A New Era of Targeting the Ancient Gatekeepers of the Immune System: Toll-Like Agonists in the Treatment of Allergic Rhinitis and Asthma
Bibliographic record
Abstract
Toll-like receptors (TLR) belong to a large family of pattern recognition receptors known as the ancient 'gatekeepers' of the immune system. TLRs are located at the first line of defense against invading pathogens as well as aeroallergens, making them interesting targets to modulate the natural history of respiratory allergy. Agonists of TLRs have been widely employed in therapeutic or prophylactic preparations useful for asthma/allergic rhinitis (AR) patients. MPL® (a TLR4 agonist) and the CpG oligodeoxynucleotide of 1018 ISS, a TLR9 agonist, show strong immunogenicity effects that make them appropriate adjuvants for allergy vaccines. Targeting the TLRs can enhance the efficacy of specific allergen immunotherapy, currently the only available 'curative' treatment for respiratory allergies. In addition, intranasal administration of AZD8848 (a TLR7 agonist) and VTX-1463 (a TLR8 agonist) as stand-alone therapeutics have revealed efficacy in the relief of the symptoms of AR patients. No anaphylaxis has been so far reported with such compounds targeting TLRs, with the most common adverse effects being transient and local irritation (e.g. redness, swelling and pruritus). Many other compounds that target TLRs have been found to suppress airway inflammation, eosinophilia and airway hyper-responsiveness in various animal models of allergic inflammation. Indeed, in the future a wide variability of TLR agonists and even antagonists that exhibit anti-asthma/AR effects are likely to emerge.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.005 |
| Insufficient payload (model declined to judge) | 0.004 | 0.002 |
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".