Multimerization of cRGD Peptides by Click Chemistry: Synthetic Strategies, Chemical Limitations, and Influence on Biological Properties
Bibliographic record
Abstract
Integrin α(ν)β(3) is overexpressed on endothelial cells of growing vessels as well as on several tumor types, and so integrin-binding radiolabeled cyclic RGD pentapeptides have attracted increasing interest for in vivo imaging of α(ν)β(3) integrin expression by positron emission tomography (PET). Of the cRGD derivatives available for imaging applications, systems comprising multiple cRGD moieties have recently been shown to exhibit highly favorable properties in relation to monomers. To assess the synthetic limits of the cRGD-multimerization approach and thus the maximum multimer size achievable by using different efficient conjugation reactions, we prepared a variety of multimers that were further investigated in vitro with regard to their avidities to integrin α(ν)β(3.) The synthesized peptide multimers containing increasing numbers of cRGD moieties on PAMAM dendrimer scaffolds were prepared by different click chemistry coupling strategies. A cRGD hexadecimer was the largest construct that could be synthesized under optimized reaction conditions, thus identifying the current synthetic limitations for cRGD multimerization. The obtained multimeric systems were conjugated to a new DOTA-based chelator developed for the derivatization of sterically demanding structures and successfully labeled with (68)Ga for a potential in vivo application. The evaluated multimers showed very high avidities-increasing with the number of cRGD moieties-in in vitro studies on immobilized α(ν)β(3) integrin and U87MG cells, of up to 131- and 124-fold, respectively, relative to the underivatized monomer.
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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) | 0.000 | 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, 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".