Probabilistic Reference and 10% Effect Concentrations for Characterizing Inhalation Non-cancer and Developmental/Reproductive Effects for 2,160 Substances.

Chemicals assessment and management frameworks rely on regulatory toxicity values, which are based on points of departure (POD) identified following rigorous dose-response assessments. Yet, regulatory PODs and toxicity values for inhalation exposure (i.e., reference concentrations [RfCs]) are available for only ∼200 chemicals. To address this gap, we applied a workflow to determine surrogate inhalation route PODs and corresponding toxicity values, where regulatory assessments are lacking. We curated and selected inhalation in vivo data from the U.S. EPA's ToxValDB and adjusted reported effect values to chronic human equivalent benchmark concentrations (BMCh) following the WHO/IPCS framework. Using ToxValDB chemicals with existing PODs associated with regulatory toxicity values, we found that the 25th %-ile of a chemical's BMCh distribution (PODp25BMCh) could serve as a suitable surrogate for regulatory PODs (Q2 ≥ 0.76, RSE ≤ 0.82 log10 units). We applied this approach to derive PODp25BMCh for 2,095 substances with general non-cancer toxicity effects and 638 substances with reproductive/developmental toxicity effects, yielding a total coverage of 2,160 substances. From these PODp25BMCh, we derived probabilistic RfCs and human population effect concentrations. With this work, we have expanded the number of chemicals with toxicity values available, thereby enabling a much broader coverage for inhalation risk and impact assessment.

Two-Stage Machine Learning-Based Approach to Predict Points of Departure for Human Noncancer and Developmental/Reproductive Effects.

Chemical points of departure (PODs) for critical health effects are crucial for evaluating and managing human health risks and impacts from exposure. However, PODs are unavailable for most chemicals in commerce due to a lack of in vivo toxicity data. We therefore developed a two-stage machine learning (ML) framework to predict human-equivalent PODs for oral exposure to organic chemicals based on chemical structure. Utilizing ML-based predictions for structural/physical/chemical/toxicological properties from OPERA 2.9 as features (Stage 1), ML models using random forest regression were trained with human-equivalent PODs derived from in vivo data sets for general noncancer effects (n = 1,791) and reproductive/developmental effects (n = 2,228), with robust cross-validation for feature selection and estimating generalization errors (Stage 2). These two-stage models accurately predicted PODs for both effect categories with cross-validation-based root-mean-squared errors less than an order of magnitude. We then applied one or both models to 34,046 chemicals expected to be in the environment, revealing several thousand chemicals of moderate concern and several hundred chemicals of high concern for health effects at estimated median population exposure levels. Further application can expand by orders of magnitude the coverage of organic chemicals that can be evaluated for their human health risks and impacts.

Near-field exposures and human health impacts for organic chemicals in interior paints: A high-throughput screening.

Interior paints contain organic chemicals that might be harmful to painters and building residents. This study aims to develop a high-throughput approach to screen near-field human exposures and health impacts related to organic chemicals in interior paints. We developed mass balance models for both water- and solvent-based paints, predicting emissions during wet and dry phases. We then screened exposures and risks, focusing on Sri Lanka where residential houses are frequently repainted. These models accurately predict paint drying time and indoor air concentrations of organic chemicals. Exposures of both painter and household resident were estimated for 65 organic chemicals in water-based and 26 in solvent-based paints, considering 12 solvents. Chemicals of concerns (CoCs) were identified, and maximum acceptable chemical contents (MACs) were calculated. Water-based paints generally pose lower health risks than solvent-based paints but might contain biocides of high concern. The total human health impact of one painting event on all household adults ranges from 1.5 × 10-3 to 2.1 × 10-2 DALYs for solvent-based paints, and from 4.1 × 10-4 to 9.5 × 10-3 DALYs for water-based paints. The present approach is a promising way to support the formulation of safer paint, and is integrated in the USEtox scientific consensus model for use in life cycle assessment, chemical substitution and risk screening.

Probabilistic Points of Departure and Reference Doses for Characterizing Human Noncancer and Developmental/Reproductive Effects for 10,145 Chemicals.

BACKGROUND: Regulatory toxicity values used to assess and manage chemical risks rely on the determination of the point of departure (POD) for a critical effect, which results from a comprehensive and systematic assessment of available toxicity studies. However, regulatory assessments are only available for a small fraction of chemicals. OBJECTIVES: Using in vivo experimental animal data from the U.S. Environmental Protection Agency's Toxicity Value Database, we developed a semiautomated approach to determine surrogate oral route PODs, and corresponding toxicity values where regulatory assessments are unavailable. METHODS: We developed a curated data set restricted to effect levels, exposure routes, study designs, and species relevant for deriving toxicity values. Effect levels were adjusted to chronic human equivalent benchmark doses (BMDh). We hypothesized that a quantile of the BMDh distribution could serve as a surrogate POD and determined the appropriate quantile by calibration to regulatory PODs. Finally, we characterized uncertainties around the surrogate PODs from intra- and interstudy variability and derived probabilistic toxicity values using a standardized workflow. RESULTS: The BMDh distribution for each chemical was adequately fit by a lognormal distribution, and the 25th percentile best predicted the available regulatory PODs [R2≥0.78, residual standard error (RSE)≤0.53 log10 units]. We derived surrogate PODs for 10,145 chemicals from the curated data set, differentiating between general noncancer and reproductive/developmental effects, with typical uncertainties (at 95% confidence) of a factor of 10 and 12, respectively. From these PODs, probabilistic reference doses (1% incidence at 95% confidence), as well as human population effect doses (10% incidence), were derived. DISCUSSION: In providing surrogate PODs calibrated to regulatory values and deriving corresponding toxicity values, we have substantially expanded the coverage of chemicals from 744 to 8,023 for general noncancer effects, and from 41 to 6,697 for reproductive/developmental effects. These results can be used across various risk assessment and risk management contexts, from hazardous site and life cycle impact assessments to chemical prioritization and substitution. https://doi.org/10.1289/EHP11524.

Advancing exposure data analytics and repositories as part of the European Exposure Science Strategy 2020-2030.

High-quality and comprehensive exposure-related data are critical for different decision contexts, including environmental and human health monitoring, and chemicals risk assessment and management. However, exposure-related data are currently scattered, frequently of unclear quality and structure, not readily accessible, and stored in various-partly overlapping-data repositories, leading to inefficient and ineffective data usage in Europe and globally. We propose strategic guidance for an integrated European exposure data production and management framework for use in science and policy, building on current and future data analysis and digitalization trends. We map the existing exposure data landscape to requirements for data analytics and repositories across European policies and regulations. We further identify needs and ways forward for improving data generation, sharing, and usage, and translate identified needs into an operational action plan for European and global advancement of exposure data for policies and regulations. Identified key areas of action are to develop consistent exposure data standards and terminology for data production and reporting, increase data transparency and availability, enhance data storage and related infrastructure, boost automation in data management, increase data integration, and advance tools for innovative data analysis. Improving and streamlining exposure data generation and uptake into science and policy is crucial for the European Chemicals Strategy for Sustainability and European Digital Strategy, in line with EU Data policies on data management and interoperability.

Semi-automated harmonization and selection of chemical data for risk and impact assessment.

Chemical data for thousands of substances are available for safety, risk, life cycle and substitution assessments, as submitted for example under the European Registration, Evaluation, Authorisation and Restriction of Chemicals (REACH) Regulation. However, to widely disseminate reported physicochemical properties as well as human and ecological exposure and toxicological data for use in various science and policy fields, systematic methods for data harmonization and selection are necessary. In response to this need, we developed a semi-automated method for deriving appropriate substance property values as input for various assessment frameworks with different requirements for resolution and data quality. Starting with data reported for a given substance and property, we propose a set of aligned data selection and harmonization criteria to obtain a representative mean value and related confidence intervals per chemical-property combination. The proposed method was tested on a set of octanol-water partition coefficients (Kow) for an illustrative set of 20 substances, reported under the REACH regulation as example data source. Our method is generally applicable to any set of substances, and can assess specific distributions in quality and variability across reported data. Further research can likely extend our method for mining information from text fields and adapt it to available data reported or collected from other sources and other substance properties to improve the reliability of input data for risk and impact assessments.

Estimating mouthing exposure to chemicals in children's products.

BACKGROUND: Existing models for estimating children's exposure to chemicals through mouthing currently depends on the availability of chemical- and material-specific experimental migration rates, only covering a few dozen chemicals. OBJECTIVE: This study objective is hence to develop a mouthing exposure model to predict migration into saliva, mouthing exposure, and related health risk from a wide range of chemical-material combinations in children's products. METHODS: We collected experimental data on chemical migration from different products into saliva for multiple substance groups and materials, identifying chemical concentration and diffusion coefficient as main properties of influence. To predict migration rates into saliva, we adapted a previously developed migration model for chemicals in food packaging materials. We also developed a regression model based on identified chemical and material properties. RESULTS: Our migration predictions correlate well with experimental data (R2 = 0.85) and vary widely from 8 × 10-7 to 32.7 µg/10 cm2/min, with plasticizers in PVC showing the highest values. Related mouthing exposure doses vary across chemicals and materials from a median of 0.005 to 253 µg/kgBW/d. Finally, we combined exposure estimates with toxicity information to yield hazard quotients and identify chemicals of concern for average and upper bound mouthing behavior scenarios. SIGNIFICANCE: The proposed model can be applied for predicting migration rates for hundreds of chemical-material combinations to support high-throughput screening.

Exposure and Toxicity Characterization of Chemical Emissions and Chemicals in Products: Global Recommendations and Implementation in USEtox.

PURPOSE: Reducing chemical pressure on human and environmental health is an integral part of the global sustainability agenda. Guidelines for deriving globally applicable, life cycle based indicators are required to consistently quantify toxicity impacts from chemical emissions as well as from chemicals in consumer products. In response, we elaborate the methodological framework and present recommendations for advancing near-field/far-field exposure and toxicity characterization, and for implementing these recommendations in the scientific consensus model USEtox. METHODS: An expert taskforce was convened by the Life Cycle Initiative hosted by UN Environment to expand existing guidance for evaluating human toxicity impacts from exposure to chemical substances. This taskforce evaluated advances since the original release of USEtox. Based on these advances, the taskforce identified two major aspects that required refinement, namely integrating near-field and far-field exposure and improving human dose-response modeling. Dedicated efforts have led to a set of recommendations to address these aspects in an update of USEtox, while ensuring consistency with the boundary conditions for characterizing life cycle toxicity impacts and being aligned with recommendations from agencies that regulate chemical exposure. The proposed framework was finally tested in an illustrative rice production and consumption case study. RESULTS AND DISCUSSION: On the exposure side, a matrix system is proposed and recommended to integrate far-field exposure from environmental emissions with near-field exposure from chemicals in various consumer product types. Consumer exposure is addressed via submodels for each product type to account for product characteristics and exposure settings. Case study results illustrate that product-use related exposure dominates overall life cycle exposure. On the effect side, a probabilistic dose-response approach combined with a decision tree for identifying reliable points of departure is proposed for non-cancer effects, following recent guidance from the World Health Organization. This approach allows for explicitly considering both uncertainty and human variability in effect factors. Factors reflecting disease severity are proposed to distinguish cancer from non-cancer effects, and within the latter discriminate reproductive/developmental and other non-cancer effects. All proposed aspects have been consistently implemented into the original USEtox framework. CONCLUSIONS: The recommended methodological advancements address several key limitations in earlier approaches. Next steps are to test the new characterization framework in additional case studies and to close remaining research gaps. Our framework is applicable for evaluating chemical emissions and product-related exposure in life cycle assessment, chemical alternatives assessment and chemical substitution, consumer exposure and risk screening, and high-throughput chemical prioritization.

Chemicals of concern in plastic toys.

We present a list of Chemicals of Concern (CoCs) in plastic toys. We started from available studies reporting chemical composition of toys to group plastic materials, as well as to gather mass fractions and function of chemicals in these materials. Chemical emissions from plastic toys and subsequent human exposures were then estimated using a series of models and a coupled near-field and far-field exposure assessment framework. Comparing human doses with reference doses shows high Hazard Quotients of up to 387 and cancer risk calculated using cancer slope factors of up to 0.0005. Plasticizers in soft plastic materials show the highest risk, with 31 out of the 126 chemicals identified as CoCs, with sum of Hazard Quotients >1 or child cancer risk >10-6. Our results indicate that a relevant amount of chemicals used in plastic toy materials may pose a non-negligible health risk to children, calling for more refined investigations and more human- and eco-friendly alternatives. The 126 chemicals identified as CoCs were compared with other existing regulatory prioritization lists. While some of our chemicals appear in other lists, we also identified additional priority chemicals that are not yet covered elsewhere and thus require further attention. We finally derive for all considered chemicals the maximum Acceptable Chemical Content (ACC) in the grouped toy plastic materials as powerful green chemistry tool to check whether chemical alternatives could create substantial risks.

Extrapolation Factors for Characterizing Freshwater Ecotoxicity Effects.

Various environmental and chemical assessment frameworks including ecological risk assessment and life cycle impact assessment aim at evaluating long-term ecotoxicity effects. Chronic test data are reported under the European Registration, Evaluation, Authorization and Restriction of Chemicals (REACH) regulation for various chemicals. However, chronic data are missing for a large fraction of marketed chemicals, for which acute test results are often available. Utilizing acute data requires robust extrapolation factors across effect endpoints, exposure durations, and species groups. We propose a decision tree based on strict criteria for curating and selecting high-quality aquatic ecotoxicity information available in REACH for organic chemicals, to derive a consistent set of generic and species group-specific extrapolation factors. Where ecotoxicity effect data are not available at all, we alternatively provide extrapolations from octanol-water partitioning coefficients as suitable predictor for chemicals with nonpolar narcosis as mode of action. Extrapolation factors range from 0.2 to 7 and are higher when simultaneously extrapolating across effect endpoints and exposure durations. Our results are consistent with previously reported values, while considering more endpoints, providing species group-specific factors, and characterizing uncertainty. Our proposed decision tree can be adapted to curate information from additional data sources as well as data for other environments, such as sediment ecotoxicity. Our approach and robust extrapolation factors help to increase the substance coverage for characterizing ecotoxicity effects across chemical and environmental assessment frameworks. Environ Toxicol Chem 2019;38:2568-2582. © 2019 SETAC.

Toward harmonizing ecotoxicity characterization in life cycle impact assessment.

Ecosystem quality is an important area of protection in life cycle impact assessment (LCIA). Chemical pollution has adverse impacts on ecosystems on a global scale. To improve methods for assessing ecosystem impacts, the Life Cycle Initiative hosted by the United Nations Environment Programme established a task force to evaluate the state-of-the-science in modeling chemical exposure of organisms and the resulting ecotoxicological effects for use in LCIA. The outcome of the task force work will be global guidance and harmonization by recommending changes to the existing practice of exposure and effect modeling in ecotoxicity characterization. These changes will reflect the current science and ensure the stability of recommended practice. Recommendations must work within the needs of LCIA in terms of 1) operating on information from any inventory reporting chemical emissions with limited spatiotemporal information, 2) applying best estimates rather than conservative assumptions to ensure unbiased comparison with results for other impact categories, and 3) yielding results that are additive across substances and life cycle stages and that will allow a quantitative expression of damage to the exposed ecosystem. We describe the current framework and discuss research questions identified in a roadmap. Primary research questions relate to the approach toward ecotoxicological effect assessment, the need to clarify the method's scope and interpretation of its results, the need to consider additional environmental compartments and impact pathways, and the relevance of effect metrics other than the currently applied geometric mean of toxicity effect data across species. Because they often dominate ecotoxicity results in LCIA, we give metals a special focus, including consideration of their possible essentiality and changes in environmental bioavailability. We conclude with a summary of key questions along with preliminary recommendations to address them as well as open questions that require additional research efforts. Environ Toxicol Chem 2018;37:2955-2971. © 2018 SETAC.