Targeted Data-Independent Acquisition and Mining Strategy for Trace Drug Metabolite Identification Using Liquid Chromatography Coupled with Tandem Mass Spectrometry

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• 作者：Yan Gao ; Ruiping Zhang ; Jinfa Bai ; Xuejun Xia ; Yanhua Chen ; Zhigang Luo ; Jing Xu ; Yang Gao ; Yuling Liu ; Jiuming He ; Zeper Abliz
• 刊名：Analytical Chemistry
• 出版年：2015
• 出版时间：August 4, 2015
• 年：2015
• 卷：87
• 期：15
• 页码：7535-7539
• 全文大小：332K
• ISSN：1520-6882

文摘

Detection and identification of unknown or low-level drug-related metabolites in complex biological materials is an ongoing challenge. A highly selective and sensitive method could be a possible solution. Here, we proposed a targeted data-independent acquisition and mining (TDIAM) strategy for the rapid identification of trace drug metabolites using ultra-high-performance liquid chromatography coupled with high-resolution tandem mass spectrometry (UHPLC-HRMS/MS). In this strategy, raw data is acquired by a novel tm-MS scan, which contains an interleaved full MS scan with a targeted mass range and a product ion scan by selecting all ions in the targeted mass range as precursor ions. For efficient discovery of metabolites, raw data are analyzed by a new postacquisition processing method, Molecule- and Fragmentation-driven Mass Defect Filters (MF-MDFs), which was developed based on the fragmentation of parent drug to pick out molecular ions and fragment ions of potential metabolites from the complex matrix. When applying the proposed strategy to paclitaxel metabolism research, we successfully identified 10 metabolites, among which six were not previously reported. The results demonstrated that TDIAM greatly improved throughput, detective sensitivity, and selectivity and, more importantly, yielded almost the same spectrum as traditional HRMS/MS. Therefore, TDIAM provides structure-enriched evidence to confirm the existence and elucidate the structures of metabolites. This strategy is suitable for identification of metabolites present at low concentrations in a complex matrix, and it has the potential to provide an efficient, sensitive, and labor-saving solution for drug metabolite research.