EFFECT OF CENTRALLY AND PERIPHERALLY ACTING GABAB AGONISM ON THE COUGH REFLEX
Bibliographic record
Abstract
Background: Currently there are no effective licensed anti-tussive therapies. Understanding how the neuronal mechanisms mediating the cough reflex in animal models translate to humans is important for the development of effective therapies. Pre-clinical studies suggest that the activation of GABAB receptors in both the peripheral and central nervous systems inhibit cough. Objective: To compare the effect of central and peripherally acting GABAB agonists (lesogaberan and baclofen) on the cough reflex in healthy volunteers. Methods: We performed a single center, double-blind, double-dummy, three-way crossover trial in healthy controls comparing single doses of lesogaberan (120mg MR), with baclofen (40mg) and placebos. Cough responses to inhaled capsaicin were assessed at screening and 2h post-dose on each study day. The primary endpoint was the maximum number of coughs evoked at any concentration of capsaicin (Emax) and the secondary endpoint was the concentration evoking 50% of the maximal response (ED50). Results: Fifteen patients enrolled onto the study (median age 29 (IQR 25-44) years; 7 females, mean BMI 24.6(±3.0). Lesogaberan treatment produced a small, statistically significant increase in Emax compared with placebo [mean 13.4coughs (95%CI 10.1-17.9) vs. 11.8coughs (8.8-15.9), p=0.04], but had no effect on ED50 [geometric mean 47.4µM (95%CI 24.4-91.7) vs 37.6 µM (95%CI 19.2-73.5), p=0.37]. In contrast, baclofen had no significant effect on Emax (11.1, 95%CI 8.1-15.4) (p=0.23), but significantly increased ED50 compared with placebo (geometric mean 75.2µM (95%CI 37.2-151.8), p=0.002). Conclusion: This data suggests the anti-tussive actions of GABAB agonists, in healthy volunteers, occur in the central rather than the peripheral nervous system.
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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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".