{"response":{"award":[{"abstractText":"The broader impact/commercial potential of this STTR project will lead to the development of engineered antibodies for that can be used to provide passive immunity and treatment to patients infected with COVID-19. These neutralizing antibodies can also be administered as preventative measures for populations at high risk of contracting COVID-19. Such engineered antibodies present a wider range of potential solutions than those produced naturally in the human body, potentially allowing more effective solutions. The proposed combination of high-throughput screening, next-generation-sequencing and AI-based antibody design allows systematic exploration of vast ranges of antibody sequences. This platform technology will be highly responsive to future outbreaks of novel coronaviruses or mutated forms of existing coronaviruses. The technology will be a platform technology which is would be useful going forward for other therapeutics for different diseases beyond coronavirus. Solutions in this space are highly relevant due to the current ongoing COVID-19 pandemic.\r\n\r\nThis STTR Phase I project proposes to greatly enable AI and machine learning antibody engineering approaches by providing the needed antibody sequence mutation binding data that will take AI-based antibody engineering to a new level. Currently available antibody datasets number in the thousands of datapoints and this project proposes to generate datasets that number in the tens of millions. The project will also be generating both positive and negative antibody binding data, leading to higher performing learned antibody binding models. This project allows testing the hypothesis that synthetic antibodies can be the equal of, or better than, naturally occurring antibodies for neutralizing SARS-CoV-2 infectivity. Nature has its own set of rules and limitations for generating antibodies and the propsoals' approach could potentially develop a much wider range of antibody variations. This work will be laser-focused on discovering a number of high-affinity antibodies targeting the receptor binding domain (RBD) of the SARS-CoV-2 spike protein through the combination of yeast-display, high-throughput FACS sorting and next-generation-sequencing. Combining these high-throughput data generation workflows with the latest deep neural networks will lead to a new methodology that can quickly and efficiently discover high performing antibodies, both for the current pandemic and others that may follow.\r\n\r\nThis 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.","activeAwd":"false","agency":"NSF","awardAgencyCode":"4900","awardee":"PROTABIT, LLC","awardeeAddress":"111 WAVERLY DR","awardeeCity":"PASADENA","awardeeCountryCode":"US","awardeeDistrict":"28","awardeeDistrictCode":"CA28","awardeeName":"Protabit LLC","awardeePhone":"6268447348","awardeeStateCode":"CA","awardeeZipCode":"911052513","cfdaNumber":"47.084","coPDPI":["Stephen L Mayo steve@mayo.caltech.edu"],"date":"05/21/2020","dirAbbr":"TIP","divAbbr":"TI","estimatedTotalAmt":"256000","expDate":"11/30/2020","fundAgencyCode":"4900","fundProgramName":"STTR Phase I","fundsObligated":["FY 2020 = $256,000.00"],"fundsObligatedAmt":"256000","histAwd":"false","id":"2027586","initAmendmentDate":"05/21/2020","latestAmendmentDate":"05/21/2020","managingPec":"150500","orgCodeDir":"15000000","orgCodeDiv":"15030000","orgLongName":"Directorate for Technology, Innovation, and Partnerships","orgLongName2":"Translational Impacts","orgUrl":"https://beta.nsf.gov/tip/ti","parentUeiNumber":"","pdPIName":"Barry D Olafson","perfAddress":"1200 E California Blvd","perfCity":"Pasadena","perfCountryCode":"US","perfDistrict":"28","perfDistrictCode":"CA28","perfLocation":"California Institute of Technology","perfStateCode":"CA","perfZipCode":"911250001","pi":["Barry D Olafson barry.olafson@protabit.com"],"piEmail":"barry.olafson@protabit.com","piFirstName":"Barry","piId":"269883529","piLastName":"Olafson","piMiddeInitial":"D","poEmail":"","poName":"Anna Brady-Estevez","poPhone":"","primaryProgram":["010N2021DB R&RA CARES Act DEFC N"],"progEleCode":"150500","program":"COVID-19 Research, Software Services and Applications","progRefCode":"096Z, 8032","projectOutComesReport":"
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This NSF Phase I project has combined computational protein design (CPD), ultra-high throughput protocols to assay the binding affinities of millions of antibodies against the spike protein of SARS-COV-2, and AI and machine learning to select a number of antibody sequences  that indicate a high-probability of strong binding and therefore likely high potency. These antibody sequences were then expressed individually as full-length antibodies and tested in pseudovirus neutralization assays for their ability to suppress infection of the SARS-CoV-2 virus. Using this process, we have been able to identify a number of antibodies that are 10-50-fold more potent than most of the best neutralizing antibodies that have been described in the published literature. The two existing antibody treatments that have been given Emergency Use Authorization (EUA) are being dosed at either 2,800 or 8,000 milligrams. The goal of this project is to ultimately engineer antibodies that are 100-fold more potent so that significantly smaller doses can be used. Ultimately having doses that could be administered by injection rather than infusion would allow for much wider use of this treatment. Lower doses will also lead to a much lower cost per treatment. These smaller, more affordable doses could potentially lead to reoccurring prophylactic treatments for populations at high risk of contracting COVID-19.

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New SARS-COV-2 variants are raising concerns that mutations may occur that could reduce or completely abrogate the effectiveness of the current COVID-19 vaccines. We have recently tested a number of our better performing full-length antibodies and discovered that one of them is highly effective against the UK variant (B.1.1.7) (IC50 = 0.1 ng/ml in a pseduovirus neutralization assay). This result demonstrates the additional benefits of having a number of high-performing antibodies available to screen for activity against these newly emerging variants.

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The South African variant (B.1.351) is the most concerning recent variant. The E484K mutation greatly diminishes the effectiveness of antibodies binding to this region of the spike protein. Our initial screen of full-length antibodies engineered for the original Wuhan strain (Wuhan-Hu-1) did not give any encouraging results, so we are in the process of i) rescreening the 3-million-member library for different antibody variants that did not bind to Wuhan-Hu-1, and ii) using CPD to design a new library against a modeled structure of the B.1.351 spike protein and repeating the design cycle that led to improved neutralizing variants for Wuhan-Hu-1. Having already been through this process for Wuhan-Hu-1, we will likely have variants to test in less than one month for the B.1.351 variant.

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This work has led to the refinement of an AI-based antibody engineering platform technology that can discover high-affinity antibodies for a number of protein targets in a relatively short period of time. It we be at the ready for any newly emerging variants of SARS-COV-2, at the ready for any other coronaviruses, and have applications for Ebola and other highly contagious and deadly viruses. In the future we will also be exploring applications in immuno-oncology and other diseases.

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\n\t\t\t\t \tLast Modified: 03/01/2021
\n\t\t\t\t\tModified by: Barry D Olafson

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","publicAccessMandate":"1","startDate":"06/01/2020","title":"STTR Phase I: COVID-19: AI-based Development of Neutralizing Antibodies for SARS-CoV-2","transType":"Standard Grant","ueiNumber":"ZMG1FJD9SQ64"}],"metadata":{"offset":0,"rpp":25,"totalCount":1}}}