Pharmacodynamic and pharmacokinetic profiles of a neurotensin receptor type 2 (NTS2) analgesic macrocyclic analog
Bibliographic record
Abstract
The current opioid crisis highlights the urgent need to develop safe and effective pain medications. Thus, neurotensin (NT) compounds represent a promising approach, as the antinociceptive effects of NT are mediated by activation of the two G protein-coupled receptor subtypes (i.e., NTS1 and NTS2) and produce potent opioid-independent analgesia. Here, we describe the synthesis and pharmacodynamic and pharmacokinetic properties of the first constrained NTS2 macrocyclic NT(8–13) analog. The Tyr11 residue of NT(8–13) was replaced with a Trp residue to achieve NTS2 selectivity, and a rationally designed side-chain to side-chain macrocyclization reaction was applied between Lys8 and Trp11 to constrain the peptide in an active binding conformation and limit its recognition by proteolytic enzymes. The resulting macrocyclic peptide, CR-01-64, exhibited high-affinity for NTS2 (Ki 7.0 nM), with a more than 125-fold selectivity over NTS1, as well as an improved plasma stability profile (t1/2 > 24 h) compared with NT (t1/2 ~ 2 min). Following intrathecal administration, CR-01-64 exerted dose-dependent and long-lasting analgesic effects in acute (ED50 = 4.6 µg/kg) and tonic (ED50 = 7.1 µg/kg) pain models as well as strong mechanical anti-allodynic effects in the CFA-induced chronic inflammatory pain model. Of particular importance, this constrained NTS2 analog exerted potent nonopioid antinociceptive effects and potentiated opioid-induced analgesia when combined with morphine. At high doses, CR-01-64 did not cause hypothermia or ileum relaxation, although it did induce mild and short-term hypotension, all of which are physiological effects associated with NTS1 activation. Overall, these results demonstrate the strong therapeutic potential of NTS2-selective analogs for the management of pain.
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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) | 0.001 | 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".