Indole Cytosolic Phospholipase A2A Inhibitors Biology Essay

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The production of prostaglandins, thromboxanes and leukotrienes, mediators of inflammation, are initiated by the release of arachidonic acid (AA) from cellular membranes under the action of a phospholipase A2. Selected enzymes and receptors in AA pathway have been targeted to treat pain and asthma in the past years.1 Clinical trials have confirmed that PGs are proinflammatory and potentiate pain, and cyclooxygenase 2 (COX-2)-selective non-steroidal anti-inflammatory drugs (NSAIDs), such as celecoxib and rofecoxib can block the conversion of AA to PGs.2 Rofecoxib was approved by the Food and Drug Administration (FDA) in 1999 in the treatment of osteoarthritis, acute pain conditions, and dysmenorrhoea. However, in 2004, Merck voluntarily withdrew rofecoxib from the market because of concerns about increased risk of heart attack and stroke associated with long-term, high-dosage use.

To develop more effective and less toxic anti-inflammatory drugs, and due to the potential importance of inhibiting arachidonate release, the development of phospholipase A2 inhibitors received great attention in recent years. Cytosolic phospholipase A2a (cPLA2a), discovered in 1991,3 is the phospholipase responsible for the selective generation of arachidonic acid in vivo. A cPLA2a inhibitor can inhibit the production of PG's and LT's, thus provides a novel therapeutic with applications in many disease states. Based on the structure of Ecopladib (Figure 1), Wyeth's lead compound as sub-micromolar inhibitor of cPLA2a in the GLU micelle and rat whole blood assays reported in 2007,4 McKew, etal recently reported a series of indole cytosolic phospholipase A2a inhibitors, among which 19 (Efipladib) and 29 (WAY-196025) are shown to be potent and selective inhibitors of cPLA2a in isolated enzyme assays, cell based assays, and rat and human whole blood assays (Figure1). 7-Hydroxycoumarinyl-g-linolenate (GLU) micelle assay paradigm which allows micellar oresentation of the inhibitor and substrate to the enzyme 5 have been validated and a series of potent and selective cPLA2a inhibitors have been developed based on these assays.

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Figure 1. Structures of Ecopladib, Epifladib and WAY-196025.

Three-dimensional quantitative structure-activity relationship (3D-QSAR) has been widely applied in rational ligand-based drug design 6 in recent years. Comparative molecule field analysis (CoMFA) 7[15] and comparative molecule similarity indices analysis (CoMSIA) 8[16] correlate the 3D structures of molecules and their experimental biological activities based on statistical results.9 CoMFA calculates steric fields and electrostatic fields using Lennard-Jones potential and Coulomb potentials, respectively 10 and CoMSIA employs a Gaussian function to assess steric, electrostatic, hydrophobic, and hydrogen bond donor/acceptor fields 11. In this paper, we established CoMFA and CoMSIA models using a data set consisting of 34 compounds with high GLU Micelle binding affinity from recent publication 12 [9]. We believe that the construction of 3D-QSAR models based on highly active indole cytosolic phospholipase A2a inhibitors is significant for the future rational development of novel cPLA2a inhibitors.13

A data set of 34 compounds was taken from the recent publication,12 in which all the compounds were tested for their binding affinities in GLU micelle assay. The IC50 values were converted into corresponding pIC50 (-log IC50) values. The test set of six compounds (marked with asterisks) were selected from the data set randomly. Molecular models were generated using Tripos Sybyl 7.2 package on a Linux system.

3D molecular structures were built using SKETCH module. Structural energy minimization was performed using the tripos force field with a distance dependent dielectric and conjugate gradient method until a gradient convergence of 0.005 kcal/mol was achieved. Gasteiger-Hückel charges were assigned. Compound 30 was taken as the template for its highest activity and the other compounds were superimposed to it using atom-based alignment (Figure 2).

Figure 2. Structural alignment of the data set.

The default Tripos settings were used to perform the CoMFA and CoMSIA analysis. In CoMFA models, steric and electrostatic interactions were calculated using a sp3 carbon atom as steric probe and a +1 charge as electrostatic probe. The aligned molecules were kept in a 3D grid spacing of 2.0 Å in the x, y, z directions. The default cutoff of 30 kcal/mol was set to truncate the steric field and the electrostatic field energy. In CoMSIA models, the steric, electrostatic, hydrophobic, hydrogen-bond donor and acceptor properties were evaluated using the standard settings with 1 Å radius, +1.0 charge, and hydrophobic and hydrogen-bond property values of +1. The default attenuation factor α value of 0.3 determines the steepness of the Gaussian function 14.

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PLS technique was employed to linearly correlate the CoMFA and CoMSIA fields with corresponding experimental values of the binding affinities (pKIC50) for the data set of the compounds. Leave-one-out (LOO) cross-validation method was employed to assess the internal predictive ability of the models. Cross-validation determines the optimal number of principal components, which results in the lowest standard error of prediction and the highest cross-validated q2. The predictive ability of the CoMFA and CoMSIA models was determined from a set of six test molecules not included in the model generation. The predictive correlation coefficient (r2pred) was defined using Eq. (1).

r2pred = (SD -PRESS)/SD (1)

where SD is the sum of squared deviations between the biological activity of the test set and the mean activity of the training set molecules and the PRESS is the sum of squared deviations between predicted and observed activity values for every molecule in the test set 7.

The structures of the data set, the observed and predicted values of pEC50 by CoMFA and CoMSIA models are listed in Table 1. The statistical results of the three CoMFA models are listed in Table 2. The cross-validated r2 (rcv2) obtained by CoMFA and CoMSIA are 0.69 and 0.63, with the standard error of estimate 0.129 and 0.136, conventional r2 of 0.981 and 0.979, F value of 225.28 and 203.55, r2pred 0.92 and 0.89 respectively. Both of the models show that electrostatic contributions are more important than steric contributions. Hydrophobic, donor and acceptor contribution in CoMSIA model are 0.11, 0.32 and 0.24, respectively. Based on these values, both of the CoMFA and CoMSIA models have good quality and predictive capability. The relationships between the predicted values and observed values for the non-cross-validated analysis of CoMFA and CoMSIA models are showed in Figure 3. The linearity of the plots demonstrates a good correlation for the two models developed in the study.

Table 1. Observed and predicted activities of training set and test set compounds by CoMFA and CoMSIA.

Paper9

R1

R2

R3

Observed

IC50

Ppredicted IC50by CoMFA

Ppredicted IC50by CoMSIA

1

H

H

6.96

6.92

7.03

2

H

H

4.48

4.50

4.47

3*

H

H

5.15

5.38

5.41

4

H

H

5.70

5.63

5.63

5

H

H

6.72

6.73

6.72

6

H

H

7.15

7.18

7.18

7

H

H

7.70

7.79

7.62

8*

H

H

7.52

7.13

7.03

9

H

H

6.17

5.97

6.03

10

H

H

5.37

5.75

5.73

11

2-NO2

H

7.07

7.06

7.11

12

3-NO2

H

6.96

6.85

6.91

13*

4-NO2

H

6.80

6.53

6.99

14

2-CN

H

7.22

7.07

7.11

15

2-CN

H

7.52

7.52

7.56

16

H

H

7.15

7.01

6.98

17

H

H

7.70

7.71

7.60

18

3-Cl

4-Cl

6.82

6.90

6.94

19

3-Cl

4-Cl

7.40

7.35

7.32

20*

3-Cl

4-Cl

7.70

7.93

7.89

21

3-Cl

4-Cl

7.40

7.56

7.55

22

2-Cl

H

7.30

7.22

7.11

23

2-Cl

H

7.70

7.76

7.86

24

2-Cl

H

8.00

8.06

8.11

25

2-Cl

H

8.00

7.97

8.15

26*

2-Me

H

7.22

7.11

7.02

27

2-Me

H

7.52

7.56

7.62

28

2-Me

6-Me

7.52

7.48

7.50

29

2-Me

6-Me

8.00

7.96

8.00

30

2-Me

6-Me

8.40

8.38

8.23

31*

2-F

6-F

6.74

6.90

6.60

32

2-F

6-F

7.22

7.43

7.14

33

2-F

6-F

7.70

7.69

7.69

34

2-F

6-F

7.15

7.03

7.10

Table 2. Summary of CoMFA and CoMSIA analytical results.

CoMFA

CoMSIA

Cross-validated r2(rcv2)

0.69

0.63

Standard error of estimate

0.129

0.136

Conventional r2

0.981

0.979

Optimal component

5

5

F value

225.283

203.553

r2pred

0.92

0.89

Relative steric contribution

0.38

0.06

Relative electrostatic contribution

Relative hydrophobic contribution

Relative donor contribution

Relative acceptor contribution

0.62

-

-

-

0.26

0.11

0.32

0.25

Figure 3. Correlation plots of predicted pIC50 versus observed pIC50 from the training set (solid blue diamonds) and test set (solid magenta squares) for CoMFA and CoMSIA models.

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To visualize the results of the CoMFA and CoMSIA fields, 3D coefficient contour maps were generated (Figure 4). These fields provide more detailed information of the binding pockets of the target, emphasizing the key structural features required for the binding affinities. In the CoMFA and CoMSIA steric field, the green (sterically favorable) and yellow (sterically unfavorable) contours represent 80 % and 20 % level contributions, respectively. The red (negative charge favorable) and blue (negative charge unfavorable) contours in CoMFA and CoMSIA represent 80 % and 20 % level contributions. In CoMSIA model, the white (hydrophobic favorable) and orange (hydrophobic unfavorable), cyan (donor favorable) and purple (donor unfavorable), magenta (acceptor favorable) and red (acceptor unfavorable) contours represent 80 % and 20 % level contributions, respectively. From the electrostatic contour map, the red contour near negative oxygen atom in SO2 group can explain the high activities of compounds 7, 8, 17, 20, 24, 25, 30, 33 and 34. Moreover, compounds 7, 17, 20, 24, 30 and 33 show high activities than compounds 6, 16, 19, 23, 29 and 32, respectively, whose CH2 units were replaced by SO2 with other structural units unchanged. The red contour around positive methyl group explains the very low activity of compound 10. The red contours near negative COOH, SO2NH and Cl groups validate the importance to their activities of the three functional groups. In steric contour map, the green contours in 2, 6-position of phenyl ring explain the high activities of compounds 26, 27, 28, 29 and 30, in which sterically hindered methyl groups are presented. Green contour near Cl shows the big chloro atom is favored to the activity in this position. The yellow contour around methyl group explains the lower activities of compound 10 compared to 9, in which sterically methyl group is unfavorable. In CoMSIA donor contour map, the cyan contours around COOH and SO2NH explain the importance of the two hydrogen bond donor structural units. The purple contour around NH group in compound 9 explains its lower activity compared to compound 1, because donor functional group NH is unfavorable in this position. In acceptor contour map, the magenta contours around oxygen and nitro atom explain the importance of acceptor atoms in these positions.

Figure 4. Contour maps for the steric and electrostatic contributions of CoMFA, and donor and acceptor contributions of CoMSIA.

In this paper, we successfully constructed CoMFA and CoMSIA models of indole cytosolic phospholipase A2a inhibitors based on recent publications. The two models show good statistical results and predictive capability in terms of cross-validated r2 and conventional r2, and r2pred. The reliability of the models was verified by the compounds in the test set. Both of the models show that electrostatic contributions are more important than steric contributions. In CoMSIA model, donor and acceptor contributions are also important. The CoMFA and CoMSIA contour maps explain the

Biological activities of the series of compounds and the importance of some functional groups, such as SO2, COOH, Cl and SO2NH. We believe that the models built up here will provide useful information for the novel design of new indole cytosolic phospholipase A2a inhibitors.