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The Search for Human Alpha‐Amylase Inhibitors as Therapeutics for Diabetes and Obesity

2010· article· en· W130587520 on OpenAlexafffundabout
Gary D. Brayer, Leslie K. Williams, Chris A. Tarling, Kate Woods, Chunmin Li, Ran Zhang, AmirAli Mahpour, Raymond J. Andersen, Stephen G. Withers

Bibliographic record

VenueThe FASEB Journal · 2010
Typearticle
Languageen
FieldMedicine
TopicNatural Antidiabetic Agents Studies
Canadian institutionsUniversity of British Columbia
FundersCanadian Institutes of Health Research
KeywordsDiabetes mellitusPostprandialMedicineEnzymeDrug discoveryObesityDiseaseAlpha-amylasePharmacologyBioinformaticsChemistryBiochemistryInternal medicineEndocrinologyBiologyAmylase

Abstract

fetched live from OpenAlex

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 distilled prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.001
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesScience and technology studies
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Bench or experimental · Consensus signal: Bench or experimental
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.023
Threshold uncertainty score0.999

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0020.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0000.000

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.

Opus teacher head0.027
GPT teacher head0.316
Teacher spread0.289 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

Study designBench or experimental
Domainnot available
GenreEmpirical

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".

Quick stats

Citations1
Published2010
Admission routes3
Has abstractyes

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