Good practices for the automated production of 18F-SiFA radiopharmaceuticals
Bibliographic record
Abstract
Abstract Background The positron emitting isotope fluorine-18 (18F) possesses almost ideal physicochemical properties for the development of radiotracers for diagnostic molecular imaging employing positron emission tomography (PET). 18F in its nucleophilic anionic 18F− form is usually prepared by bombarding an enriched 18O water target with protons of various energies between 5 and 20 MeV depending on the technical specifications of the cyclotron. Large thick-target yields between 5 and 14 GBq/µA can be obtained, enough to prepare large batches of radiotracers capable to serve a considerable contingent of patients (50 + per clinical batch). The overall yield of the radiotracer however depends on the efficiency of the 18F labeling chemistry. The Silicon Fluoride Acceptor chemistry (SiFA) has introduced a convenient and highly efficient way to provide clinical peptide-based 18F-radiotracers in a kit-like procedure matching the convenience of 99mTc radiopharmaceuticals. Main body A radiotracer’s clinical success primarily hinges on whether its synthesis can be automated. Due to its simplicity, the SiFA chemistry, which is based on isotopic exchange (18F for 19F), does not only work in a manual setup but has been proven to be automatable, yielding large batches of 18F-radiotracers of high molar activity (Am). The production of SiFA radiotracer can be centralized and the radiopharmaceutical be distributed via the “satellite” principle, where one production facility economically serves multiple clinical application sites. Clinically validated tracers such as [18F]SiTATE and [18F]Ga-rhPSMA-7/-7.3 have been synthesized in an automated synthesis unit under good manufacturing practice conditions and used in large patient cohorts. Communication of common guidelines and practices is warranted to further the dissemination of SiFA radiopharmaceuticals and to give easy access to this technology. Conclusion This current review highlights the most recent achievements in SiFA radiopharmaceutical automation geared towards large batch production for clinical application. Best practice advice and guidance towards a facilitated implementation of the SiFA technology into new and already operating PET tracer production facilities is provided. A brief outlook spotlights the future potential of SiFA radiochemistry within the landscape of non-canonical labeling chemistries.
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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 | 0.028 | 0.028 |
| Meta-epidemiology (narrow) | 0.002 | 0.002 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.006 | 0.003 |
| Science and technology studies | 0.002 | 0.003 |
| Scholarly communication | 0.004 | 0.003 |
| Open science | 0.005 | 0.004 |
| Research integrity | 0.003 | 0.004 |
| Insufficient payload (model declined to judge) | 0.015 | 0.041 |
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".