Enabling endpoint development for interventional clinical trials in individuals with Angelman syndrome: a prospective, longitudinal, observational clinical study (FREESIAS)

Abstract

Background: Angelman syndrome (AS) is a rare neurodevelopmental disorder characterized by the absence of a functional UBE3A gene, which causes developmental, behavioral, and medical challenges. While currently untreatable, comprehensive data could help identify appropriate endpoints assessing meaningful improvements in clinical trials. Herein are reported the results from the FREESIAS study assessing the feasibility and utility of in-clinic and at-home measures of key AS symptoms.

Methods: Fifty-five individuals with AS (aged < 5 years: n = 16, 5-12 years: n = 27, ≥ 18 years: n = 12; deletion genotype: n = 40, nondeletion genotype: n = 15) and 20 typically developing children (aged 1-12 years) were enrolled across six USA sites. Several clinical outcome assessments and digital health technologies were tested, together with overnight 19-lead electroencephalography (EEG) and additional polysomnography (PSG) sensors. Participants were assessed at baseline (Clinic Visit 1), 12 months later (Clinic Visit 2), and during intermittent home visits.

Results: The participants achieved high completion rates for the clinical outcome assessments (adherence: 89-100% [Clinic Visit 1]; 76-91% [Clinic Visit 2]) and varied feasibility of and adherence to digital health technologies. The coronavirus disease 2019 (COVID-19) pandemic impacted participants' uptake of and/or adherence to some measures. It also potentially impacted the at-home PSG/EEG recordings, which were otherwise feasible. Participants achieved Bayley-III results comparable to the available natural history data, showing similar scores between individuals aged ≥ 18 and 5-12 years. Also, participants without a deletion generally scored higher on most clinical outcome assessments than participants with a deletion. Furthermore, the observed AS EEG phenotype of excess delta-band power was consistent with prior reports.

Conclusions: Although feasible clinical outcome assessments and digital health technologies are reported herein, further improved assessments of meaningful AS change are needed. Despite the COVID-19 pandemic, remote assessments facilitated high adherence levels and the results suggested that at-home PSG/EEG might be a feasible alternative to the in-clinic EEG assessments. Taken altogether, the combination of in-clinic/at-home clinical outcome assessments, digital health technologies, and PSG/EEG may improve protocol adherence, reduce patient burden, and optimize study outcomes in AS and other rare disease populations.

Keywords: Angelman syndrome; Clinical outcome assessments; Clinical trials; Digital health technology; EEG; Endpoint development; Natural history; Sleep; UBE3A.

Fig. 1

FREESIAS key domains of interest in AS and study design. A Key symptoms of interest in AS as identified in a previously published AS disease concept model. B FREESIAS study design. The study consisted of two in-clinic visits 12 months apart (Clinic Visit 1 and Clinic Visit 2) and three at-home visits with Home Visit 1 and 3 around the time of Clinic Visit 1 and 2, respectively and Home Visit 2 at 3 months after Home Visit 1. AS Angelman syndrome, COA clinical outcome assessment

Fig. 2

Adherence/completed assessments for sleep diaries, sleep mat, actigraph, and overnight EEG. Adherence to sleep diary completion in participants with AS (A) and TDC (B). Adherence was defined as the percentage of days within the 52-week observational period in which a diary entry was recorded. Adherence to the sleep mat in participants with AS (C) and TDC (D). Shaded area in graphs A–D represents ± 1 SD. Sleep mat usage was defined as the percentage of days per week within the 52-week observational period in which a sleep mat recording was started. The number of participants included in the adherence calculation for both the sleep diary and sleep mat decreased over the 52 weeks due to either withdrawal, rollover to clinical drug trials, or early completion (see “N” above x-axis). E The percentage of nights that the actigraph was used in the Actigraphy Sleep Monitoring Period for those participants for which any data were collected (AS deletion n = 31; AS nondeletion n = 11; TDC n = 13). F Representation of wearing location of the actigraph during the home monitoring period with unknown location (black); right wrist (red); right ankle (orange); left wrist (yellow); left ankle (green); chest (light blue); abdomen (dark blue); and no actigraphy (black). The actigraph was worn for up to 10 days at home preceding and up to 2 days after the first home EEG visit. G Number of participants who completed overnight EEG recordings with usable data of at least 5 h duration at Home Visit 1, 2, and 3. AS Angelman syndrome, EEG Electroencephalogram, SD Standard deviation, TDC Typically developing children

Fig. 3

Bayley-III profiles obtained using FREESIAS and AS-NHS data. Mean Bayley-III raw scores in participants with AS deletion and nondeletion aged 1–12 years, from FREESIAS and the AS-NHS: A Cognitive domain, B Expressive Communication domain, C Receptive Communication domain, D Fine Motor domain, E Gross Motor domain. F EEG data in participants with AS deletion, nondeletion, and TDC from FREESIAS, AS-NHS, and BCH. Axes are plotted on logarithmic scales. G Kaplan–Meyer analysis of cumulative seizure data, stratified by underlying genotype and data source, in participants with AS deletion and nondeletion from FREESIAS and the AS-NHS. The table indicates the number of participants at risk for each group at 12-month intervals. One FREESIAS participant that presented their first seizure at over 18 years old was excluded from this analysis in order to allow for a direct comparison to the AS-NHS analysis that included pediatric population exclusively. AS Angelman syndrome, AS-NHS Angelman Syndrome Natural History Study, Bayley-III Bayley Scales of Infant and Toddler Development® – Third Edition, BCH Boston Children’s Hospital, EEG Electroencephalogram, TDC Typically developing children

Fig. 4

Study expectations and output from feedback questionnaires. EEG electroencephalogram