ORIGINAL RESEARCH
Common binding requirements of PPAR-a/d/c pan agonists:quantitative structure–activity relationship analysisof indanylacetic acid derivatives carrying 4-thiazolyl-phenoxytail group
Sarvesh Paliwal • Divya Yadav • Rakesh Yadav •
Vandana Kaushik • Shailendra Paliwal
Received: 13 August 2010 / Accepted: 5 February 2011
� Springer Science+Business Media, LLC 2011
Abstract A QSAR study on a series of indanylacetic acid
derivatives with activity against PPAR-a, d, and c was made
using combination of various physiochemical descriptors.
Several statistical regression expressions were obtained
using stepwise multiple linear regression (MLR) analysis
and partial least square (PLS) method. The highly predictive
and validated models generated through classical 2D
molecular descriptors provided deeper insights about the
binding of these small molecules to the human nuclear
receptor PPAR-a, d, and c. The results reveal that dipole
moment and number of hydrogen bond donors are important
descriptors in determining effective binding of PPAR ago-
nists to all three subtypes.
Keywords Antidiabetic � Pan agonists � 2D-QSAR �TSAR � Indanylacetic acid derivatives carrying
4-thiazolyl-phenoxy tail groups
Introduction
Type 2 diabetes (T2 DM) is a complex metabolic disorder
that affects between 6 and 20% of the population in western
industrialized societies (Lin and Sun, 2010). Type 2 diabetes
is characterized by hyperglycemia, insulin resistance, and
defects in insulin secretion and is usually associated with
dyslipidemia, hypertension, and obesity (Henke, 2004).
Initially, in type 2 diabetes, insulin-stimulated glucose
transport in skeletal muscle is impaired and as compensation,
pancreatic b cells display augmented secretion of insulin,
resulting in hyperinsulinemia (Takahashi et al., 1993).
Peripheral insulin resistance, in combination with impairment
in the early phase of insulin secretion, results in hyperglyce-
mia. In the end stage of type 2 diabetes, changes in insulin
signaling such as insulin’s inability to inhibit hepatic gluco-
neogenesis, along with deterioration of pancreatic b cell
function and b cell ‘‘exhaustion’’ occurs (Wang et al., 2004).
The peroxisome proliferator-activated receptors (PPARs)
are ligand-activated transcription factors belonging to the
nuclear receptor super family (Willson et al., 2000).
There are three PPAR subtypes encoded by distinct genes:
PPAR-a, PPAR-d, and PPAR-c. All three receptors are
important regulators in multiple physiological path-
ways, such as glucose homeostasis, fatty acid metabolism,
inflammation, and cellular differentiation (Kordik and Reitz,
1999; Lin et al., 1999, 2005). PPAR-a, which is activated by
polyunsaturated fatty acids and fibrates, is implicated in
regulation of lipid metabolism, lipoprotein synthesis, and
metabolism and inflammatory response in liver and other
tissues (Cheng et al., 2010). PPAR-a is highly expressed in
tissues with high fatty acid oxidation, in which it controls a
comprehensive set of genes that regulates most aspects of
lipid catabolism (Tenenbaum et al., 2005). PPAR-c exists as
two isoforms, c1 and c2, which are derived from the same
gene by alternative promoter splicing and differ only at
their N-terminal (Yanase et al., 1997). PPAR-c is mostly
expressed in adipose tissue, where it is essential in adipocyte
differentiation and controls fatty acid levels, increasing tri-
glyceride synthesis and storage within adipocytes (Rosen
et al., 1999). Activation of PPAR-c improves the condition
of insulin resistance, and therefore PPAR-c became a pri-
mary target in treatment of type 2 diabetes (Auwerx, 1999).
PPAR-d enhances fatty acid catabolism and energy
uncoupling in adipose tissue and muscle, and it suppresses
macrophage-derived inflammation. Its combined activities
S. Paliwal (&) � D. Yadav � R. Yadav � V. Kaushik � S. Paliwal
Department of Pharmacy, Banasthali University, Banasthali,
Rajasthan 304022, India
e-mail: [email protected]
123
Med Chem Res
DOI 10.1007/s00044-011-9599-z
MEDICINALCHEMISTRYRESEARCH
in these and other tissues make it a multifaceted therapeutic
target for the metabolic syndrome with the potential to
control weight gain, enhance physical endurance, improve
insulin sensitivity, and ameliorate atherosclerosis (Barish
et al., 2006). Thiazolidinediones (TZDs) are most impor-
tantly studied for drug discovery process and have been used
as PPARc agonists to treat diabetes mellitus. However, TZDs
has certain mechanism-based side effects associated with it
like weight gain, edema, and increased risk of myocardial
infraction etc. (Mudaliar and Hery, 2002).
Role of other PPAR subtypes i.e., a and d in controlling
type 2 diabetes has also been established. Earlier efforts to
develop hybrid molecules (PPAR a and c agonists) like
tesaglitazar, ragaglitazar, and muraglitazar have shown
promising results but were discontinued because of toxicity
problems (Balfour and Plosker, 1999; Fagerberg et al.,
2005). A pan-agonist, capable of stimulating the three
PPARs as a group, would be expected to be particularly
useful in the treatment of T2DM from the standpoints of
both efficacy and reduction in the additional risk factors
associated with polypharmacy (Artis et al., 2009).
Therefore, it will be beneficial to optimize this new class
of pan PPAR agonist using quantitative structure–activity
relationship (QSAR) modeling to identify the important
molecular properties for the effective binding of pan ago-
nist to PPAR-a, PPAR-d, and PPAR-c receptors.
There are numerous examples in the literature for the
successful use of classical descriptors in QSAR (Topliss,
1993; Hanch et al., 1963). In view of this, we decided to
develop models from classical QSAR descriptors using
MLRs and partial least square methods to establish the
individual and common structural requirement for effective
binding of agonists to all three PPAR subtypes.
The findings of this study will certainly aid in the design
of more potent PPAR pan agonists with improved activity
and reduced mechanism-based side effects of traditional
PPAR-c agonists.
Materials and methods
Generation of preliminary structures
The chemical structures of all the molecules of indanyl-
acetic acid derivatives carrying 4-thiazolyl-phenoxy tail
groups (Rudolph et al., 2007) were sketched and their
geometries were cleaned using stand-alone module of
Discovery Studio (version 2.0) and were loaded via.mol
files into the work sheet of TSAR. TSAR is an integrated
analysis package for the interactive investigation of
QSARs. Experimentally determined EC50 values of the
compounds were taken from studies reported in the liter-
ature and were converted into negative logarithm.
Defining substituents and three-dimensional optimized
structure building
The series had four major substituents (R1, R2, R3, and R4,
shown in Table 4) that were defined using ‘‘define sub-
stituents’’ option in the TSAR worksheet’s toolbar (version
3.3; Accelrys Inc., Oxford, England). In TSAR, molecular
structures are represented with a variety of descriptors and
the activity–descriptor relationship is computed by various
standard statistical tools such as multiple linear regression
(MLR), partial least squares (PLS), etc., and finally, the
output is displayed in the form of a model highlighting
substituent points that are strongly correlated with the
pharmacological properties under investigation. All the
loaded structures and their substituents were than con-
verted into high-quality three-dimensional (3D) molecular
structures using Cornia make 3D option. TSAR has in-built
program CORINA (Dalby et al., 1992), which was used to
convert all the molecular structures and substitutions to
their 3D structures. The 3D structure concept was devel-
oped by Hiller (Hendrickson et al., 1993). Since the 3D
structure of a molecule is closely related to a large variety
of chemical, physical, and biological properties, the CO-
RINA automatically generates 3D atomic co-ordinates
from the constitution of a molecule as expressed by a
connection table or linear string (Sadowski and Gasteiger,
1993).
Cosmic module was used to optimize the structure of
compounds. Cosmic calculates molecular energies by
summing bond length, bond angle, torsion angle, van der
Waals, and coulombic terms for all appropriate sets of
atoms (Wylie and Vinter, 1994). These calculations
involve the valence electrons of the atoms of the molecule.
These were later developed from semi-empirical molecular
orbital (MO) calculations. The calculations were termi-
nated when the energy difference or the energy gradient
were smaller than 1 9 10-5 and 1 9 10-10 kcal/mol,
respectively (Kovatcheva et al., 2003).
Calculation of descriptors and data reduction
The aim of calculating molecular descriptors is to provide
all the useful information about all the chemical structures
and respective substituents to build a good and predictive
QSAR model. TSAR can calculate up to 500 descriptors
(topological, geometrical, and electrostatic) derived from
the whole structures as well as substitution of the com-
pounds under consideration. Since the large pool of
descriptors was calculated, there is a significant require-
ment of data reduction to eliminate the chance correlation.
Correlation matrix was used to reduce the number of
descriptors and to identify the best subset of descriptors
with minimum intercorrelation.
Med Chem Res
123
Correlation coefficient describes the degree of linear
correlation between two variables (Paliwal et al., 2010b).
Pair-wise correlation coefficients were calculated for all the
pair of descriptors. If the intercorrelation coefficient [0.5
was detected, then the descriptor with high correlation with
biological activity was kept and others were discarded.
This is because the correlation coefficient values (which is
a measure of the fit of the regression model) closer to 1.0
represent the better fit of the model. Therefore, the
descriptor having correlation more closer to 1.0 was
selected out of two descriptors (Rameshwar et al., 2006). In
the next phase, the data reduction was performed on the
remaining descriptors on the basis of t value using back-
ward elimination technique. The stepwise regressions were
developed and the descriptors having lower t value were
discarded from the data set (Paliwal et al., 2010a).
Data set preparation and statistical analyses
The molecules of the series (Table 1; Rudolph et al., 2007)
were divided into training set and the test set. Training set
was used to build linear models so that an accurate rela-
tionship could be found between structures and biological
activity. The compounds of the test set were not included in
building up the model, instead they were set aside to check
the predictive power of the developed model. The training
set for PPAR-a agonists consisted of 52 compounds and the
test set of six compounds (17c, 17x, 34d, 34n, 34x, and
34ah). After a long procedure of data reduction, a set of
descriptors was obtained, which showed high correlation
with the biological activity and very less correlation among
themselves, which means they are independent of each
other. This set of parameters consist of four descriptors
which include inertia moment 2 length (whole molecule),
bond dipole moment (substituent 3), kier chi V6 (path)
index (whole molecule), and number of H-bond donor
(whole molecule). All the parameters obtained were taken
into confidence by the model developed showing their
major influence on the biological activity.
In the QSAR studies, the existence of outliers is fre-
quently observed. These outliers are fitted far apart from
the regression line meaning that their observed biological
activity is higher than the predicted one by the QSAR
technique, or may point toward experimental or even a
typographical error. It is also possible that these outliers act
by distinct mode of action (Furusjo et al., 2006). In this set
of molecules, four compounds namely 34m, 34ae, 34ai,
and 29h were detected as outliers because these compounds
were found not to fit to either the training or test set mol-
ecule. This fact was evident through their residuals value
which was more than two orders of magnitude, and also
when these three molecules were included in the training
set, the difference between r2 and rcv2 was greater than 0.4.
This was observed every time the data set was randomly
partitioned into training and test set molecules. Further-
more, the removal of these four molecules resulted in a best
model. All these observations proved that those molecules
were outliers and hence deleted.
For PPAR-d agonists, the training set consisted of 63
compounds and the test set of 10 compounds (17g, 17n,
17u, 29d, 34b, 34i, 34p, 34w, 34ad, and 34ak). More than
250 molecular descriptors were calculated depending on
the structural input. Data reduction was carried out in the
similar manner and a final set of least inter-correlated
parameters were obtained. This set of parameters consist of
six descriptors which included molecular mass (substituent
4), dipole moment X component (substituent 2), bond
dipole moment (substituent 3), total lipole (substituent 3),
lipole Y component (substituent 4), and number of H-bond
donor (substituent 3). All the parameters obtained were
taken into confidence by the model. Six outliers namely
17a, 17h, 29e, 29i, 34z, and 34ac were then detected and
deleted to obtain statistically significant results.
For PPAR-c agonists, 59 compounds were taken in the
training set. The test set consisted of eight compounds (17f,
17q, 29a, 34a, 34i, 34o, 34z, and 34ah). For this set also, more
than 250 molecular descriptors were calculated. Data
reduction was carried out, and finally a set of four descriptors
was obtained which included dipole moment Z component
(substituent 3), number of H-bond donor (whole molecule),
Vamp surface area (whole molecule), and Vamp polarization
XZ (whole molecule). All the parameters obtained were
taken into confidence by the model. Four outliers namely
17g, 34l, 34ad, and 34ai were detected and deleted.
Regression for all the three data sets was performed
using the two methods MLR and PLS. These were
employed to search for relationships between biological
activity data and the structure (Luco and Ferretti, 1997).
The MLR method calculates QSAR equations by per-
forming standard multivariable regression calculations
using multiple variables in a single equation (Besalu,
2007). When MLR is used, it is assumed that the variables
are independent (least inter-correlated with each other)
(Tarko and Ivanciuc, 2001). Values for F-to-enter and
F-to-leave were set to 4. The cross-validation analysis was
performed using the leave-one-out (LOO) method where
one compound is removed from the data set and its activity
is calculated using the model derived from the rest of
the data set. Statistical significance of the regression
equations were tested on the basis of conventional regres-
sion coefficient (r2) (Hawkins et al., 2003), Fischer’s ratio
(F) (Dessalew, 2008), and the standard error of estimate
(s) (Dessalew, 2009). The PLS regression method carries
out regression using the latent variables from the inde-
pendent and dependent data that are along their axes of the
greatest variation and are most highly correlated (Paliwal
Med Chem Res
123
Table 1 Structure and biological activities of indanylacetic acid analogs containing substituted phenyl tail groups against PPAR-a, PPAR-d, and
PPAR-c
O O
O
OHR1
R2
R3
R4
S. no. Compound no. R1 R2 R3 R4 hPPAR-aEC50
hPPAR-dEC50
hPPAR-cEC50
1. 17a H H H H – 44 –
2. 17b H H Et H 1980 8.9 6200
3. 17c H H CF3 H 1610 4.2 5580
4. 17d H H OCF3 H 6800 13 1020
5. 17e H H OMe H – 6.7 3000
6. 17f H H OEt H – 6.6 940
7. 17g H H CN H 5700 7.3 637
8. 17h H H Ph H – 43 –
9. 17i H Me H H – 56 8000
10. 17j H Me Me H – 67 –
11. 17k H OMe H H – 117 –
12. 17l Me H Me H 9360 4.6 2180
13. 17m n-Pr H H H – 2.4 1520
14. 17n n-Pr H CF3 H 1140 1.6 303
15. 17o n-Pr H CN H 3950 25 570
16. 17p n-Pr H OPh H – 1.9 –
17. 17q OMe H Me H – 7.5 1670
18. 17r OMe H Et H 700 2.7 1650
19. 17s OMe H CN H 7500 14 700
20. 17t Oet H Me H – 2.2 1240
21. 17u H HN N
NR3
H – 11 555
22. 17v H H N
N NR3
NHCOCH3 9200 367 5600
23. 17w H HN
N
NR3
Cl 6850 2230 8700
24. 17x H H N
N
S
R3
F3C Me 520 3.4 158
25. 29a H H S
R3
H – 5.9 650
Med Chem Res
123
Table 1 continued
S. no. Compound no. R1 R2 R3 R4 hPPAR-aEC50
hPPAR-dEC50
hPPAR-cEC50
26. 29b H H O
R3
H 3270 3.8 4100
27. 29c H H
NH R3
H 6600 1.5 740
28. 29d H H N
R3
H 1690 2.3 550
29. 29e H H N
R3
MeO
H – 43 485
30. 29f H H N
NR3
H 7280 11 970
31. 29g H HN
NR3
OMeMeO H – 5.6 300
32. 29h H H
N
R3
CH3 H 5260 22 550
33. 29i H H
N
R3
F3C H 2900 0.95 1800
34. 34a n-Pr H
N
S
R3
H 111 1.6 48
35. 34b OMe H
N
S
R3
H 255 0.58 374
36. 34c OMe H
N
S
R3
CH3 H 268 2.7 960
37. 34d H H
N
S
R3
Et H 1280 4.4 874
38. 34e n-Pr H
N
S
R3
Et H 147 11 45
39. 34f OMe H
N
S
R3
Et H 254 2.5 65
40. 34g n-Pr H
N
S
R3
t-Bu H 87 11 18
Med Chem Res
123
Table 1 continued
S. no. Compound no. R1 R2 R3 R4 hPPAR-aEC50
hPPAR-dEC50
hPPAR-cEC50
41. 34h n-Pr H
N
S
R3
F3C H 197 3.3 55
42. 34i OMe H
N
S
R3
F3C H 437 2.9 327
43. 34j H H
N
S
R3
CH3
CH3H 1250 2.9 280
44. 34k OMe H
N
S
R3
CH3
CH3H 83 4 33
45. 34l H H
N
S
R3
H 112 1.3 1200
46. 34m n-Pr H
N
S
R3
H 328 3.5 94
47. 34n OMe H
N
S
R3
H 53 4.1 116
48. 34o H H
N
S
R3
H 391 2.2 294
49. 34p n-Pr H
N
S
R3
H 43 1.9 12
50. 34q OMe H
N
S
R3
H 44 3.3 84
51. 34r n-Pr H
N
S
R3
O
H 101 4.0 42
52. 34s OMe H
N
S
R3
O
H 528 1.3 181
53. 34t OMe H
N
S
R3
H 526 5.2 214
Med Chem Res
123
Table 1 continued
S. no. Compound no. R1 R2 R3 R4 hPPAR-aEC50
hPPAR-dEC50
hPPAR-cEC50
54. 34u H H
N
S
R3
CH3
COCH3 H 2530 6.9 480
55. 34v n-Pr H
N
S
R3
CH3
COCH3 H 175 18 17
56. 34w OMe H
N
S
R3
CH3
COCH3 H 560 3.4 49
57. 34x n-Pr H
N
S
R3
CH3
CONMe2 H 140 4.4 20
58. 34y OMe H
N
S
R3
CH3
CONMe2 H 803 2.1 –
59. 34z h H
N
S
R3
CH3
COOHH 10000 218 312
60. 34aa n-Pr H
N
S
R3
CH3
COOH H 1690 46 238
61. 34ab OMe H
N
S
R3
CH3
COOH H 10000 28 1000
62. 34ac n-Pr H
N
S
R3
HOOC
HOH2C
H 6490 12 891
63. 34ad n-Pr H
N
S
R3
H
HOOCH2C
H 2480 4.6 27
64. 34ae H H
N
S
R3
H
MeO
H 5800 1.3 300
65. 34af OMe H
N
S
R3
H
MeO
H 755 3.0 69
Med Chem Res
123
et al., 2009). PLS regression can be used with more than
one dependent variable (Cramer, 1993). Under these con-
ditions, it generates robust QSAR equation to that of MLR
(Wold, 2001). Statistical significance of PLS equation was
evaluated on the basis of square of regression coefficient
(r2) and statistical significance value.
Result and discussion
PPAR-a
The multiple regression analysis technique was employed
for 52 compounds of training set using TSAR Version 3.3.
Regression equations were sought to relate the biological
activity to possible combinations of the parameters that are
inertia moment 2 length (whole molecule), bond dipole
moment (substituent 3), kier chi V6 (path) (whole mole-
cule), and number of H-bond donors (whole molecule).
The reliability of the statistical model was improved by
removing from the training set some compounds having
large residual values and acting as outliers in the model.
The best model is represented by the equation
Original equation (using MLR Method)
Y ¼ 0:30520052 � X1þ 0:11244157 � X2þ 0:58785892
� X3� 0:97143288 � X4� 4:2909122 ð1Þ
Standardized equation (using MLR Method)
Y ¼ 0:35752192 � S1þ 0:19632365 � S2þ 0:26657918
� S3� 0:41714776 � S4� 2:8677993 ð2Þ
Table 1 continued
S. no. Compound no. R1 R2 R3 R4 hPPAR-aEC50
hPPAR-dEC50
hPPAR-cEC50
66. 34ag H H
N
S
R3
H
EtO
H 100 1.2 753
67. 34ah n-Pr H
N
S
R3
H
EtO
H 105 2 41
68. 34ai OMe H
N
S
R3
H
EtO
H 1350 3.4 920
69. 34aj n-Pr H
N
S
R3
H
i-PrO
H 47 2.3 45
70. 34ak OMe H
N
S
R3
H
i-PrO
H 180 3 44
71. 34al n-Pr H
N
S
R3
Me
EtO
H 61 3.2 34
72. 34am OMe H
N
S
R3
Me
EtO
H 150 6 177
73. 34an OMe H
N
S
R3
Et
EtO
H 520 5.4 362
Med Chem Res
123
where X1 is inertia moment 2 length, X2 is bond dipole
moment, X3 is kier chi V6 (path), X4 is number of H-bond
donors, and Y is the biological activity.
The following results were obtained and analyzed.
r ¼ 0:9209; r2 ¼ 0:8481; r2cv ¼ 0:8134;
S ¼ 0:3094; F ¼ 60:0293
where ‘r’ is the relative measure of quality of fit of model.
Value of ‘r’ when 1.00 is considered to be perfect, and it
indicates that experimental results are reasonably well
(Paliwal et al., 2011). The value of ‘r’ indicates that the
model developed here is of good quality. r2 explains the
percent data represented by particular equation. The value
of r2 is 0.8481 which explains 84.8% variance in biological
activity. rcv2 (cross validated r2) is used as a diagnostic tool
to evaluate the predictive power of an equation. In the
model studied, the value of rcv2 is 0.8134 which shows that
the predictive power of the regression equation is excellent.
‘S’ is the standard error of the generated regression equa-
tion. A low value of S, that is 0.3094, shows a low possi-
bility of error in the regression. The F test reflects the ratio
of variance explained by the model and variance due to
error in the regression. A high value of F test, 60.0293,
indicates that the model is statistically significant (Prasad
et al., 2008).
The regression equation was also obtained by using the
PLS method. This analysis was carried out to cross validate
the results of MLR, as for a well-defined problem, both
MLR and PLS should have comparable results (Cramer,
1993). The results of the PLS as shown in Eq. 3 were also
evaluated on the basis of r2 and statistical significance of
the model.
Regression equation (using PLS Method)
Y ¼ 0:25293291 � X1þ 0:11146676 � X2þ 0:71208793
� X3� 0:95368648 � X4� 4:3017125 ð3Þ
Statistical significance = 0.9202, rcv2 = 0.8199, Fraction of
variance explained = 0.8444, E statistic = 0.7037.
The experimentally determined log EC50 values for the
selected training and test sets compounds and their pre-
dicted values along with their residuals are shown in
Tables 2 and 3. Four parameters entered in the final model
which showed high correlation with the biological activity
and least correlation are among them.
The inertia moment parameterizes the mass distribution
along an inertia axis of the molecule: the inclusion of this
parameter in the QSAR may point to a role for 3D charac-
teristics. Inertia moment 2 length indicates the strength and
orientation behaviors of molecule in electrostatic field. It is a
steric parameter showing its relation to the bulkiness of the
molecule. Regression equation shows that it is positively
correlated to the biological activity. Compounds like those
from 17s to 29c show an increase in the agonistic activity
with increase in the value of inertia moment 2 length.
Bond dipole moment uses the idea of electric dipole
moment to measure the polarity of a chemical bond within
a molecule. This vector can be physically interpreted as the
movement undergone by electrons when the two atoms are
placed at a distance ‘d’ apart and allowed to interact, the
electrons will move from their free-state positions to be
localized more around the more electronegative atom
(Karelson, 2000). Bond dipole moment at substituents 3 is
found to be positively correlated with the biological
activity which can be seen clearly in compounds like those
from 29b to 29h.
Kier Chi V6 is a topological parameter. Topological
parameters help us to differentiate the molecules according
mostly to their size, degree of branching, flexibility, and
overall shape. Kier chi V6 is a numeric descriptor from
molecular topology, which reflects the molecular identities,
bonding environment and the number of hydrogen bonds
(Hall et al., 1991). Through the data observed and studied,
it is shown that when the kier chi V6 value is increased, the
biological activity also increases and the vice versa.
The number of H-bond donor is related to the acidic
value of the molecule. A greater acidic value of a func-
tional group shows its high tendency of donating the
hydrogen. It is negatively correlated to the biological
activity. Compounds from 34ac to 34ag show that as the
hydrogen donating groups are introduced in the molecules,
their biological activity decreases.
PPAR-d
Regression equations were obtained for PPAR-d agonists
using the MLR and PLS methods. The equations showed
the relation between biological activity and possible com-
binations of the parameters such as molecular mass (sub-
stituent 4), dipole moment X component (substituent 2),
bond dipole moment (substituent 3), total dipole (sub-
stituent 3), dipole Y component (substituent 4), number
of H-bond donors (substituent 3), etc.. By the removal
of six outliers (17a, 17h, 29e, 34z, 34ac, and 29i) from
the training set, the reliability of the model developed
was improved. The best model is represented by the
Eqs. 4 and 5.
Original equation (regression equation)
Y ¼ �0:058438107 � X1þ 7:2915244 � X2
þ 0:13574496 � X3þ 0:18539773 � X4
� 9:9617958 � X5� 0:68246806 � X6
� 0:65587264 ð4Þ
Standardized equation (regression equation)
Med Chem Res
123
Table 2 Actual versus
predicted activity and
corresponding residuals for the
training set of PPAR-a agonist
Compound no. Actual activity Predicted activity Residual value
MLR PLS MLR PLS
17b -3.2966 -3.4594 -3.4529 0.16273 0.15627
17d -3.8325 -3.6803 -3.6809 -0.15222 -0.15156
17g -3.7559 -3.9922 -3.9932 0.23632 0.23733
17l -3.9713 -3.4463 -3.4382 -0.52502 -0.53312
17n -3.0569 -2.8052 -2.8879 -0.25166 -0.16897
17o -3.5966 -3.2202 -3.2872 -0.37636 -0.30935
17r -2.8451 -3.1198 -3.146 0.27468 0.30094
17s -3.8951 -3.6426 -3.6828 -0.25248 -0.21228
17v -3.9638 -4.0471 -4.0882 0.083345 0.12442
17w -3.8357 -3.886 -3.8722 0.050336 0.036523
29b -3.5145 -3.4632 -3.4376 -0.05134 -0.076891
29c -3.8195 -3.5954 -3.5364 -0.22415 -0.28314
29d -3.2279 -3.5048 -3.4836 0.27695 0.2557
29f -3.8621 -3.7083 -3.679 -0.15388 -0.18315
29i -3.4624 -3.2676 -3.2582 -0.19481 -0.20421
34a -2.0453 -2.4079 -2.4458 0.36254 0.40043
34b -2.4065 -2.7544 -2.7854 0.34783 0.37882
34c -2.4281 -2.6301 -2.6541 0.202 0.22601
34e -2.1673 -2.0781 -2.1025 -0.089187 -0.064855
34f -2.4048 -2.4342 -2.4496 0.029323 0.044774
34g -1.9395 -1.6441 -1.6885 -0.29542 -0.25101
34h -2.2945 -2.0748 -2.1426 -0.21971 -0.15192
34i -2.6405 -2.5319 -2.5739 -0.10855 -0.066631
34j -3.0969 -2.8979 -2.8643 -0.19901 -0.23261
34k -1.9191 -2.4769 -2.4874 0.55786 0.56835
34l -2.0492 -2.5651 -2.4535 0.5159 0.4043
34o -2.5922 -2.4532 -2.3173 -0.13902 -0.27486
34p -1.6335 -1.5412 -1.4812 -0.092238 -0.15231
34q -1.6435 -1.9862 -1.9017 0.34271 0.25823
34r -2.0043 -1.6823 -1.6641 -0.32202 -0.34024
34s -2.5159 -2.1455 -2.0995 -0.37034 -0.4164
34t -2.721 -2.3434 -2.3116 -0.37762 -0.4094
34u -3.4031 -3.2894 -3.2349 -0.11371 -0.16824
34v -2.243 -2.4187 -2.4332 0.1757 0.19014
34w -2.7482 -2.7629 -2.7703 0.014751 0.022144
34y -2.9047 -2.5846 -2.5971 -0.32015 -0.30766
34z -4 -4.1781 -4.1154 0.17813 0.1154
34aa -3.2279 -3.2818 -3.2922 0.053938 0.064345
34ab -4 -3.7427 -3.7258 -0.25727 -0.27423
34ac -3.8122 -4.07 -4.072 0.25777 0.25978
34ad -3.3945 -3.0449 -3.0761 -0.34953 -0.31833
34af -2.8779 -2.6273 -2.6678 -0.25069 -0.21019
34ag -2 -2.6119 -2.6662 0.61187 0.66619
34aj -1.6721 -2.0251 -2.0861 0.35299 0.414
34ak -2.2553 -2.3667 -2.4214 0.11141 0.16608
34al -1.7853 -2.1771 -2.197 0.39173 0.41166
34am -2.1761 -2.6862 -2.671 0.51015 0.49487
34an -2.716 -2.3014 -2.2808 -0.41458 -0.43517
Med Chem Res
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Y ¼ �0:52054793 � S1þ 0:28532961 � S2
þ 0:22632501 � S3þ 0:1459609 � S4
� 0:28188571 � S5� 0:15374762 � S6
� 0:78155333 ð5Þ
where X1 is molecular mass, X2 is dipole moment X com-
ponent, X3 is bond dipole moment, X4 is total dipole, X5 is
dipole Y component, X6 is number of H-bond donors, and
Y is the biological activity.
The following statistical results were obtained and
analyzed.
r ¼ 0:9156; r2 ¼ 0:8383; r2cv ¼ 0:8064;
S ¼ 0:2612; F ¼ 43:2059
A higher value of r, 0.9156 was obtained. The value of
r2 is 0.8383 which shows 83.8% variance in biological
activity. The value of rcv2 is 0.8064 which shows that the
predictive power of the regression equation is quite good.
A low value of S, that is 0.2612, shows low possibility of
error and better accuracy in the regression. A high value of
F test, 43.2059, indicates that the model is statistically
significant.
The regression equation was also obtained by using the
PLS method.
Regression equation (using the PLS Method)
Y ¼ �0:05543635 � X1þ 7:0303268 � X2
þ 0:14978759 � X3þ 0:22127286 � X4
� 0:97033253 � X5� 0:66681492 � X6
� 0:69186704 ð6Þ
Statistical significance = 1.0397, rcv2 = 0.7973, Frac-
tion of variance explained = 0.8352, E statistic = 0.6561.
The experimentally determined log EC50 values for the
selected training and the test sets of compounds and their
predicted values with their residuals are shown in Tables 4
and 5.
Six parameters entered the final model and their corre-
lation with the biological activity and among themselves
were evaluated.
Molecular mass (substituent 4) is a steric parameter that
is related to the bulkiness of the molecule. From the
regression equation, it can be observed that it is negatively
correlated, that is, an increase or decrease in the molecular
mass of substituent 4 would have an inversely proportional
relation with the biological activity, which is clearly pro-
ven when compounds 17v and 17w are compared.
The dipole moment X component and the bond dipole
moment are 3D electronic descriptor that indicates the
strength and orientation behavior of a molecule in an
electrostatic field. It is estimated by utilizing partial atomic
charges and atomic coordinates. A dipole moment that is
incorrectly oriented could well result in a drop in activity
(Todeschini and Consonni, 2000). Both the parameters are
positively related to the biological activity; hence, dipole
moment at substituents 2 and bond dipole moment at
substituents 3 would have a positive effect on the biolog-
ical activity of the molecules.
Total dipole (substituent 3) and dipole Y component
(substituent 4) are the measure of lipophilic distribution of
the molecule (Paliwal et al., 2010c). Total dipole substit-
uents 3 is positively correlated with the biological activity
and dipole Y component substituents 4 is negatively cor-
related with the biological activity. Therefore, they will be
showing their respective effects on activity with alteration
in their values.
The number of H-bond donor would have the same
effect as in the case of PPAR-a. Only difference here is that
the substituent 3 would be showing the effect on change.
Therefore, introduction of a more acidic group would have
a negative effect on the biological activity.
PPAR-c
Regression equation obtained shows a relation between
biological activity and the descriptors left after data
reduction, dipole moment Z component (Substituent 3), the
number of H-bond donors (whole molecule), Vamp surface
area (whole molecule), and Vamp polarization XZ (whole
molecule). Four outliers, 17g, 34l, 34ad, and 34ai were
detected and deleted.
Original equation (regression equation)
Y ¼ 0:31012508 � X1� 0:63417757 � X2þ 0:0130607
� X3þ 0:074484117 � X4� 8:3246679 ð7Þ
Standardized equation (regression equation)
Y ¼ 0:10927414 � S1� 0:2338963 � S2þ 0:68478632
� S3þ 0:17914371 � S4� 2:5183637 ð8Þ
where X1 is the dipole moment Z component, X2 is the
number of H-bond donors, X3 is the Vamp surface area,
Table 3 Actual versus predicted activity and corresponding residuals
for the test set of PPAR-a agonist
Compound Actual activity Predicted activity
MLR PLS
17c -3.2068 -3.7365 -3.7302
17x -2.716 -3.0675 -3.0724
34d -3.1072 -3.2085 -3.1215
34n -1.7243 -2.1303 -2.0646
34x -2.1461 -2.2263 -2.2484
34ah -2.0212 -2.123 -2.1785
Med Chem Res
123
Table 4 Actual versus predicted activity and corresponding residuals for the training set of PPAR-d agonist
Compound no. Actual activity Predicted activity Residual value
MLR PLS MLR PLS
17b -0.94939 -0.55694 -0.53557 -0.39245 -0.41382
17c -0.62325 -0.90167 -0.90886 0.27842 0.28561
17d -1.1139 -0.8312 -0.83201 -0.28274 -0.28193
17e -0.82607 -0.67325 -0.65986 -0.15282 -0.16621
17f -0.81954 -0.59792 -0.57978 -0.22162 -0.23976
17i -1.7482 -1.7395 -1.9557 -0.0086714 -0.20753
17j -1.8261 -1.7339 -1.9627 -0.092163 0.13659
17k -2.0682 -2.1468 -2.4498 0.078638 0.38166
17l -0.66275 -0.55996 -0.53871 -0.10279 -0.12404
17m -0.38021 -0.65085 -0.63531 0.27063 0.2551
17o -1.3979 -1.1618 -1.1936 -0.23613 -0.20433
17p -0.27875 -0.68679 -0.67666 0.40804 0.39791
17q -0.87506 -0.55997 -0.53872 -0.31509 -0.33634
17r -0.43136 -0.55657 -0.53518 0.12521 0.10381
17s -1.1461 -1.1612 -1.193 0.015116 0.046864
17t -0.34242 -0.5606 -0.53938 0.21818 0.19696
17v -2.5647 -2.5969 -2.4256 0.032286 -0.13909
17w -3.3483 -3.3107 -2.4832 -0.03759 -0.86507
17x -0.53147 -0.49244 -1.2056 -0.039033 0.67417
29a -0.77085 -0.765 -0.76067 -0.005851 -0.010185
29b -0.57978 -0.59984 -0.58152 0.020062 0.001745
29c -0.17609 -0.39115 -0.36401 0.21506 0.18792
29f -1.0414 -0.74575 -0.74709 -0.29564 -0.2943
29g -0.74818 -0.45782 -0.44025 -0.29036 -0.30793
29h -0.34242 -0.18798 -0.14431 -0.15444 -0.19811
34a -0.20412 -0.67082 -0.65934 0.4667 0.45522
34c -0.43136 -0.50242 -0.48158 0.071059 0.050222
34d -0.64345 -0.48298 -0.4612 -0.16047 -0.18225
34e -1.0414 -0.4823 -0.46052 -0.55909 -0.58087
34f -0.39794 -0.48268 -0.46089 0.084743 0.062945
34g -1.0414 -0.42059 -0.3957 -0.6208 -0.64569
34h -0.51851 -0.81038 -0.81285 0.29186 0.29434
34j -0.46239 -0.56736 -0.54838 0.10497 0.08599
34k -0.60205 -0.56801 -0.54907 -0.034036 -0.052977
34l -0.11394 -0.55787 -0.53789 0.44393 0.42395
34m -0.54403 -0.55787 -0.53678 0.012741 0.0072446
34n -0.61278 -0.55676 -0.53678 -0.056026 -0.076007
34o -0.34242 -0.5735 -0.55464 0.23108 0.21222
34q -0.51851 -0.57341 -0.55456 0.054892 0.036042
34r -0.60206 -0.39293 -0.36416 -0.20913 -0.2379
34s -0.11394 -0.39086 -0.36191 0.27692 0.24797
34t -0.716 -0.59797 -0.58404 -0.11803 -0.13194
34u -0.83885 -0.95504 -0.97929 0.11619 0.14044
34v -1.2553 -0.95888 -0.98344 -0.29639 -0.27183
34x -0.64345 -0.80329 -0.81668 0.15984 0.17323
34y -0.32222 -0.80512 -0.81862 0.4829 0.4964
34aa -1.6628 -1.4473 -1.5116 -0.21542 -0.15119
Med Chem Res
123
X4 is the Vamp Polarization XZ, and Y is the biological
activity.
The following parameters were calculated from the
equation and the results were evaluated.
r ¼ 0:9277; r2 ¼ 0:8607; r2cv ¼ 0:8132;
S ¼ 0:2908; F ¼ 77:2111:
The regression equation gave the value of r (correlation
coefficient) as 0.9277. The value of r2 is 0.8607 which
shows 86% variance in biological activity. The value of rcv2
is 0.8132 which shows that the predictive power of the
regression equation is quite good. A low value of S, that is
0.2908, shows low possibility of error in the regression. A
high value of F test, 77.2111, indicates that the model is
statistically significant.
The regression equation was also obtained by using the
PLS method.
Regression equation (using the PLS Method)
Y ¼ 0:28956234 � X1� 0:65458554 � X2
þ 0:013025655 � X3þ 0:074447379 � X4
� 8:2845993 ð9Þ
Statistical significance = 0.9406, rcv2 = 0.8428, Frac-
tion of variance explained = 0.8605, E statistic = 0.7186.
The experimentally determined log EC50 values for the
selected training and the test sets of compounds and their
predicted values with their residuals are shown in Tables 6
and 7.
The four parameters highly correlated with the biolog-
ical activity were used to generate equation and analyzed
for their relative impacts on the activity of the molecules.
The dipole moment Z component (substituent 3) here
again indicates the strength and orientation behavior of a
molecule in an electrostatic field. It is estimated by utiliz-
ing partial atomic charges and atomic coordinates. Studies
show that as the dipole moment Z component value of
compounds (like from 34u to 34ab) is increased, the bio-
logical activity also increases as it is positively correlated
to biological activity.
The number of H-bond donor (whole molecule) again is
negatively correlated to the biological activity. Compounds
from 34aa to 34ac show a decrease in the biological
activity when the number of H-bond donor increases.
Vamp polarization XZ is an optional semi-empirical
molecular orbital package used to calculate electrostatics
properties and perform structure optimizing. The polariz-
ability is the conversion factor between an applied electric
field and the induced dipole moment. The static polariz-
ability is directly proportional to the total number of
valence electrons in the molecule, or in other words,
polarizability is linearly related to the number of valence
electrons on each atom (Cronin and Schultz, 2001). Vamp
surface area and Vamp polarization XZ both are positively
correlated to the biological activity as shown by the
regression equation. Therefore, there will be an increase in
Table 5 Actual activity versus predicted activity and corresponding
residuals for the test set of PPAR-d agonist
Compound no. Actual activity Predicted activity
MLR PLS
17g -0.86332 -1.1618 -1.2387
17n -0.20412 -0.90154 -0.95381
17u -1.0414 -0.89949 -0.94194
29d -0.36173 -0.56728 -0.56392
34b -0.23657 -0.67103 -0.69448
34i -0.4624 -0.81174 -0.84683
34p -0.27875 -0.57333 -0.58348
34w -0.53148 -0.95824 -0.98917
34ad -0.66276 -1.0995 -1.0438
34ak -0.47712 -0.45558 -0.42287
Table 4 continued
Compound no. Actual activity Predicted activity Residual value
MLR PLS MLR PLS
34ab -1.4471 -1.4475 -1.5117 0.00035476 0.06454
34ae -0.11394 -0.47377 -0.45957 0.35983 0.34565
34af -0.47712 -0.47373 -0.45955 -0.0033863 -0.01757
34ag -0.079181 -0.4441 -0.42759 0.36492 0.34841
34ah -0.30103 -0.44462 -0.42817 0.14359 0.12714
34ai -0.53148 -0.44411 -0.42716 -0.087365 -0.10386
34aj -0.36173 -0.45609 -0.44043 0.094367 0.078703
34al -0.50515 -0.58756 -0.57727 0.082407 0.072123
34am -0.77815 -0.58818 -0.578 -0.18998 -0.20016
34an -0.73239 -0.40494 -0.38516 -0.32746 -0.35083
Med Chem Res
123
Table 6 Actual versus predicted activity and corresponding residuals for the training set of PPAR-c agonist
Compound no. Actual value Predicted activity Residual value
MLR PLS MLR PLS
17b -3.79 -3.4102 -3.4091 -0.37976 -0.38086
17c -3.74 -3.5408 -3.5357 -0.19923 -0.20431
17d -3 -3.4229 -3.4183 0.42292 0.4183
17e -3.47 -3.5156 -3.5082 0.045601 0.038234
17i -3.9 -3.7625 -3.7569 -0.13747 -0.14307
17l -3.33 -3.5292 -3.5238 0.1992 0.19377
17m -3.18 -3.1832 -3.179 0.0031633 0.00096893
17n -2.48 -2.7074 -2.7044 0.22744 0.22437
17o -2.75 -2.8967 -2.8931 0.14674 0.14309
17r -3.21 -3.0598 -3.0593 -0.15015 -0.15075
17s -2.84 -3.3223 -3.3235 0.48232 0.48353
17t -3.09 -3.0912 -3.0869 0.0012481 -0.0030615
17u -2.74 -3.1462 -3.1444 0.40615 0.40435
17v -3.74 -3.5673 -3.596 -0.17268 -0.14397
17w -3.93 -3.5156 -3.4958 -0.41437 -0.4342
17x -2.19 -2.4083 -2.4013 0.21833 0.21132
29b -3.61 -3.3653 -3.3599 -0.24472 -0.25008
29c -2.86 -2.8372 -2.8506 -0.022825 -0.0093758
29d -2.74 -2.7699 -2.7674 0.029855 0.027359
29e -2.68 -2.6038 -2.6298 -0.076242 -0.050163
29f -2.98 -3.4136 -3.413 0.43359 0.43299
29g -2.47 -2.621 -2.6217 0.15104 0.15174
29h -2.74 -2.7777 -2.7673 0.037665 0.027294
29i -3.25 -2.8786 -2.8739 -0.37138 -0.37614
34b -2.57 -2.7544 -2.7539 0.18439 0.1839
34c -2.98 -2.4904 -2.4874 -0.4896 -0.49258
34d -2.94 -2.4951 -2.4894 -0.44492 -0.45062
34e -1.65 -1.7173 -1.7217 0.067252 0.071656
34f -1.81 -1.9271 -1.9316 0.1171 0.12157
34g -1.25 -1.077 -1.0891 -0.17302 -0.16091
34h -1.74 -1.8942 -1.8939 0.15418 0.15392
34j -2.44 -2.4405 -2.4392 0.00048579 0.00081038
34k -1.51 -2.0166 -2.0175 0.50655 0.50753
34m -1.97 -1.74 -1.7428 -0.22996 -0.22724
34n -2.06 -1.9909 -1.993 -0.069116 -0.067039
34p -1.07 -1.5473 -1.5478 0.47729 0.47783
34q -1.92 -1.7632 -1.7637 -0.15684 -0.15629
34r -1.62 -1.6756 -1.6802 0.055553 0.060185
34s -2.25 -1.1859 -1.862 -0.39102 -0.38802
34t -2.33 -1.93 -1.9304 -0.39996 -0.39958
34u -2.68 -2.2285 -2.2272 -0.45147 -0.45279
34v -1.23 -1.6044 -1.6039 0.37436 -0.37389
34w -1.69 -1.7993 -1.7968 0.10935 0.1068
34x -1.3 -1.4551 -1.4415 0.15505 0.14147
34aa -2.37 -2.3107 -2.3306 -0.05933 -0.039445
34ab -3 -2.8436 -2.8653 -0.15644 -0.13467
34ac -2.94 -3.1456 -3.1714 0.20564 0.23145
Med Chem Res
123
the biological activity as the value of Vamp parameters in
increased.
Indanylacetic acid is found to have activity against all
the three PPAR receptors, namely, a, d, and c which means
that it has certain common binding properties with all the
three receptors.
In the case of all the three receptors, we find that dipole
moment and a number of H-bond donor properties are
common and this produces an effect on the biological
activity whereas all the other descriptors are different in all
the three cases. This shows that despite the close structural
relationship between these three receptor subtypes, all the
parameters related to biological activity were not the same
because of the subtle differences between residues lining
the ligand-binding pockets of the receptors.
PPAR ligands establish several hydrophobic interactions
with the three arms of the Y-shaped ligand-binding
domain. The three arms are arm-I, arm-II, and arm-III. The
hydrophilic head group of the PPAR agonist interacts with
arm-I; the hydrophobic tail of the PPAR ligand is either
buried in the arm-II or in arm-III; and Arm-III is the
entrance region of the pocket (Markt et al., 2007). PPAR-ahas the largest and the most hydrophobic pocket, followed
by PPAR-c and PPAR-d (Ebdrup et al., 2003). The
descriptors entering in all the three equations clearly sup-
ports the fact that lipophilic and hydrophilic interactions
are important for eliciting the PPAR-a, d, and c agonistic
response. Earlier findings suggest that hydrogen bonding
substantially contributes in establishing interaction of all
the three PPAR subtypes with its agonists (Zoete et al.,
2007). However, in our studies the number of H-bond
donor is negatively related to the activity. This clearly
suggests that if higher numbers of H-bond donating groups
are present, then they may form bonding with amino acids
or proteins around the entry point of the pocket (arm III)
which will restrict the entry of the agonist into the pocket.
Therefore, we conclude that an optimum number of
H-bond donors are necessary for binding of the agonists to
the pockets of all the three subtypes.
Conclusion
The QSAR study was successfully carried out on a series of
compounds acting as pan (PPAR-a, d, and c) agonists.
Fifty-eight molecules of indanylacetic acid analogs con-
taining substituted phenyl tail groups showed activity
against PPAR-a receptor, 73 molecules against PPAR-dreceptor, and 63 molecules against PPAR-c agonist. A
significant model possessing a good correlative and pre-
dictive ability was developed. The rcv2 value for the model
developed against PPAR—a, d, and c receptors are
0.8134, 0.8064, and 0.8132, respectively. A tetra-para-
metric regression equation was obtained using the MLR
and PLS methods for PPAR-a. A hexa-parametric regres-
sion equation was obtained using the MLR and PLS
methods for PPAR-d. A tetra-parametric regression equa-
tion was again obtained for PPAR-c agonists. These
equations show the strong relation between the biological
activity and the descriptors entering the model.
Table 6 continued
Compound no. Actual value Predicted activity Residual value
MLR PLS MLR PLS
34ae -2.47 -2.6336 -2.6309 0.16362 0.16087
34af -1.83 -2.2933 -2.2912 0.46334 0.46123
34ag -2.87 -2.3678 -2.3663 -0.50222 -0.50373
34aj -1.65 -1.47 -1.4649 -0.18003 -0.18508
34ak -1.64 -2.0483 -2.0418 0.40826 0.40177
34al -1.53 -1.5143 -1.5098 -0.015655 -0.020192
34am -2.24 -1.9844 -1.9835 -0.25562 -0.2565
34an -2.25 -2.1464 -2.122 0.10363 -0.12798
Table 7 Actual activity versus predicted activity and corresponding
residuals for the test set of PPAR-c agonist
Compound
no.
Actual
activity
Predicted activity
MLR PLS
17f -2.97 -3.3719 -3.3623
17q -3.22 -3.5077 -3.5023
29a -2.81 -3.3178 -3.3215
34a -1.68 -2.5052 -2.5021
34i -2.51 -2.1185 -2.1156
34o -2.46 -2.1565 -2.156
34z -2.49 -3.1016 -3.1202
34ah -1.61 -1.6496 -1.6495
Med Chem Res
123
Acknowledgment The computational resources were provided by
Banasthali University, and the authors thank the Vice Chancellor, for
extending the necessary facilities.
Conflict of interest The authors report no conflict of interest. The
authors alone are responsible for the contents and writing of the paper.
References
Artis DR, Lin JJ, Zhang C, Wang W, Mehra U, Perreault M, Erbe D,
Krupka HI, England BP, Arnold J (2009) Scaffold-based
discovery of indeglitazar, a PPAR pan-active anti-diabetic agent.
Proc Natl Acad Sci USA 106:262–267
Auwerx J (1999) PPAR c, the ultimate thrifty gene. Diabetologia 42:
1033–1049
Balfour B, Plosker GL (1999) Rosiglitazone. Drugs 57:921–930
Barish GD, Narkar VA, Evans RM (2006) PPAR d: a dagger in the
heart of the metabolic syndrome. J Clin Investig 116:590–597
Besalu E (2007) Trends and plot methods in MLR studies. J Chem Inf
Model 47:751–760
Cheng CF, Chen HH, Lin H (2010) Role of PPARa and its agonist in
renal diseases. PPAR Res. doi:10.1155/2010/345098
Cramer RD (1993) Partial Least Square (PLS): its strength and
limitations. Perspect Drug Discov Des 1:269–278
Cronin MTD, Schultz TW (2001) Development of quantitative
structure–activity relationships for the toxicity of aromatic
compounds to Tetrahymena pyriformis: comparative assessment
of the methodologies. Chem Res Toxicol 14:1284–1295
Dalby A, Nourse JG, Hounshell WD, Gushurst AKI, Grier DL,
Leland BA, Laufer J (1992) Description of several chemical
structure file formats used by computer programs developed at
Molecular Design Limited. J Chem Inf Comput Sci 32:244–255
Dessalew N (2008) QSAR study on piperidine carboxamides as
antiretroviral agents: an insight into the structural basis for HIV
coreceptor antagonist activity. QSAR Comb Sci 27:901–912
Dessalew N (2009) Investigation of the structural requirement for
inhibiting HIV integrase: QSAR study. Acta Pharm 59:31–43
Ebdrup S, Petterson I, Rasmussen HB, Deussen HJ, Frost JA,
Mortensen SB, Fleckner J, Pridal L, Nygaard L, Sauerberg P
(2003) Synthesis and biological and structural characterization of
the dual acting peroxisome proliferator-activated receptor a/cagonist Ragaglitazar. J Med Chem 46:1306–1317
Fagerberg B, Edwards S, Halmos T, Lopatynski J, Schuster H (2005)
Tesaglitazar, a novel dual peroxisome proliferator-activated
receptor alpha/gamma agonist, dose-dependently improves the
metabolic abnormalities associated with insulin resistance in a
non-diabetic population. Diabetologia 48:1716–1725
Furusjo E, Svenson A, Rahmberg M, Andersson M (2006) The
importance of outlier detection and training set selection for
reliable environmental QSAR predictions. Chemosphere 63:
99–108
Hall LH, Mohney BK, Kier LB (1991) The electrotopological state:
an atom index for QSAR. Quant Struct Act Relat 10:43–51
Hanch C, Muir RM, Fujita T, Maloney P, Geiger E, Streich M (1963)
Correlation of biological activity of plant growth regulators and
chloromycetin derivatives with Hammett constants and partition
coefficient. J Am Chem Soc 85:2817–2824
Hawkins DM, Basak SC, Mills D (2003) Assessing model fit by cross-
validation. J Chem Inf Comput Sci 43:579–586
Hendrickson MA, Nicklaus MC, Milne GWA, Zaharevitz D (1993)
D.CONCORD and CAMBRIDGE: comparison of computer-
generated chemical structures with X-ray crystallographic data.
J Chem Inf Comput Sci 33:155
Henke BR (2004) Peroxisome proliferators-activated receptor a/cdual agonists for the treatment of type 2 diabetes. J Med Chem
47:4118–4127
Karelson M (2000) Molecular descriptors in QSAR/QSPR. Wiley
Interscience, New York, pp 220–221
Kordik CP, Reitz AB (1999) Pharmacological treatment of obesity:
therapeutic strategies. J Med Chem 42:181–201
Kovatcheva A, Buchbauer G, Golbraikh A, Wolschann P (2003)
QSAR modeling of r-campholenic derivatives with sandalwood
odor. J Chem Inf Comput Sci 43:259–266
Lin Y, Sun Z (2010) Current views on type 2 diabetes. J Endocrinol
204:1–11. doi:10.1677/JOE-09-0260
Lin Q, Ruuska SE, Shaw NS, Dong D, Noy N (1999) Ligand
selectivity of the peroxisome proliferator-activated receptor a.
Biochemistry 38:185–190
Liu K, Xu L, Berger JP, MacNaul KL, Zhou G, Doebber TWM,
Forrest J (2005) Discovery of a novel series of peroxisome
proliferator-activated receptor a/c dual agonists for the treatment
of type 2 diabetes and dyslipidemia. J Med Chem 48:2262–2265
Luco JM, Ferretti FH (1997) QSAR based on multiple linear
regression and partial least square methods for the anti-HIV
activity of a large group of HEPT derivatives. J Chem Inf
Comput Sci 37:392–401
Markt P, Schuster D, Kirchmair J, Laggner C, Langer T (2007)
Pharmacophore modeling and parallel screening for PPAR ligands.
J Comput Aided Mol Des 21:575–590. doi:10.1007/s10822-
007-9140-0
Mudaliar S, Hery RR (2002) PPAR agonists in health and disease:
pathophysiologic and clinical overview. Curr Opin Endocrinol
Diabetes 9:285–302
Paliwal S, Narayan A, Paliwal S (2009) Quantitative structure activity
relationship analysis of dicationic diphenylisoxazole as potent
anti-trypanosomal agents. QSAR Comb Sci 28:1367–1375
Paliwal SK, Pal M, Siddiqui AA (2010a) Quantitative structure
activity relationship analysis of angiotensin II AT1 receptor
antagonist. Med Chem Res 19:475–489
Paliwal SK, Singh S, Kumari S, Siddiqui AA, Paliwal SK (2010b)
QSAR studies of imidazo [1,5a]quinoxalines amides, carbamates
and urea as potent GABA modulators. Indian J Chem B 49B:
554–560
Paliwal SK, Das S, Yadav D, Saxena M, Paliwal SK (2010c)
Quantitative structure activity relationship (QSAR) of N6-substi-
tuted adenosine receptor agonists as potential antihypertensive
agents. Med Chem Res. doi:10.1007/s00044-010-9478-z
Paliwal S, Seth D, Yadav D, Paliwal S, Yadav R (2011) Development
of a robust QSAR model to predict the affinity of pyrrolidine
analogs for dipeptidyl peptidase IV (DPP-IV). J Enzym Inhib
26:129–140. doi:10.3109/14756361003777057
Prasad YR, Kumar PR, Smiles DJ, Babu PA (2008) QSAR studies on
chalcone derivatives as antibacterial agents against Bacilluspumilis. Arkivoc 11:266–276
Rameshwar N, Krishna K, Kumar BA, Parthasarathy T (2006) QSAR
studies of N 1-(5-chloro-2-pyridyl)-2-{[4-(alkylmethyl)benzoyl]
amino}-5-chlorobenzamide analogs. Bioorg Med Chem 14:
319–325
Rosen ED, Sarraf P, Troy AE, Bradwin G, Moore K, Milstone DS,
Spiegelman BM, Mortensen RM (1999) PPARc is required for
the differentiation of adipose tissue in vivo and in vitro. Mol Cell
4:611–617. doi:10.1016/S1097-2765(00)80211-7
Rudolph J, Chen L, Majumdar D, Bullock WH, Burns M, Claus T, Dela
Cruz FE, Daly M, Ehrgott FJ, Johnson JS, Livingston JN,
Schoenleber RW, Shapiro J, Yang L, Tsutsumi M, Ma X (2007)
Indanylacetic acid derivatives carrying 4-thiazolyl-phenoxy tail
groups, a new class of potent PPAR a/d/c pan agonists: synthesis,
structure–activity relationship, and in vivo efficacy. J Med Chem
50:984–1000
Med Chem Res
123
Sadowski J, Gasteiger J (1993) From atoms and bonds to three-
dimensional atomic coordinates: automatic model builders.
Chem Rev 93:2567–2581
Takahashi M, Eto M, Makino I (1993) Peripheral insulin resistance
precedes the onset of hyperglycemia in spontaneously diabetic
Chinese hamsters of Asahikawa colony. Diabetes Res Clin Pract
20:101–109
Tarko L, Ivanciuc O (2001) QSAR modeling of the anticonvulsant
activity of phenylacetanilides with preclav (property evaluation by
class variables). Match Commun Math Comput Chem 44:201–214
Tenenbaum A, Motro M, Fisman EZ (2005) Dual and pan—peroxisome
proliferators-activator receptors (PPAR) co-agonism: the bezafi-
brate lessons. Cardiovasc Diabetol 4:14
Todeschini R, Consonni V (2000) Handbook of molecular descrip-
tors: methods and principles in medicinal chemistry. Wiley-
VCH, Weinheim
Topliss JG (1993) Some observation on classical QSAR. Perspect
Drug Discov Des 1:253–268
Wang M, Ross SA, Gulve EA (2004) Chemistry and biochemistry of
type 2 diabetes. Chem Rev 104:1255–1282
Willson TM, Brown PJ, Sternbach DD, Henke BR (2000) The PPARs:
from orphan receptors to drug discovery. J Med Chem 43:
527–550
Wold S (2001) PLS-regression: a basic tool of chemometrics.
Chemom Intell Lab Syst 58:109–130
Wylie WA, Vinter JG (1994) In: Gardner M (ed) Molecular modeling
and drug design. Macmillan, London
Yanase T, Yashiro T, Takitani K, Kato S, Taniguchi S, Takayanagi R,
Nawata H (1997) Differential expression of PPAR c1 and c2
isoforms in human adipose tissue. Biochem Biophys Res Commun
233:320–324. doi:10.1006/bbrc.1997.6446
Zoete V, Grosdidier A, Michielin O (2007) Peroxisome proliferator-
activated receptor structures: ligand specificity, molecular switch
and interactions with regulators. Biochim Biophys Acta Mol Cell
Biol Lipid 1771:915–925. doi:10.1016/j.bbalip.2007.01.007
Med Chem Res
123
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