Emerging therapeutic approaches for treating abdominal pain
Bibliographic record
Abstract
There is an urgent need for analgesics to treat pain that lacks the serious side effects of existing drugs, such as conventional opioids and nonsteroidal anti-inflammatory drugs. Most side effects arise from the non-selective actions of these drugs at sites where the pain is not generated because of the ubiquitous expression of the drug targets in the body regardless of the underlying disease. In this narrative review, we explore 2 mechanistic approaches focusing on visceral nociceptive neurons that have the potential to limit side effects while preserving efficacy. Strategy 1 demonstrates how mechanistic pain studies underlying a specific disorder, such as irritable bowel syndrome, can identify targets specifically upregulated in that condition. We discuss recent findings regarding 2 neuroactive mediators, histamine and proteases, including novel intestinal sources, signalling pathways, and intracellular synergistic actions that could serve as potential therapeutic targets. Strategy 2 examines how acidic microenvironments unique to the sites of inflammation where pain is generated, such as in inflammatory bowel disease, can be exploited. pH-sensitive analgesics have been developed that inhibit μ-opioid receptors at sites of inflammation where tissue pH is low, ie, 6.5, while showing no activity at other sites where tissue pH is normal, ie, 7.4. Collectively, these studies highlight the value of investigating the mechanisms underlying specific disorders, which can lead to novel biomarkers and therapeutic strategies that can enhance the specificity of the new therapies.
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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 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.003 |
| Insufficient payload (model declined to judge) | 0.007 | 0.003 |
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".