3D-QSAR of Amino-substituted Pyrido[3,2B]Pyrazinones as PDE-5 Inhibitors

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O R I G I N A L R E S E A R C H

3D-QSAR of amino-substituted pyrido[3,2B]pyrazinonesas PDE-5 inhibitors

Omprakash Tanwar • Rikta Saha • M. Mumtaz Alam •

Mymoona Akhtar

Received: 16 August 2010 / Accepted: 19 November 2010

Ó Springer Science+Business Media, LLC 2010

Abstract A 3D-QSAR study on amino-substituted pyr-

ido[3,2b]pyrazinones as PDE-5 inhibitors was successfullyperformed by means of pharmacophore mapping using

PHASE module of Schrodinger-9. The 3D-QSAR obtained

from AADHRR-183 hypothesis was found to be statisti-

cally good with r 2= 0.95 and q

2= 0.81 taking PLS factor

4. The statistical significance of the model was also con-

firmed by a high value of Fisher ratio of 85.1 and a very

low value of RMSE 0.29. One of the other parameters

which signify the model predictivity is Pearson R. Its value

of 0.91 shows that the correlation between predicted and

observed activities for the test set compounds is excellent.

Hydrophobic groups are important for PDE-5 inhibition

while H-bond donor groups are less favorable for the same.

Electron withdrawing groups are favorable if include at

ring A in the structures while unfavorable at other sites.

Thus, it can be assumed that the present QSAR analysis is

enough to demonstrate PDE-5 inhibition with the help of

AADHRR-183 hypothesis and will help in designing novel

and potent PDE-5 inhibitors.

Keywords 3D-QSAR Á Pharmacophore Á PHASE Á

Phosphodiesterases Á Pyrido[3,2b]pyrazinones Á

Schrodinger

Abbreviations

3D-QSAR Three-dimensional quantitative structure–

activity relationship

2D-QSAR Two-dimensional quantitative structure–

activity relationshipPDEs Phosphodiesterases

RMSE Root mean squared error

cAMP Cyclic monophosphate

cGMP Cyclic guanosine monophosphate

PLS Partial least square

LOO Leave one out

r 2 Cross-validated correlation coefficient

q2 Internal predictivity

Introduction

The Three-dimensional quantitative structure–activity

relationship (3D-QSAR) involves analysis of the quanti-

tative relationship between the biological activity of a set

of compounds and their three-dimensional structural

properties, using statistical correlation methods. Three-

dimensional QSAR approach is one of the most powerful

techniques which come in the class of indirect drug design.

Lead optimization without receptor 3D structure is one of

the most important applications of 3D-QSAR. It allows 3D

visual analysis for spatial arrangement of structural

features with biological activity thus is advantageous

over 2D-QSAR where model data has to be taken into

consideration.

PDEs are the enzymes which are responsible for the

degradation of cyclic monophosphate (cAMP) and cyclic

guanosine monophosphate (cGMP) that are imperative

intracellular second messengers (Francis and Corbin, 1999).

Erectile dysfunction is the most commonly encountered

form of sexual dysfunction in men and can cause significant

O. Tanwar Á R. Saha Á M. M. Alam Á M. Akhtar (&)

Drug Design and Medicinal Chemistry Lab, Department

of Pharmaceutical Chemistry, Faculty of Pharmacy, Jamia

Hamdard, New Delhi 110062, India

e-mail: [email protected]

Med Chem Res

DOI 10.1007/s00044-010-9523-y

MEDICINALCHEMISTRYRESEARCH



scoring methods to identify CHPs for a series of molecules

which have particular target specificity. Each hypothesis

conveys a particular 3D conformation of a set of ligands in

which the ligands are going to bind to the receptor. The

best hypothesis is then correlated with known biological

activity values to generate a 3D-QSAR model which

identifies the whole structural features of molecules that

govern activity (Samantha et al., 2008; Almerico et al.,2010).

Active analog approach was used to identify a CPH

which has been applied in generating significant 3D-QSAR

models (Narkhede and Degani, 2007; Lather et al., 2008;

Mahipal et al., 2010). The common pharmacophores were

culled from the conformations of the set of active ligands

using a tree-based partitioning technique which groups

together similar pharmacophores according to their inter-

site distances. A tree depth of five with initial box size of 25.6 A and final box size of 0.8 A was used (Samantha

Table 1 Structures of the compounds of the selected series with their inhibition data

NN

N

O

R1

NH

R2

F

X

Comp.

codeX2R1R

– log IC50

PDE-5

PDE009 OMe

O N

H 8.89

PDE010 OMe

O N

F 8.70

PDE011

N

O

H 8.60

PDE012

N

O

H 7.69

PDE013

F

F

O

H 8.57

PDE014 H H 7.55

PDE015 O

H 8.52

PDE016 O N

H 8.34

Med Chem Res



Table 1 continued

PDE017 O N

F 8.80

PDE018

N

H 7.87

PDE019N

O

H 8.46

PDE020

O

O N

F 7.28

PDE021 O

O

H 6.83

PDE022 O

O

H 7.65

PDE023 O

O N

H 7.48

PDE024 O

H 6.65

NN

N

O

HN

R2

Ar

rA2R

PDE025O N

O

N

9.00

Med Chem Res



Table 1 continued

PDE026 O

N

9.05

PDE027N

O

N

OMe

9.52

PDE028 O

N

OMe

9.52

PDE029 O N

N

OMe

9.40

PDE030 O

N

N

OMe

9.52

PDE031 O

N

N

OH

8.54

PDE032O N

O

N

7.44

PDE033 O

N

7.47

PDE034N

O

N

OMe

7.84

PDE035 O

N

OMe

8.64

PDE036O N

N

OMe

8.62

PDE037O N

N

OMe

7.57

PDE038O N

N

OH

6.27

PDE isoforms inhibition -logIC50 values (in molar units)

Med Chem Res



et al., 2008; Phase3.1, Schrodinger, LLC, 2009). The

resulting pharmacophore was then scored and ranked. The

scoring was done to identify the best candidate hypothesis,

and which provided an overall ranking of all the hypoth-

eses. The scoring algorithm included the contributions

from the alignment of site points and vectors, volume

overlap, selectivity, number of ligands matched, relative

conformational energy, and activity. A detailed description

about the scores can be found in the methodology research

article where PHASE is described (Dixon et al., 2006). The

selected AADHRR-183 hypothesis with various scores is

summarized in Table 2.

The best pharmacophore hypothesis AADHRR-183

(Fig. 2) was selected for further QSAR study. The above-

mentioned 3D pharmacophore hypothesis (Fig. 2a)

encompass the following features: two hydrogen bond

acceptor (A) in pink color, one hydrogen bond donors (D)

in sky blue color, one hydrophobic group (H) in green color

and Two Aromatic ring (R) in yellow color. The 2D rep-

resentation of the AADHRR-183 hypothesis is given in

Fig. 2b. The 2D representation shows that the secondary

amino group near to pyrazinones ring is hydrogen bonddonor (D), N atom of pyridine and C=O group of pyrazi-

none ring are two hydrogen bond acceptors (A), substitu-

tion at ring A is hydrophobic groups (H), and rings A and B

are two aromatic ring (R) are the pharmacophoric elements

of AADHRR-183 hypothesis.

Building of QSAR model

In this study, a significant 3D-QSAR model was generated

using AADHRR-183 hypothesis. For QSAR model gener-

ation, training and test partition was done by randomselection method. Atom-based model selection criterion

was chosen for model building (Shah et al., 2010). PLS

factor was set as 04, the maximum number of PLS factors

in each model can be 1/5 the total number of training set

molecules. More the PLS factor value, more will be the

reliability of models. Various models have been generated

and the best model was selected on the basis of the sta-

tistical significance given below.

Table 2 Score of different parameters of the hypothesis AADHRR-

183

S. No. Parameters Score

1. Survival 8.4

2. Survival inactive 5.831

3. Post hoc 3.65

4. Site 0.915. Vector 0.983

6. Volume 0.755

7. Selectivity 2.297

8. Must matches 14

9. Energy 0

10. Activity 8.52

11. Inactive 2.569

Survival weighted combination of the vector, site, volume, and sur-

vival scores, and a term for the number of matches, a large value of

survival score indicates the better fitness of the active ligands on the

common pharmacophore and validates the model

Survival inactive survival score for actives with a multiple of thesurvival score for inactive subtracted

Post hoc this score is the result of rescoring

Site score, this score measures how closely the site points are

superimposed in an alignment to the pharmacophore of the structures

that contribute to the hypothesis, based on the RMS deviation of the

site points of a ligand from those of the reference ligand

Vector alignment score

Volume measures how much the volume of the contributing structures

overlap when aligned on the pharmacophore

Selectivity estimate of the rarity of the hypothesis, High selectivity

means that the hypothesis is more likely to be unique to the actives

Matches number of actives that match the hypothesis

Energy relative energy of the reference ligand in kcal/molActivity of the reference ligand

Inactive survival score of inactives

N

N

N

O

N

N

O

NH

O

Acceptor

Acceptor

Donor

Ring1 Ring1

A

B

Hydrophobic

B

A

Fig. 2 a Common pharmacophore for active ligands [two hydrogen

bond acceptor ( A) in pink color, one hydrogen bond donors ( D) in sky

blue color, one hydrophobic group ( H ) in green color and two

aromatic ring ( R) in yellow color] and b 2D representation of

pharmacophore (Color figure online)

Med Chem Res



Result and discussion

A 3D-QSAR study has been performed successfully on the

series of substituted pyrido[3,2b]pyrazinone derivatives to

understand the effect spatial arrangement of structural

features on PDE-5 inhibition. Result of the 3D-QSAR canbe visualized from Fig. 3. The blue cubes in 3D plots of the

3D pharmacophore regions refer to ligand regions in which

the specific feature is important for better activity, whereas

the red cubes demonstrates that particular structural feature

or functional group is not essential for the activity or likely

to reason for decreased binding potential. The statistical

results of 3D-QSAR study are summarized in Table 3.

The reliability of the present 3D-QSAR analysis can be

justified by the fact that all statistical measures are sig-

nificant to any level. The model express 99% variance

exhibited by pyrido[3,2b]pyrazinones, which is near to one

and signifying a very close agreement of fitting points onthe regression line for the observed and PHASE predicted

activity. The fitness graph can be visualized from Fig. 4

and the observed and PHASE predicted activity data are

summarized in Table 4. Validity of the model can be

expressed by internal predictivity (q2 = 0.81) which is

obtained by leave-one-out (LOO) or leave n out method.

The q2 by leave-one-out method is more reliable and robust

statistical parameter than r 2 because it is obtained by

external validation method of dividing the dataset into

training and test set. The large value of F (85.1) indicates a

statistically significant regression model, which is sup-

ported by the small value of the variance ratio (P), anindication of a high degree of confidence. Further small

values of standard deviation of the regression (0.23) and

RMSE (0.29) make an obvious implication that the data

used for model generation are best for the QSAR analysis.

Apart from the above-mentioned features, PLS factor also

confirms the reliability of the model. In this study, number

of PLS factor was taken as 4 and for each increment it

gives one equation and there should be stepwise

improvement each time the model generated. In addition to

Fig. 3 QSAR visualization of various substituents affect: a electron

withdrawing feature, b hydrogen-bond donor, and positive (c) and

negative (d) hydrophobic effects (Color figure online)

Table 3 3D-QSAR statistical parameters

PLS

factors

SD r 2

F P RMSE q2

Pearson

R

1 0.584 0.6248 35 7.195e-

06

0.4672 0.5024 0.7301

2 0.4171 0.8177 44.9 4.048e-

08

0.4459 0.5467 0.7645

3 0.3196 0.8984 56 1.271e-

09

0.3323 0.7482 0.8778

4 0.2309 0.9498 85.1 1.95e-

11

0.2887 0.81 0.9127

SD standard deviation of the regression, r 2 for the regression, F var-

iance ratio. Large values of F indicate a more statistically significant

regression, P significance level of variance ratio. Smaller values

indicate a greater degree of confidence, RMSE root-mean-square

error, q2 for the predicted activities, Pearson R value for the corre-

lation between the predicted and observed activity for the test set

Med Chem Res



the above parameters it is interesting to note that active

ligands are closely fitted to the regression line and inactive

ligands are scattered (Fig. 5).

Figure 3 shows 3D-pharmacophore regions around

compounds. For the selected pharmacophore blue and red

cubes represent favorable and unfavorable regions,

respectively. Molecular substitutions which increase the

number of blue cubes will definitely lead to increasesbinding affinity of the molecules towards PDE-5 inhibition,

while molecular substitutions which increase the number of

red cubes will lead to decreased activity.

Figure 3a represents electron withdrawing characteristic

for the selected hypothesis. Visual analysis of Fig. 3a

demonstrates that the throng of the blue cubes at the ring A

site is pointing out the positive potential of electron with-

drawing characteristic of the molecules and is requisite for

the activity. It can be suggested that addition of appropriate

electron withdrawing groups at the ring A and at the side of

methoxy group will append the PDE-5 inhibition, whereas

the addition of electron withdrawing groups at ring B siteand at pyran ring site will lead to decreased receptor

binding which in turn will result in lower potency of

compounds. The potency of the compounds can be

increased by addition of small electron withdrawing groups

like fluoro, chloro, bromo, etc., at para and meta positions

on ring A.

Figure 3b illustrates H-donor characteristic for the

selected hypothesis. The crowd of red cube suggests that

addition of hydrogen bond donor groups at ring A site will

lead to decreased PDE-5 inhibition. Similar diminished

potential of the compounds can be found if hydrogen bond

donor groups are added at NH at ring B site. While proper

orientation of hydrogen bond donor groups may slightly

lead to increased inhibition toward PDE-5 as indicated by

fewer blue cubes.

Figure 3c and d demonstrating the effect of positive and

negative hydrophobic potential, respectively. It can bededuced from figure that hydrophobic groups are well

tolerated at cyclohexyl moiety on ring B (blue cubes),

while the substitution of hydrophobic groups at ring A site

and pyridine ring are unacceptable (red cubes) or may

hinder the binding of the molecules to the receptor active

site and will result in decreased PDE-5 inhibition. An

interesting finding of the present analysis is that H atom (on

NH group) of pyrazinone ring should substituted only with

hydrophobic groups as there is dense crowd of blue cube is

present. At the same time no hydrophobic substituents

should be present on pyridine ring for better PDE-5 inhi-

bition. It is clear that cyclohexyl ring can be substitutedwith more hydrophobic rings like substituted cyclohexyl

rings, saturated naphthalene rings, etc., along with the

some hydrophobic groups at ring A.

Although PDE-6 and PDE-11 activities were also

reported for some ligands, but no significant statistics have

been developed so they are not reported in the present. It

may also be due to the fact that the molecules are more

selective towards PDE-5 as compared to PDE-6 and PDE-

11. Thus, it can be assumed that this study shows the

Fig. 4 Fitness graph between

observed activity versus PHASE

predicted activity for training

and test set compounds

Med Chem Res



selectivity towards and will help in designing more selec-

tive PDE-5 inhibitors.

Summary and conclusion

A meaningful 3D-QSAR study was derived for the series

of amino-substituted pyrido[3,2b]pyrazinones as PDE-5

inhibitors, to identify how three-dimensional arrange-

ments of various substituents will affect the PDE-5 inhi-

bition. Selected model is very much significant to draw

unambiguous inferences. The statistical parameter values

are well above the acceptance limits. So it is easy to draw

clear inference to design novel compounds for better

PDE-5 inhibition. The 3D-QSAR model discussed above

explains how and at what extent electron withdrawing,

hydrophobic, and H-donor properties should be modified

to achieve better PDE-5 inhibition. The model shows that

PDE-5 inhibition can be increased, if the hydrophobic

character at ring B is supplemented by appropriate

hydrophobic functional groups. Hydrophobic group-

substituted cyclohexyl rings or naphthalene rings at B ring

can provide potent PDE-5 inhibitors. The present finding

suggests that H-bond donor substituents are mostly

unfavorable in the structures. Thus, it is clear that inclu-

sion of such groups will lead to deceased PDE-5 inhibi-

tion. Electron withdrawing characteristic is beneficial at

ring A, whereas electron withdrawing groups at pyrane

ring and at pyrazinone are unfavorable. This suggests that

inclusion of flouro, chloro, bromo, etc., at ring A will

provide better compounds. Thus, it is clear from this

study that it is possible to develop novel and potent PDE-

5 inhibitors if suitable substituents are added to the parent

structures.

Table 4 Fitness and PHASE

predicted activity data for all

compounds

Ligand name QSAR set PLS factors PHASE predicted

activity

Pharm set Fitness

PDE009 Training 1 2 3 4 9.06 Active 2.67


PDE011 Test 1 2 3 4 8.21 Active 2.83

PDE012 Training 1 2 3 4 7.93 Inactive 2.83

PDE013 Training 1 2 3 4 8.39 Active 2.78PDE014 Training 1 2 3 4 7.53 Inactive 2.76

PDE015 Test 1 2 3 4 8.54 Active 3





PDE020 Test 1 2 3 4 7.23 Inactive 2.66









PDE029 Test 1 2 3 4 9.4 Active 2.52


PDE031 Test 1 2 3 4 8.77 Active 2.35








Med Chem Res



Acknowledgments The authors are thankful to University Grants

Commission, New Delhi, India for providing financial support. The

authors also wish to acknowledge the team of Schro dinger for pro-

viding software facility.

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Fig. 5 Alignment of active (a) and inactive (b) ligands to the

pharmacophore

Med Chem Res

3D-QSAR of Amino-substituted Pyrido[3,2B]Pyrazinones as PDE-5 Inhibitors

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