Non-neutralizing antibodies to influenza A matrix-protein-2-ectodomain are broadly effective therapeutics and resistant to viral escape mutations
Bibliographic record
Abstract
Influenza A viruses remain a global health threat, yet no universal antibody therapy exists. Clinical programs have centered on neutralizing mAbs, only to be thwarted by strain specificity and rapid viral escape. We instead engineered three non-neutralizing IgG2a mAbs that target distinct, overlapping epitopes within the conserved N terminus of the M2 ectodomain (M2e). Combined at low dose, this "triple M2e-mAb" confers robust prophylactic and therapeutic protection in mice challenged with diverse human and zoonotic IAV strains, including highly pathogenic variants. Therapeutic efficacy depends on Fc-mediated effector activity via FcγRI, FcγRIII, and FcγRIV, rather than in vitro neutralization. Serial passaging in triple M2e-mAb-treated immunocompetent and immunodeficient hosts failed to generate viral escape mutants. Our findings redefine the influenza-specific antibody therapeutic design and support Fc-optimized, non-neutralizing M2e-mAbs as a broadly effective, mutation-resistant, off-the-shelve therapy with direct relevance to human pandemic preparedness.
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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 | 0.000 |
| 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 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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 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".