Clinical trials have never been more in the public eye than in the last year, as the world watched the development of vaccines against covid-19, the disorder at the middle of these 2020 coronavirus pandemic. Discussions of research phases, efficacy, and side effects dominated the information. The most distinctive quality of the vaccine trials was their rate. Since the vaccines are intended for worldwide distribution, the research population is, basically, everyone. That exceptional feature means that recruitment enough folks for the trials hasn’t become the barrier 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 chemotherapy and immunotherapy agents for ovarian and liver cancer and certain types of brain tumors. Borys estimates that fewer than 10percent 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 find out whether they’re safe and effective before they are approved for general use. However, the route from research design to acceptance is long, winding, and more expensive. Today,researchers are utilizing artificial intelligence and advanced data analytics to hasten the process, reduce expenses, and get effective remedies more swiftly to people who desire them. And they’re tapping into an underused but quickly expanding resource: data on patients from previous trials
Building external controllers
Clinical trials usually involve two groups, or”arms”: an evaluation or experimental arm which receives the treatment under investigation, and a control arm that does not. A control arm might get no treatment at all, a placebo or the current quality of care for your disorder being treated, based on which type of treatment has been studied and what it is being compared with under the study protocol. It’s simple to observe the recruitment issue for investigators studying therapies for cancer and other fatal ailments: patients using a life-threatening condition need help now. While they may be happy to take a risk on a new therapy,”the last thing they want is to be randomized to a control arm,” Borys says. Combine that reluctance with the necessity to recruit patients that have comparatively infrequent diseases–for example, a form of breast cancer characterized by a specific genetic marker–and the opportunity to recruit enough people can stretch out for weeks, or perhaps years. Nine out of 10 clinical trials worldwide–not just for cancer but for all kinds of ailments –can’t recruit enough people inside their target timeframes. Some trials fail altogether for lack of sufficient participants.
What if researchers did not need to recruit a control group whatsoever and could supply the experimental treatment to everyone who consented to be in the study? Celsion is researching this kind of approach with New York-headquartered Medidata, which offers management software and electronic data capture for over half of the world’s clinical trials, serving most important pharmaceutical and medical device companies, in addition to academic medical centers. Developed by French software company Dassault Systèmes at 2019, Medidata has compiled an enormous”big data” resource: comprehensive information from more than 23,000 trials and nearly 7 million sufferers going back about 10 years.
The idea would be to reuse data from patients in past trials to create”external control arms.” T