Kinetic and equilibrium analyses of [123I]epidepride binding to striatal and extrastriatal dopamine D2 receptors

Masahiro Fujita, John P. Seibyl, N. Paul L.G. Verhoeff, Masanori Ichise, Ronald M. Baldwin, Sami S. Zoghbi, Cyrill Burger, Julie K. Staley, Nallakkandi Rajeevan, Dennis S. Charney, Robert B. Innis

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Quantitative SPECT measures of dopamine D2 like receptors with [123I]epidepride is complicated by its high affinity and lipophilic metabolites. The purpose of this study was to use both parent (P) and lipophilic metabolites (M) as input functions in a kinetic paradigm and in comparison to the results of equilibrium studies. Kinetic studies on eleven healthy human subjects, ages 32 ± 10 were performed following i.v. injection of ~370 MBq of [123I]epidepride. Images were acquired for 13.5 ± 1.0 hours. Equilibrium studies were done on seven of eleven subjects with a bolus injection of ~140 MBq, bolus/infusion ratio of 10 hours, and infusion for 30-32 hours. High (striatum) and low (temporal cortex) density regions were studied. Two (P and M) and one (P) input function models were applied in the kinetic studies. In receptor-rich regions, the distribution volumes in nondisplaceable compartments were fixed to those in cerebellum. In addition, in the two input function model, K1/(P)/K1/(M) was fixed to the values in the cerebellum. The one input function model provided V3/(') values (=f1·B(max)/(')/K(D)) which were consistent with those obtained in equilibrium studies in both receptor-rich regions, while the two input function model provided consistent values only in striatum. Poor identifiability of the rate constants of metabolites seemed to be the source of errors in the two input function model. These results suggest that correct V3/(') values can be obtained with the one input function model both in high- and low-density regions.

Original languageEnglish (US)
Pages (from-to)290-304
Number of pages15
Issue number4
StatePublished - Dec 15 1999


  • Compartment modeling
  • Lipophilic metabolites

ASJC Scopus subject areas

  • Cellular and Molecular Neuroscience


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