Design, Synthesis, and <i>In Vitro</i> Characterization of Proteolytically-Stable Opioid-Neurotensin Hybrid Peptidomimetics
Bibliographic record
Abstract
High Resolution Image Download MS PowerPoint Slide Linking an opioid to a nonopioid pharmacophore represents a promising approach for reducing opioid-induced side effects during pain management. Herein, we describe the optimization of the previously reported opioid-neurotensin hybrids (OPNT-hybrids), SBL-OPNT-05 & -10, containing the μ-/δ-opioid agonist H-Dmt- d -Arg-Aba-β-Ala-NH 2 and NT(8–13) analogs optimized for NTS2 affinity. In the present work, the constrained dipeptide Aba-β-Ala was modified to investigate the optimal linker length between the two pharmacophores, as well as the effect of expanding the aromatic moiety within constrained dipeptide analogs, via the inclusion of a naphthyl moiety. Additionally, the N -terminal Arg residue of the NT(8–13) pharmacophore was substituted with β 3 h Arg. For all analogs, affinity was determined at the MOP, DOP, NTS1, and NTS2 receptors. Several of the hybrid ligands showed a subnanomolar affinity for MOP, improved binding for DOP compared to SBL-OPNT-05 & -10, as well as an excellent NTS2-affinity with high selectivity over NTS1. Subsequently, the G αi1 and β-arrestin-2 pathways were evaluated for all hybrids, along with their stability in rat plasma. Upon MOP activation, SBL-OPNT-13 and -18 were the least effective at recruiting β-arrestin-2 ( E max = 17 and 12%, respectively), while both compounds were also found to be partial agonists at the G αi1 pathway, despite improved potency compared to DAMGO. Importantly, these analogs also showed a half-life in rat plasma in excess of 48 h, making them valuable tools for future in vivo investigations.
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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 | 0.000 |
| 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".