Optimal Drug Regimen and Combined Drug Therapy and Its Efficacy in the Treatment of COVID-19: A Within-Host Modeling Study.

The COVID-19 pandemic has resulted in more than 524 million cases and 6 million deaths worldwide. Various drug interventions targeting multiple stages of COVID-19 pathogenesis can significantly reduce infection-related mortality. The current within-host mathematical modeling study addresses the optimal drug regimen and efficacy of combination therapies in the treatment of COVID-19. The drugs/interventions considered include Arbidol, Remdesivir, Interferon (INF) and Lopinavir/Ritonavir. It is concluded that these drugs, when administered singly or in combination, reduce the number of infected cells and viral load. Four scenarios dealing with the administration of a single drug, two drugs, three drugs and all four are discussed. In all these scenarios, the optimal drug regimen is proposed based on two methods. In the first method, these medical interventions are modeled as control interventions and a corresponding objective function and optimal control problem are formulated. In this framework, the optimal drug regimen is derived. Later, using the comparative effectiveness method, the optimal drug regimen is derived based on the basic reproduction number and viral load. The average number of infected cells and viral load decreased the most when all four drugs were used together. On the other hand, the average number of susceptible cells decreased the most when Arbidol was administered alone. The basic reproduction number and viral load decreased the most when all four interventions were used together, confirming the previously obtained finding of the optimal control problem. The results of this study can help physicians make decisions about the treatment of the life-threatening COVID-19 infection.

Optimal Control Studies on Age Structured Modeling of COVID-19 in Presence of Saturated Medical Treatment of Holling Type III.

COVID-19 pandemic has caused the most severe health problems to adults over 60 years of age, with particularly fatal consequences for those over 80. In this case, age-structured mathematical modeling could be useful to determine the spread of the disease and to develop a better control strategy for different age groups. In this study, we first propose an age-structured model considering two different age groups, the first group with population age below 30 years and the second with population age above 30 years, and discuss the stability of the equilibrium points and the sensitivity of the model parameters. In the second part of the study, we propose an optimal control problem to understand the age-specific role of treatment in controlling the spread of COVID -19 infection. From the stability analysis of the equilibrium points, it was found that the infection-free equilibrium point remains locally asymptotically stable when R 0 < 1 , and when R 0 is greater than one, the infected equilibrium point remains locally asymptotically stable. The results of the optimal control study show that infection decreases with the implementation of an optimal treatment strategy, and that a combined treatment strategy considering treatment for both age groups is effective in keeping cumulative infection low in severe epidemics. Cumulative infection was found to increase with increasing saturation in medical treatment.

Surface Plasmon-Coupled Dual Emission Platform for Ultrafast Oxygen Monitoring after SARS-CoV-2 Infection.

The outbreak of the COVID-19 pandemic has had a major impact on the health and well-being of people with its long-term effect on lung function and oxygen uptake. In this work, we present a unique approach to augment the phosphorescence signal from phosphorescent gold(III) complexes based on a surface plasmon-coupled emission platform and use it for designing a ratiometric sensor with high sensitivity and ultrafast response time for monitoring oxygen uptake in SARS-CoV-2-recovered patients. Two monocyclometalated Au(III) complexes, one having exclusively phosphorescence emission (λPL = 578 nm) and the other having dual emission, fluorescence (λPL = 417 nm) and phosphorescence (λPL = 579 nm), were studied using the surface plasmon-coupled dual emission (SPCDE) platform for the first time, which showed 27-fold and 17-fold enhancements, respectively. The latter complex having the dual emission was then used for the fabrication of a ratiometric sensor for studying the oxygen quenching of phosphorescence emission with the fluorescence emission acting as an internal standard. Low-cost poly (methyl methacrylate) (PMMA) and biodegradable wood were used to fabricate the microfluidic chips for oxygen monitoring. The sensor showed a high sensitivity with a limit of detection ∼ 0.1%. Furthermore, real-time oxygen sensing was carried out and the response time of the sensor was calculated to be ∼0.2 s. The sensor chip was used for monitoring the oxygen uptake in SARS-CoV-2-recovered study participants, to assess their lung function post the viral infection.

Within-host mathematical modeling on crucial inflammatory mediators and drug interventions in COVID-19 identifies combination therapy to be most effective and optimal

The unprecedented Covid-19 pandemic has resulted in more than 14.75 million infections and 6, 10, 839 deaths in 212 countries. Appropriate interventions can decrease the rate of Covid-19 related mortality. Fast track clinical trials around the world are addressing the efficacy of individual pharmaceutical agent acting at various stages of pathogenesis. However, lessons learnt while dealing with past viral epidemics mandates, simultaneous use of such drugs in combination amongst different populations. Mathematical modelling studies can be extremely helpful in understanding the efficacy of drugs both individually and in combination. The current within-host mathematical model studies the natural history of Covid-19 in terms of complex interplay of virus replication and behaviour of host immune response. Additionally it studies the role of various drugs at various stages of pathogenesis. The model was validated by generating two-parameter heat plots, representing the characteristics of Covid-19, the sensitivity analysis identified the crucial parameters. The efficacy of interventions was assessed by optimal control problem. The model dynamics exhibited disease-free equilibrium and the infected equilibrium with their stability, based on the reproduction number R0, the transcritical bifurcation observed at R0=1. The burst rate and the natural death rate of the virus were observed as most significant parameters in the life-threatening Covid-19 pneumonia. The antiviral drugs affecting viral replication and those modulating the immune response, reduce the infected cells and viral load significantly. However, the yield was optimal and most effective when the combination therapy involving one or more antiviral and one or more immunomodulating drugs were administered together. These findings may help physicians with early decision making in treatment of life-threatening Covid-19 infection.