Imaging Tumor Vasculature Noninvasively with Positron Emission Tomography and RGD Peptides Labeled with Copper 64 Using the Bifunctonal Chelates DOTA, Oxo-DO3A. and PCTA
Bibliographic record
Abstract
Two novel bifunctional chelates, 3,6,9,15-tetraazabicyclo[9.3.1]pentadeca-1(15),11,13-triene-3,6,9-triacetic acid (PCTA) and 1-oxa-4,7,10-triazacyclododecane-4,7,10-triacetic acid (Oxo-DO3A), were found to radiolabel antibodies with copper 64 (64Cu) well for positron emission tomography (PET). In this study, the same chelators were used to radiolabel peptides with 64Cu for PET imaging of angiogenesis. PCTA, Oxo-DO3A, and 1,4,7,10-tetraazacyclododecane-N,N',N'',N'''-tetraacetic acid (DOTA) were conjugated to cyclic-(RGDyK), and their binding affinities were confirmed. Conditions for 64Cu radiolabeling were optimized for maximum yield and specific activity. The in vitro stability of the radiolabeled compounds was challenged with serum incubation. PET studies were carried out in a non-αvβ3-expressing tumor model to evaluate the compounds' specificity for proliferating tumor vasculature and their in vivo pharmacokinetics. The PCTA and Oxo-DO3A bioconjugates were labeled with 64Cu at higher effective specific activity and radiochemical yield than the DOTA bioconjugate. In the imaging studies, all the 64Cu bioconjugates could be used to visualize the tumor and the radiotracer uptake was blocked with cyclic-(RGDyK). Target uptake of each bioconjugate was similar, but differences in other tissues were observed. 64Cu-PCTA-RGD showed the best clearance from nontarget tissue and the highest tumor to nontarget ratios. PCTA was the most promising bifunctional chelate for 64Cu peptide imaging and warrants further investigation.
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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".