Scientists from MIT, Brigham and Women’s Hospital, and Duke University have developed a method to track how oral drugs pass through the human digestive tract. This research is considered key as it can help predict potential drug interactions which improve patient treatment. The research used a combination of tissue models and machine learning algorithms.
Oral drugs pass through the digestive tract with the help of transporter proteins. Many drugs depend on the same transporters, causing them to interfere with one another. By identifying which transporters are used by specific drugs, potentially damaging interactions can be avoided.
The researchers adapted a tissue model developed in 2020 to measure drug absorbability. To understand the role of individual transporters, the researchers used strands of RNA to knock down the expression of each transporter. They tested 23 routine drugs and identified the transporters each drug used. A machine-learning model was then trained using that data and the information from several drug databases. The model was then used to make predictions about which drugs would interact with which transporters.
The newly developed approach found that the absorption of a common antibiotic, doxycycline, was affected by digoxin, a heart failure drug; levetiracetam, an antiseizure medication, and tacrolimus, an immunosuppressant. It was also found that doxycycline could interfere with warfarin, a popular blood-thinning medication.
While these findings have immediate applications for current drug use, the method will also be key for future drug development. Identifying potential interactions could lead to more effective drug design and prevent health problems.
Overall, the researchers hope this research will make drugs safer and more effective. The study is unique in its combination of tissue models and machine learning and offers promising potential for the advancement of drug development and patient safety.
