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.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.000 | 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) | 0.000 | 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".