An optimization study on solid lipid nanoparticles using artificial neural network

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dc.authorid0000-0002-3210-3747
dc.contributor.authorYurdasiper, Aysu
dc.contributor.authorAksu, Buket
dc.contributor.authorÜstündağ Okur, Neslihan
dc.contributor.authorGökçe, Evren Homan
dc.date.accessioned10.07.201910:49:13
dc.date.accessioned2019-07-10T20:01:38Z
dc.date.available10.07.201910:49:13
dc.date.available2019-07-10T20:01:38Z
dc.date.issued2017
dc.departmentİstanbul Medipol Üniversitesi, Eczacılık Fakültesi, Eczacılık Teknolojisi Bölümü, Farmasötik Teknoloji Ana Bilim Dalı
dc.descriptionWOS: 000399260300016
dc.description.abstractCommon use of supportive programs in finding the best in R&D studies provides positive results and thus ensures benefits to companies in terms of cost and time. The aim of this work was to develop, evaluate and optimize solid lipid nanoparticles (SLNs) formulations by applying the artificial neural network (ANN) programme to achieve the best combination of materials. SLNs have been produced by high-pressure homogenization, and the formulations have been characterized for their mean particle size, polydispersity index and zeta potential. SLN formulations were evaluated with INForm V5.1 program to optimize the best-fit formulation. According to ANN evaluation, S-PT8 formulation including 50% Compritol 888 ATO, 38% Poloxamer 188 and 12% Tween 80 mixture was found to be the most promising formulation in terms of parameters tested. It has been shown that artificial intelligence could be used to improve our understanding of the critical quality parameters that contribute to the overall quality of the drug product.
dc.description.sponsorshipTUBITAK [112S292]en_US
dc.description.sponsorshipThis study was supported by TUBITAK Projects No: 112S292. The authors would like to thank to Ege University, Faculty of Pharmacy, Pharmaceutical Sciences Research Center (FABAL).en_US
dc.identifier.citationYurdasiper, A., Aksu, B., Üstündağ Okur, N. ve Gökçe, E. H. (2017). An optimization study on solid lipid nanoparticles using artificial neural network. Latin American Journal of Pharmacy, 36(1), 115-121.
dc.identifier.endpage121
dc.identifier.issn0326-2383
dc.identifier.issue1
dc.identifier.scopusqualityN/A
dc.identifier.startpage115
dc.identifier.urihttps://hdl.handle.net/20.500.12511/3379
dc.identifier.volume36
dc.identifier.wosqualityQ4
dc.indekslendigikaynakWeb of Science
dc.indekslendigikaynakScopus
dc.language.isoen
dc.publisherColegio Farmaceuticos Provincia De Buenos Aires
dc.relation.ecinfo:eu-repo/grantAgreement/TUBITAK/SOBAG/112S292
dc.relation.ispartofLatin American Journal of Pharmacyen_US
dc.relation.publicationcategoryMakale - Uluslararası Hakemli Dergi - Kurum Öğretim Elemanı
dc.rightsinfo:eu-repo/semantics/embargoedAccess
dc.subjectArtificial Neural Network
dc.subjectOptimization
dc.subjectPharmaceutical Development
dc.subjectSolid Lipid Nanoparticles (SLNs)
dc.titleAn optimization study on solid lipid nanoparticles using artificial neural network
dc.typeArticle

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