A Novel SNEDDS Formulation of Swietenia mahagoni Jacq and Peperomia pellucida L: Antidiabetic Potential and Molecular Docking Insights
Bibliographic record
Abstract
The present study aims to develop and characterize a Self-Nanoemulsifying Drug Delivery System (SNEDDS) formulation containing a combination of Swietenia mahagoni Jacq.(mahogany) seed extract and Peperomia pellucida L. (pepper elder) leaf extract as a potential antidiabetic therapy.Physicochemical characterization demonstrated that the best SNEDDS possessed a homogeneous globule size distribution (11.29 ± 0.88 nm), a zeta potential of -10.33 mV, and a polydispersity index (PDI) value of 0.35 0.29, indicating excellent stability and uniformity of the nanoemulsion system.Gas Chromatography-Mass Spectrometry (GC-MS) analysis identified four major fatty acid methyl esters, namely methyl oleate (61.17%), methyl palmitate (8.07%), methyl stearate (6.20%), and methyl linoleate (6.48%), demonstrating that the oil phase was rich in monounsaturated fatty acids (MUFA), particularly oleic acid.These bioactive lipids contribute to enhanced insulin sensitivity and reduced inflammation, supporting the antidiabetic mechanism of the formulation.In vivo studies in diabetic rats showed that SNEDDS formulation significantly reduced blood glucose levels, with the highest reduction observed in the SMSC1A (FB 1:1) group, showing a glucose-lowering effect of up to approximately 82% compared to post-induction levels.Molecular docking analysis using the free fatty acid form (9-octadecenoic acid) against protein tyrosine phosphatase 1B (PTP1B) provided supportive mechanistic insight into the potential role of unsaturated fatty acids in modulating insulin signaling.Overall, the results indicate that the developed SNEDDS enhances the delivery of bioactive phytoconstituents and exhibits promising antidiabetic potential.
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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".