Capturing aqueous 18F-fluoride with boron: One-step 18F-labeling of dimeric cycloRGD at high specific activity for functional PET imaging of tumors in mice
Bibliographic record
Abstract
112 Objectives Arylboronates enable rapid aqueous 18F-labeling in one step via the creation of a highly polar 18F-aryltrifluoroborate anion (18F-ArBF3-) [1-6]. Here we show rapid, one-step labeling at high specific activity (> 20 GBq/μmol) of a molecularly complex and clinically trialed ligand - bisRGD that successfully showed specific tumor uptake by PET-CT. Methods [c(RGDfK)]2E was conjugated to an arylborimidine, to give the precursor [c(RGDfK)]2E-ArB(dan), which was aliquoted, stored at -20 C. For labeling, 20 μg precursor was mixed with 1-3 GBq of 18F-activity under aqueous conditions for only 15 minutes. HPLC purification (19 minutes) provided radiochemically pure [c(RGDfK)]2E-18F-ArBF3- in a total synthsesis time of less than 45 minutes with isolated yields on the order of 10%. Eight mice (pre-blocked/unblocked) with U87 xenograft tumors were injected with [c(RGDfK)]2E-18F-ArBF3- (n=4) for ex vivo tissue dissection. Four mice (pre-blocked/unblocked) were also imaged with PET-CT. Results The [c(RGDfK)]2E-ArB(dan) is converted in 15 min to 18F-labeled [c(RGDfK)]2E-18F-ArBF3- and HPLC purified in 19 min in RCY9s of ~10% (n = 3) at specific activities of 27-89 GBq/μmol. Biodistribution shows tumor-to-blood and tumor-to-muscle ratios of >9 and >6 while pre-blocking showed high tumor specificity. PET images show good contrast between tumor and non-target tissues confirming the biodistribution data. Conclusions Microgram quantities of an arylborimidine-RGD peptide are rapidly converted in aqueous conditions to an 18F-ArBF3-bioconjugate in a single step, in good yield, at high specific activity in near record time ( Research Support Canadian Cancer Society
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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 | 0.001 |
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".