Accelerating Adverse Outcome Pathway Development Using Publicly Available Data Sources

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.

The adverse outcome pathway: A multifaceted framework supporting 21st century toxicology

“…In addition to novel applications, innovative scientific approaches are being identified/ employed in support of basic AOP development. For example, recent efforts have focused on the identification, development and evaluation of new AOPs based on 'omic and/or HTP data, systems/network modeling, and/or repositories of curated toxicity information [52][53][54][55]. Increasing the "library" of available AOPs is critically important to supporting the varied applications of the framework in the future.…”

Drug Metabolism in Preclinical Drug Development: A Survey of the Discovery Process, Toxicology, and Computational Tools

“…The AOP Knowledge Base (AOP-KB) is the primary collaborative effort currently underway to catalog crowdsourced experimentally derived AOPs (www.aopkb.org). Current efforts in computationally predicted AOPs (cpAOPs) were recently reviewed by Oki et al [103]. …”

Big data in food safety: An overview

“…Two examples of these are Gene Expression Omnius (GEO) (Clough and Barrett, 2016) and ArrayExpress (Kolesnikov et al, 2015). Presently, a comprehensive knowledge base is being developed as part of the Organisation for Economic Co-operation and Development (OECD) Adverse Outcome Pathway (AOP) program (http://aopkb.org/) that will serve as a central repository for exploratory analyses and predicting human health risks (Oki et al, 2016).…”