在高通量选试验中对化学度的模型预测的准确性
Meredith N Scherer1,2, Madison Feshuk1, James M Armitage3
1Center for Computational Toxicology and Exposure, U.S. Environmental Protection Agency, Research Triangle Park, North Carolina 27711, United States.
Environmental science & technology
|February 2, 2026
概括
数学模型通过预测细胞内化学度来提高体外-体内抽取 (IVIVE) 的准确性. 这增强了毒理学预测,因为它考虑了测试系统中的化学物质分布.
科学领域:
- 毒理学 毒理学 毒理学
- 药理动力学 药理动力学
- 计算化学的计算化学
背景情况:
- 在体外-体外抽取 (IVIVE) 旨在从体外数据中预测整个动物的影响.
- 目前的IVIVE方法通常依赖于名义度,忽视了测试系统内的化学物质分布.
- 这可能导致细胞内度的估计不准确,从而导致毒性预测不可靠.
研究的目的:
- 评估数学配置模型在预测IVIVE.IVIVE细胞内化学度方面的准确性.
- 为了比较Armitage等人的表现. (2021) 和克莱默等人. (2010) 模型与名义度相对应.
主要方法:
- 利用153个实验性细胞内度测量对43种化学物质从12项研究和新的数据.
- 评估了两个体外处置模型:Armitage等人. (2021) 和克莱默等人. (2010年) 在2010年.
- 将模型预测与实验确定的细胞内度和名义度进行比较.
主要成果:
- 阿米塔奇模型 (RMSLE = 1.12) 和克莱默模型 (RMSLE = 1.30) 提供了比名义度 (RMSLE = 1.45) 更准确的细胞内度预测.
- 这些模型在估计可在体外引起影响的化学物质的自由度时显示出更高的准确性.
结论:
- 在体外化学物质分布的数学建模显著提高了IVIVE对毒理学评估的准确性.
- 这些处置模型提供了一个有价值的工具来改进剂量推断,并改善潜在不良影响的预测.
- 用扩展数据集进行进一步的研究是有必要的,以充分利用这些建模方法的潜力.
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