ORIGINAL RESEARCH

QSAR and pharmacophore modeling of diverse aminothiazolesand aminopyridines for antimalarial potency againstmultidrug-resistant Plasmodium falciparum

Rahul Balasaheb Aher • Kunal Roy

Received: 2 August 2013 / Accepted: 6 March 2014

� Springer Science+Business Media New York 2014

Abstract Artemisinin antimalarials are the frontline and

effective drugs used worldwide for the treatment of

deadly Plasmodium falciparum malaria. But the recent

reports of artemisinin resistance have created the urgent

need to discover new molecules against single and mul-

tidrug-resistant strains of P. falciparum. In this back-

ground, we have developed here 2D-quantitative

structure–activity relationship (2D-QSAR) and 3D-phar-

macophore models using aminothiazole and aminopyri-

dine compounds for their activity against multidrug-

resistant strain (k1) of P. falciparum. Based on the

internal (Q2), external (Rpred2 ), overall validation

(r2mðOverallÞ) metrics, and number of descriptors used for

model development, a QSAR equation developed from a

genetic function algorithm having both linear and spline

terms was found to be the best model (Q2 = 0.675;

Rpred2 = 0.720; r2

mðOverallÞ= 0.617). The pharmacophore

models were developed in order to unveil the structural

requirements for the activity, and to classify the com-

pounds into more active and less active antimalarials

against the multidrug-resistant strain (k1) of P. falcipa-

rum. The best pharmacophore model (Hypo-1) with a

correlation coefficient of 0.932 showed one hydrogen

bond acceptor, one hydrophobic aliphatic, and two ring

aromatic features as the essential structural requirements

for the antimalarial activity. The pharmacophore model

(Hypo-1) also shows 86.00 % correct classification of

more active compounds of the test set against the multi-

drug-resistant (k1) strain of P. falciparum. Both the

models could be utilized further for the prediction of

antimalarial potency of aminothiazole and aminopyridine

compounds against multidrug-resistant P. falciparum.

Keywords Aminothiazoles � Aminopyridines �Multidrug-resistance � Plasmodium falciparum �QSAR � 3D-pharmacophore

Introduction

Artemisinin-based combination therapies are the recom-

mended first-line treatments for falciparum malaria in the

countries of the endemic disease. But the recent sign of

decline of efficacy of artemisinin-based combination ther-

apy and artesunate monotherapy in western Cambodia

raises the serious alarms for global malaria control (Don-

dorp et al., 2009). According to the WHO malaria report of

2012, there were about 219 million cases of malaria in

2010, with an estimated 660,000 deaths (World Malaria

Report, 2012). Therefore, there is an urgent need to

develop new molecules with novel mode of action.

Drug discovery program is a multidisciplinary effort for

the lead identification and optimization of druggable can-

didates. The higher cost, time, and disappointing pace of

approvals of new molecules create a pressure on the

pharmaceutical industries to improve the efficiency of the

drug discovery cycle. Hence, different chemoinformatic

tools are being used in the drug discovery projects, so as to

Electronic supplementary material The online version of thisarticle (doi:10.1007/s00044-014-0997-x) contains supplementarymaterial, which is available to authorized users.

R. B. Aher � K. Roy (&)

Drug Theoretics and Cheminformatics Laboratory, Department

of Pharmaceutical Technology, Jadavpur University,

Kolkata 700032, India

e-mail: kunalroy_in@yahoo.com; kroy@pharma.jdvu.ac.in

URL: http://sites.google.com/site/kunalroyindia/

123

Med Chem Res

DOI 10.1007/s00044-014-0997-x

MEDICINALCHEMISTRYRESEARCH

optimize the number of molecules to be synthesized

and analyzed. Quantitative structure–activity relationship

(QSAR) is one such efficient chemoinformatic tools, which

aims to find the consistent structure–activity relationship

with the development of predictive models. Such predictive

models could be utilized to determine the biological

activity of newer compounds prior to synthesis and

experimental testing.

The aminothiazole and aminopyridine scaffolds were

reported against the multidrug-resistant strain (k1) of P.

falciparum (Gonzalez Cabrera et al., 2012; Paquet et al.,

2012), though their target information is not available in

the literature. The k1 strain is mainly resistant to three

marketed drugs, namely chloroquine, pyrimethamine, and

cycloguanil (Wenzel et al., 2010). In the present work, we

have utilized the ligand-based approaches of 2D-QSAR

and 3D-pharmacophore model development for the ami-

nothiazole and aminopyridine compounds. For this, we

have combined two datasets of diverse aminothiazoles and

aminopyridines scaffolds, which were tested previously

against the multidrug-resistant strain (k1) of P. falciparum,

by the same research group (Gonzalez Cabrera et al., 2012;

Paquet et al., 2012), and with the same assay protocol. The

structural diversity in the molecules (non-congeneric) is

indicated by the wide range of activity from 7 to

42,000 nM. The model developed from such non-conge-

neric series of compounds would be always much useful,

since it could be utilized for predicting the activity of a

varied range of compounds of similar chemical domain.

There are some previous reports of the development of

QSAR and pharmacophore modeling using varied scaffolds

against the multidrug-resistant strain of P. falciparum (k1).

These scaffolds includes tryptanthrins (Bhattacharjee et al.,

2004), alkoxylated chalcones (Xue et al., 2004), pentami-

dines (Athri et al., 2010), 3-carboxyl-4(1H)-quinolones (Li

et al., 2013), 7-chloro-4-aminoquinolines (Sahu et al.,

2011), prodiginines (Mahajan et al., 2013), etc. But there

are no previous reports of the development of 2D-QSAR

Table 1 General structural features of aminothiazole and aminopyridine derivatives

General structures Compound nos. General structures Compound nos.

S

NHN

O

NN

H2N

1 NH2N S O

O

O

FF

F

21

NN

Ph

HN

O

R

2–7 N

R

H2N S O

O

22–52

NN

Ph

HN

O

N

S

R

8–12 NO

N

NH2

R

54–82

S

NNH

RO

H2N13–20 N

H2N R

R1

83–88

Med Chem Res

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and 3D-pharmacophore models using aminothiazole and

aminopyridine scaffolds against, the multidrug-resistant

strain of P. falciparum (k1).

Materials and methods

Development of 2D-QSAR models

Dataset and descriptors

The dataset comprises a non-congeneric series of com-

pounds which includes thiazole amides, thiazole ureas, and

3,5-diaryl-2-aminopyridines scaffolds. A total set of 87

compounds was collected from the two publications,

reported by the same research group (Gonzalez Cabrera

et al., 2012; Paquet et al., 2012). The general structures of

aminothiazole and aminopyridine are given in Table 1.

Detailed structural features along with antimalarial potency

against multidrug-resistant (k1) P. falciparum of the

compounds are given in Table S1 of the Supplementary

material section. The structures of all the compounds were

drawn in ChemDraw software (CS ChemDraw 5.0) in .mol

format, and used for the descriptor calculation by

employing Cerius2 software (Cerius2 version 4.10). All the

descriptors were calculated using a Descriptor? module of

the Cerius2 software. The calculated descriptors include

topological (E-state index, Balaban index, kappa shape

index, molecular connectivity index, subgraph count,

information content indices), structural (H-bond donor,

H-bond acceptor, Rotlbonds, MW, chiral centers), spatial

(radius of gyration, Jurs, Area, PMImag, Density, Vm),

electronic (dipole-mag, HOMO, LUMO, Sr), and thermo-

dynamic (ALogP, ALogP98, AlogP_atypes, MolRef, MR,

LogP) variables. A total set of 247 descriptors was calcu-

lated, and the thinning of the descriptor matrix was done

based on variance criteria (variance \ 0.0001) using the

Cerius2 software. Finally, 152 descriptors were utilized for

the cluster analysis.

Cluster analysis

The clustering technique is a rational method of selection

of training and test set compounds. The standardized

descriptor matrix was used for k-mean clustering division

using the SPSS software (SPSS 9.0). The total dataset

(n = 87) was divided into a training set (n = 63, 72 % of

the total number of compounds) for the model develop-

ment, and test set (n = 24, 28 % of the total number of

compounds) for the external validation, based on the

clusters obtained from the clustering technique (Roy et al.,

2012). The splitting was done in such a way that both the

sets cover the total chemical space of the whole dataset.

Model development and validation

The biological activity data in IC50 values were converted

to a negative logarithm (pIC50) value, and used as the

dependent variable, while the computed descriptors were

used as an independent variables. The QSAR models were

developed using stepwise multiple linear regression (MLR)

with the stepping criteria F = 4 for inclusion and F = 3.9

for exclusion using MINITAB software (MINITAB 14).

The genetic function approximation (GFA) analysis was

also performed in order to select the best descriptors by

using the same training and test sets division. It was per-

formed using the QSAR module of Cerius2 software on a

Silicon Graphics O2 workstation running under the IRIX

6.5 operating system. The mutation probabilities were kept

at 50 % with 5,000 iterations. Both the linear and spline

terms were used for the model development.

The models were validated by both internal and external

validation tools. The internal validation deals with the

predictive ability of a model based on the training set

compounds, while the external validation deals with the

predictive ability of model for the test set compounds. The

quality of internal validation was judged by a cross-vali-

dated squared correlation coefficient (Q2) based on the

observed and predicted activity of training set compounds.

The high value of Q2 is considered as an indicator of high

predictive ability of the model. The quality of external

validation was determined by calculating the Rpred2 value

for the test set compounds. The acceptable value for both

Q2 and Rpred2 should be more than 0.5. We have also cal-

culated the additional metrics such as r2m and Drm

2 for the

training, test, and overall sets for determining the statistical

significance, predictive potential, and robustness of the

developed models (Roy et al., 2012). The validation of

developed models was also checked by Golbraikh–Tropsha

Fig. 1 Plot showing distribution of more active and less active

compounds in the training and test sets (pharmacophore model)

according to the activity threshold (pIC50: 3.523)

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criteria (Golbraikh and Tropsha, 2002). The equations

(S1–S17) utilized for the calculation of internal, external,

overall validation, and Golbraikh–Tropsha parameters are

given in the supplementary material section.

Randomization

We have also performed the Y-randomization tests for

both the model and process randomization in order to

check the non-randomness of the developed models. In

case of process randomization, the dependent variable

column entries are scrambled by keeping the entire

descriptor matrix intact for random model development

(with fresh selection of variables). In case of model ran-

domization, the dependent variable is scrambled and the

new model is developed using the same set of variables as

present in the original nonrandom model. The process

randomization is generally carried out at 95 % confidence

level, and the model randomization at 99 % confidence

level. If the correlation coefficient of the nonrandom model

is significantly greater than the average value of the cor-

relation coefficient of randomized model, then the model is

considered to be robust and statistically significant.

Moreover, we have also computed the corrected Rp2 (cRp

2)

Table 2 Observed and predicted activity obtained from stepwise MLR (Eq. 1), GFA spline (Eq. 2), and GFA linear/spline (Eq. 3) models for the

training set compounds

Comp. no. Observed pIC50 (mM) Predicted pIC50 (mM) Comp. no. Observed pIC50 (mM) Predicted pIC50 (mM)

Eq. 1 Eq. 2 Eq. 3 Eq. 1 Eq. 2 Eq. 3

1 4.097 2.742 2.989 4.224 49 2.666 3.312 3.129 3.379

3 2.152 2.704 2.774 2.893 50 2.951 2.769 3.116 2.352

5 1.960 2.244 2.520 2.446 51 4.149 4.432 4.321 4.312

6 2.306 2.404 2.290 2.177 52 4.523 4.394 4.277 4.237

8 2.370 3.055 3.058 2.880 54 2.597 2.817 3.116 2.959

10 2.830 2.792 2.965 2.620 55 4.357 4.124 3.829 3.829

11 3.721 2.693 2.998 3.425 57 2.975 3.346 3.344 3.281

12 3.174 2.787 2.929 2.389 58 3.963 3.898 3.725 3.829

13 1.467 2.261 2.415 2.010 60 3.780 3.860 3.250 3.497

15 2.156 2.506 2.881 2.938 61 3.090 3.056 3.116 3.222

16 1.955 1.518 1.473 1.416 63 3.026 3.665 3.463 3.017

17 1.371 1.354 1.112 1.096 64 2.051 2.022 2.075 2.725

19 1.368 1.711 1.475 1.470 65 1.976 3.095 2.622 3.162

20 1.427 1.815 1.129 1.577 66 2.822 1.943 2.203 2.165

22 4.292 3.526 3.566 3.700 68 2.695 2.550 2.713 3.436

24 2.956 3.365 3.417 3.055 69 2.670 2.920 2.783 2.843

25 3.553 3.439 3.328 3.435 71 2.520 3.326 3.390 3.288

27 4.538 3.914 4.012 4.115 72 3.389 3.047 2.929 3.018

29 4.602 3.682 3.553 3.859 73 3.548 3.131 3.253 3.084

30 3.772 3.785 3.691 3.879 74 3.924 3.200 3.405 3.079

31 3.268 3.520 3.691 3.836 75 2.796 3.320 3.479 3.105

32 3.810 3.455 3.565 3.337 76 3.793 3.394 3.729 3.623

34 4.046 4.004 3.905 3.745 78 3.680 3.575 3.807 3.699

35 3.198 4.316 4.039 3.981 79 3.520 3.845 3.730 3.625

36 4.721 4.626 4.724 4.365 80 3.460 4.001 4.120 3.829

38 2.671 3.252 3.280 3.427 81 4.244 3.865 3.789 3.682

39 3.087 3.825 3.360 3.835 82 4.131 3.846 3.918 3.806

40 4.699 3.834 3.767 3.850 83 4.921 4.635 4.702 4.501

41 3.824 3.785 3.846 3.626 85 4.959 4.446 4.368 4.357

44 3.229 3.722 3.691 3.851 86 4.770 4.989 5.044 4.501

45 3.284 2.964 2.710 2.592 88 5.137 4.882 5.021 5.433

47 3.658 3.333 3.691 3.727

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metric to check the non-randomness and acceptability of

the developed models (Mitra et al., 2010).

Development of 3D-pharmacophore models

The target of aminothiazole and aminopyridine scaffolds

being unknown, so we have tried to determine the required

pharmacophoric features responsible for the inhibitory

activity with an indirect approach, i.e., by deriving a phar-

macophoric model. The antimalarial potency against P.

falciparum in terms of IC50 values was used as the dependent

variable for the pharmacophore development. We have

selected the test set for QSAR model as the training set for the

pharmacophore development and vice versa. This was done

to cross-check whether both the sets cover the complete

chemical space of whole dataset or not. The validated

pharmacophore could be utilized further to predict the

activity of unknown compounds of similar chemical domain.

Generation of pharmacophore model

The structures of all the compounds in .mol format were

converted into a single SDF file by the Open Babel

software (Boyle et al., 2011), and used as an input file

for the conformation generation. After conformation

generation, the pharmacophore was developed by the

HypoGen module of Discovery Studio (Li et al., 2000).

Pharmacophore development was carried out from the

training set compounds, setting different parameters in

the automatic generation procedure in the Discovery

Studio software (Accelry’s Discovery Studio 2.1), such

as activity uncertainty 2.0; maximum five features

including hydrogen bond acceptors (HBA), hydrogen

bond donors (HBD), hydrophobic aliphatic (HYAl),

hydrophobic aromatic, and ring aromatic regions (RA);

and 2.97 A for the interfeature spacing, by the BEST

method of poling algorithm.

Table 3 Observed and predicted activity obtained from stepwise MLR (Eq. 1), GFA spline (Eq. 2) and GFA linear/spline (Eq. 3) models for the

test set compounds

Comp. no. Observed pIC50 (mM) Predicted pIC50 (mM) Comp. no. Observed pIC50 (mM) Predicted pIC50 (mM)

Eq. 1 Eq. 2 Eq. 3 Eq. 1 Eq. 2 Eq. 3

2 2.190 2.676 2.861 2.471 42 4.097 4.332 3.769 4.114

4 1.990 2.320 2.569 2.793 43 4.481 4.534 4.495 4.098

7 2.833 3.069 3.398 2.863 46 3.481 3.955 3.821 4.165

9 2.533 3.514 2.946 2.302 48 2.620 3.942 3.802 4.146

14 1.436 1.147 0.973 1.063 56 4.167 3.521 3.549 3.785

18 1.653 1.985 1.944 2.059 59 4.481 4.018 3.553 3.790

21 4.310 3.568 3.565 3.802 62 3.971 3.327 3.310 3.402

23 3.087 3.382 3.691 3.183 67 3.319 2.941 3.077 3.556

26 3.921 3.959 3.815 3.720 70 4.009 3.150 3.225 3.152

28 3.301 3.612 3.634 3.476 77 3.538 3.644 3.962 3.449

33 3.745 3.061 3.248 3.664 84 4.721 4.976 5.164 4.501

37 4.237 3.963 3.710 3.902 87 5.000 4.654 4.655 5.433

Table 4 Comparison of statistical and validation parameters of different 2D-QSAR models

Eq.

no.

Type of

model

Descriptors R2 Q2 Rpred2 r2

mðtestÞ Drm(test)2 r2

mðOverallÞ Drm(Overall)2

1 Stepwise

MLR

SC-2, Atype _C_25, Atype _O_60, AlogP98,

Dipole-mag, CHI-V-3_C

0.705 0.637 0.700 0.578 0.17 0.591 0.200

2 GFA spline \-0.199-Jurs-FPSA-2[, \2.65-Atype_O_60[, \1.039-

Dipole-mag[, \Atype_C_25?1.69[0.736 0.689 0.668 0.544 0.174 0.609 0.195

3 GFA linear

and spline

\0.43-Radofgyration[, Atype_O_60, \Dipole-mag

?0.911[, Atype_N_67

0.724 0.675 0.720 0.612 0.119 0.617 0.180

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Pharmacophore mapping and validation

The developed model was validated by mapping the

entire test set molecules on the developed pharmaco-

phore. It was performed by using the same setting as

employed for a pharmacophore generation. The predict-

ability of a model to classify both more active and less

active compounds has been determined by classifying the

molecules with an activity threshold of 300 nM. The

training and test sets cover not only the total chemical

space of the whole dataset but also have uniform dis-

tribution of more active and less active compounds. The

training set (n = 24) consist of 13 more active and 11

less active compounds, while test set (n = 63) consist of

28 more active and 35 less active compounds. The plot

of distribution values of the inhibitory activity (pIC50)

of the compounds within the training and test sets along

with activity threshold is shown in Fig. 1. Different

qualitative validation parameters were computed in order

to check the quality of the model, to ideally distinguish

between two classes for both the sets. The validation

parameters include sensitivity, specificity, accuracy, pre-

cision, F-measure, recall, and G-means. These validation

parameters depend on the four different quantities,

namely true positives, true negatives, false positives, and

false negatives, and were calculated from the confusion

matrix based on the observed and predicted activity

values. The model is considered to be robust, if all the

validation parameters values are greater than 50 % for

both the sets. The equations for the calculation of dif-

ferent qualitative validation parameters are given in the

supplementary material section (S19-S25).

We have also performed the Fischer randomization test

(F-test), to check whether the obtained model is by chance

or not. It was carried out by scrambling the activity data of

training set molecules and by employing the same settings

as used for the pharmacophore development at 95 % con-

fidence interval. The actual model is considered to be

obtained by chance, if the results of randomized models are

better than the actual one.

Results and discussion

2D-QSAR analysis

QSAR models were developed by using a training set of

63 compounds and by utilizing different chemometric tools

(stepwise regression, GFA spline and GFA linear?spline).

These models were validated rigorously using different

validation and statistical metrics in search of robust and

statistically significant models. The external validation was

performed by predicting the activity of test set compounds

Fig. 2 a Pharmacophore hypothesis (Hypo-1) with one hydrogen

bond acceptor (HBA), one hydrophobic aliphatic (HYAl), and two

ring aromatic (RA) features and interfeature distance (A); b Mapping

of the most active compound 87 of the training set (pharmacophore

mapping) on the Hypo-1; c Mapping of the least active compound 14

(with two features missing) of the training set (pharmacophore

mapping) on Hypo-1

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using the developed models. The best QSAR models

obtained by different tools are given below.

QSAR model using stepwise multiple linear regression

pIC50ðmMÞ ¼ 3:355þ 0:288�00 SC�200 þ 0:61�00

Atype C 2500 � 0:266�00 Atype O 60

00

� 0:253�00 A log P9800 � 0:248�00

Dipole�mag00 þ 0:28�00 CHI�V�3 C

00

ð1Þ

NTraining = 63; R2 = 0.705, Ra2 = 0.674, Q2 = 0.637,

Rpred2 = 0.700, r2

mðtrainingÞ = 0.590, Drm(training)2 = 0.210;

NTest = 24; r2mðtestÞ = 0.578, Drm(test)

2 = 0.17, r2mðOverallÞ =

0.591, Drm(Overall)2 = 0.20, S = 0.550.

QSAR model using GFA spline term

pIC50ðmMÞ ¼ 1:50� 0:841�\� 0:199�00

Jurs�FPSA�200[ þ 0:327\2:65�00

Atype O 6000[ þ 0:245\1:039�00

Dipole�mag00[ þ 0:560\

00

Atype C 2500 þ 1:69 [

ð2Þ

NTraining = 63; R2 = 0.736, Ra2 = 0.717, Q2 = 0.689,

Rpred2 = 0.668, r2

mðtrainingÞ = 0.633, Drm(training)2 = 0.20;

NTest = 24; r2mðtestÞ = 0.544, Drm(test)

2 = 0.174, r2mðOverallÞ =

0.609, Drm(Overall)2 = 0.195, S = 0.512.

QSAR model using GFA linear and spline terms

pIC50ðmMÞ ¼ 4:201� 1:027�\0:43�00

RadofGyration00[ � 0:382�00 Atype O 60

00

� 0:237�\00Dipole�mag

00 þ 0:911 [

þ 0:171�00 Atype N 6700

ð3Þ

NTraining = 63; R2 = 0.724, Ra2 = 0.705, Q2 = 0.675,

Rpred2 = 0.720, r2

mðtrainingÞ = 0.618, Drm(training)2 = 0.210;

NTest = 24; r2mðtestÞ = 0.612, Drm(test)

2 = 0.119, r2mðOverallÞ =

0.617, Drm(Overall)2 = 0.180, S = 0.523.

In Eq. 1, SC-2 and CHI-V-3_C are topological

descriptors; Atype_C_25, Atype_O_60, and AlogP98 are

atom-type logP fragments (molecular hydrophobicity), and

Dipole-mag is the dipole moment (electronic descriptor).

The SC-2 index is the number of second-order subgraphs

Table 5 Results of process and model randomization tests

Eq.

no.

Type of model Model

randomization at

99 % confidence

level

Process

randomization at

95 % confidence

level

R2 Rr2 cRp

2 R2 Rr2 cRp

2

2 GFA spline 0.735 0.073 0.698 0.735 0.262 0.589

3 GFA

linear ? spline

0.724 0.012 0.718 0.724 0.249 0.587

Table 6 Results of pharmacophore development for antimalarial

activity against multidrug-resistant P. falciparum; Config.

cost = 16.105; null cost = 193.357; fixed cost = 86.903

Hypothesis Features Total

cost

Dcosta Dcostb rmsd correlation

(R)

c1 HBA,

HYAl,

RA,

RA

103.046 90.311 16.143 1.158 0.932

2 HBA,

HYAl,

RA,RA

118.637 74.72 31.734 1.594 0.868

3 HBA,

HYAl,

RA,RA

119.086 74.271 32.183 1.633 0.860

4 HBA,

HBD,

RA

119.773 73.584 32.87 1.654 0.856

5 HYA,

RA,

RA

120.356 73.001 33.453 1.669 0.854

6 HYA,

RA,

RA

120.705 72.652 33.802 1.664 0.855

7 HBA,

HBD,

RA

120.828 72.529 33.925 1.677 0.852

8 HBA,

HYAl,

RA,

RA

120.855 72.502 33.952 1.648 0.858

9 HBA,

RA,

RA

121.129 72.228 34.226 1.688 0.850

10 HBA,

HYAl,

RA,

RA

121.189 72.168 34.286 1.640 0.859

Dcosta : (null cost - total cost)

Dcostb: (total cost - fixed cost)c Best hypothesis

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in the molecular graph. It indicates the number of pairs of

connected edges. As the value of an SC-2 index increases,

the size of the molecule also increases. The most active

molecule (88) with an SC-2 index value of 49 and the least

active molecule (19) with an SC-2 index value of 22

suggest that, as the size of the molecule increases, the

activity also increases. The CHI-V-3_C is a Kier & Hall

valence-modified connectivity index, which takes into

consideration of the electronic configuration of atoms

represented by the vertex with four skeletal atoms in a

trigonal relationship. This structural motif is generally

appears only in trifluoromethane and tetrafluoromethane

fragments in the datasets (Cerius2 QSAR? 4.5 manual,

2000). The presence of trifluoromethyl (–CF3) group in the

aminopyridine compounds contributes positively to the

inhibitory activity. All the compounds possessing a tri-

fluoromethyl fragment have pIC50 values greater than 3 log

unit. The compounds 64 and 65, despite having –CF3

fragments, show lower activity. This may be due to that

although these compounds have –CF3 fragments, they have

low second-order subgraph count (SC-2). This shows that

the compounds with both higher values of an SC-2 and

CHI-V-3_C indices have higher antimalarial activity as

observed in case of compounds 83–88. This indicates that

the topological descriptors can encode the required struc-

tural features for the activity with positive contributions of

higher molecular size and the presence of trifluoromethyl

group. The dipole moment is a 3D electronic descriptor

which indicates the strength and orientation behavior of a

molecule in an electrostatic field. It is estimated by uti-

lizing partial atomic charges and atomic coordinates. As

the dipole moment increases, the polarity of the molecule

also increases and vice versa. The negative contribution of

dipole moment indicates that, hydrophobicity is favorable

for the activity, which is also shown by the positive con-

tribution of Atype_C_25 and CHI-V-3_C indices. The

hydrophobicity requirement for the activity is also

confirmed by the presence of at least two aromatic

rings (Pharamcophoric features; vide infra) in all the

compounds.

The hydrophobic parameter Atype_C_25 contributes

positively, while Atype_O_60 and AlogP98 contribute

negatively to the activity. The Atype_C_25 index is related

to the tertiary carbon atom (R–CR–R) of the benzene ring

(Ghose et al., 1998). The compound with more number of

tertiary carbon atoms of benzene ring was found to be the

most active (compound 88) than the least active compound

with fewer number of tertiary carbon atoms (compound

19). The descriptor Atype_O_60 is related to the groups of

type Al–O–Ar, Ar2O, and R–O–R. The presence of di-

aryloxy or arylalkyloxy groups in compounds 64 (CH3–O–

py and ph–O–CF3), 66 (two CH3–O–py), and 69 (CH3–O–

py) decreases the antimalarial activity as compared to the

most active compound (88), which does not possess any of

these groups.

We have also performed the GFA analysis to improve

the results of stepwise regression model. The equation

obtained by GFA spline is given in Eq. 2. In Eq. 2, the

Jurs-FPSA-2 index is a fractional charged partial surface

area (spatial descriptor). This is calculated by dividing total

charge weighted positive surface area by the total molec-

ular solvent-accessible surface area. The negative contri-

bution of Jurs-FPSA-2 suggests that, an ionic interaction

involving positive charges of the ligand is not favorable for

interaction with the receptor protein. The descriptors Aty-

pe_C_25, Atype_O_60, and Dipole-mag in Eq. 2 have same

Table 7 Observed and estimated antimalarial activity against P. falciparum of the training set compounds based on Hypo-1

Comp. no. Training set Activity scale Comp. no. Training set Activity scale

Observed

(IC50 nM)

Estimated

(IC50 nM)

Observed Estimated Observed

(IC50 nM)

Estimated

(IC50 nM)

Observed Estimated

2 6,450 10,845 L L 42 80 207 H H

4 10,230 14,694 L L 43 33 541 H L

7 1,470 2,096 L L 46 330 500 L L

9 2,930 469 L L 48 2,400 243 L H

14 36,620 20,159 L L 56 68 60 H H

18 22,210 21,193 L L 59 33 29 H H

21 49 55 H H 62 107 96 H H

23 818 516 L L 67 480 319 L L

26 120 465 H L 70 98 277 H H

28 500 518 L L 77 290 147 H H

33 180 112 H H 84 19 28 H H

37 58 98 H H 87 10 19 H H

Compounds with IC50 B 300 nM: more active (H) and IC50 [ 300 nM: less active (L)

Med Chem Res

123

type of contributions to the activity as observed in the

stepwise regression equation. In order to improve the results

further, we have combined the linear and spline terms in

GFA analysis (Eq. 3). In Eq. 3, the RadofGyration index is

a size descriptor (spatial descriptor) for the distribution of

atomic masses in a molecule. It measures the molecular

compactness, i.e., smaller values are observed when most of

the atoms are close to the center of mass. The equation

(S18) for the calculation of radius of gyration is given in the

supplementary material section. The negative contribution

of RadofGyration suggests that the antimalarial activity

increases with increase in the molecular compactness. As a

molecule becomes more and more compact, more will be

the possibility of its entering into the active site and con-

sequent increase in the antimalarial activity. The Aty-

pe_N_67 index is atomic logP contribution due to Al–NH–

Al group. The presence of secondary nitrogen as observed

in the piperazine ring of compounds 87 and 88 suggests that,

its presence is responsible for the high antimalarial activity.

The descriptors Atype_O_60 and Dipole-mag have similar

type of contributions to the activity as observed in the

stepwise regression and GFA spline equations. The

observed and predicted activity of the training and test sets

compounds, computed from different models, are given in

Table 8 Observed and estimated antimalarial activity against P. falciparum of the test set compounds using Hypo-1

Comp. no. Test set Activity scale Comp. no. Test set Activity scale

Observed

(IC50 nM)

Estimated

(IC50 nM)

Observed Estimated Observed

(IC50 nM)

Estimated

(IC50 nM)

Observed Estimated

1 80 1,683 H L 49 2,160 210 L H

3 7,050 14,190 L L 50 1,120 695 L L

5 10,960 4,204 L L 51 71 207 H H

6 4,940 1,660 L L 52 30 553 H L

8 4,270 647 L L 54 2,527 20 L H

10 1,480 1,693 L L 55 44 52 H H

11 190 1,797 H L 57 1,059 54 L H

12 670 836 L L 58 109 67 H H

13 34,110 4,752 L L 60 166 28 H H

15 6,980 11,643 L L 61 812 56 L H

16 11,100 11,590 L L 63 942 39 L H

17 42,540 40,722 L L 64 8,898 49 L H

19 42,870 40,491 L L 65 10,576 31 L H

20 37,450 13,263 L L 66 1,508 24 L H

22 51 98 H H 68 2,018 311 L L

24 1,106 596 L L 69 2,140 316 L L

25 280 86 H H 71 3,018 315 L L

27 29 359 H L 72 408 319 L L

29 25 88 H H 73 283 104 H H

30 169 231 H H 74 119 91 H H

31 540 455 L L 75 1,598 20 L H

32 155 43 H H 76 161 17 H H

34 90 43 H H 78 209 169 H H

35 634 153 L H 79 302 28 L H

36 19 31 H H 80 347 66 L H

38 2,132 28 L H 81 57 141 H H

39 818 449 L L 82 74 26 H H

40 20 93 H H 83 12 17 H H

41 150 208 H H 85 11 18 H H

44 590 390 L L 86 17 37 H H

45 520 536 L L 88 7 18 H H

47 220 206 H H

Compounds with IC50 B 300 nM: more active (H) and IC50 [ 300 nM: less active (L)

Med Chem Res

123

Tables 2 and 3. The comparison of different statistical and

validation parameters of different models is given in

Table 4. The developed models are not obtained by chance,

and this was confirmed by the model and process random-

ization tests at 95 and 99 % confidence levels. The squared

average correlation coefficients of the random models (Rr2)

for the model and process randomization test were found to

be less than the squared correlation coefficient (R2) of the

corresponding nonrandom models. Moreover, the robust-

ness of models was also confirmed by the high values of cRp2

([0.5). The results of randomization tests are given in

Table 5. All the three models show the acceptable criteria

for statistical validation. But the quality of stepwise

regression model depends on six independent variables,

while the quality of GFA models depends on only four

variables. Among the developed models, the model devel-

oped by GFA linear and spline terms (Eq. 3) was found to

be the best model based on the quality of statistical metrics

and number of independent variables present in the model.

3D-Pharmacophore model

Ten different pharmacophore hypotheses were obtained

from a training set of 24 compounds. The pharmacophore

model (Hypo-1) with a high correlation coefficient (r: 0.932),

lower root mean square deviation (rmsd: 1.15), error 85.79,

and weight 1.14 was found to be of the acceptable quality.

The configuration cost was also within the recommended

range, which indicates that all the generated models have

been thoroughly analyzed. The actual cost for Hypo-1 is

much closer to the fixed cost with only a difference of 16.14

bits, which indicates the true correlation of the data. Again,

there is a large difference of 90.31 bits between the actual

cost and the null cost for Hypo-1. Hence, Hypo-1 was found

to be the best one among the ten hypotheses with one HBA,

one HYAl, and two ring aromatic features (Fig. 2a). The

results of ten pharmacophore hypotheses against multidrug-

resistant P. falciparum are given in Table 6. The external

predictability of the model has been done by mapping the test

set molecules on the Hypo-1 with the same settings as

employed for the pharmacophore generation by the BEST

method. All the molecules were mapped completely. The

classification ability of the model to classify compounds into

more active and less active antimalarials was checked by

comparing the observed activity with predicted activity by

the classification based technique. For this purpose, the

compounds with IC50 values B 300 nm were classified as

more actives and compounds with IC50 values [ 300 nM as

less actives. The observed and estimated activity of the

training and test sets compounds using Hypo-1 are given in

Tables 7 and 8, respectively. The values of different vali-

dation parameters for training as well as test sets are given in

Table 9. The values of validation parameters for both the

training and test sets are greater than 62.00 %, which suggest

the robustness and acceptability of the developed model. The

model correctly classified 11 out of 13 compounds as actives

(84.62 %) and 10 out of 11 (90.91 %) compounds as less

actives for the training set. For the test set, the model cor-

rectly classified 24 out of 28 compounds (85.71 %) as more

actives and 22 out of 35 (62.85 %) compounds as less active

antimalarials. The model reveals better classification of the

more active compounds for the test set. So, the Hypo-1 model

is best suited for the classification of more active antimala-

rials against multidrug-resistant P. falciparum.

All the compounds have at least two ring aromatic

features, of which one ring is either pyrazole/pyridine/

thiazole and the other is phenyl or other heterocycle. These

two RA features are the preliminary requirements for the

activity against multidrug-resistant P. falciparum strain.

These two RA features are also in accordance with the

Atype_C_25 and SC-2 indices of the 2D-QSAR models

(Eqs. 1, 2). The most active compound of the training set

(87, IC50:10 nM) mapped completely on Hypo-1 with all

the four features (Fig. 2b). The two pyridine rings lie in the

RA region, the trifluoromethyl (–CF3) group in the HYAl

region, and the carbonyl oxygen in the hydrogen bond

acceptor region. The least active compound (14, IC50:

36,620 nM) of the training set lacks one HBA and one

HYAl feature, and thus does not map completely (Fig. 2c).

The absence of these two features decreases the antima-

larial potency by 1,000 times as compared to the activity of

compound 87. The two ring aromatic features including

one aminopyridine and the other pyridine/phenyl, one

hydrophobic aliphatic as –CF3 group, and one hydrogen

bond acceptor as carbonyl group flanked with one phenyl

and piperazine ring are responsible for the highest anti-

malarial potency. These features are present only in com-

pounds 87 and 88, due to which these compounds show

Table 9 Different qualitative validation parameters of Hypo-1 model obtained by classification of more active and less active compounds for the

training and test sets

Training/Test Qualitative validation parameters (%)

No. of compounds Sensitivity Specificity Recall Accuracy Precision F-measure G-means

Training set (28 % compounds) 24 84.62 90.91 84.62 87.50 91.67 88.00 87.71

Test set (72 % compounds) 63 85.71 62.86 85.71 73.02 64.86 73.85 73.40

Med Chem Res

123

activity B10 nM. The HYAl feature (–CF3) of pharmaco-

phore are also in accordance with the CHI-V-3_C

descriptor of the 2D-QSAR model (Eq. 1). The replace-

ment of HYAl (–CF3 group) with ethereal oxygen R–O–Ar

(Atype_O_60) reduces the antimalarial potency as

observed in the negative contribution of Atype_O_60 index

(Eqs. 1–3). The structures of the most active and least

active compounds of the training set, along with the

pharmacophoric features and QSAR descriptors are given

in Fig. 3. The F-test confirms the non-randomness of the

developed pharmacophore (Hypo-1). This was confirmed

by a higher correlation coefficient (R: 0.932) of the actual

model than the average correlation coefficient of random

models (Rr: 0.64), and by also the closeness of total cost of

the actual model (103.04) to the fixed cost (86.90), rather

than the average cost of randomized models (158.79).

Moreover, the high value of cRp2 ([0.5) (0.713) further

confirms the robustness of the model. The original and

randomized total cost values of hypotheses for F-test are

given in Fig. S1 of the Supplementary materials section.

In summary, this study suggests that both the models

could be utilized for quantitative (2D-QSAR Eq. 3) as well

as qualitative (pharmacophore) prediction of antimalarial

activity of similar class of compounds as used in this study

against the deadly multidrug-resistant P. falciparum.

Conclusion

We have developed here 2D-QSAR and 3D-pharmaco-

phore models using aminothiazole and aminopyridine

compounds for their activity against multidrug-resistant

strain (k1) of P. falciparum. The selected dataset was

important because of two aspects. Firstly, it comprises

novel scaffolds (aminothiazole and aminopyridines) for

which there are no previous reports of development of

QSAR/pharamcohphore modeling against the multidrug-

resistant strain of P. falciparum. Secondly, the compounds

show inhibitory activity against multidrug-resistant strain

(k1) of P. falciparum, the further study of which is

important in a scenario of increasing P. falciparum resis-

tance. The study of QSAR and pharmacophore modeling

would be important in order to rationally identify and

design the inhibitors of these two classes, in the absence of

the target information. The 3D-pharmacophoric study

unveiled four different pharmacophoric features namely

one HBA, one HYAl, and two RA features contributing to

the antimalarial potency against multidrug-resistant strain

of P. falciparum. The two ring aromatic features (one

aminopyridine/pyrazole/pyridine/thiazole rings and the

other phenyl/other heterocycle) are the minimum structural

features for the activity, while the HBA (carbonyl group)

and HYAl (–CF3 group) features contribute to the potency

of the compounds. The absence of HBA and HYAl features

in the structure results in a significant decline of antima-

larial potency. Thus, these pharamcophoric features could

be helpful in designing compounds against multidrug-

resistant strain (k1) of P. falciparum. The developed

regression and pharmacophore models are statistically

significant and robust for the prediction of antimalarial

activity of aminothiazoles and aminopyridines against the

multidrug-resistant strain of P. falciparum. These models

can also be utilized for screening of compounds within the

applicability domain for discovering novel leads against

multidrug-resistant P. falciparum.

Acknowledgments The authors are thankful to the University

Grants Commission (UGC), New Delhi for providing financial

assistance in the form of a major research project (KR).

References

Athri P, Wenzler T, Tidwell R, Bakunova SM, Wilson WD (2010)

Pharmacophore model for pentamidine analogs active against

Plasmodium falciparum. Eur J Med Chem 45(12):6147–6151

Accelry’s Discovery Studio 2.1. http://accelrys.com/products/

discovery-studio/

Bhattacharjee AK, Hartell MG, Nichols DA, Hicks RP, Stanton B, van

Hamont JE, Milhous WK (2004) Structure-activity relationship

study of antimalarial indolo [2, 1-b] quinazoline-6, 12-diones

(tryptanthrins). Three dimensional pharmacophore modeling and

identification of new antimalarial candidates. Eur J Med Chem

39(1):59–67

Boyle NM, Banck M, James CA, Morley C, Vandermeersch T,

Hutchison GR (2011) Open Babel: an open chemical toolbox.

J Cheminform 3(1):1–14

Cerius2 QSAR ? 4.5. manual (2000): 28-35

Fig. 3 Structures of the most active and least active compounds of

the training set with pharmacophoric features and QSAR descriptor

fragments

Med Chem Res

123

Cerius 2 version 4.10 is a product of Accelrys, Inc., San Diego, USA.

http://www.accelrys.com/cerius2

CS ChemDraw version 5.0. http://www.camsoft.com

Dondorp AM, Nosten F, Yi P, Das D, Phyo AP, Tarning J, Lwin KM,

Ariey F, Hanpithakpong W, Lee SJ (2009) Artemisinin resis-

tance in Plasmodium falciparum malaria. N Engl J Med 361(5):

455–467

Ghose AK, Viswanadhan VN, Wendoloski JJ (1998) Prediction of

hydrophobic (lipophilic) properties of small organic molecules

using fragmental methods: an analysis of ALOGP and CLOGP

methods. J Phys Chem A 102(21):3762–3772

Golbraikh A, Tropsha A (2002) Beware of q2 ! J Mol Graph Model

20(4):269–276

Gonzalez Cabrera D, Douelle F, Younis YE, Feng TS, LeManach C,

Nchinda AT, Street LJ, Scheurer C, Kamber J, White KL (2012)

Structure-activity relationship studies of orally active antimalarial

3, 5-substituted 2-aminopyridines. J Med Chem 55:11022–11030

Li H, Sutter J, Hoffmann R (2000) HypoGen: An automated system

for generating predictive 3D pharmacophore models. Pharma-

cophore perception, development, and use in drug design.

International University, La Jolla, pp 171–189

Li J, Li S, Bai C, Liu H, Gramatica P (2013) Structural requirements

of 3-carboxyl-4 (1H)-quinolones as potential antimalarials from

2D and 3D QSAR analysis. J Mol Graphics Model 44:266–277

Mahajan DT, Masand VH, Patil KN, Hadda TB, Rastija V (2013)

Integrating GUSAR and QSAR analyses for antimalarial activity

of synthetic prodiginines against multi drug resistant strain. Med

Chem Res 22(5):2284–2292

Mitra I, Saha A, Roy K (2010) Exploring quantitative structure–

activity relationship studies of antioxidant phenolic compounds

obtained from traditional Chinese medicinal plants. Mol Simul

36:1067–1079

MINITAB 14 is a Statistical software of Minitab Inc., USA, http://

www.minitab.com

Paquet T, Gordon R, Waterson D, Witty MJ, Chibale K (2012)

Antimalarial aminothiazoles and aminopyridines from pheno-

typic whole-cell screening of a SoftFocusA� library. Future Med

Chem 4(18):2265–2277

Roy K, Mitra I, Kar S, Ojha PK, Das RN, Kabir H (2012)

Comparative studies on some metrics for external validation of

QSPR models. J Chem Inf Model 52(2):396–408

Sahu NK, Sharma MC, Mourya V, Kohli DV (2011) QSAR studies of

some side chain modified 7-chloro-4-aminoquinolines as anti-

malarial agents. Arabian J. Chem (in press). doi:10.1016/j.arabjc.

2010.12.005

SPSS 9.0 is statistical software of SPSS Inc., USA. http://www.spss.

com

Wenzel NI, Chavain N, Wang Y, Friebolin W, Maes L, Pradines B,

Lanzer M, Yardley V, Brun R, Herold-Mende C (2010)

Antimalarial versus cytotoxic properties of dual drugs derived

from 4-aminoquinolines and Mannich bases: interaction with

DNA. J Med Chem 53(8):3214–3226

World Malaria Report 2012; http://www.who.int/malaria/publications/

world_malaria_report_2012/en/ Accessed on 12 June 2013

Xue CX, Cui SY, Liu MC, Hu ZD, Fan BT (2004) 3D QSAR studies

on antimalarial alkoxylated and hydroxylated chalcones by

CoMFA and CoMSIA. Eur J Med Chem 39(9):745–753

Med Chem Res

123