Quantification of Alectinib in spiked rabbit plasma using liquid chromatography- electro spray ionization-tandem mass spectrophotometry: An application to pharmacokinetic study
Bibliographic record
Abstract
The current technique was developed to estimate the amount of alectinib present in spiked rabbit plasma using liquid chromatographic mass spectrometry. The liquid-liquid extraction method was used, and chromatographic separation was carried out on a C18 (4.6mm id x 50mm) analytical column with a mobile phase consisting of acetonitrile and water with 0.1% formic acid at a volume ratio of 75:25. Alectinib's product m/z +483.2 (parent) 396.1 (product) and the internal standard m/z +447.5 (parent) 380.3 (product) were both obtained using positive ion mode. The calibration curve was linear from 0.5 to 600 ng/ml. The percentage extraction recovery (98.15% → 98.86%), demonstrated excellent matrix and analyte selectivity (% interference = 0), and satisfactory stability study results in all types (% nominal 94.94% → 99.63%). The intra and interday accuracy with % nominal 97 → 98.8%, precision % CV ≤ 2% in all quality control levels. The rabbit model's pharmacokinetic parameters were examined, and alectinib's area under the curve (AUC 0—∞) was 4269 ± 8.13 hr.ng/ml. The half-life of elimination (t1/2) is 8.52 ± 6.66 hours. The currently established approach was used in rabbit blood samples for pharmacokinetic investigations of commercial formulations since it was thought to be a novel, verified bioanalytical method based on experimental results.
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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 | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
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".