How Much Evidence Is Enough? Research Sponsor Experiences Seeking Regulatory Acceptance of Digital Health Technology-Derived Endpoints.

Introduction: Digital health technologies (DHTs) provide opportunities for real-time data collection and assessment of patient function. However, use of DHT-derived endpoints in clinical trials to support medical product labelling claims is limited. Methods: From November 2020 through March 2021, the Clinical Trials Transformation Initiative (CTTI) conducted a qualitative descriptive study using semi-structured interviews with sponsors of clinical trials that used DHT-derived endpoints. We aimed to learn about their experiences, including their interactions with regulators and the challenges they encountered. Using applied thematic analysis, we identified barriers to and recommendations for using DHT-derived endpoints in pivotal trials. Results: Sponsors identified five key challenges to incorporating DHT-derived endpoints in clinical trials. These included (1) a need for additional regulatory clarity specific to DHT-derived endpoints, (2) the official clinical outcome assessment qualification process being impractical for the biopharmaceutical industry, (3) a lack of comparator clinical endpoints, (4) a lack of validated DHTs and algorithms for concepts of interest, and (5) a lack of operational support from DHT vendors. Discussion/Conclusion: CTTI shared the interview findings with the US Food and Drug Administration (FDA) and the European Medicines Agency (EMA) and during a multi-stakeholder expert meeting. Based on these discussions, we provide several new and revised tools to aid sponsors in using DHT-derived endpoints in pivotal trials to support labelling claims.

Assessment of adherence to, and persistence with, an electromechanical autoinjector for subcutaneous interferon beta-1a injections for multiple sclerosis treatment over 3 years.

BACKGROUND: Self-administration of subcutaneous interferon beta-1a (sc IFN ß-1a) can be achieved with the RebiSmart® electromechanical autoinjector. This study investigated adherence to, and duration of persistence with, the newest version of the device (v1.6) among 2644 people receiving sc IFN ß-1a for multiple sclerosis (MS). RESEARCH DESIGN AND METHODS: This retrospective, observational study utilized data from RebiSmart® devices, recorded on the MSdialog database, between January 2014 and November 2019. Adherence and persistence were evaluated over a 3-year period and assessed in relation to age, sex, injection type, and injection depth. RESULTS: The population of RebiSmart® users (N = 2644) comprised of 1826 (69.1%) females and mean age was 39 (range 16-83) years. Adherence to RebiSmart® use and data transfer to the MSdialog database was consistently high (mean 91.7%; range 86.8-92.6%), including across all variables (81.6-100%). Mean (±SD) persistence during the study period was 1.35 ± 1.06 years, with a maximum recorded persistence of 5.1 years. In multivariate analysis, the longest durations of persistence were observed among older individuals and males (p < 0.0001 and p = 0.0078, respectively). CONCLUSIONS: People living with MS were highly adherent to use of the RebiSmart® device, with higher persistence generally observed for older and/or male individuals.

It is important for people living with multiple sclerosis (MS) to take their medication regularly ­ and to keep doing so ­ in order to control their symptoms. Some people with MS receive a medication called interferon beta-1a (Rebif®) as a subcutaneous injection (given just under the skin), and the RebiSmart® electromechanical autoinjector was designed to help them to self-inject such medication. This study aimed to find out whether people were using the RebiSmart® device as often as they should be, and how long they continued to use it for. Information was taken from the MSdialog database, which recorded peoples' use of the RebiSmart® device between January 2014 and November 2019. Records for 2644 people using the device were analyzed. Results showed that the RebiSmart® device was used most of the time (around 91.7%). On average, people kept using the device for around a year and 4 months before stopping. This duration was generally longer for men compared with women, and longer for older people than younger people. These results increase our understanding of how people are using the RebiSmart® device to treat their MS symptoms.

Integrated Digital Health Solutions in the Management of Growth Disorders in Pediatric Patients Receiving Growth Hormone Therapy: A Retrospective Analysis.

Digital health has seen rapid advancements over the last few years in helping patients and their healthcare professionals better manage treatment for a variety of illnesses, including growth hormone (GH) therapy for growth disorders in children and adolescents. For children and adolescents requiring such therapy, as well as for their parents, the treatment is longitudinal and often involves daily injections plus close progress monitoring; a sometimes daunting task when young children are involved. Here, we describe our experience in offering devices and digital health tools to support GH therapy across some 40 countries. We also discuss how this ecosystem of care has evolved over the years based on learnings and advances in technology. Finally, we offer a glimpse of future planned enhancements and directions for digital health to play a bigger role in better managing conditions treated with GH therapy, as well as model development for adherence prediction. The continued aim of these technologies is to improve clinical decision making and support for GH-treated patients, leading to better outcomes.

Use of machine learning to identify patients at risk of sub-optimal adherence: study based on real-world data from 10,929 children using a connected auto-injector device.

BACKGROUND: Our aim was to develop a machine learning model, using real-world data captured from a connected auto-injector device and from early indicators from the first 3 months of treatment, to predict sub-optimal adherence to recombinant human growth hormone (r-hGH) in patients with growth disorders. METHODS: Adherence to r-hGH treatment was assessed in children (aged < 18 years) who started using a connected auto-injector device (easypod™), and transmitted injection data for ≥ 12 months. Adherence in the following 3, 6, or 9 months after treatment start was categorized as optimal (≥ 85%) versus sub-optimal (< 85%). Logistic regression and tree-based models were applied. RESULTS: Data from 10,929 children showed that a random forest model with mean and standard deviation of adherence over the first 3 months, infrequent transmission of data, not changing certain comfort settings, and starting treatment at an older age was important in predicting the risk of sub-optimal adherence in the following 3, 6, or 9 months. Sensitivities ranged between 0.72 and 0.77, and specificities between 0.80 and 0.81. CONCLUSIONS: To the authors' knowledge, this is the first attempt to integrate a machine learning model into a digital health ecosystem to help healthcare providers to identify patients at risk of sub-optimal adherence to r-hGH in the following 3, 6, or 9 months. This information, together with patient-specific indicators of sub-optimal adherence, can be used to provide support to at-risk patients and their caregivers to achieve optimal adherence and, subsequently, improve clinical outcomes.

High Engagement of Patients Monitored by a Digital Health Ecosystem Indicates Significant Improvements of Key r-hGH Treatment Metrics.

The early adoption of digital health solutions in the treatment of growth disorders has enabled the collection and analysis of more than 10 years of real-world data using the easypod™ connect platform. Using this rich dataset, we were able to study the impact of engagement on three key treatment-related outcomes: adherence, persistence of use, and growth. In total, data for 17,906 patients were available. The three features, regularity of injection (≤2h vs >2h), change of comfort setting (yes/no), and opting-in to receive injection reminders (yes/no), were used as a proxy for engagement. Patients were assigned to the low-engagement group (n=1,752) when all of their features had the low-engagement flag (>2h, no, no) and to the high-engagement group (n=1,081) when all of their features had the high-engagement flag (≤2h, yes, yes). The low-engagement group was down-sampled to 1,081 patients (subsample of n=37 for growth) using the iterative proportional fitting algorithm. Statistical tests were used to study the impact of engagement to the outcomes. The results show that all three outcomes were significantly improved by a factor varying from 1.8 up to 2.2 when the engagement level was high. These results should encourage the promotion of engagement and associated behaviors by both patients and healthcare professionals.

Digital Health in the Management of Pediatric Growth Hormone Therapy - 10 Years of Developments.

Growth hormone administration is approved for use in a number of growth failure conditions in children. Optimal growth outcome requires continuous daily injections over many years, leading to problems of persistence and adherence with therapy. The easypod™ connect ecosystem enables electronic monitoring of injection and dose history, transmitted to a secure cloud database via the internet or cellular networks. Thus, healthcare providers can easily monitor adherence with therapy and be alerted to problems. The growlink™ patient app has been added to the ecosystem to provide solutions that can engage and educate patients and their families/caregivers. growlink™ also allows patients to self-report height and weight, enabling healthcare providers to track growth progression. The patient support program, TuiTek and the easypod™ Augmented Reality (AR) app are being developed within the ecosystem to support telehealth services, increase disease awareness and reduce therapy-related anxiety. easypod™ connect provides objective assessments of adherence, shown to be maintained at a high level over several years, and analyses showed that increased adherence was significantly associated with a better growth outcome. Studies have identified factors that influence persistence and adherence with GH therapy via the easypod™ connect ecosystem. These novel technologies are generating solutions that enable data-driven personalized care for children with growth disorders and optimize long-term clinical outcomes.

Insulin Aggregation at a Dynamic Solid-Liquid-Air Triple Interface.

Therapeutic proteins are privileged in drug development because of their exquisite specificity, which is due to their three-dimensional conformation in solution. During their manufacture, storage, and delivery, interactions with material surfaces and air interfaces are known to affect their stability. The growing use of automated devices for handling and injection of therapeutics increases their exposure to protocols involving intermittent wetting, during which the solid-liquid and liquid-air interfaces meet at a triple contact line, which is often dynamic. Using a microfluidic setup, we analyze the effect of a moving triple interface on insulin aggregation in real time over a hydrophobic surface. We combine thioflavin T fluorescence and reflection interference microscopy to concomitantly monitor insulin aggregation and the morphology of the liquid as it dewets the surface. We demonstrate that insulin aggregates in the region of a moving triple interface and not in regions submitted to hydrodynamic shear stress alone, induced by the moving liquid. During dewetting, liquid droplets form on the surface anchored by adsorbed proteins, and the accumulation of amyloid aggregates is observed exclusively as fluorescent rings growing eccentrically around these droplets. The fluorescent rings expand until the entire channel surface sweeped by the triple interface is covered by amyloid fibers. On the basis of our experimental results, we propose a model describing the growth mechanism of insulin amyloid fibers at a moving triple contact line, where proteins adsorbed at a hydrophobic surface are exposed to the liquid-air interface.