Towards regulatory endorsement of drug development tools to promote the application of model-informed drug development in Duchenne muscular dystrophy.

Drug development for rare diseases is challenged by small populations and limited data. This makes development of clinical trial protocols difficult and contributes to the uncertainty around whether or not a potential therapy is efficacious. The use of data standards to aggregate data from multiple sources, and the use of such integrated databases to develop statistical models can inform protocol development and reduce the risks in developing new therapies. Achieving regulatory endorsement of such models through defined pathways at the US Food and Drug Administration and European Medicines Authority allows such tools to be used by the drug development community for defined contexts of use without further need for discussion of the underlying model(s). The Duchenne Regulatory Science Consortium (D-RSC) has brought together multiple stakeholders to develop a clinical trial simulation tool for Duchenne muscular dystrophy using such an approach. Here we describe the work of D-RSC as an example of how such an approach may be effective at reducing uncertainty in drug development for rare diseases, and thus bringing effective therapies to patients faster.

SOHO State-of-the-Art Updates and Next Questions: Treatment for Newly Diagnosed Peripheral T-Cell Lymphomas.

Although a rare subset of non-Hodgkin lymphomas, peripheral T-cell lymphomas (PTCL) account for a disproportionate proportion of patient mortality. Conventional therapies are derived from experience treating aggressive B-cell lymphomas and center around CHOP-based chemotherapy. However, due to the unique biology and diverse subtypes of PTCL, most patients fail to durably respond to this approach and 5-year survival is only 20% to 30%. There have been multiple attempts to improve outcomes for patients with PTCL. Among the more successful strategies are the use of consolidative autologous stem cell transplant, the augmentation of CHOP with etoposide (CHOEP), and the use of brentuximab vedotin in CD30-positive PTCL. Advances in the understanding of histology-specific biology has cultivated enthusiasm to evaluate hypomethylating agents, histone deacetylate inhibitors, and phosphoinositol-3-kinase inhibitors in the frontline setting. Improvements in monitoring disease response and prognostication including the use of cell-free DNA, mutational profiling, and interim PET/CT imaging are also on the horizon. For patients with acute T-cell leukemia/lymphoma, the use of mogamulizumab-based therapy in the frontline setting may lead to advances in care. The true impact of these new-era therapies will only be elucidated as clinical practices incorporate the rapidly changing evidence.

Trapdoor endarterectomy for coral reef plaque of the paravisceral aorta in the modern era.

Coral reef atherosclerosis of the paravisceral aorta is a rare disease whose description is confined to before contemporary vascular surgical techniques. This study aims to describe the characteristics and outcomes of patients with coral reef aorta treated with trapdoor endarterectomy at a single high-volume quaternary referral center since 2010. From 2010 to 2022, 14 patients with coral reef aorta were treated with trapdoor endarterectomy. The patient data were obtained via a retrospective medical record review. The patients were predominantly women (79%) with a median age of 65 years (interquartile range [IQR], 60-70 years). The patients universally had a tobacco smoking history and hypertension. More than 85% had previously diagnosed carotid stenosis. Two patients (14%) had undergone prior aortofemoral reconstruction, and one patient (7%) had undergone prior axillobifemoral bypass. The most common presenting symptoms were claudication (71%), chronic mesenteric ischemia (50%), and renovascular hypertension (43%). Of the 14 patients, 8 (57%) underwent isolated endarterectomy and 6 (43%) underwent concomitant aortobifemoral bypass. In addition, 13 patients (93%) required a supraceliac aortic clamp position with a median clamp time of 23 minutes (IQR, 20-30 minutes). The median estimated blood loss was 1650 mL (IQR, 1025-3000 mL). A cell saver was used in 13 procedures (93%), with a median transfusion of 563 mL (IQR, 231-900 mL). The median operative time was 341 minutes (IQR, 315-416 minutes). Eight patients (57%) experienced acute kidney injury in the postoperative period with a peak creatinine of 1.96 mg/dL (IQR, 1.50-2.84 mg/dL). The median length of stay was 11 days (IQR, 6-16 days), with an intensive care unit stay of 4 days (IQR, 2-7 days). One patient (7%) required reoperation in the immediate perioperative period for a retroperitoneal hematoma. The postoperative ankle brachial index increased from a median of 0.58 (right) and 0.57 (left) bilaterally in the preoperative period to 1.09 (right) and 1.10 (left) postoperatively. Eight patients (57%) had follow-up data available for >2 years postoperatively, with five patients (36%) having follow-up data available for >3 years. Two major adverse cardiac events were reported at the last follow-up. One patient reported mild recurrent symptoms of chronic mesenteric ischemia during 3 years of postoperatively, with no concurrent imaging findings or loss of patency found on computed tomography angiography. Symptomatic coral reef atherosclerosis of the paravisceral aorta is a complex disease rarely encountered even at high-volume referral centers. These patients can be expected to experience short-term postoperative morbidity and require intensive care. Despite these challenges, trapdoor endarterectomy is a safe and effective procedure for coral reef aorta, and most patients achieve dramatic symptomatic improvement with durable results.

Determinants of Public Opinion Toward Gender-Affirming Surgery in the United States.

Purpose: National polling data indicate that Americans support the right of transgender persons to undergo gender-affirming surgery (GAS). It remains unknown whether public perceptions of GAS differ depending on patient subpopulations, anatomical site, or insurance coverage and whether the public widely believes that transgender people will regret GAS. Methods: We built a Qualtrics™ survey derived from an online validated 2017 Ipsos survey and distributed it to American adults through Amazon Mechanical Turk. Associations of demographic characteristics with perception of GAS were determined using multinomial logistic regression. Results: Respondents (n=312) were predominantly non-Hispanic White (69.2%), held a bachelor's degree (64.7%), and reported an annual income of $25,000 to $74,999 (64.4%). Approximately half of respondents identified as socially liberal (50.3%); 34.0% as socially conservative; and 15.7% as neither. Respondents supported a right to GAS independent of anatomy and insurance. Support for transgender children (62%) was less than for adult transgender men (84%) and women (83%). Despite supporting a right to GAS, respondents agreed that transgender adults (67%) and children (74%) would regret GAS. Education was the strongest predictor of support for GAS rights. Socially conservative respondents were significantly more likely than nonideological or liberal respondents to believe that transgender people would regret GAS. Conclusion: This large online sample of American adults with diverse ideologies demonstrated support for GAS independent of anatomical site and insurance. Support of GAS for transgender children is robust, although lower than support for adults. Despite broad support, most laypersons believe that transgender people would regret GAS.

Type 1 diabetes prevention clinical trial simulator: Case reports of model-informed drug development tool.

Clinical trials seeking to delay or prevent the onset of type 1 diabetes (T1D) face a series of pragmatic challenges. Despite more than 100 years since the discovery of insulin, teplizumab remains the only FDA-approved therapy to delay progression from Stage 2 to Stage 3 T1D. To increase the efficiency of clinical trials seeking this goal, our project sought to inform T1D clinical trial designs by developing a disease progression model-based clinical trial simulation tool. Using individual-level data collected from the TrialNet Pathway to Prevention and The Environmental Determinants of Diabetes in the Young natural history studies, we previously developed a quantitative joint model to predict the time to T1D onset. We then applied trial-specific inclusion/exclusion criteria, sample sizes in treatment and placebo arms, trial duration, assessment interval, and dropout rate. We implemented a function for presumed drug effects. To increase the size of the population pool, we generated virtual populations using multivariate normal distribution and ctree machine learning algorithms. As an output, power was calculated, which summarizes the probability of success, showing a statistically significant difference in the time distribution until the T1D diagnosis between the two arms. Using this tool, power curves can also be generated through iterations. The web-based tool is publicly available: https://app.cop.ufl.edu/t1d/. Herein, we briefly describe the tool and provide instructions for simulating a planned clinical trial with two case studies. This tool will allow for improved clinical trial designs and accelerate efforts seeking to prevent or delay the onset of T1D.

Adipose Tissue in Lymphedema: A Central Feature of Pathology and Target for Pharmacologic Therapy.

Lymphedema is a chronic condition of impaired lymphatic flow that results in limb swelling and debilitation. The pathophysiology of lymphedema is characterized by lymphatic stasis that triggers inflammation, fibrosis, and adipose tissue deposition in the extremities. Most often, this condition occurs in cancer survivors in the years after treatment with combinations of surgery, radiation, or chemotherapy, with the major risk factor being lymph node dissection. Interestingly, obesity and body mass index are independent risk factors for development of lymphedema, suggesting interactions between adipose and lymphatic tissue biology. Currently, treatment of lymphedema involves palliative approaches, including compression garments and physical therapy, and surgical approaches, including liposuction, lymphovenous bypass, and vascularized lymph node transfer. Emerging lymphedema therapies that focus on weight loss or reducing inflammation have been tested in recent clinical trials, yielding mixed results with no effect on limb volumes or changes in bioimpedance measurements. These studies highlight the need for novel therapeutic strategies that target the driving forces of lymphedema. In this light, animal models of lymphedema demonstrate a role of adipose tissue in the progression of lymphedema and suggest these processes may be targeted in the treatment of lymphedema. Herein, we review both conventional and experimental therapies for lymphedema as well as the defining characteristics of its pathophysiology. We place emphasis on the aberrant fibroadipose tissue accumulation in lymphedema and propose a new approach to experimental treatment at the level of adipocyte metabolism.

Rapid Functional Recovery After Thoracic Outlet Decompression in a Series of Adolescent Athletes With Chronic Atraumatic Shoulder-Girdle Pain, Scapular Winging/Dyskinesis, and Normal Electrodiagnostic Studies.

BACKGROUND: Spontaneous shoulder-girdle pain and scapular winging/dyskinesis can be caused by several neuromuscular disorders identifiable by electrodiagnostic studies (EDX). We describe a group of adolescent athletes with this clinical presentation but normal EDX, followed by later development of neurogenic thoracic outlet syndrome (NTOS). METHODS: We identified patients referred for evaluation of NTOS that had a history of chronic atraumatic shoulder-girdle pain, scapular winging/dyskinesis, and normal EDX. Each was refractory to conservative management and underwent supraclavicular decompression and brachial plexus neurolysis for NTOS. Functional disability was quantified by Quick Disabilities of the Arm, Shoulder, and Hand (QuickDASH) scores. RESULTS: There were 5 female patients with a mean age at symptom onset of 14.2 ± 0.4 years, including spontaneous severe pain in the shoulder, scapula, and arm, along with prominent scapular winging/dyskinesis, and normal EDX. Symptoms had persisted for 18.9 ± 4.0 months prior to referral, with pronounced upper extremity disability (mean QuickDASH, 54.6 ± 6.9). By 3 months after surgical treatment for NTOS, all 5 patients experienced near-complete symptom resolution, including scapular winging/dyskinesis, with markedly improved function (mean QuickDASH, 2.2 ± 1.3) and a return to normal activity. CONCLUSIONS: A subset of patients with chronic atraumatic shoulder-girdle pain, scapular winging/dyskinesis, and normal EDX may develop dynamic brachial plexus compression characteristic of NTOS, exhibiting an ischemic "Sunderland-zero" nerve conduction block for which surgical decompression can result in rapid and substantial clinical improvement. The presence of surgically treatable NTOS should be considered for selected patients with long-standing scapular winging/dyskinesis who fail conservative management.

Disease progression joint model predicts time to type 1 diabetes onset: Optimizing future type 1 diabetes prevention studies.

Clinical trials seeking type 1 diabetes prevention are challenging in terms of identifying patient populations likely to progress to type 1 diabetes within limited (i.e., short-term) trial durations. Hence, we sought to improve such efforts by developing a quantitative disease progression model for type 1 diabetes. Individual-level data obtained from the TrialNet Pathway to Prevention and The Environmental Determinants of Diabetes in the Young natural history studies were used to develop a joint model that links the longitudinal glycemic measure to the timing of type 1 diabetes diagnosis. Baseline covariates were assessed using a stepwise covariate modeling approach. Our study focused on individuals at risk of developing type 1 diabetes with the presence of two or more diabetes-related autoantibodies (AAbs). The developed model successfully quantified how patient features measured at baseline, including HbA1c and the presence of different AAbs, alter the timing of type 1 diabetes diagnosis with reasonable accuracy and precision (<30% RSE). In addition, selected covariates were statistically significant (p < 0.0001 Wald test). The Weibull model best captured the timing to type 1 diabetes diagnosis. The 2-h oral glucose tolerance values assessed at each visit were included as a time-varying biomarker, which was best quantified using the sigmoid maximum effect function. This model provides a framework to quantitatively predict and simulate the time to type 1 diabetes diagnosis in individuals at risk of developing the disease and thus, aligns with the needs of pharmaceutical companies and scientists seeking to advance therapies aimed at interdicting the disease process.

Development of a model-based clinical trial simulation platform to optimize the design of clinical trials for Duchenne muscular dystrophy.

Early clinical trials of therapies to treat Duchenne muscular dystrophy (DMD), a fatal genetic X-linked pediatric disease, have been designed based on the limited understanding of natural disease progression and variability in clinical measures over different stages of the continuum of the disease. The objective was to inform the design of DMD clinical trials by developing a disease progression model-based clinical trial simulation (CTS) platform based on measures commonly used in DMD trials. Data were integrated from past studies through the Duchenne Regulatory Science Consortium founded by the Critical Path Institute (15 clinical trials and studies, 1505 subjects, 27,252 observations). Using a nonlinear mixed-effects modeling approach, longitudinal dynamics of five measures were modeled (NorthStar Ambulatory Assessment, forced vital capacity, and the velocities of the following three timed functional tests: time to stand from supine, time to climb 4 stairs, and 10 meter walk-run time). The models were validated on external data sets and captured longitudinal changes in the five measures well, including both early disease when function improves as a result of growth and development and the decline in function in later stages. The models can be used in the CTS platform to perform trial simulations to optimize the selection of inclusion/exclusion criteria, selection of measures, and other trial parameters. The data sets and models have been reviewed by the US Food and Drug Administration and the European Medicines Agency; have been accepted into the Fit-for-Purpose and Qualification for Novel Methodologies pathways, respectively; and will be submitted for potential endorsement by both agencies.

Innovations in Therapy Development for Rare Diseases Through the Rare Disease Cures Accelerator-Data and Analytics Platform.

Rare diseases impact the lives of an estimated 350 million people worldwide, and yet about 90% of rare diseases remain without an approved treatment. New technologies have become available, such as gene and oligonucleotide therapies, that offer great promise in treating rare diseases. However, progress toward the development of therapies to treat these diseases is hampered by a limited understanding of the course of each rare disease, how changes in disease progression occur and can be effectively measured over time, and challenges in designing and running clinical trials in diseases where the natural history is poorly characterized. Data that could be used to characterize the natural history of each disease has often been collected in various ways, including in electronic health records, patient-report registries, clinical natural history studies, and in past clinical trials. However, each data source contains a limited number of subjects and different data elements, and data is frequently kept proprietary in the hands of the study sponsor rather than shared widely across the rare disease community. The Rare Disease Cures Accelerator-Data and Analytics Platform (RDCA-DAP) is an FDA-funded effort to overcome these persistent challenges. By aggregating data across all rare diseases and making that data available to the community to support understanding of rare disease natural history and inform drug development, RDCA-DAP aims to accelerate the regulatory approval of new therapies. RDCA-DAP curates, standardizes, and tags data across rare disease datasets to make it findable within the database, and contains a built-in analytics platform to help visualize, interpret, and use it to support drug development. RDCA-DAP will coordinate data and tool resources across non-profit, commercial, and for-profit entities to serve a diverse array of rare disease stakeholders that includes academic researchers, drug developers, FDA reviewers and of course patients and their caregivers. Drug development programs utilizing the RDCA-DAP will be able to leverage existing data to support their efforts and reach definitive decisions on the efficacy of their therapeutics more efficiently and more rapidly than ever.

Leveraging Real-World Data for EMA Qualification of a Model-Based Biomarker Tool to Optimize Type-1 Diabetes Prevention Studies.

The development of therapies to prevent or delay the onset of type 1 diabetes (T1D) remains challenging, and there is a lack of qualified biomarkers to identify individuals at risk of developing T1D or to quantify the time-varying risk of conversion to a diagnosis of T1D. To address this drug development need, the T1D Consortium (i) acquired, remapped, integrated, and curated existing patient-level data from relevant observational studies, and (ii) used a model-based approach to evaluate the utility of islet autoantibodies (AAs) against insulin/proinsulin autoantibody, GAD65, IA-2, and ZnT8 as biomarkers to enrich subjects for T1D prevention. The aggregated dataset was used to construct an accelerated failure time model for predicting T1D diagnosis. The model quantifies presence of islet AA permutations as statistically significant predictors of the time-varying probability of conversion to a diagnosis of T1D. Additional sources of variability that greatly improved the accuracy of quantifying the time-varying probability of conversion to a T1D diagnosis included baseline age, sex, blood glucose measurements from the 120-minute timepoints of oral glucose tolerance tests, and hemoglobin A1c. The developed models represented the underlying evidence to qualify islet AAs as enrichment biomarkers through the qualification of novel methodologies for drug development pathway at the European Medicines Agency (EMA). Additionally, the models are intended as the foundation of a fully functioning end-user tool that will allow sponsors to optimize enrichment criteria for clinical trials in T1D prevention studies.

Identifying and characterising sources of variability in digital outcome measures in Parkinson's disease.

Smartphones and wearables are widely recognised as the foundation for novel Digital Health Technologies (DHTs) for the clinical assessment of Parkinson's disease. Yet, only limited progress has been made towards their regulatory acceptability as effective drug development tools. A key barrier in achieving this goal relates to the influence of a wide range of sources of variability (SoVs) introduced by measurement processes incorporating DHTs, on their ability to detect relevant changes to PD. This paper introduces a conceptual framework to assist clinical research teams investigating a specific Concept of Interest within a particular Context of Use, to identify, characterise, and when possible, mitigate the influence of SoVs. We illustrate how this conceptual framework can be applied in practice through specific examples, including two data-driven case studies.

Recommendations to Optimize the Use of Volumetric MRI in Huntington's Disease Clinical Trials.

Volumetric magnetic resonance imaging (vMRI) has been widely studied in Huntington's disease (HD) and is commonly used to assess treatment effects on brain atrophy in interventional trials. Global and regional trajectories of brain atrophy in HD, with early involvement of striatal regions, are becoming increasingly understood. However, there remains heterogeneity in the methods used and a lack of widely-accessible multisite, longitudinal, normative datasets in HD. Consensus for standardized practices for data acquisition, analysis, sharing, and reporting will strengthen the interpretation of vMRI results and facilitate their adoption as part of a pathobiological disease staging system. The Huntington's Disease Regulatory Science Consortium (HD-RSC) currently comprises 37 member organizations and is dedicated to building a regulatory science strategy to expedite the approval of HD therapeutics. Here, we propose four recommendations to address vMRI standardization in HD research: (1) a checklist of standardized practices for the use of vMRI in clinical research and for reporting results; (2) targeted research projects to evaluate advanced vMRI methodologies in HD; (3) the definition of standard MRI-based anatomical boundaries for key brain structures in HD, plus the creation of a standard reference dataset to benchmark vMRI data analysis methods; and (4) broad access to raw images and derived data from both observational studies and interventional trials, coded to protect participant identity. In concert, these recommendations will enable a better understanding of disease progression and increase confidence in the use of vMRI for drug development.

Standardized Data Structures in Rare Diseases: CDISC User Guides for Duchenne Muscular Dystrophy and Huntington's Disease.

Interest in drug development for rare diseases has expanded dramatically since the Orphan Drug Act was passed in 1983, with 40% of new drug approvals in 2019 targeting orphan indications. However, limited quantitative understanding of natural history and disease progression hinders progress and increases the risks associated with rare disease drug development. Use of international data standards can assist in data harmonization and enable data exchange, integration into larger datasets, and a quantitative understanding of disease natural history. The US Food and Drug Administration (FDA) requires the use of Clinical Data Interchange Consortium (CDISC) Standards in new drug submissions to help the agency efficiently and effectively receive, process, review, and archive submissions, as well as to help integrate data to answer research questions. Such databases have been at the core of biomarker qualification efforts and fit-for-purpose models endorsed by the regulators. We describe the development of CDISC therapeutic area user guides for Duchenne muscular dystrophy and Huntington's disease through Critical Path Institute consortia. These guides describe formalized data structures and controlled terminology to map and integrate data from different sources. This will result in increased standardization of data collection and allow integration and comparison of data from multiple studies. Integration of multiple data sets enables a quantitative understanding of disease progression, which can help overcome common challenges in clinical trial design in these and other rare diseases. Ultimately, clinical data standardization will lead to a faster path to regulatory approval of urgently needed new therapies for patients.

Open Data Revolution in Clinical Research: Opportunities and Challenges.

Efforts for sharing individual clinical data are gaining momentum due to a heightened recognition that integrated data sets can catalyze biomedical discoveries and drug development. Among the benefits are the fact that data sharing can help generate and investigate new research hypothesis beyond those explored in the original study. Despite several accomplishments establishing public systems and guidance for data sharing in clinical trials, this practice is not the norm. Among the reasons are ethical challenges, such as privacy of individuals, data ownership, and control. This paper creates awareness of the potential benefits and challenges of sharing individual clinical data, how to overcome these challenges, and how as a clinical pharmacology community we can shape future directions in this field.

Challenges in Alzheimer's Disease Drug Discovery and Development: The Role of Modeling, Simulation, and Open Data.

Alzheimer's disease (AD) is the leading cause of dementia worldwide. With 35 million people over 60 years of age with dementia, there is an urgent need to develop new treatments for AD. To streamline this process, it is imperative to apply insights and learnings from past failures to future drug development programs. In the present work, we focus on how modeling and simulation tools can leverage open data to address drug development challenges in AD.

Machine Learning in Drug Discovery and Development Part 1: A Primer.

Artificial intelligence, in particular machine learning (ML), has emerged as a key promising pillar to overcome the high failure rate in drug development. Here, we present a primer on the ML algorithms most commonly used in drug discovery and development. We also list possible data sources, describe good practices for ML model development and validation, and share a reproducible example. A companion article will summarize applications of ML in drug discovery, drug development, and postapproval phase.

Hippocampal Neuroimaging-Informed Clinical Trial Enrichment Tool for Amnestic Mild Cognitive Impairment Using Open Data.

Our goal was to assess the enrichment utility of hippocampal volume (HV) as an enrichment biomarker in amnestic mild cognitive impairment (aMCI) clinical trials, and, hence, develop an HV neuroimaging-informed clinical trial enrichment tool. Modeling of integrated longitudinal patient-level data came from open-access natural history studies in patients diagnosed with aMCI-the Alzheimer's Disease Neuroimaging Initiative (ADNI)-1 and ADNI-2-and indicated that a decrease of 1 cm3 with respect to the analysis dataset median baseline intracranial volume-adjusted HV (ICV-HV; ~ 5 cm3 ) is associated with > 50% increase in disease progression rate as measured by the Clinical Dementia Rating Scale-Sum of Boxes. Clinical trial simulations showed that the inclusion of aMCI subjects with baseline ICV-HV below the 84th or 50th percentile allowed an approximate reduction in trial size of at least 26% and 55%, respectively. This clinical trial enrichment tool can help design more efficient and informative clinical trials.

Development of a Disease Progression Model for Leucine-Rich Repeat Kinase 2 in Parkinson's Disease to Inform Clinical Trial Designs.

A quantitative assessment of Parkinson's disease (PD) progression is critical for optimizing clinical trials design. Disease progression model was developed using pooled data from the Progression Marker Initiative study and the Incidence of Cognitive Impairment in Cohorts with Longitudinal Evaluation in Parkinson's Disease study. Age, gender, concomitant medication, and study arms were predictors of baseline. A mutation in the leucine-rich repeat kinase 2 (LRRK2) encoding gene was associated with the disease progression rate. The progression rate in subjects with PD who carried LRRK2 mutation was slightly slower (~0.170 points/month) than that in PD subjects without the mutation (~0.222 points/month). For a nonenriched placebo-controlled clinical trial, approximately 70 subjects/arm would be required to detect a drug effect of 50% reduction in the progression rate with 80% probability, whereas 85, 93, and 100 subjects/arm would be required for an enriched clinical trial with 30%, 50%, and 70% subjects with LRRK2 mutations, respectively.

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.