Validation of an HPLC method for quantification of anti-inflammatory markers in an ethanolic extract of Sahastara and its anti-inflammatory activity in vitro
Bibliographic record
Abstract
Background and purpose: Sahastara (SHT) is a traditional Thai medicine for the treatment of musculoskeletal and joint pain. It consists of 21 plant components. A previous study demonstrated the anti-inflammatory activity of SHT on inhibition of nitric oxide production and prostaglandin E 2 (PGE 2 ) production, however, inhibitory effects on tumor necrosis factor-alpha (TNF-α) has not been reported. In this study, we evaluated the anti-inflammatory activity of SHT on inhibitory effects on TNF-α and PGE 2 production and presented an analytical method for validation of SHT. Experimental approach: Anti-inflammatory activity was evaluated by inhibitory activity on TNF-α and PGE 2 production in RAW264.7 cells. The validated procedure was conducted according to ICH guidelines. The validated parameters were specificity/selectivity, linearity, range, the limit of detection (LOD), and limit of quantitation (LOQ). Findings/Results: Ethanolic extract of SHT exerted inhibitory activity on PGE 2 production in RAW264.7 cells with IC 50 16.97 ± 1.16 μg/mL. Myristica frangrans seed extract showed the highest inhibitory activity on PGE 2 production. Piper retrofractum extract showed the highest inhibitory activity on TNF-α production. For the HPLC method, all validated parameters complied with standard requirements. Each analyzed peak showed good selectivity with a baseline resolution greater than 1.51. The linearity of all compounds was > 0.999. The % recovery of all compounds was within 98.0-102.0%. The precision of all compounds was less than 2.0% CV. Conclusion and implications: Ethanolic extracts of SHT possess anti-inflammatory activity by inhibition of TNF-α and PGE 2 production in vitro . This study provides support for the traditional use of SHT. The validated results showed good specificity/selectivity, linearity, precision, and accuracy with appropriate LOD and LOQ. This study is the first report on the validation of the HPLC method of SHT for use as quality control of the SHT extract.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.018 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".