Single amino acid chelates (SAAC): a strategy for the design of technetium and rhenium radiopharmaceuticals
Bibliographic record
Abstract
Radiolabeled biomolecules can be used to visualize a variety of diseases through interaction with specific cell receptors. A key step is the introduction of a molecular entity that allows facile labeling with the medically useful radionuclide (99m)Tc without significant alteration of the structure and function of the biomolecule. One strategy focuses on the design of single amino acid chelates (SAACs), novel bifunctional chelators constructed from derivatized amino acids or amino acid analogues. The chelating terminus of the SAAC has been designed for effective coordination to the {(99m)Tc(CO)(3)}(+) core, while the other terminus allows incorporation into any position along a peptide sequence or into a variety of biomolecules. In applications to peptidic materials, the approach affords significant flexibility in the choice of donors for (99m)Tc coordination combined with the considerable advantages of routine solid phase synthetic techniques. The methodology allows libraries of peptide-based (99m)Tc(i) and (186,188)Re(i) radiopharmaceuticals to prepared using conventional automated peptides synthesis. Other biomolecules, including nucleosides, carbohydrates, folic acid and vitamin B12 are also readily modified using analogous methods. The approach also allows the preparation of isostructural (99m)Tc and Re complexes for the correlation of in vivo and in vitro imaging studies.
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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 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 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 | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| 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".