Discordance between preclinical and clinical testing of NaV1.7-selective inhibitors for pain
Bibliographic record
Abstract
ABSTRACT: The voltage-gated sodium channel NaV1.7 plays an important role in pain processing according to genetic data. Those data made NaV1.7 a popular drug target, especially since its relatively selective expression in nociceptors promised pain relief without the adverse effects associated with broader sodium channel blockade. Despite encouraging preclinical data in rodents, NaV1.7-selective inhibitors have not yet proven effective in clinical trials. Discrepancies between preclinical and clinical results should raise alarms. We reviewed preclinical and clinical reports on the analgesic efficacy of NaV1.7-selective inhibitors and found critical differences in several factors. Putting aside species differences, most preclinical studies tested young male rodents with limited genetic variability, inconsistent with the clinical population. Inflammatory pain was the most common preclinical chronic pain model whereas nearly all clinical trials focused on neuropathic pain despite some evidence suggesting NaV1.7 channels are not essential for neuropathic pain. Preclinical studies almost exclusively measured evoked pain whereas most clinical trials assessed average pain intensity without distinguishing between evoked and spontaneous pain. Nearly all preclinical studies gave a single dose of drug unlike the repeat dosing used clinically, thus precluding preclinical data from demonstrating whether tolerance or other slow processes occur. In summary, preclinical testing of NaV1.7-selective inhibitors aligned poorly with clinical testing. Beyond issues that have already garnered widespread attention in the pain literature, our results highlight the treatment regimen and choice of pain model as areas for improvement.
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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 | 0.005 | 0.006 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 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".