Multitarget ligands that comprise opioid/nonopioid pharmacophores for pain management: Current state of the science
Bibliographic record
Abstract
Chronic pain, which affects more than one-third of the world's population, represents one of the greatest medical challenges of the 21st century, yet its effective management remains sub-optimal. The 'gold standard' for the treatment of moderate to severe pain consists of opioid ligands, such as morphine and fentanyl, that target the µ-opioid receptor (MOP). Paradoxically, these opioids also cause serious side effects, including constipation, respiratory depression, tolerance, and addiction. In addition, the development of opioid-use disorders, such as opioid diversion, misuse, and abuse, has led to the current opioid crisis, with dramatic increases in addiction, overdoses, and ultimately deaths. As pain is a complex, multidimensional experience involving a variety of pathways and mediators, dual or multitarget ligands that can bind to more than one receptor and exert complementary analgesic effects, represent a promising avenue for pain relief. Indeed, unlike monomodal therapeutic approaches, the modulation of several endogenous nociceptive systems can often result in an additive or even synergistic effect, thereby improving the analgesic-to-side-effect ratio. Here, we provide a comprehensive overview of research efforts towards the development of dual- or multi-targeting opioid/nonopioid hybrid ligands for effective and safer pain management. We reflect on the underpinning discovery rationale by discussing the design, medicinal chemistry, and in vivo pharmacological effects of multitarget antinociceptive compounds.
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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) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 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".