Quantitative structure retention relationship modeling in liquid chromatography method for separation of candesartan cilexetil and its degradation products
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 / HPLCSource:
Chemometrics and Intelligent Laboratory Systems, 2015, 140, 92-101Publisher:
- Elsevier Science BV, Amsterdam
Funding / projects:
- Synthesis, Quantitative Structure and Activity Relationship, Physico-Chemical Characterisation and Analysis of Pharmacologically Active Substances (RS-MESTD-Basic Research (BR or ON)-172033)
DOI: 10.1016/j.chemolab.2014.11.005
ISSN: 0169-7439
WoS: 000349062500010
Scopus: 2-s2.0-84913553965
Collections
Institution/Community
PharmacyTY - JOUR AU - Golubović, Jelena AU - Protić, Ana AU - Zečević, Mira AU - Otašević, Biljana PY - 2015 UR - https://farfar.pharmacy.bg.ac.rs/handle/123456789/2403 AB - 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. PB - Elsevier Science BV, Amsterdam T2 - Chemometrics and Intelligent Laboratory Systems T1 - Quantitative structure retention relationship modeling in liquid chromatography method for separation of candesartan cilexetil and its degradation products VL - 140 SP - 92 EP - 101 DO - 10.1016/j.chemolab.2014.11.005 ER -
@article{ author = "Golubović, Jelena and Protić, Ana and Zečević, Mira and Otašević, Biljana", year = "2015", 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.", publisher = "Elsevier Science BV, Amsterdam", journal = "Chemometrics and Intelligent Laboratory Systems", title = "Quantitative structure retention relationship modeling in liquid chromatography method for separation of candesartan cilexetil and its degradation products", volume = "140", pages = "92-101", doi = "10.1016/j.chemolab.2014.11.005" }
Golubović, J., Protić, A., Zečević, M.,& Otašević, B.. (2015). Quantitative structure retention relationship modeling in liquid chromatography method for separation of candesartan cilexetil and its degradation products. in Chemometrics and Intelligent Laboratory Systems Elsevier Science BV, Amsterdam., 140, 92-101. https://doi.org/10.1016/j.chemolab.2014.11.005
Golubović J, Protić A, Zečević M, Otašević B. Quantitative structure retention relationship modeling in liquid chromatography method for separation of candesartan cilexetil and its degradation products. in Chemometrics and Intelligent Laboratory Systems. 2015;140:92-101. doi:10.1016/j.chemolab.2014.11.005 .
Golubović, Jelena, Protić, Ana, Zečević, Mira, Otašević, Biljana, "Quantitative structure retention relationship modeling in liquid chromatography method for separation of candesartan cilexetil and its degradation products" in Chemometrics and Intelligent Laboratory Systems, 140 (2015):92-101, https://doi.org/10.1016/j.chemolab.2014.11.005 . .