In silico experiments were used to replicate how the human immune system deals with the COVID-19 virus.
“It’s not that in-silico trials should replace clinical trials,” Layton said.
“A model is a simplification, but it can help us whittle down the drugs for clinical trials. Clinical trials are expensive and can cost human lives. Using models helps narrow the drug candidates to the ones that are best for safety and efficacy.”
Researchers captured the results of different treatments that were used on COVID-19 patients in clinical trials. Their results are remarkably consistent with live data on COVID infections and treatments.
The simulated model and the live trial both showed Remdesivir to be biologically effective but clinically questionable, unless administered shortly after viral infection.
The model might also work for current and future variants of concern. The researchers anticipate the virus will continue to undergo mutation, which could precipitate new waves of infection.
“As we learn more about different variants of concern, we can change the model’s structure or parameters to simulate the interaction between the immune system and the variants,” Sadria said. “And we can then predict if we should apply the same treatments or even how the vaccines might work as well.”
Researchers received a rapid response grant from the Canadian Institute of Health Research on COVID variants. The UHN team will conduct simulations to understand the spread of COVID variants in Canada.
Source: Medindia
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