Are digital DCTs the answer for rare diseases?

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Enrolling enough patients in clinical trials has long been a challenge in pharma, made all the more complex when developing drugs for rare diseases. After all, the average time to accurately diagnose a rare disease is four to five years, which creates an additional hurdle to clinical trial recruitment.

Ivan Jarry, CEO, ObvioHealth

Permission granted by Ivan Jarry.

Compounding the problem is the fact that rare disease patients are spread far and wide, often with just one patient in any geographic area. As Ivan Jarry, CEO of ObvioHealth, points out, the difficulties companies experience with recruitment inevitably result in higher drug development costs.

“Being able to identify and recruit more patients in the rare disease space would lower the cost of clinical trials and give people access to potential lifesaving treatments,” Jarry says.

Decentralized clinical trials (DCTs) have accelerated in recent years in part due to the pandemic, which has heightened the need for virtual healthcare options. DCTs have also gained steam across the industry as a way to streamline recruitment and generate data-driven insights. Now, that potential is also being more fully realized in the rare disease space. But to leverage this option, pharma companies still need to find the patients.

With that goal in mind, digital health technologies are changing the game, simplifying how data is acquired, collected and processed, and even how patients are identified and enrolled.

“Most sites will never recruit enough people, but if you can enroll physicians who may only have one or two patients, handle the logistics and accountability aspects for them, and just have them do the assessment, you can potentially recruit many more principal investigators, Jarry says.

Connecting the dots with data

The approach ObvioHealth has adopted is to tap into real-world data derived from anonymized patient medical records, run algorithms to identify and diagnose those patients, then provide tools that allow patients to conduct many of the assessments and measurements from home to largely avoid site visits .

Through its healthcare software partner, Dedalus, ObvioHealth can access patient records from connected health systems, without having to move data from the electronic health records (EHRs) to a centralized location — essentially data at the edge of the hospital system. The query identifies patients and aggregates anonymized information.

“By querying the data in these site networks, we can more readily gain access to the information and go deep in terms of clinical data points that we’re looking at,” says Craig Gravina, chief technology officer at ObvioHealth. “What we’ve observed is that rare diseases tend to be misdiagnosed or undiagnosed, and we’re finding patterns in the data that essentially connect the dots between previous misdiagnosis and that eventually get diagnosed with a rare disease. This is ground-breaking because many of these patients would not have known that they are potentially eligible for certain clinical trials.”

Taylor Major, implementation project manager at ObvioHealth, says one example is with breast implant associated lymphoma, which is often missed because when a plastic surgeon conducts surgery to remove the encapsulated implant, they often see it as a complication of the procedure, rather than an indication of the disease.

“Being able to identify patients with [a] particular mutation to join a trial that is testing an alternative to platinum-based chemotherapy would not have been possible before.”

Craig Gravina

Chief technology officer, ObvioHealth

“When you look at those lab reports after the patient presents with lymphoma, you see that there were atypical cells present on the capsule itself,” Major says. “Using technology such as machine learning and algorithms lets us find those connections between medical history amongst similar patients, in this case by reviewing a plastic surgeon’s lab reports from capsulectomies that are performed and finding patterns in patients that ended up developing lymphoma. You can then identify patients for clinical trials by looking across a wider group of plastic surgeons.”

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