Action and Disposition of the β3-Agonist Nebivolol in the Presence of Inflammation; An Alternative to Conventional β1-Blockers
Bibliographic record
Abstract
Inflammation reduces pharmacological response to β1-blockers by down-regulating the target receptor protein. This may contribute to the sub-optimal response to pharmacotherapy with β-blockers. Nebivolol is a third generation β-adrenoceptor (AR) blocker with high selectivity for blocking β1 and β3-agonistic properties. We studied whether response to nebivolol is also reduced by inflammation. Male Sprague-Dawley rats (Inflamed; Mycobacterium butyricum induced) and Control (healthy) were orally administered single doses of 2 mg/kg nebivolol (n=5) or 25 mg/kg propranolol (positive control, n=7-8); ECG recorded for PR and RR interval measurements; serial blood samples were collected for pharmacokinetic assessment. Subsequently, the myocardial β1, β2 and β3-AR levels were measured in homogenized hearts. For propranolol, inflammation resulted in increased concentration but reduced response and down-regulation of β1- AR. The action and disposition of nebivolol were, however, unaffected by inflammation despite the reduced β1-AR levels. The levels of β2 and β3-AR were unaffected by inflammation. The consistency of response to nebivolol despite inflammation may be due to the predominance of contribution of β2 and β3-AR. The lack of an inhibitory effect of inflammation on the clearance of nebivolol is suggestive of mechanisms other than an efficient hepatic metabolism for its low bioavailability. If extrapolated to human, nebivolol may be a more effective cardiovascular drug when inflammatory conditions are present.
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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 | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".