Artificial Intelligence Allows the Selection Of 30 Million Possible Drugs Against SARS-CoV-2 (Medicine)

Mayo Clinic researchers and collaborators used computer simulation and artificial intelligence (AI) to select 30 million potential drugs that block the SARS-CoV-2 virus, which causes COVID-19. In the work published in Biomolecules , researchers accelerated drug discovery to better identify and study the most promising targets, as they are interested in discovering new treatments for COVID-19 .

“A multi-drug platform was used to select the ones that might work. The analysis was done with drugs clinically tested and licensed by the US Food and Drug Administration, as well as other novel compounds. Thanks to the computational power of advanced technology, it was possible to determine the best drug from a composite library for further investigation, ”says Dr. Thomas Caulfield , a molecular neuroscientist at Mayo Clinic and an expert author on the paper.

The studies were carried out using a computer simulation called silicon detection (which means on the computer) and validated through biological experiments with live viruses. This type of research uses digital databases and mathematical concepts to identify potentially useful drug compounds. Other types of research are carried out in cell lines, which is known as in vitro , or they are carried out in living organisms such as mice or humans and is known as in vivo.

The researchers started with 30 million drug compounds. Virtual assessment tools predicted the behavior of various drug compounds and showed the pattern of how they would interact with particulate biological targets of SARS-CoV-2. Selection with silicon reduced the compounds to 25. Then, for further analysis and laboratory testing, the researchers conducted a pilot study of all 25 compounds against infectious SARS-CoV-2 in human cell cultures, and then they tested for a common problem with drugs, which is toxicity.

Because one of the liver’s tasks is to clean the blood, including the drug components, the team created a model of the human liver on a honeycomb-shaped surface that was no larger than the size of a pencil eraser. The researchers were able to predict that all of those 25 compounds would be safe for the human liver.

‘The goal is to deactivate the infection and restore the cells to health. What we want is to aggressively target the SARS-CoV-2 duplication cycle from several fronts to inhibit entry and spread of the virus, ”says Dr. Caulfield.

The researchers hope that a combination of drugs, similar to a drug cocktail used in the treatment of HIV, will complement the vaccination against COVID-19. Dr. Caufield says the next step is to move forward on the basis of the new discoveries. The researchers plan to test the combination of drugs to obtain pairs that act in synergy and are more powerful against the virus than a single compound.

“This discovery opens the way for the future creation of drugs and clinical trials to accelerate the administration of possible drugs,” concludes the doctor.

Dr. Caulfield led the drug selection team, which included colleagues from Mayo Clinic in Florida and Mayo Clinic in Rochester, as well as researchers from Brigham and Women’s Hospital (affiliated with Harvard Medical School) and the University of California at Riverside. Funding for this study came from the National Institutes of Allergy and Infectious Diseases, part of the National Institutes of Health, and the Center for Personalized Medicine at Mayo Clinic. For a full list of authors, funding information, and conflict of interest statements, see the article in Biomolecules .

This article and others regarding more studies are in the Mayo Clinic research publication Discovery’s Edge .


Reference: Coban, M.A.; Morrison, J.; Maharjan, S.; Hernandez Medina, D.H.; Li, W.; Zhang, Y.S.; Freeman, W.D.; Radisky, E.S.; Le Roch, K.G.; Weisend, C.M.; Ebihara, H.; Caulfield, T.R. Attacking COVID-19 Progression Using Multi-Drug Therapy for Synergetic Target Engagement. Biomolecules 2021, 11, 787. https://doi.org/10.3390/biom11060787


Provided by Mayo Clinic

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