Cambridge scientists have identified 200 approved drugs that are expected to work against COVID-19, of which only 40 are currently being tested in COVID-19 clinical trials.
In a study published today in Scientific advances, a team led by researchers from the Milner Therapeutics Institute and the Gurdon Institute at Cambridge University used a combination of computational biology and machine learning to create a complete map of proteins involved in SARS-CoV-2 infection—Proteins that help the virus enter host cell to those generated as a result of infection. When examining this network using artificial intelligence (AI), were able to identify the key proteins involved in the infection, as well as the biological pathways that could be targeted by drugs.
To date, most small molecule and antibody approaches to treating COVID-19 are drugs that are currently the subject of clinical trials or have already undergone clinical trials and been approved. Much of the focus has been on various key targets of viruses or hosts, or on pathways (such as inflammation) where pharmacological treatment could be used as an intervention.
The team used computer modeling to conduct a “virtual display” of nearly 2,000 approved drugs and identified 200 approved drugs that could be effective against COVID-19. Forty of these drugs have already entered clinical trials, which researchers say support the approach they have taken.
When the researchers tested a subset of these drugs involved in viral replication, they found that two in particular, one antimalarial. drugs and a type of medication used to treat rheumatoid arthritis—We were able to inhibit the virus, providing initial validation of its data-based approach.
Professor Tony Kouzarides, director of the Milner Therapeutics Institute, who led the study, said: “Looking at the thousands of proteins that play a role in SARS-CoV-2 infection, either actively or as as a result of infections, we have been able to create a network that discovers the relationship between these proteins.
“We then used the latest machine learning and computer modeling techniques to identify 200 approved drugs that could help us treat COVID-19. Of those, 160 had not been related to this infection before. it could give many more weapons to our armory to fight the virus. “
By analyzing artificial neural networks, the team classified drugs based on the overall role of their targets in SARS-CoV-2 infection: those that targeted viral replication and those that directed the immune response. They then took a subset of those involved in viral replication and tested them using human- and non-human-derived cell lines.
Of note are two drugs, sulfasalazine (used to treat conditions such as rheumatoid arthritis and Crohn’s disease) and proguanil (and the antimalarial drug), which the team showed a reduced SARS-CoV-2 viral replication in cells, increasing the possibility of their potential use to prevent infections or to treat COVID-19.
Dr. Namshik Han, head of Computational Research and AI at the Milner Therapeutics Institute, added: “Our study has provided us with unexpected information about the mechanisms underlying COVID-19 and has provided us with some promising drugs that could be reused for any of the two treatments Although we adopted a data-based approach (essentially allowing intelligent algorithms to artificially interrogate data sets), we validated our findings in the laboratory, confirming the power of our approach.
“We hope this resource of potential drugs will accelerate the development of new drugs against COVID-19. We believe our approach will be useful in responding quickly to new variants of SARS-CoV2 and other new pathogens that could lead to future pandemics.”
N. Han et al., “Identification of SARS-CoV-2-induced pathways reveals drug replacement strategies.” Scientific advances (2021). avances.sciencemag.org/lookup…1126/sciadv.abh3032.
Citation: Scientists identify 160 new drugs that could be reused against COVID-19 (2021, June 30) recovered on June 30, 2021 at https://medicalxpress.com/news/2021-06-scientists-drugs-repurposed- covid-.html
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