#scope2023 is living up to its mission of driving innovation in clinical trials. Here are a few key takeaways that stood out from the expert panel at the Novel Evidence for Regulatory Success breakout session.
 | Moderator: Michelle Crouthamel, PhD, Head, Digital Sciences, AbbVie, Inc.In this session, the panelists discussed the scope of novel evidence, including RWE, Digital Biomarkers, and Novel Digital Endpoints. There has been a proliferation and adoption of RWE and DHT in the life cycle of drug development to improve outcome measurements and accelerate medicine development. The Panelists shared lessons learned and dissected successful cases to identify key “must haves” on how to use novel evidence to support regulatory decisions. |
 | Panelist: Jennifer Goldsack, Executive Director, Digital Medicine Society (DiMe)Takeaway: The paradigm of high-performing tech, heterogeneous algorithms and clinically relevant datasets will expand to include other data sources, such as imaging and tissue data. Jen foresees 2 digital endpoint labels by year-end.Advancing the adoption of these types of measures will require the creation of more educational programs for stakeholders. |
 | Panelist: Christopher Boone, PhD, Global Head, Health Economics & Outcomes Research, AbbVie, Inc.Takeaway: Pharma is experiencing an awakening with collaboration and beginning to de-silo data sources with wearables, digital tech, and genomic data. Sponsors are growing increasingly confident and beginning to embrace novel data sources.Sponsors need to upscale or rescale talent to understand the digital endpoints space. |
 | Panelist: Amy Abernethy, MD, PhD, President of Product Development and Chief Medical Officer at Verily; Former Principal Deputy Commissioner, FDATakeaway: Overlaying sensor data and ePRO provides a more complete view of the patient’s experience. Leveraging consumer-grade (easier to implement) and research-grade sensors to gauge differences and similarities in data output helps researchers create a standard. Science matters!Qualitative data really matters. Understand the quality of the underlying longitudinal data. All data is messy, and scientists need to cross-check data over time. You can’t get away with not doing the science!
The challenge for adoption is building scalable infrastructure. Endpoints must be continuously validated. |