Bifunctional Peptide-Based Opioid Agonist–Nociceptin Antagonist Ligands for Dual Treatment of Acute and Neuropathic Pain
Bibliographic record
Abstract
Drugs able to treat both nociceptive and neuropathic pain effectively without major side effects are lacking. We developed a bifunctional peptide-based hybrid (KGNOP1) that structurally combines a mu-opioid receptor agonist (KGOP1) with antinociceptive activity and a weak nociceptin receptor antagonist (KGNOP3) with anti-neuropathic pain activity. We investigated KGNOP1-related behavioral effects after intravenous administration in rats by assessing thermal nociception, cold hyperalgesia in a model of neuropathic pain induced by chronic constriction injury of the sciatic nerve, and plethysmography parameters including inspiratory time (TI) and minute ventilation (VM) in comparison to the well-known opioid analgesics, tramadol and morphine. Time-course and dose-dependent effects were investigated for all behavioral parameters to determine the effective doses 50% (ED50). Pain-related effects on cold hyperalgesia were markedly increased by KGNOP1 as compared to KGNOP3 and tramadol (ED50: 0.0004, 0.32, and 12.1 μmol/kg, respectively), whereas effects on thermal nociception were significantly higher with KGNOP1 as compared to morphine (ED50: 0.41 and 14.7 μmol/kg, respectively). KGNOP1 and KGOP1 produced a larger increase in TI and deleterious decrease in VM in comparison to morphine and tramadol (ED50(TI): 0.63, 0.52, 12.2, and 50.9 μmol/kg; ED50(VM): 0.57, 0.66, 10.6, and 50.0 μmol/kg, respectively). Interestingly, the calculated ratios of anti-neuropathic pain/antinociceptive to respiratory effects revealed that KGNOP1 was safer than tramadol (ED50 ratio: 5.44 × 10 vs 0.24) and morphine (ED50 ratio: 0.72 vs 1.39). We conclude that KGNOP1 is able to treat both experimental neuropathic and nociceptive pain, more efficiently and safely than tramadol and morphine, respectively, and thus should be a candidate for future clinical developments.
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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".