Use of Quantitative Structure - Activity Relationship (QSAR) and ADMET prediction studies as screening methods for design of benzyl urea derivatives for anti-cancer activity

Article abstract of DOI:10.3109/14756366.2010.506437

2D and 3D quantitative structure-activity relationship studies have been carried out for establishing a correlation between the structural properties of benzyl urea derivatives and their anti-tumour activities. From this correlation, the new chemical entities were designed, and their activity and absorption, distribution, metabolism, excretion, and toxicity properties were also predicted. Finally, the most promising compounds from these screening were synthesized and biologically evaluated for their anti-cancer properties. Compound 1-(2, 4-dimethylphenyl)-3, 3-dimethyl-1-(2-nitrobenzyl) urea (7d) showed significant anti-proliferative activity (at 100 μg/mL) in human cancer cell lines-T-cell leukemia (Jurkat J6), myelogenous leukemia (K562), and breast cancer (MCF-7) compared to reference standard 5-flurouracil.

Full text of DOI:10.3109/14756366.2010.506437

Journal of Enzyme Inhibition and Medicinal Chemistry, 2011; 26(3): 319–331
© 2011 Informa UK, Ltd.
ISSN 1475-6366 print/ISSN 1475-6374 online
DOI: 10.3109/14756366.2010.506437
RESEARCH ARTICLE
Use of Quantitative Structure–Activity Relationship (QSAR)
and ADMET prediction studies as screening methods for
design of benzyl urea derivatives for anti-cancer activity
Deepak Lokwani1, Shashikant Bhandari1, Radha Pujari2, Padma Shastri2, Ganesh shelke2, and
Vidya Pawar1
1Department of Pharmaceutical Chemistry, AISSMS College of Pharmacy, Pune, India, and 2National Centre for Cell
Science (NCCS), Ganeshkhind, Pune, India
Abstract
2D and 3D quantitative structure–activity relationship studies have been carried out for establishing a correlation
between the structural properties of benzyl urea derivatives and their anti-tumour activities. From this correlation,
the new chemical entities were designed, and their activity and absorption, distribution, metabolism, excretion,
and toxicity properties were also predicted. Finally, the most promising compounds from these screening were
synthesized and biologically evaluated for their anti-cancer properties. Compound 1-(2, 4-dimethylphenyl)-3,
3-dimethyl-1-(2-nitrobenzyl) urea (7d) showed significant anti-proliferative activity (at 100 µg/mL) in human cancer
cell lines-T-cell leukemia (Jurkat J6), myelogenous leukemia (K562), and breast cancer (MCF-7) compared to reference
standard 5-flurouracil.
Keywords: QSAR, k-nearest neighbour–molecular field analysis (kNN–MFA), ADMET, Anti-cancer
Introduction
which pharmacological intervention is feasible, there are
none that have generated as much widespread interest,
Cancer drug discovery has undergone a remarkable series
of changes over the last decade. e first generation of
anti-cancer drugs such as deoxyribonucleic acid (DNA)–
alkylating and cross-linking agents, anti-metabolites,
topoisomerase inhibitors and anti-tubulin agents have
been traditionally focused on targeting DNA processing
as have the protein tyrosine kinases (PTKs)4.
PTKs protein over expression or gene amplification,
mutation, or rearrangement have been demonstrated
in multiple malignancies including cancers of the head
and neck, ovary, cervix, bladder, prostate, esophagus,
stomach, brain, breast, endometrium, colon, and lung5,6
.
and cell division and were almost all cytotoxic agents1,2
.
erefore design of inhibitors toward PTKs is an attractive
In an attempt to avoid unpleasant side effects (bone mar-
row suppression and gastrointestinal, cardiac, hepatic,
and renal toxicities) associated with these conventional
anti-cancer drugs, a new class of anti-cancer drugs known
as molecularly targeted agents was being developed that
work by targeting a biochemical pathway or protein that
is unique to or upregulated in cancer cells3. Such agents
are typically less toxic than drugs in the older classes and
can be given for long-term oral therapy with the objective
of treating cancer as a chronic disease. Among all these
non-traditional (non-DNA-directed) cancer targets for
approach for development of new therapeutic agents7,8
.
Most of the tumours initially respond to PTKs but majority
of them become resistant to the drug treatment9,10. e
mechanism underlying acquired drug resistances are not
well understood11.
Our interest have been focused on a special group of
compounds containing a urea moiety in their structures,
as urea derivatives were synthesized largely in recent
years and have become of particular interest to chemists
and biologists because of their wide range of biological
Address for Correspondence: Shashikant Bhandari, Department of Pharmaceutical Chemistry, AISSMS College of Pharmacy, Near RTO,
Kennedy Road, Pune-411001, Maharashtra, India. E-mail: drugdesign1@gmail.com
(Received 07 March 2010; revised 01 July 2010; accepted 02 July 2010)
319

320 D. Lokwani et al.
activities such as anti-convulsant activity12, colchicine-
binding antagonist13 and chemokine receptor CXCR3
antagonist14. Some of urea derivatives exhibit cell growth
inhibition on numerous cancer cell lines and are report-
ed to be potent inhibitors of the PTKs activity of a num-
ber of transmembrane growth factor receptors such as
1, 3-disubstituted urea derivatives15,16, primaquine urea
derivatives17, alkyl[3-(2-chloroethyl)ureido]benzene18,
4,5-disubstituted thiazolyl urea derivatives19. erefore,
search for lead compound is still a continuous quest for
the researchers working in this area.
Designed compounds were screened by two types of
screening methods for finding of new compounds with
anti-cancer activity: (i) Lipinski′s rule and prediction of
activity using regression equation generated by 2D-QSAR
studies and (ii) prediction of absorption, distribution,
metabolism, excretion, and toxicity (ADMET) properties.
Seven compounds were selected from results of molecu-
lar modeling studies and were synthesized. e com-
pounds were screened for examining their anti-cancer
effect on human T-cell leukemia cell lines—Molt-4 and
Jurkat J6, myelogenous leukemia cell line—K562 and
breast cancer cell line—MCF 7.
Computational models are one of the powerful tools
to design highly active molecules20,21 that are able to pre-
dict structures and the biological activities of anti-cancer
compounds. Many QSAR studies22–26 were developed and
published as screening methods for design of new chem-
ical entities (NCE′s). As shown in Figure 1, to further
explore the structural requirements of selected series of
benzyl urea derivatives for their anti-proliferative activity,
two methods of QSAR, 2D-QSAR and 3D-QSAR were car-
ried out and NCEs were designed using results of QSAR
model. 2D-QSAR and 3D-QSAR studies were performed
using multiple linear regression (MLR) analysis27 and k-
nearest neighbour–molecular field analysis (kNN–MFA),
respectively. e kNN–MFA methodology relies upon a
simple distance learning approach. In this method an
unknown member is classified according to the majority
of its kNNs in the training set. e nearness is measured
by an appropriate distance metrics (e.g. a molecular
similarity measure calculated using field interactions of
molecular structures). e standard kNN–MFA method28
was implemented simply as follows: (i) the distances
between an unknown object (u) and all other objects in
the training set were calculated; (ii) the k objects were
selected from the training set most similar to object u,
according to the calculated distances; and (iii) the object
u was classified with the group to which the majority of
the k objects belong. An optimal k value is selected by
optimization through the classification of a test set of
samples or by leave-one-out (LOO) cross-validation. e
variables and optimal k values were chosen using differ-
ent variable selection methods. Here we have used simu-
lated annealing (SA)29 as variable selection method.
Experimental part
QSAR studies
All QSAR studies were performed using V-Life Molecular
Design Suite Software, version 3.530. Biological activ-
ity expressed in terms of IC50 was converted in to pIC50
(pIC50 = log1/IC50). Both 2D- and 3D-QSAR models were
generated using a training set of 20 molecules with var-
ied chemical and biological activities. Test set of four
molecules with distributed biological activity was used to
assess the predictive power of generated QSAR models.
e sphere exclusion method31 was used for the selection
of molecules in training and test sets.
Uni-Column statistics for training set and test set were
generated to check correctness of selection criteria for
trainings and test set molecules (Table 1). Selection of
molecules in the training set and test is a key and impor-
tant feature of any QSAR model, therefore due care was
taken in such a way that biological activities of all com-
pounds in test set lie within the maximum and minimum
value range of biological activities of training set of com-
pounds (Table 1).
e maximum and minimum value in training and
test set were compared in a way that
1. the maximum value of pIC50 of test set should be less
than or equal to maximum value of pIC50 of training
set.
2. the minimum value of pIC50 of test set should be higher
than or equal to minimum value of pIC50 of training set.
2D QSAR
studies
Database
Design of
NCE’s
3D QSAR
studies
Lipinski’s rule
& prediction of
activity
ADMET
prediction
Best
protocol
Experimental
validation
Figure 1. Flowchart of screening methods for new lead compounds.
Journal of Enzyme Inhibition and Medicinal Chemistry

Use of QSAR and ADMET prediction studies 321
Table 1. Uni-Column statistics for training and test set of compounds.
Column name
pIC50
Average (mean)
−1.1457
Max*
0.5690
−0.0790
Min*
−1.8760
−1.5490
Std Dev
0.6964
0.6269
Sum
Training set
Test set
−22.9140
−3.2500
pIC50
−0.8125
*Higher the value of pIC50 (Greater +ve value), higher is the potency.
is observation showed that test set was interpolative
and derived within the minimum—maximum range of
training set.
e mean and standard deviation pIC50 values of sets
of training and test provide insights to relative difference
of mean and point density distribution of two sets.
—Z score calculated by the randomization test (higher
is better); best_ran_q2 highest q2 value in the randomiza-
tion test (as low as compared to q2); best_ran_r2 highest
r2 value in the randomization test (as low as compared to
r2); α-statistical significance parameter by randomization
test (<0.01).
1. Mean in test set were found to be higher in test set
than mean in training set indicating that presence
of relatively more active molecules as compared to
inactive ones.
2. Higher standard deviation in training set indicated
that training set has widely distributed activity of
molecules as compared to test set molecules.
Validation of QSAR studies
Modelsgeneratedby2Dand3D-QSARstudieswerecross-
validated using standard LOO procedure28. e cross-
validated r2 (q2) value was calculated using equation 1,
where yi and ŷi are the actual and predicted activities of
the ith molecule, respectively, and ymean is the average
activity of all molecules in the training set. Both summa-
tions are over all molecules in the training set. Because
the calculation of the pair-wise molecular similarities,
and hence the predictions were based upon the current
trial solution, the q2 obtained is indicative of the predic-
tive power of the current kNN–MFA model.
e most widely used MLR analysis was used to correlate
biological activities with physicochemical properties of
the selected series of compounds. Molecules were opti-
mized by Merck Molecular Force Field (MMFF) energy
minimization method. A total of 287 2D descriptors were
computed using V-Life Molecular Design Suite Software,
which include various physicochemical descriptors; Bau-
mann alignment-independent topological descriptors32
and MMFF atom type count descriptor. After computa-
tion of all descriptors, invariable descriptors that have no
variation in their values were removed. 2D-QSAR equa-
tions were generated by MLR analysis using SA variable
selection method.
e 3D-QSAR studies were performed by kNN–MFA
using SA variable selection method. kNN–MFA method
requires suitable alignment of given set of molecules af-
ter optimization, alignment was carried out by template
based alignment method (Figure 2). Molecular align-
ment was used to visualize the structural diversity in the
given set of molecules. is was followed by generation
of common rectangular grid around the molecules. e
steric and electrostatic interaction energies were com-
puted at the lattice points of the grid using a methyl probe
of charge +1. e resulting set of aligned molecules was
then used to build 3D-QSAR models and information′s
generated were used to predict activity of those designed
molecules that have the similar template or set of atoms.
All generated QSAR models were evaluated and best
model was selected using following statistical measures:
n, number of molecules (>20 molecules); k, number of
descriptors in a model (statistically n/5 descriptors in a
model); df, degree of freedom (n − k − 1) (higher is bet-
ter); r2, square of regression (>0.7); q2, cross-validated r2
(>0.5); pred_r2 − r2 for external test set (>0.5); SEE, stan-
dard error of estimate (smaller is better); F-test, F-test
for statistical significance of the model (higher is better,
for same set of descriptors and compounds); F_prob.
Alpha—Error probability (smaller is better); Z score,
^
(yi y )2
q2 =1
i
(1)
2
(yi ymean
)
All cross-validation studies were performed by consider-
ing the fact that a value of q2 is above 0.5 indicating high
predictive power of generated QSAR models.
External validation29 of generated models was carried
out by predicting the activity of test set of compounds.
e predicted r2 (pred_r2) value was calculated using
equation 2, where yi and ŷi are the actual and predicted
activities of the ith molecule in test set, respectively, and
ymean is the average activity of all molecules in the training
set. Both summations are over all molecules in the test
set. e pred_r2 value is indicative of the predictive power
of the current kNN–MFA model for external test set.
^
(yi y )2
pred_r2 =1
i
(2)
2
(yi ymean
)
e statistical significance of the QSAR model for an
actual data set was further evaluated by randomization
test. e robustness of the QSAR models for experimen-
tal training sets was examined by comparing generated
model with those derived for random data sets. Random
sets were generated by rearranging biological activities
of the training set molecules. e significance of the
models hence obtained was derived based on calculated
Z-score28,29
.
ADMET prediction
ADMET properties were calculated using Discovery Stu-
dio (DS) 2.1, Accelrys software33. DS provides methods
© 2011 Informa UK, Ltd.

322 D. Lokwani et al.
and various amines (4a-4g) (40 mmol) were added to
the reaction mixture and stirred at 50–60°C for 5–6 h.
e reaction mixture was then added to 20mL ice water,
organic layer was separated, and the aqueous layer was
extracted with ethyl acetate (2 × 10 mL). e organic layer
was combined and washed with saturated NaHCO3, dried
with anhydrous Na2SO4 for 0.5 h and concentrated under
vacuum.
1-benzyl-1-(2-chlorobenzyl)-3,3-dimethylurea (7a)
Yield: 59.10% (Liquid), bp 113–115°C. FTIR (KBr) cm−1:
3095 (Ar C–H), 2976 (Aliphatic C–H), 1712 (C=O), 1314
(ter. C-N); 1081 (C-Cl). 1H NMR (300 MHz, DMSO, δ
ppm): 2.82 (s, 6H, CH3), 4.58 (s, 4H, CH2), 7.26–7.72 (m,
9H, Aromatic). Mass: m/z 302 (M+), 303 (M+1) +. 13C NMR
(100 MHz, DMSO, δ ppm): 37.2 (-N(CH3)2), 50.7 (-CH2-N-
CH2), 127.2 (Ph C-4), 128.8 (Ph C-3,5), 129.8 (Ph C-2,6),
130.2 (Cl-Ph C-3,4,5), 131.3 (Cl-Ph C-6), 132.8 (Cl-Ph
C-2), 134.4 (Ph C-1), 137.3 (Cl-ph C-1), 168.1 (C=O). El-
emental Analysis calculated for C17H19ClN2O: C, 67.54; H,
6.29; N, 9.27. Found: C, 67.46; H, 6.38; N, 9.35.
Figure 2. Alignment of substituted benzyl urea derivatives using
template based alignment method.
for assessing the disposition and potential toxicity of a
ligand within an organism. e ADMET protocols in DS
contain published models that are used to compute and
analyze ADMET properties. In addition, DS apply spe-
cific rules to remove ligands that are not likely drug like,
unsuitable leads, etc. based on the presence or absence
and frequency of certain chemical groups.
1-benzyl-3,3-dimethyl-1-(2-nitrobenzyl)urea (7b)
Yield: 90.13% (Liquid), bp 107–109°C. FTIR (KBr) cm−1:
3041 (Ar C–H), 2815 (Aliphatic C–H), 1716 (C=O), 1533
1
(N-O), 1345 (ter C-N), 863 (Ar C-N). H NMR (300 MHz,
DMSO, δ ppm): 2.91 (s, 6H, CH3), 4.63 (s, 4H, CH2), 7.21–
Synthetic studies
782 (m, 9H, Aromatic). Mass: m/z 313 (M+), 314 (M+1)
All the reactions were carried out with dry, freshly distilled
solvents under anhydrous conditions, unless otherwise
noted. Fourier transform infrared (FTIR) spectra of the
compounds were recorded using KBr on a Varian 640 FTIR
spectrophotometerandarereportedincm−1. 1HNMRspec-
tra were recorded on a Varian Mercury YH300 (300MHz
FT NMR) spectrophotometer using tetramethylsilane
as an internal reference (chemical shift represented in δ
ppm). Mass spectra were recorded on GC-MS QP5050A
System (benchtop quadrupole mass spectrophotometer).
Purity of the compounds was checked on thin layer
chromatography plates using silica gel G as stationary
phase and was visualized in UV light at 254nm.
+
.
13C NMR (100 MHz, DMSO, δ ppm): 37.3 (-N(CH3)2),
50.9 (-CH2-N-CH2), 126.7 (NO2-Ph C-3,4), 127.3 (Ph C-4),
128.8 (Ph C-3,5), 129.9 (Ph C-2,6), 130.3 (NO2-Ph C-6),
132.5 (NO2-Ph C-5), 134.6 (Ph C-1), 137.4 (NO2-Ph C-1),
144.1 (NO2-ph C-2), 168.7 (C=O). Elemental Analysis cal-
culated for C17H19N3O3: C, 65.15; H, 6.07; N, 13.41. Found:
C, 65.09; H, 5.98; N, 13.53.
1-(2-chlorobenzyl)-3,3-dimethyl-1-(naphthalen-1-yl)
urea (7c)
Yield: 41.53% (Liquid), bp 118–120°C. FTIR (KBr) cm−1:
3029 (Ar C–H), 2923 (Aliphatic C–H), 1722 (C=O), 1319
(ter. C-N); 1085 (C-Cl). 1H NMR (300 MHz, DMSO, δppm):
2.81 (s, 6H, CH3), 4.63 (s, 4H, CH2), 7.16–7.29 (m, 4H, Aro-
matic), 7.51–7.65 (m, 7H, Naphthyl). Mass: m/z 338 (M+),
Synthesis of secondary amines15,16 (4a-4g)
Aldehydes (1a-1g) (0.1 mol) were dissolved in 25 mL of
ethanol and amines (2a-2g) (0.1 mol) were added to the
solution. e reaction mixture was refluxed for 1–2h.
NaBH4 (0.05 mmol) was added to the reaction mixture
solution slowly and stirred under 50°C for 2–3 h. e
mixture was evaporated under vacuum and dissolved in
ethylacetoacetate (30 mL). e solution was washed with
20 mL water twice, dried over anhydrous sodium sulfate
and evaporated.
+
339 (M+1) . 13C NMR (100 MHz, DMSO, δ ppm): 37.6
(-N(CH3)2), 49.9 (-N-CH2), 108.1 (Naphthyl C-2), 120.3
(Naphthyl C-4,9), 125.5 (Naphthyl C-3,6,7,8), 128.1 (Ph
C-3,4,5,6), 131.7 (Ph C-2), 133.5 (Naphthyl C-5,10), 136.1
(Naphthyl C-1), 138.1 (Ph C-1), 168.5 (C=O). Elemental
Analysis calculated for C20H19ClN2O: C, 71.00; H, 5.62; N,
8.28. Found: C, 72.02; H, 5.53; N, 8.37.
1-(2,3-dimethylphenyl)-3,3-dimethyl-1-(2-nitrobenzyl)
urea (7d)
Synthesis of benzyl urea derivatives15,16 (7a–7g)
Yield: 41.89% (Liquid), bp 114–115°C. FTIR (KBr) cm−1:
3012 (Ar C–H), 2881 (Aliphatic C–H), 1721 (C=O), 1523
e mixture of CH2Cl2 (15 mL), dry DMF (5) (3 mL, 40
mmol) and SOCl2 (6) (7 mL, 0.10 mol) was stirred at
70°C for 4 h and cooled for 24 h. e solvents and excess
SOCl2 were then removed under reduced pressure. e
residue dissolved in CH2Cl2 (15 mL). Dry pyridine (4 mL)
1
(N-O); 1329 (ter C-N), 859 (Ar C-N). H NMR (300 MHz,
DMSO, δ ppm): 2.20 (s, 3H, CH3), 2.33 (s, 3H, CH3), 2.80
(s, 6H, CH3), 4.82 (s, 2H, CH2), 7.41–7.52 (m, 3H, Aro-
Journal of Enzyme Inhibition and Medicinal Chemistry

Use of QSAR and ADMET prediction studies 323
matic), 7.57–7.73 (m, 3H, Aromatic). Mass: m/z 327 (M+),
Anti-proliferative activity by MTT assay
+
328 (M+1) . 13C NMR (100 MHz, DMSO, δ ppm): 18.5
e human T-cell leukemia cell lines (Molt-4 and Jurkat
J6), myelogenous leukemia cell line (K562) and breast
cancer cell line (MCF-7) were procured from ATCC
(American type culture collection). Molt-4, Jurkat J6, and
K562 were maintained in RPMI 1640 medium and MCF-7
cell line in Eagle′s minimum essential medium supple-
mented with 10% fetal calf serum and 100 U of penicillin
and streptomycin. e cell lines were cultured and main-
tained in a humidified atmosphere of 5% CO2 at 37°C.
e anti-proliferative activity of the compounds was
determined by MTT (3-(4,5-Dimethylthiazol-2-yl)-2,5-
diphenyltetrazolium bromide) assay, using 5-flurouracil
as reference drug. Cell lines were seeded at a density of
25,000 cells/well/100 µL in a 96 well tissue culture plate
for suspension cultures and 5000 cells /well in a 96 well
tissue culture plate for adherent cultures. Suspension
cells were treated with compounds 7a–7g within 1 h of
seeding whereas adherent cells were allowed to adhere
for 12 h and medium was replaced. e compounds at
concentration of 25, 50, 100, and 200 µg/mL were used in
the volume of 10 µL/well. After the incubation of 48 h, the
cell survival was determined by addition of MTT solu-
tion (10 µL/well of 5 mg/mL MTT in phosphate-buffered
saline) and incubated for 4 h at 37oC in 5% CO2. For sus-
pension cells, the formazon crystals were solubilized by
adding 10% sodium dodecyl sulfate in 0.1N HCL, incu-
bated overnight and optical absorbance was measured
at 570–650 nm. For adherent cells, the medium contain-
ing MTT was aspirated; DMSO (100µL/well) was added
and optical absorbance was measured after 10 min at
570 nm.
(2,4 dimethyl), 37.1 (-N(CH3)2), 50.7(-N-CH2), 127.5 (di-
methyl Ph C-3,5,6), 128.7 (Ph C-3,4,5,6), 130.5 (dimethyl
Ph C-2,4), 132.1 (Ph C-1), 136.6 (dimethyl Ph C-1), 144.3
(Ph C-2), 169.5 (C=O). Elemental Analysis calculated for
C18H21N3O3: C, 66.04; H, 6.47; N, 12.84. Found: C, 66.10; H,
6.34; N, 12.76.
1-(2-ethylbenzyl)-3,3-dimethyl-1-(naphthalen-1-yl)
urea (7e)
Yield: 83.13% (Liquid), bp 122–124°C. FTIR (KBr) cm−1:
3025 (Ar C–H), 2934 (Aliphatic C–H), 1717 (C=O), 1359 (ter
C-N). 1H NMR (300MHz, DMSO, δ ppm): 1.19–1.35 (t, 3H,
CH3, J=8.1 Hz), 2.58–2.74 (q, 2H, CH2, J=8.1 Hz), 2.93 (s, 6H,
CH3), 4.92 (s, 2H, CH2), 7.21–7.36 (m, 4H, Aromatic), 7.47–
+
7.76 (m, 7H, Naphthyl). Mass: m/z 332 (M+), 333 (M+1) .
13C NMR (100MHz, DMSO, δ ppm): 14.3 (-CH2CH3), 26.3
(-CH2CH3), 37.4 (-N(CH3)2), 49.8 (-N-CH2), 108.3 (Naphthyl
C-2), 120.5 (Naphthyl C-4,9), 124.3 (Ph C-3,4,5,6), 125.8
(Naphthyl C-3,6,7,8), 128.2 (Ph C-2), 132.9 137.1 (Naph-
thyl C-1), (Naphthyl C-5,10), 138.3 (Ph C-1), 168.4 (C=O).
Elemental Analysis calculated for C22H24N2O: C, 79.51; H,
7.22; N, 8.43. Found: C, 79.44; H, 7.10; N, 8.55.
1-(3-ethoxy-4-hydroxybenzyl)-3,3-dimethyl-1-
(naphthalen-1-yl)urea (7f)
Yield: 62.50% (Liquid), bp 103–105°C. FTIR (KBr) cm−1:
3589 (Ar OH), 3098 (Ar C–H), 2859 (Aliphatic C–H), 1716
1
(C=O), 1391 (ter C-N); 1329 (ter C-N), 1256 (C-O-C). H
NMR (300 MHz, DMSO, δ ppm): 1.24–1.34 (t, 3H, CH3,
J = 8.1 Hz), 2.76 (s, 6H, CH3), 4.03–4.17 (q, 2H, CH2, J = 8.2
Hz), 4.77 (s, 2H, CH2), 5.59 (s, 1H, OH), 7.16–7.23 (m, 3H,
Aromatic), 7.37–7.52 (m, 7H, Naphthyl). Mass: m/z 364
Results and discussion
+
(M+), 365 (M+1) . 13C NMR (100 MHz, DMSO, δ ppm):
15.1 (-CH2CH3), 37.2 (-N(CH3)2), 50.4 (-N-CH2), 66.2
(-CH2CH3), 108.4 (Naphthyl C-2), 117.1(Ph C-2,5), 120.1
(Naphthyl C-4,9), 127.3 (Ph C-3,6,7,8), 128.1 (Ph C-1),
132.4 (Naphthyl C-5,10), 133.5 (Ph C-1), 136.8 (Naphthyl
C-1), 147.5 (Ph C-3,4), 169.2 (C=O). Elemental Analy-
sis calculated for C22H24N2O3: C, 72.52; H, 6.59; N, 7.69.
Found: C, 72.63; H, 6.49; N, 7.62.
QSAR studies
A series of total 24 compounds for which absolute IC50
values reported15 was used for correlating chemical com-
position (structure) with their anti-proliferative activity.
Several 2D-QSAR and 3D-QSAR models were generated
for training set of 20 compounds using MLR and SA
kNN–MFA (SA kNN–MFA) method, respectively. e best
QSAR model was selected on the basis of value of Statis-
tical 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 models were validated and tested for its pre-
dictability using an external test set of four compounds.
Statistical results generated by both 2D and 3D-QSAR
analysis showed that both QSAR model have good inter-
nal as well as external predictability (Table 2).
e generated QSAR models were evolved by repeating
the MLR and kNN–MFA methods to check the accuracy
and precision of both the methods. e frequency of use
of a particular descriptor in the population of equations
indicated the relevant contributions of the descriptors.
e best 2D-QSAR model had five contributing descrip-
tors including constant (equation 3)
1-(2,3-dimethylphenyl)-1-(2-ethylbenzyl)-3,3-
dimethylurea (7g)
Yield: 73.73% (Liquid), bp 124–125°C. FTIR (KBr) cm−1:
2998 (Ar C–H), 2917 (Aliphatic C–H), 1693 (C=O), 1320
1
(ter C-N). H NMR (300 MHz, DMSO, δ ppm): 1.14–1.26
(t, 3H, CH3), 2.02 (s, 3H, CH3, J = 7.9 Hz), 2.21 (s, 3H, CH3),
2.62-2.78 (q, 3H, CH3, J = 7.9 Hz), 2.29 (s, 6H, CH3), 4.84
(S, 2H, CH2), 7.11–7.43 (m, 6H, Aromatic). Mass: m/z 310
+
(M+), 311 (M+1) . 13C NMR (100 MHz, DMSO, δ ppm):
13.9 (-CH2CH3), 17.6 (2,4 dimethyl), 26.3 (-CH2CH3), 38.2
(-N(CH3)2), 49.8 (-N-CH2), 126.2 (Ph C-2,4,5,6), 128.1 (di-
methyl Ph C-3,4,5), 133.2 (dimethyl Ph C-2,4), 136.1 (Ph
C-1,3), 137.2 (dimethyl Ph C-1), 167.4 (C=O). Elemental
Analysis calculated for C20H26N2O: C, 77.38; H, 8.44; N,
9.02. Found: C, 77.23; H, 8.35; N, 9.09.
© 2011 Informa UK, Ltd.

324 D. Lokwani et al.
scriptor that specifies the count of number of nitrogen
atoms (single, double, or triple bonded) separated from
any oxygen atom (single or double bonded) by 2 bond
distances in a molecule. is descriptor suggested the re-
quirement of urea molecule in benzyl urea pharmacoph-
ore for biological activity. e next influential descriptor
was MMFF_6 (contribution ∼30% for biological activity)
that was inversely proportional to the activity. is is an
atom type count descriptor based on MMFF atom types
and their count in each molecule and ‘6′ indicated num-
ber of times that atom type has been found in a given
molecule.
e descriptor chiv1 (contribution ∼24% for biologi-
cal activity) was found to be directly proportional to the
activity and showed the role of atomic valence connectiv-
ity index (order 1) that is calculated as the sum of 1/√vivj
over all bonds between heavy atoms i and j where i < j.
Finally, the descriptor governing variation in the activity
was T_C_O_7 (contribution approximately 5% for biolog-
ical activity) and was found to be inversely proportional
to the activity, which indicated that the presence of sub-
stituents with direct attachment of oxygen on aromatic
ring (e.g. OH) may be unfavorable or detrimental for the
anti-cancer activity.
Table 2. Statistical results of 2D-QSAR equation generated by
MLR method and 3D-QSAR model generated by SA kNN–MFA for
benzyl urea derivatives.
Results
Sr. Statistical
No. parameter
2D-QSAR
0.8787
3D-QSAR
1.
2.
3.
4.
5.
6.
7.
8.
9.
r2
r2SE
q2
q2SE
Pred_r2
Pred_r2SE
F-Test
α q2
—
—
0.2411
0.7215
0.2378
0.5728
0.2162
19.914
0.0100
0.6276
0.2843
0.9106
0.2199
—
—
Best-Rand q2 0.2458
—
10. Z-score q2
11
12 Nearest
neighbour
11 Contributing 1. chiv1 (+ 0.6694)
2.6730
20
—
N
20
—
4
1. S_715(0.3304, 6.0782)
descriptors 2. MMFF_6 (−0.8569) 2. E_232 (-0.0255,
3. T_C_O_7 (−0.1222) 0.1432)
4.T_N_O_2 (+1.1713) 3. E_1064 (0.1346,
0.1345)
pIC50 =0.6694 chiv10.8569 MMFF_6
O.1222 T_C_O_7
3D-QSAR was used to optimize the electrostatic and
steric requirements around benzyl urea pharmacophore.
3D data points were generated that contribute to SA kNN–
MFA 3D-QSAR model, are shown in Figure 4B. e range
of property values for the generated data points helped
for the design of potent NCEs. e range was based on
the variation of the field values at the chosen points using
the most active molecule and its nearest neighbour set.
e points generated in SA kNN–MFA 3D-QSAR model
are E_232 (-0.0255, 0.1432), S_715 (0.3304, 6.0782),
and E_1064 (0.1346, 0.1345) i.e. electrostatic and steric
interaction field at lattice points 232, 715, and 1064, re-
spectively. ese points were suggested the significance
and requirement of electrostatic and steric properties
as mentioned in the ranges in parenthesis for structure-
activity relationship and maximum biological activity of
benzyl urea derivatives.
Negative and positive values in electrostatic field
descriptors indicated the requirement of nega-
tive and positive electrostatic potential respectively
for enhancing the biological activity of benzyl
urea derivatives. Therefore electronegative and
electropositive substituents were preferred at the posi-
tion of generated data points E_232 (-0.0255, 0.1432)
and E_1064 (0.1346, 0.1345) respectively around ben-
zyl urea pharmacophore.
(3)
1.1713 T_N_O_24.7880
Results of 3D-QSAR indicated the requirement of two
electropositive groups and one steric group around ben-
zyl urea pharmacophore for anti-cancer activity (Table 2).
e kNN–MFA QSAR method explores formally the active
analogue approach, which implies that compounds dis-
play similar profiles of pharmacological activities. In this
method, the activity of each compound is predicted as
average activity of k most chemically similar compounds
from that data set. e generated 3D-QSAR model
showed a good correlation between the experimental ob-
tained and computationally predicted activity (Figure 3).
Residual values obtained by subtraction of predicted
activities from experimental biological activities were
found to near to zero indicating the good predictability of
selected QSAR Model (Table 3). e plots of observed vs.
predicted activity for the optimal cross-validated kNN–
QSAR model are depicted in Figure 3.
Interpretation of QSAR studies
It is simple to interpret 2D-QSAR MLR equation where
each descriptor′s contribution can be seen by the magni-
tude and sign of its regression coefficient. A descriptor′s
coefficient magnitude shows its relative contribution
with respect to other descriptors and sign indicates
whether it is directly (+) or inversely (−) proportional to
the activity. e developed MLR equation 2 indicated
that the descriptor T_N_O_2 played most significantly
important role (contribution ∼41% for biological activity)
for anti-proliferative activity of benzyl urea derivatives
(Figure 4A). T_N_O_2 is an alignment-independent de-
Similarly the positive values of steric descriptors sug-
gested the requirement of sterically bulky groups at the
position of generated data point S_715 (0.3304, 6.0782)
around benzyl urea pharmacophore for maximum activ-
ity. us the KNN–MFA models leads to identification of
various local interacting molecular features responsible
for activity variation and hence provide direction for
design of new molecules in a convenient way.
Journal of Enzyme Inhibition and Medicinal Chemistry

Use of QSAR and ADMET prediction studies 325
1
0.5
0
0
−0.2
−0.4
−0.6
−0.8
−1
0
1
2
3
4
5
Observed
activity
Observed
activity
0
5
10
15
20
25
Predicted
activity
−0.5
−1
Predicted
activity
Linear
(predicted
activity)
−1.2
−1.4
−1.5
−1.8
Linear
(predicted
activity)
−1.5
−2
−2.5
(A)
(B)
Figure 3. Comparison of observed activity vs predicted activity for training set (A) and test set (B) of compounds.
Table 3. Structure of training and test sets of compounds along with observed and predicted activity.
R1
R2
R
N
N
CH3
O
R3
CH3
Observed activity
SA kNN–MFA
Predicted activity
Mol. No.
1
R
R1
R2
H
R3
H
H
H
H
IC50 (µM)
0.42
0.27
40.7
24.9
71.3
3.88
2.92
33.5
11.8
7.5
PIC50
Residual
−0.0828
0.0248
−0.1339
0.3487
−0.2572
0.1754
0.2476
−0.1631
−0.662
0.199
-4-propylmorpholine
-4-ethylmorpholine
-2-methylfuran
NO2
NO2
NO2
NO2
Cl
0.3768
0.4596
0.5438
2
H
0.5686
3
H
−1.6095
−1.3961
−1.8530
−0.5888
−0.4653
−1.5250
−1.0718
−0.8750
−1.8756
−1.8273
−0.3504
−0.0791
−1.5490
−1.4149
−1.4563
−1.0334
−0.9294
−1.5843
−1.6074
−1.1303
−1.2695
−1.6211
−1.4756
-1.7448
−1.5958
−0.7637
−0.7129
−1.3619
−0.409
4
-ethylpiperidine-1-carboxylate
-2-methylfuran
H
5
H
OH
OH
OH
OH
H
-4-ethylmorpholine
-4-propylmorpholine
-ethylpiperidine-1-carboxylate
-4-ethylmorpholine
-4-propylmorpholine
-2-methylfuran
Cl
H
6*
7
Cl
H
8
Cl
H
9
H
OH
OH
OH
OH
10
11
12
13
14*
15*
16
17
18*
19
20
21
22
23
24
H
H
−1.074
H
H
75.1
67.2
2.24
1.20
35.4
26.0
28.6
10.8
8.5
−1.5603
−1.6452
−0.7603
−0.4080
−1.4951
−1.048
−0.3153
−0.1821
0.4099
0.3289
−0.0539
−0.366
0.3831
0.0577
0.4638
0.1438
0.1094
0.6005
0.4067
−0.1514
-ethylpiperidine-1-carboxylate
-4-ethylmorpholine
-4-propylmorpholine
-2-methylfuran
H
H
H
NO2
NO2
NO2
NO2
H
H
H
H
H
H
-ethylpiperidine-1-carboxylate
-2-methylfuran
H
H
NO2
NO2
NO2
NO2
Br
OH
OH
OH
OH
OH
OH
OH
OH
−1.8394
−1.0911
−1.3932
−1.7281
−1.7168
−1.7308
−1.6762
−1.4703
-4-ethylmorpholine
-4-propylmorpholine
-ethylpiperidine-1-carboxylate
-2-methylfuran
H
H
H
38.4
40.5
13.5
18.6
41.8
H
-4-ethylmorpholine
-4-propylmorpholine
-ethylpiperidine-1-carboxylate
Br
H
Br
H
Br
H
*Compounds in test set.
© 2011 Informa UK, Ltd.

326 D. Lokwani et al.
2. number of Hydrogen Bond donor (B) (<5)
3. number of Rotatable Bond (R) (<10)
4. XlogP (X) (<5)
5. molecular weight (W) (<500 g/mol)
6. polar surface area (S) is (<140 A0)
Design and screening of NCEs
e information obtained from 2D and 3D-QSAR studies
were used to optimize the electrostatic and steric require-
ments around the benzyl urea nucleus for enhancing the
anti-cancer activity (Figure 4C).
Descriptors generated in 2D-QSAR equation signified
the importance of urea group for anti-cancer activity of
compounds. Similarly electrostatic and steric points gen-
erated around common template or pharmacophore in
3D-QSAR suggested substitution of sterically bulky group
at N-R2 position around urea and electropositive group at
R1 position around phenyl ring.
e NCEs were designed by CombiLib tool of V-Life
Molecular Design Suite Software using pharmacophore
shown in Figure 4C. CombiLib tool passed the designed
compounds through Lipinski′s screen to ensure drug-
like pharmacokinetic profile of the designed compounds.
e Lipinski′s screen parameters used as filters are:
More than 100 molecules were generated using CombiLib
tool that follows the Lipinski′s rule, but we have selected
only 30 most active molecules on the basis of their activ-
ity, predicted using MLR equation (Table 4).
In Table 4, Compounds qualifying all required pa-
rameters set for Lipinski′s screen/filter are indicated by
ADRXWS strings. e columns containing the Lipinski′s
screen score and strings of alphabets, ADRXWS indicated
that all six Lipinski′s screen parameters are satisfied by
corresponding compound and depending on number of
requirement satisfied, screen score varied in between 1
and 6. It was concluded from predicted activity of de-
signed compound that presence of electropostive group
at R1 position mainly at ortho and para position of phenyl
1. number of Hydrogen Bond Acceptor (A) (<10)
Contribution of 2D descriptor
50%
E_232 (-0.0255 , 0.1432)
41.53%
40%
30%
23.44%
20%
10%
0%
E_1064 (0.1346 , 0.1385)
S_715 (0.3304 , 6.0782)
Chiv1
MMFF_6
T_C_0_7
T_N_0_2
−10%
−20%
−30%
−40%
−4.57%
−30.46%
(A)
(B)
Urea required for activity
O
CH3
N
N
R1
R2
CH3
Electropositive groups and
no direct oxygen group attached
Sterically bulky groups
(C)
Figure 4. Results of QSAR studies. (A) Contributions of 2D descriptors for biological activity developed using MLR equation (2D-QSAR).
(B) Contributions of electrostatic and steric 3D data points towards biological activity developed using kNN–MFA method (3D-QSAR). (C)
Pharmacophore requirement around benzyl urea.
Journal of Enzyme Inhibition and Medicinal Chemistry

Use of QSAR and ADMET prediction studies 327
Table 4. Structure of designed NCEs along with predicted activity obtained by MLR equation generated by 2D-QSAR.
Sr. No. Compound code
R1
R2
Screen result
ADRXWS
ADRXWS
ADRXWS
ADRXWS
ADRXWS
ADRXWS
ADRXWS
ADRXWS
ADRXWS
ADRXWS
ADRXWS
ADRXWS
ADRXWS
ADRXWS
ADRXWS
ADRXWS
ADRXWS
ADRXWS
ADRXWS
ADRXWS
ADRXWS
ADRXWS
ADRXWS
ADRXWS
ADRXWS
ADRXWS
ADRXWS
ADRXWS
ADRXWS
ADRXWS
Screen score
Predicted activity
0.413
1
E5
A1
J2
2-NO2
2-CH3
Benzyl
6
6
6
6
6
6
6
6
6
6
6
6
6
6
6
6
6
6
6
6
6
6
6
6
6
6
6
6
6
6
2
Nephthyl
0.356
3
2-C2H5
2-NO2
2-CH3
2,4-Dimethyphenyl
2,4-Dimethyphenyl
Benzyl
0.291
4
E2
A5
J8
0.146
5
−0.0198
−0.0921
−0.1992
−0.2112
−0.2214
−0.2256
−0.2318
−0.2377
−0.2412
−0.2489
−0.2801
−0.3125
−0.3890
−0.4114
−0.4223
−0.4569
−0.4987
−0.5239
−0.5673
−0.6166
−0.7709
−0.9876
−0.9943
−1.2319
−1.2879
−1.1341
6
2-C2H5
2-CH3
1-Morpholine
1-Morpholine
Nephthyl
7
A8
C1
J1
8
3-OCH3 4-OH
2-C2H5
2-NO2
2-NO2
2-Cl
9
Nephthyl
10
11
12
13
14
15
16
17
18
19
20
21
22
23
24
25
26
27
28
29
30
E1
E8
H1
A6
H5
J7
Nephthyl
1-Morpholine
Nephthyl
2-CH3
2-Furan
2-Cl
Benzyl
2-C2H5
2-CH3
2-Pyrrol
A2
H8
E7
E6
C2
C5
D8
C8
D1
F1
D5
H2
D6
F5
J6
2,4-Dimethyphenyl
1-Morpholine
2-Pyrrol
2-Cl
2-NO2
2-NO2
3-OCH3 4-OH
3-OCH3 4-OH
2-OH
2-Furan
2,4-Dimethyphenyl
Benzyl
1-Morpholine
1-Morpholine
Nephthyl
3-OCH3 4-OH
2-OH
3-NO2
2-OH
Nephthyl
Benzyl
2-Cl
2,4-Dimethyphenyl
2-Furan
2-OH
3-NO2
2-C2H5
Benzyl
2-Furan
ring and presence of sterically bulky group like benzyl,
naphthyl at R2 position significantly enhanced the activ-
ity of compounds.
probability of heptotoxicity, whereas all designed com-
pounds were found to be non-toxic to liver.
In next screening step, ADMET properties of all de-
signed compounds were predicted and compared with
predicted ADMET properties of standard compounds us-
ing DS, Accelrys software (Table 5). Prediction of ADMET
properties was used as last screen to sort out those com-
pounds that already followed Lipinski′s rule and showed
good predicted activity. DS defined the prediction level
for all ADMET properties. For ADMET Absorption, there
are four prediction level: 0 – Good, 1 – Moderate, 2 – Poor,
3 – Very Poor, for ADMET plasma protein binding (PPB)
level: 0 – binding is <90%, 1 – binding is >90%, 2 – bind-
ing is >95%, for ADMET Cytochrome P450 2D6 (CYP2D6)
Probability level: <0.5 – Unlikely to inhibit CYP2D6 en-
zyme (Non-inhibitors of CYP2D6), >0.5 – Likely to inhibit
CYP2D6 enzyme (Inhibitors of CYP2D6) and for ADMET
probability level: < 0.5 – Unlikely to cause dose-depen-
dent liver injuries (Non-toxic), >0.5 – Likely to cause
dose-dependent liver injuries (Toxic).
Chemistry
e seven compounds (Table 5) that followed the all
screening criteria were selected and synthesized. e
synthetic route is outlined in Scheme 1. In the first step,
condensation reaction of aldehydes (1a-1g) with various
amines (2a-2g) gave the Schiff bases (3a-3g). Reduc-
tion of the latter with sodium borohydride afforded the
corresponding secondary amines (4a-4g). In second
step, Vilsmeier–Haack reaction was carried out, in which
dry dimethyl formamide (5) in thionyl chloride (6) and
dichloromethane was refluxed for 4 h and excessive
SOCl2 was evaporated. e residue was then dissolved
in dichloromethane, and stirred with various secondary
amines (4a-4g) obtained in the first step dissolved in
dry pyridine and CH2Cl2. As a result, benzyl urea deriva-
tives (7a–7g) were synthesized. e structures of various
synthesized compounds were assigned on the basis of
different chromatographic, spectral and qualitative and
quantitative organic analytical studies. e physical data,
All designed compounds showed good absorption,
PPB level and found non-inhibitors of CYP2D6 enzyme.
As shown in Table 5, Gefitinib and 5-flurouracil showed
1
FTIR, HNMR and mass spectral data for all synthesized
compounds are reported in experimental protocols.
© 2011 Informa UK, Ltd.

328 D. Lokwani et al.
Table 5. Prediction of ADMET properties by using discovery studio, accelrys.
Sr. No. Compound code ADMET absorption level ADMET PPB level ADMET CYP2D6 probability ADMET hepatotoxicity probability
1
C-1
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
1
2
2
0
0
2
1
0
2
2
2
2
2
1
2
2
1
2
0
0.772
0.455
0.297
0.435
0.772
0.584
0.415
0.366
0.841
0.455
0.841
0.613
0.485
0.405
0.782
0.514
0.663
0.019
0.655
0.456
0.529
0.357
0.596
0.589
0.357
0.317
0.609
0.43
2
E-5
3
E-2
4
E-7
5
F-1
6
J-2
7
D-6
8
H-5
9
H-1
10
11
12
13
14
15
16
17
18
F-5
J-1
0.642
0.602
0.609
0.072
0.682
0.655
0.973
0.834
C-5
C-2
A-8
E-1
Imatinib
Gefitinib
5-Flurouracil
R2
R2
CHO
N
H
N
(i)
(ii)
R2NH2
2a-2g
R1
R1
R1
+
4a-4g
1a-1g
3a-3g
H
O
Cl
O
O
O
S
Cl
(iii)
H3C
H3C
H3C
+
S
+
S
H3C
H3C
+
N
O
Cl
N
H
N
O
Cl
Cl
Cl
H3C
6
5
(iv)
4a-4g
O
CH3
N
N
R1
CH3
R2
7a–7g
Scheme 1. Synthetic route for benzyl urea derivatives. Reagents & condition: (i) ethanol, reflux, 1–2 h (ii) NaBH4, 50°C, 2–3h (iii) dry CH2Cl2,
70°C, 4h (iv) dry CH2Cl2, dry pyridine, 60°C, 5–6h.
was used as reference compound. e results are depict-
ed in Table 6 and Figure 5.
Anti-proliferative activity
All synthesized compounds were evaluated for their ef-
fect on proliferation in Molt-4, Jurkat J6, MCF-7, and
K562 cell lines by MTT colorimetric assay. Compounds
were tested at 25, 50, 100, and 200 g/mL concentrations
and their % decrease in cell proliferation were calculated
by considering untreated controls as 100%. 5-Flurouracil
Molt-4 cell line did not respond to any of the seven
compounds tested but in K562 cell line, all compounds
showed anti-proliferative activity. In case of Jurkat J6,
and MCF-7, only compound 7d exhibited significant
anti-proliferative effect. Compound 7d showed 35% and
Journal of Enzyme Inhibition and Medicinal Chemistry

Use of QSAR and ADMET prediction studies 329
Table 6. Anti-proliferative activity of compounds 7a–7g in cancer cell lines.
% Inhibition
Compound code
7a (H-5)
R1
R2
Conc (µg/mL)
25
Molt-4 (%)
0
Jurkat J6 (%)
3.9
MCF-7 (%)
8.85
10.07
10.07
—
K562 (%)
—
-2-Cl
-Benzyl
-Benzyl
50
6.32
10.42
—
7.45
7.78
—
6.71
9.59
17.80
—
100
200
25
7b (E-5)
7c (H-1)
7d (E-2)
7e (J-1)
-2-NO2
-2-Cl
4.44
6.5
0.52
—
4.82
4.6
17.3
9.22
4.80
—
50
0
100
200
25
0.65
—
6.19
16.66
—
-1-Naphthyl
0
2.08
3.61
5.5
7.50
5.54
2.11
—
50
4.66
0.30
—
14.92
12.91
7.94
—
100
200
25
—
-2-NO2
-2-C2H5
-2,4-dimethylphenyl
-1-Naphthyl
7.83
3.91
7.98
—
9.4
10.81
11.91
17.67
—
50
18
13.08
42.05
45.37
—
100
200
25
35
—
0
4.82
10.05
11.73
—
0
50
0.30
3.46
—
0
10.64
13.78
18.15
—
100
200
25
7.99
—
7f (C-1)
-3-OC2H5-4-OH -1-Naphthyl
0
18.09
10.05
16.55
—
0.52
17.79
2.36
—
50
4.59
2.48
—
9.773
12.91
18.67
—
100
200
25
7g (J-2)
-2-C2H5
-2,4-dimethylphenyl
6.6
7.68
0
14.36
12.39
14.3
—
0
50
0
3.40
11.43
20.49
—
100
200
25
0
—
—
5-Flurouracil
—
—
65
40
4.93
15.71
21.59
—
50
64
41
13.35
7.76
8.55
100
200
61
37
—
—
17.7% decrease in cell proliferation in Molt-4 and Jurkat
cell lines respectively at concentration of 100 µg/mL, and
was comparable with positive control-5-flurouracil that
showed 37%, and 21.59% decrease in cell proliferation,
respectively. Compound 7d exhibited good anti-prolif-
erative activity (42.05%) as compared to that of 5-flurou-
racil (7.76%) at concentration of 100 µg/mL in K562 cell
line. ese results suggested that presence of NO2 group
at ortho position of phenyl ring R1 position and 2,4-
dimethylphenyl at R2 position improved the cytotoxicity
in MCF -7, Jurkat J6, and K562 cell line. is also indi-
cated that presence of –Cl, and C2H5 at ortho position of
phenyl ring at R1 position and benzyl and naphthyl group
at R2 position showed poor cytotoxic activity. From QSAR
results, it was found that substituents at R1 position with
direct oxygen substituents may decrease the cytotoxic
activity but these compounds showed good binding af-
finity at EGFR receptor found by docking results, hence
compound 7f containing OC2H5 at meta position and OH
at para position of phenyl ring at R1 position was taken
further for experimental validation, it show comparable
cytotoxic activity with 5-flurouracil in MCF-7 but poor
in vitro anti-tumour activity against Jurkat J6. erefore,
it was revealed that presence of oxygen group at phenyl
ring at R1 position may decrease the activity. Also sub-
stituents at ortho position of phenyl ring at R1 position
rather than meta and para position may help in increas-
ing the in vitro anti-proliferative activity.
Conclusion
In this study, we tested the possibility of developing
NCE′s using QSAR and ADMET predicting studies. e
proposed 3D and 2D-QSAR models, due to the high inter-
nal and external predictive ability, can therefore act as a
useful aid to the costly and time consuming experiments
for determining electrostatic and steric requirement
around benzyl urea pharmacophore that is required to
achieve better anti-proliferative activity and thus aided
in generation of diverse combinatorial library. Prediction
of ADMET properties of designed compound helped in
selecting the compounds having drug-like properties. A
screening approach has thus facilitated the identifica-
tion of suitable compounds from designed library for
© 2011 Informa UK, Ltd.

330 D. Lokwani et al.
(A) 70%
(B) 70%
60%
50%
40%
30%
20%
10%
0%
60%
50%
40%
30%
20%
10%
0%
25
µg/mL
25
µg/mL
50
µg/mL
50
µg/mL
100
µg/mL
100
µg/mL
7a 7b 7c 7d 7e 7f 7g 5-FU
7a 7b 7c 7d 7e 7f 7g 5-FU
Compound code
Compound code
(C)
(D)
70%
60%
50%
40%
30%
20%
10%
0%
70%
60%
50%
40%
30%
20%
10%
0%
25
25
µg/mL
µg/mL
50
µg/mL
100
µg/mL
100
µg/mL
200
µg/mL
7a 7b 7c 7d 7e 7f 7g 5-FU
Compound code
7a 7b 7c 7d 7e 7f 7g 5-FU
Compound code
Figure 5. Effect of compounds 7a–7g on (A) Molt-4, (B) Jurkat J6, (C) MCF-7, and (D) K562 cell lines on cell proliferation determined by
MTT assay.
5. Krause DS, Van Etten RA. Tyrosine kinases as targets for cancer
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6. Hirsch FR, Varella-Garcia M, Bunn PA Jr, Di Maria MV, Veve R,
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ies were cross verified by testing the cytotoxic activity of
designed compounds in four cell line; Molt-4, Jurkat J6,
Bremmes RM, Barón AE, Zeng C, Franklin WA. Epidermal growth
MCF-7, and K562. Compound 7d was found to be the
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at phenyl ring at R1 position and sterically bulky group at
R2 like 2, 4-dimethylphenyl position can help the benzyl
urea pharmacophore for inhibiting the tumour cells.
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Declaration of interest
e authors report no conflicts of interest.
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