Приказ основних података о документу

dc.creatorĐuriš, Jelena
dc.creatorCirin-Varađan, Slobodanka
dc.creatorAleksić, Ivana
dc.creatorĐuriš, Mihal
dc.creatorCvijić, Sandra
dc.creatorIbrić, Svetlana
dc.date.accessioned2021-06-02T08:09:46Z
dc.date.available2021-06-02T08:09:46Z
dc.date.issued2021
dc.identifier.issn1999-4923
dc.identifier.urihttps://farfar.pharmacy.bg.ac.rs/handle/123456789/3905
dc.description.abstractCo-processing (CP) provides superior properties to excipients and has become a reliable option to facilitated formulation and manufacturing of variety of solid dosage forms. Development of directly compressible formulations with high doses of poorly flowing/compressible active pharmaceutical ingredients, such as paracetamol, remains a great challenge for the pharmaceutical industry due to the lack of understanding of the interplay between the formulation properties, process of compaction, and stages of tablets’ detachment and ejection. The aim of this study was to analyze the influence of the compression load, excipients’ co-processing and the addition of paracetamol on the obtained tablets’ tensile strength and the specific parameters of the tableting process, such as (net) compression work, elastic recovery, detachment, and ejection work, as well as the ejection force. Two types of neural networks were used to analyze the data: classification (Kohonen network) and regression networks (multilayer perceptron and radial basis function), to build prediction models and identify the variables that are predominantly affecting the tableting process and the obtained tablets’ tensile strength. It has been demonstrated that sophisticated data-mining methods are necessary to interpret complex phenomena regarding the effect of co-processing on tableting properties of directly compressible excipients.
dc.publisherMDPI AG
dc.relationinfo:eu-repo/grantAgreement/MESTD/inst-2020/200026/RS//
dc.relationinfo:eu-repo/grantAgreement/MESTD/inst-2020/200161/RS//
dc.rightsopenAccess
dc.rights.urihttps://creativecommons.org/licenses/by/4.0/
dc.sourcePharmaceutics
dc.subjectNeural networks
dc.subjectCo-processed excipients
dc.subjectCompaction analysis
dc.subjectLactose
dc.subjectLipid excipients
dc.subjectMachine learning
dc.subjectMonohydrate
dc.subjectMultilayer perceptron
dc.subjectSensitivity analysis
dc.subjectTensile strength
dc.titleApplication of machine-learning algorithms for better understanding of tableting properties of lactose co-processed with lipid excipients
dc.typearticle
dc.rights.licenseBY
dcterms.abstractAлексић, Ивана; Ибрић, Светлана; Ђуриш, Јелена; Цирин-Варађан, Слободанка; Ђуриш, Михал; Цвијић, Сандра;
dc.citation.volume13
dc.citation.issue5
dc.citation.rankM21
dc.identifier.wos000662452600001
dc.identifier.doi10.3390/pharmaceutics13050663
dc.identifier.scopus2-s2.0-85106191656
dc.identifier.fulltexthttps://farfar.pharmacy.bg.ac.rs/bitstream/id/8992/Application_of_Machine-Learning_pub_2021.pdf
dc.type.versionpublishedVersion


Документи

Thumbnail

Овај документ се појављује у следећим колекцијама

Приказ основних података о документу