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

Building external controls

Clinical trials usually involve at least two groups, or “arms”: a test or experimental arm that receives the treatment under investigation, and a control arm that doesn’t. A control arm may receive no treatment at all, a placebo or the current standard of care for the disease being treated, depending on what type of treatment is being studied and what it’s being compared with under the study protocol. It’s easy to see the recruitment problem for investigators studying therapies for cancer and other deadly diseases: patients with a life-threatening condition need help now. While they might be willing to take a risk on a new treatment, “the last thing they want is to be randomized to a control arm,” Borys says. Combine that reluctance with the need to recruit patients who have relatively rare diseases—for example, a form of breast cancer characterized by a specific genetic marker—and the time to recruit enough people can stretch out for months, or even years. Nine out of 10 clinical trials worldwide—not just for cancer but for all types of conditions—can’t recruit enough people within their target timeframes. Some trials fail altogether for lack of enough participants.

What if researchers didn’t need to recruit a control group at all and could offer the experimental treatment to everyone who agreed to be in the study? Celsion is exploring such an approach with New York-headquartered Medidata, which provides management software and electronic data capture for more than half of the world’s clinical trials, serving most major pharmaceutical and medical device companies, as well as academic medical centers. Acquired by French software company Dassault Systèmes in 2019, Medidata has compiled an enormous “big data” resource: detailed information from more than 23,000 trials and nearly 7 million patients going back about 10 years.

The idea is to reuse data from patients in past trials to create “external control arms.” These groups serve the same function as traditional control arms, but they can be used in settings where a control group is difficult to recruit: for extremely rare diseases, for example, or conditions such as cancer, which are imminently life-threatening. They can also be used effectively

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By: MIT Technology Review Insights
Title: Clinical trials are better, faster, cheaper with big data
Sourced From: www.technologyreview.com/2021/06/10/1025897/clinical-trials-are-better-faster-cheaper-with-big-data/
Published Date: Thu, 10 Jun 2021 14:00:00 +0000

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