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

Q1 Computer Science Digital Biomarkers Pub Date : 2020-11-26 eCollection Date: 2020-01-01 DOI:10.1159/000512500
Diane Stephenson, Robert Alexander, Varun Aggarwal, Reham Badawy, Lisa Bain, Roopal Bhatnagar, Bastiaan R Bloem, Babak Boroojerdi, Jackson Burton, Jesse M Cedarbaum, Josh Cosman, David T Dexter, Marissa Dockendorf, E Ray Dorsey, Ariel V Dowling, Luc J W Evers, Katherine Fisher, Mark Frasier, Luis Garcia-Gancedo, Jennifer C Goldsack, Derek Hill, Janice Hitchcock, Michele T Hu, Michael P Lawton, Susan J Lee, Michael Lindemann, Ken Marek, Nitin Mehrotra, Marjan J Meinders, Michael Minchik, Lauren Oliva, Klaus Romero, George Roussos, Robert Rubens, Sakshi Sadar, Joseph Scheeren, Eiichi Sengoku, Tanya Simuni, Glenn Stebbins, Kirsten I Taylor, Beatrice Yang, Neta Zach
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Abstract

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.

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建立竞争前共识,通过监管科学促进数字健康技术的使用,支持帕金森病药物开发。
目前急需创新工具来加快新型治疗方法的评估和后续审批,以减缓、阻止或逆转帕金森病(PD)的无情发展。对于药物开发管道中的众多候选药物来说,早期干预疾病的治疗方法是优先考虑的问题。目前还缺乏灵敏、客观但又能在临床上解释的测量方法,而这些测量方法可以捕捉到疾病有意义的方面。这给新疗法的开发带来了重大挑战,而患者临床表现的显著异质性以及帕金森病许多体征和症状的波动性又加剧了这一挑战。数字健康技术(DHT),如智能手机应用程序、可穿戴传感器和数字日记,可以在自然生活环境中对帕金森病的体征和症状进行客观、远程和频繁的测量,从而有可能弥补其中的许多不足。在 COVID-19 大流行的大环境下,有效实施此类战略的紧迫感更加强烈。为了在药物开发研究中采用这些技术,需要监管机构就实施适当技术(包括数字传感器数据的收集、处理和解读)的最佳实践达成共识。目前正在发起越来越多的合作倡议,以确定在帕金森病临床试验中推进使用 DHT 的有效方法。关键路径研究所的帕金森病关键路径联盟就是一个典型的例子,在该联盟中,利益相关者就如何在帕金森病临床试验中有效使用 DHT 与监管机构进行了集体接触。包括美国食品药品管理局和欧洲药品管理局在内的全球监管机构正在鼓励通过多方利益相关者联盟提高数据驱动参与的效率。为此,我们回顾了如何通过整合知识、专业技术和数据共享来最大限度地提高效率,从而最有效地推动 DHT 的发展。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
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来源期刊
Digital Biomarkers
Digital Biomarkers Medicine-Medicine (miscellaneous)
CiteScore
10.60
自引率
0.00%
发文量
12
审稿时长
23 weeks
期刊最新文献
The State of Digital Biomarkers in Mental Health. The Imperative of Voice Data Collection in Clinical Trials. eHealth and mHealth in Antimicrobial Stewardship Programs. Detecting Longitudinal Trends between Passively Collected Phone Use and Anxiety among College Students. Video Assessment to Detect Amyotrophic Lateral Sclerosis.
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