You are here:
Mechanistic effect models: A brief history to highlight benefits and obstacles in using them for chemical risk assessment.
Citation:
Accola, C., V. Forbes, N. Galic, Sandy Raimondo, A. Schmolke, AND M. Vaugeois. Mechanistic effect models: A brief history to highlight benefits and obstacles in using them for chemical risk assessment. 2022 Society of Environmental Toxicology and Chemistry North America Annual Meeting, Pittsburg, PA, November 13 - 17, 2022.
Impact/Purpose:
This presentation will open the session "Benefits and obstacles in using mechanistic effects models for chemical risk assessments" at the annual meeting of the Society of Environmental Toxicology and Chemistry in Pittsburg, PA in November 2022. The session chairs (authors of current abstract) have invited speakers from government (includine US EPA Office of Pesticide Programs), academia, and private sectors to present perspectives, benefits, and obstacles of using mechanistic effects models in ecological risk assessment of chemical contaminants.
Description:
Although mechanistic effect models are widely recognized as potentially valuable tools in Ecological Risk Assessment (ERA), the endpoints they yield are different from those that have been traditionally applied and the lack of “bright lines” has prevented them from being used as standard tools for ERA and pesticide registrations. This talk aims to start a thorough discussion among all the parties involved in ERA to understand how we could enhance the use of mechanistic effect models in different regulatory contexts. To this end, we present a brief history of mechanistic effect models for ERA and give an overview of their goals within the ERA context. We show some examples of effect models developed within EPA and highlight the differences with other models developed by other sectors (academia, consulting companies, or industry). In this context, we suggest focusing on a few important points: 1) Define standardized outputs of interest, such as population abundance, population decline, recovery, or extinction probability and how these outputs can be applied in decision making, 2) Determine which scenarios (and exposure magnification factors) should be included in a standard suite of models, 3) Find agreement on those model features deemed essential to represent populations such that risks can be adequately assessed, and 4) Underline the importance of identifying model use and objectives before model development and ensure transparency and consistency in the overall process. We also tackle some common issues linked to model acceptance and conceptual misunderstandings. For example, because models can include different processes, they are usually developed on a case-by-case basis, which can be problematic for ensuring consistency in decision criteria. Moreover, there are different points of view regarding how to best use data obtained from surrogate species and how to deal with model uncertainty. This session intends to foster dialog among stakeholders to ensure the use of the best available science in a standardized way to improve ecological risk assessment.
