BACKGROUND: Positron Emission Tomography (PET) imaging of the Synaptic Vesicle glycoprotein (SV) 2A is a new tool to quantify synaptic density. [18F]UCB-H was one of the first promising SV2A-ligands to be labelled and used in vivo in rodent and human, while limited information on its pharmacokinetic properties is available in the non-human primate. Here, we evaluate the reliability of the three most commonly used modelling approaches for [18F]UCB-H in the non-human cynomolgus primate, adding the coupled fit of the non-displaceable distribution volume (VND) as an alternative approach to improve unstable fit. The results are discussed in the light of the current state of SV2A PET ligands. RESULTS: [18F]UCB-H pharmacokinetic data was optimally fitted with a two-compartment model (2TCM), although the model did not always converge (large total volume of distribution (VT) or large uncertainty of the estimate). 2TCM with coupled fit K1/k2 across brain regions stabilized the quantification, and confirmed a lower specific signal of [18F]UCB-H compared to the newest SV2A-ligands. However, the measures of VND and the influx parameter (K1) are similar to what has been reported for other SV2A ligands. These data were reinforced by displacement studies using [19F]UCB-H, demonstrating only 50% displacement of the total [18F]UCB-H signal at maximal occupancy of SV2A. As previously demonstrated in clinical studies, the graphical method of Logan provided a more robust estimate of VT with only a small bias compared to 2TCM. CONCLUSIONS: Modeling issues with a 2TCM due to a slow component have previously been reported for other SV2A ligands with low specific binding, or after blocking of specific binding. As all SV2A ligands share chemical structural similarities, we hypothesize that this slow binding component is common for all SV2A ligands, but only hampers quantification when specific binding is low.
BACKGROUND: Positron Emission Tomography (PET) imaging of the Synaptic Vesicle glycoprotein (SV) 2A is a new tool to quantify synaptic density. [18F]UCB-H was one of the first promising SV2A-ligands to be labelled and used in vivo in rodent and human, while limited information on its pharmacokinetic properties is available in the non-human primate. Here, we evaluate the reliability of the three most commonly used modelling approaches for [18F]UCB-H in the non-human cynomolgus primate, adding the coupled fit of the non-displaceable distribution volume (VND) as an alternative approach to improve unstable fit. The results are discussed in the light of the current state of SV2A PET ligands. RESULTS: [18F]UCB-H pharmacokinetic data was optimally fitted with a two-compartment model (2TCM), although the model did not always converge (large total volume of distribution (VT) or large uncertainty of the estimate). 2TCM with coupled fit K1/k2 across brain regions stabilized the quantification, and confirmed a lower specific signal of [18F]UCB-H compared to the newest SV2A-ligands. However, the measures of VND and the influx parameter (K1) are similar to what has been reported for other SV2A ligands. These data were reinforced by displacement studies using [19F]UCB-H, demonstrating only 50% displacement of the total [18F]UCB-H signal at maximal occupancy of SV2A. As previously demonstrated in clinical studies, the graphical method of Logan provided a more robust estimate of VT with only a small bias compared to 2TCM. CONCLUSIONS: Modeling issues with a 2TCM due to a slow component have previously been reported for other SV2A ligands with low specific binding, or after blocking of specific binding. As all SV2A ligands share chemical structural similarities, we hypothesize that this slow binding component is common for all SV2A ligands, but only hampers quantification when specific binding is low.
Authors: Chao Zheng; Daniel Holden; Ming-Qiang Zheng; Richard Pracitto; Kyle C Wilcox; Marcel Lindemann; Zachary Felchner; Li Zhang; Jie Tong; Krista Fowles; Sjoerd J Finnema; Nabeel Nabulsi; Richard E Carson; Yiyun Huang; Zhengxin Cai Journal: Eur J Nucl Med Mol Imaging Date: 2021-11-11 Impact factor: 10.057
Authors: Takuya Toyonaga; Arman Fesharaki-Zadeh; Stephen M Strittmatter; Richard E Carson; Zhengxin Cai Journal: Front Neurosci Date: 2022-03-08 Impact factor: 4.677