Ac-225 radiochemistry through the lens of [225Ac]Ac-DOTA-TATE
Bibliographic record
Abstract
BACKGROUND: Targeted alpha therapy with Ac-225 showed to be effective in treating metastatic cancers. However, the complex decay chain requires optimized radiolabeling and quality control. This study aims to determine critical parameters and establish optimal labeling and accurate measuring techniques for radiochemical yield and purity with DOTA-TATE as a model molecule. Ac-225 sources were analyzed for metals (ΣFe, Zn, Cu) and quantified by UPLC. Optimization of radiolabeling kinetics for clinical conditions was performed in regards to temperature (20-90 °C), heating time (5-60 min), pH (2.5-10, with/without excess of metal ions), buffers, quenchers, volume (0.1-10 mL) and molar activity (90-540 kBq/nmol). The quality control was investigated using radio-TLC/HPLC by changing gradient to evaluate peak separation, radiolysed peptide and impurity separation. RESULTS: Metal ingrowth was observed in Ac-225 stocks (n = 3), (time of arrival: 17.9, 36.8 and 101.4 nmol per 10 MBq). Optimal radiochemical yields were achieved with > 80 °C (20 min) at pH 8.5 (15 mM TRIS) up to 270 kBq. Labeling at a high pH showed a higher RCY, even in presence of an excess of metals. High stability (RCP > 90%) was achieved after addition of quenchers (cysteine, methionine, ascorbate, histidine, or gentisic acid (35 mM)) up to 24 h. For optimal determination of the radiochemical purity (indirect HPLC) fifty fractions are required. CONCLUSION: The quality of Ac-225 labeled DOTA-radiopharmaceuticals is highly dependent on the pH and stabilization (buffer/quencher). Within this research it is demonstrated that optimized quality control methods and accurate measurement of the radiolabeling kinetics are crucial to ensure safe implementation for patient treatment.
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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 | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 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".