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dc.creatorChansanroj, Krisanin
dc.creatorPetrović, Jelena
dc.creatorIbrić, Svetlana
dc.creatorBetz, Gabriele
dc.date.accessioned2019-09-02T11:24:08Z
dc.date.available2019-09-02T11:24:08Z
dc.date.issued2011
dc.identifier.issn0928-0987
dc.identifier.urihttp://farfar.pharmacy.bg.ac.rs/handle/123456789/1504
dc.description.abstractArtificial neural networks (ANNs) were applied for system understanding and prediction of drug release properties from direct compacted matrix tablets using sucrose esters (SEs) as matrix-forming agents for controlled release of a highly water soluble drug, metoprolol tartrate. Complexity of the system was presented through the effects of SE concentration and tablet porosity at various hydrophilic-lipophilic balance (HLB) values of SEs ranging from 0 to 16. Both effects contributed to release behaviors especially in the system containing hydrophilic SEs where swelling phenomena occurred. A self-organizing map neural network (SOM) was applied for visualizing interrelation among the variables and multilayer perceptron neural networks (MLPs) were employed to generalize the system and predict the drug release properties based on HLB value and concentration of SEs and tablet properties, i.e., tablet porosity, volume and tensile strength. Accurate prediction was obtained after systematically optimizing network performance based on learning algorithm of MLP. Drug release was mainly attributed to the effects of SEs, tablet volume and tensile strength in multi-dimensional interrelation whereas tablet porosity gave a small impact. Ability of system generalization and accurate prediction of the drug release properties proves the validity of SOM and MLPs for the formulation modeling of direct compacted matrix tablets containing controlled release agents of different material properties.en
dc.publisherElsevier Science BV, Amsterdam
dc.relationMinistry of Science and Technological Development, Republic of Serbia
dc.rightsrestrictedAccess
dc.sourceEuropean Journal of Pharmaceutical Sciences
dc.subjectControlled releaseen
dc.subjectMatrix tableten
dc.subjectSucrose estersen
dc.subjectNeural networken
dc.subjectSwellingen
dc.subjectHydrophilic-lipophilic propertyen
dc.titleDrug release control and system understanding of sucrose esters matrix tablets by artificial neural networksen
dc.typearticle
dc.rights.licenseARR
dcterms.abstractИбрић, Светлана; Бетз, Габриеле; Петровић, Јелена; Цхансанрој, Крисанин;
dc.citation.volume44
dc.citation.issue3
dc.citation.spage321
dc.citation.epage331
dc.citation.other44(3): 321-331
dc.citation.rankM21
dc.identifier.wos000296930000017
dc.identifier.doi10.1016/j.ejps.2011.08.012
dc.identifier.pmid21878388
dc.identifier.scopus2-s2.0-80053909625
dc.identifier.rcubconv_2546
dc.type.versionpublishedVersion


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