Image-based analysis for drug discovery and repurposing
Call: ERC-2017-PoC Project Reference: 790423 Principal Investigator: Michael Boutros Host Institution: Deutsches Krebsforschungszentrum
Drug discovery and development has become a lengthy and resource-intensive process which is characterized by high attrition rates of candidate molecules. Candidate molecules are often identified in high-throughput screening experiments that capture only a small fraction of its biological activities and too many candidates fail in clinical development due to unwanted side-effects and lack of a therapeutic window. In addition, many approved drugs harbor unrecognized therapeutic efficacy in other indications that were not covered during development. Necessary deep characterization of candidate molecules during pre-clinical development demands further efficient and cost-effective methods and data rich assays. Providing a solution to compare phenotypic measurements to a reference database can facilitate the profiling of unwanted effects and the identification of drugs with potential for repurposing. High-content imaging provides a cost-effective solution to capture a broad range of biological responses, however, analysis of such data requires extensive expertise and data processing pipelines. This project has the following objectives: (i) to develop a cloud-based infrastructure for analyses of image-based drug screening (WP1), (ii) to perform a proof-of-concept screen to create a reference database and a showcase for commercialization (WP2), (iii) to conduct market analyses and development of a business plan for a spin-off company (WP3). The ERC Proof of Concept Grant will thus enable us, based on methods pioneered in our ERC Advanced Grant, to develop and perform a proof-of-concept as well as to develop an innovative knowledge base and cloud-IT product for drug development and repositioning.