Discovery of Potent Soluble Epoxide Hydrolase (sEH) Inhibitors by

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Discovery of Potent Soluble Epoxide Hydrolase (sEH) Inhibitors by Pharmacophore-Based Virtual Screening Birgit Waltenberger, Ulrike Garscha, Veronika Temml, Josephine Liers, Oliver Werz, Daniela Schuster, and Hermann Stuppner J. Chem. Inf. Model., Just Accepted Manuscript • DOI: 10.1021/acs.jcim.5b00592 • Publication Date (Web): 16 Feb 2016 Downloaded from http://pubs.acs.org on February 20, 2016

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Journal of Chemical Information and Modeling

Discovery of Potent Soluble Epoxide Hydrolase (sEH) Inhibitors by Pharmacophore-Based Virtual Screening Birgit Waltenberger†, Ulrike Garscha┴, Veronika Temml†, Josephine Liers┴, Oliver Werz┴, Daniela Schuster‡*, and Hermann Stuppner† †, ‡

Institute of Pharmacy (Pharmacognosy†/ Pharmaceutical Chemistry‡) and Center for

Molecular Biosciences Innsbruck (CMBI), University of Innsbruck, Innrain 80-82, A-6020 Innsbruck, Austria. ┴

Chair of Pharmaceutical/Medicinal Chemistry, Institute of Pharmacy, University of Jena, Philosophenweg 14, D-07743 Jena, Germany.

*Corresponding author.

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ABSTRACT There is an increasing interest in the development of soluble epoxide hydrolase (sEH) inhibitors, which block the degradation of endogenous anti-inflammatory epoxyeicosatrienoic acids. Within this study, a set of pharmacophore models for sEH inhibitors was developed. The Specs database was virtually screened and a cell-free sEH activity assay was used for the biological investigation of virtual hits. In total, out of 48 tested compounds, 19 were sEH inhibitors with IC50 < 10 µM, representing a prospective true positive hit rate of 40%. Six of these compounds displayed IC50 values in the low nanomolar range. The most potent compound 21, a urea derivative, inhibited sEH with an IC50 = 4.2 nM. The applied approach also enabled the identification of diverse chemical scaffolds, e.g. the pyrimidinone derivative 29 (IC50 = 277 nM). The generated pharmacophore model set therefore represents a valuable tool for the selection of compounds for biological testing.


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INTRODUCTION The arachidonic acid (AA) cascade comprises of a group of metabolic pathways that produce endogenous bioactive lipid mediators, which regulate multiple biological processes such as inflammation. These pathways are activated upon the release of the polyunsaturated fatty AA by cytosolic phospholipase A2 (cPLA2) from membrane phospholipids. AA is then metabolized by different oxygenases including cyclooxygenases (COXs), lipoxygenases (LOs), and cytochrome P450 (CYP450) enzymes. COX enzymes convert AA to prostaglandin H2 (PGH2), which is enzymatically transformed to prostaglandins, prostacyclins, and thromboxanes.1 LO enzymes dioxygenate AA to hydroperoxyeicosatetraenoic acids (HPETE), where 5-LO can metabolize 5HPETE to leukotriene A4 (LTA4), the precursor of pro-inflammatory leukotrienes.2 CYP450 enzymes transform AA to the pro-inflammatory 20-hydroxyeicosatetraenoic acid (20-HETE)3 as well as to the anti-inflammatory epoxyeicosatrienoic acids (EETs) (Figure 1).4-5 EETs contribute to the homeostatic equilibrium of biological processes with numerous effects. These include antiinflammatory6-7, analgesic8-9, fibrinolytic10, anti-migratory11, and proliferative12-13 features; and they are important contributors to cardiovascular physiology.4-5, 14 However, EETs are rapidly metabolized by soluble epoxide hydrolase (sEH) to the corresponding dihydroxyeicosatetraenoic acids (DiHETEs) with primarily pro-inflammatory properties.15-16 Inhibitors of sEH block the degradation of EETs to DiHETEs and therefore significantly increase EET concentrations in plasma and tissues.17-19 Such a pharmacological profile, that is, stabilization of EETs and blockade of DiHETE synthesis, is proposed to be of therapeutic benefit in several pathological disorders and could lead to novel therapies, as demonstrated in various animal models of disease.5, 20 Thus, there is an increasing interest in the development and preclinical evaluation of novel sEH inhibitors. Although several inhibitors of sEH have been identified and pharmacologically characterized in vitro, only a few inhibitors have reached clinical trials. After a successful phase I clinical study,21 3 ACS Paragon Plus Environment


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the sEH inhibitor 1 (AR9281; IC50 = 7 nM22, Figure 2) failed in a phase IIa study due to lack of efficacy.20 A small phase IIa clinical study examined the effect of the well-known sEH inhibitor 2 (AUDA; IC50 = 3.2-100 nM,23 Figure 2), which is commonly used as experimental sEH reference inhibitor, on the vascular tone.24 Two phase I clinical studies with compound 3 (GSK2256294, IC50 = 27 pM25, Figure 2) have recently been completed. More information about these clinical trials is given in the Supporting Information Material. Note that so far, no sEH inhibitor is available on the market.

Figure 1. The AA cascade and target of sEH inhibitors.


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Figure 2. sEH inhibitors that reached clinical trials.

X-ray crystal structures of human sEH provided insights into the nature of the catalytic pocket of the enzyme. Tyr383 and Tyr466 in the catalytic pocket of sEH activate the EET epoxide ring opening by Asp335, resulting in an intermediate ester, which is rapidly hydrolyzed into DiHETEs (see Figure 3 and Figure 4A).26 Many of the sEH inhibitors comprise a urea group, such as compounds 1 and 2, or an amide function, such as 3. Crystallographic studies have shown that the urea and the amide group, respectively, bind to the sEH active site.27-30 The carbonyl oxygen interacts with Tyr383 and Tyr466 and the N-H with Asp335 via hydrogen bonds (Figure 4B). This illustrates that the catalytically crucial residues also play a pivotal role in inhibitor binding. Aside from this urea or amide binding site at the center of the binding pocket, there are two hydrophobic areas, located deep inside and at the entry of the ligand binding pocket. The smaller grove leading inside the enzyme up to Phe497 can typically accommodate small apolar groups such as a phenyl, hexyl, or cyclohexyl. The second hydrophobic pocket leading to the entry site residue Met503 is typically filled by larger hydrophobic groups such as longer aliphatic chains, polyphenyls, heterocycles, or admantyl groups (Figure 4C).5 5 ACS Paragon Plus Environment


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Figure 3. Catalytic mechanism of sEH. The epoxide group of the EET binds to the active site of the enzyme. In particular, Tyr383 and Tyr466 form hydrogen bonds with the epoxide oxygen, while the carboxylic acid of Asp335 opens the epoxide and forms an intermediate ester. The oxygen of the epoxide binds hydrogen, likely from one of the tyrosines. A water molecule attacks the carbonyl of the ester of the hydroxyl alkyl-enzyme intermediate, releasing the DiHETE product and the original enzyme.


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Figure 4. Catalytic pocket of human sEH. (A) The catalytic triad formed by Tyr383, Tyr466, and Asp335 is shown in ball and stick style; the binding pocket was visualized with LigandScout. (B) Binding pocket comprising the sEH inhibitor 8 (PDB entry 3koo). The protein-ligand interaction pattern is color-coded: hydrogen bond acceptor (red sphere), hydrogen bond donor (green arrow), hydrophobic (yellow sphere). The carbonyl oxygen of the urea group interacts with Tyr383 and Tyr466 via hydrogen bonds, and the N-H forms a hydrogen bond with Asp335. (C) The overall shape of the binding pocket with a hydrophobic pocket leading up to Phe497, the catalytic triad at the center of the pocket, and the larger cavity at the entry site leading up to Met503.



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In recent years, molecular modeling approaches have been applied to discover new sEH inhibitors on several occasions. In 2011, Tanaka et al.27 identified 68 new sEH inhibitors (out of a 735 compound focused library) in a docking-based virtual screening study. They analyzed several physicochemical parameters of their hit compounds, such as size and lipophilicity, to find a compound with high ligand efficiency. The lead compound was then further optimized to yield a more potent compound with a good ADMET profile. In a similar approach in 2012, Xing et al.31 constructed a combinatorial library based on a benzoxazole lead structure that had been cocrystallized with sEH. This library was then evaluated by docking and by analyzing the hydrogen bond binding patterns of the various candidates. Roughly 90% (343) of the synthesized compounds (383) from the benzoxazole scaffold proved to be active in a biological assay. In 2012, Moser et al.32 developed a pharmacophore model based on 13 co-crystallized inhibitors of sEH. Protein Ligand Interaction Fingerprints (PLIF) were employed to create a common shape of the binding pocket consisting of exclusion volumes. A nine feature pharmacophore was created in MOE to represent the binding pattern with a special focus on the catalytic triad as described by Morisseau and Hammock.26 The model found 3,191 virtual hits (VHs) out of a 436,012 compounds virtual library. This hit list was further narrowed down to 89 hits by applying filters in accordance with e.g. Lipinski’s rule of five. Nine of these compounds were experimentally tested, of which two displayed an inhibition above 50% at a concentration of 30 µM. This pharmacophore was then further refined and used successfully in a second study by Moser et al.33 to find dual 5-LO/sEH inhibitors. The aim of our present study was to contribute to the development of this potential therapeutic strategy by finding new inhibitors of sEH using an in silico approach. We created multiple structure- and ligand-based pharmacophore models to cover different putative binding modes of


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the ligands, thereby covering the active space more thoroughly as in previous studies and successfully identifying new, potent sEH inhibitors.

RESULTS AND DISCUSSION Workflow. Based on X-ray crystal structures of small molecule inhibitors in complex with sEH and structural information of sEH inhibitors from literature, several pharmacophore models were generated. The first aim of the pharmacophore model generation process was to create a model set, which finds as many active molecules in a dataset containing published sEH inhibitors and as few confirmed inactive compounds from the literature as possible.34-35 To accomplish this aim, preferentially structure-based models were used, but for complementation also ligand-based models were employed. For an optimal coverage of the active chemical space of the inhibitors, two pharmacophore programs (LigandScout (LS)36 version 3.03 and Discovery Studio (DS)37 version 3.0) based on different screening algorithms were used.38 Out of all generated pharmacophore models, the ones performing best in the theoretical validation were employed for the prospective search for sEH inhibitors (Figure 5).



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Figure 5. Study design comprising pharmacophore model development, theoretical, and experimental validation.


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Databases Generation. A comprehensive literature survey for known sEH inhibitors and confirmed inactive compounds was performed. Out of the published structures, 68 chemically diverse inhibitors with IC50 values from the subnanomolar range up to 13,400 nM (Supporting Information Material Figure S1) as well as 192 confirmed inactive compounds (Supporting Information Material Figure S2) were collected in 3D molecular databases. These molecular libraries were used as test sets for the theoretical validation of the pharmacophore models. For the subsequent experimental validation of the theoretically successfully validated pharmacophore models, the Specs database (version 01/2012) was downloaded from the vendor homepage (www.specs.net) and 3D multiconformational databases comprising 202,879 entries were generated in both software environments used in this work (LS and DS). Pharmacophore

Model

Generation

and

Theoretical

Validation.

Structure-based

pharmacophore models for sEH inhibitors were generated using LS and the co-crystallized sEHinhibitor complexes 3pdc31, 3ans27, 3ant27, 3otq30, 3koo29, 3i1y28, 3i2828, 1zd539, and 1vj540 (Figure 6) from the PDB.41 For the amino acids in the binding pocket, two different numberings are in use in these X-ray crystal structure complexes of human sEH proteins with sEH inhibitors. Within this study, we referred to the amino acid numbering described by Eldrup et al.28



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Figure 6. Small molecule inhibitors of human sEH in X-ray crystal structure complexes used for the generation of structure-based pharmacophore models.

For each PDB complex, a structure-based pharmacophore model was generated automatically in the structure-based module of LS. These models were then refined to optimize their predictive performance.42 In a first model optimization step, active compounds were fitted into the model and features were deleted or adjusted in size to allow as many actives as possible to map the 12 ACS Paragon Plus Environment

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pharmacophore and to exclude inactive compounds. Features signifying interactions with key residues essential for inhibition such as polar interactions with Tyr383, Tyr466, and Asp335, were retained during this process. Sometimes it was feasible to generate more than one model out of a PDB entry, because the automatically generated pharmacophore comprised too many chemical features derived from several protein-ligand interactions and was thus too restrictive. By creating two or more models with reduced numbers of features displaying different key interactions from the crystal structure, a broader range of actives could be covered. In contrast, if the model automatically generated from a PDB entry did not constitute a suitable base for a model used in a prospective screening, e.g. because it contained too few chemical features and was consequently very general, thus, recognizing many active but also many inactive compounds, it was discarded. Models were also deleted if they did not find any active compounds from the test set. In the second optimization step, exclusion volumes were added to prevent inactive compounds that sterically clash with the protein to be mapped by the pharmacophore. This optimization aimed at reducing the chance of finding inactive compounds, while active compounds were still correctly recognized. In total, based on nine PDB entries, 22 structure-based pharmacophore models were generated. Out of these 22 models, one model was derived from the PDB entry 3ans, two from 3i1y, three from 3koo and 1vj5, respectively, four models from 3ant and 3otq, respectively, and five from 1zd5, whereas the PDB entries 3pdc and 3i28 did not retrieve a suitable pharmacophore model, respectively. The resulting model set was able to recognize 55 out of 68 active test set compounds (Figures 5 and 7).



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Figure 7. Workflow of the pharmacophore model generation approach.

a

In each workflow step, bold numbers indicate the generation of new pharmacophore models.

Because the structure-based models were not able to cover a part of the active compounds, additionally three ligand-based pharmacophore models were generated using LS (Supporting Information Material). For the training sets, first of all, compound 1139, the ligand from the PDB entry 1zd5, was selected because it is the only ligand of all protein-ligand complexes used for the generation of structure-based models, which was not found by any of the 22 structure-based pharmacophore models. In addition, four other compounds with aliphatic side chains or ring systems were selected, which were hardly recognized by the structure-based models despite their high inhibitory activity on sEH. Thus, compounds 11,39 13,43 14,44 and 1545 (training set A, Figure 14 ACS Paragon Plus Environment

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8) and 1139 and 1646 (training set B, Figure 8) were used for calculating shared and merged feature pharmacophore models. The resulting three models were equipped with exclusion volume coats and optimized as described above. All 25 pharmacophore models combined were able to find 58 out of 68 active test set compounds (Figures 5 and 7). The remaining 10 structures contained aliphatic chains and ring systems that were not recognized as hydrophobic by LS version 3.03. Figure 8. Training Set Compounds Used for Pharmacophore Model Generation. Compound 11, whose coordinates were used as a reference in the model generation approach, is shown in Figure 6.



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In general, the application of more than one pharmacophore modeling software tool in parallel is expected to yield a more diverse hit list and to retrieve an increased number of active chemical scaffolds.47-48 A direct comparison of the performance of LS and DS revealed that due to different screening algorithms and feature definitions, these software programs often retrieve vastly different results, even if the models are built based on the same training compounds and comprise the same chemical features. Thus, in order to obtain a more comprehensive hit list, which covers the active chemical space more completely, the usage of more than one modeling software program is suggested.38 We therefore used DS to create in addition three analogous structure-based and two ligand-based models, for a more complete coverage of the active chemical space of sEH inhibitors from our literature data set. For optimization in DS, the same dataset already used in LS was employed. Since DS and LS use different algorithms, the conformers of the dataset compounds were calculated in DS before screening. The feature sizes were adjusted to maximize the yield of actives and to minimize the number of inactive molecules found by the model. Following this approach, 67 out of 68 inhibitors of the test set could be found by the whole set of 30 pharmacophore models (Figures 5 and 7). These 30 pharmacophore models were further evaluated aiming at the selection of the best models for virtual screening of the Specs database (www.specs.net). The goal was to reduce the number of models in the collection and at the same time to improve the quality of the model set. To evaluate the quality of the individual pharmacophore models, the ability of the models to retrieve sEH inhibitors, but not inactive compounds was determined. Therefore, in addition to the active compounds test set, also the test set with inactives, comprising confirmed inactive structures from literature, was virtually screened using LS and DS, respectively. As a measure of the discriminatory power, the enrichment factor (EF) of each pharmacophore model was calculated as described below. Pharmacophore models with low EF values were eliminated from the model 16 ACS Paragon Plus Environment

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collection. If multiple models found the same active compounds, only the most restrictive model was retained. Following this approach, the best eight pharmacophore models were selected. This final collection, consisting of five structure-based (Figure 9) and three ligand-based models (Figure 10), was able to find 65 out of 68 sEH inhibitors (Supporting Information Material Table S1) and 79 out of 192 inactive compounds (in comparison to 127 out of 192 inactives retrieved by all 30 models; Supporting Information Material Table S2), yielding an overall EF of 1.73 (45% of the maximum EF of 3.82). The high number of compounds found by the model collection compared to the low numbers of compounds recognized by the individual models (Table 1) shows their high complementarity. The three sEH inhibitors not identified by the model collection were 17, 18, and 19 with reported IC50 values of 100,49 100,44 and 2.5 nM,50 respectively, probably representing distinct binding modes not reflected by the models (Figure 11). A detailed description of the finally selected pharmacophore models is provided in the Supporting Information Material.

Figure 9. 3D (left) and 2D (right) charts of structure-based pharmacophore models 1 (A), 2 (B), 3 (C), 4 (D), and 5 (E) for sEH inhibitors in the sEH binding pocket. The interactions were visualized with the following color code: hydrogen bond acceptor (red arrow), hydrogen bond donor (green arrow), hydrophobic interaction (yellow sphere), aromatic ring feature interaction (purple sphere and arrow), exclusion volume (grey sphere).



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Figure 10. Ligand-based pharmacophore models for sEH inhibitors. (A) Pharmacophore model 6; the chemical features were visualized using LS; color code: hydrogen bond acceptor (red sphere), hydrogen bond donor (green arrow), hydrophobic interaction (yellow sphere), exclusion volume (grey sphere). (B) Pharmacophore model 7 and (C) model 8; features were visualized in DS with the following color code: blue spheres signify hydrophobic features, red spheres mark hydrogen bond acceptor features and pink spheres and arrows represent directed hydrogen bond donor features.



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Table 1. Information on finally selected pharmacophore models: PDB entries used for the generation, numbers of recognized active and inactive compounds from the test set, EFs, ROCAUCs, and numbers of VHs from the Specs database. Pharmacophore

1

2

3

4

5

6

7

8

3ant

3otq

3i1y

3i1y

3ko

Ligand

Ligand

Ligand

27

30

28

28

o29

-based

-based

-based

model

model

model

31

45 (31)c 26

Sum

model PDB entry

Actives recognized, 18 %

(number

actives

15

of (12)a (10)

19

16

4

(13)

(11)a (3)a

(21)b

65

(18)d

recognized

out of the 68 actives in the database) 3 (6) 2 (3) 5

Inactives recognized,

%

6

(10)

(11)

0 (0) 13 (24)

19 (36)

11 (22)

79

(number of inactives recognized out of the 192 inactives in the database EF (% of max. EF)

2.55

2.94

2.16

1.91

3.82

1.78

1.77

1.72

1.73

(67

(77

(57

(50

(100

(47%)

(46%)

(45%)

(45

%)

%)

%)

%)

%)


%)

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0.57

ROC AUC

0.57

0.57

0.54

7

5

16,0

11,6

12,7

89

68

76

8 Number from

of the

VHs 13,7 Specs 79

0.52

0.597

including the training compound

b

including two training compounds

c

including four training compounds

d

including one training compound

0.580

0.78 1

55

1,637

database a

0.507

Figure 11. Inhibitors of sEH not identified by the model collection.


44

3,638

47,9 51


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The interactions of the sEH protein with potential ligands occupying the features of the structurebased pharmacophore models were calculated using LS. Despite similarities of the models in chemical features, they all depict slightly different interaction patterns that can occur within the sEH binding site, as shown in Table 2. With the exception of pharmacophore model 2, all of the models contain hydrogen bond features with the catalytic triad Asp335, Tyr383, and Tyr466. Pharmacophore model 2 comprises hydrogen bonds with the two tyrosine residues but not with Asp335. All five models differ in their description of the two hydrophobic side pockets, the shorter large cavity leading up to Phe497, and the longer one leading to Met503. In model 1, both side arms are represented by a single hydrophobic feature (H), respectively; the one closer to the catalytic triad represents the shorter cavity, the other one represents the larger side pocket. In contrast, model 2 has a larger H feature in the shorter cavity, located in a different direction within the broad pocket. A H/aromatic ring (RA) feature is placed on the other side of the catalytic triad, but very close to it, so that ligands that do not fill the full volume of the longer cavity can also be found. Model 5 does not contain a feature in the larger arm at all, but defines the shorter pocket with three H features close to the triad, demanding a short wide hydrophobic substituent on this side. In a similar manner, model 3 describes also only the shorter arm of the binding pocket, but with two H features, which are in a different location than the H features of model 5. Model 4 derived from the same PDB entry contains a H feature on both sides of the triad close to the hydrogen bonding residues, respectively, and consequently retrieves smaller ligands that do not fill the side pockets to their full extend. Inhibitors of sEH found by all of these models show that each of the represented binding modes can lead to active compounds. The key element of binding in sEH is the catalytic triad, while there are no polar interactions in the two side pockets. These can accommodate a wide range of differently sized substituents as long as they are hydrophobic. This


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variability in size is well represented within our set of pharmacophore models. A single model would have difficulties to allow all the variations while remaining restrictive.

Table 2. Interactions of amino acids in the sEH protein with potential ligands occupying the structure-based pharmacophore models. Amino acid

Model 1

Phe267

Ha

Model 2

Model 3

Model 4 Model 5 H

H

Pro268 Asp335

H

HBDb

Trp336

HBD

HBD

H, ARc

H

HBD

Met339

H

H

H

Thr360

H

H

H

Ile363

H

Tyr383

H, HBAd

H, HBA

Phe387

H

H

H

Leu408

H

H

H

H

H

H

H, HBA

H, HBA

HBA

H

Leu417 Met419

H

H

Leu428

H

H

Tyr466

HBA

HBA

H

H HBA

HBA

HBA H

Phe497 H, He

Val498 23 ACS Paragon Plus Environment

H


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H

Leu499

H

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H

H

Met503

H, H

Trp525 12

Total number of amino acids

11

7

H 12

10

involved in the interaction a

H

b

HBD hydrogen bond donor

c

RA

d

HBA hydrogen bond acceptor

e

H,H hydrophobic interaction with two different parts of the ligand

hydrophobic interaction

aromatic ring feature interaction

Selection of Compounds for Biological Testing. The pharmacophore models were experimentally validated using substances commercially available from Specs, The Netherlands. The Specs database (version 01/2012, comprising 202,920 compounds) was screened with the final model collection, which led to hit lists between 44 and 16,089 compounds, respectively (Table 1). The retrieved VHs were ranked according to their geometric fit values. For each pharmacophore model, the six compounds with the highest fit values were selected. These structures were inspected for reactive and non-drug-like groups. Compounds comprising such groups were discarded and replaced by the next ranked ones. The hits were also visually inspected in the environment of the binding pocket as mapped by the pharmacophore. Hits that produced obvious steric clashes were removed. Furthermore, in order to gain more information about the quality of the pharmacophore models, we aimed to test a set of structurally diverse compounds. Therefore, in case of highly similar VHs in one hit list, only the hit with the highest fit value was selected, whereby the others were removed. Finally, six VHs per pharmacophore model were selected, purchased from Specs 24 ACS Paragon Plus Environment

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and subjected to biological testing (compounds 20 - 38 and S246 – S274, Table 3, Figure 12, Figure S3).

Table 3. Inhibition of human recombinant sEH by compounds 20 – 38 and S246 – S274 in a fluorescence-based cell-free assay. Data (means ± SEM, triplicate determinations in three independent experiments) are given as residual activity in % of control (100%, vehicle, uninhibited control) or IC50 values. Pharmacophore

Cpd.

Pharmacophore fit sEH activity sEH activity sEH

model

No.

score,

IC50

relative % of control % of control [nM]

(absolute) values

at 10 µM

at 0.1 µM

1

20

0.97 (57.99)

48.3 ± 14.2

91.5 ± 13.2

n.d.

1

21

0.96 (57.81)

-0.7 ± 0.3

3.7 ± 1.6

4.2 ± 1.7

1

S246

0.96 (57.74)

70.9 ± 10.6

93.5 ± 11.3

n.d.a

1

S247

0.96 (57.81)

146.8 ± 16.1

81.9 ± 9.7

n.d.

1

S248

0.97 (57.98)

70.3 ± 9.6

102.9 ± 1.1

n.d.

1

S249

0.96 (57.74)

73.8 ± 7.0

85.9 ± 3.1

n.d.

2

22

0.98 (58.59)

8.2 ± 3.1

20.1 ± 1.2

22.7 ± 3.6

2

23

0.98 (58.51)

13.4 ± 2.5

51.9 ± 4.6

40.7 ± 5.0

2

S250

0.98 (58.54)

103.5 ± 12.0

85.7 ± 5.2

n.d.

2

S251

0.98 (58.59)

74.3 ± 10.2

85.6 ± 11.5

n.d.

2

S252

0.97 (58.46)

87.7 ± 5.6

98.8 ± 8.2

n.d.

2

S253

0.97 (58.46)

90.9 ± 3.6

83.3 ± 7.3

n.d.



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3

24

0.96 (57.77)

2.7 ± 1.3

45.7 ± 6.0

54.3 ± 20.2

3

25

0.96 (57.50)

1.6 ± 1.9

45.8 ± 11.3

52.8 ± 8.6

3

26

0.96 (57.55)

49.1 ± 10.6

98.7 ± 5.6

n.d.

3

S254

0.96 (57.54)

68.4 ± 6.7

91.3 ± 5.0

n.d.

3

S255

0.96 (57.50)

94.6 ± 5.2

85.7 ± 5.3

n.d.

3

S256

0.96 (57.55)

94.7 ± 1.9

105.8 ± 6.2

n.d.

4

27

0.97 (58.05)

34.5 ± 8.7

85.1 ± 6.9

n.d.

4

S257

0.97 (58.01)

80.8 ± 11.6

90.2 ± 7.8

n.d.

4

S258

0.97 (58.16)

82.0 ± 10.7

90.3 ± 16.2

n.d.

4

S259

0.97 (58.07)

99.6 ± 3.5

90.6 ± 7.0

n.d.

4

S260

0.97 (58.07)

82.3 ± 2.6

92.5 ± 5.9

n.d.

4

S261

0.97 (58.06)

118.9 ± 7.6

102.7 ± 8.2

n.d.

5

28

0.95 (66.44)

21.3 ± 11.1

97.2 ± 1.0

n.d.

5

29

0.94 (65.87)

4.2 ± 1.4

51.7 ± 6.3

277 ± 6.2

5

S262

0.94 (65.87)

52.0 ± 12.4

85.1 ± 5.5

n.d.

5

S263

0.95 (66.15)

91.1 ± 2.8

98.1 ± 4.1

n.d.

5

S264

0.96 (67.25)

73.1 ± 11.8

88.7 ± 10.2

n.d.

5

S265

0.94 (66.10)

62.5 ± 10.9

88.6 ± 10.0

n.d.

6

30

0.95 (47.69)

10.6 ± 3.7

84.4 ± 13.6

n.d.

6

31

0.95 (47.69)

41.1 ± 5.7

106.5 ± 10.1

n.d.

6

32

0.96 (47.89)

8.5 ± 3.7

55.0 ± 6.2

732 ± 30.9

6

33

0.95 (47.72)

1.1 ± 0.5

41.2 ± 5.5

70.9 ± 14.6

6

S266

0.96 (47.83)

101.3 ± 1.7

92.5 ± 3.1

n.d.


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6

S267

0.96 (47.79)

83.7 ± 8.3

78.3 ± 7.3

n.d.

7

34

2.12E-06 (1.06E-05)

44.5 ± 2.5

86.4 ± 6.4

n.d.

7

35

5.80E-09 (2.90E-08)

29.8 ± 5.5

82.2 ± 7.7

n.d.

7

S268

9.62E-09 (4.81E-08)

88.3 ± 9.9

82.8 ± 9.3

n.d.

7

S269

6.22E-08 (3.11E-07)

111.7 ± 13.8

106.5 ± 9.3

n.d.

7

S270

1.59E-07 (7.93E-07)

76.4 ± 2.6

90.6 ± 7.1

n.d.

7

S271

1.82E-08 (9.08E-08)

56.9 ± 8.0

88.5 ± 3.2

n.d.

8

36

0.75 (4.48)

27.6 ± 5.0

85.1 ± 1.2

n.d.

8

37

0.64 (3.85)

7. 5 ± 2.7

73.8 ± 9.2

n.d.

8

38

0.70 (4.19)

23.7 ± 14.4

71.8 ± 10.2

n.d.

8

S272

0.68 (4.05)

67.3 ± 4.0

86.3 ± 5.4

n.d.

8

S273

0.58 (3.50)

89.5 ± 2.7

73.1 ± 6.0

n.d.

8

S274

0.62 (3.74)

94.0 ± 4.6

103.5 ± 1.7

n.d.

n.a.c

2b

n.a.

n.d.

12.1 ± 2.0

10.6 ± 1.1

a

n.d. not determined

b

control inhibitor (AUDA)

c

n.a. not applicable

Figure 12. Chemical structures of identified sEH inhibitors. Threshold: Red box, highly active, < 10% remaining sEH activity at 10 µM; purple box, moderately active, ≥ 10 and < 30% remaining sEH activity at 10 µM; blue box, weakly active, ≥ 30 and < 50% remaining sEH activity at 10 µM.



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Results of Experimental Evaluation. The 48 selected compounds were investigated in a fluorescence-based cell-free sEH assay, which is based on the sEH-mediated conversion of the non-fluorescent

compound

3-phenyl-cyano(6-methoxy-2-naphthalenyl)methyl

ester-2-

oxiraneacetic acid (PHOME) to the fluorescent 6-methoxy-naphtaldehyde as described in Experimental Section. In a first screening, all compounds were tested at a final concentration of 0.1 and 10 µM, respectively. The sEH inhibitor 2 was used as reference compound,23 and DMSO as vehicle control. As shown in Table 3, eight compounds (i.e., 21, 22, 24, 25, 29, 32, 33, and 37) displayed strong inhibition of sEH at 10 µM, leading to less than 10% remaining enzyme activity (highly active). Six compounds (i.e., 23, 28, 30, 35, 36, and 38) led to a remaining enzyme activity of 10 to 30% (moderately active) and five compounds (i.e., 20, 26, 27, 31, and 34) inhibited the enzyme activity to between 30 and 50% (weakly active), respectively. In total, 19 compounds were found to be active with expected IC50 values below 10 µM, giving a total hit rate of 40% active compounds within the virtual screening hit lists. All eight pharmacophore models were involved in the recognition of these 19 active compounds. At a concentration of 10 µM, out of six VHs, every model was able to recognize at least one sEH inhibitor leading to less than 35% remaining enzyme activity. Among the 19 active compounds, eight of them (i.e., 21, 22, 23, 24, 25, 29, 32, and 33) also repressed sEH activity at a concentration of 0.1 µM by at least 45% and were thus analyzed in more detail in concentration response studies. All of these compounds showed a concentration-dependent inhibition of sEH activity, with 21 being the most potent sEH inhibitor with an IC50 = 4.2 nM (Figure 13). Compounds 22, 23, 24, 25, and 33 revealed IC50 values in the range of 22.7 and 70.9 nM, whereas compounds 29 and 32 were less active in this test system with IC50 values of 277 and 732 nM, respectively. 29 ACS Paragon Plus Environment


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Figure 13. Concentration-dependent inhibition of human sEH by compound 21; n = 3.

The highly active compound 21 (IC50 = 4.2 nM), a urea derivative which has comparable or even slightly improved potency as the positive control 2 (IC50 = 10.6 nM), was identified by the LSderived structure-based pharmacophore model 1. This model additionally recognized the weakly active compound 20 (IC50 ≤ 10 µM), showing a hit rate of 33% (two biologically active compounds out of six tested VHs). Note that 21 (at 1000 nM) failed to significantly inhibit related enzymes within the AA cascade such as cPLA2, LTA4 hydrolase, 5-LO from neutrophils, and 12-LO from platelets in cell-free or cell-based assays (data not shown), suggesting that the potent inhibitory effect against sEH is rather specific. The structure-based pharmacophore model 2 led to two potent sEH inhibitors with IC50 = 22.7 (22) and 40.7 nM (23), respectively. Similarly, also model 5 revealed two active compounds (28, moderately active, and 29, IC50 = 277 nM). Model 3 recognized two highly active inhibitors with IC50 = 54.3 (24) and 52.8 nM (25), respectively, and one weakly active inhibitor with an IC50 around 10 µM (26). Model 4 retrieved the weakly active compound 27. Although the latter two models were derived from the same PDB entry and refined using the same software, the screening results of the models were different. Pharmacophore model 6, which was created with LS following a ligand-based approach, led to four active compounds, two of them were efficient in the nanomolar range with IC50 = 732 (32) and 70.9 nM (33). A third hit was moderately (30) and a fourth one 30 ACS Paragon Plus Environment

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weakly active (31). Together, this resulted in a hit rate of 67% active compounds out of six VHs tested. The DS-derived ligand-based pharmacophore model 7 found one moderately (35) and one weakly active compound (34). Therefore, although models 6 and 7 were generated based on the same training set compounds and both models retrieved similar EFs in the theoretical validation, they led to the identification of different sEH inhibitors. Moreover, although both models retrieved active compounds, model 6 showed a better performance in the experimental validation compared to model 7 in terms of the activity of the identified inhibitors. The second DS-derived ligand-based model 8 recognized one highly active (37) and two moderately active sEH inhibitors (36 and 38). All eight pharmacophore models were therefore successfully experimentally validated with hit rates between 17 and 67%, respectively. In contrast to Moser et al., who identified two (out of nine) sEH inhibitors with an inhibition above 50% at 30 µM using one pharmacophore model based on 13 co-crystallized inhibitors,32 we developed a series of more restrictive pharmacophore models to cover the active space in combination. Thereby, a combined hit rate of 40% could be achieved, which confirms the predictive power of this approach. Many of the identified sEH inhibitors comprise a urea or an amide group. However, not all of the 19 active compounds reveal these well-known moieties that are typical for sEH inhibitors. Our list of actives includes also other chemical scaffolds, such as 29, which comprises a pyrimidinone group. This proves that the pharmacophore models, although created based on compounds with urea or amide functions, are capable of scaffold hopping. A chemical structure novelty analysis performed using SciFinder® revealed that 29 is less than 65% similar to known sEH inhibitors. Compounds 22, 28, and 38, which comprise an acyl hydrazide moiety, are less than 75, 70, and 85% similar to known sEH inhibitors, respectively. Even many of the identified sEH inhibitors with urea or amide groups do not show a high similarity to known sEH inhibitors due to their substitution patterns, which are novel for sEH inhibitors (Supporting Information Table S4). 31 ACS Paragon Plus Environment


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The pharmacophore models, which identified these diverse active sEH inhibitors, show similarities in their chemical feature compositions (see Table 2, Figure 9, and Figure 10). All models anchor their ligands to the catalytic amino acids in the binding site by two or three hydrogen bonds, as typically formed by the urea or amide moieties of the well-studied sEH inhibitors. On both sides of the binding site, hydrophobic pockets can accommodate the ligands too. Most models show hydrophobic features on both sides of the catalytic center. However, as shown in models 3, 5, and 6, it seems to be sufficient to occupy only one of these hydrophobic pockets to be accepted as a sEH inhibitor (also compare the chemical structure of compound 18). To further investigate the putative binding modes of the tested compounds, they were fitted into the binding site in a molecular docking simulation. For most active compounds, which contain urea, amide, or hydrazide groups, the docking poses confirmed the binding pattern predicted by the pharmacophore models. However, the binding modes of the diverse scaffolds observed in this virtual screening deserve some attention. In 20 (see Figure 14A), the key feature was a thiourea functionality with neighboring carbonyl functionality. A nitrogen of the thiourea group formed a hydrogen bond with Asp335, while the oxygen of the neighboring carbonyl interacted with Tyr466, matching the typical bonding pattern of sEH in the active site. An additional HBD interaction with Gln384 was formed by the amide functionality. While both the thiourea functionality and the amide functionality are known to inhibit sEH,45 their combination would represent a novel scaffold of sEH inhibitors. Compound 27 (see Figure 14B), another active of the same compound class, displayed a very similar interaction pattern. For active 29 (see Figure 14C), an amino-substituted pyrimidinone containing a thioether functionality, another binding mode with sEH was predicted. In the docking simulation, Tyr466 formed a HBA interaction with the sulfur atom of the thioether moiety. Interactions with Tyr383 and Gln383 were formed by the amino functionality at the heterocycle. Finally, a nitrogen from the pyrimidinone ring formed a HBD interaction with Asp335. 32 ACS Paragon Plus Environment

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This example confirms afresh that pharmacophore models based on well-known inhibitors can perform scaffold hopping and retrieve diverse active structures from large compound databases. However, further studies are required to confirm the predicted molecular binding patterns from these docking experiments.



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Figure 14. Inhibitors of sEH with diverse scaffolds, fitted into the sEH binding site in a molecular docking simulation. The interactions were visualized with LS, color code: HBA (red arrow), HBD (green arrow), H (yellow sphere). (A) 20, (B) 27, (C) 29.

CONCLUSIONS Within this study, a pharmacophore modeling and virtual screening approach led to the identification of 19 sEH inhibitors with a total hit rate of 40% active compounds. The eight created and experimentally validated pharmacophore models are therefore valuable tools for the selection 34 ACS Paragon Plus Environment

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of compounds for biological testing. The identified sEH inhibitors might possibly contribute to the development of novel lead structures for the treatment of inflammatory disorders. Future studies will address the analysis of the pharmacological profile of the most potent compounds, especially 21 with an IC50 = 4.2 nM, in more detail. Moreover, the model collection will be applied to virtual screening of further compound databases to search for novel sEH inhibitors from different sources, e.g. natural products.

EXPERIMENTAL SECTION Hardware and Software Specifications. Molecular modeling studies were performed on an Intel Pentium Core 8 and an Intel 2 Quad CPU, both running Windows 7. Model Generation and Dataset Preparation. The pharmacophore models were generated with LS36 3.03 software and DS37 3.0 using default settings. For use in LS, all datasets were transformed into 3D multi-conformational databases in the LS ldb file format with the omega-fast51-52 algorithm implemented in LS, automatically generating a maximum of 25 conformers for each compound. Within DS, the fast setting was used to calculate 100 conformers per compound. The models were created either structure-based or ligand-based. Structure-based models are calculated based on the structural data of a co-crystallized inhibitor-protein complex, while ligandbased models are contrived by 3D alignment of different active molecules. There are two ways to generate ligand-based pharmacophores in LS: In the shared feature mode, only features, which are present in all aligned molecules, are represented, while in the merged feature mode, all features available in the base molecules are combined to one multi-feature pharmacophore.



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Compilation of Test Set Molecules for Theoretical Model Validation. A dataset of sEH active compounds was compiled from literature. Only compounds that had been tested in a cell-free activity assay with an IC50 below 15 µM were selected for the dataset. The final set comprised of 68 compounds27-31, 39-40, 43-46, 49-50, 53-74 is shown in the Supporting Information (Figure S1, Table S1). In addition, 192 inactive compounds44, 46, 49, 57, 59, 64, 68, 75-80 were also collected in a dataset (Figure S2, Table S2). Compounds were counted as inactive if their IC50 was above 50 µM. These two datasets were employed to train and optimize the automatically generated models to find the active compounds and reject the confirmed inactive structures. Pharmacophore Model Refinement and Theoretical Validation. The automatically generated models were optimized by removing features and adapting feature sizes to yield most of the active compounds from the test set and exclude the confirmed inactives. As a measure of the discriminatory power of the individual pharmacophore models, the EF was calculated. The EF is a ratio formed by the true positives among all recovered hits (yield of actives) divided by the share of actives in the database (DB) (see equation 1).81-82 An overview over the enrichment metrics of the selected models can be found in Table 1. 𝐸𝐹 =

(𝑡𝑟𝑢𝑒 𝑝𝑜𝑠𝑖𝑡𝑖𝑣𝑒𝑠)/(𝑡𝑜𝑡𝑎𝑙 𝑛𝑢𝑚𝑏𝑒𝑟 𝑜𝑓 ℎ𝑖𝑡𝑠) (𝑎𝑐𝑡𝑖𝑣𝑒𝑠 𝑖𝑛 𝐷𝐵)/(𝑡𝑜𝑡𝑎𝑙 𝑛𝑢𝑚𝑏𝑒𝑟 𝑜𝑓 𝑐𝑜𝑚𝑝𝑜𝑢𝑛𝑑𝑠 𝑖𝑛 𝐷𝐵)

Equation 1. Calculation of the EF. ROC-AUC values were calculated using the ‘Calculate ROC Curve' protocol available within DiscoveryStudio 4.0. Virtual Screening of the Specs Database and Selection of Test Compounds. The Specs virtual database (www.specs.net), containing 202,920 molecules in version 01/2012, was downloaded and conformational libraries were calculated for LS (25 conformers, fast) and DS (100 conformers, fast). Virtual screening was performed using default settings. The purity of the selected test 36 ACS Paragon Plus Environment

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compounds was determined by the provider using HPLC coupled to MS (HPLC-MS) and/or 1HNMR spectroscopy. The melting behavior of the substances was assessed using a Reichert Thermovar polarization microscope (Reichert, Vienna) equipped with a Kofler hot stage (Reichert, Vienna). Measurements were performed using a heating rate of 10 K/min. The analysis data of the identified sEH inhibitors are available in the Supporting Information. Docking. The docking simulation was performed in GOLD (version 5.2).83 The ligands were energetically minimized in DS (version 3.0)37 prior to the simulation. The docking was performed on the crystal structure 3ant, which shows the enzyme in complex with 6 (IC50 = 8.5 nM).27 The binding site was defined in a 7 Å radius around the original ligand in the [A] chain of the protein. As a scoring function, the CHEMPLP score implemented in GOLD was used. To evaluate the workflow, a re-docking of the original ligand was performed. The best ranked resulting pose had an rmsd of 1.185 in comparison to the crystallized structure. Screening against PAINS. The 19 compounds that were active in the biological assay were screened

with

the

Pan

Assay

Interference

Compounds

(PAINS)

remover

tool

(http://cbligand.org/PAINS/login.php). Aside from 25, all compounds passed the filter. Compound 25 was found to be in the PAINS group 30: anil_di_alk_a (478). Anilated dialkyles are described as putative PAINS because tertiary anilines are problematic in alpha-sceenings.84 Since we used a different type of assay, we can exclude that the compound came up as a false positive. Expression and Purification of sEH. Human recombinant sEH was expressed and purified as described.85-86 Briefly, Sf9 insect cells were cultured in suspension at 27 °C to a density of 1 × 106 cells and infected with the recombinant baculovirus at a multiplicity of infection of 0.1 virus/ cell. The recombinant virus was kindly provided by Dr. B. Hammock, University of California, Davis, CA. 72 h post transfection, cells were harvested, washed in PBS, disrupted in buffer (50 mM 37 ACS Paragon Plus Environment


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NaHPO4, pH 8, 300 mM NaCl, 10% glycerol, 1 mM EDTA, 1 mM PMSF, 10 µg/ml leupeptin, and 60 µg/ml soybean trypsin inhibitor) by sonication, 3 × 10 sec at 4°C. The crude cell suspension was centrifuged for 1 h, 100,000×g, 4 °C, and the supernatant was used for purification of sEH by affinity chromatography, utilizing benzylthio-sepharose.85 After the column material was equilibrated with washing buffer (50 mM NaHPO4, pH 8, 1 mM EDTA), the affinity gel matrix was incubated with the 100,000×g supernatant for 30 min, at 4 °C under gentle rotation. The batch was poured into a column and the non-binding fraction was collected using gravity flow. Afterwards, the column was washed with an equal amount of washing buffer containing 0.5 M NaCl, to remove loosely bound proteins. Finally, sEH was eluted by 0.5 mM 4-fluorochalcone oxide in PBS containing 1 mM DTT and 1 mM EDTA. To remove the eluent from the protein, the eluted enzyme solution was dialyzed and concentrated using Millipore Amicon-Ultra-15 centrifugal filter units and wash buffer. The purity of sEH protein was verified by SDS-PAGE. Cell-free sEH Activity Assay. Enzyme activity of sEH was determined by a fluorescence-based assay using the non-fluorescent compound PHOME (Cayman Chemical, Ann Arbor, MI), which is converted by sEH to the fluorescent 6-methoxy-naphtaldehyde. Test compound/vehicle or control inhibitor AUDA (Cayman Chemical, Ann Arbor, MI) were pre-incubated with sEH in assay buffer (25 mM Tris HCl, pH 7, 0.1 mg/ml BSA) for 10 min at room temperature. PHOME was added at a final concentration of 50 µM and incubated for 60 min in the dark. The reaction was stopped by ZnSO4 and the fluorescence was read at 465 nm emission after excitation at 330 nm. Potential fluorescence of the tested compounds at this excitation was determined prior to the reaction, and was subtracted from the read out when applicable.


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Statistics. Data obtained from in vitro experiments are expressed as mean ± S.E.M. of triplicate determinations performed in three independent experiments at different days. IC50 values were calculated by nonlinear regression using SigmaPlot87 9.0 one site binding competition. Supporting Information. Information on clinical trials, chemical structures of all active and inactive compounds of the sEH inhibitor test sets, their IC50 values described in literature, results of the virtual screening of the test sets with the final pharmacophore model set, information on ligand-based pharmacophore modeling, generation and description of finally selected pharmacophore models, purity, melting point ranges, LC-MS, and 1H-NMR data of identified sEH inhibitors, chemical structures of compounds from the Specs database that showed < 50% sEH inhibition in the enzyme assay, and the results of a novelty analysis of the identified sEH inhibitors. This material is available free of charge via the internet at http://pubs.acs.org.

CORRESPONDING AUTHOR INFORMATION *Phone: +43 512 507 58253. E-mail: [email protected].

ACKNOWLEDGMENT This work was supported by the Austrian Science Fund (FWF) National Research Network project “Drugs from Nature Targeting Inflammation” (subprojects S10703 and S10711), the Standortagentur Tirol (TWF), and the Erika Cremer Habilitation Program from the University of Innsbruck. We also appreciate Elisabeth Gstrein for determination of the melting point ranges.



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ABBREVIATIONS USED AA, arachidonic acid; COX, cyclooxygenase; cPLA2, cytosolic phospholipase A2; CYP450, cytochrome P450; DB, database; DiHETE, dihydroxyeicosatetraenoic acid; DS, Discovery Studio; EET, epoxyeicosatrienoic acid; EF, enrichment factor; H, hydrophobic interaction; HBA, hydrogen bond acceptor; HBD, hydrogen bond donor; HETE, hydroxyeicosatetraenoic acid; LO, lipoxygenase; LS, LigandScout; LTA4, leukotriene A4; PAINS, Pan Assay Interference Compounds; PGH2, prostaglandin H2; PLIF, Protein Ligand Interaction Fingerprints; RA, aromatic ring feature interaction; sEH, soluble epoxide hydrolase; VH, virtual hit.


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Discovery of Potent Soluble Epoxide Hydrolase (sEH) Inhibitors by

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