Aprepitant for Cough in Lung Cancer. A Randomized Placebo-controlled Trial and Mechanistic Insights
Bibliographic record
Abstract
Abstract Rationale Effective cough treatments are a significant unmet need in patients with lung cancer. Aprepitant is a licensed treatment for nausea and vomiting, which blocks substance P activation of NK-1 (neurokinin 1) receptors, a mechanism also implicated in cough. Objectives To assess aprepitant in patients with lung cancer with cough and evaluate mechanisms in vagal nerve tissue. Methods Randomized double-blind crossover trial of patients with lung cancer and bothersome cough. They received 3 days of aprepitant or matched placebo; after a 3-day washout, patients crossed to the alternative treatment. The primary endpoint was awake cough frequency measured at screening and Day 3 of each treatment; secondary endpoints included patient-reported outcomes. In vitro, the depolarization of isolated guinea pig and human vagus nerve sections in grease-gap recording chambers, indicative of sensory nerve activation, was measured to evaluate the mechanism. Measurements and Main Results Twenty patients with lung cancer enrolled, with a mean age 66 years (±7.7); 60% were female and 80% had non–small cell cancer, 50% had advanced stage, and 55% had World Health Organization performance status 1. Cough frequency improved with aprepitant, reducing by 22.2% (95% confidence interval [CI], 2.8–37.7%) over placebo while awake (P = 0.03), 30.3% (95% CI, 12.7–44.3) over 24 hours (P = 0.002), and 59.8% (95% CI, 15.1–86.0) during sleep (P = 0.081). Patient-reported outcomes all significantly improved. Substance P depolarized both guinea pig and human vagus nerve. Aprepitant significantly inhibited substance P–induced depolarization by 78% in guinea pig (P = 0.0145) and 94% in human vagus (P = 0.0145). Conclusions Substance P activation of NK-1 receptors appears to be an important mechanism driving cough in lung cancer, and NK-1 antagonists show promise as antitussive therapies.
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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 | 0.005 | 0.003 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.004 | 0.003 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.003 | 0.002 |
| Insufficient payload (model declined to judge) | 0.006 | 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".