[<sup>nat/44</sup>Sc(pypa)]<sup>−</sup>: Thermodynamic Stability, Radiolabeling, and Biodistribution of a Prostate-Specific-Membrane-Antigen-Targeting Conjugate
Bibliographic record
Abstract
44 Sc is an attractive positron-emitting radionuclide for PET imaging; herein, a new complex of the Sc 3+ ion with nonmacrocyclic chelator H 4 pypa was synthesized and characterized with high-resolution electrospray-ionization mass spectrometry (HR-ESI-MS), as well as different nuclear magnetic resonance (NMR) spectroscopic techniques ( 1 H, 13 C, 1 H– 13 C HSQC, 1 H– 13 C HMBC, COSY, and NOESY). In aqueous solution (pH = 7), [Sc(pypa)] − presented two isomeric forms, the structures of which were predicted using density functional theory (DFT) calculation with a small energy difference of 22.4 kJ/mol, explaining their coexistence. [Sc(pypa)] − was found to have superior thermodynamic stability (pM = 27.1) compared to [Sc(AAZTA)] − (24.7) and [Sc(DOTA)] − (23.9). In radiolabeling, [ 44 Sc][Sc(pypa)] − formed efficiently at RT in 15 min over a range of pH (2–5.5), resulting in a complex that is highly stable (>99%) in mouse serum over at least six half-lives of scandium-44. Similar labeling efficiency was observed with the PSMA (prostate-specific membrane antigen)-targeting H 4 pypa-C7-PSMA617 at pH = 5.5 (RT, 15 min), confirming negligible disturbance from the bifunctionalization on scandium-44 scavenging. Moreover, the kinetic inertness of the radiocomplex was proved in vivo. Surprisingly, the molar activity was found to have profound influence on the pharmacokinetics of the radiotracers where lower molar activity drastically reduced the background accumulations, particularly, kidney, and thus, yielded a much higher tumor-to-background contrast.
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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".