Microwave-assisted Radiosynthesis of the Hypoxia Marker 1-&#945;-D-(5- Deoxy-5-[<sup>18</sup>F]fluoroarabinofuranosyl)-2-nitroimidazole ([<sup>18</sup>F]FAZA)
Bibliographic record
Abstract
UNLABELLED: The routine manufacture of most short-lived positron-emitting radiopharmaceuticals (PERs) involves conventional heating to accelerate the radiolabeling process. Nucleophilic radiofluorination reactions are generally slow at lower temperatures, and are accompanied by thermal decomposition of both precursor and product at higher temperatures. This necessitates HPLC purification and results in lower recovered radiochemical yields (rRCYs). [(18)F]FAZA, a PER for clinical imaging of focal tissue hypoxia, is routinely manufactured in-house in 3-12% rRCY using a Health Canada approved conventional heating procedure. The microwave-assisted (MW) radiosynthesis of [(18)F]FAZA is now reported. METHODS: Manual (MRDS) and automated (ASU) reagent delivery systems coupled to a commercial MW unit were built in-house. The MW unit controlled power, irradiation time and monitored reaction temperature (Tmax control), while the acetylAZA tosylate precursor and QMA Accel(TM) cartridge eluent reagents (K2CO3, K2.2.2) were dispensed by the MRDS or ASU. The radiofluorination yields (RFYs) and the chemical and radiochemical TLC profiles of the post-labeling reaction mixtures were compared to those obtained using the conventional heating production method and to those reported for optimized literature methods. RESULTS: MW RFYs for [(18)F]FAZA reached >76% (n=3) in 3 min. Post-labeling analysis of the MW-assisted reaction mixtures demonstrated cleaner UV and radiochemical TLC profiles than those obtained from conventional heating in routine production runs; the relatively clean MW reactions allowed rapid HPLC isolation of [(18)F]FAZA in overall rRCYs of 55±4%. CONCLUSIONS: In practical terms, the MW process provided only small gains in reaction time and RFY, but produced only a few secondary impurities, thereby improving the rRCY in comparison to conventional heating methods. These findings provide a rationale for adaptation of the MW-assisted method for the routine production of clinical [(18)F]FAZA.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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) | 0.002 | 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".