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Abstract

While Positron Emission Tomography (PET) images with the 5-HT_1B receptor specific radioligand [¹¹C]AZ10419369 show pronounced uptake in the pituitary region, and experimental studies support 5-HT_1B receptor involvement in pituitary hormonal release, this uptake remains unquantified. In the present study we applied invasive and non-invasive models to evaluate pituitary 5-HT_1B receptor binding. Ten subjects underwent PET with [¹¹C]AZ10419369, of which six participated in three additional PET-examinations after pretreatment with increasing doses of the 5-HT_1B receptor antagonist AZD3783. While [¹¹C]AZ10419369 binding in the brain displayed dose-dependent reductions after AZD3783, no dose-dependent inhibition of binding was observed for the pituitary. Distribution volume ratios were plotted against occupancy values to graphically estimate regional differences in non-displaceable binding, thereby allowing for estimation of BP_ND in extracerebral regions. Using this method, baseline pituitary BP_ND appears to be negligible, implying that most of the pituitary [¹¹C]AZ10419369 uptake is comprised of free or nonspecifically bound radioligand. Our findings highlight potential pitfalls when assuming that conspicuous regional radioligand uptake indicates presence of specific binding.

Keywords

blood-brain barrier kinetic modeling pituitary

Introduction

The serotonin 1B (5-HT_1B) receptor has been implicated in the pathophysiology of mood disorders¹ and migraine,² that is, conditions both featuring disturbances in pituitary hormone signaling.^3–6 5-HT_1B receptor involvement in pituitary gland hormone release is supported by experimental data: 5-HT_1B receptor agonists increase growth hormone (GH) concentration in rat pituitary tissue⁷ and raise serum concentrations of GH and prolactin (PRL) in vivo in humans.^8,9

Human 5-HT_1B receptors can be quantified in vivo using positron emission tomography (PET) and 5-HT_1B receptor-specific radioligands such as [¹¹C]AZ10419369¹⁰ or [¹¹C]P943.¹¹ Both [¹¹C]P943¹² and [¹¹C]AZ10419369 (Figure 1) exhibit conspicuously high uptake in the pituitary region. Nevertheless, pituitary 5-HT_1B receptor density or binding has not been characterized in humans. The lack of pituitary autoradiography data may be explained by the difficulty involved in removing pituitary tissue from the surrounding sella turcica during standard brain autopsy.

Figure 1.

SUV image of a representative baseline PET examination showing high [¹¹C]AZ10419369 uptake in the pituitary region.

The degree to which the high uptake in the pituitary region reflects specific binding can be assessed by performing PET before and after administration of a competing 5-HT_1B receptor ligand. The change in signal is then used to estimate the proportion of occupied receptors,¹³ an approach referred to as an occupancy study. AZD3783 is a high affinity 5-HT_1B receptor antagonist which displays dose-dependent 5-HT_1B receptor occupancy both in vitro and in vivo,^14,15 making it suitable for this purpose.

The pituitary gland lies outside the blood-brain barrier (BBB),^16,17 implying that the concentration of pituitary parent [¹¹C]AZ10419369 and its radioactive metabolites may differ from the concentration in brain. In this regard, [¹¹C]AZ10419369 is well suited for pituitary quantification since it displays negligible formation of radioactive metabolites during a PET examination.^10,18 Non-invasive quantification methods avoid arterial cannulation and cumbersome plasma sampling but require a reference tissue with a non-displaceable volume of distribution (V_ND) identical to that of the target region. Although [¹¹C]AZ10419369 has been validated for non-invasive quantification of 5-HT_1B receptors using the cerebellum as reference,¹⁰ the pituitary gland largely consists of glandular tissue, while the cerebellum is comprised of neuronal tissue, making the assumption of identical V_ND tenuous. Nevertheless, the cerebellum has proven successful as reference region in measurement of pituitary dopamine D₂ receptor occupancy,¹⁹ warranting assessment of its use as a normalizing factor in pituitary 5-HT_1B receptor quantification.

The aim of this study was to quantify pituitary 5-HT_1B receptor binding in healthy volunteers examined with [¹¹C]AZ10419369 before and after pretreatment with different doses of AZD3783. Several methods were applied. The specificity of pituitary binding was assessed in terms of changes in distribution volume (V_T) and non-displaceable binding potential (BP_ND) before and after AZD3783 administration. Changes in pituitary BP_ND and V_T after AZD3783 pretreatment were compared to the corresponding BP_ND and V_T changes in brain regions of interest. An estimate of pituitary specific binding adjusted for regional variation in V_ND was obtained based on the relationship between brain 5-HT_1B receptor occupancy and distribution volume ratios (DVRs) calculated for the pituitary relative to the cerebellum at different doses of AZD3783.

Materials and methods

Subjects and study design

The data used in this study were drawn from previous projects investigating brain [¹¹C]AZ10419369 binding before and after AZD3783 administration^10,15

This study received approval from the Medical Products Agency, the Radiation Safety Committee, and the Ethical Review Board in Stockholm reference number 2006/331-31/2 and 2007/89-31/2. The study was performed in accordance with ICH/Good Clinical Practice, AstraZeneca policy of bioethics and the ethical principles described in the 1964 Declaration of Helsinki. Informed consent was obtained from all subjects prior to study initiation.

Ten healthy men aged 21–34 years underwent a baseline PET with [¹¹C]AZ10419369. Subsequently, in six of these subjects (subjects 1–6) three additional PET exams with [¹¹C]AZ10419369 were performed after an oral dose of AZD3783. The AZD3783 dose was gradually increased at each occasion.

T1-weighted Magnetic Resonance Imaging (MRI) was performed using a 1.5T General Electrics Signa (GE, Milwaukee, WI, USA) system. The Siemens ECAT EXACT HR PET system was used to measure radioactivity in a 93 min series of consecutive time frames (9 × 20s, 3 × 60s, 3 × 180s and 13 × 360s frames). [¹¹C]AZ10419369 was prepared as previously described.¹⁰ An arterial input function was collected using automated arterial blood sampling for 5 min followed by manual samples drawn at each frame. Injected radioactivity, molar activity, and injected mass for each PET, are available in the supplement (Table s1), alongside PET data reconstruction parameters. See original publications for further details.^10,15

Image analysis

Regions of interest

The following brain regions of interest (ROIs) employed in the analysis were chosen based on their moderate to high 5-HT_1B receptor binding in the literature^10,20–23: Anterior Cingulate Cortex (ACC), Occipital Cortex (OC), Orbitofrontal Cortex (OFC), and the dorsal and ventral Striatum (STR). Cerebral gray matter (GM) was used to calculate brain occupancy in the graphical estimation of specific pituitary 5-HT_1B receptor binding. All brain ROIs were defined on MRI images employing the automated FreeSurfer analysis suite (version 6, http://surfer.nmr.mgh.harvard.edu/).

PET images were motion corrected using a frame-by-frame rigid-body registration.²⁴ T1-weighted MRI images were co-registered to a time-weighted summated PET-image using Statistical Parametric Mapping version 12 (SPM12, Wellcome Department of Cognitive Neurology, University College, London, UK). Regional time-activity curves (TACs) were derived by using the resulting co-registration matrix to project ROIs onto the realigned dynamic PET-image.

Given negligible cerebellar 5-HT_1B receptor binding,²⁵ a modified²⁶ FreeSurfer cerebellar ROI was used as reference region. Because the FreeSurfer suite lacks automated delineation of the pituitary gland, the pituitary ROI was delineated using the following approach. First, using the MR data a small cuboid mask, reliably containing the pituitary gland, was produced by using the landmarks of the optic chiasm, the most ventral part of the frontal cortex, and the AC-plane derived from the FreeSurfer labels. Using the previously obtained co-registration matrix, this mask was moved into individual PET-space of the baseline examination, where a version of a previously described automated method²⁷ identified the most intense voxel within the mask. In an iterative process, neighboring voxels were added by order of highest count to ensure a contiguous ROI mask. The process was terminated once the mask reached the predefined size of 420 mm³, corresponding to the mean pituitary volume in the age group of our subjects.²⁸

Quantification of [¹¹C]AZ10419369 binding

Distribution volumes (V_T) were calculated using the one tissue compartment model (1-TCM) with the plasma radioactivity concentration of [¹¹C]AZ10419369 as input function.²⁹ Two-tissue compartment (2-TC) estimation of V_T and rate constants was omitted as the 1-TCM has been shown to be sufficient for describing [¹¹C]AZ10419369 kinetics.¹⁰ Observations of high pituitary blood flow in animals,^30,31 and the positive correlation between cerebral blood flow (CBF) and cerebral blood volume (CBV) found in humans,³² suggest that the blood volume fraction (V_B) of the pituitary may be substantially larger than that of the brain. As kinetic models may produce biased results if not properly corrected for tissue blood volumes,^33,34 distribution volumes were estimated with V_B as a free parameter.

BP_ND based on the cerebellum ROI as a reference region was calculated employing the simplified reference tissue model (SRTM).³⁵ To compensate for the putative bias from regional variations in V_B, we also applied a modified version of the simplified reference tissue model (SRTM-V)³⁶ with the pituitary V_B set as a free parameter fitted by non-linear least squares estimation. Reference tissue V_B was set to the mean cerebellar V_B estimated with 1-TCM.

Occupancy calculations

Receptor occupancy (%) was calculated from the obtained brain regional BP_ND values using the following equation:

O c c u p a n c y = (1 - \frac{B P_{N D (d r u g)}}{B P_{N D (b a s e l i n e)}}) \times 100

(1)

Where BP_{ND drug} and BP_{ND baseline} refers to BP_ND values estimated after and before AZD3783 administration respectively.

Graphical estimation of BP_ND from DVRs and gray matter occupancy

There is substantial inter- and intra-subject variation in [¹¹C]AZ10419369 brain V_T, which has been suspected to result from variability in [¹¹C]AZ10419369 plasma protein binding.¹⁰ This variation was also apparent for the pituitary. Distribution volume ratios (DVRs), obtained by normalizing the target region distribution volume to the distribution volume of a reference region within the brain, avoid the issue of variability in radioligand protein binding. However, bias may still be introduced by differences in target and reference region non-displaceable binding (i.e. V_ND) which may be problematic with regards to the pituitary ROI. We herein propose a graphical method to estimate discrepancies in reference and target V_ND based on the relationship between DVR and brain occupancy.

For any ROI, the relationship between distribution volumes and BP_ND may be described as follows:

V_{T} = V_{N D} (B P_{N D} + 1)

(2)

Assuming negligible BP_ND in the reference region, integration of DVR in equation (2) can be expressed as:

D V R_{(T / R)} = \frac{V_{N D (T)}}{V_{T (R)}} (B P_{N D} + 1)

(3)

Where V_ND(T) and V_T(R) denote the non-displaceable distribution volumes of the target and the distribution volume of the reference region, respectively. Because [¹¹C]AZ10419369 has negligible specific binding in the cerebellum ROI employed as reference, V_T(R) is in the following represented by V_ND(R). Variation in occupancy can be accounted for by the replacement of BP_ND with BP_{ND Baseline} * (1 − O) where O is the fractional receptor occupancy:

D V R = \frac{V_{N D (T)}}{V_{N D (R)}} (1 + B P_{N D B a s e l i n e} (1 - O))

(4)

In this evaluation, O has been calculated from the cerebral gray matter BP_ND. Assuming 100% occupancy can be reached at high doses of competitor drug, a plot of x = 1 − O and y = DVR produces a linear relationship with the y-intercept equal to the ratio between the target and reference region non-displaceable distribution volumes (V_ND(T)/V_ND(R)). In such a plot, the V_ND(T)/V_ND(R) ratio can be estimated using least squares regression and BP_{ND Baseline} can then be obtained from the slope of the line that is, V_ND(T)/V_ND(R) × BP_{ND Baseline}. In this way, if the assumption of equal V_ND in both the target and reference region holds, then the y-intercept should be equal to 1. If it is higher or lower than 1, then it implies that the target region has either higher or lower V_ND than the reference region respectively. Another requirement of this approach is equal free passage across the BBB for the competitor drug. This assumption appears reasonable given the close agreement between the unbound plasma concentration required for 50% occupancy K_i,plasma in vivo¹⁵ and in vitro¹⁴ of AZD3783.

Statistical analysis

Invasive and reference model outcomes were correlated using Pearson’s correlation coefficient.

Statistical analysis and kinetic modeling were performed using kinfitr³⁷ in R, version 4.2.1 “Funny-Looking Kid.”³⁸

Results

Subject 10 was excluded from analysis due to an aberrant shape of the blood and plasma curves suspected to have resulted from extravasation of injected radioligand. Due to technical problems,^10,15 data were not available for subject 3 at the 40 mg dose or subject 6 at the 4 mg and 20 mg doses of AZD3783. The fraction of unchanged [¹¹C]AZ10419369 at the end of PET was ⩾97% across subjects.

Regional [¹¹C]AZ10419369 binding and displacement after AZD3783 pretreatment

Baseline average pituitary [¹¹C]AZ10419369 V_T estimated with 1-TCM was 1.63 ± 1.12 (mean ± SD) with mean pituitary and cerebellar V_B fitted to 13.08 and 7.39% respectively. Pituitary and cerebral ROI V_T values were highly correlated (r = 0.91 − 0.93, p < 0.001) but AZD3783 pretreatment did not dose-dependently reduce V_T within the brain or the pituitary (Figure 2). For two subjects (subject 2 and 5) V_T even increased across ROIs compared to baseline at the lowest dose of AZD3783. This was surprising given the dose-dependent reduction in brain radioactivity that was visible at equilibrium on cerebral time activity curves normalized for injected radioactivity and body weight (Figure 3). No relationship between intrasubject variation in injected mass and V_T was observed (Supplement Table s1). In the cerebellum ROI, where AZD3783 effects on 5-HT_1B receptor binding should be negligible, the within individual average V_T coefficient of variation (CV) was 0.44. This was notably higher than the average within individual cerebellar radioactivity normalized to injected radioactivity and weight CV of 0.12.

Figure 2.

Regional outcome measures at different doses of AZD3783.

Figure 3.

Time activity Curves (TACs) of subject 2 occipital cortex and pituitary radioactivity standardized to injected radioactivity and weight.

Baseline pituitary BP_ND estimated using SRTM and SRTM-V were 2.42 ± 0.93 and 3.27 ± 1.36 respectively which is higher than the [¹¹C]AZ10419369 BP_ND previously reported for brain regions, save the pallidum.^10,21,23 While 5-HT_1B BP_ND decreased dose-dependently in response to AZD3783 within the brain, no dose-dependent inhibition of pituitary 5-HT_1B receptor BP_ND was observed (Figure 2). Although pituitary V_B was estimated to 15.02% on average using SRTM-V, thereby violating the SRTM assumption of equal V_B in target and reference regions,³³ BP_ND values estimated using both models were closely correlated (r = 0.94, p < 0.001) and equally unresponsive to AZD3783. Considering that dose-dependent reductions of 5-HT_1B receptor BP_ND were absent specifically in the pituitary ROI, we do not consider SRTM or SRTM-V BP_ND to be suitable measures of specific 5-HT_1B receptor binding in this structure. Pituitary occupancy calculated from SRTM BP_ND at different doses of AZD3783 are provided in the supplement (Table s2) along with the occupancy results for ROIs within the brain (Table s3).

Graphical estimation of V_ND ratios and pituitary specific binding

The V_ND ratio was not estimated for subject 6 as the graphical analysis would have relied on only two data points. The mean V_ND ratio in the examined within-brain ROIs for the remaining five subjects was 1.02 ± 0.15 (mean ± SD) in the ACC, 1.09 ± 0.26 (mean ± SD) in the OFC, 1.10 ± 0.20 in the OC and 1.03 ± 0.10 in the STR. The mean pituitary V_ND ratio was 4.04 ± 1.03 corresponding to a mean baseline pituitary BP_ND of 0.07 ± 0.39 calculated from the slopes of the lines for each subject (Figure 4). Standard errors (SEs) for the pituitary DVR employed in the estimation of pituitary BP_ND baseline, are available in the supplement (Table s4).

Figure 4.

Distribution volume ratios (DVR) for subject 1–5 plotted against cerebral gray matter occupancy estimated from SRTM.

Discussion

In this PET analysis we aimed to quantify pituitary 5-HT_1B receptors in human subjects using the 5-HT_1B receptor specific radioligand [¹¹C]AZ10419369. The specificity of [¹¹C]AZ10419369 binding was assessed by measuring the extent to which [¹¹C]AZ10419369 could be displaced by 5-HT_1B receptor-specific antagonist AZD3783. While all six subjects pretreated with AZD3783 displayed dose-dependent reductions in BP_ND in brain regions, no evident dose-dependent inhibition of radioligand binding was observed for the pituitary.

A critical assumption of reference tissue models is the existence of a reference region with a V_ND equal to that of the target region. At 100% occupancy, where the contribution of V_S to V_T is negligible, the DVR for any such two regions becomes a ratio of the target to reference V_ND. For regions where the reference-based model assumption of uniform V_ND is valid, this ratio is therefore expected to be 1 at 100% occupancy. Indeed, in our graphical estimation of the V_ND ratio in cerebral ROIs the V_ND ratio ranged from 1.02 ± 0.15 (mean ± SD) in the ACC to 1.10 ± 0.20 in the OC corroborating the validity of the cerebellum as a reference region for [¹¹C]AZ10419369 binding quantification within the brain. By contrast, the pituitary to cerebellum V_ND ratio was 4.04 ± 1.03, indicating a pituitary V_ND over four times that of the reference region. Given this large V_ND mismatch, reference tissue-based BP_ND was deemed an unsuitable outcome parameter for the pituitary.

Pituitary and cerebral ROIs differ in their composition and physiology in several respects that might have implications for their respective non-specific binding. Firstly, the pituitary ROI is largely comprised of glandular rather than neuronal tissue, meaning that the distribution volume of [¹¹C]AZ10419369 non-specifically bound to cellular constituents might well be quite different from that of the brain. Secondly, although studies examining active BBB efflux of [¹¹C]AZ10419369 in humans are lacking, rats, mice, and guinea pigs all display 2–3 fold increases in [¹¹C]AZ10419369 brain uptake after pretreatment with ABC transporter inhibitor cyclosporin.³⁹ In contrast, cyclosporin pretreatment has no significant effect on pituitary uptake of ¹¹C-metoclopramide in humans,⁴⁰ consistent with the absence of ABC-mediated efflux in this region. Active extrusion of radioligand from brain, but not pituitary, tissue would be expected to contribute to a lower cerebellar V_ND relative to that of the pituitary, leading reference-tissue methods to overestimate pituitary BP_ND.

The strong correlation between V_T values for regions inside and outside the BBB may indicate variability related to the common denominator that is, blood. Cerebellar V_T showed substantially larger within-subject variability than cerebellar radioactivity normalized to weight and injected radioactivity, indicating contribution of variability from a source other than free radioligand. To our knowledge, measurements of the intrasubject variability in [¹¹C]AZ10419369 f_p has not been published, however, between-subject variability has been found to be considerable, with a CV of 38%.²⁰ Furthermore, the structural similarity¹⁵ between AZD3783 and [¹¹C]AZ10419369 implies that AZD3783 could compete with radioligand plasma protein binding possibly contributing to the inconsistent effects on V_T by AZD3783 observed here. Both subjects with marked increase in V_T at first dose of antagonist also displayed a concurrent large increase (>100% increase) in K1 across regions indicative of increased blood extraction. Increases in radioligand f_p and K1 have been previously observed after administration of unlabeled tracer leading to masking of displacement of specific brain binding.^41,42 In the present data set, the f_p of [¹¹C]AZ10419369 was not measured as the method was not yet implemented locally at the time of data collection. Since we could not confirm, or correct for, the suspected variability in f_p in the present study, the use of V_T in the quantification of 5-HT_1B receptor binding was considered inappropriate.

After adjusting for the high pituitary-to-cerebellum V_ND ratio, mean baseline pituitary 5-HT_1B receptor BP_ND was 0.07 ± 0.39 (mean ± SD) compared to the unadjusted value of 2.42 ± 0.93 calculated using SRTM with the cerebellum as reference. This result agrees with simulations demonstrating that if the V_ND in the target region substantially exceeds that of the reference region, reference tissue models markedly overestimate target BP_ND, particularly in regions with low specific binding.³³

We demonstrated that plotting fractional gray matter occupancy against DVR can be used to assess regional variation in V_ND. Many mathematical models used for quantification and post-quantification analysis of PET imaging data assume spatially homogeneous V_ND across the brain, however some degree of regional variation in V_ND is to be expected, and has been demonstrated even for radioligands with a validated reference region.⁴³ The graphical approach outlined above can be used in testing the extent to which this assumption is met. Moreover, although we derived DVR from distribution volumes, it could just as well be obtained from reference tissue approaches, thereby eliminating the need for arterial blood sampling.

The absence of [¹¹C]AZ10419369 protein binding measurements is a limitation of this study, as variability in f_p could contribute to within-subject changes in V_T, and may partly explain the inconsistent AZD3783 effects on V_T observed here. Extending the graphical V_ND ratio approach to the pituitary region required assuming that AZD3783 displaces [¹¹C]AZ10419369 equally across the BBB. Regional variation in drug occupancy has been observed for radioligands with an affinity for more targets than the drug under investigation.^44,45 [¹¹C]AZ10419369 binds selectively to the 5-HT_1B receptor albeit with affinity also for the 5-HT_1D receptor. However, autoradiography indicates very low densities of the 5-HT_1D receptor in the CNS⁴⁶ and its contribution to [¹¹C]AZ10419369 binding is considered negligible.¹⁵ Additionally, the in vitro AZD3783 K_i,plasma closely matches the K_i,plasma estimated based on occupancy of central 5-HT_1B receptors^14,15 which would be unlikely if the BBB substantially affected AZD3783 displacement of specifically bound [¹¹C]AZ10419369. Given the small size and high uptake of the pituitary ROI, partial volume effects (PVE) will underestimate pituitary V_T and BP_ND. However, because ROI size, and thus PVE, will be consistent within subjects, we do not expect PVE to substantially confound the analysis of pituitary [¹¹C]AZ10419369 displacement by AZD3783. As partial volume correction has been known to introduce bias and amplify noise,⁴⁷ we opted against its use in this study. The initial frame duration of 20s is longer than used in some studies to obtain estimates of blood volumes and pituitary V_B SEs exceeded those of other parameters (Supplement Table s4). However, the very high correlation between SRTM-V and SRTM-derived BP_ND (r = 0.94, p < 0.001), suggests that greater precision in V_B estimates would not alter the main finding of lack of pituitary [¹¹C]AZ10419369 displacement. Finally, although our SEs indicate pituitary V_T and DVR are estimated with acceptable precision, we cannot exclude occupancy of a small population of pituitary 5-HT_1B receptors below detection sensitivity.

In conclusion, we could not demonstrate displacement of 5-HT_1B receptor radioligand [¹¹C]AZ10419369 in the pituitary by the high affinity, 5-HT_1B receptor selective antagonist AZD3783. By plotting pituitary DVR against gray matter occupancy, we show that this is likely explained by low specific [¹¹C]AZ10419369 binding in the pituitary, although the existence of a pituitary [¹¹C]AZ10419369 off-target binding site not shared with AZD3783 cannot be completely ruled out. Our results highlight some of the pitfalls in estimating radioligand binding outside the brain and underscore the importance of validating non-invasive models when applied to targets suspected of having properties that differ from those of the reference region. The distinctive physiology of the pituitary, including its high vascularization and capillary permeability, may be relevant also with regards to other tracers considering that high pituitary uptake is a feature of several radioligands^12,48,49 and poor pituitary displacement in blocking studies has been observed previously.^50,51 Future PET studies employing plasma radioactivity corrected for protein binding as input function and autoradiography studies of pituitary tissue samples are needed to confirm low specific 5-HT_1B receptor binding in the pituitary region.

Supplemental Material

sj-docx-1-jcb-10.1177_0271678X261417194 – Supplemental material for Quantification of pituitary 5-HT1B receptors with positron emission tomography: Negligible specific binding despite conspicuous uptake

Supplemental material, sj-docx-1-jcb-10.1177_0271678X261417194 for Quantification of pituitary 5-HT1B receptors with positron emission tomography: Negligible specific binding despite conspicuous uptake by Martin Gärde, Katarina Varnäs, Johan Lundberg, Jonas Svensson, Lars Farde, Granville J Matheson and Mikael Tiger in Journal of Cerebral Blood Flow & Metabolism

Footnotes

Acknowledgements

We gratefully acknowledge all of the volunteer subjects for their contributions to this study. We also acknowledge AstraZeneca for supplying the 5-HT_1B receptor antagonist AZD3783. Lastly, we want to acknowledge the staff at the Karolinska PET group for their expertise in radioligand production and PET-procedures.

Author contribution

MG performed data analysis and wrote the first draft of the manuscript. MT contributed to manuscript preparation and study design. KV contributed to the data analysis and manuscript preparation. GJM contributed to study design, data analysis, and manuscript preparation. JL contributed to study design and manuscript preparation. JS contributed to the development of the automated pituitary ROI delineation method and manuscript preparation. LF contributed to study design and manuscript preparation.

Declaration of conflicting interests

The authors declared no potential conflicts of interest with respect to the research, authorship, and/or publication of this article.

Funding

The authors disclosed receipt of the following financial support for the research, authorship, and/or publication of this article: This work was supported by grants from the Söderström-König foundation; the Swedish Society of Medicine; the Swedish state under the agreement between the Swedish government and the county councils (ALF 20190429); the Ingrid & Fredrik Thuring foundation; the Swedish Brain foundation (Hjärnfonden); Karolinska Institutet; the Strategic Research Area Neuroscience (StratNeuro); the Swedish Medical Association supported MG and Region Stockholm supported MG (Psykiatri Nordväst) and MT (clinical research appointment). The collection of the PET-data used in this study was funded in part by AstraZeneca Pharmaceuticals. Funders had no role in data acquisition, data analysis, manuscript preparation, study design, or decision to publish.

Ethical considerations

This study received approval from the Medical Products Agency, the Radiation Safety Committee, and the Ethical Review Board in Stockholm reference number 2006/331-31/2 and 2007/89-31/2. The study was performed in agreement with ICH/Good Clinical Practice, AstraZeneca policy of bioethics, and the ethical principles described in the 1964 Declaration of Helsinki.

Consent to participate

Informed consent was obtained for all subjects prior to study initiation.

ORCID iDs

Martin Gärde

Johan Lundberg

Jonas Svensson

Mikael Tiger

Data availability

The data supporting the findings of this study is available on request after consultation with AstraZeneca Pharmaceuticals.

Supplemental material

Supplemental material for this article is available online.

References

Tiger

Varnäs

Okubo

, et al. The 5-HT1B receptor - a potential target for antidepressant treatment. Psychopharmacology 2018; 235: 1317–1334.

Diener

H-C

Charles

Goadsby

, et al. New therapeutic approaches for the prevention and treatment of migraine. Lancet Neurol 2015; 14: 1010–1022.

Yatham

Steiner

Neuroendocrine probes of serotonergic function: a critical review. Life Sci 1993; 53: 447–463.

Rainero

Govone

Gai

, et al. Is migraine primarily a metaboloendocrine disorder? Curr Pain Headache Rep 2018; 22: 36.

Peres

Sanchez del Rio

Seabra

, et al. Hypothalamic involvement in chronic migraine. J Neurol Neurosurg Psychiatry 2001; 71: 747–751.

Lucki

The spectrum of behaviors influenced by serotonin. Biol Psychiatry 1998; 44: 151–162.

Papageorgiou

Denef

Stimulation of growth hormone release by 5-hydroxytryptamine (5-HT) in cultured rat anterior pituitary cell aggregates: evidence for mediation by 5-HT2B, 5-HT7, 5-HT1B, and ketanserin-sensitive receptors. Endocrinology 2007; 148: 4509–4522.

Whale

Bhagwagar

Cowen

PJ.

Zolmitriptan-induced growth hormone release in humans: mediation by 5-HT1D receptors?

Psychopharmacology 1999; 145: 223–226.

de Rezende

Garcia-Leal

Graeff

, et al. The 5-HT1D/1B receptor agonist sumatriptan enhances fear of simulated speaking and reduces plasma levels of prolactin. J Psychopharmacol 2013; 27: 1124–1133.

10.

Varnäs

Nyberg

Halldin

, et al. Quantitative analysis of [¹¹C]AZ10419369 binding to 5-HT_1B receptors in human brain. J Cereb Blood Flow Metab 2011; 31: 113–123.

11.

Gallezot

J-D

Nabulsi

Neumeister

, et al. Kinetic modeling of the serotonin 5-HT_1B receptor radioligand [¹¹C]P943 in humans. J Cereb Blood Flow Metab 2010; 30: 196–210.

12.

Savli

Bauer

Mitterhauser

, et al. Normative database of the serotonergic system in healthy subjects using multi-tracer PET. NeuroImage 2012; 63: 447–459.

13.

Takano

Varrone

Gulyás

, et al. Guidelines to PET measurements of the target occupancy in the brain for drug development. Eur J Nucl Med Mol Imaging 2016; 43: 2255–2262.

14.

Zhang

Zhou

Wang

, et al. Preclinical pharmacology and pharmacokinetics of AZD3783, a selective 5-hydroxytryptamine 1B receptor antagonist. J Pharmacol Exp Ther 2011; 339: 567–578.

15.

Varnäs

Nyberg

Karlsson

, et al. Dose-dependent binding of AZD3783 to brain 5-HT_1B receptors in non-human primates and human subjects: a positron emission tomography study with [¹¹C]AZ10419369. Psychopharmacology 2011; 213: 533–545.

16.

Duvernoy

Risold

P-Y.

The circumventricular organs: an atlas of comparative anatomy and vascularization. Brain Res Rev 2007; 56: 119–147.

17.

Boron

Boulpaep

EL.

Medical physiology, 2e Updated Edition E-Book: with STUDENT CONSULT Online Access. Philadelphia, PA; Elsevier Health Sciences, 2012.

18.

Pierson

Andersson

Nyberg

, et al. [¹¹C]AZ10419369: a selective 5-HT_1B receptor radioligand suitable for positron emission tomography (PET). Characterization in the primate brain. Neuroimage 2008; 41: 1075–1085.

19.

Arakawa

Okumura

Ito

, et al. Positron emission tomography measurement of dopamine D₂ receptor occupancy in the pituitary and cerebral cortex: relation to antipsychotic-induced hyperprolactinemia. J Clin Psychiatry 2010; 71: 1131–1137.

20.

Nord

Finnema

Halldin

, et al. Effect of a single dose of escitalopram on serotonin concentration in the non-human and human primate brain. Int J Neuropsychopharmacol 2013; 16: 1577–1586.

21.

Nord

Finnema

Schain

, et al. Test–retest reliability of [¹¹C]AZ10419369 binding to 5-HT_1B receptors in human brain. Eur J Nucl Med Mol Imaging 2014; 41: 301–307.

22.

Nord

Cselenyi

Forsberg

, et al. Distinct regional age effects on [¹¹C]AZ10419369 binding to 5-HT_1B receptors in the human brain. NeuroImage 2014; 103: 303–308.

23.

Tiger

Farde

Rück

, et al. Low serotonin1B receptor binding potential in the anterior cingulate cortex in drug-free patients with recurrent major depressive disorder. Psychiatry Res Neuroimaging 2016; 253: 36–42.

24.

Schain

Tóth

Cselényi

, et al. Improved mapping and quantification of serotonin transporter availability in the human brainstem with the HRRT. Eur J Nucl Med Mol Imaging 2013; 40: 228–237.

25.

Varnäs

Halldin

Hall

Autoradiographic distribution of serotonin transporters and receptor subtypes in human brain. Hum Brain Mapp 2004; 22: 246–260.

26.

Svensson

Schain

Plavén-Sigray

, et al. Validity and reliability of extrastriatal [¹¹C]raclopride binding quantification in the living human brain. NeuroImage 2019; 202: 116143.

27.

Svensson

Svanborg

Plavén-Sigray

, et al. Serotonin transporter availability increases in patients recovering from a depressive episode. Transl Psychiatry 2021; 11: 264.

28.

Yadav

Singhal

Chauhan

, et al. MRI evaluation of size and shape of normal pituitary gland: age and sex related changes. J Clin Diagn Res 2017; 11(12): 1–4.

29.

Farde

Eriksson

Blomquist

, et al. Kinetic analysis of central [¹¹C]raclopride binding to D₂-dopamine receptors studied by PET—a comparison to the equilibrium analysis. J Cereb Blood Flow Metab 1989; 9: 696–708.

30.

Jakubowski

Blood supply, blood flow and autoregulation in the adenohypophysis, and altered patterns in oestrogen-induced adenomatous hyperplasia. Br J Neurosurg 1995; 9: 331–346.

31.

Page

Funsch

Brennan

, et al. Regional neurohypophyseal blood flow and its control in adult sheep. Am J Physiol 1981; 241: R36–R43.

32.

Leenders

Perani

Lammertsma

, et al. Cerebral blood flow, blood volume and oxygen utilization: normal values and effect of age. Brain 1990; 113: 27–47.

33.

Salinas

Searle

Gunn

RN.

The simplified reference tissue model: model assumption violations and their impact on binding potential. J Cereb Blood Flow Metab 2015; 35: 304–311.

34.

Schain

Fazio

Mrzljak

, et al. Revisiting the Logan plot to account for non-negligible blood volume in brain tissue. EJNMMI Res 2017; 7: 66.

35.

Lammertsma

Hume

SP.

Simplified reference tissue model for PET receptor studies. NeuroImage 1996; 4: 153–158.

36.

Tomasi

Edison

Bertoldo

, et al. Novel reference region model reveals increased microglial and reduced vascular binding of 11C-(R)-PK11195 in patients with Alzheimer’s disease. J Nucl Med 2008; 49: 1249–1256.

37.

Tjerkaski

Cervenka

Farde

, et al. Kinfitr — an open-source tool for reproducible PET modelling: validation and evaluation of test-retest reliability. EJNMMI Res 2020; 10: 77.

38.

R Core Team. R: a language and environment for statistical computing, https://www.R-project.org/ (accessed 28 October 2022).

39.

Tóth

Häggkvist

Varrone

, et al. ABC transporter-dependent brain uptake of the 5-HT_1B receptor radioligand [¹¹C]AZ10419369: a comparative PET study in mouse, rat, and guinea pig. EJNMMI Res 2014; 4: 64.

40.

Tournier

Bauer

Pichler

, et al. Impact of P-glycoprotein function on the brain kinetics of the weak substrate 11C-metoclopramide assessed with PET imaging in humans. J Nucl Med 2019; 60: 985–991.

41.

Phan

J-A

Landau

Jakobsen

, et al. Radioligand binding analysis of α₂ adrenoceptors with [¹¹C]yohimbine in brain in vivo: Extended Inhibition Plot correction for plasma protein binding. Sci Rep 2017; 7: 15979.

42.

Hillmer

Zheng

M-Q

, et al. PET imaging evaluation of [¹⁸F]DBT-10, a novel radioligand specific to α₇ nicotinic acetylcholine receptors, in nonhuman primates. Eur J Nucl Med Mol Imaging 2016; 43: 537–547.

43.

Kågedal

Varnäs

Hooker

, et al. Estimation of drug receptor occupancy when non-displaceable binding differs between brain regions – extending the simplified reference tissue model. Brit J Clinical Pharma 2015; 80: 116–127.

44.

De Laat

Morris

. A local-neighborhood Lassen plot filter for creating occupancy and non-displaceable binding images. J Cereb Blood Flow Metab 2021; 41: 1379–1389.

45.

Martinez

Hwang

D-R

Mawlawi

, et al. Differential occupancy of somatodendritic and postsynaptic 5HT_1A receptors by pindolol: a dose-occupancy study with [¹¹C]WAY 100635 and positron emission tomography in humans. Neuropsychopharmacol 2001; 24: 209–229.

46.

Varnäs

Hall

Bonaventure

, et al. Autoradiographic mapping of 5-HT_1B and 5-HT_1D receptors in the post mortem human brain using [³H]GR 125743. Brain Res 2001; 915: 47–57.

47.

Azimi

Rahmim

Arabi

, et al. Critical review of partial volume correction methods in PET and SPECT imaging: benefits, pitfalls, challenges, and future outlook. Eur J Nucl Med Mol Imaging. Epub ahead of print 5 November 2025. DOI: 10.1007/s00259-025-07612-5.

48.

Cumming

Marton

Lilius

, et al. A survey of molecular imaging of opioid receptors. Molecules 2019; 24: 4190.

49.

Olsson

Halldin

Swahn

C-G

, et al. Quantification of [¹¹C]FLB 457 binding to extrastriatal dopamine receptors in the human brain. J Cereb Blood Flow Metab 1999; 19: 1164–1173.

50.

Jensen

Olsen

Pedersen

, et al. Effect of monoamine oxidase inhibition on amphetamine-evoked changes in dopamine receptor availability in the living pig: a dual tracer PET study with [¹¹C]harmine and [¹¹C]raclopride. Synapse 2006; 59: 427–434.

51.

Marzano

Jakobsen

Salinas

, et al. Radiosynthesis and evaluation of 1-substituted 3-(2,3-dihydro-1H-inden-2-yl)-6-(1-ethylpropyl)-(3R,6R)-2,5-piperazinedione derivatives as PET tracers for imaging the central oxytocinergic system. J Labelled Comp Radiopharm 2017; 60: 556–565.

Quantification of pituitary 5-HT 1B receptors with positron emission tomography: Negligible specific binding despite conspicuous uptake

Abstract

Keywords

Introduction

Materials and methods

Subjects and study design

Image analysis

Regions of interest

Quantification of [11C]AZ10419369 binding

Occupancy calculations

Graphical estimation of BPND from DVRs and gray matter occupancy

Statistical analysis

Results

Regional [11C]AZ10419369 binding and displacement after AZD3783 pretreatment

Graphical estimation of VND ratios and pituitary specific binding

Discussion

Supplemental Material

sj-docx-1-jcb-10.1177_0271678X261417194 – Supplemental material for Quantification of pituitary 5-HT1B receptors with positron emission tomography: Negligible specific binding despite conspicuous uptake

Footnotes

Acknowledgements

Author contribution

Declaration of conflicting interests

Funding

Ethical considerations

Consent to participate

ORCID iDs

Data availability

Supplemental material

References

Quantification of [¹¹C]AZ10419369 binding

Graphical estimation of BP_ND from DVRs and gray matter occupancy

Regional [¹¹C]AZ10419369 binding and displacement after AZD3783 pretreatment

Graphical estimation of V_ND ratios and pituitary specific binding