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ullrika at the uncertainty show

Ullrika Sahlin

Senior lecturer

ullrika at the uncertainty show

Causal, predictive or observational? Different understandings of key event relationships for adverse outcome pathways and their implications on practice

Author

  • Zheng Zhou
  • Jeroen Pennings
  • Ullrika Sahlin

Summary, in English

The Adverse Outcome Pathways (AOPs) framework is pivotal in toxicology, but the, terminology describing Key Event Relationships (KERs) varies within AOP guidelines.This study examined the usage of causal, observational and predictive terms in AOP, documentation and their adaptation in AOP development. A literature search and text, analysis of key AOP guidance documents revealed nuanced usage of these terms, with KERs often described as both causal and predictive. The adaptation of, terminology varies across AOP development stages. Evaluation of KER causality often, relies targeted blocking experiments and weight-of-evidence assessments in the, putative and qualitative stages. Our findings highlight a potential mismatch between,terminology in guidelines and methodologies in practice, particularly in inferring,causality from predictive models. We argue for careful consideration of terms like, causal and essential to facilitate interdisciplinary communication. Furthermore, integrating known causality into quantitative AOP models remains a challenge.

Department/s

  • Centre for Environmental and Climate Science (CEC)
  • Computational Science for Health and Environment
  • BECC: Biodiversity and Ecosystem services in a Changing Climate

Publishing year

2025

Language

English

Publication/Series

Environmental Toxicology and Pharmacology

Volume

113

Document type

Article

Publisher

Elsevier

Topic

  • Pharmacology and Toxicology
  • Occupational Health and Environmental Health
  • Probability Theory and Statistics

Keywords

  • Key event relationships
  • Adverse outcome pathways
  • Causal inference
  • Predictive
  • Modeling
  • Non-animal methods
  • Next generation risk assessment

Status

Published

Research group

  • Computational Science for Health and Environment

ISBN/ISSN/Other

  • ISSN: 1382-6689