QSAR modeling of toxicity of diverse organic chemicals to Daphnia magna using 2D and 3D descriptors

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.

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.

Unveiling first report on in silico modeling of aquatic toxicity of organic chemicals to Labeo rohita (Rohu) employing QSAR and q-RASAR

Labeo rohita, a fish species within the Carp family, holds significant dietary and aquacultural importance in South Asian countries. However, the habitats of L. rohita often face exposure to various harmful pesticides and organic compounds originating from industrial and agricultural runoff. It is challenging to individually investigate the effects of each potentially harmful compound. In such cases, in silico techniques like Quantitative Structure-Activity Relationship (QSAR) and quantitative Read-Across Structure-Activity Relationship (q-RASAR) can be employed to construct algorithmic models capable of simultaneously assessing the toxicity of numerous compounds. We utilized the US EPA's ToxValDB database to curate data regarding acute median lethal concentration (LC50) toxicity for L. rohita. The experimental variables included study type (mortality), study duration (ranging from 0.25 h to 4 h), exposure route (static, flowthrough, and renewal), exposure method (drinking water), and types of chemicals (industrial chemicals and pharmaceuticals). Using this dataset, we developed regression-based QSAR and q-RASAR models to predict chemical toxicity to L. rohita based on chemical descriptors. The key descriptors for predicting the toxicity of L. rohita in the regression-based QSAR model include F05[S-Cl], SpMax_EA(ri), s4_relPathLength_2, and SpDiam_AEA(ed). These descriptors can be employed to estimate the toxicity of untested compounds and aid in the development of compounds with lower toxicity based on the presence or absence of these descriptors. Both the QSAR and q-RASAR models serve as valuable tools for understanding the chemicals' structural features responsible for toxicity and for filling gaps in aquatic toxicity data by predicting the toxicity of newly untested compounds in relation to L. rohita. Finally, the developed best model was employed to predict 297 external chemicals, the most toxic substances to L. rohita were identified as cyhalothrin, isobornyl thiocyanatoacetate, and paclobutrzol, while the least toxic ones included ethyl acetate, ethylthiourea, and n-butyric acid.

First report on chemometric modeling of tilapia fish aquatic toxicity to organic chemicals: Toxicity data gap filling

The Toxic Substances Control Act (TSCA) mandates the Environmental Protection Agency (EPA) to document chemicals entering the US. Due to the vast range of toxicity endpoints, experimental toxicological study for all chemicals is impossible to conduct. To address this, in silico methods like QSAR and read-across are strategically used to prioritize testing for chemicals lacking ecotoxicity data. Aquatic toxicity is one of the most critical endpoints directly related to aquatic species, mainly fish, followed by direct to indirect effects on humans through drinking water and fish as food, respectively. Therefore, we have employed the ToxValDB database to curate acute LC50 toxicity data for three Tilapia species covering two different genera, an ideal species for aquatic toxicity testing. Employing the curated dataset, we have developed multiple robust and predictive QSAR and quantitative read-across structure-activity relationship (q-RASAR) models for Tilapia zillii, Oreochromis niloticus, and Oreochromis mossambicus which helped to understand the toxicological mode of action (MoA) of the modeled chemicals and predict the aquatic toxicity of new untested chemicals followed by toxicity data gap filling. The best three QSAR models showed encouraging statistical quality in terms of determination coefficient R2 (0.94, 0.74, and 0.77), cross-validated leave-one-out Q2 (0.90, 0.67 and 0.70), and predictive capability in terms of R2pred (0.95, 0.77, and 0.74) for T. zillii, O. niloticus, and O. mossambicus datasets, respectively. The developed best mathematical models were used for the prediction of aquatic toxicity in terms of pLC50 for 297 untested organic chemicals across three major Tilapia species ranging from 1.841 to 8.561 M in terms of environmental risk assessment.

Are we ready to combat the ecotoxicity of COVID-19 pharmaceuticals? An in silico aquatic risk assessment

To fight COVID-19 with uncountable medications and bioproducts throughout the world has taken us to another challenge of ecotoxicity. The indiscriminate usage followed by improper disposal of unused antibacterials, antivirals, antimalarials, immunomodulators, angiotensin II receptor blockers, corticosteroids, anthelmintics, anticoagulants etc. can lead us to an unimaginable ecotoxicity in the long run. A series of studies already identified active pharmaceutical ingredients (APIs) of the mentioned therapeutic classes and their metabolites in aquatic bodies as well as in wastewater treatment plants. Therefore, an initial ecotoxicity assessment of the majorly used pharmaceuticals is utmost requirement of the present time. The present in silico risk assessment study is focused on the aquatic toxicity prediction of 81 pharmaceuticals where 77 are most-used pharmaceuticals for COVID-19 throughout the world based on the literature along with one drug nirmatrelvir [PF-07321332] approved for emergency use by US-FDA and three other molecules under clinical trial. The ecotoxicity of the studied compounds were predicted based on the three aquatic species fish, algae and crustaceans employing the highest quality QSAR models available from the literature as well as using ECOSAR and QSAR Toolbox. To compare the toxicity thresholds, we have also used 4 control pharmaceuticals based on the worldwide occurrence from river, lake, STP, WWTPs, influent and effluent followed by high reported aquatic toxicity over the years as per the literature. Based on the statistical comparison, we have proposed top 3 pharmaceuticals used for the COVID-19 most toxic to the aquatic environment. The study will provide confident predictions of aquatic ecotoxicity data related to abundant use of COVID-19 drugs. The major aim of the study is to fill up the aquatic ecotoxicity data gap of major medications used for COVID-19.

Quantitative multi-species toxicity modeling: Does a multi-species, machine learning model provide better performance than a single-species model for the evaluation of acute aquatic toxicity by organic pollutants?

The toxicological profile of any chemical is defined by multiple endpoints and testing procedures, including representative test species from different trophic levels. While computer-aided methods play an increasingly important role in supporting ecotoxicology research and chemical hazard assessment, most of the recently developed machine learning models are directed towards a single, specific endpoint. To overcome this limitation and accelerate the process of identifying potentially hazardous environmental pollutants, we are introducing an effective approach for quantitative, multi-species modeling. The proposed approach is based on canonical correlation analysis that finds a pair(s) of uncorrelated, linear combinations of the original variables that best defines the overall variability within and between multiple biological responses and predictor variables. Its effectiveness was confirmed by the machine learning model for estimating acute toxicity of diverse organic pollutants in aquatic species from three trophic levels: algae (Pseudokirchneriella subcapitata), daphnia (Daphnia magna), and fish (Oryzias latipes). The multi-species model achieved a favorable predictive performance that were in line with predictive models derived for the aquatic organisms individually. The chemical bioavailability and reactivity parameters (n-octanol/water partition coefficient, chemical potential, and molecular size and volume) were important to accurately predict acute ecotoxicity to the three aquatic organisms. To facilitate the use of this approach, an open-source, Python-based script, named qMTM (quantitative Multi-species Toxicity Modeling) has been provided.

Computational prediction of Lee retention indices of polycyclic aromatic hydrocarbons by using machine learning

Given the difficult of experimental determination, quantitative structure-property relationship (QSPR) and deep learning (DL) provide an important tool to predict physicochemical property of chemical compounds. In this paper, partial least squares (PLS), genetic function approximation (GFA), and deep neural network (DNN) were used to predict the Lee retention index (Lee-RI) of PAHs in SE-52 and DB-5 stationary phases. Four molecular descriptors, molecular weight (MW), quantitative estimate of drug-likeness (QED), atomic charge weighted negative surface area (Jurs_PNSA_3), and relative negative charge (Jurs_RNCG) were selected to construct regression models based on genetic algorithm. For SE-52, PLS model showed best prediction power, followed by DNN and GFA. The relative error (RE), root mean square error (RMSE), and regression coefficient (R² ) of best PLS regression model are 1.228%, 5.407, and 0.980. For DB-5, DNN model showed best prediction power, followed by GFA and PLS. The RE, RMSE and R² of best DNN regression model for DB-5-1 and DB-5-2 are 1.058%, 4.325%, 0.976%, 0.821%, 3.795%, and 0.970%, respectively. The three regression models not only show good predictive ability, but also highlight the stability and ductility of the models.

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.

Relationships Between Aquatic Toxicity, Chemical Hydrophobicity, and Mode of Action: Log Kow Revisited

Relationships between toxicity and chemical hydrophobicity have been known for nearly 100 years in mammals and fish, typically using the log of the octanol:water partition coefficient (Kow). The current study reassessed the influence of mode of action (MOA) on acute aquatic toxicity-log Kow relationships using a comprehensive database of 617 organic chemicals with curated and standardized acute toxicity data that did not exceed solubility limits, their consensus log Kow values, and weight of evidence-based MOA classifications (including 6 broad and 26 specific MOAs). A total of 166 significant (p < 0.05) log Kow-toxicity models were developed across six taxa groups that included QSARs for 5 of the broad and 13 of the specific MOAs. In this study, we demonstrate that QSARs based on MOAs can significantly increase LC50 prediction accuracy for specific acting chemicals. Prediction accuracy increases when QSARs are built based on highly specific MOAs, rather than broad MOA classifications. Additionally, we demonstrate that building QSAR models with chemicals in specific MOA groupings, rather than broader MOA groups leads to significantly better estimates. We also evaluated the differences between models developed from mass-based (µg/L) and mole-based (µmol/L) toxicity data and demonstrate that both are suitable for QSAR development with no clear trend in greater model accuracy. Overall, the results reveal that, despite high variance in all taxa and MOA groups, specific MOA-based models can improve the accuracy of aquatic toxicity predictions over more general groupings.Please check and confirm that the authors and their respective affiliations have been correctly identified and amend if necessary.The affiliations are correct.

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.

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.

A QSTR model for toxicity prediction of pesticides towards Daphnia magna

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

Generating accurate in silico predictions of acute aquatic toxicity for a range of organic chemicals: Towards similarity-based machine learning methods

There has been an increase in the use of non-animal approaches, such as in silico and/or in vitro methods, for assessing the risks of hazardous chemicals. A number of machine learning algorithms link molecular descriptors that interpret chemical structural properties with their biological activity. These computer-aided methods encounter several challenges, the most significant being the heterogeneity of datasets; more efficient and inclusive computational methods that are able to process large and heterogeneous chemical datasets are needed. In this context, this study verifies the utility of similarity-based machine learning methods in predicting the acute aquatic toxicity of diverse organic chemicals on Daphnia magna and Oryzias latipes. Two similarity-based methods were tested that employ a limited training dataset, most similar to a given fitting point, instead of using the entire dataset that encompasses a wide range of chemicals. The kernel-weighted local polynomial approach had a number of advantages over the distance-weighted k-nearest neighbor (k-NN) algorithm. The results highlight the importance of lipophilicity, electrophilic reactivity, molecular polarizability, and size in determining acute toxicity. The rigorous model validation ensures that this approach is an important tool for estimating toxicity in new or untested chemicals.

Aquatic toxicity (Pre)screening strategy for structurally diverse chemicals: global or local classification tree models?

With an ever-increasing number of synthetic chemicals being manufactured, it is unrealistic to expect that they will all be subjected to comprehensive and effective risk assessment. A shift from conventional animal testing to computer-aided methods is therefore an important step towards advancing the environmental risk assessments of chemicals. The aims of this study are two-fold: firstly, it examines the relationships between structural and physicochemical features of a diverse set of organic chemicals, and their acute aquatic toxicity towards Daphnia magna and Oryzias latipes using a classification tree approach. Secondly, it compares the efficiency and accuracy of the predictions of two modeling schemes: local models that are inherently restricted to a smaller subset of structurally-related substances, and a global model that covers a wider chemical space and a number of modes of toxic action. The classification tree-based models differentiate the organic chemicals into either 'highly toxic' or 'low to non-toxic' classes, based on internal and external validation criteria. These mechanistically-driven models, which demonstrate good performance, reveal that the key factors driving acute aquatic toxicity are lipophilicity, electrophilic reactivity, molecular polarizability and size. A comparative analysis of the performance of the two modeling schemes indicates that the local models, trained on homogeneous data sets, are less error prone, and therefore superior to the global model. Although the global models showed worse performance metrics compared to the local ones, their applicability domain is much wider, thereby significantly increasing their usefulness in practical applications for regulatory purposes. This demonstrates their advantage over local models and shows they are an invaluable tool for modeling heterogeneous chemical data sets.

Machine Learning in Predictive Toxicology: Recent Applications and Future Directions for Classification Models

In recent times, machine learning has become increasingly prominent in predictive toxicology as it has shifted from in vivo studies toward in silico studies. Currently, in vitro methods together with other computational methods such as quantitative structure-activity relationship modeling and absorption, distribution, metabolism, and excretion calculations are being used. An overview of machine learning and its applications in predictive toxicology is presented here, including support vector machines (SVMs), random forest (RF) and decision trees (DTs), neural networks, regression models, naïve Bayes, k-nearest neighbors, and ensemble learning. The recent successes of these machine learning methods in predictive toxicology are summarized, and a comparison of some models used in predictive toxicology is presented. In predictive toxicology, SVMs, RF, and DTs are the dominant machine learning methods due to the characteristics of the data available. Lastly, this review describes the current challenges facing the use of machine learning in predictive toxicology and offers insights into the possible areas of improvement in the field.

Chemometrics for Selection, Prediction, and Classification of Sustainable Solutions for Green Chemistry—A Review

In this review, we present the applications of chemometric techniques for green and sustainable chemistry. The techniques, such as cluster analysis, principal component analysis, artificial neural networks, and multivariate ranking techniques, are applied for dealing with missing data, grouping or classification purposes, selection of green material, or processes. The areas of application are mainly finding sustainable solutions in terms of solvents, reagents, processes, or conditions of processes. Another important area is filling the data gaps in datasets to more fully characterize sustainable options. It is significant as many experiments are avoided, and the results are obtained with good approximation. Multivariate statistics are tools that support the application of quantitative structure–property relationships, a widely applied technique in green chemistry.

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.

Exploring QSAR models for assessment of acute fish toxicity of environmental transformation products of pesticides (ETPPs)

Environmental transformation products of pesticides (ETPPs) have a great deal of ecological impact owing to their ability to cause toxicity to the aquatic organisms, which can then be translated to the humans. The limited experimental data on biochemical and toxic effects of ETPPs, the high test costs together with regulatory limitations and the international push to reduce animal testing encourage greater dependence on predictive in silico techniques like quantitative structure-activity relationship (QSAR) models. The aim of the present work was to explore the key structural features, which regulate the toxicity towards fishes, for 85 ETPPs using a partial least squares (PLS) regression based chemometric model developed according to Organisation for Economic Co-operation and Development (OECD) guidelines. The model was extensively validated using both internal and external validation metrics, and the results so obtained justify the reliability and usefulness of the developed model (Q² = 0.648, R²_pred or Q²_F1 = 0.734 and Q²_F2 = 0.733). From the developed model, we can conclude that lipophilicity, polarity, presence of branching and the functional form of O-atom in the transformed structures of pesticides are the important features that are to be considered during ecotoxicity assessment of ETPPs. The information obtained from the descriptors of the developed model could be utilized in the future for assessing ETPPs with the benefit of providing an early warning of their potentially detrimental effect on fishes for regulatory purposes.

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.

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.

Integrating in silico models and read-across methods for predicting toxicity of chemicals: A step-wise strategy

In silico methods and models are increasingly used for predicting properties of chemicals for hazard identification and hazard characterisation in the absence of experimental toxicity data. Many in silico models are available and can be used individually or in an integrated fashion. Whilst such models offer major benefits to toxicologists, risk assessors and the global scientific community, the lack of a consistent framework for the integration of in silico results can lead to uncertainty and even contradictions across models and users, even for the same chemicals. In this context, a range of methods for integrating in silico results have been proposed on a statistical or case-specific basis. Read-across constitutes another strategy for deriving reference points or points of departure for hazard characterisation of untested chemicals, from the available experimental data for structurally-similar compounds, mostly using expert judgment. Recently a number of software systems have been developed to support experts in this task providing a formalised and structured procedure. Such a procedure could also facilitate further integration of the results generated from in silico models and read-across. This article discusses a framework on weight of evidence published by EFSA to identify the stepwise approach for systematic integration of results or values obtained from these "non-testing methods". Key criteria and best practices for selecting and evaluating individual in silico models are also described, together with the means to combining the results, taking into account any limitations, and identifying strategies that are likely to provide consistent results.

QSAR-based investigation on antibiotics facilitating emergence and dissemination of antibiotic resistance genes: A case study of sulfonamides against mutation and conjugative transfer in Escherichia coli

Antibiotic resistance genes (ARGs), which are emerging environmental contaminants, have posed great threats to global public health. Although extensive efforts have been undertaken to investigate ARG pollution, little attention has been paid to the structural information of antibiotics when exploring their impact on the emergence and dissemination of ARGs. In this study, setting Escherichia coli (E. coli) as the test organism, the effects of sulfonamides (SAs) on growth, mutation frequency and conjugative transfer frequency were tested, and quantitative structure-activity relationship (QSAR) was used to quantitatively analyze the promotion of SAs on these biological effects and explore their possible mechanism. The constructed QSAR models reveal that SAs may increase expression of the FtsZ protein and pili in E. coli via binding to the SdiA protein, ultimately leading to SAs facilitation of growth, mutation frequency and conjugative transfer frequency. The results indicate that SAs can produce selective pressure on E. coli to promote the emergence and dissemination of ARGs. This study provides reference data for further investigation of the emergence and dissemination of ARGs under antibiotic exposure and a new perspective for the mechanistic exploration of ARG pollution.

Insights into pesticide toxicity against aquatic organism: QSTR models on Daphnia Magna

The toxicities of agrochemicals to non-target aquatic organisms are key items in chemical ecological risk assessment. However, it is still an urgent need to develop new tools to assess the agrochemical aquatic toxicity efficiently and accurately. In this work, QSTR studies were performed on a data set containing 639 diverse pesticides with measured EC₅₀ toxicity against Daphnia magna, by using five machine learning methods combined with seven fingerprints and a set of molecular descriptors. The imbalance problem of the data set was successfully solved by clustering analysis. The top-10 QSTR models displayed greater predicative abilities than ECOSAR. The optimal model, Ext-SVM, showed the best performance in 10-fold cross validation (Q_high=0.807, Q_moderate=0.806, Q_low=0.755, Q_total=0.794), and also in the test set verification (Q_high=0.865, Q_moderate=0.783, Q_low=0.931, Q_total=0.848). The relevance of the key physical-chemical properties with the toxicity was also investigated, in which the MW, a_np, logP(o/w), GCUT_SLOGP_1, chilv and SMR_VSA7 values displayed positive correlation with Daphnia magna toxicity, whereas the logS and a_don showed negative correlation. The robust QSTR models provided efficient tools for assessing agrochemical aquatic toxicity, and the revealed different physical-chemical properties between the high and low toxic compounds might be useful in the discovery and design of low aquatic toxic pesticides.

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

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

In silico estimation of chemical aquatic toxicity on crustaceans using chemical category methods

With industrial development and eventual commercial use, environmental chemicals through accidental spills and effluents appear more frequently in aquatic ecosystems and may produce an enormous effect on water, soil, wildlife and human health. Therefore, aquatic toxicity becomes an increasingly important endpoint in the evaluation of the environmental impact of chemicals. In this study, based on ECOTOX database, a large data set containing 824 diverse compounds with experimental 48 h EC50 values on crustaceans was compiled. A series of in silico models were then developed using six machine learning methods combined with seven types of molecular fingerprints. Performance of these models was measured by an external validation set, involving 246 molecules. The best model proposed is MACCS fingerprint and SVM algorithm with high accuracy of 0.87 for external validation set. Additionally, we proposed five structural alerts identified by information gain and substructure frequency analysis for mechanistic interpretation. The models and structural alerts can provide critical information and useful tools for a priori evaluation of chemical aquatic toxicity in environmental hazard assessment.

A Review of Recent Advances towards the Development of (Quantitative) Structure-Activity Relationships for Metallic Nanomaterials

Gathering required information in a fast and inexpensive way is essential for assessing the risks of engineered nanomaterials (ENMs). The extension of conventional (quantitative) structure-activity relationships ((Q)SARs) approach to nanotoxicology, i.e., nano-(Q)SARs, is a possible solution. The preliminary attempts of correlating ENMs' characteristics to the biological effects elicited by ENMs highlighted the potential applicability of (Q)SARs in the nanotoxicity field. This review discusses the current knowledge on the development of nano-(Q)SARs for metallic ENMs, on the aspects of data sources, reported nano-(Q)SARs, and mechanistic interpretation. An outlook is given on the further development of this frontier. As concluded, the used experimental data mainly concern the uptake of ENMs by different cell lines and the toxicity of ENMs to cells lines and Escherichia coli. The widely applied techniques of deriving models are linear and non-linear regressions, support vector machine, artificial neural network, k-nearest neighbors, etc. Concluded from the descriptors, surface properties of ENMs are seen as vital for the cellular uptake of ENMs; the capability of releasing ions and surface redox properties of ENMs are of importance for evaluating nanotoxicity. This review aims to present key advances in relevant nano-modeling studies and stimulate future research efforts in this quickly developing field of research.

QSPR/QSAR Analyses by Means of the CORAL Software

In this chapter, the methodology of building up quantitative structure—property/activity relationships (QSPRs/QSARs)—by means of the CORAL software is described. The Monte Carlo method is the basis of this approach. Simplified Molecular Input-Line Entry System (SMILES) is used as the representation of the molecular structure. The conversion of SMILES into the molecular graph is available for QSPR/QSAR analysis using the CORAL software. The model for an endpoint is a mathematical function of the correlation weights for various features of the molecular structure. Hybrid models that are based on features extracted from both SMILES and a graph also can be built up by the CORAL software. The conceptually new ideas collected and revealed through the CORAL software are: (1) any QSPR/QSAR model is a random event; and (2) optimal descriptor can be a translator of eclectic information into an endpoint prediction.

Monte Carlo-based quantitative structure-activity relationship models for toxicity of organic chemicals to Daphnia magna

Quantitative structure-activity relationships (QSARs) for toxicity of a large set of 758 organic compounds to Daphnia magna were built up. The simplified molecular input-line entry system (SMILES) was used to represent the molecular structure. The Correlation and Logic (CORAL) software was utilized as a tool to develop the QSAR models. These models are built up using the Monte Carlo method and according to the principle "QSAR is a random event" if one checks a group of random distributions in the visible training set and the invisible validation set. Three distributions of the data into the visible training, calibration, and invisible validation sets are examined. The predictive potentials (i.e., statistical characteristics for the invisible validation set of the best model) are as follows: n = 87, r²  = 0.8377, root mean square error = 0.564. The mechanistic interpretations and the domain of applicability of built models are suggested and discussed. Environ Toxicol Chem 2016;35:2691-2697. © 2016 SETAC.

Towards cheminformatics-based estimation of drug therapeutic index: Predicting the protective index of anticonvulsants using a new quantitative structure-index relationship approach

The overall efficacy and safety profile of a new drug is partially evaluated by the therapeutic index in clinical studies and by the protective index (PI) in preclinical studies. In-silico predictive methods may facilitate the assessment of these indicators. Although QSAR and QSTR models can be used for predicting PI, their predictive capability has not been evaluated. To test this capability, we developed QSAR and QSTR models for predicting the activity and toxicity of anticonvulsants at accuracy levels above the literature-reported threshold (LT) of good QSAR models as tested by both the internal 5-fold cross validation and external validation method. These models showed significantly compromised PI predictive capability due to the cumulative errors of the QSAR and QSTR models. Therefore, in this investigation a new quantitative structure-index relationship (QSIR) model was devised and it showed improved PI predictive capability that superseded the LT of good QSAR models. The QSAR, QSTR and QSIR models were developed using support vector regression (SVR) method with the parameters optimized by using the greedy search method. The molecular descriptors relevant to the prediction of anticonvulsant activities, toxicities and PIs were analyzed by a recursive feature elimination method. The selected molecular descriptors are primarily associated with the drug-like, pharmacological and toxicological features and those used in the published anticonvulsant QSAR and QSTR models. This study suggested that QSIR is useful for estimating the therapeutic index of drug candidates.

Development of Quantitative Structure-Activity Relationship Models for Predicting Chronic Toxicity of Substituted Benzenes to Daphnia Magna

The chronic toxicity of anthropogenic molecules such as substituted benzenes to Daphnia magna is a basic eco-toxicity parameter employed to assess their environmental risk. As the experimental methods are laborious, costly, and time-consuming, development in silico models for predicting the chronic toxicity is vitally important. In this study, on the basis of five molecular descriptors and 48 compounds, a quantitative structure-property relationship model that can predict the chronic toxicity of substituted benzenes were developed by employing multiple linear regressions. The correlation coefficient (R (2)) and root-mean square error (RMSE) for the training set were 0.836 and 0.390, respectively. The developed model was validated by employing 10 compounds tested in our lab. The R EXT (2) and RMSE EXT for the validation set were 0.736 and 0.490, respectively. To further characterizing the toxicity mechanism of anthropogenic molecules to Daphnia, comparative molecular field analysis (CoMFA) and comparative molecular similarity indices analysis (CoMSIA) models were developed.

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.

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.

Development of economic consequence methodology for process risk analysis

A comprehensive methodology for economic consequence analysis with appropriate models for risk analysis of process systems is proposed. This methodology uses loss functions to relate process deviations in a given scenario to economic losses. It consists of four steps: definition of a scenario, identification of losses, quantification of losses, and integration of losses. In this methodology, the process deviations that contribute to a given accident scenario are identified and mapped to assess potential consequences. Losses are assessed with an appropriate loss function (revised Taguchi, modified inverted normal) for each type of loss. The total loss is quantified by integrating different loss functions. The proposed methodology has been examined on two industrial case studies. Implementation of this new economic consequence methodology in quantitative risk assessment will provide better understanding and quantification of risk. This will improve design, decision making, and risk management strategies.

QSPR/QSAR Analyses by Means of the CORAL Software

In this chapter, the methodology of building up quantitative structure—property/activity relationships (QSPRs/QSARs)—by means of the CORAL software is described. The Monte Carlo method is the basis of this approach. Simplified Molecular Input-Line Entry System (SMILES) is used as the representation of the molecular structure. The conversion of SMILES into the molecular graph is available for QSPR/QSAR analysis using the CORAL software. The model for an endpoint is a mathematical function of the correlation weights for various features of the molecular structure. Hybrid models that are based on features extracted from both SMILES and a graph also can be built up by the CORAL software. The conceptually new ideas collected and revealed through the CORAL software are: (1) any QSPR/QSAR model is a random event; and (2) optimal descriptor can be a translator of eclectic information into an endpoint prediction.

Validation and extension of a similarity-based approach for prediction of acute aquatic toxicity towards Daphnia magna

Quantitative structure-activity relationship (QSAR) models for predicting acute toxicity to Daphnia magna are often associated with poor performances, urging the need for improvement to meet REACH requirements. The aim of this study was to evaluate the accuracy, stability and reliability of a previously published QSAR model by means of further external validation and to optimize its performance by means of extension to new data as well as a consensus approach. The previously published model was validated with a large set of new molecules and then compared with ChemProp model, from which most of the validation data were taken. Results showed better performance of the proposed model in terms of accuracy and percentage of molecules outside the applicability domain. The model was re-calibrated on all the available data to confirm the efficacy of the similarity-based approach. The extended dataset was also used to develop a novel model based on the same similarity approach but using binary fingerprints to describe the chemical structures. The fingerprint-based model gave lower regression statistics, but also less unpredicted compounds. Eventually, consensus modelling was successfully used to enhance the accuracy of the predictions and to halve the percentage of molecules outside the applicability domain.

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.

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.

Quantitative structure-toxicity relationship of the aquatic toxicity for various narcotic pollutants using the norm indexes

The aquatic toxicity value of hazardous contaminants plays an important role in the risk assessments of aquatic ecosystems. The following study presents a stable and accurate structure-toxicity relationship model based on the norm indexes for the prediction of toxicity value (log(LC50)) for 190 diverse narcotic pollutants (96 h LC50 data for Poecilia reticulata). Research indicates that this new model is very efficient and provides satisfactory results. The suggested prediction model is evidenced by R(2) (square correlation coefficient) and ARD (average relative difference) values of 0.9376 and 10.45%, respectively, for the training set, and 0.9264 and 13.90% for the testing set. Comparison results with reference models demonstrate that this new method, based on the norm indexes proposed in this work, results in significant improvements, both in accuracy and stability for predicting aquatic toxicity values of narcotic pollutants.

Partial least square and k-nearest neighbor algorithms for improved 3D quantitative spectral data-activity relationship consensus modeling of acute toxicity

A diverse set of 154 chemicals that included US Food and Drug Administration-regulated compounds tested for their aquatic toxicity in Daphnia magna were modeled by a 3-dimensional quantitative spectral data-activity relationship (3D-QSDAR). Two distinct algorithms, partial least squares (PLS) and Tanimoto similarity-based k-nearest neighbors (KNN), were used to process bin occupancy descriptor matrices obtained after tessellation of the 3D-QSDAR space into regularly sized bins. The performance of models utilizing bins ranging in size from 2 ppm × 2 ppm × 0.5 Å to 20 ppm × 20 ppm × 2.5 Å was explored. Rigorous quality-control criteria were imposed: 1) 100 randomized 20% hold-out test sets were generated and the average R(2) test of the respective models was used as a measure of their performance, and 2) a Y-scrambling procedure was used to identify chance correlations. A consensus between the best-performing composite PLS model using 0.5 Å × 14 ppm × 14 ppm bins and 10 latent variables (average R(2) test  = 0.770) and the best composite KNN model using 0.5 Å × 8 ppm × 8 ppm and 2 neighbors (average R(2) test  = 0.801) offered an improvement of about 7.5% (R(2) test consensus  = 0.845). Projection of the most frequently occurring bins on the standard coordinate space indicated that the presence of a primary or secondary amino group-substituted aromatic systems-would result in an increased toxic effect in Daphnia. The presence of a second aromatic ring with highly electronegative substituents 5 Å to 7 Å apart from the first ring would lead to a further increase in toxicity.

Synthesis, antifeedant activity against Coleoptera and 3D QSAR study of alpha-asarone derivatives

For the first time, a set of 56 compounds representing structural derivatives of naturally occurring alpha-asarone as an antifeedants against stored product pests Sitophilus granarius L., Trogoderma granarium Ev., and Tribolium confusum Duv., were subjected to the 3D QSAR studies. Three-dimensional quantitative structure-activity relationships (3D-QSAR) for 56 compounds, including 15 newly synthesized, were performed using comparative molecular field analysis s-CoMFA and SOM-CoMSA techniques. QSAR was conducted based on a combination of biological activity (against Coleoptera larvae and beetles) and various geometrical, topological, quantum-mechanical, electronic, and chromatographic descriptors. The CoMSA formalism coupled with IVE (CoMSA-IVE) allowed us to obtain highly predictive models for Trogoderma granarium Ev. larvae. We have found that this novel method indicates a clear molecular basis for activity and lipophilicity. This investigation will facilitate optimization of the design of new potential antifeedants.

Structure-activity analysis of harmful algae inhibition by congeneric compounds: case studies of fatty acids and thiazolidinediones

The occurrence of harmful algal blooms has been increasing significantly around the world. In order to ensure the safety of drinking water, procedures to screen potential materials as effective algicides are needed, and predictive methods which save both the labor and time compared with traditional experimental approaches, are particularly desirable. In this study, data from previous studies on the algal-growth inhibitory action of two kinds of compounds, namely, the action of fatty acids and thiazolidinediones on the harmful algae Heterosigma akashiwo and Chattonella marina, were modeled using multiple linear regression (MLR) based on quantitative structure-activity relationships (QSAR). The models were shown to have highly predictive ability and stability, and provided insight into the inhibitory mechanisms of congeneric compounds. The main descriptors in the fatty-acid models were the Connolly accessible area and the number of rotatable bonds, illustrating that molecular surface area and shape are important in their algicidal actions. In the thiazolidinedione models, the critical volume, octanol-water partition coefficient (LogP), and Connolly solvent-excluded volume were found to be significant, indicating that hydrophobicity, substituent group size, and mode of action are mechanistically important. Our results showed the algicidal activity of a series of compounds on different algae could be modeled, and each model is efficacious for compounds that fall into the application domain of the QSAR model. This work demonstrates how reliable predictions of the algicidal activity of novel compounds and explanations of their inhibitory mechanisms can be obtained.

Quantitative structure-activity relationship to predict acute fish toxicity of organic solvents

REACH regulation requires ecotoxicological data to characterize industrial chemicals. To limit in vivo testing, Quantitative Structure-Activity Relationships (QSARs) are advocated to predict toxicity of a molecule. In this context, the topic of this work was to develop a reliable QSAR explaining the experimental acute toxicity of organic solvents for fish trophic level. Toxicity was expressed as log(LC50), the concentration in mmol.L(-1) producing the 50% death of fish. The 141 chemically heterogeneous solvents of the dataset were described by physico-chemical descriptors and quantum theoretical parameters calculated via Density Functional Theory. The best subsets of solvent descriptors for LC50 prediction were chosen both through the Kubinyi function associated with Enhanced Replacement Method and a stepwise forward multiple linear regressions. The 4-parameters selected in the model were the octanol-water partition coefficient, LUMO energy, dielectric constant and surface tension. The predictive power and robustness of the QSAR developed were assessed by internal and external validations. Several techniques for training sets selection were evaluated: a random selection, a LC50-based selection, a balanced selection in terms of toxic and non-toxic solvents, a solvent profile-based selection with a space filling technique and a D-optimality onions-based selection. A comparison with fish LC50 predicted by ECOSAR model validated for neutral organics confirmed the interest of the QSAR developed for the prediction of organic solvent aquatic toxicity regardless of the mechanism of toxic action involved.

Comparison of fate profiles of PAHs in soil, sediments and mangrove leaves after oil spills by QSAR and QSPR

First order removal rates for 15 polyaromatic hydrocarbons (PAHs) in soil, sediments and mangrove leaves were compared in relation to the parameters used in fate transport analyses (i.e., octanol-water partition coefficient, organic carbon-water partition coefficient, solubility, diffusivity in water, HOMO-LUMO gap, molecular size, molecular aspect ratio). The quantitative structure activity relationships (QSAR) and quantitative structure property relationships (QSPR) showed that the rate of disappearance of PAHs is correlated with their diffusivities in water as well as molecular volumes in different media. Strong correlations for the rate of disappearance of PAHs in sediments could not be obtained in relation to most of the parameters evaluated. The analyses showed that the QSAR and QSPR correlations developed for removal rates of PAHs in soils would not be adequate for sediments and plant tissues.

Evaluation of joint toxicity of nitroaromatic compounds and copper to Photobacterium phosphoreum and QSAR analysis

The individual toxicities of Cu and 11 nitroaromatic compounds to Photobacterium phosphoreum were determined. The toxicity was expressed as the concentrations causing a 50% inhibition of bioluminescence after 15 min exposure (IC(50)). To evaluate the joint effect between the metal ion and the 11 nitroaromatic compounds, the joint toxicity of Cu and 11 nitroaromatic compounds were measured at different Cu concentrations (0.2IC(50), 0.5IC(50) and 0.8IC(50)), respectively. The result shows that the binary joint effect between Cu and nitroaromatic compounds is mainly simple addition at the low Cu concentration (0.2IC(50)). However, an antagonism effect, 55% and 64%, was observed between Cu and 11 nitroaromatic compounds for Cu at medium and high concentrations (0.5IC(50) and 0.8IC(50)). Quantitative structure-activity relationship (QSAR) analysis was performed to study the joint toxicity for the 11 nitroaromatic compounds. The result shows that the toxicity of nitroaromatic compounds is related to descriptors of Connolly solvent-excluded volume (CSEV) and dipolarity/polarizability (S) at low Cu concentration. On the other hand, the toxicity is related to Connolly accessible area (CAA) at medium and high Cu concentrations. The result indicates that different QSAR models on complex mixtures need to be developed to assess the ecological risk in real environments. Using single toxic data to evaluate the toxic effect of mixtures may result in wrong conclusions.