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The Chemometric Study and Quantitative Structure Retention Relationship Modeling of Liquid Chromatography Separation of Ziprasidone Components

Authorized Users Only
2012
Authors
Nikolić, Katarina
Pavlović, Marija
Smolinski, Adam
Agbaba, Danica
Article (Published version)
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Abstract
Optimization of the experimental conditions of a novel HPLC method for determination of the impurity levels with ziprasidone (in bulk substance and pharmaceutical dosage forms) was performed with use of Multi-Layer Perceptron (MLP) Artificial Neural Networks (ANN) and Response Surface Plots. The obtained experimental conditions were further used to test a set of 20 reversed-phase columns for their selectivity towards ziprasidone components by use of the principal component analysis (PCA) and hierarchical clustering analysis (HCA). The obtained HPLC retention times of ziprasidone and its impurities (Imp I-V) along with the computed molecular parameters of the examined compounds were further used in the Quantitative Structure Retention Relationship (QSRR) study. The performed QSRR study has selected the LogD(pH) (1.5), LogD(pH 2.5), LogD(pH 4.0), LogP, MS, and SAS parameters as descriptors of the chromatographic behavior of ziprasidone components. The developed QSRR model can be very use...ful in the t(R) prediction for the ziprasidone derivatives (impurities, degradation products, and metabolites). As the performed LC-MS study of the test solution has confirmed that the unknown impurity (t(R): 11.270 min) in the test solution is the TS1, one from two candidates predicted by QSRR (TS1 and TS5), the high prediction potential of the created QSRR models has been proved.

Keywords:
ANN / HCA / HPLC / PCA / PLS / QSRR / ziprasidone
Source:
Combinatorial Chemistry & High Throughput Screening, 2012, 15, 9, 730-744
Publisher:
  • Bentham Science Publ Ltd, Sharjah
Funding / projects:
  • Synthesis, Quantitative Structure and Activity Relationship, Physico-Chemical Characterisation and Analysis of Pharmacologically Active Substances (RS-172033)

DOI: 10.2174/138620712803519699

ISSN: 1386-2073

PubMed: 22934948

WoS: 000314821100006

Scopus: 2-s2.0-84870406306
[ Google Scholar ]
10
9
URI
https://farfar.pharmacy.bg.ac.rs/handle/123456789/1670
Collections
  • Radovi istraživača / Researchers’ publications
Institution/Community
Pharmacy
TY  - JOUR
AU  - Nikolić, Katarina
AU  - Pavlović, Marija
AU  - Smolinski, Adam
AU  - Agbaba, Danica
PY  - 2012
UR  - https://farfar.pharmacy.bg.ac.rs/handle/123456789/1670
AB  - Optimization of the experimental conditions of a novel HPLC method for determination of the impurity levels with ziprasidone (in bulk substance and pharmaceutical dosage forms) was performed with use of Multi-Layer Perceptron (MLP) Artificial Neural Networks (ANN) and Response Surface Plots. The obtained experimental conditions were further used to test a set of 20 reversed-phase columns for their selectivity towards ziprasidone components by use of the principal component analysis (PCA) and hierarchical clustering analysis (HCA). The obtained HPLC retention times of ziprasidone and its impurities (Imp I-V) along with the computed molecular parameters of the examined compounds were further used in the Quantitative Structure Retention Relationship (QSRR) study. The performed QSRR study has selected the LogD(pH) (1.5), LogD(pH 2.5), LogD(pH 4.0), LogP, MS, and SAS parameters as descriptors of the chromatographic behavior of ziprasidone components. The developed QSRR model can be very useful in the t(R) prediction for the ziprasidone derivatives (impurities, degradation products, and metabolites). As the performed LC-MS study of the test solution has confirmed that the unknown impurity (t(R): 11.270 min) in the test solution is the TS1, one from two candidates predicted by QSRR (TS1 and TS5), the high prediction potential of the created QSRR models has been proved.
PB  - Bentham Science Publ Ltd, Sharjah
T2  - Combinatorial Chemistry & High Throughput Screening
T1  - The Chemometric Study and Quantitative Structure Retention Relationship Modeling of Liquid Chromatography Separation of Ziprasidone Components
VL  - 15
IS  - 9
SP  - 730
EP  - 744
DO  - 10.2174/138620712803519699
UR  - conv_2775
ER  - 
@article{
author = "Nikolić, Katarina and Pavlović, Marija and Smolinski, Adam and Agbaba, Danica",
year = "2012",
abstract = "Optimization of the experimental conditions of a novel HPLC method for determination of the impurity levels with ziprasidone (in bulk substance and pharmaceutical dosage forms) was performed with use of Multi-Layer Perceptron (MLP) Artificial Neural Networks (ANN) and Response Surface Plots. The obtained experimental conditions were further used to test a set of 20 reversed-phase columns for their selectivity towards ziprasidone components by use of the principal component analysis (PCA) and hierarchical clustering analysis (HCA). The obtained HPLC retention times of ziprasidone and its impurities (Imp I-V) along with the computed molecular parameters of the examined compounds were further used in the Quantitative Structure Retention Relationship (QSRR) study. The performed QSRR study has selected the LogD(pH) (1.5), LogD(pH 2.5), LogD(pH 4.0), LogP, MS, and SAS parameters as descriptors of the chromatographic behavior of ziprasidone components. The developed QSRR model can be very useful in the t(R) prediction for the ziprasidone derivatives (impurities, degradation products, and metabolites). As the performed LC-MS study of the test solution has confirmed that the unknown impurity (t(R): 11.270 min) in the test solution is the TS1, one from two candidates predicted by QSRR (TS1 and TS5), the high prediction potential of the created QSRR models has been proved.",
publisher = "Bentham Science Publ Ltd, Sharjah",
journal = "Combinatorial Chemistry & High Throughput Screening",
title = "The Chemometric Study and Quantitative Structure Retention Relationship Modeling of Liquid Chromatography Separation of Ziprasidone Components",
volume = "15",
number = "9",
pages = "730-744",
doi = "10.2174/138620712803519699",
url = "conv_2775"
}
Nikolić, K., Pavlović, M., Smolinski, A.,& Agbaba, D.. (2012). The Chemometric Study and Quantitative Structure Retention Relationship Modeling of Liquid Chromatography Separation of Ziprasidone Components. in Combinatorial Chemistry & High Throughput Screening
Bentham Science Publ Ltd, Sharjah., 15(9), 730-744.
https://doi.org/10.2174/138620712803519699
conv_2775
Nikolić K, Pavlović M, Smolinski A, Agbaba D. The Chemometric Study and Quantitative Structure Retention Relationship Modeling of Liquid Chromatography Separation of Ziprasidone Components. in Combinatorial Chemistry & High Throughput Screening. 2012;15(9):730-744.
doi:10.2174/138620712803519699
conv_2775 .
Nikolić, Katarina, Pavlović, Marija, Smolinski, Adam, Agbaba, Danica, "The Chemometric Study and Quantitative Structure Retention Relationship Modeling of Liquid Chromatography Separation of Ziprasidone Components" in Combinatorial Chemistry & High Throughput Screening, 15, no. 9 (2012):730-744,
https://doi.org/10.2174/138620712803519699 .,
conv_2775 .

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