COVID-19 researchers utilize mathematical models and computer simulations

Researchers investigating potential treatments for COVID-19 have turned to using mathematical models and computer simulations to open new frontiers in drug trials. Scientists are using computer models to simulate COVID-19 infections on a cellular level, which is the basic structural level of the human body. The new virtual models allow researchers to conduct virtual trials for different drugs and vaccines, allowing for pre-assessment of drug and vaccine efficacy against the virus.Researchers from the University of Waterloo are conducting "in silico" experiments to replicate how the human immune system fights the COVID-19 virus. The term "in silico" refers to trials that are conducted on silicon computer chips rather than "in vitro" or "in vivo" meeting in test tubes or in living organisms, respectively.

Researchers are clear that they don't believe in-silico trials should or could replace clinical trials. Professor Anita Layton says the model is a simplification that can help narrow down drugs for use in clinical trials. Layton notes that clinical trials are very expensive and can result in human deaths. Leveraging the models, researchers can narrow drug candidates to the ones that are best for safety and efficacy.

The team of scientists are one of the first groups to work on these particular models and capture the results of different treatments used on COVID-19 patients in clinical trials. The results they obtained using the virtual trials were consistent with lab data on COVID-19 infections and treatments.

One example the group gives is the use of the treatment Remdesivir with its simulation. Remdesivir is a drug that the WHO used in humans. The simulation and live trials showed the drug was biologically effective but was clinically questionable unless it was administered very shortly after viral infection. The new tool is seen as a benefit in fighting the virus in the future as it's expected to continue to mutate, potentially leading to new infections.