Putting Theory into Practice by Developing a Novel Digital Health Technology-Derived Endpoint in Sleep Quality

Sleep disturbances associated with menopause (SDM) are common and bothersome, but there are currently no specifically licensed treatments, and studies thus far have used different methodologies to measure sleep quality. Among those, digital health technologies (DHTs) present an innovative approach that supports patient-centric drug development by providing insights into how a patient responds to treatment in real-world settings. DHTs therefore may offer a solution to provide unobtrusive objective measurement of SDM. Here we describe the joint development of a novel DHT-derived endpoint for assessing sleep quality in menopausal women through a collaborative approach from evidence generation to analytical, clinical, and usability validation based on regulatory guidance.

To demonstrate the fit-for-purpose of the novel DHT-derived endpoint, Bayer (drug developer), Sleepiz AG (DHT provider), and DEEP Measures (collaboration platform provider) partnered and applied established frameworks to leverage prior work while compiling comprehensive data, conducting a gap analysis, and curating evidence in the DEEP Measures collaboration platform based on and in preparation for discussions with health authorities. Initial regulatory feedback from health authorities provided useful input and supported the study design on the incorporation of the DHT-derived endpoint into the clinical development program of elinzanetant. Through collaborative efforts between the drug developer and the DHT provider, the novel DHT-derived endpoint (Sleepiz One+ for continuous, home-based measurement of wake after sleep onset in SDM and other sleep parameters) was implemented as an exploratory endpoint in a phase 2 pilot study where data to demonstrate fit-for-purpose were generated and validated against polysomnography, the gold-standard objective measure for sleep. The study outcomes alongside the results of the gap analyses and leveraging prior work were then structured systematically in the DEEP Measures platform. Data were organized according to the DEEP Stack model (which included information on the measurement definition, target solution profile, and instrumentation), and these facilitated the integration of our outputs directly into the regulatory package used for following health authority interactions to drive the acceptance of the novel endpoint.

We outline how various stakeholders collaborated to leverage prior evidence, interacted with regulatory authority, and incorporated a novel DHT-derived endpoint into clinical development programs. Evidence and data generated in the present project have the potential to build the basis for further endpoint and DHT development and validation.