Delivery system for mefenamic acid based on the nanocarrier layered double hydroxide: Physicochemical characterization and evaluation of anti-inflammatory and antinociceptive potential
Bibliographic record
Abstract
PURPOSE: The anionic form of the drug mefenamic acid intercalated into the nanocarrier layered double hydroxide (LDH-Mef) was evaluated by anti-inflammatory and antinociceptive assays. METHODS: The LDH-Mef material was characterized by a set of physicochemical techniques, which was supported by Density Functional Theory calculations. The pharmacological effects of LDH-Mef (40 wt% of drug) were evaluated by hemolytic, anti-inflammatory activity and antinociceptive assays. RESULTS: In vivo assays were conducted for the first time in order to assess the LDH-Mef potential. The hemolytic effects decreased for the intercalated Mef as demonstrated by the higher tolerated hemolytic concentration (1.83 mM) compared to mefenamic acid (MefH), 0.48 mM. Pretreatment of animals with MefH or LDH-Mef reduced carrageenan-, dextran sulfate- and PGE2-induced paw edema. MefH or LDH-Mef also decrease total leucocytes and neutrophil counts of the peritoneal cavity after inflammation induction with carrageenan. In the nociception model, oral pretreatment with LDH-Mef reduced mechanical hypernociception carrageenan-induced after 3-4h and also the number of writhings induced by acetic acid. CONCLUSIONS: This work shows the increase of the anti-inflammatory and antinociceptive potential of the drug confined into the LDH, as well as, its hemolytic effect.
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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".