Contributing lab leader: Craig Lipset
Addressing the challenges within clinical trials is critical to the effective and efficient use of new medicines. These challenges include recruitment barriers, cost-effectiveness, patient leakage, and overall patient experience. The integration of digital transformation has become a pivotal force for today’s lab leaders that not only promises enhanced efficiency but also holds the potential to revolutionize recruitment strategies for clinical trials.
In this interview with Craig Lipset, we discuss how technology is changing the way we think about clinical trials and how it is being used to address the aforementioned key challenges for lab leaders. As clinical research enters this exciting new era, our discussion also sheds light on the strategies and innovations that are reshaping the future of healthcare research.
Q: By allowing primary care providers and labs to be involved in clinical trials and data collection, how will this improve the overall patient experience for those partaking in clinical trials?
Craig Lipset: There are several ways in which involving local healthcare providers, and nearby laboratories, in clinical research can significantly enhance access and the overall patient experience:
Q: Recruitment for clinical trials is trending downwards as we look for more targeted patient groups. How can labs make research and clinical trials more accessible and help us cross the proverbial bridge toward higher patient engagement?
Craig Lipset: Across disease areas, we're already starting to see systemic change in the research community and how it is engaging patients early in the study design and planning process.
Much of that starts with active listening. How do we consistently include the voice of patients in how we're planning, designing, and simulating our study protocols, and based on those insights, take action where possible?
When we do start by listening to patients, we inevitably hear first-hand the challenges of access given the demanding visit schedule of a typical clinical trial protocol. From time off from work, childcare responsibilities, travel, and logistics barriers, many of these barriers disproportionately impact the underserved and exacerbate inequities in research.
One response to this challenge has been to find new locations that are trusted and accessible for patients to participate in research – whether for assessments such as phlebotomy and vital signs or for other measures considered routine care. Laboratories, pharmacies, and community clinics are just a few of the “new locations” emerging for today’s clinical trials and are often enhanced with technology to help guide through necessary steps with real-time training.
These new doors opening for research participation help address recruitment and representation challenges and create new opportunities for lab leaders to engage in clinical trials.
Q: If we are moving toward making clinical trials more accessible to the wider patient populations, how can primary care providers and laboratories be more involved in this process?
Craig Lipset: Historically, we've had only two roles for physicians in clinical trials:
What we're starting to see today, secondary to our use of video and decentralized approaches, is this opportunity for expanding our community of healthcare providers and locations that can support research studies.
For example, the FDA in the United States released its draft guidance on decentralized trials earlier this year. For the first time, they are describing the role of the healthcare provider, a stakeholder in the community, as one that can provide support for routine care activities involved in the clinical trial.
This transformation will open up access for more patients because we know that patients want to learn about clinical research from their community healthcare provider, such as a primary care doctor, pharmacist, or lab team that has been helping them to manage a chronic condition. That's where they want to learn about all care options including research participation.
We can now remove the limitation of which physicians and all healthcare providers feel confident sharing research opportunities, by opening up access to routine care activities happening within all healthcare settings. We've addressed a key misaligned incentive and can now enable community physicians to keep control and access to their patient relationships. The same is true for many laboratories, where specimens can now be acquired locally and more easily for patients.
This is also going to unlock opportunities to use real-world data in exciting and new ways, with the ability to use artificial intelligence to improve and accelerate matches for study recruitment. Many data sets are anonymous and deidentified, which limits the ability to recontact an individual patient to invite them to participate in clinical trials. Reaching an individual patient often requires us to work with healthcare providers in the frontline with patient relationships, and the combination of data and analytics coupled with new ways to engage providers in research presents exciting opportunities.
Q: How exactly do you see clinical trials combining both traditional translational medicine with digital measurements and digital diagnostics? And what role will the laboratory play in collecting digital measurements?
Craig Lipset: Data and how it’s brought together represents a significant change in how we develop medicines. Data, whether obtained through medical records, patient self-reporting, digital devices, diagnostics, or harnessed with artificial intelligence and algorithms, is at the core of this transformation. Endpoints play a pivotal role in the world of research, serving as the yardstick for gauging efficacy and safety, the very essence of why research studies are initiated.
In many cases, our endpoints are out of date and are reliant on subjective in-clinic assessments of patients. There are ways to leverage smartphones, wearables, connected devices, and other sensors, and start to digitize measurements to make them more reproducible, and more reliable.
Take, for instance, the classic example of the six-minute walk test, commonly used in conditions like congestive heart failure and movement disorders. This test necessitates patients to visit the clinic and walk back and forth for six minutes while physiological data is monitored. However, this approach can now be greatly improved through the utilization of connected devices and wearables, allowing for continuous, extended monitoring exceeding the six-minute window.
Oftentimes, the data we generate from a digital measure requires us to have additional contextual data. It's too easy for us to rely just on a digital measurement and lose important context.
For example, imagine I have a clinical research study of a new medicine in rheumatoid arthritis, where patients are given a wearable device because I believe that they'll be more active if I can address their condition. If I start to see patients where that activity tracker is showing less activity, does it mean my drug isn't working?
The answer is no. In a recent research study, we found that patients who had lost the ability to knit due to having rheumatoid arthritis, were now able to knit again. Without that contextual data, the results would have been different because the activity tracker made it look like they were moving less because they were sitting and knitting.
This amplifies the importance of contextual data when we're looking at new types of digital measurements. That type of contextual data not only comes in the form of asking patients but also from laboratory and other assessments that need to be captured alongside our new digital measurements.
The most powerful endpoints going forward will be ones to leverage AI and start to create contextually driven combinations of insights, that can take that digital measurement along with laboratory and self-reported data, and help us to get a far more thorough, insightful, and complete picture of change in that patient, whether for safety or efficacy.
Q: Which role within the clinical study would be responsible for collecting these digital measurements, and then communicating that with the physician who then takes the outputs to determine the reason behind the trend, positive or negative?
Craig Lipset: In today's environment, research studies will often have a laboratory involved in collecting specimens and analyzing data. We may also have a separate vendor involved that's specifically responsible for deploying an innovative new digital solution. That data is then flowed back through that vendor, often in parallel to the lab.
Those different data streams come together in the form of a study database, or perhaps even upstream of there, if we're starting to look at algorithms that bring this data together in more effective and efficient ways.
Now, how does that change the role of the laboratory in that process? It makes their data more meaningful. Having that data in a faster, more real-time manner is significant because it's going to feed into algorithms that are important for monitoring safety and efficacy not just in isolation, but in combination with other data streams. So temporality and speed are going to become more important for those data sources.
It also means that there could be opportunities for the lab to be more of a data integrator. And that may prove to be the case for some labs that want to bring together these disciplines.
Pharmaceutical companies have a group called translational medicine supporting when a potential drug progresses from lab research to clinical trials. This dedicated unit carefully strategizes to identify the biomarkers and novel measurement techniques essential for gauging the drug's efficacy and safety within the clinical setting.
Translational medicine is now complemented by a digital medicine division operating in tandem, sharing a comparable mission and scope. However, they possess a different set of tools at their disposal, and their engineers may look different in terms of being more electrical and mechanical rather than biological. But even with a different set of engineering tools, these digital teams still embed clinical experts to look at measurement opportunities at how to develop, qualify, and validate innovative new digital measurements. Together, they confront the common challenge of refining the transition from lab to clinic, but with a primary focus on harnessing digital innovations to enhance the measurement processes.
It may be that leaders in the laboratory space are helping to drive that convergence.
If you want to hear more from Craig Lipset about the future of clinical trials, then check out Revolutionizing clinical trials: A streamlined, smarter, and more rational future.