Researchers at East Lansing’s MSU Use Computational Process to Find Existing Drugs to Treat COVID-19

Researchers at East Lansing’s Michigan State University have developed a computational process for identifying existing drugs that may be repurposed to fight COVID-19 without needing access to the virus itself. The lab usually uses artificial intelligence and big data to discover therapeutics for cancers.
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MSU has developed a computational process for finding existing drugs that may be repurposed to fight COVID-19. // Photo courtesy of Michigan State University

Researchers at East Lansing’s Michigan State University have developed a computational process for identifying existing drugs that may be repurposed to fight COVID-19 without needing access to the virus itself. The lab usually uses artificial intelligence and big data to discover therapeutics for cancers.

When a virus infects a human cell, it hijacks the reproductive capabilities to replicate and survive, therefore interfering with the activity of a host cell’s genes. Each virus leaves a unique imprint on the cell at a certain point of infection, known as a gene expression signature, that is detectable by laboratory technologies.

“We wanted to find a drug that could block the gene expression change in the host cells, hoping to mitigate disease progression and alleviate symptoms,” says Bin Chen, assistant professor in the Department of Pediatrics and Human Development and the Department of Pharmacology and Toxicology, as well as leader of the Chen Lab.

Scientists around the world knew almost nothing about the new virus, and access to live virus samples was limited, Chen says. Based on a number of publicly available datasets, Chen and his team surmised that other members of the coronavirus family could approximate the gene expression signature of the new virus.

Using the lab’s existing library of FDA-approved or clinically investigated drugs and an established drug prediction pipeline, the team examined thousands of potential drug candidates through a methodology of scoring, rating, and ranking potential candidates against known gene expression signatures.

“Fortunately, we found a number of drugs that could be effective,” Chen says. “But we needed to do more. We needed biological validation.”

In collaboration with researchers at the University of Texas Medical Branch, Chen tested the top-rated drug candidates on kidney cells derived from an African green monkey, a common cell line used in toxicology and virology research. The cells were first treated with the drug and later infected by COVID-19.

“We sent 10 drugs to them, and it turns out that four drugs were able to prevent the virus-induced effects,” Chen says. “Unfortunately, the drugs, which are used to treat cancer, are also rather toxic. But the concept worked. Our process worked. We can now find more potential drugs to reverse the impacts of the virus and keep those less toxic drugs for further investigation.

“I’m very appreciative of how open the scientific community has been. We knew very little at the beginning, but scientists have been making their work available to the community so that we can act.”

Researchers in South Korea tested 35 FDA-approved drugs for antiviral effectiveness against COVID-19 samples. Of these, 14 positive drugs overlapped with Chen’s screening library and were also ranked high by the methodology. The data also externally validated the predictive ability of Chen’s discover process.

“We need to release this data to the public. Other laboratories across the world may be able to learn from our work,” Chen says. “They can select new compounds to investigate. There are so many drugs to screen. We alone cannot test them all.”

The study is titled “Reversal of Infected Host Gene Expression Identifies Repurposed Drug Candidates for COVID-19” and appears in bioRxiv. The study, like many being shared during the pandemic on preprint archives such as bioRxiv, has not yet undergone peer review.

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