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Predicting Effective Drug Combinations For TB

Predicting Effective Drug Combinations For TB

Predicting Effective Drug Combinations For TB

Predicting Effective Drug Combinations For TB

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Tuberculosis (TB) is a bacterial disease that has plagued people for millennia. In the 21st century, it’s still a formidable killer. According the World Health Organization, 1.5 million people died of TB in 2014; of those, about 480,000 had multidrug-resistant TB (MDR-TB), which is resistant to at least 2 of the most potent TB drugs available.

The bacterium that causes TB, Mycobacterium tuberculosis (Mtb), can withstand attacks by antibacterial drugs through a variety of mechanisms. Because of this resilience, TB drug therapy can stretch for months or even years, involve numerous drugs with harsh side effects, and still fail to cure the disease.

Researchers have been trying to understand how Mtb dodges drug attacks. They could use this knowledge to help predict which drugs or drug combinations are most likely to overcome the bug’s evasive maneuvers.

In previous work, scientists mapped the gene regulatory network in Mtb. In this study, researchers used this network model to investigate how Mtb can become tolerant to the relatively new anti-TB drug bedaquiline. The Seattle-based team was led by Dr. Nitin Baliga at the Institute for Systems Biology and Dr. David Sherman at the Center for Infectious Disease Research. The study was funded in part by NIH’s National Institute of Allergy and Infectious Diseases (NIAID). It appeared online in Nature Microbiology on June 6, 2016.

The team found that more than 1,100 genes were expressed differently in Mtb treated with bedaquiline compared to untreated bacteria. Using network analysis, the researchers pinpointed 2 key regulatory genes whose activation appeared to coordinate the changes that drive Mtb into a drug-tolerant state. When either of these genes was disrupted, the bacteria were once again susceptible to the drug in laboratory experiments.

Based on their system-wide understanding of how Mtb responds to bedaquiline, the researchers predicted which drug would be effective to use in combination with bedaquiline. The anti-TB drug pretomanid inhibits 1 of the 2 key gene networks activated by bedaquiline. In laboratory experiments, the team found that when pretomanid was used with bedaquiline the drugs acted synergistically to kill TB bacteria.

The drug combination is already being tested in several clinical trials, the researchers note. Their gene network analysis approach has helped illuminate the mechanism behind the combination’s effectiveness.

“The incredibly large number of possible drug combinations taken together with the difficulty of growing Mtb in the laboratory make discovery of effective combination therapy extremely challenging," Baliga says. "We hope that our systems-based strategy will accelerate TB drug discovery by helping researchers prioritize combinations that are more likely to be effective.”