Using a machine-learning algorithm, researchers can predict interactions that could interfere with a drug’s effectiveness.
Any drug that is taken orally must pass through the lining of the digestive tract. Transporter proteins found on cells that line the GI tract help with this process, but for many drugs, it’s unknown which of those transporters they use to exit the digestive tract.
Identifying the transporters used by specific drugs could help to improve patient treatment because if two drugs rely on the same transporter, they can interfere with each other and should not be prescribed together.
Researchers at MIT, Brigham and Women’s Hospital, and Duke University have now developed a multipronged strategy to identify the transporters used by different drugs. Their approach, which makes use of both tissue models and machine-learning algorithms, has already revealed that a commonly prescribed antibiotic and a blood thinner can interfere with each other.
“One of the challenges in modeling absorption is that drugs are subject to different transporters. This study is all about how we can model those interactions, which could help us make drugs safer and more efficacious, and predict potential toxicities that may have been difficult to predict until now,” says Giovanni Traverso, an associate professor of mechanical engineering at tissue model they had developed in 2020 to measure a given drug’s absorbability. This experimental setup, based on pig intestinal tissue grown in the laboratory, can be used to systematically expose tissue to different drug formulations and measure how well they are absorbed.
To study the role of individual transporters within the tissue, the researchers used short strands of DOI: 10.1038/s41551-023-01128-9
The research was funded, in part, by the U.S.