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Critical Review of Read-Across Potential in Testing for Endocrine-Related Effects in Vertebrate Ecological Receptors.

Margaret E McArdleElaine L FreemanJane P StaveleyLisa S OrtegoKatherine K CoadyLennart WeltjeArnd WeyersJames R WheelerAudrey J Bone
Published in: Environmental toxicology and chemistry (2020)
Recent regulatory testing programs have been designed to evaluate whether a chemical has the potential to interact with the endocrine system and could cause adverse effects. Some endocrine pathways are highly conserved among vertebrates, providing a potential to extrapolate data generated for one vertebrate taxonomic group to others (i.e., biological read-across). To assess the potential for biological read-across, we reviewed tools and approaches that support species extrapolation for fish, amphibians, birds, and reptiles. For each of the estrogen, androgen, thyroid, and steroidogenesis (EATS) pathways, we considered the pathway conservation across species and the responses of endocrine-sensitive endpoints. The available data show a high degree of confidence in the conservation of the hypothalamus-pituitary-gonadal axis between fish and mammals and the hypothalamus-pituitary-thyroid axis between amphibians and mammals. Comparatively, there is less empirical evidence for the conservation of other EATS pathways between other taxonomic groups, but this may be due to limited data. Although more information on sensitive pathways and endpoints would be useful, current developments in the use of molecular target sequencing similarity tools and thoughtful application of the adverse outcome pathway concept show promise for further advancement of read-across approaches for testing EATS pathways in vertebrate ecological receptors. Environ Toxicol Chem 2020;39:739-753. © 2020 The Authors. Environmental Toxicology and Chemistry published by Wiley Periodicals, Inc. on behalf of SETAC.
Keyphrases
  • human health
  • single molecule
  • big data
  • electronic health record
  • risk assessment
  • transcription factor
  • randomized controlled trial
  • genetic diversity
  • drug induced
  • data analysis