The Use of the Density Threshold Value as a Shape Descriptor on the Toxicity of Benzene Derivatives (original) (raw)
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Study on quantitative structure–toxicity relationships of benzene derivatives acting by narcosis
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Quantitative structure-toxicity relationship (QSTR) studies play an important role in toxicity forecasting, which is widely used in the study of modern compounds. Benzoic acid derivatives are an important type of organic chemicals. Most of them may cause serious public health and environmental problems. The effect of quantum-chemistry parameters on the toxicity of benzoic acid derivatives in rats via oral 50% lethal dose (LD 50 ) was studied by QSTR, and a model to predict the toxicity of benzoic acid derivatives was constructed. In order to obtain an accurate model, cross factors were considered and a model for predicting the toxicity of benzoic acid derivatives in rats via oral LD 50 was built using a linear regression method (correlation 0.990). The novel model is À log toxi ¼ 0:144 LogP À 0:0269SAG þ 0:0000127HoF À 0:000377PE, R 2 = 0.990, C(p) = 4.000, mean square error (MSE) = 0.785. The model demonstrated good forecasting ability.
Environmental Toxicology and Chemistry, 1998
The quantitative shape-activity relationship (QShAR) methodology, based on accurate three-dimensional electron densities and detailed shape analysis methods, has been applied to a Lemna gibba photoinduced toxicity data set of 16 polycyclic aromatic hydrocarbon (PAH) molecules. In the first phase of our studies, a shape fragment QShAR database of PAHs was developed. The results provide a very good match to toxicity based on a combination of the local shape features of single rings in comparison to the central ring of anthracene and a more global shape feature involving larger molecular fragments. The local shape feature appears as a descriptor of the susceptibility of PAHs to photomodification and the global shape feature is probably related to photosensitization activity.
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.
Structure-toxicity models exist at the intersection of biology, chemistry and statistics. The connection of these three subjects has permitted the development of structure-activity relationships as an accepted sub-discipline in toxicology. The next decade will see an increased use of (quantitative) structureactivity relationships (QSARs) to predict toxicity for new and existing chemicals. Much of the focus will be on their application to reduce or replace animal use in toxicological testing for the regulation of existing chemicals (e.g. in the REACH legislation) 1 . The official birth date of QSAR is considered to be 1962, when Hansch et al. 2 published a paper which showed a correlation between biological activity and octanol-water partition coefficient. Quantitative structure-activity relationship models have another ability which is obtaining a deeper knowledge about the mechanism of biological activity. Quantitative structure-activity relationships represent predictive models derived from application of statistical tools correlating biological activity (including therapeutic and toxic) of chemicals (drugs/toxicants/environmental pollutants) with descriptors representative of molecular structure and/or property. The
Asian Journal of Chemistry, 2013
Structure-toxicity models exist at the intersection of biology, chemistry and statistics. The connection of these three subjects has permitted the development of structure-activity relationships as an accepted sub-discipline in toxicology. The next decade will see an increased use of (quantitative) structureactivity relationships (QSARs) to predict toxicity for new and existing chemicals. Much of the focus will be on their application to reduce or replace animal use in toxicological testing for the regulation of existing chemicals (e.g. in the REACH legislation) 1 . The official birth date of QSAR is considered to be 1962, when Hansch et al. 2 published a paper which showed a correlation between biological activity and octanol-water partition coefficient. Quantitative structure-activity relationship models have another ability which is obtaining a deeper knowledge about the mechanism of biological activity. Quantitative structure-activity relationships represent predictive models derived from application of statistical tools correlating biological activity (including therapeutic and toxic) of chemicals (drugs/toxicants/environmental pollutants) with descriptors representative of molecular structure and/or property. The
Chemosphere, 2001
The log K ow and log S w values of 14 substituted benzaldehyde compounds were determined by the shake-¯ask method. Acute toxicities of 14 substituted benzaldehyde compounds to Daphnia magna were recorded. Their capacity factors (k H) were determined by reversed phased high-performance liquid chromatography (RP-HPLC) on C 18 column and methanol±water eluent. Molecular connectivity indices, the linear solvation energy relationships (LSER) parameters, and quantum chemical parameters were calculated for the tested chemicals and used to develop quantitative structure-retention relationship (QSRR) and quantitative structure-property/activity relationship (QSPR/QSAR). Results demonstrated that the molecular connectivity indices, LSER parameters, and quantum chemical parameters could be used to predict the k H for compounds studied, LSER method was more accurate. The results also show that chromatographic retention data, log k H , can be used to predict log K ow and log S w for tested compounds. The log k H w can be directly utilized as hydrophobic descriptors to predict the toxicity to D. Magna for benzaldehyde compounds.
Quantitative Structure-Activity Relationships in Drug Design, Predictive Toxicology, and Risk Assessment, 2015
Developing effective structure-activity/property/toxicity relationships (QSAR/QSPR/QSTR) is very helpful in predicting biological activity, property, and toxicity of a given set of molecules. Regular change in these properties with the structural alteration is the main reason to obtain QSAR/QSPR/QSTR models. The advancement in making different QSAR/QSPR/QSTR models to describe activity, property, and toxicity of various groups of molecules is reviewed in this chapter. The successful implementation of Conceptual Density Functional Theory (CDFT)-based global as well as local reactivity descriptors in modeling effective QSAR/QSPR/QSTR is highlighted.
Modeling of the acute toxicity of benzene derivatives by complementary QSAR methods
A data set containing acute toxicity values (96-h LC 50 ) of 69 substituted benzenes for fathead minnow (Pimephales promelas) was investigated with two Quantitative Structure-Activity Relationship (QSAR) models, either using or not using molecular descriptors, respectively. Recursive Neural Networks (RNN) derive a QSAR by direct treatment of the molecular structure, described through an appropriate graphical tool (variable-size labeled rooted ordered trees) by defining suitable representation rules. The input trees are encoded by an adaptive process able to learn, by tuning its free parameters, from a given set of structureactivity training examples. Owing to the use of a flexible encoding approach, the model is target invariant and does not need a priori definition of molecular descriptors. The results obtained in this study were analyzed together with those of a model based on molecular descriptors, i.e. a Multiple Linear Regression (MLR) model using CROatian MultiRegression selection of descriptors (CROMRsel). The comparison revealed interesting similarities that could lead to the development of a combined approach, exploiting the complementary characteristics of the two approaches.
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.