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Commercially confidentialJ.FINN
AI in Pharma Summit
BUILDING THE DIGITAL ECOSYSTEM FOR
TOMORROW’S CLINICAL TRIALS
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▪ $30-40 million average cost (Phase 1+2+3)
▪ Average drop out rate 30%
▪ Average cost per new recruit $22k
▪ 80% of all trials do not complete
Harnessing mobile human data can:
▪ Inform better trial design
▪ Better understand outcomes
▪ Improve clinical likelihood of success
SITUATION
Clinical Trials
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The stress response pathway
OVERVIEW
Chronic illness
e.g. heart disease, stroke, diabetes, UC, respiratory
Chronic stress
multiple factors
Pathophysiological
effects
Co-morbidity
of depression
cardiovascular
metabolic
respiratory
immune
nervous and
endocrine
Clinical trials involving chronic conditions must factor in the co-morbidity of stress
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CHALLENGE
Clinical insight during trials
Stress impacts behaviour and physiology:
▪ regime compliance may be compromised
▪ drug effect may be changed
What if you could develop an ML-driven
stress monitoring system to provide deeper
clinical insight during trials?
Continuous monitoring can:
▪ mitigate the impact of stress on compliance
▪ enable investigation of stress as a
confounding factor
▪ inform better adaptive trial design through real
time data
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1. Design an ML-driven ecosystem for
non-invasively assessing stress during
clinical trials (CTs)
2. Design user-friendly wearable and CT data
collection app
3. Design a widget for CTMS dashboards to
present ML stress predictions
▪ produced an ML-driven numerical estimator for
stress
▪ consulted with psychiatrists, neuroscientists and
clinical trial experts to generate a robust design
To develop an unobtrusive digital system, we
had to be as obtrusive as possible
Verum Goals
SYSTEM OVERVIEW
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GETTING THE RIGHT COMBINATION OF SENSORS
Biometric sensor data Data collection
ECG (500 Hz)
EDA (500 Hz)
EMG (500 Hz)
Respiration (500 Hz)
Skin Temp
Air Temp and Humidity
Pupil Tracking
Accelerometer
Sleep
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Self-reporting Data collection
BEHAVIOURAL AND CONTEXTUAL DATA GATHERING
Numeric Rating Scale
State Trait Anxiety Index
Perceived Stress Scale
Free Text Input
Brief Voice Analysis
Log Intake
Log Activity
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Trained to self-reported stress
▪ Trial size n=10, 4 days per person
Tested:
▪ Different ML techniques
– Normal linear regression (baseline)
– Ridge regression
– Random forest regression
– Support Vector Regression
▪ Different sensor combinations (256)
▪ Different methods of imputing missing data
Developing the machine learning algorithm
0
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05:0
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Str
ess
Leve
l
Time
A day in the life
Reported_Stress Predicted_Stress
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Our ML prediction of stress levels aligns well with self-reported stress
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Str
ess L
evel
Time
A day in the life (2)
Reported_Stress Predicted_Stress
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Stress experiment – 2 hr
VALIDATION
▪ Calming period (VR)
▪ Stress inducing (VR, Stroop, alphabet transposition, mathematical questions)
▪ Calming period
▪ Stress questionnaires asked at each stage
10:05 10:20 10:35 10:50 11:05 11:20 11:35 11:50
Elicited Stress
Stress period Relax period
Predicted Stress
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▪ The best results used 3 different pathways
▪ Pragmatic system will operate with 2 sensors
▪ Design challenges to building out a digital ecosystem
– 14 skill sets, 36 people
▪ Standard ML techniques must be adapted to cope with
data at very different rates
▪ Improved performance would require a data set
balanced between high and low stress events
– requires data synthesis/extension e.g via GANs
Voice
Error = 3.4
ECG/EMG
Error = 5.9
EDA
Error = 7.8
2.7 2.7
4.7
2.2
Project Insights
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Sensors integrated into user-friendly wearables
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Bringing the AI prediction to existing CTMS dashboard
CLINICAL TRIAL DASHBOARD WIDGET
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Benefits of the Verum platform
CLINICAL TRIALS OF THE FUTURE
An AI-driven stress monitoring system can provide deeper clinical
insight during trials, helping clinicians make more informed decisions.
Verum can:
▪ Mitigate the impact of stress on compliance
▪ Enable investigation of stress as a confounding factor
▪ Enable better profiling of drug effects and adverse events
▪ Enable study monitors to reach out to patients swiftly to respond to
adverse events
▪ Inform better adaptive trial design through bigger, real-time
contextualised data sets
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