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Food for thought … A toxicology ontology roadmap

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Barry Hardy1, Gordana Apic2, Philip Carthew3, Dominic Clark4, David Cook5, Ian Dix5,6,Sylvia Escher7, Janna Hastings4, David J. Heard8, Nina Jeliazkova9, Philip Judson10, Sherri Matis-Mitchell5, Dragana Mitic2, Glenn Myatt11, Imran Shah12, Ola Spjuth13, Olga Tcheremenskaia14, Luca Toldo15, David Watson10, Andrew White3, and Chihae Yang16
1 Douglas Connect and OpenTox, Zeiningen, Switzerland;
2 Cambridge Cell Networks, Cambridge, UK;
3 Unilever, Sharnbrook, Beds, UK;
4 EMBL-EBI, European Bioinformatics Institute, Cambridgeshire, UK;
5 AstraZeneca, Macclesfield, Cheshire, UK;
6 Pistoia Alliance;
7 Fraunhofer Institute for Toxicology and Experimental Medicine, Hannover, Germany;
8 Novartis, Basle, Switzerland;
9 Ideaconsult Ltd, Sofia, Bulgaria;
10 Lhasa Ltd., Leeds, UK;
11 Leadscope, Columbus, OH, USA;
12 US EPA, Research Triangle Park, NC, USA;
13 University of Uppsala, Uppsala, Sweden;
14 Istituto Superiore di Sanità, Rome, Italy;
15 Merck KGaA, Darmstadt, Germany;
16 Altamira, Columbus, OH, USA


Foreign substances can have a dramatic and unpredictable adverse effect on human health. In the development of new therapeutic agents, it is essential that the potential adverse effects of all candidates be identified as early as possible. The field of predictive toxicology strives to profile the potential for adverse effects of novel chemical substances before they occur, both with traditional in vivo experimental approaches and increasingly through the development of in vitro and computational methods which can supplement and reduce the need for animal testing. To be maximally effective, the field needs access to the largest possible knowledge base of previous toxicology findings, and such results need to be made available in such a fashion so as to be interoperable, comparable, and compatible with standard toolkits. This necessitates the development of open, public, computable, and standardized toxicology vocabularies and ontologies so as to support the applications required by in silico, in vitro, and in vivo toxicology methods and related analysis and reporting activities. Such ontology development will support data management, model building, integrated analysis, validation and reporting, including regulatory reporting and alternative testing submission requirements as required by guidelines such as the REACH legislation, leading to new scientific advances in a mechanistically-based predictive toxicology.

Numerous existing ontology and standards initiatives can contribute to the creation of a toxicology ontology supporting the needs of predictive toxicology and risk assessment. Additionally, new ontologies are needed to satisfy practical use cases and scenarios where gaps currently exist. Developing and integrating these resources will require a well-coordinated and sustained effort across numerous stakeholders engaged in a public-private partnership. In this communication, we set out a roadmap for the development of an integrated toxicology ontology, harnessing existing resources where applicable. We describe the stakeholders’ requirements analysis from the academic and industry perspectives, timelines, and expected benefits of this initiative, with a view to engagement with the wider community.

ALTEX 29(2), 129-137
DOI: 10.14573/altex.2012.2.129

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