Passivity-Based Inverse Optimal Impulsive Control for Influenza Treatment in the Host

Hernández-Mejía, Gustavo; Alanis, Alma Y.; Hernández‐González, Miguel; Findeisen, Rolf; Hernandez‐Vargas, Esteban A.

doi:10.1109/tcst.2019.2892351

Cited by 33 publications

(27 citation statements)

References 49 publications

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“…Furthermore, several benefits were reported for treatment if provided during 12 hours post MERS-CoV infection ( de Wit et al., 2020 ). Our study here mainly addressed T cell responses, therefore, future modelling attempts should be directed to establish a more detailed model of antibody production and cross-reaction ( Hernandez-Mejia & Hernandez-Vargas, 2020 ) as well as in silico testing of different antivirals ( Hernandez-Mejia, Alanis, Hernandez-Gonzalez, Findeisen, & Hernandez-vargas, 2019 ).…”

Section: Discussionmentioning

confidence: 99%

“…Ultimately, previous modelling efforts here and from others ( Gonçalves, Bertrand, Ke, Comets, de Lamballerie, Malvy, Pizzorno, Terrier, Calatrava, Mentré, Smith, Perelson, Guedj, 2020 , Goyal, Cardozo-Ojeda, Schiffer, 2020 , Pinky, Dobrovolny, 2020 ) to represent SARS-CoV-2 could be further extended with control theoretical approaches such as optimal control and model predictive control to schedule drug candidate as well as immune modulators. Control theory in cooperation with Pharmacokinetic (PK)/Pharmacodynamic (PD) modelling have served for optimizing therapies in HIV and influenza infection ( Chang, Astolfi, 2008 , Hernandez-Mejia, Alanis, Hernandez-Gonzalez, Findeisen, Hernandez-vargas, 2019 , Rivadeneira, Caicedo, Ferramosca, Gonzalez, 2017 , Rivadeneira, Moog, Stan, Brunet, Raffi, Ferré, Costanza, Mhawej, Biafore, Ouattara, Ernst, Fonteneau, Xia, 2014 ).…”

Section: Discussionmentioning

confidence: 99%

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In-host Mathematical Modelling of COVID-19 in Humans

Hernandez‐Vargas

Hernández

2020

Annual Reviews in Control

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183

196

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COVID-19 pandemic has underlined the impact of emergent pathogens as a major threat for human health. The development of quantitative approaches to advance comprehension of the current outbreak is urgently needed to tackle this severe disease. Considering different starting times of infection, mathematical models are proposed to represent SARS-CoV-2 dynamics in infected patients. Based on the target cell limited model, the within-host reproductive number for SARS-CoV-2 is consistent with the broad values of human influenza infection. The best model to fit the data was including immune cell response, which suggests a slow immune response peaking between 5 to 10 days post-onset of symptoms. The model with the eclipse phase, time in a latent phase before becoming productively infected cells, was not supported. Interestingly, all models predict that SARS-CoV-2 may replicate very slowly in the first days after infection, and viral load could be below detection levels during the first 4 days post infection. A quantitative comprehension of SARS-CoV-2 dynamics and the estimation of standard parameters of viral infections is the key contribution of this pioneering work. These models can serve for future evaluation of control theoretical approaches to tailor new potential drugs against COVID-19.

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Section: Discussionmentioning

confidence: 99%

Section: Discussionmentioning

confidence: 99%

In-host Mathematical Modelling of COVID-19 in Humans

Hernandez‐Vargas

Hernández

2020

Annual Reviews in Control

Self Cite

183

196

View full text Add to dashboard Cite

show abstract

“…Basic in-host infection dynamic models usually include the susceptible cells, infected cells, and the pathogen particles ( Ciupe & Heffernan, 2017 ). Among the most used mathematical models, the target cell-limited model has been employed to represent and control HIV infection ( Legrand, Comets, Aymard, Tubiana, Katlama, Diquet, 2003 , Perelson, Kirschner, De Boer, 1993 , Perelson, Ribeiro, 2013 ), influenza ( Baccam, Beauchemin, Macken, Hayden, Perelson, 2006 , Hernandez-Mejia, Alanis, Hernandez-Gonzalez, Findeisen, Hernandez-Vargas, 2019 , Larson, Dominik, Rowberg, Higbee, 1976 , Smith, Perelson, 2011 ), Ebola ( Nguyen, Binder, Boianelli, Meyer-Hermann, & Hernandez-Vargas, 2015 ), dengue ( Nikin-Beers, Ciupe, 2015 , Nikin-Beers, Ciupe, 2018 ) among others.…”

Section: Sars-cov-2 In-host Mathematical Modelmentioning

confidence: 99%

“…Even when the equilibrium states are known, a formal stability analysis is needed to understand the model behavior and, mainly, to design efficient control strategies. Note that the target cell model has been employed previously taking into account pharmacodynamic (PD) and pharmacokinetic (PK) models of antiviral therapies ( Boianelli, Sharma-Chawla, Bruder, Hernandez-Vargas, 2016 , Hernandez-Mejia, Alanis, Hernandez-Gonzalez, Findeisen, Hernandez-Vargas, 2019 ), and this can be potentially done also for COVID-19.…”

Section: Introductionmentioning

confidence: 99%

Characterization of SARS-CoV-2 dynamics in the host

Abuin

Anderson

Ferramosca

et al. 2020

Annual Reviews in Control

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While many epidemiological models were proposed to understand and handle COVID-19 pandemic, too little has been invested to understand human viral replication and the potential use of novel antivirals to tackle the infection. In this work, using a control theoretical approach, validated mathematical models of SARS-CoV-2 in humans are characterized. A complete analysis of the main dynamic characteristic is developed based on the reproduction number. The equilibrium regions of the system are fully characterized, and the stability of such regions is formally established. Mathematical analysis highlights critical conditions to decrease monotonically SARS-CoV-2 in the host, as such conditions are relevant to tailor future antiviral treatments. Simulation results show the aforementioned system characterization.

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“…To quantify bacterial resistance, the minimum inhibitory concentration (MIC) is used to measure the lowest concentration of an antibiotic to prevent bacterial replication [4]. Previous control engineering works [20] formulated the scheduling of drugs in an impulsive framework. While this is an excellent framework for single therapy, the complexity increase when different therapies are considered.…”

Section: Assumptionmentioning

confidence: 99%

Switching Logistic Maps to Design Cycling Approaches Against Antimicrobial Resistance

Hernandez‐Vargas

Parra‐Rojas

Olaru

2020

Preprint

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Antimicrobial resistance is a major threat to global health and food security today. Scheduling cycling therapies by targeting phenotypic states associated to specific mutations can help us to eradicate pathogenic variants in chronic infections. In this paper, we introduce a logistic switching model in order to abstract mutation networks of collateral resistance. We found particular conditions for which unstable zero-equilibrium of the logistic maps can be stabilized through a switching signal. That is, persistent populations can be eradicated through tailored switching regimens.Starting from an optimal-control formulation, the switching policies show their potential in the stabilization of the zero-equilibrium for dynamics governed by logistic maps. However, employing such switching strategies, deserve a specific characterization in terms of limit behaviour. Ultimately, we use evolutionary and control algorithms to find either optimal and sub-optimal switching policies. Simulations results show the applicability of Parrondo's Paradox to design cycling therapies against drug resistance.

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Passivity-Based Inverse Optimal Impulsive Control for Influenza Treatment in the Host

Cited by 33 publications

References 49 publications

In-host Mathematical Modelling of COVID-19 in Humans

In-host Mathematical Modelling of COVID-19 in Humans

Characterization of SARS-CoV-2 dynamics in the host

Switching Logistic Maps to Design Cycling Approaches Against Antimicrobial Resistance

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