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 intention is to speed up drug repurposing, which can involve a mix of serendipity and time-consuming and expensive randomised clinical trials.
The US 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. Though this study focused on proposed repurposing of drugs to prevent heart failure and stroke in patients with coronary artery disease, the framework is flexible and could be applied to most diseases.
Ping Zhang, one of the team, said: “This shows how AI can be used to test a drug on a patient, speed up hypothesis generation and even potentially speed up a clinical trial.”