利用多种 H3N2 流感病毒变异株的接种前抗体滴度预测接种后反应

Hannah Stacey, Michael A. Carlock, James D. Allen, Hannah B. Hanley, Shane Crotty, Ted M. Ross, Tal Einav
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摘要

尽管对流感病毒进行了数十年的研究,但我们仍然缺乏对接种疫苗如何重塑每个人的抗体反应的预测性了解,这阻碍了我们设计更好疫苗的努力。在此,我们将 1997-2021 年间的 15 项 H3N2 流感疫苗研究(共包含 20,000 个数据点)合并在一起,证明接种前的抗体滴度可以预测接种后的反应。除了针对疫苗菌株的血凝抑制滴度(HAI)外,最能预测接种前反应的特征是针对历史流感变异株的 HAI,而年龄、性别、体重指数、疫苗剂量、接种日期或地理位置的预测力较小。即使疫苗成分发生变化或使用了不同的灭活疫苗配方,由此产生的模型也能预测未来的反应。疫苗接种前的一个特征--近期变异株的 HAI 峰值之间的时间--可以区分接种后反应的大小,准确率为 73%。作为进一步测试,2022-2023 年进行了四项疫苗研究,涉及两个地理位置和三种流感疫苗类型。这些数据集形成了一个盲预测挑战,计算团队只收到接种前的数据,但预测接种后反应的误差为 2.2 倍,与实验检测的 2 倍内在误差相当。这种方法为更好地利用当前的流感疫苗铺平了道路,尤其是对那些反应最弱的人。
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Leveraging Pre-Vaccination Antibody Titers across Multiple Influenza H3N2 Variants to Forecast the Post-Vaccination Response
Despite decades of research on the influenza virus, we still lack a predictive understanding of how vaccination reshapes each person's antibody response, which impedes efforts to design better vaccines. Here, we combined fifteen prior H3N2 influenza vaccine studies from 1997-2021, collectively containing 20,000 data points, and demonstrate that a person's pre-vaccination antibody titers predicts their post-vaccination response. In addition to hemagglutination inhibition (HAI) titers against the vaccine strain, the most predictive pre-vaccination feature is the HAI against historical influenza variants, with smaller predictive power derived from age, sex, BMI, vaccine dose, the date of vaccination, or geographic location. The resulting model predicted future responses even when the vaccine composition changed or a different inactivated vaccine formulation was used. A pre-vaccination feature ‒ the time between peak HAI across recent variants ‒ distinguished large versus small post-vaccination responses with 73% accuracy. As a further test, four vaccine studies were conducted in 2022-2023 spanning two geographic locations and three influenza vaccine types. These datasets formed a blinded prediction challenge, where the computational team only received the pre-vaccination data yet predicted the post-vaccination responses with 2.2-fold error, comparable to the 2-fold intrinsic error of the experimental assay. This approach paves the way to better utilize current influenza vaccines, especially for individuals who exhibit the weakest responses.
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