Pharmacology and quantitative structure-activity relationships of imidazolylpropylguanidines with mepyramine-like substructures as non-peptide neuropeptide Y Y<sub>1</sub>receptor antagonists
Bibliographic record
Abstract
The design of non-peptide, Y1-selective antagonists of neuropeptide Y (NPY) as pharmacological tools is in progress and is increasingly important as therapeutic applications are expected. Starting from the potent histamine H2agonist and weak NPY Y1antagonist arpromidine, 16 imidazolylpropylguanidine derivatives were synthesized and tested for Y1antagonistic activity (inhibition of NPY-stimulated Ca2+increase in human erythroleukemic cells), where the pheniramine-like moiety of arpromidine was replaced with 2-pyridylaminoalkyl, benzyl-(2-pyridyl)aminoalkyl, and phenyl-(2-pyridyl)alkylaminoalkyl partial structures derived from mepyramine. The pA2values of the most active compounds are in the range of 6.2-6.5. Quantitative structure-activity relationships (QSAR) were investigated by fragment regression analysis. Results indicate that a tetramethylene spacer between the guanidino group and the amino nitrogen is optimal. For an at least moderate degree of Y1antagonistic activity, a second benzyl or phenyl group must be present in addition to the 2-pyridyl ring. At this second group, hydrophobic substituents such as 3,4-di-Cl and 4-Br further enhance Y1antagonism. The most active derivative additionally bears a 5-Br substituent at the 2-pyridyl moiety. Structure-activity relationships suggest that the compounds might be able to partially imitate the role of NPY when interacting with Y1receptors and thus behave as moderate non-peptide NPY Y1antagonists.Key words : neuropeptide Y Y1antagonists, imidazolylpropylguanidines, quantitative structure-activity relationships.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 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".