7,070 research outputs found
Hybrid spreading mechanisms and T cell activation shape the dynamics of HIV-1 infection
HIV-1 can disseminate between susceptible cells by two mechanisms: cell-free
infection following fluid-phase diffusion of virions and by highly-efficient
direct cell-to-cell transmission at immune cell contacts. The contribution of
this hybrid spreading mechanism, which is also a characteristic of some
important computer worm outbreaks, to HIV-1 progression in vivo remains
unknown. Here we present a new mathematical model that explicitly incorporates
the ability of HIV-1 to use hybrid spreading mechanisms and evaluate the
consequences for HIV-1 pathogenenesis. The model captures the major phases of
the HIV-1 infection course of a cohort of treatment naive patients and also
accurately predicts the results of the Short Pulse Anti-Retroviral Therapy at
Seroconversion (SPARTAC) trial. Using this model we find that hybrid spreading
is critical to seed and establish infection, and that cell-to-cell spread and
increased CD4+ T cell activation are important for HIV-1 progression. Notably,
the model predicts that cell-to-cell spread becomes increasingly effective as
infection progresses and thus may present a considerable treatment barrier.
Deriving predictions of various treatments' influence on HIV-1 progression
highlights the importance of earlier intervention and suggests that treatments
effectively targeting cell-to-cell HIV-1 spread can delay progression to AIDS.
This study suggests that hybrid spreading is a fundamental feature of HIV
infection, and provides the mathematical framework incorporating this feature
with which to evaluate future therapeutic strategies
A stochastic multi-scale model of HIV-1 transmission for decision-making: application to a MSM population.
BackgroundIn the absence of an effective vaccine against HIV-1, the scientific community is presented with the challenge of developing alternative methods to curb its spread. Due to the complexity of the disease, however, our ability to predict the impact of various prevention and treatment strategies is limited. While ART has been widely accepted as the gold standard of modern care, its timing is debated.ObjectivesTo evaluate the impact of medical interventions at the level of individuals on the spread of infection across the whole population. Specifically, we investigate the impact of ART initiation timing on HIV-1 spread in an MSM (Men who have Sex with Men) population.Design and methodsA stochastic multi-scale model of HIV-1 transmission that integrates within a single framework the in-host cellular dynamics and their outcomes, patient health states, and sexual contact networks. The model captures disease state and progression within individuals, and allows for simulation of therapeutic strategies.ResultsEarly ART initiation may substantially affect disease spread through a population.ConclusionsOur model provides a multi-scale, systems-based approach to evaluate the broader implications of therapeutic strategies
Predicting the outcomes of treatment to eradicate the latent reservoir for HIV-1
Massive research efforts are now underway to develop a cure for HIV
infection, allowing patients to discontinue lifelong combination antiretroviral
therapy (ART). New latency-reversing agents (LRAs) may be able to purge the
persistent reservoir of latent virus in resting memory CD4+ T cells, but the
degree of reservoir reduction needed for cure remains unknown. Here we use a
stochastic model of infection dynamics to estimate the efficacy of LRA needed
to prevent viral rebound after ART interruption. We incorporate clinical data
to estimate population-level parameter distributions and outcomes. Our findings
suggest that approximately 2,000-fold reductions are required to permit a
majority of patients to interrupt ART for one year without rebound and that
rebound may occur suddenly after multiple years. Greater than 10,000-fold
reductions may be required to prevent rebound altogether. Our results predict
large variation in rebound times following LRA therapy, which will complicate
clinical management. This model provides benchmarks for moving LRAs from the
lab to the clinic and can aid in the design and interpretation of clinical
trials. These results also apply to other interventions to reduce the latent
reservoir and can explain the observed return of viremia after months of
apparent cure in recent bone marrow transplant recipients and an
immediately-treated neonate.Comment: 8 pages main text (4 figures). In PNAS Early Edition
http://www.pnas.org/content/early/2014/08/05/1406663111. Ancillary files: SI,
24 pages SI (7 figures). File .htm opens a browser-based application to
calculate rebound times (see SI). Or, the .cdf file can be run with
Mathematica. The most up-to-date version of the code is available at
http://www.danielrosenbloom.com/reboundtimes
Viral Load Monitoring of Antiretroviral Therapy, cohort viral load and HIV transmission in Southern Africa: A Mathematical Modelling Analysis
In low-income settings, treatment failure is often identified using CD4 cell count monitoring. Consequently, patients remain on a failing regimen, resulting in a higher risk of transmission. We investigated the benefit of routine viral load monitoring for reducing HIV transmission
Estimation of constant and time-varying dynamic parameters of HIV infection in a nonlinear differential equation model
Modeling viral dynamics in HIV/AIDS studies has resulted in a deep
understanding of pathogenesis of HIV infection from which novel antiviral
treatment guidance and strategies have been derived. Viral dynamics models
based on nonlinear differential equations have been proposed and well developed
over the past few decades. However, it is quite challenging to use experimental
or clinical data to estimate the unknown parameters (both constant and
time-varying parameters) in complex nonlinear differential equation models.
Therefore, investigators usually fix some parameter values, from the literature
or by experience, to obtain only parameter estimates of interest from clinical
or experimental data. However, when such prior information is not available, it
is desirable to determine all the parameter estimates from data. In this paper
we intend to combine the newly developed approaches, a multi-stage
smoothing-based (MSSB) method and the spline-enhanced nonlinear least squares
(SNLS) approach, to estimate all HIV viral dynamic parameters in a nonlinear
differential equation model. In particular, to the best of our knowledge, this
is the first attempt to propose a comparatively thorough procedure, accounting
for both efficiency and accuracy, to rigorously estimate all key kinetic
parameters in a nonlinear differential equation model of HIV dynamics from
clinical data. These parameters include the proliferation rate and death rate
of uninfected HIV-targeted cells, the average number of virions produced by an
infected cell, and the infection rate which is related to the antiviral
treatment effect and is time-varying. To validate the estimation methods, we
verified the identifiability of the HIV viral dynamic model and performed
simulation studies.Comment: Published in at http://dx.doi.org/10.1214/09-AOAS290 the Annals of
Applied Statistics (http://www.imstat.org/aoas/) by the Institute of
Mathematical Statistics (http://www.imstat.org
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