School of Pharmaceutical Sciences, Shandong University, Jinan, PR China.
School of Pharmaceutical Sciences, Shandong University, Jinan, PR China.
Anal Chim Acta. 2017 Nov 15;993:38-46. doi: 10.1016/j.aca.2017.08.047. Epub 2017 Sep 13.
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.
联合药物在复杂基质中的代谢产物谱分析是一个巨大的挑战。开发一种有效的数据分析技术，能够同时从背景基质和联合药物相关信号中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质谱（HRMS）的数据挖掘策略，用于快速全面地鉴定人血浆和尿液中联合药物的代谢产物。模型药物组合为维拉帕米-厄贝沙坦（VER-IRB），广泛用于临床治疗高血压。首先，质量亏损过滤（MDF）作为一种靶向数据分析工具，除了那些具有相似 MDF 值的代谢产物外，效果显著。其次，基于精确质量的背景扣除（BS）作为一种非靶向数据分析工具，能够从全扫描 MS 数据集恢复除了埋藏在背景噪声和/或联合药物相关信号中的痕量代谢产物之外的 VER-IRB 的所有相关代谢产物。第三，新的环双键（RDB；结构中元素的价态值）过滤器能够在更敏感的全扫描 MS 色谱图中显示丰富的结构信息；然而，它去除背景噪声的能力较低，难以区分具有 RDB 覆盖的代谢产物。第四，一种综合策略，即非靶向 BS 后接 RDB，对具有不同 RDB 值的 VER 和 IRB 的代谢产物鉴定有效。首先使用 BS 去除大部分基质信号。然后，通过在母体药物周围施加预设的 RDB 值/范围并选择核心子结构，从剩余的背景基质和联合药物相关信号中分离出每个母体药物的代谢产物离子。同时，使用 MDF 恢复具有相似 RDB 的潜在代谢产物。结果，在人血浆和尿液中发现了 74 种 VER-IRB 代谢产物，其中 10 种代谢产物以前未在人体内报道过。结果表明，联合使用基于精确质量的多种数据分析技术，即非靶向背景扣除，然后与靶向质量亏损过滤并行的环双键过滤，可以成为快速鉴定联合药物代谢产物的有用工具。
