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dc.creatorMillen, Nada
dc.creatorKovačević, A
dc.creatorĐuriš, Jelena
dc.creatorIbrić, Svetlana
dc.date.accessioned2019-09-02T12:09:42Z
dc.date.available2019-09-02T12:09:42Z
dc.date.issued2019
dc.identifier.issn1872-5120
dc.identifier.urihttp://farfar.pharmacy.bg.ac.rs/handle/123456789/3292
dc.description.abstractPurpose: Optimal particle size distribution (PSD) is an important factor in wet granulation in order to achieve appropriate powder flow, compactibility, and content uniformity. Parameters like D50 and surface area (SA) are used to define PSD but both are only able to compare separate fractions of a granulate. In this work, we made an attempt to characterize PSD of a final dry granulate blend and suggest novel parameters (determination coefficient R2 and trend line slope of a PSD model) to quantitatively describe PSD. Method: The significance of these parameters was tested using machine learning. Laboratory-scale samples were used for training and commercial-scale samples for testing a model. Several machine learning techniques were used to further examine the importance of these input variables using a large data set from wet granulation scale-up study. Results: The Gradient Boosted Regression Trees (GBRT) algorithm had the lowest root mean square error (RMSE) values for the several responses studied (tablet tensile strength, tablet diameter and thickness, compaction work, decompaction work, and net work). The GBRT model for tablet tensile strength had an R2 model value of 0.87 and was not overfitted. The importance of input variables R2 and a was proven by the stepwise regression model’s p value (0.0003) and GBRT importance score (0.37 and 0.44, respectively). The GBRT model was the most successful in predicting decompaction work (R2 model = 0.97) with the least regularization effect. Conclusion: The proposed parameters can be used in PSD characterization and applied in critical quality attributes (CQA) prediction and wet granulation scale-up.en
dc.publisherSpringer New York LLC
dc.relationinfo:eu-repo/grantAgreement/MESTD/Integrated and Interdisciplinary Research (IIR or III)/47003/RS//
dc.rightsrestrictedAccess
dc.sourceJournal of Pharmaceutical Innovation
dc.subjectMachine learning modelingen
dc.subjectParticle size distributionen
dc.subjectScale-upen
dc.subjectWet granulationen
dc.titleMachine Learning Modeling of Wet Granulation Scale-up Using Particle Size Distribution Characterization Parametersen
dc.typearticle
dc.rights.licenseARR
dcterms.abstractМиллен, Нада; Ковачевић, A; Ђуриш, Јелена; Ибрић, Светлана;
dc.citation.rankM23
dc.identifier.wos000591430400004
dc.identifier.doi10.1007/s12247-019-09398-0
dc.identifier.scopus2-s2.0-85068957595
dc.identifier.rcubconv_4537
dc.type.versionpublishedVersion


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