Citation:

Paini, A., I. Campia, M. Cronin, D. Asturiol, L. Ceriani, T. Exner, W. Gao, C. Gomes, J. Kruisselbrink, M. Martens, B. Meek, D. Pamies, J. Pletz, S. Scholz, A. Schuttler, N. Spinu, D. Villeneuve, C. Wittwehr, A. Worth, AND M. Luijten. Towards a qAOP Framework to Revolutionise Toxicology - Linking Data to Decisions. Computational Toxicology. Elsevier B.V., Amsterdam, Netherlands, 21:100195, (2022). https://doi.org/10.1016/j.comtox.2021.100195

Impact/Purpose:

This product reports on results of a workshop held in October 2019. The purpose of the workshop was to explore electronic resources that could provide quantitative understanding which would support more predictive use of non-animal data in chemical safety assessments. The target audience was the scientific community involved in developing quantitative extrapolation models based on adverse outcome pathways. A general step-wise approach to guide quantitative adverse outcome pathway development is suggested. Learnings from the workshop are intended to aid other scientists interested in developing quantitative extrapolation models anchored to adverse outcome pathway knowledge.

Description:

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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