Current and emerging “at-site” pain medications: a review
Bibliographic record
Abstract
The myriad pain pathophysiology has intrigued and challenged humanity for centuries. In this regard, the traditional pain therapies such as opioids and nonsteroidal anti-inflammatory drugs have been highly successful in treating acute and chronic pain. However, their drawback includes adverse events such as psychotropic effects, addiction potential, and gastrointestinal toxicities, to mention a few. These factors combined with the likelihood of an increase in chronic pain conditions due to an aging population calls for the development of novel mechanism-based or "site-specific" agents to target novel pain pathways. In this regard, rapid progress has been made in understanding the molecular mechanisms of novel pain targets such as cannabinoid receptors, fatty acid hydrolase, voltage-gated and ligand-gated ion channels such as P2 receptors, transient receptor potential channels and glial cell modulators. Accordingly, preclinical studies indicate that the site-specific/selective agents exhibit sufficient efficacy and reduced side effects such as lack of psychotropic effects indicating their clinical potential. This review provides a brief summary of some "at-site" pain targets and their role in the pain pathophysiology, and describes the efforts in developing some small molecules as novel pain therapeutics.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| 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) | 0.010 | 0.005 |
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".