The application of a novel high-resolution mass spectrometry-based analytical strategy to rapid metabolite profiling of a dual drug combination in humans
The application of a novel high-resolution mass spectrometry-based analytical strategy to rapid metabolite profiling of a dual drug combination in humans
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应用新型高分辨率质谱分析策略对人体双药组合进行快速代谢物分析
DOI：
10.1016/j.aca.2017.08.047
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发表时间：
2017-11-15
影响因子：
6.2
通讯作者：
Liu, Huixiang
中科院分区：
文献类型：
--
作者：
Xing, Jie;Zang, Meitong;Liu, Huixiang
复方药物在复杂基质中的代谢物谱分析是一个很大的挑战。开发一种有效的数据挖掘技术，从背景基质和组合药物相关信号中同时提取一种母体药物的代谢产物，可能是一种解决方案。本研究提出了一种新的基于高分辨率质谱（HRMS）的数据挖掘策略，以快速和全面的代谢物鉴定的组合药物在人体内。模型药物组合为临床上广泛用于治疗高血压的维拉帕米-厄贝沙坦（VER-IRB）。首先，质量缺陷滤波器（mass defect filter，简称mf）作为一种有针对性的数据挖掘工具，除了那些具有相似mf值的代谢物外，其工作是有效的。第二，准确的基于质量的背景减除（BS），作为一种非靶向的数据挖掘工具，能够从全扫描MS数据集中恢复VER-IRB的所有相关代谢物，除了隐藏在背景噪声和/或组合药物相关信号中的痕量代谢物。第三，新型环双键（RDB;结构中元素的价值）过滤器，可以显示更灵敏的全扫描MS色谱图中丰富的结构信息，但它具有较低的去除背景噪声的能力，并且难以区分具有RDB覆盖的代谢物。第四，综合战略，非靶向BS，然后是RDB，是有效的代谢物鉴定VER和IRB，这具有不同的RDB值。首先使用BS去除大部分基质信号。然后通过在母体药物和选定的核心子结构周围施加预设的RDB值/范围，从剩余的背景基质和组合的药物相关信号中分离每种母体药物的代谢物离子。平行地，使用HPLC回收具有相似RDB的潜在代谢物。因此，在人血浆和尿液中共发现74种VER-IRB代谢产物，其中10种代谢产物先前在人体中未报告。结果表明，结合准确的质量为基础的多种数据挖掘技术，即，非靶向背景扣除，然后环双键过滤与靶向质量缺陷过滤并行，可以是用于组合药物的快速代谢物谱分析的有价值的工具。(C)2017爱思唯尔B. V.保留所有权利。
Metabolite profiling of combination drugs in complex matrix is a big challenge. Development of an effective data mining technique for simultaneously extracting metabolites of one parent drug from both background matrix and combined drug-related signals could be a solution. This study presented a novel high resolution mass spectrometry (HRMS)-based data-mining strategy to fast and comprehensive metabolite identification of combination drugs in human. The model drug combination was verapamil-irbesartan (VER-IRB), which is widely used in clinic to treat hypertension. First, mass defect filter (MDF), as a targeted data mining tool, worked effectively except for those metabolites with similar MDF values. Second, the accurate mass-based background subtraction (BS), as an untargeted data-mining tool, was able to recover all relevant metabolites of VER-IRB from the full-scan MS dataset except for trace metabolites buried in the background noise and/or combined drug-related signals. Third, the novel ring double bond (RDB; valence values of elements in structure) filter, could show rich structural information in more sensitive full-scan MS chromatograms; however, it had a low capability to remove background noise and was difficult to differentiate the metabolites with RDB coverage. Fourth, an integrated strategy, i.e., untargeted BS followed by RDB, was effective for metabolite identification of VER and IRB, which have different RDB values. Majority of matrix signals were firstly removed using BS. Metabolite ions for each parent drug were then isolated from remaining background matrix and combined drug-related signals by imposing of preset RDB values/ranges around the parent drug and selected core substructures. In parallel, MDF was used to recover potential metabolites with similar RDB. As a result, a total of 74 metabolites were found for VER-IRB in human plasma and urine, among which ten metabolites have not been previously reported in human. The results demonstrated that the combination of accurate mass-based multiple data-mining techniques, i.e., untargeted background subtraction followed by ring double bond filtering in parallel with targeted mass defect filtering, can be a valuable tool for rapid metabolite profiling of combination drug. (C) 2017 Elsevier B.V. All rights reserved.