# Accurate prediction of neutralization capacity from deep mining of SARS-CoV-2 serology

> **NIH NIH R21** · NEW YORK UNIVERSITY SCHOOL OF MEDICINE · 2020 · $466,125

## Abstract

ABSTRACT
The goal of this project is to establish an accurate and sensitive method for predicting the neutralization
capacity against SARS-CoV-2 of serum samples by deep mining of antibody profiles. The COVID-19 pandemic
remains a global threat with nearly seven million cases and 400K deaths. In the absence of effective vaccines
and therapeutics, immunity against SARS-CoV-2 is a main mechanism of protection against SARS-CoV-2
(re)infection. Our recent studies of convalescent serum samples revealed that their levels of neutralization
capacity vary greatly (over 100-fold) and only a small subset has high neutralization capacity. Because viral
neutralization assays are inherently low throughput, it is unrealistic to apply it to a high-risk population such as
hospital workers in a timely manner. Unfortunately, there is only moderate correlation between the
neutralization capacity and the level of anti-SARS-CoV-2 antibody levels determined using standard ELISA.
Clearly, we still do not understand what types of antibodies contribute to viral neutralization. Our overarching
hypothesis to be tested in this project is that by examining the antibody profile in patient serum more deeply
and quantitatively in terms of antigens, epitopes and antibody types, we will be able to identify quantitative
predictive markers for viral neutralization. To this end, we will develop multiplex assay for SARS-CoV-2
serology that will enable us to deeply characterize the antibody profile. We will then develop a predictive
algorithm by utilizing. We have assembled a team of experts with truly complementary skills in antibody
characterization, virology and data mining. We have access to a large number of convalescent serum samples,
which will enable us to critically validate our technology. We will expeditiously execute the following aims. (1)
We will develop multiplex serology assay for SARS-CoV-2 that can profile up to 15 antibody-antigen
interactions in a single reaction. The main technical innovation is the introduction of multi-dimensional flow
cytometry. We will produce multiple antigens including Spike, receptor-binding domain and nucleocapsid
protein, and their natural and designed variants. We will refine and validate the assay using a large panel of
convalescent serum samples. (2) We will develop an improved viral neutralization assay to better quantify the
neutralization capacity. (3) We will develop a predictive algorithm for neutralization capacity that utilizes the
antibody profiles from our multiplex assay. This analysis will identify serology parameters that contribute to
neutralization. The end products of this project will include a high-throughput serology assay that gives far-
richer antibody profiles than the current standard accompanied with an accurate predictive algorithm. Together,
this platform will help advance a fundamental understanding of SARS-CoV-2 infection as well as the
development of vaccines and therapeutics against this formidable pathogen.

## Key facts

- **NIH application ID:** 10195613
- **Project number:** 1R21AI158997-01
- **Recipient organization:** NEW YORK UNIVERSITY SCHOOL OF MEDICINE
- **Principal Investigator:** SHOHEI KOIDE
- **Activity code:** R21 (R01, R21, SBIR, etc.)
- **Funding institute:** NIH
- **Fiscal year:** 2020
- **Award amount:** $466,125
- **Award type:** 1
- **Project period:** 2020-08-19 → 2022-07-31

## Primary source

NIH RePORTER: https://reporter.nih.gov/project-details/10195613

## Citation

> US National Institutes of Health, RePORTER application 10195613, Accurate prediction of neutralization capacity from deep mining of SARS-CoV-2 serology (1R21AI158997-01). Retrieved via AI Analytics 2026-05-23 from https://api.ai-analytics.org/grant/nih/10195613. Licensed CC0.

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