digital biomarkers for huntington disease
TRANSCRIPT
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Digital Biomarkersfor Huntington Disease
Friday, November 411:15am-12:15pm
Chair: Ralf Reilmann, MDGeorge Huntington Institut
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Presenters
HSG 2016: DISCOVERING OUR FUTURE
Max Little, PhD (virtual attendee)Aston University
Spyros Papapetropoulos, MD, PhDTeva Pharmaceuticals
Gaurav Sharma, PhDUniversity of Rochester
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Objective measurement of HDsymptoms using smartphonesDr Max Little ([email protected]) Research Director, NumericAnalysis LtdAssociate Professor, Aston University, UK Senior Research Fellow, Oxford University, UK Visiting Associate Professor, MIT, US
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Smartphones as serious toolsfor symptom measurementKey aims:• Reducing logistical difficulties for measurement of HD
symptoms• Improve objectivity (repeatability, reliability) of testing
methodology• Enable high-frequency measurement• Improve quality and frequency of follow-up measurements
in clinical trials
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Structured smartphone tests:hardware and protocol
Raw sensor data collected using specialised Android smartphone softwareUsers performed specific test protocols in clinic:• Gait, balance (accelerometry)• Tapping, reaction time (touchscreen)• Voice (microphone)
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Accelerometry pre-processing • Smartphone orientation
identification (top left)• Orientation signal in
spherical coordinates (top right)
• Impulsive events extracted from dynamic acceleration (bottom left)
• Residual signal (bottom right)
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Objective-HD pilot study cohortstatistics
Age Gender MOCA UHDRStotal motor score
Controls (N=5) 54 (21) 40% male 28 (1) 0 (0)
HD (N=15) 57 (7) 60% male 21 (4) 42 (13)
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Gait test results • Gait low-frequency
spectral entropy feature
• Validation: strongly correlated with 10m walking test time (left)
• Discriminates controls from HD (right, Cohen’s d=1.2)
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Balance test results • Balance dynamic acceleration
magnitude interquartile range feature
• Discriminates controls from HD(Cohen’s d=1.2)
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Touchscreen data pre-processing• Left/right tapping clusters identified from x-y touchscreen
coordinates• Extract: tap timing events, tap placement statistics from
cluster properties
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Tapping test results • Tapping time
coefficient of variation feature (horizontal)
• Tapping cluster placement spread feature (vertical)
• Discriminates controls from HD
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Combining tests • Predict UHDRS total motor
score, linear regression• 14 features from tapping,
gait, balance tests• Select features using single
feature regression significance
• Optimal model 2 or 5 features (top)
• Prediction error ~10 UHDRSpoints (bottom)
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Conclusions• Small pilot study: Smartphone-based testing
discriminates controls from HD across tapping, balance and gait
• Smartphone-based gait test validates against standard 10m walking test
• Smartphone-based testing can predict UHDRS total motor score within ~10 UHDRS points
• First steps on the road to using standard smartphones as serious tools in clinical and research practice in HD
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Spyros Papapetropoulos MD, PhDVP, Neurodegenerative diseases and
Movement Disorders
Implementing Innovation:Rewiring Clinical Research
October 27th – 30th, 2016 ◦ Boca Raton, Florida
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Digital Technology Disrupted the World as we knew it
15Yesterday’s advantage will be replaced by today’s trends
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Steve Jobs, Co-Founder of Apple
“The biggest innovations of the 21st century will be at the intersection of biology and technology. A new era is
beginning.”
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CommonPedometerGyroscope
AccelerometerGeomagneticSleep activity
Heart rate
SpecialtyPulse oximetry
Sun exposureECG, EEG, EMG
PK, Respiratory rateHeart Rate Variability
Pulse rateStress
Brain Activity Sweat
Blood pressure Skin temperature Skin conductance
Activity (steps)Climbing/elevation
GyroscopeAmbient light sensor
AccelerometerGestureProximity
Tracking chipRGB light sensor
Barometric pressureOutdoor Temperature
HumidityVoiceGait
Urine analysisWeight
Blood PressureGlucose
Water qualityInfrared
Outdoor temperatureVoice
Ocular pressureOther
Smartphones, Wearable Devices and Health Sensors are capable of quantifying health and disease
On SMART device Wearable Portable
Objective, Real world, eSource, Remote, Real time, Continuous
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Patients are looking for a change– By 2021, average person will
have 3 personal smart devices– Relationship with physician/site
changed– Self motivated to find answers– Direct to patient marketing has
changed expectations
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A sad reality – the cascade of drug development
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Clinical trials have been centered around the site for decades mimicking delivery of healthcare
Sponsor
CRO
IRB
FDA
Patients
Site
Long, difficult to enroll and execute, expensive clinical trials with high failure rates and inconclusive data – Is it the drug or the trial?
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The missed opportunities of traditional trials
A 6-month trial = 4,380 individual patient hoursOnly ~ 50 hours at a clinical site
4.330 hrs of missed dataPatient and family burden
Costs
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Technology is creating a new research paradigm inside and outside the clinic
22
CTI@TevaClinical Trial Transformati
on
Smart
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Making our trials smart - generating more insights!
23
Social MediaTelemedicineTrainingGamification
Smart PillsBYOD - ePRO Closed loop delivery
Adherence
Biometric Monitoring
Electronic consenting
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In 2017 Teva will incorporate virtual visits (and more) into its existing studies
24Adapted from M Alsumidaie, Applied Clinical Trials 2013
At home drug delivery
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Adding to the expanding Clinical Research Toolbox
Small Molecules and Biologics for Disease Modification
Effective Symptomatic Therapies
Novel Mechanisms, Pathways and delivery methods
Personalized medicine (-omics, imaging)
Biomarkers Throughout the Drug Development Process
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The new clinical research paradigm will disrupt healthcare
New technologies support real time, continuous, self care/monitoring
Source: Tectonic Shifts in Healthcare. James R Mault MD, FACS VP & Chief Medical Officer Qualcommm
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That can be leveraged by ALL stakeholders
Opportunities to Meet Stakeholder Needs
PHYSICIANS PATIENTS
PROVIDERS PAYERS
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Open PRIDE Digital Health Sub-studyA Teva-Intel Collaboration
Case Study
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Background
– Motor symptoms in HD are typically evaluated by physicians using a rating scale; UHDRS-TMS
– Clinician-rated scales are inherently subjective and may lead to intra- and inter-rater variability, and to a substantial placebo effect
– Easy-to-use digital health solutions can supplement clinical evaluation by providing rich, reliable, and sensitive datasets during and between clinic visits
– May allow objective real-time monitoring of symptoms and progression, treatment customization and reduce patient and caregiver burden
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Open-PRIDE Digital Health Sub-study
–Exploratory sub-study– 60 Patients; Enrolment starts in 2016
–Delivery: The HD Algorithm– Detect and quantify Chorea
– Co-Developed by Intel and Teva
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Validation Data Gathering (In-Clinic and @Home)
31
– Devices are continuously collecting data for the entire 6 months days of the trial
– Each device collects 3D accelerometer data that reflects the intensity and direction of movements of the device
Pebble Smart Watch
iPhoneSmartphone
X - Forward
Z - Down
Y - Right
Major hurdle for algorithm development: Filter normal from abnormal movement
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Open-PRIDE Digital Health Sub-study*
32
Manage the Disease using Data
Data for Analysis
Researcher
INSIGHT / VALUE
Patient and Clinician Clinically
Meaningful Data
Smart Watch
Smart Phone interface
Disease platform
Big Data Analytics
(*) Almost Virtual; A Medical IoT Setting
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The mobile application
Medication diary Pop-up reminders Testing Instructions
Chorea severity rating
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In-Clinical Assessments
1.Timed Up and Go (TUG) test2.Sitting at rest (2 minutes) with arms
relaxed3.Sitting at rest (1 minute) with arms
extended 4.Standing at rest (30 seconds) 5.Ten Meter Walking Test6.Drinking from a cup test (repetitive
5 motions)7.Pronation-supination test (30 secs)
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At-Home Assessment
Assessment Tasks1. Sitting at rest (2 min) with arms
relaxed2. Standing at rest (30 sec)
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THANK YOU!
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“Digital Biomarkers” for Huntington's Disease using Multiple Body affixed,
Light weight Sensors
Sensor MD Team†
University of Rochester
†Represented by: Gaurav Sharma
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MC10 BioStampRC Sensor: Specifications and Advantages
Mode Sampling Rate
Dynamic Range
Recording Time (Max)
Accelerometer (Accel.)
31.25,50,100,200 Hz
2,4, or 8G 8-35 hours
ECG 125,250 Hz 0.2 V 17 hours
EMG 250 Hz 0.2 V 17 hours
Accel.+ECG 50 Hz(Accel.),125, 250 Hz (ECG)
2,4, or 8G(Accel), 0.2V
(ECG)
11-22 hours
Accel.+EMG 50 Hz(Accel.) 2,4, or 8G(Accel), 0.2V
(EMG)
11 hours
Gyro.+Accel. 25,50,100,250 2,4,8,16 2-4 hoursHz G(Accel) Off,
250,500,1000,2000 /sec(Gyro)
● Light weight (7 grams)
● Unobtrusive, body affixable
● Low power
● Long recording time
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Pilot Study Overview
● Focus on motor symptoms in Huntington's and Parkinson's Diseases (HD/PD)●
●
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10 HD, 4 pHD, 16 PD, and 15 Controls enrolled Five accelerometers for each participantIn clinic assessment + two day in home recording
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Body affixed vs Body worn Sensors
More than 93% of participants are●
●
●
●
Comfortable with sensors Experience no interference with daily activitiesPleased with overall experience Ready to re enroll in future
Contrast with body worn sensors●
●
●
●
………...
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Advantages of Multiple Sensors
● Potential for better/more information through
● Targeted selection of individual sensors for analysis
● Joint exploitation across sensors● Allow for effective motion analysis without being invasive to individuals' privacy (as compared to video alternatives)
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Preliminary Study Results
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Lack of Coordination in HD (walk)
Normalized vector cross
correlation of the sensor data
from left leg and right leg for
control
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Lack of Coordination in HD (walk)
Normalized vector cross
correlation of the sensor data
from left leg and right leg for HD
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Lack of Coordination in HD (walk)
Normalized vector cross
correlation of the sensor data
from left leg and right leg
Control vs HD
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Lack of Coordination in HD (walk)
Scatter plot Control vs HD
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Step Duration Identification
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Effect of Medication on HD
For one individual●
●
On/off TetraBenazine Three 10 m walk tests, each
● Mean step duration (HD off) = 0.67 seconds
● Mean step duration (HD on) = 0.55 seconds
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On/Off Medication for Parkinson'sPatient with severe at rest tremors
Spectrograms of principal acceleration component
On-medication (Levodopa)
Off-medication ((LLevodopa)
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On/Off Medication for Parkinson'sPatient with severe at rest tremors
Relative power in characteristic 5Hz band and first harmonic
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On/Off Medication for Parkinson'sPatient with mild at rest tremors
Spectrograms of principal acceleration component
Off-medication (Levodopa)
On-medication (Levodopa)
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On/Off Medication for Parkinson'sPatient with mild at rest tremors
Relative power in characteristic 5Hz band and first harmonic
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Summary● The time is ripe for broad adoption of sensors health data
analytics●
●
Light weight, body affixed, low power, long duration recording abilities Effective in combination with data analytics/signal processing
●
●
Multiple sensors are advantageous: analysis can target specific individual sensors or exploit jointly
Preliminary analyses show clear signatures of clinically observed motor symptoms in Huntington's and Parkinson's
●
●
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Lack of limb co ordination in HD: apparent in cross correlation analysis between sensors on left and right legsSlowing of gait in HD upon going off medication apparent in auto correlation analysis of chest sensorAt rest tremors in PD apparent in spectral analysis of the hand sensors, also impact of medication
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More Information
● Come see our poster:▪ “Wearable Sensors for the Objective Measurement
of Motor Features of Huntington Disease a Pilot Study”, Jamie Adams et al, Presidential Boardroom A, Nov. 4, 10:30 am 11 am and 2:45 pm 3:15 pm and Nov. 5, 11:30 am – 12:15 pm (Presented by: Mulin Xiong)
▪ Catch us for a conversation▪ We're looking for partners to take the work
further
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Team Members
Karthik Dinesh Mulin Xiong Jamie Adams
Nirav Sheth A.J. Aranyosi Ray Dorsey
Gaurav Sharma
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Thank You