在危险品分类中使用急性口服毒性计算模型
Chandrika Moudgal1, Lennart T Anger1, Wolfgang Muster2
1Safety Assessment, Genentech Inc, South San Francisco, CA, USA.
Toxicology and industrial health
|October 20, 2023
概括
在模型准确预测低急性口服毒性 (AOT) 药物化合物,识别94-97%的非危险货物. 这减少了对动物试验的需求,并简化了运输流程.
科学领域:
- 毒理学 毒理学 毒理学
- 计算化学的计算化学
- 监管科学 监管科学
背景情况:
- 急性口服毒性 (AOT) 数据对于化学危害分类,职业安全和运输法规至关重要.
- 对于缺乏AOT数据的新型化合物来说,准确识别危险物品 (DGs) 是一个挑战,导致昂贵和低效的航运实践.
- 在 silico AOT 模型提供了一个有前途的替代传统体内测试,与3R原则 (替代,减少,改进) 保持一致.
研究的目的:
- 评估两种已建立的in silico AOT模型在预测药物化合物的急性口服毒性方面的性能.
- 确定这些模型是否适用于识别未被归类为危险品的化合物.
- 为在产品管辖和监管决策中应用in silico AOT模型提供建议.
主要方法:
- 利用了210种药物化合物的专有历史体内AOT数据.
- 评估了Leadscope AOT模型套件和协作急性毒性建模套件的预测准确性.
- 将模型预测与已建立的全球协调系统 (GHS) 关于急性毒性和GD分类的标准进行比较.
主要成果:
- 在形模型准确地确定了94%和97%的化合物不是危险物品 (GHS类别4,5和未分类).
- 这些模型在识别具有低急性口服毒性潜力 (LD50 > 300 mg/kg) 的药物化合物方面表现出高可靠性.
- 研究结果表明,这些模型适合用于初步危险评估和优先考虑进一步测试的化合物.
结论:
- 在 silico AOT 模型是可靠的工具来评估新药化合物的急性毒性潜力.
- 这些模型可以显著减少对体内试验的依赖,支持毒理学中的3R原则.
- 实施这些模型可以优化监管合规性,提高安全性,提高化学品运输和产品管理的效率.
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