Evaluating digital medicine ingestion data from seriously mentally ill patients with a Bayesian Hybrid Model

Diffusion dimensionality modeling of subcutaneous/intramuscular absorption of antibodies and long-acting injectables

To evaluate the role of diffusion process dimensionality in drug absorption following subcutaneous or intramuscular administration. The diffusion dimensionality model is based on analytical solutions of the 1-, 2- or 3-dimensional diffusion equations for a bolus input linked to a central compartment with first-order elimination. The model equations were reparameterized to contain three parameters for the time needed for the drug diffusion from the administration site, drug absorption into the central compartment, and the elimination rate constant. The diffusion dimensionality models were challenged with previously published subcutaneous absorption data for 13 antibody drugs and insulin lispro, and the long-acting injectable antipsychotic drugs: subcutaneous Perseris™, intramuscular Invega Sustenna®, Risperdal Consta®, and olanzapine. The Bayesian information criterion was used for model selection. The solution to the diffusion equation for a bolus dose administration is strongly dependent on the number of dimensions. The maximal concentration is lowest for the 3-dimensional diffusion equation. The pharmacokinetic profiles of all 13 antibodies were satisfactorily approximated by a diffusion dimensionality model. Three antibodies (CNTO5825, ACE910 and ustekinumab) were best described by the 2-dimensional diffusion equation. The 2- and 3-dimensional diffusion equations were equivalent for ABT981, guselkumab, adalimumab, nemolizumab, omalizumab, and secukinumab. Golimumab, DX2930, AMG139, and mepolizumab were best described by the 3-dimensional diffusion equation. All the long-acting antipsychotic dosage forms except Risperdal Consta were modeled satisfactorily. Diffusion dimensionality models are a parsimonious and effective approach for modeling drug absorption profiles of subcutaneously and intramuscularly administered small molecule and protein drugs and their dosage forms.

Safety Evaluation in Iterative Development of Wearable Patches for Aripiprazole Tablets With Sensor: Pooled Analysis of Clinical Trials

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.

Cost-Effectiveness of Aripiprazole Tablets with Sensor versus Oral Atypical Antipsychotics for the Treatment of Schizophrenia Using a Patient-Level Microsimulation Modeling Approach

Objective

Strategies designed to track drug ingestion may improve medication adherence and clinical outcomes in adults with schizophrenia. This study aimed to estimate the cost-effectiveness of aripiprazole tablets with sensor (AS; Abilify MyCite^®) versus generic oral atypical antipsychotics (AAPs) in schizophrenia from the United States payer and societal perspectives over 12 months.

Methods

An individual-level microsimulation was developed to generate individual trajectories using data from a phase 3b multicenter, open-label, mirror-image trial in adults with schizophrenia treated prospectively for 6 months with AS. The patient's clinical characteristics and outcomes were computed as a function of the Positive and Negative Syndrome Scale (PANSS) scores. Direct and indirect medical cost estimates were sourced from the literature; EuroQol 5-Dimensions (EQ-5D) utilities were derived using risk equations based on patient and clinical characteristics. Scenario analyses were also conducted to assess outcomes under the assumption of treatment durability over 12 months.

Results

Over 12 months, AS showed a 12.2% improvement in PANSS score. AS had an incremental cost of $2168 and incremental cost savings of $22,343 from the payer and societal perspectives, respectively, with an incremental quality-adjusted life-year (QALY) gain of 0.0298 versus oral AAPs. Further, AS resulted in a 28.2% reduction in hospitalizations over 12 months. At a willingness-to-pay of $100,000 per QALY, the net monetary benefit over 12 months was $25,323 from the payer perspective. Under the assumption of the durability of the treatment effect of AS, the findings were similar to those of the base case analyses, though with greater cost savings and QALYs gained with AS. The results from the sensitivity analyses were consistent with those of the base case analysis.

Conclusion

AS may be a cost-effective strategy, with lower costs and improved quality of life among patients with schizophrenia over 12 months, from the payer and societal perspectives.

Generative models for age, race/ethnicity, and disease state dependence of physiological determinants of drug dosing

Dosing requires consideration of diverse patient-specific factors affecting drug pharmacokinetics and pharmacodynamics. The available pharmacometric methods have limited capacity for modeling the inter-relationships and patterns of variability among physiological determinants of drug dosing (PDODD). To investigate whether generative adversarial networks (GANs) can learn a generative model from real-world data that recapitulates PDODD distributions. A GAN architecture was developed for modeling a PDODD panel comprised of: age, sex, race/ethnicity, body weight, body surface area, total body fat, lean body weight, albumin concentration, glomerular filtration rate (EGFR), urine flow rate, urinary albumin-to-creatinine ratio, alanine aminotransferase to alkaline phosphatase R-value, total bilirubin, active hepatitis B infection status, active hepatitis C infection status, red blood cell, white blood cell, and platelet counts. The panel variables were derived from National Health and Nutrition Examination Survey (NHANES) data sets. The dependence of GAN-generated PDODD on age, race, and active hepatitis infections was assessed. The continuous PDODD biomarkers had diverse non-normal univariate distributions and bivariate trend patterns. The univariate distributions of PDODD biomarkers from GAN simulations satisfactorily approximated those in test data. The joint distribution of the continuous variables was visualized using three 2-dimensional projection methods; for all three methods, the points from the GAN simulation random variate vectors were well dispersed amongst the test data. The age dependence trend patterns in GAN data were similar to those in test data. The histograms for R-values and EGFR from GAN simulations overlapped extensively with test data histograms for the Hispanic, White, African American, and Other race/ethnicity groups. The GAN-simulated data also mirrored the R-values and EGFR changes in active hepatitis C and hepatitis B infection. GANs are a promising approach for simulating the age, race/ethnicity and disease state dependencies of PDODD.

Healthcare Provider Engagement with a Novel Dashboard for Tracking Medication Ingestion: Impact on Treatment Decisions and Clinical Assessments for Adults with Schizophrenia

Purpose

Schizophrenia is a severe, chronic condition accounting for disproportionate healthcare utilization. Antipsychotics can reduce relapse rates, but the characteristics of schizophrenia may hinder medication adherence. A phase 3b open-label clinical trial used aripiprazole tablets with sensor (AS; includes pills with ingestible event-marker, wearable sensor patches and smartphone application) in adults with schizophrenia. This post hoc analysis explored how healthcare providers' (HCPs) usage of a dashboard that provided medication ingestion information impacted treatment decisions and clinical assessments.

Patients and Methods

Participants used AS for 3-6 months. HCPs were instructed to check the dashboard regularly, identify features used, and report impact on treatment decisions. After stratifying HCPs by frequency of dashboard checks and resulting treatment decisions, changes from baseline were calculated for Positive and Negative Syndrome Scale (PANSS), Clinical Global Impression (CGI)-Severity of Illness and CGI-Improvement (CGI-I), and Personal and Social Performance (PSP), and compared using Mann-Whitney U-tests and rank-biserial correlation coefficient (r) effect sizes.

Results

To ensure sufficient opportunity for AS engagement, 113 participants who completed ≥3 months on study were analyzed. HCPs most often accessed dashboard data regarding medication ingestion and missed doses. HCPs recommended adherence counseling and participant education most often. Participants whose HCPs used the dashboard more and recommended adherence counseling and participant education (n=61) improved significantly more than participants with less dashboard-active HCPs (n=49) in CGI-I mean score (2.9 versus 3.4 [p=0.004]), total PANSS (mean change: -9.2 versus -3.1 [p=0.0002]), PANSS positive subscale (-3.2 versus -1.5 [p=0.003]), PANSS general subscale (-4.3 versus -1.2 [p=0.02]), and Marder factor for negative symptoms (-1.9 versus 0.0 [p=0.03]). Most HCPs found the dashboard easy to use (74%) and helpful for improving conversations with participants about their treatment plan and progress (78%).

Conclusion

This provider dashboard may facilitate discussions with patients about regular medication-taking, which can improve patient outcomes.

Machine learning-guided, big data-enabled, biomarker-based systems pharmacology: modeling the stochasticity of natural history and disease progression

The incidence of systemic and metabolic co-morbidities increases with aging. The purpose was to investigate a novel paradigm for modeling the orchestrated changes in many disease-related biomarkers that occur during aging. A hybrid strategy that integrates machine learning and stochastic modeling was evaluated for modeling the long-term dynamics of biomarker systems. Bayesian networks (BN) were used to identify quantitative systems pharmacology (QSP)-like models for the inter-dependencies for three disease-related datasets of metabolic (MB), metabolic with leptin (MB-L), and cardiovascular (CVB) biomarkers from the NHANES database. Biomarker dynamics were modeled using discrete stochastic vector autoregression (VAR) equations. BN were used to derive the topological order and connectivity of a data driven QSP model structure for inter-dependence of biomarkers across the lifespan. The strength and directionality of the connections in the QSP models were evaluated using bootstrapping. VAR models based on QSP model structures from BN were assessed for modeling biomarker system dynamics. BN-restricted VAR models of order 1 were identified as parsimonious and effective for characterizing biomarker system dynamics in the MB, MB-L and CVB datasets. Simulation of annual and triennial data for each biomarker provided good fits and predictions of the training and test datasets, respectively. The novel strategy harnesses machine learning to construct QSP model structures for inter-dependence of biomarkers. Stochastic modeling with the QSP models was effective for predicting the age-varying dynamics of disease-relevant biomarkers over the lifespan.

Characterization of activity behavior using a digital medicine system and comparison to medication ingestion in patients with serious mental illness

Activity patterns can be important indicators in patients with serious mental illness. Here, we utilized an accelerometer and electrocardiogram incorporated within a digital medicine system, which also provides objective medication ingestion records, to explore markers of patient activity and investigate whether these markers of behavioral change are related to medication adherence. We developed an activity rhythm score to measure the consistency of step count patterns across the treatment regimen and explored the intensity of activity during active intervals. We then compared these activity features to ingestion behavior, both on a daily basis, using daily features and single-day ingestion behavior, and at the patient-level, using aggregate features and overall ingestion rates. Higher values of the single-day features for both the activity rhythm and activity intensity scores were associated with higher rates of ingestion on the following day. Patients with a mean activity rhythm score greater than the patient-level median were also shown to have higher overall ingestion rates than patients with lower activity rhythm scores (p = 0.004). These initial insights demonstrate the ability of digital medicine to enable the development of digital behavioral markers that can be compared to previously unavailable objective ingestion information to improve medication adherence.

A Rest Quality Metric Using a Cluster-Based Analysis of Accelerometer Data and Correlation With Digital Medicine Ingestion Data: Algorithm Development

Background

Adherence to medication regimens and patient rest are two important factors in the well-being of patients with serious mental illness. Both of these behaviors are traditionally difficult to record objectively in unsupervised populations.

Objective

A digital medicine system that provides objective time-stamped medication ingestion records was used by patients with serious mental illness. Accelerometer data from the digital medicine system was used to assess rest quality and thus allow for investigation into correlations between rest and medication ingestion.

Methods

Longest daily rest periods were identified and then evaluated using a k-means clustering algorithm and distance metric to quantify the relative quality of patient rest during these periods. This accelerometer-derived quality-of-rest metric, along with other accepted metrics of rest quality, such as duration and start time of the longest rest periods, was compared to the objective medication ingestion records. Overall medication adherence classification based on rest features was not performed due to a lack of patients with poor adherence in the sample population.

Results

Explorations of the relationship between these rest metrics and ingestion did seem to indicate that patients with poor adherence experienced relatively low quality of rest; however, patients with better adherence did not necessarily exhibit consistent rest quality. This sample did not contain sufficient patients with poor adherence to draw more robust correlations between rest quality and ingestion behavior. The correlation of temporal outliers in these rest metrics with daily outliers in ingestion time was also explored.

Conclusions

This result demonstrates the ability of digital medicine systems to quantify patient rest quality, providing a framework for further work to expand the participant population, compare these rest metrics to gold-standard sleep measurements, and correlate these digital medicine biomarkers with objective medication ingestion data.

Hummingbird Study: Results from an Exploratory Trial Assessing the Performance and Acceptance of a Digital Medicine System in Adults with Schizophrenia, Schizoaffective Disorder, or First-Episode Psychosis

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 (NCT03568500; 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.

Detection of Behavioral Anomalies in Medication Adherence Patterns Among Patients With Serious Mental Illness Engaged With a Digital Medicine System

BACKGROUND

Adherence to medication is often represented in the form of a success percentage over a period of time. Although noticeable changes to aggregate adherence levels may be indicative of unstable medication behavior, a lack of noticeable changes in aggregate levels over time does not necessarily indicate stability. The ability to detect developing changes in medication-taking behavior under such conditions in real time would allow patients and care teams to make more timely and informed decisions.

OBJECTIVE

This study aims to develop a method capable of identifying shifts in behavioral (medication) patterns at the individual level and subsequently assess the presence of such shifts in retrospective clinical trial data from patients with serious mental illness.

METHODS

We defined the term adherence volatility as "the degree to which medication ingestion behavior fits expected behavior based on historically observed data" and defined a contextual anomaly system around this concept, leveraging the empirical entropy rate of a stochastic process as the basis for formulating anomaly detection. For the presented methodology, each patient's evolving behavior is used to dynamically construct the expectation bounds for each future interval, eliminating the need to rely on model training or a static reference sequence.

RESULTS

Simulations demonstrated that the presented methodology identifies anomalous behavior patterns even when aggregate adherence levels remain constant and highlight the temporal dependence inherent in these anomalies. Although a given sequence of events may present as anomalous during one period, that sequence should subsequently contribute to future expectations and may not be considered anomalous at a later period-this feature was demonstrated in retrospective clinical trial data. In the same clinical trial data, anomalous behavioral shifts were identified at both high- and low-adherence levels and were spread across the whole treatment regimen, with 77.1% (81/105) of the population demonstrating at least one behavioral anomaly at some point in their treatment.

CONCLUSIONS

Digital medicine systems offer new opportunities to inform treatment decisions and provide complementary information about medication adherence. This paper introduces the concept of adherence volatility and develops a new type of contextual anomaly detection, which does not require an a priori definition of normal and allows expectations to evolve with shifting behavior, removing the need to rely on training data or static reference sequences. Retrospective analysis from clinical trial data highlights that such an approach could provide new opportunities to meaningfully engage patients about potential shifts in their ingestion behavior; however, this framework is not intended to replace clinical judgment, rather to highlight elements of data that warrant attention. The evidence provided here identifies new areas for research and seems to justify additional explorations in this area.

Hummingbird Study: a study protocol for a multicentre exploratory trial to assess the acceptance and performance of a digital medicine system in adults with schizophrenia, schizoaffective disorder or first-episode psychosis

INTRODUCTION

In 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/ANALYSIS

This 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 DISSEMINATION

Approval 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 NUMBER

NCT03568500; EudraCT2017-004602-17; Pre-results.