From The Editor | April 20, 2022

What Does The Future Hold For Decentralized Clinical Trials?

Ed Miseta

By Ed Miseta, Chief Editor, Clinical Leader

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What can we expect to see from decentralized trials in the next three years, and what are the challenges sponsor companies will need to overcome? A webinar hosted by IBM Watson hoped to answer that question and many others. In part 1 of this article the panel looked at precision medicine and real-world data. In this article the panel discusses the future of decentralized trials, how they will evolve, the role of technology, capabilities, and cost factors.

The discussion featured Lorraine Marchand, general manager of life sciences at IBM Watson Health; Nimita Limaye, research VP, Life Sciences R&D Strategy and Technology at IDC Health Insights; and Greg Cunningham, director of the RWE Center of Excellence at Eli Lilly and Company.

The Role Of Technology

With all the talk about precision medicine, RWD, and decentralized trials, it’s important to remember that technology will be the game changer and enabler that brings the needed data together. While the industry attempts to figure out the right use cases, bringing together the right data will be paramount. Data interoperability will be the game changer enabling future innovation.

Looking at decentralized clinical trials, the panel notes there are a couple of opportunities for improvement. The first revolves around how the clinical trial infrastructure has evolved over the years. Trials that used to have a site focus are now evolving to a patient focus. That move will require building a new set of tools to enable the transition. For example, the new patient focus may require novel trial recruitment and screening. How will sponsors find patients and screen them to see if are a fit for the protocol? Another example is patient consent to trials, which will require tools like eConsent to perform the process in a more remote manner.

Another exciting development is the use of technologies like ePRO to quickly and accurately capture patient data using apps, devices, and sensors. Those technologies will generate more data about the patient from the patient. RWD will enable companies to quickly identify patients and improve protocols. Today drug developers can run scenarios looking at the impact of changing a cut point on a lab or a biomarker to see how the action would impact the potential enrollment of patients.

The use of RWD can also enable the recruitment of patients as pharma companies move away from conducting trials at the large academic centers which have been used in the past. This may enable companies to acquire more patients from the rural areas of countries who are not near the large academic centers. This will also bring more diversity to the recruitment process.

Further innovation will also be needed to support the evolution of decentralized trials. The goal for the industry will be to continue to build on what has been learned over the last two years. The COVID-19 pandemic created more of an attitude revolution than a digital revolution. The technology solutions had already existed prior to COVID, but the industry needed proof that the technology and the decentralized model worked. What telehealth did for healthcare is what the decentralized trial model is now doing for clinical studies: showing the public what is available and drug developers what is possible.

Decentralized Trials Will Continue To Evolve

The panel believes companies are still evolving in terms of their maturity with decentralized trials.  Most companies are discussing them, but many are still not mature in their understanding of them. There are companies that want to perform them but are still unsure how to go about doing it. They have questions such as how to create a flexible and scalable model, how to integrate the patient voice into the process, and what key performance indicators (KPIs) need to be defined. Those organizations will need to develop a scoring model to assess where they are today and define how they are going to move forward to the next level.

The panel noted there are still misconceptions that exist around decentralized trials and perhaps the most common one is that sites will eventually go away. With the move to decentralized trials in the last two years, that does not seem to be happening.

The second misconception is that we may see a decrease in patient engagement and retention as the result of the lack of investigator engagement. While that issue remains a concern, assessments conducted thus far indicate patients are happier with a decentralized or hybrid model. In fact, one speaker noted sites have increased investments in technology by 50% to support decentralized models.

The final misconception is that a decentralized approach can be applied to every clinical trial. The panel believes are far from the day when every trial will become decentralized. Sponsor companies will need to find the right use cases for them.

This will involve figuring out the advantages and disadvantages of decentralized trials. These trials will not supplant the site, the investigator, or the patient/investigator relationship. Companies will determine where the technologies best fit. Clinical science will always need to be high touch combined with high tech. The high touch will have to lead, forcing companies to determine the appropriate balance. The industry will figure out high-value and meaningful work for site staff to conduct while allowing technology to take care of the lower-value work.

What Capabilities Will Be Needed?

Sponsor companies and CROs must still determine the in-house capabilities needed to pivot from conventional to decentralized trial models. Marchand notes discussions with pharma executives about decentralized models takes her back to the early days of risk-based monitoring (RBM). When it was first being implemented, it brought about huge organizational change. That change requires a mental reset, the restructuring of workflows, and the redefining of processes.

“When you're implementing a new model, organizational change management is a big piece because you're disturbing existing structures and methods,” she says. “Executives will need to build buy-in for this new model across the entire organization. Employees need to see the value added by it, where they fit into it, how workflows will change, and how their role will be impacted. Without that buy-in, the implementations will struggle.”

Marchand also believes companies will need to advance patient access, ownership, and control over their own data. There are now health portals for patients to view their data records, images, and labs. Blockchain technology will be critical in allowing patients to be able to pull their own data together to take advantage of clinical trial opportunities. She notes this approach is good for patients and the technology is available to make it a reality.

Is There A Cost Advantage?

Companies contemplating the move to a decentralized model may wonder how the cost compares to traditional clinical trials. Companies that have already implemented this approach are early in the learning curve and it may be too early for them to make cost comparisons.

There could potentially be 20 stakeholders involved in the implementation, all trying to learn the new model and understand how it will be implemented. Organizations are still defining their KPIs and discussing what should be measured, how to measure it, who to partner with, and acquiring the needed technologies.

As with any new technology, early implementations may see higher costs. As the models begin to scale and as workflows are established and streamlined, the cost efficiencies will become apparent.

Clinical trials have traditionally been budgeted on a per-patient basis. In each therapeutic area there is an average cost to find a patient and execute the trial requirements. A per patient stipend (or average cost) is derived by determining the difficulty of reaching the patient and the complexity of the trial. Eventually companies will determine how to adjust workflows, speed patient recruitment, collect data more efficiently, and ease the burden on sites. When that happens, the cost equations will change, and companies will see their costs decline.