在基准剂量估计之前基于历史数据的信息对毒基因组学的影响
1Department of Environmental and Occupational Health, School of Public Health - Bloomington, Indiana University, Bloomington, Indiana 47405, United States.
Chemical research in toxicology
|July 26, 2023
概括
使用贝叶斯方法整合历史数据可以改善毒基因组剂量反应建模. 这种方法提高了基准剂量 (BMD) 估计的准确性,并减少了环境化学品毒性评估中的不确定性.
科学领域:
- 环境毒理学环境毒理学
- 基因组学就是基因组学.
- 计算毒理学计算毒理学
背景情况:
- 高通量毒基因组学对于评估环境化学毒性至关重要.
- 剂量反应分析中的有限数据导致参数和基准剂量 (BMD) 估计的不确定性.
- 通过先前分布整合历史数据的贝叶斯方法提供了一个潜在的解决方案,但研究不足.
研究的目的:
- 评估信息先验在基因组剂量反应建模和BMD估计中的有效性.
- 为了确定基因和途径水平的BMD估计的可信的信息先验.
- 评估特定时间信息先验对BMD估计的影响.
主要方法:
- 对于七种连续剂量反应模型,得出了一个一般信息的先验和八个特定时间的信息先验.
- 利用贝叶斯的方法来整合历史数据.
- 进行基于真实数据的模拟,以评估 BMD 估计与或没有信息先验.
主要成果:
- 导出的信息先验显示对用于诱导的特定数据集的敏感性.
- 与非信息先验相比,特定时间的信息先验改善或维持了BMD估计的准确性.
- 有信息的先验显著降低了不确定性,并略微增强了与尖端终点衍生的起点的相关性.
结论:
- 基于历史数据的信息先验在毒基因组学中为BMD估计提供了显著的好处.
- 该研究系统地检查了信息先验的影响,突出了它们在推进毒基因组学实践中的有用性.
- 可信的信息先验提高了可靠性,并减少了毒性评估中的不确定性.
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