QSAR STUDIES ON POLYCHLORINATED AROMATIC COMPOUNDS USING TOPOLOGICAL DESCRIPTORS

QSAR STUDIES ON POLYCHLORINATED AROMATIC COMPOUNDS USING TOPOLOGICAL DESCRIPTORS

Kumar Nandan*¹, KumarRanjan ³,Md. Belal Ahmad¹ and Baidyanath Sah ²

Department of Chemistry ¹, Department of Mathematics², T.N.B. College, T.M. Bhagalpur University, Bhagalpur-812007, Bihar, India

Department of Chemistry, BUIT, Barkatullah University ³, Bhopal, Madhya Pradesh, India

ABSTRACT: Polychlorinated dibenzo-p-dioxins(PCDDs) and Polychlorinated dibenzofurans(PCDFs) represent a large group of industrial and byproduct compound is resistance to chemical and biological degradation and highly toxic.A series of 18 PCDD and  PCDF compounds is subjected to QSTR studies by using differnt molecular descripters. The dependent variable used in this study represents log(EC₅₀) values (EC₅₀-median effective concentration during a bioassay).The regression analysis of the data has shown that the toxicity of the compound can be modeled excellently in multi-parametric model.Its resulting model exhibited good R²value up to 0.8938. The present work contribute in the identification of those compounds which are very toxic and polluting our environment.

QSAR, QSTR, PCDD, PCDF, MFA,EC₅₀ toxicity

INTRODUCTION: QSTR andQSAR studies have often been carried out by using regression analysis the biological toxicity are being modeled using a set of molecular descriptor. In an earlier, report by Mihaela Caprioara and Mircea V Diudea¹a QSAR studies on a group of some polychlorinated aromatic compounds.

The introduction of chlorine in PCDD and PCDF gives rise to the more potent pharmacophore.Due to their lipophlic nature PCDD and PCDF concentrate in adipose and hepatic tissues and can persist in an individual for extended lengths of time.With heavier congeners,it may stay with an individual for decades because they are resistant to thermal,metabolic and environmental breakdown.

These are persistant organic polutants(POPs) that can enter water bodies and eventually sink into the sediment through various transporation routes.These POPs havebeen of a great concern due to their elevated concentrations and wide distribution;they pose not only an environmentel risk through accumulation in human tissues and fluids^2-5.

QSAR can fill the data gap of organic pollutants,decreasses experimental expenses and,in particular,reduce animal testing.They have been widely used in research on the acute toxicity⁶,mixture toxicity⁷,endocrine disrupting activities^8,9 and photoinduced toxicity of organic compounds.

In QSAR, we seek to uncover correlations of biological activity with molecular structure with Quantative structure activity relationship (QSPR); we extend the same notion to general chemical property predication and just biological activity. In either case, the relationship is most often expressed by a linear equation that related molecular properties, X, Y .......to the desired activityA_i for compounds i.

A_i = mx_i+ ny_i + oz_i + b

Where m, n and o are the linear slopes that express the correlation of the particular molecular property with the activity of the compound and b is a constant. If only one molecular property is important, for example molecular volume, than above eqn. reduces to the simple form of a straight line, A_i = mx_i + b. The slopes and the constant are often calculated using multiple linear regression (MLR) which is analogous with regular linear regression when there is just one independent variable. In constructing graph theoretical schemes to traditional QSAR methods ¹⁰the graph theoretical approach involves (a rather small set of) structural or graph invariants.

In QSTR, one uses statistical methods in order to select critical descriptors and demonstrate a structure – toxicity correlation. In graph theory, one manipulates a structure algebraically, using partial order and ranking based on selected standards of course, graph theoretical descriptors also yield structure property or structure activity correlations.

The authors have developed a QSTR models to predict toxicity of some PCDD and PCDFderivatives. The negative logarithm ofEC₅₀ (logEC₅₀) was used as the biological activity in QSAR studies.

MATERIAL AND METHODS:

Methodology: This methodology used is to transform the chemical structure in to its molecular graph. This can be done by depleting all the Carbon- hydrogen atom as well as hetro atom hydrogen bonds of chemical structure. In the present investigation, initially, we have used a set of distance based topological indices and physico-chemical parameter.

Statistical Analysis: Based on our earlier studies we have used the  Correlation and Rregression Analysis ^11-12.

1. Correlation Analysis: Correlation analysis of biological activity, topological indices and physicochemical parameter was carried out- Inter-Correlated parameter were eliminated stepwise depending on their individual correlation with the biological activity. All possible combinations of parameters were considered for multiple regression analysis.
2. Regression Analysis: Multiple regression analysis a programmed carried out by ‘Multi Regress’ using stepwise regression methodology carried out. It was carried out using a computer program, graph pad and NCSS software, In order to obtain appropriate models; we used the maximum R² Method. In addition we also calculation the quality factorQ, as the ratio of correlation coefficient (R) and the standard error of estimation (Se) i.e. Q= R/Se. Finally, the cross-validation method was used to establish the predictive potential of our models.

Molecular Descriptors: The topological parameterWinner indices(W)andBalban indices(J) in MFA-QSAR equation  specify the regions of different compounds in the training set, leading to either an increase or decrease in activities ¹³.

1. Winner indices (W): Winner is defined as the sum over all bonds of the product of the number of vertices on each side of the bond. Mathematically, W index is defined as:

Where d_ij is the shortest distance between two vertices (atoms) s and j

2. Balban indices (J): The Balban index J is defined by;

J= J(G)

Where M is the number of bonds in a graph G and µ is the cyclomatic number of G.

3. Physico-Chemical Parameters: We have used Chemsketch program of ACD lab 27 for the calculation of Physico-chemical parameters. These parameter used in present work includes molecular weight (MW), Molecular refraction (MR), Molecular volume (MV), Parachor () Surface Tension (), Density (d), Index of Refraction (), Polarizability (α). Attempt has also been mode to combine the physicochemical with distance based topological indices with a hope of obtaining models with better statistics

 Software: Allmolecular modeling studies were carried out using HYPERCHEM (version 7.5) and DRAGON software. The structures of molecules were drawn using Chemsketch software. NCSS Inc. is a leading worldwide provider of predictive analytics software and solutions.

RESULT AND DISCUSSION: The basic PCDD and PCDF pharmacophore used in the present studies is shown in table 1.

TABLE 1: STRUCTURE OF COMPOUNDS ACTIVITY AND THEIR MOLECULAR DESCRIPTORS

β= Refractivity;α = Plorizability; W= Winner indices; J= Balban

The correlation matrix as recorded in table 2 is important in the sense that it represents inter relationship between the observed value of the variable on one side and its estimated value expressed in terms of the dependent variables on the other. These tables show that multiparametric model involving pair is good.

TABLE 2: CORRELATION MATRIX FOR THE INTER-CORRELATION OF STRUCTURE DESCRIPTORS AND THEIR CORRELATION WITH THEACTIVITY

TABLE 3: RESULT OF PROPOSED MODELS USING MAXIMUM – R² METHOD

We have carried out stepwise multiple regression analysis for modeling of Compound no 18. The results of regression analysis are presented in table 3 which shows possible correlationequation.

Final equation;

-1.618(±0.225)α+26.940(±3.518)J+0.02800(±0.0047)W-23.945

Thenumbers accompanying descriptors in the equation represent their positions in three-dimensional MFA grid. We have carried out step wise multiple regression analysis for modeling of compound no. 18.

FIG. 1: ALIGNMENT OF THE COMPOUNDS 18 USED IN THE TRAINING SET OF QSAR ANALYSIS

 In order to confirm the above-mentioned finding we have estimated Q-value and observed that it is highest for model. At this stage, It is interesting to comments an adjustable R² ( ) Coefficients. It takes into accounts of adjustment of R² therefore If a variable is added that does not contribute its fair share, the  will actually decline. If  always increases then an independent variable is added. On other side  will decreases, this means the added variable does not reduce the unexplained variation enough to offset the loss of degrees of freedom. In our case,  value increases with increasing number of parameters. This indicates that the new parameters have a fair share in the proposed model. Further support in out favors in obtained by estimating EC₅₀ and compares the same with observed EC₅₀ value. Such a comparison is demonstrated in table 4. We observed that the estimated value is very close to theobserved values.

TABLE 4: COMPARISONS OF OBSERVED AND ESTIMATED EC₅₀

The most active molecule no-18 was used for MFA model. A common substructure-based alignment was adopted in the present study, which attempted to align molecules to the template molecule on a common backbone. Finally, we have plotted a graph between observed and calculated value, which yielded predictive correlation co-efficient.

FIG. 2: PLOT OF OBSERVED VS. ESTIMATED ACTIVITY EC₅₀

CONCLUSION: On the basis of above observation it leads to the conclusion that the activity logEC₅₀ of the present set of compounds can be successfully modelled using molecular descriptors. It was also observed that out of the molecular descriptors used α, β, W and J   are most useful for this purpose. The MFA equation suggested that (-ve)sign of descriptor is disfavour the activity while (+Ve) sign of W and J parameters indicated that they favored activity.

QSAR for regulatory purpose should be defined domain of applicability apprapriate measure of goodness-of-fit, robustness and predictive power.Our results open very interestingperspectives regarding polychlorinated aromatic compounds with  toxicity.

ACKNOWLEDGEMENTS: One the author (Kumar Nandan) is highly obliged and thankful Dr.Sunita Gupta, dept. of chemistry, A.P.S. University Rewa,India, for introducing him to the fascinating field of Chemical Topology, Graph Theory and statistical work

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    How to cite this article:

    Nandan K, Ranjan K, Ahmad MB and Sah B: QSAR studies on Polychlorinated Aromatic compounds using Topological descriptors. Int J Pharm Sci Res 2013: 4(7); 2691-2695. doi: 10.13040/IJPSR. 0975-8232.4(7).2691-95

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