Craig Warren Davis

The assessment of humans indirectly exposed to chemicals via the environment (HvE) is an assessment element of the Registration, Evaluation, Authorisation, and Restriction of Chemicals (REACH) regulation. The European Union System for the Evaluation of Substances (EUSES) is the default screening tool, aimed at prioritizing chemicals for further refinement/higher tier assessment. This review summarizes the approach used in EUSES, evaluates the state of the science in human exposure modeling via the environment, and identifies areas for further research to strengthen the confidence and applicability of EUSES for assessing HvE. It confirms that EUSES v2.2 does serve as a conservative screening tool for identifying potential human risk due to HvE (via consumption of crops, meat and milk, fish, drinking water, and inhalation). However, certain submodels within EUSES have not been updated for at least two decades. For example, for highly soluble or highly hydrophobic or ionized organic substances, substance parameters are estimated based on outdated predictive models. We recommend to also update the REACH Technical Guidance to highlight possible refinements in HvE assessments as well as the integration of measured and (bio)monitoring data. Addressing limitations in the EUSES applicability domain, particularly for highly soluble and highly hydrophobic organic substances and ionized organics, would improve its applicability. We identified that some HvE submodels in EUSES could be readily updated to improve screening-level assessment in EUSES. In addition, updating the EUSES “food basket” using recent European Union food consumption data is crucial to accurately reflect recent dietary trends. Further research is required for prediction of leaf crop and drinking water exposure to better reflect the fate of chemicals in the environment. In particular for ionizable substances, research focused on QSAR (Quantitative Structure Acitivity Relationship) development and experimental measurement of fate properties is necessary to enhance the confidence of EUSES assessments.

Technical complexity associated with biodegradation testing, particularly for substances of unknown or variable composition, complex reaction products, or biological materials (UVCB), necessitates the advancement of non-testing methods such as quantitative structure–property relationships (QSPRs). Models for describing the biodegradation of petroleum hydrocarbons (HCs) have been previously developed. A critical limitation of available models is their inability to capture the variability in biodegradation rates associated with variable test systems and environmental conditions. Recently, the Hydrocarbon Biodegradation System Integrated Model (HC-BioSIM) was developed to characterize the biodegradation of HCs in aquatic systems with the inclusion of key test system variables. The present study further expands the HC-BioSIM methodology to soil and sediment systems using a database of 2195 half-life (i.e., degradation time [DT]50) entries for HCs in soil and sediment. Relevance and reliability criteria were defined based on similarity to standard testing guidelines for biodegradation testing and applied to all entries in the database. The HC-BioSIM soil and sediment models significantly outperformed the existing biodegradation HC half-life (BioHCWin) and virtual evaluation of chemical properties and toxicities (VEGA) quantitative Mario Negri Institute for Pharmacological Research (IRFMN) models in soil and sediment. Average errors in predicted DT50s were reduced by up to 6.3- and 8.7-fold for soil and sediment, respectively. No significant bias as a function of HC class, carbon number, or test system parameters was observed. Model diagnostics demonstrated low variability in performance and high consistency of parameter usage/importance and rule structure, supporting the generalizability and stability of the models for application to external data sets. The HC-BioSIM provides improved accuracy of Persistence categorization, with correct classification rates of 83.9%, and 90.6% for soil and sediment, respectively, demonstrating a significant improvement over the existing BioHCWin (70.7% and 58.6%) and VEGA (59.5% and 18.5%) models. Environ Toxicol Chem 2024;00:1–12. © 2024 Concawe. Environmental Toxicology and Chemistry published by Wiley Periodicals LLC on behalf of SETAC.

Hydraulic fracturing (HF) is commonly used to enhance onshore recovery of oil and gas during production. This process involves the use of a variety of chemicals to support the physical extraction of oil and gas, maintain appropriate conditions downhole (e.g., redox conditions, pH), and limit microbial growth. The diversity of chemicals used in HF presents a significant challenge for risk assessment. The objective of the present study is to establish a transparent, reproducible procedure for estimating 5th percentile acute aquatic hazard concentrations (e.g., acute hazard concentration 5th percentiles [HC5s]) for these substances and validating against existing toxicity data. A simplified, grouped target site model (gTSM) was developed using a database (n = 1696) of diverse compounds with known mode of action (MoA) information. Statistical significance testing was employed to reduce model complexity by combining 11 discrete MoAs into three general hazard groups. The new model was trained and validated using an 80:20 allocation of the experimental database. The gTSM predicts toxicity using a combination of target site water partition coefficients and hazard group-based critical target site concentrations. Model performance was comparable to the original TSM using 40% fewer parameters. Model predictions were judged to be sufficiently reliable and the gTSM was further used to prioritize a subset of reported Permian Basin HF substances for risk evaluation. The gTSM was applied to predict hazard groups, species acute toxicity, and acute HC5s for 186 organic compounds (neutral and ionic). Toxicity predictions and acute HC5 estimates were validated against measured acute toxicity data compiled for HF substances. This case study supports the gTSM as an efficient, cost-effective computational tool for rapid aquatic hazard assessment of diverse organic chemicals. Environ Toxicol Chem 2024;43:1161–1172. © 2024 ExxonMobil Petroleum and Chemical BV. Environmental Toxicology and Chemistry published by Wiley Periodicals LLC on behalf of SETAC.

Widespread global use of polymers and plastic has led to increased stakeholder interest in the health & environmental assessments of these materials. Formal frameworks (i.e., RISK21, Canadian ERC) may require specific data types, developed with prescribed methods, and largely focused on non-polymeric substances. These data are not always available and can result in inaction, or conservative decision making. This is due to the propensity for regulatory or policy decisions to rely primarily on hazard information, without comprehensively considering exposure information. Assessing risk and making risk-based decisions requires deliberate consideration of a complex, and often dynamic, suite of information. For risk assessments that are highly challenging (e.g., large numbers of substances), approaches which screen and prioritize data inputs is a practical methodology for parsing out and addressing complexity in a stepwise approach. This framework for screening and prioritizing additives and polymer-associated chemistries (APAC) was developed to parameterize the large, diverse set of chemistries that are utilized in the manufacture and processing of polymers by considering relative chemical hazards and potential polymer-related exposure profiles together to form the initial basis for a practical and informed risk assessment. The framework leverages existing datasets, mines global databases, and utilizes predictive modeling methodology in a tiered approach to move toward the future goal of fit-for-purpose polymers risk assessments, and is intended to be an accessible approach for all stakeholders with interest in polymers risk assessment.The purpose of communicating this abbreviated draft approach via the Center for Open Science is to allow for rapid, high-level dissemination of key framework elements to allow for public feedback via the platform. Feedback on potential for broad utility by stakeholders across a range of experience levels, as well as potential for framework uptake related to risk-based initiatives, are of particular interest. Input from stakeholders in the form of the survey response(s) (https://app.sli.do/event/eXhC9yG6WQrQnFuFzb2NsR) may be considered for incorporation into the finalized, peer-review submission of the full framework.

Since recognizing the importance of bioavailability for understanding the toxicity of chemicals in sediments, mechanistic modeling has advanced over the last 40 years by building better tools for estimating exposure and making predictions of probable adverse effects. Our review provides an up-to-date survey of the status of mechanistic modeling in contaminated sediment toxicity assessments. Relative to exposure, advances have been most substantial for non-ionic organic contaminants (NOCs) and divalent cationic metals, with several equilibrium partitioning-based (Eq-P) models having been developed. This has included the use of Abraham equations to estimate partition coefficients for environmental media. As a result of the complexity of their partitioning behavior, progress has been less substantial for ionic/polar organic contaminants. When the EqP-based estimates of exposure and bioavailability are combined with water-only effects measurements, predictions of sediment toxicity can be successfully made for NOCs and selected metals. Both species sensitivity distributions and toxicokinetic and toxicodynamic models are increasingly being applied to better predict contaminated sediment toxicity. Furthermore, for some classes of contaminants, such as polycyclic aromatic hydrocarbons, adverse effects can be modeled as mixtures, making the models useful in real-world applications, where contaminants seldomly occur individually. Despite the impressive advances in the development and application of mechanistic models to predict sediment toxicity, several critical research needs remain to be addressed. These needs and others represent the next frontier in the continuing development and application of mechanistic models for informing environmental scientists, managers, and decisions makers of the risks associated with contaminated sediments. Environ Toxicol Chem 2024;43:1778–1794. © 2023 SETAC. This article has been contributed to by U.S. Government employees and their work is in the public domain in the USA.

Quantitative structure–property relationship (QSPR) models for predicting primary biodegradation of petroleum hydrocarbons have been previously developed. These models use experimental data generated under widely varied conditions, the effects of which are not captured adequately within model formalisms. As a result, they exhibit variable predictive performance and are unable to incorporate the role of study design and test conditions on the assessment of environmental persistence. To address these limitations, a novel machine-learning System-Integrated Model (HC-BioSIM) is presented, which integrates chemical structure and test system variability, leading to improved prediction of primary disappearance time (DT50) values for petroleum hydrocarbons in fresh and marine water. An expanded, highly curated database of 728 experimental DT50 values (181 unique hydrocarbon structures compiled from 13 primary sources) was used to develop and validate a supervised model tree machine-learning model. Using relatively few parameters (6 system and 25 structural parameters), the model demonstrated significant improvement in predictive performance (root mean square error = 0.26, R2 = 0.67) over existing QSPR models. The model also demonstrated improved accuracy of persistence (P) categorization (i.e., “Not P/P/vP”), with an accuracy of 96.8%, and false-positive and -negative categorization rates of 0.4% and 2.7%, respectively. This significant improvement in DT50 prediction, and subsequent persistence categorization, validates the need for models that integrate experimental design and environmental system parameters into biodegradation and persistence assessment. Environ Toxicol Chem 2022;41:1359–1369. © 2022 ExxonMobil Biomedical Sciences, Inc. Environmental Toxicology and Chemistry published by Wiley Periodicals LLC on behalf of SETAC.

With the advent of global regulations for safer detergent and an emphasis on a shift toward more environmentally friendly formulations, the environmental profile of surfactant chemistries have moved to the forefront of product formulation and design. The two cornerstones of surfactant environmental profiles are the ability to biodegrade in the natural environment and the ecological hazard profile. The objectives of this article are to describe biodegradation and aquatic toxicity data for a series of branched oxo‐alcohol ethoxylate (AEO) surfactants; to apply the target lipid model (TLM) for deriving model‐based threshold hazard concentrations (HC5) of AEO; and, finally, to accurately determine aquatic classifications for AEO surfactants for use in regulatory classification frameworks. Biodegradation results indicate a high level of biodegradability of branched AEO, with C8–C13‐rich oxo‐alcohols with 1–20 mol of ethoxylate meeting the readily biodegradable criteria. Results from acute and chronic toxicity tests indicated comparable or lesser aquatic toxicity versus linear AEO structures previously reported in the literature. The TLM model, applied a priori, resulted in good agreement with acute toxicity data (RMSE = 0.49) and is comparable to the root mean square errors (RMSE) previously determined for other narcotic chemicals (RMSE = 0.46–0.57). Model errors for invertebrates and fish were smaller than those for algae, with the TLM systematically overpredicting acute and chronic classification of two of seven branched AEO. Furthermore, TLM‐predicted HC5 values were determined to be sufficiently conservative, with 100% of observed chronic data (N = 79) falling above the HC5 threshold values, providing a useful tool for the risk assessment of AEO.