Antinociceptive properties of shikonin: in vitro and in vivo studies
Bibliographic record
Abstract
Shikonin possess a diverse spectrum of pharmacological properties in multiple therapeutic areas. However, the nociceptive effect of shikonin is not largely known. To investigate the antinociceptive potential of shikonin, panel of GPCRs, ion channels, and enzymes involved in pain pathogenesis were studied. To evaluate the translation of shikonin efficacy in vivo, it was tested in 3 established rat pain models. Our study reveals that shikonin has significant inhibitory effect on pan sodium channel/N1E115 and NaV1.7 channel with half maximal inhibitory concentration (IC50) value of 7.6 μmol/L and 6.4 μmol/L, respectively, in a cell-based assay. Shikonin exerted significant dose dependent antinociceptive activity at doses of 0.08%, 0.05%, and 0.02% w/v in pinch pain model. In mechanical hyperalgesia model, dose of 10 and 3 mg/kg (intraperitoneal) produced dose-dependent analgesia and showed 67% and 35% reversal of hyperalgesia respectively at 0.5 h. Following oral administration, it showed 39% reversal at 30 mg/kg dose. When tested in first phase of formalin induced pain, shikonin at 10 mg/kg dose inhibited paw flinching by ∼71%. In all studied preclinical models, analgesic effect was similar or better than standard analgesic drugs. The present study unveils the mechanistic role of shikonin on pain modulation, predominantly via sodium channel modulation, suggesting that shikonin could be developed as a potential pain blocker.
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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) | 0.001 | 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 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".