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Two recent studies found that the virus that causes COVID-19 is developing resistance to two antiviral drugs commonly used for treatment in patients with compromised immune systems: remdesivir and nirmatrelvir. The first study from Cornell University and US National Institutes of Health researchers, which specifically looked at immunocompromised patients, found that the virus developed a reduced sensitivity to the drugs, while the second study from University of Pittsburgh, Brigham and Women’s Hospital, Stanford University, and Harvard University researchers also found resistant mutations, with the effect more evident in the immunocompromised and those who had received nirmatrelvir. These results raise concerns as the threat of antimicrobial resistance globally grows.
Researchers from the University of California, Riverside; the University of California, Irvine; and the Yale School of Medicine have designed a new synthetic compound, MED6-189, which is effective against drug-sensitive and drug-resistant Plasmodium falciparum malaria strains in vitro and in mice engineered to have human blood. MED6-189, inspired by a compound extracted from marine sponges, works by targeting and disrupting a specialized part of P. falciparum cells, as well as the pathways used to transport materials within the cells, thereby preventing the pathogen from developing resistance. The next steps include continued optimization of the MED6-189 and further confirmation of its mechanisms of action, but, if the compound continues to demonstrate success, this research could lead to the development of an important tool to address the rising challenge of antimalarial drug resistance.
Last week, the US Department of Health and Human Services through the Advanced Research Projects Agency for Health announced funding for the Transforming Antibiotic R&D with Generative AI to stop Emerging Threats project that will use artificial intelligence (AI) to speed up the discovery and development of new classes of antibiotics to meet the urgent need for new drugs amid the growing threat of antibiotic resistance. While conventional antibiotic discovery methods require time-consuming manual screening and testing of potential molecular compounds, most of which do not advance into further development, this project will employ deep learning to develop new methods to identify biomolecules with potential and use generative AI to expand the number of potential antibiotic candidates.