Ryan Zurakowski - Profile on Academia.edu (original) (raw)
Papers by Ryan Zurakowski
Modelling HIV-1 2-LTR dynamics following raltegravir intensification
Journal of clinical microbiology, 2012
We present a simple computational model of measurement accuracy for single-copy sensitivity assay... more We present a simple computational model of measurement accuracy for single-copy sensitivity assays (SCA) of HIV RNA that was developed from first principles. The model shows that the SCA is significantly right-skewed. Measured virus concentrations of 1 and 10 virions/ml had overlapping 95% confidence intervals and were statistically indistinguishable.
Replicating genetically modified adenoviruses have shown promise as a new treatment approach agai... more Replicating genetically modified adenoviruses have shown promise as a new treatment approach against cancer. Recombinant adenoviruses replicate only in cancer cells which contain certain mutations, such as the loss of functional p53, as is the case in the virus ONYX-015. The successful entry of the viral particle into target cells is strongly dependent on the presence of the main receptor for adenovirus, the coxsackie- and adeno-virus receptor (CAR). This receptor is frequently down-regulated in highly malignant cells, rendering this population less vulnerable to viral attack. It has been shown that use of MEK inhibitors can up-regulate CAR expression, resulting in enhanced adenovirus entry into the cells. However, inhibition of MEK results in G1 cell cycle arrest, rendering infected cells temporarily unable to produce virus. This forces a tradeoff. While drug mediated up-regulation of CAR enhances virus entry into cancer cells, the consequent cell cycle arrest inhibits production of new virus particles and the replication of the virus. Optimal control-based schedules of MEK inhibitor application should increase the efficacy of this treatment, maximizing the overall tumor toxicity by exploiting the dynamics of CAR expression and viral production. We introduce two mathematical models of these dynamics and show simple optimal control based strategies which motivate this approach
PloS one, 2011
The development of resistant strains of HIV is the most significant barrier to effective long-ter... more The development of resistant strains of HIV is the most significant barrier to effective long-term treatment of HIV infection. The most common causes of resistance development are patient noncompliance and pre-existence of resistant strains. In this paper, methods of antiviral regimen switching are developed that minimize the risk of pre-existing resistant virus emerging during therapy switches necessitated by virological failure. Two distinct cases are considered; a single previous virological failure and multiple virological failures. These methods use optimal control approaches on experimentally verified mathematical models of HIV strain competition and statistical models of resistance risk. It is shown that, theoretically, order-of-magnitude reduction in risk can be achieved, and multiple previous virological failures enable greater success of these methods in reducing the risk of subsequent treatment failures.
Biomedical Engineering Online, 2011
Background Mathematical models of the immune response to the Human Immunodeficiency Virus demonst... more Background Mathematical models of the immune response to the Human Immunodeficiency Virus demonstrate the potential for dynamic schedules of Highly Active Anti-Retroviral Therapy to enhance Cytotoxic Lymphocyte-mediated control of HIV infection. Methods In previous work we have developed a model predictive control (MPC) based method for determining optimal treatment interruption schedules for this purpose. In this paper, we introduce a nonlinear observer for the HIV-immune response system and an integrated output-feedback MPC approach for implementing the treatment interruption scheduling algorithm using the easily available viral load measurements. We use Monte-Carlo approaches to test robustness of the algorithm. Results The nonlinear observer shows robust state tracking while preserving state positivity both for continuous and discrete measurements. The integrated output-feedback MPC algorithm stabilizes the desired steady-state. Monte-Carlo testing shows significant robustness to modeling error, with 90% success rates in stabilizing the desired steady-state with 15% variance from nominal on all model parameters. Conclusions The possibility of enhancing immune responsiveness to HIV through dynamic scheduling of treatment is exciting. Output-feedback Model Predictive Control is uniquely well-suited to solutions of these types of problems. The unique constraints of state positivity and very slow sampling are addressable by using a special-purpose nonlinear state estimator, as described in this paper. This shows the possibility of using output-feedback MPC-based algorithms for this purpose.
Exploiting immune response dynamics in HIV therapy
UMI, ProQuest ® Dissertations & Theses. The world's most comprehensive collection of dis... more UMI, ProQuest ® Dissertations & Theses. The world's most comprehensive collection of dissertations and theses. Learn more... ProQuest, Exploiting immune response dynamics in HIV therapy. by Zurakowski, Ryan Mark, Ph.D ...
PLoS One, 2012
Mathematical models based on ordinary differential equations (ODE) have had significant impact on... more Mathematical models based on ordinary differential equations (ODE) have had significant impact on understanding HIV disease dynamics and optimizing patient treatment. A model that characterizes the essential disease dynamics can be used for prediction only if the model parameters are identifiable from clinical data. Most previous parameter identification studies for HIV have used sparsely sampled data from the decay phase following the introduction of therapy. In this paper, model parameters are identified from frequently sampled viral-load data taken from ten patients enrolled in the previously published AutoVac HAART interruption study, providing between 69 and 114 viral load measurements from 3-5 phases of viral decay and rebound for each patient. This dataset is considerably larger than those used in previously published parameter estimation studies. Furthermore, the measurements come from two separate experimental conditions, which allows for the direct estimation of drug efficacy and reservoir contribution rates, two parameters that cannot be identified from decay-phase data alone. A Markov-Chain Monte-Carlo method is used to estimate the model parameter values, with initial estimates obtained using nonlinear least-squares methods. The posterior distributions of the parameter estimates are reported and compared for all patients.
… Control Conference, 2004. …, 2004
The dynamics of the human immune response to infection are nonlinear and complex, and analysis of... more The dynamics of the human immune response to infection are nonlinear and complex, and analysis of these models can yield surprising, counterintuitive insights. In this paper, we explore one such insight concerning the treatment of HIV infection. In a previous paper, we introduced a model predictive control (MPC) based method of determining treatment schedules that would induce a transition to a state in which the patient's immune system controlled the viral infection without the need for further treatment. In this paper, we show how introducing additional, non HIV-specific target cells (cells which act as hosts for the HIV virus) can yield faster convergence with less transient damage to the patient's immune system.
… Control Conference (ACC), …, 2010
In previous work, we have developed optimalcontrol based approaches that seek to minimize the ris... more In previous work, we have developed optimalcontrol based approaches that seek to minimize the risk of subsequent virological failure by "pre-conditioning" the viral load during therapy switches. These techniques result in the transient susceptibility of the total viral load, and rely on finding the minimum of a dip in viral load and switching before viral load rebound. Model uncertainty necessitates a closed-loop approach to minimum-finding. Blood measurements are costly in terms of money, inconvenience and risk. In this paper, we introduce an iterative parameter estimation approach to find the viral load minimum, and measure the degree of optimality of minimum-seeking under conditions of measurement noise. We evaluate the cost-savings of this approach in terms of number of samples saved from a constant measurement rate.
… Control Conference (ACC), …, 2010
In previous work, we have developed optimalcontrol based approaches that seek to minimize the ris... more In previous work, we have developed optimalcontrol 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.
Journal of process …, 2011
Evolution has long been understood as the driving force for many problems of medical interest. Th... more 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.
American Control Conference, 2006, 2006
Recently developed models of the interaction of the human immune system and the Human Immunodefic... more Recently developed models of the interaction of the human immune system and the Human Immunodeficiency Virus (HIV) suggest the possibility of using interruptions of Highly Active Anti-Retroviral Therapy (HAART) to simulate a theraputic vaccine and induce Cytotoxic Lymphocyte (CTL) mediated control of HIV infection. In previous work we have developed a model predictive control (MPC) based method for determining optimal treatment interruption schedules for this purpose. In this paper, we introduce a nonlinear observer for the HIV-immune response system and an integrated outputfeedback MPC approach for implementing the treatment interruption scheduling algorithm using the easily available viral load measurements. We explore the robustness of this method to inevitable variations in the patient model, and discuss the implications of the results.
… Conference, 2004. 5th …, 2004
Feedback-based treatment scheduling for HIV patients is summarized. The feedback schedules are de... more Feedback-based treatment scheduling for HIV patients is summarized. The feedback schedules are developed using a dynamic HIV infection model that has appeared in the literature. The theory behind the feedback schedules is nonlinear model predictive control. This branch of nonlinear control theory is reviewed, limitations are pointed out, and recent developments are summarized. It is indicated how these developments provide a flexible, robust design tool for the HIV treatment scheduling problem.
Journal of theoretical biology, 2007
Replicating genetically modified adenoviruses have shown promise as a new treatment approach agai... more Replicating genetically modified adenoviruses have shown promise as a new treatment approach against cancer. Recombinant adenoviruses replicate only in cancer cells which contain certain mutations, such as the loss of functional p53, as is the case in the virus ONYX-015. The successful entry of the viral particle into target cells is strongly dependent on the presence of the main receptor for adenovirus, the coxsackie-and adeno-virus receptor (CAR). This receptor is frequently downregulated in highly malignant cells, rendering this population less vulnerable to viral attack. It has been shown that use of MEK inhibitors can up-regulate CAR expression, resulting in enhanced adenovirus entry into the cells. However, inhibition of MEK results in G1 cell cycle arrest, rendering infected cells temporarily unable to produce virus. This forces a tradeo1. While drug mediated upregulation of CAR enhances virus entry into cancer cells, the consequent cell cycle arrest inhibits production of new virus particles and the replication of the virus. Optimal control-based schedules of MEK inhibitor application should increase the efficacy of this treatment, maximizing the overall tumor toxicity by exploiting the dynamics of CAR expression and viral production. We introduce a mathematical model of these dynamics and show simple optimal control based strategies which motivate this approach.
… Control Conference, 2009. …, 2009
Since 1996, the National Institutes of Health and other organizations have recommended offering H... more 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.
American Control Conference, 2008, 2008
Although Highly Active Antiretroviral Therapy (HAART) provides a powerful strategy for HIV treatm... more Although Highly Active Antiretroviral Therapy (HAART) provides a powerful strategy for HIV treatment, it has been shown that HAART cannot eradicate all viruses in patients because of the existence of long-term reservoir. With the use of HAART, resistant strains develop and become the dominant species. Because the number of independent treatment regimens is limited, once resistance to all available drug classes arises, the patient will die. In this paper, we propose a drug switching strategy to minimize resistance risk of resistance and preserve long-term control of the HIV infection based on a simple model of HIV infection with persistent viral reservoirs.
Decision and Control, 2007 46th …, 2007
The development of multi-drug resistant strains of HIV remains the primary reason for treatment f... more The development of multi-drug resistant strains of HIV remains the primary reason for treatment failure and progression to AIDS in the United States. The failure of a particular multi-drug regimen necessitates a switch to a new multi-drug regimen. We use a simple model of the interaction of resistant strains to show that the transition to the new regimen involves a significant risk of strains resistant to the new regimen emerging, and that treatment interruptions using the failing regimen can be used to decrease this risk.
Proceedings of the American Control …, 2003
Using recently developed models of the interaction of the human immune system and the Human Immun... more Using recently developed models of the interaction of the human immune system and the Human Immunodeficiency Virus (HIV), we have developed a model predictive control (MPC) based method for determining optimal treatment interruption schedules. These schedules use interruptions of Highly Active Anti-Retroviral Therapy (HAART) to induce immune control of the virus without the need for continued treatment, as suggested by the models. In this paper, we discuss the medical motivation for this work, introduce the MPC-based method and show simulation results, and discuss future work necessary to implement the method.
Journal of Theoretical Biology, 2006
Recently developed models of the interaction of the human immune system and the human immunodefic... more Recently developed models of the interaction of the human immune system and the human immunodeficiency virus (HIV) suggest the possibility of using interruptions of highly active anti-retroviral therapy (HAART) to simulate a therapeutic vaccine and induce cytotoxic lymphocyte (CTL) mediated control of HIV infection. We have developed a model predictive control (MPC) based method for determining optimal treatment interruption schedules for this purpose. This method provides a clinically implementable framework for calculating interruption schedules that are robust to errors due to measurement and patient variations. In this paper, we discuss the medical motivation for this work, introduce the MPC-based method, show simulation results, and discuss future work necessary to implement the method.
Treatment interruptions to decrease risk of resistance emerging during therapy switching in HIV treatment
I. INTRODUCTION The development of resistance to a particular drug regimen necessitates a change ... more I. INTRODUCTION The development of resistance to a particular drug regimen necessitates a change in regimen, and there are, for every ap-plication, only a limited number of drug regimens available. If resistance develops to every available drug regimen, the patient will die. It is ...
Modelling HIV-1 2-LTR dynamics following raltegravir intensification
Journal of clinical microbiology, 2012
We present a simple computational model of measurement accuracy for single-copy sensitivity assay... more We present a simple computational model of measurement accuracy for single-copy sensitivity assays (SCA) of HIV RNA that was developed from first principles. The model shows that the SCA is significantly right-skewed. Measured virus concentrations of 1 and 10 virions/ml had overlapping 95% confidence intervals and were statistically indistinguishable.
Replicating genetically modified adenoviruses have shown promise as a new treatment approach agai... more Replicating genetically modified adenoviruses have shown promise as a new treatment approach against cancer. Recombinant adenoviruses replicate only in cancer cells which contain certain mutations, such as the loss of functional p53, as is the case in the virus ONYX-015. The successful entry of the viral particle into target cells is strongly dependent on the presence of the main receptor for adenovirus, the coxsackie- and adeno-virus receptor (CAR). This receptor is frequently down-regulated in highly malignant cells, rendering this population less vulnerable to viral attack. It has been shown that use of MEK inhibitors can up-regulate CAR expression, resulting in enhanced adenovirus entry into the cells. However, inhibition of MEK results in G1 cell cycle arrest, rendering infected cells temporarily unable to produce virus. This forces a tradeoff. While drug mediated up-regulation of CAR enhances virus entry into cancer cells, the consequent cell cycle arrest inhibits production of new virus particles and the replication of the virus. Optimal control-based schedules of MEK inhibitor application should increase the efficacy of this treatment, maximizing the overall tumor toxicity by exploiting the dynamics of CAR expression and viral production. We introduce two mathematical models of these dynamics and show simple optimal control based strategies which motivate this approach
PloS one, 2011
The development of resistant strains of HIV is the most significant barrier to effective long-ter... more The development of resistant strains of HIV is the most significant barrier to effective long-term treatment of HIV infection. The most common causes of resistance development are patient noncompliance and pre-existence of resistant strains. In this paper, methods of antiviral regimen switching are developed that minimize the risk of pre-existing resistant virus emerging during therapy switches necessitated by virological failure. Two distinct cases are considered; a single previous virological failure and multiple virological failures. These methods use optimal control approaches on experimentally verified mathematical models of HIV strain competition and statistical models of resistance risk. It is shown that, theoretically, order-of-magnitude reduction in risk can be achieved, and multiple previous virological failures enable greater success of these methods in reducing the risk of subsequent treatment failures.
Biomedical Engineering Online, 2011
Background Mathematical models of the immune response to the Human Immunodeficiency Virus demonst... more Background Mathematical models of the immune response to the Human Immunodeficiency Virus demonstrate the potential for dynamic schedules of Highly Active Anti-Retroviral Therapy to enhance Cytotoxic Lymphocyte-mediated control of HIV infection. Methods In previous work we have developed a model predictive control (MPC) based method for determining optimal treatment interruption schedules for this purpose. In this paper, we introduce a nonlinear observer for the HIV-immune response system and an integrated output-feedback MPC approach for implementing the treatment interruption scheduling algorithm using the easily available viral load measurements. We use Monte-Carlo approaches to test robustness of the algorithm. Results The nonlinear observer shows robust state tracking while preserving state positivity both for continuous and discrete measurements. The integrated output-feedback MPC algorithm stabilizes the desired steady-state. Monte-Carlo testing shows significant robustness to modeling error, with 90% success rates in stabilizing the desired steady-state with 15% variance from nominal on all model parameters. Conclusions The possibility of enhancing immune responsiveness to HIV through dynamic scheduling of treatment is exciting. Output-feedback Model Predictive Control is uniquely well-suited to solutions of these types of problems. The unique constraints of state positivity and very slow sampling are addressable by using a special-purpose nonlinear state estimator, as described in this paper. This shows the possibility of using output-feedback MPC-based algorithms for this purpose.
Exploiting immune response dynamics in HIV therapy
UMI, ProQuest ® Dissertations & Theses. The world's most comprehensive collection of dis... more UMI, ProQuest ® Dissertations & Theses. The world's most comprehensive collection of dissertations and theses. Learn more... ProQuest, Exploiting immune response dynamics in HIV therapy. by Zurakowski, Ryan Mark, Ph.D ...
PLoS One, 2012
Mathematical models based on ordinary differential equations (ODE) have had significant impact on... more Mathematical models based on ordinary differential equations (ODE) have had significant impact on understanding HIV disease dynamics and optimizing patient treatment. A model that characterizes the essential disease dynamics can be used for prediction only if the model parameters are identifiable from clinical data. Most previous parameter identification studies for HIV have used sparsely sampled data from the decay phase following the introduction of therapy. In this paper, model parameters are identified from frequently sampled viral-load data taken from ten patients enrolled in the previously published AutoVac HAART interruption study, providing between 69 and 114 viral load measurements from 3-5 phases of viral decay and rebound for each patient. This dataset is considerably larger than those used in previously published parameter estimation studies. Furthermore, the measurements come from two separate experimental conditions, which allows for the direct estimation of drug efficacy and reservoir contribution rates, two parameters that cannot be identified from decay-phase data alone. A Markov-Chain Monte-Carlo method is used to estimate the model parameter values, with initial estimates obtained using nonlinear least-squares methods. The posterior distributions of the parameter estimates are reported and compared for all patients.
… Control Conference, 2004. …, 2004
The dynamics of the human immune response to infection are nonlinear and complex, and analysis of... more The dynamics of the human immune response to infection are nonlinear and complex, and analysis of these models can yield surprising, counterintuitive insights. In this paper, we explore one such insight concerning the treatment of HIV infection. In a previous paper, we introduced a model predictive control (MPC) based method of determining treatment schedules that would induce a transition to a state in which the patient's immune system controlled the viral infection without the need for further treatment. In this paper, we show how introducing additional, non HIV-specific target cells (cells which act as hosts for the HIV virus) can yield faster convergence with less transient damage to the patient's immune system.
… Control Conference (ACC), …, 2010
In previous work, we have developed optimalcontrol based approaches that seek to minimize the ris... more In previous work, we have developed optimalcontrol based approaches that seek to minimize the risk of subsequent virological failure by "pre-conditioning" the viral load during therapy switches. These techniques result in the transient susceptibility of the total viral load, and rely on finding the minimum of a dip in viral load and switching before viral load rebound. Model uncertainty necessitates a closed-loop approach to minimum-finding. Blood measurements are costly in terms of money, inconvenience and risk. In this paper, we introduce an iterative parameter estimation approach to find the viral load minimum, and measure the degree of optimality of minimum-seeking under conditions of measurement noise. We evaluate the cost-savings of this approach in terms of number of samples saved from a constant measurement rate.
… Control Conference (ACC), …, 2010
In previous work, we have developed optimalcontrol based approaches that seek to minimize the ris... more In previous work, we have developed optimalcontrol 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.
Journal of process …, 2011
Evolution has long been understood as the driving force for many problems of medical interest. Th... more 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.
American Control Conference, 2006, 2006
Recently developed models of the interaction of the human immune system and the Human Immunodefic... more Recently developed models of the interaction of the human immune system and the Human Immunodeficiency Virus (HIV) suggest the possibility of using interruptions of Highly Active Anti-Retroviral Therapy (HAART) to simulate a theraputic vaccine and induce Cytotoxic Lymphocyte (CTL) mediated control of HIV infection. In previous work we have developed a model predictive control (MPC) based method for determining optimal treatment interruption schedules for this purpose. In this paper, we introduce a nonlinear observer for the HIV-immune response system and an integrated outputfeedback MPC approach for implementing the treatment interruption scheduling algorithm using the easily available viral load measurements. We explore the robustness of this method to inevitable variations in the patient model, and discuss the implications of the results.
… Conference, 2004. 5th …, 2004
Feedback-based treatment scheduling for HIV patients is summarized. The feedback schedules are de... more Feedback-based treatment scheduling for HIV patients is summarized. The feedback schedules are developed using a dynamic HIV infection model that has appeared in the literature. The theory behind the feedback schedules is nonlinear model predictive control. This branch of nonlinear control theory is reviewed, limitations are pointed out, and recent developments are summarized. It is indicated how these developments provide a flexible, robust design tool for the HIV treatment scheduling problem.
Journal of theoretical biology, 2007
Replicating genetically modified adenoviruses have shown promise as a new treatment approach agai... more Replicating genetically modified adenoviruses have shown promise as a new treatment approach against cancer. Recombinant adenoviruses replicate only in cancer cells which contain certain mutations, such as the loss of functional p53, as is the case in the virus ONYX-015. The successful entry of the viral particle into target cells is strongly dependent on the presence of the main receptor for adenovirus, the coxsackie-and adeno-virus receptor (CAR). This receptor is frequently downregulated in highly malignant cells, rendering this population less vulnerable to viral attack. It has been shown that use of MEK inhibitors can up-regulate CAR expression, resulting in enhanced adenovirus entry into the cells. However, inhibition of MEK results in G1 cell cycle arrest, rendering infected cells temporarily unable to produce virus. This forces a tradeo1. While drug mediated upregulation of CAR enhances virus entry into cancer cells, the consequent cell cycle arrest inhibits production of new virus particles and the replication of the virus. Optimal control-based schedules of MEK inhibitor application should increase the efficacy of this treatment, maximizing the overall tumor toxicity by exploiting the dynamics of CAR expression and viral production. We introduce a mathematical model of these dynamics and show simple optimal control based strategies which motivate this approach.
… Control Conference, 2009. …, 2009
Since 1996, the National Institutes of Health and other organizations have recommended offering H... more 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.
American Control Conference, 2008, 2008
Although Highly Active Antiretroviral Therapy (HAART) provides a powerful strategy for HIV treatm... more Although Highly Active Antiretroviral Therapy (HAART) provides a powerful strategy for HIV treatment, it has been shown that HAART cannot eradicate all viruses in patients because of the existence of long-term reservoir. With the use of HAART, resistant strains develop and become the dominant species. Because the number of independent treatment regimens is limited, once resistance to all available drug classes arises, the patient will die. In this paper, we propose a drug switching strategy to minimize resistance risk of resistance and preserve long-term control of the HIV infection based on a simple model of HIV infection with persistent viral reservoirs.
Decision and Control, 2007 46th …, 2007
The development of multi-drug resistant strains of HIV remains the primary reason for treatment f... more The development of multi-drug resistant strains of HIV remains the primary reason for treatment failure and progression to AIDS in the United States. The failure of a particular multi-drug regimen necessitates a switch to a new multi-drug regimen. We use a simple model of the interaction of resistant strains to show that the transition to the new regimen involves a significant risk of strains resistant to the new regimen emerging, and that treatment interruptions using the failing regimen can be used to decrease this risk.
Proceedings of the American Control …, 2003
Using recently developed models of the interaction of the human immune system and the Human Immun... more Using recently developed models of the interaction of the human immune system and the Human Immunodeficiency Virus (HIV), we have developed a model predictive control (MPC) based method for determining optimal treatment interruption schedules. These schedules use interruptions of Highly Active Anti-Retroviral Therapy (HAART) to induce immune control of the virus without the need for continued treatment, as suggested by the models. In this paper, we discuss the medical motivation for this work, introduce the MPC-based method and show simulation results, and discuss future work necessary to implement the method.
Journal of Theoretical Biology, 2006
Recently developed models of the interaction of the human immune system and the human immunodefic... more Recently developed models of the interaction of the human immune system and the human immunodeficiency virus (HIV) suggest the possibility of using interruptions of highly active anti-retroviral therapy (HAART) to simulate a therapeutic vaccine and induce cytotoxic lymphocyte (CTL) mediated control of HIV infection. We have developed a model predictive control (MPC) based method for determining optimal treatment interruption schedules for this purpose. This method provides a clinically implementable framework for calculating interruption schedules that are robust to errors due to measurement and patient variations. In this paper, we discuss the medical motivation for this work, introduce the MPC-based method, show simulation results, and discuss future work necessary to implement the method.
Treatment interruptions to decrease risk of resistance emerging during therapy switching in HIV treatment
I. INTRODUCTION The development of resistance to a particular drug regimen necessitates a change ... more I. INTRODUCTION The development of resistance to a particular drug regimen necessitates a change in regimen, and there are, for every ap-plication, only a limited number of drug regimens available. If resistance develops to every available drug regimen, the patient will die. It is ...