ORIGINAL RESEARCH

Modeling of antitubercular activity of biphenyl analogsof 2-nitroimidazo[2,1-b][1,3]oxazine to rationalize theiractivity profile

Sourav Kalra • Ankit Kumar • Manish K. Gupta

Received: 29 June 2012 / Accepted: 7 November 2012

� Springer Science+Business Media New York 2012

Abstract The antitubercular activity of ortho-, meta- and

para-substituted biphenyl analogs of 2-nitroimidazo[2,1-

b][1,3]oxazine has been analyzed through combinatorial

protocol in multiple linear regression using physicochemical

and structural descriptors obtained from MOE software. The

different electronic, hydrophobic, and steric descriptors have

been identified for the model development. The study indi-

cates that the ortho substituents with high LUMO (electron

acceptor) and less dipole moment are favorable for the

activity. The meta and para positions are mainly influenced

by steric parameters. Here, the molar refractivity and volume

surface area descriptors suggested that the small substituents

are conducive for the activity. Also, the para substituents

with negatively charged polar surface area are unfavorable

for the activity. The identified physicochemical descriptors

also indicate the nature of receptor surface in Mycobacterium

tuberculosis for this class of compounds. The models and

participating descriptors suggest that the substituted biphe-

nyl analogs of 2-nitroimidazo[2,1-b][1,3]oxazine hold scope

for further modification in the optimization of antitubercular

activity.

Keywords Antitubercular agents �2-Nitroimidazo[21-b][13]oxazine � QSAR � CP-MLR

Introduction

The mycobacterium, Mycobacterium tuberculosis (M. tb.),

is the causative pathogen for tuberculosis (TB) and

responsible for the morbidity and mortality of significant

population worldwide. The World Health Organization

(WHO) estimated that approximately 1.4 million people

died from TB in 2010 including 70,000 children (TB facts,

2012). Although, an effective multidrug treatment for a

period of 6–9 months is available for the TB, but the

deviation from the schedule and poor patient compliance

led to the development of Multi Drug-Resistant (MDR) and

Extensively Drug-Resistant (XDR) tuberculosis (Ginsberg

and Spigelman, 2007). The development of MDR-TB and

XDR-TB is of major concern because the Mycobacterium

lost susceptibility to the all effective and newer drugs. The

MDR and XDR-TB appear as life threatening in nature and

are difficult and expensive to treat (Migliori et al., 2010).

The WHO estimated that up to 650,000 people were

infected with drug-resistant forms of TB in 2010 (TB facts,

2012). Also, the TB is a major threat for the people living

with HIV and other weaken immune systems. Therefore, its

control is a high-priority task for both, the developed as

well as developing countries. This represents an urgent

need and demand for the development of potent and safe

drugs for the effective treatment of drug-sensitive and

drug-resistant TB.

During the past few decades, many Mycobacterium

enzymes as well as cellular mechanisms have been proposed

as viable targets to overcome the problem of MDR-TB (Lou

and Zhang, 2010). Some important lead molecules have been

identified with efficacy against both replicating (aerobic) and

non-replicating (non-aerobic) form of M. tb. (Tripathi et al.,

2012). The search for newer drugs for TB led to the discovery

of the 2-nitroimidazo-[2,1-b][1,3]oxazines (PA-824, a,

Electronic supplementary material The online version of thisarticle (doi:10.1007/s00044-012-0348-8) contains supplementarymaterial, which is available to authorized users.

S. Kalra � A. Kumar � M. K. Gupta (&)

Molecular Modeling and Pharmacoinformatics Lab,

Department of Pharmaceutical Chemistry,

ISF College of Pharmacy, Moga 142001, India

e-mail: mkgupta5@gmail.com

123

Med Chem Res

DOI 10.1007/s00044-012-0348-8

MEDICINALCHEMISTRYRESEARCH

Fig. 1) (Stover et al., 2000) and the related 6-nitroimi-

dazo[2,1-b][1,3]oxazoles (OPC-67683, b, Fig. 1) (Mat-

sumoto et al., 2006; Sasaki et al., 2006). These drugs showed

activity against both replicating and non-replicating form of

M. tb in animal models (Ma et al., 2006; Rivers and Mancera,

2008). Currently, both ‘a’ and ‘b’ are under the clinical trials

for the treatment of TB (Guillemont et al., 2009; Tam et al.,

2009). A study by Tasneen et al. (2008) showed that the

combination of ‘a’ at 100 mg/kg with pyrazinamide was as

effective as the standard first-line combination of rifampin,

isoniazid, and pyrazinamide, but the emergence of strains

resistant to ‘a’ was observed. In order to improve pharma-

cological profile over both a and b, the Palmer et al. (2010)

synthesized a series of biphenyl analogs of 2-nitroimi-

dazo[2,1-b][1,3]oxazine (analogs of a, Fig. 1c; Table 1) and

evaluated for their efficacy against replicating (MABA) and

non-replicating (LORA) form of M. tb. In this study, some

analogs have been found many fold more active than the

parent drug ‘a’ (Palmer et al., 2010).

Rationale drug design approaches, which include quan-

titative structure–activity relationship (QSAR) and molec-

ular modeling protocols, cull out structural and functional

requirements of chemical entities desirable for biological

response. Addressing this aspect, the QSAR and molecular

modeling protocols project the optimal structural and func-

tional requirements for the desirable biological response.

Establishing a correlation between the structure and the

associated activity is a prior measure for identifying the

structural/functional requirements of the activity. In these

studies, parameterization of chemical structure plays a piv-

otal role. In enumeration of chemical structures, it is

important to note that in isolation, a data point is only a

qualified number. A collection of such qualified numbers

makes a variable or descriptor. In mathematical models each

and every (independent; X) variable communicate with the

target (dependent; Y) variable. A meaningful communication

between X and Y variables result in the evolution of models

with predictive value.

With this philosophy, a QSAR study has been contemplated

on biphenyl analogs of 2-nitroimidazo[2,1-b][1,3]oxazine

(Fig. 1c; Table 1) to rationalize their antitubercular activity

profile (Palmer et al., 2010). For the study, different empirical,

physiochemical, and structural descriptors of the compounds

have been opted from MOE software (MOE, 2011). In the

present study, the QSAR models have been developed using the

variable selection procedure, combinatorial protocol in multi-

ple linear regression (CP-MLR) which is a variable selection

procedure for model development in QSAR and QSPR studies

(Prabhakar, 2003; Gupta and Prabhakar, 2006; Saquib et al.,

2007).The results are presented here.

N

N

O

OO2N A B

C

OCF3

N

NO2N

OO

NO

OCF3

(a) (b)

N

N

O

OO2N A

D

B X

C

(c)

Fig. 1 (a) and (b) are

antitubercular agents in clinical

trials; (c) general structure of

biphenyl analogs of

2-nitroimidazo[2,1-b][1,3]-

oxazine; (•) indicates ortho/

meta/para substitutions

Table 1 Antitubercular activity of ortho-linked biphenyl analogs

N

N

O

OO2N

X

A B

C

D

S. no. X -logMIC

MABA LORA

Observedb Eq. 1c Observedb Eq. 2c

1 H 6.193 5.818 5.602 5.510

2 2-OCF3 5.920 6.163 5.208 5.341

3 3-CN 6.096 5.672 5.086 5.075

4a 3-F 6.207 6.123 5.523 5.419

5 3-OCF3 5.721 5.933 5.201 5.076

6a 3-SMe 6.091 5.856 5.495 5.470

7 4-COCH3 4.769 5.147 4.444 4.884

8 4-CN 5.075 5.014 4.721 5.010

9a 4-F 5.920 5.822 5.469 5.407

10 4-OCF3 5.638 5.935 5.367 5.440

11 4-SMe 5.886 6.043 5.469 5.645

12a 3-F,4-Ome 5.744 6.098 5.420 5.516

13 3,4 benzo 5.795 5.463 5.509 5.215

a Test set compounds (four comp.)b Observed activity (Palmer et al., 2010)c Predicted activities from QSAR models

Med Chem Res

123

Materials and methods

Data set

From the literature report, a database of 115 biphenyl

analogs of 2-nitroimidazo[2,1-b][1,3]oxazine along with

their antitubercular activity as logarithm of the inverse of

minimum inhibitory concentration (-logMIC where MIC

in moles per liter determined under aerobic (MABA) or

anaerobic (LORA) conditions) have been considered for

the investigation (Table 1) (Palmer et al., 2010). These

analogs were synthesized by aryl substitution at ortho

(Table 1, 13 compounds), meta (Table 2, 22 compounds)

and para position (Table 3, 80 compounds) of phenyl ring

of ‘a’. The database structures of compounds were gener-

ated in molecular operating environment (MOE) software

version 2011 (MOE, 2011). For the structure database, the

minimum energy conformation of 77 was identified

through its systematic conformational analysis in MOE

with default parameter settings followed by energy mini-

mization (force field: MMFF94x; rms gradient 0.01)

(Halgren, 1996). The remaining structures of the database

were generated from this template of 83 by appending

appropriate changes to it followed by structural refinement

by MMFF94x method. The structure database was used in

MOE to compute various 2D- and 3D-descriptors of the

chemical space. They include physicochemical properties,

surface areas, atom and bond counts, topological descrip-

tors, H-bond donor/acceptor features, partial charges, and

potential energies. It has resulted in 316 descriptors to

qualify the chemical space of the compounds used in study

(Tables 1, 2, 3). Further, for external validation of QSAR

models, the compound datasets has been divided into

training and test using single linkage hierarchical clustering

of all the descriptors (Tables 1, 2, 3). The QSAR analysis

and model development for the compounds of each table

(Tables 1, 2, 3) were carried out separately. The compu-

tational procedure for the model development is briefly

described below

QSAR procedure

The CP-MLR method has been used to generate QSAR

models. The CP-MLR is a ‘filter’based variable selection

procedure for model development in QSAR studies (Prabha-

kar et al., 2004; Gupta et al., 2005; Saquib et al., 2007). Its

procedural aspects and implementation are discussed in some

of the recent publications (Gupta et al., 2005; Saquib et al.,

2007; Prabhakar et al., 2006; Gupta et al., 2005; Sharma et al.,

2009a, b; Kumar et al., 2012). It involves a combinatorial

strategy with appropriately placed ‘filters’ interfaced with

MLR and extracts diverse models having unique combination

of descriptors from the dataset. In CP-MLR, the filter-1 seeds

the variables by way of limiting inter-parameter correlations

to predefined level (default acceptable value B0.3); filter-2

controls the variables entry to a regression equation through t-

values of coefficients (threshold value C2.0) which confer

more than 95 % regression coefficients significant level; fil-

ter-3 provides comparability of equations with different

number of variable in terms of square-root of adjusted mul-

tiple correlation coefficient of regression equation ‘r-bar’ and

filter-4 estimates the consistency of the equation in terms of

cross-validated R2 or Q2 with ‘‘leave-one-out’’ (LOO) cross-

validation as default option (threshold value 0.3 B Q2 B1.0).

At the first stage of the study, for the filters-1, 2, 3, and 4 of CP-

MLR the thresholds were assigned as 0.3, 2.0, 0.71, and

Table 2 Antitubercular activity of meta-linked biphenyl analogs

N

N

O

OO2N A B

C

D X

S. no. X -logMIC

MABA LORA

Observedb Eq. 3c Observedb Eq. 4c

14 H 7.022 6.822 5.678 5.374

15 2-OCF3 6.337 6.557 5.481 5.502

16 3-CF3 6.469 6.932 5.538 5.779

17a 3-CN 6.770 6.770 5.432 5.491

18 3-F 6.745 6.872 5.569 5.562

19 3-OH 6.523 6.527 5.432 5.331

20a 3-OCF3 6.398 6.398 5.456 5.631

21 3-O(CH2)3OH 6.237 6.263 5.602 5.563

22 3-O(CH2)3N-morph 5.854 6.136 4.959 4.963

23 3-SMe 7.155 7.025 5.854 5.739

24a 4-COCH3 6.523 6.523 5.721 5.673

25 4-CF3 6.921 6.859 5.538 5.614

26 4-CN 6.638 6.625 5.319 5.544

27a 4-F 7.114 7.113 5.420 5.549

28a 4-OH 6.268 6.268 5.237 5.389

29 4-OCF3 6.959 6.663 5.658 5.601

30 4-OCF2H 7.222 7.244 5.553 5.656

31 4-O(CH2)3OH 6.444 6.318 5.824 5.470

32 4-O(CH2)3N-morph 6.161 5.937 4.959 5.125

33a 4-SMe 7.022 7.022 6.032 5.833

34 3-F,4-OMe 6.721 6.652 5.658 5.663

35a 3,4 benzo 6.921 6.921 5.495 5.559

a Test set compounds (seven comp.)b Observed activity (Palmer et al., 2010)c Predicted activities from QSAR models

Med Chem Res

123

0.3 B Q2 B 1.0, respectively to develop baseline models

(1–3 descriptor models). The QSAR analysis and model

development for the compounds of each table (Table 1, 2, 3)

were carried out separately. The base line models offer

information rich descriptors corresponding to the activity

under investigation (Gupta et al., 2005). In second stage, the

descriptors identified in the first stage were reused to develop

higher models. Since filter-1 in CP-MLR controls the asso-

ciation of variables, setting a higher value for this filter

facilitates the free mixing of descriptors. Therefore, to

develop three and four-parameter models, the threshold value

of filter-1 of CP-MLR was set to 0.79. All the identified

models were reassessed for the chance correlations if any, by

repeated randomization of the activity (Prabhakar et al.,

2004). Each identified model was subjected to 100 simula-

tion runs with scrambled activity and the emerging correla-

tions were counted to express the percent chance correlation

of the model under examination. The proposed models were

validated through test set. The predictive ability of the

models was further evaluated by calculating their overall

metric values. The metric is represented by the

following equation:

r2m ¼ r2ð1�

ffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffi

r2 � r20Þ

q

Here r2 and r20 are correlations between the observed and

predicted values with and without intercept, respectively,

for the least squares regression lines. The metric r2m values

help to circumvent the overestimation of the quality of

prediction due to a wide response range (Y-range) (Ojha

et al., 2011; Roy et al., 2012).

Result and discussion

QSAR models for ortho-linked biphenyl analogs

The QSAR analysis and model development for the com-

pounds of each table (Table 1–3) were carried out sepa-

rately. The following are selected models for MABA

(Eq.1) and LORA (Eq.2) activities of compounds of

Table 1

� logMICMABA ¼ 3:212 0:754ð ÞAM1 LUMO

� 0:227 0:074ð ÞMNDO dipoleþ 10:013

n ¼ 9; r ¼ 0:889;Q2 ¼ 0:546;Q2L3O ¼ 0:605;

s ¼ 0:247;F ¼ 11:333;

FIT ¼ 1:744;LOF ¼ 0:132;AIC ¼ 0:122;

ryrand S:Dð Þ ¼ 0:468 0:183ð Þ;R2T ¼ 0:618 ð1Þ

� logMICLORA ¼ 2:170 0:477ð ÞAM1 LUMO

� 0:039 0:013ð ÞPEOE VSA� 5þ 6:784

n ¼ 9; r ¼ 0:941;Q2 ¼ 0:648;Q2L3O ¼ 0:705;

s ¼ 0:149;F ¼ 23:152;

FIT ¼ 3:562;LOF ¼ 0:048;AIC ¼ 0:045;

ryrand S:Dð Þ ¼ 0:408 0:172ð Þ;R2T ¼ 0:935 ð2Þ

In all regression equations, n is number of compounds, r is

correlation coefficient, Q2 is cross-validated R2 from leave-

one-out (LOO) procedure, Q2L3O is cross-validated R2 from

leave-three-out procedure (where a group of three compounds

are randomly kept outside the analysis each time in such a way

that all compounds are in the predictive groups for once), S is

standard error of the estimate and F is F-ratio between the

variances of calculated and observed activities. The values

given in the parentheses (in regression equation) are the 95 %

confidence limits of the regression coefficients. The ryrand

(S.D) is the mean correlation coefficient of the regressions in

the activity (Y) randomization study with its standard

deviation from 100 simulations. In the randomization study,

none of the identified models has shown any chance

correlation. Additional statistical parameters such as, the

‘‘Akaike’s information criterion’’ (AIC) (Akaike, 1973;

Akaike, 1974) the Kubinyi function ‘FIT’ (Kubinyi, 1994a;

Kubinyi, 1994b) and the Friedman’s ‘‘lack of fit’’ (LOF)

(Friedman, 1990) have also been calculated to further validate

the derived QSAR models. The AIC takes into account the

statistical goodness of fit and the number of parameters that

have to be estimated to achieve that ‘degree of fit’. The

Kubinyi function ‘FIT’ closely related to the F-value, proved

to be a useful parameter for assessing the quality of the models.

The model that produces the lowest AIC value and highest FIT

value is considered potentially the most useful. The LOF

factor takes into account the number of terms used in the

equation and is not biased, as are other indicators, toward large

number of parameters (Sharma et al., 2009a) The equations 1

to 2 have been validated with a test set of four compounds

(R2T = R2 of test set compounds). The test set predictions are

in agreement with their experimental values (Table 1).

Now in terms of physical meaning of descriptors, the

AM1_LUMO (Eqs. 1 and 2) represents the energy (eV) of the

lowest unoccupied molecular orbital (LUMO) of substituent

groups, calculated using the AM1 Hamiltonian in MOPAC

(MOE, 2011). The LUMO is the innermost orbital that has

room to accept electrons. The substituent groups with high

LUMO energy would have greater tangency to accept the

electrons than with lower LUMO energy. Its positive corre-

lation with MABA (r = 0.677; Table 4) and LORA

(r = 0.844) activities indicate that the ortho-substituent with

high AM1_LUMO energy are conducive for both activities.

The MNDO_dipole (Eq. 1) refers to the dipole moment

Med Chem Res

123

Table 3 Antitubercular activity of para- linked biphenyl analogs

N

N

O

OO2N A

D

C

B

X

S. no. X -logMIC

MABA LORA

Observedb Eq. 5c Observedb Eq. 6c

36 H 7.347 7.080 5.409 5.791

37 2-CF3 7.114 7.161 5.796 5.831

38a 2-CHO 7.097 6.949 6.108 5.766

39 2-F 6.721 7.243 6.108 6.000

40 2-Cl 6.824 7.232 5.824 5.906

41a 2-OH 6.921 6.673 5.337 5.584

42 2-OMe 7.187 6.884 5.854 5.906

43 2-OEt 7.523 6.877 6.086 5.862

44a 2-O(CH2)3OH 6.469 6.226 5.569 5.436

45 2-OCF3 7.456 7.155 6.013 5.886

46 2-OPh 7.222 6.864 5.745 5.900

47a 2-SMe 7.060 7.028 5.854 5.944

48 3-iPr 6.854 6.879 5.377 5.619

49 3-Ph 6.509 7.065 6.056 5.940

50a 3-CF3 7.174 7.335 5.886 5.835

51 3-CHO 6.854 6.965 6.244 5.712

52 3-CN 6.921 7.049 5.886 5.839

53 3-CONH2 5.553 5.641 5.174 4.962

54 3-F 7.347 7.281 5.658 6.088

55a 3-Cl 7.222 7.313 5.796 5.908

56 3-OH 6.854 6.706 5.509 5.607

57 3-OMe 6.569 7.039 5.721 5.925

58 3-O(CH2)2OH 6.337 6.478 5.268 5.45

59a 3-O(CH2)3OH 6.745 6.510 5.495 5.495

60 3-O(CH2)2NMe2 5.824 6.402 5.824 5.633

61 3-OCF3 7.114 7.327 5.854 5.949

62 3-OCH2Ph 6.921 7.087 6.056 5.983

63 3-SMe 7.114 7.181 6.022 5.988

64 3-NH2 6.921 6.662 5.854 5.566

65a 3-NO2 6.886 7.534 6.119 5.972

66 4-iPr 7.000 6.943 – –

67 4-tBu 7.022 6.693 – –

68 4-Ph 7.046 7.169 – –

69 4-CF3 7.523 7.440 5.854 5.828

70a 4-CH2OH 6.268 6.411 5.432 5.518

71 4-CH2OtBu 7.114 6.696 5.509 5.269

72 4-CH2NHPh 7.222 7.128 6.201 5.954

73a 4-CHO 6.699 7.181 6.000 5.781

Table 3 continued

S. no. X -logMIC

MABA LORA

Observedb Eq. 5c Observedb Eq. 6c

74 4-CN 7.602 7.083 6.237 5.789

75a 4-CONH2 5.678 5.743 4.824 5.092

76 4-COCH3 7.398 7.166 6.137 5.836

77 4-F 7.824 7.273 5.854 5.984

78a 4-Cl 7.824 7.353 5.569 5.824

79 4-OH 6.194 6.635 5.367 5.561

80 4-OMe 7.187 6.953 5.167 5.903

81a 4-OiPr 6.602 6.960 5.420 5.786

82 4-OPh 7.398 7.133 5.569 5.954

83 4-O(CH2)2OH 6.260 6.478 5.481 5.421

84 4-O(CH2)3OH 6.222 6.531 5.658 5.460

85a 4-O(CH2)3N-morph 7.013 6.170 6.000 5.533

86 4-OCF2H 7.301 7.360 6.114 6.042

87a 4-OCF3 7.456 7.395 5.886 5.970

88 4-SMe 7.125 7.292 6.022 5.954

89 4-SO2Me 6.174 5.636 4.854 4.649

90a 3-NH2 6.310 6.609 5.721 5.537

91 2-Cl,4-CF3 7.523 7.412 5.854 5.945

92a 2-Cl,4-OCF3 7.398 7.251 6.108 5.996

93 2-Cl,6-OMe 7.260 6.905 6.131 5.918

94 2-F,4-OCF3 7.347 7.495 6.143 6.059

95a 2-F,6-OMe 6.886 6.988 6.092 5.946

96 2,6 diMe 6.409 6.845 6.032 5.823

97a 2,6 diOMe 6.509 6.722 6.000 5.874

98 3,4 diF 7.523 7.441 5.921 6.101

99 3-Cl, 4-CF3 7.222 7.711 6.013 5.956

100 3-Cl, 4-OCF3 7.523 7.609 6.046 6.007

101 3-OCF3,4-Cl 7.398 7.507 6.022 5.986

102a 3-CF3,4-Cl 7.456 7.510 5.721 5.860

103 3-NO2,4-OCF3 7.347 7.275 5.796 5.721

104 3-F,4-OMe 7.398 7.065 5.721 5.945

105 3-F,4-OCF3 7.523 7.619 6.469 6.035

106a 3-OMe,4-F 7.347 6.831 5.824 5.787

107 3-OCF2H,4-Cl 7.523 7.518 5.959 6.100

108 3-OH,4-Cl 6.699 6.832 5.678 5.608

109 3,5 diOMe 6.553 6.844 6.102 5.935

110 2-OMe, 3,5diF 7.398 7.262 6.065 5.996

111a 2,6 diMe,4-OMe 6.237 6.636 6.027 5.954

112 3,4,5 triF 7.398 7.630 6.119 6.099

113a 3,5 diMe,4-OH 6.523 6.168 5.824 5.525

114 3,5 diF,4-OMe 7.347 6.952 5.886 5.856

115a 3,4-benzo 7.347 6.168 6.065 6.088

a Test set compounds (24 comp.)b Observed activity (Palmer et al., 2010)c Predicted activities from QSAR models

Med Chem Res

123

calculated using the MNDO Hamiltonian [MOPAC] (MOE,

2011) Its negative correlation with MABA activity indicates

that the functional group that increases the dipole moment or

which have high polarity are detrimental for the activity. The

PEOE_VSA-5 (Eq. 2) is the partial equalization of orbital

electronegativities (PEOE) descriptor which calculates

atomic partial charges (Gasteiger and Marsili, 1980). It shows

the partial charge distribution range over the connected atoms.

The PEOE_VSA-5 represents surface areas with partial neg-

ative charges in the ranges of -0.30 to -0.25. This parameter

showed negative influence on the activity (LORA).

QSAR models for meta-linked biphenyl analogs

The equations 3 and 4 explain the MABA and LORA

activities, respectively, for the compounds of Table 2

(meta-substituted analogs). The different Surface Areas

descriptors based on an accessible van der Waals surface

area (A2) calculation for each atom, along with SlogP and/

or SMR as physicochemical property have taken part in

modeling the activity.

� logMICMABA ¼ �0:005 0:001ð ÞSlogP VSA3

þ 0:025 0:008ð ÞSMR VSA3� 13:330 3:445ð Þ� vsurf CW5þ 9:332

n ¼ 15; r ¼ 0:911;Q2 ¼ 0:728;Q2L3O ¼ 0:718;

s ¼ 0:181; F ¼ 17:802

FIT ¼ 2:225;LOF ¼ 0:067;AIC ¼ 0:057;

ryrand S:Dð Þ ¼ 0:4168 0:157ð Þ;R2T ¼ 0:711 ð3Þ

� logMICLORA ¼ �0:004 0:001ð ÞSMR VSA6

� 1:031 0:428ð ÞFCASAþ � 57:611 18:16ð Þ� vsurf CW7þ 9:632

n ¼ 15; r ¼ 0:880;Q2 ¼ 0:577;Q2L3O ¼ 0:554;

s ¼ 0:139; F ¼ 17:802

FIT ¼ 1:579;LOF ¼ 0:067;AIC ¼ 0:034;

ryrand S:Dð Þ ¼ 0:420 0:133ð Þ;R2T ¼ 0:708 ð4Þ

The SlogP_VSA3 and SMR_VSA3 (Eq. 3) are sum of

atomic ‘logPo/w’ and molar refractivity (MR), respectively,

associated with accessible van der Waals surface area in the

range of 0.0–0.1. In Eq. 3, the SlogP_VSA3 is the highest

correlating descriptor to the activity (r = –0.741, MABA)

which suggests that hydrophobic surface is detrimental for

the activity. The SMR_VSA3 (r = 0.146) is less influential

descriptor when compared to the SlogP_VSA3. Its positive

correlation with activity indicates the role of nonspecific

dispersion interactions to the activity. The vsurf_CW5 and

vsurf_CW7 are VolSurf descriptor with the capacity factor

of order of 5 and 7, respectively. There negative

correlations with MABA and LORA activities indicate the

detrimental effect of bulkiness of substituent to the activity

and suggest for smaller substituent over large one for better

activity (MOE, 2011).

The SMR_VSA6 (sum of atomic MR with accessible

surface area in the range 0.485–0.56, Eq. 4, r = -0.735) also

indicate the steric effect of substituents and suggest for the

smaller group for better activity. The descriptor FCASA?

represents the fractional water accessible surface area of all

atoms with positive partial charge. This descriptor showed

negative influence on the activity.

QSAR models for para-linked biphenyl analogs

The QSAR equations 5 and 6 explain the MABA and

LORA activities respectively, correspond to the com-

pounds of Table 3.

� logMICMABA ¼ �0:043 0:007ð Þ PEOE VSA� 6

� 26:108 4:403ð Þvsurf CW6� 0:662 0:164ð ÞAM1 LUMO

� 5:274 1:466ð ÞGCUT SMR 3þ 25:159

n ¼ 56; r ¼ 0:819; Q2 ¼ 0:597; Q2L3O ¼ 0:601;

s ¼ 0:286; F ¼ 26:012;

FIT ¼ 1:445; LOF ¼ 0:102; AIC ¼ 0:098;

ryrand S:Dð Þ ¼ 0:265 0:086ð Þ;R2T ¼ 0:557 ð5Þ

Table 4 Correlation of descriptors present in QSAR models with the MABA and LORA activities

Table 1 Table 2 Table 3

Descriptors MABA LORA Descriptors MABA LORA Descriptors MABA LORA

AM1_LUMO 0.67 0.84 FCASA ? -0.47 -0.39 AM1_LUMO -0.35 -0.22

MNDO_DIPOLE -0.39 -0.02 SMR_VSA3 0.14 -0.21 BCUT_SLOGP_3 -0.02 -0.21

PEOE_VSA_5 -0.71 -0.70 SMR_VSA6 -0.75 -0.74 GCUT_PEOE_1 -0.07 -0.06

SLOGP_VSA3 -0.74 -0.79 GCUT_SMR_3 -0.27 -0.16

VSURF_CW5 -0.65 -0.42 PEOE_VSA-6 -0.55 -0.58

VSURF_CW7 0.23 -0.191 VSURF_CW6 -0.41 -0.35

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� logMICLORA ¼ �0:042 0:006ð Þ PEOE VSA� 6

� 13:425 3:661ð Þvsurf CW6

� 6:743 3:156ð ÞBCUT SLOGP 3

� 4:577 1:927ð Þ GCUT PEOE 1þ 23:987

n ¼ 53; r ¼ 0:741;Q2 ¼ 0:453;Q2L3O ¼ 0:452;

s ¼ 0:245; F ¼ 14:646;

FIT ¼ 0:849;LOF ¼ 0:076;AIC ¼ 0:072;

ryrand S:Dð Þ ¼ 0:262 0:089ð Þ;R2T ¼ 0:572 ð6Þ

The PEOE_VSA-6 and vsurf_CW6 are common descriptors

in both equations. Here, the PEOE_VSA-6 (a partial charge

descriptor) represents thepolar surfaceareas with partialnegative

charges less than -0.30. This indicates that the negatively

charged polar surface area in not conducive for both activities.

The negative correlation of AM1_LUMO suggest for the

substituent with low LUMO energy for better activity. The

PEOE_VSA-6 and AM1_LUMO are electronic descriptors and

thus, also reflect the nature of complimentary binding cavity of

the putative receptor. The vsurf_CW6 (a VolSurf descriptor with

capacity factor of order 6) and GCUT_SMR_3 (a GCUT

descriptors, shows atomic contribution to the molar refractivity)

highlight the effect of bulkiness of substituent to the activity.

Their negative coefficient to the activity highlights the steric

effect of substituent to the activity. The similar observation was

observed with the substituent at meta position (Table 2). The

bulkier group decreases the energy of drug–receptor interactions

by steric effects. In contrary, the smaller substituent may fit well

at the receptor binding pocket and hence would be better for the

activity. The remaining descriptors are relatively less significant

to the activities. The metric r2m values of the developed models

(Eqs. 1–6) have been found within the reasonable limit (Table 5)

and thus, ensures the good predictive ability of the models.

Conclusion

A QSAR study have been carried out on ortho-, meta-, and

para-substituted biphenyl analogs of 2-nitroimidazo[2,1-

b][1,3]oxazine to rationalize their antitubercular activity.

The different electronic and steric/hydrophobic descriptors

have been identified for model development. The QSAR

analysis indicates that the ortho-substituents with ability to

accept the electrons (electron acceptor functional groups;

high LUMO) and less dipole moment would be conducive

for the activity (Table 1). The descriptors for the meta

position (Table 2) suggest that small functional groups are

favorable while bulky and hydrophobic groups are detri-

mental for the activity. The similar trend was observed

for the substituents at para position (Table 3). The bulky

substituents cause the steric hindrance and hence decrease

the energy of drug receptor interactions. Also, the sub-

stituents with negatively charged polar surface area are

unfavorable for the activity. The identified physicochem-

ical descriptors also indicate the nature of receptor surface

in M. tb. for this class of compounds. The study highlights

the influence of physicochemical properties of functional

group at ortho, meta, and para positions of biphenyl

analogs of 2-nitroimidazo[2,1-b][1,3]oxazine, which in

turn, may help to improve their antitubercular activity

profile.

Acknowledgments Authors thank Dr. Y. S. Prabhakar, Scientist,

CDRI, Lucknow (UP) India for providing software for the QSAR

study. The support and facilities provided by Chairman, ISF College

of Pharmacy, Moga (PB) India is greatly acknowledged.

Conflict of interest The authors declare that they have no conflict

of interest.

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