U-M Researchers in Ann Arbor Use Artificial Intelligence to Find Tuberculosis Treatments

A software tool created by researchers at Ann Arbor’s University of Michigan can predict how current drugs, including unlikely candidates, can be combined in new ways to create more effective treatments for tuberculosis. The research is in response to a shortage of new tuberculosis drugs in the pipeline.
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U-M researchers have developed a software that predicts the effectiveness of drug combinations for tuberculosis. // Photo courtesy of the University of Michigan

A software tool created by researchers at Ann Arbor’s University of Michigan can predict how current drugs, including unlikely candidates, can be combined in new ways to create more effective treatments for tuberculosis. The research is in response to a shortage of new tuberculosis drugs in the pipeline.

“This could replace our traditional trial-and-error system for drug development that is comparatively slow and expensive,” says Sriram Chandrasekaran, U-M assistant professor of biomedical engineering, who leads the research.

INDIGO, short for Inferring Drug INteractions using chemoGenomics and Orthology, the software tool has shown that the potency of tuberculosis drugs can be amplified when they are teamed with antipsychotics or antimalarials.

“This tool can accurately predict the activity of drug combinations, including synergy, where the activity of the combination is greater than the sum of the individual drugs,” says Shuyi Ma, a research scientist at the University of Washington and a first author of the study. “It also accurately predicts antagonism between drugs, where the activity of the combination is lesser. In addition, it also identifies the genes that control these drug responses.”

INDIGO has identified a five-drug combination, a four-drug combination, a combination of two antibiotics that are typically antagonistic but can be made highly synergistic by adding a third drug called Clofazimine, and more. All three groupings were in the top 0.01 percent of synergistic combinations identified by INDIGO.

“Successful combinations identified by INDIGO, when tested in a lab setting, showed synergy 88.8 percent of the time,” says Chandrasekaran.

Tuberculosis kills 1.8 million people each year and is the world’s deadliest bacterial infection. There are 28 drugs used to treat it, and they can be combined into 24,000 three- or four-drug combinations. If a new pair of drugs is added to the mix, the potential combinations increases to 32,000.

Multidrug-resistant strains of the disease are spreading, increasing the need for new drug combinations. INDIGO offers a faster way to find them. The system uses a database of previously published research broken down and quantified by the authors, along with information on the properties of hundreds of drugs.

The research results appear in mBio. The work was supported by the National Institutes of Health and the U-M Precision Health and MCubed initiatives.

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