Identification of urinary metabolites of imperatorin with a single run on an LC/Triple TOF system based on multiple mass defect filter data acquisition and multiple data mining techniques

Abstract

The detection of drug metabolites, especially for minor metabolites, continues to be a challenge because of the complexity of biological samples. Imperatorin (IMP) is an active natural furocoumarin component originating from many traditional Chinese herbal medicines and is expected to be pursued as a new vasorelaxant agent. In the present study, a generic and efficient approach was developed for the in vivo screening and identification of IMP metabolites using liquid chromatography-Triple TOF mass spectrometry. In this approach, a novel on-line data acquisition method mutiple mass defect filter (MMDF) combined with dynamic background subtraction was developed to trace all probable urinary metabolites of IMP. Comparing with the traditionally intensity-dependent data acquisition method, MMDF method could give the information of low-level metabolites masked by background noise and endogenous components. Thus, the minor metabolites in complex biological matrices could be detected. Then, the sensitive and specific multiple data-mining techniques extracted ion chromatography, mass defect filter, product ion filter, and neutral loss filter were used for the discovery of IMP metabolites. Based on the proposed strategy, 44 phase I and 7 phase II metabolites were identified in rat urine after oral administration of IMP. The results indicated that oxidization was the main metabolic pathway and that different oxidized substituent positions had a significant influence on the fragmentation of the metabolites. Two types of characteristic ions at m/z 203 and 219 can be observed in the MS/MS spectra. This is the first study of IMP metabolism in vivo. The interpretation of the MS/MS spectra of these metabolites and the proposed metabolite pathway provide essential data for further pharmacological studies of other linear-type furocoumarins.

An integrated strategy for the systematic characterization and discovery of new indole alkaloids from Uncaria rhynchophylla by UHPLC/DAD/LTQ-Orbitrap-MS

“…Data post-processing techniques such as extracted ion chromatography (EIC or XIC) and neutral loss filtering (NLF) can then be used in the analysis of the vast amounts of MS and MS n data acquired. For instance, NLF of 176 Da was able to select licorice saponins or the phase II metabolites of a drug which possesses a glucuronic acid residue [14,15]. Additionally, due to the increasing use of highresolution mass spectrometers, mass defect filtering (MDF) of complex MS data has been applied for the selective detection of compounds of interest, including drugs, their metabolites, endogenous ingredients, and naturally occurring plant metabolites [16,17].…”

Exploration of individuality in drug metabolism by high-throughput metabolomics: The fast line for personalized medicine

“…The metabolic pathways of a drug could be predicted on the basis of the knowledge-based information available for similar structures [15]. Both approaches demonstrate effectiveness for drug metabolite identification for in vitro and in vivo models [14,15].…”

“…High-throughput metabolomics has been widely used as a powerful instrument to study drug metabolism and, at the moment, is applied mostly at the early development stages of drug discovery to understand primary mechanisms of drug clearance, drug pharmacokinetics and pharmacodynamics [14,15,19]. However, application of metabolomics to understand variability of drug metabolism among individuals on a subpopulation scale has been rare.…”

“…Most drugs are actively metabolised by the total cellular enzymatic machine to produce dozens of metabolites [13][14][15], some of which might be specific for patient subgroups [9]. In addition to being bioactive and indispensable from primary drug action mechanisms, other metabolites could be responsible for adverse side effects [5].…”

Post-acquisition data processing for the screening of transformation products of different organic contaminants. Two-year monitoring of river water using LC-ESI-QTOF-MS and GCxGC-EI-TOF-MS

“…MDF as a data-processing approach enables the segregation of drug-related compounds from exogenous interferences based on the unique mass defect of metabolites (Zhu et al 2011;Qiao et al 2013). Different biotransformation processes may have distinct mass defect values, but most of them may fall within a predictably narrow range.…”