Safety Evaluation in Iterative Development of Wearable Patches for Aripiprazole Tablets With Sensor: Pooled Analysis of Clinical Trials
Bibliographic record
Abstract
BACKGROUND: Wearable sensors in digital health may pose a risk for skin irritation through the use of wearable patches. Little is known about how patient- and product-related factors impact the risk of skin irritation. Aripiprazole tablets with sensor (AS, Abilify MyCite; Otsuka America Pharmaceutical, Inc) is a digital medicine system indicated for the treatment of patients with schizophrenia, bipolar I disorder, and major depressive disorder. AS includes aripiprazole tablets with an embedded ingestible event marker, a wearable sensor attached to the skin through a wearable patch, a smartphone app, and a web-based portal. To continuously improve the final product, successive iterations of wearable patches were developed, including raisin patch version 4 (RP4), followed by disposable wearable sensor version 5 (DW5), and then reusable wearable sensor version 2 (RW2). OBJECTIVE: This analysis pooled safety data from clinical studies in adult participants using the RP4, DW5, and RW2 wearable patches of AS and evaluated adverse events related to the use of wearable patches. METHODS: Safety data from 12 studies in adults aged 18-65 years from May 2010 to August 2020 were analyzed. All studies evaluated safety, with studies less than 2 weeks also specifically examining human factors associated with the use of the components of AS. Healthy volunteers or patients with schizophrenia, bipolar I disorder, or major depressive disorder were enrolled; those who were exposed to at least 1 wearable patch were included in the safety analysis. Adverse events related to the use of a wearable patch were evaluated. Abrasions, blisters, dermatitis, discoloration, erythema, irritation, pain, pruritus, rash, and skin reactions were grouped as skin irritation events (SIEs). All statistical analyses were descriptive. RESULTS: The analysis included 763 participants (mean [SD] age 42.6 [12.9] years; White: n=359, 47.1%; and male: n=420, 55%). Participants were healthy volunteers (n=269, 35.3%) or patients with schizophrenia (n=402, 52.7%), bipolar I disorder (n=57, 7.5%), or major depressive disorder (n=35, 4.6%). Overall, 13.6% (104/763) of the participants reported at least 1 SIE, all of which were localized to the wearable patch site. Incidence of ≥1 patch-related SIEs was seen in 18.1% (28/155), 14.2% (55/387), and 9.2% (28/306) of participants who used RP4, DW5, and RW2, respectively. Incidence of SIE-related treatment discontinuation was low, which is reported by 1.9% (3/155), 3.1% (12/387), and 1.3% (4/306) of participants who used RP4, DW5, and RW2, respectively. CONCLUSIONS: The incidence rates of SIEs reported as the wearable patch versions evolved from RP4 through RW2 suggest that information derived from reported adverse events may have informed product design and development, which could have improved both tolerability and wearability of successive products. TRIAL REGISTRATION: Clinicaltrials.gov NCT02091882, https://clinicaltrials.gov/study/NCT02091882; Clinicaltrials.gov NCT02404532, https://clinicaltrials.gov/study/NCT02404532; Clinicaltrials.gov NCT02722967, https://clinicaltrials.gov/study/NCT02722967; Clinicaltrials.gov NCT02219009, https://clinicaltrials.gov/study/NCT02219009; Clinicaltrials.gov NCT03568500, https://clinicaltrials.gov/study/NCT03568500; Clinicaltrials.gov NCT03892889, https://clinicaltrials.gov/study/NCT03892889.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.070 | 0.114 |
| Meta-epidemiology (narrow) | 0.003 | 0.001 |
| Meta-epidemiology (broad) | 0.010 | 0.036 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.003 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".