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Quantitative structure retention relationship modeling in liquid chromatography method for separation of candesartan cilexetil and its degradation products

Authorized Users Only
2015
Authors
Golubović, Jelena
Protić, Ana
Zečević, Mira
Otašević, Biljana
Article (Published version)
Metadata
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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.
Keywords:
QSRR / Artificial neural networks / Candesartan cilexetil / Forced degradation studies / HPLC
Source:
Chemometrics and Intelligent Laboratory Systems, 2015, 140, 92-101
Publisher:
  • Elsevier Science BV, Amsterdam
Projects:
  • Synthesis, Quantitative Structure and Activity Relationship, Physico-Chemical Characterisation and Analysis of Pharmacologically Active Substances (RS-172033)

DOI: 10.1016/j.chemolab.2014.11.005

ISSN: 0169-7439

WoS: 000349062500010

Scopus: 2-s2.0-84913553965
[ Google Scholar ]
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10
URI
http://farfar.pharmacy.bg.ac.rs/handle/123456789/2403
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  • Radovi istraživača / Researchers’ publications
Institution
Pharmacy

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