Institute of Pharmaceutical Science and Technology and College of Pharmacy, Hanyang University, Ansan 15588, Republic of Korea.
Collaborative Mass Spectrometry Innovation Center, Skaggs School of Pharmacy and Pharmaceutical Sciences, University of California San Diego, La Jolla, California 92093, United States.
Anal Chem. 2022 Jan 18;94(2):1456-1464. doi: 10.1021/acs.analchem.1c04925. Epub 2022 Jan 5.
Molecular networking (MN) has become a popular data analysis method for untargeted mass spectrometry (MS)/MS-based metabolomics. Recently, MN has been suggested as a powerful tool for drug metabolite identification, but its effectiveness for drug metabolism studies has not yet been benchmarked against existing strategies. In this study, we compared the performance of MN, mass defect filtering, Agilent MassHunter Metabolite ID, and Agilent Mass Profiler Professional workflows to annotate metabolites of sildenafil generated in an in vitro liver microsome-based metabolism study. Totally, 28 previously known metabolites with 15 additional unknown isomers and 25 unknown metabolites were found in this study. The comparison demonstrated that MN exhibited performances comparable or superior to those of the existing tools in terms of the number of detected metabolites (27 known metabolites and 22 unknown metabolites), ratio of false positives, and the amount of time and effort required for human labor-based postprocessing, which provided evidence of the efficiency of MN as a drug metabolite identification tool.
分子网络（MN）已成为一种用于非靶向质谱（MS）/MS 代谢组学的流行数据分析方法。最近，MN 被提议作为一种强大的药物代谢物鉴定工具，但它在药物代谢研究中的有效性尚未与现有策略进行基准比较。在这项研究中，我们比较了 MN、质量缺陷过滤、安捷伦 MassHunter 代谢物 ID 和安捷伦 Mass Profiler Professional 工作流程在注释基于体外肝微粒体的代谢研究中生成的西地那非代谢物方面的性能。总共在这项研究中发现了 28 种先前已知的代谢物，加上 15 种额外的未知异构体和 25 种未知代谢物。该比较表明，MN 在检测到的代谢物数量（27 种已知代谢物和 22 种未知代谢物）、假阳性率以及基于人工的后期处理所需的时间和精力方面，与现有工具的性能相当或更优，这为 MN 作为药物代谢物鉴定工具的效率提供了证据。
