One of the greatest challenges in treating tuberculosis—the top infectious killer worldwide—is the bacterium’s ability to shapeshift rapidly and become resistant to multiple drugs.
Identifying resistant strains quickly and choosing the right antibiotics to treat them remains difficult for several reasons, including the bacterium’s propensity to grow slowly in the lab, which can delay drug-sensitivity test results by as much as six weeks after initial diagnosis.
New tests that can quickly and reliably detect resistance to the most commonly used drugs before a patient begins treatment are urgently needed to improve outcomes and help curb the spread of the infection.
Now, a Harvard undergraduate applied math student working with biomedical researchers at Harvard Medical School’s Blavatnik Institute has designed a computer program that sets the stage for the development of such tests.
The computer program, described April 29 in EBioMedicine, can accurately predict a TB strain’s resistance to 10 first- and second-line drugs in a tenth of a second and with greater precision than similar models.
If incorporated in clinical tests, the model could make resistance detection both faster and more accurate, overcoming deficiencies in current resistance-testing methods that either take too long to yield definitive results or do not reliably detect the presence of drug resistance.
“Drug-resistant forms of TB are hard to detect, hard to treat and portend poor outcomes for patients,” said senior study author Maha Farhat, assistant professor of biomedical informatics at Harvard Medical School and a pulmonary medicine specialist at Massachusetts General Hospital. “The ability to rapidly detect the full profile of resistance upon diagnosis is critical both to improving individual patient outcomes and in reducing the spread of the infection to others.”
The new model will be available online soon as an added feature to Harvard Medical School’s genTB tool, which analyzes TB data and predicts TB drug resistance.