A conceptual DFT approach towards analysing toxicity (original) (raw)
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Analyzing Toxicity Through Electrophilicity
Molecular Diversity, 2006
The toxicological structure-activity relationships are investigated using conceptual DFT based descriptors like global and local electrophilicities. In the present work the usefulness of electrophilicity in predicting toxicity of several polyaromatic hydrocarbons (PAH) is assessed. The toxicity is expressed through biological activity data (pIC50) defined as molar concentration of those chemicals necessary to displace 50% of radiolabeled tetrachlorodibenzo-p-dioxin (TCDD) from the arylhydrocarbon (Ah) receptor. The experimental toxicity values (pIC50) for the electron acceptor toxin like polychlorinated dibenzofurans (PCDF) are taken as dependent variables and the DFT based global descriptor electrophilicity index (ω) is taken as independent variable in the training set. The same model is then tested on a test set of polychlorinated biphenyls (PCB). A good correlation is obtained which vindicates the importance of these descriptors in the QSAR studies on toxins. These toxins act as electron acceptors in the presence of biomolecules whereas aliphatic amines behave as electron donors some of which are also taken into account for the present work. The toxicity values of the aliphatic amines in terms of the 50% inhibitory growth concentration (IGC50) towards ciliate fresh-water protozoa Tetrahymena pyriformis are considered. Since there is no global nucleophilicity we apply local nucleophilicity (ωmax+) as the descriptor in this case of training set. The same regression model is then applied to a test set of amino alcohols. Although the correlation is very good the statistical analysis reflects some cross validation problem. As a further check the amines and amino alcohols are used together to form both the training and the test sets to provide good correlation. It is demonstrated that the toxicity of several toxins (both electron donors and acceptors) in the gas and solution phases can be adequately explained in terms of global and local electrophilicities. Amount of charge transfer between the toxin and the biosystem, simulated as nucleic acid bases and DNA base pairs, indicates the importance of charge transfer in the observed toxicity. The major strength of the present analysis vis-à-vis the existing ones rests on the fact that it requires only one descriptor having a direct relationship with toxicity to provide a better correlation. Importance of using the information from both the toxin and the biosystem is also analyzed.
Toxicity analysis of benzidine through chemical reactivity and selectivity profiles: a DFT approach
2003
Chemical reactivity descriptors based on density functional theory are useful in analyzing the toxicities and in identifying the reactive sites of the molecular systems. In the present investigation the global reactivity profiles such as electronegativity, chemical hardness, polarizability, electrophilicity index and local selectivity profiles like condensed electrophilicity of benzidine are calculated using B3LYP/6-31G* including both Hartree-Fock and density functional theory based exchange functionals (B3LYP) in order to gain deeper insights into the toxic nature of this compound. Both global and local electrophilicity have been found to be adequate in explaining respectively the overall toxicity and the most probable site of reactivity. Interaction between benzidine and nucleic acid (NA) base/selected base pairs and Aryl Hydrocarbon Hydroxylase (AHH) receptors are determined using Parr's formula. The charge transfer involved in the formation of adducts is also qualitatively studied. The results revealed that benzidine acts as an electron-donating agent in their interaction with biomolecules. The planarity and electron affinity are the criteria influencing the toxic nature of benzidine.
Determinants of Molecular Reactivity as Criteria for Predicting Toxicity: Problems and Approaches
Environmental Health Perspectives, 1985
We discuss the physicochemical basis for mechanisms of action of toxic chemicals and theoretical methods that can be used to understand the relation to the structure of these chemicals. Molecular properties that determine the chemical reactivity of the compounds are proposed as parameters in the analysis of such structure-activity relationships and as criteria for predicting potential toxicity. The theoretical approaches include quantitative methods for structural superposition of molecules and for superposition of their reactivity characteristics. Applications to polychlorinated hydrocarbons are used to illustrate both rigid superposition methods, and methods that take advantage of structural flexibility. These approaches and their results are discussed and compared with methods that afford quantitative structural comparisons without direct superposition, with special emphasis on the need for efficient automated methods suitable for rapid scans of large structural data bases. Quantum mechanical methods for the calculation of molecular properties that can serve as reactivity criteria are presented and illustrated. Special attention is given to the electrostatic properties of the molecules such as the molecular electrostatic potential, the electric fields, and the polarizability terms calculated from perturbation expansions. The practical considerations related to the rapid calculation of these properties on relevant molecular surfaces (e.g., solventor reagent-accessible surfaces) are discussed and exemplified, stressing the special problems posed by the structural variety of toxic substances and the paucity of information on their mechanisms of action. The discussion leads to a rationale for the use of the combination of theoretical methods to reveal discriminant criteria for toxicity and to analyze the initial steps in the metabolic processes that could yield toxic products.
2010
In the present study, the density functional B3LYP/6-311G** level of theory was used to compute and map the molecular surface electrostatic potentials of a group of substituted nitrobenzenes to identify common features related to their subsequent toxicities. Several statistical properties including potentials’ extrema (Vmin, Vmax), molecular volume, surface area, polar surface area, along with different energies were computed. A little linear correlation was revealed between Vmin and surface area, and systems’ toxicities. Another computations employed quantitative structure– property relationships model in CODESSA package to correlate toxicities with calculated descriptors. Statistically, the most significant correlation is a five-parameter equation with correlation coefficient, R values of 0.962, and the cross-validated correlation coefficient, RCV=0.950. The obtained models allowed us to reveal toxic activity of nitrobenzenes.
Chemosphere, 2007
DFT-B3LYP method with 6-31G ** basis set was employed to fully optimize the electronic structures of 135 polychlorinated dibenzofurans and parent compound, namely dibenzofuran. It was demonstrated that polarizability anisotropy and mean polarizability could change sensitively and systematically with chlorine number and substitution pattern. And new quantitative structure-activity relationships (QSARs) focused on the binding affinities of aryl hydrocarbon receptor (AhR), aryl hydrocarbon hydroxylase (AHH) and 7-ethoxyresorufin O-deethylase (EROD) induction potencies of PCDFs were developed. It was concluded that polarizability anisotropy in conjunction with hyperpolarizabilties and hyper-order electric moments, e.g. octupole moments could well interpret the variation of toxicity of different congeners and dispersion interaction should be the leading form among various interactions. Although the terms of hyperpolarizabilities and hyper-order electric moments were not the same significant ones as polarizability anisotropy, the longrange interactions characterized by them should not be ignored in explaining the toxicity.
Toxicity analysis of polychlorinated dibenzofurans through global and local electrophilicities
2006
Toxicity of polychlorinated dibenzofurans are correlated with global and local electrophilicities calculated through DFT/6-31G(d) method with B3LYP functionals using both Mulliken and Hirshfeld population analysis schemes. An excellent correlation is observed between the experimental binding affinity values of 31 polychlorinated dibenzofurans with AhR receptors and a linear combination of global and local electrophilicity values. Motivation. To verify the importance of global and local electrophilicities in analyzing toxicity within a QSAR parlance. Method. Calculation ω and + k ω of 31 polychlorinated dibenzofurans using Becke's three parameter hybrid density functional, B3LYP, with 6-31G(d) basis set and Mulliken and Hirshfeld population analysis schemes. Results. A linear relation between the experimental binding affinities (50 pIC) of polychlorinated biphenyls with biosystems and a linear combination of global and local electrophilicity values is observed. Conclusions. The global and local electrophilicities together can explain the toxicities of polychlorinated dibenzofurans. The beautiful correlation between the binding affinity of these toxins with biosystems and a linear combination of global and local electrophilicity values confirms the importance of charge transfer in analyzing the origin of toxicity.
Journal of Chemical Information and Modeling, 2005
The quality of quantitative structure-activity relationship (QSAR) models depends on the quality of their constitutive elements including the biological activity, statistical procedure applied, and the physicochemical and structural descriptors. The aim of this study was to assess the comparative use of ab initio and semiempirical quantum chemical calculations for the development of toxicological QSARs applied to a large and chemically diverse data set. A heterogeneous collection of 568 organic compounds with 96 h acute toxicity measured to the fish fathead minnow (Pimephales promelas) was utilized. A total of 162 descriptors were calculated using the semiempirical AM1 Hamiltonian, and 121 descriptors were compiled using an ab initio (B3LYP/6-31G**) method. The QSARs were derived using multiple linear regression (MLR) and partial least squares (PLS) analyses. Statistically similar models were obtained using AM1 and B3LYP calculated descriptors supported by the use of the logarithm of the octanol-water partition coefficient (log K ow). The main difference between the models derived by both MLR and PLS with the two sets of quantum chemical descriptors was concentrated on the type of descriptors selected. It was concluded that for large-scale predictions, irrespective of the mechanism of toxic action, the use of precise but time-consuming ab initio methods does not offer considerable advantage compared to the semiempirical calculations and could be avoided.
Pharmaceuticals
The preclinical drug discovery stage often requires a large amount of costly and time-consuming experiments using huge sets of chemical compounds. In the last few decades, this process has undergone significant improvements by the introduction of quantitative structure-activity relationship (QSAR) modelling that uses a certain percentage of experimental data to predict the biological activity/property of compounds with similar structural skeleton and/or containing a particular functional group(s). The use of machine learning tools along with it has made life even easier for pharmaceutical researchers. Here, we discuss the toxicity of certain sets of bioactive compounds towards Pimephales promelas and Tetrahymena pyriformis in terms of the global conceptual density functional theory (CDFT)-based descriptor, electrophilicity index (ω). We have compared the results with those obtained by using the commonly used hydrophobicity parameter, logP (where P is the n-octanol/water partition co...
Chemical Biology & Drug Design
Quantitative structure-activity relationship (QSAR) aims at establishing a relationship between the molecular structure and the various behavioral characteristics of chemical compounds. QSARs represent predictive models based on the application of statistical tools which construct a mathematical equation for a set of homologous molecules for their specific biological behavior (activity/property/toxicity) using information about their chemical structure in terms of molecular descriptors (Roy, Kar, & Das, 2015). QSAR models primarily focus on their application in the reduction in use of experimental animals (Commission of the European Communities, 2001) as done in conventional toxicological testing. These methods are also used in grouping and regulation of existing chemical compounds (e.g., REACH legislation; Worth et al., 2007). QSAR was brought into light in 1962 when Hansch, Maloney, Fujita, & Muir (1962) published a paper showing a correlation between biological activity and n-octanol/water partition coefficient. A wide range of descriptors, such as thermodynamic potentials, quantum chemical and electronic energy descriptors, topology, shape, and hydrophobicity, have been used in the development of different QSARs (Karelson, Lobanov, & Katritzky, 1996). QSAR studies in biochemistry mainly use quantum chemical descriptors such as polarizabilities, dipole moments, charges, orbital energies, and frontier orbital densities to predict different biological activities (