Beyond adverse outcome pathways: making toxicity predictions from event networks, SAR models, data and knowledge. Issue 1 (22nd January 2021)

• Record Type: Journal Article
• Authors: Ball, Thomas; Barber, Christopher G; Cayley, Alex; Chilton, Martyn L; Foster, Robert; Fowkes, Adrian; Heghes, Crina; Hill, Emma; Hill, Natasha; Kane, Steven; Macmillan, Donna S; Myden, Alun; Newman, Daniel; Polit, Artur; Stalford, Susanne A; Vessey, Jonathan D
• Is Part Of: Toxicology research. Volume 10:Issue 1(2021)
• Page Start: 102
• Page End: 122
• Publication Date: 2021-01-22
• Subjects: AOP -- Reasoning -- Predictive toxicology; Toxicology -- Periodicals; 615.9005
• DOI: 10.1093/toxres/tfaa099
• Languages: English
• ISSNs: 2045-452X

Abstract

Abstract: Adverse outcome pathways have shown themselves to be useful ways of understanding and expressing knowledge about sequences of events that lead to adverse outcomes (AOs) such as toxicity. In this paper we use the building blocks of adverse outcome pathways—namely key events (KEs) and key event relationships—to construct networks which can be used to make predictions of the likelihood of AOs. The networks of KEs are augmented by data from and knowledge about assays as well as by structure activity relationship predictions linking chemical classes to the observation of KEs. These inputs are combined within a reasoning framework to produce an information-rich display of the relevant knowledge and data and predictions of AOs both in the abstract case and for individual chemicals. Illustrative examples are given for skin sensitization, reprotoxicity and non-genotoxic carcinogenicity.