Synthesis of <i>p</i>-(Di-<i>tert</i>-butyl[<sup>18</sup>F]fluorosilyl)benzaldehyde ([<sup>18</sup>F]SiFA-A) with High Specific Activity by Isotopic Exchange: A Convenient Labeling Synthon for the <sup>18</sup>F-Labeling of N-amino-oxy Derivatized Peptides
Bibliographic record
Abstract
The syntheses of different (18)F-labeled peptides using the highly effective labeling synthon p-(di- tert-butylfluorosilyl) benzaldehyde ([ (18)F]SiFA-A) for the development of (18)F-radiopharmaceuticals for oncological positron emission tomography (PET) is reported. The novel and mild labeling technique for the radiosynthesis of [ (18)F]SiFA-A, based on an unexpectedly efficient isotopic (19)F- (18)F exchange, yielded the (18)F-synthon [ (18)F]SiFA-A in almost quantitative yields in high specific activities between 225 and 680 GBq/micromol (6081-18 378 Ci/mmol) without applying HPLC purification. The [ (18)F]SiFA-A was finally used to label the N-terminal amino-oxy (N-AO) derivatized peptides AO-Tyr (3)-octreotate (AO-TATE), cyclo(fK(AO-N)RGD and N-AO-PEG 2-[D-Tyr-Gln-Trp-Ala-Val-betaAla-His-Thi-Nle-NH 2] (AO-BZH3, a bombesin derivative) in high radiochemical yields. Density functional theory (DFT) calculations confirmed high efficiency of the isotopic exchange, which is predicted to proceed via a pentacoordinate siliconate intermediate dissociating immediately to form the radiolabeled [ (18)F]SiFA-A.
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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) | 0.001 | 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".