Evolutionarily smart vaccine strain selection for proactive vaccinology

用于主动疫苗学的进化智能疫苗株选择

• 批准号: MR/Y004337/1
• 负责人: Derek Smith
• 金额: $ 1091.51万
• 依托单位: University of Cambridge
• 依托单位国家: 英国
• 项目类别: Research Grant
• 财政年份: 2024
• 资助国家: 英国
• 起止时间: 2024 至 无数据
• 项目状态: 未结题
• 来源: https://gtr.ukri.org/projects?ref=MR%2FY004337%2F1
• 关键词: Evolutionarily; smart; vaccine; strain; selection

For viruses, such as SARS-CoV-2, that can change over time to escape immunity, keeping a vaccine up to date, and effective against current variants is a substantial challenge. Circulating viruses are constantly changing and are thus a moving target, that is easy for the variants in the vaccine to fall behind. Before SARS-CoV-2, the only vaccine for which the variants in the vaccine are routinely updated to track the evolution of the virus is the influenza virus vaccine. For influenza, decades of research and practice have resulted in a WHO assessment and strain recommendation system that functions well, but can still be substantially improved. The equivalent system to influenza vaccine strain selection for SARS-CoV-2 is still in its infancy. Our consortium will build directly on the partners' expertise in both influenza and SARS-CoV-2 to optimize the SARS-CoV-2 vaccine strain selection process to best protect the UK population that is at risk and will continue to be vaccinated against COVID. This project builds on the vaccination strategy outlined in our advisory paper to the UK Government SAGE committee titled 'Setting up medium-and long-term vaccine strain selection and immunity management for SARS-CoV-2'.To achieve our goals it is necessary to be able to accurately determine 'antigenic' differences among SARS-CoV-2 variants. Antigenic differences are the changes in the virus that result in escape from immunity raised by earlier vaccination or infection. We will test SARS-CoV-2 variants from the UK and around the world to generate, and keep current throughout the project, 'antigenic maps' to determine, at high resolution, the antigenic relationships among SARS-CoV-2 variants. Further, we will horizon-scan and proactively explore how the virus might further evolve using a combination of three methods. 1. Surveillance of UK and global variation in collaboration with the UK Health Security Agency project partner and colleagues world-wide involved in surveillance including the US Centers for Disease Control and US National Institutes of Health. 2. Identifying genetic changes in the virus that reveal early signs of being advantageous by analyzing patterns of parallel evolution in the global sequence surveillance data. 3. Generating in the laboratory variants of the spike protein of the virus (not live virus) with which we experimentally test the antigenic and characteristics of amino acid substitutions ahead of the current evolution. In combination with this virological surveillance we will also do 'serological surveillance' in which we track the antibody immunity in a cohort of 800 individuals for which we have highly reliable vaccination and infection history from the start of the pandemic and will continue to track during this project. This serological surveillance will allow us to both measure the selection pressure on the virus to escape immunity, and to estimate the population immunity to new variants that might evolve, and thus determine which variants the current population has least immunity to, and thus to which it is most at-risk.We will then test alternate vaccine strain selection choices to find those that best build immunity in the part of antigenic space that most needs it. There is substantial optimization that can be done here because such vaccination is being done in the context of prior immunity to earlier variants. We will thus select and test vaccine strains using a combination computational models, animal models, and in final stages experimental medicine in humans. This work will be tightly integrated with, and contribute substantially to, the related european, US, and WHO global vaccine strain selection processes.

对于诸如SARS-COV-2之类的病毒，可以随着时间的流逝而变化以逃避免疫力，保持疫苗的最新和对当前变体有效，这是一个重大挑战。循环病毒正在不断变化，因此是一个移动的靶标，这对于疫苗中的变体很容易落在后面。在SARS-COV-2之前，通常会更新疫苗中的疫苗以跟踪该病毒的演变。对于流感，数十年的研究和实践导致了WHO评估和应变推荐系统，该系统功能良好，但仍然可以大大改善。 SARS-COV-2的流感疫苗菌株选择的等效系统仍处于起步阶段。我们的财团将直接建立在伙伴在流感和SARS-COV-2方面的专业知识上，以优化SARS-COV-2疫苗菌株选择过程，以最好地保护有风险的英国人口，并将继续对Covid进行疫苗接种。该项目建立在我们向英国政府鼠尾草委员会的咨询文件中概述的疫苗接种策略，标题为“建立中型和长期的疫苗菌株选择和SARS-COV-2”的免疫力管理。为了实现我们的目标，有必要在SARS-COV-2变体之间准确地确定“抗原性”差异。抗原性差异是病毒的变化，导致通过早期疫苗接种或感染而产生的免疫力逃脱。我们将测试来自英国和世界各地的SARS-COV-2变体，以生成整个项目的“抗原图”，以确定SARS-COV-2变体之间的抗原关系。此外，我们将使用三种方法的组合进行地平线扫描，并主动探索该病毒如何进一步发展。 1。与英国卫生安全局项目合作伙伴和全球同事合作对英国的监视和全球差异，包括美国疾病控制中心和美国国立卫生研究院。 2.鉴定病毒中的遗传变化，这些变化揭示了通过分析全局序列监视数据中平行演化模式的早期迹象。 3。在病毒（非活病毒）的实验室变体中产生，我们通过该变体在当前进化之前通过实验测试氨基酸取代的抗原和特征。结合这种病毒学监测，我们还将进行“血清学监测”，在该研究中，我们在800个个体中跟踪抗体免疫，从大流行开始开始，我们将拥有高度可靠的疫苗接种和感染历史，并将在该项目期间继续跟踪。这种血清学监测将使我们既可以衡量病毒的选择压力，以逃避免疫，并估算人口对可能发展的新变体的免疫力，从而确定哪些变体的当前人群对当前的种群的免疫力最低，因此它是最处于危险中的最不受欢迎的，我们将最能选择替代疫苗的选择，从而可以选择替代疫苗的选择。可以在此处进行实质性优化，因为在先前对早期变体的免疫力的背景下进行了这种疫苗接种。因此，我们将使用组合计算模型，动物模型以及在人类的最终阶段中选择和测试疫苗菌株。这项工作将与相关的欧洲，美国以及全球疫苗菌株选择过程紧密地集成并为实质性贡献。