Quantitative Structure Use Relationships: Highlights from a technical summit meeting.

The Quantitative Structure Use Relationship (QSUR) Summit, held on November 2-4, 2022, focused on advancing the development, refinement, and use of QSURs to support chemical substance prioritization and risk assessment and mitigation. QSURs utilize chemical structures to predict the function of a chemical within a formulated product or an industrial process. This presumed function can then be used to develop chemical use categories or other information necessary to refine exposure assessments. The invited expert meeting was attended by 38 scientists from Canada, Finland, France, the UK, and the USA, representing government, business, and academia, with expertise in exposure science, chemical engineering, risk assessment, formulation chemistry, and machine learning. Workshop discussions emphasized the importance of collection and sharing of data and quantification of relative chemical quantities to progress QSUR development. Participants proposed collaborative approaches to address key challenges, including mechanisms for aggregating information while still protecting proprietary product composition and other confidential business information. Discussions also led to proposals for applications beyond exposure and risk modeling, including sustainable formulation discovery. In addition, discussions continue to construct, conduct, and circulate case studies tied to various specific problem formulations in which QSURs supply or derive information on chemical functions, concentrations, and exposures.

Cutting-edge computational chemical exposure research at the U.S. Environmental Protection Agency.

Exposure science is evolving from its traditional "after the fact" and "one chemical at a time" approach to forecasting chemical exposures rapidly enough to keep pace with the constantly expanding landscape of chemicals and exposures. In this article, we provide an overview of the approaches, accomplishments, and plans for advancing computational exposure science within the U.S. Environmental Protection Agency's Office of Research and Development (EPA/ORD). First, to characterize the universe of chemicals in commerce and the environment, a carefully curated, web-accessible chemical resource has been created. This DSSTox database unambiguously identifies >1.2 million unique substances reflecting potential environmental and human exposures and includes computationally accessible links to each compound's corresponding data resources. Next, EPA is developing, applying, and evaluating predictive exposure models. These models increasingly rely on data, computational tools like quantitative structure activity relationship (QSAR) models, and machine learning/artificial intelligence to provide timely and efficient prediction of chemical exposure (and associated uncertainty) for thousands of chemicals at a time. Integral to this modeling effort, EPA is developing data resources across the exposure continuum that includes application of high-resolution mass spectrometry (HRMS) non-targeted analysis (NTA) methods providing measurement capability at scale with the number of chemicals in commerce. These research efforts are integrated and well-tailored to support population exposure assessment to prioritize chemicals for exposure as a critical input to risk management. In addition, the exposure forecasts will allow a wide variety of stakeholders to explore sustainable initiatives like green chemistry to achieve economic, social, and environmental prosperity and protection of future generations.

Computational estimates of daily aggregate exposure to PFOA/PFOS from 2011 to 2017 using a basic intake model.

BACKGROUND: Human exposure to per- and polyfluoroalkyl substances has been modeled to estimate serum concentrations. Given that the production and use of these compounds have decreased in recent years, especially PFOA and PFOS, and that additional concentration data have become available from the US and other industrialized countries over the past decade, aggregate median intakes of these two compounds were estimated using more recent data. METHODS: Summary statistics from secondary sources were collected, averaged, and mapped for indoor and outdoor air, water, dust, and soil for PFOA and PFOS to estimate exposures for adults and children. European dietary intake estimates were used to estimate daily intake from food. RESULTS: In accordance with decreased concentrations in media, daily intake estimates among adults, i.e., 40 ng/day PFOA and 40 ng/day PFOS, are substantially lower than those reported previously, as are children's estimates of 14 ng/day PFOA and 17 ng/day PFOS. Using a first-order pharmacokinetic model, these results compare favorably to the National Health and Nutrition Examination Survey serum concentration measurements. CONCLUSION: Concomitant blood concentrations support this enhanced estimation approach that captures the decline of PFOA/PFOS serum concentration over a decade.

The chemical landscape of high-throughput new approach methodologies for exposure.

The rapid characterization of risk to humans and ecosystems from exogenous chemicals requires information on both hazard and exposure. The U.S. Environmental Protection Agency's ToxCast program and the interagency Tox21 initiative have screened thousands of chemicals in various high-throughput (HT) assay systems for in vitro bioactivity. EPA's ExpoCast program is developing complementary HT methods for characterizing the human and ecological exposures necessary to interpret HT hazard data in a real-world risk context. These new approach methodologies (NAMs) for exposure include computational and analytical tools for characterizing multiple components of the complex pathways chemicals take from their source to human and ecological receptors. Here, we analyze the landscape of exposure NAMs developed in ExpoCast in the context of various chemical lists of scientific and regulatory interest, including the ToxCast and Tox21 libraries and the Toxic Substances Control Act (TSCA) inventory. We examine the landscape of traditional and exposure NAM data covering chemical use, emission, environmental fate, toxicokinetics, and ultimately external and internal exposure. We consider new chemical descriptors, machine learning models that draw inferences from existing data, high-throughput exposure models, statistical frameworks that integrate multiple model predictions, and non-targeted analytical screening methods that generate new HT monitoring information. We demonstrate that exposure NAMs drastically improve the coverage of the chemical landscape compared to traditional approaches and recommend a set of research activities to further expand the development of HT exposure data for application to risk characterization. Continuing to develop exposure NAMs to fill priority data gaps identified here will improve the availability and defensibility of risk-based metrics for use in chemical prioritization and screening. IMPACT: This analysis describes the current state of exposure assessment-based new approach methodologies across varied chemical landscapes and provides recommendations for filling key data gaps.

Exposure Prioritization (Ex Priori): A Screening-Level High-Throughput Chemical Prioritization Tool.

To estimate potential chemical risk, tools are needed to prioritize potential exposures for chemicals with minimal data. Consumer product exposures are a key pathway, and variability in consumer use patterns is an important factor. We designed Ex Priori, a flexible dashboard-type screening-level exposure model, to rapidly visualize exposure rankings from consumer product use. Ex Priori is Excel-based. Currently, it is parameterized for seven routes of exposure for 1108 chemicals present in 228 consumer product types. It includes toxicokinetics considerations to estimate body burden. It includes a simple framework for rapid modeling of broad changes in consumer use patterns by product category. Ex Priori rapidly models changes in consumer user patterns during the COVID-19 pandemic and instantly shows resulting changes in chemical exposure rankings by body burden. Sensitivity analysis indicates that the model is sensitive to the air emissions rate of chemicals from products. Ex Priori's simple dashboard facilitates dynamic exploration of the effects of varying consumer product use patterns on prioritization of chemicals based on potential exposures. Ex Priori can be a useful modeling and visualization tool to both novice and experienced exposure modelers and complement more computationally intensive population-based exposure models.

Inadvertently Generated PCBs in Consumer Products: Concentrations, Fate and Transport, and Preliminary Exposure Assessment.

Although commercial polychlorinated biphenyl (PCB) production was banned in 1979 under the Toxics Substance Control Act, inadvertent generation of PCBs through a variety of chemical production processes continues to contaminate products and waste streams. In this research, a total of 39 consumer products purchased from local and online retailer stores were analyzed for 209 PCB congeners. Inadvertent PCBs (iPCBs) were detected from seven products, and PCB-11 was the only congener detected in most of the samples, with a maximum concentration exceeding 800 ng/g. Emission of PCB-11 to air was studied from one craft foam sheet product using dynamic microchambers at 40 °C for about 120 days. PCB-11 migration from the product to house dust was also investigated. The IAQX program was then employed to estimate the emissions of PCB-11 from 10 craft foam sheets to indoor air in a 30 m3 room at 0.5 h-1 air change rate for 30 days. The predicted maximum PCB-11 concentration in the room air (156.8 ng/m3) and the measured concentration in dust (20 ng/g) were applied for the preliminary exposure assessment. The generated data from multipathway investigation in this work should be informative for further risk assessment and management for iPCBs.

Incorporating human exposure information in a weight of evidence approach to inform design of repeated dose animal studies.

Human health risks from chronic exposures to environmental chemicals are typically estimated from potential human exposure estimates and dose-response data obtained from repeated-dose animal toxicity studies. Various criteria are available for selecting the top (highest) dose used in these animal studies. For example, toxicokinetic (TK) and toxicological data provided by shorter-term or dose range finding studies can be evaluated in a weight of evidence approach to provide insight into the dose range that would provide dose-response data that are relevant to human exposures. However, there are concerns that a top dose resulting from the consideration of TK data may be too low compared to other criteria, such as the limit dose or the maximum tolerated dose. In this paper, we address several concerns related to human exposures by discussing 1) the resources and methods available to predict human exposure levels and the associated uncertainty and variability, and 2) the margin between predicted human exposure levels and the dose levels used in repeated-dose animal studies. A series of case studies, ranging from data-rich to data-poor chemicals, are presented to demonstrate that expected human exposures to environmental chemicals are typically orders of magnitude lower than no-observed-adverse-effect levels/lowest-observed-adverse-effect levels (NOAELs/LOAELs) when available (used as conservative surrogates for top doses). The results of these case studies support that a top dose based, in part, on TK data is typically orders of magnitude higher than expected human exposure levels.

Opportunities and challenges related to saturation of toxicokinetic processes: Implications for risk assessment.

Top dose selection for repeated dose animal studies has generally focused on identification of apical endpoints, use of the limit dose, or determination of a maximum tolerated dose (MTD). The intent is to optimize the ability of toxicity tests performed in a small number of animals to detect effects for hazard identification. An alternative approach, the kinetically derived maximum dose (KMD), has been proposed as a mechanism to integrate toxicokinetic (TK) data into the dose selection process. The approach refers to the dose above which the systemic exposures depart from being proportional to external doses. This non-linear external-internal dose relationship arises from saturation or limitation of TK process(es), such as absorption or metabolism. The importance of TK information is widely acknowledged when assessing human health risks arising from exposures to environmental chemicals, as TK determines the amount of chemical at potential sites of toxicological responses. However, there have been differing opinions and interpretations within the scientific and regulatory communities related to the validity and application of the KMD concept. A multi-stakeholder working group, led by the Health and Environmental Sciences Institute (HESI), was formed to provide an opportunity for impacted stakeholders to address commonly raised scientific and technical issues related to this topic and, more specifically, a weight of evidence approach is recommended to inform design and dose selection for repeated dose animal studies. Commonly raised challenges related to the use of TK data for dose selection are discussed, recommendations are provided, and illustrative case examples are provided to address these challenges or refute misconceptions.

Integrating Personal Air Sensor and GPS to Determine Microenvironment-Specific Exposures to Volatile Organic Compounds.

Personal exposure to volatile organic compounds (VOCs) from indoor sources including consumer products is an understudied public health concern. To develop and evaluate methods for monitoring personal VOC exposures, we performed a pilot study and examined time-resolved sensor-based measurements of geocoded total VOC (TVOC) exposures across individuals and microenvironments (MEs). We integrated continuous (1 min) data from a personal TVOC sensor and a global positioning system (GPS) logger, with a GPS-based ME classification model, to determine TVOC exposures in four MEs, including indoors at home (Home-In), indoors at other buildings (Other-In), inside vehicles (In-Vehicle), and outdoors (Out), across 45 participant-days for five participants. To help identify places with large emission sources, we identified high-exposure events (HEEs; TVOC > 500 ppb) using geocoded TVOC time-course data overlaid on Google Earth maps. Across the 45 participant-days, the MEs ranked from highest to lowest median TVOC were: Home-In (165 ppb), Other-In (86 ppb), In-Vehicle (52 ppb), and Out (46 ppb). For the two participants living in single-family houses with attached garages, the median exposures for Home-In were substantially higher (209, 416 ppb) than the three participant homes without attached garages: one living in a single-family house (129 ppb), and two living in apartments (38, 60 ppb). The daily average Home-In exposures exceeded the estimated Leadership in Energy and Environmental Design (LEED) building guideline of 108 ppb for 60% of the participant-days. We identified 94 HEEs across all participant-days, and 67% of the corresponding peak levels exceeded 1000 ppb. The MEs ranked from the highest to the lowest number of HEEs were: Home-In (60), Other-In (13), In-Vehicle (12), and Out (9). For Other-In and Out, most HEEs occurred indoors at fast food restaurants and retail stores, and outdoors in parking lots, respectively. For Home-In HEEs, the median TVOC emission and removal rates were 5.4 g h-1 and 1.1 h-1, respectively. Our study demonstrates the ability to determine individual sensor-based time-resolved TVOC exposures in different MEs, in support of identifying potential sources and exposure factors that can inform exposure mitigation strategies.

Toward setting public health guidelines for chemicals in indoor settled dust?

Indoor settled dust may result in substantial human exposure to chemicals, especially by ingestion following hand-to-mouth or hand-to-object-to-mouth contact. As with other environmental media related to exposure, dust may thus be subject to regulation. An international scientific workshop was convened in Paris in September 2019 firstly to assess the relevance for public health of setting guidelines for indoor settled dust, and secondly to discuss scientific and technical challenges related to such guidelines. The main discussions and conclusions, with consensus achieved, are reported herein. Discussions concerned general considerations, objectives and definitions, relevance for a health-based guideline, units of measure, and finally derivation of the guideline. These points should be addressed when considering an indoor settled dust guideline as part of a policy to reduce exposure indoors to a given chemical or group of chemicals.

Enhancing life cycle chemical exposure assessment through ontology modeling.

In its 2014 report, A Framework Guide for the Selection of Chemical Alternatives, the National Academy of Sciences placed increased emphasis on comparative exposure assessment throughout the life cycle (i.e., from manufacturing to end-of-life) of a chemical. The inclusion of the full life cycle greatly increases the data demands for exposure assessments, including both the quantity and type of data. High throughput tools for exposure estimation add to this challenge by requiring rapid accessibility to data. In this work, ontology modeling was used to bridge the domains of exposure modeling and life cycle inventory modeling to facilitate data sharing and integration. The exposure ontology, ExO, is extended to describe human exposure to consumer products, while an inventory modeling ontology, LciO, is formulated to support automated data mining. The core ontology pieces are connected using a bridging ontology and discussed through a theoretical example to demonstrate how data from LCA can be leveraged to support rapid exposure modeling within a life cycle context.

Advances on a Decision Analytic Approach to Exposure-Based Chemical Prioritization.

The volume and variety of manufactured chemicals is increasing, although little is known about the risks associated with the frequency and extent of human exposure to most chemicals. The EPA and the recent signing of the Lautenberg Act have both signaled the need for high-throughput methods to characterize and screen chemicals based on exposure potential, such that more comprehensive toxicity research can be informed. Prior work of Mitchell et al. using multicriteria decision analysis tools to prioritize chemicals for further research is enhanced here, resulting in a high-level chemical prioritization tool for risk-based screening. Reliable exposure information is a key gap in currently available engineering analytics to support predictive environmental and health risk assessments. An elicitation with 32 experts informed relative prioritization of risks from chemical properties and human use factors, and the values for each chemical associated with each metric were approximated with data from EPA's CP_CAT database. Three different versions of the model were evaluated using distinct weight profiles, resulting in three different ranked chemical prioritizations with only a small degree of variation across weight profiles. Future work will aim to include greater input from human factors experts and better define qualitative metrics.

Establishing a system of consumer product use categories to support rapid modeling of human exposure.

Consumer product categorizations for use in predicting human chemical exposure provide a bridge between product composition data and consumer product use pattern information. Furthermore, the categories reflect other factors relevant to developing consumer product exposure scenarios, such as microenvironment of use (e.g., indoors or outdoors), method of application/form of release (e.g., spray versus liquid), release to various media, removal processes (e.g., rinse-off or wipe-off), and route-specific exposure factors (dermal surface areas of application, fraction of release in respirable form). While challenging, developing harmonized product categories can generalize the factors described above allowing for rapid parameterization of route-specific exposure scenario algorithms for new chemical/product applications and efficient utilization of new data on product use or composition. This can be accomplished via mapping product categories to likewise categorized release and use patterns or exposure factors. Here, hierarchical product use categories (PUCs) for consumer products that provide such mappings are presented and crosswalked with other internationally harmonized product categories for consumer exposure assessment. The PUCs were defined by applying use and exposure scenario information to the products in EPA's Chemical and Products Database (CPDat). This paper demonstrates how these PUCs are being used to rapidly parameterize algorithms for scenario-specific use, fate, and exposure in a probabilistic aggregate model of human exposure to chemicals used in consumer products. The PUCs provide a generic representation of consumer products for use in exposure assessment and provide an efficient framework for flexible and rapid data reporting and consumer exposure model parameterization.

Qualitative approach to comparative exposure in alternatives assessment.

Most alternatives assessments (AAs) published to date are largely hazard-based rankings, thereby ignoring potential differences in human and/or ecosystem exposures; as such, they may not represent a fully informed consideration of the advantages and disadvantages of possible alternatives. Building on the 2014 US National Academy of Sciences recommendations to improve AA decisions by including comparative exposure assessment into AAs, the Health and Environmental Sciences Institute's (HESI) Sustainable Chemical Alternatives Technical Committee, which comprises scientists from academia, industry, government, and nonprofit organizations, developed a qualitative comparative exposure approach. Conducting such a comparison can screen for alternatives that are expected to have a higher or different routes of human or environmental exposure potential, which together with consideration of the hazard assessment, could trigger a higher tiered, more quantitative exposure assessment on the alternatives being considered, minimizing the likelihood of regrettable substitution. This article outlines an approach for including chemical ingredient- and product-related exposure information in a qualitative comparison, including ingredient and product-related parameters. A classification approach was developed for ingredient and product parameters to support comparisons between alternatives as well as a methodology to address exposure parameter relevance and data quality. The ingredient parameters include a range of physicochemical properties that can impact routes and magnitude of exposure, whereas the product parameters include aspects such as product-specific exposure pathways, use information, accessibility, and disposal. Two case studies are used to demonstrate the application of the methodology. Key learnings and future research needs are summarized. Integr Environ Assess Manag 2018;00:000-000. © 2018 The Authors. Integrated Environmental Assessment and Management published by Wiley Periodicals, Inc. on behalf of Society of Environmental Toxicology & Chemistry (SETAC).

Integrating exposure to chemicals in building materials during use stage.

PURPOSE: There do not currently exist scientifically defensible ways to consistently characterize the human exposures (via various pathways) to near-field chemical emissions and associated health impacts during the use stage of building materials. The present paper thus intends to provide a roadmap which summarizes the current status and guides future development for integrating into LCA the chemical exposures and health impacts on various users of building materials, with a focus on building occupants. METHODS: We first review potential human health impacts associated with the substances in building materials and the methods used to mitigate these impacts, also identifying several of the most important online data resources. A brief overview of the necessary steps for characterizing use stage chemical exposures and health impacts for building materials is then provided. Finally, we propose a systematic approach to integrate the use stage exposures and health impacts into building material LCA and describe its components, and then present a case study illustrating the application of the proposed approach to two representative chemicals: formaldehyde and methylene diphenyl diisocyanate (MDI) in particleboard products. RESULTS AND DISCUSSION: Our proposed approach builds on the coupled near-field and far-field framework proposed by Fantke et al. (Environ Int 94:508-518, 2016), which is based on the product intake fraction (PiF) metric proposed by Jolliet et al. (Environ Sci Technol 49:8924-8931, 2015), The proposed approach consists of three major components: characterization of product usage and chemical content, human exposures, and toxicity, for which available methods and data sources are reviewed and research gaps are identified. The case study illustrates the difference in dominant exposure pathways between formaldehyde and MDI and also highlights the impact of timing and use duration (e.g., the initial 50 days of the use stage vs. the remaining 15 years) on the exposures and health impacts for the building occupants. CONCLUSIONS: The proposed approach thus provides the methodological basis for integrating into LCA the human health impacts associated with chemical exposures during the use stage of building materials. Data and modeling gaps which currently prohibit the application of the proposed systematic approach are discussed, including the need for chemical composition data, exposure models, and toxicity data. Research areas that are not currently focused on are also discussed, such as worker exposures and complex materials. Finally, future directions for integrating the use stage impacts of building materials into decision making in a tiered approach are discussed.

Consensus Modeling of Median Chemical Intake for the U.S. Population Based on Predictions of Exposure Pathways.

Prioritizing the potential risk posed to human health by chemicals requires tools that can estimate exposure from limited information. In this study, chemical structure and physicochemical properties were used to predict the probability that a chemical might be associated with any of four exposure pathways leading from sources-consumer (near-field), dietary, far-field industrial, and far-field pesticide-to the general population. The balanced accuracies of these source-based exposure pathway models range from 73 to 81%, with the error rate for identifying positive chemicals ranging from 17 to 36%. We then used exposure pathways to organize predictions from 13 different exposure models as well as other predictors of human intake rates. We created a consensus, meta-model using the Systematic Empirical Evaluation of Models framework in which the predictors of exposure were combined by pathway and weighted according to predictive ability for chemical intake rates inferred from human biomonitoring data for 114 chemicals. The consensus model yields an R2 of ∼0.8. We extrapolate to predict relevant pathway(s), median intake rate, and credible interval for 479 926 chemicals, mostly with minimal exposure information. This approach identifies 1880 chemicals for which the median population intake rates may exceed 0.1 mg/kg bodyweight/day, while there is 95% confidence that the median intake rate is below 1 µg/kg BW/day for 474572 compounds.

A Model for Risk-Based Screening and Prioritization of Human Exposure to Chemicals from Near-Field Sources.

Exposure- and risk-based assessments for chemicals used indoors or applied to humans (i.e., in near-field environments) necessitate an aggregate exposure pathway framework that aligns chemical exposure information from use sources to internal dose and eventually to their potential for health effects. Such a source-to-effect continuum is advocated to balance the complexity of human exposure and the insufficiency of relevant data for thousands of existing and emerging chemicals. Here, we introduce the Risk Assessment, IDentification And Ranking-Indoor and Consumer Exposure (RAIDAR-ICE) model, which establishes an integrated framework to evaluate human exposure due to indoor use and direct application of chemicals to humans. As a model evaluation, RAIDAR-ICE faithfully reproduces exposure estimates inferred from biomonitoring data for 37 chemicals with direct and indirect near-field sources. RAIDAR-ICE generates different rankings for 131 chemicals based on different exposure- and risk-based assessment metrics, demonstrating its versatility for diverse chemical screening goals. When coupled with a far-field RAIDAR model, the near-field RAIDAR-ICE model enables assessment of aggregate human exposure. Overall, RAIDAR-ICE is a powerful tool for high-throughput screening and prioritization of human exposure to neutral organic chemicals used indoors.

Aggregate Exposure Pathways in Support of Risk Assessment.

Over time, risk assessment has shifted from establishing relationships between exposure to a single chemical and a resulting adverse health outcome, to evaluation of multiple chemicals and disease outcomes simultaneously. As a result, there is an increasing need to better understand the complex mechanisms that influence risk of chemical and non-chemical stressors, beginning at their source and ending at a biological endpoint relevant to human or ecosystem health risk assessment. Just as the Adverse Outcome Pathway (AOP) framework has emerged as a means of providing insight into mechanism-based toxicity, the exposure science community has seen the recent introduction of the Aggregate Exposure Pathway (AEP) framework. AEPs aid in making exposure data applicable to the FAIR (i.e., findable, accessible, interoperable, and reusable) principle, especially by (1) organizing continuous flow of disjointed exposure information;(2) identifying data gaps, to focus resources on acquiring the most relevant data; (3) optimizing use and repurposing of existing exposure data; and (4) facilitating interoperability among predictive models. Herein, we discuss integration of the AOP and AEP frameworks and how such integration can improve confidence in both traditional and cumulative risk assessment approaches.

Refining the aggregate exposure pathway.

Advancements in measurement technologies and modeling capabilities continue to result in an abundance of exposure information, adding to that currently in existence. However, fragmentation within the exposure science community acts as an obstacle for realizing the vision set forth in the National Research Council's report on Exposure Science in the 21st century to consider exposures from source to dose, on multiple levels of integration, and to multiple stressors. The concept of an Aggregate Exposure Pathway (AEP) was proposed as a framework for organizing and integrating diverse exposure information that exists across numerous repositories and among multiple scientific fields. A workshop held in May 2016 followed introduction of the AEP concept, allowing members of the exposure science community to provide extensive evaluation and feedback regarding the framework's structure, key components, and applications. The current work briefly introduces topics discussed at the workshop and attempts to address key challenges involved in refining this framework. The resulting evolution in the AEP framework's features allows for facilitating acquisition, integration, organization, and transparent application and communication of exposure knowledge in a manner that is independent of its ultimate use, thereby enabling reuse of such information in many applications.

Conceptual Framework To Extend Life Cycle Assessment Using Near-Field Human Exposure Modeling and High-Throughput Tools for Chemicals.

Life Cycle Assessment (LCA) is a decision-making tool that accounts for multiple impacts across the life cycle of a product or service. This paper presents a conceptual framework to integrate human health impact assessment with risk screening approaches to extend LCA to include near-field chemical sources (e.g., those originating from consumer products and building materials) that have traditionally been excluded from LCA. A new generation of rapid human exposure modeling and high-throughput toxicity testing is transforming chemical risk prioritization and provides an opportunity for integration of screening-level risk assessment (RA) with LCA. The combined LCA and RA approach considers environmental impacts of products alongside risks to human health, which is consistent with regulatory frameworks addressing RA within a sustainability mindset. A case study is presented to juxtapose LCA and risk screening approaches for a chemical used in a consumer product. The case study demonstrates how these new risk screening tools can be used to inform toxicity impact estimates in LCA and highlights needs for future research. The framework provides a basis for developing tools and methods to support decision making on the use of chemicals in products.