Planning of patient-specific drug-specific optimal HIV treatment strategies

Hadjiandreou, Marios M.; Conejeros, Raúl; Wilson, D. Ian

doi:10.1016/j.ces.2009.06.009

Cited by 12 publications

(30 citation statements)

References 35 publications

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“…Since ZDV (C 3 ) has a greater side effect than the other two drugs, its concentration is fixed to a minimum value. 20 By fixing the concentration of C 3 , concentrations of the remaining drugs are related to drug efficacies via the following equations: 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 13…”

Section: Controllers Tuning Using the Pharmacological Conceptsmentioning

confidence: 99%

“…For implementing the control law (20) in the absence of state measurements, the following control law can be used.…”

Section: Coupling the Observer With The Feedback Controllermentioning

confidence: 99%

“…18,20 In addition minimum and maximum values were considered for each drug concentration to To obtain the optimal values of controller parameters, for a selected set of controller parameters, the efficacies are evaluated for the period of treatment. Using equations (35) and (36), C 1 and C 2 are obtained.…”

Section: Controllers Tuning Using the Pharmacological Conceptsmentioning

confidence: 99%

See 2 more Smart Citations

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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Section: Controllers Tuning Using the Pharmacological Conceptsmentioning

confidence: 99%

“…For implementing the control law (20) in the absence of state measurements, the following control law can be used.…”

Section: Coupling the Observer With The Feedback Controllermentioning

confidence: 99%

Section: Controllers Tuning Using the Pharmacological Conceptsmentioning

confidence: 99%

See 1 more Smart Citation

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

Hajizadeh

Shahrokhi

2015

Ind. Eng. Chem. Res.

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“…In an attempt to resolve this, mathematical modeling can test different assumptions and provide new insights into questions that cannot be answered by clinical or experimental studies. Furthermore, mathematical modeling can serve as a powerful tool in the quest for computing optimal treatment strategies due to their ability to estimate the patient's response to therapy [2]. The optimal control of (HIV-1) pathogenesis and immunology remains an extraordinarily challenging task that encompasses several different areas of knowledge.…”

Section: Introductionmentioning

confidence: 99%

Modeling and stability analysis of HIV-1 as a time delay fuzzy T–S system via LMIs

Abbasi

Beheshti

Mohraz

2015

Applied Mathematical Modelling

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“…Immune system is a combination of cells, molecules and different tissues that has the responsibility for preventing and eradicating the disease by identifying and killing pathogens and tumour cells [1]. CD4 + T cells are a subgroup of lymphocytes that play an important role in the immune system.…”

Section: Introductionmentioning

confidence: 99%

On the design of human immunodeficiency virus treatment based on a non‐linear time‐delay model

Batmani

Khaloozadeh

2014

IET Systems Biology

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Mathematical modelling and methods from control theory can be employed to find appropriate drug regimens in human immunodeficiency virus (HIV) treatment. In this study, using a non-linear time-delay model, the authors design some suboptimal highly active antiretroviral therapy (HAART) [http://www.en.wikipedia.org/wiki/Protease_inhibitor_%28pharmacology%29] regimens for patients with HIV. The non-linear delayed model is used to describe the dynamical interactions between HIV and human immune system in the presence of HAART. Based on the model, a set point tracking problem is defined in order to set the number of susceptible CD4+T cells to a desired value. To solve this set point tracking problem in a suboptimal way, the authors introduce a new method which is able to consider constraints on the amount of drug dosage. It is proved that the proposed method is able to set the number of susceptible CD4+T cells to the desired value. Simulation results confirm that the method is efficient even in the presence of parametric uncertainties.

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Planning of patient-specific drug-specific optimal HIV treatment strategies

Cited by 12 publications

References 35 publications

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

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

Modeling and stability analysis of HIV-1 as a time delay fuzzy T–S system via LMIs

On the design of human immunodeficiency virus treatment based on a non‐linear time‐delay model

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