Inhibition of carbohydrate digestive enzymes by a complementary essential oil blend: in silico and mixture design approaches
Bibliographic record
Abstract
Background The increasing demand for natural alternatives in diabetes treatment has driven research into plant-derived metabolites, particularly essential oils (EOs) with bioactive properties. This study aims to optimize an EO mixture for inhibiting two key enzymes involved in glucose digestion: pancreatic α-amylase and intestinal α-glucosidase. Methods Essential oils were extracted from three Moroccan medicinal plants: false yellowhead ( Inula viscosa L.), rose geranium ( Pelargonium graveolens L'Hér.), and lemongrass ( Cymbopogon citratus (DC.) Stapf.). Gas chromatography-mass spectrometry (GC-MS) analysis identified key metabolites in each EO. A statistical mixture design was employed to evaluate different EO ratios for their inhibitory effects on α-amylase and α-glucosidase. Additionally, density functional theory (DFT) calculations and molecular docking simulations were conducted to assess the key metabolites' electronic properties and interaction potential with target enzymes. Results GC-MS analysis identified 32 metabolites in P . graveolens , with citronellol (18.67%), eucalyptol (13.30%), and 2-octen-1-ol (8.12%) as major components. I . viscosa contained 18 metabolites, dominated by 2-camphanol acetate (51.12%) and camphol (19.32%), while C. citratus had 23 metabolites, with α-citral (24.70%) and 2-isopropenyl-5-methylhex-4-enal (29.25%) as key constituents. The optimal formulation for α-glucosidase inhibition was a binary mixture of 73% C. citratus and 27% P. graveolens . In contrast, the best blend for α-amylase inhibition consisted of 56% P. graveolens and 44% I. viscosa . DFT calculations confirmed the electrophilic nature of key metabolites, supporting their potential for enzyme interaction. Molecular docking simulations suggested that these phytochemicals could exhibit stronger inhibitory effects than acarbose, a widely used antidiabetic drug. Conclusion These findings highlight the potential of optimized EO formulations as natural alternatives for managing hyperglycemia and developing novel diabetes therapies.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| 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".