<sup>18</sup>F-Labeled Silicon-Based Fluoride Acceptors: Potential Opportunities for Novel Positron Emitting Radiopharmaceuticals
Bibliographic record
Abstract
BACKGROUND: Over the recent years, radiopharmaceutical chemistry has experienced a wide variety of innovative pushes towards finding both novel and unconventional radiochemical methods to introduce fluorine-18 into radiotracers for positron emission tomography (PET). These "nonclassical" labeling methodologies based on silicon-, boron-, and aluminium-(18)F chemistry deviate from commonplace bonding of an [(18)F]fluorine atom ((18)F) to either an aliphatic or aromatic carbon atom. One method in particular, the silicon-fluoride-acceptor isotopic exchange (SiFA-IE) approach, invalidates a dogma in radiochemistry that has been widely accepted for many years: the inability to obtain radiopharmaceuticals of high specific activity (SA) via simple IE. METHODOLOGY: The most advantageous feature of IE labeling in general is that labeling precursor and labeled radiotracer are chemically identical, eliminating the need to separate the radiotracer from its precursor. SiFA-IE chemistry proceeds in dipolar aprotic solvents at room temperature and below, entirely avoiding the formation of radioactive side products during the IE. SCOPE OF REVIEW: A great plethora of different SiFA species have been reported in the literature ranging from small prosthetic groups and other compounds of low molecular weight to labeled peptides and most recently affibody molecules. CONCLUSIONS: The literature over the last years (from 2006 to 2014) shows unambiguously that SiFA-IE and other silicon-based fluoride acceptor strategies relying on (18)F(-) leaving group substitutions have the potential to become a valuable addition to radiochemistry.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 0.002 |
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".