Prediction of the anti-cancer activity of spiro derivatives of parthenin based on molecular modeling methods and docking

ORIGINAL RESEARCH

Prediction of the anti-cancer activity of spiro derivativesof parthenin based on molecular modeling methods and docking

Zahra Garkani-Nejad • Mehri Shahhoseini

Received: 10 February 2013 / Accepted: 16 January 2014

� Springer Science+Business Media New York 2014

Abstract A quantitative structure–activity relationship

(QSAR) study has been done on the anti-cancer activity

(IC50) of 66 spiro derivatives of parthenin against three

human cancer cell lines, SW-620, DU-145, and PC-3.

QSAR models were based on multiple linear regression

(MLR), partial least square, support vector regression

(SVR), and Levenberg–Marquardt back propagation arti-

ficial neural network (ANN-LM). First, stepwise MLR was

employed as a descriptor selection procedure. Then selec-

ted descriptors were used as inputs for SVR and ANN

models. Comparison of the results indicates that the SVR

and ANN methods have better predictive power than other

methods. Finally, an ANN model was developed using

common molecular descriptors in three MLR models of

PC-3, DU-145, and SW-620 cell lines including hydration

energy (HE), G2v, and H3u, simultaneously. In order to

show the effect of HE on anti-cancer activity, docking of

spiro derivatives of parthenin with Nf-jB transcription

factor has been done.

Keywords Quantitative structure–activity relationship

(QSAR) � Anti-cancer activity � Parthenin �Spiro derivatives � Docking

Introduction

In recent years, the anti-cancer property of various ses-

quiterpenes has attracted a great deal of interest and

extensive research works have been carried out to charac-

terize the anti-cancer activity and the molecular mecha-

nisms of sesquiterpenoids (Gershenzon and Dudareva,

2007). Sesquiterpene lactones (SLs) are the active con-

stituents of many medicinal plants from the Asteraceae

family. Parthenin is a sesquiterpene lactone (SL) that iso-

lated from Parthenium hysterophorus L. (Picman, 1986),

has found interest due to its medicinal properties like anti-

cancer, antibacterial, antiamoebic, anti-inflammatory, lipid

peroxidation inhibition, and trypanocidal activity (Fraga,

2006; Modzelewska et al., 2005; Ramos et al., 2001;

Sharma and Bhutani, 1988; Talakal et al., 1995; Kim et al.,

2005).

Several novel spiro derivatives of parthenin have been

synthesized by the dipolar cycloaddition using various

dipoles such as benzonitrile oxides, nitrones, and azides.

Majority of the compounds exhibited improved anti-cancer

activity compared to the parthenin, when screened for their

in vitro cytotoxicity against three human cancer cell lines

including SW-620, DU-145, and PC-3 (Mahendhar et al.,

2011).

Sesquiterpene lactones (SLs) are potent anti-inflamma-

tory substances. The anti-inflammatory effect of these

compounds could be partly explained by the inhibition of

the transcription factor of NF-jB. Whether they inhibit the

DNA binding of NF-jB, the activation of the IjB-kinase,

or both is still a matter of debate (Garcıa-Pineres et al.,

2004).

NF-jB is a central mediator of the human immune

response. In the majority of cell types, this protein is

composed of a p50 and a p65 subunit. It is retained in an

Z. Garkani-Nejad (&)

Chemistry Department, Faculty of Science, Shahid Bahonar

University, Kerman, Iran

e-mail: [email protected]; [email protected]

M. Shahhoseini

Chemistry Department, Faculty of Science, Vali-e-Asr

University, Rafsanjan, Iran

123

Med Chem Res

DOI 10.1007/s00044-014-0920-5

MEDICINALCHEMISTRYRESEARCH

inactive cytoplasmic complex by binding to IjB, its

inhibitory subunit. A large variety of inflammatory condi-

tions, such as bacterial and viral infections as well as

inflammatory cytokines, rapidly induce NF-jB activity.

Active NF-jB is released from the cytoplasmic complex

by phosphorylation, ubiquitination and degradation of the

IjB subunit. The activated factor then translocates to the

nucleus where it stimulates the transcription of its target

genes (Mahendhar et al., 2011).

Using helenalin and parthenolide (types of Sesquiter-

pene lactones) as models, it has been well established that

DNA binding of NF-jB is prevented by alkylation of

cysteine 38 in the p65/NF-jB subunit, which is considered

to be the general mechanism for SL bearing a,b-unsatu-

rated carbonyl structures (Levin et al., 2001).

Quantitative structure–activity relationship (QSAR) is an

important tool to keep the number of synthesized and tested

compounds at a minimum in the process of development of

new drugs (Martin, 1978). The purpose of QSAR is to obtain

the quantitative correlation of molecular structure with bio-

logical activity and to predict the biological activities for

novel compounds. QSAR should help to characterize those

structural features that are responsible for biological activity

and the information is crucial for drug design (Sames and

Taylor, 1990). QSAR is mathematical equations for calcu-

lation of biological activity from molecular descriptors

(physicochemical properties) (Beebe et al., 1998; Ferreira

et al., 1999).

At the present work, relationship between the structure of

spiro derivatives of parthenin and their anti-cancer activities

against three human cancer cell lines, SW-620, DU-145,

and PC-3 has been considered using different linear and

nonlinear chemometrics methods. In the first step, for each

cell lines, models were performed using multiple linear

regression (MLR), partial least squares (PLS) and support

vector regression (SVR) as linear methods. Then Leven-

berg–Marquardt back propagation artificial neural network

(ANN-LM) applied as a nonlinear modeling method.

Experimental

Data set

The half maximal inhibitory concentration (IC50) values of

parthenin and 66 spiro derivatives of parthenin against

three human cancer cell lines, SW-620, DU-145, and PC-3

are taken from the literature (Mahendhar et al., 2011). The

IC50 values were converted to the corresponding log IC50

and used as dependent variable in this QSAR study. The

compounds classified as four structural groups (A, B, C,

and D). The chemical structure of these compounds has

been listed in Table 1.

Molecular descriptors

The main step in every QSAR study is choosing and cal-

culating the structural descriptors as numerical encoded

parameters representing the chemical structures. In the

present work the molecular descriptors were generated

using DRAGON (version 3.0, 2003) and HYPERCHEM

(version 7.0, 2002) softwares. Descriptors with constant or

almost constant values for all molecules were eliminated.

In addition, pairs of variables with a correlation coefficient

greater than 0.90 were classified as inter correlated and

only one of them were considered in developing the

models. A total of 658 descriptors were considered for

further investigations after discarding the descriptors with

constant and inter correlated ones.

Multiple linear regressions (MLR)

A stepwise MLR procedure was used for model develop-

ment. For regression analysis, data set was divided into two

groups of training and test sets for each cell lines, SW-620,

DU-145, and PC-3. The molecules included in these sets

were selected randomly. The training set, consist of 47

molecules, was used for the model generation using the

CLEMENTINE software package (2008). The test set,

consist of 20 molecules, was used to evaluate the generated

models.

It is clear that many MLR models will result using

stepwise multiple regression procedure. Among them, we

have to choose the best one. It is common to consider some

statistical parameters such as the number of descriptors,

correlation coefficient (R) and standard error (SE), for this

purpose. The best MLR model is one that has high R value,

low SE, the least number of descriptors and high ability for

prediction. The best selected model for each cell lines, SW-

620, DU-145, and PC-3 are presented in Tables 2, 3, and 4.

We have chosen seven descriptors for cell lines SW-620

and PC-3 and six descriptors for cell line DU-145 as the

optimum number of parameters. Calculated values of anti-

cancer activities for training and test sets using MLR

models are shown in Tables 5 and 6.

Partial least squares (PLS)

PLS is a linear modeling technique where information in

the descriptor matrix X is projected onto a small number of

underlying (‘‘latent’’) variables called PLS components

referred to as latent variables. The matrix Y is simulta-

neously used in estimating the ‘‘latent’’ variables in X that

will be most relevant for predicting the Y variables.

At the present work, the modeling by PLS method was

performed using MINITAB (2008). For regression analysis,

data set for each cell lines, SW-620, DU-145, and PC-3 were

Med Chem Res

123

Table 1 Different groups of compounds used in anti-cancer activity modeling

Structure No. R No. R

1

B

2 2,6-Cl–C6H3 12 3-OMe, 4-OH–C6H3

3 4-MeO–C6H4 13 2-F–C6H4

4 4-Cl–C6H4 14 3-NO2–C6H4

5 2-Br–C6H4 15 2-Cl–C6H4

6 –C6H5 16 2-Me–C6H4

7 4-NMe2–C6H4 17 3-Me–C6H4

8 4-HO–C6H4 18 4-Me–C6H4

9 2-NO2–C6H4 19 4-F–C6H4

10 4-NO2–C6H4 20 4-CN–C6H4

11 3,4-OMe–C6H3 21 9-Anthracene–

C

22 4-Br–C6H4 (92 %) 40 2,6-F–C6H3 (70 %)

23 4-Br–C6H4 (8 %) 41 2,6-F–C6H3 (30 %)

24 4-Cl–C6H4 (93 %) 42 3-Cl–C6H4 (90 %)

25 4-Cl–C6H4 (7 %) 43 3-Cl–C6H4 (10 %)

26 4-F–C6H4 (94 %) 44 3-F–C6H4 (90 %)

27 4-F–C6H4 (6 %) 45 3-F–C6H4 (8 %)

28 4-CN–C6H4 (93 %) 46 3-Br, 4-MeO–C6H3 (95 %)

29 4-CN–C6H4 (7 %) 47 3-Br, 4-MeO–C6H3 (5 %)

30 4-MeO–C6H4 (95 %) 48 2-Cl–C6H4 (93 %)

31 4-MeO–C6H4 (5 %) 49 2-Cl–C6H4 (7 %)

32 4-Me–C6H4 (90 %) 50 2-F–C6H4 (92 %)

33 4-Me–C6H4 (10 %) 51 2-F–C6H4 (8 %)

34 2-Me–C6H4 (94 %) 52 2,3,4,5,6-F–C6 (90 %)

35 2-Me–C6H4 (6 %) 53 2,3,4,5,6-F–C6 (10 %)

36 C6H5 (97 %) 54 3-MeO–C6H4 (91 %)

37 C6H5 (3 %) 55 3-MeO–C6H4 (9 %)

38 2,6-Cl–C6H3 (65 %) 56 4-NO2–C6H4 (90 %)

39 2,6-Cl–C6H3 (35 %) 57 4-NO2–C6H4 (10 %

D

58 C6H5 63 2-NO2–C6H4

59 3-Cl–C6H4 64 3-NO2–C6H4

60 4-MeO–C6H4 65 3-COOMe–C6H4

61 2-MeO–C6H4 66 4-Me–C6H4

62 3-Me–C6H4 67 2-Me–C6H4

Med Chem Res

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separated into two groups of training and test sets

(Tables 5, 6). The number of significant factors for the PLS

algorithm was determined using the cross-validation

method. With cross-validation, one sample was kept out

(leave-one-out) of the calibration and used for prediction.

The process was repeated so that each of the samples was

kept out once. The predicted values of left-out samples were

then compared to the observed values using prediction error

sum of squares (PRESS). The PRESS obtained in the cross-

validation was calculated each time that a new principal

component (PC) was added to the model. The optimum

number of PLS factors is the one that minimized PRESS.

Calculated values of anti-cancer activities for training and

test sets using PLS models are shown in Tables 5 and 6.

Table 2 MLR model for cell line SW-620

Descriptor definition Descriptor type Symbol Regression

coefficient

Standard

deviation

Lowest eigenvalue n = 1 of Burden matrix/weighted

by atomic masses

BCUT BELm1 -7.322 0.031

Signal 25/weighted by van der Waals volume. 3D-MORSE Mor25v 0.9407 0.130

Signal 18/weighted by Sanderson electronegativity 3D-MORSE Mor18e -0.367 0.266

T total size index/weighted by I-state WHIM Ts -0.08839 2.928

2nd component symmetry directional WHIM index/weighted

by van der Waals volume

WHIM G2v -18.21 0.006

H autocorrelation of lag 3/unweighted GETAWAY H3u -0.3495 0.517

Hydration energy Quantum chemical HE -0.1008 2.636

Constant 22.65 3.816

Table 3 MLR model for cell line DU-145


coefficient

Standard

deviation

Randic-type eigenvector-based index from adjacency matrix Topological VRA1 -0.00126 172.185

Signal 25/weighted by mass 3D-MORSE Mor25m 0.167 0.153

Signal 30/weighted by van der Waals volume 3D-MORSE Mor30v -0.3912 0.092



WHIM G2v -28.14 0.006


Hydration energy Quantum chemical HE -0.07451 2.636

Constant 10.419 1.193

Table 4 MLR model for cell line PC-3


coefficient

Standard

deviation

H autocorrelation of lag 0/weighted by Sanderson

electronegativity

GETAWAY H0e 1.676 0.111

Signal 13/weighted by mass 3D-MORSE Mor13m 0.3554 0.383

Signal 04/weighted by mass 3D-MORSE Mor04m -0.0754 0.832

Moran autocorrelation of lag 5 weighted by Sanderson

electronegativity

2D Autocorrelations MATS5e -7.726 0.032



WHIM G2v -30.65 0.006


Hydration energy Quantum chemical HE 0.0344 2.636

Constant 6.074 2.290

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Table 5 Experimental and predicted log IC50 values by MLR, PLS, ANN, ANNa, and SVR models for training set for three cell lines, SW-620,

DU-145, and PC-3

No. SW-620 DU-145

Exp. MLR PLS ANN ANNa SVR Exp. MLR PLS ANN ANNa SVR

1 4.588 4.655 4.604 4.572 4.761 4.637 4.493 4.673 4.553 4.523 4.636 4.442

2 4.633 4.708 4.685 4.644 4.450 4.674 4.580 4.527 4.554 4.500 4.476 4.529

3 4.613 4.393 4.334 4.627 4.998 4.563 4.301 4.185 4.383 4.274 4.741 4.251

5 4.643 4.547 4.662 4.727 4.705 4.643 4.255 4.506 4.322 4.363 4.527 4.468

6 4.657 4.506 4.527 4.684 4.374 4.607 4.600 4.483 4.442 4.487 4.431 4.493

7 3.708 3.853 3.914 3.704 3.690 3.848 3.653 3.987 3.721 3.676 3.963 3.812

8 4.949 5.070 4.822 4.961 4.769 5.00 4.968 4.933 4.798 4.953 4.690 4.919

9 4.602 4.498 4.621 4.603 4.657 4.553 4.531 4.587 4.612 4.558 4.614 4.582

10 3.954 3.925 4.156 3.994 4.191 4.004 3.845 3.933 4.121 3.906 4.111 3.895

11 4.934 4.764 5.045 4.937 4.795 4.885 4.924 4.399 4.875 4.868 4.694 4.334

12 4.944 5.113 4.990 4.922 4.813 4.995 4.851 4.910 4.966 4.841 4.726 4.901

14 4.690 4.369 4.431 4.701 4.837 4.641 4.708 4.575 4.410 4.700 4.734 4.658

17 4.176 4.132 3.982 4.096 4.231 4.126 4.447 4.364 4.293 4.264 4.336 4.362

18 3.398 3.947 3.757 3.402 3.569 3.891 4.146 4.112 4.335 4.159 4.369 4.197

19 3.556 4.265 4.103 3.608 4.394 4.294 4.114 4.378 4.329 4.303 4.446 4.369

20 4.724 4.452 4.501 4.721 4.807 4.650 4.415 4.447 4.287 4.440 4.713 4.482

21 3.633 3.635 3.575 3.662 3.784 3.683 3.663 3.895 3.644 3.602 3.801 3.713

23 3.908 3.646 3.840 3.849 3.536 3.858 3.623 3.588 3.814 3.674 3.686 3.673

24 3.875 3.695 3.825 3.830 3.814 3.825 3.845 3.785 3.779 3.873 3.812 3.820

25 3.708 3.779 3.853 3.832 3.815 3.812 3.778 3.820 3.654 3.724 3.813 3.828

26 3.863 3.702 3.729 3.835 3.814 3.814 3.806 3.783 3.688 3.829 3.813 3.826

29 3.699 3.994 3.942 3.702 3.723 3.809 3.845 3.914 3.742 3.845 3.766 3.888

30 3.863 3.978 3.983 3.766 3.804 3.813 3.806 3.723 3.825 3.685 3.808 3.756

31 3.623 3.800 3.627 3.672 3.760 3.740 3.851 3.932 3.715 3.878 3.820 3.902

32 3.940 3.589 3.575 3.960 3.834 3.889 3.908 3.757 3.777 3.867 3.823 3.815

33 3.954 3.923 4.021 3.954 3.787 4.004 3.799 3.820 3.732 4.053 3.828 3.813

34 4.079 3.789 3.821 3.929 3.828 4.011 3.653 3.858 3.713 3.847 3.819 3.869

37 3.778 3.693 3.754 3.741 3.812 3.760 3.813 3.870 3.815 3.828 3.811 3.878

38 3.477 3.757 3.518 3.364 3.595 3.745 3.954 3.632 3.806 3.918 3.723 3.803

40 3.699 3.921 3.912 3.799 3.815 3.785 4.079 3.855 3.968 3.826 3.814 3.874

41 3.857 3.959 3.832 3.872 3.810 3.832 3.954 3.915 3.975 3.864 3.818 3.904

42 3.785 3.862 3.806 3.877 3.814 3.862 3.869 3.825 3.790 3.845 3.813 3.854

44 3.681 3.815 3.857 3.900 3.812 3.888 3.699 3.834 3.756 3.754 3.811 3.860

45 3.708 3.651 3.712 3.568 3.815 3.738 3.908 3.825 3.783 3.768 3.813 3.858

47 3.954 3.828 3.969 3.887 3.808 3.822 3.732 3.726 3.818 3.846 3.819 3.732

50 3.954 3.961 3.910 3.899 3.813 3.904 3.613 3.849 3.747 3.782 3.812 3.846

51 4.009 3.903 4.031 3.956 3.660 3.909 3.778 3.696 3.756 3.858 3.762 3.829

53 3.740 3.565 3.519 3.762 3.805 3.690 3.785 3.919 4.091 3.837 3.822 3.835

55 3.820 3.952 3.860 3.786 3.803 3.831 4.041 3.916 4.054 3.964 3.814 3.904

57 3.708 3.869 3.712 3.737 3.808 3.657 3.778 3.915 3.742 3.763 3.825 3.828

58 4.943 4.682 4.840 4.936 4.732 4.761 4.497 4.561 4.560 4.526 4.540 4.582

59 4.685 4.631 4.653 4.630 4.705 4.649 4.597 4.504 4.515 4.479 4.551 4.528

62 4.176 4.266 4.079 4.160 4.330 4.226 4.796 4.496 4.481 4.726 4.536 4.510

64 4.785 4.561 4.582 4.785 4.825 4.736 4.477 4.529 4.641 4.568 4.306 4.427

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Table 5 continued

No. SW-620 DU-145

Exp. MLR PLS ANN ANNa SVR Exp. MLR PLS ANN ANNa SVR

65 4.477 4.582 4.462 4.445 4.360 4.527 4.267 4.353 4.226 4.287 4.418 4.317

66 4.505 4.378 4.439 4.510 4.461 4.395 4.556 4.493 4.523 4.569 4.547 4.506

67 4.342 4.439 4.389 4.367 4.159 4.392 4.477 4.497 4.433 4.532 4.513 4.527

No. PC-3

Exp. MLR PLS ANN ANNa SVR

1 4.605 4.772 4.609 4.721 4.670 4.655

2 4.748 4.506 4.625 4.716 4.429 4.698

3 4.544 4.211 4.457 4.551 4.704 4.322

5 4.708 4.653 4.758 4.692 4.545 4.657

6 4.663 4.536 4.523 4.652 4.376 4.613

7 3.799 4.421 4.122 3.802 3.707 4.236

8 4.301 4.249 4.485 4.296 4.383 4.293

9 4.568 4.606 4.566 4.579 4.403 4.619

10 4.079 4.204 4.058 4.079 3.930 4.129

11 4.398 4.653 4.125 4.403 4.412 4.448

12 4.204 4.265 4.282 4.261 4.405 4.254

14 4.740 4.591 4.630 4.738 4.443 4.690

17 4.823 4.498 4.636 4.827 4.840 4.504

18 4.146 4.220 4.077 4.126 4.142 4.196

19 4.431 4.513 4.397 4.433 4.397 4.481

20 4.505 4.538 4.449 4.509 4.455 4.547

21 3.690 3.653 3.670 3.701 3.815 3.640

23 3.362 3.539 3.617 3.363 3.657 3.412

24 3.892 3.987 3.906 3.993 3.840 3.942

25 4.079 3.972 4.020 4.056 3.841 3.927

26 3.756 3.923 3.819 3.957 3.840 3.806

29 3.959 3.914 3.938 3.947 3.771 4.004

30 3.756 3.639 3.765 3.836 3.832 3.806

31 3.778 3.898 3.983 3.982 3.782 3.828

32 3.929 3.800 3.891 3.960 3.855 3.930

33 3.959 3.870 3.848 3.936 3.806 4.009

34 4.176 3.651 4.122 3.953 3.851 3.965

37 3.954 3.936 3.739 3.966 3.838 4.004

38 3.380 3.518 3.473 3.373 3.309 3.412

40 3.398 3.629 3.433 3.100 3.341 3.421

41 3.362 3.544 3.521 3.387 3.333 3.412

42 3.914 3.997 4.098 3.990 3.840 3.954

44 3.681 3.821 3.783 3.662 3.839 3.769

45 3.954 3.911 3.835 3.870 3.840 3.793

47 3.908 3.979 4.012 3.953 3.831 3.947

50 3.431 3.675 3.603 3.106 3.839 3.601

51 3.398 3.328 3.071 3.398 3.763 3.448

53 3.699 3.499 3.824 3.688 3.827 3.649

55 3.954 3.659 3.821 3.918 3.828 3.755

57 3.968 4.069 4.040 3.959 3.829 4.018

58 4.025 4.328 4.410 4.109 4.573 4.428

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Support vector regression (SVR)

One of the QSAR models were based on support vector

machines (SVM) method that is a relatively new alternative

to the existing linear and nonlinear multivariate calibration

approaches in chemometrics (Belousov et al., 2002; This-

sen et al., 2003). SVM was originally proposed by Vapnik

and Chervonenkis (1974) and Vapnik (1998) and devel-

oped to solve pattern recognition and classification prob-

lems. But their principles can be extended easily to the task

of regression and time series prediction. A nonlinear

mapping is defined to map the input data (training data set)

into the so-called high dimensional feature space (which

may have infinite dimensions). Then, in the high dimen-

sional feature space, there theoretically exists a linear

function, to formulate the nonlinear relationship between

input data and output data. Such linear function named

SVR. SVR is the most common application form of SVM

that is a powerful technique for predictive data analysis

with many applications to varied areas of study. It is hard

to determine the type of functions for specific data patterns.

However, the Gaussian RBF kernel is not only easier to

implement, but also capable to nonlinearly map the training

data into an infinite dimensional space, thus, it is suitable to

deal with nonlinear relationship problems. Therefore, the

Gaussian RBF kernel function is specified in this study.

Table 5 continued

No. PC-3

Exp. MLR PLS ANN ANNa SVR

59 4.935 4.552 4.660 4.962 4.577 4.696

62 4.789 4.503 4.536 4.781 4.470 4.659

64 4.004 3.946 3.989 3.960 4.308 4.054

65 4.004 4.162 4.097 3.964 4.354 4.031

66 4.398 4.298 4.282 4.369 4.513 4.348

67 4.097 4.217 4.138 4.092 4.006 4.222

ANNa ANN with common molecular descriptors between three models of cell lines, PC-3, DU-145, and SW-620

Table 6 Experimental and predicted log IC50 values for Test set of cell lines SW-620, DU-145, and PC-3 using MLR, PLS, and SVR models

No. SW-620 DU-145 PC-3

Exp. MLR PLS SVR Exp. MLR PLS SVR Exp. MLR PLS SVR

4 4.708 4.400 4.468 4.433 4.633 4.684 4.702 4.826 4.690 4.393 4.457 4.372

13 4.041 4.135 4.113 4.153 4.380 4.13 3.975 4.038 3.914 4.145 4.421 4.194

15 3.954 4.249 3.907 4.229 4.279 4.205 4.133 4.082 4.079 4.172 4.437 4.237

16 3.699 4.145 3.959 4.111 4.079 4.468 4.295 4.464 4.000 4.430 4.218 4.268

22 3.826 3.800 3.946 3.896 3.724 3.751 3.651 3.840 3.763 3.757 3.814 3.800

27 3.778 3.663 3.512 3.722 3.699 3.921 3.871 3.805 4.079 3.800 3.711 3.814

28 3.681 3.958 3.882 3.829 3.699 3.843 3.828 4.010 3.681 3.903 3.759 3.885

35 3.954 3.812 3.924 4.006 3.708 3.869 3.977 3.974 4.176 3.885 3.657 3.857

36 3.724 3.821 3.839 3.824 3.748 3.882 3.853 3.991 3.756 3.868 3.779 3.879

39 3.699 3.768 3.493 3.854 3.799 3.693 3.578 3.597 3.623 3.692 3.861 3.793

43 4.021 3.888 3.991 3.959 3.778 4.033 4.037 3.987 4.079 3.880 3.931 3.839

46 3.833 3.945 3.89 3.896 3.799 3.727 3.658 3.986 3.756 3.753 3.811 3.790

48 3.740 4.127 4.037 4.008 3.778 3.696 3.718 3.804 3.544 3.934 3.902 3.907

49 3.964 4.014 3.971 3.835 3.613 3.865 3.708 3.817 3.799 3.852 3.617 3.782

52 3.477 3.479 3.447 3.621 3.968 3.463 3.697 3.616 3.748 3.774 3.936 3.723

54 3.875 3.997 3.794 3.893 4.000 3.623 4.029 3.718 4.079 3.825 3.971 3.835

56 3.756 3.930 3.671 3.786 3.748 3.989 3.838 3.993 3.924 3.905 3.692 3.755

60 4.477 4.722 4.448 4.647 4.892 3.788 4.141 3.627 4.079 4.446 4.792 4.444

61 4.675 4.655 4.714 4.663 4.431 4.647 4.910 4.821 4.716 4.515 4.444 4.502

63 4.519 4.714 4.639 4.712 4.447 4.374 4.002 4.258 4.000 4.629 4.514 4.616

Med Chem Res

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The performances of SVM for regression depend on the

combination of several parameters: capacity parameter C, eof e -insensitive loss function, and c controlling the

amplitude of the Gaussian function. C is a regularization

parameter that controls the tradeoff between maximizing

the margin and minimizing the training error. If C is too

small, then inadequate strain will be placed on fitting the

training data. If C is too large, then the algorithm will over-

fit the training data. To make the learning process stable, a

large value should be set up for C.

In this work, the SVR evaluations were carried out using

the SVM toolbox in CLEMENTINE software (2008). Data

set was divided into two groups of training set and test set

for each cell lines, SW-620, DU-145, and PC-3 (Tables 5,

6). Selected descriptors using MLR models were employed

as inputs. After that, the kernel function should be deter-

mined, which represents the sample distribution in the

mapping space. In this work, the RBF (radial basis func-

tion) kernel was chosen. The RBF SVR method fits a

nonlinear function onto the data, again aiming for maxi-

mum flatness. The RBF kernel is also often named a

Gaussian kernel since the kernel function is the same as the

Gaussian distribution function. Calculated values of anti-

cancer activities for training and test sets using SVR

models are shown in Tables 5 and 6.

Artificial neural networks (ANN)

The ANN-LM applied as a nonlinear model in two stages.

In the first step, ANN was designed using descriptors that

were selected using MLR models for cell lines PC-3, DU-

145, and SW-620, respectively with [7–7–1], [6–7–1], and

[7–7–1] structures. In the next step, ANN was designed

using common descriptors between three cell lines PC-3,

DU-145, and SW-620 with a [3–6–3] structure. In this step,

G2v, H3u, and hydration energy (HE) were used as inputs

of ANN.

Data set for each cell lines, SW-620, DU-145, and PC-3

were divided into three groups of training, test and vali-

dation sets (Tables 5, 7). The training set, consisting of 47

molecules, was used for the model generation. However,

the test set, consisting of 10 molecules, was used to take

care of the overtraining. The validation set, consisting of 10

molecules, was used to evaluate the generated model. The

ANN program was run in MATLAB software (2008). The

network was trained using the training set for optimization

of the weights and bias values. The proper number of nodes

in the hidden layer was determined by training the network

with a different number of nodes in the hidden layer. The

SE value measures how good the outputs are in comparison

with the target values. It should be noted that for evaluating

the over fitting, the training of the network for the pre-

diction of log IC50 must stop when the SE of the test set

begins to increase while the SE of training set continues to

decrease. Therefore, training of the network was stopped

when overtraining began.

Calculated values of anti-cancer activities for training,

test and validation sets using ANN models are shown in

Tables 5 and 7.

Molecular docking

Molecular docking was carried out by AutoDock 4.3

(2010) to understand the detailed binding model for the

active site of the receptor with its ligands. Autodock is a

flexible ligand–protein docking program which basically

runs as a two steps procedure: the calculation of the map of

interactions of the binding site with some general atom

types (performed with autogrid) and the posing of the

ligand respecting this map of interaction (performed with

autodock). In our study, the posing of the ligand respecting

this map of interaction was performed. For determining the

appropriate binding conformations of studied compounds

and check the main factors affecting the activity, docking

study was performed for spiro derivatives of parthenin with

the most anti-cancer activity (21, 7) and derivatives with

weak anti-cancer activity (11) using AutoDock program. In

order to show the effect of HE descriptor, spiro derivatives

of parthenin docked with Nf-jB transcription factor. The

three dimensional structure of NF-jB heterodimeric pro-

tein complexed with IjBa was retrieved from the RCSB

Protein Data Bank (PDB entry code: 1NFI). At the

beginning of the docking, all water molecules and IjBawere removed, the hydrogen atoms were added to the

protein (NF-jB) and all atom force field charges and atom

types were assigned. Preparation, refinement and minimi-

zation were done on the structure of protein in a standard

procedure, and then ligand preparation procedure per-

formed and docked ligand with protein.

Results and discussion

Regression analysis

The main aim of the present work was developing QSAR

models to predict biological activity of spiro derivatives of

parthenin. The best selected descriptors using stepwise MLR

procedure are shown in Tables 2, 3, and 4 for each cell lines

SW-620, DU-145, and PC-3, respectively. The calculated

values using MLR models are indicated in Tables 5 and 6,

for training and test sets, respectively. Also, the statistical

parameters of MLR models are shown in Tables 8, 9, and 10

for each cell lines SW-620, DU-145, and PC-3.

In PLS with cross-validation, one sample was kept out

(leave-one-out) of the calibration and used for prediction.

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123

The process was repeated so that each of the samples was

then compared to the observed values of PRESS. The cal-

culated values using PLS method are indicated in Tables 5

and 6, for training and test sets, respectively. Also, the sta-

tistical parameters of PLS model are shown in Tables 8, 9,

and 10 for each cell lines SW-620, DU-145, and PC-3.

Comparison of MLR and PLS models indicates that

MLR yields models that are simpler and easier to interpret

than PLS, because PLS perform regression on latent vari-

ables that do not have physical meaning. Due to the

colinearity problem in MLR analysis, one may remove the

collinear descriptors before MLR model development.

MLR equations can describe the structure activity rela-

tionships well but some information will be discarded in

MLR analysis. On the other hand, methods such as PLS

regression can handle the collinear descriptors and

Table 7 Experimental and predicted log IC50 values for test and validation sets of cell lines SW-620, DU-145, and PC-3 using ANN and ANNa

models

Cell line No. SW-620 DU-145 PC-3

Set Exp. ANN ANNa Exp. ANN ANNa Exp. ANN ANNa

Test 13 4.041 4.058 4.103 4.380 4.396 4.283 3.914 3.913 4.189

15 3.954 3.977 4.145 4.279 4.29 4.307 4.079 4.083 4.220

22 3.826 3.895 3.814 3.724 3.739 3.812 3.763 3.772 3.839

28 3.681 3.705 3.723 3.699 3.78 3.766 3.681 3.674 3.771

35 3.954 4.002 3.822 3.708 3.754 3.824 4.176 3.957 4.142

43 4.021 3.955 3.814 3.778 3.847 3.812 4.079 4.032 3.840

49 3.964 3.885 3.809 3.613 3.595 3.818 3.799 3.957 3.833

54 3.875 3.852 3.804 4.00 3.884 3.807 4.079 3.926 4.032

56 3.756 3.770 3.813 3.748 3.788 3.830 3.924 3.927 3.831

61 4.675 4.655 4.763 4.431 4.475 4.547 4.716 4.717 4.587

Validation 4 4.708 4.628 4.417 4.633 4.293 4.460 4.690 4.726 4.415

16 3.699 3.724 4.148 4.079 4.082 4.275 4.000 3.994 4.151

27 3.778 3.738 3.814 3.699 3.734 3.812 4.079 3.951 3.840

36 3.724 3.883 3.810 3.748 3.844 3.811 3.756 3.961 3.837

39 3.699 3.802 3.606 3.799 3.807 3.731 3.623 3.628 3.720

46 3.833 3.886 3.810 3.799 4.036 3.818 3.756 3.742 3.833

48 3.740 3.753 3.813 3.778 3.739 3.813 3.544 3.595 3.839

52 3.477 3.492 3.822 3.968 3.948 3.819 3.748 3.754 3.845

60 4.477 4.464 4.275 4.892 4.860 4.369 4.079 4.083 4.295

63 4.519 4.517 4.415 4.447 4.360 4.456 4.00 3.948 4.005

ANNa ANN using common molecular descriptors with (3–6–3) structure

Table 8 Statistical parameters for training, test, and validation sets

using MLR, SVR, PLS, and ANN models for cell line SW-620

Model Training Validation Test

R2 SE R2 SE R2 SE

MLR 0.788 0.213 0.726 0.188

PLS 0.867 0.171 0.823 0.151

SVR 0.893 0.153 0.810 0.153

ANN 0.980 0.065 0.979 0.060 0.970 0.047


using MLR, SVR, PLS, and ANN models for cell line DU-145


R2 SE R2 SE R2 SE

MLR 0.839 0.161 0.703 0.202

PLS 0.888 0.135 0.946 0.088

SVR 0.882 0.139 0.779 0.178

ANN 0.942 0.093 0.895 0.120 0.968 0.058


using MLR, SVR, PLS, and ANN models for cell line PC-3


R2 SE R2 SE R2 SE

MLR 0.773 0.206 0.564 0.230

PLS 0.876 0.154 0.853 0.120

SVR 0.905 0.135 0.554 0.237

ANN 0.960 0.092 0.934 0.087 0.880 0.102

Med Chem Res

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therefore better predictive models will be obtained by PLS

method. Comparison of statistical parameters in Tables 8,

9, and 10 clearly indicates the superiority of PLS over that

of the MLR models.

Developed MLR models consider two purposes. First, a

stepwise MLR procedure was used to select the suitable

variables. The second purpose of developing MLR model

was to assess the linear relationship between these

descriptors and the anti-cancer activity parameters.

In order to examine the relative importance, as well as

the contribution of each descriptor in the model, the value

of the mean effect (Mf) was calculated for each descriptor.

In the case of the MLR, the mean effect show the role of

each descriptors in predicting the log IC50 of the biological

activity. Mean effect is defined as:

Mfj ¼bj

Pni dij

Pmj bj

Pni dij

: ð1Þ

Mfj represents the mean effect for the considered descriptor

j, bj is the coefficient of the descriptor j, dij stands for the

value of the target descriptors for each molecule and,

eventually, m is the descriptor number in the model. The

MF value indicates the relative importance of a descriptor,

compared with the other descriptors in the model.

It can be seen from Tables 2, 3, and 4 that G2v, HE, and

H3u are common molecular descriptors in three cell lines

models. Therefore, we considered the effect of these

descriptors in more detail. Figure 1a–c shows the mean

effect of G2v, HE, and H3u in MLR models for PC-3, DU-

145, and SW-620 cell lines.

Interpretation of descriptors

The first descriptor is G2v, which is a WHIM descriptor.

The WHIM descriptors are statistical indices calculated on

the projections of the atoms along principal axes. Thus these

descriptors are built in such a way that they acquire relevant

3D information regarding molecular size, shape, symmetry,

and atom distribution with respect to invariant reference

frames. WHIM descriptors describe global directional

symmetry index that contains mean information content of

the symmetry indices along each principle component.

The G2v is a second component symmetry directional

WHIM index/weighted by atomic van der Waals volumes.

G2v can represent molecular symmetry. The descriptor G2v

negatively correlated with log IC50. A small G2v value

means a high degree of structural uniformity and molecular

symmetry. Generally, an increase in molecular symmetry

tends to increase the log IC50 values. The effect of this

descriptor can be investigated on phenyl ring substituted on

the nitrogen atom. Difference between C and D groups of

compounds is that the compounds of C group have a phenyl

ring on the nitrogen atom, but D group of compounds do not

have this substitution. This difference decrease G2v values

in compounds of C group. In these compounds because of

existence phenyl ring on nitrogen atom, structural unifor-

mity and molecular symmetry increase, G2v values

decrease and the biological response will be weaken. But in

B and D groups of compounds, lower symmetry along with

the second component, increase G2v values and thus, the

biological responses in these groups improve.

Fig. 1 a–c Mean effects of common molecular descriptors for cell

lines. a SW-620, b DU-145, and c PC-3

Fig. 2 Structure of compounds of C group

Med Chem Res

123

H3u is one of the GETAWAY type descriptors, which

appeared in MLR models for three cell lines, SW-620, DU-

145, and PC-3. GETAWAY defined by applying some tra-

ditional matrix operators, concepts of the information theory

and spatial autocorrelation formulas, weighting the molecule

atoms in such a way as to account for atomic mass, polar-

izability, van der Waals volume, and electronegativity.

H3u defined as H autocorrelation of lag 3/unweighted

that is related to the size and location of the atom in the

molecule. By increasing the size of the atom and the dis-

tance between an atom and the center of the molecule, the

value of this descriptor increases. As shown in Fig. 1, the

mean effect of H3u for three cell lines has negative sign,

which indicates that IC50 is inversely related to this

descriptor. Therefore, increasing the size of molecules and

the distance between an atom and the center of the mole-

cule, leads to decrease in IC50 values.

The effect of atomic size in H3u descriptor is significant

in compounds number 6 (B group), 36 (C group) and 58 (D

group). In these three compounds, substitution R is a phenyl

ring. Among these compounds, maximum volume and

molecular mass are respectively, for compounds 36, 6, and

58. In fact, C group of compounds have larger size (volume

and mass), thus, H3u values for these compounds increased,

IC50 values decreased and anti-cancer activity improved.

The effect of atom distance from the center of molecule

(spiro carbon) is significant in compounds number 16, 17,

and 18. In these compounds the position of methyl group

respectively are Ortho, Meta and Para. Maximum H3u

values respectively are for 18, 17, and 16 compounds.

Because of increasing the distance of methyl group from

the center of molecule in compound 18, H3u values

increased. In fact, large groups in Para position increase

anti-cancer activities. Compound 21 show high anti-cancer

property, because of placing large group (anthracene) in

Para position.

Also, the effect of atom distance from center of mole-

cule (spiro carbon) for H3u descriptor, can be observed in

compounds of C group (Fig. 2).

Compounds of C group are mixture of diastereomers

that activities of them are measured. In minor diastereomer

(Fig. 2) H3u value is more than major diastereomer. In

minor diastereomer spatial orientation of spiro carbon is

out of screen and the spatial orientation of Ar0 (phenyl ring

and substitutions), is inside screen. In fact, in term of space,

spiro carbon and Ar0 are in contrast and distance of them

increase, thus H3u values increase. So, in minor

Fig. 3 Crystallographic structure of NF-jB heterodimeric protein

(p65—cyan and p50—green) complexed with IjBa (magenta) (Color

figure online)

Fig. 4 Docking of compound

21 with Nf-jB factor

Med Chem Res

123

diastereomers, IC50 values are decreased and anti-cancer

properties of these compounds are improved.

HE is other descriptor that entered in three MLR mod-

els. HE is a physicochemical property that is a measure of

the energy released when water molecules surround certain

molecules. Higher the HE of the compounds, greater is the

solubility of the compound in water. Further, the extent of

hydration depends upon the size of the compounds. Smaller

the size of the compound, more highly it is hydrated.

Presence of HE means that the mechanism of molecules

against anti-cancer activity is dependent on a hydration

process which is related to solubility.

HE is related directly with the number of hydrogen bond

acceptor atoms. With increasing the number of hydrogen

Fig. 5 Docking of compound 7with Nf-jB factor

Fig. 6 Docking of compound

11 with Nf-jB factor

Med Chem Res

123

bond acceptor atoms, the HE values are increased (more

negative). This descriptor has positive effect in cell lines

DU-145 and SW-620. Thus, with increasing the HE, IC50

values increase and anti-cancer properties reduce in cell

lines DU-145 and SW-620. This descriptor has very small

negative mean effect for cell line PC-3 that has been

ignored.

To showing the effect of HE, the hydrogen bond

between Transcription factor Nf-jB with spiro derivatives

of parthenin has been investigated.

Nf-jB factor is in dimer form and in the most cells, this

protein is composed of subunit P65 and P50. Structure of Nf-

jB protein is shown in Fig. 3. In this figure Nf-jB protein is

complexed with Ijb factor. Anti-cancer activity of spiro

derivatives of parthenin are performed through hydrogen

bonding with the Nf-jB transcription factor (subunit P65).

Computational docking of the interaction of spiro

derivatives of parthenin with Nf-jB factor were performed

with Auto Dock (2010) program. Compound 21 is one of

the most active spiro derivatives of parthenin that has been

docked with Nf-jB factor. The HE of this molecule is low.

Nf-jB–P65 expression is completely inhibited by com-

pound 21. It can be presumed that Nf-jB–P65 expression is

inhibited either on transcriptional level or translational

level. According to Fig. 4, this molecule has three hydro-

gen bonds with Nf-jB transcription factor. This molecule

has two hydrogen bonds with Lys37 that one bond is

through oxygen atom of nitrile oxide ring and another bond

is through of oxygen atom of cyclopentenone ring. The

other hydrogen bond of this molecule is through hydroxyl

group of cyclopentenone ring with Glu39. Docking of

compound 21 with Lys37 and Glu39 leads to blocking and

alkylation Cys38. Thus, compound 21 inhibits Nf-jB fac-

tor completely.

Compound 7 is the other most active spiro derivatives of

parthenin that the HE of this molecule is low. According to

Fig. 5, this molecule has three hydrogen bonds with Nf-jB

transcription factor. Docking of this compound with Nf-jB

transcription factor is similar to compound 21.

Compound 11 shows weak anti-cancer activity. In this

compound, increasing HE leads to reducing anti-cancer

activity. According to Fig. 6, this molecule has three

hydrogen bonds with Nf-jB transcription factor. This

molecule has two Hydrogen bonds with Glu39 through

hydroxyl group of benzene ring and one hydrogen bond

with Lys37 through Nitrogen atom of nitrile oxide ring.

Hydroxyl group of the benzene ring has the strong desire to

make hydrogen bond. For this reason, this hydroxyl group

docked to Glu39 with two hydrogen bonds. So by

increasing HE in spiro derivatives of parthenin, hydroxyl

group of cyclopentenone ring and Oxygen atom of cyclo-

pentenone ring do not participate in hydrogen bond.

Therefore, Cys38 does not block well and binding of

molecules with Nf-jB factor is not doing well and anti-

cancer activity is reduced.


for ANN model by using common descriptors between three cell lines

PC-3, DU-145, and SW-620

Cell line Training Validation Test

R2 SE R2 SE R2 SE

SW-620 0.817 0.200 0.750 0.227 0.844 0.113

DU-145 0.818 0.172 0.802 0.200 0.868 0.120

PC-3 0.773 0.208 0.715 0.131 0.746 0.138

Fig. 7 a–c Plot of experimental log IC50 of spiro derivatives of

parthenin against the calculated values using LM-ANN model for cell


Med Chem Res

123

Investigation of nonlinearity

In order to investigate the nonlinear interactions between

different parameters in the MLR model, an ANN and SVR

model were developed to predict the IC50 of spiro deriv-

atives of parthenin. The ANN and SVR were generated

using the descriptors appearing in the MLR model as input.

Calculated values of anti-cancer activities for different sets

of studied compounds using SVR and ANN models are

shown in Tables 5, 6, and 7. Based on the data given in

Tables 8, 9, and 10, comparison between the results

obtained by the MLR, PLS, SVR and ANN methods clearly

indicates the superiority of ANN and SVR over that of the

MLR and PLS models.

In the next step, ANN modeling was done using three

common molecular descriptors for SW-620, DU-145, and

PC-3 cell lines, simultaneously. Calculated values of anti-

cancer activities for training, test and validation sets using

this ANN model are shown in Tables 5 and 7. Statistical

parameters for this model are shown in Table 11. As can be

seen from this table, common molecular descriptors are

able to account 81.7, 81.8, and 77.3 % of variances of anti-

cancer activity screening of spiro derivatives of parthenin,

respectively, for each cell lines.

Plots of the calculated against the experimental log IC50

values using ANN and SVR methods are shown in

Figs. 7a–c and 8a–c, respectively, for each cell lines SW-

620, DU-145, and PC-3.

Conclusion

In the present research study, a quantitative structure

activity relationship (QSAR) was developed for the pre-

diction of anti-cancer activity of spiro derivatives of par-

thenin as novel anti-cancer agents using different linear

(MLR and PLS) and nonlinear (SVR and ANN) modeling

methods.

The proposed methods indicate that three structural

parameters including G2v, HE, and H3u are related to anti-

cancer activity of spiro derivatives of parthenin and are

able to predict the anti-cancer activity of these compounds.

It can be concluded that designed models would be

expected to estimate IC50 values for new compounds

which experimental values are unknown.

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Documents

Prediction of the anti-cancer activity of spiro derivatives of parthenin based on molecular modeling methods and docking