A convenient solid-phase synthesis methodology for preparing peptide-derived molecular imaging agents Synthesis, characterization, and in vitro screening of Tc(I) chemotactic peptide conjugates
Bibliographic record
Abstract
A versatile solid-phase synthesis strategy for preparing peptidechelate conjugates was developed. The methodology was optimized using a series of ligands, designed to bind Tc(I)/Re(I), and a chemotactic peptide fMFL, which was exploited as a model targeting vector. The peptide derivatives were prepared in parallel using a conventional automated peptide synthesizer in multi-milligram quantities, which provided sufficient material to perform complete characterization, radiolabelling, and in vitro screening studies. Because of the robust nature of the metalchelate complexes, the Re complex of a chelatepeptide conjugate was prepared on the resin using the same methodology employed to prepare the free ligand conjugates. As such, the reported methodology is amenable to the preparation of libraries of novel Tc radiopharmaceutical ligands and their corresponding Re reference standards in which several factors, including peptide sequence, site of derivatization, and both the type and length of the spacer, can be easily varied.Key words: radiopharmaceuticals, technetium, rhenium, peptides, solid-phase synthesis.
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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) | 0.001 | 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 | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".