Synthesis and comparative evaluation of novel 64Cu-labeled high affinity cell-specific peptides for positron emission tomography imaging of tumor vasculature
Source abstract
Abstract
Tumor angiogenesis, the formation of new tumor blood supply, has been recognized as a hallmark of cancer and represents an important target for clinical management of various angiogenesis-dependent solid tumors. Previously, by screening a bacteriophage peptide library we have discovered the FHT-peptide sequence that binds specifically to bone marrow-derived tumor vasculature with high affinity. Here in an effort to determine the potential of the FHT-peptide for in vivo positron emission tomography (PET) imaging of aggressive tumor vasculature we studied four FHT-derivatives: NOTA-FHT, NOTA-(FHT)2, NOTA-PEG-FHT, and NOTA-PEG-(FHT)2. These peptide analogs were synthesized, labeled with the PET radionuclide (64)Cu, and characterized side-by-side with small animal PET and computed tomography imaging (microPET/CT) at 1 h, 4 h, and 24 h post injection in a subcutaneous Lewis lung carcinoma (LLC) tumor model. Because of its excellent in vivo kinetic properties and high tumor-to-background ratio, the (64)Cu-NOTA-FHT radiopeptide was selected for more detailed evaluation. Blocking studies with excess of unlabeled peptide showed specific and peptide mediated (64)Cu-NOTA-FHT tumor uptake. Biodistribution experiments in the same tumor model confirmed microPET/CT imaging results. Human radiation absorbed dose extrapolated from rodent biodistribution of (64)Cu-NOTA-FHT revealed favorable dosimetry profile. The findings from this investigation warrant further development of (64)Cu-NOTA-FHT as a potential targeted diagnostic radiopharmaceutical for PET imaging of aggressive tumor vasculature.
Source: europepmc · Quality: candidate unverified · Source record ↗
Research context
OpenAlex topics: Monoclonal and Polyclonal Antibodies Research · Radiopharmaceutical Chemistry and Applications · Glycosylation and Glycoproteins Research
SciVX discovery tags are terminology-based. OpenAlex topics are source-provided classifications.
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