Enantioselective assay of nimodipine in human plasma using liquid chromatography–tandem mass spectrometry
Bibliographic record
Abstract
Abstract Nimodipine is a dihydropyridine calcium channel blocker that exhibits higher selectivity toward cerebral blood vessels compared with other members of the same class. It has been shown to improve outcomes and prevent delayed cerebral ischemia in the setting of aneurysmal subarachnoid hemorrhage, a life‐threatening brain bleed. Nimodipine is a chiral compound and it is marketed as a racemic mixture of (+) ‐R and (−)‐ S enantiomers. (−)‐ S ‐Nimodipine is approximately twice as potent a vasorelaxant as the racemic mixture and is more rapidly eliminated than the (+) ‐R counterpart following oral dosing. Few analytical procedures have been reported to determine nimodipine enantiomers in biological samples; however, the reported methods were time‐consuming, involved multistep extraction procedures and required large sample volumes. Herein, we present an LC–MS/MS method for quantifying nimodipine enantiomers in human plasma using a small sample volume (0.3 ml) and a single liquid–liquid extraction step. The peak area ratios were linear over the tested concentration ranges (1.5–75 ng/ml) with r 2 > 0.99. The intraday CV and percentage error were within ±14% while the interday values were within ±13%, making this analytical method feasible for research purposes and 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 | 0.002 | 0.003 |
| Meta-epidemiology (narrow) | 0.002 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| 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".