New Diarylmethylpiperazines as Potent and Selective Nonpeptidic δ Opioid Receptor Agonists with Increased In Vitro Metabolic Stability
Bibliographic record
Abstract
Nonpeptide delta opioid agonists are analgesics with a potentially improved side-effect and abuse liability profile, compared to classical opioids. Andrews analysis of the NIH nonpeptide lead SNC-80 suggested the removal of substituents not predicted to contribute to binding. This approach led to a simplified lead, N, N-diethyl-4-[phenyl(1-piperazinyl)methyl]benzamide (1), which retained potent binding affinity and selectivity to the human delta receptor (IC(50) = 11 nM, mu/delta = 740, kappa/delta > 900) and potency as a full agonist (EC(50) = 36 nM) but had a markedly reduced molecular weight, only one chiral center, and increased in vitro metabolic stability. From this lead, the key pharmacophore groups for delta receptor affinity and activation were more clearly defined by SAR and mutagenesis studies. Further structural modifications on the basis of 1 confirmed the importance of the N, N-diethylbenzamide group and the piperazine lower basic nitrogen for delta binding, in agreement with mutagenesis data. A number of piperazine N-alkyl substituents were tolerated. In contrast, modifications of the phenyl group led to the discovery of a series of diarylmethylpiperazines exemplified by N, N-diethyl-4-[1-piperazinyl(8-quinolinyl)methyl]benzamide (56) which had an improved in vitro binding profile (IC(50) = 0.5 nM, mu/delta = 1239, EC(50) = 3.6 nM) and increased in vitro metabolic stability compared to SNC-80.
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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 | 0.001 |
| 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".