Machine learning is revolutionizing the field of drug absorption, offering new insights into how orally administered medications interact with the body. A recent study conducted by researchers from Brigham and Women’s Hospital and MIT has developed a model that uses machine learning to analyze the movement of drugs through tissues and predict interactions with drug transporters.
Drug transporters are membrane proteins responsible for carrying compounds across the intestinal tract, playing a crucial role in drug absorption and efficacy. When a drug binds to multiple transporters, it may face challenges in crossing the gut barrier, potentially leading to reduced absorption and effectiveness. Furthermore, interactions between different drugs and their transporters can result in harmful side effects.
Through their innovative approach, the researchers were able to identify previously unknown drug-transporter interactions and potential interactions between different medications. By testing their model on pig tissue with 50 approved and investigational drugs, they uncovered 58 new drug-transporter interactions and over 1.8 million potential interactions between drugs.
Dr. Giovanni Traverso, senior author of the study and gastroenterologist at Brigham’s Division of Gastroenterology, Hepatology, and Endoscopy, emphasized the potential of their model to accelerate drug discovery and improve safety considerations when combining different medications. The findings of this study, published in Nature Biomedical Engineering, highlight the significant impact of machine learning on enhancing our understanding of drug absorption mechanisms.
