The nociceptin receptor as a potential target in drug design.
Bibliographic record
Abstract
There are now four types of opioid receptors. The new designations OP(1), OP(2) and OP(3) correspond, respectively, to the classic delta-, kappa- and micro-nomenclature. OP(4) was previously known as ORL(1), the receptor for the endogenous heptadecapeptide nociceptin/orphanin FQ. Although the cellular effects of nociceptin resemble those of conventional OP(1), OP(2), and OP(3) opioid agonists, its effects on nociceptive processes are quite different. Nociceptin produces spinal analgesia but appears to antagonize the effects of opioids. Following the recent synthesis of the nonpeptide OP(4) agonist Ro-64-6198 by Hoffmann-La Roche and the nonpeptide OP(4) antagonist J-113397 by Banyu, the nociceptin-OP(4) system now represents a viable and intriguing new target for drug design. OP(4) agonists may be of use in the management of neuropathic pain, anxiety, anorexia, epilepsy, drug dependence, male impotence, hypertension, cerebral ischemia and neurogenic bladder. They may also serve as novel diuretics and to help to reduce gastrointestinal motility. OP(4) antagonists may be of use as general analgesics and in the improvement of memory function. This review covers the recent exciting progress in this field, compares the actions of OP(4) agonists and antagonists with those of classic opioids, and seeks to predict some of the untoward effects that may be seen with such drugs.
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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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 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 | 0.002 |
| 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".