QSRR Modeling for Metabolite Standards Analyzed by Two Different Chromatographic Columns Using Multiple Linear Regression
Chrysostomi Zisi,
Ioannis Sampsonidis,
Stella Fasoula,
Konstantinos Papachristos,
Michael Witting,
Helen G. Gika,
Panagiotis Nikitas,
Adriani Pappa-Louisi
Affiliations
Chrysostomi Zisi
Department of Chemistry, Aristotle University of Thessaloniki, 54124 Thessaloniki, Greece
Ioannis Sampsonidis
Infrastructure and Environment Research Division, School of Engineering, University of Glasgow, Rankine Building, Oakfield Avenue, G12 8LT Glasgow, United Kingdom
Stella Fasoula
Department of Chemistry, Aristotle University of Thessaloniki, 54124 Thessaloniki, Greece
Konstantinos Papachristos
Department of Chemistry, Aristotle University of Thessaloniki, 54124 Thessaloniki, Greece
Michael Witting
Helmholtz Zentrum München, Research Unit Analytical BioGeoChemistry, Ingolstaedter Landstrasse 1, D-85764 Neuherberg, Germany
Helen G. Gika
Department of Medicine, Aristotle University of Thessaloniki, 54124 Thessaloniki, Greece
Panagiotis Nikitas
Department of Chemistry, Aristotle University of Thessaloniki, 54124 Thessaloniki, Greece
Adriani Pappa-Louisi
Department of Chemistry, Aristotle University of Thessaloniki, 54124 Thessaloniki, Greece
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, tR(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 tR(R) was considered.
