Modernizing the clinical trial landscape is a high priority at the US Food and Drug Administration (FDA). In a 2019 press release, former FDA Commissioner Scott Gottlieb, MD, cited the push towards precision medicine as one of the drivers of this initiative. “The advent of precision medicine is challenging the entire medical research ecosystem to develop more efficient approaches to testing and developing diagnostics and therapeutics,” he noted.
Digital technology is changing the healthcare landscape every day. Clinical trial design can take advantage of these changes and utilize technology to shift to a decentralized research model. Read more below.
More recently, in a 2020 press release, FDA Commissioner Stephen Hahn said that many of the changes the agency will implement “represent an acceleration of where we were headed before.” This includes support for decentralized clinical trials, greater use of telemedicine technology in clinical trials, and work related to laboratory-developed tests.”
Decentralized clinical trials increase opportunities for drug developers. They reach more patients across wider geographic areas, improving sponsors’ abilities to study, for example, extremely rare diseases or patients with a specific genetic biomarker. To conduct these types of trials though, sponsors must embrace the power of technology—something that some sponsors are still reluctant to do.
Survey results published by DT Consulting indicate that clinical trial professionals still hesitate to integrate digital technology into their processes. Results also showed that up to 43% of clinical trial sites are not utilizing digital support tools, and the sites that are using these tools are using them only for patient recruitment. Cost, complexity, and identification of the right technologies are among the top-rated challenges in adopting digital technologies into clinical trials.
Artificial intelligence (AI) use in healthcare isn’t new—so leveraging AI technology for decentralized clinical trials is a logical next step. AI and machine learning can help sponsors make various decisions, like determining the ideal population for a particular study or the ideal primary and secondary endpoints to optimize results. AI might also have applicability in guiding outreach and recruitment strategies, bringing sponsors to the right participants and, subsequently, reducing participant dropout during the trial process.
Because the use of technology to recruit patients may ultimately result in larger numbers of clinical trial participants, another potential application for AI is parsing the much larger amounts of data collected by researchers. We can also use AI to monitor and enhance patient adherence to trial protocols, including survey completion or use of wearables.
Watches, Sensors, and Ingestibles
Digital wearables are one of the easiest technologies to incorporate into clinical research. These devices range from wristbands to watches, sensors, and smart fabrics. They present a low barrier of entry and a simple learning curve. Smartwatch-powered EKG technology is already being used in trials to detect atrial fibrillation and acute coronary syndrome. A clinical trial sponsored by Duke University is evaluating how we can use wearable device data to aid early detection of COVID-19.
The role of these technologies in clinical trials is clear. In the decentralized trial landscape, PRA partnered with Janssen Pharmaceuticals to conduct the first-of-its-kind heart failure drug approval trial through a completely decentralized model, CHIEF-HF. The Phase IIIb, double-blind study requires participants to wear a Fitbit device and use a smartphone to communicate with researchers and collect study data.
One benefit of wearables is the ability for researchers to gather data in real time. This continuous collection of data goes a long way to eliminating in-person study visits, where the data collected is only a snapshot of the participant at a single point in time. Sponsors who incorporate multiple wearables—for example, an Apple Watch with a digital glucose sensor—can collect multiple types of data, allowing researchers to draw more robust conclusions.
Similar to wearables are ingestibles: ingestible sensors consumed by participants that communicate with tablet software to monitor outcomes. The first drug approved with this mechanism was digital aripiprazole (Abilify Mycite™). Adaptation of this digital tool has been slow, but future use of ingestibles might be one way to track medication adherence across trial populations and across conditions from infectious diseases to oncology.
PRA’s Mobile Health Platform (MHP) engages with patients wherever they are. Our app-based platform allows for some assessments such as consent, scales and diaries to be done remotely.Learn more about our MHP.
How to Choose the Best Digital Technology
Sponsors should know what it is that they want to measure. That is, determine if the trial is measuring clinical, biological, physical, or functional data, and then select the best digital technology to capture that information. The selection of the technology should be “specification-driven and collaborative,” engaging both technology manufacturers and patients, as well as key clinical trial staff and stakeholders. Each technology should be validated and verified—including an assessment of accuracy, precision, consistency, and uniformity—to ensure that the data is both high quality and objective.
2018 Recommendations from the Clinical Trials Transformation Initiative (CTTI) outlined some key considerations that sponsors should keep in mind when selecting digital technology for a clinical trial, decentralized or not.
For sponsors who are still wary of the decentralized model due to the reliance on technology, CTTI recommends that feasibility studies be conducted before implementing a particular technology in a large-scale study. In addition to quelling sponsor concerns, feasibility studies can collect important data from patients on the tolerability, acceptability, and usability of the technology in question.
Digital technology is changing the healthcare landscape every day. Clinical trial design can take advantage of these changes and utilize technology to shift to a decentralized research model. Artificial intelligence and wearables are two of the most common technologies that are powering the shift to a decentralized, digital research paradigm.
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