More than 600 million people around the world have been infected with the newly emerged SARS-CoV-2 virus since the beginning of December 2019, and more than 6 million people have lost their lives as a result (as of September 2022). In addition to this, severe cases of COVID-19 have resulted in irreparable organ damage in a significant number of patients.
Researchers have had a hard time coming up with new ways to treat the new coronavirus and its many different forms, especially since traditional drug development and discovery can take years. A researcher at Michigan State University and his team are using high-tech methods to determine if drugs already on the market can treat new COVID variants.
Bin Chen, an associate professor at the College of Human Medicine, said that the covid-19 virus keeps evolving. With the help of artificial intelligence and large data sets, current medications can be redesigned for new uses.
How Bin Chen started the procedureÂ
In order to overcome obstacles, Chen assembled a group of scholars from all over the world who are experts in a wide variety of fields, spanning from biology to computer science. First, Chen and his team searched through 1,700 host transcriptomic profiles, which came from patient tissues, cell cultures, and mouse models, in order to find the unique coronavirus gene expression signatures. These host transcriptomic profiles were obtained from publically available databases. These signals shed light on the same biological characteristics shared by COVID-19 and its variations.
How the team worked
Even though there are potentially hundreds of different signatures, only a small subset of these signatures are actually informative in drug prediction. This strong meta-signature of CoV-hijacking host transcriptome change was produced as a result of our data-driven technique, which was able to elegantly extract those informative signatures.
Despite the fact that this signature was originally created from SARS and MERS data, it is still able to reclassify SARS-CoV-2 infected patient samples and control patient samples in many independent datasets.
The team used a computer program to screen a drug library of FDA-approved or investigational drugs to find candidates that could correct the expression of signature genes and further prevent the coronavirus from replicating by using the virus’s signature to determine which genes needed to be suppressed and which genes needed to be activated.
IMD-0354 was the only innovative candidate that made it through phase I of clinical testing for the treatment of atopic dermatitis which was found to have been developed by Chen and his colleagues.
In further research, a team from Korea found that it was 90 times more effective against six different COVID-19 variations than remdesivir, which was the first medicine that was approved to treat COVID-19.
In addition, the research group discovered that IMD-0354 enhanced the immune response pathways present in the host cells, which prevented the virus from replicating itself. Their MoA analysis suggests that IMD-0354 activates type I interferon antiviral response and targets multiple pathways involved in the viral life cycle, suggesting the power of this system-based approach for drug discovery and the importance of robust pathological relevant CoV signatures. This pipeline might provide more robust CoV signatures as a forward-looking tool to enable drug discovery for new variants and future potential pandemics by iteratively updating CoV infection profiles and anti-CoV drug profiles.
The researchers used the knowledge they gained to investigate a medicine called IMD-1041, which is a precursor to the drug IMD-0354. A chemical known as a prodrug is one that is inert but can be converted into a drug that is active within the body.
According to Chen, IMD-1041 is much more intriguing as it is orally accessible and has been examined for chronic obstructive pulmonary disease. Chronic obstructive pulmonary disease refers to a set of lung illnesses that restrict airflow and make it difficult to breathe.
Chen added that the structure of IMD-1041 has not been made public, they are constructing a new artificial intelligence platform to generate fresh compounds and they are hopeful that these novel compounds will be able to be tested and evaluated in more advanced animal models.
The research was published in the journal Science(Check the spelling)
Jing Xing, who is now a young investigator at the Chinese Academy of Sciences, and Rama Shankar, both senior postdoctoral scholars in the Chen lab, led the charge on this project with assistance from scientists at Institute Pasteur Korea, The Shanghai Institute of Materia Medica, The University of Texas Medical Branch, Spectrum Health in Grand Rapids ,and Stanford University.
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