Researchers have developed a mathematical model to simulate SARS-CoV-2 infection dynamics within hosts and the action of potential antiviral treatments. The model may thus facilitate rapid in silico testing of potential COVID-19 treatments and vaccines.
Treatments against SARS-CoV-2
The ever-increasing death toll and emergence of new variants from SARS-CoV-2 highlights an urgent need for better intervention strategies. Moreover, despite the steady rollout of vaccines, there is still no efficacious antiviral treatment.
Clinical trials are necessary to validate the safety and efficacy of potential therapies and vaccines. However, these trials are costly, labour-intensive and time-consuming. Conversely, in silico drug testing would help rapidly narrow down the drug candidates that are safest and most efficacious. This would reduce the costs and risks associated with clinical trials and increase the likelihood of the rapid development of a treatment against SARS-CoV-2.
By this rationale, a team of researchers at the University of Waterloo developed a detailed mathematical model to capture the within-host dynamics of SARS-CoV-2. Understanding the interactions between the host immune system and SARS-CoV-2 provides insight into the efficacy of potential therapies.
Modelling within-host SARS-CoV-2 infection dynamics
Previously, researchers at the University of Pittsburg have developed a dynamical model simulating the human immune response to influenza infection. In this study, recently published in Viruses, researchers adapted the model to describe the dynamics of SARS-CoV-2 infection in a human host.
The model describes how the human innate and adaptive immune systems control circulating SARS-CoV-2 viral loads (Figure 1). It captures target cells and immune components, such as antigen-presenting cells, plasma cells, antibodies and interferons. Other parameters in the model included natural death of immune cells, rates of cell infection and death, and antibody production rate. Ultimately, the model simulated the human immune response against SARS-CoV-2 in silico.
For validation, the researchers compared the modelled data with measured data from two clinical studies. The first dataset includes throat swab and sputum sample data, while the second contains sputum viral loads. The predicted viral load dynamics shared key similarities with the clinical data: the viral load peaks 7 days after symptoms start showing and the viral load nears zero 10 days after.
Virtual clinical trials
The model’s parameters can be modified to simulate the effects of different therapies used on patients in clinical trials. To test this, the researchers applied the model to simulate three potential treatments against SARS-CoV-2: the drug – Remdesivir, host cell entry inhibitors and convalescent plasma transfusion.
From the model, the predicted antiviral effects of Remdesivir treatment were consistent with those observed in experimental data. Overall, the simulation results indicated that all three treatments are effective at clearing the virus, but only if administered shortly after infection. These findings are consistent with those generated through clinical trials.
As such, the model has development potential to act as a virtual clinical trial, thereby enabling the possibility of assessing drug and vaccine efficacies against SARS-CoV-2 before in vivo trials. In lieu of emerging SARS-CoV-2 variants, the model’s parameters may be altered to simulate the immune response against different variants. Furthermore, the model may predict whether the same treatments or vaccines are similarly efficacious against different variants.
Limitations of SARS-CoV-2 infection dynamics model
Given that the immune response against SARS-CoV-2 is still poorly characterised, many of the model’s parameters are derived from influenza infection. Therefore, the model may not accurately capture the response of the different immune components to SARS-CoV-2 infection and treatments. As with all models, the present model is likely over-simplistic, which may limit its clinical relevance. Nevertheless, as researchers discover more about SARS-CoV-2 infections, new parameters may be incorporated into the model to improve its accuracy.
Summary
In this study, researchers have developed and validated a mathematical model capturing within-host SARS-CoV-2 infection dynamics. The model can be extended to simulate the action of antiviral treatments. Despite its simplifications, this work establishes an in silico platform to rapidly assess the efficacy of potential therapies and vaccines. The proposed model may facilitate the selection of likely-successful drug candidates to be tested in clinical trials. Crucially, it may aid in the development of an efficacious antiviral treatment against SARS-CoV-2.
Image credit: kjpargeter – Freepik
Reference: Sadria, M.; Layton, A.T. Modeling within-Host SARS-CoV-2 Infection Dynamics and Potential Treatments. Viruses 2021, 13, 1141. https://doi.org/10.3390/v13061141
