دورية أكاديمية
أعرض في EDS

Identifying xenobiotic metabolites with in silico prediction tools and LCMS suspect screening analysis.

التفاصيل البيبلوغرافية
العنوان: Identifying xenobiotic metabolites with in silico prediction tools and LCMS suspect screening analysis.
المؤلفون: Boyce M; Center for Computational Exposure, Office of Research and Development, U.S. Environmental Protection Agency, Research Triangle Park, Durham, NC, United States., Favela KA; Southwest Research Institute, San Antonio, TX, United States., Bonzo JA; Thermo Fisher Scientific, South San Francisco, CA, United States., Chao A; Center for Computational Exposure, Office of Research and Development, U.S. Environmental Protection Agency, Research Triangle Park, Durham, NC, United States., Lizarraga LE; Center for Public Health and Environmental Assessment, Office of Research and Development, U.S. Environmental Protection Agency, Cincinnati, OH, United States., Moody LR; Thermo Fisher Scientific, South San Francisco, CA, United States., Owens EO; Center for Public Health and Environmental Assessment, Office of Research and Development, U.S. Environmental Protection Agency, Cincinnati, OH, United States., Patlewicz G; Center for Computational Exposure, Office of Research and Development, U.S. Environmental Protection Agency, Research Triangle Park, Durham, NC, United States., Shah I; Center for Computational Exposure, Office of Research and Development, U.S. Environmental Protection Agency, Research Triangle Park, Durham, NC, United States., Sobus JR; Center for Computational Exposure, Office of Research and Development, U.S. Environmental Protection Agency, Research Triangle Park, Durham, NC, United States., Thomas RS; Center for Computational Exposure, Office of Research and Development, U.S. Environmental Protection Agency, Research Triangle Park, Durham, NC, United States., Williams AJ; Center for Computational Exposure, Office of Research and Development, U.S. Environmental Protection Agency, Research Triangle Park, Durham, NC, United States., Yau A; Southwest Research Institute, San Antonio, TX, United States., Wambaugh JF; Center for Computational Exposure, Office of Research and Development, U.S. Environmental Protection Agency, Research Triangle Park, Durham, NC, United States.
المصدر: Frontiers in toxicology [Front Toxicol] 2023 Jan 18; Vol. 5, pp. 1051483. Date of Electronic Publication: 2023 Jan 18 (Print Publication: 2023).
نوع المنشور: Journal Article
اللغة: English
بيانات الدورية: Publisher: Frontiers Research Foundation Country of Publication: Switzerland NLM ID: 101777990 Publication Model: eCollection Cited Medium: Internet ISSN: 2673-3080 (Electronic) Linking ISSN: 26733080 NLM ISO Abbreviation: Front Toxicol Subsets: PubMed not MEDLINE
أسماء مطبوعة: Original Publication: Lausanne, Switzerland : Frontiers Research Foundation, [2019]-
مستخلص: Understanding the metabolic fate of a xenobiotic substance can help inform its potential health risks and allow for the identification of signature metabolites associated with exposure. The need to characterize metabolites of poorly studied or novel substances has shifted exposure studies towards non-targeted analysis (NTA), which often aims to profile many compounds within a sample using high-resolution liquid-chromatography mass-spectrometry (LCMS). Here we evaluate the suitability of suspect screening analysis (SSA) liquid-chromatography mass-spectrometry to inform xenobiotic chemical metabolism. Given a lack of knowledge of true metabolites for most chemicals, predictive tools were used to generate potential metabolites as suspect screening lists to guide the identification of selected xenobiotic substances and their associated metabolites. Thirty-three substances were selected to represent a diverse array of pharmaceutical, agrochemical, and industrial chemicals from Environmental Protection Agency's ToxCast chemical library. The compounds were incubated in a metabolically-active in vitro assay using primary hepatocytes and the resulting supernatant and lysate fractions were analyzed with high-resolution LCMS. Metabolites were simulated for each compound structure using software and then combined to serve as the suspect screening list. The exact masses of the predicted metabolites were then used to select LCMS features for fragmentation via tandem mass spectrometry (MS/MS). Of the starting chemicals, 12 were measured in at least one sample in either positive or negative ion mode and a subset of these were used to develop the analysis workflow. We implemented a screening level workflow for background subtraction and the incorporation of time-varying kinetics into the identification of likely metabolites. We used haloperidol as a case study to perform an in-depth analysis, which resulted in identifying five known metabolites and five molecular features that represent potential novel metabolites, two of which were assigned discrete structures based on in silico predictions. This workflow was applied to five additional test chemicals, and 15 molecular features were selected as either reported metabolites, predicted metabolites, or potential metabolites without a structural assignment. This study demonstrates that in some-but not all-cases, suspect screening analysis methods provide a means to rapidly identify and characterize metabolites of xenobiotic chemicals.
Competing Interests: The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.
(Copyright © 2023 Boyce, Favela, Bonzo, Chao, Lizarraga, Moody, Owens, Patlewicz, Shah, Sobus, Thomas, Williams, Yau and Wambaugh.)
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فهرسة مساهمة: Keywords: in vitro; mass-spectrometry; metabolism; suspect-screening analysis; xenobiotics
تواريخ الأحداث: Date Created: 20230206 Latest Revision: 20230207
رمز التحديث: 20230207
مُعرف محوري في PubMed: PMC9889941
DOI: 10.3389/ftox.2023.1051483
PMID: 36742129
قاعدة البيانات: MEDLINE
الوصف
تدمد:2673-3080
DOI:10.3389/ftox.2023.1051483