Towards scientific validated digital biomarkers measured ...€¦ · Towards scientific validated...
Transcript of Towards scientific validated digital biomarkers measured ...€¦ · Towards scientific validated...
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Towards scientific validated digital biomarkers measured by patient's own smart devices: cases studies from Parkinson's disease and Multiple Sclerosis
Christian Gossens, PhD, MBA, Global Head Digital Biomarkers, Roche pREDISCTM Autumn 2018 Conference, Marina Del Rey, 15 October 2018
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Digital Operational Efficiency
Why «Digital» in Clinical Development?Digital is new normal!
DigitalTranslational Science
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Continuous data from mobile sensorsCollect, process, analyse and add to clinical knowledge
Accelerometer
GyroscopeGPS
Touch Magnetometer
HC 0 1 2 3 4
-1
0
1
2****
Sound
Light
Clinical knowledge
Data processing & analysis
And more
Connectivity
Sensors
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Two case studies
Parkinson’s Disease (PD)Remote Monitoring
Distributed February 2015
Multiple Sclerosis (MS)Remote Monitoring
Distributed November 2016
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RG7935/PRX002 Ph1 Parkinson’s disease case study 44 subjects completed daily assessments for 6 months, starting Feb. 2015
Daily Active Tests
TremorBrady-kinesia
Rigidity/Postural Instability
Phonation Postural Rest Tapping Balance Walking
Active Tests
Passive Monitoring
Motor behavior in everyday life
Gait MobilityProvided phone Secure storage and data
processing
Transferred by WIFI
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Active Test Example 1: GaitHow does the incoming data look like?
Active Tests
Voice Tremor DexterityBradykinesia: Lower Body
Phonation Rest Postural Tapping Balance Walking
Active Tests
Passive Monitoring
Motor behavior in everyday life
Gait Mobility
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Accelerometer and gyroscope data from Gait testIllustrative example
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Active Test Example 2: BalanceHow sensitive are sensors in a normal Smartphone?
Active Tests
Voice Tremor DexterityBradykinesia: Lower Body
Phonation Rest Postural Tapping Balance Walking
Active Tests
Passive Monitoring
Motor behavior in everyday life
Gait Mobility
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Balance: Visualizing swayIllustrative example
“Healthy” tester: not much sway
Acceleration left/right
Acc
ele
rati
on
fo
rwar
d/b
ackw
ard
s
Patient: a lot of sway
Acceleration left/right
Acc
ele
rati
on
fo
rwar
d/b
ackw
ard
s
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Sensor measures correlate with clinical gold standard (MDS-UPDRS)Example: Rest Tremor
Sen
sor
dat
a fe
atu
re (
me
an o
ver
2 w
ee
ks)
Skew
nes
so
f ac
cele
rati
on
mag
nit
ud
es
HC 0 1 2 3 4
-1
0
1
2****
Physician rating for Rest Tremor(MDS-UPDRS)
3
2
1
0
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Frequent sampling enabled measurement of symptoms before/after sporadic clinic visits
‘We only see a snapshot of a patient’s clinical status during the exam –there is so much more we would need to know.’ (Investigator)
HC 0 1 2 3 4
-1
0
1
2****
3
2
1
0
Sen
sor
dat
a fe
atu
re (
me
an o
ver
2 w
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ks)
Skew
nes
so
f ac
cele
rati
on
mag
nit
ud
es
Physician rating for Rest Tremor(MDS-UPDRS)
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Sensors detect significant rest tremor in patients clinically scored as having no tremor (‘0’)
Heightened sensitivity to motor symptoms will help measure progression, especially in prodromal patients
HC 0 1 2 3 4
-1
0
1
2****
3
2
1
0
Sen
sor
dat
a fe
atu
re (
me
an o
ver
2 w
ee
ks)
Skew
nes
so
f ac
cele
rati
on
mag
nit
ud
es
Physician rating for Rest Tremor(MDS-UPDRS)
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Parkinson’s disease case studyContinuous measurement picks up treatment effect fast and accurately
Sen
sor
feat
ure
:Ti
me
fro
m t
ap t
o t
ap (
s)
Test: Dexterity Gait
Feature: Tapping Time Stride-Time
p-value <0.001 <0.001
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Unlocking insights from passive monitoring data Routinely using machine learning and high-performance computing to extract unprecedented insights
?Time
Acc
eler
atio
n s
enso
r
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Unlocking insights from passive monitoring data Routinely using machine learning and high-performance computing to extract unprecedented insights
Time
Acc
ele
rati
on
Human Activity Recognition Model
Trained with 50 hours of activity data (categorized datasets)
90 mins to process 1’200 GB
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Measuring effects of disease on everyday motor behavior Activity in daily life outside the clinic: Parkinson’s patients differ from controls
HC
PD
0 .5
1 .0
1 .5
2 .0
ST
S (
pe
r h
ou
r)
* * *
Sit-to-stand transitions
STS
tran
siti
on
s p
er h
ou
r
Healthy control Parkinson’s disease
Augmentation
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RG7935/PRX002 Ph1 Digital Biomarker analysisFirst research article published in Movement Disorders
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Acknowledgements
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The Roche PD Mobile Application V2was just presented at MDS 2018 meeting (Hongkong, October 6)
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Two case studies
Parkinson’s Disease (PD)Remote Monitoring
Distributed February 2015
Multiple Sclerosis (MS)Remote Monitoring
Distributed November 2016
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FloodlightSee beyond the surface
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Identifying sub-clinical disease & progressing MS 365 days/year with active tests and passive monitoring
Legend
Day in the life of a patient with chronic stable symptoms
Day with a visit to the clinic/physician
Day with worseningsymptoms
Patients’ recall period
365 days in the life of a patient with MS: in current clinical practice a patient may only see their physician twice for around 10 minutes.
Remote monitoring promises to change this. Disease activity can be measured throughout the year, enabling better-informed treatment decisions.
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FLOODLIGHT study design60 patients with MS, 20 controls
Mulero et al. 2017 Annual Meeting of the Consortium of Multiple Sclerosis Centers, May 24-27, Poster QL19, New Orleans, Louisiana
Site visit
Week
Day
1
2
3
4
5
6
7
1 2 4 6 11 14 15 16 19 20 21 247 93 5 8 10 12 13 17 18 22 23
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FLOODLIGHT study design
Mulero et al. 2017 Annual Meeting of the Consortium of Multiple Sclerosis Centers, May 24-27, Poster QL19, New Orleans, Louisiana
Oral Symbol Digit Modalities Test (SDMT)
Various Clinical/PRO Rating Scales
Timed 25-Foot Walk (T25-FW)
Berg Balance Scale (BBS)
Nine hole peg test (9HPT)
Week
Day
1
2
3
4
5
6
7
1 2 4 6 11 14 15 16 19 20 21 247 93 5 8 10 12 13 17 18 22 23
Site visit Clinical/PRO rating scales
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FLOODLIGHT study design
Mulero et al. 2017 Annual Meeting of the Consortium of Multiple Sclerosis Centers, May 24-27, Poster QL19, New Orleans, Louisiana
Week
Day
1
2
3
4
5
6
7
1 2 4 6 11 14 15 16 19 20 21 247 93 5 8 10 12 13 17 18 22 23
Site visit Clinical/PRO rating scales Active test
Daily Mood Question
(DMQ)
SymptomTracker (ST)
Multiple Sclerosis
Impact Scale(MSIS)-29
Symbol Digit Modalities
Test (SDMT)
Pinching Test Draw a Shape Test
Static Balance
Test (SBT)
Five U-Turn Test (5UTT)
Two-Minute Walk Test (2MWT)
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FLOODLIGHT study design
Mulero et al. 2017 Annual Meeting of the Consortium of Multiple Sclerosis Centers, May 24-27, Poster QL19, New Orleans, Louisiana
Week
Day
1
2
3
4
5
6
7
1 2 4 6 11 14 15 16 19 20 21 247 93 5 8 10 12 13 17 18 22 23
Site visit Clinical/PRO rating scales Active test
Daily Mood Question
(DMQ)
SymptomTracker (ST)
Multiple Sclerosis
Impact Scale(MSIS)-29
Symbol Digit Modalities
Test (SDMT)
Pinching Test Draw a Shape Test
Static Balance
Test (SBT)
Five U-Turn Test (5UTT)
Two-Minute Walk Test (2MWT)
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FLOODLIGHT study design
Mulero et al. 2017 Annual Meeting of the Consortium of Multiple Sclerosis Centers, May 24-27, Poster QL19, New Orleans, Louisiana
Week
Day
1
2
3
4
5
6
7
1 2 4 6 11 14 15 16 19 20 21 247 93 5 8 10 12 13 17 18 22 23
Site visit Clinical/PRO rating scales Active test Passive monitoring
Gait Behaviour Mobility Pattern
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Three pillars of our Digital Biomarker analysis
1. Adherence
Patients collect data regularly
2. Agreement
Sensor data correlates with clinical scales
3. Augmentation
Sensor data providesnovel insights beyond clinical scales
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Adherence to active tests and passive monitoring is good and stable over 24 weeks
Active tests
Ave
rage
# o
f te
sts
per
wee
k
Study week
Active testsActive tests (excluding Two-Minute Walking Test)Two-Minute Walking Test
Passive monitoring
Ave
rage
dai
ly h
ou
rs o
f p
assi
ve m
on
ito
rin
g (h
)
Study week
SmartphoneSmartwatch
Mulero et al. 2017 Annual Meeting of the Consortium of Multiple Sclerosis Centers, May 24-27, Poster QL19, New Orleans, Louisiana
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Smartphones allow for modernized and remote assessmentsExample 1: Pinching test “Squeeze a Shape”
Clinical anchorSmartphone-based task
Test rationale:
• To assess fine distal motor manipulation (gripping & grasping, muscle weakness), motor control and impaired hand-eye coordination
Patients are asked to:
• Pinch tomatoes as fast as possible for 30 seconds
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Pinching test discriminates healthy controls from MS patients with normal hand/arm function
Montalban et al. 2018 ECTRIMS Meeting, 10–12 October, Berlin, Germany
‡ p<0.001; * p<0.059HPT= 9-hole peg test; MS= multiple sclerosis
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Smartphones allow for modernized and remote assessmentsExample 2: Turning speed in “5 U-Turn Test” (5UTT)
Test rationale:
• U-Turns can be used to assess certain features of gait and balance
• Smartphone and smartwatch sensors can measure change step counts, speed and asymmetry during U-Turns
Patients are asked to:
• Do at least 5 U-turns while walking between two points
Timed 25 Foot Walk
Smartphone-based task Clinical anchor
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Turning speed in U-turns while walking correlateswith Timed 25-Foot Walk Test at baseline(and also with Expanded Disability Status Scale)
T25-FW: Timed 25 Foot Walk
Montalban et al. 2018 ECTRIMS Meeting, 10–12 October, Berlin, Germany
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Augmentation:An example journey of a patient with MS in the FLOODLIGHT trial
2 4 6 8 14 16 18 20 22 24 26 28 30
FEB
MAR
APR
MAY
JUN
JUL
Mo
nth
Day in Month
10 12
Screening visit(patient skipped active test)
EDSS: 3.5; T25-FW: 4.9s
12 week follow upEDSS: 3.5; T25-FW: 6.6s
A day in the study Site visit
Termination visitEDSS: 3.5; T25-FW: 10.3s
Mulero et al. 2017 ECTRIMS-ACTRIMS Meeting, 25–28 October, Poster P1226, Paris, France
A day in the study Site visit Reported relapse onset
Smartphone reported relapse onset (PRO)
EDSS: Expanded Disability Status ScaleT25-FW: Timed 25 Foot Walk
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2 4 6 8 14 16 18 20 22 24 26 28 30
FEB
MAR
APR
MAY
JUN
JUL
Mo
nth
Day in Month
10 12
Screening visit(patient skipped active test)
EDSS: 3.5; T25-FW: 4.9s
Termination visitEDSS: 3.5; T25-FW: 10.3s
* Performance based on patient’s 5 U-Turn Test (5UTT) U-Turn speed distribution
5UTT U-Turn speed Performance (°/second)*
Good (> 79.4) Average (67.3 < x ≤ 79.4) Poor (≤ 67.3) Test not performed
12 week follow upEDSS: 3.5; T25-FW: 6.6s
Smartphone reported relapse onset (PRO)
Augmentation:An example journey of a patient with MS in the FLOODLIGHT trial
Mulero et al. 2017 ECTRIMS-ACTRIMS Meeting, 25–28 October, Poster P1226, Paris, France
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Acknowledgements
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Digital Biomarkers rapidly built into clinical research programs
2018+Feb 2015 2016
Parkinson’s diseasePhase 1
Multiple Sclerosis(Floodlight)
Parkinson’s diseasePhase 2
Spinal Muscular Atrophy
Huntington’s disease
2017
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Working with the community to build sets of robust digital outcome measures
Parkinson’s disease Multiple Sclerosis
https://www.roche.com/sustainability/open-sourcing-health.htmhttps://floodlightopen.comhttps://www.bloomberg.com/news/articles/2018-10-09/sneaking-into-patients-pockets-one-medical-app-at-a-time
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FDA launched Program to apply Digital Health to Drugs
• “… We’re also working to … develop digital biomarkersas drug development tools.”
• “… to enable these opportunities, we need clear policies for how the reviewand validation of digital health toolscan be baked into drug development programs.”
https://www.fda.gov/NewsEvents/Speeches/ucm605697.htm
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Doing now what patients need next
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Doing now what patients need next