Inhibition of Aldose Reductase by Ginsenoside Derivatives via a Specific Structure Activity Relationship with Kinetics Mechanism and Molecular Docking Study
Bibliographic record
Abstract
This present work is designed to evaluate the anti-diabetic potential of 22 ginsenosides via the inhibition against rat lens aldose reductase (RLAR), and human recombinant aldose reductase (HRAR), using DL-glyceraldehyde as a substrate. Among the ginsenosides tested, ginsenoside Rh2, (20S) ginsenoside Rg3, (20R) ginsenoside Rg3, and ginsenoside Rh1 inhibited RLAR significantly, with IC50 values of 0.67, 1.25, 4.28, and 7.28 µM, respectively. Moreover, protopanaxadiol, protopanaxatriol, compound K, and ginsenoside Rh1 were potent inhibitors of HRAR, with IC50 values of 0.36, 1.43, 2.23, and 4.66 µM, respectively. The relationship of structure–activity exposed that the existence of hydroxyl groups, linkages, and their stereo-structure, as well as the sugar moieties of the ginsenoside skeleton, represented a significant role in the inhibition of HRAR and RLAR. Additional, various modes of ginsenoside inhibition and molecular docking simulation indicated negative binding energies. It was also indicated that it has a strong capacity and high affinity to bind the active sites of enzymes. Further, active ginsenosides suppressed sorbitol accumulation in rat lenses under high-glucose conditions, demonstrating their potential to prevent sorbitol accumulation ex vivo. The findings of the present study suggest the potential of ginsenoside derivatives for use in the development of therapeutic or preventive agents for diabetic complications.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 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".