In vitro and in vivo Characterization of New Formulations of St. John's Wort Extract with Improved Pharmacokinetics and Anti-nociceptive Effect
Bibliographic record
Abstract
The main purpose of the present study was to develop a novel formulation of St. John's Wort (SJW) extract with the aim of improving its pharmacokinetics and anti-nociceptive effect. Several formulations of SJW were prepared, including cyclodextrin inclusion (SJW-CD), solid dispersion (SJW-SD), dry-emulsion (SJW-DE), and nano-emulsion (SJW-NE). Physicochemical properties of SJW formulations were characterized with a focus on the morphology, dissolution behavior, colloidal properties, and dispersion stability in water. Although all the SJW formulations and SJW extract itself exhibited fine dissolution behavior in water, SJW extract and most formulations tended to cream, aggregate, or flocculate after dispersion in distilled water. In contrast, there were no significant changes in appearance and particle size of the SJW-NE for at least a few weeks, suggesting that SJW-NE was the most stable form as a carrier of SJW in the present study. After oral administration of the SJW-NE formulation (5.2 mg hyperforin/kg) in mice, higher hyperforin exposure in plasma (1188 ± 41 nM·h) and the brain (52.9 ± 1.6 pmol/g tissue·h) was observed with 2.8- and 1.3-fold increases of the area under the concentration curve from 0 to 6 hours (AUC(0-6)) compared to those of the SJW extract (417 ± 41 nM·h in plasma and 41.6 ± 1.5 pmol/g tissue·h in the brain). In the formalin test for scoring properties of the first and second phases of the pain response in mice, single oral administration of SJW-NE significantly reduced the nociceptive response compared with SJW extract. From these findings, the NE approach might be efficacious in improving the oral bioavailability and anti-nociceptive effect of SJW extract.
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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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 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 | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".