scholarly journals Advanced Adverse Outcome Pathways Potentially Bridging the Pathogenesis of COVID-19

2021 ◽  
Author(s):  
Youngjun kim ◽  
Chang Gyun Park ◽  
Sang Rak Lim ◽  
Indong Jun ◽  
Yong Oh Lee
Keyword(s):  
Adverse Outcome ◽  
Viral Disease ◽  
New Drugs ◽  
Adverse Outcome Pathway ◽  
Limited Information ◽  
Angiotensin Converting Enzyme 2 ◽  
Global Concern ◽  
Toxicity Mechanisms ◽  
Key Events ◽  
Adverse Outcome Pathways

Increasing global concern over COVID-19 has recently brought greater attention to studies due to the ease of person-to-person transmission and the current lack of effective antiviral therapy. Here, we proposed the application of the adverse outcome pathway (AOP) framework to support re-search on the pathogenesis of viral disease. We first constructed adverse outcome pathways (AOPs) applicable to COVID-19 management to understand whether the infection causes severe acute respiratory distress. Based on the AOP framework where mechanistic elucidation of the pathway from the interaction of chemicals (or viruses) to apical endpoints is represented, our COVID-19 AOP indicated that the molecular initiating event (MIE) was angiotensin-converting enzyme 2 (ACE2) interaction, and the key events (KEs) were the increased pro-inflammatory cytokines in immune cells, with increased mortality as an apical adverse outcome (AO). However, there is still limited information on the toxicity mechanisms of AOPs in COVID-19; therefore, detailed KEs and AOs on toxicity mechanisms will be required to fill these gaps in the data. This study demonstrated that the COVID-19 AOP framework is a suitable tool to design new drugs and to integrate crowded-sourced information for the battle against the COVID-19 pandemic.

scholarly journals Providing Adverse Outcome Pathways from the AOP-Wiki in Semantic Web Format to Increase Usability and Accessibility of the Content

2021 ◽  
Author(s):  
Marvin Martens ◽  
Chris Evelo ◽  
Egon Willighagen
Keyword(s):  
Resource Description Framework ◽  
Adverse Outcome ◽  
Semantic Annotation ◽  
Adverse Outcome Pathway ◽  
Dublin Core ◽  
Description Framework ◽  
Federated Queries ◽  
Key Events ◽  
Adverse Outcome Pathways ◽  
Resource Description

<div>The AOP-Wiki is the main environment for the development and storage of Adverse Outcome Pathways. These Adverse Outcome Pathways describe mechanistic information about toxicodynamic processes and can be used to develop effective risk assessment strategies. However, it is challenging to automatically and systematically parse, filter, and use its contents. We explored solutions to better structure the AOP-Wiki content and to link it with chemical and biological resources. Together this allows more detailed exploration which can be automated.</div><div><br></div><div>We converted the complete AOP-Wiki content into Resource Description Framework. We used over twenty ontologies for the semantic annotation of property-object relations, including the ChemInformatics Ontology, Dublin Core, and the Adverse Outcome Pathway Ontology. The latter was used over 8,000 times. Furthermore, over 3,500 link-outs were added to twelve chemical databases and over 6,500 link-outs to four gene and protein databases. </div><div><br></div><div>SPARQL queries can be used against the Resource Description Framework to answer biological and toxicological questions, such as listing measurement methods for all Key Events leading to an Adverse Outcome of interest. The full power that the use of this new resource provides becomes apparent when combining the content with external databases using federated queries. For example, we can link genes related to Key Events with molecular pathway on WikiPathways in which they occur and find all Adverse Outcome Pathways caused by stressors that are part of a particular chemical group. Overall, the AOP-Wiki Resource Description Framework allows new ways to explore the rapidly growing Adverse Outcome Pathway knowledge and makes the integration of this database in automated workflows possible.</div>

scholarly journals Providing Adverse Outcome Pathways from the AOP-Wiki in Semantic Web Format to Increase Usability and Accessibility of the Content

2021 ◽  
Author(s):  
Marvin Martens ◽  
Chris Evelo ◽  
Egon Willighagen
Keyword(s):  
Resource Description Framework ◽  
Adverse Outcome ◽  
Semantic Annotation ◽  
Adverse Outcome Pathway ◽  
Dublin Core ◽  
Description Framework ◽  
Federated Queries ◽  
Key Events ◽  
Adverse Outcome Pathways ◽  
Resource Description

<div>The AOP-Wiki is the main environment for the development and storage of Adverse Outcome Pathways. These Adverse Outcome Pathways describe mechanistic information about toxicodynamic processes and can be used to develop effective risk assessment strategies. However, it is challenging to automatically and systematically parse, filter, and use its contents. We explored solutions to better structure the AOP-Wiki content and to link it with chemical and biological resources. Together this allows more detailed exploration which can be automated.</div><div><br></div><div>We converted the complete AOP-Wiki content into Resource Description Framework. We used over twenty ontologies for the semantic annotation of property-object relations, including the ChemInformatics Ontology, Dublin Core, and the Adverse Outcome Pathway Ontology. The latter was used over 8,000 times. Furthermore, over 3,500 link-outs were added to twelve chemical databases and over 6,500 link-outs to four gene and protein databases. </div><div><br></div><div>SPARQL queries can be used against the Resource Description Framework to answer biological and toxicological questions, such as listing measurement methods for all Key Events leading to an Adverse Outcome of interest. The full power that the use of this new resource provides becomes apparent when combining the content with external databases using federated queries. For example, we can link genes related to Key Events with molecular pathway on WikiPathways in which they occur and find all Adverse Outcome Pathways caused by stressors that are part of a particular chemical group. Overall, the AOP-Wiki Resource Description Framework allows new ways to explore the rapidly growing Adverse Outcome Pathway knowledge and makes the integration of this database in automated workflows possible.</div>

Adverse outcome pathways, key events, and radiation risk assessment

2020 ◽  
pp. 1-18
Author(s):  
R. Julian Preston ◽  
Werner Rühm ◽  
Edouard I. Azzam ◽  
John D. Boice ◽  
Simon Bouffler ◽  
...  
Keyword(s):  
Risk Assessment ◽  
Adverse Outcome ◽  
Radiation Risk ◽  
Key Events ◽  
Adverse Outcome Pathways

scholarly journals Systematic Organization of COVID-19 Data Supported by the Adverse Outcome Pathway Framework

2021 ◽  
Author(s):  
Penny Nymark ◽  
Magdalini Sachana ◽  
Sofia Batista-Leite ◽  
Jukka Sund ◽  
Catharine E Krebs ◽  
...  
Keyword(s):  
World Wide ◽  
Adverse Outcome ◽  
Management Strategies ◽  
Research Data ◽  
Adverse Outcome Pathway ◽  
Research Support ◽  
Broad Application ◽  
The World ◽  
Adverse Outcome Pathways ◽  
Expected Outcomes

Adverse Outcome Pathways (AOP) provide structured frameworks for systematic organization of research data and knowledge. The AOP framework follows a set of key principles that allow for broad application across diverse disciplines related to human health, including toxicology, pharmacology, virology and medical research. The COVID-19 pandemic engages a great number of scientists world-wide and data is increasing with exponential speed. Diligent data management strategies are employed but approaches for systematically organizing the data-derived information and knowledge are lacking. We believe AOPs can play an important role in improving interpretation and efficient application of scientific understanding of COVID-19. Here, we outline a newly initiated effort to streamline collaboration between scientists across the world towards development of AOPs for COVID-19, and describe the overarching aims of the effort, as well as the expected outcomes and research support that they will provide.

A methodology for developing key events to advance nanomaterial-relevant adverse outcome pathways to inform risk assessment

2020 ◽  
pp. 1-24
Author(s):  
Sabina Halappanavar ◽  
James D. Ede ◽  
Indrani Mahapatra ◽  
Harald F. Krug ◽  
Eileen D. Kuempel ◽  
...  
Keyword(s):  
Risk Assessment ◽  
Adverse Outcome ◽  
Key Events ◽  
Adverse Outcome Pathways

scholarly journals Systematic Organization of COVID-19 Data Supported by the Adverse Outcome Pathway Framework

2021 ◽  
Vol 9 ◽  
Author(s):  
Penny Nymark ◽  
Magdalini Sachana ◽  
Sofia Batista Leite ◽  
Jukka Sund ◽  
Catharine E. Krebs ◽  
...  
Keyword(s):  
World Wide ◽  
Adverse Outcome ◽  
Management Strategies ◽  
Research Data ◽  
Adverse Outcome Pathway ◽  
Research Support ◽  
Broad Application ◽  
The World ◽  
Adverse Outcome Pathways ◽  
Expected Outcomes

Adverse Outcome Pathways (AOP) provide structured frameworks for the systematic organization of research data and knowledge. The AOP framework follows a set of key principles that allow for broad application across diverse disciplines related to human health, including toxicology, pharmacology, virology and medical research. The COVID-19 pandemic engages a great number of scientists world-wide and data is increasing with exponential speed. Diligent data management strategies are employed but approaches for systematically organizing the data-derived information and knowledge are lacking. We believe AOPs can play an important role in improving interpretation and efficient application of scientific understanding of COVID-19. Here, we outline a newly initiated effort, the CIAO project (https://www.ciao-covid.net/), to streamline collaboration between scientists across the world toward development of AOPs for COVID-19, and describe the overarching aims of the effort, as well as the expected outcomes and research support that they will provide.

scholarly journals The AOPOntology: A Semantic Artificial Intelligence Tool for Predictive Toxicology

2018 ◽  
Author(s):  
Lyle D. Burgoon
Keyword(s):  
Artificial Intelligence ◽  
High Throughput ◽  
Network Theory ◽  
Adverse Outcome ◽  
Adverse Outcome Pathway ◽  
Causal Network ◽  
Potential Toxicity ◽  
Key Events ◽  
Outcome Predictions

AbstractIntroductionToxicology needs artificial intelligence tools that can automate the prediction of toxicity. Today we are at an interesting nexus. We have thousands of chemicals in the environment that lack regulatory thresholds for determining risk. New high throughput in vitro testing methods are becoming available to test these chemicals. Causal Adverse Outcome Pathway Networks (CAOPN) are emerging that will allow us to make predictions based on perturbations of specific key events within the network. The AOPOntology was developed as infrastructure for this nexus, providing the ability to model and marry the data from the in vitro tests for the thousands of chemicals and place them within the CAOPN framework to facilitate adverse outcome predictions.Materials and MethodsThe AOPN is a functional specialized ontology that creates classes that model biological pathways and CAOPNs. Adverse outcome predictions are based on mathematical determinations of key events that are sufficient to infer adverse outcomes will occur, or biological information. These sufficiency relationships are captured in the AOPOntology and used by the semantic reasoners to make predictions.ResultsThe AOPOntology version 1.0 architecture is in place, and a CAOPN for steatosis demonstrates how causal network theory is used to make predictions. The AOPOntology is available at https://github.com/DataSciBurgoon/aop-ontology.DiscussionThe AOPOntology is a knowledge base for CAOPNs that one can use to make predictions about a chemical’s potential toxicity using in vitro high throughput and other assays.ConclusionsUsing CAOPNs and causal network theory one is able to predict potential toxicity for chemicals using in vitro high throughput and various high content screens.

scholarly journals Representing the Process of Inflammation as Key Events in Adverse Outcome Pathways

2018 ◽  
Vol 163 (2) ◽  
pp. 346-352 ◽  
Author(s):  
Daniel L Villeneuve ◽  
Brigitte Landesmann ◽  
Paola Allavena ◽  
Noah Ashley ◽  
Anna Bal-Price ◽  
...  
Keyword(s):  
Adverse Outcome ◽  
Key Events ◽  
Adverse Outcome Pathways
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