ORI GINAL RESEARCH

QSAR analysis of some novel sulfonamidesincorporating 1,3,5-triazine derivatives as carbonicanhydrase inhibitors

Abhishek Kumar Jain • Ravichandran Veerasamy •

Ankur Vaidya • Vishnukanth Mourya •

Ram Kishore Agrawal

Received: 5 March 2009 / Accepted: 10 September 2009 / Published online: 6 October 2009

� Birkhauser Boston 2009

Abstract A QSAR study on a series of sulfonamides incorporating 1,3,5-triazine

derivatives was performed to explore the physicochemical parameters responsible for

their inhibitory activity of carbonic anhydrase I (CA-I) inhibitors. Physicochemical

parameters were calculated using WIN CAChe 6.1. Stepwise multiple linear regression

analysis was carried out to derive QSAR models, which were further evaluated for

statistical significance and predictive power by internal and external validation. The best

QSAR model was selected, having a correlation coefficient R = 0.932, a standard error

of estimation SEE = 0.224, and a cross-validated squared correlation coefficient

Q2 = 0.825. The predictive ability of the selected model was also confirmed by leave-

one-out cross-validation. The QSAR model indicates that the descriptors (polarizability,

DVY, 0v) play an important role in CA-I inhibition. The results of the present study may

be useful in the design of more potent sulfonamide substituted 1,3,5-triazine derivatives

as CA-I inhibitors.

Keywords QSAR � Sulfonamides incorporating 1,3,5-triazine derivatives �Carbonic anhydrase I inhibitors � Multiple linear regression

A. K. Jain � A. Vaidya � R. K. Agrawal (&)

Pharmaceutical Chemistry Research Laboratory, Department of Pharmaceutical Sciences,

Dr. Hari Singh Gour University, Sagar, MP 470 003, India

e-mail: dragrawal2001@yahoo.co.in

A. K. Jain

e-mail: abhishek_rawat2105@yahoo.co.in

R. Veerasamy

Faculty of Pharmacy, AIMST University, Semeling 08100, Kedah, Malaysia

V. Mourya

Government College of Pharmacy, Osmanpura, Aurangabad, Maharashtra, India

Med Chem Res (2010) 19:1191–1202

DOI 10.1007/s00044-009-9262-0

MEDICINALCHEMISTRYRESEARCH

Introduction

Carbonic anhydrases (CAs) are ubiquitous metallic-enzymes that catalyze the

hydration of carbon dioxide and the dehydration of bicarbonate. This reaction is

ubiquitous in nature, involving the interchange of gaseous and ionic species crucial

to a wide range of physiological and biochemical processes, being fundamental, for

example, in respiration, renal tubular acidification, and bone resorption (Deutsch,

1981). The CAs are present in prokaryotes and eukaryotes, where they are encoded

by four distinct, evolutionarily unrelated gene families: the a-CAs (present in

vertebrates, Bacteria, algae, and cytoplasm of green plants), the b-CAs (predom-

inantly found in Bacteria, algae, and chloroplasts of both mono- and dicotyledons),

the c-CAs (mainly present in Archaea and some Bacteria), and the d-CAs (present in

some marine diatoms, respectively) (Supuran and Scozzafava, 2000, 2003; Smith

and Ferry, 2000; Liljas et al., 1994; Lane and Morel, 2000).

There are 11 active CA isozymes known in human (Hilvo et al., 2005), some of

which act in cytosol (I, II, and III), others that are membrane-bound isozymes (IV,

VII, IX, XII, and XIV), a mitochondrial isozyme (V), and one secreted salivary

isozyme (VI). CA-I is known to have a low catalytic activity compared with CA-II

(Emmett and Tashian, 1996) a medium affinity for sulfonamides (Tripp et al.,2001). Sulfonamides, which are the most important CA-Is (such as the clinically

used derivatives acetazolamide, methazolamide, ethoxzolamide, dichlorophena-

mide, dorzolamide, and brinzolamide) (Leval et al., 2004; Sugrue, 2000), provoke a

wide range of deleterious side effects due to inhibition of the enzyme present in

other tissues (kidneys, lungs, red cells, stomach) (Maren, 1997).

The presence of these isozymes in so many tissues and in a number of different

isoforms represents an attractive objective for the design of inhibitors with

biomedical applications. The purpose of the present study was to investigate the

physicochemical parameters of a novel series of sulfonamide containing 1,3,5-

triazine derivatives responsible for the inhibition of cytosolic and tumor-associated

CA-I isozyme activity, to explore the correlation between them. It is expected to

garner more information for designing novel triazine-containing sulfonamide

derivatives for CA-I inhibition activities. Quantitative structure–activity relationship

(QSAR) studies are tools for predicting end points of interest in organic molecules

acting as drugs (Karelson, 2000). Many physiological activities of a molecule can be

related to their composition and structure. Molecular descriptors, which are

numerical representations of molecular structures, are used for performing QSAR

analysis (Tropsha et al., 2003).

The present group of authors has developed a few QSAR models to predict the

biological activity of different groups of compounds (Jain et al., 2008, 2009;

Ravichandran and Agarwal, 2007; Ravichandran et al., 2007a–c, 2008a–d; Sahu

et al., 2007, 2008).

There is high structural diversity and a sufficient range of biological activity in

the selected series of sulfonamide derivatives containing triazine moieties, which

led us to choose this series of compounds for our QSAR studies. We carried out

QSAR analysis and established QSAR models to guide further structural

1192 Med Chem Res (2010) 19:1191–1202

optimization and predict the potency and physiochemical properties of clinical drug

candidates.

Experimental

All of the molecular modeling studies reported herein were performed using Win

CAChe 6.1 (Product of Fujitsu Pvt. Ltd., Japan; http://www.cachesoftware.com/

contacts/japan.shtml) modeling software, and QSAR models were executed with

STATISTICA 6 (Softstat, Inc., Tulsa, OK, USA) software.

A data set of 38 compounds for CRTh2 receptor antagonists was used for the

present QSAR study (Garaj et al., 2005) (Table 1). The molar concentrations of the

compounds required to produce inhibition at the enzyme site (nM) were converted

to free-energy-related negative logarithmic values for the QSAR study. All 38

compounds were built on the workspace of molecular modeling software WIN

CAChe 6.1 (Fujitsu Pvt. Ltd.). Energy minimization was done by geometry

optimization of molecules using MM3 (Molecular Mechanics) followed by

MOPAC-AM1 (Austin model) using root mean square gradients of 0.1 and 0.001,

respectively. Physicochemical properties were calculated in the project leader file of

the software. These properties were fed manually into the statistical software

program STATISTICA 6 and a correlation matrix (Table 2) was constructed to

select the parameters having very low intercorrelation and maximum correlation

with activity. This was followed by stepwise linear regression analysis to obtain the

best model. In the present study the calculated descriptors were related to

topological, thermodynamic, spatial, electronic, etc. (Table 3). All physicochemical

parameter data will be provided upon request.

QSAR model development and validation

The above properties were fed manually into the statistical software program

STATISTICA 6 and a correlation matrix was constructed to select the parameters

having very low intercorrelation and maximum correlation with activity (Table 2).

This was followed by stepwise multiple linear regression analysis to achieve the

best models. Statistical measures used were number of compounds in regression n,

correlation coefficient R, correlation coefficient R2, F-test (Fischer’s value) for

statistical significance, and standard error of estimation SEE (S); the standard error

is an absolute measure of quality of fit and should have a low value for the

regression to be significant. Validation parameters considered were cross-validated

R2 or Q2, standard deviation based on predicted residual sum of squares (SPRESS), Q2

cross-validated correlation coefficient, and correlation matrix to show intercorre-

lation among the parameters.

Internal validation was carried out by the leave-one-out (LOO) method using the

statistical software STATISTICA. The cross-validated correlation coefficient, Q2,

was calculated using the following equations:

Med Chem Res (2010) 19:1191–1202 1193

Q2 ¼ 1 � PRESS

PN

i¼1

yi � ymð Þ2

PRESS ¼XN

i¼1

ypred;i � yi

� �2

where

yi is the activity for training-set compounds

ym is the mean observed value, corresponding to the mean of the values for

each cross-validation group

ypred,i is the predicted activity for yi

The predictive ability of the selected model was also confirmed by external R2

and R2CVext.

R2CVext ¼ 1�

Ptest

i¼1

yexp � ypred

� �2

Ptest

i¼1

yexp � �ytr

� �2

where

�ytr is the averaged value for the dependent variable for the training set

Table 1 Sulfonamides incorporating 1,3,5-triazine derivatives, with observed, calculated, and predicted

biological activity data

N

N

N

S OO

NH2

NH

NHR NHR1

n

N

N

N

S OO

NH

NRR1 NRR1

n

N

N

N

S OO

NH

PhO OPh

n

N

N

N

S OO

NH

Cl NH

COORm

n

NH2 NH2 NH2

8a-8x 9a-9i 10-12 13-16

Compound No. R, R1 N m hCA-I (nM) Log(1/IC50) (mM)

Obs. Calc. Pred. by LOO

method

8a H 1 – 87 -1.939 -1.645 -1.602

8b H 2 – 95 -1.977 -1.794 -1.775

8c NH2 0 – 93 -1.968 -1.851 -1.820

1194 Med Chem Res (2010) 19:1191–1202

Table 1 continued

Compound No. R, R1 N m hCA-I (nM) Log(1/IC50) (mM)

Obs. Calc. Pred. by LOO

method

8d NH2 1 – 104 -2.017 -1.902 -1.880

8e NH2 2 – 113 -2.053 -1.930 -1.918

8f Et 1 – 105 -2.021 -1.958 -1.955

8g Et 2 – 112 -2.049 -2.053 -2.052

8h i-Pr 0 – 235 -2.371 -2.182 -2.175

8i i-Pr 1 – 247 -2.392 -2.099 -2.084

8j i-Pr 2 – 241 -2.382 -2.180 -2.169

8k n-Pr 0 – 368 -2.565 -2.521 -2.510

8m n-Pr 1 – 425 -2.623 -2.415 -2.388

8n n-Pr 2 – 540 -2.732 -2.464 -2.434

8o n-Bu 0 – 561 -2.748 -2.663 -2.653

8p n-Bu 1 – 622 -2.793 -2.560 -2.544

8q n-Bu 2 – 613 -2.787 -2.532 -2.523

8r Et2NCH2CH2 0 – 558 -2.746 -2.594 -2.575

8s [HN(CH2CH2)2N]CH2CH2 0 – 1560 -3.193 -3.391 -3.421

8t [HN(CH2CH2)2N]CH2CH2 1 – 1635 -3.213 -3.322 -3.341

8u

N

OHHO

H3C

CH2

0 – 3500 -3.544 -3.462 -3.440

8v

N

OHHO

H3C

CH2

1 – 4200 -3.623 -3.523 -3.496

8x

N

OHHO

H3C

CH2

2 – 8500 -3.929 -3.694 -3.624

Med Chem Res (2010) 19:1191–1202 1195

The robustness of QSAR models was checked by Y-randomization test. In this

technique, new QSAR models were developed by shuffling the dependent variable

vector randomly and keeping the original independent variable as such. New QSAR

models are expected to have low R2 and Q2 values. If the opposite happens, then an

acceptable QSAR model cannot be obtained for the specific modeling method and

data.

In addition, we estimated Pogliani’s (1994, 2000) quality factor, Q. This quality

factor (Q) is defined as the ratio of the correlation coefficient (R) to the SEE, i.e.,

Q = R/S. Obviously, higher the R, and the lower the S, the higher will be the Qvalues, and thus the better will be the quality of correlation and predictive power of

the model.

Table 2 Correlation matrix: variance inflation factor of the physicochemical parameters and biological

activity

Variable Polarizability 0v DVY Log(1/IC50)

Polarizability 10v 0.207 1

DVY 0.168 0.122 1

Log(1/IC50) -0.877 -0.822 -0.358 1

Table 1 continued

Compound No. R, R1 N m hCA-I (nM) Log(1/IC50) (mM)

Obs. Calc. Pred. by LOO

method

9a Me, Me 0 – 63 -1.7993 -1.875 -1.882

9b Me, Me 1 – 75 -1.875 -1.908 -1.910

9c Me, Me 2 – 76 -1.880 -2.011 -2.019

9d Et, Et 0 – 128 -2.107 -2.318 -2.328

9e Et, Et 1 – 130 -2.113 -2.248 -2.260

9f Et, Et 2 – 156 -2.193 -2.353 -2.362

9g Me, n-Pr 0 – 176 -2.245 -2.199 -2.196

9h Me, n-Pr 1 – 180 -2.255 -2.164 -2.151

9i Me, n-Pr 2 – 198 -2.296 -2.338 –2.344

10 – 0 – 875 -2.942 -3.189 -3.273

11 – 1 – 631 -2.800 -2.956 -2.973

12 – 2 – 549 -2.739 -3.023 -3.073

13 – 1 1 35 -1.5440 -1.799 -1.819

14 – 2 1 33 -1.518 -1.911 -1.932

15 – 2 1 39 -1.591 -1.939 -1.962

16 – 2 2 31 -1.491 -2.092 -2.141

1196 Med Chem Res (2010) 19:1191–1202

Results and discussion

In the present study the authors tried to develop the best QSAR model to explain the

correlation between the physicochemical parameters and the CA-I inhibitory

activity of sulfonamides incorporating 1,3,5-triazine derivatives. A data set of 38

compounds of a reported series (Garaj et al., 2005) of CA-I and -II inhibitors was

used for the present QSAR study (Table 1). A QSAR analysis was performed using

Win CAChe 6.1 and STATISTICA. Stepwise linear regression analysis resulted in

three statistically significant QSAR models.

log 1=IC50ð Þ ¼ 0:167 �0:238ð Þ � 0:0591 �0:005ð ÞPol

n ¼ 38;R ¼ 0:878; S ¼ 0:496;F ¼ 74:767;R2 ¼ 0:770;

Q2 ¼ 0:749;Q ¼ 1:770; SPRESS ¼ 0:298 ð1Þ

log I=IC50ð Þ ¼ �0:174 �0:212ð Þ � 0:177 �0:028ð ÞPolþ 0:328 �0:077ð Þ0vn ¼ 38;R ¼ 0:921; S ¼ 0:237;F ¼ 97:920;R2 ¼ 0:848;

Q2 ¼ 0:825;Q ¼ 3:88; SPRESS ¼ 0:251 ð2Þ

log I=IC50ð Þ ¼ �0:234 �0:202ð Þ � 0:158 �0:028ð ÞPol

þ 0:280 �0:076ð Þ0v� 0:0268 �0:012ð Þn ¼ 38;R ¼ 0:932; S ¼ 0:224;F ¼ 120;R2 ¼ 0:886;

Q2 ¼ 0:838;Q ¼ 4:160; SPRESS ¼ 0:2414 ð3Þ

Table 3 Descriptors calculated

in the present studyDescriptor Definition

CME Conformational minimum energy

CI0 Zero-order connectivity index

CI1 First-order connectivity index

CI2 Second-order connectivity index

DM Dipole moment

TE Total energy

HF Heat of formation at its current geometry after

optimization of structure

HOMO Highest occupied molecular orbital energy

LUMO Lowest unoccupied molecular orbital energy

LOGP Octanol–water partition coefficient

MR Molar refractivity

SI1 Shape index order 1

SI2 Shape index order 2

SI3 Shape index order 30v Zero-order valance connectivity index1v First-order valance connectivity index2v Second-order valance connectivity index

LM Lambda max (UV absorption maximum)

Med Chem Res (2010) 19:1191–1202 1197

where IC50 is the molar concentration of drug required for 50% inhibition of the

CA-I enzyme; n, number of data points; R, correlation coefficient; S, standard error

of regression; F, = F-ratio of calculated-to-observed value variances; Q2, cross-

validated R2; Q, quality factor; Pol, polarizabilty; DVY, dipole vector Y; and 0v,

zero-order valance connectivity index.

Of the above three models, model 3 was selected as the best on the basis of the

high Q2, R, R2, F, and Q values and low S and SPRESS values. The calculated and

predicted (LOO) activities of the compounds by model 3 are listed in Table 1.

Model 3 shows a good correlation coefficient (R) of 0.932 between the descriptors

polarizability, zero-order valance connectivity index, dipole vector Y, and hCA-I

inhibitor activity. The R2 of 0.886 explains 88.6% of the variance in biological

activity.

This model also indicates statistical significance [99.9% with F = 120 and the

high Q value of 4.160. The cross-validated squared correlation coefficient of this

model is 0.825, which shows the good internal prediction power of this model.

Models 1 and 2 show the good correlation coefficients (R) of 0.878 and 0.921

between descriptors (polarizability), (polarizability, zero-order valance connectivity

index) and CA-I enzyme inhibition activity. The R2 of 0.770 and 0.848 explains

77.2% and 84.8% of the variance in biological activity, respectively. This model

also indicates statistical significance [99.9% with F = 74.767 and 97.920 and Qvalues of 1.770 and 3.88. The cross-validated squared correlation coefficients for

models 1 and 2 were 0.749 and 0.825, which shows the good internal prediction

power of this model. Consequently model 3 can be considered the most suitable

model, with both high statistical significant and excellent predictive ability; the

predictive ability of model 3 was also confirmed by external R2CVext: The robustness

of the selected model was checked by Y-randomization test. The low R2 and Q2

values indicate (data not shown) that the good results with our original model are

not due to a chance correlation or structural dependence of the training set. The

predictive ability of this model was also confirmed by external cross-validation

(Eq. 4). Consequently Eq. 3 can be considered the most suitable model, with both

high statistical significance and excellent predictive ability.

The selected model was externally validated by randomly selecting a training set

of 29 compounds and a test set of 9 compounds (8a, 8f, 8 m, 8 s, 8t, 9b, 9 h, 10,

15). QSAR analysis was performed for the training set, and model 4 was developed.

This model was used to predict the biological activities of the test-set compounds

(Table 4). The correlation between observed and calculated activity and between

observed and predicted activity (LOO) are shown in Figs. 1 and 2, respectively.

log 1=IC50ð Þ ¼ �0:131 �0:227ð Þ � 0:180 �0:036ð ÞPol

þ 0:332 �0:097ð Þ0v� 0:0315 �0:014ð ÞDVY

n ¼ 29;R ¼ 0:934;R2cvext ¼ 0:873;Q ¼ 4:04;Q2 ¼ 0:837;

S ¼ 0:225; SPRESS ¼ 0:246;F ¼ 57:395 ð4Þ

The variables used in the selected model have no mutual correlation. This model

showed the good correlation coefficient (R) of 0.934 between the descriptors dipole

1198 Med Chem Res (2010) 19:1191–1202

Table 4 Descriptors and observed and calculated activities for training and test set

Compound No. Polarizability 0v DVY Obs. Calc. Pred.

8a 29.304 11.131 -3.646 -1.939 * -1.595

8b 30.954 11.839 -0.385 -1.977 -1.777

8c 31.351 11.424 -4.961 -1.968 -1.842

8d 31.93 12.131 0.934 -2.017 -1.898

8e 34.063 12.839 -3.204 -2.053 -1.917

8f 37.095 14.391 -3.798 -2.021 * -1.910

8g 39.035 15.098 -4.32 -2.049 -2.030

8h 40.129 15.424 -2.525 -2.371 -2.176

8i 41.124 16.131 -4.073 -2.392 -2.072

8j 42.767 16.839 -3.358 -2.382 -2.156

8k 40.188 15.098 6.384 -2.565 -2.575

8m 41.165 15.805 4.058 -2.628 * -2.421

8n 42.814 16.512 3.579 -2.732 -2.492

8o 43.946 16.512 4.327 -2.748 -2.719

8p 44.914 17.219 2.149 -2.793 -2.591

8q 46.587 17.926 -1.362 -2.787 -2.548

8r 46.915 17.512 -5.309 -2.746 -2.620

8s 56.498 20.649 0.768 -3.193 * -3.469

8t 56.736 21.356 4.189 -3.213 * -3.385

8u 61.457 22.72 -4.167 -3.544 -3.552

8v 64.6 24.35 -3.366 -3.623 -3.604

8x 66.052 25.057 1.857 -3.929 -3.796

9a 36.499 14.164 -5.752 -1.799 -1.837

9b 37.514 14.871 -3.128 -1.875 * -1.847

9c 39.178 15.578 -1.688 -1.880 -1.980

9d 44.061 16.992 -4.227 -2.107 -2.312

9e 45.086 17.7 -5.497 -2.113 -2.222

9f 46.715 18.407 -3.78 -2.193 -2.335

9g 44.049 17.319 -5.174 -2.245 -2.171

9h 44.636 18.026 -2.545 -2.255 * -2.102

9i 46.761 18.733 -1.204 -2.296 -2.317

10 47.882 16.86 4.402 -2.942 * -3.290

11 48.274 17.567 0.761 -2.800 -3.038

12 50.482 18.274 -2.342 -2.739 -3.104

13 34.377 13.673 -1.204 -1.544 -1.760

14 36.503 14.38 -2.18 -1.518 -1.879

15 38.554 15.341 -3.155 -1.591 * -1.878

16 38.338 15.087 1.152 -1.491 -1.880

Med Chem Res (2010) 19:1191–1202 1199

vector Y, polarizability, zero-order valance connectivity index, and CA-I inhibitor

activity. The R2 value of 0.872 explains 87.2% of the variance in biological activity.

The predicted R2 value of the test set was 0.936.

The positive contribution of the zero-order valance connectivity index to the

biological activity showed that increased values of these parameters led to better

CA-1 inhibition properties. The negative coefficient of polarizability indicated that

an increase in that property is detrimental to biological activity and the negative

coefficient of dipole vector Y is conducive to activity; in short, according to model 4

the CA-I inhibition activity of compounds is increased if the valance connectivity

index zero is increased, polarizability decreased, and dipole vector Y decreased.

Thus we conclude that the biological activity will be increased if substituents that

bring about changes in the molecule as mentioned above are attached to it. Based on

the QSAR model developed, new CA-I inhibitor derivatives can be designed, with

caution.

-4

-3.5

-3

-2.5

-2

-1.5

-1

-0.5

0-4.5 -4 -3.5 -3 -2.5 -2 -1.5 -1 -0.5 0

Observed activity [log(1/IC50 )]

Cal

cula

ted

acti

vity

[ log

(1/I

C50

)]

Fig. 1 Correlation between experimental and calculated activities for model 4

-4

-3.5

-3

-2.5

-2

-1.5

-1

-0.5

0

-3.5 -3 -2.5 -2 -1.5 -1 -0.5 0

Observed activity [log(1/IC50)]

Cal

cula

ted

acti

vity

[lo g

(1/I

C50

)]

Fig. 2 Correlation between calculated and predicted activities for model 4

1200 Med Chem Res (2010) 19:1191–1202

Acknowledgment A. K. Jain is grateful to the AICTE for providing a fellowship for this work.

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