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 SARS-CoV-2 等病毒来说，它们会随着时间的推移而发生变化以逃避免疫，因此保持疫苗处于最新状态并有效对抗当前变种是一项巨大的挑战。循环的病毒不断变化，因此是一个移动的目标，很容易让疫苗中的变种落后。在 SARS-CoV-2 之前，唯一定期更新疫苗变体以追踪病毒进化的疫苗是流感病毒疫苗。对于流感，数十年的研究和实践已经形成了世界卫生组织的评估和毒株推荐系统，该系统运行良好，但仍然可以大幅改进。与 SARS-CoV-2 流感疫苗株选择相当的系统仍处于起步阶段。我们的联盟将直接利用合作伙伴在流感和 SARS-CoV-2 方面的专业知识来优化 SARS-CoV-2 疫苗株选择流程，以最好地保护处于危险中并将继续接种新冠疫苗的英国人口。该项目以我们向英国政府 SAGE 委员会提交的题为“建立 SARS-CoV-2 中长期疫苗株选择和免疫管理”的咨询文件中概述的疫苗接种策略为基础。为了实现我们的目标，有必要能够准确确定 SARS-CoV-2 变体之间的“抗原”差异。抗原差异是病毒的变化，导致病毒逃避早期疫苗接种或感染所产生的免疫力。我们将测试来自英国和世界各地的 SARS-CoV-2 变体，以生成“抗原图谱”，并在整个项目中保持最新状态，从而以高分辨率确定 SARS-CoV-2 变体之间的抗原关系。此外，我们将结合三种方法进行地平线扫描并主动探索病毒如何进一步进化。 1. 与英国卫生安全局项目合作伙伴以及世界各地参与监测的同事（包括美国疾病控制中心和美国国立卫生研究院）合作，监测英国和全球的变化。 2. 通过分析全球序列监测数据中的平行进化模式，识别病毒中揭示优势早期迹象的基因变化。 3. 在实验室中生成病毒（非活病毒）刺突蛋白的变体，我们用其在当前进化之前通过实验测试抗原和氨基酸取代的特征。结合病毒学监测，我们还将进行“血清学监测”，跟踪 800 名个体的抗体免疫力，我们从大流行开始就拥有高度可靠的疫苗接种和感染史，并将在该项目期间继续跟踪。这种血清学监测将使我们能够测量病毒逃避免疫的选择压力，并估计群体对可能进化的新变种的免疫力，从而确定当前群体对哪些变种的免疫力最小，从而确定哪些变种的风险最大。然后，我们将测试替代疫苗株选择选择，以找到那些在最需要的抗原空间部分最能建立免疫力的疫苗株。这里可以进行大量的优化，因为这种疫苗接种是在对早期变体的先前免疫的背景下进行的。因此，我们将结合计算模型、动物模型以及最后阶段的人体实验医学来选择和测试疫苗株。这项工作将与相关的欧洲、美国和世卫组织全球疫苗株选择过程紧密结合，并为其做出重大贡献。