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

被引：14
|
作者：
Golubovic, Jelena B. [1 ]
Protic, Ana D. [1 ]
Zecevic, Mira L. [1 ]
Otasevic, Biljana M. [1 ]
机构：
[1] Univ Belgrade, Fac Pharm, Dept Drug Anal, Vojvode Stepe 450, Belgrade 11152, Serbia
来源：
CHEMOMETRICS AND INTELLIGENT LABORATORY SYSTEMS | 2015年 / 140卷
关键词：
QSRR; Artificial neural networks; Candesartan cilexetil; Forced degradation studies; HPLC; ARTIFICIAL NEURAL-NETWORKS; PREDICTION; QSRR;
D O I：
10.1016/j.chemolab.2014.11.005
中图分类号：
TP [自动化技术、计算机技术];
学科分类号：
0812 ;
摘要：
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. (C) 2014 Elsevier B.V. All rights reserved.
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页码：92 / 101
页数：10
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