Imagine trying to stop a thief by changing the locks on the door instead of chasing the thief himself. This project finds the 'locks' inside our own cells that viruses need to get in or survive. By using AI and high-tech imaging, they can quickly find existing medicines that act as these new locks to block many different viruses at once.
Developing new antivirals is slow and risky because viruses evolve quickly and develop resistance. Traditional genome-wide screens often produce inconsistent results, delaying the discovery of effective treatments.
A computational and experimental pipeline combining machine learning, high-density cell arrays, and primary cell cultures to identify and validate host-directed antiviral drugs.
If you are a drug repurposing firm dealing with the high cost of new drug discovery — this project developed a computational and experimental pipeline that identifies existing approved drugs to treat emerging viruses. This speeds up the path to clinical use since safety is already confirmed.
If you are a genomics service provider dealing with inconsistent results in genome-wide screens — this project developed High Density Cell Arrays that provide more detailed readouts than standard pooled screens. This allows for more precise identification of drug targets.
If you are a clinical research organization dealing with pandemic preparedness — this project developed an 'Expedited Arm' for rapid efficacy testing and clinical trials. This ensures a ready-to-operate system for new outbreaks.
Based on available project data, specific pricing or cost structures for the pipeline are not provided.
The project focuses on the identification and validation pipeline, including an 'Expedited Arm' for repurposing and clinical trials, which serves as a scalable model for pandemic preparedness.
Based on available project data, there are no specific details regarding IP ownership or licensing agreements.
The project runs from 2024-01-01 to 2028-12-31, aiming to identify at least one broad-spectrum antiviral drug within this period.
It integrates machine learning, proteomics, and high-density cell arrays to create a sustainable pipeline that can be activated at the early stage of a new outbreak.
The consortium is well-balanced for translational research, consisting of 15 partners across 7 countries. With a 20% industry ratio (3 companies, including 3 SMEs), the project maintains a strong academic foundation (7 universities, 5 research institutes) while ensuring a direct path to commercial application through SME involvement.
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