<i>In Vivo</i> Radioimaging of Bradykinin Receptor B1, a Widely Overexpressed Molecule in Human Cancer
Bibliographic record
Abstract
The bradykinin receptor B1R is overexpressed in many human cancers where it might be used as a general target for cancer imaging. In this study, we evaluated the feasibility of using radiolabeled kallidin derivatives to visualize B1R expression in a preclinical model of B1R-positive tumors. Three synthetic derivatives were evaluated in vitro and in vivo for receptor binding and their ability to visualize tumors by PET. Enalaprilat and phosphoramidon were used to evaluate the impact of peptidases on tumor visualization. While we found that radiolabeled peptides based on the native kallidin sequence were ineffective at visualizing B1R-positive tumors, peptidase inhibition with phosphoramidon greatly enhanced B1R visualization in vivo. Two stabilized derivatives incorporating unnatural amino acids ((68)Ga-SH01078 and (68)Ga-P03034) maintained receptor-binding affinities that were effective, allowing excellent tumor visualization, minimal accumulation in normal tissues, and rapid renal clearance. Tumor uptake was blocked in the presence of excess competitor, confirming that the specificity of tumor accumulation was receptor mediated. Our results offer a preclinical proof of concept for noninvasive B1R detection by PET imaging as a general tool to visualize many human 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 | 0.000 | 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".