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Ullrika Sahlin. Foto.

Ullrika Sahlin

Universitetslektor

Ullrika Sahlin. Foto.

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

Författare

  • 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.

Avdelning/ar

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

Publiceringsår

2025

Språk

Engelska

Publikation/Tidskrift/Serie

Environmental Toxicology and Pharmacology

Volym

113

Dokumenttyp

Artikel i vetenskaplig tidskrift

Förlag

Elsevier

Ämne

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

Nyckelord

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

Aktiv

Published

Forskningsgrupp

  • Computational Science for Health and Environment

ISBN/ISSN/Övrigt

  • ISSN: 1382-6689