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dc.creatorStanojević, Gordana
dc.creatorMedarević, Đorđe
dc.creatorAdamov, Ivana
dc.creatorPešić, Nikola
dc.creatorKovačević, Jovana
dc.creatorIbrić, Svetlana
dc.date.accessioned2021-01-26T09:39:35Z
dc.date.available2021-01-26T09:39:35Z
dc.date.issued2020
dc.identifier.issn1420-3049
dc.identifier.urihttps://farfar.pharmacy.bg.ac.rs/handle/123456789/3771
dc.description.abstractVarious three-dimensional printing (3DP) technologies have been investigated so far in relation to their potential to produce customizable medicines and medical devices. The aim of this study was to examine the possibility of tailoring drug release rates from immediate to prolonged release by varying the tablet thickness and the drug loading, as well as to develop artificial neural network (ANN) predictive models for atomoxetine (ATH) release rate from DLP 3D-printed tablets. Photoreactive mixtures were comprised of poly(ethylene glycol) diacrylate (PEGDA) and poly(ethylene glycol) 400 in a constant ratio of 3:1, water, photoinitiator and ATH as a model drug whose content was varied from 5% to 20% (w/w). Designed 3D models of cylindrical shape tablets were of constant diameter, but different thickness. A series of tablets with doses ranging from 2.06 mg to 37.48 mg, exhibiting immediate- and modified-release profiles were successfully fabricated, confirming the potential of this technology in manufacturing dosage forms on demand, with the possibility to adjust the dose and release behavior by varying drug loading and dimensions of tablets. DSC (differential scanning calorimetry), XRPD (X-ray powder diffraction) and microscopic analysis showed that ATH remained in a crystalline form in tablets, while FTIR spectroscopy confirmed that no interactions occurred between ATH and polymers.
dc.publisherMDPI
dc.relationinfo:eu-repo/grantAgreement/MESTD/inst-2020/200161/RS//
dc.rightsopenAccess
dc.sourceMolecules (Basel, Switzerland)
dc.subjectadditive manufacturing
dc.subjectdigital light processing (DLP)
dc.subjectneural networks
dc.subjectoptimization
dc.subjectpersonalized therapy
dc.subjectrelease rate
dc.subjectthree-dimensional (3D) printing
dc.titleTailoring Atomoxetine Release Rate from DLP 3D-Printed Tablets Using Artificial Neural Networks: Influence of Tablet Thickness and Drug Loading
dc.typearticle
dc.rights.licenseBY
dcterms.abstractИбрић, Светлана; Aдамов, Ивана; Ковачевић, Јована; Станојевић, Гордана; Медаревић, Ђорђе; Пешић, Никола;
dc.citation.volume26
dc.citation.issue1
dc.citation.rankM22~
dc.identifier.wos000606262700001
dc.identifier.doi10.3390/molecules26010111
dc.identifier.scopus2-s2.0-85099242524
dc.identifier.fulltexthttp://farfar.pharmacy.bg.ac.rs/bitstream/id/8559/Tailoring_Atomoxetine_Release_pub_2020.pdf
dc.type.versionpublishedVersion


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