Impact of dianionic and dicationic linkers on tumor uptake and biodistribution of [<sup>64</sup>Cu]Cu/NOTA peptide‐based gastrin‐releasing peptide receptors antagonists
Bibliographic record
Abstract
In this study, we investigated for the first time the influence of 2‐aminoethyl‐piperazine‐1‐carboxylic acid (APCA) and amino‐hexanedioic‐1‐acid (AHDA) on tumor uptake and elimination kinetics of [64Cu]‐radiolabeled gastrin releasing peptide receptors (GRPR) antagonists. Three GRPR antagonists containing the RM26 sequence were synthesized and conjugated with NOTA via different linkers (LK): polyethylene glycol (PEG–neutral), APCA (dicationic) or AHDA (dianionic). The NOTA‐LK‐RM26 peptides were radiolabeled with 64Cu to assess their pharmacokinetic and positron emission tomography (PET) imaging properties using PC3 tumor‐bearing athymic nude mice. The inhibition constants (Ki) of the 3 natCu/NOTA‐LK‐RM26 peptides bearing PEG, dicationic and dianionic linkers were 0.98 ± 0.48 nM, 0.95 ± 0.21 nM, and 17.97 ± 2.79 nM, respectively. The [64Cu] NOTA‐LK‐RM26 conjugates were prepared with labeling yields superior to 95% and specific activities of 67 to 77 TBq/mmol. The 3 radiopeptides were stable in vivo and showed GRPR‐specific uptake in pancreas with a very fast washout of this tissue observed for [64Cu]‐NOTA‐AHDA‐RM26 peptide. Results from imaging studies displayed specific PC3 tumor uptake for both [64Cu]‐NOTA‐APCA‐ and AHDA‐RM26, similar kidney elimination and fast liver washout. Considering their adequate imaging characteristics, [64Cu]‐NOTA‐LK‐RM26 bearing APCA‐ and AHDA‐linkers are promising candidates for GRPR‐targeted PET imaging prostate 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) | 0.001 | 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".