This is the authors' abstract. We don't add key points for this paper.
Background: Many diseases, including cancer, ischemia and tumors show degrees of tissue hypoxia. Therefore, it is useful to detect tissue hypoxia with compounds that can penetrate and remain in hypoxic cells in order to diagnose cancer at early stages. Peptides with low molecular weight, susceptible to redox environment, with easier penetration into tissues and low antigenic characteristics are good candidates, as compared to antibodies and proteins. Therefore, radiolabeling small lipophilic peptides containing a nitro group with Tc to enter and remain in the hypoxic cell for imaging hypoxic tissue is a good strategy for earlier detection of tumors, which leads to more effective and satisfactory treatment. Materials and Methods: In this study, some tripeptides were synthesized with added para nitro benzoic acid (PNBA) group at their N-terminal using solid phase peptide synthesis (SPPS) method with Fmoc strategy using Wang resin. The peptides were labeled with Tc and their characterizations were determined using chromatographic techniques and partition coefficient experiments. A hypoxia Jar system was used to examine cellular uptake of the labeled peptides exposed to MCF-7 cells in normal and hypoxic conditions for 30 to 120 min. Results: Synthesis of the peptides resulted in good yields (70% to 78%). The synthesized peptides and radiopeptides were stable in normal saline and human plasma for at least 4 h and 6 h, respectively. Radiochemical purity (RCP) of the synthesized labeled peptides was ≈94% with lipophilicity (Log P) ≈-0.006. Furthermore, labeled peptides exposed to MCF-7 cells could remain in hypoxic cells longer than those in normal cells. Conclusion: Considering the results obtained in this study, the peptide derivatives labeled with Tc, appear to be appropriate agents for the detection and imaging of hypoxic tissues.
Answers are grounded in this paper and cite their sources.
No takes yet. Share an insight, caveat, or question.
Tehrnai et al. (2019) studied this question.
Synapse has enriched 10 related papers. Consider them for comparative context: