Improving the stability of peptidic radiotracers by the introduction of artificial scaffolds: which structure element is most useful?
Bibliographic record
Abstract
Peptidic radiotracers are highly potent substances for the specific in vivo imaging of various biological targets with Single Photon Emission Computed Tomography and Positron Emission Tomography. However, some radiolabeled peptides such as bombesin analogs were shown to exhibit only a limited stability, hampering a successful target visualization. One option to positively influence the stability of radiolabeled peptides is the introduction of certain artificial molecular scaffolds. In order to comparatively assess the influence of different structure elements on the stability of radiolabeled peptides and to identify those structure elements being most useful for peptide radiotracer stabilization, several monomeric and dimeric bombesin derivatives were synthesized, exhibiting differing molecular designs and the chelator NODAGA for (68) Ga-labeling. The radiolabeled peptides were evaluated regarding their in vitro stability in human serum to determine the influence of the introduced molecular scaffolds on the peptides' serum stabilities. The results of the evaluations showed that the introduction of scaffold structures and the overall molecular design have a substantial impact on the stabilities of the resulting peptidic radiotracers. But besides some general trends found using certain scaffold structures, the obtained results point to the necessity to empirically assess their influence on stability for each susceptible peptidic radiotracer individually.
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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 | 0.001 |
| 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".