(Naphthalen-1-yloxy)-acetic acid benzylidene/(1-phenylethylidene)-hydrazide derivatives: Synthesis, antimicrobial evaluation, and QSAR studies

Article abstract of DOI:10.1007/s00044-011-9776-0

A series of (naphthalen-1-yloxy)-acetic acid hydrazides (1-36) was synthesized and screened, in vitro, for antibacterial, antifungal, and antiviral activities. The results of antiviral activity showed that none of the tested compounds was active against viruses at subtoxic concentrations. Further, the antimicrobial screening results demonstrated that compounds having 3,4,5-trimethoxy benzaldehyde (18), o-Br, p-CN (31), and m-NO₂ acetophenone (32) substituents were the most active ones against tested strains. QSAR investigations revealed that multi-target QSAR models were effective in describing the antimicrobial activity. Springer Science+Business Media, LLC 2011.

Full text of DOI:10.1007/s00044-011-9776-0

MEDICINAL
CHEMISTRY
RESEARCH
Med Chem Res (2012) 21:2526–2547
DOI 10.1007/s00044-011-9776-0
ORIGINAL RESEARCH
(Naphthalen-1-yloxy)-acetic acid benzylidene/(1-phenyl-
ethylidene)-hydrazide derivatives: synthesis, antimicrobial
evaluation, and QSAR studies
•
Rakesh Narang Balasubramanian Narasimhan
•
Sunil Sharma
Received: 11 May 2011 / Accepted: 28 July 2011 / Published online: 18 August 2011
ꢀ Springer Science+Business Media, LLC 2011
Abstract A series of (naphthalen-1-yloxy)-acetic acid
hydrazides (1–36) was synthesized and screened, in vitro,
for antibacterial, antifungal, and antiviral activities. The
results of antiviral activity showed that none of the tested
compounds was active against viruses at subtoxic con-
centrations. Further, the antimicrobial screening results
demonstrated that compounds having 3,4,5-trimethoxy
benzaldehyde (18), o-Br, p-CN (31), and m-NO2 aceto-
phenone (32) substituents were the most active ones
against tested strains. QSAR investigations revealed that
multi-target QSAR models were effective in describing the
antimicrobial activity.
estimated that 68 million will die of AIDS by 2020 (Gupta
et al., 2007). Over the past decade, great strides have been
made in the treatment of HIV infection through use of
drug combinations known as highly active anti-retroviral
therapy (HAART). HAART regimens usually include
three or more drugs with inhibitory activity against either
HIV reverse transcriptase (RT) or HIV protease. Cur-
rently, most HAART regimens include a backbone of two
nucleos(t)ide reverse-transcriptase inhibitors (N(t)RTIs)
with a third agent added from the non-nucleoside reverse-
transcriptase inhibitor (NNRTI) or protease inhibitor
classes (PI) (Boojamra et al., 2008). Nevertheless, long-
term side effects and virus–drug resistance emergence,
together with compliance problems in accurately adhering
to the often complicated time schedules of drug uptake,
have made it imperative to develop additional drugs,
preferably acting at a novel target in the replication cycle
of the virus.
Keywords Hydrazides ꢀ Antiviral ꢀ Antibacterial ꢀ
Antifungal ꢀ QSAR
Introduction
Research and development of potent and effective
antimicrobial agents represents one of the most important
advances in therapeutics, not only in control of serious
infections, but also in prevention and treatment of some
infectious complications of other therapeutic modalities
such as cancer chemotherapy and surgery. Over the past
decade, fungal infection become an important complication
and a major cause of morbidity and mortality in immuno-
compromised individuals such as those suffering from
tuberculosis, cancer, or AIDS and in organ transplant cases
(Rostom et al., 2009). Further, in recent years, much
attention has been focused on addressing the problem of
multi-drug resistant (MDR) bacteria and fungi resulting
from widespread use and misuse of classical antimicrobial
agents (Akbas and Berber, 2005). Such serious global
health problem demands a renewed effort seeking the
development of new antimicrobial agents effective against
The human immunodeficiency virus (HIV), the causative
agent of acquired immunodeficiency syndrome (AIDS),
was identified in the early 1980s (Sakamoto et al., 2007).
There are now more than 40 million people living with
AIDS worldwide and globally 24.8 million people have
died of AIDS since the beginning of epidemic and it is
R. Narang ꢀ S. Sharma
Department of Pharmaceutical Sciences, Guru Jambheshwar
University of Science and Technology, Hisar 125 001, India
B. Narasimhan (&)
Faculty of Pharmaceutical Sciences, Maharshi Dayanand
University, Rohtak 124 001, India
e-mail: naru2000us@yahoo.com
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Med Chem Res (2012) 21:2526–2547
2527
pathogenic microorganisms resistant to currently available
treatments.
spectra were recorded as KBr pellet on Perkin Elmer FTIR
spectrometer. The wave number is given in cm-1
.
Quantitative structure–activity relationships (QSARs)
are among the most widely used techniques in rational
drug design (Bhonsle et al., 2007). The major goal of
QSAR is to formulate mathematical relationships between
physicochemical properties of compounds and their
experimentally determined in vitro biological activities.
Thus, the derived QSAR model can be subsequently used
to predict the biological activities of new derivatives. A
good QSAR model both enhances our understanding of
the specifics of drug action and provides a theoretical
foundation for lead optimization (Kubinyi, 1997). More-
over, QSAR techniques increase the probability of success
with reduced time and cost in drug discovery (Pasha
et al., 2007).
General procedure for synthesis of (naphthalen-1-
yloxy)-acetic acid benzylidene/(1-phenyl-ethylidene)-
hydrazides (1–36)
The mixture of 2-naphthoxy acetic acid (0.08 mol) and
ethanol (0.80 mol) was refluxed in the presence of a few
drops of concentrated sulfuric acid till the completion of
reaction. Then the reaction mixture was added to 200 ml
ice cold water and the residual acid was removed by
treatment with sodium bicarbonate. The ester formed was
extracted with ether. The ether layer was separated which
on evaporation yielded the crude ester, which was recrys-
tallized from alcohol. Hydrazine hydrate (99%)
(0.015 mol) was added in ethanolic solution of ester
(0.01 mol) synthesized above and refluxed for 8 h. The
reaction mixture was then cooled and the precipitates of
acid hydrazide were filtered off, washed with water, dried,
and recrystallized from ethanol. Then the solution of acid
hydrazide (0.01 mol) and appropriate aldehydes/acetophe-
nones (0.01 mol) in ethanol was refluxed for 3–4 h. The
precipitated title compounds were then filtered off, washed
with water, and recrystallized from ethanol.
Hydrazone derivatives have attracted continuing inter-
est over the years because of their wide range of bio-
logical activities, viz., antimicrobial (Bayrak et al., 2009),
antimycobacterial (Nayyar et al., 2007), antitumour
(Lembege et al., 2008), anti-inflammatory (Bhandari
et al., 2008), trypanocidal (Leite et al., 2006), antiviral
(Osama et al., 2009), and antimalarial activities (Gemma
et al., 2006).
Therefore, the target compounds were rationalized so as
to comprise the hydrazone pharmacophores that are
believed to be responsible for the biological significance of
some relevant chemotherapeutic agents. The substitution
pattern of such hydrazone derivatives was carefully selec-
ted so as to confer a different electronic environment to the
molecules. We described previously the antimicrobial
profile of hydrazide–hydrazone derivatives of cinnamic
acid, benzoic acid, and their derivatives, some of which
have exhibited a remarkable antimicrobial activity (Kumar
et al., 2008, 2009, 2010a, b). Motivated by the aforemen-
tioned findings and with the aim of obtaining new anti-
microbial compounds we hereby report the synthesis,
antiviral, antibacterial, antifungal, and QSAR studies of
(naphthalen-1-yloxy)-acetic acid benzylidene/(1-phenyl-
ethylidene)-hydrazide derivatives.
(Naphthalen-1-yloxy)-acetic acid (2-chloro-benzylidene)-
hydrazide (2)
Mp (ꢁC) 145–148; Yield 85.4%; 1H NMR (400 MHz,
DMSO) d ppm: 11.60 (s, 1H, NH), 8.46 (s, 1H, CH of
N=CH), 7.29–8.16 (m, 11H, ArH), 5.24 (s, 2H, CH2); IR
(KBr pellets): cm-1 3200.95 (N–H str.), 3053.47 (C–H str.,
aromatic), 2915.03 (C–H str., aliphatic), 1667.80 (C=O
str.), 1252.55 (C=C str., aromatic), 755.20 (C–Cl str.),
835.16 (C–H out-of-plane bending, naphthalene).
(Naphthalen-1-yloxy)-acetic acid (3-chloro-benzylidene)-
hydrazide (3)
Mp (ꢁC) 183–186; Yield 68.8%; 1H NMR (400 MHz,
DMSO) d ppm: 11.75 (s, 1H, NH), 8.01 (s, 1H, CH of
N=CH), 7.20–7.81 (m, 11H, ArH), 5.26 (s, 2H, CH2); IR
(KBr pellets): cm-1 3201.00 (N–H str.), 3083.34 (C–H str.,
aromatic), 2968.58 (C–H str., aliphatic), 1682.96 (C=O
str.), 1269.22 (C=C str., aromatic), 757.09 (C–Cl str.),
820.75 (C–H out-of-plane bending, naphthalene).
Experimental
Melting points were determined in open glass capillaries on
a sonar melting point apparatus and are 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
(400 MHz) at 298 K, in appropriate deuterated solvents.
Chemical shifts were reported as d (ppm) relative to tet-
ramethylsilane (TMS) as internal standard. Infrared (IR)
(Naphthalen-1-yloxy)-acetic acid (4-chloro-benzylidene)-
hydrazide (4)
Mp (ꢁC) 203–206; Yield 69.7%; 1H NMR (400 MHz,
DMSO) d ppm: 11.70 (s, 1H, NH), 8.01 (s, 1H, CH of
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Med Chem Res (2012) 21:2526–2547
(Naphthalen-1-yloxy)-acetic acid (4-nitro-benzylidene)-
hydrazide (11)
N=CH), 7.18–7.82 (m, 11H, ArH), 5.24 (s, 2H, CH2); IR
(KBr pellets): cm-1 3172.07 (N–H str.), 3078.52 (C–H str.,
aromatic), 2959.90 (C–H str., aliphatic), 1684.89 (C=O
str.), 1271.14 (C=C str., aromatic), 748.41 (C–Cl str.),
819.78 (C–H out-of-plane bending, naphthalene).
Mp (ꢁC) 193–196; Yield 71.4%; 1H NMR (400 MHz,
DMSO) d ppm: 11.86 (s, 1H, NH), 8.09 (s, 1H, CH of
N=CH), 7.17–7.95 (m, 11H, ArH), 5.27 (s, 2H, CH2); IR
(KBr pellets): cm-1 3220.22 (N–H str.), 3061.68 (C–H str.,
aromatic), 2950.50 (C–H str., aliphatic), 1676.54 (C=O
str.), 1563.80 (C=C str., aromatic), 1531.63 (NO2 asym.
str.), 1357.2 (NO2 sym. str.), 800.32 (C–H out-of-plane
bending, naphthalene), 510.10 (C–Br str.).
(Naphthalen-1-yloxy)-acetic acid (4-bromo-benzylidene)-
hydrazide (7)
Mp (ꢁC) 161–164; Yield 85.6%; 1H NMR (400 MHz,
DMSO) d ppm: 11.73 (s, 1H, NH), 8.02–8.33 (s, 1H, CH of
N=CH), 7.22–7.90 (m, 11H, ArH), 4.80-5.28 (s, 2H, CH2);
IR (KBr pellets): cm-1 3176.00 (N–H str.), 3055.50 (C–H
str., aromatic), 2961.65 (C–H str., aliphatic), 1686.97 (C=O
str.), 1426.74 (C=C str., aromatic), 809.34 (C–H out-of-
plane bending, naphthalene), 510.10 (C–Br str.).
(Naphthalen-1-yloxy)-acetic acid (2-hydroxy-benzylidene)-
hydrazide (12)
Mp (ꢁC) 201–203; Yield 77.8%; 1H NMR (400 MHz,
DMSO) d ppm: 11.85 (s, 1H, NH), 11.17 (s, 1H, OH), 8.57
(s, 1H, CH of N=CH), 6.86–7.84 (m, 11H, ArH), 4.81 (s,
2H, CH2); IR (KBr pellets): cm-1 3380.60 (O–H str.),
3178.70 (N–H str.), 3084.30 (C–H str., aromatic), 2921.15
(C–H str., aliphatic), 1680.53 (C=O str.), 1471.40 (C=C
str., aromatic), 1255.70 (C–O str., phenol), 755.80 (C–H
out-of-plane bending, naphthalene).
(Naphthalen-1-yloxy)-acetic acid (4-fluoro-benzylidene)-
hydrazide (8)
Mp (ꢁC) 182–185; Yield 68.9%; 1H NMR (400 MHz,
DMSO) d ppm: 11.66 (s, 1H, NH), 8.03 (s, 1H, CH of
N=CH), 7.14–7.83 (m, 11H, ArH), 5.24 (s, 2H, CH2); IR
(KBr pellets): cm-1 3179.79 (N–H str.), 3079.49 (C–H str.,
aromatic), 2969.82 (C–H str., aliphatic), 1683.93 (C=O
str.), 1498.75 (C=C str., aromatic), 1262.46 (C–F str.),
755.16 (C–H out-of-plane bending, naphthalene).
(Naphthalen-1-yloxy)-acetic acid (4-hydroxy-benzylidene)-
hydrazide (14)
Mp (ꢁC) 212–215; Yield 69.3%; 1H NMR (400 MHz,
DMSO) d ppm: 11.35 (s, 1H, NH), 9.57 (s, 1H, OH), 7.92 (s,
1H, CH of N=CH), 7.15–7.78 (m, 11H, ArH), 5.21 (s, 2H,
CH2); IR (KBr pellets): cm-1 3296.49 (N–H str.), 3043.80
(C–H str., aromatic), 2932.89 (C–H str., aliphatic), 1699.36
(C=O str.), 1598.09 (C=C str., aromatic), 1245.10 (C–O str.,
phenol), 836.18 (C–H out-of-plane bending, naphthalene).
(Naphthalen-1-yloxy)-acetic acid (2-nitro-benzylidene)-
hydrazide (9)
Mp (ꢁC) 167–170; Yield 81.7%; 1H NMR (400 MHz,
DMSO) d ppm: 11.97 (s, 1H, NH), 8.48 (s, 1H, CH of
N=CH), 7.32–8.12 (m, 11H, ArH), 5.23 (s, 2H, CH2); IR
(KBr pellets): cm-1 3201.97 (N–H str.), 3086.24 (C–H str.,
aromatic), 2941.57 (C–H str., aliphatic), 1697.43 (C=O
str.), 1260.54 (C=C str., aromatic), 1519.97 (NO2 asym.
str.), 1425.46 (NO2 sym. str.), 749.38 (C–H out-of-plane
bending, naphthalene).
(Naphthalen-1-yloxy)-acetic acid (3,4-dimethoxy-
benzylidene)-hydrazide (17)
Mp (ꢁC) 165–168; Yield 70.2%; 1H NMR (400 MHz,
DMSO) d ppm: 11.52 (s, 1H, NH), 7.95 (s, 1H, CH of
N=CH), 6.89–7.91 (m, 10H, ArH), 5.24 (s, 2H, CH2),
3.85–3.86 (s, 6H, OCH3); IR (KBr pellets): cm-1 3191.36
(N–H str.), 3060.20 (C–H str., aromatic), 2942.53 (C–H str.,
aliphatic), 1680.07 (C=O str.), 1599.06 (C=C str., aromatic),
1244.14 (C–O-C asym. str.), 1165.05 (C–O–C sym. str.),
735.87 (C–H out-of-plane bending, naphthalene).
(Naphthalen-1-yloxy)-acetic acid (3-nitro-benzylidene)-
hydrazide (10)
Mp (ꢁC) 168–171; Yield 73.6%; 1H NMR (400 MHz,
DMSO) d ppm: 11.89 (s, 1H, NH), 8.52 (s, 1H, CH of
N=CH), 7.22–8.49 (m, 12H, ArH), 5.29 (s, 2H, CH2); IR
(KBr pellets): cm-1 3186.54 (N–H str.), 3039.94 (C–H str.,
aromatic), 2923.25 (C–H str., aliphatic), 1679.11 (C=O
str.), 1266.32 (C=C str., aromatic), 1536.37 (NO2 asym.
str.), 1352.16 (NO2 sym. str.), 821.71 (C–H out-of-plane
bending, naphthalene).
(Naphthalen-1-yloxy)-acetic acid (4-hydroxy-3-methoxy-
benzylidene)-hydrazide (19)
Mp (ꢁC) 138–141; Yield 78.6%; 1H NMR (400 MHz,
DMSO) d ppm: 11.51 (s, 1H, NH), 9.58 (s, 1H, OH),
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Med Chem Res (2012) 21:2526–2547
2529
(Naphthalen-1-yloxy)-acetic acid [1-(2-hydroxy-phenyl)-
ethylidene]-hydrazide (34)
7.93–8.24 (s, 1H, CH of N=CH), 6.28–7.90 (m, 10H, ArH),
4.78–5.27 (s, 2H, CH2), 3.80–3.81 (s, 3H, OCH3); IR (KBr
pellets): cm-1 3464.09 (OH str.), 3190.36 (N–H str.),
3090.73 (C–H str., aromatic), 2972.80 (C–H str., aliphatic),
1682.65 (C=O str.), 1521.46 (C=C str., aromatic), 1248.30
(C–O–C asym. str.), 1158.84 (C–O-C sym. str.), 761.28
(C–H out-of-plane bending, naphthalene).
Mp (ꢁC) 154–157; Yield 77.5%; 1H NMR (400 MHz,
DMSO) d ppm: 12.97 (s, 1H, OH), 11.07 (s, 1H, NH),
6.81–7.77 (m, 12H, ArH), 4.86 (s, 2H, CH2), 2.37 (s, 3H,
CH3); IR (KBr pellets): cm-1 3472.02 (O–H str.),
3379.43 (N–H str.), 3050.55 (C–H str., aromatic), 2927.10
(C–H str., aliphatic), 1692.61 (C=O str.), 1384.95 (C=C
str., aromatic), 763.84 (C–H out-of-plane bending,
naphthalene).
(Naphthalen-1-yloxy)-acetic acid (4-methyl-benzylidene)-
hydrazide (23)
Mp (ꢁC) 196–199; Yield 87.5%; 1H NMR (400 MHz,
DMSO) d ppm: 11.54 (s, 1H, NH), 7.99 (s, 1H, CH of
N=CH), 7.16–7.87 (m, 11H, ArH), 5.23 (s, 2H, CH2), 2.14
(s, 3H, CH3); IR (KBr pellets): cm-1 3180.00 (N–H str.),
3053.40 (C–H str., aromatic), 2963.15 (C–H str., aliphatic),
1689.40 (C=O str.), 1401.59 (C=C str., aromatic), 777.38
(C–H out-of-plane bending, naphthalene).
(Naphthalen-1-yloxy)-acetic acid [1-(4-hydroxy-phenyl)-
ethylidene]-hydrazide (35)
Mp (ꢁC) 182–185; Yield 80.5%; 1H NMR (400 MHz,
DMSO) d ppm: 10.65 (s, 1H, NH), 9.47 (s, 1H, OH),
6.77–7.94 (m, 12H, ArH), 5.25 (s, 2H, CH2), 2.12 (s, 3H,
CH3); IR (KBr pellets): cm-1 3382.26 (N–H str.),
3180.00 (N–H str.), 3051.96 (C–H str., aromatic), 1695.59
(C=O str.), 1457.46 (C=C str., aromatic), 1255.93 (C–O
str., phenol), 753.85 (C–H out-of-plane bending,
naphthalene).
(Naphthalen-1-yloxy)-acetic acid [1-(4-chloro-phenyl)-
ethylidene]-hydrazide (28)
Mp (ꢁC) 152–155; Yield 78.5%; 1H NMR (400 MHz,
DMSO) d ppm: 10.91 (s, 1H, NH), 7.15-7.80 (m, 12H,
ArH), 5.26 (s, 2H, CH2), 2.30 (s, 3H, CH3); IR (KBr pel-
lets): cm-1 3363.61 (N–H str.), 3050.90 (C–H str., aro-
matic), 2924.11 (C–H str., aliphatic), 1694.21 (C=O str.),
1434.48 (C=C str., aromatic), 827.88 (C–H out-of-plane
bending, naphthalene), 744.88 (C–Cl str.).
Evaluation of antimicrobial activity (determination
of minimum inhibitory concentration)
Antibacterial assay
A 24-h fresh culture was obtained by inoculation of
respective bacteria in double strength nutrient broth I.P.
(Pharmacopoeia of India, 2007) followed by incubation at
37 1ꢁC. The stock solution (100 lg/ml) of synthesized
substituted benzylidene hydrazide derivatives was serially
diluted in tubes (Cappucino and Sherman, 1999) con-
taining 1 ml of sterile double strength nutrient broth I.P.
to get a concentration of 50 to 3.125 lg/ml and then
inoculated with 100 ll of suspension of respective
organisms in sterile saline (S. aureus, B. subtilis and
E. coli). The inoculated tubes were incubated at 37 1ꢁC
for 24 h and minimum inhibitory concentrations (MICs)
were determined.
(Naphthalen-1-yloxy)-acetic acid [1-(4-bromo-phenyl)-
ethylidene]-hydrazide (30)
Mp (ꢁC) 175–178; Yield 79.6%; 1H NMR (400 MHz,
DMSO) d ppm: 10.72 (s, 1H, NH), 7.52–7.84 (m, 12H,
ArH), 5.26 (s, 2H, CH2), 2.31 (s, 3H, CH3); IR (KBr pel-
lets): cm-1 3190.34 (N–H str.), 3061.14 (C–H str., aro-
matic), 2964.45 (C–H str., aliphatic), 1686.35 (C=O str.),
1406.80 (C=C str., aromatic), 804.10 (C–H out-of-plane
bending, naphthalene), 508.70 (C–Br str.).
(Naphthalen-1-yloxy)-acetic acid [1-(3-nitro-phenyl)-
ethylidene]-hydrazide (32)
Antifungal assay
Mp (ꢁC) 170–173; Yield 65.7%; 1H NMR (400 MHz,
DMSO) d ppm: 11.12 (s, 1H, NH), 8.62 (s, 1H, CH of
N=CH), 7.16–7.87 (m, 11H, ArH), 5.31 (s, 2H, CH2), 2.13
(s, 3H, CH3); IR (KBr pellets): cm-1 3380.40 (N–H str.),
3061.16 (C–H str., aromatic), 2918.42 (C–H str., aliphatic),
1713.83 (C=O str.), 1517.08 (NO2 asym. str.), 1353.12
(NO2 sym. str.), 1248.00 (C=C str., aromatic), 759.02 (C–H
out-of-plane bending, naphthalene).
The antifungal activity of synthesized hydrazide deriva-
tives against the fungal species C. albicans and A. niger
was determined by serial dilution method similar to anti-
bacterial assay using Sabouraud dextrose broth I.P. The
inoculated tubes were incubated at 37 1 and 25 1ꢁC
for a period of 2 and 7 days in case of C. albicans and
A. niger, respectively.
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QSAR studies
12.0) software package (SPSS for Windows, 1999). The cal-
culated descriptors were collected in a data matrix whose
number of rows and columns were the number of molecules
and descriptors, respectively. Linear regression (LR) and
MLRs were used to derive the QSAR equations and feature
selection was performed using the SPSS software package.
The resulted models were validated by leave-one-out (LOO)
cross-validation method (Schaper, 1999). The statistical
qualities of the equations were judged by the parameters like
correlation coefficient (r), cross-validated r2 (q2), standard
error ofestimate (s), and varianceratio (F) at specified degrees
of freedom and some other statistical parameters were
employed to confirm their predictability and robustness.
Theory of step wise multiple linear regression (MLR)
For the construction of QSAR models, most often a linear
model is sought, which can be expressed as the following
equation (Damme and Bultinck, 2010)
A ¼ k1 ꢁ D1 þ k2 ꢁ D2 þ constant
Here A stands for the biological activity, D1 and D2 stands
for the value of different molecular descriptor retained in the
QSAR, k1 and k2 is the regression constant belonging to
descriptor D1 and D2, and C is a constant. The core
machinery of deriving a QSAR are statistical algorithms
that pick the most discriminating descriptors for each
problem from a large pool of possible descriptors and yield
the coefficients k. Obviously, in order to develop a QSAR,
one first needs a set of molecules for which the activities are
known as well as a set of descriptors from which usually only
a small fraction will be kept in the final QSAR. Obviously,
the better one can describe the molecule in terms of a set of
molecular descriptors, the bigger the chance of a better
QSAR. As a consequence, there has been a lot of research on
the development of new descriptors that may be better at
representing different aspects of molecular behavior, e.g.,
reactivity. Note that extending the pool of descriptors from
which to pick the significant descriptors for the problem
considered is fruitful, provided the descriptors are not
correlated. Hence, increasing the number of descriptors
retained in the QSAR model has to be done with great care to
avoid overfitting (Damme and Bultinck, 2010).
Calculation of statistical parameters
The developed QSAR models were validated by the cal-
culation of following statistical parameters : probable error
of the coefficient of correlation (PE), least square error
(LSE), Friedman’s lack of fit measure (LOF), standard
error of prediction (SEP), quality value (Q), and sum of
squares of response values (SSY) (Mandloi et al., 2005;
Pinheiro et al., 2004).
These parameters were calculated from the following
equations.
ꢀ
ꢁ
p
PE ¼ 2 1 ꢂ r2 =3 n
where r correlation coefficient and n number of compounds
used.
X
2
LSE ¼
ðYobs ꢂ YcalcÞ
where Yobs and Ycalc are the observed and calculated values.
2
Descriptor generation
LOF ¼ LSE=f1 ꢂ ðC þ d ꢀ p=nÞg
where LSE, least square error; C number of descriptors ?1;
p, number of independent parameters; n, number of
compounds used; d, smoothing parameter which controls
the bias in the scoring factor between equations with
different number of terms and was kept 1.0.
A Pentium IV personal computer (CPU at 1.96 GHz) with
windows XP operating system was used. The two-dimen-
sional structures of synthesized hydrazide derivatives were
drawn by chemoffice 6.0 software to calculate whole molec-
ular structure-based descriptors. The structure of molecules
are first pre-optimized with the Molecular Mechanics Force
Field (MM?) procedure included in Hyperchem 6.03
(Hyperchem 6.0, 1993) and the resulting geometries are fur-
ther refined by means of the semiempirical method PM3
(parametric Method-3). The structures were minimized with
theconjugategradientmethoduntilthegradientis\0.01 kcal/
p
SEP ¼ LSE=n
The quality value, Q is given by
Q ¼ r=Se
where Q, quality value; r, correlation coefficient; and Se,
standard error.
˚
mol A. This lowest energy structure was used for each mol-
The predictive ability of QSAR models was also quan-
tified in terms of q2, which is defined as
ecule tocalculate physicochemicalproperties using TSAR3.3
software for Windows (TSAR 3D Version 3.3, 2000).
n
X
o
X
2
2
q2¼ 1 ꢂ
ðYobs ꢂ YcalcÞ =
ðYobs ꢂ YmeanÞ
Data screening and model building
The low value of PE, LSE, LOF, and SEP and high value
of Q and q2 are the essential criteria for qualifying the
model as the best one.
The selected descriptors from each class and the experimental
data were analyzed by the stepwise regression SPSS (version
123

Med Chem Res (2012) 21:2526–2547
2531
Variation inflation factor (Goodarzi et al., 2010) is
employed to determine the multicolinearity between the
physicochemical parameters. The VIF and tolerance value
is calculated as
with 100 cell culture inhibitory dose-50 (CCID50) of virus
(1 CCID50 being the virus dose to infect 50% of the cell
cultures) in the presence of varying concentrations (100,
20, 4, … lg/ml) of the test compounds. Viral cytopathicity
was recorded as soon as it reached completion in the
control virus-infected cell cultures that were not treated
with the test compounds.
VIF ¼ 1=1 ꢂ r2
Tolerance ¼ 1=VIF
where r2 is the squared multiple correlation coefficient of
one parameter effect on the remaining parameter.
In the resubstitution test, three statistical parameters (r,
RMSE, and e-) were used to evaluate the performance
(Zhao et al., 2007). The regression coefficient r is defined
by
Results and discussion
Chemistry
Regression coefficient ðrÞ
The synthesis of the intermediate and target compounds
(1–36) was performed according to reactions outlined in
Scheme 1. The 2-naphthoxy acetic acid was refluxed with
ethanol in the presence of a few drops of sulfuric acid to
yield the ethyl ester. The ethyl ester was refluxed with
hydrazine hydrate in ethanol to yield the acid hydrazide,
which was then condensed with corresponding aromatic
aldehydes (1–26)/acetophenones (27–36) to yield the target
substituted hydrazides. The physicochemical characteris-
tics of synthesized compounds are presented in Table 1.
1H NMR and IR spectral data are in agreement with the
proposed structures of all newly synthesized compounds.
The appearance of singlet signal ranging from d
10.65–11.97 ppm in compounds 1–36 confirmed the pres-
ence of NH group of synthesized hydrazide derivatives.
The presence of aromatic hydrogens was confirmed by the
multiplet signal in the range of d 7.0–9.0 ppm. The one
proton of N=CH group appeared as singlet in the range of d
7.95–8.48 ppm in the synthesized compounds confirmed
the formation of hydrazone derivatives, which is formed by
removal of two hydrogen molecules from the hydrazide
derivatives and one oxygen molecule from benzaldehydes
or acetophenones. The appearance of singlet signals around
d 5.25 ppm revealed the presence of CH2 group in syn-
thesized derivatives. The presence of singlet signal around
d 2.12–2.37 ppm confirmed the presence of CH3 group of
acetophenones in the synthesized compounds (27–36).
Further, in compounds 17 and 19, singlet signal at d
3.85–3.86 ppm and d 3.80–3.81 ppm depicted the presence
of 6 and 3 hydrogens of methoxy groups, respectively. The
singlet signal at d 11.17, 9.57, 9.58, 12.97, and 9.47 ppm in
compounds 12, 14, 19, 34, and 35 confirmed the presence
of hydroxyl group in their structures. The three protons
appeared as singlet at d 2.14 ppm confirmed the presence
of methyl group in compound 23. The highest d 11.97 ppm
and least d 10.65 ppm of NH proton indicated the presence
of electron withdrawing NO2 (causes deshielding of pro-
tons) group and electron donating OH (causes shielding of
protons) group in compounds 9 and 35, respectively.
n
The root mean square of errors (RMSE) is calculated
ꢂ
ꢃo
X
X
p
2
2
¼
1 ꢂ
ðYcalc ꢂ YobsÞ =
ðYcalc ꢂ YmeanÞ
with equation
n
To illustrate the predictive accuracy more explicitly, the
o
X
p
2
RMSE ¼
ðYcalc ꢂ YobsÞ =n
e- (absolute average error) is defined by equation
X
eꢂ ¼
jYcalc ꢂ Yobsj=n
Evaluation of anti-HIV activity
The anti-HIV activity and cytotoxicity were evaluated
against wild-type HIV-1 (IIIB) and HIV-2 (ROD) in MT-4
cell cultures using the 3-(4,5-dimethylthiazol-2-yl)-2,5-
diphenyltetrazolium bromide (MTT) method (Pauwels
et al., 1988). Briefly, virus stocks were titrated in MT-4
cells and expressed as the 50% cell culture infective dose
(CCID50). MT-4 cells were suspended in culture medium at
1 9 105 cells/ml and infected with HIV at a multiplicity of
infection of 0.02. Immediately after viral infection, 100 ll
of the cell suspension was placed in each well of a flat-
bottomed microtiter tray containing various concentrations
of the test compounds. After 4-day incubation at 37ꢁC, the
number of viable cells was determined using the MTT
method. Compounds were tested in parallel for their
cytotoxic effects in uninfected MT-4 cell cultures.
Antiviral assays
The antiviral assays [except anti-human immunodeficiency
virus (HIV) assays] were based on inhibition of virus-
induced cytopathicity in HEL [varicella-zoster virus (TK?
VZV strain and TK- VZV strain) and cytomegalovirus
(AD-169 strain and Davis strain)] cell culture. Confluent
cell cultures in microtiter 96-well plates were inoculated
123

2532
Med Chem Res (2012) 21:2526–2547
O
OH
O
OC2H5
O
NHNH2
O
NHN
C
H
R
O
O
O
O
EtOH, H+
NH2NH2.H2O
R-CHO
[24, 25, 26]
R5
R4
R5
R1
R4
O
C CH3
R3
CHO
R3
R1
R1
R2
R1
R2
O
NHN
C
H
R3
O
NHN
C
R3
CH3
R4
R4
O
O
[1-23]
[27-36]
Comp.
R1
H
Cl
H
H
Br
H
R2
H
H
Cl
H
H
R3
H
H
H
Cl
H
H
R4
R
-
-
-
-
Comp.
R1
H
H
H
H
H
-
R2
OH
OH
R3
R4
H
H
R
-
-
-
-
H
H
H
H
H
H
OCH3
OC2H5
OH
SCH3
CH3
-
1
2
3
4
5
6
19
20
21
22
23
24
OCH3
OCH3
H
H
-
H
H
-
-
-
-
Br
C
H
H
H
Br
H
-
-
-
-
-
7
25
HO
H
H
F
H
-
-
-
-
-
8
26
N
H
NO2
H
H
OH
H
H
H
OCH3
H
H
NO2
H
H
OH
H
H
H
OCH3
OCH3
H
H
NO2
H
H
H
H
H
H
H
H
OCH3
H
OCH3
-
-
-
-
-
-
-
-
-
-
H
H
Br
H
Br
H
H
OH
H
OH
H
H
H
H
H
NO2
H
H
H
H
H
Cl
H
Br
CN
H
NO2
H
OH
OH
H
H
H
H
H
H
H
H
H
H
-
-
-
-
-
-
-
-
-
-
9
27
28
29
30
31
32
33
34
35
36
10
11
12
13
14
15
16
17
18
H
OH
OCH3
H
OCH3
OCH3
H
Scheme 1 Scheme for the synthesis of substituted (naphthalen-1-yloxy)-acetic acid benzylidene/(1-phenyl-ethylidene)-hydrazides
Furthermore, the absence of singlet signal corresponding
to COOH in the NMR spectra of synthesized compounds
(1–36) supports the complete reaction of 2-naphthoxy
acetic acid.
moiety in the synthesized compounds. The appearance of
stretching band around 3200–3400 cm-1 indicated the
presence of NH linkage of amide bond hydrazidederivatives.
The presence of peaks slightly above and below 3000 cm-1
indicated the presence of aromatic and aliphatic protons in
the synthesized compounds, respectively. The characteristic
C–Cl str. bands were observed at 755.20, 757.09, 748.41, and
744.88 cm-1 in compounds 2, 3, 4, and 28, respectively.
The presence of C=O functional group was marked by the
appearance of a stretching band around 1700 cm-1, which is
the characteristic of an amide linkage. The presence of IR
band around 800 cm-1 showed the presence of naphthalene
123

Med Chem Res (2012) 21:2526–2547
2533
presence of aromatic rings. In compounds 17 and 19,
stretching bands in the region of 1160 cm-1 (symmetric
C–O–C stretching) and 1245 cm-1 (asymmetric C–O–C
stretching) revealed the presence of methoxy groups.
Moreover, the aromatic nitro stretching around 1350 cm-1
(symmetric NO2 stretching) and 1540 cm-1 (asymmetric
NO2 stretching) depicted the presence of nitro functional
group in synthesized compounds 9, 10, 11, and 32.
Table 1 Physicochemical properties of synthesized (naphthalen-1-
yloxy)-acetic acid benzylidene/(1-phenyl-ethylidene)-hydrazide
derivatives
Comp. Mol. formula
Mol. wt. Mp (ꢁC) Rfa
304 176–179 0.42 86.9
19H15ClN2O2 339 145–148 0.50 85.4
% Yield
1
C19H16N2O2
2
C
C
C
C
C
C
C
C
C
C
C
C
C
C
C
C
C
C
C
C
C
C
C
C
C
C
C
C
C
C
C
C
C
C
C
3
19H15ClN2O2 339
19H15ClN2O2 339
19H15BrN2O2 383
19H15BrN2O2 383
19H15BrN2O2 383
183–186 0.61 68.8
203–206 0.54 69.7
138–141 0.62 74.5
140–143 0.74 72.4
161–164 0.37 85.6
182–185 0.48 68.9
167–170 0.69 81.7
168–171 0.68 73.6
193–196 0.45 71.4
201–204 0.77 77.8
170–173 0.69 82.1
212–215 0.47 69.3
163–166 0.60 80.5
163–166 0.79 62.6
165–168 0.58 70.2
136–139 0.43 85.6
138–141 0.64 78.6
166–169 0.49 77.8
167–170 0.38 80.3
169–172 0.58 85.7
196–199 0.47 87.5
165–168 0.38 70.4
157–160 0.71 80.0
186–189 0.58 71.1
180–183 0.57 72.0
152–155 0.59 78.5
166–169 0.62 79.9
175–178 0.50 79.6
162–165 0.45 75.7
170–173 0.51 65.7
211–214 0.59 72.5
154–157 0.61 77.5
182–185 0.68 80.5
162–165 0.73 74.3
4
5
Cytotoxic and antiviral activity
6
7
All the synthesized compounds were evaluated for antiviral
activity and cytotoxicity. The inhibitory concentration of
compounds was expressed as the concentration that caused
50% inhibition of viral cytopathogenicity (EC50). The cyto-
toxic concentration (CC50) of the compounds was monitored
based on the growth of non-infected cells by the MTT method
and corresponded to the compound concentration required to
cause 50% cell death (Table 2) or on microscopically visible
alteration of cell morphology (Tables 3, 4).
8
19H15FN2O2
19H15N3O4
19H15N3O4
19H15N3O4
19H16N2O3
19H16N2O3
19H16N2O3
20H18N2O3
21H20N2O4
21H20N2O4
22H22N2O5
20H18N2O4
21H20N2O4
21H20N2O5
20H18N2O2S
20H18N2O2
21H18N2O2
23H18N2O3
21H17N3O2
20H18N2O2
322
349
349
349
320
320
320
334
364
364
394
350
364
380
350
328
330
370
343
318
9
10
11
12
13
14
15
16
17
18
19
20
21
22
23
24
25
26
27
28
29
30
31
32
33
34
35
36
a
Generally, none of the compounds were inhibitory to
HIV viruses tested at subtoxic concentrations (Table 2).
Although in general most compounds were poorly cyto-
toxic to the MT-4 cell cultures (CC50:[50 lg/ml), several
compounds showed pronounced cytotoxicity. In particular,
compounds 12, 20, 25, and 28 showed better cytotoxicity
(CC50: 5.19, 6.23, 2.12, and 4.07 lg/ml) as compared to
other synthesized derivatives, respectively.
It was interesting to notice that the position of the OH on
the phenyl group is of crucial importance for eventual
toxicity in the MT-4 cell cultures. The presence an OH in
ortho position (12, 25, and 34) results in high toxicity
(CC50: 5.19, 2.12, and 11.25 lg/ml), in para position (14)
showed moderate toxicity (CC50: 26.50 lg/ml) and an OH
at meta position (13) resulted in poor toxicity (CC50:
32.63 lg/ml) pointing to the free OH at ortho position as a
prerequisite for cytotoxic activity. Further, the presence of
both alkoxy and OH groups (compounds 19 and 20)
improved the cytotoxicity (CC50: 7.05 and 6.23 lg/ml).
The compound synthesized from the p-chloroacetophenone
(28) was also found to be highly toxic (CC50: 4.07 lg/ml)
against selected HIV strains.
20H17ClN2O2 353
20H17BrN2O2 397
20H17N3O4
20H17N3O4
20H18N2O3
20H18N2O3
20H18N2O4
363
363
334
334
350
20H17BrN2O2 397
21H16BrN3O2 422
TLC Mobile Phase—hexane:ethyl acetate 3:7
Similar type of results were also observed in case of
varicella-zoster virus (VZV) cultured in human embryonic
lung (HEL) cells, compounds having hydroxy groups par-
ticularly at meta and para position showed high toxicity,
i.e., 13, 14, and 19 having EC50 equal to or less than
20 lg/ml. Further, compound 28 was found to be highly
cytotoxic (EC50: 2.50 lg/ml against TK? VZV strain;
3.24 lg/ml against TK- VZV strain and CC50: 7.1 lg/ml)
synthesized from p-chloro acetophenone (Table 3). None
of the compound was found to be active against cyto-
megalovirus (Table 4).
Further, the appearance of C–Br and C–F stretching
bands at 510.10 and 1262.46 cm-1 in compounds 7 and 8
showed the presence of bromo and flouro groups in their
structures, respectively. The presence of OH group in
compounds 12, 14, and 35 was confirmed by C–O stretching
band around 1250 cm-1. The skeletal C=C– stretching
bands were observed in the range of 1200–1500 cm-1 in the
spectra of the synthesized compounds, which represents the
123

2534
Med Chem Res (2012) 21:2526–2547
Table 2 Cytostatic and anti-HIV activity of synthesized (naphthalen-
1-yloxy)-acetic acid benzylidene/(1-phenyl-ethylidene)-hydrazide
derivatives
Table 3 Cytotoxicity and anti-virus activity of synthesized (naph-
thalen-1-yloxy)-acetic acid benzylidene/(1-phenyl-ethylidene)-hydra-
zide derivatives against varicella-zoster virus in human embryonic
lung (HELL) cells
Comp.
ECa50 (lg/ml)
CCb50 (lg/ml)
Comp.
ECa50 (lg/ml)
CCb50 (lg/ml)
HIV-1(IIIB)
HIV-2 (ROD)
TK? VZV
strain
TK- VZV
strain
(MCC)b
CCc50
(lg/ml)
1
[96.60
[56.20
[89.93
[87.60
[11.43
[87.43
[106.43
[65.05
[12.18
[12.10
[87.85
[5.19
[96.60
[56.20
[89.93
[87.60
[11.43
[87.43
[106.43
[65.05
[12.18
[12.10
[87.85
[5.19
C96.60
2
56.20 1.52
89.93 8.75
87.60 7.67
11.43 0.26
87.43 15.44
106.43 15.58
65.05 12.10
12.18 0.51
12.10 0.10
87.85 6.94
5.19 3.15
32.63 12.99
26.50 6.90
76.90 3.17
19.25 2.59
14.65 1.95
13.40 1.11
7.05 3.30
6.23 3.15
16.28 2.63
68.33 19.77
83.88 12.97
82.68 28.47
2.12 0.35
51.53 22.51
104.53 10.31
4.07 3.78
79.77 20.40
77.60 5.11
12.10 0.71
12.08 1.29
89.28 11.20
11.25 0.66
68.78 4.26
59.90 2.60
[4.0
1
[20
[20
[100
[100
[4
[20
[20
[100
[100
[4
100
100
[100
[100
20
64.5
62.7
58.6
100
3
2
4
3
5
4
6
5
100
7
6
[20
[100
[20
[4
[20
[100
[20
[4
100
[100
100
20
48.6
100
8
7
9
8
71.6
100
10
11
12
13
14
15
16
17
18
19
20
21
22
23
24
25
26
27
28
29
30
31
32
33
34
35
36
Nevirapine
9
10
11
12
13
14
15
16
17
18
19
20
21
22
23
24
25
26
27
28
29
30
31
32
33
34
35
36
Acyclovir
Brivudin
a
[4
[4
20
61.2
14.2
3.57
62.7
60.6
100
[4
[4
20
[32.63
[26.50
[76.90
[19.25
[14.65
[13.40
[7.05
[32.63
[26.50
[76.90
[19.25
[14.65
[13.40
[7.05
[0.8
9.2
[0.8
16.2
10.5
[4
4
100
C20
20
9.6
[4
[4
[20
[20
17.4
20
C20
C20
100
100
20
56.9
52.6
47.9
27.7
18.1
52.8
[100
80.1
100
[4
12.5
13.8
[4
[6.23
[6.23
[16.28
[68.33
[83.88
[62.68
[2.12
[16.28
[68.33
[83.88
[62.68
[2.12
[4
16.9
[20
[20
[20
[0.8
[20
[20
2.50
[4
[20
[20
[20
[20
[0.8
[20
[20
3.24
[4
100
100
100
100
4
[51.53
[104.53
[4.07
[51.53
[104.53
[4.07
1.94
[100
20
100
100
C4
[79.77
[77.60
[12.10
[12.08
[89.28
[11.25
[68.78
[59.90
[79.77
[77.60
[12.10
[12.08
[89.28
[11.25
[68.78
[59.90
7.1
20
16.2
15.3
12.7
10.5
[100
4
[4
[4
20
[4
[4
20
[4
[4
20
[20
7.0
[20
7.9
100
100
C4
[4
[0.8
[100
23
11.1
74.8
120
0.050 0.011 [4.0
58.5
0.44
0.0093
[100
[100
[100
Zidovudine 0.002 0.001 0.0009 0.0005 [25
a
50% Effective concentration or compound concentration
required to inhibit HIV-induced cytopathicity in MT-4 cell cul-
tures by 50%
20
173
Effective concentration required to reduce virus plaque formation
by 50%. Virus input was 20 plaque forming units (PFU)
b
50% Cytotoxic concentration or compound concentration required
to reduce viability of the MT-4 cell cultures by 50% using the MTT
methodology
b
Minimum cytotoxic concentration that causes a microscopically
detectable alteration of cell morphology
c
Cytotoxic concentration required to reduce cell growth by 50%
123

Med Chem Res (2012) 21:2526–2547
2535
Antibacterial and antifungal activity
Table 4 Cytotoxicity and anti-virus activity of synthesized (naph-
thalen-1-yloxy)-acetic acid benzylidene/(1-phenyl-ethylidene)-hydra-
zide derivatives against cytomegalovirus in human embryonic lung
(HELL) cells
The (naphthalen-1-yloxy)-acetic acid benzylidene/(1-phe-
nyl-ethylidene)-hydrazide (1–36) derivatives were evalu-
ated for their in vitro antibacterial activity against
Gram-positive Staphylococcus aureus, Bacillus subtilis,
Gram-negative Escherichia coli and antifungal activity
against Candida albicans and Aspergillus niger by serial
tube dilution method (Cappucino and Sherman, 1999)
using norfloxacin and fluconazole as reference standards
for antibacterial and antifungal activity, respectively, and
the results are presented in Table 5.
Comp.
ECa50 (lg/ml)
CCb50 (lg/ml)
AD-169
strain
Davis
strain
(MCC)b
CCc50
(lg/ml)
1
[20
[20
[20
[20
[4
[20
[20
[20
[20
[20
[20
[100
[20
[4
100
100
100
100
C20
100
[100
100
20
64.5
62.7
58.6
100
2
3
4
5
100
Double strength nutrient broth I.P. and Sabouraud dex-
trose broth I.P. have been employed as media for growth of
bacterial and fungal strains, respectively.
6
[20
[100
[20
[4
48.6
100
7
8
71.6
100
In case of B. subtilis compounds 18, 19, 20, and 21
emerged as effective antibacterial agents having pMICbs
values 1.50, 1.45, 1.46, and 1.48, respectively. With regard
to activity against S. aureus, the best activity was displayed
by compounds 6, 12, 13, 14, 18, and 32 having pMICsa
values more than 1.70. Whereas, analogues 18, 31, and 36
were found to be most active with pMICec values 1.50,
1.53, and 1.75, respectively, against E. coli. Among the
tested bacterial strains, S. aureus showed relatively high
sensitivity towards the tested compounds.
9
10
[4
[20
[4
C20
20
61.2
14.2
3.57
62.7
60.6
100
11
[4
12
[4
[4
20
13
[20
[4
[20
[4
100
20
14
15
[4
[4
20
16
[4
[20
[20
[20
[20
[20
[20
[20
[4
C20
C20
C20
100
100
100
C100
20
56.9
52.6
47.9
27.7
18.1
52.8
[100
80.1
100
17
[4
18
[4
On the other hand, investigation of antifungal activity
revealed that the compound 31 has shown marked inhibi-
tory potential against C. albicans having pMICca value
2.13, which is comparable to standard drug fluconazole
(pMICca = 2.64). For antifungal activity against A. niger,
compounds 18, 29, and 31 have shown better antifungal
potential having pMICan values 1.50, 1.50, and 1.53,
respectively, than the other synthesized derivatives. It can
be seen from Table 5 that synthesized hydrazides have
higher antifungal potential against C. albicans as compared
to A. niger. Further, the results of antimicrobial activity
revealed that compounds 18 and 31 displayed maximum
inhibitory effect on the growth of tested bacterial and
fungal strains as compared to other synthesized derivatives.
The obtained result revealed that the nature of substit-
uents have a considerable impact on antibacterial and
antifungal activities of target hydrazones and following
structure–activity relationship (SAR) can be deduced.
19
[20
[20
[20
[20
[4
20
21
22
23
24
[20
[0.8
[20
[4
[20
[0.8
[20
[4
100
4
25
1.94
[100
20
26
100
20
27
28
[0.8
[4
4
C4
7.1
29
[4
20
16.2
15.3
12.7
10.5
[100
4
30
[4
[4
20
31
[4
[4
20
32
[4
[4
20
33
[20
[4
[20
[4
100
20
34
35
[4
[4
20
11.1
74.8
60.5
35.4
36
[100
2.68
0.80
[100
1.79
0.30
[100
[100
[100
Ganciclovir
Cidofovir
a
Structure–activity relationship
1. The analysis of antimicrobial studies indicated that the
compounds having methoxy group (compounds 15–
19, 21) except compound 15 were endowed with high
antimicrobial activity against all the selected strains.
This is supported by results of Emami et al. (2008).
Effective concentration required to reduce virus plaque formation
by 50%. Virus input was 100 plaque forming units (PFU)
b
Minimum cytotoxic concentration that causes a microscopically
detectable alteration of cell morphology
c
Cytotoxic concentration required to reduce cell growth by 50%
123

2536
Med Chem Res (2012) 21:2526–2547
Table 5 Antimicrobial activity
(lm/ml) of synthesized
Comp.
pMICbs
pMICsa
pMICec
pMICca
pMICan
pMICb
pMICf
pMICam
(naphthalen-1-yloxy)-acetic
acid benzylidene/(1-phenyl-
ethylidene)-hydrazides
1
0.78
0.83
0.53
0.53
0.88
0.58
0.58
1.11
1.14
1.14
1.14
1.11
1.11
1.11
0.82
1.16
1.16
1.50
1.45
1.46
1.48
0.85
0.52
1.12
0.57
1.14
0.82
1.15
1.20
0.90
1.23
1.16
1.16
0.82
1.13
1.15
0.32
0.64
2.61b
0.78
1.13
0.83
1.43
1.19
1.79
0.58
1.41
1.45
1.14
0.84
1.71
1.71
1.71
0.82
1.16
1.46
1.80
1.15
1.16
1.18
0.85
1.12
1.12
0.87
0.84
0.82
1.45
1.20
0.60
1.23
1.76
1.16
1.13
1.13
1.15
0.36
0.94
2.61b
0.78
0.83
0.53
0.53
0.88
0.58
0.58
1.41
1.45
1.45
0.84
1.41
1.41
1.41
0.82
1.46
1.46
1.50
1.45
1.46
1.48
0.85
0.52
1.12
0.87
0.84
0.82
1.15
1.20
0.60
1.53
1.46
1.46
1.43
1.43
1.75
0.39
0.94
2.61b
0.78
0.83
0.53
0.53
0.88
0.58
0.58
0.81
1.45
1.45
1.45
1.41
1.41
1.71
0.82
1.46
1.46
1.50
1.15
1.46
1.78
1.45
0.52
1.72
0.87
0.84
0.82
1.75
1.50
0.60
2.13
1.76
1.46
0.82
1.43
1.45
0.42
0.64
2.64c
0.78
0.83
0.53
0.53
0.88
0.58
0.58
0.81
1.45
0.84
0.54
1.11
1.41
1.11
0.82
1.46
1.46
1.50
1.15
1.46
1.48
0.54
0.82
0.82
0.87
0.84
0.82
0.85
1.50
0.60
1.53
1.46
0.86
0.82
1.43
1.45
0.36
0.64
2.64c
0.78
0.93
0.63
0.83
0.98
0.98
0.58
1.31
1.35
1.24
0.94
1.41
1.41
1.41
0.82
1.26
1.36
1.60
1.35
1.36
1.38
0.85
0.72
1.12
0.77
0.94
0.82
1.25
1.20
0.70
1.33
1.46
1.26
1.13
1.23
1.35
0.30
0.84
2.61
0.78
0.83
0.53
0.53
0.88
0.58
0.58
0.81
1.45
1.15
1.00
1.26
1.41
1.41
0.82
1.46
1.46
1.50
1.15
1.46
1.63
1.00
0.67
1.27
0.87
0.84
0.82
1.30
1.50
0.60
1.83
1.61
1.16
0.82
1.43
1.45
0.36
0.64
2.64
0.78
0.89
0.59
0.71
0.94
0.82
0.58
1.11
1.39
1.20
0.96
1.35
1.41
1.41
0.82
1.34
1.40
1.56
1.27
1.40
1.48
0.91
0.70
1.18
0.81
0.90
0.82
1.27
1.32
0.66
1.53
1.52
1.22
1.00
1.31
1.39
0.31
0.76
2.62
2
3
4
5
6
7
8
9
10
11
12
13
14
15a
16
17
18
19
20
21
22
23
24
25
26
27a
28
29a
30a
31
32
33
34a
35a
36a
SD
NAH
Std.
SD standard deviation, NAH
2-naphthoxy acetic acid
hydrazide
a
b
Outliers, Norfloxacin,
Fluconazole
c
2. The results of antimicrobial activity showed that
ortho substituted chloro derivative (compound 2) has
higher antimicrobial potency as compared to that of
meta and para substituted (compounds 3 and 4,
respectively) chloro derivatives. This fact is sup-
ported by the observations of Guven et al. (2007). In
contrary, compound 28 synthesized from p-chloro
acetophenone has shown marked antimicrobial
potential against selected microorganisms except
against A. niger.
3. It is noteworthy that the introduction of OH group
(compounds 12–14 and 35–36) gave new compounds
with potent antimicrobial activity. This observation
reveals the fact that presence of OH substituent
increases the antimicrobial potential probably by form-
ing hydrogen bond with the target biomacromolecule.
123

Med Chem Res (2012) 21:2526–2547
2537
Fig. 1 Structural requirements
for the antimicrobial activity of
synthesized hydrazide
derivatives
Essential for both antibacterial and antifungal acctivities
OCH3, o-Cl, p-Cl (on acetophenone), OH and NO2 groups
improve antimicrobial activity
NO2 and Br acetophenone derivatives > NO2 and Br
benzaldehyde derivatives
O
O
H
C
NHN
p-F group improves antibacterial activity
p-CH3, p-SCH3, o-OH naphthalene, indolyl groups and
unsbstituted derivatives decrease the antimicrobial activity
Replacement with cinnamaldehyde improves antimicrobial activity
This fact is supported by the observations of Vicni et al.
(2002).
in antimicrobial activity of the synthesized com-
pounds. This may be due to the increase in lipophil-
icity of the molecules which may allow them to
easily penetrate the microbial membrane.
4. Compound 24 derived from cinnamaldehyde, has
displayed better activity as compared to unsubstituted
derivative (1). This may be due to the presence of
extended conjugation in cinnamaldehyde portion and
aromatic nucleus of 2-naphthoxy acetic acid, which
may be involved in binding of synthesized compound
with the target site. This fact is supported by one of
our earlier studies (Narasimhan et al., 2007).
10. The aforementioned results indicated the fact that
different structural requirements are essential for a
compound to be selected as antibacterial or antifungal
agent. This is similar to the results obtained by
Sortino et al. (2007). The SAR results are summa-
rized in Fig. 1.
5. The presence of electron withdrawing NO2 group in
compounds 9–11, 32, and 33 make them highly
active antibacterial and antifungal agents. However,
it is interesting to note that compounds synthesized
from nitro acetophenones (32 and 33) were found to
be more active than compounds synthesized from
nitro benzaldehydes (9–11). The role of electron
withdrawing group in increasing the antimicrobial
activity is similar to the results of Sharma et al.
(2004).
QSAR studies
Development of one-target QSAR models (ot-QSAR)
In the present study, a classical Hansch approach using the
linear free energy relationship (LFER) model described by
Hansch and Fujita (1964) was employed to build the QSAR
equations for identifying the substituent effect of synthe-
sized compounds (1–36) against the five tested microbial
strains.
6. The presence of electron withdrawing fluorine group
(compound 8) in benzylidene moiety enhances the
growth inhibition activity against bacterial strains as
compared to unsubstituted analogue (compound 1).
7. The substitution of bromine moiety in the synthe-
sized analogues (compounds 5, 6, 7, and 30) lower
the effectiveness against both bacteria and fungi.
This behavior evidences that the introduction of
bulky substituent significantly decrease the antimi-
crobial potency as mentioned by Zani et al. (2004). In
contrary, in compounds 29 and 31 the presence of
o-Br and o-Br–m-CN acetophenones resulted in
marked improvement in antimicrobial activity par-
ticularly against fungal strains.
Before using the antimicrobial activity data determined
as MIC for QSAR study, they should be transformed to
-log MIC or pMIC in lm/ml (Table 5) to get all the values
positive, normal distribution of errors and to get LFER of
these data with physicochemical properties (Zhao et al.,
2007). Depending on the intercorrelation among the inde-
pendent descriptors and also their individual correlation
with antimicrobial activity, different possible combinations
of parameters were subjected to LR and MLR analysis.
The different molecular parameters (independent
variables), viz., thermodynamic, spatial, electronic, and
topological parameters (Hansch and Fujita, 1964; Hansch
et al., 1973; Kier and Hall, 1973; Randic 1975; Balaban
1982; Wiener, 1947; Randic, 1993) were calculated using
the molecular package TSAR 3.3 for windows (TSAR
3D Version 3.3, 2000) for substituted hydrazide deriva-
tives are summarized in Table 6 and their respective
values corresponding to their compounds are presented in
Table 7.
8. As regard the SARs, the introduction of thiomethyl
(22), methyl (23), 2-hydroxyl naphthalene (25), and
indolyl (26) moieties did not noticeably enhance
growth inhibition potency of synthesized derivatives.
9. In general, the replacement of NH2 group (compound
NAH)
with
benzylidene/(1-phenyl-ethylidene)
moities (compounds 1–36) led to noticeable increase
123

2538
Med Chem Res (2012) 21:2526–2547
Table 6 Brief description of some molecular descriptors used in the present study
S. no.
1
Descriptor type
Topological
Molecular description
Kier’s molecular connectivity (0v, 0vv, 1v, vv, v, 2vv), Kier’s alpha shape (ja1) topological indices,
Kier’s shape (j) topological indices, Randic topological index (R), Balaban topological index (J),
and Wiener topological index (W)
1
2
2
Quantum
Total energy (Te), Ionization potential (I.P.), energies of highest occupied molecular orbital (HOMO)
and lowest unoccupied molecular orbital (LUMO), dipole moment (l) and electronic energy (Ele.E)
3
4
5
Thermodynamic
Spatial
log P (Octanol–water partition coefficient), Molar refractivity (MR)
Molecular surface area, molar volume (Vm)
Molecular weight (MW)
Structural
Thermodynamic parameters describe free energy change
during drug receptor complex formation. Spatial parame-
ters are the quantified steric features of drug molecules
required for its complimentary fit with receptor. Electronic
parameters describe weak non-covalent bonding between
drug molecules and receptor (Silakari et al., 2008). Further,
the topological descriptors are based on graph/structure
concepts and geometric features such as shape, size, and
branching (Lather and Madan, 2005). Units of the energy
and dipole were electron volts (eV), and atomic units (a.u.),
respectively (Dai et al., 1999).
Table 7 Value of QSAR descriptors of synthesized hydrazide
derivatives used in regression analysis
Comp. log P 0v
1v
j1
j2
B
Nu.E.
HOMO
1
4.02 15.91 11.33 17.81 9.47 1.15 20701.50 -8.45
4.54 16.78 11.74 18.78 9.63 1.16 22712.80 -8.42
4.54 16.78 11.72 18.78 9.63 1.14 22151.20 -8.49
4.54 16.78 11.72 18.78 9.63 1.14 22148.30 -8.49
4.81 16.78 11.74 18.78 9.63 1.16 22652.80 -8.42
4.81 16.78 11.72 18.78 9.63 1.14 22092.90 -8.50
4.81 16.78 11.72 18.78 9.63 1.14 22090.30 -8.49
4.16 16.78 11.72 18.78 9.63 1.14 22279.60 -8.49
3.97 18.36 12.65 20.73 10.52 1.19 27696.10 -8.73
3.97 18.36 12.63 20.73 10.52 1.14 25218.80 -8.60
3.97 18.36 12.63 20.73 10.52 1.14 25219.60 -8.60
3.73 16.78 11.74 18.78 9.63 1.16 22881.70 -8.47
3.73 16.78 11.72 18.78 9.63 1.14 22316.70 -8.46
3.73 16.78 11.72 18.78 9.63 1.14 22312.20 -8.46
3.77 17.48 12.26 19.75 10.36 1.14 23945.90 -8.42
3.51 19.06 13.21 21.70 11.25 1.20 30088.30 -8.50
3.51 19.06 13.21 21.70 11.25 1.16 27780.60 -8.45
3.26 20.64 14.16 23.66 12.14 1.20 32430.60 -8.51
3.48 18.36 12.67 20.73 10.52 1.15 25897.90 -8.44
3.82 19.06 13.17 21.70 11.25 1.15 27597.00 -8.44
3.23 19.93 13.62 22.68 11.41 1.18 30287.30 -8.52
4.11 17.48 12.26 19.75 10.36 1.14 23705.90 -8.24
4.49 16.78 11.72 18.78 9.63 1.14 22269.90 -8.44
4.43 17.32 12.33 19.75 10.98 1.10 23046.00 -8.43
4.74 19.35 13.72 21.24 10.35 1.02 28469.50 -8.45
3.79 17.77 12.81 19.32 9.47 0.99 25455.10 -8.36
3.65 16.78 11.74 18.78 9.63 1.18 23987.30 -8.44
4.17 17.65 12.13 19.75 9.80 1.19 24239.30 -8.45
4.44 17.65 12.13 19.75 9.80 1.19 23679.90 -8.58
3.61 19.23 13.04 21.70 10.68 1.18 27147.60 -8.62
3.61 19.23 13.04 21.70 10.68 1.19 27475.40 -8.90
3.37 17.65 12.15 19.75 9.80 1.21 24946.90 -8.79
3.37 17.65 12.13 19.75 9.80 1.19 24546.60 -8.77
3.08 18.52 12.54 20.73 9.97 1.21 26786.00 -8.70
4.44 17.65 12.15 19.75 9.80 1.21 24761.60 -8.73
4.31 19.23 13.08 21.70 10.68 1.22 26179.90 -8.61
2
3
4
5
6
7
8
The substituents have to be used at least 3 times at the
compounds for QSAR analysis. At the compounds 4, 24,
25, and 26 there are some substituents used only once. So
these compounds are not suitable for a QSAR work and so
not included in QSAR studies. Further, it is noteworthy that
no statistically significant equations were obtained for the
remaining 32-data set as the value of regression coefficient
(r) is very less. The regression analysis of antimicrobial
activity with different molecular parameters indicated that
compounds 15, 27, 29, 30, 34, 35, and 36 gave high resi-
dues, i.e., the difference between observed and calculated
pMIC. A compound was considered as an outlier for
deriving a particular model when the residual value
exceeded twice the standard error of estimate of the model
(Prasanna et al., 2005). Furthermore, in multivariate sta-
tistics, it is common to define following types of outliers
(Furusjo et al., 2006).
9
10
11
12
13
14
15
16
17
18
19
20
21
22
23
24
25
26
27
28
29
30
31
32
33
34
35
36
1. X outliers. Briefly, a substance is an X outlier if the
molecular descriptors for this substance do not lie in
the same range as the (rest of the) training data.
2. Y outliers are only defined for training or test samples.
They are substances for which the reference value of
response is invalid.
In view of above facts, a data set of 25 members, was
generated for the model development and a marked
improvement in the quality of correlations (r as high as
0.863) was observed.
The reference drugs norfloxacin and fluconazole were
not included in model generation as they belong to
123

Med Chem Res (2012) 21:2526–2547
2539
Table 8 Correlation matrix for antibacterial activity of synthesizes hydrazides against B. subtilis
log P
MR
0v
0vv
j1
ja1
R
T.E.
I.E.
LUMO
HOMO
pMICbs
log P
MR
0v
1.000
-0.336
-0.660
-0.270
-0.655
-0.585
-0.651
0.614
1.000
0.861
0.974
1.000
0vv
0.800
0.999
1.000
j1
0.868
0.805
0.890
1.000
ja1
0.928
0.983
0.985
0.998
1.000
R
0.876
0.995
0.814
0.987
-0.957
0.171
1.000
T.E.
I.E.
-0.802
-0.007
0.020
-0.980
0.291
-0.752
-0.024
0.120
-0.979
0.262
-0.973
0.217
1.000
-0.366
-0.141
0.366
-0.298
0.227
1.000
LUMO
HOMO
pMICbs
-0.148
-0.291
0.753
-0.135
-0.262
0.747
-0.045
-0.171
0.693
-0.105
-0.217
0.739
-0.498
-1.000
0.260
1.000
0.498
0.007
0.024
0.298
1.000
-0.831
0.443
0.391
-0.766
-0.025
-0.260
1.000
Table 9 Correlation of molecular descriptors with antimicrobial (antibacterial and antifungal) activity
pMICbs
pMICsa
pMICec
pMICca
pMICan
pMICb
pMICf
pMICam
log P
MR
0v
0vv
1v
1vv
2v
2vv
-0.831
0.443
0.753
0.391
0.739
0.232
0.743
-0.064
0.747
0.693
0.592
0.435
0.739
0.461
0.730
-0.766
-0.025
-0.260
-0.348
-0.015
0.142
-0.841
0.242
0.599
0.196
0.579
0.040
0.603
-0.229
0.590
0.516
0.398
0.225
0.579
0.504
0.555
-0.611
0.023
-0.398
-0.766
0.385
0.622
0.302
0.584
0.254
0.662
0.049
0.606
0.547
0.400
0.288
0.584
0.560
0.576
-0.573
-0.137
-0.437
-0.735
0.399
0.648
0.369
0.645
0.164
0.610
-0.091
0.646
0.601
0.527
0.302
0.645
0.609
0.611
-0.626
0.169
-0.354
-0.786
0.248
0.571
0.229
0.552
0.053
0.566
-0.180
0.562
0.505
0.394
0.206
0.552
0.514
0.527
-0.586
0.077
-0.330
-0.827
0.431
0.698
0.365
0.673
0.236
0.704
-0.014
0.687
0.629
0.503
0.324
0.673
0.639
0.651
-0.657
-0.003
-0.439
-0.838
0.346
0.655
0.304
0.632
0.144
0.654
-0.107
0.645
0.585
0.463
0.271
0.632
0.596
0.608
-0.643
0.044
-0.395
0.030
0.127
-0.106
0.134
-0.146
0.133
j1
ja1
ja2
ja3
R
0.119
0.054
-0.094
0.127
B
0.346
W
0.100
T.E.
LUMO
HOMO
-0.155
0.188
-0.172
different structural series. A correlation analysis was per-
formed on all the descriptors, depending on the inter-
correlation among the independent descriptors and also
their individual correlation with biological activity. A
correlation matrix constructed for antibacterial activity
against B. subtilis is presented in Table 8. The correlations
of different molecular descriptors with antibacterial and
antifungal activity are presented in Table 9. In general,
high colinearity (r [ 0.8) was observed between different
parameters. High interrelationship was observed between
HOMO and I.E. (r = 1.000), 1v and R (r = 0.995) and low
interrelationship was observed between MR and LUMO
(r = 0.020). Any combination of these descriptors in
multiple regression analysis may result with model suf-
fering from multi-colinearty. The correlation between
antimicrobial activity and molecular descriptors (Table 9)
indicated the predominance of partition coefficient
(log P) in describing the antimicrobial activity of synthe-
sized compounds.
In order to corroborate the validity of the derived QSAR
models, LOO method was used (Schaper, 1999). In LOO
method each compound is eliminated once, a model is
derived from the remaining compounds and the eliminated
compounds are predicted from this model. The same
123

2540
Med Chem Res (2012) 21:2526–2547
Table 10 Statistical parameters values calculated for the derived models for modeling antimicrobial activity of synthesized hydrazide
derivatives
Eq. no.
SDEP
PRESS
SPRESS
SSY
r2
RQR
Q
RMSE
e-
VIF
PE
LSE
LOF
SEP
1
2
3
4
5
6
7
8
9
0.161
0.201
0.290
0.248
0.234
0.177
0.208
0.187
0.168
0.627
0.974
2.028
1.477
1.320
0.755
1.045
1.058
0.679
0.241
0.312
0.341
0.268
0.280
0.225
0.307
0.320
0.258
0.741
1.118
2.325
1.698
1.635
0.894
1.210
1.060
0.797
0.690
0.705
0.586
0.539
0.587
0.617
0.684
0.745
0.701
0.920
0.939
0.898
0.872
0.835
0.887
0.925
0.909
0.927
5.036
4.102
2.584
2.901
3.127
4.337
3.883
4.426
4.866
0.158
0.197
0.284
0.243
0.230
0.174
0.204
0.167
0.164
0.003
0.004
0.007
0.006
0.005
0.003
0.005
0.004
0.003
–
0.041
0.039
0.055
0.061
0.055
0.051
0.042
0.034
0.040
0.627
0.974
2.020
1.480
1.320
0.755
1.040
0.697
0.679
0.627
0.974
2.020
1.480
0.330
0.755
1.040
0.174
0.679
0.032
0.039
0.057
0.049
0.046
0.035
0.041
0.033
0.033
–
–
–
2.421
–
–
3.922
–
procedure is repeated after elimination of another com-
pound until all the compounds have been eliminated once.
Further, the statistical qualities of the developed models
were judged by the parameters like correlation coefficient
(r), the cross-validated correlation coefficient (q2), standard
error of estimate (s), and Fischer’s statistics (F) at specified
degrees of freedom. Sum of squared prediction errors
called predictive residual sum of squares (PRESS) statistic
also called as LSE is calculated as the sum of squares of the
differences between predicted and observed values of the
activity. Standard deviation of prediction (SPRESS), stan-
dard deviation of error of prediction (SDEP), absolute
average error (e-), root mean square error (RMSE), quality
value (Q), square of correlation coefficient (r2), probable
error of the coefficient of correlation (PE), Friedman’s lack
of fit measure (LOF), standard error of prediction (SEP),
and sum of squares of response values (SSY) were calcu-
lated for each model and taken as an estimate of the pre-
dictability of the developed models (Table 10).
As the coefficient of log P in Eq. 1 is negative, therefore
the antibacterial activity against B. subtilis will increase
with decrease in value of log P. This is clearly, evident
from Table 7 that compounds 18, 19, 20, and 21 having
low log P values, 3.26, 3.48, 3.82, and 3.23, have high
antibacterial activity values 1.50, 1.45, 1.46, and 1.48
(Table 5), respectively. Similarly, compounds 3 and 23
having high log P values, i.e., 4.54 and 4.49 (Table 7),
have minimum antibacterial activity values, i.e., 0.53 and
0.52, respectively, against B. subtilis. Similarly, Eqs. 2, 3,
and 4 demonstrated that antimicrobial potential of E. coli,
C. albicans, and A. niger is negatively correlated with
log P, respectively. The negative coefficient of log P indi-
cated that an increase in the log P of molecule hinders the
ligand to pass through cell membrane and thus decrease in
activity.
Progress in the use of QSAR methods has shown the
importance of the hydrophobic or lipophilic nature of
biologically active molecules. The lipophilicity modifies
the penetration of bioactive molecules through the apolar
cell membranes. This property is usually characterized by
the partition coefficient (log P), which is essentially
determined from distribution studies of the compound
between an immiscible polar and non-polar solvent pair.
This quantitative descriptor of lipophilicity (log P) is one
of the key determinants of pharmacokinetic properties.
Knowing the exact values for this parameter, it is possible
to predict the inhibitory activity of the drugs (Podunavac-
Kuzmanovic et al., 2008a).
ot-QSAR model for antibacterial activity against B. subtilis
pMICbs ¼ ꢂ0:488 log P þ 2:980
n ¼ 25 r ¼ 0:831 q2 ¼ 0:635 s ¼ 0:165 F ¼ 51:38
ð1Þ
For model 1, the cross-validated r2 (q2) and square of
correlation coefficient (r2) are 0.635 and 0.690, which
means that the resulted QSAR model could predict 63.5%
and explain 69.0% of variances, respectively. Here, r2,
which presents the explained variance for given set, was
also used to determine the goodness of model’s fit per-
formance. Eq. 1 also indicated statistical significance of
[99.9% with F value of 51.38. The value of q2 obtained by
LOO method is greater than 0.5 of Eq. 1 qualifies it to be a
valid model according to recommendations of Golbraikh
and Tropsha (2002).
log P is the logarithm of the ratio of the concentrations
of the un-ionized solute in two solvents, which is calcu-
lated according to following equation, where o is octanol
and w is un-ionized water.
log Po=w ¼ logð½soluteoꢃ=½solutewꢃÞ
The hydrophobic effect is the major driving force
for the binding of drugs to their receptor targets in
123

Med Chem Res (2012) 21:2526–2547
2541
pharmacodynamics, and is based on the log P contribution
of each atom. Each atom in a molecule contributes to the
log P by the amount of its atomic parameter multiplied by
the degree of exposure to the surrounding solvent (Park
et al., 2008).
predicted by the rules derived from all the other com-
pounds except the predicted one. In other words, in the
LOO test each compound is singled out as an independent
sample and used to examine the predictive method. Both
tests of resubstitution and LOO were used to evaluate the
prediction of models developed in the current study. In the
resubstitution test, three statistical parameters, viz.,
regression coefficient (r), root mean square error (RMSE),
and absolute average error (e-) were used (Table 10) to
evaluate the performance (Zhao et al., 2007).
To systematically assess a prediction algorithm, a reli-
able validation is required. Usually, a predictive method is
evaluated by the predictive results for a training data set
and testing data set, respectively. According to the statis-
tical terminology, the former is called a test of resubstitu-
tion, and the latter is a test of cross-validation. By the test
of resubstitution, the value of each compound in a training
data set is predicted using the rules derived from the same
set. This test gives somewhat optimistic error estimate
because the same compounds are used to derive the pre-
diction rules and to predict themselves. On the other hand,
a cross-validation test for an independent testing data set is
also needed because it can reflect the generalizing effec-
tiveness of a predictive method. Of various cross-validation
tests, the LOO test is thought to be a reliable one. By the
LOO test, the statistical parameter of the derived model is
In addition, the prediction performance of the built
models must be estimated to build a successful QSAR
model. The comparison of observed and predicted anti-
bacterial activities is presented in Table 11. It can be seen
from the results that the observed and predicted antibac-
terial activities lie close to each other as evidenced by their
low residual values. Further, the plot of LR predicted
pMICbs against observed pMICbs (Fig. 2) also favors the
model expressed by Eq. 1.
To investigate the existence of a systemic error in
developing the QSAR model, the residuals pMICbs values
Table 11 Comparison of observed and predicted antibacterial and antifungal activity obtained by ot-QSAR models
Comp.
pMICbs using Eq. 1
pMICec using Eq. 2
pMICca using Eq. 3
pMICan using Eq. 5
Obs.
Pre.
Res.
Obs.
Pre.
Res.
Obs.
Pre.
Res.
Obs.
Pre.
Res.
1
0.78
0.83
0.53
0.88
0.58
0.58
1.11
1.14
1.14
1.14
1.11
1.11
1.11
1.16
1.16
1.50
1.45
1.46
1.48
0.85
0.52
1.15
1.23
1.16
1.16
1.02
0.77
0.77
0.63
0.63
0.63
0.95
1.04
1.04
1.04
1.16
1.16
1.16
1.27
1.27
1.39
1.28
1.11
1.40
0.97
0.79
0.95
1.22
1.34
1.34
-0.24
0.06
0.78
0.83
0.53
0.88
0.58
0.58
1.41
1.45
1.45
0.84
1.41
1.41
1.41
1.46
1.46
1.50
1.45
1.46
1.48
0.85
0.52
1.15
1.53
1.46
1.46
1.13
0.80
0.80
0.63
0.63
0.63
1.04
1.16
1.16
1.16
1.31
1.31
1.31
1.45
1.45
1.61
1.47
1.25
1.63
1.07
0.83
1.03
1.39
1.54
1.54
-0.35
0.03
0.78
0.83
0.53
0.88
0.58
0.58
0.81
1.45
1.45
1.45
1.41
1.41
1.71
1.46
1.46
1.50
1.15
1.46
1.78
1.45
0.52
1.75
2.13
1.76
1.46
1.22
0.86
0.86
0.67
0.67
0.67
1.12
1.25
1.25
1.25
1.42
1.42
1.42
1.58
1.58
1.75
1.60
1.36
1.77
1.16
0.89
1.12
1.51
1.68
1.68
-0.44
-0.03
-0.33
0.21
0.78
0.83
0.53
0.88
0.58
0.58
0.81
1.45
0.84
0.54
1.11
1.41
1.11
1.46
1.46
1.50
1.15
1.46
1.48
0.54
0.82
0.85
1.53
1.46
0.86
0.87
0.72
0.72
0.61
0.61
0.61
0.87
1.08
1.07
1.07
1.04
1.04
1.04
1.34
1.34
1.58
1.28
1.21
1.52
0.97
0.74
0.92
1.28
1.25
1.25
-0.09
0.11
2
3
-0.24
0.25
-0.27
0.25
-0.19
0.27
5
6
-0.05
-0.05
0.16
-0.05
-0.05
0.37
-0.09
-0.09
-0.31
0.20
-0.03
-0.03
-0.06
0.37
7
8
9
0.10
0.29
10
11
12
13
14
16
17
18
19
20
21
22
23
28
31
32
33
0.10
0.29
0.20
-0.23
-0.53
0.07
0.10
-0.32
0.10
0.20
-0.05
-0.05
-0.05
-0.11
-0.11
0.11
-0.01
-0.01
0.29
0.10
0.37
0.10
0.07
0.01
-0.12
-0.12
-0.25
-0.45
0.10
0.12
0.01
0.12
-0.11
-0.02
0.21
-0.08
-0.13
0.25
0.17
0.35
0.08
-0.15
-0.22
-0.31
0.12
0.01
-0.04
-0.43
0.08
-0.12
-0.27
0.20
0.29
-0.37
0.63
-0.07
0.25
0.01
0.14
0.62
-0.18
-0.18
-0.08
-0.08
0.08
0.21
-0.22
-0.39
123

2542
Med Chem Res (2012) 21:2526–2547
1.6
synthesized derivatives against E. coli is inversely propor-
tional to the value of log P. This is evidenced by the anti-
bacterial activity data of substituted hydrazides (Table 5)
and their log P values (Table 7). Compounds 18 and 21 with
low log P values of 3.26 and 3.23 (Table 7) have maxi-
mum antibacterial activity against E. coli (compound 18,
pMICec = 1.50; compound 21, pMICec = 1.48, Table 5).
Similarly, the compounds 3 and 23 with high log P (Table 7)
values have minimum antibacterial activity (pMICec = 0.53
and 0.52, Table 5). In the case of S. aureus the regression
analysis did not yield valid ot-QSAR model.
1.4
1.2
1.0
.8
ot-QSAR model for antifungal activity against C. albicans
.6
.4
.6
.8
1.0
1.2
1.4
1.6
pMICca ¼ ꢂ0:699 log P þ 4:034
Observed pMICbs
n ¼ 25 r ¼ 0:765 q2 ¼ 0:526 s ¼ 0:296 F ¼ 32:61
ð3Þ
Fig. 2 Plot of observed pMICbs versus predicted pMICbs for LR
model by Eq. 1
ot-QSAR model for antifungal activity against A. niger
.4
.3
pMICan ¼ ꢂ0:543 log P þ 3:185
n ¼ 25 r ¼ 0:734 q2 ¼ 0:501 s ¼ 0:253 F ¼ 26:96
ð4Þ
.2
.1
The QSAR models elicited in Eqs. 3 and 4 once again
indicated the importance of log P (r = 0.766 and 0.734)
in demonstrating antifungal activity of substituted benzyl-
idene hydrazide derivatives against C. albicans and
A. niger, respectively.
0.0
-.1
-.2
-.3
-.4
pMICan ¼ ꢂ0:403 log P þ 0:1431v þ 0:861
n ¼ 25 r ¼ 0:766 q2 ¼ 0:490 s ¼ 0:245 F ¼ 15:67
ð5Þ
In MLR analysis, the coeffcient of regression
.4
.6
.8
1.0
1.2
1.4
1.6
Observed pMICbs
Fig. 3 Plot of observed pMICbs versus residual pMICbs for LR model
by Eq. 1
(r) increases to 0.766 (Eq. 5) from 0.734 (Eq. 4, in LR)
1
on addition of v to log P, thus leading to an increase in
predictability of mt-QSAR models, which can be observed
from low residual values (Table 11). Similar to Eq. 1 the
high q2 values (q2 [ 0.5) obtained by LOO technique and
observance of low residual values (Table 11) indicated the
validity and predictability of Eqs. 2, 3, and 4.
were plotted against the observed pMICbs values in Fig. 3.
The propagation of the residual values on both sides of zero
indicates that no systemic error exists in the development
of LR model (Jalali-Heravi and Kyani, 2004).
The cross-validated correlation coefficient (r2cv [ 0.5)
values obtained for the best QSAR models indicated their
reliability in predicting the antimicrobial activity of substi-
tuted hydrazide derivatives. In case of A. niger the r2cv (q2)
value is equal to 0.5, which shows that the developed model
is an invalid one. But one should not forget the recommen-
dations of Golbraikh and Tropsha (2002) 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, the QSAR model for
ot-QSAR model for antibacterial activity against E. coli
pMICec ¼ ꢂ0:631 log P þ 3:66
n ¼ 25 r ¼ 0:841 q2 ¼ 0:662 s ¼ 0:205 F ¼ 55:21
ð2Þ
For model 2 the value of r2 and q2 is 0.705 (Table 10) and
0.662, which means the QSAR model is able explain 70.5%
and predict 66.2% variances of the antimicrobial activity,
respectively. Further, the coefficient of log P in Eq. 2 is
negative, indicating that the antibacterial activity of
123

Med Chem Res (2012) 21:2526–2547
2543
A. niger (Eq. 4) is a valid one. Moreover, PRESS is a good
estimate of the real prediction error of the model. Its value
less than SSY indicate that the proposed models have good
predictive power (Kumar et al., 2006). Thus, in view of this,
all the models proposed by us (Eqs. 1–5) are statistically
significant which can be easily clarified by their values listed
in Table 10. The internal consistency of the developed
models using q2 can also assessed with the ratio RQR =
q2/r2 (Sivaprakasam et al., 2006). Considering RQR for the
QSAR models represented by Eqs. 1–5 (Table 10), it is
pleasing that the lowest value is RQR = 0.835 for QSAR
model 5 and there is a gradual, roughly monotonic increase
across the series to RQR = 0.939. Thus, the QSAR models
are statistically robust.
each target. However, very recently the interest has been
increased in 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 antibacterial and antifungal activity (Prado-Prado et al.,
2008; Gonzalez-Diaz et al., 2007, 2008; Gonzalez-Diaz and
Prado-Prado, 2008; Cruz-Monteagudo et al., 2007).
In the present study we have attempted to develop three
different types of mt-QSAR models, viz., mt-QSAR model
for describing antibacterial activity of synthesized com-
pounds 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
(overall antibacterial and antifungal) activity of substituted
benzylidene hydrazides against all the above mentioned
microorganisms.
Generally for QSAR studies, the biological activities of
compounds should span 2–3 orders of magnitude. But in
the present study the range of activities of the 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 very high as evidenced by the low
residual values (Table 11). 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 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 (Narasimhan et al., 2007; Sharma et al., 2006;
Hatya et al., 2006; Kumar et al., 2006). When biological
activity data lie in the narrow range, the presence of min-
imum standard deviation of the biological activity justifies
its use in QSAR studies (Narasimhan et al., 2007, Kumar
et al., 2007). Further, according to recommendation of Kim
et al. (2007) if the standard deviation s is not much larger
than 0.3, the models are acceptable. Hence, the minimum
standard deviation (Table 5) observed in the antimicrobial
activity data justifies its use in QSAR studies.
In order to develop mt-QSAR models, initially we have
calculated the average antibacterial activity, antifungal
activity, and antimicrobial activity values of substituted
hydrazide derivatives which are presented in Table 5. These
average activity values were also correlated with the
molecular descriptors of synthesized compounds (Table 9).
The mt-QSAR model for antibacterial activity revealed
the importance of partition coeffcient (log P) in describing
the antibacterial activity of substituted benzylidene
hydrazides (Eq. 6).
mt-QSAR model for antibacterial activity
pMICb ¼ ꢂ0:455 log P þ 2:964
n ¼ 25 r ¼ 0:785 q2 ¼ 0:547 s ¼ 0:181 F ¼ 37:08
ð6Þ
It is worth to mention here that in case of ot-QSAR,
developed model of S. aureus can not accurately predict
antibacterial activity as the regression analysis did not
yield valid QSAR model (r = 0.348), whereas in case of
mt-QSAR, developed model (Eq. 6, r = 0.785) are valid
and can be used to predict the antibacterial activity
including S. aureus of synthesized hydrazide derivatives.
Further, the low values of PE, LSE, LOF, SEP, SSY,
Spress, SDEP, RMSE, and e- and high values of Q, r2, and
q2 (Table 10) revealed the statistical significance of the
model described by Eqs. 1–5. In case of S. aureus statis-
tical significant model can not be developed as the value of
regression coefficient (r) is very less (Table 9).
mt-QSAR model for antifungal activity
Development of multi-target QSAR (mt-QSAR) model
pMICf ¼ ꢂ0:621 log P þ 3:600
According to the above 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, which are almost in all the literature,
become unpractical or at less complicated to use when we
have to predict to each compound results for more than one
target. In these cases we have to develop one ot-QSAR for
n ¼ 25 r ¼ 0:827 q2 ¼ 0:633 s ¼ 0:213 F ¼ 49:85
ð7Þ
pMICf ¼ ꢂ0:591 log P ꢂ 0:554J ꢂ 2:280
n ¼ 25 r ¼ 0:852 q2 ¼ 0:623 s ¼ 0:195
F ¼ 28:03
ð8Þ
123

2544
Med Chem Res (2012) 21:2526–2547
The mt-QSAR model for antifungal activity signifies the
importance of partition coefficient (log P) in describing
antifungal activity. Further, the coefficient of log P in the
Eq. 7 is negative, indicating that the antifungal activity of
synthesized derivatives is inversely proportional to the
value of log P. This is evidenced by the antifungal activity
data of substituted hydrazides (Table 5) and their
log P values (Table 7). The value of q2 is 0.633 which
means that the developed model could predict 63.3%
variance of antifungal activity. Further, during MLR study,
there is slight increase in value of regression coefficient
(r = 0.827, Eq. 7 to r = 0.852, Eq. 8), hence Eq. 8 is the
most predictive one for describing the antifungal potential
of synthesized hydrazide derivatives.
Table 12 Comparison of observed and predicted antibacterial and
antifungal activity obtained by mt-QSAR models
Comp. pMICb
using Eq. 6
pMICf
using Eq. 8
pMICam
using Eq. 9
Obs. Pre. Res.
Obs. Pre. Res.
Obs. Pre. Res.
1
0.78 1.13 -0.35 0.78 1.04 -0.26 0.78 1.12 -0.34
0.93 0.90 0.03 0.83 0.87 -0.04 0.89 0.85 0.04
0.63 0.90 -0.27 0.53 0.76 -0.23 0.59 0.85 -0.26
2
3
5
0.98 0.77
0.98 0.77
0.21 0.88 0.73
0.15 0.94 0.71
0.23
0.11
6
0.21 0.58 0.62 -0.04 0.82 0.71
7
0.58 0.77 -0.19 0.58 0.63 -0.05 0.58 0.71 -0.13
8
1.31 1.07
1.35 1.15
1.24 1.15
0.24 0.81 0.97 -0.16 1.11 1.05
0.06
0.24
0.05
9
0.20 1.45 1.30
0.09 1.15 1.03
0.15 1.39 1.15
0.12 1.20 1.15
Topological indices (viz., Balaban index (J)) are
numerical parameters of a graph which characterize its
topology and are usually graph invariant. Topological
indices are used in the development of QSARs in which the
biological activity or other properties of molecules are
correlated with their chemical structure. Topological
descriptors are derived from hydrogen-suppressed molec-
ular graphs, in which the atoms are represented by vertices
and the bonds by edges. The connections between the
atoms can be described by various types of topological
matrices (e.g., distance or adjacency matrices), which can
be mathematically manipulated so as to derive a single
number, usually known as graph invariant, graph-theoret-
ical index, or topological index (Gonzalez-Diaz et al.,
2008).
10
11
12
13
14
16
17
18
19
20
21
22
23
28
31
32
33
0.94 1.15 -0.21 1.00 1.06 -0.06 0.96 1.15 -0.19
1.41 1.26
1.41 1.26
1.41 1.26
0.15 1.26 1.28 -0.02 1.35 1.27
0.08
0.14
0.14
0.15 1.41 1.17
0.15 1.41 1.18
0.24 1.41 1.27
0.23 1.41 1.27
1.26 1.36 -0.10 1.46 1.54 -0.08 1.34 1.39 -0.05
1.36 1.36
1.60 1.48
0.00 1.46 1.36
0.10 1.40 1.39
0.01
0.04
0.12 1.50 1.67 -0.17 1.56 1.52
1.35 1.38 -0.03 1.15 1.33 -0.18 1.27 1.41 -0.14
1.36 1.22 0.14 1.46 1.15 0.31 1.40 1.23 0.17
1.38 1.49 -0.11 1.63 1.61
0.85 1.09 -0.24 1.00 0.97
0.02 1.48 1.54 -0.06
0.03 0.91 1.08 -0.17
0.72 0.92 -0.20 0.67 0.80 -0.13 0.70 0.88 -0.18
1.25 1.06
1.33 1.32
1.46 1.43
0.19 1.30 1.16
0.01 1.83 1.46
0.14 1.27 1.05
0.37 1.53 1.34
0.22
0.19
0.06
0.03 1.61 1.66 -0.05 1.52 1.46
mt-QSAR models for antimicrobial activity
1.26 1.43 -0.17 1.16 1.56 -0.40 1.22 1.46 -0.24
pMICam ¼ ꢂ0:520log P þ 3:220
n ¼ 25 r ¼ 0:837 q2 ¼ 0:650 s ¼ 0:172
F ¼ 54:09
ð9Þ
1.6
1.4
1.2
1.0
.8
Similarly, as in aforementioned QSAR studies, the devel-
oped mt-QSAR model Eq. 9 revealed the importance of
log P in demonstrating the overall antimicrobial activity of
synthesized derivatives. This is evidenced by antimicrobial
activity data (Table 5) and their log P values (Table 7).
In order to confirm our results we have predicted the
antibacterial, antimicrobial, and antifungal activity values
using mt-QSAR Eqs. 6, 8, and 9 (Table 12). The compar-
ison of observed and predicted values (Table 12) demo-
strated that they are close to each other, evidenced by their
low residual values. Further, the results are supported by
plot of observed pMICam and predicted pMICam (Fig. 4).
To investigate the existence of a systemic error in devel-
oping the QSAR model, the residuals pMICam values were
plotted against the observed pMICam values in Fig. 5. The
propagation of the residual values on both sides of zero
.6
.4
.6
.8
1.0
1.2
1.4
1.6
Observed pMICam
Fig. 4 Plot of observed pMICam versus predicted pMICam for LR
model by Eq. 9
123

Med Chem Res (2012) 21:2526–2547
2545
.4
.3
synthesized successfully in appreciable yields and screened
for their in vitro antiviral (against HIV-I, HIV-II, VZV, and
cytomegalovirus strains) and antimicrobial activities
against bacterial strains S. aureus, B. subtilis, E. coli, and
fungal strains C. albicans and A. niger. The antiviral
evaluation depicted that none of the synthesized derivatives
inhibited the replication of tested virus strains at subtoxic
concentrations. However, the presence of OH group
improved the cytotoxic activity against HIV (at ortho
position) and varicella-zoster virus (at meta and para
position) as compared to other synthesized derivatives. The
SAR studies indicated that the introduction of 3,4,5-tri-
methoxy benzaldehyde (18), o-Br, p-CN (31), and m-NO2
acetophenone (32) substituents improved the antimicrobial
activity of synthesized hydrazide derivatives. Moreover, to
understand the relationship between physicochemical
parameters and antimicrobial activity of substituted
hydrazide derivatives, QSAR investigation was performed
by development of ot- and mt-QSAR models. The multi-
target QSAR models were found to be more effective in
describing the antimicrobial activity of substituted hydra-
zides than the one-target models. Further, it revealed the
importance of the partition coefficient (log P) in describing
the antimicrobial activity of substituted hydrazides. The
validity of models obtained by linear and MLRs are clearly
evidenced by the high q2 values and the low residual
activity obtained for the developed QSAR models.
.2
.1
0.0
-.1
-.2
-.3
-.4
.4
.6
.8
1.0
1.2
1.4
1.6
Observed pMICam
Fig. 5 Plot of observed pMICam versus residual pMICam for LR
model by Eq. 9
indicates that no systemic error exists in the development
of LR model.
The non-collinearity between the descriptors was tested
through variance inflation factor (VIF) and tolerance. In
practice, when VIF [ 5 or higher, or if the tolerance
remains under the value 0.20, then this would indicate that
there exists multicollinearity among the descriptors.
Table 10 shows that there is no significant correlation
between the selected descriptors (VIF \ 5); hence, there is
no multicollinearity problem on the selected subset of
molecular descriptors which indicated that MLR models
expressed by Eqs. 5 and 8 (Table 10) are statistically valid
(Goodarzi et al., 2010).
Acknowledgments The authors (RN) are highly thankful to the
Department of Technical Education, Haryana for providing research
fellowship during research work. Antiviral activity tests were carried
out at the Rega Institute for Medical Research (Leuven, Belgium).
Further, the statistical quality of the developed models
(Eqs. 6–9) was gaged by the low values of parameters like
PE, LSE, LOF, SEP, SSY, Spress, SDEP, RMSE, and e- and
high value of quality value Q, r2, and q2 (Table 10).
In general from the above-mentioned ot- and mt-QSAR
models, it can be concluded that strong influence of the
partition coefficient, log P, is important for the antimicrobial
activity of synthesized hydrazide derivatives. This evidence
was clearly described in lipid theory advanced by Meyer and
Overton (Podunavac-Kuzmanovic et al., 2008b). According
to this theory, log P is a measure of hydrophobicity, which is
important for the penetration and distribution of the drug, but
also for the interaction of the drug with receptors. Therefore,
it can be suggested that lipophilic properties have to be
checked for designing of potent antimicrobial agents as they
are decisive factors for their activity.
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