Quantitative structure-activity relationship predicting toxicity of pesticides towards Daphnia magna

Global pesticide usage reaching 2.7 million metric tons annually, brings a grave threat to non-target organisms, especially aquatic organisms, resulting in serious concerns. Predicting aquatic toxicity of pesticides towards Daphnia magna is significant. In this work, random forest (RF) algorithm, together with ten Dragon molecular descriptors, was successfully utilized to develop a quantitative structure-activity/toxicity relationship (QSAR/QSTR) model for the toxicity pEC50 of 745 pesticides towards Daphnia magna. The optimal QSTR model (RF Model I) based on the RF parameters of ntree = 50, mtry = 3 and nodesize = 5, yielded R2 = 0.877, MAE = 0.570, rms = 0.739 (training set of 596 pEC50), R2 = 0.807, MAE = 0.732, rms = 0.902 (test set of 149 pEC50), and R2 = 0.863, MAE = 0.602, rms = 0.774 (total set of 745 pEC50), which are accurate and satisfactory. The optimal RF model is comparable to other published QSTR models for Daphnia magna, although the optimal RF model possessed a small descriptor subset and dealt with a large dataset of pesticide toxicity pEC50. Thus, the investigation in this work provides a reliable, applicable QSTR model for predicting the toxicity pEC50 of pesticides towards Daphnia magna.

Prioritization of the ecotoxicological hazard of PAHs towards aquatic species spanning three trophic levels using 2D-QSTR, read-across and machine learning-driven modelling approaches

Polycyclic aromatic hydrocarbons (PAHs) represent a common group of environmental pollutants that endanger various aquatic organisms via various pathways. To better prioritize the ecotoxicological hazard of PAHs to aquatic environment, we used 2D descriptors-based quantitative structure-toxicity relationship (QSTR) to assess the toxicity of PAHs toward six aquatic model organisms spanning three trophic levels. According to strict OECD guideline, six easily interpretable, transferable and reproducible 2D-QSTR models were constructed with high robustness and reliability. A mechanistic interpretation unveiled the key structural factors primarily responsible for controlling the aquatic ecotoxicity of PAHs. Furthermore, quantitative read-across and different machine learning approaches were employed to validate and optimize the modelling approach. Importantly, the optimum QSTR models were further applied for predicting the ecotoxicity of hundreds of untested/unknown PAHs gathered from Pesticide Properties Database (PPDB). Especially, we provided a priority list in terms of the toxicity of unknown PAHs to six aquatic species, along with the corresponding mechanistic interpretation. In summary, the models can serve as valuable tools for aquatic risk assessment and prioritization of untested or completely new PAHs chemicals, providing essential guidance for formulating regulatory policies.

In silico prediction and a systematic toxicology-based in vivo investigation uncovering the mechanism of aquatic toxicity caused by beta-lactam antibiotics

Recently, beta-lactam antibiotics have gained attention as significant contributors to public health and environmental issues due to their potential toxicity. Our study employed machine learning to develop a model for assessing the aquatic toxicity of beta-lactam antibiotics on zebrafish. Notably, aztreonam (AZT), a synthetic monobactam and a subclass of beta-lactam antibiotics, demonstrated developmental effects in zebrafish embryos comparable to cephalosporins, indicating a potential for toxicity. Using a systems toxicology-based approach, we identified apoptosis and metabolic disorders as the primary pathways affected by AZT and its impurity F exposure. During the administration of monobactams, we noted that ctsbb, nos2a, and dgat2, genes associated with apoptosis and the metabolic pathway, exhibited significant differential expression. Molecular docking studies were conducted to ascertain the binding affinity between monobactam compounds and their potential targets-Ctsbb, Nos2a, and Dgat2. Furthermore, our research revealed that monobactams influence pre-mRNA alternative splicing, resulting in disruptions in the expression of genes involved in hair cells, brain, spinal cord, and fin regeneration (e.g., krt4, krt5, krt17, cyt1). Notably, we observed a correlation between the levels of rpl3 and rps7 genes, both important ribosomal proteins, and the detected alternative splicing events. Overall, this study enhances our understanding of the toxicity of beta-lactam antibiotics in zebrafish by demonstrating the developmental effects of monobactams and uncovering the underlying mechanisms at the molecular level. It also identifies potential targets for further investigation into the mechanisms of toxicity and provides valuable insights for early assessment of biological toxicity associated with antibiotic pollutants.

q-RASTR modelling for prediction of diverse toxic chemicals towards T. pyriformis

A series of diverse organic compounds impose serious detrimental effects on the health of living organisms and the environment. Determination of the structural aspects of compounds that impart toxicity and evaluation of the same is crucial before public usage. The present study aims to determine the structural characteristics of compounds for Tetrahymena pyriformis toxicity using the q-RASTR (Quantitative Read Across Structure-Toxicity Relationship) model. It was developed using RASTR and 2-D descriptors for a dataset of 1792 compounds with defined endpoint (pIGC50) against a model organism, T. pyriformis. For the current study, the whole dataset was divided based on activity/property into the training and test sets, and the q-RASTR model was developed employing six descriptors (three latent variables) having r2, Q2F1 and Q2 values of 0.739, 0.767, and 0.735, respectively. The generated model was thoroughly validated using internationally recognized internal and external validation criteria to assess the model's dependability and predictability. It was highlighted that high molecular weight, aromatic hydroxyls, nitrogen, double bonds, and hydrophobicity increase the toxicity of organic compounds. The current study demonstrates the applicability of the RASTR algorithm in QSTR model development for the prediction of toxic chemicals (pIGC50) towards T. pyriformis.

Global classification models for predicting acute toxicity of chemicals towards Daphnia magna

Molecular descriptors reflecting structural information on hydrophobicity, reactivity, polarizability, hydrogen bond and charged groups, were used to predict the toxicity (pLC50) of chemicals towards Daphnia magna with global quantitative structure-activity/toxicity relationship (QSAR/QSTR) models. A sufficiently large dataset including 1517 chemical toxicity to Daphnia magna was divided into a training set (758 pLC50) and a test set (759 pLC50). By applying random forest algorithm, two classification models, Class Model A and Class Model B were developed, having prediction accuracy, sensitivity and specificity above 85% for Class 1 (with pLC50 ≤ 4.48) and Class 2 (with pLC50 > 4.48). The Class Model A was based on nine molecular descriptors and RF parameters of nodesize = 1, ntree = 80 and mtry = 2, and yielded accuracy of 92.3% (training set), 85.6% (test set) and 88.9% (total data set). Class Model B was based on ten descriptors and parameters, nodesize = 1, ntree = 90 and mtry = 2, produced accuracy of 88.3% (training set), 86.8% (test set) and 87.5% (total data set). The two classification models were satisfactory compared with other classification model reported in the literature, although classification models in this work dealt with more samples. Thus, the two classification models with a larger applicability domain provided efficient tools for assessing chemical aquatic toxicity towards Daphnia magna.

Conceptual DFT, machine learning and molecular docking as tools for predicting LD50 toxicity of organothiophosphates

CONTEXT

Several descriptors from conceptual density functional theory (cDFT) and the quantum theory of atoms in molecules (QTAIM) were utilized in Random Forest (RF), LASSO, Ridge, Elastic Net (EN), and Support Vector Machines (SVM) methods to predict the toxicity (LD₅₀) of sixty-two organothiophosphate compounds. The A-RF-G1 and A-RF-G2 models were obtained using the RF method, yielding statistically significant parameters with good performance, as indicated by R² values for the training set (R²_Train) and R² values for the test set (R²_Test), around 0.90.

METHODS

The molecular structure of all organothiophosphates was optimized via the range-separated hybrid functional ωB97XD with the 6-311 +  + G** basis set. Seven hundred and eighty-seven descriptors have been processed using a variety of machine learning algorithms: RF LASSO, Ridge, EN and SVM to generate a predictive model. The properties were obtained with Multiwfn, AIMALL and VMD programs. Docking simulations were performed by using AutoDock 4.2 and LigPlot + programs. All the calculations in this work are carried out in Gaussian 16 program package.

First report on ecotoxicological QSTR and I-QSTR modeling for the prediction of acute ecotoxicity of diverse organic chemicals against three protozoan species

The recent years have witnessed an upsurge of interest to assess the toxicity of organic chemicals exhibiting harmful impacts on the environment. In this investigation, we have developed regression-based quantitative structure-toxicity relationship (QSTR) models against three protozoan species (Entosiphon sulcantum, Uronema parduczi, and Chilomonas paramecium) using three sets of descriptor combinations such as ETA indices only, non-ETA descriptors only, and both ETA and non-ETA descriptors to examine the key structural features that determine the toxic properties of protozoa. The interspecies models (i-QSTRs) were also generated for efficient data gap-filling of toxicity databases. The statistical results of the validated models in terms of both internal and external validation metrics suggested that the models are statistically reliable and robust. Additionally, using these validated models, we screened the DrugBank database containing 11,300 pharmaceuticals for assessing the ecotoxicological properties. The features appearing in the models suggested that nonpolar characteristics, electronegativity, hydrogen bonding, π-π, and hydrophobic interactions are responsible for chemical toxicity toward protozoan. The validated models may be utilized for the development of eco-friendly drugs & chemicals, data gap-filling of toxicity databases for regulatory purposes and research, as well as to decrease the use of toxic and hazardous chemicals in the environment.

Predicting the ecotoxicity of endocrine disruptive chemicals: Multitasking in silico approaches towards global models

Manufactured substances known as endocrine disrupting chemicals (EDCs) released in the environment, through the use of cosmetic products or pesticides, can cause severe eco and cytotoxicity that may induce trans-generational as well as long-term deleterious effects on several biological species at relatively low doses, unlike other classical toxins. As the need for effective, affordable and fast EDCs environmental risk assessment has become increasingly pressing, the present work introduces the first moving average-based multitasking quantitative structure-toxicity relationship (MA-mtk QSTR) modeling specifically developed for predicting the ecotoxicity of EDCs against 170 biological species belonging to six groups. Based on 2301 data-points with high structural and experimental diversity, as well as on the usage of various advanced machine learning methods, the novel most predictive QSTR models display overall accuracies > 87 % in both training and prediction sets. However, maximum external predictivity was achieved when a new multitasking consensus modeling approach was applied to these models. Additionally, the developed linear model provided means to investigate the determining factors for eliciting higher ecotoxicity by the EDCs towards different biological species, identifying several factors such as solvation, molecular mass and surface area as well as the number of specific molecular fragments (e.g.: aromatic hydroxy and aliphatic aldehyde). The resource to non-commercial open-access tools to develop the models is a useful step towards library screening to speed up regulatory decision on discovery of safe alternatives to reduce the hazards of EDCs.

Development of reliable quantitative structure-toxicity relationship models for toxicity prediction of benzene derivatives using semiempirical descriptors

The Health and environmental hazards of benzene and nitrobenzene (NB) derivatives have remained a topic of interest of researchers. In silico methods for prediction of toxicity of chemicals have proved their worth in accurate forecast of environmental as well as health toxicity and are strongly recommended by regulatory authorities. Two quantitative structure-toxicity relationship (QSTR) models explaining Scenedesmus obliquus toxicity trends among 39 benzene derivatives and Tetrahymena pyriformis toxicity of 103 NB and 392 benzene derivatives are developed using semiempirical quantum chemical parameters. The best constructed QSTR models have good fitting ability (R² = 0.8053, 0.7591, and 0.8283) and robustness (Q²_LOO = 0.7507, 0.7227, and 0.8194; Q²_LMO = 0.7338, 0.7153, and 0.8172). The external predictivity of all the models are quite good (R²_EXT = 0.8256, 0.9349, and 0.8698). Electronegativity, Cosmo volume, total energy, and molecular weight are responsible for the increase and decrease of toxicity of benzene derivatives against S. obliquus while electronegativity, electrophilicity index, the heat of formation, total energy, hydrophobicity, and cosmo volume are responsible for modulation of toxicity of NB and benzene derivatives toward T. pyriformis. These models fulfill the requirements of all the five OECD principles.

Aquatic toxicity prediction of diverse pesticides on two algal species using QSTR modeling approach

With the aim of identification of toxic nature of the diverse pesticides on the aquatic compartment, a large dataset of pesticides (n = 325) with experimental toxicity data on two algal test species (Pseudokirchneriella subcapitata (PS) (synonym: Raphidocelis subcapitata, Selenastrum capricornutum) and Scenedemus subspicatus (SS)) was gathered and subjected to quantitative structure toxicity relationship (QSTR) analysis to predict aquatic toxicity of pesticides. The QSTR models were developed by multiple linear regressions (MLRs), and the genetic algorithm (GA) was used for the variable selection. The developed GA-MLR models were statistically robust enough internally (Q²_LOO = 0.620-0.663) and externally (Q²F_n = 0.693-0.868, CCC_ext = 0.843-0.877). The leverage approach of applicability domain (AD) and prediction reliability indicator assured the reliability of the developed models. The mechanistic interpretation highlighted that the presence of SO₂, F and aromatic rings influenced the toxicity of pesticides towards PS species while the presence of alkyl, alkyl halide, aromatic rings and carbonyl was responsible for the toxicity of pesticides towards SS species. Additionally, we have reported the application of developed models to pesticides without experimental value and the cumulative toxicity of pesticides on the aquatic environment by using principal component analysis (PCA). The reliable prediction and prioritization of toxic compounds from the developed models will be useful in the aquatic toxicity assessment of pesticides.

In Silico Prediction of the Toxicity of Nitroaromatic Compounds: Application of Ensemble Learning QSAR Approach

In this work, a dataset of more than 200 nitroaromatic compounds is used to develop Quantitative Structure-Activity Relationship (QSAR) models for the estimation of in vivo toxicity based on 50% lethal dose to rats (LD₅₀). An initial set of 4885 molecular descriptors was generated and applied to build Support Vector Regression (SVR) models. The best two SVR models, SVR_A and SVR_B, were selected to build an Ensemble Model by means of Multiple Linear Regression (MLR). The obtained Ensemble Model showed improved performance over the base SVR models in the training set (R² = 0.88), validation set (R² = 0.95), and true external test set (R² = 0.92). The models were also internally validated by 5-fold cross-validation and Y-scrambling experiments, showing that the models have high levels of goodness-of-fit, robustness and predictivity. The contribution of descriptors to the toxicity in the models was assessed using the Accumulated Local Effect (ALE) technique. The proposed approach provides an important tool to assess toxicity of nitroaromatic compounds, based on the ensemble QSAR model and the structural relationship to toxicity by analyzed contribution of the involved descriptors.

Random forest algorithm-based accurate prediction of chemical toxicity to Tetrahymena pyriformis

The random forest (RF) algorithm, together with ten Dragon descriptors, was used to develop a quantitative structure-toxicity/activity relationship (QSTR/QSAR) model for a larger data set of 1792 chemical toxicity pIGC₅₀ towards Tetrahymena pyriformis. The optimal RF (ntree =300 and mtry =3) model yielded root mean square (rms) errors of 0.261 for the training set (1434 chemicals) and 0.348 for the test set (358 chemicals). Compared with other QSTR models reported in the literature, the optimal RF model in this paper is more accurate. The feasibility of applying the RF algorithm to predict chemical toxicity pIGC₅₀ towards Tetrahymena pyriformis has been verified.

Toxicity prediction of 1,2,4-triazoles compounds by QSTR and interspecies QSTTR models

1,2,4-triazole derivatives exhibit various biological activities, including antibacterial and antifungal properties. On the other hand, these chemicals may have unique cumulative and harmful effects on living organisms. The goal of this work is to use quantitative structure-toxicity relationship (QSTR) and interspecies quantitative toxicity-toxicity relationship (iQSTTR) models to predict the acute toxicity of 1,2,4-triazole derivatives. The QSTR models were generated by multiple linear regression (MLR) following the OECD recommendations for QSAR model development and validation. The iQSTTR models were constructed using data on acute oral toxicity in rats and mice, as well as the 2D descriptor. The application domain (AD) analysis was used to identify model outliers and determine if the forecast was credible. Six QSTR models were successfully constructed in rats and mice using various delivery methods, and the scatter plots demonstrated excellent consistency across training and test sets. According to external and internal validation criteria, all six QSTR models may be broadly accepted; however, the orally administered mice model was the optimum one among the six species. Several chemicals with leverage values above the requirements were identified as response or structural outliers in the training sets for six QSTR and two iQSTTR models. All outliers, however, fell slightly outside the threshold or had low prediction errors, which may have had little impact on the capacity to forecast and were therefore preserved in the final models. In fact, neither the QSTR nor the iQSTTR test sets contained any response outliers. Additionally, all external and internal validation results for the iQSTTR models were approved, with the iQSTTR models outperforming the comparable QSTR models, which are deemed more dependable. The QSTR and iQSTTR models performed well in predicting toxicity using test sets, which would be beneficial in evaluating and synthesizing newly discovered 1,2,4-triazoles derivatives with low toxicity and environmental hazard.

Quantitative Structure-Neurotoxicity Assessment and In Vitro Evaluation of Neuroprotective and MAO-B Inhibitory Activities of Series N'-substituted 3-(1,3,7-trimethyl-xanthin-8-ylthio)propanehydrazides

The neurotoxic, neuroprotective and MAO-B inhibitory effects of series N'-substituted 3-(1,3,7-trimethyl-xanthin-8-ylthio)propanehydrazides are evaluated. The results indicate compounds N'-(2,3-dimethoxybenzylidene)-3-(1,3,7-trimethyl-2,6-dioxo-2,3,6,7-tetrahydro-1H-purin-8-ylthio)propanehydrazide (6k) and N'-(2-hydroxybenzylidene)-3-(1,3,7-trimethyl-2,6-dioxo-2,3,6,7-tetrahydro-1H-purin-8-ylthio)propanehydrazide (6l) as most perspective. The performed QSTR analysis identified that the decreased lipophilicity and smaller dipole moments of the molecules are the structural features ensuring lower neurotoxicity. The obtained results may be used as initial information in the further design of (xanthinyl-8-ylthio)propanhydrazides with potential hMAOB inhibitory effect and pronounced neuroprotection.

CORAL: Development of a hybrid descriptor based QSTR model to predict the toxicity of dioxins and dioxin-like compounds with correlation intensity index and consensus modelling

In the present study, ninety-five halogenated dioxins and related chemicals (dibenzo-p-dioxins, dibenzofurans, biphenyls, and naphthalene) with endpoint pEC₅₀ were used to develop twelve quantitative structure toxicity relationship (QSTR) models using inbuilt Monte Carlo algorithm of CORAL software. The hybrid optimal descriptor of correlation weights (DCW) using a combination of SMILES and HSG (hydrogen suppressed graph) was employed to generate QSTR models. Three target functions i.e. TF₁ (W_IIC=W_CII=0), TF₂ (W_IIC= 0.3 & W_CII=0) and TF₃ (W_IIC= 0.0 &W_CII=0.3) were employed to develop robust QSTR models and the statistical outcomes of each target function were compared with each other. The correlation intensity index (CII) was found a reliable benchmark of the predictive potential for QSTR models. The numerical value of the determination coefficient of the validation set of split 1 computed by TF₃ was found highest (R_Valid²=0.8438). The fragments responsible for the toxicity of dioxins and related chemicals were also identified in terms of the promoter of increase/decrease for pEC₅₀. Three random splits (Split 1, Split 2 and Split 4) were selected for the extraction of the promoter of increase/decrease for pEC₅₀. In the last, consensus modelling was performed using the intelligent consensus tool of DTC lab (https://dtclab.webs.com/software-tools). The original consensus model, which was created by combining four distinct models employing the split 4 arrangement, was more predictive for the validation set and the numerical value of the determination coefficient of the test set (validation set) was increased from 0.8133 to 0.9725. For the validation set of split 4, the mean absolute error (MAE 100%) was also lowered from 0.513 to 0.2739.

Nano-read-across predictions of toxicity of metal oxide engineered nanoparticles (MeOx ENPS) used in nanopesticides to BEAS-2B and RAW 264.7 cells

The demand for nutrients and new technologies has increased with population growth. The agro-technological revolution with metal oxide engineered nanoparticles (MeOx ENPs) has the potential to reform the resilient agricultural system while maintaining the security of food. When utilized extensively, MeOx ENPs may have unintended toxicological effects on both target and non-targeted species. Since limited information about nanopesticides' pernicious effects is available, in silico modeling can be done to explore these issues. Hence, in the present work, we have applied computational modeling to explore the influence of metal oxide nanoparticles on the toxicity of bronchial epithelial (BEAS-2B) and murine myeloid (RAW 264.7) cells to bridge the data gap relating to the toxicity of MeOx NPs. Initially, partial least squares (PLS) regression models were developed applying the Small Dataset Modeler software (http://teqip.jdvu.ac.in/QSAR_Tools/DTCLab/) using four datasets having effective concentration (EC₅₀%) as the endpoints and employing only periodic table descriptors. To further explore the predictions, we applied a read-across approach using the descriptors selected in the QSAR models. Also, the inter-endpoint cytotoxicity relationship modeling (quantitative toxicity-toxicity relationship or QTTR) was conducted. It was found that the result obtained by nano-read-across provided a similar level of accuracy as provided by QSAR. The information derived from the PLS models of both the cell lines suggested that metal cation formation, and bond-forming capacity influence the toxicity whereas the presence of metal has an influential impact on the ecotoxicological effects. Thus, it is feasible to design safe nanopesticides that could be more effective than conventional analogs.

A QSTR model for toxicity prediction of pesticides towards Daphnia magna

Because of the large amount of pesticides discharged into rivers, adverse effects could be induced to aquatic organisms. Daphnia magna is often used as an indicator organism to evaluate the toxicity of pesticides. In this study, a quantitative structure-toxicity relationship (QSTR) model was established based on norm descriptors for predicting the acute toxicity of pesticides to Daphnia magna. The model results showed the good predictability (R_training² = 0.8045, R_testing² = 0.8224). The validation results of internal validation, external validation, Y-randomization test and application domain analysis demonstrated the model's stability, reliability and robustness. Therefore, the above results indicate that norm descriptors might be universal for describing the relationship between the toxicity and structures of pesticides compounds. Moreover, some pesticides' toxicities without experimental data were also predicted by this model.

Modeling and mechanistic understanding of cytotoxicity of metal oxide nanoparticles (MeOxNPs) to Escherichia coli: categorization and data gap filling for untested metal oxides

Metal oxide nanoparticles (MeO_xNPs) production is expected to increase every year exponentially, and their potential to cause adverse effect to the environment and human health will also expand rapidly. Hence, risk assessment of nanoparticles (NPs) is necessary to design ecosafe products. However, experimental ecotoxicological assessments are time-consuming requiring a lot of resources. Therefore, researchers rely on alternative in silico approaches to predict the behavior of NPs in the biological system. Quantitative structure - toxicity relationship (QSTR) has been adopted as a potential method to predict the cytotoxicity of untested NPs. Hence, in the present study, multiple linear regression (MLR) models were developed using 17 MeO_xNPs on Escherichia coli (E. coli) bacteria cells under both light and dark conditions. The models were developed applying Small Dataset Modeler software, version 1.0.0 (http://teqip.jdvu.ac.in/QSAR_Tools/DTCLab/) which generates models with a limited number of data points. Periodic table-based descriptors (both 1st and 2nd generation) were used for the modeling purpose. Two statistically significant MLR models based on photo-induced toxicity (Q(LOO)2= 0.612, R² = 0.726) and dark-based toxicity (Q(LOO)2= 0.627, R² = 0.770) were developed. From the developed models, we interpreted that increase in valency and oxidation state of the metal will decrease the cytotoxicity whereas the atomic radius of the metal and electronegativity of MeO_xNPs influence the toxicity toward E. coli cells. The MLR models were validated using different internal validation metrics. Additionally, we have collected 42 MeO_xNPs as an external set to observe the predictive power of the two developed MLR models and categorize them into toxic and non-toxic classes. The chemical features selected in the developed models are important for understanding the mechanisms of nanotoxicity. Thus, the developed models can be a scientific basis for designing safer NPs.

Ecotoxicological QSTR and QSTTR Modeling for the Prediction of Acute Oral Toxicity of Pesticides against Multiple Avian Species

The ever-increasing use of pesticides in response to the rising agricultural demand has threatened the existence of nontarget organisms like avian species, disrupting the global ecological integrity. Therefore, it is critical to protect and restore different endangered bird species from the perspective of ecosystem safety. In the present work, we have developed regression-based two-dimensional quantitative structure toxicity relationship (2D QSTR) and quantitative structure toxicity-toxicity relationship (QSTTR) models to estimate the toxicity of pesticides on five different avian species following the Organization for Economic Co-operation and Development (OECD) guidelines. Rigorous validation has been performed using different statistical internal and external validation parameters to ensure the robustness and interpretability of the developed models. From the developed models, it can be stated that the presence of electronegative and lipophilic features greatly enhance pesticide toxicity, whereas the hydrophilic characters are shown to have a detrimental impact on the toxicity of pesticides. Moreover, the developed QSTTR models have been employed to the in silico toxicity prediction of 124, 154, and 250 pesticides against bobwhite quail, ring-necked pheasant, and mallard duck species, respectively, extracted from the Office of Pesticides Program (OPP) Pesticide Ecotoxicity Database. The information obtained from the modeled descriptors might be used for pesticide risk assessment in the future, with the added benefit of providing an early caution of their possible negative impact on birds for regulatory purposes.

Hazard assessment of beta-lactams: Integrating in silico and QSTR approaches with in vivo zebrafish embryo toxicity testing

Antibiotics have emerged as a well-known representative of pharmaceuticals and personal care products (PPCPs) by causing public health and environmental problems due to their potential toxicity. β-lactams are the most commonly used antibiotics in the world. This study used zebrafish embryos to evaluate the toxicity of β-lactams. The results showed that 23 β-lactam compounds induced malformation and death in a concentration-response manner. Moreover, this study established and validated quantitative structure-toxicity relationship (QSTR) models for the toxicity of β-lactams in zebrafish. These models performed well and fast in the prediction of the acute toxicity of β-lactams. Structural interpretation indicated that the β-lactam ring, the thiazolidine/dihydrothiazine rings, the side chains, and spatial configuration are the main factors responsible for the toxicity of β-lactams. The results from our previous studies and this study also revealed that the potential biological risks caused by β-lactams and their degradation products could not be ignored. This study provided important data for further environmental risk assessment of β-lactams and regulatory purposes.

Identification of concealed structural alerts using QSTR modeling for Pseudokirchneriella subcapitata

In the present work, QSTR modeling was conducted for microalga Pseudokirchneriella subcapitata using a data set of 271 molecules belonging to different types of chemical classes for the prediction of EC₅₀ for 72 hr based assays. The balanced QSTR model encompasses seven easily interpretable molecular descriptors and possesses statistical robustness with high predictive ability. This Genetic Algorithm Multi-linear regression (GA-MLR) model was subjected to internal validation, Y-randomization test, applicability domain analysis, and external validation as per the recommended OECD guidelines. The newly developed model fulfilled the threshold values for more than 20 recommended validation parameters including R² = 0.72, Q²_LOO = 0.70, etc. The developed QSTR model was successful in identifying the type of hybridization or specific type of atoms of previously reported and newer structural alerts. Thus, the model could be useful for data gap filling and expanding mechanistic interpretation of toxicity for different chemicals.

In vivo and in silico evaluations of survival and cardiac developmental toxicity of quinolone antibiotics in zebrafish embryos (Danio rerio)

Quinolones are ranked as the second most commonly used class of antibiotics in China, despite their adverse clinical and environmental effects. However, information on their cardiac developmental toxicity to zebrafish is limited. This study investigates the relationships between different quinolone structures and toxicity in zebrafish embryos using in vivo and in silico methods. All of the experimentally tested quinolones show cardiac developmental toxicity potential and present mortality and teratogenic effects in a dose-dependent manner. Theoretically, the acute toxicity values predicted using quantitative structure-toxicity relationship (QSTR) modeling based on previously reported LC₅₀ values are in good agreement with the in vivo results. Further investigation demonstrates that the hormetic concentration response of some quinolones may be related to methylation on the piperazine ring at the C-7 position. The amino group at the C-5 position, the methylated or ethylated piperazine group at the C-7 position, halogens at the C-8 position and a cyclopropyl ring at N1 position may be responsible for cardiac developmental toxicity. In terms of survival (key ecological endpoint), the naridine ring is more toxic than the quinoline ring. This combined approach can predict the acute and cardiac developmental toxicity of other quinolones and impurities.

Predictive classification-based QSTR models for toxicity study of diverse pesticides on multiple avian species

Protection and restoration of different endangered bird species from pesticide exposure is crucial from the point of safety assessment of ecosystem. Toxicity predictions or risk assessment of pesticides by chemometric tools is one of the challenging fields in recent era. In the present study, classification-based quantitative structure toxicity relationship (QSTR) models were developed for a large dataset (516) of diverse pesticides on multiple avian species mallard duck, bobwhite quail, and zebra finch according to the Organization for Economic Co-operation and Development guidelines. The QSTR models were developed by linear discriminant analysis method with genetic algorithm for feature selection from 2D descriptors using QSAR-Co software. Different statistical metrics assured the reliability and robustness of the developed models. External compound prediction highlighted predictive nature of the models. The mechanistic interpretation suggested that presence of phosphate, halogens (Cl, Br), ether linkage, and NCOO influence the avian toxicity. Furthermore, model reliability was checked by the application of the standardization approach of the applicability domain (AD). Finally, the developed models provided a priori toxic and non-toxic classification for unknown pesticides (inside AD), with particular emphasis on organophosphate pesticides. The interspecies toxicity correlation and predictions encouraged for their further applicability for the fulfilment of data gaps in vital missing species.

Quantitative structure-toxicity relationship model for acute toxicity of organophosphates via multiple administration routes in rats and mice

Organophosphates (OPs) are highly toxic compounds, with widespread application in agricultural and chemical industries, whose introduction into the environment poses serious hazards to humans and ecological systems. To assess and ultimately mitigate these hazards, this study predicted the acute toxicity of OPs according to their chemical structure and administration route. The acute toxicity data of 161 OPs in two species via six different administration routes were manually collected and used to develop a series of quantitative structure-toxicity relationship (QSTR) models with robust and practical predictive abilities. The random forest algorithm was used to develop the models, employing both quantum chemical and two-dimensional descriptors according to OECD guidelines. Correlation results and feature similarities indicated that whereas acute toxicity data from rats and mice via the same administration route were combinable for modeling, data from different routes were not. Six QSTR models for each route in a single species and two QSTR models for a single route in the two species were constructed, achieving practical predictive performance. Despite significant variances in their datasets, the prediction models could predict the acute toxicity of novel or unknown OPs, realize rapid assessment, and provide guidance for regulatory decisions to reduce the hazards of OPs.

In vivo toxicity of nitroaromatic compounds to rats: QSTR modelling and interspecies toxicity relationship with mouse

Nitroaromatic compounds (NACs) in the environment can cause serious public health and environmental problems due to their potential toxicity. This study established quantitative structure-toxicity relationship (QSTR) models for the acute oral toxicity of NACs towards rats following the stringent OECD principles for QSTR modelling. All models were assessed by various internationally accepted validation metrics and the OECD criteria. The best QSTR model contains seven simple and interpretable 2D descriptors with defined physicochemical meaning. Mechanistic interpretation indicated that van der Waals surface area, presence of C-F at topological distance 6, heteroatom content and frequency of C-N at topological distance 9 are main factors responsible for the toxicity of NACs. This proposed model was successfully applied to a true external set (295 compounds), and prediction reliability was analysed and discussed. Moreover, the rat-mouse and mouse-rat interspecies quantitative toxicity-toxicity relationship (iQTTR) models were also constructed, validated and employed in toxicity prediction for true external sets consisting of 67 and 265 compounds, respectively. These models showed good external predictivity that can be used to rapidly predict the rat oral acute toxicity of new or untested NACs falling within the applicability domain of the models, thus being beneficial in environmental risk assessment and regulatory purposes.

Is intraspecies QSTR model answer to toxicity data gap filling: Ecotoxicity modeling of chemicals to avian species

Interspecies model represents an established approach for the response data gap filling for regulatory agencies and researchers. We propose a novel approach of intraspecies modeling within the animals of the same species, instead of animals from different species. The proposed intraspecies model is capable of more precise extrapolation of data than the interspecies model, as animals under the same species share a similar mechanism of action (MOA) and target sites for the response. Along with the advantage of better prediction over the interspecies model, the intraspecies model has all the significant features like recognition of MOA, species-specific toxicity, reduction of animal experimentation, and money and time. To establish and test the intraspecies modeling approach, we have modeled ecotoxicity of organic chemicals to three avian species: Anas platyrhynchos, Colinus virginianus, and Phasianus colchicus. The intraspecies models offer to identify the mechanistic interpretation of the ecotoxicity of the studied chemicals along with the toxicity data gap filling. The success of the intraspecies modeling relies on connecting the missing dots of toxicity for the regulatory purposes, especially when there is a scarcity of ecotoxicity experimental data and in silico models for avian species.

A unified in silico model based on perturbation theory for assessing the genotoxicity of metal oxide nanoparticles

Nanomaterials (NMs) are an ever-increasing field of interest, due to their wide range of applications in science and technology. However, despite providing solutions to many societal problems and challenges, NMs are associated with adverse effects with potential severe damages towards biological species and their ecosystems. Particularly, it has been confirmed that NMs may induce serious genotoxic effects on various biological targets. Given the difficulties of experimental assays for estimating the genotoxicity of many NMs on diverse biological targets, development of alternative methodologies is crucial to establish their level of safety. In silico modelling approaches, such as Quantitative Structure-Toxicity Relationships (QSTR), are now considered a promising solution for such purpose. In this work, a perturbation theory machine learning (PTML) based QSTR approach is proposed for predicting the genotoxicity of metal oxide NMs under various experimental assay conditions. The application of such perturbation approach to 6084 NM-NM pair cases, set up from 78 unique NMs, afforded a final PTML-QSTR model with an accuracy better than 96% for both training and test sets. This model was then used to predict the genotoxicity of some NMs not included in the modelling dataset. The results for this independent data set were in excellent agreement with the experimental ones. Overall, that thus suggests that the derived PTML-QSTR model is a reliable in silico tool to rapidly and cost-efficiently assess the genotoxicity of metal oxide NMs. Finally, and most importantly, the model provides important insights regarding the mechanism of the genotoxicity triggered by these NMs.

Quantitative Structure-Toxicity Relationship analysis of combined toxic effects of lignocellulose-derived inhibitors on bioethanol production

This study performed a Quantitative Structure-Toxicity Relationship (QSTR) model to evaluate the combined toxicity of lignocellulose-derived inhibitors on bioethanol production. Compared with all the control groups, the combined systems exhibited lower conductivity values, higher oxidation-reduction potential values, as well as maximum inhibition rates. These results indicated that the presence of combined inhibitors had a negative effect on the bioethanol fermentation process. Meanwhile, QSTR model was excellent for evaluating the combined toxic effects at lower ferulic acid concentration (([1:4] × IC50)) and (([1:1] × IC50)), due to higher R² values (0.994 and 0.762), lower P values (0.000 and 0.023) and relative error values (less than 30%). The obtained results also showed that the combined toxic effects of ferulic acid and representative lignocellulose-derived inhibitors were relevant to different molecular descriptors. Meanwhile, the interactions of combined inhibitors were weaker when ferulic acid was at low concentration ([1:4] × IC50).

Multiple toxicity endpoint-structure relationships for substituted phenols and anilines

Quantitative structure-toxicity relationship (QSTR) models with the same mathematical structure were proposed for predicting the multiple toxicity endpoints of substituted phenols and anilines towards Chlorella vulgaris (C. vulgaris) based on the norm indexes. Four aquatic toxicity endpoints including growth inhibition concentrations of IC₅₀, IC₂₀, LOEC and NOEC towards C. vulgaris were involved in the modeling work. The results indicated that the developed models could produce satisfactory predictive results for the four different toxicity endpoints with high squared correlation coefficients (R²). Leave-one-out cross validation, Y-randomized validation and application domain analysis demonstrated the accuracy, robustness and reliability of these models. Accordingly, the results obtained in this work suggested that it might be possible to develop QSTR models with the same mathematical structure for predicting multiple toxicity endpoints successfully via norm index descriptors.

Molecular docking and pharmacokinetic evaluation of natural compounds as targeted inhibitors against Crz1 protein in Rhizoctonia solani

Crz1p regulates Calcineurin, a serine-threonine-specific protein phosphatase, in Rhizoctonia solani. It has attracted consideration as a novel target of antifungal therapy based on studies in numerous pathogenic fungi, including, Cryptococcus neoformans, Candida albicans and Aspergillus fumigatus. To investigate whether Calcineurin can be a useful target for the treatment of Crz1 protein in R. solani causing wet root rot in Chickpea. The work presented here reports the in-silico studies of Crz1 protein against natural compounds. This study Comprises of quantitative structure-toxicity relationship (QSTR) and quantitative structure-activity relationship (QSAR). All compounds showed high binding energy for Crz1 protein through molecular docking. Further, a pharmacokinetic study revealed that these compounds had minimal side effects. Biological activity spectrum prediction of these compounds showed potential antifungal properties by showing significant interaction with Crz1. Hence, these compounds can be used for the prevention and treatment of wet root rot in Chickpea.

Revisiting fish toxicity of active pharmaceutical ingredients: Mechanistic insights from integrated ligand-/structure-based assessments on acetylcholinesterase

The release of active pharmaceutical ingredients (APIs) into the environment is of great concern for aquatic ecosystem as many of these chemicals are designed to exert biological activity. Hence, their impact on non-target organisms like fish would not be surprising. In this respect, we revisited fish toxicity data of pharmaceuticals to generate linear and non-linear quantitative structure-toxicity relationships (QSTRs). We predicted fish lethality data from the validated QSTR models for 120 APIs with no experimental fish toxicity data. Toxicity of APIs on aquatic organisms is not fully characterized. Therefore, to provide a mechanistic insight for the assessment of API's toxicity to fish, the outcome of the derived QSTR models was integrated with structure-based toxicophore and molecular docking studies, utilizing the biomarker enzyme acetylcholinesterase originating from fish Torpedo californica (TcAChE). Toxicophore virtual screening of 60 chemicals with pT > 0 identified 23 hits as potential TcAChE binders with binding free energies ranging from -6.5 to -12.9 kcal/mol. The TcAChE-ligand interaction analysis revealed a good nesting of all 23 hits within TcAChE binding site through establishing strong lipophilic and hydrogen bonding interactions with the surrounding key amino acid residues. Among the chemicals passing the criteria of our integrated approach, majority of APIs belong noticeably to the Central Nervous System class. The screened chemicals displayed not only comprehensive toxicophore coverage, but also strong binding affinities according to the docking calculations, mainly due to interactions with TcAChE's key amino acid residues Tyr121, Tyr130, Tyr334, Trp84, Phe290, Phe330, Phe331, Ser122, and Ser200. Moreover, we propose here that binding of pharmaceuticals to AChE might have a potential in triggering molecular initiating events for adverse outcome pathways (AOPs), which in turn can play an important role for future screening of APIs lacking fish lethality data.

Predicting Cytotoxicity and Enzymatic Activity of Diverse Chemicals Using Goldfish Scale Tissue and Topminnow Hepatoma Cell Line-based Data

Quantitative structure-toxicity relationship (QSTR) models were built for two in vitro endpoints: cytotoxicity and enzymatic activity of diverse chemicals to goldfish (Crassius auratus) scale tissue (GFS) and topminnow (Poeciliopsis lucida) hepatoma cell line (PLHC-1), respectively. The data sets were based on experimental cytotoxicity measured with uptake of 3-amino-7-dimethylamino-2-methylphenazine hydrochloride dye (Neutral Red assay) representing lysosomal damage and enzymatic activity measured with Ethoxyresorufin-O-deethylase (EROD) induction potency. The descriptors were calculated with DRAGON 6 and SPARTAN 10 software packages. Descriptor selection was made by 'All Subset' and Genetic Algorithm-based features implemented in QSARINS software. The proposed QSTR models validated both internally and externally. Additionally, the QSTR models generated for cytotoxicity and EROD induction potency were used to predict the relevant endpoint values for external set chemicals with structural coverage of 95.0 % and 92.1 %, respectively. A strong correlation of experimental in vivo fish lethality data with predicted in vitro cytotoxicity and EROD induction potency values for external set chemicals was found. It was concluded that the proposed QSTR models might be useful to provide an initial screening and prioritization for these diverse chemicals. Also, regarding the strong correlations between predicted in vitro and experimental in vivo data, the use of QSTR predictions as an alternative to the acute fish toxicity assessment can be claimed.

Chemometric modeling of aquatic toxicity of contaminants of emerging concern (CECs) in Dugesia japonica and its interspecies correlation with daphnia and fish: QSTR and QSTTR approaches

The contaminants of emerging concern (CEC) are universally detected in surface water and soil. They can affect the wild life, and their subsequent translocation through the food chain can affect human health, which is an issue of serious concern. Very few amounts of ecotoxicological data are available on the environmental behavior and ecotoxicity of CEC, thus modeling approaches are essential to bridge the existing gap in experimental data. In this present study, we have developed quantitative structure-toxicity relationship (QSTR) models using a data set of 75 compounds for the prediction of aquatic ecotoxicity of CECs on fresh water planarian (Dugesia japonica) by partial least squares (PLS) regression algorithm using simple molecular descriptors selected by genetic algorithm approach. We also explore the correlations between toxicity against D. japonica and those against daphnia (D. magna) and fish (P. promelas), and these were improved on addition of a few molecular descriptors (B08[C-O] and B09[N-O] in case of daphnia and C-006 and H-052 in case of fish) which allowed us to develop predictive interspecies quantitative structure toxicity-toxicity relationship (QSTTR) models, allowing to extrapolate data from one endpoint to another endpoint. The QSTR (Q²_LOO ranging from 0.630 to 0.720 and R²_pred ranging from 0.723 to 0.798) and QSTTR (Q²_LOO = 0.60 and 0.67, R²_pred = 0.88 and 0.84) models have desirable statistical qualities and acceptable internal and external validation measures, meeting rigorous criteria of different validation metrics and showing acceptability for regulatory purposes as proposed by Organization for Economic Cooperation and Development (OECD). Consensus predictions were also performed based on multiple models generated in this study by using the "Intelligent Consensus Predictor" (ICP) tool to enhance the prediction quality for external set compounds.

Single or mixture halogenated chemicals? Risk assessment and developmental toxicity prediction on zebrafish embryos based on weighted descriptors approach

Halogenated chemicals including perfluoroalkyl substances (PFASs) represent an emerging class of endocrine-disrupting pollutants for human populations across the globe. Distress related to their environmental fate and toxicity has initiated several research projects, but the amount of experimental data available for these pollutants is limited. The objective of this study is to assess the toxicity of potentially "safer" alternatives, in relation to their existing counterparts. Developmental toxicity data on zebrafish (Danio rerio) embryos of single and tertiary halogenated mixtures were modeled employing quantitative structure-toxicity relationship (QSTR) tool. The computed models are then employed for toxicity prediction of theoretically generated binary and tertiary mixtures (which have no experimental data) to check their possible threshold and mode of toxicity for future risk assessment. Further, for toxicity screening, we have prepared a huge external dataset consists of single (24), binary (276) and tertiary (2024) mixtures of PFASs. It was accomplished by combination method and predicted through developed models for interpretation of toxicity threats for individuals and mixtures along with identification of diverse range and combination of toxicity thresholds. We found that chemicals in mixtures displayed concentration addition of individual chemical suggesting a similar mode of toxic action and non-interaction of chemicals. Not only that, mixtures of halogenated compounds including PFASs showed less toxicity than their single counterparts and the obtained toxicity trend is: Single chemical > Binary mixture > Tertiary mixture.

On the prediction of cytotoxicity of diverse chemicals for topminnow (Poeciliopsis lucida) hepatoma cell line, PLHC-1$

Two data sets on the cytotoxicity of diverse chemicals to topminnow (Poeciliopsis lucida) hepatoma cell line (PLHC-1) were modelled with quantitative structure-toxicity relationship (QSTR). The data sets are based on 3-amino-7-dimethylamino-2-methylphenazine hydrochloride (NR) and 3-(4,5-dimethylthiazol-2-yl)-2,5-diphenyl tetrazolium bromide (MTT) assays representing lysosomal damage and metabolic impairment, respectively. The descriptors were calculated with DRAGON 6 and SPARTAN 10 software packages. Descriptor selection was made by 'all subset' and genetic algorithm-based features implemented in QSARINS software. The proposed QSTR models were validated both internally and externally. For both endpoints, statistically satisfactory QSTR models were generated with n_Tr = 39; r²_Tr = 0.782; RMSE_Tr = 0.466; n_Test = 18; r²_Test = 0.799; RMSE_Test = 0.360 for NR-based model and n_Tr = 32; r²_Tr = 0.775; RMSE_Tr = 0.460; n_Test = 10; r²_Test = 0.864; RMSE_Test = 0.290 for MTT-based model. Additionally, the QSTR models generated for NR and MTT endpoints were used to predict the cytotoxicity of an external set of 657 and 652 diverse chemicals with structural coverage of 98.6% and 98.3%, respectively. A moderate correlation was observed between the experimental in vivo and predicted in vitro values for external set chemicals. The QSTR models may provide an initial, rapid screening and prioritization of these diverse chemicals for the acute fish toxicity assessment and reduce the need for extensive in vivo toxicity testing.

Toxicity of contaminants of emerging concern to Dugesia japonica: QSTR modeling and toxicity relationship with Daphnia magna

Freshwater planarian Dugesia japonica has a critical ecological importance owing to its unique properties. This study presents for the first time an in silico approach to determine a priori the acute toxicity of contaminants of emerging concern towards D. japonica. Quantitative structure-toxicity/toxicity-toxicity relationship (QSTR/QTTR) models provided here allow producing reliable information using the existing data, thus, reducing the demand of in vivo and in vitro experiments, and contributing to the need for a more holistic approach to environmental safety assessment. Both models are promising for being notably simple and robust, meeting rigorous validation metrics and the OECD criteria. The QTTR model based on the available Daphnia magna data might also contribute to the US EPA Interspecies Correlation Estimation web application. Moreover, the proposed models were applied on hundreds of environmentally significant chemicals lacking experimental D. japonica toxicity data and predicted toxicity values were reported for the first time. The models presented here can be used as potential tools in toxicity assessment, screening and prioritization of chemicals and development of risk management measures in a scientific and regulatory frame.

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.

Ecotoxicological modelling of cosmetics for aquatic organisms: A QSTR approach

In this study, externally validated quantitative structure-toxicity relationship (QSTR) models were developed for toxicity of cosmetic ingredients on three different ecotoxicologically relevant organisms, namely Pseudokirchneriella subcapitata, Daphnia magna and Pimephales promelas following the OECD guidelines. The final models were developed by partial least squares (PLS) regression technique, which is more robust than multiple linear regression. The obtained model for P. subcapitata shows that molecular size and complexity have significant impacts on the toxicity of cosmetics. In case of P. promelas and D. magna, we found that the largest contribution to the toxicity was shown by hydrophobicity and van der Waals surface area, respectively. All models were validated using both internal and test compounds employing multiple strategies. For each QSTR model, applicability domain studies were also performed using the "Distance to Model in X-space" method. A comparison was made with the ECOSAR predictions in order to prove the good predictive performances of our developed models. Finally, individual models were applied to predict toxicity for an external set of 596 personal care products having no experimental data for at least one of the endpoints, and the compounds were ranked based on a decreasing order of toxicity using a scaling approach.

Design, Synthesis, and Antibacterial Activities of Novel Heterocyclic Arylsulphonamide Derivatives

Design, synthesis, and antibacterial activities of a series of arylsulphonamide derivatives as probable peptide deformylase (PDF) inhibitors have been discussed. Compounds have been designed following Lipinski's rule and after docking into the active site of PDF protein (PDB code: 1G2A) synthesized later on. Furthermore, to assess their antibacterial activity, screening of the compound was done in vitro conditions against Gram-positive and Gram-negative bacterial strains. In silico, studies revealed these compounds as potential antibacterial agents and this fact was also supported by their prominent scoring functions. Antibacterial results indicated that these molecules possessed a significant activity against Staphylococcus aureus, Bacillus cereus, Pseudomonas aeruginosa, and Escherichia coli with MIC values ranging from 0.06 to 0.29 μM. TOPKAT results showed that high LD₅₀ values and the compounds were assumed non-carcinogenic when various animal models were studied computationally.

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.

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.

QSAR DataBank repository: open and linked qualitative and quantitative structure-activity relationship models

Background

Structure–activity relationship models have been used to gain insight into chemical and physical processes in biomedicine, toxicology, biotechnology, etc. for almost a century. They have been recognized as valuable tools in decision support workflows for qualitative and quantitative predictions. The main obstacle preventing broader adoption of quantitative structure–activity relationships [(Q)SARs] is that published models are still relatively difficult to discover, retrieve and redeploy in a modern computer-oriented environment. This publication describes a digital repository that makes in silico (Q)SAR-type descriptive and predictive models archivable, citable and usable in a novel way for most common research and applied science purposes.

Description

The QSAR DataBank (QsarDB) repository aims to make the processes and outcomes of in silico modelling work transparent, reproducible and accessible. Briefly, the models are represented in the QsarDB data format and stored in a content-aware repository (a.k.a. smart repository). Content awareness has two dimensions. First, models are organized into collections and then into collection hierarchies based on their metadata. Second, the repository is not only an environment for browsing and downloading models (the QDB archive) but also offers integrated services, such as model analysis and visualization and prediction making.

Conclusions

The QsarDB repository unlocks the potential of descriptive and predictive in silico (Q)SAR-type models by allowing new and different types of collaboration between model developers and model users. The key enabling factor is the representation of (Q)SAR models in the QsarDB data format, which makes it easy to preserve and share all relevant data, information and knowledge. Model developers can become more productive by effectively reusing prior art. Model users can make more confident decisions by relying on supporting information that is larger and more diverse than before. Furthermore, the smart repository automates most of the mundane work (e.g., collecting, systematizing, and reporting data), thereby reducing the time to decision.

Graphical abstract

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.

Comparative toxicity of new halophenolic DBPs in chlorinated saline wastewater effluents against a marine alga: halophenolic DBPs are generally more toxic than haloaliphatic ones

Using seawater for toilet flushing effectively reduces the consumption of precious freshwater resources, yet it introduces bromide and iodide ions into a wastewater treatment system, which may form bromo- and iodo-disinfection byproducts (DBPs) during chlorination of the wastewater effluent. Most of the newly identified DBPs in chlorinated wastewater effluents were halophenolic compounds. It has been reported that the newly identified bromo- and iodo-phenolic DBPs were generally significantly more toxic to a heterotrophic marine polychaete than the commonly known haloacetic acids and trihalomethanes. This has raised a concern over the discharge of chlorinated saline wastewater effluents into the marine ecosystem. In this study, the toxicity of new halophenolic DBPs and some haloaliphatic DBPs was tested against an autotrophic marine alga, Tetraselmis marina. The alga and polychaete bioassays gave the same toxicity orders for many groups of halo-DBPs. New halophenolic DBPs also showed significantly higher toxicity to the alga than the commonly known haloacetic acids, indicating that the emerging halophenolic DBPs deserve more attention. However, two bioassays did exhibit a couple of disparities in toxicity results, mainly because the alga was capable of metabolizing some (nitrogenous) halophenolic DBPs. A quantitative structure-toxicity relationship was developed for the halophenolic DBPs, by employing three physicochemical descriptors (log K(ow), pKa and molar topological index). This relationship presented the toxicity mechanism of the halophenolic DBPs to T. marina and gave a good prediction of the algal toxicity of the tested halophenolic DBPs.

Periodic table-based descriptors to encode cytotoxicity profile of metal oxide nanoparticles: a mechanistic QSTR approach

Nanotechnology has evolved as a frontrunner in the development of modern science. Current studies have established toxicity of some nanoparticles to human and environment. Lack of sufficient data and low adequacy of experimental protocols hinder comprehensive risk assessment of nanoparticles (NPs). In the present work, metal electronegativity (χ), the charge of the metal cation corresponding to a given oxide (χox), atomic number and valence electron number of the metal have been used as simple molecular descriptors to build up quantitative structure-toxicity relationship (QSTR) models for prediction of cytotoxicity of metal oxide NPs to bacteria Escherichia coli. These descriptors can be easily obtained from molecular formula and information acquired from periodic table in no time. It has been shown that a simple molecular descriptor χox can efficiently encode cytotoxicity of metal oxides leading to models with high statistical quality as well as interpretability. Based on this model and previously published experimental results, we have hypothesized the most probable mechanism of the cytotoxicity of metal oxide nanoparticles to E. coli. Moreover, the required information for descriptor calculation is independent of size range of NPs, nullifying a significant problem that various physical properties of NPs change for different size ranges.

Periodic table-based descriptors to encode cytotoxicity profile of metal oxide nanoparticles: a mechanistic QSTR approach

Nanotechnology has evolved as a frontrunner in the development of modern science. Current studies have established toxicity of some nanoparticles to human and environment. Lack of sufficient data and low adequacy of experimental protocols hinder comprehensive risk assessment of nanoparticles (NPs). In the present work, metal electronegativity (χ), the charge of the metal cation corresponding to a given oxide (χox), atomic number and valence electron number of the metal have been used as simple molecular descriptors to build up quantitative structure-toxicity relationship (QSTR) models for prediction of cytotoxicity of metal oxide NPs to bacteria Escherichia coli. These descriptors can be easily obtained from molecular formula and information acquired from periodic table in no time. It has been shown that a simple molecular descriptor χox can efficiently encode cytotoxicity of metal oxides leading to models with high statistical quality as well as interpretability. Based on this model and previously published experimental results, we have hypothesized the most probable mechanism of the cytotoxicity of metal oxide nanoparticles to E. coli. Moreover, the required information for descriptor calculation is independent of size range of NPs, nullifying a significant problem that various physical properties of NPs change for different size ranges.

Predictive modeling of chemical toxicity towards Pseudokirchneriella subcapitata using regression and classification based approaches

Biodiversity nurturing may be a valuable pathway in controlling chemical stress on the ecosystem. In the present work, in silico studies have been performed to develop regression based quantitative structure toxicity relationship (QSTR) models using a data set containing 105 organic chemicals for the prediction of 48-h chemical toxicity towards Pseudokirchneriella subcapitata. Classification based linear discriminant analysis (LDA) was also performed to distinguish chemicals into toxic and nontoxic groups using the same data set. The developed models were found to possess good predictive quality in terms of internal, external and overall validation parameters. The regression based QSTR model suggests that second order molecular connectivity index (molecular size and lipophilicity), density (aromaticity), relative shape of molecules (cyclicity/aromaticity), and specific molecular fragments of the chemicals are important properties of chemicals to exert their toxicity on P. subcapitata. The classification based LDA QSTR model suggested that fused ring aromatic systems, secondary carbon atom fragments, second order valence molecular connectivity indices (molecular size and branching) and molecular weight are the distinguishing features to differentiate chemicals into toxic and nontoxic groups.

Exploring QSTR modeling and toxicophore mapping for identification of important molecular features contributing to the chemical toxicity in Escherichia coli

Biodiversity deprivation can affect functions and services of the ecosystem. Changes in biodiversity alter ecosystem processes and change the resilience of ecosystems to ecological changes. Bacterial communities are the main form of biomass in the ecosystem and one of largest populations on the planet. Bacterial communities provide important services to biodiversity. They break down pollutants, municipal waste and ingested food, and they are the primary route for recycling of organic matter to plants and other autotrophs, conversion of inorganic matter into new biological tissue using sunlight, management of energy crisis through use of biofuel. In the present study, computational chemistry and statistical modeling have been used to develop mathematical equations which can be applied to calculate toxicity of new/unknown chemicals/biofuels/metabolites in Escherichia coli. 2D and 3D descriptors were generated from molecular structure of compounds and mathematical models have been developed using genetic function approximation followed by multiple linear regression (GFA-MLR) method. Model validity was checked through defined internal (R(2)=0.751 and Q(2)=0.711), and external (Rpred(2)=0.773) statistical parameters. Molecular features responsible for toxicity were also assessed through 3D toxicophore study. The toxicophore-based model was validated (R=0.785) using qualitative statistical metrics and randomization test (Fischer validation).

Study on QSTR of benzoic acid compounds with MCI

Quantitative structure-toxicity relationship (QSTR) plays an important role in toxicity prediction. With the modified method, the quantum chemistry parameters of 57 benzoic acid compounds were calculated with modified molecular connectivity index (MCI) using Visual Basic Program Software, and the QSTR of benzoic acid compounds in mice via oral LD₅₀ (acute toxicity) was studied. A model was built to more accurately predict the toxicity of benzoic acid compounds in mice via oral LD₅₀: 39 benzoic acid compounds were used as a training dataset for building the regression model and 18 others as a forecasting dataset to test the prediction ability of the model using SAS 9.0 Program Software. The model is LogLD₅₀ = 1.2399 × ⁰J^A +2.6911 × ¹J^A – 0.4445 × J^B (R² = 0.9860), where ⁰J^A is zero order connectivity index, ¹J^A is the first order connectivity index and J_B = ⁰J^A × ¹J^A is the cross factor. The model was shown to have a good forecasting ability.

A QSAR/QSTR Study on the Environmental Health Impact by the Rocket Fuel 1,1-Dimethyl Hydrazine and its Transformation Products

QSAR/QSTR modelling constitutes an attractive approach to preliminary assessment of the impact on environmental health by a primary pollutant and the suite of transformation products that may be persistent in and toxic to the environment. The present paper studies the impact on environmental health by residuals of the rocket fuel 1,1-dimethyl hydrazine (heptyl) and its transformation products. The transformation products, comprising a variety of nitrogen containing compounds are suggested all to possess a significant migration potential. In all cases the compounds were found being rapidly biodegradable. However, unexpected low microbial activity may cause significant changes. None of the studied compounds appear to be bioaccumulating.Apart from substances with an intact hydrazine structure or hydrazone structure the transformation products in general display rather low environmental toxicities. Thus, it is concluded that apparently further attention should be given to tri- and tetramethyl hydrazine and 1-formyl 2,2-dimethyl hydrazine as well as to the hydrazones of formaldehyde and acetaldehyde as these five compounds may contribute to the overall environmental toxicity of residual rocket fuel and its transformation products.