The Antiinflammatory Action and Pharmacokinetics of a Novel Glucosamine-based Di-Peptide Aminosugar
Bibliographic record
Abstract
PURPOSE: We have previously shown favorable in vitro gut permeability for three novel di-peptide esters of glucosamine (GlcN) likely facilitated by the peptide transporter 1 (PepT1). Herein, we report the development of a novel assay for the determination of bioavailability of the peptide ester of interest, the anti-inflammatory properties of a glycine-valine ester derivative of GlcN (GVG) as well as its pharmacokinetics under healthy and inflammatory conditions. METHODS: A pre-column derivatization (with 9-fluorenylmethoxycarbonyl) HPLC assay was developed to study bioavailability of GVG, GlcN or cleaved GlcN in the rats that were cannulated in their right jugular vein for blood collection. The compounds of interest were orally administered to both healthy and arthritic rats. Serial blood samples and urine were collected and assayed for the compounds. The stability of the GVG was also tested after incubation with the rat feces. Efficacy of GVG was tested in inflamed rats (injection of 0.2 mL of Mycobacterium butyricum in squalene) following GVG (20 and 30 mg/kg/day GlcN equivalent) or GlcN (20 and 90 mg/kg/day) administration. Arthritis index was calculated at the end of the experiment. RESULTS: The assay was linear (ranged between 0.05-20 μg/mL) and reproducible (intra- and inter-day<10%). Among the tested compounds, only GVG showed a significantly higher plasma concentrations and urinary excretion than GlcN (≈3-fold increase). GVG showed a favorable stability in the rat feces. Adjuvant arthritis was completely prevented with doses greater than 20 mg/kg/day with GVG being 3-fold more potent than GlcN. CONCLUSION: The examined glycine-valine-GlcN di-peptide aminosugar is a potent anti-inflammatory compound due to its favorable properties to deliver GlcN into the systemic circulation.
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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) | 0.000 | 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 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".