Modulating the endocannabinoid pathway as treatment for peripheral neuropathic pain: a selected review of preclinical studies
Bibliographic record
Abstract
Chemotherapy-induced neuropathic pain is a distressing and commonly occurring side effect of many commonly used chemotherapeutic agents, which in some cases may prevent cancer patients from being able to complete their treatment. Cannabinoid based therapies have the potential to manage or even prevent pain associated with this syndrome. Pre-clinical animal studies that investigate the modulation of the endocannabinoid system (endogenous cannabinoid pathway) are being conducted to better understand the mechanisms behind this phenomenon. Five recent pre-clinical studies identified from Medline published between 2013 and 2016 were selected for review. All studies evaluated the effect of small-molecule agonists or antagonists on components of the endocannabinoid system in rats or mice, using cisplatin or paclitax-el-induced allodynia as a model of chemotherapy-induced neuropathic pain. Activation of the cannabinoid receptor-2 (CB-2) receptor by AM1710 blocked paclitaxel-induced mechanical and cold allodynia in one study. Four studies investigating the activation of both cannabinoid receptor-1 (CB-1) and CB-2 receptors by dual-agonists (WIN55,21 and CP55,940), or by the introduction of inhibitors of endocannabinoid metabolisers (URB597, URB937, JZL184, and SA-57) showed reduction of chemotherapy-induced al-lodynia. In addition, their results suggest that anti-allodynic effects may also be mediated by additional receptors, including TRPV1 and 5-hydroxytryptamine (5-HT1A). Pre-clinical studies demon-strate that the activation of endocannabinoid CB-1 or CB-2 receptors produces physiological effects in animal models, namely the reduction of chemotherapy-induced allodynia. These studies also provide in-sight into the biological mechanism behind the therapeutic utility of cannabis compounds in managing chemotherapy-induced neuropathic pain, and provide a basis for the conduct of future clinical studies in patients of this population.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.003 | 0.002 |
| Bibliometrics | 0.004 | 0.004 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 0.001 |
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".