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Record W2107383745 · doi:10.1126/science.1232552

Mechanism-Based Covalent Neuraminidase Inhibitors with Broad-Spectrum Influenza Antiviral Activity

2013· article· en· W2107383745 on OpenAlexafffund
Jin‐Hyo Kim, Ricardo Resende, Tom Wennekes, Hongming Chen, Nicole Bance, Sabrina Buchini, Andrew G. Watts, Pat Pilling, Victor A. Streltsov, Martin Petric, Richard Liggins, Susan Barrett, Jennifer L. McKimm‐Breschkin, Masahiro Niikura, Stephen G. Withers

Bibliographic record

VenueScience · 2013
Typearticle
Languageen
FieldMedicine
TopicInfluenza Virus Research Studies
Canadian institutionsCentre for Drug Research and DevelopmentProvincial Health Services AuthoritySimon Fraser UniversityUniversity of British Columbia
FundersMedical Research CouncilCanadian Institutes of Health ResearchNederlandse Organisatie voor Wetenschappelijk OnderzoekAustralian SynchrotronMichael Smith Health Research BCNational Health and Medical Research CouncilPfizer
KeywordsNeuraminidaseMechanism (biology)VirologyBroad spectrumNeuraminidase inhibitorChemistryVirusBiologyMedicineCoronavirus disease 2019 (COVID-19)Combinatorial chemistryPhysics

Abstract

fetched live from OpenAlex

Influenza antiviral agents play important roles in modulating disease severity and in controlling pandemics while vaccines are prepared, but the development of resistance to agents like the commonly used neuraminidase inhibitor oseltamivir may limit their future utility. We report here on a new class of specific, mechanism-based anti-influenza drugs that function through the formation of a stabilized covalent intermediate in the influenza neuraminidase enzyme, and we confirm this mode of action with structural and mechanistic studies. These compounds function in cell-based assays and in animal models, with efficacies comparable to that of the neuraminidase inhibitor zanamivir and with broad-spectrum activity against drug-resistant strains in vitro. The similarity of their structure to that of the natural substrate and their mechanism-based design make these attractive antiviral candidates.

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 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 score0.164
Threshold uncertainty score0.610

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0000.002
Scholarly communication0.0000.001
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 head0.048
GPT teacher head0.345
Teacher spread0.297 · 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

Citations197
Published2013
Admission routes2
Has abstractyes

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