Quantitative Analysis of a Novel Systems-predicted Neutralizing Antibody Titer Assay 2305470

Abstract Introduction Using systems serology, it is possible to create an assay that quantifies a human receptor’s binding levels to a virus receptor binding protein in the presence of neutralizing antibodies. For example, human influenza A virus predominantly binds to alpha 2’6-linked sialic acids, while avian influenza A prefers alpha 2’3-linked sialic acids. Modeling this binding in the presence of neutralizing antibodies using a systems serology approach would allow us to quantify neutralization breadth and capacity at a throughput and scale not previously known. Methods Using four parameter or Michaelis-Menton fitted models, we can quantify the complete binding and complete inhibition of binding for virus receptor binding proteins and human receptor pairs at the multiplex level. From this, percent inhibition can be quantified, and a systems-predicted neutralizing antibody (SNAb) titer can be calculated. This titer was compared against existing neutralization assays and readouts for validation. This was done using serum and lavage samples from vaccinated/boosted non-human primates. Results Neutralizing antibody levels, whether they were measured by microneutralization assay, hemagglutinin inhibition assay, or SNAb were tightly correlated, and showed dependence on route of vaccination. The SNAb assay could successfully multiplex hemagglutinin molecules to quantify the predicted neutralization of twelve analytes simultaneously. The SNAb assay was performance-validated and did not show an overcounting or undercounting of neutralizing antibody titers. Conclusion Neutralizing antibody levels can be quantified in an efficient way using a systems-based approach. This allows for the multiplexing of readouts in a high-throughput assay. The assay itself is built on four-parameter or Michaelis-Menton binding modeling, which does not bias the titer compared to other commonly used methods. Funding Source National Institutes of Health Grant CA260476, PO1AI165072, and U19AI135995, and National Institutes of Health Contract 75N93021C00029 Topic Categories Technological Innovations in Immunology (TECH)