Substance P – Structure-Activity Studies and the Development of Antagonists
Bibliographic record
Abstract
Various attempts to developing antagonists for substance P (SP) and related peptides, based on our past experience with angiotensin, kinins, and neurotensin, were unsuccessful. The particular features of SP, namely the high activity of C-terminal partial sequences in some pharmacological tests were used to develop one hexa and several octapeptide antagonists. Weak antagonists were obtained with a single modification, namely the replacement of Leu10 with trp in the sequences SP (6-11), SP (4-11) and SP (1-11). The affinity of octa and undecapeptide antagonists could be increased by using two (in positions 7 and 9 or 7 and 10) or 3 (in position 7, 9 and 10) substitutions of the natural residues with trp. Affinity of antagonists was further increased by replacing Met11 with either Nle or Phe. These new compounds showed some selectivity, [pro4, trp7 ,9, Nle11 ]-SP (4-11) being more potent in the rabbit mesenteric vein than all other octapeptides described in the present study; on the other hand, [pro4, trp7 ,9,10,Phe11]-SP (4-11) was found to be the most potent antagonist of SP and related peptides in the guinea pig ileum and the guinea pig trachea. Both compounds were similarly active in the dog carotid artery. Undecapeptide antagonists, bearing the same structural modifications as the octapeptides, were found to be stimulant in the guinea pig trachea and relaxant in the dog carotid artery. The agonistic property was eliminated by repeated applications of the compounds in guinea pig tracheae, and therefore the compounds could be tested as antagonists. The undecapeptides were found to be much more active antagonists against kasinin and eledoisin than against SP and physalaemin. The data obtained with the octa and undecapeptide antagonists of SP have been used for identification and characterization of SP receptors in various smooth muscles. It appears that SP and related peptides may exert their numerous pharmacological effects by activating more than one receptor type.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| 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 | 0.001 |
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".