Time filter

Source Type

Agency: Cordis | 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: Cordis | 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.

Muhlberger I.,Emergentec Biodevelopment GmbH
Methods in molecular biology (Clifton, N.J.) | Year: 2011

Progress in experimental procedures has led to rapid availability of Omics profiles. Various open-access as well as commercial tools have been developed for storage, analysis, and interpretation of transcriptomics, proteomics, and metabolomics data. Generally, major analysis steps include data storage, retrieval, preprocessing, and normalization, followed by identification of differentially expressed features, functional annotation on the level of biological processes and molecular pathways, as well as interpretation of gene lists in the context of protein-protein interaction networks. In this chapter, we discuss a sequential transcriptomics data analysis workflow utilizing open-source tools, specifically exemplified on a gene expression dataset on familial hypercholesterolemia.

Mayer B.,Emergentec Biodevelopment GmbH
Diabetologia | Year: 2016

Medications approved for diabetes-associated renal and cardiovascular morbidities and candidate drugs currently in development are subject to substantial variability in drug response. Heterogeneity on a molecular phenotype level is not apparent at clinical presentation, which means that inter-individual differences in drug effect at the molecular level are masked. These findings identify the need for optimising patient phenotyping via use of molecular biomarkers for a personalised therapy approach. Molecular diversity may, on the one hand, result from the effect of genetic polymorphisms on drug transport, metabolism and effective target modulation. Equally relevant, differences may be due to molecular pathologies. The presence of distinct molecular phenotypes is suggested by classifiers aimed at modelling progressive disease. Such functions for prognosis incorporate a complex set of clinical variables or a multitude of molecular markers reflecting a diverse set of molecular disease mechanisms. This information on disease pathology and the mechanism of action of the drug needs to be systematically integrated with data on molecular biomarkers to develop an experimental tool for personalising medicine. The large amount of molecular data available for characterising diabetes-associated morbidities allows for elucidation of molecular process model representations of disease pathologies. Selecting biomarker candidates on such grounds and, in turn identifying their association with progressive disease allows for the identification of molecular processes associated with disease progression. The molecular effect of a drug can also be modelled at a molecular process level, and the integration of disease pathology and drug effect molecular models reveals candidate biomarkers for assessing drug response. Such tools serve as enrichment strategies aimed at adding precision to drug development and use. © 2016 Springer-Verlag Berlin Heidelberg

Wiesinger M.,Emergentec Biodevelopment GmbH
Methods in molecular biology (Clifton, N.J.) | Year: 2011

Cross-Omics studies aimed at characterizing a specific phenotype on multiple levels are entering the -scientific literature, and merging e.g. transcriptomics and proteomics data clearly promises to improve Omics data interpretation. Also for Systems Biology the integration of multi-level Omics profiles (also across species) is considered as central element. Due to the complexity of each specific Omics technique, specialization of experimental and bioinformatics research groups have become necessary, in turn demanding collaborative efforts for effectively implementing cross-Omics. This setting imposes specific emphasis on data sharing platforms for Omics data integration and cross-Omics data analysis and interpretation. Here we describe a software concept and methodology fostering Omics data sharing in a distributed team setting which next to the data management component also provides hypothesis generation via inference, semantic search, and community functions. Investigators are supported in data workflow management and interpretation, supporting the transition from a collection of heterogeneous Omics profiles into an integrated body of knowledge.

Discover hidden collaborations