3D-QSAR of Amino-substituted Pyrido[3,2B]Pyrazinones as PDE-5 Inhibitors
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Transcript of 3D-QSAR of Amino-substituted Pyrido[3,2B]Pyrazinones as PDE-5 Inhibitors
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O R I G I N A L R E S E A R C H
3D-QSAR of amino-substituted pyrido[3,2B]pyrazinonesas PDE-5 inhibitors
Omprakash Tanwar • Rikta Saha • M. Mumtaz Alam •
Mymoona Akhtar
Received: 16 August 2010 / Accepted: 19 November 2010
Ó Springer Science+Business Media, LLC 2010
Abstract A 3D-QSAR study on amino-substituted pyr-
ido[3,2b]pyrazinones as PDE-5 inhibitors was successfullyperformed by means of pharmacophore mapping using
PHASE module of Schrodinger-9. The 3D-QSAR obtained
from AADHRR-183 hypothesis was found to be statisti-
cally good with r 2= 0.95 and q
2= 0.81 taking PLS factor
4. The statistical significance of the model was also con-
firmed by a high value of Fisher ratio of 85.1 and a very
low value of RMSE 0.29. One of the other parameters
which signify the model predictivity is Pearson R. Its value
of 0.91 shows that the correlation between predicted and
observed activities for the test set compounds is excellent.
Hydrophobic groups are important for PDE-5 inhibition
while H-bond donor groups are less favorable for the same.
Electron withdrawing groups are favorable if include at
ring A in the structures while unfavorable at other sites.
Thus, it can be assumed that the present QSAR analysis is
enough to demonstrate PDE-5 inhibition with the help of
AADHRR-183 hypothesis and will help in designing novel
and potent PDE-5 inhibitors.
Keywords 3D-QSAR Á Pharmacophore Á PHASE Á
Phosphodiesterases Á Pyrido[3,2b]pyrazinones Á
Schrodinger
Abbreviations
3D-QSAR Three-dimensional quantitative structure–
activity relationship
2D-QSAR Two-dimensional quantitative structure–
activity relationshipPDEs Phosphodiesterases
RMSE Root mean squared error
cAMP Cyclic monophosphate
cGMP Cyclic guanosine monophosphate
PLS Partial least square
LOO Leave one out
r 2 Cross-validated correlation coefficient
q2 Internal predictivity
Introduction
The Three-dimensional quantitative structure–activity
relationship (3D-QSAR) involves analysis of the quanti-
tative relationship between the biological activity of a set
of compounds and their three-dimensional structural
properties, using statistical correlation methods. Three-
dimensional QSAR approach is one of the most powerful
techniques which come in the class of indirect drug design.
Lead optimization without receptor 3D structure is one of
the most important applications of 3D-QSAR. It allows 3D
visual analysis for spatial arrangement of structural
features with biological activity thus is advantageous
over 2D-QSAR where model data has to be taken into
consideration.
PDEs are the enzymes which are responsible for the
degradation of cyclic monophosphate (cAMP) and cyclic
guanosine monophosphate (cGMP) that are imperative
intracellular second messengers (Francis and Corbin, 1999).
Erectile dysfunction is the most commonly encountered
form of sexual dysfunction in men and can cause significant
O. Tanwar Á R. Saha Á M. M. Alam Á M. Akhtar (&)
Drug Design and Medicinal Chemistry Lab, Department
of Pharmaceutical Chemistry, Faculty of Pharmacy, Jamia
Hamdard, New Delhi 110062, India
e-mail: [email protected]
Med Chem Res
DOI 10.1007/s00044-010-9523-y
MEDICINALCHEMISTRYRESEARCH
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scoring methods to identify CHPs for a series of molecules
which have particular target specificity. Each hypothesis
conveys a particular 3D conformation of a set of ligands in
which the ligands are going to bind to the receptor. The
best hypothesis is then correlated with known biological
activity values to generate a 3D-QSAR model which
identifies the whole structural features of molecules that
govern activity (Samantha et al., 2008; Almerico et al.,2010).
Active analog approach was used to identify a CPH
which has been applied in generating significant 3D-QSAR
models (Narkhede and Degani, 2007; Lather et al., 2008;
Mahipal et al., 2010). The common pharmacophores were
culled from the conformations of the set of active ligands
using a tree-based partitioning technique which groups
together similar pharmacophores according to their inter-
site distances. A tree depth of five with initial box size of 25.6 A and final box size of 0.8 A was used (Samantha
Table 1 Structures of the compounds of the selected series with their inhibition data
NN
N
O
R1
NH
R2
F
X
Comp.
codeX2R1R
– log IC50
PDE-5
PDE009 OMe
O N
H 8.89
PDE010 OMe
O N
F 8.70
PDE011
N
O
H 8.60
PDE012
N
O
H 7.69
PDE013
F
F
O
H 8.57
PDE014 H H 7.55
PDE015 O
H 8.52
PDE016 O N
H 8.34
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Table 1 continued
PDE017 O N
F 8.80
PDE018
N
H 7.87
PDE019N
O
H 8.46
PDE020
O
O N
F 7.28
PDE021 O
O
H 6.83
PDE022 O
O
H 7.65
PDE023 O
O N
H 7.48
PDE024 O
H 6.65
NN
N
O
HN
R2
Ar
rA2R
PDE025O N
O
N
9.00
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Table 1 continued
PDE026 O
N
9.05
PDE027N
O
N
OMe
9.52
PDE028 O
N
OMe
9.52
PDE029 O N
N
OMe
9.40
PDE030 O
N
N
OMe
9.52
PDE031 O
N
N
OH
8.54
PDE032O N
O
N
7.44
PDE033 O
N
7.47
PDE034N
O
N
OMe
7.84
PDE035 O
N
OMe
8.64
PDE036O N
N
OMe
8.62
PDE037O N
N
OMe
7.57
PDE038O N
N
OH
6.27
PDE isoforms inhibition -logIC50 values (in molar units)
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et al., 2008; Phase3.1, Schrodinger, LLC, 2009). The
resulting pharmacophore was then scored and ranked. The
scoring was done to identify the best candidate hypothesis,
and which provided an overall ranking of all the hypoth-
eses. The scoring algorithm included the contributions
from the alignment of site points and vectors, volume
overlap, selectivity, number of ligands matched, relative
conformational energy, and activity. A detailed description
about the scores can be found in the methodology research
article where PHASE is described (Dixon et al., 2006). The
selected AADHRR-183 hypothesis with various scores is
summarized in Table 2.
The best pharmacophore hypothesis AADHRR-183
(Fig. 2) was selected for further QSAR study. The above-
mentioned 3D pharmacophore hypothesis (Fig. 2a)
encompass the following features: two hydrogen bond
acceptor (A) in pink color, one hydrogen bond donors (D)
in sky blue color, one hydrophobic group (H) in green color
and Two Aromatic ring (R) in yellow color. The 2D rep-
resentation of the AADHRR-183 hypothesis is given in
Fig. 2b. The 2D representation shows that the secondary
amino group near to pyrazinones ring is hydrogen bonddonor (D), N atom of pyridine and C=O group of pyrazi-
none ring are two hydrogen bond acceptors (A), substitu-
tion at ring A is hydrophobic groups (H), and rings A and B
are two aromatic ring (R) are the pharmacophoric elements
of AADHRR-183 hypothesis.
Building of QSAR model
In this study, a significant 3D-QSAR model was generated
using AADHRR-183 hypothesis. For QSAR model gener-
ation, training and test partition was done by randomselection method. Atom-based model selection criterion
was chosen for model building (Shah et al., 2010). PLS
factor was set as 04, the maximum number of PLS factors
in each model can be 1/5 the total number of training set
molecules. More the PLS factor value, more will be the
reliability of models. Various models have been generated
and the best model was selected on the basis of the sta-
tistical significance given below.
Table 2 Score of different parameters of the hypothesis AADHRR-
183
S. No. Parameters Score
1. Survival 8.4
2. Survival inactive 5.831
3. Post hoc 3.65
4. Site 0.915. Vector 0.983
6. Volume 0.755
7. Selectivity 2.297
8. Must matches 14
9. Energy 0
10. Activity 8.52
11. Inactive 2.569
Survival weighted combination of the vector, site, volume, and sur-
vival scores, and a term for the number of matches, a large value of
survival score indicates the better fitness of the active ligands on the
common pharmacophore and validates the model
Survival inactive survival score for actives with a multiple of thesurvival score for inactive subtracted
Post hoc this score is the result of rescoring
Site score, this score measures how closely the site points are
superimposed in an alignment to the pharmacophore of the structures
that contribute to the hypothesis, based on the RMS deviation of the
site points of a ligand from those of the reference ligand
Vector alignment score
Volume measures how much the volume of the contributing structures
overlap when aligned on the pharmacophore
Selectivity estimate of the rarity of the hypothesis, High selectivity
means that the hypothesis is more likely to be unique to the actives
Matches number of actives that match the hypothesis
Energy relative energy of the reference ligand in kcal/molActivity of the reference ligand
Inactive survival score of inactives
N
N
N
O
N
N
O
NH
O
Acceptor
Acceptor
Donor
Ring1 Ring1
A
B
Hydrophobic
B
A
Fig. 2 a Common pharmacophore for active ligands [two hydrogen
bond acceptor ( A) in pink color, one hydrogen bond donors ( D) in sky
blue color, one hydrophobic group ( H ) in green color and two
aromatic ring ( R) in yellow color] and b 2D representation of
pharmacophore (Color figure online)
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Result and discussion
A 3D-QSAR study has been performed successfully on the
series of substituted pyrido[3,2b]pyrazinone derivatives to
understand the effect spatial arrangement of structural
features on PDE-5 inhibition. Result of the 3D-QSAR canbe visualized from Fig. 3. The blue cubes in 3D plots of the
3D pharmacophore regions refer to ligand regions in which
the specific feature is important for better activity, whereas
the red cubes demonstrates that particular structural feature
or functional group is not essential for the activity or likely
to reason for decreased binding potential. The statistical
results of 3D-QSAR study are summarized in Table 3.
The reliability of the present 3D-QSAR analysis can be
justified by the fact that all statistical measures are sig-
nificant to any level. The model express 99% variance
exhibited by pyrido[3,2b]pyrazinones, which is near to one
and signifying a very close agreement of fitting points onthe regression line for the observed and PHASE predicted
activity. The fitness graph can be visualized from Fig. 4
and the observed and PHASE predicted activity data are
summarized in Table 4. Validity of the model can be
expressed by internal predictivity (q2 = 0.81) which is
obtained by leave-one-out (LOO) or leave n out method.
The q2 by leave-one-out method is more reliable and robust
statistical parameter than r 2 because it is obtained by
external validation method of dividing the dataset into
training and test set. The large value of F (85.1) indicates a
statistically significant regression model, which is sup-
ported by the small value of the variance ratio (P), anindication of a high degree of confidence. Further small
values of standard deviation of the regression (0.23) and
RMSE (0.29) make an obvious implication that the data
used for model generation are best for the QSAR analysis.
Apart from the above-mentioned features, PLS factor also
confirms the reliability of the model. In this study, number
of PLS factor was taken as 4 and for each increment it
gives one equation and there should be stepwise
improvement each time the model generated. In addition to
Fig. 3 QSAR visualization of various substituents affect: a electron
withdrawing feature, b hydrogen-bond donor, and positive (c) and
negative (d) hydrophobic effects (Color figure online)
Table 3 3D-QSAR statistical parameters
PLS
factors
SD r 2
F P RMSE q2
Pearson
R
1 0.584 0.6248 35 7.195e-
06
0.4672 0.5024 0.7301
2 0.4171 0.8177 44.9 4.048e-
08
0.4459 0.5467 0.7645
3 0.3196 0.8984 56 1.271e-
09
0.3323 0.7482 0.8778
4 0.2309 0.9498 85.1 1.95e-
11
0.2887 0.81 0.9127
SD standard deviation of the regression, r 2 for the regression, F var-
iance ratio. Large values of F indicate a more statistically significant
regression, P significance level of variance ratio. Smaller values
indicate a greater degree of confidence, RMSE root-mean-square
error, q2 for the predicted activities, Pearson R value for the corre-
lation between the predicted and observed activity for the test set
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the above parameters it is interesting to note that active
ligands are closely fitted to the regression line and inactive
ligands are scattered (Fig. 5).
Figure 3 shows 3D-pharmacophore regions around
compounds. For the selected pharmacophore blue and red
cubes represent favorable and unfavorable regions,
respectively. Molecular substitutions which increase the
number of blue cubes will definitely lead to increasesbinding affinity of the molecules towards PDE-5 inhibition,
while molecular substitutions which increase the number of
red cubes will lead to decreased activity.
Figure 3a represents electron withdrawing characteristic
for the selected hypothesis. Visual analysis of Fig. 3a
demonstrates that the throng of the blue cubes at the ring A
site is pointing out the positive potential of electron with-
drawing characteristic of the molecules and is requisite for
the activity. It can be suggested that addition of appropriate
electron withdrawing groups at the ring A and at the side of
methoxy group will append the PDE-5 inhibition, whereas
the addition of electron withdrawing groups at ring B siteand at pyran ring site will lead to decreased receptor
binding which in turn will result in lower potency of
compounds. The potency of the compounds can be
increased by addition of small electron withdrawing groups
like fluoro, chloro, bromo, etc., at para and meta positions
on ring A.
Figure 3b illustrates H-donor characteristic for the
selected hypothesis. The crowd of red cube suggests that
addition of hydrogen bond donor groups at ring A site will
lead to decreased PDE-5 inhibition. Similar diminished
potential of the compounds can be found if hydrogen bond
donor groups are added at NH at ring B site. While proper
orientation of hydrogen bond donor groups may slightly
lead to increased inhibition toward PDE-5 as indicated by
fewer blue cubes.
Figure 3c and d demonstrating the effect of positive and
negative hydrophobic potential, respectively. It can bededuced from figure that hydrophobic groups are well
tolerated at cyclohexyl moiety on ring B (blue cubes),
while the substitution of hydrophobic groups at ring A site
and pyridine ring are unacceptable (red cubes) or may
hinder the binding of the molecules to the receptor active
site and will result in decreased PDE-5 inhibition. An
interesting finding of the present analysis is that H atom (on
NH group) of pyrazinone ring should substituted only with
hydrophobic groups as there is dense crowd of blue cube is
present. At the same time no hydrophobic substituents
should be present on pyridine ring for better PDE-5 inhi-
bition. It is clear that cyclohexyl ring can be substitutedwith more hydrophobic rings like substituted cyclohexyl
rings, saturated naphthalene rings, etc., along with the
some hydrophobic groups at ring A.
Although PDE-6 and PDE-11 activities were also
reported for some ligands, but no significant statistics have
been developed so they are not reported in the present. It
may also be due to the fact that the molecules are more
selective towards PDE-5 as compared to PDE-6 and PDE-
11. Thus, it can be assumed that this study shows the
Fig. 4 Fitness graph between
observed activity versus PHASE
predicted activity for training
and test set compounds
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selectivity towards and will help in designing more selec-
tive PDE-5 inhibitors.
Summary and conclusion
A meaningful 3D-QSAR study was derived for the series
of amino-substituted pyrido[3,2b]pyrazinones as PDE-5
inhibitors, to identify how three-dimensional arrange-
ments of various substituents will affect the PDE-5 inhi-
bition. Selected model is very much significant to draw
unambiguous inferences. The statistical parameter values
are well above the acceptance limits. So it is easy to draw
clear inference to design novel compounds for better
PDE-5 inhibition. The 3D-QSAR model discussed above
explains how and at what extent electron withdrawing,
hydrophobic, and H-donor properties should be modified
to achieve better PDE-5 inhibition. The model shows that
PDE-5 inhibition can be increased, if the hydrophobic
character at ring B is supplemented by appropriate
hydrophobic functional groups. Hydrophobic group-
substituted cyclohexyl rings or naphthalene rings at B ring
can provide potent PDE-5 inhibitors. The present finding
suggests that H-bond donor substituents are mostly
unfavorable in the structures. Thus, it is clear that inclu-
sion of such groups will lead to deceased PDE-5 inhibi-
tion. Electron withdrawing characteristic is beneficial at
ring A, whereas electron withdrawing groups at pyrane
ring and at pyrazinone are unfavorable. This suggests that
inclusion of flouro, chloro, bromo, etc., at ring A will
provide better compounds. Thus, it is clear from this
study that it is possible to develop novel and potent PDE-
5 inhibitors if suitable substituents are added to the parent
structures.
Table 4 Fitness and PHASE
predicted activity data for all
compounds
Ligand name QSAR set PLS factors PHASE predicted
activity
Pharm set Fitness
PDE009 Training 1 2 3 4 9.06 Active 2.67
PDE010 Training 1 2 3 4 8.97 Active 2.62
PDE011 Test 1 2 3 4 8.21 Active 2.83
PDE012 Training 1 2 3 4 7.93 Inactive 2.83
PDE013 Training 1 2 3 4 8.39 Active 2.78PDE014 Training 1 2 3 4 7.53 Inactive 2.76
PDE015 Test 1 2 3 4 8.54 Active 3
PDE016 Training 1 2 3 4 8.54 Active 2.84
PDE017 Training 1 2 3 4 8.65 Active 2.82
PDE018 Training 1 2 3 4 8.03 Inactive 2.92
PDE019 Training 1 2 3 4 8.62 Active 2.89
PDE020 Test 1 2 3 4 7.23 Inactive 2.66
PDE021 Training 1 2 3 4 6.92 Inactive 2.58
PDE022 Test 1 2 3 4 7.41 Inactive 2.57
PDE023 Training 1 2 3 4 7.12 Inactive 2.64
PDE024 Training 1 2 3 4 6.57 Inactive 2.63
PDE025 Training 1 2 3 4 8.86 Active 2.24
PDE026 Training 1 2 3 4 8.83 Active 2.32
PDE027 Training 1 2 3 4 9.53 Active 2.39
PDE028 Training 1 2 3 4 9.43 Active 2.51
PDE029 Test 1 2 3 4 9.4 Active 2.52
PDE030 Training 1 2 3 4 9.46 Active 2.71
PDE031 Test 1 2 3 4 8.77 Active 2.35
PDE032 Training 1 2 3 4 7.36 Inactive 2.2
PDE033 Training 1 2 3 4 7.22 Inactive 2.34
PDE034 Test 1 2 3 4 8.4 Inactive 2.5
PDE035 Training 1 2 3 4 8.68 Active 2.44
PDE036 Training 1 2 3 4 8.54 Active 2.45
PDE037 Training 1 2 3 4 7.39 Inactive 2.5
PDE038 Training 1 2 3 4 6.83 Inactive 2.27
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Acknowledgments The authors are thankful to University Grants
Commission, New Delhi, India for providing financial support. The
authors also wish to acknowledge the team of Schro dinger for pro-
viding software facility.
References
Almerico AM, Tutone M, Lauria A (2010) 3D-QSAR pharmacophore
modeling and in silico screening of new Bcl-xl inhibitors. Eur JMed Chem 45:4774–4782
Black KL, Yin D, Ong JM, Hu J, Konda BM, Wang X, Ko MK,
Bayan JA, Sacapano MR, Espinoza A, Irvin DK, Shu Y (2008)
PDE5 inhibitors enhance tumor permeability and efficacy of
chemotherapy in a rat brain tumor model. Brain Res 1230:
290–302
Burnett AL (2005) Phosphodiesterase 5 mechanisms and therapeutic
applications. Am J Cardiol 96:29–31
Dixon SL, Smondyrev AM, Knoll EH, Rao SN, Shaw DE, Friesner
RA (2006) PHASE: a new engine for pharmacophore perception,
3D QSAR model development, and 3D database screening: 1.
Methodology and preliminary results. J Comput Aided Mol Des
20:647–671
Eardley I, Donatucci C, Corbin J, El-Meliegy A, Hatzimouratidis K,
McVary K, Munarriz R, Lee SW (2010) Pharmacotherapy forerectile dysfunction. J Sex Med 7:524–540
Francis SH, Corbin JD (1999) Cyclic nucleotide-dependent protein
kinases: intracellular receptors for cAMP and cGMP action. Crit
Rev Clin Lab Sci 36:275–328
Haning H, Niewohner U, Schenke T, Es-Sayed M, Schmidt G, Lampe
T, Bischoff E (2002) Imidazo[5, 1-f][1, 2, 4]triazin-4(3H)-ones,
a new class of potent PDE 5 inhibitors. Bioorg Med Chem Lett
12:865–868
Hosogai NH, Hamada K, Tomita M, Nagashima A, Takahashi T,
Sekizawa T, Mizutani T, Urano Y, Kuroda A, Sawada K, Ozaki
T, Seki J, Goto T (2001) FR226807: a potent and selective
phosphodiesterase type 5 inhibitor. Eur J Pharmacol 428:
295–302
Lather V, Kristam R, Saini JS, Karthikeyan NA, Balaji VN (2008)
QSAR models for prediction of glycogen synthase kinase-3b
inhibitory activity of indirubin derivatives. QSAR Comb Sci
27:718–728
LigPrep, version 2.3, Schrodinger, LLC, New York, NY, 2009
MacroModel, version 9.7, Schrodinger, LLC, New York, NY, 2009
Mahipal, Tanwar OP, Karthikeyan C, Moorthy NSHN, Trivedi P
(2010) 3D-QSAR of aminophenyl benzamide derivatives as
histone deacetylase inhibitors. Med Chem Res 6:277–285
Moreland RB, Goldstein I, Kim NN, Traish A (1999) Sildenafil
citrate, a selective phosphodiesterase type 5 inhibitor: research
and clinical implications in erectile dysfunction. TEM 10:97–
104
Narkhede SS, Degani MS (2007) Pharmacophore refinement and 3D-
QSAR studies of histamine H3 antagonists. QSAR Comb Sci
26:744–753
Owen DR, Walker JK, Jon Jacobsen E, Freskos JN, Hughes RO,
Brown DL, Bell AS, Brown DG, Phillips C, Mischke BV,
Molyneaux JM, Fobian YM, Heasley SE, Moon JB, Stallings
WC, Joseph Rogier D et al (2009) Identification, synthesis and
SAR of amino substituted pyrido[3, 2b]pyrazinones as potent
and selective PDE5 inhibitors. Bioorg Med Chem Lett 19:
4088–4091
PHASE-3.1, Schrodinger, LLC, New York, NY, 2009
Samantha L, Cesare M, Aleksey K, Mattia S, Stefano M, Elena C,
Dorotea R, Alessandro C (2008) SAR and QSAR study on 2-
aminothiazole derivatives, modulators of transcriptional repres-
sion in Huntington’s disease. Bioorg Med Chem 16:5695–5703
Shah UA, Deokar HS, Kadam SS, Kulkarni VM (2010) Pharmaco-
phore generation and atom-based 3D-QSAR of novel 2-(4-
methylsulfonylphenyl)pyrimidines as COX-2 inhibitors. Mol
Divers 14:559–568
Terrett NK, Bell AS, Brown D, Ellis P (1996) Sildenafil (viagra), a
potent and selective inhibitor of type 5 cgmp phosphodiesterase
with utility for the treatment of male erectile dysfunction. Bioorg
Med Chem Lett 6:1819–1824
Wall ME, Francis SH, Corbin JD, Grimes K, Jannetta RR, Kotera J,
Macdonald BA, Gibson RR, Trewhella J (2003) Mechanisms
associated with cGMP binding and activation of cGMP-depen-
dent protein kinase. PNAS 100:2380–2385
Wallace WD (2005) Available and future treatments for erectile
dysfunction. Clinical Cornerstone 1:37–44
Fig. 5 Alignment of active (a) and inactive (b) ligands to the
pharmacophore
Med Chem Res