The application of high-resolution mass spectrometry-based data-mining tools in tandem to metabolite profiling of a triple drug combination in humans

• Published in: Analytica chimica acta 2015-10, Vol.897, p.34-44
• Main Authors: Xing, Jie, Zang, Meitong, Zhang, Haiying, Zhu, Mingshe
• Format: Article
• Language: English
• Subjects: 2-Pyridinylmethylsulfinylbenzimidazoles - blood; 2-Pyridinylmethylsulfinylbenzimidazoles - metabolism; 2-Pyridinylmethylsulfinylbenzimidazoles - urine; Adolescent; Adult; Background subtraction; Clarithromycin - blood; Clarithromycin - metabolism; Clarithromycin - urine; Combination drugs; Data Mining - methods; Drug Combinations; Healthy Volunteers; High resolution mass spectrometry; Humans; Male; Mass defect filter; Mass Spectrometry; Metabolite identification; Metabolomics - methods; Metronidazole - blood; Metronidazole - metabolism; Metronidazole - urine; Young Adult
• ISSN: 0003-2670; 1873-4324
• DOI: 10.1016/j.aca.2015.09.034

Patients are usually exposed to multiple drugs, and metabolite profiling of each drug in complex biological matrices is a big challenge. This study presented a new application of an improved high resolution mass spectrometry (HRMS)-based data-mining tools in tandem to fast and comprehensive metabolite identification of combination drugs in human. The model drug combination was metronidazole-pantoprazole-clarithromycin (MET-PAN-CLAR), which is widely used in clinic to treat ulcers caused by Helicobacter pylori. First, mass defect filter (MDF), as a targeted data processing tool, was able to recover all relevant metabolites of MET-PAN-CLAR in human plasma and urine from the full-scan MS dataset when appropriate MDF templates for each drug were defined. Second, the accurate mass-based background subtraction (BS), as an untargeted data-mining tool, worked effectively except for several trace metabolites, which were buried in the remaining background signals. Third, an integrated strategy, i.e., untargeted BS followed by improved MDF, was effective for metabolite identification of MET-PAN-CLAR. Most metabolites except for trace ones were found in the first step of BS-processed datasets, and the results led to the setup of appropriate metabolite MDF template for the subsequent MDF data processing. Trace metabolites were further recovered by MDF, which used both common MDF templates and the novel metabolite-based MDF templates. As a result, a total of 44 metabolites or related components were found for MET-PAN-CLAR in human plasma and urine using the integrated strategy. New metabolic pathways such as N-glucuronidation of PAN and dehydrogenation of CLAR were found. This study demonstrated that the combination of accurate mass-based multiple data-mining techniques in tandem, i.e., untargeted background subtraction followed by targeted mass defect filtering, can be a valuable tool for rapid metabolite profiling of combination drugs in vivo. [Display omitted] •Metabolite identification of a triple drug combination in humans using high resolution mass spectrometry (HRMS).•Metabolite detection using untargeted background subtraction (BS) followed by improved mass defect filtering (MDF).•Both novel metabolite MDF templates based on BS-processed data and common MDF templates were used.•Unusual metabolites were characterized based on HR-MSn data.