Hansch analysis of substituted benzoic acid benzylidene/furan-2-yl-methylene hydrazides as antimicrobial agents

Article abstract of DOI:10.1016/j.ejmech.2008.10.034

A series of substituted hydrazide derivatives have been synthesized and screened for their in vitro antimicrobial activities against five representative microorganisms. The results of antimicrobial study indicated that the presence of electron withdrawing groups on the benzoic acid moiety improved antimicrobial activity. Further, the presence of heterocyclic ring furan does not improve the antimicrobial activity of substituted hydrazides. To understand the relationship between physicochemical parameters and antimicrobial activity of substituted hydrazide derivatives, QSAR investigation was performed by the development of one-target and multi-target models. The multi-target model was found to be effective in describing the antimicrobial activity of substituted hydrazides in comparison to the one-target models. Further, it indicated the importance of the topological parameter, valence third order molecular connectivity index (³χ^v) and the electronic parameter, energy of highest occupied molecular orbital (HOMO) in describing the antimicrobial activity of substituted hydrazides.

Full text of DOI:10.1016/j.ejmech.2008.10.034

European Journal of Medicinal Chemistry 44 (2009) 1853–1863
Contents lists available at ScienceDirect
European Journal of Medicinal Chemistry
journal homepage: http://www.elsevier.com/locate/ejmech
Original article
Hansch analysis of substituted benzoic acid benzylidene/furan-2-yl-methylene
hydrazides as antimicrobial agents
,
a
a
Pradeep Kumar a, Balasubramanian Narasimhan b , Deepika Sharma , Vikramjeet Judge ,
*
Rakesh Narang a
a Department of Pharmaceutical Sciences, Guru Jambheshwar University of Science and Technology, Hisar 125001, India
b Faculty of Pharmaceutical Sciences, Maharshi Dayanand University, Rohtak 124001, India
a r t i c l e i n f o
a b s t r a c t
Article history:
A series of substituted hydrazide derivatives have been synthesized and screened for their in vitro
antimicrobial activities against five representative microorganisms. The results of antimicrobial study
indicated that the presence of electron withdrawing groups on the benzoic acid moiety improved
antimicrobial activity. Further, the presence of heterocyclic ring furan does not improve the antimicrobial
activity of substituted hydrazides. To understand the relationship between physicochemical parameters
and antimicrobial activity of substituted hydrazide derivatives, QSAR investigation was performed by the
development of one-target and multi-target models. The multi-target model was found to be effective in
describing the antimicrobial activity of substituted hydrazides in comparison to the one-target models.
Further, it indicated the importance of the topological parameter, valence third order molecular
Received 24 July 2008
Received in revised form
25 October 2008
Accepted 31 October 2008
Available online 5 November 2008
Keywords:
Substituted hydrazides
Antibacterial activity
Antifungal activity
QSAR
connectivity index (3
(HOMO) in describing the antimicrobial activity of substituted hydrazides.
Ó 2008 Elsevier Masson SAS. All rights reserved.
c
v) and the electronic parameter, energy of highest occupied molecular orbital
1. Introduction
In view of the above and in continuation of our ongoing research
program in the field of synthesis, antimicrobial and QSAR studies of
medicinally important compounds [15–25], in the present study we
hereby report the synthesis, antimicrobial and QSAR evaluation of
substituted benzoic acid benzylidene/furan-2-yl-methylene hydrazides.
The development of antimicrobial agents (antibacterials and
antifungals) to treat infections has been one of the most notable
medical achievements of the past century. The advances in medical
care are threatened, however, by a natural phenomenon known as
‘‘antimicrobial resistance.’’ The increased use of antibacterial and
antifungal agents in recent years has resulted in the development of
resistance to these drugs with important implications for
morbidity, mortality and health care costs. In spite of a large
number of antibiotics and chemotherapeutics available for medical
use, the antimicrobial resistance created a substantial need of new
class of antimicrobial agents in the last decades [1–3]. Hydrazide-
hydrazones form a class of compounds possessing a wide range of
biological activities viz. antimicrobial [4], antimycobacterial [5],
antitumour [6], anti-inflammatory [7], trypanocidal [8], leishma-
nicidal [9], anti-HIV [10], inhibitor of anthrax lethal factor [11],
antidiabetic [12] and antimalarial agents [13].
2. Chemistry
The synthesis of compounds 1–26 followed the general pathway
elicited in Scheme 1. The substituted benzoic acid was treated with
thionyl chloride to get the corresponding acid chloride which in
turn was reacted with hydrazine hydrate to get the corresponding
acid hydrazide. The treatment of hydrazide with different
substituted aldehydes gave the corresponding substituted benzoic
acid benzylidene/furan-2-yl-methylene hydrazides (1–26). The
physicochemical characteristics of synthesized compounds are
presented in Supplementary data. The structures of compounds 1–
26 were found in agreement with the assigned molecular structure
confirmed by their consistent IR and NMR spectra.
Quantitative structure–activity relationship (QSAR) is a meth-
odology used to correlate biological property of molecule with
molecular descriptors derived from chemical structures. It is
a mathematical model of statistically validated correlation between
the chemical structures and their activity profile [14].
3. Results and discussion
3.1. Antimicrobial activity
The antimicrobial activity of the synthesized compounds was
determined by tube dilution method [26]. In case of Staphylococcus
* Corresponding author. Tel.: þ91-1262-272535; fax: þ91-1262-274133.
E-mail address: naru2000us@yahoo.com (B. Narasimhan).
0223-5234/$ – see front matter Ó 2008 Elsevier Masson SAS. All rights reserved.
doi:10.1016/j.ejmech.2008.10.034

1854
P. Kumar et al. / European Journal of Medicinal Chemistry 44 (2009) 1853–1863
R2
R1
R2
R1
SOCl2
+
COOH
R3
COCl + NH2NH2
R3
2h
R4
R5
R4
R5
30min
R2
R1
R2
R1
R3
CONHN=CH
X
R3
CONHNH2 + OHC
X
3 h
R4
R5
R4
R5
Scheme 1. Scheme for syntheses of substituted benzoic acid benzylidene/furan-2-yl-methylene hydrazides.
aureus, compounds 17 and 25 were found to be most active with
pMIC value of 2.65 and 2.67 respectively. Against Bacillus subtilis,
compounds 12 and 17 emerged as the most potential candidates. In
the case of Escherichia coli, the dinitro compound (18) and
compound with chloro and nitro substituents (25) were found to be
active. Compound 18 emerged as most active one against Candida
albicans. Against Aspergillus niger compounds 18 and 22 emerged as
effective antifungal agents. The results of antimicrobial studies are
presented in Table 1 (pMIC values for ot-QSAR model development)
and Table 2 (average pMIC values for mt-QSAR model development).
Here and thereafter, n – number of data points, r – multiple
correlation coefficient, q2 – cross-validated r2 obtained by leave one
out (LOO) technique, s – standard error of the estimate and F –
Fischer statistics.
The topological index, 3cv, signifies the degree of branching,
connectivity of atoms and the unsaturation in the molecule which
accounts for variation in activity [35]. The positive coefficient of 3cv
in Eq. (1) indicates that there is a positive correlation between the
antibacterial activity of substituted hydrazides and 3cv. This is
evidenced by the antibacterial activity data of substituted hydra-
zides (Table 1) and their 3 v values (Table 3). Compounds 17 and 25
c
3.2. QSAR studies
with maximum 3cv values of 0.64 and 0.64 respectively have
maximum antibacterial activity against S. aureus (Compound 17,
pMICsa ¼ 2.65; compound 25, pMICsa ¼ 2.67). Similarly the
3.2.1. Development of one-target QSAR model
In order to identify substituent effect on antimicrobial activity,
quantitative structure–activity relationship (QSAR) studies of title
compounds were performed. Biological activity data (MIC) were
calibrated to their logarithmic values (pMIC) in micromoles and are
listed in Table 1. The compounds were analyzed by physicochem-
ical-based QSAR (Hansch) approach using different physicochem-
ical parameters [27–33] (Supplementary data) as independent
and pMIC values as dependent variables. These QSAR descriptors
of substituted hydrazides were calculated using the molecular
package TSAR 3.3 for Windows [34] and the values of selected
descriptors used in the regression analysis are presented in Table 3.
A correlation analysis was performed on all the descriptors,
depending on the intercorrelation among the independent
descriptors and also their individual correlation with antimicrobial
activity. The correlation matrix elicited in Table 4 indicates the
correlation of antibacterial activity of substituted hydrazides with
S. aureus. The correlation matrix depicted in Table 4 indicated that
there is a high autocorrelation (r > 0.7) observed between the
molecular descriptors except with log P. The high and low auto-
correlations were observed between k1 and 0c (r ¼ 0.999) and
compound 1 having minimum 3 v value has minimum antibacterial
c
activity (compound 1, 3cv ¼ 0.32, pMICsa ¼ 1.61).
Squared correlation coefficient (r2) of 0.828 in Eq. (1) explains
82.8% variance in antibacterial activity against S. aureus. Eq. (1)
also indicated the statistical significance of >99.9% with F value of
116.18. Similarly the cross-validation of obtained Eq. (1) was
subsequently checked by employing ‘‘leave one out’’ (LOO)
method. The q2 value of Eq. (1) (q2 ¼ 0.795) qualifies it to be
a valid model according to the recommendations of Golbraikh and
Trophsa [36].
In order to confirm our results we have predicted the antibac-
terial activity of substituted hydrazides against S. aureus using Eq.
(1). The comparison of observed and predicted values (Supple-
mentary data) demonstrated that they are close to each other
evidenced by the low residual activity values. Further, it is sup-
ported by the plot of pMICsa observed vs pMICsa predicted (Fig. 1).
To determine the existence of systemic error in the model
development we have plotted pMICsa observed against pMICsa
residual values (Fig. 2). The propagation of residuals on both sides
of zero indicates that there is no systemic error in the development
of QSAR model [37]. The QSAR models elicited in Eqs. (2)–(5) were
developed to predict the antimicrobial activity of substituted
hydrazide derivatives against B. subtilis, E. coli, C. albicans and
A. niger.
log P and 1
c
(r ¼ ꢀ0.005) respectively.
The antibacterial activity of substituted hydrazides against S.
aureus is best described using the molecular descriptor, 3cv
(r ¼ 0.910, Eq. (1), Table 5).
3.2.1.1. QSAR model for antibacterial activity against S. aureus.
3.2.1.2. QSAR model for antibacterial activity against B. subtilis.
pMICsa ¼ 3:3813cv þ 0:485
n ¼ 26, r ¼ 0.910, r2 ¼ 0.828, q2 ¼ 0.795, s ¼ 0.126, F ¼ 116.18.
(1)
pMICbs ¼ 1:8393
cv þ 1:478
(2)
n ¼ 26, r ¼ 0.843, r2 ¼ 0.712, q2 ¼ 0.648, s ¼ 0.096, F ¼ 59.38.

P. Kumar et al. / European Journal of Medicinal Chemistry 44 (2009) 1853–1863
1855
NO2
R1,R4,R5=H; R2,R3= OCH3;
O
1
2
3
4
5
6
7
8
X =
14 R1,R2,R4,R5=H; R3=NO2;
15 R1,R2,R4,R5=H; R3=Cl;
X =
NO2
R1,R3,R5=H;R2,R4=NO2;
O
X =
X =
NO2
R1,R3,R5=H;R2,R4=NO2;
OMe
X =
16 R1,R4,R5=H; R2,R3= OCH3; X =
17 R2,R3,R5=H; R1=Cl; R4=NO2; X =
18 R1,R3,R5=H;R2,R4=NO2; X =
19 R1,R4,R5=H; R2,R3= OCH3; X =
20 R1,R2,R4,R5=H;R3=CH3; X =
NO2
R1,R2,R4,R5=H;R3=CH3;
OMe
X =
R1,R2,R4,R5=H; R3=NH2 ;
OMe
OMe
OMe
X =
R1,R2,R4,R5=H; R3=NO2;
OMe
OMe
OMe
X =
R1,R4,R5=H; R2,R3= OCH3;
OMe
OMe
OMe
X =
R1,R2,R4,R5=H;R3=CH3;
OMe
OMe
O
X =
21 R1,R2,R4,R5=H; R3=NH2 ;
22 R2,R3,R4,R5=H;R1=Br;
23 R1,R3,R4,R5=H; R2=NO2;
24 R1,R2,R4,R5=H; R3=NO2;
X =
X =
X =
X =
R1,R3,R5=H;R2,R4=NO2;
NO
2
OMe
OMe
9
X =
R1,R2,R4,R5=H;R3=CH3;
NO
2
OMe
OMe
10 X =
R1,R2,R4,R5=H; R3=NH2 ;
NO
2
OMe
OMe
11
12
X =
R2,R3,R4,R5=H;R1=Br;
NO
2
OMe
OMe
X =
25 R2,R3,R5=H; R1=Cl; R4=NO2; X =
OMe
OMe
13 R1,R3,R4,R5=H; R2=NO2;
26 R1,R2,R4,R5=H; R3=Cl;
X =
NO
2
X =
Scheme 1. (continued).
3.2.1.3. QSAR model for antibacterial activity against E. coli.
As in the case of S. aureus Eq. (2) describes the importance of
valence third order molecular connectivity index (3 v) in demon-
strating antibacterial activity of substituted hydrazide derivatives
against B. subtilis. Here also similar to S. aureus, positive correlation
has been observed with 3cv and antibacterial activity against
B. subtilis.
The model depicted in Eq. (3) describes the statistically signifi-
cant relationship between Kiers alpha shape topological index ka3
in describing the antibacterial activity of substituted hydrazide
derivatives against E. coli. In this case also the positive relationship
was observed between ka3 and antibacterial activity against E. coli.
The model described in Eq. (4) indicated the importance of
topological parameter, Balaban topological index (r ¼ 0.815, Eq. (4))
in demonstrating antifungal activity of substituted hydrazide
derivatives against C. albicans. The model described in Eq. (5)
c
ka
pMICec ¼ 0:227 3 þ 1:527
(3)
(4)
(5)
n ¼ 26, r ¼ 0.899, r2 ¼ 0.809, q2 ¼ 0.762, s ¼ 0.044, F ¼ 101.86.
3.2.1.4. QSAR model for antifungal activity against C. albicans.
pMICca ¼ 1:046B þ 0:042
n ¼ 26, r ¼ 0.815, r2 ¼ 0.663, q2 ¼ 0.613, s ¼ 0.034, F ¼ 47.41.
3.2.1.5. QSAR model for antifungal activity against A. niger.
pMICan ¼ 0:162HOMO þ 3:269
n ¼ 26, r ¼ 0.614, r2 ¼ 0.377, q2 ¼ 0.278, s ¼ 0.107, F ¼ 14.52.

1856
P. Kumar et al. / European Journal of Medicinal Chemistry 44 (2009) 1853–1863
Table 1
The electronic parameter HOMO, which denotes the energy of
highest occupied molecular orbital directly relates to the electron
affinity and characterizes the sensibility of the molecule towards an
attack by electrophile [38]. The contribution of HOMO in describing
antifungal activity may be attributed to the interaction of
substituted hydrazide derivatives with electrophilic amino acid
residue of fungi [39].
Antimicrobial activity of substituted hydrazide derivatives for the development of
one-target QSAR model.
Comp
pMICsa
pMICbs
pMICec
pMICca
pMICan
1
2
3
4
5
6
7
8
1.61
1.99
2.20
2.24
1.63
1.68
2.17
1.89
2.34
2.14
1.98
2.35
1.99
1.99
2.59
2.05
2.65
2.38
2.07
2.17
1.94
2.52
2.05
1.99
2.67
2.61
0.30
2.61a
1.98
2.29
2.42
2.32
2.24
2.25
2.58
2.19
2.48
2.40
2.29
2.65
2.30
2.09
2.59
2.33
2.65
2.51
2.34
2.58
2.29
2.35
2.28
2.34
2.62
2.61
0.18
2.61a
2.30
2.47
2.64
2.54
2.54
2.56
2.58
2.28
2.63
2.47
2.56
2.49
2.61
2.61
2.59
2.63
2.65
2.68
2.64
2.58
2.49
2.63
2.63
2.63
2.67
2.61
0.10
2.61a
1.64
1.69
1.74
1.63
1.63
1.62
1.68
1.56
1.76
1.59
1.66
1.75
1.70
1.70
1.69
1.66
1.75
1.78
1.74
1.68
1.68
1.76
1.64
1.72
1.76
1.71
0.06
2.64b
1.87
1.69
1.80
1.88
1.80
1.98
1.68
1.90
1.76
1.85
1.80
1.75
1.70
1.70
1.69
1.72
1.59
2.08
2.04
1.75
1.90
2.06
1.75
1.75
1.95
2.01
0.13
2.64b
Similar to Eq. (1) the high q2 values (q2 > 0.5) obtained by leave
one out technique and the observance of low residual values
(Supplementary data) indicated the validity and predictability of
Eqs. (2)–(4). Further their predictability was supported by the plot
of their predicted pMIC against observed pMIC (Figs. 3–5).
The cross-validation of the models was also done by leave one
out (LOO) technique. The cross-validated correlation coefficient
(q2 > 0.5) values obtained for the best QSAR models indicated their
reliability in predicting the antimicrobial activity of substituted
hydrazide derivatives. In the case of A. niger the q2 value is less than
0.5 which shows that the developed model is an invalid one. But
one should not forget the recommendations of Golbraikh and
Tropsha [36] who have recently reported that the only way to
estimate the true predictive power of a model is to test their
ability to predict accurately the biological activities of compounds.
As the observed and predicted values are close to each other
(Supplementary data), the QSAR model for A. niger (Eq. (5)) is a
valid one.
It is important to note that Eqs. (1)–(5) are derived using the
entire data set and no outliers were found during model develop-
ment. Even though the sample size and the ‘Rule of Thumb’ allowed
us to go for development of tetra-parametric model in multiple
linear regression analysis, the high multico-linearity among the
parameters restricted us for mono-parametric model. The ‘rule of
thumb’ gives information about the number of parameters to be
selected for regression analysis in QSAR based on the number of
compounds [21]. According to this rule for QSAR model develop-
ment one should select one parameter for a five-compound data set.
The multi-colinearity occurs when two independent variables
are correlated with each other. One should note that the change in
signs of the coefficients, a change in the values of previous coeffi-
cient, change of significant variable into insignificant one or an
increase in standard error of the estimate on addition of an addi-
tional parameter to the model are indications of high interrela-
tionship among descriptors [21].
9
10
11
12
13
14
15
16
17
18
19
20
21
22
23
24
25
26
SD
Std.
a
Norfloxacin.
Fluconazole.
b
indicated the importance of electronic parameter, energy of highest
occupied molecular orbital (HOMO) (r ¼ 0.614, Eq. (5)) in demon-
strating antifungal activity of substituted hydrazide derivatives
against A. niger. This is evidenced by the high HOMO values (ꢀ8.70,
ꢀ8.35, ꢀ8.38, Table 3) of compounds 18, 19 and 22 having high
activity values (2.08, 2.04 and 2.06, Table 1).
Table 2
Antimicrobial activity of substituted hydrazide derivatives for the development of
multi-target QSAR model.
Generally for QSAR studies, the biological activities of
compounds should span 2–3 orders of magnitude. But in the
present study the range of antimicrobial activities of the synthe-
sized compounds is within one order of magnitude. But it is
important to note that the predictability of the QSAR models
developed in the present study is high evidenced by the low
residual values (Supplementary data) This is in accordance with
results suggested by Bajaj et al. [40], who stated that the reliability
of the QSAR model lies in its predictive ability even though the
activity data are in the narrow range. Further, recent literature
reveals that the QSAR has been applied to describe the relationship
between narrow range of biological activity and physicochemical
properties of the molecules [41,42]. When biological activity data
lie in the narrow range, the presence of minimum standard devi-
ation of the biological activity justifies its use in QSAR studies
[19,22]. The minimum standard deviation (Table 1) observed in the
antimicrobial activity data justifies its use in QSAR studies.
Comp
pMICb
pMICf
pMICam
1
2
3
4
5
6
7
8
1.96
2.25
2.42
2.37
2.14
2.16
2.44
2.12
2.48
2.34
2.28
2.50
2.30
2.23
2.59
2.34
2.65
2.52
2.35
2.44
2.24
2.50
2.32
2.32
2.65
2.61
1.76
1.69
1.77
1.76
1.72
1.80
1.68
1.73
1.76
1.72
1.73
1.75
1.70
1.70
1.69
1.69
1.67
1.93
1.89
1.72
1.79
1.91
1.70
1.74
1.86
1.86
1.88
2.03
2.16
2.12
1.97
2.02
2.14
1.96
2.19
2.09
2.06
2.20
2.06
2.02
2.23
2.08
2.26
2.29
2.17
2.15
2.06
2.26
2.07
2.09
2.33
2.31
9
10
11
12
13
14
15
16
17
18
19
20
21
22
23
24
25
26
3.2.2. Development of multi-target model
According to the above ot-QSAR (one-target QSAR) models one
should use five different equations with different errors to predict
the activity of a new compound against the five microbial species.
The ot-QSAR models, which are almost in all the literature, become
unpractical or at less complicated to use when we have to predict

P. Kumar et al. / European Journal of Medicinal Chemistry 44 (2009) 1853–1863
1857
Table 3
Values of selected descriptors used in the regression analysis.
Comp
log P
MR
0c
HOMO
1c
9.74
1cv
2c
7.97
2cv
3c
3cv
ka1
ka2
ka3
1
1.87
2.28
3.08
3.64
2.39
3.13
3.39
2.84
3.29
3.85
2.60
4.17
3.33
3.33
3.90
2.87
3.85
2.83
2.42
3.39
2.14
3.71
2.87
2.87
3.39
3.44
73.06
74.79
88.86
79.25
78.91
81.53
85.71
65.18
89.72
80.11
79.77
82.69
82.39
82.39
79.87
88.00
87.20
95.32
93.60
85.71
85.37
88.29
88.00
88.00
92.80
85.48
1.56
1.62
1.60
1.49
1.49
1.51
1.56
1.48
1.64
1.55
1.55
1.59
1.57
1.56
1.55
1.60
1.61
1.65
1.60
1.57
1.57
1.61
1.59
1.58
1.64
1.57
ꢀ8.64
ꢀ9.12
ꢀ8.94
ꢀ8.51
ꢀ8.45
ꢀ8.80
ꢀ8.69
ꢀ8.63
ꢀ9.91
ꢀ9.37
ꢀ8.90
ꢀ9.42
ꢀ9.69
ꢀ9.73
ꢀ9.48
ꢀ9.17
ꢀ9.69
ꢀ8.70
ꢀ8.35
ꢀ8.35
ꢀ8.29
ꢀ8.38
ꢀ8.56
ꢀ8.59
ꢀ8.56
ꢀ8.42
5.83
5.78
6.82
6.23
6.02
6.32
6.76
5.19
6.79
6.21
5.99
6.71
6.29
6.29
6.30
6.85
6.81
7.35
7.40
6.76
6.55
7.26
6.85
6.85
7.36
6.86
3.78
3.97
4.72
4.34
4.09
4.28
4.68
3.58
4.80
4.42
4.17
4.90
4.36
4.36
4.53
4.62
4.91
5.07
4.88
4.68
4.44
5.17
4.62
4.62
5.18
4.79
0.94
1.61
1.81
1.10
1.10
1.31
1.23
0.90
2.11
1.40
1.40
1.31
1.61
1.61
1.40
1.44
1.81
1.94
1.27
1.23
1.23
1.14
1.44
1.44
1.64
1.23
0.32
0.43
0.52
0.47
0.39
0.41
0.52
0.37
0.57
0.51
0.44
0.62
0.45
0.45
0.54
0.46
0.64
0.57
0.47
0.52
0.44
0.63
0.46
0.46
0.64
0.55
14.68
15.86
18.56
14.51
14.47
16.04
16.43
11.83
19.15
15.10
15.06
15.57
16.63
16.63
15.38
17.97
17.90
20.49
19.31
16.43
16.39
16.90
17.97
17.97
19.24
16.72
7.17
3.89
4.24
5.10
4.32
4.30
4.59
4.53
3.41
5.16
4.34
4.32
4.32
4.62
4.62
4.49
4.81
4.77
5.33
4.98
4.53
4.51
4.52
4.81
4.81
4.94
4.68
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
10.47
11.90
9.69
9.53
10.68
8.22
8.22
9.12
6.96
8.36
7.04
7.01
7.61
7.90
5.62
8.27
6.92
6.90
7.26
7.50
7.50
7.13
8.47
7.89
9.21
9.46
7.90
7.87
8.25
8.47
8.47
8.84
8.11
9.69
10.60
10.63
8.25
8.94
7.08
11.42
8.96
8.96
8.87
9.88
9.86
8.96
9.85
10.40
11.40
9.83
8.94
8.94
8.85
9.85
9.84
10.38
8.94
12.27
10.06
10.06
10.08
10.97
10.97
10.06
11.55
11.38
12.85
12.12
10.63
10.63
10.65
11.55
11.55
11.96
10.63
each compound results for more than one target. In these cases we
have to develop one ot-QSAR for each target. However, very
recently the interest has been increased in the development of
multi-target QSAR (mt-QSAR) models. In opposition to ot-QSAR,
the mt-QSAR model is a single equation that considers the nature of
molecular descriptors which are common and essential for
describing the antimicrobial activity [43–47].
In the present study we have attempted to develop three
different types of mt-QSAR (multi-target QSAR) models viz. mt-
QSAR model for describing antibacterial activity of synthesized
compounds against S. aureus, B. subtilis and E. coli, mt-QSAR model
for describing antifungal activity of synthesized compounds against
C. albicans and A. niger as well a common mt-QSAR model for
describing the antimicrobial activity of substituted hydrazide
derivatives against all the aforementioned microorganisms.
In order to develop mt-QSAR models initially we have calculated
which are presented in Table 2. These average activity values
were correlated with the molecular descriptors of synthesized
compounds (Table 5). The data depicted in Table 5 indicated that
the pMICb, pMICf and pMICam are equally correlated with the
molecular descriptors as compared to ot-MIC values i.e. pMICsa,
pMICbs, pMICec, pMICca and pMICan
.
The mt-QSAR model for antibacterial activity signified the
importance of topological parameter valence third order molecular
connectivity index 3cv in describing the antibacterial activity of
substituted hydrazides (Eq. (6)).
3.2.2.1. mt-QSAR model for antibacterial activity.
pMICb ¼ 1:9923cv þ 1:386
(6)
n ¼ 26, r ¼ 0.935, r2 ¼ 0.875, q2 ¼ 0.846, s ¼ 0.062, F ¼ 167.35.
the average antibacterial activity [pMICb ¼ (pMICsa þ pMICbs
þ
The mt-QSAR model for antifungal activity of substituted hydra-
zides indicated the importance of topological parameter, valence zero
order molecular connectivity index, 0cv and electronic parameter,
energy of highest occupied molecular orbital, HOMO (Eq. (7)).
pMICec)/3], antifungal activity [pMICf ¼ (pMICca þ pMICan)/2] and
antimicrobial activity values [pMICam ¼ (pMICsa þ pMICbs
þ
pMICec þ pMICca þ pMICan)/5] of substituted hydrazide derivatives
Table 4
Correlation matrix for pMICsa with molecular descriptors.
pMICsa
1.000
log P
MR
0c
0cv
1c
0.356
ꢀ0.005
0.910
0.994
0.835
1.000
1cv
2c
2cv
3c
3cv
k1
k2
0.297
k3
ka1
ka2
0.363
ka3
pMICsa
log P
MR
0c
0.692
1.000
0.495
0.173
1.000
0.381
0.029
0.883
1.000
0.522
0.143
0.952
0.790
1.000
0.585
0.262
0.964
0.781
0.989
0.819
1.000
0.399
0.107
0.786
0.971
0.647
0.941
0.657
1.000
0.759
0.500
0.902
0.711
0.906
0.726
0.953
0.639
1.000
0.404
0.193
0.570
0.834
0.387
0.772
0.418
0.941
0.476
1.000
0.910
0.728
0.637
0.491
0.630
0.467
0.710
0.507
0.888
0.488
1.000
0.372
0.018
0.893
0.999
0.804
0.997
0.793
0.963
0.716
0.816
0.482
1.000
0.289
0.107
0.779
0.929
0.596
0.907
0.618
0.957
0.582
0.891
0.407
0.925
0.869
1.000
0.426
0.029
0.936
0.982
0.887
0.993
0.872
0.914
0.786
0.731
0.532
0.987
0.983
0.864
1.000
0.438
0.149
0.936
0.953
0.825
0.957
0.839
0.913
0.785
0.761
0.551
0.957
0.952
0.938
0.952
0.887
1.000
ꢀ0.047
0.932
0.964
0.874
0.986
0.852
0.878
0.727
0.666
0.412
0.973
1.000
ꢀ0.034
0.951
0.874
0.964
0.918
0.935
0.737
0.799
0.471
0.461
0.889
0.959
0.711
0.945
1.000
0cv
1c
1cv
2c
2cv
3c
3cv
k1
k2
k3
ka1
ka2
ka3

1858
P. Kumar et al. / European Journal of Medicinal Chemistry 44 (2009) 1853–1863
Table 5
Correlation of molecular descriptors with antimicrobial activity.
Mol. descriptor
pMICsa
pMICbs
pMICec
pMICca
pMICan
pMICb
pMICf
pMICam
log P
MR
0c
0.692
0.495
0.381
0.522
0.356
0.585
0.399
0.759
0.404
0.910
0.372
0.297
0.289
0.426
0.363
0.438
0.356
0.535
0.341
ꢀ0.385
ꢀ0.216
ꢀ0.181
0.652
0.498
0.343
0.488
0.324
0.555
0.358
0.717
0.362
0.844
0.336
0.281
0.299
0.384
0.336
0.437
0.324
0.420
0.307
ꢀ0.305
ꢀ0.127
ꢀ0.125
0.329
0.875
0.800
0.761
0.806
0.807
0.766
0.801
0.630
0.614
0.804
0.811
0.812
0.814
0.777
0.900
0.806
0.542
0.795
ꢀ0.719
ꢀ0.427
ꢀ0.110
0.232
0.751
0.749
0.745
0.738
0.764
0.726
0.802
0.628
0.738
0.745
0.694
0.606
0.779
0.704
0.722
0.738
0.815
0.709
ꢀ0.764
ꢀ0.451
ꢀ0.171
ꢀ0.187
0.212
0.041
0.338
0.094
0.295
ꢀ0.087
0.202
ꢀ0.247
0.048
0.058
0.161
ꢀ0.089
0.142
0.299
0.092
0.094
0.032
0.106
0.014
0.249
0.614
0.690
0.625
0.489
0.613
0.469
0.683
0.499
0.839
0.478
0.935
0.482
0.421
0.425
0.532
0.472
0.575
0.469
0.554
0.452
ꢀ0.463
ꢀ0.248
ꢀ0.171
ꢀ0.084
0.479
0.323
0.592
0.367
0.560
0.197
0.488
0.013
0.322
0.337
0.411
0.150
0.426
0.541
0.359
0.367
0.338
0.367
ꢀ0.278
0.055
0.497
0.595
0.695
0.536
0.713
0.530
0.766
0.512
0.887
0.444
0.926
0.533
0.498
0.432
0.601
0.575
0.622
0.530
0.596
0.514
0cv
1c
1cv
2c
2cv
3c
3cv
k1
k2
k3
ka1
ka2
ka3
R
B
W
Te
LUMO
HOMO
ꢀ0.501
ꢀ0.219
ꢀ0.029
3.2.2.2. mt-QSAR model for antifungal activity.
hydrazides respectively. The low residual activity values indicated
the high predictability of above mentioned equations which is clear
from the plot of their observed pMIC and predicted pMIC (Figs.
6–8). The propagation of activity values on both sides of zero, in the
plot of observed pMICam against residual pMICam, indicated that
there was no systemic error in the development of mt-QSAR
models (Fig. 9).
It is important to note that residual activity values in the case of
mt-QSAR model for antimicrobial activity are less when compared
to the residuals of one-target models as well the multi-target
models for antibacterial and antifungal activities (Table 6). So this
mt-QSAR equation (Eq. (8)) can be used to predict the activity of
substituted hydrazides against different microbial species. Further
it indicated the importance of topological parameter, valence third
order molecular connectivity index, 3cv and electronic parameter,
energy of highest occupied molecular orbital, HOMO in describing
the antimicrobial activity of substituted hydrazides.
pMICf ¼ 0:0310cv þ 0:056HOMO þ 1:879
(7)
n ¼ 26, r ¼ 0.697, r2 ¼ 0.486, q2 ¼ 0.353, s ¼ 0.056, F ¼ 10.86.
Further, the mt-QSAR model for antimicrobial activity of
substituted hydrazides demonstrated the involvement of topolog-
ical parameter, valence third order molecular connectivity index,
3cv and electronic parameter, energy of highest occupied molecular
orbital, HOMO in describing the antimicrobial activity (Eq. (8)).
3.2.2.3. mt-QSAR model for antimicrobial activity.
pMICam ¼ 1:3673cv þ 0:042HOMO þ 1:819
(8)
n ¼ 26, r ¼ 0.943, r2 ¼ 0.890, q2 ¼ 0.854, s ¼ 0.039, F ¼ 93.01.
The predictability of Eqs. (6)–(8) was verified by calculating the
antibacterial, antifungal and antimicrobial activities for substituted
It is important to mention here that the development of
multi-target models exhibited the following advantages: (a). There
is an improvement in statistical parameters when compared to
2.8
2.6
2.4
2.2
2.0
1.8
1.6
1.4
2.8
2.6
2.4
2.2
2.0
1.8
1.6
1.4
1.6
1.8
2.0
2.2
Observed pMICsa
2.4
2.6
2.8
1.6
1.8
2.0
2.2
Observed pMICsa
2.4
2.6
2.8
Fig. 1. Plot of predicted pMICsa activity values against the experimental pMICsa values
for the linear regression developed model by Eq. (1).
Fig. 2. Plot of residual pMICsa values against the experimental pMICsa values.

P. Kumar et al. / European Journal of Medicinal Chemistry 44 (2009) 1853–1863
1859
2.7
2.6
2.5
2.4
2.3
2.2
2.1
2.0
1.8
1.7
1.6
1.5
1.5
1.6
1.7
1.8
Observed pMICca
1.9
2.0
2.1
2.2
2.3
Observed pMICbs
2.4
2.5
2.6
2.7
Fig. 5. Plot of predicted pMICca activity values against the experimental pMICca values
for the linear regression developed model by Eq. (4).
Fig. 3. Plot of predicted pMICbs activity values against the experimental pMICbs values
for the linear regression developed model by Eq. (2).
one-target models (improvement in r, q2, etc. cf. Eq. (8) with Eqs.
(1)–(7)). (b). No change in the trend of molecular descriptors has
been observed i.e. the both 3cv and HOMO showed the same
positive correlation with antimicrobial activity as observed in the
case of one-target models. (c). There was a significant decrease in
residual values when compared to the residual values observed in
one-target QSAR models i.e. mt-QSAR model has better predict-
ability than the ot-QSAR model.
(2) In general, presence of electron withdrawing (NO2) substitu-
ents on the benzylidene moiety favors the antimicrobial
activity of title compounds against organisms under test.
(3) For activity against S. aureus, on the benzylidene moiety both
electron withdrawing (compound 17) and electron donating
(compound 25) groups favored the antibacterial activity. The
contrary result observed with the compound containing elec-
tron withdrawing group 17 may be due the fact that it has 3cv
value of 0.64 which is equivalent to the most active compound
25 (Table 3). In the case of B. subtilis, the presence of electron
withdrawing groups (compounds 12 and 17) on the benzyli-
dene moiety favored the antibacterial activity. Whereas in the
case of E. coli, presence of electron donating groups on the
benzylidene moiety favored the antibacterial activity. The
above results suggested that there must be different structural
requirements, which are needed for compounds to be active
against different microorganisms [49].
3.3. SAR studies
From the results of antimicrobial activity following structure–
activity relationships can be derived.
(1) In general, all the compounds which emerged as most active
compounds have electron withdrawing substituents on the
benzoic acid moiety. The role of electron withdrawing group in
improving antimicrobial activities is supported by the studies
of Sharma et al. [48].
From the results of antifungal activity, following conclusions
can be withdrawn.
2.8
2.7
2.6
2.5
2.4
2.3
2.2
2.7
2.6
2.5
2.4
2.3
2.2
2.1
2.0
2.2
2.3
2.4 2.5
Observed pMICec
2.6
2.7
1.9
2.0
2.1
2.2
2.3
2.4
Observed pMICb
2.5
2.6
2.7
Fig. 4. Plot of predicted pMICec activity values against the experimental pMICec values
Fig. 6. Plot of predicted pMICb activity values against the experimental pMICb values
for the linear regression developed model by Eq. (3).
for the linear regression developed model by Eq. (6).

1860
P. Kumar et al. / European Journal of Medicinal Chemistry 44 (2009) 1853–1863
1.9
1.8
1.7
1.6
.1
0.0
-.1
1.8
1.6
1.7
1.8
Observed pMICf
1.9
2.0
1.9
2.0
2.1
Observed pMICam
2.2
2.3
2.4
Fig. 9. Plot of residual pMICam values against the experimental pMICam values.
Fig. 7. Plot of predicted pMICf activity values against the experimental pMICf values for
the linear regression developed model by Eq. (7).
(4) Even though in the present study we have attempted to
synthesize benzylidene and furan-2-yl-methylene hydrazide
derivatives for their antimicrobial activity, the presence of
furan does not significantly improve the antimicrobial activity
of the synthesized compounds.
(a) Influence of substituents on the benzoic acid moiety on the
antifungal activity:
(i) For a compound to be active against C. albicans, it does not
require the presence of halogen group on the benzoic acid
moiety.
(ii) Presence of halogen as well as electron withdrawing NO2
group on the benzoic acid moiety is essential for a compound
to be active against A. niger (compounds 18 and 22).
(iii) Among the halogens (Cl and Br), the presence of bromo
group on the benzoic acid moiety is responsible for
improving the antifungal activity of synthesized compounds.
(b) Influence of substituents on the benzylidene moiety on the
antifungal activity:
(i) In contrast to antibacterial activity, the presence of electron
withdrawing NO2 group on the benzylidene moiety is not
favorable for the title compounds (18 and 22) to be active
against tested fungal strains.
(ii) Presence of methoxy groups at meta and para position
of benzylidene moiety favored the antifungal activity
(Compounds 18 and 22).
The results of QSAR studies indicated that there is a positive
correlation of the topological parameter, valence third order
molecular connectivity index (3 v) and the electronic parameter,
c
energy of highest occupied molecular orbital (HOMO) with anti-
microbial activity of substituted hydrazides. The topological index,
3cv, signifies the degree of branching, connectivity of atoms and
the unsaturation in the molecule which accounts for variation in
activity [35]. This was evidenced by the high antimicrobial activity
of compounds 18, 22 and 25 which have two electron donating
groups in comparison to other compounds i.e. their presence
increases the branching of the molecule as well as increases the
electron density which in turn increases the HOMO values of the
compounds and makes them to be the most effective ones against
the organisms under test. The SAR results are summarized in
Fig. 10.
4. Conclusion
2.4
In conclusion, a series of substituted hydrazide derivatives have
been synthesized and their in vitro antimicrobial activities were
evaluated against five representative microorganisms. The results
of antimicrobial study indicated that the presence of electron
withdrawing groups on the benzoic acid moiety improved anti-
microbial activity and the presence of both electron withdrawing
and donating groups on benzylidene moiety improved antimi-
crobial activity. Further, the presence of heterocyclic ring furan
does not improve the antimicrobial activity of substituted hydra-
zides. To understand the relationship between physicochemical
parameters and antimicrobial activity of substituted hydrazide
derivatives, QSAR investigation was performed by the develop-
ment of one-target and multi-target models. The multi-target
model was found to be effective in describing the antimicrobial
activity of substituted hydrazides in comparison to the one-target
models. Further, it indicated the importance of the topological
2.3
2.2
2.1
2.0
1.9
1.8
1.8
1.9
2.0
2.1
Observed pMICam
2.2
2.3
2.4
parameter, valence third order molecular connectivity index (3cv
)
and the electronic parameter, energy of highest occupied molec-
ular orbital (HOMO) in describing the antimicrobial activity of
substituted hydrazides. These results are similar to the results
Fig. 8. Plot of predicted pMICam activity values against the experimental pMICam
values for the linear regression developed model by Eq. (8).

P. Kumar et al. / European Journal of Medicinal Chemistry 44 (2009) 1853–1863
1861
Table 6
Comparison of residual values obtained by one-target and multi-target QSAR models.
Comp
Residual activity
pMICsa
pMICbs
pMICec
pMICb
pMICca
pMICan
pMICf
pMICam
1
2
3
4
5
6
7
8
0.04
0.06
ꢀ0.09
0.03
ꢀ0.02
ꢀ0.01
0.04
0.02
0.15
0.03
ꢀ0.11
ꢀ0.02
ꢀ0.04
0.03
L0.06
0.02
L0.01
0.06
L0.03
L0.04
0.03
ꢀ0.04
ꢀ0.04
0.03
0.03
0.03
0.00
ꢀ0.10
ꢀ0.02
ꢀ0.01
ꢀ0.10
0.14
0.02
L0.01
0.00
0.01
L0.02
0.05
L0.10
0.05
0.04
0.02
0.02
0.02
0.01
0.01
L0.01
L0.08
L0.06
0.10
0.03
L0.08
0.00
L0.02
0.01
0.00
0.02
L0.04
0.00
L0.02
L0.01
0.01
L0.04
0.01
L0.08
0.02
L0.02
0.06
0.01
0.00
0.05
0.06
L0.03
L0.02
L0.07
L0.02
0.00
ꢀ0.05
0.18
ꢀ0.19
ꢀ0.19
ꢀ0.06
0.16
ꢀ0.06
ꢀ0.07
0.01
ꢀ0.23
ꢀ0.03
ꢀ0.03
0.27
0.04
ꢀ0.01
0.00
0.01
0.02
ꢀ0.18
ꢀ0.02
ꢀ0.07
ꢀ0.04
0.05
0.00
ꢀ0.03
0.03
9
ꢀ0.04
ꢀ0.01
0.01
L0.03
L0.06
0.02
L0.12
0.01
L0.06
0.13
0.04
0.00
0.10
0.10
10
11
12
13
14
15
16
17
18
19
20
21
22
23
24
25
26
ꢀ0.08
ꢀ0.01
0.04
0.01
0.03
ꢀ0.02
0.03
ꢀ0.02
0.01
ꢀ0.01
ꢀ0.22
0.12
0.01
0.04
0.03
0.00
0.01
ꢀ0.04
ꢀ0.06
ꢀ0.11
0.22
0.03
0.04
0.01
0.04
ꢀ0.06
ꢀ0.02
0.02
0.02
0.01
0.08
ꢀ0.06
0.04
L0.01
0.04
0.02
ꢀ0.04
ꢀ0.02
0.01
0.02
ꢀ0.01
ꢀ0.01
0.03
0.01
0.00
0.13
ꢀ0.06
ꢀ0.05
ꢀ0.08
0.01
0.15
0.03
ꢀ0.16
ꢀ0.02
0.15
ꢀ0.13
ꢀ0.13
0.07
ꢀ0.01
ꢀ0.28
ꢀ0.04
0.02
0.00
0.12
ꢀ0.06
L0.03
L0.13
0.02
0.02
0.03
0.08
0.08
0.01
0.01
0.02
0.02
ꢀ0.07
L0.10
L0.06
0.03
ꢀ0.05
0.03
0.08
0.00
0.02
0.02
0.09
0.27
0.13
0.11
0.05
observed in previous studies [17,20] where the antimicrobial
activity was highly correlated with topological parameters, espe-
cially with molecular connective indices. The design of new
chemical entities with high 3cv and HOMO values than the
substituted hydrazides included in the present study may result in
novel compounds with improved antibacterial and antifungal
activity as the aforementioned parameters are positively correlated
with the antimicrobial activity.
5.1. General procedure for the synthesis of substituted benzoic acid
benzylidene/furan-2-yl-methylene hydrazides (1–26)
Thionyl chloride 32.8 g (0.3 mol) was added to substituted
benzoic acid (0.25 mol) in a round bottom flask. After addition, the
mixture was refluxed for 2 h. The excess of thionyl chloride was
removed by distillation. To the solution of 0.05 mol of substituted
acid chloride in methanol is added 0.1 mol of hydrazine hydrate.
The mixture was refluxed for 30 min. Then the reaction mixture
was cooled and the resultant precipitate (substituted benzoic
acid hydrazide) was collected, washed with distilled water and
recrystallized from ethanol. A solution of 0.05 mol of substituted
aldehyde [substituted benzaldehyde (3–7, 9–26)/furan-2-aldehyde
(1–2, 8)] in ethanol was added to a solution of corresponding
0.05 mol of substituted benzoic acid hydrazides in 50 ml ethanol.
The mixture was refluxed on a water bath for 3 h. Then the reaction
mixture was allowed to cool at room temperature and the precip-
itate obtained was filtered, dried and recrystallized from ethanol.
5. Experimental
Starting materials were obtained from commercial sources and
were used without further purification. Solvents were dried by
standard procedures. Reaction progress was observed by thin
layer chromatography making use of commercial silica gel plates
(Merck), Silica gel F254 on aluminum sheets. Melting points were
determined in open capillary tubes on a Sonar melting point
apparatus and are uncorrected. 1H nuclear magnetic resonance
(1H NMR) spectra were determined by Bruker Avance II 400 NMR
spectrometer in appropriate deuterated solvents and are
5.1.1. Compound 5
Mp (ꢁC) 158–161; Yield – 44.6%; 1H NMR (CDCl3):
d 3.85 (s, 3H,
expressed in parts per million (d, ppm) downfield from tetrame-
OCH3), 6.94–7.57 (m, 4H, CH of ArNH2), 7.74–8.77 (m, 4H, CH of
ArOCH3); IR (KBr pellets, cmꢀ1): 1625.9 (C]O str., secondary
amide), 3084.1 (CH str., aromatic), 2990.1 (CH, str., aliphatic), 1265.6
(C–N str., aryl primary amine).
thylsilane (internal standard) NMR data are given as multiplicity
(s, singlet; d, doublet; t, triplet; m, multiplet) and number of
protons. Infrared (IR) spectra were recorded on a Shimadzu FTIR
spectrometer.
S. aureus - NO2, Cl
NO2, OCH3 - S. aureus
B. subtilis - NO2, Br, Cl
R
- B. subtilis
NO2
R'
E. coli - NO2
N
H
N
OCH3 - E. coli, C. albicans, A. niger
O
A. niger - NO2, Br
Fig. 10. Structural requirements for the antimicrobial activity of substituted benzoic acid benzylidene/furan-2-yl-methylene hydrazides.

1862
P. Kumar et al. / European Journal of Medicinal Chemistry 44 (2009) 1853–1863
5.1.2. Compound 8
[52]. The predictive powers of the equation were validated by the
Mp (ꢁC) 229–231; Yield – 16.4%; 1H NMR (CDCl3):
d
2.17 (s, 3H,
determination of cross-validated r2 (q2) using leave one out (LOO)
CH3 of ArCH3), 7.0 (m, 2H, CH of C3 and C4 of furan), 7.44 (d, 1H, CH
of C5 of furan), 7.26–7.76 (m, 4H, CH of ArCH3), 9.0 (s, 1H, NH); IR
(KBr pellets, cmꢀ1): 1027.4 (Ring breathing, 2-substituted furan),
1630.5 (C]O str., secondary amide), 3055.6 (CH str., aromatic),
2960.1 (CH, str., aliphatic), 948.5 (CH out of plane bending, 2-
substituted furan).
cross-validation method.
Appendix. Supplementary data
Supplementary data associated with this article can be found in
the online version, at doi:10.1016/j.ejmech.2008.10.034.
5.1.3. Compound 14
Mp (ꢁC) 69–72; Yield – 49.7%; 1H NMR (CDCl3):
d 7.27–8.22 (m,
References
4H, CH of mArNO2), 8.23–8.30 (m, 4H, CH of pArNO2), 8.70 (s, 1H,
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3077.2 (CH str., aromatic), 1348.0 (NO2 symmetric str., ArNO2),
1525.1 (NO2 asymmetric str., ArNO2), 857.4 (C–N str., ArNO2).
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