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ORIGINAL RESEARCH
3D-QSAR studies of chromone derivatives as iron-chelating agent
Narumol Phosrithong • Jiraporn Ungwitayatorn
Received: 27 March 2013 / Accepted: 10 August 2013 / Published online: 22 August 2013
� Springer Science+Business Media New York 2013
Abstract The metal-chelating activity of a series of 48
chromone compounds, evaluated by ferrous (Fe2?) che-
lating test, were subjected to 3D-QSAR studies using
comparative molecular field analysis (CoMFA) and com-
parative molecular similarity indices analysis (CoMSIA).
The best CoMFA model obtained from HF/6-31G*
geometry optimization and field fit alignment gave cross-
validated r2 (q2) = 0.582, non-cross-validated r2 = 0.975.
The best CoMSIA model gave q2 = 0.617, non-cross-val-
idated r2 = 0.917. The resulted CoMFA and CoMSIA
contour maps proposed the Fe2?-chelating sites of chro-
mone compounds compared with those of quercetin.
Keywords Chromone derivatives �Iron-chelating activity � CoMFA � CoMSIA
Introduction
Flavonoids are natural phenyl-substituted chromones
mainly found in fruits, vegetables, and beverages such as
tea and red wine. Several researches in these compounds
have grown in consequence of their reported numerous
properties (Havsteen, 1983), such as anti-inflammatory
(Gabor, 1986; Harborne and Williams, 2000), anti-allergic
(Gabor, 1986), antimicrobial (Harborne and Williams,
2000), estrogenic (Harborne and Williams, 2000; Breinholt
et al., 2000), anti-HIV (Hu et al., 1994; Jesus and Leo-
nardo, 2002), and anticancer activities (Ren et al., 2003;
Ramos, 2007). Recent interests in natural antioxidants have
been stimulated by potential health benefits arising from
the antioxidant activity of flavonoids (Bors and Saran,
1987; Ji and Zhang, 2006; Teixeira et al., 2005). Flavo-
noids like many other polyphenols are very effective rad-
ical scavengers because they are highly reactive hydrogen
or electron donors (Cotelle, 2001; Pannala et al., 2001;
Yang et al., 2001). It is also known that they can chelate
transition metals such as Fe2?, Cu2?, Zn2?, and Mg2? (van
Acker et al., 1996, 1998). The ability of flavonoids to
chelate Fe2? is very important for their antioxidant activity
and would have a double, synergistic action, which would
make them extremely powerful antioxidant (Haenen et al.,
1993). The cytoprotective activity of catechin, quercetin,
and diosmetin could be ascribed to their widely known
antiradical property and also to their strong iron-chelating
effectiveness. These findings increase further the prospects
for the development and clinical application of these potent
antioxidants (Morel et al., 1993).
In the previous studies, we have designed, synthesized a
series of chromone derivatives and evaluated for various
biological activities. It was found that our synthesized chro-
mone compounds-exhibited potent HIV-1 protease (HIV-1
PR) inhibitory activity (Ungwitayatorn et al., 2000, 2011). A
three-dimensional quantitative structure–activity relationship
(3D-QSAR) study using CoMFA and CoMSIA of this chro-
mone series regarding their HIV-1 PR inhibitory activity was
performed (Ungwitayatorn et al., 2004). More recently, they
were investigated for DNA topoisomerase I inhibitory (Ma-
icheen et al., 2013) and antioxidant activities (Phosrithong
et al., 2012). The measurement of Fe2?-chelating activity
showed that 7,8-dihydroxy-2-(30-trifluoromethylphenyl)-3-
(300-trifluoromethylbenzoyl) chromone, 32 (Table 1) was
N. Phosrithong
Faculty of Pharmacy, Siam University, 38 Petkasem Road,
Bangkok 10160, Thailand
J. Ungwitayatorn (&)
Faculty of Pharmacy, Mahidol University, 447 Sri-Ayudhya
Road, Bangkok 10400, Thailand
e-mail: [email protected]
123
Med Chem Res (2014) 23:1037–1045
DOI 10.1007/s00044-013-0710-5
MEDICINALCHEMISTRYRESEARCH
Table 1 Structures and Fe2? chelating activity of the studied chromone derivatives
O
OR5
R6
R7
R8
R2
R3
Compd. R2 R3 R5 R6 R7 R8 IC50 (lM)
1 Phenyl H H H H OH 189.79 ± 1.58
2 CH3 H H H OH OH 322.78 ± 4.66
3 Benzyl H H H OH OH 174.20 ± 1.74
4 Phenyl H H H OH OH 186.70 ± 4.18
5 CH3 H H H OH H 238.11 ± 3.41
6 Benzyl H H H OH H 401.30 ± 1.65
7 Benzyl CH3 H H OH H 145.52 ± 2.09
8 Phenyl H H H OH H 241.72 ± 2.98
9 Phenyl CH3 H H OH H 765.93 ± 1.27
10 40-(NO2)-Phenyl H H H OH H 146.98 ± 9.73
11 30-(CF3)-Phenyl H H H OH H 184.43 ± 1.10
12 40-(F)-Phenyl H H H OH H 215.33 ± 0.99
13 30,50-(diNO2)-Phenyl H H H OH H 196.18 ± 0.58
14 30-(Cl)-Phenyl H H H OH H 178.42 ± 2.76
15 30,40-(diCl)-Phenyl H H H OH H 170.41 ± 9.36
16 40-(t-butyl)-Phenyl H H H OH H 163.03 ± 3.83
17 30-(CF3)-Phenyl H OH H OH H 261.25 ± 2.43
18 40-(F)-Phenyl H OH H OH H 356.58 ± 3.82
19 30,40-(diF)-Phenyl H OH H OH H 241.77 ± 3.07
20 40-(t-butyl)-Phenyl H OH H OH H 120.86 ± 2.29
21 40-(NO2)-Phenyl H OH H OH H 119.10 ± 1.10
22 30,50-(diNO2)-Phenyl H OH H OH H 105.93 ± 7.06
23 30-(Cl)-Phenyl H OH H OH H 233.62 ± 1.67
24 30,40-(diCl)-Phenyl H OH H OH H 224.86 ± 11.09
25 40-(OCH3)-Phenyl H OH H OH H 177.57 ± 11.11
26 30-(OCH3)-Phenyl H OH H OH H 238.25 ± 5.77
27 30-(OCH3)-Phenyl H H OH H H 426.39 ± 5.78
28 30-(Cl)-Phenyl H H OH H H 412.33 ± 7.07
29 40-(F)-Phenyl H H OH H H 371.54 ± 2.40
30 30-(CF3)-Phenyl H H OH H H 286.44 ± 6.97
31 40-(t-butyl)-Phenyl H H OH H H 436.92 ± 9.51
32 30-(CF3)-Phenyl 30 0-(CF3)-benzoyl H H OH OH 60.05 ± 0.31
33 30-(Cl)-Phenyl 30 0-(Cl)-benzoyl H H OH OH 140.46 ± 0.60
34 30-(OCH3)-Phenyl 30 0-(OCH3)-benzoyl H H OH OH 146.69 ± 1.44
35 40-(F)-Phenyl 40 0-(F)-benzoyl H H OH OH 133.86 ± 1.62
36 40-(NO2)-Phenyl 40 0-(NO2)-benzoyl H H OH OH 81.67 ± 1.18
37 40-(OCH3)-Phenyl 40 0-(OCH3)-benzoyl H H OH OH 82.35 ± 1.40
38 30,40-(diF)-Phenyl 30 0,40 0-(diF)-benzoyl H H OH H 147.38 ± 2.01
39 30-(CF3)-Phenyl 30 0-(CF3)-benzoyl H H OH H 119.85 ± 1.37
40 30-(Cl)-Phenyl 30 0-(Cl)-benzoyl H H OH H 295.77 ± 18.47
41 30-(OCH3)-Phenyl 30 0-(OCH3)-benzoyl H H OH H 108.34 ± 2.73
1038 Med Chem Res (2014) 23:1037–1045
123
the most active compound with IC50 = 60.05 ± 0.31 lM
which was much more potent than butylated hydroxytoluene
(BHT) (IC50 = 321.34 ± 9.79 lM), vitamin E (IC50 =
169.79 ± 1.01 lM) and trolox (IC50 = 338.64 ± 4.81 lM)
from the same experiment (Phosrithong et al., 2012). To
explore the relationships between the structures of these
chromone compounds and their Fe2?-chelating activity, a
three-dimensional quantitative structure–activity relation-
ship (3D-QSAR) study using CoMFA and CoMSIA were
performed.
CoMFA samples the steric (Lennard-Jones) and elec-
trostatic (Coulombic) fields surrounding a set of ligands
and constructs a 3D-QSAR model by correlating these 3D
steric and electrostatic fields with the corresponding
observed activities (Cramer et al., 1988). CoMSIA has the
basic principle same as that of CoMFA, but includes some
additional fields, i.e., hydrophobicity, hydrogen-bond
donor, and hydrogen-bond acceptor (Klebe et al., 1994;
Klebe and Abraham, 1999). The fields obtained from both
CoMFA and CoMSIA were evaluated by a partial least
squares (PLS) analysis, with a cross-validation procedure,
which was employed to select relevant components from
the large set of data to build up the best QSAR equation.
The resulted contour maps of CoMFA and CoMSIA fields
are used as visual guides for design of the new and more
potent compounds.
Materials and methods
Biological activity data of chromone derivatives
The metal-chelating activity of chromone derivatives was
determined by the Fe2?-chelating method (Dinis et al.,
1994). In the presence of chelating agents, red color of
Fe2?–ferrozine complex formation is interrupted. Mea-
surement of color reduction, therefore, allows the estima-
tion of the chelating activity of sample (Yamaguchi et al.,
2000). The percentage of inhibition of the Fe2?–ferrozine
complex formation was calculated using the following
equation:
Ferrous irons-chelating effect %ð Þ¼ Acontrol � Asample
�Acontrol
� �� 100
where Acontrol is the absorbance of the control (containing
FeCl2 and ferrozine), and Asample is the absorbance of tested
compound. IC50 (concentration of test compounds needed
to reduce the absorption at 562 nm by 50 %) was obtained
by interpolation from linear regression plot between con-
centration and percentage of Fe2?-chelating effect and
expressed as lM. The IC50 values expressed in units of
micromolar concentration were transformed to molar and
then converted to pIC50 (-logIC50). A total of 48 chro-
mone compounds were used as data sets, of which 11
compounds were chosen as a test set while the remaining
37 compounds were treated as a training set. The selected
test set represented a range of activity similar to that of the
training set and was used to evaluate the predictive power
of the CoMFA and CoMSIA models.
Generating the 3D structures
The 3D structures of all studied compounds were modeled
with SYBYL 8.1 molecular modeling program (Tripos
Associates, Saint Louis, MO) on Indigo Elan workstation
(Silicon Graphics Inc., Mountain View, CA) using sketch
approach. The fragment libraries in SYBYL database
containing of small molecules were used as building blocks
for construction of larger ones. Each structure was first
energy minimized using the standard Tripos force field
(Powell method and 0.05 kcal/mol A energy gradient
convergence criteria) and electrostatic charge was assigned
by Gasteiger–Huckel method.
Geometry optimization
The minimized structures were further optimized by
semiempirical AM1 using MOPAC 6.0 or by ab initio HF/
6-31G* using Gaussian 03W program package. The fully
geometrical optimized structures were used in the follow-
ing 3D QSAR studies.
Table 1 continued
Compd. R2 R3 R5 R6 R7 R8 IC50 (lM)
42 40-(F)-Phenyl 40 0-(F)-benzoyl H H OH H 118.87 ± 0.74
43 40-(NO2)-Phenyl 40 0-(NO2)-benzoyl H H OH H 124.09 ± 3.63
44 40-(OCH3)-Phenyl 40 0-(OCH3)-benzoyl H H OH H 114.36 ± 4.84
45 40-(t-butyl)-Phenyl 40 0-(t-butyl)-benzoyl H H OH H 243.68 ± 12.09
46 30-(OCH3)-Phenyl 30 0-(OCH3)-benzoyl OH H OH H 122.31 ± 0.49
47 40-(NO2)-Phenyl 40 0-(NO2)-benzoyl OH H OH H 144.67 ± 2.43
48 40-(t-butyl)-Phenyl 40 0-(t-butyl)-benzoyl H OH H H 246.79 ± 1.69
Med Chem Res (2014) 23:1037–1045 1039
123
Structural alignments
Two different structural alignments were performed: (i) The
MOPAC geometry optimized structures were aligned on the
template molecule, chromone 32, which is the most active
molecule among the given set. All chromone derivatives
were aligned by the Align Database command available in
SYBYL. Chromone nucleus was used as substructure to
evaluate the best fit. Substructural overlap assumes that the
molecules share a common core of atoms which is over-
lapped in each of the molecules of the database. (ii) The HF/
6-31G* optimized structures were aligned by field fit func-
tion in SYBYL. The field fit alignment of molecules was
based on trying to increase field similarity within a series of
studied molecules. The rms differences in the sum of steric
and electrostatic interaction energies averaged across all
(possibly weighted) lattice points, between that molecule
and the template was minimized to find the best fit. Chro-
mone 32 was used as the template molecule.
CoMFA and CoMSIA setup
CoMFA and CoMSIA were performed using the QSAR
option in SYBYL 8.1. In CoMFA, the cubic grid space was
generated around molecules in the training set based on the
molecular volume of the structures. A sp3-carbon atom was
probed with a ?1.0 U charge, 2.0 A grid spacing, and the
default 30 kcal/mol energy cutoff for steric and electro-
static fields.
CoMSIA was performed using five physicochemical
properties (steric, electrostatic, hydrophobic, hydrogen-
bond donor, and acceptor) were evaluated using common
probe atom with 1 A radius, charge ?1.0, hydrophobicity
?1.0, hydrogen-bond donor and acceptor properties ?1.0.
Similarity indices were calculated using Gaussian-type
distance dependence between the probe and the atoms of
the molecules of the data set. This functional form requires
no arbitrary definition of cutoff limits, and similarity
indices can be calculated at all grid points inside and
outside the molecule. The value of the attenuation factor awas set to 0.3.
PLS methodology was used for all 3D-QSAR analyses.
The grid had a resolution of 2.0 A and extended beyond the
molecular dimensions by 4.0 A in all directions. Column
filtering was set to 2.0 kcal/mol. CoMFA and CoMSIA
models were developed using the conventional stepwise
procedure. The optimum number of components used to
derive the non-validated model was defined as the number of
components leading to the highest cross-validated r2 (q2) and
the lowest standard error of prediction (SEP). The q2 values
were derived after ‘‘leave-one-out’’ cross-validation. The
non-cross-validated models were assessed by the explained
variance r2, standard error of estimate (S) and F ratio. The
non cross-validated analyses were used to make predictions
of pIC50 of the chromone derivatives from the test set and to
display the coefficient contour maps. The actual versus
predicted pIC50 were fitted by linear regression, and the
‘‘predictive’’ r2, S, and F ratio were determined.
Results and discussion
In our previous study, the metal-chelating activity of
chromone derivatives was determined by the ferrous ion
(Fe2?)-chelating method (Phosrithong et al., 2012). A few
reports of structure–activity relationship (SAR) studies of
Table 2 CoMFA results (grid space 2.0 A, column filtering 2.0 kcal/
mol and energy cut off 30.0 kcal/mol)
AM1 HF/6-31G*
Align database Field fit
Cross-validation
Optimal components 5 6
q2 0.472 0.582
S 0.186 0.168
Non-cross-validation
r2 0.892 0.975
S 0.084 0.041
F 51.267 193.774
Contribution
Steric 0.586 0.540
Electrostatic 0.414 0.460
Table 3 The actual, predicted activities, and the residuals from the
best CoMFA model
Compd. pIC50
Actual Predicted Residual
Training set
1 3.723 3.702 0.021
3 3.762 3.755 0.007
6 3.397 3.423 -0.026
8 3.616 3.606 0.010
9 3.115 3.083 0.032
10 3.833 3.870 -0.037
11 3.733 3.770 -0.037
12 3.667 3.599 0.068
13 3.707 3.735 -0.028
14 3.749 3.729 0.020
15 3.769 3.749 0.020
16 3.788 3.778 0.010
18 3.437 3.523 -0.086
19 3.616 3.662 -0.046
1040 Med Chem Res (2014) 23:1037–1045
123
Fe2?-chelating agents are available (Leopoldini et al.,
2006; Kalinowski et al., 2008) and in those they were not
the subject of 3D-QSAR studies. The objective of this
study was to determine the relationships between the
structures of chromone derivatives and their Fe2?-chelating
activity. In the absence of information regarding the bio-
logical target, indirect ligand-based approaches 3D-QSAR,
i.e., CoMFA and CoMSIA can assist in clarifying the SAR.
CoMFA study
Since the alignment of the compound is one of the critical
step for CoMFA study, in this study we have aligned the
ligands onto a template molecule chromone 32 using two
alignment rules (Table 2). We initially performed analysis
using highest occupied molecular orbital (HOMO), lowest
unoccupied molecular orbital (LUMO) and ClogP in
addition to CoMFA (steric and electrostatic) fields vari-
ables. It was found that inclusion of more physicochemical
properties resulted in both cross-validated r2 (q2) and non-
cross-validated r2 less than 0.4 (data not shown). For a
reliable predictive model, the cross-validated correlation
coefficient, q2, should be more than 0.5 (Golbraikh and
Tropsha, 2002). Hence, only traditional CoMFA fields
were used as variable descriptors. The HF/6-31G* opti-
mized structures and field fit alignment has achieved the
required q2 value criteria. This model yielded q2 = 0.582
and non-cross-validated r2 = 0.975, where as the AM1
optimization with SYBYL Align Database produced lower
q2 (0.472) and r2 (0.892) values (Table 2).
The actual (experimental), the predicted (calculated)
pIC50 and the residuals of the predictions are shown in
Table 3. The scattered plots of the actual and predicted
pIC50 of chromones in the training set and test set are
shown in Fig. 1a.
CoMSIA study
The CoMSIA study was performed using the same PLS
protocol and stepwise procedure as in the CoMFA. The
Fig. 1 Actual versus predicted
pIC50 (-logIC50) for the
training set (filled square) and
the test set (filled triangle).
a CoMFA. b CoMSIA
Table 3 continued
Compd. pIC50
Actual Predicted Residual
20 3.918 3.950 -0.032
21 3.921 3.904 0.017
22 3.976 3.997 -0.021
23 3.632 3.627 0.005
24 3.649 3.634 0.015
26 3.623 3.611 0.012
27 3.370 3.380 -0.010
28 3.385 3.399 -0.014
29 3.430 3.422 0.008
30 3.543 3.514 0.029
31 3.359 3.329 0.030
32 4.221 4.198 0.023
33 3.852 3.875 -0.023
34 3.834 3.898 -0.064
35 3.871 3.808 0.063
36 4.088 4.067 0.021
37 4.084 4.086 -0.002
39 3.921 3.927 -0.006
40 3.529 3.628 -0.099
43 3.907 3.846 0.061
44 3.941 3.887 0.054
45 3.612 3.612 0.000
46 3.913 3.903 0.010
Test set
2 3.836 3.778 0.058
4 3.731 3.849 -0.118
5 3.623 3.554 0.069
7 3.836 3.649 0.187
17 3.583 3.987 -0.404
25 3.750 3.829 -0.079
38 3.832 3.630 0.202
41 3.966 3.698 0.268
42 3.925 3.592 0.333
47 3.840 4.021 -0.181
48 3.607 3.407 0.200
Med Chem Res (2014) 23:1037–1045 1041
123
best CoMSIA model, which gave q2 = 0.617 and
r2 = 0.917, was also obtained from the HF/6-31G* opti-
mized structures and field fit alignment (Table 4). The
actual, predicted pIC50, and the residuals of the predictions
are shown in Table 5. The scattered plots of the actual and
predicted pIC50 of compounds in the training set and test
set from the best CoMSIA model are illustrated in Fig. 1b.
The statistical outcomes and the linearity of the scattered
plots indicate the high fitting and predictive abilities of
both CoMFA and CoMSIA models.
CoMFA and CoMSIA contour maps
The QSARs produced by CoMFA and CoMSIA models,
which are usually represented as 3D ‘‘coefficient contour
maps’’ are shown in Figs. 2 and 3, respectively. The
molecular structure of chromone 32 was displayed inside
the field as the reference structure. The contour maps from
the best CoMFA and CoMSIA models show common
features. The steric contour maps of CoMFA (Fig. 2a) and
CoMSIA (Fig. 3a) indicate that the steric groups are pre-
ferred at C-30 and C300 (shown as green contours). The
electrostatic contour maps from CoMFA (Fig. 2b) and
CoMSIA (Fig. 3b) models indicate that the electronegative
Table 4 CoMSIA statistical results
AM1 HF/6-31G*
Align database Field fit
Cross-validation
Optimal components 3 6
q2 0.509 0.617
Non-cross-validation
r2 0.781 0.917
S 0.116 0.075
F 39.312 54.970
Contribution
Steric 0.317 0.525
Electrostatic 0.683 0.475
Hydrophobic – –
H-donor – –
H-acceptor – –
Table 5 The actual, predicted activities, and the residuals of
CoMSIA
Compounds pIC50
Actual Predicted Residual
Training set
1 3.723 3.682 0.041
3 3.762 3.801 -0.039
6 3.397 3.398 -0.001
8 3.616 3.630 -0.014
9 3.115 3.079 0.036
10 3.833 3.838 -0.005
11 3.733 3.771 -0.038
12 3.667 3.636 0.031
13 3.707 3.772 -0.065
14 3.749 3.624 0.125
15 3.769 3.661 0.108
16 3.788 3.831 -0.043
18 3.437 3.607 -0.170
19 3.616 3.713 -0.097
20 3.918 3.905 0.013
21 3.921 3.922 -0.001
22 3.976 3.869 0.107
23 3.632 3.608 0.024
24 3.649 3.610 0.039
26 3.623 3.656 -0.033
27 3.370 3.415 -0.045
28 3.385 3.426 -0.041
29 3.430 3.447 -0.017
30 3.543 3.524 0.019
31 3.359 3.338 0.021
32 4.221 4.166 0.055
33 3.852 3.830 0.022
Table 5 continued
Compounds pIC50
Actual Predicted Residual
34 3.834 3.952 -0.118
35 3.871 3.760 0.111
36 4.088 4.039 0.049
37 4.084 4.116 -0.032
39 3.921 4.002 -0.081
40 3.529 3.676 -0.147
43 3.907 3.855 0.052
44 3.941 3.903 0.038
45 3.612 3.570 0.042
46 3.913 3.855 0.058
Test set
2 3.836 4.037 -0.201
4 3.731 3.850 -0.119
5 3.623 3.844 -0.221
7 3.836 3.631 0.205
17 3.583 3.974 -0.391
25 3.750 3.958 -0.208
38 3.832 3.627 0.205
41 3.966 3.807 0.159
42 3.925 3.600 0.325
47 3.840 3.881 -0.041
48 3.607 3.318 0.289
1042 Med Chem Res (2014) 23:1037–1045
123
substituent (the red contours) are preferred at C-3 and C-4
of ring C, C-5 and C-6 of ring A. Electropositive sub-
stituent (the blue contours) should be around C-7 and C-8
of ring A. These 3D-QSAR results concerning electro-
negative groups at C-3, C-4, and C-5 of chromones
increasing the activity support the finding that 3-OH,
O
O
OH
HO
O
CF3
CF3
1
2
43
56
78
2'3'
4'
5'
6'
2"
3"4"
5"
6"
A
B
C
D
(a) (b)
Chromone 32
Fig. 2 The CoMFA contour
maps: a steric contour map.
b Electrostatic contour map.
The green contour refers to
sterically favored region; the
yellow contours indicate
disfavored areas. The blue
contour indicates region where
electropositive substituent is
favored and red contour refers
to region where electronegative
substituent is favored (Color
figure online)
(a) (b)
O
O
OH
HO
O
CF3
CF3
1
2
43
56
78
2'3'
4'
5'
6'
2"
3"4"
5"
6"
A
B
C
D
Chromone 32
Fig. 3 The CoMSIA contour
maps: a steric contour map.
b Electrostatic contour map.
The green contour suggests that
more bulky substituent
improves the activity, the yellow
contour indicates that more
steric bulk is unfavorable for the
activity. The blue contour
indicates that the electropositive
substituent increases the
activity, the red contour
indicates that the
electronegative substituent is
favorable for the activity (Color
figure online)
Med Chem Res (2014) 23:1037–1045 1043
123
4-oxo, and 5-OH groups of quercetin are the favored
coordinating sites for the iron cation (Leopoldini et al.,
2006). Quercetin is a powerful chelating agent which was
reported that both neutral and deprotonated structures can
chelate with Fe2?. The Fe2? chelating sites in chromone
structures were proposed as illustrated in Fig. 4, comparing
with chelating sites of quercetin. The 4-keto and benzoyl
oxygen of chromone 32, 4-keto, 5-OH and benzoyl oxygen
of chromone 46 were proposed as Fe2?-chelating sites,
corresponding to 3-OH, 4-keto, 5-OH of quercetin.
As shown in Table 1, chromone compounds with either
3-substituted benzoyl and 5-OH groups (chromones 46–47,
IC50 = 122.31–144.67 lM) or 3-substituted benzoyl sub-
stituent (chromones 32–48, IC50 = 60.05–295 lM)
exhibited good activity. Chromone derivatives with IC50
higher than 400 lM, such as chromones 6, 9, 27–28, and
31, were compounds whose structures bearing no benzoyl
substituent at C-3 (R3 = H, CH3) and 5-OH group. These
experimental results corresponded to the 3-D QSAR elec-
trostatic contour maps. The resulting SAR was also in
agreement with the previous studies reported that the
compounds with structures containing two or more of the
following functional groups, i.e., OH, SH, COOH, C=O,
NR2, –S– and –O– showed metal-chelation activity (Gul-
cin, 2006; Fiorucci et al., 2007).
Conclusion
In this study, the ligand-based 3D-QSAR, CoMFA ,and
CoMSIA have been successfully applied to a set of novel
chromone series. Statistical parameters illustrate the
established CoMFA and CoMSIA models are reliable. The
contour maps provide structure–Fe2?-chelating activity
relationships and suggest key structural features that may
help to design new compounds with improved activities.
The potent Fe2? chelators might be useful for the treatment
of patients with iron-overload disease and cancer, as well
as neurodegenerative and chronic inflammatory diseases.
Acknowledgments This research is supported by the Office of the
Higher Education Commission and Mahidol University under the
National Research Universities Initiative. The authors thank High
Performance Computer Center (HPCC), National Electronics and
Computer Technology Center (NECTEC) of Thailand for providing
SYBYL version 8.1 facilities.
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O
O
O
O
HO
Fe+Fe+
O
O
HO
O
Fe+
O
Fe+
OCH3
OCH3
O
O O
Fe+
CF3
CF3HO
OH
(a) (b)
(c)
Fig. 4 Proposed Fe2?-chelating
sites for a chromone 32,
b chromone 46 compared to
c quercetin
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