Imagine your body's immune system is like a security team; some people have a team that overreacts, while others are too slow to respond to a virus. This work studies why people react so differently to COVID-19, flu, and RSV by looking at their genetic blueprints and blood samples. By using tiny lab-grown lung models, they can test how different people might respond to a treatment before ever giving it to a patient.
Current respiratory virus treatments are generic and supportive because we don't know why some patients crash while others recover. This leads to inefficient healthcare spending and higher mortality rates.
A database of 4,400+ patient samples and machine learning models to predict disease outcomes based on immune and genetic data.
If you are a vaccine developer dealing with low efficacy across diverse populations — this project developed deep immunological phenotypes and genetic datasets that help identify which groups need specific vaccine formulations.
If you are a diagnostics company dealing with the inability to predict patient outcomes — this project developed machine learning models and predictive tools that can help personalize treatment based on a patient's immune profile.
If you are a drug discovery firm dealing with high failure rates in clinical trials — this project developed lung-on-chip and organoid systems to validate therapeutic targets before human testing.
Based on available project data, the project aims to make findings available on a dedicated website for clinicians and researchers, but specific pricing for commercial licenses is not mentioned.
The project uses machine learning and bioinformatics to integrate large datasets, which are inherently scalable, though the physical organoid models are currently for validation purposes.
Based on available project data, the results are intended to be FAIR, open, and reusable to support public health resilience, though specific patent filings are not listed.
The project focuses on creating interoperable datasets and tools that can be integrated into European health infrastructures to guide clinical management.
The project period runs from 2022-08-01 to 2026-07-31, with data currently being generated from cohorts in Denmark and Spain.
The consortium is heavily weighted toward research and academia, with 4 universities and 4 research institutes. While there is only one industrial partner (representing a 9% industry ratio), the presence of the Statens Serum Institut as coordinator provides strong institutional backing and access to large-scale biobanking and clinical cohorts across 5 countries.
Contact Statens Serum Institut in Denmark
Contact us to identify specific licensing opportunities for the REACT predictive models.
