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dc.creatorMadžarević, Marijana
dc.creatorMedarević, Đorđe
dc.creatorVulović, Aleksandra
dc.creatorŠušteršič, Tijana
dc.creatorĐuriš, Jelena
dc.creatorFilipović, Nenad
dc.creatorIbrić, Svetlana
dc.date.accessioned2019-11-15T10:41:40Z
dc.date.available2019-11-15T10:41:40Z
dc.date.issued2019
dc.identifier.issn1999-4923
dc.identifier.urihttps://farfar.pharmacy.bg.ac.rs/handle/123456789/3466
dc.description.abstractThe aim of this work was to investigate eects of the formulation factors on tablet printability as well as to optimize and predict extended drug release from cross-linked polymeric ibuprofen printlets using an artificial neural network (ANN). Printlets were printed using digital light processing (DLP) technology from formulations containing polyethylene glycol diacrylate, polyethylene glycol, and water in concentrations according to D-optimal mixture design and 0.1% w/w riboflavin and 5% w/w ibuprofen. It was observed that with higher water content longer exposure time was required for successful printing. For understanding the eects of excipients and printing parameters on drug dissolution rate in DLP printlets two dierent neural networks were developed with using two commercially available softwares. After comparison of experimental and predicted values of in vitro dissolution at the corresponding time points for optimized formulation, the R2 experimental vs. predicted value was 0.9811 (neural network 1) and 0.9960 (neural network 2). According to dierence f1 and similarity factor f2 (f1 = 14.30 and f2 = 52.15) neural network 1 with supervised multilayer perceptron, backpropagation algorithm, and linear activation function gave a similar dissolution profile to obtained experimental results, indicating that adequate ANN is able to set out an input–output relationship in DLP printing of pharmaceutics.en
dc.publisherMDPI
dc.relationinfo:eu-repo/grantAgreement/MESTD/Technological Development (TD or TR)/34007/RS//
dc.relationinfo:eu-repo/grantAgreement/MESTD/Integrated and Interdisciplinary Research (IIR or III)/41007/RS//
dc.relationinfo:eu-repo/grantAgreement/MESTD/Basic Research (BR or ON)/174028/RS//
dc.rightsopenAccess
dc.sourcePharmaceutics
dc.subjectAdditive manufacturing
dc.subjectDigital light processing technology
dc.subjectNeural networks
dc.subjectOptimization
dc.subjectPrediction
dc.subjectPrintlets
dc.subjectThree-dimensional printing
dc.titleOptimization and prediction of ibuprofen release from 3D DLP printlets using artificial neural networksen
dc.typearticle
dc.rights.licenseCC BY
dcterms.abstractФилиповић, Ненад; Ибрић, Светлана; Маджаревић, Маријана; Медаревић, Ђордје; Шуштершич, Тијана; Ђуриш, Јелена; Вуловић, Aлександра;
dc.citation.volume11
dc.citation.issue10
dc.citation.spage1
dc.citation.epage16
dc.citation.rankaM21
dc.identifier.wos000498392300057
dc.identifier.doi10.3390/pharmaceutics11100544
dc.identifier.scopus2-s2.0-85074050369
dc.identifier.fulltexthttps://farfar.pharmacy.bg.ac.rs/bitstream/id/7271/Optimization_and_Prediction_pub_2019.pdf
dc.type.versionpublishedVersion
dc.type.versionpublishedVersion


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