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| Funder | National Science Foundation (US) |
|---|---|
| Recipient Organization | University of Arkansas |
| Country | United States |
| Start Date | Apr 01, 2025 |
| End Date | Mar 31, 2026 |
| Duration | 364 days |
| Number of Grantees | 1 |
| Roles | Principal Investigator |
| Data Source | National Science Foundation (US) |
| Grant ID | 2505779 |
This I-Corps project is based on the translation from lab to market of a cost-effective computational tool that optimizes immunoassay development - a critical process the detection of cancer, infectious diseases, and autoimmune disorders. This tool addresses the inefficiencies in current immunoassay development by leveraging rigorous physical models to simulate and predict immunoassay performance before conducting costly lab experiments.
By replacing guesswork with a data-driven, computational approach, this technology enables faster, more precise, and cost-effective development of highly accurate diagnostic tests. The impact extends beyond scientific innovation, as this technology has the potential to accelerate testing, expand test availability, and ultimately improve patient outcomes.
By making immunoassay development more efficient, the commercialization of this discovery has the potential to enhance public health and help bring life-saving treatments to patients faster.
This I-Corps project utilizes experiential learning coupled with a first-hand investigation of the industry ecosystem to assess the translation potential of a physics-based tool that improves immunoassay development. This tool uses advanced computational methods, utilizing free energy calculations based on molecular dynamics simulations, to predict how strongly antibodies and antigens bind.
Traditional immunoassay design relies on trial and error, requiring lengthy and expensive laboratory experiments that slow the creation of new diagnostic tests for cancer, infectious diseases, and autoimmune disorders. By applying statistical physics and efficient computational models, this approach improves the accuracy of binding affinity predictions while significantly reducing the time and cost required for immunoassay development.
Ultimately, this technology could make diagnostic tests more reliable, faster to develop, and more affordable, benefiting both the healthcare industry and patients.
This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.
University of Arkansas
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