as matrix substance. in Journal of Controlled Release. 2002;82(2-3):213-222.
doi:10.1016/S0168-3659(02)00044-5 .
Ibrić, Svetlana, Jovanović, M, Đurić, Zorica, Parojčić, Jelena, Solomun, Ljiljana, "The application of generalized regression neural network in the modeling and optimization of aspirin extended release tablets with Eudragit (R) RS PO as matrix substance" in Journal of Controlled Release, 82, no. 2-3 (2002):213-222,
https://doi.org/10.1016/S0168-3659(02)00044-5 . .

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