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Digital Biomarkers for Huntington Disease

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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.

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Digital Biomarkers for Huntington Disease

  1. 1. Digital Biomarkers for Huntington Disease Friday, November 4 11:15am-12:15pm Chair: Ralf Reilmann, MD George Huntington Institut
  2. 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. 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. 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. 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. 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. 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. 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. 9. Balance test results • Balance dynamic acceleration magnitude interquartile range feature • Discriminates controls from HD (Cohen’s d=1.2)
  10. 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. 11. Tapping test results • Tapping time coefficient of variation feature (horizontal) • Tapping cluster placement spread feature (vertical) • Discriminates controls from HD
  12. 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. 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. 14. Spyros Papapetropoulos MD, PhD VP, Neurodegenerative diseases and Movement Disorders Implementing Innovation: Rewiring Clinical Research October 27th – 30th, 2016 ◦ Boca Raton, Florida
  15. 15. Digital Technology Disrupted the World as we knew it 15Yesterday’s advantage will be replaced by today’s trends
  16. 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. 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. 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. 19. A sad reality – the cascade of drug development
  20. 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. 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. 22. Technology is creating a new research paradigm inside and outside the clinic 22 CTI@TevaClinical Trial Transformation Smart
  23. 23. Making our trials smart - generating more insights! 23 Social MediaTelemedicineTrainingGamification Smart PillsBYOD - ePRO Closed loop delivery Adherence Biometric Monitoring Electronic consenting
  24. 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. 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. 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. 27. That can be leveraged by ALL stakeholders Opportunities to Meet Stakeholder Needs PHYSICIANS PATIENTS PROVIDERS PAYERS
  28. 28. Open PRIDE Digital Health Sub-study A Teva-Intel Collaboration Case Study
  29. 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. 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. 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. 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. 33. The mobile application Medication diary Pop-up reminders Testing Instructions Chorea severity rating
  34. 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. 35. At-Home Assessment Assessment Tasks 1. Sitting at rest (2 min) with arms relaxed 2. Standing at rest (30 sec) 35
  36. 36. THANK YOU!
  37. 37. “Digital Biomarkers” for Huntington's Disease using Multiple Bodyaffixed, Lightweight Sensors Sensor MD Team† University of Rochester †Represented by: Gaurav Sharma
  38. 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. 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. 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. 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. 42. Preliminary Study Results
  43. 43. Lack of Coordination in HD (walk) Normalized vector cross correlation of the sensor data from left leg and right leg for control
  44. 44. Lack of Coordination in HD (walk) Normalized vector cross correlation of the sensor data from left leg and right leg for HD
  45. 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. 46. Lack of Coordination in HD (walk) Scatter plot Control vs HD
  47. 47. Step Duration Identification
  48. 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. 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. 50. On/Off Medication for Parkinson's Patient with severe at rest tremors Relative power in characteristic 5Hz band and first harmonic
  51. 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. 52. On/Off Medication for Parkinson's Patient with mild at rest tremors Relative power in characteristic 5Hz band and first harmonic
  53. 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. 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. 55. Team Members Karthik Dinesh Mulin Xiong Jamie Adams Nirav Sheth A.J. Aranyosi Ray Dorsey Gaurav Sharma
  56. 56. Thank You

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