Martin Thomas, Thompson Helen, Thorbek Pernille, Ashauer Roman
University of York , Environment Department , Heslington, York YO10 5NG , United Kingdom.
Syngenta, Jealott's Hill International Research Centre Bracknell , Berkshire RG42 6EY , United Kingdom.
Chem Res Toxicol. 2019 Nov 18;32(11):2281-2294. doi: 10.1021/acs.chemrestox.9b00294. Epub 2019 Nov 1.
Ecological risk assessment is carried out for chemicals such as pesticides before they are released into the environment. Such risk assessment currently relies on summary statistics gathered in standardized laboratory studies. However, these statistics extract only limited information and depend on duration of exposure. Their extrapolation to realistic ecological scenarios is inherently limited. Mechanistic effect models simulate the processes underlying toxicity and so have the potential to overcome these issues. Toxicokinetic-toxicodynamic (TK-TD) models operate at the individual level, predicting the internal concentration of a chemical over time and the stress it places on an organism. TK-TD models are particularly suited to addressing the difference in exposure patterns between laboratory (constant) and field (variable) scenarios. So far, few studies have sought to predict sublethal effects of pesticide exposure to wild mammals in the field, even though such effects are of particular interest with respect to longer term exposure. We developed a TK-TD model based on the dynamic energy budget (DEB) theory, which can be parametrized and tested solely using standard regulatory studies. We demonstrate that this approach can be used effectively to predict toxic effects on the body weight of rats over time. Model predictions separate the impacts of feeding avoidance and toxic action, highlighting which was the primary driver of effects on growth. Such information is relevant to the ecological risk posed by a compound because in the environment alternative food sources may or may not be available to focal species. While this study focused on a single end point, growth, this approach could be expanded to include reproductive output. The framework developed is simple to use and could be of great utility for ecological and toxicological research as well as to risk assessors in industry and regulatory agencies.
在农药等化学物质释放到环境之前,会对其进行生态风险评估。目前,此类风险评估依赖于标准化实验室研究中收集的汇总统计数据。然而,这些统计数据仅提取了有限的信息,并且取决于暴露持续时间。将其外推到实际生态情景中存在固有的局限性。机理效应模型模拟毒性背后的过程,因此有潜力克服这些问题。毒代动力学-毒效动力学(TK-TD)模型在个体层面运行,预测化学物质随时间的体内浓度及其对生物体造成的压力。TK-TD模型特别适合解决实验室(恒定)和野外(可变)情景之间暴露模式的差异。到目前为止,很少有研究试图预测野外农药暴露对野生哺乳动物的亚致死效应,尽管就长期暴露而言,此类效应特别令人关注。我们基于动态能量收支(DEB)理论开发了一个TK-TD模型,该模型仅使用标准监管研究即可进行参数化和测试。我们证明,这种方法可以有效地用于预测大鼠体重随时间的毒性效应。模型预测区分了摄食回避和毒性作用的影响,突出了哪个是影响生长的主要驱动因素。此类信息与化合物所带来的生态风险相关,因为在环境中,目标物种可能有也可能没有替代食物来源。虽然本研究侧重于单一终点——生长,但这种方法可以扩展到包括生殖产出。所开发的框架易于使用,对于生态和毒理学研究以及行业和监管机构的风险评估人员可能具有很大的实用价值。