A new strategy to decrease risk of resistance emerging during therapy switching in HIV treatment

Luo, Rutao; Zurakowski, Ryan

doi:10.1109/acc.2008.4586804

Cited by 13 publications

(24 citation statements)

References 8 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…We develop a generalized multi-strain model in section II-B, which includes a description of the mutation process during HIV evolution. In section III, simulation results using the therapy switching strategy proposed by Luo and Zurakowski [16][17] to minimize the risk of drug resistance occurrence illustrate the model and its utility. Conclusions are drawn in the last section.…”

Section: Introductionmentioning

confidence: 99%

“…Therefore, the goal of restraining the drug resistance occurrence can be achieved by minimizing the number of infected cells at the time of starting a new therapy. In 2007, Luo and Zurakowski proposed that a pattern of structured treatment interruptions using the failing regimen preceding the introduction of the new regimen can significantly decrease the risk of resistance emerging to the new regimen [16] [17].…”

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

A generalized multi-strain model of HIV evolution with implications for drug-resistance management

Luo

Piovoso

Zurakowski

2009

2009 American Control Conference

Self Cite

View full text Add to dashboard Cite

Since 1996, the National Institutes of Health and other organizations have recommended offering Highly Active Antiretroviral Therapy (HAART) to all patients infected with HIV. Although HAART provides a powerful strategy for HIV treatment, it does not prevent completely the development of multi-drug resistant strains, and drug resistance is the primary reason for treatment failure. A better control of drug-resistance risk is critical for the success of long-term antiviral therapy in HIV patients. Recent research focuses on how to develop new drugs, but little has been done to control resistance risk by using an appropriate treatment regime. In this paper, we propose a generalized multi-strain model of HIV evolution with viral mutations. Based on this model, we suggest a drug switching strategy to minimize resistance risk and preserve long-term control of the HIV infection for the case in which the patient only has one kind of drug-resistance virus. Though simulations, this model can also be used for detecting and minimizing the resistance risk for the patients who develops multiple drugregimen resistance.

show abstract

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

A generalized multi-strain model of HIV evolution with implications for drug-resistance management

Luo

Piovoso

Zurakowski

2009

2009 American Control Conference

Self Cite

View full text Add to dashboard Cite

show abstract

“…But the competition relationship between wild-type virus and resistant virus offers a possible way to control drug-resistance risk. In the previous works [13], [14], [15], [16], we proposed a novel idea to control drug-resistance risk by using structured treatment interruptions (STIs) properly.…”

Section: Optimal Treatment Switchingmentioning

confidence: 99%

“…In this paper, we have developed a Monte-Carlo technique for testing the robustness of our previously proposed viral-load preconditioning procedure [13], [14], [15], [16] to likely variations in patient dynamics. In order to implicitly account for important parameter dependencies, we use the stochastic distribution of parameters generated by MCMC parameter identification applied to data from 12 HIV patients as the set from which parameter sets are randomly chosen.…”

Section: A Conclusionmentioning

confidence: 99%

“…In our previous work [13], [14], [15], [16], we have developed optimal treatment scheduling algorithms which seek to minimize the risk of subsequent virological failure by "preconditioning" the viral load prior to introducing a novel antiretroviral combination. This is accomplished by exploiting the residual efficacy of the failed antiretrovirals, either through interrupted therapy or through the use of novel combinations.…”

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

Modeling-error robustness of a viral-load preconditioning strategy for HIV treatment switching

Luo

Piovoso

Zurakowski

2010

Proceedings of the 2010 American Control Conference

Self Cite

View full text Add to dashboard Cite

In previous work, we have developed optimal-control based approaches that seek to minimize the risk of subsequent virological failure by "pre-conditioning" the viral load during therapy switches. In this paper, we use Monte-Carlo methods to evaluate the sensitivity of an open-loop implementation of these approaches to modeling errors. To account for hidden parameter dependencies, we use parameter distributions obtained from the convergence of Bayesian parameter estimation techniques applied to sets of clinical data obtained during serial therapy interruptions as the distribution from which the Monte-Carlo method samples.

show abstract

Controlling the evolution of resistance

Luo¹,

Cannon²,

Hernandez³

et al. 2011

Journal of Process Control

Self Cite

View full text Add to dashboard Cite

Evolution has long been understood as the driving force for many problems of medical interest. The evolution of drug resistance in HIV and bacterial infections is recognized as one of the most significant emerging problems in medicine. In cancer therapy, the evolution of resistance to chemotherapeutic agents is often the differentiating factor between effective therapy and disease progression or death. Interventions to manage the evolution of resistance have, up to this point, been based on steady-state analysis of mutation and selection models. In this paper, we review the mathematical methods applied to studying evolution of resistance in disease. We present a broad review of several classical applications of mathematical modeling of evolution, and review in depth two recent problems which demonstrate the potential for interventions which exploit the dynamic behavior of resistance evolution models. The first problem addresses the problem of sequential treatment failures in HIV; we present a review of our recent publications addressing this problem. The second problem addresses a novel approach to gene therapy for pancreatic cancer treatment, where selection is used to encourage optimal spread of susceptibility genes through a target tumor, which is then eradicated during a second treatment phase. We review the recent in Vitro laboratory work on this topic, present a new mathematical model to describe the treatment process, and show why model-based approaches will be necessary to successfully implement this novel and promising approach.

show abstract

A new strategy to decrease risk of resistance emerging during therapy switching in HIV treatment

Cited by 13 publications

References 8 publications

A generalized multi-strain model of HIV evolution with implications for drug-resistance management

A generalized multi-strain model of HIV evolution with implications for drug-resistance management

Modeling-error robustness of a viral-load preconditioning strategy for HIV treatment switching

Controlling the evolution of resistance

Contact Info

Product

Resources

About