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: paliwalsarvesh@yahoo.com

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