ORIGINAL RESEARCH

Pharmacophore-based in silico high-throughput screeningto identify novel topoisomerase-I inhibitors

Supriya Singh • Sucheta Das • Anubhuti Pandey •

Swapnil Sharma • Sarvesh Paliwal

Received: 22 June 2012 / Accepted: 31 January 2013

� Springer Science+Business Media New York 2013

Abstract Topoisomerase-I (TOP-I) has emerged as a

potential target for the design and development of anti-

cancer compounds. TOP-I inhibitors have shown promise

in the treatment of various cancers including renal cell

cancer, whose exact cause is yet to be known. Recent

studies indicate that indenoisoquinolines can provide

greater stability to drug-topoisomerase-DNA cleavage

complexes, which makes them a more appropriate anti-

cancer class of compounds compared to camptothecin. In

view of such significance, a three-dimensional pharmaco-

phore model has been developed using a training set of 36

indenoisoquinoline-based topoisomerase inhibitors. The

validated best model consists of three chemical features:

one hydrophobic, one positive ionizable, and one ring

aromatic with good correlation values of r(training)2 = 0.827

and r(test)2 = 0.702. Furthermore, 98 % validation by Cat-

Scramble method and a good r2 of 0.703 from 22 external

test set compounds have testified the universal applicability

of the generated model. Validated three feature pharma-

cophore model has been used to screen the chemical

database from the National Cancer Institute (NCI) leading

to the identification of 17 druggable TOP-I inhibitors

which can be raised into drug candidates after further

evaluation.

Keywords TOP-I � Cancer � Pharmacophore � NCI

Introduction

Cancer is a major health problem and one of the leading

causes of death worldwide. About 13 % of all human

deaths worldwide are caused due to cancer. In India, about

0.9 million new cancer cases are detected every year. Renal

cancer is the third most common urologic malignancy

(Rekha et al., 2008) and the seventh most common cancer

overall (Thakur and Jain, 2011). It is considered as a silent

cancer, as it does not show any symptoms until it reaches

beyond the kidneys. Renal cell carcinoma corresponds to

2–3 % of all cancers (Canamares et al., 2012) with the

highest incidence occurring in western countries. During

the last two decades, there has been an annual increase of

about 2 % in the occurrence of renal cancer both world-

wide and in Europe (Lindblad, 2004). In the last 30 years,

only few drugs have shown some activity against advanced

renal cancer (Scherr et al., 2011).

DNA topoisomerase-I (TOP-I) has emerged as a popular

target for cancer treatment. Topoisomerases are universal

enzymes involved in diverse cellular processes, such as

replication, recombination, transcription, and repair (Wang,

1996, 2002; Fortune and Osheroff, 2000; Champoux, 2001;

Wilstermann and Osheroff, 2003). Camptothecin was the

first agent identified as a TOP-I inhibitor from the Chinese

tree Camptotheca acuminate. However, it was discontinued

in the 70s because of severe side effects and lack of under-

standing of the drug’s mechanism of action. Although, the

camptothecin derivatives currently in the clinic possess

potent antitumor activity, they have a major limitation that

they are inactivated within minute at physiological pH by

lactone E ring opening. In view of this, a variety of het-

erocyclic aromatic and intercalating non-camptothecin

TOP-I inhibitors have been evaluated in clinical trials

(Jaxel et al., 1989).

S. Singh � S. Das � A. Pandey � S. Sharma � S. Paliwal (&)

Department of Pharmacy, Banasthali University,

Banasthali 304022, Rajasthan, India

e-mail: paliwalsarvesh@yahoo.com

123

Med Chem Res

DOI 10.1007/s00044-013-0526-3

MEDICINALCHEMISTRYRESEARCH

Indenoisoquinolines have shown several advantages

over the camptothecin derivatives. In addition to possess-

ing high antiproliferative activity, indenoisoquinoline-

based compounds are chemically more stable. They also

have greater stability in their drug–enzyme–DNA cleavage

complexes (Kohlhagen et al., 1998; Yoshinari et al., 1999).

The present study has been conducted with the aim to

explore the structural requirements for potent TOP-I

inhibitors and to identify novel lead compounds with

potential as anticancer agents. A pharmacophore model has

been constructed employing renal cancer activity of

indenoisoquinoline series of compounds. The validated and

predictive pharmacophore model has been used to mine the

National Cancer Institute (NCI) chemical compound data-

base for identification of structurally diverse novel TOP-I

inhibitors.

Methods

Data compilation

The biological activity data with sufficient structural

diversity and over four orders of magnitude, reported as

GI50 (lM) has been obtained from the literature (Nagarajan

et al., 2004, 2006; Morrell et al., 2007a, b). The chemical

structures of all the inhibitors are given in Table 1. All

compounds have been built using ISIS Draw 2.5, imported

to Accelry’s Discovery Studio 2.0 (DS 2.0) and energy

minimized to the closest local minima using the general-

ized CHARMM-like force field as implemented in the

software program.

Conformational analysis

The single conformer 3D structures have been used as the

starting point for conformational analysis. The conforma-

tional space of each inhibitor has been extensively sampled

using the poling algorithm. Catalyst provides three types of

conformational analysis: Fast, Best, and Ceasar. Both Best

and Fast uses a version of the CHARMM force field for

energy calculations and a poling mechanism for forcing the

search into unexplored regions of conformer space. Fast

generation takes less time, but Best generation provides

more complete coverage of conformational space by opti-

mizing the conformation in both torsional and Cartesian

space. Moreover, Best searches the conformational space

more extensively than Fast, particularly ring conforma-

tions, and it applies more stringent minimization proce-

dures. Ceasar is 5–20 times faster and slightly better at

reproducing the ligand conformations than Catalyst Fast.

Fast and Ceasar perform better in high-throughput

screenings, while the Best method is recommended for

generating conformers that would be used as input for

developing automated hypotheses (Watts et al., 2010).

In the present study, diverse conformations of the

compounds have been generated using ‘‘Best’’ conforma-

tional approach, specifying 255 as the maximum number of

conformers under the constraint of 20 kcal/mol energy

threshold above the estimated global minimum based on

the use of the CHARMM force field.

Training and test set selection criteria

The most critical aspect in the generation of a pharmaco-

phore hypothesis is selection of the training set. As a

minimum requirement, training set should include at least

16 compounds to assure statistical significance and to avoid

any chance correlation in the pharmacophore model. The

training set consisting of 36 structurally diverse indeno-

isoquinoline-based TOP-I inhibitors have been carefully

selected with biological activities spanning over 4 orders of

magnitude. The remaining compounds having both struc-

tural diversity and biological activity variation have been

used as test set to validate the developed pharmacophore

model.

Pharmacophore generation methodology

A pharmacophore is described by a set of functional fea-

tures such as hydrophobic (HY), hydrogen bond donor

(HBD), hydrogen bond acceptor (HBA), hydrogen bond

acceptor lipid (HBA_L), and positively and negatively

ionizable sites distributed over a 3D space. The hydrogen-

bonding features are vectors, whereas all other functions

are points. The feature mapping protocol in Catalyst gen-

erates all possible pharmacophore features including HBA,

HBA_L, HBD, HY, HY (aliphatic), HY (aromatic), nega-

tive charge, negative ionizable (NI), positive charge,

positive ionizable (PI), and ring aromatic (RA).

The hypogen module of catalyst was used to generate

pharmacophore models. Pharmacophore generation was

carried out by setting function weight to 0.302, mapping

coefficient to 0, and resolution to 297 pm. The uncertainty

value was set to 3, which represents the ratio range of

uncertainty in the activity value based on the expected sta-

tistical straggling of biological data collection. Six com-

pounds (1_43, 1_95, 2_13, 2_24, 3_37, and 4_13) detected

as outliers from both the training as well as the test set were

excluded from the dataset. The implemented protocol

returned top ten hypotheses which were further analyzed for

their statistical significance on the basis of cost function

analysis, correlation coefficient, root mean-square deviation

(RMSD), Cat-scrambling, and internal and external test set

prediction. Out of ten generated hypotheses, the best one was

chosen on the basis of statistical fitness.

Med Chem Res

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Table 1 Chemical structures of the indenoisoquinoline derivatives as TOP-I inhibitors

N

O

O

R1

R2 R3

R4

Name of

compound

R1 R2 R3 R4 Activity

GI M50 (µ )

1_35 –H –NO2 Br O 11.7

1_37 –H –NO2 Br –CH3 46.8

1_38 –H –NO2 Br S 0.891

1_40 –H –NO2 Cl –H 26.9

1_41 –H –NO2 Cl –F 27.5

1_42 –H –NO2 Cl –Cl 2.51

1_43 –H –NO2 Cl –Br 0.019

1_46 –H –NO2 Cl N 5.89

1_49 –H –NO2 BrS

O O 6.92

1_50 –H –NO2 N3 O 5.25

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Table 1 continued

1_52 –H –NO2 N3–CH3 58.9

1_53 –H –NO2 N3 S 1.05

1_55 –H –NO2 N3–H 72.4

1_56 –H –NO2 N3–F 0.302

1_59 –H –NO2 N3–I 3.89

1_61 –H –NO2 N3 N 46.8

1_62 –H –NO2 NH2 O 0.055

1_63 –H –NO2 NH2–C2H5 1.48

1_64 –H –NO2 NH2–CH3 0.229

1_65 –H –NO2 NH2–SMe 0.162

1_66 –H –NO2 NH20.437

1_67 –H –NO2 NH2–H 0.009

1_68 –H –NO2 NH2–F 0.034

1_86 –OCH3 –OCH3 BrNH

O

13.8

1_87 –OCH3 –OCH3 Cl O 5.5

1_90 –OCH3 –OCH3 Br –F 15.5

Name of

compound

R1 R2 R3 R4 Activity

GI M50 (µ )

Med Chem Res

123

Table 1 continued

1_92 –OCH3 –OCH3 Cl

N 22.9

1_94 –OCH3 –OCH3 N3 N

H

O

50.1

1_95 –OCH3 –OCH3 N3

–NH2 0.148

1_97 –OCH3 –OCH3 N3

–OCH3 64.6

1_99 –OCH3 –OCH3 N3

–H 13.8

1_100 –OCH3 –OCH3 N3

–F 8.32

1_102 –OCH3 –OCH3 N3

N 15.5

1_104 –OCH3 –OCH3 NH2

–NH2 2.57

1_105 –OCH3 –OCH3 NH2

–N(CH3)2 17

1_106 –OCH3 –OCH3 NH2

–OCH3 0.141

1_107 –OCH3 –OCH3 NH2

–C2H5 1.74

1_108 –OCH3 –OCH3 NH2

–H 0.794

1_109 –OCH3 –OCH3 NH2

–F 4.36

1_110 –OCH3 –OCH3 NH2

O

O

15.1

1_111 –OCH3 –OCH3 NH2

N 16.6

1_112 –OCH3 –OCH3 NH2

–NO2 11.7

2_6 –H –H Br

–H 7

Name of

compound

R1 R2 R3 R4 Activity

GI M50 (µ )

Med Chem Res

123

Table 1 continued

2_7 –H –H NH2

–H 0.16

2_8 –H –H

N

–H 0.91

2_9 –H –H

N

N

–H 1.66

2_10 –H –NO2 N3

–H 72.4

2_11 –H –NO2 NH2

–H 1.102

2_12 –H –NO2

N

–H 4.17

2_13 –H –NO2

N

N

–H 0.015

2_24 –H –NO2 I

–OCH3 0.309

2_27 –H –H

NO

–H 21.4

2_28 –H –H N3

–H 25.1

2_29 –H –H

NO

–NO2 0.309

2_31 –H –H

NO

–OCH3 4.07

2_32 –H –NO2 HN

OH

–H 0.229

2_33 –H –NO2 HN

OH –OCH3 0.012

2_34 –H –H HN

OH –OCH3 0.158

Name of

compound

R1 R2 R3 R4 Activity

GI M50 (µ )

Med Chem Res

123

Table 1 continued

2_35 –H –H HN

OH –H 0.269

2_37 –H –H

N

–OCH3 0.245

2_39 –H –H N

N

–OCH3 0.676

3_20 –H –H HN

N

O

O

–H 1.55

3_21 –H –H N

N

O

O

–H 0.589

3_32 –H –H

NN

N

O

NHO

HNO

O

O

O

–H 39.8

3_34 –H –H

N

O

NHO

N

N O

OO

HN

O

–H 36.3

3_37 –H –H

N

HN

O

O

–H 11

Name of

compound

R1 R2 R3 R4 Activity

GI M50 (µ )

Med Chem Res

123

3_38 –H –H

NNH

O

O

–H 0.132

3_39 –H –H

N

O

O

HN

NH

–H 0.178

3_42 –H –HN N

N

O

O

–H 15.5

3_45 –H –HN

O

O

HN

NH

HN

–H 0.017

3_49 –OCH3 –OCH3

N

O

O

HN

HN

–H 0.028

3_50 –OCH3 –OCH3

N

O

O

NH

NH

–H 0.24

3_51 –H –NO2

N

O

O

HN

HN

–H 0.123

3_52 –H –NO2

NNH

NH

O

O

–H 1.17

3_53 –OCH3 –OCH3 NH2NH

NH O O

33

Name of

compound

R1 R2 R3 R4 Activity

GI M50 (µ )

Table 1 continued

Med Chem Res

123

Table 1 continued

4_5 –OCH3 –OCH3

NOH

H HO O

4_6 –H –H NH

OH–H 0.95

4_9 –H –H NH3 –H 0.49

4_10 –H –HN O

–H 93.3

4_11 –H –HN NH

–H 2.19

4_13 –H –H NH3 –H 0.16

4_15 –H –H HN NH2

NH2

–H 0.23

4_16 –H –HN

–H 0.91

4_17 –H –H

N

N

H –H 1.66

4_20 –H –HN

H2N

N

H

–H 2

4_22 –H –H NH3 –H 0.04

4_33 –H –HBr O O

0.23

4_35 –H –H NH3 O O0.19

4_36 –H –HCl

–H 4.39

Name of

compound

R1 R2 R3 R4 Activity

GI M50 (µ )

Med Chem Res

123

Virtual screening

The best 3D pharmacophore was used as a search query to

screen the chemical database from the NCI to retrieve new

chemical entities as potent TOP-I inhibitors. Hits obtained were

subjected to Lipinski’s rule of five (Lipinski et al., 1997). This

led to retrieval of compounds having no violation of the Lipin-

ski’s rule of five and good estimated activity (less than 1 lM).

Table 1 continued

4_37 –H –HOH

–H 16.4

4_38 –H –HOH

–H 6.49

4_39 –OCH3 –OCH3NH3 O O

0.36

4_40 –H –H NH3–OCH3 0.43

4_41 –OCH3 –OCH3 NH3 O O0.31

4_42 –OCH3 –OCH3

N O O0.9

4_43 –OCH3 –OCH3

O O3.6

Name of

compound

R1 R2 R3 R4 Activity

GI M50 (µ )

Table 2 The cost values,

correlation coefficients (r),

RMSD, and features for the top

ten hypotheses (Hypo 0–Hypo

10)

Hypothesis Total cost Cost difference RMSD Correlation Features

1 156.29 68.109 1.096 0.909 HY, PI, RA

2 171.252 53.147 1.472 0.789 HY, PI, RA

3 174.449 49.95 1.531 0.770 HY, PI, RA

4 177.974 46.425 1.604 0.743 HY, PI, RA

5 179.277 45.122 1.619 0.738 HY, PI, RA

6 184.184 40.215 1.688 0.713 HY, PI, RA

7 187.156 37.243 1.755 0.681 HY, PI, RA

8 193.013 31.386 1.837 0.643 HY, PI, RA

9 194.002 30.397 1.850 0.636 HY, PI, RA

10 194.424 29.975 1.863 0.629 HY, PI, RA

Med Chem Res

123

Results and discussion

Construction of pharmacophore model

HypoGen pharmacophore models were generated using 36

training set compounds with antiproliferative activity

against the human renal cancer cell line. Hypotheses were

generated using structural information, conformational

models, and chemical features. The first hypothesis

(Hypo1) was considered as the best pharmacophore model

on the basis of high correlation coefficient of 0.909 and

high cost difference of 68.109. The Hypo1 consisted of

three features: one HY, one PI, and one RA.

Assessment of pharmacophore model

The HypoGen module in Catalyst performs two important

theoretical cost calculations that determine the success of

any pharmacophore hypothesis. One is known as the ‘‘fixed

cost,’’ representing the simplest model that fits all data

perfectly, and the second is known as ‘‘null cost,’’ which

represents the highest cost of a pharmacophore with no

features and which estimates activity to be the average of

the activity data of the training set molecules. Because the

null hypothesis is an ‘‘empty’’ hypothesis with no features,

there is no contribution of the weight and configuration

costs. All the analytical cost values represented in bits have

been calculated by the HypoGen module during pharma-

cophore generation.

The top-ranked pharmacophore model (Hypo1) showed

the best predictive power and statistical significance

described by the high squared correlation coefficient (r2 =

0.827), low root mean-square deviation (RMSD = 1.096),

weight (3.920), and error cost (142.703) satisfying the

acceptable range suggested in the cost analysis of the

Catalyst procedure (Lu et al., 2007). The low values of

error cost and RMSD represented the good quality of the

correlation between the estimated and the actual activity

data. The configuration cost was 9.667, indicating that all

generated models have been thoroughly analyzed. The cost

difference between total and fixed costs for the best

hypothesis was only 14.30 bits, indicating the high prob-

ability of the true correlation of the data.

It is a well known fact that, lower the cost difference

between the total and fixed costs, higher the probability is

for true correlation of the data. Also a cost difference of

68.109 between the total cost (156.29) and the null cost

(224.399) indicates a 68 % chance of representing a true

correlation in the data. The cost values, correlation coef-

ficients (r), RMSD, and features for the top ten hypotheses

are listed in Table 2.

Hypo1, identified as the best hypothesis estimated the

activity of the training set molecules accurately. All the

compounds are classified by their activity as highly active

(\0.5 lM, ???), moderately active (0.5–10 lM, ??),

and inactive ([10 lM, ?). Table 3 represents the actual

and predicted renal cancer cell line TOP-I inhibitory

activity of the 36 training set molecules based on the best

hypothesis. Out of the 36 training set compounds, two

Table 3 The actual and predicted TOP-I inhibitory activity of 36

training set molecules based on the best hypothesis

Name Actual

activity

Predicted

activity

Fit

value

Actual

activity scale

Predicted

activity scale

1_67 0.009 0.057 7.072 +++ +++

2_33 0.012 0.183 6.567 +++ +++

3_45 0.017 0.029 7.37 +++ +++

4_22 0.04 0.223 6.482 +++ +++

1_62 0.055 0.093 6.859 +++ +++

3_38 0.132 0.068 7 +++ +++

1_106 0.141 0.764 5.947 +++ ++

2_34 0.158 0.265 6.407 +++ +++

3_39 0.178 0.132 6.711 +++ +++

1_64 0.229 0.13 6.717 +++ +++

4_15 0.23 0.124 6.737 +++ +++

2_37 0.245 0.297 6.358 +++ +++

2_35 0.269 0.107 6.802 +++ +++

1_56 0.302 7.278 4.968 +++ ++

4_40 0.43 0.388 6.242 +++ +++

2_8 0.91 0.433 6.194 ++ +++

3_52 1.17 0.443 6.184 ++ +++

3_20 1.55 1.085 5.795 ++ ++

1_42 2.51 9.423 4.856 ++ ++

1_104 2.57 0.829 5.912 ++ ++

4_36 4.39 17.007 4.599 ++ ++

1_50 5.25 11.939 4.753 ++ +

1_87 5.5 7.127 4.977 ++ +

2_6 7 9.218 4.865 ++ ++

1_112 11.7 15.811 4.631 + +

1_35 11.7 12.053 4.749 + +

1_110 15.1 12.478 4.734 + +

1_90 15.5 8.524 4.899 + ++

1_92 22.9 7.271 4.968 + ++

1_40 26.9 9.618 4.847 + ++

3_53 33 4.07 5.22 + ++

3_34 36.3 14.547 4.667 + +

1_37 46.8 11.03 4.787 + +

1_61 46.8 14.321 4.674 + +

1_97 64.6 12.24 4.742 + +

4_10 93.3 12.744 4.725 + +

Med Chem Res

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highly active compounds are predicted as moderate, two

moderate compounds are predicted as active and two as

inactive, and four inactive compounds are predicted as

moderate. Consequently, for 26 of 36 training set com-

pounds, the predicted GI50 (lM) values are within the same

activity scale as the experimental values in the training set.

Fig. 1 The plot for training set

compounds showing correlation

between actual and predicted

activity

Fig. 2 The plot for internal test

set compounds showing

correlation between actual and

predicted activity

Fig. 3 Graph of 98 %

CatScrambled cost data. None

of the outcome hypotheses had a

lower cost score than the initial

(best) hypothesis, Hypo1

Med Chem Res

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Table 4 Structures of 22 indenoisoquinoline derivatives as TOP-I inhibitors used for external validation

Name of

compounds

Structure of compounds Actual activity GI50

(µM)

Predicted

activity GI50

(µ )M

6_5

N

O

O

OH

35.7 19.549

6_6

N

O

O

OH

16.4 16.167

6_7

N

O

O

OH

6.49 13.446

6_8

N

O

O

Cl

4.39 17.015

6_9

N

O

O

Br

7 9.217

Med Chem Res

123

Table 4 continued

6_15c

N

O

O

Cl

O

OMeO

MeO

24 7.325

6_15d

N

O

O

Br

O

OMeO

MeO

5.83 7.464

6_15e

N

O

O

Br

MeO

MeO

OMe

OMe

3.15 13.571

6_16b

N

O

O

MeO

MeON3

O

O

4.53 18.113

6_17a

N

O

O

NH3

OMe

OMe

H

H

+

0.43 0.388

Name of

compounds

Structure of compounds Actual activity GI50

(µM)

Predicted

activity GI50

(µ )M

Med Chem Res

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Table 4 continued

6_17b

N

O

O

MeO

MeONH2

O

O

+

0.31 1.284

6_18b

N

O

O

NH OH

O

O

2.07 0.336

6_18c

N

O

O

NH

OH

O

O

MeO

MeO

0.01 0.162

6_18d

N

O

O

NH

O

O

MeO

MeO

OH

0.01 0.703

Name of

compounds

Structure of compounds Actual activity GI50

(µM)

Predicted

activity GI50

(µ )M

Med Chem Res

123

Table 4 continued

6_18f

N

O

O

NH

O

O

MeO

MeO

OH

0.15 0.764

6_18g

N

O

O

NH

O

O

MeO

MeO

OH

0.11 0.49

6_19b

N

O

O

NH2

O

O

MeO

MeO

OH+

0.11 0.4

6_19c

N

O

O

NH2

MeO

MeO

OMe

OMe

OH+

0.23 1.076

Name of

compounds

Structure of compounds Actual activity GI50

(µM)

Predicted

activity GI50

(µ )M

Med Chem Res

123

Table 4 continued

6_19d

N

O

O

NH2

MeO

MeO

OMe

OMe

OH+

0.28 1.076

6_25

N

O

O

NH

MeO

MeO

OH

0.15 0.105

6_27a

N

O

CH3

MeO

MeO

O

O 43.6 9.564

6_27c

N

O

MeO

MeO

O

O 21.7 10.615

Name of

compounds

Structure of compounds Actual activity GI50

(µM)

Predicted

activity GI50

(µ )M

Med Chem Res

123

The plot for training set compounds showing correlation

between actual and predicted renal cell line TOP-I inhibitor

activity is depicted in Fig. 1.

Pharmacophore validation

Internal test set

Test set validation is one of the obligatory steps to establish

the competency of the generated pharmacophore model for

prediction accuracy. In order to validate the pharmacophore

hypothesis, we have used a test set consisting of 53 molecules

with variation in anti-proliferative activity against renal

cancer cell line. All molecules in the test set have been built,

minimized, and subjected to conformational analysis like the

molecules in the training set. Test set prediction has been

observed in terms of the squared correlation coefficient (r2),

which is 0.702 (Fig. 2). The high r2 value indicates a good

correlation between the actual and estimated activities. The

agreement between the actual and predicted activity of test

Fig. 4 The plot for external test

set compounds showing

correlation between actual and

predicted activity

Fig. 5 Pharmacophoric

features obtained from the best

hypothesis, Hypo1:

a pharmacophoric features;

b inter-atomic distances

between pharmacophoric

features

Fig. 6 Mapping analysis of the

most active and the least active

compound on pharmacophore

model: a the most active

compound, 1_67, showed best

fit with all the three features;

b the least active compound,

4_10, showed poor fit with

mapping of two out of the three

pharmacophore features

Med Chem Res

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Table 5 Mapping of pharmacophoric feature with marketed and clinical trial drug candidates

Name of comopunds Mapping of drugs along with generated pharmacophores

replica

Fit value

Afeletecan

4.965

Gimatecan

4.962

SN-38

4.962

Camptothecin

4.958

Irinotecan

4.919

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Table 5 continued

9-amino-

camptothecin

4.505

Topotecan

4.5

Rubitecan

4.474

Belotecan

4.461

Exatecan

3.888

Name of comopunds Mapping of drugs along with generated pharmacophores

replica

Fit value

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set compounds testifies the soundness of Hypo1. This vali-

dation provides an added confidence in the usability of the

selected pharmacophore.

Fischer validation

To further evaluate the statistical relevance of the phar-

macophore hypotheses generated from the training set

molecules, the CatScramble module in Catalyst has been

used which is based on the principle of Fisher’s randomi-

zation test. In this cross validation test, thorough random-

ization of the training set is used to validate and derive the

significance of the generated best model. These random-

ized spreadsheets should yield hypotheses with lesser sta-

tistical significance than the original model to suggest that

the original hypothesis represents a true correlation. 98 %

Fischer validation has been applied to the developed model

to minimize the possibility of adopting fortuitous phar-

macophores. The results of the F-randomization test are

shown in Fig. 3. The data of cross validation clearly indi-

cate that the statistical values of Hypo1 are better than

other random hypotheses, as revealed by the lowest total

cost and the highest correlation coefficient, which verifies

that Hypo1 has not been obtained by chance and there is

98 % possibility for Hypo1 to represent a true correlation

in the training set activity data.

External test set

A pharmacophore model is claimed to be best when it not

only predicts the activity of the training and internal test set

compounds but also predicts the activities of external

molecules. So, the selected pharmacophore model has been

further validated by an external test set consisting of known

TOP-I inhibitors with experimental GI50 values. In total, 22

compounds have been selected for external set showing

diversity in the activity range from 0.01 to 43.6 lM

(Table 4) (Cushman et al., 2000). The activities of all the

external test set compounds have been estimated using

Hypo1 with the squared correlation coefficient, r2 of 0.703.

An r2 value of more than 0.5 between the actual and esti-

mated values renders the model to be good (Frimayanti

et al., 2011). The graphical representation of actual versus

estimated activity of external test set compounds is

depicted in Fig. 4.

Pharmacophore mapping

The obtained pharmacophoric features and their interfea-

ture distances are shown in Fig. 5a, b, respectively. Map-

ping of the most active compound, 1_67, shows the best fit

with all the three features (Fig. 6a). The HY feature is

mapped by the oxygen atom of the carbonyl group, PI is

mapped by the nitrogen atom of the amine group, and the

RA feature is mapped by benzene ring. On the contrary, the

least active compound, 4_10, shows a poor fit with map-

ping of two out of the three pharmacophore features

(Fig. 6b). In this case, the HY and RA features are mapped

on the benzene ring whereas PI feature is missing. The

most active compound in the dataset assumes a confor-

mation that allows proper mapping of all the features of the

generated hypothesis, whereas the least active compound is

unable to map PI.

In addition to this, the pharmacophore has also been

mapped on some of the clinically approved marketed drugs

and clinical trial candidates like Irinotecan, Topotecan,

Belotecan, SN-38, Lurtotecan, Rubitecan, Exatecan, Cam-

ptothecin, Afeletecan, Gimatecan, and 9-amino-camptothecin

Table 5 continued

Lurotecan 2.055

Name of comopunds Mapping of drugs along with generated pharmacophores

replica

Fit value

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123

Table 6 Hits obtained from NCI database

Name Mapping Estimated Fit value

NSC 17153

0.04 7.228

NSC 3607

0.046 7.172

NSC 32583

0.051 7.125

NSC 11966

0.052 7.112

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Table 6 continued

NSC 23679

0.056 7.08

NSC 24114

0.056 7.078

NSC 8582

0.057 7.071

NSC 23681

0.067 7.003

NSC 32480

0.073 6.969

Name Mapping Estimated Fit value

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123

(Table 5). It has been found that none of the compounds

mapped the PI feature; however, all the other features (one HY

and one RA) are mapped. Afeletecan is mapped with a

maximum fit value of 4.965.

Database screening

Database screening deals with the quick search of large

libraries of chemical structures in order to identify those

structures which are most likely to bind to target, typically

a protein receptor or enzyme (Kurogi and Guner, 2001;

Oloff et al., 2005). The pharmacophore-based best model

has been used to screen the 260071 compounds of NCI

database which returned 295 hits. Lipinski’s rule of five has

been applied to screen druggable compounds, which led to

the selection of final 64 compounds, among which 17 have

high activity span of 0.04–0.081 and fit values ranging

from 7.228 to 6.924, namely, NSC 17153, NSC 3607, NSC

32583, NSC 11966, NSC 23679, NSC 24114, NSC 8582,

NSC 23681, NSC 32480, NSC 13454, NSC 31334, NSC

18762, NSC 18418, NSC 15412, NSC 32478, NSC 33424,

and NSC 8571 (Table 6).

The most active compound NSC 17153 with estimated

activity of 0.04 and fit value of 7.228 showed mapping with

all the three features. PI was mapped to the amine group

whereas HY and RA mapped to the benzene ring. The

second most active compound NSC 3607 (estimated

activity = 0.046, fit value = 7.172) showed mapping of

NH group present in six membered ring with PI feature,

ethyl chain with HY feature, and benzene ring with RA

feature. Similarly, all the 17 compounds showed mapping

with all the features.

Conclusions

The generated pharmacophore model showed high corre-

lation values for both the training (r2 = 0.827) as well as

the internal test set (r2 = 0.702). The model was also

validated by external test set with an r2 of 0.703. The

results demonstrated that the HY, RA, and PI features

Table 6 continued

NSC 13454 0.075 6.954

NSC 31334 0.079 6.933

NSC 18762 0.081 6.924

Name Mapping Estimated Fit value

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influence significantly to the TOP-I inhibitory activity. The

whole procedure of pharmacophore modeling along with

database screening carried out on the NCI database resulted

in the retrieval of 17 novel ligands with TOP-I inhibitory

activity which is a potential subject of further investigation.

Acknowledgments The authors thank the Department of Science

and Technology, New Delhi and the Vice Chancellor, Banasthali

University, for extending all the necessary facilities. The authors also

thank Dr. Monali Bhattacharya, Department of English, Banasthali

University, for her support.

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