SBIR Phase II:COVID-19 Rapid Sensing Using Structural DNA Biosensors

The broader impact of this Small Business Innovation Research (SBIR) Phase I project will be the development of a platform technology for creating rapid virus diagnostics that directly recognizes the virus surface protein pattern from a patient sample and generates accurate results within minutes. Current standards for high-fidelity viral pathogen diagnostics require complex instruments, technical expertise to run the instruments, andhours to produce and interpret results. The proposed platform creates a virus-specific biosensor that selectively binds to the target virus and produces visible results without time-consuming pre-processing or expensive instruments. Lower cost tests and faster sample-to-result turnovers could result in more effective control of disease spread.This project seeks to develop a highly functional, sensitive, and specific diagnostic for the detection of coronavirus. This technology is based on the company’s Pattern-Recognition Enhanced Sensing and Therapeutics (PEST) concept. The solution is a first-in-class diagnostics that uses algorithmically-designed structural DNA to form a trap that may detect and selectively bind a signature pattern of the pathogen. This recognition and binding may generate visual signals without the need of DNA/RNA preprocessing or amplification associated with the current generation of molecular tests (polymerase chain reactions, PCRs). This project will involve building a preclinical prototype of PEST-enabled lateral flow based COVID-19 rapid diagnostics with a goal of providing results for each sample within 5 minutes.This fast test result will be followed by preclinical validation to determine the test’s specificity, limits of detection, and implement mechanism to improve the assay specificity and to avoid cross-reaction to other virus types.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.