Matching chelators to radiometals for radiopharmaceuticals
Bibliographic record
Abstract
Radiometals comprise many useful radioactive isotopes of various metallic elements. When properly harnessed, these have valuable emission properties that can be used for diagnostic imaging techniques, such as single photon emission computed tomography (SPECT, e.g.(67)Ga, (99m)Tc, (111)In, (177)Lu) and positron emission tomography (PET, e.g.(68)Ga, (64)Cu, (44)Sc, (86)Y, (89)Zr), as well as therapeutic applications (e.g.(47)Sc, (114m)In, (177)Lu, (90)Y, (212/213)Bi, (212)Pb, (225)Ac, (186/188)Re). A fundamental critical component of a radiometal-based radiopharmaceutical is the chelator, the ligand system that binds the radiometal ion in a tight stable coordination complex so that it can be properly directed to a desirable molecular target in vivo. This article is a guide for selecting the optimal match between chelator and radiometal for use in these systems. The article briefly introduces a selection of relevant and high impact radiometals, and their potential utility to the fields of radiochemistry, nuclear medicine, and molecular imaging. A description of radiometal-based radiopharmaceuticals is provided, and several key design considerations are discussed. The experimental methods by which chelators are assessed for their suitability with a variety of radiometal ions is explained, and a large selection of the most common and most promising chelators are evaluated and discussed for their potential use with a variety of radiometals. Comprehensive tables have been assembled to provide a convenient and accessible overview of the field of radiometal chelating agents.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 0.004 |
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".