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U.S. Food and Drug Administration

Dynamic Headspace Gas Chromatography Mass Spectrometry (DHS-GC/MS) Method for Sensitive Detection of Volatiles in Saline Extracts

Catalog of Regulatory Science Tools to Help Assess New Medical Devices 

This regulatory science tool (RST) is a lab method that is intended to assist in the detection and quantification of volatiles in aqueous extracts using dynamic headspace gas chromatography-mass spectrometry.

Technical Description

This tool offers information on an alternative approach to extract volatiles through dynamic headspace (DHS) and analyze them using gas chromatography/mass spectrometry (GC/MS) techniques. It specifically focuses on screening for volatile extractables in saline extracts of medical devices and device materials, as outlined in the associated peer-reviewed publication [1]. The tool provides step by step procedures on sample preparation, sample extraction, and data processing, including identification and semi-quantification information for the compounds found in the extracts, to enhance the user-friendly application of this tool.

Intended Purpose 

This tool provides a method for analysis of volatiles sensitive to detection and quantification in aqueous device extracts.

Testing

  • Dynamic headspace (DHS) method developments were conducted using a GERSTEL Multi Purpose Sampler (MPS) with headspace capabilities, attached to an Agilent 7890B GC-5977B MS system. The DHS extraction was optimized based on incubation temperature, trapping volume/ time, adsorbent type, drying time, and split ratio for high volume (5 mL) samples. Details on optimized parameters are listed with Appendix A.
  • The tool was assessed using the following volatile standard mixtures:
    • Residual Solvents Class 3 – Mix A (24 components, 5000 µg mL⁻¹ each suspended in N, N-Dimethylformamide)
    • 8260B MegaMix Calibration Mix (76 components, 2000 µg mL⁻¹ each suspended in P & T Methanol)
  • Method performance was compared with commonly used static headspace (SHS) GC-MS analysis. More than an order of magnitude sensitivity was obtained with the method developed in this RST compared with the SHS method.
  • The RST was tested using saline extracts of various medical device materials such as Poly (acrylonitrile-butadiene styrene) blend (ABS), nitrile butadiene (BUNA) rubber sheets, Polyvinyl chloride (PVC) pellets containing 10% nonionic detergent, 0.25% Zinc dibutyldithiocarbamate (ZDBC) polyurethane films and high-density polyethylene (HDPE).
  • Additionally, medical devices/components such as nasal oxygen cannula (NOC) and nebulizer parts (Neb), which included mouthpiece, tee, flexhose, and kink-resistant tubing were also extracted and analyzed using the optimized DHS method as described in Wijeweera et al.’s Dynamic Headspace GC–MS Method to Detect Volatile Extractables from Medical Device Materials [1].

Limitations

The functionality of the tool is restricted to the analysis of volatiles found in aqueous extracts from devices or volatile standard mixtures that are diluted in aqueous solvents such as saline. It is not suitable for the analysis of extracts using semi-polar or non-polar solvents, nor for assessing elemental impurities. The tool has been specifically optimized and qualified for a maximum volume of 5 mL of extracts in the headspace vial (20 mL). Increased aqueous sample volumes can lead to inlet pressure shutdown due to trapped water vapor freezing in the inlet causing interruptions to the sequence run. Careful selection of optional drying parameters and incorporation of thermal desorption blank runs between sample runs can alleviate the issue. To enhance the precision and accuracy of the RST and to establish an analytical evaluation threshold (AET), it is recommended to include additional volatile organic compounds (VOCs) in the analytical process. 

Supporting Documentation

  1. 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 2, 1125–1142 (2024). https://doi.org/10.1007/s44174-023-00145-1
  2. Appendix A

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