Xing Jie, Zang Meitong, Zhang Haiying, Zhu Mingshe
School of Pharmaceutical Sciences, Shandong University, Jinan, China.
School of Pharmaceutical Sciences, Shandong University, Jinan, China.
Anal Chim Acta. 2015 Oct 15;897:34-44. doi: 10.1016/j.aca.2015.09.034. Epub 2015 Oct 13.
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.
患者通常会接触多种药物，在复杂生物基质中对每种药物进行代谢物谱分析是一项巨大挑战。本研究展示了一种改进的基于高分辨率质谱（HRMS）的数据挖掘工具的新应用，该工具串联使用，可快速、全面地鉴定人体内联合用药的代谢物。模型药物组合为甲硝唑-泮托拉唑-克拉霉素（MET-PAN-CLAR），该组合在临床上广泛用于治疗幽门螺杆菌引起的溃疡。首先，质量缺陷过滤器（MDF）作为一种靶向数据处理工具，当为每种药物定义合适的MDF模板时，能够从全扫描MS数据集中恢复人血浆和尿液中MET-PAN-CLAR的所有相关代谢物。其次，基于精确质量的背景扣除（BS）作为一种非靶向数据挖掘工具，除了几种痕量代谢物外，效果良好，这些痕量代谢物被埋在剩余的背景信号中。第三，一种综合策略，即先进行非靶向BS，然后进行改进的MDF，对MET-PAN-CLAR的代谢物鉴定有效。除痕量代谢物外，大多数代谢物在BS处理数据集的第一步中被发现，这些结果导致为后续MDF数据处理设置合适的代谢物MDF模板。痕量代谢物通过MDF进一步恢复，MDF使用了常见的MDF模板和基于新代谢物的MDF模板。结果，使用该综合策略在人血浆和尿液中总共发现了44种MET-PAN-CLAR的代谢物或相关成分。发现了新的代谢途径，如PAN的N-葡萄糖醛酸化和CLAR的脱氢。本研究表明，串联使用基于精确质量的多种数据挖掘技术，即先进行非靶向背景扣除，然后进行靶向质量缺陷过滤，可成为体内联合用药快速代谢物谱分析的有价值工具。
