Opening the amino acid toolbox for peptide‐based NTS2‐selective ligands as promising lead compounds for pain management
Bibliographic record
Abstract
Chronic pain is one of the most critical health issues worldwide. Despite considerable efforts to find therapeutic alternatives, opioid drugs remain the gold standard for pain management. The administration of μ-opioid receptor (MOR) agonists is associated with detrimental and limiting adverse effects. Overall, these adverse effects strongly overshadow the effectiveness of opioid therapy. In this context, the development of neurotensin (NT) ligands has shown to be a promising approach for the management of chronic and acute pain. NT exerts its opioid-independent analgesic effects through the binding of two G protein-coupled receptors (GPCRs), NTS1 and NTS2. In the last decades, modified NT analogues have been proven to provide potent analgesia in vivo. However, selective NTS1 and nonselective NTS1/NTS2 ligands cause antinociception associated with hypothermia and hypotension, whereas selective NTS2 ligands induce analgesia without altering the body temperature and blood pressure. In light of this, various structure-activity relationship (SAR) studies provided findings addressing the binding affinity of ligands towards NTS2. Herein, we comprehensively review peptide-based NTS2-selective ligands as a robust alternative for future pain management. Particular emphasis is placed on SAR studies governing the desired selectivity and associated in vivo results.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| 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) | 0.005 | 0.003 |
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".