Harnessing <b>α</b>-Emitting Radionuclides for Therapy: Radiolabeling Method Review
Bibliographic record
Abstract
Targeted α-therapy (TAT) is an emerging powerful tool treating late-stage 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 α-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 α-emitters and are at various stages of development and community adoption. Isotopes such as 149Tb, 212/213Bi, 212Pb (for 212Bi), 225Ac, and 226/227Th have found suitable chelators, although further studies, especially in vivo studies, are required. For others, including 223Ra, 230U, and, arguably 211At, the ideal chemistry remains elusive. This review summarizes the methods reported to date for the incorporation of 149Tb, 211At, 212/213Bi, 212Pb (for 212Bi), 223Ra, 225Ac, 226/227Th, and 230U into radiopharmaceuticals, with a focus on new discoveries and remaining challenges.
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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) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 0.003 |
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".