Quantifying Neutralizing Antibodies in Patients with COVID-19 by a Two-Variable Generalized Additive Model

Area of Science:

• * Immunology and Virology
• * Biostatistics and Mathematical Modeling
• * Infectious Disease Diagnostics

Background:

• * The conventional virus neutralization test (NT) for quantifying severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) neutralizing antibodies is time-consuming and poses biosafety risks.
• * There is an urgent need for faster, safer methods to assess COVID-19 immunity and vaccine efficacy, especially with proposals for
• * The viral spike protein subunit 1 (S1) and its receptor-binding domain (RBD) are key targets for neutralizing antibodies.
• * The RBD is the primary binding site, while S1 encompasses additional important antibody binding regions.

Purpose of the Study:

• * To develop and validate an enzyme-linked immunosorbent assay (ELISA) as a surrogate method for quantifying SARS-CoV-2 neutralizing antibody titers.
• * To establish a rapid and reliable diagnostic tool for assessing COVID-19 immunity and potential vaccine effectiveness.
• * To provide a mathematical model for analyzing ELISA results to accurately predict NT values.

Main Methods:

• * Development of an ELISA assay detecting SARS-CoV-2 neutralizing antibodies based on the binding affinity of S1 and RBD proteins.
• * Analysis of 144 clinical specimens (COVID-19 patients and healthy donors) using both the developed ELISA and the conventional NT.
• * Application of spline regression and a two-variable generalized additive model to correlate ELISA results with NT values.

Main Results:

• * The developed ELISA method demonstrated a high correlation (0.917) between predicted and actual NT values when analyzed using the generalized additive model.
• * The ELISA results precisely reflected the neutralizing antibody titers obtained from the traditional NT.
• * The analytical model accurately quantified neutralizing antibody levels, serving as a reliable surrogate.

Conclusions:

• * The developed ELISA, coupled with a two-variable generalized additive model, provides a rapid, accurate, and safe surrogate for quantifying SARS-CoV-2 neutralizing antibody titers.
• * This methodology offers a significant advancement in serological testing for SARS-CoV-2 infection and has strong potential for assessing vaccine efficacy.
• * The innovative approach combines biological assays with mathematical analysis, paving the way for improved diagnostic platforms in infectious disease research.