The first pharmacophore model for potent G protein-coupled receptor 119 agonist

European Journal of Medicinal Chemistry 46 (2011) 2901e2907

Contents lists available at ScienceDirect

European Journal of Medicinal Chemistry journal homepage: http://www.elsevier.com/locate/ejmech

Original article

The first pharmacophore model for potent G protein-coupled receptor 119 agonist Xiaoyun Zhu 1, Dandan Huang 1, Xiaobu Lan, Chunlei Tang, Yan Zhu, Jing Han, Wenlong Huang*, Hai Qian* Centre of Drug Discovery, China Pharmaceutical University, 24 Tongjiaxiang, Nanjing, Jiangsu 210009, China

a r t i c l e i n f o

a b s t r a c t

Article history: Received 20 December 2010 Received in revised form 23 March 2011 Accepted 4 April 2011 Available online 13 April 2011

G protein-coupled receptor 119 (GPR119) has emerged as arguably one of the most exciting targets for the treatment of type 2 diabetes mellitus in the new millennium. Pharmacophore models were developed by using Discovery Studio V2.1 with a training set of 24 GPR119 agonists. The best hypothesis consisting of five features, namely, two hydrogen bond acceptors and three hydrophobic features, has a correlation coefficient of 0.969, cost difference of 62.68, RMS of 0.653, and configuration cost of 15.24, suggesting that a highly predictive pharmacophore model was successfully obtained. The application of the model shows great success in predicting the activities of the 25 known GPR119 agonists in our test set with a correlation coefficient of 0.933. Ó 2011 Elsevier Masson SAS. All rights reserved.

Keywords: GPR119 agonists Pharmacophore HypoGen Discovery Studio Type 2 diabetes

1. Introduction Type 2 diabetes mellitus (T2DM) is emerging as a disease of staggering proportions in the 21st century, with an estimated 300 million cases worldwide projected by 2020 [1]. Historically, treatment regimens for T2DM have shown significant effectiveness for improving glucose homeostasis; however, increasing failure to maintain glycemic control is observed after about 2 years of therapy [2,3]. Modulators of G protein-coupled receptors (GPCRs), which represent one of the most successful target classes in drug discovery [4], have been identified as prime candidates for type 2 diabetes and associated disorders for new treatment [5,6]. GPR119 has been described as a class A (rhodopsin-type) orphan GPCR without close primary sequence relative in the human genome [7]. The initial results with prototypical potent and selective, orally available, synthetic GPR119 agonists indicate that these compounds could be potential therapies for diabetes and related metabolic disorders by i) stimulating glucose-dependent insulin secretion; ii) inducing the release of Glucose-dependent insulinotropic peptide (GIP) and glucagon-like peptide-1 (GLP-1); iii) protecting pancreatic b cells through raised cAMP levels; and iv) reducing body weight and food intake [8e10]. * Corresponding authors. Tel.: þ86 25 83271302; fax: þ86 25 83271480. E-mail addresses: [email protected] (W. Huang), [email protected] (H. Qian). 1 These authors contributed equally to this work. 0223-5234/$ e see front matter Ó 2011 Elsevier Masson SAS. All rights reserved. doi:10.1016/j.ejmech.2011.04.014

Agonists of GPR119 have emerged from pharmaceutical discovery efforts to identify an improved GLP-1 therapeutic by combining the convenience oral dosage of DPP-IV inhibitors and the pharmacological robustness of GLP-1 receptor agonists. The field of GPR119 agonist research has progressed far enough for some companies to push compounds for the clinical use. For example, GlaxoSmithKline (GSK) and Metabolex have announced that Phase II clinical trials with GSK-1292263 and MBX-2982 have been completed in 2010, respectively [11]. Arena and partner Ortho-McNeil initiated the earliest FTIH studies with APD668 and have recently progressed APD597 into Phase I clinical trials [11]. With the passage of the GPR119 agonist clinical candidates into phase trials and confirmatory reports of clinical proof of concept with respect to glycemic control and incretin release, the spotlight has been set for a new class of therapeutics for type 2 diabetes and associated obesity [12]. In the present study, we have generated pharmacophore models using Discovery Studio V2.1 for a diverse set of molecules as GPR119 agonist with an aim to obtain the pharmacophore model that would provide a hypothetical picture of the chemical features responsible for activity. Further employment of the best pharmacophore model will be used as a 3D query for searching large databases to identify GPR119 agonist and also to utilize this pharmacophore model as a predictive tool for estimating biological activity of GPR119 agonist through virtual screening or molecular designing on the basis of structuree activity analysis.

2902

X. Zhu et al. / European Journal of Medicinal Chemistry 46 (2011) 2901e2907

2. Materials and methods

structural diversity and wide coverage of molecular bioactivities in terms of EC50 ranging from 0.5 nM to 4360 nM (Fig. 1 and Table 1). To validate our pharmacophore hypothesis, 25 compounds with available EC50 values were used as a test set (Fig. 2 and Table 2) [21].

2.1. Selection of molecules A set of 49 different compounds tested with the same assay (CHO: CRE reporter assay) has been collected from different references [11e20]. The datasets are divided into a training set and a test set. We selected 24 compounds as training set with the following rules: i) Both training and test sets should cover the widest possible range of molecular bioactivities (EC50); ii) Both the highly active and low active compounds should be included. iii) Both training and test sets should cover a diversity of structure. The training set consists of 24 compounds with significant

O

Cl

S O

O S O N

N

N

O

N

N

HN

N

O

O

O S O

O O

4

S

N

O

N N

N

N N NO2

N N N

O

N N

NO2

N

O

O

N

O

O

O

N

N

N

O

N

N

16

O

O

O

N N

N

N

F

F

O

F

N

18

O

O

N N

O

O

N O

O

F

O

N

19

14

15

N N N N

N

O S O

N

F

F

O

F

F

O

F

N

20

N

21

F

O

N

N

N

O

N

N N

N

13

N N N N

N

17

N

N

N

N N

N

N

N

N

N

OH

O S O N

N

N N

F

O

N

N

11

10

9

F

O O S

N N N

N

OH

F

F

O

12

F

N

N

N

N N

O

8

O

7

F

O

N

N

N

O S

6 F

N

N

O

F HN

N

N

5

O O S N

N N

N

N

N

N

O

3

2

N

O

O

N

1

N

O

N

N

N N

O

O

O S O

N

N

O

N

N

N

F

O

N

HN

O

Before starting the pharmacophore generation process, conformational analysis of the molecules was performed using the poling algorithm [22]. The poling algorithm eliminates much of the redundancy in conformation generation and improves the coverage of conformational space. The number of conformers

O S O N

O O

N H

O O

O

S

O

N

N

O S O N

2.2. Diverse conformation generation

F

N

N

F

O

23

Fig. 1. Chemical structures of the 24 training set molecules applied to HypoGen pharmacophore generation.

F

O

N

N N

22

F

O

N N

O S

O

N

24

N

X. Zhu et al. / European Journal of Medicinal Chemistry 46 (2011) 2901e2907

2903

Table 1 Actual and estimated activity of the training set molecules based on the pharmacophore model Hypo1. Index

Compound no.

Fit valueb

Exp.EC50 nM

Predicted EC50 nM

Errora

Experimental scalec

1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24

21 16 23 24 18 17 20 22 19 8 6 12 15 2 10 4 7 5 9 11 13 14 1 3

9.77 9.74 9.78 9.01 9.48 8.88 9.04 8.57 8.26 7.48 8.00 6.90 6.98 7.26 7.01 7.21 6.26 6.65 6.76 6.60 6.53 6.45 6.72 6.28

0.5 0.7 1 1 2 5 5 28 29 81.8 87 200 360 396 400 514 544.6 666 1200 1300 1800 1800 4010 4360

0.78 0.83 0.76 4.47 1.50 5.99 4.16 12.29 24.98 152.43 45.32 576.71 482.60 253.47 444.87 280.14 2538.33 1033.10 787.90 1162.41 1349.26 1641.03 862.26 2422.33

þ1.5 þ1.2 1.3 þ4.7 1.4 þ1.2 1.2 2.1 1.2 þ2.0 2.0 þ3.3 þ1.4 1.6 þ1.1 1.8 þ4.7 þ1.6 1.8 1.1 1.1 1.3 4.9 1.8

þþþ þþþ þþþ þþþ þþþ þþþ þþþ þþþ þþþ þþþ þþþ þþ þþ þþ þþ þþ þþ þþ þ þ þ þ þ þ

þþþ þþþ þþþ þþþ þþþ þþþ þþþ þþþ þþþ þþ þþþ þþ þþ þþ þþ þþ þ þ þþ þ þ þ þþ þ

a Difference between the predicted and experimental values. “þ” indicates that the predicted EC50 is higher than the experimental EC50; “” indicates that the predicted EC50 is lower than the experimental EC50; a value of 1 indicates that the predicted EC50 is equal to the experimental EC50. b Fit value indicates how well the features in the pharmacophore overlap the chemical features in the molecule. c Activity scale: EC50 < 100 nM, þþþ (highly active); 100 nM  EC50 < 1000 nM, þþ (moderately active); EC50  1000 nM, þ (low active).

generated for each compound was limited to a maximum of 255 with an energy range of 20 kcal mol1. In Discovery Studio V2.1, the BEST method provides complete and improved coverage of conformational space by performing a rigorous energy minimization and optimizing the conformations in both torsional and cartesian space using the poling algorithm, while FAST generation only searches for conformations in torsion space and so takes less time [23,24]. The conformers of training set were generated using the BEST methods. 2.3. Generation of the 3D pharmacophore For the present work, pharmacophore models were developed using the HypoGen module implemented in Discovery Studio 2.1 [23] with the conformers generated for the molecules in the training set (n ¼ 24). Hypothesis generation in Discovery Studio has three steps, which are constructive phase, subtractive phase, and optimization phase, respectively. Uncertainty influences the first step, called the constructive phase, in the hypothesis generating process [25]. The default uncertainty value of 3 was used for the compound activity, representing the ratio of the uncertainty range of measured SAR Pharmacophore Generation module/Discovery Studio (DS), which was used to construct pharmacophore model using hydrogen bond acceptor (HBA), hydrogen bond donor (HBD), and hydrophobic (H) chemical features [21,26,27]. 2.4. HypoGen mode Pharmacophores were computed by HypoGen encoded and top 10 hypotheses (Table 3) were exported finally. The best hypothesis (Hypo1, Fig. 3) consisted of two HBA and three H with a high correlation coefficient of 0.969, cost difference of 62.68, low RMS of 0.653, and configuration cost of 15.24. The fixed and the null cost values are 97.095 and 164.965, respectively. A value of 40e60 bits between them for a pharmacophore hypothesis may indicate that it

has 75e90% probability of correlating the data [23]. The greater the difference between the cost of the generated hypothesis and the cost of the null hypothesis, the less likely it is that the hypothesis reflects a chance correlation. The results of our study demonstrate that we have successfully developed a reliable pharmacophore model with high predictivity. 3. Pharmacophore model validation Validation of a quantitative model was performed in order to determine whether the developed model was able to identify active structures and to forecast their activities precisely. There are several methods to confirm the quality of pharmacophore like preparing test set, Fischer’s randomization method, goodness of fit (GF) etc. 3.1. Fischer’s validation The statistical significance of Hypo1 was estimated using Fischer’s randomization test [24]. This was done by scrambling the activity data of the training set molecules and assigning them new values, and then generating pharmacophore hypotheses using the same features and parameters originated for Hypo1. To achieve the confidence level of 95%, 19 random spreadsheets were generated. The significance of the hypotheses was calculated using the following formula [1  (1 þ X)/Y]  100, Here, X ¼ 0 and Y ¼ (19 þ 1), S ¼ [1  ((1 þ 0)/(19 þ 1))]  100% ¼ 95% [23,28]. Fig. 4 clearly shows that the Hypo1 hypothesis was not generated by chance, but meaningful and successful. 3.2. Prediction with test set molecules In addition to the estimation of activity of the 24 training set molecules, the pharmacophore model should also be able to estimate the activity of new compounds. The conformers

2904

X. Zhu et al. / European Journal of Medicinal Chemistry 46 (2011) 2901e2907

HO O S O N

N

N

N

N

HN

N

O O S O N

O S O N

N

O

O

N

N

N N

N N

N

N

N

N O

N O S O

N O

O

O

O

N

S

O

F

O

O

N

N

NH

N O

O

35 F F

N

N

O

N N

O

O

N

N O S O

N O S O

36

37

O

NH2

N

N

O

N N

N

N

N

N O S O

N N N

N

O

N

N

N O

O

N

39

38 N

O

HN

N O S O

N N

F

O

O

40

N

N

N

N

F

F F

O

F

N

N

45

N N

F O

F

F

F

O

F

N N

N N

44

O

O

42

41

N

N

N

N O

F F

N O

N O

N N

43

O

O S O N

N

O N

N N

34

O

S O N

O

S

33

32

O

N N N

N

F

31

N O S O

O

O

N

N N

N O S O

O

O

N

O

O

N

N

N

N

O

N

O

NH

N N

30

29

O

O

N

N

28

27

26

25

N N

N

N

N

O

O N

N

N

N

N

O

O

O

O

N

N

N

O

O

O

O

O S O N

O N S O N

O

46

N

N

N N 47

O 48

N

O

49

Fig. 2. Chemical structures of the 25 test set molecules.

generated for the test set molecules (Fig. 2 and Table 2) were selected and mapped using the corresponding pharmacophore models with the BEST methods. The correlation coefficient (r) for the test set given by Hypo1 was 0.933. Test set compounds were classified on the basis of their activity values: þþþ, EC50 < 100 nM (highly active); þþ, 100 nM  EC50 < 1000 nM (moderately active); þ, EC50  1000 nM (inactive). The actual and estimated GPR119 agonists activities of the 25 compounds based on Hypo1 are listed in Table 2. All the compounds except

compounds 31, 38, 43, and 48 were classified correctly (Table 2). The discrepancy between the actual and estimated activity observed for the four compounds was only about one-order of magnitude, which might be an artifact of the program that uses different number of degrees of freedom for these compounds to mismatch the pharmacophore model. This result was used for further legalization of Hypo1 and we also suggest that the Hypo1 not only fits for training set compounds but also for the external compounds [28].

X. Zhu et al. / European Journal of Medicinal Chemistry 46 (2011) 2901e2907

2905

Table 2 Actual and estimated activity of the test set molecules based on the pharmacophore model Hypo1. Index

Compound No.

Fit valueb

Exp.EC50 nM

Predicted EC50 nM

Errora

Experimental scalec

Predicted scalec

1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25

25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49

7.90 8.28 8.17 7.03 5.58 7.15 6.55 6.21 6.48 6.42 6.26 6.36 6.51 6.19 5.48 6.02 7.25 6.23 6.18 7.88 7.53 8.93 7.71 8.52 7.90

7 5 27 204 2600 300 200 2000 1800 1200 1000 1000 1600 800 2800 2000 900 2100 700 9 22 3 92 4 62

58 24 31 428 11936 327 1293 2827 1522 1737 2501 1980 1403 2935 15116 4352 259 2665 2991 60 136 5 89 14 57

þ8.2 þ4.8 þ1.2 þ2.1 þ4.6 þ1.1 þ6.5 þ1.4 1.2 þ1.4 þ2.5 þ2.0 1.1 þ3.7 þ5.4 þ2.2 3.5 þ1.3 þ4.3 þ6.6 þ6.2 þ1.8 1.0 þ3.5 1.1

þþþ þþþ þþþ þþ þ þþ þþ þ þ þ þ þ þ þþ þ þ þþ þþ þþ þþþ þþþ þþþ þþþ þþþ þþþ

þþþ þþþ þþþ þþ þ þþ þ þ þ þ þ þ þ þ þ þ þþ þþ þ þþþ þþ þþþ þþþ þþþ þþþ

a Difference between the predicted and experimental values. “þ” indicates that the predicted EC50 is higher than the experimental EC50; “” indicates that the predicted EC50 is lower than the experimental EC50; a value of 1 indicates that the predicted EC50 is equal to the experimental EC50. b Fit value indicates how well the features in the pharmacophore overlap the chemical features in the molecule. c Activity scale: EC50 < 100 nM, þþþ (highly active); 100 nM  EC50 < 1000 nM, þþ (moderately active); EC50  1000 nM, þ (low active).

3.3. Decoy set Decoy set was generated to evaluate the efficiency of Hypo1 by computing Goodness of fit score (GF) and Enrichment Factor (EF). Decoy set contains 38 active compounds of GPR119 agonists and 1500 inactive compounds. A set of 38 active compounds tested with the same assay (CHO: CRE reporter assay) has been collected from different references [11e20]. The screening was performed using the Ligand Pharmacophore Mapping module in Discovery Studio, with the conformers generated for the decoy set molecules using the FAST methods. Total number of compounds in the hit list (Ht), number of active percent of yields (%Y), percent ratio of actives in the hit list (%A), false negatives, false positives EF and GF are listed in Table 4. The false positives, true negatives EF and GF are 1, 1, 39.4 and 0.97, respectively, indicating Hypo1 with high efficiency of the screening.

Table 3 Results of top 10 pharmacophore hypotheses generated using training set. Hypo no.

Total cost Cost RMSb Correlation Featuresc differencea

Hypo1 Hypo2 Hypo3 Hypo4 Hypo5 Hypo6 Hypo7 Hypo8 Hypo9 Hypo10

102.28 106.17 107.03 109.09 109.65 110.02 110.68 110.81 111.14 111.16

62.68 58.80 57.94 55.87 55.32 54.94 54.29 54.15 53.82 53.81

0.65 0.79 0.82 0.93 0.96 1.03 1.05 1.05 0.97 1.04

0.97 0.96 0.95 0.94 0.93 0.92 0.92 0.92 0.93 0.92

HBA, HBA, HBA, HBA, HBA, HBA, HBA, HBA, HBA, HBA,

The phase II clinical trials with GSK-1292263 as a GPR119 agonist has been completed in May 2010. The structure of GSK1292263 (Fig. 5) was disclosed in Drug Report (THOMSON PHARMAÒ). GSK-1292263 was selected from 1538 compounds by using Hypo1. The Fit-Value and Estimate of GSK-1292263 that was aligned in Hypo1 shown in Fig. 5 are 8.8 and 7.7 (nM), respectively. The result suggests that Hypo1 perfectly fits GPR119 agonists in trials. 3.4. Virtual screening The validated pharmacophore model, Hypo1, was used as a search query to retrieve molecules with novel and desired chemical features from the NCI database and Maybridge database which consists of 60,000 compounds. Compounds that had HypoGen estimated activity EC50 < 500 nM were considered as the rather active compounds. The 20 compounds satisfied the specified cutoff value. According to the rule-of-five model,

Max. fit

HBA, H, H, H 10.60 H, H, H 10.09 H, H, H 10.29 H, H, H 10.17 H, H, H 10.01 HBA, H, H, H 8.90 H, H, H, H 11.20 H, H, H, H 11.54 H, H, H 10.80 H, H, H 9.77

a Cost difference ¼ null cost  total cost. Null cost ¼ 294.92. Fixed cost ¼ 101.34. Configuration cost ¼ 16.12. All cost units are in bits. Configuration cost: a fixed cost which depends on the complexity of the hypothesis space being optimized. b RMS, the deviation of the log (estimated activities) from the log (measured activities) normalized by the log (uncertainties). c HBA, hydrogen bond acceptor; H, hydrophobic feature.

Fig. 3. The best pharmacophore model Hypo1 where H and HBA are illustrated in cyan and green, respectively. (For interpretation of the references to colour in this figure legend, the reader is referred to the web version of this article.)

2906

X. Zhu et al. / European Journal of Medicinal Chemistry 46 (2011) 2901e2907

Fig. 4. The difference in costs between HypoGen runs and the scrambled runs. The 95% confidence level was selected.

Table 4 Statistical parameter from screening test set molecules. No

Parameter

Values

1 2 3 4 5 6 7 8 9 10

Total number of molecules in database (D) Total number of actives in database (A) Total number of hit molecules from the database (Ht) Total number of active molecules in hit list (Ha) %Yield of actives [(Ha/Ht)  100] %Ratio of actives [(Ha/A)  100] False negatives [A  Ha] False Positives [Ht  Ha] Goodness of fit scorea (GF) Enrichment Factorb (EF)

1538 38 38 37 97.40% 97.40% 1 1 0.97 39.4

a [(Ha/4HtA)(3A þ Ht)  (1  ((Ht  Ha)/(D  A)))]. GF score of 0.6e0.7 indicates a very good model. b (Ha  D)/(Ht  A).

compounds were considered likely to be well-absorbed when they possess LogP < 5, molecular weight < 500, number of H-bond donors < 5, and number H-bond acceptors < 10. There were 8 compounds satisfied Lipinski’s rule among 20 compounds which would be proceeded for further evaluation in future study [28,29]. 4. Conclusion In this study, chemical based pharmacophore modeling of GPR119 agonists has been created by using DS. The best HypoGen model in terms of predictive values consisted of two HBA and three H. Test set, Decoy set and Fischer’s validation methods have been used to validate the pharmacophore model. For predicting activity, the correlation coefficient of Hypo1 with training and test sets were 0.933 and 0.933 respectively. The Fit-Value and Estimate activity of GSK-1292263, which have completed phase II clinical trials as a GPR119 agonist, based on Hypo1 in Decoy set are 8.8 and 7.7 (nM), respectively. From the overall analysis, we conclude that the Hypo1 pharmacophore truly reflects the features of GPR119 agonist. Thus, the pharmacophore generated can be used to evaluate how well any newly designed compound maps on the pharmacophore before undertaking any further study including synthesis, and also used as a three-dimensional query in database searches to identify compounds with diverse structures that can potentially agonist GPR119. Acknowledgment The work was supported by the Important National Science and Technology Specific Projects (2009ZX09102-033), the National Natural Science Foundation of China (No. 30772647) and the Fundamental Research Funds for the Central Universities of China (No. 2J10023). References

Fig. 5. Best pharmacophore model Hypo1 aligned to GSK1292263. Pharmacophore features are colour coded (H, cyan and HBA, green). (For interpretation of the references to colour in this figure legend, the reader is referred to the web version of this article.).

[1] [2] [3] [4]

J.C. Seidell, Br. J. Nutr. 83 (Suppl. 1) (2000) S5eS8. U.P.D.S. Group, Lancet 352 (1998) 837e853. R.M. Jones, J.N. Leonard, Annu. Rep. Med. Chem. 44 (2009) 149e170. S. Schlyer, R. Horuk, Drug. Discov. Today. 11 (2006) 481e493.

X. Zhu et al. / European Journal of Medicinal Chemistry 46 (2011) 2901e2907 [5] C. Bjenning, H. Al-Shamma, W. Thomsen, J. Leonard, D. Behan, Curr. Opin. Investig. Drugs. 5 (2004) 1051e1062. [6] H.B. Schioth, CNS Neurol. Disord. Drug Targets 5 (2006) 241e249. [7] R. Fredriksson, P.J. Hoglund, D.E. Gloriam, M.C. Lagerstrom, H.B. Schioth, FEBS. Lett. 554 (2003) 381e388. [8] M.C.T. Fyfe, J.G. McCormack, H.A. Overton, M.J. Procter, C. Reynet, Expert Opin. Drug Discovery 3 (2008) 403e413. [9] F. Rodriguez de Fonseca, M. Navarro, R. Gomez, L. Escuredo, F. Nava, J. Fu, E. Murillo-Rodriguez, A. Giuffrida, J. LoVerme, S. Gaetani, S. Kathuria, C. Gall, D. Piomelli, Nature 414 (2001) 209e212. [10] S.R. Madiraju, V. Poitout, Endocrinology 148 (2007) 2598e2600. [11] Y. Wu, J.D. Kuntz, A.J. Carpenter, J. Fang, H.R. Sauls, D.J. Gomez, C. Ammala, Y. Xu, S. Hart, S. Tadepalli, Bioorg. Med. Chem. Lett. 20 (2010) 2577e2581. [12] R.M. Jones, J.N. Leonard, D.J. Buzard, J. Lehmann, Expert Opin. Ther. Pat. 19 (2009) 1339e1359. [13] E.R.L. Gerald, WO2010006191, 2010. [14] R.K.A. Wacker, A. Dean, WO2010009183, 2010. [15] E.R. Alper Phillip, WO2009105722, 2009. [16] C.C. Azimioara Mihai, WO2009105717, 2009. [17] R.K. Wacker Dean, WO2009012277, 2009.

2907

[18] F.J.W. Dean, WO2008137436, 2008. [19] G. Semple, B. Fioravanti, G. Pereira, I. Calderon, J. Uy, K. Choi, Y. Xiong, A. Ren, M. Morgan, V. Dave, W. Thomsen, D.J. Unett, C. Xing, S. Bossie, C. Carroll, Z.L. Chu, A.J. Grottick, E.K. Hauser, J. Leonard, R.M. Jones, J. Med. Chem. 51 (2008) 5172e5175. [20] S.D.P. Gillespie, US20090286812, 2009. [21] A. Lu, J. Zhang, X. Yin, X. Luo, H. Jiang, Bioorg. Med. Chem. Lett. 17 (2007) 243e249. [22] A. Smellie, S.L. Teig, P. Towbin, J. Comput. Chem. 16 (1995) 171e187. [23] Accelrys Inc., Discovery Studio 2.1, San Diego, 2010. [24] I. Mitra, A. Saha, K. Roy, J. Mol. Model. 16 (2010) 1585e1596. [25] O. Güner, Pharmacophore Perception, Development, and Use in Drug Design (1999) Intl Univ Line. [26] R. Sarma, S. Sinha, M. Ravikumar, M. Kishore Kumar, S.K. Mahmood, Eur. J. Med. Chem. 43 (2008) 2870e2876. [27] T. Equbal, O. Silakari, G. Rambabu, M. Ravikumar, Bioorg. Med. Chem. Lett. 17 (2007) 1594e1600. [28] S. Sakkiah, S. Thangapandian, S. John, Y.J. Kwon, K.W. Lee, Eur. J. Med. Chem. 45 (2010) 2132e2140. [29] P.C. Hawkins, A.G. Skillman, A. Nicholls, J. Med. Chem. 50 (2007) 74e82.