AI Identifies Effective Tuberculosis Multi-Drug Combinations

U.S. researchers have used machine learning to predict the effectiveness of multi-drug treatment combinations for tuberculosis (TB), which could help in the design of new therapy regimens. By examining study data from TB drug pairs in vitro, they were able to predict how three or four drugs could affect treatment in vivo and work out rules governing drug choices among these pairwise building blocks that would create effective multi-drug therapies. "Using the design rules we've established and tested, we can substitute one drug pair for another drug pair and know with a high degree of confidence that the drug pair should work in concert with the other drug pair to kill the TB bacteria in the rodent model," explained researcher Bree Aldridge, associate professor of molecular biology and microbiology at Tufts University School of Medicine in Boston, Massachusetts. "The selection process we developed is both more streamlined and more accurate in predicting success than prior processes, which necessarily considered fewer combinations." The research, published in the journal Cell Reports Medicine, follows an earlier study released this week showing that a deep learning program can be as effective as radiologists in identifying tuberculosis on chest X-rays.