Co-reporter:Attia Afzal, Yunxi Zhong, Muhammad Sarfraz, Ying Peng, Longsheng Sheng, Zimei Wu, Jianguo Sun, Guangji Wang
Journal of Chromatography A 2016 Volume 1444() pp:74-85
Publication Date(Web):29 April 2016
DOI:10.1016/j.chroma.2016.03.068
•Commercial software packages were coupled with LC/IT-TOFMS for data mining.•Segmented MSn data acquisition was qualified for molecular ion of interest.•Conjugated and phase I metabolites were detected in gender-based biological fluids.•Maximum metabolites were identified by bilary route in male rats at 2 h post dosing.Asulacrine (ASL) is a broad-spectrum, antitumor drug whose data are promising for the treatment of breast and lung cancers; however, a high incidence of phlebitis hampered its further development. Phlebitis is associated with generation of reactive species. Asulacrine donates electrons and produces oxidative stress in chemical reactions. It was expected that ASL would actively metabolize to oxidized products through reactive intermediates and produce more products in vivo than reported and thus cause phlebitis. A comprehensive study was planned to investigate in vivo metabolism of ASL, using high-resolution mass spectrometry LC/IT-TOF MS in positive mode. Metabolites were detected by different software by applying annotated detection strategy. The possible metabolites and their product ions were simultaneously detected by segmented data acquisition to get accurate mass values. Segmented data acquisition improved signal-to-noise (S/N) ratio, which was helpful to detect metabolites and their fragments even when present in trace amounts. A total of 21 metabolites were detected in gender-based biological fluids and characterized by comparing their accurate mass values, fragmentation patterns, and relative retention times with that of ASL. Among previously reported glucuronosylation metabolites, some oxidation, hydroxylation, carboxylation, demethylation, hydrogenation, glutamination, and acetylcysteine conjugation were detected for the first time. Twenty metabolites were tentatively identified by using the annotated strategy for data acquisition and post-data mining.