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Int J Mol Sci. 2012;13(3):3820-46. doi: 10.3390/ijms13033820. Epub 2012 Mar 21.

Inroads to predict in vivo toxicology-an introduction to the eTOX Project.

Author information

1
Lhasa Ltd., 22-23 Blenheim Terrace, Woodhouse Lane, Leeds, LS2 9HD, UK; E-Mails: Katharine.Briggs@lhasalimited.org (K.B.); David.Watson@lhasalimited.org (D.K.W.).

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will be a database consisting of preclinical toxicity data for drug compounds or candidates extracted from previously unpublished, legacy reports from thirteen European and European operation-based pharmaceutical companies.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:

Data Integration; Decision Support System; Expert Systems; Knowledge Management; Manual Curation; QSAR; computational models; data sharing; histopathology; in silico toxicity; in vitro toxicity; in vivo toxicity; ontology; predictive toxicology

PMID:
22489185
PMCID:
PMC3317745
DOI:
10.3390/ijms13033820
[Indexed for MEDLINE]
Free PMC Article

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