Neuraminidase inhibitor resistance in influenza: a clinical perspective
Bibliographic record
Abstract
PURPOSE OF REVIEW: Neuraminidase inhibitors (NAIs), including oseltamivir, zanamivir, and peramivir, is the main class of antiviral available for clinical use. As such, development of resistance toward these agents is of great clinical and public health concern. RECENT FINDINGS: At present, NAI resistance remains uncommon among the circulating viruses (oseltamivir <3.5%, zanamivir <1%). Resistance risk is slightly higher in A(H1N1) than A(H3N2) and B viruses. Resistance may emerge during drug exposure, particularly among young children (<5 years), the immunocompromised, and individuals receiving prophylactic regimens. H275Y A(H1N1) variant, showing high-level oseltamivir resistance, is capable of causing outbreaks. R294K A(H7N9) variant shows reduced inhibition across NAIs. Multi-NAI resistance has been reported in the immunocompromised. SUMMARY: These findings highlight the importance of continuous surveillance, and assessment of viral fitness and transmissibility of resistant virus strains. Detection can be challenging, especially in a mix of resistant and wild-type viruses. Recent advances in molecular techniques (e.g. targeted mutation PCR, iART, ddPCR, pyrosequencing, next-generation sequencing) have improved detection and our understanding of viral dynamics. Treatment options available for oseltamivir-resistant viruses are limited, and susceptibility testing of other NAIs may be required, but non-NAI antivirals (e.g. polymerase inhibitors) that are active against these resistant viruses are in late-stage clinical development.
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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 | 0.009 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.002 |
| Insufficient payload (model declined to judge) | 0.000 | 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 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".