Stereospecific analytical method development and preliminary <i>in vivo</i> pharmacokinetic characterization of pinostrobin in the rat
Bibliographic record
Abstract
The complete pharmacokinetic disposition of the chiral flavonoid (±) pinostrobin remains unknown without the development of an analytical method of detection and quantitation of its individual enantiomers. Resolution of the enantiomers of pinostrobin was achieved using as simple high-performance liquid chromatographic method. A Chiralpak(®) AD-RH column was employed to perform baseline separation with UV detection at 287 nm. The standard curves were linear ranging from 0.5 to 100 µg/mL for each enantiomer. The limit of quantification was 0.5 µg/mL. Precision and accuracy of the assay was < 15% (RSD) and was with a bias <15% for all points on the calibration curve. The assay was applied successfully to stereoselective serum disposition of pinostrobin enantiomers in rats. Both enantiomers had a serum half-life of ~7 h. They also shared similar values of volume of distribution (V(d) S-pinostrobin, 8.2 L/kg; V(d) R-pinostrobin, 8.9 L/kg), total clearance (S-pinostrobin CL(total), 0.959 L//h/kg; R-pinostrobin CL(total), 1.055 L//h/kg), and area under the curve (S-pinostrobin AUC(inf), 23.16 µg h/mL; R-pinostrobin AUC(inf), 21.296 µg h/mL). The large volume of distribution suggests extensive distribution of pinostrobin into tissues.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| 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 | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 0.002 |
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".