Analysis of Treatment for HIV-infected Patients Considering CD4 T Cell Count in STI

Ki, Park; Chung, Han Byul; Chung, Chung

doi:10.1109/sice.2006.315831

Cited by 2 publications

(3 citation statements)

References 13 publications

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“…Further, there is strong evidence that drug effectiveness does depend on the treatment progression (Buchbinder et al, 1994; Park et al, 2006; Perelson et al, 1999), and the longer treatment the less drug effectiveness. Note that drugs not only provides an effective treatment of infections with HIV, but also plays an important role in virus dynamics.…”

Section: Discussionmentioning

confidence: 99%

“…Hadjiandreou et al (Hadjiandreou et al, 2011) formulated the non-adaptive STI therapy into a dynamic optimization problem and showed that STIs could control disease progression. Park et al (Park et al, 2006) used a mathematical model to describe an ‘incomplete’ adaptive STI strategy, in which the drug dose was administered (interrupted) when the healthy CD4 T cell count was less (greater) than a threshold. When the healthy CD4 T cell count was between the two thresholds, the non-adaptive STI strategy is maintained.…”

Section: Introductionmentioning

confidence: 99%

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Piecewise HIV virus dynamic model with CD4+ T cell count-guided therapy: I

Tang

Xiao

Wang³

et al. 2012

Journal of Theoretical Biology

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The strategies of structured treatment interruptions (STIs) of antiretroviral therapies have been proposed for clinical management of HIV infected patients, but clinical studies on STIs failed to achieve a consistent conclusion for this strategy. To evaluate the STI strategies, in particular, CD4+ T cell count-guided STIs, and explain these controversial conclusions from different clinical studies, in this paper we propose to use piecewise HIV virus dynamic models to quantitatively explore the STI strategies and investigate their dynamic behaviors. Our analysis results indicate that CD4+ T cell counts can be maintained above a safe level using the STI with a single threshold or a threshold window. Numerical simulations show that the CD4+ T cell counts either fluctuate or approach a stable level for a patient, depending on the prescribed upper or lower threshold values. In particular, the CD4+ T cell counts can be stabilized at a desired level if the threshold policy control is applied. The durations of drug-on and drug-off are very sensitive to the prescribed upper or lower threshold levels, which possibly explains why the on-off strategy with fixed schedule or a STI strategy with frequent switches is associated with the high rate of failure. Our findings suggest that it is critical to carefully choose the thresholds of CD4+ T cell count and individualize the STIs for each individual patient based on initial CD4+ T cell counts.

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Section: Discussionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Piecewise HIV virus dynamic model with CD4+ T cell count-guided therapy: I

Tang

Xiao

Wang³

et al. 2012

Journal of Theoretical Biology

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“…Further, Rosenberg et al pointed out that it is needed to include drug resistance and CD4 cell counts when making the structured treatment [27]. In 2006, Park et al developed a mathematical model to investigate the impact of finite times of structured treatment guided by CD4 cell counts on HIV patients [28]. Then, Tang et al [29] formulated a piecewise system to describe the CD4 cell-guided STIs, quantitatively explored STI strategies, and explained some controversial conclusions from different clinical studies.…”

Section: Mathematical Problems In Engineeringmentioning

confidence: 99%

Global Dynamics of a Virus-Immune System with Virus-Guided Therapy and Saturation Growth of Virus

Xiao

2018

Mathematical Problems in Engineering

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We considered a piecewise virus-immune dynamic model to investigate the effectiveness of the HIV virus loads-guided structured treatment interruptions (STIs). To better describe the biological reality, we extended the existing models by taking the carrying capacity of the virus loads into consideration to indicate the saturated growth of virus loads. We initially investigated the sliding dynamics of the proposed model and then obtained the global dynamics of the proposed model. Our main results showed that the system can exhibit very complex and diverse dynamic behaviors including a globally asymptotically stable equilibrium, bistable equilibra, and tristable equilibria, depending on the dynamics of the subsystems and the threshold level. In particular, an interesting result indicated that, with a proper threshold condition, the virus-guided therapy policy can successfully control the virus loads far below its carrying capacity and maintain the activity of the immune system for the case that the effector cells always go to zero without therapy or with continuous therapy. The finding suggested that the optimal strategy should be individual-based due to coexistence of multiple stable steady states, depending on the threshold conditions and the initial levels of viral loads and effector cells of the patients.

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Analysis of Treatment for HIV-infected Patients Considering CD4 T Cell Count in STI

Cited by 2 publications

References 13 publications

Piecewise HIV virus dynamic model with CD4+ T cell count-guided therapy: I

Piecewise HIV virus dynamic model with CD4+ T cell count-guided therapy: I

Global Dynamics of a Virus-Immune System with Virus-Guided Therapy and Saturation Growth of Virus

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