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QSRR Modeling for Metabolite Standards Analyzed by Two Different Chromatographic Columns Using Multiple Linear Regression
Modified quantitative structure retention relationships (QSRRs) are proposed and applied to describe two retention data sets: A set of 94 metabolites studied by a hydrophilic interaction chromatography system under organic content gradient conditions and a set of tryptophan and its major metabolites...
Autores principales: | , , , , , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
MDPI
2017
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC5372210/ https://www.ncbi.nlm.nih.gov/pubmed/28208794 http://dx.doi.org/10.3390/metabo7010007 |
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author | Zisi, Chrysostomi Sampsonidis, Ioannis Fasoula, Stella Papachristos, Konstantinos Witting, Michael Gika, Helen G. Nikitas, Panagiotis Pappa-Louisi, Adriani |
author_facet | Zisi, Chrysostomi Sampsonidis, Ioannis Fasoula, Stella Papachristos, Konstantinos Witting, Michael Gika, Helen G. Nikitas, Panagiotis Pappa-Louisi, Adriani |
author_sort | Zisi, Chrysostomi |
collection | PubMed |
description | Modified quantitative structure retention relationships (QSRRs) are proposed and applied to describe two retention data sets: A set of 94 metabolites studied by a hydrophilic interaction chromatography system under organic content gradient conditions and a set of tryptophan and its major metabolites analyzed by a reversed-phase chromatographic system under isocratic as well as pH and/or simultaneous pH and organic content gradient conditions. According to the proposed modification, an additional descriptor is added to a conventional QSRR expression, which is the analyte retention time, t(R)(R), measured under the same elution conditions, but in a second chromatographic column considered as a reference one. The 94 metabolites were studied on an Amide column using a Bare Silica column as a reference. For the second dataset, a Kinetex EVO C18 and a Gemini-NX column were used, where each of them was served as a reference column of the other. We found in all cases a significant improvement of the performance of the QSRR models when the descriptor t(R)(R) was considered. |
format | Online Article Text |
id | pubmed-5372210 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2017 |
publisher | MDPI |
record_format | MEDLINE/PubMed |
spelling | pubmed-53722102017-04-10 QSRR Modeling for Metabolite Standards Analyzed by Two Different Chromatographic Columns Using Multiple Linear Regression Zisi, Chrysostomi Sampsonidis, Ioannis Fasoula, Stella Papachristos, Konstantinos Witting, Michael Gika, Helen G. Nikitas, Panagiotis Pappa-Louisi, Adriani Metabolites Article Modified quantitative structure retention relationships (QSRRs) are proposed and applied to describe two retention data sets: A set of 94 metabolites studied by a hydrophilic interaction chromatography system under organic content gradient conditions and a set of tryptophan and its major metabolites analyzed by a reversed-phase chromatographic system under isocratic as well as pH and/or simultaneous pH and organic content gradient conditions. According to the proposed modification, an additional descriptor is added to a conventional QSRR expression, which is the analyte retention time, t(R)(R), measured under the same elution conditions, but in a second chromatographic column considered as a reference one. The 94 metabolites were studied on an Amide column using a Bare Silica column as a reference. For the second dataset, a Kinetex EVO C18 and a Gemini-NX column were used, where each of them was served as a reference column of the other. We found in all cases a significant improvement of the performance of the QSRR models when the descriptor t(R)(R) was considered. MDPI 2017-02-09 /pmc/articles/PMC5372210/ /pubmed/28208794 http://dx.doi.org/10.3390/metabo7010007 Text en © 2017 by the authors. Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (http://creativecommons.org/licenses/by/4.0/). |
spellingShingle | Article Zisi, Chrysostomi Sampsonidis, Ioannis Fasoula, Stella Papachristos, Konstantinos Witting, Michael Gika, Helen G. Nikitas, Panagiotis Pappa-Louisi, Adriani QSRR Modeling for Metabolite Standards Analyzed by Two Different Chromatographic Columns Using Multiple Linear Regression |
title | QSRR Modeling for Metabolite Standards Analyzed by Two Different Chromatographic Columns Using Multiple Linear Regression |
title_full | QSRR Modeling for Metabolite Standards Analyzed by Two Different Chromatographic Columns Using Multiple Linear Regression |
title_fullStr | QSRR Modeling for Metabolite Standards Analyzed by Two Different Chromatographic Columns Using Multiple Linear Regression |
title_full_unstemmed | QSRR Modeling for Metabolite Standards Analyzed by Two Different Chromatographic Columns Using Multiple Linear Regression |
title_short | QSRR Modeling for Metabolite Standards Analyzed by Two Different Chromatographic Columns Using Multiple Linear Regression |
title_sort | qsrr modeling for metabolite standards analyzed by two different chromatographic columns using multiple linear regression |
topic | Article |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC5372210/ https://www.ncbi.nlm.nih.gov/pubmed/28208794 http://dx.doi.org/10.3390/metabo7010007 |
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