Discovery of Novel Acyl Coenzyme A: Cholesterol Acyltransferase Inhibitors: Pharmacophore-Based Virtual Screening, Synthesis and Pharmacology

Article abstract of DOI:10.1111/j.1747-0285.2012.01384.x

The present study describes ligand-based pharmacophore modeling of a series of structurally diverse acyl coenzyme A cholesterol acyltransferase inhibitors. Quantitative pharmacophore models were generated using HypoGen module of Discovery Studio 2.1, whereby the best pharmacophore model possessing two hydrophobic, one ring aromatic, and one hydrogen bond acceptor feature for inhibition of acyl coenzyme A cholesterol acyltransferase showed a very good correlation coefficient (r=0.942) along with satisfactory cost analysis. Hypo1 was also validated by test set and cross-validation methods. Developed models were found to be predictive as indicated by low error values for test set molecules. Virtual screening against Maybridge database using Hypo1 was performed. The two most potent compounds (47 and 48; predicted IC₅₀=1nm) of the retrieved hits were synthesized and biologically evaluated. These compounds showed 86% and 88% inhibition of acyl coenzyme A cholesterol acyltransferase (at 10μg/mL) with IC₅₀ value of 3.6 and 2.5nm, respectively. As evident from the close proximity of biological data to the predicted values, it can be concluded that the generated model (Hypo1) is a reliable and useful tool for lead optimization of novel acyl coenzyme A cholesterol acyltransferase inhibitors. A ligand-based pharmacophore modeling of a series of structurally diverse acyl CoA cholesterol acyltransferase (ACAT) inhibitors was carried out. Two most potent compounds (47 and 48; predicted IC₅₀=1nm) of the retrieved hits from virtual screening were synthesized and biologically evaluated. The results demonstrated that these compounds showed 86% and 88% inhibition of ACAT (at 10μg/mL) with IC₅₀ value of 3.6 and 2.5nm, respectively.

Full text of DOI:10.1111/j.1747-0285.2012.01384.x

ª 2012 John Wiley & Sons A/S
doi: 10.1111/j.1747-0285.2012.01384.x
Chem Biol Drug Des 2012; 80: 107–113
Research Article
Discovery of Novel Acyl Coenzyme A: Cholesterol
Acyltransferase Inhibitors: Pharmacophore-Based
Virtual Screening, Synthesis and Pharmacology
Mahesh T. Chhabria1,*, Pathik S.
factor for its development (1,2). Hyperlipidemia is associated with
the increased levels of serum cholesterol, which is the most signifi-
cant risk factor for the development of atherosclerosis. An attractive
target for hyperlipidemia is inhibition of acyl coenzyme A choles-
Brahmkshatriya1, Bhushan M. Mahajan1,
Urvesh B. Darji1 and Gaurang B. Shah2
1Department of Pharmaceutical Chemistry, L. M. College of
terol acyltransferase (ACAT), an enzyme which catalyzes cholesterol
esterification and plays an important role in lipoprotein assembly,
dietary cholesterol absorption, and intracellular cholesterol metabo-
lism (3). Two types of ACAT enzymes, viz., ACAT-1 and ACAT-2 have
been identified in various mammals including human and mouse.
ACAT-1 can accumulate in macrophages and smooth muscle cells to
produce foam cells, leading to plaque initiation and atherosclerotic
progression (4). On the other hand, the selective distribution of
ACAT-2 in the endoplasmic reticulum of liver and intestine seems to
suggest that this isoenzyme could operate in a specialized manner,
for example in intestinal cholesterol absorption and in lipoprotein
secretion (4). In the small intestine, ACAT facilitates the absorption
of exogenous cholesterol, which is incorporated into chylomicrons
(5,6). In the liver, ACAT plays an important role in the assembly of
very low density lipoprotein (VLDL), which is secreted into the blood
(7–9). This clearly suggests that inhibition of ACAT remains to be
an attractive target to the medicinal chemists for discovery of new
antihyperlipidemic agents.
Pharmacy, Navrangpura, Ahmedabad 380 009, Gujarat, India
2K. B. Institute of Pharmaceutical Education and Research, Gh-6,
Sector-23, Gandhinagar 382 023, Gujarat, India
*Corresponding author: Mahesh T. Chhabria,
mahesh.chhabria@rediffmail.com
The present study describes ligand-based pharma-
cophore modeling of
a series of structurally
diverse acyl coenzyme A cholesterol acyltransfer-
ase inhibitors. Quantitative pharmacophore mod-
els were generated using HypoGen module of
Discovery Studio 2.1, whereby the best pharmaco-
phore model possessing two hydrophobic, one
ring aromatic, and one hydrogen bond acceptor
feature for inhibition of acyl coenzyme A choles-
terol acyltransferase showed a very good correla-
tion coefficient (r = 0.942) along with satisfactory
cost analysis. Hypo1 was also validated by test set
and cross-validation methods. Developed models
were found to be predictive as indicated by low
error values for test set molecules. Virtual screen-
ing against Maybridge database using Hypo1 was
performed. The two most potent compounds (47
and 48; predicted IC50 = 1 nM) of the retrieved hits
were synthesized and biologically evaluated.
These compounds showed 86% and 88% inhibition
of acyl coenzyme A cholesterol acyltransferase (at
10 lg ⁄ mL) with IC50 value of 3.6 and 2.5 nM,
respectively. As evident from the close proximity
of biological data to the predicted values, it can
be concluded that the generated model (Hypo1) is
a reliable and useful tool for lead optimization of
novel acyl coenzyme A cholesterol acyltransferase
inhibitors.
Pharmacophore generation helps to generate a set of minimal
structural features required for biological activity which then can
be used as a query tool for virtual screening and database search-
ing for exploration of new chemical scaffolds for diverse therapeu-
tic classes. Pharmacophore models are typically used when some
active compounds have been identified, but the three-dimensional
(3D) structure of the target protein or receptor is unknown. There
are several small molecule ACAT inhibitors reported in the litera-
ture, which suggest that pharmacophore modeling could be an
alternate method of rational design of ACAT inhibitors. HypoGen is
quantitative method for generation of pharmacophore. The Hypo-
Gen algorithm tries to find hypotheses that are common among
the active compounds of the training set, but do not reflect the
inactive ones, thus constructing a model that correlates best with
measured activities and that consists of as few features as possi-
ble. More information on hypogen can be found in the literature
(10–14).
Key words: ACAT inhibitors, cost analysis, predictive pharmaco-
phore, validation, virtual screening
Received 23 July 2011; revised 28 January 2012 and accepted for publi-
cation 25 February 2012
In continuation of our search for a new biological lead with potent
ACAT inhibitory properties (15), a pharmacophore study on structur-
ally diverse ACAT inhibitors was carried out to gain better insights
into structural requirements for ACAT inhibition.
Coronary heart disease (CHD) is the major cause of death in most
of the western countries and atherosclerosis is the leading risk
107

Chhabria et al.
of any hypothesis should be close to the fixed cost. If a returned
cost (total cost) differs from the null hypothesis by 40–60 bits, it is
highly probable that the hypothesis has 75–90% chance of repre-
senting the true correlation of the data (Discovery Studio 2.1 docu-
mentationa).
Materials and Methods
Preparation of data set
For the pharmacophore modeling studies, a set of 46 ACAT inhibi-
tors were selected from the literature (16–31) and divided into a
training set (21 molecules, Chart S1) and a test set (25 molecules,
Chart S2) based on principles of structural diversity and wide cover-
age of the activity range (between 0.002 and 230 lM, six orders of
magnitude). Structures of all compounds in this study were
sketched using the Visualizer module of Discovery Studio 2.1. The
CHARMm force field was used for calculation of the potential
energy. Energy minimizations of all compounds were done using the
Smart Minimizer method, which uses the Steepest Descent method,
followed by the conjugate Gradient method for faster convergence
towards a local minimum, until the root mean square gradient value
becomes smaller than 0.001 kcal ⁄ mol followed by geometry optimi-
zation using the semi-empirical MOPAC-AM1 method.
Test set validation
Test set validation is a type of external validation method. In addi-
tion to validating the predictive ability of training set molecules,
the pharmacophore model should also estimate the activity of new
compounds. Therefore, a set of 25 compounds (22–46) were
included in the test set, which were not included in the training
set. These compounds cover wide range of activity range spanning
from 0.005 to 97 lM.
Fischer’s randomization test
To evaluate the statistical relevance of the model, the Fischer's
randomization test was applied. The purpose of the Fischer ran-
domization test is to validate the strong correlation between chemi-
cal structures and biological activity. The activity values of the
training set molecules are reassigned by randomization using the
Fischer's randomization test and new spreadsheets are created.
The number of spreadsheets depends on what level of statistical
significance one wants to achieve. These randomized spreadsheets
should yield hypotheses without statistical significance; otherwise,
the original model was also obtained randomly. To achieve a statis-
tical significance level of 95%, 19 random spreadsheets were gen-
erated.
Pharmacophore generation
A pharmacophore is a representation of generalized molecular fea-
tures including 3D [hydrophobic (HY) groups, charges ⁄ ionizable
groups and hydrogen bond donors ⁄ acceptors], 2D (substructures)
and 1D (physical or biological properties) aspects that are consid-
ered to be responsible for a desired biological activity. Selection
of feasible features is very important. Each feature is defined by a
chemical function, location and orientation in 3D space, tolerance
in location, and weight. HypoGen allows a maximum of five fea-
tures from a set of eleven features which are hydrogen bond
acceptor (HBA), hydrogen bond acceptor lipid (HBAL), hydrogen
bond donor (HBD), HY, hydrophobic aliphatic (HYAI), hydrophobic
aromatic (HYAr), positively (PC) and negatively (NC) charged, posi-
tively (PI) and negatively (NI) ionisable and ring aromatic (RA). Tak-
ing into account the chemical features of the compounds included
in the training set, three features were selected in the hypothesis
generation: hydrogen bond acceptor (HBA), HY, and RA. These
three features were used to generate the best ten hypotheses
from the training set using a default uncertainty value of 3, repre-
senting the ratio of the uncertainty range of measured biological
activity against the actual activity for each compound. The mini-
mum and maximum number of features varied to achieve a statis-
tically significant model. The best model was generated using
HBA, min 1 and max 3; HY, min 2 and max 2; and RA, min 1 and
max 3. The generated pharmacophore has three features and four
points, viz. one HBA, two HY, and one RA. The best hypothesis is
called as Hypo 1.
Chemistry
Melting points were determined in open capillaries in a micropro-
cessor based melting point apparatus model VMP-D (Veego make)
and are uncorrected. Infrared spectra were recorded in KBr using a
8400S Shimadzu Fourier Transform spectrophotometer. NMR Proton
Nuclear Magnetic Resonance spectra were taken on Bruker Avance
400 spectrophotometer at 400 MHz and the chemical shifts are
given as parts per million (d ppm) downfield from tetramethylsilane
(TMS) as the internal standard. Mass spectra (ESI) were obtained
on Perkin–Elmer LC-MS PE Sciex API ⁄ 65.
General procedure for synthesis of ethyl
disubstituted-4-[((2-(phenylcarbonyl)phenyl)
carbonyl)oxy]benzoate
To a solution of benzophenone-2-carboxylic acid (56) (10 mmol) in
dichloromethane (10 mL) at 0 ꢀC, a solution of ethyl 3,5-disubsti-
tuted-4-hydroxybenzoate (55) (11 mmol) in dichloromethane (10 mL)
was added. The reaction mixture was allowed to stir for 15 min at
0 ꢀC after which DMAP (1 mmol) and DCC (11 mmol) were added
to the reaction mixture in one lot. The reaction mixture was
allowed to stir for another 30 min at room temperature. The precip-
itates of dicyclohexylurea were filtered off and the filtrate was con-
centrated under vacuum to afford crude product which was
recrystallized using a mixture of methanol–water to afford pure
product.
Pharmacophore validation
To validate the reliability and accuracy of the generated 3D pharma-
cophore models, cost analysis, test set activity prediction and Fi-
scher's randomization (Y scrambling) test studies were performed.
Cost analysis
The quality of HypoGen models can be described in terms of fixed
cost, null cost, and total cost (32). As a good model, the total cost
108
Chem Biol Drug Des 2012; 80: 107–113

Pharmacophore Mapping of ACAT Inhibitors
Ethyl 3,5-dibromo-4-({[2-(phenylcarbonyl)phenyl]
carbonyl}oxy)benzoate (47)
described above. The second run resulted in improvement of corre-
lation (r) from 0.886 to 0.912, but an increase in configuration cost
from 16.56 to 17.15, having a total cost of 117.664. The model
was discarded as the configuration cost exceeded the normal per-
missible limit of 17. Therefore, to achieve further improvement,
another HypoGen run (Model 3) was carried out using pharmaco-
phoric features min 1, max 3; HY min 2, max 2 and RA min 1,
max 3. The correlation (r) was found to be 0.942 with a better
configuration cost (16.45). The cost analysis shows that total cost,
null cost, and fixed cost are 106.014, 176.924 and 88.81 respec-
tively. The difference between total cost and null cost is 70.91
bits, indicating that the generated model exhibits ꢀ90% probabil-
ity of true correlation. The difference between total cost and fixed
cost is 17.203 (<20 indicates good correlation, Discovery Studio
2.1 documentation). The best model was generated using HBA,
min 1 and max 3; HY, min 2 and max 2; and RA, min 1 and max
3. The generated pharmacophore is three features and four point
pharmacophore, which contains one HBA, two HY and one RA.
The best hypothesis is called as Hypo 1. The results for the Hypo-
gen model (Model 3) are shown in Table 1, along with its statis-
tics and cost analysis.
Off-White solid. Yield: 75%. Mp 130–134 ꢀC; 1H NMR (CDCl3,
400 MHz): 8.40–8.38 (1H, dd; J = 8.8Hz, J = 8.0 Hz) (8.15, 2H, s),
7.80–7.66 (4H, m), 7.56–7.50 (2H, m), 7.42–7.38 (t, 2H; J = 7.6 Hz),
4.40 (2H, q; J = 7.4 Hz), 1.38 (3H, t; J = 7.4 Hz) anal C23H16Br2O5,
Mass calcd. 529.94; MS (ESI+) m ⁄ z 531.2 (M + H+; 100%).
Ethyl 3,5-dichloro-4-({[2-(phenylcarbonyl)phenyl]
carbonyl}oxy)benzoate (48)
Off-White solid. Yield: 51%. Mp 115–119 ꢀC; 1H NMR (CDCl3,
400 MHz): 8.39–8.38 (1H, dd; J = 8.8 Hz, J = 7.8 Hz), 7.96 (2H, s),
7.81–7.67 (4H, m), 7.56–7.50 (2H, m), 7.43–7.39 (t, 2H; J = 7.5 Hz),
4.36 (2H, q; J = 7.1 Hz), 1.37 (3H, t; J = 7.2 Hz) anal C23H16Cl2O5,
Mass calcd. 442.04; MS (ESI+) m ⁄ z 443.4 (M + H+; 100%).
ACAT inhibition assay
The ACAT enzyme inhibitory activity was assayed using the method
described by Lada et al. (33) with the following modifications. The
reaction mixture included 0.03 mL of rat liver microsome fraction,
0.5 mL of KH2PO4 buffer (0.2 M, pH 7.4) containing (15 mg ⁄ mL)
bovine serum albumin, and 0.03 mL of DMSO vehicle with 0.03 mL
of test drugs in three different concentrations, viz. 1, 10,
100 lg ⁄ mL. The reaction mixture was incubated at 37 ꢀC for
10 min after which 0.02 mL of palmitoyl Co-A (Sigma Aldrich, St
Louis, MO, USA) was added, and the reaction was run at 37 ꢀC for
5 min. The reaction was stopped by the addition of 2:1 chloroform-
methanol, phases were separated, and the organic phase was con-
centrated. The residue was dissolved in 0.5 mL of 2-propanol con-
taining 10% Triton X-100, followed by addition of 0.3 mL of free
cholesterol decomposition reagent. Both the above-mentioned
reagents were part of a commercially available kit (Span Diagnos-
tics, India). The reaction mixture was then incubated at 37 ꢀC for
15 min after which 0.15 mL of cholesteryl ester measurement
reagent was added, and the reaction mixture was again incubated
at 37 ꢀC for 15 min. The enzyme activity was measured by estima-
tion of the reaction followed from the increase in absorbance at
600 nm.
Pharmacophore model validation
Fischer’s randomization (CatScramble) study
With the help of the Catscramble program, the experimental activi-
ties of compounds in the training set were scrambled randomly and
the resulting training set was used for a HypoGen run. The results
of the Fischer test are shown in Table 2; the data clearly suggest
that none of the generated hypotheses after randomization have a
cost value lower than that of Hypo1, and none of the hypotheses
had correlation higher than that of the Hypo1. This suggests that
there is a 95% chance for the best hypothesis to represent a true
correlation in the training set activity data.
Test set validation
Hypo1 was further validated using a test set of 25 compounds
which were structurally distinct from those included in the training
Results and Discussion
Table 1: Results of pharmacophore hypothesis generated using
training set molecules (Model 3)a
Pharmacophore model
Hypothesis no.
Total costa
D Cost
RMSD
Correlation (r)
The first HypoGen run was carried out using pharmacophoric
parameters, HBA min 1, max 3; HY min 2, max 2 and RA min 1,
max 3. FAST conformation generation was applied with the mini-
mum conformation set as 255 and other parameters were kept at
their default settings. The model (Model 1) showed relatively good
correlation (r = 0.886) between the actual and estimated activity,
and a higher difference among the total cost and null cost
(D = 78.558). The configuration cost was 16.56, as it was <17, it
is considered that all the generated hypothesis were not because
of the chance correlation. To further improve the cost and correla-
tion coefficients, another HypoGen run (Model 2) was carried out
using pharmacophoric features, HBA min 1, max 3; HY min 1, max
2 and RA min 1, max 3. Other parameters were kept the same as
1
2
3
4
5
6
7
8
9
106.014
113.108
114.794
114.976
115.19
115.833
115.89
116.509
116.942
117.291
70.91
63.816
62.13
61.948
61.734
61.091
61.034
60.415
59.982
59.633
1.9038
1.4067
1.4512
1.4582
1.5444
1.5648
1.5308
1.5775
1.5930
1.6085
0.942
0.899
0.893
0.892
0.875
0.872
0.878
0.869
0.867
0.864
10
aNull cost of 10 top-scored hypothesis is 176.924, fixed cost value is
88.811, and configuration cost is 16.45.
Chem Biol Drug Des 2012; 80: 107–113
109

Chhabria et al.
set. All the compounds were classified on the bases of their activity
scale, as highly active compounds (<0.1 lM denoted by +++), mod-
erately active compounds (0.1–10 lM denoted by ++), and ⁄ or inac-
tive compounds (>10 lM denoted by +). As shown in Table 3, all
highly active compounds predicted correctly, only two moderately
active compounds (35 and 39) were predicted to be inactive, and
only two inactive compounds (42 and 44) were predicted to be
moderately active. Furthermore, a regression analysis of the experi-
mental and predicted values of inhibitory activity of the test set
compounds gives a very good correlation coefficient of 0.955, indi-
cating a good predictive ability (Figure 1).
Table 2: Results Fischer's randomization test using CatScramble
protocol
Hypothetical No.
Total cost
Correlation (r)
Hypo 1
106.01
157.35
167.86
129.83
154.99
159.32
122.49
150.47
154.57
156.69
140.19
158.15
142.12
152.51
143.78
141.07
158.83
146.68
150.54
121.57
0.9425
0.6207
0.5081
0.8204
0.6182
0.5716
0.8500
0.6356
0.6188
0.6152
0.7128
0.5439
0.7409
0.6239
0.7793
0.7070
0.6200
0.7090
0.6592
0.8390
Random 1
Random 2
Random 3
Random 4
Random 5
Random 6
Random 7
Random 8
Random 9
Random 10
Random 11
Random 12
Random 13
Random 14
Random 15
Random 16
Random 17
Random 18
Random 19
Database searching
To identify novel and potent ACAT inhibitors, virtual screening was
performed using the best flexible database search tool in Discovery
Studio 2.1. The best pharmacophore model was used as a query to
search a commercial database Maybridge. In the present study, best
flexible conformation generation was used to find out the hit from
database. Interestingly, over ninety compounds fitted well with fea-
tures of Hypo1. Results of some of the top hits are summarized in
Table 4 along with their predicted IC50 and fit values (See Chart S3
for structures). It should be noted that only compounds 47–54
(Table 4) are found to be highly active (estimated IC50 < 100 nM)
and rest of the compounds, although with good fit values, were
only moderately active (IC50 > 100 nM). Hence, only the top eight
compounds (47–54) are shown in Table 4. Pharmacophore mapping
of the most potent compound (2) with Hypo1 (Model 3) is shown in
Figure 2A. Mapping of synthesized compounds (47 and 48) are
shown in Figure 2B and 2C. The pharmacophoric distances between
Table 3: Actual and estimated activity comparison of test set molecules using Hypo1
Compound No.
Experimental IC50 (lM)
Estimated IC50 (lM)
Errora
Fit valueb
Experimental scalec
Estimated scale
22
23
24
25
26
27
28
29
30
31
32
33
34
35
36
37
38
39
40
41
42
43
44
45
46
0.005
0.006
0.01
0.01
0.01
0.012
0.013
0.015
0.022
0.026
0.052
0.062
0.53
0.74
0.79
1
1.2
8.7
8.8
12
12
12
15
49
0.001
0.001
0.001
0.001
0.001
0.003
0.001
0.001
0.001
0.001
0.018
0.007
0.346
10.832
0.484
2.111
2.844
98.875
4.292
)5
)6
)10
)10
)10
)4
)13
)15
11.236
11.27
11.362
11.315
11.44
10.658
11.343
11.2
11.125
11.42
9.912
10.323
8.62
7.125
8.475
7.835
7.706
6.165
7.527
6.161
8.01
5.992
8.303
5.286
9.875
+++
+++
+++
+++
+++
+++
+++
+++
+++
+++
+++
+++
++
++
++
++
++
++
++
+
+
+++
+++
+++
+++
+++
+++
+++
+++
+++
+++
+++
+++
++
+
++
++
++
+
++
+
++
)22
)26
)2.89
)8.86
)1.53
14.63
)1.63
2.111
2.37
11.36
)2.05
8.31
)8.5
12.26
)20.86
15.26
19.86
99.829
1.411
147.164
0.719
747.611
1926.63
+
+
+
+
+
++
+
+
97
a+ means that the estimated IC50 is higher than the experimental IC50; means that the estimated IC50 is lower than the experimental IC50; a value of 1 indi-
cates that the estimated IC50 is equal to the experimental IC50.
bFit value indicates how well the features in the pharmacophore map the chemical features in the molecule.
cActivity scale: +++, IC50 £ 0.01 lM (highly active); ++, 0.01 lM < IC50 £ 0.1 lM (moderately active); +, IC50 > 0.1 lM (low active).
110
Chem Biol Drug Des 2012; 80: 107–113

Pharmacophore Mapping of ACAT Inhibitors
Chemistry
the features are shown in Figure 2D. These promising results led
us to synthesize and evaluate the two most potent compounds 47
and 48 (Scheme 1) for their ability to inhibit ACAT.
The synthetic route for compounds 47 and 48 is outlined in
Scheme 1. Benzophenone-2-carboxylic acid (56) was obtained using
phthalic anhydride and benzene as starting materials by Friedel
Craft's acylation, according to the literature (34). Acid-catalyzed
esterification of corresponding acids afforded ethyl 3,5-dichloro 4-
hydroxybenzoate (55a) and ethyl 3,5-dibromo-4-hydroxybenzoate
(55b). Coupling of compound 55a and 55b with 56 in presence
of DCC and DMAP afforded target compounds 47 and 48 respec-
tively.
ACAT inhibitory activity
Both the synthesized compounds were tested for their inhibitory prop-
erties toward ACAT extracted from male Sprague-Dawley rat liver mi-
crosomes. Their activity, expressed as percentage inhibition at
10 lg⁄ mL, is shown in Table 5. Compound 47 showed 86% inhibi-
tion whereas compound 48 showed 88% inhibition of rat liver micro-
somal ACAT. The IC50 values were obtained by the Logit method
(35,36) and were determined from the results of at least three inde-
pendent tests. IC50 values of 47 and 48 were found to be 3.6 and
2.5 nM, respectively, which were found to be very close to the pre-
dicted value indicating that pharmacophore-based ACAT inhibitor
design was a rational protocol in lead generation. Two of the most
potent compounds (47 and 48) predicted by virtual screening (fit
value 11.134 and 11.002, respectively) were found to be highly
potent in vitro when tested for their ability to inhibit rat liver micro-
somal ACAT. As expected, the compounds essentially possessed
highly lipophilic functionalities (two ester linkages, three phenyl rings;
AlogP = 6.199 and 6.367, respectively (37) and a few hydrogen bond
acceptor atoms (carbonyl oxygen) which was found to be the key
pharmacophoric requirements as predicted by Hyop1. Interestingly,
Figure 1: Correlation of experimental versus estimated activities
by Hypo1 for the test set.
Table 4: List of compounds retrieved in 3D database searching
Compound No.
Estimated IC50 (lM)
Fit value
47
48
49
50
51
52
53
54
0.001
0.001
0.004
0.013
0.022
0.024
0.056
0.067
11.134
11.002
10.565
10.042
9.812
9.782
9.469
9.333
A
B
Figure 2: The best Hypogen
pharmacophore model mapping (A)
With the most active (IC50 = 0.002
lM) compound 2; (B) With the
compound 47 (Predicted IC50 = 0.001
lM); (C) With the compound 48
(Predicted IC50 = 0.001 lM). (D) 3D
spatial relationship and pharmaco-
hporic distances of Hypo 1. Pharma-
cophore features are color coded
(green, HBA; orange, ring aromatic
and cyan hydrophobic).
C
D
Chem Biol Drug Des 2012; 80: 107–113
111

Chhabria et al.
O
O
R
DCC, DMAP
MDC, rt
O
O
O
O
O
+
OH
R
R
R O
OH
O
(55a) R = Br
(55b) R = Cl
(47) R = Br
(48) R = Cl
(56)
Scheme 1: Synthetic route for the synthesis of target compounds 47 and 48.
Table 5: Results of ACAT inhibition of synthesized compounds
(47 and 48)
References
Compound No.
Conc. (lg ⁄ mL)
% Inhibitiona
Logitb
IC50 (lM)c
1. Chhabria M.T., Brahmkshatriya P.S. (2006) Novel targets for the
treatment of atherosclerosis. Curr Opin Invest Drugs;7:820–825.
2. Brahmkshatriya P.S., Chhabria M.T., Jani M.H. (2006) Recent
developments in the treatment of atherosclerosis. J Enzyme
Inhib Med Chem;21:1–15.
3. Kushwaha R.S., Vandeberg J.F., Rodriguez R., Vandeberg J.L.
(2004) Cholesterol absorption and hepatic acyl-coenzyme A:cho-
lesterol acyltransferase activity play major roles in lipemic
response to dietary cholesterol and fat in laboratory opossums.
Metabolism;53:817–822.
47
48
10
10
86.2
88.14
0.7956
0.8761
0.0036
0.0025
a% inhibition = [(standard conc. ) test conc.) ⁄ standard conc.] · 100.
bLogit = log [(% inhibition ⁄ (100 ) % Inhibition)].
cpIC50 = Logit ) logC; where, C is molar concentration.
logP values of both the compounds were found to be very close to
that of the well-known ACAT inhibitor CI-976 (logP = 6.17). The target
compounds thus stand out as promising novel leads for further struc-
tural optimization and pharmacological studies.
4. Rudel L.L., Lee R.G., Cockman T.L. (2001) Acyl coenzyme A: cho-
lesterol acyltransferase types 1 and 2: structure and function in
atherosclerosis. Curr Opin Lipidol;12:121–127.
5. Field F.J., Salome R.G. (1982) Effect of dietary fat saturation,
cholesterol and cholestyramine on acyl-CoA: cholesterol acyl-
transferase activity in rabbit intestinal microsomes. Biochim Bio-
phys Acta;712:557–570.
6. Heider J.G., Pickens C.E., Kelly L.A. (1983) Role of acyl CoA:cho-
lesterol acyltransferase in cholesterol absorption and its inhibi-
tion by 57-118 in the rabbit. J Lipid Res;24:1127–1134.
7. Drevon C.A., Engelhorn S.C., Steinberg D. (1980) Secretion of
very low density lipoproteins enriched in cholesteryl esters by
cultured rat hepatocytes during simulation of intracellular cho-
lesterol esterification. J Lipid Res;21:1065–1071.
8. Khan B., Wilcox H.G., Heimberg M. (1989) Cholesterol is
required for secretion of very-low-density lipoprotein by rat liver.
Biochem J;258:807–816.
9. Cianflone K.M., Yasruel Z., Rodriguez M.A., Sniderman A.D.
(1990) Regulation of apoB secretion from HepG2 cells: evidence
for a critical role for cholesteryl ester synthesis in the response
to a fatty acid challenge. J Lipid Res;31:2045–2055.
10. Mishra R.K., Singh J. (2010) Generation, validation and utilizai-
ton of three-dimensional pharmacophore model for EP3 antago-
nists. J Chem Inf Model;50:1502–1509.
11. Brogi S., Kladi M., Vagias C., Papazafiri P., Roussis V., Tafi A.
(2009) Pharmacophore modelling of qualitative prediction of an-
tiestrogenic activity. J Chem Inf Model;49:2489–2497.
12. Krovat E.M., Langer T. (2003) Non-peptide angiotensin II receptor
antagonists: chemical feature based pharmacophore identifica-
tion. J Med Chem;46:716–726.
Conclusions
In the present study, predictive pharmacophore models were devel-
oped for a series of structurally diverse ACAT inhibitors using Hypo-
Gen. The best model (Hypo1) has four pharmacophore features viz.
one hydrogen bond acceptor, two HY and one RA showed good cor-
relation (r = 0.942). Validation of the generated hypothesis was car-
ried out using cost analysis (Dcost = 70.91; configuration
cost = 16.45) and the Fischer randomization test. The predictive abil-
ity of generated hypotheses was also checked using a set of 25 test
compounds (r = 0.955). Results showed that a majority of compounds
were predicted correctly, with lower error values and good fit values.
The best hypothesis (Hypo 1; model 3) was used as a 3D query for
searching the Maybridge database, which identified structurally
diverse scaffolds with activity ranging from 1 to 170 nM as plausible
leads for the design of novel ACAT inhibitors. With the help of these
lead molecules, several compounds were predicted and planned for
the synthesis. Compounds 47 and 48 were synthesized and screened
for ACAT inhibitory activity on liver microsomes preparations of male
Sprague-Dawley rat. The activity of the synthesized compounds was
found to be very close to the predicted activity, suggesting a high
predictive ability of the generated pharmacophore model. Binding
assays for ACAT subtypes and a lead optimization study of the identi-
fied lead molecules are underway in our laboratory.
Acknowledgments
13. Funk O.F., Kettmann V., Drimal J., Langer T. (2004) Chemical
function based pharmacophore generation of Endothelin-A selec-
tive receptor antagonists. J Med Chem;47:2750–2760.
Authors acknowledge University Grants Commission, Govt. of India,
for the financial support.
112
Chem Biol Drug Des 2012; 80: 107–113

Pharmacophore Mapping of ACAT Inhibitors
14. Barreca M.L., Ferro S., Rao A., Luca L.D., Zappala M., Monforte
A.-M., Debyser Z., Witvrouw M., Chimirri A. (2005) Pharmaco-
phore-based design of HIV-1 integrase strand-transfer inhibitors.
J Med Chem;48:7084–7088.
15. Chhabria M.T., Mahajan B.M., Brahmkshatriya P.S. (2010) QSAR
study of a series of acyl coenzyme A (CoA): cholesterol acyl-
transferase inhibitors using genetic function approximation. Med
Chem Res;???:???–???. Online first, DOI: 10.1007/s00044-010-
9413-3.
16. Picard J.A., O'Brien P.M., Sliskovic D.R., Anderson M.K., Bousley
R.F., Hemelehle K.L., Krause B.R., Stanfield R.L. (1996) Inhibitors
of Acyl-CoA: cholesterol O-Acyltransferase. Structure-activity
relationships of several series of compounds derived from N-
chlorosulfonylisocyanate. J Med Chem;39:1243–1252.
17. O' Brien P.M., Sliskocic D.R., Picard J.A., Lee H.T., Purchase C.F.,
Roth B.D., White A.D. et al. (1996) Inhibitors of Acyl-CoA: cho-
lesterol O-Acyltransferase. Synthesis and pharmacological activ-
ity of (€)-2-dodecyl-phenyl-N-(2,4,6,trimethoxy)-2H-tetrazole-5-
acetamide and structurally related tetrazole amide derivatives. J
Med Chem;39:2354–2366.
and structure–activity relationship studies of a new series of tri-
substituted imidazoles. J Med Chem;37:3511–3522.
30. Dugar S., Clader J.W., Burrier R.E., Kogan T.P. (1993) N-Oleoyl-
1,2,3,4- tetrahydroquinolines as confomationally restricted inhibi-
tors of Acyl-CoA: cholesterol Acyl Transferase (ACAT). Bioorg
Med Chem Lett;3:571–576.
31. Boswell G.A., Li H.Y., Delucca I., Billheimer J.T., Drummond S.,
Gillies P.J., Robinson C. (1996) Novel 4,4-bis(trifluoromethyl) imi-
dazoles as stereospecific and orally active Acyl CoA: cholesterol
Acyltransferase (ACAT) inhibitors and antihypercholesterolemic
agents. Bioorg Med Chem Lett;6:885–888.
32. Debnath A.K. (2002) Pharmacophore mapping of a series of 2,4-
diamino-5-deazapteridine inhibitors of Mycobacterium avium
complex dihydrofolate reductase. J Med Chem;45:41–53.
33. Largis E., Wang C., BeVaries V., Schaffe F. (2004) CL277,082; A
novel inhibitor of ACAT catalysed cholesterol esterification and
cholesterol absorption. J Lipid Res;45:396–401.
34. Furniss B.S., Hannaford A.J., Smith P.W.G., Tatchell A.R. (2006)
Vogel's Textbook of Practical Organic Chemistry, 5th edn. Lon-
don, UK: Pearson Publishing Corp.
18. White A.D., Purchase C.F., Picard J.A., Anderson M.K., Mueller
S.B., Bocan T.M.A., Bousley R.F., Hammelehle K.L., Krause B.R.,
Lee P., Stanfield R.L., Reindel J.F. (1996) Heterocyclic amides:
inhibitors of Acyl-CoA:cholestrol O-Acyltransferase with hypocho-
lesterolemic activity in several species and antiatherosclerotic
activity in the rabbit. J Med Chem;39:3908–3919.
35. Wang Y., Ai J., Wang Y., Chen Y., Wang L., Liu G., Geng M.,
Zhang A. (2011) Synthesis and c-Met kinase inhibition of 3,5-
disubstituted and 3,5,7-trisubstituted quinolines: identification of
3-(4-acetylpiperazin-1-yl)-5-(3-nitrobenzylamino)-7- (trifluorometh-
yl)quinoline as a novel anticancer agent. J Med Chem;54:2127–
2142.
19. Sliskovic D.R., Picard J.A., O'Brien P.M., Liao P., Roark W.H.,
Roth B.D., Anderson M.A. et al. (1998) a–Substituted malonester
amides: tools to define the relationship between ACAT inhibitors
and adrenal toxicity. J Med Chem;41:682–690.
20. Giovannoni M.P., Piaz V.D., Kwon B.M., Kim Y.K., Toma L., Bar-
locco D., Bernini F., Canavesi M. (2001) 5-6-Diphenylpyridazine
derivatives as Acyl- CoA:cholesterol acyltransferase inhibitors. J
Med Chem;44:4292–4295.
36. Manvar A., Malde A., Verma J., Virsodia V., Mishra A., Upadhy-
ay K., Acharya H., Coutinho E., Shah A. (2008) Synthesis, anti-
tubercular activity and 3D-QSAR study of coumarin-4-acetic acid
benzylidene hydrazides. Eur J Med Chem;43:2395–2403.
37. Ghose A.K., Crippen G. (1986) Atomic physicochemical parame-
ters for three-dimensional structure-directed quantitative struc-
ture-activity relationships I. Partition coefficient as a measure of
hydrophobicity. J Comp Chem;7:565–577.
21. Ohishi K., Aiyama R., Hatano H., Yoshida Y., Wada Y., Yokai W.,
Sawada H., Watanabe T., Yokokura T. (2001) Strucure-activity
relationships of N-(3,5-dimethoxy-4-n-octyloxycinnamoyl)-N9-(3,4-
dimethylphenyl)piperazine and analogues as inhibitors of Acyl
CoA:cholesterol O-Acyltransferase. Chem Pharm Bull;49:830–839.
22. Toma L., Giovannoni M.P., Vergelli C., Piaz V.D., Kwon B.M., Kim
Y.K., Gelain A., Barlocco A. (2002) Novel 3-arylamino- and 3-cy-
cloalkylamino 5,6-diphenyl-pyridazines active as ACAT Inhibitors.
Arch Pharm Pharm Med Chem;11:563–566.
23. Lee H.T. (1996) Sulphonate ACAT inhibitors. U. S. Patent
5,510,379, April, 23.
24. Lee H.T. (1997) Tetrazole Alkyl Amide ACAT Inhibitors. U. S. Pat-
ent 5,646,170. July, 8.
Notes
aAccelrys Inc., San Diego, CA, USA.
Supporting Information
Additional Supporting Information may be found in the online ver-
sion of this article:
Chart S1. Chemical structures of training set compounds (1–21)
with their IC50 values (in parentheses).
25. Dugar S. (1994) 1,2-Disubstituted Ethyl Amides as Inhibitors of
ACAT. WO94 ⁄ 06784, March, 31.
26. Lee H.T., Picard J.A., Sliskovic D.R. (1996) b–Carboxy Sulfon-
amide ACAT Inhibitors. U. S. Patent 5,491,170, February, 13.
27. Roark W.H. (2000) Phosphonamide ACAT inhibitors. U. S. Patent
6,017,905, January, 25.
Chart S2. Chemical structures of test set compounds (22–46)
with their IC50 values (in parentheses).
Chart S3. Structures of hits (47–54) found as query in virtual
screening.
28. O'Brien P.M., Sliskovic D.R. (1995) Amide tetrazole ACAT inhibi-
tors. U. S. Patent 5,461,049, October, 24.
29. Higley C., Wilde R.G., Maduskuie T.P., Johnson A.L., Pennev P.,
Billheimer J.T., Robinson C.S., Gillies P.J., Wexler R.R. (1994)
Acyl CoA:cholesterol Acyltransferase (ACAT) inhibitors: synthesis
Please note: Wiley-Blackwell is not responsible for the content or
functionality of any supporting materials supplied by the authors.
Any queries (other than missing material) should be directed to the
corresponding author for the article.
Chem Biol Drug Des 2012; 80: 107–113
113