First-Generation Radiolabeled Cyclic Peptides for Molecular Imaging of Platelet-Derived Growth Factor Receptor α
Bibliographic record
Abstract
Occult nodal spread and metastatic disease require longstanding imaging and biochemical assessments for thyroid cancer, a disease that has a propensity for diffuse, small-volume disease. We have developed a 64 Cu-labeled platelet-derived growth factor receptor α (PDGFRA) antibody for immuno-PET of PDGFRA in metastatic papillary thyroid cancer (PTC). The present work describes the discovery of small cyclic PDGFRA-targeting peptides, their binding features, and radiolabeling with positron emitter gallium-68 ( 68 Ga) for in vitro and in vivo characterization in thyroid cancer models. Phage-display technology with two separate libraries and seven different cell lines was used through three rounds of biopanning as well as flow cytometry and comparative analysis with recombinant protein to select specific peptide sequences. Phenotypic binding analysis was completed by using phosphorylation and cell migration assays. In vitro protein binding was analyzed with thermophoresis and flow cytometry using the fluorescent-labeled PDGFRA peptide. Peptide candidates were modified with the NOTA chelator for radiolabeling with 68 Ga. In vitro cell uptake was studied in various thyroid cancer cell lines. In vivo studies of 68 Ga-labeled peptides included metabolic stability and PET imaging. From the original library (10 13 compounds), five different peptide groups were identified based on biopanning experiments with and without the α subunit of PDGFR, leading to ∼50 peptides. Subsequent phenotypic screening revealed two core peptide sequences ( CP16 and CP18 ) that demonstrated significant changes in the level of PDGFRA phosphorylation and cell migration. Alanine scan sublibraries were created from these two lead peptide sequences, and peptides were radiolabeled using 68 Ga-GaCl 3 at pH 4.5, resulting in RCP > 95% within 34–40 min, including SPE purification. Cyclic peptide CP18.5 showed the strongest effects on cell migration, flow cytometry, and binding by visual interference color assay. 68 Ga-labeled PDGFRA-targeting peptides showed elevated cell and tumor uptake in models of thyroid cancer, with 68 Ga-NOTA-CP18.5 being the lead candidate. However, metabolic stability in vivo was compromised for 68 Ga-NOTA-CP18.5 vs 68 Ga-NOTA-CP18 but without impacting tumor uptake or clearance profiles. First-generation radiolabeled cyclic peptides have been developed as novel radiotracers, particularly 68 Ga-NOTA-CP18.5, for the molecular imaging of PDGFRA in thyroid cancer.
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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".