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Virtual dose mapping for radiation sterilization

Award Information
Agency: Department of Health and Human Services
Branch: Food and Drug Administration
Contract: 2R44FD007584-02
Agency Tracking Number: R44FD007584
Amount: $661,773.00
Phase: Phase II
Program: SBIR
Solicitation Topic Code: FDA
Solicitation Number: PA22-176
Timeline
Solicitation Year: 2022
Award Year: 2023
Award Start Date (Proposal Award Date): 2023-09-01
Award End Date (Contract End Date): 2025-08-31
Small Business Information
39655 Eureka Drive
Newark, CA 94560-4806
United States
DUNS: 184609621
HUBZone Owned: No
Woman Owned: No
Socially and Economically Disadvantaged: No
Principal Investigator
 TOBIAS FUNK
 (510) 592-3000
 tfunk@tripleringtech.com
Business Contact
 BRIAN WILFLEY
Phone: (650) 906-7829
Email: bwilfley@tripleringtech.com
Research Institution
N/A
Abstract

Project Summary
This grant is in response to PA-22-176, an omnibus call from NIH, FDA, and CDC. Therein the FDA expresses
the need to develop computer modelling tools that help with regulatory decision making.
Sterilization is the terminal step of medical device development, and sterilization validation is a regulatory re-
quirement. We are developing a Monte Carlo modelling tool that can produce virtual dose maps for radiation
sterilization, thereby helping medical companies make important decisions about sterilization that will also affect
the regulatory pathway early in the medical device development cycle. As modelling only requires a CAD model
of the device and not a manufactured device (which is required for dose mapping at a contract sterilizer), mod-
elling can be used early to iterate on the design of the device and its packing. Such a modelling tool will allow
medical device companies to shorten and de-risk the design process with respect to sterilization.
Monte Carlo modelling libraries such as Geant4 have the ability to accurately model radiation transport and dose
deposition. Because of this, Geant4 is widely used in high energy physics and radiation treatment, but so far it
is rarely used for radiation sterilization. The reason for this is the lack of user friendliness, which stems from its
flexible architecture and the slow simulation speeds. Furthermore, Geant4 does not provide the ability to load
full CAD models, and thus makes it rather cumbersome to model entire medical devices.
In Phase I of this FDA SBIR grant, we showed that Geant4 can be deployed in the cloud. We have developed a
prototype of a web user interface to configure simulations and to review modeled results. Our cloud tool can load
full CAD models and accurately reproduce measured dose maps. We showed a 100x increase of simulation
speed compared to a single computer due to seamless scaling in the cloud.
In Phase II we will build on our accomplishments of Phase I and extend this tool to be capable of modeling dose
maps from X-ray and gamma sterilization. We will develop parametric radiation source models that can be cali-
brated with qualification data provided by the contract sterilizer. We will refine our user interface and give the
user the ability to compare the different radiation sterilization technologies. We will also implement the ability to
model virtual dosimeters to facilitate direct comparison with measurements. In user studies we will show that the
tool is user-friendly and can be used to streamline decisions necessary for regulatory filings.
Our modelling tool will be geared towards engineers at medical device companies and does not require an
advanced background in software engineering or radiation physics. Rather, the engineer will be guided through
simple steps through the simulation process. The engineer can review the results in a user-friendly viewer and
determine the dose uniformity ratio. We anticipate that our tool will offer significant benefits to the medical device
development community.

* Information listed above is at the time of submission. *

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