Integrating human health and ecological data into cumulative risk assessment
Integrating human health and ecological data into cumulative risk assessment through the Aggregate Exposure Pathway and Adverse Outcome Pathway frameworks
Cumulative risk assessment (CRA) methods promote the use of a conceptual site model (CSM) to apportion exposures and integrate risk from relevant stressors across different species. Integration is important to provide a more complete assessment of risk, but evaluating endpoints across multiple species can be challenging due to differences in data availability, exposure, and physiology among organisms. The Adverse Outcome Pathway (AOP) framework can inform CRA by describing biological mechanisms of action for chemicals from molecular initiating events, through key events (KEs), to adverse outcomes (AOs). However, AOPs do not consider the exposures that an organism may encounter. The Aggregate Exposure Pathway (AEP) framework was created to track stressors from sources, through key exposure states, to a target site exposure. This work presents techniques for the application of a joint AEP-AOP construct that can act as a mechanistic, source-to-outcome structure to inform a CSM and evaluate risk by considering real-world exposure. We use a case study focused on perchlorate, an environmental contaminant found in various media, to demonstrate how this construct supports the integration of human health and ecological endpoints, and can facilitate visualization of risk across multiple species. Computational models and dose-response data were used to evaluate the effects of perchlorate exposure in eight vertebrates and four invertebrates. We observed a dose-response concordance across key events and species, reflecting the conserved nature of the biology affected by perchlorate. Results suggested that endpoints in frogs and rats (Xenopus laevis and Rattus sp., respectively) may be more sensitive to perchlorate exposure than AOs examined in other organisms, but also highlighted gaps for some species such as fish (Danio rerio and Gambusia holbrooki). The AEP-AOP construct advances CRA by 1) organizing data, 2) providing a mechanistic framework for integrating data on chemicals with similar effects and on endpoints across species, 3) highlighting data gaps, and 4) facilitating analyses and visualizations of risk. The need for a common ontology for KEs across species is discussed.
The views expressed in this abstract are those of the authors and do not necessarily reflect the views or policies of the U.S. Environmental Protection Agency.