Structure-based Drugs Design Studies on Spleen

Letters in Drug Design & Discovery, v. 13, p. 845-858, 2016. Send Orders for Reprints to [email protected] Letters in Drug Design & Discovery, 2016, 13, 1-14

1

RESEARCH ARTICLE

Structure-based Drugs Design Studies on Spleen Tyrosine Kinase Inhibitors Letícia Cristina Assisa, Letícia Santos Garciaa, Daiana Teixeira Mancinia, Tamiris Maria Assisa, Daniela Rodrigues Silvaa, Giovanna Gajo Cardosoa, Alexandre Alves de Castroa, Teodorico Castro Ramalhoa,b and Elaine Fontes Ferreira da Cunha*,a a b

Departmentof Chemistry, Federal University of Lavras, C.P. 3037, CEP 37200-000, Lavras, Minas Gerais; Brazil; Center for Basic and Applied research, University Hradec Kralove, Hradec Kralove, Czech Republic

ARTICLEHISTORY Received: March 09, 2016 Revised: July 15, 2016 Accepted: July 19, 2016 DOI: 10.2174/1570180813666160725095 118

Abstract: A quantitative structure-activity relationship analysis has been applied to a series of 97 imidazopyridine analogous Spleen tyrosine kinase (Syk) inhibitors, the enzyme responsible for the signal transduction of classic immunoreceptors. The deregulation of Syk is associated with several pathologies, among which uncontrolled tumor cell growth stands out. The most advanced Syk inhibitor, fostamatinib, has proven efficient in multiple therapeutic indications, but its clinical evolution is still in process. In this context it is necessary to search for new potent inhibitors andin this work we have developed and validated 4D-QSAR models in order to obtain pharmacophoricfeatures that can enhance the potency of the imiE.F.F. da Cunha dazopyridine compounds. The conformations obtained by molecular dynamic simulation were overlapped in a virtual three dimensional box comprised of 1 Å cells, according to the six trial alignments. The models were generated by a combined genetic algorithm (GA) and partial least squares (PLS) regression technique. The best models generated show good adjusted crossvalidate value (q2adjusted) and correlation coefficient value (R2). Analyzing the descriptors it can be observethat the nonpolar substituents are detrimental for activity of these compounds, suggesting hydrophilic regions in the Syk active site.

Keywords: Molecular modeling; QSAR; molecular dynamics; genetic function approximation; Spleen tyrosine kinase; imidazopyridine. 1. INTRODUCTION Spleen tyrosine kinase (EC:2.7.10.2), also known as Syk, is a non-receptor cytoplasmic tyrosine kinase [1] mainly expressed in most hematopoietic cells, including platelets, B cells, mast cells [2, 3] and can also be expressed in other tissues such as the epithelial tissue, fibroblasts, neuronal cells and other cell types [4, 5]. This kinase was first recognized as a 40 kDaproteolytic fragment derived from a p72 tyrosine kinase present in spleen and lung. In 1991, the Syk was cloned from pig spleen with 72kDa [6]. Mechanism of Syk action is incompletely understood, but studies have shown positive results in the treatment of allergy, autoimmune diseases and B cell lineage malignancies [7]. A promising Syk inhibitor, fostamatinib (Fig. 1), is a prodrug of the active compound tamatinib [8]. It demonstrated efficacy in several indications, but its clinical development has been broken because adverse effects such as the limitation of dosage effects, hypertension, gastrointestinal upset (nausea and

* Address correspondence to this author at the Dept. of Chemistry, C.P. 3037, CEP 37200-000, Lavras, Minas Gerais, Brazil; Tel: +55 35 38295129; E-mail: [email protected] 1570-1808/16 $58.00+.00

diarrhea), and neutropenia [9]. Entospletinib (GS-9973, Fig. 1) is a new hugely selective inhibitor of Syk and is in the Phase 2 [9]. The co-crystal structure of the Syk kinase domain with entrospletinib can be found in the Protein Data Bank (PDB code: 4PV0) [9]. In2011, Gilead ScienceInc developed imidazopyridine derivatives able to inhibit Syk activity [10]. Several theoretical tools, such as comparative modeling, molecular dynamics, QSAR (quantitative structure activity relationship) and docking are used in medicinal chemistry to propose new molecular targets in order to direct the design of new drug candidates for the treatment of various diseases [11-13]. Among these tools, a widely used method is the QSAR study. This method is based on the concept that the differences observed in the molecular structure ofa congener series can be quantitatively correlated with its biological activity [14, 15]. Among the QSAR methods known, the 4DQSAR method developed by Hopfinger and coworkers [16] is advantageous because it incorporates the molecular flexibility and freedom of alignment with the average of the three-dimensional conventional descriptors found in traditional 3D-QSAR methods [17], allowing the identification of ©2016 Bentham Science Publishers

2 Letters in Drug Design & Discovery, 2016, Vol. 13, No. 9

Assis et al.

conformation that maximizes the expected activity using the best 4D-QSAR models. In the presented work, 97 imidazopyridine derivative Syk inhibitors [10] have been chosen for further studies using mathematical models. O

OH P

OH

O H N

O

N N

O

H N

N

F

O

O

2.3.Alignment and Interaction Pharmacophore Elements Definition

O Fostamatinib

O N

NH N

N H N

N

N

H N

O

Entospletinib H N N

H N

O

N F

O

tion (MDS) process in order to generate a conformational ensemble profile (CEP) for each compound, thereby investigating the conformational flexibility of the ligand. The analysis was performed on the Molsim software [20]. The temperature for the MDS was set at 300 K, close to the assay temperature, with a simulation sampling time of 100 ps, and intervals of 0.001 ps. The MDS calculations were carried out applying a distance-dependent dielectric function in order to try to model the solvent effect in the absence of explicit solvent.

O

O Tamatinib

Fig. (1). Structures of Syk inhibitors.

2. MATERIALS AND METHOD 2.1. Biological Data Table 1 lists the 97 imidazopyridine compounds [10] that were used in this study. The IC50 (half maximal inhibitory concentration) values (nM) were converted to molar units and subsequently expressed as negative logarithm of units (pIC50 = -logIC50). The Syk crystal structure including crystallization water and N-{6-[(2S)-2-methylpyrrolidin – 1yl]pyridin- 2-yl} – 6 –phenylimidazo[1,2-b]pyridazin-8amine (0VG) was retrieved from Protein Data Bank (PDB code: 4FZ6) database with a resolution of 1.85Å [19]. 0VG, co-crystallized, was used as reference in the constructed of the three-dimensional structure of each compound. The 4DQSAR models were developed using a training data set of 77 compounds, and externally validated using 20 compounds, selected from the original 97 compounds (Table 1). Multiple pairs of training and test sets were generated. It is possible observe the imidazo-oxazine group (Compound 19) extends beyond the hinge residues towardsolvent, which explains the importance of the polar substituents at this position. 2.2. Molecular Dynamic Simulation The optimized 3D structures of the compounds in the Syk active site were subjected to a molecular dynamics simula-

The CEP of each compound generated in the MDS was inserted in a cubic grid box (resolution of 1.0 Å) using different alignments (Table 2) [14,17]. According to the 4DQSARmethodology [20] the descriptors are generated from different types of atoms, which are classified into seven types of interaction pharmacophoric elements (IPE), depending on the type of interaction that each atom is capable of performing [17]. In this work we considered the IPEs: nonpolar (np); polar-positive charge density (p+); polar-negative charge density (p-); hydrogen bond acceptor (hba); hydrogen bond donor (hbd); and aromatic systems (ar)[14]. The grid cell occupancy profiles for each of the chosen IPEs were then computed and used as the basis set of trial QSAR descriptors. The occupancy of grid cell (x, y, z), where x, y, and z define the location in cartesian space of the cell, in time in the MDS ensemble sampling for the IPE type of compound. The occupancy measures can be “normalized” dividing the values by number of sampling steps. 2.4. 4D-QSAR Model Calculation Partial least-squares (PLS) regression analysis was performed as a data reduction fit between the observed dependent variable measures and the corresponding set of grid cell occupancy descriptors (GCOD) values. After these procedures, the genetic function approximation, GFA, developed by Rogers [21] was used to generate the QSAR models. For validation of the QSAR models, the following strategies were adopted: -

Internal validation using leave-one-out crossvalidation (q2 or r2cv) [22]. Adjusted q2: models with q2adj greater than 0.5 are considered good predictive models [15]. External validation using the R2pred value. Modified equation for r2pred giving importance to the difference between r2 and r20 (observed and predicted values with the zero axis intersection). Values greater than 0.5 can be a good indicator of a good external predictability [23].

(

rm2 = r 2 1

r 2 – r02

)

(1)

- Y-randomization: consists of the exchange of the values of the independent variables randomly. For an acceptable QSAR model the Yrand value must be less than the correlation coefficient of the non-randomized design.

Now Tetrazolo[1,5-a]quinoline Derivatives

Table 1.

Letters in Drug Design & Discovery, 2016, Vol. 13, No. 9

3

Chemical structure and the respective values of pIC50 (M) of the 97 compounds derived from imidazopyridines. The bold and underlined represent the selected molecules to form the test group, the other molecules, participate in the training group of QSAR-4D methodology. O

N

HN

O

N

O

HN

N

HN

H N

N

O

N

N

N

N

H N

N

N

N

N

N

S 1 [8.420]

2 [6.527]

O

O

N

HN

3 [7.740]

O HN

O

N

O

HN

N

N N

O

N H N

N

OH

N

N

H N

N N

N

4 [7.650]

N

O

NH2 5 [7.876]

6 [7.625]

N

N

N HN

HN

N

N

HN N

N

N

H N

N

O

N

N

H N

N

N

N

O

7[8.208]

8[6.261]

NH2

9[6.766]

O

N

N

N

N HN

HN HN

N N

N H N

N

H N

N

N

N

N

10 [6.740]

O

N

N

N

N

11 [8.180]

12 [7.392]


Assis et al.

Table 1. contd…

N

N

N

N N

HN

HN

HN

N

N

N H N

N

N

H N

N

N

N

N

N

O

13 [6.813]

14 [7.450]

15 [6.727]

OH

HN

N

N

N

N

OH

N

NH2

HN HN

N

N N

H N

N

N

H N

N

H N

N

N N

16[7.636]

17[8.222]

18[8.046]

O

O

O

N

N

N

N

N HN

H N

N

N

HN

H N

N

N

HN

N

N

N

H N

N N

N

19[8.700]

N

20[7.886]

21[8.301]

OH

O N

HN

HN

H N

N

N N

N N

N

N HN

N

N

N

H N

N

O

H N

N

N

N

O 22[7.410]

23[7.824]

24[8.301]



5

Table 1. contd…

OH

O

OH O

N

N

N HN

HN

N H N

N

H N

N

H N

N N

N

25 [7.699]

N

26 [8.097]

27 [8.097]

O

O N

N

N

S

OH

N

HN

N

N

N

N H N

N

O

N

HN

HN

OH

HN

N

N

N

N

H N

N

H N

N N

N

N

30 [6.536] 28[7.824]

29 [8.301]

O

O N

N

N

N

HN

N

HN

N

HN N

N

H N

N

N

H N

N N

N

N

N H

N

31[7.347]

32[7.634]

33[7.420]

O N N

N

HN

HN

N HN

N

N H N

N

N

N

H N

N

OH

N

O

H N

N

N N

N

OH 34[7.979]

35[7.599]

36[8.070]


Assis et al.

Table 1. contd…

O O

N

OH

N

N

N

N

N

HN

HN

N

HN

N

H N

N

N

H N

N H N

N

N

OH

OH 37 [8.367]

38 [6.860]

OH

N

39 [7.602]

N

N

OH

N

N

N

HN

O

N

HN

N

N N

H N

HN

H N

H N

N

O

O

N N

N

40 [7.268]

42[7.745]

41 [7.886]

O OH

N N

N

OH

N

N

OH

N HN

HN

N

HN N

N

N

H N

N H N

N

H N

N

O N

N 43[8.585]

44 [7.364]

O

O

O N

45 [7.682]

S

N O

S

N

O

N

N

N

N

N H N

HN

HN

HN

O

N

N N

N

N

H N

N N

N

O

N H 46 [8.000]

47 [8.432]

48 [7.810]



7

Table 1. contd… 49 [7.947]

50 [7.622]

51 [7.962]

O O

N N

HN

N

HN

N

N

N

N O

N

HN

N

N

N N

H N

N

N

N

HN HN

O 52 [8.113]

53 [7.967]

54[8.553]

O

N

N N

HN

HN

N

N

N

OH

HN

OH N

N

N

H N

N

N H N

N

O

O

N OH

HN 55 [7.382]

56 [8.149]

57 [7.706]

O

N

N

N

N

O

N

OH

HN N

N

HN

HN N

N

H N

N

H N

N

H N

N

N

N

O

58 [7.349]

59 [7.307]

N

NH HN

N

N

60 [7.893]

O NH

HN HN

N

N

N N N

N N

NH NH

NH

N O 61[7.585]

62 [7.886]

63 [7.657]


Assis et al.

Table 1. contd…

O

N

N

N OH

HN HN

HN

N

N

N

N

N

N

N H N

N

N

NH

NH

N

N

O

OH 64 [7.721]

66[7.770]

N

N

N

HN

OH

65 [7.036]

N

N

N

HN

N

N

N

HN

N N

N

N H N

N

H N

N

NH

O

N 67 [7.770]

68 [7.377]

69 [7.149]

N

HN

O

N

N

N

N

HN

N

N

N

N

H N

N

N

HN H N

N

N N NH

70 [6.521]

N H

N

O

OH 71 [6.054]

72 [6.780]

N NH

O

N

N

N

HN

HN

HN

N

N

N

N

N

N

H N

N N

N

O

N

N

NH

O

N

O 73 [5.988]

74 [6.893]

75 [7.260]



9

Table 1. contd…

N N

HN

N

N N

N

N

HN

N

HN

N N

N H N

N

N

H N

N

H N

N N

76 [6.374]

77 [7.979]

78[6.565]

O N

N

N

N

N HN

HN

N

N

HN

N

N

N

N

N

H N

N

N

H N

N

O

Cl OH

79 [6.274]

80 [6.495]

81 [6.567]

N

N

N

N

N

N HN

HN

N

HN

N

N

N

H N

H N

N

O

Cl

H N

N

N

N

N

Cl

N

Cl

OH 82 [7.097]

83 [7.495]

84 [6.668]

N

N

N

N

N HN

N

N

HN

N

N

HN

N

N

N

N

N

H N

N Cl

H N

N N

Cl

O

Cl

OH 85 [6.556]

86 [7.854]

87 [7.770]


Assis et al.

Table 1. contd…

N

N N

N

HN

N

HN

N

N

N

HN

N

N N

N

H N

N

N

O

Cl

OH

N

O

OH

88 [6.661]

89 [7.824]

N

N

90 [7.921]

N

O

N HN

S

HN

H N

N

Cl

Cl

N

N

N

N

H N

N

H N

N

HN

N

N H N

N

N

F 91 [7.143]

92[7.310]

93 [7.167]

O

N

N

N

N

HN

HN

N

N

N

HN

N

H N

N

H N

N

N

O

94 [7.579]

95 [6.245]

96 [6.122]

H3C

N

HN

N

N

N

H N

N

N

N O

O

97 [7.364]

N HO

O

N

H N

N

N H2N

N

N

N

N

HN

O

0VG

N


Table 2.


11

Three letters (a, b, c) directing the carbon atoms used in the 4D-QSAR defining six tested alignments. Compound 19 (pIC50 = 8.700), the most potent inhibitor of the series, was used to define the atom letter code.

O N N a

HN

H N

N N

b

N

c

Alignment

1st Atom

2st Atom

1

a

b

c

2

a

c

b

3

b

a

c

4

b

c

a

5

c

a

b

6

c

b

A

-R2p: penalizes the model r2 for the difference between squared mean correlation coefficient (r2Y-rand) of randomized models and squared correlation coefficient (r2) of the nonrandomized model predictability [23].

Rp2 = r 2 r 2 – rY2–rand

(2)

3. RESULTS AND DISCUSSION During genetic function approximation analyses several models or equations were generated. Table 3 presents only the best model of each alignment. A good QSAR model depends of the quality of biological data, the choice of descriptors, variable selection, statistical methods and validation. In accord with Veerasamy et al. [18] a QSAR model is predictive if 2 R 2 0.7; q 2 > 0.5; Rpred > 0.6; Rp2 > 0.5; rm2 > 0.5 . An illustration of the results obtained for each model studied is given in Table 3. Analyzing the qadj values, it can be observed that all models showed a good predictive ability. Model 2 showed the highest q2adj value. Models 1, 2 and 3 showed r2 value greater than or equal to 0.7, indicating that they are correlated and can be used to calculate the activity of the test set [15]. In the case of external validation, in the equations 2 above it can be seen that Model 1 shows the best Rpred , Rp2 ,

3st Atom

explain the difference in the activity of Compounds 84 and 85 or 88 and 89, for example, the presence of the methyl group in Compound 85, making it less active that Compound 84. It is believed that the N-H group favors hydrogen bond interaction with Syk. This claim is substantiated by the studies of Currie [9], in which the importance of indazole N-H hydrogen bonding with the Asp512 residue of the Syk active site were mentioned. This observation can be confirmed thought GCOD (0, 9, -2, np). It has the highest frequency of occupation for Compound 76 and is located on the methyl group binding to the 1H-indole ring. When this compound is compared with 68 we can see that the absence of the methyl group increases the activity of Compound 68. GCOD (0, 10, 3 np) is located close to the oxygen atom of the ethanol group of Compounds 39, 50, 56, 65, 66, 70, 84, 85, 88, and 89. Since this GCOD has a negative regression coefficient and nonpolar IPE, the presence of the substituents, as such a hydroxyl group, in this coordinate favor the activity of the ligands. For example, Compound 84 is more potent than the similar 81.

and r 2 values. Fig. 2 presents the graphic representation of m the 3D-pharmacophore embedded in 4D-QSAR Model 1 using the most potent compound this series as a reference. It is important to observe that all compounds in Table 1 contain the imidazopyrazine ring offering the ability to stabilize the inhibitor-Syk complex. The GCOD (0, 6, -2, np) has a negative coefficient value and nonpolar type IPE. It has high frequency of occupancy for the compounds 80, 85 and 88 (Table 1) and is located close to the 1-methyl-1H-indazole ring. This descriptor can

Fig. (2). Compound 19 with the descriptors generated by Model 1. Each GCOD is described as “x, y, z, IPE”, which represents the Cartesian coordinatesof the selectedgrid cell(x,y, z)andthe respectiveatom type(IPE). The describedGCODsare:(1) (0, 6, -2, np), (2) (0, 3, -3, np), (3) (0, 10,3, np), (4) (2, 1, -8, np), (5) (-1, 9, 0, np), (6) (0, -3, -5,np) (7) (0, 0,-3,ahb), (8) (0, 9,-2, np), (9) (1,-1,-4, np), (10) (-1, 6, 3, np) and (11) (0,1,-3, np).


Assis et al.

Table 3. The best 4D-QSAR models. Alignment 1

pIC50 = 5.06 - 2.10 (0,6,-2, np)- 2.65 (0,3,-3,np) -1.15 (0,

Model 1

- 1.25(2,1,-8,np) -1.21(-1,9,0,np) -3.29 (0,-3,-5,np) -1.11 - 2.44 (0,9,-2,np) + 0.87 (1,-1,-4,np) - 0.93 (-1,6,3,np) - 1

Alignment 2 Model 2

pIC50 = 4.44 -4.06 (0,5,7,np) -1.78 (0,-3,13,np) -3.20 (2,9,3 np) -1.49 (1,3,10,np) + 0.73(0,9,4,np) + 0.63(-1,9,5,np) – 0.72(1,10,6,np) +1.53(1,10,5,np) -1.00(0,3,3,abh) – 0.669(0,2,5,np)+0.39 (0,-2,10)

Alignment 3

pIC50 = 4.61 -1.58 (-1,6,7,1) -3.58 (-1,3,8,np) -1.99 (-2,1,1

Model 3

-1.16 (-1, 10,5,np) -1.18(0,-3,10,np) -2.30(0,8,5,np) -1.13 +0.73(-1,-1,11,np) +0.73(0,-2,10,np)

Alignment 4

pIC50 = 4.51 + 3.26 (2,-8,11,np) -7.19 (2,-7,8,np) – 2.90(1

Model 4

-2.16 (0,-6,-2,np) -1.93 (0,-4,1,np) -1.18 (0,0,6,ar) +0.64 ( +0.95 (0,-4,9,np) + 0.78 (-1,-3,9,np) -0.65 (0,-4,-3,np) -0.

Alignment 5

pIC50 = 3.68 + 3.48 (-1,-8,-6,np) – 2.85 (-1,-7,-4,np) -2.49

Model 5

+ 1.52 (0,-4,-3,np) -1.11(-1,-4,5,np) -0.71(0,-1,0,abh) +0.3 + 1.04 (1,-3,-4,np) +0.75 (0,5,5,np) + 0.66 (0,-1,8,abh)

Alignment 6

pIC50 = 4.41 – 6.01 (0,5,-1,np) - 4.55 (1,11,-4,np) + 1.00

Model 6

- 2.08 (1,-3,2,np) – 6.91(0,0,-3,np) + 3.21 (0,-1,-2,np) – 0 - 0.57 (-1,3,-3,np)+ 0.86 (-1,10,1,np) + 0.73 (0,-2,-4,np)

GCOD (-1,6,3, np) is located in the 1H-indazole ring with highest frequency of occupation for Compound 77, however substituents in the imidazopyridine ring may be indirectly responsible for the effects of the GCODs. Compounds 67 (pIC50 = 7.770) and 77 (pIC50 = 7.979) show (a) methyl and ethyl group(s) bonded to the imidazopyridine ring, respectively, and the former is more active than the latter. During MDS the indazole ring of Compound 67 remained in the same plane as the imidazopyridine ring while that in Compound 77 this was not observed. This effect also

occurs with GCOD (-1, 9, 0, np) with the highest frequency of occupation for Compound 79. Replacing the ethyl group from the imidazopyridine ring with chlorine (Compound 82) the potency of the compound increases. The GCODs (0, 3, -3, np), (2, 1, -8, np), (0, -3, -5, np), and (0, 1, -3, np), have negative coefficients and represent nonpolar IPEs, in other words, hydrophobic substituents in these coordinates affect the activity of the compounds. The GCODs (0, -3, -5, np) and (2, 1, -8, np), are located in all compounds with ethyl/isopropyl group bonded to the pyrazine ring. However, GCODs (0, 3, -3, np) and (0, 1,-3, np) are located in all compounds with a/the 1-ethyl-1Hpyrazolegroup, for example, Compounds 8 (pIC50= 6.261) and 9 (pIC50 = 6.766). GCOD (0, 0, -3, ahb) had a negative coefficient value and hydrogen bond acceptor IPE. It is located on the 2-methoxy-pyrimidinegroup with high frequency of occupation in Compound 12. These descriptors suggest a hydrogen bond acceptor region in the receptor close this ring. Thus, imidazopyridine derivatives with hydrogen bond donor substituents in this position favor the activity. In order to better understand the interaction mode between imidazopyridine and Syk, docking studies for Compound 19, 0VG and entospletinib were carried out (Fig. 3). The potential binding sites were calculated and a cavity of 355.0 A3 (surface = 1155.0 A2) was observed close to Leu377, GLy378, Ser379, Phe382, Thr384, Val385, Ala400, Val01, Lys402, Ile403, Met424, Val433, Arg434, Leu446, Met448, GLu449, Met450, ALa451, Glu452, Leu453, Gly454, Pro455, Lys458, Leu501, Ser511 and Asp512. After docking calculations, the binding orientations of ligands into the active site were predicted and the following parameters (Table 4) were then calculated: (a) energy score values used during docking; (b) total interaction energy between ligand and Syk; and (c) hydrogen bond energy values between ligand and Syk. Compound 19 showed energy values more stable than 0VG and entospletinib. The structures of the three compounds are shown in Fig. 3. Hydrogen bonding was observed between the Syk and two compounds: 19 interacted with Ala451, Lys402 and Asp512; 0VG interacted with Ala451. CONCLUSION 4DQSAR was capable of providing predictive models which are useful to predict bioactivity of Syk compounds

Table 4. Validation parameters of different models. Model

R2

RMSEc

q2

q2adj

LSE

LOF

R2pred

RMSEp

Rm 2

Yrand

R2p

1

0.75

0.29

0.62

0.60

0.10

0.23

0.68

0.20

0.56

0.24

0.53

2

0.73

0.30

0.66

0.64

0.11

0.25

0.64

0.22

0.51

0.24

0.52

3

0.70

0.31

0.64

0.62

0.12

0.23

0.59

0.23

0.47

0.20

0.50

4

0.69

0.33

0.60

0.58

0.12

0.28

0.66

0.21

0.28

0.31

0.43

5

0.68

0.33

0.63

0.61

0.13

0.27

0.63

0.22

0.53

0.40

0.37

6

0.68

0.31

0.59

0.57

0.13

0.27

0.48

0.26

0.56

0.37

0.39


Table 5.


13

Estimated energy score values used for the evaluation of docking poses; total interaction energy between ligand and Syk; hydrogen bond energy values between ligand and Syk (energies in kcal mol1). Compound

Escore

Einter

EHbond

19

-128.8

-131.1

-3.5

0VG

-119.6

-126.3

-2.5

entospletinib

-117.1

-126.7

0

A

B

Fig. (3). Docking poses for the three compounds analyze: A) 19 (black) and 0VG (gray); B) 19 (black) and entospletinib (gray).

inhibitors. Model 1 revealed some regions at the imidazopyridine analogs of particular interest for access in future steps of the drug discovery, for example the indazole N-H region of Compound 19 favoring a/the hydrogen bond with Syk and polar substituents at the imidazo-oxazine position favoring hydrophilic interaction.

p-

=

polar-negative charge density

hba

=

hydrogen bond acceptor

hbd

=

hydrogen bond donor

ar

=

aromatic systems

GCOD

=

grid cell occupancy descriptors

LIST OF ABBREVIATIONS

CONFLICT OF INTEREST

QSAR ship

=

quantitative structure activity relation-

The authors confirm that there is no conflict of interest in this article.

Syk

=

Spleen tyrosine kinase

GA

=

genetic algorithm

ACKNOWLEDGEMENTS

PLS

=

partial least squares

q2

=

leave-one-out cross-validation

q2adj

=

adjusted leave-one-out cross-validation

2

R

=

coefficient of determination

RMSEC

=

root mean square error of calibration

We are grate ful to “Conselho Nacional de Desenvolvimento Científico e Tecnológico” (CNPq, Brazil) and to “Fundação de Amparo à Pesquisa do Estado de Minas Gerais” (FAPEMIG, Brazil) for fellowship support. We thank A. J. Hopfinger who kindly supplied the 4D-QSAR program for academic use. This work was also supported by Excellence project FIM.

RMSEP

=

root-mean-square error for prediction

LSE

=

least-square error

REFERENCES

LOF

=

lack-of-fit

[1]

IC50

=

half maximal inhibitory concentration

MDS

=

molecular dynamics simulation

CEP

=

conformationalensembleprofile

IPE

=

interaction pharmacophoric elements

np

=

nonpolar

p+

=

polar-positive charge density

[2]

[3]

Kaur, M., Singh, M., & Silakari, O. (2013). Inhibitors of switch kinase ‘spleen tyrosine kinase’in inflammation and immunemediated disorders: a review.European journal of medicinal chemistry, 67, 434-446. doi:10.1016/j.ejmech.2013.04.070 Pamuk, O. N., & Tsokos, G. C. (2010). Spleen tyrosine kinase inhibition in the treatment of autoimmune, allergic and autoinflammatory diseases.Arthritis research & therapy, 12(6), 1. doi: 10.1186/ar3198 Castillo, M., Forns, P., Erra, M., Mir, M., López, M., Maldonado, M., ... & Vidal, B. (2012). Highly potent aminopyridines as Syk kinase inhibitors.Bioorganic & medicinal chemistry letters, 22(17), 5419-5423. doi:10.1016/j.bmcl.2012.07.045

14 Letters in Drug Design & Discovery, 2016, Vol. 13, No. 9 [4]

[5]

[6]

[7] [8]

[9]

[10] [11]

[12]

[13]

Riccaboni, M., Bianchi, I., & Petrillo, P. (2010). Spleen tyrosine kinases: biology, therapeutic targets and drugs. Drug discovery today, 15(13), 517-530. doi:10.1016/j.drudis.2010.05.001 Lucas, M. C., Goldstein, D. M., Hermann, J. C., Kuglstatter, A., Liu, W., Luk, K. C., ... & Xie, W. (2012). Rational design of highly selective spleen tyrosine kinase inhibitors. Journal of medicinal chemistry, 55(23), 10414-10423. DOI: 10.1021/jm301367c Kaur, M., Singh, M., & Silakari, O. (2013). Inhibitors of switch kinase ‘spleen tyrosine kinase’in inflammation and immunemediated disorders: a review.European journal of medicinal chemistry, 67, 434-446. doi:10.1016/j.ejmech.2013.04.070 Mócsai, A., Ruland, J., & Tybulewicz, V. L. (2010). The SYK tyrosine kinase: a crucial player in diverse biological functions. Nature Reviews Immunology,10(6), 387-402. doi:10.1038/nri2765 Braselmann, S., Taylor, V., Zhao, H., Wang, S., Sylvain, C., Baluom, M., ... & Wong, B. R. (2006). R406, an orally available spleen tyrosine kinase inhibitor blocks fc receptor signaling and reduces immune complex-mediated inflammation. Journal of Pharmacology and Experimental Therapeutics, 319(3), 998-1008. doi:10.1124/jpet.106.109058 Currie, K. S., Kropf, J. E., Lee, T., Blomgren, P., Xu, J., Zhao, Z., ... & Maciejewski, P. (2014). Discovery of GS-9973, a selective and orally efficacious inhibitor of spleen tyrosine kinase. Journal of medicinal chemistry, 57(9), 3856-3873. doi: 10.1021/jm500228a Blomgren, P.; Currie, K.S.;Kropf, J.E.; Lee, S.H.; Mitchell, S.A.; Schmitt, A.C.; Xu, J.; Zhao, Z. (2011). WO2011112995. da Cunha, E. F., Ramalho, T. D. C., de Alencastro, R. B., & Maia, E. R. (2004). Interactions of 5-deazapteridine derivatives with Mycobacterium tuberculosis and with human dihydrofolate reductases. Journal of Biomolecular Structure and Dynamics, 22(2), 119-130. doi: 10.1080/07391102.2004.10506988 Muri, E. M. F., Gomes Jr, M., Costa, J. S., Alencar, F. L., Sales Jr, A., Bastos, M. L., ... & Williamson, J. S. (2004). Nt-Boc-amino acid esters of isomannide Potential inhibitors of serine proteases. Amino acids, 27(2), 153-159. doi: 10.1007/s00726-004-0121-5 da Cunha, E. F. F., Martins, R. C. A., Albuquerque, M. G., & de Alencastro, R. B. (2004). LIV-3D-QSAR model for estrogen recep-

Assis et al.

[14]

[15] [16]

[17]

[18]

[19] [20] [21]

[22]

[23]

tor ligands. Journal of Molecular Modeling, 10(4), 297-304. doi: 10.1007/s00894-004-0198-5 Silva, D. R.; Ramalho, T. C.; da Cunha,E. F. F. (2014). 4D-QSAR model for compounds with binding affinity towards dopamine D2 receptors.Letters in Drug Design & Discovery, 11(5), 649-664. Caldas, G. B., Ramalho, T. C., & da Cunha, E. F. (2014). Application of 4D-QSAR studies to a series of benzothiophene analogs. Journal of molecular modeling, 20(10), 1-10. Hopfinger, A. J., Wang, S., Tokarski, J. S., Jin, B., Albuquerque, M., Madhav, P. J., & Duraiswami, C. (1997). Construction of 3DQSAR models using the 4D-QSAR analysis formalism. Journal of the American Chemical Society, 119(43), 10509-10524. doi: 10.1021/ja9718937 da Cunha, E.F.F.; Albuquerque, M.G.; Antune, O.A.C.; Alencastro, R.B. (2005). 4DQSAR Models of HOE/BAY793 Analogues as HIV1 Protease Inhibitors. QSAR & Combinatorial Science,24(2), 240-253. doi: 10.1002/qsar.200430893 Veerasamy, R., Rajak, H., Jain, A., Sivadasan, S., Varghese, C. P., & Agrawal, R. K. (2011). Validation of QSAR models-strategies and importance.International Journal of Drug Design & Discovery, 3, 511-519. Berman, H. M., Westbrook, J., Feng, Z., Gilliland, G., Bhat, T. N., Weissig, H., ... & Bourne, P. E. (2000). The protein data bank. Nucleic acids research,28(1), 235-242. 4D-QSAR User’s Manual v.1.00.The Chem21 Group Inc.,1780 Wilson Dr., Lake forest, IL 60045, 1997. Rogers, D., & Hopfinger, A. J. (1994). Application of genetic function approximation to quantitative structure-activity relationships and quantitative structure-property relationships. Journal of Chemical Information and Computer Sciences, 34(4), 854-866. doi: 10.1021/ci00020a020 Senese, C. L., Duca, J., Pan, D., Hopfinger, A. J., & Tseng, Y. J. (2004). 4D-fingerprints, universal QSAR and QSPR descriptors. Journal of chemical information and computer sciences, 44(5), 1526-1539. Roy, P.P.; Paul, S.; Mitra, I.; Roy, K. (2009). On two novel parameters for validation of predictive QSAR models. Molecules, 14(5), 1660-1701. Doi: 10.3390/molecules14051660