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代谢组学:从定量构效关系到定量构效毒性关系,一种用于早期检测毒理学效应和基于生物学的化学分组的工具。

Metabolomics: a tool for early detection of toxicological effects and an opportunity for biology based grouping of chemicals-from QSAR to QBAR.

机构信息

BASF SE, Experimental Toxicology and Ecology, Z 470, D-67056 Ludwigshafen, Germany.

出版信息

Mutat Res. 2012 Aug 15;746(2):144-50. doi: 10.1016/j.mrgentox.2012.01.006. Epub 2012 Jan 26.

DOI:10.1016/j.mrgentox.2012.01.006
PMID:22305969
Abstract

BASF has developed a Metabolomics database (MetaMap(®) Tox) containing approximately 500 data rich chemicals, agrochemicals and drugs. This metabolome-database has been built based upon 28-day studies in rats (adapted to OECD 407 guideline) with blood sampling and metabolic profiling after 7, 14 and 28 days of test substance treatment. Numerous metabolome patterns have been established for different toxicological targets (liver, kidney, thyroid, testes, blood, nervous system and endocrine system) which are specific for different toxicological modes of action. With these patterns early detection of toxicological effects and the underlying mechanism can now be obtained from routine studies. Early recognition of toxicological mode of action will help to develop new compounds with a more favourable toxicological profile and will also help to reduce the number of animal studies necessary to do so. Thus this technology contributes to animal welfare by means of reduction through refinement (2R), but also has potential as a replacement method by analyzing samples from in vitro studies. With respect to the REACH legislation for which a large number of animal studies will need to be performed, one of the most promising methods to reduce the number of animal experiments is grouping of chemicals and read-across to those which are data rich. So far mostly chemical similarity or QSAR models are driving the selection process of chemical grouping. However, "omics" technologies such as metabolomics may help to optimize the chemical grouping process by providing biologically based criteria for toxicological equivalence. "From QSAR to QBAR" (quantitative biological activity relationship).

摘要

巴斯夫开发了一个代谢组学数据库(MetaMap(®) Tox),其中包含大约 500 种富含数据的化学物质、农药和药物。这个代谢组数据库是基于大鼠 28 天的研究(适应 OECD 407 指南)构建的,在测试物质处理后的第 7、14 和 28 天进行血液采样和代谢谱分析。已经为不同的毒理学靶标(肝脏、肾脏、甲状腺、睾丸、血液、神经系统和内分泌系统)建立了许多代谢组模式,这些模式针对不同的毒理学作用模式具有特异性。通过这些模式,可以从常规研究中获得毒理学效应和潜在机制的早期检测。早期识别毒理学作用模式将有助于开发具有更有利毒理学特征的新化合物,并有助于减少进行此类研究所需的动物研究数量。因此,这项技术通过改进(2R)减少来促进动物福利,并且通过分析来自体外研究的样品也具有替代方法的潜力。就 REACH 法规而言,需要进行大量的动物研究,减少动物实验数量最有希望的方法之一是对化学品进行分组,并将其与数据丰富的化学品进行推断。到目前为止,化学相似性或 QSAR 模型主要用于驱动化学品分组的选择过程。然而,代谢组学等“组学”技术可以通过提供基于生物学的毒理学等效性标准来帮助优化化学分组过程。“从 QSAR 到 QBAR”(定量生物活性关系)。

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