<i>N</i>-Guanidyl and <i>C</i>-Tetrazole Leu-Enkephalin Derivatives: Efficient Mu and Delta Opioid Receptor Agonists with Improved Pharmacological Properties
Bibliographic record
Abstract
Leu-enkephalin and d -Ala 2 -Leu-enkephalin were modified at their N- and C-termini with guanidyl and tetrazole groups. The resulting molecules were prepared in solution or by solid phase peptide synthesis. The affinity of the different analogues at mu (MOP) and delta opioid receptors (DOP) was then assessed by competitive binding in stably transfected DOP and MOP HEK293 cells. Inhibition of cAMP production and recruitment of β-arrestin were also investigated. Finally, lipophilicity (logD 7.4 ) and plasma stability of each compound were measured. Compared to the native ligands, we found that the replacement of the terminal carboxylate by a tetrazole slightly decreased both the affinity at mu and delta opioid receptors as well as the half-life. By contrast, replacing the ammonium at the N-terminus with a guanidyl significantly improved the affinity, the potency, as well as the lipophilicity and the stability of the resulting peptides. Replacing the glycine residue with a d -alanine in position 2 consistently improved the potency as well as the stability of the analogues. The best peptidomimetic of the whole series, guanidyl-Tyr- d -Ala-Gly-Phe-Leu-tetrazole, displayed sub-nanomolar affinity and an increased lipophilicity. Moreover, it proved to be stable in plasma for up to 24 h, suggesting that the modifications are protecting the compound against protease degradation.
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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) | 0.000 | 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 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".