Editorial Commentary: Antiretroviral Therapy: The “When to Start” Debates

Cohen, Calvin; Boyle, Brian A

doi:10.1086/425934

Cited by 10 publications

(6 citation statements)

References 17 publications

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“…The best time to start a patient on HAART is an open question (Ahdieh-Grant et al 2003, Cohen and Boyle 2004, Harrington and Carpenter 2000, Ho 1995, Hoffman and Mulcahy 2007, Holmberg et al 2004, Jeffrey et al 2003, Lepri et al 2001, Mauskopf et al 2005, O'Shaughnessy et al 2000, Phillips et al 2003, Schackman et al 2002a, Tebas et al 2001. According to Dr. Anthony Fauci, the director of the National Institute of Allergy and Infectious Diseases, the question of when to initiate therapy is "the most important question in HIV therapy" (Hoffman and Mulcahy 2007, p. 176).…”

Section: Introductionmentioning

confidence: 99%

The Optimal Time to Initiate HIV Therapy Under Ordered Health States

Shechter

Bailey

Schaefer

et al. 2008

Operations Research

132

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The question of when to initiate HIV treatment is considered the most important question in HIV care today. Benefits of delaying therapy include avoiding the negative side effects and toxicities associated with the drugs, delaying selective pressures that induce the development of resistant strains of the virus, and preserving a limited number of treatment options. On the other hand, the risks of delayed therapy include the possibility of irreversible damage to the immune system, development of AIDS-related complications, and death. We use Markov decision processes to develop the first HIV optimization models that aim to maximize the expected lifetime or quality-adjusted lifetime of a patient. We prove conditions that establish structural properties of the optimal solution and compare them to our data and results. Model solutions, based on clinical data, support a strategy of treating HIV earlier in its course as opposed to recent trends toward treating it later.

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Section: Introductionmentioning

confidence: 99%

The Optimal Time to Initiate HIV Therapy Under Ordered Health States

Shechter

Bailey

Schaefer

et al. 2008

Operations Research

132

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“…When HIV enters the bloodstream, it primarily targets crucial components of the immune system [8], specifically, CD4+ T-cells or helper T-cells, whose function is to assist the response to bodily infections by releasing chemicals that signal other immune system cells, such as CD8+ (killer) T-cells, to kill infected cells or infectious particles [9,10,8,11,12,13]. HIV is capable of infecting other immune cells, such as macrophages [14], but the primary targets of infection are the CD4+ T-cells [15].…”

Section: Introductionmentioning

confidence: 99%

Sparse Modeling of Human Actions from Motion Imagery

Castrodad

Sapiro

2012

Int J Comput Vis

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In treating Human Immunodeficiency Virus (HIV) infection, strict adherence to drug therapy is crucial in maintaining a low viral load, but the high dosages required for this often have toxic side effects which make perfect adherence to Antiretroviral Therapy (ART) unsustainable. The imperfect patient adherence to ART and the development of resistant strains in the viral load has led to the development of alternative treatments that incorporate immunological response. This paper investigates theoretically and numerically the effect of immune effectors, such as the cytotoxic lymphocyte (CTL), in modeling HIV pathogenesis; our results suggest the significant impact of the immune response on the control of the virus during primary infection. Qualitative aspects (including positivity, boundedness, stability, uncertainty, and sensitivity analysis) are addressed. Additionally, by introducing drug therapy, we analyze numerically the model to assess the effect of treatment consisting of a combination of several antiretroviral drugs. Nevertheless, even in the presence of drug therapy, ongoing viral replication can lead to the emergence of drug-resistant virus variances. This fact is addressed in our model by including two viral strains, wild-type and drug-resistant. Our results show that the inclusion of the CTL compartment produces a higher rebound for an individual's healthy helper T-cell compartment than does drug therapy alone. Furthermore, we quantitatively characterize successful drugs or drug combination scenarios for both strains of virus.

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“…AIDS refers to a condition that afflicts the body when the immune system is so severely weakened that it is unable to fight even small infections. When HIV enters the bloodstream, it primarily targets crucial components of the immune system [10], specifically, CD4+ T-cells or helper T-cells, whose function is to assist the response to bodily infections by releasing chemicals that signal other immune system cells, such as CD8+ (killer) T-cells, to kill infected cells or infectious particles [2,7,10,11,20,38]. HIV is capable of infecting other immune cells, such as macrophages [24], but the primary targets of infection are the CD4+ T-cells [17].…”

Section: Introductionmentioning

confidence: 99%

A mathematical model for HIV treatment with time-varying antiretroviral therapy

Tarfulea

2011

International Journal of Computer Mathematics

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In treating the human immunodeficiency virus (HIV) infection, strict adherence to drug therapy is crucial for maintaining a low viral load, but the high dosages required for this often have toxic side effects which make perfect adherence to antiretroviral therapy (ART) unsustainable. Even in the presence of drug therapy, ongoing viral replication can lead to the emergence of drug resistance. In this paper, we investigate the effect of immune effectors in modelling HIV pathogenesis during ART, showing a higher rebound for healthy T-cell concentration than drug therapy alone. A periodic model of bang-bang type and a pharmacokinetic model are employed to estimate the drug efficacies. We numerically investigate how time-varying drug efficacy due to drug dosing regimen and/or suboptimal adherence affects the antiviral response and how it affects the emergence of drug resistance. Moreover, we qualitatively characterize successful drugs or drug combination scenarios.

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Editorial Commentary: Antiretroviral Therapy: The “When to Start” Debates

Cited by 10 publications

References 17 publications

The Optimal Time to Initiate HIV Therapy Under Ordered Health States

The Optimal Time to Initiate HIV Therapy Under Ordered Health States

Sparse Modeling of Human Actions from Motion Imagery

A mathematical model for HIV treatment with time-varying antiretroviral therapy

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