It is essential to understand the pattern of immune responses in COVID-19 in order to develop an effective vaccine or antiviral therapeutics that target the optimal severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) antigens. Such interventions may help to contain the pandemic by preventing viral spread or reinfection or at least to reduce the severity of disease following infection. A recent study published on the preprint server bioRxiv* seeks to establish the pattern of antibody reactivity in COVID-19 convalescent plasma (CCP), which is widely being used as a therapy for seriously ill COVID-19 patients.
Convalescent plasma is a treatment approach with a lengthy historical background. It has been used in previous influenza, SARS, and other deadly viral outbreaks to induce passive immunity in the recipients during the active phase of infection. Its efficacy is partly at least due to the presence of neutralizing Ab against the causative virus, which prevents its replication and thus blunts the severity of the clinical condition.
Many researchers are conducting clinical trials of various designs in order to characterize the true efficacy of CCP and lay down the optimal regime for its use. For instance, the kind of antibody isotypes, and their level of expression, are essential parameters in determining the impact of CCP on COVID-19.
The current study examined the use of a testing technology called a Coronavirus Antigen Microarray (COVAM) to produce a profile of the reactivity characteristics of antibodies in CCP before its use in a given patient.
Standard Serologic Testing is Binary
At present, routine serologic testing produces a binary outcome and measures titers of a single antibody isotype against a single antigen or epitope, based on whether the titer is above or below a threshold that is more or less arbitrary. Typically, the assay does not report the actual reactivity of the antibody, whether above or below the predefined threshold.
The cutoff is set using established and validated positive and negative controls for the specific antibody or antibody set. Some assays use a mixture of antigens, with secondary antibodies that target various primary antibody isotypes. They generate an aggregate antibody value; therefore, that is represented as a binary outcome, either positive or negative.
What is COVAM?
The COVAM platform refers to a low-cost multiplex assay format that enables antibody testing in a high-throughput workflow. It uses a set of multiple validated purified antigens from a spectrum of coronavirus and non-coronavirus antigens, printed on nitrocellulose-coated slides for the serological assay. This includes 11 antigens from SARS-CoV-2, along with 5 each from SARS- and MERS-CoV, and 12 from seasonal coronaviruses. In addition, 35 come from other acute respiratory viruses.
COVAM is part of a chemometrics approach that quantifies the intensity levels of multiple specific signals independently in order to classify antibody-containing samples. However, in the current era of advanced genomic tools, sophisticated multivariant analytic tools are brought into play as well, using machine learning, pattern recognition, and cluster analysis.
This helps to develop a genomic signature, including both the genome sequence and the pattern of gene expression. These methods are also useful in clinical diagnostic tests.
The COVAM assay can be run to understand what antibodies are in the serum sample, which in turn reveals what viruses the patient has been exposed to and monitors dynamic changes in the antibody profile over time. Its design may allow large-scale studies to understand the epidemiology of COVID-19, revealing the prevalence and the titer of various antibodies in the population at different points of the epidemic.
Agreement Between COVAM and Other Assays
The researchers classified the COVAM results using two computational approaches as reactive or non-reactive. When compared with an established FDA EUA VITROS CoV2T chemiluminescence immunoassay from Ortho and with a plaque neutralization test (RVPNT) assay, they found that the COVAM and RVPNT assays showed 93% and 96% agreement with the VITROS CoV2T results. The highest level of correlation was with the antigens that contain the receptor-binding domain (RBD) but not the S2 domain.
They also found that COVAM-detected antibodies that target the nucleocapsid N protein and S2 spike domain show poor concordance with either of the other two assays. They explain this in terms of the known dependence of neutralization on the presence of inhibitory antibodies targeting the S1 RBD at its ACE2-binding site.
Earlier Studies Suggest Efficacy of CCP
It is important to look for antibody titers in plasma before assuming that it has therapeutic efficacy. An earlier Expanded Access Protocol (EAP) clinical trial looks at the use of more than 65,000 CCP units in over 35,000 COVID-19 patients. It shows that all CCP is not the same in terms of antibody efficacy.
Thus, only a subset of patients who received CCP under the EAP experienced significant improvement in the clinical outcome, albeit of a small magnitude. This was associated with higher antibody titers and early CCP administration. The upshot of the trial is that it became clear that only placebo-controlled, prospective and randomized clinical trials (RCTs) can provide a satisfactory answer as to whether the administration of CCP is a valid therapeutic approach in COVID-19.
COVAM Classification of CCP
In the current COVAM multiplex assay, these tools are applied to identify patterns in plasma samples based on the independent measurement of multiple antibodies and isotypes with different titers targeting several SARS-CoV-2 antigens. The current study uses COVAM to classify plasma before it is used to treat COVID-19 patients. Existing criteria include the donor’s positivity for viral RNA by polymerase chain reaction (PCR), the severity of disease, and the time from symptom onset or from recovery to donation of CCP.
The use of COVAM is based on the measurement of the antibody levels as well as the neutralization titers, using pseudotyped viruses bearing SARSC-CoV-2 antigens. In this way, it is possible to set a threshold above which the clinical usefulness of CCP can be gauged before it is given to a patient urgently in need of passive immunity.
The current study used CCP from 99 donors in the period between April 4 and June 5, 2020, all from the US.
Three CCP Groups
The researchers found a large variation in the type and level of antibodies to the SARS-CoV-2 antigens, especially four of them, with several different groups emerging from the analysis.
The researchers found that there were three distinct groups of IgG reactivity patterns with respect to the COVAM NP, Spike, S1, S2, and RBD antigens. In Group 1, there were high antibody levels against the N, S1, and full-length Spike protein antigens. Group 2 and Group 3 had intermediate spike-targeting antibody reactivity and very low anti-N and anti-S1 reactivity, respectively.
In particular, one subcluster of group 3 is seen to have practically no anti- SARS-CoV-2 antibodies detectable by COVAM. Moreover, there were two or more subgroups within each of the major groups. These groups were not evident with the other assays, even after adjusting the thresholds of reactivity.
These differences are due to the fact that these assays detect different patterns of antigens in the serum. In other words, the infection prompts a polyclonal response with a range of specific antibodies.
Implications and Future Directions
The researchers say that more work is needed to identify the correlation between these groups and the effectiveness of the CCP as COVID-19 therapy. As more knowledge is gained about the relative clinical importance of each of the major groups of antibodies in the course of the disease, it may even become possible to select CCP for administration based on a rapid analysis of the antibody profile, looking for specific subsets within any of the three groups.
Again, this type of multi-antigen array could help to generate a vaccine-induced antibody profile for preclinical and clinical studies, to further understand the range and depth of serologic responses following immunization, and to define protective antibody responses. Understanding how antibody types and intensities correlate with clinical severity could be useful in predicting the course of vaccine breakthrough infections as well.