Predicting multiple ecotoxicological profiles in agrochemical fungicides: a multi-species chemoinformatic approach

Machine Learning-Based Toxicological Modeling for Screening Environmental Obesogens

The emerging presence of environmental obesogens, chemicals that disrupt energy balance and contribute to adipogenesis and obesity, has become a major public health challenge. Molecular initiating events (MIEs) describe biological outcomes resulting from chemical interactions with biomolecules. Machine learning models based on MIEs can predict complex toxic end points due to chemical exposure and improve the interpretability of models. In this study, a system was constructed that integrated six MIEs associated with adipogenesis and obesity. This system showed high accuracy in external validation, with an area under the receiver operating characteristic curve of 0.78. Molecular hydrophobicity (SlogP_VSA) and direct electrostatic interactions (PEOE_VSA) were identified as the two most critical molecular descriptors representing the obesogenic potential of chemicals. This system was further used to predict the obesogenic effects of chemicals on the candidate list of substances of very high concern (SVHCs). Results from 3T3-L1 adipogenesis assays verified that the system correctly predicted obesogenic or nonobesogenic effects of 10 of the 12 SVHCs tested, and identified four novel potential obesogens, including 2-benzotriazol-2-yl-4,6-ditert-butylphenol (UV-320), 4-(1,1,5-trimethylhexyl)phenol (p262-NP), 2-[4-(1,1,3,3-tetramethylbutyl)phenoxy]ethanol (OP1EO) and endosulfan. These validation data suggest that the screening system has good performance in adipogenic prediction.

The first report on the assessment of maximum acceptable daily intake (MADI) of pesticides for humans using intelligent consensus predictions

Direct or indirect consumption of pesticides and their related products by humans and other living organisms without safe dosing may pose a health risk. The risk may arise after a short/long time which depends on the nature and amount of chemicals consumed. Therefore, the maximum acceptable daily intake of chemicals must be calculated to prevent these risks. In the present work, regression-based quantitative structure-activity relationship (QSAR) models were developed using 39 pesticides with maximum acceptable daily intake (MADI) for humans as the endpoint. From the statistical results (R2 = 0.674-0.712, QLOO2 = 0.553-0.580, Q(F1)2 = 0.544-0.611, and Q(F2)2 = 0.531-0.599), it can be inferred that the developed models were robust, reliable, reproducible, accurate, and predictive. Intelligent Consensus Prediction (ICP) was employed to improve the external predictivity (Q(F1)2 =0.579-0.657 and Q(F2)2 = 0.563-0.647) of the models. Some of the chemical markers responsible for toxicity enhancement are the presence of unsaturated bonds, lipophilicity, presence of C< (double bond-single bond-single bonded carbon), and the presence of sulphur and phosphate bonds at the topological distances 1 and 6, while the presence of hydrophilic groups and short chain fragments reduces the toxicity. The Pesticide Properties Database (PPDB) (1694 pesticides) was also screened with the developed models. Hence, this research work will be helpful for the toxicity assessment of pesticides before their synthesis, the development of eco-friendly and safer pesticides, and data-gap filling reducing the time, cost, and animal experimentation. Thus, this study might hold promise for future potential MADI assessment of pesticides and provide a meaningful contribution to the field of risk assessment.

Evaluation of the Aquatic Toxicity of Several Triazole Fungicides

Fungicides play an important role in crop protection, but they have also been shown to adversely affect non-target organisms, including those living in the aquatic environment. The aim of the present study is to combine experimental and computational approaches to evaluate the effects of flutriafol, metconazole, myclobutanil, tebuconazole, tetraconazole and triticonazole on aquatic model organisms and to obtain information on the effects of these fungicides on Lemna minor, a freshwater plant, at the molecular level. The EC50 (the half-maximum effective concentration) values for the growth inhibition of Lemna minor in the presence of the investigated fungicides show that metconazole (EC50 = 0.132 mg/L) and tetraconazole (EC50 = 0.539 mg/L) are highly toxic, tebuconazole (EC50 = 1.552 mg/L), flutriafol (EC50 = 3.428 mg/L) and myclobutanil (EC50 = 9.134 mg/L) are moderately toxic, and triticonazole (EC50 = 11.631 mg/L) is slightly toxic to this plant. The results obtained with the computational tools TEST, ADMETLab2.0 and admetSAR2.0 also show that metconazole and tetraconazole are toxic to other aquatic organisms: Pimephales promelas, Daphnia magna and Tetrahymena pyriformis. A molecular docking study shows that triazole fungicides can affect photosynthesis in Lemna minor because they strongly bind to C43 (binding energies between -7.44 kcal/mol and -7.99 kcal/mol) and C47 proteins (binding energies between -7.44 kcal/mol and -8.28 kcal/mol) in the reaction center of photosystem II, inhibiting the binding of chlorophyll a to these enzymes. In addition, they can also inhibit glutathione S-transferase, an enzyme involved in the cellular detoxification of Lemna minor.

Predictionof Environmental FateandToxicityofInsecticidesUsing Multi-Target QSAR Approach

Ecotoxicological risk assessments form the foundation of regulatory decisions for industrial chemicals used in various sectors. In this study, a multi-target-QSAR model established by a backpropagation neural network trained with the Levenberg-Marquardt (LM) algorithm was used to construct a statistically robust and easily interpretable Mt-QSAR model with high external predictability for the simultaneous prediction of the environmental fate in form of octanol-water partition coefficient (LogP), (BCF) and acute oral toxicity in mammals and birds (LD50rat ) and (LD50bird ) for a wide range of chemical structural classes of insecticides. Principal component analysis was performed on descriptors selected by the SW-MLR method, and the selected PCs were used for constructing the SW-MLR-PCA-ANN model. The developed well-trained model (RMSE=0.83, MPE=0.004, CCC=0.82, IIC=0.78, R2 =0.69) was statistically robust as indicated by the external validation parameters (RMSE=0.93, MPE=0.008, CCC=0.77, IIC=0.68, R2 =0.61). The AD of the developed Mt-QSAR model was also defined to identify the most reliable predictions. Finally, the missing values in the dataset for the aforementioned targets were predicted using the constructed Mt-QSAR model. The proposed approach can be used for simultaneous prediction of the environmental fate of new insecticides, especially ones that haven't been tested yet.

Quantitative Read-across structure-activity relationship (q-RASAR): A new approach methodology to model aquatic toxicity of organic pesticides against different fish species

We have developed quantitative toxicity prediction models for organic pesticides of agricultural importance considering different fish species using a novel quantitative Read-across structure-activity relationship (q-RASAR) approach. The current study uses experimental (Log 1/LC50) data of organic pesticides to various fish species, including Rainbow trout (RT: Oncorhynchus mykiss: 715 data points), Lepomis (LP: Lepomis macrochirus: 136 data points), and Miscellaneous (Pimephales promelas, Brachydanio rerio: 226 data points). This study has also discussed the validation of the developed models and the analysis of structural features that are important for aquatic toxicity towards fishes. The read-across-derived similarity, error, and concordance measures (RASAR descriptors) have been extracted from the preliminary 0D-2D descriptors; the combined pool of RASAR and selected 0D-2D descriptors have been used to develop the final models by employing partial least squares algorithm. All the q-RASAR models are acceptable in terms of goodness of fit, robustness, and external predictivity, superseding the quality of the respective QSAR models, as seen from the computed validation metrics. The q-RASAR is an effective approach that has the potential to be used as a good alternative way to enhance external predictivity, interpretability, and transferability for aquatic toxicity prediction as well as ecotoxicity potential identification.

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.

Antioxidant, histopathological and biochemical outcomes of short-term exposure to acetamiprid in liver and brain of rat: The protective role of N-acetylcysteine and S-methylcysteine

The present study was conducted to investigate the protective effects of N-Acetyl-L-cysteine (NAC) and S-methyl- L-cysteine (SMC) against hepatic oxidative stress and brain damage induced by acetamiprid (ACP) in rats, which were evaluated by histopathological changes, measuring serum biomarkers and antioxidant defense systems. In this study, 42 rats were randomly divided into 6 groups and administered by intraperitoneally for one week: the control group, the sham group (normal saline), ACP alone (5 mg/kg) (group1), NAC alone (160 mg/kg) (group2), ACP + SMC (100 mg/kg) (group3), ACP + NAC (group 4) and ACP + NAC + SMC (group 5). Our results showed that acetamiprid induces liver injures including infiltration of inflammatory cells, congestion and altered histo-architecture and brain damages including gliosis, hyperemia and necrosis. The biochemical analyses showed that acetamiprid significantly altered the structural and biochemical profiles of liver which may be due to the loss of integrity of cell membranes. Furthermore, antioxidant parameters results of ACP group revealed that glutathione (GSH) and total antioxidant capacity (TAC) levels decreased significantly, while lipid peroxidation (LPO) content and glutathione-S-transferase (GST) and catalase (CAT) activities increased in both tissues (P < 0.05), suggesting tissue oxidative damage, which was also confirmed histopathological. Conversely, administration of NAC and SMC ameliorated LPO, GSH content and antioxidant enzymes system considerably (P < 0.05) in both tissues. Moreover, NAC and SMC administration also improved liver and brain malfunction. These results indicate that both NAC and in to a lesser amount SMC have a potent antioxidant protection in both tissues of rat against ACP-induced oxidative stress.

The unreliability of the reliability criteria in the estimation of QSAR for skin sensitivity: A pun or a reliable law?

Some new products, which include common personal-care products, drugs, household items, can be hazardous in aspect personal care products/cosmetics and their ingredients (i.e. the above can effect human skin). International organizations (e.g. the Organisation for Economic Co-operation and Development-OECD) recommend evaluating individual ingredients when assessing the safety of personal care or cosmetic products. Thus, checking up that "popular at the market" substances are non-toxic, do not penetrate into or through normal or compromised human skin, and therefore, pose no risk to human health is an essential element of modern toxicology. The development of reliable models of toxicological endpoints is a tool to carry out the above checking up via quantitative structure-activity relationships (QSARs). The reliability of the QSAR is the current task of mathematical statistics. Recently, the index of ideality of correlation (IIC) and correlation intensity index (CII) were suggested as criteria of predictive potential (i.e. reliability) of QSAR-models. Here, the abilities of these criteria were studied for the case of building up models for skin sensitivity (LLNA, local lymph node assay). Computational experiments have confirmed that the IIC demonstrates an obvious ability to improve the predictive potential of models of skin sensitization. The applying of the CII for the case of skin sensitization also improves the quality of the model. However, the best models for skin sensitization were observed if the above-mentioned criteria are applied jointly (n = 268; R² = 0.60; RMSE = 0.63).

Acetamiprid-induced Cyto- and Genotoxicity in the AR42J Pancreatic Cell Line

Objectives

Neonicotinoid insecticides, 30% of insecticides marketed worldwide, have selective toxicity on insects through α4p2 nicotinic acetylcholine receptors. Although it is known that acetamiprid exerts toxicity on several organ systems, its toxic effects on the pancreas and its mechanism of action have not been clarified yet. Therefore, in the present study, the cytotoxic and genotoxic potentials of acetamiprid on the AR42J pancreatic cell line were evaluated.

Materials and Methods

The (3-[4,5-dimethylthiazol-2-yl]-2,5 diphenyl tetrazolium bromide) (MTT) assay and comet assay were conducted for the cyto- and genotoxicity evaluations, respectively. Reactive oxygen species (ROS) production was assessed by flow cytometry and glutathione (GSH) levels were determined by ELISA for oxidative damage potential, which is thought to be an underlying mechanism of cyto-/genotoxic effects.

Results

To reveal the dose-response relationship the concentration range of 1-6 mM was selected for the assays. Cell viability decreased in a dose-dependent manner and the inhibitory concentration 50 value was calculated as 12.61 mM by the MTT assay. Acetamiprid induced DNA damage in all concentrations tested in a dose-depending manner. The mean tail intensity values were 3.84 and ≤32.96 for the control and exposure groups, respectively. There was no significant difference for ROS production; however, the GSH level was reduced at the highest concentration.

Conclusion

It is thought that the present study will contribute to the literature due to the lack of data on the potential toxic effects of acetamiprid on the pancreas. To better understand acetamiprid toxicity, further studies including a wide range of mechanistic parameters are needed.

Combined Machine Learning and Molecular Modelling Workflow for the Recognition of Potentially Novel Fungicides

Novel machine learning and molecular modelling filtering procedures for drug repurposing have been carried out for the recognition of the novel fungicide targets of Cyp51 and Erg2. Classification and regression approaches on molecular descriptors have been performed using stepwise multilinear regression (FS-MLR), uninformative-variable elimination partial-least square regression, and a non-linear method called Forward Stepwise Limited Correlation Random Forest (FS-LM-RF). Altogether, 112 prediction models from two different approaches have been built for the descriptor recognition of fungicide hit compounds. Aiming at the fungal targets of sterol biosynthesis in membranes, antifungal hit compounds have been selected for docking experiments from the Drugbank database using the Autodock4 molecular docking program. The results were verified by Gold Protein-Ligand Docking Software. The best-docked conformation, for each high-scored ligand considered, was submitted to quantum mechanics/molecular mechanics (QM/MM) gradient optimization with final single point calculations taking into account both the basis set superposition error and thermal corrections (with frequency calculations). Finally, seven Drugbank lead compounds were selected based on their high QM/MM scores for the Cyp51 target, and three were selected for the Erg2 target. These lead compounds could be recommended for further in vitro studies.

Interspecific Variation in Nematode Responses to Metals

Performing toxicity testing on multiple species with differing degrees of evolutionary relatedness can provide important information on how chemical sensitivity varies among species and can help pinpoint the biological drivers of species sensitivity. Such knowledge could ultimately be used to design better multispecies predictive ecological risk assessment models and identify particularly sensitive species. However, laboratory toxicity tests involving multiple species can also be resource intensive, especially when each species has unique husbandry conditions. We performed lethality tests with 2 metals, copper chloride and zinc chloride, on 5 different nematode species, which are nested in their degree of evolutionary relatedness: Caenorhabditis briggsae, Caenorhabditis elegans, Oscheius myriophila, Oscheius tipulae, and Pristionchus pacificus. All species were successfully cultured and tested concurrently with limited resources, demonstrating that inexpensive, multispecies nematode toxicity testing systems are achievable. The results indicate that P. pacificus is the most sensitive to both metals. Conversely, C. elegans is the least sensitive species to copper, but the second most sensitive to zinc, indicating that species relationships do not necessarily predict species sensitivity. Toxicity testing with additional nematode species and types of chemicals is feasible and will help form more generalizable conclusions about relative species sensitivity. Environ Toxicol Chem 2020;39:1006-1016. © 2020 SETAC.

In silico prediction of drug-induced developmental toxicity by using machine learning approaches

Some drugs and xenobiotics have the potential to disturb homeostasis, normal growth, differentiation, development or behavior during prenatal development or postnatally until puberty. Assessment of the developmental toxicity is one of the important safety considerations incorporated by international regulatory agencies. In this investigation, seven machine learning methods, including naïve Bayes, support vector machine, recursive partitioning, k-nearest neighbor, C4.5 decision tree, random forest and Adaboost, were used to build binary classification models for developmental toxicity. Among these models, the naïve Bayes classifier represented the best predictive performance and stability, which gave 91.11% overall prediction accuracy, 91.50% balanced accuracy and 0.818 MCC for the training set, and generated 83.93% concordance, 81.85% balanced accuracy and 0.627 MCC for the test set. The application domains were analyzed, and only one chemical in the test set was identified as outside the application domain. In addition, 10 important molecular descriptors related to developmental toxicity were selected by the genetic algorithm, which may contribute to explanation of the mechanisms of developmental toxicants. The best naïve Bayes classification model should be employed as alternative method for qualitative prediction of chemical-induced developmental toxicity in early stages of drug development.

Chemometric modeling of Daphnia magna toxicity of agrochemicals

Over the past few years, the ecotoxicological hazard potential of agrochemicals has received much attention in the industries and regulatory agencies. In the current work, we have developed quantitative structure-activity relationship (QSAR) models for Daphnia magna toxicities of different classes of agrochemicals (fungicides, herbicides, insecticides and microbiocides) individually as well as for the combined set with the application of Organization for Economic Co-operation and Development (OECD) recommended guidelines. The models for the individual data sets as well as for the combined set were generated employing only simple and interpretable two-dimensional descriptors, and subsequently strictly validated using test set compounds. The validated individual models were used to generate consensus models, with the objective to improve the prediction quality and reduced prediction errors. All the individual models of different classes of agrochemicals as well as the global set of agrochemicals showed encouraging statistical quality and prediction ability. The general observations from the derived models suggest that the toxicity increases with lipophilicity and decreases with polarity. The generated models of different classes of agrochemicals and also for the combined set should be applicable for data gap filling for new or untested agrochemical compounds falling within the applicability domain of the developed models.

Fungicides: An Overlooked Pesticide Class?

Fungicides are indispensable to global food security and their use is forecasted to intensify. Fungicides can reach aquatic ecosystems and occur in surface water bodies in agricultural catchments throughout the entire growing season due to their frequent, prophylactic application. However, in comparison to herbicides and insecticides, the exposure to and effects of fungicides have received less attention. We provide an overview of the risk of fungicides to aquatic ecosystems covering fungicide exposure (i.e., environmental fate, exposure modeling, and mitigation measures) as well as direct and indirect effects of fungicides on microorganisms, macrophytes, invertebrates, and vertebrates. We show that fungicides occur widely in aquatic systems, that the accuracy of predicted environmental concentrations is debatable, and that fungicide exposure can be effectively mitigated. We additionally demonstrate that fungicides can be highly toxic to a broad range of organisms and can pose a risk to aquatic biota. Finally, we outline central research gaps that currently challenge our ability to predict fungicide exposure and effects, promising research avenues, and shortcomings of the current environmental risk assessment for fungicides.

Cardiotoxicity and myocardial infarction-associated DNA damage induced by thiamethoxam in vitro and in vivo: Protective role of Trigonella foenum-graecum seed-derived polysaccharide

The risk of pesticides on the human health and environment has drawn increasing attention. Today, new tools are developed to reduce pesticide adverse effects. This study aimed to evaluate the toxicity induced by, thiamethoxam (TMX), and the cytoprotective effect of a novel polysaccharide, named fenugreek seed water polysaccharide (FWEP) in vitro using H9c2 cardiomyoblastes and in vivo using Wistar rat model. Animals were assigned into four groups per eight rats each: group 1 served as a control group, group 2 received TMX, group 3, and group 4 received both FWEP and TMX tested at two doses (100 and 200 mg/kg, respectively). Regarding the in vitro study, our results demonstrated that TMX induced a decrease in H9c2 cell viability up to 70% with the highest concentration. In vivo, TMX injection induced marked heart damage noted by a significant increase in plasma lactate dehydrogenase, creatine phosphokinase, troponin-T, aspartate amino transferase activities, cholesterol, and triglyceride levels. Concomitant alterations in cardiac antioxidant defense system revealed depletion in the levels of glutathione and non-protein thiol and an increase in the activity of superoxide dismutase, catalase, and glutathione peroxidase. Similarly, a significant increase in heart lipid, malondialdehyde, advanced oxidation protein product and in protein carbonyls levels was also noted. In addition, heart tissues histo-architecture displayed major presence of apoptosis and necrosis as confirmed by DNA degradation. However, supplementation with FWEP alleviated heart oxidative damage and genotoxicity. In this manner, ABTS radical-scavenging activity, linoleic acid oxidation tests and heart genomic and DNA nicking assay had proved FWEP strong antioxidant potential. In conclusion, FWEP provided significant protection against TMX-induced heart injury, and could be a useful and efficient agent against cardiotoxicity and atherosclerosis.

In silico prediction of pesticide aquatic toxicity with chemical category approaches

Aquatic toxicity is an important issue in pesticide development. In this study, using nine molecular fingerprints to describe pesticides, binary and ternary classification models were constructed to predict aquatic toxicity of pesticides via six machine learning methods: Naïve Bayes (NB), Artificial Neural Network (ANN), k-Nearest Neighbor (kNN), Classification Tree (CT), Random Forest (RF) and Support Vector Machine (SVM). For the binary models, local models were obtained with 829 pesticides on rainbow trout (RT) and 151 pesticides on lepomis (LP), and global models were constructed on the basis of 1258 diverse pesticides on RT and LP and 278 on other fish species. After analyzing the local binary models, we found that fish species caused influence in terms of accuracy. Considering the data size and predictive range, the 1258 pesticides were also used to build global ternary models. The best local binary models were Maccs_ANN for RT and Maccs_SVM for LP, which exhibited accuracies of 0.90 and 0.90, respectively. For global binary models, the best model was Graph_SVM with an accuracy of 0.89. Accuracy of the best global ternary model Graph_SVM was 0.81, which was a little lower than that of the best global binary model. In addition, several substructural alerts were identified including nitrobenzene, chloroalkene and nitrile, which could significantly correlate with pesticide aquatic toxicity. This study provides a useful tool for an early evaluation of pesticide aquatic toxicity in environmental risk assessment.

QSAR-Based Studies of Nanomaterials in the Environment

Nanotechnology is a newly emerging field, posing substantial impacts on society, economy, and the environment. In recent years, the development of nanotechnology has led to the design and large-scale production of many new materials and devices with a vast range of applications. However, along with the benefits, the use of nanomaterials raises many questions and generates concerns due to the possible health-risks and environmental impacts. This chapter provides an overview of the Quantitative Structure-Activity Relationships (QSAR) studies performed so far towards predicting nanoparticles' environmental toxicity. Recent progresses on the application of these modeling studies are additionally pointed out. Special emphasis is given to the setup of a QSAR perturbation-based model for the assessment of ecotoxic effects of nanoparticles in diverse conditions. Finally, ongoing challenges that may lead to new and exciting directions for QSAR modeling are discussed.

Hematological, biochemical, and toxicopathic effects of subchronic acetamiprid toxicity in Wistar rats

Acetamiprid is one of the most widely used neonicotinoids. This study investigates toxic effects of repeated oral administration of three doses of acetamiprid (1/20, 1/10, and 1/5 of LD₅₀) during 60 days. For this, male Wistar rats were divided into four different groups. Hematological, biochemical, and toxicopathic effects of acetamiprid were evaluated. According to the results, a significant decrease in the body weight gain at the highest dose 1/5 of LD₅₀ of acetamiprid was noticed. An increase in the relative liver weight was also observed at this dose level. The hematological constituents were affected. A significant decrease in RBC, HGB, and HCT in rats treated with higher doses of acetamiprid (1/10 and 1/5 of LD₅₀) was noted. However, a significant increase in WBC and PLT were observed at the same doses. Furthermore, acetamiprid induced liver toxicity measured by the increased activities of aspartate aminotransferase (AST), alanine aminotransferase (ALT), alkaline phosphates (ALPs), and lactate dehydrogenase (LDH) which may be due to the loss of hepatic membrane architecture and hepatocellular damage. In addition, exposure to acetamiprid resulted in a significant decrease in the levels of superoxide dismutase and catalase activities (p ≤ 0.01) with concomitant increase in lipid peroxidation in rat liver. These findings highlight the subchronic hepatotoxicity of acetamiprid.

In silico target prediction for elucidating the mode of action of herbicides including prospective validation

The rapid emergence of pesticide resistance has given rise to a demand for herbicides with new mode of action (MoA). In the agrochemical sector, with the availability of experimental high throughput screening (HTS) data, it is now possible to utilize in silico target prediction methods in the early discovery phase to suggest the MoA of a compound via data mining of bioactivity data. While having been established in the pharmaceutical context, in the agrochemical area this approach poses rather different challenges, as we have found in this work, partially due to different chemistry, but even more so due to different (usually smaller) amounts of data, and different ways of conducting HTS. With the aim to apply computational methods for facilitating herbicide target identification, 48,000 bioactivity data against 16 herbicide targets were processed to train Laplacian modified Naïve Bayesian (NB) classification models. The herbicide target prediction model ("HerbiMod") is an ensemble of 16 binary classification models which are evaluated by internal, external and prospective validation sets. In addition to the experimental inactives, 10,000 random agrochemical inactives were included in the training process, which showed to improve the overall balanced accuracy of our models up to 40%. For all the models, performance in terms of balanced accuracy of≥80% was achieved in five-fold cross validation. Ranking target predictions was addressed by means of z-scores which improved predictivity over using raw scores alone. An external testset of 247 compounds from ChEMBL and a prospective testset of 394 compounds from BASF SE tested against five well studied herbicide targets (ACC, ALS, HPPD, PDS and PROTOX) were used for further validation. Only 4% of the compounds in the external testset lied in the applicability domain and extrapolation (and correct prediction) was hence impossible, which on one hand was surprising, and on the other hand illustrated the utilization of using applicability domains in the first place. However, performance better than 60% in balanced accuracy was achieved on the prospective testset, where all the compounds fell within the applicability domain, and which hence underlines the possibility of using target prediction also in the area of agrochemicals.

Use of big data in drug development for precision medicine

Drug development has been a costly and lengthy process with an extremely low success rate and lack of consideration of individual diversity in drug response and toxicity. Over the past decade, an alternative "big data" approach has been expanding at an unprecedented pace based on the development of electronic databases of chemical substances, disease gene/protein targets, functional readouts, and clinical information covering inter-individual genetic variations and toxicities. This paradigm shift has enabled systematic, high-throughput, and accelerated identification of novel drugs or repurposed indications of existing drugs for pathogenic molecular aberrations specifically present in each individual patient. The exploding interest from the information technology and direct-to-consumer genetic testing industries has been further facilitating the use of big data to achieve personalized Precision Medicine. Here we overview currently available resources and discuss future prospects.

Building up a QSAR model for toxicity toward Tetrahymena pyriformis by the Monte Carlo method: A case of benzene derivatives

Data on toxicity toward Tetrahymena pyriformis is indicator of applicability of a substance in ecologic and pharmaceutical aspects. Quantitative structure-activity relationships (QSARs) between the molecular structure of benzene derivatives and toxicity toward T. pyriformis (expressed as the negative logarithms of the population growth inhibition dose, mmol/L) are established. The available data were randomly distributed three times into the visible training and calibration sets, and invisible validation sets. The statistical characteristics for the validation set are the following: r(2)=0.8179 and s=0.338 (first distribution); r(2)=0.8682 and s=0.341 (second distribution); r(2)=0.8435 and s=0.323 (third distribution). These models are built up using only information on the molecular structure: no data on physicochemical parameters, 3D features of the molecular structure and quantum mechanics descriptors are involved in the modeling process.

A Quantitative Structure Activity Relationship for acute oral toxicity of pesticides on rats: Validation, domain of application and prediction

Quantitative Structure Activity Relationship (QSAR) models are expected to play an important role in the risk assessment of chemicals on humans and the environment. In this study, we developed a validated QSAR model to predict acute oral toxicity of 329 pesticides to rats because a few QSAR models have been devoted to predict the Lethal Dose 50 (LD50) of pesticides on rats. This QSAR model is based on 17 molecular descriptors, and is robust, externally predictive and characterized by a good applicability domain. The best results were obtained with a 17/9/1 Artificial Neural Network model trained with the Quasi Newton back propagation (BFGS) algorithm. The prediction accuracy for the external validation set was estimated by the Q(2)ext and the root mean square error (RMS) which are equal to 0.948 and 0.201, respectively. 98.6% of external validation set is correctly predicted and the present model proved to be superior to models previously published. Accordingly, the model developed in this study provides excellent predictions and can be used to predict the acute oral toxicity of pesticides, particularly for those that have not been tested as well as new pesticides.

Quantitative Structure-Antifungal Activity Relationships for cinnamate derivatives

Quantitative Structure-Activity Relationships (QSAR) are established with the aim of analyzing the fungicidal activities of a set of 27 active cinnamate derivatives. The exploration of more than a thousand of constitutional, topological, geometrical and electronic molecular descriptors, which are calculated with Dragon software, leads to predictions of the growth inhibition on Pythium sp and Corticium rolfsii fungi species, in close agreement to the experimental values extracted from the literature. A set containing 21 new structurally related cinnamate compounds is prepared. The developed QSAR models are applied to predict the unknown fungicidal activity of this set, showing that cinnamates like 38, 28 and 42 are expected to be highly active for Pythium sp, while this is also predicted for 28 and 34 in C. rolfsii.

Exploring the role of quantum chemical descriptors in modeling acute toxicity of diverse chemicals to Daphnia magna

Various quantum-mechanically computed molecular and thermodynamic descriptors along with physico-chemical, electrostatic and topological descriptors are compared while developing quantitative structure-activity relationships (QSARs) for the acute toxicity of 252 diverse organic chemicals towards Daphnia magna. QSAR models based on the quantum-chemical descriptors, computed with routinely employed advanced semi-empirical and ab-initio methods, along with the electron-correlation contribution (CORR) of the descriptors, are analyzed for the external predictivity of the acute toxicity. The models with reliable internal stability and external predictivity are found to be based on the HOMO energy along with the physico-chemical, electrostatic and topological descriptors. Besides this, the total energy and electron-correlation energy are also observed as highly reliable descriptors, suggesting that the intra-molecular interactions between the electrons play an important role in the origin of the acute toxicity, which is in fact an unexplored phenomenon. The models based on quantum-chemical descriptors such as chemical hardness, absolute electronegativity, standard Gibbs free energy and enthalpy are also observed to be reliable. A comparison of the robust models based on the quantum-chemical descriptors computed with various quantum-mechanical methods suggests that the advanced semi-empirical methods such as PM7 can be more reliable than the ab-initio methods which are computationally more expensive.

Computational ecotoxicology: simultaneous prediction of ecotoxic effects of nanoparticles under different experimental conditions

Nanotechnology has brought great advances to many fields of modern science. A manifold of applications of nanoparticles have been found due to their interesting optical, electrical, and biological/chemical properties. However, the potential toxic effects of nanoparticles to different ecosystems are of special concern nowadays. Despite the efforts of the scientific community, the mechanisms of toxicity of nanoparticles are still poorly understood. Quantitative-structure activity/toxicity relationships (QSAR/QSTR) models have just started being useful computational tools for the assessment of toxic effects of nanomaterials. But most QSAR/QSTR models have been applied so far to predict ecotoxicity against only one organism/bio-indicator such as Daphnia magna. This prevents having a deeper knowledge about the real ecotoxic effects of nanoparticles, and consequently, there is no possibility to establish an efficient risk assessment of nanomaterials in the environment. In this work, a perturbation model for nano-QSAR problems is introduced with the aim of simultaneously predicting the ecotoxicity of different nanoparticles against several assay organisms (bio-indicators), by considering also multiple measures of ecotoxicity, as well as the chemical compositions, sizes, conditions under which the sizes were measured, shapes, and the time during which the diverse assay organisms were exposed to nanoparticles. The QSAR-perturbation model was derived from a database containing 5520 cases (nanoparticle-nanoparticle pairs), and it was shown to exhibit accuracies of ca. 99% in both training and prediction sets. In order to demonstrate the practical applicability of our model, three different nickel-based nanoparticles (Ni) with experimental values reported in the literature were predicted. The predictions were found to be in very good agreement with the experimental evidences, confirming that Ni-nanoparticles are not ecotoxic when compared with other nanoparticles. The results of this study thus provide a single valuable tool toward an efficient prediction of the ecotoxicity of nanoparticles under multiple experimental conditions.

Escalating chronic kidney diseases of multi-factorial origin in Sri Lanka: causes, solutions, and recommendations

During the last two decades, Sri Lanka, located close to the equator, has experienced an escalating incidence of chronic kidney disease (CKD) of unknown aetiology (CKDue) in dry zonal areas. Similar incidences of unusual CKDs have been reported in the dry zonal, agricultural areas of several other equatorial countries. In Sri Lanka, the incidence of CKDue is highest in the North Central Province (NCP), where approximately 45 % of the country's paddy fields are located. However, in recent years, the disease has spread into areas adjacent to as well as distant from the NCP. The cause of CKD in Sri Lanka is unknown, and may likely due to interactions of different potential agents; thus, CKD is of multi-factorial origin (CKD-mfo). These factors include, the negative effects from overuse of agrochemicals. Nevertheless, the potential interactions and synergism between probable agents have not been studied. This systematic review discusses the proposed hypotheses and causes of CKD-mfo in Sri Lanka, and ways to decrease the incidence of this disease and to eradicate it, and provide some recommendations. During the past decade, a number of groups have investigated this disorder using different methodologies and reported various correlations, but failed to find a cause. Research has focussed on the contamination of water with heavy metals, agrochemicals, hard water, algae, ionicity, climate change, and so forth. Nevertheless, the levels of any of the pollutants or conditions reported in water in NPC are inconsistent not correlated with the prevalence of the disease, and are too low to be the sole cause of CKD-mfo. Meanwhile, several nephrotoxins prevalent in the region, including medications, leptospirosis, toxic herbs, illicit alcohol, locally grown tobacco, and petrochemicals, as well as the effects of changed habits occured over the past four decades have not been studied to date. Taken together, the geographical distribution and overall findings indicate that combinations of factors and/or their interactions are likely to precipitate CKD-mfo, which kills more than 5,000 people annually in Sri Lanka; most victims are middle-aged male farmers. Much anecdotal evidence from this region suggests that consumption of contaminated water is the most likely source of this deadly disease. Although the aetiology is unknown, prevention of this "environmentally acquired" disease seems relatively straightforward. Solutions include (a) preventing environmental pollution, (b) stopping the irresponsible use and decreasing the usage of agrochemicals, and encouraging the use of environmentally friendly agricultural methods, (c) taking proper precautions when using agrochemicals and safe disposal of their containers, (d) changing the risky behaviour of farmers and educating them to preserve the environment, and (e) providing clean potable water to all affected regions. Implementing a well-coordinated, in-depth, region-wide, broad-based research study together with a long-term effective surveillance programme across the country is essential to curbing this disease. Unless firm actions are taken promptly, more than three million healthy people in the country, live in agricultural regions, are at risk for contracting CKD-mfo and succumb to premature deaths, which are preventable.

Computer-aided nanotoxicology: assessing cytotoxicity of nanoparticles under diverse experimental conditions by using a novel QSTR-perturbation approach

Nowadays, the interest in the search for new nanomaterials with improved electrical, optical, catalytic and biological properties has increased. Despite the potential benefits that can be gathered from the use of nanoparticles, only little attention has been paid to their possible toxic effects that may affect human health. In this context, several assays have been carried out to evaluate the cytotoxicity of nanoparticles in mammalian cells. Owing to the cost in both resources and time involved in such toxicological assays, there has been a considerable increase in the interest towards alternative computational methods, like the application of quantitative structure-activity/toxicity relationship (QSAR/QSTR) models for risk assessment of nanoparticles. However, most QSAR/QSTR models developed so far have predicted cytotoxicity against only one cell line, and they did not provide information regarding the influence of important factors rather than composition or size. This work reports a QSTR-perturbation model aiming at simultaneously predicting the cytotoxicity of different nanoparticles against several mammalian cell lines, and also considering different times of exposure of the cell lines, as well as the chemical composition of nanoparticles, size, conditions under which the size was measured, and shape. The derived QSTR-perturbation model, using a dataset of 1681 cases (nanoparticle-nanoparticle pairs), exhibited an accuracy higher than 93% for both training and prediction sets. In order to demonstrate the practical applicability of our model, the cytotoxicity of different silica (SiO2), nickel (Ni), and nickel(ii) oxide (NiO) nanoparticles were predicted and found to be in very good agreement with experimental reports. To the best of our knowledge, this is the first attempt to simultaneously predict the cytotoxicity of nanoparticles under multiple experimental conditions by applying a single unique QSTR model.

Escalating chronic kidney diseases of multi-factorial origin in Sri Lanka: causes, solutions, and recommendations

During the last two decades, Sri Lanka, located close to the equator, has experienced an escalating incidence of chronic kidney disease (CKD) of unknown aetiology (CKDue) in dry zonal areas. Similar incidences of unusual CKDs have been reported in the dry zonal, agricultural areas of several other equatorial countries. In Sri Lanka, the incidence of CKDue is highest in the North Central Province (NCP), where approximately 45 % of the country's paddy fields are located. However, in recent years, the disease has spread into areas adjacent to as well as distant from the NCP. The cause of CKD in Sri Lanka is unknown, and may likely due to interactions of different potential agents; thus, CKD is of multi-factorial origin (CKD-mfo). These factors include, the negative effects from overuse of agrochemicals. Nevertheless, the potential interactions and synergism between probable agents have not been studied. This systematic review discusses the proposed hypotheses and causes of CKD-mfo in Sri Lanka, and ways to decrease the incidence of this disease and to eradicate it, and provide some recommendations. During the past decade, a number of groups have investigated this disorder using different methodologies and reported various correlations, but failed to find a cause. Research has focussed on the contamination of water with heavy metals, agrochemicals, hard water, algae, ionicity, climate change, and so forth. Nevertheless, the levels of any of the pollutants or conditions reported in water in NPC are inconsistent not correlated with the prevalence of the disease, and are too low to be the sole cause of CKD-mfo. Meanwhile, several nephrotoxins prevalent in the region, including medications, leptospirosis, toxic herbs, illicit alcohol, locally grown tobacco, and petrochemicals, as well as the effects of changed habits occured over the past four decades have not been studied to date. Taken together, the geographical distribution and overall findings indicate that combinations of factors and/or their interactions are likely to precipitate CKD-mfo, which kills more than 5,000 people annually in Sri Lanka; most victims are middle-aged male farmers. Much anecdotal evidence from this region suggests that consumption of contaminated water is the most likely source of this deadly disease. Although the aetiology is unknown, prevention of this "environmentally acquired" disease seems relatively straightforward. Solutions include (a) preventing environmental pollution, (b) stopping the irresponsible use and decreasing the usage of agrochemicals, and encouraging the use of environmentally friendly agricultural methods, (c) taking proper precautions when using agrochemicals and safe disposal of their containers, (d) changing the risky behaviour of farmers and educating them to preserve the environment, and (e) providing clean potable water to all affected regions. Implementing a well-coordinated, in-depth, region-wide, broad-based research study together with a long-term effective surveillance programme across the country is essential to curbing this disease. Unless firm actions are taken promptly, more than three million healthy people in the country, live in agricultural regions, are at risk for contracting CKD-mfo and succumb to premature deaths, which are preventable.

Assessing predictive uncertainty in comparative toxicity potentials of triazoles

Comparative toxicity potentials (CTPs) quantify the potential ecotoxicological impacts of chemicals per unit of emission. They are the product of a substance's environmental fate, exposure, and hazardous concentration. When empirical data are lacking, substance properties can be predicted. The goal of the present study was to assess the influence of predictive uncertainty in substance property predictions on the CTPs of triazoles. Physicochemical and toxic properties were predicted with quantitative structure-activity relationships (QSARs), and uncertainty in the predictions was quantified with use of the data underlying the QSARs. Degradation half-lives were based on a probability distribution representing experimental half-lives of triazoles. Uncertainty related to the species' sample size that was present in the prediction of the hazardous aquatic concentration was also included. All parameter uncertainties were treated as probability distributions, and propagated by Monte Carlo simulations. The 90% confidence interval of the CTPs typically spanned nearly 4 orders of magnitude. The CTP uncertainty was mainly determined by uncertainty in soil sorption and soil degradation rates, together with the small number of species sampled. In contrast, uncertainty in species-specific toxicity predictions contributed relatively little. The findings imply that the reliability of CTP predictions for the chemicals studied can be improved particularly by including experimental data for soil sorption and soil degradation, and by developing toxicity QSARs for more species.

Fragment-based optimization of small molecule CXCL12 inhibitors for antagonizing the CXCL12/CXCR4 interaction

The chemokine CXCL12 and its G protein-coupled receptor (GPCR) CXCR4 are high-priority clinical targets because of their involvement in metastatic cancers (also implicated in autoimmune disease and cardiovascular disease). Because chemokines interact with two distinct sites to bind and activate their receptors, both the GPCRs and chemokines are potential targets for small molecule inhibition. A number of chemokines have been validated as targets for drug development, but virtually all drug discovery efforts focus on the GPCRs. However, all CXCR4 receptor antagonists with the exception of MSX-122 have failed in clinical trials due to unmanageable toxicities, emphasizing the need for alternative strategies to interfere with CXCL12/CXCR4-guided metastatic homing. Although targeting the relatively featureless surface of CXCL12 was presumed to be challenging, focusing efforts at the sulfotyrosine (sY) binding pockets proved successful for procuring initial hits. Using a hybrid structure-based in silico/NMR screening strategy, we recently identified a ligand that occludes the receptor recognition site. From this initial hit, we designed a small fragment library containing only nine tetrazole derivatives using a fragment-based and bioisostere approach to target the sY binding sites of CXCL12. Compound binding modes and affinities were studied by 2D NMR spectroscopy, X-ray crystallography, molecular docking and cell-based functional assays. Our results demonstrate that the sY binding sites are conducive to the development of high affinity inhibitors with better ligand efficiency (LE) than typical protein-protein interaction inhibitors (LE ≤ 0.24). Our novel tetrazole-based fragment 18 was identified to bind the sY21 site with a K(d) of 24 μM (LE = 0.30). Optimization of 18 yielded compound 25 which specifically inhibits CXCL12-induced migration with an improvement in potency over the initial hit 9. The fragment from this library that exhibited the highest affinity and ligand efficiency (11: K(d) = 13 μM, LE = 0.33) may serve as a starting point for development of inhibitors targeting the sY12 site.

Chemoinformatics for rational discovery of safe antibacterial drugs: simultaneous predictions of biological activity against streptococci and toxicological profiles in laboratory animals

Streptococci are a group of Gram-positive bacteria which are responsible for causing many diverse diseases in humans and other animals worldwide. The high prevalence of resistance of these bacteria to current antibacterial drugs is an alarming problem for the scientific community. The battle against streptococci by using antimicrobial chemotherapies will depend on the design of new chemicals with high inhibitory activity, having also as low toxicity as possible. Multi-target approaches based on quantitative-structure activity relationships (mt-QSAR) have played a very important role, providing a better knowledge about the molecular patterns related with the appearance of different pharmacological profiles including antimicrobial activity. Until now, almost all mt-QSAR models have considered the study of biological activity or toxicity separately. In the present study, we develop by the first time, a unified multitasking (mtk) QSAR model for the simultaneous prediction of anti-streptococci activity and toxic effects against biological models like Mus musculus and Rattus norvegicus. The mtk-QSAR model was created by using artificial neural networks (ANN) analysis for the classification of compounds as positive (high biological activity and/or low toxicity) or negative (otherwise) under diverse sets of experimental conditions. Our mtk-QSAR model, correctly classified more than 97% of the cases in the whole database (more than 11,500 cases), serving as a promising tool for the virtual screening of potent and safe anti-streptococci drugs.

PTID: an integrated web resource and computational tool for agrochemical discovery

SUMMARY

Although in silico drug discovery approaches are crucial for the development of pharmaceuticals, their potential advantages in agrochemical industry have not been realized. The challenge for computer-aided methods in agrochemical arena is a lack of sufficient information for both pesticides and their targets. Therefore, it is important to establish such knowledge repertoire that contains comprehensive pesticides' profiles, which include physicochemical properties, environmental fates, toxicities and mode of actions. Here, we present an integrated platform called Pesticide-Target interaction database (PTID), which comprises a total of 1347 pesticides with rich annotation of ecotoxicological and toxicological data as well as 13 738 interactions of pesticide-target and 4245 protein terms via text mining. Additionally, through the integration of ChemMapper, an in-house computational approach to polypharmacology, PTID can be used as a computational platform to identify pesticides targets and design novel agrochemical products.

AVAILABILITY

http://lilab.ecust.edu.cn/ptid/.

CONTACT

hlli@ecust.edu.cn; xhqian@ecust.edu.cn

SUPPLEMENTARY INFORMATION

Supplementary data are available at Bioinformatics online.

QSAR study of liver specificity of carcinogenicity of N-nitroso compounds

The quantitative structure-activity relationship (QSAR) of N-nitroso compounds (NOCs) for rat liver was developed by a topological sub-structural molecular-descriptors (TOPS-MODE) approach to predict non-liver-carcinogenic and liver-carcinogenic N-nitroso compounds based on a data set of 108 NOCs. Three descriptors calculated solely from the molecular structures of the compounds were selected by enhanced replacement method (ERM) and were weighted, respectively, with atomic weight, bond dipole moments and Abraham solute descriptor partition between water and aqueous solvent systems to indicate the importance of their roles in liver specificity. A detailed discussion on these three descriptors was carried out, and the contributions of different fragments to rat-liver specificity and the interactions among fragments were analyzed. Such results can offer some useful theoretical references for understanding the chemical structural and biological factors related to the liver-specific biological activity of NOCs.