Synthesis and Evaluation of 99m Tc-Labeled PSMA-Targeted Tracers Based on the Lys-Urea-Aad Pharmacophore for Detecting Prostate Cancer with Single Photon Emission Computed Tomography.
Kelly LuChengcheng ZhangZhengxing ZhangHsiou-Ting KuoNadine ColpoFrançois BénardKuo-Shyan LinPublished in: Molecules (Basel, Switzerland) (2023)
Prostate-specific membrane antigen (PSMA) is a well-validated prostate cancer marker but reported PSMA-targeted tracers derived from the Lys-urea-Glu pharmacophore including the clinically validated [ 99m Tc]Tc-EDDA/HYNIC-iPSMA have high off-target uptake in kidneys, spleen, and salivary glands. In this study, we synthesized and evaluated three novel 99m Tc-labeled PSMA-targeted tracers and investigated if the tracers derived from the Lys-urea-Aad pharmacophore could have minimized uptake in off-target organs/tissues. In vitro competition binding assays showed that compared with HYNIC-iPSMA, the three novel ligands had slightly weaker PSMA binding affinity (average K i = 3.11 vs. 8.96-11.6 nM). Imaging and ex vivo biodistribution studies in LNCaP tumor-bearing mice showed that [ 99m Tc]Tc-EDDA/HYNIC-iPSMA and the three novel tracers successfully visualized LNCaP tumor xenografts in SPECT images and were excreted mainly via the renal pathway. The average tumor uptake at 1 h post-injection varied from 5.40 to 18.8%ID/g, and the tracers derived from the Lys-urea-Aad pharmacophore had much lower uptake in the spleen and salivary glands. Compared with the clinical tracer [ 99m Tc]Tc-EDDA/HYNIC-iPSMA, the Lys-urea-Aad-derived [ 99m Tc]Tc-EDDA-KL01127 had lower background uptake and superior tumor-to-background contrast ratios and is therefore promising for clinical translation to detect prostate cancer lesions with SPECT.
Keyphrases
- prostate cancer
- pet imaging
- pet ct
- computed tomography
- radical prostatectomy
- molecular docking
- molecular dynamics
- positron emission tomography
- cancer therapy
- machine learning
- magnetic resonance
- drug delivery
- metabolic syndrome
- mass spectrometry
- adipose tissue
- deep learning
- insulin resistance
- optical coherence tomography
- contrast enhanced
- convolutional neural network
- dna binding