Clinical trials are better, faster, cheaper with big data


“One of the most difficult parts of my job is to enrich patients in studies,” says Nicholas Borys, chief medical officer of Lawrenceville, NJ, Celsion biotechnology company, which develops next-generation chemotherapy and immunotherapy agents for breast cancers. liver and ovary and certain types of brain tumors. Borys estimates that less than 10% of cancer patients are enrolled in clinical trials. “If we could get up to 20% or 30%, we might have had several cancers conquered by now.”

Clinical trials test new drugs, devices and procedures to determine if they are safe and effective before they are approved for general use. But the path from study design to approval is long, winding and expensive. Today, researchers use artificial intelligence and advanced data analysis to accelerate the process, reduce costs and get effective treatments faster than those who need it. And they are exploiting an exploited 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 treatment under investigation, and a control arm that does not. A control arm may receive no treatment, a placebo or current standard of care for the disease being treated, depending on what type of treatment is being studied and what is compared to the study protocol. It is easy to see the recruitment problem for researchers studying therapies for cancer and other deadly diseases: patients with a life-threatening situation need help now. While they may be willing to take a risk for a new treatment, “the last thing they want is to be randomized to a control arm,” Borys says. It combines that reluctance with the need to recruit patients with relatively rare diseases – for example, a form of breast cancer characterized by a specific genetic marker – and the time to recruit enough people can be extended by months, or even years. Nine out of 10 clinical trials around the world – not just for cancer, but for all types of conditions – are unable to recruit enough people on their target terms. Some tests fail together due to lack of enough participants.

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

The idea is to reuse patient data in past trials to create “external control weapons”. These groups serve the same function as traditional control arms, but can be used in environments where a control group is difficult to recruit: for extremely rare diseases, for example, or conditions such as cancer, which are imminent. life-threatening. They can also be used effectively for “one-arm” procedures, making a control group impractical: for example, to measure the effectiveness of an implanted device or a surgical procedure. Perhaps its most valuable immediate use is to perform rapid preliminary trials, to assess whether a treatment is worth following until the complete clinical trial.


Medidata uses artificial intelligence to search its database and find patients who have served as controls in past treatment trials for a certain situation to create its own version of external control arms. “We can accurately select these historical patients and match the experimental arm of the current day with the historical test data,” says Arnaub Chatterjee, senior vice president for products, Acorn AI at Medidata. (Acorn AI is Medidata’s data and analytics division.) Processes and patients are tracked by study objectives – so-called endpoints, such as reduced mortality or how long patients remain free of cancer – and for other aspects of the study design, such as the type of data collected at the beginning of the study and on the road.

When we create an external control arm, “We do everything we can to mimic an ideal randomized controlled trial,” says Ruthie Davi, vice president of data science, Acorn AI at Medidata. The first step is to search the database for potential candidates for the control arm with key eligibility criteria from the investigative process: for example, the type of cancer, key characteristics of the disease and how advanced it is, and if it is the first time the patient is being treated. It’s essentially the same process used to select control patients in a standard clinical study — except the data recorded at the start of the past trial, rather than the current one, is used to determine eligibility, Davi says. “We have found historical patients who qualify for the trial if they exist today.”

Download u complete report.

This content was produced by Insights, the custom content arm of MIT Technology Review. It was not written by the editor of the MIT Technology Review.

Source link


Read More

Leave a Reply

Your email address will not be published. Required fields are marked *

Back to top button