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一个化学信息学工作流程来选择代表性TSCA化学品用于新方法方法 (NAM) 选.

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这项研究开发了一种化学信息工作流程,从TSCA清单中选择318种不同的化学物质,用于使用新方法 (NAM) 的体外查. 这种方法有助于填补新的化学评估的数据缺口.

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科学领域:

  • 环境化学环境化学
  • 毒理学 毒理学 毒理学
  • 计算化学的计算化学

背景情况:

  • 有毒物质控制法 (TSCA) 要求对化学品进行危险和暴露评估.
  • 新化学品通报中的数据缺口往往需要预测方法.
  • 新方法方法 (NAMs) 提供了加强化学评估的机会.

研究的目的:

  • 开发和应用化学信息工作流程,以选择用于体外查的代表性化学品.
  • 创建一个案例研究,以适应性NAMs为基础的方法,用于新的化学评估.
  • 从TSCA清单中识别候选化学品,用于危险和暴露评估.

主要方法:

  • 使用ClassyFire进行化学分类和层次聚类,以定义180个终端结构类别.
  • 根据选历史,物理化学性质,采购可能性和分析可接受性,过了TSCA库存.
  • 被排除在外的物质具有潜在的反应性或不稳定性问题.
  • 选择了符合选标准的结构代表性化学品.

主要成果:

  • 开发了一个工作流程,从TSCA库存中识别一组318种候选化学品.
  • 这些候选物具有结构多样性,适合用于体外查.
  • 使用in silico工具来预测所选候选物质的危险概况.

结论:

  • 开发的化学信息工作流提供了一个可适应的策略,用于为基于NAM的评估选择各种化学集.
  • 这种方法支持在TSCA下缺乏数据的新化学品评估.
  • 选择的318种化学品代表了未来毒理学研究的宝贵资源.