Synergistic Effect of Acarbose–Chlorogenic Acid on α-Glucosidase Inhibition: Kinetics and Interaction Studies Reveal Mixed-Type Inhibition and Denaturant Effect of Chlorogenic Acid
Bibliographic record
Abstract
The α-glucosidase inhibitory mechanism of chlorogenic acid was evaluated in the presence of an antidiabetic drug, acarbose. Enzyme kinetics showed the mode of inhibition of the chlorogenic acid–acarbose combination to be either mixed inhibition (competitive and noncompetitive inhibition) or solely competitive inhibition, depending on the dominating inhibitor in the dual system. Despite weaker inhibition by chlorogenic acid, supplementation with acarbose exhibited a synergistic effect on α-glucosidase inhibition, with acarbose equivalent activity exceeding the concentration of acarbose present. Fluorescence quenching studies indicated an increased affinity of chlorogenic acid in the presence of acarbose with an effective quenching constant increasing from 1.6 ± 0.11 × 10 4 to 3.9 ± 0.32 × 10 4 M –1 . Furthermore, acarbose did not affect the static binding mode or the number of chlorogenic acid bound per α-glucosidase molecule. This chlorogenic acid–acarbose dual inhibition system highlights the potential for antidiabetic nutraceuticals as adjuvant therapy for acarbose-based treatments in diabetes management and, to a broader extent, reveals that nutraceuticals can significantly modify or regulate drug-disease state dynamics.
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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) | 0.001 | 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".