Quantification of the glycine transporter 1 in rhesus monkey brain using [¹⁸F]MK-6577 and a model-based input function

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• 作者：Sandra M. Sanabria-Bohó ; rquez ; Aniket D. Joshi ; Marie Holahan ; Lori Daneker ; Kerry Riffel ; Mangay Williams ; Wenping Li ; Jacquelynn J. Cook ; Terence G. Hamill ; ^{terence_hamill@merck.com}
• 关键词：[18F]MK-6577 ; Glycine transporter 1 (GlyT1) ; Positron emission tomography ; Tracer kinetic modeling ; Model-based input function ; Lassen plot
• 刊名：NeuroImage
• 出版年：2012
• 出版时间：1 February 2012
• 年：2012
• 卷：59
• 期：3
• 页码：2589-2599
• 全文大小：1424 K

文摘

Background

Glycine transporter 1 (GlyT1) inhibitors have emerged as potential treatments for schizophrenia due to their potentiation of NMDA receptor activity by modulating the local concentrations of the NMDA co-agonist glycine. [¹⁸F]MK-6577 is a potent and selective GlyT1 inhibitor PET tracer. Although differences in ligand kinetics can be expected between non-human primates and humans, the tracer pre-clinical evaluation can provide valuable information supporting protocol design and quantification in the clinical space. The main objective of this work was to evaluate the in vivo kinetics of [¹⁸F]MK-6577 in rhesus monkey brain. Additionally, a method for estimating the tracer input function from the tracer brain tissue kinetics and venous sampling was validated. This technique was applied for determination of the dose-occupancy relationship of a GlyT1 inhibitor in monkey brain.

Methods

Compartmental and Logan graphical analysis were utilized for quantification of the [¹⁸F]MK-6577 binding using the measured tracer arterial input function. The stability of the tracer volume of distribution relative to scan length was assessed. The proposed model-based input function method takes advantage of the agreement between the tracer concentration in arterial and venous plasma from ~ 5 min. The approach estimates the initial peak of the input curve by adding a gamma like function term to the measured venous curve. The parameters of the model function were estimated by simultaneously fitting several brain time activity curves to a compartmental model.

Results

Good agreement was found between the model-based and the measured arterial plasma curve and the corresponding distribution volumes. The Logan analysis was the preferred method of analysis providing reliable and stable volume of distribution and occupancy results using a 90 and possibly 60 min scan length.

Conclusion

The model-based input function method and Logan analysis are well suited for quantification of [¹⁸F]MK-6577 binding and GlyT1 occupancy in monkey brain.