Computational modeling and simulation (CM&S) has the potential to revolutionize medical devices by accelerating innovation and providing comprehensive evidence of long-term safety. For example, CM&S can provide performance benchmarks, assess design parameter interdependencies, evaluate a variety of use conditions, provide visualization of complex processes and become a core element of device submissions and approvals. This presentation will begin with an overview of the use of CM&S throughout the orthopaedic implant lifecycle, followed by a review of the current regulatory direction regarding the use of CM&S in device submissions. Next, a series of case studies based on a variety of orthopaedic implants will demonstrate the application of CM&S at various phases of the product lifecycle in more detail. The examples will also highlight the effects of modeling assumptions on model credibility and some verification and validation best practices. This presentation will position CM&S as a credible and common means for device companies and FDA to demonstrate the safety of medical devices, and thereby ensure safety, reduce cost and accelerate the pathway toward “first in the world” access to products in the U.S.

2. 14 June, 2017
Leveraging Computational
Modeling and Simulation for
Device Design
Marc Horner, Ph.D.
Technical Lead, Healthcare
ANSYS, Inc.
Mehul Dharia
Principal Research Engineer
Zimmer Biomet

3. •This session will review the following aspects of computational
modeling and simulation (CM&S) as it relates to the total
product lifecycle of orthopaedic products:
–Review CM&S throughout the orthopaedic implant lifecycle
–Overview of the regulatory direction regarding CM&S for
device submissions
–Examples of ways in which computer modeling transforms
product development, including examples that demonstrate
the contemporary regulatory framework
–Opportunities and challenges in the use of computer models
Takeaways

4. 4
Phases in the Design Cycle
• Conceptualization
• Concept Development
• Verification & Validation
• Marketing Claims
• Post-Market Evaluation

5. 5
Simulation in the Design Cycle
• Conceptualization
– Anatomical fit
• Verification & Validation
– Strength (Performance)
– Contact Mechanics (Wear)
– Disassociation (Constraints, Locking mechanisms)
– Stability (Fixation)
– MRI, Packaging, etc.
• Surgical Guidance
– Optimal use of product
• Marketing Claims
– Comparison of designs (“selling” the Science)
• Post-Market Evaluation
– Evaluate unforeseen situations
Implant heating during MRI
Relationship
between implant
position and µ-
motion
Verma et al.
Pre-ORS (2014)

6. Regulatory Pathway
for CM&S

7. 7
Addressing Regulatory Uncertainty
Computational modeling was established as a center-level initiative
by CDRH in 2011.

8. • Leverage “Big Data” for regulatory decision-making
• Modernize biocompatibility and biological risk evaluation of device materials
• Leverage real-world evidence and employ evidence synthesis across multiple
domains in regulatory decision-making3
• Advance tests and methods for predicting and monitoring medical device
clinical performance
• Develop methods and tools to improve and streamline clinical trial design
• Develop computational modeling technologies to support regulatory
decision-making
• Enhance the performance of Digital Health and medical device cybersecurity
• Reduce healthcare associated infections by better understanding the
effectiveness of antimicrobials, sterilization and reprocessing of medical
devices
• Collect and use patient input in regulatory decision-making
• Leverage precision medicine and biomarkers for predicting medical device
performance, disease diagnosis and progression
2017 Regulatory Science Priorities
“Design for Clean”
MDDTs

9. 9
Model Reporting
* issued September 20, 2016
Summarizes information to be included in a CM&S Report
Scope:
•Fluid Mechanics and Mass Transport
•Solid Mechanics
•Electromagnetics and Optics
•Ultrasound
•Heat Transfer
Report Sections:
•Governing Equations • System Properties
•System Conditions • System Discretization
•Numerical Implementation • Validation

11. 11
Standards Committee
– Provide procedures for assessing and
quantifying the accuracy and credibility of
computational models and simulations.
ASME V&V Standards Committee
V&V in Computational
Modeling and Simulation
V&V 10 - Verification and
Validation in Computational
Solid Mechanics
V&V 20 - Verification and
Validation in Computational
Fluid Dynamics and Heat
Transfer
V&V 30 - Verification and
Validation in Computational
Simulation of Nuclear System
Thermal Fluids Behavior
V&V 40 - Verification and
Validation in Computational
Modeling of Medical Devices
V&V 50 - Verification and
Validation of Computational
Modeling for Advanced
Manufacturing

12. 12
V&V 40 Charter
– Provide procedures to standardize
verification and validation for
computational modeling of medical
devices
– Charter approved in January 2011
Motivating Factors
– Regulated industry with limited ability to
validate clinically
– Increased emphasis on modeling to
support device safety and/or efficacy
– Use of modeling hindered by lack of V&V
guidance and expectations within medical
device community
ASME V&V 40 Overview
V&V in Computational
Modeling and Simulation
V&V 10 - Verification and
Validation in Computational
Solid Mechanics
V&V 20 - Verification and
Validation in Computational
Fluid Dynamics and Heat
Transfer
V&V 30 - Verification and
Validation in Computational
Simulation of Nuclear System
Thermal Fluids Behavior
V&V 40 - Verification and
Validation in Computational
Modeling of Medical Devices
V&V 50 - Verification and
Validation of Computational
Modeling for Advanced
Manufacturing

13. The V&V40 guide outlines a process for making risk-informed
determinations as to whether a computational model is
credible for decision-making for a specified context of use.
Risk-Informed Credibility Assessment
Framework

14. The question of interest describes the specific question, decision or
concern that is being addressed.
Context of use defines the specific role and scope of the computational
model used to inform that decision.
Question of Interest
and Context of Use

15. Model risk is the possibility that the model
may lead to a false/incorrect conclusion about
device performance, resulting in adverse
outcomes.
- Model influence is the contribution of the
computational model to the decision relative
to other available evidence.
- Decision consequence is the significance
of an adverse outcome resulting from an
incorrect decision.
* Blood pump image courtesy Mark Goodin, SimuTech Group
Risk Assessment

16. Model credibility refers to the
trust in the predictive
capability of the computational
model for the COU.
Trust can be established
through the collection of V&V
evidence and by
demonstrating the applicability
of the V&V activities to
support the use of the CM for
the COU.
Credibility Factors
Verification Validation
Applicability
Code Solution Model Comparator
Output
Assessment
SoftwareQuality
Assurance
NumericalAlgorithm
Verification
DiscretizationError
UseError
NumericalSolverError
SystemConfiguration
SystemProperties
BoundaryConditions
GoverningEquations
SampleCharacterization
ControlOverTestConditions
MeasurementUncertainty
Equivalencyofinputand
outputtypes
Rigorof
OutputComparison
Relevanceofthe
QuantitiesofInterest
Applicabilityto
theContextofUse
Credibility Assessment

17. Examples

18. The Path Forward
Assessing
Computational Model
Credibility through
Verification and
Validation:
Application to
Medical Devices
currently in DRAFT form
“Develop computational modeling technologies
to support regulatory decision-making”
Hierarchical
ValidationofCM&S

19. Examples

20. 20
Conceptualization
Anatomical Fit
• “Better conform to anatomy” → “Better clinical outcomes”
• ZiBRA*:
– Morphological Analysis
– Statistical Shape Analyses
– Automated Landmark Detection & Virtual Surgery
– Component Placement Optimization
– Implant Fit Assessment
• Extensive digital anatomic library
– Captures ethnic and gender variation across the global population
– Caucasian / African American / European / Indian / Chinese / Japanese / Korean
Zimmer Biomet Internal Software

21. 21
Anatomical Fit
Tibial Baseplate
• Compromise Between
– Proper Rotation (kinematics)
– Minimum Overhang (impingement)
– Optimal Coverage (stability)
• Subtle shape differences between ethnicities and genders
Dai et al, J Ortho Res 31; 2013

22. 22
Anatomical Fit
Tibial Baseplate
optimizes the “compromise” between kinematics,
impingement and fixation aspects
Zimmer Biomet Persona
Tibial Baseplate
• One design for the global population

23. Strength Testing
Total Ankle Replacement (TAR)

24. 24
Strength Testing
Based on Standard
TKA Tibial Baseplate THA Stem
• What if a Standard is not specific enough?
ASTM F1800-12 ISO 7206-4

25. 25
Total Ankle Replacement
Strength Testing
•Standard provides guidance
– Does not provide specifics for strength testing
•Method
– Develop biomechanical loading rationale
– Input to Simulation
– Determine worst case condition from simulation
– Develop test
Trabecular Metal
(TM)
Trabecular Metal
(TM)
Talar
Component
Tibial Tray
HXPE
Zimmer Biomet
Trabecular Metal Total Ankle
Dharia et al, World Congress of Biomechanics, 2014
Talus
Tibia

26. 26
Biomechanical Input
Forces & Kinematics
• Joint Forces Axial Compressive Load
•Flexion/Extension Internal/External Rotation
•Anterior/Posterior Translation
– obtained from Bell et al., 1997
Seireg & Arvikar, J Biomech, 1975
Procter, J Biomech, 1982
Anderson et al, J Biomech, 2001
Stauffer et al, Clin Orthop Rel Res, 1977
Lamoreux , Bull Prosthet Res, 1971
Bahr et al, Knee Surg, 1998
Singer et al, JBJS, 2013
Stauffer et al, Clin Orthop Rel Res, 1977

27. 27
Biomechanical Input
Load and Motion Curves
• Combined Loading
Dharia et al, Ortho. Research Society, 2013

28. 28
Physiological Model
Tibia & Talus
Dharia et al, Ortho. Research Society, 2013
Model
Tibia
Model
Talus
Model

29. 29
Tibial Insert
Stress Results
Individual Components
Dharia et al, World Congress of Biomechanics, 2014
Tibial Baseplate
41% 45%
Talus Component

30. 30
Fatigue Test
Physiologically Motivated Inputs
• Test Orientations
– 41% & 45% Gait Positions for Tibia & Talus assemblies
– Apply axial load
– 10 Mc test
Dharia et al, World Congress of Biomechanics, 2014
Tibia Talus

31. 31
Foot
Physiologically Motivated Inputs ??
• Hallux Valgus
– Open Wedge Osteotomies
• Osteotomy Cut, Open Wedge
• Place Spacer/Implant(s)
• Loading??
www.arthrex.com
Defect Correction

32. 32
Musculoskeletal Model
Loading through 1st Metatarsal
• Kinematic Foot Model
– 26 segments (bones)
– Contains bones, muscles, ligaments, joints
– 75 Forces through 1st Metatarsal
Al-Munnajed et al, J Biomech Eng., March 2016, Vol. 138
Y
Z
X
Ligaments Muscles
Dharia et al, BMES/FDA Frontiers in Medical Device, 2016

33. 33
Patient & Surgical Variability
Surgical Guidance
• 5 Osteotomy Planes
– Defined using the ZiBRATM Anatomical Modeling System*
•Neutral (N): perpendicular to long axis
•5° in abduction (AB)
•5° in adduction (AD)
•5° in dorsiflexion (DF)
•5° in plantar-flexion (PF)
Dharia et al, BMES/FDA Frontiers in Medical Device, 2016
*Bischoff et al., ASME/FDA Frontiers in Medical Devices, 2013
Compressive
Force
Flexion/Extension
Moment

34. 34
Proximal Tibial Locking Plate
Optimal Screw Configurations
• Potential Screw Configurations
– Models A & D has hole 6 unsecured
Dharia et al., Orthopaedic Research Society, 2006

35. 35
Optimal Screw Configurations
Surgical Guidance
• Maximum Principal Stress
– Peak stress at unsecured hole 6 in Models A & D.
Dharia et al., Orthopaedic Research Society, 2006

36. Contact Mechanics
Total Ankle Replacement (TAR)

37. 37
Contact Mechanics
Contact Area & Pressure (CAREA/CPRESS)
• Edge Loading
– Cause
•Deformity, V/V Malalignment, Congruency
– Effect
– Point or edge loading on polyethylene
– Increased wear
– Catastrophic failure
Easley, JBJS Am 2011;93:1455-1468
Espinosa, JBJS Am 2010 Laflamme, AOFAS 2012Assal, F&A Intl 2003

38. 38
Test Setup
ASTM F 2665-09
– Contact Area and Contact Pressure should be
determined at various flexion angles
• 0°, ±10°, ±15° tibiotalar flexion angles
•800 N load
AP View ML View
Dharia et al, World Congress of Biomechanics, 2014

39. 39
Results
CAREA/CPRESS
• Mean Contact Area
• Contact Pressure - Comparison to Predicate
Contact
Area
Contact
Pressure
New Design
Predicate Design
Dharia et al, World Congress of Biomechanics, 2014

40. 40
How are these Results Relevant?
CAREA/CPRESS
– Does not represent physiological condition - tested at constant 800N load.
– All the load and motion profiles (IE, AP, Axial loads etc.) are not captured at
the tested flexion angles.
– The known worst case gait position (41%) is not tested.
– Simulation can provide better insights.
Contact Area Contact Pressure
Dharia et al, World Congress of Biomechanics, 2014

41. 41
CAREA/CPRESS Comparison
Neutral Implantation
• Comparison to Predicates
– Fixed Bearing and Mobile Bearing
Fixed Bearing
Predicate
Mobile Bearing
Predicate
Dharia et al, American Orthopaedic Foot & Ankle Soc., 2011 Dharia et al, American Orthopaedic Foot & Ankle Soc., 2013

42. Fixation

43. 43
Micromotion
Reverse Shoulder Arthroplasty
• Stability predictions in RSA
Zimmer Biomet Comprehensive Reverse Shoulder System
Subsidence
Lift-off
Normalized
Micromotion
Dharia et al, Intl Society of Technology & Arthroplasty, 2016

44. 44
Total Ankle Replacement
Clinical Outcomes
• Low Survivability
– 78% to 95% @ 5 years
– Revision rate >double of THA, TKA
•High Revision Rates (loosening)
– 26% (Australian Registry, 2013)
– 48% (New Zealand Registry, 2013)
– 50% (Swedish Registry, 2013)
– 68% (Daniels et al., 2014)
• Design Features Affecting Loosening
– Fixation features (Keel etc.)
– Fixation Approach (cemented, cementless)
– Bony Support
Bonnin et al., 2004; Henricson et al, 2007; Hosman et al., 2007
Labek et al., 2011
Bischoff et al., Orthopaedic Research Society, 2016

45. 45
Bony Support
Flat vs Anatomical Cut
• Assumption: ↑Bony Support, ↑Stability, ↑Load Transfer
• Hypothesis: Anatomical Cut → ↑Bony Support
– ↑Bony Density (HU); ↑Surface Area (SA)
•CT Data: ~0.5mm slice thickness
Brigido and DiDomenica, 2016
Source Ethnicity Talus count Tibia count Matched pairs
Total cohort Caucasian, Korean,
Japanese, Indian
N=52
34M / 18F
N=81
56M / 25F
N=30
23M / 7F
Bischoff et al., Orthopaedic Research Society, 2016

46. 46
Bony Support
Method
• Tibia
• Talus
•Output
– Normalized HU (Density)
– Normalized SA (surface area)
– Normalized Bony Support (HU*SA)
Articulation
axis
2mm depth
4mm depth
6mm depth
Resection depth
defined based on high
point of talar dome
Resection depth defined
based on distal
center of tibia
6mm depth
4mm depth
2mm depth
Anatomic
HU↑
HU ↓FlatFlat
Bischoff et al., Orthopaedic Research Society, 2016

47. 47
Bony Support
Results
Observations:
1.Boney support is statistically significantly increased for anatomic cuts relative to
flat cuts at all cut depths, for tibia and talus
2.Depth of cut most significantly influences boney support for flat cuts of talus
(~90% increase from 2-6mm), attributed to increased SA with depth
Tibia Talus
Bischoff et al., Orthopaedic Research Society, 2016

48. Disassociation
Locking Mechanism (Knee)

49. 49
Background
Tibial Tray Anterior Liftoff
•Locking Mechanism strength
– Disassociation of Tibial Component
from Tibial Tray
– Measure Tibial Component Lift-off
distance
•Question of Interest
– Does the locking mechanism of a
posterior-stabilized TKA design have
sufficient strength to withstand
posteriorly directed loads?
Zimmer Biomet TKA

50. 50
Tibial Tray Anterior Liftoff
Scope
• 2 Tray Sizes
– Small & Medium
• Model & Experiment
• Output Comparison
– Rank Order & Absolute Values
Rail
Height
Anterior
Rail
Rail Height
Tibial Tray
Tibial
Spine
3°
Posterior
Slope
Articular
Surface
Dharia et al, ASME Verification & Validation Symposium, 2014

51. 51
Tibial Tray Anterior Liftoff
Model & Experiment
• Model Experiment
Load on anterior
tibial spine
Dharia et al, ASME Verification & Validation Symposium, 2014

52. 52
Tibial Tray Anterior Liftoff
Results
• The ratio (Medium/small) of predicted
versus measured load compared within
2.2%.
– Model is validated for Rank Ordering sizes
• Model vs Exp Absolute Values
– 1.5% for medium
– 3.5% for small
– Model is validated to use in lieu of testing
• Submit 510(k) of new (similar) design
– Outcome?
Size Measured Force (N) Predicted Force (N) % difference
Medium Average 744.1 733 1.5%
Small Average 426.6 412 3.5%
Ratio, medium/small 1.74 1.78 2.2%
Dharia et al, ASME Verification & Validation Symposium, 2014
ModelExperiment

53. 53
Tibial Tray Anterior Liftoff
V&V 40 Approach
• How Good is Good Enough?
– Depends on COU
– Risk informed credibility requirement
• What is the Decision Consequence?
• What is the Model Influence?
– What additional information is available?
• Predicate device
• Testing on predicate device and/or new device
– Plan V&V activities accordingly
• Computer Model & Comparator (e.g. Experiment)

54. 54
Context Of Use (COU)
Tibial Tray Anterior Liftoff
•Differentiation
– Based on additional information available (outside of model)
•Predicate device, Benchtop Testing
• COU1, Performance evaluation without testing: The tibial component anterior
liftoff is evaluated exclusively using the computational model.
• COU2, Performance evaluation with testing: The model is used to predict the
worst-case size across the proposed product portfolio in terms of tibial component
anterior liftoff, and this worst case is then physically tested.
• COU3, Superiority evaluation without testing: The model is used to predict the
tibial component anterior liftoff across all sizes in the proposed product portfolio,
with no associated benchtop testing. Results are benchmarked against similar
modeling results from a successful predicate device.
No Predicate Device Predicate Device
None COU1 COU3
Worst Case COU2 COU4 (a,b)
Matrix of Proposed COUs
Existence of Predicate Device
Benchtop Testing

55. 55
Context Of Use (COU)
Tibial Tray Anterior Liftoff
• COU4, Superiority evaluation with testing: Model predictions of tibial
component anterior liftoff are supported by benchtop testing, and evaluation of the
proposed product portfolio is benchmarked against that of a predicate device.
– This may occur in multiple ways.
No Predicate Device Predicate Device
None COU1 COU3
Worst Case COU2 COU4 (a,b)
Matrix of Proposed COUs
Existence of Predicate Device
Benchtop Testing

56. 56
Context Of Use (COU)
Examples
• COU1: Tibial component liftoff is evaluated exclusively using the computational
model. No predicate device exists to compare with the computed results. No
bench testing will be performed for this device.
• COU2: A worst case size of a new design family will be determined for tibial
component liftoff using computational model, which will then be tested in
laboratory to ensure that it meets functional requirements. No predicate device
exists.
• COU3: Tibial component liftoff of new device and a predicate device is evaluated
using the computational model. No bench testing will be performed.
• COU4a: A worst case size of a new design family will be determined for tibial
component liftoff using computational model, which will then be tested in
laboratory to compare with test results of a predicate device.
• COU4b: A worst case size for a new and a predicate design will be determined for
tibial component liftoff using computational model. The worst design will then be
tested in laboratory to ensure that it meets functional requirements.
No Predicate Device Predicate Device
None COU1 COU3
Worst Case COU2 COU4 (a,b)
Matrix of Proposed COUs
Existence of Predicate Device
Benchtop Testing

57. 57
Model Risk
•Decision Consequence
– Revision Surgery
• Independent of model
•Model Influence
– LOW: Results from the model are a negligible factor in the decision associated
with the question being answered. (COU4)
– HIGH: Results from the model are the primary factor in the decision associated
with the question being answered (COU1)
Lower
Higher
COU1
COU1
COU1-4
COU4
COU4

58. 58
V&V Activities
Credibility Factors
•Two modeling assumptions
– Polyethylene Material
– Component Size & Locking Region
Geometry
Credibility Factors
Software Quality Assurance
Numerical Code Verification
Discretization Error*
Numerical Solver Error
Use Error
Model Form
Model Input
Test Samples
Test Conditions
Equivalency of Input
Parameters
Output Comparison*
Relevance of the Quantities of
Interest *
Relevance of the Validation
Activities to the COU*
Applicability
Activities
Verification
Code
Calculation
Validation
Computational
Model
Comparator
Assessment

59. 59
V&V Activities
Model Validation – Model Form
•Constitutive polyethylene material
model
– Several material models available in literature
– How does selected material model impacts
model predictions
• May not justify further quantification
• May have to try one or more material models
to:
– Quantify impact on predictions
– Increase confidence that decision related to COU
is not impacted by material model selection
Lower
Risk
Higher
Risk
Credibility Factors
Software Quality Assurance
Numerical Code Verification
Discretization Error
Numerical Solver Error
Use Error
Model Form
Model Input
Test Samples
Test Conditions
Equivalency of Input
Parameters
Output Comparison
Relevance of the Quantities of
Interest
Relevance of the Validation
Activities to the COU
Applicability
Activities
Verification
Code
Calculation
Validation
Computational
Model
Comparator
Assessment

60. 60
V&V Activities
Model Validation – Model Input
System Configuration
•Component Size
•Variation in Locking Region Geometry
– Sensitivity Analyses on Tolerance in
individual component size
• Nominal dimensions
• LMC, MMC
• LMC, MMC
– Both Tibial Component and Tbial Tray
– All component sizes
Lower
Risk
Higher
Risk
Credibility Factors
Software Quality Assurance
Numerical Code Verification
Discretization Error
Numerical Solver Error
Use Error
Model Form
Model Input
Test Samples
Test Conditions
Equivalency of Input
Parameters
Output Comparison
Relevance of the Quantities of
Interest
Relevance of the Validation
Activities to the COU
Applicability
Activities
Verification
Code
Calculation
Validation
Computational
Model
Comparator
Assessment

61. 61
V&V Activities
Model Validation – Model Input
System Conditions
•Insertion of Poly Tibial into Metal Tray
• No Interference Fit
• Interference Fit to capture residual stress
• May have to model the insertion process
Quantify the sensitivity of the modeling assumptions
on modeling predictions
Lower
Risk
Higher
Risk
Credibility Factors
Software Quality Assurance
Numerical Code Verification
Discretization Error
Numerical Solver Error
Use Error
Model Form
Model Input
Test Samples
Test Conditions
Equivalency of Input
Parameters
Output Comparison
Relevance of the Quantities of
Interest
Relevance of the Validation
Activities to the COU
Applicability
Activities
Verification
Code
Calculation
Validation
Computational
Model
Comparator
Assessment

62. 62
V&V Activities
Comparator Validation – Test Samples
•Quantification of locking region
geometry
• Use production parts
• Inspect key parameters
– Understand which tolerance band is tested
• Specifically produce parts
– At targeted dimension within tolerance
band
Lower
Risk
Higher
Risk
Credibility Factors
Software Quality Assurance
Numerical Code Verification
Discretization Error
Numerical Solver Error
Use Error
Model Form
Model Input
Test Samples
Test Conditions
Equivalency of Input
Parameters
Output Comparison
Relevance of the Quantities of
Interest
Relevance of the Validation
Activities to the COU
Applicability
Activities
Verification
Code
Calculation
Validation
Computational
Model
Comparator
Assessment

63. 63
V&V Activities
Validation Assessment – Equivalency of Input Parameters
•Tibiofemoral Contact
•Tibial Tray – Poly Contact
• Apply load through contact patch
– Use Constraints to mimic Tray
• Model Femoral & Tibial Tray as a rigid body
• Model the femoral and Tibial Tray component
Lower
Risk
Higher
Risk
Credibility Factors
Software Quality Assurance
Numerical Code Verification
Discretization Error
Numerical Solver Error
Use Error
Model Form
Model Input
Test Samples
Test Conditions
Equivalency of Input
Parameters
Output Comparison
Relevance of the Quantities of
Interest
Relevance of the Validation
Activities to the COU
Applicability
Activities
Verification
Code
Calculation
Validation
Computational
Model
Comparator
Assessment

64. • Computational modeling is extensively used throughout the total product life cycle.
– Not just to simulate testing, but also to “drive” test methods
• With advancement in computational technologies (both h/w and s/w), CM&S is expanding
to several “non-traditional” disciplines (MRI labeling, drop-testing, morphological analysis,
patient-specific modeling, etc.)
• Researchers are already working on developing tools for using modeling as a surrogate for
clinical studies (in silico patients) and innovative manufacturing processes, such as additive
manufacturing
• FDA guidance is already available for reporting computational modeling studies in the
regulatory submissions.
• After 6+ years of efforts involving multiple members from academia, FDA, and industry, a
V&V standard for using computer models in medical devices is expected to release in the
latter half of 2017.
– A similar guidance from FDA is in works as well
• Efforts are ongoing to expand these V&V efforts by involving regulatory bodies outside of
US (important because devices are made for global population)
Conclusions

65. THANK YOU!
marc.horner@ansys.com
mehul.dharia@zimmerbiomet.com