Automated synthesis and dosimetry of 6-deoxy-6-[(18)F]fluoro-D-fructose (6-[(18)F]FDF): a radiotracer for imaging of GLUT5 in breast cancer.
Bibliographic record
Abstract
6-Deoxy-6-[(18)F]fluoro-D-fructose (6-[(18)F]FDF) is a promising PET radiotracer for imaging GLUT5 in breast cancer. The present work describes GMP synthesis of 6-[(18)F]FDF in an automated synthesis unit (ASU) and dosimetry calculations to determine radiation doses in humans. GMP synthesis and dosimetry calculations are important prerequisites for first-in-human clinical studies of 6-[(18)F]FDF. The radiochemical synthesis of 6-[(18)F]FDF was optimized and adapted to an automated synthesis process using a Tracerlab FXFN ASU (GE Healthcare). Starting from 30 GBq of cyclotron-produced n.c.a. [(18)F]fluoride, 2.9 ± 0.1 GBq of 6-[(18)F]FDF could be prepared within 50 min including HPLC purification resulting in an overall decay-corrected radiochemical yield of 14 ± 3% (n = 11). Radiochemical purity exceeded 95%, and the specific activity was greater than 5.1 GBq/μmol. Sprague-Dawley rats were used for biodistribution experiments, and dynamic and static small animal PET experiments. Biodistribution studies served as basis for allometric extrapolation to the standard man anatomic model and normal organ-absorbed dose calculations using OLINDA/EXM software. The calculated human effective dose for 6-[(18)F]FDF was 0.0089 mSv/MBq. Highest organ doses with a dose equivalent of 0.0315 mSv/MBq in a humans were found in bone. Injection of 370 MBq (10 mCi) of 6-[(18)F]FDF results in an effective whole body radiation dose of 3.3 mSv in humans, a value comparable to that of other (18)F-labeled PET radiopharmaceuticals. The optimized automated synthesis under GMP conditions, the good radiochemical yield and the favorable human radiation dosimetry estimates support application of 6-[(18)F]FDF in clinical trials for molecular imaging of GLUT5 in breast cancer patients.[This corrects the article on p. 248 in vol. 4.].
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| 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".