Quantifying Nocturnal Scratch in Atopic Dermatitis: A Machine Learning Approach Using Digital Wrist Actigraphy.

Nocturnal scratching substantially impairs the quality of life in individuals with skin conditions such as atopic dermatitis (AD). Current clinical measurements of scratch rely on patient-reported outcomes (PROs) on itch over the last 24 h. Such measurements lack objectivity and sensitivity.

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.

Deep Learning-Based Psoriasis Assessment: Harnessing Clinical Trial Imaging for Accurate Psoriasis Area Severity Index Prediction.

Introduction: Image-based machine learning holds great promise for facilitating clinical care; however, the datasets often used for model training differ from the interventional clinical trial-based findings frequently used to inform treatment guidelines. Here, we draw on longitudinal imaging of psoriasis patients undergoing treatment in the Ultima 2 clinical trial (NCT02684357), including 2,700 body images with psoriasis area severity index (PASI) annotations by uniformly trained dermatologists.

Methods: An image-processing workflow integrating clinical photos of multiple body regions into one model pipeline was developed, which we refer to as the "One-Step PASI" framework due to its simultaneous body detection, lesion detection, and lesion severity classification.

Developing a Novel Measurement of Sleep in Rheumatoid Arthritis: Study Proposal for Approach and Considerations.

The development of novel digital endpoints (NDEs) using digital health technologies (DHTs) may provide opportunities to transform drug development. It requires a multidisciplinary, multi-study approach with strategic planning and a regulatory-guided pathway to achieve regulatory and clinical acceptance. Many NDEs have been explored; however, success has been limited.

Developing Smartphone-Based Objective Assessments of Physical Function in Rheumatoid Arthritis Patients: The PARADE Study.

Background: Digital biomarkers that measure physical activity and mobility are of great interest in the assessment of chronic diseases such as rheumatoid arthritis, as it provides insights on patients' quality of life that can be reliably compared across a whole population.

Objective: To investigate the feasibility of analyzing iPhone sensor data collected remotely by means of a mobile software application in order to derive meaningful information on functional ability in rheumatoid arthritis patients.

Methods: Two objective, active tasks were made available to the study participants: a wrist joint motion test and a walk test, both performed remotely and without any medical supervision.

Optimizing the Use of Electronic Data Sources in Clinical Trials: The Technology Landscape.

TransCelerate has created an initiative to facilitate the industry's movement toward optimal use of electronic data sources for clinical research. Although guidance and standards have been in place for some time, gaps remain. Consequently, transcription among electronic systems continues to be the norm.

Using a ResearchKit Smartphone App to Collect Rheumatoid Arthritis Symptoms From Real-World Participants: Feasibility Study.

Background: Using smartphones to enroll, obtain consent, and gather self-reported data from patients has the potential to enhance our understanding of disease burden and quantify physiological impact in the real world. It may also be possible to harness integral smartphone sensors to facilitate remote collection of clinically relevant data.

Objective: We conducted the Patient Rheumatoid Arthritis Data From the Real World (PARADE) observational study using a customized ResearchKit app with a bring-your-own-device approach.

Optimizing the Use of Electronic Data Sources in Clinical Trials: The Landscape, Part 1.

Background: TransCelerate BioPharma has created the eSource Initiative with the intent to facilitate the industry's movement toward optimal usage of electronic data sources. Although guidance and standards have been in place for some time, data collection methods and technology have not been utilized to their fullest capability, and transcription between electronic systems continues to be the norm.

Methods: The TransCelerate approach for the eSource Initiative is to understand the current landscape and highlight factors that are influencing the adoption of new technologies.

Mobile health: the power of wearables, sensors, and apps to transform clinical trials.

Mobile technology has become a ubiquitous part of everyday life, and the practical utility of mobile devices for improving human health is only now being realized. Wireless medical sensors, or mobile biosensors, are one such technology that is allowing the accumulation of real-time biometric data that may hold valuable clues for treating even some of the most devastating human diseases. From wearable gadgets to sophisticated implantable medical devices, the information retrieved from mobile technology has the potential to revolutionize how clinical research is conducted and how disease therapies are delivered in the coming years.

A DNA Hypomethylation Signature Predicts Antitumor Activity of LSD1 Inhibitors in SCLC.

Epigenetic dysregulation has emerged as an important mechanism in cancer. Alterations in epigenetic machinery have become a major focus for targeted therapies. The current report describes the discovery and biological activity of a cyclopropylamine containing inhibitor of Lysine Demethylase 1 (LSD1), GSK2879552.

Macrocyclic inhibitors of beta-secretase: functional activity in an animal model.

A macrocyclic inhibitor of beta-secretase was designed by covalently cross-linking the P1 and P3 side chains of an isophthalamide-based inhibitor. Macrocyclization resulted in significantly improved potency and physical properties when compared to the initial lead structures. More importantly, these macrocyclic inhibitors also displayed in vivo amyloid lowering when dosed in a murine model.