Generation and Characterization of Recombinant Pandemic Influenza A(H1N1) Viruses Resistant to Neuraminidase Inhibitors
Bibliographic record
Abstract
BACKGROUND: Neuraminidase inhibitors (NAIs) play a key role in the management of influenza epidemics and pandemics. Given the novel pandemic influenza A(H1N1) (pH1N1) virus and the restricted number of approved anti-influenza drugs, evaluation of potential drug-resistant variants is of high priority. METHODS: Recombinant pH1N1 viruses were generated by reverse genetics, expressing either the wild-type or any of 9 mutant neuraminidase (NA) proteins (N2 numbering: E119G, E119V, D198G, I222V, H274Y, N294S, S334N, I222V-H274Y, and H274Y-S334N). We evaluated these recombinant viruses for their resistance phenotype to 4 NAIs (oseltamivir, zanamivir, peramivir, and A-315675), NA enzymatic activity, and replicative capacity. RESULTS: The E119G and E119V mutations conferred a multidrug resistance phenotype to many NAIs but severely compromised viral fitness. The oseltamivir- and peramivir-resistance phenotype was confirmed for the H274Y and N294S mutants, although both viruses remained susceptible to zanamivir. Remarkably, the I222V mutation had a synergistic effect on the oseltamivir- and peramivir-resistance phenotype of H274Y and compensated for reduced viral fitness, raising concerns about the potential emergence and dissemination of this double-mutant virus. CONCLUSIONS: This study highlights the importance of continuous monitoring of antiviral drug resistance in clinical samples as well as the need to develop new agents and combination strategies.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".