Synthesis, antitubercular activity, and QSAR analysis of substituted nitroaryl analogs: Chalcone, pyrazole, isoxazole, and pyrimidines

Article abstract of DOI:10.1007/s00044-012-0385-3

In the present investigation, 4-nitroacetophenone, on condensation with appropriate aldehydes in ethanolic sodium hydroxide solution, yielded the corresponding chalcones. These corresponding chalcones were reacted with hydrazine hydrate, urea, thiourea, a

Full text of DOI:10.1007/s00044-012-0385-3

MEDICINAL
CHEMISTRY
RESEARCH
Med Chem Res (2013) 22:3863–3880
DOI 10.1007/s00044-012-0385-3
ORIGINAL RESEARCH
Synthesis, antitubercular activity, and QSAR analysis
of substituted nitroaryl analogs: chalcone, pyrazole,
isoxazole, and pyrimidines
•
Revathi A. Gupta Satish G. Kaskhedikar
Received: 6 June 2012 / Accepted: 29 November 2012 / Published online: 14 December 2012
Ó Springer Science+Business Media New York 2012
Abstract In the present investigation, 4-nitroacetophe-
none, on condensation with appropriate aldehydes in etha-
nolic sodium hydroxide solution, yielded the corresponding
chalcones. These corresponding chalcones were reacted with
hydrazine hydrate, urea, thiourea, and hydroxylamine hydro-
chloride, which led to the formation of corresponding novel
pyrazole, pyrimidine, and isooxazole derivatives, respec-
tively. All newly synthesized compounds were evaluated for
their antimycobacterial activities against Mycobacterium
tuberculosis using agar dilution. The results indicated that
most of the compounds demonstrated better in vitro antitu-
bercular activity, and the compound 1g has a MIC of 1.56
lg mL-1. QSAR analysis revealed that molecular descrip-
tors mean electrotopological state (Ms) and average valance
connectivity index chi-0 (0vAv) contributed positively while
3-path Kier alpha-modified shape index (3ja) contributed
negatively. The objective of our study is to generate new
leads and to optimize their structure to display the potent
efficacy.
an important undertaking in medicinal chemistry research.
The chemistry of chalcones has generated intensive scientific
interest due to their biological and industrial applications.
Chalcones are natural biocides and are well-known interme-
diates in the synthesis of heterocyclic compounds exhibiting
various biological activities. The extensive literature survey
was carried out to find out the different types of molecular
structures which have been developed till date and method-
ologies used for rational designing of anti-mycobacterial
agents. The literature review reveals that chalcone derivatives
exhibit diverse pharmacological activities such as antimicro-
bial (Prasad et al., 2008), anticancer (Jevwon et al., 2005),
antitubercular (Shivakumar et al., 2005), and antiviral
(Churkin et al., 1982) etc.
The a, b-unsaturated ketonic group which is responsible
for bactericidal activity of chalcones is also of great use in
further chemical modification into various heterocyclic
moieties. Therefore, chalcones are considered as most
important molecules for the synthesis of various hetero-
cyclic systems. The diverse properties of chalcones have
prompted us to synthesize them to study their antimyco-
bacterial activity.
Keywords Tuberculosis ꢀ Chalcones ꢀ
Antimycobacterial ꢀ QSAR ꢀ SQ-MLR
Tuberculosis (TB) is by far the most frequently encoun-
tered mycobacterial disease in the world. Mycobacteria
are ubiquitous organisms that are becoming increasingly
important intracellular pathogens that establish an infection
in oxygen-rich macrophages of the lung (O’Brien and Nunn,
2001). About 32 % of the world’s population is currently
infected with TB. Every year, approximately eight to nine
million of these infected people develop clinical pulmonary
TB leading to nearly three million annual deaths (Corbett
et al., 2003; Dye et al., 1999; World Health Organization,
2003). If the present trend continues, TB is likely to claim
more than 30 million lives within the next decade (WHO
Report, 2007).
Introduction
The design as well as identification of new molecules for the
treatment of diseases such as the tuberculosis and cancer is
R. A. Gupta (&) ꢀ S. G. Kaskhedikar
Medicinal Chemistry Laboratory, Department of Pharmacy,
Shri G. S. Institute of Technology and Science,
23 Park Road, Indore 452003, MP, India
e-mail: sivarevathi@rediffmail.com
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The treatment of infectious diseases still remains an
(nitrosubstituted compounds) using the reducing power of
nicotinamide adenine dinucleotide (NAD(P)H). Microor-
ganisms with appropriate nitroreductases act on nitroaryls to
produce a highly reactive electrophilic intermediate and this
is postulated to affect DNA as the reduced intermediates of
nitroaryls do. Other evidence suggests that the reduced nit-
roaryls bind to bacterial ribosomes and prevent protein
synthesis (Edwards, 1993; Bryant et al., 1981).
important and challenging problem because of a combi-
nation of factors including emerging infectious disease and
the increasing number of multidrug-resistant microbial
pathogens. In spite of a large number of antibiotics and
chemotherapeutics available for medical use, at the same
time, the emergence of old and new antibiotic resistance
created in the last decades revealed a substantial medical
need for new classes of antimicrobial agents (Kaplancikli
et al., 2008). Moreover, the emergence of multidrug-
resistant (MDR) strains of Mycobacterium tuberculosis,
which is insensitive to one or more of the first-line drugs,
isoniazid and rifampicin, has further worsened the situation
(Alland et al., 1994; Jacobs, 1994; Weltman and Rose,
1994). Furthermore, the association of TB and HIV
infections has caused an urgent need in search of alterna-
tive chemotherapeutics for M. tuberculosis infection (Lin
et al., 2001, 2002).
Several in silico techniques are utilized in the process of
drug design and development of antitubercular agents. One
such technique is quantitative structure activity relationship
(QSAR), has been traditionally perceived as means of
establishing correlation between trends in chemical struc-
ture modification and respective changes of biological
activity (Yao et al., 2004). This quantitative technology
can be utilized to improve the structure of the inhibitor
molecule and to interpret the improved structure in terms of
favorable biological interactions (Hansch and Leo, 1995).
Thus, the use of predictive computational (in silico) QSAR
models allows the biological properties of virtual structures
to be predicted, and a more informed choice of target to be
selected for synthesis (Painea et al., 2010). The use of
computational approaches for the estimation of the activity
of various molecules as drug candidates prior to their
synthesis can save the resources and accelerate the drug
discovery procedure (Zhoua et al., 2007). We carried out
QSAR analysis and established QSAR models to guide
further structural optimization and predict the potency and
physiochemical properties of clinical drug candidates.
The current work describes the synthesis of novel
chalcone moiety and their structural modification into
various heterocyclic derivatives like pyrazole, isoxazole,
oxopyrimidine, and thiopyrimidine, etc., with respect to
their antimycobacterial activity against M. tuberculosis and
their QSAR analysis has been explored.
It is well known that no new anti-TB drug has been dis-
covered in the last 40 years. A major concern is the rise of
multidrug-resistant-TB (MDR-TB); fatality rates are much
higher with MDR-TB and they are up to 100-fold more
expensive to treat. Even more serious is the emergence of
extensively drug-resistant TB (XDR-TB) that has been
confirmed in more than 45 countries. This increase of
MDR-TB and the emergence of XDR-TB provide the ratio-
nale to search for new antimycobacterial drugs (Gupta and
Kaskhedikar, 2011). Therefore, in order to control the rapid
spread of TB, there is an urgent need for new anti-TB drugs
with unique modes of action and improved properties such as
enhanced activity against MDR strains, reduced toxicity and
shortened duration of therapy (Global Alliance for TB Drug
Development, 2001; Smith et al., 2004, Duncan, 2003).
Our research efforts toward the development of novel
anti-TB agents are in the direction of discovering new
classes of compounds, which are structurally different from
known anti-TB drugs. Several structural classes of antitu-
berculosis agents includes substituted chalcones, couma-
rines, pyrimidines, quinolones etc. which are reported to
exhibit promising activities against drug-resistant and
drug-sensitive strains of tuberculi (Nayyar and Jain, 2005).
To the best of our knowledge, there has been no previous
report of various nitro heterocyclic derivatives and parent
chalcones as antitubercular agents. However, there are
numerous examples of nitrogen-containing heterocycles
being used to treat TB, for example clofazimine, isoniazid,
and pyrazinamide.
Experimental
All the chemicals used in the synthesis of compounds were
of synthetic grade, and they were procured from Sigma,
SD-fine, Himedia and E. Merck. All the melting points
were taken in open capillaries tube and are uncorrected.
The purity of compounds was checked routinely by thin-
layer chromatography (TLC) (0.2-mm thickness) using
silica gel, G-coated aluminum, plates (Merck), and spots
were visualized by exposing the dry plates in iodine vapors
and UV detector (long and short wavelength). As quanti-
tative yields of chalcone derivatives 1–5 were obtained,
neither crystallization nor flash column chromatographic
purification (using silica gel # 240-400 mesh) is necessary.
Infrared (IR) spectra (kmax in cm-1) were recorded on an
Perkin Elmer FT-IR using KBr technique. 1H-nuclear
The antitubercular activity of nitroaryl/heteroaryls is
mainly due to the metabolic reduction of their nitro group by
a class of enzymes called nitroreductases. The nitroreductase
family comprises a group of flavin mononucleotide or flavin
adenine dinucleotide-dependent enzymes that are able to
metabolize nitroaromatic and nitroheterocyclic derivatives
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1
magnetic resonance (NMR) spectra were observed on a
BRUKER DRX-300 (300 MHz) NMR instrument using
dimethyl sulfoxide (DMSO)-d6 as solvent and TMS as
internal reference (chemical shifts in d ppm). Mass spectra
were obtained on a JEOL SX 102/DA-6000 instrument
using fast atom bombardment (FAB) technique.
1332 (sym, Ar–NO2); H NMR DMSO (d in PPM): 8.38
(s, 4H, Ar–H), 8.02–7.95 (m, 3H, Ar–H and CH=CH),
7.83–7.77 (d, 1H, CH=CH, J = 15.5 Hz), 7.57–7.54
(m, 2H, Ar–H); FAB–MS: m/z 288 (M?H?).
3-(3-Chlorophenyl)-1-(4-nitrophenyl)prop-2
-en-1-one (1d)
General procedure
M.p. 106–108 °C; Yield—70 %; IR (cm-1) (m in cm-1):
3102 (CH), 1667 (C=O), 1603 (Ar–C=C), 1520 (asym,
Ar–NO2), 1341 (sym, Ar–NO2); 1H NMR DMSO (d in
PPM): 8.37-8.34 (d, 4H, Ar–H, J = 9 Hz), 7.95-7.87 (m, 3H,
Ar–H and CH=CH), 7.63-7.47 (m, 3H, Ar–H and CH=CH);
FAB–MS: m/z 288 (M?H?).
General synthesis of chalcones (1a–1i, 6 and 7)
Substituted aromatic aldehydes (10 mmol) and p-nitro ace-
tophenone (10 mmol) were dissolved in ethanol (25 mL)
and the mixture was cooled to 5–10 °C in ice bath. 50 %
sodium hydroxide solution (2.5 mL) was added slowly to the
reaction mixture with constant stirring. Reaction mixture
was stirred for another 2–4 h until the entire mixture
becomes very cloudy. The progress of the reaction was
monitored through TLC. The mixture was kept overnight in
refrigerator. Reaction mixture was poured slowly into cru-
shed ice, acidified with dilute hydrochloric acid. The pre-
cipitate was filtered, washed with ice-cold water, dried, and
purified by flash column chromatography.
3-(3,4-Dimethoxyphenyl)-1-(4-nitrophenyl)prop-
2-en-1-one (1e)
M.p. 176–178 °C; Yield—65 %; IR (m in cm-1): 2969
(CH), 1660 (C=O), 1604 (Ar–C=C), 1515 (asym, Ar–NO2),
1346 (sym, Ar–NO2), 1236 (OCH3); 1H NMR DMSO (d in
PPM): 8.39–8.32 (m, 4H, Ar–H), 7.84–7.73 (m, 2H, Ar–H),
7.56–7.55 (d, 1H, CH=CH, J = 3.3 Hz), 7.44–7.42 (d, 1H,
CH=CH, J = 6 Hz), 7.05–7.03 (d, 1H, Ar–H, J = 6.1 Hz),
3.86 (s, 3H, OCH3), 3.82 (s, 3H, OCH3); FAB–MS: m/z 314
(M?H?).
3-(3-Methoxyphenyl)-1-(4-nitrophenyl)prop-2-en-1-
one (1a)
3-(2,5-Dimethoxyphenyl)-1-(4-nitrophenyl)prop-
M.p. 116–118 °C; Yield—80 %; IR (m in cm-1): 1657
(C=O), 1602 (Ar–C=C), 1516 (asym, Ar–NO2), 1340 (sym,
2-en-1-one (1f)
1
Ar–NO2), 1256 (–OCH3); H NMR DMSO (d in PPM):
M.p. 118–120 °C; Yield—68 %; IR (m in cm-1): 2971
(CH), 1661 (C=O), 1602 (Ar–C=C), 1512 (asym, Ar–NO2),
1341 (sym, Ar–NO2), 1238 (OCH3); 1H NMR DMSO (d in
PPM): 8.39–8.34 (m, 4H, Ar–H), 7.56–7.54 (d, 1H,
CH=CH, J = 6 Hz), 7.45–7.42 (d, 1H, CH=CH, J = 9
Hz), 6.95–6.79 (m, 3H, Ar–H), 3.77 (s, 3H, OCH3), 3.65
(s, 3H, OCH3); FAB–MS: m/z 314 (M?H?).
8.37–8.31 (d, 2H, Ar–H, J = 16.8 Hz), 8.16–8.13 (d, 2H,
Ar–H, J = 9 Hz), 7.84–7.78 (d, 1H, CH=CH, J = 17.1
Hz), 7.53–7.48 (d, 1H, CH=CH, J = 15.3 Hz), 7.43–6.99
(m, 4H, Ar–H), 3.87 (s, 3H, OCH3); FAB–MS: m/z 283
(M?).
3-(4-Methoxyphenyl)-1-(4-nitrophenyl)prop-2-
en-1-one (1b)
3-(2,4-Dichlorophenyl)-1-(4-nitrophenyl)prop-
2-en-1-one (1g)
M.p. 172–174 °C; Yield—81 %; IR (m in cm-1): 1655
(C=O), 1595 (Ar–C=C), 1526 (asym, Ar–NO2), 1333 (sym,
M.p. 200–202 °C; Yield—75 %; IR (m in cm-1): 3096 (CH),
1666 (C=O), 1596 (Ar–C=C), 1520 (asym, Ar–NO2), 1331
1
Ar–NO2), 1247 (–OCH3); H NMR DMSO (d in PPM):
1
8.38–8.29 (m, 4H, Ar–H), 7.89–7.83 (d, 2H, CH=CH,
J = 16.5 Hz), 7.79–7.75 (d, 2H, Ar–H, J = 12 Hz),
7.04–7.02 (d, 2H, Ar–H, J = 6 Hz), 3.83 (s, 3H, OCH3);
FAB–MS: m/z 283 (M?).
(sym, Ar–NO2); H NMR DMSO (d in PPM): 8.39 (s, 4H,
Ar–H), 8.29–8.27 (d, 1H, Ar–H, J = 6 Hz), 8.08–8.02
(m, 2H, Ar–H) 7.78–7.77 (d, 1H, CH=CH, J = 3 Hz), 7.60-
7.57 (d, 1H, CH=CH, J = 9 Hz); FAB–MS:m/z 322 (M?H?).
3-(4-Chlorophenyl)-1-(4-nitrophenyl)prop-
3-(4-Fluorophenyl)-1-(4-nitrophenyl)prop-
2-en-1-one (1c)
2-en-1-one (1h)
M.p. 154–156 °C; Yield—72 %; IR (m in cm-1): 3097
(CH), 1662 (C=O), 1606 (Ar–C=C), 1520 (asym, Ar–NO2),
M.p. 194–196 °C; Yield—75 %; IR (m in cm-1): 1609
(Ar–C=C), 1510 (asym, Ar–NO2), 1334 (sym, Ar–NO2),
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1
1160 (Ar–F); H NMR DMSO (d in PPM): 8.40–8.33 (m,
4H, Ar–H), 8.03–8.00 (m, 2H, Ar–H), 7.90 (s, 1H,
CH=CH), 7.83 (s, 1H, CH=CH), 7.36–7.30 (m, 2H, Ar–H);
FAB–MS: m/z 272 (M?H?).
J = 4.1, 7.2 Hz), 2.96–2.88 (dd, 1H, CH2, J = 4.8,
17.2 Hz); FAB–MS: m/z 298 (M?H?).
5-(4-Chlorophenyl)-3-(4-nitrophenyl)-4,5-dihydro-1H-
pyrazole (2c)
3-(3,4,5-Trimethoxyphenyl)-1-(4-nitrophenyl)prop-2-
M.p. 162–164 °C; Yield—62 %; IR (m in cm-1): 3310
(NH), 3056 (CH), 1599 (Ar–C=C), 1504 (asym, Ar–NO2),
en-1-one (1i)
1
M.p. 158–160 °C; Yield—71 %; IR (m in cm-1): 2968
(CH), 1668 (C=O), 1602 (Ar–C=C), 1507 (asym, Ar–NO2),
1323 (sym, Ar–NO2), 1250 (OCH3); 1H NMR DMSO (d in
PPM): 8.40–8.33 (m, 4H, Ar–H), 7.90–7.86 (d, 1H,
CH=CH, J = 12 Hz), 7.76–7.72 (d, 1H, CH=CH,
J = 12.2 Hz), 7.26 (s, 2H, Ar–H), 3.86 (s, 6H, OCH3),
3.72 (s, 3H, OCH3); FAB–MS: m/z 344 (M?H?).
1337 (sym, Ar–NO2); H NMR DMSO (d in PPM): 8.25
(bs, 1H, NH), 8.23–8.19 (d, 2H, Ar–H, J = 11.4 Hz),
7.84–7.81 (d, 2H, Ar–H, J = 8.7 Hz), 7.74–7.69 (m, 2H,
Ar–H), 7.30–7.26 (m, 2H, Ar–H), 5.01–4.97 (dd, 1H, CH,
J = 4.4, 9.9 Hz), 3.54–3.49 (dd, 1H, CH2, J = 3.9,
13.2 Hz), 2.95–2.88 (dd, 1H, CH2, J = 4, 17.4 Hz); FAB–
MS: m/z 302 (M?H?).
5-(3-Chlorophenyl)-3-(4-nitrophenyl)-4,5-dihydro-1H-
General synthesis of pyrazoles (2a–2j and 8)
pyrazole (2d)
Hydrazine hydrate (10 mmol), substituted chalcones (1a–1i,
10 mmol) and sodium acetate (10 mmol) were taken in
ethanol (12 mL) and mixture was refluxed for 6–8 h. The
progress of the reaction was monitored through TLC. After
completion of the reaction, mixture was concentrated under
reduced pressure and poured into ice water. The precipitate
was filtered, washed with water, and dried. The crude
product was purified by silica gel column chromatography to
afford the corresponding pyrazole derivatives (2).
M.p. 186–188 °C; Yield—61 %; IR (m in cm-1): 3314
(NH), 3081 (CH), 1594 (Ar–C=C), 1507 (asym, Ar–NO2),
1339 (sym, Ar–NO2); 1H NMR DMSO (d in PPM):
8.38–8.10 (m, 3H, Ar–H and NH), 7.94–7.80 (m, 2H,
Ar–H), 7.58–7.20 (m, 4H, Ar–H), 5.03–4.95 (dd, 1H, CH,
J = 4, 18.1 Hz), 3.54–3.48 (dd, 1H, CH2, J = 4.2,
12.9 Hz), 3.10–3.02 (dd, 1H, CH2, J = 4.4, 17.2 Hz);
FAB–MS: m/z 302 (M?H?).
5-(3,4-Dimethoxyphenyl)-3-(4-nitrophenyl)-4,5-dihydro-
5-(3-Methoxyphenyl)-3-(4-nitrophenyl)-4,
1H-pyrazole (2e)
5-dihydro-1H-pyrazole (2a)
M.p. 89–91 °C; Yield—66 %; IR (m in cm-1): 3339 (NH),
2953 (CH), 1593 (Ar–C=C), 1507 (asym, Ar–NO2), 1307
M.p. 224–226 °C; Yield—65 %; IR (m in cm-1): 3308
(NH), 2955 (C–H), 1596 (Ar–C=C), 1506 (asym, Ar–NO2),
1336 (sym, Ar–NO2), 1257 (–OCH3);1H NMR DMSO (d in
PPM): 8.40 (bs, 1H, NH), 8.32–8.29 (d, 2H, Ar–H,
J = 9 Hz), 8.15–8.11 (m, 2H, Ar–H), 8.04–8.01 (d, 1H,
Ar–H, J = 9.1 Hz), 7.52–7.36 (m, 2H, Ar–H), 6.95–6.93
(d, 1H, Ar–H, J = 6 Hz), 5.01-4.96 (dd, 1H, CH, J = 3.6,
11.1 Hz), 3.67 (s, 3H, OCH3), 3.52–3.48 (dd, 1H, CH2,
J = 3.6, 10.8 Hz), 2.94–2.86 (dd, 1H, CH2, J = 4.4,
17.3 Hz); FAB–MS: m/z 298 (M?H?).
1
(sym, Ar–NO2), 1257 (–OCH3); H NMR DMSO (d in
PPM): 8.38–8.20 (m, 3H, Ar–H and NH), 7.83–7.80 (d, 2H,
Ar–H, J = 9 Hz), 6.99–6.85 (m, 3H, Ar–H), 4.96–4.88
(dd, 1H, CH, J = 4.3, 17.8 Hz), 3.85–3.80 (dd, 1H, CH2,
J = 5.7, 8.4 Hz), 3.78 (s, 3H, OCH3), 3.74 (s, 3H, OCH3),
2.97–2.89 (dd, 1H, CH2, J = 4.2, 17.5 Hz); FAB–MS: m/z 328
(M?H?).
5-(2,5-Dimethoxyphenyl)-3-(4-nitrophenyl)-4,5-dihydro-
1H-pyrazole (2f)
5-(4-Methoxyphenyl)-3-(4-nitrophenyl)-4,
5-dihydro-1H-pyrazole (2b)
M.p. 150–152 °C; Yield—64 %; IR (m in cm-1): 3364
(NH), 1595 (Ar–C=C), 1533 (asym, Ar–NO2), 1339 (sym,
Ar–NO2), 1259 (–OCH3);1H NMR DMSO (d in PPM):
8.19–8.17 (d, 2H, Ar–H, J = 6 Hz), 8.09 (bs, 1H, NH),
7.81–7.79 (d, 2H, Ar–H, J = 5.7 Hz), 6.95–6.79 (m, 3H,
Ar–H), 5.14–5.08 (dd, 1H, CH, J = 7.3, 10.4 Hz), 3.76
(s, 3H, OCH3), 3.67 (s, 3H, OCH3), 3.51–3.44 (dd, 1H,
CH2, J = 4.8, 15.6 Hz), 2.81–2.74 (dd, 1H, CH2, J = 8.1,
15.3 Hz); FAB–MS: m/z 328 (M?H?).
M.p. 124–126 °C; Yield—67 %; IR (m in cm-1): 3318
(NH), 2953 (C–H), 1600 (Ar–C=C), 1509 (asym, Ar–NO2),
1339 (sym, Ar–NO2), 1253 (–OCH3); 1H NMR DMSO
(d in PPM): 8.34–8.29 (m, 3H, Ar–H and NH), 8.14–8.09
(m, 2H, Ar–H), 7.78–7.73 (m, 2H, Ar–H), 6.95–6.93
(d, 2H, Ar–H, J = 6 Hz), 4.99–4.95 (dd, 1H, CH, J = 4.2,
10.2 Hz), 3.73 (s, 3H, OCH3), 3.51–3.48 (dd, 1H, CH2,
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5-(2,4-Dichlorophenyl)-3-(4-nitrophenyl)-4,5-dihydro-1H-
General synthesis of dihydropyrimidinone derivatives
pyrazole (2g)
(3a–3j and 9)
M.p. 88–90 °C; Yield—68 %; IR (m in cm-1): 3281 (NH),
2951 (CH), 1595 (Ar–C=C), 1506 (asym, Ar–NO2), 1336
(sym, Ar–NO2), 1080 (Ar–Cl); 1H NMR CDCl3 (d in
PPM): 8.28–8.24 (d, 2H, Ar–H, J = 11.4 Hz), 7.79–7.76
(d, 2H, Ar–H, J = 9 Hz), 7.57–7.54 (d, 1H, Ar–H,
J = 9.3 Hz), 7.43–7.42 (d, 1H, Ar–H, J = 3.6 Hz), 7.26
(s, 1H, Ar–H), 5.40–5.33 (dd, 1H, CH, J = 8.5, 12.3 Hz),
3.72–3.66 (dd, 1H, CH2, J = 4.4, 13.1 Hz), 2.98–2.81 (dd,
1H, CH2, J = 9.8, 11.8 Hz); FAB–MS: m/z 336 (M?H?).
Substituted chalcone (1a–1i) (20 mmol) and urea (20 mmol)
were dissolved in ethanol (20 mL) and ethanolic sodium
hydroxide (0.2 g in 2.5 mL) was added to reaction mixture.
The mixture was refluxed for 8–10 h with constant stirring.
The progress of the reaction was monitored through TLC.
After completion of reaction, mixture was cooled and poured
into ice-cold water with constant stirring for an hour. The
mixture was kept in refrigerator for 24 h. The precipitate was
filtered, washed with water, dried. The crude product was
purified by silica gel column chromatography to afford the
corresponding pyrimidin-2(1H)-one derivatives (3).
5-(4-Fluorophenyl)-3-(4-nitrophenyl)-4,5-dihydro-1H-
pyrazole (2h)
M.p. 140–142 °C; Yield—64 %; IR (m in cm-1): 3277
(NH), 1600 (Ar–C=C), 1506 (asym, Ar–NO2), 1333 (sym,
Ar–NO2), 1230 (Ar–F); 1H NMR DMSO (d in PPM): 8.25
(bs, 1H, NH), 8.22–8.19 (d, 2H, Ar–H, J = 8.7 Hz),
7.83–7.80 (d, 2H, Ar–H, J = 9 Hz), 7.74–7.39 (m, 2H,
Ar–H), 7.20–7.15 (m, 2H, Ar–H), 5.00–4.95 (dd, 1H, CH,
J = 4.6, 10.7 Hz), 3.54–3.47 (dd, 1H, CH2, J = 4.1,
16.1 Hz), 2.94–2.87 (dd, 1H, CH2, J = 8.6, 11.6 Hz);
FAB–MS: m/z 286 (M?H?).
6-(3-Methoxyphenyl)-4-(4-nitrophenyl)-5,6-
dihydropyrimidin-2(1H)-one (3a)
M.p. 185–187 °C; Yield—67 %; IR (m in cm-1): 3174
(NH), 2968 (C–H), 1647 (C=O), 1595 (Ar–C=C), 1520
(asym, Ar–NO2), 1311 (sym, Ar–NO2), 1258 (–OCH3); 1H
NMR DMSO (d in PPM): 8.63–8.35 (m, 3H, Ar–H and
NH), 8.20–8.16 (d, 2H, Ar–H, J = 12.3 Hz), 7.56–7.39
(m, 3H, Ar–H), 7.26–7.20 (m, 1H, Ar–H), 5.11–5.03
(m, 2H, CH2), 4.76 (m, 1H, CH), 3.74 (s, 3H, OCH3);
FAB–MS: m/z 326 (M?H?).
3-(4-Nitrophenyl)-5-(3,4,5-trimethoxyphenyl)-4,
5-dihydro-1H-pyrazole (2i)
6-(4-Methoxyphenyl)-4-(4-nitrophenyl)-5,6-
M.p. 148–150 °C; Yield—68 %; IR (m in cm-1): 3354
(NH), 2943 (CH), 1592 (Ar–C=C), 1506 (asym, Ar–NO2),
1328 (sym, Ar–NO2), 1235 (–OCH3); 1H NMR DMSO
(d in PPM): 8.24–8.21 (m, 3H, Ar–H and NH), 7.84–7.81
(d, 2H, Ar–H, J = 8.7 Hz), 6.71 (s, 2H, Ar–H), 4.97–4.82
(dd, 1H, CH, J = 9.6, 12.6 Hz), 3.77 (s, 6H, OCH3), 3.64
(s, 3H, OCH3), 3.54–3.49 (dd, 1H, CH2, J = 4.8, 12 Hz),
3.00–2.90 (dd, 1H, CH2, J = 11.4, 16.5 Hz); FAB–MS: m/z 358
(M?H?).
dihydropyrimidin-2(1H)-one (3b)
M.p. 204–206 °C; Yield—70 %; IR (m in cm-1): 3184
(NH), 2973 (C–H), 1653 (C=O), 1595 (Ar–C=C), 1507
(asym, Ar–NO2), 1339 (sym, Ar–NO2), 1255 (–OCH3); 1H
NMR DMSO (d in PPM): 8.6 (s, 1H, NH), 8.32–8.26
(d, 2H, Ar–H, J = 17.4 Hz), 8.17–8.14 (m, 2H, Ar–H),
7.78–7.72 (m, 2H, Ar–H), 6.95–6.91 (d, 2H, Ar–H,
J = 11.6 Hz), 5.23–5.11 (m, 2H, CH2), 4.76 (m, 1H, CH),
3.73 (s, 3H, OCH3); FAB–MS: m/z 326 (M?H?).
5-(4-Bromophenyl)-3-(4-nitrophenyl)-4,5-dihydro-1H-
pyrazole (2j)
6-(4-Chlorophenyl)-4-(4-nitrophenyl)-5,6-
M.p. 138–140 °C; Yield—60 %; IR (m in cm-1): 3334
(NH), 1595 (Ar–C=C), 1506 (asym, Ar–NO2), 1338 (sym,
Ar–NO2); 1H NMR DMSO (d in PPM): 8.33–8.24 (m, 3H,
Ar–H and NH), 7.83–7.80 (d, 2H, Ar–H, J = 9 Hz),
7.57–7.55 (d, 2H, Ar–H, J = 6.3 Hz), 7.35–7.32 (d, 2H,
Ar–H, J = 9.6 Hz), 5.00–4.94 (dd, 1H, CH, J = 7.9,
12 Hz), 3.57–3.52 (dd, 1H, CH2, J = 4.7, 12.2 Hz),
2.95–2.86 (dd, 1H, CH2, J = 11.4, 15.5 Hz); FAB–MS:
m/z 346 (M?H?).
dihydropyrimidin-2(1H)-one (3c)
M.p. 166–168 °C; Yield—71 %; IR (m in cm-1): 3195
(NH), 2935 (C–H), 1652 (C=O), 1593 (Ar–C=C), 1521
1
(asym, Ar–NO2), 1339 (sym, Ar–NO2); H NMR DMSO
(d in PPM): 8.41 (bs, 1H, NH), 8.32–8.21 (m, 4H, Ar–H),
8.02–7.95 (m, 2H, Ar–H), 7.57–7.54 (m, 2H, Ar–H),
5.12–5.07 (m, 2H, CH2), 4.85 (m, 1H, CH); FAB–MS:
m/z 330 (M?H?).
123

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Med Chem Res (2013) 22:3863–3880
6-(3-Chlorophenyl)-4-(4-nitrophenyl)-5,6-
J = 11.4 Hz), 7.83–7.80 (d, 2H, Ar–H, J = 8.1 Hz),
7.74–7.39 (m, 2H, Ar–H), 7.20–7.15 (m, 2H, Ar–H),
5.31–5.18 (m, 2H, CH2), 4.47 (m, 1H, CH); FAB–MS:
m/z 314 (M?H?).
dihydropyrimidin-2(1H)-one (3d)
M.p. 176–178 °C; Yield—68 %; IR (m in cm-1): 3119
(NH), 2951 (C–H), 1652 (C=O), 1595 (Ar–C=C), 1520
1
(asym, Ar–NO2), 1319 (sym, Ar–NO2); H NMR DMSO
6-(4-Bromophenyl)-4-(4-nitrophenyl)-5,6-
(d in PPM): 8.48–8.10 (m, 5H, Ar–H and NH), 7.81–7.40
(m, 4H, Ar–H), 5.43–5.32 (m, 2H, CH2), 4.81 (m, 1H, CH);
FAB–MS: m/z 330 (M?H?).
dihydropyrimidin-2(1H)-one (3i)
M.p. 178–180 °C; Yield—65 %; IR (m in cm-1): 3210
(NH), 2951 (C–H), 1655 (C=O), 1601 (Ar–C=C), 1514
1
(asym, Ar–NO2), 1333 (sym, Ar–NO2); H NMR DMSO
6-(3,4-Dimethoxyphenyl)-4-(4-nitrophenyl)-5,6-
dihydropyrimidin-2(1H)-one (3e)
(d in PPM): 8.62 (s, 1H, NH), 8.39–8.33 (m, 4H, Ar–H),
7.89–7.85 (d, 2H, Ar–H, J = 10.8 Hz), 7.69–7.63 (d, 2H,
Ar–H, J = 8.7 Hz), 5.19–5.11 (m, 2H, CH2), 4.73 (m, 1H,
CH); FAB–MS: m/z 374 (M?H?).
M.p. 204–206 °C; Yield—66 %; IR (m in cm-1): 3313
(NH), 2967 (CH), 1648 (C=O), 1595 (Ar–C=C), 1507
(asym, Ar–NO2), 1334 (sym, Ar–NO2), 1262 (–OCH3); 1H
NMR DMSO (d in PPM): 8.43–8.16 (m, 3H, Ar–H and
NH), 7.83–7.80 (d, 2H, Ar–H, J = 9 Hz), 6.99–6.85
(m, 3H, Ar–H), 5.21–5.13 (m, 2H, CH2), 4.59 (m, 1H, CH),
3.78 (s, 3H, OCH3), 3.74 (s, 3H, OCH3); FAB–MS:
m/z 356 (M?H?).
6-(4-(Dimethylamino)phenyl)-4-(4-nitrophenyl)-5,6-
dihydropyrimidin-2(1H)-one (3j)
M.p. 220–222 °C; Yield—60 %; IR (m in cm-1): 3148
(NH), 2967 (C–H), 1653 (C=O), 1586 (Ar–C=C), 1516
(asym, Ar–NO2), 1339 (sym, Ar–NO2), 1238 (–OCH3); 1H
NMR DMSO (d in PPM): 8.78 (bs, 1H, NH), 8.32–8.24
(m, 2H, Ar–H), 7.80–7.76 (d, 2H, Ar–H, J = 12 Hz),
7.29–7.20 (m, 2H, Ar–H), 6.80–6.68 (m, 2H, Ar–H),
5.15–5.10 (m, 2H, CH2), 4.49 (m, 1H, CH), 2.93 (s, 6H,
N(CH3)2); FAB–MS: m/z 339 (M?H?).
6-(2,5-Dimethoxyphenyl)-4-(4-nitrophenyl)-5,6-
dihydropyrimidin-2(1H)-one (3f)
M.p. 230–232 °C; Yield—69 %; IR (m in cm-1): 3174
(NH), 2998 (C–H), 1652 (C=O), 1595 (Ar–C=C), 1505
(asym, Ar–NO2), 1339 (sym, Ar–NO2), 1221 (–OCH3); 1H
NMR DMSO (d in PPM): 8.79 (bs, 1H, NH), 8.19–8.17
(d, 2H, Ar–H, J = 6.3 Hz), 7.82–7.79 (d, 2H, Ar–H,
J = 8.4 Hz), 6.95–6.79 (m, 3H, Ar–H), 4.85–4.79 (m, 2H,
CH2), 4.46 (m, 1H, CH), 3.75 (s, 3H, OCH3), 3.67 (s, 3H,
OCH3); FAB–MS: m/z 356 (M?H?).
Synthesis of 6-(substituted phenyl)-5,6-dihydro-4-(4-
nitrophenyl)pyrimidine-2(1H)-thione (4a–4j and 10)
Substituted chalcone (1a–1i) (20 mmol) and thiourea
(20 mmol) were dissolved in ethanol (20 mL) and etha-
nolic sodium hydroxide (0.2 g in 2.5 mL) was added to
reaction mixture. The mixture was refluxed for 8–10 h with
constant stirring. The progress of the reaction was moni-
tored through TLC. After completion of reaction, mixture
was cooled and poured into ice-cold water with constant
stirring for an hour. The mixture was kept in refrigerator
for 24 h. The precipitate was filtered, washed with water,
dried. The crude product was purified by silica gel column
chromatography to afford the corresponding dihydropy-
rimidine-2(1H)-thione derivatives (4).
6-(2,4-Dichlorophenyl)-4-(4-nitrophenyl)-5,6-
dihydropyrimidin-2(1H)-one (3g)
M.p. 190–192 °C; Yield—68 %; IR (m in cm-1): 3252
(NH), 2935 (C–H), 1647 (C=O), 1590 (Ar–C=C), 1507
1
(asym, Ar–NO2), 1316 (sym, Ar–NO2); H NMR DMSO
(d in PPM): 8.43–8.20 (m, 3H, Ar–H and NH), 7.79–7.76
(d, 2H, Ar–H, J = 9 Hz), 7.57–7.54 (d, 1H, Ar–H,
J = 8.7 Hz), 7.43–7.42 (d, 1H, Ar–H, J = 3.6 Hz), 7.26
(s, 1H, Ar–H), 5.01–4.92 (m, 2H, CH2), 4.63 (m, 1H, CH);
FAB–MS: m/z 364 (M?H?).
6-(3-Methoxyphenyl)-4-(4-nitrophenyl)-5,6-
dihydropyrimidine-2(1H)-thione (4a)
6-(4-Fluorophenyl)-4-(4-nitrophenyl)-5,6-
M.p. 158–160 °C; Yield—65 %; IR (m in cm-1): 3243
(NH), 2935 (C–H), 1583 (Ar–C=C), 1514 (asym, Ar–NO2),
1316 (sym, Ar–NO2), 1259 (–OCH3), 1178 (C=S); 1H
NMR DMSO (d in PPM): 9.12 (bs, 1H, NH), 8.43–8.35
(m, 2H, Ar–H), 8.20–8.17 (d, 2H, Ar–H, J = 8.7 Hz),
7.56–7.39 (m, 3H, Ar–H), 7.26–7.20 (m, 1H, Ar–H),
dihydropyrimidin-2(1H)-one (3h)
M.p. 212–214 °C; Yield—74 %; IR (m in cm-1): 3252
(NH), 2929 (C–H), 1669 (C=O), 1594 (Ar–C=C), 1508
1
(asym, Ar–NO2), 1311 (sym, Ar–NO2); H NMR DMSO
(d in PPM): 8.81 (bs, 1H, NH), 8.22–8.18 (d, 2H, Ar–H,
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Med Chem Res (2013) 22:3863–3880
3869
5.07–4.99 (m, 2H, CH2), 4.51 (m, 1H, CH), 3.74 (s, 3H,
OCH3); FAB–MS: m/z 342 (M?H?).
1329 (sym, Ar–NO2), 1260 (–OCH3), 1176 (C=S); 1H
NMR DMSO (d in PPM): 9.05 (bs, 1H, NH), 8.19–8.14
(m, 2H, Ar–H), 7.81–7.79 (d, 2H, Ar–H, J = 6 Hz),
6.95–6.79 (m, 3H, Ar–H), 5.11–5.03 (m, 2H, CH2), 4.71
(m, 1H, CH), 3.77 (s, 3H, OCH3), 3.68 (s, 3H, OCH3);
FAB–MS: m/z 372 (M?H?).
6-(4-Methoxyphenyl)-4-(4-nitrophenyl)-5,6-
dihydropyrimidine-2(1H)-thione (4b)
M.p. 226–228 °C; Yield—67 %; IR (m in cm-1): 3204
(NH), 2933 (C–H), 1595 (Ar–C=C), 1510 (asym, Ar–NO2),
1332 (sym, Ar–NO2), 1247 (–OCH3), 1178 (C=S); 1H
NMR DMSO (d in PPM): 9.21 (s, 1H, NH), 8.42–8.29 (m,
2H, Ar–H), 8.15–8.11 (m, 2H, Ar–H), 7.79–7.73 (m, 2H,
Ar–H), 6.95–6.93 (d, 2H, Ar–H, J = 6 Hz), 5.08–5.03
(m, 2H, CH2), 4.57 (m, 1H, CH), 3.73 (s, 3H, OCH3);
FAB–MS: m/z 342 (M?H?).
6-(4-Fluorophenyl)-4-(4-nitrophenyl)-5,6-
dihydropyrimidine-2(1H)-thione (4g)
M.p. 168–170 °C; Yield—74 %; IR (m in cm-1): 3234
(NH), 2976 (C–H), 1595 (Ar–C=C), 1507 (asym, Ar–NO2),
1
1331 (sym, Ar–NO2), 1175 (C=S); H NMR DMSO (d in
PPM): 9.41 (bs, 1H, NH), 8.34–8.19 (m, 2H, Ar–H),
7.85–7.79 (d, 2H, Ar–H, J = 15.6 Hz), 7.74–7.39 (m, 2H,
Ar–H), 7.19–7.14 (m, 2H, Ar–H), 5.17–5.12 (m, 2H, CH2),
4.74 (m, 1H, CH); FAB–MS: m/z 330 (M?H?).
6-(4-Chlorophenyl)-4-(4-nitrophenyl)-5,6-
dihydropyrimidine-2(1H)-thione (4c)
M.p. 204–206 °C; Yield—61 %; IR (m in cm-1): 3198
(NH), 3026 (C–H), 1592 (Ar–C=C), 1508 (asym, Ar–NO2),
6-(4-Bromophenyl)-4-(4-nitrophenyl)-5,6-
dihydropyrimidine-2(1H)-thione (4h)
1
1339 (sym, Ar–NO2), 1181 (C=S); H NMR DMSO (d in
PPM): 9.38 (bs, 1H, NH), 8.32–8.19 (m, 4H, Ar–H),
8.01–7.92 (m, 2H, Ar–H), 7.55–7.52 (m, 2H, Ar–H),
5.14–5.06 (m, 2H, CH2), 4.67 (m, 1H, CH); FAB–MS:
m/z 346 (M?H?).
M.p. 184–186 °C; Yield—69 %; IR (m in cm-1): 3184
(NH), 2942 (C–H), 1601 (Ar–C=C), 1509 (asym, Ar–NO2),
1
1339 (sym, Ar–NO2), 1181 (C=S); H NMR DMSO (d in
PPM): 9.42 (s, 1H, NH), 8.49–8.33 (m, 4H, Ar–H),
7.89–7.86 (d, 2H, Ar–H, J = 9 Hz), 7.69–7.65 (d, 2H,
Ar–H, J = 11.1 Hz), 5.18–5.11 (m, 2H, CH2), 4.73 (m, 1H,
CH); FAB–MS: m/z 390 (M?H?).
6-(3-Chlorophenyl)-4-(4-nitrophenyl)-5,6-
dihydropyrimidine-2(1H)-thione (4d)
M.p. 153–155 °C; Yield—70 %; IR (m in cm-1): 3160
(NH), 2969 (C–H), 1591 (Ar–C=C), 1519 (asym, Ar–NO2),
4-(4-Nitrophenyl)-6-(3,4,5-trimethoxyphenyl)-5,6-
1
dihydropyrimidine-2(1H)-thione (4i)
1331 (sym, Ar–NO2), 1181 (C=S); H NMR DMSO (d in
PPM): 8.78–8.10 (m, 5H, Ar–H and NH), 7.76–7.41
(m, 4H, Ar–H), 5.23–5.08 (m, 2H, CH2), 4.79 (m, 1H, CH);
FAB–MS: m/z 346 (M?H?).
M.p. 155–157 °C; Yield—67 %; IR (m in cm-1): 3217
(NH), 2930 (C–H), 1592 (Ar–C=C), 1511 (asym, Ar–NO2),
1311 (sym, Ar–NO2), 1219 (–OCH3), 1174 (C=S); 1H
NMR DMSO (d in PPM): 9.21 (s, 1H, NH), 8.24–8.21
(d, 2H, Ar–H, J = 9 Hz), 7.87–7.83 (d, 2H, Ar–H,
J = 11.7 Hz), 6.74 (s, 2H, Ar–H), 5.14–5.06 (m, 2H, CH2),
4.63 (m, 1H, CH), 3.77 (s, 6H, OCH3), 3.64 (s, 3H, OCH3);
FAB–MS: m/z 402 (M?H?).
6-(3,4-Dimethoxyphenyl)-4-(4-nitrophenyl)-5,6-
dihydropyrimidine-2(1H)-thione (4e)
M.p. 149–151 °C; Yield—72 %; IR (m in cm-1): 3368
(NH), 2924 (C–H), 1594 (Ar–C=C), 1509 (asym, Ar–NO2),
1328 (sym, Ar–NO2), 1261 (–OCH3), 1174 (C=S); 1H
NMR DMSO (d in PPM): 8.73–8.15 (m, 3H, Ar–H, NH),
7.83–7.80 (d, 2H, Ar–H, J = 8.4 Hz), 6.99–6.85 (m, 3H,
Ar–H), 5.14–5.05 (m, 2H, CH2), 4.64 (m, 1H, CH), 3.78
(s, 3H, OCH3), 3.74 (s, 3H, OCH3); FAB–MS: m/z 372
(M?H?).
6-(4-(Dimethylamino)phenyl)-4-(4-nitrophenyl)-5,6-
dihydropyrimidine-2(1H)-thione (4j)
M.p. 193–195 °C; Yield—61 %; IR (m in cm-1): 3351
(NH), 2923 (C–H), 1595 (Ar–C=C), 1522 (asym, Ar–NO2),
1
1334 (sym, Ar–NO2), 1170 (C=S); H NMR DMSO (d in
6-(2,5-Dimethoxyphenyl)-4-(4-nitrophenyl)-5,6-
PPM): 8.89 (s, 1H, NH), 8.37–8.31 (m, 2H, Ar–H and CH),
7.79–7.75 (d, 2H, Ar–H, J = 12.3 Hz), 7.28–7.21 (m, 2H,
Ar–H), 6.78–6.66 (m, 2H, Ar–H), 5.08–5.01 (m, 2H, CH2),
4.46 (m, 1H, CH), 2.97 (s, 6H, N(CH3)2); FAB–MS: m/z 355
(M?H?).
dihydropyrimidine-2(1H)-thione (4f)
M.p. 138–140 °C; Yield—65 %; IR (m in cm-1): 3240
(NH), 2995 (C–H), 1591 (Ar–C=C), 1524 (asym, Ar–NO2),
123

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Med Chem Res (2013) 22:3863–3880
General synthesis of 5-(substituted phenyl)-3-(4-
5-(3,4-Dimethoxyphenyl)-3-(4-nitrophenyl)-4,5-
nitrophenyl)isoxazole (5a–5f)
dihydroisoxazole (5d)
Substituted chalcone (1a–1k) (10 mmol) and hydroxyl-
amine hydrochloride (10 mmol) were taken in ethanol
(10 mL). 50 % ethanolic potassium hydroxide solution
(2 mL) was added drop wise to the reaction mixture with
stirring at rt. The reaction mixture was refluxed for
10–18 h. The progress of the reaction was monitored
through TLC. After completion of reaction, mixture was
cooled and acidified with glacial acetic acid. The excess
solvent was removed under reduced pressure and concen-
trate was poured into ice water. The precipitate was fil-
tered, washed with water, dried. The crude product was
purified by silica gel column chromatography to afford the
corresponding isoxazole derivatives (5).
M.p. 106–108 °C; Yield—75 %; IR (m in cm-1): 2924
(CH), 1594 (Ar–C=C), 1507 (asym, Ar–NO2), 1306 (sym,
Ar–NO2), 1259 (OCH3); 1H NMR DMSO (d in PPM):
8.37–8.32 (m, 4H, Ar–H), 7.15–6.93 (m, 3H, Ar–H),
5.78–5.73 (dd, 1H, CH, J = 6, 9.9 Hz), 3.98–3.89 (dd, 1H,
CH2, J = 11.2, 15.6 Hz), 3.76 (s, 3H, OCH3), 3.67 (s, 3H,
OCH3), 3.52–3.43 (dd, 1H, CH2, J = 8.2, 18.1 Hz); FAB–
MS: m/z 329 (M?H?).
5-(2,4-Dichlorophenyl)-3-(4-nitrophenyl)-4,5-
dihydroisoxazole (5e)
M.p. 160–162 °C; Yield—67 %; IR (m in cm-1): 3012
(CH), 1590 (Ar–C=C), 1520 (asym, Ar–NO2), 1344 (sym,
5-(3-Methoxyphenyl)-3-(4-nitrophenyl)-4,5-
1
Ar–NO2); H NMR DMSO (d in PPM): 8.29–8.25 (d, 2H,
dihydroisoxazole (5a)
Ar–H, J = 11.6 Hz), 7.99–7.93 (m, 2H, Ar–H), 7.54–7.51
(d, 1H, Ar–H, J = 9.3 Hz), 7.41–7.36 (d, 1H, Ar–H,
J = 14.1 Hz), 7.24 (s, 1H, Ar–H), 5.86–5.79 (dd, 1H, CH,
J = 8.2, 12.7 Hz), 3.96–3.88 (dd, 1H, CH2, J = 10.5,
13.8 Hz), 3.53–3.45 (dd, 1H, CH2, J = 9, 15.6 Hz); FAB–
MS: m/z 337 (M?H?).
M.p. 174–176 °C; Yield—72 %; IR (m in cm-1): 3032
(CH), 1609 (Ar–C=C), 1520 (asym, Ar–NO2), 1349 (sym,
Ar–NO2), 1244 (OCH3);1H NMR DMSO (d in PPM):
8.38–8.31 (m, 4H, Ar–H), 7.92–7.87 (d, 1H, Ar–H,
J = 13.8 Hz), 7.53–7.39 (m, 2H, Ar–H), 6.96–6.93 (d, 1H,
Ar–H, J = 9 Hz), 5.80–5.74 (dd, 1H, CH, J = 7.9,
11.7 Hz), 3.97–3.89 (dd, 1H, CH2, J = 10.9, 17.3 Hz),
3.76 (s, 3H, OCH3), 3.53–3.45 (dd, 1H, CH2, J = 8.1,
15.9 Hz); FAB–MS: m/z 299 (M?H?).
5-(4-Fluorophenyl)-3-(4-nitrophenyl)-4,5-dihydroisoxazole
(5f)
M.p. 157–159 °C; Yield—64 %; IR (m in cm-1): 2921
(CH), 1603 (Ar–C=C), 1508 (asym, Ar–NO2), 1342 (sym,
Ar–NO2), 1225 (Ar–F); 1H NMR DMSO (d in PPM):
8.44–8.41 (d, 2H, Ar–H, J = 8.1 Hz), 8.20–8.17 (d, 2H,
Ar–H, J = 8.4 Hz), 8.02–7.96 (m, 2H, Ar–H), 7.60–7.39
(m, 2H, Ar–H), 5.88–5.81 (dd, 1H, CH, J = 8.2, 11.8 Hz),
3.99–3.89 (dd, 1H, CH2, J = 10.9, 17.3 Hz), 3.54–3.45
(dd, 1H, CH2, J = 9, 17.3 Hz); FAB–MS: m/z 287
(M?H?).
5-(4-Chlorophenyl)-3-(4-nitrophenyl)-4,5-
dihydroisoxazole (5b)
M.p. 122–124 °C; Yield—74 %; IR (m in cm-1): 3034
(CH), 1612 (Ar–C=C), 1511 (asym, Ar–NO2), 1335 (sym,
Ar–NO2); 1H NMR DMSO (d in PPM): 8.37–8.32 (m, 4H,
Ar–H), 7.77–7.71 (m, 2H, Ar–H), 7.31–7.27 (m, 2H,
Ar–H), 5.75–5.71 (dd, 1H, CH, J = 6.2, 7.5 Hz),
3.96–3.89 (dd, 1H, CH2, J = 8.3, 15.5 Hz), 3.54–3.45 (dd,
1H, CH2, J = 8.4, 16.1 Hz); FAB–MS: m/z 303 (M?H?).
Evaluation of antimycobacterial activity
Determination of minimum inhibitory concentration (MIC)
5-(3-Chlorophenyl)-3-(4-nitrophenyl)-4,5-
dihydroisoxazole (5c)
The synthesized compounds after confirmation of their
structures were subjected to biological evaluation. The
MIC was determined by agar dilution method. The pro-
cedure followed for anti-TB activity mainly involves the
use of Middlebrook 7H11 agar with OADC growth sup-
plement and standard strain of M. tuberculosis H37Rv
(ATCC 27294). The basal medium is prepared and steril-
ized by autoclaving. The stock solution of the test
M.p. 138–140 °C; Yield—70 %; IR (m in cm-1): 3023
(CH), 1595 (Ar–C=C), 1516 (asym, Ar–NO2), 1319 (sym,
Ar–NO2); 1H NMR DMSO (d in PPM): 8.37–8.32 (m, 4H,
Ar–H), 7.56–7.24 (m, 4H, Ar–H), 5.81–5.73 (dd, 1H, CH,
J = 9, 15 Hz), 3.98–3.89 (dd, 1H, CH2, J = 10.8,
16.2 Hz), 3.55–3.48 (dd, 1H, CH2, J = 8.2, 14.3 Hz);
FAB–MS: m/z 303 (M?H?).
123

Med Chem Res (2013) 22:3863–3880
3871
compounds and standard drugs (isoniazid, rifampicin, and
ethambutol) was prepared in DMSO. Twofold serial dilu-
tions of each test compound/drug were incorporated into
Middlebrook 7H11 agar medium with OADC growth
supplement. Inoculum of M. tuberculosis H37Rv were
prepared from fresh Middlebrook 7H11 agar slants with
OADC growth supplement adjusted to 1 mg mL-1 in
Tween 80 (0.05 %) saline diluted to 10-2 to give a con-
centration of approximately 107 cfu mL-1. The tubes were
incubated at 37 °C. Along with this one growth control
without compound and drug controls were also set up. The
tubes are inspected for growth twice a week for a period of
3 weeks and final readings were recorded after 28 days.
quality of the regression equations were analyzed by
parameters such as quality factor (QF), pair-wise correlation
(PWC), variance inflation factor (VIF), probable error of
coefficient of determination (PE), outlier (Zvalue), Akaike’s
information criterion (AIC) and Kubinyi function (FIT).
The robustness of the regression equation was ascertained
through bootstrapping analysis and response scrambling
analysis (Y-randomization test) techniques. Bootstrapping
squared correlation coefficient (r2BS) is the average squared
correlation coefficient of subset of compounds used in
regression, while bootstrapping standard deviation (rB2 S_STD
)
is the standard deviation of n run data of bootstrapping
method. Response scrambling analysis was evaluated by
parameters like randomized squared correlation coefficient
(r2RAND_MAX), mean randomized squared correlation coeffi-
cient (r2RAND_MEAN), standard deviation of randomized
squared correlation coefficient (r2RAND_STD) and chance
correlation factor (Chance).
QSAR study
In an attempt to determine the role of structural features which
appears to influence the observed activity of chalcone and
their substituted nitroaryl derivatives (1a–1i, 2a–2j, 3a–3j,
4a–4j and 5a–5f), QSAR studies were performed using
SQ-MLR model. The pMIC value of the biological activity data
(excluding six compounds, which MIC value numerically not
well defined) was used as dependent variable in QSAR study.
The dataset was divided into training set (30 compounds; TR-1
to TR-30) and test set (9 compounds; TE-1 to TE-9). These
were correlated with different molecular descriptors like con-
stitutional, topological, geometrical, charge, GETAWAY
(Geometry, Topology and Atoms-Weighted AssemblY),
WHIM (Weighted Holistic Invariant Molecular descriptors),
3D-MoRSE (3D-Molecular Representation of Structure based
on Electron diffraction), molecular walk counts, BCUT
descriptors, 2D-Autocorrelations, aromaticity indices, Randic
molecular profiles, radial distribution functions.
Result and discussion
Chemistry
In this study, a series of 45 new compounds were synthe-
sized. Scheme 1 illustrates the synthetic route for the
preparation of target compounds. Chalcones are synthe-
sized by Claisen–Schmidt condensation. In Claisen–
Schmidt condensation, an aromatic aldehyde react with an
aryl methyl ketones/methyl ketones in the presence of a
base to form an a,b-unsaturated ketone (enone). In the
initial step, chalcones were synthesized by condensing
4-nitroacetophenone with appropriate aromatic aldehydes
in ethanolic sodium hydroxide solution at room tempera-
ture (1a–1i). From the chalcone derivatives, the various
heterocyclic derivatives like pyrazole (2a–2j), oxopyrimi-
dine (3a–3j), thiopyrimidine (4a–4j), and isoxazole (5a–5f)
were synthesized via cyclocondensation with hydrazine
hydrate, urea, thiourea, and hydroxylamine hydrochloride,
respectively. The purity of the compounds was checked via
TLC using various mobile bases and the compounds of this
study were identified by spectral data.
Molecular modeling study was carried out through
CS ChemOffice (CS ChemOffice, 6.0, 2000), DRAGON
(Todeschini and Consonni, 2001), and VALSTAT (Gupta
et al., 2004) software. The structure of substituted nitroaryl
analogs were subjected to energy minimization using
molecular mechanics (MM2) until the RMS gradient value
-1. The energy
˚
A
became smaller than 0.419 kJ mol-1
minimized molecules were subjected to re-optimization via
AM1 method until the RMS gradient attains a value smaller
than 0.419 J mol-1
-1
˚
A
using MOPAC. The geometry
Both analytical and spectral data (1H NMR, IR spec-
troscopy, Mass Spectroscopy) of all the synthesized com-
pounds were in full agreement with the proposed structures.
In the 1H NMR spectra, the signals of the respective protons
of the prepared derivatives were verified on the basis of
their chemical shifts and multiplicities.
optimization of the lowest energy structure was carried out
using EF routine. The QSAR descriptors of substituted
nitroaryl analogs were calculated using the molecular
package DRAGON. Further, the regression analysis was
performed using the statistical program VALSTAT.
The statistical fitness of the regression equations were
assessed by parameters like Pearson correlation coefficient
(r), coefficient of determination (r2), explained variance
(r2adj), standard error of estimate (SEE), and sequential
Fischer test (F) at specified degree of freedom (df). The
Antimycobacterial activity
The synthesized compounds were screened for their anti-
mycobacterial activity in vitro against M. tuberculosis
123

3872
Med Chem Res (2013) 22:3863–3880
R
Scheme 1 General synthetic
scheme of nitroaryl derivatives
NH2NH2.H2O
CH3COONa/C2H5OH
NO2
HN
O
C
N
R
OHC
O2N
CH3
2a-2j,8
O
N
NH
C2H5OH/NaOH
O C(NH2)2
R
NaOH/C2H5OH
O2N
3a-3j, 9
O
R
S
O2N
C CH CH
N
NH
1a-1i, 6,7
S
C(NH2)2
R
NaOH/C2H5OH
O2N
R
4a-4j, 10
NH2OH.HCl
KOH/C2H5OH
NO2
O
N
5a-5f
(MTB) H37Rv by agar dilution method. The MIC is defined
as the minimum concentration of compound required to
give complete inhibition of bacterial growth. MICs of the
synthesized compounds along with the standard drugs for
comparison are reported in Table 1.
for the activity, while electron releasing groups (i.e., –OCH3,
–N(CH3)2) diminish the activity. Di-substituted halogen
containing compounds are more potent as compare to mono-
substituted halogen compounds. 3-methoxy, 4-methoxy, 2,5-
dimethoxy, 3,4-dimethoxy, 3,4,5-trimetoxy and N,N-dimeth-
ylamino derivatives are comparatively less potent than 4-flouro,
3-chloro, 4-chloro, 4-bromo and 2,4-dichloro derivatives
of chalcone, pyrazole, isoxazole, pyrimidin-2(1H)-one, and
pyrimidine-2(1H)-thione.
Among the chalcones, 3-(2,4-dichlorophenyl)-1-(4-nitro
phenyl)prop-2-en-1-one (1g) and 3-(4-fluorophenyl)-1-
(4-nitrophenyl)prop-2-en-1-one (1h) shows MIC value
1.56 and 3.13 lg mL-1, respectively. Chalcone analogs
having 3-methoxy (1a), 4-methoxy (1b), 3-chloro (1d),
4-chloro (1c), 2,5-dimethoxy (1f), 3,4-dimethoxy (1e) and
3,4,5-trimethoxy (1i) substitution shows MIC value more
than or equal to 12.5 lg mL-1. In pyrazole analogs, com-
pounds 2g and 2h shows MIC value 12.5 lg mL-1, whereas
the rest of the analogs shows MIC value more than or equal
to 25.0 lg mL-1. 6-(4-fluorophenyl)-4-(4-nitrophenyl)-5,
6-dihydropyrimidin-2(1H)-one (3h) produced inhibition at
6.25 lg mL-1 as compared to thiopyrimidines (4g) exhib-
ited inhibition at 12.5 lg mL-1. On the other hand, the
analog of substituted methoxy, 2,4-dichloro, bromo and
p-dimethylamino shows relatively moderate-to-low anti-
mycobacterial activity.
Model and discussions
Quantitative models were developed by means of structural
contribution considering regression methodology. The
multivariant expressions were developed on the basis of
adjustable correlation coefficient (r2adj) (Gupta et al., 2008).
This parameter explains statistical significance of incor-
porated physicochemical descriptors in SEQ-MLR. r2adj takes
into account of adjustment of coefficient of determination.
If r2adj value decline by the addition of a physicochemical
descriptor to the equation it is indicated that descriptor was
not contributed fairly. Adjustable correlation coefficient is
a measure of % explained variation of regression expres-
sion, whereas r2 always increases when an independent
variable is added. Study has furnished uni and bi-variant
expression with moderate correlation coefficient Eqs. (1)
Preliminary structure activity relationship analysis revealed
that chalcone analogs are more potent in comparison to
substituted pyrazole, isoxazole, pyrimidin-2(1H)-one, and
pyrimidine-2(1H)-thione derivatives. Substitution of electron
withdrawing groups (i.e., –F, –Cl, –Br) onphenylringfavorable
123

Med Chem Res (2013) 22:3863–3880
3873
Table 1 Structure and Anti-mycobacterial activity data of nitroaryl
analogs
Table 1 continued
Comp. no
R
MIC (lg mL-1 a
)
MIC (lM)b pMICc
4d
3-Cl
25
72.46
134.77
134.77
37.99
4.138
3.869
3.869
4.418
4.492
3.601
3.848
–
4e
3,4-OCH3
2,5-OCH3
4-F
50
4f
50
4g
12.5
12.5
4h
4-Br
32.05
4i
3,4,5-OCH3 100
249.38
141.24
[168.91
83.33
4j
4-N(CH3)2
50
5a
3-OCH3
[50
25
5b
4-Cl
4.080
–
5c
3-Cl
[25
25
[83.33
76.69
5d
3,4-OCH3
4.116
4.428
4.357
6.447
6.924
5.117
5e
2,4-Cl
12.5
12.5
0.049
0.098
1.56
37.31
5f
4-F
–
44.01
Isoniazid
Rifampicin
Ethambutol
a
0.357
–
0.119
–
7.635
Comp. no
R
MIC (lg mL-1 a
)
MIC (lM)b pMICc
Minimum inhibitory concentration against M. tuberculosis in lg/
mL
1a
1b
1c
1d
1e
1f
3-OCH3
4-OCH3
4-Cl
25
88.34
[88.34
43.55
4.054
–
[25.0
12.5
12.5
[25.0
25
b
Minimum inhibitory concentration in lM
4.362
4.362
–
c
Negative logarithmic minimum inhibitory concentration in mole
3-Cl
43.55
3,4-OCH3
2,5-OCH3
2,4-Cl
4-F
[79.87
79.87
4.098
5.315
4.938
4.138
3.470
–
and (2) respectively but the r2adj value is increasing sig-
1g
1h
1i
1.56
3.13
4.84
nificantly from uni to bi-variant expression.
11.55
pMIC ¼ ꢁ 2:164ðꢂ 0:313ÞBELv5 þ 6:763
n ¼ 30; r ¼ 0:794; ra2dj ¼ 0:617;
3,4,5-OCH3 25
72.89
2a
2b
2c
2d
2e
2f
3-OCH3
4-OCH3
4-Cl
100
338.98
[338.98
83.61
[100
25
SEE ¼ 0:257; F ¼ 47:698
ð1Þ
4.079
4.079
3.813
3.813
4.427
4.355
3.852
4.139
3.510
3.811
4.419
4.419
3.548
–
3-Cl
25
83.61
pMIC¼1:943ðꢂ0:275ÞMsþ6:623ðꢂ0:973ÞATS8vꢁ3:922
n¼30; r ¼0:897; ra2dj ¼0:791; SEE¼0:2190;
F ¼55:854
3,4-OCH3
2,5-OCH3
2,4-Cl
4-F
50
153.85
153.85
37.43
50
ð2Þ
2g
2h
2i
12.5
12.5
Significant improvement in r2adj value emphasizes to
explore the higher variant expressions like tri-variant
expressions. Proposed model should have to satisfy both
statistical quality and predictive power. Therefore, all
the developed tri-variant expressions were validated through
leave-n-out cross-validation method, bootstrapping tech-
nique, and response scrambling analysis test. Equation (3)
which fulfills all the corroboration criteria up to signi-
ficant echelon was considered as best QSAR model for
M. tuberculosis.
44.17
3,4,5-OCH3 50
140.85
72.67
2j
4-Br
25
3a
3b
3c
3d
3e
3f
3-OCH3
4-OCH3
4-Cl
100
50
307.69
153.85
37.99
12.5
12.5
100
[100
12.5
6.25
12.5
100
100
100
12.5
3-Cl
37.99
3,4-OCH3
2,5-OCH3
2,4-Cl
4-F
281.69
[281.69
34.34
3g
3h
3i
4.462
4.697
4.474
3.527
3.531
3.531
4.439
19.97
0
pMIC ¼ 3:979ðꢂ0:332ÞMs þ 14:607ðꢂ1:838ÞvAv
4-Br
33.42
3
3j
4-N(CH3)2
3-OCH3
4-OCH3
4-Cl
295.86
293.26
293.26
36.23
ꢁ 0:399ðꢂ0:083Þs ꢁ 13:004
n ¼ 30; r ¼ 0:921; ra2dj ¼ 0:831;
4a
4b
4c
SEE ¼ 0:171; F ¼ 48:392
ð3Þ
123

3874
Med Chem Res (2013) 22:3863–3880
It is necessary that the selected equations should have
both the statistical quality as well as better predictive
power. The aforementioned discussion and the regression
parameters are good enough to establish the quality of
model. However, the simplest parameter to decide the
predictive power of the model is the quality factor QF
(Pogliani, 1994, 1996). Selected QSAR equation has good
statistical quality with significant value of QF which indi-
cates best predictive power of the model.
Table 2 Pair wise correlation (PWC) and variance inflation factor
(VIF) values of the descriptors used in QSAR model
Equation
Descriptors
VIF
PWC
Ms
0vAv
3js
3
Ms
1.836
2.149
1.295
1.000
0.630
0.019
0vAv
3js
1.000
0.382
1.000
Quality parameters : QF ¼ 5:385; PE ¼ 0:018;
FIT ¼ 3:722; LOF ¼ 1:294; AIC ¼ 0:038;
PWC \ 0:635; VIF \ 2:220; Outlier ¼ Nil:
Attempt was also made to investigate goodness of fit for
the model using statistical parameter called probable error
of correlation (PE) (Mandloi et al., 2005). Selected QSAR
equation has multiple regression coefficient (r) value much
greater than 6PE. Thus, the correlation attempted is defi-
nitely good.
Validation parameters : rB2S ¼ 0:855; rB2S
¼ 0:081;
STD
Chance \ 0:001; rR2AND
¼ 0:452;
STD
MAX
Additional statistical parameters, such as the Akaike’s
information criterion (AIC) (Akaike, 1973, 1974) the
Kubinyi function (FIT) (Kubinyi, 1994a, b) and the Fried-
man’s lack of fit (LOF) (Friedman, 1990), have also been
calculated to further validate the equation. The model that
produces the lowest AIC value and highest FIT value is
considered potentially the most useful and the best. The LOF
factor takes into account the number of terms used in the
equation and is not biased, as are other indicators, toward
large number of parameters. The decreased values of
parameters AIC and LOF and increased value of FIT have
further shown the superiority of this model.
rR2AND
¼ 0:104; rR2AND
¼ 0:077; 1Q2 ¼ 0:797;
MEAN
1SPRESS ¼ 0:198; 1SDEP ¼ 0:184; 10Q2 ¼ 0:771;
10SPRESS ¼ 0:197; 10SDEP ¼ 0:196; rP2REDðn¼9Þ ¼ 0:672:
The statistically best model (Eq. 3) for anti-mycobacterial
activity against M. tuberculosis with a coefficient of determi-
nation (r2 = 0.848), which accounts for more than 83.1 % of
the explain variance in the activity was considered for further
study.
A high correlation coefficient alone is not enough to
select the equation as a model; hence various statistical
approaches were employed to confirm the robustness and
the practical applicability of the equations. Equations were
screened through various internal and external statistical
validation techniques. Internal statistical significance level
of the equations was confirmed using sequential Fischer
test. Equation (3) shows significance level more than
99.9 % as it exceeded the tabulated F(3,26, a 0.001) = 8.269.
High values of the F indicate that the model is statistically
significant. The equation was further tested for outlier by
the Z-score method (Gupta et al., 2010) and no compound
was found to be an outlier, suggesting that the equation is
able to explain the structurally diverse analogs of the
series.
The value of bootstrapping squared correlation coeffi-
cient (r2BS) and the bootstrapping standard deviation implies
that the equations were proper representatives of the group
of analogs. The r2BS is at par with the coefficient of deter-
mination (r2) with low value of bootstrapping standard error
suggested the robustness of the model. The value of chance
correlation factor (Chance\0.001) revealed that the results
were not based on prospective correlation. Similarly, max
randomized r2 (r2RAND_MAX), mean randomized r2 (rRAND_-
2) and randomized standard deviation (r2RAND_STD
)
MEAN
values are also supporting that the results are not based on
chance correlation. The internal consistency of the training
set was confirmed through leave-n-out cross-validation
method. Model showed good internal consistency in leave-
one-out and leave-ten-out techniques (1Q2 = 0.797 and
10Q2 = 0.771). Leave-one-out (1Q2) and leave-ten-out
(10Q2) values found to be significantly more than 0.5, which
minimizes the risk of finding a significant explanatory
equation for the biological activity just by mere chance.
These may not be applicable for the analogs, which were
never used in the generation of the correlation. Therefore,
predictive power of selected model was further confirmed
by a test set of nine compounds. The robustness and
wide applicability of the model was further explained by a
significant r2pred (n=9) value (0.672) of the test set data. In
order to confirm the results, the comparison of experimental
The interdependency of physicochemical properties for
each equation was checked by pair wise correlation anal-
ysis (PWC) to confirm inimitable contribution of the
properties to the expression (Table 2). From the correlation
matrix, it was observed pair wise correlation values are
£0.630 between the descriptors suggested that independent
contribution. To further check the multi-colinearity prob-
lem between the descriptors variance inflation factor (VIF)
(Chatterjee et al., 2000; Shapiro and Guggenheim, 1998)
analysis was performed (Table 2). VIF values for the
model is less than 2, which is much lesser than critical
value 10, indicating that these models reach the statistical
requirements and that there is no co-linearity problem.
123

Med Chem Res (2013) 22:3863–3880
3875
Table 3 Experimental,
Comp. no
Training (TR) set
pMIC
Expa
calculated, calculated (leave-
one-out), residual and Z score
data of nitroaryl analogs
training set compounds
Calb
Resccal
Zvalue
Cald(LOO)
ReseCal(LOO)
1a
1c
1f
TR-1
4.054
4.362
4.098
5.315
4.938
4.079
4.079
3.470
4.355
4.139
3.813
4.427
3.811
4.419
4.419
4.697
4.474
3.548
3.531
4.492
4.138
3.869
4.439
3.869
3.848
3.601
4.116
4.080
4.428
4.357
4.117
4.575
4.138
5.071
4.886
4.019
4.019
3.583
4.303
4.320
3.663
4.513
3.877
4.324
4.324
4.574
4.579
3.919
3.750
4.440
4.185
3.789
4.185
3.789
3.617
3.772
3.763
4.145
4.599
4.429
-0.063
-0.213
-0.040
0.244
-0.386
-1.315
-0.247
1.505
4.124
4.601
4.145
4.965
4.865
4.012
4.012
3.606
4.293
4.369
3.645
4.525
3.885
4.319
4.319
4.546
4.592
3.959
3.769
4.423
4.188
3.779
4.169
3.779
3.575
3.809
3.731
4.152
4.623
4.446
-0.070
-0.239
-0.047
0.350
TR-2
TR-3
1g
1h
2c
2d
2a
2h
2j
TR-4
TR-5
0.052
0.322
0.073
TR-6
0.060
0.368
0.067
TR-7
0.060
0.368
0.067
TR-8
-0.113
0.052
-0.695
0.324
-0.136
0.063
TR-9
TR-10
TR-11
TR-12
TR-13
TR-14
TR-15
TR-16
TR-17
TR-18
TR-19
TR-20
TR-21
TR-22
TR-23
TR-24
TR-25
TR-26
TR-27
TR-28
TR-29
TR-30
-0.181
0.151
-1.120
0.931
-0.230
0.169
2e
2g
3b
3d
3c
3h
3i
-0.086
-0.067
0.095
-0.528
-0.412
0.585
-0.098
-0.074
0.100
0.095
0.585
0.100
0.123
0.760
0.151
-0.106
-0.371
-0.219
0.052
-0.652
-2.291
-1.351
0.321
-0.118
-0.410
-0.238
0.069
3e
4b
4h
4d
4e
4c
4f
-0.047
0.080
-0.288
0.494
-0.050
0.090
a
Experimental data of the
compounds used in generation
of model
0.254
1.571
0.271
b
0.080
0.494
0.090
Calculated data of the
compounds using model
4j
0.232
1.430
0.273
c
4i
-0.170
0.352
-1.052
2.176
-0.208
0.385
Residual value of calculated
data
d
5d
5b
5e
5f
Calculated leave-one-out data
of the compounds using model
-0.065
-0.170
-0.072
-0.401
-1.052
-0.445
-0.072
-0.194
-0.089
e
Residual value of calculated
leave-one-out data
and predicted (LOO) and predicted data (Tables 3, 4)
demonstrated that they are close to each other evidenced by
the low residual activity values. Further their fitness and
internal predictability was supported by the plot of experi-
mental pMIC against calculated pMIC and experimental
pMIC against predicted (LOO) pMIC (Figs. 1, 2) respec-
tively, while external predictivity was supported by the plot
of experimental pMIC against predicted pMIC (Fig. 3). In
general, the selected model fulfills the statistical validation
criteria to a significant extent.
Table 4 Experimental, predicted and residual values of nitroaryl
analogs test set compounds
Comp. no
Test (TE) set
pMIC
Expa
Predb
Rescpred
1d
1i
TE-1
TE-2
TE-3
TE-4
TE-5
TE-6
TE-7
TE-8
TE-9
4.362
4.138
3.813
3.852
3.510
4.462
3.527
4.418
3.531
4.575
4.124
3.663
3.673
3.877
4.725
3.774
4.437
3.750
-0.213
0.013
2f
2i
0.151
0.179
3a
3g
3j
4g
4a
a
-0.368
-0.263
-0.248
-0.019
-0.219
QSAR analysis revealed that molecular descriptors
mean electrotopological state (Ms) and average valance
connectivity index chi-0 (0vAv) contributed positively,
while 3-path Kier alpha-modified shape index (3ja) con-
tributed negatively to the anti-mycobacterial activity of the
analogs.
Experimental data of the test set compounds
Predicted data of the test set compounds
Residual value of test set compounds
b
c
The mean electrotopological state (Ms) is derived from
Kier–Hall electrotopological state index, (Kier and Hall,
123

3876
Med Chem Res (2013) 22:3863–3880
Fig. 1 A plot of experimental and calculated pMIC of training set
compounds using selected model
Fig. 3 A plot of experimental and predicted pMIC of test set
compounds using selected model
measure of the electronic accessibility of an atom and can be
interpreted as a probability of interaction with another
molecule. However, the index cannot be considered a pure
electronic descriptor: it is, in fact, a descriptor of atom polarity
andsteric accessibility. This corresponds to an electronegativity
equalization principle and means that the sum of the
electrotopological states in the molecule depends on only the
number and type of atoms, not on their mutual interactions.
Positive contribution of mean electrotopological state
(Ms) revealed that electronic interactions with macromole-
cule are crucial for the activity and electron withdrawing
groups are favor for the anti-mycobacterial activity.
Intermolecular encounters of molecules, governed by the
pattern of atoms, and bonds, these structure features are
expected to have a relationship to activity arising from
intermolecular interactions and may also function to provide
discrimination among multiple structural classes. Second,
atom-type and bond-type descriptors encode information on
specific molecule features such as atom and bond types
associated with important functional groups. Molecular
connectivity is a method of molecular structure quantitation
in which weighted counts of substructure fragments are
incorporated into numerical indices. Structural features such
as size, branching, unsaturation, heteroatom content, and
cyclicity are encoded. 0vAv is average valence connectivity
index chi-0 (0v) (Kier, 1980; Kier and Hall, 1983, 1986).
0vAv mathematically calculated as
Fig. 2 A plot of experimental and predicted (leave-one-out) pMIC of
training set compounds using selected model
1990, 1999) and its gives information related to the elec-
tronic and topological state of the atom in the molecule.
The mean electrotopological state (Ms) is calculated by
dividing Kier–Hall electrotopological state (Ss) by the
number of nonhydrogen atoms (nSK).
Ms ¼ Ss=nSK
Ss depend on intrinsic state of theithatomandfieldeffecton
the ith atom calculated as perturbation of the intrinsic state of
the ith atom by all other atoms in the molecule. The intrinsic
state of an atom is the ratio of p and lone pair electrons to the
count of the r bonds in the molecular graph for the considered
atom. Therefore, the intrinsic state reflects the possible
partitioning of non-r electrons influence along the paths
starting from the considered atom; the less partitioning of the
electron influence, the more available are the valence electrons
for intermolecular interactions. Large positive values of Ss
relate to atoms of high electronegativity and/or terminal atoms
or atoms that lie on the mantle of the molecule; small or
negative Ss values correspond to atoms possessing only
r electrons and/or buried in the interior of the molecule or
close to higher electronegative atoms. Therefore, the Ss is a
ꢀ
ꢁ
NS
1=2
ðZkv ꢁ HkÞ
X Y
mþ1
k¼1
1
dkv
mvv
¼
where dvk ¼
ðZk ꢁ Zkv ꢁ 1Þ
i¼1
dvk is valence connectivity for the kth atom in the molecular
graph; Ns is the number of non-hydrogen atoms in the
molecule; Zk is the total number of electrons in the kth
atom; Zkv is the number of valence electrons in the kth atom;
Hk is the number of hydrogen atoms directly attached to the
kth non-hydrogen atom; m is 0 for atomic valence con-
nectivity indices. The 0th order v index holds little struc-
tural information. Only the presence of the nearest
123

Med Chem Res (2013) 22:3863–3880
3877
neighbor to each atom is captured. However, dv distin-
guishes between multiple and single bonds. It’s also dis-
tinction between element rows.
Structure of designed compounds (6–10) and their predicted
activity are shown in Table 5. The required absorption
characteristic of the designed compound was checked to
confirm their bioavailability using Lipinski rule (Table 6).
The designed compounds were synthesized by above-
mentioned methods. Anti-mycobacterial activity of the
proposed synthesized compounds was determined using agar
dilution method. The experimental and model predicted
pMIC of these compounds (6–10) are shown in Table 5 and
Fig. 4. The experimental activity data of the proposed
compounds was found to be comparable with that of the
predicted activity using model. This data suggested that the
QSAR models could be further used for designing of new
analogs.
v index encode degree of skeletal branching and
0
1
molecular size. Low order indices v and v increase with
molecular size and decrease with increased branching.
0
Increase in v, which reflects the increasing size of the
molecule skeleton. 0vv index is highly inter-correlated
0
with molecular surface area and volume. vAv is average
0
of the vv index, positive contribution of this descriptor
revealed that bulkier substitutions favor for the activity.
These groups might be helpful in favorable enthalpic
interactions.
Kier alpha-modified shape (3ja) (Kier, 1986) descriptor
accounts the different shape contribution of heteroatoms
and hybridization states of the molecules. It is a function of
the number of atoms and their bonding relationship. The
negative contribution revealed that branched molecule
favor for the activity in comparison with non-branching or
when it is located at the extremities of a graph.
3-(4-Bromophenyl)-1-(4-nitrophenyl)prop-2-en-1-one (6)
M.p. 152–154 °C; Yield—75 %; IR (m in cm-1): 1658
(C=O), 1603, 1487 (Ar–C=C), 1514 (asym, Ar–NO2), 1318
1
(sym, Ar–NO2); H NMR DMSO (d in PPM): 8.39–8.33
Designed compounds
(m, 4H, Ar–H), 8.01–7.97 (d, 1H, CH=CH, J = 11.6 Hz),
7.89–7.87 (d, 2H, Ar–H, J = 6 Hz), 7.69–7.67 (d, 2H,
Ar–H, J = 6 Hz), 7.53–7.48 (d, 1H, CH=CH,
J = 14.6 Hz); FAB–MS: m/z 332 (M?H?).
On the basis of contributing parameters, newer analogs were
designed and the activity was predicted through model.
Table 5 Structure, predicted
Comp. no
Structure
MIC
pMIC
pMIC and experimental MIC
and pMIC of proposed
synthesized compound
lg mL-1
lM
9.428
Experimental
Predicted
4.856
O
C
6
7
3.13
5.026
O2N
O2N
CH CH
CH CH
Br
N
O
C
25
84.459
4.074
3.935
3.456
N
8
100
322.581
3.489
NO2
HN
N
N
O
NH
OCH3
OCH3
9
100
259.740
3.585
4.481
3.877
4.598
O2N
OCH3
S
N
NH
Cl
10
12.5
32.895
O2N
Cl
123

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Med Chem Res (2013) 22:3863–3880
Table 6 Lipinski data for
proposed compounds
Comp. Mol. formula
no
H-bond
donors
H-bond
acceptors
Mol.
wt.
CLogP Lipinski
number
Absorption
character
6
C15H10BrNO3
0
0
1
1
1
1
1
5
2
332.15 4.253
296.32 3.557
310.35 3.195
385.37 3.160
380.25 4.806
4
4
4
4
4
Good
Good
Good
Good
Good
7
C
C
C
C
17H16N2O3
17H18N4O2
19H19N3O6
8
9
10
16H11Cl2N3O2S 1
4-(4-Nitrophenyl)-6-(3,4,5-trimethoxyphenyl)-5,6-
dihydropyrimidin-2(1H)-one (9)
M.p. [240 °C; Yield—68 %; IR (m in cm-1): 3172 (NH),
2973 (C–H), 1651 (C=O), 1589 (Ar–C=C), 1506 (asym,
1
Ar–NO2), 1334 (sym, Ar–NO2), 1239 (–OCH3); H NMR
DMSO (d in PPM): 8.24–8.21 (m, 3H, Ar–H and NH),
7.84–7.80 (d, 2H, Ar–H, J = 11.8 Hz), 6.72 (s, 2H, Ar–H),
5.27–5.13 (m, 2H, CH2), 4.56 (1H, m, CH), 3.77 (s, 6H,
OCH3), 3.64 (s, 3H, OCH3); FAB–MS: m/z 386 (M?H?).
6-(2,4-Dichlorophenyl)-4-(4-nitrophenyl)-5,6-
Fig. 4 A plot of experimental and predicted pMIC of proposed
compounds obtained from selected model
dihydropyrimidine-2(1H)-thione (10)
M.p. 208–210 °C; Yield—69 %; IR (m in cm-1): 3131
(NH), 2932 (C–H), 1597 (Ar–C=C), 1515 (asym, Ar–NO2),
1
1331 (sym, Ar–NO2), 1172 (C=S); H NMR DMSO (d in
PPM): 9.28 (s, 1H, NH), 8.28–8.20 (m, 2H, Ar–H),
7.81–7.76 (m, 2H, Ar–H), 7.58–7.54 (d, 1H, Ar–H,
J = 11.7 Hz), 7.44–7.42 (d, 1H, Ar–H, J = 6 Hz), 7.26
(s, 1H, Ar–H), 5.31–5.20 (m, 2H, CH2), 4.78 (m, 1H, CH);
FAB–MS: m/z 380 (M?H?).
3-(4-(Dimethylamino) phenyl)-1-(4-nitrophenyl)prop-2-en-
1-one (7)
M.p. 210–212 °C; Yield—67 %; IR (m in cm-1): 3047
(C–H), 2909 (C–H), 1653 (C=O), 1598 (Ar–C=C), 1520
(asym, Ar–NO2), 1340 (sym, Ar–NO2); 1H NMR DMSO (d
in PPM): 8.36–8.28 (m, 4H, Ar–H), 7.75–7.71 (m, 3H,
Ar–H and CH=CH), 7.64–7.60 (d, 1H, CH=CH,
J = 11.7 Hz), 6.77–6.74 (d, 2H, Ar–H, J = 8.7 Hz), 3.02
(s, 6H, N(CH3)2); FAB–MS: m/z 297 (M?H?).
Conclusion
In conclusion, nitroaryl derivatives of chalcone, pyrazole,
isoxazole, and pyrimidine were synthesized and evaluated as
anti-tubercular agents. Preliminary structure activity rela-
tionship analysis revealed that chalcone analogs are more
potent in comparison to substituted pyrazole, isoxazole,
pyrimidin-2(1H)-one, and pyrimidine-2(1H)-thione deriva-
tives. QSAR analysis was performed on set of compounds
and key features were identified for the anti-tubercular
activity. On the basis of contributing descriptors, newanalogs
were designed, synthesized, and evaluated. The predicted
activity of these compounds is comparable to their experi-
mental pMIC. The newly synthesized chalcones exhibited
promising anti-tubercular activities against M. tuberculosis.
It can be concluded that developed QSAR model could be
used for the prediction of newer analogs and further study.
N,N-dimethyl-4-(3-(4-nitrophenyl)-4,5-dihydro-1H-
pyrazol-5-yl)aniline (8)
M.p. 108–110 °C; Yield—63 %; IR (m in cm-1): 3389
(NH), 2930 (C–H), 1590 (Ar–C=C), 1508 (asym, Ar–NO2),
1336 (sym, Ar–NO2); 1H NMR DMSO (d in PPM):
8.27–8.20 (m, 2H, Ar–H), 7.79–7.76 (d, 2H, Ar–H,,
J = 9 Hz), 7.28–7.20 (m, 2H, Ar–H), 6.80–6.68 (m, 2H,
Ar–H), 6.22 (bs, 1H, NH), 4.97–4.90 (dd, 1H, CH, J = 9.0,
10.8 Hz), 3.48–3.39 (dd, 1H, CH2, J = 11.0, 16.4 Hz),
3.09–3.06 (dd, 1H, CH2, J = 8.0, 9.6 Hz), 2.94 (s, 6H,
N(CH3)2); FAB–MS: m/z 311 (M?H?).
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3879
Acknowledgments The authors are grateful to the director of Shri
G. S. Institute of Technology and Science, Indore for providing
facilities for this work. The author R.A.G. is grateful to UGC, New
Delhi, for providing senior research fellowship.
Jacobs RF (1994) Multiple drug resistant tuberculosis. Clin Infect Dis
19:1–10
Jevwon S, Liv CT, Sao LT, Weng JR, Ko HH (2005) Synthetic
chalcones as potential anti-inflammatory and cancer chemopre-
ventive agents. Eur J Med Chem 40:103–112
Conflict of interest The authors report no conflict of interest.
Kaplancikli ZA, Turan-Zitouni G, Ozdemir A, Revial G (2008) New
triazole and triazolothiadiazine derivatives as possible antimi-
crobial agents. Eur J Med Chem 43:155–159
Kier LB (1980) Use of molecular negentropy to encode structure
governing biological activity. J Pharm Sci 69:807–810
Kier LB (1986) Shape indexes of orders. One and three from
molecular graphs. Quant Struct Act Relat 5:1–7
References
Kier LB, Hall LH (1983) Estimation of substituent group electronic
influence from molecular connectivity delta values. Quant Struct
Act Relat 2:163–167
Akaike H (1973) Information theory and an extension of the
minimum likelihood principle. Akademiai Kiado, Budapest,
pp 267–281
Kier LB, Hall LH (1986) Molecular connectivity in structure–activity
analysis. Wiley, Chichester, p 262
Akaike H (1974) A new look at the statistical model identification.
IEEE Trans Autom Control 19:716–723
Kier LB, Hall LH (1990) An electrotopological state index for atoms
in molecules. Pharm Res 7:801–807
Alland D, Kalkut GE, Moss AR, McAdam RA, Hahn JA, Bosworth W,
Drucker E, Bloom BR (1994) Transmission of tuberculosis in New
York city—an analysis by DNA fingerprinting and conventional
epidemiologic methods. N Engl J Med 330:1710–1716
Bryant D, McCalla D, Leeksma M, Laneuville P (1981) Type I
nitroreductases of Escherichia coli. Can J Microbiol 27:81–86
Chatterjee S, Hadi A, Price B (2000) Regression analysis by
examples, 3rd edn. Wiley, New York
Churkin YD, Panfilova LV, Boreko EI (1982) Synthesis and antiviral
activity of unsaturated ketones of thiopene series. Pharm Chem
16:103–105
Corbett EL, Watt CJ, Walker N, Maher BMD, Williams BG,
Raviglione MC, Dye C (2003) The growing burden of tubercu-
losis—global trends and interactions with the HIV epidemic.
Arch Intern Med 163:1009–1021
Kier LB, Hall LH (1999) Molecular structure description: the
electrotopological state. Academic Press, London, p 246
Kubinyi H (1994a) Variable selection in QSAR studies I. An
evolutionary algorithm. Quant Struct Act Relat 13:285–294
Kubinyi H (1994b) Variable selection in QSAR Studies II. A highly
efficient combination of systematic search and evolution. Quant
Struct Act Relat 13:393–401
Lin YM, Flavin MT, Cassidy CS, Mar A, Chen FC (2001)
Biflavonoids as novel antituberculosis agents. Bioorg Med Chem
Lett 11:2101–2104
Lin YM, Zhou Y, Flavin MT, Zhou LM, Nie W, Chen FC (2002)
Chalcones and flavonoids as anti-tuberculosis agents. Bioorg
Med Chem 10:2795–2802
Mandloi D, Joshi S, Khadikar PV, Khosla N (2005) QSAR study on
the antibacterial activity of some sulfa drugs: building blockers
of Mannich bases. Bioorg Med Chem Lett 15:405–411
Nayyar A, Jain R (2005) Recent advances in new structural classes of
anti-tuberculosis agents. Curr Med Chem 12:1873–1876
O’Brien RJ, Nunn PP (2001) The need for new drugs against
tuberculosis. Am J Respir Crit Care Med 163:1055–1058
Painea SW, Barton P, Bird J, Denton R, Menochet K, Smith A,
Tomkinson NP, Chohan KK (2010) A rapid computational filter
for predicting the rate of human renal clearance. J Mol Graph
Model 29:529–537
CS ChemOffice (2000) Version 6.0, Cambridge Soft Corporation,
Software Publishers Association, 1730 M Street, Suite 700,
Washington DC. 20036 (202) 452–1600, USA
Duncan K (2003) Progress in TB drug development and what is still
needed. Tuberculosis 83:201–207
Dye C, Scheele S, Dolin P, Pathania V, Raviglione MC (1999) Global
burden of tuberculosis: estimated incidence, prevalence, and
mortality by country. JAMA 282:677–686
Edwards DI (1993) Nitroimidazole drugs-action and resistance mech-
anisms. I. Mechanisms of action. J Antimicrob Chemother 31:9–20
Friedman J (1990) Multivariate adaptive regression splines. Technical
Report No. 102. Laboratory for Computational Statistics.
Stanford University, Stanford
Pogliani L (1994) Structure property relationships of amino acids and
some dipeptides. Amino Acids 6:141–153
Pogliani L (1996) Modeling with special descriptors derived from a
medium-sized set of connectivity indices. J Phys Chem 100:
18065–18077
Prasad YR, Kumar PP, Kumar PR (2008) Synthesis and antimicrobial
activity of some new chalcones of 2-acetyl pyridine. Eur J Chem
5:144–148
Global Alliance for TB Drug Development (2001) Scientific blueprint for
tuberculosis drug development. Tuberculosis 81 (Suppl. 1):1–52
Gupta RA, Kaskhedikar SG (2011) Insights into the structural
requirement of 6-nitroquinolone-3-carboxylic acids as antimy-
cobacterial agent: chemometric approaches. Med Chem Res. doi:
10.1007/s00044-011-9930-8
Shapiro S, Guggenheim B (1998) Inhibition of oral bacteria by
phenolic compounda. Part. 1. QSAR analysis using molecular
connectivity. Quant Struct Act Relat 17:327–337
Gupta AK, Babu MA, Kaskhedikar SG (2004) VALSTAT: validation
program for quantitative structure activity relationship studies.
Indian J Pharm Sci 66:396–402
Shivakumar PM, Geetha Babu SM, Mukesh D (2005) QSAR studies
on chalcones and flavonoids as antitubercular agents using
genetic function approximation (GFA) method. Chem Pharm
Bull 55:44–49
Gupta AK, Gupta RA, Soni LK, Kaskhedikar SG (2008) Exploration
of physicochemical properties and molecular modelling studies
of 2-sulfonyl-phenyl-3-phenyl-indole analogs as cyclooxygen-
ase-2 inhibitors. Eur J Med Chem 43:1297–1303
Smith CV, Sharma V, Sacchettini JC (2004) TB drug discovery:
addressing issues of persistence and resistance. Tuberculosis
84:45–55
Todeschini R, Consonni V (2001) DRAGON-Software for the
calculation of molecular descriptors, rel. 1.12 for Windows
Weltman AC, Rose DN (1994) Tuberculosis susceptibility patterns,
predictors of multidrug resistance, and implications for initial
Gupta AK, Sabarwal N, Agrawal YP, Prachand S, Jain S (2010)
Insights through AM1 calculations into the structural require-
ment of 3,4,6-substituted-2-quinolone analogs towards FMS
kinase inhibitory activity. Eur J Med Chem 45:3472–3479
Hansch C, Leo A (1995) Exploring QSAR, fundamentals and
applications in chemistry and biology. American Chemical
Society, Washington, DC, pp 90–100
123

3880
Med Chem Res (2013) 22:3863–3880
therapeutic regimens at a New York City hospital. Arch Intern
Med 154:2161–2167
WHO. WHO Report (2007) http://www.who.int/tb/publications/
global_report/2007/pdf/full.pdf. Accessed Dec 2010
World Health Organization (2003) The WHO Global Tuberculosis
Program. http://www.who.int/gtb/. Accessed Sept 2009
Yao XJ, Panaye JP, Doucet JP, Zhang RS, Chen HF, Lice MC,
Hu ZD, Fan BT (2004) Comparative study of QSAR/QSPR
correlations using support vector machines, radial basis function
neural networks, and multiple linear regression. J Chem Inf
Comput Sci 44:1257–1266
Zhoua YP, Cai CB, Huanb S, Jiang JH, Wu HL, Shen GL, Yu RQ
(2007) QSAR study of angiotensin II antagonists using robust
boosting partial least square regression. Anal Chim Acta
593:68–74
123