Electrochemical Radiofluorination of Small Molecules: New Advances
Bibliographic record
Abstract
Abstract The development of new 18F‐based radiopharmaceuticals constantly demands innovations in the search for new radiofluorination methods. [18F]fluoride is the simplest and most convenient chemical form of the isotope for the synthesis of 18F‐based radiopharmaceuticals. The ease of production and handling, as well as the possibility of obtaining high molar activities, makes it the preferred choice for radiofluorination. However, the use of [18F]fluoride in late‐stage radiofluorination comes with challenges, especially for the radiolabeling of electron‐rich molecules where SN2 and SNAr reactions are not suitable. New developments in fluorination chemistry have been extensively studied to overcome these difficulties. Selective electrochemical oxidation of precursors, using a controlled potential, is one method to create reactive intermediates and overcome the activation energy required for nucleophilic fluorination of electron‐rich moieties. This method has been used for years in cold fluorination of organic molecules and more recently has been adapted as an alternative to traditional radiofluorination methods. Although relatively young, this field stands out as a promising route for the synthesis of new PET probes as well as fluorinated pharmaceuticals. This review focuses on recent advances in electrochemical radiofluorination as an alternative for the late‐stage radiolabeling of organic molecules.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| 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".