US-based scientists are using artificial intelligence (AI) to find new uses for existing medications with the aim of speeding up drug repurposing.
Researchers at Ohio State University has developed a machine-learning method that crunches large amounts of data to help determine which existing medications could improve outcomes in diseases for which they are not prescribed.
The researchers created a framework that combines 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 team has said that the framework is flexible and could be applied to most diseases.
Ping Zhang, assistant professor of computer science and engineering and biomedical informatics at Ohio State, said: “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.
“But we will never replace the physician – drug decisions will always be made by clinicians.”
The research team used insurance claims data on almost 1.2 million heart-disease patients, which provided information on their assigned treatment, disease outcomes and various values for potential confounders.
The deep learning algorithm also has the power to take into account the passage of time in each patient’s experience – for every visit, prescription and diagnostic test. The model input for drugs is based on their active ingredients.
Applying what is called causal inference theory, the researchers categorised, for the purposes of this analysis, the active drug and placebo patient groups that would be found in a clinical trial. The model tracked patients for two years – and compared their disease status at that end point to whether or not they took medications, which drugs they took and when they started the regimen.
The model yielded nine drugs considered likely to provide therapeutic benefits to lower the risk of heart failure and stroke in coronary artery disease patients, three of which are currently in use.
Zhang added: “With causal inference, we can address the problem of having multiple treatments. We don’t answer whether drug A or drug B works for this disease or not, but figure out which treatment will have the better performance
“My motivation is applying this, along with other experts, to find drugs for diseases without any current treatment. This is very flexible, and we can adjust case-by-case. The general model could be applied to any disease if you can define the disease outcome.”