Heavy Alkaline Earth Radiometals for Cancer Theranostics: Coordination and Radiochemistry of Radium-223 and Barium-131 with Kryptofix 22-Based Chelators
Bibliographic record
Abstract
The possibility of pairing the α-emitter 223 Ra for targeted α therapy with the γ-emitter 131 Ba for SPECT imaging could unlock novel theranostic options in cancer management. However, the lack of stable in vivo chelation for Ra 2+ /Ba 2+ remains a key barrier to clinical use. Four macrocyclic chelators were herein developed by functionalizing 1,10-diaza-18-crown-6 (Kryptofix 22) with donor groups tailored to Ra 2+ /Ba 2+: 2-pyridylphosphonic acid (macrophospho), malonic acid (macromal), catechol (macrocat), and 1,2-HOPO (macroHOPO). The thermodynamic and structural properties of their Ba 2+ and Ra 2+ complexes were explored in aqueous solution through potentiometry, NMR spectroscopy, X-ray crystallography and DFT calculations. Macromal gave the highest stability constant known so far for a 1:1 Ba 2+ -to-ligand fully deprotonated complex (logβ = 16.6), even higher than that of Ba 2+ -macropa, the current state-of-the-art chelator for 223 Ra/ 131 Ba. The experimental complex stability followed the order macromal > macropa ≫ macrophospho ∼ macroHOPO > macrocat. Concentration-, temperature-, pH-, and time-dependent radiolabeling were carried out using 223 Ra derived from Xofigo residues and cyclotron-produced 131 Ba. Although quantitative 223 Ra/ 131 Ba incorporation was not achieved, this work expands the scarce coordination chemistry and radiochemistry of the two heaviest alkaline earth (radio)metals.
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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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 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".