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TargetMI

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A Multi-Omics Approach for Novel Drug Targets, Biomarkers and Risk Algorithms fo...

A Multi-Omics Approach for Novel Drug Targets, Biomarkers and Risk Algorithms for Myocardial Infarction In TargetMI we propose a high throughput multi-omic approach for rapid discovery of novel drug targets, biomarkers and risk algorithms, applied here to atherosclerosis, myocardial infarction (MI) and their risk factors. Cardiovasc... ver más

30/09/2028

UNIVERSITY OF MALT...

4M€

Presupuesto del proyecto: 4M€

Líder del proyecto

UNIVERSITA TA MALTA No se ha especificado una descripción o un objeto social para esta compañía.

TRL 4-5

Fecha límite participación Sin fecha límite de participación.

Financiación concedida El organismo HORIZON EUROPE notifico la concesión del proyecto el día 2023-10-01

HORIZON-EIC-2022-PATHFINDERCHALLENGES-01-01: EIC Pathfinder Challenge: Carbon di... Scope:Climate change, global warming and water/soil pollution are unprecedented challenges for the p...

I+D

Cerrada hace 2 años

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UNIVERSITY OF MALTA

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Duración del proyecto: 59 meses Fecha Inicio: 2023-10-01
Fecha Fin: 2028-09-30

Líder del proyecto

UNIVERSITA TA MALTA No se ha especificado una descripción o un objeto social para esta compañía.

TRL 4-5

Presupuesto del proyecto 4M€

Fecha límite de participación Sin fecha límite de participación.

Descripción del proyecto In TargetMI we propose a high throughput multi-omic approach for rapid discovery of novel drug targets, biomarkers and risk algorithms, applied here to atherosclerosis, myocardial infarction (MI) and their risk factors. Cardiovascular disease is a major cause of death and morbidity worldwide. The causes of MI are highly complex involving genetic, lifestyle and environmental factors. Whilst much research effort has been invested in attempting to decipher these factors, clinical applications of findings are disappointingly few. We will harness four -omic datasets (whole genome, transcriptomic, metabolomic and proteomic data) on 1000 highly phenotyped samples of the Maltese Acute Myocardial Infarction (MAMI) Study. These were collected from cases, controls and relatives of cases (including 80 families) with meticulous attention to preanalytical variables. We will identify intermediate phenotypes associated with risk of MI and its associated risk factors. Using a combination of approaches including extreme phenotype and family-based approaches we will identify variants which robustly influence these intermediate phenotypes. The genes thus identified are potential drug targets that influence risk of MI via an intermediate phenotype and are applicable across all populations. They will be validated through various approaches including computational analysis, (using Mendelian randomisation and 10 year follow-up data), and functional work that includes using zebrafish as an animal model. Machine learning algorithms will be used to analyse the multi-layered data to identify novel biomarkers and risk algorithms, including polygenic risk scores, for early risk prediction in the clinic. Quantitative targeted proteomic assays will be developed for further validation in other cohorts facilitating clinical use. Besides the increase in knowledge on the molecular etiology of MI, this powerful integrated strategy will bring rapid clinical translation of unprecedented multi-omic data.

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