Current Environmental Health Reports

, Volume 3, Issue 1, pp 53–63 | Cite as

Accelerating Adverse Outcome Pathway Development Using Publicly Available Data Sources

  • Noffisat O. Oki
  • Mark D. Nelms
  • Shannon M. Bell
  • Holly M. Mortensen
  • Stephen W. EdwardsEmail author
Mechanisms of Toxicity (CJ Mattingly, Section Editor)
Part of the following topical collections:
  1. Topical Collection on Mechanisms of Toxicity


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.


Adverse outcome pathways (AOPs) Computationally predicted AOPs (cpAOPs) Risk assessment Data mining Toxicity pathways 



The authors thank all the members of the AOP-KB development team for their helpful discussions and the following for feedback on an early draft of this paper: Kevin Crofton, Lyle Burgoon, Hristo Aladjov, Ed Perkins, Natalia Garcia-Reyero, and Clemens Wittwehr. They also thank Ingrid Druwe and Michelle Angrish for their helpful comments on the manuscript. The information in this document has been funded wholly (or in part) by the US Environmental Protection Agency. It has been subjected to review by the National Health and Environmental Effects Research Laboratory and approved for publication. Approval does not signify that the contents reflect the views of the agency, nor does mention of trade names or commercial products constitute endorsement or recommendation for use.

Compliance with Ethical Standards

Conflict of Interest

Noffisat Oki, Mark Nelms, Shannon Bell, Holly Mortensen, and Stephen Edwards declare that they have no conflict of interest.

Human and Animal Rights and Informed Consent

This article does not contain any studies with human or animal subjects performed by any of the authors.


Papers of particular interest, published recently, have been highlighted as: • Of importance

  1. 1.
    Martić-Kehl MI, Schibli R, Schubiger PA. Can animal data predict human outcome? Problems and pitfalls of translational animal research. Eur J Nucl Med Mol Imaging. 2012;39(9):1492–6.CrossRefPubMedPubMedCentralGoogle Scholar
  2. 2.
    Russell WMS, Burch RL. The principles of humane experimental technique. Methuen 1959.Google Scholar
  3. 3.
    United States Congress. ICCVAM authorization act of 2000. 2000 [cited 2015 08/03/2015]; available from:
  4. 4.
    Meek ME, Armstrong VC. The assessment and management of industrial chemicals in Canada. In: Leeuwen CJV, Vermeire TG, editors. Risk assessment of chemicals. Netherlands: Springer; 2007. p. 591–621.CrossRefGoogle Scholar
  5. 5.
    Commission;, E., Corrigenda of Regulation (EC) No 1907/2006 concerning the Registration, Evaluation, Authorisation and Restriction of Chemicals (REACH). Official Journal of the European Union, 2006b. EC 1907/2006.Google Scholar
  6. 6.
    Commission;, E., Regulation (EC) No 1907/2006 of the European Parliament and of the Council of 18 December 2006 concerning the Registration, Evaluation, Authorisation and Restriction of Chemicals (REACH). Official Journal of the European Union 2006a. EC 1907/2006.Google Scholar
  7. 7.
    National Research Council. Toxicity testing in the 21st century: a vision and a strategy. Washington, D.C.: The National Academies; 2007. p. 216.Google Scholar
  8. 8.
    Ankley GT, Bennet RS, Erickson RJ, et al. Adverse outcome pathways: a conceptual framework to support ecotoxicology research and risk assessment. Environ Toxicol Chem. 2010;29(3):730–41.Google Scholar
  9. 9.
    Garcia-Reyero NL. Are adverse outcome pathways here to stay? Environ Sci Technol. 2014;49(1):3–9.CrossRefPubMedGoogle Scholar
  10. 10.
    Villeneuve DL, Crump D, Garcia-Reyero N, et al. Adverse outcome pathway development II: best practices. Toxicol Sci. 2014;142(2):321–30.Google Scholar
  11. 11.
    OECD. Users’ handbook supplement to the guidance document for developing and assessing AOPs. 2014 [cited 2015 7/13/2015]; available from:
  12. 12.
    Becker RA, Ankley GT, Edwards SW, et al. Increasing scientific confidence in adverse outcome pathways: application of tailored Bradford-Hill considerations for evaluating weight of evidence. Regul Toxicol Pharmacol. 2015;72(3):514–37.Google Scholar
  13. 13.•
    Villeneuve DL, Crump D, Garcia-Reyero N, et al. Adverse outcome pathway (AOP) development I: strategies and principles. Toxicol Sci. 2014;142(2):312–20. This paper outlines the current principles for defining AOPs and discusses the different phases of AOP development ranging from putative AOPs to fully described quantitative AOPs.Google Scholar
  14. 14.
    Tollefsen KE, Scholz S, Cronin MT, et al. Applying adverse outcome pathways (AOPs) to support integrated approaches to testing and assessment (IATA). Regul Toxicol Pharmacol. 2014;70(3):629–40.Google Scholar
  15. 15.
    Groh KJ, Carvalho RN, Chipman JK, et al. Development and application of the adverse outcome pathway framework for understanding and predicting chronic toxicity: I. Challenges and research needs in ecotoxicology. Chemosphere. 2015;120:764–77.Google Scholar
  16. 16.
    OECD. Guidance document on developing and assessing adverse outcome pathways, P.A.B. Environment Directorate; Joint Meeting of the Chemicals Committee and the Working Party on Chemicals, Editor. 2013, Organisation for Economic Co-operation and Development: Paris, France.Google Scholar
  17. 17.
    Pearl J. Causality: models, reasoning and inference. Cambridge University Press; 2009. p 478.Google Scholar
  18. 18.•
    Kleinstreuer N et al. A computational model predicting disruption of blood vessel development. PLoS Comput Biol. 2013;9(4):e1002996. This paper provides a recent example of using computational approaches to build AOPs based on an existing scaffold.CrossRefPubMedPubMedCentralGoogle Scholar
  19. 19.
    Kleinstreuer NC, Dix D, Rountree M, et al. Environmental impact on vascular development predicted by high-throughput screening. Environ Health Perspect. 2011;119(11):1596–603.Google Scholar
  20. 20.
    Perkins EJ, Chipman K, Edwards S, et al. Reverse engineering adverse outcome pathways. Environ Toxicol Chem. 2011;30(1):22–38.Google Scholar
  21. 21.
    Schadt EE, Friend SH, Shaywitz DA. A network view of disease and compound screening. Nat Rev Drug Discov. 2009;8(4):286–95.CrossRefPubMedGoogle Scholar
  22. 22.
    Edwards SW, Preston RJ. Systems biology and mode of action based risk assessment. Toxicol Sci. 2008;106(2):312–8.CrossRefPubMedGoogle Scholar
  23. 23.
    AbdulHameed MD, Tawa GJ, Kumar K, et al. Systems level analysis and identification of pathways and networks associated with liver fibrosis. PLoS ONE. 2014;9(11):e112193.Google Scholar
  24. 24.
    Agrawal R, Imielinski T, Swami A, et al. Mining association rules between sets of items in large databases. SIGMOD Rec. 1993;22(2):207–16.Google Scholar
  25. 25.
    Agrawal R, Srikant R. Fast algorithms for mining association rules in large databases. In: Proceedings of the 20th International Conference on Very Large Data Bases. 1994, Morgan Kaufmann Publishers Inc. p. 487–499.Google Scholar
  26. 26.
    Bell SM, Edwards SW. Identification and prioritization of relationships between environmental stressors and adverse human health impacts. Environ Health Perspect. 2015.Google Scholar
  27. 27.•
    Bell SM, Edwards SW. Building associations between markers of environmental stressors and adverse human health impacts using frequent itemset mining. In: Proceedings of the 2014 SIAM International Conference on Data Mining. 2014. p. 551–559. This paper discusses the use of frequent itemset mining in an environmental context. The approach is analogous to the use of frequent itemset mining for cpAOP development once the transactions have been defined based on the chemical.Google Scholar
  28. 28.
    Davis AP, Grondin CJ, Lennon-Hopkins K, et al. The comparative toxicogenomics database’s 10th year anniversary: update 2015. Nucleic Acids Res. 2015;43(Database issue):D914–20.Google Scholar
  29. 29.
    Kolesnikov N, Hastings E, Keays M, et al. ArrayExpress update—simplifying data submissions. Nucleic Acids Res. 2015;43(D1):D1113–6.Google Scholar
  30. 30.
    Edgar R, Domrachev M, Lash AE. Gene expression omnibus: NCBI gene expression and hybridization array data repository. Nucleic Acids Res. 2002;30(1):207–10.CrossRefPubMedPubMedCentralGoogle Scholar
  31. 31.
    Judson RS, Houck KA, Kavlock RJ, et al. In vitro screening of environmental chemicals for targeted testing prioritization: the ToxCast project. Environ Health Perspect. 2010;118(4):485–92.Google Scholar
  32. 32.
    Ganter B, Tugendreich S, Pearson CI, et al. Development of a large-scale chemogenomics database to improve drug candidate selection and to understand mechanisms of chemical toxicity and action. J Biotechnol. 2005;119(3):219–44.Google Scholar
  33. 33.
    Igarashi Y, Nakatsu N, Yamashita T, et al. Open TG-GATEs: a large-scale toxicogenomics database. Nucleic Acids Res. 2015;43(Database issue):D921–7.Google Scholar
  34. 34.
    Wishart DS, Jewison T, Guo AC, et al. HMDB 3.0—the human metabolome database in 2013. Nucleic Acids Res. 2013;41(Database issue):D801–7.Google Scholar
  35. 35.
    Wishart DS, Knox C, Guo AC, et al. DrugBank: a comprehensive resource for in silico drug discovery and exploration. Nucleic Acids Res. 2006;34 suppl 1:D668–72.Google Scholar
  36. 36.
    Whirl-Carrillo M, McDonagh EM, Hebert JM, et al. Pharmacogenomics knowledge for personalized medicine. Clin Pharmacol Ther. 2012;92(4):414–7.Google Scholar
  37. 37.
    Kanehisa M, Goto S. KEGG: Kyoto encyclopedia of genes and genomes. Nucleic Acids Res. 2000;28(1):27–30.CrossRefPubMedPubMedCentralGoogle Scholar
  38. 38.
    Kanehisa M, Goto S, Sato Y, et al. Data, information, knowledge and principle: back to metabolism in KEGG. Nucleic Acids Res. 2014;42(Database issue):D199–205.Google Scholar
  39. 39.
    Milacic M, Haw R, Rothfels K, et al. Annotating cancer variants and anti-cancer therapeutics in Reactome. Cancers. 2012;4(4):1180–211.Google Scholar
  40. 40.
    Mi H, Muruganujan A, Thomas PD. PANTHER in 2013: modeling the evolution of gene function, and other gene attributes, in the context of phylogenetic trees. Nucleic Acids Res. 2013;41(Database issue):D377–86.CrossRefPubMedPubMedCentralGoogle Scholar
  41. 41.
    Stark C, Breitkreutz BJ, Reguly T, et al. BioGRID: a general repository for interaction datasets. Nucleic Acids Res. 2006;34(Database issue):D535–9.Google Scholar
  42. 42.
    Sandelin A, Alkema W, Engstrom P, et al. JASPAR: an open-access database for eukaryotic transcription factor binding profiles. Nucleic Acids Res. 2004;32(Database issue):D91–4.Google Scholar
  43. 43.
    Hume MA, Barrera LA, Gisselbrecht SS, et al. UniPROBE, update 2015: new tools and content for the online database of protein-binding microarray data on protein–DNA interactions. Nucleic Acids Res. 2015;43(Database issue):D117–22.Google Scholar
  44. 44.
    Hamosh A, Scott AF, Amberger JS, et al. Online Mendelian Inheritance in Man (OMIM), a knowledgebase of human genes and genetic disorders. Nucleic Acids Res. 2005;33 suppl 1:D514–7.Google Scholar
  45. 45.
    Groth P, Pavlova N, Kalev I, et al. PhenomicDB: a new cross-species genotype/phenotype resource. Nucleic Acids Res. 2007;35(Database issue):D696–9.Google Scholar
  46. 46.
    Ramos EM, Hoffman D, Junkins HA, et al. Phenotype–Genotype Integrator (PheGenI): synthesizing genome-wide association study (GWAS) data with existing genomic resources. Eur J Hum Genet. 2014;22(1):144–7.Google Scholar
  47. 47.
    Eppig JT, Blake JA, Bult CJ, et al. The Mouse Genome Database (MGD): facilitating mouse as a model for human biology and disease. Nucleic Acids Res. 2015;43(Database issue):D726–36.Google Scholar
  48. 48.
    Smith CM, Finger JH, Hayamizu TF, et al. The mouse Gene Expression Database (GXD): 2014 update. Nucleic Acids Res. 2014;42(Database issue):D818–24.Google Scholar
  49. 49.
    Smith CM, Finger JH, Hayamizu TF, et al. GXD: a community resource of mouse gene expression data. Mamm Genome, 2015.Google Scholar
  50. 50.
    Howe DG, Bradford YM, Conlin T, et al. ZFIN, the zebrafish model organism database: increased support for mutants and transgenics. Nucleic Acids Res. 2013;41(D1):D854–60.Google Scholar
  51. 51.
    Harris TW, Baran J, Bieri T, et al. WormBase 2014: new views of curated biology. Nucleic Acids Res. 2014;42(D1):D789–93.Google Scholar
  52. 52.
    dos Santos G, Schroeder AJ, Goodman JL, et al. FlyBase: introduction of the Drosophila melanogaster Release 6 reference genome assembly and large-scale migration of genome annotations. Nucleic Acids Res. 2014. doi: 10.1093/nar/gku1099.
  53. 53.
    Martin MT, Judson RS, Reif DM, et al. Profiling chemicals based on chronic toxicity results from the US EPA ToxRef Database. Environ Health Perspect. 2009;117(3):392–9.Google Scholar
  54. 54.
    Abeyruwan S, Vempati UD, Kucuk-McGinty H, et al. Evolving BioAssay Ontology (BAO): modularization, integration and applications. J Biomed Sem. 2014;5 Suppl 1:S5.Google Scholar
  55. 55.
    The Gene Ontology Consortium. Gene ontology consortium: going forward. Nucleic Acids Res. 2015;43(D1):D1049–56.CrossRefPubMedCentralGoogle Scholar
  56. 56.
    Smith CL, Eppig JT. The mammalian phenotype ontology: enabling robust annotation and comparative analysis. Wiley Interdiscip Rev Syst Biol Med. 2009;1(3):390–9.CrossRefPubMedPubMedCentralGoogle Scholar
  57. 57.
    Goecks J, Nekrutenko A, Taylor J, et al. Galaxy: a comprehensive approach for supporting accessible, reproducible, and transparent computational research in the life sciences. Genome Biol. 2010;11(8):R86.Google Scholar
  58. 58.
    Giardine B, Riemer C, Hardison RC, et al. Galaxy: a platform for interactive large-scale genome analysis. Genome Res. 2005;15(10):1451–5.Google Scholar
  59. 59.
    Blankenberg D, Von Kuster G, Coraor N, et al. Galaxy: a web-based genome analysis tool for experimentalists. Current Protocols in Molecular Biology, 2010: 19.10.1–19.10. 21.Google Scholar
  60. 60.
    Berthold MR, Cebron N, Dill F, et al. KNIME: the Konstanz Information Miner. Data analysis, machine learning and applications. Berlin: Springer; 2008. p. 319–26.Google Scholar
  61. 61.
    Shannon PT, Reiss DJ, Bonneau R, et al. The Gaggle: an open-source software system for integrating bioinformatics software and data sources. BMC Bioinf. 2006;7:176.Google Scholar
  62. 62.
    Smedley D, Haider S, Durinck S, et al. The BioMart community portal: an innovative alternative to large, centralized data repositories. Nucleic Acids Res. 2015;43(W1):W589–98.Google Scholar
  63. 63.
    Huber W, Carey VJ, Gentleman R, et al. Orchestrating high-throughput genomic analysis with bioconductor. Nat Methods. 2015;12(2):115–21.Google Scholar
  64. 64.
    R Core Team. R: a language and environment for statistical computing. Vienna, Austria; 2014. URL, 2015.

Copyright information

© Springer International Publishing AG (outside the USA) 2016

Authors and Affiliations

  • Noffisat O. Oki
    • 1
    • 2
  • Mark D. Nelms
    • 1
    • 2
  • Shannon M. Bell
    • 1
    • 2
    • 3
  • Holly M. Mortensen
    • 4
  • Stephen W. Edwards
    • 2
    Email author
  1. 1.Oak Ridge Institute for Science and EducationOak RidgeUSA
  2. 2.Integrated Systems Toxicology Division, National Health and Environmental Effects Research Laboratory, Office of Research and DevelopmentU.S. Environmental Protection AgencyResearch Triangle ParkUSA
  3. 3.ILS/Contractor Supporting the NTP Interagency Center for the Evaluation of Alternative Toxicological Methods (NICEATM)Research Triangle ParkUSA
  4. 4.Genomics Research Core, National Health and Environmental Effects Research Laboratory, Office of Research and DevelopmentU.S. Environmental Protection AgencyResearch Triangle ParkUSA

Personalised recommendations