Efficient Identification of High-Titer Anti-Severe Acute Respiratory Syndrome Coronavirus 2 (SARS-CoV-2) Antibody Plasma Samples by Pooling Method. Archives of pathology & laboratory medicine Nguyen, K. D., Wirz, O. F., Röltgen, K., Pandey, S., Tolentino, L., Boyd, S. D., Pham, T. D. 2021

Abstract

The ongoing coronavirus disease 2019 (COVID-19) pandemic caused by the severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) has elicited a surge in demand for serological testing to identify previously infected individuals. In particular, antibody testing is crucial in identifying COVID-19 convalescent plasma (CCP), which has been approved by the Food and Drug Administration (FDA) under the Emergency Use Authorization (EUA) for use as passive immune therapy for hospitalized patients infected with COVID-19. Currently, high-titer CCP can be qualified by Ortho's Vitros COVID-19 IgG antibody test (VG).To explore the use of an efficient testing method to identify high-titer CCP for use in treating COVID-19 infected patients and track COVID-19 positivity over time.We evaluated an ELISA-based method that detects antibodies specific to the SARSCoV-2 receptor binding domain (RBD) with individual and pooled plasma samples and compared its performance against VG. Using the pooled RBD-ELISA (P-RE) method, we also screened over 10,000 longitudinal healthy blood donor samples to assess seroprevalence.P-RE demonstrates 100% sensitivity in detecting FDA-defined high-titer samples when compared to VG. Overall sensitivity of P-RE when compared to VG and our individual sample RBD-ELISA (I-RE) were 83% and 56%, respectively. When screening 10,218 healthy blood donor samples by P-RE, we found the seroprevalence correlated with the local infection rates with a correlation coefficient of 0.21 (P< .001).Pooling plasma samples can be used to efficiently screen large populations for individuals with high-titer anti-RBD antibodies, important for CCP identification.

View details for DOI 10.5858/arpa.2021-0215-SA

View details for PubMedID 34101801