Stretching Your Dollar: Methods and Tools for Increasing the Productivity of Clinical Trials

Stretching Your Dollar: Methods and Tools for Increasing the Productivity of Clinical Trials

Recent Tufts data show clinical trials typically take nearly twice as long to complete enrollment as predicted at launch and 48% of trial sites miss enrollment targets.  Delays in trials shrink the returns from patent exclusivity and increase R&D costs.  New methods such as adaptive and batch trial designs, as well as explosive growth in digital tools, such as Apple’s ResearchKit, have transformed the trial design choices available.  These innovations, and others, provide new levers for boosting productivity of clinical trials—with positive implications for improving cash burn rates and accelerating speed-to-market.

At the 14th Annual BIO Investor Forum in San Francisco, a panel of clinical trial experts came together to discuss recent innovations in trial design choices now available through new digital tools. The panel was moderated by Lawrence Klein, Associate Principal, McKinsey & Company, who opened up the discussion with a discussion of R&D ROI, which has declined 3 fold in the past few years, and is a significant challenge in the industry. Recent research indicates that the cost of new drug approval has increased to a staggering $2.6B, in part because the cost of clinical trials is rising. Thirty percent of trials fail because they never reach their enrollment goals. How to increase participation in clinical trials is a critical challenge for the industry. A promising new approach is patient facing clinical tools, which is a disruptive model that makes it easier for patients to participate in clinical trials.

Klein then introduced the panelists, which were:

  • Noah Craft MD, PhD, DTM&H, Co-Founder & CEO, Science 37, Inc.
  • Avi Kulkarni, PhD, Managing Director and Senior Vice President, Quintiles Advisory Services
  • Michael V. McConnell, MD, MSEE, Professor of Medicine, Director of Cardiovascular Health Innovation and PI of the MyHeart Counts Health research study, Stanford School of Medicine
  • Matt Noble, Senior Director, Product Management, Medidata Solutions
  • Komathi Stem, Strategic Innovation Leader, Genentech

Klein began by asking Craft what he saw as the pain points of the traditional clinical model as someone who has worked on both traditional clinical trials and with new digital approaches. Craft replied that at Science 37, they are reinventing the clinical trial model. In the new model, trials are not done at research centers anymore. They can now be done at the patient’s homes. The reason that this is an advantage is because it addresses many of the barriers to entry that exist in traditional clinical trials, which are: awareness, trust, and location. Many patients would prefer to stay with their current doctors, and doctors are often not interested in having their patients that are on a treatment regimen enroll in a clinical trial. Under the new model, patients can stay with their doctors, and doctors are able to stay more involved in the care of their patients. The increased enrollment then helps companies with their clinical trials, which results in a win-win situation for all parties involved.

Kulkarni raised the question of whether there are other challenges besides awareness, trust and location to overcome with this new approach. Craft agreed that there were, and cited how having hundreds of research sites can pose challenges due to people being trained in different ways. However, it can also present an opportunity for reporting adverse events 24/7.

McConnell discussed MyHeart Counts, which is a Stanford app that brings the clinical trial to the patient. It uses the ResearchKit platform, which is an open source software iPhone toolkit. It allows you to build mobile research studies, making it easier for companies to create phone-based clinical trials. Studies can leverage the sensors in the phone like GPS, and can pull in data from connected devices. The patient can get access to how their data compares with that of others. The app also offers improvements in the traditional informed consent process in that the patient can go through the consent screens at their own pace more easily than when reading and signing a written document.

Klein asked: What can you learn from this type of study that you can’t learn as easily in traditional models? McConnell pointed out that in his work on cardiovascular disease, he found that it captures information on what people are doing on a daily basis more easily and accurately than surveys that rely on recall. People overestimate how much activity they do on surveys, but the phone actually tracks it through the sensors. This allows for more accurate and granular data.

Stem was asked for her perspective, and she said that we are still doing clinical trials in essentially the same way as we have done 50 years ago, even as we are seeing innovations happening in the clinical care sphere. There is even a new universe opening up of patients generating their own data. What needs to happen now is we need to start pushing our teams to push the envelope on clinical trials as well. Someone needs to step forward and take the lead; we can’t all be “fast followers.”

Klein asked her if she saw resistance to clinical trial innovation, and Stem said she had. She explained that we are not like the tech industry; we can’t just push out software updates our released products like they can. However, we need to nurture “safe sandboxes” where innovation can occur, and protect this innovation from the “antibodies” of the company who will try to kill it off. She pointed out that the FDA has issued guidance on eConsent, so it is a little strange to see so few companies using it. Klein agreed, adding that it is scary to see the FDA taking the lead on an issue.

Another key issue discussed during the panel was the challenge of shifting tests to the point of care. Kulkarni described “hugely invasive” tests such as in-vitro diagnostics as “the water’s edge.” However, Stem pointed out that mobile nurses can be utilized to do the types of testing that it would be unrealistic to expect patients to do themselves. Craft agreed, adding that there was value in “radical collaboration” where mobile nurses can be brought to patient’s homes through the technology, making a “wildly complex operation” into a seamless experience.

Klein asked the panel: Where is the tech currently at in the industry? Can we actually use this, or is it still in development? The panel had somewhat differing opinions on the use of consumer-grade devices. Noble said that he has seen the use of both consumer and medical grade devices, and it really depends on your needs. He pointed out that there are tools that are designed to replace clinical trial tools, but there are also tools that can collect data never captured before in traditional clinical trials. For example, sensors can now be used on the Apple Watch to sense vomiting. In the past, sleep studies were hard because patients had cameras and lights on them and that affected sleep, but now the patient can sleep with a wearable and it creates better data. However, Stem pointed out that consumer devices have been more unpredictable than medical grade devices, so her company is moving away from consumer devices.

Many of the panelists agreed on the need for good analytics on the collected data. McConnell said that a good analytics platform has help cluster data in ways that is very difficult to do by hand.

Another issue raised by the panelists is that while the new technologies can eliminate barriers to clinical trial recruitment, there is still a need for recruitment efforts. McDonnell pointed out that recruitment was easier when done through a phone app, but you still need to think about how to get them to do initial download. There may be a role for the marketing of studies, such as social media and advertising on the sides of buses. Stem pointed out that there is also no substitute for understanding the patient journey. She mentioned a case where researchers tried to figure out why patients weren’t showing up for a global pandemic study; it turned out that when people got sick, they go to primary doctor, and not to the specialists they were using to recruit. Understanding this allowed them to shift recruitment to primary care physicians, and get much better results.


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