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Design and Selection Parameters to Accelerate the Discovery of Novel Central Nervous System Positron Emission Tomography (PET) Ligands and Their Application in the Development of a Novel Phosphodiesterase 2A PET Ligand Lei Zhang,*,† Anabella Villalobos,† Elizabeth M. Beck,† Thomas Bocan,§ Thomas A. Chappie,† Laigao Chen,§ Sarah Grimwood,∥ Steven D. Heck,⊥ Christopher J. Helal,‡ Xinjun Hou,† John M. Humphrey,‡ Jiemin Lu,‡ Marc B. Skaddan,§ Timothy J. McCarthy,§ Patrick R. Verhoest,† Travis T. Wager,† and Kenneth Zasadny§ †

Neuroscience Medicinal Chemistry, Pfizer Inc., Cambridge, Massachusetts 02139, United States Neuroscience Medicinal Chemistry, Pfizer Inc., Groton, Connecticut 06340, United States § BioImaging Center, Precision Medicine, Pfizer Inc., Groton, Connecticut 06340, United States ∥ Neuroscience Research Unit, Pfizer Inc., Cambridge, Massachusetts 02139, United States ⊥ Data Analytical Group, Groton Center of Chemistry, Groton, Connecticut 06340, United States ‡

S Supporting Information *

ABSTRACT: To accelerate the discovery of novel small molecule central nervous system (CNS) positron emission tomography (PET) ligands, we aimed to define a property space that would facilitate ligand design and prioritization, thereby providing a higher probability of success for novel PET ligand development. Toward this end, we built a database consisting of 62 PET ligands that have successfully reached the clinic and 15 radioligands that failed in late-stage development as negative controls. A systematic analysis of these ligands identified a set of preferred parameters for physicochemical properties, brain permeability, and nonspecific binding (NSB). These preferred parameters have subsequently been applied to several programs and have led to the successful development of novel PET ligands with reduced resources and timelines. This strategy is illustrated here by the discovery of the novel phosphodiesterase 2A (PDE2A) PET ligand 4-(3-[18F]fluoroazetidin-1-yl)-7methyl-5-{1-methyl-5-[4-(trifluoromethyl)phenyl]-1H-pyrazol-4-yl}imidazo[5,1-f ][1,2,4]triazine, [18F]PF-05270430 (5).

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INTRODUCTION

PET imaging can be used as a diagnostic to assess disease stage and progression in longitudinal studies.3 To enable accurate clinical RO measurement, the availability of a PET ligand with favorable attributes is essential. With the rapid expansion of PET imaging applications in preclinical research and clinical drug development, effective CNS PET ligands have been developed for a wide range of targets including G-protein coupled receptors (GPCRs), ion channels, transporters, enzymes, and amyloid plaques (Figure 1). However, PET ligand development continues to be a fairly lengthy and costly process, partially due to the array of attributes required for an effective PET ligand and the empirical nature of the PET ligand discovery process. From a chemical structure point of view, PET ligand candidates should have structure moieties amenable to efficient incorporation of short-lived positron emission radionuclides

Positron emission tomography (PET) imaging is a noninvasive method that provides high resolution and quantitative information on specific target areas.1 For central nervous system (CNS) targets, PET is most frequently used to quantify the concentration of drug binding to a specific pharmacological target, providing receptor occupancy (RO) measurements. PET RO data can be effectively used in several ways to inform critical drug discovery and development decisions. For example, in conjunction with a biomarker for pharmacological effect, PET RO data can be used to determine whether a given mechanism has been adequately tested in the clinic (Proof of Mechanism).2 PET RO data can also be used to establish welldefined translatable clinical No Go criteria if the target RO cannot be achieved at a maximum tolerated dose. Furthermore, PET RO data are extremely useful in optimizing compound dose selection for accurate evaluation of efficacy and side effect profiles. Finally, in addition to providing RO measurements, © 2013 American Chemical Society

Received: February 28, 2013 Published: May 7, 2013 4568

dx.doi.org/10.1021/jm400312y | J. Med. Chem. 2013, 56, 4568−4579

Journal of Medicinal Chemistry

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Optimization of brain permeability may be achieved at the design stage by focusing on favorable physicochemical and absorption, distribution, metabolism, and excretion (ADME) properties.8 With regard to minimizing NSB, a common notion within the PET field is that lower lipophilicity generally leads to reduced NSB, with log P or log D being the most tracked physicochemical parameters. However, the often narrowly defined optimal ranges (log P 1.5−2.59 or log D 1−310) restrict the scope of potential candidates for PET ligand development. As an alternative, development of tools to predict NSB has become an area of increased focus in recent years, with efforts ranging from experimental (vesicle electrokinetic chromatography11 or in vitro no-wash autoradiography12) to computational modeling approaches13 being reported. While these analytical and computational methods may indeed be helpful in identifying compounds with low NSB, the process remains resource-intensive and empirical in nature. In our strategy to prospectively design and select successful novel PET ligands in an accelerated timeline, we aimed to identify design parameters predictive of favorable brain permeability and reduced NSB risk by utilizing in silico models in combination with parameters that can be experimentally determined in a high-throughput fashion. Such design parameters, in conjunction with favorable potency and selectivity, should allow alignment of desirable PET attributes in one molecule, thereby accelerating the selection of novel CNS PET ligands biased toward a higher probability of success. In particular, this strategy would apply to the design and selection of carbon-11 and fluorine-18 small molecule PET ligands.

Figure 1. CNS PET Ligand Database: target type distribution of validated CNS PET ligands (“Yes” category, N = 62) and negative controls (“No” category, N = 15).

such as carbon-11 (T1/2 = 20.4 min) and/or fluorine-18 (T1/2 = 109.7 min). In terms of pharmacology, a viable PET ligand must be potent and selective toward its intended pharmacological target. The maximum concentration of target binding sites (Bmax) is a key parameter for consideration, as it dictates the level of affinity (Kd) required for a successful radioligand. Most clinically successful PET ligands have an in vitro binding potential (Bmax/Kd) ≥ 10.4 Considering the low expression levels of most brain targets, PET ligands typically demand more stringent potency criteria than do drug candidates, with subnanomolar affinity often required.5 The requisite selectivity over other targets is dependent not only on relative affinities but also on the brain distribution and expression levels of competing targets. Aiming for a high level of selectivity (>30- to 100-fold) is recommended, particularly against targets that are highly expressed in brain [e.g., the dopamine transporter (DAT)6] and colocated with the target of interest. In addition to the aforementioned potency and selectivity criteria, a suitable CNS PET ligand should be brain-penetrant and exhibit low nonspecific binding (NSB) to avoid radioactivity signals in brain white matter and to achieve the requisite signal-to-noise ratio. Furthermore, a suitable PET ligand should not form brain permeable radioactive metabolites in vivo, which would be detrimental to the quantification of PET data due to the inability to distinguish signals from different radioactive chemical entities. More specifically, for fluorine-18 labeled PET ligands, extensive defluorination should be avoided as this would lead to high uptake of radioactivity in the skull and resulting spillover radio signal contamination in the underlying brain regions such as the neocortex. Poor brain permeability and high NSB are among the most frequent causes for failure in CNS PET ligand development.7

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CNS PET LIGAND DATABASE Our approach to optimize the design and selection of novel PET ligands, particularly around brain permeability and NSB properties, was underpinned by a systematic analysis of the physicochemical properties and ADME profiles of both successful and unsuccessful CNS PET ligands. Toward this end, a database of CNS PET ligands was built, which comprised 62 successful ligands validated for human clinical use and 15 PET ligands that failed in late stage development, primarily due to high NSB, as negative controls.14 As illustrated in Figure 1, the validated CNS PET ligands used in the analysis (“Yes” category) encompassed various target types, with G-protein coupled receptors (GPCRs) (63%), ion channels (6%), and transporters (21%) among the most populated target types.

Table 1. Physicochemical Properties, Transformed Function Utilized, Weighting, and Parameter Ranges for CNS MPO and CNS PET MPO CNS MPOa

CNS PET MPOb

properties

transformation (T0)

weight

more desirable range (T0 = 1.0)

less desirable range (T0 = 0.0)

more desirable range (T0 = 1.0)

less desirable range (T0 = 0.0)

ClogP ClogD MW TPSA HBD pKa

monotonic decreasing monotonic decreasing monotonic decreasing hump function monotonic decreasing monotonic decreasing

1.0 1.0 1.0 1.0 1.0 1.0

ClogP ≤ 3 ClogD ≤ 2 MW ≤ 360 40 < TPSA ≤ 90 HBD ≤ 0.5 pKa ≤ 8

ClogP > 5 ClogD > 4 MW > 500 TPSA ≤ 20; TPSA > 120 HBD > 3.5 pKa > 10

ClogP ≤ 2.8 ClogD ≤ 1.7 MW ≤ 305.3 44.8 < TPSA ≤ 63.3 HBD ≤ 1 pKa ≤ 7.2

ClogP > 4.0 ClogD > 2.8 MW > 350.5 TPSA ≤ 32.3; TPSA > 86.2 HBD > 2 pKa > 9.5

a

See ref 8 for the definitions of CNS MPO inflection values. bCNS PET MPO inflection values are defined by a statistical analysis of the physicochemical properties of 119 marketed CNS drugs.17 Median values and the 75th percentile values are used to define the more desirable and less desirable ranges, respectively, for ClogP, ClogD, MW, HBD, and pKa. The more desirable range of TPSA is defined by the median value (44.8) and the 75th percentile value (63.3), while the less desirable range is defined by 25th percentile (32.3) and 90th percentile (86.2) values. 4569



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MDR1 BA/AB ratio),20 and unbound fraction in brain (Fu_b) and plasma (Fu_p),21 as surrogate measures of brain permeability and NSB. On the basis of the wealth of experimental data from these high-throughput screening assays, which provide large training sets of data points for structurally diverse compounds, corresponding high-performing in silico models have been developed. For example, a calculated RRCK (cRRCK) in silico model was developed based on the RRCK passive permeability data for a diverse set of >100000 compounds.22 The prediction performance of the cRRCK model is continuously monitored prospectively, typically providing results within 2-fold of experimental values for 80% of new compounds meeting the prediction confidence cutoff. Such in silico models are particularly useful in that they offer opportunities for prospective design of drug-like molecules, with reasonable confidence in their pharmacokinetic (PK) properties, prior to synthesis. ADME values determined via high-throughput assays and/or in silico model-derived ADME values were used in the analysis of the PET ligand database.23

These are also the three target types (73%, 20%, and 7%, respectively) represented by the 15 failed PET ligands (“No” category) selected for our analysis. The physicochemical properties and ADME end points for this set of 77 ligands were either calculated using in silico models or experimentally derived. The results were analyzed utilizing a Spotfire visualization tool to identify key differences between these two categories of ligands as a way to define the desired property space for CNS PET ligands and facilitate ligand design, prioritization, and selection.

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PHYSICOCHEMICAL PROPERTIES For analysis of the physicochemical properties of the aforementioned 77 CNS PET ligands, we explored the use of a previously described central nervous system multiparameter optimization (CNS MPO) design tool8 based on a set of six physicochemical properties commonly used by medicinal chemists in compound design: ClogP (calculated partition coefficient), ClogD (calculated distribution coefficient at pH = 7.4), MW (molecular weight), TPSA (topological polar surface area),15 HBD (number of hydrogen bond donors), and pKa (ionization constant of the most basic center). The CNS MPO design tool, which utilizes the concept of transformed functions, employs monotonic decreasing functions for ClogP, ClogD, MW, HBD, and pKa and a hump function for TPSA. Desirable ranges were defined based on the authors’ and Pfizer scientists’ medicinal chemistry experience for each physicochemical property in the corresponding transformed functions, which were given desirability scores ranging from 0.0 to 1.0 (see Table 1). The summation of the individual transformed functions representing each of the six physicochemical properties yielded the final CNS MPO desirability score for each compound, which could range from 0.0 to 6.0. The majority of marketed CNS drugs (74% of 119 drugs analyzed) have a CNS MPO > 4. A higher CNS MPO score was also shown to lead to a higher probability of successfully aligning CNS drug-like properties such as permeability, clearance, and safety in a single molecule.8 For the purpose of the CNS PET ligand analysis, we used the published CNS MPO desirability scores and a modified version of this tool, CNS PET MPO.16 The inflection points and desirable ranges for the CNS PET MPO tool were derived exclusively from the physicochemical property data associated with the marketed CNS drugs described in the CNS MPO paper.17 These inflection values were taken primarily from the actual median and the 75th percentile values exhibited by marketed CNS drugs and differ from the values used in the CNS MPO tool (see Table 1). The option of modifying the inflection points in the CNS MPO tool enhances the flexibility of this method and consequently its utility in assessing property space for more narrowly defined medicinal chemistry data sets such as the ones associated with CNS PET ligands.

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RESULTS AND DISCUSSION Results of the physicochemical property analysis for the set of 77 PET ligands utilizing the CNS MPO and CNS PET MPO desirability tools are shown in Figure 2. This analysis confirmed

Figure 2. CNS MPO and CNS PET MPO comparison of validated CNS PET ligands (“Yes” category) and negative controls (“No” category).

that the majority of ligands in the “Yes” category indeed had CNS MPO scores >4 (85%). However, a CNS MPO score >4 offered limited distinction from the ligands in the “No” category, as this score also captured more than half of the negative controls (60%). It is worth mentioning that PET ligands with CNS MPO scores >5 only appeared in the “Yes” category (45% of 62 PET ligands), with none in the “No” category. On the other hand, the more stringent CNS PET MPO tool offered an improved distinction between the two categories of PET ligands over the original CNS MPO. As shown in Figure 2, CNS PET MPO scores >3 captured the majority of the “Yes” category (79%) while CNS PET MPO ≤ 3 captured the majority of the “No” category (67%), suggesting that this parameter may be useful when designing novel PET compounds. To understand the origin of the distinction offered by the CNS PET MPO tool, we first examined the distribution pattern

ADME PROPERTIES

With regard to the analysis of ADME space associated with the set of 77 CNS PET ligands, we became interested in ADME end points that help assess brain permeability and NSB. A number of high-throughput in vitro ADME assays have been developed that are routinely used in screening compounds to guide drug discovery efforts in an efficient manner.18 In particular, we focused on high-throughput assays measuring passive permeability [e.g., RRCK Papp AB],19 P-gp liability (e.g., 4570



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of the three physicochemical property parameters within the tool that are related to lipophilicity: ClogD, ClogP, and TPSA (Figure 3). ClogD and ClogP offered similar distinctions

Figure 4. Analysis of RRCK Papp AB and MDR1 BA/AB properties of validated CNS PET ligands (“Yes” category) and negative controls (“No” category).

for brain permeability of PET ligands. Compounds in the “No” category exhibited similar values (Figure 4): 73% of the ligands in the “No” category had RRCK Papp AB values of >5 × 10−6 cm/s and 80% had MDR1 BA/AB ratios ≤2.5. These results were not surprising, as the set of ligands in the negative control group failed primarily due to high NSB rather than poor brain permeability. However, when we compared properties across ligands in the “Yes” and “No” categories, the fraction unbound in brain (Fu_b) emerged as the most pronounced differentiator. As illustrated in Figure 5, an overwhelming majority (87%) of the

Figure 3. Distribution of calculated physicochemical properties, ClogD, ClogP, and TPSA, for validated PET ligands (“Yes” category) and negative controls (“No” category).

between PET ligands in the two different categories. Most of the clinically validated ligands displayed ClogD values ≤3 (47 out of 62, 76%) and ClogP values ≤4 (50 out of 62, 80%), while most of the failed ligands displayed ClogD values >3 (10 out of 15, 67%) and ClogP values >4 (10 out of 15, 67%). It is also worth noting that the literature values for preferred Log D (1−3) or ClogP ranges (1.5−2.5), partially driven by brain permeability considerations, appear to be overly stringent for PET ligand selection. In contrast, TPSA offered minimal distinction between successful and failed PET ligands, with the majority of successful (46 out of 62, 74%) and failed (13 out of 15, 87%) ligands residing in a range between 20 and 60 Å2. Like TPSA, the remaining three physicochemical parameters (MW, HBD, and pKa) exhibited no apparent trends upon similar analysis (data not shown). On the basis of the ClogD and ClogP ranges associated with the set of successful and failed PET ligands, and the inflection points and corresponding scores associated with the CNS MPO tools (Table 1), the CNS PET MPO tool is able to better capture differences in the overall desirability scores for the two groups of ligands. We next examined the in vitro ADME parameters RRCK and MDR1 BA/AB, which are related to brain permeability (Figure 4). With regard to passive permeability, a total of 92% of the ligands in the “Yes” category had RRCK Papp AB values >5 × 10 −6 cm/s, consistent with moderate to high passive permeability [RRCK Papp AB > 10, high permeability, 68%; 5 < RRCK Papp AB ≤ 10, moderate permeability, 24%]. In terms of P-gp liability, 87% of the ligands in the “Yes” category had MDR1 BA/AB ratios ≤2.5, consistent with low P-gp liability or efflux. These observations confirmed that a favorable combination of passive permeability and P-gp liability determined by RRCK Papp AB values and MDR1 BA/AB ratios, respectively, could indeed serve as a reasonable predictor

Figure 5. Analysis of fractions unbound in brain (Fu_b) and fractions unbound in plasma of validated PET ligands (“Yes” category) and negative controls (“No” category).

ligands in the “No” category had Fu_b ≤ 0.05, while only 33% of the ligands in the “Yes” category were in this range. This result suggests that unbound brain fraction could be used as a potential predictor of NSB, with values >0.05 preferred. Because unbound brain fraction could be a reflection of a compound’s tendency to bind brain proteins and tissues, it is 4571



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Figure 6. CNS PET MPO scores versus alignment of in vitro ADME properties for CNS PET ligands and a broader compound set. (A) PET ligand set. (B) Broad Pfizer compound set.

Figure 7. Preferred design and selection parameters for novel CNS PET ligands.

not entirely surprising that this parameter appears to predict risk of NSB.24 To a lesser extent, a similar trend was observed in the analysis of unbound plasma fractions (Fu_p). The majority of the ligands (60%) in the “Yes” category had Fu_p > 0.15, while only a small fraction (20%) of the ligands in the “No” category resided in this space. Taken together, these observations suggest that compounds with Fu_b > 0.05 and Fu_p > 0.05, preferably Fu_p > 0.15, should be targeted whenever possible, as such compounds would be at lower risk of NSB (60% of the ligands in the “Yes” category fulfill these criteria in contrast to 20% in the “No” category). Compounds with Fu_b ≤ 0.05, especially if Fu_p is also ≤0.05 (only 16% of the “Yes” category), should be avoided as they would be at risk of high levels of NSB. Prompted by the larger CNS PET MPO scores associated with the successful CNS PET ligand set and our understanding of the preferred in vitro ADME properties (Fu_b, RRCK and MDR), we investigated whether higher CNS PET MPO scores

(>3) would lead to an increased probability of aligning all three preferred ADME attributes in one molecule. Each PET ligand was assessed for the following desirable criteria: Fu_b > 0.05 for lower risk of NSB, RRCK Papp AB > 5 × 10−6 cm/s for moderate to high permeability, and MDR1 BA/AB < 2.5 for low P-gp liability. An alignment count of 3 was assigned to the ligand if all three criteria were met, 2 was assigned if only two out of three criteria were met, and so on. As illustrated in Figure 6A, within the set of 77 PET ligands, CNS PET MPO scores >3 clearly increased the probability of aligning the three key ADME attributes in one molecule. For the ligands with CNS PET MPO scores >3, 62% showed alignment of all three desired ADME attributes, compared to only 19% of the ligands with CNS PET MPO scores ≤3. Expansion of the analysis to a broader data set consisting of all Pfizer compounds with experimental data available for the three ADME parameters (Fu_b, RRCK, and MDR) revealed a similar trend (Figure 6B). For compounds with CNS PET MPO scores >3, 49% showed 4572



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Figure 8. Alignment of the proposed set of criteria with 10 high-performing CNS PET ligands.

Utilization of the Proposed PET Design Parameters in the Development of a Novel PDE2A PET Ligand. To further evaluate whether the aforementioned PET design parameters (Figure 7) would be applicable in a prospective manner for novel PET ligand development, we applied them to the design and prioritization process for a selective phosphodiesterase 2A (PDE2A) PET ligand. PDE2A is a dual-substrate PDE that hydrolyzes two key secondary messenger molecules, cyclic guanosine monophosphate (cGMP) and cyclic adenosine monophosphate (cAMP). The highest levels of PDE2A expression are found within the limbic and basal ganglia brain circuitry;26 inhibitors of PDE2A increase cyclic nucleotide levels in these key brain regions and could thus potentially improve cognitive processes.27 We therefore sought to develop a highly selective PDE2A PET ligand to serve as a translational tool that would enable both preclinical pharmacology studies as well as clinical evaluation, including target occupancy measurements, of novel PDE2A inhibitors. Toward this end, we applied the CNS PET design and selection parameters to the prioritization of a Pfizer collection of PDE2A inhibitors.28 As illustrated in Figure 9, a collection of over a thousand compounds was quickly narrowed down to ∼150 using filters of MDR1 BA/AB ≤ 2.5, RRCK Papp AB > 5, followed by cFu_b > 0.05 and cFu_p > 0.05, and CNS PET MPO > 3. A potency cutoff of 10 nM further reduced the field to ∼20 compounds, eight of which contained structural moieties that were amenable to either [11C] or [18F] radiolabeling. Potency ranking of these eight compounds yielded compound 1 as a promising lead that possessed a number of desirable characteristics for a PET tracer: reasonable potency (IC50 = 2.30 nM), exquisite PDE selectivity (>500-fold over all other PDEs), excellent physicochemical properties (CNS PET MPO = 4.94), good brain permeability (RRCK Papp AB = 21.5 × 10−6 cm/s, MDR1 BA/AB ratio = 1.46), and low risk of NSB based on experimentally obtained unbound fractions (rat brain fraction unbound, rFu_b = 9.5%; rat

an alignment score of 3 compared to only 19% for compounds with CNS PET MPO scores ≤3. This analysis indicates that CNS PET MPO scores >3 indeed would facilitate alignment of in vitro ADME attributes that are key to the success of a given CNS PET ligand and should therefore be monitored appropriately in PET ligand design. In summary, we have identified a set of preferred physicochemical and ADME profiles to inform the design and selection of novel PET ligand candidates (Figure 7). In addition to the existing knowledge of in vitro binding potential (Bmax/Kd > 10) and selectivity (>30- to 100-fold) criteria, one would ideally target CNS PET MPO scores >3 to optimize physicochemical properties, RRCK Papp AB > 5 × 10−6cm/s to ensure good passive permeability, MDR1 BA/AB < 2.5 to minimize P-gp liability, and Fu_b and Fu_p > 0.05, preferably Fu_p > 0.15, to minimize risk of NSB. In addition, CNS PET MPO scores >3 were found to increase the probability of aligning all three key ADME parameters, thereby increasing the overall probability of success for CNS PET ligand development. As an additional test, we further examined this set of desired design parameters against the profiles of 10 high-performing CNS PET ligands,25 which were a subset of the broader database of 77 PET ligands. Selection of these 10 ligands was based on their frequency of use, acceptance in the field, robust test-retest reliability results, and good properties for quantification. Figure 8 highlights the alignment of the proposed set of criteria with the properties of these high-performing CNS PET ligands: all 10 met criteria for RRCK values, and 9 out of 10 compounds met the criteria of MDR1 BA/AB ≤ 2.5, Fu_b > 0.05, and CNS PET MPO > 3. Importantly, none of these 10 ligands fell within the defined high risk space for NSB (Fu_b ≤ 0.05 and Fu_p ≤ 0.05). These results suggest that the proposed design parameters have the potential to guide and accelerate novel CNS PET ligand development efforts toward higher performing ligands rather than marginal ones. 4573



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Table 2. PDE2A Activities and Calculated ADME Properties of PET-Specific Analogues 2−7 compd

human PDE2A IC50 (nM)a

CNS PET MPO

cRRCK (× 10−6 cm/s)

cMDR1 BA/AB

cFu_b

cFu_p

2 3 4 5 6 7

0.82 0.58 1.08 0.53 2.01 1.72

4.55 4.55 4.55 4.81 4.55 4.83

25.5 23.5 23.9 21.5 21.7 28.4

1.36 1.87 1.48 1.32 1.57 1.12

0.21 0.21 0.18 0.10 0.12 0.07

0.35 0.29 0.38 0.23 0.30 0.25

a IC50 values obtained from a cGMP-stimulated human recombinant full-length PDE2A enzymatic assay; Values represent the mean of at least three experiments.

which were achieved from a common intermediate 829 via a straightforward two-step sequence: Suzuki coupling was followed by conversion to triazolotriazine intermediates, which were treated in situ with azetidines 10. The human PDE2A potencies of analogues 2−7 are shown in Table 2. In general, all analogues showed similar or improved potency compared to 1. The most potent analogues, 3 and 4(3-fluoroazetidin-1-yl)-7-methyl-5-{1-methyl-5-[4(trifluoromethyl)phenyl]-1H-pyrazol-4-yl}imidazo[5,1-f ][1,2,4]triazine (PF-05270430, 5) were selected for further in vitro ADME and in vivo PK evaluation. As predicted by in silico models, both compounds demonstrated high RRCK Papp AB(>10 × 10−6 cm/s) and low MDR1 BA/AB ratio (≤2.5), as well as Fu_b and Fu_p consistent with low predicted risk for NSB (Table 3). Compound 5 was selected for further study based on its higher in vivo brain permeability (B/P_free of 0.5 for 5 vs B/P_free of 0.2 for 3). Evaluation of 5 in a PDE selectivity panel30 and a broad CEREP panel31 confirmed its exquisite selectivity against other targets (>1800-fold). In light of its favorable profile, compound 5 was advanced to radiochemistry development. As illustrated in Scheme 2,

Figure 9. Data mining process for the identification of PDE2A PET leads.

plasma fraction unbound, rFu_p = 32%; human plasma fraction unbound, hFu_p = 23%). Using compound 1 as a lead, we carried out a focused PETspecific structure−activity relationship (SAR) effort targeting a small set of analogues, with the main goal of increasing potency to maximize in vivo binding potential while maintaining the desirable PET design attributes around physicochemical, ADME, and NSB properties. This effort (Scheme 1) consisted of: (a) designing compounds within the proposed preferred PET property space, guided by in silico models (CNS PET MPO, cRRCK, cMDR, cFu_b, and cFu_p; see Table 2), (b) incorporating structural moieties that have been shown to be beneficial to PDE2A inhibition activity in a related system,28 and (c) introducing functional groups such as methoxy (2−4, 6) and fluoroazetidine (5−7) as structural handles for [11C] and [18F] radiolabeling. A total of six analogues (2−7) with suitable overall properties were targeted, and the syntheses of

Scheme 1. Design and Synthesis of Six PET-Specific PDE2A Analoguesa

a

Reagents and conditions: (a) 2 mol % Pd(PPh3)4, K3PO4, EtOH/water, reflux; (b) 1,2,4-triazole, POCl3, Et3N, CH3CN, then Et3N, 10, CH2Cl2. 4574



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Table 3. In Vitro and in Vivo PK Properties of Compounds 3 and 5 PK properties

3

5

RRCK Papp AB (10−6 cm/s) MDR1 BA/AB rFu_p (rat) (%)a rFu_b (rat) (%)b hFu_p (%)c B/P_total (rat)d B/P_free (rat)

42 1.23 29 17 23 0.34 0.2

21 1.71 24 7.7 17 1.56 0.5

a

Fraction unbound in rat plasma. bFraction unbound in rat brain. Fraction unbound in human plasma. dRatio of total drug levels in rat brain and plasma; determined from pharmacokinetics in Wistar-Han rats 0−2 h following a single 1 mg/kg intravenous (IV) dosing. eRatio of free drug levels in rat brain and plasma; calculated based on B/ P_total and fractions unbound in rat brain and plasma.

c

Scheme 2. Synthesis of [18F]5a

Figure 10. (a) Representative baseline time−activity curves (TACs) from [18F]5 in cynomolgus monkeys. (b) Representative baseline standard uptake value (SUV) images in transverse and coronal views, summed between 0 and 60 min of PET imaging.

primate using a mutual information algorithm (Figure 10b). As shown in Figure 10, both the TACs for brain regions of interest and the baseline SUV images, summed between 0 and 60 min, demonstrated rapid and high uptake of [18F]5 in striatum (putamen and caudate) and low uptake in cerebellum, consistent with the distribution pattern of PDE2A enzyme.32 No skull uptake due to defluorination was observed. Employing cerebellum as a reference region for NSB, [18F]5 was calculated to have an in vivo binding potential of 1.51 ± 0.18 (n = 2) in striatum using a simplified reference tissue model. Importantly, the signal in striatum was blocked by a nonradioactive structurally related PDE2A inhibitor in a dose-responsive manner (data not shown). The blocking study in primates to measure PDE2A target occupancy, and additional data including rat dosimetry and radioactive metabolite analysis, will be reported in a future publication. Overall, these data indicate that [18F]5 is a promising PET ligand that could be used for imaging PDE2A in vivo.

a Reagents and conditions: (a) 2 mol % Pd(PPh3)4, 4-trifluoromethylphenyl boronic acid, K3PO4, EtOH/water, reflux, 40%; (b) 1,2,4triazole, POCl3, Et3N, CH3CN, then Et3N, 4-hydroxyazetidine hydrochloride salt, CH2Cl2, 87%; (c) TsCl, Et3N, CH2Cl2, 57%; (d) TBA[18F], t-amyl alcohol, 110 °C, 30 min, 99.5% radiochemical purity, 540 ± 152 GBq/μmol (14592 ± 4095 Ci/mmol) specific activity.

hydroxyazetidine 11 was synthesized following a similar twostep sequence to that described above from triazinone intermediate 8. Subsequent tosylation yielded the tosylate precursor 12. Nucleophilic displacement of the tosylate using [18F]tetrabutylammonium fluoride in t-amyl alcohol produced [18F]5 reproducibly (7.3 ± 1.9% radiochemical yield, decaycorrected to starting [18F]fluoride in the reactor, n = 8) in high specific activity (>500 GBq/μmol at end of synthesis) and radiochemical purity (>99.5%). PET ligand [18F]5 was subsequently evaluated in cynomolgus monkeys for baseline and displacement studies. Primates were maintained under anesthesia with isoflurane and injected intravenously with an average of 215 MBq (5.8 mCi) of [18F]5, and dynamic brain scans were performed with a Siemens Focus220 PET scanner for 90 min. Time−activity curves (TACs) were generated for brain regions of interest defined in the reference PET images such as frontal cortex, putamen, caudate, and cerebellum (Figure 10a). Reconstructed images were scaled to standardized uptake values (SUV) and coregistered to an individual reference PET image for each

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CONCLUSIONS The identification of novel PET ligands in an accelerated fashion with reduced resources is an important area of focus for CNS medicinal chemistry. Neuroimaging with PET ligands is a noninvasive clinical methodology that is frequently used to quantify the concentration of drugs reaching the pharmacological target, as it provides a direct measure of RO. In addition, PET ligands can be used to assess disease stage and progression. As part of our efforts to accelerate the identification of novel PET ligands, we undertook an analysis of validated PET ligands and negative controls to define a series of design parameters which could lead to the selection of novel ligands with higher probability of success. Physicochemical properties as summarized by a CNS PET MPO tool and ADME end points such as permeability and P-gp efflux, together with unbound fraction in brain and plasma as indirect measures of nonspecific binding, were identified as key 4575



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or Biotage disposable columns on a CombiFlash Companion or Biotage Horizon automatic purification system. Microwave reactions were carried out in a microwave reactor manufactured by Smith Creator of Personal Chemistry. Purification by mass-triggered HPLC was carried out using Waters XTerra PrepMS C18 columns, 5 μm, 30 mm × 100 mm steel. Compounds were presalted as TFA salts and diluted with dimethyl sulfoxide (1 mL). Samples were purified by mass-triggered collection using mobile phases of 0.1% trifluoroacetic acid in water and acetonitrile with a gradient of 100% aqueous to 100% acetonitrile over 10 min at a 20 mL per min flow rate. Elemental analyses were performed by QTI, Whitehouse, NJ. All target compounds were analyzed using ultra high-performance liquid chromatography/ultraviolet/evaporative light scattering detection coupled to time-of-flight mass spectrometry (UHPLC/UV/ELSD/ TOFMS). Unless otherwise noted, all tested compounds were found to be ≥95% pure by this method. UHPLC/MS Analysis. UHPLC was performed on a Waters ACQUITY UPLC system (Waters, Milford, MA), which was equipped with a binary solvent delivery manager, column manager, and sample manager coupled to ELSD and UV detectors (Waters, Milford, MA). Detection was performed on a Waters LCT premier XE mass spectrometry (Waters, Milford, MA). The instrument was fitted with an Acquity BEH (Bridged Ethane Hybrid) C18 column (30 mm × 2.1 mm, 1.7 μm particle size, Waters, Milford, MA) operated at 60 °C. Mobile phase A, 0.05% trifluoroacetic acid in water (v/v); Mobile phase B, 0.05% trifluoroacetic acid in acetonitrile (v/v). Gradient: 5.0% to 95% B, linear over 4.0 min. Flow rate: 2 mL/min. General Synthetic Procedure for PDE2 Analogues 2−7. A 1.0 M solution of potassium phosphate (2.5 equiv) in water was added to a suspension of 8 (1.0 equiv) and the aryl boronic acid (1.5 equiv) in ethanol (0.13 M). Nitrogen was bubbled through the mixture for 10 min, and the reaction was warmed to 70 °C over 10 min with consistent nitrogen flow through the solution. Tetrakis(triphenylphosphine)palladium(0) (1 mol %) was added in one portion, and the reaction was heated at 100 °C for 4 h, then was cooled to room temperature and filtered through Celite, eluting with dichloromethane. The filtrate was concentrated and diluted with water. The aqueous layer was extracted with ethyl acetate, and the combined organic layers were dried over sodium sulfate and concentrated. The crude product was purified by flash column silica chromatography, eluting with 2−8% dichloromethane in methanol, to yield the corresponding triazinone 9. Phosphorus oxychloride (3.0 equiv) was added to a suspension of 1,2,4-triazole (5.0 equiv) in acetonitrile (0.05 M) at 0 °C. Triethylamine (6.0 equiv) was added dropwise to the reaction mixture at 0 °C, and the mixture was allowed to stir for 5 min at room temperature. The triazinone 9 (1.0 equiv) was added to the mixture as a solid, and the reaction was heated at 80 °C for 12 h, then was cooled to room temperature and concentrated. After addition of water, the aqueous layer was extracted with ethyl acetate. The combined organic layers were washed with brine, dried over sodium sulfate, and concentrated. The crude solid was diluted with dichloromethane (0.06 M), triethylamine (3.0 equiv), and the azetidine hydrochloride 10 (1.0 equiv) were added, and the mixture was stirred at room temperature for 12 h. Water was added, and the aqueous layer was extracted with ethyl acetate. The combined organic layers were washed with brine, dried over sodium sulfate, and concentrated. The crude product was purified by flash column silica chromatography, eluting with 5−10% methanol in dichloromethane, and then recrystallized from ethanol to yield the desired PDE2 analogues 2−7 and intermediate 11. 4-(Azetidin-1-yl)-5-[5-(2-fluoro-4-methoxyphenyl)-1-methyl-1Hpyrazol-4-yl]-7-methylimidazo[5,1-f ][1,2,4]triazine (2). 1H NMR (400 MHz, CD3OD) δ 2.22−2.31 (m, 2H), 2.56 (s, 3H), 3.6−3.9 (br m, 2H), 3.78 (s, 3H), 3.82 (br s, 3H), 4.0−4.3 (v br m, 2H), 6.71 (dd, J = 8.6, 2.4 Hz, 1H), 6.82 (dd, J = 12.4, 2.4 Hz, 1H), 7.69 (s, 1H), 7.71 (s, 1H). LCMS m/z = 394, LCMS-ELSD purity = 100%. HPLC retention time = 0.47 min. HPLC method: UPLC, 0.05% TFA 95−5 to 5−95 water−ACN. 4-(Azetidin-1-yl)-5-[5-(4-methoxy-2-methylphenyl)-1-methyl-1Hpyrazol-4-yl]-7-methylimidazo[5,1-f ][1,2,4]triazine (3). 1H NMR

parameters in this analysis of a set of 77 PET ligands. These properties, coupled with pharmacological parameters such as in vitro binding potential and selectivity, as shown in Figure 7, could be used to prioritize and accelerate the identification of PET ligands. The prospective use of this approach was illustrated by efforts leading to the identification of [18F]5 as the first highly selective PDE2A PET ligand.33 Incorporation of these design parameters, aided by in silico models, enabled SAR efforts to be carried out in a highly rational and focused manner, yielding a suitable PET ligand in a single design cycle with a small number of analogues. We envision that these design parameters and overall approach for the identification of novel PET ligands may be applicable across a broad range of brain targets. Future application of this strategy will result in additional refinement of this methodology and further optimize the target range of design parameters for successful PET ligands.

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EXPERIMENTAL SECTION

CNS MPO and CNS PET MPO. The CNS MPO tool has been previously described.8 A monotonic decreasing function was used for ClogP, ClogD, MW, HBD and pKa, and a hump function was used for TPSA. More desirable and less desirable ranges were defined for each physicochemical property in the corresponding transformed function, which was given a desirability score ranging from 0.0 to 1.0. The transformed function definitions associated with the CNS MPO tool are shown in Table 1. The summation of the individual transformed functions representing each of the six physicochemical properties yielded the final CNS MPO desirability score, which can range from 0 to 6. CNS PET MPO uses the same set of physicochemical properties and transformation functions but with different inflection values (Table 1). The revised inflection values were taken from the median values, the 25th percentile and the 75th percentile values exhibited by marketed CNS drugs.17 A CNS PET MPO calculator in Excel format is included as part of the Supporting Information. In Silico ADME models. RRCK passive permeability (cRRCK), MDR1 BA/AB efflux (cMDR1 BA/AB), fraction unbound in plasma (cFu_p), and fraction unbound in brain (cFu_b) in silico models were built internally at Pfizer using the Cubist algorithm from RuleQuest Research. Descriptors used include MOE and E-state descriptors, ClogP, and structural fragments. The size of the data set in these models is roughly 132000, 103000, 5200, and 2700 compounds, respectively. The performance of these in silico models was tested by an 80/20 split of the data set, where the model was trained on the 80% and validated on the 20%. Performance of the model was assessed by determining the % predicted within 2-fold of experimental values. Measurement of Recombinant Human PDE2A Inhibition by SPA Technology. The activities of the test substances on human fulllength PDE2A3 enzyme were determined using the [3H]cGMP scintillation proximity assay (SPA) modified from the Amersham TRKQ7100 instructions (GE Healthcare, USA). PDE2A3 protein was obtained from FLAG purification of sf21 insect cells using standard affinity purification procedures for this tag (anti-FLAG M2, SigmaAldrich). The SPA assay was performed using PDE SPA yttrium silicate beans (Perkin-Elmer RPNQ0024), which bind preferentially to the linear nucleotide GMP, compared to the cyclic nucleotide cGMP. The [3H]GMP product was detected using a Wallac MicroBeta scintillation counter. The reaction time was chosen with respect to the amount of time in which 10−20% of substrates were hydrolyzed by the enzyme. The corresponding IC50 values of the compounds for the inhibition of PDE2A activities were determined from the concentration−effect curves by means of nonlinear regression. Chemistry. General Methods. Solvents and reagents were of reagent grade and were used as supplied by the manufacturer. All reactions were run under a N2 atmosphere. Organic extracts were routinely dried over anhydrous Na2SO4. Concentration refers to rotary evaporation under reduced pressure. Chromatography refers to flash chromatography using disposable RediSep Rf 4 to 120 g silica columns 4576



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(400 MHz, CDCl3) δ 2.06 (s, 3H), 2.24−2.32 (m, 2H), 2.56 (s, 3H), 3.67 (s, 3H), 3.77 (s, 3H), 3.8−4.2 (br m, 4H), 6.71−6.73 (m, 2H), 7.17−7.19 (m, 1H), 7.65 (s, 1H), 7.75 (s, 1H). LCMS m/z = 390, LCMS-ELSD purity = 100%. HPLC retention time = 0.48 min. HPLC method: UPLC 0.05% TFA 95−5 to 5−95 water−ACN. 4-(Azetidin-1-yl)-5-[5-(2,3-difluoro-4-methoxyphenyl)-1-methyl1H-pyrazol-4-yl]-7-methylimidazo[5,1-f ][1,2,4]triazine (4). 1H NMR (400 MHz, CDCl3) δ 2.21−2.30 (m, 2H), 2.63 (s, 3H), 3.4−4.4 (br m, 4H), 3.84 (d, J = 1.6 Hz, 3H), 3.89 (s, 3H), 6.68 (ddd, J = 8.8, 7.6, 1.8 Hz, 1H), 7.02 (ddd, J = 8.8, 7.6, 2.3 Hz, 1H), 7.68 (s, 1H), 7.77 (s, 1H). LCMS m/z = 412.1 LCMS-ELSD purity = 100%. HPLC retention time = 0.49 min. HPLC method: UPLC 0.05% TFA 95−5 to 5−95 water−ACN. 4 - (3 - F lu o ro az e ti di n - 1 - y l )- 7 - m e t h y l - 5- { 1- m e thy l- 5 - [4 (trifluoromethyl)phenyl]-1H-pyrazol-4-yl}imidazo[5,1-f ][1,2,4]triazine (5). 1H NMR (400 MHz, CDCl3) δ 2.71 (s, 3H), 3.8−4.0 (br m, 2H), 3.93 (s, 3H), 4.0−4.3 (br m, 2H), 5.13−5.33 (m, JHF = 57.0 Hz, 1H), 7.62 (AB quartet, JAB = 8.1 Hz, ΔνAB = 35.7 Hz, 4H), 7.70 (s, 1H), 7.86 (s, 1H). GCMS m/z 431 (M). LCMS m/z = 432, LCMSELSD purity = 100%. HPLC retention time = 0.59 min. HPLC method: UPLC 0.05% TFA 95−5 to 5−95 water−ACN. Anal. Calcd for C20H17F4N7: C, 55.68%; H, 3.97%; N, 22.73%. Found: C, 55.48%; H, 3.92%; N, 22.52%. 4-(3-Fluoroazetidin-1-yl)-5-{5-[2-methoxy-4-(trifluoromethyl)phenyl]-1-methyl-1H-pyrazol-4-yl}-7-methylimidazo[5,1-f ][1,2,4]triazine (6). 1H NMR (400 MHz, CDCl3) δ 2.64 (s, 3H), 3.78 (s, 3H), 3.89 (br s, 3H), 3.9−4.5 (br m, 4H), 5.11−5.31 (m, JHF = 57.3 Hz, 1H), 7.16 (br s, 1H), 7.18 (br d, J = 8 Hz, 1H), 7.41 (br d, J = 8 Hz, 1H), 7.67 (s, 1H), 7.79 (s, 1H). LCMS m/z = 462.2, LCMS-ELSD purity = 100%. HPLC retention time = 0.59 min. HPLC method: UPLC 0.05% TFA 95−5 to 5−95 water−ACN. 5-[5-(4-Chloro-2-fluorophenyl)-1-methyl-1H-pyrazol-4-yl]-4-(3fluoroazetidin-1-yl)-7-methylimidazo[5,1-f ][1,2,4]triazine (7). 1H NMR (400 MHz, CDCl3) δ 2.68 (s, 3H), 3.83 (s, 3H), 3.85−4.3 (br m, 4H), 4.95−5.30 (m, JHF = 56 Hz, 1H), 7.14−7.19 (m, 2H), 7.43−7.47 (m, 1H), 7.69 (s, 1H), 7.84 (s, 1H). LCMS m/z = 415, LCMS-ELSD purity = 93%. HPLC retention time = 0.56 min. HPLC method: UPLC 0.05% TFA 95−5 to 5−95 water−ACN. 1-(7-Methyl-5-{1-methyl-5-[4-(trifluoromethyl)phenyl]-1H-pyrazol-4-yl}imidazo[5,1-f ][1,2,4]triazin-4-yl)azetidin-3-ol (11). 1H NMR (400 MHz, CDCl3) δ 2.59 (s, 3H), 3.2−3.3 (br m, 2H), 3.55−3.70 (br m, 2H), 3.88 (s, 3H), 4.50−4.56 (m, 1H), 7.49 (d, J = 8 Hz, 2H), 7.59 (br d, J = 8 Hz, 2H), 7.61 (s, 1H), 7.75 (s, 1H). LCMS m/z = 330, LCMS-ELSD purity = 100%. HPLC retention time = 2.42 min. 1-(7-Methyl-5-{1-methyl-5-[4-(trifluoromethyl)phenyl]-1H-pyrazol-4-yl}imidazo[5,1-f ][1,2,4]triazin-4-yl)azetidin-3-yl 4-methylbenzenesulfonate (12). Triethylamine (1.29 mL, 9.0 mmol, 3 equiv) and p-toluenesulfonyl chloride (744 mg, 3.9 mmol, 1.3 equiv) were added to a solution of 11 (1.29 g, 3.0 mmol, 1.0 equiv) in dichloromethane (20 mL). The reaction mixture was stirred at room temperature for 12 h. The mixture was diluted with ethyl acetate and water. The layers were separated, and the organic layer was washed with brine, dried over sodium sulfate, and concentrated. The crude product was purified by flash chromatography (silica gel, 0−4% methanol in dichloromethane). The resulting off-white powder was recrystallized from dichloromethane/n-heptane to yield the desired tosylate intermediate 12 in 57% yield. 1H NMR (400 MHz, CDCl3) characteristic peaks: δ 2.45 (s, 3H), 2.63 (s, 3H), 3.75−4.15 (br m, 4H), 3.93 (s, 3H), 4.90 (m, 1H), 7.33−7.38 (m, 2H), 7.57 (d, J = 8.2 Hz, 2H), 7.59 (s, 1H), 7.62 (d, J = 8.2 Hz, 2H), 7.71−7.75 (m, 2H). LCMS m/z = 584.3, LCMS-ELSD purity = 100%. Anal. Calcd for C27H24F3N7O3S: C, 55.57%; H, 4.15%; N, 16.80%. Found: C, 55.33%; H, 4.12%; N, 16.56%. Radiosynthesis of [18F]5. Oxygen-18 enriched (97%) water was purchased from Huayi Isotopes (cat. no. WT-98−5), Taiyo Nippon Sanso Corp. (cat. no. F03−0027), or Sigma-Aldrich (cat. no. 329878) . All solvents were purchased from Sigma-Aldrich. Radiochemistry was performed on a GE TracerLab FX F−N synthesis module. [18F]5 was purified by HPLC available on the GE TracerLab FX F−N module, consisting of a Sykam S-1021 pump, a Knauer K-2001 UV detector (λ = 254 nm) in series with a Berthold β+-flow detector, on a

Phenomenex Hydro-RP column (10 × 250 mm, 4 μm) equipped with a Phenomenex semiprep Security Guard holder (cat. no. AJ0−7220 with cartridge AJ0−7512) at 5 mL/min with 40% v/v acetonitrile/ water (modified with 0.1% formic acid) as the mobile phase. Quality control analysis of [18F]5 for radiochemical and chemical purity was performed on an Agilent 1100 using a Phenomenex Hydro-RP column (4.6 mm × 150 mm, 5 μm) and 40% acetonitrile/0.1% formic acid (1.0 mL/min) as the mobile phase. The identity of the labeled compound was confirmed by HPLC coinjection with authentic material. The specific activity was determined by injection of an aliquot of the final solution with known radioactivity on the analytical HPLC system described above. The area of the UV peak measured at 254 nm corresponding to the carrier product was measured and compared to a standard curve relating mass to UV absorbance. Radioactivity was measured with a Capintec CRC-15 PET dose calibrator. No-carrier-added [18F]fluoride was prepared by proton irradiation (70 min, 60 μamp beam) of a 2.4 mL tantalum target [18O(p,n)18F]. The semiprep HPLC column was equilibrated with the mobile phase 15−30 min at 3 mL/min during the beam. The radioactivity was unloaded from the target and delivered to the glass V-vial on the GE TracerLab FX F−N synthesis module. The [18F]fluoride was trapped on a Waters QMA Plus Light cartridge (cat. no. 023531; pretreated with 5 mL 0.2 M potassium phosphate, then dried under an N2 stream). The [18F]fluoride was eluted with a solution of 1 mL of 0.03 M tetrabutylammonium mesylate in methanol. The methanol was evaporated at 100 °C in vacuo under a stream of helium until dry (about 3.5 min). The reactor was then cooled to 40 °C while still under vacuum. The residue from the drying step was dissolved in a solution of 12 (5 mg) in 2-methyl-2-butanol (t-amyl alcohol, 0.6 mL) and heated at 110 °C for 30 min. The reaction mixture was cooled to 90 °C, and the 2-methyl-2-butanol was evaporated in vacuo under helium (∼1 min). The reaction mixture was cooled to 30 °C, and the residue was dissolved in a mixture of aqueous sodium hydroxide (1 M, 0.5 mL) and acetonitrile (0.5 mL) and stirred for 3 min. The mixture was diluted with aqueous formic acid (1 M, 0.5 mL) and 50% acetonitrile/0.1% formic acid (1.0 mL). This solution was passed through a Waters Alumina N Sep-Pak Light (part no. 23561) into an intermediate vial. The contents of the intermediate vial were then purified by semiprep chromatography (vide supra). The fraction containing [18F]5 was collected between 14.7 and 16.2 min and diluted with 60 mL water, followed by trapping on a Waters 30 mg Oasis HLB vac cartridge (WAT186001879). The cartridge was washed with water (3 mL), then eluted with ethanol (0.5 mL) and pH 7.4 phosphate-buffered saline (4.5 mL). The final formulation was filtered (Millipore Durapore PVDF 13 mm, cat. no. SLGSV013SL) and collected in a sterile 10 mL Hollister−Stier crimp-sealed vial, with a small aliquot (0.4 mL) set aside in a sterile 2 mL QC vial for analytical HPLC analysis on the Agilent system (vide supra). Averages over eight reactions: starting radioactivity (reactor following elution of fluoride), 75.9 ± 10.4 GBq (2050 ± 281 mCi); time of synthesis (from end of beam), 81 min; product radioactivity, 3.22 ± 0.67 GBq (87 ± 18 mCi); radiochemical yield, 7.3 ± 1.9%, decay-corrected to starting [18F]fluoride in the reactor; specific activity, 540 ± 152 GBq/μmol (14592 ± 4095 Ci/mmol); radiochemical purity, 99.5 ± 0.3%; chemical purity, 93.3 ± 6.5%. PET Imaging Protocol. This study employed two male nonhuman primates (NHPs, 5- to 6-year-old cynomolgus monkeys) fasted overnight prior to PET imaging. In preparation for PET scanning, NHPs were premedicated with ketamine (10 mg/kg, IM) and glycopyrrolate (0.01 mg/kg, IM), intubated, and maintained under isoflurane anesthesia (1.5−2.0% in oxygen) for the duration of the scan procedure. An intravenous (IV) catheter was placed into the saphenous vein in the leg for tracer injection/blood sampling. Before radiotracer injection, a 515 s transmission scan was acquired for attenuation and scatter correction using a 57Co point-source. Baseline PET scans were conducted with [18F]5 administered IV as a bolus dose targeting an injected radioactivity of 37 MBq/kg (1.0 mCi/kg). Dynamic PET scans of 90 min were conducted in a Focus 220 microPET scanner (Siemens Medical Systems) and initiated on 4577



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administration of the radiotracer. PET data was histogrammed into 16 image frames (five 1 min frames, five 5 min frames, six 10 min frames) over the 90 min dynamic scan duration. The Focus 220 scanner consists of 48 rings of detectors axially. 3D sinograms were created with corrections for random coincidences and global. Attenuation and scatter correction maps were created from the measured 57Co transmission scan (515 s duration). PET images were reconstructed into a 256 × 256 × 95 volume with x, y pixel dimension of 0.9 mm and slice thickness of 0.8 mm with corrections for detector normalization, decay, attenuation, and scatter using OSEM3D fastMAP iterative image reconstruction using inverse Fourier rebinning for fully 3D PET (Siemens Medical Systems). Reconstructed images were scaled to standardized uptake value (SUV) and coregistered to an individual reference PET image for each NHP using a mutual information algorithm. Time−activity curves were generated for regions of interest defined in the reference PET images for frontal cortex, putamen, caudate, and cerebellum. PDE2A binding potential was calculated for each tissue using the simplified reference tissue model34 implemented in PMOD software (PMOD Technologies) employing cerebellum as a reference region of nonspecific binding.

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(3) Frank, R. A.; Långström, B.; Antoni, G.; Montalto, M. C.; Agdeppa, E. D.; Mendizabal, M.; Wilson, I. A.; Vanderheyden, J.-L. The imaging continuum: bench to biomarkers to diagnostics. J. Labelled Compd. Radiopharm. 2007, 50, 746−769. (4) Patel, S.; Gibson, R. In vivo site-directed radiotracers: a minireview. Nucl. Med. Biol. 2008, 35, 805−815. (5) Laruelle, M.; Slifstein, M.; Huang, Y. Y. Positron emission tomography: imaging and quantification of neurotransporter availability. Methods 2002, 27, 287−299. (6) Fowler, J. S.; Volkow, N. D.; Wang, G.-J.; Gatley, S. J.; Logan, J. [11C]Cocaine: PET studies of cocaine pharmacokinetics, dopamine transporter availability and dopamine transporter occupancy. Nucl. Med. Biol. 2001, 28, 561−572. (7) Pike, V. W. PET radiotracers: crossing the blood−brain barrier and surviving metabolism. Trends Pharmacol. Sci. 2009, 30, 431−440. (8) Wager, T. T.; Hou, X.; Verhoest, P. R.; Villalobos, A. Moving beyond Rules: The Development of a Central Nervous System Multiparameter Optimization (CNS MPO) Approach to Enable Alignment of Druglike Properties. ACS Chem. Neurosci. 2010, 1, 435− 449. (9) Cunningham, V. J.; Parker, C. A.; Rabiner, E. A.; Gee, A. D.; Gunn, R. N. PET studies in drug development: methodological considerations. Drug Discovery Today: Technol. 2005, 2, 311−315. (10) Van de Waterbeemd, H.; Camenisch, G.; Folkers, G.; Chretien, J. R.; Raevsky, O. A. Estimation of Blood−Brain Barrier Crossing of Drugs Using Molecular Size and Shape, and H-Bonding Descriptors. J. Drug Targeting 1998, 6, 151−165. (11) Jiang, Z.; Reilly, J.; Everatt, B.; Briard, E. A rapid vesicle electrokinetic chromatography method for the in vitro prediction of non-specific binding for potential PET ligands. J. Pharm. Biomed. Anal. 2011, 54, 722−729. (12) Patel, S.; Hamill, T.; Hostetler, E.; Burns, H. D.; Gibson, R. E. An In Vitro Assay for Predicting Successful Imaging Radiotracers. Mol. Imaging Biol. 2003, 5, 65−71. (13) (a) Rosso, L.; Gee, A. D.; Gould, I. R. Ab initio Computational Study of Positron Emission Tomography Ligands Interacting with Lipid Molecule for the Prediction of Nonspecific Binding. J. Comput. Chem. 2008, 29, 2397−2405. (b) Guo, Q.; Brady, M.; Gunn, R. N. A Biomathematical Modeling Approach to Central Nervous System Radioligand Discovery and Development. J. Nucl. Med. 2009, 50, 1715−1723. (14) For selection of the negative control compounds and their references, please see the Supporting Information. (15) Ertl, P.; Rohde, B.; Selzer, P. Fast Calculation of Molecular Polar Surface Area as a Sum of Fragment-Based Contributions and Its Applications to the Prediction of Drug Transport Properties. J. Med. Chem. 2000, 43, 3714−3717. (16) This modified version of the CNS MPO tool, derived from analysis of marketed CNS drugs, was named as CNS PET MPO to reflect its utility in PET ligand design and prioritization. A CNS PET MPO calculator is included as part of the Supporting Information. (17) Wager, T. T.; Chandrasekaran, R. Y.; Hou, X.; Troutman, M. D.; Verhoest, P. R.; Villalobos, A.; Will, Y. Defining Desirable Central Nervous System Drug Space through the Alignment of Molecular Properties, in Vitro ADME and Safety Attributes. ACS Chem. Neurosci. 2010, 1, 420−434. (18) Whalen, K.; Gobey, J.; Janiszewski, J. A centralized approach to tandem mass spectrometry method development for high-throughput ADME screening. Rapid Commun. Mass Spectrom. 2006, 20, 1497− 1503. (19) RRCK cells were generated in-house (Pfizer Inc., Groton, CT, USA) as a subclone of Mardin−Darby canine kidney wild-type (MDCK-WT) cells that displayed low expression of endogenous P-gp (approximately 1−2% of MDCK-WT cells, based on mRNA level). For detailed information, see: Callegari, E.; Malhotra, B.; Bungay, P. J.; Webster, R.; Fenner, K. S.; Kempshall, S.; LaPerle, J. L.; Michel, M. C.; Kay, G. G. A comprehensive non-clinical evaluation of the CNS penetration potential of antimuscarinic agents for the treatment of overactive bladder. Br. J. Clin. Pharmacol. 2011, 72, 235−246.

ASSOCIATED CONTENT

S Supporting Information *

The full data set of the PET ligand database, an extended version of Table 2 with individual parameters of CNS PET MPO (ClogP, ClogD, MW, HBD, TPSA, and pKa), and a CNS PET MPO calculator in Excel format. This material is available free of charge via the Internet at http://pubs.acs.org.

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AUTHOR INFORMATION

Corresponding Author

*Phone: +1 617 395 0640. Fax: +1 860 686 5285. E-mail: Lei. Zhang3@pfizer.com. Notes

The authors declare no competing financial interest.

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ACKNOWLEDGMENTS We thank Rebecca O’Connor and Mary C. MacDougall for providing the PDE2A activity and PDE selectivity data and the Pfizer ADME technology group for generating the in vitro pharmacokinetic data and developing the in silico RRCK passive permeability, MDR efflux, and plasma and brain fractions unbound models. We also thank Laura Blumberg, Brian S. Bronk, and Clive Brown-Proctor for helpful discussions and suggestions.

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ABBREVIATIONS USED cGMP, cyclic guanosine monophosphate; ClogD, calculated distribution coefficient; MDCK, Mardin−Darby canine kidney; MPO, multiparameter optimization; NSB, nonspecific binding; PDE, phosphodiesterase; PET, positron emission tomography; RRCK, RR canine kidney; RO, receptor occupancy; SUV, standard uptake value; TAC, time−activity curve

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REFERENCES

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(20) Feng, B.; Mills, J. B.; Davidson, R. E.; Mireles, R. J.; Janiszewski, J. S.; Troutman, M. D.; de Morais, S. M. In Vitro P-glycoprotein Assays to Predict the in Vivo Interactions of P-glycoprotein with Drugs in the Central Nervous System. Drug Metab. Dispos. 2008, 36, 268− 275. (21) Di, L.; Umland, J. P.; Chang, G.; Huang, Y.; Lin, Z.; Scott, D. O.; Troutman, M. D.; Liston, T. E. Species Independence in Brain Tissue Binding Using Brain Homogenates. Drug Metab. Dispos. 2011, 39, 1270−1277. (22) Keefer, C. E.; Kauffman, G. W.; Gupta, R. An interpretable probability-based confidence metric for continuous QSAR models. J. Chem. Inf. Model. 2013, 53, 368−383. (23) In silico model-derived ADME values were employed in the analysis, except for compounds that were part of the Pfizer compound collection (21 of the 62 ligands in the “Yes” category and 5 of the 15 ligands in the “No” category), in which case experimentally measured values were used. Information on the origin of data for specific compounds can be found in the Supporting Information. (24) Cella, C. V.; Woodhouse, S. M.; Lewis, R. A.; Fray, A. E. A novel method for the assessment of potential PET ligands using an in vitro assay of non-specific binding. FENS Abstr. 2004, 2, A059.4. (25) The top 10 performing PET ligands were selected by Pfizer’s PET imaging group, who was blinded to our analysis, based on their experience and literature understanding of PET ligands frequency of use, acceptance in the field, robust test-retest reliability results, and good properties for quantification. Representative references for the 10 high performing CNS PET ligands include: (a) [11C]Raclopride: Ribeiro, M.-J.; Ricard, M.; Bourgeois, S.; Lièvre, M.-A.; Bottlaender, M.; Gervais, P.; Dollé, F.; Syrota, A. Biodistribution and radiation dosimetry of [11C]raclopride in healthy volunteers. Eur. J. Nucl. Med. Mol. Imaging 2005, 32, 952−958. (b) [18F]Fallypride: Siessmeier, T.; Zhou, Y.; Buchholz, H.-G.; Landvogt, C.; Vernaleken, I.; Peil, M.; Schirrmacher, R.; Rösch, F.; Schreckenberger, M.; Wong, D. F.; Cumming, P.; Gründer, G.; Bartenstein, P. Parametric Mapping of Binding in Human Brain of D2 Receptor Ligands of Different Affinities. J. Nucl. Med. 2005, 46, 964−972. (c) [11C]Flumazenil: Lassen, N. A.; Bartenstein, P. A.; Lammertsma, A. A.; Prevett, M. C.; Turton, D. R.; Luthra, S. K.; Osman, S.; Bloomfield, P. M.; Jones, T.; Patsalos, P. N.; O’Connell, M. T.; Duncan, J. S.; Andersen, J. V. Benzodiazepine receptor quantification in vivo in humans using [11C]Flumazenil and PET: application of the steady-state principle. J. Cereb. Blood Flow Metab. 1995, 15, 152−165. (d) [11C]DASB: Houle, S.; Ginovart, N.; Hussey, D.; Meyer, J. H.; Wilson, A. A. Imaging the serotonin transporter with positron emission tomography: initial human studies with [11C]DAPP and [11C]DASB. Eur. J. Nucl. Med. 2000, 27, 1719−1722. (e) [11C]β-CFT: Rinne, J. O.; Laihinen, A.; Någren, K.; Ruottinen, H.; Ruotsalainen, U.; Rinne, U. K. PET examination of the monoamine transporter with [11C]β-CIT and [11C]β-CFT in early Parkinson’s disease. Synapse 1995, 21, 97−103. (f) [11C]MDL100907: Andree, B.; Nyberg, S.; Ito, H.; Ginovart, N.; Brunner, F.; Jaquet, F.; Halldin, C.; Farde, L. Positron Emission Tomographic Analysis of Dose-Dependent MDL 100,907 Binding to 5-Hydroxytryptamine-2A Receptors in the Human Brain. J. Clin. Psychopharmacol. 1998, 18, 317−323. (g) [11C]WAY100635: Farde, L.; Ito, H.; Swahn, C. G.; Pike, V. W.; Halldin, C. Quantitative analyses of carbonyl-carbon-11-WAY-100635 binding to central 5-hydroxytryptamine-1A receptors in man. J. Nucl. Med. 1998, 39, 1965−1971. (h) [11C]Carfentanil: Frost, J. J.; Douglass, K. H.; Mayberg, H. S.; Dannals, R. F.; Links, J. M.; Wilson, A. A.; Ravert, H. T.; Crozier, W. C.; Wagner, H. N. Multicompartmental analysis of [11C]carfentanil binding to opiate receptors in humans measured by positron emission tomography. J. Cereb. Blood Flow Metab. 1989, 9, 398−409. (i) [11C] PHNO: Gurevich, E. V.; Joyce, J. N. Distribution of Dopamine D3 Receptor Expressing Neurons in the Human Forebrain: Comparison with D2 Receptor Expressing Neurons. Neuropsychopharmacology 1999, 20, 60−80. (j) [11C]ABP688: Ametamey, S. M.; Treyer, V.; Streffer, J.; Wyss, M. T.; Schmidt, M.; Blagoev, M.; Hintermann, S.; Auberson, Y.; Gasparini, F.; Fischer, U. C.; Buck, A. Human PET 4579


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