Biotechnological

Communication

Biosci. Biotech. Res. Comm. 10(1): 63-71 (2017)

Development of 3D QSAR based pharmacophore model

for neuraminidase in In uenza A Virus

Sudha Singh, Anvita Gupta Malhotra, Mohit Jha and Khushhali M. Pandey*

Department of Biological Science and Engineering Maulana Azad National Institute of Technology, Bhopal

(India)

ABSTRACT

A number of new advances in computer - aided drug designing have reduced the effective cost and time involved

in drug discovery. However, the quest for more effective compounds often faces stiff challenge due to increased

drug resistance. Pharmacophore modeling has emerged as a method with a lot of potential and is increasingly used

for designing new molecules by using available knowledge of activity of compounds. 3D Quantitative Structure -

Activity Relationship (QSAR) based pharmacophore modeling is a reliable method for developing new chemical

moieties. In uenza A virus results in acute respiratory infection with serious consequences for the elderly and high

– risk patients. Neuraminidase inhibitors are the well - known drugs that are frequently used against in uenza virus.

The current work has focused on developing a 3D QSAR based pharmacophore model for neuraminidase enzyme by

using a dataset of known inhibitors. The best quantitative pharmacophore model selected was made of one hydrogen

bond acceptor, one hydrogen bond donor and hydrophobic aliphatic features with high correlation value of 0. 917.

Pharmacophore model was cross - validated by Fischer randomization and leave - one - out method to check the reli-

ability of model. The  ndings can prove out to be quite helpful in screening new molecules against neuraminidase.

KEY WORDS: NEURAMINIDASE, DISCOVERY STUDIO, 3D - QSAR, PHARMACOPHORE, COMPUTER – AIDED DRUG DESIGN

ARTICLE INFORMATION:

*Corresponding Author: menaria.khushhali@gmail.com

Received 27

Nov, 2016

Accepted after revision 21

Feb, 2017

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Online Contents Available at: http//www.bbrc.in/

INTRODUCTION

In uenza virus is a member of Orthomyxoviridae fam-

ily of viruses. Based on differences in the nucleoprotein

(NP) and matrix (M1) protein, this virus is classi ed into

three major categories - A, B and C. In uenza A, known

for the infection of mammalian species, is divided into

18 HA subtypes (H1 - H17) and 11 NA (N1 - N9). This

division is based on serological reactivates of surface

proteins, hemagglutinin (HA) and neuraminidase (NA)

(Ferguson et al., 2015, Ducatez et al., 2015). Various

types of in uenza viruses have signi cant difference in

64 DEVELOPMENT OF 3D QSAR BASED PHARMACOPHORE MODEL FOR NEURAMINIDASE IN INFLUENZA A VIRUS BIOSCIENCE BIOTECHNOLOGY RESEARCH COMMUNICATIONS

Sudha Singh et al.

their host range and pathogenicity. They infect a variety

of animals including pigs, horses, whales, dogs, bats and

birds (Nayak et al., 2010, Rajao and Vincent, 2015).

Infection caused by In uenza virus, commonly

known as  u, is responsible for acute respiratory infec-

tion, with signi cant morbidity in the population and

mortality in the elderly and high–risk patients. It is

also a prominent cause of disability and death and is

therefore a serious public health issue (Kobasa et al.,

2004). One of the main limitations in new drug dis-

covery is drug resistance in mutant strains (Renzette et

al., 2014). M2 protein and Neuraminidase (NA) are the

two main drug targets for commonly available drugs.

M2 protein inhibitors like amantadine and rimantadine

have narrow spectrum of activity, hence provide lim-

ited protection(Hay et al., 1985, Hastings et al., 1996,

Mammen et al., 1995, Colman, 1989, De Clercq, 2001).

Another target Neuraminidase (NA) is a glycoprotein. It

acts as an enzyme and participates in the release of the

progeny virus from infected cells(Gong et al., 2007, Var-

ghese and Colman, 1991). Two well - known Neurami-

nidase inhibitors are Zanamivir and Oseltamivir. Zan-

amivir is known for excellent anti-viral activity during

intranasal administration but is not too effective when

delivered systemically. Oral bioavailability of Zanamivir

is low and easily eliminated by renal excretion(Ryan et

al., 1995). Oseltamivir is orally active but the associated

side effects like vomiting, nausea and several allergic

reactions do not augur well for a promising drug(Burch

et al., 2009). Few other Neuraminidase inhibitors such

as Peramivir, Laninamivir are in phase III clinical trials

(Hata et al., 2014). Peramivir shows less oral bioavail-

ability as compared to Oseltamivir(de Jong et al., 2014).

So the situation is worrying and there is a need to design

and identify new effective compounds for chemotherapy

of in uenza virus infection.

Pharmacophore modeling is one of the most impor-

tant and extensively used method in ligand - based drug

design. There are various studies in literature where

pharmacophore modeling was used as an effective tool

to understand the important features for well - known

target inhibitors. The pharmacophore model is widely

acknowledged as a balanced quantitative model that

can be used to explore common chemical characteristics

among a considerable number of structures with great

diversity. Quali ed pharmacophore model could also be

used as a query for searching chemical databases to  nd

new chemical entities. Quantitative Structure-Activity

Relationship (QSAR) is an effective statistical method

used to design new chemical moieties from the previ-

ous knowledge of activity of known compounds(John et

al., 2010, Li et al., 2015). Different classes of inhibitors

could be useful in digging out valuable information for

developing new potent NA inhibitors.

This study aims to construct the chemical features

based on pharmacophore models for neuraminidase. A

high correlation quantitative pharmacophore model was

generated, using observed structure-activity relationship

of known neuraminidase inhibitors. The pharmacophore

modeling was successfully applied for the development

of new model and validated with available methods.

This work is signi cant in connection with discovery of

new molecules and may contribute to the development

of more effective chemical moieties.

MATERIALS AND METHODS

(1) Selection of Data Set Compounds

3D QSAR method is one of the ligand – based phar-

macophore modeling strategies used for the discovery

of new effective compounds [12]. This strategy diverges

from the usual pharmacophore approach in the num-

ber of training set compounds’ requirement and need of

experimental activity values predicted through similar

bioassay conditions, etc. A data set of 46 compounds

was retrieved from BRENDA database and literature

and the redundancy was removed (Schomburg et al.,

2004). Out of 46 compounds, 18 diverse compounds

were selected for training data set with the experimental

activity values (IC

) ranging from 0.0032 nM to 8640

nM and structural diversity. These compounds were also

utilized in pharmacophore cross - validation.

(2) Compound Preparation and Conformation Generation

The ChemSketch Version 12 was used to design the

2D structures of compounds and conversion of these

compounds to 3D structures was done with the help of

Accelrys Discovery Studio 3.1. Hydrogen atoms were

then added to these prepared compounds and veri ed

later. This was followed by energy minimization pro-

cess using Smart Minimizer that carries out 1000 steps

of steepest descent. This is then followed by applica-

tion of conjugate gradient algorithms with convergence

gradient of 0.001 kcal/mol. After completion of energy

minimization, a number of acceptable conformers were

generated for every training set compound within DS

Diverse conformation generation module employing the

poling algorithm for conformational analysis. The pol-

ing algorithm eliminates the chances of redundancy in

conformation generation and this, in turn, improves the

coverage of the conformational space. Within an energy

range of 20kcal/mol above the global energy minimum,

maximum number of conformers generated for each

compound was limited to 255 (Schuster et al., 2006,

Bharatham et al., 2007, Neves et al., 2009). This practi-

cally means that the difference in energy values among

BIOSCIENCE BIOTECHNOLOGY RESEARCH COMMUNICATIONS DEVELOPMENT OF 3D QSAR BASED PHARMACOPHORE MODEL FOR NEURAMINIDASE IN INFLUENZA A VIRUS 65

Sudha Singh et al.

different conformers of a particular compound was <

20kcal/mol.

(3) Generation of Pharmacophore Models

Ligand – based pharmacophore modeling is divided into

two types methodologies one is common feature phar-

macophore modeling utilized the common features pre-

sent only in the most active compounds and another

one based on 3D QSAR pharmacophore–the new design

compounds activity estimated by using pharmacophore

models, generation of this model by used the most active

and inactive compounds’ chemical features with phar-

macological activity. The training set compounds fea-

tures identify by feature mapping protocol available in

DS. The values of Uncertainty and the minimum inter

– feature distances were set respectively to 2 and 2Å. In

DS -3D QSAR pharmacophore generation used the Fea-

ture mapping protocol identi ed hydrogen bond accep-

tor (HBA), hydrogen bond donor (HBD), hydrophobic

aliphatic (HY-AL), hydrophobic aromatic (HY-AR) and

ring aromatic (RA) features with other default values to

generate ten pharmacophore models.

Biological activity of compounds that is directly rel-

atively contributed to each feature of the model has a

certain weight. The process of HypoGen pharmacophore

model generation divided into three major steps - the con-

structive phase, the subtractive phase and the optimiza-

tion phase(Kurogi and Guner, 2001, Kansal et al., 2010). In

constructive phase of Hypotheses identi ed the common

maximum number of active set compounds. HypoGen all

combinations of pharmacophore features using for deter-

mines all possible pharmacophore con gurations Apart

from this, the hypotheses must  t a minimum subset of

features of the remaining most active compounds. The

end of the constructive phase coincides with generation

of a large database of pharmacophore con gurations.

The subtractive phase, on the other hand, goes through

elimination of all phramacophore con gurations that also

exist in the least active set of molecules. The least active

molecules here are considered to be those compounds

whose activity levels are less by 3.5 orders of magnitude

than that of the most active compound, though this order

is not  xed and can be modi ed in accordance with the

activity of the training set.

The errors in activity estimates obtained through

regression and complexity serve as adequate basis for

scoring the hypotheses. The hypotheses scores get further

improved in the optimization phase. This phase employs

a simulated annealing approach. The activity prediction

is optimized by considering variation of features and/

or locations. HypoGen stops after reaching the point

beyond which no further score improvement is possible.

It then provides top scoring 10 unique pharmacophore

models. The reliability of these models is assesses on the

basis of different cost parameters. The overall cost of a

model consists of the weight cost, the error cost, and the

con guration cost. The weight cost shows a Gaussian

increase pattern, the error cost is an indication of the

difference between estimated and measured activities of

the training set and the con guration cost is a quantita-

tive measure of the hypothesis space entropy.

The generation of pharmacophore models also

involves calculation of three additional cost values – the

 xed cost, the total cost, and the null cost. The  xed

cost is the least possible cost that represents the simplest

hypothetical model that provides a perfect  t for the

data. Fixed costs consist of minimum achievable error,

weight cost and the constant con guration cost. The

null cost, on the other hand, is the maximum cost of a

pharmacophore and calculates the average of activity

data of training set molecules. It matches with the maxi-

mum error cost. To generate a pharmacophore model, a

total of ten cost values along with their  xed and null

cost were estimated. Ideally, the model should have a

low  xed cost and high null cost values. Alongside, the

difference between the total and  xed values should

be minimum whereas the difference between total and

null values should be maximum (Sundarapandian et al.,

2010, Sanam et al., 2009). Further, regression analysis

was performed employing HypoGen for predicting activ-

ity of the training set compounds. This study was done

using the relationship of geometric  t value V/s the

negative logarithm of activity. The activity prediction is

directly proportional to the geometric  tness. Other sta-

tistical parameters, namely - correlation coef cient and

root mean square deviation (RMSD) were also computed.

Finally, the model with high cost difference and correla-

tion coef cient with low RMSD was selected.

(4) Pharmacophore Cross Validation

The models were cross validated to assess their ability to

predict the activity of any new compound. The identi ed

best model was validated via two approaches based on

derived cost modules - the Fischer randomization test and

leave – one - out method. All the cost values are stated

in bits and 75 - 90% correlation is proposed by a differ-

ence of 40 - 60 bits. The Fischer randomization approach

for validation of the pharmacophore model involved

construction of 19 random spread sheets with 95% con-

 dence level (Sarma et al., 2008, Thangapandian et al.,

2011a). In this study the correlation between the biologi-

cal activity and the chemical structures is tested by rand-

omizing the activity data of training set compounds. The

models were generated using the same parameters which

were used to build the original model but the activity val-

ues were randomized. The second is the leave – one - out

method, where 18 pharmacophore models were generated

with the same parameters used for generating original

Sudha Singh et al.

66 DEVELOPMENT OF 3D QSAR BASED PHARMACOPHORE MODEL FOR NEURAMINIDASE IN INFLUENZA A VIRUS BIOSCIENCE BIOTECHNOLOGY RESEARCH COMMUNICATIONS

pharmacophore model but leaving one compound at a

time from the training set compounds. This is done to

state the effect of every single training set compound in

the generation of selected pharmacophore model (Stoll

et al., 2002, Zampieri et al., 2009).

RESULT AND DISCUSSION

PHARMACOPHORE GENERATION

A training set with 18 compounds is used for the gener-

ation of ten pharmacophore models. Structures of these

training set compounds are shown in (Figure 1). These

models were generated by using HBA, HBD, HY-AL,

HY-AR and RA features from the Feature Mapping

Protocol(Arooj et al., 2013). All the selected pharmaco-

phore models consisted of either HBA or HBD or both,

with HY-AL or HY-AR. Total cost values ranged from

94.22 to 98.28.

The pharmacophore generation run in this study

revealed  xed cost value and null cost value as 77.44

and 157.052 respectively. The analysis of ten generated

pharmacophores models indicates that the total cost

value for the  rst model (Hypo 1) is the closest to the

 xed cost value vis – à - vis other models. The cost dif-

ference between the null cost and total cost value of the

 rst pharmacophore model is 62.83 (Table 1). A cost dif-

ference value between 40 and 60 signi es that the phar-

macophore model correlates the experimental and pre-

dicted activity. Herein, the cost difference value of Hypo

1 signi es the correlation between the experimental and

predicted activity values of more than 90% of the train-

ing set compounds(Vuorinen et al., 2014, Kandakatla

and Ramakrishnan, 2014). The Hypo 1 pharmacophore

model, being the best, was selected and consisted of two

HBA, one HBD and one HY-AL features (Figure 2).

Further investigation of the generated pharmacoph-

ore models was based on the selected ten pharmacoph-

FIGURE 1. 2D Structure of the training set compounds. 2D Chemical struc-

tures of the 18 training set with their experimental IC50 values

Sudha Singh et al.

BIOSCIENCE BIOTECHNOLOGY RESEARCH COMMUNICATIONS DEVELOPMENT OF 3D QSAR BASED PHARMACOPHORE MODEL FOR NEURAMINIDASE IN INFLUENZA A VIRUS 67

Table 1. Statistical Results of the 10 Pharmacophore Hypothesis generated by Hypo Gen

Algorithm

Hypothesis Total cost Cost difference RMSD Correlation Features

Hypo 1 94.2211 62.8309 1.30795 0.917666 HBA HBD HBD HY-AL

Hypo 2 95.2269 61.8251 1.36582 0.909478 HBA HBD HBD HY-AL

Hypo 3 96.7312 60.3208 1.40736 0.903947 HBA HBD HBD HY-AL

Hypo 4 97.0164 60.0356 1.41827 0.902366 HBA HBD HBD HY-AL

Hypo 5 97.34 59.712 1.44001 0.898952 HBA HBD HBD HY-AL

Hypo 6 97.9418 59.1102 1.50229 0.888384 HBA HBD HBD HY-AL

Hypo 7 98.251 58.801 1.50229 0.888384 HBA HBD HBD HY-AL

Hypo 8 98.2555 58.7965 1.50857 0.887742 HBA HBD HBD HY-AL

Hypo 9 98.2683 58.7837 1.5014 0.889011 HBA HBD HBD HY-AL

Hypo 10 98.2848 58.7672 1.49708 0.889783 HBA HBD HBD HY-AL

Null cost = 157.052;  xed cost = 77.44; con guration cost = 15.77

Cost difference = null cost – total cost.

HBA, hydrogen bond acceptor; HBD, hydrogen bond donor; hydrophobic aliphatic

Table 2. Experimental and Estimated IC

Values of the Training Set Compounds based on Best

Pharmacophore.

Name IC

nM Error Fit value Activity scale

Experimental Estimated Experimental Estimated

Compound 1 0.0032 0.012 2.6 9.65 ++++ ++++

Compound 2 0.3 0.34 3.9 8.21 ++++ ++++

Compound 3 0.9 3.7 2.8 7.17 ++++ ++++

Compound 4 22 850 21 4.81 +++ ++

Compound 5 26 170 13 5.52 +++ +++

Compound 6 36 12 -4.1 6.68 +++ +++

Compound 7 32 56 -1.3 5.99 +++ +++

Compound 8 49 66 -1.3 5.92 +++ +++

Compound 9 140 19 -7.6 6.45 +++ +++

Compound 10 210 38 6.16 1.1 ++ +++

Compound 11 280 240 1.1 5.36 ++ ++

Compound 12 650 750 -1.6 4.86 ++ ++

Compound 13 810 550 -1.8 5 ++ ++

Compound 14 980 620 -2.2 4.95 ++ ++

Compound 15 1020 560 -2.8 4.99 + ++

Compound 16 1920 5300 5.9 4.01 + +

Compound 17 5840 730 -15 4.87 + ++

Compound 18 8640 1800 -3.9 4.48 + +

Positive value indicates that the estimate IC

is higher than the experimental IC

; negative value indicates that the

estimate IC

is lower than the experimental IC

Fit value indicates how well the features in the pharmacophore map the chemical features in the compound

Activity scale: IC

≤ 10 nM (Most active, ++++); 10< IC

≤ 200 nM (Active, +++); 200< IC

≤ 1000 nM (Moderately

active, ++); > 1000 nM

(inactive, +)

ore models having correlation values greater than 0.889.

Out of them, the top four pharmacophore models corre-

lated the activity data with high correlation values that

were higher than 0.9. These results indicate the capabil-

ity of the pharmacophore model to predict the activity

of the training set compounds. Hypo 1 showed the high-

est correlation coef cient value of 0.9, thus highlighting

its strong predictive ability (Muthusamy et al., 2015).

RMSD values calculated for the top  ve pharmacoph-

ore models were less than 1.5 which supports our  nd-

Sudha Singh et al.

68 DEVELOPMENT OF 3D QSAR BASED PHARMACOPHORE MODEL FOR NEURAMINIDASE IN INFLUENZA A VIRUS BIOSCIENCE BIOTECHNOLOGY RESEARCH COMMUNICATIONS

FIGURE 2. The Best HypoGen Pharmacophore

Model, Hypo 1. (a) Chemical features present in

Hypo 1 (b) 3D Spatial Arrangement and the Dis-

tance Constraints between the Chemical Features.

Green color represents HBA, magenta color repre-

sents HBD and cyan color represents HY-AL.

FIGURE 3. Pharmacophore Mapping: Mapping of the most active compounds

1, IC50 = 0.0032 mapped on the best pharmacophore model; (b) the least

active compound (compound 18, IC50 = 8640) mapped on the best pharmaco-

phore model (Hypo 1). In the pharmacophore model - green represents HBA,

magenta represents HBD and cyan represents HY-AL features.

ings (Niu et al., 2014). Hypo 1 was developed with better

statistical values, such as higher correlation, large cost

difference, and low RMSD (1.30795). Hypo 1 has pre-

dicted the experimental activity values of training set

compounds with high correlation. All compounds in the

training set were categorized into four different groups

based on their experimental activity (IC

) values: most

active (IC

≤ 10 nM, ++++), active (10 < IC

≤ 200 nM,

+++), moderately active (200 < IC

≤ 1000 nM, ++), and

inactive (IC

> 1000 nM, +).

The predictive ability of Hypo 1 on training set com-

pounds is shown in (Table 2). In accordance with the

Hypo1 activity values, 15 out of 18 compounds in the

training set were predicted within their experimental

activity scale whereas compounds 10, 15, and 17 were

over estimated as active. None of the calculated error

values representing the ratio between the experimental

and predicted activity values were more than one order

of magnitude. All of the three most active compounds in

the training set were predicted very close to their activ-

ity values indicating the predictive ability of Hypo 1.

The most active compounds in training set mapped all

the features of Hypo 1 whereas the other compounds

missed at least one of the pharmacophoric features.

The pharmacophore mapping of the most active

and the least active compounds is shown in (Figure

3). Among top four hypotheses, Hypo 1 is the best

model over others which have also shown a high cor-

relation value (0.91) with HBA, HBD and HY-AL fea-

tures (Thangapandian et al., 2011b). The energy values

of the conformations of the most active compounds in

the training set used in model generation were lower in

Hypo 1 but relatively higher in Hypo 2. This analysis

also supported the reliability of Hypo 1 along with the

high correlation coef cient.

PHARMACOPHORE CROSS VALIDATION

(a) Fisher Randomization Test

The Fisher randomization test used for testifying and

validating Hypo 1 indicates that this pharmacophore

model does not occur due to the random correlation

(Singh and Singh, 2013). The experimental activities of

the training set were picked randomly and the resulting

Sudha Singh et al.

BIOSCIENCE BIOTECHNOLOGY RESEARCH COMMUNICATIONS DEVELOPMENT OF 3D QSAR BASED PHARMACOPHORE MODEL FOR NEURAMINIDASE IN INFLUENZA A VIRUS 69

training set was used in HypoGen with the parameters

chosen for the original pharmacophore generation. A set

of 19 random spread sheets was generated to achieve a

95% con dence level that the best pharmacophore Hypo

1 was not generated by chance (Sakkiah and Lee, 2012)

shown in (Table 3). None of the randomly generated

pharmacophore models during Fisher randomization

test has scored better statistical parameters than Hypo 1.

Though four random pharmacophores scored a correla-

tion value higher than 0.9 (i. e. than Hypo 1).

(b) Leave – one – out method

Leave – one - out method was used for  nal validation(Niu

et al., 2013). This method is used to verify if the correla-

tion between the experimental and predicted activities

is primarily dependent on one particular molecule in

the training set, or otherwise. This is done by apply-

ing recursive iteration on the pharmacophore model by

excluding one molecule in every iteration cycle. The 18

HypoGen calculations were carried out under conditions

that were identical to the ones used in the generation of

original pharmacophore model Hypo 1. 18 new train-

ing sets, each containing 17 molecules, were derived.

The correlation coef cients of newly generated phar-

macophore models were computed. A positive result

emerges if none of the correlation coef cients of newly

generated pharmacophore models is higher or too lower

to that of Hypo 1. The  ndings establish that none of

the 18 new models generated by this method has any

signi cant difference vis – a - vis Hypo 1. This result

enhances the con dence level of Hypo 1regarding the

non – dependence of correlation coef cient on any par-

ticular compound in the training set.

CONCLUSION

The present study is an attempt to generate a quantita-

tive pharmacophore model for neuraminidase enzyme

by employing a dataset of known inhibitors. A model

(Hypo 1) was developed based on the training set com-

pounds with high chemical structure diversity and sig-

ni cant divergence in biological activity values (IC

The best pharmacophore model was selected on the basis

of various parameters like cost difference, correlation co

– ef cient, and the validation results. All these valida-

tion procedures have shown and con rmed the strength

of the selected model Hypo 1. These validation results

Table 3. Fisher Randomization test results.

Validation no Total cost Null cost Cost Diff. correlation

Original hypothesis

Hypo 1 94.22 157.052 62.83 0.917666

Randomized

hypothesis

Trail 1 103.517 157.052 53.535 0.871634

Trail 2 109.558 157.052 47.494 0.854541

Trail 3 108.675 157.052 48.377 0.84838

Trail 4 107.26 157.052 49.792 0.860989

Trail 5 108.599 157.052 48.453 0.856582

Trail 6 132.103 157.052 24.949 0.655598

Trail 7 115.443 157.052 41.609 0.805429

Trail 8 112.835 157.052 44.217 0.804367

Trail 9 122.52 157.052 34.532 0.724558

Trail 10 112.816 157.052 44.236 0.885913

Trail 11 113.451 157.052 43.601 0.801793

Trail 12 115.03 157.052 42.022 0.868995

Trail 13 101.264 157.052 55.788 0.89344

Trail 14 105.574 157.052 51.478 0.919629

Trail 15 115.66 157.052 41.392 0.784862

Trail 16 110.194 157.052 46.858 0.906808

Trail 17 114.316 157.052 42.736 0.779937

Trail 18 127.639 157.052 29.413 0.780038

Trail 19 127.029 157.052 24.95 0.692584

Sudha Singh et al.

70 DEVELOPMENT OF 3D QSAR BASED PHARMACOPHORE MODEL FOR NEURAMINIDASE IN INFLUENZA A VIRUS BIOSCIENCE BIOTECHNOLOGY RESEARCH COMMUNICATIONS

throw interesting opportunities for further database

screening to identify the small molecule which can be

used in neuraminidase inhibitor design and may provide

leads in the world’s  ght against In uenza A Virus.

ACKNOWLEDGEMENT

The authors are thankful to Maulana Azad National

Institute of Technology (MANIT), Bhopal, and MHRD,

GOI, for providing  nancial assistance. Special thanks

are due to Dr. Ajay Pandey, a faculty member at MANIT,

Bhopal, for his valuable support, help, and guidance

during the preparation of this manuscript.

CONFLICT OF INTEREST

The authors declare that they have no con ict of

interest.

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