Formulation design and physicochemical evaluation of an anti-inflammatory hydrogel patch containing Crinum asiaticum L. extract
Bibliographic record
Abstract
Background and purpose: Crinum asiaticum L. has long been used in Thai traditional medicine to treat osteoarthritis and inflammation by placing it on painful areas without further formulation design which is suboptimal for therapeutic use. Thus, this research aims to formulate a topical hydrogel patch containing C. asiaticum L. extracts (CAE) for anti-inflammatory effects. Experimental approach: The hydrogel patches are made from carrageenan, locust bean gum, with glycerin as a plasticizer and contain CAE formulated by using response surface methodology based on Box-Behnken design for design, determination of the effect of independent factors on the tensile strength, and optimization of the hydrogel patch formulation. In vitro release and skin permeation studies using a modified Franz diffusion cell and anti-inflammatory activity were evaluated. Findings/Results: The optimized CAE hydrogel patch showed a good correlation between predicted and observed tensile strength values and exerted its maximum cumulative lycorine release and permeation at 69.38 ± 2.78% and 48.51 ± 0.45%, respectively which were fit to Higuchi's kinetic model. The release rates were found to decrease with an increase in the polymer proportion of carrageenan and locust bean gum. In addition, the patch exerted potent in vitro anti-inflammatory activity with an IC 50 value of 21.36 ± 0.78 μg/mL. Conclusion and implication: The optimized CAE hydrogel patch application was successfully formulated with excellent mechanical properties, cumulative release, permeation, and anti-inflammatory effects. Thus, it has the potential to be further developed as a herbal application to relieve pain and inflammation. The in vivo anti-inflammatory effect of this delivery system should be further investigated.
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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 | 0.009 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| 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 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".