QSAR-Based Estimation of Species Sensitivity Distribution Parameters: An Exploratory Investigation

Environmental persistence, bioaccumulation, and hazards of chemicals in e-cigarette e-liquids: short-listing chemicals for risk assessments

BACKGROUND/METHODS

Increased use and sales of e-cigarettes raises concerns about the potential environmental impacts throughout their life-cycle. However, few available research studies focus on the environmental impacts and ecotoxicity of e-cigarettes. In this study, we short-list e-liquid chemicals from published literature that should be considered in future environmental impact and risk assessments. We used a combination of available laboratory bioassays-based data and predictive methods (eg, Structure-Activity Relationships) to characterise the hazards of the e-liquid chemicals (environmental persistence, bioaccumulation, and aquatic toxicity including hazardous concentration values (concentration affecting specific proportion of species)) for short-listing.

RESULTS

Of the 421 unique e-liquid chemicals compiled from literature, 35 are US Environmental Protection Agency's hazardous constituents, 42 are US Food and Drug Administration's harmful or potentially harmful constituents in tobacco products and smoke, and 20 are listed as both. Per hazard characteristics, we short-listed 81 chemicals that should be considered for future environmental impact and risk assessments, including tobacco-specific compounds (eg, nicotine, N'-nitrosonornicotine), polycyclic aromatic hydrocarbons (eg, chrysene), flavours (eg, (-)caryophyllene oxide), metals (eg, lead), phthalates (eg, di(2-ethylhexyl)phthalate) and flame retardants (eg, tris(4-methylphenyl)phosphate).

IMPLICATIONS

Our findings documenting various hazardous chemicals in the e-liquids underscore the importance of awareness and education when handling or disposing of e-liquids/e-cigarettes and aim to inform strategies to prevent and reduce hazards from e-cigarettes. This includes any scenario where e-liquids can come into contact with people or the environment during e-liquid storage, manufacturing, use, and disposal practices. Overall, our study characterises the environmental hazards of e-liquid chemicals and provides regulators and researchers a readily available list for future ecological and health risk assessments.

Characterizing Freshwater Ecotoxicity of More Than 9000 Chemicals by Combining Different Levels of Available Measured Test Data with In Silico Predictions

Ecotoxicological impacts of chemicals released into the environment are characterized by combining fate, exposure, and effects. For characterizing effects, species sensitivity distributions (SSDs) estimate toxic pressures of chemicals as the potentially affected fraction of species. Life cycle assessment (LCA) uses SSDs to identify products with lowest ecotoxicological impacts. To reflect ambient concentrations, the Global Life Cycle Impact Assessment Method (GLAM) ecotoxicity task force recently recommended deriving SSDs for LCA based on chronic EC10s (10% effect concentration, for a life-history trait) and using the 20th percentile of an EC10-based SSD as a working point. However, because we lacked measured effect concentrations, impacts of only few chemicals were assessed, underlining data limitations for decision support. The aims of this paper were therefore to derive and validate freshwater SSDs by combining measured effect concentrations with in silico methods. Freshwater effect factors (EFs) and uncertainty estimates for use in GLAM-consistent life cycle impact assessment were then derived by combining three elements: (1) using intraspecies extrapolating effect data to estimate EC10s, (2) using interspecies quantitative structure-activity relationships, or (3) assuming a constant slope of 0.7 to derive SSDs. Species sensitivity distributions, associated EFs, and EF confidence intervals for 9862 chemicals, including data-poor ones, were estimated based on these elements. Intraspecies extrapolations and the fixed slope approach were most often applied. The resulting EFs were consistent with EFs derived from SSD-EC50 models, implying a similar chemical ecotoxicity rank order and method robustness. Our approach is an important step toward considering the potential ecotoxic impacts of chemicals currently neglected in assessment frameworks due to limited test data. Environ Toxicol Chem 2024;43:1914-1927. © 2024 The Author(s). Environmental Toxicology and Chemistry published by Wiley Periodicals LLC on behalf of SETAC.

Prioritisation of data-poor pharmaceuticals for empirical testing and environmental risk assessment

There are more than 3,500 active pharmaceutical ingredients (APIs) on the global market for human and veterinary use. Residues of these APIs eventually reach the aquatic environment. Although an environmental risk assessment (ERA) for marketing authorization applications of medicinal products is mandatory in the European Union since 2006, an ERA is lacking for most medicines approved prior to 2006 (legacy APIs). Since it is unfeasible to perform extensive ERA tests for all these legacy APIs, there is a need for prioritization of testing based on the limited data available. Prioritized APIs can then be further investigated to estimate their environmental risk in more detail. In this study, we prioritized more than 1,000 APIs used in Europe based on their predicted risk for aquatic freshwater ecosystems. We determined their risk by combining an exposure estimate (Measured or Predicted Environmental Concentration; MEC or PEC, respectively) with a Predicted No Effect Concentration (PNEC). We developed several procedures to combine the limited empirical data available with in silico data, resulting in multiple API rankings varying in data needs and level of conservativeness. In comparing empirical with in silico data, our analysis confirmed that the PEC estimated with the default parameters used by the European Medicines Agency often - but not always - represents a worst-case scenario. Comparing the ecotoxicological data for the three main taxonomic groups, we found that fish represents the most sensitive species group for most of the APIs in our list. We furthermore show that the use of in silico tools can result in a substantial underestimation of the ecotoxicity of APIs. After combining the different exposure and effect estimates into four risk rankings, the top-ranking APIs were further screened for availability of ecotoxicity data in data repositories. This ultimately resulted in the prioritization of 15 APIs for further ecotoxicological testing and/or exposure assessment.

Adopting a toxic unit model paradigm in design, analysis and interpretation of oil toxicity testing

The lack of a conceptual understanding and unifying quantitative framework to guide conduct and interpretation of laboratory oil toxicity tests, has led investigators to divergent conclusions that can confuse stakeholders and impede sound decision-making. While a plethora of oil toxicity studies are available and continue to be published, due to differences in experimental design, results between studies often cannot be compared. Furthermore, much resulting data fails to advance quantitative effect models that are critically needed for oil spill risk and impact assessments. This paper discusses the challenges posed when evaluating oil toxicity test data based on traditional, total concentration-based exposure metrics and offers solutions for improving the state of practice by adopting a unifying toxic unit (TU) model framework. Key advantages of a TU framework is that differences in test oil composition, sensitivity of the test organism/endpoint, and toxicity test design (i.e., type of test) can be taken into quantitative account in predicting aquatic toxicity. This paradigm shift is intended to bridge the utility of laboratory oil toxicity tests with improved assessment of effects in the field. To illustrate these advantages, results from literature studies are reassessed and contrasted with conclusions obtained based on past practice. Using instructive examples, model results are presented to explain how dissolved oil composition and concentrations and resulting TUs vary in WAFs prepared using variable loading or dilution test designs and the important role that unmeasured oil components contribute to predicted oil toxicity. Model results are used to highlight how the TU framework can serve as a valuable aid in designing and interpreting empirical toxicity tests and provide the data required to validate/refine predictive toxicity models. To further promote consistent exposure and hazard assessment of physically and chemically dispersed oil toxicity tests recommendations for advancing the TU framework are presented.

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.

Revisiting assessment factors for species sensitivity distributions as a function of sample size and variation in species sensitivity

To use species sensitivity distributions (SSDs) for ecological risk assessment, there are various uncertainties, which require applying assessment factors (AFs) accordingly. This study aims to quantify the uncertainty of estimating statistical distributions. Given a management goal of protecting 95% of species, the concentration that affects 5% of the species (HC5) is estimated. Since the true concentration affecting 5% of the species (population HC5) is unknown, the estimated HC5 is divided by an AF to derive the predicted no-effect concentration (PNEC), which is set as the protection goal, to compensate for the deviation in the estimated HC5 from the population HC5. Although the deviation between these two HC5 values depends on the sample size and the variation in sensitivity (standard deviation of the distribution) among species, there has been little discussion of how to quantify the degree of uncertainty. By assuming that toxicity values are a random sample from a lognormal distribution, we mathematically analyzed the SSD to derive the magnitude of AF needed to achieve a given protection goal (as an example, the protection of 95% of species with a probability of 95%). We successfully derived an equation that explicitly relates the magnitude of AF to the sample size and the variation in species sensitivity, providing a new basis to statistically determine the magnitude of AF for ecological risk assessments.

Can Chemical Toxicity in Saltwater Be Predicted from Toxicity in Freshwater? A Comprehensive Evaluation Using Species Sensitivity Distributions

Species sensitivity distributions (SSDs) play an important role in ecological risk assessment. Estimating SSDs requires toxicity data for many species, but reports on saltwater species are often limited compared to freshwater species. This limitation can constrain informed management of saltwater quality for the protection of marine ecosystems. We investigated the relationships between the parameters (i.e., mean and standard deviation [SD]) of freshwater and saltwater log-normal SSDs to determine how accurately saltwater toxicity could be estimated from freshwater toxicity test data. We estimated freshwater and saltwater SSDs for 104 chemicals with reported acute toxicity data for five or more species and compared their means, SDs, and hazardous concentrations for 5% of the species (HC5) derived from the acute SSDs. Standard major axis regression analyses generally showed that log-log relationships between freshwater and saltwater SSD means, SDs, and HC5 values were nearly 1:1. In addition, the ratios of freshwater-to-saltwater SSD means and HC5 values for most of the 104 chemicals fell within the range 0.1-10. Although such a strong correlation was not observed for SSD SDs (r²  < 0.5), differences between freshwater and saltwater SSD SDs were relatively small. These results indicate that saltwater acute SSDs can be reasonably estimated using freshwater acute SSDs. Because the differences of the means and SDs between freshwater and saltwater SSDs were larger when the number of test species used for SSD estimation was lower (i.e., five to seven species in the present study), obtaining toxicity data for an adequate number of species will be key to better approximation of a saltwater acute SSD from a freshwater acute SSD for a given chemical. Environ Toxicol Chem 2022;41:2021-2027. © 2022 The Authors. Environmental Toxicology and Chemistry published by Wiley Periodicals LLC on behalf of SETAC.

In Silico Methods for Environmental Risk Assessment: Principles, Tiered Approaches, Applications, and Future Perspectives

This chapter aims to introduce the reader to the basic principles of environmental risk assessment of chemicals and highlights the usefulness of tiered approaches within weight of evidence approaches in relation to problem formulation i.e., data availability, time and resource availability. In silico models are then introduced and include quantitative structure-activity relationship (QSAR) models, which support filling data gaps when no chemical property or ecotoxicological data are available. In addition, biologically-based models can be applied in more data rich situations and these include generic or species-specific models such as toxicokinetic-toxicodynamic models, dynamic energy budget models, physiologically based models, and models for ecosystem hazard assessment i.e. species sensitivity distributions and ultimately for landscape assessment i.e. landscape-based modeling approaches. Throughout this chapter, particular attention is given to provide practical examples supporting the application of such in silico models in real-world settings. Future perspectives are discussed to address environmental risk assessment in a more holistic manner particularly for relevant complex questions, such as the risk assessment of multiple stressors and the development of harmonized approaches to ultimately quantify the relative contribution and impact of single chemicals, multiple chemicals and multiple stressors on living organisms.

A QSAR-ICE-SSD Model Prediction of the PNECs for Per- and Polyfluoroalkyl Substances and Their Ecological Risks in an Area of Electroplating Factories

Per- and polyfluoroalkyl substances (PFASs) are a class of highly fluorinated aliphatic compounds that are persistent and bioaccumulate, posing a potential threat to the aquatic environment. The electroplating industry is considered to be an important source of PFASs. Due to emerging PFASs and many alternatives, the acute toxicity data for PFASs and their alternatives are relatively limited. In this study, a QSAR–ICE–SSD composite model was constructed by combining quantitative structure-activity relationship (QSAR), interspecies correlation estimation (ICE), and species sensitivity distribution (SSD) models in order to obtain the predicted no-effect concentrations (PNECs) of selected PFASs. The PNECs for the selected PFASs ranged from 0.254 to 6.27 mg/L. The ΣPFAS concentrations ranged from 177 to 983 ng/L in a river close to an electroplating industry in Shenzhen. The ecological risks associated with PFASs in the river were below 2.97 × 10⁻⁴.

Aquatic toxicity of waterpipe wastewater chemicals

INTRODUCTION

The recent increase in U.S. popularity and use prevalence of water pipe (WP) tobacco smoking raises concerns about the potential environmental impacts of WP waste disposal and the need for strategies to reduce such impacts. The U.S. Food and Drug Administration (FDA) is required to assess the environmental impacts of its tobacco regulatory actions per the U.S. National Environmental Policy Act (NEPA). The purpose of this study was to identify and quantify specific chemical constituents in WP wastewater and to determine their potential aquatic toxicity.

METHODS

Using a modified Beirut smoking regimen, five different WP charcoal brands (n = 70) and ten WP tobacco brands (n = 35) were smoked separately using a WP smoking machine in which smoke was passed through the WP base water. We analyzed and quantified specific chemical constituents in the WP bowl wastewater through standardized U.S. Environmental Protection Agency's (EPA) Hazardous Waste Test Methods. We then characterized the ecological hazard for acute and chronic aquatic toxicity posed by the specific chemicals through compilations of Globally Harmonized System of Classification and Labelling of Chemicals (GHS) and hazardous concentration values (concentration affecting 50% of the species).

RESULTS

Among the list of 31 specific chemicals analyzed, we detected 22 and 11 chemicals in wastewater from WP tobacco and WP charcoal smoking, respectively. Nearly half of the 22 WP wastewater chemicals were classified as "very toxic" or "toxic" for acute and chronic aquatic toxicity per GHS classification. The most hazardous compounds with acute and chronic toxicity in aquatic organisms include acrolein, acrylonitrile, and metals (cadmium, lead, chromium, nickel, cobalt) found in both WP tobacco and charcoal wastewater, and N-nitrosonornicotine, nicotine, crotonaldehyde and selenium were additionally found in WP tobacco wastewater. All the identified chemicals are considered harmful or potentially harmful constituents in tobacco products and tobacco smoke per FDA's list, and seventeen of them represent hazardous waste per EPA's list.

CONCLUSION

Our study expands the identification and quantifies several WP wastewater chemical constituents. It characterizes the ecological hazard of these chemicals and identifies chemicals of concern, aiding our evaluation of the environmental impacts of WP waste products. Our results add to the existing evidence that WP wastewater is a source of toxins that could affect water quality and aquatic organisms, and bioaccumulate in the environment if disposed of into public sewers, on the ground, or in an onsite septic system. These findings highlight the importance of concerted efforts to raise awareness of appropriate WP waste disposal practices in both retail and residential settings, and applicable regulatory compliance requirements for WP retailer establishments, thereby limiting hazards from WP wastewater.

Estimating species sensitivity distributions on the basis of readily obtainable descriptors and toxicity data for three species of algae, crustaceans, and fish

Estimation of species sensitivity distributions (SSDs) is a crucial approach to predicting ecological risks and water quality benchmarks, but the amount of data required to implement this approach is a serious constraint on the application of SSDs to chemicals for which there are few or no toxicity data. The development of statistical models to directly estimate the mean and standard deviation (SD) of the logarithms of log-normally distributed SSDs has recently been proposed to overcome this problem. To predict these two parameters, we developed multiple linear regression models that included, in addition to readily obtainable descriptors, the mean and SD of the logarithms of the concentrations that are acutely toxic to one algal, one crustacean, and one fish species, as predictors. We hypothesized that use of the three species' mean and SD would improve the accuracy of the predicted means and SDs of the logarithms of the SSDs. We derived SSDs for 60 chemicals based on quality-assured acute toxicity data. Forty-five of the chemicals were used for model fitting, and 15 for external validation. Our results supported previous findings that models developed on the basis of only descriptors such as log K OW had limited ability to predict the mean and SD of SSD (e.g., r 2 = 0.62 and 0.49, respectively). Inclusion of the three species' mean and SD, in addition to the descriptors, in the models markedly improved the predictions of the means and SDs of SSDs (e.g., r 2 = 0.96 and 0.75, respectively). We conclude that use of the three species' mean and SD is promising for more accurately estimating an SSD and thus the hazardous concentration for 5% of species in cases where limited ecotoxicity data are available.

Can We Reasonably Predict Chronic Species Sensitivity Distributions from Acute Species Sensitivity Distributions?

Estimation of species sensitivity distributions (SSDs) is an essential way to estimate the hazardous concentration for 5% of the species (HC5) and thus to derive a "safe" concentration. Here, we examined whether we can reasonably predict SSDs based on chronic no-observed-effect concentration or level (chronic SSDs) from SSDs based on acute median effective/lethal concentration (acute SSDs) by analyzing log-normal SSDs of 150 chemicals. Chronic SSD means were, on average, 10 times lower than acute SSD means. The standard deviations (SDs) of acute and chronic SSDs closely overlapped. Our detailed analysis suggests that the acute SSD SD can be used as an initial estimate of the chronic SSD SD if the number of tested species is ≥10. There were no significant differences in the ratios of chronic to acute SSD means or SDs among three different modes of action. The HC5 of chronic SSDs was, on average, 10 times lower than the acute SSD HC5. We suggest that multiplication of the acute HC5 by a factor of 0.1 is a defensible way to obtain a first approximation of the chronic HC5, particularly when relative ecological risks of chemicals are being evaluated. Further study is needed to develop methods for a more accurate estimation of chronic SSDs.

Chemical pollution imposes limitations to the ecological status of European surface waters

Aquatic ecosystems are affected by man-made pressures, often causing combined impacts. The analysis of the impacts of chemical pollution is however commonly separate from that of other pressures and their impacts. This evolved from differences in the data available for applied ecology vis-à-vis applied ecotoxicology, which are field gradients and laboratory toxicity tests, respectively. With this study, we demonstrate that the current approach of chemical impact assessment, consisting of comparing measured concentrations to protective environmental quality standards for individual chemicals, is not optimal. In reply, and preparing for a method that would enable the comprehensive assessment and management of water quality pressures, we evaluate various quantitative chemical pollution pressure metrics for mixtures of chemicals in a case study with 24 priority substances of Europe-wide concern. We demonstrate why current methods are sub-optimal for water quality management prioritization and that chemical pollution currently imposes limitations to the ecological status of European surface waters. We discuss why management efforts may currently fail to restore a good ecological status, given that to date only 0.2% of the compounds in trade are considered in European water quality assessment and management.

"Quantifying the precision of ecological risk: Misunderstandings and errors in the methods for assessment factors versus species sensitivity distributions"

The science of species sensitivity distributions (SSDs) is a blend of statistical theory, ecotoxicological testing, study reliability, and biodiversity. The utility of SSDs has been well reviewed and they are viewed as a high tier assessment tool in environmental risk assessment and other disciplines. SSDs seek to improve upon probabilistic extrapolation of laboratory (and sometimes field) collected ecotoxicity data for environmental protection by modeling the diversity of multiple experimental results in the form of a single statistical distribution which reduces or eliminates the need for extrapolation with deterministic assessment factors. SSDs thus depend heavily on both statistical and biological knowledge. In this commentary we review recently published literature identifying areas of improvement based on fundamental statistical theory or application in environmental assessment contexts. We reveal that sound application of SSDs relies heavily upon a grasp of probability distributions, how asymmetric confidence intervals are derived for distributions common to SSDs, the influence of sample size on parameter estimation, and how these are collectively applied across the myriad of regulatory systems globally. Statisticians and ecotoxicologists are inextricably bound together in the goal of actually improving hazard assessment using both probabilistic and deterministic methodologies.