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Mechanistic Effect Modeling Approach for the Extrapolation of Species Sensitivity

机译：物种敏感性外推的机械效应建模方法

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摘要

In the higher-tier environmental risk assessment of chemicals, species sensitivity distributions (SSDs) are used to statistically describe differences in sensitivity between species and derive community level endpoints. SSDs are usually based on the results from short-term laboratory experiments performed under constant environmental conditions. However, different species may be kept at different "optimal" temperatures, which influence their apparent sensitivity and thus the derivation of endpoints. Also, the extrapolation capacity of SSDs is largely limited to the tested species and conditions. Time-variable exposures and effects at higher levels of biological organization, including biological interactions, are not considered. The quantitative effect prediction at higher tiers would ultimately require the extrapolation of toxicokinetics and toxicodynamics to untested species and the involvement of population and community modeling. In this regard, we tested a toxicokinetic-toxicodynamic modeling approach to mechanistically consider and correct endpoints for ambient temperature and demonstrate the significance for SSDs. We explored correlations in toxicokinetic-toxicodynamic model parameters which would allow for the extrapolation of sensitivities to untested species. Finally, we illustrate the applicability of the approach for higher level effect predictions using an individual-based model. Our results suggest that mechanistic effect modeling approaches can reduce the uncertainties in higher tier effect assessments related to knowledge gaps.
机译：在较高层级的化学品环境风险评估中，物种敏感度分布（SSD）用于统计描述物种之间的敏感度差异并得出社区水平的终点。 SSD通常基于在恒定环境条件下进行的短期实验室实验的结果。但是，不同的物种可能会保持在不同的“最佳”温度下，这会影响它们的表观敏感性，从而影响端点的推导。而且，SSD的外推能力在很大程度上限于测试的物种和条件。没有考虑在生物组织的较高水平上的时变暴露和影响，包括生物相互作用。更高层次的定量效应预测最终将需要将毒物动力学和毒物动力学外推至未经测试的物种，并需要种群和社区建模的参与。在这方面，我们测试了毒物动力学-毒物动力学建模方法，以机械方式考虑和校正环境温度的端点，并证明了SSD的重要性。我们探索了毒物动力学-毒物动力学模型参数之间的相关性，从而可以将敏感性外推至未经测试的物种。最后，我们说明了基于个人模型的方法对更高水平的效果预测的适用性。我们的结果表明，机械效应建模方法可以减少与知识缺口相关的更高层效应评估的不确定性。

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  • 来源
    《Environmental Science & Technology》 |2019年第16期|9818-9825|共8页
  • 作者

    Gergs Andre; Rakel Kim J.; Liesy Dino; Zenker Armin; Classen Silke;

  • 作者单位

    Res Inst Ecosyst Anal & Assessment Gaiac Kackertstr 10 D-52072 Aachen Germany|Bayer AG Alfred Nobel Str 50 D-40789 Monheim Germany;

    Univ Koblenz Landau Inst Environm Sci Fortstr 7 D-76829 Landau Germany;

    Univ Appl Sci & Arts Northwestern Switzerland Sch Life Sci Inst Ecopreneurship Hofackerstr 30 CH-4132 Muttenz Switzerland;

  • 收录信息 美国《科学引文索引》(SCI);美国《工程索引》(EI);美国《生物学医学文摘》(MEDLINE);美国《化学文摘》(CA);
  • 原文格式 PDF
  • 正文语种 eng
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