Navigating the Use of Digital Health in Virtual Trials

Digital health is moving fast, as the FDA recently released a new guidance document on the initiative. Digital health companies are an important aspect, as the FDA will be leveraging companies (i.e., from Pre-Cert) to develop the digital health framework. In this interview, Dr. Michelle Longmire, CEO of Medable, will discuss her perspective on the advancements of digital health in clinical trials.

Moe Alsumidaie: Can you describe a virtual trial? What are some of the challenges and benefits?

Michelle Longmire

Michelle Longmire: A virtual trial is a new process where we take the standard clinical trial (involving numerous clinical visits) and conduct some part, or potentially the entire trial virtually. In a virtual trial, a visit where you may undergo a clinical assessment is now not done in the clinic visit, but is done via telemedicine or via a remote patient reported assessment, such as in a mobile app. In a clinical trial you have a couple of different components: the patient reported data, assessments and physiologic or biological data. Some of the benefits of a virtual trial include the ability to capture one or all of these data points without having the patient come in to the clinic. This opens the door for participation to a much broader audience, especially for patients who live far away from a study site. Currently, approximately 3% of potential patients that are eligible to participate in a clinical trial enroll. A virtual trial can broaden the rate of enrollment and enable a broader patient representation into a study. Another benefit of virtual trials, is that they provide more and higher quality data. When you have a participant filling a diary or recounting specific events that are pertinent to the trial, you have an element of retrospective bias or inaccurate data capture. The intention of a virtual trial is to improve the accuracy of data collection by enabling patient reporting at the moment in which an event occurred. For example, if the patient had an adverse event like a headache, they can self-report, or capture the adverse event at that specific moment, while they are experiencing symptomology. Patients can also capture a broader set of contextual data; where was the patient when they had that episode? Were they traveling? Were they sleep deprived? We can use the contextual data from the patient's life to better inform the events surrounding the adverse event. On the other hand, there are several challenges to virtual trial execution. The technology used to activate virtual trials enable patients, however, some technologies are not fit-for-purpose, or the patients will not use the device either because it is not comfortable, or impacts their quality of life in some way. Another challenge is industry acceptance among biopharmaceutical companies, and regulatory agencies; is the data captured in a virtual trial as accurate and validated as standard of care measurements and existing validated methods? If the patient reports the data directly from their home, how do we know that it’s not a family member? There are a lot of questions around ensuring that the data capture is validated.

MA: How are FDA’s Initiatives in Digital Health Advancing Innovation in the Field?

ML: FDA’s recent announcements on the Pre-Cert program, and the release of new guidance documents are important and interesting moves by the FDA, which signifies commitment to the advancement in digital health. To elaborate, companies who are directly involved in FDA’s Pre-Cert program can take what's being put out by the FDA on this topic on software and medical devices, and directly leverage evolution in digital health. This move by the FDA is good, as we can begin developing a framework for things like digital biomarkers, and how to apply them in clinical trials. Our company is very interested in the domain because in virtual trials, you are gathering digital data, which can be used to generate predictive models that can supplement a better understanding of the drug’s efficacy, or analytically identify patient safety events. For example, let’s say we are studying an immuno-oncology trial and are using biometric, and physiologic data coming from the participants via a validated wearable device. This data can open opportunities for generating digital signatures that can predict an adverse reaction or event or what would be a reliable and specific signal that could be used by providers for clinical study reports. If such systems are implemented on a scalable level, biopharmaceutical companies and investigators can inspect and intervene based on predefined and validated key risk and performance digital indicators.

MA: In the emerging world of digital health, how can study teams navigate the best digital health tools/devices for their studies? What approaches would you recommend?

ML: Let’s say you are doing a virtual trial and are looking at a specific therapeutic area. You are dealing with an antipsychotic medication with a known cardiovascular risk, so you want to capture an ECG to ensure a normal electrocardiogram. The question becomes which ECG to use. Is there a validated in-home ECG? Simple things like glucose and blood tests? What would be accepted on trial by the FDA? These remain big questions. There are traditional FDA cleared devices and devices that have been used in endpoint data collection at clinics and study sites. But, now that we are decentralizing and making remote clinical trials possible, we open the door to new devices that could play this role. Right now, what the community really needs is an evolving list of devices that are best for certain physiologic data capture and where does that stand in the approval process. Everyone has something to work from in collecting these devices, and we maintain this list internally in our company for which we are often asked, but, this information should be a community resource. If the FDA were to publicize these devices, the act would help inform the community about what is acceptable, and promote virtual trial design.

MA: Medable goes further by offering a system that collects, aggregates, and portrays the data in a beneficial way for researchers. How do companies, such as Medable, that leverage digital health tools to implement studies, fit into running a digital health study?

ML: In each study we ask ourselves what set of tools are going to enable the best digital representation of the participant. Say you have a study for a psychiatric medication. You want to capture patient reported outcomes and environmental data, such as urban air quality and social determinants of disease. Those would be well-captured by a mobile device, so a patient can use an application for patient reported outcomes and location can be leveraged for those other contextual data pieces. Next, we look at what physiologic data needs to be captured and then we implement a validated format for capturing that physiologic data. We also help researchers implement adherence technology and programs. For example, there are connected pill bottles that can send a virtual signal that the pill bottle was opened and the time the patient reported taking the medications. Additionally, we look at what digital tools can be leveraged for gathering blood data. For example, if you want to look at pharmacokinetic levels of medication in a patient's blood, traditionally that task is performed in a lab setting, but now you can do a finger prick at home and just put a little drop of blood onto a card and that can be recorded via a mobile device, and read digitally at a remote lab. Finally, you want a system that aggregates and prepares the data for analysis by researchers. Within that, you want an analytics tool that essentially standardizes and normalizes data across the varied data types, so you can make sense of the confluence of different types of information and types of data. It is one thing to collect data from patients via digital devices, but, an entirely different approach, when you are able to comprehensively evaluate your data and medical signals in one system.

Moe Alsumidaie, MBA, MSF is Chief Data Scientist at Annex Clinical, and Editorial Advisory Board member for and regular contributor to Applied Clinical Trials.