Modeling how antibody responses may determine the efficacy of COVID-19 vaccines
Modeling how antibody responses may determine the efficacy of COVID-19 vaccines
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DOI：
10.1038/s43588-022-00198-0
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发表时间：
2022-02-01
期刊：
影响因子：
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通讯作者：
Dixit, Narendra M.
中科院分区：
文献类型：
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作者：
Padmanabhan, Pranesh;Desikan, Rajat;Dixit, Narendra M.
预测新冠肺炎疫苗的效力将有助于疫苗的开发和使用战略，鉴于其供应有限，这一点非常重要。在这里，我们开发了一个多尺度数学模型，该模型提出了新冠肺炎疫苗有效性和它们引发的中和抗体反应之间的机制联系。我们假设所有NAB的集合将构成一个形状空间，并且个体的反应是来自该空间的随机样本。我们通过分析已报道的类似于80nabs的体外剂量-反应曲线，构建了形状空间。我们从空间中抽取NAB亚群，概括了恢复期患者的反应。我们假设接种疫苗会引起类似的NAB反应。我们开发了一个宿主内SARS-CoV-2动态模型，将其应用于虚拟患者群体，并调用上述NAB响应，预测疫苗效果。我们的预测定量地捕捉到了临床试验的效果。因此，我们的研究提出了新冠肺炎疫苗看似合理的机制基础，并为建立这些疫苗产生了可测试的假设。
Predicting the efficacy of COVID-19 vaccines would aid vaccine development and usage strategies, which is of importance given their limited supplies. Here we develop a multiscale mathematical model that proposes mechanistic links between COVID-19 vaccine efficacies and the neutralizing antibody (NAb) responses they elicit. We hypothesized that the collection of all NAbs would constitute a shape space and that responses of individuals are random samples from this space. We constructed the shape space by analyzing reported in vitro dose-response curves of similar to 80 NAbs. Sampling NAb subsets from the space, we recapitulated the responses of convalescent patients. We assumed that vaccination would elicit similar NAb responses. We developed a model of within-host SARS-CoV-2 dynamics, applied it to virtual patient populations and, invoking the NAb responses above, predicted vaccine efficacies. Our predictions quantitatively captured the efficacies from clinical trials. Our study thus suggests plausible mechanistic underpinnings of COVID-19 vaccines and generates testable hypotheses for establishing them.