An Ultrafast Ultrahigh-Performance Liquid Chromatography Coupled With Tandem Mass Spectrometry Method for Cannabidiol Monitoring in Pediatric Refractory Epilepsy
Bibliographic record
Abstract
BACKGROUND: Cannabidiol (CBD) is a nonpsychoactive natural product that has been increasingly used as a promising new drug for the management of neurological conditions such as refractory epilepsy. Development of rapid and sensitive methods to quantitate CBD is essential to evaluate its pharmacokinetics in humans, particularly in children. The objective of this work was to develop and validate an ultrafast ultrahigh-performance liquid chromatography coupled with tandem mass spectrometry (UHPLC-MS/MS) method for CBD quantitation that is capable of detecting major CBD and tetrahydrocannabinol (THC) metabolites in the plasma of pediatric refractory epilepsy patients. METHODS: Eight-point CBD calibration curves were prepared using 60 µL of plasma from healthy volunteers. Samples were analyzed in a Shimadzu Nexera X2 UHPLC system, which was coupled to a Sciex QTRAP 6500 mass spectrometer. Chromatography was optimized in acetonitrile (ACN)/water with a 70%-90% gradient of ACN in 2 minutes. Multiple reaction monitoring transitions of major CBD and THC metabolites were optimized in patient plasma. RESULTS: The optimized UHPLC-MS/MS method was validated for the linear range (1-300 ng/mL) of CBD (r2 = 0.996). The limit of quantification and limit of detection were 0.26 and 0.86 ng/mL, respectively. Accuracy and precision met the acceptable validation limits. CBD recovery and matrix effects were 83.9 ± 13.9% and 117.4 ± 4.5%, respectively. The method was successfully applied to quantify CBD and detect the major CBD and THC metabolites in clinical samples. 7-COOH-CBD was the most intensely detected metabolite followed by glucuronide conjugates. CONCLUSIONS: A simple and sensitive method for rapidly monitoring CBD and identifying relevant metabolites was developed. Its applicability in samples from children treated for epilepsy was demonstrated, making it an excellent alternative for performing pharmacokinetic studies.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 0.001 |
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".