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Toward an AOP Network-Based Tiered Testing Strategy for the Assessment of Thyroid Hormone Disruption.

Dries KnapenEvelyn StinckensJenna E CavallinGerald T AnkleyHenrik HolbechDaniel L VilleneuveLucia Vergauwen
Published in: Environmental science & technology (2020)
A growing number of environmental pollutants are known to adversely affect the thyroid hormone system, and major gaps have been identified in the tools available for the identification, and the hazard and risk assessment of these thyroid hormone disrupting chemicals. We provide an example of how the adverse outcome pathway (AOP) framework and associated data generation can address current testing challenges in the context of fish early life stage tests, and fish tests in general. We demonstrate how a suite of assays covering biological processes involved in the underlying toxicological pathways can be implemented in a tiered screening and testing approach for thyroid hormone disruption, using the levels of assessment of the OECD's Conceptual Framework for the Testing and Assessment of Endocrine Disrupting Chemicals as a guide.
Keyphrases
  • early life
  • risk assessment
  • heavy metals
  • human health
  • machine learning
  • big data
  • climate change
  • deep learning
  • drug induced
  • data analysis
  • network analysis
  • life cycle