Rapid, one‐step, high yielding <sup>18</sup>F‐labeling of an aryltrifluoroborate bioconjugate by isotope exchange at very high specific activity
Bibliographic record
Abstract
A rapid, single step, aqueous 18F‐labeling method that proceeds under mild conditions to provide radiotracers in high radiochemical yield and at high specific activity represents a long‐standing challenge. Arylboronates capture aqueous 18F‐fluoride ion in buffered pH 2–3 at moderate temperature to provide a highly polar 18F‐ArBF3− anion. Similarly, 19F‐18F isotope exchange on a 19F‐ArBF3− should create an 18F‐ArBF3−. We hypothesized that this reaction would proceed in volumes that would be amenable to the high levels of 18F‐activity used in clinical hospitals. In order to measure both radiochemical and chemical yields, along with specific activity, we linked an alkyne‐19F‐ArBF3− to rhodamine azide by standard click chemistry to afford a precursor Rh‐19F‐ArBF3−. This precursor was aliquoted in portions of 50 nmol and lyophilized for on‐demand use. Using robotic manipulators in a hot cell, we combined >29.6 GBq (800 mCi) and 50 nmol of the Rh‐19F‐ArBF3− in aqueous dimethylformamide at buffered pH 2–3. Following mild heating (40 °C) for 10‐15 min, the reaction was quenched and analyzed. We observed radiochemical yields of 50% and specific activities of nearly 555 GBq/µmol (15 Ci/µmol). Similar radiochemical yields and slightly lower specific activities were also obtained with ~400 mCi (n = 2). With radiochemical yields in the hundreds of millicuries and specific activities that are 3–10‐fold higher than most radiotracers, this method is very attractive method for preparing clinically useful radiotracers. Moreover, the ability to produce tracers at extraordinarily high specific activities expands the distribution time window from production labs to distant positron emission tomography scanners. Copyright © 2012 John Wiley & Sons, Ltd.
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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) | 0.001 | 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 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".