Quantitative structure -retention relationship modeling of selected antipsychotics and their impurities in green liquid chromatography using cyclodextrin mobile phases
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 complexesSource:
Analytical and Bioanalytical Chemistry, 2018, 410, 10, 2533-2550Publisher:
- Springer Heidelberg, Heidelberg
Funding / projects:
DOI: 10.1007/s00216-018-0911-3
ISSN: 1618-2642
PubMed: 29442144
WoS: 000427797800008
Scopus: 2-s2.0-85041902003
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Institution/Community
PharmacyTY - 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 . .