posted on 2022-01-05, 15:33authored byJun Sang Yu, Louis-Félix Nothias, Mingxun Wang, Dong Hyun Kim, Pieter C. Dorrestein, Kyo Bin Kang, Hye Hyun Yoo
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.