Preclinical Animal Models to Investigate the Role of Nav1.7 Ion Channels in Pain
Bibliographic record
Abstract
Chronic pain is a maladaptive neurological disease that remains a major global healthcare problem. Voltage-gated sodium channels (Navs) are major drivers of the excitability of sensory neurons, and the Nav subtype 1.7 (Nav1.7) has been shown to be critical for the transmission of pain-related signaling. This is highlighted by demonstrations that gain-of-function mutations in the Nav1.7 gene SCN9A result in various pain pathologies, whereas loss-of-function mutations cause complete insensitivity to pain. A substantial body of evidence demonstrates that chronic neuropathy and inflammation result in an upregulation of Nav1.7, suggesting that this channel contributes to pain transmission and sensation. As such, Nav1.7 is an attractive human-validated target for the treatment of pain. Nonetheless, a lack of subtype selectivity, insufficient efficacy, and adverse reactions are some of the issues that have hindered Nav1.7-targeted drug development. This review summarizes the pain behavior profiles mediated by Nav1.7 reported in multiple preclinical models, outlining the current knowledge of the biophysical, physiological, and distribution properties required for a Nav1.7 inhibitor to produce analgesia.
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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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".