Central Composite Design Tool Application for Optimizing Methanolic Leaves Extract of Ceiba Pentandra L. Ethosome Suspension Gel with In silico, In vitro, and In vivo Anti-inflammatory Effects
Bibliographic record
Abstract
Introduction: The goal of this research is to develop a gel formulation from the leaf extract of Ceiba pentandra L. and to evaluate its anti-inflammatory properties using in silico, in vitro, and in vivo approaches. The in silico anti-inflammatory effects of the gel were validated by in vitro and in vivo studies. Methods: A Central Composite Design (CCD) was applied to optimize the extract suspension. Anti-inflammatory activity has been compared with Indomethacin molecules in PDB ID:4IK7. Further, absorption, distribution, metabolism, excretion, and toxicity analysis have been performed to ensure the therapeutic potential and its safety for the drug development process. Results: The extracted gel has been characterized by performing Fourier transformer infrared, zeta potential, particle size, scanning electron microscope, and entrapment efficiency. Furthermore, the formulation was evaluated by assessing its viscosity, spreadability, and pH. Discussion: An in vitro study of all nine extract suspensions was conducted to determine the drug content at 295 nm. The optimized suspension has shown the maximum percentage of drug release (83.43%) in 09 hours of study. Anti-inflammatory effects of extract and gel are studied by animal studies using formalin to induce paw inflammation. Conclusion: The results of the study conclude that the gel formulation exhibits stronger antiinflammatory activity compared to the extract, and molecular docking studies support the therapeutic potential of the extract’s bioactive molecules. ADMET analysis ensures the therapeutic effects and its safety.
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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 | 0.002 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.009 | 0.001 |
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".