Synthesis and characterization of a disubstituted piperazine derivative with T-type channel blocking action and analgesic properties
Bibliographic record
Abstract
BACKGROUND: T-type calcium channels are important contributors to signaling in the primary afferent pain pathway and are thus important targets for the development of analgesics. It has been previously reported that certain piperazine-based compounds such as flunarizine are able to inhibit T-type calcium channels. Thus, we hypothesized that novel piperazine compounds could potentially act as analgesics. RESULTS: Here, we have created a series of 14 compound derivatives around a diphenyl methyl-piperazine core pharmacophore. Testing their effects on transiently expressed Cav3.2 calcium channels revealed one derivative (3-((4-(bis(4-fluorophenyl)methyl)piperazin-1-yl)methyl)-4-(2-methoxyphenyl)-1,2,5-oxadiazole 2-oxide, compound 10e) as a potent blocker. 10e mediate tonic block of these channels with an IC50 of around 4 micromolar. 10e also blocked Cav3.1 and Cav3.3 channels, but only weakly affected high-voltage-activated Cav1.2 and Cav2.2 channels. Intrathecal delivery of 10e mediated relief from formalin and complete Freund's adjuvant induced inflammatory pain that was ablated by genetic knockout of Cav3.2 channels. CONCLUSIONS: Altogether, our data identify a novel T-type calcium channel blocker with tight structure activity relationship (SAR) and relevant in vivo efficacy in inflammatory pain conditions.
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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) | 0.002 | 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".