Project description
Accelerating drug development for chronic diseases
Much of biomedical research has, for good reasons, focused on diseases that shorten our life, but this has often left millions of patients suffering from less severe diseases without safe and effective treatments. Examples include inflammatory skin diseases like rosacea or Raynaud’s syndrome, both affecting about 1 in 20 people worldwide. Funded by the European Research Council, the GenDrug project addresses the lack of safe and effective drugs for these common diseases, despite their significant impact on healthcare. By developing innovative algorithms, the research team will integrate genomic data from large-scale studies with real-world data from electronic health records to identify potent drug targets and opportunities for drug repurposing. The researchers will harness deep learning models to gain insights into disease biology, ultimately fostering drug development.
Objective
There are thousands of common non-communicable diseases (NCDs) that lack safe and effective drugs despite accounting for most health care expenses and years lived with disability. Me and my team will address this unmet clinical need by developing innovative algorithms that generate and integrate evidence from massive scale genomic studies with real-world data based on millions of patients from electronic health records (EHRs) to identify potent drug targets and opportunities for drug repurposing. To achieve this aim, we will 1) harness ethnically diverse biobanks (>500,000 participants) with whole-exome/genome sequencing and EHR linkage powered by deep learning models to gain new insights into the aetiology of neglected NCDs that are needed for rational drug design, 2) create a genetically anchored biomedical knowledge graph that incorporates rich functional genomic data from single-cell studies with drug characteristics to predict promising drug targets using deep graph neural networks, and 3) establish convergence of genetic and real-world evidence of proposed drug targets by emulating clinical trials in multiple large EHR datasets (>50 million patients). Unique access to diverse hospital cohorts and a clinical trial unit at one of the largest European hospitals, the Charit Universittsmedizin Berlin, will further accelerate clinical translation for selected examples. With GenDrug, we aim to build a community resource to enable and accelerate drug development using big data for hundreds to thousands of diseases that currently lack safe and effective treatments.
Fields of science (EuroSciVoc)
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CORDIS classifies projects with EuroSciVoc, a multilingual taxonomy of fields of science, through a semi-automatic process based on NLP techniques.
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Programme(s)
- HORIZON.1.1 - European Research Council (ERC) Main Programme
Topic(s)
Funding Scheme
HORIZON-ERC - HORIZON ERC GrantsHost institution
10117 Berlin
Germany