Precompetitive Consensus Building to Facilitate the Use of Digital Health Technologies to Support Parkinson Disease Drug Development through Regulatory Science

Innovative tools are urgently needed to accelerate the evaluation and subsequent approval of novel treatments that may slow, halt, or reverse the relentless progression of Parkinson disease (PD). Therapies that intervene early in the disease continuum are a priority for the many candidates in the drug development pipeline. There is a paucity of sensitive and objective, yet clinically interpretable, measures that can capture meaningful aspects of the disease. This poses a major challenge for the development of new therapies and is compounded by the considerable heterogeneity in clinical manifestations across patients and the fluctuating nature of many signs and symptoms of PD. Digital health technologies (DHT), such as smartphone applications, wearable sensors, and digital diaries, have the potential to address many of these gaps by enabling the objective, remote, and frequent measurement of PD signs and symptoms in natural living environments. The current climate of the COVID-19 pandemic creates a heightened sense of urgency for effective implementation of such strategies. In order for these technologies to be adopted in drug development studies, a regulatory-aligned consensus on best practices in implementing appropriate technologies, including the collection, processing, and interpretation of digital sensor data, is required. A growing number of collaborative initiatives are being launched to identify effective ways to advance the use of DHT in PD clinical trials. The Critical Path for Parkinson’s Consortium of the Critical Path Institute is highlighted as a case example where stakeholders collectively engaged regulatory agencies on the effective use of DHT in PD clinical trials. Global regulatory agencies, including the US Food and Drug Administration and the European Medicines Agency, are encouraging the efficiencies of data-driven engagements through multistakeholder consortia. To this end, we review how the advancement of DHT can be most effectively achieved by aligning knowledge, expertise, and data sharing in ways that maximize efficiencies.

[1]  S. Hendrix,et al.  Clinically Meaningful Outcomes in Early Alzheimer Disease: A Consortia-Driven Approach to Identifying What Matters to Patients , 2017, Therapeutic innovation & regulatory science.

[2]  J. Cedarbaum,et al.  Targeted Therapies for Parkinson's Disease: From Genetics to the Clinic , 2018, Movement disorders : official journal of the Movement Disorder Society.

[3]  M Daumer,et al.  Considerations for development of an evidence dossier to support the use of mobile sensor technology for clinical outcome assessments in clinical trials. , 2020, Contemporary clinical trials.

[4]  S. Buckman-Garner,et al.  The Role of Public–Private Partnerships in Catalyzing the Critical Path , 2017, Clinical and translational science.

[5]  S. Masand,et al.  Accelerating Adoption of Patient-Facing Technologies in Clinical Trials: A Pharmaceutical Industry Perspective on Opportunities and Challenges , 2019, Therapeutic innovation & regulatory science.

[6]  B. Bloem,et al.  The Coronavirus Disease 2019 Crisis as Catalyst for Telemedicine for Chronic Neurological Disorders. , 2020, JAMA neurology.

[7]  P. Brown,et al.  Predicting motor, cognitive & functional impairment in Parkinson's , 2019, Annals of clinical and translational neurology.

[8]  Christine Manta,et al.  Digital Measures That Matter to Patients: A Framework to Guide the Selection and Development of Digital Measures of Health , 2020, Digital Biomarkers.

[9]  E. Ray Dorsey,et al.  Verily and Its Approach to Digital Biomarkers , 2017, Digital Biomarkers.

[10]  Ida Sim,et al.  Mobile Devices and Health. , 2019, The New England journal of medicine.

[11]  D. Berg,et al.  Cognitive changes in prodromal Parkinson's disease: A review , 2017, Movement disorders : official journal of the Movement Disorder Society.

[12]  R. C. Helmich,et al.  The Personalized Parkinson Project: examining disease progression through broad biomarkers in early Parkinson’s disease , 2019, BMC Neurology.

[13]  K. Kieburtz,et al.  New drugs for Parkinson's disease: The regulatory and clinical development pathways in the United States , 2018, Movement disorders : official journal of the Movement Disorder Society.

[14]  Martha Brumfield,et al.  The Critical Path Institute: transforming competitors into collaborators , 2014, Nature Reviews Drug Discovery.

[15]  B. Bloem,et al.  The Emerging Evidence of the Parkinson Pandemic , 2018, Journal of Parkinson's disease.

[16]  Francesca Cerreta,et al.  Digital technologies for medicines: shaping a framework for success , 2020, Nature reviews. Drug discovery.

[17]  Yoshihiro Kokubo,et al.  Global, regional, and national burden of Parkinson's disease, 1990–2016: a systematic analysis for the Global Burden of Disease Study 2016 , 2018, The Lancet Neurology.

[18]  Anirvan Ghosh,et al.  Evaluation of smartphone‐based testing to generate exploratory outcome measures in a phase 1 Parkinson's disease clinical trial , 2018, Movement disorders : official journal of the Movement Disorder Society.

[19]  Ariel V. Dowling,et al.  Verification, analytical validation, and clinical validation (V3): the foundation of determining fit-for-purpose for Biometric Monitoring Technologies (BioMeTs) , 2020, npj Digital Medicine.

[20]  Max A. Little,et al.  Technology in Parkinson's disease: Challenges and opportunities , 2016, Movement disorders : official journal of the Movement Disorder Society.

[21]  Alberto J Espay,et al.  Disease Modification in Parkinson's Disease: Current Approaches, Challenges, and Future Considerations , 2018, Movement disorders : official journal of the Movement Disorder Society.

[22]  Z. Mari,et al.  The Promise of Telemedicine for Movement Disorders: an Interdisciplinary Approach , 2018, Current Neurology and Neuroscience Reports.

[23]  Suchi Saria,et al.  Metadata Concepts for Advancing the Use of Digital Health Technologies in Clinical Research. , 2019, Digital biomarkers.

[24]  Jacqueline Corrigan-Curay,et al.  Real-World Evidence and Real-World Data for Evaluating Drug Safety and Effectiveness. , 2018, JAMA.

[25]  K. Taylor,et al.  Outcome measures based on digital health technology sensor data: data- and patient-centric approaches , 2020, npj Digital Medicine.

[26]  I. Clay,et al.  Qualification opinion on stride velocity 95th centile as a secondary endpoint in Duchenne Muscular Dystrophy measured by a valid and suitable wearable device , 2019 .

[27]  S. Barlas The 21st Century Cures Act: FDA Implementation One Year Later: Some Action, Some Results, Some Questions. , 2018, P & T : a peer-reviewed journal for formulary management.

[28]  Janet Woodcock,et al.  The FDA critical path initiative and its influence on new drug development. , 2008, Annual review of medicine.

[29]  John Wilbanks,et al.  Indicators of retention in remote digital health studies: a cross-study evaluation of 100,000 participants , 2019, npj Digital Medicine.

[30]  Sean Khozin,et al.  Developing and adopting safe and effective digital biomarkers to improve patient outcomes , 2019, npj Digital Medicine.

[31]  Max A. Little,et al.  Smartphone motor testing to distinguish idiopathic REM sleep behavior disorder, controls, and PD , 2018, Neurology.

[32]  Shyamal Patel,et al.  Clinical feasibility of a wearable, conformable sensor patch to monitor motor symptoms in Parkinson's disease. , 2019, Parkinsonism & related disorders.

[33]  M. Ringel,et al.  Innovation in Regulatory Science Is Meeting Evolution of Clinical Evidence Generation , 2019, Clinical pharmacology and therapeutics.

[34]  Paolo Bonato,et al.  A roadmap for implementation of patient‐centered digital outcome measures in Parkinson's disease obtained using mobile health technologies , 2019, Movement disorders : official journal of the Movement Disorder Society.

[35]  Cindy Howry,et al.  Selection of and Evidentiary Considerations for Wearable Devices and Their Measurements for Use in Regulatory Decision Making: Recommendations from the ePRO Consortium. , 2017, Value in health : the journal of the International Society for Pharmacoeconomics and Outcomes Research.

[36]  M. Horne,et al.  Evaluation of the Parkinson’s KinetiGraph in monitoring and managing Parkinson’s disease , 2017, Expert review of medical devices.

[37]  Dimitrios Athanasiou,et al.  European regulators’ views on a wearable-derived performance measurement of ambulation for Duchenne muscular dystrophy regulatory trials , 2019, Neuromuscular Disorders.

[38]  Thierry Troosters,et al.  Walking-related digital mobility outcomes as clinical trial endpoint measures: protocol for a scoping review , 2020, BMJ Open.

[39]  Arthur W. Toga,et al.  Precompetitive Data Sharing as a Catalyst to Address Unmet Needs in Parkinson’s Disease , 2015, Journal of Parkinson's disease.

[40]  Norbert Schuff,et al.  The Parkinson's progression markers initiative (PPMI) – establishing a PD biomarker cohort , 2018, Annals of clinical and translational neurology.

[41]  M D Kruizinga,et al.  Development of Novel, Value-Based, Digital Endpoints for Clinical Trials: A Structured Approach Toward Fit-for-Purpose Validation , 2020, Pharmacological Reviews.

[42]  Thomas Voit,et al.  A Movement Monitor Based on Magneto-Inertial Sensors for Non-Ambulant Patients with Duchenne Muscular Dystrophy: A Pilot Study in Controlled Environment , 2016, PloS one.

[43]  Z. Mari,et al.  Implementation of Telemedicine for Urgent and Ongoing Healthcare for Patients with Parkinson's Disease During the COVID-19 Pandemic: New Expectations for the Future. , 2020, Journal of Parkinson's disease.

[44]  R. Roubenoff,et al.  How soon will digital endpoints become a cornerstone for future drug development? , 2019, Drug discovery today.

[45]  M. Hallett,et al.  Past, present, and future of Parkinson's disease: A special essay on the 200th Anniversary of the Shaking Palsy , 2017, Movement disorders : official journal of the Movement Disorder Society.

[46]  D. Hill,et al.  The Qualification of an Enrichment Biomarker for Clinical Trials Targeting Early Stages of Parkinson’s Disease , 2019, Journal of Parkinson's disease.

[47]  Günther Deuschl,et al.  The new definition and diagnostic criteria of Parkinson's disease , 2016, The Lancet Neurology.