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Leveraging Technology to Develop New Trial Endpoints

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Page 3 of 3 APPLIED CLINICAL TRIALS 31 December 2018 mHE ALTH meaningful to patients and/or the treatment of their condition; and faithfully measures the construc t intended. Ability to detect change: Sensitive enough to de- tect change when a change exists. Endpoint interpretability: The change in the end- point deemed meaningful to patients is understood (e.g., minimally clinically important difference [MCID] or individual responder definition). Conclusions There is huge potential for thinking differently about how existing technologies can be repurposed to enable novel measurements for health outcomes and health status in patients. The increased insights obtained through more frequent home-based mea- surement, and new objec tive outcome data that was not possible before, enables sponsors to build a far richer and more insightful picture of intervention ef fects, which will aid early decision-making and contribute to labelling claims in the future. While these remain exploratory in nature and more work is needed to pro- vide the level of validation around these new endpoints, they have great potential to aid drug development and regulator y decision- making, and may also have value in the care and management of patients in routine care. The life sciences industry should adopt a culture of facilitating the exploration of new technolog y implementation within trials in an exploratory way, and aim to share experience, information, and access to the technologies showing most promise. Only through extended use will sufficient data and experience of using these new endpoints be accumulated to enable their acceptance in regulatory decision-making. References 1. Byrom B, Mc Carthy M, Schueler P, Muehlhausen W. Brain Monitoring Devices in Neuroscience Clinical Research: The Potential of Remote Monitoring Using Sensors, Wearables, and Mobile Devices. Clinical Pharmacology and Therapeutics, 2018. 2. Katholic University of Leuven. Campus Insight Magazine. www.kuleu- (May 2010). 3. Looney, D.P. et al. The in-the-ear recording concept user-centered and wearable brain monitoring. IEEE Pulse 3(6), 32–42 (2012). 4. Prichep, L.S., John, E.R., Howard, B., Merkin, H., Hiesiger, E.M. Evalu- ation of the pain matrix using EEG source localization: a feasibility study. Pain Med. 12(8), 1241-8 (2011). 5. dos Santos Pinheiro, E.S. et al. Electroencephalographic Patterns in Chronic Pain: A Systematic Review of the Literature. PLoS ONE 11(2): e0149085 (2016). 6. 7. Lipsmeier F et al. Evaluation of smartphone-based testing to generate exploratory outcome measures in a phase 1 Parkinson's disease clini- cal trial. Mov Disord 2018; 33: 1287-1297. 8. Breedon, P., Byrom, B., Siena, L., Muehlhausen, W. Enhancing the Mea- surement of Clinical Outcomes Using Microsoft Kinect. International Conference on Interactive Technologies and Games (iTAG) 2016. IEEE Xplore. (2016). 9. Jintronix. TNJH-Case-Study.pdf 10. Breedon P et al. Face to face: An interactive facial exercise system for stroke patients with facial weakness, In (Rehabilitation: Innovations and Challenges in the Use of Virtual Reality Technologies; Eds: Wendy Powell, Albert "Skip" Rizzo, Paul M. Sharkey, Joav Merrick; Nova Sci- ence Publishers, 2017. 11. Breedon P, Byrom B, Siena L, Muehlhausen W. (2016) Enhancing the Measurement of Clinical Outcomes Using Microsoft Kinect. Interac- tive Technologies and Games (iTAG), 2016 International Conference on 2016 Oct 26 (pp. 61-69). 12. Byrom B, Walsh D, Muehlhausen W. New Approaches to Measuring Health Outcomes - Leveraging a Gaming Platform. Journal for Clinical Studies 2016; 8(6):40-42. 13. Lee SK et al. Measurement of shoulder range of motion in patients with adhesive capsulitis using a Kinect. PLOS ONE, 2015. 14. Byrom, B. et al. Selection of and Evidentiary Considerations for Wearable Devices and Their Measurements for Use in Regulatory Decision Making: Recommendations from the ePRO Consortium. Value Health (2018). Bill Byrom, PhD, is VP, Product Strategy and Innovation, CRF Bracket Endpoints Developed for Wearables Source: Byrom Figure 2. Evidence to support clinical outcomes assessments derived from novel technology sensors. 14 For personal, non-commercial use

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