Neurotensin Analogues Containing Cyclic Surrogates of Tyrosine at Position 11 Improve NTS2 Selectivity Leading to Analgesia without Hypotension and Hypothermia
Bibliographic record
Abstract
Abstract Neurotensin (NT) exerts its analgesic effects through activation of the G protein-coupled receptors NTS1 and NTS2. This opioid-independent antinociception represents a potential alternative for pain management. While activation of NTS1 also induces a drop in blood pressure and body temperature, NTS2 appears to be an analgesic target free of these adverse effects. Here, we report modifications of NT at Tyr11 to increase selectivity toward NTS2, complemented by modifications at the N-terminus to impair proteolytic degradation of the biologically active NT(8–13) sequence. Replacement of Tyr11 by either 6-OH-Tic or 7-OH-Tic resulted in a significant loss of binding affinity to NTS1 and subsequent NTS2 selectivity. Incorporation of the unnatural amino acid β3hLys at position 8 increased the half-life to over 24 h in plasma. Simultaneous integration of both β3hLys8 and 6-OH-Tic11 into NT(8–13) produced a potent and NTS2-selective analogue with strong analgesic action after intrathecal delivery in the rat formalin-induced pain model with an ED50 of 1.4 nmol. Additionally, intravenous administration of this NT analogue did not produce persistent hypotension or hypothermia. These results demonstrate that NT analogues harboring unnatural amino acids at positions 8 and 11 can enhance crucial pharmacokinetic and pharmacodynamic features for NT(8–13) analogues, i.e., proteolytic stability, NTS2 selectivity, and improved analgesic/adverse effect ratio.
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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 | 0.000 |
| 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".