PHARMACEUTICAL NANOTECHNOLOGY: FORMULATION AND IN VIVO EVALUATION OF CURCUMIN-LOADED NANOSUSPENSIONS FOR ENHANCED ANTI-INFLAMMATORY EFFICACY
Bibliographic record
Abstract
Curcumin, a natural polyphenol derived from Curcuma longa, is well-regarded for its potent anti-inflammatory properties. However, its therapeutic application is severely hampered by its extremely low aqueous solubility and poor oral bioavailability, which leads to suboptimal absorption and limited clinical efficacy. Pharmaceutical nanotechnology offers a promising strategy to overcome these biopharmaceutical challenges. This research aimed to formulate a stable curcumin nanosuspension to significantly enhance its dissolution rate and bioavailability, and to subsequently evaluate its improved anti-inflammatory efficacy in an in vivo model. A curcumin nanosuspension was prepared using the high-pressure homogenization technique, stabilized with Poloxamer 188. The formulation was characterized for particle size, polydispersity index (PDI), and zeta potential. An in vivo anti-inflammatory study was conducted using the carrageenan-induced paw edema model in Wistar rats, comparing the efficacy of the nanosuspension against a conventional coarse curcumin suspension. The optimized nanosuspension exhibited a narrow particle size distribution with a mean diameter of 210 nm and a zeta potential of -28.5 mV, indicating good physical stability. The in vivo evaluation demonstrated that the curcumin nanosuspension produced a significantly greater inhibition of paw edema (72.4%) compared to the coarse curcumin suspension (28.1%) at the same dose (p < 0.01). Formulating curcumin into a nanosuspension is a highly effective strategy for overcoming its inherent bioavailability limitations. This nanotechnological approach dramatically enhances curcumin’s anti-inflammatory activity, validating its potential as a powerful therapeutic agent for inflammatory conditions.
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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".