Research ArticleClinical Investigations

Optimized Quantification of Translocator Protein Radioligand 18F-DPA-714 Uptake in the Brain of Genotyped Healthy Volunteers

Sonia Lavisse, Daniel García-Lorenzo, Marie-Anne Peyronneau, Benedetta Bodini, Claire Thiriez, Bertrand Kuhnast, Claude Comtat, Philippe Remy, Bruno Stankoff and Michel Bottlaender
Journal of Nuclear Medicine July 2015, 56 (7) 1048-1054; DOI: https://doi.org/10.2967/jnumed.115.156083

Abstract

Translocator protein (TSPO) is expressed at a low level in healthy brain and is upregulated during inflammatory processes that may occur in neurodegenerative diseases. Thus, TSPO may be a suitable in vivo indicator of neurodegeneration. Here, we quantified the 18F-DPA-714 radioligand in healthy TSPO-genotyped volunteers and developed a method to eliminate the need for invasive arterial blood sampling. Methods: Ten controls (7 high-affinity binders [HABs] and 3 mixed-affinity binders [MABs]) underwent 18F-DPA-714 PET with arterial and venous sampling. 18F-DPA-714 binding was quantified with a metabolite-corrected arterial plasma input function, using the 1- and 2-tissue-compartment models (TCMs) as well as the Logan analysis to estimate total volume distribution (VT) in the regions of interest. Alternative quantification methods were tested, including tissue-to-plasma ratio or population-based input function approaches normalized by late time points of arterial or venous samples. Results: The distribution pattern of 18F-DPA-714 was consistent with the known distribution of TSPO in humans, with the thalamus displaying the highest binding and the cerebellum the lowest. The 2-TCM best described the regional kinetics of 18F-DPA-714 in the brain, with good identifiability (percentage coefficient of variation < 5%). For each region of interest, VT was 47.6% ± 6.3% higher in HABs than in MABs, and estimates from the 2-TCM and the Logan analyses were highly correlated. Equilibrium was reached at 60 min after injection. VT calculated with alternative methods using arterial samples was strongly and significantly correlated with that calculated by the 2-TCM. Replacement of arterial with venous sampling in these methods led to a significant but lower correlation. Conclusion: Genotyping of subjects is a prerequisite for a reliable quantification of 18F-DPA-714 PET images. The 2-TCM and the Logan analyses are accurate methods to estimate 18F-DPA-714 VT in the human brain of both HAB and MAB individuals. Population-based input function and tissue-to-plasma ratio with a single arterial sample are promising alternatives to classic arterial plasma input function. Substitution with venous samples is promising but still requires methodologic improvements.

Neuroinflammation is implicated in many neurodegenerative diseases and microglial activation is suspected to play a role in the central nervous system (1). The identification of a PET radiotracer that can detect the microglial activation could prove to be a useful noninvasive approach to facilitate the characterization and understanding of neurodegeneration.

The translocator protein (TSPO) (18 kDa), which is located in the outer mitochondrial membrane, is widely distributed in many peripheral tissues and in the central nervous system (2). The abundance of TSPO is low in normal brain tissue but is markedly elevated in neuroinflammatory brain diseases. TSPO is thus considered a promising target for the early imaging of microglial activation. The 11C-PK11195 ligand was the first described TSPO radioligand and has been extensively used in animal models and in patients with neurodegenerative diseases over the past 2 decades. Nevertheless, this ligand has many limitations, including strong nonspecific and plasma protein binding and low brain uptake. A second generation of TSPO radioligands with improved properties has since been developed, including 11C-PBR28 ([methyl-11C]N-acetyl-N-(2-methoxybenzyl)-2-phenoxy-5-pyridinamine), 18F-PBR111 (2-(6-chloro-2-(4-(3-18F-fluoropropoxy)phenyl)imidazo[1,2-a]pyridin-3-yl)-N,N-diethylacetamide), 18F-FEDAA1106 (N-(5-fluoro-2-phenoxyphenyl)-N-(2-18F-fluoroethyl-5-methoxybenzyl)acetamide), 18F-FEPPA (N-2-(2-18F-fluoroethoxy)benzyl-N-(4-phenoxypyridin-3-yl)acetamide), 18F-PBR06 (N-(2,5-dimethoxybenzyl)-2-18F-fluoro-N-(2-phenoxyphenyl)acetamide), 11C-DPA-713 (N,N-diethyl-2-[2-(4-11C-methoxyphenyl)-5,7-dimethyl-pyrazolo[1,5-a]pyrimidin-3-yl]-acetamide), and 18F-DPA-714 (N,N-diethyl-2-(2-(4-(2-18F-fluoroethoxy)phenyl)-5,7-dimethylpyrazolo[1,5-a]pyrimidin-3-yl)acetamide). Although some ligands radiolabeled with 11C are highly specific for TSPO, with many advantages over the 11C-PK11195, their short half-life considerably complicates their widespread use.

Among the 18F-labeled compounds, 18F-FEDAA1106 has showed slow kinetics in healthy individuals and Alzheimer disease patients (3) not suitable for in vivo imaging and quantification of TSPO. 18F-PBR06 (4) demonstrated favorable properties in healthy humans but produced brain-penetrant radiolabeled metabolites that could bias the binding quantification. 18F-FEPPA and 18F-PBR111 displayed favorable properties for imaging in healthy humans (5,6), although 18F-FEPPA is rapidly metabolized.

Recent preclinical studies (7–9) have identified 18F-DPA-714 as a promising TSPO ligand for in vivo imaging, and 18F-DPA-714 has been used to quantify TSPOs in healthy subjects (10) and in amyotrophic lateral sclerosis, Alzheimer, or stroke patients (11–13). In most studies, quantification was performed without arterial samples or metabolite analysis and was based either on a simplified compartmental model (the cerebellum as a reference region (10,12)) or on a cluster analysis (10,11,13).

Interestingly, Owen et al. (14) demonstrated in humans that second-generation TSPO tracers target 2 binding sites that lead to 3 affinity patterns: high-, low-, and mixed-affinity binders (HABs, LABs, and MABs, respectively). The recent TSPO radioligands, therefore, have to be evaluated by accounting for the TSPO genotype of each subject, which has not been the case so far for 18F-DPA-714.

Here, we evaluated the ability of 18F-DPA-714 to quantify the TSPO in the brain of 10 healthy subjects with known TSPO genotype. Using arterial blood sampling and metabolite correction, we sought to determine the compartmental model that best described 18F-DPA-714 cerebral uptake. We investigated whether the total distribution volume (VT) could be properly identified and differed as a function of the TSPO genotype. We determined the scan duration needed for stable VT estimates. Last, we studied whether a population-based input function (PBIF) method or the use of venous sampling could provide accurate quantification while avoiding the need for invasive arterial blood sampling.

MATERIALS AND METHODS

Study Design

Ten healthy volunteers (4 women and 6 men; age ± SD, 43.5 ± 15.7 y) were included. Written informed consent was obtained from all participants. All individuals were genotyped for the rs6971 polymorphism within the TSPO gene, and genetic analysis revealed 7 HABs and 3 MABs. Each participant underwent T1-weighted MR imaging (to delineate anatomic boundaries for region-of-interest [ROI] analysis) and 18F-DPA-714 PET scanning. A 18F-DPA‐714 bolus was injected intravenously (201.4 ± 26.3 MBq), and the dynamic PET acquisition lasted 90 min. During acquisition, sequential arterial and venous blood was sampled to generate an arterial input function (AIF) and venous input function.

Supplemental data (available at http://jnm.snmjournals.org) provide details on volunteers, genetic analysis procedure, 18F-DPA-714 synthesis, and imaging protocols.

18F-DPA-714 PET Quantification

Reconstructed dynamic data were realigned for motion correction according to the process of frame-to-reference image registration in Pmod (version 3.5; PMOD Technologies Ltd.).

All T1 MR images were automatically segmented using Freesurfer 5.3, which is documented and freely available for download online (http://surfer.nmr.mgh.harvard.edu/). Selected segmented ROIs were those described as having a moderate, low, or high density of TSPO in normal conditions (15): thalamus; caudate; putamen; hippocampus; white matter; cerebellum; and occipital, parietal, frontal, and temporal cortices. Data from left and right regions were averaged.

After coregistration (BrainVISA), the MR imaging–defined ROIs were applied to the dynamic PET scan to derive regional time–activity curves.

The 1- and 2-tissue-compartment model (1-TCM and 2-TCM, respectively) were applied to the dynamic PET data (Pmod). Blood volume correction was fixed at 5%. The outcome parameters VT, Vs (specific volume of distribution), and kinetic rate constants were estimated for each region. Logan graphical analysis was tested with the fit starting at 30 min.

To investigate the scanning duration required for reproducible estimates of VT, a time-stability analysis was performed (2-TCM) by analyzing reduced scan durations ranging from 90 (VT-90 min) to 30 min, with a 10-min step.

To simplify the quantification, the equilibrium state between the parent plasma P and the free ligand F compartments (and between free F and bound B ligand compartments) was estimated from the ratio of the concentrations in these 2 compartments (F/P and B/F) in the 2-TCM. At equilibrium, the ratio of brain tissue to AIF (ratioAIF) corresponds to the definition of the VT parameter and is proportional to the receptor density (16). From equilibrium, these ratios were individually calculated and were compared with the corresponding VT extracted from 2-TCM analysis (VT-2TCM).

The equivalence between unchanged 18F-DPA-714 concentrations in venous and arterial plasma was also tested for each individual. At equivalence, the ratio obtained between the brain tissue and the unchanged venous plasma activity concentrations was calculated (ratiovein) and compared with VT-2TCM.

More details on 18F-DPA-714 PET data quantification and PBIF method are described in the supplemental data.

PBIF

We used a leave-one-out approach based on all subjects: for each subject, a standard input function (SIF) was created by averaging the AIF of the 9 other subjects. The SIF was then normalized by the value of the individual AIF at time Tα (AIFTα) so that the SIF was dimensionless.

To replace the arterial sample with the venous samples for the normalization of the SIF, we defined a linear function f(x) to estimate the value of AIF at time Tα based on venous samples. To evaluate the accuracy of the PBIF method, VT was assessed with the Logan graphical model using as input the original AIF (VT-Logan), the arterial-based input function (VT-PBIF-AIF), or the venous-based input function (VT-PBIF-vein).

Statistical Analysis

Data are presented as the mean ± SD. Goodness of fit by the nonlinear least-squares method was evaluated using the Akaike information criterion (AIC). The SE of estimation was expressed as a percentage of the rate constant (coefficient of variation [%COV]). AIC estimates from the 1- and 2-TCM were compared with a Wilcoxon paired t test for each ROI. VT values between MABs and HABs were compared using a Mann–Whitney U test. Coefficients of correlation (Spearman) were evaluated between the different parameters. Statistical analysis was conducted with R (R Project for Statistical Computing). The threshold of significance was set to a P value of less than 0.05.

RESULTS

18F-DPA-714 Distribution

All subjects showed fairly uniform 18F-DPA-714 binding in the brain, with higher levels of radioactivity in HABs than in MABs (Fig. 1). The thalamus region demonstrated the highest 18F-DPA-714 uptake.

FIGURE 1.
FIGURE 1.

18F-DPA-714 representative SUV PET images (summed from 60 to 90 min) showing axial, sagittal, and coronal views of HAB (A) and MAB (B) individual.

The concentration of the ligand in tissue peaked within the first 4 min and washed out with different elimination rates depending on the genetic group and ROI (Fig. 2). The peak was slightly higher, and washout was faster in MABs than in HABs.

FIGURE 2.
FIGURE 2.

Time–activity-curves of SUV expressed as mean ± SD in several brain structures of MAB (A) and HAB (B) subjects.

Between 60 and 90 min, the thalamus exhibited the highest washout (mean standardized uptake value [SUV], ∼0.75 and ∼1.11 for MAB and HAB groups, respectively), followed by the parietal and frontal cortices (∼0.63 and ∼0.90), the hippocampus, white matter, occipital cortex (∼0.59 and ∼0.79), putamen (∼0.56 and ∼0.88) and caudate, cerebellum, and temporal cortex (∼0.51 and ∼0.69).

In plasma, the metabolism of 18F-DPA-714 was relatively slow, with an unchanged fraction reaching 54.8% ± 9.7% of the total plasma activity at 90 min in arterial samples. Similar values were obtained in the venous plasma with 54.5% ± 9.6% at 90 min. The difference between the venous and arterial unchanged fraction was low in each individual: 3.1% ± 1.8% at 90 min, except for 1 participant who displayed a much higher unchanged fraction in venous plasma (+19%) than in arterial plasma. This individual was excluded from the analysis based on venous data.

After a rapid distribution phase (peak of 7.3 ± 3.2 SUV at 1 min), the unchanged radiotracer in arterial plasma showed slow elimination kinetics with low concentrations and small interindividual variability (0.22 ± 0.08 SUV at 30), with no SUV difference between HABs and MABs (respectively, 0.133 ± 0.037 and 0.137 ± 0.080 between 60 and 90 min).

In venous plasma, the elimination kinetics occurred at the same rate as in arterial plasma, with lower values (24% ± 13%) and higher interindividual variability.

18F-DPA-714 Quantification

The 2-TCM fitted correctly the kinetics of the radiotracer and identified the best AIC scores (33.5 ± 16.5). The VT parameter was identified with a %COV for VT-2TCM ranging between 2.1% and 4.9%, depending on the ROI. This model estimated the rate constants with moderate identifiability (mean, 8.2, 19.0, 25.3 and 17.4 %COV for K1, k2, k3, and k4, respectively).

Fitting with the 1-TCM deviated from the measured PET data (Fig. 3), and this model gave a significantly higher AIC score (49.2 ± 20.7) than the 2-TCM (P < 0.004).

FIGURE 3.
FIGURE 3.

Model-fit comparisons. Example of 1- and 2-TCM fit to parietal (A) and occipital cortex (B) for one HAB subject.

Therefore, the unconstrained 2-TCM best described the kinetics of both specific and nonspecific 18F-DPA-714 binding (Table 1).

View this table:
TABLE 1

Rate Constant Estimates from 2-TCM Analysis

VT was higher in HABs (+45.9% ± 4.8%) than MABs, with a trend toward significance in the thalamus and white matter regions (P = 0.055) despite the small number of subjects. Figure 4 shows the variability in the VT estimates, which is substantial in the HAB group. No correlation between the age of subjects and VT values in the HAB group was found (−0.2 < r < −0.61, depending on ROI).

FIGURE 4.
FIGURE 4.

VT estimates in different ROIs for HAB and MAB groups. Data are expressed as mean ± SD.

The nondisplaceable distribution volume VND (K1/k2) and Vs (Vs = VT – VND) were estimated for each region. When both HABs and MABs were considered, VND estimates ranged from 0.82 mL/cm3 in the thalamus to 1.13 mL/cm3 in the caudate, with an average of 0.975 ± 0.08 mL/cm3 across regions (mean VND was 0.77 and 1.07 mL/cm3 in MABs and HABs, respectively). VND was equal to 1.02 mL/cm3 across all regions and individuals, using the polymorphism plot method developed by Guo et al. (5). For both genetic groups, Vs was lowest in the caudate and highest in the thalamus (Table 2). The ratio of specific to total uptake of 18F-DPA-714 was approximately 80% in the thalamus and the hippocampus regions, approximately 65% in the occipital, parietal, frontal, and temporal cortices, and only 50% in the caudate. This ratio was similar for HABs and MABs.

View this table:
TABLE 2

VT, Vs, and BPnd Estimates from 2-TCM and Logan Analysis

The time-stability analysis showed that VT-60 min was similar to VT-90 min (VT-60 min > 98% of the VT-90 min value in all ROIs for MABs and HABs), and covariance was stable (+2.29% higher at 60 than at 90 min). VT-60 min and VT-90 min were highly correlated when all subjects were pooled (r = 0.92 for the white matter, r = 0.59 for the putamen, r > 0.90 for other ROIs, P < 0.001). When the duration of the scan was reduced to 30 min, VT identifiability was poorer (%COV > 10) and showed higher bias to VT-90 min (Fig. 5).

FIGURE 5.
FIGURE 5.

VT time-stability assessment for unconstrained 2-TCM in MAB and HAB groups.

VT-Logan values were highly correlated with those obtained with the 2-TCM (r > 0.98, P < 10−8; Table 2): the mean underestimation of the VT with this approach was 2.07% and 3.01% over all ROIs for MABs and HABs, respectively.

As a prerequisite of the alternative quantification methods, B/F and F/P were calculated and were found to rapidly increase with time, reaching a plateau at 30 min (Fig. 6) for each subject. They both were stable at 60 min, with a mean variation of 2% between 60 and 90 min for all individuals and ROIs. No difference was observed between HABs and MABs. 18F-DPA-714 therefore reached a true equilibrium after 60 min after injection. The ratioAIF was calculated for the last 30 min (60–90 min) for each individual: it was highly correlated with the VT-2TCM for all regions (r = 0.93 ± 0.05, P < 10−3; Fig. 7) and was 29.9% ± 5.0% higher than the VT-2TCM. The ratiovein was 72.7% ± 6.6% higher than the VT-2TCM across all ROIs, and both parameters were found correlated as well (r = 0.83 ± 0.06, P = 0.01).

FIGURE 6.
FIGURE 6.

Equilibrium assessment showing B/F and F/Pl with time. Dotted line represents 100% of mean ratio. Data are expressed as mean ± SD.

FIGURE 7.
FIGURE 7.

Correlations of VT obtained with 2-TCM and 2 alternative methods: PBIF (VT-PBIF-AIF) (A) and tissue-to-plasma ratio (ratioAIF) (B) for all individuals (n = 10).

In the PBIF method, the correlation between the AIF points and the area under the curve was highest at 40 min (r = 0.96, P < 10−5). This time point was chosen as Tα to normalize the SIF. VT-PBIF-AIF was strongly correlated with VT-Logan and VT-2TCM (r = 0.97 ± 0.03, P = 0.02; Fig. 7). The difference between VT-PBIF-AIF and VT-Logan values was 2.2% ± 5.8%. VT-PBIF-vein and VT-Logan (difference of 2.4% ± 29.3%) showed a mean correlation of 0.67 ± 0.10, which was significant in the caudate, putamen, thalamus, white matter, and frontal and parietal cortex regions.

DISCUSSION

We quantified 18F-DPA-714 uptake in the brain of TSPO-genotyped healthy individuals.

We found that the 2-TCM described the kinetics of the tracer better than the 1-TCM, with good identifiability of the VT, VND, and Vs parameters. Furthermore, the Logan approach was highly correlated with the 2-TCM and accurately estimated VT. This approach can be further applied at the voxel level to produce parametric VT maps. Our observations are consistent with studies regarding TSPO radioligands showing that the 2-TCM is superior to the 1-TCM (5,6,12,17). The range of our VT estimates (3–5 mL/cm3) was similar to that found for 18F-DPA-714 (12), 11C-PRB28, and 18F-PBR111 in HABs (5,15) and for 11C-DPA-713 (17). Our estimates in the ROIs were consistent with the known distribution of TSPO in the human brain and with previous studies on 11C-PK11195, 11C-DPA713, 11C-PBR28, and 18F-FEPPA (6,15,17,18): the biodistribution of 18F-DPA-714 was widespread and similar in the different cortical regions, but activity concentration was always higher in the thalamus than in the other cerebral regions.

The mean VND in the brain was 0.975 mL/cm3, further confirmed by the polymorphism plot method (5). This value was in the same range as the VND of the 18F-PBR111 tracer (1.18 mL/cm3) but was smaller than the VND of 11C-PRB28 (between 1.6 and 2 (15)), indicating that 18F-DPA-714 has lower nonspecific binding than 11C-PRB28. All these data suggest that 18F-DPA-714 is a highly specific ligand for TSPO.

We compared the binding of 18F-DPA-714 in 2 genetic groups using VT outcome measures. It was not known whether the different binding states reported for 11C-DPA-713 (19) also exist for 18F-DPA-714. However, we expected 18F-DPA-714 to exhibit differential affinity for TSPO, as observed for all second-generation TSPO ligands examined to date, because 18F-DPA-714 and 11C-DPA-713 are structurally similar (20). We found approximately 50% difference in VT between HABs and MABs, which was higher than the difference seen with the 18F-FEPPA tracer (between 15% and 30% (21)) but within the same range as the one with 11C-PBR28 (15).

The difference in VT between HABs and MABs was evident from the shape of the time–activity curves in each affinity group. In addition, one LAB individual was also initially included in our study, and the corresponding time–activity curve clearly differed from the time–activity curves of the other 2 groups, suggesting that the VT parameter of LABs is different from that of HABs and MABs (Supplemental Fig. 1). Arterial sampling was not performed for this subject.

Our data confirm that 18F-DPA-714 binding is influenced by the Ala147Thr polymorphism of TSPO, demonstrating the need to genotype each subject to stratify analysis and to accurately interpret 18F-DPA-714 binding.

In the HABs, VT showed high variability that could be related to K1 higher variability in that group, compared with MABs (Table 1). Such variability in HABs has already been reported for other tracers, namely 18F-PBR111 (5) and 18F-FEPPA (22). As described in Guo et al. (5), we checked here if age or injected mass could influence the binding of radiotracers to TSPO but we found no correlation between those variables and ligand binding. This result was consistent with the findings of Suridjan et al. (21), who previously determined that age did not influence the binding of the 18F-FEPPA tracer in a large HAB cohort (n = 22).

Arterial blood sampling to quantify 18F-DPA-714 is invasive and unsuitable for routine use in patients. Therefore, the reference-based quantitative method (simplified reference tissue model) with the cerebellum as a reference region may be an easier alternative method for data analysis (10,12). Although Golla et al. found a good correlation between the binding potential (BPnd) estimated in the simplified reference tissue model and the associated VT-2TCM values, here we could not always reliably estimate the BPnd parameter using same method. In addition, when correctly estimated, BPnd clearly did not correlate with our VT-2TCM estimates, likely meaning that Vs in the cerebellum is not negligible and the use of this region as a reference can bias the quantification. Moreover, TSPO is widely distributed in the entire brain, thus no anatomic region can be considered devoid of TSPO binding sites.

We tested 3 methods to limit the burden of the 18F-DPA-714 PET scan: (1) we tried to reduce the scan duration and we performed the quantification with (2) venous blood samples only or (3) a single arterial blood sample. Our analyses showed that equilibrium was attained by 60 min, suggesting that acquisition time can be reduced to 60 min in clinical trials. Moreover, the small increase in VT between 60 and 90 min suggests that radiometabolites potentially entering the brain do not substantially contribute to VT, as previously described (23). Indeed, we recently showed that one 18F-DPA-714 radiometabolite contributes to about 15% of brain radioactivity after 120 min in rats. And this value is likely to be much lower in humans at 90 min because metabolism is much slower in humans than in rodents.

The distribution volume calculated after equilibrium as ratioAIF was strongly correlated with VT estimated by the 2-TCM. This method could be therefore used with an acquisition starting 60 min after radiopharmaceutical injection and lasting only 30 min. The ratiovein (to avoid using arterial invasive procedure) was correlated with VT-2TCM, but the correlation was high in the same individual while we observed a higher variability between subjects. This finding is probably explained by the variability in the ratio in venous samples between subjects, despite a stable arteriovenous ratio during analysis (60–90 min). Further work is in progress to better understand and to reduce the variability of the venous sampling. Furthermore, although both ratios were correlated with VT-2TCM, they both overestimated the VT value by approximately 20% and 70%, respectively.

The results of the PBIF method based on one arterial sample or venous samples were highly consistent with those of the full AIF and gave VT estimates (VT-PBIF-AIF and VT-PBIF-vein) close to the VT-2TCM, with only approximately 2% difference. As expected, the variability of the equilibrium between arterial and vein input functions led to substantial variability in the PBIFvein method (∼30% SD for the difference between VT-PBIF-vein and VT-2TCM). However, our method based on f(x) minimized the difference between venous and arterial samples and provided a similar error for both PBIFvein and PBIFartery methods. Although this result is encouraging, the number of individuals (n = 9) is insufficient to draw definitive conclusions about the utility of a method using only vein sampling. Further work on a larger cohort of HABs and MABs will help us to improve the f(x) function.

CONCLUSION

18F-DPA-714 is highly specific for TSPO and can be quantified using the 2-TCM and the Logan graphical approach. Radiolabeling with 18F renders this ligand suitable for widespread use. It was crucial first to characterize and to quantify this ligand in genotyped healthy controls before using it in patients. Indeed, we showed that 18F-DPA-714 binding is influenced by the TSPO polymorphism, consistent with all second-generation TSPO radioligands. Genotyping subjects should be therefore systematically performed before clinical interpretation of 18F-DPA-714 PET scans in neuroinflammatory studies of various neuropathologies.

DISCLOSURE

The costs of publication of this article were defrayed in part by the payment of page charges. Therefore, and solely to indicate this fact, this article is hereby marked “advertisement” in accordance with 18 USC section 1734. This work was supported by France Parkinson (NCT02319382), ANR-08-MNPS-016-02 (NCT02305264), ANR-10-IAIHU-06, a grant from “Investissement d'Avenir-ANR-11-INBS-0011”—NeurATRIS, CEA, and ECTRIMS. No other potential conflict of interest relevant to this article was reported.

Acknowledgments

We are grateful to the SHFJ staff and to Irène Buvat for careful reading of the manuscript. We thank Lena Guillot, Isabelle Rebeix, Sylvie Forlani, and Luce Dauphinot for the genetic analysis. We also thank Philippe Gervais and Frédéric Dollé for the qualification of the clinical production.

Footnotes

  • ↵* Contributed equally to this work.

  • Published online May 29, 2015.

REFERENCES

  1. 1.↵
    1. Glass CK,
    2. Saijo K,
    3. Winner B,
    4. Marchetto MC,
    5. Gage FH
    . Mechanisms underlying inflammation in neurodegeneration. Cell. 2010;140:918–934.
    OpenUrlCrossRefPubMed
  2. 2.↵
    1. Venneti S,
    2. Lopresti BJ,
    3. Wiley CA
    . The peripheral benzodiazepine receptor (translocator protein 18kDa) in microglia: from pathology to imaging. Prog Neurobiol. 2006;80:308–322.
    OpenUrlCrossRefPubMed
  3. 3.↵
    1. Varrone A,
    2. Mattsson P,
    3. Forsberg A,
    4. et al
    . In vivo imaging of the 18-kDa translocator protein (TSPO) with [18F]FEDAA1106 and PET does not show increased binding in Alzheimer's disease patients. Eur J Nucl Med Mol Imaging. 2013;40:921–931.
    OpenUrlCrossRefPubMed
  4. 4.↵
    1. Fujimura Y,
    2. Zoghbi SS,
    3. Simeon FG,
    4. et al
    . Quantification of translocator protein (18 kDa) in the human brain with PET and a novel radioligand, 18F-PBR06. J Nucl Med. 2009;50:1047–1053.
    OpenUrlAbstract/FREE Full Text
  5. 5.↵
    1. Guo Q,
    2. Colasanti A,
    3. Owen DR,
    4. et al
    . Quantification of the specific translocator protein signal of 18F-PBR111 in healthy humans: a genetic polymorphism effect on in vivo binding. J Nucl Med. 2013;54:1915–1923.
    OpenUrlAbstract/FREE Full Text
  6. 6.↵
    1. Rusjan PM,
    2. Wilson AA,
    3. Bloomfield PM,
    4. et al
    . Quantitation of translocator protein binding in human brain with the novel radioligand [18F]-FEPPA and positron emission tomography. J Cereb Blood Flow Metab. 2011;31:1807–1816.
    OpenUrlCrossRefPubMed
  7. 7.↵
    1. Boutin H,
    2. Prenant C,
    3. Maroy R,
    4. et al
    . [18F]DPA-714: direct comparison with [11C]PK11195 in a model of cerebral ischemia in rats. PLoS ONE. 2013;8:e56441.
    OpenUrlCrossRefPubMed
  8. 8.
    1. Chauveau F,
    2. Van Camp N,
    3. Dolle F,
    4. et al
    . Comparative evaluation of the translocator protein radioligands 11C-DPA-713, 18F-DPA-714, and 11C-PK11195 in a rat model of acute neuroinflammation. J Nucl Med. 2009;50:468–476.
    OpenUrlAbstract/FREE Full Text
  9. 9.↵
    1. Lavisse S,
    2. Guillermier M,
    3. Herard AS,
    4. et al
    . Reactive astrocytes overexpress TSPO and are detected by TSPO positron emission tomography imaging. J Neurosci. 2012;32:10809–10818.
    OpenUrlAbstract/FREE Full Text
  10. 10.↵
    1. Arlicot N,
    2. Vercouillie J,
    3. Ribeiro MJ,
    4. et al
    . Initial evaluation in healthy humans of [18F]DPA-714, a potential PET biomarker for neuroinflammation. Nucl Med Biol. 2012;39:570–578.
    OpenUrlCrossRefPubMed
  11. 11.↵
    1. Corcia P,
    2. Tauber C,
    3. Vercoullie J,
    4. et al
    . Molecular imaging of microglial activation in amyotrophic lateral sclerosis. PLoS ONE. 2012;7:e52941.
    OpenUrlCrossRefPubMed
  12. 12.↵
    1. Golla SS,
    2. Boellaard R,
    3. Oikonen V,
    4. et al
    . Quantification of [F]DPA-714 binding in the human brain: initial studies in healthy controls and Alzheimer's disease patients. J Cereb Blood Flow Metab. 2015; 35:766–772.
    OpenUrlCrossRefPubMed
  13. 13.↵
    1. Ribeiro MJ,
    2. Vercouillie J,
    3. Debiais S,
    4. et al
    . Could (18) F-DPA-714 PET imaging be interesting to use in the early post-stroke period? EJNMMI Res. 2014;4:28–35.
    OpenUrlCrossRefPubMed
  14. 14.↵
    1. Owen DR,
    2. Howell OW,
    3. Tang SP,
    4. et al
    . Two binding sites for [3H]PBR28 in human brain: implications for TSPO PET imaging of neuroinflammation. J Cereb Blood Flow Metab. 2010;30:1608–1618.
    OpenUrlCrossRefPubMed
  15. 15.↵
    1. Owen DR,
    2. Guo Q,
    3. Kalk NJ,
    4. et al
    . Determination of [11C]PBR28 binding potential in vivo: a first human TSPO blocking study. J Cereb Blood Flow Metab. 2014;34:989–994.
    OpenUrlCrossRefPubMed
  16. 16.↵
    1. Innis RB,
    2. Cunningham VJ,
    3. Delforge J,
    4. et al
    . Consensus nomenclature for in vivo imaging of reversibly binding radioligands. J Cereb Blood Flow Metab. 2007;27:1533–1539.
    OpenUrlCrossRefPubMed
  17. 17.↵
    1. Endres CJ,
    2. Pomper MG,
    3. James M,
    4. et al
    . Initial evaluation of 11C-DPA-713, a novel TSPO PET ligand, in humans. J Nucl Med. 2009;50:1276–1282.
    OpenUrlAbstract/FREE Full Text
  18. 18.↵
    1. Turkheimer FE,
    2. Edison P,
    3. Pavese N,
    4. et al
    . Reference and target region modeling of [11C]-(R)-PK11195 brain studies. J Nucl Med. 2007;48:158–167.
    OpenUrlAbstract/FREE Full Text
  19. 19.↵
    1. Coughlin JM,
    2. Wang Y,
    3. Ma S,
    4. et al
    . Regional brain distribution of translocator protein using [11C]DPA-713 PET in individuals infected with HIV. J Neurovirol. 2014;20:219–232.
    OpenUrlCrossRefPubMed
  20. 20.↵
    1. Owen DR,
    2. Gunn RN,
    3. Rabiner EA,
    4. et al
    . Mixed-affinity binding in humans with 18-kDa translocator protein ligands. J Nucl Med. 2011;52:24–32.
    OpenUrlAbstract/FREE Full Text
  21. 21.↵
    1. Suridjan I,
    2. Rusjan PM,
    3. Voineskos AN,
    4. et al
    . Neuroinflammation in healthy aging: a PET study using a novel translocator protein 18kDa (TSPO) radioligand, [18F]-FEPPA. Neuroimage. 2014;84:868–875.
    OpenUrlCrossRefPubMed
  22. 22.↵
    1. Mizrahi R,
    2. Rusjan PM,
    3. Kennedy J,
    4. et al
    . Translocator protein (18 kDa) polymorphism (rs6971) explains in-vivo brain binding affinity of the PET radioligand [18F]-FEPPA. J Cereb Blood Flow Metab. 2012;32:968–972.
    OpenUrlCrossRefPubMed
  23. 23.↵
    1. Peyronneau MA,
    2. Saba W,
    3. Goutal S,
    4. et al
    . Metabolism and quantification of [18F]DPA-714, a new TSPO positron emission tomography radioligand. Drug Metab Dispos. 2013;41:122–131.
    OpenUrlAbstract/FREE Full Text
  • Received for publication February 24, 2015.
  • Accepted for publication May 12, 2015.
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