1. Office of Science and Engineering Laboratories Excellence in Regulatory Science Modeling and Simulation (M&S) for Medical Device Innovation: FDA’s Initiatives Tina Morrison, PhD Deputy Director, Division of Applied Mechanics Office of Science and Engineering Laboratories May 23, 2016

2. Office of Science and Engineering Laboratories Excellence in Regulatory Science Center for Devices and Radiological Health (CDRH) Center for Devices and Radiological Health Office of the Center Director Jeffrey Shuren, MD, JD Office of In Vitro Diagnostic and Radiation Health Office of Surveillance and Biometrics Office of Compliance Office of Communication and Education Office of Management Operations Office of Science and Engineering Laboratories Office of Device Evaluation

3. Office of Science and Engineering Laboratories Excellence in Regulatory Science 3 “ The mission of CDRH is to protect and promote the public health. … We facilitate medical device innovation by advancing regulatory science, providing industry with predictable, consistent, transparent, and efficient regulatory pathways, and assuring consumer confidence in devices marketed in the U.S.” CDRH Mission

4. Office of Science and Engineering Laboratories Excellence in Regulatory Science To advance the use and increase the acceptance of M&S for regulatory decision making … Need better communication of M&S studies Need continued research to address challenges Need frameworks and approaches for determining the level of evidence needed to support M&S 4 Applicability Framework FDA Guidance Medical Device Development Tools Medical Device Innovation Consortium ASME V&V40 OSEL Research

5. Office of Science and Engineering Laboratories Excellence in Regulatory Science Objective of the Panel Introduce key initiatives where FDA is working side-by-side with industry to advance the use of M&S I.FDA Guidance Documents II.OSEL Research Efforts III.Medical Device Innovation Consortium, Leonardo Angelone, Ph.D. IV.ASME V&V40 Subcommittee Prasanna Hariharan, Ph.D. V.Applicability Framework Pras Pathmanathan, Ph.D. VI.Medical Device Development Tools Program Maureen Dreher, Ph.D. 5

6. Office of Science and Engineering Laboratories Excellence in Regulatory Science Oct 2015 ASME V&V40 1 st Ballot of Guide Oct 2015 ASME V&V40 1 st Ballot of Guide May 2013 MDIC M&S Project App May 2013 MDIC M&S Project App Timeline 6 Jan 2011 ASME V&V40 Formed Jan 2011 ASME V&V40 Formed Jun 2011 FDA RRCM Formed Jun 2011 FDA RRCM Formed Dec 2012 MDIC MOU Signed Dec 2012 MDIC MOU Signed May 2014 MDIC M&S Summit May 2014 MDIC M&S Summit Sep 2014 FDA MDDT Pilot Begins Sep 2014 FDA MDDT Pilot Begins Jan 2014 FDA Draft Guidance Reporting M&S Jan 2014 FDA Draft Guidance Reporting M&S Jan 2013 ASME V&V40 RAM/CAM Jan 2013 ASME V&V40 RAM/CAM Nov 2012 FDA MDDT Draft Guidance Nov 2012 FDA MDDT Draft Guidance Sep 2013 FDA/ASME M&S Conf Sep 2013 FDA/ASME M&S Conf May 2015 FDA/BMES M&S Conf May 2015 FDA/BMES M&S Conf Aug 2014 MDIC Launches Mock Submission Aug 2014 MDIC Launches Mock Submission Summer 2016 FDA Final Guidance Reporting M&S Summer 2016 FDA Final Guidance Reporting M&S Jan 2017 FDA Draft Guidance Credible M&S Jan 2017 FDA Draft Guidance Credible M&S TODAY BMES/FDA M&S Conf TODAY BMES/FDA M&S Conf Dec 2015 FDA MDDT Begins Program Transition Dec 2015 FDA MDDT Begins Program Transition Spring 2017 ASME V&V40 Final Credibility Guide Spring 2017 ASME V&V40 Final Credibility Guide Jun 2013 FDA Public Mtg Credible M&S FDA Library Jun 2013 FDA Public Mtg Credible M&S FDA Library Fall 2016 MDIC M&S Summit Fall 2016 MDIC M&S Summit 2011 2012 2014 20132015 2017

7. Office of Science and Engineering Laboratories Excellence in Regulatory Science OSEL Research Scientists Podium presentations May 24 th Numerical Modeling of Temperature Dynamics During Photoacoustic Tomography: The Role of Melanin Content May 25 th Towards Patient-Specific Modeling of IVC Filter Performance Realistic Electromagnetic Modeling of Deep-Brain Stimulation Patients for MRI Safety Studies OSEL’s Electronic Booth Snapshots of 22 projects on M&S at OSEL 7

8. Office of Science and Engineering Laboratories Excellence in Regulatory Science OSEL Research Scientists Poster presentations  Using Virtual Testing to Evaluate RF Heating of Small Conductive Implants During MRI  Increased RF Heating of Implanted Medical Devices Due to the Ground Plane Effect  Computational Assessment of Implant Fatigue Failure: Application to Coronary Stenting  A Comparison of Community-Based Guidelines and Standards for the Credible Use of Computational Methods in Healthcare 8

9. Office of Science and Engineering Laboratories Excellence in Regulatory Science Objective of the Panel 9 Introduce key initiatives where FDA is working side-by-side with industry to advance the use of M&S I.FDA Guidance II.OSEL Research Efforts III.Medical Device Innovation Consortium, Leonardo Angelone, Ph.D. IV.ASME V&V40 Subcommittee Prasanna Hariharan, Ph.D. V.Applicability Framework Pras Pathmanathan, Ph.D. VI.Medical Device Development Tools Program Maureen Dreher, Ph.D.

10. Office of Science and Engineering Laboratories Excellence in Regulatory Science Medical Device Innovation Consortium Computer Modeling and Simulation Program 10

11. Office of Science and Engineering Laboratories Excellence in Regulatory Science Medical Device Regulatory Evaluation 11 Sources of Scientific Evidence Human Animal Laboratory Computer Human Animal Laboratory Computer Virtual Patient Credit: MDIC Modeling and Simulation Project TODAYFUTURE

12. Office of Science and Engineering Laboratories Excellence in Regulatory Science MDIC M&S Project Goals 1.Advance medical device innovation, and evaluate new and emerging technologies 2.Develop state of the art preclinical methods for assessing device safety and performance 3.Develop novel ways to use clinical data in evaluating medical devices 4.Define, standardize and educate the medical device community on validation requirements for the use of M&S in device development and regulatory submission 12 Heart & Vasculature Orthopedics Blood Damage Neuro Stimulation MR Induced Heating Library of Models/Data Clinical Trials Powered by M&S

13. Office of Science and Engineering Laboratories Excellence in Regulatory Science Virtual Patient with the Medical Device Innovation Consortium Physical Modeling Probabilistic Modeling Clinically Relevant Predictions Credit: MDIC Modeling and Simulation Project 13

14. Office of Science and Engineering Laboratories Excellence in Regulatory Science Leonardo Angelone, Ph.D. Research biomedical Engineer Expertise in electromagnetism, radiofrequency safety and magnetic resonance imaging 14

15. Office of Science and Engineering Laboratories Excellence in Regulatory Science MDIC Magnetic Resonance Heating Working Group Scope: Improve public health: access to MRI for patients with medical devices Monthly calls About 15 participants from ~10 groups Main objectives:  Share information  Identify & Discuss new projects

16. Office of Science and Engineering Laboratories Excellence in Regulatory Science ASME Verification and Validation (V&V 40) (Bardot & Horner) Medical Device Development Tool Program (Lochner, FDA) Joint Working group ISO 10974 (MR labeling for Active implanted Medical Devices at 1.5T) Ongoing research at FDA/CDRH Office of Science and Engineering Laboratories Share Information

17. Office of Science and Engineering Laboratories Excellence in Regulatory Science Model RF coils Model Human Anatomy * EM/SAR across different MRI vendors * EM/SAR across different human model * EM/SAR across different landmarks head Pelvis thorax Legs [Andreas Christ et al., Phys Med Biol 2010] Deliverables: Submission for Medical Device Development Tool A computational modeling framework for tissue heating during MRI: Radiofrequency Exposure maps to evaluate implant heating New projects (FDA Critical Path) Contacts: Leonardo.Angelone@fda.hhs.gov Maria.Iacono@fda.hhs.gov Sunder.Rajan@fda.hhs.gov 17

18. Office of Science and Engineering Laboratories Excellence in Regulatory Science Verification & Validation Computational Modeling of Medical Devices ASME V&V40 Subcommittee is a standards organization with more than 60 industry partners Balloted first document last Fall: Assessing Credibility of Computational Modeling and Simulation Results through Verification and Validation: Application to Medical Devices 18

19. Office of Science and Engineering Laboratories Excellence in Regulatory Science Prasanna Hariharan, Ph.D. Research Mechanical Engineer Expertise in fluid mechanics and heat transfer 19

20. Office of Science and Engineering Laboratories Excellence in Regulatory Science ASME Credibility Assessment Standard Guide for assessing credibility of Computational modeling and simulation to support a decision  How to assess the sufficiency of the V&V activities for a computational model. Key aspects  Risk informed credibility assessment methodology  Rigor of V & V is a function of model risk o Model risk: Possibility of CM&S leading to incorrect decision that might result in an adverse outcome  No discussion about how to do V&V 20

21. Office of Science and Engineering Laboratories Excellence in Regulatory Science ASME Credibility Assessment Standard 21 Risk informed credibility assessment process Example: Is the blood pump safe from hemolysis? Could include other test modalities such as In vitro testing in vivo testing Clinical trials Is the blood pump safe from hemolysis?

22. Office of Science and Engineering Laboratories Excellence in Regulatory Science ASME Credibility Assessment Standard 22 Use CFD to modify pump design to reduce shear stress and hemolysis Other test methods used: In vitro hemolysis testing of the final design COU: Defines the specific role and scope of the CM&S used to address a question of interest. Example: Risk informed credibility assessment process

23. Office of Science and Engineering Laboratories Excellence in Regulatory Science Risk informed credibility assessment process ASME Credibility Assessment Standard 23 Model risk: Risk to the patient if the model results leads to incorrect decision - -Influence: Influence of the model in the decision making process - -Consequence: Consequence to the patient if the model output is wrong? Example: Hemolysis prediction in blood pump Influence: Intent of CM is to change the pump design and minimize the hemolysis; the design change will be finally assessed through in-vitro testing Consequence: Incorrect design could lead to hemolysis during clinical use and patient injury

24. Office of Science and Engineering Laboratories Excellence in Regulatory Science Risk informed credibility assessment process ASME Credibility Assessment Standard 24 Credibility Factors 16 factors identified Verification Are the equations solved correctly Validation How well the computational model replicates the validation experiments Applicability How different is the validation from COU

25. Office of Science and Engineering Laboratories Excellence in Regulatory Science Applicability Framework The context of use of M&S for medical products usually involves the clinical setting and/or patients The inability to perform direct validation hinders the broader acceptance and reliability of computational modeling. Direct validation is a validation study with a comparator that has been closely designed to match to the COU. Indirect validation is a validation study with a comparator for which there are significant differences to the setting of the COU. 25

26. Office of Science and Engineering Laboratories Excellence in Regulatory Science Pras Pathmanathan, Ph.D. Applied Mathematician Expertise in cardiac modeling, verification, validation and uncertainty quantification 26

27. Office of Science and Engineering Laboratories Excellence in Regulatory Science EXAMPLE: MR safety of patients with implanted devices Context of Use (COU) vs Validation It can be very difficult to perform validation of computational models with medical applications The validation setting may be very different to the COU setting, because of ethical, technological or financial reasons Even if validation results are good, it is important to consider the applicability of the model and validation evidence to the COU 27 Lucano et al. IEEE Transactions in Biomedical Engineering, 2016 courtesy of Maria Iacono, FDA COU simulationValidation experiment and simulation

28. Office of Science and Engineering Laboratories Excellence in Regulatory Science Applicability framework – aims 28 Problem: How applicable is the model, and the validation results, to the COU? Problem: How applicable is the model, and the validation results, to the COU? This question is very broad and difficult to answer. Our aim is to develop a systematic and comprehensive approach to answering this question. The proposed framework is used to break down the above question into a series of tractable questions These questions can then be addressed using supporting evidence or subject matter expertise.

29. Office of Science and Engineering Laboratories Excellence in Regulatory Science Overview of applicability framework 29 Validation comparator R-V Model & simulations used for validation M-V Reality being modelled R-COU Model & simulations used for COU M-COU Context of use Sources of Evidence Primary validation evidence Subject matter expertise Historical validation evidence (e.g. for related models) Established theory Other validation evidence ΔMΔM Primary validation evidence ΔRΔR Directly comparable ?

30. Office of Science and Engineering Laboratories Excellence in Regulatory Science Medical Device Development Tools Why are we qualifying tools? 30 Tools considered and evaluated on a case-by-case basis Qualified for regulatory purposes, within a defined context of use TODAYFUTURE

31. Office of Science and Engineering Laboratories Excellence in Regulatory Science Maureen Dreher, Ph.D. Research biomedical engineer Expertise in mechanical durability and degradation 31

32. Office of Science and Engineering Laboratories Excellence in Regulatory Science MDDT Types 32 Clinical Outcome Assessment (COA)   Patient selection   Clinical study outcomes Objective and subjective Non-clinical Assessment Method (NAM)   New method to measure or predict a parameter of interest   Replace/ reduce animal testing Biomarker Test (BIO)   Objective measure of biologic process or response to an intervention   Patient selection   Predict or identify outcomes

33. Office of Science and Engineering Laboratories Excellence in Regulatory Science What is a MDDT Context of Use? 33 Device or Product Area Specific Role of the MDDT Regulatory Evaluation Context of use defines the boundaries within which evidence & justification supports tool use Stage of Development

34. Office of Science and Engineering Laboratories Excellence in Regulatory Science Proposal (PP) - -Submitter (tool developer) proposes tool and FDA determines eligibility for the pilot Pre-Qualification (PQP) - -FDA engages submitter as we evaluate qualification protocol and plans Qualification (QP) - -FDA makes qualification decision based on evidence and COU and works with submitter to make the tool public Current Pilot Process Overview Pilot program implemented September 2014 to pressure test system, inform draft guidance revisions, qualify tools

35. Office of Science and Engineering Laboratories Excellence in Regulatory Science Tool Benefit and Context of Use: Perspectives MDDT Benefit  FDA benefit not clearly communicated  FDA participation in defining benefits Context of Use  Tool not necessarily developed for regulatory use  Broad – difficult to implement tool into regulatory review practice  CDRH can help with COU modifications  Advice  Don’t shy way from iterating the COU 35

36. Office of Science and Engineering Laboratories Excellence in Regulatory Science Qualification Process: Perspectives MDDT developers can have little experience interacting with CDRH And CDRH has limited experience interacting with tool developers  Customer base for tool developers differs from that of medical device industry  Tool developer regulatory resources can be limited  Informal interaction during and between each phase MDDT developers can have limited access to validation data 36

37. Office of Science and Engineering Laboratories Excellence in Regulatory Science NAMs Lead Review: Perspectives CDRH Challenges  Unclear expectations internally on extent of validation data necessary for qualification  Industry raises issues that were not necessarily finalized during initial program conception o For example, software version control MDDT Developer Challenges  Not privy to data supporting device review decisions  Concern regarding proprietary information and public summary of qualification 37

38. Office of Science and Engineering Laboratories Excellence in Regulatory Science Proposals, N = 27 Pre-Qualification, N = 12 Qualification, N = 3 Qualified, N = TBD Current status of MDDT Pilot Common reasons for not advancing to next stage Broad, poorly defined context of use Low public health impact Not used in device evaluation Unclear qualification plan Protocols don’t support context of use Data doesn’t support context of use Disadvantages outweigh advantages

39. Office of Science and Engineering Laboratories Excellence in Regulatory Science Find our posters for more information 39