1,2,3,4-Tetrahydroquinoline-Based Selective Human Neuronal Nitric Oxide Synthase (nNOS) Inhibitors: Lead Optimization Studies Resulting in the Identification of<i>N</i>-(1-(2-(Methylamino)ethyl)-1,2,3,4-tetrahydroquinolin-6-yl)thiophene-2-carboximidamide as a Preclinical Development Candidate
Bibliographic record
Abstract
Numerous studies have shown that selective nNOS inhibitors could be therapeutic in many neurological disorders. Previously, we reported a series of 1,2,3,4-tetrahydroquinoline-based potent and selective nNOS inhibitors, highlighted by 1 ( J. Med. Chem. 2011 , 54 , 5562 - 5575 ). Despite showing activity in two rodent pain models, 1 suffered from low oral bioavailability (18%) and moderate hERG channel inhibition (IC(50) = 4.7 μM). To optimize the properties of 1, we synthesized a small focused library containing various alkylamino groups on the 1-position of the 1,2,3,4-tetrahydroquinoline scaffold. The compounds were triaged based on their activity in the NOS and hERG manual patch clamp assays and their calculated physicochemical parameters. From these studies, we identified 47 as a potent and selective nNOS inhibitor with improved oral bioavailability (60%) and no hERG channel inhibition (IC(50) > 30 μM). Furthermore, 47 was efficacious in the Chung model of neuropathic pain and has an excellent safety profile, making it a promising preclinical development candidate.
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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) | 0.001 | 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 | 0.001 |
| 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".