Quantitative Structure-toxicity Relationship Research Articles

Free and Open Source Chemistry Software in Research of Quantitative Structure-Toxicity Relationship of Pesticides

Pesticides are toxic chemicals aimed for the destroying pest on crops. Numerous data evidence about toxicity of pesticides on aquatic organisms. Since pesticides with similar properties tend to have similar biological activities, toxicity may be predicted from structure. Their structure feature and properties are encoded my means of molecular descriptors. Molecular descriptors can capture quite simple two-dimensional (2D) chemical structures to highly complex three-dimensional (3D) chemical structures. Quantitative structure-toxicity relationship (QSTR) method uses linear regression analyses for correlation toxicity of chemical with their structural feature using molecular descriptors. Molecular descriptors were calculated using open source software PaDEL and in-house built PyMOL plugin (PyDescriptor). PyDescriptor is a new script implemented with the commonly used visualization software PyMOL for calculation of a large and diverse set of easily interpretable molecular descriptors encoding pharmacophoric patterns and atomic fragments. PyDescriptor has several advantages like free and open source, can work on all major platforms (Windows, Linux, MacOS). QSTR method allows prediction of toxicity of pesticides without experimental assay. In the present work, QSTR analysis for toxicity of a dataset of mixtures of 5 classes of pesticides comprising has been performed.

Predictive Quantitative Structure Toxicity Relationship Study on Avian Toxicity of Some Diverse Agrochemical Pesticides by Monte Carlo Method

Application of pesticides may have serious adverse consequences in environment. Birds are one of the most important non-target species that are harmed by agricultural chemical pesticides. In the current study, Monte Carlo optimization based Quantitative Structure Toxicity Relationship (QSTR) analyses were performed on a dataset containing diverse chemical pesticides with toxicity data determined on Bobwhite quail. Hybrid models containing SMILES and graph based descriptors were developed on three different training and test set combinations. The best model was selected based on validation statistics on internal training (n = 96) and external test (n = 31) as well as validation (n=25) sets. The best model was thoroughly analyzed to understand structural requirements of the chemical pesticides for higher avian toxicity. The models developed in the current analyses may be useful to design novel less toxic pesticides.

Density Functional Theory (B3LYP/6-31G*) Study of Toxicity of Polychlorinated Dibenzofurans

Quantitative Structure Toxicity Relationship (QSTR) study was applied to a dataset of 35 polychlorinated dibenzofurans (PCDFs) to investigate the relationship between toxicities of the compounds and their structures by employing Density Functional Theory (DFT) (B3LYP/6-31G*) method to compute their quantum molecular descriptors. The model was built using Genetic Function Algorithm (GFA) approach. The model (N= 24, Friedman LOF = 0.361, squared correlation coefficient (R²) = 0.963, R²adj = 0.955, cross-validation correlation coefficient (Q²) = 0.889, external prediction ability (R²_pred) = 0.8286, P-value of optimization at P_95% < 0.05) of the best statistical significance was selected. The accuracy of the model was evaluated through Leave one out (LOOV) cross-validation, external validation using test set molecules, Y-randomization and applicability domain techniques. The results of the present study are expected to be useful to the environmental regulatory agencies locally and internationally in the area of environmental risk assessment of toxicity of Polychlorinated dibenzofurans (PCDFs) and other related Polychlorinated aromatic compounds/ pollutants that fall within the model’s applicability domain.

In silico ADME/T and 3D QSAR analysis of KDR inhibitors

Tyrosine kinases (KDR) have been considered as a potential targets for the design of new anticancer agents. Recently, a series of N-4-chlorophenylnaphthamides has been reported with KDR inhibitory activity. In order to demonstrate the pharmacokinetics and the relationship between the structures and their inhibition of KDR, 3D-QSAR and in silico ADME/T analysis were performed on a dataset of 13 compounds. Quantum chemical parameters such as LUMO energy, HOMO energy, ionization energy (I), electron affinity (A), chemical potential (μ), hardness (η) and electrophilicity (χ) of the compounds are calculated by using semi-empirical SCF-MO method at PM3 level of theory and various SlogP descriptors from MOE software. In silico ADME/T analysis was performed by using different software’s Discovery Studio 2.5, TOPKAT and QikProp 4.3. From in silico ADME and Toxicity studies, it was revealed that selected derivatives have good oral absorptions and metabolisms with no BBB penetration. QSTR (Quantitative Structure Toxicity Relationship) studies by using TOPAK in various computational animal models, showed high LD50 values and the compounds are found to be noncarcenogenic. Moreover, three different QSAR models were generated by Partial Least Squares (PLS) Regression method having correlation coefficient (Q2) of 0.86382, 0.84372, and 0.82629, respectively. These models conclude a significant relationship of KDR inhibition with dipole moment (D), LUMO energy (ELUMO), hardness (η) and electrophilicity (χ), and hydrophobicity of compounds, regarding N-4-chlorophenylnaphthamides.

An in silico approach to cytotoxicity of pharmaceuticals and personal care products on the rainbow trout liver cell line RTL-W1.

The authors constructed novel, robust, and validated linear Quantitative Structure-Toxicity Relationship (QSTR) models in line with Organisation of Co-operation and Development (OECD) criteria using 2 cytotoxicity data sets which were obtained from the Alamar Blue and 5-carboxyfluorescein diacetate acetoxymethyl ester (CFDA-AM) assays. The data sets comprise the cytotoxic effect of structurally diverse and widely used pharmaceuticals, synthetic musks, and industrial chemicals on the rainbow trout (Oncorhynchus mykiss) liver cell line RTL-W1. Common descriptors defined the relationship between structure and cytotoxicity for both the Alamar Blue and the CFDA-AM assays which measure the metabolic activity and membrane integrity, respectively. Only the statistical parameters of the best Alamar Blue-based model were given (nTR = 13; R2 = 0.839; the root-mean-square error of the training set [RMSETR ] = 0.261; nTEST = 5; R2TEST = 0.903; RMSETEST = 0.181; CCCTEST = 0.939). The proposed QSTR model was able to predict the cytotoxicity of 101 diverse chemicals on the RTL-W1 cell line with 91% structural coverage. The authors found that in vitro-derived cytotoxicity data are promising predictors of in vivo fish toxicity and may provide an initial, rapid screening tool for acute fish toxicity assessment and reduce the need for extensive in vivo toxicity testing. Environ Toxicol Chem 2017;36:1162-1169. © 2016 SETAC.

An in silico algal toxicity model with a wide applicability potential for industrial chemicals and pharmaceuticals.

The authors modeled the 72-h algal toxicity data of hundreds of chemicals with different modes of action as a function of chemical structures. They developed mode of action-based local quantitative structure-toxicity relationship (QSTR) models for nonpolar and polar narcotics as well as a global QSTR model with a wide applicability potential for industrial chemicals and pharmaceuticals. The present study rigorously evaluated the generated models, meeting the Organisation for Economic Co-operation and Development principles of robustness, validity, and transparency. The proposed global model had a broad structural coverage for the toxicity prediction of diverse chemicals (some of which are high-production volume chemicals) with no experimental toxicity data. The global model is potentially useful for endpoint predictions, the evaluation of algal toxicity screening, and the prioritization of chemicals, as well as for the decision of further testing and the development of risk-management measures in a scientific and regulatory frame. Environ Toxicol Chem 2017;36:1012-1019. © 2016 SETAC.

Prediction of acute toxicity of phenol derivatives using multiple linear regression approach for Tetrahymena pyriformis contaminant identification in a median-size database

In this article, the modeling of inhibitory grown activity against Tetrahymena pyriformis is described. The 0-2D Dragon descriptors based on structural aspects to gain some knowledge of factors influencing aquatic toxicity are mainly used. Besides, it is done by some enlarged data of phenol derivatives described for the first time and composed of 358 chemicals. It overcomes the previous datasets with about one hundred compounds. Moreover, the results of the model evaluation by the parameters in the training, prediction and validation give adequate results comparable with those of the previous works. The more influential descriptors included in the model are: X3A, MWC02, MWC10 and piPC03 with positive contributions to the dependent variable; and MWC09, piPC02 and TPC with negative contributions. In a next step, a median-size database of nearly 8000 phenolic compounds extracted from ChEMBL was evaluated with the quantitative-structure toxicity relationship (QSTR) model developed providing some clues (SARs) for identification of ecotoxicological compounds. The outcome of this report is very useful to screen chemical databases for finding the compounds responsible of aquatic contamination in the biomarker used in the current work.

Quantitative structure-toxicity relationships and molecular highlights about Aedes aegypti larvicidal activity of monoterpenes and related compounds

Dengue is a viral tropical disease transmitted to humans via the bite of Aedes aegypti L. (Diptera: Culicidae) mosquitoes. Since there is no specific treatment for dengue, the only method for preventing virus transmission is to control its vector by using environmental management and chemical methods. In this work, we propose a quantitative structure-toxicity relationships study of 31 monoterpenes and structurally related compounds which are bioactive against Aedes aegypti larvae, aiming to identify structural features responsible for the compounds activity, with the goal to support lead optimization. After conformational studies and complete geometry optimizations in DFT level, 1666 descriptors were calculated by E-Dragon online platform. The ordered predictors selection algorithm selected nine important descriptors, which were reduced to three after further analysis (R8e+, MAXDN, nCt), considering twenty-four and seven compounds in training and test sets, respectively. The validation parameters of final MLR modeling for these variables were: R2 = 0.83; Q2loo = 0.77; Q2ext = 0.83, showing that a robust and predictive model was obtained.

Can Toxicity for Different Species be Correlated?

Experimental evaluation of the toxicity of a compound is an expensive practice, and it requires sacrifice of a large number of animals. As a consequence, in silico techniques for predictive toxicology are taking the central stage of attention of the scientific community. Interspecies quantitative structure-toxicity relationship (i-QSTR) modeling provides a tool for estimation of contaminant's sensitivity with known levels of uncertainty for a diverse pool of species. It is capable of extrapolating data for one toxicity endpoint to another toxicity endpoint when the data for the second species are unavailable. The emerging i-QSTR approach can overcome the cost of multiple toxicity tests, improve the understanding of the mechanism of toxic action (MOA) of chemicals for different organisms and endpoints and is very useful in order to fill the data gaps where toxicity value for a particular compound is absent for a specific endpoint.

Predicting the acute neurotoxicity of diverse organic solvents using probabilistic neural networks based QSTR modeling approaches

Organic solvents are widely used chemicals and the neurotoxic properties of some are well established. In this study, we established nonlinear qualitative and quantitative structure-toxicity relationship (STR) models for predicting neurotoxic classes and neurotoxicity of structurally diverse solvents in rodent test species following OECD guideline principles for model development. Probabilistic neural network (PNN) based qualitative and generalized regression neural network (GRNN) based quantitative STR models were constructed using neurotoxicity data from rat and mouse studies. Further, interspecies correlation based quantitative activity–activity relationship (QAAR) and global QSTR models were also developed using the combined data set of both rodent species for predicting the neurotoxicity of solvents. The constructed models were validated through deriving several statistical coefficients for the test data and the prediction and generalization abilities of these models were evaluated. The qualitative STR models (rat and mouse) yielded classification accuracies of 92.86% in the test data sets, whereas, the quantitative STRs yielded correlation (R2) of >0.93 between the measured and model predicted toxicity values in both the test data (rat and mouse). The prediction accuracies of the QAAR (R2 0.859) and global STR (R2 0.945) models were comparable to those of the independent local STR models. The results suggest the ability of the developed QSTR models to reliably predict binary neurotoxicity classes and the endpoint neurotoxicities of the structurally diverse organic solvents.

Modeling the toxicity of chemical pesticides in multiple test species using local and global QSTR approaches.

The safety assessment processes require the toxicity data of chemicals in multiple test species and thus, emphasize the need for computational methods capable of toxicity prediction in multiple test species. Pesticides are designed toxic substances and find extensive applications worldwide. In this study, we have established local and global QSTR (quantitative structure-toxicity relationship) and ISC QSAAR (interspecies correlation quantitative structure activity-activity relationship) models for predicting the toxicities of pesticides in multiple aquatic test species using the toxicity data in crustacean (Daphnia magna, Americamysis bahia, Gammarus fasciatus, and Penaeus duorarum) and fish (Oncorhynchus mykiss and Lepomis macrochirus) species in accordance with the OECD guidelines. The ensemble learning based QSTR models (decision tree forest, DTF and decision tree boost, DTB) were constructed and validated using several statistical coefficients derived on the test data. In all the QSTR and QSAAR models, Log P was an important predictor. The constructed local, global and interspecies QSAAR models yielded high correlations (R2) of >0.941; >0.943 and >0.826, respectively between the measured and model predicted endpoint toxicity values in the test data. The performances of the local and global QSTR models were comparable. Furthermore, the chemical applicability domains of these QSTR/QSAAR models were determined using the leverage and standardization approaches. The results suggest for the appropriateness of the developed QSTR/QSAAR models to reliably predict the aquatic toxicity of structurally diverse pesticides in multiple test species and can be used for the screening and prioritization of new pesticides.

Skin sensitization model by quantitative structure–toxicity relationships (QSTR) approach

Skin sensitization model by quantitative structure–toxicity relationships (QSTR) approach

Toxicity prediction of PHDDs and phenols in the light of nucleic acid bases and DNA base pair interaction

The applicability of Density Functional Theory (DFT) based descriptors for the development of quantitative structure-toxicity relationships (QSTR) is assessed for two different series of toxic aromatic compounds, viz., polyhalogenated dibenzo-p-dioxins (PHDDs) and phenols (PHs). A series of 20 compounds each for PHDDs and PHs with their experimental toxicities (IC50 and IGC50) is chosen in the present study to develop DFT based efficient quantum chemical parameters (QCPs) for explaining the toxin potential of the considered compounds. A systematic analysis to find out the electron donation/acceptance nature of these selected compounds with the considered model biosystems, viz., nucleic acid (NA) bases and DNA base pairs, is performed to identify potential QCPs. Accordingly, PHDDs is found to be electron acceptors whereas phenols as donors, during their interaction with biosystems. Two parameter regression model is carried out comprising global charge transfer (ΔN), and local Fukui Function’s for nucleophilic attack (fk+) for PHDDs and the same for electrophilic attack (fk−) in case of PHs. It is heartening to note that our chosen descriptors, viz, charge transfer (ΔN) and Fukui Function (fk±) plays a crucial role by explaining more than 90% of the observed toxic behavior (in terms of correlation-coefficient, R) of PHDDs and PHs. The developed QCPs, viz., ΔN and fk± can be added as the new descriptors in the QSTR parlance.

Predicting aquatic toxicities of chemical pesticides in multiple test species using nonlinear QSTR modeling approaches

In this study, we established nonlinear quantitative-structure toxicity relationship (QSTR) models for predicting the toxicities of chemical pesticides in multiple aquatic test species following the OECD (Organization for Economic Cooperation and Development) guidelines. The decision tree forest (DTF) and decision tree boost (DTB) based QSTR models were constructed using a pesticides toxicity dataset in Selenastrum capricornutum and a set of six descriptors. Other six toxicity data sets were used for external validation of the constructed QSTRs. Global QSTR models were also constructed using the combined dataset of all the seven species. The diversity in chemical structures and nonlinearity in the data were evaluated. Model validation was performed deriving several statistical coefficients for the test data and the prediction and generalization abilities of the QSTRs were evaluated. Both the QSTR models identified WPSA1 (weighted charged partial positive surface area) as the most influential descriptor. The DTF and DTB QSTRs performed relatively better than the single decision tree (SDT) and support vector machines (SVM) models used as a benchmark here and yielded R2 of 0.886 and 0.964 between the measured and predicted toxicity values in the complete dataset (S. capricornutum). The QSTR models applied to six other aquatic species toxicity data yielded R2 of >0.92 (DTF) and >0.97 (DTB), respectively. The prediction accuracies of the global models were comparable with those of the S. capricornutum models. The results suggest for the appropriateness of the developed QSTR models to reliably predict the aquatic toxicity of chemicals and can be used for regulatory purpose.

Computational Study of Quinolone Derivatives to Improve their Therapeutic Index as Anti-malaria Agents: QSAR and QSTR.

Malaria is a parasitic disease caused by five different species of Plasmodium. More than 40% of the world's population is at risk and malaria annual incidence is estimated to be more than two hundred million, malaria is one of the most important public health problems especially in children of the poorest parts of the world, annual mortality is about 1 million. The epidemiological status of the disease justifies to search for control measures, new therapeutic options and development of an effective vaccine. Chemotherapy options in malaria are limited, moreover, drug resistant rate is high. In spite of global efforts to develop an effective vaccine yet there is no vaccine available. In the current study, a series of quinolone derivatives were subjected to quantitative structure activity relationship (QSAR) and quantitative structure toxicity relationship (QSTR) analyses to identify the ideal physicochemical characteristics of potential anti-malaria activity and less cytotoxicity. Quinolone with desirable properties was built using HyperChem program, and conformational studies were performed through the semi-empirical method followed by the PM3 force field. Multi linear regression (MLR) was used as a chemo metric tool for quantitative structure activity relationship modeling and the developed models were shown to be statistically significant according to the validation parameters. The obtained QSAR model reveals that the descriptors PJI2, Mv, PCR, nBM, and VAR mainly affect the anti-malaria activity and descriptors MSD, MAXDP, and X1sol affect the cytotoxicity of the series of ligands.

Chemical and toxicological characterization of halobenzoquinones, an emerging class of disinfection byproducts.

Halobenzoquinones (HBQs), a new class of disinfection byproducts (DBPs), occur widely in treated drinking water and recreational water. The main concern regarding human exposure to DBPs stems from epidemiological studies that have consistently linked the consumption of chlorinated drinking water with an increased risk of developing bladder cancer. The U.S. Environmental Protection Agency and Health Canada have set regulations on the amount of DBPs in drinking water to minimize the risk. However, these regulated DBPs do not account for the increased risk of bladder cancer because they have different target organs or lower magnitudes of risk based on animal carcinogenesis studies. Because of the pervasive exposure to DBPs, identification of DBPs relevant to human health has become one of the important research targets to address DBP-associated health concerns. Quantitative structure-toxicity relationship (QSTR) analysis has predicted HBQs to be potential bladder carcinogens. Therefore, this perspective focuses on the chemical and toxicological characterization of HBQs. In vitro cytotoxicity experiments have shown that HBQs induce greater cytotoxicity and/or greater developmental toxicity than most of the regulated DBPs. Cellular mechanistic studies indicate that HBQs are capable of producing reactive oxygen species (ROS) either within cells or in solution, depleting cellular glutathione levels, and influencing cellular antioxidant enzymes, which further induces oxidative stress and oxidative damage to cellular proteins and DNA. Oxidative damage to DNA was demonstrated in the form of significant increases in cellular levels of 8-hydroxydeoxyguanosine (8-OHdG), DNA strand breaks, and apurinic/apyrimidinic (AP) sites. HBQs can also form DNA adducts, affect genome-wide DNA methylation, and inhibit DNA repair enzymes. These findings demonstrate that HBQs are highly cytotoxic and potentially genotoxic and carcinogenic, although in vivo data corroborating this is not available. To fully understand the potential adverse health effects and cancer risk due to HBQ exposure, multidisciplinary research is required regarding human exposure, health risk assessment, and toxicological mechanisms of HBQs.

New avenue in the treatment of temporal lobe epilepsy by classical anti-epileptics: A hypothetical establishment of executioner Caspase 3 inactivation by molecular modeling.

Patients with temporal lobe epilepsy (TLE) are prescribed first-line antiepileptic drugs and surgery to the management of this disorder. Unfortunately, the surgical treatment has been shown to be beneficial for the selected patients but fails to provide a seizure-free outcome in 20–30% of TLE patients. In our present study, we investigate the possibilities of marketed antiepileptic drugs in a different manner to improve the present situation in TLE. Molecular docking simulation study and various open source computational tools were used to perform the study. AutoDock 4.2 MGL tools, Pymol visualize tools, Patch dock server, and Swarm Dock servers (protein-protein docking) were used to perform the molecular modeling. FTsite and computed atlas of surface topography of protein open source server were used to understand the pocket and ligand binding information respectively. Toxtree application was used to determine the toxicity profile of the drug by Cramers rule. The obtained molecular docking models (Caspase 3, Procaspase 8, and Fas-associated death domain [FADD]) with selected compounds (Clonazepam, Clobazepam, and Retigabine) showed promising trio blocking event of FADD, Caspase 3, and Procaspase 8 (−6.66 kcal, −8.1 kcal, 6.46 kcal) by Clonazepam respectively. Protein-protein interaction study (Swarm Dock, Patch Dock server) indicated promising results that helped to establish our hypothesis. Toxtree showed a quantitative structure toxicity relationship report that helps to clarify the toxicity of the selected compounds. Clonazepam showed a trio inhibition property that may lead to develop a new era of the new generation benzodiazepine prototype drugs in the future. Filtered compounds will further process for higher in vitro, in vivo models for better understanding of the mechanism.

Cytotoxicity towards CCO cells of imidazolium ionic liquids with functionalized side chains: Preliminary QSTR modeling using regression and classification based approaches

Within this work we evaluated the cytotoxicity towards the Channel Catfish Ovary (CCO) cell line of some imidazolium-based ionic liquids containing different functionalized and unsaturated side chains. The toxic effects were measured by the reduction of the WST-1 dye after 72h exposure resulting in dose- and structure-dependent toxicities. The obtained data on cytotoxic effects of 14 different imidazolium ionic liquids in CCO cells, expressed as EC50 values, were used in a preliminary quantitative structure–toxicity relationship (QSTR) study employing regression- and classification-based approaches. The toxicity of ILs towards CCO was chiefly related to the shape and hydrophobicity parameters of cations. A significant influence of the quantum topological molecular similarity descriptor ellipticity (ε) of the imine bond was also observed.

QSTR Study for the Carcinogenicity of Alkylbenzenes

Carcinogenicity is an important toxicological endpoint which poses a great concern being the major determinants of health problem, a quantitative structure toxicity relationship (QSTR) study was performed for the prediction of the carcinogenicity of alkylbenzenes. The molecular descriptors of alkylbenzenes have been calculated with semi-empirical AM1 and E-dragon methods, and QSTR model for mice carcinogenic model of alkylbenzenes were developed using multiple linear regression (MLR) analysis.

Predicting Carcinogenicity of Anilines by Quantitative Structure-Toxicity Relationship

Carcinogenicity is an important toxicological endpoint which poses a great concern being the major determinants of cancers and tumours. Anilines possess such toxic properties as they can form various electrophilic intermediates and adducts with biological systems. In the present work, the molecular descriptors of anilines have been calculated with semi-empirical AM1 and E-dragon methods, and a quantitative structure–toxicity relationships (QSTR) model for carcinogenic potency (pTD50) model of anilines was developed with multiple linear regression (MLR) analysis. The validation results through the test set indicate that the proposed model is robust and satisfactory. The QSTR study suggests that the molecular structure and the electronegativity of chemicals are closely related to the Carcinogenicity.