November 21, 2019
With a shortage of new tuberculosis drugs in the pipeline, a software tool from the University of Michigan can predict how current drugs—including unlikely candidates—can be combined in new ways to create more effective treatments.
“This could replace our traditional trial-and-error system for drug development that is comparatively slow and expensive,” said Sriram Chandrasekaran, a U-M assistant professor of biomedical engineering.
Dubbed 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,” said Shuyi Ma, a research scientist in the David Sherman lab at the University of Washington. “It also accurately predicts antagonism between drugs, where the activity of the combination is lesser. In addition, also identifies the genes that control these drug responses.”
Among the combinations INDIGO identified as showing a strong likelihood of effectiveness against tuberculosis were:
- A five-drug combination of tuberculosis drugs Bedaquiline, Clofazimine, Rifampicin, Clarythromycin with the antimalarial drug P218.
- A four-drug combination of Bedaquiline, Clofazimine, Pretomanid and the antipsychotic drug Thioridazine.
- A combination of antibiotics Moxifloxacin, Spectinomycin – two drugs that are typically antagonistic but can be made highly synergistic by the addition of a third drug, Clofazimine.
All three groupings were in the top .01% 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,” Chandrasekaran said.
Tuberculosis kills 1.8 million people each year and is the world’s deadliest bacterial infection. There are 28 drugs currently used to treat tuberculosis, and those can be combined into 24,000 three or four drug combinations. If a pair of new drugs is added to the mix, that increases potential combinations to 32,000. These numbers make developing new treatment regimens time-consuming and expensive. At the same time, multi-drug resistant strains are rapidly spreading.
At a time when new drugs are in short supply to deal with old-but-evolving diseases, this tool presents a new way to utilize medicine’s current toolbox. Answers may already be out there, and INDIGO’s outside-the-box approach represents a faster way of finding them.
INDIGO utilizes a database of previously published research, broken down and quantified by the authors, along with detailed information on the properties of hundreds of drugs.
The team’s research results appear in mBio. The paper is titled “Transcriptomic Signatures Predict Regulators of Drug Synergy and Clinical Regimen Efficacy against Tuberculosis.” This work was supported by the National Institutes of Health and the University of Michigan Precision Health and MCubed initiatives.
- By James Lynch, Michigan Engineering, Research News & Feature Writer, (734) 763-1652
- Source article: https://news.engin.umich.edu/2019/11/how-ai-can-design-new-tuberculosis-drug-regimens/