PDF(Pubmed)
Abstract：
While grouping/read-across is widely used to fill data gaps, chemical registration dossiers are often rejected due to weak category justifications based on structural similarity only. Metabolomics provides a route to robust chemical categories via evidence of shared molecular effects across source and target substances. To gain international acceptance, this approach must demonstrate high reliability, and best-practice guidance is required. The MetAbolomics ring Trial for CHemical groupING (MATCHING), comprising six industrial, government and academic ring-trial partners, evaluated inter-laboratory reproducibility and worked towards best-practice. An independent team selected eight substances (WY-14643, 4-chloro-3-nitroaniline, 17α-methyl-testosterone, trenbolone, aniline, dichlorprop-p, 2-chloroaniline, fenofibrate); ring-trial partners were blinded to their identities and modes-of-action. Plasma samples were derived from 28-day rat tests (two doses per substance), aliquoted, and distributed to partners. Each partner applied their preferred liquid chromatography-mass spectrometry (LC-MS) metabolomics workflows to acquire, process, quality assess, statistically analyze and report their grouping results to the European Chemicals Agency, to ensure the blinding conditions of the ring trial. Five of six partners, whose metabolomics datasets passed quality control, correctly identified the grouping of eight test substances into three categories, for both male and female rats. Strikingly, this was achieved even though a range of metabolomics approaches were used. Through assessing intrastudy quality-control samples, the sixth partner observed high technical variation and was unable to group the substances. By comparing workflows, we conclude that some heterogeneity in metabolomics methods is not detrimental to consistent grouping, and that assessing data quality prior to grouping is essential. We recommend development of international guidance for quality-control acceptance criteria. This study demonstrates the reliability of metabolomics for chemical grouping and works towards best-practice.
摘要：
虽然分组/跨读被广泛用于填补数据空白，由于仅基于结构相似性的类别理由薄弱，化学注册档案经常被拒绝。代谢组学通过源物质和靶物质共享分子效应的证据，提供了一条通往强大化学类别的途径。为了获得国际认可，这种方法必须证明高可靠性，需要最佳实践指导。用于化学分组(匹配)的MetAbolomics环试验，包括六个工业，政府和学术环审判合作伙伴，评估实验室间的可重复性，并致力于最佳实践。一个独立的小组选择了八种物质（WY-14643，4-氯-3-硝基苯胺，17α-甲基睾酮，群勃龙,苯胺,二氯丙-p,2-氯苯胺,非诺贝特）；环试验合作伙伴对他们的身份和作用方式视而不见。血浆样本来自28天的大鼠试验（每种物质两种剂量），等分,分发给合作伙伴。每个合作伙伴都应用他们首选的液相色谱-质谱（LC-MS）代谢组学工作流程来获取，process,质量评估，统计分析并将其分组结果报告给欧洲化学品管理局，确保环试验的盲检条件。六个合伙人中的五个,其代谢组学数据集通过了质量控制，正确识别了将八种测试物质分为三类，对于雄性和雌性老鼠。引人注目的是，即使使用了一系列代谢组学方法,这也是实现的.通过评估研究内质量控制样本，第六个伙伴观察到技术差异很大，无法对这些物质进行分组。通过比较工作流，我们得出结论，代谢组学方法中的一些异质性对一致的分组并不有害，在分组之前评估数据质量是至关重要的。我们建议制定质量控制验收标准的国际指南。这项研究证明了代谢组学用于化学分组的可靠性，并致力于实现最佳实践。
