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A framework for the development of targeted gas chromatography mass spectrometry (GC-MS) methods: Synthetic cannabinoids

NCJ Number
304636
Journal
Journal of Forensic Sciences Volume: 66 Issue: 5 Dated: September 2021 Pages: 1908-1918
Author(s)
Edward Sisco; Amber Burns; Arun S. Moorthy
Date Published
September 2021
Length
11 pages
Annotation

This paper outlines a six-step, data-driven, framework to develop and evaluate gas chromatography mass spectrometry (GC-MS) methods for targeted classes of drugs.

Abstract

With the increased presence of novel psychoactive substances (NPSs) in casework, drug analysis has become more challenging. To address these challenges, new screening technologies with improved specificity are being implemented, allowing for the creation and adoption of targeted confirmatory analyses that produce more conclusive results. The process used in the current project emphasizes maximizing retention time differences (to minimize the potential for retention time acceptance windows to overlap) and understanding the trade-offs between sensitivity and reproducibility using a test solution containing pairs of compounds that are difficult to distinguish. The method is then evaluated by expanding the panel of compounds analyzed, identifying limitations in compound discrimination, comparing to current methods, and analyzing representative casework to establish usability. To demonstrate this framework, a method for synthetic cannabinoids was created. The developed method utilizes a DB-200 column and an isothermal temperature program. It was found that sensitivity could be adjusted, without compromising reproducibility, by altering the split ratio and injection volume. The targeted method successfully differentiated 50 cannabinoids based on either retention time differences or mass spectral dissimilarity – determined using a newly developed spectral comparison test. Compared to a general method used for casework, the targeted method was an order of magnitude more sensitive, a minute shorter, and provided major increases in retention time differences. This framework can be implemented and adapted to develop targeted methods for other applications or compound classes. (Publisher Abstract)

Date Published: September 1, 2021