Imaging Bradykinin B1 Receptor with<sup>68</sup>Ga-Labeled [des-Arg<sup>10</sup>]Kallidin Derivatives: Effect of the Linker on Biodistribution and Tumor Uptake
Bibliographic record
Abstract
Bradykinin B1 receptor (B1R) that is overexpressed in cancers but minimally expressed in normal healthy tissues represents an attractive biomarker for the development of cancer imaging agents. The goal of this study was to evaluate the effect of different linkers on the pharmacokinetics and tumor uptake of a B1R-targeting radio-peptide sequence, 68Ga-DOTA-linker-Lys-Arg-Pro-Hyp-Gly-Cha-Ser-Pro-Leu. Four peptides, SH01078, P03034, P04115, and P04168, with 6-aminohexanoic acid, 9-amino-4,7-dioxanonanoic acid, Gly-Gly, and 4-amino-(1-carboxymethyl)piperidine, respectively, as the linker were synthesized and evaluated. In vitro competition binding assays showed that the Ki values of SH01078, P03034, P04115, and P04168 were 27.8±4.9, 16.0±1.9, 11.4±2.5, and 3.6±0.2 nM, respectively. Imaging and biodistribution studies were performed in mice bearing both B1R-positive HEK293T::hB1R and B1R-negative HEK293T tumors. All tracers showed mainly renal excretion with excellent tumor visualization and minimal background activity except for kidneys and bladder. The average uptake of 68Ga-labeled SH01078, P03034, and P04115 in HEK293T::hB1R tumor was similar (1.96-2.17%ID/g) at 1 h postinjection. 68Ga-P04168 generated higher HEK293T::hB1R tumor uptake (4.15±1.13%ID/g) and lower background activity, leading to a >2-fold improvement in HEK293T::hB1R tumor-to-background (HEK293T tumor, blood, muscle, and liver) contrasts over those of 68Ga-labeled SH01078, P03034, and P04115. Our results indicate that the choice of linker affects binding affinity, pharmacokinetics, and tumor targeting. The use of the cationic 4-amino-(1-carboxymethyl)piperidine linker improved tumor visualization, and the resulting 68Ga-P04168 might be promising for clinical application for imaging B1R-expressing tumors with positron emission tomography.
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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".