Multi-drug therapy design for HIV-1 infection using nonlinear model predictive control

Pinheiro, João; Lemos, João M.

doi:10.1109/med.2011.5983037

Cited by 6 publications

(8 citation statements)

References 15 publications

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“…Health care schemes have been developed for individual patients to treat them in a way that matches their specific reaction to a disease or infection. These schemes show increasing interest in immunology and virology by providing experimental approach to mathematical models of infectious diseases [7]. Control techniques have been used to design treatment therapies for dynamical models of HIV infection in such a way that the manipulated variable is the drug control and output is the viral load.…”

Section: Introductionmentioning

confidence: 99%

“…A control law has been implemented to reduce the effect of nonlinearities in the system model. A nonlinear model predictive control (NMPC) has been used for multi-drug therapy design for HIV infection [7]. An Extended state Kalman filter has been used for state estimation as in NMPC which required the knowledge of state variables but the problem is that all state variables cannot be measured directly.…”

Section: Introductionmentioning

confidence: 99%

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Integral Backstepping and Synergetic Control for Tracking of Infected Cells During Early Antiretroviral Therapy

et al. 2019

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Human Acquired Immune Deficiency Syndrome or Human Immunodeficiency Virus (HIV) Infection is a disease caused by a lentivirus known as the HIV virus. It alters the human immune system making him/her vulnerable to diseases and infections. The susceptibility worsens if the syndrome is not controlled through proper drug injection. In the literature, the researchers have proposed different control methodologies of drug injection so that the infected cells may reach the desired reference value. In this paper, nonlinear control algorithms based on the integral backstepping approach and synergetic control have been proposed to reduce steady-state error for robust tracking of infected helper T-cells to a set reference level for a deterministic model of HIV virus. The model is based on the mass balance of helper T-cells and viral load. System stability analysis has been proved with the help of a suitable Lyapunov-based theory. The viral load has been suppressed to zero. Both the numerical analysis and the simulations have been performed to validate the performance of proposed controllers. The proposed controllers have been compared with each other, with generic backstepping and backstepping embedded with sliding mode control techniques. They have been used to check the effect of efficiency of the drug on the control input. The simulations have been performed on MATLAB/Simulink. INDEX TERMS Acquired immune deficiency syndrome (AIDS), human immunodeficiency virus (HIV), highly active antiretroviral therapy (HAART), synergetic control, generic backstepping, steady-state error, antiretroviral therapy.

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Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Integral Backstepping and Synergetic Control for Tracking of Infected Cells During Early Antiretroviral Therapy

et al. 2019

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“…To solve the first problem, a state observer can be incorporated in the control design. 21,[35][36][37]46 To handle the second problem, input (drug) limitations should be considered as a constraint in designing the controller. 20,30 To the best of authors' knowledge, a control strategy which considers these two limitations in the control design and its closed-loop stability has been established is not proposed for the HIV treatment in the literature.…”

Section: Introductionmentioning

confidence: 99%

Observer-Based Output Feedback Linearization Control with Application to HIV Dynamics

Hajizadeh

Shahrokhi

2015

Ind. Eng. Chem. Res.

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This paper presents the feedback linearization control of HIV infection. A multi-input multi-output (MIMO) dynamic nonlinear HIV infection model for this purpose has been used. For this purpose, three widely used drugs are considered. A Luenberger-like nonlinear observer (LNO) is designed for estimation of unavailable states. To minimize the side effects of drugs, the concentration of ZDV which has the highest side effect is fixed to a minimum value and the external controllers parameters are obtained by maximizing an objective function. In the control design, limitations on drug consumption and unavailability of all states are taken into account. The closed-loop stability has been established in the presence of the observer dynamics and input limitations. Robustness of the proposed treatment strategy against model parameter uncertainties, noise, or sparse measurements has been demonstrated through simulation study. Finally, by including pharmacological models, an impulsive control strategy for HIV treatment has been considered.

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“…Model predictive control (MPC) is a method often used in clinical applications for planing of optimal treatment [ 2 - 5 ] mainly due to its inherent capability to handle clinically relevant constraints and to take into account multiple variables at the same time. A predictive model is the most important part of MPC, which is deployed to estimate patient's response to the therapy.…”

Section: Introductionmentioning

confidence: 99%

A data-driven acute inflammation therapy

2013

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Acute inflammation is a severe medical condition defined as an inflammatory response of the body to an infection. Its rapid progression requires quick and accurate decisions from clinicians. Inadequate and delayed decisions makes acute inflammation the 10th leading cause of death overall in United States with the estimated cost of treatment about $17 billion annually. However, despite the need, there are limited number of methods that could assist clinicians to determine optimal therapies for acute inflammation. We developed a data-driven method for suggesting optimal therapy by using machine learning model that is learned on historical patients' behaviors. To reduce both the risk of failure and the expense for clinical trials, our method is evaluated on a virtual patients generated by a mathematical model that emulates inflammatory response. In conducted experiments, acute inflammation was handled with two complimentary pro- and anti-inflammatory medications which adequate timing and doses are crucial for the successful outcome. Our experiments show that the dosage regimen assigned with our data-driven method significantly improves the percentage of healthy patients when compared to results by other methods used in clinical practice and found in literature. Our method saved 88% of patients that would otherwise die within a week, while the best method found in literature saved only 73% of patients. At the same time, our method used lower doses of medications than alternatives. In addition, our method achieved better results than alternatives when only incomplete or noisy measurements were available over time as well as it was less affected by therapy delay. The presented results provide strong evidence that models from the artificial intelligence community have a potential for development of personalized treatment strategies for acute inflammation.

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Multi-drug therapy design for HIV-1 infection using nonlinear model predictive control

Cited by 6 publications

References 15 publications

Integral Backstepping and Synergetic Control for Tracking of Infected Cells During Early Antiretroviral Therapy

Integral Backstepping and Synergetic Control for Tracking of Infected Cells During Early Antiretroviral Therapy

Observer-Based Output Feedback Linearization Control with Application to HIV Dynamics

A data-driven acute inflammation therapy

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