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 HIV/AIDS epidemic, targeting prevention interventions, and design and evaluation of HIV prevention trials.

Public Health Relevance

This project will evaluate and optimize methods that can be used to determine HIV incidence (the rate of new HIV infections) from cross-sectional surveys of single blood samples collected from individuals. These methods are needed to monitor the HIV/AIDS epidemic, to identify populations at high risk of HIV infection, to target HIV prevention efforts, and to design and evaluate HIV prevention trials. RELEVANCE This project is relevant to surveillance of the HIV/AIDS epidemic, identification of populations at increased risk of HIV acquisition, design of HIV prevention trials, and evaluation of the efficacy of interventions for HIV prevention.

Agency
National Institute of Health (NIH)
Institute
National Institute of Allergy and Infectious Diseases (NIAID)
Type
Research Project (R01)
Project #
5R01AI095068-06
Application #
9379836
Study Section
AIDS Clinical Studies and Epidemiology Study Section (ACE)
Program Officer
Sharma, Usha K
Project Start
2011-08-01
Project End
2021-10-31
Budget Start
2017-11-01
Budget End
2018-10-31
Support Year
6
Fiscal Year
2018
Total Cost
Indirect Cost
Name
Johns Hopkins University
Department
Pathology
Type
Schools of Medicine
DUNS #
001910777
City
Baltimore
State
MD
Country
United States
Zip Code
21205
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