Topical analgesics for neuropathic pain: Preclinical exploration, clinical validation, future development
Bibliographic record
Abstract
Topical analgesics applied locally to skin or to specialized compartments modify pain by actions on sensory nerve endings and/or adjacent cellular elements. With this approach, there are low systemic drug levels, good tolerability and few drug interactions, and combination with oral formulations is feasible. The goal of this review is to provide an overview of the potential for topical analgesics to contribute to improved management of neuropathic pain. Mechanistic and preclinical studies indicate much potential for development of novel topical analgesics for neuropathic pain. In humans, two topical analgesics are approved for use in post-herpetic neuralgia (lidocaine 5% medicated plaster, capsaicin 8% patch), and there is evidence for efficacy in other neuropathic pain conditions. Comparative trials indicate similar efficacy between topical and oral analgesics. Not all individuals respond to topical analgesics, and there is interest in determining factors (patient factors, sensory characteristics) which might predict responsiveness to topical analgesics. There is a growing number of controlled trials and case reports of investigational agents (vasodilators, glutamate receptor antagonists, α2-adrenoreceptor agonists, antidepressants, centrally acting drugs), including combinations of several agents, indicating these produce pain relief in neuropathic pain. There is interest in compounding topical analgesics for neuropathic pain, but several challenges remain for this approach. Topical analgesics have the potential to be a valuable additional approach for the management of neuropathic pain.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.003 | 0.002 |
| 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) | 0.005 | 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".