Using mathematical modeling and control to develop structured treatment interruption strategies for HIV infection

Rosenberg, Eric S.; Davidian, Marie; Banks, Harvey Thomas

doi:10.1016/j.drugalcdep.2006.12.024

Cited by 20 publications

(15 citation statements)

References 68 publications

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“…Rosenberg et al [71], and Banks et al [72] discussed how a variety of data sources with models based on ordinary differential equations can be used to build a dynamical systems model for use in estimating an optimal DTR in AIDS treatment. In this setting the treatment is a continuous dose of antiviral therapy, and the optimal DTR is chosen to bring the dynamical system to its “steady state”.…”

Section: Discussionmentioning

confidence: 99%

Dynamic Treatment Regimes

Chakraborty

Murphy

2014

Annu. Rev. Stat. Appl.

194

152

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A dynamic treatment regime consists of a sequence of decision rules, one per stage of intervention, that dictate how to individualize treatments to patients based on evolving treatment and covariate history. These regimes are particularly useful for managing chronic disorders, and fit well into the larger paradigm of personalized medicine. They provide one way to operationalize a clinical decision support system. Statistics plays a key role in the construction of evidence-based dynamic treatment regimes – informing best study design as well as efficient estimation and valid inference. Due to the many novel methodological challenges it offers, this area has been growing in popularity among statisticians in recent years. In this article, we review the key developments in this exciting field of research. In particular, we discuss the sequential multiple assignment randomized trial designs, estimation techniques like Q-learning and marginal structural models, and several inference techniques designed to address the associated non-standard asymptotics. We reference software, whenever available. We also outline some important future directions.

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Section: Discussionmentioning

confidence: 99%

Dynamic Treatment Regimes

Chakraborty

Murphy

2014

Annu. Rev. Stat. Appl.

194

152

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“…In the study [26], Adams et al analyzed the efficacy of STIs with fixed moments through the optimal control theory. 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].…”

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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“…The survey paper [37] details the design of STI strategies using HIV models and emphasizes that the use of models may be beneficial in a clinical design scenario. In [38] the authors build upon the original work of [39] and use an in-host HIV model to simulate many possible different STI treatments.…”

Section: Optimal Sti Therapymentioning

confidence: 99%

An Optimal Control Approach to Structured Treatment Interruptions for HIV Patients: A Personalized Medicine Perspective

Attarian¹,

Tran²

2017

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Highly Active Antiretroviral Therapy (HAART) has changed the course of human immunodeficiency virus (HIV) treatments since its introduction. However, for many patients, long term continuous HAART is expensive and can include problems with drug toxicity and side effects, as well as increased drug resistance. Because of these reasons, some HIV infected patients will voluntarily terminate HAART. Some of these patients will also interrupt the continuous prescribed therapies for short or long periods. After discontinuing HAART, patients will usually experience a rapid increase in viral load coupled with an immediate decline in CD4+ counts. The canonical example of a patient undergoing unsupervised breaks in HAART is that of the "Berlin patient". In this case, the patient was able to control viral load in the absence of treatment by cycling HAART on and off due to non-related infections. Due to this patient, interest in the use of structured treatment interruptions (STI) as a mechanism to regulate an HIV infection piqued. This paper describes an optimal control approach to determine STI regimen for HIV patients. The optimal STI was implemented in the context of the receding horizon control (RHC) using a mathematical model for the in-vivo dynamics of an HIV type 1 infection. Using available clinical data, we calibrate the model by estimating on a patient specific basis, a best estimable set of parameters using sensitivity analysis and subset selection. We demonstrate how customized STI protocols can be designed through the variation of control parameters on a patient specific basis.

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Using mathematical modeling and control to develop structured treatment interruption strategies for HIV infection

Cited by 20 publications

References 68 publications

Dynamic Treatment Regimes

Dynamic Treatment Regimes

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

An Optimal Control Approach to Structured Treatment Interruptions for HIV Patients: A Personalized Medicine Perspective

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