# Quantitative and Molecular Characterization of HIV Persistence and Rebound in Early and Very-Early ART Treated Children

> **NIH NIH P01** · BOSTON COLLEGE · 2021 · $283,129

## Abstract

Project Summary
The overarching goal of this proposal is to better understand the dynamics of HIV-1 persistence in perinatal
infection with very early treatment (VET) within 48 hours of life, early treatment by 2-3 months of age combined
with long-term suppression, and when initial ART is combined with broadly neutralizing antibodies, to facilitate
viral remission. Eliminating the resting memory CD4+ T cell latent HIV-1 reservoir is the major challenge for
eradicating HIV-1 from treated individuals. VET and early treatment of perinatal infection leads to a dramatic
reduction in HIV-1 reservoir size that may enable sustained HIV-1 remission, but requires additional studies
including mathematical modelling of reservoir dynamics. The proposed project will address a critical barrier to
assessing HIV remission and cure studies in perinatally infected children and adolescents, with direct application
to the rapidly evolving and exciting field of HIV cure. The studies have direct relevance to the research mission
of the National Institutes of Health where finding ways to achieve HIV-1 viral remission or cure is a top research
priority.

## Key facts

- **NIH application ID:** 10246902
- **Project number:** 7P01AI131365-05
- **Recipient organization:** BOSTON COLLEGE
- **Principal Investigator:** Deborah Persaud
- **Activity code:** P01 (R01, R21, SBIR, etc.)
- **Funding institute:** NIH
- **Fiscal year:** 2021
- **Award amount:** $283,129
- **Award type:** 7
- **Project period:** 2017-08-11 → 2024-07-31

## Primary source

NIH RePORTER: https://reporter.nih.gov/project-details/10246902

## Citation

> US National Institutes of Health, RePORTER application 10246902, Quantitative and Molecular Characterization of HIV Persistence and Rebound in Early and Very-Early ART Treated Children (7P01AI131365-05). Retrieved via AI Analytics 2026-05-21 from https://api.ai-analytics.org/grant/nih/10246902. Licensed CC0.

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