Estimation and Prediction With HIV-Treatment Interruption Data

Adams, Brian M.; Banks, Harvey Thomas; Davidian, Marie; Rosenberg, Eric S.

doi:10.1007/s11538-006-9140-6

Cited by 50 publications

(73 citation statements)

References 21 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Here we explored the hypothesis that potent early treatment not only drove viral loads below the limit of detection but also interrupted the formation of the latent reservoir, so that upon cessation of ART, the adaptive immune response was then able to control infection. To investigate this hypothesis, we created a mathematical model of viral dynamics encompassing both effector cell and latent reservoir dynamics, extending previously published models (24,34,35,56). Though HIV-specific antibody responses may also play a role in PTC, here we focused on cell-mediated responses, including the possibility of immune exhaustion.…”

Section: Discussionmentioning

confidence: 99%

“…Our model of effector cell dynamics, which underlies the possibility of an individual having two distinct viral load setpoints, is relatively simple and based on prior work (34,35). A number of more complex models that incorporate additional immune system features, such as the role CD4 + T cells as both helper cells and target cells, precursor and effector CD8 + cells, as well as more explicit models of immune impairment have also been shown to exhibit coexistence of two stable viral set-points (58-60).…”

Section: Discussionmentioning

confidence: 99%

“…34, based on the model developed in ref. 35, we assume effector cells, E, kill productively infected cells, I, at rate mEI, where m is the CTL killing rate constant.…”

Section: Methodsmentioning

confidence: 99%

“…If the rate of generation of new productively infected cells is small, the adaptive immune response mounted by post-treatment controllers may be sufficiently strong to control infection. We explore this hypothesis using a mathematical model, a variant of the standard viral dynamic model of HIV infection and treatment (31)(32)(33), which incorporates the dynamics of the latent reservoir and explicit CTL effector cell dynamics (34,35). We show that in certain parameter regimes, our model predicts viral rebound and reestablishment of chronic HIV infection after therapy is stopped (Fig.…”

mentioning

confidence: 99%

See 3 more Smart Citations

Post-treatment control of HIV infection

Conway

Perelson

2015

Proc. Natl. Acad. Sci. U.S.A.

173

250

View full text Add to dashboard Cite

Antiretroviral therapy (ART) for HIV is not a cure. However, recent studies suggest that ART, initiated early during primary infection, may induce post-treatment control (PTC) of HIV infection with HIV RNA maintained at <50 copies per mL. We investigate the hypothesis that ART initiated early during primary infection permits PTC by limiting the size of the latent reservoir, which, if small enough at treatment termination, may allow the adaptive immune response to prevent viral rebound (VR) and control infection. We use a mathematical model of within host HIV dynamics to capture interactions among target cells, productively infected cells, latently infected cells, virus, and cytotoxic T lymphocytes (CTLs). Analysis of our model reveals a range in CTL response strengths where a patient may show either VR or PTC, depending on the size of the latent reservoir at treatment termination. Below this range, patients will always rebound, whereas above this range, patients are predicted to behave like elite controllers. Using data on latent reservoir sizes in patients treated during primary infection, we also predict population-level VR times for noncontrollers consistent with observations.

show abstract

Section: Discussionmentioning

confidence: 99%

Section: Discussionmentioning

confidence: 99%

“…34, based on the model developed in ref. 35, we assume effector cells, E, kill productively infected cells, I, at rate mEI, where m is the CTL killing rate constant.…”

Section: Methodsmentioning

confidence: 99%

mentioning

confidence: 99%

See 2 more Smart Citations

Post-treatment control of HIV infection

Conway

Perelson

2015

Proc. Natl. Acad. Sci. U.S.A.

173

250

View full text Add to dashboard Cite

show abstract

“…We utilize both sensitivity analysis and identifiability analysis methods to compute patient specific best-estimatable subsets of parameters. In [21] an identifiability analysis was conducted on an identical model, however the authors used a different, ad-hoc subset selection procedure.…”

Section: Introductionmentioning

confidence: 99%

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

Attarian¹,

Tran²

2017

View full text Add to dashboard Cite

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.

show abstract

Receding Horizon Control of HIV

David

Tran

Banks

2010

Optim Control Appl Methods

Self Cite

View full text Add to dashboard Cite

This paper describes a model of the immunologic response of the human immunodeficiency virus (HIV) in individuals. It then illustrates how a Receding Horizon Control (RHC) methodology can be used to drive the system to a stable equilibrium in which a strong immune response controls the viral load in the absence of drug treatment. We also illustrate how this feedback methodology can overcome unplanned treatment interruptions, inaccurate or incomplete data and imperfect model specification. We consider how ideas from stochastic estimation can be used in conjunction with RHC to create a robust treatment methodology. We then consider the performance of this methodology over random simulations of the previously considered clinical conditions.

show abstract

Estimation and Prediction With HIV-Treatment Interruption Data

Cited by 50 publications

References 21 publications

Post-treatment control of HIV infection

Post-treatment control of HIV infection

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

Receding Horizon Control of HIV

Contact Info

Product

Resources

About