51. Identification of flurochloridone metabolites in rat urine using liquid chromatography/high resolution mass spectrometry
- Author
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Liming Tang, Qian Xu, Chao Feng, Suhui Zhang, Dasheng Lu, Chunhua Wu, Yu ’e Jin, Guoquan Wang, Jianwen She, Yuanjie Lin, Shihong Liu, Zhijun Zhou, and Dongli Wang
- Subjects
Male ,0301 basic medicine ,Metabolite ,In silico ,Urine ,Urinalysis ,Orbitrap ,Mass spectrometry ,Tandem mass spectrometry ,01 natural sciences ,Biochemistry ,Analytical Chemistry ,law.invention ,03 medical and health sciences ,chemistry.chemical_compound ,Tandem Mass Spectrometry ,law ,Animals ,Biotransformation ,Chromatography ,010401 analytical chemistry ,Organic Chemistry ,Flurochloridone ,General Medicine ,Pyrrolidinones ,Rats ,0104 chemical sciences ,030104 developmental biology ,chemistry ,Urine sample ,Chromatography, Liquid - Abstract
It is of great interest to develop strategic methods to enable chemicals' metabolites to be accurately and rapidly screened and identified. To screen and identify a category of metabolites with distinct isotopic distribution, this study proposed a generic strategy using in silico metabolite prediction plus accurate-mass-based isotopic pattern recognition (AMBIPR) and library identification on the data acquired via the data dependent MS/MS scan of LC-Q Exactive Orbitrap mass spectrometry. The proposed method was evaluated by the analysis of flurochloridone (FLC) metabolites in rat urine sample collected from toxicity tests. Different from the traditional isotopic pattern recognition (IPR) approach, AMBIPR here was performed based on the potential metabolites predicted via in silico metabolite prediction tools. Thus, the AMBIPR treated FLC data was only associated with FLC metabolites, consequently not only avoiding great efforts made to remove FLC-unrelated information and reveal FLC metabolites, but also increasing the percent of positive hits. Among the FLC metabolite peaks screened using AMBIPR, 87% of them (corresponding 97 metabolites and 49 biotransformation) were successfully identified via multiple MS identification techniques packaged in an established FLC's metabolites library based on Mass Frontier. Noteworthy, 34 metabolites (89%) were identified without distinct naturally isotopic distribution. The universal strategic approach based on background subtraction (BS) and mass defect filtering (MDF) was used to evaluate the AMBIPR and no more false positive and negative metabolites were detected. Furthermore, our results revealed that AMBIPR is very effective, inherently sensitive and accurate, and is easily automated for the rapidly screening and profiling chemicals related metabolites.
- Published
- 2016