Experts identify steps to expand, improve SARS-CoV-2 serology tests

More than 300 scientists and clinicians from the federal government, industry and academia published a report of their conclusions and recommendations on COVID-19 serology studies online in Immunity. The group gathered for an online workshop in May to discuss the role of serology testing in understanding and responding to the COVID-19 public health crisis and to explore strategies to address key scientific knowledge opportunities and gaps in the emerging field.

Serology tests for COVID-19 are designed to detect antibodies against SARS-CoV-2, the virus that causes COVID-19. While such tests do not diagnose active infection, they can indicate prior infection with SARS-CoV-2 that may have been missed because a person did not experience significant symptoms or access testing while infected.

The COVID-19 Serology Studies workshop was convened by an interagency working group comprised of experts from the U.S. Department of Health and Human Services--including scientists at the National Institute of Allergy and Infectious Diseases (NIAID), the National Cancer Institute (NCI), and the National Heart, Lung and Blood Institute (NHLBI), parts of the National Institutes of Health, as well as the Centers for Disease Control and Prevention and the Biomedical Advanced Research and Development Authority--and the Department of Defense. Attendees assessed efforts to better understand the implications of serology test results, to produce and validate test kits, and to quantify undetected cases of SARS-CoV-2 infection.

Attendees recommended that additional research is needed to determine if and to what extent a positive antibody test means a person may be protected from reinfection with SARS-CoV-2. Attendees emphasized that until such data is available, serology tests should not be used as a stand-alone tool to make decisions about personal safety related to SARS-CoV-2 exposure. Researchers are now pursuing studies in humans and in animal models to better understand SARS-CoV-2 immunity. Attendees noted that such understanding could help identify optimal donors of convalescent plasma that potentially could be used to help treat those with severe COVID-19.

Researchers from NCI reviewed progress in their effort to independently validate SARS-CoV-2 serology tests on behalf of the U.S. Food and Drug Administration. Attendees also proposed strategies to expand the accuracy and capacity of these tests to distinguish between naturally acquired and vaccine-induced antibodies, which will be critical to evaluating COVID-19 vaccine candidates.

Both community-based and large-scale serology surveillance efforts--such as the RESPONSE study sponsored by NIAID and NHLBI--are collecting critical data to improve epidemiological models and inform public health decision-making. Ideally, attendees noted, federal partners will expand this activity to establish an interactive serological database that will help public health officials monitor and quickly respond to changes in SARS-CoV-2 infection patterns.

Source:
Journal reference:

Lerner, A.M., et al. (2020) The COVID-19 Serology Studies Workshop: Recommendations and Challenges. Immunity. doi.org/10.1016/j.immuni.2020.06.012.

Comments

The opinions expressed here are the views of the writer and do not necessarily reflect the views and opinions of AZoLifeSciences.
Post a new comment
Post

While we only use edited and approved content for Azthena answers, it may on occasions provide incorrect responses. Please confirm any data provided with the related suppliers or authors. We do not provide medical advice, if you search for medical information you must always consult a medical professional before acting on any information provided.

Your questions, but not your email details will be shared with OpenAI and retained for 30 days in accordance with their privacy principles.

Please do not ask questions that use sensitive or confidential information.

Read the full Terms & Conditions.

You might also like...
blotRig Tool Transforms Western Blot Design and Statistical Analysis for Reproducibility