Aprepitant for Cough in Lung Cancer. A Randomized Placebo-controlled Trial and Mechanistic Insights
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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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Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.008 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 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) | 0.000 | 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 it