Tc-labeled PD-L1 nanobody was prepared and its potential as a SPECT/CT imaging agentfor the visualization of tumor PD-L1 ex
pression was performed via a xenograft nude mouse model.
Methods:
The PD-L1 nanobody was radiolabeled with
99m
Tc(CO) 3 (H 2 O) 3 . The radiolabeling yields and stabilities were studied by radio-active instant thin layer chromatography (radio-ITLC) and high performance liquid chromatography (HPLC). Micro-SPECT/CT imaging was performed at 30
90 min after intravenous injection of
99m
Tc-PD-L1 nanobody in HCC827 and A549 as PD-L1 expression positive and negative groups
respectively.
Results:
The radiochemical yield of
99m
Tc-PD-L1 nanobody was above 95%
and was stable within 3 h both in phosphate buffered saline (PBS) and human serum at 37℃. SPECT images of 99m Tc-PD-L1 nanobody in tumor models showed that HCC827 tumor lesions were visualized with high contrast at 30
90 min post-injection
while no obvious radioactive signal could be found in A549 tumor lesions
suggesting that the formed
99m
Tc-PD-L1 nanobody could show the PD-L1 expression in tumors and had favorable specificity invivo. Moreover
the
99m
Tc-PD-L1 was mainly accumulated in kidneys and eliminated from the body through urinary system
while other tissues
including liver
spleen
heart
lung and soft tissues
displayed low accumulation and no obvious radioactivity was found in thyroid and gastrointestinal system
indicating the beneficial biodistribution of
99m
Tc-PD-L1.
Conclusion:
99m
Tc-PD-L1 nanobody was successfully prepared with a high radiochemical purity and stability
and could be used as a tumor- specific ligand for SPECT imaging of tumor PD-L1 expression.
Site-specific development and preclinical evaluation of 68Ga-THP-ACN376, a CLDN18.2-targeted nanobody probe
The predictive value of CT radiomics model for immunotherapy efficacy in non-small cell lung cancer
In vitro and in vivo evaluation of targeted probe 68 Ga-NOTA-376 constructed by non-site-specific labeling method for CLDN18.2
Research progress in predicting EGFR mutation of NSCLC patients using machine learning based on medical imaging
The treatment outcome and prognosis of CT-guided radiofrequency ablation for elderly patients with non-small cell lung cancer who cannot be treated with surgery
Related Author
Hua ZHU
Zhi YANG
Yuwen YANG
Chang LIU
Wufei CHEN
Ying DONG
LI Dapeng
LIU Chang
Related Institution
Department of Nuclear Medicine, Peking University Cancer Hospital & Institute, State Key Laboratory of Holistic Integrative Management of Gastrointestinal Cancers, NMPA Key Laboratory for Research and Evaluation of Radiopharmaceuticals, National Medical Products Administration, Beijing Key Laboratory of Carcinogenesis and Translational Research
Institute of Medical Technology, Peking University Health Science Center
Department of Radiology, Huadong Hospital, Fudan University