<sup>18</sup>F-Trifluoroborate Derivatives of [Des-Arg<sup>10</sup>]Kallidin for Imaging Bradykinin B1 Receptor Expression with Positron Emission Tomography
Bibliographic record
Abstract
Bradykinin B1 receptor (B1R) is involved in pain and inflammation pathways and is upregulated in inflamed tissues and cancer. Due to its minimal expression in healthy tissues, B1R is an attractive target for the development of therapeutic agents to treat inflammation, chronic pain, and cancer. The goal of this study is to synthesize and compare two (18)F-labeled peptides derived from potent B1R antagonists B9858 and B9958 for imaging B1R expression with positron emission tomography (PET). Azidoacetyl-B9858 2 and azidoacetyl-B9958 3 were synthesized by a solid-phase approach and subsequently clicked to ammoniomethyl-trifluoroborate (AmBF3)-conjugated alkyne 1 to obtain AmBF3-B9858 and AmBF3-B9958, respectively. AmBF3-B9858 and AmBF3-B9958 bound B1R with high affinity, with Ki values at 0.09 ± 0.08 and 0.46 ± 0.03 nM, respectively, as measured by in vitro competition binding assays. (18)F labeling was performed via an (18)F-(19)F isotope exchange reaction. The radiofluorinated tracers were obtained within a synthesis time of 30 min and with 23-32% non-decay-corrected radiochemical yield, >99% radiochemical purity, and 43-87 GBq/μmol specific activity at the end of the synthesis. PET imaging and biodistribution studies were carried out in mice bearing both B1R-positive (B1R(+)) HEK293T::hB1R and B1R-negative (B1R(-)) HEK293T tumors. Both tracers cleared rapidly from most organs/tissues, mainly through the renal pathway. High uptake in B1R(+) tumors ((18)F-AmBF3-B9858: 3.94 ± 1.24% ID/g, tumor-to-muscle ratio 21.3 ± 4.33; (18)F-AmBF3-B9958: 4.20 ± 0.98% ID/g, tumor-to-muscle ratio 48.6 ± 10.7) was observed at 1 h postinjection. These results indicate that (18)F-AmBF3-B9858 and (18)F-AmBF3-B9958 are promising agents for the in vivo imaging of B1R expression with PET.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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) | 0.002 | 0.001 |
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".