J Pharmacokinet Pharmacodyn (2013) 40:267–279 DOI 10.1007/s10928-013-9320-6
REVIEW PAPER
Modeling of PET data in CNS drug discovery and development Katarina Varna¨s • Andrea Varrone Lars Farde
•
Received: 18 March 2013 / Accepted: 26 April 2013 / Published online: 10 May 2013 Ó Springer Science+Business Media New York 2013
Abstract Positron emission tomography (PET) is increasingly used in drug discovery and development for evaluation of CNS drug disposition and for studies of disease biomarkers to monitor drug effects on brain pathology. The quantitative analysis of PET data is based on kinetic modeling of radioactivity concentrations in plasma and brain tissue compartments. A number of quantitative methods of analysis have been developed that allow the determination of parameters describing drug pharmacokinetics and interaction with target binding sites in the brain. The optimal method of quantification depends on the properties of the radiolabeled drug or radioligand and the binding site studied. We here review the most frequently used methods for quantification of PET data in relation to CNS drug discovery and development. The utility of PET kinetic modeling in the development of novel CNS drugs is illustrated by examples from studies of the brain kinetic properties of radiolabeled drug molecules. Keywords Positron emission tomography Kinetic modeling Microdosing Receptor occupancy
Introduction Positron emission tomography (PET) is a non-invasive imaging technology increasingly used in CNS drug K. Varna¨s (&) A. Varrone L. Farde Department of Clinical Neuroscience, Karolinska Institutet, Stockholm, Sweden e-mail:
[email protected] L. Farde AstraZeneca Translational Science Center at Karolinska Institutet, Stockholm, Sweden
discovery and development [1–3]. In PET studies of novel CNS drugs an estimate of brain exposure can be achieved either directly, by studying the drug molecule as labeled with a PET radionuclide (microdosing studies), or indirectly, by examination of the competition of a drug candidate for radioligand binding to the target (occupancy studies). Using the direct approach PET has been applied to study brain disposition of a wide range of therapeutic drugs as well as of novel compounds developed for the treatment of CNS disorders [4–19]. This approach is often referred to as PET microdosing since injection of a small amount of radiolabeled drug is generally sufficient to obtain a good signal. In this context, the term microdose is defined, according to the European Medicines Agency [20] and the Food and Drug Administration [21], as less than one hundredth of the dose expected to induce pharmacologic effects, or up to 100 lg. Importantly, PET microdosing can be applied in early stages of drug development to assess plasma and brain pharmacokinetics without the requirement of the extensive safety evaluations associated with therapeutic doses [22]. Despite radiolabeling of numerous drug candidates for microdosing, only a few of them have showed suitable binding affinity and selectivity to visualize specific binding to targets. Therefore suitable radioligands have to be implemented or developed to allow for measurement of target occupancy. Target occupancy per se confirms that the drug binds specifically to a target in brain, thus representing an indirect estimate of drug CNS concentration. The indirect approach is commonly applied for selection of dosage regimen for novel drug candidates. Identification of neurochemical biomarkers for brain pathology remains a challenge in neuroscience research. Validation and quantification of radiotracers for disease biomarkers is therefore an important objective of current PET imaging projects. Indeed PET research has enabled
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the development of tracers for detection of biochemical abnormalities in neurodegenerative disorders, including Alzheimer’s and Parkinson’s diseases (for reviews see [23–26]). Such disease biomarkers can be applied for diagnostic purposes, and to follow disease progression and the effect of pharmacologic intervention. By contrast, PET studies of disorders such as schizophrenia and depression are still hampered by the lack of suitable disease biomarkers. The study of disease biomarkers and CNS drug exposure rely on accurate quantification of kinetic parameters describing the underlying interaction between drug molecules and brain biochemistry. In a quantitative analysis of PET data, the outcome measures are commonly partition coefficients for radioactivity in tissues (or compartments) to that in a reference fluid, such as plasma, at steady state. The brain to plasma partition coefficient can be directly calculated by employing an infusion paradigm to achieve steady state concentrations of the radioactive molecule in brain and plasma [27]. However, the most common approach is to administer the radiotracer as a bolus injection. Although steady state of the system is not achieved in a bolus injection study, the partition coefficients can be estimated by taking advantage of time and applying mathematical models to the data. Over the years, a number of models for interpretation and quantification of CNS PET imaging data have been developed. As the suitability of the models will depend on the characteristics of the radiolabeled molecule and the target studied, the optimal method for quantitative analysis needs to be validated as part of characterization of any new radioligand. Such characterization commonly involves initial kinetic modeling to describe the kinetic behavior of the radiolabeled drug or radioligand, and subsequently simplified methods to more routinely estimate the extent of binding at the target of interest. In the following, tracer denotes both a radioligand and a radiolabeled drug molecule. The term tracer implies that the amount of the compound administered is very small and does not influence the molecular interactions studied. We here present an overview of commonly applied models for quantification of CNS PET imaging data. Comprehensive overviews of general models for quantification of neuroreceptor binding have been provided elsewhere [28–31] whereas the aim of the present review is primarily to describe commonly used PET quantification models in relation to CNS drug discovery and development.
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number of tissue compartments. The procedure requires arterial blood sampling and detection of radioactive metabolites to generate a time curve corresponding to the concentration of unchanged (parent) tracer in plasma. At the early phase of PET data acquisition an automated blood sampling system is commonly used to capture the rapid changes in blood radioactivity following a bolus injection [32]. Subsequently, samples for measurement of blood and plasma radioactivity are manually drawn. Generation of the arterial input function commonly involves several curve fitting steps, including correction for dispersion, blood to plasma transformation and correction for radioactive metabolites, as only parent tracer is assumed to contribute to the radioactivity detected within the brain. The regional time–activity curves The reconstructed PET images contain information regarding the local brain radioactivity concentration, corrected for radioactive decay, in each image volume element, or voxel, over a consecutive series of time frames. For the purpose of definition of anatomical regions of interest (ROIs) brain magnetic resonance (MR) images are commonly acquired and coregistered with PET images. The local time–activity curve of a selected ROI is subsequently defined as the average voxel radioactivity concentration in each of a consecutive series of time frames. Rationale for compartment analysis
PET kinetic modeling in neuroscience research
As will be described in this section the brain time–activity curves provide information regarding the total radioactivity concentration corresponding to tracer in different tissue pools, or compartments. The objective of a PET quantitative analysis is to provide a mathematical model that, given a measured input function (the arterial plasma curve), can be applied to describe the observed time curves for regional brain radioactivity and differentiate the tissue compartments (TCs, Fig. 1). The commonly applied methods for quantification of PET data are based on kinetic models describing transfer of a tracer between different TCs. The TCs represent kinetically separate pools assumed to be homogeneous in tracer concentration. A further assumption of the compartment model is that the biological processes studied are in steady state during the measurement, and consequently that the derived parameters are constant over time. In this section commonly used models assuming different compartment configurations are presented.
The arterial input function
The compartment model and derived parameters
A PET kinetic model analysis uses the arterial plasma concentration as an input function to describe tracer kinetics in a
Most quantitative approaches are derived from a model configuration with three TCs corresponding to the free
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Fig. 1 The objective of a PET compartment analysis is to provide a mathematical model that, given a measured arterial input function, can be applied to describe the observed time curves for regional brain radioactivity
tracer in tissue, non-specifically bound tracer and tracer specifically bound to target sites. A common assumption is that, due to rapid steady state between the free and nonspecifically bound tracer, the kinetics for these two compartments cannot be distinguished, thus implying that the unbound fraction in brain (fND) is constant over time [33]. The two compartments are therefore commonly approximated by one single compartment, referred to as the nondisplaceable compartment [33] (Fig. 2a). It is further assumed that the unbound fraction of tracer in plasma (fp) is constant over time. K1 and k2 correspond to the influx and efflux rate constants, respectively, for tracer transfer across the blood–brain barrier (BBB), and k3 and k4 describe the transfer between the specifically bound and the non-displaceable compartment. K1 is given in units of volume of blood or plasma per volume of tissue per unit time (mL cm-3 min-1) whereas k2, k3 and k4 all have the unit of min-1. The rate of change in tracer concentration over time for the two tissue compartments can be described by the following differential equations: dCND ðtÞ ¼ K1 Cp ðtÞ ðk2 þ k3 ÞCND ðtÞ þ k4 CS ðtÞ; dt
ð1Þ
dCS ðtÞ ¼ k3 CND ðtÞ k4 CS ðtÞ; dt
ð2Þ
Fig. 2 Kinetic compartment models used in PET quantitative analysis. a Two-tissue compartment model. b One-tissue compartment model. c Two-tissue compartment model with irreversible binding to the second compartment
where CP(t), CND(t), and CS(t), correspond to tracer concentration in plasma and in the non-displaceable and specifically bound compartment, respectively. The total concentration in brain tissue (CT(t)) is defined as
concentration in brain tissue can be estimated from the total radioactivity detected by the PET system, CPET(t), by using the radioactivity concentration in arterial blood, Ca(t), as an estimate of the intracerebral blood concentration according to the following equation
CT ðtÞ ¼ CND ðtÞ þ CS ðtÞ:
CPET ðtÞ ¼ ð1 Vb ÞCT ðtÞ þ Vb Ca ðtÞ;
ð3Þ
In addition to radioactivity in brain tissue the signal detected by the PET system also includes a contribution from radioactivity in cerebral vasculature. Radioactivity
ð4Þ
where Vb corresponds to the fractional volume of blood in tissue. To estimate Vb it can either be fitted together with other model parameters, or obtained from estimates of the
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cerebral blood volume reported in the literature [32, 34, 35]. Assuming that there is no specific binding, or that CS and CND equilibrate rapidly and can be combined in one single compartment, CT(t), the model can be further simplified to a 1-TC model with two rate constants, K1 and k2 (Fig. 2b). For the 1-TC model the rate of change in tracer concentration is defined by Eq. (5). dCT ðtÞ ¼ K1 CP ðtÞ k2 CT ðtÞ: dt
ð5Þ
The rate constants are estimated by nonlinear least squares curve fitting, and can be subsequently used to calculate a parameter termed the volume of distribution (VT). It is important to note that in the PET literature the concept of a distribution volume is different from the definition used in clinical pharmacokinetics. The distribution volume is defined as the partition coefficient corresponding to the local concentration of radioactivity in a target organ or compartment relative to that in plasma. For instance, the total distribution volume, VT, corresponds to the steady state concentration ratio of total regional radioactivity to that in plasma whereas the non-displaceable distribution volume (VND) is the ratio of concentration in the non-displaceable compartment to that in plasma. The PET distribution volume, VT, can be calculated as follows: For the 1-TC model: VT ¼
K1 ; k2
and for the 2-TC model: K1 k3 VT ¼ 1þ : k2 k4
ð6Þ
ð7Þ
In PET studies of drug targets or disease biomarkers in the CNS a primary goal is to estimate the concentration of available binding sites (Bavail). PET studies are commonly conducted using a tracer with high specific radioactivity, which does not allow Bavail to be differentiated from the affinity of the radiotracer (KD). At such conditions an index of the available binding sites can, however, be provided from the steady state concentration ratio of the specifically bound concentration to that of a reference concentration, which is either the free concentration in plasma, total concentration in plasma or non-displaceable concentration in tissue [36]. Such ratios are commonly denoted binding potentials (BPs). In the early PET literature different definitions have been used to describe the outcome measure used to quantify the extent of specifically bound tracer. To account for discrepancies in terms and definitions used in the literature a consensus nomenclature has more recently been recommended [36].
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Thus for instance the binding ratio relative to nondisplaceable tracer concentration is denoted BPND, and corresponds to the ratio of k3/k4 obtained by compartment analysis. In terms of in vitro parameters BPND is equivalent to the ratio of the density of available binding sites to the equilibrium dissociation constant, KD, multiplied by fND [36]. The 2-TC model commonly yields accurate estimates of VT. However, the model is computationally demanding and the individual rate constants are normally less reliable. Thus, for some tracers it has not been possible to obtain biologically meaningful estimations of BPND (k3/k4) using this approach (see e.g. [37–39]). Graphical analyses The kinetic methods described above conventionally use nonlinear least squares curve fitting procedures to estimate rate constants. As the fitting procedure is computationally demanding the methods are commonly used for analysis of averaged radioactivity in selected ROIs, but are less suitable for voxel level analysis for generation of parametric images. To overcome these limitations graphical analysis methods have been derived based on linear transformation of the data. Using the graphical method developed by Logan et al. [40], VT can be calculated using the following equation RT RT CP ðtÞdt 0 CT ðtÞdt ¼ VT 0 þ b: ð8Þ CT ðTÞ CT ðTÞ It has been shown that, after a certain time the plot of RT versus becomes 0 CT ðtÞdt=CT ðTÞ 0 CP ðtÞdt=CT ðTÞ linear and VT can be obtained, by ordinary least squares estimation, from the slope of the curve. The model is computationally simple and stable, and no a priori assumptions are required regarding the underlying number of compartments. A limitation with the graphical approach described above is noise-induced bias due to the tissue radioactivity term CT(T) in the independent variable and correlated noise in the left and right hand sides of the equation resulting in underestimation of VT, particularly in regions having a high density of binding sites [41]. For this reason, alternative linear models based on multilinear rearrangement of Eq. (8) have been suggested [42]. RT
Irreversible binding All of the models described above were developed for reversibly binding tracers. For tracers that bind irreversibly to their targets, such as some enzyme inhibitors, the rate constant k4, describing dissociation from binding sites is assumed to be negligible [43]. For such tracers a 2-TC model with three estimable parameters, K1, k2, and k3, has
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been applied (Fig. 2c; [44]. Here the k3 rate constant is the parameter of interest describing the rate of binding to the target. However, the parameter kk3, where k corresponds to K1/k2, has been shown to be more reproducible than k3 alone [45] and is frequently used for the quantitative analysis of irreversibly binding tracers [46–48]. Reference region approaches Kinetic modeling using arterial plasma as input function is considered to be the gold standard method for quantification of PET data. The modeling requires arterial blood sampling, which may be associated with subject discomfort and be technically demanding. In order to avoid the need for arterial blood sampling several non-invasive quantification models have been developed. In the non-invasive models concentration of tracer in the non-displaceable compartment is approximated by radioactivity concentration in a reference region, showing negligible specific binding to the target of interest. The reference region approaches were originally adapted from equations commonly used to interpret radioligand binding to receptors in vitro [49]. In vitro experiments allow for sufficient time to assure equilibrium conditions between ligand and binding site. In PET studies, however, the tracer is most commonly administered as an intravenous bolus injection and true equilibrium of the entire system is not achieved. Theoretically, binding equilibrium can be achieved at the time when the peak in specific binding occurs, i.e. when dCS(t)/dt in Eq. (2) is 0 [32]. In the quantitative approach referred to as the peak equilibrium method, specific binding is approximated by CT(t) minus concentration in a reference region (CREF(t)). By fitting a sum of exponentials to the curve for specific binding, the time t0 for which peak equilibrium occurs can be defined when d(CT(t) - CREF(t))/dt = 0. Thus, at time of peak specific binding, t0 , BPND is approximated by the ratio (CT(t0 ) - CREF(t0 ))/CREF(t0 ). Another ratio method is based on the assumption that the clearance rates from tissue and plasma are similar at late times after bolus injection, and thus that the ratio of brain and plasma time–activity curves reaches a constant value. In this method the ratio for specific binding over that for the reference region is calculated for a time interval immediately before the end of PET data acquisition [50]. A reference tissue model based on compartmental models uses the radioactivity concentration in a reference region as an indirect input function [51]. Using this Simplified Reference Tissue Model BPND can be estimated by nonlinear least square estimation, under the assumption that the K1/k2 is the same in target and reference regions and that the regional time–activity curves can be described by a 1-TC model [51].
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Reference tissue models have also been derived based on the linear transformations applied for methods requiring arterial data [52, 53]. The methods are computationally rapid and are therefore well suited for parametric imaging [54]. The ratio of distribution volumes (DVR = VT/VND) in target and reference regions is estimated. Based on the assumption that K1/k2 is the same in target and reference regions BPND can be calculated as DVR - 1. Advantages and limitations of PET methods for quantification have been discussed by Ikoma et al. [55]. Kinetic modeling provides an unbiased estimate of VT, but commonly, the individual rate constants are not reliably identified. Graphic models are stable and reproducible, but may be prone to noise-induced bias. Reference models are easy to implement, and may increase reliability of parameter estimates, but may yield biased estimates if the model assumptions are violated. The suitability of the models will thus depend on the characteristics of the radiotracer and the binding site studied, and needs to be evaluated as part of the characterization of any new PET tracer.
Modeling of CNS drug disposition by PET microdosing PET allows for the detection of concentrations of radiolabeled molecules in tissue at the low pM range. The high sensitivity of PET thereby enables the study of drug pharmacokinetics in human subjects after administration of sub-pharmacologic doses (microdoses) without the requirement of the extensive safety evaluations associated with therapeutic dosing of the drug [2, 22, 56, 57]. Important information can also be provided from microdosing studies in non-human primates as previous evidence support the validity of PET studies in these species for predicting CNS drug disposition in humans. In a microdosing PET study of a candidate drug pharmacokinetic parameters can be derived based on descriptive statistics obtained from analysis of the time curves for radioactivity concentration in plasma and brain (Fig. 3). The time curves for the plasma and brain radioactivity concentrations are commonly expressed in relation to the total amount of radioactivity injected, or as the standardized uptake value (SUV), which is defined as the local radioactivity per gram tissue divided by the administered radioactivity per gram body weight. An SUV of 1 is the concentration corresponding to even distribution of radioactivity throughout the body. The majority of therapeutic CNS drugs examined with PET have shown maximum brain concentration in brain to be above 2 % of the total radioactivity injected [3]. As illustrated in the previous section parameters describing CNS drug disposition can be estimated by applying kinetic modeling to the brain time–activity curves
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model can be selected by comparing goodness of the fit, using statistical tests such as the F test or Akaike information criterion. A common observation for many tracers is that a 2-TC model is required even in tissues having negligible specific binding [7]. The parameter K1 describes the entry of a tracer to the brain. According to the model developed by Renkin [58] and Crone [59], K1 can be expressed in terms of the cerebral blood flow (F) and the extraction fraction (E): K1 ¼ FE; E ¼ 1 eðPS=FÞ ;
Fig. 3 Time curves for radioactivity in plasma, corrected for metabolites, and brain, and the brain/plasma radioactivity ratio after administration of intravenous microdoses of three 11C-labeled drug candidates
using the arterial plasma concentration as input function. The modeling process commonly involves fitting 1- and 2-TC models to the data. Subsequently the most suitable
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ð9Þ ð10Þ
where PS is the permeability–surface area product. The parameter PS is a measure of BBB permeability, but has not been commonly reported in the PET literature as determination of PS requires direct measurement of cerebral blood flow. Drugs used for treatment of CNS pathology have shown K1 values in the range of 0.4–0.6 mL min-1 cm-3 [12, 14, 15]. Verapamil and loperamide are drugs with restricted entry into the CNS due to being substrates of the P-glycoprotein (P-gp) drug efflux pump. Consistent with this property K1 values reported in PET studies for such compounds have been considerably lower than those for CNS drugs (0.03 and 0.05 mL min-1 cm-3 [7, 60]). Indeed, in PET studies using the P-gp radiotracers [11C]verapamil and [11C]N-desmethyl-loperamide, the influx constant K1 and the PS have been shown to increase after pharmacologic inhibition of P-gp [61–63]. In Fig. 3 plasma and brain time–activity curves, as well as the curves for the corresponding brain/plasma concentration ratios, are shown for three potential CNS drugs that were radiolabeled with 11C and evaluated using PET in non-human primates. The comparison serves to illustrate differences in brain kinetic behavior commonly observed in PET studies of novel CNS drugs. The measurements were conducted under conditions where a high dose of the unlabeled compound had been administered in order to minimize specific binding. Compounds A and B showed concentration in brain above 1 SUV whereas brain concentrations for compound C were markedly lower than 1. When applying kinetic compartment modeling to the data a 2-TC model was found to be required for compounds A and B, whereas a 1-TC model was found to be sufficient to describe the curves for compound C. Compounds A and B showed a similar rate of entry to the CNS (K1 0.31 and 0.28 mL min-1 cm-3, respectively). As assessed by k2 (0.30 and 0.064 min-1, respectively) the rate of elimination from brain was found to be faster for compound B than that for compound A. When estimating CNS drug exposure using PET microdosing it is of crucial interest to achieve information regarding the unbound tracer concentration in the brain,
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since only the unbound drug molecules can interact with the intended target [64, 65]. The contribution of specifically bound tracer can be minimized by selecting a brain region having negligible specific binding to target sites. As described in the previous section the non-displaceable volume of distribution (VND) corresponds to the ratio at steady state of the tracer concentration in the free and nonspecifically bound compartment (CND) to the plasma tracer concentration, and can be estimated for a region devoid of specific binding sites [7, 32, 66]. The PET parameter VND is thus a parameter closely related to the total brain/plasma partition coefficient, KP, used in the fields of drug metabolism and pharmacokinetics [67]. VND does not take nonspecific binding of tracer to brain tissue and plasma proteins into account. However, an estimate of the unbound brain/plasma partition coefficient can be obtained by combining information derived from PET studies with in vitro measures of the unbound fractions in plasma and brain [7]. VND can be expressed as a function of the concentration of free drug in brain (CFT) and plasma (CFP), and the plasma and brain unbound fractions [7, 67] as described in Eq. (11) VND ¼
CND fP CFT ¼ : CP fND CFP
ð11Þ
Thus, if estimates of fp and fND are available the unbound brain/plasma concentration ratio can be calculated from VND. fp can be estimated from measurements of the plasma protein binding of the tracer by equilibrium dialysis in vitro. fND cannot be measured with PET, but estimates of this parameter can be obtained in vitro, using equilibrium dialysis of the compound between buffer and brain homogenates [68] or brain slices [69]. CFT/CFP provides information of the distributional impairment over the BBB since under freely diffusible steady state conditions this value should be close to unity. However, if the radioactive drug is a substrate of efflux proteins in the BBB, CFT/CFP will be lower than unity [64, 70]. If CFT/CFP is known then the free brain concentrations at targeted therapeutic doses can be predicted. Moreover, provided that an in vitro estimate of KD is available target occupancy at intended therapeutic doses can be calculated as described further below. The usefulness of applying PET for estimating unbound brain concentrations is supported by a recent investigation of 36 radiolabeled test compounds in pig [7]. By combining PET imaging data with equilibrium dialysis estimates of fp and fND, the authors demonstrated that compounds expected to enter the brain by passive diffusion show VND values consistent with that predicted by fp/fND. Conversely for the P-gp substrate [11C]loperamide, VND was shown to be markedly lower than the ratio of unbound fractions [7].
The compounds in Fig. 3 differed markedly with respect to their total brain/plasma partition coefficients. Distribution volumes as obtained using the 2-TC model were 10 for compound A and 1.4 for compound B. However, when taking differences in unbound fractions into account both compounds showed unbound brain/plasma partition coefficients close to 1 (1.4 and 0.89, respectively). For compound C the distribution volume obtained using the 1-TC model was 0.20. After correction for unbound fractions in brain and plasma, the estimated unbound brain/plasma ratio was found to be considerably lower than one (ca. 0.025). For most CNS drugs evaluated using PET microdosing specific binding to targets could not be demonstrated. For instance in a PET study with [11C]-labeled BMS-181101, a serotonergic compound under clinical development for the treatment of depression, VT values were homogeneous across brain regions and remained at a similar level after administration of [11C]BMS-181101 at high and low specific radioactivity [18]. However, there are some examples of CNS drugs as well as of drug candidates under development, that have been successfully used to identify binding to targets using PET microdosing. The dopamine D2 receptor antagonist raclopride was initially under development as an antipsychotic drug. Carbon-11-labeled raclopride was in parallel found to be suitable for quantifying specific binding to this receptor [16, 49] and is now a widely used radioligand for applied PET studies. AZD9272, a non-competitive antagonist at the metabotropic glutamate receptor 5 under development as a potential CNS drug [71], has been labeled with carbon11 and evaluated in human subjects at tracer dose alone and after administration of different doses of AZD9272 [19]. As evidenced in studies after increasing doses of unlabeled AZD9272, the binding of AZD9272 was found to be saturable. Also, a nonlinear mixed effects model was applied and improved the identification of individual kinetic parameters [19]. There are a few additional examples of marketed CNS drug compounds that have been radiolabeled and studied using PET kinetic modeling to quantify the extent of binding to their targets in human subjects. One example is the tricyclic anti-depressant doxepin, which can be used for quantification of the binding to H1 histamine receptors [13], another is the selective serotonin reuptake inhibitor sertraline, that has been used to study the brain distribution of serotonin transporter binding sites [14], and finally the acetylcholinesterase inhibitor donepezil [15]. PET may also be applied for the quantitative evaluation of drugs that bind irreversibly to their target. An example is the monoamine oxidase B (MAO B) enzyme inhibitor L-deprenyl (selegiline), which has been labeled with carbon-11, and used in PET studies to image the brain distribution of this enzyme [72]. Consistent with irreversible
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binding to MAO B a 2-TC model with irreversible binding to the second compartment may be applied to describe the regional [11C]L-deprenyl time–activity curves [44, 73]. In these studies administration of reversible MAO B inhibitors was found to reduce the binding of [11C]L-deprenyl to the enzyme, thereby confirming specific binding. The microdosing concept is based on the assumption that pharmacokinetic parameters derived after administration of microdoses of a drug can be extrapolated to the far higher pharmacologic doses. The limited number of studies in the literature supports the utility of microdosing for predicting plasma pharmacokinetics at pharmacologic doses [74–76]. Brain pharmacokinetic dose linearity has indeed been confirmed in a number of PET studies in human subjects examined after administration of microdoses of radioactive drug and after administration of microdoses in combination with a higher dose of nonradioactive drug [2, 3, 57]. It has moreover been shown for [11C]raclopride, that the ratio of brain to blood radioactivity is similar at microdoses and pharmacologic doses [77]. A study comparing brain pharmacokinetics of [11C]verapamil at microdose and pharmacologic dose showed no statistically significant differences in the influx and efflux rate constants between the two conditions [60]. The relationship between brain and plasma exposure could, however, be expected to be nonlinear if plasma protein binding or transport mechanisms across the BBB is saturable. Further PET investigations of additional therapeutic drugs are therefore required to confirm dose linearity of brain pharmacokinetics. It should further be noted, that in PET studies it is not possible to distinguish parent radioactive drug from radioactive metabolites having sufficient lipophilicity to cross the BBB. Hence, the presence of radioactive metabolites in brain tissue will contribute to the signal detected by the PET system. Characterization of the metabolism of the radiolabeled drug is therefore of crucial importance for correct interpretation of results from PET examination [78].
Modeling of target occupancy using PET PET receptor occupancy studies allow for the evaluation of whether the drug candidate enters into the CNS and also whether it reaches and binds to specific sites. This approach requires the use of an established radioligand to evaluate the degree of drug-induced occupancy after administration at therapeutic doses. When applied in conjunction with clinical studies this methodology is commonly used to enable the selection of doses required for the aimed level of occupancy at the binding site. In an occupancy study multiple PET measurements are undertaken, at baseline and post-drug administration.
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Occupancy is calculated as the percentage reduction in radioligand binding after drug administration as compared with baseline. Commonly used PET radioligands have been found to have negligible binding in a brain region that can be used as a reference region in the brain and BPND can be calculated using reference tissue models (see above description). However also for targets without a reference region it is possible to estimate occupancy from the VT values obtained using kinetic modeling or graphical analysis at baseline and after drug administration [79]. Interestingly, it has been demonstrated that the equations describing radioligand binding to targets in vitro, can be derived from the differential equations used in PET kinetic modeling [32]. An estimate of the in vivo affinity of the drug for the binding site can thus be provided from the relationship between receptor occupancy and drug plasma concentration, Cp, Occupancy ¼
Occmax CP ; Ki;plasma þ CP
ð12Þ
where Occmax is the maximal occupancy induced by the drug, and Ki,plasma the inhibition constant corresponding to plasma concentration required for half-maximum receptor occupancy [80]. This parameter can be used for translation of findings from preclinical models to human studies [81]. PET has been extensively used to determine the occupancy range required for therapeutic efficacy of CNS drugs. Early studies using this methodology established the occupancy at dopamine D2 receptors required for therapeutic efficacy of antipsychotic drugs [82–84]. More recently the availability of radioligands for a number of CNS drug binding sites enables the determination of the level of occupancy required for therapeutic efficacy at additional targets [85–90]. These studies suggest that in general antagonists require high (60–90 %) receptor occupancy whereas the required occupancy level for agonist drugs may vary, depending on the target system and intrinsic efficacy of the drug [91]. Thus in the evaluation of compounds binding to established drug targets, the occupancy–efficacy relationships derived from clinical PET studies may serve as guidelines for prediction of the dose range required for the intended pharmacologic effect. For the study of novel drug targets occupancy at efficacious doses in animal models are commonly used for prediction of occupancy at exposure required for clinical efficacy. Therefore to validate the use of animal models for prediction of human in vivo pharmacology it is necessary to compare plasma exposure–occupancy relationships across species. A number of translational PET imaging studies have found a good correspondence between Ki,plasma values in preclinical species and humans. For example, the Ki,plasma value of fluvoxamine at the serotonin transporter has been shown to be similar in
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rodents (6 ng/mL [92]) and in humans (5 ng/mL [93]). Also the 5-HT1B receptor antagonist AZD3783 (Fig. 4) shows a good correspondence between monkey (Ki,plasma = 26 nmol/L) and human (Ki,plasma = 21 nmol/L) in vivo affinity estimates [81]. Other studies have, however, shown between-species differences in apparent in vivo affinity. The GABAA receptor compound TPA023 has for instance been shown to have similar in vivo affinity at central benzodiazepine receptor sites in several preclinical species, but to be slightly more potent in humans [94]. These species differences in in vivo affinity were not predicted from the Ki values at rat and human GABAA receptors determined in vitro [95], thus illustrating the value of translational PET studies for the assessment of CNS target engagement. The 5-HT1A receptor antagonist NAD-299 has shown about 10-fold higher in vivo affinity in cynomolgus monkeys [96] than in human subjects [97], a difference that could be accounted for by a 10-fold higher unbound fraction of NAD-299 in monkey than in human plasma [97]. These examples illustrate that, although studies in preclinical species are useful for predicting initial clinical doses, subsequent studies in human subjects are important to confirm the exposure–occupancy relationships derived from preclinical studies.
Finally biomarkers for disease pathophysiology have more recently been developed for neurodegenerative diseases. Whereas microdosing and occupancy studies aim at understanding the fate of a drug in the human body, studies of disease biomarkers demonstrate effect of drugs on CNS pathology. Imaging of the dopamine system using PET and SPECT is widely used as a biomarker for nigro-striatal dopamine deficit in Parkinson’s disease [24–26]. The availability of imaging tracers for both pre- and postsynaptic markers of the dopamine system has enabled studies applied to monitor the effect of pharmacologic treatment on the progression of dopaminergic deficit [24–26]. A number of PET imaging biomarkers have been developed that allow the quantification and visualization of brain amyloid deposition in patients with Alzheimer’s disease [23]. Among the available amyloid tracers [11C]labeled Pittsburgh Compound B ([11C]PiB [98, 99]) has been widely used in applied studies. The cerebellar binding profile of this tracer has been shown to be similar in Alzheimer’s disease patients and control subjects, allowing for non-invasive quantification methods, based on cerebellum as a reference region [99]. In a recent double-blind,
Fig. 4 Dose-dependent binding of AZD3783 to brain 5-HT1B receptors in a non-human primate (upper panel) and a human subject (lower panel). Color-coded PET images showing distribution of radioactivity in the non-human primate brain after injection of [11C]AZ10419369, at baseline and following intravenous
administration of AZD3783. Fused MR and PET images are shown for a human subject after injection of [11C]AZ10419369, at baseline and after oral administration of AZD3783. Summation images from 3 to 93 min are shown. Image intensity is presented in SUV units. For further details see [81]
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placebo-controlled study it was shown that treatment with a humanized anti-amyloid-b monoclonal antibody may decrease cortical [11C]PiB binding in Alzheimer’s disease patients [100]. These findings support the usefulness of amyloid imaging as a biomarker in future studies monitoring the effect of pharmacologic intervention.
Conclusion PET kinetic models are widely applied in drug discovery and development for evaluation of CNS drug disposition and for validation of disease biomarkers and tracers for novel targets. By using these models in the study of novel drugs, information can be provided regarding CNS pharmacokinetics as well as binding to drug targets within the brain. Future development of disease biomarkers and radioligands for novel drug binding sites will require implementation of models that allow for quantitative analysis of radiotracer binding at these molecular targets. As the microdosing technology is increasingly used in drug discovery and development, PET pharmacokinetic modeling of data derived from studies using radiolabeled drug molecules constitutes an important tool in the characterization of novel drugs. Acknowledgments The authors gratefully acknowledge Martin Schain for the very helpful comments and discussions related to the contents reviewed in this article.
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