Effect of roll compaction on granule size distribution of microcrystalline cellulose-mannitol mixtures : computational intelligence modeling and parametric analysis

2017
journal article
article
13
dc.abstract.enDry granulation using roll compaction is a typical unit operation for producing solid dosage forms in the pharmaceutical industry. Dry granulation is commonly used if the powder mixture is sensitive to heat and moisture and has poor flow properties. The output of roll compaction is compacted ribbons that exhibit different properties based on the adjusted process parameters. These ribbons are then milled into granules and finally compressed into tablets. The properties of the ribbons directly affect the granule size distribution (GSD) and the quality of final products; thus, it is imperative to study the effect of roll compaction process parameters on GSD. The understanding of how the roll compactor process parameters and material properties interact with each other will allow accurate control of the process, leading to the implementation of quality by design practices. Computational intelligence (CI) methods have a great potential for being used within the scope of quality by design approach. The main objective of this study was to show how the computational intelligence techniques can be useful to predict the GSD by using different process conditions of roll compaction and material properties. Different techniques such as multiple linear regression, artificial neural networks, random forest, Cubist and k-nearest neighbors algorithm assisted by sevenfold cross-validation were used to present generalized models for the prediction of GSD based on roll compaction process setting and material properties. The normalized root-mean-squared error and the coefficient of determination (R2) were used for model assessment. The best fit was obtained by Cubist model (normalized root-mean-squared error =3.22%, R2=0.95). Based on the results, it was confirmed that the material properties (true density) followed by compaction force have the most significant effect on GSD.pl
dc.affiliationWydział Farmaceutyczny : Zakład Technologii Postaci Leku i Biofarmacjipl
dc.cm.date2020-01-07
dc.cm.id81320
dc.contributor.authorKazemi, Pezhmanpl
dc.contributor.authorKhalid, Mohammad Hassanpl
dc.contributor.authorPerez Gago, Anapl
dc.contributor.authorKleinebudde, Peterpl
dc.contributor.authorJachowicz, Renata - 129780 pl
dc.contributor.authorSzlęk, Jakub - 162262 pl
dc.contributor.authorMendyk, Aleksander - 130937 pl
dc.date.accessioned2020-01-17T09:15:22Z
dc.date.available2020-01-17T09:15:22Z
dc.date.issued2017pl
dc.date.openaccess0
dc.description.accesstimew momencie opublikowania
dc.description.physical241-251pl
dc.description.points35pl
dc.description.versionostateczna wersja wydawcy
dc.description.volume11pl
dc.identifier.doi10.2147/DDDT.S124670pl
dc.identifier.eissn1177-8881
dc.identifier.projectROD UJ / OPpl
dc.identifier.urihttps://ruj.uj.edu.pl/xmlui/handle/item/140065
dc.languageengpl
dc.language.containerengpl
dc.rightsUdzielam licencji. Uznanie autorstwa - Użycie niekomercyjne 3.0*
dc.rights.licenceCC-BY-NC
dc.rights.urihttp://creativecommons.org/licenses/by-nc/3.0/legalcode*
dc.share.typeotwarte czasopismo
dc.subject.encomputational intelligencepl
dc.subject.enmillingpl
dc.subject.enroll compactionpl
dc.subject.endry granulationpl
dc.subject.enneural networkpl
dc.subject.enCubistpl
dc.subtypeArticlepl
dc.titleEffect of roll compaction on granule size distribution of microcrystalline cellulose-mannitol mixtures : computational intelligence modeling and parametric analysispl
dc.title.journalDrug Design, Development and Therapypl
dc.typeJournalArticlepl
dspace.entity.typePublication
dc.abstract.enpl
Dry granulation using roll compaction is a typical unit operation for producing solid dosage forms in the pharmaceutical industry. Dry granulation is commonly used if the powder mixture is sensitive to heat and moisture and has poor flow properties. The output of roll compaction is compacted ribbons that exhibit different properties based on the adjusted process parameters. These ribbons are then milled into granules and finally compressed into tablets. The properties of the ribbons directly affect the granule size distribution (GSD) and the quality of final products; thus, it is imperative to study the effect of roll compaction process parameters on GSD. The understanding of how the roll compactor process parameters and material properties interact with each other will allow accurate control of the process, leading to the implementation of quality by design practices. Computational intelligence (CI) methods have a great potential for being used within the scope of quality by design approach. The main objective of this study was to show how the computational intelligence techniques can be useful to predict the GSD by using different process conditions of roll compaction and material properties. Different techniques such as multiple linear regression, artificial neural networks, random forest, Cubist and k-nearest neighbors algorithm assisted by sevenfold cross-validation were used to present generalized models for the prediction of GSD based on roll compaction process setting and material properties. The normalized root-mean-squared error and the coefficient of determination (R2) were used for model assessment. The best fit was obtained by Cubist model (normalized root-mean-squared error =3.22%, R2=0.95). Based on the results, it was confirmed that the material properties (true density) followed by compaction force have the most significant effect on GSD.
dc.affiliationpl
Wydział Farmaceutyczny : Zakład Technologii Postaci Leku i Biofarmacji
dc.cm.date
2020-01-07
dc.cm.id
81320
dc.contributor.authorpl
Kazemi, Pezhman
dc.contributor.authorpl
Khalid, Mohammad Hassan
dc.contributor.authorpl
Perez Gago, Ana
dc.contributor.authorpl
Kleinebudde, Peter
dc.contributor.authorpl
Jachowicz, Renata - 129780
dc.contributor.authorpl
Szlęk, Jakub - 162262
dc.contributor.authorpl
Mendyk, Aleksander - 130937
dc.date.accessioned
2020-01-17T09:15:22Z
dc.date.available
2020-01-17T09:15:22Z
dc.date.issuedpl
2017
dc.date.openaccess
0
dc.description.accesstime
w momencie opublikowania
dc.description.physicalpl
241-251
dc.description.pointspl
35
dc.description.version
ostateczna wersja wydawcy
dc.description.volumepl
11
dc.identifier.doipl
10.2147/DDDT.S124670
dc.identifier.eissn
1177-8881
dc.identifier.projectpl
ROD UJ / OP
dc.identifier.uri
https://ruj.uj.edu.pl/xmlui/handle/item/140065
dc.languagepl
eng
dc.language.containerpl
eng
dc.rights*
Udzielam licencji. Uznanie autorstwa - Użycie niekomercyjne 3.0
dc.rights.licence
CC-BY-NC
dc.rights.uri*
http://creativecommons.org/licenses/by-nc/3.0/legalcode
dc.share.type
otwarte czasopismo
dc.subject.enpl
computational intelligence
dc.subject.enpl
milling
dc.subject.enpl
roll compaction
dc.subject.enpl
dry granulation
dc.subject.enpl
neural network
dc.subject.enpl
Cubist
dc.subtypepl
Article
dc.titlepl
Effect of roll compaction on granule size distribution of microcrystalline cellulose-mannitol mixtures : computational intelligence modeling and parametric analysis
dc.title.journalpl
Drug Design, Development and Therapy
dc.typepl
JournalArticle
dspace.entity.type
Publication

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