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Файл:Ординатура / Хирургия / Библиотека им академика М.И. Перельмана / Книга_5865_Библиотеки_им_академика_М_И_Перельмана.pdf
Quantitative Structure-Activity/Property/Toxicity Relationships
Table 4. Polychlorinated biphenyls with identity number (ID) representing the substitution pattern
ID X
28 Cl H H H H H Cl Cl Cl H
29 Cl Cl Cl H H H Cl Cl H H
30 Cl H Cl Cl H H Cl Cl H H
31 Cl Cl Cl Cl H H Cl Cl H H
32 Cl H Cl H H Cl H Cl H H
33 Cl H Cl H Cl H Cl Cl Cl H
(Reprinted with permission from Roy et al., 2006c. Copyright © 2006, Springer Science+Business Media, Inc.).
2
X
3
X
4
X
5
X
6
X2’ X3’ X4’ X5’ X6’
Figure 3. Variation of charge transfer (ΔN) with (A) electrophilicity index (ω), (B) the experimental
biological activity (pIC
Observed versus calculated pIC
(ω) and N with guanine (For C, the regression model is: pIC
(Reprinted with permission from Roy et al., 2006c. Copyright © 2006, Springer Science+Business Media, Inc.).
) during interaction of polychlorinated dibenzo furan with guanine and (C)
50
values of polychlorinated dibenzo furan using electrophilicity index
50
= 4.033*ω − 13.596*ΔN − 5.954)
50
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Quantitative Structure-Activity/Property/Toxicity Relationships
Table 5. Experimental and calculated biological activity (pIC50) in gas phase for the training set of
polychlorinated dibenzofurans and polychlorinated biphenyls
ID ω (eV) ω
+
(eV) Observed pIC
max
50
Calculated pIC
1 2.763 0.888 4.061 3.800
2 2.699 0.720 3.429 3.583
3 3.101 0.883 4.125 4.947
4 3.180 0.893 4.103 5.216
5 3.366 1.306 6.123 5.847
6 3.425 1.007 4.653 6.047
7 3.331 1.156 5.396 5.728
8 3.486 1.488 6.858 6.254
9 3.659 1.551 7.255 6.841
10 3.748 1.551 7.379 7.144
11 3.769 1.633 7.657 7.215
12 3.785 1.824 8.444 7.269
13 3.952 1.717 8.194 7.836
14 3.967 1.683 7.911 7.887
15 4.005 1.756 8.147 8.016
16 4.046 1.902 8.943 8.155
17 4.263 1.612 7.587 8.892
18 4.293 1.756 8.376 8.994
19 3.828 1.621 7.610 7.415
20 3.642 1.57 7.379 6.784
21 3.657 1.718 7.954 6.835
22 3.989 1.629 7.657 7.962
23 3.988 1.629 7.657 7.958
24 4.010 1.575 7.313 8.033
25 3.657 1.718 7.954 6.835
26 3.989 1.621 7.623 7.962
27 3.988 1.622 7.623 7.958
28 3.109 0.476 5.584 4.975
29 3.329 0.432 6.134 5.721
30 3.424 0.440 5.762 6.044
31 3.597 0.461 6.057 6.631
32 2.866 0.318 4.442 4.150
33 3.112 0.406 4.577 4.985
(Reprinted with permission from Roy et al., 2006c. Copyright © 2006, Springer Science+Business Media, Inc.).
50
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147
Quantitative Structure-Activity/Property/Toxicity Relationships
Figure 4. (A) Calculated versus observed values of biological activity (pIC
(B) predicted versus observed values of biological activity (pIC
(Reprinted with permission from Roy et al., 2006c. Copyright © 2006, Springer Science+Business Media, Inc.).
) for test set of PCBs in gas phase
50
) for training set of PCDFs,
50
log(IGC
−1
) = −0.0703 ω + 0.7706 ω
50
N=97, R=0.898, R
2
= 0.804, R
adj
−
− 6.10
Omax
2
= 0.795, SD=0.290
CV
−05
EHF 0.6179 (36)
and that for the complete set of aromatic acceptors as
log(IGC
−1
) = 0.6612 ω − 1.2673 ω
50
N=77, R=0.902, R
2
= 0.811, R
adj
+
− 4.10
Cmax
2
= 0.800, SD=0.359
CV
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−05
EHF 1.1752 (37)
Quantitative Structure-Activity/Property/Toxicity Relationships
-1
Table 6. Experimental and calculated toxicity (log(IGC
)) values against T. pyriformis of the selected
50
chloroanilines at the B3LYP/6-311++G(d,p) method
System Log(IGC
Observed
2-CA -0.17 -0.09 -0.08
3-CA 0.22 -0.02 0.24
4-CA 0.05 0.01 0.04
2,5-C2A 0.58 0.72 -0.14
3,4-C2A 0.56 0.67 -0.11
3,5-C2A 0.71 0.75 -0.04
2,4,5-C3A 1.30 1.44 -0.14
2,3,4,5-C4A 1.96 1.83 0.13
2,3,5,6-C4A 1.76 1.66 0.10
a
The values from reference 179. bDifference between the experimental and calculated toxicity (log(IGC
CA indicates the position of chloro group and CxA indicates the presence of x number of chloro groups.
(Reprinted with permission from Padmanabhan et al., 2006a. Copyright © 2006, American Chemical Society).
The corresponding plots of exp. log(IGC
a
−1
) vs. calc. log(IGC
50
-1
) Residual
50
Calculated
-1
)) values. The number before
50
−1
) are displayed in Figure 6.
50
b
Three-parameter regression models based on global electrophilicity (ω), local philicity descriptor at
−
the O site (ω
) in case of phenol derivatives (aromatic donors) and at the C site (ω
O
max
of nitrobenzene and benzonitrile derivatives (aromatic acceptors) and total Hartree-Fock energy (E
+
C
max
) in cases
HF
could predict toxicity towards the T. pyriformis larger than 80% of cross-validation variance of data of
these systems.
)
Figure 5. A plot between experimental and calculated toxicity (log(IGC
-1
) values of the selected chlo-
50
roanilines against T. pyriformis
(Reprinted with permission from Padmanabhan et al., 2006a. Copyright © 2006, American Chemical Society).
149
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Quantitative Structure-Activity/Property/Toxicity Relationships
−1
Figure 6. Experimental versus calculated log (IGC
) values for aromatic donors (a) and for aromatic
50
acceptors (b)
(Reprinted with permission from Roy et al., 2006b. Copyright © 2006 WILEY-VCH Verlag GmbH & Co. KGaA, Weinheim).
4.6 QSPR Model for the Prediction of Lipophilic Behavior
of Polychlorinated Biphenyl Derivatives
Padmanabhan et al. (2006b) successfully developed effective QSPR model for the prediction of the
lipophilic behaviour (logK
derivatives by using semi-empirical AM1 method. The effectiveness of a QSPR model depends on the
accuracy in the selection of appropriate descriptors. CDFT based global descriptors like ω was employed
in addition to the other descriptors viz., energy of the lowest unoccupied molecular orbital (E
number of chlorine substituents (N
set of 100 PCB derivatives as dependent variable and these three descriptors along with their several
combinations as independent variables, the regression models were developed (see Table 7). It was found
that each descriptor individually could describe lipophilic behavior quite well (r
the three-parameter model based on the combination of the ω, E
This model was able to predict largest variation in data (r
squared correlation coefficient (r
between exp. logK
vs. calc. logKow for the training set of 100 PCBs and the data set of 133 PCBs. In
ow
both cases, the three-parameter regression models gave excellent correlation coefficient, r value of 0.956
for these cases. Therefore, it clearly showed the efficacy of these descriptors in explaining lipophilic
behavior of PCBs.
) of a set having large number (133) of polychlorinated biphenyl (PCB)
ow
) and
LUMO
). Assuming experimental logKow values (Patil, 1991) of the training
Cl
2
= 0.81-0.88); however
and NCl provided the best prediction.
LUMO
2
= 0.914) with leave-one-out cross-validated
2
= 0.909) having lowest SD value of 0.225. Figure 7 depicts the plots
cv
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Quantitative Structure-Activity/Property/Toxicity Relationships
Table 7. Regression models for logK
S.No. Regression Equation r
1 LogK
2 LogK
3 LogK
4 LogK
5 LogK
6 LogK
7 LogK
(Reprinted with permission from Padmanabhan et al., 2006b. Copyright © 2006, Elsevier).
= 4.617–75.219*E
ow
= 4.234 + 0.406*N
ow
= −2.446 + 81.693*ω 0.877 0.874 0.266
ow
= 4.262 + 0.243*NCl − 36.119*E
ow
= −10.523 + 91.620*E
ow
= −0.038 + 0.173*NCl + 51.015*ω 0.906 0.902 0.234
ow
= −5.967 + 0.115*NCl + 58.070*E
ow
using various descriptors for the training set of 100 PCB congeners
ow
2
LUMO
Cl
LUMO
+ 176.280*ω 0.905 0.901 0.235
LUMO
+ 121.280*ω 0.914 0.909 0.225
LUMO
0.811 0.806 0.330
0.842 0.838 0.302
0.892 0.888 0.251
2
r
cv
SD
4.7 QSPR Models to Predict the Enthalpy of Vaporization
of 209 Polychlorinated Biphenyl Congeners
The ω, E
tion (Δ
et al., 2007). A small data set containing only 17 PCBs, whose experimental Δ
(Puri et al., 2001) was considered in building regression models. In general, data set is used to divide
into training set and test set but here due to small number of data set, it was only considered as the training set. The different regression models based on the exp. Δ
individual ω, E
along with the obtained r
and NCl were further employed to model QSPR in order to predict the enthalpy of vaporiza-
LUMO
) of a set of 209 PCBs by using semi-empirical AM1 level of computations (Padmanabhan
vapHm
values were known,
vapHm
values as dependent parameters and
vapHm
and NCl desciptors or their different combinations as the independent parameters
LUMO
2
2
, r
and SD values are provided in Table 8.
cv
Figure 7. Experimental versus calculated values of logKow with electrophilicity, E
and NCl as de-
LUMO
scriptors for the model constructed using (a) the training set of 100 PCBs; (b) the data set of 133 PCBs
(Reprinted with permission from Padmanabhan et al., 2006b. Copyright © 2006, Elsevier).
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Quantitative Structure-Activity/Property/Toxicity Relationships
Table 8. Regression models for vaporization enthalpy (Δ
a
scriptors for the dataset of 17 PCBs
S.No. Regression Equation r
1 Δ
2 Δ
3 Δ
4 Δ
5 Δ
6 Δ
7 Δ
a
Experimental data as obtained from ref 184.
(Reprinted with permission from Padmanabhan et al., 2007. Copyright © 2007 WILEY-VCH Verlag GmbH & Co. KGaA, Weinheim).
= 73.8 – 936.5*E
vapHm
= 65.2 + 6.1*N
vapHm
= −27.5 + 1159.4*ω 0.921 0.904 3.9
vapHm
= 67.0 + 4.4*NCl − 309.0*E
vapHm
= 12 + 2.9*NCl + 654.9*ω 0.951 0.918 3.2
vapHm
= −151.0 + 2583.6*ω + 1240.4*E
vapHm
= −103.6 + 1.7*NCl + 994.7*E
vapHm
LUMO
Cl
LUMO
LUMO
+ 2009.0*ω 0.976 0.948 2.3
LUMO
The regression equation developed by each individual descriptor could predict Δ
(298.15 K) in kJ/mol) using various de-
vapHm
2
0.819 0.764 5.9
0.908 0.879 4.2
0.928 0.884 3.9
0.968 0.943 2.6
2
r
cv
with high r2
vapHm
SD
value (0.82-0.92); however the best correlation was provided by the three-parameter regression equation developed by ω, E
0.976) having leave-one-out (LOO) cross-validated squared correlation coefficient, r
This model could explain Δ
prescribed by Puri et al. (2001), where they obtained r
and NCl desciptors, which described the maximum variation in the data (r2 =
LUMO
value with higher efficiency (in terms of higher r
vapHm
2
value of 0.852. Another advantage of this model
cv
2
value of 0.948.
cv
2
) than that model
cv
was that it could provide excellent result even based on least expensive AM1 level of computations.
Now, another set of 27 PCDFs with their experimentally reported Δ
values (Nakajoh et al.,
vapHm
2006) were considered to further test this model. Here 22 PCBs were taken as training set and remaining
five PCBs as test set. It was found that the best regression equation in the previous set could describe
2
maximum variation in the data (r
2
coefficient, r
value of 0.856. It was also capable of predicting the Δ
cv
= 0.915) having leave-one-out cross-validated squared correlation
values of the test set within
vapHm
4.2 kJ/mol error bars. Now including these five test set candidates in the data set, for all 27 PCBs, this
model was able to represent the Δ
exp. Δ
vapHm
and calc. Δ
values for both the first set and the second set are given in Figure 12. In
vapHm
values with r2 = 0.858 and r
vapHm
2
= 0.758. The plots between the
cv
case of the first set, correlation coefficient (r) was 0.988 and in the second set it was 0.926. Based on
this model, the Δ
values for the remaining 183 PCB derivatives were predicted (see Figure 8).
vapHm
4.8 QSAR Model in Predicting Biological Activity of
Testosterone and Estrogen Derivatives
Parthasarathi et al. (2004b) further employed electrophilicity to assess its efficacy as a probable biological activity descriptor in modeling QSAR for testosterone and estrogen derivatives. The geometry
optimizations of all the systems were carried out at the semi-empirical AM1 level whereas the single
point energy calculations at the B3LYP/6-31G(d) level were performed with those geometries obtained
at the AM1 level. The biological activity of various testosterone derivatives was described by means of
relative binding affinity (RBA) (Saartok et al., 1984), androgenic potency (Counsell et al., 1962), relative
androgenic activity (Liao et al., 1973), therapeutic index (Sala & Baldratti, 1957), testosterone-binding
globulins (TeBG) binding affinity (Cramer et al., 1988), relative competition indices (Liao et al., 1973),
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Quantitative Structure-Activity/Property/Toxicity Relationships
Figure 8. Exp. vs. calc. Δ
values for the dataset of (a) 17 PCBs, (b) 27 PCBs, in the general set for
vapHm
(c) 193 PCBs and (d) 183 PCBs
(Reprinted with permission from Padmanabhan et al., 2007. Copyright © 2007 WILEY-VCH Verlag GmbH & Co. KGaA,
Weinheim).
binding affinity for rat ventral prostate receptor protein (Wolff, 1955) and myotrophic to androgenic
potency in temporal activity (Kochakian, 1976) (MAPT) whereas the same for estrogen derivatives was
measured in terms of RBA values (Gao et al., 1999). In Figure 13, some representative plots between
calculated ω and different biological activities of testosterone derivatives are presentated. In every case,
a linear correlation between them was found; however, the correlations of RBA, relative competition
indices and myotrophic to androgenic potency in temporal activity with ω were found to be better than
the others. The corresponding regression models used to generate the plots were inserted within the
2
Figures (see Figure 9). In all cases R
values were found to range from 0.61 to 0.90. It was also noted
that B3LYP result was better than that of AM1 in predicting biological activity.
Similarly, in case of estrogen derivatives ω could predict their biological activity towards RBA hav-
2
value in the range of 0.73 and 0.87. Therefore, it clearly showed that ω could predict biological
ing R
activity of these systems quite efficiently.
Roy et al. (2007) also showed that the number of atoms in a molecule (N
) along with ω values could
A
explain the biological activities of testosterone and estrogen derivatives reasonably well (R ≈ 0.8).
Pasha et al. (2005) and Singh et al. (2004) developed QSAR models based on frontier orbitals infor-
mation (E
HOMO
, E
) and different CDFT based descriptors like η, S, χ, μ as well as different energy
LUMO
descriptors like total energy, heat of formation, electronic energy, and the highest negatively charge on
an atom to predict different biological activities of testosterone and estrogen derivatives. They found
that the regression models made by η and different energy descriptors possess better predicitivity than
the other combinations. They argued that η represents the situation in these cases in a better way due
to the associated maximum hardness principle. Srivastava et al. (2005) further showed that some other
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153
Quantitative Structure-Activity/Property/Toxicity Relationships
Figure 9. Relationship between various biological activities of testosterone derivatives with electrophilicity index
(Reprinted with permission from Parthasarathi et al., 2004b. Copyright © 2004, Elsevier).
descriptors like IP and EA of an atom in a molecule, atomic softness could predict the biological activity of testosterone derivatives quite well, The differences in softness values of estrogen derivatives and
lysine, histidine, tyrosine and cysteine receptors were also found to be an effective parameter in describing
the biological activities of estrogen derivatives (Pasha et al., 2005). Rokhina & Suri (2012) further built
QSPR to represent the estrogenicity of the natural estrogen hormones. They found that the information
of frontier molecular orbital energies is very important to interpret this activity.
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Quantitative Structure-Activity/Property/Toxicity Relationships
5. GROUP PHILICITY TOWARDS MODELING QSAR
In addition to the conventional global electrophilicity Padmanabhan et al. (2006c) used group philicity
+
−
, ω
(ω
(CPs). The data set was made by 18 CPs, which was divided into training set having 16 CPs and test set
containing two CPs. Then various regression models were developed by these descriptors in order to
describe the experimental toxicity (log(1/EC
Daphnia magna taking log(1/EC
variables (see Table 9). It was found that two-parameter regression equation made by ω
capable of describing maximum variation data (88.7%) having large r
found to be the least for this model. This model was also found to predict the experimental log(1/EC
value for the test set within ± 0.05 error bar. Now, regression analyses were carried out for all 18 CPs
(data set) (see Table 9). In this set also, the combination of ω
tion data (88.9%) along with r
composed by ω, ω
this model was 0.812. The plots of calc. log(1/EC
which shows a correlation coefficient of 0.942 for the best model. Further, these descriptors were used to
explain the experimental toxicity of CPs against B. rerio, and Bacillus. It was found that in case against
B. rerio, three-parameter regression model (ω, ω
) computed at the B3LYP/6-31G(d) level to model QSAR for ecotoxicity of chlorophenols
g
g
)) (Devillers & Chambon, 1986) of the training set against
+
and ω
g
50
) as a dependent variable whereas ω, ω
50
2
cv
+
and ω
2
value of 0.838. The second best model was three-parameter regression
cv
−
, which could also predict 88.9% variation data, however, the r
g
) vs exp. Log(1/EC50) are given in Figure 10(A),
50
+
and ω
g
g
−
), being a very good model, could explain
g
g
+
and ω
g
−
as independent
g
value of 0.826. The SD was also
−
could explain maximum varia-
+
and ω
g
2
−
was
g
value for
cv
50
)
Table 9. Regression models for the toxicity [log(1/EC
)] against D. magna using various descriptors
50
for a training Set of 16 CPs and data set of 18 CPs
S.No. Regression Equation r
For training set
1 log(1/EC
2 log(1/EC
3 log(1/EC
4 log(1/EC
5 log(1/EC
6 log(1/EC
7 log(1/EC
) = –2.2 + 198.0*ω
50
) = –0.9+ 28.7*ω 0.731 0.671 0.259
50
) = −1.4 + 24.8*ω + 42.0*ω
50
) = –3.8 + 545.6*ω
50
) = –3.5 + 3.5*ω + 491.3*ω
50
) = −4.0 − 0.6*ω + 492.3*ω
50
) = −4.0 + 485.3*ω
50
For data set
1 log(1/EC
2 log(1/EC
3 log(1/EC
4 log(1/EC
5 log(1/EC
6 log(1/EC
7 log(1/EC
) = –2.2 + 196.2*ω
50
) = –0.8+ 28.4*ω 0.704 0.644 0.260
50
) = −1.6 + 22.4*ω + 65.5*ω
50
) = –3.8 + 545.5*ω
50
) = –3.6 + 3.3*ω + 496.4*ω
50
) = −3.9 − 0.6*ω + 477.3*ω
50
) = −4.0 + 484.6*ω
50
(Reprinted with permission from Padmanabhan et al., 2006c. Copyright © 2006, American Chemical Society).
−
g
+
g
+
+ 41.9*ω
g
−
g
+
g
+
+ 41.4*ω
g
−
g
+
g
+
+ 43.26*ω
g
−
g
−
g
+
g
+
+ 40.2*ω
g
−
g
−
g
−
g
2
2
r
cv
SD
0.515 0.374 0.348
0.741 0.630 0.264
0.874 0.835 0.177
0.877 0.802 0.178
0.887 0.792 0.182
0.887 0.826 0.175
0.529 0.409 0.326
0.731 0.634 0.254
0.876 0.842 0.167
0.879 0.817 0.171
0.889 0.812 0.170
0.889 0.838 0.164
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155