Harnessing <b>α</b>-Emitting Radionuclides for Therapy: Radiolabeling Method Review
Bibliographic record
Abstract
Targeted a-therapy (TAT) is an emerging powerful tool treating latestage cancers for which therapeutic options are limited. At the core of TAT are targeted radiopharmaceuticals, where isotopes are paired with targeting vectors to enable tissue-or cell-specific delivery of a-emitters. DOTA (1,4,7,10-tetraazacyclododecane-1,4,7,10-tetraacetic acid) and DTPA (diethylenetriamine pentaacetic acid) are commonly used to chelate metallic radionuclides but have limitations. Significant efforts are underway to develop effective stable chelators for a-emitters and are at various stages of development and community adoption. Isotopes such as 149 Tb, 212/213 Bi, 212 Pb (for 212 Bi), 225 Ac, and 226/227 Th have found suitable chelators, although further studies, especially in vivo studies, are required. For others, including 223 Ra, 230 U, and, arguably 211 At, the ideal chemistry remains elusive. This review summarizes the methods reported to date for the incorporation of 149 Tb, 211 At, 212/213 Bi, 212 Pb (for 212 Bi), 223 Ra, 225 Ac, 226/227 Th, and 230 U into radiopharmaceuticals, with a focus on new discoveries and remaining challenges.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.003 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.005 | 0.001 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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 teacher head, 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".