History of EPI Suite™ and future perspectives on chemical property estimation in US Toxic Substances Control Act new chemical risk assessments

Prioritization of organic contaminants in China's groundwater based on national-scale monitoring data and their persistence, bioaccumulation, and toxicity

There has been increasing concern regarding the adverse environmental and health effects of organic pollutants. A list of priority control organic pollutants (PCOPs) can provide regulatory frameworks for the use and monitoring of organic compounds in the environment. In this study, 20,010 groundwater samples were collected from 15 "first level" groundwater resource zones in China. Fifty (50) organic compounds were analyzed based on their prevalence, occurrence, and physicochemical properties (persistence, bioaccumulation, and toxicity). Results showed that 16 PCOPs, including 12 pesticides, 3 aromatic hydrocarbons (AHs), and 1 phthalate ester, were recognized. Pesticides and AHs accounted for 75 % and 18.75 % of the high-priority pollutants, respectively. There were significant differences in PCOPs between confined and phreatic groundwater. Higher concentrations of pesticides were mainly detected in phreatic groundwater. PCOPs detected in samples from the 15 groundwater resource zones were mainly pesticides and AHs. The groundwater data indicate that the organic compounds detected in the Yellow River Basin (YRB), Yangtze River Basin (YZB), Liaohe River Basin (LRB), and Songhua River Basin (SRB) are mainly categorized as Q1 (high priority) and Q2 (medium priority) pollutants based on the contaminants ranking system in China. The findings from this study offer a snapshot of the wide distribution of PCOPs in the surveyed regions, and are expected to establishing treatment and prevention measures at both the regional and national levels in China.

Grouping strategies for assessing and managing persistent and mobile substances

Background

Persistent, mobile and toxic (PMT), or very persistent and very mobile (vPvM) substances are a wide class of chemicals that are recalcitrant to degradation, easily transported, and potentially harmful to humans and the environment. Due to their persistence and mobility, these substances are often widespread in the environment once emitted, particularly in water resources, causing increased challenges during water treatment processes. Some PMT/vPvM substances such as GenX and perfluorobutane sulfonic acid have been identified as substances of very high concern (SVHCs) under the European Registration, Evaluation, Authorisation and Restriction of Chemicals (REACH) regulation. With hundreds to thousands of potential PMT/vPvM substances yet to be assessed and managed, effective and efficient approaches that avoid a case-by-case assessment and prevent regrettable substitution are necessary to achieve the European Union's zero-pollution goal for a non-toxic environment by 2050.

Main

Substance grouping has helped global regulation of some highly hazardous chemicals, e.g., through the Montreal Protocol and the Stockholm Convention. This article explores the potential of grouping strategies for identifying, assessing and managing PMT/vPvM substances. The aim is to facilitate early identification of lesser-known or new substances that potentially meet PMT/vPvM criteria, prompt additional testing, avoid regrettable use or substitution, and integrate into existing risk management strategies. Thus, this article provides an overview of PMT/vPvM substances and reviews the definition of PMT/vPvM criteria and various lists of PMT/vPvM substances available. It covers the current definition of groups, compares the use of substance grouping for hazard assessment and regulation, and discusses the advantages and disadvantages of grouping substances for regulation. The article then explores strategies for grouping PMT/vPvM substances, including read-across, structural similarity and commonly retained moieties, as well as the potential application of these strategies using cheminformatics to predict P, M and T properties for selected examples.

Conclusions

Effective substance grouping can accelerate the assessment and management of PMT/vPvM substances, especially for substances that lack information. Advances to read-across methods and cheminformatics tools are needed to support efficient and effective chemical management, preventing broad entry of hazardous chemicals into the global market and favouring safer and more sustainable alternatives.

Hazardous radical-coupled transformation products of benzophenone-3 formed during manganese dioxide treatment

Radical-coupled transformation products (TPs) have been identified as the byproducts of various transformation processes, including both natural attenuation and artificial treatments, of phenolic micropollutants. Benzophenone-3 (BP-3), an organic UV filter of emerging concern, has been previously reported with ubiquitous occurrence in the natural environment and water bodies. Current research has demonstrated how TPs are formed from BP-3 when it is treated with manganese oxide (MnO2). The ecological and toxicological risks of these TPs have also been assessed. Polymerization of BP-3 through radical coupling was observed as the major pathway by which BP-3 is transformed when treated with MnO2. These radical-coupled TPs haven't shown further degradation after formation, suggesting their potential persistence once occurred in the environment. In silico experiments predict the radical-coupled TPs will increase in mobility, persistence and ecotoxicity. If true, they also represent an ever-increasing threat to the environment, ecosystems and, most immediately, aquatic living organisms. In addition, radical-coupled TPs produced by MnO2 transformation of BP-3 have shown escalated estrogenic activity compared to the parent compound. This suggests that radical coupling amplifies the toxicological impacts of parent compound. These results provide strong evidence that radical-coupled TPs with larger molecular sizes are having potential adverse impacts on the ecosystem and biota.

Emerging micropollutants in aquatic ecosystems and nanotechnology-based removal alternatives: A review

There is a significant concern about the accessibility of uncontaminated and safe drinking water, a fundamental necessity for human beings. This concern is attributed to the toxic micropollutants from several emission sources, including industrial toxins, agricultural runoff, wastewater discharges, sewer overflows, landfills, algal blooms and microbiota. Emerging micropollutants (EMs) encompass a broad spectrum of compounds, including pharmaceutically active chemicals, personal care products, pesticides, industrial chemicals, steroid hormones, toxic nanomaterials, microplastics, heavy metals, and microorganisms. The pervasive and enduring nature of EMs has resulted in a detrimental impact on global urban water systems. Of late, these contaminants are receiving more attention due to their inherent potential to generate environmental toxicity and adverse health effects on humans and aquatic life. Although little progress has been made in discovering removal methodologies for EMs, a basic categorization procedure is required to identify and restrict the EMs to tackle the problem of these emerging contaminants. The present review paper provides a crude classification of EMs and their associated negative impact on aquatic life. Furthermore, it delves into various nanotechnology-based approaches as effective solutions to address the challenge of removing EMs from water, thereby ensuring potable drinking water. To conclude, this review paper addresses the challenges associated with the commercialization of nanomaterial, such as toxicity, high cost, inadequate government policies, and incompatibility with the present water purification system and recommends crucial directions for further research that should be pursued.

Aquatic toxicity and fate of styrene oligomers in the environment

Styrene oligomers (SOs) are ubiquitous contaminants that appear in the environment, sometimes to significant extent (see section 3.1). Despite the ongoing international debate on the human health risks posed by SOs, to the best of my knowledge, there are no studies on the aquatic toxicity and environmental fates (biodegradation and atmospheric degradation) of SOs in the environment. This study is to predict the aquatic toxicity and environmental fate of SOs by using the US EPA EPI suite program as an in-silico method. For better understanding, the risks and fates of SOs are compared with those of the well-known bisphenol A (BPA) and styrene monomer (SM or styrene). As a result of this study, SOs are predicted to be relatively more toxic than BPA and SM to aquatic and terrestrial organisms in the freshwater, marine, and terrestrial environments. In particular, the biodegradability of SOs is predicted to be relatively very slow in the environment, and most SOs are more likely to be effectively decomposed by hydroxyl radicals than by ozone in the atmosphere. As a result, this study can contribute to motivating understanding of the aquatic toxicity and fate of ubiquitous SOs in the environment.

Systematic computational toxicity analysis of the ozonolytic degraded compounds of azo dyes: Quantitative structure-activity relationship (QSAR) and adverse outcome pathway (AOP) based approach

The present study identifies and analyses the degraded products of three azo dyes (Reactive Orange 16, Reactive Red 120, and Direct Red 80) and proffers their in silico toxicity predictions. In our previously published work, the synthetic dye effluents were degraded using an ozonolysis-based Advanced Oxidation Process. In the present study, the degraded products of the three dyes were analysed using GC-MS at endpoint strategy and further subjected to in silico toxicity analysis using Toxicity Estimation Software Tool (TEST), Prediction Of TOXicity of chemicals (ProTox-II), and Estimation Programs Interface Suite (EPI Suite). Several physiological toxicity endpoints, such as hepatotoxicity, carcinogenicity, mutagenicity, cellular and molecular interactions, were considered to assess the Quantitative Structure-Activity Relationships (QSAR) and adverse outcome pathways. The environmental fate of the by-products in terms of their biodegradability and possible bioaccumulation was also assessed. Results of ProTox-II suggested that the azo dye degradation products are carcinogenic, immunotoxic, and cytotoxic and displayed toxicity towards Androgen Receptor and Mitochondrial Membrane Potential. TEST results predicted LC50 and IGC50 values for three organisms Tetrahymena pyriformis, Daphnia magna, and Pimephales promelas. EPISUITE software via the BCFBAF module surmises that the degradation products' bioaccumulation (BAF) and bioconcentration factors (BCF) are high. The cumulative inference of the results suggests that most degradation by-products are toxic and need further remediation strategies. The study aims to complement existing tests to predict toxicity and prioritise the elimination/reduction of harmful degradation products of primary treatment procedures. The novelty of this study is that it streamlines in silico approaches to predict the nature of toxicity of degradation by-products of toxic industrial affluents like azo dyes. These approaches can assist the first phase of toxicology assessments for any pollutant for regulatory decision-making bodies to chalk out appropriate action plans for their remediation.

Toward Property-Based Regulation

An expanding web of adverse impacts on people and the environment has been steadily linked to anthropogenic chemicals and their proliferation. Central to this web are the regulatory structures intended to protect human and environmental health through the control of new molecules. Through chronically insufficient and inefficient action, the current chemical-by-chemical regulatory approach, which considers regulation at the level of chemical identity, has enabled many adverse impacts to develop and persist. Recognizing the link between fundamental physicochemical properties and hazards, we describe a new paradigm─property-based regulation. By regulating physicochemical properties, we show how governments can delineate and enforce safe chemical spaces, increasing the scalability of chemical assessments, reducing the time and resources to regulate a substance, and providing transparency for chemical designers. We highlight sparse existing property-based approaches and demonstrate their applicability using bioaccumulation as an example. Finally, we present a path to implementation in the United States, prescribing roles and steps for government, nongovernmental organizations, and industry to accelerate this transition, to the benefit of all.

Animal-derived products in science and current alternatives

More than fifty years after the 3Rs definition and despite the continuous implementation of regulatory measures, animals continue to be widely used in basic research. Their use comprises not only in vivo experiments with animal models, but also the production of a variety of supplements and products of animal origin for cell and tissue culture, cell-based assays, and therapeutics. The animal-derived products most used in basic research are fetal bovine serum (FBS), extracellular matrix proteins such as Matrigel™, and antibodies. However, their production raises several ethical issues regarding animal welfare. Additionally, their biological origin is associated with a high risk of contamination, resulting, frequently, in poor scientific data for clinical translation. These issues support the search for new animal-free products able to replace FBS, Matrigel™, and antibodies in basic research. In addition, in silico methodologies play an important role in the reduction of animal use in research by refining the data previously to in vitro and in vivo experiments. In this review, we depicted the current available animal-free alternatives in in vitro research.

A multiple linear regression approach to the estimation of carboxylic acid ester and lactone alkaline hydrolysis rate constants

Pesticides, pharmaceuticals, and other organic contaminants often undergo hydrolysis when released into the environment; therefore, measured or estimated hydrolysis rates are needed to assess their environmental persistence. An intuitive multiple linear regression (MLR) approach was used to develop robust QSARs for predicting base-catalyzed rate constants of carboxylic acid esters (CAEs) and lactones. We explored various combinations of independent descriptors, resulting in four primary models (two for lactones and two for CAEs), with a total of 15 and 11 parameters included in the CAE and lactone QSAR models, respectively. The most significant descriptors include pKa, electronegativity, charge density, and steric parameters. Model performance is assessed using Drug Theoretics and Cheminformatics Laboratory's DTC-QSAR tool, demonstrating high accuracy for both internal validation (r2 = 0.93 and RMSE = 0.41-0.43 for CAEs; r2 = 0.90-0.93 and RMSE = 0.38-0.46 for lactones) and external validation (r2 = 0.93 and RMSE = 0.43-0.45 for CAEs; r2 = 0.94-0.98 and RMSE = 0.33-0.41 for lactones). The developed models require only low-cost computational resources and have substantially improved performance compared to existing hydrolysis rate prediction models (HYDROWIN and SPARC).

Sustainability insights into the synthesis of engineered nanomaterials - Problem formulation and considerations

Engineered nanomaterials (ENMs) have been introduced into the market for a wide range of applications. As per the literature review, the fabrication of new generations of ENMs is starting to comply with environmental, economic, and social criteria in addition to technical aspects to meet sustainability criteria. At this stage, identification of the appropriate criteria for the synthesis of ENMs is critical because the technologies already developed at the lab scales are being currently transferred to pilot and full scales. Hence, the development of scientific-based methodologies to identify, screen, and prioritize the involved criteria is highly necessary. In the present manuscript, a fuzzy-Delphi methodology is adopted to identify the main criteria and sub-criteria encompassing the sustainable fabrication of ENMs, and to explore the "degree of consensus" among the experts on the relative importance of the mentioned criteria. The "health and safety risks" respecting the equipment and the materials, solvent used, and availability of "green experts" were identified as the most critical criteria. Furthermore, although all the criteria were identified as being important, some criteria, such as "solvent" and "raw materials cost", raised a lower degree of consensus, indicating that various "degrees of uncertainties" still exist regarding the level of importance of the studied criteria.

Photolysis of Gas-Phase Atmospherically Relevant Monoterpene-Derived Organic Nitrates

Organic nitrates (ONs) can impact spatial distribution of reactive nitrogen species and ozone formation in the atmosphere. While photolysis of ONs is known to result in the release of NO₂ back to the atmosphere, the photolysis rate constants and mechanisms of monoterpene-derived ONs (MT-ONs) have not been well constrained. We investigated the gas-phase photolysis of three synthetic ONs derived from α-pinene, β-pinene, and d-limonene through chamber experiments. The measured photolysis rate constants ranged from (0.55 ± 0.10) × 10^-5 to (2.3 ± 0.80) × 10^-5 s^-1 under chamber black lights. When extrapolated to solar spectral photon flux at a solar zenith angle of 28.14° in summer, the photolysis rate constants were in the range of (4.1 ± 1.4) × 10^-5 to (14 ± 6.7) × 10^-5 s^-1 (corresponding to lifetimes of 2.0 ± 0.96 to 6.8 ± 2.4 h) and (1.7 ± 0.60) × 10^-5 to (8.3 ± 4.0) ×10^-5 s^-1 (3.3 ± 1.6 to 17 ± 6.0 h lifetimes) by using wavelength-dependent and average quantum yields, respectively. Photolysis mechanisms were proposed based on major products detected during photolysis. A zero-dimensional box model was further employed to simulate the photolysis of α-pinene-derived ON under ambient conditions. We found that more than 99% of α-pinene-derived ON can be converted to inorganic nitrogen within 12 h of irradiation and ozone was formed correspondingly. Together, these findings show that photolysis is an important atmospheric sink for MT-ONs and highlight their role in NO_x recycling and ozone chemistry.

In silico package models for deriving values of solute parameters in linear solvation energy relationships

Environmental partitioning influences fate, exposure and ecological risks of chemicals. Linear solvation energy relationship (LSER) models may serve as efficient tools for estimating environmental partitioning parameter values that are commonly deficient for many chemicals. Nonetheless, scarcities of empirical solute parameter values of LSER models restricted the application. This study developed and evaluated in silico methods and models to derive the values, in which excess molar refraction, molar volume and logarithm of hexadecane/air partition coefficient were computed from density functional theory; dipolarity/polarizability parameter, solute H-bond acidity and basicity parameters were predicted by quantitative structure-activity relationship models developed with theoretical molecular descriptors. New LSER models on four physicochemical properties relevant with environmental partitioning (n-octanol/water partition coefficients, n-octanol/air partition coefficients, water solubilities, sub-cooled liquid vapour pressures) were constructed using the in silico solute parameter values, which exhibited comparable performance with conventional LSER models using the empirical solute parameter values. The package models for deriving the LSER solute parameter values, with advantages that they are free of instrumental determinations, may lay the foundation for high-throughput estimating environmental partition parameter values of diverse organic chemicals.

A study using QSAR/QSPR models focused on the possible occurrence and risk of alloxydim residues from chlorinated drinking water, according to the EU Regulation

Any active substance with phytosanitary capacity intended to be marketed in Europe must pass exhaustive controls to assess its risk before being marketed and used in European agriculture. Since the implementation of Regulation (EC) No 1107/2009, agrochemical companies have been obliged to study the formation of pesticide transformation products (TPs) during the treatment of drinking water containing pesticide residues. However, there is no consensus on how to address this requirement. In this research work, the open literature collection on alloxydim was used to propose potential chlorination paths from alloxydim isomers. Furthermore, several QSAR/QSPR models have been used to fill the of knowledge gap relative to some key parameters in the physico-chemical, environmental and ecotoxicological areas of potential alloxydim TPs from chlorinated water for which little information exists. In this way, it has been possible to estimate the state of aggregation of these TPs (they exist mainly as liquids) as well as their ease of transit between the different phases, to predict their possible behaviour in the three environmental compartments (e.g., thermophysical properties point to a change in their evolution with respect to the parent alloxydim isomers) and to anticipate their potential risk to human and animal health (e.g., all of them cause developmental toxicity). These and other results highlight that the hazards of several TPs, i.e., both chlorinated and nonchlorinated from parent alloxydim or from those obtained after cleavage of the N - O bond and the subsequent reaction with chlorine, should be seriously considered. The obtained results reopen the debate on the implications of the use of QSAR/QSPR models for pesticide risk assessment in the legislative framework.

In Silico Prediction of Chemical Aquatic Toxicity by Multiple Machine Learning and Deep Learning Approaches

Fish is one of the model animals used to evaluate the adverse effects of a chemical exposed to the ecosystem. However, its low throughput and relevantly high expense make it impossible to test all new chemicals in manufacture. Hence using in silico models to prioritize compounds to be tested has been widely applied in environmental risk assessment and drug discovery. In this study, we constructed the local predictive models for four fish species, including bluegill sunfish, rainbow trout, fathead minnow, and sheepshead minnow, and the global models with all four fish data. A total of 1874 unique compounds with their labels, i.e. toxic (LC₅₀ < 10 ppm) or nontoxic were collected from ECOTOX and literature. Both conventional machine learning methods and the deep learning architecture, graph convolutional network (GCN), were used to build predictive models. The classification accuracy of the best local model for each fish species was higher than 0.83. For the global models, two strategies including consistency prediction and probability threshold were adopted to improve the predictive capability at the cost of limiting applicability domain. For 63% of compounds in domain, the accuracy was around 0.97. By comparison of the deep learning and machine learning methods, we found that the single-task GCN showed specific advantages in performance and multi-task GCN showed no advantages over the conventional machine learning methods. The data and models are available on GitHub (https://github.com/ChemPredict/ChemicalAquaticToxicity).

Long-Chain PFASs-Free Omniphobic Membranes for Sustained Membrane Distillation

Omniphobic membranes possessing high wetting resistance have been created for the treatment of challenging hypersaline feedwaters with low surface tension through membrane distillation (MD). However, virtually all such membranes are fabricated with long-chain per- and polyfluoroalkyl substances (PFASs, ≥8 fluorinated carbons). The environmental risks and high bioaccumulation potential of long-chain PFASs have raised increasing concerns. Developing highly wetting-resistant MD membranes while avoiding the use of long-chain PFASs is essential to improve the viability of MD for resilient and sustainable water purification. We demonstrate that MD membranes with exceptional wetting resistance can be designed through the combination of hierarchically structured membranes consisting of re-entrant texture at different length scales and (ultra)short-chain fluorocarbons, which have lower acute toxicity and bioaccumulation potentials than long-chain PFASs. Our hierarchically structured membrane with three-tier micro/nanostructure fabricated with short-chain fluorocarbon possesses superior wetting resistance, which is comparable to or higher than the long-chain PFASs-based omniphobic membranes reported in the literature. Furthermore, the hierarchically structured membranes fabricated with ultrashort-chain fluorocarbons display improved wetting resistance against feedwaters with low surface tension. Our findings indicate that long-chain PFASs are not required when designing wetting-resistant membranes and that the balance between sustainability and wetting resistance should be tailored to the wetting potential of the feedwater.

Emerging frontiers in microbe-mediated pesticide remediation: Unveiling role of omics and In silico approaches in engineered environment

The overuse of pesticides for augmenting agriculture productivity always comes at the cost of environment, biodiversity, and human health and has put the land, water, and environmental footprints under severe threat throughout the globe. Underpinning and maximizing the microbiome functions in pesticide-contaminated environments has become a prerequisite for a sustainable environment and resilient agriculture. It is imperative to elucidate the metabolic network of the microbial communities and environmental variables at the contaminated site to predict the best strategy for remediation and soil microbe-pesticide interactions. High throughput next-generation sequencing and in silico analysis allow us to identify and discern the members and characteristics of core microbiomes at the contaminated site. Integration of modern high throughput multi-omics investigations and informatics pipelines provide novel approaches and pathways to capitalize on the core microbiomes for enhancing environmental functioning and mitigation. The role of eco-genomics tools in visualising the microbial network, taxonomy, functional potential, and environmental variables in contaminated habitats is discussed in this review. The integrated role of the potential microbe identification as individual or consortia, mechanistic approach for pesticide degradation, identification of responsible enzymes/genes, and in silico approach is emphasized for the prospects of the area.

A Data Resource for Prediction of Gas-Phase Thermodynamic Properties of Small Molecules

The thermodynamic properties of a substance are key to predicting its behavior in physical and chemical systems. Specifically, the enthalpy of formation and entropy of a substance can be used to predict whether reactions involving that substance will proceed spontaneously under conditions of constant temperature and pressure, and if they do, what the heat and work yield of those reactions would be. Prediction of enthalpy and entropy of substances is therefore of value for substances for which those parameters have not been experimentally measured. We developed a database of 2869 experimental values of enthalpy of formation and 1403 values for entropy for substances composed of stable small molecules, derived from the literature. We developed a model for predicting enthalpy of formation and entropy from semiempirical quantum mechanical calculations of energy and atom counts, and applied the model to a comprehensive database of 16,417 small molecules. The database of small-molecule thermodynamic properties will be useful for predicting the outcome of any process that might involve the generation or destruction of volatile products, such as atmospheric chemistry, volcanism, or waste pyrolysis. Additionally, the collected experimental thermodynamic values will be of value to others developing models to predict enthalpy and entropy.

Molecular Docking as a Tool to Examine Organic Cation Sorption to Organic Matter

Molecular docking simulations were performed to examine the structural effects of organic cations on their sorption to organic matter. A set of benzylamine compounds was used to assess the sorption trends arising from the systematic structural differences between ring or nitrogen substituents. Binding simulations were performed using AutoDock 4.2 with Schulten's proposed soil organic matter as a representative organic matter structure. The calculated binding energies for the sorbate compounds correlated strongly with the measured sorption energies for Pahokee peat, indicating that the simulated binding energies and their associated sorbate orientations were representative of the experimental conditions. Graphical docking orientations showed primary, secondary, and tertiary aminium compounds to form hydrogen-bond interactions with deprotonated carboxylic acid groups in a pocket of the organic matter structure. Quaternary ammonium compounds formed pi-pi or cation-pi interactions with the aromatic groups elsewhere in the same organic matter pocket. Ring substituents showed no clear trends in sorption energies with the substituent group type for primary aminium compounds. Rather, substituent groups altered the simulated van der Waals, electrostatic, hydrogen-bond, and desolvation energy contributions to the overall sorption energies, in part because of the variations in docking orientations between compounds. Increasing methyl substitution of the aminium nitrogen group was associated with an increase in van der Waals energy contributions and a decrease in electrostatic energy contributions to the overall compound sorption energies because of aminium charge delocalization into methyl substituents and steric hindrance from methyl substituents to form specific interactions. The findings illustrate how molecular docking can be used to explore the effects of organic cation structure on sorption interactions with organic matter.

Pharmacokinetic Tools and Applications

Drug toxicity, as well as therapeutic activity, is contingent upon the parent drug, or a derivative thereof, reaching the relevant site of action in the body, at sufficient concentration, over a given period of time. Thus, the potential to truly elicit an effect is governed by both the intrinsic activity/toxicity of the drug (or its transformation products) and its pharmacokinetic profile. As the pharmaceutical industry has become increasingly aware of the role of pharmacokinetics in determining drug activity and toxicity, the range of software, both freely available and commercial, to predict relevant properties has proliferated. Such tools can be considered on three different levels, applicable at different stages within the drug development process and providing increasing detail and relevance of information. Level (i) is the prediction of fundamental physicochemical properties that can be used to screen vast virtual libraries of potential candidates. Level (ii), predicting the individual absorption, distribution, metabolism, and excretion (ADME) characteristics of potential drugs, can also be applied to many compounds simultaneously. Level (iii), predicting the concentration-time profile of a drug in blood or specific tissues/organs for individuals or a population, is the most sophisticated level of prediction, applied to fewer candidates. In this chapter, in silico tools for predicting ADME-relevant properties, across these three levels, and the applications of this information, are described using exemplar, freely available resources. Further resources are signposted but not all are considered in detail as the purpose here is more to provide an introduction to the capabilities and practicalities of the tools, rather than to provide an exhaustive review of all the tools available.

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.

Deriving Human Health and Aquatic Life Water Quality Criteria in the United States for Bioaccumulative Substances: A Historical Review and Future Perspective

Methods used to derive water quality regulations for persistent, bioaccumulative, and toxic substances (PBTs) in the United States have evolved substantially over the past 50 yr, leveraging current understandings and assumptions about the nature and magnitude of partitioning and accumulation of substances in water, sediments, and organisms. In the United States and across the world, environmental regulations continue to evolve into more refined water quality criteria protective of aquatic life and human health. The present review provides historical context on the establishment of aquatic life and human health water quality criteria in the United States by compiling information from regulatory agencies and peer-reviewed literature on methods used to characterize and quantify bioaccumulation of substances in aquatic organisms and humans. Primary data needs and assumptions for various methods, as well as their application in setting criteria by the US Environmental Protection Agency over the past half century, are highlighted. Our review offers an important retrospective on the data and methods used to derive water quality criteria for PBTs and provides commentary on the future of US criteria development. Environ Toxicol Chem 2021;40:2394-2405. © 2021 SETAC.

Trends in predictive biodegradation for sustainable mitigation of environmental pollutants: Recent progress and future outlook

The feasibility of in-silico techniques, together with the computational framework, has been applied to predictive bioremediation aiming to clean-up contaminants, toxicity evaluation, and possibilities for the degradation of complex recalcitrant compounds. Emerging contaminants from different industries have posed a significant hazard to the environment and public health. Given current bioremediation strategies, it is often a failure or inadequate for sustainable mitigation of hazardous pollutants. However, clear-cut vital information about biodegradation is quite incomplete from a conventional remediation techniques perspective. Lacking complete information on bio-transformed compounds leads to seeking alternative methods. Only scarce information about the transformed products and toxicity profile is available in the published literature. To fulfill this literature gap, various computational or in-silico technologies have emerged as alternating techniques, which are being recognized as in-silico approaches for bioremediation. Molecular docking, molecular dynamics simulation, and biodegradation pathways predictions are the vital part of predictive biodegradation, including the Quantitative Structure-Activity Relationship (QSAR), Quantitative structure-biodegradation relationship (QSBR) model system. Furthermore, machine learning (ML), artificial neural network (ANN), genetic algorithm (GA) based programs offer simultaneous biodegradation prediction along with toxicity and environmental fate prediction. Herein, we spotlight the feasibility of in-silico remediation approaches for various persistent, recalcitrant contaminants while traditional bioremediation fails to mitigate such pollutants. Such could be addressed by exploiting described model systems and algorithm-based programs. Furthermore, recent advances in QSAR modeling, algorithm, and dedicated biodegradation prediction system have been summarized with unique attributes.

Interactions of fluoroquinolone antibiotics with sodium hypochlorite in bromide-containing synthetic water: Reaction kinetics and transformation pathways

Seven popular fluoroquinolone antibiotics (FQs) in synthetic marine aquaculture water were subject to sodium hypochlorite (NaClO) disinfection scenario to investigate their reaction kinetics and transformation during chlorination. Reactivity of each FQ to NaClO was following the order of ofloxacin (OFL) > enrofloxacin (ENR) > lomefloxacin (LOM) > ciprofloxacin (CIP) ~ norfloxacin (NOR) >> pipemedic acid (PIP), while flumequine did not exhibit reactivity. The coexisting chlorine ions and sulfate ions in the water slightly facilitated the oxidation of FQs by NaClO, while humic acid was inhibitable to their degradation. The bromide ions promoted degradation of CIP and LOM, but restrained oxidation of OFL and ENR. By analysis of liquid chromatography with tandem mass spectrometry (LC-MS/MS), eight kinds of emerging brominated disinfection byproducts (Br-DBPs) caused by FQ_S were primarily identified in the chlorinated synthetic marine culture water. Through density functional theory calculation, the highest-occupied molecular orbital (HOMO) and the lowest-unoccupied molecular orbital (LUMO) characteristic as well as the charge distribution of the FQs were obtained to clarify transformation mechanisms. Their formation involved decarboxylation, ring-opening/closure, dealkylation and halogenation. Chlorine substitution occurred on the ortho-position of FQs's N4 and bromine substitution occurred on C8 position. The piperazine ring containing tertiary amine was comparatively stable, while this moiety with a secondary amine structure would break down during chlorination. Additionally, logK_ow and logBAF of transformation products were calculated by EPI-Suite^TM to analyze their bioaccumulation. The values indicated that Br-DBPs are easier to accumulate in the aquatic organism relative to their chloro-analogues and parent compounds.

Determination of environmental properties and toxicity of octyl-dimethyl-para-aminobenzoic acid and its degradation products

Octyl-dimethyl-para-aminobenzoic acid (ODPABA) is one of compounds of emerging concern. It undergoes transformations under the influence of oxidizing or chlorinating agents and UV radiation forming products with different properties. There is very little experimental data concerning the environmental fate of ODPABA and its transformation products. Therefore, the purpose of the studies was to determine environmental parameters: water solubility, soil - water partition coefficient, octanol - air partition coefficient, bioconcentration factor as well as half-life in air, water and soil. Based on the results obtained, the persistence and migration possibilities of ODPABA and its transformation products in the aquatic environment were estimated. Moreover, the ecological toxicity of oxidation and chlorination products was investigated. Microtox®, Daphtoxkit F® and Artoxkit M® tests were used to determine toxicity. LC₅₀ for Fish and Daphnia magna was calculated by Ecosar module. Studies have shown that as a result of ODPABA transformations, chloroorganic products are formed, which are lipophilic, are bioconcentrated in organic matter, are characterized by significant environmental persistence, can spread over considerable distances and are toxic. Oxidation products have significantly smaller impact on the environment. They are characterized by higher water solubility, lower bioconcentration factor and are less toxic.

Sandy beaches as hotspots of bisphenol A

Bisphenol A (BPA) poses a serious environmental threat and health concern. This study presents the global monitoring of BPA on oceanic sandy beaches. According to monitoring results, many beach sands contain a harmful concentrations of BPA. Likewise, styrene oligomers (SOs), anthropogenic chemicals derived from polystyrene plastics, show similar concentrations as BPA. This study shows a strong, positive correlation between BPA and SOs. The results indicate that probably BPA-containing materials including micro- and nano-plastics can be an important source of BPA to the sand beaches. Therefore, BPA presents potential health risks to people spending considerable time on the beach.

Current Progress and Future Directions in Gas-Phase Metal-Organic Framework Thin-Film Growth

Deposition of materials as a thin film is important for various applications, such as sensors, microelectronic devices, and membranes. There have been breakthroughs in gas-phase metal-organic framework (MOF) thin-film growth, which is more applicable to micro- and nanofabrication processes and also less harmful to the environment than solvent-based methods. Three different types of gas-phase MOF thin film deposition methods have been developed using chemical vapor deposition (CVD), atomic layer deposition (ALD), and physical vapor deposition (PVD)-CVD combined techniques. The CVD-based method basically converts metal oxide layers into MOF thin films by exposing the surface to ligand vapor. The ALD-based method allows growing MOF thin films following layer-by-layer (LBL) growth by sequentially exposing gas-phase metal and ligand precursors. The PVD-CVD method uses PVD for metal deposition and CVD for ligand deposition, which is similar to LBL growth. These gas-phase growth methods can broaden the use of MOFs in diverse areas. Herein, the current progress of gas-phase MOF thin film growth is discussed and future directions suggested.

Development of models predicting biodegradation rate rating with multiple linear regression and support vector machine algorithms

Biodegradation is a significant process for removing organic chemicals from water, soil and sediment environments, and therefore biodegradability is critical to evaluate the environmental persistence of organic chemicals. In this study, based on a dataset with 171 compounds, four quantitative structure-activity relationship (QSAR) models were developed for predicting primary and ultimate biodegradation rate rating with multiple linear regression (MLR) and support vector machine (SVM) algorithms. Two MLR models were built with a dataset with carbon atom number ≤9, and two SVM models were built with a dataset with carbon atom number >9. In the MLR models, n_ArX (number of X on aromatic ring) is the most important descriptor governing primary and ultimate biodegradation of organic chemicals. For the SVM models, determination coefficient (R²) values, cross-validated coefficients (Q²_LOO) and external validation coefficient (Q²_ext) values are over 0.9, indicating the SVM models have satisfactory goodness-of-fit, robustness and external predictive abilities. The applicability domains of these models were visualized by the Williams plot. The developed models can be used as effective tools to predict biodegradability of organic chemicals.

Prediction of Soil Adsorption Coefficient in Pesticides Using Physicochemical Properties and Molecular Descriptors by Machine Learning Models

The soil adsorption coefficient (K_OC ) plays an important role in environmental risk assessment of pesticide registration. Based on this risk assessment, applied and registered pesticides can be allowed in the European Union. Almost 1 yr is required to study and obtain the K_OC value of a pesticide. Furthermore, acquiring the K_OC requires a large cost. It is necessary to efficiently estimate the K_OC value in the early stages of pesticide development. In the present study, the experimental values of physicochemical properties and molecular descriptors of chemical structures were collected to develop a quantitative structure-property relationship (QSPR) model, and the prediction performance of the model was evaluated. More specifically, we compared the accuracies of models based on a gradient boosting decision tree, multiple linear regression, and support vector machine. The experimental results suggest that it is possible to develop a QSPR model with high accuracy using both the molecular descriptors calculated from the structural formula and experimental values of physicochemical properties from open literature and databases. Comparing to the previously established models, we achieved high prediction accuracy, fitness, and robustness by only using freeware. Therefore, our developed QSPR models can be useful preliminary risk assessment in the early developmental stages of pesticides. Environ Toxicol Chem 2020;39:1451-1459. © 2020 SETAC.

Screening New Persistent and Bioaccumulative Organics in China's Inventory of Industrial Chemicals

Over a third of the world's annual chemical production and sales occur in China. Thus, knowledge of the properties of the substances produced and emitted there is important from a global perspective. The chemical Inventory of Existing Chemical Substances of China (IECSC) lists over 45 000 chemicals. When compared to the North American and European chemical inventories, 6916 substances were found to be unique to the IECSC. We retrieved structural information for 14 938 organic chemicals in the IECSC and determined their overall environmental persistence , bioaccumulation factor (BAF), and long-range transport potential (transfer efficiency) using in silico approaches with the goal of identifying new chemicals with properties that might lead to global contamination issues. Overall, 10% of the 14 938 chemicals were unique to the IECSC and their environmental persistence and BAF were statistically higher than the values for the rest of the IECSC chemicals. We prioritized 27 neutral organic compounds predicted to have prolonged environmental persistence, and high potential for bioaccumulation and long-range transport when compared with polychlorinated biphenyls as a benchmark. We also identified 69 organofluorine compounds with three or more perfluorinated moieties, unique to the IECSC. Screening approaches and results from this study help to identify and prioritize those to be considered in further environmental modeling and monitoring assessments.

A deep learning approach for the blind logP prediction in SAMPL6 challenge

Water octanol partition coefficient serves as a measure for the lipophilicity of a molecule and is important in the field of drug discovery. A novel method for computational prediction of logarithm of partition coefficient (logP) has been developed using molecular fingerprints and a deep neural network. The machine learning model was trained on a dataset of 12,000 molecules and tested on 2000 molecules. In this article, we present our results for the blind prediction of logP for the SAMPL6 challenge. While the best submission achieved a RMSE of 0.41 logP units, our submission had a RMSE of 0.61 logP units. Overall, we ranked in the top quarter out of the 92 submissions that were made. Our results show that the deep learning model can be used as a fast, accurate and robust method for high throughput prediction of logP of small molecules.

Air-Water Partitioning of Biomass-Burning Phenols and the Effects of Temperature and Salinity

Biomass burning (BB) emits organic gases that, with chemical aging, can form secondary organic aerosol (SOA) in both the gas and aqueous phases. One class of biomass-burning emissions, phenols, are of interest because they react rapidly in the aqueous phase to efficiently form SOA, which might affect climate and human health. However, while measurements exist for the air-water partitioning constants of some simple phenols, Henry's law constants (K_H) are unknown for more complex BB phenols. In this work, we use a custom-built apparatus to measure K_H for a suite of biomass-burning phenols that span a wide range of air-water partitioning coefficients. Comparing our measurements to predicted values from EPI Suite shows that this model consistently overestimates K_H unless a suitable measured phenol K_H value is included to adjust the calculations. In addition, we determine the effect of five salts on phenol partitioning by measuring the Setschenow coefficients (K_S). Across the eight phenols we examined, values of K_S depend primarily on salt identity and descend in the order (NH₄)₂SO₄ > NaCl > NH₄Cl ≥ KNO₃ > NH₄NO₃. Lastly, we use our K_H and K_S results to discuss the aqueous processing of biomass-burning phenols in cloud/fog water versus aerosol liquid water.

Property Estimation of Per- and Polyfluoroalkyl Substances: A Comparative Assessment of Estimation Methods

To accurately predict the environmental fate of per- and polyfluoroalkyl substances (PFAS), high-quality physicochemical property data are required. Because such data are often not available from experiments, assessment of the accuracy of existing property estimation models is essential. The quality of predicted physicochemical property data for a set of 25 PFAS was examined using COSMOtherm, EPI Suite, the estimation models accessible through the US Environmental Protection Agency's CompTox Chemicals Dashboard, and Linear Solvation Energy Relationships (LSERs) available through the UFZ-LSER Database. The results showed that COSMOtherm made the most accurate acid dissociation constant and air-water partition ratio estimates compared with literature data. The OPEn structure-activity/property Relationship App (OPERA; developed through the CompTox Chemicals Dashboard) estimates of vapor pressure and dry octanol-air partition ratios were the most accurate compared with other models of interest. Wet octanol-water partition ratios were comparably predicted by OPERA and EPI Suite, and the organic carbon soil coefficient and solubility were well predicted by OPERA and COSMOtherm. Acid dissociation of the perfluoroalkyl acids has a significant impact on their physicochemical properties, and corrections for ionization were included where applicable. Environ Toxicol Chem 2020;39:775-786. © 2020 SETAC.

Physicochemical properties of styrene oligomers in the environment

Currently, styrene oligomers (SOs) are persistent contaminants that are present in the environment globally. SOs are artificial substances originating from styrene-based polymer materials, mainly including PS plastic, resin, and rubber. However, the behavior of SOs in the environment is not well-understood yet due to the scarcity of experimental data. The objective of this study was to use in-silico tool to estimate key physicochemical properties of these SOs. The US EPA EPI suite program was used to predict SOs' physicochemical properties including solubility, vapor pressure, LogKow, Henry's constant, LogKoc, and fugacity-based multimedia mass balance. Although styrene monomer (SM) and SOs have structural similarity, the physicochemical properties of SOs are significantly different from those of SM, a precursor of SOs. In particular, it is estimated that as much as the heavy molecular weight, most SOs persist for comparable periods of time in a sandy environment. Although there is uncertainty, this preliminary in-silico study provides a sufficient reason to assure an experimental study to better determine properties of SOs.

Prediction of Partition Coefficients of Environmental Toxins Using Computational Chemistry Methods

The partitioning of compounds between aqueous and other phases is important for predicting toxicity. Although thousands of octanol-water partition coefficients have been measured, these represent only a small fraction of the anthropogenic compounds present in the environment. The octanol phase is often taken to be a mimic of the inner parts of phospholipid membranes. However, the core of such membranes is typically more hydrophobic than octanol, and other partition coefficients with other compounds may give complementary information. Although a number of (cheap) empirical methods exist to compute octanol-water (log k _OW) and hexadecane-water (log k _HW) partition coefficients, it would be interesting to know whether physics-based models can predict these crucial values more accurately. Here, we have computed log k _OW and log k _HW for 133 compounds from seven different pollutant categories as well as a control group using the solvation model based on electronic density (SMD) protocol based on Hartree-Fock (HF) or density functional theory (DFT) and the COSMO-RS method. For comparison, XlogP3 (log k _OW) values were retrieved from the PubChem database, and KowWin log k _OW values were determined as well. For 24 of these compounds, log k _OW was computed using potential of mean force (PMF) calculations based on classical molecular dynamics simulations. A comparison of the accuracy of the methods shows that COSMO-RS, KowWin, and XlogP3 all have a root-mean-square deviation (rmsd) from the experimental data of ≈0.4 log units, whereas the SMD protocol has an rmsd of 1.0 log units using HF and 0.9 using DFT. PMF calculations yield the poorest accuracy (rmsd = 1.1 log units). Thirty-six out of 133 calculations are for compounds without known log k _OW, and for these, we provide what we consider a robust prediction, in the sense that there are few outliers, by averaging over the methods. The results supplied may be instrumental when developing new methods in computational ecotoxicity. The log k _HW values are found to be strongly correlated to log k _OW for most compounds.

Development of Prediction Models on Base-Catalyzed Hydrolysis Kinetics of Phthalate Esters with Density Functional Theory Calculation

Many phthalate esters (PAEs) are chemicals of high production volume and of toxicological concern. The second-order rate constant for base-catalyzed hydrolysis ( k_B) is a key parameter for assessing environmental persistence of PAEs. However, the k_B values for most PAEs are lacking, and the experimental determination of k_B encounters various difficulties. Herein, density functional theory (DFT) methods were selected by comparing empirical k_B values of five PAEs and five carboxylic acid esters with the DFT-calculated ones. Results indicate that PAEs with cyclic side chains are more vulnerable to base-catalyzed hydrolysis than PAEs with linear alkyl side chains, followed by PAEs with branched alkyl side chains. By combining experimental and DFT-calculated second-order rate constants for base-catalyzed hydrolysis of one side chain in PAEs ( k_{B_side chain}), quantitative structure-activity relationship models were developed. The models can differentiate PAEs with the departure of the leaving group (or the nucleophilic attack of OH^-) as the rate-determining step in the hydrolysis and estimate k_B values, which provides a promising way to predict hydrolysis kinetics of PAEs. The half-lives of the investigated PAEs were calculated and vary from 0.001 h to 558 years (pH = 7∼9), further illustrating the necessity of prediction models for hydrolysis kinetics in assessing the environmental persistence of chemicals.

Transformation of norfloxacin during the chlorination of marine culture water in the presence of iodide ions

The antibacterial agent norfloxacin (NOR) and sodium hypochlorite (NaClO), which are both widely used in marine culture, react with each other to form the halogenated disinfection byproducts (X-DBPs). The effects of the water characteristics and iodide concentration on the reaction kinetics were investigated. The results showed that the reaction rate of NOR with NaClO increases from 0.0586 min^-1 to 0.1075 min^-1 when the iodide concentration was changed from 0 μg^-1 to 50 μg^-1. This demonstrated the enhancement of NOR oxidation in the presence of iodide ions. Four novel iodinated DBPs (I-DBPs) were identified in the marine culture water. Iodine substitutions occurred at the C3 and C8 positions of NOR. The formation mechanisms of X-DBPs in the marine culture water were proposed based on the intermediate and final products. NOR may undergo a ring-opening reaction, a de-carbonyl reaction and substitution to form intermediates and finally generate the X-DBPs. Furthermore, the predicted logK_OW and logBCF values of the I-DBPs were higher than that of the Br-DBPs and Cl-DBPs. The AOX concentration in the synthetic water samples decreased in the following order: seawater (8.49 mg L^-1) > marine culture water (4.05 mg L^-1) > fresh water (1.89 mg L^-1). The amount of AOX also increased with the increase in iodide concentration. These results indicated that the I-DBPs were more toxic than their brominated and chlorinated analogues.

The environmental fate of synthetic organic chemicals

This article focuses on the routes of transport and abiotic processes involved in the environmental transformation of synthetic organic chemicals and how molecular structure controls the products and lifetimes of several important classes of organic chemicals. The chapter also discusses the current methods to reliably determine the rates and products of degradation of new chemicals based on combinations of chemical structure and environmental processes as well as use of laboratory and field measurements. Methods are also discussed for use of structure activity relations for this purpose.

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 and Classification Study on Prediction of Acute Oral Toxicity of N-Nitroso Compounds

To better understand the mechanism of in vivo toxicity of N-nitroso compounds (NNCs), the toxicity data of 80 NNCs related to their rat acute oral toxicity data (50% lethal dose concentration, LD50) were used to establish quantitative structure-activity relationship (QSAR) and classification models. Quantum chemistry methods calculated descriptors and Dragon descriptors were combined to describe the molecular information of all compounds. Genetic algorithm (GA) and multiple linear regression (MLR) analyses were combined to develop QSAR models. Fingerprints and machine learning methods were used to establish classification models. The quality and predictive performance of all established models were evaluated by internal and external validation techniques. The best GA-MLR-based QSAR model containing eight molecular descriptors was obtained with Q²loo = 0.7533, R² = 0.8071, Q²ext = 0.7041 and R²ext = 0.7195. The results derived from QSAR studies showed that the acute oral toxicity of NNCs mainly depends on three factors, namely, the polarizability, the ionization potential (IP) and the presence/absence and frequency of C⁻O bond. For classification studies, the best model was obtained using the MACCS keys fingerprint combined with artificial neural network (ANN) algorithm. The classification models suggested that several representative substructures, including nitrile, hetero N nonbasic, alkylchloride and amine-containing fragments are main contributors for the high toxicity of NNCs. Overall, the developed QSAR and classification models of the rat acute oral toxicity of NNCs showed satisfying predictive abilities. The results provide an insight into the understanding of the toxicity mechanism of NNCs in vivo, which might be used for a preliminary assessment of NNCs toxicity to mammals.

In silico environmental chemical science: properties and processes from statistical and computational modelling

Quantitative structure-activity relationships (QSARs) have long been used in the environmental sciences. More recently, molecular modeling and chemoinformatic methods have become widespread. These methods have the potential to expand and accelerate advances in environmental chemistry because they complement observational and experimental data with "in silico" results and analysis. The opportunities and challenges that arise at the intersection between statistical and theoretical in silico methods are most apparent in the context of properties that determine the environmental fate and effects of chemical contaminants (degradation rate constants, partition coefficients, toxicities, etc.). The main example of this is the calibration of QSARs using descriptor variable data calculated from molecular modeling, which can make QSARs more useful for predicting property data that are unavailable, but also can make them more powerful tools for diagnosis of fate determining pathways and mechanisms. Emerging opportunities for "in silico environmental chemical science" are to move beyond the calculation of specific chemical properties using statistical models and toward more fully in silico models, prediction of transformation pathways and products, incorporation of environmental factors into model predictions, integration of databases and predictive models into more comprehensive and efficient tools for exposure assessment, and extending the applicability of all the above from chemicals to biologicals and materials.

(Q)SARs to predict environmental toxicities: current status and future needs

The current state of the art of (Quantitative) Structure-Activity Relationships ((Q)SARs) to predict environmental toxicity is assessed along with recommendations to develop these models further. The acute toxicity of compounds acting by the non-polar narcotic mechanism of action can be well predicted, however other approaches, including read-across, may be required for compounds acting by specific mechanisms of action. The chronic toxicity of compounds to environmental species is more difficult to predict from (Q)SARs, with robust data sets and more mechanistic information required. In addition, the toxicity of mixtures is little addressed by (Q)SAR approaches. Developments in environmental toxicology including Adverse Outcome Pathways (AOPs) and omics responses should be utilised to develop better, more mechanistically relevant, (Q)SAR models.