在中通过机器学习和计算系统生物学的毒剂的危险评估
Shu Luan1, Chao Ji2, Gregory M Zarus2
1School of Biological Sciences, Georgia Institute of Technology, Atlanta, GA 30332, USA.
Toxics
|January 28, 2026
概括
计算模型在环境化学物质中确定了潜在的耳毒剂. 这种方法有助于优先考虑化学物质进行进一步的耳毒性风险评估,重点关注甲状腺介导的途径.
科学领域:
- 环境健康 环境健康
- 毒理学 毒理学 毒理学
- 计算生物学 计算生物学
背景情况:
- 化学物质和药物可以在所有生命阶段引起听力损失 (耳毒性).
- 环境暴露引起了人们对潜在的耳毒性影响的担忧.
研究的目的:
- 应用计算机建模来识别危险和优先考虑有关耳毒性的化学物质.
- 使用in silico模型选环境化学品以检测潜在的耳毒性质.
主要方法:
- 在76种环境化学物质的制药诱导的耳毒性模型中进行评估.
- 使用指纹,相似性评估和机器学习分类.
- 使用PCB 177和米托坦的系统生物学框架构建了一个耳毒性通路.
主要成果:
- 一个共识计算模型实现了高精度 (0.95测试;0.90验证).
- 76种化学物质中有18种被确定为潜在的耳毒剂.
- 预测的重叠机制,包括甲状腺刺激激素受体信号干扰由米托和PCB 177.
结论:
- 综合计算建模有效支持用于耳毒性风险评估的虚拟查.
- 研究结果表明,甲状腺介导的通路可能会导致化学物质引起的听力损伤.
- 这种方法有助于为进一步的耳毒性评估优先考虑化学物质.
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