hansch analysis of novel pyrimidine derivatives as highly potent and specific cox-2 inhibitors

ORIGINAL RESEARCH

Hansch analysis of novel pyrimidine derivatives as highly potentand specific COX-2 inhibitors

Ashish Khare • Shashank Trivedi • H. Rajak •

R. S. Pawar • U. K. Patil • P. K. Singour

Received: 29 July 2010 / Accepted: 12 January 2011 / Published online: 4 February 2011

� Springer Science+Business Media, LLC 2011

Abstract A QSAR study on novel Pyrimidine derivatives

as specific COX-2 inhibitory agents was performed with 69

(59 training ? 10 test) compounds. Molecular modeling

studies were performed using chemoffice 6.0 supplied by

cambridgesoft. The sketched structures were subjected to

energy minimization and the lowest energy structure was

used to calculate the physiochemical properties. The

regression analysis was carried out using a computer pro-

gram called SYSTAT 10.2. The best models were selected

from the various statistically significant equations. The

study revealed that the hydrogen bond donar groups at

position-4 enhances the activity, electron with-drawing

groups at position-2 reduces the activity, electron donating

groups at position-6 enhances the activity. The analysis

resulted in QSAR equation, which suggests that, n = 59,

r = 0.957, r2 = 0.915, adjusted squared multiple R =

0.901, Standard error of estimate(s) = 0.294 & validated

r2(q2) = 0.642. This study can help in rational drug design

and synthesis of new selective cyclooxygenase-2 inhibitor

with predetermined affinity.

Keywords QSAR analysis � Cyclooxygenase �Pyrimidine ring

Introduction

The non-steroidal anti-inflammatory drugs (NSAIDs) are

among the most commonly medications in the world

(Zarghi et al., 2009a). Their anti-inflammatory activity is

due to inhibition of cyclooxygenases (COXs), which

catalyze the bioconversion of arachidonic acid to inflam-

matory prostaglandins (PGs) (Zarghi et al., 2009b).

Prostaglandins such as PGE2 are produced in the cyclo-

oxygenase pathway of the arachidonic acid cascade by the

action of the isoenzymes COX-1 and COX-2 (Schuhly

et al., 2009).

Prostaglandins are among the most important mediators

of inflammation. They promote blood vessel dilation and

vascular permeability, causing the typical redness, heat and

swelling phenomena involved in inflammation. Moreover,

they promote pain transmission from nociceptors to the

brain by increasing the sensitivity of the nerve endings.

However, prostaglandins also play a cytoprotective role in

the gastrointestinal tract and they are necessary for nor-

mal platelet aggregation and renal function (Girgis and

Barsoum, 2009).

The success of NSAIDs in treatment of various

inflammatory disorders validated inhibition of COX

enzyme as a highly suitable target in anti-inflammatory

therapies. However, the gastrointestinal toxicities associ-

ated with widespread use of NSAIDs proved to be a major

problem during long-term therapy (Zebardast et al., 2009).

Although COX-2 is concerned to be the main isoenzyme

related to inflammation, most NSAIDs in the market today

block both forms of COX isoenzymes. Side effects such as

gastrointestinal pain have been associated with NSAID use

due to the inhibition of COX-1 (Kouatly et al., 2009).

The identification of cyclooxygenase-2 (COX-2) and the

subsequent introduction of the COX-2 selective inhibitor

A. Khare � S. Trivedi � R. S. Pawar � U. K. Patil �P. K. Singour (&)

Computational & Synthetic Chemistry Division,

Department of Pharmaceutical Chemistry, VNS Institute

of Pharmacy, VNS Campus, Vidhya Vihar, Berkheda Nathu,

Neelbud, Bhopal, Madhya Pradesh, India

e-mail: [email protected]

H. Rajak

SLT Institute of Pharmacy, Pharmaceutical Chemistry Division,

Guru Gashidas University, Bilaspur, Chhattisgarh, India

123

Med Chem Res (2012) 21:672–680

DOI 10.1007/s00044-011-9566-8

MEDICINALCHEMISTRYRESEARCH

NSAID drugs were thought to be a major breakthrough,

with the expectation of a significant reduction in gastro-

intestinal (GI) side effects (Sondhi et al., 2008). The dif-

ferential tissue distribution of cyclooxygenase-1 (COX-1)

and cyclooxygenase-2 (COX-2) provides a rationale for the

development of selective COX-2 inhibitors as anti-

inflammatory-analgesic agents that lack the GI side effects

exhibited by traditional NSAIDs (Navidpour et al., 2007).

COX-2 is induced in response to proinflammatory con-

ditions, while COX-1 is constitutive and responsible for the

maintenance of physiological homeostasis, such as gas-

trointestinal integrity and renal function. Selective inhibi-

tion of COX-2 provides a new class of anti-inflammatory

agents with significantly reduced side effects such as gas-

trointestinal ulcer and renal dysfunction. The initial pos-

tulate that a selective COX-2 inhibitor would reduce

inflammation without causing gastric irritation was vali-

dated following the introduction of selective COX-2

inhibitors such as celecoxib and rofecoxib. However, it was

subsequently observed that selective COX-2 inhibitors may

alter the balance in the cyclooxygenase pathway resulting

in a decrease in the level of the vasodilatory and anti-

aggregatory prostacyclin (PGI2), relative to an increase in

the level of the prothrombotic tromboxane A2 (TxA2),

leading to increased incidences of an adverse cardiovas-

cular thrombotic event (Moreau et al., 2006).

The active sites of COX-1 and COX-2 are very similar.

However, the COX-2 ligand binding domain has an addi-

tional hydrophobic pocket making it more spacious: Ile523

is exchanged for Val523 in COX-2. Furthermore, Ile434

and His513 from the second shell are exchanged for

Val434 and Arg513 contributing to the enlargement of the

ligand binding site. The presence of this small cavern

allows for the design of specific inhibitors versus COX-2.

Vice versa, no highly selective COX-1 inhibitor has been

reported yet because all COX-1 inhibitors also fit well into

the COX-2 active site (Schuster et al., 2010).

Tricyclic molecules possessing as a common feature

1,2-diaryl substitution on a central heterocyclic or carbo-

cyclic ring system represent a major class of selective

COX-2 inhibitor. Pyrimidine used as template for the

synthesis of new selective COX-2 inhibitors. The main aim

of our research program was to discover new selective

COX-2 inhibitors. The substitution pattern of these com-

pounds is substantially different from that of previously

reported pyrimidine-based COX-2 inhibitors (Orjales et al.,

2008).

Current research has focused on developing safer

NSAIDs-selective COX-2 inhibitors (Hu et al., 2003). The

development of drugs from this class of compounds

through lead optimization or through sophisticated com-

puter-aided drug design (CADD) techniques. The present

QSAR study on various Pyrimidines attempts to address

this need by arriving at the physico-chemical properties

required for high specific COX-2 inhibitory activity in the

form of a mathematical equation, according to the Hansch

type of analysis. This study should, therefore, help in

designing newer molecules with better specific COX-2

inhibitory activity.

Experimental section

Data set

In QSAR analysis, it is imperative that the biological data

be both accurate and precise to develop a meaningful

model. The overall performance of the current method used

for QSAR study is critically depends on the selection of

compounds for series used to build the classifier model.

The most critical aspect of the construction of the series is

to warrant a great molecular diversity in this data set.

Different substituted pyrimidine derivatives were pre-

viously evaluated for the inflammatory response with their

biological activities as COX-2 assay values obtained from

the human whole blood (HWB) assay (Orjales et al., 2008).

On the basis of diversity between reported biological

activities, this series of compounds has been selected for

QSAR analysis. COX-2 inhibitory activity has been

expressed as IC50 values in nM units which represents the

concentration of drug that inhibits 50% of COX-2 enzyme.

The values were converted to negative logarithms (pIC50)

(Hui-Ding, 2009) in order to reduce the skewness of the

data set and obtain a linear relationship in the QSAR

equation are summarized in Tables 1, 2, 3, 4, and 5.

Molecular structure generation

The studies of pyrimidine derivatives were performed

using chemoffice CS Chem Office 2003 version 6.0 sup-

plied by Cambridge Software Company, USA. All the

molecules were sketched using Chem Draw Ultra module.

The two-dimensional (2D) structures were transformed into

three dimensional (3D) structures by using the Chem3D

Ultra module. The resulting 3D structures were then sub-

jected to an energy-minimization by using the molecular

mechanics (MM2) method. The energy minimized mole-

cules were re-optimizing using molecular orbital package

(MOPAC). The numerical descriptors are responsible for

encoding important features of the structure of the mole-

cules and can be categorized as electronic, steric, and

thermodynamic characters.

The thermodynamic, spatial, electronic, and topological

descriptors were calculated for QSAR analysis. The ther-

modynamic parameters describe free energy change during

drug receptor complex formation. Spatial parameters were

Med Chem Res (2012) 21:672–680 673

123

Table 1 In vitro COX-2 inhibitory activities of compounds 1–20

Compound R1 R2 IC50 B.A. 1 CH3 71 -1.85125

2 NH2 S 157.6 -2.19755

3 CH3 S

2.1 -0.32221

4* NH2 S

51.9 -1.71516

5* CH3 S

46.5 -1.66745

6 NH2 N

3330 -3.52244

7 CH3 N

298.5 -2.47494

8* NH2 O

2790 -3.4456

9 CH3 O

140 -2.14612

10 NH2 S

140.4 -2.14736

11 CH3 S

293.4 -2.46746

12 NH2 803.5 -2.90498

13 CH3 1480 -3.17026

14 CH3 N

S

83.6 -1.9222

15 CH3 N 28.8 -1.45939

16 CH3 728.6 -2.86248

17 CH3

789.8 -2.89751

18 CH3 475.9 -2.67751

19* CH3 238.5 -2.37748

20 CH3 32.9 -1.51719

N

N

CF3

SO2R1

NH

R2

* Compound of test set

674 Med Chem Res (2012) 21:672–680

123

quantified for steric feature of drug molecules required for

its complimentary fit with the receptor. Electronic param-

eters describe weak non-covalent bonding between drug

molecules and the receptor.

Division of test and training set

It is proven that the only way to estimate the true predictive

power of a model is to test it on a sufficiently large col-

lection of compounds from an external test set. The test set

must include not less than five compounds, whose activities

and structure must cover the range of activities and struc-

tures of compounds from the training set. This application

is necessary for obtaining trustful statistics for comparison

between the observed and predictive activities for these

compounds. In this series 10 compounds were selected as a

test set. This set used for the validation of model.

Statistical analysis

Statistical methods are an essential component of QSAR

work. They help to build models, estimate a model’s

predictive abilities, and find relationships and correla-

tions among variables and activities. The contribution of

descriptors to biological activity (BA) was studied using

simple linear regression analysis by SYSTAT 10.2

Software (2002) and, due to the problem of collinearity

among descriptors, different combinations of descriptors

were subjected to sequential and stepwise multiple

regression analysis. The pearson intercorrelation matrix of

the descriptors of QSAR model 5 is given in Table 6. The

regression methods are used to build a model in the form of

an equation that gives relationship between dependent

variable (usually activity) and independent variable

(‘‘descriptors’’). The model can then be used to predict

activities for new molecules.

Results and discussion

When data set of 69 compounds was subjected to step-

wise multiple linear regression analysis, in order to

develop QSAR model, several model were obtained. The

final set of equation was obtained using 59 compounds

and the best equation was obtained by using the optimal

combination of descriptors. Descriptors were selected for

the final equation having intercorrelation coefficient

below 0.5 were considered. The best QSAR model has

characters of large F, low error s, low P value, r2 and q2

value close to 1, as well as P \ 0.001. The large F

means proposed regression model fits the data well. The

low error means less standard deviation of the sampling

distribution associated with the estimation method. The

lower the P value, more ‘‘significant’’ the result is, in the

sense of statistical significance. The r2 and q2 value close

to 1 means model explained well the activity variations in

the compounds.

The stepwise development of model along with changes

in statistical qualities on gradual addition of descriptors

was done.

Model 1

BA ¼ 2:154 �0:850ð Þ þ 1:801 �0:411ð ÞLUMO

� 0:000 �0:000ð ÞGP� 0:007 �0:002ð ÞBP

� 0:067 �0:037ð Þ VDW

� 0:011 �0:003ð ÞTE

n = 59, r = 0.842, r2 = 0.709, adjusted squared multiple

R = 0.682, s = 0.527, F = 25.876, P = 0.

Model 1 explains only 70.9% variance in the COX-2

inhibitory activity. It shows that descriptor Low unoccu-

pied molecular orbital (LUMO) contribute positively;

where as Gamma polarizability (GP), b polarizability (BP),

Van der Waals Energy (vdW), Total energy (TE) contrib-

ute negatively towards COX-2 inhibitory activity. It is not

a very good significant equation, therefore new model

required for good explained variance.

Table 2 In vitro COX-2 inhibitory activities of compounds 21–26 in

HWB assay

N

N

CF3

SO2CH3

XR2

R3

( )n

Compound X n R3 R2 IC50 B.A.

21 NCH3 1 H Ph 454.5 -2.65753

22 NH 1 CH3 Ph 521.8 -2.7175

23 S 1 H Ph 527.8 -2.72246

24 S 1 H Thiophen-2-yl 4.1 -0.61278

25 NH 2 H Thiophen-2-yl 1,210 -3.08278

26 NH 2 H 1-Methyl-1H-

ptrrol-2-yl

3,980 -3.59988

Med Chem Res (2012) 21:672–680 675

123

Model 2

BA ¼ 7:315 �2:441ð Þ þ 0:624 �0:241ð ÞHOMO

� 0:000 �0:000ð ÞGP� 0:119 �0:067ð ÞD� 0:010 �0:002ð ÞBP� 0:123 �0:042ð ÞVDW

� 0:019 �0:004ð ÞTE� 0:188 �0:050ð ÞDDE

þ 0:000 �0:000ð ÞPMX


R = 0.704, s = 0.509, F = 18.208, P = 0.


inhibitory activity. It shows that descriptor highest occu-

pied molecular orbital (HOMO), principal moment of

inertia-axis (PMX) contribute positively; where as Gamma

polarizability (GP), Dipole (D), b polarizability (BP), van

der Waals Energy (vdW), Total energy (TE), Dipole–

Dipole energy (DDE) contribute negatively towards COX-

2 inhibitory activity. It is not a very good significant

equation, therefore new model required for good explained

variance.

Model 3

BA ¼ 0:664 �0:731ð Þ � 0:000 �0:000ð ÞGP

þ 0:000 �0:000ð ÞEE� 0:005 �0:002ð ÞBP

þ 0:016 �0:004ð ÞAP� 0:008 �0:003ð ÞTE

þ 0:128 �0:061ð ÞNVDW


R = 0.725, s = 0.491, F = 26.437, P = 0.


inhibitory activity. It is not a very good significant

equation, therefore new model required for good

explained variance. In this equation, Electronic Energy

(EE), a polarizability (AP), Non-vander Waals

Energy (NVDW) contribute positively, where as GP, BP,

Table 3 In vitro COX-2 inhibitory activities of compounds 27–38 in HWB assay

N

N

CF3

SO2CH3

NHR

N

N

CF3

SO2CH3

NH

SR

Ia Ib

Compound I R IC50 B.A.

27 Ia 4-CH3 48.4 -1.68484

28 Ia 4-F 77.2 -1.88761

29 Ia 2-CH3 5,720 -3.75739

30 Ia 3-CH3 1,930 -3.28555

31 Ia 3,5-DiF 300.3 -2.47755

32 Ia 4-CF3 1,920 -3.2833

33 Ia 4-OCH3 272.3 -2.43504

34 Ia 4-OH 635 -2.80277

35 Ia 4-NH2 211.3 -2.32489

36* Ib 3-CH3 527.8 -2.72246

37 Ib 5-CH3 11.7 -1.06818

38 Ib 5-Cl 5.4 -0.73239


676 Med Chem Res (2012) 21:672–680

123

TE contribute negatively towards COX-2 inhibitory

activity.

Model 4

BA ¼ � 1:326 �0:367ð Þ � 0:000 �0:001ð ÞGP

� 0:007 �0:001ð ÞBP� 0:092 �0:027ð ÞVDW

� 0:008 �0:002ð ÞBEþ 0:191 �0:071ð ÞPþ 0:000 �0:000ð ÞPMX þ 0:007 �0:001ð ÞHOF


R = 0.862, s = 0.347, F = 52.923, P = 0.


inhibitory activity. It is satisfactory significant equation,

therefore new model required for good explained variance.

This equation shows Partition coefficient (P), PMX, Heat

of Formation (HOF) contribute positively, where as GP,

BP, vdW, bend energy (BE) contribute negatively towards

COX-2 inhibitory activity.


N

N

R4

SO2CH3

NH

N

N

R4

SO2CH3

NH

S

Ia Ib

Compound I R4 IC50 B.A.

39 Ia iPr 25.3 -1.40312

40 Ib iPr 9.8 -0.99122

41 Ia tBu 93.9 -1.97266

42 Ib tBu 31.1 -1.49276

43 Ia OCH3 5.1 -0.70757

44 Ib OCH3 0.4 -0.39794

45 Ia Cl 10.9 -1.03742

46 Ib Cl 1.2 -0.07918

47 Ia SC2H5 24.1 -1.38201

48 Ib SC2H5 1.2 -0.07918

49 Ia SO2C2H5 90.7 -1.9576

50 Ib SO2C2H5 144.5 -2.15986

51* Ia OH 49.3 -1.69284

52* Ib OH 79.4 -1.89982

53 Ia NHiPr 273.8 -2.43743

54* Ib NHiPr 10.3 -1.01283

55* Ib H 9.2 -0.96378

56 Ib NEt2 6.1 -0.78532

57 Ib SOEt 37.6 -1.57518

58 Ib OEt 0.3 -0.52287

59 Ib OCH2CH2OCH3 2.4 -0.38021

60 Ib O-Cyclopentyl 7.5 -0.87506


Med Chem Res (2012) 21:672–680 677

123

Model 5

BA ¼ 1:554 �0:700ð Þ � 0:000 �0:001ð ÞGP

� 0:007 �0:001ð ÞBP� 0:089 �0:022ð ÞVDW

� 0:008 �0:002ð ÞBEþ 0:308 �0:065ð ÞP� 0:382 �0:083ð ÞMRþ 0:001 �0:000ð ÞPMX

þ 0:009 �0:001ð ÞHOF


R = 0.901, s = 0.294 and q2 = 0.642.

The r2-value accounts for 91% variance in observed

activity value. Therefore, model 5 is the best equation in

the QSAR study. The graph between experimental BA and

predicted BA of training set compounds by using model 5

is shown in Fig. 1. The r2 value can be easily increased by

increasing the number of descriptors in the model, so cross

validated correlation coefficient (q2) was used as a

parameter to select the optimum number of descriptors.

The variations in cross validation correlation coefficient

(q2) as a function of number of descriptors are shown in

Table 6 Pearson correlation matrix

B.A. GP BP VDW BE P MR PMX HOF

B.A. 1.000

GP -0.642 1.000

BP -0.528 0.294 1.000

VDW -0.154 0.262 0.134 1.000

BE -0.154 0.032 -0.225 -0.195 1.000

P -0.047 0.125 0.138 0.449 -0.088 1.000

MR 0.159 0.042 -0.080 0.339 -0.060 0.385 1.000

PMX -0.065 0.064 0.143 0.190 0.076 0.180 0.452 1.000

HOF 0.643 -0.120 -0.212 0.127 -0.128 -0.089 0.331 -0.264 1.000


N

N

R4

SO2CH3

NH

R5

R

N

N

R4

SO2CH3

NH

S

R5

R

Ia Ib

Compound I R R4 R5 IC50 B.A.

61 Ia H CF3 CH3 18 -1.25527

62 Ib H CF3 CH3 38.2 -1.58206

63 Ib H CF3 C2H5 120.9 -2.08242

64 Ia 4-CH3 Cl H 44.4 -1.64738

65 Ia 4-CH3 Cl CH3 36.7 -1.56466

66* Ia 4-F CF3 CH3 22.4 -1.35024

67 Ia 4-F CF3 C2H5 102.8 -2.01199

68 Ia 4-F Cl H 33.7 -1.52762

69 Ia 4-F Cl CH3 68.8 -1.83758


678 Med Chem Res (2012) 21:672–680

123

Fig. 2. The study revealed that the hydrogen bond donar

groups at position-4 enhances the activity, electron with-

drawing groups (e.g., -OMe, -OEt) at position-2 reduces

the activity, electron donating groups at position-6 enhan-

ces the activity. Model shows that PMX is a spatial

descriptor, which explains the significance of orientation

and conformation rigidity of the molecule. The positive

coefficient of these descriptor suggest the presence of

bulky substituent oriented towards X-axis of the molecules

will give better activity. The lipophilic parameter, partition

coefficient (P), denotes direct relationship to solubility in

aqueous phase, to membrane permeation, and its entropic

contribution to binding & it is positively correlated means

groups which are lipophilic nature have enhance the

activity of the compound. Molar refractivity (MR), a steric

parameter, which is negatively correlated, indicates that

sterically bulky substituent would reduce the binding

affinity. b polarizability (BP) is an electronic property

which shows second order polarizability coefficients &

Gamma polarizability (GP) is a third order polarizability

coefficients and these are negatively correlated. Heat of

Formation (HOF) is responsible for the stability of the

compounds and it is positively correlated. Anything which

can affect the bond properties and strength of the bonds in

the molecule can affect the value of HOF of that molecule.

Of them, the number of atoms and number of the bonds and

order of the bonds and number of non-organic elements

(heavy atoms) in a molecule directly affect on the value of

HOF. Number of atoms which are commonly existed in all

molecules such as oxygen and fluorine atoms, and even

heavy atoms affect HOF of a molecule. Decreases in the

number of these atoms in a molecule, increases HOF of

that molecule. The bend energy (BE) and van der Waals

energy (vdW), a thermodynamic property, denotes the sum

of the angle-bending terms of the force-field equation, and

it is negatively correlated, which is indicative of defor-

mation of the structure. Figure 3 denote the residual curve

of test compounds, which shows the variation in observed

and predicted biological activity in test set compounds.

The developed QSAR model can be utilized for the

further designing of new compounds belonging to the class

of NSAIDs to exhibit good COX-2 inhibitory activity, it

may be bind to the COX-2 ligand binding domain Val523,

Val434, and Arg513 which contributing to the enlargement

of the ligand binding site, as it reveals the various physico-

chemical parameters that play important roles in exhibiting

potential COX-2 inhibitory activity.

Acknowledgments Authors are thankful to institute for providing

the necessary facilities and guidance to carry out this research.

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hansch analysis of novel pyrimidine derivatives as highly potent and specific cox-2 inhibitors

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