Validation of a Microsampling-Compatible LC-MS/MS Method for Cannabinoid Quantitation in Whole Blood
Bibliographic record
Abstract
Abstract Recently developed dried blood sampling methods for cannabinoid quantitation require small blood volumes, making them microsampling-compatible, but have notable limitations including hematocrit-related bias for dried blood spots (DBS) and higher consumable costs for volumetric absorptive microsampling (VAMS®). To address these issues, we developed a highly sensitive liquid chromatography–tandem mass spectrometry (LC-MS/MS) method capable of quantifying cannabinoids in 50 µL of liquid whole blood, providing a practical microsampling alternative to dried blood approaches. Using liquid–liquid extraction (LLE) with sodium hydroxide alkalinization and acetonitrile precipitation, followed by quantitative analysis on an Agilent 6495 liquid chromatography-triple quadrupole (LC-TQ) mass spectrometer, we achieved lower limits of quantitation (LLOQs) of 0.10 ng/mL for Δ9-tetrahydrocannabinol (THC) and cannabinol (CBN), 0.20 ng/mL for cannabigerol (CBG), 0.30 ng/mL for cannabidiol (CBD), 0.50 ng/mL for 11-hydroxy-THC (11-OH-THC), and 5.0 ng/mL for 11-nor-9-carboxy-THC (THC-COOH). Calibration was linear from the LLOQ to 300 ng/mL for all analytes. To our knowledge, this is the first validated approach for cannabinoid quantitation in less than 100 µL of liquid whole blood with an LLOQ for THC comparable to that with the most sensitive LC-MS/MS methods using standard blood volumes. The achieved LLOQs for other cannabinoids are also suitable for forensic toxicology applications. We anticipate particular utility for obtaining evidence from suspected impaired drivers at the roadside when paired with finger-prick sampling and liquid blood microcollection tubes.This approach enables measurement of THC levels at the time of driving and thereby overcoming current limitations, including the decrease in THC levels that occurs if blood sampling is delayed, the requirement for larger sample volumes (≥100 µL), and dependence on trained phlebotomists for venipuncture.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.005 | 0.006 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.002 |
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 source (direct Gemma or distilled Codex), 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".