An Analytical Platform for Near Real-Time Drug Landscape Monitoring using Paraphernalia Residues
Meghan Appley, Liz Robinson, Allison Thomson, Erin Russell, Edward Sisco
Deaths attributed to drug overdoses are constantly on the rise, but drug trends are frequently changing and often differ across geographical regions. Current analytical techniques are limited in their abilities to rapidly identify drugs that would inform both public health and law enforcement officials about the evolving drug landscape. The work presented here outlines an analytical platform that utilizes ambient ionization mass spectrometry and additional techniques (e.g., tandem mass spectrometry) to analyze trace residues from drug paraphernalia to quickly detect both drugs and cutting agents. To demonstrate proof-of-concept, samples collected from syringe service programs throughout the state of Maryland were analyzed by direct analysis in real time – mass spectrometry (DART-MS) to provide rapid, near complete chemical profiles (drugs, cutting agents, and other compounds of interest). To obtain a more complete chemical profile, it was found that a small subset of samples (7.5 %) benefited from additional analysis by direct analysis in real time – tandem mass spectrometry (DART-MS/MS) or liquid chromatography – tandem mass spectrometry (LC-MS/MS). This additional analysis enabled confirmation of the presence or absence of questioned compounds, assisted in identification of new compounds, and provided isomer differentiation without hindering the rapid reporting of results. This analytical platform utilizing DART-MS and, where necessary, tandem mass spectrometry techniques, was found to detect a wide range of drugs and cutting agents in a manner that can better inform public health and public safety personnel about the drug landscape in "near real-time".
, Robinson, L.
, Thomson, A.
, Russell, E.
and Sisco, E.
An Analytical Platform for Near Real-Time Drug Landscape Monitoring using Paraphernalia Residues, Forensic Chemistry, [online], https://doi.org/10.1016/j.forc.2023.100489, https://tsapps.nist.gov/publication/get_pdf.cfm?pub_id=935802
(Accessed September 26, 2023)