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Towards a qAOP Framework for Predictive Toxicology - Linking Data to Decisions

Paini, A, Campia, I, Cronin, M, Pletz, J and Spinu, N Towards a qAOP Framework for Predictive Toxicology - Linking Data to Decisions. Computational Toxicology. ISSN 2468-1113 (Accepted)

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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.

Item Type: Article
Subjects: R Medicine > RA Public aspects of medicine > RA1190 Toxicology. Poisions
R Medicine > RM Therapeutics. Pharmacology
Divisions: Pharmacy & Biomolecular Sciences
Publisher: Elsevier
Date Deposited: 11 Oct 2021 11:07
Last Modified: 20 Oct 2021 11:22
URI: https://researchonline.ljmu.ac.uk/id/eprint/15625

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