The Search for Human Alpha‐Amylase Inhibitors as Therapeutics for Diabetes and Obesity
Bibliographic record
Abstract
Diabetes and obesity lead to a significantly reduced quality of life, with an increased risk of serious complications including cardiovascular disease, hypertension, stroke, kidney failure and nerve damage. Human pancreatic alpha‐amylase (HPA) provides a unique opportunity for the development of potential therapeutic agents for the treatment of these conditions. This enzyme plays a vital role in the breakdown of starch in the diet, and its activity has been correlated to postprandial blood glucose levels, the control of which is essential for maintaining quality of life for diabetic patients. Nonetheless, the discovery of specific high affinity inhibitors for HPA has proven elusive and the currently available therapies that target this enzyme cause many deleterious side effects due to their activity on a wide range of glycosidases. In an attempt to identify new inhibitors of HPA, we have screened over 80,000 pure chemicals and crude biological extracts. This has resulted in the exciting discovery of montbretin A, a glycosylated acyl flavonol that acts as a competitive HPA inhibitor with a K i of 8.1 nM. Structural characterization of the binding mode of fragments of the montbretin A molecule have been undertaken and a model of montbretin A binding proposed. This work is supported by the Canadian Institutes of Health Research.
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 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 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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".