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Dynamic Headspace GC–MS Method to Detect Volatile Extractables from Medical Device Materials

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Abstract

This study addresses the critical issue of detecting volatile extractables released during the clinical use of medical devices which can pose a serious threat to patient health. ISO 10993-18:2020 introduces the concept of the Analytical Evaluation Threshold (AET) to determine the necessary analytical sensitivity for detecting these extractables. Compounds at or above the AET must be reported for toxicological risk assessment. Currently, Static Headspace (SHS) analysis serves as a supplementary technique for volatile analysis, but its variable signal responses and limited sensitivity have hindered the establishment of a defined AET. In response to this challenge, this study explores the potential of Dynamic Headspace (DHS) gas chromatography-mass spectrometry (GC–MS) analysis to achieve the required sensitivity levels for robust toxicological risk assessment of volatile compounds from medical devices. The development of the DHS method involves optimizing several parameters, including incubation temperature, trapping volume/time, adsorbent type, and drying time. The study rigorously evaluates DHS extraction performance and compares it to traditional SHS and Stir Bar Sorptive Extraction (SBSE) GC–MS methods. By applying these newly developed methods to saline extracts from various medical devices and materials, the study demonstrates that DHS achieves over a tenfold increase in sensitivity compared to conventional volatile organic compound (VOC) analysis methods. This enhanced sensitivity enables the quantification of a significantly greater number of VOCs, thereby improving safety assessments for volatiles present in medical devices. With optimized conditions and robust data, DHS methods emerge as a valuable tool for VOC analysis to support comprehensive toxicological risk assessments in the context of medical device safety evaluation.

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Acknowledgements

This research was funded in part by CDRH Critical Path initiative and an appointment to the Research Participation Program administered by the Oak Ridge Institute for Science and Education (ORISE) through an interagency agreement between the U.S. Department of Energy and the U.S. FDA. Special thanks go to Drs. Keaton Nahan, Robert Elder and Adrijo Chakraborti for their help in project initiation and data processing. The authors would like to acknowledge Drs. Michael Eppihimer and Victoria Nawaby for their guidance and support throughout the project.

Disclaimer

Findings and conclusions in this paper have not been formally disseminated by the Food and Drug Administration and should not be construed to represent any agency determination or policy. The mention of commercial products, their sources, or their use in this study is not to be construed as either an actual or implied endorsement of such products by the US Department of Health and Human Services.

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Authors and Affiliations

  1. Center for Devices and Radiological Health, U.S. Food and Drug Administration, 10903 New Hampshire Avenue, Silver Spring, MD, 20993, USA

    Milani Wijeweera Patabandige, Jacob Hill, Amali Herath, Isabella Naimi & Samanthi Wickramasekara

  2. Division of Biology, Chemistry and Materials Science (DBCMS), Office of Science and Engineering Laboratories (OSEL), Center for Devices and Radiological Health (CDRH), US Food and Drug Administration, 10903 New Hampshire Avenue, Bldg. 64, Rm 3064, Silver Spring, MD, 20993, USA

    Samanthi Wickramasekara

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  1. Milani Wijeweera Patabandige
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Correspondence to Samanthi Wickramasekara.

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Wijeweera Patabandige, M., Hill, J., Herath, A. et al. Dynamic Headspace GC–MS Method to Detect Volatile Extractables from Medical Device Materials. Biomedical Materials & Devices (2024). https://doi.org/10.1007/s44174-023-00145-1

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