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Metabolite identification of the antimalarial naphthoquine using liquid chromatography–tandem high‐resolution mass spectrometry in combination with multiple data‐mining tools
Author(s) -
Sun Yanhong,
Wang Shuqi,
Ji Jianbo,
Zhai Guangxi,
Xing Jie
Publication year - 2018
Publication title -
biomedical chromatography
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.4
H-Index - 65
eISSN - 1099-0801
pISSN - 0269-3879
DOI - 10.1002/bmc.4207
Subject(s) - chemistry , chromatography , metabolite , orbitrap , tandem mass spectrometry , protein precipitation , solid phase extraction , mass spectrometry , liquid chromatography–mass spectrometry , metabolomics , biochemistry
Naphthoquine (NQ) is one of important partner drugs of artemisinin‐based combination therapy (ACT), which is recommended for the treatment of uncomplicated Plasmodium falciparum. NQ shows a high cure rate after a single oral administration. It is absorbed quickly (time to peak concentration 2–4 h) and has a long elimination half‐life (255 h). However, the metabolism of NQ has not been clarified. In this work, the metabolite profiling of NQ was studied in six liver microsomal incubates (human, cynomolgus monkey, beagle dog, mini pig, rat and CD1 mouse), seven recombinant CYP enzymes (1A2, 2B6, 2C8, 2C9, 2C19, 2D6 and 3A4) and rat (plasma, urine, bile and feces) using liquid chromatography tandem high‐resolution LTQ‐Orbitrap mass spectrometry (HRMS n ) in conjunction with online hydrogen/deuterium exchange. The biological samples were pretreated by protein precipitation and solid‐phase extraction. For data processing, multiple data‐mining tools were applied in tandem, i.e. background subtraction and followed by mass defect filter. NQ metabolites were characterized by accurate MS/MS fragmentation characteristics, the hydrogen/deuterium exchange data and c Log P simulation. As a result, five phase I metabolites (M1–M5) of NQ were characterized for the first time. Two metabolic pathways were involved: hydroxylation and N ‐oxidation. This study demonstrates that LC‐HRMS n in combination with multiple data‐mining tools in tandem can be a valuable analytical strategy for rapid metabolite profiling of drugs.