Identification and quantification of both isomers of hexahydrocannabinol, (9 <i>R</i> )‐hexahydrocannabinol and (9 <i>S</i> )‐hexahydrocannabinol, in three different matrices by mass spectrometry
Bibliographic record
Abstract
CONTEXT: -THC), has gained recent attention due to its presence in seized materials across Europe. Sold legally in various forms, HHC poses potential health risks, particularly as a legal alternative to THC in some countries. Despite its historical description in the 1940s, limited toxicology data, pharmacological understanding, and analytical methods for HHC exist. METHOD: This study proposes analytical techniques using mass spectrometry to detect, identify, and quantify (9R)-HHC and (9S)-HHC, concurrently with THC and CBD in various matrices, including oral fluid, whole blood, and seized material. Three distinct methods were employed for different matrices: GC/MS for seized material, GC/MS/MS for whole blood, and UHPLC/MS/MS for oral fluid. Methods were validated qualitatively for oral fluid with a FLOQSwab® device and quantitatively in whole blood and seized material according to Peters et al's recommendations and ICH guidelines. RESULTS: systematically >0.992. These methods were applied to authentic cases, including seized materials and biological samples from traffic control (whole blood and oral fluid). In seized materials, (9R)-HHC levels ranged from 2.09% to 8.85% and (9R)-HHC/(9S)-HHC ratios varied from 1.36 to 2.68. In whole blood sample, (9R)-HHC and (9S)-HHC concentrations were, respectively, 2.38 and 1.39 ng/mL. For all analyzed samples, cannabinoids such as THC and CBD were also detected. CONCLUSION: This research contributes analytical insights into differentiating and simultaneously analyzing (9R)-HHC and (9S)-HHC, using widely applicable mass spectrometric methods. The study emphasizes the need for vigilance among toxicologists, as new semisynthetic cannabinoids continue to emerge in Europe, with potential health implications. The findings underscore the importance of reliable analytical methods for monitoring these compounds in forensic and clinical settings.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".