An efficient design framework for developing and demonstrating high reliability in an autoinjector for medical drug delivery

Greater product reliability is required by the U.S. Food and Drug Administration (FDA) for medical devices designed especially for emergency-use drug delivery systems such as autoinjectors (AIs; EpiPens). To achieve high levels of reliability, manufacturers are implementing design frameworks and their associated methods, which leverage design outcome prediction approaches such as Design for Six Sigma (DFSS) in lieu of the ‘Build, Test, Fix’ (BTF) frameworks that are widely used in the medical device industry. In this context, these two design frameworks, which are currently used by design teams and fit this purpose, are analyzed in the present study. The objective of this study is to compare the frameworks relative to their ability to yield suitably reliable designs and to understand whether changes can be made to these methodologies to improve the time and cost of meeting these high standards. For this purpose, a fundamental shift in how fault tree analysis (FTA) is used to model failures is leveraged to drive the system reliability level to the design targets meeting the mandates of the FDA. The results show the study design outcome prediction framework decreased the time needed to converge on device reliability targets on average by 61% from lead design concept selection to fault model verification in the intervention vs. the control study groups. The findings of this study may contribute to the use of tight mapping of critical parameters to the fault model to develop highly reliability medical device products.

This article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if changes were made. The images or other third party material in this article are included in the article’s Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article’s Creative Commons licence and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this licence, visit http://creativecommons.org/licenses/by/4.0/.