Bifunctional μ/δ Opioid Peptides: Variation of the Type and Length of the Linker Connecting the Two Components
Bibliographic record
Abstract
On the basis of evidence that opioid compounds with a mixed μ agonist/δ antagonist profile may produce an antinociceptive effect with low propensity to induce side effects, bifunctional opioid peptides containing the μ agonist H-Dmt-d-Arg-Phe-Lys-NH(2) ([Dmt(1) ]DALDA; Dmt = 2',6'-dimethyltyrosine) connected tail-to-tail via various α,ω-diaminoalkyl- or diaminocyclohexane linkers to the δ antagonists H-Tyr-TicΨ[CH(2) -NH]Cha-Phe-OH (TICP[Ψ]; Cha = cyclohexylalanine, Tic = 1,2,3,4-tetrahydroisoquinoline-3-carboxylic acid), H-Dmt-Tic-OH or H-Bcp-Tic-OH (Bcp = 4'-[N-((4'-phenyl)phenethyl)carboxamido]phenylalanine) were synthesized and pharmacologically characterized in vitro. Bifunctional [Dmt(1) ]DALDA→NH-(CH(2) )(n) -NH←TICP[Ψ] compounds (n = -12) showed decreasing μ and δ receptor binding affinities with increasing linker length. As expected, several of the bifunctional peptides were μ agonist/δ antagonists with low nanomolar μ and δ receptor binding affinities. However, compounds with unexpected opioid activity profiles, including a μ partial agonist/δ partial agonist, μ antagonist/δ antagonists and μ agonist/δ agonists, were also identified. These results indicate that the binding affinities and intrinsic efficacies of these bifunctional compounds at both receptors depend on the length and type of the linker connecting the μ and δ components. An important recommendation emerging from this study is that the in vitro activity profiles of bifunctional compounds containing an agonist and an antagonist component connected via a linker need to be determined prior to their pharmacological evaluation in vivo.
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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".