Computational Modeling and Simulation to get to Market Faster
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The document discusses the advantages of using computational modeling and simulation in the development of medical devices, emphasizing time and cost savings along with enhanced product quality. It highlights critical design questions, the limitations of physical prototypes, and the regulatory implications of preclinical data requirements. The presentation concludes that effective modeling and simulation, augmented by cloud computing, can streamline the regulatory process and provide effective data visualization for better communication of complex concepts.
![A L I T T L E A B O U T M E
A R L E N W A R D
15 years in Medical Device R&D [Valleylab/Covidien/Medtronic]
BS and MS degrees in Mechanical Engineering from University
of Colorado
PhD in Mechanical Engineering from Colorado State University
PHD, PE
Dissertation: “Improvements to Transurethral
Resection of Prostate (TURP) Electrosurgical
Devices through Finite Element Modeling”
Licensed PE in Colorado
17 US and 50+ Worldwide Patents](https://image.slidesharecdn.com/arlenward10xmdtx4518-180411021947/85/Computational-Modeling-and-Simulation-to-get-to-Market-Faster-3-320.jpg)
Computational Modeling and Simulation to get to Market Faster
- 1. M O D E L I N G A N D S I M U L A T I O N T O G E T T O M A R K E T F A S T E R 10X MEDICAL DEVICE/MDTX CONFERENCE SECAUCUS, NJ APRIL 5, 2018 ARLEN K. WARD, PHD, PE
- 2. W H Y A R E W E H E R E ? Leveraging computational modeling and simulation will save time, save money, and result in a demonstrably better product.
- 3. A L I T T L E A B O U T M E A R L E N W A R D 15 years in Medical Device R&D [Valleylab/Covidien/Medtronic] BS and MS degrees in Mechanical Engineering from University of Colorado PhD in Mechanical Engineering from Colorado State University PHD, PE Dissertation: “Improvements to Transurethral Resection of Prostate (TURP) Electrosurgical Devices through Finite Element Modeling” Licensed PE in Colorado 17 US and 50+ Worldwide Patents
- 4. D E S I G N Q U E S T I O N S What power will provide the desired surgical result for our new device concept? What are the performance expectations in human trials for our device that has been developed using animal models? Is our sensor placement optimized? If we change our design to improve the device manufacturability, will it change the surgical effect in unintended ways?
- 5. Physical prototypes are limited to how a technology is implemented, not just what is being tested. The existing DOE approach is also limited to what effects can be discerned within the noisy results of tissue. H o w d o w e t r a n s l a t e d e s i r e d m e d i c a l e f f e c t b a c k t o e n g i n e e r i n g r e q u i r e m e n t s ? T H E P R O B L E M E n g i n e e r i n g M e d i c a l E f f e c t P o w e r G e o m e t r y T i m e D i v i d e T i s s u e H e m o s t a s i s A b l a t e
- 6. W H A T A R E W E T R Y I N G T O A D D R E S S ? Trying to optimize instrument design through in vivo and in vitro tissue testing. T I M E C O N S U M I N G E X P E N S I V E D I F F I C U L T Studies suggest a 30% variation between porcine renal artery sources
- 7. I N S I L I C O P R E C L I N I C A L D A T A G O A L
- 8. S I N G L E P H Y S I C S F E A
- 9. M U L T I P H Y S I C S Complicated real-world problems Interaction between multiple physics Complex material behaviors Multiphysics FEA
- 10. W H Y M O D E L I N G A N D S I M U L A T I O N ?
- 11. SIMULATION REDUCES PROTOTYPING TIME Multiple design parameters + Multiple options for each = Exponential number of iterations
- 12. S E N S I T I V I T Y A N A L Y S I S Design tolerances for production Design options Troubleshooting Cost saving Product development space Technology Evaluation Optimization Monte Carlo simulation Assign distribution to inputs Run hundreds or thousands of iterations Analyze impact on outputs
- 13. R E G U L A T O R Y I M P A C T Increasing preclinical data requirements Equipment interoperability Patient size Increasing preclinical data requirements Field questions from FDA subject matter experts (SMEs) Address with simulation data Cut preclinical testing requirements FDA Guidance- Reporting of Computational Modeling Studies in Medical Device Submissions (Sept 2016) ASME V&V 40- Verification and Validation in Computational Modeling of Medical Devices (est. 2018)
- 14. R E G U L A T O R Y S I M U L A T I O N F R A M E W O R K
- 15. D A T A V I S U A L I Z A T I O N Customers or Nontechnical Audience Explain Device Function Intuitive Grasp of Concepts
- 16. C O M P U T A T I O N A L R E Q U I R E M E N T S Realistic simulations were computationally expensive Supercomputers Clusters ...and financially expensive In-house Experts IT Overhead
- 17. C L O U D R E S O U R C E S
- 18. C A V E A T S F O R M O D E L I N G A N D S I M U L A T I O N Every simulation requires experimental validation and convergence tests Can’t model what we don’t understand
- 19. I M P A C T A N D O P P O R T U N I T Y
- 20. I M P A C T A N D O P P O R T U N I T Y
- 21. T H E T A K E A W A Y Modeling and simulation significantly reduces time to market for medical device development 01. 02. Modeling and simulation can reduce the regulatory load required by the FDA 03. 04. Data visualization is an effective tool for communicating complex concepts Data centers and cloud computing puts computational modeling and simulation within reach of a company of any size
- 22. T H A N K Y O U ! A R L E N W A R D arlen@sysinsighteng.com 720.744.0059 @sysinsight WWW.SYSTEMINSIGHTENGINEERING.COM