Novel dimeric DOTA-coupled peptidic Y1-receptor antagonists for targeting of neuropeptide Y receptor-expressing cancers
Bibliographic record
Abstract
BACKGROUND: Several peptide hormone receptors were identified that are specifically over-expressed on the cell surface of certain human tumors. For example, high incidence and density of the Y1 subtype of neuropeptide Y (NPY) receptors are found in breast tumors. Recently, we demonstrated that the use of potent radiolabeled somatostatin or bombesin receptor antagonists considerably improved the sensitivity of in vivo imaging when compared to agonists. We report here on the first DOTA-coupled peptidic Y1 receptor affine dimer antagonists. METHODS: Based on a Y1 affine dimeric peptide scaffold previously reported to competitively antagonize NPY-mediated processes, we have developed new dimeric DOTA-coupled Y1 receptor affine antagonists for scintigraphy and radiotherapy. These dimeric peptides were tested for their specific binding to Y1 expressed in SK-N-MC cells and Y2 expressed in SH-SY5Y as well as for their ability to mediate cAMP production in SK-N-MC cells. RESULTS: Introduction of two DOTA moieties at the N-termini of the dimeric NPY analogs as well as the double Asn29 replacement by Dpr(DOTA) or Lys(DOTA) (6 and 10) moiety dramatically reduced binding affinity. However, asymmetric introduction of the DOTA moiety in one segment of the peptidic heterodimer (8 and 11) resulted in suitable antagonists for receptor targeting with high binding affinity for Y1. All compounds were devoid of Y2 binding affinity. CONCLUSIONS: The design and the in vitro characterization of the first DOTA-coupled dimeric NPY receptor antagonist with high affinity and selectivity for Y1 over Y2 are described. This compound may be an excellent candidate for the imaging of Y1-positive tumors and their treatment.
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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 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".