# HIV incidence testing in an evolving epidemic:  Identification of accurate multi-assay algorithms that include serosignatures from a novel antibody profiling system.

> **NIH NIH R01** · JOHNS HOPKINS UNIVERSITY · 2021 · $695,133

## Abstract

PROJECT SUMMARY
Accurate HIV incidence estimates are critical for monitoring the HIV/AIDS epidemic and evaluating
interventions for HIV prevention. We have developed multi-assay algorithms (MAAs) that provide accurate
incidence estimates. However, there are new challenges in this field of research. With increasing use of
antiretroviral drugs for HIV treatment and prevention and a push towards early treatment initiation, more
individuals, including those with recent infection, will be virally suppressed. This will impact cross-sectional
incidence testing: higher rates of viral suppression will increase misclassification with standard serologic
incidence assays; low viral load (VL) will no longer serve as biomarker for non-recent infection; and use of HIV
diversity assays for incidence testing will be problematic, since it may not be possible to analyze samples with
low VLs. Our hypothesis is that well-characterized samples, novel assays, and statistical modeling can be
used to develop methods that provide accurate cross-sectional incidence estimates in the evolving landscape
of HIV treatment and prevention. The Specific Aims of this project are:
Aim 1: Expand a repository of well-characterized samples with information on the duration of HIV infection;
 use these samples to evaluate performance of HIV incidence assays. Our repository includes >17,000
 samples from individuals with known duration of infection. We will continue to expand this repository,
 focusing on key populations and settings with high rates of viral suppression. These samples repository will
 be used to evaluate serologic HIV incidence assays.
Aim 2: Use massively multiplexed VirScan assay to identify serosignatures that discriminate between recent
 and non-recent HIV infection. VirScan uses phage display, immuno-precipitation, and next generation
 sequencing to measure antibody reactivity to >3,300 HIV peptides. We will test samples from Aim 1 with
 VirScan and will use the data to identify “serosignatures” that distinguish between recent and non-recent
 infection, independent of VL. We will also use VirScan data to develop multi-peptide immunoassays (EIAs).
Aim 3: Develop MAAs for HIV incidence estimation and validate the top-performing MAAs using independent
sample sets from cohort studies and clinical trials with known HIV incidence. Data from Aims 1 and 2 will be
 used to identify MAAs that maximize accuracy and minimize cost of cross-sectional incidence testing. The
 performance of MAAs and VirScan-based EIAs will be validated by comparing incidence estimates obtained
 with these methods to those observed in longitudinal follow-up in cohorts and clinical trials.
Based on our prior work and preliminary data, we believe that these studies will identify accurate, cost-
effective methods for cross-sectional HIV incidence estimation for use in diverse populations and settings.
This work will have direct public health benefit, providing improved methods for surveillance of the HI...

## Key facts

- **NIH application ID:** 10053294
- **Project number:** 5R01AI095068-09
- **Recipient organization:** JOHNS HOPKINS UNIVERSITY
- **Principal Investigator:** SUSAN H ESHLEMAN
- **Activity code:** R01 (R01, R21, SBIR, etc.)
- **Funding institute:** NIH
- **Fiscal year:** 2021
- **Award amount:** $695,133
- **Award type:** 5
- **Project period:** 2011-08-01 → 2022-10-31

## Primary source

NIH RePORTER: https://reporter.nih.gov/project-details/10053294

## Citation

> US National Institutes of Health, RePORTER application 10053294, HIV incidence testing in an evolving epidemic:  Identification of accurate multi-assay algorithms that include serosignatures from a novel antibody profiling system. (5R01AI095068-09). Retrieved via AI Analytics 2026-05-21 from https://api.ai-analytics.org/grant/nih/10053294. Licensed CC0.

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