Suppressing Protodeboronation in Cu‐Mediated <sup>19</sup>F/<sup>18</sup>F‐Fluorination of Arylboronic Acids: A Mechanistically Guided Approach Towards Optimized PET Probe Development
Bibliographic record
Abstract
Abstract Fluorinated arenes play a crucial role in drug discovery, specialty materials, and medical imaging. Although several variants for Cu‐mediated nucleophilic fluorination of arylboronic acids and derivatives have been developed, these protocols rarely address the occurrence and control of protodeboronation, which greatly complicates product separation and can compromise the effectiveness of a radiotracer for in vivo imaging. Consequently, simpler and more efficient procedures are needed to allow rapid 18 F/ 19 F‐fluorination of both arylboronic acids and esters while minimizing protodeboronation. Mechanistic controls revealed that in addition to a high temperature, strong donor ligands such as acetonitrile and pyridine accentuate a Cu‐mediated protodeboronation. This observation guided the optimization of a ligandless procedure, with t ‐BuOH as solvent, to activate fluoride under milder conditions at lower temperatures minimizing protodeboronation. Additionally, a new copper salt, Cu(ONf) 2 was employed to further improve the fluorination efficiency. A large range of functional groups are tolerated under the new procedure, which is complete within 30 minutes at a temperature of 60 °C, and affords fluorinated arenes and heteroarenes in 39 % to 84 % yield. With minimal modifications, the protocol can also be applied in 18 F‐radiofluorination, affording radiochemical conversions (RCCs) between 17 and 54 % with minimal protodeboronation compared to previously established protocols.
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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) | 0.000 | 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 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
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".