QSAR models for predicting toxicity of polychlorinated dibenzo-p-dioxins and dibenzofurans using quantum chemical descriptors

Quantum Chemical Investigation of Polychlorinated Dibenzodioxins, Dibenzofurans and Biphenyls: Relative Stability and Planarity Analysis

All the possible polychlorinated aromatic compounds in the classes of dibenzodioxins (PCDDs), dibenzofurans (PCDFs), and biphenyls (PCBs) were studied by the quantum chemical methods of HF/6-311++G(d,p), B3LYP/6-311++G(d,p), and MP2/cc-pVTZ. The calculated stabilities and structures of these compounds were compared with the available data on their abundance and toxicity. Prediction models for trends in energy and planarity among these congeners were proposed. The results discussed here can help contribute to the understanding of the role of dioxin-like compounds (DLCs) in the environment.

QSAR models for the acute toxicity of 1,2,4-triazole fungicides to zebrafish (Danio rerio) embryos

In recent decades, the 1,2,4-triazole fungicides are widely used for crop diseases control, and their toxicity to wild lives and pollution to ecosystem have attracted more and more attention. However, how to quickly and efficiently evaluate the toxicity of these compounds to environmental organisms is still a challenge. In silico method, such like Quantitative Structure-Activity Relationship (QSAR), provides a good alternative to evaluate the environmental toxicity of a large number of chemicals. At the present study, the acute toxicity of 23 1,2,4-triazole fungicides to zebrafish (Danio rerio) embryos was firstly tested, and the LC₅₀ (median lethal concentration) values were used as the bio-activity endpoint to conduct QSAR modelling for these triazoles. After the comparative study of several QSAR models, the 2D-QSAR model was finally constructed using the stepwise multiple linear regression algorithm combining with two physicochemical parameters (logD and μ), an electronic parameter (Q_N1) and a topological parameter (X^vPC₄). The optimal model could be mathematically described as following: pLC₅₀ = -7.24-0.30X^vPC₄ + 0.76logD - 26.15Q_N1 - 0.08μ. The internal validation by leave-one-out (LOO) cross-validation showed that the R²_adj (adjusted noncross-validation squared correlation coefficient), Q² (cross-validation correlation coefficient) and RMSD (root-mean-square error) was 0.88, 0.84 and 0.17, respectively. The external validation indicated the model had a robust predictability with the q² (predictive squared correlation coefficient) of 0.90 when eliminated tricyclazole. The present study provided a potential tool for predicting the acute toxicity of new 1,2,4-triazole fungicides which contained an independent triazole ring group in their molecules to zebrafish embryos, and also provided a reference for the development of more environmentally-friendly 1,2,4-triazole pesticides in the future.

Understanding the toxic potencies of xenobiotics inducing TCDD/TCDF-like effects

Toxic potencies of xenobiotics such as halogenated aromatic hydrocarbons inducing 2,3,7,8-tetrachlorodibenzo-p-dioxin/2,3,7,8-tetrachlorodibenzofuran (TCDD/TCDF)-like effects were investigated by quantitative structure-toxicity relationships (QSTR) using their aryl hydrocarbon receptor (AhR) binding affinity data. A descriptor pool was created using the SPARTAN 10, DRAGON 6.0 and ADMET 8.0 software packages, and the descriptors were selected using QSARINS (v.2.2.1) software. The QSTR models generated for AhR binding affinities of chemicals with TCDD/TCDF-like effects were internally and externally validated in line with the Organization of Economic Co-operation and Development (OECD) principles. The TCDD-based model had six descriptors from DRAGON 6.0 and ADMET 8.0, whereas the TCDF-based model had seven descriptors from DRAGON 6.0. The predictive ability of the generated models was tested on a diverse group of chemicals including polychlorinated/brominated biphenyls, dioxins/furans, ethers, polyaromatic hydrocarbons with fused heterocyclic rings (i.e. phenoxathiins, thianthrenes and dibenzothiophenes) and polyaromatic hydrocarbons (i.e. halogenated naphthalenes and phenanthrenes) with no AhR binding data. For the external set chemicals, the structural coverage of the generated models was 90% and 89% for TCDD and TCDF-like effects, respectively.

Molecular Polarization Potential as a Molecular Descriptor to Predict Biological Activity of Dioxins and Dibenzofurans

Molecular interaction potential (MIP) maps are a powerful tool to develop structure-activity relationships of a series of compounds. In the present study we have studied the effect of molecular polarization on the description of the toxicity of a series of dioxins and benzofurans using their MIPs. Specifically, we have used principal component analysis in an exploratory way to understand the common structural features that describe the toxicity of these molecules through the analysis of their MIPs and each of their components, i.e.; molecular electrostatic and polarization potentials. Moreover, we have developed a predictive model using PLS that permits to evaluate the toxicity of compounds belonging to any of the families of compounds studied in the present work on the basis of their molecular electrostatic and interaction potentials.

Molecular docking and 3D-QSAR studies on the glucocorticoid receptor antagonistic activity of hydroxylated polychlorinated biphenyls

The glucocorticoid receptor (GR) antagonistic activities of hydroxylated polychlorinated biphenyls (HO-PCBs) were recently characterised. To further explore the interactions between HO-PCBs and the GR, and to elucidate structural characteristics that influence the GR antagonistic activity of HO-PCBs, molecular docking and three-dimensional quantitative structure-activity relationship (3D-QSAR) studies were performed. Comparative molecular similarity indices analysis (CoMSIA) was performed using both ligand- and receptor-based alignment schemes. Results generated from the receptor-based model were found to be more satisfactory, with q(2) of 0.632 and r(2) of 0.931 compared with those from the ligand-based model. Some internal validation strategies (e.g. cross-validation analysis, bootstrapping analysis and Y-randomisation) and an external validation method were used respectively to further assess the stability and predictive ability of the derived model. Graphical interpretation of the model provided some insights into the structural features that affected the GR antagonistic activity of HO-PCBs. Molecular docking studies revealed that some key residues were critical for ligand-receptor interactions by forming hydrogen bonds (Glu540) and hydrophobic interactions with ligands (Ile539, Val543 and Trp577). Although CoMSIA sometimes depends on the alignment of the molecules, the information provided is beneficial for predicting the GR antagonistic activities of HO-PCB homologues and is helpful for understanding the binding mechanisms of HO-PCBs to GR.

Consensus toxicity factors for polychlorinated dibenzo-p-dioxins, dibenzofurans, and biphenyls combining in silico models and extensive in vitro screening of AhR-mediated effects in human and rodent cells

Consensus toxicity factors (CTFs) were developed as a novel approach to establish toxicity factors for risk assessment of dioxin-like compounds (DLCs). Eighteen polychlorinated dibenzo-p-dioxins, dibenzofurans (PCDD/Fs), and biphenyls (PCBs) with assigned World Health Organization toxic equivalency factors (WHO-TEFs) and two additional PCBs were screened in 17 human and rodent bioassays to assess their induction of aryl hydrocarbon receptor-related responses. For each bioassay and compound, relative effect potency values (REPs) compared to 2,3,7,8-tetrachlorodibenzo-p-dioxin were calculated and analyzed. The responses in the human and rodent cell bioassays generally differed. Most notably, the human cell models responded only weakly to PCBs, with 3,3',4,4',5-pentachlorobiphenyl (PCB126) being the only PCB that frequently evoked sufficiently strong responses in human cells to permit us to calculate REP values. Calculated REPs for PCB126 were more than 30 times lower than the WHO-TEF value for PCB126. CTFs were calculated using score and loading vectors from a principal component analysis to establish the ranking of the compounds and, by rescaling, also to provide numerical differences between the different congeners corresponding to the TEF scheme. The CTFs were based on rat and human bioassay data and indicated a significant deviation for PCBs but also for certain PCDD/Fs from the WHO-TEF values. The human CTFs for 2,3,4,7,8-pentachlorodibenzofuran, 1,2,3,4,7,8-hexachlorodibenzofuran, 1,2,3,4,6,7,8-heptachlorodibenzo-p-dioxin, and 1,2,3,4,7,8,9-heptachlorodibenzofuran were up to 10 times greater than their WHO-TEF values. Quantitative structure-activity relationship models were used to predict CTFs for untested WHO-TEF compounds, suggesting that the WHO-TEF value for 1,2,3,7,8-pentachlorodibenzofuran could be underestimated by an order of magnitude for both human and rodent models. Our results indicate that the CTF approach provides a powerful tool for condensing data from batteries of screening tests using compounds with similar mechanisms of action, which can be used to improve risk assessment of DLCs.

Synthesis, antifungal activity, and QSAR study of novel trichodermin derivatives

In an attempt to discover more potential antifungal agents, in this study, 21 novel trichodermin derivatives containing conjugated oxime ester (5a-5u) were designed and synthesized and were screened for in vitro antifungal activity. The bioassay tests showed that some of them exhibited good inhibitory activity against the tested pathogenic fungi. Compound 5a exhibited better activity against Pyricularia oryzae and Sclerotonia sclerotiorum than trichodermin, and compound 5j showed particular activity against P.oryzae and Botrytis cinerea. The quantitative structure-activity relationship (QSAR) indicated that log P and hardness were two critical parameters for the biological activities. The result suggested that these would be potential lead compounds for the development of fungicides with further structure modification.

Complementary PLS and KNN algorithms for improved 3D-QSDAR consensus modeling of AhR binding

Multiple validation techniques (Y-scrambling, complete training/test set randomization, determination of the dependence of R2test on the number of randomization cycles, etc.) aimed to improve the reliability of the modeling process were utilized and their effect on the statistical parameters of the models was evaluated. A consensus partial least squares (PLS)-similarity based k-nearest neighbors (KNN) model utilizing 3D-SDAR (three dimensional spectral data-activity relationship) fingerprint descriptors for prediction of the log(1/EC50) values of a dataset of 94 aryl hydrocarbon receptor binders was developed. This consensus model was constructed from a PLS model utilizing 10 ppm x 10 ppm x 0.5 Å bins and 7 latent variables (R2test of 0.617), and a KNN model using 2 ppm x 2 ppm x 0.5 Å bins and 6 neighbors (R2test of 0.622). Compared to individual models, improvement in predictive performance of approximately 10.5% (R2test of 0.685) was observed. Further experiments indicated that this improvement is likely an outcome of the complementarity of the information contained in 3D-SDAR matrices of different granularity. For similarly sized data sets of Aryl hydrocarbon (AhR) binders the consensus KNN and PLS models compare favorably to earlier reports. The ability of 3D-QSDAR (three dimensional quantitative spectral data-activity relationship) to provide structural interpretation was illustrated by a projection of the most frequently occurring bins on the standard coordinate space, thus allowing identification of structural features related to toxicity.

Molecular docking, molecular dynamics simulation, and structure-based 3D-QSAR studies on the aryl hydrocarbon receptor agonistic activity of hydroxylated polychlorinated biphenyls

The binding interactions between hydroxylated polychlorinated biphenyls (HO-PCBs) and the aryl hydrocarbon receptor (AhR) are suspected of causing toxic effects. To understand the binding mode between HO-PCBs and AhR, and to explore the structural characteristics that influence the AhR agonistic activities of HO-PCBs, the combination of molecular docking, three-dimensional quantitative structure-activity relationship (3D-QSAR), and molecular dynamics (MD) simulations was performed. Using molecular docking, the HO-PCBs were docked into the binding pocket of AhR, which was generated by homology modeling. Comparative molecular similarity index analysis (CoMSIA) models were subsequently developed from three different alignment rules. The optimum 3D-QSAR model showed good predictive ability (q(2)=0.583, R(2)=0.913) and good mechanism interpretability. The statistical reliability of the CoMSIA model was also validated. In addition, molecular docking and MD simulations were applied to explore the binding modes between the ligands and AhR. The results obtained from this study may lead to a better understanding of the interaction mechanism between HO-PCBs and AhR.

In silico investigations of anti-androgen activity of polychlorinated biphenyls

Polychlorinated biphenyls (PCBs) have attracted great concern as global environmental pollutants and representative endocrine disruptors. In this work, a molecular model study combining three-dimensional quantitative structure-activity relationship (3D-QSAR), molecular docking, and molecular dynamics (MD) simulations was performed to explore the structural requirement for the anti-androgen activities of PCBs and to reveal the binding mode between the PCBs and androgen receptor (AR). The best comparative molecular similarity indices analysis (CoMSIA) model, obtained from receptor-based alignment, shows leave-one-out cross-validated correlation coefficient (q(2)) of 0.665 and conventional correlation coefficient (R(2)) of 0.945. The developed model has a highly predictive ability in both internal and external validation. Furthermore, the interaction mechanisms of PCBs to AR were analyzed by molecular docking and MD simulation. Molecular docking indicated that all the PCBs in the data set docked in a hydrophobic pocket. The Binding free energies calculated by Molecular mechanics-Poisson Boltzmann surface area (MM-PBSA) not only exhibited a good correlation with the experimental activity, but also could explain the activity difference of the studied compounds. The binding free energy decomposition analysis indicates that the van der Waals interaction is the major driving force for the binding process.

On the use of electronic descriptors for QSAR modelling of PCDDs, PCDFs and dioxin-like PCBs

The electronic properties of 29 polychlorinated dibenzo-p-dioxins and dibenzofurans and dioxin-like polychlorinated biphenyls that have been included in the toxic equivalency factor system have been investigated and used to derive quantum mechanical (QM) chemical descriptors for QSAR modelling. Their utility in this context was investigated alongside descriptors based on ultraviolet absorption data and traditional 2D descriptors including log K(ow), polarizability, molecular surface properties, van der Waals volume and selected connectivity indices. The QM descriptors were calculated using the semi-empirical AM1 method and the density functional theory method B3-LYP/6-31G**. Atom-specific and molecular quantum chemical descriptors were calculated to compare the electronic properties of dioxin-like compounds regardless of their chemical class, with particular emphasis on the lateral positions. Multivariate analysis revealed differences between the chemical classes in terms of their electronic properties and also highlighted differences between congeners. The results obtained demonstrated the importance of considering molecular orbital energies, but also indicated that the ratios of the coefficients of the highest occupied molecular orbital (HOMO) and the lowest unoccupied molecular orbital (LUMO) at the lateral carbons were important. In addition, the digitalized UV spectra contained chemical information that provided crucial insights into dioxin-like activity.

In silico study on hydroxylated polychlorinated biphenyls as androgen receptor antagonists

Hydroxylated polychlorinated biphenyls (HO-PCBs), major metabolites of PCBs, may have the potential to disrupt androgen hormone homeostasis. However, there is a lack of systematic investigation into the intermolecular interaction mechanism between HO-PCBs and the androgen receptor (AR). In this study, the combination of three-dimensional quantitative structure-activity relationship (3D-QSAR), molecular docking, and molecular dynamics (MD) simulations was performed to elucidate structural characteristics that influence the anti-androgen activity of HO-PCBs, and to provide a better understanding of the binding modes between HO-PCBs and AR. A predictive comparative molecular field analysis (CoMFA) model was developed with good robustness and predictive ability. Graphical interpretation of the model provided some insights into the structural features that affect the anti-androgen activity of HO-PCBs. The hydrogen bond interaction with Gln711, and hydrophobic interactions with residues in the hydrophobic pocket played important roles in the binding of ligand with receptor. These results are expected to be beneficial to predict anti-androgen activities of other HO-PCBs and provided possible clues for further elucidation of the binding mechanism of HO-PCBs with AR.