Development of Non-covalent Chemical Probes for ??, ?? and ?? Opioid Receptors
Bibliographic record
Abstract
Opioids are well-known pain relievers and one of several therapeutic options for pain management. Although opioids are very effective analgesics, continued use and abuse can lead to physical dependence and withdrawal symptoms. Their side effects, such as constipation, respiratory depression, and the development of tolerance, addiction liability and abuse, highlight the need for safer opioid prescribing practices. The development of new chemical probes will assist in further understanding opioid-receptor interactions and will serve to advance this field of research. As a part of a collaborative Ontario Tech University-Purdue Pharma program, the synthesis of opioid derivatives for use as chemical probes was undertaken to better understand the structure-activity relationships of opioid receptors. \nThe initial efforts were focused on the preparation of an essential building block 1-bromocodeine. The bromination of codeine affords an attractive platform for further functionalization and the synthesis of other opioid derivatives. Here we describe the development of a new methodology for the large-scale bromination of codeine under mild conditions, which was applied for the preparation of a broad range of codeine derivatives. The developed protocol is safer and has a higher yield than the commonly employed procedure for the bromination of codeine, which involves toxic HBr gas. \nIn addition, our efforts toward the synthesis of natural product Bismorphine A revealed that bismorphine and pseudomorphine have the same structure. Consequently, we successfully prepared a series of novel opioid derivatives, which appear to have a high affinity for MOR (Mu Opioid Receptor). The synthesis and biological activities of a series of opioid derivatives are reported.
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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 | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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".