SBIR Phase I: Leveraging machine learning to enable generalized phage therapy for pulmonary infections

SBIR 第一阶段：利用机器学习实现肺部感染的通用噬菌体疗法

• 批准号: 2126731
• 负责人: Robert McBride
• 金额: $ 25.6万
• 依托单位: FELIX BIOTECHNOLOGY, INC.
• 依托单位国家: 美国
• 项目类别: Standard Grant
• 财政年份: 2021
• 资助国家: 美国
• 起止时间: 2021-09-01 至 2022-08-31
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=2126731&HistoricalAwards=false
• 关键词: SBIR; Phase; Leveraging; machine; learning

The broader impact /commercial potential of this Small Business Innovation Research (SBIR) Phase I project is to develop a new therapy for bacterial infections, especially those resistant to current antibiotics, which have generated antibiotic-resistant “super-bug” bacterial infections that cannot be treated easily. Bacteriophages (‘phages’) are viruses that only infect specific bacteria and cannot infect humans. Phages kill harmful bacteria, but they currently do not work well as general solutions that can be prescribed broadly because each phage only kills a subset of bacteria; therefore a unique phage may be required for different people with the same infection. This project develops new technology to understand how phages target bacteria. It uses machine learning to determine the parts of each phage responsible for killing specific bacteria, in order to make phages for broad use in treating infections. This innovation is a key competitive advantage, and helps both national health and defense by creating new treatments for antibiotic-resistant infections, which cost $64 billion annually and may become the next major pandemic. This Small Business Innovation Research (SBIR) Phase I project will develop machine learning algorithms that identify genetic determinants of host range in phages in order to engineer phage to have expanded host range. The widespread evolution of multidrug-resistant infections is a major threat to global health, and traditional antibiotics have significant adverse effects on patients and their microbiomes. Phages can solve this global health challenge, but the inability to expand and tune phage host-range to create a generalizable therapeutic remains a key barrier to commercial success. This project will leverage machine learning and proprietary high throughput phage characterization methods to generate maps of phage-host interactions to identify genes that determine phage host range, and use novel engineering techniques to validate these genetic determinants of host range. The expected outputs are twofold: 1) a machine learning model for predicting variants, genes, or genomic regions that determine phage host range and 2) an engineered phage with expanded host range. This work will further scientific understanding of phage biology and phage-host interactions, while also providing a platform to develop phages with tunable host range for therapeutic, agricultural, and environmental applications.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.

这项小型企业创新研究（SBIR）I期项目的更广泛的影响 /商业潜力是为细菌感染开发一种新的疗法，尤其是那些对当前抗生素具有抗药性的疗法，这些抗生素具有抗生素耐药性的“超级腹膜”细菌感染，无法轻易治疗。噬菌体（“噬菌体”）是仅感染特定细菌并且无法感染人类的​​病毒。噬菌体会杀死有害细菌，但目前它们的运作效果不佳，因为可以广泛规定的一般解决方案，因为每个噬菌体都只会杀死细菌的一部分。因此，患有相同感染的不同人可能需要独特的噬菌体。该项目开发了新技术，以了解噬菌体如何靶向细菌。它使用机器学习来确定负责杀死特定细菌的每个噬菌体的部分，以便在治疗感染方面进行广泛使用。这项创新是一个关键的竞争优势，并通过为抗生素耐药性感染创造新的治疗方法来帮助国家卫生和防御，每年花费640亿美元，并可能成为下一个主要的大流行。这项小型企业创新研究（SBIR）I阶段项目将开发机器学习算法，以识别噬菌体中寄主范围的遗传决定剂，以设计噬菌体以扩大宿主范围。多药耐药感染的宽度演变是对全球健康的主要威胁，传统的抗生素对患者及其微生物组有重大不利影响。噬菌体可以解决这一全球健康挑战，但是无法扩展和调整噬菌体宿主范围来创建可推广的疗法仍然是商业成功的关键障碍。该项目将利用机器学习和专有的高吞吐量噬菌体特征方法来生成噬菌体 - 宿主相互作用的地图，以识别确定噬菌体宿主范围的基因，并使用新颖的工程技术来验证这些宿主范围的这些遗传确定剂。预期输出是双重的：1）用于预测确定噬菌体宿主范围的变体，基因或基因组区域的机器学习模型，以及2）具有扩展宿主范围的工程噬菌体。这项工作将进一步科学地了解噬菌体生物学和噬菌体 - 霍斯特相互作用，同时还提供了一个平台，以使用可调的宿主范围来开发用于治疗，农业和环境应用的可调节宿主范围。该奖项反映了NSF的法定任务，并被认为是通过使用基金会的智力和更广泛影响的评估来审查CRITERIA的评估来通过评估来获得的支持。