Alpha-particle Emitting Radiopharmaceuticals in Targeted Therapy and Diagnostics: Challenges and Opportunities
Bibliographic record
Abstract
Targeted Radionuclide Therapy (TRT) demonstrates significant therapeutic efficacy and survival benefits, especially for late-stage metastatic cancers with limited conventional therapy options. The majority of TRT radiopharmaceuticals rely on beta-emitting radionuclides, but more recently the global radiopharmaceutical community has come to appreciate the advantages of alpha-emitting radionuclides. Targeted alpha therapy (TAT) is now a fast-growing area of TRT, focused on the identification, development, and translation of alpha-emitting radiopharmaceuticals, several of which have started to show promising results in early-stage clinical trials. In this chapter, we provide an overview of the physical and chemical nature of alpha-emitting radionuclides relevant for the development and translation of TAT radiopharmaceuticals. Considerations for the production, chemistry, bioconjugation, and radiopharmaceutical formulation are all discussed to help equip the reader to better understand the cross-disciplinary physical, chemical, and biological factors that influence radiopharmaceutical development.
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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 | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.003 |
| Insufficient payload (model declined to judge) | 0.019 | 0.015 |
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".