A computationally designed hemagglutinin stem-binding protein provides <i>in vivo</i> protection from influenza in the ferret model.
Bibliographic record
Abstract
Abstract Influenza viruses cause thousands of deaths worldwide and remain a prominent public health issue. Currently, the influenza vaccine is the best tool available at protecting against infection, but the correct strains are hard to predict and the vaccines do not always work. More effective antiviral drugs are needed. Protection against influenza by broadly neutralizing antibodies targeted to a highly conserved region in the hemagglutinin (HA) stem shows great potential for immunotherapy. We have investigated the protective efficacy of an engineered protein, HB36.6, that was computationally designed to bind with high affinity to the same region in the HA stem as targeted by broadly neutralizing antibodies. Studies in the mouse model demonstrated that intranasal delivery of HB36.6 affords protection in mice lethally challenged with diverse strains of influenza, both when administered as a single dose of 6.0 mg/kg up to 48 hrs prior to challenge and significantly reduces disease when administered as a daily therapeutic after challenge. The current study was designed to evaluate HB36.6 in the ferret model, comparing prophylactic and therapeutic efficacy of this new antiviral against high dose influenza aerosol challenge. Similar to data obtained in the mouse model, results from these studies demonstrated significant reduction in viral load and clinical signs of disease in ferrets treated with HB36.6 compared to untreated controls or ferrets treated with Tamiflu. Together, these results show that binding of HB36.6 to the influenza HA stem region alone was sufficient to reduce the viral infection and disease process in vivo. These studies demonstrate a potential new class of antivirals for influenza.
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 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 | 0.000 |
| 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".