Society of Environmental Toxicology and Chemistry

1.04 - Bridging Innovation and Interpretation: New Approach Methodologies (NAMs) and 3R Alternatives in Environmental Toxicology

Description

Keywords: Animal Alternatives, High Throughput

As global regulatory frameworks increasingly prioritize reducing animal testing, the development and validation of New Approach Methodologies (NAMs) has become central to environmental toxicology. NAMs, including organ-on-chip platforms, microfluidics, 3D cell cultures, in silico models, and toxicogenomics, offer human- and ecologically relevant insights into toxicity mechanisms. These methods generate high-resolution datasets that do not always align with simplified endpoints from traditional in vivo studies (e.g., LOECs, NOECs). Understanding the concordance, divergence, and mechanistic basis of these differences is essential for regulatory translation, risk assessment, and broader adoption of NAMs. This session will bring together researchers, regulators, and industry scientists to explore both technological advances in NAMs and their interpretation and application in environmental toxicology. It aims to foster discussion on how NAMs can refine, replace, or reduce animal testing while improving mechanistic understanding, predictive power, and regulatory relevance. Key topics include: • showcase innovative NAMs, such as advanced in vitro assays, organ-on-chip systems, toxicogenomics, and AI-driven predictive toxicology • compare NAM outcomes with traditional in vivo results, highlighting both concordance and divergence, and explore mechanistic explanations for observed differences • examine implications for hazard classification, risk characterization, and regulatory thresholds, including case studies where NAMs have supported or challenged regulatory decisions. • discuss reproducibility, inter-species variability, and uncertainty in interpreting NAMs data. • explore Weight-of-Evidence (WoE) and Integrated Approaches to Testing and Assessment (IATA) that integrate NAMs with legacy datasets. We particularly welcome case studies demonstrating how NAMs inform or improve regulatory decisions, examples where NAMs and in vivo data diverge or align, and discussions of “outlier” results that challenge conventional interpretation.

Chairs

Zhiling Guo University of Birmingham United Kingdom
Luigi Margiotta-Casaluci King's College London United Kingdom
Pu Xia University of Birmingham United Kingdom