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2 May 2023
Clinical trials and wearablesA Pistoia Alliance Debates webinar
Chaired by Richard Lingard, Dotmatics
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32 May 2023
Panelists
Pistoia Alliance Debates: Clinical trials and wearables
Richard Lingard, Head of Commercial Operations, Dotmatics has a chemistry background and has been working in the life sciences industry for over two decades, focusing on the business side of drug discovery technology and services. Richard has enjoyed a number of international commercial roles at research supporting organisations: Thomson Reuters and Biovia, as well as research organisations like Argenta Discovery and Merck & Co Inc. As a Management & Chemical and Sciences graduate of UMIST he is still pleased to be using his degree in everyday working life.
Marie Mc Carthy MSc, MBA. Director Product Innovation, is part of the multidisciplinary Innovation Team at ICON PLC. She has specific responsibility for developing solutions in the direct to patient paradigm. Her previous role was that of EU Sales and Marketing Manager with Philips Respironics, building awareness of the value of Actigraphy endpoints among Clinicians and Researchers.
Christian Gossens is leading the Early Development Workflow team in Roche’s Research and Early Development Informatics organization. His team covers workflows in Proteomics and Genetics & Genomics over Clinical Pharmacology to Clinical Operations. He is currently focusing to drive innovation with digital tools for patient and investigator recruitment and engagement.
Matt Jones has over 16 years' experience of working in Research and development and informatics groups within the Pharmaceutical industry. Matt joined Glaxo Wellcome/GSK in 1998 as a scientific software engineer after completing his PhD in synthetic organic chemistry at the University of Bath. He moved through technical leadership, project and programme management roles before leaving in 2014 to join Tessella. Matt is currently helping lead and drive the Tessella advanced analytics strategy, to bring our cross domain experience to bear on the challenges that life science and modern pharma faces.
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dotmaticsknowledge solutions
. . .
Clinical trials and wearablesRichard LingardSVP Global Commercial Operations
Dotmatics Ltd
dotmaticsknowledge solutions
.. .
• Consumer wearables prevalent – Low cost – him availability– High compliance rate – people want them
• Data capture ongoing – Adapt to clinical trials– App usage/ Custom App
• Advantages/ Hurdles– Cost, compliance, monitoring– FDA / EMA approval
Consumer wearables for clinical trials
dotmaticsknowledge solutions
.. .
• Butterfly effect - wearables- Clinical trials changed forever?
Disruptive
Wearables and Clinical Trials
27th Jan 2016
Name: Marie Mc Carthy
Title: Director Product Innovation
Wearables
By 2019 245 million wearable devices sold CCS Insights latest Wearable Tech
Value of Wearables in Clinical Trials
Trial of the Future
Patient EngagementPatient BurdenReal Time DataReal world DataNew Data
Considerations
Clinical Question
Fit for Purpose
Device/Vendor
Data Transfer Data Management
Wearables are so 1970’s !
Outcome Measures
12
80% CNS
90 Trials
82% 1°and 2°endpoint
Bringing remote patient monitoring into clinical trialsA Parkinson’s Disease case study in Roche pRED
Christian Gossens, PhD, MBA, Global Head Early Development WorkflowsPistoia Alliance Debates “Clinical trials and wearables”, 21 January 2016
Smartphones are (nearly) everywhereBut not yet in clinical trials!Disruptive consumer adoption…
… and an opportunity for drug development?
14Source: Spiegel Online
Disruptive adoption of smartphones Opens a new space of opportunies for clinical research
15
How is the patient doing outside the clinic?Measuring disease progression continuously
16
Towards prediction of today’s clinical gold standard using smartphone data
17
Sensor data
UPDRS*Clinical parameter
of interest
Train model using Machine
Learning algorithm on UPDRS*
1.
Sensor data
2. Predict UPDRS*
using model developed above
* UPDRS: Unified Parkinson Disease Rating Scale
Remote patient monitoring in a clinical trialFour-fold motivation for Roche
Get app workflow into the clinic
Ensure uptake and adherence
Use active tests to track disease progression
Develop frictionless monitoring
18
End to end workflow established in clinical trialFrom smartphone distribution through to data analysis
Automatic encrypted data upload via WIFI
The Roche Parkinson’s Disease mobile appThe patient’s daily routineActive Test (in the morning)
Dexterity test
Passive Monitoring(throughout the day)
Data upload
Smartphone sensors provide rich raw dataData read out for Active Tests & Passive Monitoring
21
Y
X
Z
Main data from magnetometer, accelerometer and gyroscope:• Recordings in three directions• Sampling rate: ~60Hz
Other data • Touch data• Voice recording (only during
30s voice tests)• Light• Location (GPS, Wifi)• Battery level
Many building blocks must be fit together to deliver a scientifically meaningful “tool”From technology, workflows through to data analysis
22
A cross-functional team is requiredFrom legal through to clinical operations
Remote patient monitoring in a clinical trialFour-fold motivation for Roche
Get app workflow into the clinic
Ensure uptake and adherence
Use active tests to track disease progression
Develop frictionless monitoring
24
“Big” data is being collected as we speakOver 1500 active tests &140 GB of passive monitoring data
25
Time
GB
sens
or d
ata
colle
cted
by
pati
ents
May ‘15
December ‘15
Colours: data accumulated by different patients
0
140
Adherence is supported with proactive monitoringExample: replacing fast-draining batteries
Batte
ry d
rain
age
Time
How battery drainage per hour gets worse over time for the selected
smartphone
26
Batte
ry d
rain
age
Smartphones used by patients
Battery drainage for smartphones in trial
Screening Middle of the trial
Remote patient monitoring in a clinical trialFour-fold motivation for Roche
Get app workflow into the clinic
Ensure uptake and adherence
Use active tests to track disease progression
Develop frictionless monitoring
27
In progress
Automatic data cleaning is a prerequisite An example from the voice test, applied to over 1500 records
For automated data cleaning, the sound waves are segmented to remove:• Phone beep before first
phonation• Anything after first
phonation (quiet time, breathing)
Ampl
itude
Pitc
h (H
z)
The “useful” segmentInitial
beep
Voice amplitude and pitch
Pow
er o
n sm
artp
hone
(am
ount
of
ener
gy a
t di
ffer
ent
freq
uenc
ies)
Frequency (Hz)
Postural tremor power spectrum
HzDeuschl et al (1998). Movement Disorders
Longitudinal is key: Patient tremor severity varies from day to day (on/off)
No visible tremor
Plainly visible tremor
Frequency (Hz)29
Smartphone sensors compete with high cost, stationary clinical equipmentFirst results in line with medical literature
Roche is paving the way for “Digital Biomarkers”We have developed the capabilities and a scalable approach
Technology & workflows
Data analysis
Program management
30
Learn more on Roche.com or YouTube“Roche app measures Parkinson's disease fluctuations”
31
Take a look: https://www.youtube.com/watch?v=d2Ofu-Bf_p8
Doing now what patients need next
Matt Jones – Pistoia – Jan 2016
Wearables – Data standards for clinical applications
Rise of the consumer wearable
Pistoia IP3 Proposalhttps://main.qmarkets.org/live/pistoia/node/1588
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• But a great opportunity to learn and share information
Discussion and Audience Q&A
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