Antiviral Role of Toll-Like Receptor-3 Agonists Against Seasonal and Avian Influenza Viruses
Bibliographic record
Abstract
The divergence and antigenic shifts in influenza viruses represent significant challenges for the development of effective vaccines and antiviral drugs against influenza viruses. In view of current challenges and/or deficiencies in the influenza pandemic influenza preparedness, novel antiviral strategies which are robust and can respond to constant viral mutations, are particularly needed to combat future pandemic threats. Toll-like receptor-3 (TLR-3) is an integral part of the host's innate immune system and serves as an important signaling pathway for the recognition of dsRNA for the triggering of antiviral and inflammatory responses to combat viral infections. This review examines dsRNA including Poly ICLC and liposome-encapsulated Poly ICLC (LE Poly ICLC) as TLR-3 agonists for their antiviral activity against seasonal and highly pathogenic avian influenza (HPAI) viruses. Furthermore, their roles in attenuating the antiviral and inflammatory cytokines in the host will also be explored. Preclinical studies in experimental animals suggest Poly ICLC and liposome-encapsulated Poly ICLC are safe and offer broad-spectrum protection against both seasonal and HPAI viruses, as well as other respiratory viruses including respiratory syncytial virus and SARS. Preliminary results from recent studies suggest these drugs up-regulate the production of interferons (-alpha, -beta, and -gamma), and tumor necrosis factor (TNF-alpha) but downregulate some proinflammatory cytokines including IL-2 and IL-4. Taken together, these results suggest these TLR-3 agonists have a promising role to play as safe, effective and broad-spectrum anti-influenza drugs that could complement other antiviral drugs to combat seasonal, zoonotic and pandemic influenza viruses. The clinical safety of these drugs and their efficacy in pre-clinical studies may provide sufficient justification for regulatory agencies to consider their fast track development for use in future outbreaks of pandemic influenza or of other emerging respiratory pathogens.
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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 | 0.000 |
| 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".