Combined Interactions with I<sub>1</sub>-, I<sub>2</sub>-Imidazoline Binding Sites and α<sub>2</sub>-Adrenoceptors To Manage Opioid Addiction
Bibliographic record
Abstract
Tolerance and dependence associated with chronic opioid exposure result from molecular, cellular, and neural network adaptations. Such adaptations concern opioid and nonopioid systems, including α 2 -adrenoceptors (α 2 -ARs) and I 1 - and I 2 -imidazoline binding sites (IBS). Agmatine, one of the hypothesized endogenous ligands of IBS, targeting several systems including α 2 -ARs and IBS, proved to be able to regulate opioid-induced analgesia and to attenuate the development of tolerance and dependence. Interested in the complex pharmacological profile of agmatine and considering the nature of its targets, we evaluated two series of imidazolines, rationally designed to simultaneously interact with I 1 -/I 2 -IBS or I 1 -/I 2 -IBS/α 2 -ARs. The compounds showing the highest affinities for I 1 -/I 2 -IBS or I 1 -/I 2 -IBS/α 2 -ARs have been selected for their in vivo evaluation on opiate withdrawal syndrome. Interestingly, 9, displaying I 1 -/I 2 -IBS/α 2 -ARs interaction profile, appears more effective in reducing expression and acquisition of morphine dependence and, therefore, might be considered a promising tool in managing opioid addiction.
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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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.000 |
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".