A message from the editor

Dear Colleagues:

Welcome to the annual CNS Summit Abstracts of Poster Presentations supplement to Innovations in Clinical Neuroscience. We are pleased to provide you with this reference guide to some of the innovative research presented during the CNS Summit 2022 event. The supplement is also available online by visiting www.innovationscns.com.

This supplement is just a small representation of the cutting-edge research, innovative ideas, and collaborative efforts shared via the CNS Summit platform. The annual CNS Summit and its year-round programming are designed to encourage and facilitate open channels of communication and data sharing across all disciplines of medical research—with the ultimate goal of achieving optimal patient outcomes.  

In this abstract supplement, we’ve organized CNS Summit 2022 poster abstracts into the following groups for your convenience and easy reference:

  • Artificial Intelligence (AI)/Machine-based Learning
  • Assessment Devices and Tools
  • Biomarkers
  • Decentralized and Virtual Clinical Trials
  • Investigative Drug Compounds and Therapies
  • Treatment Devices and Tools
  • Trial Methodology
  • Wearables and Mobile Applications (APPs)

You will also find an alphabetical index by poster title on pages 29 and 30 of this publication.

We hope you find the CNS Summit 2022 Abstracts of Poster Presentations supplement to Innovations in Clinical Neuroscience informative and that it provides you with a useful snapshot of the research being presented at the annual CNS Summit event. Visit www.cnssummit.org for more information.

As always, we welcome your feedback and participation.

Sincerely,

Amir Kalali, MD

Editor, Innovations in Clinical Neuroscience

Table of Contents

Artificial Intelligence/Machine-based Learning

  • AI-driven COVID-19 mRNA vaccine degradation prediction
  • Balance scan – automation of Timed Up and Go (TUG) 
  • Evidence-based clinical trial feasibility and protocol design using artificial intelligence (AI)
  • Reducing sample sizes by up to 30% in clinical trials for mood disorders via enhanced primary endpoint reliability using audio-visual multimodal machine learning
  • The development of an automated EEG-based machine learning pipeline for the detection of Alzheimer’s disease: a proof of concept for novel clinical trial biomarkers
  • Tools for rapid development of EEG-based pharmacodynamic biomarkers 
  • TwinRCT: novel AI-based solution to reduce sample size of control arms

Assessment Devices and Tools

  • Circadian rhythm-related endpoints derived from actigraphy: validation of Somno-Art 
  • Q-FiberMapper: a fully automated reconstruction of major white-matter bundles with extraction of quantitative imaging-markers for white matter integrity
  • Reliability of real world digital end-points of functional neurophysiology 
  • Using ecological momentary assessment to measure real-world effects of a combined computerized cognitive and functional skills training program in mild cognitive impairment
  • Utility of an optimized 10-item pediatric PANSS: Comparison to 30-item PANSS in a large multi-site, industry-sponsored trial for adolescent schizophrenia 

Biomarkers

  • Assessing effects of heavy alcohol drinking on results from clinical trials in persons with schizophrenia by retrospective use of an alcohol biomarker
  • EEG biomarkers: novel insights for characterizing neuropathology and quantifying treatment outcomes in psychiatric disorders

Decentralized and Virtual Clinical Trials

  • A decentralized, prospective, observational study to collect real-world data from patients with myasthenia gravis using smartphones
  • Deploying a virtual site in a decentralized fibromyalgia study
  • High interest in decentralized clinical trial participation among CNS disorder sufferers
  • How decentralized clinical trials made a global Alzheimer’s trial possible during the pandemic
  • How mobile research nurses are making possible the largest fully decentralized trial for a vulnerable population 
  • Inside the pharmacodynamic toolbox: how questionnaires, models, and tests of cognition can accelerate the development of CNS-active drugs 

Investigative Drug Compounds and Therapies

  • A follow-up study on the differences in Yale Global Tic Severity Scale (YGTSS) and additional outcome measures in a cohort of participants in a Phase IIb trial for the treatment of Tourette’s syndrome before and after restrictions related to the COVID-19 pandemic: a continuation study assessing group differences following positive results for the IIb study
  • A single, ascending dose study of PCN-101 (R-ketamine) and the clinical design of a Phase IIa study for treatment-resistant depression (TRD)
  • Metabolic mechanism in migraine: tricaprilin, a ketogenic agent
  • Results from the Phase I small pharma trial of SPL026 in psychedelic-naïve healthy volunteers
  • Safety, tolerability, and pharmacokinetics of BRII-296, an extended-release injectable aqueous suspension formulation of brexanolone in healthy adult subjects 
  • Two randomized, double-blind, placebo-controlled trials of adjunctive troriluzole, a novel glutamate modulating agent, in obsessive compulsive disorder

Treatment Devices and Tools

  • A basket trial for accelerated discovery of novel neuroscience-based digital treatments for chronic pain 
  • Factors affecting Positive and Negative Syndrome Scale (PANSS) change between screening and baseline in acute schizophrenia clinical trials: an exploratory analysis
  • Longitudinal ecological momentary assessments of the behavioral indicators of avolition in schizophrenia identify changes that are correlated with clinical ratings of negative symptoms 
  • Symptom and physical function reporting by caregivers as predictors of adverse clinical outcomes in cancer patients

Trial Methodology

  • An end-to-end cloud solution for managing multisite imaging clinical trials
  • Are we missing opportunities by misinterpreting FDA guidance?  
  • Can targeting recruitment to treatment-seeking participants improve speed or quality?
  • Comparison of electronic health records (EHRs) and electronic source data (EDC) in clinical research trials: a retrospective review
  • Do experience and credentials impact clinical trial certification scoring performance?
  • Ecological momentary assessment for prediction and monitoring of clinical trials in neuropsychiatric conditions
  • Educational needs of CNS clinicians: the challenging reality of modern CNS trials
  • Frequent exclusion of patients with comorbidities in Alzheimer’s research contributes to unrepresentative participant demographics
  • Furthering the validity of the placebo-control reminder script: essential perspectives from the end user
  • Importance of representation in participant recruitment and retention for generating robust evidence from real-world data
  • Investigation of post-pandemic patient perception toward clinical trials
  • Measuring the impacts of caregiving in schizophrenia 
  • Optimizing study delivery mix for diversity, enrollment velocity, and cost
  • Patient Insights Board informed consent engagement case study
  • Pediatric caregiver research perspectives survey: highlighting key barriers to participation in pediatric clinical research 
  • Perception of wellbeing: Does being in a clinical trial influence how patients with cancer view themselves?
  • Quality of life data, collected in the home, demonstrate high internal consistency, high resolution, and the ability to monitor clinical trial participants
  • Remote administration of MADRS is equal in quality to in-person administration: evidence from two parallel depression trials
  • Research-driven recruitment for social anxiety study 
  • Study design for a feasibility exploration of digital biomarkers as reader comprehension and engagement measures of informed consent 
  • The impact of Mini-Mental State Examination (MMSE) score on MMSE assessment duration: an exploratory analysis 
  • The positive impact of telehealth genetic counseling in rare disease clinical trial recruitment
  • The utility of data tokenization in clinical trials 
  • Update on Alzheimer’s research-related global challenges, opportunities, solutions, and evolving innovations 
  • Who has done what and where? Which CNS population attempts the most protocol violations and what are those protocol violations? 

Wearables and Mobile Applications (APPs)

  • A study to evaluate passive collection of sensor data in participants undergoing consciousness-altering therapeutic sessions for treatment of psychiatric illness
  • Addressing the unmet needs of Alzheimer’s disease with real-world digital clinical measures 
  • Administering ePROs using Datacubed Health’s mobile app improves adherence in a siteless, virtual, longitudinal study
  • Association between patient-reported sleep quality and passive sleep measurements in patients with psychiatric disorders using mobile-based questionnaires and wearable sensors
  • Implementing wearable technologies for in-home assessment of cognitive and event-related potential responses after sleep and wakefulness in Alzheimer’s disease
  • Mobile app care utility: patients’ and caregivers’ perception
  • Older adults engage with optional, interactive elements of a mobile app
  • Reception of a behavioral science-based, patient-centered mobile app in a diverse cohort
  • Results from a survey of biopharmaceuticals on attitudes toward the use of wearables and sensors in clinical research
  • Smart walking training devices and methodology to alleviate the course of the disease and validate the effects of drugs on Parkinson’s patients
  • Using digital technology to increase patient retention and improve compliance for better clinical trial management  

Artificial Intelligence (AI)/Machine-based Learning

AI-driven COVID-19 mRNA Vaccine Degradation Prediction

Author: Abhijit Ray

Background/Objective: Messenger RNA (mRNA) vaccines have emerged as a promising treatment for the coronavirus disease-2019 (COVID-19) pandemic, but such a solution has its challenges. One such issue is the mRNA vaccine’s molecular stability, which requires that it be kept under certain environmental conditions that restrict its global outreach in packages, such as disposable syringes, distributed worldwide using refrigeration.Designing an environmentally stable mRNA vaccine that can withstand shipment worldwide is a challenge, since a single slit or puncture can render the complete dose of the vaccine useless. If not kept under certain environmental conditions and left unmonitored, mRNA vaccines tend to degrade rapidly. To address this problem, agencies currently store mRNA vaccines under strict refrigeration, thus limiting their global reach.The objective was to develop a hybrid deep learning model that can efficiently predict mRNA vaccine degradation rate from RNA sequences, thus aiding researchers and scientists in designing and developing a more stable mRNA vaccine in the future

Results: Research presented here discusses the capability of the in-house developed hybrid deep learning model. 

Conclusion: The model was developed with a performance of 0.2430 mean columnwise root-mean-squared error (MCRMSE) score on the test data. 

Funding/financial disclosures: Not provided.

Balance Scan – Automation of Timed Up and Go (TUG)

Authors: Jerry Winniczek, PhD; Meghan Conroy; Rhonda Taubin, MD; Kevin Christenson, PT

Affiliations: All authors are with CaptureProof, Inc.

Background/Objective: Falls represent a challenge for medicine, and gait is a clear visual biomarker of a patient’s health. It is proposed that the gait is the visual fingerprint of movement and that computer vision and artificial intelligence (AI) identify biomarkers and objective measurements of health. These biomarkers are used in research and patient care. 

Design: This study used the Timed Up and Go (TUG), a standard for triaging fall risk. A seated patient rose, walked 3m, and returned to the seat. TUG was recorded on the CaptureProof mobile application in the hospital and at home. CaptureProof’s proprietary machine learning (ML) algorithm monitored patient movement, computed TUG total time, and also included metrics of body motion, such as side-to-side listing and cadence of walk.  

Results: Among all ages, 39 percent of patients passed the TUG test with a TUG time of less than 11 seconds, a category considered low risk. Among patients over the age of 65 years, 53 percent were at low or moderate risk of falling. Assisted devices (walkers and canes) accounted for 20 percent of subjects; 88 percent had TUG times over 20 seconds. Agreement between human frame-by-frame analysis versus the ML-automated algorithm was 77 percent for all cases and 91 percent for TUG times under 15 seconds. The major source of error was attributed to large hospital gowns, which reduced the precision of detecting the human form. 

Conclusion: Automated ML-based measurement of TUG times provides greater accuracy and objectivity. Additionally, other motion parameters can be extracted. Two of these include side-to-side listing and turn-to-sit time. In addition to TUG time, there are 13 other biomarkers CaptureProof tracks. 

Funding/financial disclosures: Funding and support provided by Intermountain Healthcare. Current employees of CaptureProof, Inc., RT and KC, are employees of Intermountain Healthcare. 

Evidence-based Clinical Trial Feasibility and Protocol Design Using Artificial Intelligence

Authors: Ying Ding, PhD;1 Ganesh Padmanabhan;2 Kris Nair3 

Affiliations: 1School of Information, Department of Population Health, Dell Medical School, University of Texas at Austin; 2Chief Executive Officer, Autonomize Inc.; 3Chief Operating Officer, Clinical Ops, Autonomize, Inc.

Background/Objective: This study focused on how Autonomize, Inc. used AI and machine learning (ML) techniques to explore publicly available biomedical research and clinical trials information to predict evidence-based clinical study feasibility decisions around protocol design and site selection. 

Design: There are 430,807 clinical studies on clinicaltrials.gov. There are about 110409 clinical trial publications collected by the PubMed literature database, which have detailed documentation of the performed clinical trials and their scientific outcomes. Currently, the clinical trial and PubMed literature databases are not meaningfully connected. Furthermore, the knowledge embedded in the unstructured textual data in both datasets has not been extracted and linked. Connecting dots can transform and empower knowledge discovery. In this project, we applied BioBert to extract diseases from titles and abstracts of clinical trials and genes and methods from the abstracts of PubMed articles. We built the Clinical Study Insights tool by fusing the PubMed knowledge graph and clinical trial knowledge graph through joint biological entities and authors of articles/primary investigators (PIs) of clinical trials. 

Results: We could provide evidence-based insights from previous and ongoing clinical trials at the granularity of disease conditions, observations, and eligible criteria and summarize the insights from the related PubMed literature about genes, methods, and scientific outputs. The tool allowed for answers to questions such as which PIs are experts in running successful clinical trials for a particular condition; understanding the impact of eligibility criteria to the clinical measures and enrollment challenges; or identifying in-licensing opportunities for drugs in Phase IV for a particular therapeutic area based on published research, among other uses. 

Conclusion: Autonomize’s Clinical Study Insights produces insights for evidence-based decision making for clinical trial protocol comparison, trial design and optimization, site selection, and competitive intelligence analysis of past, ongoing, and future clinical trials. 

Funding/financial disclosures: Not provided.

Reducing Sample Sizes by up to 30% in Clinical Trials for Mood Disorders via Enhanced Primary Endpoint Reliability Using Audio-visual Multimodal Machine Learning

Authors: Jeremy Jones,1 Marcelo Cicconet,1 Ben Barone,1 Katie Aafjes-van Doorn,1,2 Jeff Cohn,1,3 Marc Aafjes1

Affiliations: 1Deliberate AI, 2Yeshiva University, 3University of Pittsburgh

Background/Objective: Suboptimal inter-rater reliability of clinician-reported outcome assessments (ClinROs) in central nervous system (CNS) clinical trials attenuates signal detection and statistical power and contributes to comparatively high failure rates. Much effort is put into developing digital biomarkers—for instance, using speech or oculometrics—that could act as surrogate endpoints. However, most only weakly and nonlinearly correlate to outcomes and struggle with clinical validation.

Our objective was to develop multimodal machine learning (ML) models that combined a comprehensive set of behavioral and peripheral physiological biomarkers that directly predict United States (US) Food and Drug Administration (FDA)-preferred primary endpoints for mood disorders, Hamilton Depression Rating Scale (HAM-D), and Hamilton Anxiety Rating Scale (HAM-A). By pairing ML with human raters, we aimed to improve the effective reliability of endpoints and increase statistical power, thus reducing the necessary size of cohorts. 

Design: Over 100 HAM-D and HAM-A clinical interviews were conducted, across multiple settings, with a full range of mood disorder severity. An extensive set of facial expressions, face and head dynamics, vital signs, oculometrics, voice, linguistics, and physiological features were extracted from interview audio-video recordings. ML models were developed to predict HAM-D and HAM-A scores. Analytical validation was accomplished by testing on an independent test set.

Results: The HAM-D model achieved an intraclass correlation coefficient (ICC) of 0.882 (root-mean-square error [RMSE]: 2.89 and mean absolute error [MAE]: 2.35). The HAM-A model achieved an ICC of 0.874 (RMSE: 3.36 and MAE: 2.57). 

Conclusion: The models outperformed typical observed rater reliability. By combining human raters with ML models, effective reliability of endpoints can be increased and result in sample size reduction of 10 to 30 percent. 

Funding/financial disclosures: This research was funded by Deliberate AI, and all authors are employed by and/or hold equity stakes in Deliberate AI. 

The Development of an Automated EEG-based Machine Learning Pipeline for the Detection of Alzheimer’s Disease: A Proof of Concept for Novel Clinical Trial Biomarkers

Author: Nicholas Chedid, MD

Affiliations: SynapseBio, Inc.

Background/Objective: The purpose of this study was to develop an electroencephalography (EEG)-based automated machine learning (ML) pipeline for detecting Alzheimer’s disease (AD) that solves the following problems: 1) lack of automated and unbiased removal of EEG artifacts, 2) dependence on a high level of expertise in data preprocessing and ML for nonautomated processes, 3) need for very large sample sizes and accurate EEG source localization using high-density electrode systems, and 4) reliance on black box ML approaches, such as deep neural nets with unexplainable feature selection.

Design: EEGs were collected from 23 healthy patients and 18 patients with AD. EEG data were transformed from the time domain to the frequency domain. A support-vector machine pipeline automatically detected and removed artifacts. Power spectral density (PSD) analysis was performed, and PSD features were selected based on statistical analysis. These features were input into a logistic regression model. 

Results: We reached a mean accuracy of 81 percent for classifying patients as healthy or with AD (area under the curve [AUC]: 87%, precision: 78%, recall: 75%).

Conclusion: We developed a fully automated EEG ML-based model for differentiating healthy subjects from patients with AD. The novelty in our approach is twofold: 1) having “transparent” ML techniques, as opposed to black box deep learning methods, and 2) preprocessing EEGs in an automated manner to remove artifacts, enabling reproducible, rigorous, and scalable results. These novel aspects enabled proof-of-concept data, despite a small sample size. Our current and future work entails applying this automated pipeline to develop biomarkers to enhance safety, patient selection, and assessment of treatment efficacy in AD clinical trials. 

Funding/financial disclosures: NC is an employee of SynapseBio, Inc.

Tools for Rapid Development of EEG-based Pharmacodynamic Biomarkers 

Authors: Jay Pathmanathan,1 Stephen Johnson,1 Eric P. Hanson,1 David Little,1 Kolia Sadeghi,1 Kevin Zheng,1 Kim Laberinto,1 Michelle Fogerson,1 Hannah Roberts,1 Dhany Tjiptarto,1 Jarret Revels,1 Alex Arslan,1 David Kleinschimdt,1 Philip Alday,1 Sydney Cash,1,2 Alex Chan,1 Ciara Martin,1 Brandon Westover,1,2 Jacob Donoghue1

Affiliations: 1Beacon Biosignals, Boston, MA; 2Massachusetts General Hospital, Harvard University, Boston, MA

Background/Objective: We aimed to develop methods for rapid development and deployment of electroencephalography (EEG)/polysomnography (PSG)-based biomarkers of central nervous system (CNS) targeting therapeutics.

Design: This was a descriptive assessment and retrospective case-control study.

Results: While EEG offers coarse insight into CNS activity, its noninvasiveness, wide availability, and chronic recording make it a potentially valuable biomarker of CNS activity. Naïve interpretation methods (visual inspection and simple power spectral analyses), data collection limitations (requiring placement by trained technicians), and technical limitations (including proprietary data formats and archaic data transfer systems) have been barriers to the implementation of EEG biomarkers. Beacon Biosignals has developed infrastructure and machine learning (ML) algorithms for data interpretation, coupled with a database of over 45,000 subjects with a variety of CNS diseases and over 5,000 controls, to rapidly develop and deploy EEG as a biomarker of neurological disease (and therapeutic effect). We demonstrated the potential of this system using Alzheimer’s disease (AD) as a model. Recent data has shown the value of EEG as a biomarker of cortical irritability and sleep derangements that predict AD severity. Our database was used to rapidly analyze data on 196 subjects with AD (90 EEGs and 106 PSGs) and controls. 

Conclusion: Streamlined data constructs, ML analysis pipelines, and massive datasets allows for rapid characterization of neurophysiological data and identification of potential CNS biomarkers. Here, we used AD as a model to demonstrate the feasibility of such a system, noting the presence of discharges and sleep pathology in the AD cohort that serve as biomarkers of disease severity, treatment efficacy, and therapeutic targets. 

Funding/financial disclosures: Beacon Biosignals is a commercial entity focused on the analysis of time series neurophysiological data and the development neurophysiological biomarkers. Beacon Biosignals funded this work.

TwinRCT: Novel AI-based Solution to Reduce Sample Size of Control Arms

Authors: Jose Luis Olmos-Serrano, David P. Miller

Affiliations: All authors are with Unlearn.AI, San Francisco, CA.

Background/Objective: The aim of this study was to describe a novel twin randomized, controlled trial (TwinRCT™), which integrates prognostic digital twins (machine learning [ML]-generated clinical predictions of a subject’s longitudinal prognosis) into clinical trial designs to reduce sample size of control arms.

Design: TwinRCTs use external data and disease-specific deep learning models to optimize the covariate adjustment process using a novel statistical approach (Prognostic Covariate Adjustment [PROCOVA]) to reduce sample size. To illustrate the advantages of TwinRCTs, we used a deep learning model that was trained on data from the Pooled Resource Open-Access Amyotrophic Lateral Sclerosis (ALS) Clinical Trials Database (PRO-ACT). After the model was finalized, the PROCOVA procedure was applied to data from a Phase II clinical trial (NCT00349622) led by Merit Cudkowicz obtained from the National Institute of Neurological Disorders and Stroke (NINDS) to demonstrate the potential benefit of running a similar trial as a TwinRCT.

Results: Assuming ALS Functional Rating Scale-Revised (ALSFRS-R) change from baseline as the primary endpoint, we estimated that adjusting for prognostic digital twins resulted in a reduction in the number of subjects randomized to the control arm in a comparable future trial by up to 22 percent, compared to the standard sample size calculation, for which no covariate adjustment was assumed.

Conclusion: TwinRCTs enable a more accurate estimation of treatment effect, an increase in statistical power, and a reduction in sample size of clinical trials, without introducing bias. Moreover, PROCOVA, the foundation of a TwinRCT, has received a qualification opinion from the European Medicines Agency (EMA). 

Funding/financial disclosures: JLO-S and DPM have received personal compensation for serving as employees of Unlearn.AI, Inc.

Assessment Devices and Tools

Circadian Rhythm-related Endpoints Derived from Actigraphy: Validation of Somno-Art 

Authors: Georg Dorffner,1,2 Manuel Eder,2 Bruno Muller,3 Gil Fuchs,3 Laurie Thiesse,3 Antoine Viola,3 Manuel Kemethofer2

Affiliations: 1Medical University of Vienna, Institute for Artificial Intelligence, Vienna, Austria; 2The Siesta Group, Vienna, Austria; 3PPRS, Colmar, France

Background/Objective: This study aimed to validate circadian rhythm-related endpoints derived from actimetry recorded with Somno-Art (SA) against two United States (US) Food and Drug Administration (FDA)-cleared actigraphy devices commonly used in clinical trials, MotionWatch8 (MW8) and ActTrust (AT). 

Design: Eighteen healthy subjects wore the devices for one week on their nondominant arms. MW8 and AT were worn on the wrist, and SA was worn on the upper forearm. Five nonparametric circadian rhythm analysis (NPCRA) variables were calculated: 1) interdaily stability (IS), 2) intradaily variability (IV), 3) start hour of the five least (L5ST) active hours per 24-hour period, 4) start hour of the most (M10ST) active hours per 24-hour period, and 5) relative amplitude (RA). AT and MW8 were analyzed with their proprietary algorithms, SA with our own algorithms. The different position of the SA device was accounted for by a previously validated scaling factor. For comparison, intraclass correlation coefficients (ICCs) were calculated. An ICC above 0.75 was considered as good agreement. 

Results: While ICC for all variables between all three devices (ranging from 0.77 [RA] to 0.99 [L5ST]) and between MW8 and SA (ranging from 0.89 [IV] to 1.0 [L5ST]) was good to very good, this was only the case for IV (0.93), IS (0.97), and L5ST (0.99) when comparing AT and SA. M10ST (0.57) and RA (0.58) showed low agreement between those two devices.

Conclusion: Results indicate that each variable showed good agreement when comparing all three devices, especially between SA and MW8. Two variables showed low agreement between SA and AT, but this was also the case between MW8 and AT. Thus, we conclude that the NPCRA endpoints derived from SA are reliable. 

Funding/financial disclosures: GD, ME, and MK are employees of The Siesta Group, a service provider for measuring electrophysiological and actigraphic signals in clinical trials. GD is also a shareholder of The Siesta Group. BM, GF, LT, and AV are employees of PPRS, the manufacturer of Somno-Art.

Q-FiberMapper: A Fully Automated Reconstruction of Major White-matter Bundles with Extraction of Quantitative Imaging-markers for White Matter Integrity

Authors: Tommy Boshkovski, Oscar Peña-Nogales, Marc Ramos, Paulo Rodrigues, Vesna Prčkovska, Kire Trivodaliev

Affiliations: All authors are with QMENTA, Boston, MA.

Background/Objective: Manual segmentation of white matter (WM) bundles is a labor-intensive and error-prone task that can only be performed by trained experts. However, automatic reconstruction methods that use predefined landmarks and rules for the tractography showed comparable accuracy. Despite their advantages, translation into a clinical setting is still problematic, as clinical data require tailored preprocessing. To address these issues, we introduced Q-FiberMapper (QFM), which automatically dissects 33 major bundles and extracts imaging biomarkers from clinical magnetic resonance imaging (MRI) data. All of this makes QFM useful for detection and monitoring of WM diseases, such as multiple sclerosis, traumatic brain injury, and psychiatric disorders.

Design: QFM begins by standardizing the input data and preprocessing them using state-of-the-art algorithms. Then, QFM reconstructs a total of 33 WM bundles. For each bundle, a lateralization index and fractional anisotropy profile are calculated. Then, the results are shown in an easy-to-read report that summarizes all of the information about the shape, profile, and lateralization index of the bundle. To validate QFM, we used the Human Connectome Project and Information-eXtraction-from-Images datasets.

Results: QFM streamlined the process of data processing, analysis, and biomarker extraction. QFM’s bundle reconstructions showed a good agreement for the shape and volume of the bundles. Additionally, we found that the population-wise lateralization index of the tracts is in line with the neuroanatomy literature. 

Conclusion: QFM is a reproducible, fully automated bundle reconstruction pipeline that can detect and monitor WM diseases, increase the quality of imaging endpoints, and reduce the time and cost in large studies, such as clinical trials.

Funding/financial disclosures: The presenters are employed by and hold options of QMENTA.

Reliability of Real World Digital End-points of Functional Neurophysiology 

Author: Brian Murphy, PhD 

Affiliations: Cumulus Neuroscience

Background/Objective: Direct and reliable measurement of brain function in trials of central nervous system (CNS) therapies usually requires a clinic visit for modalities, such as positron emission tomography (PET), functional magnetic resonance imaging (fMRI), and electroencephalography (EEG). Decentralized trials can improve cohort diversity, costs, and time-to-decision, but rely on indirect measures of brain function (e.g., at-home digital measurement of self-reports and cognitive tasks). Here, we evaluated data quality of real-world, functionally specific EEG in terms of test-retest reliability. 

Design: Cumulus Neuroscience provides a patient-friendly platform for the synchronized at-home recording of dry wireless EEG signals and validated tasks that probe core cognitive functions. A group of adults (n=30; mean age: 25.6 years) used the device on a daily basis over a number of weeks for task-driven ERPs and resting quantitative EEG (qEEG). Test-retest reliability was quantified with intraclass correlation (ICC; 3,1). 

Results: In-clinic qEEG metrics of peak alpha band power had an ICC reliability of 0.8 to 0.9, which is competitive with published clinical studies. Single at-home qEEG recordings had lower ICCs of 0.7 to 0.8 but could reach the 0.8 to 0.9 range by aggregating over two at-home sessions. Single-session visual event-related potential (ERP) metrics had ICCs of about 0.6 in-lab and at-home but rose above 0.8 with aggregation, again similar to high-quality published studies.

Conclusion: Carefully designed, task-driven EEG suited to autonomous use by patients and caretakers may provide a solution to the dilemma between the objectivity and depth of understanding that functional brain measurement brings and the scalability and frequency of sampling of digital endpoints. Analysis of test-retest reliability indicates that a small number of sessions with a self-applied wearable can provide clinical grade data. 

Funding/financial disclosures: BM is a founder, employee, and shareholder at Cumulus Neuroscience.

Using Ecological Momentary Assessment to Measure Real-World Effects of a Combined Computerized Cognitive and Functional Skills training Program in Mild Cognitive Impairment

Authors: Philip D. Harvey, PhD;1,2,3 Sara Czaja;2,4 John Saber;3 Peter Kallestrup2

Affiliations: 1University of Miami Miller School of Medicine, Miami, FL; 2I-Function, Inc., Miami, FL; 3EMA Wellness, Boston, MA; 4Weil Cornell Medical Center

Background/Objective: Pharmacological treatments for mild cognitive impairment (MCI) have not been successful, leading to computerized cognitive training (CCT) efforts. CCT alone does not improve everyday functioning, so skills training is needed. Combining commercially available CCT and novel computerized functional skills training software (FUNSAT) leads to gains in functional skills and cognition across both no cognitive impairment (NC) and MCI. Previously, training was conducted in-person, and real-world transfer was not assessed. The current study uses fully remote training delivered twice weekly for 12 weeks with ecological momentary assessment (EMA), targeting trained and untrained everyday skills.

Design: A total of 120 participants with MCI or NC constituted the full sample. Participants were randomized equally to skills alone or combined training. Outcomes included training gains, performance on untrained cognitive abilities: BACS, untrained functional skills, VFCAT, and real-world functioning, with three EMA surveys per week. 

Results. Fifty-five patients with NC and 65 patients with MCI participated in the study. Of these, 32 patients randomized to combined training and 33 to skills alone completed training. Improvement in time to completion from baseline ranged from 41 to 56 percent across tasks, with all tasks also showing a minimum improvement in error rates of 80 percent across groups. A total of 2,880 EMA surveys were answered (mean: 3 per training week). The proportion of surveys where the respondents reported accessing the internet since the last survey increased by 35 percent, and using social media doubled. There was a 54-percent increase in the proportion of surveys where respondents reported sending a text message and a 20-percent increase in surveys reporting mobile phone use. 

Conclusion. A fully remote functional skills and CCT training program is feasible and shows efficacy. EMA assessment of everyday activities manifested excellent adherence and generated a large survey database. Importantly, participants were found to engage in both trained and untrained technology-related activities. 

Disclosures: This research was funded by National Institute of Aging Grants 2 R44 AG0572381 and R44 AG074818 to PK. EMA assessment software was provided by EMA Wellness, Inc.

Utility of an Optimized 10-item Pediatric PANSS: Comparison to 30-item PANSS in a Large Multisite Industry-sponsored Trial for Adolescent Schizophrenia 

Authors: Joan Busner, PhD;1,2 Eric A. Youngstrom, PhD;3 Joshua A. Langfus;3 Robert L. Findling, MD, MBA;2 David G. Daniel, MD1

Affiliations: 1Signant Health; 2Virginia Commonwealth University School of Medicine, Department of Psychiatry; 3University of North Carolina, Chapel Hill

Background/Objective: The Positive and Negative Syndrome Scale (PANSS) is a lengthy 30-item psychosis measure designed for adults. Our group has developed a 10-item PANSS for pediatric trials, based on psychometric analyses of the National Institute of Mental Health (NIMH) Treatment of Early Onset Schizophrenia Study (TEOSS). Our 10-item PANSS compared well to the 30-item version and was reliable and sensitive to treatment changes in that study. In the present study, we attempted to replicate the findings using an independent, multisite, placebo-controlled trial.

Design: Unblinded data from the six-week, double-blind, parallel group, acute-phase data from the Johnson & Johnson-sponsored positive paliperidone study were accessed from the Yale Open Data Access (YODA) secure data environment. The trial included 201 12- to 17-year-old participants randomly allocated to placebo or one of three fixed doses of paliperidone. Analyses included mixed regressions using random intercepts and partial eta-squared as the effect size estimate for time, treatment, and time x treatment interaction effects. 

Results: The 10- and 30-item versions had similar average inter-item correlations, as well as similar partial eta-squared values for time, treatment, and time x treatment. Last observation carried forward (LOCF) analyses found similar 10- versus 30-item separation from placebo on multiple identical arms; week-by-week effect sizes for the 10- and 30-item versions were similar. 

Conclusion: The 10-item version of the PANSS was replicated well in an independent, larger, adolescent sample using double-blind, randomized, controlled trial data. The similar sensitivity to treatment effects is particularly promising, given the decrease in scale length, required rater training, interview length, and respondent burden.

Funding/financial disclosures: JB and DGD are full time employees of Signant Health. JAL has no financial disclosures. Financial disclosures of EAY and RLF are available on request. Presented in part at the ISCTM Autumn Conference, September 8 to 9, Boston, MA.

Biomarkers

Assessing Effects of Heavy Alcohol Drinking on Results from Clinical Trials in Persons with Schizophrenia by retrospective Use of an Alcohol Biomarker

Authors: Martin S. Mumenthaler,1 Raymond F. Anton,2 Tao Shi,1 Larry D. Alphs1

Affiliations: 1Denovo Biopharma; 2Department of Psychiatry, Medical University of South Carolina, Charleston, SC

Background/Objective: Alcohol misuse can significantly impact clinical trial results with Type-2 errors and adverse events. Alternatively, the study drug may impact drinking behavior and affect treatment response. The selective mGlu2/3 agonist— pomaglumetad methionil (Poma)—showed mixed results in clinical trials of patients with schizophrenia, possibly due to variable comorbid alcohol use disorder (AUD) frequency, with secondary effects of Poma on drinking behavior. Carbohydrate deficient transferrin (CDT), a validated biomarker, can identify heavy alcohol drinking (HAD) behavior in blood samples with high specificity. We describe a methodology applying CDT that allows retrospective assessment of drug effects on patients with schizophrenia with comorbid AUD, despite the absence of alcohol use measures in the original trials. 

Design: We describe retrospective CDT assays in blood samples from prior clinical trials to determine which participants with schizophrenia demonstrated HAD (likely meeting AUD criteria). This permits determination of whether Poma improved psychotic symptoms more in patients with schizophrenia and HAD, compared to patients with schizophrenia without HAD, based on the change of schizophrenic symptoms (Positive and Negative Syndrome Scale [PANSS], Clinical Global Impression [CGI]). Anticipating a Cohen’s effect size D of 0.3, and with 30 percent of patients testing CDT-positive, we have 84-percent power to detect an effect (difference in PANSS and CGI scores) in 364 available patient samples.

Results: The study is ongoing. 

Conclusion: Retrospective application of CDT allows evaluation of the impact of HAD on treatment results from comorbid patients with historically higher rates of AUD. This data should inform better subject assessment/selection and/or a way to evaluate targeted treatment responses in persons with schizophrenia and comorbid HAD. 

Funding/financial disclosures: Not provided.

EEG Biomarkers: Novel Insights for Characterizing Neuropathology and Quantifying Treatment Outcomes in Psychiatric Disorders

Authors: Chris Berka, Amir Meghdadi, Natasha Kovacevic

Affiliations: All authors are with Advanced Brain Monitoring (ABM).

Background/Objective: Resting state (RS) electroencephalogram (EEG) and event-related potentials (ERPs) provide functional assessment of the neural activity characteristic of psychiatric diseases and quantify treatment efficacy.

Design: Patients with major depressive disorder (MDD) or posttraumatic stress disorder (PTSD) were evaluated during RS/ERP and compared to matched healthy controls (HCs). ABM’s wireless StatX24 system with standard 10-20 montage acquired EEG during RS (eyes open/closed) and three-choice vigilance task (3CVT) was used.

Results: Partial least squares (PLS) analysis of differences between patients with MDD and HC resting state EEG power spectra was significant (p<0.01), including decreases in alpha (8–12Hz) and increases in beta/gamma (15–40Hz) and delta/theta (2–5Hz); alpha deficits correlated with severity of depression symptoms in the MDD group. 

PLS analysis of 3CVT ERPs revealed significant differences between patients with MDD and HC. Early attentional ERP components (<250ms) in MDD showed significant latency and amplitude deviations, suggesting difficulty allocating attentional resources. ERP amplitudes around 200ms in the frontocentral region were correlated with symptom severity in MDD. In combination, these EEG biomarkers significantly differentiated patients with MDD from HCs and provided a statistically reliable association with depression severity.

Compared to HCs, the PTSD group showed a significant decrease in relative alpha (8–13Hz) (t-test, p<0.00001) and a significant reduction in amplitude of the 3CVT P400 component (p<0.01), which might indicate impaired cognition/decision-making. Attention-related N100/P200 showed increased amplitude in PTSD (p=0.01), which might possibly be a biomarker for hypervigilance. 

Conclusion: The evaluation of new therapeutics for psychiatric disorders can be streamlined using EEG-based precision biomarkers to assess target engagement and treatment efficacy. EEG RS and ERP are easily deployed, cost effective, noninvasive, and scalable for multisite clinical trials.

Funding/financial disclosures: All authors are employees of ABM and receive compensation for their work. CB is also a shareholder of ABM.

Decentralized and Virtual Clinical Trials

A Decentralized, Prospective, Observational Study to Collect Real-world Data from Patients with Myasthenia Gravis Using Smartphones

Authors: Sandra Steyaert, Meelis Lootus, Chethan Sarabu, Zeenia Framroze, Harriet Dickinson, Emily Kunka, Jean-Christophe Steels, Nirav R. Shah, Francesca Rinaldo

Background/Objective: We report results from a three-month, prospective, observational study in adults with myasthenia gravis (MG) using fully decentralized methods to assess the feasibility of real-world data collection from smartphones for patients with this rare disease. Using an application (app) designed for the study (available on iOS and Android), participants reported their daily symptoms, symptom severity, exacerbation status, and health-related quality of life using digital versions of the MG-Activities of Daily Living (MG-ADL) and MG-Quality of Life (MG-QoL) assessments. Participants could also make optional connections to contribute secondary, passive data streams (e.g., daily step count). 

Design: The study enrolled and onboarded 113 participants across 37 states in the United States (US). Mean age of participants was 53.6 years (standard deviation [SD]: 14.0), 60 percent were female, and 73 percent (n=82) completed the study. 

Results: Participants were representative of clinically observed age and sex distributions for MG. During the study, 45 participants (39.8%) reported MG exacerbations, with an average of 6.3 exacerbation days per participant over the 90-day study period. Median MG-ADL scores during self-reported exacerbation and nonexacerbation periods were 5 (interquartile range: 2–8, range: 0–24) and 0 (interquartile range: 0–0, range: 0–15), respectively, with an association between MG-ADL scores and exacerbation status. Machine learning methods were applied to the data to examine symptom signatures and clustering during exacerbation and nonexacerbation periods.

Conclusion: Overall, the study demonstrated that it is feasible to collect real-world data from patients with MG, which might provide enhanced visibility into the natural history of the disease to guide clinical management and future therapeutic development. 

Funding/financial disclosures: Funded by UCB Pharma, in collaboration with Smart Omix by Sharecare. 

Deploying a Virtual Site in a Decentralized Fibromyalgia Study

Author: Dave Hanaman

Affiliations: President, Founder, Chief Commercial Officer, Curavit Clinical Research 

Background/Objective: Swing Therapeutics engaged Curavit Clinical Research to employ a virtual site in a pivotal clinical trial to test a digital therapeutic for patients with fibromyalgia who have not found any medical relief with traditional therapies.

Design: The study was designed to enroll up to 300 participants with a fibromyalgia diagnosis who will be recruited from approximately 15 to 30 North American sites, including Curavit’s virtual clinical site. This is a fully decentralized clinical trial (DCT) design with web-based patient recruitment, supported by a comprehensive prescreening website. The study includes a clinical research coordinator for live screening, e-consent, and enrollment. A primary investigator and multiple subinvestigators located in multiple time zones are also employed to provide scheduling convenience for the patient televisits.

Results: Protocols written for physical, site-based trials require alteration to fit the unique structural issues associated with a virtual site. The greatest challenges are screening and study subject calendar management. Recruiting a large number of study participants in a timely manner is significantly more effective with a virtual site.

Conclusion: The audience will learn best practices in running a fully decentralized trial; how issues such as patient enrollment screening, drug tests, and fail rates are addressed; and review lessons learned. While DCTs are still fairly new, the entire industry is looking for case studies and best practices, which this presentation will provide.

Funding/financial disclosures: Not provided.

High Interest in Decentralized Clinical Trial Participation Among CNS Disorder Sufferers

Authors: Matthew Heidman,1 Analice Costa,1 Susan M. Dallabrida2 

Affiliations: 1SPRIM US, LLC, Orlando, FL; 2SPRIM US, LLC, Boston, MA 

Background/Objective: Central nervous system (CNS) disorder study participation requirements, such as frequent site visits and doctor evaluations, can result in poor study protocol adherence and high dropout rates, negatively impacting study data and results. This study sought to elucidate the inclination of patients with CNS disorders to participate in decentralized clinical trials (DCT).

Design: A digital survey was issued for 30 days to patients with chronic pain, depression, bipolar disorder, anxiety disorder, migraines, and insomnia. When considering study participation, respondents were asked their preferences for 1) nurse visits to their home, 2) doctor visits, or 3) learning how to take their own vital signs at home and using a smartphone application (app) to enter data. Secondly, respondents were asked their preferences for 1) being sent devices and doing everything at home or 2) doctor visits. 

Results: Respondents for each condition included chronic pain (n=105), depression (n=188), bipolar disorder (n=53), anxiety disorder (n=172), migraines (n=83), and insomnia (n=86). Eighty-eight, 86, 85, 84, 82, and 78 percent of respondents, respectively, indicated they would prefer to learn how to take their own vital signs at home and use an app to enter data. Eighty-two, 82, 91, 83, 84, and 78 percent of respondents, respectively, indicated they would prefer to be sent devices and do everything at home.

Conclusion: Among patients with CNS disorders, there was a clear interest and strong inclination toward participating in DCTs and learning and executing study procedures from the home over traditional clinical trial procedures. By utilizing a DCT model among patients with CNS disorders, investigators have the potential to decrease participant burden, increase satisfaction, and improve participant retention and data quality. 

Funding/financial disclosures: All authors are employees of SPRIM US, LLC.

How Decentralized Clinical Trials Made a Global Alzheimer’s Trial Possible During the Pandemic

Author: Ellen Weiss

Affiliations: Vice President, In-Home Solutions, Decentralized Clinical Trials, PCM TRIALS, Home Nursing Provider

Background/Objective: The COVID-19 pandemic threatened the future of many clinical trials, including a global Phase III clinical trial focused on preclinical Alzheimer’s disease. To retain participants in this fragile patient population, despite the restrictions of the pandemic, decentralized clinical trial (DCT) methods were adopted, including the participation of mobile research nurses to execute home visits 

Design: The trial required one-hour intravenous (IV) infusions every month for 4.5 years during the double-blind treatment period, with the option for an additional four years during the open-label treatment period. Investigational product (IP) was shipped to participants and reconstituted by mobile research nurses. Project management teams coordinated IP delivery and ancillary infusion supplies, separating logistics from clinical responsibilities. The same nurses were assigned to participants to create familiarity and comfort. 

Results: To date, more than 900 home visits have been conducted and 889 infusions completed. Patients were accepting of receiving infusions at home at a rate higher than the sponsor had anticipated at the outset of the pandemic. Visits began within seven weeks from the time of project kickoff.

Conclusion: Patient interest and comfort with having nurses infuse them at home kept them on regimen and compliant to a demanding protocol, despite the challenges of the pandemic. Demand for DCTs has continued to increase, even after COVID-19 fears have subsided somewhat.

Funding/financial disclosures: None to report. 

How Mobile Research Nurses are Making Possible the Largest Fully Decentralized Trial for a Vulnerable Population 

Author: Ellen Weiss

Affiliations: Vice President, In-Home Solutions, Decentralized Clinical Trials, PCM TRIALS, Home Nursing Provider 

Background/Objective: Many neurological diseases cause mobility issues that make it difficult for patients to participate in traditional, site-based clinical trials. Parkinson’s disease, in particular, puts patients at high risk for falls and fractures. Would a single infusion of zoledronic acid, approved by the United States (US) Food and Drug Administration (FDA) to treat osteoporosis by increasing bone strength, reduce fractures in people with Parkinson’s disease?

Design: The TOPAZ trial is seeking to enroll 3,500 participants over the age of 60 years, making it one of the largest double-blind, placebo-controlled, randomized, fully decentralized clinical trials (DCTs) ever attempted in people with parkinsonism. Due to mobility constraints of the study population, it was necessary to design a study that could recruit participants from across a wide geography and develop a multistep protocol done entirely from participants’ homes. 

Results: The patient-centric, convenient nature of the study enabled participant recruitment from a broad geographic area to enhance diversity and be more representative of real-world patients. The study is achieving an estimated cost savings of 80 percent per participant, compared with a trial conducted at a clinical site, due to lower overhead, and a home-based study forces researchers to engage only in those activities necessary for the study. 

Conclusion: Mobile research nurses are necessary for certain studies, including those involving patients with a disabling disease. 

Funding/financial disclosures: None to report.

Inside the Pharmacodynamic Toolbox: How Questionnaires, Models, and Tests of Cognition can Accelerate the Development of CNS-active Drugs 

Authors: Denise Milovan, PhD;1 Beatrice Setnik, PhD1,2

Affiliations: 1Altasciences, Laval, QC, Canada; 2Department of Pharmacology and Toxicology, University of Toronto, Toronto, ON, Canada

Background/Objective: Despite the small number of participants typically included in early phase clinical trials, strategic design and use of pharmacodynamic measures can greatly assist to appropriately characterize a new drug entity early in the clinical development stages. Assessing safety and potential efficacy early in development can identify what future studies might be needed to support drug approval. In certain cases, such study data can be instrumental in supporting waivers of dedicated studies. This poster reviews various models related to pain and other psychiatric conditions, including testing batteries of cognition, memory, attention, psychomotor function, and pain/analgesia. A customized approach for each of the major therapeutic areas is reviewed.

Design: Evaluating a more in-depth pharmacological profile of a drug can help uncover important benefits of safety and efficacy that can be further investigated in larger late-stage trials and provide important product differentiation features. Various validated models and strategically selected measures can be included as early as in first-in-human studies to provide meaningful data.  

Conclusion: The selection of appropriate models requires an evaluation of a drug’s targeted indication, mechanism of action, and pharmacology. The use of models within a clinical trial requires consideration of staff/subject training, consistency of administration, controlled environment, and frequency of administration. 

Funding/financial disclosures: Not provided.

Investigative Drug Compounds and Therapies

A Follow-up Study on the Differences in Yale Global Tic Severity Scale (YGTSS) and Additional Outcome Measures in a Cohort of Participants in a Phase-2b Trial for the Treatment of Tourette’s Syndrome Before and After Restrictions Related to the COVID-19 Pandemic: A Continuation Study Assessing Group Differences Following Positive Results for the 2b Study

Authors: C. Yavorsky,1 A. Mahableshwarkar2

Affiliations: 1Valis Bioscience, 2Emalex Bioscience

Background/Objective: This continuation study follows positive results of the Phase IIb study and aims to supplement findings with additional scale data and unblinded group membership. The goal is to further assess the potential impact of COVID-19 restrictions on symptom severity in additional domains captured by the Children’s Depression Rating Scale–Revised (CDRS-R); Children’s Yale-Brown Obsessive Compulsive Scale (CYBOCS); Swanson, Nolan, and Pelham (SNAP-IV); Pediatric Anxiety Scale (PARS); and Clinical Global Impression Scale–Severity (CGI-S). 

Design: Data was partitioned using the date of March 11, 2020. Baseline outcome measure data was analyzed. Additionally, individual item scores were considered across the two time periods, as were treatment group assignments. Descriptive statistics were computed using SPSS 27.0 and included mean, standard deviation, variance, and distribution measures. 

Results: YGTSS mean values under consideration did not appear to change significantly between the time periods before and after COVID-19 restrictions began, with total scores of 67.2 and 66.7, respectively. Mean baseline CY-BOCS scores of 7.45 (before) and 7.55 (after) also showed similar stability to PARS (11.7 before and 12 after) and CDRS (29 before and 27.5 after), with similar stability noted in the CGI-S. 

Conclusion: While clinically, it might be typical for symptoms of Tourette’s syndrome to be susceptible to stressors, this was not demonstrated in relation to the COVID-19 pandemic by any significant differences in primary tic assessments or related/comorbid disorders. There did not appear to be significant differences in YGTSS, CY-BOCS, PARS, CDRS, or CGI-S domain scores or individual item scores before or after COVID-19 restrictions were in place. 

Funding/financial disclosures: CY is a full-time employee of Valis Biosciences, Inc. AM is Chief Medical Officer of Emalex Bioscience, Inc. 

A Single, Ascending Dose Study of PCN-101 (R-ketamine) and the Clinical Design of a Ph2a Study for Treatment Resistant Depression (TRD)

Authors: Terence A. Kelly, PhD; Maju Mathews, MD

Affiliations: All authors are with Perception Neuroscience, Inc.

Objectives: The aim of this study is to assess R-ketamine for treatment-resistant depression (TRD). 

Perception Neuroscience, an atai Life Sciences company, is developing the R-isomer of ketamine (PCN-101, R-ketamine, arketamine) for the treatment of depressive and other mental health disorders. This poster will briefly review published nonclinical pharmacology that supports the concept and then discuss the results of a single-ascending dose study of PCN-101 in healthy volunteers that evaluated safety, pharmacokinetics, sedation, and dissociation. This presentation will also outline the clinical design of a Phase IIa proof-of-concept study in TRD that is expected to be completed by the end of 2022. 

Design: This is a double-blind, randomized, placebo-controlled, multicenter study comprised three phases: screening (up to 2 weeks [Day -15–Day -2]), in-clinic treatment (Day -1–Day 2; including double-blind treatment [Day 1]), and post-treatment follow-up (7 and 14 days after infusion on Days 8 and 15, respectively). A total of 93 adult subjects with TRD will be randomly allocated in equal cohorts of 31 subjects per arm to the three arms of the study in a blinded manner.

Results: Results from the Phase IIa study will be available in early 2023.

Conclusion: R-ketamine is a potential new addition for the treatment of TRD.

Funding/financial disclosures: None to report.

Metabolic Mechanism in Migraine: Tricaprilin, a Ketogenic Agent

Authors: Lilian Chow, Julia Presanis, Nikki McIntyre, Samuel Henderson, Marc Cantillon

Affiliations: All authors are with Cerecin Australia Pty Ltd.

Background/Objective: This was a pilot study to evaluate tricaprilin for the prevention of migraine. Increasing evidence suggests that migraine headaches have an underlying metabolic etiology. In particular, the induction of ketosis has been implicated in correcting metabolic defects found in patients with migraines. Tricaprilin is a ketogenic agent that can induce ketosis without the need for changes to diet. 

Design: This was a double-blind, randomized, placebo-controlled, three-month study of up to 60g/day tricaprilin. The study was designed to include two parts: Part 1 was a small pilot study for accurate sample size calculation, and Part 2 was a fully powered proof-of-concept study. The primary endpoint was the change from baseline in the number of migraine headache days (MHDs) during Month 3. Participants were to have 4 to 24 MHDs in the baseline period. 

Results: Due to formulation tolerability issues, Part 2 was not conducted. Results from Part 1 (nonpowered pilot phase) are presented. A total of 81 participants were randomized and dosed in the study (tricaprilin: n=40, placebo: n=41), and 61 participants (31 per arm) had evaluable efficacy data. For Part 1, there was no meaningful difference in the primary endpoint between treatment arms during Month 3. During Month 2, a mean improvement of -2.75 days was observed in favor of tricaprilin. The withdrawal rate was 45.0 and 53.7 percent (tricaprilin and placebo, respectively). Treatment-emergent adverse events, the majority being gastrointestinal, occurred in both active and placebo arms (90.0% and 82.9%, respectively). 

Conclusion: Results of the pilot study suggest directional promise over 2 to 3 months for tricaprilin, and a new formulation will be used for larger, fully-powered Phase II/III studies. 

Funding/financial disclosures: Not provided.

Results from the Phase I Small Pharma Trial of SPL026 in Psychedelic-naïve Healthy Volunteers

Author: Dr. Tiffanie Benway

Affiliations: Small Pharma Ltd.

Background/Objective: Small Pharma is conducting the world’s first regulated Phase I/IIa clinical trial of SPL026 (N,N-dimethyltryptamine [DMT] fumarate), a psychedelic compound with a short-acting experience, in combination with psychotherapy, in major depressive disorder (MDD). 

Design: We have completed the Phase I component, a double-blind, single-ascending dose study, assessing safety, tolerability, pharmacokinetics (PK), and pharmacodynamics (PD) of four intravenous (IV) doses of SPL026 with therapy in psychedelic-naïve healthy volunteers (6 active and 2 placebo per cohort). 

Results: SPL026 was safe and well tolerated, with no serious adverse events and few drug-related adverse events across all dose cohorts. Detailed PK profiles for DMT were elucidated, and a model was developed to understand and predict the PK profiles of various doses of SPL026. DMT is rapidly metabolized, with a half-life of about 11 minutes, which underpins the observed short psychedelic experiences of up to 30 minutes. Following PK/PD analysis, a strong exposure-response relationship for both the intensity and quality of the psychedelic experience was demonstrated. In addition, no correlation between exposure versus age, weight, or body mass index (BMI) of subjects was shown. 

Conclusion: We have developed a safe and well-tolerated IV infusion protocol and accompanying psychotherapy model for administration of SPL026 to elicit, in a dose-predictive way, a significant psychedelic experience with the potential to provide therapeutic efficacy in a number of mental health disorders. Psychedelics, such as DMT, offer the potential of an alternative treatment paradigm to the traditional selective serotonin reuptake inhibitor (SSRI) or psychotherapy models currently available to patients. The results of the Phase IIa component are due in the coming months. 

Funding/financial disclosures: TB is an employee of Small Pharma Ltd. 

Safety, Tolerability, and Pharmacokinetics of BRII-296, An Extended-Release Injectable Aqueous Suspension Formulation of Brexanolone in Healthy Adult Subjects

Authors: Ji Ma,1 Leela Vrishabhendra,2 Yujin Wang,1 Chi-Chi Peng,1 Michael Watkins,1 Chetana Trivedi,1 Xuelian Wei,1 Lijie Zhong,1 Kamlesh Patel,1 Jean-Luc Girardet,1 Aleksandar Skuban,1 Claudia Prilliman,1 David Margolis,1 Li Yan,1 Lianhong Xu1

Affiliations: 1Brii Biosciences Limited; 2Medpace, Inc.

Background/Objective: Postpartum depression (PPD) is the most common psychological condition impacting mothers after giving birth, affecting approximately 900,000 people in the United States (US) each year. The only approved treatment option for PPD—brexanolon­—remains limited and is associated with significant disruptions to daily life, including required hospitalization. BRII-296 is an extended-release, injectable, aqueous suspension formulation of brexanolone in development for treatment of PPD. BRII-296 formulation and route of administration is designed to provide advantages over the currently approved brexanolon­. This study will determine if BRII-296 via intramuscular (IM) administration can achieve brexanolone exposure associated with efficacy in PPD, while maintaining a favorable safety and tolerability profile. 

Design: This was a Phase I, open-label, single-ascending dose escalation, safety, tolerability, and pharmacokinetic (PK) study of BRII-296 at dose levels up to 600mg given to 116 subjects (up to 10 healthy adult subjects per cohort). Local injection site reactions (ISRs) were prophylactically treated. 

Results: IM administration of BRII-296 was generally well tolerated in healthy adult subjects at dose levels up to 600mg. No serious adverse events (AEs) were reported, and no clinically significant systemic AEs were observed, including sedation or loss of consciousness. ISRs occurred at doses at and above 100mg. Prophylactic treatment with the selected steroid controlled ISRs to grade 1 (mild) or grade 2 (moderate). 

Maximum plasma exposure to brexanolone was quickly achieved and sustained in a dose-dependent manner across 30 to 600mg for 14 days. 

Conclusion: PK and safety profiles support further assessment of BRII-296 in patients with PPD. A Phase II study is expected to be initiated. 

Funding/financial disclosures: Not provided.

Two Randomized, Double-Blind, Placebo-Controlled Trials of Adjunctive Troriluzole, A Novel Glutamate Modulating Agent, in Obsessive Compulsive Disorder

Authors: Olson, Aguiar, Rybicki, Smith, Mccormack, Donahue, Coric, Munivar

Affiliations: All authors are with Biohaven Pharmaceuticals.

Background/Objective: The aim of this is to describe the design, scientific rationale, and demographic characteristics of the studies. 

Design: Following up on a clinical signal seen in a Phase II proof-of-concept study, two identical Phase III studies will randomize 700 international participants for 10 weeks in a double-blind, placebo-controlled design. Subjects must have a diagnosis of obsessive compulsive disorder (OCD) for one year with inadequate response to an ongoing standard-of-care medication, as defined by a Yale-Brown Obsessive Compulsive Scale (Y-BOCS) scores of 22 at screening and baseline. The primary endpoint is the change from baseline to Week 10 in the Y-BOCS. 

Results: Demographic analysis of the subjects randomized as of August 1, 2022, revealed that the majority of subjects randomized were women (68%). Subjects between the ages of 18 to 39 years comprised 57 percent of those randomized. Subjects reported baseline Y-BOCS score ranges of 22 to 23, 24 to 27, 28 to 31, and 32 to 40 (12%, 41%, 31%, and 16%, respectively). The majority of subjects reported between 2 to 10 OCD history years (55%), while 20 and 17 percent reported between 11 to 20 and over 21 OCD history years, respectively. The studies began enrollment in December 2020 and are currently ongoing. 

Conclusion: The studies (NCT04641143 and NCT04693351) will investigate the efficacy and safety of troriluzole for patients with OCD. The majority of reported baseline Y-BOCS scores fall within the 24 to 27 range, indicating a severe range symptoms, with the highest scores in the 40 to 59 years of age group. Thus, as subject age increases, the relative proportion of individuals reporting more severe Y-BOCS scores also appears to increase.  

Funding/financial disclosures: Not provided.

Treatment Devices and Tools

A Basket Trial for Accelerated Discovery of Novel Neuroscience-based Digital Treatments for Chronic Pain 

Authors: Samantha Adler, PhD;1 Jacqueline Lutz, PhD;1 Vitaly Napadow, PhD;2 Brendan D. Hare, PhD;1 Alankar Gupta, MD, MBA;1 Shaheen Lakhan, MD, PhD1

Affiliations: 1Click Therapeutics, Inc., New York, NY; 2Department of Physical Medicine and Rehabilitation, Spaulding Rehabilitation Hospital, Harvard Medical School, Boston, MA

Background/Objective: Digital Neuro-Activation and Modulation (DiNaMoTM) are targeted, neuroscience-based digital treatments that activate and modulate neural systems underlying central nervous system (CNS)-based disorders. Basket trials offer the potential to estimate effects of novel therapies across several indications with shared underlying, transdiagnostic features, such as chronic pain. However, basket trials are not commonly used outside of oncology. We designed a basket trial to evaluate a novel DiNaMo that deploys personalized attention bias modification training (ABMT) for participants with chronic pain due to diverse indications. 

Design: This is a randomized, virtual, basket trial to evaluate ABMT for chronic pain due to rheumatoid arthritis, diabetic neuropathy, fibromyalgia, or irritable bowel syndrome. Patients must report three or more months of chronic pain due to their primary diagnosis. Patients will be randomized to four weeks of either the treatment application (app; ABMT treatment and daily pain tracking), or the digital control app (daily pain tracking). Each indication will include 30 individuals (N=120) and will be randomized 3:1 (treatment app:digital control app). The primary endpoint will be the change in the Brief Pain Inventory Interference subscale from baseline to end of treatment. Secondary endpoints include pain catastrophizing, pain intensity, mood, and quality of life measures. 

Results: This study is ongoing and does not yet have results. 

Conclusion: To our knowledge, this is the first study to apply a basket trial approach to evaluate a neuroscience-based digital therapeutic method. This approach could allow for more efficient discovery and validation of effective treatments for conditions sharing transdiagnostic features.

Funding/financial disclosures: All authors, except VN, have equity interest and are currently employed by Click Therapeutics, Inc. VN is a consultant for Cala Health, Inc. and Click Therapeutics, Inc.

Factors Affecting Positive and Negative Syndrome Scale (PANSS) Change Between Screening and Baseline in Acute Schizophrenia Clinical Trials: An Exploratory Analysis

Authors: Alan Kott, Xingmei Wang, David G. Daniel

Affiliations: All authors are with Signant Health. 

Background/Objective: Positive and Negative Syndrome Scale (PANSS) total score changes, both improvement and worsening, are often observed in the screening period in patients entering acute schizophrenia clinical trials. In the current analysis, we explored factors associated with symptom instability during the screening period. 

Design: Data were pooled from six placebo-controlled acute schizophrenia clinical trials. Change between screening and baseline was calculated for each subject, and a multiple linear regression model was fitted with the PANSS change as the dependent variable and PANSS severity at screening, study, country, site, and whether a rater changed between screening and baseline, as independent variables.

Results: With increasing screening PANSS severity, the PANSS change from screening to baseline decreased. A 10-point increase in severity was associated with a reduction between screening and baseline of 2.2 points (p<0.05). Twenty-two percent of the total PANSS change variance could be explained by the differences between the individual sites.

Conclusion: Our analyses indicate that changes in the PANSS between screening and baseline can be at least partially explained by the screening PANSS severity and, to a larger degree, by specific site factors. Our model, however, failed to explain most of the variance observed. Specific subject characteristics that were not explored could impact the PANSS change during the washout in the screening period. Over 20 percent of variance could be explained by specific site characteristics, which indicates a relatively large variability between individual sites and underlines the necessity of rigorous between-site standardization and constant site oversight through data analytics and other unobtrusive means. 

Funding/financial disclosures: This research was financially supported by Signant Health. All authors are Signant Health employees. 

Longitudinal Ecological Momentary Assessments of the Behavioral Indicators of Avolition in Schizophrenia Identify changes that are Correlated with Clinical Ratings of Negative Symptoms 

Authors: JR Owen,1,2 C Sauder,2 S Chaturvedi,2 SD Targum,1,2 PD Harvey1,3

Affiliations: 1EMA Wellness, Inc.; 2Karuna Therapeutics; 3University of Miami Miller School of Medicine 

Background/Objective: Reduced emotional experience in schizophrenia is associated with social deficits. Observational studies of negative symptoms have employed ecological momentary assessment (EMA) to capture behavioral features of avolition and asociality, including time spent home and alone and passive and unproductive activities. We report data from the first half of a 12-month longitudinal study of EMA, combined with ratings of negative symptoms, during a safety study of an antipsychotic medication in development. 

Design: Stable outpatients with schizophrenia entered a 12-month, open-label study, with monthly ratings with the Positive and Negative Syndrome Scale (PANSS) and 16-item Negative Symptom Assessment (NSA-16). EMA surveys were delivered in seven-day bursts, three surveys per day each month. The surveys queried location and social context, positive and negative affect (PA and NA), and activities for “the last hour.” Participants also wore an actigraph. Three NSA items were selected for analysis because they closely defined avolition. 

Results: A total of 4,138 completed EMA surveys came from 54 subjects with clinical assessments. Momentary PA increased significantly, predicting fewer surveys at home, more steps, more productive activities, and fewer passive and unproductive activities. More surveys that answered home, alone, and engaging in unproductive activities shared 21 percent of the variance with higher “reduced activity” scores and 31 percent of the variance with lower ratings of “reduced sense of purpose.” More surveys that answered home and alone and fewer productive activities shared 29 percent of the variance with “reduced social drive.” 

Conclusion: This study is the first to document that pharmacologically induced changes in behavioral indicators of avolition can be detected with EMA. The EMA results correlated with clinician ratings of the primary predictors of social deficits in schizophrenia.

Financial/funding disclosures: PDH has worked as a consultant for Karuna Therapeutics in addition to his activities with EMA Wellness. 

Symptom and Physical Function Reporting by Caregivers as Predictors of Adverse Clinical Outcomes in Cancer Patients

Authors: Elad Neeman,*1 Reem Yunis,*2 Ai Kubo,1 Sara Aghaee,1 Jennifer Shieh,1 Tess Veuthey,1 Stephanie Fonda,3 Raymond Liu,1 Ingrid Oakley-Girvan2

*Shared co-first authorship

Affiliations: 1Kaiser Permanente Northern California, Oakland, CA; 2Medable, Inc., Palo Alto, CA; 3Estenda, Conshohcken, PA

Background/Objective: Informal caregivers are essential partners in the delivery of care and can often accurately identify and report symptoms and physical function of their patients. We assessed whether such reporting by caregivers is predictive of adverse outcomes in patients with cancer.

Design: In this prospective study, adult patients with cancer on active, systemic, intravenous (IV) therapies and their informal caregivers were recruited from Kaiser Permanente cancer centers. Using the TOGETHERCareTM study application (app), caregivers completed weekly National Institutes of Health Patient-Reported Outcomes Measurement Information Systems (NIH-PROMIS) scores to report patients’ physical function and Patient-Reported Outcomes version of the Common Terminology Criteria for Adverse Events (PRO-CTCAE) to report patients’ symptoms. Patients’ adverse clinical outcomes were abstracted from the medical record and included emergency department (ED) visits or hospitalizations, Grade 3 to 4 adverse events (AEs), and treatment delays within one month following the active study period, as well as mortality and hospice referrals within six months following the active study period. Simple univariate logistic regressions were used to correlate caregiver reports with mortality and hospice referrals, and quasi-Poisson regressions were used for the other adverse outcome measures. 

Results: Fifty-two patient-caregiver dyads were included in this analysis. Almost 75 percent of the patients had Stage III or IV cancer. Caregivers predominantly identified as male (61.5%), spouse/partner (76.9%), and non-Hispanic White (63.5%). At least one adverse outcome was experienced by 36.5 percent of patients. Caregiver-reported PRO-CTCAE consistently predicted ED/hospitalizations and mortality, while caregiver-reported NIH-PROMIS scores predicted hospice referrals.

Conclusion: The results suggest that caregivers reliably capture and report their patients’ symptoms and physical function, and their reporting could help provide early predictions of adverse events. 

Funding/financial disclosures: This study was funded in part by the NIH, the National Cancer Institute (NCI) through the Small Business Innovation Research (SBIR) program (HHSN261201700030C and HHSN261201800010C).  

Trial Methodology

An End-to-end Cloud Solution for Managing Multisite Imaging Clinical Trials 

Authors: Tim Peeters, Paulo Rodrigues, Vesna Prčkovska

Affiliations: All authors are with QMENTA, Boston, MA.

Background/Objective: Imaging clinical trials are complex processes that include many labor-intensive and error-prone steps. We propose a centralized, cloud-based platform to boost productivity, improve quality, and maximize resource usage, and thus drastically decrease the invested time and money.

Design: Our proposed solution centralizes secure data collection and offers features to ensure privacy, protocol compliance, and quality assurance (QA). It also offers artificial intelligence (AI) biomarkers and central review functionalities. For this analysis, we picked six studies representative of typical imaging studies. One study was retrospective, and the other five (including a Phase II trial) had a duration of 1 to 5 years, varied in size from 10 to 20 sites, and had 100 to 4,500 subjects per study. 

Results: Compared to cost estimates without using a centralized cloud platform, required computational and staffing resources were reduced. The Phase II trial with 100 patients had a site burden reduction of 75 percent and a 50-percent cost reduction. All studies benefited from centralized secure data collection, which improved privacy, protocol compliance, and QA. AI biomarkers improved reproducibility and reduced errors and delays. Two of the studies produced the publication or validation of a new biomarker, and one study resulted in changed clinical treatment.

Conclusion: To run a time- and cost-effective imaging clinical trial, it is essential to use a single, centralized, end-to-end platform. It decreases site burden and resources needed to analyze medical images. The quality of data is increased, and errors and delays are reduced.

Funding/financial disclosures: The presenters are employed by and hold stock or options of QMENTA. 

Are We missing Opportunities by Misinterpreting FDA Guidance? 

Authors: Scott Stout, Ted Barduson, Jonathan Helfgott 

Affiliations: MedVector, Johns Hopkins University, Healthcare Innovation Catalysts, formerly United States (US) Food and Drug Administration (FDA)

Background/Objective: Can “alternative locations,” such as noninvestigator providers, facilitate clinical trial appointments without violating FDA guidance?

Design: From an FDA compliance perspective, when engaging a participant using telemedicine, is the patient location relevant to the source data? The FDA Guidance for Industry: Electronic Source Data in Clinical Investigations states, “Many data elements (e.g., blood pressure, weight, temperature, pill count, etc.) … can be entered directly into the electronic case report form (eCRF) by an authorized data originator.…For these data elements, the eCRF is the source.”

Results: The FDA has established that the source data remain unchanged when utilizing an alternative patient location. Furthermore, the FDA does not recognize a difference between a participant who is physically present versus a participant who is virtually present via telemedicine. This is verified by Jonathan Helfgott, former FDA Associate Office Director and author of the eSource FDA guidance; “Using technology to connect patients from alternate locations to a primary investigator (PI) site does not trigger any new clinical sites or clinical investigators. The FDA is open to new technologies that make it easier for patients to participate in clinical research, whether it be from their home, a local clinic, or a provider’s office.”

Conclusion: Clinical trial sponsors are missing huge opportunities by misinterpreting FDA guidance. Encouraging participation through alternative locations aligns incentives between treating physicians and investigator teams, finally enabling “clinical trials as a care option.” This middle ground between decentralized clinical trials and the traditional clinical trial model creates immediate access to the missing majority trapped behind physicians who don’t refer to clinical trials. 

Funding/financial disclosures: Not provided.

Can Targeting Recruitment to Treatment-Seeking Participants Improve Speed or Quality? 

Authors: J Engler,1 D Thorpe,1 M Evans,1 D Fagundo,1 S Starling,1 S Nicholas,1 H Zandi,1 G Sachs2

Affiliations: 1Adams Clinical, 2Signant Health

Background/Objective: Major depressive disorder (MDD) trial recruitment is challenging, given that depression symptoms impact on screening visit attendance. To address this, we asked, “Does the ‘promise to refer’ and give treatment resources for potential participants impact show rates and participant quality for MDD trials?” 

Design: Prospective participants were randomly assigned via Facebook A/B testing to view either standard advertisement content (about MDD and clinical trials) or advertisements emphasizing that everyone would receive treatment resources and referrals, regardless of trial eligibility. Both groups were offered phone screening interviews, followed by in-person prescreening appointments. For the experimental group, recruiters and clinicians reiterated the promise of resources and referrals. We compared the two groups on phone screening rates, prescreening rates, and study eligibility. 

Results: Participants in the experimental group were significantly more likely than the control group to complete a phone screening [22% vs. 16%, χ2 (1, n=1,358)=6.46, p=0.011] and have a prescreening visit scheduled [83% vs. 68%, χ2 (1, n=265)=8.03, p=0.005]. The effects on prescreening visit attendance and trial eligibility was trending but did not reach statistical significance. 

Conclusion: Our results suggest that articulating and delivering treatment resources and referrals has the potential to speed up trial recruitment by increasing participants’ likelihood of completing phone screenings and in-person prescreening visits. 

Funding/financial disclosures: The authors report no conflicts of interest for this work; all aside from GS are current employees of Adams Clinical, an independent central nervous system (CNS) research site that conducts self-sponsored and industry-sponsored pharmaceutical trials. GS is a full-time employee of Signant Health. 

Comparison of Electronic Health Records (EHRs) and Electronic Source Data (EDC) in Clinical Research Trials: A Retrospective Review 

Authors: Elena A. Christofides, MD, FACE; Amelia Tian; Ada Zhu; Olivia Dennis; Nicole Mastacouris

Background/Objective: The objective of this study was to compare the medical history documented in the electronic health records (EHRs) versus electronic data capture (EDC) of clinical trial subjects to assess the role that principal investigators play in data transfer from EHR to EDC.

Design: Accurate documentation of medications and medical history is a critical component in ensuring the integrity of subject data in clinical research trials. With a mandate to use EHRs in healthcare settings, there has been a parallel movement toward integrating EHR and EDC software in clinical trials to improve efficiency and accuracy of data entry. However, there is increasing evidence that EHR data tend to be erroneous. The present study was a retrospective review comparing the medications and medical history documented in the EHR versus EDC of subjects in active, ongoing clinical research studies to assess the validity of the assumption of the utility of using EHR data directly. 

Results: Our results showed significant data deviation from the EHR to EDC, where 98 percent of all records were modified for clarity in some capacity. Only 31.3 percent of all medication records were concordant, and only 45.7 percent of all medical problem records were concordant.

Conclusion: These results suggest that automated transfer of EHR records into the EDC produces factually incorrect and erroneous research records. It is clear that principal investigators play a crucial role in parsing out incomplete, inaccurate, and irrelevant information when transferring data from the EHR to EDC.

Funding/financial disclosures: None to report.

Do Experience and Credentials Impact Clinical Trial Certification Scoring Performance? 

Authors: David Daniel, Xingmei Wang, Andrei Iacob, Emanuel Pintillii, Alan Kott

Affiliations: All authors are with Signant Health.

Background/Objective: In the current analysis, we addressed the impact of credentials and experience on certification scoring performance in schizophrenia clinical trials. 

Design: Experience and credentials data were collected from 957 raters intending to rate the Positive and Negative Syndrome Scale (PANSS) in acute schizophrenia trials. Scoring certification performance was assessed using the total number of item deviations from the acceptable score range. A generalized linear mixed model was fitted for the number of item deviations. 

Results: Using the backward selection process, the model was simplified to include only the years of clinical trial experience. With increasing clinical trial experience, the number of item deviations decreased. The model predicted about five-percent reduction in item deviations for every five additional years of clinical trial experience. None of the other explored variables meaningfully impacted certification performance. 

Conclusion: Our post-hoc analysis of almost 1,000 raters revealed a significant impact of rater experience on rating performance during certification. Among the experience factors, years of clinical trial experience impacted scoring performance the most. There are several important limitations to the current analysis. For example, this is a retrospective analysis, and the experience data provided by the raters cannot be confidently verified. Future analyses will address the relationship between clinical and scale experience and in-study rating and interview quality.

Funding/financial disclosures: The post-hoc analyses in this abstract were paid for by Signant Health. All authors are employees of Signant Health.

Ecological Momentary Assessment for Prediction and Monitoring of Clinical Trials in Neuropsychiatric Conditions 

Authors: C Bower,1 PD Harvey1,2

Affiliations: 1EMA Wellness, Inc.; 2University of Miami Miller School of Medicine 

Background/Objective: Technology-based momentary assessments offer the opportunity to collect information with increased validity, as well as sampling data predictive of successful completion of clinical trials. Specifically, ecological momentary assessment (EMA) offers the opportunity to repeatedly sample clinical trial participants to assess their general adherence prior to randomization and other elements of adherence (e.g., medication) after randomization. It is critical to evaluate the validity of pre-randomization sampling, both in terms of predicting adherence during the trial and not excessively excluding eligible participants on the basis of symptoms.

Design: We collected EMA data on participants with schizophrenia (n=163) and bipolar depression (n=158) three times per day for 30 days with a survey that sampled location, social context, mood psychotic symptoms, and activities. Immediately prior to the survey period, participants were rated with the Positive and Negative Syndrome Scale (PANSS) or Montgomery-Asberg Depression Rating Scale (MADRS). We examined the predictive correlation between EMA adherence after one and seven days as a predictor of adherence over the overall protocol and examined whether nonadherent patients differed in symptom severity.

Results: The correlation between number of surveys answered on Day 1 and Days 1 to 7 was r=0.60 in both groups, and the correlation between Day 1 to 7 adherence and the entire protocol was r=0.80 and r=0.79, respectively. The correlation between baseline PANSS scores and adherence over up to 90 surveys was r=0.03, with MADRS scores correlating at r=0.09.

Conclusion: Early nonadherence was highly correlated with eventual nonadherence, with essentially no correlation between baseline symptoms and early or eventual nonadherence. EMA surveys have the potential of identifying potentially nonadherent participants with apparently limited risk of exclusion based on symptom severity.

Funding/financial disclosures: None to report.

Educational Needs of CNS Clinicians: The Challenging Reality of Modern CNS Trials 

Authors: Brian McGowan, PhD; Joel Selzer, MBA

Affiliations: Presenters are employed by ArcheMedX, Inc.

Background/Objective: This study aimed to understand the educational needs of central nervous system (CNS) clinicians.

Design: Learning data was analyzed to determine the mastery of each clinician learner based on their demonstrated knowledge, competence, and confidence across more than 150 on-demand training activities covering CNS conditions. Data included 1,000,000 question responses from 100,000 clinician learners. Mastery is a composite measure of a clinician learner’s knowledge and competence, plus their confidence in applying that knowledge in practice. 

Results: There were significant variations in mastery among CNS clinicians. The resulting analyses revealed that less than 1 in 7 clinicians demonstrated baseline mastery across a range of CNS-related topics and competencies. 

In clinical areas, such as Alzheimer’s disease, migraine, major depressive disorder (MDD), and multiple sclerosis (MS), data consistently demonstrated that more than 80 percent of clinicians lacked the clinical mastery to formulate a differential diagnosis, develop individualized treatment plans, or navigate the complexity of CNS care teams to provide appropriate treatment. 

In the Alzheimer’s disease/dementia data, the analyses found more than 3 in 4 clinicians demonstrated deficiencies in accurately diagnosing patients, more than 4 in 5 clinicians demonstrated deficiencies in implementing new and emerging treatments, and fewer than 1 in 4 clinicians could confidently navigate collaborative care of patients. 

Conclusion: Learning data reveals significant variations in mastery among CNS-treating clinicians. These variations can delay patient enrollment, increase clinical trial costs, and worsen healthcare outcomes. Identifying and understanding clinician mastery is a critical step in planning for study startup, delivering effective study training, and achieving trial milestones and endpoints.

Funding/financial disclosures: Data was provided by ArcheMedX, Inc.

Frequent Exclusion of Patients with Comorbidities in Alzheimer’s Research Contributes to Unrepresentative Participant Demographics 

Authors: Lauren M. Vamos,1 Thomas E. Barnett,2 Brandon Li3

Affiliations: 1Department of Anthropology, University of Toronto; 2Department of Health Science, Keiser University; 3Power Life Sciences

Background/Objective: African Americans are more likely to be diagnosed with Alzheimer’s disease than any other population in the United States (US). As such, this population should be overrepresented in Alzheimer’s disease research. Instead, a total of four percent of participants in trials on clinicaltrials.gov that reported race identified as Black or African American, while 80 percent of participants identified as non-Hispanic White. While abundant factors in trial design, site management, and recruitment processes are responsible for this trend of underrepresentation, one factor not frequently mentioned in the literature is the impact of comorbidities on underrepresented populations’ abilities to participate in research. 

Design: This study aimed to quantify how African American individuals are excluded from research participation by comparing the frequency of terms relating to heart disease in the inclusion versus exclusion criteria of all interventional Alzheimer’s disease trials on clinicaltrials.gov. 

Results: African Americans are more likely to experience heart diseases, including hypertension and stroke, and as such might be disproportionately affected by heart disease-related exclusion criteria. Terms relating to heart disease appeared 220 times in the inclusion criteria of every Alzheimer’s disease trial on clinicaltrials.gov and 1,649 times in the exclusion criteria. These results indicated that people with a history of heart disease are 7.5 times more likely to be expressly excluded from Alzheimer’s disease research than expressly included. 

Conclusion: When considering innovative ways to improve representation of minority populations in research, it is imperative to consider the impact of potentially overexclusionary criteria on the demographics of those who will ultimately be able to participate.

Funding/financial disclosures: These findings draw on research supported by the Power Life Sciences Research Grant.

Furthering the Validity of the Placebo-Control Reminder Script: Essential Perspectives from the End User 

Authors: EA Cohen,1 HA Hassman,1 DP Walling,2 VM Grindell,2 K Wyka,3 BA English,1,2 JM Lobb,1 D Hough,1 CL Blanchard,1 SM Iglesias,1 L Ereshefsky,1,2,4

Affiliations: 1CenExel Hassman Research Institute; 2CenExel Collaborative Neuroscience Network; 3The City University of New York, Graduate School of Public Health and Health Policy; 4Retired Professor, The University of Texas

Background/Objective: The Placebo-Control Reminder Script (PCRS) is an empirically validated tool found to significantly reduce the placebo and nocebo effects in clinical trials by having a rater read a paragraph to the participant that reviews known factors causing these phenomena. To investigate further validation of the PCRS, and as indicated by instrument gold standard validation methods, the current study surveyed PCRS site raters regarding the instrument’s content. 

Design: A 16-item PCRS user survey was sent via SurveyMonkey to obtain raters’ perspectives on the tool’s design and construct to evaluate face and content validity. Sponsors who licensed the PCRS provided raters’ email addresses. A link to the survey was sent to the raters either by the first author of this poster or by the sponsor. All PCRS responders were informed of why they were receiving the survey, how their email addresses were obtained, the voluntariness and anonymity of survey completion, and the five-minute expected duration of completion. 

Results: The PCRS user survey was completed by 169 separate raters (percent response could not be calculated because three sponsors sent the questionnaire to their raters, and we were not provided with their email addresses), with a majority of the tool reportedly used in depression (78%) and schizophrenia (45%) trials. Using Chi-squared goodness of fit test, significantly more raters reported agreeing (rather than being neutral or disagreeing) with the PCRS user survey’s face and content validity questions, including its appropriate wording, phrasing, content, and length aimed to reduce the placebo effect (all factors yielded p<0.001). 

Conclusion: The PCRS has significant face and content validity, with its administrators deeming the instrument highly effective regarding its stated aim of minimizing the placebo response.

Funding/financial disclosures: None to report.

Importance of Representation in Participant Recruitment and Retention for Generating Robust Evidence from Real-world Data 

Author: Abhishek Pratap

Background/Objective: The aim of this study was to investigate key factors that might impact equitable recruitment and long-term retention of the target population in fully remote decentralized studies.

Design: This was a cross-study comparison of participant-level recruitment and retention data from 10 large, fully remote studies with over 100,000 participants with varying demographics, socioeconomic indicators, and disease severity. 

Results: Evaluation of over 1.2 million days of individual-level engagement data collected from smartphones and wearable technologies highlighted several factors that could impact the quantity and quality of real-world data collected in fully remote settings. Some of the common factors that led to differential engagement patterns across multiple studies included 1) demographic and socioeconomic indicators of participants, 2) self vs. clinical referral to the study, 3) compensation for participation, and 4) having the clinical condition of interest in the study. Additionally, several study-specific factors influencing long-term data collection were seen. These included 1) variation in engagement linked to recruitment platforms, 2) timing and frequency of incentive distribution, 3) device heterogeneity that also led to technical variation in data collection, and, most notably, 4) severity of the disease. 

Conclusion: Underrepresentation of the target population in real-world data can make resulting evidence inapplicable to those who were not included in the trials and, therefore, biased. Our findings can help inform the development of data-driven best practices to recruit and retain the target population in a representative and equitable manner.

Funding/financial disclosures: Not provided.

Investigation of Post-pandemic Patient Perception Toward Clinical Trials

Author: Iqra Hasan,1 Taso Callanan,2 Hayden Messina,2 Jacqueline Kestenbaum.3 Overseen By: Jeffrey T. Apter,1,2 Kaylee M. White1,4

Affiliations: 1Princeton Medical Institute, Princeton, NJ; 2Princeton University, Princeton, NJ; 3The College of New Jersey, Ewing Township, NJ; 4The Chicago School of Professional Psychology, Chicago, IL

Background/Objective: The objective of this poster was to understand patient perception about clinical trial participation after the onset of the COVID-19 pandemic. Many believe that the widely publicized production of vaccines has influenced patient perception, and we aimed to understand any other factors that impact whether patients go through with a clinical trial after prescreening. Using the patient database at Princeton Medical Institute, we emailed a survey to 689 patients who screened for either psychiatric or neurologic trials. 

Design: We designed three surveys: one for patients with neurologic disorders, one for neurologic caregivers, and one for patients with psychiatric disorders, namely major depressive disorder (MDD), posttraumatic stress disorder (PTSD), binge eating disorder (BED), social anxiety disorder, and smoking cessation. For data analysis, we received 55 complete responses that we analyzed to understand patient impressions about trusting medications on the market compared to study drugs, willingness to join trials after hearing about the pandemic, and telehealth appointments. 

Results: One result that stood out was that 85.5 percent of all respondents reported that increased awareness of COVID-19 vaccine development did not change their willingness to join our trials. Additionally, we learned that younger patients in the psychiatric population preferred telehealth appointments (82%) over older patients in the neurologic population (40%). 

Conclusion: The main reason for these results is the convenience that telehealth provides by eliminating challenging factors, such as transportation and scheduling. Future research can build on these findings by using a stronger sample size that captures all patient viewpoints on participating in clinical trials. 

Funding/financial disclosures: None to report.

Measuring the Impacts of Caregiving in Schizophrenia

Authors: Holly Krasa, MSc;1,2 Kelly Birch, MPH;3 Theresa Frangiosa, MBA;1,2 Andrew Palsgrove;2 Melissa Culhane Maravic, PhD, MPH3

Affiliations: 1Schizophrenia & Psychosis Action Alliance, Alexandria, VA; 2Blue Persimmon Group, LLC, Washington, DC; 3PRECISIONheor, LLC, New York, NY

Background/Objective: The aim of this study was to characterize humanistic and financial burden of informal caregivers of individuals with schizophrenia in the United States (US). 

Design: An online survey was administered to self-identified, unpaid, informal caregivers of adults diagnosed with schizophrenia or schizoaffective disorder in the US. Questions included caregiver and care recipient characteristics, out-of-pocket (OOP) costs for major life events (MLEs) (e.g., arrest) and everyday expenses; humanistic burden measured by the Burden Assessment Scale (BAS); and career, educational, and personal impacts. Responses were summarized with descriptive statistics and by logistic regression for predictors of caregiver impacts. 

Results: A total of 200 caregivers aged 42.3±13.4 years, 54.0 percent being male, 65.5 percent being White, 24.5 being Black or African American, and 11.0 being Hispanic or Latinx, completed the survey. Caregivers reported an average of 36.1±33 hours of care weekly, and 49.5 percent of care recipients lived with caregivers. Moderate-to-severe symptoms in the past month (67.5%) and MLEs in the past year (84.0%) were common. 

Caregivers frequently paid for care recipient expenses for MLEs (n=110, mean 12-month OOP cost: $3,909±$6,113) and everyday needs (n=174, mean 30-day OOP cost: $1,809±$3,492) without reimbursement. Living with caregiver, at least one MLE in the past year, and higher income predicted 30-day OOP costs (p<0.05). High humanistic burden (mean BAS score: 49.6±13.3) was associated with care recipient living with caregiver, receiving substance abuse treatment, attempting suicide in past year, and higher six-month symptom severity (p<0.05). 

Conclusion: Impacts of informal caregiving in schizophrenia are significant and should be considered during product development.

Funding/financial disclosures: MCM and KB are employees of PRECISIONheor and received funding for the conduct of this research. HK, TF, and AP are employees of Blue Persimmon Group and received funding for the conduct of this research. HK and TF are members of the board of directors for the Schizophrenia & Psychosis Action Alliance, the nonprofit organization funding this research. 

Optimizing Study Delivery Mix for Diversity, Enrollment Velocity, and Cost 

Authors: Elisa Cascade, Sandy Robbins

Affiliations: All authors are with Science 37

Background/Objective: The benefits of decentralized clinical trials (DCTs), enrollment velocity, and access are driving a permanent shift in study conduct. In this poster, we summarized output from a cost/value model of traditional sites, plus a DCT metasite.

Design: Science 37 collaborated with Hobson & Company, an expert in valuing new technologies, to develop a model of DCT methods. Based on customer/expert interviews, literature, and market data, the model identifies value drivers and, for each driver, defines the impact on the study price. Model parameters can be tailored to the study, benefits toggled on/off, and impact adjusted accordingly.

Results: The most common customer objectives and model outputs to date are: 

Optimize diversity with cost neutrality/savings by adding a DCT metasite. For example, a Phase II hypertension study with 50 sites/350 patients would maintain cost/time neutrality but increase access/diversity by adding a DCT metasite in lieu of 50 percent of traditional sites.

Optimize access and enrollment velocity with increased costs by adding a DCT metasite and maintaining current sites. For example, adding a DCT metasite to a Phase III infectious disease study with 350 sites/18,500 patients added 12 percent in cost but increased access and decreased enrollment by eight months.

Other factors to consider include protocol suitability for a DCT and risk of reliance on a single delivery method.

Conclusion: Considering model outputs and other risk factors, we concluded that sponsors looking to maximize value should consider a mix of site and DCT delivery methods.

Funding/financial disclosures: Not provided.

Patient Insights Board Informed Consent Engagement Case Study 

Author: Alicia Staley

Affiliations: Medidata Solutions

Background/Objective: The informed consent process is often one of the first and most critical introduction points a patient might have to the clinical trial process; however, the consent process can be tedious and overwhelming for patients, with complex and difficult-to-understand forms. To best support clinical trial participants, sponsors should invest more time and effort in developing simplified informed consent forms (ICF).

Design: Over four weeks, 10 advocates from the Medidata Patient Insights Board (PIB) engaged directly with an existing Top 25 pharmaceutical client. The PIB advocates were randomized to review either a long- or short-form ICF, followed by a discussion between the advocate and sponsor, simulating a consent process. An online survey with the same questions was sent to a separate group of patient advocates to gather written input and capture feedback without sponsor interaction.

Results: Results were analyzed within the interview and survey groups for quantitative metrics on length, time to completion, and likelihood of participation. A between-group analysis was performed to identify differences in responses based on form length and modalities of administration. Qualitative feedback was also collated and summarized into high-level themes, such as patient pain points, areas of opportunity, and suggestions for document and process improvement. 

Conclusion: The sponsor took the recommendations of Medidata’s Patient Insights team and redesigned their ICFs to prioritize the layout of important information within the template, refine the messaging and graphics for complex concepts, and establish the right balance of thoroughness and concise wording.

Funding/financial disclosures: Not provided.

Pediatric Caregiver Research Perspectives Survey: Highlighting Key Barriers to Participation in Pediatric Clinical Research 

Authors: Lara Finlayson,1 Lynda Gargan,2 Luke Kramer,3 Adam Simmons1

Affiliations: 1Alkermes, Inc., Waltham, MA; 2National Federation of Families, Rockville, MD; 3Evolution Research Group, New Providence, NJ

Background/Objective: Efforts to increase awareness of pediatric clinical research are critical, given that there might be a greater need for new treatment options for serious mental illness, as can be inferred by the upward trend of concerning mental health behaviors reported in adolescents. To better understand caregiver considerations around clinical research, we developed a survey for caregivers of children with a serious mental illness.

Design: The anonymous survey was distributed by the National Federation of Families, a family-run organization focused on the issues of youth with emotional, behavioral, or mental health needs and substance use challenges. The responses were tabulated using descriptive statistical analysis.

Results: Seventy-three responses were received across respondents from 24 different states. Several key barriers were noted, including lack of awareness of clinical research opportunities, not having the time or ability to travel to appointments, not feeling well-informed about all potential medication options, and stigma around clinical research in children. There was a recognition of the importance of clinical research and a desire to be more informed of clinical research opportunities. 

Conclusion: Many of the barriers to pediatric clinical research participation identified in the survey can be addressed with minimal accommodations. While stigma still exists, there is agreement that clinical research is important, and caregivers want to know more about research studies. Education of stakeholders through local advocacy and mental health organizations could help to reduce the stigma and increase awareness around clinical research and the vital role it serves in bringing new medications to children.

Funding/financial disclosures: LF is an employee of Alkermes and may have stock in the company. LG has no disclosures to report. LK is currently employed full-time by Evolution Research Group and has no other contracts or affiliations with other groups. He recently received a stipend from a presentation with Otsuka at the American Society for Clinical Pathology meeting. AS is an employee of Alkermes and may have stock in the company.

Perception of Wellbeing: Does Being in a Clinical Trial Influence How Patients with Cancer View themselves? 

Author: Sarah D. Atkinson, MD

Background/Objective: The objective of this study was to understand how patients perceive their wellbeing during their participation in a clinical trial and investigate if there was a change in perception at the termination of their participation in the trial.

Design: Participants were drawn from seven open-label oncology trials, three for breast cancer, two for prostate cancer, and two for colon cancer. The 16-item Quality of Life Scale (QoLS) was administered at the time of enrollment, midway through the clinical trial, at the termination of the clinical trial, and three months after participation if the reason for ending participation was not due to disease progression.

Results: Data was collected from 125 individuals, with an age range of 50 to 90 years, and 65 percent were female. Several items appeared to be influenced by participation in a clinical trial. Health, being physically fit and vigorous was rated a 1 or 2 by 80 percent at initiation of the trial, dropping to 35 percent at the mid- and endpoints of participation, and then rising to 75 percent of participants reporting a decline in their perception. This finding was in the light of no measurable change in their overall health status and cancer. Overall, respondents trended toward improvement in 10 items during their participation in a clinical trial, and this trend reversed at three months post-participation.

Conclusion: The act of participating in a clinical trial might significantly influence patient perception of their illness and wellbeing.

Funding/financial disclosures: None to disclose.

Quality of Life Data Collected in the Home Demonstrate High Internal Consistency, High Resolution, and the Ability to Monitor Clinical Trial Participants 

Authors: Tirumala Devi Kodavanti, Ben Ogorek, Alan Menius 

Affiliations: All authors with Spencer Health Solutions, Morrisville, NC. 

Background/Objective: Understanding quality of life (QoL) in the home is an important clinical endpoint. Previous studies demonstrated that QoL projections, administered via a medicine-dispensing smart hub, can be created from pulse surveys. This study examined the internal consistency reliability of this QoL instrument. We studied how time affects internal consistency reliability and compared the instrument to continuous QoL projections. 

Design: QoL responses were collected daily from January to June 2022 for 2,105 patients. Quarterly QoL scores were created for each patient, representing three categories: general health, activity, and emotion, as both additive scales and projections via a state-space model. For each construct and two quarters, internal consistency was measured by Cronbach’s alpha. Mean changes in QoL score by quarter were compared to the corresponding projection score for each patient. 

Results: Patients responded to daily QoL questions at a rate of 90 percent. General health and emotion categories indicated good internal consistency (alpha=0.804–0.845). The activity category indicated lower internal consistency (alpha=0.5). Changes to quarterly QoL scores were comparable to QoL projections, with agreement of greater than 80 percent in all three categories, demonstrating adequate signal for quantifying QoL. 

Conclusion: Intermittent pulse survey responses collected in the home demonstrated high internal consistency reliability for QoL constructs. QoL projections can be used to localize changepoints and increase resolution. These findings further support the use of pulse survey scoring as a means of collecting QoL data, especially for monitoring participants in decentralized clinical trials. 

Funding/financial disclosures: None to report.

Remote Administration of MADRS is Equal in Quality to In-Person Administration: Evidence from Two Parallel Depression Trials 

Authors: Dr. Christopher Reist, MD, MBA; Gary Sachs; Jason Bain; Thuy Le Nguyen; Xingmei Wang; Marcela Roy; Vikas Mohan Sharma; Petra Reksoprodjo; Alan Kott; Jan Wruck 

Affiliations: Science 37; Signant Health; Boehringer Ingelheim Pharmaceuticals, Inc.; Boehringer Ingelheim International GmbH

Background/Objective: The quality of Montgomery-Asberg Depression Rating Scale (MADRS) ratings in two Boehringer Ingelheim-sponsored clinical Phase II trials was compared, one conducted as a fully decentralized clinical trial (DCT) and the other as a global traditional site-based (TRAD) trial. 

Design: Both trials, with nearly identical designs, evaluated the efficacy of adjunctive treatment for patients with major depressive disorder (MDD) with insufficient response to antidepressant monotherapy. The primary outcome measure was the MADRS conducted at eight time points. DCT clinician assessments were conducted remotely, utilizing telehealth, while the TRAD study used site-based raters. Raters in both studies were trained and certified using identical materials prior to study start-up by Signant Health, which also conducted quality reviews of all screening and baseline interviews.

Results: The DCT had 86 patients (44 randomized), and the TRAD had 286 patients screened (146 randomized) at the time of data analysis. Cronbach’s alpha for baseline (V1) showed DCT alpha of 0.82 and TRAD alpha of 0.71. Subsequent visits showed a steady increase for both the DCT (alpha=0.91) and TRAD (alpha=0.89) at V8.

The DCT had lower total MADRS mean scores and lower variation (standard deviation [SD]) at each timepoint than the TRAD. The need for interventions (clinical contacts) by Signant Health reviewers was 11 percent, placing it in the top half of all combined sites. 

Conclusion: This is the first opportunity to report on quality of depression symptom assessment, using MADRS, conducted remotely in direct comparison to a parallel site-based trial. Standard metrics suggest quality is similar to face-to-face methods. The DCT was above average in terms of administration and scoring. 

Funding/financial disclosures: Authors and co-authors of this abstract are employees of Science 37, Signant Health, and Boehringer Ingelheim. Study sponsor (funding): Boehringer Ingelheim International GmbH.

Research-driven Recruitment for Social Anxiety Study 

Authors: Matthew Heidman,1 Dan Brenner,2 Susan M Dallabrida3

Affiliations: 1SPRIM US, LLC, Orlando, FL; 21nHealth, Orlando, FL; 3SPRIM US, LLC, Boston, MA

Background/Objective: A Phase III, interventional, 20-site social anxiety disorder study was behind pace on recruiting140 participants, who were required to perform two public speaking challenges, and needed to expedite recruitment efforts. The sponsor was utilizing vendor-leveraging Facebook, Google search, and television advertisements and requested support in reaching recruitment goals. 

Design: Multiple social media platforms were utilized (Facebook, Google search, and TikTok) to provide an omnichannel campaign with a strong focus on advertisements that spoke to patients with social anxiety using their own language, making the patients, rather than the study, the center of the message. Internet-based social anxiety discourse was thoroughly researched and utilized to develop customized creative elements, such as a shift away from stock photography of negative emotions to colorful, positive, engaging images with clear messaging about who should consider the study. Additionally, advertisements focused on language that reflected multiple motivation points (e.g., improving condition, getting a diagnosis, conquering public speaking fear) in headlines and advertisement copy, efficient prescreener development, and the ability to add SMS messaging to the communication pathways to account for patients’ hesitance to answer a spontaneous phone call.  

Results: From onset in Quarter 2 (Q2) 2022, the study saw a 115-percent increase in informed consent form (ICF) completion of qualified participants from Q1 over the three-month period. 

Conclusion: Utilizing this methodology to better understand and communicate with a demographic prior to recruitment, clinical trial sponsors can be more confident in meeting recruitment deadlines and the accuracy of cost estimates. It lowers the risk of costs rising due to the need for campaign extension. It is important to focus on “site activation” by creating processes that a) bring the patient into the site’s hands through a patient-centric journey and b) implement systems that push site personnel to engage with patients faster and more efficiently.

Funding/financial disclosures: SD and MH are employees of SPRIM US, LLC, and DB is an employee of 1nHealth.

Study Design for a Feasibility Exploration of Digital Biomarkers as Reader Comprehension and Engagement Measures of Informed Consent 

Authors: Aaron J. Masino,1 Joel W. Schwartz,2 Jacob Epifano,1 Sarah M. Kark,1 Stephanie Caamaño,1 Michelle A. Worthington,3 Richard H. Christie,1 Leslie A. Shinobu2 

Affiliations: 1AiCure, New York, NY; 2Systems Neuroscience, Neuroscience Thematic Research Center, Bristol-Myers Squibb, Cambridge, MA; 3Department of Psychology, Yale University, New Haven, CT

Background/Objective: Our primary objective was to develop digital biomarkers (DBMs) for reader comprehension and engagement for informed consent forms (ICFs). Here, we describe the design, population, and planned analyses for a study of DBMs to leverage prosodic features to predict comprehension and engagement.

Design: English-speaking individuals aged 18 to 75 years were eligible for inclusion. Participants videoed themselves using a smartphone while reading out loud. Participants first read The Caterpillar (speech disorder assessment), followed by three ICF excerpts of varying difficulty (Flesch Reading Ease score 26–80). ICF order was randomized. Participants reported difficulty, engagement, and interest for The Caterpillar and the last ICF passage. Recordings were processed to extract vocal acoustics, movement, and facial expressivity measures.

Results: The study population (n=40) was 57.5 percent female, 87.5 percent White, and 90 percent non-Hispanic or Latinx, with a mean (standard deviation [SD]) age of 41.2 (11.5) years. Participant education ranged from high school (GED equivalence) to advanced degree, with a majority at the high school level. Mean reported reading difficulty was equivalent across ICF passages, when scaled by reported difficulty for The Caterpillar. Participant-reported interest and engagement with the ICF passages were high for most participants. 

Conclusion: To our knowledge, this is the first time facial or vocal DBMs have been considered as readability measures. We plan to analyze correlations of localized DBM changes with designed prosody inflection points in The Caterpillar. We will then determine if such localized correlations exist in the ICF readings and whether they are associated with potentially problematic regions in the ICF.

Funding/financial disclosures: AJM, JE, SMK, SC, and RHC are employees of and have ownership interests in AiCure. JWS and LAS are employees of Bristol-Myers Squibb.

The Impact of Mini-Mental State Examination (MMSE) Score on MMSE Assessment Duration. An Exploratory Analysis

Authors: Alan Kott; Xingmei Wang; David Miller

Affiliations: All authors are with Signant Health.

Background/Objective: Mini-Mental State Examination (MMSE) assessments usually take around 7 to 8 minutes. However, in our blinded data analytical programs, we observed a large variation in the MMSE assessment duration. In the current analysis, we wanted to explore the impact of cognitive functioning, as measured by the MMSE score, on MMSE assessment duration. 

Design: All MMSE data were pooled from four clinical trials in Alzheimer’s disease. MMSE assessment durations were obtained from Electronic Clinical Outcome Assessment (eCOA) timestamps. A generalized estimating equations model estimating the MMSE assessment duration was fitted to the data, with MMSE score entering the model in a linear, quadratic, and cubic term and subject-rater dyad treated as a panel. 

Results: A total of 41,762 MMSE duration data were used. A curvilinear relationship was identified between MMSE score and MMSE assessment duration. The model predicted the MMSE assessment to take around 6.5 minutes with an MMSE score of 30, to take the longest at an MMSE score of 5, and then decrease slightly as MMSE scores dropped below 5. 

Conclusion: Using exact eCOA-obtained MMSE assessment duration meta-data, we found a significant curvilinear relationship between the severity of cognitive impairment and assessment duration, correcting for subject rater factors. As expected, durations were the shortest in the most intact population and increased with increasing cognitive impairment down to an MMSE score of 5, beyond which a slight decrease in assessment duration was observed. Our results may serve as a reference to estimate subject burden and be used in analytical programs to identify possibly problematic MMSE assessments. 

Funding/financial disclosures: This research was financially supported by Signant Health. All authors are Signant Health employees. 

The Positive Impact of Telehealth Genetic Counseling in Rare Disease Clinical Trial Recruitment

Author: Sean W. Sigmon

Affiliations: Vice President Business Development of Life Sciences at InformedDNA

Background/Objective: Telehealth genetic counseling is a proven method of clinical genomics care delivery. With the expanding number of genomics-based clinical trials, we proposed a model of utilizing genomics expertise to address rare disease trial pain points and increase trial success.

Design: One of the biggest challenges facing rare disease trials is the timely recruitment of eligible patients. Traditional recruitment methods are not as effective for rare disease trials due to the small, geographically dispersed patient populations required for trial participation and the often-limited condition expertise within the medical community. In this poster, we discuss proven methods in developing a nationwide, telehealth genetic counseling and testing program that supports rare disease trials in mitigating these challenges. 

Results: Certified genetic counselors were uniquely qualified to work with primary investigators to develop a strategic, multi-phased approach to patient identification and screening. Once screened, genetic testing could be facilitated remotely for patients meeting relevant criteria. In addition, we discussed the benefits of providing posttest results disclosure, reviewing implications for family members, discussing trial options, and facilitating referrals to clinical trial sites. Additionally, genetic counselors offered guidance to referring clinical providers by providing medical management recommendations for patients who test positive and specific guidance regarding negative or uncertain genetic test results. 

Conclusion: Employing a combination of these strategies through a telehealth genetic counseling program can begin to address the common challenges associated with recruitment and retention for rare disease trials. 

Funding/financial disclosures: SWS is a full-time employee of InformedDNA.

The Utility of Data Tokenization in Clinical Trials 

Authors: Devin Gilliam, Paul Petraro, Carla Heywood, Christian Niyonkuru, Ling Zhang, Devin Gilliam, Gordon Cummins 

Background/Objective: The purpose of this study was to evaluate the utility of anonymized data linking technology using token algorithms to supplement clinical trial data with real-world data sources. 

Design: Phase II clinical trials were selected to utilize data tokenization via a token algorithm software to assess the available data sources to be linked to existing patient information collected in the trials. Most token algorithms utilize complete personal identifiable information (PII), including first/last name, date of birth, sex, and address (including zip code). Local legal and data privacy considerations must be identified and carefully evaluated. By implementing a token identification (ID) algorithm, PII was transformed into an unrecognizable token ID that was not linkable to the patient. As global regulations vary around the collection of PII, only the central nervous system (CNS) trial (United States [US] only) contained all required PII elements, while the CD trial only contained the patient’s email address. Key to the analysis will be determining the number and type of PII required to maximize the utility of a token algorithm. 

Results: Of the 136 patients currently enrolled in the CNS trial to date, all required PII data elements have been made available and utilized. To date, the CD clinical trial has enrolled 33 patients with the availability of an email address for all participants. These results open various potential real-world data types to be evaluated for efficient linkage to the core clinical dataset. This provides additional insights to be considered as part of the overall real-world evidence strategy. 

Conclusion: The ability to tokenize trial data in the development lifecycle allows for earlier access to real-world data on specific patient populations, enabling the potential to validate real-world populations prior to marketing authorization. This approach also provides the ability to link additional data directly into the clinical trial programs.

Funding/financial disclosures: Not provided.

Update on Alzheimer’s Research-Related Global Challenges, Opportunities, Solutions, and Evolving Innovations 

Author: Charles S. Wilcox, PhD, MPA, MBA

Affiliations: Praxis Research Consulting, Alzheimer’s Association Orange County, Alzheimer’s Impact Movement (AIM)

Background/Objective: Alzheimer’s Disease International (ADI) estimates that more than 55 million people live with dementia worldwide, with up to 90 percent going undiagnosed in some low- and middle-income countries. The World Health Organization (WHO) is targeting 50 percent of countries to diagnose 50 percent of the estimated number of people with dementia by 2025. Our recent literature review identified evolving strategies-in-common to bolster recruitment in Alzheimer’s disease (AD) trials and enhance diversity, equity, and inclusion (DEI), facilitating better access to care as well. 

Design: The Alzheimer’s Association and the WHO are unified in seeking solutions to common challenges, including barriers to diagnosis, increasing demand for diagnosis, and a lack of access to specialized tests. We also identified the top five clinician-prioritized opportunities, which included DEI as the second-highest priority.

Results: As a blood test for dementia is the top (global) clinician-cited priority, five such tests now being utilized were highlighted, plus a magnetic resonance imaging (MRI)-based machine learning (ML) system, to reliably detect early signs of AD in 98 percent of cases. Cognitive assessment via telemedicine and online self-assessment was rapidly gaining traction. Acceptance within the context of a clinical trial was more often to supplement, and not supplant, face-to-face assessments. Practitioner-, societal-, and self-stigma remained significant barriers to diagnosis, along with cultural norms.

Conclusion: Clinicians around the world are seeking more than a new chemical entity (e.g., drug) for their anti-AD armamentarium. A validated blood test, simple and accessible self-assessment tools, and scales for telemedicine, along with more culturally and linguistically diverse scales, are needed as well. 

Funding/financial disclosures: None to report.

Who has Done What and Where: Which CNS Population Attempts the Most Protocol Violations and What are Those Protocol Violations 

Authors: Matt Michalik; Benjamin Rogowsky; Kerri K. Weingard, ANP, MSN; Mitchell D. Efros, MD

Affiliations: All authors are employed by or consult for Verified Clinical Trials (VCT).

Background/Objective: The objective of this abstract was to differentiate the incidence and prevalence of specific inclusion and exclusion protocol violations (IEPVs) sorted by a specific central nervous system (CNS) health condition. 

Design: The VCT database was utilized to retrospectively detect and prevent IEPVs in a series of clinical trials conducted in the United States (US) since 2017. A near-instantaneous response by the VCT research subject database registry could identify any psychiatry IEPVs revealed after authentication and comparison of the subject’s research history to the protocol criteria via proprietary algorithm. Data was analyzed using Excel, SQL, and Power BI. 

Results: Among the 35,421 verifications into psychiatric studies, subjects attempted to verify into a subsequent study 9,438 times. Subsequent verifications resulted in 1,386 potential protocol violations, which VCT prevented. Subjects with a CNS condition who attempted to enter a subsequent trial triggered a potential protocol violation 30.7 percent of the time. This ranged from 7.92 percent for subjects with depression, to 29.8 percent for subjects with schizophrenia, to 54.6 percent for subjects with posttraumatic stress disorder (PTSD). 

Conclusion: Protocol violations in CNS trials carry the potential for major consequences, including lost time and money, skewed study data, increased placebo rates, and failure of the trial. 

Funding/financial disclosures: Not provided.

Wearables and Mobile Applications (Apps)

A Study to Evaluate Passive Collection of Sensor Data in Participants Undergoing Consciousness-Altering Therapeutic Sessions for Treatment of Psychiatric Illness 

Authors: Todd M. Solomon, PhD;1 Matus Hajduk, MSc;1 Martin Majernik, MSc;1 Jamileh Jemison, MD;1 Alex Deschamps, LMSW;1 Jenna Scoggins, MSHS;1 Adam Kolar, MSc;1 Miguel Pinheiro, PhD;1 Peter Dubec;1 Ondrej Skala;1 Daniel R. Karlin, MD;1 Robert Barrow, MA;1 Christianna Mariano, BS;2 Amanda Tinkelman, MD2 

Affiliations: 1Mind Medicine, Inc.; 2Brooklyn Minds Psychiatry/Curated Mental Health 

Background/Objective: The present study (MSMS001) aimed to evaluate the feasibility of using digital device(s) and novel applications to passively collect data during an esketamine session, assess the quality of the collected data, and examine the acceptability of using the hardware to collect data from the point of view of both patients and practitioners.

Design: MindMed was developing a hardware and software platform via the Software as Medical Device (SaMD) pathway to be used by healthcare providers (HCPs) during therapy sessions involving pharmaceutical products with consciousness-altering effects. The system gathered patient data obtained with the following devices: a smartwatch, a smartphone, and a United States (US) Food and Drug Administration (FDA)-cleared noninvasive blood pressure measurement device. All data inputs were collected via an application, which also passively collected motion, position, physiological, and audio data from patients undergoing esketamine treatment sessions.

Results: Results indicated that the system provided adequate feasibility data with regard to the number and quality of data points recorded and uploaded, with greater than 90 percent data coverage across data modalities. Further, questionnaire data regarding the usability of the system indicated that subjects were overwhelmingly tolerant of the devices used during their treatment sessions. Finally, data collected utilizing the MSMS™ aligned with other physiologic and clinical data routinely collected during treatment sessions.

Conclusion: To our knowledge, this is the first time a comprehensive, multimodal data collection system has been deployed during consciousness-altering therapy sessions. Our data indicate that the system was able to adequately capture data using existing consumer hardware and that the product was acceptable to use and aligned with other clinical data.

Funding/financial disclosures: TS, MH, MM, JJ, AD, JS, AK, MP, PD, OS, DK, and RB are employed by Mind Medicine, Inc. CM and AT were employed at Brooklyn Minds Psychiatry and Curated Mental Health at the time of this study.

Addressing the Unmet Needs of Alzheimer’s Disease with Real-world Digital Clinical Measures 

Authors: Jen Blankenship,1 Shelby Bachman,1 Michael Busa,2 Corinna Serviente,2 Ieuan Clay,1 Kate Lyden1

Affiliations: 1VivoSense, Inc., Newport Coast, CA; 2University of Massachusetts, Amherst, MA

Background/Objective: Alzheimer’s disease (AD) is a devastating neurodegenerative disease that progresses to impact every dimension of a patient’s life. With no cure and limited options to manage symptoms, research is underway to develop drugs that impact the most burdensome aspects of AD, memory problems, and ability to function independently.

Design: Many clinical trials in drug development assess these aspects of health by measuring cognitive and physical function; however, current approaches are reliant on self-report, which can be burdensome to patients, and are not representative of real-world function. To ensure that new therapies address the needs of patients with AD, drug development tools (DDTs) that comprehensively capture aspects of daily living that matter to patients are paramount. 

Results: Digital clinical measures have the potential to address the limitations of currently available evidence-generation tools by directly measuring how patients feel and function in real-world environments with wearable and connected technologies. Such technologies can be deployed remotely, passively, and continuously to enable a new class of patient-centric endpoints aligned with FDA’s Patient Focused Drug Development initiatives. Real-world digital clinical measures that capture the dynamic day-to-day variation of disease manifestation are a departure from the status quo and have the potential to substantially advance drug development in AD. 

Conclusion: This poster will focus on the opportunities for digital clinical measures in AD drug development. Specifically, we will summarize what is known regarding the meaningful aspects of health in AD, describe limitations of current DDTs, and highlight how novel digital clinical measures can address existing gaps and capture everyday patient functioning. 

Funding/financial disclosures: Not provided.

Administering ePROs Using Datacubed Health’s Mobile App Improves Adherence in a Siteless, Virtual, Longitudinal Study 

Authors: Xinrui Jiang, PhD, MA; Michelle Cinguina, BS; Marie Onakomaiya, PhD, MPH 

Affiliations: All authors are with Datacubed Health.

Background/Objective: The goal of this study was to compare adherence between groups completing electronic patient-reported outcomes (ePROs) on the Datacubed application (app), Datacubed Web, a third-party website, and a traditional paper survey format. We hypothesized that reducing participant friction and increasing motivation by administering ePROs using Datacubed’s behavioral science-based mobile app would result in higher adherence among retained participants.

Design: This was a six-month, entirely remote longitudinal study. A total of 284 participants completed weekly ePROs for six months; 176 participants were retained until the end of the study. Adherence was assessed based on data from retained participants. 

Results: A main effect of modality was found in both retention [F(3,280)=8.18, p<0.0001] and adherence [F(3,176)=9.10, p<0.0001]. The Datacubed app and third-party website were associated with significantly longer retention than the paper format (p<0.001). The Datacubed app was associated with significantly higher adherence than the third-party website (p<0.001). All electronic formats had significantly higher adherence than the paper format (p<0.05). No other differences were found (p>0.05). 

Conclusion: Datacubed’s behavioral science-based mobile app provided significantly higher ePRO adherence, compared to the third-party web-based modality without behavioral science features. This suggests that the motivational and user-friendly aspects of the app improve participant adherence.

Funding/financial disclosures: This study was funded by Datacubed Health.

Association Between Patient-reported Sleep Quality and Passive Sleep Measurements in Patients with Psychiatric Disorders Using Mobile-based Questionnaires and Wearable Sensors 

Authors: Joseph Stanton, Mykel Robble, Candice Blacknall, Hailey Lefkofsky, Marcus Badgeley

Affiliations: All authors are with Tempus Labs, Inc.

Background/Objective: The purpose of this study was to compare patient-reported and passive-sensor-based measures of sleep in patients with a psychiatric diagnosis.

Design: This was a retrospective study of patients being treated for psychiatric disease who received the Tempus nP pharmacogenomics assay and used the TempusPRO application (app). We extracted patient-reported sleep scores from standard psychiatric assessments and a one-question daily score (“check-in”). Sleep from the two weeks prior to an assessment was summarized with average sleep duration and variance. The association between sleep and cardiac sensor data was tested using Pearson correlation. 

Results: We analyzed a subset of 1,191 patients that used TempusPRO between January 2021 and June 2022. Of this subset, 1,096 patients completed daily check-in surveys, and 544 completed assessments reviewing the prior two weeks’ sleep. Self-reported sleep difficulty on assessments was negatively associated with patients’ average duration of sleep and positively associated with the variation in sleep duration over the prior two weeks (n=177). Daily sleep quality scores were positively correlated with sleep duration (Spearman estimate 0.08, p=0.002). More sleep was associated with higher heart rate variability and lower resting heart rate (r2: 0.1–0.15).

Conclusion: We found expected associations between passive-sensor data and self-reported sleep data, as well as between sleep and cardiac sensor data. Self-reported data measurements are more sparse and often more difficult to capture, compared to passive-sensor data. Mobile sensor data can complement patient-reported sleep quality and potentially be used in the absence of self-reported data. 

Funding/financial disclosures: All the authors are employees of Tempus Labs, Inc. 

Implementing Wearable Technologies for In-home Assessment of Cognitive- and Event-related Potential Responses After Sleep and Wakefulness in Alzheimer’s Disease 

Authors: Renée Decaro,1 John Dyer,2 Joel W. Schwartz,3 Katherine Turk,1 Brian Murphy,2 Leslie Shinobu,3 Andrew E. Budson1

Affiliations: 1Boston University School of Medicine; 2Cumulus Neuroscience, Ltd.; 3Bristol Myers Squibb

Background/Objective: The purpose of this study was to explore circadian effects on memory consolidation and underlying neurophysiology in young healthy adults, old healthy adults, and patients with Alzheimer’s disease (AD) by using two wearable technologies in a repeated, longitudinal, at-home paradigm that capture key cognitive and electrophysiologic responses.

Design: A participant home-administered, cognitive, event-related potential (ERP) platform (Cumulus Neuroscience) was administered over a two-week period to healthy younger adults, healthy older adults, and older adult patients with AD. Resting state electroencephalography (EEG) and recognition memory coupled with ERP recordings were tested twice daily, once in the morning following approximately 12 hours of sleep/rest and once at end of day before retiring for the night. This pilot of a novel protocol explored whether each wearable technology generated data that was sufficiently reproducible and sensitive to track and interpret changes in episodic memory consistent with known changes expected based on age and AD status. We used sleep versus normal daily activities as “state of change” contexts. Subjective reports of sleep, together with smartwatch actigraphy (Empatica, Inc), were used to estimate the duration and intensity of the rest/sleep period versus daytime activity.

Results: We reported on the feasibility of implementation of the experimental paradigm and whether the sensors employed reliably interpretable yield data. We shared data on recruitment rates and subject feedback regarding tolerability of the Cumulus Headset and Empatica watch. 

Conclusion: The home-based experimental paradigm was well tolerated, with excellent adherence to the study design. Both sensors yielded experimental datasets of similar quality to those obtained in in-house studies. Planned analyses focus on establishing whether this experimental paradigm yields data with sufficient sensitivity and reproducibility to confirm past observations regarding the effect of age and AD on memory consolidation.

Funding/financial disclosures: Not provided.

Mobile App Care Utility: Patients’ and Caregivers’ Perception 

Authors: Ingrid Oakley-Girvan,1 Reem Yunis,1 Ai Kubo,2 Elaine Kurtovich,2 Sara Aghaee,2 Maya Ramsey,2 Michelle Longmire,1 Elad Neeman,2 Raymond Liu2

Affiliations: 1Medable, Inc., Palo Alto, CA; 2Kaiser Permanente Northern California, Oakland, CA 

Background/Objective: We assessed the perspectives of patients with cancer and their caregivers on the utility of a mobile application (app) combined with a smartwatch for collection of specific outcomes and to provide clinically actionable data. 

Design: From October 12, 2020, to April 30, 2021, we enrolled dyads consisting of a cancer patient on intravenous (IV) chemotherapy and their informal caregiver in a decentralized clinical trial (DCT). All participants owned an Apple iPhone 6 or higher. Patients used an Apple Watch 3 or 4 and downloaded a mobile app (DigiBioMarCTM). Caregivers downloaded a mobile app (TOGETHERCareTM). Participants used their respective app for 28 days, completing specific surveys and activity requests in the mobile app; physical activity data was collected via the smartwatch. At the end of the usage period, questions were asked in a video interview about participants’ perspectives on app use and what they believed could be impacted if their doctor were to receive the app data. 

Results: Fifty-four dyads were enrolled, and 50 completed interviews. Perceived impacts (strongly agreed or agreed by patient:caregiver respectively) included 68 percent:70 percent felt that patients would take better care of themselves; 73 percent:61 percent felt that using the app would encourage them to be more physically active; 80 percent:77 percent felt that use of the app would improve communication with the doctor; 80 percent:75 percent felt that the doctor would know earlier if something was not right; and 64 percent:80 percent felt the doctor would intervene sooner if something were not right. 

Conclusion: Combining low-burden, home-based, remote digital measurements with patient- and observer-reported outcomes has promise to improve care during and outside of clinical trials.

Funding/financial disclosures: This study was funded in part by the National Institutes of Health (NIH), the National Cancer Institute (NCI) through the Small Business Innovation Research (SBIR) program (HHSN261201700030C and HHSN261201800010C).

Older Adults Engage with Optional, Interactive Elements of a Mobile App 

Authors: Marie Onakomaiya, PhD, MPH;1 Silas Lee, BS;2 Michelle Cinguina1,3 

Affiliations: 1Datacubed Health, Brooklyn, NY; 2Department of Epidemiology and Biostatistics, CUNY SPH Graduate School of Public Health and Health Policy; 3Rutgers

Background/Objective: Patient engagement in clinical trials is increasingly recognized as playing a critical role in participant compliance and retention. Datacubed Health’s patient-centric mobile application (app) was designed using behavioral science principles that increase engagement and motivation. However, engagement can be influenced by age, especially with technology. Therefore, we analyzed engagement metrics to evaluate how participants in different age groups interacted with optional, gamified engagement features in the app.

Design: Engagement metrics were obtained from 113 adults in a decentralized, longitudinal study conducted by a large academic medical center. There were four age groups: 20 to 24 years, 25 to 34 years, 35 to 54 years, and 55 years and older. Average age was 40 years. The key engagement indicator was “total number of interactions with app engagement features.” This was normalized to the number of electronic patient-reported outcomes (ePROs) per participant. 

Results: While all participants interacted with the optional engagement features in the app, the total number of interactions significantly increased with age [F(1, 111)=7.74, p=0.006]. Number of interactions was highest in the 55 years and older age group. Second, participants in the 55 years and older age group had four interactions with optional engagement features per ePRO completed, compared to one interaction per ePRO for those in the 20 to 24 years group. 

Conclusion: In a random sample of healthy, adult volunteers, participants who were aged 55 years or older were most likely to interact with optional, gamified elements of a mobile application designed around behavioral science-driven patient engagement. 

Funding/financial disclosures: This evaluation was funded by Datacubed Health.

Reception of a Behavioral, Science-Based, Patient-Centered Mobile App in a Diverse Cohort 

Authors: Elias Boroda, PhD;1 Silas Lee, BS;1,2 Marie Onakomaiya, PhD, MPH1

Affiliations: 1Datacubed Health, Brooklyn, NY; 2Department of Epidemiology and Biostatistics, CUNY SPH Graduate School of Public Health and Health Policy

Background/Objective: Increasing clinical trial diversity effectively and efficiently remains a challenge for the pharmaceutical industry. Datacubed Health’s application (app) is an innovative app that features in-app characters and avatars designed to enhance participants’ ability to identify with the study environment and thereby improve study engagement. This study examined the reception of the app across a variety of demographic factors in a diverse cohort.

Design: A group of 87 participants completed a comprehensive usability survey about their subjective experiences with the in-app characters and avatars in the Datacubed app. The sample represented diversity across age, sex, ethnic background, and education level. The survey responses were collected on a 5-point, agree/disagree Likert scale.

Results: The three primary questions examined in this analysis were 1) “I feel that I can identify with the characters in the app,” 2) “I could create an avatar that I identify with,” and 3) “There are an appropriate amount of skin colors in the avatar selection.” Most participants responded favorably to the three questions, with 74 percent of total responses being either “strongly agree” or “agree.” Only eight percent of responses were “disagree” or “strongly disagree.” There were no significant differences across age, sex, ethnic/racial background, or level of education (p>0.05 for all comparisons). 

Conclusion: These results indicate that a diverse group of Datacubed’s app users can identify with the characters in the app and feel represented by the avatar options available to them. Facilitating participant identification with the app experience and study participation overall could enhance engagement and increase study compliance.

Funding/financial disclosures: This evaluation was funded by Datacubed Health. 

Results from a Survey of Biopharmaceuticals on Attitudes Toward the Use of Wearables and Sensors in Clinical Research 

Author: Rohini Kumar

Affiliations: Principal Sensor Solutions Leader, Scientific Data Organization, Parexel International

Background/Objective: This study aimed to better understand the current and future environment for sensors and wearables use in clinical research.

Design: We conducted a survey of 146 individuals working at biopharmaceutical companies in the United States (US), Europe, and China about their use of, and attitudes toward, sensors in clinical trials. Additionally, deep-dive interviews with 10 US-based respondents were conducted.

Results: Among users of wearable devices in clinical trials, heart rate and blood pressure were the top two identified measurements, mentioned by over half of respondents. While wearables made up 20 percent of the budget, they were expected to become the gold standard in clinical trials. Eighty-nine percent of current users of wearable devices in clinical trials said that patient feedback has been positive. The ability to monitor data in real-time or near real-time was the biggest potential data analysis benefit to including wearable sensors in a clinical trial, while collecting data that can only be captured by wearables was the strongest driver for use of wearable devices in clinical trials. When considering which specific wearable device to use in a clinical trial, ease of use for the patient and data quality were the two top attributes.

Conclusion: Clinical trial sponsors are increasingly exploring the use of sensors and wearables in clinical research. Sensors are now being used in Phase I to IV trials, and by 2024, small and emerging pharmaceutical companies are expected to use wearables in a higher proportion of clinical trials than large pharmaceutical companies. Respondents see improved data quality, obtaining previously unavailable data, and better patient compliance as the top three benefits of including wearable devices in clinical trials. In summary, the pharmaceutical industry views wearable devices as a paradigm shift in clinical trials, allowing them to see more comprehensive patient data and improving the patient experience.

Funding/financial disclosures: Not provided.

Smart Walking Training Devices and Methodology to Alleviate the Course of the Disease and Validate the Effects of Drugs on Parkinson’s Patients 

Authors: ILUUM, Ltd., Tallinn, Estonia 

Affiliations: Tallinn University of Technology 

Background/Objective: The purpose of this study was to provide a smart device that, via a methodology, helps validate the effects and effectiveness of drugs on patients with Parkinson’s disease during aerobic endurance movement/physical activity (walking), while the human operator controls and doses the methodology their self. 

Design: Wireless walking devices (working title: Smart Poles) that measure, when walking, how much workload (i.e., thrust amount of muscle activity) the human operator does to their self, meanwhile utilizing arm thrust application to expand the balance surface and improve coordinated walking ability independently via a feedback system. 

Results: These smart walking devices and methodology allowed the human operator to monitor and adjust their individual treatment and performance according to their capability and the real-time feedback system.

The devices simultaneously measured the dynamic force of the body sides (in Newtons), features and characteristics of the gait cycle of the upper limbs (left and right hand separately), agility, hand amplitude (in degrees), hand tremor, and endurance of aerobic activity.

Conclusion: The smart devices (poles), when used with the methodology (poles used during walking to push the body forward), forms a system which allows measurement and use of the applied arm thrust amount of muscle activity (force) to validate the effects and effectiveness of drugs on patients with Parkinson’s disease during aerobic endurance movement/physical activity (walking). It allows for an independent treatment for patients with Parkinson’s disease outside of a laboratory or rehabilitation center, while alleviating the course of the disease.

Funding/financial disclosures: This is an original concept for a patent-based technology currently under development, and there are no funding or presenter conflicts to report.

Using Digital Technology to Increase Patient Retention and Improve adherence for Better Clinical Trial Management 

Author: Tarra Shingler

Affiliations: SVP Global Business Solutions, StudyKIKl

Background/Objective: The aim of this study was to demonstrate how sponsors and sites can leverage patient-facing technologies, specifically mobile applications (apps), to facilitate study success. 

Design: A sponsor was conducting a Phase III trial for molluscum contagiosum. The first two trials failed. The sponsor was able to identify that the issue was not the efficacy of the product, but that patients were falling out of adherence at Weeks 8 to 12. Looking to leverage digital technology, the sponsor contracted StudyKIK to develop a custom study mobile app to support retention and encourage adherence for the third trial attempt. 

Fifty-five clinic sites enrolled 891 patients. Site staff assisted patients with the initial download of the mobile app at Visit 1 to assist with patient onboarding. Push notifications were used for appointment reminders, visit expectations, and surveys to enhance patient use of the app. The frequency of these notifications increased between Weeks 8 to 12, which in the past two trials had seen the highest rate of patients falling out of compliance. 

Results: After two failed Phase III trials, the third attempt, which leveraged the use of a mobile app, was successful. The app saw a push notification open rate of 94 percent and a high level of study compliance, with less than five percent of patients missing daily treatments.  

Conclusion: The sponsor is now advancing the product toward a New Drug Application submission to bring it to market and to patients in need of effective therapy.

Funding/financial disclosures: TS is employed by StudyKIK.