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根据致畸学数据计算基准剂量。

Calculation of benchmark doses from teratology data.

作者信息

Auton T R

机构信息

ZENECA Central Toxicology Laboratory, Alderley Park, Macclesfield, Cheshire, United Kingdom.

出版信息

Regul Toxicol Pharmacol. 1994 Apr;19(2):152-67. doi: 10.1006/rtph.1994.1014.

Abstract

The benchmark dose approach has several potential advantages over the no observed adverse effect level (NOAEL) as a basis for risk assessment of toxic chemicals, based upon animal toxicity data. The practical use of the benchmark dose has been evaluated by applying dose-response models to an extensive historical database of teratology bioassays. Doses corresponding to 1 and 5% increases in incidence of lesions are calculated and compared to NOAELs. The statistical accuracy of these estimates was determined by calculating confidence intervals. The lower confidence limit on the 5% benchmark dose (LED05) is found to be comparable to the NOAEL for most datasets, and slightly higher on average. Benchmark doses at the 1% level could not be estimated accurately (i.e., they had wide confidence intervals) for a significant fraction of the datasets. LED01 values were lower on average than the NOAEL. Based on these results, it is concluded that benchmark doses for a 5% increases in incidence can be calculated for most datasets, and could be used as a satisfactory basis for risk assessment, e.g., to set reference doses or acceptable daily intakes. An exception occurs when the benchmark dose exceeds the highest dose of the study. This is only likely to occur when the chemical causes a small, but significant, increase in a finding that is uncommon in untreated animals.

摘要

基于动物毒性数据,作为有毒化学品风险评估的基础,基准剂量法相对于未观察到有害作用水平(NOAEL)具有若干潜在优势。通过将剂量反应模型应用于大量畸形学生物测定的历史数据库,对基准剂量的实际应用进行了评估。计算与病变发生率增加1%和5%相对应的剂量,并与NOAEL进行比较。通过计算置信区间来确定这些估计值的统计准确性。发现对于大多数数据集,5%基准剂量(LED05)的较低置信限与NOAEL相当,平均略高。对于相当一部分数据集,无法准确估计1%水平的基准剂量(即它们具有较宽的置信区间)。LED01值平均低于NOAEL。基于这些结果,可以得出结论,对于大多数数据集,可以计算出发病率增加5%时的基准剂量,并可将其用作风险评估的令人满意的基础,例如设定参考剂量或可接受的每日摄入量。当基准剂量超过研究的最高剂量时,会出现例外情况。这仅可能在化学物质导致未处理动物中不常见的发现出现小但显著的增加时发生。

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