Abstract
IntroductionMass spectrometry is the current technique of choice in studying drug metabolism. High-resolution mass spectrometry in combination with MS/MS gas-phase experiments has the potential to contribute to rapid advances in this field. However, the data emerging from such fragmentation spectral files pose challenges to downstream analysis, given their complexity and size.ObjectivesThis study aims to detect and visualize antihypertensive drug metabolites in untargeted metabolomics experiments based on the spectral similarity of their fragmentation spectra. Furthermore, spectral clusters of endogenous metabolites were also examined.MethodsHere we apply a molecular networking approach to seek drugs and their metabolites, in fragmentation spectra from urine derived from a cohort of 26 patients on antihypertensive therapy. The mass spectrometry data was collected on a Thermo Q-Exactive coupled to pHILIC chromatography using data dependent analysis (DDA) MS/MS gas-phase experiments.ResultsIn total, 165 separate drug metabolites were found and structurally annotated (17 by spectral matching and 122 by classification based on a clustered fragmentation pattern). The clusters could be traced to 13 drugs including the known antihypertensives verapamil, losartan and amlodipine. The molecular networking approach also generated clusters of endogenous metabolites, including carnitine derivatives, and conjugates containing glutamine, glutamate and trigonelline.ConclusionsThe approach offers unprecedented capability in the untargeted identification of drugs and their metabolites at the population level and has great potential to contribute to understanding stratified responses to drugs where differences in drug metabolism may determine treatment outcome.Electronic supplementary materialThe online version of this article (doi:10.1007/s11306-016-1064-z) contains supplementary material, which is available to authorized users.
Highlights
Mass spectrometry is the current technique of choice in studying drug metabolism
This study aims to detect and visualize antihypertensive drug metabolites in untargeted metabolomics experiments based on the spectral similarity of their fragmentation spectra
The mass defect filtering (MDF) approach uses drugs and core template filters and a set of commonly found transformations in drug metabolism such as hydroxylation, methylation, and decarboxylation and their calculated ‘mass defect shifts’, i.e., the fractional difference of the reactant and the product. With these filters and mass defects, potential drug metabolites can be found within a larger set of detected compounds in high-resolution mass spectrometry (HR-MS) data combined with datadependent analysis (DDA) fragmenting the most abundant ions entering the mass spectrometer
Summary
Mass spectrometry is the current technique of choice in studying drug metabolism. High-resolution mass spectrometry in combination with MS/MS gas-phase experiments has the potential to contribute to rapid advances in this field. The MDF approach uses drugs and core template filters and a set of commonly found transformations in drug metabolism such as hydroxylation, methylation, and decarboxylation and their calculated ‘mass defect shifts’, i.e., the fractional difference of the reactant and the product. With these filters and mass defects, potential drug metabolites can be found within a larger set of detected compounds in high-resolution mass spectrometry (HR-MS) data combined with datadependent analysis (DDA) fragmenting the most abundant ions (i.e., the TopN ions) entering the mass spectrometer. MS2Analyzer can be used to screen for spectra that contain pre-determined drug product ions, as well as commonly found losses caused by biotransformation of drugs, such as glucuronidation (i.e., 176.0321 Da) and sulfation (i.e., 79.9568 Da), which can be added into the search to aid in metabolite annotation
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