Agency: European Commission | Branch: FP7 | Program: CP-IP | Phase: HEALTH-2009-2.4.5-2 | Award Amount: 15.74M | Year: 2010
Chronic kidney disease (CKD) affects up to 10% of the population. Besides eventual progression towards end stage renal disease CKD impacts the patients quality of life by causing serious comorbidities including cardiovascular complications and bone metabolism disorders. On the everyday clinical level early stage diagnosis and tailored treatment of CKD are still inadequate. In addition, CKD seems not to have reached its appropriate emplacement in an epidemiological and healthcare perspective yet, and the pathophysiology of the disease on a molecular and cellular level is not well enough understood. Our sysKID consortium was installed for precisely addressing these issues: To unravel the molecular and cellular mechanisms of chronic kidney disease development, combine this information with clinical risk factors, and on this basis delineate chronic kidney disease biomarkers. These markers will allow us to perform preclinical studies of novel therapy approaches for halting disease progression, and will provide us with the materials for development and clinical evaluation of tools for early stage diagnosis as well as prognosis and treatment monitoring. sysKID assures a successful implementation of these goals by a truly international consortium of 27 leading research groups. We combine clinical know how, provide access to a huge chronic kidney disease sample and clinical data pool, and build a Systems Biology framework for chronic kidney disease by integrating molecular and cellular biology, computational biology, statistics and epidemiology. Our expert group is further complemented by a high level advisory board covering science, product development, and the patients perspective. sysKID implementation is structured for completing pre-clinical Proof of Concept studies of novel chronic kidney disease therapy regimes, and further for completing clinical evaluation of an epidemiological screening tool as well as of early stage chronic kidney disease diagnostic kits.
Agency: European Commission | Branch: FP7 | Program: CP-FP | Phase: HEALTH.2012.1.2-1 | Award Amount: 4.60M | Year: 2012
With the advent of Omics technologies capable of profiling substantial fractions of molecular entities from the genome down to the metabolome level a significant boost in understanding disease pathophysiology, consequently providing tailored diagnostics and therapy, was expected. With impressive results for selected diseases, cancer still sees significant shortcomings. One reason for this clinical situation is certainly the apparent heterogeneity of cancers, manifesting in clinical presentation and outcome, but also on the personalized molecular level. Within DIPROMON we selected bladder cancer as a prototypical example for the need of personalization efforts in therapy, and we plan of using this clinically highly challenging disease phenotype for establishing a general workflow for enabling personalization strategies towards rational decision on tailoring therapeutic interventions for defined patient groups. DIPROMON on its basis follows a computational Systems Biology approach for delineating statistical rules for patient segmentation, resting on patient-specific biomarker profiles interpreted in tight integration with patient-specific clinical data. DIPROMON will establish a workflow including quality control aspects for i) selecting biomarker panels for given clinical phenotypes, ii) providing modular instrumentation for measuring the profiles in a clinical setting, and iii) deriving statistics-driven rules for patient segmentation regarding optimal therapy. DIPROMON will finally validate the concept in bladder cancer.
Agency: European Commission | Branch: FP7 | Program: CP-FP | Phase: HEALTH.2011.2.4.1-2 | Award Amount: 3.96M | Year: 2012
About 75% of advanced epithelial ovarian cancer (EOC) patients respond to first-line surgery and chemotherapy but most relapse and ultimately acquire platinum resistance which soon leads to death. Relapsed high grade serous ovarian cancer (HGSOC) is the single main cause of EOC-related morbidity and mortality (despite the fact that HGSOC is highly chemosensitive). We hypothesize that the primary tumour includes a small population of resistant cells that are ultimately responsible for relapse and that by targeting this population front-line we may prolong disease-free survival or even achieve cure. OCTIPS will use unique retrospective and novel prospective paired tumour samples collected at the time of diagnosis and relapse to identify and validate molecules and pathways responsible for relapse. This identification will employ cutting edge high throughput multiplatform analyses such as next generation sequencing, mRNA and miRNA expression arrays and SNP array. Known and newly defined molecules or pathways will be evaluated in innovative integrated cancer model systems, utilising cell lines and avian egg and murine xenografts. New therapies to target these molecules and pathways will be developed and validated in these model systems. In order to translate these findings into patient benefit, agents that target the relapsing cell population will be tested for tolerability, efficacy, ability to combine with first line chemotherapy and then in randomised first line trials by the OCTIPS consortium. By translating the clinical observation of treatment failures into innovative cancer models that mimic relapsed ovarian cancer, we will validate improved front-line therapeutic strategies to help prolong patient survival. The impact of this application is that it defines a highly rigorous approach to integrate the bedside to bench to bedside paradigm, leading to novel prognosis-changing strategies for the treatment of ovarian cancer patients.
Agency: European Commission | Branch: FP7 | Program: CP-FP | Phase: HEALTH.2011.2.4.2-2 | Award Amount: 8.11M | Year: 2011
EU-MASCARA is a collaborative project that aims to improve diagnosis of cardiovascular diseases and prediction of cardiovascular risk by analysing a panel of biomarkers. EU-MASCARA aims to examine genetic, proteomic and metabolomic markers together with markers of inflammation, oxidative stress and cardiac remodelling to study their incremental diagnostic and predictive value over and above existing diagnostic and predictive algorithms. For this purpose a large number of cohorts from different European regions, both patient and population cohorts, that have been accurately assessed for cardiovascular phenotypes are readily available to the consortium. Access to clinical samples and to standardised cardiovascular phenotypes will be granted by a strong clinical platform as one of the key work packages of EU-MASCARA. Both cross-sectional and prospective analyses will be performed that will result in the development of improved risk prediction scores. The consortium is heavily supported by contributions of SMEs in key areas of the proposed research: biomarker testing, data handling and analysis, assay development and project management. EU-MASCARA is further characterised by a strong integrative approach both within and across work packages, with results from one task informing strategies of research in other tasks. With a dedicated bioinformatics and health economic platform the most robust biomarkers will be selected and analysed for their benefit in clinical practice. EU-MASCARA will rigorously validate biomarkers that have been proposed to be associated with cardiovascular disease and risk across different disease entities and also in independent general population samples. The most robust biomarkers will be implemented in novel biochip based assays for clinical use.
Agency: European Commission | Branch: H2020 | Program: MSCA-RISE | Phase: MSCA-RISE-2015 | Award Amount: 387.00K | Year: 2016
Cardiovascular disease (VD) is the leading cause of mortality and morbidity in Europe and worldwide. The objective of the PRETREAT consortium is to generate a joint SME/academic European preclinical platform for providing services for detection of VD and drug development. This platform will combine the use of urinary and/or blood peptidomics in humans and in preclinical animal models of VD, together with bioinformatics and systems biology, in order to better detect, stratify and decipher the molecular mechanisms of VD, develop new animal models with high similarity to human disease, and provide new tools for obtaining information on novel drug targets. PRETREAT builds on the FP7 project Sysvasc (systems biology to identify molecular targets for vascular disease treatment, 2014-2018) and the combined expertise of the PRETREAT partners in clinical proteomics, proteomics, animal models and system medicine as the pillars to implement this platform. The work will be carried out in an extensive exchange program totaling 27 secondments. Main objectives during the secondments include establishment of the link between urinary markers and the pathophysiology of VD, identification of additional animal VD models with similarity to human VD, development of humanized body fluid readouts in VD models, establishment of a VD protein-centric database in order to automatically link urinary peptides to in situ changes and provide information on drug targets, and provide proof-of-concept of the utility of the proposed platform. These secondments will in parallel serve to keep information flowing within the project, increase individual research efficiency and create a multi-disciplinary working chain, train personnel, and prepare the sustainability of the results after the project. To complete training, 3 monthly webinars and four workshops are planned.
Agency: European Commission | Branch: FP7 | Program: CP-IP | Phase: HEALTH-2007-1.3-1 | Award Amount: 16.43M | Year: 2008
The overall aim of Predict-IV is to develop strategies to improve the assessment of drug safety in the early stage of development and late discovery phase, by an intelligent combination of non animal-based test systems, cell biology, mechanistic toxicology and in-silico modelling, in a rapid and cost effective manner. A better prediction of the safety of an investigational compound in early development will be delivered. Margins-of-safety will be deduced and the data generated by the proposed approach may also identify early biomarkers of human toxicity for pharmaceuticals. The results obtained in Predict-IV will enable pharmaceutical companies to create a tailored testing strategy for early drug safety. The project will integrate new developments to improve and optimize cell culture models for toxicity testing and to characterize the dynamics and kinetics of cellular responses to toxic effects in vitro. The target organs most frequently affected by drug toxicity will be taken into account, namely liver and kidney. Moreover, predictive models for neurotoxicty are scarce and will be developed. For each target organ the most appropriate cell model will be used. The approach will be evaluated using a panel of drugs with well described toxicities and kinetics in animals and partly also in humans. This approach will be highly advantageous as it will allow a direct comparison between the in vivo to the in vitro data. A parallel analysis of several dynamic and kinetic models with a broad spectrum of endpoints should allow for the identification of several relevant biomarkers of toxicity. Inter-individual susceptibilities will be taken into account by integrating the polymorphisms of the major drug metabolizing enzymes and correlating the observed effects in the human cell models with their genotype. Environmental influences on cellular toxicity to these compounds will also be evaluated using hypoxic stress as a relevant test model.
Echeverria P.C.,University of Geneva |
Bernthaler A.,Emergentec Biodevelopment GmbH |
Dupuis P.,University of Geneva |
Mayer B.,Emergentec Biodevelopment GmbH |
Picard D.,University of Geneva
PLoS ONE | Year: 2011
Understanding the functions of proteins requires information about their protein-protein interactions (PPI). The collective effort of the scientific community generates far more data on any given protein than individual experimental approaches. The latter are often too limited to reveal an interactome comprehensively. We developed a workflow for parallel mining of all major PPI databases, containing data from several model organisms, and to integrate data from the literature for a protein of interest. We applied this novel approach to build the PPI network of the human Hsp90 molecular chaperone machine (Hsp90Int) for which previous efforts have yielded limited and poorly overlapping sets of interactors. We demonstrate the power of the Hsp90Int database as a discovery tool by validating the prediction that the Hsp90 co-chaperone Aha1 is involved in nucleocytoplasmic transport. Thus, we both describe how to build a custom database and introduce a powerful new resource for the scientific community. © 2011 Echeverría et al.