The ECOTOXicology Knowledgebase: A Curated Database of Ecologically Relevant Toxicity Tests to Support Environmental Research and Risk Assessment.

The need for assembled existing and new toxicity data has accelerated as the amount of chemicals introduced into commerce continues to grow and regulatory mandates require safety assessments for a greater number of chemicals. To address this evolving need, the ECOTOXicology Knowledgebase (ECOTOX) was developed starting in the 1980s and is currently the world's largest compilation of curated ecotoxicity data, providing support for assessments of chemical safety and ecological research through systematic and transparent literature review procedures. The recently released version of ECOTOX (Ver 5, www.epa.gov/ecotox) provides single-chemical ecotoxicity data for over 12,000 chemicals and ecological species with over one million test results from over 50,000 references. Presented is an overview of ECOTOX, detailing the literature review and data curation processes within the context of current systematic review practices and discussing how recent updates improve the accessibility and reusability of data to support the assessment, management, and research of environmental chemicals. Relevant and acceptable toxicity results are identified from studies in the scientific literature, with pertinent methodological details and results extracted following well-established controlled vocabularies and newly extracted toxicity data added quarterly to the public website. Release of ECOTOX, Ver 5, included an entirely redesigned user interface with enhanced data queries and retrieval options, visualizations to aid in data exploration, customizable outputs for export and use in external applications, and interoperability with chemical and toxicity databases and tools. This is a reliable source of curated ecological toxicity data for chemical assessments and research and continues to evolve with accessible and transparent state-of-the-art practices in literature data curation and increased interoperability to other relevant resources. Environ Toxicol Chem 2022;41:1520-1539. © 2022 SETAC. This article has been contributed to by US Government employees and their work is in the public domain in the USA.

Development of an adverse outcome pathway for acetylcholinesterase inhibition leading to acute mortality.

Adverse outcome pathways (AOPs) are designed to describe linkages of key events within a biological pathway that result in an adverse outcome associated with chemical perturbation of a well-defined molecular initiating event. Risk assessors have traditionally relied on data from apical endpoints (e.g., mortality, growth, reproduction) to derive benchmark values for use in determining the potential adverse impacts of chemicals. One goal in building reliable and well-characterized AOPs is to identify relevant in vitro assays and/or in vivo biomarkers that could be used in screening the potential hazard of substances, thereby reducing costs and increasing the number of chemicals that can be evaluated in a timely fashion. The purpose of this review article is to build an AOP for substances with a molecular initiating event of acetylcholinesterase inhibition leading to acute mortality following guidance developed by the Organisation for Economic Cooperation and Development. In contrast to most other AOPs developed to date, in which coverage is for a relatively limited taxonomic group or life stage, this AOP is applicable to a wide range of species at multiple life stages. Furthermore, while development of most AOPs has relied on data for a few model chemicals, the AOP described in the present review captures information from a large number of studies with a diversity of organophosphate and carbamate insecticides.

Molecular target sequence similarity as a basis for species extrapolation to assess the ecological risk of chemicals with known modes of action.

It is not feasible to conduct toxicity tests with all species that may be impacted by chemical exposures. Therefore, cross-species extrapolation is fundamental to environmental risk assessment. Recognition of the impracticality of generating empirical, whole organism, toxicity data for the extensive universe of chemicals in commerce has been an impetus driving the field of predictive toxicology. We describe a strategy that leverages expanding databases of molecular sequence information together with identification of specific molecular chemical targets whose perturbation can lead to adverse outcomes to support predictive species extrapolation. This approach can be used to predict which species may be more (or less) susceptible to effects following exposure to chemicals with known modes of action (e.g., pharmaceuticals, pesticides). Primary amino acid sequence alignments are combined with more detailed analyses of conserved functional domains to derive the predictions. This methodology employs bioinformatic approaches to automate, collate, and calculate quantitative metrics associated with cross-species sequence similarity of key molecular initiating events (MIEs). Case examples focused on the actions of (a) 17α-ethinyl estradiol on the human (Homo sapiens) estrogen receptor; (b) permethrin on the mosquito (Aedes aegypti) voltage-gated para-like sodium channel; and (c) 17ß-trenbolone on the bovine (Bos taurus) androgen receptor are presented to demonstrate the potential predictive utility of this species extrapolation strategy. The examples compare empirical toxicity data to cross-species predictions of intrinsic susceptibility based on analyses of sequence similarity relevant to the MIEs of defined adverse outcome pathways. Through further refinement, and definition of appropriate domains of applicability, we envision practical and routine utility for the molecular target similarity-based predictive method in chemical risk assessment, particularly where testing resources are limited.

Prediction of aquatic toxicity mode of action using linear discriminant and random forest models.

The ability to determine the mode of action (MOA) for a diverse group of chemicals is a critical part of ecological risk assessment and chemical regulation. However, existing MOA assignment approaches in ecotoxicology have been limited to a relatively few MOAs, have high uncertainty, or rely on professional judgment. In this study, machine based learning algorithms (linear discriminant analysis and random forest) were used to develop models for assigning aquatic toxicity MOA. These methods were selected since they have been shown to be able to correlate diverse data sets and provide an indication of the most important descriptors. A data set of MOA assignments for 924 chemicals was developed using a combination of high confidence assignments, international consensus classifications, ASTER (ASessment Tools for the Evaluation of Risk) predictions, and weight of evidence professional judgment based an assessment of structure and literature information. The overall data set was randomly divided into a training set (75%) and a validation set (25%) and then used to develop linear discriminant analysis (LDA) and random forest (RF) MOA assignment models. The LDA and RF models had high internal concordance and specificity and were able to produce overall prediction accuracies ranging from 84.5 to 87.7% for the validation set. These results demonstrate that computational chemistry approaches can be used to determine the acute toxicity MOAs across a large range of structures and mechanisms.

Adverse outcome pathways: a conceptual framework to support ecotoxicology research and risk assessment.

Ecological risk assessors face increasing demands to assess more chemicals, with greater speed and accuracy, and to do so using fewer resources and experimental animals. New approaches in biological and computational sciences may be able to generate mechanistic information that could help in meeting these challenges. However, to use mechanistic data to support chemical assessments, there is a need for effective translation of this information into endpoints meaningful to ecological risk-effects on survival, development, and reproduction in individual organisms and, by extension, impacts on populations. Here we discuss a framework designed for this purpose, the adverse outcome pathway (AOP). An AOP is a conceptual construct that portrays existing knowledge concerning the linkage between a direct molecular initiating event and an adverse outcome at a biological level of organization relevant to risk assessment. The practical utility of AOPs for ecological risk assessment of chemicals is illustrated using five case examples. The examples demonstrate how the AOP concept can focus toxicity testing in terms of species and endpoint selection, enhance across-chemical extrapolation, and support prediction of mixture effects. The examples also show how AOPs facilitate use of molecular or biochemical endpoints (sometimes referred to as biomarkers) for forecasting chemical impacts on individuals and populations. In the concluding sections of the paper, we discuss how AOPs can help to guide research that supports chemical risk assessments and advocate for the incorporation of this approach into a broader systems biology framework.

Overview of data and conceptual approaches for derivation of quantitative structure-activity relationships for ecotoxicological effects of organic chemicals.

The use of quantitative structure-activity relationships (QSARs) in assessing potential toxic effects of organic chemicals on aquatic organisms continues to evolve as computational efficiency and toxicological understanding advance. With the ever-increasing production of new chemicals, and the need to optimize resources to assess thousands of existing chemicals in commerce, regulatory agencies have turned to QSARs as essential tools to help prioritize tiered risk assessments when empirical data are not available to evaluate toxicological effects. Progress in designing scientifically credible QSARs is intimately associated with the development of empirically derived databases of well-defined and quantified toxicity endpoints, which are based on a strategic evaluation of diverse sets of chemical structures, modes of toxic action, and species. This review provides a brief overview of four databases created for the purpose of developing QSARs for estimating toxicity of chemicals to aquatic organisms. The evolution of QSARs based initially on general chemical classification schemes, to models founded on modes of toxic action that range from nonspecific partitioning into hydrophobic cellular membranes to receptor-mediated mechanisms is summarized. Finally, an overview of expert systems that integrate chemical-specific mode of action classification and associated QSAR selection for estimating potential toxicological effects of organic chemicals is presented.

An overview of the use of quantitative structure-activity relationships for ranking and prioritizing large chemical inventories for environmental risk assessments.

Ecological risk assessments for chemical stressors are used to establish linkages between likely exposure concentrations and adverse effects to ecological receptors. At times, it is useful to conduct screening risk assessments to assist in prioritizing or ranking chemicals on the basis of potential hazard and exposure assessment parameters. Ranking of large chemical inventories can provide evidence for focusing research and/or cleanup efforts on specific chemicals of concern. Because of financial and time constraints, data gaps exist, and the risk assessor is left with decisions on which models to use to estimate the parameter of concern. In this review, several methods are presented for using quantitative structure-activity relationships (QSARs) in conducting hazard screening or screening-level risk assessments. The ranking methods described include those related to current regulatory issues associated with chemical inventories from Canada, Europe, and the United States and an example of a screening-level risk assessment conducted on chemicals associated with a watershed in the midwest region of the United States.

Mining environmental toxicology information: web resources.

Environmental toxicology is the study of the ecological effects of anthropogenic substances released into the environment. It is a relatively diverse field addressing impacts to aquatic and terrestrial organisms and communities. The determination of potential risk associated with toxic agents is of interest to government regulators, industry, researchers, private organizations and citizen groups. In assessing the ecological risk associated with a chemical stressor, it is important to establish linkages between likely exposure concentrations and adverse effects to ecological receptors. To do so requires access to reliable information resources. The proper application of such data requires familiarity with the scientific literature and keeping abreast of new and emerging issues as well as state-of-the-art research findings and methods. In addition, an understanding of government regulations as they relate to environmental issues is also of primary interest. The advent of the Web has made these tools accessible at computer desktops. This review focuses on currently available free Web resources related to environmental toxicology, specifically those which address available empirical data sources, predictive tools and publications of interest such as standard test methods, guidance documents and governmental regulations.