Development of a Universal Influenza Vaccine

通用流感疫苗的开发

• 批准号: 10080771
• 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/10080771
• 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; 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; Insecta; Manuals; Measures; Medical; Methods; Modeling; Modification; Mus; Mutation; Pattern Recognition; Population; Preparation; Process; Productivity; Proteins; Quality of life; Recombinants; Seasons; Sequence Analysis; Series; Serological; 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 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 按残疾调整后衡量，流感病毒（flu）在所有传染病的疾病负担中排名最高 季节性流行病每年导致全球 200,000-500,000 人死亡。 据估计，美国每年季节性流感的直接损失约为 $26B 至 $87B 此外，至少有六种已知的流感大流行已成为医疗费用以及工作和生产力损失。 全球人类灾难，最著名的是 1918 年的西班牙流感大流行，导致世界上 3-5% 的人死亡 流感感染率、传播率和严重程度的任何降低都会大大减少。 我们的医疗保健支出，每年改善数百万人的生活质量。 每年根据对哪些循环流感病毒株可能在某个地区流行的预测来配制疫苗 这些疫苗的有效性每年都会根据疫苗的流通情况而有所不同。 由于流感的多样性，疫苗设计变得复杂。 菌株，每种菌株都具有快速进化的显性抗原表位（“诱饵”表位），可在很大程度上刺激菌株- 一种合理抗原设计的策略，称为免疫重新聚焦技术（IRT）， 涉及引入突变，降低这些诱饵表位的免疫原性，从而改变 针对更广泛保守的亚显性表位的免疫反应先前已应用过这种 IRT。 方法在其他病毒抗原（例如 HRV 和 RSV F 蛋白）方面取得了一些显着的成功，现在我们 使用 H1、H3 和 B 疫苗株作为亲本，重点关注主要流感表面抗原糖蛋白 HA 设计适当修饰的抗原的预期工作通常需要长期的努力。 然而，我们最近开发了许多潜在候选人的试错测试过程。 ANATOPE 自动化 B 细胞表位预测软件包，其算法参数使用 我们的算法识别表位的成功率明显高于人工智能方法。 这一突破使我们能够分配免疫原性“强度”。 对特定抗原表面斑块进行评分，并将进一步指导和加速突变体的设计 在本申请中，我们提出将免疫反应重新集中到跨菌株保守表位的抗原。 设计和测试合理设计的 HA 抗原的免疫原性，这些抗原含有以下两种突变：1) 抑制显性菌株限制性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优势表位的免疫原性。 向上的研究将评估雪貂挑战研究中合理设计的抗原，并准备方法 作为一种通用疫苗转化为人类，不需要每年重新配制。