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

Alicia Paini, Ivana Campia, Mark T D Cronin, David Asturiol, Lidia Ceriani, Thomas E Exner, Wang Gao, Caroline Gomes, Johannes Kruisselbrink, Marvin Martens, M E Bette Meek, David Pamies, Julia Pletz, Stefan Scholz, Andreas Schüttler, Nicoleta Spînu, Daniel L Villeneuve, Clemens Wittwehr, Andrew Worth*, Mirjam Luijten

*Corresponding author for this work

Research output: Contribution to journalArticleAcademic

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.

Original languageEnglish
Article number100195
Number of pages14
JournalComputational Toxicology
Volume21
DOIs
Publication statusPublished - Feb 2022

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