Rapid Thermodynamically Stable Complex Formation of [<sup>nat/111</sup>In]In<sup>3+</sup>, [<sup>nat/90</sup>Y]Y<sup>3+</sup>, and [<sup>nat/177</sup>Lu]Lu<sup>3+</sup> with H<sub>6</sub>dappa
Bibliographic record
Abstract
A phosphinate-bearing picolinic acid-based chelating ligand (H 6 dappa) was synthesized and characterized to assess its potential as a bifunctional chelator (BFC) for inorganic radiopharmaceuticals. Nuclear magnetic resonance (NMR) spectroscopy was employed to investigate the chelator coordination chemistry with a variety of nonradioactive trivalent metal ions (In 3+, Lu 3+, Y 3+, Sc 3+, La 3+, Bi 3+ ). Density functional theory (DFT) calculations explored the coordination environments of aforementioned metal complexes. The thermodynamic stability of H 6 dappa with four metal ions (In 3+, Lu 3+, Y 3+, Sc 3+ ) was deeply investigated via potentiometric and spectrophotometric (UV–vis) titrations, employing a combination of acidic in-batch, joint potentiometric/spectrophotometric, and ligand–ligand competition titrations; high stability constants and pM values were calculated for all four metal complexes. Radiolabeling conditions for three clinically relevant radiometal ions were optimized ([ 111 In]In 3+, [ 177 Lu]Lu 3+, [ 90 Y]Y 3+ ), and the serum stability of [ 111 In][In(dappa)] 3– was studied. Through concentration-, time-, temperature-, and pH-dependent labeling experiments, it was determined that H 6 dappa radiolabels most effectively at near-physiological pH for all radiometal ions. Furthermore, very rapid radiolabeling at ambient temperature was observed, as maximal radiolabeling was achieved in less than 1 min. Molar activities of 29.8 GBq/μmol and 28.2 GBq/μmol were achieved for [ 111 In]In 3+ and [ 177 Lu]Lu 3+, respectively. For H 6 dappa, high thermodynamic stability did not correlate with kinetic inertness—lability was observed in serum stability studies, suggesting that its metal complexes might not be suitable as a BFC in radiopharmaceuticals.
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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) | 0.001 | 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".