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Predictions of time to HIV viral rebound following ART suspension that incorporate personal biomarkers.

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.

SUBMITTER: Conway JM

PROVIDER: S-EPMC6682162 | biostudies-literature | 2019 Jul

REPOSITORIES: biostudies-literature

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Predictions of time to HIV viral rebound following ART suspension that incorporate personal biomarkers.

Conway Jessica M JM Perelson Alan S AS Li Jonathan Z JZ

PLoS computational biology 20190724 7

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 ...[more]

PMID: 31339888

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Project description:ObjectiveHIV cure research urgently needs to identify pre-analytic treatment interruption (ATI) biomarkers of time-to-viral-rebound and viral setpoint to mitigate the risk of ATI and accelerate development of a cure. We previously reported that galactosylated IgG glycans, G2, negatively correlate with cell-associated HIV DNA and RNA during antiretroviral therapy (ART). We hypothesized that this and other plasma glycomic traits can predict time-to-viral-rebound and viral setpoint upon ART cessation.DesignWe profiled the circulating glycomes (plasma and bulk IgG) of two geographically distinct cohorts: Philadelphia Cohort - 24 HIV-infected, ART-suppressed individuals who had participated in an open-ended ATI study without concurrent immunomodulatory agents. Johannesburg Cohort - 23 HIV-infected, ART-suppressed individuals who had participated in a 2-week ATI.MethodsCapillary electrophoresis and lectin microarray were used for glycomic analyses. Cox proportional-hazards model and log-rank test were used for statistical analyses.ResultsHigher pre-ATI levels of the IgG glycan, G2, were significantly associated with a longer time-to-viral-rebound (hazard ratio = 0.12, P = 0.05). In addition to G2, we identified several predictive glycomic traits in plasma, for example, levels of FA2BG1, a non-sialylated, core-fucosylated glycan, associated with a longer time-to-viral-rebound (hazard ratio = 0.023, P = 0.05), whereas FA2G2S1, a sialylated glycan, associated with a shorter time-to-viral-rebound (hazard ratio = 24.1, P = 0.028). Additionally, pre-ATI plasma glycomic signatures associated with a lower viral setpoint, for example, T-antigen (Galβ1-3GalNAc) (r = 0.75, P = 0.0007), or a higher viral setpoint, for example, polylactosamine (r = -0.58, P = 0.01). These results were initially validated in the Johannesburg Cohort.ConclusionWe describe first-in-class, non-invasive, plasma and IgG glycomic biomarkers that inform time-to-viral-rebound and viral setpoint in two geographically distinct cohorts.

Project description:Monitoring the efficacy of novel reservoir-reducing treatments for HIV is challenging. The limited ability to sample and quantify latent infection means that supervised antiretroviral therapy (ART) interruption studies are generally required. Here we introduce a set of mathematical and statistical modeling tools to aid in the design and interpretation of ART-interruption trials. We show how the likely size of the remaining reservoir can be updated in real-time as patients continue off treatment, by combining the output of laboratory assays with insights from models of reservoir dynamics and rebound. We design an optimal schedule for viral load sampling during interruption, whereby the frequency of follow-up can be decreased as patients continue off ART without rebound. While this scheme can minimize costs when the chance of rebound between visits is low, we find that the reservoir will be almost completely reseeded before rebound is detected unless sampling occurs at least every two weeks and the most sensitive viral load assays are used. We use simulated data to predict the clinical trial size needed to estimate treatment effects in the face of highly variable patient outcomes and imperfect reservoir assays. Our findings suggest that large numbers of patients-between 40 and 150-will be necessary to reliably estimate the reservoir-reducing potential of a new therapy and to compare this across interventions. As an example, we apply these methods to the two "Boston patients", recipients of allogeneic hematopoietic stem cell transplants who experienced large reductions in latent infection and underwent ART-interruption. We argue that the timing of viral rebound was not particularly surprising given the information available before treatment cessation. Additionally, we show how other clinical data can be used to estimate the relative contribution that remaining HIV+ cells in the recipient versus newly infected cells from the donor made to the residual reservoir that eventually caused rebound. Together, these tools will aid HIV researchers in the evaluating new potentially-curative strategies that target the latent reservoir.

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Predictions of time to HIV viral rebound following ART suspension that incorporate personal biomarkers.

Publications

Predictions of time to HIV viral rebound following ART suspension that incorporate personal biomarkers.

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