Analgesic Properties of Opioid/NK1 Multitarget Ligands with Distinct in Vitro Profiles in Naive and Chronic Constriction Injury Mice
Bibliographic record
Abstract
The lower efficacy of opioids in neuropathic pain may be due to the increased activity of pronociceptive systems such as substance P. We present evidence to support this hypothesis in this work from the spinal cord in a neuropathic pain model in mice. Biochemical analysis confirmed the elevated mRNA and protein level of pronociceptive substance P, the major endogenous ligand of the neurokinin-1 (NK1) receptor, in the lumbar spinal cord of chronic constriction injury (CCI)-mice. To improve opioid efficacy in neuropathic pain, novel compounds containing opioid agonist and neurokinin 1 (NK1) receptor antagonist pharmacophores were designed. Structure-activity studies were performed on opioid agonist/NK1 receptor antagonist hybrid peptides by modification of the C-terminal amide substituents. All compounds were evaluated for their affinity and in vitro activity at the mu opioid (MOP) and delta opioid (DOP) receptors, and for their affinity and antagonist activity at the NK1 receptor. On the basis of their in vitro profiles, the analgesic properties of two new bifunctional hybrids were evaluated in naive and CCI-mice, representing models for acute and neuropathic pain, respectively. The compounds were administered to the spinal cord by lumbar puncture. In naive mice, the single pharmacophore opioid parent compounds provided better analgesic results, as compared to the hybrids (max 70% MPE), raising the acute pain threshold close to 100% MPE. On the other hand, the opioid parents gave poor analgesic effects under neuropathic pain conditions, while the best hybrid delivered robust (close to 100% MPE) and long lasting alleviation of both tactile and thermal hypersensitivity. The results presented emphasize the potential of opioid/NK1 hybrids in view of analgesia under nerve injury conditions.
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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) | 0.001 | 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".