SBIR Phase I: Developing a Rapid Antibody Generation Platform for Emerging COVID-19 Variants

SBIR 第一阶段：开发针对新兴 COVID-19 变体的快速抗体生成平台

• 批准号: 2033772
• 负责人: Randolph Lopez Barrezueta
• 金额: $ 25.6万
• 依托单位: A-Alpha Bio, Inc.
• 依托单位国家: 美国
• 项目类别: Standard Grant
• 财政年份: 2020
• 资助国家: 美国
• 起止时间: 2020-09-01 至 2021-12-31
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=2033772&HistoricalAwards=false
• 关键词: SBIR; Phase; Developing; Rapid; Antibody

The broader impact of this Small Business Innovation Research (SBIR) Phase I project is to improve and accelerate the development of antibody therapies to fight the COVID-19 pandemic. Antibody therapies are in development to target SARS-CoV-2, the virus responsible for COVID-19, but emerging virus mutations may result in resistance and require the development of new therapies. Antibody therapies typically enter the clinic after a year of development or longer, and the medical and economic consequences of this delay are severely felt throughout the world. To reduce the time to produce an antibody against an emerging SARS-CoV-2 mutant, this project develops a computational platform trained using massive experimental datasets to rapidly predict the therapeutic potency. This platform will enable drugs against SARS-CoV-2 mutants to more rapidly reach the clinic, saving thousands of lives. Moreover, the proposed platform can be utilized to predict therapeutic efficacy against future coronavirus strains unassociated with the COVID-19 pandemic, providing an invaluable tool to fight future pandemics.The proposed project will demonstrate the feasibility of using quantitative and library-on-library protein interaction datasets to train machine learning models for predicting antibody binding to novel SARS-CoV-2 variants. Existing approaches to build computational predictions for antibody drug development have been limited to few target variants, since datasets with binding measurements against hundreds or thousands of targets are not available. This project involves optimizing and validating a cell-based platform for generating a sufficient quantity and quality of antibody-antigen binding data for training computational models. The platform uses genetically engineered yeast cells and next generation sequencing to link protein interaction strength with cellular mating frequency. To demonstrate feasibility, large multi-chain antibody libraries will be genomically integrated in yeast and enriched for binding to SARS-CoV-2 and related coronaviruses. Next, a large network of antibody-antigen interactions will be measured and validated for quantitative accuracy by comparing to biophysical measurements. Finally, the resulting data will be used to train machine learning models and evaluate their predictive power using cross-validation. Training of computational models with sufficient predictive power will demonstrate the feasibility of using quantitative and library-on-library binding data coupled with machine learning to develop a platform for rapid antibody development to a novel SARS-CoV-2 mutant.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.

这项小企业创新研究（SBIR）第一阶段项目的更广泛影响是改进和加速抗体疗法的开发，以对抗COVID-19大流行。针对导致COVID-19的病毒SARS-CoV-2的抗体疗法正在开发中，但新出现的病毒突变可能导致耐药性，需要开发新的疗法。抗体疗法通常在开发一年或更长时间后才进入临床，这种延误的医疗和经济后果在全世界都受到严重影响。为了减少生产针对新出现的SARS-CoV-2突变体的抗体的时间，该项目开发了一个使用大量实验数据集训练的计算平台，以快速预测治疗效力。该平台将使针对SARS-CoV-2突变体的药物能够更快地到达诊所，挽救数千人的生命。此外，该平台可用于预测与COVID-19大流行无关的未来冠状病毒株的治疗效果，为应对未来的大流行提供宝贵的工具。拟议的项目将证明使用定量和库间蛋白质相互作用数据集来训练机器学习模型的可行性，以预测抗体与新型SARS-CoV-2变体的结合。建立抗体药物开发的计算预测的现有方法仅限于少数靶标变体，因为没有针对数百或数千个靶标的结合测量数据集。该项目包括优化和验证一个基于细胞的平台，以生成足够数量和质量的抗体-抗原结合数据，用于训练计算模型。该平台使用基因工程酵母细胞和下一代测序将蛋白质相互作用强度与细胞交配频率联系起来。为了证明可行性，将在酵母中基因组整合大型多链抗体文库，并进行富集以与SARS-CoV-2和相关冠状病毒结合。接下来，将测量抗体-抗原相互作用的大型网络，并通过与生物物理测量相比较来验证定量准确性。最后，结果数据将用于训练机器学习模型，并使用交叉验证评估其预测能力。具有足够预测能力的计算模型的训练将证明使用定量和库对库结合数据与机器学习相结合的可行性，以开发针对新型SARS-CoV-2突变体的快速抗体开发平台。该奖项反映了美国国家科学基金会的法定使命，并通过使用基金会的知识价值和更广泛的影响审查标准进行评估，被认为值得支持。