Department of Environmental and Occupational Health, School of Public Health - Bloomington, Indiana University, Bloomington, Indiana 47405, United States.
Chem Res Toxicol. 2023 Aug 21;36(8):1345-1354. doi: 10.1021/acs.chemrestox.3c00088. Epub 2023 Jul 26.
High-throughput toxicogenomics as an advanced toolbox of Tox21 plays an increasingly important role in facilitating the toxicity assessment of environmental chemicals. However, toxicogenomic dose-response analyses are typically challenged by limited data, which may result in significant uncertainties in parameter and benchmark dose (BMD) estimation. Integrating historical data via prior distribution using a Bayesian method is a useful but not-well-studied strategy. The objective of this study is to evaluate the effectiveness of informative priors in genomic dose-response modeling and BMD estimation. Specifically, we aim to identify plausible informative priors and evaluate their effects on BMD estimates at both gene and pathway levels. A general informative prior and eight time-specific (from 3 h to 29 d) informative priors for seven commonly used continuous dose-response models were derived. Results suggest that the derived informative priors are sensitive to the specific data sets used for elicitation. Real data-based simulations indicate that BMD estimation with the time-specific informative priors can achieve increased or equivalent accuracy, significantly decreased uncertainty, and a slightly enhanced correlation with the points of departure estimated from apical end points than the counterparts with noninformative priors. Overall, our study systematically examined the effects of historical data-based informative priors on BMD estimates, highlighting the benefits of plausible information priors in advancing the practice of toxicogenomics.
高通量毒代基因组学作为 Tox21 的先进工具箱，在促进环境化学物毒性评估方面发挥着越来越重要的作用。然而，毒代基因组学剂量-反应分析通常受到数据有限的挑战，这可能导致参数和基准剂量（BMD）估计存在显著的不确定性。通过贝叶斯方法使用先验分布整合历史数据是一种有用但研究不足的策略。本研究旨在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。具体来说，我们旨在确定合理的信息先验，并评估它们在基因和途径水平上对 BMD 估计的影响。为七种常用的连续剂量-反应模型推导了一个通用信息先验和八个时间特定（从 3 小时到 29 天）信息先验。结果表明，推导的信息先验对用于启发的特定数据集敏感。基于真实数据的模拟表明，使用时间特定的信息先验进行 BMD 估计可以实现更高或等效的准确性、显著降低的不确定性，以及与从顶极终点估计的离差点的相关性略有增强，优于非信息先验的对应物。总体而言，本研究系统地研究了历史数据信息先验对 BMD 估计的影响，强调了合理信息先验在推进毒代基因组学实践中的益处。
