Novel Radiolabeled Peptides for Breast and Prostate Tumor PET Imaging: <sup>64</sup>Cu/and <sup>68</sup>Ga/NOTA-PEG-[<scp>d</scp>-Tyr<sup>6</sup>,βAla<sup>11</sup>,Thi<sup>13</sup>,Nle<sup>14</sup>]BBN(6–14)
Bibliographic record
Abstract
Bombesin (BBN)-based radiolabeled peptides exhibit promising properties for targeted imaging of gastrin-releasing peptide receptors (GRPR)-positive tumors. The aim of this study was to evaluate with positron emission tomography (PET) the pharmacokinetic and imaging properties of two novel BBN-based radiolabeled peptides, (64)Cu/and (68)Ga/NOTA-PEG-BBN(6-14), for diagnosis of breast and prostate cancers using small animal models. Competitive binding assays on T47D breast and PC3 prostate cancer cells showed that the affinity for GRPR depends on the complexed metal and can vary up to a factor of about 3; (64)Cu/NOTA-PEG-BBN(6-14) was found to have the lowest inhibition constant (1.60 ± 0.59 nM). (64)Cu/and (68)Ga/NOTA-PEG-BBN(6-14) presented similar cell uptake on T47D and PC3 cells and were stable in vivo. Biodistribution studies of radiolabeled peptides carried out in Balb/c and tumor-bearing Balb/c nude mice showed that (64)Cu/NOTA-PEG-BBN(6-14) presented higher GRPR-mediated uptake in pancreas and adrenal glands, but comparable PC3 tumor uptake as (68)Ga/NOTA-PEG-BBN(6-14). Finally, receptor-dependent responses were observed during blocking studies with unlabeled peptide in both biodistribution and small-animal PET imaging studies. Our results confirmed the dependence of the affinity and pharmacokinetics of BBN-based radiopeptides on the complexed radiometal. Interspecies differences between mouse and human GRPR binding properties were also noted in these preclinical studies. Considering their good imaging characteristics, both (64)Cu/NOTA-PEG-BBN(6-14) and (68)Ga/NOTA-PEG-BBN(6-14) are promising candidates for GRPR-targeted PET imaging of breast and prostate cancers.
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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 | 0.001 | 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".