Before covid-19, tuberculosis was the most dangerous infection in the world, killing more than 1.5 million people annually. The problem prompted Sriram Chandrasekaran to build AI tools to identify potent drug combinations to treat it. His goal is to boost the effectiveness of existing antibiotics to combat drug resistance among TB patients.
Drug-resistant infections occur when people don’t finish their course of treatment or are treated incorrectly. They can also occur when people come in contact with a patient infected with drug-resistant bacteria. While a typical TB treatment regimen lasts six to nine months, a drug-resistant case takes 18 to 24 months to treat. Chandrasekaran wants to drastically reduce this timeline. Curing patients faster could also save thousands of dollars in treatment costs.
Chandrasekaran’s systems predict the effectiveness of various drug combinations for TB. “We’ve found some really surprising ones,” he says, including an antipsychotic drug that would enhance the potency of existing antibiotics. He and his team confirmed the results against the TB bacterium in the lab.
Many drugs work in the lab but aren’t effective in the body, and Chandrasekaran wanted to make sure his algorithms take this into account. One system he built simulates characteristics of the infection site—for example, how much oxygen it gets or whether amino acids are present, which can affect a drug’s effectiveness. Chandrasekaran’s lab is now identifying promising drug combinations for use in clinical trials of treatment against drug-resistant TB.