This display of virus fragments alerts the body that the cell is infected and activates the immune system to kill and eliminate the cell before the virus can spread. In addition, the infected cells will also produce molecules, called interferons, which directly interfere with the process of viral replication to slow down the reproduction rate.
Interferons also send a handy warning signal to nearby cells to alert them of the growing viral threat. Learn More. The best defense against viruses, however, is to stop the infection in its tracks. This immune mechanism is made possible not by the cells themselves, but by antibodies which can identify and eliminate viruses before they start the infection cycle. Over the course of our lives, our bodies produce thousands of different types of antibodies that comprise our antibody-mediated immune response.
Antibodies are proteins that are produced by B cells, which are a specialized type of blood cell. Once produced, these antibodies patrol our circulatory system and tissues, ready to deal with the pathogens. Antibodies have several mechanisms to prevent infections. They can either neutralize viruses directly to prohibit their entry into the host cell, or they can crowd around a virus to increase its visibility to other immune cells.
Once bound to a virus, antibodies can also tag the virus for phagocytes, which in turn ingest and destroy the pathogen. Imagine the arms as thousands of different jigsaw-puzzle pieces that give each antibody a unique shape. Each of those antibody jigsaw-puzzle pieces has the potential to fit specific virus antigens while fitting poorly with others.
The better the antibody and antigen fit, the higher their affinity to each other. In other words, the stronger they bind to each other the more effective the antibody is at preventing infection by the virus. Figure 1: The better the antibody and antigen fit, the stronger they bind to each other the more effective the antibody is at preventing infection by the virus. To infect, viruses must first enter a healthy cell.
They accomplish cell entry by binding to receptor molecules on the surface of their host cells. Once the virus spike protein binds to the receptors of the lung cells, the virus enters and begins to replicate.
Virus-neutralizing antibodies are designed to interfere with this binding event. In fact, it displays an even better fit than the receptor itself, resulting in the virus surface becoming covered by antibodies. The steepness of this relationship between protective efficacy and neutralization level is determined by the parameter k. Note that the distribution of neutralization level for convalescent individuals has a mean of zero by definition that is, the log of the mean of neutralization titers for convalescent individuals normalized by itself.
Therefore the proportion of the vaccinated population for a study, s , that will be protected will be given by. The above integral is the so-called logistic-normal integral and the mean of the logit-normal distribution, which has no analytical solution Therefore, we use a simple numerical approximation left Riemann sum. The above model of protection was fitted to data on the protective efficacy of vaccines from phase 3 and another large cohort study of convalescent individuals.
The likelihood of observing the number of infected individuals in the control and vaccinated groups for each study, given some parameters, is. However, we wish to fit all studies simultaneously, and so the total likelihood of observing the data in all studies, given some parameters, is. The standard error s. This is, in most cases, the ratio of two values directly reported in the immunogenicity studies.
However, this approach does not account for situations in which the neutralization assay had neutralization titers below the LOD, therefore we also used a second method, in which we estimated this value by extracting the neutralization titers from the figures in each immunogenicity study Supplementary Table 1 and computing the mean neutralization titer for vaccinated and convalescent individuals in each study using censoring regression equation 1.
Additionally, although it was in principle possible to compute the standard deviation of neutralization levels for each study as above , these appeared somewhat confounded by the varying numbers of individuals between studies, hence we fitted the above model using 1 the standard deviation estimates for each study, 2 one standard deviation from one larger study to which we had direct access to raw data 3 that is, no manual data extraction required , and 3 an estimate of the standard deviation for all studies pooled together.
The two different methods of estimating the mean neutralization level for each study and the three methods of estimating the standard deviation of the neutralization levels give rise to six versions of the above model. All of these versions of the model were fitted, and the estimated protective levels were very similar Extended Data Fig. The above modeling approach assumed that neutralization levels were normally distributed. Here, we present a method for determining a protective threshold that is free of assumptions regarding the distribution of neutralization levels.
This model assumes that there is a protective neutralization level, T , above which individuals will be protected from infection and below which individuals will be susceptible. The protective efficacies observed in phase 3 clinical trials of vaccinated individuals and another large cohort study of convalescent individuals 1 ; Supplementary Table 2 are denoted by E s. These represent the proportion of individuals in each study, s , who should possess a neutralization level above the protective threshold.
It follows then that the number of individuals above the protective threshold in study s is a function of T , which we denote K s T. Therefore, the probability of observing K s T individuals above the protective threshold, given that there were N s individuals in the immunogenicity study which are much smaller than the phase 3 studies , is given by.
To determine one protective threshold using the results of all efficacy studies in this paper, we construct a likelihood function. Note that this likelihood function is discontinuous as the threshold T is varied. Therefore, we evaluate this likelihood function with the threshold T set equal to all observed neutralization levels n i across all studies, and find the value of T that maximizes this likelihood Extended Data Fig.
This method determines a protective level at which the proportion of individuals with neutralization levels above the threshold is in greatest agreement with the observed protective efficacy of that vaccine. Equation 8 is the likelihood function that should be adopted when neutralization measurements are not affected by an LOD.
In the case that some neutralization levels are below the LOD, the likelihood function is adjusted as follows:. C s is the number of censored values in study s and Q is the cumulative binomial distribution function. This later term considers the probability that as many as all of the censored values were below the threshold T given the protective efficacy of the study E s. Resampling was performed so as to preserve the total number of neutralization levels in each study.
These randomly generated samples of the original data were then fitted in the same way as described above, which generated 1, corresponding estimates of the protective neutralization level. To determine the ability of the model to predict the efficacy of a vaccine, we performed a leave-one-out analysis in which we systematically excluded one of the vaccine studies or the convalescent study and performed the same model-fitting procedure described above.
Using the model fitted on the subset of the studies we then estimated the efficacy of the vaccine that was left out from the fitted model. This leave-one-out analysis was performed for all versions of the logistic model that is, using the six methods of estimating the mean neutralization level and standard deviation outlined above. The predicted efficacy for each vaccine and convalescence obtained while leaving the study out are plotted against the reported efficacy in Fig.
In Fig. That is, these represent. We also tested if the protective neutralization level was different between mild and severe infection by fitting the combined dataset with two different mathematical models. A number of studies have analyzed the decay in neutralization titer in convalescent subjects.
These studies have generally shown a rapid early decay that slows with time 3 , 4 , 5 , 59 , We identified one study by Widge et al. Note that this restricted convalescent time course was used only so that we could compare decay in vaccination and convalescence over a similar time course time-limited by the vaccination data.
This is the estimate that was used in the predictive model presented in Figs. The decay in efficacy with time Fig. Further information on research design is available in the Nature Research Reporting Summary linked to this article. Lumley, S. Kim, J. Wheatley, A. Gaebler, C. Nature , — Dan, J. Science , eabf Wang, P. Hobson, D. The role of serum haemagglutination-inhibiting antibody in protection against challenge infection with influenza A2 and B viruses.
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Widge, A. Rodda, L. Cell , — Pradenas, E. Med NY 2 , — Chia, W. Chen, R. Wang, Z. Wu, K. Serum neutralizing activity elicited by mRNA vaccine. Liu, Y. Neutralizing activity of BNTb2-elicited serum. Zhou, D. Sette, A. Rydyznski Moderbacher, C. Juno, J. Published Online: March 19, Author Contributions: Dr Suthar had full access to all of the data in the study and takes responsibility for the integrity of the data and the accuracy of the data analysis. Acquisition, analysis, or interpretation of data: Edara, Hudson, Xie, Suthar.
Critical revision of the manuscript for important intellectual content: All authors. Conflict of Interest Disclosures: None reported.
Disclaimer: The findings and conclusions in this report are those of the authors and do not necessarily represent the official position of the Centers for Disease Control and Prevention. Stephens, MD, Evan J. No one received financial compensation for his or her contributions.
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Neutralizing activity of BNTb2-elicited serum: preliminary report. Published online February 17, This study describes changes in blood donor demographics and seroreactivity after testing of blood donations for severe acute respiratory syndrome coronavirus 2 SARS-CoV-2 antibodies began and was publicized in the US in mid-June Manish M.
Patel, MD; Natalie J. Thornburg, PhD; William B. Stubblefield, MD; H. Coughlin, PhD; Leora R. This study compares titers of binding and neutralizing antibodies after a single mRNA coronavirus vaccine dose in health care workers previously infected with SARS-CoV Frieman, PhD; Anthony D.
Harris, MD; Mohammad M. Sajadi, MD. This cohort study evaluates evidence of SARS-CoV-2 infection based on diagnostic nucleic acid amplification test among patients who tested positive versus negative for antibodies.
Raymond A. Rassen, ScD; Carly A. Cronin, PhD; Alison L. Lowy, MD; Norman E. Sharpless, MD; Lynne T. This interim analysis of an ongoing randomized trial evaluates the efficacy of 2 inactivated coronavirus vaccines for preventing symptomatic COVID in healthy adults and adverse events after immunization. This Medical News article describes how ultrapotent antibodies discovered in patients who recovered from COVID could be key players in new treatments and vaccines.
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