Jun 5, 2023 10:45 AM - Jun 5, 2023 11:05 AM, Meena Swaminathan, Chemical Sciences, Section Presentation
The goal of this work is to present a sample preparation method for detecting a variety of cannabis exposure biomarkers in exhaled breath condensate (EBC) and oral fluid (OF) matrices.
The increase in cannabis consumption for its perceived medical benefits and recreational utilization has led many U.S. states to legalize cannabis products. There is a current challenge in forensic toxicology to identify biomarkers of cannabis exposure, other than the commonly detected delta-9-tetrahydrocannabinol (Δ9-THC) and its metabolites, with simple sample collection procedures to evaluate cannabis consumption patterns.
EBC and OF are alternative, non-invasive matrices that hold promise for presence and possible detection of cannabis exposure. EBC consists of condensed water vapor and volatile and non-volatile compounds. Potential advantages of EBC as a matrix include ease of collection and presence of a wide variety of analytes, including markers of respiratory disease, metabolites, and drug compounds. EBC is mainly used in clinical settings and the presence of drugs has been detected, however, there are no reports on cannabinoid detection. OF is currently being explored as a matrix for cannabis exposure analysis. The current sample preparation methods for OF and EBC include the use of protein precipitation, solid phase extraction (SPE), and lyophilization for EBC. However, these approaches require rigorous method development, are time consuming, and utilize instrumentation that may not be widely available.
This work focuses on developing simple and fast sample preparation methods to quantify cannabinoids and metabolites as potential cannabis exposure biomarkers in EBC and OF using a previously optimized liquid chromatography triple quadrupole mass spectrometer (LC-QqQ-MS) method in dynamic multiple reaction monitoring (dMRM) mode. The developed and optimized methods will be employed in the analysis of authentic specimens.
A total of 25 cannabinoids and metabolites were targeted for analysis, including six internal standards. An Agilent 1290 UHPLC coupled to a 6460 LC-QqQ-MS with electrospray ionization in positive mode was utilized for this work. Chromatographic separation used a Zorbax 120 EC-C18 column (3.0 x 100 mm, 1.8 μm) and a step gradient. OF was collected using Quantisal™ (Immunalysis) and EBC was collected using RTube™ (Respiratory Research) devices. After collection of OF, the pads were placed in either the provided buffer, or 3 mL cold (-20˚C) acetonitrile (ACN) to evaluate which solvent yielded higher recoveries and reproducibility. Prior to sample preparation, the pads were removed from the buffer or solvent and the solution was filtered. For OF, direct injection, evaporation/reconstitution, and protein precipitation were tested. For evaporation/reconstitution, a 1 mL aliquot was evaporated to dryness and reconstituted in ACN. For protein precipitation, cold ACN was added to a 1 mL aliquot, then evaporated to dryness and reconstituted in ACN. For EBC, evaporation/reconstitution and protein precipitation were tested as described previously with a 500 µL aliquot.
For OF, extraction with ACN followed by evaporation/reconstitution yielded best recoveries and reproducibility. The recoveries ranged from 34-121%, with most >70%, except for cannabigerol monoethyl ether (34%), cannabichromene (48%), and cannabicyclol, cannabinol, and 11-nor-cannabinol-9-carboxy-acid (~60%). For EBC, the mean recoveries were not significantly different for the two sample preparation methods. Recoveries for acidic cannabinoids and metabolites were substantially higher (80-90%) than for the neutral cannabinoids (<10%).
Simple, fast, and reliable sample preparation methods were developed for acidic cannabinoids and metabolites in OF and EBC. These methods will be applied in the detection of cannabis exposure biomarkers in ongoing research involving OF and EBC samples obtained from a human cohort of medicinal and recreational cannabis smokers with different user profiles.