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dc.creatorIbrić, Svetlana
dc.creatorJovanović, Milica
dc.creatorĐurić, Zorica
dc.creatorParojčić, Jelena
dc.creatorSolomun, Ljiljana
dc.creatorLucić, Branka
dc.date.accessioned2019-09-02T11:09:29Z
dc.date.available2019-09-02T11:09:29Z
dc.date.issued2007
dc.identifier.issn0022-3573
dc.identifier.urihttp://farfar.pharmacy.bg.ac.rs/handle/123456789/922
dc.description.abstractThis study had two aims. Firstly, we wanted to model the effects of the percentage of Eudragit RS PO and compression pressure as the most important process and formulation variables on the time course of drug release from extended-release matrix aspirin tablets. Secondly, we investigated the possibility of predicting drug stability and shelf-life using an artificial neural network (ANN). Ten types of matrix aspirin tablets were prepared as model formulations and were stored in stability chambers at 60 degrees C, 50 degrees C, 40 degrees C and 30 degrees C and controlled humidity. Samples were removed at predefined time points and analysed for acetylsalicylic acid (ASA) and salicylic acid (SA) content using stability-indicating HPLC. The decrease in aspirin content followed apparent zero-order kinetics. The amount of Eudragit RS PO and compression pressure were selected as causal factors. The apparent zero-order rate constants for each temperature were chosen as output variables for the ANN. A set of output parameters and causal factors were used as training data for the generalized regression neural network (GRNN). For two additional test formulations, Arrhenius plots were constructed from the experimentally observed and GRNN-predicted results. The slopes of experimentally observed and predicted Arrhenius plots were tested for significance using Student's t-test. For test formulations, the shelf life (t(95%)) was then calculated from experimentally observed values (t(95%) 82.90 weeks), as well as from GRNN-predicted values (t(95%) 81.88 weeks). These results demonstrate that GRNN networks can be used to predict ASA content and shelf life without stability testing for formulations in which the amount of polymer and tablet hardness are within the investigated range.en
dc.publisherPharmaceutical Press-Royal Pharmaceutical Soc Great Britian, London
dc.rightsrestrictedAccess
dc.sourceJournal of Pharmacy and Pharmacology
dc.titleGeneralized regression neural networks in prediction of drug stabilityen
dc.typearticle
dc.rights.licenseARR
dcterms.abstractЂурић, Зорица; Соломун, Љиљана; Луцић, Бранка; Паројчић, Јелена; Јовановић, Милица; Ибрић, Светлана;
dc.citation.volume59
dc.citation.issue5
dc.citation.spage745
dc.citation.epage750
dc.citation.other59(5): 745-750
dc.citation.rankM23
dc.identifier.wos000246885300017
dc.identifier.doi10.1211/jpp.59.5.0017
dc.identifier.pmid17524242
dc.identifier.scopus2-s2.0-34447330195
dc.identifier.rcubconv_1855
dc.type.versionpublishedVersion


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