2d-qsar study of 1,4-benzodiazepine-2-ones as potent anti-trypanosomal agents
TRANSCRIPT
ORIGINAL RESEARCH
2D-QSAR study of 1,4-benzodiazepine-2-ones as potentanti-trypanosomal agents
Neelesh Maheshwari • Anju Goyal •
Sourabh Jain
Received: 8 June 2012 / Accepted: 11 April 2013
� Springer Science+Business Media New York 2013
Abstract 2D-QSAR is one of the oldest methods of
structure activity relationship analysis which provide many
breakthroughs in the medicinal field. This method can
provide a direct correlation between various physico-
chemical properties of a molecule with its structural char-
acteristics. The correlations established by this method are
based on the relative contribution of various descriptors. In
the present study, this technique is utilized to develop a
correlation equation between the anti-human African try-
panosomiasis activity and the physicochemical parameters
depicted by various descriptors utilized within the study.
This series consists of 67 compounds with widespread range
of activity between 0.78 and 3.13 lM. In addition, an
effective correlation was obtained between SaasCE-index,
Slogp, NitrogensCount, 0PathCount, Chi4pathCluster,
CarbonsCount, and the biologic activity. These contributed
descriptors illustrate that various physicochemical proper-
ties of the molecules responsible for the activities. The
model suggests that the hydrophobicity has positive impact
on the activity, while the presence of electronegative atom
especially nitrogen and flexibility of the molecules also
provide favorable effect on the activity.
Keywords Human African trypnosomiasis � WHO �QSAR � Hydrophobicity � 1,4-Benzodiazepine-2-ones
Introduction
Human African trypanosomiasis (HAT), also known as
sleeping sickness is a parasitic disease transmitted by the
bite of the ‘‘Glossina insect’’, commonly known as Tsetse
fly (WHO, 1998). Annually one million people are at risk
due to this disease. Approximately 300,000 new cases per
year in Africa, with less than 30,000 cases diagnosed and
treated, as estimated by WHO expert committee. About
48,000 people died in 2008 caused by this disease. It has
been known for many years that chronic Trypanosomiasis
infection leads to CNS invasion by the parasite. This leads
to an inflammatory reaction within the brain which pro-
vides symptom-like psychosis leading to deteriorated sit-
uation. Currently, using therapies for HAT are several
decades old and are not surprisingly have many drawbacks
viz. high toxicity, prohibitive costs, undesirable routes of
administration as well as poor efficacy (Welburn et al.,
2009). Even though, the discovery of novel molecules and
treatment strategy are important for this disease. Hence, in
the present studies a series of 1,4-benzodiazepine-2-ones
has biologic activity in the range of 0.78–3.13 lM against
HAT considered to investigate the physiochemical features
responsible for the biologic activity (Spencer et al., 2011).
The quantitative structural activity relationship analysis
(QSAR) was employed to perform the structural feature
analysis.
QSAR has proven to be a major tool in drug discovery to
explore ligand-receptor/enzyme interactions, especially
when either the structural details of the target are unknown
or protein binding data of ligand is unavailable. 2D-QSAR
does not involve complex alignment or assumptions on
conformations; therefore, they can easily be applied to
large compound sets, both in model building and in model
application to new compounds. In such methods one has
N. Maheshwari � A. Goyal
B.N. College of Pharmacy, Udaipur, Rajasthan, India
e-mail: [email protected]
S. Jain (&)
School of Pharmaceutical Sciences, Rajiv Gandhi Technological
University, Airport Bypass Road Gandhinagar, Bhopal,
Madhya Pradesh, India
e-mail: [email protected]
123
Med Chem Res
DOI 10.1007/s00044-013-0592-6
MEDICINALCHEMISTRYRESEARCH
the choice among a wide variety of molecular descriptors
independent on 3D conformation, e.g., topological
descriptors, simple molecular properties, e.g., molecular
weight, ClogP or atomic partial charges. Hence, in this
study the 2D QSAR analysis was performed on the 1,4-
benzodiazepine-2-ones compounds (Kubinyi et al., 1993;
Gupta and Kapoor, 1995).
Material and method
The data set compounds of 1,4-benzodiazepine-2-ones
derivatives considered for the present study is given in
Table 1 (Spencer et al., 2011). The data set comprised of
67 compounds, in which only 46 compounds have well-
defined biologic activities against the target. Hence, those
46 compounds were considered for the QSAR model
development. The biologic activities expressed as minimal
inhibitory concentration (MIC) in lM concentration, were
converted to their molar units and subsequently to free
energy related negative logarithmic state, i.e., -Log
(1/MIC50) (Golbraikh and Tropsha, 2002; Cronin and
Schultz, 2003).
Initially, data set (46 compounds) was divided into two
sets as the training set containing 25 compounds and the
remaining 21 compounds have been grouped as test set. The
test and training sets were divided randomly with the con-
sideration of equal distribution of actives or in-actives in
both sets. In this study, we have used 40 % of the com-
pounds as test set, which can effectively validate the gen-
erated model. The computational studies were performed on
V-life MDS (Molecular Design Suite)TM 3.5 (Vlifesciences,
V-Life, MDS TM 3.5, 2011, www.vlifesciences.com)
software. Each compound was subjected to energy optimi-
zation by batch optimization using Merck Molecular Force
Field (MMFF), fixing Root Mean Square Gradients (RMS)
to 0.01 kcal/mol A. The optimized batch of analogs were
selected for calculation of the physiochemical descriptors
using V-life MDS suite. The obtained descriptor pool was
reduced by eliminating out the descriptors with constant and
near-constant values. Further diminution in the descriptor
pool has been done by ousting the descriptors that are
degenerated and difficult to interpret. The remaining topo-
logical and electrotopological descriptors have been taken
into account for the correlation analysis. The descriptors,
which showed high intercorrelation and less correlation
with biologic activity was removed. Simulated annealing
methodology was applied for the variable selection. QSAR
model was generated using partial least square regression
(PLSR) method using V-life molecular design suite (MDS).
PLSR method is intensively used in QSAR analysis and this
approach leads to stable, correct, and highly predictive
models (Hoskuldsson, 1988; Eriksson et al., 2001). The rule
of thumb describe that multiple regression analysis gener-
ally requires sufficiently more compounds than parameters
(three to six times the number of parameters under con-
sideration). In the present study, four compounds for a
descriptor was adopted for limiting the number of descrip-
tors in the model (Moorthy et al., 2011a, 2012). The pro-
gram computes the best model based on squared correlation
coefficient r2, crossed validated q2, F test, and pred_r2. The
calculated Ftest value is large margin of difference with the
tabulated value at 99.99 % significance. The lower standard
error of pred_r2se, q2_se, and r2_se show absolute quality of
fitness for the QSAR model. The optimal generated QSAR
model was validated to investigate its predictive ability by
cross validation (Jack-Knife method or leave one out) and
external validation methods. These validation studies pro-
vide more statistical parameters to interpretate the predic-
tive capacity of the model. The high pred_r2 and low
pred_r2se show high predictive ability of the model. The
squared correlation coefficient (or coefficient of multiple
determination) r2, is a relative measure of quality of fitness
by the regression equation (Moorthy et al., 2011b, 2011c;
Kubinyi, 1995). Similarly, it represents the part of the
variation in the observed data explained by the regression.
The correlation coefficient values closer to 1.0 represent the
better fit of the regression.
Result and discussion
QSAR study of a series of 1,4-benzodiazepine-2-ones were
performed using partial least square regression analysis.
Among the number of models generated, the following
model has been selected as significant model for further
studies.
BA ¼ �2:2280½ � þ �0:1303½ �SaasCE-index
þ 0:6054½ �S logPþ 0:4821½ �Nitrogens Countþ �0:0759½ �0PathCountþ 0:0207½ �Chi4pathCluster
þ 0:0326½ �CarbonCount
n = 25, Degree of freedom = 18, r2 = 0.8233, q2 =
0.6605, Ftest = 93.9796, r2 se = 0.3100, q2 se = 0.4297,
pred_r2 = 0.5271, pred_r2se = 0.3980.
The derived QSAR model shows good correlation
(r2 = 0.8233) between the biologic activity and physio-
chemical descriptors such as SaasCE-index, Slog P,
Nitrogens Count, 0PathCount, Chi4pathCluster, and Car-
bonCount. The low standard error of r2_se = 0.3100
demonstrates the accuracy of the model. The significant
cross validated correlation coefficient (q2 = 0.6605) and
low q2_se = 0.4297 values reflect the internal predictive
power of the instant QSAR model. However, a high q2
value does not necessarily provide a suitable representation
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123
Table 1 Inhibition data of 1,4-benzodiazepin-2-one with anti-trypanosomanl activity
N
NO
R4
R1
R2
R3
Serial no. Compound no. R1 R2 R3 R4 (MIClM) (-log10MIC)
1 5a Ph H H H 400 3.398
2 6a Ph (S)-iPr H H 100 4
3 8a Ph (S)-Bn H H 6.25 5.204
4 11a (S)-iPr H H H 410 3.387
5 12a (S)-iPr (S)-Bn H H 50 4.301
6 13a c-C6H11 (S)-iPr H H 25 4.602
7 14a c-C6H11 (S)-iPr H NO2 12.5 4.903
8 15a Ph (S)-Bn H H 12.5 4.903
9 16a 2Py (S)-Bn H H 100 4
10 18a Ph (S)-iPr Me H 100 4
11 19a Ph (S)-iPr Bn H 200 3.699
12 20a Ph (S)-iPr 2-CH2BiPh H 12.5 4.903
13 21a Ph (S)-iPr 8-CH2QUIN H 25 4.602
14 24a Ph (S)-Bn Me H 50 4.301
15 25a Ph (S)-Bn Bn H 6.25 5.204
16 26a Ph (S)-Bn 4-CH2BiPh H 6.25 5.204
17 27a Ph (S)-Bn 3-CH2BiPh H 25 4.602
18 28a Ph (S)-Bn 2-CH2BiPh H 6.25 5.204
19 29a Ph (S)-Bn 8-CH2Quin H 25 4.602
20 30a Ph (S)-Bn CH2CN H 3.1 5.509
21 32a Ph (S)-Bn CH2COOH H 50 4.301
22 35a Ph (S)-CH2OBn 4-CH2BiPh H 25 4.602
23 36a Ph (S)-CH2OBn 3-CH2BiPh H 25 4.602
24 37a Ph (S)-CH2OBn 8-CH2Quin H 12.5 4.903
25 38a iPr H Me H 400 3.398
26 39b iPr (S)-Bn Me H 25 4.602
27 40b c-C6H11 (S)-Bn Bn H 6.25 5.204
28 49b Ph (S)-Bn 4-NH2C6H4CH2 H 12.5 4.903
29 50b Ph (S)-Bn 3-NH2C6H4CH2 H 6.25 5.204
30 51b Ph (S)-Bn 2-NH2C6H4CH2 H 6.25 5.204
31 52b c-C6H11 (S)-iPr 4-NH2C6H4CH2 H 6.25 5.204
32 53b c-C6H11 (S)-iPr 4-NH2C6H4CH2 NH2 6.25 5.204
33 54b c-C6H11 (S)-Bn 4-NH2C6H4CH2 H 6.25 5.204
34 55b c-C6H11 (S)-Bn 3-NH2C6H4CH2 H 6.25 5.204
35 56b 2-Py (S)-Bn 4-NH2C6H4CH2 H 25 4.602
36 57b c-C6H11 (S)-iPr H NH2 100 4
37 58b Ph (S)-Bn 4-CH2C6H4–HC(=NH)NH2 H 0.78 6.108
38 59b Ph (S)-Bn 3-CH2C6H4–HC(=NH)NH2 H 0.78 6.108
39 60b Ph (S)-Bn 2-CH2C6H4–HC(=NH)NH2 H 1.56 5.807
40 61b c-C6H11 (S)-iPr 4-CH2C6H4–HC(=NH)NH2 H 6.25 5.204
41 62b c-C6H11 (S)-iPr 4-CH2C6H4–HC(=NH)NH2 NHC(=NH)NH2 0.78 6.108
42 63b c-C6H11 (S)-Bn 4-CH2C6H4–HC(=NH)NH2 H 0.78 6.108
Med Chem Res
123
of the real predictive power of the model for HAT inhib-
itory ligands. Hence, we have validated the model with an
external test set. The external predictive power of the
model was assessed by predicting pMIC50 values of the 21
test set molecules, which were not included in the QSAR
model development. Another parameter used to test the
predictive power of test set compound is pred_r2. The
model predicted the compounds with high pred_r2 =
0.5271 and low pred_r2se = 0.3980, showed good external
predictive power of the model. The correlation between the
observed and predicted biologic activity of both test and
training set compounds are graphically depicted in Fig. 1.
The inter-correlation among the selected descriptors was
very less due to auto scaling and cross correlation limit
permitted was 0.6. The maximum residual value for test set
compounds is 0.78 which showed (Fig. 2) good correlation
between calculated and experimental activities (Table 2).
The model incorporates six physicochemical descriptors
such as SaasCE-index, Slog P, NitrogensCount, 0PathCount,
Chi4pathCluster, and CarbonCount and their corresponding
values provided in Table 3 and graphically represented in
Fig. 3.
Fig. 1 Graph of actual versus predicted activity
Fig. 2 Residual activity of various compounds depicting the difference between actual and predicted activity
Table 1 continued
Serial no. Compound no. R1 R2 R3 R4 (MIClM) (-log10MIC)
43 64b c-C6H11 (S)-Bn 3-CH2C6H4–HC(=NH)NH2 H 1.56 5.807
44 65b 2-Py (S)-Bn 4-CH2C6H4–HC(=NH)NH2 H 3.13 5.504
45 66b c-C6H11 (S)-iPr H NHC(=NH)NH2 1.56 5.807
46 67b Ph CH2CH2CH2NHC(=NH)NH2 H H 400 3.398
Only molecule used for the development of QSAR are shown in the table according to serial number provided in the articlea denotes training set compoundb denotes test set compounds
Med Chem Res
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The descriptor SaasCE-index is an electrotopological
state indices signifies the number of carbon atoms con-
nected with one single-bond along with two aromatic
bonds. Its negative contribution in the model suggests that
those carbon atoms detrimental for the activity. In the
studied compounds, those compounds possessed such
carbon atoms attached with 1,4-benzodiazepine-2-one
nucleus exhibited low HAT inhibition, for example mol-
ecule 38 and 67 with activity 400 lM. Slog P is the log of
the octanol/water partition coefficient (including implicit
hydrogens). This property is an atomic contribution model
that calculates the log P from the given structure; i.e., the
correct protonation state (Moorthy et al., 2012). Its posi-
tive value suggests that the inclusion of hydrophobic/
aromatic substituents in the nucleus has better interaction
in the target. The highly active compounds in the series
possessed aromatic or cyclic rings in their structures
(Fig. 4).
NitrogensCount signifies the number of nitrogen atoms
in a compound and its positive contribution in the model
suggests that the presence of nitrogen atoms in the mole-
cule increases the anti-HAT activity. It is evidenced by the
presence of various phenyl guanidine substitutions in the
compounds of the series has significant activity (0.78 lM)
(Fig. 4). 0PathCount signifies total number of fragments of
zero-order (atoms) in a compound. In a very simple way, it
implies the number of substitution on carbon atom other
than H (hydrogen). This will give an account of molecular
connectivity indices or Chi indices. In more typical terms,
it can provide an estimate of sigma bonds present in the
molecule and the availability of sigma electrons (Kier and
Hall, 1986). Its negative contribution to the activity sug-
gests that there should be less number of fragments of zero-
order (atoms) in a compound or in other words more
substituted molecule will give better results in terms of
activity. The most active compounds of the series have the
least number of 0PathCounts (Fig. 4).
Chi4pathCluster signifies the molecular connectivity
index of 4th order pathcluster. In general terms, it signifies
the number of isopentane equivalent or the fourth-order
fragments attached to the molecule (Kier and Hall, 1986).
In very simple terms, it can be linked to log P value.
Increasing the substitution can rise the lipophilicity of the
molecule and hence can give positive influence toward
lipophilic receptor cavity with better interactions. A
positive value suggests that increase in the molecular
connectivity index positively influences activity. As the
descriptor signifies, the same is depicted by the most
active compounds with the highest Chi4pathClusters
(Fig. 4).
CarbonsCount signifies the number of carbon atoms in a
compound. This provides us an estimate of carbon atoms in
Table 2 Compound with their actual activity, predicted activity, and
residual activity
Serial
no.
Compound
no.
Actual
activity
Predicted
activity
Residual
activity
1 5 4.6020 4.9153 -0.3133
2 6 4.9030 4.5492 0.3538
3 8 4.9030 4.9552 -0.0522
4 11 6.1080 6.2817 -0.1737
5 12 5.2040 4.7913 0.4127
6 13 4.6020 4.8706 -0.2686
7 14 5.8070 5.8283 -0.0213
8 15 5.2040 5.2065 -0.0025
9 16 4.6020 4.8514 -0.2494
10 18 5.8070 5.4338 0.3732
11 19 4.6020 4.2526 0.3494
12 20 4.9030 4.9091 -0.0061
13 21 3.3980 3.4771 -0.0791
14 24 4.3010 4.2367 0.0643
15 25 4.0000 4.2866 -0.2866
16 26 5.2040 5.1861 0.0179
17 27 5.2040 5.1701 0.0339
18 28 4.0000 4.0041 -0.0041
19 29 3.6990 4.5330 -0.834
20 30 4.6020 4.4281 0.1739
21 32 4.3010 4.2831 0.0179
22 35 5.2040 5.0331 0.1709
23 36 4.9030 4.6945 0.2085
24 37 4.9030 4.7831 0.1199
25 38 4.6020 4.6077 -0.0057
26 39 3.3870 3.5054 -0.1184
27 40 4.0000 3.9724 0.0276
28 49 5.2040 4.7973 0.4067
29 50 4.6020 5.1678 -0.5658
30 51 5.5090 4.5794 0.9296
31 52 4.3010 3.8048 0.4962
32 53 4.6020 4.8465 -0.2445
33 54 5.2040 5.2266 -0.0226
34 55 3.3980 3.4935 -0.0955
35 56 5.2040 4.8092 0.3948
36 57 5.2040 4.9352 0.2688
37 58 5.2040 5.1993 0.0047
38 59 4.0000 4.5183 -0.5183
39 60 6.1080 5.4050 0.703
40 61 6.1080 5.5163 0.5917
41 62 5.2040 5.5543 -0.3503
42 63 6.1080 5.8184 0.2896
43 64 5.5040 5.5372 -0.0332
44 65 5.8070 5.5867 0.2203
45 66 3.3980 5.1995 -1.8015
46 67 5.2040 4.2686 0.9354
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Table 3 Compound with their descriptors values
Serial no. Compound no. Slog P SaasCE-index 0PathCount CarbonsCount Nitrogens count chi4pathCluster
1 5 2.0838 1.908148 15 12 2 2.233746
2 6 3.6426 1.94796 19 16 2 2.813308
3 8 4.0327 2.051735 21 18 2 3.237234
4 11 4.4641 2.026415 24 18 3 3.626102
5 12 4.5669 2.081084 22 19 2 2.755218
6 13 3.1287 1.978584 22 18 3 2.755218
7 14 3.525 2.967271 22 19 2 3.922112
8 15 4.6953 3.082174 25 22 2 3.760799
9 16 6.6105 6.323317 34 31 2 5.102198
10 18 4.7073 3.906093 32 28 3 5.102198
11 19 4.0592 3.011295 23 20 2 3.430516
12 20 5.2295 3.126197 26 23 2 3.269203
13 21 6.8946 5.088241 35 32 2 4.486059
14 24 7.1447 6.40133 35 32 2 4.552769
15 25 7.1447 6.393146 35 32 2 4.610602
16 26 5.2415 3.960983 33 29 3 4.610602
17 27 3.9529 2.648154 25 21 3 3.269203
18 28 3.514 2.305762 26 21 2 3.487248
19 29 6.7712 6.225565 37 33 2 4.600881
20 30 6.7712 6.179926 37 33 2 4.552769
21 32 4.868 3.778195 35 30 3 4.610602
22 35 2.1081 2.030833 16 13 2 3.016991
23 36 3.6669 2.070645 20 17 2 3.488605
24 37 5.7615 2.302892 26 23 2 3.269203
25 38 5.0599 4.696872 30 26 3 4.06632
26 39 2.4761 2.818151 18 15 2 2.175657
27 40 5.0599 4.623982 30 26 3 4.002376
28 49 5.0599 4.525576 30 26 3 4.25014
29 50 5.0577 3.929108 29 25 3 4.557916
30 51 4.6399 4.410669 30 25 4 4.911049
31 52 5.5919 3.974662 30 26 3 4.06632
32 53 5.5919 3.909391 30 26 3 4.002376
33 54 4.4549 4.320482 30 25 4 4.06632
34 55 3.6149 2.592203 22 18 3 3.590367
35 56 4.7830 4.579382 33 27 5 4.300301
36 57 4.7830 4.484231 33 27 5 4.247328
37 58 3.5007 2.833845 21 18 2 3.237234
38 59 4.7830 4.371874 33 27 5 4.386805
39 60 4.7808 3.832753 32 26 5 4.791896
40 61 4.0862 4.140346 36 27 8 5.389981
41 62 5.3150 3.878306 33 27 5 4.300301
42 63 5.3150 3.793991 33 27 5 4.247328
43 64 4.1780 4.202993 33 26 6 4.300301
44 65 4.7808 3.578289 32 26 5 4.753341
45 66 4.0827 3.177635 32 26 5 3.946747
46 67 4.0349 2.877869 22 19 2 2.755218
Med Chem Res
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the molecule with a particular arrangement that are
responsible for the activity while the nature of carbon
atoms is well known. However, this descriptor can provide
a correlation between the number of carbon atoms, their
nature and activity. The positive contribution of Carbons-
Count indicates that increasing the number of carbon atoms
positively influences the activity and increasing log P. This
is obvious that the most active molecules of the series have
highest number of carbon atoms (Fig. 4).
Conclusion
Summarizing the above information, it may be inferred
that 2D-QSAR model is generated with 1,4-benzodiaze-
pine-2-ones derivatives against HAT inhibitory activity
having reliable predictive power. The validation methods
provided significant statistical parameters with q2 [ 0.5,
which shows that the models is considered as predictive
model. In addition, low error values (root mean square
error, standard deviation) support the model and increase
its significance. The result of the study suggests that
increase in log P, number of nitrogen containing substi-
tutions (Nitrogen count), number of 3� carbon substitu-
tions (molecular connectivity index of fourth order), and
number of carbon atoms (Carboncount) will increase anti-
HAT activity. The carbon atoms connected with one sin-
gle-bond along with two atomic bonds can have a nega-
tive influence on activity but relatively to a lesser extent.
But fragments of zero-order (atoms) in a compound will
negatively influence activity. The findings derived from
this analysis along with other molecular modeling studies
will be helpful in designing of the new potent HAT
inhibitors of clinical utility.
Acknowledgments All the author gratefully acknowledge V-life
sciences for providing there valuable software for such study. One
of the author Sourabh Jain wishes to thanks AICTE New Delhi
for providing post Graduate scholarship during the period. We
also acknowledge Dr. N�S. Hari Narayana Moorthy, Professor,
Fig. 3 Contribution chart of
various descriptors utilized in
the study
Fig. 4 Most active compounds of the series
Med Chem Res
123
Department of Chemistry & Biochemistry, Faculty of Sciences,
University of Porto, 687, Rua de Campo Alegre, Porto-4169-007,
Portugal for providing their support in editing and proofreading of
this article.
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