Enhancing life cycle chemical exposure assessment through ontology modeling

Application of read-across methods as a framework for the estimation of emissions from chemical processes

The read-across method is a popular data gap filling technique with developed application for multiple purposes, including regulatory. Within the US Environmental Protection Agency's (US EPA) New Chemicals Program under Toxic Substances Control Act (TSCA), read-across has been widely used, as well as within technical guidance published by the Organization for Economic Co-operation and Development, the European Chemicals Agency, and the European Center for Ecotoxicology and Toxicology of Chemicals for filling chemical toxicity data gaps. Under the TSCA New Chemicals Review Program, US EPA is tasked with reviewing proposed new chemical applications prior to commencing commercial manufacturing within or importing into the United States. The primary goal of this review is to identify any unreasonable human health and environmental risks, arising from environmental releases/emissions during manufacturing and the resulting exposure from these environmental releases. The authors propose the application of read-across techniques for the development and use of a framework for estimating the emissions arising during the chemical manufacturing process. This methodology is to utilize available emissions data from a structurally similar analogue chemical or a group of structurally similar chemicals in a chemical family taking into consideration their physicochemical properties under specified chemical process unit operations and conditions. This framework is also designed to apply existing knowledge of read-across principles previously utilized in toxicity estimation for an analogue or category of chemicals and introduced and extended with a concurrent case study.

Tracking end-of-life stage of chemicals: A scalable data-centric and chemical-centric approach

Chemical flow analysis (CFA) can be used for collecting life-cycle inventory (LCI), estimating environmental releases, and identifying potential exposure scenarios for chemicals of concern at the end-of-life (EoL) stage. Nonetheless, the demand for comprehensive data and the epistemic uncertainties about the pathway taken by the chemical flows make CFA, LCI, and exposure assessment time-consuming and challenging tasks. Due to the continuous growth of computer power and the appearance of more robust algorithms, data-driven modelling represents an attractive tool for streamlining these tasks. However, a data ingestion pipeline is required for the deployment of serving data-driven models in the real world. Hence, this work moves forward by contributing a chemical-centric and data-centric approach to extract, transform, and load comprehensive data for CFA at the EoL, integrating cross-year and country data and its provenance as part of the data lifecycle. The framework is scalable and adaptable to production-level machine learning operations. The framework can supply data at an annual rate, making it possible to deal with changes in the statistical distributions of model predictors like transferred amount and target variables (e.g., EoL activity identification) to avoid potential data-driven model performance decay over time. For instance, it can detect that recycling transfers of 643 chemicals over the reporting years (1988 to 2020) are 29.87%, 17.79%, and 20.56% for Canada, Australia, and the U.S. Finally, the developed approach enables research advancements on data-driven modelling to easily connect with other data sources for economic information on industry sectors, the economic value of chemicals, and the environmental regulatory implications that may affect the occurrence of an EoL transfer class or activity like recycling of a chemical over years and countries. Finally, stakeholders gain more context about environmental regulation stringency and economic affairs that could affect environmental decision-making and EoL chemical exposure predictions.

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.

Predicting Chemical End-of-Life Scenarios Using Structure-Based Classification Models

Analyzing chemicals and their effects on the environment from a life cycle viewpoint can produce a thorough analysis that takes end-of-life (EoL) activities into account. Chemical risk assessment, predicting environmental discharges, and finding EoL paths and exposure scenarios all depend on chemical flow data availability. However, it is challenging to gain access to such data and systematically determine EoL activities and potential chemical exposure scenarios. As a result, this work creates quantitative structure-transfer relationship (QSTR) models for aiding environmental managment decision-making based on chemical structure-based machine learning (ML) models to predict potential industrial EoL activities, chemical flow allocation, environmental releases, and exposure routes. Further multi-label classification methods may improve the predictability of QSTR models according to the ML experiment tracking. The developed QSTR models will assist stakeholders in predicting and comprehending potential EoL management activities and recycling loops, enabling environmental decision-making and EoL exposure assessment for new or existing chemicals in the global marketplace.

Exposure forecasting - ExpoCast - for data-poor chemicals in commerce and the environment

Estimates of exposure are critical to prioritize and assess chemicals based on risk posed to public health and the environment. The U.S. Environmental Protection Agency (EPA) is responsible for regulating thousands of chemicals in commerce and the environment for which exposure data are limited. Since 2009 the EPA's ExpoCast ("Exposure Forecasting") project has sought to develop the data, tools, and evaluation approaches required to generate rapid and scientifically defensible exposure predictions for the full universe of existing and proposed commercial chemicals. This review article aims to summarize issues in exposure science that have been addressed through initiatives affiliated with ExpoCast. ExpoCast research has generally focused on chemical exposure as a statistical systems problem intended to inform thousands of chemicals. The project exists as a companion to EPA's ToxCast ("Toxicity Forecasting") project which has used in vitro high-throughput screening technologies to characterize potential hazard posed by thousands of chemicals for which there are limited toxicity data. Rapid prediction of chemical exposures and in vitro-in vivo extrapolation (IVIVE) of ToxCast data allow for prioritization based upon risk of adverse outcomes due to environmental chemical exposure. ExpoCast has developed (1) integrated modeling approaches to reliably predict exposure and IVIVE dose, (2) highly efficient screening tools for chemical prioritization, (3) efficient and affordable tools for generating new exposure and dose data, and (4) easily accessible exposure databases. The development of new exposure models and databases along with the application of technologies like non-targeted analysis and machine learning have transformed exposure science for data-poor chemicals. By developing high-throughput tools for chemical exposure analytics and translating those tools into public health decisions ExpoCast research has served as a crucible for identifying and addressing exposure science knowledge gaps.

The Human Disease Ontology 2022 update

The Human Disease Ontology (DO) (www.disease-ontology.org) database, has significantly expanded the disease content and enhanced our userbase and website since the DO’s 2018 Nucleic Acids Research DATABASE issue paper. Conservatively, based on available resource statistics, terms from the DO have been annotated to over 1.5 million biomedical data elements and citations, a 10× increase in the past 5 years. The DO, funded as a NHGRI Genomic Resource, plays a key role in disease knowledge organization, representation, and standardization, serving as a reference framework for multiscale biomedical data integration and analysis across thousands of clinical, biomedical and computational research projects and genomic resources around the world. This update reports on the addition of 1,793 new disease terms, a 14% increase of textual definitions and the integration of 22 137 new SubClassOf axioms defining disease to disease connections representing the DO’s complex disease classification. The DO’s updated website provides multifaceted etiology searching, enhanced documentation and educational resources.

Catalyzing Knowledge-Driven Discovery in Environmental Health Sciences through a Community-Driven Harmonized Language

Harmonized language is critical for helping researchers to find data, collecting scientific data to facilitate comparison, and performing pooled and meta-analyses. Using standard terms to link data to knowledge systems facilitates knowledge-driven analysis, allows for the use of biomedical knowledge bases for scientific interpretation and hypothesis generation, and increasingly supports artificial intelligence (AI) and machine learning. Due to the breadth of environmental health sciences (EHS) research and the continuous evolution in scientific methods, the gaps in standard terminologies, vocabularies, ontologies, and related tools hamper the capabilities to address large-scale, complex EHS research questions that require the integration of disparate data and knowledge sources. The results of prior workshops to advance a harmonized environmental health language demonstrate that future efforts should be sustained and grounded in scientific need. We describe a community initiative whose mission was to advance integrative environmental health sciences research via the development and adoption of a harmonized language. The products, outcomes, and recommendations developed and endorsed by this community are expected to enhance data collection and management efforts for NIEHS and the EHS community, making data more findable and interoperable. This initiative will provide a community of practice space to exchange information and expertise, be a coordination hub for identifying and prioritizing activities, and a collaboration platform for the development and adoption of semantic solutions. We encourage anyone interested in advancing this mission to engage in this community.

Data engineering for tracking chemicals and releases at industrial end-of-life activities

Performing risk evaluation is necessary to determine whether a chemical substance presents an unreasonable risk of injury to human health or the environment across its life cycle stages. Data gathering, reconciliation, and management for supporting risk evaluation are time-consuming and challenging, especially for end-of-life (EoL) activities due to the need for proper reporting and traceability. A data engineering framework using publicly-available databases to track chemicals in waste streams generated by industrial activities and transferred to other facilities across different U.S. locations for waste management is implemented. The analysis tracks chemicals in waste streams generated at industrial processes and handling at off-site facilities and then estimates releases from EoL activities. The final product of this effort is a framework that identifies a set of chemical, activity, and industry sector categories as well as hazardous waste flows, emission factors, and uncertainty indicators to describe EoL activities. This framework helps to identify EoL exposure scenarios that would otherwise not be evaluated. As a case study, methylene chloride, one of the first ten chemicals to undergo risk evaluation under the amended U.S. Toxic Substances Control Act, was evaluated with results highlighting potential additional exposure scenarios.

Assessing Human Exposure to SVOCs in Materials, Products, and Articles: A Modular Mechanistic Framework

A critical review of the current state of knowledge of chemical emissions from indoor sources, partitioning among indoor compartments, and the ensuing indoor exposure leads to a proposal for a modular mechanistic framework for predicting human exposure to semivolatile organic compounds (SVOCs). Mechanistically consistent source emission categories include solid, soft, frequent contact, applied, sprayed, and high temperature sources. Environmental compartments are the gas phase, airborne particles, settled dust, indoor surfaces, and clothing. Identified research needs are the development of dynamic emission models for several of the source emission categories and of estimation strategies for critical model parameters. The modular structure of the framework facilitates subsequent inclusion of new knowledge, other chemical classes of indoor pollutants, and additional mechanistic processes relevant to human exposure indoors. The framework may serve as the foundation for developing an open-source community model to better support collaborative research and improve access for application by stakeholders. Combining exposure estimates derived using this framework with toxicity data for different end points and toxicokinetic mechanisms will accelerate chemical risk prioritization, advance effective chemical management decisions, and protect public health.

Advancing systematic-review methodology in exposure science for environmental health decision making

Systematic review (SR) is a rigorous methodology applied to synthesize and evaluate a body of scientific evidence to answer a research or policy question. Effective use of systematic-review methodology enables use of research evidence by decision makers. In addition, as reliance on systematic reviews increases, the required standards for quality of evidence enhances the policy relevance of research. Authoritative guidance has been developed for use of SR to evaluate evidence in the fields of medicine, social science, environmental epidemiology, toxicology, as well as ecology and evolutionary biology. In these fields, SR is typically used to evaluate a cause-effect relationship, such as the effect of an intervention, procedure, therapy, or exposure on an outcome. However, SR is emerging to be a useful methodology to transparently review and integrate evidence for a wider range of scientifically informed decisions and actions across disciplines. As SR is being used more broadly, there is growing consensus for developing resources, guidelines, ontologies, and technology to make SR more efficient and transparent, especially for handling large amounts of diverse data being generated across multiple scientific disciplines. In this article, we advocate for advancing SR methodology as a best practice in the field of exposure science to synthesize exposure evidence and enhance the value of exposure studies. We discuss available standards and tools that can be applied and extended by exposure scientists and highlight early examples of SRs being developed to address exposure research questions. Finally, we invite the exposure science community to engage in further development of standards and guidance to grow application of SR in this field and expand the opportunities for exposure science to inform environment and public health decision making.