Design, synthesis, antimicrobial, anticancer evaluation, and QSAR studies of 4-(substituted benzylidene-Amino)-1,5-dimethyl-2-phenyl-1,2-dihydropyrazol-3- ones

Article abstract of DOI:10.1007/s00044-011-9906-8

A series of 4-(substituted benzylidene-Amino)-1,5-dimethyl-2-phenyl-1,2- dihydropyrazol-3-ones (1-17) was synthesized and tested in vitro for its antimicrobial and anticancer potentials. The biological screening results indicated that compounds having m-c

Full text of DOI:10.1007/s00044-011-9906-8

MEDICINAL
CHEMISTRY
RESEARCH
Med Chem Res (2012) 21:3863–3875
DOI 10.1007/s00044-011-9906-8
ORIGINAL RESEARCH
Design, synthesis, antimicrobial, anticancer evaluation, and QSAR
studies of 4-(substituted benzylidene-amino)-1,5-dimethyl-2-
phenyl-1,2-dihydropyrazol-3-ones
•
Sumit Sigroha Balasubramanian Narasimhan Pradeep Kumar
•
•
•
Anurag Khatkar Kalavathy Ramasamy Vasudevan Mani
•
•
•
Rakesh Kumar Mishra Abu Bakar Abdul Majeed
Received: 12 July 2011 / Accepted: 14 November 2011 / Published online: 11 December 2011
Ó Springer Science+Business Media, LLC 2011
Abstract A series of 4-(substituted benzylidene-amino)-
1,5-dimethyl-2-phenyl-1,2-dihydropyrazol-3-ones (1–17)
was synthesized and tested in vitro for its antimicrobial and
anticancer potentials. The biological screening results
indicated that compounds having m-chloro substituent on
benzaldehyde portion showed antimicrobial potential,
whereas compounds having chloro, methoxy, and hydroxyl
groups showed anticancer potential. The quantitative
structure activity relationship (QSAR) studies indicated the
importance of topological parameter, valence first order
molecular connectivity index in describing antifungal
activity. The developed QSAR models, however, were
statistically insignificant with reference to anticancer
activity of the synthesized compounds.
Introduction
Extensive use of antibiotics has led to the development of
multidrug-resistant microbial pathogens. This highlights
the incessant need for the development of new classes of
antimicrobial agents as well as the alteration of known
drugs in such a way as to reduce their resistance to
pathogens, while retaining their physiological action
(Guzeldemirci et al., 2010).
Undoubtedly, cancer ranks high among human diseases
and is still in dire need of effective therapy. Lack of a wide
array of anticancer drugs to capitalize on new discoveries
regarding tumor genesis, coupled with the unique growth
patterns of diverse repertoires of cancer, have driven the
attention of researchers toward the discovery of novel
anticancer agents (Abdel-Jalil et al., 2010).
Keywords 4-Aminoantipyrine derivatives ꢀ
Antimicrobial ꢀ Anticancer ꢀ QSAR
Antipyrine (2,3-dimethyl-1-phenyl-3-pyrazolin-5-one)
was the first pyrazolone derivative used in the manage-
ment of pain and inflammation (Bondock et al., 2008).
Review of the literature in the last decade has revealed
that antipyrine and 4-aminoantipyrine derivatives possess
a number of biological activities viz. antimicrobial
(Bayrak et al., 2010; Rostom et al., 2009), antiviral
(Rusinov et al., 2005), antioxidant (Santos et al., 2010;
Bashkatova et al., 2005), anticancer (Rosu et al., 2010),
antipyretic (Shchegolkov et al., 2006), analgesic, and anti-
inflammatory (Mahle et al., 2010; Rubtsov et al., 2002)
activities.
S. Sigroha ꢀ B. Narasimhan (&) ꢀ P. Kumar ꢀ A. Khatkar
Faculty of Pharmaceutical Sciences, Maharshi Dayanand
University, Rohtak 124001, India
e-mail: naru2000us@yahoo.com
K. Ramasamy
Quantitative structure activity relationship (QSAR)
study provides medicinal chemists valuable information
that is useful for drug design and prediction of drug
activity. QSAR models are mathematical equations which
construct a relationship between chemical structures and
their biological activities as a linear regression model in the
form y = Xb ? e. This equation may be used to describe a
Collaborative Drug Discovery Research Group, Faculty of
Pharmacy, Campus Puncak Alam, Universiti Teknologi MARA
(UiTM), 42300 Bandar Puncak Alam, Selangor, Malaysia
V. Mani ꢀ R. K. Mishra ꢀ A. B. A. Majeed
Brain Research Laboratory, Faculty of Pharmacy, Campus
Puncak Alam, Universiti Teknologi MARA (UiTM), 42300
Bandar Puncak Alam, Selangor, Malaysia
123

3864
Med Chem Res (2012) 21:3863–3875
set of predictor variables (X) with a predicted variable
(y) by means of a regression vector (b) (Sabet et al., 2010).
Schiff bases are considered to be the most important
group of compounds in medicinal chemistry due to their
preparative accessibility, structural variety, and wide bio-
logical profile (Rosu et al., 2010). With this in view and in
continuation of our study on exploring the biological pro-
file of schiff bases (Kumar et al., 2010a, b, 2011; Judge
et al., 2011a, b; Narang et al., 2011a, b), we hereby report
the synthesis, antimicrobial, anticancer evaluation, and
QSAR studies of 4-(substituted benzylidene-amino)-1,
5-dimethyl-2-phenyl-1,2-dihydropyrazol-3-ones (1–17).
(MEOD): 2.43 (s, 3H, –NCH3), 6.99–7.80 (m, 8H, ArH),
9.48 (s, 1H, –N=CH).
Compound 3
Mp(°C) 86–88; Yield—83.26%; IR (KBr pellets) cm-1
1,643 (C=O str., Ar.), 1,592 (C=N str., N=CH), 1,578 (C=C
skeletal str., phenyl), 1,486 (C=C str., antipyrine);
1
1,135(C–O–C str.); H NMR (MEOD): 6.88–7.67 (m, 9H,
ArH), 9.68 (s, 1H, –N=CH), 2.51 (s, 3H, –NCH3), 3.25(s,
3H, –OCH3), 2.53 (s, 3H, –NCH3).
Compound 4
Experimental
Mp(°C) 208–210; Yield—81.64%; IR (KBr pellets) cm-1
1,615 (C=O str., Ar.), 1,590 (C=N str., N=CH), 1,556 (C=C
skeletal str., phenyl), 1,487 (C=C str., antipyrine); 1,245
Melting points were determined in open capillary tubes on
a sonar melting point apparatus and were uncorrected.
Reaction progress was monitored by thin layer chroma-
tography on silica gel sheets (Merck silica gel–G). 1H
nuclear magnetic resonance (1H NMR) spectra were
recorded on Bruker Avance II 400 NMR spectrometer
using appropriate deuterated solvents and are expressed in
parts per million (d, ppm) downfield from tetramethylsili-
ane (internal standard). Infrared (IR) spectra were recorded
on Perkin Elmer FTIR spectrometer using KBr pellets.
1
(OH in plane bending); H NMR (MEOD): 6.75–7.64 (m,
9H, ArH), 9.54 (s, 1H, –N=CH), 2.55 (s, 3H, –NCH3).
Compound 5
Mp(°C) 216–218; Yield—87.48%; IR (KBr pellets) cm-1
1,645 (C=O str., Ar.), 1,588 (C=N str., N=CH), 1,564 (C=C
skeletal str., phenyl), 1,485 (C=C str., antipyrine) 750
(C–Cl str.); 1H NMR (MEOD): 2.55 (s, 3H, –NCH3),
7.39–8.24 (m, 9H, ArH), 10.12 (s, 1H, –N=CH), 3.30(s,
3H, –OCH3).
General procedure for synthesis of 4-(substituted
benzylidene-amino)-1,5-dimethyl-2-phenyl-1,
2-dihydropyrazol-3-ones (1–17)
Compound 6
A solution of 0.05 mol of appropriate benzaldehyde in
ethanol was added to a solution of 0.05 mol of 4-amino-
antipyrine in 50 ml ethanol, and the mixture was refluxed
for 4–5 h. The reaction mixture was then allowed to cool at
room temperature, and the precipitate obtained was filtered,
dried, and recrystallized from ethanol.
Mp(°C) 176–178; Yield—77.52%; IR (KBr pellets) cm-1
1,647 (C=O str., Ar.), 1,608 (C=N str., N=CH), 1,574 (C=C
skeletal str., phenyl), 1,484 (C=C str., antipyrine); 1,128
1
(C–O–C str.); H NMR (MEOD): 2.34 (s, 3H, –NCH3),
6.96–7.73(m, 9H, ArH), 9.53 (s, 1H, –N=CH).
Compound 7
Compound 1
Mp(°C) 256–258; Yield—94.66%; IR (KBr pellets) cm-1
1,646 (C=O str., Ar.), 1,594 (C=N str., N=CH), 1,573 (C=C
skeletal str., phenyl), 1,487 (C=C str., antipyrine); 1,136
Mp(°C) 220–222; Yield—81.45%; IR (KBr pellets) cm-1
1,647 (C=O str., Ar.), 1,608 (C=N str., N=CH), 1,579 (C=C
skeletal str., phenyl), 1,487 (C=C str., antipyrine), 1,369
(C–N str., aryl 30 amine) 1,310 (C–N str., aryl 30 amine);
1H NMR (MEOD): 2.48 (s, 3H, –NCH3), 6.79–7.71 (m,
9H, ArH), 9.37 (s, 1H, –N=CH), 3.21 (s, 6H, –N(CH3)2).
1
(C–O–C str.), H NMR (MEOD): 2.33 (s, 3H, –NCH3),
6.90–7.68 (m, 8H, ArH), 9.42 (s, 1H, –N=CH), 3.47(s, 6H,
–OCH3).
Compound 8
Compound 2
Mp(°C) 211–213; Yield—88.44%; IR (KBr pellets) cm-1
1,625 (C=O str., Ar.), 1,578 (C=N str., N=CH), 1,517 (C=C
skeletal str., phenyl), 1,486 (C=C str., antipyrine); 1,256
(OH in plane bending), 1,130(C–O–C str.); 1H NMR
(MEOD): 2.50 (s, 3H, –NCH3), 6.86–7.59 (m, 8H, ArH),
Mp(°C) 171–173; Yield—92.76%; IR (KBr pellets) cm-1
1,629 (C=O str., Ar.), 1,595 (C=N str., N=CH), 1,575 (C=C
skeletal str., phenyl), 1,484 (C=C str., antipyrine); 1,210
(OH in plane bending), 1,150(C–O–C str.); 1H NMR
123

Med Chem Res (2012) 21:3863–3875
3865
Compound 15
9.42 (s, 1H, –N=CH), 3.33(s, 3H, –OCH3); 4.88 (s,
1H –OH).
Mp(°C) 179–181; Yield—84.54%; IR (KBr pellets) cm-1
1,602 (C=O str., Ar.), 1,576 (C=N str., N=CH), 1,528 (C=C
skeletal str., phenyl), 1,491 (C=C str., antipyrine); 1,238
Compound 9
1
Mp(°C) 192–194; Yield—84.75%; IR (KBr pellets) cm-1
1,648 (C=O str., Ar.), 1,591 (C=N str., N=CH), 1,572 (C=C
skeletal str., phenyl), 1,483 (C=C str., antipyrine), 1,136
(OH in plane bending); H NMR (MEOD): 2.22 (s, 3H,
–NCH3), 6.71–7.79 (m, 9H, ArH), 4.62(s, 1H, –OH).
1
Compound 16
(C–O–C str.); H NMR (MEOD): 2.52 (s, 3H, –NCH3),
7.00–8.09 (m, 9H, ArH), 9.92 (s, 1H, –N=CH), 3.33 (s, 3H,
–OCH3).
Mp(°C) 210–212; Yield—78.24%; IR (KBr pellets) cm-1
1,643 (C=O str., Ar.), 1,590 (C=N str., N=CH), 1,565 (C=C
skeletal str., phenyl), 1,484 (C=C str., antipyrine) 747
(C–Cl str.); 1H NMR (MEOD): 2.33 (s, 3H, –NCH3),
7.60–7.84 (m, 9H, H of ArH), 9.91 (s, 1H, N=CH).
Compound 10
Mp(°C) 195–197; Yield—88.30%; IR (KBr pellets) cm-1
1,645 (C=O str., Ar.), 1,592 (C=N str., N=CH), 1,568 (C=C
skeletal str., phenyl), 1,489 (C=C str., antipyrine); 1,522
(NO2 str.), 1H NMR (MEOD): 2.66 (s, 3H, –NCH3),
7.46–8.74 (m, 9H, ArH), 9.54 (s, 1H, –N=CH).
Compound 17
Mp(°C) 230–232; Yield—76.34%; IR (KBr pellets) cm-1
1,649 (C=O str., Ar.), 1,577 (C=N str., N=CH), 1,502 (C=C
skeletal str., phenyl), 1,488 (C=C str., antipyrine), 1,123
Compound 11
1
(C–O–C str.); H NMR (MEOD): 2.50 (s, 3H, –NCH3),
6.86–7.59 (m, 7H, ArH), 9.42 (s, 1H, –N=CH), 3.33(s, 9H,
–OCH3).
Mp(°C) 231–233; Yield—76.87%; IR (KBr pellets) cm-1
1,648 (C=O str., Ar.), 1,594 (C=N str., N=CH), 1,563 (C=C
skeletal str., phenyl), 1,481 (C=C str., antipyrine); 1H NMR
(MEOD): 2.52 (s, 3H, –NCH3), 7.42–7.85 (m, 10H, ArH),
9.56 (s, 1H, N=CH), 3.25 (s, 6H, –N(CH3)2).
Evaluation of antimicrobial activity
Determination of MIC
Compound 12
The antimicrobial activity of the synthesized compounds
was tested against Gram-positive bacteria: Staphylcococcus
aureus MTCC 2901, Bacillus sublitis MTCC 2063, Gram-
negative bacterium: Escherichia coli MTCC 1652 and
fungal strains: Candida albicans MTCC 227 and Asper-
gillus niger MTCC 8189 using tube dilution method
(Cappucino and Sherman, 1999). Dilutions of test and
standard compounds were prepared in double strength
nutrient broth—I.P. (bacteria) or Sabouraud dextrose broth
I.P. (fungi) (Pharmacopoeia of India, 2007). The samples
were incubated at 37°C for 24 h (bacteria), at 25°C for 7
days (A. niger), and at 37°C for 48 h (C. albicans), and the
results were recorded in terms of MIC.
Mp(°C) 195–197; Yield—73.28%; IR (KBr pellets) cm-1
1,662 (C=O str., Ar.), 1,589 (C=N str., N=CH), 1,486 (C=C
str., antipyrine); 1,246 (OH in plane bending), 1,137(C–O–
C str.); 1H NMR (MEOD): 3.46 (s, 3H, –OCH3), 7.42–7.84
(m, 8H, ArH), 9.55 (s, 1H, –N=CH), 4.96 (s, 1H, –OH).
Compound 13
Mp(°C) 135–137; Yield—90.10%; IR (KBr pellets) cm-1
1,647 (C=O str., Ar.), 1,592 (C=N str., N=CH), 1,571 (C=C
skeletal str., phenyl), 1,482 (C=C str., antipyrine) 749(C–Cl
str.); 1H NMR(MEOD): 7.41–8.23 (m, 9H, ArH), 9.95
(s, 1H, –N=CH), 2.57 (s, 3H, –NCH3).
Determination of MBC/MFC
Compound 14
The minimum bactericidal concentration (MBC) and fun-
gicidal concentration (MFC) were determined by subcul-
turing 100 ll of culture from each tube (which remained
clear in the MIC determination) into fresh medium. MBC
and MFC values represent the lowest concentration of a
compound that produces a 99.9% end point reduction
(Rodriguez-Arguelles et al., 2005).
Mp(°C) 274–276; Yield—92.80%; IR (KBr pellets) cm-1
1,643 (C=O str., Ar.), 1,581 (C=N str., N=CH), 1,562 (C=C
skeletal str., phenyl), 1,487 (C=C str., antipyrine) 748
1
(C–Cl str.); H NMR (MEOD): 7.26–7.58 (m, 8H, ArH),
9.49 (s, 1H, –N=CH), 2.51 (s, 3H, –NCH3).
123

3866
Med Chem Res (2012) 21:3863–3875
QSAR studies
aldehydes with 4-aminoantipyrine (Scheme 1). The phys-
icochemical characteristics of the synthesized compounds
are presented in Table 1.
The structures of 4-(substituted benzylidene-amino)-1,
5-dimethyl-2-phenyl-1,2-dihydropyrazol-3-ones were first
pre-optimized using Molecular Mechanics Force Field
(MM?) procedure included in Hyperchem 6.03 (1993), and
the resulting geometries were further refined by means of the
semiempirical method PM3 (Parametric Method-3). A gra-
The structures of the synthesized compounds (1–17) were
ascertained on the basis of their consistent IR and NMR
spectral characteristics. The formation of Schiff bases was
confirmed by the presence of stretching bands in the range of
1,608–1,576 cm-1. The presence of C=O functional group
of antipyrine was marked by the appearance of stretching
bands ranging from 1,662 to 1,602 cm-1. The presence
of C=C skeletal stretching bands in the region of
1,589–1,502 cm-1 indicated the presence of phenyl nucleus.
The C–Cl stretching bands at 750–747 cm-1 depicted the
presence of chloro functional group in compounds 5, 13, 14,
and 16. The appearance of aromatic stretching bands in the
range of 1,137–1,128 cm-1 in compounds 3, 6, 7, 8, 9,
and 17 indicated the presence of methoxy group in their
structures. The appearance of stretching band around
1,256–1,210 cm-1 indicated the presence of hydroxyl group
in compounds 2, 4, 8, and 15. The presence of aromatic nitro
stretching band at 1,522 cm-1 (asymmetric NO2 stretching)
depicted the presence of nitro functional group in
compound 10.
˚
dient norm limit of 0.01 kcal/A was used for the geometry
optimization. The lowest energy structure of each molecule
was used to calculate physicochemical properties using
TSAR 3.3 software for Windows (TSAR 3D Version 3.3,
2000). Further, the regression analysis was performed using
the SPSS software package (SPSS for Windows, 1999).
Determination of anticancer activity
Human colorectal DLD-1 (ATCC CCL-221), HT29 (ATCC
HTB-38), HCT116 (ATCC CCL-247), and murine leuke-
mia, P388 (ATCC TIB 63) cancer cell lines were purchased
from the American Type Culture Collection (ATCC),
Manassas, VA, USA. All cell lines were cultured in RPMI
1640 (Sigma) supplemented with 10% heat inactivated fetal
bovine serum (FBS) (PAA Laboratories) and 1% penicillin/
streptomycin (PAA Laboratories). Cultures were maintained
in a humidified incubator at 37°C in an atmosphere of 5%
CO2. Cytotoxicity of synthesized compounds at various
concentrations was assessed using 3-(4,5-dimethylthiazol-
2-yl)-2,5-diphenyl tetrazolium bromide (MTT) (Sigma)
assay, as described by Mosmann, 1983 but with minor
modification, following 72 h of incubation. Assay plates
were read using a spectrophotometer at 520 nm. Data gen-
erated were used to plot a dose–response curve of which the
concentration of test compounds required to kill 50% of cell
population (IC50) was determined. Cytotoxic activity was
expressed as the mean IC50 of three independent experiments
(Mosmann, 1983).
The formation of Schiff bases was confirmed by the
appearance of singlet signal around d 9.37–10.12 ppm. The
appearance of singlet signal in the range of d 2.22–
2.66 ppm confirmed the presence of N-CH3 group of
4-amino antipyrine. The appearance of singlet signal in the
range of d 3.25–3.47 ppm in compounds 3, 6, 7, 8, 9, and
17 indicated the presence of methoxy group in their
structures. The appearance of multiplet signal around d
6.71–8.74 ppm depicted the presence of aromatic protons.
Antimicrobial activity
The synthesized compounds were evaluated for their in
vitro antibacterial activity against S. aureus, B. subtilis,
E. coli, and antifungal activity against C. albicans and
A. niger by tube dilution method using norfloxacin (anti-
bacterial) and fluconazole (antifungal) as reference stan-
dards, and the results are presented in Table 2.
Results and discussion
Chemistry
Among the synthesized compounds, 4-[(3-chloro-ben-
zylidene)-amino]-1,5-dimethyl-2-phenyl-1,2-dihydro-pyra-
zol-3-one (16) was found to be effective against S. aureus,
B. subtilis, E. coli, and C. albicans with pMIC values
of 1.72, 1.72, 2.02, and 2.02, respectively (Table 2).
A series of 4-(substituted benzylidene-amino)-1,5-dime-
thyl-2-phenyl-1,2-dihydropyrazol-3-ones (1–17) were syn-
thesized through reaction of the corresponding aromatic
Scheme 1 Scheme for the
synthesis of 4-(substituted
benzylidene-amino)-1,
5-dimethyl-2-phenyl-1,
2-dihydropyrazol-3-ones
R5
R5
R1
R4
R2
OHC
R1
R4
R3
N
N
N
N
+
R3
NH2
N
O
O
R2
(1-17)
123

Med Chem Res (2012) 21:3863–3875
3867
Table 1 Physicochemical characteristics of the synthesized 4-(substituted benzylidene-amino)-1,5-dimethyl-2-phenyl-1,2-dihydropyrazol-
3-ones
Comp.
R1
R2
R3
R4
R5
M. formula
M. wt.
m.p.
Rf valuea
% Yield
1
H
H
N(CH3)2
OH
OCH3
H
H
H
H
H
H
H
H
H
H
H
H
H
H
H
H
H
H
H
C20H22N4O
C20H21N3O3
C19H19N3O2
334
351
321
307
326
321
351
337
321
336
291
337
326
360
307
326
381
231–233
220–222
171–173
86–88
0.80
0.62
0.62
0.84
0.84
0.64
0.80
0.76
0.80
0.90
0.70
0.60
0.70
0.80
0.80
0.96
0.82
76.87
81.45
92.76
83.26
73.28
90.10
92.80
81.64
87.48
77.52
84.54
78.24
94.66
88.44
76.34
84.75
88.30
2
H
OC2H5
H
H
3
H
H
4
H
OH
H
H
C18H17N3O2
5
Cl
H
H
H
C18H16ClN3O
195–197
135–137
274–276
208–210
216–218
176–178
179–181
210–212
256–258
211–213
230–232
192–194
195–197
6
OCH3
OCH3
OCH3
H
H
H
C19H19N3O2
C20H21N3O3
C19H19N3O3
C19H19N3O2
C18H16N4O3
7
H
OCH3
OH
H
H
8
H
H
9
OCH3
H
H
10
11
12
13
14
15
16
17
a
NO2
H
H
H
H
H
H
C18H17N3O
OH
H
OCH3
H
H
H
C19H19N3O3
Cl
H
C
C
C
C
18H16ClN3O
18H15Cl2N3O
18H17N3O2
Cl
H
H
Cl
H
H
OH
H
H
H
Cl
H
18H16ClN3O
H
OCH3
OCH3
OCH3
C21H23N3O4
TLC mobile phase—Toluene:Chloroform (7:3)
Table 2 Antimicrobial activity
Comp.
pMICsa
1.13
pMICbs
pMICec
pMICca
pMICan
pMICab
pMICaf
pMICam
(pMIC in lM/ml) of the
synthesized 4-(substituted
benzylidene-amino)-1,
5-dimethyl-2-phenyl-1,
2-dihydropyrazol-3-ones
1
1.13
1.15
1.11
1.39
1.42
1.11
1.15
1.13
1.41
1.43
1.07
1.13
1.12
1.16
1.39
1.72
1.48
0.19
2.61b
1.43
1.45
1.41
1.39
1.42
1.41
1.45
1.43
1.41
1.43
1.37
1.43
1.12
1.46
1.39
2.02
1.48
0.17
2.61b
1.43
1.45
1.41
1.39
1.42
1.41
1.45
1.43
1.41
1.43
1.37
1.43
1.42
1.46
1.39
2.02
1.48
0.15
2.64c
1.43
1.45
1.41
1.39
1.42
1.41
1.45
1.43
1.41
1.43
1.37
1.13
1.42
1.46
1.69
1.42
1.48
0.10
2.64c
1.23
1.25
1.21
1.29
1.32
1.21
1.25
1.23
1.41
1.33
1.17
1.23
1.12
1.26
1.39
1.82
1.48
0.16
–
1.43
1.45
1.41
1.39
1.42
1.41
1.45
1.43
1.41
1.43
1.37
1.28
1.42
1.46
1.54
1.72
1.48
0.09
–
1.31
1.33
1.29
1.33
1.36
1.29
1.33
1.31
1.41
1.37
1.25
1.25
1.24
1.34
1.45
1.78
1.48
0.13
–
2
1.15
1.11
1.09
1.12
1.11
1.15
1.13
1.41
1.13
1.07
1.13
1.12
1.16
1.39
1.72
1.48
0.18
2.61b
3
4
5
6
7
8
9
10
11
12a
13
14
15a
16a
17
S.D.
Std.
a
Outliers
b
Norfloxacin
c
Fluconazole
Anticancer activity
4-[(4-Hydroxy-benzylidene)-amino]-1,5-dimethyl-2-phenyl-1,
2-dihydro-pyrazol-3-one (15) was effective against
A. niger (pMIC = 1.69). The results of MBC/MFC studies
(Table 3) revealed that the synthesized compounds were
bacteriostatic and fungistatic in action as their MFC and
MBC values were threefold higher than their MIC values
(Emami et al., 2004).
The anticancer activity of the synthesized 4-(substituted
benzylidene-amino)-1,5-dimethyl-2-phenyl-1,2-dihydropyr-
azol-3-ones was determined against human colorectal
DLD-1, HT29, HCT116, and murine leukemia, P388
cancer cell lines using 3-(4,5-dimethylthiazol-2-yl)-2,
123

3868
Med Chem Res (2012) 21:3863–3875
Table 3 MBF/MFC of the synthesized 4-(substituted benzylidene-
amino)-1,5-dimethyl-2-phenyl-1,2-dihydropyrazol-3-ones
Table 4 Cytotoxicity (pIC50) of the synthesized 4-(substituted ben-
zylidene-amino)-1,5-dimethyl-2-phenyl-1,2-dihydropyrazol-3-ones
against human colon and murine leukemia cancer cell lines
Comp.
Minimum bactericidal/fungicidal concentration (lM/ml)
S. aureus B. subtilis E. coli C. albicans A. niger
Compound
pIC50 (lM/ml)
P388 DLD-1
HCT116
HT29
1
[0.15
[0.14
[0.16
[0.16
[0.15
[0.16
[0.14
[0.15
[0.16
[0.15
[0.17
[0.15
[0.15
[0.14
[0.16
[0.15
[0.13
[0.15
[0.14
[0.16
[0.16
[0.15
[0.16
[0.14
[0.15
[0.16
[0.15
[0.17
[0.15
[0.15
[0.14
[0.16
[0.15
[0.13
[0.15
[0.14
[0.16
[0.16
[0.15
[0.16
[0.14
[0.15
[0.16
[0.15
[0.17
[0.15
[0.15
[0.14
[0.16
[0.15
[0.13
[0.15
[0.14
[0.16
[0.16
[0.15
[0.16
[0.14
[0.15
[0.16
[0.15
[0.17
[0.15
[0.15
[0.14
[0.16
[0.15
[0.13
0.15
[0.14
[0.16
[0.16
[0.15
0.16
1
0.33
-0.06
0.14
0.08
-0.03
0.24
0.21
0.07
0.03
0.24
0.33
0.49
0.44
0.23
1.05
0.23
0.57
0.19
0.04
0.28
1.34
0.48
0.53
0.23
0.78
0.51
0.81
0.22
0.27
1.15
0.13
0.07
0.62
0.51
0.75
0.41
0.57
0.56
1.07
2
2
0.53
0.73
3
3
0.01
4
4
-0.36
-0.49
0.98
-0.43
-0.38
-0.08
-0.17
-0.12
0.03
5
5
6
6
7
[0.14
[0.15
[0.16
[0.15
[0.17
[0.15
0.15
7
0.85
8
8
0.50
9
9
0.64
10
10
11
12
13
14
15
16
17
5-FU
0.31
0.31
11
0.62
-0.03
NA
12
-0.07
-0.19
-0.44
0.27
13
0.20
14
[0.14
[0.16
[0.15
0.13
0.90
15
-0.19
-0.14
-0.30
2.03
16
0.26
17
0.30
Standard
0.019a
0.019a
0.019a
0.040b
0.040b
1.66
a
Norfloxacin
Fluconazole
b
Data represent mean values of three replicates
NA Not able to obtain IC50 after three independent tests
Structure–activity relationship
5-diphenyl tetrazolium bromide (MTT) assay, and the
results are presented in Table 4.
1. Compound 16, that possess electron-withdrawing
m-chloro substituent on aromatic aldehyde portion
emerged as the most potent candidate against S. aureus,
B. subtilis, E. coli, and C. albicans. The role of electron-
withdrawing group in improving antimicrobial activity
is supported by the studies of Sharma et al., (2004). It is
important to note that incubation temperature was the
same for all three bacterial species as well as C. albicans
(a fungus). This similarity in the incubation conditions
may be the reason for the higher activity of compound 16
against C. albicans and the bacterial species.
4-[(3-Methoxy-benzylidene)-amino]-1,5-dimethyl-2-phen
yl-1,2-dihydro-pyrazol-3-one (6) showed potent activity
against the P388 cancer cell line (pIC50 = 0.98 lM/ml,
Table 4). Against the DLD-1 cancer cell line, 4-[(2,
4-dichloro-benzylidene)-amino]-1,5-dimethyl-2-phenyl-1,
2-dihydro-pyrazol-3-one (14) emerged as the most effec-
tive one with a pIC50 value of 0.90 lM/ml. 4-[(2-Hydroxy-
3-methoxy-benzylidene)-amino]-1,5-dimethyl-2-phenyl-1,
2-dihydro-pyrazol-3-one (12) showed potent activity
against HCT116 cancer cell line (pIC50 = 1.05 lM/ml).
Compound, 4-[(2-methoxy-benzylidene)-amino]-1,5-dime-
thyl-2-phenyl-1,2-dihydro-pyrazol-3-one (9) exhibited potential
2. In the case of antifungal activity against A. niger,
compound 15 with p-hydroxy group on benzaldehyde
portion was found to be the most active one. Further-
more, it is important to note that the presence of OH
group may be effective in forming hydrogen bonding
at the target site. The role of electron releasing group
in improving antifungal activity is supported by the
studies of Emami et al., (2008).
anticancer activity against HT29 cancer cell line (pIC50
=
1.15 lM/ml). All the synthesized compounds, however,
were less active in comparison with the standard drug
5-fluorouracil (5-FU) except compound
9 (pIC50 =
1.15 lM/ml) which was almost equivalent to that of 5-FU
(pIC50 = 1.07 lM/ml) against HT29. This indicated that
compound 9 be selected as a lead compound for the
development of novel anticancer agents.
3. In the case of anticancer activity against DLD-1 cell
line, compound 14 with 2,4-dichloro moiety proved to
be the most active one among the synthesized
123

Med Chem Res (2012) 21:3863–3875
3869
Fig. 1 Structural requirements
for the antimicrobial and
anticancer activities of the
synthesized 4-(substituted
benzylidene-amino)-1,
m-Cl group
Increase antibacterial
activity
Electron
withdrawing
groups
2,4 Dichloro group
Increase anticancer
activity against DLD-1
cell lines
N
R
N
5-dimethyl-2-phenyl-1,
2-dihydropyrazol-3-ones
N
p-OH group
Increase antifungal activity
against A. niger
O
3-Methoxy
group
Increase anticancer
activity against P388
cell lines
Electron
donating
groups
2-Hydroxy-3-
methoxy group
Increase anticancer
activity against HCT116
cell lines
2-Methoxy
group
Increase anticancer
activity against HT29
cell lines
Table 5 QSAR descriptors used in the study
antipyrine derivatives. Compounds 6 and 9 that have
m- and o-methoxy groups, respectively, were found to
have higher anticancer activity against P388 and HT29
cell lines, respectively, whereas compound 12 having
2-hydroxy, 3-methoxy group emerged as the most
effective against the HCT116 cell line. This showed
that different structural requirements exhibit selectivity
against different cancer cell lines.
S.no. QSAR descriptor
Type
1
2
log P
Lipophilic
Zero order molecular connectivity
indices (0v)
Topological
3
4
5
6
7
First order molecular connectivity
indices (1v)
Topological
Topological
Topological
Topological
Topological
Second order molecular connectivity
indices (2v)
The aforementioned findings are summarized in Fig. 1.
QSAR study for antimicrobial activity
Valence zero order molecular connectivity
indices (0vv)
Valence first order molecular connectivity
indices (1vv)
In order to identify the substituent effect on antimicrobial
activity, QSAR study was undertaken using linear free
energy relationship model (LFER) as described by Hansch
and Fujita (Hansch and Fujita, 1964). Biological activity
(MIC values) was first transformed into pMIC values (i.e.,
–log MIC) and then used as dependent variable in QSAR
study. The different molecular descriptors and values of
selected descriptors used in the present study are depicted
in Tables 5 and 6, respectively.
Valence second order molecular connectivity
indices (2vv)
8
Kier’s alpha first order shape indice (ja1)
Kier’s alpha second order shape indice (ja2)
Kier’s first order shape indice (j1)
Randic topological index
Topological
Topological
Topological
Topological
Topological
Topological
Topological
Electronic
9
10
11
12
13
14
15
16
17
Balaban topological index
Wiener’s topological index
Kier’s second order shape indice (j2)
Ionization potential
Our earlier studies (Kumar et al., 2011, Judge et al.,
2011a, b; Narang et al., 2011a, b) indicated that the multi-
target QSAR (mt-QSAR) models are better than the
one-target QSAR (ot-QSAR) models in describing anti-
microbial activity. In the present study, therefore, we
developed mt-QSAR models to describe the antimicrobial
activity of synthesized 4-(substituted benzylidene-amino)-
1,5-dimethyl-2-phenyl-1,2-dihydropyrazol-3-ones.
Dipole moment (l)
Electronic
Energy of highest occupied
molecular orbital (HOMO)
Electronic
18
Energy of lowest unoccupied
molecular orbital (LUMO)
Electronic
19
20
21
Total energy (Te)
Electronic
Electronic
Steric
Nuclear Energy (Nu. E)
Molar refractivity (MR)
According to ot-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, however, is not practical to
be used, when results of each compound is predicted for
more than one target. In these cases one ot-QSAR for each
target must be developed. This subsequently led to an
increased interest in the development of mt-QSAR
(mt-QSAR) models. As opposed to ot-QSAR, the mt-QSAR
model is a single equation that considers the nature of
molecular descriptors which are common and essential in
describing the antibacterial and antifungal activities
123

3870
Med Chem Res (2012) 21:3863–3875
(Gonzalez-Diaz et al., 2008; Cruz-Monteagudo et al.,
2007; Gonzalez-Diaz et al., 2007; Gonzalez-Diaz and
Prado-Prado, 2008).
model, whereas their removal improved the r value sig-
nificantly with 1vv (r = 0.942, Eq. 2) during regression
analysis. In multivariate statistics, it is common to define
three types of outliers (Furusjo et al., 2006).
In the light of the above, we have attempted to develop
three different mt-QSAR models, viz., mt-QSAR model
that describes antibacterial and antifungal activities as well
as a common mt-QSAR model that describes antimicrobial
(overall antibacterial and antifungal) activity of the syn-
thesized compounds.
1. X/Y relation outliers are substances for which the
relationship between the descriptors (X variables) and
the dependent variables (Y variables) are not the same
as in the (rest of the) training data.
2. X outliers are substances molecular descriptors of
which do not lie in the same range as the (rest of the)
training data.
In order to develop mt-QSAR models, we have calcu-
lated the average antibacterial, antifungal, and antimicro-
bial activities of antipyrine derivatives which are presented
in Table 2. These average activity values were correlated
with molecular descriptors of the synthesized compounds
(Table 7).
3. Y outliers are only defined for training or test samples.
They are substances for which the reference value of
response is invalid.
In the present study, compounds 12, 15, and 16 were
identified as outliers as their presence resulted in a loss of
correlation (r = 0, Eq. 1) with 1vv which never entered the
There was no difference in the activity (Table 2) as well
as the molecular descriptor range (Table 6) of the outliers
(12, 15, and 16) when compared to other antipyrine
Table 6 Values of selected
Comp.
log P
1vv
R
J
W
Nu. E
I.P.
LUMO
HOMO
l
parameters used in QSAR
studies of 4-(substituted
benzylidene-amino)-1,
5-dimethyl-2-phenyl-1,
2-dihydropyrazol-3-ones
1
2.64
2.18
2.23
1.97
2.92
2.23
2.10
1.84
2.23
2.08
2.36
1.84
2.92
3.47
1.97
2.92
1.98
8.14
8.36
7.64
7.25
7.63
7.64
8.17
7.78
7.64
7.61
7.11
7.78
7.62
8.14
7.62
8.70
8.70
11.99
12.52
11.61
11.08
11.09
11.61
12.56
12.02
11.63
11.99
10.68
12.04
11.08
11.49
11.08
13.51
13.51
1.38
1.37
1.38
1.39
1.43
1.37
1.40
1.40
1.44
1.37
1.41
1.41
1.40
1.43
1.41
1.43
1.43
1628.00
1778.00
1439.00
1236.00
1220.00
1407.00
1774.00
1578.00
1375.00
1580.00
1088.00
1546.00
1252.00
1388.00
1236.00
2129.00
2129.00
25410.60
27521.50
23669.60
22102.80
22310.90
23758.70
27595.50
25758.10
24372.10
25096.60
20434.00
26129.00
21860.70
23831.60
21936.00
31999.10
31999.10
8.02
8.36
8.39
8.60
8.68
8.56
8.31
8.38
8.56
8.87
8.55
8.51
8.62
8.74
8.66
8.52
8.52
-0.61
-0.76
-0.71
-0.83
-0.71
-0.76
-0.75
-0.78
-0.50
-1.23
-0.75
-0.86
-0.90
-0.85
-0.88
-0.87
-0.87
-8.02
-8.36
-8.39
-8.60
-8.68
-8.56
-8.31
-8.38
-8.56
-8.87
-8.55
-8.51
-8.62
-8.74
-8.66
-8.52
-8.52
3.03
2.68
4.07
3.98
2.42
3.62
4.08
3.02
3.24
9.26
2.99
1.54
3.89
3.42
4.48
4.97
4.97
2
3
4
5
6
7
8
9
10
11
12a
13
14
15a
16a
17
a
Outliers
Table 7 Correlation matrix for the antibacterial, antifungal, and antimicrobial activities of synthesized compounds with selected molecular
descriptors
pMICab
1.000
pMICaf
pMICam
log P
1vv
J
Nu. E
0.595
I.P.
0.206
LUMO
pMICab
pMICaf
pMICam
log P
1vv
0.436
1.000
0.986
0.579
1.000
-0.279
0.107
0.410
0.942
0.544
0.030
1.000
0.472
0.139
0.453
0.314
0.083
1.000
-0.007
-0.202
-0.044
0.068
0.835
0.692
-0.104
0.168
-0.233
1.000
-0.403
0.887
0.238
-0.364
0.246
0.026
J
0.025
0.370
Nu. E
I.P.
1.000
-0.323
1.000
-0.080
-0.601
1.000
LUMO
123

Med Chem Res (2012) 21:3863–3875
3871
The topological index, v, signifies the degree of branching,
connectivity of atoms, and unsaturation in the molecule
which accounts for variation in activity (Gupta et al.,
2003).
derivatives. This indicated a fact that the outliers belong to
the category of Y outliers (substances for which the ref-
erence value of response is invalid) (Furusjo et al., 2006).
The antifungal activity of the antipyrine derivatives is
best described by the topological parameter, valence first-
order molecular connectivity index (1vv) (r = 0.942,
Table 7, Eq. 2)
From the correlation matrix (Table 7), it is observed that
the electronic parameter nuclear energy (Nu. E) was
effective in describing antibacterial activity of the synthe-
sized compounds (Eq. 3).
LR mt-QSAR model for antifungal activity
pMICaf ¼ 1:441;
LR mt-QSAR model for antibacterial activity
n ¼ 17; r ¼ 0; q2 ¼ ꢁ0:131; s ¼ 0:089; F
ð1Þ
pMICab ¼ 0:00002Nu: E þ 0:802;
n ¼ 14; r ¼ 0:594; q2 ¼ 0:187; s ¼ 0:078; F
¼ ꢁ1; SSE ¼ 0:127; PRESS ¼ 0:144; r2 ¼ 0
¼ 6:56; SSE ¼ 0:073; PRESS ¼ 0:135; r2 ¼ 0:353
pMICaf ¼ 0:0621vv þ 0:941;
ð3Þ
n ¼ 14; r ¼ 0:942; q2 ¼ 0:849; s ¼ 0:009; F
¼ 94:86; SSE ¼ 0:001; PRESS ¼ 0:002; r2 ¼ 0:888
In the case of antifungal activity, the coefficient of nuclear
energy in Eq. 3 is positive which signifies that the anti-
bacterial activity of the synthesized compounds positively
correlated to nuclear energy. This is evident from the
antibacterial activity data of the synthesized antipyrine
derivatives (Table 2), and their nuclear energy values
(Table 6).
ð2Þ
were and hereafter, n is the number of data points; r is the
correlation coefficient; q2 is the cross validated r2 obtained
by leave one out method; s is the standard error of the
estimate; and F is the Fischer statistics. SSE is the sum of
squared errors of prediction, PRESS is the predicted
residual error sum of squares, and r2 is the squared
correlation coefficient.
The addition of topological parameter, Balaban index
(J) to electronic parameter, nuclear energy (Nu. E) signifi-
cantly improved the correlation coefficient from 0.594 to
0.750 (Eq. 4).
The antifungal activity of the synthesized compounds
is positively correlated to valence first-order molecular
connectivity index (1vv) i.e., activity of the synthesized
compounds will increase with increase in value of valence
first-order molecular connectivity index (1vv). This is
evident from the antifungal activity data of the synthe-
MLR mt-QSAR model for antibacterial activity
pMICab ¼ 1:783J þ 0:00002Nu: E ꢁ 1:685;
n ¼ 14; r ¼ 0:750; q2 ¼ 0:296; s ¼ 0:067; F
¼ 7:08; SSE ¼ 0:049; PRESS ¼ 0:080; r2 ¼ 0:563
1
sized compounds (Table 2) and their vv values (Table 6).
Compound 17 which has a high 1vv value of 8.70
(Table 6) exhibited maximum antifungal activity (pMI-
Caf = 1.48, Table 2), whereas compound 11 with a min-
imum 1vv value of 7.11 (Table 6), showed minimum
antifungal activity (pMICaf = 1.37, Table 2). It was also
evident that the outliers (12, 15, and 16) did not dem-
ð4Þ
The topological parameters signify the degree of branch-
ing, connectivity of atoms, and the unsaturation in the
molecule which accounts for variation in activity. The
topological parameter, Balaban index J = J(G) of G is
defined as
1
onstrate a linear relationship with their vv and antifungal
activity values.
X
J ¼ M=ðl þ 1Þ
ðdi ꢀ djÞꢁ0:5
The molecular connectivity index, an adjacency-based
topological index proposed by Randic, is denoted by v and
is defined as the sum over all the edges (ij) as per the
following:
Bonds
where M is the number of bonds in G, l is the cyclomatic
number of G,, and di (i = 1,2,3, N; N is the number of
vertices in G) is the distance sum. The cyclomatic number
l = l(G) of a cyclic graph G is equal to the minimum
number of edges necessary to be erased from G to
transform it into the related acyclic graph. In case of
monocyclic graph, l = 1; otherwise, it is calculated by
means of the following expression (Balaban, 1982).
n
X
v
ðViVjÞꢁ1=2
i¼1
where Vi and Vj are the degrees of adjacent vertices i and j,
respectively, and n is the number of vertices in a hydrogen-
suppressed molecular structure (Lather and Madan, 2005).
123

3872
Med Chem Res (2012) 21:3863–3875
Table 8 Observed and predicted antimicrobial activity obtained by
mt-QSAR models
l ¼ M ꢁ N þ 1:
The mt-QSAR model of antimicrobial activity (Eq. 5)
depicted the importance of electronic parameter, nuclear
energy (Nu. E), in describing antimicrobial activity of the
synthesized compounds.
Comp. pMICab
Obs. Pre. Res.
pMICaf
pMICam
Obs. Pre. Res.
Obs. Pre. Res.
1
1.23 1.24 -0.01 1.43 1.45 -0.02 1.31 1.32 -0.01
1.25 1.27 -0.02 1.45 1.46 -0.01 1.33 1.34 -0.01
2
LR mt-QSAR model for antimicrobial activity
3
1.21 1.22 -0.01 1.41 1.41
0.00 1.29 1.30 -0.01
4
1.29 1.21
1.32 1.28
1.21 1.20
0.08 1.39 1.39
0.04 1.42 1.41
0.01 1.41 1.41
0.00 1.33 1.29
0.01 1.36 1.33
0.00 1.29 1.29
0.04
0.03
0.00
pMICam ¼ 0:00002Nu: E þ 0:972;
5
n ¼ 14; r ¼ 0:692; q2 ¼ 0:219; s ¼ 0:046; F
6
¼ 11:04; SSE ¼ 0:026; PRESS ¼ 0:039; r2 ¼ 0:479
7
1.25 1.33 -0.08 1.45 1.45
1.23 1.29 -0.06 1.43 1.42
0.00 1.33 1.38 -0.05
0.01 1.31 1.35 -0.04
8
ð5Þ
9
1.41 1.33
1.33 1.22
0.08 1.41 1.41
0.11 1.43 1.41
0.00 1.41 1.37
0.02 1.37 1.30
0.04
0.07
Further, in search of a better QSAR model, topological
parameter, Balaban topological index (J), was coupled with
electronic parameter, nuclear energy (Nu. E). This change
resulted in a significant improvement of the r value
(r = 0.818, Eq. 6) as well as its q2 value (q2 = 0.475) which
was almost equal to the qualifying value i.e., q2 = 0.5.
10
11
12a
13
14
15a
16a
17
a
1.17 1.20 -0.03 1.37 1.38 -0.01 1.25 1.28 -0.03
1.23 1.35 -0.12 1.28 1.42 -0.14 1.25 1.35 -0.10
1.12 1.21 -0.09 1.42 1.41
1.26 1.30 -0.04 1.46 1.45
0.01 1.24 1.29 -0.05
0.01 1.34 1.35 -0.01
1.39 1.26
1.82 1.50
1.48 1.45
0.13 1.54 1.41
0.32 1.72 1.48
0.03 1.48 1.48
0.13 1.45 1.29
0.24 1.78 1.46
0.00 1.48 1.47
0.16
0.32
0.01
MLR mt-QSAR model for antimicrobial activity
Outliers
pMICam ¼ 1:125J þ 0:00001Nu: E ꢁ 0:598;
n ¼ 14; r ¼ 0:818; q2 ¼ 0:475; s ¼ 0:038; F
¼ 11:15; SSE ¼ 0:016; PRESS ¼ 0:026; r2 ¼ 0:670
1.50
1.48
1.46
1.44
1.42
1.40
ð6Þ
The QSAR model expressed by Eq. 2 was cross-validated by
its high q2 values (q2 = 0.849) obtained by leave one out
(LOO) method. The value of q2 greater than 0.5 is the basic
requirement for a QSAR model to be considered valid
(Golbraikh and Tropsha, 2002). As the observed and pre-
dicted values are close to each other (Table 8), the mt-QSAR
model for antifungal activity (Eq. 2) is the valid one. The
plot of predicted pMICaf against observed pMICaf (Fig. 2)
also favors the developed model expressed by Eq. 2. The
low PRESS and SSE values also supported the validity of
Eq. 2. Further, the plot of observed pMICaf versus residual
pMICaf (Fig. 3) indicated that there was no systemic error in
model development as the propagation of residuals was
observed on both sides of zero (Kumar et al., 2007).
1.38
1.36
1.36
1.38
1.40
1.42
1.44
1.46
1.48
1.50
Observed pMICaf
Fig. 2 Plot of observed pMICaf against predicted pMICaf by Eq. 2
Even though the low residual values were observed in
the case of antibacterial and antimicrobial activity predic-
tions using Eqs. 4 and 6, respectively (Table 8), the q2
values derived for these equations by leave one out method
were less than 0.5, which rendered these models invalid. In
summary, the mt-QSAR models [Eqs. 1–6] indicated that
antifungal activity of the synthesized antipyrine derivatives
is governed by the topological parameter, valence first-
order molecular connectivity index (1vv).
but the application was not possible in the present study as
the compounds were not divided into test and training sets.
The predictive r2 can be calculated by using the formula
(Roy et al., 2009).
h
i
2
2
rp2red ¼ 1 ꢁ RðYpredðtestÞ ꢁ YtestÞ =RðYtest ꢁ YMeanðtrainingÞÞ
In general, for QSAR studies, the biological activities of
compounds should span 2–3 orders of magnitude. In the
present study, however, the range of antimicrobial activities
of the synthesized compounds is within one order of
In general, calculation of predictive r2 (rp2red) is a useful
statistical parameter for the validation of QSAR models,
123

Med Chem Res (2012) 21:3863–3875
3873
.03
standard deviation of the biological activity justifies its use
in QSAR studies (Narasimhan et al., 2007). The minimum
standard deviation (Table 2) observed in the antimicrobial
activity justifies its use in QSAR studies.
.02
.01
QSAR study for anticancer activity
0.00
-.01
In order to identify the substituent effect on anticancer
activity of the synthesized compounds, QSAR study was
undertaken using linear free energy relationship model
(LFER) as described by Hansch and Fujita (Hansch and
Fujita, 1964). Biological activity data determined as IC50
values were first transformed into pIC50 values on molar
basis and then used as dependent variable in QSAR
study. Preliminary analysis was carried out by finding out
the correlation between anticancer activities of the anti-
pyrine derivatives with their molecular descriptors
(Table 9).
-.02
-.03
1.36
1.38
1.40
1.42
1.44
1.46
1.48
1.50
Residual pMICaf
Fig. 3 Plot of observed pMICaf against residual pMICaf by Eq. 2
Table 9 Correlation between different molecular descriptors with
anticancer activity of the synthesized compounds
None of the parameters showed appreciable correlation
with anticancer activity of the synthesized compounds
(Table 9). We attempted both linear and multiple linear
regressions in the development of valid QSAR models to
predict the anticancer potential of the synthesized com-
pounds. However, we were unable to develop the valid
QSAR models, as none of the derived ot-QSAR models
was statistically significant (Eqs. 7–10).
Parameter
pIC50 (lM/mL)
P388 DLD-1
HCT116
HT29
0.227
Avg
log P
0v
0vv
1v
1vv
2v
2vv
3v
0.597
0.576
0.008
0.185
-0.086
-0.084
-0.120
-0.133
-0.053
0.022
-0.001
0.119
0.127
0.136
0.123
0.086
0.049
-0.019
-0.040
0.126
0.157
0.150
0.123
0.154
0.150
0.136
0.006
0.098
-0.121
0.018
0.167
-0.018
0.084
0.267
0.122
-0.137
-0.015
-0.139
0.010
0.123
0.338
-0.043
0.200
0.069
The poor correlation of different molecular descriptors
with anticancer activity prevented the development of
mt-QSAR model using the average anticancer activity.
0.142
0.118
-0.146
0.074
-0.267
-0.245
-0.544
0.298
0.405
0.354
0.119
-0.105
0.123
3vv
0.525
0.154
ot-QSAR model for anticancer activity against P388
cell lines
j1
-0.012
-0.102
0.013
-0.103
-0.179
-0.212
-0.086
-0.156
-0.177
-0.120
0.422
-0.140
-0.150
-0.194
-0.092
-0.083
-0.102
-0.139
0.333
j2
0.435
j3
0.388
pIC50ðP388Þ ¼ ꢁ0:637 log P þ 1:814; n ¼ 14; r ¼ 0:597;
q2 ¼ 0:171; s ¼ 0:408; F ¼ 6:65; SSE ¼ 1:999;
ja1
ja2
ja3
R
0.217
0.049
0.323
-0.017
0.130
PRESS ¼ 2:570; r2 ¼ 0:356
ð7Þ
0.238
0.338
-0.043
-0.004
-0.028
-0.117
0.331
ot-QSAR model for anticancer activity against DLD-1
cell lines
J
-0.349
0.314
W
-0.145
-0.039
0.475
-0.184
0.121
Te
-0.141
-0.447
0.242
pIC50ðDLDꢁ1Þ ¼ 0:410 log P ꢁ 0:961; n ¼ 14; r ¼ 0:575;
q2 ¼ 0:389; s ¼ 0:277; F ¼ 5:95; SSE ¼ 0:926;
I.P.
LUMO
HOMO
l
0.126
-0.317
-0.331
0.216
-0.227
-0.475
0.369
0.442
0.447
-0.126
-0.337
PRESS ¼ 1:924; r2 ¼ 0:331
ð8Þ
0.044
ot-QSAR model for anticancer activity against HCT-116
cell lines
magnitude. This is in accordance with results suggested by
Bajaj et al. (2005) who stated that the reliability of the
QSAR model lies in its predictive ability, even though the
activity data are narrow in range. When biological activity
data lie in the narrow range, the presence of minimum
pIC50ðHCT116Þ ¼ 0:396I:P: ꢁ 3:135; n ¼ 14; r ¼ 0:475;
q2 ¼ 0:0002; s ¼ 0:159; F ¼ 3:49; SSE ¼ 0:305;
PRESS ¼ 0:394; r2 ¼ 0:225
ð9Þ
123

3874
Med Chem Res (2012) 21:3863–3875
Markov model for the distribution of 1300 chemicals to 38
different environmental or biological systems. J Comput Chem
28(11):1909–1923
ot-QSAR model for anticancer activity against HT-29
cell lines
Emami S, Falahati M, Banifafemi A, Shafiee A (2004) Stereoselective
synthesis and antifungal activity of (Z)-trans-3-azolyl-2-methyl-
chromanone oxime ethers. Bioorgan Med Chem 12:5881–5889
Emami S, Foroumadi A, Falahati M, Lotfali E, Rajabalian S,
Ebrahimi FS, Shafiee A (2008) 2-Hydroxyphenacyl azoles and
related azolium derivatives as antifungal agents. Bioorg Med
Chem Lett 18:141–146
pIC50ðHT29Þ ¼ 0:816 LUMO þ 1:131; n ¼ 14; r ¼ 0:442;
q2 ¼ 0:185; s ¼ 0:282; F ¼ 2:91; SSE ¼ 0:960;
PRESS ¼ 1:414; r2 ¼ 0:195
ð10Þ
Conclusion
Furusjo E, Svenson A, Rahmberg M, Andersson M (2006) The
importance of outlier detection and training set selection for
reliable environmental QSAR predictions. Chemosphere
63:99–108
A series of 4-(substituted benzylidene-amino)-1,5-dime-
thyl-2-phenyl-1,2-dihydropyrazol-3-ones (1–17) was syn-
thesized and screened in vitro for its antimicrobial and
anticancer activities. The antimicrobial results indicated
that 4-[(3-chloro-benzylidene)-amino]-1,5-dimethyl-2-phenyl-1,
2-dihydro-pyrazol-3-one (16) was the most effective anti-
microbial agent among the synthesized compounds. The
anticancer screening results indicated that compound 9
(pIC50 = 1.15 lM/ml) which was effective at a concen-
tration closer to that of the reference drug, 5-FU
(pIC50 = 1.07 lM/ml) against HT29 cell line, may be used
as a lead compound for the development of novel anti-
cancer agents. The QSAR studies for antimicrobial activity
demonstrated the importance of topological parameter,
valence first-order molecular connectivity index (1vv), in
describing antifungal activity of the synthesized com-
pounds. The poor correlation of molecular descriptors with
anticancer activity prevented the development of valid
QSAR model for its prediction.
Golbraikh A, Tropsha A (2002) Beware of q2! J Mol Graphics Model
20:269–276
Gonzalez-Diaz H, Prado-Prado FJ (2008) Unified QSAR and
network-based computational chemistry approach to antimicro-
bials, part 1: multispecies activity models for antifungals.
J Comput Chem 29(4):656–667
Gonzalez-Diaz H, Vilar S, Santana L, Uriarte E (2007) Medicinal
chemistry and bioinformatics—current trends in drugs discovery
with networks topological indices. Curr Top Med Chem
7(10):1015–1029
Gonzalez-Diaz H, Gonzalez-Diaz Y, Santana L, Ubeira FM, Uriarte E
(2008) Networks and connectivity indices. Proteomics 8(4):750–
778
Gupta SP, Kumar AN, Nagazappa AN, Kumar D, Kumaran S (2003)
A quantitative structure-activity relationship study on a novel
class of calcium-entry blockers: 1-[(4-(aminoalkoxy)phenyl)sul-
phonyl]indolizines. J Med Chem 38:867–873
Guzeldemirci NU, Ilhan E, Kucukbasmaci O, Satana D (2010)
Synthesis and antimicrobial evaluation of new 3-alkyl/aryl-2-
[((a, a-diphenyl-a-hydroxy)acetyl)hydrazono]-5-methyl-4-thia-
zolidinones. Arch Pharm Res 33:17–24
Hansch C, Fujita T (1964) p-r-p Analysis. A method for the
correlation of biological activity and chemical structure. J Am
Chem Soc 86:1616–1626
References
Hyperchem 6.0 (1993) Hypercube, Inc., Florida
Judge V, Narasimhan B, Ahuja M, Sriram D, Yogeeswari P, Clercq
ED, Pannecouque C, Balzarini J (2011a) Synthesis, antimyco-
bacterial, antiviral, antimicrobial activities, and QSAR studies of
isonicotinic acid-1-(substituted phenyl)-ethylidene/cycloheptyli-
dene hydrazides. Med Chem Res. doi:10.1007/s00044-011-
9705-2
Judge V, Narasimhan B, Ahuja M, Sriram D, Yogeeswari P, Clercq
ED, Pannecouque C, Balzarini J (2011b) Isonicotinic acid
hydrazide derivatives: synthesis, antimicrobial activity, and
QSAR studies. Med Chem Res. doi:10.1007/s00044-011-
9662-9
Abdel-Jalil RJ, Momani EQE, Hamad M, Voelter W, Mubarak MS,
Smith BH, Peters DG (2010) Synthesis, antitumor activity and
electrochemical behavior of some piperazinyl amidrazones.
Monatsh Chem 141:251–258
Bajaj S, Sambi SS, Madan AK (2005) Prediction of anti-inflammatory
activity of N-arylanthranilic acids: computational approach using
refined Zagreb Indices. Croat Chem Acta 78(2):165–174
Balaban AT (1982) Highly discriminating distance based topological
indices. Chem Phys Lett 89:399–404
Bashkatova NV, Korotkova EI, Karbainov YA, Yagovkin AY,
Bakibaev AA (2005) Electrochemical, quantum-chemical and
antioxidant properties of antipyrine and its derivatives. J Pharm
Biomed Anal 37:1143–1147
Kumar A, Narasimhan B, Kumar D (2007) Synthesis, antimicrobial,
and QSAR studies of substituted benzamides. Bioorgan Med
Chem 15:4113–4124
Bayrak H, Demirbas A, Demirbas N, Karaoglu SA (2010) Cyclization
of some carbothioamide derivatives containing antipyrine and
triazolemoieties and investigation of their antimicrobial activi-
ties. Eur J Med Chem 45:4726–4732
Bondock S, Rabie R, Etman HA, Fadda AA (2008) Synthesis and
antimicrobial activity of some new heterocycles incorporating
antipyrine moiety. Eur J Med Chem 43:2122–2129
Cappucino JG, Sherman N (1999) Microbiology—a laboratory
manual. Addison Wesley, California, p 263
Cruz-Monteagudo M, Gonzalez-Diaz H, Aguero-Chapin G, Santana
L, Borges F, Dominguez ER, Podda G, Uriarte E (2007)
Computational chemistry development of a unified free energy
Kumar D, Judge V, Narang R, Sangwan S, Clercq ED, Balzarini J,
Narasimhan B (2010a) Benzylidene/2-chlorobenzylidene hydra-
zides: synthesis, antimicrobial activity, QSAR studies and
antiviral evaluation. Eur J Med Chem 45:2806–2816
Kumar P, Narasimhan B, Yogeswari P, Sriram D (2010b) Synthe-
sis and antitubercular activities of substituted benzoic acid N’-
(substituted benzylidene/furan-2-ylmethylene)-N-(pyridine-3-
carbonyl)-hydrazides. Eur J Med Chem 45:6085–6089
Kumar D, Narang R, Judge V, Kumar D, Narasimhan B (2011)
Antimicrobial evaluation of 4-methylsulfanyl benzylidene/
3-hydroxy benzylidene hydrazides and QSAR studies. Med
Chem Res. doi:10.1007/s00044-010-9543-7
123

Med Chem Res (2012) 21:3863–3875
3875
Lather V, Madan AK (2005) Topological models for the prediction of
anti-HIV activity of dihydro (alkylthio) (naphthylmethyl) oxo-
pyrimidines. Bioorgan Med Chem 13:1599–1604
Mahle F, Guimaraes TR, Meira AV, Correa R, Cruz RCB, Cruz AB,
Nunes RJ, Cechinel-Filho V, Campos-Buzzi FD (2010) Synthe-
sis and biological evaluation of N-antipyrine-4-substituted
anti-inflammatory, analgesic and antimicrobial agents. Bioorgan
Med Chem 17:882–895
Rosu T, Negoiu M, Pasculescu S, Pahontu E, Poirier D, Gulea A
(2010) Metal-based biologically active agents: synthesis, char-
acterization, antibacterial and antileukemia activity evaluation of
Cu(II), V(IV) and Ni(II) complexes with antipyrine-derived
compounds. Eur J Med Chem 45:774–789
Roy PP, Paul S, Mitra I, Roy K (2009) Two novel parameters for
validation of predictive QSAR models. Molecules 14:1660–1701
Rubtsov AE, Makhmudov RR, Kovylyaeva NV, Prosyanik NI,
Bobrov AV, Zalesov VV (2002) Synthesis, anti-inflammatory
and analgesic activity of 4-antipyrine derivatives. Pharm Chem J
36:31–35
Rusinov VL, Kovalev IS, Kozhevnikov DN, Ustinova MM, Chupak-
hin ON, Pokrovskii AG, Ilicheva TN, Belanov EF, Bormotov NI,
Serova OA, Volkov ON (2005) Synthesis and antiviral activity
of 2-amino-3-ethoxycarbonylpyrazine derivatives. Pharm Chem
J 39(12):12–16
amino-3-chloromaleimide derivatives. Eur
45:4761–4768
J Med Chem
Mosmann T (1983) Rapid colorimetric assay for cellular growth and
survival: application to proliferation and cytotoxicity assays.
J Immunol Methods 65(1–2):55–63
Narang R, Narasimhan B, Sharma S, Sriram D, Yogeeswari P, Clercq
ED, Pannecouque C, Balzarini J (2011a) Synthesis, antimyco-
bacterial, antiviral, antimicrobial activities, and QSAR studies of
nicotinic acid benzylidene hydrazide derivatives. Med Chem
Res. doi:10.1007/s00044-011-9664-7
Narang R, Narasimhan B, Sharma S, Sriram D, Yogeeswari P, Clercq
ED, Pannecouque C, Balzarini
J (2011b) Nicotinic acid
benzylidene/phenyl-ethylidene hydrazides: synthesis and anti-
microbial evaluation and QSAR studies. Lett Drug Des Discov
8:733–749
Sabet R, Mohammadpour M, Sadeghi A, Fassihi A (2010) QSAR
study of isatin analogues as in vitro anti-cancer agents. Eur J
Med Chem 45:1113–1118
Narasimhan B, Judge V, Narang R, Ohlan S, Ohlan R (2007)
Quantitative structure–activity relationship studies for prediction
of antimicrobial activity of synthesized 2,4-hexadienoic acid
derivatives. Bioorg Med Chem Lett 17:5836–5845
Pharmacopoeia of India (2007) Controller of Publications, Ministry of
Health Department, Government of India, New Delhi, vol. I: 37
Rodriguez-Arguelles MC, Lopez-Silva EC, Sanmartin J, Pelagatti P,
Zani F (2005) Copper complexes of imidazole-2-, pyrrole-2- and
indol-3-carbaldehyde thiosemicarbazones: inhibitory activity
against fungi and bacteria. J Inorg Biochem 99:2231–2239
Rostom SAF, El-Ashmawy IM, Razik HAAE, Badr MH, Ashour
HMA (2009) Design and synthesis of some thiazolyl and
thiadiazolyl derivatives of antipyrine as potential non-acidic
Santos PMP, Antunes AMM, Noronha J, Fernandes E, Vieira AJSC
(2010) Scavenging activity of aminoantipyrines against hydroxyl
radical. Eur J Med Chem 45:2258–2264
Sharma P, Rane N, Gurram VK (2004) Synthesis and QSAR studies
of pyrimido[4,5-d]pyrimidine-2,5-dione derivatives as potential
antimicrobial agents. Bioorg Med Chem Lett 14:4185–4190
Shchegolkov EV, Khudina O, Anikina LV, Burgart YV, Saloutin VI
(2006) Synthesis, analgesic and antipyretic activity of 2-(antip-
yrin-4-yl)hydrazones of 1,2,3-triketones and their derivatives.
Pharm Chem J 40:27–29
SPSS for Windows (1999) version 10.05, SPSS Inc., Bangalore, India
TSAR 3D Version 3.3 (2000) Oxford Molecular Limited
123