Daphnia and fish toxicity of (benzo)triazoles: validated QSAR models, and interspecies quantitative activity-activity modelling

In silico aquatic toxicity prediction of chemicals toward Daphnia magna and fathead minnow using Monte Carlo approaches

The fast-increasing use of chemicals led to large numbers of chemical compounds entering the aquatic environment, raising concerns about their potential effects on ecosystems. Therefore, assessment of the ecotoxicological features of organic compounds on aquatic organisms is very important. Daphnia magna and Fathead minnow are two aquatic species that are commonly tested as standard test organisms for aquatic risk assessment and are typically chosen as the biological model for the ecotoxicology investigations of chemical pollutants. Herein, global quantitative structure-toxicity relationship (QSTR) models have been developed to predict the toxicity (pEC(LC)50) of a large dataset comprising 2106 chemicals toward Daphnia magna and Fathead minnow. The optimal descriptor of correlation weights (DCWs) is calculated using the notation of simplified molecular input line entry system (SMILES) and is used to construct QSTR models. Three target functions, TF1, TF2, and TF3 are utilized to generate 12 QSTR models from four splits, and their statistical characteristics are also compared. The designed QSTR models are validated using both internal and external validation criteria and are found to be reliable, robust, and excellently predictive. Among the models, those generated using the TF3 demonstrate the best statistical quality with R2 values ranging from 0.9467 to 0.9607, Q2 values ranging from 0.9462 to 0.9603 and RMSE values ranging from 0.3764 to 0.4413 for the validation set. The applicability domain and the mechanistic interpretations of generated models were also discussed.

Unveiling critical structural features for effective HDAC8 inhibition: a comprehensive study using quantitative read-across structure-activity relationship (q-RASAR) and pharmacophore modeling

Histone deacetylases constitute a group of enzymes that participate in several biological processes. Notably, inhibiting HDAC8 has become a therapeutic strategy for various diseases. The current inhibitors for HDAC8 lack selectivity and target multiple HDACs. Consequently, there is a growing recognition of the need for selective HDAC8 inhibitors to enhance the effectiveness of therapeutic interventions. In our current study, we have utilized a multi-faceted approach, including Quantitative Structure-Activity Relationship (QSAR) combined with Quantitative Read-Across Structure-Activity Relationship (q-RASAR) modeling, pharmacophore mapping, molecular docking, and molecular dynamics (MD) simulations. The developed q-RASAR model has a high statistical significance and predictive ability (Q2F1:0.778, Q2F2:0.775). The contributions of important descriptors are discussed in detail to gain insight into the crucial structural features in HDAC8 inhibition. The best pharmacophore hypothesis exhibits a high regression coefficient (0.969) and a low root mean square deviation (0.944), highlighting the importance of correctly orienting hydrogen bond acceptor (HBA), ring aromatic (RA), and zinc-binding group (ZBG) features in designing potent HDAC8 inhibitors. To confirm the results of q-RASAR and pharmacophore mapping, molecular docking analysis of the five potent compounds (44, 54, 82, 102, and 118) was performed to gain further insights into these structural features crucial for interaction with the HDAC8 enzyme. Lastly, MD simulation studies of the most active compound (54, mapped correctly with the pharmacophore hypothesis) and the least active compound (34, mapped poorly with the pharmacophore hypothesis) were carried out to validate the observations of the studies above. This study not only refines our understanding of essential structural features for HDAC8 inhibition but also provides a robust framework for the rational design of novel selective HDAC8 inhibitors which may offer insights to medicinal chemists and researchers engaged in the development of HDAC8-targeted therapeutics.

Derivation of water quality criteria for paraquat, bisphenol A and carbamazepine using quantitative structure-activity relationship and species sensitivity distribution (QSAR-SSD)

The risk assessment of an expanding array of emerging contaminants in aquatic ecosystems and the establishment of water quality criteria rely on species sensitivity distribution (SSD), necessitating ample multi-trophic toxicity data. Computational methods, such as quantitative structure-activity relationship (QSAR), enable the prediction of specific toxicity data, thus mitigating the need for costly experimental testing and exposure risk assessment. In this study, robust QSAR models for four aquatic species (Rana pipiens, Crassostrea virginica, Asellus aquaticus, and Lepomis macrochirus) were developed using leave-one-out (LOO) screening variables and the partial least squares algorithm to predict toxicity data for paraquat, bisphenol A, and carbamazepine. These predicted data can be integrated with experimental data to construct SSD models and derive hazardous concentration for 5 % of species (HC5) for the criterion maximum concentration. The chronic water quality criterion for paraquat, bisphenol A, and carbamazepine were determined at 6.7, 11.1, and 3.5 μg/L, respectively. The QSAR-SSD approach presents a viable and cost-effective method for deriving water quality criteria for other emerging contaminants.

Phenolic Acid-β-Cyclodextrin Complexation Study to Mask Bitterness in Wheat Bran: A Machine Learning-Based QSAR Study

The need to solvate and encapsulate hydro-sensitive molecules drives noticeable trends in the applications of cyclodextrins in the pharmaceutical industry, in foods, polymers, materials, and in agricultural science. Among them, β-cyclodextrin is one of the most used for the entrapment of phenolic acid compounds to mask the bitterness of wheat bran. In this regard, there is still a need for good data and especially for a robust predictive model that assesses the bitterness masking capabilities of β-cyclodextrin for various phenolic compounds. This study uses a dataset of 20 phenolic acids docked into the β-cyclodextrin cavity to generate three different binding constants. The data from the docking study were combined with topological, topographical, and quantum-chemical features from the ligands in a machine learning-based structure-activity relationship study. Three different models for each binding constant were computed using a combination of the genetic algorithm (GA) and multiple linear regression (MLR) approaches. The developed ML/QSAR models showed a very good performance, with high predictive ability and correlation coefficients of 0.969 and 0.984 for the training and test sets, respectively. The models revealed several factors responsible for binding with cyclodextrin, showing positive contributions toward the binding affinity values, including such features as the presence of six-membered rings in the molecule, branching, electronegativity values, and polar surface area.

Development of an effective QSAR-based hazard threshold prediction model for the ecological risk assessment of aromatic hydrocarbon compounds

The insufficient hazard thresholds of specific individual aromatic hydrocarbon compounds (AHCs) with diverse structures limit their ecological risk assessment. Thus, herein, quantitative structure-activity relationship (QSAR) models for estimating the hazard threshold of AHCs were developed based on the hazardous concentration for 5% of species (HC5) determined using the optimal species sensitivity distribution models and on the molecular descriptors calculated via the PADEL software and ORCA software. Results revealed that the optimal QSAR model, which involved eight descriptors, namely, Zagreb, GATS2m, VR3_Dzs, AATSC2s, GATS2c, ATSC2i, ω, and Vm, displayed excellent performance, as reflected by an optimal goodness of fit (R2adj = 0.918), robustness (Q2LOO = 0.869), and external prediction ability (Q2F1 = 0.760, Q2F2 = 0.782, and Q2F3 = 0.774). The hazard thresholds estimated using the optimal QSAR model were approximately close to the published water quality criteria developed by different countries and regions. The quantitative structure-toxicity relationship demonstrated that the molecular descriptors associated with electrophilicity and topological and electrotopological properties were important factors that affected the risks of AHCs. A new and reliable approach to estimate the hazard threshold of ecological risk assessment for various aromatic hydrocarbon pollutants was provided in this study, which can be widely popularised to similar contaminants with diverse structures.

Significance of Triazole in Medicinal Chemistry: Advancement in Drug Design, Reward and Biological Activity

One of the triazole tautomers, 1,2,4-triazole derivatives, has a wide range of biological activities that suggest its potential therapeutic utility in medicinal chemistry. These actions include anti-inflammatory, anti-cancer, anti-bacterial, anti-tuberculosis, and anti-diabetic effects. Using computational simulations and models, we investigate the structure-activity relationships of 1,2,4-triazoles, showing how various modifications to the triazole core yield a variety of clinical therapeutic benefits. The review highlights the anti-inflammatory effect of 1,2,4-triazoles in relation to their ability to disrupt significant inflammatory mediators and pathways. We present in-silico data that illuminate the triazoles' capacity to inhibit cell division, encourage apoptosis, and stop metastasis in a range of cancer models. This review looks at the bactericidal and bacteriostatic properties of 1,2,4-triazole derivatives, with a focus on their potential efficacy against multi-drug resistant bacterial infections and their usage in tuberculosis therapy. In order to better understand these substances' potential anti-diabetic benefits, this review also looks at how they affect glucose metabolism regulation and insulin responsiveness. Coordinated efforts are required to translate the efficacy of 1,2,4-triazole compounds in preclinical models into practical therapeutic benefits. Based on the information provided, it can be concluded that 1,2,4-triazole derivatives are a promising class of diverse therapeutic agents with potential utility in a range of disorders. Their development and improvement might herald a new era of medical care that will be immensely advantageous to both patients and the medical community as a whole. This comprehensive research, which is further reinforced by in-silico investigations, highlights the great medicinal potential of 1,2,4-triazoles. Additionally, this study encourages more research into these substances and their enhancement for use in pharmaceutical development.

Towards safer pesticide management: A quantitative structure-activity relationship based hazard prediction model

Pesticides are recognized as common environmental contaminants. The potential pesticide hazard to non-target organisms, including various mammal species, is a global concern. The global problem requires a comprehensive risk assessment. To assess the toxic effects of pesticides at the early stage, a toxicological risk analysis is conducted to determine pesticide hazard levels. World Health Organization (WHO) has established five pesticide hazard classes based on lethal dose (LD50) values to perform these assessments. In this paper, we have developed one-vs-all quantitative structure-activity relationship (OvA-QSAR) models using five machine-learning techniques with the selected optimum molecular descriptors. Descriptor selection was conducted based on correlation to evaluate the relevance and significance of individual features in our dataset. Our OvA-QSAR model was built using a dataset obtained from the WHO, covering a wide range of chemical pesticides. These models can predict the hazard category for a pesticide within the five available categories. Notably, our experiments demonstrate the outstanding performance and robustness of the Random Forest (RF) model in addressing the challenge of multi-class classification with the selected descriptors.

Chronic aquatic toxicity assessment of diverse chemicals on Daphnia magna using QSAR and chemical read-across

We have modeled here chronic Daphnia toxicity taking pNOEC (negative logarithm of no observed effect concentration in mM) and pEC50 (negative logarithm of half-maximal effective concentration in mM) as endpoints using QSAR and chemical read-across approaches. The QSAR models were developed by strictly obeying the OECD guidelines and were found to be reliable, predictive, accurate, and robust. From the selected features in the developed models, we have found that an increase in lipophilicity and saturation, the presence of electrophilic or electronegative or heavy atoms, the presence of sulphur, amine, and their related functionality, an increase in mean atomic polarizability, and higher number of (thio-) carbamates (aromatic) groups are responsible for chronic toxicity. Therefore, this information might be useful for the development of environmentally friendly and safer chemicals and data-gap filling as well as reducing the use of identified toxic chemicals which have chronic toxic effects on aquatic ecosystems. Approved classes of drugs from DrugBank databases and diverse groups of chemicals from the Chemical and Product Categories (CPDat) database were also assessed through the developed models.

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.

Thiazolidinedione-triazole conjugates: design, synthesis and probing of the α-amylase inhibitory potential

Aim: The primary objective of this investigation was the synthesis, spectral interpretation and evaluation of the α-amylase inhibition of rationally designed thiazolidinedione-triazole conjugates (7a-7aa). Materials & methods: The designed compounds were synthesized by stirring a mixture of thiazolidine-2,4-dione, propargyl bromide, cinnamaldehyde and azide derivatives in polyethylene glycol-400. The α-amylase inhibitory activity of the synthesized conjugates was examined by integrating in vitro and in silico studies. Results: The investigated derivatives exhibited promising α-amylase inhibitory activity, with IC50 values ranging between 0.028 and 0.088 μmol ml-1. Various computational approaches were employed to get detailed information about the inhibition mechanism. Conclusion: The thiazolidinedione-triazole conjugate 7p, with IC50 = 0.028 μmol ml-1, was identified as the best hit for inhibiting α-amylase.

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.

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.

A novel procedure for predicting chronic toxicities and ecological risks of perfluorinated compounds in aquatic environment

Perfluorinated compounds (PFCs) can pose adverse effect on aquatic species and community structure. However, little is known about how the characteristics of molecules of PFCs affect their chronic toxic potencies to aquatic species, and the species sensitivity distributions (SSDs) and ecological risk assessments of PFCs are hampered by limited available data of chronic toxicity. In the present study, a novel procedure is proposed to obtain the ecological risk of PFCs using existing exposure concentrations of PFCs and SSDs integrated with the chronic toxicity prediction through robust QSAR models. The results showed that the energy of the lowest unoccupied molecular orbital (E_LUMO) exhibited the strongest correlation with the chronic toxicities of 15 PFCs (R² > 0.844, F > 16.206, p < 0.05). SSDs of 15 PFCs on eight species were first constructed, and the SSD fitting parameters were significantly correlated with E_LUMO (R² > 0.610, F > 19.471, p < 0.05). The QSAR-SSDs support the evaluation of hazardous criteria of PFCs for which data are lacking. Given environmental exposure distributions (EEDs) of the national presence of PFCs in aquatic systems in China, the QSAR-SSDs models allow the development of the ecological risk assessment for PFCs. This way, it was concluded that negligible environmental risk (defined as 5% of the species being potentially exposed to concentrations able to cause effects in < 5% of the case) could be expected from exposure to PFCs in surface waters in China. This method may be helpful for providing an evidence-based approach to guide the risk management for PFCs in aquatic environment.

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.

Prediction of acute toxicity to Daphnia magna and interspecific correlation: a global QSAR model and a Daphnia-minnow QTTR model

Acute toxicity is an important basis for the assessment of hazardous chemicals, but currently there is a huge data gap in chemical toxicity information. The in silico Quantitative Structure Activity Relationship (QSAR) models can use the existing experimental data information to predict the missing chemical toxicity information data and thus reduce animal testing. In the present study, a global QSAR model for the prediction of acute Daphnia magna toxicity has been developed based on the five principles proposed by the Organization for Economic Co-operation and Development (OECD). Moreover, a Daphnia-minnow (referring specifically to the fathead minnow) Quantitative Toxicity-Toxicity Relationship (QTTR) prediction model has been developed based on the present study and our previous work on fathead minnow (Pimephales promelas). Both the QSAR and QTTR prediction models have good goodness-of-fit, robustness, and predictive ability. Finally, the acute toxicity mode of action (MOA) for fathead minnow and Daphnia magna was compared by toxicity ratio based on interspecies toxicity data. By comparison, Daphnia magna was found more sensitive to anilines and phosphorothioates than fathead minnow. The present models can fill the acute toxicity data gap and contribute to the chemicals risk assessment and priority setting.

Acute and chronic toxicities of prothioconazole and its metabolite prothioconazole-desthio in Daphnia magna

Current research on prothioconazole (PTC), a broad-spectrum triazole fungicide, mainly focuses on its efficacy and residues; only a few studies have been assessing its toxicological effects. Using acute and chronic toxicity tests, we assessed the effects of PTC and its metabolite prothioconazole-desthio (PTCd) on the inhibition of the activity, growth, and reproduction of Daphnia magna. A dose-response relationship was established to determine sensitive biological indicators. In the acute and chronic toxicity tests, the 48-h EC₅₀ (concentration for 50% of the maximal effect) of PTC and PTCd for D. magna were 2.82 and 5.19 mg/L and 0.0807 and 0.132 mg/L, respectively; in the latter test, PTC was 1.64 times more toxic than PTCd. Acute-to-chronic toxicity ratios were calculated using chronic toxicity data; the ratios were 227 and 27.5 for PTC and PTCd, respectively. Our results indicate that both PTC and PTCd affect the growth and reproduction of D. magna and that the toxicity of PTC is greater than that of PTCd. In conclusion, the metabolites of PTD are toxic to D. magna at certain concentrations, and their environmental risks should not be neglected.

Deriving the predicted no effect concentrations of 35 pesticides by the QSAR-SSD method

The widespread use of pesticides results in their frequent detection in water bodies and other environmental media. Pesticide residues may cause certain risks to the environment and human health, and reliable predicted no effect concentrations (PNEC) must be obtained when assessing environmental risks. Species sensitivity distribution (SSD) is an important method for the derivation of chemical PNECs. Construction of the SSD model requires sufficient toxicity data to various species including at least eight families in three phyla, suitable nonlinear fitting functions and assessment factors (AFs) with certain uncertainty. However, most chemicals could not collect sufficient species toxicity data, while some chemicals had sufficient species toxicity data but could not find suitable fitting functions, thus hindering the construction of effective SSD models. To this end, the established QSAR models were applied to predict toxicity of chemicals to specific species to fill in the toxicity data gaps required for SSD and selecting multiple nonlinear functions to optimize the SSD model. Combined with QSAR and SSD methods, a new method of PNEC derivation was developed and successfully applied to the derivation of PNEC for 35 pesticides. Three QSAR models were used to predict the toxicities of six pesticides with few toxicity data. Nine two-parameter nonlinear functions were used to fit the toxicity-cumulative probability data one by one to determine the optimal SSD models. The hazardous concentrations at the cumulative probability of 5% and 10%, i. e, HC₅ and HC₁₀, respectively, were calculated by the optimal SSD model. The assessment factor used to determine the PNEC of the chemical based on the HC₁₀ was derived from the quantitative correlation between HC₁₀ and HC₅ of pesticides found in this study. When the toxicity data are insufficient, it may be more appropriate to calculate the PNECs of chemicals using HC₁₀ than using HC₅.

QSAR study of antituberculosis activity of oxadiazole derivatives using DFT calculations

Mycobacterium tuberculosis (Mtb) is the causative agent of infectious diseases worldwide. Oxadiazole derivatives have many biological activities and can be a good alternative to antimicrobial drugs. In this study, the quantitative structure-activity relationship (QSAR) of fifty-one novel oxadiazoles derivatives has been studied using the density functional theory (DFT) and statistical methods. Becke's three-parameter hybrid method and the Lee-Yang-Parr B3LYP functional employing 6-31++G (d) basis set are used to calculated quantum chemical descriptors using Gaussian09 software. The other descriptors including Lipinski, physicochemistry, topological, etc. were calculated using Chembio3d software. Statistically, the best correlation between the independent variables and the PMIC as the dependent variable was a 6-variable equation for which the correlation coefficient were as follows R² = 0.86 and R = 0.93. Also, the values of MAE = 0.003 and Q²_CV = 0.9 confirm the acceptability of the obtained model. The obtained equation shows that NRB, energy gap (ΔE), Henry's law constant, O-C, and C-N bonds length, and the Free Gibbs energy have the highest correlation with the anti-Tb activity.

New Models to Predict the Acute and Chronic Toxicities of Representative Species of the Main Trophic Levels of Aquatic Environments

To assess the impact of chemicals on an aquatic environment, toxicological data for three trophic levels are needed to address the chronic and acute toxicities. The use of non-testing methods, such as predictive computational models, was proposed to avoid or reduce the need for animal models and speed up the process when there are many substances to be tested. We developed predictive models for Raphidocelis subcapitata, Daphnia magna, and fish for acute and chronic toxicities. The random forest machine learning approach gave the best results. The models gave good statistical quality for all endpoints. These models are freely available for use as individual models in the VEGA platform and for prioritization in JANUS software.

Quantitative Structure-Activity Relationship (QSAR) Studies on the Toxic Effects of Nitroaromatic Compounds (NACs): A Systematic Review

Nitroaromatic compounds (NACs) are ubiquitous in the environment due to their extensive industrial applications. The recalcitrance of NACs causes their arduous degradation, subsequently bringing about potential threats to human health and environmental safety. The problem of how to effectively predict the toxicity of NACs has drawn public concern over time. Quantitative structure-activity relationship (QSAR) is introduced as a cost-effective tool to quantitatively predict the toxicity of toxicants. Both OECD (Organization for Economic Co-operation and Development) and REACH (Registration, Evaluation and Authorization of Chemicals) legislation have promoted the use of QSAR as it can significantly reduce living animal testing. Although numerous QSAR studies have been conducted to evaluate the toxicity of NACs, systematic reviews related to the QSAR modeling of NACs toxicity are less reported. The purpose of this review is to provide a thorough summary of recent QSAR studies on the toxic effects of NACs according to the corresponding classes of toxic response endpoints.

QSARINS-Chem standalone version: A new platform-independent software to profile chemicals for physico-chemical properties, fate, and toxicity

The new software QSARINS-Chem standalone version is a multiplatform tool, freely downloadable, for the in silico profiling of multiple properties and activities of organic chemicals. This software, which is based on the concept of the QSARINS-chem module embedded in the QSARINS software, has been fully redesigned and redeveloped in the Java™ language. In addition to a selection of models included in the old module, the new software predicts biotransformation rates and aquatic toxicities of pharmaceuticals and personal care products in multiple organisms, and offers a suite of tools for the analysis of predictions. Furthermore, a comprehensive and transparent database of molecular structures is provided. The new QSARINS-Chem standalone version is an informative and solid tool, which is useful to support the assessment of the potential hazard and risks related to organic chemicals and is dedicated to users which are interested in the application of QSARs to generate reliable predictions.

Support vector machine-based model for toxicity of organic compounds against fish

Predicting the toxicity of chemicals to various fish species through chemometric approach is crucial for ecotoxicological assessment of existing as well as not yet synthesized chemicals. This paper reports a quantitative structure-activity/toxicity relationship (QSAR/QSTR) model for the toxicity pLC₅₀ of organic chemicals against various fish species. Only six descriptors were used to develop the QSTR model, by applying support vector machine (SVM) together with genetic algorithm. The QSTR model was trained and established on a sufficiently large data set of 840 organic compounds and evaluated with a test set (281 compounds). Compared with other QSTRs reported in the literature, the optimal SVM model for fish toxicity produces better statistical results with determination coefficients R² above 0.70 for both the training set and test set, although the QSTR model in this work possesses fewer molecular descriptors. Applying SVM and genetic algorithm to develop the QSTR model for pLC₅₀ of organic compounds against various fish species is successful.

Solar photo-Fenton treatment of the anti-cancer drug anastrozole in different aqueous matrices at near-neutral pH: Transformation products identification, pathways proposal, and in silico (Q)SAR risk assessment

Anastrozole (ANZ) is a breast cancer drug that was introduced onto the pharmaceutical market in the 1990s and is still one of the most widely consumed cytotoxic compounds. Due to the persistence of the drug, its continued presence after passing through wastewater treatment plants can lead to harm to aquatic environments. The present study investigates use of the solar photo-Fenton (SPF) process applied for ANZ degradation, considering the fate of ANZ and its transformation products (TPs). The SPF process was performed using different concentrations of ferrous iron (Fe²⁺) and H₂O₂ in solutions produced with deionized water (DW) and hospital wastewater (HWW), at pH close to neutrality. When solar irradiation in the SPF process was carried out the best ANZ removal rates were found under the following conditions: (i) for the DW matrix, [ANZ]₀ = 50 μg L^-1, [Fe²⁺] = 5 mg L^-1, and [H₂O₂]₀ = 25 mg L^-1, achieving 95% primary ANZ elimination; (ii) for the HWW matrix, [ANZ]₀ = 50 μg L^-1, [Fe²⁺] = 10 mg L^-1(multiple additions), and [H₂O₂]₀ = 25 mg L^-1, achieving 51% primary ANZ elimination. LC-QTOF MS analysis allowed to identify tentatively five transformation products (TPs) formed during the ANZ degradation process in DW, and two TPs when HWW was used. The main proposed degradation pathways were demethylation and hydroxylation. Different in silico models free available (quantitative) structure-activity relationship ((Q)SAR) software were used to predict the ecotoxicities and environmental fates of ANZ and the TPs. The in silico (Q)SAR predictions indicated that ANZ and the TPs were non-biodegradable compounds. In silico (Q)SAR predictions for mutagenicity and carcinogenicity end-points identified some TPs that require further study. Attention is drawn to the formation of several TPs for which statistical and rule-based positive alerts for mutagenic activities were found, requiring further confirmatory in vitro validation tests.

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.

Ecosystem ecology: Models for acute toxicity of pesticides towards Daphnia magna

Quantitative structure - activity relationships (QSARs) which are obtained with a representation of the molecular architecture via simplified molecular input-line entry system (SMILES) are applied to build up predictive models of acute toxicity of pesticides towards Daphnia magna. The acute toxicity towards Daphnia magna is an adequate measure of the ecological impact of various substances. The Monte Carlo technique is the basis to build up the above QSAR models. The statistical quality of suggested models is good: the best model is characterized by n = 103, R² = 0.76, RMSE = 0.91 (training set); n = 53, R² = 0.82, RMSE = 0.87 (validation set). The approach provides the mechanistic interpretation (e.g. aromaticity and branching of carbon skeleton are promoters of increase for toxicity towards Daphnia magna in the case of the examined set of pesticides). The approach is attractive to build up predictive models since instead of a large number of different molecular descriptors the corresponding model is based on solely one optimal descriptor calculated with SMILES and all necessary calculations can be done using the CORAL software available on the Internet (http://ww.insilico.eu/coral).

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.

Modelling quantitative structure activity-activity relationships (QSAARs): auto-pass-pass, a new approach to fill data gaps in environmental risk assessment under the REACH regulation

Reviewing the toxicological literature for over the past decades, the key elements of QSAR modelling have been the mechanisms of toxic action and chemical classes. As a result, it is often hard to design an acceptable single model for a particular endpoint without clustering compounds. The main aim here was to develop a Pass-Pass Quantitative Structure-Activity-Activity Relationship (PP QSAAR) model for direct prediction of the toxicity of a larger set of compounds, combing the application of an already predicted model for another species, and molecular descriptors. We investigated a large acute toxicity data set of five aquatic organisms, fish, Daphnia magna, and algae from the VEGA-Hub, as well as Tetrahymena pyriformis and Vibrio fischeri. The statistical quality of the models encouraged us to consider this alternative for the prediction of toxicity using interspecies extrapolation QSAAR models without regard to the toxicity mechanism or chemical class. In the case of algae, the use of activity values from a second species did not improve the models. This can be attributed to the weak interspecies relationships, due to different aquatic toxicity mechanisms in species.

Study of the photodegradation of 2-chlorobenzoic acid by TiO2 and the effects of eutrophicated water on the reaction

The photodegradation of 2-chlorobenzoic acid (2-CBA) in suspensions of TiO₂ was examined under different operational parameters. The optimal condition could be obtained through the experiment, i.e. that the concentration of 2-CBA was 30 mg/L and the dosing quantity of TiO₂ was 0.01 g under UV light in the case of pH 3.5. Above reaction process was in accordance with first order kinetics model. The influence on photocatalytic degradation caused by typical anions in eutrophicated water body such as NO₃ ^- and H₂PO₄ ^- was explored in this work, which revealed that both two anions had inhibitory effect on the degradation process. In addition, alcohol was introduced into the process to identify the degradation mechanism of 2-CBA with TiO₂, and the reaction route of 2-CBA could be predicted through the analysis on the intermediate.

Consensus QSAR models estimating acute toxicity to aquatic organisms from different trophic levels: algae, Daphnia and fish

We report new consensus models estimating acute toxicity for algae, Daphnia and fish endpoints. We assembled a large collection of 3680 public unique compounds annotated by, at least, one experimental value for the given endpoint. Support Vector Machine models were internally and externally validated following the OECD principles. Reasonable predictive performances were achieved (RMSE_ext = 0.56-0.78) which are in line with those of state-of-the-art models. The known structural alerts are compared with analysis of the atomic contributions to these models obtained using the ISIDA/ColorAtom utility. A benchmarking against existing tools has been carried out on a set of compounds considered more representative and relevant for the chemical space of the current chemical industry. Our model scored one of the best accuracy and data coverage. Nevertheless, industrial data performances were noticeably lower than those on public data, indicating that existing models fail to meet the industrial needs. Thus, final models were updated with the inclusion of new industrial compounds, extending the applicability domain and relevance for application in an industrial context. Generated models and collected public data are made freely available.

Effect of the structural factors of organic compounds on the acute toxicity toward Daphnia magna

The acute toxicity of organic compounds towards Daphina magna was subjected to QSAR analysis. The two-dimensional simplex representation of molecular structure (2D SiRMS) and the support vector machine (SVM), gradient boosting (GBM) methods were used to develop QSAR models. Adequate regression QSAR models were developed for incubation of 24 h. Their interpretation allowed us to quantitatively describe and rank the well-known toxicophores, to refine their molecular surroundings, and to distinguish the structural derivatives of the fragments that significantly contribute to the acute toxicity (LC₅₀) of organic compounds towards D. magna. Based on the results of the interpretation of the regression models, a molecular design (modification) of highly toxic compounds was performed in order to reduce their hazard. In addition, acceptable classification QSAR models were developed to reliably predict the following mode of action (MOA): specific and non-specific toxicity of organic compounds towards D. magna. When interpreting these models, we were able to determine the structural fragments and the physicochemical characteristics of molecules that are responsible for the manifestation of one of the modes of action. The on-line version of the OCHEM expert system (https://ochem.eu), HYBOT descriptors, and the random forest and SVM methods were used for a comparative QSAR investigation.

Principles of QSAR Modeling

At the end of her academic career, the author summarizes the main aspects of QSAR modeling, giving comments and suggestions according to her 23 years' experience in QSAR research on environmental topics. The focus is mainly on Multiple Linear Regression, particularly Ordinary Least Squares, using a Genetic Algorithm for variable selection from various theoretical molecular descriptors, but the comments can be useful also for other QSAR methods. The need for rigorous validation, also external, and for applicability domain check to guarantee predictivity and reliability of QSAR models is particularly highlighted. The commented approach is the “predictive” one, based on chemometrics, and is usefully applied to the prioritization of environmental pollutants. All the discussed points and the author's ideas are implemented in the software QSARINS, as a legacy to the QSAR community.

Conlecs: A novel procedure for deriving the concentration limits of chemicals outside the criteria of human drinking water using existing criteria and species sensitivity distribution based on quantitative structure-activity relationship prediction

Water quality criteria (WQC) for an increasing number of emerging chemicals need to be developed to protect human health and biological safety. Existing species sensitivity distribution (SSD) methods can only be used to help establish WQC for ecological protection, and cannot be extended to the protection of human beings from various hazards. In this study, a novel procedure called Conlecs is proposed to derive the concentration limits (ConLs) of pesticides outside the criteria for human drinking water (CHDW) using the existing criteria of pesticides and SSD integrated with the toxicity prediction achieved through robust QSAR models. Optimal SSD models of four pesticides (within the CHDW) and two pesticides (outside the CHDW) on 12 species were first constructed, and the existing ConLs of four pesticides within the CHDW were then utilized to select the most suitable species for the optimal proportions to avoid human hazards (PHH), allowing the ConLs of two pesticides outside the CHDW to be derived.

Quantitative structure-activity relationship between the toxicity of amine surfactant and its molecular structure

With the extensive applications and ongoing world demand, more and more amine surfactants are discharged into natural environment. However, the database about toxicity of amine surfactants is incomplete, which is not beneficial to environmental protection process. In this paper, the toxicity of 20 amine surfactants on Daphnia magna were tested to extend the toxicity data of amine surfactants. Besides, 35 molecular structure descriptors including quantum parameters, physicochemical parameters and topological indices were chosen and calculated as independent variables to develop the quantitative structure-activity relationship (QSAR) model between the toxicity of amine surfactants and their molecular structure by genetic function approximation (GFA) algorithm. According to statistical analysis, a robust model was built with the determination coefficient of (R²) was 0.962 and coefficient determinations of cross-validation (R_cv²) was 0.794. Meanwhile, external validation was implemented to evaluate the QSAR model. The result of coefficient determinations of cross-validation (R_ext²) for external validation was calculated as 0.942, illustrating the model has great goodness-of-fit and good prediction ability.

Effective extrapolation models for ecotoxicity of benzene, toluene, ethylbenzene, and xylene (BTEX)

Benzene homologues have significant toxic effects to aquatic organisms. In this study, the acute toxicity data of benzene, toluene, ethylbenzene and xylene (BTEX) were collected and screened, and the toxicity extrapolation model of paired BTEX was established. The results showed that except the correlation between benzene and xylene was not strong due to insufficient data, the linear correlation of the other five paired BTEX was good (p < 0.01), and R² fitted by the four out of five paired BTEX was greater than 0.85. The cross validation showed that ethylbenzene-xylene model was optimal, and for most species (81.8%), the established five BTEX models had a prediction error of less than 10%. Also, these extrapolation models were validated by experimental results of Pseudorasbora parva. The difference between the predicted and measured values of the acute toxicity of BTEX was less than 1 fold, which indicated that the extrapolation model had high accuracy.

Norm Index-Based QSAR Model for Acute Toxicity of Pesticides Toward Rainbow Trout

The aquatic ecological environment is being threatened from overuse of pesticides, and the aquatic toxicity toward rainbow trout (Oncorhynchus mykiss) plays a significant role in environmental risk assessment of agrochemicals. In the present study, 2 norm index formulas were developed, from which several norm descriptors were derived. A quantitative structure-activity relationship (QSAR) model was established for the prediction of acute toxicity (median lethal concentration) toward rainbow trout of various pesticides. Results indicated that the present QSAR model presented an R² of 0.8053. Meanwhile, internal validation (Q_LOO²  = 0.7606), external validation (R_training²  = 0.8011, R_testing²  = 0.8108), Y-randomization test, and applicability domain analysis further demonstrated the stability, reliability, and wide application domain of the present QSAR model. Accordingly, these norm descriptors might be applicable to the structures of pesticides for predicting the acute toxicity to aquatic organism. Environ Toxicol Chem 2020;39:352-358. © 2019 SETAC.

The Azoles in Pharmacochemistry: Perspectives on the Synthesis of New Compounds and Chemoinformatic Contributions

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Due to their versatile biological activity, Azoles are widely studied in pharmacochemistry. It is possible to use them in many applications and in studies aimed at discovering antiparasitic, antineoplastic, antiviral, antimicrobial compounds; and in the production of materials for treatment of varied pathologies. Based on their biological activity, our review presents several studies that involve this class of organic compounds. A bibliographic survey of this type can effectively contribute to pharmaceutical sciences, stimulating the discovery of new compounds, and structural improvements to biological profiles of interest. In this review, articles are discussed involving the synthesis of new compounds and chemoinformatic contributions. Current applications of azoles in both the pharmaceutical and agri-business sectors are well known, yet as this research highlights, azole compounds can also bring important contributions to the fight against many diseases. Among the heterocyclics, azoles are increasingly studied by research groups around the world for application against tuberculosis, HIV, fungal and bacterial infections; and against parasites such as leishmaniasis and trypanosomiasis. Our hope is that this work will help arouse the interest of research groups planning to develop new bioactives to fight against these and other diseases.

Prediction of the Auto-Ignition Temperatures of Binary Miscible Liquid Mixtures from Molecular Structures

A quantitative structure-property relationship (QSPR) study is performed to predict the auto-ignition temperatures (AITs) of binary liquid mixtures based on their molecular structures. The Simplex Representation of Molecular Structure (SiRMS) methodology was employed to describe the structure characteristics of a series of 132 binary miscible liquid mixtures. The most rigorous “compounds out” strategy was employed to divide the dataset into the training set and test set. The genetic algorithm (GA) combined with multiple linear regression (MLR) was used to select the best subset of SiRMS descriptors, which significantly contributes to the AITs of binary liquid mixtures. The result is a multilinear model with six parameters. Various strategies were employed to validate the developed model, and the results showed that the model has satisfactory robustness and predictivity. Furthermore, the applicability domain (AD) of the model was defined. The developed model could be considered as a new way to reliably predict the AITs of existing or new binary miscible liquid mixtures, belonging to its AD.

Toxicity of some prevalent organic chemicals to tadpoles and comparison with toxicity to fish based on mode of toxic action

Although mode of action (MOA) plays a key role in the understanding of the toxic mechanism of chemicals, the MOAs of class-based compounds to tadpoles have not been investigated. To explore the MOAs, acute toxicity (expressed as log 1/LC₅₀) to Rana chensinensis tadpoles were determined and molecular descriptors were calculated. Quantitative structure-activity relationship (QSAR) showed that toxicity to tadpoles is closely related to the chemical octanol/water partition coefficient (log K_OW), energy of the lowest unoccupied molecular orbital (E_LUMO), and number of hydrogen bond donors and acceptors (NH_DA), representing the bio-uptake potential in tadpoles, the electrophilicity and hydrogen bonding capacity with target site(s), respectively. Comparison of the toxicity values between tadpoles and fish revealed that there were no significant differences for the overlapping compounds (average residual = 0.29 between tadpole and fish toxicity) with P values of interspecies correlation substantially less than 0.001. Classification of MOAs for the class-based compounds based on the excess toxicity calculated from toxicity ratio suggested that baseline, less inert compounds and some reactive or specifically-acting compounds share same MOAs between tadpoles and fish. Fish and tadpoles can serve as surrogates for each other in the safety evaluation of organic pollutants.

Development of classification models for predicting chronic toxicity of chemicals to Daphnia magna and Pseudokirchneriella subcapitata

Both the acute toxicity and chronic toxicity data on aquatic organisms are indispensable parameters in the ecological risk assessment priority chemical screening process (e.g. persistent, bioaccumulative and toxic chemicals). However, most of the present modelling actions are focused on developing predictive models for the acute toxicity of chemicals to aquatic organisms. As regards chronic aquatic toxicity, considerable work is needed. The major objective of the present study was to construct in silico models for predicting chronic toxicity data for Daphnia magna and Pseudokirchneriella subcapitata. In the modelling, a set of chronic toxicity data was collected for D. magna (21 days no observed effect concentration (NOEC)) and P. subcapitata (72 h NOEC), respectively. Then, binary classification models were developed for D. magna and P. subcapitata by employing the k-nearest neighbour method (k-NN). The model assessment results indicated that the obtained optimum models had high accuracy, sensitivity and specificity. The model application domain was characterized by the Euclidean distance-based method. In the future, the data gap for other chemicals within the application domain on their chronic toxicity for D. magna and P. subcapitata could be filled using the models developed here.

QSAR model for predicting the toxicity of organic compounds to fathead minnow

In this work, a new norm descriptor is proposed based on atomic properties. A quantitative structure-activity relationship (QSAR) model for predicting the toxicity of organic compounds to fathead minnow is further developed by norm descriptors. Results indicate that this new model based on the norm descriptors has satisfactory predictive results with the squared correlation coefficient (R²) and squared relation coefficient of the cross validation (Q²) of 0.8174 and 0.7923, respectively. Combining with Y-randomization test, applicability domain test, and comparison with other references, calculation results indicate that the QSAR model performs well both in the stability and the accuracy with wide application domain, which might be further used effectively for the safe and risk assessment of various organics.

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.

Predictive ecotoxicity of MoA 1 of organic chemicals using in silico approaches

Persistent organic products are compounds used for various purposes, such as personal care products, surfactants, colorants, industrial additives, food, pesticides and pharmaceuticals. These substances are constantly introduced into the environment and many of these pollutants are difficult to degrade. Toxic compounds classified as MoA 1 (Mode of Action 1) are low toxicity compounds that comprise nonreactive chemicals. In silico methods such as Quantitative Structure-Activity Relationships (QSARs) have been used to develop important models for prediction in several areas of science, as well as aquatic toxicity studies. The aim of the present study was to build a QSAR model-based set of theoretical Volsurf molecular descriptors using the fish acute toxicity values of compounds defined as MoA 1 to identify the molecular properties related to this mechanism. The selected Partial Least Squares (PLS) results based on the values of cross-validation coefficients of determination (Q_cv²) show the following values: Q_cv² = 0.793, coefficient of determination (R²) = 0.823, explained variance in external prediction (Q_ext²) = 0.87. From the selected descriptors, not only the hydrophobicity is related to the toxicity as already mentioned in previously published studies but other physicochemical properties combined contribute to the activity of these compounds. The symmetric distribution of the hydrophobic moieties in the structure of the compounds as well as the shape, as branched chains, are important features that are related to the toxicity. This information from the model can be useful in predicting so as to minimize the toxicity of organic compounds.

Descriptor Selection via Log-Sum Regularization for the Biological Activities of Chemical Structure

The quantitative structure-activity relationship (QSAR) model searches for a reliable relationship between the chemical structure and biological activities in the field of drug design and discovery. (1) Background: In the study of QSAR, the chemical structures of compounds are encoded by a substantial number of descriptors. Some redundant, noisy and irrelevant descriptors result in a side-effect for the QSAR model. Meanwhile, too many descriptors can result in overfitting or low correlation between chemical structure and biological bioactivity. (2) Methods: We use novel log-sum regularization to select quite a few descriptors that are relevant to biological activities. In addition, a coordinate descent algorithm, which uses novel univariate log-sum thresholding for updating the estimated coefficients, has been developed for the QSAR model. (3) Results: Experimental results on artificial and four QSAR datasets demonstrate that our proposed log-sum method has good performance among state-of-the-art methods. (4) Conclusions: Our proposed multiple linear regression with log-sum penalty is an effective technique for both descriptor selection and prediction of biological activity.

Relationship between acute and chronic toxicity for prevalent organic pollutants in Vibrio fischeri based upon chemical mode of action

Chemicals show diverse modes of action (MOAs) in aquatic organisms depending upon acute and chronic toxicity evaluations. Here, toxicity data for Vibrio fischeri involving 52 compounds for acute and chronic toxicity were used to determine the congruence of acute and chronic toxicity for assessing MOAs. Using toxic ratios, most of the compounds categorized into MOAs that included baseline, less inert or reactive compounds with acute toxicity were also categorized as baseline, less inert or reactive compounds with chronic toxicity. However, significantly different toxic effects were observed with acute and chronic toxicity for the reactive and specific-acting compounds. The acute-chronic toxic ratios were smaller and less variable for the baseline and less inert compounds, but were greater and more variable for the reactive and specific-acting compounds. Baseline and less inert compounds share same MOAs, but reactive and specific-acting compounds have different MOAs between acute and chronic toxicity. Bioconcentration processes cannot reach an equilibrium for highly hydrophilic and ionized compounds with short-term exposure, resulting in lower toxicity compared to long-term exposure. Pronounced differences for the antibiotics were not only due to the difference in bioconcentration, but also due to a predicted difference in MOAs during acute and chronic exposures.

QSAR models for predicting acute toxicity of pesticides in rainbow trout using the CORAL software and EFSA's OpenFoodTox database

Optimal (flexible) descriptors were used to establish quantitative structure - activity relationships (QSAR) for toxicity of pesticides (n=116) towards rainbow trout. A heterogeneous set of hundreds of pesticides has been used, taken from the EFSA's chemical Hazards Database: OpenFoodTox. Optimal descriptors are preparing from simplified molecular input-line entry system (SMILES). So-called, correlation weights of different fragments of SMILES are calculating by the Monte Carlo optimization procedure where correlation coefficient between endpoint and optimal descriptor plays role of the target function. Having maximum of the correlation coefficient for the training set, one can suggest that the optimal descriptor calculated with these correlation weights can correlate with endpoint for external validation set. This approach was checked up with three different distributions into the training (≈85%) set and external validation (≈15%) set. The statistical characteristics of these models are (i) for training set correlation coefficient (r²) ranges 0.72-0.81, and root mean squared error (RMSE) ranges 0.54-1.25; (ii) for external (validation) set r² ranges 0.74-0.84; and RMSE ranges 0.64-0.75. Computational experiments have shown that presence of chlorine, fluorine, sulfur, and aromatic fragments is promoter of increase for the toxicity.