Identification of a Suitable Peptidic Molecular Platform for the Development of NPY(Y<sub>1</sub>)R‐Specific Imaging Agents
Bibliographic record
Abstract
Abstract NPY(Y1)R (neuropeptide Y receptor subtype 1) is an important target structure for tumor‐specific imaging and therapy as this receptor subtype is overexpressed in very high density and incidence especially in human breast cancer. Targeting this receptor with radiolabeled truncated analogues of the endogenous ligand NPY (neuropeptide Y) has, however, not yet resulted in satisfactory imaging results when using positron emission tomography (PET). This can be attributed to the limited stability of these PET imaging agents caused by their fast proteolytic degradation. Although highly promising NPY analogues were developed, their stability has only been investigated in very few cases. In this systematical work, we comparatively determined the stability of the five most promising truncated analogues of NPY that were developed over the last years, showing the highest receptor affinities and subtype selectivities. The stability of the peptides was assessed in human serum as well as in a human liver microsomal stability assay; these gave complementary results, thus demonstrating the necessity to perform both assays and not just conventional serum stability testing. Of the tested peptides, only [Lys(lauroyl)27,Pro30,Lys(DOTA)31,Bip32,Leu34]NPY27‐36 showed high stability against peptidase degradation; thus this is the best‐suited truncated NPY analogue for the development of NPY(Y1)R‐specific imaging agents.
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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".