Quantitative structure-activity relationships studies for prediction of antimicrobial activity of synthesized disulfonamide derivatives

Article abstract of DOI:10.1007/s00044-010-9304-7

A new series of disulfonamides were synthesized and assayed as antimicrobial agents against Staphylococcus aureus, Bacillus cereus, and Escherichia coli. The quantitative structure-activity relationship analysis (QSAR) was applied to find out the correlat

Full text of DOI:10.1007/s00044-010-9304-7

MEDICINAL
CHEMISTRY
RESEARCH
Med Chem Res (2011) 20:175–183
DOI 10.1007/s00044-010-9304-7
ORIGINAL RESEARCH
Quantitative structure–activity relationships studies for prediction
of antimicrobial activity of synthesized disulfonamide derivatives
¨
•
•
•
Saliha Alyar Neslihan Ozbek Ku¨bra Kuzukıran
Nurcan Karacan
Received: 15 June 2009 / Accepted: 13 January 2010 / Published online: 2 February 2010
Ó Springer Science+Business Media, LLC 2010
Abstract A new series of disulfonamides were synthe-
sized and assayed as antimicrobial agents against Staphy-
lococcus aureus, Bacillus cereus, and Escherichia coli.
The quantitative structure–activity relationship analysis
(QSAR) was applied to find out the correlation between
experimentally evaluated antimicrobial activities with
various parameters of the compounds using stepwise
multiple liner regression method. The QSAR analysis
revealed that the third-order average connectivity index
antimicrobials—the sulfonamides or sulfa drugs—targeted
one of these enzymes, namely, dihydropteroate synthase
(Babaoglu et al., 2004; Woods, 1940). These sulfa drugs
are still widely used, usually in combination with dihy-
drofolate reductase inhibitors, to treat common bacterial
infections such as, urinary tract infections, nocardiosis,
toxoplasmosis, blepharitis, conjunctivitis, septicemia, acute
sinusitis, and chronic bronchitis, particularly in patients
with penicillin allergy. In contrast to other types of medi-
cation, antibiotics ultimately lose their effectiveness as
they are used over time and resistant strains of bacteria
(Iliades et al., 2003; Lerner, 1998). In addition, the threat
of bioterrorism using agents, such as weaponized Bacillus
anthracis and Yersinia pestis, highlight the need for
continuing research in infectious diseases and the search
for new therapeutic agents (Greenfield and Bronze, 2003).
Quantitative structure–activity relationships (QSAR),
establishing correlation between trends in chemical struc-
ture modification and respective changes of biological
activity have been broadly used for the past few years
mainly in medical research (Hansch and Leo, 1995; Hansch
et al., 1995). This methodology allows cost savings by
reducing the laboratory resources needed and the time
required to create and investigate new drugs with certain
desired biological activity. A large number of QSAR
studies for sulfonamides have been developed for different
biological properties, such as antimicrobial activity (Temiz
et al., 2008), antifungal activity (Saiz et al., 2007; Deokar
et al., 2008), antimalarial activity (Agrawal et al., 2001),
antitumor activity (Jaiswal et al., 2004; Samanta et al.,
2004), carbonic anhydrase inhibitors (Khadikar et al.,
2005; Balaban et al., 2004), COX-2 inhibitors (Silakari
et al., 2008), HIV-1 integrase inhibitors (Kumar Sahu
et al., 2007; Ravichandran et al., 2007), matrix metallo-
proteinase (MMP) inhibitors (Gupta and Kumaran, 2005),
ð vAÞ was found to have negative correlation. The best
3
QSAR models were further validated by leave-one-out
method of cross-validation.
Keywords Antimicrobial activity ꢀ Sulfonamides ꢀ
Disulfonamides ꢀ QSAR
Introduction
Folate cofactors are essential components of several
enzymatic reactions occurring in nearly all cells and are
essential for life. Indeed, the first chemically synthesized
S. Alyar
Department of Chemistry, Science and Art Faculty,
C¸ ankırı Karatekin University, 18100 C¸ ankırı, Turkey
¨
N. Ozbek
Department of Primary Education, Faculty of Education,
Ahi Evran University, 40001 Kırs¸ehir, Turkey
K. Kuzukıran ꢀ N. Karacan (&)
Department of Chemistry, Science and Art Faculty,
Gazi University, 06500 Ankara, Turkey
e-mail: nkaracan@gazi.edu.tr
123

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Med Chem Res (2011) 20:175–183
and b3-adrenergic receptor agonist (Hanumantharao et al.,
2005; Kashaw et al., 2003).
Biology Department, and Refik Saydam Hygiene Center
Culture Collection. Bacterial strains were cultured over-
night at 310 K in Nutrient Broth. These stock cultures were
stored in the dark at 277 K during the survey.
Recently there has been considerable interest in new
sulfonamides possessing antimicrobial activity (Patel et al.,
2007; El-Sharief and Al-Raqa, 2007; Ezabadi et al., 2008).
In our previous studies, aliphatic and aromatic disulfona-
mides were synthesized and evaluated for antimicrobial
activity (Ozbek et al., 2007a, b; Alyar and Karacan, 2009;
Alyar et al., 2007). In addition, methanesulfonic acid
hydrazides and their hydrazones were obtained and
screened for their antimicrobial and cytotoxic activity
(Ienco et al., 1999; Dodoff et al., 1999; Alyar et al., 2008;
Ozbek et al., 2009). Metal carbonyl complexes of these
sulfonyl hydrazones were also reported (Ozdemir et al.,
2003; Sert et al., 2004; Ozdemir et al., 2004; Ozdemir
et al., 2006; Ozdemir and Olgun, 2008; Senturk et al.,
2007; Senturk et al., 2003). In this article, as part of our
ongoing studies, a series of novel aromatic sulfonamide
derivatives were synthesized and characterized by ele-
mental analysis and standard physicochemical methods.
Their antimicrobial activity was screened against three
bacteria, Staphylococcus aureus, Bacillus cereus, and
Escherichia coli, by microdilution method. The QSAR
analysis of the compounds was performed using various
physicochemical and topological descriptors.
Microdilution broth susceptibility assay was used
(Koneman et al., 1997). Stock solution of the test com-
pounds were prepared in 10% dimethylsulfoxide (DMSO),
and then serial dilutions of the test compounds were made
in a concentration range from 25 to 1400 lg/ml. The 96-
well plates were prepared by dispensing into each well
95 ll of nutrient broth and 5 ll of the inoculum. Then,
100 ll from each of the test compounds initially prepared
at the concentration of 1400 lg/ml was added into the first
wells. The last well containing 195 ll of nutrient broth
without compound and 5 ll of the inoculum on each strip
was used as negative control. The final volume in each well
was 200 ll. The contents of the wells were mixed and the
micro plates were incubated at 37°C for 24 h. All of the
compounds tested in this study were screened two times
against each microorganism. The MIC was defined as the
lowest concentration of the compounds to inhibit the
growth of microorganisms.
Disk diffusion method
Bacterial susceptibility testing was performed by the disk
diffusion method according to the guidelines of Clinical
and Laboratory Standards Institute (CLSI) (Bauer et al.,
1966). The sterilized (autoclaved at 393 K for 30 min),
liquefied Mueller–Hinton agar (313–323 K) was inoculated
with the suspension of the microorganism (matched to 0.5
McFarland) and poured into a Petri dish to give a depth of
3–4 mm. The paper disks impregnated with the test com-
pounds (60 lg) were placed on the solidified medium.
Disks were placed on agar plates, and the cultures were
incubated at 310 K for 24 h for bacteria. Inhibition zones
formed on the medium were evaluated in millimeters.
Ciprofloxacin (5 lg/disk) and Ampicillin (10 lg/disk)
were used as standard drugs (positive control). DMSO-
poured disk was used as negative control. Each assay in
this experiment was repeated twice.
Experimental
Synthesis of the compounds
The nucleophilic substitution reaction of the aliphatic
diamines with sulfonyl chlorides were carried out as fol-
lows: Tetrahydrofuran (THF) solution of aliphatic diamines
was added slowly dropwise to the THF solution of sulfonyl
chlorides, maintaining the temperature between -5 and
-10°C. Then, the reaction mixture was stirred for 24 h at
room temperature (completion of the reaction was moni-
tored by TLC). After the completion of the reaction, the
solvent was evaporated in vacuum. The solid residue was
purified by column chromatography. The synthesized
compounds were characterized by various spectrochemical
techniques, and the data were found to be in agreement
with those of the assigned molecular structures. The
physicochemical characteristics and the molecular struc-
ture of our compounds are tabulated in Table 1.
Result and discussion
In vitro antibacterial activity
Antimicrobial screening methods
In vitro antibacterial activity of the synthesized compounds
was assayed on bacterial strains, Gram-positive S. aureus,
B. cereus, and Gram negative E. coli with microdilution
method. Antibacterial activity data (MIC) were converted
Microdilution assays
E. coli ATCC 11230, S. aureus ATCC 25923, and B. cereus
RSKK 863 cultures were obtained from Gazi University,
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Med Chem Res (2011) 20:175–183
177
Table 1 Physicochemical data of disulfonamides derivatives
H
N
O
S
O
S
H
N
R
(CH2)n
R
O
O
Compound
n
R
Molecular formula
Mol Wt
Mp (°C)
Yield (%)
Training set
1
2
3
4
2
3
4
2
3
4
2
3
4
2
2
3
4
5
2
3
4
5
2
3
4
3
4
Ph–
Ph–
Ph–
C
C
C
14H16N2O4S2
15H18N2O4S2
16H20N2O4S2
340.41
354.44
368.47
367.47
382.49
396.52
368.47
382.49
396.52
368.47
382.49
396.52
400.46
216.27
230.30
244.32
258.35
244.32
258.35
244.32
258.35
272.38
286.40
300.43
314.46
328.49
173–174
1174–175
175–176
158–161
173–174
183–184
158–161
173–174
183–184
158–161
173–174
183–184
195–196
74–75
72
77
85
72
72
72
75
78
78
80
82
80
80
75
85
85
75
75
75
72
72
75
88
84
88
75
2
3
4
4-CH3–ph–
4-CH3–ph–
4-CH3–ph–
2-CH3–ph–
2-CH3–ph–
2-CH3–ph–
3-CH3–ph–
3-CH3–ph–
3-CH3–ph–
4-CH3O–ph–
CH3–
C16H20N2O4S2
C17H22N2O4S2
C18H24N2O4S2
C16H20N2O4S2
C17H22N2O4S2
C18H24N2O4S2
C16H20N2O4S2
C17H22N2O4S2
C18H24N2O4S2
C16H20N2O6S2
C4H12N2O4S2
C5H14N2O4S2
C6H16N2O4S2
C7H18N2O4S2
C6H16N2O4S2
C7H18N2O4S2
C8H20N2O4S2
C9H22N2O4S2
C8H20N2O4S2
C9H22N2O4S2
C10H24N2O4S2
C11H26N2O4S2
5
6
7
8
9
10
11
12
13
14
15
16
17
18
19
20
21
22
23
24
25
26
CH3–
77–78
CH3–
79–80
CH3–
81–82
CH3–CH2–
CH3–CH2–
CH3–CH2–
CH3–CH2–
CH3–CH2–CH2–
CH3–CH2–CH2–
CH3–CH2–CH2–
CH3–CH2–CH2–
78–79
80–81
82–83
84–85
94–95
121–122
126–128
116–118
118–119
CH3–CH2–CH2–
CH2–
C12H28N2O4S2
Test set
27
3
5
5
2
4-CH3O–ph–
Ph–
C17H22N2O6S2
17H22N2O4S2
C11H26N2O4S2
10H24N2O4S2
414.49
382.49
214.46
300.43
200–201
177–178
129–130
120–121
80
80
75
75
28
C
29
CH3–CH2–CH2–
30
CH3–CH2–CH2–
CH2–
C
31
32
33
34
2
3
2
3
3-CH3O–ph–
3-CH3O–ph–
2-CH3O–ph–
2-CH3O–ph–
C16H20N2O6S2
C17H22N2O6S2
C16H20N2O6S2
C17H22N2O6S2
400.46
414.49
400.46
414.49
195–196
200–201
195–196
200–201
80
80
80
80
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Table 2 Comparison of observed and predicted antimicrobial activity of the compounds
pMIC
Escherichia coli
Bacillus cereus
Staphylococcus aureus
Obs.
Pre.
Res.
Obs.
Pre.
Res.
Obs.
Pre.
Res.
1
3.510
3.490
3.480
3.530
3.520
3.500
3.700
3.680
3.640
3.580
3.570
3.550
3.560
2.990
2.970
2.950
2.940
2.720
2.700
2.690
2.670
3.258
3.104
3.119
3.116
2.857
3.537
3.522
3.508
3.537
3.508
3.493
3.670
3.654
3.625
3.581
3.566
3.551
3.522
3.012
2.997
2.968
2.954
2.662
2.662
2.678
2.677
3.158
3.128
3.114
3.056
3.041
0.027
0.032
0.028
0.007
0.012
0.007
0.032
0.026
0.015
0.001
0.004
0.001
0.038
0.022
0.027
0.018
0.014
0.058
0.038
0.011
0.007
0.100
0.024
0.005
0.060
0.184
3.370
3.360
3.358
3.400
3.380
3.350
3.560
3.542
3.505
3.480
3.440
3.410
3.460
2.876
2.864
2.840
2.828
2.660
2.642
2.638
2.580
3.179
3.058
2.950
2.917
2.850
3.408
3.394
3.381
3.408
3.381
3.166
3.342
3.328
3.298
3.254
3.240
3.225
3.196
2.682
2.668
2.638
2.624
2.330
2.330
2.345
2.345
2.829
2.800
2.785
2.726
2.712
0.038
0.034
0.023
0.008
0.001
0.184
0.218
0.214
0.207
0.226
0.200
0.185
0.264
0.194
0.196
0.202
0.204
0.330
0.312
0.293
0.235
0.350
0.258
0.165
0.191
0.138
3.380
3.285
3.240
3.400
3.300
3.260
3.638
3.600
3.540
3.522
3.480
3.470
3.490
2.990
2.925
2.900
2.880
2.660
2.642
2.637
2.610
3.003
3.009
2.933
2.861
2.829
3.404
3.391
3.370
3.404
3.370
3.360
3.527
3.513
3.486
3.445
3.432
3.418
3.391
2.917
2.904
2.877
2.863
2.592
2.592
2.606
2.606
3.053
3.025
3.012
2.958
2.944
0.024
0.106
0.130
0.004
0.070
0.100
0.111
0.087
0.054
0.077
0.048
0.052
0.099
0.073
0.021
0.023
0.017
0.068
0.050
0.031
0.004
0.050
0.016
0.079
0.097
0.115
2
3
4
5
6
7
8
9
10
11
12
13
14
15
16
17
18
19
20
21
22
23
24
25
26
Test set
27
28
29
30
31
32
33
34
3.511
3.496
3.191
3.120
3.600
3.590
3.720
3.711
3.508
3.478
3.099
3.070
3.566
3.551
3.698
3.683
0.003
0.018
0.092
0.050
0.034
0.039
0.022
0.028
3.420
3.200
2.780
2.830
3.580
3.550
3.660
3.600
3.420
3.200
2.780
2.830
3.435
3.422
3.559
3.545
0.039
0.048
0.010
0.089
0.145
0.128
0.101
0.055
3.460
3.380
2.960
2.974
3.520
3.500
3.580
3.550
3.370
3.350
2.998
2.971
3.431
3.418
3.554
3.540
0.090
0.030
0.038
0.003
0.089
0.082
0.026
0.010
into the corresponding pMIC values (Table 2) to get the
linear relationship in equations using the formula:
pMIC = -logMIC(lM). Compound (7) was the most
effective compound against E. coli with pMIC value of
3.70 in the training set. In general, the antimicrobial
activity of the compounds was in the following order:
length of the carbon chain between NH groups was
increased. The aromatic disulfonamides were more active
than aliphatic disulfonamides.
QSAR analysis
E: coli [ B: cereus [ S: aureus
The trend of antimicrobial activity shows that the
activity against all bacterial strains was decreased when the
In order to identify the substituent effect on the antimi-
crobial activity, QSAR studies were performed using the
linear regression analysis described by Hansch and Fujita
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Med Chem Res (2011) 20:175–183
179
(Hansch and Fujita, 1964). The compounds were analyzed
by physicochemical and topological parameters as inde-
pendent variables and pMIC values as dependent variables.
The chemical structure of molecules was set up with the
Hyperchem 7.5 program and optimized with semi-empiri-
cal PM3 method. In order to prevent the structures locating
at local minima, geometry optimization was run many
times with different starting points for each molecule.
Different quantum chemical descriptors including heat of
formation, dipole moment, and energies of the frontier
orbitals (HOMO and LUMO) were calculated by the
Hyperchem 7.5 software (HyperChem 7.5, 2002). The
topological parameters were calculated by Dragon 5.5
software (Dragon 5.5). Before generating model, intercor-
relation of descriptors was taken into account, and highly
correlated descriptors were removed. The values of the
selected descriptors used in the regression analysis are
presented in Table 3.
All the statistical analyses were performed with SPSS
software (Version 15, 2005). Leave-One-Out (LOO)
cross-validation method was used to validate the pre-
dictive powers of all QSAR equations. The abbrevia-
tions for the statistical parameters given for each
equation were: n (number of data points), r2 (squared
correlation coefficient), and s (standard error of esti-
mate),rc2v (cross-validation correlation coefficient),
SPRESS (sum of square standard error), and F (Fischer
statistics).
The parameter r2 is an indicator of the fit of the regression
equation. Fisher test value reflects the ratio of the variance
explained by the model and the variance due to the error in
the model. The goodness of fit of Eqs. 1–3 is significant,
possessing high r2 values ([0.91), small standard deviations
(s \ 0.07) with high F values, exceeding the tabulated
F1;34; 0:95 ¼ 4:23 (Draper and Smith 1966). The parameter rc2v
is an indication of the predictive capability of the model
(Jung et al., 2007; Sivakumar et al., 2007; Narasimhan et al.,
2007). High cross-validated correlation coefficient
The first step in analyzing multivariate correlation is to
construct correlation matrix, showing the interrelationship
among the independent parameters and also their individ-
ual correlation with antimicrobial activity. The correlation
matrix given in Table 4 indicates that there was high
autocorrelation (r [ 0.7) between the molecular discrip-
tors. The topological descriptors were significantly
correlated with (r [ 0.8) antimicrobial activity of the
compounds against all microorganisms. The third-order
ꢀ
ꢁ
rc2v [ 0:9 and low predictive residual sum of square
(SPRESS \ 0.076) values indicate the robustness of the
obtained QSAR models (Kadam and Roy, 2006; Tetko et al.,
2001). Since the activity profiles of the compounds on target
organisms are very similar to squared correlation coefficients
(r2 = 0.91–0.98), it is understandable that the best QSAR
models are very similar.
3
average connectivity index ð vAÞ exhibits the most corre-
Statistically, significant inverse relationship was
observed between antimicrobial activity of disulfona-
mides against all microorganisms and third-order average
lation (r [ 0.97) with all microorganisms, especially with
E. coli (r [ 0.99).
3
3
In this study, molecules were rationally divided into the
training set (1–26) and test set (27–34). 2D-QSAR analysis
was performed by stepwise multiple liner regression
method using training set. The statistically significant
QSAR models are given below:
connectivity index ð vAÞ. The negative coefficient of vA
in Eqs. 1–3 indicates that the lower the 3vA value, the
higher the antimicrobial activity. For example, the com-
pound (7) has the minimum third-order average con-
nectivity index value ð vA ¼ 0:189Þ in Table 3 and has
3
QSAR model for antimicrobial activity against E. coli
the maximum antimicrobial activity against E. coli
(pMIC = 3.70). Similarly, the compound (21) has the
maximum third order average connectivity index value
3
pMIC ¼ ꢁ14:585ðꢂ0:422Þ vA þ 6:425ðꢂ0:093Þ
ð1Þ
where n = 26, r2 = 0.9801, s = 0.0504, F = 1194,
SPRESS = 0.050, rc2v ¼ 0:994.
ð vA ¼ 0:259Þ and has the minimum antimicrobial
3
activity against S. aureus (pMIC = 2.61) in the training
set. Kier–Hall Connectivity Indices are calculated from
the Hydrogen-depleted molecular graph (Kier and Hall,
1986). One of them, the Average Connectivity index
Chi-3 is defined as
QSAR model for antimicrobial activity against B. cereus
3
pMIC ¼ ꢁ13:670ðꢂ0:408Þ vA þ 6:115ðꢂ0:089Þ
ð2Þ
where n = 26, r2 = 0.9781, s = 0.0487, F = 1124,
SPRESS = 0.048, rc2v ¼ 0:978.
ꢂ
v3A ¼ v3
b
QSAR model for antimicrobial activity against S. aureus
3
pMIC ¼ ꢁ13:537ðꢂ0:636Þ vA þ 6:085ðꢂ0:139Þ
ð3Þ
where, b is the number of bonds, the sum runs through all
bonds in the Hydrogen-depleted molecule, and for each
bond, di dj is the product of the vertex degrees of the end
where n = 26, r2 = 0.9082, s = 0.0760, F = 453,
SPRESS = 0.076, rc2v ¼ 0:947.
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Med Chem Res (2011) 20:175–183
181
atoms i and j. Connectivity indices Chi-m for 3 is defined
as
K
X
v3 ¼
ðPdiÞꢁ1=2
;
k
k¼1
where, (II di)k is the product of the vertex degrees of the
atoms that form a connected subgraph with m edges, and
K is the total number of such distinct connected sub
graphs (the H-depleted molecular graph), each having
m edges.
In order to confirm our results, we have predicted the
antimicrobial activity of the disulfonamides in the test set
using Eqs. 1–3. The comparison of the observed and the
predicted values (Table 2) demonstrated that they are
close to each other as evidenced by the low residual
activity values. Furthermore, it is supported by the plot of
pMICEc observed vs. pMICEc predicted (Fig. 1). It is
important to note that all the equations are derived using
the entire data set and no outliers were found during
model development. Even though the sample size and
‘‘Rule of Thumb’’ allowed us to go for development of
multi-parametric model in MLA, the high autocorrelation
(Table 3) among parameters restricted us to opt for
bi-parametric model (Narasimhan et al., 2006). In this
study, the range of antimicrobial activities of the disulf-
onamides is within one order of magnitude. Bajaj et al.
stated that the reliability of the QSAR models lies in its
predictive ability even though the activity data are in the
narrow range (Bajaj et al., 2005). The predictability of the
QSAR models developed in this study is high evidenced
by low residual values (Table 2).
Conclusion
In conclusion, a series of disulfonamide derivatives were
synthesized in efficient yields and screened for their in
vitro antimicrobial activity against Staphylococcus aureus,
Bacillus cereus and Escherichia coli. The SAR studies
indicate that title compounds were the most effective
against Gram-negative Escherichia coli. The trends of
antimicrobial studies showed that decreasing the length of
the carbon chain between NH groups will improve the
activity. The QSAR analysis revealed that the antimicro-
bial activity of these synthesized derivatives against
microorganism under test is mainly governed by the
topological parameters, namely, third order average con-
3
nectivity index ð vAÞ. The obtained regression modes
present a good capacity to explain the observed values of
antimicrobial activity with high statistical significance and
predictive capacity.
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Fig. 1 a Plot of predicted
pMICEc activity values against
the experimental pMICEc values
for the linear regression
developed model by Eq. 1.
b Plot of predicted pMICBc
activity values against the
experimental pMICBc values for
the linear regression developed
model by Eq. 2. c Plot of
predicted pMICSa activity
values against the experimental
pMICSa values for the linear
regression developed model by
Eq. 3
Acknowledgments The authors are thankful to TUBITAK Foun-
dation (TBAG-104T390) and Gazi University Scientific Research
Projects (BAP 05/2007-01 and 05/2006-33) for financial supports.
Deokar HS, Puranik P, Kulkarni VM (2008) QSAR analysis of
N-myristoyltransferase inhibitors: antifungal activity of benzo-
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SM, Stefanova ME (1999) Schiff bases of methanesulfonylhydr-
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sulfonamides, tosylamino acids and thiosulfonic ester derived
from autotoxin, bergapten and visnagin with biological interest.
Phosphorus Sulfur 182:1557–1580
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