Decentralized clinical trials
A framework for “direct-to-patient,” “remote,” “site-less,” and “virtual” clinical research
A year ago, the Elektra Labs team published a piece on “Software-Enabled Clinical Trials” which mapped software tools that support clinical research. Since then, a number of new clinical trial approaches have emerged, and in the following piece we’ll decrypt some of the buzzwords from “site-less” to “direct-to-patient” to “virtual” and beyond.
We depend on clinical trials to bring safe and effective drugs and devices to market.
However, many of our life-altering drugs and devices are developed on limited populations in strict laboratory-controlled settings, which may not match natural settings. Over 70 percent of potential participants live more than 2 hours away from the nearest study center — traveling to a physical clinical trial site is a burdensome, limiting engagement. Today, less than 5% of the US population participates in clinical research.
Additionally, the data collected in clinical trials — the information we hang multi-billion dollar investments on and that patients are literally living and dying by — is such a tiny snapshot of the lived experience with a disease.
Decentralized clinical trials (DCTs), which are conducted in a study participant’s home using digital tools, offer a way to make better informed decisions about the efficacy of new therapies. More sensitive, objective measures from digital technologies coupled with a greater density of information — continuously sampling multiple times a day, not just one a quarter — will help us fail faster and win cheaper (h/t Jennifer Goldsack).
“Being healthy or sick isn’t a one-shot deal; it’s a continuous process that spans a lifetime,” writes Sean Khozin, Director of the FDA INFORMED incubator in the WSJ, and the data from these digital tools may be “more valuable” than the data from traditional clinical trials, which are collected at specialized medical centers rather than in a patient’s natural environment.
Well-structured DCTs would use fewer resources to bring higher quality drugs and devices to the market at a faster rate. Our goal for this post is to provide (a) frameworks for evaluating DCTs and (b) definitions of supporting technologies — a lack of clarity and inconsistencies in language surrounding decentralized trials hinders scientific advancement.
In taking a cue from the FDA/NIH:
Effective, unambiguous communication is essential for efficient translation of promising scientific discoveries into approved medical products. Unclear definitions and inconsistent use of key terms can hinder the evaluation and interpretation of scientific evidence and may pose significant obstacles to medical product development programs. (BEST Framework)
Below is our first draft of frameworks and definitions for the community. If you have suggestions, send us a note on Twitter (@ElektraLabs) or leave a comment.
What is a decentralized clinical trial (DCT)?
Note for crypto readers — “decentralized” as defined in this post is not blockchain-related. If you’re interested in healthcare blockchains, check out the Mt. Sinai’s Center for Biomedical Blockchain Research (CBBR) Op-Ed in STAT News and this crowdsourced biomedical blockchain project list.
Two components: “where” and “how” patient data are collected
We proposed that two features of data collection that determine how “decentralized” a clinical trial is:
(1) Where are data collected? (e.g., how dependent is the trial on a “site” such as a clinic or hospital center?)
Clinical trial sites serve both patients and sponsors (e.g., the biopharma or device manufacturer). Historically, the site is where drugs, medical devices and other therapies are tested on humans — often a clinic or research hospital. A new model has emerged called “direct-to-patient” or “remote” where patient data is collected in the home or in the study participant’s natural environment.
This shift has recently accelerated as more digital tools are able to collect clinically-validated data (e.g., digital biomarkers and electronic outcomes assessments — eCOAs) and have improved operational capabilities (e.g., the ability to perform blood draws at home with mobile phlebotomists). Some clinical trials employ a hybrid approach, with some sites and some remote locations. Craig Lipset at Pfizer refers to these as “location-flexible” trials. These types of trials can maximize patient-centricity by allowing the study participant more flexibility in how she or he participates in the research.
As with any new technology, the industry is still figuring out the best language to describe emerging products and services. Some people refer to remote clinical as “site-less” clinical trials, though this phrase is a misnomer. Even Science 37, which specializes in this this type of trial, has a site: itself. Science37 is the site — the “Metasite” — which provides support and coordination for the study participants.
(2) How are the data collected? (e.g., does collection require an intermediary like a study team or phlebotomist to collect the data?)
In the past, most clinical trial data were collected via an intermediary — someone from the study team would record the information and then record it in the EDC and/or case report form.
The data could be used to test the waters and monitor a new study site or participant to inform future protocol design, or the data could be used to provide evidence to support the study endpoint. A clinical trial endpoint refers to “an event or outcome that can be measured objectively to determine whether the intervention being studied is beneficial” (NIH).
As digital tools advance, we can collect more endpoint-supporting data at home via digital surveys and sensors, and study teams can “visit” patients at home via telemedicine conference calls. This means that more of the data is participant/patient-generated.
The word “virtual” in clinical trials has a loose meaning — sometimes used to refer to “remote” studies as described above. In this case, we define fully virtual as the method for collecting clinically-validated patient data without an intermediary, and all the data are fully participant-generated.
Why are decentralized clinical trials attractive?
DCTs have a long list of benefits, many of which were published last week in STAT News First Opinion by Andreas Koester, M.D., the global head of Janssen Clinical Innovation. DCTs can recruit more people into trials, increase retention and engagement, collect more continuous data in natural settings, while shortening the study time (faster to market), and decreasing costs.
Additionally, software-driven tools like telemedicine, personalized and clinically validated digital measures from wearable sensors (e.g., digital biomarkers) and surveys (eCOA), and improved logistics and operations management (e.g., coordinating phlebotomists at home, shipping drugs to the home) have accelerated the feasibility and adoption of DCTs in recent years.
Should all studies be fully decentralized?
No — “fully decentralized” does not make sense for all types of research. Some studies may require an MRI or CT scan, and these technologies are expensive and are maintained at a clinical trial site. Also, the study protocol may require a blood draw, which is challenging to do at home with personalized tools (RIP Theranos).
The best study protocols mix and match centralized and decentralized tools based on study objectives. For instance, observational post-market studies, often called Phase IV studies, are great candidates for a fully virtual approach, which is able to capture more continuous data in natural environments. In contrast, oncology studies will likely require more of a hybrid approach with more expensive site-based equipment.
We asked Craig Lipset from Pfizer about his thoughts and he replied that “a study that is truly ‘patient-centric’ would allow the patient to choose their own adventure.” If, for example, the patient is capable and wants to supply their own blood sample, then the protocol could be set up without an intermediary (more decentralized). In contrast, many people we asked preferred to have a trained phlebotomist draw their blood (less decentralized) rather than using a home-kit.
Which organizations are supporting decentralized trials?
Pfizer’s 2011 REMOTE (Research On Electronic Monitoring of Overactive Bladder Treatment Experience) study is widely referenced as the first clinical trial of an FDA-approved pharmaceutical, which used digital technologies to recruit and manage participants entirely from their homes. This study also pioneered one of the first versions of electronic informed consent (eConsent).
Since then, a number of contract research organizations (CROs) and specialty vendors have developed products across the decentralized trial spectrum, many of which we outlined in our blog post last year on software-enabled clinical trials.
Biopharma and medical device companies (“sponsors”) commonly work with a number of third party vendors during product development (e.g., contract research organizations / CROs). Some companies are more “full-service” in their approach, and will support the sponsor to design the protocol, recruit patients, set up and manage the site, and capture the patient data.
Other companies focus in a single area, like high-quality data capture, because many digital tools for clinical trials have features that require special attention (e.g., continuous data capture from a wearable is harder to collect, store, analyze and validate than a blood test taken 1x/month for a year). With specialization, it’s more likely that generalist CROs will have to partner with a few “speciality” vendors — companies like Koneksa and Evidation — to successfully bring a product to market.
The software-enabled clinical trials landscape is moving quickly, and as organizations develop deeper product offerings a one-page landscape map is limiting, because duplicating logos gets cluttered.
To update the map over the past year, we asked organizations to add comments to our Medium post or send us an email with product update — two methods that are not scalable. So, we followed the lead of all the organizations on this map, and created a web-based version of the landscape, which we launched at DPharm this month. Check out the landscape map on the Elektra Labs website to add and update company information.
Overtime, we hope to collaborate with other catalogs like eClinicalHealth’s EDCMarket to create a more holistic view of company offerings to support software-enabled clinical trials. EDC Market’s data is becoming out of date and the team is open to finding partners to revive and expand the database.
The Clinical Trial Transformation Initiative (CTTI), a public-private partnership co-founded in 2007 by Duke University and the US FDA, is currently working on a project exploring the challenges of conducting decentralized clinical trials (DCTs) through telemedicine and mobile healthcare providers.
CTTI is digging into several topics including “telemedicine state licensing issues, FDA review division reception, and Good Clinical Practice-related issues — to help advance widespread use of mobile technologies in DCTs.” CTTI released their work this fall at DPharm in Boston last month.
We believe that many clinically-validated endpoints used in research will eventually transition into clinical care. Historically, measures that supported research (“endpoints”) and care (“outcomes”) were siloed, but we are seeing examples of these measurements merging. Many companies are working towards a universal vision of human digital measurement across the continuum of research and clinical care.
For example, Evidation Health views itself as new kind of health and measurement company that focuses on quantifying the relationship between everyday behaviors and health outcomes. They don’t see themselves as a CRO / clinical trials company, per se, though Evidation does a lot of clinical studies in their enterprise B2B business.
Clinical research provides a practical use-case to link (or validate) everyday behaviors and outcomes. For instance, many companies like Evidation, Sage Bionetworks, and Medable that are developing digital biomarkers, “consumer-generated physiological and behavioral measures collected through connected digital tools” (Rock Health), validate these tools through clinical research as a first step towards what may eventually become a validated digital diagnostic (e.g., software that diagnosis) or a digital therapeutic (e.g., software that treats/intervenes).
Similarly, a number of big tech companies are also developing digital measures for more clinical settings. Last week, the FDA cleared a “software as a medical device” (SaMD) for the Apple Watch that determines the presence of atrial fibrillation, an abnormal heart condition. This clearance provides a regulatory pathway for Apple to create more advanced diagnostics and interventions for the patient, at home, decentralized.
It’s in the early days for SaMDs and digital measures like biomarkers. We will need better tools to compare and select validated measures for both research and care. Our next post will dig into more “digital measures,” including sensors, digital biomarkers and electronic clinical outcome assessments (eCOAs). Stay tuned.
This work was supported by the Harvard-MIT Center for Regulatory Science. Thanks to Joe Dustin, Craig Lipset, Chris Benko, Jennifer Goldsack, Bill Wood, Deborah Kilpatrick, Elena Izmailova, Yusuf Ghadiali, Franklin Yang, Ankit Gordhandas, Sofia Warner, Michelle Longmire, Marni Hall, Sean Khozin, Adam Goulburn, and many others for contributing to this piece.
An aside on “real-world,” a buzzword we avoided in this piece but is important to define. The regulatory definition of real-world data is the data collected outside of a randomized control trial (RCT). Real-world evidence is the evidence derived from real-world data. If study participants contribute to some measurements at home, e.g. pain measurement via ePRO or step count wearing a sensor, in the context of an RCT, they do not constitute real-world data because the participants have been preselected for study entry by the inclusion and exclusion criteria of a given trial — and do not represent the overall population in a certain indication. This is why in on this piece on DCTs, we talked about the benefits of health-related data collected in natural settings — and not “real world” settings. In the context of drug development, the “real world data” a biopharma collects in post-marketing studies is not restricted by the inclusion/exclusion criteria. (Adapted from a conversation with Elena Izmailova at Takeda)