“In‐loop” carbonylation—A simplified method for carbon‐11 labelling of drugs and radioligands
Bibliographic record
Abstract
Abstract Transition‐metal mediated carbonylation with 11C‐labelled carbon monoxide ([11C]CO) is a versatile method for introducing 11C (t1/2 = 20.3 min) into drugs and radioligands for subsequent use in positron emission tomography (PET). The aim of the current study was to perform the 11C‐carbonylation reaction on the interior surface of a stainless‐steel loop used for high performance liquid chromatography (HPLC). In the experimental setup, cyclotron produced 11C‐labelled carbon dioxide ([11C]CO2) was converted to [11C]CO by reduction over heated Molybdenum and swept into an HPLC loop pre‐charged with the appropriate reaction mixture. Following a 5 min reaction, the radiochemical purity (RCP) and the trapping efficiency (TE) of the reaction mixture was determined. After optimization, [11C]N‐Benzylbenzamide was obtained in quantitative radiochemical yield (RCY) following a 5 min reaction at room temperature. The methodology was further applied to label [11C]benzoic acid (RCP≥99%, TE>91%), [11C]methyl benzoate (RCP≥99%, TE>93%) and [11C]phthalide (RCP≥99%, TE>88%). A set of pharmaceuticals was finally radiolabelled using non‐optimized conditions. Excellent yields were obtained for the histamine‐3 receptor radioligand [11C]AZ13198083, the oncology drug [11C]olaparib and the dopamine D2 receptor radioligand [11C]raclopride, whereas a moderate yield was observed for the high‐affinity dopamine D2 receptor radioligand [11C]FLB457. The presented “in‐loop” process proved efficient for diverse 11C‐carbonylations, providing [11C]amides, [11C]esters and [11C]carboxylic acids in moderate to excellent RCYs. Based on the advantages associated with performing the radiolabelling step as an integrated part of the purification system, this methodology may become a valuable addition to the toolbox of methodologies used for 11C‐carbonylation of drugs and radioligands for PET.
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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 | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| 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".