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dc.creatorPetrović, Jelena
dc.creatorIbrić, Svetlana
dc.creatorBetz, Gabriele
dc.creatorĐurić, Zorica
dc.date.accessioned2019-09-02T11:29:03Z
dc.date.available2019-09-02T11:29:03Z
dc.date.issued2012
dc.identifier.issn0378-5173
dc.identifier.urihttp://farfar.pharmacy.bg.ac.rs/handle/123456789/1705
dc.description.abstractThe main objective of the study was to develop artificial intelligence methods for optimization of drug release from matrix tablets regardless of the matrix type. Static and dynamic artificial neural networks of the same topology were developed to model dissolution profiles of different matrix tablets types (hydrophilic/lipid) using formulation composition, compression force used for tableting and tablets porosity and tensile strength as input data. Potential application of decision trees in discovering knowledge from experimental data was also investigated. Polyethylene oxide polymer and glyceryl palmitostearate were used as matrix forming materials for hydrophilic and lipid matrix tablets, respectively whereas selected model drugs were diclofenac sodium and caffeine. Matrix tablets were prepared by direct compression method and tested for in vitro dissolution profiles. Optimization of static and dynamic neural networks used for modeling of drug release was performed using Monte Carlo simulations or genetic algorithms optimizer. Decision trees were constructed following discretization of data. Calculated difference (f(1)) and similarity (f(2)) factors for predicted and experimentally obtained dissolution profiles of test matrix tablets formulations indicate that Elman dynamic neural networks as well as decision trees are capable of accurate predictions of both hydrophilic and lipid matrix tablets dissolution profiles. Elman neural networks were compared to most frequently used static network, Multi-layered perceptron, and superiority of Elman networks have been demonstrated. Developed methods allow simple, yet very precise way of drug release predictions for both hydrophilic and lipid matrix tablets having controlled drug release.en
dc.publisherElsevier Science BV, Amsterdam
dc.relationinfo:eu-repo/grantAgreement/MESTD/Technological Development (TD or TR)/34007/RS//
dc.rightsrestrictedAccess
dc.sourceInternational Journal of Pharmaceutics
dc.subjectMatrix tabletsen
dc.subjectNeural networksen
dc.subjectDecision treesen
dc.subjectDrug release modelingen
dc.subjectControlled releaseen
dc.titleOptimization of matrix tablets controlled drug release using Elman dynamic neural networks and decision treesen
dc.typearticle
dc.rights.licenseARR
dcterms.abstractБетз, Габриеле; Ибрић, Светлана; Петровић, Јелена; Ђурић, Зорица;
dc.citation.volume428
dc.citation.issue1-2
dc.citation.spage57
dc.citation.epage67
dc.citation.other428(1-2): 57-67
dc.citation.rankM21
dc.identifier.wos000302364600008
dc.identifier.doi10.1016/j.ijpharm.2012.02.031
dc.identifier.pmid22402474
dc.identifier.scopus2-s2.0-84859428478
dc.identifier.rcubconv_2614
dc.type.versionpublishedVersion


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