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在示例监管方案下的 PNEC 推导逻辑流程比较及其对生态毒理学阈值的影响。

Comparisons of PNEC derivation logic flows under example regulatory schemes and implications for ecoTTC.

机构信息

The Procter & Gamble Company, Cincinnati, OH, USA.

The Dow Chemical Company, Midland, MI, USA.

出版信息

Regul Toxicol Pharmacol. 2021 Jul;123:104933. doi: 10.1016/j.yrtph.2021.104933. Epub 2021 Apr 20.

DOI:10.1016/j.yrtph.2021.104933
PMID:33891999
原文链接:https://pmc.ncbi.nlm.nih.gov/articles/PMC10461128/
Abstract

Derivation of Predicted No Effect Concentrations (PNECs) for aquatic systems is the primary deterministic form of hazard extrapolation used in environmental risk assessment. Depending on the data availability, different regulatory jurisdictions apply application factors (AFs) to the most sensitive measured endpoint to derive the PNEC for a chemical. To assess differences in estimated PNEC values, two PNEC determination methodologies were applied to a curated public database using the EnviroTox Platform (www.EnviroToxdatabase.org). PNECs were derived for 3647 compounds using derivation procedures based on example US EPA and a modified European Union chemical registration procedure to allow for comparisons. Ranked probability distributions of PNEC values were developed and 5th percentile values were calculated for the entire dataset and scenarios where full acute or full chronic data sets were available. The lowest PNEC values indicated categorization based on chemical attributes and modes of action would lead to improved extrapolations. Full acute or chronic datasets gave measurably higher 5th percentile PNEC values. Algae were under-represented in available ecotoxicity data but drove PNECs disproportionately. Including algal inhibition studies will be important in understanding chemical hazards. The PNEC derivation logic flows are embedded in the EnviroTox Platform providing transparent and consistent PNEC derivations and PNEC distribution calculations.

摘要

预测无效应浓度(PNEC)的推导是环境风险评估中用于危害外推的主要确定性方法。根据数据的可用性,不同的监管管辖区会对最敏感的测量终点应用应用因子(AF),以得出化学物质的 PNEC。为了评估估计的 PNEC 值之间的差异,两种 PNEC 确定方法被应用于使用 EnviroTox 平台(www.EnviroToxdatabase.org)的公共数据库。使用基于美国环保署的示例和修改后的欧盟化学注册程序的推导程序,为 3647 种化合物推导了 PNEC。为整个数据集和具有完整急性或完整慢性数据集的情况开发了 PNEC 值的排名概率分布,并计算了第 5 个百分位数值。基于化学属性和作用模式的最低 PNEC 值表明分类将导致更好的外推。完整的急性或慢性数据集给出了可测量的更高的第 5 个百分位数 PNEC 值。藻类在可用的生态毒性数据中代表性不足,但不成比例地推动了 PNEC。包括藻类抑制研究将对了解化学危害很重要。PNEC 推导逻辑流程嵌入在 EnviroTox 平台中,提供透明和一致的 PNEC 推导和 PNEC 分布计算。

https://cdn.ncbi.nlm.nih.gov/pmc/blobs/d781/10461128/9078df72341f/nihms-1925143-f0007.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/d781/10461128/9b476a934d51/nihms-1925143-f0001.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/d781/10461128/7b2ae35b2122/nihms-1925143-f0002.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/d781/10461128/16770ce2a616/nihms-1925143-f0003.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/d781/10461128/1544f383cb62/nihms-1925143-f0004.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/d781/10461128/dd1421a91dbf/nihms-1925143-f0005.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/d781/10461128/c669d0087756/nihms-1925143-f0006.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/d781/10461128/9078df72341f/nihms-1925143-f0007.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/d781/10461128/9b476a934d51/nihms-1925143-f0001.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/d781/10461128/7b2ae35b2122/nihms-1925143-f0002.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/d781/10461128/16770ce2a616/nihms-1925143-f0003.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/d781/10461128/1544f383cb62/nihms-1925143-f0004.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/d781/10461128/dd1421a91dbf/nihms-1925143-f0005.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/d781/10461128/c669d0087756/nihms-1925143-f0006.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/d781/10461128/9078df72341f/nihms-1925143-f0007.jpg

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