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Accelerating Adverse Outcome Pathway Development Using Publicly Available Data Sources.

Noffisat O Oki1,2, Mark D Nelms1,2, Shannon M Bell1,2,3

  • 1Oak Ridge Institute for Science and Education, Oak Ridge, TN, USA.

Current Environmental Health Reports
|January 27, 2016
PubMed
Summary
This summary is machine-generated.

Developing computationally predicted adverse outcome pathways (cpAOPs) from public data can accelerate the creation of expert-derived AOPs. This approach supports high-throughput toxicity testing and reduces animal use.

Keywords:
Adverse outcome pathways (AOPs)Computationally predicted AOPs (cpAOPs)Data miningRisk assessmentToxicity pathways

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Area of Science:

  • Toxicology
  • Computational Biology
  • Environmental Science

Background:

  • Adverse Outcome Pathways (AOPs) link molecular changes to organism-level effects for toxicity testing.
  • Current AOP development is labor-intensive and time-consuming.
  • High-throughput toxicity (HTT) testing requires efficient AOP definition.

Purpose of the Study:

  • To explore the use of computationally predicted AOPs (cpAOPs) as a foundation for expert-derived AOPs.
  • To accelerate the development and increase the quality of AOPs.
  • To facilitate the adoption of HTT testing and reduce animal testing.

Main Methods:

  • Utilizing publicly available data sources for information extraction.
  • Employing computational tools for large dataset mining.
  • Leveraging cpAOPs to streamline expert AOP development.

Main Results:

  • cpAOPs can be derived from existing public data.
  • Computational mining extracts and organizes relevant information efficiently.
  • cpAOPs serve as a valuable starting point for expert review.

Conclusions:

  • cpAOPs significantly accelerate AOP development.
  • Integrating cpAOPs with expert coordination enhances AOP quantity and quality.
  • This approach is crucial for advancing HTT testing and reducing animal use in chemical safety assessment.