A simple and sensitive method for determination of vitamins D<sub>3</sub>and K<sub>1</sub>in rat plasma: application for an<i>in vivo</i>pharmacokinetic study
Bibliographic record
Abstract
PURPOSE: To develop and to validate a simple but sensitive method for determination of vitamins D3 and K1 in rat plasma. METHODS: The sample treatment included protein precipitation by cold acetonitrile, evaporation, reconstitution with methanol and filtration. The chromatography conditions included Xterra RP18 3.5 µm 4.6 × 100 mm column at ambient temperature and mobile phase consisting of methanol/water (93/7, v/v) at 0.5 mL/min flow rate. Vitamin D3 and probucol were detected at 265 nm and vitamin K1 at 239 nm. Rats were administered intravenously by 0.1 mg/kg of vitamin D3 or K1 and the blood samples were withdrawn pre-administration and at pre-determined time points post-administration. The pharmacokinetic analysis was performed using a non-compartmental approach. RESULTS: The calibration curves in rat plasma were linear up to 5000 ng/mL for both vitamins. The limit of quantification (LOQ) was 20 ng/mL for vitamin D3 and 40 ng/mL for K1. Inter- and intra-day precision and accuracy were below 15%. The pharmacokinetic parameters of vitamin D3 following intravenous administration were: AUC0-∞ = 11323 ± 1081 h × ng/mL, Vd = 218 ± 80 mL/kg, CL = 8.9 ± 0.8 mL/h/kg, t1/2 = 16.8 ± 5 h; and of vitamin K1: AUC0-∞ = 2495 ± 297 h × ng/mL, Vd = 60 ±24 mL/kg, CL = 40.5 ± 5.1 mL/h/kg, t1/2 = 1.1 ±0.5 h. CONCLUSION: The developed HPLC-UV assay is a simple and sensitive method for the determination of vitamins D3 and K1 in rat plasma. A higher dose of vitamin K1 should be used in future studies for accurate estimation of pharmacokinetic parameters. The data show the suitability of the assay for pharmacokinetic studies in rats.
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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 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.001 |
| 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".