Researchers have recently introduced a groundbreaking machine-learning model that can predict drug interactions accurately, aiding in safer prescribing practices and boosting treatment effectiveness. This innovative approach uses tissue models and advanced algorithms to identify potential interactions, ultimately enhancing patient safety.
Published in Nature Biomedical Engineering, this study was conducted by a collaborative team from MIT, Brigham and Women’s Hospital, and Duke University. The researchers focused on the complexities of the human digestive tract, particularly the crucial role of transporter proteins lining the gastrointestinal (GI) tract in drug absorption.
Lead author Giovanni Traverso, an associate professor at MIT and gastroenterologist at Brigham and Women’s Hospital, emphasized the challenge of understanding which transporters individual drugs rely on, stating that One of the challenges in modeling absorption is that drugs are subject to different transporters.
By uncovering these transporter-drug interactions, the researchers can identify potential conflicts that may impact treatment outcomes. Their approach involves using tissue models to simulate drug absorption and machine-learning algorithms to predict interactions, offering a comprehensive view of drug transport mechanisms and potential interactions.
One striking example highlighted the interaction between the antibiotic doxycycline and the blood thinner warfarin, leading to fluctuations in warfarin levels in the bloodstream. Through data analysis from patient records, additional interactions with medications like digoxin, levetiracetam, and tacrolimus were also uncovered, shedding light on previously unknown drug interactions.
This novel approach not only enhances the safety of existing drug combinations but also holds promise for optimizing the development of new drugs. By fine-tuning drug formulations to minimize interactions and improve absorption, researchers aim to enhance efficacy and safety in future medications.
Vivtex, a biotech company co-founded by MIT researchers, is leveraging this technology to develop innovative oral drug delivery systems, leading the way in applying these advancements to real-world applications.
Overall, this new machine-learning model offers a significant step forward in predicting drug interactions, ensuring safer prescribing practices and improved treatment outcomes for patients worldwide.
