Inhibition of inflammatory pain and cough by a novel charged sodium channel blocker
Bibliographic record
Abstract
Background and Purpose: Many pain-triggering nociceptor neurons express TRPV1 or TRPA1, cation-selective channels with large pores that enable permeation of QX-314, a cationic analogue of lidocaine. Co-application of QX-314 with TRPV1 or TRPA1 activators can silence nociceptors. We now describe BW-031, a novel more potent cationic sodium channel inhibitor, test whether its application alone can inhibit the pain associated with tissue inflammation, and whether this strategy can also inhibit cough. Experimental Approach: We characterized BW-031 inhibition of sodium channels and tested BW-031 in three models of inflammatory pain: rat paw inflammation produced by Complete Freund’s Adjuvant injection or surgical incision and a mouse paw UV burn model. We also tested the ability of BW-031 to inhibit cough induced by inhalation of dilute citric acid in guinea pigs. Key Results: BW-031 inhibited Nav1.7 and Nav1.1 channels with ~6-fold greater potency than QX-314 when introduced inside cells and entered capsaicin-activated TRPV1 expressing sensory neurons. BW-031 inhibited inflammatory pain in all three models, producing more effective and longer-lasting inhibition of pain than QX-314 in the mouse UV burn model. BW-031 was also effective in reducing cough counts by 78-90% when applied intratracheally under isoflurane anesthesia or by aerosol inhalation in awake guinea pigs with airway inflammation produced by ovalbumin sensitization. Conclusion and Implications: BW-031 a novel cationic sodium channel inhibitor can be applied locally as a single agent to inhibit inflammatory pain and also effectively inhibits cough in a guinea pig model of nociceptor-activated cough, suggesting a new clinical approach to treating cough.
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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 | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".