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 distilled prediction
Teacher imitationNot 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.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| 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 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
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.
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 teacher head, 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".