ArticlePLoS computational biology2019
Predictions of time to HIV viral rebound following ART suspension that incorporate personal biomarkers.
Article in PLoS computational biology, 2019. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 27 papers.
What it found
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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
27 citing papers in PubMed.
- Expression of type I interferon-associated genes at antiretroviral therapy interruption predicts HIV virological rebound.Scientific reports · 2022Trial
- CCR5-edited CD4+ T cells augment HIV-specific immunity to enable post-rebound control of HIV replication.The Journal of clinical investigation · 2021Trial
- AZD5582 robustly reactivates latently infected cells and clears the majority of those reactivated from the SIV reservoir.bioRxiv : the preprint server for biology · 2026Article
- Characterization of HIV humoral immunity during analytical treatment interruption.Journal of clinical virology : the official publication of the Pan American Society for Clinical Virology · 2026Article
- Review
- Article
- Understanding early HIV-1 rebound dynamics following antiretroviral therapy interruption: The importance of effector cell expansion.PLoS pathogens · 2024Article
- Comparative analysis of within-host dynamics of acute infection and viral rebound dynamics in postnatally SHIV-infected ART-treated infant rhesus macaques.bioRxiv : the preprint server for biology · 2024Article
- Understanding early HIV-1 rebound dynamics following antiretroviral therapy interruption: The importance of effector cell expansion.bioRxiv : the preprint server for biology · 2024Article
- DAA-mediated HCV cure reduces HIV DNA levels in HCV/HIV coinfected people.Journal of virology · 2023Article
- Making waves: Integrating wastewater surveillance with dynamic modeling to track and predict viral outbreaks.Water research · 2023Article
- Substantial uneven proliferation of CD4Journal of virus eradication · 2022Article
- Discordance between HIV-1 Population in Plasma at Rebound after Structured Treatment Interruption and Archived Provirus Population in Peripheral Blood Mononuclear Cells.Microbiology spectrum · 2022Article
- Balancing Statistical Power and Risk in HIV Cure Clinical Trial Design.The Journal of infectious diseases · 2022Article
- Article
- Nonlinear mixed-effects models for HIV viral load trajectories before and after antiretroviral therapy interruption, incorporating left censoring.Statistical communications in infectious diseases · 2022Article
- PD-1 blockade and vaccination provide therapeutic benefit against SIV by inducing broad and functional CD8Science immunology · 2021Article
- Article
- Unified model of short- and long-term HIV viral rebound for clinical trial planning.Journal of the Royal Society, Interface · 2021Article
- Article
Corrections and comments
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Authors and funding
3 authors.
Funding
Abstract
Antiretroviral therapy (ART) effectively controls HIV infection, suppressing HIV viral loads. Suspension of therapy is followed by rebound of viral loads to high, pre-therapy levels. However, there is significant heterogeneity in speed of rebound, with some rebounds occurring within days, weeks, or sometimes years. We present a stochastic mathematical model to gain insight into these post-treatment dynamics, specifically characterizing the dynamics of short term viral rebounds (≤ 60 days). Li et al. (2016) report that the size of the expressed HIV reservoir, i.e., cell-associated HIV RNA levels, and drug regimen correlate with the time between ART suspension and viral rebound to detectable levels. We incorporate this information and viral rebound times to parametrize our model. We then investigate insights offered by our model into the underlying dynamics of the latent reservoir. In particular, we refine previous estimates of viral recrudescence after ART interruption by accounting for heterogeneity in infection rebound dynamics, and determine a recrudescence rate of once every 2-4 days. Our parametrized model can be used to aid in design of clinical trials to study viral dynamics following analytic treatment interruption. We show how to derive informative personalized testing frequencies from our model and offer a proof-of-concept example. Our results represent first steps towards a model that can make predictions on a person living with HIV (PLWH)'s rebound time distribution based on biomarkers, and help identify PLWH with long viral rebound delays.
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Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.