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.

这个小企业创新研究（SBIR）第一阶段项目的更广泛的影响/商业潜力是开发一种新的治疗细菌感染的方法，特别是那些对现有抗生素具有耐药性的细菌感染，这些细菌感染已经产生了耐抗生素的“超级细菌”，不易治疗。噬菌体（“噬菌体”）是只感染特定细菌而不能感染人类的病毒。噬菌体杀死有害细菌，但它们目前还不能作为通用的解决方案，因为每个噬菌体只能杀死一小部分细菌；因此，患有相同感染的不同人可能需要一种独特的噬菌体。该项目开发新技术来了解噬菌体如何靶向细菌。它使用机器学习来确定每个噬菌体负责杀死特定细菌的部分，以便使噬菌体广泛用于治疗感染。这项创新是一项关键的竞争优势，通过为抗生素耐药性感染创造新的治疗方法，有助于国家卫生和国防。抗生素耐药性感染每年耗资640亿美元，并可能成为下一个重大流行病。这个小企业创新研究（SBIR）第一阶段项目将开发机器学习算法，以识别噬菌体中宿主范围的遗传决定因素，以便设计噬菌体以扩大宿主范围。耐多药感染的广泛演变是对全球健康的主要威胁，传统抗生素对患者及其微生物组具有重大不利影响。噬菌体可以解决这一全球健康挑战，但无法扩大和调整噬菌体宿主范围以创造可推广的治疗方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通量噬菌体表征方法来生成噬菌体-宿主相互作用的图谱，以鉴定决定噬菌体宿主范围的基因，并使用新的工程技术来验证这些宿主范围的遗传决定因素。预期的输出是双重的：1)用于预测变异、基因或决定噬菌体宿主范围的基因组区域的机器学习模型；2)具有扩展宿主范围的工程噬菌体。这项工作将进一步加深对噬菌体生物学和噬菌体-宿主相互作用的科学理解，同时也为开发具有可调节宿主范围的噬菌体提供了一个平台，用于治疗、农业和环境应用。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。