Boyce Matthew, Favela Kristin A, Bonzo Jessica A, Chao Alex, Lizarraga Lucina E, Moody Laura R, Owens Elizabeth O, Patlewicz Grace, Shah Imran, Sobus Jon R, Thomas Russell S, Williams Antony J, Yau Alice, Wambaugh John F
Center for Computational Exposure, Office of Research and Development, U.S. Environmental Protection Agency, Research Triangle Park, Durham, NC, United States.
Southwest Research Institute, San Antonio, TX, United States.
Front Toxicol. 2023 Jan 18;5:1051483. doi: 10.3389/ftox.2023.1051483. eCollection 2023.
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 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 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 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.
了解外源性物质的代谢命运有助于了解其潜在的健康风险，并有助于识别与暴露相关的标志性代谢物。对研究较少或新型物质的代谢物进行表征的需求，已将暴露研究转向非靶向分析（NTA），这种分析通常旨在使用高分辨率液相色谱-质谱联用（LCMS）对样品中的多种化合物进行分析。在这里，我们评估可疑物筛选分析（SSA）液相色谱-质谱联用技术在外源性化学物质代谢研究中的适用性。由于大多数化学物质的真实代谢物信息匮乏，因此使用预测工具生成潜在代谢物作为可疑物筛选列表，以指导选定外源性物质及其相关代谢物的鉴定。从美国环境保护局的ToxCast化学库中选择了33种物质，代表各种药物、农用化学品和工业化学品。将这些化合物在使用原代肝细胞的代谢活性测定中进行孵育，然后用高分辨率LCMS分析所得的上清液和裂解物部分。使用软件对每种化合物结构的代谢物进行模拟，然后合并以用作可疑物筛选列表。然后使用预测代谢物的精确质量来选择用于串联质谱（MS/MS）裂解的LCMS特征。在起始化学物质中，有12种在至少一个样品中以正离子或负离子模式被检测到，其中一部分被用于开发分析工作流程。我们实施了一种筛选级工作流程，用于背景扣除以及将随时间变化的动力学纳入可能代谢物的鉴定中。我们以氟哌啶醇为例进行深入分析，结果鉴定出5种已知代谢物和5种代表潜在新代谢物的分子特征，其中2种根据预测被赋予了离散结构。该工作流程应用于另外5种测试化学品，15种分子特征被选为已报道的代谢物、预测的代谢物或无结构归属的潜在代谢物。这项研究表明，在某些（但不是所有）情况下，可疑物筛选分析方法提供了一种快速识别和表征外源性化学物质代谢物的手段。
