Mechanistic investigation of the substituent effects on Si–F bond reactivity in phenyl silicon–fluoride acceptors (SiFAs)
Bibliographic record
Abstract
Silicon–fluoride acceptors (SiFAs) have emerged as a transformative technology for the development of novel peptide-based positron emission tomography tracers and theranostic radiopharmaceuticals, offering a rapid, high-yielding, robust, and operationally simple method for incorporating fluorine-18 through isotope exchange on silicon. The di- tert-butylfluoro(phenyl)silane core of SiFA is typically conjugated to biomolecules via a functional group positioned either meta- or para- to the silicon center on the phenyl ring. Although a variety of such substituents have been employed, their impact on the efficiency of 18 F-labeling and the hydrolytic stability of the Si–F bond has not been systematically evaluated. In this study, we investigate the electronic effects of commonly used meta and para substituents on the kinetics of these reactions. A Hammett analysis was employed to further elucidate the mechanisms of Si–F bond hydrolysis and to quantify the substituent effect. Furthermore, in light of the growing application of bifunctional SiFAs for incorporation into peptide chains or as linkers between peptides and chelators in radiotheranostics, we synthesized a series of 3,5-disubstituted SiFAs and characterized their physicochemical properties. Our findings provide a foundation for the rational design of novel 18 F-labeled radiopharmaceuticals based upon SiFA technology.
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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 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".