Advances in [18F]Trifluoromethylation Chemistry for PET Imaging
Bibliographic record
Abstract
Positron emission tomography (PET) is a preclinical and clinical imaging technique extensively used to study and visualize biological and physiological processes in vivo. Fluorine-18 (18F) is the most frequently used positron emitter for PET imaging due to its convenient 109.8 min half-life, high yield production on small biomedical cyclotrons, and well-established radiofluorination chemistry. The presence of fluorine atoms in many drugs opens new possibilities for developing radioligands labelled with fluorine-18. The trifluoromethyl group (CF3) represents a versatile structural motif in medicinal and pharmaceutical chemistry to design and synthesize drug molecules with favourable pharmacological properties. This fact also makes CF3 groups an exciting synthesis target from a PET tracer discovery perspective. Early attempts to synthesize [18F]CF3-containing radiotracers were mainly hampered by low radiochemical yields and additional challenges such as low radiochemical purity and molar activity. However, recent innovations in [18F]trifluoromethylation chemistry have significantly expanded the chemical toolbox to synthesize fluorine-18-labelled radiotracers. This review presents the development of significant [18F]trifluoromethylation chemistry strategies to apply [18F]CF3-containing radiotracers in preclinical and clinical PET imaging studies. The continuous growth of PET as a crucial functional imaging technique in biomedical and clinical research and the increasing number of CF3-containing drugs will be the primary drivers for developing novel [18F]trifluoromethylation chemistry strategies in the future.
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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) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.002 | 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".