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Record W2597164613

Comparison of two high affinity gallium-68-labeled bradykinin receptor antagonists for in vivo imaging of bradykinin B1 receptor expression

2016· article· en· W2597164613 on OpenAlexaff
Jinhe Pan, Zhengxing Zhang, Guillaume Amouroux, Silvia Jenni, Jutta Zeisler, Chengcheng Zhang, François Bénard, Kuo‐Shyan Lin

Bibliographic record

Venuenot available
Typearticle
Languageen
FieldMedicine
TopicPeptidase Inhibition and Analysis
Canadian institutionsBC Cancer Agency
Fundersnot available
KeywordsBradykininBiodistributionIn vivoChemistryReceptorDOTAIn vitroPeptideMolecular biologyBiochemistryChelationBiology
DOInot available

Abstract

fetched live from OpenAlex

113 Objectives The bradykinin B1 receptor (B1R) is a G protein-coupled receptor and is minimally expressed in normal tissues. Due to its overexpression in a variety of cancers, B1R is a promising cancer imaging biomarker. In this study, we synthesized and evaluated two B1R-targeting peptides, 68Ga-P05022 (68Ga-DOTA-Pip-B9858) and 68Ga-Z02176 (68Ga-DOTA-Pip-B9958), for imaging B1R expression with PET (Pip: 4-amino-(1-carboxymethyl)piperidine; B9858:Lys-Lys-Arg-Pro-Hyp-Gly-Igl-Ser-D-Igl-Oic; B9958: Lys-Lys-Arg-Pro-Hyp-Gly-Cpg-Ser-D-Tic-Cpg). Methods The DOTA-conjugated peptides were synthesized by solid phase peptide synthesis, and reacted with GaCl3 to obtain cold standards. B1R binding affinity was measured by in vitro competition binding assays. The peptides were labeled with 68Ga by microwave heating followed by HPLC purification. LogD7.4 values were determined via traditional shake flask method. PET/CT imaging and biodistribution studies were performed in NODSCID/IL2RKO mice bearing both B1R-negative (B1R-) HEK293T wild-type tumor and B1R-positive (B1R+) HEK293T::hB1R tumor. Blocking studies were performed by co-injecting the tracers with their respective cold standard (100 μg). Results P05022 and Z02176 were obtained with 12 % and 18 % overall yield. The binding affinities (Ki) of P05022 and Z02176 to hB1R were 5.5 ± 0.1 and 2.5 ± 0.8 nM, respectively. 68Ga-labeled P05022 and Z02176 were prepared in 42 - 76 % decay-corrected radiochemical yield, with > 99 % radiochemical purity and 41 - 282 GBq/μmol specific activity. Both tracers were highly hydrophilic with LogD7.4 of -2.65 ± 0.15 and -4.15 ± 0.04 for P05022 and Z02176, respectively. The PET imaging and biodistribution studies at 1-h post-injection showed extremely low background (~ 1 %ID/g or less) of both tracers. Only kidneys, bladder, and B1R+ tumor were clearly visualized in PET images. The biodistribution data showed that both tracers were excreted mainly via the renal pathway. At 1-h post-injection, B1R+ tumor uptake for 68Ga-Z02176 (28.9 ± 6.21 %ID/g) was higher than 68Ga-P05022 (11.6 ± 3.30 %ID/g). The B1R+ tumor-to-blood and B1R+ tumor-to-muscle ratios were also higher with 68Ga-Z02176 (56.1 ± 17.3 and 167 ± 57.6) compared to 68Ga-P05022 (34.3 ± 15.2 and 103 ± 30.2). Negligible uptake in B1R- tumors indicated that the uptake in B1R+ tumors was receptor mediated. In addition, co-injecting with the cold standard reduced B1R+ tumor uptake by more than 85 % for both radiotracers. Conclusions 68Ga-P05022, 68Ga-Z02176, and their cold compounds were successfully synthesized and characterized. Both tracers had high binding affinities for B1R. PET imaging and biodistribution studies revealed that 68Ga-Z02176 was superior to 68Ga-P05022 and other previously reported tracers for B1R-targeting imaging. Our results indicate that 68Ga-Z02176 warrants further studies for potential clinical translation.

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 imitation

Not 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.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Bench or experimental · Consensus signal: Bench or experimental
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.001
Threshold uncertainty score0.003

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0010.001
Open science0.0010.000
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0010.001

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.

Opus teacher head0.029
GPT teacher head0.346
Teacher spread0.317 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designBench or experimental
Domainnot available
GenreEmpirical

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".

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Citations0
Published2016
Admission routes1
Has abstractyes

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