IUPHAR review: Navigating the role of preclinical models in pain research
Bibliographic record
Abstract
Chronic pain is a complex and challenging medical condition that affects millions of people worldwide. Understanding the underlying mechanisms of chronic pain is a key goal of preclinical pain research so that more effective treatment strategies can be developed. In this review, we explore nociception, pain, and the multifaceted factors that lead to chronic pain by focusing on preclinical models. We provide a detailed look into inflammatory and neuropathic pain models and discuss the most used animal models for studying the mechanisms behind these conditions. Additionally, we emphasize the vital role of these preclinical models in developing new pain-relief drugs, focusing on biologics and the therapeutic potential of NMDA and cannabinoid receptor antagonists. We also discuss the challenges of TRPV1 modulation for pain treatment, the clinical failures of neurokinin (NK)- 1 receptor antagonists, and the partial success story of Ziconotide to provide valuable lessons for preclinical pain models. Finally, we highlight the overall success and limitations of current treatments for chronic pain while providing critical insights into the development of more effective therapies to alleviate the burden of chronic 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 | 0.005 | 0.010 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.003 | 0.004 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.003 | 0.006 |
| Insufficient payload (model declined to judge) | 0.006 | 0.004 |
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".