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Background: Current methods of measuring disease progression of neurodegenerative disorders, including Parkinson's disease (PD), largely rely on composite clinical rating scales, which are prone to subjective biases and lack the sensitivity to detect progression signals in a timely manner. Digital health technology (DHT)-derived measures offer potential solutions to provide objective, precise, and sensitive measures that address these limitations. However, the complexity of DHT datasets and the potential to derive numerous digital features that were not previously possible to measure pose challenges, including in selection of the most important digital features and construction of composite digital biomarkers. Methods: We present a comprehensive machine learning based framework to construct composite digital biomarkers for progression tracking. This framework consists of a marginal (univariate) digital feature screening, a univariate association test, digital feature selection, and subsequent construction of composite (multivariate) digital disease progression biomarkers using Penalized Generalized Estimating Equations (PGEE). As an illustrative example, we applied this framework to data collected from a PD longitudinal observational study. The data consisted of Opal™ sensor-based movement measurements and MDS-UPDRS Part III scores collected at 3-month intervals for 2 years in 30 PD and 10 healthy control participants. Results: In our illustrative example, 77 out of 235 digital features from the study passed univariate feature screening, with 11 features selected by PGEE to include in construction of the composite digital measure. Compared to MDS-UPDRS Part III, the composite digital measure exhibited a smoother and more significant increasing trend over time in PD groups with less variability, indicating improved ability for tracking disease progression. This composite digital measure also demonstrated the ability to classify between de novo PD and healthy control groups. Conclusion: Measures from DHTs show promise in tracking neurodegenerative disease progression with increased sensitivity and reduced variability as compared to traditional clinical scores. Herein, we present a novel framework and methodology to construct composite digital measure of disease progression from high-dimensional DHT datasets, which may have utility in accelerating the development and application of composite digital biomarkers in drug development. [ABSTRACT FROM AUTHOR]

The rapidly advancing field of digital health technologies provides a great opportunity to radically transform the way clinical trials are conducted and to shift the clinical trial paradigm from a site‐centric to a patient‐centric model. Merck's (Kenilworth, NJ) digitally enabled clinical trial initiative is focused on introduction of digital technologies into the clinical trial paradigm to reduce patient burden, improve drug adherence, provide a means of more closely engaging with the patient, and enable higher quality, faster, and more frequent data collection. This paper will describe the following four key areas of focus from Merck's digitally enabled clinical trials initiative, along with corresponding enabling technologies: (i) use of technologies that can monitor and improve drug adherence (smart dosing), (ii) collection of pharmacokinetic (PK), pharmacodynamic (PD), and biomarker samples in an outpatient setting (patient‐centric sampling), (iii) use of digital devices to collect and measure physiological and behavioral data (digital biomarkers), and (iv) use of data platforms that integrate digital data streams, visualize data in real‐time, and provide a means of greater patient engagement during the trial (digital platform). Furthermore, this paper will discuss the synergistic power in implementation of these approaches jointly within a trial to enable better understanding of adherence, safety, efficacy, PK, PD, and corresponding exposure‐response relationships of investigational therapies as well as reduced patient burden for clinical trial participation. Obstacle and challenges to adoption and full realization of the vision of patient‐centric, digitally enabled trials will also be discussed. [ABSTRACT FROM AUTHOR]

Atogepant is a potent, selective, oral calcitonin gene–related peptide (CGRP) receptor antagonist in development for migraine prevention. The chemical structure of atogepant is distinct from previous CGRP receptor antagonists, which were associated with elevated serum alanine aminotransferase (ALT) in clinical trials. Here, we report the safety, tolerability, and pharmacokinetics (PKs) of a once‐daily supratherapeutic dose (170 mg) of atogepant for 28 days from a randomized, double‐blind, placebo‐controlled phase I trial in healthy participants. Overall safety, hepatic safety, and plasma PK parameters were evaluated. Thirty‐four participants aged 23–55 years enrolled; 28 (82.4%) completed the study in accordance with the protocol. Multiple doses of 170 mg atogepant for 28 consecutive days were generally well‐tolerated. All adverse events (AEs; reported in 87.0% of the atogepant group; 72.7%, placebo) were mild in severity except one serious AE of subarachnoid hemorrhage due to a bicycle accident and not considered related to treatment. There were two discontinuations due to AEs, both with atogepant, one considered possibly related to treatment. Over 28 days of treatment, no participant receiving atogepant had an ALT elevation above 1.5 × upper limit of normal. Change from baseline in serum ALT levels was not different between atogepant and placebo. Atogepant is rapidly absorbed (median time to maximum plasma concentration, ~ 2 hours) with an apparent terminal half‐life of ~ 11 hours, and no evidence of accumulation after once‐daily dosing. Overall, atogepant at a high oral dose is safe and well‐tolerated in healthy participants with no clinically meaningful elevations in ALT. [ABSTRACT FROM AUTHOR]

β‐site amyloid precursor protein cleaving enzyme 1 (BACE1) is required for the production of β‐amyloid (Aβ) peptides and is considered a potential treatment target for Alzheimer's disease (AD). To support Japan's participation in the global clinical development program, we characterized the safety, pharmacokinetics (PKs), and pharmacodynamics of the BACE1 inhibitor verubecestat (MK‐8931) in 24 healthy Japanese adults in a two‐part, single‐center, randomized, placebo‐controlled phase I trial (protocol MK‐8931‐007) and compared the results with historical data from non‐Japanese subjects. Both single (20, 100, and 450 mg) and multiple (80 and 150 mg once daily for 14 days) doses of verubecestat were well tolerated. Verubecestat's PK profile was similar in Japanese and non‐Japanese subjects. Verubecestat also reduced mean cerebrospinal fluid concentrations of the Aβ proteins Aβ40, Aβ42, and soluble β fragment of amyloid precursor protein; the level of reduction was comparable between Japanese and non‐Japanese subjects. These results support the continued global development of verubecestat as a potential disease‐modifying agent for Japanese and non‐Japanese subjects who are at risk for developing AD. [ABSTRACT FROM AUTHOR]

Merck & Co, Inc (Kenilworth, NJ) is investing in approaches to enrich clinical trial data and augment decision making through use of digital health technologies, outpatient sampling, and real‐time data access. As part of this strategy, a phase I study was conducted to explore a few technologies of interest. In this fixed‐sequence two‐period trial, 16 healthy subjects were administered 50‐mg once‐daily sitagliptin packaged in a bottle that electronically captured the date and time study medication was dispensed (period 1) and in a traditional pharmacy bottle (period 2). Dried blood spot samples were collected for sitagliptin concentration analysis on select study days, both in clinic and at home, with collection time recorded using an electronic diary in period 1 and by clinic staff in period 2. Study results demonstrated the feasibility and subject acceptance of collecting digital adherence data and outpatient dried blood spot samples in clinical trials and highlighted areas for future improvements. [ABSTRACT FROM AUTHOR]