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风险评估中基准剂量法的使用指南。

Guidance on the use of the benchmark dose approach in risk assessment.

作者信息

More Simon John, Bampidis Vasileios, Benford Diane, Bragard Claude, Halldorsson Thorhallur Ingi, Hernández-Jerez Antonio F, Bennekou Susanne Hougaard, Koutsoumanis Kostas, Lambré Claude, Machera Kyriaki, Mennes Wim, Mullins Ewen, Nielsen Søren Saxmose, Schrenk Dieter, Turck Dominique, Younes Maged, Aerts Marc, Edler Lutz, Sand Salomon, Wright Matthew, Binaglia Marco, Bottex Bernard, Abrahantes Jose Cortiñas, Schlatter Josef

出版信息

EFSA J. 2022 Oct 25;20(10):e07584. doi: 10.2903/j.efsa.2022.7584. eCollection 2022 Oct.

DOI:10.2903/j.efsa.2022.7584
PMID:36304832
原文链接:https://pmc.ncbi.nlm.nih.gov/articles/PMC9593753/
Abstract

The Scientific Committee (SC) reconfirms that the benchmark dose (BMD) approach is a scientifically more advanced method compared to the no-observed-adverse-effect-level (NOAEL) approach for deriving a Reference Point (RP). The major change compared to the previous Guidance (EFSA SC, 2017) concerns the Section 2.5, in which a change from the frequentist to the Bayesian paradigm is recommended. In the former, uncertainty about the unknown parameters is measured by confidence and significance levels, interpreted and calibrated under hypothetical repetition, while probability distributions are attached to the unknown parameters in the Bayesian approach, and the notion of probability is extended to reflect uncertainty of knowledge. In addition, the Bayesian approach can mimic a learning process and reflects the accumulation of knowledge over time. Model averaging is again recommended as the preferred method for estimating the BMD and calculating its credible interval. The set of default models to be used for BMD analysis has been reviewed and amended so that there is now a single set of models for quantal and continuous data. The flow chart guiding the reader step-by-step when performing a BMD analysis has also been updated, and a chapter comparing the frequentist to the Bayesian paradigm inserted. Also, when using Bayesian BMD modelling, the lower bound (BMDL) is to be considered as potential RP, and the upper bound (BMDU) is needed for establishing the BMDU/BMDL ratio reflecting the uncertainty in the BMD estimate. This updated guidance does not call for a general re-evaluation of previous assessments where the NOAEL approach or the BMD approach as described in the 2009 or 2017 Guidance was used, in particular when the exposure is clearly lower (e.g. more than one order of magnitude) than the health-based guidance value. Finally, the SC firmly reiterates to reconsider test guidelines given the wide application of the BMD approach.

摘要

科学委员会(SC)再次确认,与用于推导参考点(RP)的未观察到有害作用水平(NOAEL)方法相比,基准剂量(BMD)方法在科学上更为先进。与先前的指南(EFSA SC,2017)相比,主要变化涉及第2.5节,其中建议从频率学派范式转变为贝叶斯范式。在前者中,未知参数的不确定性通过置信水平和显著性水平来衡量,在假设重复下进行解释和校准,而在贝叶斯方法中,概率分布与未知参数相关联,并且概率的概念被扩展以反映知识的不确定性。此外,贝叶斯方法可以模拟学习过程并反映知识随时间的积累。再次建议将模型平均作为估计BMD及其可信区间的首选方法。已对用于BMD分析的默认模型集进行了审查和修订,以便现在有一套用于定性和连续数据的单一模型集。指导读者在进行BMD分析时逐步操作的流程图也已更新,并插入了一章比较频率学派和贝叶斯范式的内容。此外,在使用贝叶斯BMD建模时,下限(BMDL)应被视为潜在的RP,而建立反映BMD估计不确定性的BMDU/BMDL比率则需要上限(BMDU)。本更新指南并不要求对先前使用NOAEL方法或2009年或2017年指南中所述的BMD方法进行的评估进行全面重新评估,特别是当暴露明显低于(例如超过一个数量级)基于健康的指导值时。最后,SC坚定地重申,鉴于BMD方法的广泛应用,应重新考虑测试指南。

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