Modelling physico-chemical properties of (benzo)triazoles, and screening for environmental partitioning

Occurrence and ecological risks of 156 pharmaceuticals and 296 pesticides in seawater from mariculture areas of Northeast China

China is the largest mariculture producer in the world. In recent years, pharmaceuticals and pesticides have been widely used in mariculture activities; however, most studies have only focused on the occurrence of limited types of antibiotics and organochlorine pesticides. It is critical to comprehensively investigate the occurrence of pharmaceuticals and pesticides in mariculture areas and assess their potential impacts on ocean ecosystems. In this study, the occurrence, distribution, and ecological risk of 484 compounds, including 296 pesticides, 156 pharmaceuticals, and 32 other substances, in the drainage ditches of culture ponds and raft-culture areas were investigated. A total of 51 compounds were detected in the mariculture area, with total concentrations ranging from 5.4 × 10² to 2.0 × 10⁴ ng/L at each sampling site. Eleven pesticides, three pharmaceuticals, and five other compounds were detected with detection frequencies of 100%. The cluster analysis indicated that mariculture is a source of herbicide pollution in coastal waters. To assess the ecological risks of the detected compounds, toxicity data collected from the database and predicted from quantitative structure activity relationship (QSAR) models were used to calculate the risk quotients and probabilistic risks. According to the risk quotients, five pollutants, including diuron, ametryn, prometryne, simetryn, and terbutryn, were estimated to pose high risks to marine organisms. The results of the probabilistic risk assessment indicated that only diuron, a biocide used in antifouling paint and mariculture, would have an adverse effect on up to 8% of the aquatic species in nearshore areas. These findings could be helpful in determining the aquatic benchmarks of pesticides and pharmaceuticals in mariculture discharge to promote the sustainable development of mariculture and ecological protection in coastal areas.

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.

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.

Effects of beta-cypermethrin and myclobutanil on some enzymes and changes of biomarkers between internal tissues and saliva in reptiles (Eremias argus)

Numerous studies suggested that reptiles are sensitive to environmental pollution and the abundance of many species are in decline. Our research is aim to assess the toxic effects of pesticide in reptiles. And we also want to supply some data about nondestructive samples for environmental risk assessment in reptiles. Lizards were orally administered a single-dose of beta-cypermethrin (BCP) or myclobutanil (MC) at the concentration of 20 mg/kg body weight (bw). The results showed that pesticides could induce changes in enzymatic activities (SOD, CAT, LDH, AChE) and MDA levels in organs or tissues of lizards. BCP could cause more severe oxidative damage than that of MC. Salivary enzymes activities showed sensitivity changes to the toxicity of pesticides. We could use saliva to reflect whether the reptiles are toxic by pesticides. We also agree that buccal swabs could be used as a tool for saliva sampling.

QSPR prediction of the hydroxyl radical rate constant of water contaminants

In advanced oxidation processes (AOPs), the aqueous hydroxyl radical (HO) acts as a strong oxidant to react with organic contaminants. The hydroxyl radical rate constant (kHO) is important for evaluating and modelling of the AOPs. In this study, quantitative structure-property relationship (QSPR) method is applied to model the hydroxyl radical rate constant for a diverse dataset of 457 water contaminants from 27 various chemical classes. The constricted binary particle swarm optimization and multiple-linear regression (BPSO-MLR) are used to obtain the best model with eight theoretical descriptors. An optimized feed forward neural network (FFNN) is developed to investigate the complex performance of the selected molecular parameters with kHO. Although the FFNN prediction results are more accurate than those obtained using BPSO-MLR, the application of the latter is much more convenient. Various internal and external validation techniques indicate that the obtained models could predict the logarithmic hydroxyl radical rate constants of a large number of water contaminants with less than 4% absolute relative error. Finally, the above-mentioned proposed models are compared to those reported earlier and the structural factors contributing to the AOP degradation efficiency are discussed.

Spatial variability versus parameter uncertainty in freshwater fate and exposure factors of chemicals

We compared the influence of spatial variability in environmental characteristics and the uncertainty in measured substance properties of seven chemicals on freshwater fate factors (FFs), representing the residence time in the freshwater environment, and on exposure factors (XFs), representing the dissolved fraction of a chemical. The influence of spatial variability was quantified using the SimpleBox model in which Europe was divided in 100 × 100 km regions, nested in a regional (300 × 300 km) and supra-regional (500 × 500 km) scale. Uncertainty in substance properties was quantified by means of probabilistic modelling. Spatial variability and parameter uncertainty were expressed by the ratio k of the 95%ile and 5%ile of the FF and XF. Our analysis shows that spatial variability ranges in FFs of persistent chemicals that partition predominantly into one environmental compartment was up to 2 orders of magnitude larger compared to uncertainty. For the other (less persistent) chemicals, uncertainty in the FF was up to 1 order of magnitude larger than spatial variability. Variability and uncertainty in freshwater XFs of the seven chemicals was negligible (k < 1.5). We found that, depending on the chemical and emission scenario, accounting for region-specific environmental characteristics in multimedia fate modelling, as well as accounting for parameter uncertainty, can have a significant influence on freshwater fate factor predictions. Therefore, we conclude that it is important that fate factors should not only account for parameter uncertainty, but for spatial variability as well, as this further increases the reliability of ecotoxicological impacts in LCA.

A best​ comprehension about the toxicity of phenylsulfonyl carboxylates in Vibrio fischeri using quantitative structure activity/property relationship methods

Aromatic sulfones comprise a class of chemicals used in agrochemical and pharmaceutical industries and as floatation and extractant agents in petrochemical and metallurgy industries. In this study, new QSA(P)R studies were carried out to predict the toxicity against Vibrio fischeri of a set of 52 aromatic sulfones. The same approach was used to evaluate the relationship between these endpoint and the water solubility, another important environmental endpoint. The study resulted in models of good statistical quality and mechanistic interpretation with a possible correlation between the two endpoints, but the toxic effect is also likely to depend on other physicochemical properties. The use of the PLS2, a method not commonly used in QSA(P)R studies, also produced models of greater reliability, and the relationship between the two endpoints was reinforced to some degree. These results are useful for better understanding the process by which these compounds exert their environmental toxicity, thus aiding in the development of industrially useful compounds with less potential environmental damage.

Prediction of the Fate of Organic Compounds in the Environment From Their Molecular Properties: A Review

A comprehensive review of quantitative structure-activity relationships (QSAR) allowing the prediction of the fate of organic compounds in the environment from their molecular properties was done. The considered processes were water dissolution, dissociation, volatilization, retention on soils and sediments (mainly adsorption and desorption), degradation (biotic and abiotic), and absorption by plants. A total of 790 equations involving 686 structural molecular descriptors are reported to estimate 90 environmental parameters related to these processes. A significant number of equations was found for dissociation process (pKa), water dissolution or hydrophobic behavior (especially through the KOW parameter), adsorption to soils and biodegradation. A lack of QSAR was observed to estimate desorption or potential of transfer to water. Among the 686 molecular descriptors, five were found to be dominant in the 790 collected equations and the most generic ones: four quantum-chemical descriptors, the energy of the highest occupied molecular orbital (EHOMO) and the energy of the lowest unoccupied molecular orbital (ELUMO), polarizability (α) and dipole moment (μ), and one constitutional descriptor, the molecular weight. Keeping in mind that the combination of descriptors belonging to different categories (constitutional, topological, quantum-chemical) led to improve QSAR performances, these descriptors should be considered for the development of new QSAR, for further predictions of environmental parameters. This review also allows finding of the relevant QSAR equations to predict the fate of a wide diversity of compounds in the environment.

Bisphenol A, nonylphenols, benzophenones, and benzotriazoles in soils, groundwater, surface water, sediments, and food: a review

Contaminants of emerging concern (CECs) are not commonly monitored in the environment, but they can enter the environment from a variety of sources. The most worrying consequence of their wide use and environmental diffusion is the increase in the possible exposure pathways for humans. Moreover, knowledge of their behavior in the environment, toxicity, and biological effects is limited or not available for most CECs. The aim of this work is to edit the state of the art on few selected CECs having the potential to enter the soil and aquatic systems and cause adverse effects in humans, wildlife, and the environment: bisphenol A (BPA), nonylphenol (NP), benzophenones (BPs), and benzotriazole (BT). Some reviews are already available on BPA and NP, reporting about their behavior in surface water and sediments, but scarce and scattered information is available about their presence in soil and groundwater. Only a few studies are available about BPs and BT in the environment, in particular in soil and groundwater. This work summarizes the information available in the literature about the incidence and behavior of these compounds in the different environmental matrices and food. In particular, the review focuses on the physical-chemical properties, the environmental fate, the major degradation byproducts, and the environmental evidence of the selected CECs.

QSARINS-chem: Insubria datasets and new QSAR/QSPR models for environmental pollutants in QSARINS

A database of environmentally hazardous chemicals, collected and modeled by QSAR by the Insubria group, is included in the updated version of QSARINS, software recently proposed for the development and validation of QSAR models by the genetic algorithm-ordinary least squares method. In this version, a module, named QSARINS-Chem, includes several datasets of chemical structures and their corresponding endpoints (physicochemical properties and biological activities). The chemicals are accessible in different ways (CAS, SMILES, names and so forth) and their three-dimensional structure can be visualized. Some of the QSAR models, previously published by our group, have been redeveloped using the free online software for molecular descriptor calculation, PaDEL-Descriptor. The new models can be easily applied for future predictions on chemicals without experimental data, also verifying the applicability domain to new chemicals. The QSAR model reporting format (QMRF) of these models is also here downloadable. Additional chemometric analyses can be done by principal component analysis and multicriteria decision making for screening and ranking chemicals to prioritize the most dangerous.

Assessing predictive uncertainty in comparative toxicity potentials of triazoles

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

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

Due to their chemical properties synthetic triazoles and benzo-triazoles ((B)TAZs) are mainly distributed to the water compartments in the environment, and because of their wide use the potential effects on aquatic organisms are cause of concern. Non testing approaches like those based on quantitative structure-activity relationships (QSARs) are valuable tools to maximize the information contained in existing experimental data and predict missing information while minimizing animal testing. In the present study, externally validated QSAR models for the prediction of acute (B)TAZs toxicity in Daphnia magna and Oncorhynchus mykiss have been developed according to the principles for the validation of QSARs and their acceptability for regulatory purposes, proposed by the Organization for Economic Co-operation and Development (OECD). These models are based on theoretical molecular descriptors, and are statistically robust, externally predictive and characterized by a verifiable structural applicability domain. They have been applied to predict acute toxicity for over 300 (B)TAZs without experimental data, many of which are in the pre-registration list of the REACH regulation. Additionally, a model based on quantitative activity-activity relationships (QAAR) has been developed, which allows for interspecies extrapolation from daphnids to fish. The importance of QSAR/QAAR, especially when dealing with specific chemical classes like (B)TAZs, for screening and prioritization of pollutants under REACH, has been highlighted.

Reply to the comment of S. Rayne on "QSAR model reproducibility and applicability: A case study of rate constants of hydroxyl radical reaction models applied to polybrominated diphenyl ethers and (benzo-)triazoles"

We appreciate the interest of Dr. Rayne on our article and we completely agree that the dataset of (benzo-)triazoles, which were screened by the hydroxyl radical reaction quantitative structure-activity relationship (QSAR) model, was not only composed of benzo-triazoles but also included some simpler triazoles (without the condensed benzene ring), such as the chemicals listed by Dr. Rayne, as well as some related heterocycles (also few not aromatic). We want to clarify that in this article (as well as in other articles in which the same dataset was screened), for conciseness, the abbreviations (B)TAZs and BTAZs were used as general (and certainly too simplified) notations meaning an extended dataset of benzo-triazoles, triazoles, and related compounds.

Modeling toxicity of mixtures of perfluorooctanoic acid and triazoles (triadimefon and paclobutrazol) to the benthic cladoceran Chydorus sphaericus

Toxicity of perfluorooctanoic acid (PFOA) and triazoles, that is, paclobutrazol (PBZ) and triadimefon (TDF), singly and in binary mixtures was assessed on the benthic cladoceran Chydorus sphaericus. Following single exposure, toxicity of PBZ was not significantly different from that of TDF as shown by the overlapping 95% confidence intervals of their median effective concentrations: EC50(PBZ) = 0.07-0.09 mM; EC50(TDF) = 0.066-0.078 mM. PFOA was far less toxic than these triazoles: EC50(PFOA) = 0.20-0.24 mM. Assuming no interactions between PFOA and the triazoles, the toxicity of PFOA in mixtures with the triazoles estimated by the concentration addition (CA) and independent action (IA) models was significantly lower than its toxicity in single exposure. This contrasted with the results for PBZ and TDF. Based on the assumption that PFOA may interact with the triazoles, different predictions were obtained by the two models. Significant alleviation of PBZ and TDF on the toxicity of PFOA was found by the extended CA model only. According to the extended IA model, PFOA had nonsignificant effects on the toxicity of PBZ while significantly increasing the toxicity of TDF. This difference is possibly related to different modes of actions for various functional groups in PBZ and TDF.

Removal of ethylenthiourea and 1,2,4-triazole pesticide metabolites from water by adsorption in commercial activated carbons

This study evaluated the adsorption capacity of ethylenthiourea (ETU) and 1H-1,2,4-triazole (1,2,4-T) for two commercial activated carbons: charcoal-powdered activated carbon (CPAC) and bovine bone-powdered activated carbon (BPAC). The tests were conducted at a bench scale, with ETU and 1,2,4-T diluted in water, for isotherm and adsorption kinetic studies. The removal of the compounds was accompanied by a total organic carbon (TOC) analysis and ultraviolet (UV) reduction analysis. The coals were characterized by their surface area using nitrogen adsorption/desorption, by a scanning electron microscopy and energy-dispersive X-ray spectroscopy (SEM/EDS) and by a zero charge point analysis (pHpcz). The results showed that adsorption kinetics followed a pseudo-second-order model for both coals, and the adsorption isotherms for CPAC and BPAC were adjusted to the Langmuir and Freundlich isotherms, respectively. The CPAC removed approximately 77% of the ETU and 76% of the 1,2,4-T. The BPAC was ineffective at removing the contaminants.

QSAR Modeling is not "Push a Button and Find a Correlation": A Case Study of Toxicity of (Benzo-)triazoles on Algae

A case study of toxicity of (benzo)triazoles ((B)TAZs) to the algae Pseudokirchneriella subcapitata is used to discuss some problems and solutions in QSAR modeling, particularly in the environmental context. The relevance of data curation (not only of experimental data, but also of chemical structures and input formats for the calculation of molecular descriptors), the crucial points of QSAR model validation and the potential application for new chemicals (internal robustness, exclusion of chance correlation, external predictivity, applicability domain) are described, while developing MLR-OLS models based on molecular descriptors, calculated by various QSAR software tools (commercial DRAGON, free PaDEL-Descriptor and QSPR-THESAURUS). Additionally, the utility of consensus models is highlighted. This work summarizes a methodology for a rigorous statistical approach to obtain reliable QSAR predictions, also for a large number of (B)TAZs in the ECHA preregistration list of REACH (even if starting from limited experimental data availability), and has evidenced some ambiguities and discrepancies related to SMILES notations from different databases; furthermore it highlighted some general problems related to QSAR model generation and was useful in the implementation of the PaDEL-Descriptor software.

Real external predictivity of QSAR models: how to evaluate it? Comparison of different validation criteria and proposal of using the concordance correlation coefficient

The main utility of QSAR models is their ability to predict activities/properties for new chemicals, and this external prediction ability is evaluated by means of various validation criteria. As a measure for such evaluation the OECD guidelines have proposed the predictive squared correlation coefficient Q(2)(F1) (Shi et al.). However, other validation criteria have been proposed by other authors: the Golbraikh-Tropsha method, r(2)(m) (Roy), Q(2)(F2) (Schüürmann et al.), Q(2)(F3) (Consonni et al.). In QSAR studies these measures are usually in accordance, though this is not always the case, thus doubts can arise when contradictory results are obtained. It is likely that none of the aforementioned criteria is the best in every situation, so a comparative study using simulated data sets is proposed here, using threshold values suggested by the proponents or those widely used in QSAR modeling. In addition, a different and simple external validation measure, the concordance correlation coefficient (CCC), is proposed and compared with other criteria. Huge data sets were used to study the general behavior of validation measures, and the concordance correlation coefficient was shown to be the most restrictive. On using simulated data sets of a more realistic size, it was found that CCC was broadly in agreement, about 96% of the time, with other validation measures in accepting models as predictive, and in almost all the examples it was the most precautionary. The proposed concordance correlation coefficient also works well on real data sets, where it seems to be more stable, and helps in making decisions when the validation measures are in conflict. Since it is conceptually simple, and given its stability and restrictiveness, we propose the concordance correlation coefficient as a complementary, or alternative, more prudent measure of a QSAR model to be externally predictive.