Mechanism-specific assay design facilitates the discovery of Nav1.7-selective inhibitors
Bibliographic record
Abstract
Many ion channels, including Nav1.7, Cav1.3, and Kv1.3, are linked to human pathologies and are important therapeutic targets. To develop efficacious and safe drugs, subtype-selective modulation is essential, but has been extremely difficult to achieve. We postulate that this challenge is caused by the poor assay design, and investigate the Nav1.7 membrane potential assay, one of the most extensively employed screening assays in modern drug discovery. The assay uses veratridine to activate channels, and compounds are identified based on the inhibition of veratridine-evoked activities. We show that this assay is biased toward nonselective pore blockers and fails to detect the most potent, selective voltage-sensing domain 4 (VSD4) blockers, including PF-05089771 (PF-771) and GX-936. By eliminating a key binding site for pore blockers and replacing veratridine with a VSD-4 binding activator, we directed the assay toward non-pore-blocking mechanisms and discovered Nav1.7-selective chemical scaffolds. Hence, we address a major hurdle in Nav1.7 drug discovery, and this mechanistic approach to assay design is applicable to Cav3.1, Kv1.3, and many other ion channels to facilitate drug discovery.
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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 | 0.002 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.002 | 0.002 |
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".