Kinetic modelling of [123I]CNS 1261—a potential SPET tracer for the NMDA receptor

Nuclear Medicine and Biology 30 (2003) 441– 454

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Kinetic modelling of [123I]CNS 1261—a potential SPET tracer for the NMDA receptor Kjell Erlandsson*,a, Rodrigo A Bressanb, Rachel S Mulligana, Roger N Gunnc, Vincent J Cunninghamd, Jonathan Owense, David Wyperf, Peter J Ella, Lyn S Pilowskya,b a

Institute of Nuclear Medicine, Royal Free and University College Medical School, Middlesex Hospital, Mortimer Street, London W1T 3AA, UK b Institute of Psychiatry, De Crespigny Park, London SE5 8AF, UK c McConnell Brain Imaging Centre, Montreal Neurological Institute, McGill University, Montreal, Quebec, Canada d IRSL, Hammersmith Hospital, Du Cane Rd., London W12 ONN, UK e West of Scotland Radionuclide Dispensary, Western Infirmary, Glasgow, UK f Department of Clinical Physics, Southern General Hospital NHS Trust, Glasgow, UK Received 23 April 2002; received in revised form 7 October 2002

Abstract N-(1-napthyl)-N⬘-(3-[123I]-iodophenyl)-N-methylguanidine ([123I]CNS 1261) is a novel SPET ligand developed for imaging the NMDA receptor intra-channel MK 801/PCP/ketamine site. Data was acquired in 7 healthy volunteers after bolus injection of [123I]CNS 1261. Kinetic modeling showed reversible tracer binding. Arterial and venous time-activity curves overlapped after 90 min. The rank order of binding was: Thalamus ⬎ striatum ⬎ cortical regions ⬎ white matter. This distribution concurs with [11C]-ketamine and [18F]-memantine PET studies [14,1]. These data provide a methodological basis for further direct in vivo challenge studies. © 2003 Elsevier Inc. All rights reserved. Keywords: NMDA receptors; [123I]CNS 1261; Ketamine; MK 801; Single photon emission tomography; Kinetic modeling

1. Introduction The intrachannel phencyclidine (PCP)/dizocilpine (MK 801)/ketamine binding site of the NMDA receptor has long been of great relevance to neurology and psychiatry (for review see [7]). Antagonism of this site has been linked to important behavioral consequences, including intoxication and psychosis [34]. Drugs acting at this site are under development for prevention of ischemia-related brain damage, as cognitive enhancers and to prevent progression of Parkinson’s disease, among many other potential applications [7]. There is therefore a pressing need to develop useful in vivo probes for this site. Non-competitive antagonists bind to the intrachannel site when the channel is open. The in vivo binding estimate is therefore assumed to reflect the distribution of functionally active receptors [25]. The dynamic properties of this receptor do not fully lend themselves to classical in vitro or post mortem studies. * Corresponding author: Tel.: ⫹(0)20-7380-9396; fax: ⫹(0)20-76370578. E-mail address: [email protected] (K. Erlandsson). 0969-8051/03/$ – see front matter © 2003 Elsevier Inc. All rights reserved. doi:10.1016/S0969-8051(02)00450-X

Attempts to develop probes for this site have suffered from a lack of selectivity (for example co-binding to sigma receptors) or poor signal to noise properties (for review see [3]). In vivo imaging with [123I]8-iodo-MK 801 was deemed unsuccessful due to high non-specific accumulation in brain white matter as a result of high lipophilicity (log D7.4 ⫽ 3.30) [30]. N,N⬘ Diarylguanidine derivatives have been shown to act as antagonists at the intrachannel NMDA receptor site [15,32]. [125I]-labeled N-(1-napthyl)-N⬘-(3-iodophenyl)-Nmethylguanidine ([125I]CNS 1261) was developed from the neuroprotective compound CNS 1102 as a potential agent for imaging the NMDA receptor in vivo [31]. CNS 1261 was found to have high affinity for the intrachannel site with a Ki value of 4.2 ⫾ 0.4 nM in the [3H]MK 801 binding assay. The selectivity of CNS 1261 for the NMDA receptor was investigated at 2 concentrations (10 nM and 1 ␮M) [31]. The Novascreen at 10nM showed no biologically significant effects (⬍20% inhibition of binding) on the 41 receptor systems investigated. At a higher concentration (1 ␮M) CNS 1261 showed marginal effects on 3 assays, the

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Na⫹ channel 2 (38%), ␣-1 adrenoreceptor (33%), and GABA uptake (21%). This was compared to the 99% inhibition observed in the [3H]MK 801 binding assay at the same concentration. The other 37 binding assays were unaffected by this concentration of CNS 1261. CNS 1261 is likely to have low affinity for sigma receptors since its lead compound, CNS 1102 has an IC50 of 2,540 nM for sigma receptors compared with 36 nM for the NMDA intrachannel site [32]. This data suggests that CNS 1261 is a selective antagonist for the MK 801 binding site of the NMDA receptor. A comparison of partition coefficients for 8-iodoMK 801 and CNS 1261 (log D7.4 ⫽ 2.19) suggests that a lower non-specific binding component should be present in CNS 1261 images. Ex vivo autoradiography experiments showed that in normal rat brain [125I]CNS 1261 uptake was similar to that previously described for [125I]MK 801. Results of displacement studies are inconclusive, with no in vivo displacement of [125I]CNS 1261 being observed in rats sacrificed within 1 h of MK 801 (0.5mg/kg) administration [24]. However, these results could be due to heterogeneous effects of MK 801 on cerebral blood flow, since no reduction in [125I]8iodo MK 801 binding was observed after 1 h with coadministration of MK 801, while a 40% reduction was observed after 4 h [9]. In ischemic rat brain [125I]CNS 1261 uptake reproduced findings previously obtained with [125I]MK 801: Increased uptake in peri-ischemic areas where increased NMDA receptor activation is a result of cell death and the subsequent liberation of glutamate. Tracer metabolism studies revealed that [125I]CNS 1261 was rapidly metabolized in vivo in rats with a half-life of 2.2 ⫾ 0.4 min. However, extracts of brain homogenates studied at 120 min post injection showed that ⬎95% of the activity detected was due to authentic [125I]CNS 1261 [31]. We report the in vivo kinetic behavior of this tracer in 7 healthy volunteers, with the aim of determining the kinetics and distribution of the tracer in normal human brain and to develop an optimized data acquisition protocol for specific drug challenges and clinical research studies in patients with neuropsychiatric disorders.

2. Material and methods 2.1. Subjects The protocol for this study was approved by the South London and Maudsley Trust Ethics Committee. Permission was obtained from the UK Administration of Radioactive Substances Advisory Committee (ARSAC). All subjects gave written informed consent for the study. The inclusion criteria for subjects were: 1) absence of past or present neurological or psychiatric illness or major physical illness requiring chronic medication; 2) no use of medications acting at the central nervous system; 3) no substance dependence according to DSM IV criteria; 4) no

pregnancy (based on disclosure during consent procedure). Nine healthy subjects met inclusion criteria for scanning, but only 7 subjects had both arterial and venous samples and are included in this paper (3 female, 4 male; ages 26.4⫾5.9 years). Five subjects were Caucasians and 2 were mixed race. 2.1.1. Preparation of [123I]CNS 1261 [123I]CNS 1261 was prepared by radioiododestannylation of N-(1-naphthyl)-N⬘-(3-tributylstannylphenyl)-N⬘methylguanidine hydrochloride, using a modification of the method reported by Owens et al. [31]. More specifically, tri-butylstannyl precursor (50 ␮g) was dissolved in methanol (100 ␮L) and sodium acetate: hydrochloric acid buffer (275␮l, pH 1.09). This solution was added to the 2.5 mL vial containing about 1 GBq sodium iodide-123 (⬃75␮L, MDS Nordion S.A., Belgium), followed by peracetic acid (50 ␮L). The reaction mixture was vortexed and after 10 min purified by semi-preparative HPLC. The eluent containing the radioiodinated product (⬃4 mL) was collected, diluted with sterile water for injection (⬃12 mL) and alkalinised with sodium hydroxide (2 M) before loading onto an activated Sep-pak C18 cartridge (Waters, UK). After washing the column with water (10 mL) the radioactivity retained on the column was recovered by reversing the flow and eluting with ethanol. The first 0.3 mL of ethanol was discarded and the following 0.5 mL, containing 80% of the loaded radioactivity, was formulated for injection by dilution with normal saline. The isolated yield of this procedure was 54%. The product was found to co-elute with reference CNS 1261 and had a radiochemical purity of ⬎99% as shown by analytical HPLC. 2.2. Data acquisition Dynamic single photon emission tomography (SPET) studies were acquired on a Prism 3000XP (Philips Medical systems, Cleveland, Oh, USA) triple-headed scanner equipped with a 153Gd transmission source. The three detectors were fitted with ultra-high resolution low energy fan beam collimators (focal length: 50 cm). Primary emission data was collected in a 15% wide energy window centred at 159 keV. Two 3% wide windows were placed on either side of the peak to detect scattered and high-energy photons. 120 projections were acquired over 360° in 128x128 matrices with a pixel size of 3.56 mm. Scanning was done with 360° rotation, and the data was subsequently divided into 3 time frames for each rotation. This was done in order to obtain a more uniform temporal sampling for each individual projection. The reconstructed resolution was ⬃8 mm (FWHM) with ramp-filter. 110-185 MBq of [123I]CNS 1261 was injected intravenously as a bolus. Scanning started at the time of injection, and continued until ⬃5.5 h post-injection in a series of six imaging sessions, starting with 90 min scan (6⫻2 min, 12⫻4 min, 3⫻10 min) and 30 min break, and then continu-

K. Erlandsson et al. / Nuclear Medicine and Biology 30 (2003) 441– 454

ing with 30 min scan (3⫻10 min) and 15 min break for the rest of the study. Four fiducial markers, each filled with approximately 0.1 MBq of 123I, were attached to the subject’s head to facilitate realignment of data acquired from the different imaging sessions. The fiducial markers were placed at the level of the orbito-meatal (OM) line, at the corners of the eyes and behind the ears. Transmission data were acquired together with emission data using the simultaneous transmission emission protocol (STEP, Philips Medical Systems, Cleveland, Oh, USA) with a 20% energy window centred at 100 keV for the transmission data and the same windows as above for primary and scattered emission data. One STEP acquisition was done for each subject, either before the injection of tracer or at the end of the study. 2.3. Blood sampling Blood and plasma kinetics of the tracer were determined from arterial samples taken manually from a radial arterial line at a rate of one sample per 5-10 s for the first 2 min post-injection (p.i.), then one sample per 10-20 s up to 5 or 10 min p.i. Additional samples were taken at 10, 15, 20, 30, 45, 60 and 90 min p.i. and then approximately every 45 min until the end of the scan. Venous sampling started at the time of injection for three subjects, at 6 min for one, and at 25 min p.i. for two subjects. The sampling schedule was the same as for arterial blood. In one of the subjects (#4) no arterial samples were taken after 90 min p.i. and no venous samples were taken at all. 2.4. Blood analysis All samples were analyzed for radioactivity concentration in whole blood and plasma. Parent radioligand and radioactive metabolites in human plasma were separated and measured by HPLC using the system reported by Mulligan et al. [27]. Plasma samples (1 mL), taken at 5, 15, 30, 60, 90, 150 min after radioligand injection, were treated with acetonitrile (9 mL), centrifuged. The resulting liquid supernatant was concentrated in vacuo, reconstituted in mobile phase and filtered before injection into an HPLC system consisting of one pump (Kontron T-414), injector (Rheodyne 1761; 1 mL loop) and a reverse-phase column (Phenomenex Jupiter 10 ␮ C18 column; 250 x 10 mm) eluted with acetonitrile–water–trifluoracetic acid (45:55:0.1 by volume) at 3 mL/min. The HPLC eluent was monitored sequentially for absorbance of light at 230 nm (Thermo Separation Products SpectraSERIES UV150) and radioactivity (in-house sodium iodide well detector linked to an ACEMate; EG & G Ortec). The fraction of unchanged [123I]CNS 1261 was determined by integrating the areas under the HPLC radioactivity curve for each sample. Reference CNS 1261 eluted on this system with a retention time of 14 min. The data was fitted to the following function:

f p共t兲 ⫽ ␣ e ⫺t/␤ ⫹ ␥

443

(1)

where fp(t) is the fraction of unchanged tracer at time t, and ␣, ␤ and ␥ are constants to be determined. Equation (1) was used for metabolite correction of the blood time-activity curves above 5 min. Below 5 min, linear interpolation was used, assuming fp(0) ⫽ 1. Plasma protein binding was not measured. For the purpose of tracer binding quantification (see below) we therefore assumed that this was similar in all subjects. 2.5. Data processing The emission data was corrected for scatter using the triple energy window method [29]. In order to improve the statistical accuracy, all scatter data for each imaging session was integrated and smoothed, and the resulting distribution scaled according to the number of counts in each time frame. This procedure was considered to be appropriate due to the slow change in the activity distribution in the brain, and due to low frequency content of the scatter data. Tomographic images were reconstructed into a 128 ⫻ 128 ⫻ 60 matrix with a voxel size of 2.03 mm ⫻2.03 mm ⫻3.56 mm. Transmission images were reconstructed using an ordered subsets implementation of the convex EM algorithm [20] with 4 iterations and 8 subsets. Emission images were reconstructed by fan-beam filtered backprojection [17] with a ramp-filter. After reconstruction, both emission and transmission images were filtered with a 3D Butterworth low-pass filter, described by B共v兲 ⫽

1 1 ⫹ 共v/q兲 2p

(2)

where B(␯) is the filter value at spatial frequency ␯, p is the filter order and q is the roll-off frequency. The values used for the filter parameters were: p ⫽ 4, q ⫽ 0.58 cm⫺1. Attenuation correction was performed using two iterations of the method of Chang [5], based on the transmission images. The measured attenuation coefficient (␮) values were scaled by a factor 0.88 to correct for the difference in photon energy of the radionuclides used for transmission (153Gd: ⬃100 keV) and emission (123I: 159 keV) scanning. The final resolution was ⬃11 mm (FWHM). For realignment of images from different sessions, the centroids of the fiducial markers were determined and 6 parameters corresponding to a rigid body transformation (3 translation and 3 rotation parameters) were determined by minimizing the mean square error in the marker positions. Realignment was performed by tri-linear interpolation. First, the transmission images were realigned to the emission data for attenuation correction purposes, and finally, all emission images were realigned so that the transaxial planes were perpendicular to the OM-plane (by minimizing the difference in axial coordinates of the markers). The reconstructed image values were transformed into activity concentration values [kBq/mL] using a calibration

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Fig. 1. Compartmental structure of the two compartmental models used: a) The 2-tissue compartment model, with one compartment for free (F) and non-specifically bound (NS) tracer and one for specifically bound tracer (SP), b) the 1-tissue compartment model, with one single compartment for free, non-specifically bound and specifically bound tracer.

factor obtained from measurement with a water-filled cylindrical phantom (diameter ⫽ 20 cm) containing a uniform distribution of 123I. A sample from the same phantom was also used for calibration of the well-counter used for measuring the activity in the blood samples. Regions of interest (ROIs) were defined, guided by an anatomical atlas [37], for the following brain regions: cerebellum, anterior cingulate, frontal cortex, hippocampus, striatum, temporal cortex, thalamus and white matter. Timeactivity curves (TACs) were generated for each ROI. The image reconstruction and processing software was implemented in IDL (Interactive Data Language, Research Systems Inc., Boulder, Co, USA). 2.6. Kinetic modeling Compartmental analysis: The tracer kinetic modeling of the radioligand was based around the compartmental model for neuroreceptor ligand binding introduced by Mintun et al. [26]. Two compartmental models were considered, firstly a two tissue compartment model (2-TC model; Fig. 1a). and secondly a one tissue compartment model (1-TC model; Fig. 1b). Both models are derived from a three tissue compartment model in which the tissue compartments consist of free, non-specifically bound and specifically bound tracer, and which assumes that the tracer can exchange freely across the blood brain barrier with the free compartment. The 2-TC model assumes that the free and non-specifically bound compartments equilibrate rapidly and may then be considered as a single compartment yielding the following parameterisation; K1 (plasma to tissue influx constant), k2⬘ (tissue to plasma efflux constant), k3⬘ ⫽ f2 kon BA (pseudo first order association rate constant: f2 is the tissue free fraction, kon is the first order bimolecular association rate constant and BA is the concentration of available binding sites) and k4 ⫽ koff (disassociation rate constant). The 1-TC model assumes that the free, non-specifically bound, and

specifically bound compartments all equilibrate rapidly and may then be considered as a single compartment yielding the following parameterisation; K1 (plasma to tissue influx constant), k2⬙ (tissue to plasma efflux constant: k2⬙ ⫽ k2⬘/(1 ⫹ k3⬘/k4)). We have assumed here that the plasma (f1) and tissue (f2) free fractions are constant during scanning, which is a valid assumption if the free and non-specifically bound components equilibrate rapidly compared to the rest of the system [19]. Both the 2-TC and 1-TC are described in terms of their associated differential equations. For the 2-TC model the rate of change of tracer concentration in the two tissue compartments are given by the following equations [26]: d 共t兲 ⫽ K 1C P共t兲 ⫺ k 2⬘C F⫹NS共t兲 C dt F⫹NS ⫺ k 3⬘C F⫹NS共t兲 ⫹ k 4C SP共t兲 d C 共t兲 ⫽ k 3⬘C F⫹NS共t兲 ⫺ k 4C SP共t兲 dt SP

(3a) (3b)

where CP(t), CF⫹NS(t) and CSP(t), are the tracer activity concentration [kBq/mL] at time t in the plasma, the free ⫹ non-specifcally bound, and the specifically bound compartments respectively, and K1 [mL/min/mL] and k⬘2, k⬘3 and k4 [min-1] are the first order rate constants. For the 1-TC model the rate of change of tracer in the tissue compartment is d C 共t兲 ⫽ K 1C P共t兲 ⫺ k 2⬙C F⫹NS⫹SP共t兲 dt F⫹NS⫹SP

(4)

where CF⫹NS⫹SP(t) is the tracer activity concentration at time t in the free ⫹ non-specifcally ⫹ specifically bound compartment, and K1 [mL/min/mL] and k⬙2 [min⫺1] are the first order rate constants. The solution of these differential equations gives an expression for the observed total tissue concentration (CT)

K. Erlandsson et al. / Nuclear Medicine and Biology 30 (2003) 441– 454

in terms of the plasma (CP) and whole blood (CB) concentrations and the system impulse response function (HN) [12]: C T共t兲 ⫽ 共1 ⫺ V B兲C P共t兲 䊟 H N共t兲 ⫹ V BC B共t兲

(5)

where VB is the fractional blood volume in brain tissue, HN( · ) is the system impulse response function for an N compartment model, and is the convolution operator. For the 2-TC model, the solution to equations (3a-b) [19] results in:

冋

K1 H 2共t兲 ⫽ 共k 3⬘ ⫹ k 4 ⫺ ␣ 1兲e ⫺␣1t ␣2 ⫺ ␣1

册

⫹ 共 ␣ 2 ⫺ k 3⬘ ⫺ k 4兲e ⫺␣2t

(6)

where

␣ 1,2 ⫽

1 关共k 2⬘ ⫹ k 3⬘ ⫹ k 4兲 2 ⫿ 冑共k 2⬘ ⫹ k 3⬘ ⫹ k 4兲 2 ⫺ 4k 2⬘k 4兴

For the 1-TC model, the solution to equation (4) [19] results in H 1共t兲 ⫽ K 1e ⫺k2⬙t

VT ⫽

冕

⬁

H N共t兲dt

(8)

0

For the 2-TC model this results in VT ⫽

冉

K1 k 3⬘ 1⫹ k 2⬘ k4

冊

(9)

and for the 1-TC model in: VT ⫽

K1 k 2"

To evaluate the influence of the acquisition time on the results, the 1-TC and 2-TC models were applied to subsets of data, representing different total acquisition times from 90 up to 330 min. VT values were calculated for the different acquisition time points, subjects and ROIs. Normalized VT values, NVT, were calculated as follows:

(10)

The set of regional TACs were fitted to the 1-TC and 2-TC models using the metabolite-corrected plasma input curves, and VT was calculated for both models (the blood volume component was fixed to 5%, VB⫽0.05). In order to assess whether the 2-TC model gave a better fit to the data than the 1-TC model, an F test was applied to the sum of squared discrepancies of both models.

V T共t兲 V T共330 min兲

N VT共t兲 ⫽

(11)

were VT(t) is the VT value obtained from analysis of the data subset corresponding to the time range [0, t]. The standard deviation of NVT was calculated across all subjects and regions for each acquisition time point (t). Graphical analysis: The ROI data was also analyzed by the graphical analysis (GA) method of Logan et al. [22], based on the following transformations of the observed tissue (CT) and plasma (CP) concentrations:

X共t兲 ⫽

(7)

The total volume of distribution (VT) is defined as the equilibrium ratio of the total concentration of tracer in tissue to the total concentration of parent tracer in plasma [mL plasma/mL tissue]. This is a robust parameter, for reversible kinetics, linearly dependent on the specific binding in a brain region and independent of blood flow [23]. Formulas for VT can be derived from equations (3a-b) and (4), assuming a zero net transfer of tracer between the different compartments, or simply from the integral of the impulse response function [12]:

445

Y共t兲 ⫽

冕

t

C p共 ␶ 兲d ␶

0

C T共t兲

冕

(12a)

t

C T共 ␶ 兲d ␶

0

C T共t兲

(12b)

For reversible tracers, the plot of Y(t) against X(t) becomes linear above some value ␰, and the slope of this line is then equal to VT. The value of ␰ was determined by visual inspection for each region, and was in general 15 min for gray matter regions and 30 min for white matter. The software for compartmental modeling and GA was implemented in IDL (Research Systems Inc., Boulder, CO, USA). Spectral analysis: The dynamic images were also analyzed by spectral analysis (SA) [6]. Spectral analysis uses the non-negative least squares algorithm to estimate the impulse response function from a set of exponential basis functions. The method was applied on a voxel by voxel basis producing parametric images of VT-values. For comparison with the other methods, the same ROIs used previously were applied to the parametric images and mean VT values were obtained for each ROI. The SA software was implemented in MATLAB (MathWorks Inc., Natick, Ma., USA). The integrated activity distribution (ADD) images and the VT images were transformed into Tailairach-space using the spatial normalization tool in SPM-99 [8]. The template used was a SPET blood flow image. The transformation parameters were determined based on the ADD images, as they have the highest signal-to-noise ratio and a distribution closer to that of blood flow. This was done to allow summation of the images of all subjects for visual display purposes.

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Fig. 2. Activity concentration in arterial (circles) and venous (squares) plasma from one normal volunteer during a [123I]CNS 1261 scan; a) Total activity concentration, 0-5 min; b) Total (filled symbols) and metabolite corrected (empty symbols) activity concentration, 0-360 min. There is a large difference initially between the arterial and venous curves, but from 2 h p.i. the two curves are in good agreement.

3. Results 3.1. Blood and plasma kinetics The specific activity of [123I]CNS 1261 was very high (⬎74GBq/mol), and the radiochemical purity was ⬎99%. While the chloroderivative was formed, its retention time enabled separation from the desired product by HPLC. Precipitation of plasma proteins with acetonitrile (1:9 v/v) resulted in a consistent recovery of ⬎90% of the total plasma radioactivity with no temporal relationship being observed. The consistent recovery over time indicates efficient extraction of the radioactive metabolites with aceto-

nitrile. Experiments showed that recovery off the HPLC over a 16 min run was ⬎95% of the injected radioactivity. Recoveries for the precipitation and the HPLC were not included in the determination of the input function. [123I]CNS 1261 was metabolized to 3, more polar radioactive metabolites that eluted with retention times of 5.2, 6.3 and 7.3 min, respectively. In Figs. 2a-b the metabolite corrected activity concentration in arterial and venous plasma are shown for one of the subjects. The high initial peak in the arterial curve is not present in the venous curve (Fig. 2a), but from 90 min p.i. the two curves are in good agreement (Fig. 2b). The fraction of parent compound in plasma is shown in Fig. 3 for one of the subjects. A good fit

K. Erlandsson et al. / Nuclear Medicine and Biology 30 (2003) 441– 454

447

Fig. 3. The fraction of total radioactivity attributed to [123I]CNS 1261 in plasma from one normal volunteer. The lines represent the curve fitted to the data.

was obtained with equation (1) to the measured values for all subjects. There was a close agreement between the parent fraction obtained from arterial and venous samples. However, the [123I]CNS 1261 rate of metabolism varied between individuals, with exponential decay coefficients (␤ in equation (1)) ranging from 23 to 71 min (mean ⫾ SD ⫽ 38 ⫾ 15 min). The final constant level (␥ in equation (1)) was fairly consistent between the subjects (12 ⫾3%). The correction for parent fraction after 150 min p.i. was based on extrapolated values using equation (1). The validity of this extrapolation was confirmed with data from three psychiatric patients, scanned subsequent to this study, for which metabolite analysis was done up to 5 h p.i. (unpublished data). 3.2. Brain tissue kinetics Fig. 4a shows TACs for thalamus and temporal cortex for one subject, as well as the fitted 1-TC model curves. The general trend for the gray matter regions is a rapid initial uptake, followed by a slow washout. White matter (not shown) had a slower uptake and washout, consistent with lower blood-flow to this region. Logan plots corresponding to the data in Fig. 4a are shown in Fig. 4b. Linear relationships were observed for all investigated brain regions. Figs. 5 shows the VT values from the 2-TC model plotted against those from the 1-TC model. The individual values for all regions in all subjects were included. The graph

shows a good correlation with a slope close to unity (slope ⫽ 1.02, R2 ⫽ 0.98). The results of the F-test indicate that there was no significant difference (p ⬎ 0.05) between the 1-TC and the 2-TC models for 45 of the 56 ROIs. The VT values from GA and SA were also compared with those from the 1-TC model (graphs not shown), with the following results: Slope ⫽ 0.96, R2 ⫽ 0.90 for GA, and slope ⫽ 1.04, R2 ⫽ 0.93 for SA. Fig. 6 shows the results from the kinetic modeling using different data subsets. The standard deviations across regions and subjects of the NVT values are shown as a function of acquisition time. The SD values obtained from the 1-TC model are much lower than those from the 2-TC model. Above 2.5 h, the values increase slowly with decreasing acquisition time, but under 2.5 h an abrupt increase is observed. The mean activity distribution image for all subjects, integrated over the entire scan and transformed into Talairachspace, is shown in Fig. 7 (transaxial, coronal and sagittal sections), and the mean VT image is shown in Fig. 8. The mean values of the rate constant K1 from the 1-TC model are shown in Fig. 9 for different brain regions, and the mean VT values from the 1-TC model in Fig. 10. The rank order of the VT values was: Thalamus ⬎ striatum ⬇ anterior cingulate ⬎ hippocampus ⬎ frontal cortex ⬇ cerebellum ⬇ temporal cortex ⬎ white matter.

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Fig. 4. a) Measured time activity curves of [123I]CNS 1261 in one volunteer for thalamus (circles) and temporal cortex (triangles) regions. The lines represent curves fitted using the 1-tissue compartment model. b) Logan-graphs of the same data. Linear relationships were observed for all investigated brain regions, consistent with reversible tracer binding.

4. Discussion We have performed the first study to evaluate the in vivo behavior in humans of [123I]CNS 1261, a potential ligand for the glutamatergic NMDA receptor, using kinetic modeling and quantitative SPET. The binding dis-

tribution resembles that found in previous in vivo PET studies [14,1], but there are differences compared to in vitro rat and human studies (see below). Issues concerning the methodology and implications for further research are discussed below.

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Fig. 5. Correlation between volume of distribution values (mL/mL) obtained by compartmental analysis with 2- and 1-tissue compartments. All regions and subjects are included. A good correlation was observed, which supports the use of the simpler 1-tissue compartment model.

Fig. 6. Standard deviation of the normalized volume of distribution (NVT) values obtained after applying the 1-tissue (diamonds) and 2-tissue (squares) compartment models to subsets of data, representing different acquisition times. The 1-tissue compartment model gives lower variability and is more stable with respect to shorter acquisition times. These data suggest data acquisition should continue for ⱖ 150 min to obtain reliable VT values.

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Fig. 7. Mean [123I]CNS 1261 integral image from 9 normal volunteers. Transaxial (bottom right), coronal (top right) and sagittal (bottom left) slices are shown which include thalamus (t), striatum (s) and hippocampus (h).

4.1. SPET methodological considerations

4.2. Non-specific binding

Correction for scatter and attenuation are essential for accurate quantitation in cerebral SPET studies [35,16]. For 123 I studies, the triple energy window method for scatter correction has the advantage of also correcting for the background due to septum penetration of high energy photons (⬃500 keV) originating from the decay of 123I. Since a significant amount of this kind of background events can be obtained from activity outside the field of view [18], it is difficult to take it into account with a model-based method. A drawback of this scatter correction method is that it results in a higher noise-level compared to other methods [28,16]. Traditionally, attenuation correction of cerebral SPET studies has been done by assuming a uniform attenuation map. However, it has been shown that higher accuracy can be obtained with a non-uniform attenuation map [10,36]. With the STEP system (Philips Medical Systems, Cleveland, Oh, USA), an accurate non-uniform attenuation map can be obtained from simultaneously acquired transmission and emission data [11].

The high VT value in white matter would suggest that there is a high level of non-specific binding. If it were assumed that white matter represents a valid measure of non-specific binding across the brain, then a large fraction of the binding in cortical regions would be attributed to non-specific binding. However, it is unclear whether binding in white matter is a true representation of the nonspecific binding in gray matter regions. It is unlikely that the high background would be due to the presence of a radioactive metabolite that could penetrate the blood brain barrier for the following reasons: The three radioactive metabolites of [123I]CNS 1261 observed in this study were all more polar than the unchanged parent, and analysis of brain homogenate from rat ex vivo studies showed that ⬎95% of the total brain radioactivity was parent radioligand [31]. 4.3. Comparisons between NMDA radioligands in vivo [11C]ketamine and [18F]memantine PET have previously been used to study NMDA receptors in vivo in humans

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Fig. 8. Mean [123I]CNS 1261 volume of distribution image from 7 normal volunteers. Transaxial (bottom right), coronal (top right) and sagittal (bottom left) slices are shown which include thalamus (t), striatum (s) and hippocampus (h).

[14,1]. Although it is not yet clear whether these ligands do, in fact, accurately estimate NMDA binding in vivo, binding of [11C]ketamine was displaced in vivo by administration of cold ketamine in cortical and sub-cortical regions [14] and [18F]memantine was displaced in mice and monkey in vivo following co-injection with MK 801 [33]. The rank order of VT values estimated with [123I]CNS 1261 is consistent with that found by these PET studies (Thalamus ⬎ striatum ⬎ cortical regions ⬎ white matter). However, this rank order differs from that found in rat ex-vivo [2] and human in vitro studies [25] with [3H]MK 801. The key discrepancy is higher binding in thalamus and striatum compared to cortical regions. This may be due to species specific differences or reflect partial volume effects in in vivo methods, or may indeed represent a difference in NMDA activation between in vitro and in vivo conditions. In contrast to the classical situations of in vivo receptor imaging studies (e.g. dopamine receptors or transporter proteins), binding to the intra-channel site of the NMDA ion channel is dependent on channel opening (use-dependent). An example of discrepancies between the in vitro and ex vivo situation, explained by high endogenous d-serine levels in vivo, have been demonstrated in a recent study imaging the glycineB site of the NMDA receptor [13]. We would not wish an emphasis on the rank

order of binding to divert attention away from important questions of specificity, displaceability and specific/nonspecific binding ratios, however the binding rank order does demonstrate remarkable consistency between different ligands for the NMDA site, different modalities and different imaging centers.

5. Conclusions We have characterized the in vivo kinetic behavior of the potential NMDA receptor ligand [123I]CNS 1261 in healthy volunteers. The following conclusions were reached for planning future studies. The arterial and venous time-activity curves were in good agreement from 90 min after injection. Venous samples can be used to give a true measure of unchanged parent fraction. The kinetics exhibited reversible binding characteristics and the total volume of distribution (VT) was the most reliable parameter for quantification. VT values obtained from the different analysis methods were well correlated. The 1-TC model can be used for quantification of receptor binding by means of the VT value with a data acquisition time ⱖ2.5 h. The rank order of the VT values were: Thalamus ⬎ striatum ⬎ cortical regions ⬎

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Fig. 9. Mean value of rate constant K1 estimated from the 1-tissue compartment model, reflecting delivery of the tracer to different brain regions. The error bars show the SD among subjects.

white matter. These in vivo data represent the first full quantification of this tracer’s behavior in humans. This is an essential step before further direct displacement challenges are performed in humans to evaluate the sensitivity and specificity of [123I]CNS 1261 binding to NMDA receptors in vivo. Further studies will investigate a bolus ⫹ infusion paradigm for large clinical studies (obviating the need for invasive and uncomfortable arterial sampling) [21].

Acknowledgments We wish to thank the following persons for their invaluable help: DC Costa, I Cullum, D Gopalan, S Gacinovic, HM Jones, and J Wong. LSP, KE and RSM were supported by a UK MRC Senior Clinical Fellowship award to LSP. RAB was supported by CAPES, Brazil and a charitable grant from Janssen Pharmaceuticals.

Fig. 10. Volume of distribution (VT) values for different brain regions estimated from the 1-tissue compartment model. VT (⫽ K1/k2⬙) is proportional to receptor binding and independent of blood flow. The error bars show the SD among subjects.

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