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Record W3034493697

Effects of linker modifications on tumor-to-kidney contrast of 68Ga-PSMA imaging radioligands

2018· article· en· W3034493697 on OpenAlexaff
Hsiou‐Ting Kuo, Jinhe Pan, Zhengxing Zhang, Joseph Lau, Helen Merkens, Chengcheng Zhang, Nadine Colpo, Kuo‐Shyan Lin, François Bénard

Bibliographic record

Venuenot available
Typearticle
Languageen
FieldMedicine
TopicProstate Cancer Treatment and Research
Canadian institutionsBC Cancer Agency
Fundersnot available
KeywordsBiodistributionLNCaPGlutamate carboxypeptidase IIProstate cancerChemistryIn vivoKidneyIn vitroCancer researchNuclear medicineMedicineCancerInternal medicineBiochemistry
DOInot available

Abstract

fetched live from OpenAlex

1142 Objectives: A number of 18F- and 68Ga-labeled radioligands targeting the prostate specific membrane antigen (PSMA) are used in the clinic to detect prostate cancer and metastases. Of these, 68Ga-PSMA-11 is the most widely used. However, the high uptake of 68Ga-PSMA-11 in kidneys can create halo-artifacts resulting in lower detection sensitivity for lesions between the kidneys. In this study we developed two 68Ga-labeled PSMA-targeting tracers based on 68Ga-PSMA-617 with the goal of improving tumor-kidney contrast compared to 68Ga-PSMA-11. Methods: PSMA-617, HTK01166 and HTK01167 were prepared on solid phase, while PSMA-11 was purchased commercially. In the structure of HTK01166 and HTK01167, the 2-naphthylalanine (2-Nal) in PSMA-617 was replaced with 2-indanylglycine (Igl) and 3,3-diphenylalanine (Dip), respectively. Cold standards were prepared and their binding affinity to PSMA was determined by in vitro competition assays. 68Ga labeling was performed in HEPES buffer (2 M, pH 5.0) with microwave heating for 1 min followed by HPLC purification. The stability of 68Ga-labeled PSMA-617, HTK01166 and HTK01167 was assessed in mouse plasma and monitored by HPLC. PET imaging and biodistribution studies were performed in mice bearing PSMA-expressing LNCap prostate cancer xenografts. Blocking studies were conducted with co-injection of DCFPyL (0.5 mg) for 68Ga-HTK01166 and 68Ga-HTK01167. Results: PSMA-617, HTK01166 and HTK01167 were synthesized in 25-39% yield and their cold Ga-complexed standards were obtained in 54-73% yield. 68Ga-labeled PSMA-11, PSMA-617, HTK01166 and HTK01167 were obtained in 46-71% average decay-corrected radiochemical yield with >99% radiochemical purity and 63-152 GBq/μmol average specific activity. 68Ga-labeled PSMA-617, HTK01166 and HTK01167 were relatively stable in mouse plasma with 90% uptake of 68Ga-HTK01166 and 68Ga-HTK01167 in LNCaP tumor xenografts at 1h pi, demonstrating their specificity for PSMA. CONCLUSION: Compared with 68Ga-PSMA-617, 68Ga-HTK01166 showed comparable PSMA binding affinity and tumor uptake, and almost 5-fold higher kidney uptake; whereas 68Ga-HTK01167 exhibited lower PSMA binding affinity, tumor uptake and kidney uptake. Compared with 68Ga-PSMA-11, 68Ga-HTK01167 had similar tumor uptake and tumor-to-blood contrast (23.8 ± 6.71 vs 20.4 ± 4.98) but higher tumor-to-muscle contrast (16.0 ± 3.79 vs 54.3 ± 16.1) and much lower kidney uptake.

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)0.001
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
Metaresearch0.0000.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0010.000

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.

Opus teacher head0.015
GPT teacher head0.316
Teacher spread0.302 · 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".

Quick stats

Citations0
Published2018
Admission routes1
Has abstractyes

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