1-(3-[<sup>18</sup>F]fluoropropyl)piperazines as model compounds for the radiofluorination of pyrido[2,3-<i>d</i>]pyrimidines
Bibliographic record
Abstract
Abstract The visualization of cyclin-dependent kinases (CDKs), which are overexpressed in multiple tumor types, with radiolabeled CDK inhibitors by means of positron emission tomographyin vivois a promising approach for tumor imaging. Pyrido[2,3-d]pyrimidines belong to a class of inhibitors, which bind with high affinity to CDK4 and CDK6. 1-(3-[18F]Fluoropropyl)-4-(4-nitrophenyl)piperazine and 1-(3-[18F]fluoropropyl)-4-(6-nitropyridin-3-yl)piperazine represent structural elements of the appropriate CDK inhibitors and were therefore chosen as model compounds for the incorporation of fluorine-18 into pyrido[2,3-d]pyrimidines. Three methods are known for the preparation of tertiary 3-[18F]fluoropropyl-amines: 1) the direct substitution of a good leaving group, 2) the two-step reaction synthesizing a 3-[18F]fluoropropyl intermediate, and 3) the utilization of aziridinium or azetidinium salts. In general, radiofluorinations using azetidinium salts lead to excellent radiochemical yields in short periods of time. For these reasons, we developed a synthesis route to tosylated piperazine precursors and established a radiolabeling approach based on the incorporation of fluorine-18 into open-chained tosylates as well as the respective azetidinium spiro compounds to yield the desired radiofluorinated piperazines successfully.
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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".