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As the 2019 coronavirus disease (COVID-19) pandemic continues to cause many difficulties for public health authorities, research is underway to understand how the virus can cause fatal effects on infected people and thus suggest effective preventive and therapeutic measures against it. .
A new study by researchers from Houston Methodist Hospital, Rutgers University and other institutions was posted on the prepress server medRxiv. * His findings show the central role that innate immunity can play in controlling the viral load of severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2), the causative pathogen of COVID-19. In doing so, the researchers emphasized the importance of timing in the therapeutic treatment of COVID-19 in determining the outcome.
Modeling of virus-receptor interactions
Mathematical modeling of SARS-CoV-2 infection has shown its importance in arriving at a better understanding of the viral dynamics and pathophysiology of the disease. Such models are based on the fact that the angiotensin converting enzyme 2 receptor (ACE2) is required for SARS-CoV-2 infection of the host cell and is modeled by current understanding of immune responses. The goal is to predict the viral load in different organs of the body.
ACE2 messenger RNA (mRNA) is found in very high levels on the surface of the alveolar epithelium of the lungs and enterocytes of the small intestine. With an incubation period of 3 to 17 days, a significant percentage of asymptomatic infection, and a large percentage of patients with only mild nonspecific infection, it is difficult to diagnose COVID-19 based on symptoms.
Therefore, such a diagnosis must be supported by the detection of viral RNA by RT-PCR tests (samples from both upper and lower airways) and lung CT scans. The significant biomarker of COVID-19 is viral load in nasal and oral swabs, and therapeutic interventions aim to reduce it to undetectable levels.
Model diagram showing system interactions. Inter-compartment connectivity diagram indicating viral transport mechanisms, cell populations, immune system agents and their interactions. The estimated characteristic times of the various transport processes are shown in brackets next to the red arrows. Notation: (V) virus, (H) healthy cells, (I) infected cells, (IFN) interferon, (Ab) antibody, (CD8 *) CD8 + effector cells and (APC) antigen presenting cells. The solid red arrows indicate the transport of the virus; solid green arrows indicate the transformation of a cell into another type; solid black arrows indicate the production of an agent; purple dashed lines indicate the interaction between two agents; and solid dark red arrows with a flat head indicate inhibition.
Usefulness of mathematical modeling in COVID-19
With limited data on viral dynamics in living organisms, mathematical modeling at various scales has been widely adopted to complement the current understanding of virus-host interactions at the molecular, cellular, tissue, organ and organism levels.
Previous models have provided useful information, such as showing that antivirals are most effective when used early in the course of the disease, before symptoms appear. Others modeled the interactions between infected and immune cells, as well as the relationship between infectivity and disease severity.
Multiscale model
The current study uses a “multiscale mechanistic model” designed to include processes from viral infection through viral spread and destruction of infected cells to immune responses. A fundamental assumption was that the rate of bile production was the limiting step in the excretion of the virus through the bile duct in the faeces.
Unknown parameters were estimated from known parameters, published clinical and in vivo data. The model was then calibrated against the published experimental data. The model assumes that viral entry into various tissues depends on the permeability of the tissue rather than the flow of blood to the tissues.
The size of the endothelial pores then decides how long it takes the virus to enter each organ. Therefore, it takes 20 times longer to penetrate the brain than the mononuclear phagocytic system (MPS) due to the large pores in the capillaries of the latter.
Viral load varies with the immune response
The model predicted viral load kinetics in various body compartments based on previously estimated parameters. Therefore, the predicted persistence of the virus in organs other than the lungs and in the blood is for approximately 17-20 days after the first symptom, which corresponds to published studies and compares well with the known duration of viral load in the upper and lower respiratory tract. tract infections. This indicates that the use of nasal swabs is valid to reflect the patient’s infected status.
Antibody production undetectable beyond 100 days?
The model also predicted that antibodies in the plasma are detectable for 100 days or more, but then fall below the detection limit. This, they say, “suggests the lack of indefinite antibody protection against reinfection and stresses the need for vaccine boosters to achieve lasting immunity. “It is also important to note that the data come from subjects in the absence of drug treatment. This makes predictions more reliable regarding viral load distribution and clearance.
Effects of reduced immunity on viral load
The researchers also looked at the effect of immunity on viral load. Each component, namely cellular, humoral, adaptive and innate immunity, was removed from the model, to model the immunocompromised states due to various chronic conditions (cancer, diabetes, autoimmune diseases), in the absence of any antiviral therapy. Viral kinetics were modeled to the point where viral load became undetectable.
With the removal of one or more components, the viral concentration in each compartment exceeded the baseline. They also found that interferons were the primary mechanism by which the virus is suppressed in the upper airways, but macrophages are primarily responsible for viral clearance in the lungs, MPS and other systemic sites, although interferons also play a role. .
Interferons promote viral clearance
This agrees with previous findings, such as the fact that interferon deficiency can cause excessive viral load and a life-threatening case of COVID-19. A subset of macrophages was also absent in severe COVID-19 but present in mild disease, indicating that they are primarily responsible for controlling the infection, just as in chronic obstructive pulmonary disease. However, the model cannot predict whether or not the disease itself affects the availability and function of macrophages.
Another finding is that adaptive immunity, both humoral and cellular, is not a major player in limiting infection. However, this does not mean that passive immunity or T cell therapy is useless. Emphasize the importance of immunity in limiting viral replication.
If immunity is defective, viral clearance is dependent on virus excretion through the bile duct, which can take weeks to reach with undetectable viral loads.
How Treatment Affects Viral Load
The researchers suggest that their results indicate the importance of manipulating interferon concentrations and viral replication to limit viral load after infection. Targeting both parameters is more efficient than monotherapy, which agrees with clinical reports. The model also suggests that viral clearance is dependent on the cytopathic death rate of infected cells and viral replication rate, but not so much on interferon production.
Viral transport to the lungs and between blood and MPS are the main determinants of viral clearance from plasma and, therefore, from other organs. The rate at which pulmonary macrophages and MPS clear the virus also affects the viral load in both the lungs and plasma, as does the rate at which viral agents travel from the lungs to the plasma.
By simulating antiviral therapy with a hypothetical remdesivir-like agent, or with interferon beta-1a, they found that the model predicted a significant reduction in viral load, positively correlated with timing of onset. The combination of both produced the fastest clearance of the virus, bringing the viral load below the limit of detection 2-3 days earlier than with monotherapy.
Conclusion
The researchers summarize: “Importantly, using our modeling platform, we can identify treatment strategies for effective viral load suppression in various clinically relevant scenarios. We also expect it to be applicable to other viruses that have shared similarities in mechanisms of infection and physical size.. “As more data comes in on changes in viral load in organs other than the lung, the model can be fine-tuned to test more therapeutic approaches.
*Important Notice
medRxiv publishes preliminary scientific reports that are not peer-reviewed and, therefore, should not be considered conclusive, guide clinical practice / health-related behavior, or treated as consolidated information.
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