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.
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 patients 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."
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.”
Once the data identifies where those patients are geographically, ObvioHealth can notify physicians that may have patients who would be eligible for a clinical trial, and those physicians can then rerun the query on their own data.
“At that point, the physician can begin the enrollment process for their patients,” Gravina says. “[Then] we can leverage the data in truly identifiable form along with any of the data we're collecting via the decentralized trial as well as data that is collected over the normal course of delivering care. So, it also becomes less cumbersome on the patient to participate in the trial.”
Enrolled patients receive an email invitation to download the app that takes them through informed consent, any training that they may need and when relevant, managing the shipping and logistics of devices that might be used for remote patient monitoring. Other digital technologies — or what the company refers to as augmented patient reported outcomes — are leveraged to provide more accurate predictions or assessments.
The future for DCTs in rare disease
Digital technologies and DCTs could improve a trial’s odds of success, the company says.
“Being able to run queries on larger datasets across different cases makes it possible to discover correlations and tap into many more patients than was possible in the past because we had a very siloed view of a subset of patients,” Jarry says. “Digitalization of clinical trials also will reduce costs, meaning researchers will be able to do more research on a larger set of patients, and that should increase the odds of a trial’s success. A third benefit is that as you're dealing with live data with remote patient monitoring, you may instantly identify things that normally would have taken months to figure out. We may also have a better understanding of why a treatment is working on a subset of the population and not on the others.”
Gravina points out that in addition to the breadth of data that EHR records offer, there will also be greater depth by leveraging ancillary systems such as pharmacy data, lab data and genetic data.
“As an example, oncology patients with the HER2 mutation tend to not respond very well to platinum-based chemotherapy,” he says. “Being able to identify patients with that particular mutation to join a trial that is testing an alternative to platinum-based chemotherapy would not have been possible before.”
A future stage of DCTs and digital clinical trials will potentially be in next-generation innovations. For example, Gravina says digital twins could be transformative in rare disease trials by allowing companies to potentially replicate a small patient population with variances to identify efficacy in a broader population.