对工业化学品生态毒性预测的物种敏感度分布建模
Kabiruddin Khan1, Nyssa Tucker2, Holli-Joi Martin2
1OpenTox Association, Basel, Switzerland; Edelweiss Connect GmbH, Basel, Switzerland; In Silico Solutions, Goa, India.
The Science of the total environment
|November 11, 2025
概括
新物种敏感度分布 (SSD) 模型预测了各种物种的化学毒性,有助于对生态风险进行评估. 这些计算工具减少了动物试验,并为监管行动优先考虑环境污染物.
科学领域:
- 环境毒理学环境毒理学
- 计算化学的计算化学
- 生态毒理学 生态毒理学
背景情况:
- 环境污染物 (ECs) 具有特定物种的毒性,挑战了传统的评估方法.
- 巨大的化学物种相互作用需要先进的计算框架来准确评估风险.
- 种类敏感度分布 (SSD) 模型统计总结毒性数据,以估计生态风险评估的危险度.
研究的目的:
- 使用广泛的生态毒性数据开发全球和特定类别的SSD模型.
- 预测未经测试的化学品的危险度 (pHC-5) 并确定毒性驱动因素.
- 支持针对PCP和农业化学品等高优先级化学品类的有针对性的降低风险的监管需求.
主要方法:
- 从美国EPA ECOTOX数据库中策划了3250个毒性条目,跨越14个分类组和四个热量级.
- 将急性 (EC50/LC50) 和慢性 (NOEC/LOEC) 终点集成到SSD模型中.
- 采用可解释的特征选择来识别毒性驱动子结构和应用模型到8449种工业化学品.
主要成果:
- 开发了强大的全球和专业的SSD模型用于生态风险评估.
- 成功预测未经测试的化学品的pHC-5值,并确定了关键的毒性驱动子结构.
- 根据模型应用,优先考虑188种高毒性工业化学品,以获得监管关注.
结论:
- 通过减少对动物试验的依赖和采用新方法方法 (NAMs) 来进行先进的生态风险评估.
- 提供了一个可扩展的化学品优先级框架,支持数据差的评估和基于证据的监管.
- 通过通过OpenTox SSDM平台公开访问的数据集和工具来促进透明度和协作.
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