Targeted ubiquitination of Na <sub>V</sub> 1.8 reduces sensory neuronal excitability
Bibliographic record
Abstract
Abstract Chronic pain and addiction are a significant global health challenge. Voltage-gated sodium channel Na V 1.8, a pivotal driver of pain signaling, is a clinically validated target for the development of novel, non-addictive pain therapeutics. Small molecule inhibitors against Na V 1.8 have shown promise in acute pain indications, but large clinical effect sizes have not yet been demonstrated and efficacy in chronic pain indications are lacking. An alternative strategy to target Na V 1.8 channels for analgesia is to reduce the number of channels that are present on nociceptor membranes. We generated a therapeutic heterobifunctional protein, named UbiquiNa V , that contains a Na V 1.8-selective binding module and the catalytic subunit of the NEDD4 E3 Ubiquitin ligase. We show that UbiquiNav significantly reduces channel expression in the plasma membrane and reduces Na V 1.8 currents in rodent sensory neurons. We demonstrate that UbiquiNa V is selective for Na V 1.8 over other Na V isoforms and other components of the sensory neuronal electrogenisome. We then show that UbiquiNa V normalizes the distribution of Na V 1.8 protein to distal axons, and that UbiquiNa V normalizes the neuronal hyperexcitability in in vitro models of inflammatory and chemotherapy-induced neuropathic pain. Our results serve as a blueprint for the design of therapeutics that leverage the selective ubiquitination of Na V 1.8 channels for 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 | 0.000 | 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) | 0.001 | 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".