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

这个小企业创新研究（SBIR）I期项目的更广泛的影响/商业潜力是开发一种新的细菌感染疗法，特别是那些对当前抗生素具有耐药性的细菌，这些抗生素产生了耐药性的“超级细菌”细菌感染，无法轻易治疗。噬菌体（Bacteriophage，缩写为“噬菌体”）是一种病毒，它只能感染特定的细菌，不能感染人类。噬菌体杀死有害细菌，但它们目前不能作为一般的解决方案，因为每个噬菌体只杀死一个细菌子集;因此，不同的人可能需要一个独特的噬菌体。该项目开发了新技术，以了解细菌如何靶向细菌。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，以便使其广泛用于治疗感染。这项创新是一项关键的竞争优势，通过为耐药性感染创造新的治疗方法，有助于国家健康和国防，耐药性感染每年花费640亿美元，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，识别宿主范围的遗传决定因素，以便工程噬菌体扩大宿主范围。多重耐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有显著的不良影响。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，以识别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和2）具有扩展的宿主范围的工程化噬菌体。这项工作将进一步科学地了解噬菌体生物学和噬菌体-宿主相互作用，同时也提供了一个平台，以开发具有可调宿主范围的噬菌体，用于治疗，农业和环境应用。该奖项反映了NSF的法定使命，并被认为值得通过使用基金会的智力价值和更广泛的影响审查标准进行评估来支持。

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.