Synthesis of a Bifunctional Cross‐Bridged Chelating Agent, Peptide Conjugation, and Comparison of <sup>68</sup>Ga Labeling and Complex Stability Characteristics with Established Chelators
Bibliographic record
Abstract
Abstract [68Ga]Ga3+ can be introduced into receptor‐specific peptidic carriers via different chelators to obtain radiotracers for Positron Emission Tomography imaging and the chosen chelating agent considerably influences the in vivo pharmacokinetics of the corresponding radiopeptides. A chelator that should be a valuable alternative to established chelating agents for 68Ga‐radiolabeling of peptides would be a backbone‐functionalized variant of the chelator CB‐DO2A. Here, the bifunctional cross‐bridged chelating agent CB‐DO2A‐GA was developed and compared to the established chelators DOTA, NODA‐GA and DOTA‐GA. For this purpose, CB‐DO2A‐GA(tBu)2 was introduced into the peptide Tyr3‐octreotate (TATE) and in direct comparison to the corresponding DOTA‐, NODA‐GA‐, and DOTA‐GA‐modified TATE analogs, CB‐DO2A‐GA‐TATE required harsher reaction conditions for 68Ga‐incorporation. Regarding the hydrophilicity profile of the resulting radiopeptides, a decrease in hydrophilicity from [68Ga]Ga‐DOTA‐GA‐TATE (logD(7.4) of −4.11±0.11) to [68Ga]Ga‐CB‐DO2A‐GA‐TATE (−3.02±0.08) was observed. Assessing the stability against metabolic degradation and complex challenge, [68Ga]Ga‐CB‐DO2A‐GA demonstrated a very high kinetic inertness, exceeding that of [68Ga]Ga‐DOTA‐GA. Therefore, CB‐DO2A‐GA is a valuable alternative to established chelating agents for 68Ga‐radiolabeling of peptides, especially when the formation of a very stable, positively charged 68Ga‐complex is pursued.
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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".