In a recent webinar, TJ Bowen (DeepLens Co-founder & Chief Scientific Officer) reviewed the impact of bottlenecks in clinical trial recruitment.
TJ was also joined by Deborah Friedman (Director of Clinical Research at a Southern California Cancer Center) and Jim Langford (Vice President of Clinical Operations at AIVITA Biomedical).
Together, Bowen, Friedman, and Langford were able to unpack the challenges faced and discuss how technology could be utilized to help free up resources within the healthcare community.
Take a look below at some of the best responses to come out of the webinar.
What are some of the current critical challenges in recruiting patients for these precision medicine studies?
Bowen: The pharmaceutical industry spends billions of dollars per drug to get these drugs to market. Every day that that drug has not launched, it costs a lot of money, just in time, that drug is not on the market. 25% of clinical trials fail to enroll fully. Almost half of the trials are delayed due to patient recruitment specifically...There’s a lot of steps involved in getting a drug through the clinical trial process. Obviously, it starts with trial design and understanding what the studies need to look like, but I think one of the big bottlenecks is really the identification and screening of those patients.
Langford: The cost of doing clinical trials has become appreciably higher. A lot of that does have to do with recruitment. The fact of the matter is that we just can't find the number of patients. I'm not surprised by the scene that only less than half meet the goals that we had set for enrolling in clinical trials. What is shocking though, is that [there is only] 5% of qualified patients…The challenge is the system is still struggling to keep up with the demand and really trying to find those patients. Because as we know, precision medicines are very helpful to the patients that they serve, but it requires more triage to find patients that actually will qualify for those precision therapies.
Friedman: It's very disappointing because not being able to reach full enrollment actually delays the entire process of getting things approved. One of the issues that we tend to see is that coordinators are extremely busy, and to screen a patient properly can take hours. When you're screening multiples, tens, hundreds of patients through a system, you're going to have lots and lots of patients who are not going to qualify, and coordinators develop a frustration level because they're looking for patients.
Let’s deep dive into the challenges more. What additional challenges are hindering patient enrollment?
Bowen: There is complex data that has to be sorted through. If you think about the way these trials work, a site might have dozens of ongoing trials, and each of those trials has dozens or hundreds of patients, which results in thousands of patient lives being covered. You then layer on the complexity that each one of those different studies might have its unique inclusion and exclusion criteria. They have to know ahead of time which specific criteria qualify or disqualify a patient for a study. Then, of course, you have starts, stops, and pauses of each of those studies. You not only have the data problem, but you have the complexity of having to know when a trial is recruiting. Patients are always being diagnosed, and staying on top of the number of patients is hard. These things really contribute to this narrow window of opportunity. A patient might not initially qualify for a study, but after some time, they might not be responding to treatment, so at that point, they might become ready for a study. All of these things add up to things that have to be tracked and monitored.
Is there a challenge surrounding limited connectivity and interoperability?
Bowen: In many cases, we still see that electronic medical records and laboratory information systems aren't always integrated. We see a lot of different third party genomic test results. Even at one hospital, you can have multiple vendors being used by different physicians to retrieve genetic results. Then, of course, when all of that comes together, you often have it sitting in unstructured data. That's hard to sort through when you're looking for a needle in a haystack. Then the last thing that we bucket it under is the ineffective manual tools. From our experience, this is an interesting challenge because what you have is all of these sophisticated prescreening requirements that are having to be managed by people. Yet, the tools available to them before the enrollment of a patient are pretty minimal.
What is the importance of trial design? How has this changed over time for new trials, like precision medicine and basket studies?
Langford: Design of study and feasibility have to go hand-in-hand. There's no point in designing a clinical trial that gives you a great scientific answer if you can't enroll any patients because of the design of the study, or the population you're looking for is so narrow that you expect a site to get one patient a quarter. You have to look at these things at the same time. You have to understand what that patient population might look like.
Bowen: It's a real challenge for people trying to run studies because you now have these complex trial designs, like basket studies. Historically, if you were a lung cancer doctor, you would know the lung cancer treatments, and probably the lung cancer trials that are available. But now that there are basket studies, that throws that traditional paradigm out the window, because you now have to know all of the molecular markers and the patient needs to be diagnosed by molecular marker, not necessarily tissue to qualify for a specific study.
Friedman: This definitely presents a challenge. Being that we're a community-based hospital, we have numerous community-based physicians who may or may not be even aware of some of the research trials that are ongoing in the community. One of the things that we've done to help bridge that gap is that we have a Director of Precision Medicine.
What do you see as the potential for technology like Deep Lens VIPER to help with the challenges of recruiting patients into trials?
Friedman: Technology can identify better quality candidates for us to screen so that my team doesn't spend hours and hours screening patients who never really should have been screened in the first place. Technology is narrowing down the sample size for them to look at, but it provides a better quality patient for them to screen. It is improving our numbers for adding additional patients to the trial. We’ve seen here at our site that with the ability to use Deep Lens’ VIPER, it's identifying better quality patients. In the initial process of setting up the trials within the VIPER system, the inclusion and exclusion criteria that we feel are the most important are selected. Then we'll go through the electronic systems to pull these patients out of the system to notify the coordinator that they have patients ready to be screened and for which particular trial they need to be screened. That's been very effective because it's giving us a better candidate to screen.
Langford: What I'm excited about is the fact that it gives me and the clinical site a platform for common communication. You can have a conversation that isn't just one-sided; it's a two-sided conversation talking about enrollment, and it opens up the level of communication that you just generally don't have in communicating with your sites. We've seen direct results from being able to talk to clinical sites regarding the use of Deep Lens’ VIPER, to help them identify patients for the clinical trials.
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