In a breakthrough development, researchers have developed a machine-learning method to determine how to improve the outcomes of existing medications for diseases these are not prescribed. The method involves crunching massive amounts of data.
In fact, the objective of the work is to expedite drug repurposing – a concept not new in the pharma sector. For example, Botox injections that were first used to treat crossed eyes now find use to treat migraine, and are a top cosmetic strategy to lessen the appearance of wrinkles.
However, to discover new uses of existing medication is a mix of few things. It is time-consuming, involves expensive randomized clinical trials, and serendipity. The success of such efforts ensure a drug deemed effective for one medical condition will be useful for some other condition as well.
Meanwhile, researchers at the Ohio State University developed a framework that combines massive datasets pertaining to patient care with high-powered computation. This leads to arrive at repurposed drug molecules and estimated effects of existing medications on predefined outcomes.
Whilst the focus of the study is to propose repurpose of drugs to prevent stroke and heart failure in patients with coronary artery disease- the flexible nature of the framework enables its application for most diseases.
“Besides this, the work showcases how artificial intelligence can be used to examine how a drug works on a patient. In addition, the framework expedites hypothesis generation and speeds up clinical trials,” said a scientific associate at the Ohio State University.
Nonetheless, drug decisions will remain with clinicians.
Notably, drug repurposing is an attractive vocation. This is because it could lower the risk related with safety testing of new medications.