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Record W1981200521 · doi:10.1021/ml400080t

Identification of Selective Nanomolar Inhibitors of the Human Neuraminidase, NEU4

2013· article· en· W1981200521 on OpenAlexafffund
Amgad Albohy, Yi Zhang, Victoria Smutova, Alexey V. Pshezhetsky, Christopher W. Cairo

Bibliographic record

VenueACS Medicinal Chemistry Letters · 2013
Typearticle
Languageen
FieldBiochemistry, Genetics and Molecular Biology
TopicGlycosylation and Glycoproteins Research
Canadian institutionsCentre Hospitalier Universitaire Sainte-JustineMcGill UniversityAlberta Glycomics CentreUniversité de MontréalUniversity of Alberta
FundersNatural Sciences and Engineering Research Council of CanadaAlberta Glycomics Centre
KeywordsNeuraminidaseEnzymeSialidaseBiochemistryChemistryIsozymeIn vitroSialic acidPotencyNeuraminidase inhibitorMedicineInternal medicine

Abstract

fetched live from OpenAlex

The human neuraminidase enzymes (hNEU) play important roles in human physiology and pathology. The lack of potent and selective inhibitors toward these enzymes has limited our understanding of their function and the development of therapeutic applications. Here we report the evaluation of a panel of compounds against the four human neuraminidase isoenzymes. Among the compounds tested, we identified the first selective, nanomolar inhibitors of the human neuraminidase 4 enzyme (NEU4). The most potent NEU4 inhibitor (5-acetamido-9-[4-hydroxymethyl[1,2,3]triazol-1-yl]-2,3,5,9-tetradeoxy-d-glycero-d-galacto-2-nonulopyranosonic acid) was found to have an inhibitory constant (K i ) of 30 ± 19 nM and was 500-fold selective for its target over the other hNEU isoenzymes tested in vitro (NEU1, NEU2, and NEU3). This is the first report of any inhibitor of hNEU with nanomolar potency, and this confirms that the 2,3-didehydro-2-deoxy-N-acetylneuraminic acid (DANA) scaffold can be exploited to develop new, potent, and selective inhibitors that target this important family of human enzymes.

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)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
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 score0.010
Threshold uncertainty score0.325

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
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 head0.006
GPT teacher head0.245
Teacher spread0.239 · 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.

The models applied no category: nothing in the taxonomy fit this work.
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

Citations51
Published2013
Admission routes2
Has abstractyes

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