Accelerating Adverse Outcome Pathway Development Using Publicly Available Data Sources

Noffisat O Oki; Mark D Nelms; Shannon M Bell; Holly M Mortensen; Stephen W Edwards

doi:10.1007/s40572-016-0079-y

Accelerating Adverse Outcome Pathway Development Using Publicly Available Data Sources

Curr Environ Health Rep. 2016 Mar;3(1):53-63. doi: 10.1007/s40572-016-0079-y.

Authors

Noffisat O Oki^{1

2}, Mark D Nelms^{1

2}, Shannon M Bell^{1

2

3}, Holly M Mortensen⁴, Stephen W Edwards⁵

Affiliations

¹ Oak Ridge Institute for Science and Education, Oak Ridge, TN, USA.
² Integrated Systems Toxicology Division, National Health and Environmental Effects Research Laboratory, Office of Research and Development, U.S. Environmental Protection Agency, 109 T.W. Alexander Drive, Mail Code: B305-01, Research Triangle Park, NC, 27709, USA.
³ ILS/Contractor Supporting the NTP Interagency Center for the Evaluation of Alternative Toxicological Methods (NICEATM), Research Triangle Park, NC, USA.
⁴ Genomics Research Core, National Health and Environmental Effects Research Laboratory, Office of Research and Development, U.S. Environmental Protection Agency, Research Triangle Park, NC, USA.
⁵ Integrated Systems Toxicology Division, National Health and Environmental Effects Research Laboratory, Office of Research and Development, U.S. Environmental Protection Agency, 109 T.W. Alexander Drive, Mail Code: B305-01, Research Triangle Park, NC, 27709, USA. edwards.stephen@epa.gov.

PMID: 26809562
DOI: 10.1007/s40572-016-0079-y

Abstract

The adverse outcome pathway (AOP) concept links molecular perturbations with organism and population-level outcomes to support high-throughput toxicity (HTT) testing. International efforts are underway to define AOPs and store the information supporting these AOPs in a central knowledge base; however, this process is currently labor-intensive and time-consuming. Publicly available data sources provide a wealth of information that could be used to define computationally predicted AOPs (cpAOPs), which could serve as a basis for creating expert-derived AOPs in a much more efficient way. Computational tools for mining large datasets provide the means for extracting and organizing the information captured in these public data sources. Using cpAOPs as a starting point for expert-derived AOPs should accelerate AOP development. Coupling this with tools to coordinate and facilitate the expert development efforts will increase the number and quality of AOPs produced, which should play a key role in advancing the adoption of HTT testing, thereby reducing the use of animals in toxicity testing and greatly increasing the number of chemicals that can be tested.

Keywords: Adverse outcome pathways (AOPs); Computationally predicted AOPs (cpAOPs); Data mining; Risk assessment; Toxicity pathways.

Publication types

Research Support, U.S. Gov't, Non-P.H.S.
Review

MeSH terms

Computer Simulation
Ecotoxicology / methods*
Humans
Information Management / methods*
Risk Assessment / methods
Toxicity Tests*