Characterization of [6]‐gingerol metabolism in rat by liquid chromatography electrospray tandem mass spectrometry
Bibliographic record
Abstract
[6]-Gingerol is a structural analog of capsaicin, an agonist of the transient receptor potential channel vanilloid 1, which is known to have therapeutic properties for the treatment of pain and inflammation. A selective and sensitive quantitative method for the determination of [6]-gingerol by HPLC-ESI/MS/MS was developed. The method consisted of a protein precipitation extraction followed by analysis using liquid chromatography electrospray tandem mass spectrometry. The chromatographic separation was achieved using a Thermo 100 × 2.1 mm C(8) column combined with an isocratic mobile phase composed of acetonitrile, water and formic acid (80:20:0.1) at a flow rate of 250 μL/min. The mass spectrometer was operating in SRM mode and an analytical range set at 20-5000 ng/mL was used to construct a calibration curve in rat plasma. The interbatch precision (%CV) and accuracy (%NOM) observed were 2.9-10.8% and 98.1-102.1% in rat plasma. Similarly, precision and accuracy in rat liver microsomal suspension were also evaluated at nominal concentrations of 1, 25 and 100 μm; the precision (%CV) was <3.4% and the accuracy (%NOM) observed ranged from 89.7 to 109.4%. An in vitro metabolic stability study using rat liver microsomes was performed to determine intrinsic clearance of [6]-gingerol. The results show slow degradation with a T(1/2) of 163 min and relatively low intrinsic clearance suggesting that phase I metabolism may not be a major contributor of the drug clearance. Further analyses were performed to characterize in vitro and in vivo metabolites. Three main phase I metabolites and four phase II metabolites were identified by HPLC-MS/MS and HPLC-MSD TOF. However, the results suggest that glucuronidation of hydroxylated [6]-gingerol is the primary metabolite excreted in rat urine.
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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) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 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".