Development of a Universal Influenza Vaccine

通用流感疫苗的开发

• 批准号: 10211103
• 负责人: David A MacLeod
• 金额: $ 29.92万
• 依托单位: BIOLOGICAL MIMETICS, INC.
• 依托单位国家: 美国
• 财政年份: 2020
• 资助国家: 美国
• 起止时间: 2020-07-05 至 2022-06-30
• 项目状态: 已结题
• 来源: https://reporter.nih.gov/project-details/10211103
• 关键词: Algorithms; Amino Acid Substitution; Animal Model; Animal Testing Alternatives; Antibodies; Antibody Binding Sites; Antibody Formation; Antibody Response; Antigens; Antiviral Agents; Artificial Intelligence; B-Lymphocyte Epitopes; Baculoviruses; Binding Sites; Biological Assay; Blood Circulation; Body mass index; California; Cells; Cellular Immunity; Cessation of life; Communicable Diseases; Computational algorithm; Computer Analysis; Computer software; Cryoelectron Microscopy; Development; Distant; Economic Burden; Engineering; Epidemic; Epitopes; Evolution; Ferrets; Follow-Up Studies; Future; Glycoproteins; Goals; Ha antigen; Health; Health Expenditures; Healthcare; Hemagglutination; Hemagglutinin; Hong Kong; Human; Immune; Immune response; Immune system; Immunity; Immunization; Influenza; Influenza A Virus, H1N1 Subtype; Influenza A Virus, H3N2 Subtype; Insecta; Manuals; Measures; Medical; Methods; Modeling; Modification; Mus; Mutation; Pattern Recognition; Population; Preparation; Process; Productivity; Proteins; Quality of life; Recombinants; Seasons; Sequence Analysis; Series; Serology test; Severities; Singapore; Site; Spanish flu; Statistical Data Interpretation; Structure; Surface; Surface Antigens; Switzerland; T-Lymphocyte; Technology; Testing; Texas; Translations; Vaccine Design; Vaccines; Validation; Variant; Viral Antibodies; Viral Antigens; Viral Physiology; Virus; Work; antiviral immunity; base; burden of illness; cross reactivity; design; disability-adjusted life years; flu; immunogenicity; improved; in silico; indexing; infection rate; influenza infection; influenza virus strain; influenza virus vaccine; influenzavirus; mutant; neutralizing antibody; new technology; novel; pandemic influenza; pathogen; prevent; programs; response; seasonal influenza; success; transmission process; universal influenza vaccine; universal vaccine; vaccine effectiveness

ABSTRACT Influenza virus (flu) ranks highest in disease burden of all infectious diseases as measured in disability-adjusted life years. Seasonal epidemics cause 200,000-500,000 worldwide deaths annually. The total economic burden of seasonal flu is estimated to range from approximately $26B to $87B each year in the US in terms of direct medical expenses and lost work and productivity. Additionally, at least six known flu pandemics have become global human catastrophes, most notably the Spanish Flu pandemic of 1918, which killed 3-5% of the world’s population. Any reduction in the infection rate, transmission, and severity of flu infection would greatly reduce our healthcare expenditures and improve the quality of life for millions of people every year. The current vaccines are formulated annually based on predictions of which circulating flu strains may be prevalent in a given season. The effectiveness of these vaccines varies from year to year based on the circulation of unexpected antigenic variants and other factors. Vaccine design is complicated the by the multiplicity of flu strains, each with rapidly-evolving dominant antigen epitopes (“decoy” epitopes) that largely stimulate strain- restricted immunity. One strategy for rational antigen design, termed Immune Refocusing Technology (IRT), involves introducing mutations that reduce the immunogenicity of these decoy epitopes thus shifting the immune response to target more widely-conserved subdominant epitopes. BMI has previously applied this IRT approach with some notable successes to other viral antigens (e.g. HRV and the RSV F protein), and we now focus on the major flu surface antigen glycoprotein HA using H1, H3, and B vaccine strains as parental antigens. The anticipated effort to design a suitably modified antigen would ordinarily involve a protracted process of trial-and-error testing of many potential candidates. However, we have recently developed the ANATOPE automated B cell epitope prediction software package with algorithm parameters tuned using methods in artificial intelligence. Our algorithm identifies epitopes with a significantly higher success rate than previously available prediction programs. This breakthrough allows us to assign immunogenicity “strength” scores to particular antigen surface patches and will further guide and accelerate the design of mutant antigens that refocus the immune response to cross-strain conserved epitopes. In this application, we propose to engineer and test the immunogenicity of rationally-designed HA antigens containing mutations that both 1) dampen the immunogenicity of dominant strain-restricted decoy epitopes and 2) enhance the immunogenicity of conserved subdominant epitopes associated with broadly neutralizing antibodies. Follow- up studies will assess the rationally-designed antigens in a ferret challenge study and prepare the approach for translation into humans as a universal vaccine that does not require annual reformulation.

抽象的 流感病毒（流感）在所有传染病中的疾病中最高 生命年。季节性发作每年导致全球200,000-500,000次死亡。总经济伯恩 季节性流感估计在美国，每年的季节性流感每年约26b至87b不等 医疗费用和失去工作和生产力。此外，至少六个已知的流感大流传学已经成为 全球人类灾难，最著名的是1918年的西班牙流感大流行，造成世界3-5％ 人口。感染率，流感感染的传播和严重程度的任何降低都将大大降低 我们的医疗保健支出并改善每年数百万人的生活质量。电流 疫苗每年根据哪些循环流感菌株在A中普遍存在制定 给定季节。这些疫苗的有效性每年都不同 意外的抗原变体和其他因素。疫苗设计因流感多样性而复杂 菌株，每种都有迅速发展的显性抗原表位（“诱饵”表位），在很大程度上刺激应变 限制免疫。理性抗原设计的一种策略，称为免疫重新聚焦技术（IRT）， 涉及引入突变，以降低这些诱饵表位的免疫原性，从而改变 免疫反应对靶标更广泛的亚辅助表位。 BMI以前已经应用了此IRT 与其他病毒抗原（例如HRV和RSV F蛋白）取得了一些显着成功的方法，我们现在 使用H1，H3和B疫苗作为父母的主要流感表面抗原糖蛋白HA 抗原。设计适当修改的抗原的预期努力通常会涉及长时间的 许多潜在候选者的反复测试过程。但是，我们最近开发了 带有算法参数的Anatope自动化B细胞表位预测软件包使用 人工智能的方法。我们的算法确定成功率明显高于 以前可用的预测程序。这一突破使我们能够分配免疫原性“力量” 得分达到特定的抗原表面斑块，并将进一步指导和加速突变体的设计 重新集中对跨加工的免疫反应的抗原构成表位。在此应用程序中，我们建议 设计和测试合理设计的HA抗原的免疫原性，其中含有两者的突变。 抑制主要应变限制诱饵表位的免疫原性，2）增强 与广泛中和抗体相关的保守亚区域表位的免疫原性。跟随- UP研究将在雪貂挑战研究中评估合理设计的抗原，并准备该方法 将人类转化为一种普遍的疫苗，不需要每年的重新重新进行。