Table2_Using in silico tools to predict flame retardant metabolites for more informative exposomics-based approaches.XLSX

Introduction: The positive identification of xenobiotics and their metabolites in human biosamples is an integral aspect of exposomics research, yet challenges in compound annotation and identification continue to limit the feasibility of comprehensive identification of total chemical exposure. Nonetheless, the adoption of in silico tools such as metabolite prediction software, QSAR-ready structural conversion workflows, and molecular standards databases can aid in identifying novel compounds in untargeted mass spectral investigations, permitting the assessment of a more expansive pool of compounds for human health hazard. This strategy is particularly applicable when it comes to flame retardant chemicals. The population is ubiquitously exposed to flame retardants, and evidence implicates some of these compounds as developmental neurotoxicants, endocrine disruptors, reproductive toxicants, immunotoxicants, and carcinogens. However, many flame retardants are poorly characterized, have not been linked to a definitive mode of toxic action, and are known to share metabolic breakdown products which may themselves harbor toxicity. As U.S. regulatory bodies begin to pursue a subclass- based risk assessment of organohalogen flame retardants, little consideration has been paid to the role of potentially toxic metabolites, or to expanding the identification of parent flame retardants and their metabolic breakdown products in human biosamples to better inform the human health hazards imposed by these compounds.
Methods: The purpose of this study is to utilize publicly available in silico tools to 1) characterize the structural and metabolic fates of proposed flame retardant classes, 2) predict first pass metabolites, 3) ascertain whether metabolic products segregate among parent flame retardant classification patterns, and 4) assess the existing coverage in of these compounds in mass spectral database.
Results: We found that flame retardant classes as currently defined by the National Academies of Science, Engineering and Medicine (NASEM) are structurally diverse, with highly variable predicted pharmacokinetic properties and metabolic fates among member compounds. The vast majority of flame retardants (96%) and their predicted metabolites (99%) are not present in spectral databases, posing a challenge for identifying these compounds in human biosamples. However, we also demonstrate the utility of publicly available in silico methods in generating a fit for purpose synthetic spectral library for flame retardants and their metabolites that have yet to be identified in human biosamples.
Discussion: In conclusion, exposomics studies making use of fit-for-purpose synthetic spectral databases will better resolve internal exposure and windows of vulnerability associated with complex exposures to flame retardant chemicals and perturbed neurodevelopmental, reproductive, and other associated apical human health impacts.

引言：在人类生物样本中准确识别外源性化合物（xenobiotics）及其代谢产物，是暴露组学（exposomics）研究的核心组成部分。然而，化合物注释与识别环节仍存在诸多挑战，制约了对人体总化学暴露进行全面识别的可行性。尽管如此，采用代谢产物预测软件、适配定量结构-活性关系（QSAR）的结构转换工作流以及分子标准数据库等虚拟（in silico）工具，可助力非靶向质谱分析中新型化合物的识别，从而能够针对更广泛的化合物集合开展人体健康危害评估。该策略在阻燃剂类化学品的研究中尤为适用。人群普遍暴露于阻燃剂中，现有研究证据表明部分此类化合物具有发育性神经毒性、内分泌干扰活性、生殖毒性、免疫毒性以及致癌性。然而，多数阻燃剂的特性尚不明确，尚未确定明确的毒性作用模式，且已知它们共享可能自身具备毒性的代谢分解产物。随着美国监管机构开始针对有机卤系阻燃剂开展基于子类的风险评估工作，目前极少考虑潜在毒性代谢产物的作用，也未扩大对人类生物样本中母体阻燃剂及其代谢分解产物的识别范围，以更好地明确此类化合物对人体健康造成的危害。

方法：本研究旨在利用公开可用的虚拟工具完成以下四项任务：1）解析拟议阻燃剂类别的结构特征与代谢归宿；2）预测首过代谢产物；3）明确代谢产物是否按照母体阻燃剂的分类模式进行聚类分离；4）评估现有质谱数据库对上述化合物的收录覆盖情况。

结果：本研究发现，美国国家科学院、工程院和医学院（National Academies of Sciences, Engineering, and Medicine, NASEM）当前定义的阻燃剂类别结构多样性显著，其成员化合物的预测药代动力学特性与代谢归宿差异极大。绝大多数阻燃剂（96%）及其预测代谢产物（99%）未被收录于质谱数据库中，这为在人类生物样本中识别此类化合物带来了挑战。不过，本研究同时证实，利用公开可用的虚拟方法，可以为尚未在人类生物样本中被发现的阻燃剂及其代谢产物构建适配特定用途的合成光谱库。

讨论：综上，利用适配特定用途的合成光谱数据库开展暴露组学研究，可更精准地解析与复杂阻燃剂暴露相关的人体内暴露情况与易感窗口期，以及由此引发的神经发育、生殖等相关顶端人体健康效应的扰动。