Research Journal of
Chemical
Vol. 4(6), 1822, June (2014)
International Science Congress Association
Comparative QSAR study of
Chincholikar P.M.
1
Department of Applied Chemistry, BUIT, Barkatullah University, Bhopal, MP, INDIA
2
Department of Chemistry, SNGGPG College, Bhopal, MP, INDIA
Available online at:
Received 19th
March
Abstract
Anthracycline, a potent anticancer molecule being used extensively in chemotherapy, though known for its efficacy
known to cause adverse effects when administered. We are trying to analyse the variations in its analogues structurally. To
study the
QSAR of anthracycline analogues by using graph theoretical indices and physicochemical properties efforts have
been made to derive a best mathematical model for the calculation of biological activity of the analogues in multivariate
form. We have used mult
iple linear regression method. Two equations were developed separately, 1. Containing graph
theoretical indices with r value 0.7038 and 2. Containing physicochemical properties as predictor variable with r value
0.8162 both models are significant to under
Keywords:
Anthracycline analogues, QSAR, drug designing.
Introduction
Anthracyclines (viz., Doxorubicin, Daunorubicin, Epirubicin,
valirubicin, etc) are the anti
cancer drugs used in
chemotherapy. The use
of these drugs have the common side
effects that were observed, in majority they are: nausea,
alopecia, bone marrow suppression, and vomiting. Doxorubicin
induced bone marrow suppression can now be reduced by the
use of hematopoietic growth factors
.
The
partition coefficient is the ratio of the concentration of a
chemical in
octanol to that in water in a two phase system at
equilibrium. The logarithm of this coefficient, log
shown to be one of the key parameters in quantitativ
activity/property relationship (QSAR/QSPR) studies
hydrophobicity and hydrophilicity of a substance is measured by
 octanol
water partition coefficient. It gives the idea of the
tendencies of hydrophobic molecules or parts of molecu
avoid water, because they are not readily accommodated in the
highly ordered hydrogen bonded structure of water
Thermodynamically Hydrophobic interaction is favoured
because of it increases the entropy of the water molecules that
accompanies the association of non
polar molecules, which
squeeze out water. There are some reports about the applications
of MLR
36
and artificial neural network
710
modeling to predict
the
octanol/water partition coefficient of anti
some previous pap
ers, reports on the application of QSAR
techniques in the development of a new, simplified approach to
prediction of compounds properties1117
. Study of log
its experimental determination is often complex and time
consuming that can be done only f
or already synthesized
compounds11
. Hence, computational methods were applied for
the prediction of this parameter.
The family composed of 23 substituted anthracyclines have
been studied, and activity analyzed is the logIC
Chemical
Sciences _________________________________
______
International Science Congress Association
Comparative QSAR study of
Anthracycline Analogues
Chincholikar P.M.
, Amlathe S.1 and Joshi S.2
Department of Applied Chemistry, BUIT, Barkatullah University, Bhopal, MP, INDIA
Department of Chemistry, SNGGPG College, Bhopal, MP, INDIA
Available online at:
www.isca.in, www.isca.me
March
2014, revised 3rd May 2014, accepted 10th June 2014
Anthracycline, a potent anticancer molecule being used extensively in chemotherapy, though known for its efficacy
known to cause adverse effects when administered. We are trying to analyse the variations in its analogues structurally. To
QSAR of anthracycline analogues by using graph theoretical indices and physicochemical properties efforts have
been made to derive a best mathematical model for the calculation of biological activity of the analogues in multivariate
iple linear regression method. Two equations were developed separately, 1. Containing graph
theoretical indices with r value 0.7038 and 2. Containing physicochemical properties as predictor variable with r value
0.8162 both models are significant to under
stand QSAR of anthracycline analogues.
Anthracycline analogues, QSAR, drug designing.
Anthracyclines (viz., Doxorubicin, Daunorubicin, Epirubicin,
cancer drugs used in
of these drugs have the common side

effects that were observed, in majority they are: nausea,
alopecia, bone marrow suppression, and vomiting. Doxorubicin

induced bone marrow suppression can now be reduced by the
The
octanol/water
partition coefficient is the ratio of the concentration of a
octanol to that in water in a two phase system at
equilibrium. The logarithm of this coefficient, log
, has been
shown to be one of the key parameters in quantitativ
e structure
activity/property relationship (QSAR/QSPR) studies
2,3. The
hydrophobicity and hydrophilicity of a substance is measured by
water partition coefficient. It gives the idea of the
tendencies of hydrophobic molecules or parts of molecu
les that
avoid water, because they are not readily accommodated in the
highly ordered hydrogen bonded structure of water
2
.
Thermodynamically Hydrophobic interaction is favoured
because of it increases the entropy of the water molecules that
polar molecules, which
squeeze out water. There are some reports about the applications
modeling to predict
octanol/water partition coefficient of anti
cancer drugs. In
ers, reports on the application of QSAR
techniques in the development of a new, simplified approach to
. Study of log
o/w and
its experimental determination is often complex and time
or already synthesized
. Hence, computational methods were applied for
The family composed of 23 substituted anthracyclines have
been studied, and activity analyzed is the logIC
50 (50%
inhibitory concentration). The topological and physicochemical
descriptors
were studied for the investigation of QSAR of the
compound. Multiple linear regressions were performed to obtain
correlation for systematic study on substituents of anthracycline
(table 1). Figure

Parent structure of anthracyclines
Methodology
Two methodologies are adopted to develop mathematical
models. In the first method graph theoretical descriptor are used
for correlation with biological activity (logIC
second method physicochemical properties are used for
correlation with biological activity (log
The multivariate regression analysis (MRA) was used to obtain
a model used in QSAR studies. Typically in such studies, after
selecting the compounds an
d the activity to be analysed, one
considers selection of potentially useful descriptors. The major
descriptor used in QSAR are divided into two classes:
theoretical descriptors, this class includes Electrotopological
state(S), Balaban J Index(J),
Szeged Index(Sz), Schultz
molecular topological Index(MTI), Wiener Index(W), Randic
connectivity Index(), etc. and ii.
(descriptor), this includes logP, Molar refractivity (MR), Molar
______
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Res. J. Chem. Sci.
18
Anthracycline Analogues
Department of Applied Chemistry, BUIT, Barkatullah University, Bhopal, MP, INDIA
Anthracycline, a potent anticancer molecule being used extensively in chemotherapy, though known for its efficacy
 but
known to cause adverse effects when administered. We are trying to analyse the variations in its analogues structurally. To
QSAR of anthracycline analogues by using graph theoretical indices and physicochemical properties efforts have
been made to derive a best mathematical model for the calculation of biological activity of the analogues in multivariate
iple linear regression method. Two equations were developed separately, 1. Containing graph
theoretical indices with r value 0.7038 and 2. Containing physicochemical properties as predictor variable with r value
inhibitory concentration). The topological and physicochemical
were studied for the investigation of QSAR of the
compound. Multiple linear regressions were performed to obtain
correlation for systematic study on substituents of anthracycline

1
Parent structure of anthracyclines
Two methodologies are adopted to develop mathematical
models. In the first method graph theoretical descriptor are used
for correlation with biological activity (logIC
50) and in the
second method physicochemical properties are used for
correlation with biological activity (log
IC50).
The multivariate regression analysis (MRA) was used to obtain
a model used in QSAR studies. Typically in such studies, after
d the activity to be analysed, one
considers selection of potentially useful descriptors. The major
descriptor used in QSAR are divided into two classes:
i. Graph
theoretical descriptors, this class includes Electrotopological
Szeged Index(Sz), Schultz
molecular topological Index(MTI), Wiener Index(W), Randic
Physicochemical properties
(descriptor), this includes logP, Molar refractivity (MR), Molar
Research Journal of Chemical Sciences ____
_
Vol. 4(6), 1822, June (2014)
International Science Congress Association
volume(MV), Parachor(Pc), Index of refracti
tension(ST) etc. Indicator parameters are also used for the
presentation of substitutent effect. Descriptors Type
Graph theoretical descriptors Wiener index
Randic connectivity Index
Balaban J Index
Szeged index
Schultz molecular
topological index
Electrotopological state
Physicochemical descriptors Partition Coefficient
Molar refractivity
Molar volume
Parachor
Index of refraction
Surface tension
The topological
indices in the study included: Wiener index
(W)16, Randic connectivity index 17
, Balaban J index (J)
Substituents on anthracyclines (ANTHs)
_
_____________________________________________
_
International Science Congress Association
volume(MV), Parachor(Pc), Index of refracti
on(IR), Surface
tension(ST) etc. Indicator parameters are also used for the
Abbreviation
W
Randic connectivity Index
J
Sz
Schultz molecular
MTI
S
Log P
MR
MV
Pc
IR
ST
indices in the study included: Wiener index
, Balaban J index (J)
18, Szeged index (Sz)19
, Electro topological state (S)
Molecular topological index (MTI)
indices were done using comp
uter program Chemsketch 5.0
(from ACD labs).
The physicochemical properties were estimated using
Chemsketch 5.0 (from ACD labs), while the logP values were
calculated using program ChemlogP. Multiple Linear
Regression method22
was used for regression analysis.
Results and Discussion
The present study deals with the QSAR on benzodiazepines.
The very first step is to calculate the desired topological indices,
and the physicochemical properties and then investigate their
utility.
The topological indices viz. logW, physicochemical properties,
Electrotopological state (S) and Schultz Molecular topological
index (MTI), were calculated using software ACD lab
(Chemsketch 5.0) and are presented in the
parameters are also given in table 2Table1
Substituents on anthracyclines (ANTHs)
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Res. J. Chem. Sci.
19
, Electro topological state (S)
20, Schultz
Molecular topological index (MTI)
21. Calculation of these
uter program Chemsketch 5.0
The physicochemical properties were estimated using
Chemsketch 5.0 (from ACD labs), while the logP values were
calculated using program ChemlogP. Multiple Linear
was used for regression analysis.
The present study deals with the QSAR on benzodiazepines.
The very first step is to calculate the desired topological indices,
and the physicochemical properties and then investigate their
The topological indices viz. logW, physicochemical properties,
Electrotopological state (S) and Schultz Molecular topological
index (MTI), were calculated using software ACD lab
(Chemsketch 5.0) and are presented in the
table 2. The indicator
.
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Table2 Topological indices, physicochemical properties and indicator parameters of anthracyclines (ANTHs) ANTHs MTI S logW logP ST Pol I
2
I
1
I
3,8
I
1,7
1 3173 48.35 2.8609 1.176 45.4 28.09 0 0 0 0
2 3173 49.85 2.8609 0.832 42.3 30.20 0 1 0 1
3 3543 56.02 2.9133 1.424 42.9 28.03 1 0 0 0
4 3543 57.52 2.9133 1.08 40.2 30.14 1 1 0 1
5 3173 44.46 2.8609 1.479 49.6 29.96 0 0 0 0
6 3173 49.85 2.8609 1.231 46.1 32.07 0 1 0 1
7 3543 52.13 2.9133 1.823 47.0 29.91 1 0 0 0
8 3543 48.24 2.9133 2.222 51.1 31.78 1 0 0 0
9 3543 51.64 2.9133 2.126 47.5 35.21 1 1 0 1
10 3543 65.6 3.0298 1.936 40.4 30.02 0 0 0 0
11 4173 45.95 2.9795 0.310 60.6 30.38 0 0 0 0
12 4523 53.62 3.0265 0.902 57.2 30.33 1 0 0 0
13 4523 55.12 3.0265 0.558 52.5 32.44 1 1 0 1
14 4523 49.73 3.0265 1.301 61.8 32.20 1 0 0 0
15 4523 51.23 3.0265 0.957 56.5 34.32 1 1 0 1
16 6119 70.87 3.1658 1.662 50.0 32.26 1 0 0 0
17 5111 58.02 3.0824 0.58 54.4 32.16 1 1 0 1
18 4173 44.35 2.9795 0.889 49.3 29.30 0 0 0 0
19 4173 45.85 2.9795 0.71 48.7 31.26 0 1 0 1
20 4523 53.52 3.0265 0.462 47.2 30.96 1 1 0 1
21 4523 48.13 3.0265 0.281 55.7 30.72 1 0 0 0
22 3629 50.35 2.9238 0.058 48.5 32.83 0 1 0 1
23 4029 58.02 2.7931 0.19 46.1 32.78 1 1 0 1
= Indicator Parameter 1 If substituents on R, I = Indicator Parameter 1 If substituents on R', I3,8 = Indicator Parameter 1 If substituents on R or R, I1,7 = Indicator Parameter 1 If substituents on R, R or on both. The correlations that are low in values of R (0.50) are not considered being statistically insignificant. Individually indices were correlated in lesser manner with the biological activity (logIC50) and same was the case with physicochemical properties of observed biological activity. The results show that the topological, physicochemical properties and biological activity when studied for univariate correlation was insufficient to describe the SAR (structure activity relationship) in quantitative manner. All the univariate correlation gives very low correlation coefficient. Out of all univariate correlations the best statistics are shown by S (r = 0.2105, s = 0.7321). The correlation coefficient in the case of bivariate correlation showed little higher but insufficient to explain structure activity relationship quantitatively and the best statistics are shown by MTI and I2 (r = 0.6567, s = 0.5631), so is the case with trivariate correlation and best statistics are shown by MTI, log W and I2 (r =0.7038, s = 0.5492). In case of tetravariate combination, the best statistics are shown by MTI, logW, I2 and I1,7 (r =0.8162, s = 0.5468). In case of pentavariate combination, the best statistics are shown by MTI, logW, S, I2 and I1,7 (r = 0.7019, s = 0.549). In cases of hexavariate correlation the results are quite good and the correlation coefficient r (0.7063) is obtained as a best one from the correlation of MTI, S I2, I1, I1,7 and logW with the BA (biological activity). The low value of standard deviation (0.5479) and high value of Fratio (8.129) supports the findings. The mathematical model from above variables is shown in equation (1). logIC50 = 7.3644 104 MTI 0.0173 S 0.9519 I 0.3466 I + 0.5664 I1,7 3.508 LogW + 10.0314 (1) The estimation of logIC50 values were essential to confirm our findings between the best suited model and the observed values. The observedand calculated logIC50 values obtained from equation (1) are presented in table III. Using the equation (1) we can describe the effect of substituents. It is observed that the substituents at 1 and 7 positions and together increase the biological activity, while the substituents at 1 and 2 positions have a negative impact on the activity quantitatively. Similarly, in case of physicochemical properties best results of univariate correlation are shown by logP (r = 0.574, s = 0.6056). Similarly for bivariate correlation the best correlation coefficient is shown by logP and I (r = 0.6776, s = 0.5491) and
Research Journal of Chemical Sciences ___________________________________________________________ ISSN 2231606XVol. 4(6), 1822, June (2014) Res. J. Chem. Sci. International Science Congress Association
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the best results for trivariate correlation are shown by logP, Pol and I(r = 0.7186, s = 0.5242). In the case of tetravariate correlation compatible results are obtained by the correlation of logP, ST, Pol, and I2 (r = 0.7695, s = 0.4860). The pentavariate correlation shows more compatibility than the tetravariate correlation by logP, ST, Pol, I, and I3,8 (r = 0.7948, s = 0.4665). For the hexavariate correlation the results are encouraging but the better results are obtained with I, I3,8, I1,7, logP, surface tension (ST) and polarizability (Pol) with the r value of 0.8153. The high value of Fratio (16.192) and the low value of standard error (0.4496) supports the above finding. The mathematical model obtained from above variables can be given by equation (2): logIC50 = 0.5520 logP 0.0628 ST + 0.1792 Pol 0.6754 I2 + 0.4947 I3,8 0.4340 I1,7 0.1839 (2) The observedand calculated values of logIC50 obtained from equation, (2) are presented in table 3. Using the equation (2) we can describe the effect of substituent. It is observed that the substituent at 3 or 8 position increases the biological activity, whereas the substitutions at position2 and together at 1 and 7position have a negative impact on biological activity. The results are little contrary to the one observed with the graph theoretical descriptors but can very well be seen that the negative impact is comparatively very low in case of substituent at positions1 and 7. Conclusion The indicator parameters were successfully used to explore the substituent effect. On the basis of equation (2) we conclude that the substituent at 2nd position has a negative impact on the biological activity quantitatively, i.e, presence of substituent at position decreases the biological activity (logIC50) and the substituent at 3rd or 8th position show the direct relationship and will help increase in biological activity quantitatively, i.e., the presence of the substituent at positions3 and 8 play a significant role to enhance the biological activity (logIC50). Based on the results and discussion made above conclusion may be drawn  no single molecular descriptors or physicochemical properties could be used individually for QSAR studies. In multivariate correlations more than the graph theoretical descriptors the physicochemical properties have shown significant correlation. Thus, physicochemical property studies are most suited for understanding QSAR of anthracyclines. Table3 Observed and calculated logIC50 of substituted anthracyclines (ANTHs) ANTHs logIC50(Obs.) logIC50
a
Residual logIC50
b
Residual
1 1.602 1.4956 0.1063 1.4366 0.1653
2 1.23 1.0841 0.1458 1.7131 0.4836
3 0.869 1.105 0.236 0.7767 0.0922
4 0.708 0.6935 0.0144 1.0548 0.3468
5 0.973 1.5629 0.5899 1.4095 0.4365
6 0.908 1.0841 0.1761 1.6672 0.7592
7 0.301 1.1722 0.8712 0.721 0.42
8 0.255 1.2395 0.9845 0.6805 0.4255
9 0.462 0.7952 0.3332 0.6001 0.1381
10 1.114 0.8771 0.2368 1.5741 0.4601
11 1 1.8575 0.8575 1.3394 0.3394
12 0.176 1.4711 1.2951 0.5934 0.4174
13 0.58 1.0596 0.4796 0.9646 0.3846
14 0.255 1.5384 1.2834 0.5339 0.2789
15 0.342 1.1269 0.7849 0.9296 0.5876
16 0.544 1.8593 1.3153 0.8485 0.3045
17 1.982 1.2464 0.7355 1.3508 0.6311
18 2.587 1.8852 0.7017 2.3358 0.2511
19 2.663 1.4737 1.1892 2.2361 0.4268
20 1.813 1.0873 0.7256 1.4195 0.3934
21 1.875 1.566 0.3089 1.0361 0.8388
22 2.58 1.1906 1.3893 2.276 0.3039
23 1.477 1.4644 0.0125 1.6139 0.1369
Research Journal of Chemical Sciences ___________________________________________________________ ISSN 2231606XVol. 4(6), 1822, June (2014) Res. J. Chem. Sci. International Science Congress Association
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