Researchers at University of California San Diego analyzed the genomes of hundreds of malaria parasites to determine which genetic variants are most likely to confer drug resistance.
University of California - San DiegoNov 28 2024 The findings, published in Science, could help scientists use machine learning to predict antimalarial drug resistance and more effectively prioritize the most promising experimental treatments for further development. The approach could also help predict treatment resistance in other infectious diseases, and even cancer.
Elizabeth Winzeler, Ph.D., Professor at UC San Diego Skaggs School of Pharmacy and Pharmaceutical Sciences and the Department of Pediatrics at UC San Diego School of Medicine "The need for new, more effective malaria treatments is urgent, but funding for malaria research and drug development is very limited," said Winzeler, who in addition to her role at UC San Diego is director of the Bill and Melinda Gates Foundation-funded Malaria Drug Accelerator.
Related Stories"Our ultimate goal is to use machine learning to help us understand which compounds have the most risk of being compromised by resistance so that we can streamline the early drug development process and ultimately get treatments into clinical trials faster," said Winzeler. "This study gives us the data needed to train these new tools."
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