Healthcare is undergoing a technological transformation, and it is imperative for the industry to leverage new technologies to generate, collect, and track novel data. Panel chaired by Ralf Reilmann of the George Huntington Institut, Muenster.

1. Digital Biomarkers
for Huntington Disease
Friday, November 4
11:15am-12:15pm
Chair:
Ralf Reilmann, MD
George Huntington Institut

2. Presenters
HSG 2016: DISCOVERING OUR FUTURE
Max Little, PhD (virtual attendee)
Aston University
Spyros Papapetropoulos, MD, PhD
Teva Pharmaceuticals
Gaurav Sharma, PhD
University of Rochester

3. Objective measurement of HD
symptoms using smartphones
Dr Max Little (maxl@mit.edu)
Research Director, NumericAnalysis Ltd
Associate Professor, Aston University, UK
Senior Research Fellow, Oxford University, UK
Visiting Associate Professor, MIT, US

4. Smartphones as serious tools
for symptom measurement
Key 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

5. Structured smartphone tests:
hardware and protocol
Raw sensor data collected using specialised
Android smartphone software
Users performed specific test protocols in
clinic:
• Gait, balance (accelerometry)
• Tapping, reaction time (touchscreen)
• Voice (microphone)

6. 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)

7. Objective-HD pilot study cohort
statistics
Age Gender MOCA UHDRS
total motor
score
Controls (N=5) 54 (21) 40% male 28 (1) 0 (0)
HD (N=15) 57 (7) 60% male 21 (4) 42 (13)

8. 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)

9. Balance test results
• Balance dynamic acceleration
magnitude interquartile range
feature
• Discriminates controls from HD
(Cohen’s d=1.2)

10. Touchscreen data pre-
processing
• Left/right tapping clusters identified from x-y touchscreen
coordinates
• Extract: tap timing events, tap placement statistics from
cluster properties

11. Tapping test results
• Tapping time
coefficient of
variation feature
(horizontal)
• Tapping cluster
placement spread
feature (vertical)
• Discriminates
controls from HD

12. 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 UHDRS
points (bottom)

13. 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 smartphonesas
serious tools in clinical and research practice in HD

14. Spyros Papapetropoulos MD, PhD
VP, Neurodegenerative diseases and
Movement Disorders
Implementing Innovation:
Rewiring Clinical Research
October 27th – 30th, 2016 ◦ Boca Raton, Florida

15. Digital Technology Disrupted the World as we knew it
15Yesterday’s advantage will be replaced by today’s trends

16. 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.”

17. Common
Pedometer
Gyroscope
Accelerometer
Geomagnetic
Sleep activity
Heart rate
Specialty
Pulse oximetry
Sun exposure
ECG, EEG, EMG
PK, Respiratory rate
Heart Rate Variability
Pulse rate
Stress
Brain Activity
Sweat
Blood pressure
Skin temperature
Skin conductance
Activity (steps)
Climbing/elevation
Gyroscope
Ambient light sensor
Accelerometer
Gesture
Proximity
Tracking chip
RGB light sensor
Barometric pressure
Outdoor Temperature
Humidity
Voice
Gait
Urine analysis
Weight
Blood Pressure
Glucose
Water quality
Infrared
Outdoor temperature
Voice
Ocular pressure
Other
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

18. 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
18

19. A sad reality – the cascade of drug development

20. 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?

21. The missed opportunities of traditional trials
A 6-month trial = 4,380 individual patient hours
Only ~ 50 hours at a clinical site
4.330 hrs of missed data
Patient and family burden
Costs

22. Technology is creating a new research paradigm
inside and outside the clinic
22
CTI@TevaClinical Trial
Transformation
Smart

23. Making our trials smart - generating more insights!
23
Social MediaTelemedicineTrainingGamification
Smart PillsBYOD - ePRO
Closed loop
delivery
Adherence
Biometric
Monitoring
Electronic
consenting

24. 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

25. 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

26. 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

27. That can be leveraged by ALL stakeholders
Opportunities to Meet Stakeholder Needs
PHYSICIANS PATIENTS
PROVIDERS PAYERS

28. Open PRIDE Digital
Health Sub-study
A Teva-Intel Collaboration
Case Study

29. 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

30. 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
30

31. 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
iPhone
Smartphone
X - Forward
Z - Down
Y - Right
Major hurdle for algorithm
development: Filter normal from
abnormal movement

32. 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 platformBig Data Analytics
(*) Almost Virtual; A Medical IoT Setting

33. The mobile application
Medication diary Pop-up reminders Testing Instructions Chorea severity
rating

34. In-Clinical Assessments
1. Timed Up and Go (TUG) test
2. Sitting at rest (2 minutes) with arms
relaxed
3. Sitting at rest (1 minute) with arms
extended
4. Standing at rest (30 seconds)
5. Ten Meter Walking Test
6. Drinking from a cup test (repetitive 5
motions)
7. Pronation-supination test (30 secs)

35. At-Home Assessment
Assessment Tasks
1. Sitting at rest (2 min) with arms
relaxed
2. Standing at rest (30 sec)
35

36. THANK YOU!

37. “Digital Biomarkers” for Huntington's
Disease using Multiple Bodyaffixed,
Lightweight Sensors
Sensor MD Team†
University of Rochester
†Represented by: Gaurav Sharma

38. 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 0.2V 17 hours
EMG 250 Hz 0.2V 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 hours
Hz G(Accel) Off,
250,500,1000,20
00 /sec(Gyro)
●
Light weight (7 grams)
●
Unobtrusive, body affixable
●
Low power
●
Long recording time

39. Pilot Study Overview
●
Focus on motor symptoms in Huntington's and
Parkinson's Diseases (HD/PD)
●
●
●
10 HD, 4 pHD, 16 PD, and 15 Controls enrolled
Five accelerometers for each participant
Inclinic assessment + two day inhome recording

40. Bodyaffixed vs Bodyworn
Sensors
More than 93% of participants are
●
●
●
●
Comfortable with sensors
Experience no interference with
daily activities
Pleased with overall experience
Ready to reenroll in future
Contrast with body worn sensors
●
●
●
●
…
…
…
...

41. 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)

42. Preliminary Study Results

43. Lack of Coordination in HD (walk)
Normalized
vector cross
correlation of the
sensor data from
left leg and right
leg for control

44. Lack of Coordination in HD (walk)
Normalized
vector cross
correlation of the
sensor data from
left leg and right
leg for HD

45. Lack of Coordination in HD (walk)
Normalized
vector cross
correlation of the
sensor data from
left leg and right
leg
Control vs HD

46. Lack of Coordination in HD (walk)
Scatter plot
Control vs HD

47. Step Duration Identification

48. Effect of Medication on HD
For one individual
●
●
On/off TetraBenazine
Three 10 m walk tests,
each
●
Mean step duration
(HDoff) = 0.67 seconds
●
Mean step duration
(HDon) = 0.55 seconds

49. On/Off Medication for Parkinson's
Patient with severe at rest tremors
Spectrograms of principal acceleration component
On-medication
(Levodopa)
Off-medication
((LLevodopa)

50. On/Off Medication for Parkinson's
Patient with severe at rest tremors
Relative power in characteristic 5Hz band and first harmonic

51. On/Off Medication for Parkinson's
Patient with mild at rest tremors
Spectrograms of principal acceleration component
Off-medication
(Levodopa)
On-medication
(Levodopa)

52. On/Off Medication for Parkinson's
Patient with mild at rest tremors
Relative power in characteristic 5Hz band and first harmonic

53. Summary
● The time is ripe for broad adoption of sensors health data
analytics
●
●
Light weight, bodyaffixed, low power, longduration 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
●
●
●
Lack of limb coordination in HD: apparent in crosscorrelation analysis
between sensors on left and right legs
Slowing of gait in HD upon going off medication apparent in auto
correlation analysis of chest sensor
At rest tremors in PD apparent in spectral analysis of the hand sensors,
also impact of medication

54. 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 am11 am and 2:45 pm3:15 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

55. Team Members
Karthik Dinesh Mulin Xiong Jamie Adams
Nirav Sheth A.J. Aranyosi Ray Dorsey
Gaurav Sharma

56. Thank You