An improved method for estimating antibody titers in microneutralization assay using green fluorescent protein

ABSTRACT

Viruses that express reporter genes upon infection have been recently used to evaluate neutralizing antibody responses, where a lack of reporter expression indicates specific virus inhibition. The traditional model-based methods using standard outcome of percent neutralization could be applied to the data from the assays to estimate antibody titers. However, the data produced are sometimes irregular, which can yield meaningless outcomes of percent neutralization that do not fit the typical curves for immunoassays, making automated or semi-high throughput antibody titer estimation unreliable. We developed a type of new outcomes model, which is biologically meaningful and fits typical immunoassay curves well. Our simulation study indicates that the new response approach outperforms the traditional response approach regardless of the data variability. The proposed new response approach can be used in similar assays for other disease models.

KEYWORDS:

• Antibody-mediated virus inhibition
• four-parameter logistic regression
• green fluorescent protein (GFP)
• hemagglutinin (HA)
• microneutralization assay
• neutralizing antibodies
• influenza virus
• nonlinear maximization

Acknowledgements

The authors thank the Statistical Center for HIV/AIDS Research and Prevention at Fred Hutchinson Cancer Research Center (especially Dr. George K. Lewis) for sharing with us their HIV neutralizing antibodies data, and the referees and the Editors for their detailed comments and valuable suggestions that greatly improved the presentation and contents of this article.

Funding

This work is supported by the NIAID Center for Biodefense Immune Modeling (HHSN272201000055C), by the NIAID Centers of Excellence for Influenza Research and Surveillance (HHSN266200700008C), by the NIAID Center for AIDS Research (Grant Number P30AI078498), and by the NIH grant R01HD071779. Research in the L.M.-S. laboratory is funded by the NIH grants RO1AI077719, R21NS075611-01, and R03AI099681-01A1, the NIAID Centers of Excellence for Influenza Research and Surveillance (HHSN266200700008C), and the University of Rochester Center for Biodefense Immune Modeling (HHSN272201000055C). SFB is currently supported by the University of Rochester Immunology Training Grant (AI 007285-26).

Additional information

Funding

This work is supported by the NIAID Center for Biodefense Immune Modeling (HHSN272201000055C), by the NIAID Centers of Excellence for Influenza Research and Surveillance (HHSN266200700008C), by the NIAID Center for AIDS Research (Grant Number P30AI078498), and by the NIH grant R01HD071779. Research in the L.M.-S. laboratory is funded by the NIH grants RO1AI077719, R21NS075611-01, and R03AI099681-01A1, the NIAID Centers of Excellence for Influenza Research and Surveillance (HHSN266200700008C), and the University of Rochester Center for Biodefense Immune Modeling (HHSN272201000055C). SFB is currently supported by the University of Rochester Immunology Training Grant (AI 007285-26).