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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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