Sulfamoylbenzoate Analogues as Putative Treatments for Chronic Neurologic Disease
Bibliographic record
Abstract
Targeting neuroinflammation, a key factor in the progression of chronic neurological disorders such as Alzheimer’s disease and epilepsy, is essential for discovering effective treatments. Recent studies indicate that Pannexin-1 (Panx-1) channels serve as the primary pathways for ATP release from degenerating and innate immune cells in the brain, triggering neuroinflammation. By blocking it with a potent channel blocker, the chronic activation of Panx-1 channels could be reduced, thereby protecting damaged and dying neurons associated with Alzheimer’s disease and epilepsy. A known channel blocker of the Panx-1 channel is the drug Probenecid, which also acts as an agonist to the TRPV2 channel. Here, we aimed to create analogues of Probenecid to identify a potent channel blocker for either the Panx-1 or the TRPV2 channel. A zebrafish model was used to screen the potency of the created analogues by inducing seizures using pentylenetetrazol, targeting epilepsy in this context. Compound 1002 is the most effective Panx-1 blocker identified in this study, achieving a two-fold decrease in seizure activity at a lower concentration of 70 µM compared to the reference dosage of PBN at 100 µM. Additionally, it showed a twelve-fold increase in seizure inhibition compared to valproic acid at 75 µM, a widely recognized treatment for epilepsy. It was further tested in a mouse model of kainic acid-induced seizures, where it exhibited a similar anti-seizure profile and significantly improved the survival rate of mice after being pretreated with the compound at a dose of 100 mg/kg. Further screening of additional analogues is currently in progress, along with electrophysiology experiments using voltage clamp and site-directed mutagenesis to determine the mechanism of action by which the compound binds and reduces seizures.
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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 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".