Chiral analytical method development and application to pre‐clinical pharmacokinetics of pinocembrin
Bibliographic record
Abstract
An analytical method enabling the detection and quantification of the individual enantiomers of racemic (±) pinocembrin is required to fully characterize its pharmacokinetic disposition. Direct resolution of the enantiomers of pinocembrin was achieved using a novel and simple reversed-phase high-performance liquid chromatography method with electrospray ionization and detection by mass spectrometry in rat serum. A Chiralcel® AD-RH column was employed to perform baseline separation with electrospray positive-mode ionization with selected ion monitoring detection. The standard curves were linear from 0.5 to 100 µg/mL for each enantiomer. The limit of quantification was 0.5 µg/mL. The assay was applied successfully to stereoselective serum disposition of pinocembrin enantiomers in rats. Pinocembrin enantiomers were detected in serum. Both enantiomers had a serum half-life of ~15 min in rats. Similar values of volume of distribution between the enantiomers were also observed: 1.76 L/kg for S-pinocembrin and 1.79 L/kg for R-pinocembrin. Total clearance was 5.527 L//h/kg for S-pinocembrin and 5.535 L/h/kg for R-pinocembrin, and the area under the curve was 1.821 µg h/mL for S-pinocembrin and 1.876 µg h/mL for R-pinocembrin. The large volume of distribution coupled with the short serum half-life suggests extensive distribution of pinocembrin into the tissues.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.004 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| 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".