Effects of common cold and concomitant administration of nasal decongestant on the pharmacokinetics and pharmacodynamics of nasal glucagon in otherwise healthy participants: <scp>A</scp> randomized clinical trial
Bibliographic record
Abstract
Aims Nasal glucagon (NG) is a nasally‐administered glucagon powder, absorbed through the nasal mucosa, designed for treatment of severe hypoglycaemia. This study evaluated the safety, pharmacokinetics (PK) and pharmacodynamics (PD) of NG in otherwise healthy participants with common colds and after recovery from cold symptoms, with and without concomitant nasal decongestant. Materials and Methods This was a single‐centre, open‐label study. Cohort 1 participants (N = 18) received 2 doses of NG: one while experiencing nasal congestion and another after recovery from cold symptoms. Cohort 2 participants (N = 18), who also had colds with nasal congestion, received a single dose of NG 2 hours after treatment with the decongestant oxymetazoline. Total symptoms score and other safety measures were assessed before and after NG administration. Results NG was well tolerated, without serious adverse events. Common adverse events (transient lacrimation, nasal discomfort, rhinorrhea and nausea) were more frequent in both Cohorts 1 and 2 during nasal congestion. Glucagon levels peaked 18 minutes post‐dose and glucose levels peaked 30 to 42 minutes post‐dose in all groups. Nasal congestion, with or without concomitant nasal decongestant, did not significantly affect PK of NG. Although glucose AUECs 0‐t was different between Cohort 1 with nasal congestion and Cohort 2, glucose concentrations at 30 minutes appeared similar in all groups. Conclusions There were no clinically relevant differences in safety or PK/PD of NG associated with nasal congestion or concomitant administration of nasal decongestant, suggesting that NG can be used to treat severe hypoglycaemia in individuals experiencing nasal congestion.
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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 | 0.002 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.002 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 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".