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Quantitative structure -retention relationship modeling of selected antipsychotics and their impurities in green liquid chromatography using cyclodextrin mobile phases

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2018
Authors
Maljurić, Nevena
Golubović, Jelena
Otašević, Biljana
Zečević, Mira
Protić, Ana
Article (Published version)
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Abstract
Applying green chromatography methods is currently one of the challenges in liquid chromatography. Among different strategies, using cyclodextrin (CD) mobile phase modifiers was applied in this paper. CDs can form inclusion complexes with a wide variety of hydrophobic organic compounds and, consequently, affect their retention behavior. CD-containing mobile phases possess complicated complexation and adsorption equilibria so retention is dependent not only on chromatographic parameters and properties of the compound but also on properties of compound-CD complex. Docking study was used to calculate association constants of the selected antipsychotics (risperidone, olanzapine, and their impurities) and beta-CD complexes and predict which part of the molecule structure will most likely incorporate into the beta-CD cavity. Quantitative structure-retention relationship model (QSRR) for selected model substances was built employing artificial neural network (ANN) technique. Reliable QSRR mod...el was obtained using molecular descriptors, complex association constants, and chromatographic factors. The multilayer perceptron network with 11-8-1 topology, trained with back propagation algorithm, showed the best performance. Root mean square error for training, validation, and test was 0.2954, 0.3633, and 0.4864, respectively. The correlation coefficient (R-2) between experimentally obtained retention factor values [k(exp)] and values computed or predicted by ANN [k(ANN)] was 0.9962 for training, 0.9927 for validation, and 0.9829 for test, indicating good predictive ability of the model. The optimized network was used for development of green chromatography method for separation of risperidone and its related impurities, as well as olanzapine and its related impurities in a relatively short run time and with low consumption of organic modifier. The developed methods were validated in accordance with ICH Q2 (R1) quideline and all parameters fulfilled the defined criteria. The greenness of the proposed methods has also been demonstrated.

Keywords:
Green chromatography / QSRR / ANN / Complex association constants / ss-CD / Inclusion complexes
Source:
Analytical and Bioanalytical Chemistry, 2018, 410, 10, 2533-2550
Publisher:
  • Springer Heidelberg, Heidelberg
Funding / projects:
  • Synthesis, Quantitative Structure and Activity Relationship, Physico-Chemical Characterisation and Analysis of Pharmacologically Active Substances (RS-172033)

DOI: 10.1007/s00216-018-0911-3

ISSN: 1618-2642

PubMed: 29442144

WoS: 000427797800008

Scopus: 2-s2.0-85041902003
[ Google Scholar ]
10
6
URI
https://farfar.pharmacy.bg.ac.rs/handle/123456789/3174
Collections
  • Radovi istraživača / Researchers’ publications
Institution/Community
Pharmacy
TY  - JOUR
AU  - Maljurić, Nevena
AU  - Golubović, Jelena
AU  - Otašević, Biljana
AU  - Zečević, Mira
AU  - Protić, Ana
PY  - 2018
UR  - https://farfar.pharmacy.bg.ac.rs/handle/123456789/3174
AB  - Applying green chromatography methods is currently one of the challenges in liquid chromatography. Among different strategies, using cyclodextrin (CD) mobile phase modifiers was applied in this paper. CDs can form inclusion complexes with a wide variety of hydrophobic organic compounds and, consequently, affect their retention behavior. CD-containing mobile phases possess complicated complexation and adsorption equilibria so retention is dependent not only on chromatographic parameters and properties of the compound but also on properties of compound-CD complex. Docking study was used to calculate association constants of the selected antipsychotics (risperidone, olanzapine, and their impurities) and beta-CD complexes and predict which part of the molecule structure will most likely incorporate into the beta-CD cavity. Quantitative structure-retention relationship model (QSRR) for selected model substances was built employing artificial neural network (ANN) technique. Reliable QSRR model was obtained using molecular descriptors, complex association constants, and chromatographic factors. The multilayer perceptron network with 11-8-1 topology, trained with back propagation algorithm, showed the best performance. Root mean square error for training, validation, and test was 0.2954, 0.3633, and 0.4864, respectively. The correlation coefficient (R-2) between experimentally obtained retention factor values [k(exp)] and values computed or predicted by ANN [k(ANN)] was 0.9962 for training, 0.9927 for validation, and 0.9829 for test, indicating good predictive ability of the model. The optimized network was used for development of green chromatography method for separation of risperidone and its related impurities, as well as olanzapine and its related impurities in a relatively short run time and with low consumption of organic modifier. The developed methods were validated in accordance with ICH Q2 (R1) quideline and all parameters fulfilled the defined criteria. The greenness of the proposed methods has also been demonstrated.
PB  - Springer Heidelberg, Heidelberg
T2  - Analytical and Bioanalytical Chemistry
T1  - Quantitative structure -retention relationship modeling of selected antipsychotics and their impurities in green liquid chromatography using cyclodextrin mobile phases
VL  - 410
IS  - 10
SP  - 2533
EP  - 2550
DO  - 10.1007/s00216-018-0911-3
ER  - 
@article{
author = "Maljurić, Nevena and Golubović, Jelena and Otašević, Biljana and Zečević, Mira and Protić, Ana",
year = "2018",
abstract = "Applying green chromatography methods is currently one of the challenges in liquid chromatography. Among different strategies, using cyclodextrin (CD) mobile phase modifiers was applied in this paper. CDs can form inclusion complexes with a wide variety of hydrophobic organic compounds and, consequently, affect their retention behavior. CD-containing mobile phases possess complicated complexation and adsorption equilibria so retention is dependent not only on chromatographic parameters and properties of the compound but also on properties of compound-CD complex. Docking study was used to calculate association constants of the selected antipsychotics (risperidone, olanzapine, and their impurities) and beta-CD complexes and predict which part of the molecule structure will most likely incorporate into the beta-CD cavity. Quantitative structure-retention relationship model (QSRR) for selected model substances was built employing artificial neural network (ANN) technique. Reliable QSRR model was obtained using molecular descriptors, complex association constants, and chromatographic factors. The multilayer perceptron network with 11-8-1 topology, trained with back propagation algorithm, showed the best performance. Root mean square error for training, validation, and test was 0.2954, 0.3633, and 0.4864, respectively. The correlation coefficient (R-2) between experimentally obtained retention factor values [k(exp)] and values computed or predicted by ANN [k(ANN)] was 0.9962 for training, 0.9927 for validation, and 0.9829 for test, indicating good predictive ability of the model. The optimized network was used for development of green chromatography method for separation of risperidone and its related impurities, as well as olanzapine and its related impurities in a relatively short run time and with low consumption of organic modifier. The developed methods were validated in accordance with ICH Q2 (R1) quideline and all parameters fulfilled the defined criteria. The greenness of the proposed methods has also been demonstrated.",
publisher = "Springer Heidelberg, Heidelberg",
journal = "Analytical and Bioanalytical Chemistry",
title = "Quantitative structure -retention relationship modeling of selected antipsychotics and their impurities in green liquid chromatography using cyclodextrin mobile phases",
volume = "410",
number = "10",
pages = "2533-2550",
doi = "10.1007/s00216-018-0911-3"
}
Maljurić, N., Golubović, J., Otašević, B., Zečević, M.,& Protić, A.. (2018). Quantitative structure -retention relationship modeling of selected antipsychotics and their impurities in green liquid chromatography using cyclodextrin mobile phases. in Analytical and Bioanalytical Chemistry
Springer Heidelberg, Heidelberg., 410(10), 2533-2550.
https://doi.org/10.1007/s00216-018-0911-3
Maljurić N, Golubović J, Otašević B, Zečević M, Protić A. Quantitative structure -retention relationship modeling of selected antipsychotics and their impurities in green liquid chromatography using cyclodextrin mobile phases. in Analytical and Bioanalytical Chemistry. 2018;410(10):2533-2550.
doi:10.1007/s00216-018-0911-3 .
Maljurić, Nevena, Golubović, Jelena, Otašević, Biljana, Zečević, Mira, Protić, Ana, "Quantitative structure -retention relationship modeling of selected antipsychotics and their impurities in green liquid chromatography using cyclodextrin mobile phases" in Analytical and Bioanalytical Chemistry, 410, no. 10 (2018):2533-2550,
https://doi.org/10.1007/s00216-018-0911-3 . .

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