Creating a Structured Adverse Outcome Pathway Knowledgebase via Ontology-Based Annotations

Ives, Cataia; Campia, Ivana; Wang, Rong-Lin; Wittwehr, Clemens; Edwards, Stephen W.

doi:10.1089/aivt.2017.0017

Cited by 56 publications

(41 citation statements)

References 41 publications

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“…As AOPs naturally evolve from linear constructs to networks of inter-related effects 21 , metabolism and interactions will become integral to the development of AOP networks within an ontology framework, a formalised way to organise AOP knowledge and capture AOP relationships. 86 This provides the possibility, for example, to include models for metabolism, particularly the ability to bind to a CYP or enzyme and the rate of binding / inhibition. In addition models will be required to predict relevant metabolites that may go elsewhere in the AOP network, or to completely independent AOPs.…”

Section: Transformations and Interaction Of Chemicalsmentioning

confidence: 99%

Relationship Between Adverse Outcome Pathways and Chemistry-BasedIn SilicoModels to Predict Toxicity

Cronin

RicharzAndrea-Nicole

2017

Applied In Vitro Toxicology

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The current landscape of Adverse Outcome Pathways (AOPs) provides a means of organising information relating to the adverse effects elicited following exposure to chemicals. As such, AOPs are an excellent driver for the development and application of in silico models for predictive toxicology allowing for the direct relationship between chemistry and adverse effects to be established. Information may be extracted from AOPs to support the creation of (quantitative) structure-activity relationships ((Q)SARs) as well as to increase confidence in grouping and read-across. Any part of an AOP can be linked to these various types of in silico models. There is, however, an emphasis on using information from known Molecular Initiating Events (MIEs) to create models including 2D and 3D structural alerts, SARs and QSARs. MIEs can be classified according to the nature of the interaction e.g. covalent reactivity, oxidative stress, phototoxicity, chronic receptor mediated, acute enzyme inhibition, unspecific, physical and other effects. Different types of MIEs require different approaches to their in silico modelling. Modelling Key Events and Key Event Relationships is useful if they represent the rate limiting step or key determinant of toxicity. Modelling of metabolism and chemical interactions will become part of AOP networks, which are also driving species-specific extrapolation and respective adaptation of models. With more information and data being captured, in silico approaches will increasingly support the application of knowledge from AOPs to build weight of evidence and support risk assessment, e.g. in the context of Integrated Assessment and Testing Approaches (IATAs).

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Section: Transformations and Interaction Of Chemicalsmentioning

confidence: 99%

Relationship Between Adverse Outcome Pathways and Chemistry-BasedIn SilicoModels to Predict Toxicity

Cronin

RicharzAndrea-Nicole

2017

Applied In Vitro Toxicology

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“…As discussed later in this article (see ontologies), existing information and descriptions of AOPs, KEs and KERs, will need to be consistent to enable the developments described above. In this context, it is relevant that the AOP-Wiki has recently been upgraded to facilitate the use of a defined set of ontologies to define ‘event components’ as consistent structured descriptions of KEs and their contexts (Ives et al 2017 ).…”

Section: The Future Trajectory Of Aopsmentioning

confidence: 99%

“…Also, there is OECD guidance on in vitro test method reporting for both guideline (OECD 2005 ), and non-guideline methods (OECD 2014 ), and draft guidance for draft good cell culture practise (OECD 2017 ). However, although databases and data-sharing initiatives do exist, a lack of interoperable standards means there remain logistical and technological barriers to the optimal exploitation of data (Burgoon 2017 ; Ives et al 2017 ). This could be addressed through greater consolidation and use of ontologies, and the accompanying development of improved bioinformatics and data mining tools; which in turn would drive greater use of available data to better define KERs and potentially allow automated linking of AOPs.…”

Section: The Future Trajectory Of Aopsmentioning

confidence: 99%

The future trajectory of adverse outcome pathways: a commentary

Sewell

Gellatly

et al. 2018

Arch Toxicol

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The advent of adverse outcome pathways (AOPs) has provided a new lexicon for description of mechanistic toxicology, and a renewed enthusiasm for exploring modes of action resulting in adverse health and environmental effects. In addition, AOPs have been used successfully as a framework for the design and development of non-animal approaches to toxicity testing. Although the value of AOPs is widely recognised, there remain challenges and opportunities associated with their use in practise. The purpose of this article is to consider specifically how the future trajectory of AOPs may provide a basis for addressing some of those challenges and opportunities.

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“…Connecting toxicity data across species with KEs can be challenging and illustrates the need for careful attention to experimental design and a common ontology across disciplines, especially when performing systematic reviews. A KE for an AOP can be described in term of specific processes and objects based on existing biological ontologies 74 . For example, the impaired growth KE in the ClO 4 − case study could be assigned the "growth" process term from the Gene Ontology 75 .…”

Section: Towards a Mechanistic Approach For Place-based Community Crasmentioning

confidence: 99%

A Case Study Application of the Aggregate Exposure Pathway (AEP) and Adverse Outcome Pathway (AOP) Frameworks to Facilitate the Integration of Human Health and Ecological End Points for Cumulative Risk Assessment (CRA)

Hines

Edwards

Conolly

et al. 2017

Environ. Sci. Technol.

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Cumulative risk assessment (CRA) methods promote the use of a conceptual site model (CSM) to apportion exposures and integrate risk from multiple stressors. While CSMs may encompass multiple species, evaluating end points across taxa can be challenging due to data availability and physiological differences among organisms. Adverse outcome pathways (AOPs) describe biological mechanisms leading to adverse outcomes (AOs) by assembling causal pathways with measurable intermediate steps termed key events (KEs), thereby providing a framework for integrating data across species. In this work, we used a case study focused on the perchlorate anion (ClO) to highlight the value of the AOP framework for cross-species data integration. Computational models and dose-response data were used to evaluate the effects of ClO in 12 species and revealed a dose-response concordance across KEs and taxa. The aggregate exposure pathway (AEP) tracks stressors from sources to the exposures and serves as a complement to the AOP. We discuss how the combined AEP-AOP construct helps to maximize the use of existing data and advances CRA by (1) organizing toxicity and exposure data, (2) providing a mechanistic framework of KEs for integrating data across human health and ecological end points, (3) facilitating cross-species dose-response evaluation, and (4) highlighting data gaps and technical limitations.

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Creating a Structured Adverse Outcome Pathway Knowledgebase via Ontology-Based Annotations

Cited by 56 publications

References 41 publications

Relationship Between Adverse Outcome Pathways and Chemistry-BasedIn SilicoModels to Predict Toxicity

Relationship Between Adverse Outcome Pathways and Chemistry-BasedIn SilicoModels to Predict Toxicity

The future trajectory of adverse outcome pathways: a commentary

A Case Study Application of the Aggregate Exposure Pathway (AEP) and Adverse Outcome Pathway (AOP) Frameworks to Facilitate the Integration of Human Health and Ecological End Points for Cumulative Risk Assessment (CRA)

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