MétaCan
Menu
Back to cohort
Record W2521156942 · doi:10.1128/jvi.01703-16

Resistance to Mutant Group 2 Influenza Virus Neuraminidases of an Oseltamivir-Zanamivir Hybrid Inhibitor

2016· article· en· W2521156942 on OpenAlexaff
Yan Wu, Feng Gao, Jianxun Qi, Yuhai Bi, Lifeng Fu, Sankar Mohan, Yuhang Chen, Xuebing Li, Bernardine M. Pinto, Christopher J. Vavricka, Po Tien, George F. Gao

Bibliographic record

VenueJournal of Virology · 2016
Typearticle
Languageen
FieldMedicine
TopicInfluenza Virus Research Studies
Canadian institutionsSimon Fraser University
Fundersnot available
KeywordsZanamivirOseltamivirNeuraminidaseNeuraminidase inhibitorVirologyBiologyVirusInfluenza A virusDrug resistanceMultiple drug resistanceAntiviral drugMicrobiologyMedicineCoronavirus disease 2019 (COVID-19)

Abstract

fetched live from OpenAlex

Influenza virus neuraminidase (NA) drug resistance is one of the challenges to preparedness against epidemic and pandemic influenza virus infections. NA N1- and N2-containing influenza viruses are the primary cause of seasonal epidemics and past pandemics. The structural and functional basis underlying drug resistance of the influenza virus N1 NA is well characterized. Yet drug resistance of the N2 strain is not well understood. Here, we confirm that replacement of N2 E119 or I222 results in multidrug resistance, and when the replacements occur together, the sensitivity to NA inhibitors (NAI) is reduced severely. Using crystallographic studies, we showed that E119 replacement results in a loss of hydrogen bonding to oseltamivir and zanamivir, whereas I222 replacement results in a change in the hydrophobic environment that is critical for oseltamivir binding. Moreover, we found that MS-257, a zanamivir-oseltamivir hybrid inhibitor, is less susceptible to drug resistance. The binding mode of MS-257 shows that increased hydrogen bonding interactions between the inhibitor and NA active site anchor the inhibitor within the active site and allow adjustments in response to active-site modifications. Such stability is likely responsible for the observed reduced susceptibility to drug resistance. MS-257 serves as a next-generation anti-influenza virus drug candidate and serves also as a scaffold for further design of NAIs. IMPORTANCE: Oseltamivir and zanamivir are the two major antiviral drugs available for the treatment of influenza virus infections. However, multidrug-resistant viruses have emerged in clinical cases, which pose a challenge for the development of new drugs. N1 and N2 subtypes exist in the viruses which cause seasonal epidemics and past pandemics. Although N1 drug resistance is well characterized, the molecular mechanisms underlying N2 drug resistance are unknown. A previous report showed that an N2 E119V/I222L dual mutant conferred drug resistance to seasonal influenza virus. Here, we confirm that these substitutions result in multidrug resistance and dramatically reduced sensitivity to NAI. We further elucidate the molecular mechanism underlying N2 drug resistance by solving crystal structures of the N2 E119V and I222L mutants and the dual mutant. Most importantly, we found that a novel oseltamivir-zanamivir hybrid inhibitor, MS-257, remains more effective against drug-resistant N2 and is a promising candidate as a next-generation anti-influenza virus drug.

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 imitation

Not 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.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: metacan-v3-hybrid-931329e0061cValidation 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 score0.001
Threshold uncertainty score0.003

Distilled classifier scores by category (both heads)

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)0.0010.000

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 head0.045
GPT teacher head0.361
Teacher spread0.316 · 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 source (direct Gemma or distilled Codex), 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

Citations32
Published2016
Admission routes1
Has abstractyes

Explore more

Same venueJournal of VirologySame topicInfluenza Virus Research StudiesFrench-language works237,207