Test-retest properties of [11C]-PXT012253 as a positron emission tomography (PET) radiotracer in healthy human brain: PET imaging of mGlu4

Test-retest properties of [11C]-PXT012253 as a positron emission tomography (PET) radiotracer in healthy human brain: PET imaging of mGlu4 | Research Square window.SnipcartSettings = { analytics: { enabled: false } }; (function() { var accessVector = localStorage.getItem('access_vector') || ''; window.dataLayer = window.dataLayer || []; if (accessVector) { window.dataLayer.push({ user: { profile: { profileInfo: { snid: accessVector } } } }); } })(); (function(w,d,s,l,i){w[l]=w[l]||[];w[l].push({'gtm.start':new Date().getTime(),event:'gtm.js'});var f=d.getElementsByTagName(s)[0],j=d.createElement(s),dl=l!='dataLayer'?'&l='+l:'';j.async=true;j.src='https://www.googletagmanager.com/gtm.js?id='+i+dl;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-K279D39R'); Browse Preprints In Review Journals COVID-19 Preprints AJE Video Bytes Research Tools Research Promotion AJE Professional Editing AJE Rubriq About Preprint Platform In Review Editorial Policies Our Team Advisory Board Help Center Sign In Submit a Preprint Cite Share Download PDF Research Article Test-retest properties of [11C]-PXT012253 as a positron emission tomography (PET) radiotracer in healthy human brain: PET imaging of mGlu4 Per Stenkrona, Ryosuke Arakawa, Jiamei Guo, Benny Bang-Andersen, and 6 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-5123848/v1 This work is licensed under a CC BY 4.0 License Status: Published Journal Publication published 14 Jun, 2025 Read the published version in EJNMMI Research → Version 1 posted 5 You are reading this latest preprint version Abstract Background The metabotropic glutamate receptor 4 (mGlu4) has been proposed as a target for Parkinson’s disease to measure levodopa-induced dyskinesia. [ 11 C]PXT012253 is a PET radioligand for mGlu4 (3.4 nM), previously characterized in non-human primates. We aimed to determine the optimal method for quantification, duration for acquisition, and test-retest reliability of the binding parameters for [ 11 C]PXT012253 in healthy volunteers. Results Six subjects (4 females) completed. [ 11 C]PXT012253 displayed high uptake and rapid wash-out. Unchanged [ 11 C]PXT012253 at 20 min was 10–20%. V T in subcortical regions was higher than in cortical regions. 2TC provided better fits than 1TC. V T by Logan GA and MA1 analysis correlated with that of 2TC-CM. MA1 showed better identifiability and standard error than Logan. The test-retest metrics in pons, putamen and thalamus showed absolute variability of V T 0.93 using the 2TC, Logan and MA1 graphical analyses. Time stability analysis showed that V T values estimated using 63 minutes of imaging were within 10% of the values obtained with 93 minutes with all three models. Conclusion [ 11 C]PXT012253 showed a high brain uptake, with rapid washout and metabolism. V T was reliably estimated using 2TC, Logan GA and MA1. The test-retest metrics showed high replicability, indicating [ 11 C]PXT012253 to be a suitable PET radioligand for mGlu4. EudraCT number: 2018-002333-37 Registered at clinicaltrials.gov NCT03826134 Registered 17 January 2019 https://www.clinicaltrials.gov/study/NCT03826134 Positron Emission Tomography (PET) Test-retest human brain [11C]PXT012253 mGlu4 Figures Figure 1 Figure 2 Figure 3 Figure 4 Figure 5 Figure 6 Figure 7 Introduction L-Glutamate is the major excitatory neurotransmitter in the mammalian CNS. It acts via two classes of receptors, ligand gated ion channels (ionotropic receptors) and G-protein coupled (metabotropic) receptors (mGluR). mGluRs are expressed in virtually every major brain region at the presynaptic site of chemical synapses and act as fine tuners of the chemical transmission [ 1 ]. This occurs at excitatory (glutamatergic), inhibitory (GABAergic), and neuromodulatory synapses (monoamines, ACh, peptides) [ 2 , 3 ]. mGluRs are subclassified into three groups based on sequence homology, G-protein coupling, and ligand selectivity. Group I include mGluRs 1 and 5, Group II includes mGluRs 2 and 3, and Group III includes mGluRs, 4, 6, 7, and 8. mGlu4 is expressed in the basal ganglia, which suggest a putative target for the treatment of neurological and psychiatric disorders such as Alzheimer’s disease, Parkinson’s disease, anxiety, depression, and schizophrenia [ 2 ]. Modulation of presynaptic mGlu4 by an allosteric ligand has been proposed as a promising therapeutic target in Parkinson's disease (PD) and levodopa-induced dyskinesia [ 4 , 5 ]. The first clinical mGlu4-positive allosteric modulator (PAM) was recently tried in PD patients (PXT002331) [ 6 ]. Consequently, there is an increasing demand for the development of novel PET radioligands targeting mGlu4. With the successful development PET radioligands for mGlu1 and mGlu5, the utility of radioligands has been demonstrated in research studies with [ 18 F]FIMX for mGlu1 [ 7 ]; [ 11 C]ABP688, [ 18 F]SP203 and [ 18 F]FPEB for mGlu5 [ 8 – 10 ], and in clinical trials with [ 18 F]FPEB [ 11 , 12 ] and [ 11 C]ABP688 for mGlu5 [ 13 , 14 ]. Various chemotypes of mGluR4 positive allosteric modulators (PAMs) have been developed. The first PET imaging ligand for mGluR4 was a carbon-11 labeled derivative of N-(methylthiophenyl)picolinamide ( 11 C-11 or 11 C-KALB012) [ 15 ] later named [ 11 C]PXT012253 [ 16 , 17 ]. It has high affinity to an allosteric site of mGlu4 (3.4 nM) and has been characterized in rodents and in non-human primates (NHP) [ 5 , 15 , 17 ]. Our previous research in NHPs found the brain uptake of PET [ 11 C]PXT012253 radiotracer was high, with a rapid washout [ 17 ]. Test-retest variability of the V T was 17%. It was uniformly blocked by an analogue PXT002331, in all gray matter regions. These results indicated that [ 11 C]PXT012253 might be a promising PET radioligand for measuring receptor occupancy of mGlu4 allosteric modulators in vivo. To determine the suitability of [ 11 C]PXT012253 application in clinical PET studies of central mGlu4, we carried out a positron emission tomography (PET) study in healthy volunteers. The aims were to determine the brain distribution of [ 11 C]-PXT012253, to identify the most reliable model to quantify [ 11 C]-PXT012253 radio tracer binding, and to estimate the intra-subject repeatability of [ 11 C]-PXT012253 binding in the brain. The validation of [ 11 C]PXT012253 as a PET radioligands for the mGlu4 will enable to study mGlu4 binding in vivo in patients, e.g. levodopa-induced dyskinesia in Parkinson’s disease and as a target for novel antipsychotic drugs in Schizophrenia. Materials and Method Ethical standards The ethical standards in conducting the present study was in accordance with the Declaration of Helsinki and the European Medicines Agency “Guideline for good clinical practice E6(R2)”. Participants and Study design The healthy participants were recruited by advertisement on social media. The study protocol was approved by the Swedish ethical review authority and the Swedish Medical Product Agency. Written informed consent was obtained from all the participants. All the subjects met the inclusion/exclusion criteria (see Appendix 1). Each subject underwent a screening visit, a structural Magnetic Resonance Imaging (sMRI) scan, and two PET measurements. The two PET measurements were performed on the same day and within 30 days from the sMRI. Subject-specific plaster helmets were constructed to ensure minimal head movement during the PET measurements. PET Measurements The synthesis of [ 11 C]PXT012253 was performed as previously reported [17]. All PET measurements were performed on the same PET-system, HRRT (Siemens/CTI), which has an effective resolution of 1.5 mm full width half maximum (FWHM) at the centre of the field of view (FOV). Before each PET measurement, a transmission scan of 6 minutes was performed with an external source of 137 Caesium emitting gamma rays for attenuation correction of the PET images. The PET measurement was performed immediately after bolus injection of the [ 11 C]PXT012253 followed by the i.v. injection of 20 mL physiological saline. Emission data was collected in list mode for 93 minutes. The list mode data was reconstructed into a series of 3D radioactivity images consisting of 38-time frames (9x10sec + 2x15sec + 3x20sec + 4x30sec + 4x60sec + 4x180sec + 12x360sec = 5580sec). The midpoint of each frame was used for the measurement of the mean concentration of radioactivity at each time point. Arterial Blood Sampling Subjects had a catheter inserted in the radial artery for arterial blood sampling and a venous catheter inserted in the antecubital vein for radiotracer injection. An Automatic Blood Sampling System (ABSS) was used to withdraw arterial blood for 10 minutes, to measure radioactivity in the arterial blood every second after injection of [ 11 C]PXT012253. In addition, manual samples of arterial blood were taken at 2, 4, 6, 8, 10, 15, 20, 30, 45, 60, 90 minutes to measure radioactivity in whole blood and for metabolite analysis. The manual samples were centrifuged to measure for radioactivity in plasma. Metabolite analysis was performed as previously reported [17]. Image Analysis PET images were analyzed using an in-house developed image analysis pipeline. First, the sMRI image was reoriented so that the axial image was parallel to the plane having the anterior and posterior commissures. The reoriented sMRI was coregistered to the mean PET image saving the transformation parameters (coregistration matrix). Regions of interest (ROIs) were defined by an Anatomical Automatic Labeling (AAL) template on each individual sMRI scan. The MRI images were segmented into gray and white matter masks. The GM mask was used to mask the AAL templates to reduce any overinclusion by the ROI’s. The ROIs selected for the quantitative analysis were thalamus, putamen, frontal cortex, cerebellum and pons. The masked ROI’s was projected and resliced to the space of the corresponding PET image using the coregistration matrix. The resliced ROIs were applied to the dynamic PET images to obtain the Time-activity curves (TACs) expressed as Standardized Uptake Values (SUV) calculated by ratio between tissue radioactivity concentration (kBq/cc) and administered radioactivity (MBq), divided by body weight (kg) Kinetic Model Analysis The kinetic modeling was performed using the PMOD 3.4 software package (PMOD Group, Zurich, Switzerland). The outcome measure was the total distribution volume ( V T ) of [ 11 C]PXT012253 for each ROI, applying different models and using the metabolite-corrected arterial input function. The models implemented were the one-tissue and two-tissue compartment models (1TCM, 2TCM), Logan graphical analysis (Logan) [18] and the multilinear analysis (MA1) [19]. Reliability among the models was estimated by comparing the Akaike information criterion (AIC) and the Model Selection Criterion (MSC). Reliability of the graphical analyses (Logan and MA1) was estimated by comparing percent Standard error of V T . The absolute intra-subject variability (VAR) of the V T was calculated by the absolute difference divided by the average difference of the two examinations according to: Statistical Analysis No statistical testing was done is this study. All summary statistics are based on descriptive methods only. Results Demographic and Exposure Data In total, 6 subjects were included (4 females). Table 1 shows a summary of demographic details and amount of injected [ 11 C]PXT012253. Mean (range) of injected radioactivity 373 MBq (297-539), molar radioactivity 1090 GBq/µmol (696-2214), injected mass 0.11 µg (0.05- 0.18). Table 1. Demographic details of subjects and injected radioactivity and mass PET 1 PET 2 Gender (M/F) Age (yr) BMI (kg/m 2 ) Injected radioactivity (MBq) Mass (µg) Injected radioactivity (MBq) Mass (µg) M 21 25.7 510 0.16 539 0.18 M 21 20.5 398 0.08 386 0.05 F 21 22.9 297 0.11 334 0.13 F 24 21.9 337 0.08 350 0.01 F 22 21.5 335 0.10 364 0.13 F 30 20.2 310 0.09 316 0.07 Mean ± SD 23.2 ± 3.52 22.1 ± 2.0 365 ± 79 0.10 ± 0.03 382 ± 81 0.11 ± 0.05 Regional brain distribution of [ 11 C]PXT012253 The AUC summation 0-30 min images demonstrated a widespread uptake of radioactivity in the brain (Fig. 1). The regional TACs showed a rapid uptake and washout of [ 11 C]PXT012253 in the gray matter regions (Fig. 2). The slowest washout was observed in the pons, followed by the thalamus, striatum, cortex and cerebellum (Fig. 2A). The washout of [ 11 C]PXT012253 from the white matter (WM) TAC was slow and after 45 min the TAC of [ 11 C]PXT012253 was higher than that the TACs of all gray matter regions . TACs in the average grey matter were similar for PET1 and PET 2 (Fig. 2B). The mean (± SD) peak SUV in the whole brain for the six subjects was 5.5 (± 0.8) for PET1 and 5.4 (± 0,5) for PET2, with the total range of 4.3-6.3. The curves of model fitting in the thalamus, putamen and pons as representative regions are shown in figure 3. 2TC, Logan and MA1 analysis provided good on visual inspection, whereas 1TC showed poor fitting. Estimates of the V T for all selected regions, based on the four different modelling approaches are shown in Table 2. V T was high in pons, medium in thalamus and striatum, and low in cortex and cerebellum. Table 2. Mean and SD of V T in selected regions (mL/ccm). 1TC 2TC Logan MA1 THA 4.4 ± 0.8 5.5 ± 0.9 5.7 ± 0.9 5.6 ± 0.9 PUT 3.6 ± 0.7 5.3 ± 1.1 5.4 ± 1.0 5.4 ± 1.0 FC 2.6 ± 0.5 4.0 ± 0.8 4.3 ± 0.9 4.2 ± 0.9 CER 2.7 ± 0.5 3.6 ± 0.6 4.0 ± 0.7 4.0 ± 0.7 PONS 5.0 ±1.0 6.1 ± 1.0 6.2 ± 1.1 6.2 ± 1.1 The 2TC showed better fitting than the 1TC based on lower Akaike information criterion (AIC; 9,8±7,7 vs 81±3,9, Mean±SD) and higher model selection criterion (MSC; 4.7±0.1 vs 2.2±0.3, Mean±SD) (Fig. 3). As for the graphical models, MA1 showed better identifiability than Logan, as smaller standard error (1.2 vs 1.7). t* of Logan and MA1 was set at 27.0 min for all subjects and regions. First, t* was estimated as 27.0 min when Max Err was 10% in the whole brain of most subjects. Next, V T was calculated when t* was fixed at 27.0 min for all subjects and regions. Logan and MA1 were well correlated with the 2TC for V T values although a slight overestimation was observed in both models (Fig. 4). Radiometabolite Analysis The HPLC chromatograms demonstrated that the major metabolites were less lipophilic than that of the parent compound making them less likely to enter the brain (Fig. 5). The metabolite curves were extrapolated to 90 minutes, using up to 45- or 60-minutes data points, by 3-exp fitting because of later time points having too low signal to noise in high-performance liquid chromatography (HPLC) due to the radioactivity decay and fast metabolism. As demonstrated in Figure 6, 10-20% parent compound was present after 20 minutes injection. Test-retest variability and ICC for V T in six subjects. Table 3 shows the test-retest variability and ICC in the selected regions for all models. Mean absolute variability was lowest in thalamus and pons and highest in frontal cortex and cerebellum. The average of absolute test-retest variability of the six subjects for the 12 regions was similar among the models, 6.2 ± 2.7% for 2TC, 6.5 ±1.3% for Logan, and 6.7 ± 1.1% for MA1. Absolute variability for V T calculated by 2TC was lower than those of Logan and MA1 for all the selected regions. The ICC for V T was similar among the selected regions and different models. The ICC confidence interval was generally smaller for 2TC compared to that of Logan and MA1. Table 3. Test-retest metrics of absolute variability and ICC for V T in selected regions for six subjects. Absolute variability (Mean ±SD) % ICC (95% CI) Regions 1TC 2TC Logan MA1 2TC Logan MA1 PONS 5.0±3.6 3.3±2.6 4.6 ±3.1 4.6 ±2.6 0.97 (0.78,1.00) 0.96 (0.54,0.99) 0.96 (0.56,0.99) THA 5.1±2.5 2.8±1.7 5.5±3.2 5.5 ±3.3 0.98 (0.87,1.00) 0.93 (0.51,0.99) 0.93 (0.51,0.99) PUT 4.3±2.7 4.2 ±5.5 6.4 ±3.4 6.8 ±2.8 0.93 (0.56,0.99) 0.92 (0.60,0.99) 0.91 (0.57,0.99) FC 2.6±2.8 6.4 ±5.3 6.9 ±2.7 7.2 ±2.4 0.95 (0.67,0.99) 0.94 (0.21,0.99) 0.94 (0.18,0.99) CER 3.6±3.7 8.0±5.8 8.2 ±1.7 8.6 ±1.6 0.83 (0.27,0.97) 0.90 (0.28,0.99) 0.90 (0.25,0.99) Time time-stability of V T is presented in Figure 7 A-C. The V T values decreased according to the reduced duration of image analysis. The proportion of V T at 63 minutes relative to 93 minutes for 2TC, Logan, and MA1 models were 92%, 93%, and 94% respectively. Figure 7D-F shows the time stability of V T variability. Absolute variability was lower at 63 minutes than at 93 minutes by Logan (5.2% vs 6.5%) and MA1 (5.1% vs 6.8%), and similar by 2TC (6.6% vs 6.1%) (Figure 7 D-F). Discussion In this study, we assessed the quantification and the repeatability of the novel mGlu4 receptor PET radioligand [ 11 C]-PXT012253 for brain imaging in six healthy volunteers. The administration of [ 11 C]-PXT012253 in doses of less than one µg was well tolerated. The results demonstrated high brain uptake and V T values (range 3.6 – 6.1 with 2TC across the selected brain regions (Table 2)) as well as good repeatability of the V T values (range 2.8-8.0 % absolute variability with 2TC among selected regions (Table 2)), providing support that [ 11 C]PXT012253 can be used as a mGlu4 PET radioligand in clinical studies. The brain uptake of [ 11 C]PXT012253 reached SUV 4.3 – 6.3 in the whole brain among the subjects, which is similar to other mGlu radioligands [7, 11, 13, 17]. There was a rapid wash-out, which is favourable for quantification of PET radioligand binding. There was a rapid metabolism of [ 11 C]PXT012253, with 10-20% parent compound left 20 minutes after injection. The regional time curve of radioactivity showed the highest uptake in thalamus, medium in striatum, and the lowest in cortical regions and cerebellum. Pons showed slightly slow kinetics, most likely due to the substantial white matter tissue component that has slower kinetics. The kinetic property in the WM indicates that in this region there is a slow equilibrating component of [ 11 C]PXT012253. To the best of our knowledge, there are no mGluR4 receptors in the WM region and the pharmacological profile of PXT012253 does not indicate binding to other targets of relevance. The WM uptake could represent nonspecific binding. In our previous study with [ 11 C]PXT012253 in NHPs, the analogue PXT002331 blocked the radioligand’s GM binding by 36% [17], and from the parametric image reported, it seemed that an effect was also observed in the WM. Therefore, off-target binding of [ 11 C]PXT012253, for instance to myelin components, cannot be excluded. The specificity of the binding should be futher evaluated by bocking studies with suitable analogues of PXT012253. The data was better described by the 2-TCM than the 1-TCM, based on lower AIC and higher MSC. In the linear graphical analysis, both Logan and MA1 analysis had good fitting to the data and would be the alternative methods considering that graphically derived V T values were well correlated to those by 2-TCM analysis despite of a slight overestimation of 6.9 % ± 11.2 (Mean ± SD) (Fig. 4). In comparison of graphical models, MA1 showed better identifiability compared to Logan, with lower % COV. The rank order of the regional TACs and V T s of [ 11 C]PXT012253 in the present clinical study agreed with that previously reported in NHPs [17] and in studies with F-18-labeled mGlu4 PET radioligands [20, 21]. Hence, the regional distribution of [ 11 C]PXT012253 binding in the present human brains was considered to represent the regional distribution of mGlu4 receptors. High repeatability of V T values was indicated by the test-retest variability below 10% in all subjects and for all models in the selected regions. Analysis of time stability of V T showed less than 10% lower V T values for 63 minutes data compared to that of 93 minutes data in the 2-TCM, Logan and MA1 models. Test-retest repeatability was better at 63 minutes than at 93 minutes by Logan and MA1, probably because of the rapid metabolism, the measurement of blood radioactivity data at later timepoints became less reliable due to low signal-to-noise. The low arterial concentration of parent compound at later time-points made the quantification of V T less reliable. These time stability data suggest that an imaging duration of at 63 min would be preferable for the PET measurement of [ 11 C]-PXT012253 binding. Limitations of the study were the low number of individuals examined and the low and unreliable counts in the later arterial blood samples. Increasing the injected radioactivity in future studies might provide better reliability, by increasing the reliability of the measurement of the parent fraction and arterial input function. Conclusion In this study, the mGlu4 PET radioligand [ 11 C]-PXT012253 was evaluated for the first time in healthy volunteers. We examined the quantification and test-retest reliability of [ 11 C]-PXT012253 binding to mGlu4 in healthy volunteers. [ 11 C]-PXT012253 showed a high brain uptake in the grey matter with peak SUV’s ranging between 3.6 – 6.1%. The slow uptake in white matter (supplements) seems not to impact the gray matter data. However, further evaluation with blocking studies is needed. The kinetics of gray matter regions was well described by 2-TCM, and test-retest variability of V T was below 10% for selected regions, similar or better than other radioligands without a reference region [22, 23]. The most reliable model for quantifying V T estimation based on the ICC values is the 2-TCM. Overall, the present results indicate that [ 11 C]-PXT012253 is a suitable PET radioligand to measure the mGlu4 receptor in the human brain. Declarations Ethics approval and consent to participate The ethical standards in conducting the present study were in accordance with the Declaration of Helsinki and the European Medicines Agency “Guideline for good clinical practice E6(R2)”. The study protocol was approved by the Swedish ethical review authority and the Swedish Medical Product Agency. Written informed consent was obtained from all the participants. Consent for publication Not applicable. Availability of data and material The datasets generated during and/or analysed during the current study are available from the corresponding author on reasonable request. Competing interests Financial interests: Author Benny Bang-Andersen is employed by H. Lundbeck A/S. The other authors declare they have no financial interests in connection with H. Lundbeck A/S. Funding This research work was commissioned with full cost coverage by H. Lundbeck A/S Authors' contributions BBA, CH, AV, RA, SN and PS contributed to the study conception and design. Material preparation, data collection and analysis were performed by Ryosuke Arakawa, Jiamei Guo, Mohammad Mahdi Moein, Zhisheng Jia and Per Stenkrona. The first draft of the manuscript was written by Per Stenkrona and Jiamei Guo and all authors commented on previous versions of the manuscript. Acknowledgements We thank the staff of the PET group at Karolinska Institutet for their assistance during this research work. References Olivero G, Vergassola M, Cisani F, Roggeri A, Pittaluga A. Presynaptic Release-regulating Metabotropic Glutamate Receptors: An Update. Curr Neuropharmacol. 2020;18:655-72. doi:10.2174/1570159X17666191127112339. Niswender CM, Conn PJ. 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Supplementary Files Appendix1.docx Supplementaryfigure.docx Cite Share Download PDF Status: Published Journal Publication published 14 Jun, 2025 Read the published version in EJNMMI Research → Version 1 posted Reviewers agreed at journal 07 Apr, 2025 Reviewers invited by journal 06 Apr, 2025 Editor assigned by journal 04 Apr, 2025 First submitted to journal 04 Apr, 2025 Editorial decision: Minor Revision 18 Nov, 2024 You are reading this latest preprint version Research Square lets you share your work early, gain feedback from the community, and start making changes to your manuscript prior to peer review in a journal. As a division of Research Square Company, we’re committed to making research communication faster, fairer, and more useful. We do this by developing innovative software and high quality services for the global research community. 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Also discoverable on Platform About Our Team In Review Editorial Policies Advisory Board Help Center Resources Author Services Accessibility API Access RSS feed Manage Cookie Preferences © Research Square 2026 | ISSN 2693-5015 (online) Privacy Policy Terms of Service Do Not Sell My Personal Information {"props":{"pageProps":{"initialData":{"identity":"rs-5123848","acceptedTermsAndConditions":true,"allowDirectSubmit":false,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":438975305,"identity":"fff4fffb-bb2a-4b2e-9a14-bed26828c718","order_by":0,"name":"Per Stenkrona","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAA2klEQVRIiWNgGAWjYDADCTBZkcBDpPoEmJYzJGthbEsgrNicvfcB488fNnmSsw8fe/BzXpoMA//hA3i1WPYcN2DmSUgrluZLSzfs3ZbDwyCRht8qgxtpDMwMCYcT5/HwmEnwbqsAauExwK/l/jMGxh8J/8FaJP/OAWrhP/+BgC1sDAw8CQcSZwO1SPM2AB3GkINXB9AvaQyHedKSE2f2sKVJyxxL42GTSMPvMHP2Y4wPf9jYJc44w3xM8k1Nsj0//+EH+B0GxAdQRNjwOwuiZRSMglEwCkYBfgAAE2E76TTKZLkAAAAASUVORK5CYII=","orcid":"https://orcid.org/0000-0002-5529-3336","institution":"Karolinska Institutet Institutionen for klinisk neurovetenskap","correspondingAuthor":true,"prefix":"","firstName":"Per","middleName":"","lastName":"Stenkrona","suffix":""},{"id":438975306,"identity":"dce57374-9ca1-4a39-af67-c3d4e78eb74f","order_by":1,"name":"Ryosuke Arakawa","email":"","orcid":"","institution":"Karolinska Institutet Institutionen for klinisk neurovetenskap","correspondingAuthor":false,"prefix":"","firstName":"Ryosuke","middleName":"","lastName":"Arakawa","suffix":""},{"id":438975307,"identity":"c3a68d4d-97c1-4644-b079-98ffaf8c875d","order_by":2,"name":"Jiamei Guo","email":"","orcid":"","institution":"Chongqing University of Medical Science Clinical College: The First Affiliated Hospital of Chongqing Medical University","correspondingAuthor":false,"prefix":"","firstName":"Jiamei","middleName":"","lastName":"Guo","suffix":""},{"id":438975308,"identity":"2df4f95c-3802-45e4-938f-a4ee86d80519","order_by":3,"name":"Benny Bang-Andersen","email":"","orcid":"","institution":"H Lundbeck A/S","correspondingAuthor":false,"prefix":"","firstName":"Benny","middleName":"","lastName":"Bang-Andersen","suffix":""},{"id":438975309,"identity":"2807bccf-872c-4ae8-8b3f-4025c31ac135","order_by":4,"name":"Sangram Nag","email":"","orcid":"","institution":"Karolinska Institutet Institutionen for klinisk neurovetenskap","correspondingAuthor":false,"prefix":"","firstName":"Sangram","middleName":"","lastName":"Nag","suffix":""},{"id":438975310,"identity":"8ba1bcf0-255c-42ac-b24b-991ecf31a863","order_by":5,"name":"Mohammad Mahdi Moein","email":"","orcid":"","institution":"Karolinska Institutet Institutionen for klinisk neurovetenskap","correspondingAuthor":false,"prefix":"","firstName":"Mohammad","middleName":"Mahdi","lastName":"Moein","suffix":""},{"id":438975311,"identity":"7b7b3d27-49f6-43e6-bb96-49388e9dc844","order_by":6,"name":"Zhisheng Jia","email":"","orcid":"","institution":"Karolinska Institutet Institutionen for klinisk neurovetenskap","correspondingAuthor":false,"prefix":"","firstName":"Zhisheng","middleName":"","lastName":"Jia","suffix":""},{"id":438975312,"identity":"adaeb430-b954-4a29-ad4a-e957abfa06a8","order_by":7,"name":"Zsolt Cselenyi","email":"","orcid":"","institution":"Karolinska Institutet Institutionen for klinisk neurovetenskap","correspondingAuthor":false,"prefix":"","firstName":"Zsolt","middleName":"","lastName":"Cselenyi","suffix":""},{"id":438975313,"identity":"b58e2f05-2400-45f9-ba23-433e2f157754","order_by":8,"name":"Christer Halldin","email":"","orcid":"","institution":"Karolinska Institutet Institutionen for klinisk neurovetenskap","correspondingAuthor":false,"prefix":"","firstName":"Christer","middleName":"","lastName":"Halldin","suffix":""},{"id":438975314,"identity":"2c4aa500-c7f8-4d03-ab12-d0c67880e1d6","order_by":9,"name":"Andrea Varrone","email":"","orcid":"","institution":"Karolinska Institutet Institutionen for klinisk neurovetenskap","correspondingAuthor":false,"prefix":"","firstName":"Andrea","middleName":"","lastName":"Varrone","suffix":""}],"badges":[],"createdAt":"2024-09-20 13:03:42","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-5123848/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-5123848/v1","draftVersion":[],"editorialEvents":[{"content":"https://doi.org/10.1186/s13550-025-01266-y","type":"published","date":"2025-06-14T15:58:04+00:00"}],"editorialNote":"","failedWorkflow":false,"files":[{"id":80139166,"identity":"f0606fb6-8f42-4280-aa4e-e2be49ae3221","added_by":"auto","created_at":"2025-04-08 11:03:10","extension":"jpg","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":79311,"visible":true,"origin":"","legend":"\u003cp\u003eSummation PET images (AUC, 0-30 min), radiographic view, superimposed on corresponding MRI images in a healthy volunteer after i.v. injection of [\u003csup\u003e11\u003c/sup\u003eC]PXT012253, at two different occasions (A and B).\u003c/p\u003e","description":"","filename":"1.jpg","url":"https://assets-eu.researchsquare.com/files/rs-5123848/v1/a4f664b751b5e639607a912a.jpg"},{"id":80139162,"identity":"eb913efe-2bf5-4af2-90dd-000b2fda6070","added_by":"auto","created_at":"2025-04-08 11:03:10","extension":"jpg","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":46023,"visible":true,"origin":"","legend":"\u003cp\u003eThe mean time-activity curves for PET1 (n = 6; A), and the mean (SD) grey matter time-activity curves for PET1 and PET 2 (n = 6; B). THA: thalamus, PUT: putamen, FC: frontal cortex, CER: cerebellum, PONS.\u003c/p\u003e","description":"","filename":"2.jpg","url":"https://assets-eu.researchsquare.com/files/rs-5123848/v1/0444a3f14c5caee852013629.jpg"},{"id":80140051,"identity":"64a11e0f-ad4f-4b66-b8aa-aa91ef9112de","added_by":"auto","created_at":"2025-04-08 11:11:10","extension":"jpg","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":66749,"visible":true,"origin":"","legend":"\u003cp\u003eThe curves of model fitting in the thalamus, putamen, and pons using 1TC \u0026amp; 2TC, Logan, and MA1 models.\u003c/p\u003e","description":"","filename":"3.jpg","url":"https://assets-eu.researchsquare.com/files/rs-5123848/v1/865aecce11af698fa158df6a.jpg"},{"id":80140475,"identity":"cfcfee30-7eff-4774-971f-3474e496bd50","added_by":"auto","created_at":"2025-04-08 11:19:11","extension":"jpg","order_by":4,"title":"Figure 4","display":"","copyAsset":false,"role":"figure","size":51512,"visible":true,"origin":"","legend":"\u003cp\u003eBland-Altman plots showing overestimation of \u003cem\u003eV\u003c/em\u003e\u003csub\u003eT\u003c/sub\u003e by graphical analysis, Logan 7.3 (± 8.0) % (A) and MA1 6.4 (± 7.8) % (B) (Mean ± SD) compared to 2TC in the five selected regions (Thalamus, Putmen, Frontal Cortex, Cerebellum and Pons) in the six subjects, each with two PET examinations.\u003c/p\u003e","description":"","filename":"4.jpg","url":"https://assets-eu.researchsquare.com/files/rs-5123848/v1/3ecdef0abd999b5f9437261a.jpg"},{"id":80139167,"identity":"6cdf90eb-7dc1-4347-8dc3-7f54ba380fcc","added_by":"auto","created_at":"2025-04-08 11:03:10","extension":"jpg","order_by":5,"title":"Figure 5","display":"","copyAsset":false,"role":"figure","size":46882,"visible":true,"origin":"","legend":"\u003cp\u003eRadio-HPLC chromatogram (S101 PET1 60 min sample)\u003c/p\u003e","description":"","filename":"5.jpg","url":"https://assets-eu.researchsquare.com/files/rs-5123848/v1/f33bde7bc35876395638c2b8.jpg"},{"id":80139172,"identity":"a7105645-a618-454b-9f63-fdaa8a9a0fa3","added_by":"auto","created_at":"2025-04-08 11:03:10","extension":"jpg","order_by":6,"title":"Figure 6","display":"","copyAsset":false,"role":"figure","size":20657,"visible":true,"origin":"","legend":"\u003cp\u003eThe mean (SD) parent fraction curves for PET1 and PET 2 (n = 6).\u003c/p\u003e","description":"","filename":"6.jpg","url":"https://assets-eu.researchsquare.com/files/rs-5123848/v1/8522b97c787f0b0efdd6bfe4.jpg"},{"id":80141467,"identity":"1c6aba36-f122-48ba-93a8-10b845d9e6a5","added_by":"auto","created_at":"2025-04-08 11:27:10","extension":"jpg","order_by":7,"title":"Figure 7","display":"","copyAsset":false,"role":"figure","size":56374,"visible":true,"origin":"","legend":"\u003cp\u003eThe mean (SD) Time Stability of \u003cem\u003eV\u003c/em\u003e\u003csub\u003eT\u003c/sub\u003e normalized to 100% at 93 min for 2TC, MA1 and Logan models (n = 6; PET1; A-C) ; the mean time-stability of absolute \u003cem\u003eV\u003c/em\u003e\u003csub\u003eT\u003c/sub\u003e variability (n = 6;\u0026nbsp; D-F)\u003c/p\u003e","description":"","filename":"7.jpg","url":"https://assets-eu.researchsquare.com/files/rs-5123848/v1/0a1510887b68a9040abf11f9.jpg"},{"id":84726544,"identity":"bf5b35ce-e192-4fe6-a026-e1f163d35b50","added_by":"auto","created_at":"2025-06-16 16:06:47","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":1220601,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-5123848/v1/ab9b0690-371e-4b04-a51f-8d4a56e03331.pdf"},{"id":80140474,"identity":"f5d390bd-0df4-4845-ac24-21461e713ead","added_by":"auto","created_at":"2025-04-08 11:19:10","extension":"docx","order_by":1,"title":"","display":"","copyAsset":false,"role":"supplement","size":17461,"visible":true,"origin":"","legend":"","description":"","filename":"Appendix1.docx","url":"https://assets-eu.researchsquare.com/files/rs-5123848/v1/37d48f265496be82ce9b16a7.docx"},{"id":80139168,"identity":"24ebc039-28cc-465d-9e3d-5c986e23fe71","added_by":"auto","created_at":"2025-04-08 11:03:10","extension":"docx","order_by":2,"title":"","display":"","copyAsset":false,"role":"supplement","size":44592,"visible":true,"origin":"","legend":"","description":"","filename":"Supplementaryfigure.docx","url":"https://assets-eu.researchsquare.com/files/rs-5123848/v1/c6f763e21fd37131b7c26ccf.docx"}],"financialInterests":"","formattedTitle":"Test-retest properties of [11C]-PXT012253 as a positron emission tomography (PET) radiotracer in healthy human brain: PET imaging of mGlu4","fulltext":[{"header":"Introduction","content":"\u003cp\u003eL-Glutamate is the major excitatory neurotransmitter in the mammalian CNS. It acts via two classes of receptors, ligand gated ion channels (ionotropic receptors) and G-protein coupled (metabotropic) receptors (mGluR). mGluRs are expressed in virtually every major brain region at the presynaptic site of chemical synapses and act as fine tuners of the chemical transmission [\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e]. This occurs at excitatory (glutamatergic), inhibitory (GABAergic), and neuromodulatory synapses (monoamines, ACh, peptides) [\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e, \u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e]. mGluRs are subclassified into three groups based on sequence homology, G-protein coupling, and ligand selectivity. Group I include mGluRs 1 and 5, Group II includes mGluRs 2 and 3, and Group III includes mGluRs, 4, 6, 7, and 8. mGlu4 is expressed in the basal ganglia, which suggest a putative target for the treatment of neurological and psychiatric disorders such as Alzheimer\u0026rsquo;s disease, Parkinson\u0026rsquo;s disease, anxiety, depression, and schizophrenia [\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e]. Modulation of presynaptic mGlu4 by an allosteric ligand has been proposed as a promising therapeutic target in Parkinson's disease (PD) and levodopa-induced dyskinesia [\u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e, \u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eThe first clinical mGlu4-positive allosteric modulator (PAM) was recently tried in PD patients (PXT002331) [\u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e]. Consequently, there is an increasing demand for the development of novel PET radioligands targeting mGlu4. With the successful development PET radioligands for mGlu1 and mGlu5, the utility of radioligands has been demonstrated in research studies with [\u003csup\u003e18\u003c/sup\u003eF]FIMX for mGlu1 [\u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e]; [\u003csup\u003e11\u003c/sup\u003eC]ABP688, [\u003csup\u003e18\u003c/sup\u003eF]SP203 and [\u003csup\u003e18\u003c/sup\u003eF]FPEB for mGlu5 [\u003cspan additionalcitationids=\"CR9\" citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e], and in clinical trials with [\u003csup\u003e18\u003c/sup\u003eF]FPEB [\u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e, \u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e] and [\u003csup\u003e11\u003c/sup\u003eC]ABP688 for mGlu5 [\u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e, \u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eVarious chemotypes of mGluR4 positive allosteric modulators (PAMs) have been developed. The first PET imaging ligand for mGluR4 was a carbon-11 labeled derivative of N-(methylthiophenyl)picolinamide (\u003csup\u003e11\u003c/sup\u003eC-11 or \u003csup\u003e11\u003c/sup\u003eC-KALB012) [\u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e] later named [\u003csup\u003e11\u003c/sup\u003eC]PXT012253 [\u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e16\u003c/span\u003e, \u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e17\u003c/span\u003e]. It has high affinity to an allosteric site of mGlu4 (3.4 nM) and has been characterized in rodents and in non-human primates (NHP) [\u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e, \u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e, \u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e17\u003c/span\u003e]. Our previous research in NHPs found the brain uptake of PET [\u003csup\u003e11\u003c/sup\u003eC]PXT012253 radiotracer was high, with a rapid washout [\u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e17\u003c/span\u003e]. Test-retest variability of the \u003cem\u003eV\u003c/em\u003e\u003csub\u003eT\u003c/sub\u003e was 17%. It was uniformly blocked by an analogue PXT002331, in all gray matter regions. These results indicated that [\u003csup\u003e11\u003c/sup\u003eC]PXT012253 might be a promising PET radioligand for measuring receptor occupancy of mGlu4 allosteric modulators in vivo.\u003c/p\u003e \u003cp\u003eTo determine the suitability of [\u003csup\u003e11\u003c/sup\u003eC]PXT012253 application in clinical PET studies of central mGlu4, we carried out a positron emission tomography (PET) study in healthy volunteers. The aims were to determine the brain distribution of [\u003csup\u003e11\u003c/sup\u003eC]-PXT012253, to identify the most reliable model to quantify [\u003csup\u003e11\u003c/sup\u003eC]-PXT012253 radio tracer binding, and to estimate the intra-subject repeatability of [\u003csup\u003e11\u003c/sup\u003eC]-PXT012253 binding in the brain. The validation of [\u003csup\u003e11\u003c/sup\u003eC]PXT012253 as a PET radioligands for the mGlu4 will enable to study mGlu4 binding in vivo in patients, e.g. levodopa-induced dyskinesia in Parkinson\u0026rsquo;s disease and as a target for novel antipsychotic drugs in Schizophrenia.\u003c/p\u003e"},{"header":"Materials and Method","content":"\u003cp\u003eEthical standards\u003c/p\u003e\n\u003cp\u003eThe ethical standards in conducting the present study was in accordance with the Declaration of Helsinki and the European Medicines Agency \u0026ldquo;Guideline for good clinical practice E6(R2)\u0026rdquo;.\u003c/p\u003e\n\u003cp\u003eParticipants and Study design\u003c/p\u003e\n\u003cp\u003eThe healthy participants were recruited by advertisement on social media. The study protocol was approved by the Swedish ethical review authority and the Swedish Medical Product Agency. Written informed consent was obtained from all the participants. All the subjects met the inclusion/exclusion criteria (see Appendix 1). Each subject underwent a screening visit, a structural Magnetic Resonance Imaging (sMRI) scan, and two PET measurements. The two PET measurements were performed on the same day and within 30 days from the sMRI. Subject-specific plaster helmets were constructed to ensure minimal head movement during the PET measurements.\u003c/p\u003e\n\u003cp\u003ePET Measurements\u003c/p\u003e\n\u003cp\u003eThe synthesis of [\u003csup\u003e11\u003c/sup\u003eC]PXT012253 was performed as previously reported [17]. All PET measurements were performed on the same PET-system, HRRT (Siemens/CTI), which has an effective resolution of 1.5 mm full width half maximum (FWHM) at the centre of the field of view (FOV). Before each PET measurement, a transmission scan of 6 minutes was performed with an external source of \u003csup\u003e137\u003c/sup\u003eCaesium emitting gamma rays for attenuation correction of the PET images. The PET measurement was performed immediately after bolus injection of the [\u003csup\u003e11\u003c/sup\u003eC]PXT012253 followed by the i.v. injection of 20 mL physiological saline. Emission data was collected in list mode for 93 minutes. The list mode data was reconstructed into a series of 3D radioactivity images consisting of 38-time frames (9x10sec + 2x15sec + 3x20sec + 4x30sec + 4x60sec + 4x180sec + 12x360sec = 5580sec). The midpoint of each frame was used for the measurement of the mean concentration of radioactivity at each time point.\u003c/p\u003e\n\u003cp\u003eArterial Blood Sampling\u003c/p\u003e\n\u003cp\u003eSubjects had a catheter inserted in the radial artery for arterial blood sampling and a venous catheter inserted in the antecubital vein for radiotracer injection. An Automatic Blood Sampling System (ABSS) was used to withdraw arterial blood for 10 minutes, to measure radioactivity in the arterial blood every second after injection of [\u003csup\u003e11\u003c/sup\u003eC]PXT012253. In addition, manual samples of arterial blood were taken at 2, 4, 6, 8, 10, 15, 20, 30, 45, 60, 90 minutes to measure radioactivity in whole blood and for metabolite analysis. The manual samples were centrifuged to measure for radioactivity in plasma. Metabolite analysis was performed as previously reported [17].\u003c/p\u003e\n\u003cp\u003eImage Analysis\u003c/p\u003e\n\u003cp\u003ePET images were analyzed using an in-house developed image analysis pipeline. First, the sMRI image was reoriented so that the axial image was parallel to the plane having the anterior and posterior commissures. The reoriented sMRI was coregistered to the mean PET image saving the transformation parameters (coregistration matrix). Regions of interest (ROIs) were defined by an Anatomical Automatic Labeling (AAL) template on each individual sMRI scan. The MRI images were segmented into gray and white matter masks. The GM mask was used to mask the AAL templates to reduce any overinclusion by the ROI\u0026rsquo;s. The ROIs selected for the quantitative analysis were thalamus, putamen, frontal cortex, cerebellum and pons. The masked ROI\u0026rsquo;s was projected and resliced to the space of the corresponding PET image using the coregistration matrix. The resliced ROIs were applied to the dynamic PET images to obtain the Time-activity curves (TACs) expressed as Standardized Uptake Values (SUV) calculated by ratio between tissue radioactivity concentration (kBq/cc) and administered radioactivity (MBq), divided by body weight (kg)\u003c/p\u003e\n\u003cp\u003eKinetic Model Analysis\u003c/p\u003e\n\u003cp\u003eThe kinetic modeling was performed using the PMOD 3.4 software package (PMOD Group, Zurich, Switzerland). The outcome measure was the total distribution volume (\u003cem\u003eV\u003c/em\u003e\u003csub\u003eT\u003c/sub\u003e) of [\u003csup\u003e11\u003c/sup\u003eC]PXT012253 for each ROI, applying different models and using the metabolite-corrected arterial input function. The models implemented were the one-tissue and two-tissue compartment models (1TCM, 2TCM), Logan graphical analysis (Logan) [18] and the multilinear analysis (MA1) [19]. Reliability among the models was estimated by comparing the Akaike information criterion (AIC) and the Model Selection Criterion (MSC). Reliability of the graphical analyses (Logan and MA1) was estimated by comparing percent Standard error of \u003cem\u003eV\u003c/em\u003e\u003csub\u003eT\u003c/sub\u003e. The absolute intra-subject variability (VAR) of the \u003cem\u003eV\u003c/em\u003e\u003csub\u003eT\u003c/sub\u003e was calculated by the absolute difference divided by the average difference of the two examinations according to:\u003c/p\u003e\n\u003cp\u003e\u003cimg src=\"data:image/png;base64,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\" width=\"282\" height=\"59\"\u003e\u003c/p\u003e\n\u003cp\u003eStatistical Analysis\u003c/p\u003e\n\u003cp\u003eNo statistical testing was done is this study. All summary statistics are based on descriptive methods only.\u003c/p\u003e"},{"header":"Results","content":"\u003cp\u003eDemographic and Exposure Data\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eIn total, 6 subjects were included (4 females). Table 1 shows a summary of demographic details and amount of injected\u0026nbsp;[\u003csup\u003e11\u003c/sup\u003eC]PXT012253. Mean (range) of injected radioactivity 373 MBq (297-539), molar radioactivity 1090 GBq/\u0026micro;mol (696-2214), injected mass 0.11 \u0026micro;g (0.05- 0.18).\u003c/p\u003e\n\u003cp id=\"_Toc9291921\"\u003eTable 1. Demographic details of subjects and injected radioactivity and mass\u003c/p\u003e\n\u003ctable border=\"1\" cellspacing=\"0\" cellpadding=\"0\" width=\"604\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 85px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 85px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 76px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\" valign=\"top\" style=\"width: 180px;\"\u003e\n \u003cp\u003e\u003cstrong\u003ePET 1\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\" valign=\"top\" style=\"width: 180px;\"\u003e\n \u003cp\u003e\u003cstrong\u003ePET 2\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 85px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eGender\u003c/strong\u003e\u003c/p\u003e\n \u003cp\u003e\u003cstrong\u003e(M/F)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 85px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eAge\u003c/strong\u003e\u003c/p\u003e\n \u003cp\u003e\u003cstrong\u003e(yr)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 76px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eBMI\u003c/strong\u003e\u003c/p\u003e\n \u003cp\u003e\u003cstrong\u003e(kg/m\u003c/strong\u003e\u003cstrong\u003e\u003csup\u003e2\u003c/sup\u003e\u003c/strong\u003e\u003cstrong\u003e)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 92px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eInjected\u003c/strong\u003e\u003c/p\u003e\n \u003cp\u003e\u003cstrong\u003eradioactivity\u003c/strong\u003e\u003c/p\u003e\n \u003cp\u003e\u003cstrong\u003e(MBq)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 87px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eMass\u003c/strong\u003e\u003c/p\u003e\n \u003cp\u003e\u003cstrong\u003e(\u0026micro;g)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 92px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eInjected\u003c/strong\u003e\u003c/p\u003e\n \u003cp\u003e\u003cstrong\u003eradioactivity\u003c/strong\u003e\u003c/p\u003e\n \u003cp\u003e\u003cstrong\u003e(MBq)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 87px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eMass\u003c/strong\u003e\u003c/p\u003e\n \u003cp\u003e\u003cstrong\u003e(\u0026micro;g)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 85px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eM\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 85px;\"\u003e\n \u003cp\u003e21\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 76px;\"\u003e\n \u003cp\u003e25.7\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 92px;\"\u003e\n \u003cp\u003e510\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 87px;\"\u003e\n \u003cp\u003e0.16\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 92px;\"\u003e\n \u003cp\u003e539\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 87px;\"\u003e\n \u003cp\u003e0.18\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 85px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eM\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 85px;\"\u003e\n \u003cp\u003e21\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 76px;\"\u003e\n \u003cp\u003e20.5\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 92px;\"\u003e\n \u003cp\u003e398\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 87px;\"\u003e\n \u003cp\u003e0.08\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 92px;\"\u003e\n \u003cp\u003e386\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 87px;\"\u003e\n \u003cp\u003e0.05\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 85px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eF\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 85px;\"\u003e\n \u003cp\u003e21\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 76px;\"\u003e\n \u003cp\u003e22.9\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 92px;\"\u003e\n \u003cp\u003e297\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 87px;\"\u003e\n \u003cp\u003e0.11\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 92px;\"\u003e\n \u003cp\u003e334\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 87px;\"\u003e\n \u003cp\u003e0.13\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 85px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eF\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 85px;\"\u003e\n \u003cp\u003e24\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 76px;\"\u003e\n \u003cp\u003e21.9\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 92px;\"\u003e\n \u003cp\u003e337\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 87px;\"\u003e\n \u003cp\u003e0.08\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 92px;\"\u003e\n \u003cp\u003e350\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 87px;\"\u003e\n \u003cp\u003e0.01\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 85px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eF\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 85px;\"\u003e\n \u003cp\u003e22\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 76px;\"\u003e\n \u003cp\u003e21.5\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 92px;\"\u003e\n \u003cp\u003e335\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 87px;\"\u003e\n \u003cp\u003e0.10\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 92px;\"\u003e\n \u003cp\u003e364\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 87px;\"\u003e\n \u003cp\u003e0.13\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 85px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eF\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 85px;\"\u003e\n \u003cp\u003e30\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 76px;\"\u003e\n \u003cp\u003e20.2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 92px;\"\u003e\n \u003cp\u003e310\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 87px;\"\u003e\n \u003cp\u003e0.09\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 92px;\"\u003e\n \u003cp\u003e316\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 87px;\"\u003e\n \u003cp\u003e0.07\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 85px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eMean \u0026plusmn; SD\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 85px;\"\u003e\n \u003cp\u003e23.2 \u0026plusmn; 3.52\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 76px;\"\u003e\n \u003cp\u003e22.1 \u0026plusmn; 2.0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 92px;\"\u003e\n \u003cp\u003e365 \u0026plusmn; 79\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 87px;\"\u003e\n \u003cp\u003e0.10 \u0026plusmn; 0.03\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 92px;\"\u003e\n \u003cp\u003e382 \u0026plusmn; 81\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 87px;\"\u003e\n \u003cp\u003e0.11 \u0026plusmn; 0.05\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n\u003c/table\u003e\n\u003cp\u003eRegional brain distribution of [\u003csup\u003e11\u003c/sup\u003eC]PXT012253\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eThe AUC summation 0-30 min images demonstrated a widespread uptake of radioactivity in the brain (Fig. 1).\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eThe regional TACs showed a rapid uptake and washout of [\u003csup\u003e11\u003c/sup\u003eC]PXT012253 in the gray matter regions (Fig. 2). The slowest washout was observed in the pons, followed by the thalamus, striatum, cortex and cerebellum (Fig. 2A). The washout of [\u003csup\u003e11\u003c/sup\u003eC]PXT012253 from the white matter (WM) TAC was slow and after 45 min the TAC of [\u003csup\u003e11\u003c/sup\u003eC]PXT012253 was higher than that the TACs of all gray matter regions\u003cs\u003e.\u0026nbsp;\u003c/s\u003e\u003c/p\u003e\n\u003cp\u003eTACs in the average grey matter were similar for PET1 and PET 2 (Fig. 2B). The mean (\u0026plusmn; SD) peak SUV in the whole brain for the six subjects was 5.5 (\u0026plusmn; 0.8) for PET1 and 5.4 (\u0026plusmn; 0,5) for PET2, with the total range of 4.3-6.3.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eThe curves of model fitting in the thalamus, putamen and pons as representative regions are shown in figure 3. 2TC, Logan and MA1 analysis provided good on visual inspection, whereas 1TC showed poor fitting. Estimates of the \u003cem\u003eV\u003c/em\u003e\u003csub\u003eT\u003c/sub\u003e for all selected regions, based on the four different modelling approaches are shown in Table 2. \u003cem\u003eV\u003c/em\u003e\u003csub\u003eT\u003c/sub\u003e was high in pons, medium in thalamus and striatum, and low in cortex and cerebellum.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eTable 2. Mean and SD of \u003cem\u003eV\u003c/em\u003e\u003csub\u003eT\u003c/sub\u003e in selected regions (mL/ccm).\u003c/p\u003e\n\u003ctable border=\"1\" cellspacing=\"0\" cellpadding=\"0\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 121px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 121px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e1TC\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 121px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e2TC\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 121px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eLogan\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 121px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eMA1\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 121px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eTHA\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 121px;\"\u003e\n \u003cp\u003e4.4 \u0026plusmn; 0.8\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 121px;\"\u003e\n \u003cp\u003e5.5 \u0026plusmn; 0.9\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 121px;\"\u003e\n \u003cp\u003e5.7 \u0026plusmn; 0.9\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 121px;\"\u003e\n \u003cp\u003e5.6 \u0026plusmn; 0.9\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 121px;\"\u003e\n \u003cp\u003e\u003cstrong\u003ePUT\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 121px;\"\u003e\n \u003cp\u003e3.6 \u0026plusmn; 0.7\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 121px;\"\u003e\n \u003cp\u003e5.3 \u0026plusmn; 1.1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 121px;\"\u003e\n \u003cp\u003e5.4 \u0026plusmn; 1.0\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 121px;\"\u003e\n \u003cp\u003e5.4 \u0026plusmn; 1.0\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 121px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eFC\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 121px;\"\u003e\n \u003cp\u003e2.6 \u0026plusmn; 0.5\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 121px;\"\u003e\n \u003cp\u003e4.0 \u0026plusmn; 0.8\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 121px;\"\u003e\n \u003cp\u003e4.3 \u0026plusmn; 0.9\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 121px;\"\u003e\n \u003cp\u003e4.2 \u0026plusmn; 0.9\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 121px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eCER\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 121px;\"\u003e\n \u003cp\u003e2.7 \u0026plusmn; 0.5\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 121px;\"\u003e\n \u003cp\u003e3.6 \u0026plusmn; 0.6\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 121px;\"\u003e\n \u003cp\u003e4.0 \u0026plusmn; 0.7\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 121px;\"\u003e\n \u003cp\u003e4.0 \u0026plusmn; 0.7\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 121px;\"\u003e\n \u003cp\u003e\u003cstrong\u003ePONS\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 121px;\"\u003e\n \u003cp\u003e5.0 \u0026plusmn;1.0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 121px;\"\u003e\n \u003cp\u003e6.1 \u0026plusmn; 1.0\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 121px;\"\u003e\n \u003cp\u003e6.2 \u0026plusmn; 1.1\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 121px;\"\u003e\n \u003cp\u003e6.2 \u0026plusmn; 1.1\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n\u003c/table\u003e\n\u003cp\u003eThe 2TC showed better fitting than the 1TC based on lower Akaike information criterion (AIC; 9,8\u0026plusmn;7,7 vs 81\u0026plusmn;3,9, Mean\u0026plusmn;SD) and higher model selection criterion (MSC; 4.7\u0026plusmn;0.1 vs 2.2\u0026plusmn;0.3, Mean\u0026plusmn;SD) (Fig. 3). As for the graphical models, MA1 showed better identifiability than Logan, as smaller standard error (1.2 vs 1.7). t* of Logan and MA1 was set at 27.0 min for all subjects and regions. First, t* was estimated as 27.0 min when Max Err was 10% in the whole brain of most subjects. Next, \u003cem\u003eV\u003c/em\u003e\u003csub\u003eT\u003c/sub\u003e was calculated when t* was fixed at 27.0 min for all subjects and regions.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eLogan and MA1 were well correlated with the 2TC for \u003cem\u003eV\u003c/em\u003e\u003csub\u003eT\u003c/sub\u003e values although a slight overestimation was observed in both models (Fig. 4).\u003c/p\u003e\n\u003cp\u003eRadiometabolite Analysis\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eThe HPLC chromatograms demonstrated that the major metabolites were less lipophilic than that of the parent compound making them less likely to enter the brain (Fig. 5).\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eThe metabolite curves were extrapolated to 90 minutes, using up to 45- or 60-minutes data points, by 3-exp fitting because of later time points having too low signal to noise in high-performance\u0026nbsp;liquid chromatography (HPLC) due to the radioactivity decay and fast metabolism. As demonstrated in Figure 6, 10-20% parent compound was present after 20 minutes\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003einjection.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eTest-retest variability and ICC for \u003cem\u003eV\u003c/em\u003e\u003csub\u003eT\u003c/sub\u003e in six subjects.\u003c/p\u003e\n\u003cp\u003eTable 3 shows the test-retest variability and ICC in the selected regions for all models. Mean absolute variability was lowest in thalamus and pons and highest in frontal cortex and cerebellum.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eThe average of absolute test-retest variability of the six subjects for the 12 regions was similar among the models, 6.2 \u0026plusmn; 2.7% for 2TC, 6.5 \u0026plusmn;1.3% for Logan, and 6.7 \u0026plusmn; 1.1% for MA1. Absolute variability for \u003cem\u003eV\u003c/em\u003e\u003csub\u003eT\u003c/sub\u003e calculated by 2TC was lower than those of Logan and MA1 for all the selected regions. The ICC for \u003cem\u003eV\u003c/em\u003e\u003csub\u003eT\u003c/sub\u003e was similar among the selected regions and different models. The ICC confidence interval was generally smaller for 2TC compared to that of Logan and MA1.\u0026nbsp;\u003c/p\u003e\n\u003cp id=\"_Toc9291933\"\u003e\u003cstrong\u003eTable 3. Test-retest metrics of absolute variability and ICC for \u003cem\u003eV\u003c/em\u003e\u003csub\u003eT\u003c/sub\u003e in selected regions for six subjects.\u003c/strong\u003e\u003c/p\u003e\n\u003ctable border=\"0\" cellspacing=\"0\" cellpadding=\"0\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 9.1945%;\"\u003e\u0026nbsp;\u003cp\u003e\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"4\" style=\"width: 37.3189%;\"\u003e\n \u003cp\u003e\u003cstrong\u003eAbsolute variability (Mean \u0026plusmn;SD) %\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"3\" style=\"width: 29.0709%;\"\u003e\n \u003cp\u003e\u003cstrong\u003eICC (95% CI)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 9.1945%;\"\u003e\n \u003cp\u003e\u003cstrong\u003eRegions\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 10.141%;\"\u003e\n \u003cp\u003e\u003cstrong\u003e1TC\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 9.0593%;\"\u003e\n \u003cp\u003e\u003cstrong\u003e2TC\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 9.0593%;\"\u003e\n \u003cp\u003e\u003cstrong\u003eLogan\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 9.0593%;\"\u003e\n \u003cp\u003e\u003cstrong\u003eMA1\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 9.7354%;\"\u003e\n \u003cp\u003e\u003cstrong\u003e2TC\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 9.7354%;\"\u003e\n \u003cp\u003e\u003cstrong\u003eLogan\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 9.7354%;\"\u003e\n \u003cp\u003e\u003cstrong\u003eMA1\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 9.1945%;\"\u003e\n \u003cp\u003e\u003cstrong\u003ePONS\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 10.141%;\"\u003e\n \u003cp\u003e\u003cstrong\u003e5.0\u0026plusmn;3.6\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 9.0593%;\"\u003e\n \u003cp\u003e\u003cstrong\u003e3.3\u0026plusmn;2.6\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 9.0593%;\"\u003e\n \u003cp\u003e\u003cstrong\u003e4.6\u0026nbsp;\u0026plusmn;3.1\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 9.0593%;\"\u003e\n \u003cp\u003e\u003cstrong\u003e4.6\u0026nbsp;\u0026plusmn;2.6\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 9.7354%;\"\u003e\n \u003cp\u003e\u003cstrong\u003e0.97 (0.78,1.00)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 9.7354%;\"\u003e\n \u003cp\u003e\u003cstrong\u003e0.96 (0.54,0.99)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 9.7354%;\"\u003e\n \u003cp\u003e\u003cstrong\u003e0.96 (0.56,0.99)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 9.1945%;\"\u003e\n \u003cp\u003e\u003cstrong\u003eTHA\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 10.141%;\"\u003e\n \u003cp\u003e\u003cstrong\u003e5.1\u0026plusmn;2.5\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 9.0593%;\"\u003e\n \u003cp\u003e\u003cstrong\u003e2.8\u0026plusmn;1.7\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 9.0593%;\"\u003e\n \u003cp\u003e\u003cstrong\u003e5.5\u0026plusmn;3.2\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 9.0593%;\"\u003e\n \u003cp\u003e\u003cstrong\u003e5.5\u0026nbsp;\u0026plusmn;3.3\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 9.7354%;\"\u003e\n \u003cp\u003e\u003cstrong\u003e0.98 (0.87,1.00)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 9.7354%;\"\u003e\n \u003cp\u003e\u003cstrong\u003e0.93 (0.51,0.99)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 9.7354%;\"\u003e\n \u003cp\u003e\u003cstrong\u003e0.93 (0.51,0.99)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 9.1945%;\"\u003e\n \u003cp\u003e\u003cstrong\u003ePUT\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 10.141%;\"\u003e\n \u003cp\u003e\u003cstrong\u003e4.3\u0026plusmn;2.7\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 9.0593%;\"\u003e\n \u003cp\u003e\u003cstrong\u003e4.2\u0026nbsp;\u0026plusmn;5.5\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 9.0593%;\"\u003e\n \u003cp\u003e\u003cstrong\u003e6.4\u0026nbsp;\u0026plusmn;3.4\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 9.0593%;\"\u003e\n \u003cp\u003e\u003cstrong\u003e6.8\u0026nbsp;\u0026plusmn;2.8\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 9.7354%;\"\u003e\n \u003cp\u003e\u003cstrong\u003e0.93 (0.56,0.99)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 9.7354%;\"\u003e\n \u003cp\u003e\u003cstrong\u003e0.92 (0.60,0.99)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 9.7354%;\"\u003e\n \u003cp\u003e\u003cstrong\u003e0.91 (0.57,0.99)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 9.1945%;\"\u003e\n \u003cp\u003e\u003cstrong\u003eFC\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 10.141%;\"\u003e\n \u003cp\u003e\u003cstrong\u003e2.6\u0026plusmn;2.8\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 9.0593%;\"\u003e\n \u003cp\u003e\u003cstrong\u003e6.4\u0026nbsp;\u0026plusmn;5.3\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 9.0593%;\"\u003e\n \u003cp\u003e\u003cstrong\u003e6.9\u0026nbsp;\u0026plusmn;2.7\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 9.0593%;\"\u003e\n \u003cp\u003e\u003cstrong\u003e7.2\u0026nbsp;\u0026plusmn;2.4\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 9.7354%;\"\u003e\n \u003cp\u003e\u003cstrong\u003e0.95 (0.67,0.99)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 9.7354%;\"\u003e\n \u003cp\u003e\u003cstrong\u003e0.94 (0.21,0.99)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 9.7354%;\"\u003e\n \u003cp\u003e\u003cstrong\u003e0.94 (0.18,0.99)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 9.1945%;\"\u003e\n \u003cp\u003e\u003cstrong\u003eCER\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 10.141%;\"\u003e\n \u003cp\u003e\u003cstrong\u003e3.6\u0026plusmn;3.7\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 9.0593%;\"\u003e\n \u003cp\u003e\u003cstrong\u003e8.0\u0026plusmn;5.8\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 9.0593%;\"\u003e\n \u003cp\u003e\u003cstrong\u003e8.2\u0026nbsp;\u0026plusmn;1.7\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 9.0593%;\"\u003e\n \u003cp\u003e\u003cstrong\u003e8.6\u0026nbsp;\u0026plusmn;1.6\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 9.7354%;\"\u003e\n \u003cp\u003e\u003cstrong\u003e0.83 (0.27,0.97)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 9.7354%;\"\u003e\n \u003cp\u003e\u003cstrong\u003e0.90 (0.28,0.99)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 9.7354%;\"\u003e\n \u003cp\u003e\u003cstrong\u003e0.90 (0.25,0.99)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n\u003c/table\u003e\n\u003cp\u003eTime time-stability of \u003cem\u003eV\u003c/em\u003e\u003csub\u003eT\u003c/sub\u003e is presented in Figure 7 A-C. The \u003cem\u003eV\u003c/em\u003e\u003csub\u003eT\u003c/sub\u003e values decreased according to the reduced duration of image analysis. The proportion of \u003cem\u003eV\u003c/em\u003e\u003csub\u003eT\u003c/sub\u003e at 63 minutes relative to 93 minutes for 2TC, Logan, and MA1 models were 92%, 93%, and 94% respectively. Figure 7D-F shows the time stability of \u003cem\u003eV\u003c/em\u003e\u003csub\u003eT\u003c/sub\u003e variability. Absolute variability was lower at 63 minutes than at 93 minutes by Logan (5.2% vs 6.5%) and MA1 (5.1% vs 6.8%), and similar by 2TC (6.6% vs 6.1%) (Figure 7 D-F).\u0026nbsp;\u003c/p\u003e"},{"header":"Discussion","content":"\u003cp\u003eIn this study, we assessed the quantification and the repeatability of the novel mGlu4 receptor PET radioligand [\u003csup\u003e11\u003c/sup\u003eC]-PXT012253 for brain imaging in six healthy volunteers. The administration of [\u003csup\u003e11\u003c/sup\u003eC]-PXT012253 in doses of less than one \u0026micro;g was well tolerated. The results demonstrated high brain uptake and \u003cem\u003eV\u003c/em\u003e\u003csub\u003eT\u003c/sub\u003e values (range 3.6 \u0026ndash; 6.1 with 2TC across the selected brain regions (Table 2)) as well as good repeatability of the \u003cem\u003eV\u003c/em\u003e\u003csub\u003eT\u003c/sub\u003e values (range 2.8-8.0 % absolute variability with 2TC among selected regions (Table 2)), providing support that [\u003csup\u003e11\u003c/sup\u003eC]PXT012253 can be used as a mGlu4 PET radioligand in clinical studies. \u003c/p\u003e\n\u003cp\u003eThe brain uptake of [\u003csup\u003e11\u003c/sup\u003eC]PXT012253 reached SUV 4.3 \u0026ndash; 6.3 in the whole brain among the subjects, which is similar to other mGlu radioligands [7, 11, 13, 17]. There was a rapid wash-out, which is favourable for quantification of PET radioligand binding. There was a rapid metabolism of [\u003csup\u003e11\u003c/sup\u003eC]PXT012253, with 10-20% parent compound left 20 minutes after injection. \u003c/p\u003e\n\u003cp\u003eThe regional time curve of radioactivity showed the highest uptake in thalamus, medium in striatum, and the lowest in cortical regions and cerebellum. Pons showed slightly slow kinetics, most likely due to the substantial white matter tissue component that has slower kinetics. The kinetic property in the WM indicates that in this region there is a slow equilibrating component of [\u003csup\u003e11\u003c/sup\u003eC]PXT012253. To the best of our knowledge, there are no mGluR4 receptors in the WM region and the pharmacological profile of PXT012253 does not indicate binding to other targets of relevance. The WM uptake could represent nonspecific binding. In our previous study with [\u003csup\u003e11\u003c/sup\u003eC]PXT012253 in NHPs, the analogue PXT002331 blocked the radioligand\u0026rsquo;s GM binding by 36% [17], and from the parametric image reported, it seemed that an effect was also observed in the WM. Therefore, off-target binding of [\u003csup\u003e11\u003c/sup\u003eC]PXT012253, for instance to myelin components, cannot be excluded. The specificity of the binding should be futher evaluated by bocking studies with suitable analogues of PXT012253. \u003c/p\u003e\n\u003cp\u003eThe data was better described by the 2-TCM than the 1-TCM, based on lower AIC and higher MSC. In the linear graphical analysis, both Logan and MA1 analysis had good fitting to the data and would be the alternative methods considering that graphically derived \u003cem\u003eV\u003c/em\u003e\u003csub\u003eT\u003c/sub\u003e values were well correlated to those by 2-TCM analysis despite of a slight overestimation of 6.9 % \u0026plusmn; 11.2 (Mean \u0026plusmn; SD) (Fig. 4). In comparison of graphical models, MA1 showed better identifiability compared to Logan, with lower % COV. \u003c/p\u003e\n\u003cp\u003eThe rank order of the regional TACs and \u003cem\u003eV\u003c/em\u003e\u003csub\u003eT\u003c/sub\u003es of [\u003csup\u003e11\u003c/sup\u003eC]PXT012253 in the present clinical study agreed with that previously reported in NHPs [17] and in studies with F-18-labeled mGlu4 PET radioligands [20, 21]. Hence, the regional distribution of [\u003csup\u003e11\u003c/sup\u003eC]PXT012253 binding in the present human brains was considered to represent the regional distribution of mGlu4 receptors. \u003c/p\u003e\n\u003cp\u003eHigh repeatability of \u003cem\u003eV\u003c/em\u003e\u003csub\u003eT\u003c/sub\u003e values was indicated by the test-retest variability below 10% in all subjects and for all models in the selected regions. Analysis of time stability of \u003cem\u003eV\u003c/em\u003e\u003csub\u003eT\u003c/sub\u003e showed less than 10% lower \u003cem\u003eV\u003c/em\u003e\u003csub\u003eT\u003c/sub\u003e values for 63 minutes data compared to that of 93 minutes data in the 2-TCM, Logan and MA1 models. Test-retest repeatability was better at 63 minutes than at 93 minutes by Logan and MA1, probably because of the rapid metabolism, the measurement of blood radioactivity data at later timepoints became less reliable due to low signal-to-noise. The low arterial concentration of parent compound at later time-points made the quantification of \u003cem\u003eV\u003c/em\u003e\u003csub\u003eT\u003c/sub\u003e less reliable. These time stability data suggest that an imaging duration of at 63 min would be preferable for the PET measurement of [\u003csup\u003e11\u003c/sup\u003eC]-PXT012253 binding. \u003c/p\u003e\n\u003cp\u003eLimitations of the study were the low number of individuals examined and the low and unreliable counts in the later arterial blood samples. Increasing the injected radioactivity in future studies might provide better reliability, by increasing the reliability of the measurement of the parent fraction and arterial input function. \u003c/p\u003e"},{"header":"Conclusion","content":"\u003cp\u003eIn this study, the mGlu4 PET radioligand [\u003csup\u003e11\u003c/sup\u003eC]-PXT012253 was evaluated for the first time in healthy volunteers. We examined the quantification and test-retest reliability of [\u003csup\u003e11\u003c/sup\u003eC]-PXT012253 binding to mGlu4 in healthy volunteers. [\u003csup\u003e11\u003c/sup\u003eC]-PXT012253 showed a high brain uptake in the grey matter with peak SUV\u0026rsquo;s ranging between 3.6 \u0026ndash; 6.1%. The slow uptake in white matter (supplements) seems not to impact the gray matter data. However, further evaluation with blocking studies is needed. The kinetics of gray matter regions was well described by 2-TCM, and test-retest variability of\u0026nbsp;\u003cem\u003eV\u003c/em\u003e\u003csub\u003eT\u003c/sub\u003e was below 10% for selected regions, similar or better than other radioligands without a reference region [22, 23]. The most reliable model for quantifying \u003cem\u003eV\u003c/em\u003e\u003csub\u003eT\u003c/sub\u003e estimation based on the ICC values is the 2-TCM. Overall, the present results indicate that [\u003csup\u003e11\u003c/sup\u003eC]-PXT012253 is a suitable PET radioligand to measure the mGlu4 receptor in the human brain.\u003c/p\u003e"},{"header":"Declarations","content":"\u003cp\u003eEthics approval and consent to participate\u003cbr\u003e\u0026nbsp;The ethical standards in conducting the present study were in accordance with the Declaration of Helsinki and the European Medicines Agency \u0026ldquo;Guideline for good clinical practice E6(R2)\u0026rdquo;. The study protocol was approved by the Swedish ethical review authority and the Swedish Medical Product Agency. Written informed consent was obtained from all the participants.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eConsent for publication\u003cbr\u003e\u0026nbsp;Not applicable.\u003c/p\u003e\n\u003cp\u003eAvailability of data and material\u003cbr\u003e\u0026nbsp;The datasets generated during and/or analysed during the current study are available from the corresponding author on reasonable request.\u003c/p\u003e\n\u003cp\u003eCompeting interests\u003cbr\u003e\u0026nbsp;Financial interests: Author Benny Bang-Andersen is employed by H. Lundbeck A/S. The other authors declare they have no financial interests in connection with H. Lundbeck A/S.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eFunding\u003cbr\u003e\u0026nbsp;This research work was commissioned with full cost coverage by H. Lundbeck A/S\u003c/p\u003e\n\u003cp\u003eAuthors\u0026apos; contributions\u003cbr\u003e\u0026nbsp;BBA, CH, AV, RA, SN and PS contributed to the study conception and design. Material preparation, data collection and analysis were performed by Ryosuke Arakawa, Jiamei Guo, Mohammad Mahdi Moein, Zhisheng Jia and Per Stenkrona. The first draft of the manuscript was written by Per Stenkrona and Jiamei Guo and all authors commented on previous versions of the manuscript.\u003c/p\u003e\n\u003cp\u003eAcknowledgements\u003cbr\u003e\u0026nbsp;We thank the staff of the PET group at Karolinska Institutet for their assistance during this research work.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\n\u003cli\u003eOlivero G, Vergassola M, Cisani F, Roggeri A, Pittaluga A. Presynaptic Release-regulating Metabotropic Glutamate Receptors: An Update. Curr Neuropharmacol. 2020;18:655-72. doi:10.2174/1570159X17666191127112339.\u003c/li\u003e\n\u003cli\u003eNiswender CM, Conn PJ. Metabotropic glutamate receptors: physiology, pharmacology, and disease. 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Mol Psychiatry. 2018;23:824-32. doi:10.1038/mp.2017.58.\u003c/li\u003e\n\u003cli\u003eKil KE, Poutiainen P, Zhang Z, Zhu A, Kuruppu D, Prabhakar S, et al. Synthesis and evaluation of N-(methylthiophenyl)picolinamide derivatives as PET radioligands for metabotropic glutamate receptor subtype 4. Bioorg Med Chem Lett. 2016;26:133-9. doi:10.1016/j.bmcl.2015.11.015.\u003c/li\u003e\n\u003cli\u003eWang J, Shoup TM, Brownell AL, Zhang Z. Improved Synthesis of the Thiophenol Precursor N-(4-Chloro-3-mercaptophenyl)picolinamide for Making the mGluR4 PET Ligand. Tetrahedron. 2019;75:3917-22. doi:10.1016/j.tet.2019.06.010.\u003c/li\u003e\n\u003cli\u003eTakano A, Nag S, Jia Z, Jahan M, Forsberg A, Arakawa R, et al. Characterization of [(11)C]PXT012253 as a PET Radioligand for mGlu(4) Allosteric Modulators in Nonhuman Primates. Mol Imaging Biol. 2019;21:500-8. doi:10.1007/s11307-018-1257-0.\u003c/li\u003e\n\u003cli\u003eLogan J, Fowler JS, Volkow ND, Wolf AP, Dewey SL, Schlyer DJ, et al. Graphical analysis of reversible radioligand binding from time-activity measurements applied to [N-11C-methyl]-(-)-cocaine PET studies in human subjects. Journal of Cerebral Blood Flow \u0026amp; Metabolism. 1990;10:740-7.\u003c/li\u003e\n\u003cli\u003eIchise M, Toyama H, Innis RB, Carson RE. Strategies to improve neuroreceptor parameter estimation by linear regression analysis. J Cereb Blood Flow Metab. 2002;22:1271-81. doi:10.1097/01.WCB.0000038000.34930.4E.\u003c/li\u003e\n\u003cli\u003eKil KE, Poutiainen P, Zhang Z, Zhu A, Choi JK, Jokivarsi K, et al. Radiosynthesis and evaluation of an 18F-labeled positron emission tomography (PET) radioligand for metabotropic glutamate receptor subtype 4 (mGlu4). J Med Chem. 2014;57:9130-8. doi:10.1021/jm501245b.\u003c/li\u003e\n\u003cli\u003eWang JF, Li YB, Takahashi K, El Fakhri G. SuFEx click chemistry enabled fast efficient F-18 labeling (5 seconds) for the understanding of drug development - A case study. J Nucl Med. 2023:731.\u003c/li\u003e\n\u003cli\u003eArakawa R, Takano A, Stenkrona P, Stepanov V, Nag S, Jahan M, et al. PET imaging of beta-secretase 1 in the human brain: radiation dosimetry, quantification, and test-retest examination of [(18)F]PF-06684511. Eur J Nucl Med Mol Imaging. 2020;47:2429-39. doi:10.1007/s00259-020-04739-5.\u003c/li\u003e\n\u003cli\u003eCollste K, Forsberg A, Varrone A, Amini N, Aeinehband S, Yakushev I, et al. Test-retest reproducibility of [(11)C]PBR28 binding to TSPO in healthy control subjects. Eur J Nucl Med Mol Imaging. 2016;43:173-83. doi:10.1007/s00259-015-3149-8.\u003c/li\u003e\n\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":false,"highlight":"","institution":"","isAcceptedByJournal":true,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":true,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"ejnmmi-research","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"ejre","sideBox":"Learn more about [EJNMMI Research](http://ejnmmires.springeropen.com/)","snPcode":"","submissionUrl":"https://www.editorialmanager.com/ejre/default.aspx","title":"EJNMMI Research","twitterHandle":"@officialEANM","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"em","reportingPortfolio":"BMC/SO AJ","inReviewEnabled":true,"inReviewRevisionsEnabled":true},"keywords":"Positron Emission Tomography (PET), Test-retest, human brain, [11C]PXT012253, mGlu4","lastPublishedDoi":"10.21203/rs.3.rs-5123848/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-5123848/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003e\u003cstrong\u003eBackground\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe metabotropic glutamate receptor 4 (mGlu4) has been proposed as a target for Parkinson’s disease to measure levodopa-induced dyskinesia. [\u003csup\u003e11\u003c/sup\u003eC]PXT012253 is a PET radioligand for mGlu4 (3.4 nM), previously characterized in non-human primates. We aimed to determine the optimal method for quantification, duration for acquisition, and test-retest reliability of the binding parameters for [\u003csup\u003e11\u003c/sup\u003eC]PXT012253 in healthy volunteers.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eResults\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eSix subjects (4 females) completed. [\u003csup\u003e11\u003c/sup\u003eC]PXT012253 displayed high uptake and rapid wash-out. Unchanged [\u003csup\u003e11\u003c/sup\u003eC]PXT012253 at 20 min was 10–20%. \u003cem\u003eV\u003c/em\u003e\u003csub\u003eT\u003c/sub\u003e in subcortical regions was higher than in cortical regions. 2TC provided better fits than 1TC. \u003cem\u003eV\u003c/em\u003e\u003csub\u003eT\u003c/sub\u003e by Logan GA and MA1 analysis correlated with that of 2TC-CM. MA1 showed better identifiability and standard error than Logan. The test-retest metrics in pons, putamen and thalamus showed absolute variability of \u003cem\u003eV\u003c/em\u003e\u003csub\u003eT\u003c/sub\u003e\u0026lt;7% and ICC \u0026gt; 0.93 using the 2TC, Logan and MA1 graphical analyses. Time stability analysis showed that \u003cem\u003eV\u003c/em\u003e\u003csub\u003eT\u003c/sub\u003e values estimated using 63 minutes of imaging were within 10% of the values obtained with 93 minutes with all three models.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eConclusion\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e[\u003csup\u003e11\u003c/sup\u003eC]PXT012253 showed a high brain uptake, with rapid washout and metabolism. \u003cem\u003eV\u003c/em\u003e\u003csub\u003eT\u003c/sub\u003e was reliably estimated using 2TC, Logan GA and MA1. The test-retest metrics showed high replicability, indicating [\u003csup\u003e11\u003c/sup\u003eC]PXT012253 to be a suitable PET radioligand for mGlu4.\u003c/p\u003e\n\u003cp\u003eEudraCT number: 2018-002333-37\u003c/p\u003e\n\u003cp\u003eRegistered at clinicaltrials.gov NCT03826134\u003c/p\u003e\n\u003cp\u003eRegistered 17 January 2019\u003c/p\u003e\n\u003cp\u003ehttps://www.clinicaltrials.gov/study/NCT03826134\u003c/p\u003e","manuscriptTitle":"Test-retest properties of [11C]-PXT012253 as a positron emission tomography (PET) radiotracer in healthy human brain: PET imaging of mGlu4","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2025-04-08 11:03:06","doi":"10.21203/rs.3.rs-5123848/v1","editorialEvents":[{"type":"communityComments","content":0},{"type":"reviewerAgreed","content":"","date":"2025-04-07T13:42:01+00:00","index":0,"fulltext":""},{"type":"reviewersInvited","content":"","date":"2025-04-06T07:37:16+00:00","index":"","fulltext":""},{"type":"editorAssigned","content":"","date":"2025-04-04T15:14:42+00:00","index":"","fulltext":""},{"type":"submitted","content":"EJNMMI Research","date":"2025-04-04T07:56:09+00:00","index":"","fulltext":""},{"type":"decision","content":"Minor Revision","date":"2024-11-18T08:03:18+00:00","index":"","fulltext":""}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"ejnmmi-research","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"ejre","sideBox":"Learn more about [EJNMMI Research](http://ejnmmires.springeropen.com/)","snPcode":"","submissionUrl":"https://www.editorialmanager.com/ejre/default.aspx","title":"EJNMMI Research","twitterHandle":"@officialEANM","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"em","reportingPortfolio":"BMC/SO AJ","inReviewEnabled":true,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"5d7fed62-6533-41a2-afb3-c7c31fc25c93","owner":[],"postedDate":"April 8th, 2025","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"published-in-journal","subjectAreas":[],"tags":[],"updatedAt":"2025-06-16T16:02:37+00:00","versionOfRecord":{"articleIdentity":"rs-5123848","link":"https://doi.org/10.1186/s13550-025-01266-y","journal":{"identity":"ejnmmi-research","isVorOnly":false,"title":"EJNMMI Research"},"publishedOn":"2025-06-14 15:58:04","publishedOnDateReadable":"June 14th, 2025"},"versionCreatedAt":"2025-04-08 11:03:06","video":"","vorDoi":"10.1186/s13550-025-01266-y","vorDoiUrl":"https://doi.org/10.1186/s13550-025-01266-y","workflowStages":[]},"version":"v1","identity":"rs-5123848","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-5123848","identity":"rs-5123848","version":["v1"]},"buildId":"8U1c8b4HqxoKbykW_rLl7","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}