Development of Heteroaromatic Silicon Fluoride Acceptors (HetSiFAs) for <sup>18</sup>F-Labeled Radiopharmaceuticals
Bibliographic record
Abstract
Abstract Silicon Fluoride Acceptors (SiFAs) enable 18F-labeling of complex peptides via isotope exchange under exceptionally mild conditions. SiFA radiopharmaceuticals have been clinically validated for diagnosing and treating cancers as tracers for positron emission tomography (PET) and radiotwin theranostics. Despite these advances, conventional phenyl SiFAs (PhSiFAs) require complex substrate engineering to mask their lipophilicity, which complicates the design of new tracers. Here, we report a new and diverse class of heteroaromatic SiFA derivatives (HetSiFAs) that are significantly less lipophilic than classical PhSiFAs. We show that HetSiFAs retain suitable stability without compromising labeling kinetics. We highlight the performance of a pyrazole-HetSiFA for rapid and efficient 18F-labeling within a model TATE-peptide and show that the resulting PET tracer clears through the kidneys with minimal defluorination in mice. Novel radiopharmaceuticals incorporating HetSiFAs can capitalize on the improved and flexible pharmacokinetic parameters and excellent 18F-fluorination efficiency offered by these scaffolds.
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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) | 0.002 | 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".