Lewis Acid-Facilitated Radiofluorination of MN3PU: A LRRK2 Radiotracer
Bibliographic record
Abstract
Background: Temperature-sensitive radiopharmaceutical precursors require lower reaction temperatures (<100 °C) during nucleophilic radiofluorination in order to avoid compound thermolysis, often resulting in sub-optimal radiochemical yields (RCYs). To facilitate nucleophilic aromatic substitution (SNAr) of nucleofuges commonly used in radiofluorination (e.g., nitro group), we explored the use of Lewis acids as nucleophilic activators to accelerate [18F]fluoride incorporation at lower temperatures, and thereby increasing RCYs for thermolabile activated precursors. Lewis acid-assisted radiofluorination was exemplified on the temperature-sensitive compound 1-(4-(4-morpholino-7-neopentyl-7H-pyrrolo[2,3-d]pyrimidin-2-yl)phenyl)-3-(6-nitropyridin-3-yl)urea (MN3PU, compound 3) targeting leucine-rich repeat kinase 2 (LRRK2), an important target in the study of Parkinson’s disease and various cancers. Methods: To a vessel containing dried K[18F]F-K222 complex, a solution of precursor MN3PU ((3), 1 mg; 1.8 μmol) and Lewis acid (6 μL of 0.2 μmol: chromium II chloride (A), ferric nitrite (B) or titanocene dichloride (C)) in 500 μL of N,N-dimethylformamide (DMF) (with 10% t-BuOH for B) were added. Reactions were stirred for 25 min at 90 °C. In parallel, reactions were conducted without the addition of Lewis acids for baseline comparison. After purification via preconditioned Sep-Pak C18 plus cartridges, aliquots were analyzed by analytical radio-HPLC. Results: Non-decay corrected radiochemical yields (ndc RCYs) for [18F]FMN3PU (7) were improved from 1.7 ± 0.7% (no addition of Lewis acids) to 41 ± 1% using Cr(II) and 37 ± 0.7% using Ti(II)-based Lewis acids, with radiochemical purities of ≥96% and molar activities (Am) of up to 3.23 ± 1.7 Ci/μmol (120 ± 1.7 GBq/μmol). Conclusion: RCYs of [18F]FMN3PU (7) improved from ~5% using conventional nucleophilic radiofluorination, up to 41 ± 1% using Lewis-acid supported SNAr.
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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".