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Ligand- and Structure-Based Drug Design of NSAIs in Breast Cancer
Figure 11. Selective potent non-steroidal aromatase inhibitors (NSAIs)
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Ligand- and Structure-Based Drug Design of NSAIs in Breast Cancer
LIGAND- AND STRUCTURE-BASED DRUG DESIGN (LBDD
AND SBDD) OF AROMATASE INHIBITORS
The LBDD may play important roles for designing potent NSAIs. The chemometric approaches include
different QSAR and pharmacophore mapping techniques. It is observed that conformational flexibility of
ligands and molecular alignment represents key techniques though these are also the main difficulties in
LBDD. Various molecular modeling tools involved in LBDD are SYBYL (Tripos Inc., 2012), HipHop
(Accelrys Inc. 2011), Hypogen (Accelrys Inc. 2011), DISCO (Martin, 2000), GASP (Jones & Willet, 2000),
GALAHAD (Tipos Inc., 2012), PHASE (Dixon et al., 2006) and MOE (Chemical Computing Group,
2013). The SBDD involves the macromolecular target structure or receptor-ligand complex. Structure-based
pharmacophore mapping involves the chemical feature of the active site and their spatial relationship. The
tool used for this purpose is ‘LigandScout’ which incorporates the macromolecule-ligand complex base
scheme (Wolber & Langer, 2005). Other softwares used for this purpose are ‘Pocket v.2’ (Chen & Lai,
2006) and ‘GBPM’ (Ortuso, 2006). The limitation of this approach is the need for the 3D-structure of the
macromolecule-ligand complex. It implies that it cannot be applied when compounds targeting the binding site of interest are unknown. This problem can be overcome by the macromolecule-based approach.
The structure-based pharmacophore method implemented in ‘Discovery Studio’ (Accelrys Inc. 2011) is a
typical macromolecule-based approach. It converts LUDI interaction maps within the protein binding sites
along with different pharmacophore features (Bohm, 1992). The main limitation of the structure-based
pharmacophore flowchart is that the interaction maps generally consist of a large number of unprioritized
‘Catalyst’ features which complicates its application for searching 3D databases. To overcome this problem,
hot-spots-guided receptor-based pharmacophores (HSPharm) is proposed (Barillari, Marcou, & Rognan,
2008). This approach may be helpful for prioritization of atoms that should be targeted for ligand binding
by machine learning algorithms with atom-based fingerprints of known ligand-binding pockets. Again, an
apoprotein-based approach, i.e., GRID molecular interaction fields (MIFs) (Tintori, 2008) may be helpful
for analyzing binding site of interest and conversion of the minimum MIFs into pharmacophore features.
However, a pharmacophore model composed of too many (>7) or too less (<3) chemical features may not
be suitable for 3D database screening. Hence, it is necessary to select limited number of chemical features
(3 to 7) to construct a practical hypothesis. However, pharmacophore approaches are one of the most
successful concepts in drug designing strategies. Combination of pharmacophore mapping followed by
other molecular modeling approaches like docking may be a good strategy for drug designing approach.
Compared with pharmacophore-based virtual screening, pharmacophore-based de novo design show
unique advantage in building novel hits. The pharmacophore based design may also be extended to lead
optimization (Brenk & Klebe, 2006), multitarget drug design (Wei et al., 2008), activity profiling (Steindl
et al., 2006) and target identification (Rollinger et al., 2004).
Docking method is an in silico technique that determines the binding site of a protein and estimates
the binding affinity of small molecule to the protein. Docking study involves the conformational and orientational degrees of freedom of a small molecule within the constraints of binding pocket of the protein.
The program uses a scoring function to select the best pose of each molecule and top scoring hits may be
identified. Docking method predicts the preferred orientation of ligands to a macromolecule when bound
to each other to form a stable complex (Lengauer & Rarey, 1996). Molecular recognition may play the
key role in promoting fundamental events like enzyme-substrate, drug-protein and drug-nucleic acid interactions. The nature of interactions like hydrogen bonding, van der Waals and electrostatic interactions
may provide a conceptual framework for designing the desired potency and specificity of potential drug
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Ligand- and Structure-Based Drug Design of NSAIs in Breast Cancer
candidates (Mohan et al. 2005). Docking method includes rigid body docking (both receptor and molecule
treated as rigid), flexible ligand docking (receptor treated as rigid but ligand kept as flexible) and flexible
docking (both receptor and ligand kept treated as flexible). Different docking algorithms used for docking
program are GOLD (genetic algorithm), AUTODOCK (Lamarckian genetic algorithm), GLIDE (Hybrid),
LigandFit (shape matching based on moments of inertia), DOCK (shape matching based on sphere images),
FlexX (incremental construction), Hammerhead (incremental construction), FRED (shape matching based
on Gaussian functions) and Surflex (dock surface-based molecular similarity) (Kroemer, 2007). Scoring
functions can be classified into three categories - knowledge-based, empirical and force field-based. Regarding the LBDD and SBDD of NSAIs, several studies were performed to design potent and specific NSAIs.
Cavalli et al. (2005) proposed a CoMFA model that represents further extension of previous ones
(Recanatini & Cavalli, 1998; Recanatini, 1996) with total of 70 NSAIs. Interestingly, 7 of the newly
added molecules carried at least one stereogenic center and showed enantioselectivity towards the biological target. In agreement with their previous models (Recanatini & Cavalli, 1998; Recanatini, 1996),
the steric regions were located around the para-CN phenyl ring of S-fadrozole, whereas the para-CN
phenyl ring of R-fadrozole partially protruded into the sterically disallowed region. Negative electrostatic
blue regions were scattered around the template, whereas positive electrostatic red regions partially
surrounded the negatively charged atoms of the para-CN phenyl ring of R-fadrozole. It indicates that
the increase of positive charge may be required for higher inhibitory activity in that region, whereas the
R-fadrazole locates its negatively charged phenyl ring. Based on that CoMFA model, some chromone
analogs were synthesized keeping in mind the pharmacophoric para-substituted benzylimidazole moiety
and to introduce an asymmetric center on the methylene carbon atom. The para-CN phenyl ring of the
R-enantiomer of the best active compound was found completely embedded in the positive steric CoMFA
contour. Its S-enantiomer placed the same moiety into a positive electrostatic region. The R-enantiomers
were always calculated more potent than the S-ones. Hence, the CoMFA model can be considered as an
example of enantioselective 3D-QSAR model (Cavalli et al., 2005). The schematic representation on
the basis of the best active chromone analog for imparting aromatase inhibition is shown in Figure 12.
Some coumarins were compared with androstenedione as aromatase inhibitors by shape similarity analy-
sis (Chen et al., 2004). It was found that replacement of 7-methoxy group with a hydroxyl group reduced
the inhibitory activity. Again, elimination of 4′-chloro group from 3-phenyl group reduced the aromatase
inhibitory activity. Hence, it is suggested that both the 7-methoxy and 3-(4′-chlorophenyl) groups are important for the anti-aromatase activity of these coumarins. In addition, 4-(4′-chlorobenzyl) group instead
of a 4-benzyl group was not able to suppress aromatase activity. In the compound like 3-(4′-chlorophenyl)7-methoxy-4-phenylcoumarin, replacement of 4-benzyl group by 4-phenyl group diminished the activity.
Hence, the critical functional groups controlling the aromatase inhibition were 4-benzyl, 3-(4′-chlorophenyl)
and 7-methoxy groups. The most potent compound, 4-benzyl-3-(4′-chlorophenyl)-7-methoxycoumarin
= 80 nM) was compared with androstenedione, which suggested that the coumarin ring mimic the A
(IC
50
and B rings of the steroidal nucleus and the 3-(4′-chlorophenyl) group mimics the D ring of the androgen
moiety. In the compound, 4-benzyl group aligns very closely to the C-19 methyl group of androstenedione.
As C-19 methyl group pointed towards the heme group of aromatase, it is supposed that the space between
the heme and C-19 was not sufficient to accommodate phenyl group, whereas benzyl group can be spatially
rotate by the methylene linkage and can accommodate at the place. The 7-methoxy group was found to
align closely to the C-3 keto function and 3-(4′-chlorophenyl) group aligned near the C-17 keto oxygen of
androstenedione. It suggested that electron withdrawing groups might be favorable at this position. The
crucial groups important for better activity are shown in Figure 13.
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Ligand- and Structure-Based Drug Design of NSAIs in Breast Cancer
Figure 12. Structural requirements of chromones for effective NSAIs
Figure 13. Crucial structural requirements of coumarins as promising NSAIs
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Ligand- and Structure-Based Drug Design of NSAIs in Breast Cancer
Some azole compounds linked to a fluorene, indenodiazine or coumarin scaffold were also designed
and synthesized (Leonetti et al., 2004). Amongst these, azole-linked coumarin derivatives were found to
be the most active. These coumarin derivatives were also more selective over 17-α-hydroxylase/17-20
lyase. CoMFA study suggested that the electronegative keto oxygen of the coumarin ring was important
for the higher activity. The compounds 1, 2, 4-triazole derivatives were less active than imidazole derivatives as it was surrounded by a negative electrostatic region. Positive steric effect was important for the
higher activity due to the lipophilic aromatic rings whereas the negative steric effect was not favorable
for the activity. Again, methoxy group containing compounds were found to be less active. This result
was in agreement with the previously published CoMFA results (Recanatini & Cavalli, 1998; Cavalli et
al., 2000). The important structural features are shown schematically in Figure 14.
Flavone derivatives were also found to be effective aromatase inhibitors (Gobbi et al., 2006; Pouget
et al., 2002). The 2D-QSAR study (Nagar et al., 2008) suggested that the presence of the positive charge
function at the atom C
tory activity. The presence of nucleophilic substitution at meta position of the ring C, i.e., in R
substitutions decreased the activity. Presence of hydrogen bond donor group at C15 was found to be
R
4
and decrease in the electron density at the atom C12 (Figure 15) favored inhibi-
1
and
2
important for the activity which was adjudged by the previous study (Cavalli et al. 2005). Substitution
in the ring A of flavone scaffold imparted hydrophobicity of these molecules. Aromatic attachment
at the atom C
was also found to be important for the activity. This result was also in agreement with
8
previous findings (Recanatini et al., 2001). The CoMFA study indicated favorable steric influence of
imidazole ring at R′′ substituent. Additionally, substitutions in the ring C favored inhibitory activity
possibly by imparting steric hindrance on that region, whereas similar substitution in the ring A (R′
position) proved to be unfavorable for the activity. The CoMSIA study suggested that the presence of
Figure 14. Important structural features of coumarin azoles required for better NSAIs
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Ligand- and Structure-Based Drug Design of NSAIs in Breast Cancer
acceptors at position 11 as well as in the imidazole-1-methyl ring, hydrophobic substituent at the atom C2
and steric hindrance due to the imidazole ring and the C rings increased the inhibitory activity. Oxygen
atom at position 11 might behave as a hydrogen bond acceptor that possibly binds with the Ser478 of
the aromatase enzyme (Pouget et al., 2002). The presence of acceptors at the atom C
imidazole ring and hydrophobic substituent in the ring C as well as steric influence at the atom C
, β-position of the
15
and
2
the ring B might decrease activity. The pharmacophore modeling study could also be correlated with the
QSAR studies. The ring A along with its substitutional pattern imparted steric hindrance that provided
hydrophobic feature of these molecules. The presence of hydrogen bond acceptor (C=O group) and the
imidazole-1-methyl ring at R′′ position of the B ring increased inhibitory activity. The pharmacophore
space modeling study suggested that two ring aromatic features, one each hydrogen bond acceptor and
hydrophobic features are necessary for the anti-aromatase property of these molecules. The distance
between the hydrophobic and the acceptor feature is 7.394 Å, whereas the two ring aromatic features
are located 9.259 Å and 8.070 Å respectively away from the hydrophobic feature. Similarly, the ring
aromatic features are located 7.938 Å and 4.119 Å away from the hydrogen bond acceptor feature. The
aromatic rings are separated by a distance of 5.474 Å. The structural and pharmacophoric requirements
of these flavones derivatives for better aromatase inhibition are represented schematically in Figure 15.
Natural phytoestrogens, such as lignans, flavones and coumestrol were found to be effective aromatase
inhibitors. A molecular dynamics simulation (MDS) study was performed to understand the mechanisms involved in the binding of various phytoestrogens to the aromatase enzyme (Karkola & Wahala,
Figure 15. Structural and pharmacophoric requirements of flavones for aromatase inhibitory activity
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Ligand- and Structure-Based Drug Design of NSAIs in Breast Cancer
2009). The simulation study was analyzed to find the essential interactions took place on binding with
the aromatase enzyme to the phytoestrogen, and were subjected to docking study to know the binding
interactions. The MDS study of the aromatase-enterolactone complex suggested that there was only an
occasional hydrogen bond formed between enterolactone to the backbone carbonyl of Glu302. A few
water molecules were present occasionally inside the active site cavity. It suggested that the character
of the active site was lipophilic in nature. There was coordination between the enterolactone carbonyl
oxygen and the heme moiety. The active site residues, i.e. Ile133, Phe134, Ala226, Leu227, Ile229,
Lys230, Tyr244, Glu302, Ile305, Ala306, Ala307, Pro308, Asp309, Thr310, Met311, Val313, Ser314,
Phe430, Cys437, Ala438, Gly439, Lys440, Leu479, His480 and Pro481 along with the heme were found
in contact with enterolactone. In case of MDS of the aromatase-α-naphthoflavone complex, interaction
between carbonyl function and the heme group was observed. Only a few water molecules were found
to form hydrogen bonding to the active site residues, such as Glu302, Lys230 and His480. The active
site residues, i.e. Leu122, Ile132, Ile133, Phe134, Phe148, Lys230, Cys299, Glu302, Met303, Ala306,
Ala307, Thr310, Met374, Phe430, Cys437, Ala438, Gly439, Leu477, Ser478, Leu479 and His480 in
addition to heme group were found in contact with α-naphthoflavone. The docking study of the lignans
showed that compounds having lactone ring have the possibility to coordinate the heme group. Optimal
coordination position and orientation would be achieved if the plane defined by the lactone group is
perpendicular to the heme plane. The strength of the coordination to the heme group was also dependent
on the tilting and torsion angles of the coordinating systems. Hence, a small change from the optimal
coordinating geometry might lead to weaker binding. The best ranked pose showed that hydrogen bonds
between the phenolic hydroxy group at the C3 atom of the ring A and the backbone carbonyl of Thr310,
the hydroxy group at the C3′ atom of the ring B and the backbone carbonyl of Glu302. All compounds
with a phenolic hydroxyl at the C4 atom formed a hydrogen bond to the backbone carbonyl of Pro481.
The higher activity was dependent probably on the hydrogen bonding between the C3 hydroxyl group
with Thr310 and a water-mediated hydrogen bond between the ring B hydroxyl group with Lys230 or
Ile305. The methoxy group at the C3′ atom might block a water-mediated hydrogen bond and lead to
decrease in the activity. The docking study of α-naphthoflavone showed to coordinate with the heme iron
via the C4 carbonyl group. The naphthyl moiety of α-naphthoflavone was located near residues Leu122,
Ile133, Phe134, Lys230, Val373, Met374, Ser478 and Leu479, and the phenyl B-ring near residues
Ile132, Phe148 and Cys299. Hence, the hydrophobic skeleton of α-naphthoflavone was accommodated
by two hydrophobic areas in the active site. A favorable π-π interaction between the aromatic tyrosine
ring of 133 and the B ring of flavonoid were also observed, whereas the unfavorable π-π interaction was
observed with the catechol hydroxyl group and the 7, 8-dihydroxy-flavones. It was found that Asp309
has not directly been involved with the binding of inhibitor. The hydroxyl group of Thr310 played a
dual character in the active site. It might serve as a hydrogen-bonding partner of water molecules for
catalytic reaction and might stabilize the link formed by Pro308 in the I-helix by hydrogen bonding to the
carbonyl group of Ala306. The steric effects of the ring B attached to the C2 atom in isoflavone shifted
the carbonyl group to an unfavorable position and angle compared to the carbonyl group in flavones.
The coordination of isoflavone was weaker compared to flavone and thus, might lead to decrease in the
activity. A hydroxy group at the C7 atom of the flavone moiety might increase the inhibitory potency
significantly due to the hydrogen bonding to Ser478. Hydroxy groups at atoms C3, C5 and C6 led to
decreased inhibitory activity. The hydroxyl group at the C6 atom was too far to form a hydrogen bond
to Ser478, and therefore less active. A hydroxy group at the ring B also led to decreased activity. In the
docking study, a hydrophobic skeleton, a sterically unhindered carbonyl functionality and a hydrogen
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Ligand- and Structure-Based Drug Design of NSAIs in Breast Cancer
bonding substituent at the C7 atom of the flavone skeleton might be necessary factors for effective binding. The docking of coumestrol showed that it could form hydrogen bonding to hydroxyl side chain of
Ser478 and carbonyl backbone of Cys299 to the phenolic hydroxy groups. The His480 was located near
the aromatic skeleton of coumestrol and the mutation to a glutamine led to the loss of π-π interactions
and hence, decreased the inhibition potency. Amino acid Asp309 was not directly involved in phytoestrogen binding and therefore, the mutation to an alanine did not directly affect the binding of coumestrol.
Schematic representation of the docking results for better activity is shown in Figure 16.
Flavonoid derivatives having anti-aromatase activity were subjected to docking study by calculating three types of scoring functions, i.e. knowledge-based, force field-based and empirical scores
(Narayana et al., 2012). The best docking pose was selected based on the interactions of the imidazole
nitrogen atom as well as the carbonyl oxygen atom to the heme portion of aromatase. As the individual
scoring functions showed low correlation with the activity (<0.44), it was not useful to identify true
hits. Thus, consensus prediction models were used for calculating aromatase inhibition. Two models
were developed. One is based on the individual descriptors of each docking protocol and second on
the different scoring functions of three docking methods. The descriptors obtained by the GOLD
and GLIDE docking methods were correlated with the activity. Different internal and external validation parameters were used for model validation. The first model showed the importance of Astex
statistical potential (ASP) and Score of internal energy [S(int)]. The ASP is the atom–atom potential
derived from the frequency of interactions between ligand and receptor atoms, i.e. heme portion,
whereas S(int) denotes the sum of ligand internal van der Waals energy and ligand torsional strain
energy. Because of the presence of heme group, it could be inferred that the metal-ligand interaction
or coordination may play a significant role of aromatase inhibition. The S(int) signifies the stabil-
Figure 16. Schematic representation of the docking results of phytoestrogens for better activity
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Ligand- and Structure-Based Drug Design of NSAIs in Breast Cancer
ity of the ligand inside the receptor active site. The second model showed the significance of the
scoring functions like ASP_Score, PMF (potential of mean force) and dock score. The ASP_Score
is the atom–atom potential derived from a database of protein-ligand complexes, which signifies
the information about the frequency of interaction between ligand and receptor atoms. It also gives
information about the statistical potential. Potential of mean force is calculated by summing up pairwise interaction terms, overall interatomic pairs of the receptor-ligand complex along with metal ion
interactions and halogen potentials. Increasing value of PMF suggested that these interactions were
important as far as the aromatase inhibition was concerned. The sum of ligand internal energy and
the receptor-ligand interaction energy are denoted by the dock score, and it implied that both of the
interaction energies were important for aromatase inhibition. To judge the accuracy of both of these
models, an external validation set was used. All these external set compounds were well validated,
and external validation parameters proved the reliability and robustness of the developed models. This
study proved that flavonoid derivatives with imidazolylmethyl substitution were more potent than
unsubstituted derivatives suggested by the docking score. Conformational and spatial arrangement
3+
of the imidazole ring toward the heme (Fe
anti-aromatase activity. In case of unsubstituted flavonoids, interaction with heme (Fe
) of the aromatase enzyme was important for higher
3+
) moiety
might be observed with cyclic ketone. The methoxy group at 7-position and the electron-withdrawing
groups with 4-phenyl substituent at 3-position of flavonoids were found to be more potent than the
unsubstituted flavonoids. This could be attributed to H-bond interaction with Ser478 and Met374
amino acids of aromatase enzyme. These models might be useful for designing new NSAIs. Structural
requirements for designing better anti-aromatase compounds of this class are shown in Figure 17.
Figure 17. Important structural features of flavonoids for potent anti-aromatase activity
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Ligand- and Structure-Based Drug Design of NSAIs in Breast Cancer
Flavonoids are found to be effective aromatase inhibitors. The QSAR and classification structure-activity
relationship (CSAR) studies were performed on some anti-aromatase flavonoid derivatives (Le Bail et al.,
2001; Pouget et al., 2002b, 2002c; Yahiaoui et al., 2004; Gobbi et al., 2006; Mohammed et al., 2011) us-
ing multiple linear regression (MLR), artificial neural network (ANN), support vector machine (SVM) and
decision tree (DT) approaches. The QSAR model developed by MLR approach showed the importance of
the number of rings (nCIC), number of hydrogen bond donors (nHDon), number of hydrogen bond acceptors (nHAcc), lipophilicity (ALogP), rotatable bond number (RBN) and dipole moment for regulating the
anti-aromatase properties of the flavonoids. These descriptors were further analyzed through ANN and SVM
approaches and found to be statistically significant. The classification DT model proved that the nCIC, RBN
and nHDon have the prime importance. This study provided that the active molecules are generally smaller,
having a higher degree of rigidity, lower polarity and charge distribution, and also provided slightly lower
electron-withdrawing tendency and higher chemical reactivity than the inactive molecules. The structural
requirements of these flavonoids for better aromatase inhibition are represented schematically in Figure 18.
The CoMFA-based virtual screening followed by molecular dynamics (MD) simulation study was
performed on a series of 45 flavonoids active against human aromatase (Awasthi et al., 2014). The model
was found to be statistically significant and used for virtual screening of flavonoids database and followed
by molecular docking study. Docking study suggested that 7-hydroxyflavanone β-D-glucopyranoside
Figure 18. Structural requirements of flavonoids for potential anti-aromatase activity
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