A closed-loop approach to antiretroviral therapies for HIV infection

Costanza, Vicente; Rivadeneira, Pablo S.; Biafore, Federico L.; D’Attellis, C.E.

doi:10.1016/j.bspc.2009.02.005

Cited by 17 publications

(17 citation statements)

References 17 publications

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“…In this regard the stability analysis is investigated in [16], [17], [25], [26], [28] and [30], and a variety of control strategies is applied to HIV-1. For instance, feedback control in [7]- [9] and [20], nonlinear theory based control in [14]- [15] and [29], model predictive control in [18], [27], optimal control in [10] [22] [31] [32] and [36] and intelligent control in [59] [63] can be noticed.…”

Section: On the Other Hand Recent Years Have Witnessed Growing Interementioning

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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Section: On the Other Hand Recent Years Have Witnessed Growing Interementioning

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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“…Costanza, et al 3 In designing the HIV treatment strategy two constraints, namely unavailability of all states and drug consumption limitation, should be considered. To solve the first problem, a state observer can be incorporated in the control design.…”

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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“…The …rst three equations (when ; (u 1 ) and (u 2 ) are kept constant, as if they were parameters) constitute a classical simpli…ed model [18], [19], [5], thus further comments on the meaning of their terms seem unnecessary at this time. The last equation for dynamics is new.…”

Section: Dynamics Of Infection and Thymus Functionmentioning

confidence: 99%

“…That explains the adjective 'Hybrid' assigned to the Dynamic Programming technique used as numerical approach to the problem in this paper. The adopted value of h = 30 days takes into consideration the observed 'peak time'(of approximately 20 days) occurring in the state variables and sensitivities (see [5]). The possibility of a hiding acute infection period is discarded this way, since at least the results of a blood analysis re ‡ecting the situation will come at some intermediate point.…”

Section: Hybrid Dynamic Programmingmentioning

confidence: 99%

“…Detailed mathematical models have been developed at cellular level [14], [28], aiming to re ‡ect all relevant interactions a¤ecting the intervening agents, to the cost of including numerous state variables, most of them di¢ cult to measure (or to observe) continuously. According to applicable …ndings of these studies, the dynamical models used by the authors in previous publications [5], [6] were modi…ed to include variations on healthy cells' regeneration rate and thymus function decay. For instance, it has turned clear that the parameter associated with the thymopoiesis in simple models should become a state variable in itself, re ‡ecting thymic function dynamics.…”

Section: Introductionmentioning

confidence: 99%

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Optimizing thymic recovery in HIV patients through multidrug therapies

Costanza

Rivadeneira

Biafore

et al. 2013

Biomedical Signal Processing and Control

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An enlarged nonlinear model for the dynamics of HIV infection and thymic function is composed to simulate and evaluate antiretroviral therapies. In addition to the relevant biological agents, an extra state variable is included, associated with the thymus capacity for healthy cells production. The methodology contemplates eventual deleterious e¤ects of drugs over children's thymus recovery. The intake of RTI and PI drugs are modelled as two independent control variables, each a¤ecting a di¤erent term in the dynamics, so extending the prevailing pure-HAART-therapy analysis. The objective function designed here is also more inclusive than usual, accounting for the costs of the two drug families involved and for the thymus deterioration, in addition to penalizing eventual virus excess and healthy cells de…cits. The search for the best combined therapy is treated as an optimal control problem. An hybrid version of Dynamic Programming for continuous and discrete variables is used to treat the problem numerically. Long time-horizons are explored, aiming to avoid typical peaks in drug prescriptions found at the beginning and end of the optimization periods. Results indicate that certain combinations of drugs are more convenient than pure protocols when the value of thymus functioning is relevant, specially for children patients.

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A closed-loop approach to antiretroviral therapies for HIV infection

Cited by 17 publications

References 17 publications

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

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

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

Optimizing thymic recovery in HIV patients through multidrug therapies

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