Scientists have developed a machine-learning method that crunches massive amounts of data to help determine which existing medications could improve outcomes in diseases for which they are not prescribed.
The intent is to speed up drug repurposing, similar to the way Botox injections, first approved to treat crossed eyes, are now used for smooth skin and migraine treatments.
Finding those new uses typically involves a mix of serendipity and time-consuming and expensive randomized clinical trials to ensure that a drug deemed effective for one disorder will be useful as a treatment for something else.
Ohio State University researchers created a framework that combines enormous patient care-related datasets with high-powered computation to arrive at repurposed drug candidates and the estimated effects of those existing medications on a defined set of outcomes.
The study focused on proposed repurposing of drugs to prevent heart failure and stroke in patients with coronary artery disease, though the framework is flexible and could be applied to most diseases.
"This work shows how artificial intelligence can be used to 'test' a drug on a patient, and speed up hypothesis generation and potentially speed up a clinical trial," says senior author Ping Zhang, assistant professor of computer science and engineering and biomedical informatics at Ohio State.
The research is described in "A Deep Learning Framework for Drug Repurposing via Emulating Clinical Trials on Real-World Patient Data," published in Nature Machine Intelligence.
From Ohio State University
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