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Using artificial intelligence to boost tuberculosis treatments

Using artificial intelligence to improve tuberculosis treatments
A medical illustration of drug-resistant, Mycobacterium tuberculosis bacteria, presented in the Centers for Disease Control and Prevention (CDC) publication entitled, Antibiotic Resistance Threats in the usa, 2019 (AR Threats Report). Credit: Medical Illustrators: Alissa Eckert; James Archer

Imagine you have 20 new compounds which have shown some effectiveness in treating an illness like tuberculosis (TB), which affects 10 million people worldwide and kills 1.5 million every year. For effective treatment, patients will have to take a mix of 3 or 4 drugs for months as well as years as the TB bacteria behave differently in various environments in cellsand in some instances evolve to become drug-resistant. Twenty compounds in three- and four-drug combinations offer nearly 6,000 possible combinations. How can you decide which drugs to check together?

In a recently available study, published in the September problem of Cell Reports Medicine, researchers from Tufts University used data from large studies that contained laboratory measurements of two-drug combinations of 12 anti-tuberculosis drugs. Using mathematical models, the team discovered a couple of rules that drug pairs have to satisfy to be potentially good treatments within three- and four-drug cocktails.

The usage of drug pairs instead of three- and four- drug combination measurement decreases significantly on the quantity of testing that should be done before moving a drug combination into further study.

“Utilizing the design rules we’ve established and tested, we are able to substitute one drug pair for another drug pair and know with a higher amount of confidence that the drug pair should work in collaboration with another drug pair to kill the TB bacteria in the rodent model,” says Bree Aldridge, associate professor of molecular biology and microbiology at Tufts University School of Medicine and of at the institution of Engineering, and an immunology and molecular microbiology program faculty member at the Graduate School of Biomedical Sciences. “The we developed is both more streamlined and much more accurate in predicting success than prior processes, which necessarily considered fewer combinations.”

The lab of Aldridge, who’s corresponding author on the paper and in addition associate director of Tufts Stuart B. Levy Center for Integrated Management of Antimicrobial Resistance, previously developed and uses DiaMOND, or diagonal measurement of n-way drug interactions, a strategy to systemically study pairwise and high-order drug combination interactions to recognize shorter, better treatment regimens for TB and potentially other transmissions. With the look rules established in this new study, researchers believe they are able to raise the speed of which scientists determine which combinations will most effectively treat tuberculosis, the next leading infectious killer on earth.

More info: Jonah Larkins-Ford et al, Design principles to put together drug combinations for effective tuberculosis therapy using interpretable pairwise drug response measurements, Cell Reports Medicine (2022). DOI: 10.1016/j.xcrm.2022.100737

Citation: Using artificial intelligence to boost tuberculosis treatments (2022, September 14) retrieved 14 September 2022 from

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