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Health Informatics- An International Journal (HIIJ) Vol.2, No.4, November 2013
PREDICTIVE COMPARATIVE QSAR ANALYSIS OF
AS 5-NITROFURAN-2-YL DERIVATIVES MYCO
BACTERIUM TUBERCULOSIS H37RV
Doreswamy1 and Chanabasayya .M. Vastrad2
1Department of Computer Science Mangalore University , Mangalagangotri-574 199, Karnataka,
2Department of Computer Science Mangalore University , Mangalagangotri-574 199, Karnataka,

*Antitubercular activity of 5-nitrofuran-2-yl Derivatives series were subjected to Quantitative Structure Activity Relationship (QSAR) Analysis with an effort to derive and understand a correlation between the biological activity as response variable and different molecular descriptors as independent variables. QSAR models are built using 40 molecular descriptor dataset. Different statistical regression expressions were got using Partial Least Squares (PLS) ,Multiple Linear Regression (MLR) and Principal Component Regression (PCR) techniques. The among these technique, Partial Least Square Regression (PLS) technique has shown very promising result as compared to MLR technique A QSAR model was build by a training set of 30 molecules with correlation coefficient (* *) of 0.8484 , significant cross validated *
*correlation coefficient (**) is 0.0939, * * is 48.5187, * *for external test set (*
_ )

* is -0.5604, coefficient of correlation of predicted data set *(
_

*) is 0.7252 and degree of freedom is 26 by *

Partial Least Squares Regression technique.

**KEYWORDS **
* TB, MLR , PLS , PCR , LOO *

**1. INTRODUCTION **

Tuberculosis in humans is generally caused by mycobacterium tuberculosis(TB). The desease is

spread by respirable droplets generated during effective expiratory manoeuvres such as coughing.

TB desease can be either active or latent[1] . The World Health Organization (WHO) asses that

within the next twenty years about thirty million people will be troubled with the bacillus [2-3].

The analytic management of TB has depends dully on a limited number of drugs such as

Isonicotinic acid, Hydrazide,Rifadin, Rimactane ,Myambutol ,Streptomycin, Ethionamide,

Pyrazinamide, Fluroquinolones etc [4]. Still with the origin of these special chemical drugs the

DOI: 10.5121/hiij.2013.2404 47
Health Informatics- An International Journal (HIIJ) Vol.2, No.4, November 2013

spread of TB has not been eradicated completely because of delayed treatment programmes

.There is now recognition that new drugs to treat TB are necessarily required, particularly for use

in shorter medication procedure than are possible with the current agents and which can be

engaged to treat multi-drug resistant and hidden disease[5].

5-nitrofuran-2-yl shows effective in vitro and in vivo antimycobacterial activity [6]. There is also

a great effort to find and develop newer, 5-nitrofuran-2-yl, and some of them might have value in

the remedy of TB [7]. Chemo informatics[26] and computer-aided drug design (CADD) are

likely to contribute to a possible solution for the dangerous situation regarding this infectious

disease by assisting in the swift identification of new effective anti-TB agents. The other way for

overcoming the absence of empirical analysis for biological systems is depends on the activity to

develop quantitative structure activity relationship (QSAR) [8] . QSAR models are mathematical

expressions formulating a relationship between chemical structures and biological activities.

These models have different capability, which is providing a deeper knowledge about the process

of biological activity. In the first step of a usual QSAR study one needs to find a set of molecular

descriptors with the higher influence on the biological activity of interest [9]. A broad scope of

molecular descriptors[10] has been used in QSAR modeling. These molecular descriptors[11]

have been categorised into different classes, including constitutional, geometrical, topological,

quantum chemical and so on. Using such an way one could predict the activities of newly

formulated compounds before a conclusion is being made whether these compounds should be

truly synthesized and tested. We examine the performance of Partial Least Squares(PLS) based

QSAR models with the results produced by Multi Linear Regression(MLR ) and Principal

Component Regression (PCR) methods to discover basic requirements for additional bettered

antitubercular activity.

**2. MATERIALS AND METHODS **

2.1 MOLECULAR DESCRIPTOR DATA SETS

A set of fourty molecule compounds relates to derivatives for mycobacterium TB(H37Rv)

inhibitors were taken from large antitubercular drug molecule databases[12] using substructure

mining tool Schrodinger Canvas 2010(Trial version) [13]. All molecules were handled by the

Vlife MDS [14] - 2D coordinates of atoms were recalculated counter ions and salts were

eliminated from molecular structures, molecules were neutralized, mesomerized and aromatized.

Data sets were then refined from duplicates. The 2D-QSAR models were produced using a

training set of thirty molecules. Predictive ability of the models was assessed by a test set of ten

molecules with consistently distributed biological activities. The observed selection of test set

molecules was made by seeing the fact that test set molecules shows a range of biological activity

similar to the training set. The actual and predicted biological activities of the training and test

set molecules are given in Table 1.

Health Informatics- An International Journal (HIIJ) Vol.2, No.4, November 2013

**Compound **
**Residual **
Health Informatics- An International Journal (HIIJ) Vol.2, No.4, November 2013
Health Informatics- An International Journal (HIIJ) Vol.2, No.4, November 2013
Health Informatics- An International Journal (HIIJ) Vol.2, No.4, November 2013
Table 1 Molecular structure with Observed and Predicted activity of 5-nitrofuran-2-yl used in training and test set using Model-1 (PLS) Expt. = Experimental activity, Pred. = Predicted activity IC50a = Compound concentration in micro mole required to inhibit growth by 50%
Health Informatics- An International Journal (HIIJ) Vol.2, No.4, November 2013

PIC50b = -Log (IC50 × 10): Training data set developed using model 1 (PLS) and Test set

is light blue shaded.

** **

2.2 BIOLOGICAL OBSERVED ACTIVITY DATA

For the evolution of QSAR models of 5-nitrofuran-2-yl ,of processes antitubercular activity in

terms of half maximum inhibitory concentration IC50 (µM) versus (H37Rv) strains were took

from the antitubercular drug molecule databases[12]. The IC50 activity data contains only

molecules that have at least exhibited some activity. The biological activity data (IC50) were

transformed in to pIC50 according to the formula pIC50 = (−log (IC50 × 10)) was used as

response values, thus correlating the data linear to the free energy change.

**2.3 DESCRIPTOR CALCULATION FOR MOLECULAR DATASET **

The VLife MDS tool used for the computation of various molecular descriptors containing

topological index (J), connectivity index (x), radius of gyration (RG), moment of inertia, Wiener

index(W), balabian index(J), centric index, hosoya index (Z), information based indices, XlogP,

logP, hydrophobicity, elemental count, path count, chain count, pathcluster count, molecular

connectivity index (chi), kappa values, electro topological state indices, electrostatic surface

properties, dipole moment, polar surface area(PSA), alignment independent descriptor

(AI)[11,14. The calculated molecular descriptors were gathered in a data matrix. The

preprocessing for the generated molecular descriptors was done by removing invariable (constant

column) and cross-correlated descriptors (with r = 0.99). which happen in total 156, 125 and 162

molecular descriptors for MLR, PCR and PLS accordingly to be used for QSAR analysis.

**2.4 CREATION OF TRAINING AND TEST SET **
The dataset of forty molecular descriptors is split s into training and test set by Sphere Exclusion (SE)[15-16] technique. In this technique initially data set splits into training and test set using sphere exclusion technique. In this technique variance value provides an idea to handle training and test set size. It needs to be adapted by trial and error until a desired split of training and test set is acquired. Increase in dissimilarity value results in increase in number of molecules in the test set. This technique is used for MLR, PCR and PLS models with pIC50 activity data as response variable and various 2D molecular descriptors computed for the molecules as independent variables.

**2.5 MODEL VALIDATION **
Model validation [17-18] is a essential manner of quantitative structure–activity relationship (QSAR) modelling. This is done to test the internal stability and predictive capability of the QSAR models. These three QSAR models were validated by the following method.

**2.5.1 INTERNAL MODEL VALIDATION **
Internal model validation was carried out using leave-one-out (LOO-Q) method. For calculating q, each sample in the training set was eliminated once and the activity of the eliminated sample was predicted by using the model developed by the remaining samples. The Q computed using the expression which explains the internal strength of a model.
Health Informatics- An International Journal (HIIJ) Vol.2, No.4, November 2013
!"# $%&)' (1)
∑($%& (")*)'
In Eq. (1), Y,-./ and Y012 indicate predicted and observed activity values accordingly and Y3.45

signify mean activity value. A model is considered acceptable when the value of Q exceeds 0.5.

**2.5.2 EXTERNAL MODEL VALIDATION **

External model validation, the activity of each sample in the test set was predicted using the

model created by the training set. The pred_r value is computed as follows.

!"#(9"&9) 9"&9)'
∑(9!):* (")*(9!):*))'
In Eq (2) Y,-./(;.2;) and Y;.2; indicate predicted and observed activity values for the test set and

Y;-4<5 indicates mean activity value of the training set. For the predictive QSAR model, the value

of pred_r must be more than 0.5.

**2.5.3 RANDOMIZATION TEST **

Randomization test or Y-scrambling is key mean of statistical validation. To assess the statistical

importance of the QSAR model for the dataset, one tail hypothesis testing is used. The strength

of the models for training sets was tested by examining these models to those derived for random

datasets. Random sets were produced by rearranging the activities of the samples in the training

set. The statistical model was determined using different randomly reorganize activities (random

sets) with the chosen molecular descriptors and the equivalent Q were computed. The

importance of the models for that reason obtained was developed based on a computed Z2>0-.

A Z score value is calculated by the following equation:

Where ℎ is the Q value computed for the dataset, µ the mean Q, and is its σ standard deviation

calculated for various iterations using models build by different random datasets. The probability

(a) of importance of randomization test is derived by comparing Z2>0-. value with Z2>0-. critical

value as stated, if Z2>0-. value is less than 4.0; otherwise it is computed by the expression as

given in the literature. For example, a Z2>0-. value more than 3.10 proposes that there is a

probability (a) of smaller than 0.001 that the QSAR model build for the dataset is random. The

randomization test proposes that all the created models have a probability of less than 1% that the

model is produced by chance.

**2.6 MULTIPLE LINEAR REGRESSION (MLR) ANALYSIS **

MLR technique used for modelling linear relationship between a response variable Y (pIC50) and

independent variables X (2D molecular descriptors). MLR is based on least squares technique: the

model is fit such that sum-of-squares of differences of actual and a predicted values are

minimized. MLR estimates the regression coefficients ( r) by applying least squares fitting

Health Informatics- An International Journal (HIIJ) Vol.2, No.4, November 2013
technique. The model builds a relationship in the form of a straight line (linear) that best estimates all the individual data points. In regression analysis, conditional mean of response variable (pIC50) Y depends on (molecular descriptors)X. MLR analysis add to this idea to include more than one independent variables. Regression expression takes the form.
where Y is a response variable, ‘b's are regression coefficients for corresponding ‘x's are

molecular descriptors(independent variables), ‘c' is a regression constant or intercept [19,25].

**2.7 PRINCIPAL COMPONENT REGRESSION (PCR) ANALYSIS **

Principal Component Regression (PCR) is a regression technique that uses principal component

analysis(PCA) when evaluating regression coefficients. PCR presents a technique for finding

structure in datasets. Its object is to group correlated variables, replacing the earlier descriptors

by new set called principal components (PCs). These PC's are uncorrelated and are developed as

a simple linear aggregation of earlier variables. It moves the data into a new set of axes such that

first few axes indicates most of the variations within the data. First PC (PC1) is expressed in the

direction of maximum variance of the whole dataset. Second PC (PC2) is the direction that

defines the maximum variance in orthogonal subspace to PC1. Consequent components are taken

orthogonal to the particular formerly chosen and defines best of remaining variance, by locating

the data on new set of axes, it can points major fundamental structures certainly. Value of each

point, when moved to a given axis, is called the PC value. PCA chooses a new set of axes for the

data. These are chosen in decreasing order of variance within the data. The aim of principal

component PCR is the computation of values of a response variable on the basis of chosen PCs of

independent variables [21].

**2.8 PARTIAL LEAST SQUARES (PLS) REGRESSION ANALYSIS **

PLS is a well known regression technique which can be used to correlate one or more response

variable (Y) to various independent variables(X) . PLS relates a matrix Y of response variables to

a matrix X of molecular descriptors. PLS is useful in conditions where the number of molecular

descriptors( independent variables) exceeds the number of samples, when X data contain

colinearties or when N is less than 5M, where N is number of samples and M is number of

response variables. PLS builds orthogonal components using existing correlations between

independent variables and corresponding outputs while also keeping most of the variance of

independent variables. Major aim of PLS regression is to predict the activity (Y) from X and to

define their common frame[22,23] . PLS is probably the least contrary of various multivariate

extensions of MLR model. PLS is a technique for constructing predictive models when factors

are many and highly collinear.

**2.9 EVALUATION OF THE QSAR MODELS **

The created QSAR models are computed using the following statistical parameters: N (Number

of samples in regression); K (Number of independent variables(molecular descriptors)); DF

(Degree of freedom); optimum component ( number of optimums); r ( the squared correlation

coefficient); F test (Fischer's Value) for statistical importance; q (cross-validated correlation

Health Informatics- An International Journal (HIIJ) Vol.2, No.4, November 2013

coefficient); pred_r ( r for external test set); Z2>0-. ( Z score computed by the randomization

test); Best_ran_r (maximal r value in the randomization test) ; Best_ran_q (maximal q value

in the randomization test) ; α ( statistical importance parameter obtained by the randomization

test). The correlation coefficient r is a respective standard of fit by the regression expression. It

expressed the part of the variation in the observed data is analyzed by the regression. Despite, a

QSAR models are examined to be predictive, if the following prerequistes are satisfied: r > 0.6 ,

q > 0.6 and pred_r > 0.5 [24] . The F-test indicates the ratio of variance described by the

model and variance due to the error in the regression. High values of the F-test indicate that

model is statistically meaningful. The reduced standard error of pred_rse , q_se and r_se

demonstrates actual value of the fitness of the model. The cross-correlation extent was set at 0.5.

**3. RESULTS **

Taining set of 30 and 10 of test set of 5-nitrofuran-2-yl having different substitution were

employed.

**3.1 CREATION OF QSAR MODELS **

3.1.1 PARTIAL LEAST SQUARES (PLS) REGRESSION ANALYSIS

The molecular descriptors were applied to PLS technique to developQSAR models by using

simulated anealing variable selection mode. PLS model is having following QSAR Eq.(5) with

five descriptors.

pIC50 =

1.8704(StsCcount) + 4.0747(chi5chain) − 0.6865(SaaaCcount) + 0.7046(SssScount) −

0.1538 (SdssCcount) + 4.9478 (5)

Table 2 Statistical parameters of PLS, MLR And PCR

** Parameters **

Health Informatics- An International Journal (HIIJ) Vol.2, No.4, November 2013
The above analysis directs to the improvement of statistically meaningful QSAR model, which allows understanding of the molecular properties/features that play an key role in governing the variation in the activities. In addition, this QSAR study allowed examining influence of very simple and easy-to-compute molecular descriptors in discovering biological activities, which could shed light on the important factors that may aid in design of new potent molecules.
All the parameters and their significance, which contributed to the specific antitubercular inhibitory activity in the generated model is discussed below.
**1. StsCcount:** This descriptor indicates the total number of carbon atoms with a triple bond and a

single bond exist in the molecule. Positive Contribution of this descriptor to the model is

31.72%.

**2.chi5chain:** This descriptor signifies a retention index for five membered ring. Positive

Contribution of this descriptor to the model is 21.99%.

**3. SaaaCcount:** This descriptor defines the total number of carbon connected with three aromatic

bonds. Negative Contribution of this descriptor to the model is -23.28%.

**4. SssScount:** This descriptor indicates the total number of sulphur atom attached with two

single bonds. Positive Contributions of this descriptor to the model is 11.95%.

**5. SdssCcount:** This descriptor defines the total number of carbon connected with one double

and two single bond. Negative Contribution of this descriptor to the model is -11.06%.

Figure 1 Observed vs. Predicted activities for training and test set molecular descriptors by Partial Least
Square model. (A) Training set (Red dots) (B) Test Set (Blue dots).
The PLS model gave correlation coefficient ( r) of 0.8484, significant cross validated correlation coefficient ( q) of 0.0939, F-test of 48.5187 and degree of freedom 26. The model is validated by α_ran_r= 0.00100, α_ran_q = 0.10000, best_ran_r = 0.56429, best_ran_q = -0.03892, Z
2>0-._-45_- = 3.43122 and Z2>0-._-45_f =1.59111. The randomization test proposes that the
created model have a probability of smaller than 1% that the model is build by chance. Statistical

Health Informatics- An International Journal (HIIJ) Vol.2, No.4, November 2013
data is presented in Table 2. The graph of observed vs. predicted activity is demonstrated in Figure 1. The descriptors which contribute for the QSAR model is demonstrated in Figure 2.
Figure 2 Percentage contribution of each descriptor in created PLS model describing variation in the
**3.1.2 MULTIPLE LINEAR REGRESSION (MLR) ANALYSIS **

The QSAR analysis by Multiple Linear Regression method with simulated annealing variable selection technique, the final QSAR model is created having five descriptors is shown in Eq. (6).
pIC50 =1.8681(± 0.2421)StsCcount + 4.0722(± 0.7599)chi5chain −0.6879(± 0.1139)SaaaCcount + 0.7033(± 0.2393)SssScount −0.1548 (± 0.0265)SdssCcount + 4.9497 (6)
MLR Model has a correlation coefficient ( r ) of 0.8484, significant cross validated correlation coefficient ( q ) of 0.0932, F test of 26.8725 and degree of freedom 24. The model is validated by α_ran_r = 0.00000 , α_ran_q = 0.10000, best_ran_r = 0.28620, best_ran_q = −0.08598 , Z
= 8.11471 andZ2>0-._-45_f = 1.31886
The randomization test proposes that the created model have a probability of smaller than 1% that the model is build by chance. The observed and predicted values with residual values are demonstrated in Table 1.Statistical data is demonstrated in Table 2.The graph of observed vs. predicted activity demonstrated is in Figure 3. The descriptors which contribute for the QSAR model are demonstrated in Figure 4. All the parameters and their significance, which contributed to the specific antitubercular inhibitory activity in the generated models are explained below.
Figure 3 Observed vs. Predicted activities for training and test set molecular descriptors from the Multiple
Linear Regression model. (A) Training set (Red dots) (B) Test Set (Blue dots).

Health Informatics- An International Journal (HIIJ) Vol.2, No.4, November 2013

**1. StsCcount:** This descriptor indicates the total number of carbon atoms with a triple bond and a

single bond present in the molecule. Positive Contribution of this descriptor to the model is

31.67%.

**2.chi5chain:** This descriptor signifies a retention index for five membered ring. Positive

Contribution of this descriptor to the model is 21.97%.

**3. SaaaCcount:** This descriptor defines the total number of carbon connected with three aromatic

bonds. Negative Contribution of this descriptor to the model is -23.32%.

**4. SssScount:** This descriptor indicates the total number of sulphur atom attached with two

single bonds. Positive Contributions of this descriptor to the model is 11.92%.

**5. SdssCcount:** This descriptor defines the total number of carbon connected with one double

and two single bond. Negative Contribution of this descriptor to the model is -11.12%.

Figure 4 Percentage contribution of each descriptor in created MLR model describing variation in the
**3.1.3 PRINCIPAL COMPONENT REGRESSION (PCR) ANALYSIS **

The molecular descriptors were applied to under goes PCR technique to create QSAR model with

Simulated anealining variable selection mode by using PCR model. The final QSAR model is

Eq. (7) was created having one descriptor as follows.

pIC50 = 1.7397StsCcount + 5.3563 (7)

The PCR model gave correlation coefficient ( r) is 0.3289, significant cross validated

correlation coefficient ( q) of -5.3805, F test of 13.7231 and degree of freedom 28. The model is

validated by α_ran_r = 0.01000, α_ran_q = 99.00000, best_ran_r = 0.32891, best_ran_q

=-0.13938 , Z

2>0-._-45_- = 2.81258 and Z2>0-._-45_f =-2.47533. The randomization test proposes
that the created model have a probability of smaller than 1% that the model is build by chance.

Health Informatics- An International Journal (HIIJ) Vol.2, No.4, November 2013
Statistical data is demonstrated in Table 2. The graph of observed vs. predicted activity is in demonstrated Figure 5 .The descriptors which contribute for the QSAR model is demonstrated in Figure 6.
Figure 5 Observed vs. Predicted activities for training and test set molecular descriptors by Principal
Component Regression model. A) Training set (Red dots) B) Test Set (Blue dots).
All the parameters and their significance, which contributed to the specific antitubercular inhibitory activity in the generated models are discussed here.
**1. StsCcount:** This descriptor indicates the total number of carbon atoms with a triple bond and a

single bond present in the molecule. Positive Contribution of this descriptor to the model is

100%.

Figure 6 Percentage contribution of each descriptor in developed PCR model describing variation in the
Health Informatics- An International Journal (HIIJ) Vol.2, No.4, November 2013

**4. CONCLUSION **

The 2D QSAR analysis were conducted with a series of 5-nitrofuran-2-yl derivatives for

mycobacterium tuberculosis(H37Rv) inhibitors , and some useful predictive models were

obtained. The physicochemical molecular descriptors were found to have an key role in

governing the change in activity. The statistical parameters demonstrate the estimation power of

QSAR model for the molecular descriptor data set from which it has been determined and

evaluate it only internally. The overall performance of prediction was found to be around 84% in

case of PLS and MLR. Among the three 2D-QSAR models (MLR, PCR, and PLS), the results of

PLS and MLR analysis showed significant predictive power and reliability as compare to PCR

technique.

**ACKNOWLEDGEMENTS **

The Authors are thankful to Dr Mahesh .B. Palkar Department of Pharmaceutical Chemistry

K.L.E Pharmacy College Hubli

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**Authors **

Doreswamy received B.Sc degree in Computer Science and M.Sc Degree in

Computer Science from University of Mysore in 1993 and 1995 respectively. Ph.D

degree in Computer Science from Mangalore University in the year 2007. After

completion of his Post-Graduation Degree, he subsequently joined and served

asLecturer in Computer Science at St. Joseph's College, Bangalore from 1996

1999.Then he has elevated to the position Reader in Computer Science at Mangalor

Universityin year 2003. He was the Chairman of the Department of Post-Graduate Studies and research in

computer science from 2003-2005 and from 2009-2008 and served at varies capacitiesin Mangalore

University at present he is the Chairman of Board of Studies and Professor in Computer Science of

Mangalore University. His areas of Research interests include Data Mining and Knowledge Discovery,

Artificial Intelligence and Expert Systems, Bioinformatics ,Molecular modelling and simulation

,Computational Intelligence ,Nanotechnology, Image Processing and Pattern recognition. He has been

granted a Major Research project entitled "Scientific Knowledge Discovery Systems (SKDS) for

Advanced Engineering Materials Design Applications" from the funding agency University Grant

Commission, New Delhi , India. He has been published about 30 contributed peer reviewed Papers at

national/International Journal and Conferences. He received SHIKSHA RATTAN PURASKAR for his

outstanding achievements in the year 2009 and RASTRIYA VIDYA SARASWATHI AWARD for

outstanding achievement in chosen field of activity in the year 2010.

**Chanabasayya.M.Vastrad** received B.E. degree and M.Tech. degree in the year

2001and 2006 respectively. Currently working towards his Ph.D Degree in Computer

Scienceand Technology under the guidance of Dr. Doreswamy in the Department of

Post-Graduate Studies and Research in Computer Science , Mangalore University.

Source: http://airccse.org/journal/hiij/papers/2413hiij04.pdf

Necrotic Enteritis: Managing without Antibiotics Dr. Linnea J. Newman Schering-Plough Animal Health (presented at the PIC's Poultry Health Conference on November 14, 2000) The medical community has expressed concern that antibiotic use in food animals may promote the development ofantibiotic-resistant strains of bacteria that could threaten the human population. While the true relationship betweenantibiotic use in animals and antibiotic-resistant bacteria in humans has yet to be determined, there has been a strong outcryfrom consumers to eliminate antibiotic use from food animal production.

C A S E R E P O R T Hellenic Journal of Atherosclerosis 1(1):65–67 Case report of rhabdomyolysis possibly associated to the interaction of ciprofloxacin with simvastatin N. Fountoulakis, L. Khafizova, M. Logothetis, G. Fanti, J.A. Papadakis Department of Internal Medicine, University Hospital of Heraklion, Heraklion Crete, Greece,