STTR Phase II: Artificial Intelligence (AI)-based Development of Neutralizing Antibodies for SARS-CoV-2

The broader impact/commercial potential of this Small Business Innovation Research (SBIR) project will lead to the development of engineered neutralizing antibodies for the SARS-CoV-2 virus that can be used as therapeutic agents to diminish the severity of a COVID-19 infection and decrease the chances of hospitalization and progressive disease. As the SARS-CoV-2 virus continues to mutate it is necessary to increase collective preparedness by generating a wide collection of neutralizing antibodies that individually provide coverage for a range of variants (Delta, Beta, Omicron). These antibodies, when administered as an antibody cocktail, may offer broad protection over a diverse population of variants. This project's approach generates neutralizing antibodies that are specifically engineered to bind to different regions of the spike protein thereby increasing the probability that one or more of the engineered antibodies will be effective against future mutated versions of the virus. The proposed combination of high-throughput screening, next-generation-sequencing and artificial intelligence (AI)-based antibody design allows systematic exploration of vast ranges of antibody sequences. This approach also has the benefit of engineering antibodies that are more potent, easier to administer, more stable under challenging environmental conditions, and less costly to manufacture, leading to therapeutics that can be more readily distributed to low-income countries. This STTR Phase II project proposes to enable AI and machine learning antibody engineering approaches by providing needed antibody sequence mutation binding data. Currently available antibody datasets number in the thousands of datapoints and the team proposes to generate datasets that number in the tens of millions. The project will also be generating both positive and negative antibody binding data, potentially leading to higher performing learned antibody binding models. This project seeks to test the hypothesis that synthetic antibodies can be the equal of, or better than, naturally occurring antibodies for neutralizing SARS-CoV-2 infectivity. This approach could potentially develop a wide range of antibody variations. The application of this AI-based antibody engineering will be focused on discovering a large array of high-affinity neutralizing antibodies targeting multiple, different regions of the SARS-CoV-2 spike protein through the combination of yeast-display, high-throughput fluorescence-activated cell sorting (FACS) and next generation sequencing. Combining these high-throughput data generation workflows with the latest deep neural networks may lead to a new methodology that can efficiently discover high performing antibodies for the current pandemic and those in the future. 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.