Accelerating Adverse Outcome Pathway Development Using Publicly Available Data Sources
- 593 Downloads
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.
KeywordsAdverse 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
- 2.Russell WMS, Burch RL. The principles of humane experimental technique. Methuen 1959.Google Scholar
- 3.United States Congress. ICCVAM authorization act of 2000. 2000 [cited 2015 08/03/2015]; available from: https://www.congress.gov/bill/106th-congress/house-bill/4281.
- 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.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.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.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
- 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.OECD. Users’ handbook supplement to the guidance document for developing and assessing AOPs. 2014 [cited 2015 7/13/2015]; available from: https://aopkb.org/common/AOP_Handbook.pdf.
- 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.•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.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.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.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.Pearl J. Causality: models, reasoning and inference. Cambridge University Press; 2009. p 478.Google Scholar
- 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.Perkins EJ, Chipman K, Edwards S, et al. Reverse engineering adverse outcome pathways. Environ Toxicol Chem. 2011;30(1):22–38.Google Scholar
- 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.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.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.Bell SM, Edwards SW. Identification and prioritization of relationships between environmental stressors and adverse human health impacts. Environ Health Perspect. 2015.Google Scholar
- 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.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.Kolesnikov N, Hastings E, Keays M, et al. ArrayExpress update—simplifying data submissions. Nucleic Acids Res. 2015;43(D1):D1113–6.Google Scholar
- 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.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.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.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.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.Whirl-Carrillo M, McDonagh EM, Hebert JM, et al. Pharmacogenomics knowledge for personalized medicine. Clin Pharmacol Ther. 2012;92(4):414–7.Google Scholar
- 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.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
- 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.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.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.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.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.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.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.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.Smith CM, Finger JH, Hayamizu TF, et al. GXD: a community resource of mouse gene expression data. Mamm Genome, 2015.Google Scholar
- 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.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.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.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.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
- 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.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.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.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.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.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.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.R Core Team. R: a language and environment for statistical computing. Vienna, Austria; 2014. URL http://www.R-project.org, 2015.