Application of dynamic neural networks in the modeling of drug release from polyethylene oxide matrix tablets
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2009
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Metapodaci
Prikaz svih podataka o dokumentuApstrakt
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.
Ključne reči:
Dynamic neural networks / Drug release modeling / Time series / Polyethylene oxides (PEOs) / Controlled releaseIzvor:
European Journal of Pharmaceutical Sciences, 2009, 38, 2, 172-180Izdavač:
- Elsevier Science BV, Amsterdam
Finansiranje / projekti:
DOI: 10.1016/j.ejps.2009.07.007
ISSN: 0928-0987
PubMed: 19632323
WoS: 000269769800011
Scopus: 2-s2.0-68849106502
Institucija/grupa
PharmacyTY - 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 . .