# Modeling of Viral Load Trajectories for HIV Cure Research

> **NIH NIH R01** · HARVARD PILGRIM HEALTH CARE, INC. · 2022 · $432,574

## Abstract

The world urgently needs to advance the HIV cure research agenda to address the persistently high global HIV
prevalence and associated mortality. Despite the success of combined antiretroviral therapy (ART) in achieving
sustained control of viral replication, the concerns about side-effects, drug-drug interactions, drug resistance and
cost call for a need to identify strategies for achieving HIV eradication or an ART-free remission. Following ART
withdrawal, patients' viral load levels usually increase rapidly to a peak followed by a dip, and then stabilize at a
viral load set point. Characterizing features of the viral rebound trajectories (e.g., time to viral rebound and viral
set points) after analytic antiretroviral treatment interruption (ATI) and identifying host, virological, and
immunological factors that are predictive of these features are central to HIV cure research. But doing so requires
addressing a variety of analytical challenges, including the non-linear viral rebound trajectories, coarsened data
due to the assay's limit of quantification, intermittent measurements of viral load values, small sample sizes from
individual studies, and high-dimensional candidate predictors. Motivated by our ongoing collaborations with HIV
cure research investigators and built on our previous work, we aim to address key methodological gaps by
leveraging data from multiple randomized studies conducted by the AIDS Clinical Trials Group and from the
Zurich Primary HIV Infection Cohort. Aim 1 proposes to develop a new set of methods for prediction of time to
viral rebound based on comprehensive history profiles, such as the rate of viral decay after ART initiation,
extending fitting algorithms and variable selection techniques developed for interval-censored outcomes. Aim 2
proposes to fit the viral rebound model using a Smoothed Simulated Pseudo Maximum likelihood method which
maximizes a smoothed simulated objective function constructed based on a Monte Carlo approximation of the
first two moments of the smoothed responses, and to develop methods to assess the association between time
to rebound and the viral set point and to simultaneously select biomarkers that affect different finer features of
the viral rebound trajectory. Aim 3 proposes to develop methods that optimally integrate data from multiple
cohorts and different phases of viral load trajectories while properly accounting for the homogeneity and
heterogeneity in covariate effects across studies. Innovation lies in the development and application of new
methods for modeling viral rebound that address various inherent challenges in analyses of available data.
Significance lies in the role of these methods in better characterizing viral rebound trajectories, identifying pre-
ATI predictors, and assessing the effects and mechanisms of novel therapeutic agents. The results of the
proposed research can inform optimal design of future ATI studies and provide new tools that can extract more
inform...

## Key facts

- **NIH application ID:** 10548503
- **Project number:** 1R01AI170254-01A1
- **Recipient organization:** HARVARD PILGRIM HEALTH CARE, INC.
- **Principal Investigator:** Rui Wang
- **Activity code:** R01 (R01, R21, SBIR, etc.)
- **Funding institute:** NIH
- **Fiscal year:** 2022
- **Award amount:** $432,574
- **Award type:** 1
- **Project period:** 2022-08-24 → 2026-07-31

## Primary source

NIH RePORTER: https://reporter.nih.gov/project-details/10548503

## Citation

> US National Institutes of Health, RePORTER application 10548503, Modeling of Viral Load Trajectories for HIV Cure Research (1R01AI170254-01A1). Retrieved via AI Analytics 2026-05-24 from https://api.ai-analytics.org/grant/nih/10548503. Licensed CC0.

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