Optimized Opioid-Neurotensin Multitarget Peptides: From Design to Structure–Activity Relationship Studies
Bibliographic record
Abstract
Abstract Fusion of nonopioid pharmacophores, such as neurotensin, with opioid ligands represents an attractive approach for pain treatment. Herein, the μ-/δ-opioid agonist tetrapeptide H-Dmt-d-Arg-Aba-β-Ala-NH2 (KGOP01) was fused to NT(8-13) analogues. Since the NTS1 receptor has been linked to adverse effects, selective MOR-NTS2 ligands are preferred. Modifications were introduced within the native NT sequence, particularly a β3-homo amino acid in position 8 and Tyr11 substitutions. Combination of β3hArg and Dmt led to peptide 7, a MOR agonist, showing the highest NTS2 affinity described to date (Ki = 3 pM) and good NTS1 affinity (Ki = 4 nM), providing a >1300-fold NTS2 selectivity. The (6-OH)Tic-containing analogue 9 also exhibited high NTS2 affinity (Ki = 1.7 nM), with low NTS1 affinity (Ki = 4.7 μM), resulting in an excellent NTS2 selectivity (>2700). In mice, hybrid 7 produced significant and prolonged antinociception (up to 8 h), as compared to the KGOP01 opioid parent compound.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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) | 0.002 | 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".