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Int. J. Mol. Sci. 2012, 13(3), 3820-3846; doi:10.3390/ijms13033820
Review

Inroads to Predict in Vivo Toxicology—An Introduction to the eTOX Project

1
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, 3,* , 2
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1 Lhasa Ltd., 22-23 Blenheim Terrace, Woodhouse Lane, Leeds, LS2 9HD, UK 2 Research Programme on Biomedical Informatics (GRIB), Fundació IMIM, Universitat Pompeu Fabra, PRBB, Dr. Aiguader 88, 08003 Barcelona, Spain 3 Department of Preclinical Safety, Novartis Institutes for Biomedical Research (NIBR), Postfach CH-4002, Basel, Switzerland 4 Molecular Networks GmbH, IZMP, Henkestr. 91, 91052 Erlangen, Germany 5 Bayer HealthCare, Investigational Toxicology, Müllerstr. 178, 13353 Berlin, Germany
* Author to whom correspondence should be addressed.
Received: 11 October 2011 / Revised: 30 January 2012 / Accepted: 14 March 2012 / Published: 21 March 2012
(This article belongs to the Special Issue Advances in Computational Toxicology)
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Abstract

There is a widespread awareness that the wealth of preclinical toxicity data that the pharmaceutical industry has generated in recent decades is not exploited as efficiently as it could be. Enhanced data availability for compound comparison (“read-across”), or for data mining to build predictive tools, should lead to a more efficient drug development process and contribute to the reduction of animal use (3Rs principle). In order to achieve these goals, a consortium approach, grouping numbers of relevant partners, is required. The eTOX (“electronic toxicity”) consortium represents such a project and is a public-private partnership within the framework of the European Innovative Medicines Initiative (IMI). The project aims at the development of in silico prediction systems for organ and in vivo toxicity. The backbone of the project will be a database consisting of preclinical toxicity data for drug compounds or candidates extracted from previously unpublished, legacy reports from thirteen European and European operation-based pharmaceutical companies. The database will be enhanced by incorporation of publically available, high quality toxicology data. Seven academic institutes and five small-to-medium size enterprises (SMEs) contribute with their expertise in data gathering, database curation, data mining, chemoinformatics and predictive systems development. The outcome of the project will be a predictive system contributing to early potential hazard identification and risk assessment during the drug development process. The concept and strategy of the eTOX project is described here, together with current achievements and future deliverables.
Keywords: predictive toxicology; in silico toxicity; in vitro toxicity; in vivo toxicity; Knowledge Management; Expert Systems; Decision Support System; Data Integration; Manual Curation; ontology; histopathology; computational models; QSAR; data sharing predictive toxicology; in silico toxicity; in vitro toxicity; in vivo toxicity; Knowledge Management; Expert Systems; Decision Support System; Data Integration; Manual Curation; ontology; histopathology; computational models; QSAR; data sharing
This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

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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. Inroads to Predict in Vivo Toxicology—An Introduction to the eTOX Project. Int. J. Mol. Sci. 2012, 13, 3820-3846.

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