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dc.creatorPetrović, Jelena
dc.creatorIbrić, Svetlana
dc.creatorBetz, Gabriele
dc.creatorParojčić, Jelena
dc.creatorĐurić, Zorica
dc.date.accessioned2019-09-02T11:18:27Z
dc.date.available2019-09-02T11:18:27Z
dc.date.issued2009
dc.identifier.issn0928-0987
dc.identifier.urihttp://farfar.pharmacy.bg.ac.rs/handle/123456789/1263
dc.description.abstractThe 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.en
dc.publisherElsevier Science BV, Amsterdam
dc.relationinfo:eu-repo/grantAgreement/MESTD/MPN2006-2010/23015/RS//
dc.rightsrestrictedAccess
dc.sourceEuropean Journal of Pharmaceutical Sciences
dc.subjectDynamic neural networksen
dc.subjectDrug release modelingen
dc.subjectTime seriesen
dc.subjectPolyethylene oxides (PEOs)en
dc.subjectControlled releaseen
dc.titleApplication of dynamic neural networks in the modeling of drug release from polyethylene oxide matrix tabletsen
dc.typearticle
dc.rights.licenseARR
dcterms.abstractБетз, Габриеле; Ђурић, Зорица; Паројчић, Јелена; Петровић, Јелена; Ибрић, Светлана;
dc.citation.volume38
dc.citation.issue2
dc.citation.spage172
dc.citation.epage180
dc.citation.other38(2): 172-180
dc.citation.rankM22
dc.identifier.wos000269769800011
dc.identifier.doi10.1016/j.ejps.2009.07.007
dc.identifier.pmid19632323
dc.identifier.scopus2-s2.0-68849106502
dc.identifier.rcubconv_2206
dc.type.versionpublishedVersion


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