Lysogenization of Staphylococcus aureus by the serotype F converting bacteriophage phi 13 results in loss of beta-toxin expression. Sequence analysis of the S. aureus beta-toxin gene (hlb), the attachment site (attP)-containing region of phi 13 DNA and the chromosome/bacteriophage DNA junctions of a phi 13 lysogen, revealed that the molecular mechanism of loss of beta-toxin expression was due to insertion of the phi 13 genome into the 5' end of hlb. The insertion site (attB) within hlb contained a 14 base pair core sequence in common with attP and both ends of the integrated linear prophage genome of a phi 13 lysogen. These findings indicate that integration of the phi 13 genome into hlb is site- and orientation-specific.
Purpose
Symptoms of psychotic disorders can complicate efforts to accurately evaluate patients’ medication ingestion. The digital medicine system (DMS), composed of antipsychotic medication co-encapsulated with an ingestible sensor, wearable sensor patches, and a smartphone application, was developed to objectively measure medication ingestion. We assessed performance and acceptance of the DMS in subjects with psychotic disorders.
Methods
This was an 8-week open-label, single-arm, multicenter, Phase 4 pragmatic study (NCT 03568500; EudraCT #2017-004602-17). Eligible adults were diagnosed with schizophrenia, schizoaffective disorder, or first-episode psychosis; were receiving aripiprazole, quetiapine, olanzapine, or risperidone; and could use the DMS with the application downloaded on a personal smartphone. The primary endpoint was good patch coverage, defined as the proportion of days over the assessment period where ≥80.0% of patch data was available, or an ingestion was detected. Exploratory endpoints included a survey on user satisfaction, used to assess acceptance of the DMS. Safety analyses included the incidence of treatment-emergent adverse events (TEAEs).
Results
From May 25, 2018 to March 22, 2019, 55 subjects were screened and 44 were enrolled. Good patch coverage was achieved on 63.4% of days assessed and the DMS generated an adherence metric of ≥80.0%, reflecting the percentage of ingestion events expected when good patch coverage was reported. Most subjects (53.5%) were satisfied with the DMS. Medical device skin irritations were the only TEAEs reported.
Conclusion
The DMS had sufficient performance in, and acceptance from, subjects with psychotic disorders and was generally well tolerated.
IntroductionIn patients with schizophrenia, medication adherence is important for relapse prevention, and effective adherence monitoring is essential for treatment planning. A digital medicine system (DMS) has been developed to objectively monitor patient adherence and support clinical decision making regarding treatment choices. This study assesses the acceptance and performance of the DMS in adults with schizophrenia, schizoaffective disorder or first-episode psychosis and in healthcare professionals (HCPs).Methods/AnalysisThis is a multicentre, 8-week, single-arm, open-label pragmatic trial designed using coproduction methodology. The study will be conducted at five National Health Service Foundation Trusts in the UK. Patients 18–65 years old with a diagnosis of schizophrenia, schizoaffective disorder or first-episode psychosis will be eligible. HCPs (psychiatrists, care coordinators, nurses, pharmacists), researchers, information governance personnel, clinical commissioning groups and patients participated in the study design and coproduction. Intervention employed will be the DMS, an integrated system comprising an oral sensor tablet coencapsulated with an antipsychotic, non-medicated wearable patch, mobile application (app) and web-based dashboard. The coencapsulation product contains aripiprazole, olanzapine, quetiapine or risperidone, as prescribed by the HCP, with a miniature ingestible event marker (IEM) in tablet. On ingestion, the IEM transmits a signal to the patch, which collects ingestion and physical activity data for processing on the patient’s smartphone or tablet before transmission to a cloud-based server for viewing by patients, caregivers and HCPs on secure web portals or mobile apps.Ethics and disseminationApproval was granted by London - City and East Research Ethics Committee (REC ref no 18/LO/0128), and clinical trial authorisation was provided by the Medicines and Healthcare products Regulatory Agency. Written informed consent will be obtained from every participant. The trial will be compliant with the International Council for Harmonisation of Technical Requirements for Pharmaceuticals for Human Use guidelines and the Declaration of Helsinki.Trial registration numberNCT03568500; EudraCT2017-004602-17; Pre-results.
The objective of this work was to adapt and evaluate the performance of a Bayesian hybrid model to characterize objective temporal medication ingestion parameters from two clinical studies in patients with serious mental illness (SMI) receiving treatment with a digital medicine system. This system provides a signal from an ingested sensor contained in the dosage form to a patient-worn patch and transmits this signal via the patient’s mobile device. A previously developed hybrid Markov-von Mises model was used to obtain maximum-likelihood estimates for medication ingestion behavior parameters for individual patients. The individual parameter estimates were modeled to obtain distribution parameters of priors implemented in a Markov chain-Monte Carlo framework. Clinical and demographic covariates associated with model ingestion parameters were also assessed. We obtained individual estimates of overall observed ingestion percent (median:75.9%, range:18.2–98.3%, IQR:32.9%), rate of excess dosing events (median:0%, range:0–14.3%, IQR:3.0%) and observed ingestion duration. The modeling also provided estimates of the Markov-dependence probabilities of dosing success following a dosing success or failure. The ingestion-timing deviations were modeled with the von Mises distribution. A subset of 17 patients (22.1%) were identified as prompt correctors based on Markov-dependence probability of a dosing failure followed by a dosing success of unity. The prompt corrector sub-group had a better overall digital medicine ingestion parameter profile compared to those who were not prompt correctors. Our results demonstrate the potential utility of a Bayesian Hybrid Markov-von Mises model for characterizing digital medicine ingestion patterns in patients with SMI.
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