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dc.creatorMillen, Nada
dc.creatorKovacević, Aleksandar
dc.creatorKhera, Lalit
dc.creatorĐuriš, Jelena
dc.creatorIbrić, Svetlana
dc.date.accessioned2019-09-02T12:08:32Z
dc.date.available2019-09-02T12:08:32Z
dc.date.issued2019
dc.identifier.issn0367-598X
dc.identifier.urihttp://farfar.pharmacy.bg.ac.rs/handle/123456789/3245
dc.description.abstractThe purpose of this extensive study is to use a quality by design (QbD) approach and multiple machine learning algorithms in facilitating wet granulation process scale-up. This study investigated the extent of influence of both formulation and process variables. Furthermore, measured responses covered compressibility, compactibility and manufacturability of a powder blend. Finally, the models developed on laboratory scale samples were tested on pilot and commercial scale runs. Tablet detachment and ejection work were calculated from force-displacement measurements. Significant numerical and categorical input variables were identified by using a stepwise regression model and their importance evaluated by using a boosted trees model. Pilot scale runs resulted in the highest tablet tensile strength and compaction work as well as the highest detachment and ejection work. Critical quality attributes (CQAs) that were the most successfully predicted were the compaction, decompaction, and net work, as well as the tablet height. The most important input variable influencing all CQAs was the compaction force. Application of the boosted regression trees model resulted in the lowest Root Mean Square Error (RMSE) values for all of the responses. This work demonstrates reliability of predictions of developed models that can be successfully used as a part of a QbD approach for wet granulation scale-up.en
dc.publisherSavez hemijskih inženjera, Beograd
dc.rightsopenAccess
dc.sourceHemijska industrija
dc.subjectquality by designen
dc.subjectartificial intelligenceen
dc.subjectcompaction worken
dc.subjectdecompaction worken
dc.subjectelastic recoveryen
dc.titleMachine learning modelling of wet granulation scale-up using compressibility, compactibility and manufacturability parametersen
dc.typearticle
dc.rights.licenseBY-NC-ND
dcterms.abstractКовацевић, Aлександар; Кхера, Лалит; Ибрић, Светлана; Ђуриш, Јелена; Миллен, Нада;
dc.citation.volume73
dc.citation.issue3
dc.citation.spage155
dc.citation.epage168
dc.citation.other73(3): 155-168
dc.citation.rankM23
dc.identifier.wos000475425200003
dc.identifier.doi10.2298/HEMIND190412017M
dc.identifier.scopus2-s2.0-85073287063
dc.identifier.fulltexthttp://farfar.pharmacy.bg.ac.rs//bitstream/id/1822/3243.pdf
dc.identifier.rcubconv_4434
dc.type.versionpublishedVersion


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