It has been about 16 months since severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) appeared in Wuhan, China, but the relationship between immunity and clinical protection from SARS-CoV-2 is still unknown. Reliable predictive models of immune protection from infection are still unavailable. Models like these are essential in identifying correlates of protection for SARS-CoV-2 vaccinations in the future.

Analyzing the relationship between neutralization levels and observed protection from SARS-CoV-2 infection

To address this gap, researchers from Australia recently examined the relationship between levels of in vitro neutralization and the observed protection obtained from SARS-CoV-2 infection. They used data from 7 currently available vaccines and convalescent cohorts. Their research is published in the journal Nature Medicine.

The researchers estimated that the neutralization level needed for 50% protection against detectable infection is 20.2% of the mean convalescent level. The estimated neutralization level for 50% protection from severe SARS-CoV-2 infection was considerably lower at 3% of the mean convalescent level.

"Neutralising antibodies are tiny Y-shaped proteins produced by our body in response to infection or vaccination. They bind to the virus, reducing its ability to infect," says Dr Deborah Cromer from the Kirby Institute.

"While we have known for some time that neutralising antibodies are likely to be a critical part of our immune response to COVID-19, we haven't known how much antibody you need for immunity. Our work is the strongest evidence to date to show that specific antibody levels translate to high levels of protection from disease."

Model predicts the relationship between efficacy against viral variants and neutralization

Modeling of the neutralization titer decay over the first 250 days post-immunization predicts a considerable loss in protection from SARS-CoV-2 infection, although protection from severe disease is largely retained. Neutralization titers against some new SARS-CoV-2 variants of concern are reduced compared to the vaccine variant. Their model predicts the relationship between efficacy against viral variants and neutralization.

This study uses available data on immune responses and protection to model the protective antibody titer and the long-term behavior of SARS-CoV-2-induced immunity. The model suggests that neutralization titer is a crucial predictor of vaccine efficacy as more vaccines become available in the future. The results show that neutralization level is strongly predictive of immune protection, and it offers an evidence-based model of SARS-CoV-2 immune protection, which will help develop vaccine strategies to control the future course of the COVID-19 pandemic.

The model developed by the researchers also predicts that immune protection from SARS infection may wane over time as the neutralization levels drop. This suggests that booster immunization may be needed within a year. However, the findings indicate that protection acquired from severe COVID-19 disease may be significantly more durable due to alternative responses, including cellular immune responses playing a significant role in fighting infection.

Findings agree with that of influenza and seasonal coronavirus infection studies

This method for estimating neutralization titer uses a similar modeling approach used to determine the protective titer for influenza infection. However, a key difference is the data usage in the models. While the COVID-19 model uses a wide range of immunogenicity and efficacy of different vaccines to determine the 50% protective titer, the influenza infection studies use data from the HAI titer of individual subjects and their infection risk in either cohort or human challenge studies to assess protection. The authors believe that it would help to validate the results of their study using a similar individual risk analysis of COVID-19 infection in the future.

The results are in agreement with influenza and seasonal coronavirus infection studies, which show that reinfection is possible a year after the initial infection, although reinfection is usually mild. Similarly, protective efficacy was found to decline by about 7% / month after influenza vaccination. They also warn that this modeling and predictions are based on several assumptions about the mechanisms and rate of immunity loss.

Dr Cromer said that this finding has the potential to change the way we conduct COVID-19 vaccine trials in the future.

"Antibody immune levels are much easier to measure than directly measuring vaccine efficacy over time. So, by measuring antibody levels across the range of new vaccine candidates during early phases of clinical trials, we can better determine whether a vaccine should be used to prevent COVID-19."

Study offers a framework for integrating available vaccination and convalescent study data

According to the authors, based on these findings, the key priorities now are standardized clinical trial protocols and the development of standardized assays to determine neutralization and immune responses.

The authors believe that the data from the study will allow more testing and validation of other immune correlates of protection. However, this study develops a framework for integrating available data from vaccination and convalescent studies to develop a tool for predicting the future of SARS-CoV-2 immunity and the COVID-19 pandemic.

“Important priorities for the field are the development of standardized assays to measure neutralization and other immune responses, as well as standardized clinical trial protocols.”

Written by

Susha Cheriyedath

Susha has a Bachelor of Science (B.Sc.) degree in Chemistry and Master of Science (M.Sc) degree in Biochemistry from the University of Calicut, India. She always had a keen interest in medical and health science. As part of her masters degree, she specialized in Biochemistry, with an emphasis on Microbiology, Physiology, Biotechnology, and Nutrition. In her spare time, she loves to cook up a storm in the kitchen with her super-messy baking experiments.