Neutralizing Antibody Levels May Predict Immune Response to COVID-19 Vaccines

By Marilynn Larkin

May 27, 2021

NEW YORK (Reuters Health) - Neutralizing antibodies were "highly predictive" of immune protection from SARS-CoV-2 in a modeling study, and could be used to predict vaccine efficacy against viral variants, researchers suggest.

"We are actively working to validate the analysis with additional data," Dr. Miles Davenport of the Kirby Institute, University of New South Wales in Sydney told Reuters Health by email. "It will be important to validate that predictions about waning vaccine efficacy/decreased protection against SARS-CoV-2 variants are borne out over time."

As reported in Nature Medicine, the team analyzed the relationship between in vitro neutralizing antibody levels and protection from severe SARS-CoV-2 infection using data from seven current vaccines (mRNA-1273, NVX-CoV2373, BNT162b2, rAd26-S+rAd5-S, ChAdOx1 nCoV-19, Ad26.COV2. S and CoronaVac) and from a study of convalescent patients up to eight months after infection.

The estimated neutralization level for 50% protection against detectable SARS-CoV-2 infection was 20.2% of the mean convalescent level.

However, the estimated neutralization level required for 50% protection from severe infection was significantly lower, at 3% of the mean convalescent level.

Modeling of the decay of the neutralization titer over the first 250 days after immunization predicted that a significant loss of protection from SARS-CoV-2 infection will occur, although protection from severe disease should be largely retained.

Further, neutralization titers against some SARS-CoV-2 variants of concern were reduced compared with the vaccine strain, and the model predicted the relationship between neutralization and efficacy against viral variants.

"A major caveat of our estimate of the relative protective level of antibodies in SARS-CoV-2 infection is that it includes aggregation of data collected from diverse neutralization assays and clinical trial designs," the authors state. "It is hoped that in the future a standardized neutralization assay may be developed and utilized, which will allow direct comparison of neutralization titers and further refinement of these analyses."

That said, they note that their results "are consistent with studies of both influenza and seasonal coronavirus infection for which reinfection is possible 1 year after the initial infection...Similarly, after influenza virus vaccination, protective efficacy is thought to decline by around 7% per month."

Dr. Davenport added, "We should be able to predict vaccine efficacy and protection from infection in the future based on the level of neutralizing antibodies present. Early waning of immunity and infection with variants is predicted to reduce protection in both vaccinated and previously infected individuals. It is likely that booster shots will be required."

Dr. Henry Cohen, Dean and Professor at Touro College of Pharmacy called the approach "promising."

"Currently," he said, "we lack standardized assays for SARS-CoV-2 immunity, so a predictive model is beneficial in helping predict immune protection."

"A few caveats to consider are that this predictive model does not take into account the protection provided by the antiviral T and B memory cells that also generate an antibody response," he noted.

In addition, he said, and as the authors state, "the model is based on seven vaccine trials that used different assays for measuring neutralization."

"Finally," he said, "the seven vaccine trials used to support this predictive model used different definitions for infection, disease, severity, hospitalization, and death. For instance, did patients become hospitalized or expire 'due to' COVID-19 disease or 'with' COVID-19?"

"Despite these limitations, this important study will spawn further studies of predictive models of COVID immunity using newly available robust phase 3 vaccine studies that will confirm and improve the reliability of the model's predictability," Dr. Cohen concluded.

Dr. John Moore, Professor of Microbiology and Immunology, Weill Cornell Medicine, also commented by email. "The approach taken in the paper is impressive," he said, "and it's already had an impact on thinking as it's been available as a preprint for a couple of months."

Nonetheless, he added, "All modeling papers are, well, models. The parameters in this one seem carefully chosen. I believe its core messages."

SOURCE: Nature Medicine, online May 17, 2021.