eTOX

eTOX is a temporary consortium established in 2010 to share and use toxicology data. It is a pre-competitive collaboration which main goal is to create and distribute tools to predict drug side-effects based on pre-clinical experiments. Aims are a better in silico predictability of potential adverse events and a decrease of the use of animals in toxicological research. eTOX is funded by the Innovative Medicines Initiative (IMI).[1]

This article is about the Toxicology consortium. For the automobile manufacturer, see Etox.

eTOX
Part of the Innovative Medicines Initiative
KeywordsDrug discovery, drug development
Project typeJoint Technology Initiative
Funding agencySeventh Framework Programme
European Federation of Pharmaceutical Industries and Associations (EFPIA)
ObjectiveDevelop in silico strategies and software tools to predict the toxicological profiles of small molecules
Project coordinatorNovartis, Bayer
ParticipantsEFPIA: AstraZeneca, Boehringer Ingelheim, Esteve, GlaxoSmithKline, Janssen Pharmaceutica, Lundbeck, Pfizer, Hoffmann-La Roche, UCB, Sanofi, Servier

Academia: Erasmus Universitair Medisch Centrum, Fraunhofer Gesellschaft, Fundació Institut Mar d'Investigacions Mèdiques, Fundación Centro Nacional de Investigaciones Oncológicas Carlos III, European Molecular Biology Laboratory, Liverpool John Moores University, Technical University of Denmark, Universitat Politècnica de Valencia, University of Leicester, Universität Wien, VU University Amsterdam

SME: Lhasa Limited, Inte:Ligand, Molecular Networks, Chemotargets, Lead Molecular Design, Synapse Research Management Partners
Budget
  • Total: 6.9 million EUR
  • Funding: 18.7 million EUR
Duration2010 – 2016
Websitewww.e-tox.net

Goals

The official title of the consortium is "Integrating bioinformatics and chemoinformatics approaches for the development of expert systems allowing the in silico prediction of toxicities (eTOX)". eTOX aims at creating in silico tools to predict the toxicity of small molecules during early stages of the drug development pipeline.[2] The consortium is assembling predictive model based on a set of toxicology reports curated by pharmaceutical companies who joined the consortium. In the spirit of the IMI, eTOX bring together private companies producing preclinical data and academic partners experts on cheminformatics, bioinformatics and natural language processing. Contribution to public domain of algorithms,[3][4][5] software and ontologies[6] is part of the road-map.

Organization

To achieve these goal, a consortium bringing together 25 pharmaceutical companies, biotech companies and university was created. The project was started in 2010, it was originally funded for 5 years by consortium partners and the European Union. The consortium did benefit from an ENSO extension and will continue until end of 2016. Overall estimated budget is 18.7 million Euros.

Partners

eTOX is collaborating with OpenPHACTS, another IMI consortium to reuse publicly available clinical data. In 2014, the IMI iPiE (Intelligence-Led Assessment of Pharmaceuticals in the Environment) consortium was started, goals are similar to eTOX, but raw data are from chemical substances collected to comply with REACH[7] European regulation. It was agreed that a close collaboration for database development, predictive systems and intellectual property issues will take place.

Outcome and dissemination

The eTox project is currently running and is scheduled to finish in December 2016. Internal work is slowly becoming visible externally: as of August 2014, 3046 reports from 4291 animal studies were manually curated and stored in a database.[8] Two thousand more are being processed, and seventy-four models were built and validated, mostly based on public data.[8] The consortium has started to disseminate information and a review article about the project has been published.[2]

The list of scientific publications (including articles and talks) is accessible at the eTOX public website.

An external newsletter is released every 3 months with the main advances in the project, since November 2011.

The future

The United States Food and Drug Administration is going to ask for electronic submission of raw data from animal studies using the Standard for Exchange of Non-clinical Data in future investigational new drug and biologic license application submissions. This is widely seen as a potential game changer for the toxicology discipline where data exchange and data mining of large data-sets is not the norm. It could allow for the automatic import to a database like the eTOX one.

gollark: ubq: did you try the electromagnetic radio? What is your preferred form of musical units?
gollark: LC aren't divisible enough for that to work nicely.
gollark: Yep! MPD has HTTP streaming capabilitioforms.
gollark: hereßy.
gollark: It uses one (2) MPD instances.

See also

References
  1. "eTOX - IMI - Innovative Medicines Initiative". europa.eu. Retrieved 27 March 2015.
  2. Briggs, K; Cases, M; Heard, D. J.; Pastor, M; Pognan, F; Sanz, F; Schwab, C. H.; Steger-Hartmann, T; Sutter, A; Watson, D. K.; Wichard, J. D. (2012). "Inroads to Predict in Vivo Toxicology-An Introduction to the eTOX Project". International Journal of Molecular Sciences. 13 (3): 3820–46. doi:10.3390/ijms13033820. PMC 3317745. PMID 22489185.
  3. Carrió, P; Pinto, M; Ecker, G; Sanz, F; Pastor, M (2014). "Applicability Domain ANalysis (ADAN): A robust method for assessing the reliability of drug property predictions". Journal of Chemical Information and Modeling. 54 (5): 1500–11. doi:10.1021/ci500172z. PMID 24821140.
  4. Gautier, L; Taboureau, O; Audouze, K (2013). "The effect of network biology on drug toxicology". Expert Opinion on Drug Metabolism & Toxicology. 9 (11): 1409–18. doi:10.1517/17425255.2013.820704. PMID 23937336.
  5. Vazquez, Miguel; Krallinger, Martin; Leitner, Florian; Valencia, Alfonso (2011). "Text Mining for Drugs and Chemical Compounds: Methods, Tools and Applications". Molecular Informatics. 30 (6–7): 506–519. doi:10.1002/minf.201100005.
  6. Hardy, B; Apic, G; Carthew, P; Clark, D; Cook, D; Dix, I; Escher, S; Hastings, J; Heard, D. J.; Jeliazkova, N; Judson, P; Matis-Mitchell, S; Mitic, D; Myatt, G; Shah, I; Spjuth, O; Tcheremenskaia, O; Toldo, L; Watson, D; White, A; Yang, C (2012). "Toxicology ontology perspectives". ALTEX. 29 (2): 139–56. doi:10.14573/altex.2012.2.139. PMID 22562487.
  7. Nicolotti, O; Benfenati, E; Carotti, A; Gadaleta, D; Gissi, A; Mangiatordi, G. F.; Novellino, E (2014). "REACH and in silico methods: An attractive opportunity for medicinal chemists". Drug Discovery Today. 19: 1757–68. doi:10.1016/j.drudis.2014.06.027. PMID 24998783.
  8. Cases, M; Briggs, K; Steger-Hartmann, T; Pognan, F; Marc, P; Kleinöder, T; Schwab, C. H.; Pastor, M; Wichard, J; Sanz, F (2014). "The eTOX Data-Sharing Project to Advance in Silico Drug-Induced Toxicity Prediction". International Journal of Molecular Sciences. 15 (11): 21136–54. doi:10.3390/ijms151121136. PMC 4264217. PMID 25405742.
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