Validation and Application of a Simple UHPLC–MS-MS Method for the Enantiospecific Determination of Warfarin in Human Urine
Bibliographic record
Abstract
A simple and rapid liquid chromatographic-tandem mass spectrometric method has been developed and validated for the enantiospecific determination of R- and S-warfarin in human urine. Warfarin enantiomers were extracted from urine using methyl tert-butyl ether. Chromatographic separation of warfarin enantiomers and the internal standard d5-warfarin was achieved using a Astec Chirobiotic V column with gradient mobile phase at a flow rate of 400 µL/min over 10 min. Detection was performed on a TSQ Quantum Ultra triple quadrupole mass spectrometer equipped with a heated electrospray ionization source. Analytes were detected in negative ionization mode using selected reaction monitoring. Calibration curves were linear with a correlation coefficient of ≥0.996 for both enantiomers over a concentration range of 5-500 ng/mL. The intra- and interday accuracy and precision for both analytes were within ±9.0%. Excellent extraction efficiency and negligible matrix effects were observed. The applicability of the method was demonstrated by successful measurement of warfarin enantiomers in urine of patients with kidney disease. The method is simple, accurate and reproducible and is currently being used to support warfarin 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 | 0.005 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 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".