Slotkin Rebecca, Kyriakides Tassos C, Yu Vinni, Chen Xien, Kundu Anupam, Gupta Shaili
Department of Medicine, Yale School of Medicine, 950 Campbell Ave, Bldg 1, Floor 5, West Haven, CT, 06516, United States, 1 2038005320.
Cooperative Studies Program Coordinating Center, VA CT Healthcare System, West Haven, CT, 06516, United States.
JMIR Form Res. 2025 Apr 22;9:e59467. doi: 10.2196/59467.
This study illustrates the development of a simple web-based application, which demonstrates the relationship between serum anti-SARS-CoV-2 S1/receptor-binding domain immunoglobulin G (IgG) and anti-SARS-CoV-2 neutralizing antibody (nAb) half-maximal inhibitory concentration (IC50) titers in a vaccinated US adult population and compares them to prior data on nAb titers at different time points after vaccination.
The objective of this study is to create an easily accessible calculator that uses the results of commercially available anti-SARS-CoV-2 serum IgG to approximate the underlying ability to neutralize SARS-CoV-2.
Our web-based application leveraged two previously published datasets. One dataset demonstrated a robust correlation between nAb and serum IgG. The other dataset measured nAb titers at specific time periods over a year-long interval following a messenger RNA vaccination primary series and booster vaccine dose. Clinical factors that were statistically significant on a forward linear regression model examining the prediction of nAb from serum IgG were incorporated in the application tool.
By combining the datasets described above, we developed a publicly available web-based application that allows users to enter a serum IgG value and determine their estimated nAb titer. The application contextualizes the estimated nAb titer with the theoretical distance from the corresponding vaccine-mediated antibody protection. Using the clinical variables that had a significant impact on how well IgG values predict nAb titers, this application allows for a patient-centered, nAb titer prediction.
This application offers an example of how we might bring the advances made in scientific research on protective antibodies post-SARS-CoV-2 vaccination into the clinical sphere with practical tools.
本研究阐述了一个简单的基于网络的应用程序的开发过程,该程序展示了美国成年接种人群中血清抗SARS-CoV-2 S1/受体结合域免疫球蛋白G(IgG)与抗SARS-CoV-2中和抗体(nAb)半数最大抑制浓度(IC50)滴度之间的关系,并将其与接种疫苗后不同时间点的nAb滴度先前数据进行比较。
本研究的目的是创建一个易于使用的计算器,利用市售抗SARS-CoV-2血清IgG的结果来近似中和SARS-CoV-2的潜在能力。
我们基于网络的应用程序利用了两个先前发表的数据集。一个数据集显示了nAb与血清IgG之间的强相关性。另一个数据集在信使核糖核酸疫苗初免系列和加强疫苗接种后的一年时间间隔内的特定时间段测量了nAb滴度。在研究从血清IgG预测nAb的正向线性回归模型上具有统计学意义的临床因素被纳入应用工具中。
通过合并上述数据集,我们开发了一个公开可用的基于网络的应用程序,允许用户输入血清IgG值并确定其估计的nAb滴度。该应用程序将估计的nAb滴度与来自相应疫苗介导的抗体保护的理论距离相关联。利用对IgG值预测nAb滴度的效果有显著影响的临床变量,此应用程序实现了以患者为中心的nAb滴度预测。
本应用程序提供了一个示例,展示了我们如何利用实用工具将SARS-CoV-2疫苗接种后保护性抗体科学研究取得的进展引入临床领域。