Topical amitriptyline and ketamine for post-herpetic neuralgia and other forms of neuropathic pain
Bibliographic record
Abstract
INTRODUCTION: Neuropathic pain (NP) has several therapeutic options but efficacy is limited and adverse effects occur, such that additional treatment options are needed. A topical formulation containing amitriptyline 4% and ketamine 2% (AmiKet) may provide such an option. AREAS COVERED: This report summarizes both published and unpublished results of clinical trials with AmiKet. In post-herpetic neuralgia (PHN), AmiKet produces a significant analgesia which is comparable to that produced by oral gabapentin. In diabetic painful neuropathy, AmiKet showed a strong trend towards pain reduction. In mixed neuropathic pain, case series reports suggest a favourable response rate, but are limited by trial characteristics. AmiKet is absorbed minimally following topical administration. Over 700 patients have now received topical AmiKet in clinical regimens, and it is well-tolerated with the adverse effects mainly being application site reactions. Both agents are polymodal, and several mechanisms may contribute to the peripheral efficacy of AmiKet. EXPERT OPINION: Topical AmiKet has the potential to be a first-line treatment option for PHN, and to be useful in other NP conditions. Furthermore, AmiKet has the potential to be an adjunct to systemic therapies, with the targeting of a peripheral compartment in addition to central sites of action representing a rational drug combination.
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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) | 0.001 | 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 | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.006 | 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".