Clinical trials have never been more in the public eye than in the past year, as the world watched the development of vaccines against covid-19, the disease at the center of the 2020 coronavirus pandemic. Discussions of study phases, efficacy, and side effects dominated the news. The most distinctive feature of the vaccine trials was their speed. Because the vaccines are meant for universal distribution, the study population is, basically, everyone. That unique feature means that recruiting enough people for the trials has not been the obstacle that it commonly is.
“One of the most difficult parts of my job is enrolling patients into studies,” says Nicholas Borys, chief medical officer for Lawrenceville, N.J., biotechnology company Celsion, which develops next-generation chemotherapy and immunotherapy agents for liver and ovarian cancers and certain types of brain tumors. Borys estimates that fewer than 10% of cancer patients are enrolled in clinical trials. “If we could get that up to 20% or 30%, we probably could have had several cancers conquered by now.”
Clinical trials test new drugs, devices, and procedures to determine whether they’re safe and effective before they’re approved for general use. But the path from study design to approval is long, winding, and expensive. Today, researchers are using artificial intelligence and advanced data analytics to speed up the process, reduce costs, and get effective treatments more swiftly to those who need them. And they’re tapping into an underused but rapidly growing resource: data on patients from past trials.
In this piece, originally published by MIT Technology Review Insights, learn how researchers are using analytics and existing patient data to ease recruitment, reduce costs, and accelerate timelines.