Combining corticosteroids and NK1R antagonists: A new drugs combination to treat allergic diseases
Bibliographic record
Abstract
Introduction. Recently, using the rat mast cell/basophil cell line RBL-2H3, a major model system for the study of FceRI intracellular signaling pathways, we found that optimal pharmacological blocking of autocrine activation of the neurokinin-1 receptor (NK1R) in response to FceRI clustering suppresses antigen-induced 50 % of maximal cell degranulation, and decreases by nearly 50 % antigen-induced maximal cell activation. Aim. To determine whether combining corticosteroids and NK1R antagonist may be a powerful therapeutic combination to control IgE-FceRI complex responses in allergic diseases. Methodology. IgE-sensitized RBL-2H3 cells were incubated with various concentrations of corticosteroids in combination or not with NK1R antagonist prior to FceRI clustering. Cells degranulation and cysteinyl-leukotrienes (Cys-LTs) production were examined. Results : Maximal concentrations of respective corticosteroids decreased by nearly 50 % allergen-induced maximal degranulation and Cys-LTs production in basophils. Pharmacological blocking of NK1R alone has also produced similar inhibitory effects in basophils. Interestingly, the combination of corticosteroids and NK1R antagonist improved both time response and concentration efficacy of corticosteroids with nearly total inhibition of basophil allergic responses. Conclusion : Combining corticosteroids and NK1R antagonist (patent WO2007/096782) is a promising therapeutic combination to increase corticosteroids efficacy while decreasing effective doses, and may give a second “breath” to corticosteroids patents that are no longer protected. Supported by the Canadian Institutes of Health Research .
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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 | 0.001 | 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) | 0.003 | 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 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".