Sigma Antagonists for Treatment of Neuropathic Pain Syndromes in Cancer Patients: A Narrative Review
Bibliographic record
Abstract
Almost 40% of cancer patients have neuropathic pain or mixed pain with a neuropathic component, which can be intense, debilitating, and challenging to treat. New studies on sigma receptors show these enigmatic ligand-binding protein chaperones may be helpful drug targets for new pharmacologic options to reduce many types of neuropathies, including chemotherapy-induced peripheral neuropathy (CIPN) and other cancer-related neuropathic pain syndromes. Our objective was to review the literature, including preclinical findings, in support of sigma-1 receptor (S1R) antagonists in reducing neuropathic pain and sigma-2 receptor (S2R) agonists for neuroprotection. The mechanisms behind these effects are not yet fully elucidated. The role of S1R antagonists in treating CIPN appears promising. In some cases, combination therapy of an opioid—which is a true analgesic—with a S1R antagonist, which is an anti-hyperalgesic and anti-allodynic agent, has been proposed. Of interest, but not well studied is whether or not S1R antagonists might be effective in treating CIPN in patients with pre-existing peripheral diabetic neuropathy. While neuropathic syndromes may occur with hematologic cancers, the role of S1R agonists may be effective. Sigma receptors are being actively studied now for a variety of conditions ranging from Alzheimer’s disease to Parkinson’s disease as well as 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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".