PESIN Multimerization Improves Receptor Avidities and <i>in Vivo</i> Tumor Targeting Properties to GRPR-Overexpressing Tumors
Bibliographic record
Abstract
The gastrin releasing peptide receptor (GRPR), being overexpressed on several tumor types, represents a promising target for specific noninvasive in vivo tumor imaging using positron emission tomography. Many of the radiolabeled bombesin analogs being applied in tumor imaging, however, suffer from shortcomings such as limited in vivo stability and poor tumor to background ratios. These obstacles can be overcome by peptide multimerization, as this approach results in constructs comprising several copies of the same peptide, thus retaining the ability to specifically bind to the target structure even if one peptide is cleaved. Furthermore, peptide multimers can result in increased binding avidities to the target, which can entail higher absolute tumor uptakes and also tumor to background levels. We therefore synthesized monomers and multimers of the peptide PESIN on dendrimer scaffolds comprising linkers of different lengths. The monomers/multimers were functionalized with the chelator NODAGA, efficiently radiolabeled with (68)Ga and evaluated in vitro regarding their GRPR binding affinity. The results show that shorter distances between the peptide moieties result in substantially higher binding affinities/avidities of the monovalent/multivalent PESIN ligands to the GRPR. Furthermore, the bivalent ligands gave the best results in terms of binding avidity, achieving a 2.5-fold increase in avidity compared to the respective monomer. Moreover, the most potent bivalent ligand showed an about 2-fold higher absolute tumor uptake and twice as high tumor-to-background ratios than the monomeric reference DOTA-PESIN in an initial animal PET study in tumor-bearing mice. Thus, besides high avidities, multivalency also positively influences the in vivo pharmacokinetics of peptide multimers.
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How this classification was reachedexpand
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 | 0.000 |
| 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 teacher head, 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".