Why adverse outcome pathways need to be FAIR

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Clemens Wittwehr
Laure-Alix Clerbaux
Stephen Edwards
Michelle Angrish
Holly Mortensen
Annamaria Carusi
Maciej Gromelski
Eftychia Lekka
Vassilis Virvilis
Marvin Martens
Luiz Olavo Bonino da Silva Santos
Penny Nymark


Adverse outcome pathways (AOPs) provide evidence for demonstrating and assessing causality between measurable toxicological mechanisms and human or environmental adverse effects. AOPs have gained increasing attention over the past decade and are believed to provide the necessary steppingstone for more effective risk assessment of chemicals and materials and moving beyond the need for animal testing. However, as with all types of data and knowledge today, AOPs need to be reusable by machines, i.e., machine-actionable, in order to reach their full impact potential. Machine-actionability is supported by the FAIR principles, which guide findability, accessibility, interoperability, and reusability of data and knowledge. Here, we describe why AOPs need to be FAIR and touch on aspects such as the improved visibility and the increased trust that FAIRification of AOPs provides.

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Wittwehr, C., Clerbaux, L.-A., Edwards, S., Angrish, M., Mortensen, H., Carusi, A., Gromelski, M., Lekka, E., Virvilis, V., Martens, M., Bonino da Silva Santos, L. O. and Nymark, P. (2023) “Why adverse outcome pathways need to be FAIR”, ALTEX - Alternatives to animal experimentation. doi: 10.14573/altex.2307131.

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