毒素知识图谱:支持化妆品的无动物风险评估。

IF 3.6 4区 生物学 Q1 MATHEMATICAL & COMPUTATIONAL BIOLOGY
Sara Sepehri, Anja Heymans, Dinja De Win, Jan Maushagen, Audrey Sanctorum, Christophe Debruyne, Robim M Rodrigues, Joery De Kock, Vera Rogiers, Olga De Troyer, Tamara Vanhaecke
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引用次数: 0

摘要

欧盟禁止对化妆品及其成分进行动物实验,再加上缺乏经过验证的无动物实验方法,这给评估化妆品潜在的重复给药器官毒性带来了挑战。为了解决这个问题,人们正在探索下一代风险评估(NGRA)等创新策略,将历史动物数据与来自非动物新方法方法论(NAMs)的新机制见解相结合。本文介绍了毒素知识图谱(TOXIN KG),一个用于检索化妆品成分毒理学信息的工具,重点是肝脏相关数据。毒素KG使用图结构语义技术,并通过本体集成毒理学数据,确保可互操作表示。主要数据来源是2009年至2019年消费者安全科学委员会发布的科学意见中关于化妆品成分的安全信息。ToxRTool自动化了毒性研究的可靠性评估,而简化分子输入线输入系统(SMILES)符号标准化了化学鉴定,通过实施经济合作与发展组织定量结构-活性关系工具箱(OECD QSAR工具箱),实现了重复剂量毒性的计算机预测。毒性过程本体,丰富了相关的生物资源库,被用来系统地表示毒理学概念。搜索过滤器允许识别可能与肝毒性有关的化妆品化合物。数据可视化是通过JavaScript库Ontodia实现的。毒素KG含有88种化妆品成分的信息,使我们能够在90天的重复给药动物研究中确定53种影响至少一种肝脏毒性参数的化合物。对于一种化合物,我们说明了毒素KG如何将这种观察与肝脏胆汁淤积作为不利结果联系起来。在从头开始的NGRA背景下,有必要使用基于人的NAMs进行后续的体外研究,以了解该化合物的生物活性和导致不良反应的分子机制。总之,毒素KG是促进化妆品安全数据可重复使用的宝贵工具,为支持基于nama的危害和风险评估提供了知识。数据库地址:https://toxin-search.netlify.app/。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
The TOXIN knowledge graph: supporting animal-free risk assessment of cosmetics.

The European Union's ban on animal testing for cosmetic products and their ingredients, combined with the lack of validated animal-free methods, poses challenges in evaluating their potential repeated-dose organ toxicity. To address this, innovative strategies like Next-Generation Risk Assessment (NGRA) are being explored, integrating historical animal data with new mechanistic insights from non-animal New Approach Methodologies (NAMs). This paper introduces the TOXIN knowledge graph (TOXIN KG), a tool designed to retrieve toxicological information on cosmetic ingredients, with a focus on liver-related data. TOXIN KG uses graph-structured semantic technology and integrates toxicological data through ontologies, ensuring interoperable representation. The primary data source is safety information on cosmetic ingredients from scientific opinions issued by the Scientific Committee on Consumer Safety between 2009 and 2019. The ToxRTool automates the reliability assessment of toxicity studies, while the Simplified Molecular Input Line Entry System (SMILES) notation standardizes chemical identification, enabling in silico prediction of repeated-dose toxicity via the implementation of the Organization for Economic Co-operation and Development Quantitative Structure-Activity Relationship Toolbox (OECD QSAR Toolbox). The ToXic Process Ontology, enriched with relevant biological repositories, is employed to represent toxicological concepts systematically. Search filters allow the identification of cosmetic compounds potentially linked to liver toxicity. Data visualization is achieved through Ontodia, a JavaScript library. TOXIN KG, filled with information for 88 cosmetic ingredients, allowed us to identify 53 compounds affecting at least one liver toxicity parameter in a 90-day repeated-dose animal study. For one compound, we illustrate how TOXIN KG links this observation to hepatic cholestasis as an adverse outcome. In an ab initio NGRA context, follow-up in vitro studies using human-based NAMs would be necessary to understand the compound's biological activity and the molecular mechanism leading to the adverse effect. In summary, TOXIN KG emerges as a valuable tool for advancing the reusability of cosmetics safety data, providing knowledge in support of NAM-based hazard and risk assessments. Database URL: https://toxin-search.netlify.app/.

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来源期刊
Database: The Journal of Biological Databases and Curation
Database: The Journal of Biological Databases and Curation MATHEMATICAL & COMPUTATIONAL BIOLOGY-
CiteScore
9.00
自引率
3.40%
发文量
100
审稿时长
>12 weeks
期刊介绍: Huge volumes of primary data are archived in numerous open-access databases, and with new generation technologies becoming more common in laboratories, large datasets will become even more prevalent. The archiving, curation, analysis and interpretation of all of these data are a challenge. Database development and biocuration are at the forefront of the endeavor to make sense of this mounting deluge of data. Database: The Journal of Biological Databases and Curation provides an open access platform for the presentation of novel ideas in database research and biocuration, and aims to help strengthen the bridge between database developers, curators, and users.
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