Metadata Framework to Support Deployment of Digital Health Technologies in Clinical Trials in Parkinson's Disease.

Sensor data from digital health technologies (DHTs) used in clinical trials provides a valuable source of information, because of the possibility to combine datasets from different studies, to combine it with other data types, and to reuse it multiple times for various purposes. To date, there exist no standards for capturing or storing DHT biosensor data applicable across modalities and disease areas, and which can also capture the clinical trial and environment-specific aspects, so-called metadata. In this perspectives paper, we propose a metadata framework that divides the DHT metadata into metadata that is independent of the therapeutic area or clinical trial design (concept of interest and context of use), and metadata that is dependent on these factors. We demonstrate how this framework can be applied to data collected with different types of DHTs deployed in the WATCH-PD clinical study of Parkinson's disease. This framework provides a means to pre-specify and therefore standardize aspects of the use of DHTs, promoting comparability of DHTs across future studies.

New Frontiers in Parkinson's Disease: From Genetics to the Clinic.

The greatest unmet therapeutic need in Parkinson's disease (PD) is a treatment that slows the relentless progression of the symptoms and the neurodegenerative process. This review highlights the utility of genetics to understand the pathogenic mechanisms and develop novel therapeutic approaches for PD. The focus is on strategies provided by genetic studies: notably via the reduction and clearance of α-synuclein, inhibition of LRRK2 kinase activity, and modulation of glucocerebrosidase-related substrates. In addition, the critical role of precompetitive public-private partnerships in supporting trial design optimization, overall drug development, and regulatory approvals is illustrated. With these great advances, the promise of developing transformative therapies that halt or slow disease progression is a tangible goal.

Big data to smart data in Alzheimer's disease: Real-world examples of advanced modeling and simulation.

Many disease-modifying clinical development programs in Alzheimer's disease (AD) have failed to date, and development of new and advanced preclinical models that generate actionable knowledge is desperately needed. This review reports on computer-based modeling and simulation approach as a powerful tool in AD research. Statistical data-analysis techniques can identify associations between certain data and phenotypes, such as diagnosis or disease progression. Other approaches integrate domain expertise in a formalized mathematical way to understand how specific components of pathology integrate into complex brain networks. Private-public partnerships focused on data sharing, causal inference and pathway-based analysis, crowdsourcing, and mechanism-based quantitative systems modeling represent successful real-world modeling examples with substantial impact on CNS diseases. Similar to other disease indications, successful real-world examples of advanced simulation can generate actionable support of drug discovery and development in AD, illustrating the value that can be generated for different stakeholders.

Big data to smart data in Alzheimer's disease: The brain health modeling initiative to foster actionable knowledge.

Massive investment and technological advances in the collection of extensive and longitudinal information on thousands of Alzheimer patients results in large amounts of data. These "big-data" databases can potentially advance CNS research and drug development. However, although necessary, they are not sufficient, and we posit that they must be matched with analytical methods that go beyond retrospective data-driven associations with various clinical phenotypes. Although these empirically derived associations can generate novel and useful hypotheses, they need to be organically integrated in a quantitative understanding of the pathology that can be actionable for drug discovery and development. We argue that mechanism-based modeling and simulation approaches, where existing domain knowledge is formally integrated using complexity science and quantitative systems pharmacology can be combined with data-driven analytics to generate predictive actionable knowledge for drug discovery programs, target validation, and optimization of clinical development.

Development of a unified clinical trial database for Alzheimer's disease.

INTRODUCTION: Data obtained in completed Alzheimer's disease (AD) clinical trials can inform decision making for future trials. Recognizing the importance of sharing these data, the Coalition Against Major Diseases created an Online Data Repository for AD (CODR-AD) with the aim of supporting accelerated drug development. The aim of this study was to build an open access, standardized database from control arm data collected across many clinical trials. METHODS: Comprehensive AD-specific data standards were developed to enable the pooling of data from different sources. Nine member organizations contributed patient-level data from 24 clinical trials of AD treatments. RESULTS: CODR-AD consists of control arm pooled and standardized data from 24 trials currently numbered at 6500 subjects; Alzheimer's Disease Assessment Scale-cognitive subscale 11 is the main outcome and specific covariates are also included. DISCUSSION: CODR-AD represents a unique integrated standardized clinical trials database available to qualified researchers. The pooling of data across studies facilitates a more comprehensive understanding of disease heterogeneity.

Modeling and simulation for medical product development and evaluation: highlights from the FDA-C-Path-ISOP 2013 workshop.

Medical-product development has become increasingly challenging and resource-intensive. In 2004, the Food and Drug Administration (FDA) described critical challenges facing medical-product development by establishing the critical path initiative [1]. Priorities identified included the need for improved modeling and simulation tools, further emphasized in FDA's 2011 Strategic Plan for Regulatory Science [Appendix]. In an effort to support and advance model-informed medical-product development (MIMPD), the Critical Path Institute (C-Path) [www.c-path.org], FDA, and International Society of Pharmacometrics [www.go-isop.org] co-sponsored a workshop in Washington, D.C. on September 26, 2013, to examine integrated approaches to developing and applying model- MIMPD. The workshop brought together an international group of scientists from industry, academia, FDA, and the European Medicines Agency to discuss MIMPD strategies and their applications. A commentary on the proceedings of that workshop is presented here.

Coalition Against Major Diseases/European Medicines Agency biomarker qualification of hippocampal volume for enrichment of clinical trials in predementia stages of Alzheimer's disease.

BACKGROUND: Regulatory qualification of a biomarker for a defined context of use provides scientifically robust assurances to sponsors and regulators that accelerate appropriate adoption of biomarkers into drug development. METHODS: The Coalition Against Major Diseases submitted a dossier to the Scientific Advice Working Party of the European Medicines Agency requesting a qualification opinion on the use of hippocampal volume as a biomarker for enriching clinical trials in subjects with mild cognitive impairment, incorporating a scientific rationale, a literature review and a de novo analysis of Alzheimer's Disease Neuroimaging Initiative data. RESULTS: The literature review and de novo analysis were consistent with the proposed context of use, and the Committee for Medicinal Products for Human Use released an opinion in November 2011. CONCLUSIONS: We summarize the scientific rationale and the data that supported the first qualification of an imaging biomarker by the European Medicines Agency.

Using a smartwatch and smartphone to assess early Parkinson's disease in the WATCH-PD study over 12 months.

Digital measures may provide objective, sensitive, real-world measures of disease progression in Parkinson's disease (PD). However, multicenter longitudinal assessments of such measures are few. We recently demonstrated that baseline assessments of gait, tremor, finger tapping, and speech from a commercially available smartwatch, smartphone, and research-grade wearable sensors differed significantly between 82 individuals with early, untreated PD and 50 age-matched controls. Here, we evaluated the longitudinal change in these assessments over 12 months in a multicenter observational study using a generalized additive model, which permitted flexible modeling of at-home data. All measurements were included until participants started medications for PD. Over one year, individuals with early PD experienced significant declines in several measures of gait, an increase in the proportion of day with tremor, modest changes in speech, and few changes in psychomotor function. As measured by the smartwatch, the average (SD) arm swing in-clinic decreased from 25.9 (15.3) degrees at baseline to 19.9 degrees (13.7) at month 12 (P = 0.004). The proportion of awake time an individual with early PD had tremor increased from 19.3% (18.0%) to 25.6% (21.4%; P < 0.001). Activity, as measured by the number of steps taken per day, decreased from 3052 (1306) steps per day to 2331 (2010; P = 0.16), but this analysis was restricted to 10 participants due to the exclusion of those that had started PD medications and lost the data. The change of these digital measures over 12 months was generally larger than the corresponding change in individual items on the Movement Disorder Society-Unified Parkinson's Disease Rating Scale but not greater than the change in the overall scale. Successful implementation of digital measures in future clinical trials will require improvements in study conduct, especially data capture. Nonetheless, gait and tremor measures derived from a commercially available smartwatch and smartphone hold promise for assessing the efficacy of therapeutics in early PD.

A biological definition of neuronal α-synuclein disease: towards an integrated staging system for research.

Parkinson's disease and dementia with Lewy bodies are currently defined by their clinical features, with α-synuclein pathology as the gold standard to establish the definitive diagnosis. We propose that, given biomarker advances enabling accurate detection of pathological α-synuclein (ie, misfolded and aggregated) in CSF using the seed amplification assay, it is time to redefine Parkinson's disease and dementia with Lewy bodies as neuronal α-synuclein disease rather than as clinical syndromes. This major shift from a clinical to a biological definition of Parkinson's disease and dementia with Lewy bodies takes advantage of the availability of tools to assess the gold standard for diagnosis of neuronal α-synuclein (n-αsyn) in human beings during life. Neuronal α-synuclein disease is defined by the presence of pathological n-αsyn species detected in vivo (S; the first biological anchor) regardless of the presence of any specific clinical syndrome. On the basis of this definition, we propose that individuals with pathological n-αsyn aggregates are at risk for dopaminergic neuronal dysfunction (D; the second biological anchor). Our biological definition establishes a staging system, the neuronal α-synuclein disease integrated staging system (NSD-ISS), rooted in the biological anchors (S and D) and the degree of functional impairment caused by clinical signs or symptoms. Stages 0-1 occur without signs or symptoms and are defined by the presence of pathogenic variants in the SNCA gene (stage 0), S alone (stage 1A), or S and D (stage 1B). The presence of clinical manifestations marks the transition to stage 2 and beyond. Stage 2 is characterised by subtle signs or symptoms but without functional impairment. Stages 2B-6 require both S and D and stage-specific increases in functional impairment. A biological definition of neuronal α-synuclein disease and an NSD-ISS research framework are essential to enable interventional trials at early disease stages. The NSD-ISS will evolve to include the incorporation of data-driven definitions of stage-specific functional anchors and additional biomarkers as they emerge and are validated. Presently, the NSD-ISS is intended for research use only; its application in the clinical setting is premature and inappropriate.

Global standardization measurement of cerebral spinal fluid for Alzheimer's disease: an update from the Alzheimer's Association Global Biomarkers Consortium.

Recognizing that international collaboration is critical for the acceleration of biomarker standardization efforts and the efficient development of improved diagnosis and therapy, the Alzheimer's Association created the Global Biomarkers Standardization Consortium (GBSC) in 2010. The consortium brings together representatives of academic centers, industry, and the regulatory community with the common goal of developing internationally accepted common reference standards and reference methods for the assessment of cerebrospinal fluid (CSF) amyloid ß42 (Aß42) and tau biomarkers. Such standards are essential to ensure that analytical measurements are reproducible and consistent across multiple laboratories and across multiple kit manufacturers. Analytical harmonization for CSF Aß42 and tau will help reduce confusion in the AD community regarding the absolute values associated with the clinical interpretation of CSF biomarker results and enable worldwide comparison of CSF biomarker results across AD clinical studies.

Leveraging the regulatory framework to facilitate drug development in Parkinson's disease.

There is an exigent need for disease-modifying and symptomatic treatment approaches for Parkinson's disease. A better understanding of Parkinson's disease pathophysiology and new insights in genetics has opened exciting new venues for pharmacological treatment targets. There are, however, many challenges on the path from discovery to drug approval. These challenges revolve around appropriate endpoint selection, the lack of accurate biomarkers, challenges with diagnostic accuracy, and other challenges commonly encountered by drug developers. The regulatory health authorities, however, have provided tools to provide guidance for drug development and to assist with these challenges. The main goal of the Critical Path for Parkinson's Consortium, a nonprofit public-private partnership part of the Critical Path Institute, is to advance these so-called drug development tools for Parkinson's disease trials. The focus of this chapter will be on how the health regulators' tools were successfully leveraged to facilitate drug development in Parkinson's disease and other neurodegenerative diseases.

Digital technologies: Innovations that transform the face of drug development.

Recently, digital health technologies (DHTs) and digital biomarkers have gained a lot of traction in clinical investigations, motivating sponsors, investigators, and regulators to discuss and implement integrated approaches for deploying DHTs. These new tools present new and unique challenges for optimal technology integration in clinical trial processes, including operational, ethical, and regulatory issues. In this paper, we gathered different perspectives to discuss challenges and perspectives from three different stakeholders: industry, US regulators, and a public-private partnership consortium. The complexities of DHT implementation, which include regulatory definitions, defining the scope of validation experiments, and the need for partnerships between BioPharma and the technology sectors, are highlighted. Most of these challenges are related to translation of DHT-derived measures into endpoints that are meaningful to clinicians and patients, participant safety, training, and retention and privacy of data. The example of the Wearable Assessments in the Clinic and Home in PD (WATCH-PD) study is discussed as an example that demonstrated the advantages of pre-competitive collaborations, which include early regulatory feedback, data sharing, and multistakeholder alignment. Future advances in DHTs are expected to spur device-agnostic measured development and incorporate patient reported outcomes in drug development. More efforts are needed to define validation experiments for a defined context of use, incentivize data sharing and development of data standards. Multistakeholder collaborations via precompetitive consortia will help facilitate broad acceptance of DHT-enabled measures in drug development.

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.

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.

Relative Meaningfulness and Impacts of Symptoms in People with Early-Stage Parkinson's Disease.

BACKGROUND: Patient perspectives on meaningful symptoms and impacts in early Parkinson's disease (PD) are lacking and are urgently needed to clarify priority areas for monitoring, management, and new therapies. OBJECTIVE: To examine experiences of people with early-stage PD, systematically describe meaningful symptoms and impacts, and determine which are most bothersome or important. METHODS: Forty adults with early PD who participated in a study evaluating smartwatch and smartphone digital measures (WATCH-PD study) completed online interviews with symptom mapping to hierarchically delineate symptoms and impacts of disease from "Most bothersome" to "Not present," and to identify which of these were viewed as most important and why. Individual symptom maps were coded for types, frequencies, and bothersomeness of symptoms and their impacts, with thematic analysis of narratives to explore perceptions. RESULTS: The three most bothersome and important symptoms were tremor, fine motor difficulties, and slow movements. Symptoms had the greatest impact on sleep, job functioning, exercise, communication, relationships, and self-concept- commonly expressed as a sense of being limited by PD. Thematically, most bothersome symptoms were those that were personally limiting with broadest negative impact on well-being and activities. However, symptoms could be important to patients even when not present or limiting (e.g., speech, cognition). CONCLUSION: Meaningful symptoms of early PD can include symptoms that are present or anticipated future symptoms that are important to the individual. Systematic assessment of meaningful symptoms should aim to assess the extent to which symptoms are personally important, present, bothersome, and limiting.

Mapping Relevance of Digital Measures to Meaningful Symptoms and Impacts in Early Parkinson's Disease.

BACKGROUND: Adoption of new digital measures for clinical trials and practice has been hindered by lack of actionable qualitative data demonstrating relevance of these metrics to people with Parkinson's disease. OBJECTIVE: This study evaluated of relevance of WATCH-PD digital measures to monitoring meaningful symptoms and impacts of early Parkinson's disease from the patient perspective. METHODS: Participants with early Parkinson's disease (N = 40) completed surveys and 1:1 online-interviews. Interviews combined: 1) symptom mapping to delineate meaningful symptoms/impacts of disease, 2) cognitive interviewing to assess content validity of digital measures, and 3) mapping of digital measures back to personal symptoms to assess relevance from the patient perspective. Content analysis and descriptive techniques were used to analyze data. RESULTS: Participants perceived mapping as deeply engaging, with 39/40 reporting improved ability to communicate important symptoms and relevance of measures. Most measures (9/10) were rated relevant by both cognitive interviewing (70-92.5%) and mapping (80-100%). Two measures related to actively bothersome symptoms for more than 80% of participants (Tremor, Shape rotation). Tasks were generally deemed relevant if they met three participant context criteria: 1) understanding what the task measured, 2) believing it targeted an important symptom of PD (past, present, or future), and 3) believing the task was a good test of that important symptom. Participants did not require that a task relate to active symptoms or "real" life to be relevant. CONCLUSION: Digital measures of tremor and hand dexterity were rated most relevant in early PD. Use of mapping enabled precise quantification of qualitative data for more rigorous evaluation of new measures.

Phosphodiesterase 9A regulates central cGMP and modulates responses to cholinergic and monoaminergic perturbation in vivo.

Cyclic nucleotides are critical regulators of synaptic plasticity and participate in requisite signaling cascades implicated across multiple neurotransmitter systems. Phosphodiesterase 9A (PDE9A) is a high-affinity, cGMP-specific enzyme widely expressed in the rodent central nervous system. In the current study, we observed neuronal staining with antibodies raised against PDE9A protein in human cortex, cerebellum, and subiculum. We have also developed several potent, selective, and brain-penetrant PDE9A inhibitors and used them to probe the function of PDE9A in vivo. Administration of these compounds to animals led to dose-dependent accumulation of cGMP in brain tissue and cerebrospinal fluid, producing a range of biological effects that implied functional significance for PDE9A-regulated cGMP in dopaminergic, cholinergic, and serotonergic neurotransmission and were consistent with the widespread distribution of PDE9A. In vivo effects of PDE9A inhibition included reversal of the respective disruptions of working memory by ketamine, episodic and spatial memory by scopolamine, and auditory gating by amphetamine, as well as potentiation of risperidone-induced improvements in sensorimotor gating and reversal of the stereotypic scratching response to the hallucinogenic 5-hydroxytryptamine 2A agonist mescaline. The results suggested a role for PDE9A in the regulation of monoaminergic circuitry associated with sensory processing and memory. Thus, PDE9A activity regulates neuronal cGMP signaling downstream of multiple neurotransmitter systems, and inhibition of PDE9A may provide therapeutic benefits in psychiatric and neurodegenerative diseases promoted by the dysfunction of these diverse neurotransmitter systems.

Combining patient-level and summary-level data for Alzheimer's disease modeling and simulation: a ß regression meta-analysis.

Our objective was to develop a beta regression (BR) model to describe the longitudinal progression of the 11 item Alzheimer's disease (AD) assessment scale cognitive subscale (ADAS-cog) in AD patients in both natural history and randomized clinical trial settings, utilizing both individual patient and summary level literature data. Patient data from the coalition against major diseases database (3,223 patients), the Alzheimer's disease neruroimaging initiative study database (186 patients), and summary data from 73 literature references (representing 17,235 patients) were fit to a BR drug-disease-trial model. Treatment effects for currently available acetyl cholinesterase inhibitors, longitudinal changes in disease severity, dropout rate, placebo effect, and factors influencing these parameters were estimated in the model. Based on predictive checks and external validation, an adequate BR meta-analysis model for ADAS-cog using both summary-level and patient-level data was developed. Baseline ADAS-cog was estimated from baseline MMSE score. Disease progression was dependent on time, ApoE4 status, age, and gender. Study drop out was a function of time, baseline age, and baseline MMSE. The use of the BR constrained simulations to the 0-70 range of the ADAS-cog, even when residuals were incorporated. The model allows for simultaneous fitting of summary and patient level data, allowing for integration of all information available. A further advantage of the BR model is that it constrains values to the range of the original instrument for simulation purposes, in contrast to methodologies that provide appropriate constraints only for conditional expectations.

Can Innovative Trial Designs in Orphan Diseases Drive Advancement of Treatments for Common Neurological Diseases?

Global regulatory agencies have transformed their approach to approvals in their processes for formal review of the safety and efficacy of new drugs. Opportunities for innovation have expanded because of the coronavirus disease 2019 (COVID-19) pandemic. Several regulatory-led initiatives have progressed rapidly during the past year, including patient-focused drug development, model-informed drug development, real-world evidence, and complex innovative trial designs. Collectively, these initiatives have accelerated the rate of approvals. Despite demands to focus on urgent needs imposed by the COVID-19 pandemic, the number of new drug approvals over the past year, particularly for rare diseases, has outpaced expectations. Advancing therapeutics for nervous system disorders requires adaptive strategies that align with rapid developments in the field. Three relentlessly progressive diseases, amyotrophic lateral sclerosis, Duchenne muscular dystrophy, and Parkinson's disease are in urgent need of new treatments. Herein, we propose new regulatory initiatives, including innovative trial designs and patient-focused drug development that accelerate clinical trial conduct while meeting critical regulatory requirements for therapeutic approval.

Application of longitudinal item response theory models to modeling Parkinson's disease progression.

The Movement Disorder Society revised version of the Unified Parkinson's Disease Rating Scale (MDS-UPDRS) parts 2 and 3 reflect patient-reported functional impact and clinician-reported severity of motor signs of Parkinson's disease (PD), respectively. Total scores are common clinical outcomes but may obscure important time-based changes in items. We aim to analyze longitudinal disease progression based on MDS-UPRDS parts 2 and 3 item-level responses over time and as functions of Hoehn & Yahr (H&Y) stages 1 and 2 for subjects with early PD. The longitudinal item response theory (IRT) modeling is a novel statistical method addressing limitations in traditional linear regression approaches, such as ignoring varying item sensitivities and the sum score balancing out improvements and declines. We utilized a harmonized dataset consisting of six studies with 3573 subjects with early PD and 14,904 visits, and mean follow-up time of 2.5 years (±1.57). We applied both a unidimensional (each part separately) and multidimensional (both parts combined) longitudinal IRT models. We assessed the progression rates for both parts, anchored to baseline H&Y stages 1 and 2. Both the uni- and multidimensional longitudinal IRT models indicate significant worsening time effects in both parts 2 and 3. Baseline H&Y stage 2 was associated with significantly higher baseline severities, but slower progression rates in both parts, as compared with stage 1. Patients with baseline H&Y stage 1 demonstrated slower progression in part 2 severity compared to part 3, whereas patients with baseline H&Y stage 2 progressed faster in part 2 than part 3. The multidimensional model had a superior fit compared to the unidimensional models and it had excellent model performance.