Researchers are using big data and AI to identify drugs already on the market that could be applied to treat new COVID-19 variants.
Finding new ways to treat the novel coronavirus and its ever-changing variants has been a challenge, especially when traditional drug development and discovery process can take years.
“The COVID-19 virus is a challenge because it continues to evolve,” says Bin Chen, an associate professor in the College of Human Medicine at Michigan State University. “By using artificial intelligence and really large data sets, we can repurpose old drugs for new uses.”
For a new study, published in the journal iScience, Chen and colleagues turned to publicly available databases to mine for the unique coronavirus gene expression signatures from 1,700 host transcriptomic profiles that came from patient tissues, cell cultures, and mouse models. These signatures revealed the biology COVID-19 and its variants share.
With the virus’s signature and knowing which genes need to be suppressed and which genes need to be activated, the team was able to use a computer program to screen a drug library consisting of FDA-approved or investigational drugs to find candidates that could correct the expression of signature genes and further inhibit the coronavirus from replicating.
Chen and his team discovered one novel candidate, IMD-0354, a drug that passed phase I clinical trials for the treatment of atopic dermatitis. A group in Korea later observed the drug is 90-fold more effective against six COVID-19 variants than remdesivir, the first drug approved to treat COVID-19.
The team further found that IMD-0354 boosted the immune response pathways in the host cells, inhibiting the virus from copying itself. Based on the new information, the researchers studied a prodrug of IMD-0354 called IMD-1041. A prodrug is an inactive substance that is metabolized within the body to create an active drug.
“IMD-1041 is even more promising as it is orally available and has been investigated for chronic obstructive pulmonary disease, a group of lung diseases that block airflow and make it difficult to breathe,” Chen says.
“Because the structure of IMD-1041 is undisclosed, we are developing a new artificial intelligence platform to design novel compounds that hopefully could be tested and evaluated in more advanced animal models.”
Two senior postdoctoral scholars in the Chen lab are lead authors of the study: Jing Xing, who recently became a young investigator at the Chinese Academy of Sciences, and Rama Shankar. Additional coauthors are from Institut Pasteur Korea; Shanghai Institute of Materia Medica; University of Texas Medical Branch; Spectrum Health in Grand Rapids, Michigan; and Stanford University.
Source: Emilie Lorditch for Michigan State University