Society of Environmental Toxicology and Chemistry

4.09.P - From Outliers to Insights: Transforming Environmental Risk Assessment through Effect Modelling

Description

In the evolving field of regulatory science, mechanistic effect modelling (MEM) has emerged as a vital tool for understanding ecotoxicological effects. This session explores how innovative MEM methodologies can advance environmental risk assessment (ERA) and support decisions regarding outlier data and non-standard methodologies. While individual-level effect modelling has gained traction, particularly after EFSA's 2018 opinion on TKTD models, the use of population-, ecosystem-, and landscape-level models in regulatory contexts remains limited. However, these models have shown their value in scientific research, particularly in screening effects across diverse environmental scenarios. Integrating ecological models into regulatory risk assessments would enable the propagation of effects from individuals to higher biological levels, accounting for landscape characteristics and other environmental factors. Effect models play a crucial role in incorporating uncertainty and variability of conditions and processes in environmental systems into risk assessments, which is closely linked to identifying outliers in datasets. By analyzing entire datasets and their variability, MEMs can identify data points that deviate, allowing for further investigation. Additionally, MEMs can assess how non-standard endpoints could propagate to population-level effects relevant to the according protection goals, ensuring risk assessments are robust and scientifically sound. This session invites contributions that showcase the innovative use of ecological models in regulatory ERA, emphasizing how these models can challenge conventional perspectives and enhance our mechanistic understanding of ecotoxicological effects. We encourage submissions that address mode of action analyses, extrapolation of effects from individuals to populations, simulations of population dynamics under various stressors, and multi-species modelling. Moreover, we aim to foster dialogue on how MEMs can be utilized in regulatory ERA beyond their role as mere refinement tools and become integral to advancing ERA. We particularly welcome studies that explore how MEMs can analyse risks overlooked or overestimated by existing regulatory frameworks, maximize the value of experimental data, or impact regulatory outcomes. Contributions discussing the role of effect models as new approach methodologies (NAMs) to reduce animal testing and bridge retrospective and prospective risk assessments are also encouraged.