A New Strategy for the Preparation of Peptide-Targeted Technetium and Rhenium Radiopharmaceuticals. The Automated Solid-Phase Synthesis, Characterization, Labeling, and Screening of a Peptide-Ligand Library Targeted at the Formyl Peptide Receptor
Bibliographic record
Abstract
A new solid-phase synthetic methodology was developed that enables libraries of peptide-based Tc(I)/Re(I) radiopharmaceuticals to be prepared using a conventional automated peptide synthesizer. Through the use of a tridentate ligand derived from N-alpha-Fmoc-l-lysine, which we refer to as a single amino acid chelate (SAAC), a series of 12 novel bioconjugates [R-NH(CO)ZLF(SAAC)G, R = ethyl, isopropyl, n-propyl, tert-butyl, n-butyl, benzyl; Z = Met, Nle] that are designed to target the formyl peptide receptor (FPR) were prepared. Construction of the library was carried out in a multiwell format on an Advanced ChemTech 348 peptide synthesizer where multi-milligram quantities of each peptide were isolated in high purity without HPLC purification. After characterization, the library components were screened for their affinity for the FPR receptor using flow cytometry where the K(d) values were found to be in the low micromolar range (0.5-3.0 microM). Compound 5j was subsequently labeled with (99m)Tc(I) and the product isolated in high radiochemical yield using a simple Sep-Pak purification procedure. The retention time of the labeled compound matched that of the fully characterized Re-analogue which was prepared through the use of the same solid-phase synthesis methodology that was used to construct the library. The work reported here is a rare example of a method by which libraries of peptide-ligand conjugates and their rhenium complexes can be prepared.
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How this classification was reachedexpand
Direct model labels (unvalidated)
Per-model category and study-design labels from the labeling rounds. They are machine output, unvalidated, and the disagreement between models ships as data. No study design here is MEDLINE-validated yet.
| Model arm | Categories | Study design | Confidence |
|---|---|---|---|
| gemma | no category Domain: not available · Genre: Empirical About the Canadian research system: no · About a Canadian topic: no | Bench or experimental | low |
| gpt | no category Domain: not available · Genre: Empirical About the Canadian research system: no · About a Canadian topic: no | Bench or experimental | high |
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| 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 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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, unvalidatedLabeled directly by 2 models reading the full record.
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".