提高对不良结果途径的定量理解:当前状态,方法和未来方向
Jaeseong Jeong1, Manvel Gasparyan1, Jinhee Choi1
1School of Environmental Engineering, University of Seoul, Seoul, Korea.
量化不良结果途径 (qAOPs) 通过整合数据和数学模型来增强化学风险评估. 本综述探讨了qAOP方法,以更好地了解化学物质暴露和保护公共健康.
科学领域:
- 毒理学 毒理学 毒理学
- 计算生物学 计算生物学
- 风险评估 风险评估
背景情况:
- 负面结果路径 (AOP) 框架提供了对化学物质暴露的机制性理解.
- 定量AOP (qAOP) 集成了定量数据和数学建模,以精确理解AOPs.
- 传统的方法往往缺乏对化学物质暴露的机制性理解.
研究的目的:
- 批判性地检查量化不良结果途径 (qAOPs) 的方法.
- 突出系统毒理学,回归建模和贝叶斯网络建模在QAOP开发中的优势,局限性和数据要求.
- 建议在监管科学中推进QAOP的战略.
主要方法:
- 审查现有的关于QAOP方法的文献.
- 对系统毒理学,回归建模和贝叶斯网络建模的分析.
- 检查用于阐明关键事件关系的实验和计算方法.
主要成果:
- 确定了三个主要的QAOP方法的优点和局限性.
- 强调需要整合实验和计算方法.
- 突出数据要求和当前QAOP开发中的挑战.
结论:
- 量化AOP提供了更精确的化学暴露及其不良结果的理解.
- 多种方法和标准化协议的整合对于推进QAOP至关重要.
- qAOP有潜力显著改变化学风险评估和监管科学.
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