Opening the amino acid toolbox for peptide‐based NTS2‐selective ligands as promising lead compounds for pain management
Why this work is in the frame
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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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Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.006 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.004 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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 it