Design, Synthesis, In Vitro, and Initial In Vivo Evaluation of Heterobivalent Peptidic Ligands Targeting Both NPY(Y1)- and GRP-Receptors—An Improvement for Breast Cancer Imaging?
Bibliographic record
Abstract
Heterobivalent peptidic ligands (HBPLs), designed to address two different receptors independently, are highly promising tumor imaging agents. For example, breast cancer has been shown to concomitantly and complementarily overexpress the neuropeptide Y receptor subtype 1 (NPY(Y1)R) as well as the gastrin-releasing peptide receptor (GRPR). Thus, radiolabeled HBPLs being able to bind these two receptors should exhibit an improved tumor targeting efficiency compared to monospecific ligands. We developed here such bispecific HBPLs and radiolabeled them with 68Ga, achieving high radiochemical yields, purities, and molar activities. We evaluated the HBPLs and their monospecific reference peptides in vitro regarding stability and uptake into different breast cancer cell lines and found that the 68Ga-HBPLs were efficiently taken up via the GRPR. We also performed in vivo PET/CT imaging and ex vivo biodistribution studies in T-47D tumor-bearing mice for the most promising 68Ga-HBPL and compared the results to those obtained for its scrambled analogs. The tumors could easily be visualized by the newly developed 68Ga-HBPL and considerably higher tumor uptakes and tumor-to-background ratios were obtained compared to the scrambled analogs in and ex vivo. These results demonstrate the general feasibility of the approach to use bispecific radioligands for in vivo imaging of breast cancer.
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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) | 0.000 | 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 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".