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Toxicity Testing in Animals01:23

Toxicity Testing in Animals

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Toxicity tests in animals are grounded on two main assumptions: first, the effects observed in laboratory animals can be extrapolated to humans, especially when adjusted for body surface area; second, high-dose exposure in animals is essential to identify potential human hazards from lower doses. This is based on the quantal dose-response concept, which faces the challenge of extrapolating results from relatively few test animals to much larger human populations. For example, a 0.01% incidence...
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相关实验视频

Updated: May 5, 2026

Diffuse Optical Spectroscopy for the Quantitative Assessment of Acute Ionizing Radiation Induced Skin Toxicity Using a Mouse Model
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基于深度学习的多模式融合方法用于预测急性皮肤毒性.

Monnishkaran Madheswaran1, Keerthana Jaganathan2, Lakshmanan Shanmugam1

  • 1Department of Mathematics, School of Advanced Sciences, Vellore Institute of Technology, Chennai, Tamil Nadu 600 127, India.

Journal of chemical information and modeling
|July 18, 2025
PubMed
概括

一个新的深度学习模型,TriModalToxNet,使用融合的分子数据准确地预测急性皮肤毒性. 这种多模式的方法为化学安全评估提供了比传统的动物试验更道德和更有效的替代方案.

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科学领域:

  • 计算毒理学计算毒理学
  • 化学信息学 化学信息学
  • 机器学习是机器学习.

背景情况:

  • 传统的急性皮肤毒性测试在很大程度上依赖于动物研究,这些研究在伦理上令人担忧,资源密集.
  • 越来越需要替代方法来支持动物试验中的3R原则 (取代,减少和改进).
  • 预测化学毒性对于制药,杀虫剂,化品和工业化学品至关重要.

研究的目的:

  • 开发和验证可靠和准确的多式联络深度学习框架,用于预测急性皮肤毒性.
  • 调查融合异质分子表示的有效性,以提高预测性能.
  • 为动物性毒性评估提供更道德和更有效的替代方案.

主要方法:

  • 提出了一个新的深度学习架构,TriModalToxNet,整合了2D分子图像,SMILES嵌入和分子指纹的功能.
  • 利用2D卷积神经网络,1D卷积神经网络和完全连接的神经网络来提取特征.
  • 在3845个化合物的数据集上训练和评估模型,使用分层的10倍交叉验证和外部验证.

主要成果:

  • TriModalToxNet在接收器操作特征曲线下实现了95%的面积,并在交叉验证中获得了91.2%的灵敏度.
  • 与单一模式基线模型 (BiModalToxNet) 相比,多式模式方法显示出更好的预测性能.
  • 外部验证证实了TriModalToxNet框架的稳定性和通用性.

结论:

  • 综合多种分子表示的多式深度学习框架可以显著提高急性皮肤毒性预测的准确性.
  • TriModalToxNet提供了一个有前途的计算工具,用于道德和高效的化学安全评估.
  • 开发的框架有可能融入监管流程和制药选管道.