Naturally Occurring Sulfonium-Ion Glucosidase Inhibitors and Their Derivatives: A Promising Class of Potential Antidiabetic Agents
Bibliographic record
Abstract
In humans, four different enzymes mediate the digestion of ingested carbohydrates. First salivary and pancreatic α-amylases, the two endoacting retaining glucosidases, break down the complex starch molecules into smaller linear maltose-oligomers (LM) and branched α-limit dextrins (αLDx). Then two retaining exoglucosidases, maltase-glucoamylase (MGAM) and sucrase-isomaltase (SI), convert those molecules into glucose in the small intestine. The small intestinal brush-border epithelial cells anchor MGAM and SI, and each contains a catalytic N- and C-terminal subunit, ntMGAM, ctMGAM, ntSI, and ctSI, respectively. All four catalytic domains have, to varying extents, α-1,4-exohydrolytic glucosidase activity and belong to the glycoside hydrolase family 31 (GH31). ntSI and ctSI show additional activity toward α-1,6 (isomaltose substrates) and α-1,2 (sucrose) glycosidic linkages, respectively. Because they mediate the final steps of starch digestion, both MGAM and SI are important target enzymes for the treatment of type-2 diabetes. Because of their potent inhibitory activities against the mammalian intestinal α-glucosidases, sulfonium-ion glucosidase inhibitors isolated from the antidiabetic herbal extracts of various Salacia species have received considerable attention recently. Thus far, researchers have isolated eight sulfonium-ion glucosidase inhibitors from Salacia species: salaprinol, salacinol, ponkoranol, kotalanol, and four of their corresponding de-O-sulfonated compounds, the structures of which comprise a 1,4-anhydro-4-thio-d-arabinitol and a polyhydroxylated acyclic side chain. Some of these compounds more strongly inhibit human intestinal α-glucosidases than the currently available antidiabetic drugs, acarbose and miglitol, and could serve as lead candidates in the treatment of type-2 diabetes. In this Account, we summarize progress in the field since 2010 with this class of inhibitors, with particular focus on their selective inhibitory activities against the intestinal glucosidases. Through structure-activity relationship (SAR) studies, we have modified the natural compounds to derive more potent, nanomolar inhibitors of human MGAM and SI. This structural optimization also yielded the most potent inhibitors known to date for each subunit. Furthermore, we observed that some of our synthetic inhibitors selectively blocked the activity of some mucosal α-glucosidases. Those results led to our current working hypothesis that selective inhibitors can dampen the action of a fast digesting subunit or subunits which places the burden of digestion on slower digesting subunits. That strategy can control the rate of starch digestion and glucose release to the body. Decreasing the initial glucose spike after a carbohydrate-rich meal and extending postprandial blood glucose delivery to the body can be desirable for diabetics and patients with other metabolic syndrome-associated diseases.
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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 | 0.000 |
| Bibliometrics | 0.001 | 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 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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".