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Artificial neural networks in the modeling and optimization of aspirin extended release tablets with Eudragit L 100 as matrix substance

rcub.bitstream.locked
2003
Authors
Ibrić, Svetlana
Jovanović, M
Đurić, Zorica
Parojčić, Jelena
Petrović, Slobodan D.
Solomun, Ljiljana
Stupar, Biljana
Article (Published version)
Metadata
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Abstract
The purpose of the present study was to model the effects of the concentration of Eudragit L 100 and compression pressure as the most important process and formulation variables on the in vitro release profile of aspirin from matrix tablets formulated with Eudragit L 100 as matrix substance and to optimize the formulation by artificial neural network. As model formulations, 10 kinds of aspirin matrix tablets were prepared. The amount of Eudragit L 100 and the compression pressure were selected as causal factors. In vitro dissolution time profiles at 4 different sampling times were chosen as responses. A set of release parameters and causal factors were used as tutorial data for the generalized regression neural network (GRNN) and analyzed using a computer. Observed results of drug release studies indicate that drug release rates vary widely between investigated formulations, with a range of 5 hours to more than 10 hours to complete dissolution. The GRNN model was optimized. The root me...an square value for the trained network was 1.12%, which indicated that the optimal GRNN model was reached. Applying the generalized distance function method, the optimal tablet formulation predicted by GRNN was with 5% of Eudragit L 100 and tablet hardness 60N. Calculated difference (f1 2.465) and similarity (f2 85.61) factors indicate that there is no difference between predicted and experimentally observed drug release profiles for the optimal formulation. This work illustrates the potential for an artificial neural network, GRNN, to assist in development of extended release dosage forms.

Keywords:
Artificial neural network / Aspirin / Controlled release / Eudragit L 100 / Matrix tablets
Source:
AAPS PharmSciTech, 2003, 4, 1
Publisher:
  • AAPS PharmSci Editorial Office

DOI: 10.1208/pt040109

ISSN: 1530-9932

Scopus: 2-s2.0-0141576818
[ Google Scholar ]
65
URI
https://farfar.pharmacy.bg.ac.rs/handle/123456789/469
Collections
  • Radovi istraživača / Researchers’ publications
Institution/Community
Pharmacy
TY  - JOUR
AU  - Ibrić, Svetlana
AU  - Jovanović, M
AU  - Đurić, Zorica
AU  - Parojčić, Jelena
AU  - Petrović, Slobodan D.
AU  - Solomun, Ljiljana
AU  - Stupar, Biljana
PY  - 2003
UR  - https://farfar.pharmacy.bg.ac.rs/handle/123456789/469
AB  - The purpose of the present study was to model the effects of the concentration of Eudragit L 100 and compression pressure as the most important process and formulation variables on the in vitro release profile of aspirin from matrix tablets formulated with Eudragit L 100 as matrix substance and to optimize the formulation by artificial neural network. As model formulations, 10 kinds of aspirin matrix tablets were prepared. The amount of Eudragit L 100 and the compression pressure were selected as causal factors. In vitro dissolution time profiles at 4 different sampling times were chosen as responses. A set of release parameters and causal factors were used as tutorial data for the generalized regression neural network (GRNN) and analyzed using a computer. Observed results of drug release studies indicate that drug release rates vary widely between investigated formulations, with a range of 5 hours to more than 10 hours to complete dissolution. The GRNN model was optimized. The root mean square value for the trained network was 1.12%, which indicated that the optimal GRNN model was reached. Applying the generalized distance function method, the optimal tablet formulation predicted by GRNN was with 5% of Eudragit L 100 and tablet hardness 60N. Calculated difference (f1 2.465) and similarity (f2 85.61) factors indicate that there is no difference between predicted and experimentally observed drug release profiles for the optimal formulation. This work illustrates the potential for an artificial neural network, GRNN, to assist in development of extended release dosage forms.
PB  - AAPS PharmSci Editorial Office
T2  - AAPS PharmSciTech
T1  - Artificial neural networks in the modeling and optimization of aspirin extended release tablets with Eudragit L 100 as matrix substance
VL  - 4
IS  - 1
DO  - 10.1208/pt040109
ER  - 
@article{
author = "Ibrić, Svetlana and Jovanović, M and Đurić, Zorica and Parojčić, Jelena and Petrović, Slobodan D. and Solomun, Ljiljana and Stupar, Biljana",
year = "2003",
abstract = "The purpose of the present study was to model the effects of the concentration of Eudragit L 100 and compression pressure as the most important process and formulation variables on the in vitro release profile of aspirin from matrix tablets formulated with Eudragit L 100 as matrix substance and to optimize the formulation by artificial neural network. As model formulations, 10 kinds of aspirin matrix tablets were prepared. The amount of Eudragit L 100 and the compression pressure were selected as causal factors. In vitro dissolution time profiles at 4 different sampling times were chosen as responses. A set of release parameters and causal factors were used as tutorial data for the generalized regression neural network (GRNN) and analyzed using a computer. Observed results of drug release studies indicate that drug release rates vary widely between investigated formulations, with a range of 5 hours to more than 10 hours to complete dissolution. The GRNN model was optimized. The root mean square value for the trained network was 1.12%, which indicated that the optimal GRNN model was reached. Applying the generalized distance function method, the optimal tablet formulation predicted by GRNN was with 5% of Eudragit L 100 and tablet hardness 60N. Calculated difference (f1 2.465) and similarity (f2 85.61) factors indicate that there is no difference between predicted and experimentally observed drug release profiles for the optimal formulation. This work illustrates the potential for an artificial neural network, GRNN, to assist in development of extended release dosage forms.",
publisher = "AAPS PharmSci Editorial Office",
journal = "AAPS PharmSciTech",
title = "Artificial neural networks in the modeling and optimization of aspirin extended release tablets with Eudragit L 100 as matrix substance",
volume = "4",
number = "1",
doi = "10.1208/pt040109"
}
Ibrić, S., Jovanović, M., Đurić, Z., Parojčić, J., Petrović, S. D., Solomun, L.,& Stupar, B.. (2003). Artificial neural networks in the modeling and optimization of aspirin extended release tablets with Eudragit L 100 as matrix substance. in AAPS PharmSciTech
AAPS PharmSci Editorial Office., 4(1).
https://doi.org/10.1208/pt040109
Ibrić S, Jovanović M, Đurić Z, Parojčić J, Petrović SD, Solomun L, Stupar B. Artificial neural networks in the modeling and optimization of aspirin extended release tablets with Eudragit L 100 as matrix substance. in AAPS PharmSciTech. 2003;4(1).
doi:10.1208/pt040109 .
Ibrić, Svetlana, Jovanović, M, Đurić, Zorica, Parojčić, Jelena, Petrović, Slobodan D., Solomun, Ljiljana, Stupar, Biljana, "Artificial neural networks in the modeling and optimization of aspirin extended release tablets with Eudragit L 100 as matrix substance" in AAPS PharmSciTech, 4, no. 1 (2003),
https://doi.org/10.1208/pt040109 . .

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