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Integrating bioinformatics and chemoinformatics approaches for the development of expert systems allowing the in silico prediction of toxicities (ETOX)
Start date: Jan 1, 2010, End date: Dec 31, 2016 PROJECT  FINISHED 

The eTOX consortium proposes to develop innovative methodological strategies and novel software tools to better predict the toxicological profiles of new molecular entities in early stages of the drug development pipeline. This will be achieved by coordinating the efforts of specialists from academia and SMEs in the wide scope of disciplines that are required to move towards a more reliable modelling of the complex relationships existingbetween molecular and in vitro information and the in vivo toxicity outcomes of drugs. The proposed strategy includes a synergetic integration of innovative approaches in the following areas:Database building and management, including masking procedures for sensitive data, with the aim of facilitating the sharing of information among pharmaceutical companies.• Ontologies and text mining techniques, with the purpose of facilitating knowledge extraction from legacy preclinical reports and biomedical literature.• Chemistry and structure-based approaches for the molecular description of the compounds considered, as well as of the biological anti-targets responsible for the secondary pharmacologies.• Prediction of absorption and metabolism since these ADME properties are often critically related to toxicological events.• Computational genomics to afford the inter-species and inter-individual variabilities that complicate the interpretation of experimental and clinical outcomes.• Systems biology approaches in order to cope with the complex biological mechanisms which govern in vivo safety problems.• Sophisticated statistical analysis tools required to derive the inevitably highly-multivariate QSAR models.• Development and validation (according to the OECD principles) of QSARs, integrative models, expert systems and meta-tools.• Linkage of toxicity preclinical data with human outcomes

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