CoMFA and CoMSIA studies of 1,2-dihydropyridine derivatives as anticancer agents

Article abstract of DOI:10.2174/157340612800786598

Taking advantage of our in-house experimental data on 3-cyano-2-imino-1, 2-dihydropyridine and 3-cyano-2-oxo-1,2-dihydropyridine derivatives as inhibitors of the growth of the human HT-29 colon adenocarcinoma tumor cell line, we have established a highly significant CoMFA and CoMSIA models (q ² _cv=0.70/0.639). The models were investigated to assure their stability and predictivity (r² _pred= 0.65/0.61) and successfully applied to design two new potential cell growth inhibitory agents with IC₅₀s in the submicromolar range.

Full text of DOI:10.2174/157340612800786598

372
Medicinal Chemistry, 2012, 8, 372-383
CoMFA and CoMSIA Studies of 1,2-dihydropyridine Derivatives as
Anticancer Agents
Ismail Salama1,2, Mohamed A. O. Abdel-Fattah1, Marwa S. Hany1, Shaimaa A. El-Sharif1,
Mahmoud A. M. El-Naggar1, Rasha M. H. Rashied1, Gary A. Piazza3 and Ashraf H. Abadi1*
1Department of Pharmaceutical Chemistry, Faculty of Pharmacy and Biotechnology, German University in Cairo,
Cairo 11835, Egypt
2Department of Pharmaceutical Chemistry, Faculty of Pharmacy, Suez Canal University, Ismailia 41522, Egypt
3Mitchell Cancer Institute, University of South Alabama, 1660 Springhill Avenue, Suite 3029, Mobile AL 36604-1405,
USA
Abstract: Taking advantage of our in-house experimental data on 3-cyano-2-imino-1, 2-dihydropyridine and 3-cyano-2-
oxo-1,2-dihydropyridine derivatives as inhibitors of the growth of the human HT-29 colon adenocarcinoma tumor cell
line, we have established a highly significant CoMFA and CoMSIA models (q2 cv =0.70/0.639). The models were investi-
gated to assure their stability and predictivity (r2 pred= 0.65/0.61) and successfully applied to design two new potential cell
growth inhibitory agents with IC50s in the submicromolar range.
Keywords: 3D-QSAR, CoMFA/CoMSIA model, 3-cyano-2-imino-1,2-dihydropyridine, 3-cyano-2-oxo-1,2-dihydropyridine.
1. INTRODUCTION
predictivity, we demonstrate the applicability of the resulting
model in guiding synthetic efforts toward highly potent anti-
cancer agents. Thus, the study involved the prediction, syn-
thesis, and biological testing of novel 3-cyano-4,6-diaryl-2-
(1H) iminopyridines (36,37). The good correspondence be-
tween the obtained experimental log IC50 values and our pre-
vious predictions is encouraging. To ensure optimal compa-
rability of the anticancer activity for all ligands, we have
obtained all biological data used in this study within our
laboratory.
There is an interest in the 3-cyano-4,6-diaryl-2-(1H)
imino or oxopyridine series of compounds due to their di-
verse pharmacological activities, such as antimicrobials
against Gram positive and Gram negative microorganisms
[1-3], antidepressant [4], cardiotonic through inhibition of
PDE3 [5] and anticancer [6-8]. Cross reactivity is a potential
possibility e.g. milrinone; is a 3-cyano-2-oxopyridine deriva-
tive that has been introduced to the clinic for the treatment of
congestive heart failure. Its mechanism of action involves
PDE3 inhibition, leading to high levels of cAMP and conse-
quent inotropic effect. Recent studies showed that PDE3,
PDE4 and PDE5 are over expressed in cancerous cells com-
pared with normal cells. In addition, cross inhibition of
PDE3 together with other PDEs may lead to inhibition of
tumor cell growth and angiogenesis [9-12].
2. METHODS
2.1. Generation of Structures and Exploration of the
Conformational Space
Structure building and refinement for the entire set of
1,2-dihydropyridine analogues (1–37, Scheme 1 and Table
1) was accomplished using SYBYLX 1.1 molecular model-
ing software [13]. Compound 1 has been selected as a tem-
plate for generating the whole series of the 1,2-
dihydropyridines.
We have reported 3- cyano -2-iminopyridine and 3-
cyano -2-oxopyridine derivatives for their anticancer activ-
ity, particularly against leukemia and colorectal HT-29 cell
lines.6-8 Taking advantage of our in-house data on inhibitors
of the growth of the human HT-29 colon adenocarcinoma
tumor cell line, we herein present a ligand-based 3D-QSAR
approach (Chart 1) in order to improve and refine our under-
standing of the molecular requirements for optimized anti-
cancer activity, as well as to establish a working model that
allows to facilitate further drug discovery efforts. We applied
a well-established protocol on our training and test set com-
prising of 30 and 5 compounds, respectively. In addition to
several techniques for monitoring the statistical quality and
We performed a grid search, using the Tripos force field
[14] with Gasteiger–Marsili charges [15], on the 4,6-phenyl-
1,2-dihydropyridine structure, iterating the bonds ,between
1,2-dihydropyridine and the two phenyl rings on 4&6 posi-
tions, in steps of 30°. The most reasonable low energy con-
former was chosen from the obtained conformations. Using
this template structure, all other ligands were derived by
modification of the aromatic moiety and the phenyl substitu-
ents at position 6 and 4 respectively. Finally, all ligands were
optimized with MOPAC using a semiempirical AM1
Hamiltonian [16] to improve the molecular geometries and
ensure better comparability of the ligand structures by
providing structures based on identical levels of calculation.
*Address correspondence to this author at the Department of Pharmaceuti-
cal Chemistry, Faculty of Pharmacy and Biotechnology, German University
in Cairo, Cairo 11835, Egypt; Tel: 202-27590716; Fax: 202-27581041;
E-mail: ashraf.abadi@guc.edu.eg
1875-6638/12 $58.00+.00
© 2012 Bentham Science Publishers

CoMFA and CoMSIA Studies of 1,2-dihydropyridine Derivatives
Medicinal Chemistry, 2012, Vol. 8, No. 3 373
NH
X
NC
NC
NH
NH
R1
R4
S
R2
R1
R3
4 and 9
1-3, 5-7, 10-12, 14-16 and 18-37
NH
NH
NC
R2
NH
NC
NH
R1
O
O
8 and 17
H3CH2CO
13
Scheme 1.
3-cyano-4,6-diaryl-2-(1H)imino or oxo-1,2-dihydropyridines
NH or O
N
NH
Ar1
Ar2
(1-35)
2. Validation
1. Development
CoMFA
CoMSIA
Models
NH
NH
N
N
NH
NH
Br
Br
OCH3
37
CI
CI
36
Chart 1.

374 Medicinal Chemistry, 2012, Vol. 8, No. 3
Salama et al.
Table 1. Data Set of 30 Compounds in the Training Set and 5 Compounds in the Test Set and their Experimental and Predicted
Activity a
a
ꢀlogIC50
logIC50
Cpd.
R1
R2
R3
R4
X
(Exp.)
CoMFA
CoMSIA
Training set
1
2
3
4
5
6
7
3-Br
2-Br
-
Cl
-
4-Cl
NH
NH
O
0.329
0.397
0.436
0.438
0.491
0.600
0.633
-0.027
-0.304
-0.439
0.482
0.308
-0.561
-0.212
0.531
0.536
-0.653
0.043
-OCH3
-
2-Br
-OCH3
-
-
4-OCH3
2-Br
-
-
-
-
-
-OCH3
-
-
4-Cl
NH
NH
NH
-0.348
-0.247
0.217
4-Br
-
5-OCH3
4-OCH3
3-Br
-
NH
8
4-Br
-
-
-
0.780
-0.666
-0.452
9
2-OCH2CH3
2-Br
-
-
-
-
-
-
-
0.807
1.016
1.017
1.060
1.080
1.114
1.113
1.322
-0.533
0.091
0.463
0.102
0.293
-0.319
0.004
0.526
0.546
0.031
0.483
-0.369
-0.129
0.303
0.119
0.446
10
11
12
13
14
15
16
Cl
Cl
-
-
NH
NH
NH
-
2-Br
-
4-Br
4-OCH2CH3
3-Br
3-CF3
4-Br
Cl
Cl
-
-
-
-
5-Cl
-
NH
O
-OH
O
17
4-Br
-
-
-
1.430
-0.424
0.044
O
NO2
18
19
20
21
22
23
24
25
26
27
28
29
30
4-Br
4-F
4-F
3-F
3-F
4-F
2-F
3-F
2-F
2-F
3-F
2-F
3-F
-OCH3
-OH
-
-
-
NH
O
1.700
1.281
1.965
1.868
1.164
1.972
0.591
1.996
1.972
1.352
1.994
0.76
0.356
0.000
0.256
0.325
-0.467
0.028
-0.444
-0.062
0.432
0.229
0.256
0.276
0.014
-0.151
0.320
0.018
0.404
0.130
0.364
-0.317
0.293
0.401
-0.132
0.084
0.458
0.289
-
-OCH3
-OCH3
-OH
-
-
NH
O
-
-
-
-
NH
NH
O
OCH2CH3
-OH
-
-
-
-
OCH2CH3
OCH2CH3
-OCH3
OCH3
-
-
-
NH
NH
O
-
-
-
-
-
-
NH
NH
NH
Cl
Cl
4-Cl
4-Cl
-
0.597

CoMFA and CoMSIA Studies of 1,2-dihydropyridine Derivatives
Medicinal Chemistry, 2012, Vol. 8, No. 3 375
Table 1. contd….
a
ꢀlogIC50
logIC50
Cpd.
R1
R2
R3
R4
X
(Exp.)
CoMFA
CoMSIA
Test set
31
32
33
34
35
4-Br
3-Br
2-F
Cl
Cl
-
-
-
-
-
4-Cl
O
O
0.681
1.113
0.813
1.779
1.969
-0.359
-0.160
0.179
-0.190
-0.466
-0.301
-0.368
0.059
-
-
-
-
OH
NH
O
3-F
-OCH2CH3
OCH3
-0.749
-0.041
2-F
NH
a ꢀ logIC50 is the error of fitted activities = [logIC50 experimental - logIC50 fitted].
2.2. Alignment
2.3. CoMFA and CoMSIA
To ensure the best comparability between molecules, all
pharmacological data of the ligands used in this study have
been measured within our laboratory. We performed a Com-
parative Molecular Field Analysis (CoMFA) evaluating the
typically used steric and electrostatic fields implemented in
SYBYL. All CoMFA calculations were accomplished using
an sp3 carbon atom with a charge of +1, a cutoff value of 30
kcal/mol for the Lennard-Jones and Coulomb-type potential,
and a constant dielectric function. The dimension of the sur-
rounding lattice (1.0 A ˚ grid spacing) was selected with a
sufficiently large margin to enclose all aligned molecules by
at least 4 A˚. Putative problems of the analysis can arise
from the absolute orientation of the molecules within the
grid space. A useful protocol to address this problem is the
AOS/APS script of Wang et al., [20] which automatically
rotates/translates the entire dataset within the lattice without
changing the relative orientation or alignment of the mole-
cules. We applied the APS protocol varying the ligands by
steps of 0.1 A ˚ in all three dimensions within a 1.0 A ˚ grid.
After each of 1000 translational steps, the PLS analysis was
repeated. This procedure gives detailed information about
the translational dependence of the CoMFA, helps to ensure
that no artificial effects are included, and provides evidence
of the robustness of the model. In the CoMSIA, all five
physicochemical descriptors (electrostatic, steric, hydropho-
bic, and hydrogen-bond donor and acceptor) were evaluated
using a common probe atom placed within a 3D grid. The
atom was set up with a radius of 1.0 A˚ and charge, hydro-
phobic interaction, and hydrogen-bond donor and acceptor
properties all equal to +1. Like in the CoMFA, the grid was
extended beyond the molecular dimensions by 4.0 A ˚ in the
x, y, and z directions and the spacing between probe points
within the grid was set at 1.0 A˚. For the attenuation factor ꢁ
controlling the steepness of the Gaussian function, the stan-
dard value of 0.3 was accepted.
In general, the alignment is one of the most challenging
aspects of 3D-QSAR. Thus, various approaches involving
different degrees of complexity exist in order to address this
problem. The spectrum of available techniques ranges from
simple atom-based fitting procedures to sophisticated bind-
ing-site guided protocols. However, none of these has
evolved to be superior over the others. We have found the
ligand-based alignment technique ASP to be very useful.
Thus, we used the module ASP as implemented in the QSAR
package TSAR [17], which allows us to perform an align-
ment by comparison of steric overlap and molecular electro-
static potentials. For deriving reasonable electrostatic poten-
tials, first, VESPA charges were calculated using the
semiempirical program package VAMP [18]. These atom
centered partial charges are obtained by a fit of the electronic
wave function to the atomic positions. Compared to other
charge schemes such as Coulson or Mulliken, they have the
advantage that the anisotropy of the electron distribution
around the molecule, especially for aromatic systems, is de-
scribed in more detail.
As the basic template onto which the other ligands are to
be superimposed, we have chosen compound 1, which shows
considerably high growth inhibitory activity on HT-29 colon
adenocarcinoma tumor cell line. To quantify the relative
orientation of two molecules, the combined similarity index
based on the Carbo index for electrostatics and the shape
similarity index to account for steric differences was evalu-
ated (with both indices weighted equally) using three Gaus-
sian functions for integration. This parameter was then opti-
mized by overlaying the centroids of the molecules and per-
forming a full translational and orientational search of each
rigid comparison molecule relative to the lead compound 1
by systematically rotating around the Cartesian x-, y-, and z-
axes in 10°steps. For each new orientation, a Simplex algo-
rithm in combination with Simulated Annealing directs the
six degrees of freedom to an alignment with optimal similar-
ity [19]. Finally, the orientation and placement of each
ligand on the template 1 featuring the highest score related to
this search algorithm was chosen to yield the TSAR-based
alignment.
2.4. Partial Least Squares (PLS) Analysis
The PLS method [21] was used to linearly correlate the
CoMFA and CoMSIA fields to biological activity values.
The cross-validated analysis was performed using the leave-
one-out (LOO) method in which one compound is removed

376 Medicinal Chemistry, 2012, Vol. 8, No. 3
Salama et al.
Table 2. Summary of the Results from Several PLS Runs after Applying Different Levels of Noise Reduction by Column Filtering
(ꢀmin
)
CoMFA
C
CoMSIA
H
PLS Run
A
B
D
E
F
G
I
J
ꢀmin
1
2
3
4
5
1
2
3
4
5
q2
0.700
0.479
0.945
0.267
208.0
4
0.700
0.465
0.947
0.265
211.9
4
0.702
0.455
0.947
0.262
215.6
4
0.704
0.443
0.947
0.263
215.1
4
0.680
0.428
0.932
0.292
170.2
4
0.637
0.533
0.964
0.233
141.1
4
0.639
0.529
0.965
0.232
142.3
4
0.642
0.528
0.966
0.229
146.4
4
0.648
0.524
0.966
0.227
150.0
4
0.655
0.515
0.967
0.226
151.4
4
cv
SPRESS
r2
S
F
Components
Descriptors
Fraction
Steric
3544
2241
1682
1393
1231
6581
4626
3503
2740
2110
0.723
0.277
0.704
0.296
0.671
0.329
0.645
0.355
0.639
0.361
0.148
0.255
0.341
0.256
0.148
0.255
0.341
0.256
0.160
0.256
0.334
0.252
0.187
0.264
0.316
0.241
0.215
0.264
0.306
0.241
Electrostatic
Hydrophob
Donor
and its activity is predicted using the model derived from the
rest of the dataset. Complementing the results obtained from
the leave-one-out cross-validation, the more robust leave-
many-out procedure was performed to ensure the
reproducibility of q2 [14].
The cross-validated q2 that resulted in minimal number of
components and lowest standard error of prediction was ac-
cepted. To speed up the analysis and reduce noise, column
filtering values (ꢀmin) were iterated between 1.0 and 5.0
kcal/mol. Thus, only columns with a standard deviation of
more than ꢀmin were used for the cross-validation, resulting
in approximately 5.2– 15.3% of the original data to be used
in the CoMFA and 4.0–12.6% used in the CoMSIA. A final
non-cross-validated analysis was performed using the opti-
mal number of previously identified components. After ob-
taining the models, the CoMFA and CoMSIA results were
graphically interpreted by field contribution maps.
of r2 pred. Based on the test set molecules; this predictive cor-
relation coefficient (r2pred) is defined as r2
= (SD -
PRESS)/SD; where SD is the sum of the squared deviations
of each biological property value from their mean and
PRESS is the sum of squared differences between the pre-
dicted and actual logIC50 values for every molecule in test
set.
pred
3. RESULTS AND DISCUSSION
3.1. CoMFA
Using our properly selected training set of 30 1,2-
dihydropyridine derivatives (Scheme 1 and Table 1), we
obtained statistically significant QSAR models (Table 2).
The initial PLS analysis of our aligned training set applying
a default ꢀmin data filter of 2 kcal/mol yielded a crossvali-
dated q2 of 0.7 with Sc/v = 0.465 using 4 components (Table
2, Panel B). Increasing the minimum level of field variation
ꢀmin to purpose a more efficient reduction of noise, only a
negligible further improvement of the statistics of the
CoMFA model is found for ꢀmin = 3.0 and 4.0 kcal/mol
(Table 2, Panels C, D). Likewise, reduction of ꢀmin to 1.0
kcal/mol shows also only negligible effects on the q2cv (Table
2, Panel A). In contrast, increasing min further to 5.0
kcal/mol results even in a reduction of the cross validated q2
to 0.68 (Table 2, Panel E), which presumably reflects that
some statistically relevant descriptors have been filtered out
at this min value. With increasing min, the number of re-
maining descriptors is noticeably decreased from 3544 to
1231, while at the same time the electrostatic contribution is
raised from 27.7% to 36.1%. Thus, these results demonstrate
that the obtained CoMFA is stable at a highly significant
level (>0.7) and not prone to deviate noticeably with varying
numbers of descriptors.
2.5. Calculation of the Predictive Correlation Coefficient
(r2 pred) and Prediction of Novel Compounds
The predictive ability of the 3D-QSAR model was de-
termined from a set of 5 compounds (31–35) that were not
included in the training set (Table 1). The compounds were
manually assigned to the training or test set ensuring a rea-
sonable range of log IC50 units and structural diversity in
both sets. These molecules were aligned using the same
method as described before, and their activities were pre-
dicted using the model generated by the training set. Accom-
panying a synthetic strategy to design some analogues of the
1,2-dihydropyridine ligands used in the training and test set,
we also predicted two ligands (36 & 37), which were subse-
quently synthesized and tested. In addition to the classical
test set, we also included these compounds in the calculation

CoMFA and CoMSIA Studies of 1,2-dihydropyridine Derivatives
Medicinal Chemistry, 2012, Vol. 8, No. 3 377
Fig. (1). Histogram showing the distribution of q2 values calculated by SAMPLS25 leave-one-out cross-validation after systematic translation
of aligned molecules within the lattice by an all placement search (APS).
As q2 is dependent on the grid spacing and the absolute
3.2. CoMSIA
cv
position of the aligned molecules within the lattice to the
CoMSIA computes the steric and electrostatic fields (as
shape and steepness of the hyperbolic Lennard-Jones and
in CoMFA), but it also calculates additional hydrophobic,
Coulomb potentials used during the analysis [22]. They
hydrogen-bond donor, and hydrogen-bond acceptor fields.
The resulting contour maps are easier to interpret than those
in CoMFA because a Gaussian function is used to determine
stated that in the case of a 2.0 A˚ lattice, important contribu-
tions to the correlation analysis could be lost due to the re-
quired arbitrary cutoff values. Despite the fact that changing
from a 2.0 to 1.0 A˚ lattice spacing results in an increase in
computing time by a factor of 8, we performed all analyses
using the smaller increment. Quantifying the translational
the distance-dependence. Therefore, the similarity indices
can also be calculated at the grid points inside the molecules,
not just outside, as it is in CoMFA [23]. Several CoMSIA
models were generated using the combinations of different
fields (Table 3). The purpose of using several combinations
of different fields is not only to increase the significance and
predictive power of the 3D-QSAR models, but also to parti-
tion the various properties into spatial locations where they
play a decisive role in determining the activity. CoMSIA, in
most instances, performs similarly to CoMFA in terms of
predictive ability. The results of the initial CoMSIA models
for different combinations are summarized in Table 3.
dependence of q2 helps to ensure that no artificial effects
cv
are included in the final CoMFA and gives evidence of the
robustness of the model. Thus, we applied a procedure pub-
lished by Wang et al. [20] which systematically translates
the aligned dataset in space, followed by a PLS analysis after
every retranslation. The histogram plot of the resulting 1000
model variants showed to be approximately corresponding to
a normal distribution of the obtained q2 values (Fig. 1). The
range of the values 0.685-0.720 is quite narrow, indicating
that our CoMFA model is rather stable toward different grid
placements.
The combined use of the steric, electrostatic, hydropho-
bic, and hydrogen-bond donor descriptors produced the best
model (q2=0.639 and r2 =0.965 with four components).
The highest q2 (0.720) was found for 5 components.
In analogy to the CoMFA, already the initial PLS analy-
sis of our aligned training set applying a default ꢁmin data
filter of 2.0 kcal/mol yielded a highly significant cross-
validated q2 of 0.639 with Sc/v = 0.529 using four compo-
nents (Table 2, Panel G). Variation of the column filtering
parameter ꢁmin shows that for increasing ꢁmin values from 1.0
to 5.0 kcal/mol, the number of descriptors is strongly re-
cv
Thus, it should be noted in this context that the increase of
q2 values by less than 5% with the use of an additional
cv
component is regularly considered inappropriate due to the
‘parsimony-principle’ [23]. Thus, we used the original mod-
els, which are in line with this rule, to derive the graphical
CoMFA maps. Modification of other CoMFA parameters,
such as changing the cutoff values for steric/electrostatic
energies, did not give any improvement of the model. The
final predicted/cross-validated versus experimental logIC50
values for model B and their residuals (ꢀlogIC50) are given
in Table 1.
duced from 6581 to 2110, while the q2 is steadily increased
cv
from 0.637 (F) to 0.655 (J). Although the q2 values of the
CoMSIA models are slightly reduced compared to those of
the CoMFA models, they are still indicating stable analyses
of high quality. Furthermore, the CoMSIA models comprise

378 Medicinal Chemistry, 2012, Vol. 8, No. 3
Salama et al.
Table 3. Summary of the CoMSIA Modelsa
PLS Statistic
SEHDA
SEHD
SED
q2
0.629
0.512
0.965
0.232
187.6
4
0.639
0.529
0.965
0.232
142.3
4
0.655
0.515
0.967
0.226
151.4
4
cv
SPRESS
r2
S
F
Components
Descriptors
Fraction
Steric
5427
4626
2907
0.118
0.259
0.189
0.232
0.201
0.148
0.255
0.341
0.256
0.158
0.267
Electrostatic
Hydrophob.
Donor
0.222
Acceptor
a S, steric; E, electrostatic; H, hydrophobic; D, donor; A, acceptor
valuable complementary information, as they offer addi-
tional explanation for the molecules different inhibitory ac-
tivities by introducing three auxiliary field types, the hydro-
phobic field, the hydrogen bond acceptor field, and the hy-
drogen bond donor field. Interestingly, while the noise re-
duction in the CoMFA model decreased the fraction of the
steric contribution from 72.3% down to 63.9%, it was in-
creased by the noise reduction in the CoMSIA model from
14.8% up to 21.5%. The other CoMSIA field types typically
contribute to the full model in the order: hydrophobic (30.6–
34.1%)> electrostatic (25.5–26.4%) > donor (24.1– 25.6%).
The final predicted/cross-validated versus experimental pKi
values for model G and their residuals (ꢀlogIC50) are given
in Table 3.
the ligands to the 10 groups. The negligible standard devia-
tion (SD) is a further indicator of the model quality. Also the
q2 L20%O-values of 0.68 ± 0.025 and 0.618± 0.032 for
CoMFA and CoMSIA, respectively, show only a small de-
crease of the mean values, although disregarding every fifth
compound. Likewise, there is only a marginal increase in the
standard deviation. Therefore, we conclude that leave-10%-
out and leave-20%-out cross-validation clearly corroborates
the good stability and robustness of the models.
The predictive power of the CoMFA (Table 2, Panel B)
and CoMSIA (Table 2, panel G) analysis was further exam-
ined using a test set of 5 compounds (31–35, Scheme 1 and
Table 1) that had been omitted from the training set. In addi-
tion to these test set ligands, we also included two 1,2-
dihydropyridine derivative (36&37) in the calculation of the
r2
value, which were synthesized accompanied by these
3.3. Advanced Cross-Validation and Assessment of
Model Predictivity
pred
QSAR predictions as described subsequently. The calcula-
tion was performed and gave better results for the CoMFA
Despite the good results obtained for CoMFA and CoM-
SIA cross-validation, of course, we are aware that there is
always the danger to overemphasize the usefulness of cross-
validation and q2 as measures of the predictive performance.
High values of q2 LOO can be regarded as a necessary, but not
a sufficient, condition for a model to possess significant pre-
dictive power. Thus; we also performed the more critical and
widely accepted leave-10%-out and leave-20%-out cross-
validations. With q2 L10%O-values (mean ± SD) of 0.696 ±
0.013 and 0.639 ± 0.017 for the CoMFA and CoMSIA, re-
spectively, we have shown that even after discarding every
tenth compound from the training set the predictivity of the
models is hardly impaired. Repeating this calculation n = 20
times for each model assesses the liability of the respective
model to random effects caused by different assignment of
with r2
0.613.
= 0.651 than for the CoMSIA model with r2
=
pred
pred
3.4. 3D Contour Maps
To visualize the information content of the derived 3D-
QSAR models, CoMFA and CoMSIA contour maps were
generated. The field energies at each lattice point were calcu-
lated as the scalar results of the coefficient and the standard
deviation associated with a particular column of the data
table (“stdev*coeff”), which was always plotted as the per-
centage of the contribution to the CoMFA or CoMSIA equa-
tion. In Figs. (2 and 3) discussed below, the isocontour dia-
grams of the field contributions (“stdev*coeff”) for different
properties calculated by the CoMFA and CoMSIA analysis

CoMFA and CoMSIA Studies of 1,2-dihydropyridine Derivatives
Medicinal Chemistry, 2012, Vol. 8, No. 3 379
Fig. (2). Stdev*coeff contour plots illustrating steric (A and B) and electrostatic features (C and D) as obtained by the final CoMFA. In (A)
and (B), regions where steric bulk will enhance affinity are shown enclosed by green contours (contribution level: 80%), whereas regions
which should be kept unoccupied to prevent decrease of affinity are contoured in yellow (20%). This is exemplified by the high activity
ligand 2 in (A) and the lower activity ligand 17in (B). In (C) and (D), red contours (contribution level: 20%) encompass regions where elec-
tron-rich fragments with negative partial charges will improve activity. Blue contours (80%) indicate regions where reduced electron density
(positive partial charges) is predicted to increase activity. Again, compounds 2 (C) and 15 (D) are used to exemplify the plots for a high and
lower affinity ligand, respectively.
are illustrated with exemplary ligands. The contour plots
may help to identify important regions where any change
may affect the binding preference. Furthermore, they may be
helpful in identifying important features contributing to in-
teractions between the ligand and the active site of an en-
zyme.
ognized to yield high logIC50 values, while any substituents,
as well as sterically demanding systems, is detrimental for
activity (exemplified by 17 in (Panel B)). This, for instance,
applies to 12 & 13, which both bear an ethoxy substituent in
position 4 of phenyl ring and have lower activity compared
to compound 2.
3.4.1. CoMFA
In panel (C), the electrostatic contour map shows a large
region of red polyhedrals and a small blue area below phenyl
ring in position 6 of 1,2-dihydropyridines, indicating that
electron-rich substituents in ortho position of phenyl ring are
beneficial for the activity as shown for 2 in panel (C), This is
also the case with 3, 5, 10 and 11. While electron rich sub-
stituent separated from meta position by one c-c bond exerts
a negative impact on the inhibitory activity as shown for 15
in panel (D). The red maps positioned on the right side are
due to phenyl rings in position 6 of 1,2-dihydropyridine with
electron rich substituents in p-position have very high activ-
ity, this is the case with 6,8 and 12.
Favored and disfavored cutoff energies were set at the
80th and 20th percentiles for both the steric and the electro-
static contributions. The presence of a substituent near a
green region, the absence of a substituent near a yellow re-
gion, the increase of a negative charge near a red region, or a
positive charge near a blue region increases the inhibitory
activity of the ligands, while the presence of a substituent
near a yellow region, the absence of a substituent near a
green region, the increase of a negative charge near a blue
region, or a positive charge near a red region decreases the
inhibitory activity. Such fields could, in principle, suggest
the way a leading structure should be modified for gaining
higher activity. Because it is a representative example of the
most active ligands, compound 2 is shown embedded as a
guide into the steric (A) and electrostatic fields (C) (Fig. 2)
resulting from a non-cross-validated CoMFA run (ꢀmin = 2.0
kcal/mol). Likewise, compound 17 is shown enclosed in the
steric (B) and compound 15 is displayed in panel (D) as a
representative of lower activity ligands. In panel (A), The
large green isopleth in the middle indicate that substituents
near this region increase inhibitory activity of the ligands.
The yellow isopleths at left near the (hetero)aryl rings in
position 4 of 1,2-dihydropyridine ring reflect a strict de-
crease in activity for all of these ‘anchor moieties’ being
dislocated into this area. This yellow isopleths reveal that
(hetero)aryl rings with no substituents are preferentially rec-
3.4.2. CoMSIA
In Fig. (3), as well as in the following discussion, the
CoMSIA contour plots are exemplified by ligands of low
and high affinity. It becomes obvious from a direct compari-
son of Figs. (2 and 3) that steric and electrostatic properties
in CoMFA and CoMSIA show a high degree of similarity,
however, a certain degree of complementary information can
be found.
3.4.2.1. Steric Contributions
In panel (A), green and yellow isopleths are drawn at a
contribution level of 80% and 20%. Areas indicated by green
contours correspond to regions where steric occupancy with
bulky groups should increase anticancer activity. Areas en-

380 Medicinal Chemistry, 2012, Vol. 8, No. 3
Salama et al.
Fig. (3). Stdev*coeff contour plots illustrating steric (A + B), electrostatic (C + D), hydrophobic (E + F), and hydrogen bond donor (G + H)
properties revealed by the final CoMSIA. For all features, one ligand with high (A: 2, C:2, E: 2, G: 2) and another with low activity (B: 17,
D: 12, F: 17, H: 22) are shown in comparison. The mesh fields represent the stdev*coeff plots, whereas the transparent surfaces indicate the
fields of the particular ligand, thus, facilitating the recognition of matching or mismatching features.
compassed by yellow isopleths should be sterically avoided;
otherwise, reduced anticancer activity can be expected.
respectively. In panels C and D, the electrostatic property
maps include 2 and 12 as examples for high and low activity
ligands, respectively. A large, red isopleth is located at the
ortho and meta positions of the right-sided phenyl rings of
ligands showing high anticancer activity (2 & 3) (Fig. 3C).
The small blue map positioned on the left side indicates
where electron-deficient substructures should be placed. For
12 & 13 the blue mesh is completely buried in the transpar-
ent red electrostatic potential at the oxygen of ethoxy groups.
The red isopleth positioned on the left side exhibits the influ-
ence of the oxygen of methoxy groups of the active ligands.
As in the CoMFA, the green isocontour in the middle
found in Fig. (3A and B) is located very near to substituent
in ortho position of phenyl group located in C-4 of 1,2-
dihydropyridines such as compound 2. The green isopleth
positioned on the right side can also be explained by a num-
ber of high activity ligands (1-3, 5, 7, 10 & 11) bearing an
ortho or meta substituent in this region. The low activity of
the 2-nitrofuryl derivative 17 could be due to the orientation
of its nitro group to the left sided lower yellow region. Ac-
cordingly, the ethoxy groups of 12 and 13 are pointing to-
ward the upper yellow area.
3.4.2.3. Hydrophobic Contributions
Yellow and orange isopleths (contribution level 80%:
20%) enclose regions favorable for hydrophobic and hydro-
philic groups, respectively. The hydrophobic effect on the
activity can be drawn from panels E and F, suggesting that
occupation of the ortho or meta positions of the phenyl ring
3.4.2.2. Electrostatic Contributions
Red and blue isopleths (contribution level 90%: 20%)
enclose regions favorable for negative and positive charge,

CoMFA and CoMSIA Studies of 1,2-dihydropyridine Derivatives
Medicinal Chemistry, 2012, Vol. 8, No. 3 381
Table 4. Inhibitory Effect of the Synthesized Compounds (36 and 37) on HT-29 Cells
NH
NH
NC
NC
NH
NH
Br
Br
OCH3
CI
CI
37
36
Cpd.
% Growth Inhibition at 50 ꢀM
Growth Inhibition IC50 (ꢀM)
36
37
99.5
0.24
2.6
91.14
on C-6 of 1,2- dihydropyridines by a hydrophobic group is
crucial for a highly active ligand, as illustrated by com-
pounds 2, 3 and 5 and 1&7, respectively, while the orange
mesh on the left is due to the methoxy group of 2, 3 and 6.
The ligands, which have very reduced activity, have 4-bromo
substituents in the small orange isopleth on the right side,
which is exemplified by ligands 16-18.
verify our aforementioned predictions by in vitro biological
testing of the ligands.
3.6. Chemistry
The general synthesis of the two compounds 36 and 37,
using the in-solution one pot synthesis approach. Briefly, the
aromatic ketone 3-bromoacetophenone, the respective aro-
matic aldehyde, ammonium acetate and malononitrile were
refluxed in ethanol for 15-24 hours. The afforded com-
pounds were purified by recrystallization from a mixture of
DMF-Ethanol in different ratios.
3.4.2.4. Hydrogen Bond Donor Contributions
The graphical interpretation of the field contributions of
the H-bond donor (from CoMSIA) is shown in panels G and
H. Cyan isopleth contour maps (contribution level 80%) are
representing the position of H-bond donor groups which fa-
vor biological activity, while purple isopleths (20%) are out-
lining the location of biologically unfavored donors. In prin-
ciple, they should highlight the areas beyond the ligands
where putative partners in the target can form a hydrogen
bond that will influence the biological activity significantly.
For the active anticancer ligand 2, the cyan transparent area
of the NH of 1,2-dihydropyridines is positioned on the cyan
mesh, however for less active ligands, the cyan transparent
near OH of 16, 19, 22 and 24 is aligned on the purple mesh.
1
In H-NMR, successful formation of the two compounds
has been indicated by the appearance of a singlet peak
around 6.90 ppm corresponding to the aromatic proton at
position 5. Compound 37 showed a singlet peak with a
chemical shift ranging between 3.82 ppm due to the methoxy
substituent at position 4 of the 1,2-dihydropyridine ring.
Mass Spectrometry of the synthesized compounds
showed the molecular ion peaks were also the base peaks
indicating the stable nature of these compounds. As the syn-
thesized compounds possess a 3-bromophenyl group, ac-
cordingly their mass spectrometry displays a common fea-
ture involving the molecular ion peak [M+] and [M+ +2] in-
dicating the isotopic nature of the bromine atom. Infrared
spectra showed a band at 3320-3480 cm-1 for the NH stretch-
ing and a band at 2210-2220 cm-1 for the CN stretching.
3.5. Prediction of Novel, Anticancer Compounds
Based on our initial approach toward the development of
anticancer compounds 6, 8 this work focused on the rational
prediction of novel anticancer ligands with improved bio-
logical activity. Consequently, we chose the highly potent 2
(logIC50 0.39 ꢀM) as an interesting lead compound for the
structural design of potential anticancer agents. We exam-
ined further substitution patterns by replacing ortho-methoxy
by an ortho & para dichloro substituents, and also transfer-
ring bromine from ortho to meta position of phenyl nucleus,
to obtain the 1,2-dihydropyridine derivative 36 and only
transferring bromine from ortho to meta position of phenyl
nucleus to obtain 37.
3.7. Biological Testing
The two synthesized compounds were tested for their in
vitro ability to inhibit the growth of human HT-29 colon
adenocarcinoma tumor cells (Table 4). The two compounds
were evaluated for the growth inhibitory activity in two
steps. The first step was determination of the percentage of
inhibition at 50 ꢀM performed in triplicate. For compounds
displaying a percentage of inhibition greater than 70 % at the
screening dose, the exact IC50 was determined by testing a
range of 10 concentrations with at least two replicate per
concentration.
When the target compounds 36 and 37 were predicted
employing the CoMFA and CoMSIA model, 36 was sug-
gested to have a logIC50 of -0.96/-0.77 as predicted from
CoMFA/CoMSIA, respectively. The other derivative 37 was
supposed to give a logIC50 of 0.25/ 0.5 by CoMFA/CoMSIA
prediction, respectively. At this point, we were ready to pro-
ceed with the synthesis of the two novel ligands 36 and 37 to
The obtained experimental results showed to be in very
good consistence with our previous CoMFA/CoMSIA pre-
dictions. All deviations were found to be below 0.5 log IC50
units.

382 Medicinal Chemistry, 2012, Vol. 8, No. 3
Salama et al.
4. CONCLUSION
matic), 7.22-7.93 (m, 4H, aromatic +-NHs), 8.04 (s, 1H,
aromatic), 8.09 (s, 1H, aromatic), 8.16-8.19 (d, 1H, aro-
matic), 8.30-8.33 (d, 1H, aromatic), 8.37 (s, 1H, aromatic);
MS-EI m/z 420 (M+; 100%), m/z 422( M++2), m/z 424(
M++4); Anal (C18H10 BrCl2N3) calcd. C 51.58, H 2.40, N
10.03 ; found C 51.88; H 2.69; N 10.27
In the current work, we have successfully established
CoMFA and CoMSIA models based on a training set of 30
ligands. We have carefully evaluated the statistical signifi-
cance of the models and found high q2 values of 0.70 (4
cv
components) and 0.639 (4 components) for CoMFA and
CoMSIA, respectively, when using the standard leave-one-
out cross-validation method. Even for the more critical
5.3. 3-Cyano-4-(2-methoxyphenyl)-6-(3-bromophenyl)-2-
imino-1,2-dihydropyridine (37)
leave-20%-out method, q2 was only slightly reduced yield-
cv
ing still highly significant mean values (n = 20 runs) of 0.696
for CoMFA and 0.639 for CoMSIA. The models were veri-
fied to be stable and robust against the variation of the un-
derlying parameters. Thus, an all placement search (APS)
yielded only CoMFA models in the rather narrow range be-
tween 0.685-0.720. Using a test set of 5 ligands, we were
able to demonstrate that the models are also predictive for
new compounds. This was extended to the successful appli-
cation of our models guiding the synthesis of novel, active
derivatives (36 & 37). The theoretical investigations pre-
sented in this study provide a valuable tool for predicting the
affinity of novel compounds and, thus, for guiding and
evaluating further structural modifications.
A mixture of 3-bromoacetophenone(0.5g, 2.5 mmol), 2-
methoxy benzaldehyde (2.5 mmol), malononitrile (0.16 g,
2.5 mmol) and ammonium acetate (1.54 g, 20 mmol) in ab-
solute ethyl alcohol (30 ml) was heated under reflux, with
stirring for 15-24 h. The reaction mixture was cooled and the
formed precipitate was filtered, washed with cold ethyl alco-
hol, allowed to dry, and crystallized from DMF/ethanol 1:2.
Yield 80%, red powder; mp 274-76 oC; IR (cm-1)
3482.3(-NH), 2205.8(-CꢁN); 1H-NMR: ꢀ 3.89 (s, 3H,-
OCH3), 7.06 (s, 1H, aromatic), 7.20-7.54 (m, 5H, aromatic),
7.61-7.64 (d, 1H, aromatic), 7.72-7.75 (d, 1H, aromatic),
7.81 (s, 1H, aromatic); MS-EI m/z 379 (M+; 100%), m/z
381( M++2); Anal. (C19H14 BrN3O) calcd. C 60.02, H 3.71, N
11.05 ; found C 60.33; H 4.04; N 11.23.
5. EXPERIMENTAL
5.1. Methods and Materials
5.4. Biology
All reactions were performed with commercially avail-
able reagents and they were used without further purifica-
tion. Solvents were dried by standard methods and stored
over molecular sieves. All reactions were monitored by thin-
layer chromatography (TLC) carried out on precoated silica
gel plates (ALUGRAM SIL G/UV254) and detection of the
components was made by short and long UV light. Melting
points were determined in open capillaries using a Buchi
Melting Point B-540 apparatus and are uncorrected. 1H NMR
spectra were recorded on Varian spectrometer at 300 MHz
using tetramethylsilane (TMS) as internal reference. Chemi-
cal shift values are given in ppm at room temperature using
DMSO-d6 as a solvent; chemical shifts (ꢀ) were reported in
parts per million (ppm) downfield from TMS; multiplicities
are abbreviated as: s: singlet; d: doublet; q: quartet; m: mul-
tiplet; dd: doublet of doublet; br s: broad singlet. Yields are
not optimized. Elemental analyses were performed by Insti-
tute of Organic Chemistry, Jena University; and the Micro-
analytical Unit, Faculty of Science, Cairo University; found
values were within ±0.4% of the theoretical ones, unless
otherwise indicated.
5.4.1. Cell Cultures
HT-29 tumor cells were obtained from ATCC. They were
grown under standard cell culture conditions at 37°C in a
humidified atmosphere with 5% CO2. Cells were grown in
RPMI 1640 containing 5% fetal bovine serum. Cell count
and viability was determined by Trypan blue staining fol-
lowed by hemocytometry. Only cultures displaying >95%
cell viability were used for experiments.
5.4.2. Growth Assays
Tissue culture treated microtiter 96-well plates were
seeded at a density of 5000 cells/well. The plates were incu-
bated for 18–24 h prior to any treatment. Cell viability was
measured 72 h after treatment by the Cell Titer Glo Assay
(Promega), which is a luminescent assay that is an indicator
of live cells as a function of metabolic activity and ATP con-
tent. The assay was performed according to manufacturer’s
specifications.
DISCLOSURE
Methods for the generation of structures, exploration of
the conformational space and alignment steps were done as
previously described in our previous publication Bioorg.
Med. Chem. 2006, 19, 5898-5912.
5.2. 3-Cyano-4-(2,4-dichlorophenyl)-6-(3-bromophenyl)-
2-imino-1,2-dihydropyridine (36)
A mixture of 3-bromoacetophenone(0.5g, 2.5 mmol), 2,4
dichlorobenzaldehyde (2.5 mmol), malononitrile (0.16 g, 2.5
mmol) and ammonium acetate (1.54 g, 20 mmol) in absolute
ethyl alcohol (30 ml) was heated under reflux, with stirring
for 15-24 h. The reaction mixture was cooled and the formed
precipitate was filtered, washed with cold ethyl alcohol, al-
lowed to dry, and crystallized from DMF/ethanol 1:2.
CONFLICT OF INTERESTS
None declared.
ACKNOWLEDGMENT
o
Yield 56%, pale brown powder; mp 189-91 C; IR (cm-1)
The authors are grateful to the Alexander von Humboldt
Foundation, Germany for partial financing of this work.
3321(-NH), 2224 (-CꢁN); 1H-NMR: ꢀ 6.95 (s, 1H, aro-

CoMFA and CoMSIA Studies of 1,2-dihydropyridine Derivatives
Medicinal Chemistry, 2012, Vol. 8, No. 3 383
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