Towards scientific validated digital biomarkers measured by patient's own smart devices: cases studies from Parkinson's disease and Multiple Sclerosis
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1 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 pred ISCTM Autumn 2018 Conference, Marina Del Rey, 15 October 2018
2 Why «Digital» in Clinical Development? Digital is new normal! Digital Operational Efficiency Digital Translational Science
3 Continuous data from mobile sensors Collect, process, analyse and add to clinical knowledge Light Touch Connectivity GPS Accelerometer Gyroscope Magnetometer Sound Sensors And more 2 *** * 1 0 Data processing & analysis -1 H C Clinical knowledge
4 Two case studies Parkinson s Disease (PD) Remote Monitoring Distributed February 2015 Multiple Sclerosis (MS) Remote Monitoring Distributed November 2016
5 RG7935/PRX002 Ph1 Parkinson s disease case study 44 subjects completed daily assessments for 6 months, starting Feb Tremor Daily Active Tests Bradykinesia Rigidity/Postural Instability Phonation Postural Rest Tapping Balance Walking Active Tests Provided phone Passive Monitoring Gait Motor behavior in everyday life Mobility Secure storage and data processing Transferred by WIFI
6 Active Test Example 1: Gait How does the incoming data look like? Active Tests Voice Tremor Dexterity Bradykinesia: Lower Body Phonation Rest Postural Tapping Balance Walking Active Tests Passive Monitoring Motor behavior in everyday life Gait Mobility
7 Accelerometer and gyroscope data from Gait test Illustrative example
8 Active Test Example 2: Balance How sensitive are sensors in a normal Smartphone? Active Tests Voice Tremor Dexterity Bradykinesia: Lower Body Phonation Rest Postural Tapping Balance Walking Active Tests Passive Monitoring Motor behavior in everyday life Gait Mobility
9 Acceleration forward/backwards Acceleration forward/backwards Balance: Visualizing sway Illustrative example Healthy tester: not much sway Patient: a lot of sway Acceleration left/right Acceleration left/right
10 Sensor data feature (mean over 2 weeks) Skewness of acceleration magnitudes Sensor measures correlate with clinical gold standard (MDS-UPDRS) Example: Rest Tremor 3 2 *** * H C Physician rating for Rest Tremor (MDS-UPDRS)
11 Sensor data feature (mean over 2 weeks) Skewness of acceleration magnitudes 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) H C Physician rating for Rest Tremor (MDS-UPDRS)
12 Sensor data feature (mean over 2 weeks) Skewness of acceleration magnitudes 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 H C Physician rating for Rest Tremor (MDS-UPDRS)
13 Sensor feature: Time from tap to tap (s) Parkinson s disease case study Continuous measurement picks up treatment effect fast and accurately Test: Dexterity Gait Feature: Tapping Time Stride-Time p-value <0.001 <0.001
14 Acceleration sensor Unlocking insights from passive monitoring data Routinely using machine learning and high-performance computing to extract unprecedented insights? Time
15 Acceleration Unlocking insights from passive monitoring data Routinely using machine learning and high-performance computing to extract unprecedented insights Trained with 50 hours of activity data (categorized datasets) 90 mins to process GB Human Activity Recognition Model Time
16 S T S (p e r h o u r) STS transitions per hour Measuring effects of disease on everyday motor behavior Activity in daily life outside the clinic: Parkinson s patients differ from controls 2.0 Sit-to-stand transitions * * * Augmentation Healthy control H C Parkinson s disease P D
17 RG7935/PRX002 Ph1 Digital Biomarker analysis First research article published in Movement Disorders
18 Acknowledgements
19 The Roche PD Mobile Application V2 was just presented at MDS 2018 meeting (Hongkong, October 6)
20 Two case studies Parkinson s Disease (PD) Remote Monitoring Distributed February 2015 Multiple Sclerosis (MS) Remote Monitoring Distributed November 2016
21 Floodlight See beyond the surface
22 Identifying sub-clinical disease & progressing MS 365 days/year with active tests and passive monitoring 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. Legend Day in the life of a patient with chronic stable symptoms Day with a visit to the clinic/physician Day with worsening symptoms Patients recall period
23 Day FLOODLIGHT study design 60 patients with MS, 20 controls Site visit Week Mulero et al Annual Meeting of the Consortium of Multiple Sclerosis Centers, May 24-27, Poster QL19, New Orleans, Louisiana
24 Day FLOODLIGHT study design Timed 25-Foot Walk (T25-FW) Berg Balance Scale (BBS) Nine hole peg test (9HPT) Oral Symbol Digit Modalities Test (SDMT) Various Clinical/PRO Rating Scales Site visit Clinical/PRO rating scales Week Mulero et al Annual Meeting of the Consortium of Multiple Sclerosis Centers, May 24-27, Poster QL19, New Orleans, Louisiana
25 Day FLOODLIGHT study design Daily Mood Question (DMQ) Symptom Tracker (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) Site visit Clinical/PRO rating scales Active test Week Mulero et al Annual Meeting of the Consortium of Multiple Sclerosis Centers, May 24-27, Poster QL19, New Orleans, Louisiana
26 Day FLOODLIGHT study design Daily Mood Question (DMQ) Symptom Tracker (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) Site visit Clinical/PRO rating scales Active test Week Mulero et al Annual Meeting of the Consortium of Multiple Sclerosis Centers, May 24-27, Poster QL19, New Orleans, Louisiana
27 Day FLOODLIGHT study design Gait Behaviour Mobility Pattern Site visit Clinical/PRO rating scales Active test Passive monitoring Week Mulero et al Annual Meeting of the Consortium of Multiple Sclerosis Centers, May 24-27, Poster QL19, New Orleans, Louisiana
28 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 provides novel insights beyond clinical scales
29 Average # of tests per week Average daily hours of passive monitoring (h) Adherence to active tests and passive monitoring is good and stable over 24 weeks Active tests Passive monitoring Active tests Active tests (excluding Two-Minute Walking Test) Two-Minute Walking Test Smartphone Smartwatch Study week Study week Mulero et al Annual Meeting of the Consortium of Multiple Sclerosis Centers, May 24-27, Poster QL19, New Orleans, Louisiana
30 Smartphones allow for modernized and remote assessments Example 1: Pinching test Squeeze a Shape Smartphone-based task Clinical anchor 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
31 Pinching test discriminates healthy controls from MS patients with normal hand/arm function p<0.001; * p<0.05 9HPT= 9-hole peg test; MS= multiple sclerosis Montalban et al ECTRIMS Meeting, October, Berlin, Germany
32 Smartphones allow for modernized and remote assessments Example 2: Turning speed in 5 U-Turn Test (5UTT) Smartphone-based task Clinical anchor Timed 25 Foot Walk 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
33 Turning speed in U-turns while walking correlates with Timed 25-Foot Walk Test at baseline (and also with Expanded Disability Status Scale) T25-FW: Timed 25 Foot Walk Montalban et al ECTRIMS Meeting, October, Berlin, Germany
34 Month Augmentation: An example journey of a patient with MS in the FLOODLIGHT trial EDSS: 3.5; T25-FW: 4.9s Screening visit (patient skipped active test) Day in Month FEB MAR APR MAY JUN JUL 12 week follow up EDSS: 3.5; T25-FW: 6.6s Termination visit EDSS: 3.5; T25-FW: 10.3s Smartphone reported relapse onset (PRO) A day in the study Site visit Reported relapse onset EDSS: Expanded Disability Status Scale T25-FW: Timed 25 Foot Walk Mulero et al ECTRIMS-ACTRIMS Meeting, October, Poster P1226, Paris, France
35 Month Augmentation: An example journey of a patient with MS in the FLOODLIGHT trial EDSS: 3.5; T25-FW: 4.9s Screening visit (patient skipped active test) Day in Month FEB MAR APR MAY JUN JUL 12 week follow up EDSS: 3.5; T25-FW: 6.6s Smartphone reported relapse onset (PRO) Termination visit EDSS: 3.5; T25-FW: 10.3s 5UTT U-Turn speed Performance ( /second)* Good (> 79.4) Average (67.3 < x 79.4) Poor ( 67.3) Test not performed * Performance based on patient s 5 U-Turn Test (5UTT) U-Turn speed distribution Mulero et al ECTRIMS-ACTRIMS Meeting, October, Poster P1226, Paris, France
36 Acknowledgements
37 Digital Biomarkers rapidly built into clinical research programs Feb Parkinson s disease Phase 1 Multiple Sclerosis (Floodlight) Parkinson s disease Phase 2 Spinal Muscular Atrophy Huntington s disease
38 Working with the community to build sets of robust digital outcome measures Parkinson s disease Multiple Sclerosis
39 FDA launched Program to apply Digital Health to Drugs We re also working to develop digital biomarkers as drug development tools. to enable these opportunities, we need clear policies for how the review and validation of digital health tools can be baked into drug development programs.
40 Doing now what patients need next
41
42 Doing now what patients need next
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