“In‐loop” <sup>18</sup>F‐fluorination: A proof‐of‐concept study
Bibliographic record
Abstract
There is a great demand to develop more cost‐efficient and robust manufacturing processes for fluorine‐18 (18F) labelled compounds and radiopharmaceuticals. Herein, we present to our knowledge the first radiofluorination “in‐loop,” where [18F]triflyl fluoride was used as the labelling agent. Initial development of the “in‐loop” [18F]fluorination method was optimized by reacting [18F]triflyl fluoride with 1,4‐dinitrobenzene to form [18F]1‐fluoro‐4‐nitrobenzene. This methodology was then applied for the syntheses of two well‐known radiopharmaceuticals, namely, [18F]T807 for imaging of tau protein and [18F]FEPPA for imaging the translocator protein 18 KDa. Both radiotracers were synthesized and formulated using an automated radiosynthesis module with nondecay corrected radiochemical yields of 27% and 29% (relative [18F]F−), respectively. The overall syntheses times for [18F]T807 and [18F]FEPPA were 65 and 55 minutes, respectively. In these cases, our “in‐loop” radiofluorination methodology enabled us to obtain equal or superior yields compared with conventional reactions in a vial. The radiochemical purities were more than 99%, and the molar activities were more than 350 GBq/μmol at the end‐of‐synthesis for both radiotracers. This novel method is simple, efficient, and allows for a reliable production of radiofluorinated compounds and radiopharmaceuticals.
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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 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 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 | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".