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Technical Brief

The Role of Computational Modeling and Simulation in the Total Product Life Cycle of Peripheral Vascular Devices

[+] Author and Article Information
Tina M. Morrison

Office of Science and Engineering Laboratories,
Center for Devices and Radiological Health,
U.S. Food and Drug Administration,
10903 New Hampshire Avenue,
Silver Spring, MD 20993
e-mail: tina.morrison@fda.hhs.gov

Maureen L. Dreher

Office of Science and Engineering Laboratories,
Center for Devices and Radiological Health,
U.S. Food and Drug Administration,
10903 New Hampshire Avenue,
Silver Spring, MD 20993
e-mail: maureen.dreher@fda.hhs.gov

Srinidhi Nagaraja

Office of Science and Engineering Laboratories,
Center for Devices and Radiological Health,
U.S. Food and Drug Administration,
10903 New Hampshire Avenue,
Silver Spring, MD 20993
e-mail: srinidhi.nagaraja@fda.hhs.gov

Leonardo M. Angelone

Office of Science and Engineering Laboratories,
Center for Devices and Radiological Health,
U.S. Food and Drug Administration,
10903 New Hampshire Avenue,
Silver Spring, MD 20993
e-mail: leonardo.angelone@fda.hhs.gov

Wolfgang Kainz

Office of Science and Engineering Laboratories,
Center for Devices and Radiological Health,
U.S. Food and Drug Administration,
10903 New Hampshire Avenue,
Silver Spring, MD 20993
e-mail: wolfgang.kainz@fda.hhs.gov

1Corresponding author.

Manuscript received August 2, 2016; final manuscript received January 23, 2017; published online May 3, 2017. Assoc. Editor: Marc Horner.This material is declared a work of the U.S. Government and is not subject to copyright protection in the United States. Approved for public release; distribution is unlimited.

J. Med. Devices 11(2), 024503 (May 03, 2017) (5 pages) Paper No: MED-16-1295; doi: 10.1115/1.4035866 History: Received August 02, 2016; Revised January 23, 2017

The total product life cycle (TPLC) of medical devices has been defined by four stages: discovery and ideation, regulatory decision, product launch, and postmarket monitoring. Manufacturers of medical devices intended for use in the peripheral vasculature, such as stents, inferior vena cava (IVC) filters, and stent-grafts, mainly use computational modeling and simulation (CM&S) to aid device development and design optimization, supplement bench testing for regulatory decisions, and assess postmarket changes or failures. For example, computational solid mechanics and fluid dynamics enable the investigation of design limitations in the ideation stage. To supplement bench data in regulatory submissions, manufactures can evaluate the effects of anatomical characteristics and expected in vivo loading environment on device performance. Manufacturers might also harness CM&S to aid root-cause analyses that are necessary when failures occur postmarket, when the device is exposed to broad clinical use. Once identified, CM&S tools can then be used for redesign to address the failure mode and re-establish the performance profile with the appropriate models. The Center for Devices and Radiological Health (CDRH) wants to advance the use of CM&S for medical devices and supports the development of virtual physiological patients, clinical trial simulations, and personalized medicine. Thus, the purpose of this paper is to describe specific examples of how CM&S is currently used to support regulatory submissions at different phases of the TPLC and to present some of the stakeholder-led initiatives for advancing CM&S for regulatory decision-making.

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References

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Figures

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Fig. 1

Total product life cycle of medical devices. Note that the phase “preclinical” refers to evaluations conducted before the clinical evaluation. This could include in vivo animal studies, in vitro bench testing, and in silico models.

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Fig. 2

Four different models (top row) can be used for regulatory evaluation of peripheral intervention and vascular surgery devices. The shading represents our interpretation of how well the models can be used for different aspects of performance, as listed in the left column. Note that while cost and time are not attributes of performance, they are important factors to consider when selecting a model for use as scientific evidence.

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