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© 2017-18 Bigfoot Biomedical | Confidential
How Machine Learning
Fuels D-Industry Solutions
Lane Desborough, Co-Founder and...
© 2017-18 Bigfoot Biomedical | Confidential
Disclaimer and Disclosure
• Lane Desborough is an employee of Bigfoot Biomedica...
© 2017-18 Bigfoot Biomedical | Confidential
Bigfoot’s Vision: solutions to reduce burden for PWD, healthcare providers, and...
© 2017-18 Bigfoot Biomedical | Confidential
Assertion 1:
Bigfoot is a “Machine Learning“
(and “Data Science”) company.
© 2017-18 Bigfoot Biomedical | Confidential
PRES0010_20181101
© 2017-18 Bigfoot Biomedical | Confidential
Machine Learning (ML) is a field of artificial
intelligence that uses statistical...
© 2017-18 Bigfoot Biomedical | Confidential
© 2017-18 Bigfoot Biomedical | Confidential
Assertion 2:
Diabetes is a data disease.
© 2017-18 Bigfoot Biomedical | Confidential
Diabetes is a Data Disease
User Data
• Demographic information
• Presentation a...
© 2017-18 Bigfoot Biomedical | Confidential
Assertion 3:
Living with diabetes would be
easy if glucose never varied.
© 2017-18 Bigfoot Biomedical | Confidential
PRES0010_20181101
© 2017-18 Bigfoot Biomedical | Confidential
Assertion 4:
~42 things contribute
to glucose variation.
© 2017-18 Bigfoot Biomedical | Confidential
PRES0010_20181101
© 2017-18 Bigfoot Biomedical | Confidential
Assertion 5:
Experimenting with humans is
dangerous, expensive,
time consuming,...
© 2017-18 Bigfoot Biomedical | Confidential
“Public Shadow Driving is Dangerous. Thousands of accidents, injuries and
casua...
© 2017-18 Bigfoot Biomedical | Confidential
Experimentation is slow, difficult, and potentially dangerous
PRES0010_20181101
© 2017-18 Bigfoot Biomedical | Confidential
Same subject and stimulus, different responses
PRES0010_20181101
© 2017-18 Bigfoot Biomedical | Confidential
Source: Morrison, T. M., Dreher, M. L., Nagaraja, S.,
Angelone, L. M., & Kainz,...
© 2017-18 Bigfoot Biomedical | Confidential
Assertion 6:
Automation transfers variation
from a place where it hurts to a
pl...
© 2017-18 Bigfoot Biomedical | Confidential
Cruise control
Thermostat
Control System
Autopilot
Automated Insulin Delivery
F...
© 2017-18 Bigfoot Biomedical | Confidential
Act
Blood
Glucose
Sense
Decide
Conceptual rendering of research device. Product...
© 2017-18 Bigfoot Biomedical | Confidential
Assertion 7:
You can’t transfer variability
if you don’t know
the nature of the...
© 2017-18 Bigfoot Biomedical | Confidential
Act
Blood
Glucose
Sense
Decide
lost BGM / strips
air in insulin
expired insulin...
© 2017-18 Bigfoot Biomedical | Confidential
Variability in glucose comes from many places
change
across
population
change
t...
© 2017-18 Bigfoot Biomedical | Confidential
Assertion 8:
The best way to build an
automated insulin delivery system
is to fi...
© 2017-18 Bigfoot Biomedical | Confidential
Blood Glucose Physiology
Feedback
Controller
Feedforward
Controller
Insulin
Res...
© 2017-18 Bigfoot Biomedical | Confidential
Replace Actual with Simulation: physiology, behavior, events, activities
change...
© 2017-18 Bigfoot Biomedical | Confidential
Assertion 9:
Simulators may be new to
medical device development,
but they are ...
© 2017-18 Bigfoot Biomedical | Confidential
“Process modeling is the single technology that has had the biggest impact on o...
© 2017-18 Bigfoot Biomedical | Confidential
h"ps://commons.wikimedia.org/wiki/File:3DiTeams_percuss_chest.JPG
Source:
https...
© 2017-18 Bigfoot Biomedical | Confidential
Assertion 10:
Simulations contain models,
which contain data,
which contain var...
© 2017-18 Bigfoot Biomedical | Confidential
Simulations contain models, which contain data, which contain variation.
A Mode...
© 2017-18 Bigfoot Biomedical | Confidential
PRES0010_20181101
© 2017-18 Bigfoot Biomedical | Confidential
Assertion 11:
Diabetes data is not normal.
© 2017-18 Bigfoot Biomedical | Confidential
Lognormal is the new the Normal
PRES0010_20181101
© 2017-18 Bigfoot Biomedical | Confidential
Blood Glucose has a lognormal distribution
1. Job, D., & Eschwege, E. (1976). ...
© 2017-18 Bigfoot Biomedical | Confidential
Diabetes Data is almost always lognormal
1. Blood glucose
2. Insulin use / TDB...
© 2017-18 Bigfoot Biomedical | Confidential
Assertion 12:
Models go through a lifecycle
on their way to providing value.
© 2017-18 Bigfoot Biomedical | Confidential
The life of a model at Bigfoot
Conception - let’s make a model
Inheritance - g...
© 2017-18 Bigfoot Biomedical | Confidential
Data sources
• Primary - data we collect at Bigfoot
• Bigfoot trial
• Operatio...
© 2017-18 Bigfoot Biomedical | Confidential
Assertion 13:
vClinic is Bigfoot’s simulator.
© 2017-18 Bigfoot Biomedical | Confidential
PRES0010_20181101
© 2017-18 Bigfoot Biomedical | Confidential
vClinic
• MATLAB / object oriented
• Very fast simulation
• 1-500 subject-days...
© 2017-18 Bigfoot Biomedical | Confidential
Example
PRES0010_20181101
© 2017-18 Bigfoot Biomedical | Confidential
Assertion 14:
Simulation complements
traditional methods of
system characteriz...
© 2017-18 Bigfoot Biomedical | Confidential
IEC60601-1
Testing
Wireless
Coexistence
Testing
Component
Interaction Analysis...
© 2017-18 Bigfoot Biomedical | Confidential
Assertion 15:
Simulations predict outcomes.
© 2017-18 Bigfoot Biomedical | Confidential
Desborough, L, Naylor, R, Block, J, Buckingham, B, Pinsker, J, Wadwa, P, Forlen...
© 2017-18 Bigfoot Biomedical | Confidential
Assertion 16:
Simulation is hard.
© 2017-18 Bigfoot Biomedical | Confidential
Gotchas
• Purpose
• Explanation vs. Prediction
• Open- vs. Closed-loop
• Implem...
© 2017-18 Bigfoot Biomedical | Confidential
Assertion 17:
FDA is a strong supporter of
Modeling and Simulation.
© 2017-18 Bigfoot Biomedical | Confidential
“The Role of
Computational Modeling
and Simulation in the Total
Product Life Cy...
© 2017-18 Bigfoot Biomedical | Confidential
FDA is a strong supporter of Modeling and Simulation
• Morrison, T. M., Pathman...
© 2017-18 Bigfoot Biomedical | Confidential
*Used with permission,
Dr. Tina Morrison,
OSEL, FDAPRES0010_20181101
© 2017-18 Bigfoot Biomedical | Confidential
✔
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✔
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✔
✔
✔
✔
✔
✔
✔
✔
✔
✔
✔
*Used with permission,
Dr. Tina Morri...
© 2017-18 Bigfoot Biomedical | Confidential
As Modeling and
Simulation matures
and becomes more
widely accepted,
the role o...
© 2017-18 Bigfoot Biomedical | Confidential
Assertion 18:
Simulation enables
simpler, safer designs.
© 2017-18 Bigfoot Biomedical | Confidential
Simulation helps reduce complexity of designs
PRES0010_20181101
© 2017-18 Bigfoot Biomedical | Confidential
Simulation helps reduce complexity of designs
PRES0010_20181101
© 2017-18 Bigfoot Biomedical | Confidential
Simulation helps reduce complexity of designs
PRES0010_20181101
© 2017-18 Bigfoot Biomedical | Confidential
Assertion 19:
Simulation is the
only practical approach
to developing a scalabl...
© 2017-18 Bigfoot Biomedical | Confidential
Modeling and Simulation
With Simulation:
• Rapidly evaluate multiple algorithm
...
© 2017-18 Bigfoot Biomedical | Confidential
Modeling and Simulation
With Simulation:
• Rapidly evaluate multiple algorithm
...
© 2017-18 Bigfoot Biomedical | Confidential
Modeling and Simulation
With Simulation:
• Rapidly evaluate multiple algorithm
...
© 2017-18 Bigfoot Biomedical | Confidential
Assertion 20:
#WeAreNotWaiting
to use Simulation to hasten
the development of o...
© 2017-18 Bigfoot Biomedical | Confidential
1. Bigfoot is a “Machine Learning“ (and “Data Science”) company
2. Diabetes is ...
© 2017-18 Bigfoot Biomedical | Confidential
and Data-Driven!
PRES0010_20181101
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Machine Learning in Diabetes Systems - Lane Desborough, Bigfoot Biomedical - DIABETESMINE UNIVERSITY

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Lane Desborough of Bigfoot Biomedical presents on utilizing Machine Learning to build a diabetes closed loop at DIABETESMINE UNIVERSITY – our new innovation program that encompasses our annual Innovation Summit and D-Data ExChange forums, held Nov.1-2 at UCSF Mission Bay.

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  • here's a video of my talk, with optional subtitles https://www.youtube.com/watch?v=y7cvvwFGESM
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Machine Learning in Diabetes Systems - Lane Desborough, Bigfoot Biomedical - DIABETESMINE UNIVERSITY

  1. 1. © 2017-18 Bigfoot Biomedical | Confidential How Machine Learning Fuels D-Industry Solutions Lane Desborough, Co-Founder and Chief Engineer Conceptual Rendering. Product under development and not for sale in the United States. PRES0010_20181101
  2. 2. © 2017-18 Bigfoot Biomedical | Confidential Disclaimer and Disclosure • Lane Desborough is an employee of Bigfoot Biomedical, Inc. • Bigfoot Biomedical, Inc. does not market or sell any medical devices (yet!). PRES0010_20181101
  3. 3. © 2017-18 Bigfoot Biomedical | Confidential Bigfoot’s Vision: solutions to reduce burden for PWD, healthcare providers, and payers Bigfoot Inject For individuals who wish to control insulin delivery through self- administration of insulin injections Bigfoot Loop For individuals who wish to automate management of diabetes through a fully integrated pump system Conceptual Rendering. Products under development and not for sale in the United States. PRES0010_20181101
  4. 4. © 2017-18 Bigfoot Biomedical | Confidential Assertion 1: Bigfoot is a “Machine Learning“ (and “Data Science”) company.
  5. 5. © 2017-18 Bigfoot Biomedical | Confidential PRES0010_20181101
  6. 6. © 2017-18 Bigfoot Biomedical | Confidential Machine Learning (ML) is a field of artificial intelligence that uses statistical techniques to give computer systems the ability to "learn" (e.g., progressively improve performance on a specific task) from data, without being explicitly programmed. Data Science is a "concept to unify statistics, data analysis, machine learning and their related methods" in order to "understand and analyze actual phenomena" with data. -Wikipedia
  7. 7. © 2017-18 Bigfoot Biomedical | Confidential
  8. 8. © 2017-18 Bigfoot Biomedical | Confidential Assertion 2: Diabetes is a data disease.
  9. 9. © 2017-18 Bigfoot Biomedical | Confidential Diabetes is a Data Disease User Data • Demographic information • Presentation and initial treatment of diabetes • Severe events • Insulin management • Glucose monitoring • General health and life style • Menstrual / pregnancy history • Socioeconomic / education status • Family history of diabetes and other autoimmune diseases Clinical Data • Diagnosis of T1D • History of severe events • Insulin management • Other medications for glucose management • Glucose monitoring • Other medical conditions • Complications • Other medications • Physical exam • Lab values • Healthcare costs Device Data • Glucose trends • Insulin usage • Carb announcements • Temporary mode use • Configuration settings • System interventions • Alarms, Alerts • Location • Sleep • Physical Activity • Heart rate • Movement • Sweat PRES0010_20181101
  10. 10. © 2017-18 Bigfoot Biomedical | Confidential Assertion 3: Living with diabetes would be easy if glucose never varied.
  11. 11. © 2017-18 Bigfoot Biomedical | Confidential PRES0010_20181101
  12. 12. © 2017-18 Bigfoot Biomedical | Confidential Assertion 4: ~42 things contribute to glucose variation.
  13. 13. © 2017-18 Bigfoot Biomedical | Confidential PRES0010_20181101
  14. 14. © 2017-18 Bigfoot Biomedical | Confidential Assertion 5: Experimenting with humans is dangerous, expensive, time consuming, and not very informative.
  15. 15. © 2017-18 Bigfoot Biomedical | Confidential “Public Shadow Driving is Dangerous. Thousands of accidents, injuries and casualties will occur when these companies move from benign and easy scenarios to complex, dangerous accident scenarios. And the cost in time and funding is untenable. One trillion public shadow driving miles would need to be ddriven at a cost of over $300B” - Michael DeKort
  16. 16. © 2017-18 Bigfoot Biomedical | Confidential Experimentation is slow, difficult, and potentially dangerous PRES0010_20181101
  17. 17. © 2017-18 Bigfoot Biomedical | Confidential Same subject and stimulus, different responses PRES0010_20181101
  18. 18. © 2017-18 Bigfoot Biomedical | Confidential Source: Morrison, T. M., Dreher, M. L., Nagaraja, S., Angelone, L. M., & Kainz, W. (2017). The Role of Computational Modeling and Simulation in the Total Product Life Cycle of Peripheral Vascular Devices. Journal of medical devices, 11(2), 024503. Poor for exploring algorithms Extremely expensive Time consuming Poor for extrapolating to normal use conditions Difficult to measure or control other sources of variability PRES0010_20181101
  19. 19. © 2017-18 Bigfoot Biomedical | Confidential Assertion 6: Automation transfers variation from a place where it hurts to a place where it doesn’t hurt as much, in order to make a human’s job easier.
  20. 20. © 2017-18 Bigfoot Biomedical | Confidential Cruise control Thermostat Control System Autopilot Automated Insulin Delivery From (Sense): To (Act): Conceptual Rendering of research device. Product under development and not for sale in the United States. PRES0010_20181101
  21. 21. © 2017-18 Bigfoot Biomedical | Confidential Act Blood Glucose Sense Decide Conceptual rendering of research device. Product under development and not for sale in the United States. PRES0010_20181101
  22. 22. © 2017-18 Bigfoot Biomedical | Confidential Assertion 7: You can’t transfer variability if you don’t know the nature of the variability.
  23. 23. © 2017-18 Bigfoot Biomedical | Confidential Act Blood Glucose Sense Decide lost BGM / strips air in insulin expired insulin occlusion horm ones CGM compression CGM drift illness activity stressm odelm ism atch de-skilling reducedvigilance expired battery carbs m iscalibrated trust lost communication BGM use error site loss lost communication CGM pullout CGM failure external insulin circadian rhythm s mode confusion HMI use error PRES0010_20181101
  24. 24. © 2017-18 Bigfoot Biomedical | Confidential Variability in glucose comes from many places change across population change throughout the day change with activities and events change over time PRES0010_20181101
  25. 25. © 2017-18 Bigfoot Biomedical | Confidential Assertion 8: The best way to build an automated insulin delivery system is to first build a simulation.
  26. 26. © 2017-18 Bigfoot Biomedical | Confidential Blood Glucose Physiology Feedback Controller Feedforward Controller Insulin Response CGM carbs mbg Disturbance Response exercise, illness, stress Setpoint Carb Response + + + + + + + - Algorithms and Simulation Model PRES0010_20181101
  27. 27. © 2017-18 Bigfoot Biomedical | Confidential Replace Actual with Simulation: physiology, behavior, events, activities change across popula.on change over .me change throughout the day change with ac.vi.es and events PRES0010_20181101
  28. 28. © 2017-18 Bigfoot Biomedical | Confidential Assertion 9: Simulators may be new to medical device development, but they are not new to other complex safety critical industries.
  29. 29. © 2017-18 Bigfoot Biomedical | Confidential “Process modeling is the single technology that has had the biggest impact on our business in the last decade” - Frank Popoff, former CEO, Dow Chemical, April 1996 PRO II 1988 • Steady state simulator for plant design Aspen SPEEDUP 1989 • Dynamic simulator for multivariable model predictive control projects Unisim 2007 • Operator training simulator for abnormal situation management my office PRES0010_20181101
  30. 30. © 2017-18 Bigfoot Biomedical | Confidential h"ps://commons.wikimedia.org/wiki/File:3DiTeams_percuss_chest.JPG Source: https://www.flickr.com/photos /rcafimagery
  31. 31. © 2017-18 Bigfoot Biomedical | Confidential Assertion 10: Simulations contain models, which contain data, which contain variation.
  32. 32. © 2017-18 Bigfoot Biomedical | Confidential Simulations contain models, which contain data, which contain variation. A Model is simplified representation used to explain the workings of a real world system Data is the foundation upon which a model is built A Simulation is a collection of models used to imitate the operation of a real-world system over time PRES0010_20181101
  33. 33. © 2017-18 Bigfoot Biomedical | Confidential PRES0010_20181101
  34. 34. © 2017-18 Bigfoot Biomedical | Confidential Assertion 11: Diabetes data is not normal.
  35. 35. © 2017-18 Bigfoot Biomedical | Confidential Lognormal is the new the Normal PRES0010_20181101
  36. 36. © 2017-18 Bigfoot Biomedical | Confidential Blood Glucose has a lognormal distribution 1. Job, D., & Eschwege, E. (1976). Estrogens, lactation and oral glucose tolerance test in the carly puerperium. Journal of Perinatal Medicine-Official Journal of the WAPM, 4(2), 95-99. 2. Koschinsky, T., Dannehl, K., & Gries, F. A. (1988). New approach to technical and clinical evaluation of devices for self-monitoring of blood glucose. Diabetes Care, 11(8), 619-629. 3. Lim, T. O., Bakri, R., Morad, Z., & Hamid, M. A. (2002). Bimodality in Blood Glucose Distribution Is it universal?. Diabetes care, 25(12), 2212-2217. 4. Chase, J. G., Shaw, G., Le Compte, A., Lonergan, T., Willacy, M., Wong, X. W., ... & Hann, C. (2008). Implementation and evaluation of the SPRINT protocol for tight glycaemic control in critically ill patients: a clinical practice change. Critical Care, 12(2), R49. 5. Rodbard, D. (2009). A semilogarithmic scale for glucose provides a balanced view of hyperglycemia and hypoglycemia. Journal of diabetes science and technology, 3(6), 1395. 6. Braithwaite, S. S., Umpierrez, G. E., & Chase, J. G. (2012). Multiplicative surrogate standard deviation: a group metric for the glycemic variability of individual hospitalized patients. Journal of diabetes science and technology, 7(5), 1319-1327. PRES0010_20181101
  37. 37. © 2017-18 Bigfoot Biomedical | Confidential Diabetes Data is almost always lognormal 1. Blood glucose 2. Insulin use / TDBD / TDD 3. HbA1c 4. Treatment response times 5. Carb Ratio 6. Insulin Sensitivity Factor 7. Meal size 8. Carb counting error 9. CGM transmission loss duration 10. Eating speed 11. Bolus frequency 12. Time between boluses 13. Fingersticks frequency 14. Weight 15. Hypo event frequency 16. Hypo hospitalization cost 17. DKA event frequency 18. DKA hospitalization cost 19. Notification frequency PRES0010_20181101
  38. 38. © 2017-18 Bigfoot Biomedical | Confidential Assertion 12: Models go through a lifecycle on their way to providing value.
  39. 39. © 2017-18 Bigfoot Biomedical | Confidential The life of a model at Bigfoot Conception - let’s make a model Inheritance - gather available data and predicates (research papers, models from partners, data, older models) Gestation - rapidly iterate and develop - stakeholder interaction, modeling, visualization, statistics, simulation Birth - publish single page lessons (SPLs), data science reports (DSRs), or engineering notebooks (ENs) Growth - instantiate the model per Bigfoot engineering work instructions (WIs) Develop requirements Create other design, review, and V & V artifacts Implement Test Leave the Nest - release the model to its environment (simulator, algorithm within firmware) PRES0010_20181101
  40. 40. © 2017-18 Bigfoot Biomedical | Confidential Data sources • Primary - data we collect at Bigfoot • Bigfoot trial • Operational • Engineering • Surveys • Secondary - data collected by others • Clinical trials • Observational • Surveys • Tertiary - summaries of data collected by others • Research papers, posters PRES0010_20181101
  41. 41. © 2017-18 Bigfoot Biomedical | Confidential Assertion 13: vClinic is Bigfoot’s simulator.
  42. 42. © 2017-18 Bigfoot Biomedical | Confidential PRES0010_20181101
  43. 43. © 2017-18 Bigfoot Biomedical | Confidential vClinic • MATLAB / object oriented • Very fast simulation • 1-500 subject-days – Laptop • 500-10,000 subject-days – Multicore Laptop / parallel processing • >10,000 subject days, i.e. 100 subjects x 3 years – AWS • Algorithms are THE SAME code as the code that runs on the Firmware • Various ways to invoke • Config files • Dependency injection • Run as inner loop of other programs (BREACH, sensitivity analysis) • Visualization / reporting / animation / metrics postprocessing PRES0010_20181101
  44. 44. © 2017-18 Bigfoot Biomedical | Confidential Example PRES0010_20181101
  45. 45. © 2017-18 Bigfoot Biomedical | Confidential Assertion 14: Simulation complements traditional methods of system characterization.
  46. 46. © 2017-18 Bigfoot Biomedical | Confidential IEC60601-1 Testing Wireless Coexistence Testing Component Interaction Analysis Test Lab Human Factors Formative Testing Human Factors Summative Testing Pivotal Clinical Trial Clinical Research Center Trial Highly Accelerated Life Testing (HALT) Power Use Measurement Security Penetration (Pen) Testing DRM (Design, Reliability, Manufacturability) Test Methods: BOM Analysis, Value Stream Mapping, EMI Testing Water Ingress (IPX) Testing Water Ingress (IPX) Testing BGM Testing Biocompatibility, Leachables, Extractables Testing Sterilization Testing CGM Testing vClinic simulates hours to years, physiology and behavior, from one subject to an entire population
  47. 47. © 2017-18 Bigfoot Biomedical | Confidential Assertion 15: Simulations predict outcomes.
  48. 48. © 2017-18 Bigfoot Biomedical | Confidential Desborough, L, Naylor, R, Block, J, Buckingham, B, Pinsker, J, Wadwa, P, Forlenza, G, O’Brien, R, Lum, J, and B. Mazlish, “Leveraging Modeling and Simulation in the Development of the Bigfoot Biomedical Automated Insulin Delivery System”, Poster, DTM 2017, Bethesda, MD. vClinic predicted CRC results very very well PRES0010_20181101
  49. 49. © 2017-18 Bigfoot Biomedical | Confidential Assertion 16: Simulation is hard.
  50. 50. © 2017-18 Bigfoot Biomedical | Confidential Gotchas • Purpose • Explanation vs. Prediction • Open- vs. Closed-loop • Implementation • Overfitting / Overparametrizing • Insufficient Fidelity • Unnecessary Complexity • Don’t forget • Stoichasticity • Sensitivity Analysis • Verification and Validation 1. Law, Averill M., W. David Kelton, and W. David Kelton. Simulation modeling and analysis. Vol. 3. New York: McGraw-Hill, 2007. 2. Shmueli, Galit. "To explain or to predict?." Statistical science(2010): 289-310. 3. Saltelli, A., Tarantola, S., Campolongo, F., and Ratto, M. (2004). Sensitivity Analysis in Practice - A Guide to Assessing Scientific Models. Wiley. 4. MacGregor, John F., Thomas J. Harris, and J. D. Wright. "Duality between the control of processes subject to randomly occurring deterministic disturbances and ARIMA stochastic disturbances." Technometrics 26.4 (1984): 389-397. 5. Sterman, John D. "A skeptic’s guide to computer models." Managing a nation: The microcomputer software catalog 2 (1991): 209-229. 6. Sargent, Robert G. "Verification and validation of simulation models."Simulation Conference (WSC), Proceedings of the 2010 Winter. IEEE, 2010. 7. Hjalmarsson, H. (2005). From experiment design to closed-loop control. Automatica, 41(3), 393-438. 8. Roy, C. J., & Oberkampf, W. L. (2011). A comprehensive framework for verification, validation, and uncertainty quantification in scientific computing. Computer methods in applied mechanics and engineering, 200(25), 2131-2144. 9. Pianosi, F., Beven, K., Freer, J., Hall, J. W., Rougier, J., Stephenson, D. B., & Wagener, T. (2016). Sensitivity analysis of environmental models: A systematic review with practical workflow. Environmental Modelling & Software, 79, 214-232. PRES0010_20181101
  51. 51. © 2017-18 Bigfoot Biomedical | Confidential Assertion 17: FDA is a strong supporter of Modeling and Simulation.
  52. 52. © 2017-18 Bigfoot Biomedical | Confidential “The Role of Computational Modeling and Simulation in the Total Product Life Cycle of Peripheral Vascular Devices” Morrison, T. M., Dreher, M. L., Nagaraja, S., Angelone, L. M., & Kainz, W. (2017). Journal of medical devices, 11(2), 024503. PRES0010_20181101
  53. 53. © 2017-18 Bigfoot Biomedical | Confidential FDA is a strong supporter of Modeling and Simulation • Morrison, T. M., Pathmanathan, P., Adwan, M., and Margerrison, E. (2018). Advancing Regulatory Science With Computational Modeling for Medical Devices at the FDA's Office of Science and Engineering Laboratories. Frontiers in Medicine, 5. • Morrison T.M. FDA Final Guidance. Reporting on Computational Modeling Studies for Medical Device Submissions. Issued September 21, 2016. https://www.fda.gov/downloads/MedicalDevices/DeviceRegulationandGuidance/GuidanceDocuments/Ucomputational modeling381813.pdf • Morrision, T.M., The emerging regulatory landscape for in silico methods in medical devices, https://figshare.com/articles/The_emerging_regulatory_landscape_for_in_silico_methods_in_medical_devices_- _Presentation_at_the_2018_VPH_Conference/7117391 • Morrison, T.M. Advancing Regulatory Science with Modeling and Simulation at FDA, https://figshare.com/articles/FDA_Grand_Rounds_How_Simulation_Can_Transform_Regulatory_Pathways/7028450 PRES0010_20181101
  54. 54. © 2017-18 Bigfoot Biomedical | Confidential *Used with permission, Dr. Tina Morrison, OSEL, FDAPRES0010_20181101
  55. 55. © 2017-18 Bigfoot Biomedical | Confidential ✔ ✔ ✔ ✔ ✔ ✔ ✔ ✔ ✔ ✔ ✔ ✔ ✔ ✔ ✔ ✔ ✔ ✔ ✔ ✔ ✔ *Used with permission, Dr. Tina Morrison, OSEL, FDAPRES0010_20181101
  56. 56. © 2017-18 Bigfoot Biomedical | Confidential As Modeling and Simulation matures and becomes more widely accepted, the role of clinical trials will transform from EXPLORATION to CONFIRMATION * *Used with permission, Dr. Tina Morrison, OSEL, FDAPRES0010_20181101
  57. 57. © 2017-18 Bigfoot Biomedical | Confidential Assertion 18: Simulation enables simpler, safer designs.
  58. 58. © 2017-18 Bigfoot Biomedical | Confidential Simulation helps reduce complexity of designs PRES0010_20181101
  59. 59. © 2017-18 Bigfoot Biomedical | Confidential Simulation helps reduce complexity of designs PRES0010_20181101
  60. 60. © 2017-18 Bigfoot Biomedical | Confidential Simulation helps reduce complexity of designs PRES0010_20181101
  61. 61. © 2017-18 Bigfoot Biomedical | Confidential Assertion 19: Simulation is the only practical approach to developing a scalable automated insulin delivery system to reduce the burden of living with insulin-requiring diabetes.
  62. 62. © 2017-18 Bigfoot Biomedical | Confidential Modeling and Simulation With Simulation: • Rapidly evaluate multiple algorithm candidates and parameters • Simulate performance of closed loop algorithms in a larger more varied population • Inform design of clinical trial protocols, predict outcomes • Predict performance over months or years of use PRES0010_20181101
  63. 63. © 2017-18 Bigfoot Biomedical | Confidential Modeling and Simulation With Simulation: • Rapidly evaluate multiple algorithm candidates and parameters • Simulate performance of closed loop algorithms in a larger more varied population • Inform design of clinical trial protocols, predict outcomes • Predict performance over months or years of use Plus: • Perform experiments in ways not possible or safe to do in in-vivo clinical trials • No IRB or exclusion criteria are necessary • No recruitment bias PRES0010_20181101
  64. 64. © 2017-18 Bigfoot Biomedical | Confidential Modeling and Simulation With Simulation: • Rapidly evaluate multiple algorithm candidates and parameters • Simulate performance of closed loop algorithms in a larger more varied population • Inform design of clinical trial protocols, predict outcomes • Predict performance over months or years of use Plus: • Perform experiments in ways not possible or safe to do in in-vivo clinical trials • No IRB or exclusion criteria are necessary • No recruitment bias • 4,000,000 times faster and less expensive than real-time (~1 cent per simulated contact-day vs. ~$1,500 per contact-hour) PRES0010_20181101
  65. 65. © 2017-18 Bigfoot Biomedical | Confidential Assertion 20: #WeAreNotWaiting to use Simulation to hasten the development of our systems.
  66. 66. © 2017-18 Bigfoot Biomedical | Confidential 1. Bigfoot is a “Machine Learning“ (and “Data Science”) company 2. Diabetes is a Data Disease 3. Living with diabetes would be easy if glucose never varied 4. ~42 things contribute to glucose variation 5. Experimenting with humans is dangerous, expensive, time consuming, and not very informative 6. Automation transfers variation from a place where it hurts to a place where it doesn’t hurt as much, in order to make a human’s job easier 7. You can’t transfer variability if you don’t know the nature of the variability 8. The best way to build an automated insulin delivery system is to first build a simulation 9. Simulators may be new to medical device development, but they are not new to other complex safety critical industries 10. Simulations contain models, which contain data, which contain variation 11. Diabetes Data is not Normal 12. Models go through a lifecycle on their way to providing value 13. vClinic is Bigfoot’s simulator 14. Simulation complements traditional methods of system characterization 15. Simulations predict outcomes 16. Simulation is hard 17. FDA is a strong supporter of Modeling and Simulation 18. Simulation enables simpler, safer designs 19. Simulation is the only practical approach to developing a scalable automated insulin delivery system to reduce the burden of living with insulin-requiring diabetes 20. #WeAreNotWaiting to use Simulation to hasten the development of our systems PRES0010_20181101
  67. 67. © 2017-18 Bigfoot Biomedical | Confidential and Data-Driven! PRES0010_20181101

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