Synthesis of Potent .ALPHA.v.BETA.3/.ALPHA.IIb.BETA.3 Dual Antagonists Tricyclic Pharmacophore-based Molecules and Their Selectivity Control
Bibliographic record
Abstract
The blockade of αIIbβ3 binding to fibrinogen is known to provide a potent antiplatelet activity. We have focused on the adhesion and migration of integrin αβ3in vascular smooth muscle cells and leucocytes. An injectable αβ3 dual antagonist would be a novel drug candidate for acute ischemic diseases. For the molecular design of novel non-peptide integrin ββ3 antagonists, we focused on spatial screening and mimetics of the RGD tripeptide, which was a key recognition site. The first piperazine analogue (4) exhibited strong αIIbβ3 activity, and replacement of piperazine by 4-aminopiperidine afforded the 6alpha;β3/βIIbβ3 dual antagonist (30). Paying attention to enhancement of activity and improvement of solubility, optimization of 30 gave some preliminary candidates modified at the central aromatic or the C-terminus. Backup compounds possessing the (3S)-aminopiperidine moiety with further improvement of solubility were synthesized. These preliminary candidates exhibited preferable ADME in rats, and some of them showed acceptable water solubility, which did not provide detectable toxicity in vitro or in mice. We also generated αβ3 selective antagonists with tricyclic pharmacophore, in order to prove that in vivo efficacy of the dual antagonist was superior to that of the avf33 selective antagonist. Finally, excellent in vivo efficacy of the candidate (81) was demonstrated in both canine ACS and PTCA models by independent groups. Regarding development of 81, a partnership was established in 2006, and a clinical trial of 81 is now being prepared in the US and Canada. The selection of an integrin research program, strategies for competing with big pharmaceutical companies, and speedy medicinal chemistry are also described.
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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 | 0.001 |
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".