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Application of dynamic neural networks in the modeling of drug release from polyethylene oxide matrix tablets

Authorized Users Only
2009
Authors
Petrović, Jelena
Ibrić, Svetlana
Betz, Gabriele
Parojčić, Jelena
Đurić, Zorica
Article (Published version)
Metadata
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Abstract
The main objective of this study was to demonstrate the possible use of dynamic neural networks to model diclofenac sodium release from polyethylene oxide hydrophilic matrix tablets. High and low molecular weight polymers in the range of 0.9-5 x 10(6) have been used as matrix forming materials and 12 different formulations were prepared for each polymer. Matrix tablets were made by direct compression method. Fractions of polymer and compression force have been selected as most influential factors on diclofenac sodium release profile. In vitro dissolution profile has been treated as time series using dynamic neural networks. Dynamic networks are expected to be advantageous in the modeling of drug release. Networks of different topologies have been constructed in order to obtain precise prediction of release profiles for test formulations. Short-term and long-term memory structures have been included in the design of network making it possible to treat dissolution profiles as time series.... The ability of network to model drug release has been assessed by the determination of correlation between predicted and experimentally obtained data. Calculated difference (f(1)) and similarity (f(2)) factors indicate that dynamic networks are capable of accurate predictions. Dynamic neural networks were compared to most frequently used static network, multi-layered perceptron, and superiority of dynamic networks has been demonstrated. The study also demonstrated differences between the used polyethylene oxide polymers in respect to drug release and suggests explanations for the obtained results.

Keywords:
Dynamic neural networks / Drug release modeling / Time series / Polyethylene oxides (PEOs) / Controlled release
Source:
European Journal of Pharmaceutical Sciences, 2009, 38, 2, 172-180
Publisher:
  • Elsevier Science BV, Amsterdam
Funding / projects:
  • Razvoj i primena in vitro i in silico metoda u biofarmaceutskoj karakterizaciji lekova BSK grupe 2 i 3 (RS-23015)

DOI: 10.1016/j.ejps.2009.07.007

ISSN: 0928-0987

PubMed: 19632323

WoS: 000269769800011

Scopus: 2-s2.0-68849106502
[ Google Scholar ]
31
24
URI
https://farfar.pharmacy.bg.ac.rs/handle/123456789/1263
Collections
  • Radovi istraživača / Researchers’ publications
Institution/Community
Pharmacy
TY  - JOUR
AU  - Petrović, Jelena
AU  - Ibrić, Svetlana
AU  - Betz, Gabriele
AU  - Parojčić, Jelena
AU  - Đurić, Zorica
PY  - 2009
UR  - https://farfar.pharmacy.bg.ac.rs/handle/123456789/1263
AB  - The main objective of this study was to demonstrate the possible use of dynamic neural networks to model diclofenac sodium release from polyethylene oxide hydrophilic matrix tablets. High and low molecular weight polymers in the range of 0.9-5 x 10(6) have been used as matrix forming materials and 12 different formulations were prepared for each polymer. Matrix tablets were made by direct compression method. Fractions of polymer and compression force have been selected as most influential factors on diclofenac sodium release profile. In vitro dissolution profile has been treated as time series using dynamic neural networks. Dynamic networks are expected to be advantageous in the modeling of drug release. Networks of different topologies have been constructed in order to obtain precise prediction of release profiles for test formulations. Short-term and long-term memory structures have been included in the design of network making it possible to treat dissolution profiles as time series. The ability of network to model drug release has been assessed by the determination of correlation between predicted and experimentally obtained data. Calculated difference (f(1)) and similarity (f(2)) factors indicate that dynamic networks are capable of accurate predictions. Dynamic neural networks were compared to most frequently used static network, multi-layered perceptron, and superiority of dynamic networks has been demonstrated. The study also demonstrated differences between the used polyethylene oxide polymers in respect to drug release and suggests explanations for the obtained results.
PB  - Elsevier Science BV, Amsterdam
T2  - European Journal of Pharmaceutical Sciences
T1  - Application of dynamic neural networks in the modeling of drug release from polyethylene oxide matrix tablets
VL  - 38
IS  - 2
SP  - 172
EP  - 180
DO  - 10.1016/j.ejps.2009.07.007
ER  - 
@article{
author = "Petrović, Jelena and Ibrić, Svetlana and Betz, Gabriele and Parojčić, Jelena and Đurić, Zorica",
year = "2009",
abstract = "The main objective of this study was to demonstrate the possible use of dynamic neural networks to model diclofenac sodium release from polyethylene oxide hydrophilic matrix tablets. High and low molecular weight polymers in the range of 0.9-5 x 10(6) have been used as matrix forming materials and 12 different formulations were prepared for each polymer. Matrix tablets were made by direct compression method. Fractions of polymer and compression force have been selected as most influential factors on diclofenac sodium release profile. In vitro dissolution profile has been treated as time series using dynamic neural networks. Dynamic networks are expected to be advantageous in the modeling of drug release. Networks of different topologies have been constructed in order to obtain precise prediction of release profiles for test formulations. Short-term and long-term memory structures have been included in the design of network making it possible to treat dissolution profiles as time series. The ability of network to model drug release has been assessed by the determination of correlation between predicted and experimentally obtained data. Calculated difference (f(1)) and similarity (f(2)) factors indicate that dynamic networks are capable of accurate predictions. Dynamic neural networks were compared to most frequently used static network, multi-layered perceptron, and superiority of dynamic networks has been demonstrated. The study also demonstrated differences between the used polyethylene oxide polymers in respect to drug release and suggests explanations for the obtained results.",
publisher = "Elsevier Science BV, Amsterdam",
journal = "European Journal of Pharmaceutical Sciences",
title = "Application of dynamic neural networks in the modeling of drug release from polyethylene oxide matrix tablets",
volume = "38",
number = "2",
pages = "172-180",
doi = "10.1016/j.ejps.2009.07.007"
}
Petrović, J., Ibrić, S., Betz, G., Parojčić, J.,& Đurić, Z.. (2009). Application of dynamic neural networks in the modeling of drug release from polyethylene oxide matrix tablets. in European Journal of Pharmaceutical Sciences
Elsevier Science BV, Amsterdam., 38(2), 172-180.
https://doi.org/10.1016/j.ejps.2009.07.007
Petrović J, Ibrić S, Betz G, Parojčić J, Đurić Z. Application of dynamic neural networks in the modeling of drug release from polyethylene oxide matrix tablets. in European Journal of Pharmaceutical Sciences. 2009;38(2):172-180.
doi:10.1016/j.ejps.2009.07.007 .
Petrović, Jelena, Ibrić, Svetlana, Betz, Gabriele, Parojčić, Jelena, Đurić, Zorica, "Application of dynamic neural networks in the modeling of drug release from polyethylene oxide matrix tablets" in European Journal of Pharmaceutical Sciences, 38, no. 2 (2009):172-180,
https://doi.org/10.1016/j.ejps.2009.07.007 . .

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