Glial-modulating agents for the treatment of pain: a systematic review
Bibliographic record
Abstract
ABSTRACT: Preclinical research supports a critical role for nervous system glia in pain pathophysiology. This systematic review of human trials of potential glia-modulating drugs for the prevention or treatment of pain followed a predefined search strategy and protocol registration. We searched for English language, randomized, double-blind trials comparing putative glia-modulating drugs to placebo or other comparators. The primary outcomes included validated participant-reported measures of pain intensity or relief and, in studies of opioid administration, measures of opioid consumption and/or opioid-related adverse effects. Twenty-six trials (2132 participants) of glial modulators (12 minocycline, 11 pentoxifylline, and 3 ibudilast) were included. Because of clinical heterogeneity related to study drug, participant population, outcome measures, and trial design, no meta-analysis was possible. Only 6 trials reported a positive effect of the treatment (pentoxifylline-4 trials; minocycline-2 trials), whereas 11 trials reported mixed results and 9 trials reported no effect. This review does not provide convincing evidence of efficacy of current pharmacological targets of nervous system glial function for pain treatment or prevention. However, in light of ample preclinical evidence of the importance of neuroimmune signalling and glial functions in pain pathophysiology, continued strategic human research is anticipated to identify (1) drugs with maximal activity as selectively targeted glial modulators, (2) the necessary timing and duration of pharmacological glial modulation needed for pain prevention or treatment for specific injuries or pain conditions, and (3) the best design of future clinical trials of glial-targeted drugs for pain treatment and/or prevention.
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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 | 0.015 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.010 | 0.008 |
| Bibliometrics | 0.005 | 0.006 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.007 | 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".