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毒素知识图:支持对化品的无动物风险评估

Sara Sepehri¹, Anja Heymans¹, Dinja De Win¹

¹Department of In Vitro Toxicology and Dermato-Cosmetology (IVTD), Vrije Universiteit Brussel, Laarbeeklaan 103, Brussels 1090, Belgium.

Database : the journal of biological databases and curation

|January 29, 2025

概括

毒素知识图 (KG) 通过整合非动物测试数据来帮助化品安全. 它确定了53种可能与肝脏毒性相关的成分,支持下一代风险评估 (NGRA).

科学领域:

毒理学毒理学毒理学
计算化学计算化学
化品科学化品科学

背景情况:

欧盟法规禁止对化品进行动物试验,因此需要使用替代毒性评估方法.
现有的非动物新方法方法 (NAM) 需要强大的数据集成和解释框架.
在没有经过验证的无动物方法的情况下,评估化品成分的重复剂量器官毒性是具有挑战性的.

研究的目的:

引入TOXIN知识图 (KG) 作为一种工具,用于检索化品成分的毒理信息,重点是肝毒性.
通过重新使用现有的安全数据,证明TOXIN KG在支持下一代风险评估 (NGRA) 中的实用性.
使用综合毒理学数据识别具有潜在肝毒性的化品成分.

主要方法:

开发了TOXIN KG,使用图形结构的语义技术和本体学来互操作地表示毒理学数据.
关于88种化品成分的消费者安全科学委员会意见 (2009-2019) 的综合数据.
使用ToxRTool进行可靠性评估,使用SMILES标记进行化学识别,以及OECD QSAR工具箱进行in silico预测.
在数据表示和可视化方面采用了ToXic过程本体学和Ontodia.

主要成果:

毒素KG成功地整合了88种化品成分的毒理数据.
在90天重复剂量动物研究中,确定了53种与至少一种肝毒性参数相关的成分.
已证明将特定化合物的数据与肝胆固醇症联系在一起作为不良结果.

结论:

毒素KG是提高化品安全数据可重复使用性的宝贵工具.
知识图支持基于NAM的化品成分危险和风险评估.
建议使用基于人类的NAM进行进一步的体外研究,以阐明毒性机制.

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