Digital Progression Biomarkers as Novel Endpoints in Clinical Trials: A Multistakeholder Perspective

Digital measures in epilepsy in low-resourced environments

INTRODUCTION

Digital measures and digital health-care delivery have been rarely implemented in lower-and-middle-income countries (LMICs), contributing to worsening global disparities and inequities. Sustainable ways to implement and use digital approaches will help to improve time to access, management, and quality of life in persons with epilepsy, goals that remain unreachable in under-resourced communities. As under-resourced environments differ in human and economic resources, no one approach will be appropriate to all LMICs.

AREAS COVERED

Digital health and tools to monitor and measure digital endpoints and metrics of quality of life will need to be developed or adapted to the specific needs of under-resourced areas. Portable technologies may partially address the urban-rural divide. Careful delineation of stakeholders and their engagement and alignment in all efforts is critically important if these initiatives are to be successfully sustained. Privacy issues, neglected in many regions globally, must be purposefully addressed.

EXPERT OPINION

Epilepsy care in under-resourced environments has been limited by the lack of relevant technologies for diagnosis and treatment. Digital biomarkers, and investigative technological advances, may finally make it feasible to sustainably improve care delivery and ultimately quality of life including personalized epilepsy care.

Improved measurement of disease progression in people living with early Parkinson's disease using digital health technologies

BACKGROUND

Digital health technologies show promise for improving the measurement of Parkinson's disease in clinical research and trials. However, it is not clear whether digital measures demonstrate enhanced sensitivity to disease progression compared to traditional measurement approaches.

METHODS

To this end, we develop a wearable sensor-based digital algorithm for deriving features of upper and lower-body bradykinesia and evaluate the sensitivity of digital measures to 1-year longitudinal progression using data from the WATCH-PD study, a multicenter, observational digital assessment study in participants with early, untreated Parkinson's disease. In total, 82 early, untreated Parkinson's disease participants and 50 age-matched controls were recruited and took part in a variety of motor tasks over the course of a 12-month period while wearing body-worn inertial sensors. We establish clinical validity of sensor-based digital measures by investigating convergent validity with appropriate clinical constructs, known groups validity by distinguishing patients from healthy volunteers, and test-retest reliability by comparing measurements between visits.

RESULTS

We demonstrate clinical validity of the digital measures, and importantly, superior sensitivity of digital measures for distinguishing 1-year longitudinal change in early-stage PD relative to corresponding clinical constructs.

CONCLUSIONS

Our results demonstrate the potential of digital health technologies to enhance sensitivity to disease progression relative to existing measurement standards and may constitute the basis for use as drug development tools in clinical research.

Digital Mobility Measures: A Window into Real-World Severity and Progression of Parkinson's Disease

BACKGROUND

Real-world monitoring using wearable sensors has enormous potential for assessing disease severity and symptoms among persons with Parkinson's disease (PD). Many distinct features can be extracted, reflecting multiple mobility domains. However, it is unclear which digital measures are related to PD severity and are sensitive to disease progression.

OBJECTIVES

The aim was to identify real-world mobility measures that reflect PD severity and show discriminant ability and sensitivity to disease progression, compared to the Movement Disorder Society-Unified Parkinson's Disease Rating Scale (MDS-UPDRS) scale.

METHODS

Multicenter real-world continuous (24/7) digital mobility data from 587 persons with PD and 68 matched healthy controls were collected using an accelerometer adhered to the lower back. Machine learning feature selection and regression algorithms evaluated associations of the digital measures using the MDS-UPDRS (I-III). Binary logistic regression assessed discriminatory value using controls, and longitudinal observational data from a subgroup (n = 33) evaluated sensitivity to change over time.

RESULTS

Digital measures were only moderately correlated with the MDS-UPDRS (part II-r = 0.60 and parts I and III-r = 0.50). Most associated measures reflected activity quantity and distribution patterns. A model with 14 digital measures accurately distinguished recently diagnosed persons with PD from healthy controls (81.1%, area under the curve: 0.87); digital measures showed larger effect sizes (Cohen's d: [0.19-0.66]), for change over time than any of the MDS-UPDRS parts (Cohen's d: [0.04-0.12]).

CONCLUSIONS

Real-world mobility measures are moderately associated with clinical assessments, suggesting that they capture different aspects of motor capacity and function. Digital mobility measures are sensitive to early-stage disease and to disease progression, to a larger degree than conventional clinical assessments, demonstrating their utility, primarily for clinical trials but ultimately also for clinical care. © 2023 The Authors. Movement Disorders published by Wiley Periodicals LLC on behalf of International Parkinson and Movement Disorder Society.

3D printing combined with biopredictive dissolution and PBPK/PD modeling optimization and personalization of pharmacotherapy: Are we there yet?

Precision medicine requires selecting the appropriate dosage regimen for a patient using the right drug, at the right time. Model-Informed Precision Dosing (MIPD) is a concept suggesting utilization of model-based prediction methods for optimizing the treatment benefit-harm balance, based on individual characteristics of the patient, disease, treatment method, and other factors. Here, we discuss a theoretical workflow comprising several elements, beginning from the physiologically based pharmacokinetic/pharmacodynamic (PBPK/PD) models, through 3D printed tablets with the model proposed dose, information range and flow, and the patient themselves. We also describe each of these elements, and the connection between them, highlighting challenges and potential obstacles.

Transforming Drug Development for Neurological Disorders: Proceedings from a Multidisease Area Workshop

Neurological disorders represent some of the most challenging therapeutic areas for successful drug approvals. The escalating global burden of death and disability for such diseases represents a significant worldwide public health challenge, and the rate of failure of new therapies for chronic progressive disorders of the nervous system is higher relative to other non-neurological conditions. However, progress is emerging rapidly in advancing the drug development landscape in both rare and common neurodegenerative diseases. In October 2022, the Critical Path Institute (C-Path) and the US Food and Drug Administration (FDA) organized a Neuroscience Annual Workshop convening representatives from the drug development industry, academia, the patient community, government agencies, and regulatory agencies regarding the future development of tools and therapies for neurological disorders. This workshop focused on five chronic progressive diseases: Alzheimer's disease, Parkinson's disease, Huntington's disease, Duchenne muscular dystrophy, and inherited ataxias. This special conference report reviews the key points discussed during the three-day dynamic workshop, including shared learnings, and recommendations that promise to catalyze future advancement of novel therapies and drug development tools.

In vivo effects of the alpha-synuclein misfolding inhibitor minzasolmin supports clinical development in Parkinson's disease

Direct targeting of alpha-synuclein (ASYN) has emerged as a disease-modifying strategy for Parkinson's disease and other synucleinopathies which is being approached using both small molecule compounds and ASYN-targeted biologics. Minzasolmin (UCB0599) is an orally bioavailable and brain-penetrant small molecule ASYN misfolding inhibitor in clinical development as a disease-modifying therapeutic for Parkinson's disease. Herein the results of preclinical evaluations of minzasolmin that formed the basis for subsequent clinical development are described. Pharmacokinetic evaluations of intraperitoneal 1 and 5 mg/kg minzasolmin in wildtype mice revealed parallel and dose-proportional exposures in brain and plasma. Three-month administration studies in the Line 61 transgenic mouse model of PD were conducted to measure ASYN pathology and other PD-relevant endpoints including markers of CNS inflammation, striatal DAT labeling and gait. Reductions in ASYN pathology were correlated with improved aspects of gait and balance, reductions in CNS inflammation marker abundance, and normalized striatal DAT levels. These findings provide support for human dose determinations and have informed the translational strategy for clinical trial design and biomarker selection for the ongoing clinical studies of minzasolmin in patients living with early-stage Parkinson's disease (ClinicalTrials.gov ID: NCT04658186; EudraCT Number 2020-003265).

Wearable device and smartphone data quantify ALS progression and may provide novel outcome measures

Amyotrophic lateral sclerosis (ALS) therapeutic development has largely relied on staff-administered functional rating scales to determine treatment efficacy. We sought to determine if mobile applications (apps) and wearable devices can be used to quantify ALS disease progression through active (surveys) and passive (sensors) data collection. Forty ambulatory adults with ALS were followed for 6-months. The Beiwe app was used to administer the self-entry ALS functional rating scale-revised (ALSFRS-RSE) and the Rasch Overall ALS Disability Scale (ROADS) surveys every 2-4 weeks. Each participant used a wrist-worn activity monitor (ActiGraph Insight Watch) or an ankle-worn activity monitor (Modus StepWatch) continuously. Wearable device wear and app survey compliance were adequate. ALSFRS-R highly correlated with ALSFRS-RSE. Several wearable data daily physical activity measures demonstrated statistically significant change over time and associations with ALSFRS-RSE and ROADS. Active and passive digital data collection hold promise for novel ALS trial outcome measure development.

Wrist-worn sensor-based measurements for drug effect detection with small samples in people with Lewy Body Dementia

INTRODUCTION

Few late-stage clinical trials in Parkinson's disease (PD) have produced evidence on the clinical validity of sensor-based digital measurements of daily life activities to detect responses to treatment. The objective of this study was to assess whether digital measures from patients with mild-to-moderate Lewy Body Dementia demonstrate treatment effects during a randomized Phase 2 trial.

METHODS

Substudy within a 12-week trial of mevidalen (placebo vs 10, 30, or 75 mg), where 70/344 patients (comparable to the overall population) wore a wrist-worn multi-sensor device.

RESULTS

Treatment effects were statistically significant by conventional clinical assessments (Movement Disorder Society-Unified Parkinson's Disease Rating Scale [MDS-UPDRS] sum of Parts I-III and Alzheimer's Disease Cooperative Study-Clinical Global Impression of Change [ADCS-CGIC] scores) in the full study cohort at Week 12, but not in the substudy. However, digital measurements detected significant effects in the substudy cohort at week 6, persisting to week 12.

CONCLUSIONS

Digital measurements detected treatment effects in a smaller cohort over a shorter period than conventional clinical assessments.

TRIAL REGISTRATION

clinicaltrials.gov, NCT03305809.

Empowering drug development: Leveraging insights from imaging technologies to enable the advancement of digital health technologies

The US Food and Drug Administration (FDA) has publicly recognized the importance of improving drug development efficiency, deeming translational biomarkers a top priority. The use of imaging biomarkers has been associated with increased rates of drug approvals. An appropriate level of validation provides a pragmatic way to choose and implement these biomarkers. Standardizing imaging modality selection, data acquisition protocols, and image analysis (in ways that are agnostic to equipment and algorithms) have been key to imaging biomarker deployment. The best known examples come from studies done via precompetitive collaboration efforts, which enable input from multiple stakeholders and data sharing. Digital health technologies (DHTs) provide an opportunity to measure meaningful aspects of patient health, including patient function, for extended periods of time outside of the hospital walls, with objective, sensor-based measures. We identified the areas where learnings from the imaging biomarker field can accelerate the adoption and widespread use of DHTs to develop novel treatments. As with imaging, technical validation parameters and performance acceptance thresholds need to be established. Approaches amenable to multiple hardware options and data processing algorithms can be enabled by sharing DHT data and by cross-validating algorithms. Data standardization and creation of shared databases will be vital. Pre-competitive consortia (public-private partnerships and professional societies that bring together all stakeholders, including patient organizations, industry, academic experts, and regulators) will advance the regulatory maturity of DHTs in clinical trials.

Wearable sensors for assessing disease severity and progression in Progressive Supranuclear Palsy

INTRODUCTION

Progressive supranuclear palsy (PSP) is an atypical parkinsonism characterized by prominent gait and postural impairment. The PSP rating scale (PSPrs) is a clinician-administered tool to evaluate disease severity and progression. More recently, digital technologies have been used to investigate gait parameters. Therefore, object of this study was to implement a protocol using wearable sensors evaluating disease severity and progression in PSP.

METHODS

Patients were evaluated with the PSPrs as well as with three wearable sensors located on the feet and lumbar area. Spearman coefficient was used to assess the relationship between PSPrs and quantitative measurements. Furthermore, sensor parameters were included in a multiple linear regression model to assess their ability in predicting the PSPrs total score and sub-scores. Finally, differences between baseline and three-month follow-up were calculated for PSPrs and each quantitative variable. The significance level in all analyses was set at ≤ 0.05.

RESULTS

Fifty-eight evaluations from thirty-five patients were analyzed. Quantitative measurements showed multiple significant correlations with the PSPrs scores (r between 0.3 and 0.7; p < 0.05). Linear regression models confirmed the relationships. After three months visit, significant worsening from baseline was observed for cadence, cycle duration and PSPrs item 25, while PSPrs item 10 showed a significant improvement.

CONCLUSION

We propose wearable sensors can provide an objective, sensitive quantitative evaluation and immediate notification of gait changes in PSP. Our protocol can be easily introduced in outpatient and research settings as a complementary tool to clinical measures as well as an informative tool on disease severity and progression in PSP.

An International Multi-Stakeholder Delphi Survey Study on the Design of Disease Modifying Parkinson's Disease Trials

BACKGROUND

Design of disease modification (DM) trials for Parkinson's disease (PD) is challenging. Successful delivery requires a shared understanding of priorities and practicalities.

OBJECTIVE

To seek stakeholder consensus on phase 3 trials' overall goals and structure, inclusion criteria, outcome measures, and trial delivery and understand where perspectives differ.

METHODS

An international expert panel comprising people with Parkinson's (PwP), care partners (CP), clinical scientists, representatives from industry, funders and regulators participated in a survey-based Delphi study. Survey items were informed by a scoping review of DM trials and PwP input. Respondents scored item agreement over 3 rounds. Scores and reasoning were summarized by participant group each round until consensus, defined as≥70% of at least 3 participant groups falling within the same 3-point region of a 9-point Likert scale.

RESULTS

92/121 individuals from 13 countries (46/69 PwP, 13/18 CP, 20/20 clinical scientists, representatives from 8/8 companies, 4/5 funders, and 1/1 regulator) completed the study. Consensus was reached on 14/31 survey items: 5/8 overall goals and structure, 1/8 Eligibility criteria, 7/13 outcome measures, and 1/2 trial delivery items. Extent of stakeholder endorsement for 428 reasons for scores was collated across items.

CONCLUSIONS

This is the first systematic multi-stakeholder consultation generating a unique repository of perspectives on pivotal aspects of DM trial design including those of PwP and CP. The panel endorsed outcomes that holistically measure PD and the importance of inclusive trials with hybrid delivery models. Areas of disagreement will inform mitigating strategies of researchers to ensure successful delivery of future trials.

The dilemma between milestones of progression versus clinical scales in Parkinson's disease

There are significant challenges in accurately documenting the progression of Parkinson's disease (PD). The disease course is highly heterogeneous, there are no validated biomarkers, and we are reliant on repeated clinical measures to assess disease state over time. Yet, the ability to chart disease progression accurately is vital in both observational and interventional study designs, where reliable measures are critical to determine whether an outcome has been met. In this chapter, we first discuss the natural history of PD, including the spectrum of clinical presentation and expected developments through the course of the disease. We then explore in detail the current strategies for measuring disease progression, which can be broadly divided into: (i) the use of quantitative clinical scales; and (ii) determination of the onset time of key milestones. We discuss the strengths and limitations of these approaches for use in clinical trials, with a particular focus on disease modification trials. The selection of outcome measures for a particular study will depend on multiple factors, but trial duration is an important determinant. Milestones are reached over a course of years rather than months, and hence clinical scales with sensitivity to change are needed for short-term studies. However, milestones represent important markers of disease stage which are not confounded by symptomatic therapies and are of critical relevance to the patient. Prolonged but low intensity follow-up beyond a limited period of treatment with a putative disease-modifying agent may allow milestones to be incorporated into evaluation of efficacy in a practical and cost-effective way.

Understanding how people with Parkinson's disease turn in gait from a real-world in-home dataset

INTRODUCTION

Turning in gait digital parameters may be useful in measuring disease progression in Parkinson's disease (PD), however challenges remain over algorithm validation in real-world settings. The influence of clinician observation on turning outcomes is poorly understood. Our objective is to describe a unique in-home video dataset and explore the use of turning parameters as biomarkers in PD.

METHODS

11 participants with PD, 11 control participants stayed in a home-like setting living freely for 5 days (with two sessions of clinical assessment), during which high-resolution video was captured. Clinicians watched the videos, identified turns and documented turning parameters.

RESULTS

From 85 hours of video 3869 turns were evaluated, averaging at 22.7 turns per hour per person. 6 participants had significantly different numbers of turning steps and/or turn duration between "ON" and "OFF" medication states. Positive Spearman correlations were seen between the Movement Disorders Society-sponsored revision of the Unified Parkinson's Disease Rating Scale III score with a) number of turning steps (rho = 0.893, p < 0.001), and b) duration of turn (rho = 0.744, p = 0.009) "OFF" medications. A positive correlation was seen "ON" medications between number of turning steps and clinical rating scale score (rho = 0.618, p = 0.048). Both cohorts took more steps and shorter durations of turn during observed clinical assessments than when free-living.

CONCLUSION

This study shows proof of concept that real-world free-living turn duration and number of turning steps recorded can distinguish between PD medication states and correlate with gold-standard clinical rating scale scores. It illustrates a methodology for ecological validation of real-world digital outcomes.

Digitizing a Therapeutic: Development of an Augmented Reality Dual-Task Training Platform for Parkinson's Disease

Augmented reality (AR) may be a useful tool for the delivery of dual-task training. This manuscript details the development of the Dual-task Augmented Reality Treatment (DART) platform for individuals with Parkinson's disease (PD) and reports initial feasibility, usability, and efficacy of the DART platform in provoking dual-task interference in individuals with PD. The DART platform utilizes the head-mounted Microsoft HoloLens2 AR device to deliver concurrent motor and cognitive tasks. Biomechanical metrics of gait and cognitive responses are automatically computed and provided to the supervising clinician. To assess feasibility, individuals with PD (N = 48) completed a bout of single-task and dual-task walking using the DART platform. Usability was assessed by the System Usability Scale (SUS). Dual-task interference was assessed by comparing single-task walking and walking during an obstacle course while performing a cognitive task. Average gait velocity decreased from 1.06 to 0.82 m/s from single- to dual-task conditions. Mean SUS scores were 81.3 (11.3), which placed the DART in the "good" to "excellent" category. To our knowledge, the DART platform is the first to use a head-mounted AR system to deliver a dual-task paradigm and simultaneously provide biomechanical data that characterize cognitive and motor performance. Individuals with PD were able to successfully use the DART platform with satisfaction, and dual-task interference was provoked. The DART platform should be investigated as a platform to treat dual-task declines associated with PD.

Patient-led development of digital endpoints and the use of computer vision analysis in assessment of motor function in rare diseases

Digital health technologies are transforming the way health outcomes are captured and measured. Digital biomarkers may provide more objective measurements than traditional approaches as they encompass continuous and longitudinal data collection and use of automated analysis for data interpretation. In addition, the use of digital health technology allows for home-based disease assessments, which in addition to reducing patient burden from on-site hospital visits, provides a more holistic picture of how the patient feels and functions in the real world. Tools that can robustly capture drug efficacy based on disease-specific outcomes that are meaningful to patients, are going to be key to the successful development of new treatments. This is particularly important for people living with rare and chronic complex conditions, where therapeutic options are limited and need to be developed using a patient-focused approach to achieve the biggest impact. Working in partnership with patient Organisation Duchenne UK, we co-developed a video-based approach, delivered through a new mobile health platform (DMD Home), to assess motor function in patients with Duchenne muscular dystrophy (DMD), a genetic, rare, muscular disease characterized by the progressive loss of muscle function and strength. Motor function tasks were selected to reflect the “transfer stage” of the disease, when patients are no longer able to walk independently but can stand and weight-bear to transfer. This stage is important for patients and families as it represents a significant milestone in the progression of DMD but it is not routinely captured and/or scored by standard DMD clinical and physiotherapy assessments. A total of 62 videos were submitted by eight out of eleven participants who onboarded the app and were analysed with pose estimation software (OpenPose) that led to the extraction of objective, quantitative measures, including time, pattern of movement trajectory, and smoothness and symmetry of movement. Computer vision analysis of video tasks to identify voluntary or compensatory movements within the transfer stage merits further investigation. Longitudinal studies to validate DMD home as a new methodology to predict progression to the non-ambulant stage will be pursued.

Deep Learning for Daily Monitoring of Parkinson's Disease Outside the Clinic Using Wearable Sensors

Now that wearable sensors have become more commonplace, it is possible to monitor individual healthcare-related activity outside the clinic, unleashing potential for early detection of events in diseases such as Parkinson's disease (PD). However, the unsupervised and "open world" nature of this type of data collection make such applications difficult to develop. In this proof-of-concept study, we used inertial sensor data from Verily Study Watches worn by individuals for up to 23 h per day over several months to distinguish between seven subjects with PD and four without. Since motor-related PD symptoms such as bradykinesia and gait abnormalities typically present when a PD subject is walking, we initially used human activity recognition (HAR) techniques to identify walk-like activity in the unconstrained, unlabeled data. We then used these "walk-like" events to train one-dimensional convolutional neural networks (1D-CNNs) to determine the presence of PD. We report classification accuracies near 90% on single 5-s walk-like events and 100% accuracy when taking the majority vote over single-event classifications that span a duration of one day. Though based on a small cohort, this study shows the feasibility of leveraging unconstrained wearable sensor data to accurately detect the presence or absence of PD.

Evaluating the Use of Digital Biomarkers to Test Treatment Effects on Cognition and Movement in Patients with Lewy Body Dementia

Background:

PRESENCE was a Phase 2 trial assessing mevidalen for symptomatic treatment of Lewy body dementia (LBD). Participants received daily doses (10, 30, or 75 mg) of mevidalen (LY3154207) or placebo for 12 weeks.

Objective:

To evaluate if frequent cognitive and motor tests using an iPad app and wrist-worn actigraphy to track activity and sleep could detect mevidalen treatment effects in LBD.

Methods:

Of 340 participants enrolled in PRESENCE, 238 wore actigraphy for three 2-week periods: pre-, during, and post-intervention. A subset of participants (n = 160) enrolled in a sub-study using an iPad trial app with 3 tests: digital symbol substitution (DSST), spatial working memory (SWM), and finger-tapping. Compliance was defined as daily test completion or watch-wearing ≥23 h/day. Change from baseline to week 12 (app) or week 8 (actigraphy) was used to assess treatment effects using Mixed Model Repeated Measures analysis. Pearson correlations between sensor-derived features and clinical endpoints were assessed.

Results:

Actigraphy and trial app compliance was > 90% and > 60%, respectively. At baseline, daytime sleep positively correlated with Epworth Sleepiness Scale score (p < 0.01). Physical activity correlated with improvement on Movement Disorder Society –Unified Parkinson Disease Rating Scale (MDS-UPDRS) part II (p < 0.001). Better scores of DSST and SWM correlated with lower Alzheimer Disease Assessment Scale –Cognitive 13-Item Scale (ADAS-Cog₁₃) (p < 0.001). Mevidalen treatment (30 mg) improved SWM (p < 0.01), while dose-dependent decreases in daytime sleep (10 mg: p < 0.01, 30 mg: p < 0.05, 75 mg: p < 0.001), and an increase in walking minutes (75 mg dose: p < 0.001) were observed, returning to baseline post-intervention.

Conclusion:

Devices used in the LBD population achieved adequate compliance and digital metrics detected statistically significant treatment effects.

Which decentralised trial activities are reported in clinical trial protocols of drug trials initiated in 2019-2020? A cross-sectional study in ClinicalTrials.gov

OBJECTIVES

Decentralised clinical trial activities-such as participant recruitment via social media, data collection through wearables and direct-to-participant investigational medicinal product (IMP) supply-have the potential to change the way clinical trials (CTs) are conducted and with that to reduce the participation burden and improve generalisability. In this study, we investigated the decentralised and on-site conduct of trial activities as reported in CT protocols with a trial start date in 2019 or 2020.

DESIGN

We ascertained the decentralised and on-site conduct for the following operational trial activities: participant outreach, prescreening, screening, obtaining informed consent, asynchronous communication, participant training, IMP supply, IMP adherence monitoring, CT monitoring, staff training and data collection. Results were compared for the public versus private sponsors, regions involved, trial phases and four time periods (the first and second half of 2019 and 2020, respectively).

SETTING

Phases 2, 3 and 4 clinical drug trial protocols with a trial start date in 2019 or 2020 available from ClinicalTrials.gov.

OUTCOME MEASURES

The occurrence of decentralised and on-site conduct of the predefined trial activities reported in CT protocols.

RESULTS

For all trial activities, on-site conduct was more frequently reported than decentralised conduct. Decentralised conduct of the individual trial activities was reported in less than 25.6% of the 254 included protocols, except for decentralised data collection, which was reported in 68.9% of the protocols. More specifically, 81.9% of the phase 3 protocols reported decentralised data collection, compared with 73.3% and 47.0% of the phase 2 and 4 protocols, respectively. For several activities, including prescreening, screening and consenting, upward trends in reporting decentralised conduct were visible over time.

CONCLUSIONS

Decentralised methods are used in CTs, mainly for data collection, but less frequently for other activities. Sharing best practices and a detailed description in protocols can drive the adoption of decentralised methods.

Virtual exam for Parkinson's disease enables frequent and reliable remote measurements of motor function

Sensor-based remote monitoring could help better track Parkinson's disease (PD) progression, and measure patients' response to putative disease-modifying therapeutic interventions. To be useful, the remotely-collected measurements should be valid, reliable, and sensitive to change, and people with PD must engage with the technology. We developed a smartwatch-based active assessment that enables unsupervised measurement of motor signs of PD. Participants with early-stage PD (N = 388, 64% men, average age 63) wore a smartwatch for a median of 390 days. Participants performed unsupervised motor tasks both in-clinic (once) and remotely (twice weekly for one year). Dropout rate was 5.4%. Median wear-time was 21.1 h/day, and 59% of per-protocol remote assessments were completed. Analytical validation was established for in-clinic measurements, which showed moderate-to-strong correlations with consensus MDS-UPDRS Part III ratings for rest tremor (⍴ = 0.70), bradykinesia (⍴ = -0.62), and gait (⍴ = -0.46). Test-retest reliability of remote measurements, aggregated monthly, was good-to-excellent (ICC = 0.75-0.96). Remote measurements were sensitive to the known effects of dopaminergic medication (on vs off Cohen's d = 0.19-0.54). Of note, in-clinic assessments often did not reflect the patients' typical status at home. This demonstrates the feasibility of smartwatch-based unsupervised active tests, and establishes the analytical validity of associated digital measurements. Weekly measurements provide a real-life distribution of disease severity, as it fluctuates longitudinally. Sensitivity to medication-induced change and improved reliability imply that these methods could help reduce sample sizes needed to demonstrate a response to therapeutic interventions or disease progression.

Opportunities and Challenges for Decentralized Clinical Trials: European Regulators' Perspective

Decentralized clinical trials (DCTs) have the potential to improve accessibility, diversity, and retention in clinical trials by moving trial activities to participants’ homes and local surroundings. In this study, we conducted semi‐structured interviews with 20 European regulators to identify regulatory challenges and opportunities for the implementation of DCTs in the European Union. The key opportunities for DCTs that were recognized by regulators include a reduced participation burden, which could facilitate the participation of underserved patients. In addition, regulators indicated that data collected in DCTs are expected to be more representative of the real world. Key challenges recognized by regulators for DCTs include concerns regarding investigator oversight and participants’ safety when physical examinations and face‐to‐face contact are limited. To facilitate future learning, hybrid clinical trials with both on‐site and decentralized elements are proposed by the respondents.

Reimagining India’s Health System: Technology Levers for Universal Health Care

Just as the COVID-19 pandemic highlighted the inadequacies of our current health systems and rekindled the debate around universal health care, the Lancet Citizens’ Commission on Reimagining India’s Health System was launched in late 2020. As a part of the commission, we articulated how technology can enable universal health care. We begin by stating the foundational values—a set of normative statements—that should underpin the use of technology in our health systems. Then, after summarising the paradigm shifts necessary to achieve citizen-centred universal health care, we articulate five ‘technology levers’ to enable those shifts. Finally, we describe the intersections and synergies between technology and the other pillars of health systems, namely, human resources, financing, governance and citizens’ engagement.

Considerations for clinical trial design and conduct in the evaluation of novel advanced therapeutics in neurodegenerative disease

The recent advances in the development of potentially disease modifying cell and gene therapies for neurodegenerative disease has resulted in the production of a number of promising novel therapies which are now moving forward to clinical evaluation. The robust evaluation of these therapies pose a significant number of challenges when compared to more traditional evaluations of pharmacotherapy, which is the current mainstay of neurodegenerative disease symptom management. Indeed, there is an inherent complexity in the design and conduct of these trials at multiple levels. Here we discuss specific aspects requiring consideration in the context of investigating novel cell and gene therapies for neurodegenerative disease. This extends to overarching trial designs that could be employed and the factors that underpin design choices such outcome assessments, participant selection and methods for delivery of cell and gene therapies. We explore methods of data collection that may improve efficiency in trials of cell and gene therapy to maximize data sharing and collaboration. Lastly, we explore some of the additional context beyond efficacy evaluations that should be considered to ensure implementation across relevant healthcare settings.