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Quantitative structure retention relationship modeling in liquid chromatography method for separation of candesartan cilexetil and its degradation products
dc.creator | Golubović, Jelena | |
dc.creator | Protić, Ana | |
dc.creator | Zečević, Mira | |
dc.creator | Otašević, Biljana | |
dc.date.accessioned | 2019-09-02T11:47:05Z | |
dc.date.available | 2019-09-02T11:47:05Z | |
dc.date.issued | 2015 | |
dc.identifier.issn | 0169-7439 | |
dc.identifier.uri | https://farfar.pharmacy.bg.ac.rs/handle/123456789/2403 | |
dc.description.abstract | Artificial neural network (ANN) is a learning system based on a computation technique, which was employed for building of the quantitative structure-retention relationship (QSRR) model for candesartan cilexetil and its degradation products. Candesartan cilexetil has been exposed to forced degradation conditions and degradation products have been subsequently identified with the assistance of HPLC-MS technique. Molecular descriptors have been computed for all compounds and were optimized together with significant chromatographic parameters employing developed QSRR models. In this way, QSRR has been used in development of HPLC stabilityindicating method, optimal conditions toward various outputs have been established and high prediction potential of the created QSRR models has been proved. | en |
dc.publisher | Elsevier Science BV, Amsterdam | |
dc.relation | info:eu-repo/grantAgreement/MESTD/Basic Research (BR or ON)/172033/RS// | |
dc.rights | restrictedAccess | |
dc.source | Chemometrics and Intelligent Laboratory Systems | |
dc.subject | QSRR | en |
dc.subject | Artificial neural networks | en |
dc.subject | Candesartan cilexetil | en |
dc.subject | Forced degradation studies | en |
dc.subject | HPLC | en |
dc.title | Quantitative structure retention relationship modeling in liquid chromatography method for separation of candesartan cilexetil and its degradation products | en |
dc.type | article | |
dc.rights.license | ARR | |
dcterms.abstract | Протић, Aна; Зечевић, Мира; Голубовић, Јелена; Оташевић, Биљана; | |
dc.citation.volume | 140 | |
dc.citation.spage | 92 | |
dc.citation.epage | 101 | |
dc.citation.other | 140: 92-101 | |
dc.citation.rank | aM21 | |
dc.identifier.wos | 000349062500010 | |
dc.identifier.doi | 10.1016/j.chemolab.2014.11.005 | |
dc.identifier.scopus | 2-s2.0-84913553965 | |
dc.type.version | publishedVersion |