The digital biomarker team at GSK implement digital health tools in clinical studies of the company’s assets in the interest of generating evidence for the success for those tools. This aligns with the goal of regulatory bodies to put more onus patient-centred research in clinical studies. More and more case studies are showing that digital measures can centre the patient experience.
The course of creating digital measurements from inception to approval has no set-in-stone guidelines. Approving a digital measurement instead relies on a consortium effort of interactions with regulatory bodies like the FDA to prove that the technology meets requirements. In this presentation, Sahin Shah, a Digital Biomarker Lead, walks us through GSK’s position on validating digital measurements toward clinical endpoints.
Concept elicitation is the first point of validation along this journey. Starting with the concept of interest, qualitative research is undertaken which involves talking to the patients, healthcare professionals, and experts and finding out what would make their experience better. Often concepts of interest will be predicated on adding degrees of objectivity to measurements that are typically subjective (patient-reported outcomes, patient diaries, or interviews).
Then the researchers need to understand the context of use, landscaping out the different types of technology which may be able to capture the concept of interest. This is typically backed up with a literature review to examine the evidence that is available to support the use of the technology of interest.
In the case of nocturnal scratch in atopic dermatitis (a scratching disease that affects patients at night), the gold standard of measurement would be polysomnography which is expensive and impractical. The alternative, however, is to use PROs and patient diaries which can be heavily biassed and inexact. Here, using wearable devices like smart watches with gyroscopes, accelerometers, and actigraphy counters could be a solution to measure night-time scratches.
Analytical validation focuses on ensuring that the technology accurately captures and processes the necessary data points. This involves verifying both the hardware and software components to ensure they consistently and accurately capture and analyse data.
Clinical validation, on the other hand, involves testing the technology in the intended target population to ensure it correlates with existing methods and accurately captures the desired data. This step is crucial for demonstrating that the technology can perform effectively in real-world clinical settings.
