Synthesis, molecular modeling study, preliminary antibacterial, and antitumor evaluation of N-substituted naphthalimides and their structural analogues

Article abstract of DOI:10.1007/s00044-012-0230-8

Carboxylic acid imides 1-26 have been synthesized and screened for their antibacterial against gram-positive and gram-negative organisms and their antitumor activity against 60 tumor cell lines taken from nine different organs. Compounds 12, 14, and 16 we

Full text of DOI:10.1007/s00044-012-0230-8

MEDICINAL
CHEMISTRY
RESEARCH
Med Chem Res
DOI 10.1007/s00044-012-0230-8
ORIGINAL RESEARCH
Synthesis, molecular modeling study, preliminary antibacterial,
and antitumor evaluation of N-substituted naphthalimides
and their structural analogues
•
Adel S. El-Azab Amer M. Alanazi Naglaa I. Abdel-Aziz
•
•
•
Ibrahim A. Al-Suwaidan Magda A. A. El-Sayed
•
•
Magda A. El-Sherbeny Alaa A.-M. Abdel-Aziz
Received: 22 January 2012 / Accepted: 11 September 2012
ꢀ Springer Science+Business Media, LLC 2012
Abstract Carboxylic acid imides 1–26 have been syn-
thesized and screened for their antibacterial against gram-
positive and gram-negative organisms and their antitumor
activity against 60 tumor cell lines taken from nine dif-
ferent organs. Compounds 12, 14, and 16 were the most
active and broad-spectrum antibacterial member in this
study. Compound 9 showed the most cytotoxicity with a
significant inhibition for renal cancer cells. 2D-QSAR
study provides details on the fine relationship linking
structure and activity and offers clues for structural modi-
fications that can improve the activity. Docking study of
the compounds 12, 14, and 16 into the active site of the
topoisomerase II DNA gyrase enzymes revealed a similar
binding mode to bound inhibitor Clorobiocin.
Keywords Naphthalimides ꢀ Cyclic imides ꢀ Synthesis ꢀ
Antibacterial activity ꢀ Antitumor evaluation ꢀ
Molecular modeling ꢀ Modeling
Introduction
Imide moiety is an integral part of structures of various
important molecules such as fumaramidmycin, granulati-
mide, isogranulatimide, and thalidomide (Maruyama et al.,
´
1975; Berlinck et al., 1998; Roberge et al., 1998; Henon
et al., 2005; Melchert and List, 2007; Collin et al., 2001).
Moreover, cyclic imides, such as succinimide, maleimide,
and phthalimide possess structural features, which confer
potential biological activity and pharmaceutical use (Zentz
et al., 2002; Laronze et al., 2005; Amr et al., 2007; Anizon
et al., 1997). The molecules contain an imide ring and
the general structure –CO–N(R)–CO–, so that they are
hydrophobic and neutral, and can, therefore, cross-biolog-
ical membranes in vivo. Naphthalimides, one type of cyclic
imides with strong hydrophobicity and desirable large p-
conjugated backbone, could easily interact with various
active targets in biological system via non-covalent forces
such as p–p stacking, and exhibit diverse biological
activities including anticancer, antibacterial, antitrypanos-
omal, analgesic, photobiological, antinociceptive potency
(Cechinel-Filho et al., 2003; Lv and Xu, 2009; Fuente
et al., 2006; Muth et al., 2007; Andricopulo et al., 2000;
Xu et al., 2003; De Souza et al., 2002). Some naphthali-
mide derivatives such as Amonafide and Elinafide for the
treatment of cancers acting by intercalating DNA have
been on the clinical trials stage (Bran˜a and Ramos, 2001).
This encourages much effort towards bioactivities of
naphthalimides, especially their antibacterial and antitumor
activities (Bran˜a and Ramos, 2001; Abuo-Rahma et al.,
A. S. El-Azab (&) ꢀ A. M. Alanazi ꢀ I. A. Al-Suwaidan ꢀ
A. A.-M. Abdel-Aziz
Department of Pharmaceutical Chemistry, College of Pharmacy,
King Saud University, P.O. Box 2457, Riyadh 11451,
Saudi Arabia
e-mail: adelazaba@yahoo.com
A. S. El-Azab
Department of Organic Chemistry, Faculty of Pharmacy,
Al-Azhar University, Cairo 11884, Egypt
N. I. Abdel-Aziz ꢀ M. A. El-Sherbeny ꢀ
A. A.-M. Abdel-Aziz (&)
Department of Medicinal Chemistry, Faculty of Pharmacy,
University of Mansoura, Mansoura 35516, Egypt
e-mail: alaa_moenes@yahoo.com
M. A. A. El-Sayed
Department of Pharmaceutical Organic Chemistry,
Faculty of Pharmacy, University of Mansoura,
Mansoura 35516, Egypt
123

Med Chem Res
2009). On the other hand, DNA gyrase is a type II topoi-
somerase found in all bacteria and controls the topological
state of DNA (Menzel and Gellert, 1994). Inhibition of
DNA gyrase blocks relaxation of supercoiled DNA,
relaxation being a requirement for transcription and repli-
cation. DNA gyrase is a selective target for antibacterial
agents, such as novobiocin and clorobiocin which act on
GyrB (Flatman et al., 2006). Apart from biological activ-
ities, imide derivatives are useful in the reactions involving
condensation, alkylation, acylation, and cyclocondensation
(Nicolaou and Mathison, 2005).
2011; El-Azab et al., 2010; Al-Omary et al., 2010), and to
predict the correct binding geometry for each ligand at the
active site, which reveals the MOE score values and
hydrogen bonds formed with the surrounding amino acids.
MOE as flexible docking program enable us to predict
favorable protein–ligand complex structures with reason-
able accuracy and speed (MOE, 2008).
Materials and methods
We have recently reported on the synthesis and bio-
logical evaluation of N-substituted cyclic imides as DNA
binding, COX-2-inhibition, and antihyperlipidemic activi-
ties (Abdel-Aziz, 2007; Abdel-Aziz et al., 2011a, b). In
view of the previous rationale and in continuation of an
ongoing program aiming at finding new structure leads
with potential antibacterial and chemotherapeutic activi-
ties, a series of cyclic imides based on naphthalimide and
its structural analogues (Fig. 1) have been synthesized and
screened for antibacterial and cytotoxic activities and the
achievement of a better biological profile. The strategy is
intended to obtain potent broad-spectrum antibacterial and
antitumor activities using traditional medicinal chemistry
techniques motivated by the comparative modeling toge-
ther with the available pharmacophore. Moreover, we
describe a 2D-QSAR analysis of the antibacterial activity
for the complete series of carboxylic acid imides as
well. Multiple linear regression (MLR) analysis corre-
lates biological activity values with various descriptors
(Abdel-Aziz et al., 2011a). Computer docking technique
plays an important role in the drug design and discovery, as
well as in the mechanistic study by placing a molecule into
the binding site of the target macromolecule in a non-
covalent fashion (El-Gamal et al., 2010; El-Sayed et al.,
Chemistry
Melting points (uncorrected) were recorded on Barnstead
Electrothermal 9100 melting apparatus. IR spectra were
1
recorded on a FT-IR Perkin-Elmer spectrometer. H NMR
and C13NMR were recorded in DMSO-d6 and CD3OD on a
Bruker 500 MHz instrument using TMS as internal stan-
dard (chemical shifts in d ppm). Microanalytical data
(C, H, and N) were performed on Perkin-Elmer 240 B
analyzer, and they agreed with proposed structures within
0.4 % of the calculated values. Mass spectra were
recorded on a Perkin-Elmer, Clarus 600 GC/MS and
Varian, TQ 320 GC/MS/MS mass spectrometers. Solvent
evaporation was performed under reduced pressure using
Buchan Rotatory Evaporator unless otherwise stated. Thin
layer chromatography was performed on precoated
(0.25 mm) silica gel GF254 plates (E. Merck, Germany);
compounds were detected with 254 nm UV lamp. Silica
gel (60–230 mesh) was employed for routine column
chromatography separations. Compounds 3, 4, 6, 10–15,
and 23 were prepared following their procedures reported
in the literature (Abdel-Aziz et al., 2005; Elshafie, 1979;
Tropak et al., 2007; MacGregor and McCluskey, 2011;
Lipophilic or amphiphilic moiety
required for cell wall penetration
Lipophilic or amphiphilic moiety
required for cell wall penetration
N
O
N
O
N
O
O
N
N
Fragment required for
hydrophobicity and large
π−conjugated backbone
O
O
Fig. 1 Proposed and designed antibacterial and antitumor carboxylic acid imides
123

Med Chem Res
Karpenko et al., 2008; Mattocks and Hutchison, 1948;
Chernick et al., 2005; Fahmy et al., 1979; Paull et al., 1984).
obtained was washed with water, dried (MgSO4), and re-
crystallized from an appropriate solvent.
General procedure for the synthesis of the proposed
imides (Scheme 1)
6-(2-Morpholinoethyl)-5H-pyrrolo[3,4-b]pyrazine-
5,7(6H)-dione (1)
A solution of substituted amine (10 mmol) and acid
anhydride (10 mmol) in glacial acetic acid (15 mL) was
heated under reflux for 12–18 h. After the evaporation of
the reaction mixture to dryness under reduced pressure, the
residue was neutralized by a solution of sodium bicar-
bonate (4 %) until effervescence ceased. The precipitate
Yield, 67 %; mp 151–152 ꢁC (methanol); IR (KBr, cm-1
)
1
m: 1755, 1700 (C=O); H NMR (DMSO-d6) d 2.50 (s, 8H,
4CH2), 3.70 (s, 4H, 2CH2), 8.99 (s, 2H, Ar–H); 13C NMR
(DMSO-d6); d 37.33, 56.00, 57.10, 68.09, 137.06, 147.54,
162.39: MS m/z: 262.2 (M?); C12H14N4O3.
O
O
O
Amine
+
Anhydrides
CH3COOH/ CH3COONa
O
N
NO2
COOH
NO2
N
O
N
O
N
CH3
N
Cl
N
N
O
O
O
O
O
O2N
O
O
O
Cl
O
Cl
O
O
O
Cl Cl
N
N
N
O
N
O
O
O
O
O
O
O
O
O
N
N
N
N
N
N
N
N
N
N
N
N
N
N
N
N
N
N
1
2
3
4
5
6
7
8
9
O
NO2
CH3
Cl
COOH
NO2
N
O
O
N
O
O
O
O
N
O2N
O
Cl
O
Cl
Cl
O
Cl
O
O
O
O
O
N
O
O
N
O
O
N
O
O
O
N
O
N
N
N
O
N
O
10
11
12
13
14
15
16
17
18
O
CO OH
NO2
CH3
N
Cl
O
O
O
N
O
N
O2N
O
Cl
O
Cl
O
Cl
Cl
O
O
O
O
O
O
N
O
O
O
O
N
N
O
O
N
N
O
N
O
O
N
19
20
21
22
23
24
25
26
Scheme 1 Synthesis of the designed carboxylic acid imides 1–26
123

Med Chem Res
6-(2-(Pyridin-2-yl)ethyl)-5H-pyrrolo[3,4-b]pyrazine-
5,7(6H)-dione (2)
2-(2,4,6-Trimethoxyphenyl)-1H-benzo[de]isoquinoline-
1,3(2H)-dione (16)
Yield, 69 %; mp 161–162 ꢁC (ethanol); IR (KBr, cm-1) m:
1770, 1710 (C=O); H NMR (DMSO-d6) d 3.02–3.05 (t,
Yield, 71 %; mp 190–191 ꢁC (acetic acid); IR (KBr, cm-1
)
1
1
m: 1798,1745 (C=O); H NMR (CD3OD) d 3.33 (s, 6H,
2CH3), 3.77 (s, 3H, CH3), 7.48–7.52 (q, 3H, Ar–H), 7.70 (s,
3H, Ar–H), 7.85–7.87 (q, 2H, Ar–H). MS m/z: 363.3 (M?);
C21H17NO5.
2H, J = 7.0 Hz, CH2), 3.69–3.73 (q, 2H, CH2), 7.19–7.22
(t, 1H, J = 5.5 Hz, Ar–H), 7.27–7.28 (d, 1H, J = 7.5 Hz,
Ar–H), 7.67–7.70 (q, 1H, Ar–H), 8.49–8.50 (d, 1H,
J = 3.5 Hz, Ar–H), 8.70 (s, 1H, Ar–H), 8.84 (s, 1H,
Ar–H); 13C NMR (DMSO-d6); d 36.93, 38.62, 121.49,
123.07, 136.46, 143.24, 143.41, 144.67, 147.39, 148.99,
158.99, 162.69; MS m/z: 254.2 (M?); C13H10N4O2.
2-(2,6-Dichloro-4-methylphenyl)-1H-
benzo[de]isoquinoline-1,3(2H)-dione (17)
Yield, 61 %; mp 159–160 ꢁC (ethanol); IR (KBr, cm-1) m:
1
1780, 1718 (C=O); H NMR (DMSO-d6) d: 1.90 (s, 3H,
6-(2,4-Dinitrophenyl)-5H-pyrrolo[3,4-b]pyrazine-
5,7(6H)-dione (5)
CH3), 7.86 (s, 2H, Ar–H), 8.02 (s, 2H, Ar–H), 8.45–8.46 (d,
2H, J = 6.5 Hz, Ar–H), 8.54–8.55 (d, 2H, J = 5.5 Hz,
Ar–H); 13C NMR (DMSO-d6); d 118.49, 127.21, 129.71,
131.36, 132.62, 135.32, 160.27, 171.93. MS m/z: 356.2
(M?); C19H11Cl2NO2.
Yield, 87 %; mp 201–201 ꢁC (DMF); IR (KBr, cm-1) m:
1
1779, 1712 (C=O); H NMR (DMSO-d6) d 8.18 (s, 1H,
Ar–H), 8.43–8.47 (t, 4H, J = 8.0 Hz, Ar–H); MS m/z:
315.2 (M?); C12H5N5O6.
2-(2,4,6-Trichlorophenyl)-1H-benzo[de]isoquinoline-
1,3(2H)-dione (18)
6-(2,4,6-Trimethoxyphenyl)-5H-pyrrolo[3,4-b]pyrazine-
5,7(6H)-dione (7)
Yield, 81 %; mp 227–228 ꢁC (ethanol); IR (KBr, cm-1) m:
1789,1727 (C=O); 1H NMR (DMSO-d6) d 7.56–7.59
(t, 3H, J = 7.5 Hz, Ar–H), 7.88–7.94 (m, 3H, Ar–H), 8.46-
8.47 (d, 1H, J = 8.0 Hz, Ar–H), 8.62–8.64 (d, 1H,
J = 6.0 Hz, Ar–H). MS m/z: 376.6 (M?); C18H8Cl3NO2.
Yield, 81 %; mp 134–135 ꢁC (methanol); IR (KBr, cm-1
)
1
m: 1720, 1700 (C=O); H NMR (CD3OD) d: 3.78 (s, 6H,
2CH3), 3.82 (s, 3H, CH3), 7.24 (s, 2H, Ar–H), 8.83 (s, 1H,
Ar–H), 9.34 (s, 1H); 13C NMR (CD3OD); d 56.53, 56.64,
61.26, 135.33, 136.15, 144.64, 145.04, 146.46, 148.66,
154.54, 163.11; MS m/z: 315.4 (M?); C15H13N3O5.
6-(2-Morpholinoethyl)-5H-dibenzo[c,e]azepine-5,7(6H)-
dione (19)
Yield, 66 %; mp 191–192 ꢁC (methanol/CH2Cl2); IR (KBr,
cm-1) m: 1781, 1720 (C=O); H NMR (DMSO-d6) d 2.48
1
6-(2,6-Dichloro-4-methylphenyl)-5H-pyrrolo
[3,4-b]pyrazine-5,7(6H)-dione (8)
(s, 4H, 2CH2), 2.57–2.60 (t, 2H, J = 6.5 Hz, CH2), 3.54 (s,
4H, 2CH2), 4.18–4.21 (t, 2H, J = 6.5 Hz, CH2), 7.76–7.77
(d, 2H, J = 7.5 Hz, Ar–H), 8.21–8.28 (d, 2H, J = 7.5 Hz,
Ar–H), 8.41–8.55 (m, 4H, Ar–H); 13C NMR (DMSO-d6); d
52.97, 53.31, 65.91, 66.26, 126.25, 126.59, 126.78, 127.12,
130.15, 130.50, 132.28, 132.63, 133.42, 133.77, 134.89,
135.24, 162.95, 163.30; MS m/z: 336.3 (M?); C20H20N2O3.
Yield, 73 %; mp 170–171 ꢁC (methanol); IR (KBr, cm-1
)
m: 1737, 1720 (C=O); 1H NMR (DMSO-d6); d 2.43 (s, 3H,
CH3), 7.65–7.69 (d, 2H, J = 8.5 Hz, Ar–H), 9.22 (s, 2H,
Ar–H); 13C NMR (DMSO-d6): d 19.71, 128.14, 131.30,
133.65, 136.89, 145.94, 150.42, 162.19; MS m/z: 308.2
(M?); C13H7Cl2N3O2.
6-(2-(Pyridin-2-yl)ethyl)-5H-dibenzo[c,e]azepine-5,7(6H)-
dione (20)
6-(2,4,6-Trichlorophenyl)-5H-pyrrolo[3,4-b]pyrazine-
5,7(6H)-dione (9)
Yield, 73 %; mp 188–189 ꢁC (Ethanol); IR (KBr, cm-1
)
1
m: 1720, 1689 (C=O); H NMR R (DMSO-d6) d 2.75–2.78
(t, 2H, J = 7.5 Hz, CH2), 4.09–4.12 (t, 2H, J = 7.5 Hz,
CH2), 6.80–6.81 (d, 1H, J = 7.0 Hz, Ar–H), 7.13–7.15
(t, 1H, J = 7.5 Hz, Ar–H), 7.25–7.36 (m, 3H, Ar–H),
7.72–7.86 (m, 3H, Ar–H), 8.09–8.15 (m, 4H, Ar–H); 13C
NMR (DMSO-d6); d 35.25, 39.17, 117.85, 120.63, 121.64,
122.46, 125.94, 126.50, 127.17, 129.29, 130.24, 130.60,
Yield, 81 %; mp 285–286 ꢁC (methanol); IR (KBr, cm-1
)
1
m: 1746, 1710 (C=O); H NMR (CD3OD) d 7.08 (s, 2H,
Ar–H), 7.79 (s, 2H, Ar–H); 13C NMR (CD3OD); d 127.91,
128.07, 130.92, 133.81, 134.68, 134.89, 142.24, 144.38,
146.96, 147.67, 162.16, 166.03; MS m/z: 328.5 (M?);
C12H4Cl3N3O2.
123

Med Chem Res
130.77, 132.45, 132.95, 134.36, 135.67, 148.16, 157.86,
159.57, 163.06; MS m/z: 328.3 (M?); C21H16N2O2.
Antibacterial methodology
MIC of the carboxylic acid imide derivatives 1–26, and the
reference drug ampicillin were performed by broth dilution
method as proposed by Clinical and Laboratory Standards
Institute (Cruickshank et al., 1975; Sahm and Washington,
1991) and interpreted using its guidelines. MIC is defined
as the lowest concentration of the drug that kills or inhibits
visible growth of microorganism. Briefly the cultures were
obtained from Muellere-Hinton broth (Difco) for all the
bacterial strains after 24 h of incubation at 37 1 ꢁC.
Testing was carried out in Muellere-Hinton broth at pH 7.4,
and the twofold serial dilution technique was applied. The
final inoculum size was 105 CFU/mL. A set of tubes con-
taining only inoculated broth was used as controls. After
incubation for 24 h at 37 1 ꢁC, the last tube with no
growth of microorganism was recorded to represent the
MIC expressed in lg/mL. The tests were performed in
duplicate and repeated.
4-(5,7-Dioxo-5H-dibenzo[c,e]azepin-6(7H)-yl)benzoic
acid (21)
Yield, 95 %; mp 197–198 ꢁC (methanol/DMF); IR (KBr,
cm-1)m:1779, 1726(C=O);1HNMR(DMSO-d6)d7.41–7.88
(m, 12H), 12.59 (s, 1H, OH); 13C NMR (DMSO-d6); d 118.13,
124.83, 126.85, 129.42, 130.34, 130.97, 141.97, 143.30,
166.90, 167.80, 168.83. MS m/z: 343.3 (M?); C21H13NO4.
6-(2,4-Dinitrophenyl)-5H-dibenzo[c,e]azepine-5,7(6H)-
dione (22)
Yield, 89 %; mp 175–177 ꢁC (Methanol); IR (KBr, cm-1
)
1
m: 1784, 1711 (C=O); H NMR (DMSO-d6) d 6.83–6.86
(t, 1H, J = 7.5 Hz, Ar–H), 7.02–7.03 (d, 2H, J = 5.0 Hz,
Ar–H), 7.21 (s, 4H, Ar–H) 7.41–7.42 (m, 2H, Ar–H), 8.48–
8.50 (d, 2H, J = 7.5 Hz, Ar–H); 13C NMR (DMSO-d6); d
113.76, 126.04, 126.72, 127.00, 129.45, 134.03, 134.84,
139.26, 140.78, 173.55. MS m/z: 389.32 (M?); C20H11N3O6.
Antitumor screening
Under a sterile condition, cell lines were grown in RPMI
1640 media (Gibco, NY, USA) supplemented with 10 %
fetal bovine serum (Biocell, CA, USA); 5 9 105 cell/mL
was used to test the growth inhibition activity of the syn-
thesized compounds. The concentrations of the compounds
ranging from 0.01 to 100 lM were prepared in phosphate
buffer saline. Each compound was initially solubilized in
dimethyl sulfoxide (DMSO), however, each final dilution
contained less than 1 % DMSO. Solutions of different
concentrations (0.2 mL) were pipetted into separate well of
a microtiter tray in duplicate. Cell culture (1.8 mL) con-
taining a cell population of 6 9 104 cells/mL was pipetted
into each well. Controls, containing only phosphate buffer
saline and DMSO at identical dilutions, were also prepared
in the same manner. These cultures were incubated in a
humidified incubator at 37 ꢁC. The incubator was supplied
with 5 % CO2 atmosphere. After 48 h, cells in each well
were diluted 10 times with saline and counted by using a
coulter counter. The counts were corrected for the dilution
(Grever et al., 1992; Monks et al., 1991; Boyd and Paull,
1995; Skehan et al., 1990).
6-(2,4,6-Trimethoxyphenyl)-5H-dibenzo[c,e]azepine-
5,7(6H)-dione (24)
Yield, 79 %; mp 288–289 ꢁC (Ethanol/DMF); IR (KBr,
1
cm-1) m: 1779, 1700 (C=O); H NMR (CD3OD) d 3.90 (s,
3H, CH3), 3.98 (s, 6H, 2CH3), 7.11 (s, 2H, Ar–H), 7.58–
7.60 (m, 8H, Ar–H); 13C NMR (CD3OD); d 56.69, 61.40,
90.07, 128.64, 128.90, 130.50, 130.90, 137.96, 140.73,
154.59, 176.51. MS m/z: 389.40 (M?); C23H19NO5.
6-(2,6-Dichloro-4-methylphenyl)-5H-dibenzo[c,e]
azepine-5,7(6H)-dionee (25)
Yield, 95 %; mp 197–198 ꢁC (Methanol); IR (KBr, cm-1
)
1
m: 1777, 1726 (C=O); H NMR (DMSO-d6) d 2.34 (s, 3H,
CH3), 7.31–7.42 (m, 10H, Ar–H); 13C NMR (DMSO-d6); d
19.96, 127.32, 129.90, 130.77, 133.06, 133.80, 135.65,
168.08. MS m/z: 385.2 (M?); C21H13Cl2NO2.
6-(2,4,6-Trichlorophenyl)-5H-dibenzo[c,e]azepine-
5,7(6H)-dione (26)
Molecular modeling methods
Yield, 94 %; mp[300 ꢁC (acetic acid); IR (KBr, cm-1) m:
Conformational search
1
1706, 1667 (C=O); H NMR (DMSO-d6) d 6.82–6.84 (q,
3H), 7.15–7.16 (q, 5H), 7.35–7.37 (q, 2H); 13C NMR
(DMSO-d6); d 128.61, 128.73, 130.48, 130.78, 137.44,
140.79, 176.05. MS m/z: 402.6 (M?); C20H10Cl3NO2.
Initial structures for the molecules 1–26 were constructed
using the HyperChem program (HyperChem, 1997). The
MM (calculations in vacuo, bond dipole option for
123

Med Chem Res
electrostatics, PolakeRibiere algorithm, and RMS gradient
of 0.01 kcal/mol) conformational searching in torsional
space was performed using the multiconformer method
(Abdel-Aziz, 2007; Abdel-Aziz et al., 2011a, b, c;
El-Gamal et al., 2010; El-Sayed et al., 2011; El-Azab et al.,
2010). Energy minima for compounds 1–26 were deter-
mined by a semi-empirical method AM1 (as implemented
in HyperChem) (Dewar et al., 1985). The conformations
thus obtained were confirmed as minima by vibrational
analysis. The energy-minimized geometry was used for the
docking calculation and the various 2D descriptors using
molecular modeling software MOE 2008.10 and VLife
MDS 3.5 (MOE 2008).
squared regression), Z score (Z score calculated by the
randomization test), best_ran_q2 (highest q2 value in the
randomization test). The regression coefficient r2 is a rel-
ative measure of fit by the regression equation. It represents
the part of the variation in the observed data that is
explained by the regression. However, a QSAR model is
considered to be predictive, if the following conditions are
satisfied: r2 [ 0.6, q2 [ 0.6, and pred_r2 [ 0.5 (Abdel-
Aziz et al., 2011a, b, c; Golbraikh and Tropsha, 2002]. The
F test reflects the ratio of the variance explained by the
model and the variance due to the error in the regression.
High values of the F test indicate that the model is statis-
tically significant. The low standard error of r2 (r2_se), q2
(q2_se), and pred_r2 (Pred_r2se) shows absolute quality of
fitness of the model.
2D-Descriptor calculations
Docking methodology
Data set
Docking studies have been performed using MOE 2008.10
(MOE 2008). With this purpose, crystal structure of topo-
isomerase II DNA gyrase, with its bound inhibitor Cloro-
biocin (PDB codes: 1KZN) was obtained from the Protein
Data Bank in order to prepare protein for docking studies.
Docking procedure was followed using the standard pro-
tocol implemented in MOE 2008.10 and the geometry of
resulting complexes was studied using the MOE’s Pose
Viewer utility.
A data set of 26 compounds of carboxylic acid imides was
used for the present 2D QSAR study. There is high struc-
tural diversity and a sufficient range of the biological
activity in the selected series of these derivatives. The
biological activity values [MIC (lmol/L)] were converted
to negative logarithmic scale (-log MIC) and subsequently
used as the dependent variable for the QSAR analysis. The
energy-minimized geometry of the compounds was used
for the calculation of the various 2D descriptors (Individ-
ual, Chi, ChiV, Path count, ChiChain, ChiVChain, Chain-
pathcount, Cluster, Pathcluster, Kapa, Element Count, Estate
number, Estate contribution, Hydophillic–hydophobic and
Polar surface area). The various alignment independent (AI)
descriptors were also calculated. For calculation of alignment,
the independent descriptor was assigned the utmost three
attributes. The first attribute was T to characterize the topol-
ogy of the molecule. The second attribute was the atom type,
and the third attribute was assigned to atoms taking part in the
double or triple bond. The pre-processing of the independent
variables (i.e., 2D descriptors) was done by removing
invariable (constant column).
Results and discussion
Chemistry
Classical condensation of amine derivatives with acid
anhydrides in refluxing acetic acid afforded the required
cyclic imides in satisfactory yields (Scheme 1). The
structures of the isolated products 1–26 were established on
the basis of their elemental and spectral analyses.
Antibacterial activity
Statistical analysis
All the synthesized compounds were subjected to the
antibacterial screening using broth dilution technique
(Cruickshank et al., 1975; Sahm and Washington, 1991).
Ampicillin was used as the standard and the minimum
inhibitory concentration (MIC) was determined. The
compounds were screened against gram-positive bacteria
namely, S. aureus and B. subtilis and gram-negative bac-
teria namely, E. coli and P. aeruginosa in nutrient broth
medium. The results are listed in Table 1. The investiga-
tion of antibacterial screening data revealed that most of
the tested compounds showed moderate to good bacterial
inhibition as compared to the standard drugs (Table 1).
The descriptors were taken as independent variables and
antibacterial activity as dependent variable. MLR method
of analysis was used to derive the 2D QSAR equations. The
developed QSAR models are evaluated using the following
statistical measures: r2 (the squared correlation coefficient),
r2se (standard error of squared correlation coefficient), F
test (Fischer’s value) for statistical significance, q2 (cross-
validated correlation coefficient), q2_se (standard error of
cross-validated square correlation coefficient), pred_r2 (r2
for external test set), pred_r2se (standard error of predicted
123

Med Chem Res
p-conjugated backbone which could easily interact with
various active targets in biological system via non-covalent
forces such as p–p stacking. Compounds having chloro
substituent on aromatic ring showed maximum antimicro-
bial potency against gram-negative organisms (MIC; 2.0–
10.0 lg/mL), which was comparable to the standard
ampicillin drug (MIC; 2.2–10.0 lg/mL). Halogen-con-
taining aromatic compounds could efficiently increase the
bioactivity due to the lipohilicity, inductive and mesomeric
effects which could affect the diffusion and interaction
with bacterial cells and tissues by enhancing solubility and
membrane permeability (Zhang and Zhou, 2011; Zhang
et al., 2010). More interestingly replacing halogen moieties
with nitro, carboxyl and methoxy groups (compounds 12–
16, 21–24), resulted in marked increase in the antibacterial
activity against gram-positive organisms. Moreover, loss of
activity was observed when replacing N-substituted phenyl
group with ethylmorpholine or ethylpyridine moieties
(compounds 1, 2, 9, 10, 19, and 20).
Table 1 Minimum inhibitory concentrations (MIC) of the designed
carboxylic acid imides using gram-positive and gram-negative
organisms
Compd. nos. S. aureus B. subtilis E. coli
MIC
P. aeruginosa
MIC (lg/mL)
MIC
(lg/mL)
MIC
(lg/mL)
(lg/mL)
1
[100.0
[100.0
8.5
[100.0
[100.0
8.0
[100.0
[100.0
10.9
10.5
10.9
9.0
[100.0
[100.0
[100.0
[100.0
[100.0
[100.0
[100.0
20.0
2
3
4
8.9
8.5
5
8.4
8.0
6
8.7
9.0
7
8.7
9.0
9.0
8
20
20.0
20.0
[100.0
[100.0
2.0
6.9
9
20
6.9
20.0
10
[100.0
[100.0
1.6
[100.0
[100.0
3.9
[100.0
[100.0
[100.0
[100.0
[100.0
[100.0
[100.0
10.0
11
12
13
2.9
2.9
4.0
14
1.8
2.2
4.0
Antitumor activity
15
2.0
2.2
2.5
16
2.0
2.2
2.5
The 12 compounds indicated in (Table 2; Fig. 2) were
selected by National Cancer Institute, Bethesda, Maryland,
USA on the basis of degree of the structure variation and
computer modeling techniques for evaluation of their
antineoplastic activity. The selected compounds were sub-
jected to in vitro anticancer assay against tumor cells in a
full panel of 60-cell lines taken from nine different organs
(lung, colon, breast, ovary, blood, kidney, skin, prostate and
brain) (Grever et al., 1992; Monks et al., 1991; Boyd and
Paull, 1995; Skehan et al., 1990). The compounds were
tested at a single dose concentration of 10 lM, and the data
were reported as mean graph of the percent growth of treated
cells, and presented as percentage growth inhibition (GI%)
(Tables 2, 3; Figs. 2, 3). The obtained results of the tested
cyclic imide derivatives 2, 5, 6, 9, 16, 17, 18, 19, 21, 22, 25,
and 26 showed moderate GI values, potential pattern of
selectivity, as well as broad-spectrum antitumor activity.
Regarding the activity toward individual cell lines; nearly all
compounds showed selective potency against renal cancer
cell line (UO-31) with GI values of 11–73 %; while non-
small cell lung cancer cell line (HOP-92) proved to be
selectively sensitive to 9 with GI value of 57.14 %. Com-
pounds 9, 18, 22, and 26 showed activity against CNS
cancer SNB-75 with GI values of 47.35, 39.13, 28.60,
and 45.60 %, respectively. Meanwhile, compound 9 showed
a remarkable activity against non small cell lung cancer
HOP-92, renal SN12C, renal UO-31, and CNS cancers
SNB-75 with GI values of 57.14, 22.68, 72.89 and 47.35 %,
respectively. Structure–activity correlation, based on the
number of cell lines proved sensitive toward each of the
synthesized individual compounds, revealed that, cyclic
17
18.2
18.0
[100.0
[100.0
2.2
20.0
20.0
[100.0
[100.0
2.0
2.2
18
2.0
10.0
19
[100.0
[100.0
4.2
[100.0
[100.0
[100.0
[100.0
[100.0
[100.0
10.0
20
21
22
2.2
2.3
4.0
23
2.5
2.5
2.5
24
2.5
2.5
2.5
25
19.5
19.0
2.2
19.2
18.5
2.2
2.6
26
2.4
10.0
Ampicillin
2.2
10.0
The tests were performed in duplicate and repeated
Most of the compounds exhibited significant antibacterial
activity against gram-positive bacteria (MIC; 1.6–2.9
lg/mL) and gram-negative E. coli (MIC; 2.0–4.2 lg/mL)
and showed no activity against P. aeruginosa. The differ-
ence in the activity was observed due to molecular diver-
sity and lipophilicty of the tested compounds. Conversion
of the cyclic structure of imide from pyrazine (compound
3, MIC, 8.0–10.9 lg/mL) into naphthalene moiety (com-
pound 12, MIC; 1.6–3.9 lg/mL) greatly increased the
antibacterial activity. Similarly conversion from compound
3 to compound 21 showed fourfold increases in activity
against gram-positive bacteria (Table 1). It is clear that
compounds 12–16, 21–24 were the most potent against
S. aureus, B. subtilis, and E. coli with the MIC range of
1.6–4.2 lg/mL. The antibacterial activity of such naph-
thalimides (15–18) and its structural analoges 21–24
may be attributed to strong hydrophobicity and large
123

Med Chem Res
active, while their replacement by three methoxy groups
Table 2 The percentages of growth inhibition of the selected eleven
compounds over the most sensitive tumor cell lines
diminished the activity as in compound 6.
Comp. HOP-92 NCI-
(%)
SN12C UO-31 SNB-75 MCF7
(%)
no
H522 (%) (%)
(%)
(%)
Molecular modeling results of antibacterial activity
2
-3.50
3.84
3.89
4.82
–
20.95
11.80
11.42
-2.75
47.35
13.16
6.64
2.87
5
-28.05
6.83 15.32
5.86 12.51
22.68 72.89
19.27 21.03
3.72 26.75
6.09 20.89
8.53 32.45
6.30 15.40
1.40 18.15
14.77 34.69
-1.37 10.78
3.56
4.31
Modeling studies are required in order to construct molec-
ular models that incorporate all experimental evidence
reported. These models are necessary to obtain a consistent
and more precise picture of the biological active mole-
cules at the atomic level and furthermore, provide new
insights that can be used to design novel therapeutic agents
(Abdel-Aziz, 2007; El-Ayaan et al., 2007; Abdel-Aziz
et al., 2005; Goda et al., 2004, 2005).
6
10.15
9
57.14 14.91
30.21 13.61
3.64
16
17
18
19
21
22
25
26
6.42
-15.52
5.32
4.37
-1.03
3.55
–
39.130
13.06
11.00
28.60
10.08
45.60
13.52 11.36
28.28
10.05
-0.39
1.96
10.05
26.35
1.58
7.66
5.50
1.31
4.08
Conformational analysis
33.57
-1.42
As an attempt to gain a better insight into the molecular
structures of the compounds 1–26 that required to 2D-
QSAR and docking calculation, conformational analysis
has been performed by use of the MM? force-field (Pro-
feta and Allinger, 1985) (calculations in vacuo, bond dipole
option for electrostatics, Polak–Ribiere algorithm, and
The showed inhibition percentages are measured at a single concen-
tration of 10 lM. % inhibition is calculated by simple abstraction of
the % activity from 100. 100 % activity is the activity of cells in
presence of test solvent only (DMSO)
imides based on pyrazine ring (2, 5, 6, and 9) are more
active antitumor than other compounds (16, 17, 18, 19, 21,
22, 25, and 26). Electro-negativity of substituent at the
aromatic rings manipulated the magnitude of activity. The
three chlorine atoms of 9 rendered the compound merely
˚
RMS gradient of 0.01 kcal/A mol) as implemented in
HyperChem (1997). The most stable conformer was fully
optimized with AM1 semi-empirical molecular orbital
calculation (Dewar et al., 1985). The global minimum was
80
60
40
20
0
HOP-92 (%)
NCI-H522 (%)
SN12C (%)
UO-31 (%)
SNB-75 (%)
MCF7 (%)
-20
-40
2
5
6
9
16
17
18
19
21
22
25
26
Tested compounds
Fig. 2 The percentages of growth inhibition of the 12 selected compounds over the most sensitive tumor cell lines
123

Med Chem Res
Table 3 The percentages of growth inhibition of compound 9 over
the full panel of tumor cell lines
Table 3 continued
Cell line type
Cell line name
Inhibition
(%)
Cell line type
Cell line name
Inhibition
(%)
CNS cancer
SF-268
SF-295
SF-539
SNB-19
SNB-75
U251
7.64
-2.42
8.66
Non-small cell lung cancer
A549/ATCC
EKVX
-11.65
8.20
HOP-92
57.14
9.41
-2.30
47.35
-7.03
NCI-H226
NCI-H23
NCI-H460
NCI-H522
COLO 205
HCC-2998
HCT-116
HCT-15
0.61
-7.91
14.91
8.27
The showed inhibition percentages are measured at a single concentration
of 10 lM. % Inhibition is calculated by simple abstraction of the %
activity from 100. 100 % Activity is the activity of cells in presence of test
solvent only (DMSO)
Colon cancer
-4.24
1.17
7.09
HT29
4.24
confirmed as true minimum not saddle point by the absence
of negative eigen value of the Hessian through frequency
calculation (Abdel-Aziz 2007; El-Sherbeny et al., 2010).
The calculation results showed that the lowest energy-
minimized structures of the compounds under investigation
exhibited a common arrangement of the N-aryl moiety,
such group arranged in an almost coplanar form to the
imide core. On the other hand, the bioactive conforma-
tion resulted from conformational analysis in the vicinity
of receptor site showed that N-aryl moiety attached
to imide core was out of the plane of imide ring by
50.0ꢁ–119.5ꢁ.
KM12
10.33
-7.54
4.48
SW-620
Breast cancer
BT-549
HS 578T
MCF7
15.33
3.64
MDA-MB-231/ATCC
MDA-MB-468
T-47D
7.23
17.77
5.37
Ovarian cancer
IGROV1
OVCAR-3
OVCAR-4
OVCAR-5
OVCAR-8
SK-OV-3
DU-145
7.24
-15.44
-0.64
-1.70
10.99
4.55
2D-QSAR study
Prostate cancer
Leukemia
18.59
22.70
3.38
PC-3
The antibacterial activities of the tested compounds 1–26
against S. aureus and E. coli, presented in -log MIC
(Table 4; calculated from lM/L concentration), were used
in the 2D-QSAR studies using VLife Molecular Design
Suite (VLife Molecular Design Suite). Compounds were
divided in training and test set. This was achieved by set-
ting aside six compounds as test set which have regularly
distributed activity. Selection of molecules in the training
set and test is a key and important feature of any QSAR
model. Therefore, the care was taken in such a way that
biological activities of all compounds in test set lie within
the maximum and minimum value range of biological
activities of training set of compounds. A Uni-Column
statistics for training set and test set were generated to
check correctness of selection criteria for trainings and test
set molecules. The maximum and minimum value in
training and set were compared in a way that:
CCRF-CEM
HL-60(TB)
K-562
1.42
10.11
1.21
MOLT-4
RPMI-8226
786-0
4.32
Renal cancer
2.39
A498
20.85
-0.27
18.04
-25.20
22.68
-4.45
72.89
-0.26
3.32
ACHN
CAKI-1
RXF393
SN12C
TK-10
UO-31
Melanoma
LOX IMVI
M14
MALME-3M
SK-MEL-2
SK-MEL-28
SK-MEL-5
UACC-62
21.15
-0.16
-3.36
-6.79
1.42
1. The maximum value of -log MIC of test set should be
less than or equal to maximum value of -log 1/MIC of
training set.
123

Med Chem Res
80
60
40
20
0
-20
-40
Non-small cell lung cancer
Colon Cancer
Breast Cancer
Ovarian Cancer
Leukemia
Renal Cancer
Melanoma
CNS cancer
Prostate
Cancer
Fig. 3 The percentages of growth inhibition of compound 9 over the full panel of tumor cell lines
2. The minimum value of –log MIC of test set should be
ꢁLog MIC ¼ þ 0:1515ðꢂ0:0080Þ T 2 O 4
ꢁ 0:1574ðꢂ0:0251Þ T C N 4
higher than or equal to minimum value of -log MIC
of training set.
ꢁ 0:0218ðꢂ0:0048Þ SaaCHE-index
This observation showed that test set was interpolative
and derived within the minimum–maximum range of
training set. The mean and standard deviation of -log MIC
values of sets of training and test provide insights to rela-
tive difference of mean and point density distribution of
two sets. 2D-QSAR models were generated for training set
of 20 compounds using MLR method. The best QSAR
model was selected on the basis of value of statistical
parameters like r2 (square of correlation coefficient for
training set of compounds), q2 (cross-validated r2), and
pred_r2 (predictive r2 for the test set of compounds). All
QSAR model was validated and tested for its predictability
using an external test set of 6 compounds. Statistical results
generated by 2D-QSAR analysis showed that QSAR
models have good internal as well as external predictabil-
ity. The results obtained for actual and predicted activity
are presented in Table 4 and the residuals were found to
be minimal (Table 4; Fig. 4). The regression equation
obtained for the different series of compounds is given
below.
þ 0:0296ðꢂ0:0108Þ T C O 7 þ 3:3214
n ¼ 21 r2 ¼ 0:9559 q2 ¼ 0:9298
F test ¼ 86:6815 r2se ¼ 0:1537
q2se ¼ 0:1939 pred r2 ¼ 0:8894
pred r2se ¼ 0:2813
•
2D-QSAR model for antibacterial activity against
E. coli (Model-2)
ꢁLog MIC ¼ þ 0:3861ðꢂ0:0041Þ 3ClusterCount
ꢁ 4:8933ðꢂ0:5388Þ chi6chain
ꢁ 0:0637ðꢂ0:0013Þ T T N 5
þ 0:0220ðꢂ0:0042Þ SsOHE-index þ 2:2525
n ¼ 21 r2 ¼ 0:9788 q2 ¼ 0:9678
F test ¼ 85:0993 r2se ¼ 0:1113
q2se ¼ 0:1373 pred r2 ¼ 0:9057
pred r2se ¼ 0:2023
A brief idea of statistical result of 2D-QSAR models, the
requirement of different physicochemical parameters and
•
2D-QSAR model for antibacterial activity against
S. aureus (Model-1)
123

Med Chem Res
Table 4 Observed, predicted antibacterial activity (-log MIC) and residual values of the carboxylic acid imides
Compd.
nos
S. aureus
E. coli
Observed
Predicted
Residual
Observed
Predicted
Residual
1
3.42
3.41
4.50
4.48
4.57
4.56
4.56
4.19
4.22
3.49
3.48
5.30
5.04
5.31
5.26
5.26
4.29
4.32
3.53
3.52
5.19
5.25
5.19
5.19
4.29
4.33
3.09
3.34
4.66
4.45
4.64
4.56
4.85
3.96
3.89
3.49
3.74
5.05
4.84
5.22
5.13
5.30
4.35
4.29
3.43
3.69
5.01
5.25
5.14
5.31
4.29
4.23
0.33
0.07
3.42
3.41
4.39
4.41
4.46
4.54
4.54
4.65
4.68
3.49
3.48
4.91
4.90
4.96
5.16
5.16
5.21
5.27
3.53
3.52
4.91
4.99
5.19
5.19
5.17
5.22
3.15
3.28
4.40
4.14
4.48
4.60
4.73
4.72
4.73
3.49
3.61
4.87
4.60
4.94
5.06
5.19
5.19
5.19
3.52
3.64
4.89
4.97
5.09
5.22
5.22
5.22
0.27
0.13
2
3
-0.16
0.03
-0.01
0.27
4
5
-0.07
0.00
-0.02
-0.06
-0.19
-0.07
-0.05
0.00
6
7
-0.29
-0.77
0.33
8
9
10
11
12
13
14
15
16
17
18
19
20
21
22
23
24
25
26
0.00
-0.26
0.25
-0.13
0.04
0.20
0.30
0.09
0.02
0.13
0.10
-0.04
-0.06
0.03
-0.03
0.02
0.08
0.10
0.01
-0.17
0.18
-0.12
0.02
0.00
0.02
0.05
0.10
-0.12
0.00
-0.03
-0.05
0.00
0.10
their contributions (positive or negative influence on
biological activity), required for potential antibacterial
activity was obtained. The regression equation so obtained
will be useful for the prediction of biological activities of
the designed series of compounds, in future.
bounded atoms (i.e., any double bonded atom) separated
from oxygen atom by 4 bonds in a molecule and T_C_O_7
which mean the count of number of carbon atoms (single
double or triple bonded) separated from any oxygen atom
(single or double bonded) by 7 bond distance in a mole-
cule. These two descriptors showed positive contribution
toward antibacterial activity against S. aureus in selected
QSAR model-1 and the contribution is approximately
58.07 and 9.93 %, respectively. Positive contribution of
these descriptors revealed the increase of antibacterial
activity against S. aureus of carboxylic acid imides with
presence of methoxy, carboxyl and nitro groups such as
compounds 12–16. AI descriptors T_C_N_4 which mean
the count of number of carbon atoms (single double or
triple bonded) separated from any nitrogen atom (single or
double bonded) by 4 bond distance in a molecule and
the descriptor SaaCHE-index is Electrotopological state
indices for number of –CH group connected with two
aromatic bonds. These two descriptors showed negative
contribution toward gram positive S. aureus in selected
The statistical best model (Model-1) for antibacterial
activity against S. aureus with a coefficient of determina-
tion (r2) = 0.9559 was considered, as the model showed an
internal predictive power (q2 = 0.9298) of 93 % and a
predictivity for the external test set (pred_r2 = 0.8894) of
about 89 %. The low standard error of r2_se = 0.1537
demonstrates accuracy of the model. The F test value of
86.6815 shows the overall statistical significance level to
be 99.99 % of the model, which means that the probability
of failure for model is 1 in 10,000. The model is validated
by a_ran_r2 = 0.00001, a_ran_q2 = 0.00001, a_ran_
pred_r2 = 0.01000. The randomization test suggests that
the developed model have a probability of less than 1 %
that the model is generated by chance. AI descriptors
T_2_O_4 which mean the count of number of double
123

Med Chem Res
models (model-1) and its contributions are approximately
20.96 and 11.05 %, respectively. Negative contribution of
this descriptor revealed the decrease of antibacterial
activity against S. aureus of carboxylic acid imides with
the presence of ethyl-2-pyridyl and ethylmorphlino groups
such as compounds 1, 2, 10, 11, 19, and 20. Moreover, the
antibacterial activity decreased with the presence of
nitrogen atoms in the core imides such as compounds 1–9
compared with naphthalimide derivatives.
order cluster in a molecule and SsOHE-index which is
electrotopological state indices for number of –OH group
connected with one single bond, showed positive contri-
bution toward E. coli with contribution of 56.6 and 5.42 %,
respectively. Such positive effect indicates that the anti-
bacterial activity against E. coli was increase with presence
of aromatic substitution such as compounds 12–18 and
21–26.
On the other hand, the statistical best model (Model-2)
for antibacterial activity against E. coli with a coefficient of
determination (r2) = 0.9788 was considered, as the model
showed an internal predictive power (q2 = 0.9678) of
97 % and a predictivity for the external test set (pred_r2
= 0.9057) of about 91 %. The low standard error of
r2_se = 0.1113 demonstrates accuracy of the model. The F
test value of 85.0993 shows the overall statistical signifi-
cance level to be 99.99 % of the model, which means that
the probability of failure for model is 1 in 10,000. The
model is validated by a_ran_r2 = 0.00001, a_ran_q2 =
0.00001, a_ran_pred_r2 = 0.01000. The randomization
test suggests that the developed model have a probability of
less than 1 % that the model is generated by chance. AI
descriptors T_T_N_5 which mean the count of number of
bond (single double or triple bonded) separated from
nitrogen atom by 5 bond distances in a molecule and the
descriptor chi6chain which signifies retention index for six
membered rings. These two descriptors showed negative
contribution toward antibacterial activity against E. coli in
selected QSAR model-2 and the contribution is approxi-
mately 17.24 and 20.74 %, respectively. Negative contri-
bution of these descriptors revealed the decrease of
antibacterial activity against E. coli of carboxylic acid
imides with presence of six member cyclic imides such as
compounds 1–9. Moreover descriptors, such as 3Cluster-
Count which signifies total number of fragments of third
Docking studies
To predict the antibacterial data on a structural basis, auto-
mated docking studies were carried out using MOE 10.2008
program (MOE 2008) installed on 2.3G Core 2 Duo, the
scoring functions and hydrogen bonds formed with the sur-
rounding amino acids are used to predict their binding modes,
their docking energy and orientation of these compounds at
the active site of the topoisomerase II DNA gyrase-B enzyme.
The protein–ligand complex was constructed based on the
X-ray structure of topoisomerase II DNA gyrase-B enzyme
with its bound inhibitor Clorobiocin (Fig. 5) available
through the RCSB Protein Data Bank (PDB entry 1KZN)
(Lafitte etal., 2002). Theactivesiteoftheenzyme wasdefined
˚
to include residues within a 10.0 A radius to any of the
inhibitor atoms. The scoring functions of the compounds were
calculated from minimized ligand protein complexes. In order
to compare the binding energy of the most active compounds
12, 14, and 16 along with inactive compound 2, we docked
these compoundsintothe empty binding siteoftopoisomerase
II DNA gyrase (1KZN), with its bound inhibitor Clorobiocin.
Figures 5 and 6 showed the docking solutions with the best
predicted binding energyfor topoisomerase IIDNA gyrase-B,
binding mode of the most active compounds 12, 14, and 16
along with inactive compound 2 as well. As a results of
molecular docking and as shown from Figs. 5 and 6, the
following results can be drawn: compound 12 reveals docking
5.5
5.0
4.5
4.0
3.5
3.0
5.5
5.0
4.5
4.0
3.5
3.0
2.5
3.0
3.5
4.0
4.5
5.0
5.5
3.0
3.5
4.0
4.5
5.0
5.5
Observed -log MIC
Observed -log MIC
Fig. 4 Plot between observed and predicted activities of the compounds against S. aureus (left panel) and E. coli (right panel) in the training set
and test set for the linear regression developed models
123

Med Chem Res
Fig. 5 The orientation of the docked molecules (red and cyan) in
DNA gyrase active pocket (upper left panel; Clorobiocin is shown as
yellow). The docked 12 (upper right panel; red), 14 (lower left panel;
cyan) and 16 (lower right panel; violet) in DNA gyrase active pocket
(H bonds are shown as green) (Color figure online)
score of -31.55 kcal/mol and forms 10 hydrogen bonds
˚
(classical and non-classical) with Glu-50 (2.59 A,
Interestingly a similar interaction trend was observed for 16
complexed with topoisomerase II DNA gyrase-B enzyme
with docking score of -28.56 kcal/mol and formed 10
hydrogen bonds with His-55, Arg-76, Glu-50, Asp-73, Asn-
46, ILe-78, and Val-43 (Fig. 5; lower right panel). Moreover,
the hydrophobic naphthalene ring of the three compounds 12,
14, and 16 was surrounded by active site amino acid residue
Asn-46 forming hydrophobic interaction through CH–p and
p–p interactions[23.29]. Onthe other hand, compound2 were
modeled in the active site of topoisomerase II DNA gyrase-B
enzyme (Fig. 6) with binding energy of -2.9 kcal/mol. The
lower interaction energy observed for 2 rationalize the
insufficient binding of such compounds into the active site of
the topoisomerase II DNA gyrase-B enzyme than that of
compounds 12, 14, and 16 (Fig. 5). The insufficient binding
can be explained in terms of the occurrence of only two weak
˚
NHꢀꢀꢀCOOH), His-55 (3.10 A, NHꢀꢀꢀCOOH), Gly-75
˚
˚
(2.45 A, CHꢀꢀꢀCOOH), Arg-76 (3.04 A, NHꢀꢀꢀCOOH), Arg-
˚
˚
76 (2.80 A, NHꢀꢀꢀCOOH), Asn-46 (2.37 A, NHꢀꢀꢀimide car-
˚
bonyl), Asp-73 (2.69 A, OHꢀꢀꢀimide carbonyl), Ile-78
˚
˚
˚
(2.41 A, CHꢀꢀꢀimide carbonyl), Val-43 (2.57 A and 2.73 A,
C=O–Ar–CH) (Fig. 5; upper right panel). Similarly, com-
pound 14 showed similar docking profile with docking score
of -30.01 kcal/mol and forms 10 classical and non-classical
˚
hydrogen bonds with Glu-50 (2.49 A, OHꢀꢀꢀNO2), Arg-76
˚
˚
(2.39 A, NHꢀꢀꢀNO2), Arg-76 (3.40 A, NHꢀꢀꢀNO2), Ile-78
˚
˚
(1.80 A, NHꢀꢀꢀNO2), Thr-165 (2.22 A, OHꢀꢀꢀNO2), Asn-46
˚
˚
(2.63 A, NHꢀꢀꢀimide carbonyl), Asp-73 (2.16 A, OHꢀꢀꢀimide
˚
carbonyl), Ile-78 (2.65 A, CHꢀꢀꢀimide carbonyl), Val-43
˚
(2.59 A and 2.50 A, C=O–Ar–CH) (Fig. 5; lower left panel).
˚
123

Med Chem Res
essential guide for the future work. Molecular docking
studies further supported the potent antibacterial inhibitory
activity of 12, 14, and 16 compared to 2 and further help
understanding the various interactions between the ligands
and enzyme active sites in detail and thereby help to design
novel potent derivatives. Further optimization of antibacte-
rial and antitumor profiling of these series are currently on
going.
Acknowledgments The authors extend their appreciation to the
Deanship of Scientific Research at King Saud University for funding
the work through the research group project No. RGP-VPP-163. The
authors would like to express their gratitude and thanks to Dr. You-
ssef Magd, Faculty of Science, Mansoura University, Egypt, for
providing the antibacterial data. The authors wish to express gratitude
to V-life Science Technologies Pvt. Ltd. for providing the trial full
version software. Our sincere acknowledgments to Chemical Com-
puting Group Inc, 1010 Sherbrooke Street West, Suite 910, Montreal,
H3A 2R7, Canada., for its valuable agreement to use the package of
MOE 2008.10 software.
Fig. 6 The orientation of the docked compound 2 (white) in DNA
gyrase active pocket (H bonds are shown as green) (Color figure online)
hydrogen bonds with ILe-78 and Asn-46 amino acid residue.
Moreover, compound 2 showed steric clash with amino acid
residues His-55, Glu-50, Met-91, and Val-120 which can give
more details about the inactivity of such compound. In short,
compounds 12, 14, and 16 can bind in the active site of
enzyme in approximately similar fashion of Clorobiocin with
an additional classical and nonclassical hydrogen bond as
described above.
References
Abdel-Aziz AA-M (2007) Novel and versatile methodology for
synthesis of cyclic imides and evaluation of their cytotoxic,
DNA binding, apoptotic inducing activities and molecular
modeling study. Eur J Med Chem 42:614–626
Abdel-Aziz AA-M, El-Subbagh HI, Kunieda T (2005) Lewis acid-
promoted transformation of 2-alkoxypyridines into 2-aminopyri-
dines and their antibacterial activity. Part 2: remarkably facile
C–N bond formation. Bioorg Med Chem 13:4929–4935
Abdel-Aziz AA-M, El-Azab AS, Attia SM, Al-Obaid AM, Al-Omar
MA, El-Subbagh HI (2011a) Synthesis and biological evaluation
of some novel cyclic-imides as hypoglycaemic, anti-hyperlipi-
demic agents. Eur J Med Chem 46:4324–4329
Conclusion
In summary, a series of substituted cyclic imide derivatives
have been synthesized successfully in appreciable yields and
screened for their in vitro antibacterial and antitumor activ-
ities. Compound 4-(1,3-dioxo-1H-benzo[de]isoquinolin-
2(3H)-yl)benzoic acid (12) is the most potent and broad-
spectrum as antibacterial against S. aureus, B. subtilis, and
E. coli with the MIC range of 1.6–3.9 lg/mL. On the other
hand, 2-(2,4,6-trichlorophenyl)-1H-benzo[de]isoquinoline-
1,3(2H)-dione (18) is only active against E. coli with the MIC
value of 2.0 lg/mL. 6-(2,4,6-Trichlorophenyl)-5H-pyrrol-
o[3,4-b]pyrazine-5,7(6H)-dione (9) is broad-spectrum antit-
umors showing effectiveness toward numerous cell lines
that belong to different tumor subpanels. Various physico-
chemical indices are helpful for the understanding of
microbiological results, as shown by our 2D-QSAR study.
The 2D-QSAR results could not be addressed to a concrete
drug–receptor interaction, but they can reveal trends in the
relationship between ligand structures and their activities for
our set of antibacterial agents. Analysis of 2D-QSAR models
provides details on the fine relationship linking structure and
activity and offers clues for structural modifications that can
improve the activity. These trends should prove to be an
Abdel-Aziz AA-M, Asiri YA, Al-Agamy MHM (2011b) Design,
synthesis and antibacterial activity of fluoroquinolones contain-
ing bulky arenesulfonyl fragment: 2D-QSAR and docking study.
Eur J Med Chem 46:5487–5497
Abdel-Aziz AA-M, El Tahir KEH, Asiri YA (2011c) Synthesis, anti-
inflammatory activity and COX-1/COX-2 inhibition of novel
substituted cyclic imides. Part 1: molecular docking study. Eur J
Med Chem 46:1648–1665
Abuo-Rahma GE, Sarhan HA, Gad GFM (2009) Design, synthesis,
antibacterial activity and physicochemical parameters of novel
N-4-piperazinyl derivatives of norfloxacin. Bioorg Med Chem
17:3879–3886
Al-Omary FA, Abou-Zeid LA, Nagi MN, Habib SE, Abdel-Aziz AA-
M, El-Azab AS, Abdel-Hamide SG, Al-Omar MA, Al-Obaid
AM, El-Subbagh HI (2010) Non-classical antifolates. Part 2:
synthesis, biological evaluation, and molecular modeling study
of some new 2,6-substituted-quinazolin-4-ones. Bioorg Med
Chem 18:2463–2849
Amr AEGE, Sabry NM, Abdulla MM (2007) Synthesis, reactions, and
anti-inflammatory activity of heterocyclic systems fused to a
thiophene moiety using citrazinic acid as synthon. Monatsh
Chem 138:699–707
ˆ
Andricopulo AD, Mu¨ller LA, Filho VC, Cani GS, Roos JF, Correa R,
Santos AR, Nunes RJ, Yunes RA (2000) Analgesic activity of
cyclic imides: 1,8-naphthalimide and 1,4,5,8-naphthalenediimide
derivatives. IL Farmaco 55:319–321
123

Med Chem Res
Anizon F, Belin L, Moreau P, Sancelme M, Voldoire A, Prudhomme
M, Ollier M, Severe D, Riou JF, Bailly C, Fabbro D, Thomas M
(1997) Syntheses and biological activities (topoisomerase inhi-
bition and antitumor and antimicrobial properties of rebeccamy-
cin analogues bearing modified sugar moieties and substituted on
the imide nitrogen with a methyl group. J Med Chem 40:3456–
3465
Berlinck RGS, Britton R, Piers E, Lim L, Roberge M, Moreira da
Rocha R, Andersen RJ (1998) Granulatimide and isogranulati-
mide, aromatic alkaloids with G2 checkpoint inhibition activity
isolated from the Brazilian ascidian Didemnum granulatum:
structure elucidation and synthesis. J Org Chem 63:9850–9856
Boyd MR, Paull KD (1995) Some practical considerations and
applications of the national cancer institute in vitro anticancer
drug discovery screen. Drug Dev Res 34:91–109
and topoisomerase IV inhibitors obtained by combinatorial
biosynthesis. Antimicrob Agents Chemother 50:1136–1142
Fuente RDL, Sonawane ND, Arumainayagam D, Verkman AS (2006)
Small molecules with antimicrobial activity against E. coli and
P. aeruginosa identified by high-throughput screening. Br J
Pharmacol 149:551–559
Goda FE, Abdel-Aziz AA-M, Attef OA (2004) Synthesis, antimicro-
bial activity and conformational analysis of novel substituted
pyridines: BF3 promoted reaction of hydrazine with 2-alkoxy
pyridines. Bioorg Med Chem 12:1845–1852
Goda FE, Abdel-Aziz AA-M, Ghoneim HA (2005) Synthesis and
biological evaluation of novel 6-nitro-5-substituted aminoquin-
olines as local anesthetic and anti-arrhythmic agents: molecular
modeling study. Bioorg Med Chem 13:3175–3183
Golbraikh A, Tropsha A (2002) Beware of q2. J Mol Graph Model
20:269–276
Bran˜a MF, Ramos A (2001) Naphthalimides as anti-cancer agents:
synthesis and biological activity. Curr Med Chem 1:237–255
ˆ
Cechinel-Filho V, Campos F, Correa R, Nunes JR, Yunes RA (2003)
Chemical aspects and therapeutic potential of cyclic imides.
Grever MR, Schepartz SA, Chabner BA (1992) The National Cancer
Institute: cancer drug discovery and development program.
Semin Oncol 19:622–638
´
´
Henon H, Messaoudi S, Hugon B, Anizon F, Pfeiffer B, Prudhomme
M (2005) Synthesis of granulatimide bis-imide analogues.
Tetrahedron 61:5599–5614
HyperChem (1997) Molecular modeling system. Release 5.1. Hyper-
cube Inc., Gainesville, Florida
Karpenko OS, IDorovs’kikh B, Lyakhov SA, Andronati SA, Zholo-
bak NM, Spivak MY, Nekhoroshkova YV, Shafran LM (2008)
Aminoalkylnaphthalimides as antiviral and interferon inducing
agents. Synthesis and properties. Ukrainica Bioorganica Acta
1:42–48
Lafitte D, Lamour V, Tsvetkov PO, Makarov AA, Klich M, Deprez P,
Moras D, Briand C, Gilli R (2002) DNA gyrase interaction with
coumarin-based inhibitors: the role of the hydroxybenzoate
isopentenyl moiety and the 50-methyl group of the noviose.
Biochemistry 41:7217–7223
Quım Nova 26:230–241
Chernick ET, Ahrens MJ, Scheidt KA, Wasielewski MR (2005)
Copper-promoted N-arylation of cyclic imides for the synthesis
of extended aromatic systems. J Org Chem 70:1486–1489
Collin X, Robert JM, Wielgosz G, Le Baut G, Bobin-Dubigeon C,
Grimaud N, Petit JY (2001) New anti-inflammatory N-pyridi-
nyl(alkyl)phthalimides acting as tumour necrosis factor-a pro-
duction inhibitors. Eur Med Chem 36:639–649
Cruickshank R, Duguid JP, Marion BP, Swain RHA (1975) Medicinal
microbiology, vol II, 12th edn. Churchill Livingstone, London,
pp 196–202
De Souza MM, Correa R, Cechinel V, Grabchev I, Bojinov V (2002)
4-Nitro-1,8-naphthalimides exhibit antinociceptive properties.
Pharmazie 57:430–431
Dewar MJS, Zoebisch EG, Healy EF, Stewart JJP (1985) AM1: a new
general purpose quantum mechanical molecular model. J Am
Chem Soc 107:3902
El-Ayaan U, Abdel-Aziz AA-M, Al-Shihry S (2007) Solvatochro-
mism, DNA binding, antitumor activity and molecular modeling
study of mixed-ligand copper(II) complexes containing the bulky
ligand: bis[N-(p-tolyl)imino]acenaphthene. Eur J Med Chem
42:1325–1333
El-Azab AS, Al-Omar MA, Abdel-Aziz AA-M, Abdel-Aziz NI,
El-Sayed MA-A, Aleisa AM, Sayed-Ahmed MM, Abdel-
Hamide SG (2010) Design, synthesis and biological evaluation
of novel quinazoline derivatives as potential antitumor agents:
molecular docking study. Eur J Med Chem 45:4188–4198
El-Gamal MI, Bayomi SM, El-Ashry SM, Said SA, Abdel-Aziz
AA-M, Abdel-Aziz NI (2010) Synthesis and anti-inflammatory
activity of novel (substituted)benzylidene acetone oxime ether
derivatives: molecular modeling study. Eur J Med Chem 45:
1403–1414
El-Sayed MA, Abdel-Aziz NI, Abdel-Aziz AA-M, El-Azab AS, Asiri
YA, ElTahir KEH (2011) Design, synthesis, and biological
evaluation of substituted hydrazone and pyrazole derivatives as
selective COX-2 inhibitors: molecular docking study. Bioorg
Med Chem 19:3416–3424
Elshafie SMM (1979) N-Substituted 2,20-diphenic acid imides. J Prakt
Chem 321:1034–1038
´
Laronze M, Boisbrun M, Leonce S, Pfeiffer B, Renard P, Lozach O,
Meijer L, Lansiaux A, Bailly C, Sapi J, Laronzea JY (2005)
Synthesis and anticancer activity of new pyrrolocarbazoles and
pyrrolo-beta-carbolines. Bioorg Med Chem 13:2263–2283
Lv M, Xu H (2009) Overview of naphthalimide analogs as anticancer
agents. Curr Med Chem 16:4797–4813
MacGregor KA, McCluskey A (2011) Ionic liquids accelerate
access to N-substituted-1,8-naphthalimides. Tetrahedron Lett 52:
767–769
Maruyama HB, Suhara Y, Suzuki-Watanabe J, Maeshima Y, Shimizu
N, Ogura-Hamada M, Fujimoto H, Takano K (1975) A new
antibiotic, fumaramidmycin. II. Isolation, structure and synthe-
ses. J Antibiot 28:648–655
Mattocks AM, Hutchison OS (1948) Local anesthetics; N-dial-
kylaminoalkylimides of naphthalic and diphenylmaleic acids.
J Am Chem Soc 70:3474
Melchert M, List A (2007) The thalidomide saga. Int J Biochem Cell
Biol 39:1489–1499
Menzel R, Gellert M (1994) The biochemistry and biology of DNA
gyrase. Adv Pharmacol 29A:39–69
MOE 2008.10. Chemical Computing Group Inc., Montreal
Monks A, Scudiero D, Skehan P (1991) Feasibility of a high-flux
anticancer drug screen using a diverse panel of cultured human
tumor cell lines. J Natl Cancer Inst 83:757–766
El-Sherbeny MA, Abdel-Aziz AA-M, Ahmed MA (2010) Synthesis
and antitumor evaluation of novel diarylsulfonylurea derivatives:
molecular modeling applications. Eur J Med Chem 45:689–697
Fahmy AFM, Aly NF, Elkomy M (1979) Synthesis of N-aryl-imides
from acid azides. Indian J Chem Sect B 17B:399–400
Flatman RH, Eustaquio A, Li SM, Heide L, Maxwell A (2006)
Structure–activity relationships of aminocoumarin-type gyrase
Muth M, Hoerr V, Glaser M, Ponte-Sucre A, Moll H, Stich A,
Holzgrabe U (2007) Antitrypanosomal activity of quaternary
naphthalimide derivatives. Bioorg Med Chem 17:1590–1593
Nicolaou KC, Mathison CJN (2005) Synthesis of imides, N-acyl
vinylogous carbamates and ureas, and nitriles by oxidation of
amides and amines with Dess-Martin periodinane. Angew Chem
44:5992–5997
123

Med Chem Res
Paull KD, Nasr M, Narayanan VL (1984) Computer assisted
structure-activity correlations. Evaluation of benzo(de)isoqino-
line-1,3-diones and related compounds as antitumor agents.
Arzneimittelforschung 34:1243–1246
Tropak MB, Blanchard JE, Withers SG, Brown ED, Mahuran D
(2007) High-throughput screening for human lysosomal beta-N-
acetyl hexosaminidase inhibitors acting as pharmacological
chaperones. Chem Biol 14:153–164
Profeta S, Allinger NL (1985) Molecular mechanics calculations on
aliphatic amines. J Am Chem Soc 107:1907–1918
V-Life Molecular Design Suite 3.5. VLife Sciences Technologies Pvt.
Ltd, Pune. www.Vlifesciences.com
Roberge M, Berlinck RGS, Xu L, Anderson HJ, Lim LY, Curman D,
Stringer CM, Friend SH, Davies P, Vincent I, Haggarty SJ, Kelly
MT, Britton R, Piers E, Anderson RJ (1998) High-throughput
assay for G2 checkpoint inhibitors and identification of the
structurally novel compound isogranulatimide. Cancer Res
58:5701–5706
Sahm DF, Washington JA (1991) Antibacterial susceptibility tests:
dilution methods. In: Ballows A, Housler WJ Jr, Herrmann KL,
Isenberg HD, Shadomy HJ (eds) Manual of clinical microbiol-
ogy, 5th edn. ASM, Washington, DC, pp 1105–1116
Xu YF, Qian XH, Yao W, Mao P, Cui JN (2003) Novel naphthalimide
hydroperoxide photonucleases: the role of thiocyclic-fused area
and the difference in spectra, photochemistry and photobiolog-
ical activity. Bioorg Med Chem 11:5427–5433
Zentz F, Valla A, Le Guillou R, Labia R, Mathot AG, Sirot D (2002)
Synthesis and antimicrobial activities of N-substituted imides. II
Farmaco 57:421–426
Zhang Y-Y, Zhou CH (2011) Synthesis and activities of naphthal-
imide azoles as a new type of antibacterial and antifungal agents.
Bioorg Med Chem Lett 21:4349–4352
Skehan P, Storeng R, Scudiero D, Monks A, McMahon J, Vistica D,
Warren JR, Bokesch H, Kenney S, Boyd MR (1990) New
colorimetric cytotoxicity assay for anticancer-drug screening.
J Natl Cancer Inst 82:1107–1112
Zhang FF, Gan LL, Zhou CH (2010) Synthesis, antibacterial and
antifungal activities of some carbazole derivatives. Bioorg Med
Chem Lett 20:1881–1884
123