Virtual reality company AppliedVR is taking an innovative approach for a new trial for its chronic pain treatment. Instead of trying to find a group of people with chronic back pain to enroll in the trial and not obtain treatment, they will pull data from an existing database of chronic pain patients to use as a comparison group, a strategy that has the potential to make clinical trials more efficient.
AppliedVR won Food and Drug Administration approval for its virtual reality system treating chronic back pain in November. Now the company is collecting more information about how the treatment works in different groups in the real world. They are partnering with healthcare data company Komodo Health on the trial. Komodo offers its customers access to a vast database of anonymous patient health records of people with a variety of health conditions, including chronic pain, that follows people over time.
The partnership allows AppliedVR to track the experience of chronic low back patients in general and compare their experience with the experience of people actively enrolled in the trial. “So now, as they go forward, they will be able to understand and demonstrate much more clearly the value of their technology and what it offers compared to traditional treatments for chronic pain,” says Web Sun, president and co-founder of Komodo Health. .
Using real-world data such as a group of patients in a trial, often known as a synthetic control arm, can make research trials more efficient: companies don’t have to do the legwork to enroll as many people in clinical trials. They can also allow all patients who decide to actively enroll in a trial to get the treatment being tested, rather than risk enrolling just to get a placebo. Synthetic control groups can also improve fairness in clinical research, says Sun. Historical mistrust of the medical system by racial minority groups and less access to health care often means that minority groups are underrepresented in clinical trials. The Komodo database has information on the race and ethnicity of patients, so research teams can zero in on specific groups, she says.
“That allows us to look at all those different subpopulations and underrepresented patient populations to see if they have different outcomes,” he says.
This approach to trial design is still new: experts are excited about its potential, but it is not used regularly. The researchers are still working to verify that it can produce results with the same precision as a standard control group and to identify what types of trials it might work best for. “The FDA is still wary of trial designs in which a synthetic control arm is intended to completely replace traditional data due to concerns that synthetic data may not match traditional data one-for-one,” Arnaub Chatterjee, senior vice president of product at health data company Medidata Acorn AI, said PharmaVoice.
But the agency is becoming more comfortable with this type of data, particularly if used in combination with more traditional patient groups, Chatterjee said. And some groups are beginning to use synthetic patient arms for studies that will form part of FDA approval applications: The FDA said in 2020 that a drug company could use a partially synthetic control arm in a trial testing a treatment against cancer.
Sun says he is optimistic that this approach to clinical trials will become more common. “Regulatory agencies are increasingly agreeing to this approach because they recognize the full challenge of testing,” he says. “It saves time and money, but more importantly, it represents the opportunity for us to accelerate the development of new treatments and bring them to market in a faster, cheaper and more representative way.”