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A case study application of the Aggregate Exposure Pathway (AEP) and Adverse Outcome Pathway (AOP) frameworks to facilitate the integration of human health and ecological endpoints for Cumulative Risk Assessment (CRA)

机译:总体暴露途径(AEP)和不良结果途径(AOP)框架的案例研究应用以促进人类健康和生态终点的整合以进行累积风险评估(CRA)

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摘要

Cumulative risk assessment (CRA) methods promote the use of a conceptual site model (CSM) to apportion exposures and integrate risk from multiple stressors. While CSMs may encompass multiple species, evaluating endpoints across taxa can be challenging due to data availability and physiological differences among organisms. Adverse Outcome Pathways (AOPs) describe biological mechanisms leading to adverse outcomes (AOs) by assembling causal pathways with measurable intermediate steps termed key events (KEs), thereby providing a framework for integrating data across species. We used a case study focused on the perchlorate anion (ClO4) to highlight the value of the AOP framework for cross-species data integration. Computational models and dose-response data were used to evaluate the effects of ClO4 in twelve species and revealed a dose-response concordance across KEs and taxa. The Aggregate Exposure Pathway (AEP) tracks stressors from sources to the exposures and serves as a complement to the AOP. We discuss how the combined AEP-AOP construct helps to maximize the use of existing data and advances CRA by 1) organizing toxicity and exposure data, 2) providing a mechanistic framework of KEs for integrating data across human health and ecological endpoints, 3) facilitating cross-species dose-response evaluation, and 4) highlighting data gaps and technical limitations.
机译:累积风险评估(CRA)方法促进使用概念性场所模型(CSM)分配风险并整合来自多个压力源的风险。虽然CSM可能包含多种物种,但是由于数据的可用性和生物体之间的生理差异,评估整个分类单元的终点可能具有挑战性。不良结果途径(AOP)通过将因果途径与可测量的中间步骤(称为关键事件(KE))组合起来,从而描述了导致不良结果(AOs)的生物学机制,从而提供了跨物种整合数据的框架。我们使用了一个针对高氯酸根阴离子(ClO4 -)的案例研究,以强调AOP框架对于跨物种数据集成的价值。计算模型和剂量反应数据被用来评估ClO4 -对十二种物种的影响,并揭示了KEs和分类单元之间的剂量反应一致性。总暴露途径(AEP)跟踪从源头到暴露的压力源,并作为AOP的补充。我们将讨论组合的AEP-AOP结构如何最大程度地利用现有数据并通过以下方法提高CRA:1)组织毒性和暴露数据; 2)提供KE的机制框架,以整合跨人类健康和生态终点的数据; 3)促进跨物种剂量反应评估,以及4)突出数据差距和技术局限性。

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