5-HT1A Receptor Agonist Befiradol Reduces Fentanyl-induced Respiratory Depression, Analgesia, and Sedation in Rats
Bibliographic record
Abstract
BACKGROUND: There is an unmet clinical need to develop a pharmacological therapy to counter opioid-induced respiratory depression without interfering with analgesia or behavior. Several studies have demonstrated that 5-HT1A receptor agonists alleviate opioid-induced respiratory depression in rodent models. However, there are conflicting reports regarding their effects on analgesia due in part to varied agonist receptor selectivity and presence of anesthesia. Therefore the authors performed a study in rats with befiradol (F13640 and NLX-112), a highly selective 5-HT1A receptor agonist without anesthesia. METHODS: Respiratory neural discharge was measured using in vitro preparations. Plethysmographic recording, nociception testing, and righting reflex were used to examine respiratory ventilation, analgesia, and sedation, respectively. RESULTS: Befiradol (0.2 mg/kg, n = 6) reduced fentanyl-induced respiratory depression (53.7 ± 5.7% of control minute ventilation 4 min after befiradol vs. saline 18.7 ± 2.2% of control, n = 9; P < 0.001), duration of analgesia (90.4 ± 11.6 min vs. saline 130.5 ± 7.8 min; P = 0.011), duration of sedation (39.8 ± 4 min vs. saline 58 ± 4.4 min; P = 0.013); and induced baseline hyperventilation, hyperalgesia, and "behavioral syndrome" in nonsedated rats. Further, the befiradol-induced alleviation of opioid-induced respiratory depression involves sites or mechanisms not functioning in vitro brainstem-spinal cord and medullary slice preparations. CONCLUSIONS: The reversal of opioid-induced respiratory depression and sedation by befiradol in adult rats was robust, whereas involved mechanisms are unclear. However, there were adverse concomitant decreases in fentanyl-induced analgesia and altered baseline ventilation, nociception, and behavior.
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How this classification was reachedexpand
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 | 0.000 | 0.001 |
| 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 | 0.000 |
| 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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, 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".