Virtual reality company AppliedVR is taking an innovative approach to a new trial for its chronic pain treatment. Rather than try to find a group of people with chronic back pain to sign up for the trial and not get the treatment, they’ll 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 got Food and Drug Administration approval for its virtual reality system that treats chronic back pain back in November. Now, the company is collecting more information about how the treatment works in different groups in the real world. They’re partnering with healthcare data company Komodo Health on the trial. Komodo offers its clients access to a vast database of anonymized patient health records from people with a range of health conditions, including chronic pain, that follows people over time.
The partnership lets AppliedVR track the experience of chronic lower back patients in general and compare their experience with the experience of people actively enrolled in the trial. “So now, as they move forward, they’ll be able to much more clearly understand and demonstrate the value of their technology and what it delivers compared to traditional chronic pain treatments,” says Web Sun, president and co-founder of Komodo Health .
Using real-world data as a patient group 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 every patient who actively decides to sign up for a trial gets the treatment being tested, rather than risking signing up only to get a placebo. Synthetic control groups can also improve equity in clinical research, Sun says. Historic mistrust in the medical system from racial minority groups and lower access to health care often means minority groups are underrepresented in clinical trials. Komodo’s database has information on patient race and ethnicity, so research teams can hone in on specific groups, he says.
“That allows us to go 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’s not in regular use. Researchers are still working to double-check that it can produce results as accurately as a standard control group and identifying which types of trials it might work best for. “The FDA is still wary of trial designs in which a synthetic control arm is meant to entirely replace traditional data due to concerns that synthetic data is not a one-to-one match to traditional data,” Arnaub Chatterjee, senior VP of product at health data company Medidata Acorn AI, told PharmaVoice.
But the agency is getting more comfortable with this type of data, particularly if it’s used in combination with more traditional patient groups, Chatterjee said. And some groups are starting to use synthetic patient arms for studies that will be part of applications for FDA approval: the FDA said in 2020 that a drug company could use a part-synthetic control arm in a trial testing a cancer treatment.
Sun says he’s optimism this approach to clinical trials will become more common. “Regulatory agencies are on board with this approach because they recognize all of the challenge with trials,” he says. “It saves time and money, but most importantly, it represents the opportunity for us to speed the development of novel therapeutics and bring them to market faster, more cheaply, and in a more representative way.”