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Journal Articles Computational Toxicology Year : 2021

Towards a qAOP framework for predictive toxicology - Linking data to decisions

Mark T.D. Cronin
  • Function : Author
David Asturiol
  • Function : Author
Lidia Ceriani
  • Function : Author
Thomas Exner
  • Function : Author
Caroline Gomes
  • Function : Author
Johannes Kruisselbrink
  • Function : Author
Marvin Martens
M.E. Bette Meek
  • Function : Author
David Pamies
  • Function : Author
Julia Pletz
  • Function : Author
Stefan Scholz
  • Function : Author
Andreas Schüttler
  • Function : Author
Nicoleta Spînu
  • Function : Author
Daniel Villeneuve
  • Function : Author
Clemens Wittwehr
  • Function : Author
Mirjam Luijten
  • Function : Author

Abstract

The adverse outcome pathway (AOP) is a conceptual construct that facilitates organisation and interpretation of mechanistic data representing multiple biological levels and deriving from a range of methodological approaches including in silico, in vitro and in vivo assays. AOPs are playing an increasingly important role in the chemical safety assessment paradigm and quantification of AOPs is an important step towards a more reliable prediction of chemically induced adverse effects. Modelling methodologies require the identification, extraction and use of reliable data and information to support the inclusion of quantitative considerations in AOP development. An extensive and growing range of digital resources are available to support the modelling of quantitative AOPs, providing a wide range of information, but also requiring guidance for their practical application. A framework for qAOP development is proposed based on feedback from a group of experts and three qAOP case studies. The proposed framework provides a harmonised approach for both regulators and scientists working in this area.
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Dates and versions

ineris-03500982 , version 1 (25-11-2022)

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Alicia Paini, Ivana Campia, Mark T.D. Cronin, David Asturiol, Lidia Ceriani, et al.. Towards a qAOP framework for predictive toxicology - Linking data to decisions. Computational Toxicology, 2021, pp.100195. ⟨10.1016/j.comtox.2021.100195⟩. ⟨ineris-03500982⟩

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