High-denticity ligands based on picolinic acid for <sup>111</sup>In radiochemistry
Bibliographic record
Abstract
Four new acyclic ligands, Bn-H3nonapa (3), H3nonapa (4), p-NO2-Bn-H3nonapa (10), and Bn-H3trenpa (7), were synthesized and studied with nonradioactive In3+ and with radioactive 111In3+. The coordination of these ligands to In3+ was confirmed by high-resolution mass spectrometry and nuclear magnetic resonance spectroscopy. Radiolabeling experiments were performed with 111In3+; these demonstrated H3nonapa (4) to be the best indium ligand of those studied herein, achieving radiochemical yields of ∼97% in 10 min at ambient temperature, and stability to transchelation in mouse serum of 44.5% ± 25.9% after 24 h. Although the radiolabeling kinetics of H3nonapa (4) were excellent, serum stability results were inferior to the previously studied ligands DOTA, DTPA, and H4octapa, suggesting that the presented ligands may find their optimum radiopharmaceutical applications with isotopes other than 111In. Owing to the high denticity of these ligands (9–10 coordinate), they may realize their potential with large ion isotopes such as 177Lu, 86/90Y, and 225Ac.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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) | 0.001 | 0.001 |
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".