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New Models for Preclinical Evaluation of Drug Efficacy in Common Solid Tumours (PREDECT)
Date du début: 1 févr. 2011, Date de fin: 30 avr. 2016 PROJET  TERMINÉ 

Traditional preclinical discovery methods, particularly for target validation, poorly predict drug efficacy, causing a high attrition rate in costly late-stage clinical trials. The PREDECT consortium will focus on complex but transferable, next generation in vitro and in vivo models for breast, prostate and lung cancers. Models will be investigated for their improved potential to validate novel therapeutic targets. Known targets, in canonical pathways, will be interrogated for induction of phenotypic, proteomic and transcriptomic changes using inhibitors. A strategy of seeking a ‘dynamic reciprocity’ of concordance between the steady and perturbed states of in vitro complex cultures, tissue slices and in vivo tumour models will be pursued by systems biology analyses. Comparison with historic gene expression and genomic data from relevant clinical materials should permit the emergence of faithful models for target validation and beyond. PREDECT is coordinated by Servier and AstraZeneca, and the managing entity of IMI JU funding is the University of Helsinki. The team assembles world-class biologists, clinicians and computational scientists from 8 prestigious EU institutes, 3 SMEs and 8 EFPIA members to develop and critically assess models for target validation. The interaction between pharmaceutical and academic participants will be proactive, through postdoc co-supervision and work package co-piloting. We propose to develop and generate a repository of advanced complex models in 3 complementary areas: (i) in vitro 2D/3D organotypic (co-)cultures, stirred bioreactor aggregates and tissue slice systems; (ii) novel (orthotopic) grafts of human and mouse tumour samples; and (iii) genetically-engineered and mosaic mouse models. The deliverables of PREDECT are expected to shift paradigms in target validation, permitting greater predictivity of drug efficacy in patient cohorts.



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