Bioimaging and Biodistribution of the Metal‐Ion‐Controlled Self‐Assembly of PYY<sub>3–36</sub> Studied by SPECT/CT
Bibliographic record
Abstract
Abstract The controlled self‐assembly of peptide‐ and protein‐based pharmaceuticals is of central importance for their mode of action and tuning of their properties. Peptide YY3–36 (PYY3–36) is a 36‐residue peptide hormone that reduces food intake when peripherally administered. Herein, we describe the synthesis of a PYY3–36 analogue functionalized with a metal‐ion‐binding 2,2’‐bipyridine ligand that enables self‐assembly through metal complexation. Upon addition of CuII, the bipyridine‐modified PYY3–36 peptide binds stoichiometric quantities of metal ions in solution and contributes to the organization of higher‐order assemblies. In this study, we aimed to explore the size effect of the self‐assembly in vivo by using non‐invasive quantitative single‐photon emission computed tomography/computed tomography (SPECT/CT) imaging. For this purpose, bipyridine‐modified PYY3–36 was radiolabeled with a chelator holding 111InIII, followed by the addition of CuII to the bipyridine ligand. SPECT/CT imaging and biodistribution studies showed fast renal clearance and accumulation in the kidney cortex. The radiolabeled bipyridyl‐PYY3–36 conjugates with and without CuII presented a slightly slower excretion 1 h post injection compared to the unmodified‐PYY3–36, thus demonstrating that higher self‐assemblies of the peptide might have an effect on the pharmacokinetics.
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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".