Christine L. Russom

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.

QSAR modelling of the ERL-D fathead minnow acute toxicity database

1. Regression analysis has been applied to examine the structure-activity relationships regarding the acute fish toxicity (96 h LC50 fathead minnow) of organic chemicals. The log P dependent baseline toxicity model has been confirmed for a data set composed of 618 compounds from 24 chemical classes associated with a putative common mode of action. 2. Covariance analysis of the discrete by class regression functions resulted in the combination of chemicals to subsets associated with their mode of action. Separate models were derived for nonpolar (Class I) and polar (Class II and III) compounds. Chemicals which are more toxic than estimated from the baseline model are identified.

Workgroup report: review of fish bioaccumulation databases used to identify persistent, bioaccumulative, toxic substances.

Chemical management programs strive to protect human health and the environment by accurately identifying persistent, bioaccumulative, toxic substances and restricting their use in commerce. The advance of these programs is challenged by the reality that few empirical data are available for the tens of thousands of commercial substances that require evaluation. Therefore, most preliminary assessments rely on model predictions and data extrapolation. In November 2005, a workshop was held for experts from governments, industry, and academia to examine the availability and quality of in vivo fish bioconcentration and bioaccumulation data, and to propose steps to improve its prediction. The workshop focused on fish data because regulatory assessments predominantly focus on the bioconcentration of substances from water into fish, as measured using in vivo tests or predicted using computer models. In this article we review of the quantity, features, and public availability of bioconcentration, bioaccumulation, and biota–sediment accumulation data. The workshop revealed that there is significant overlap in the data contained within the various fish bioaccumulation data sources reviewed, and further, that no database contained all of the available fish bioaccumulation data. We believe that a majority of the available bioaccumulation data have been used in the development and testing of quantitative structure–activity relationships and computer models currently in use. Workshop recommendations included the publication of guidance on bioconcentration study quality, the combination of data from various sources to permit better access for modelers and assessors, and the review of chemical domains of existing models to identify areas for expansion.

ASTER: an integration of the AQUIRE data base and the QSAR system for use in ecological risk assessments

Ecological risk assessments are used by the US Environmental Protection Agency (US EPA) and other governmental agencies to assist in determining the probability and magnitude of deleterious effects of hazardous chemicals on plants and animals. These assessments are important steps in formulating regulatory decisions. The completion of an ecological risk assessment requires the gathering of ecotoxicological hazard and environmental exposure information. This information is evaluated in the risk characterization section to assist in making the final risk assessment. ASTER (ASsessment Tools for the Evaluation of Risk) was designed by the US EPA Environmental Research Laboratory-Duluth (ERL-D) to assist regulators in producing assessments. ASTER is an integration of the ACQUIRE (AQUatic toxicity Information REtrieval system) and QSAR (Quantitative Structure Activity Relationships) systems. ACQUIRE is a data base of aquatic toxicity tests and QSAR is comprised of a data base of measured physicochemical properties, and various QSAR models that estimate physicochemical and ecotoxicological endpoints. ASTER will be available to international governmental agencies through the US EPA National Computing Center.

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) 17β-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.

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.

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.

Guidance for Evaluating In Vivo Fish Bioaccumulation Data

ABSTRACT Currently, the laboratory-derived fish bioconcentration factor (BCF) serves as one of the primary data sources used to assess the potential for a chemical to bioaccumulate. Consequently, fish BCF values serve a central role in decision making and provide the basis for development of quantitative structure–property relationships (QSPRs) used to predict the bioaccumulation potential of untested compounds. However, practical guidance for critically reviewing experimental BCF studies is limited. This lack of transparent guidance hinders improvement in predictive models and can lead to uninformed chemical management decisions. To address this concern, a multiple-stakeholder workshop of experts from government, industry, and academia was convened by the International Life Sciences Institute Health and Environmental Sciences Institute to examine the data availability and quality issues associated with in vivo fish bioconcentration and bioaccumulation data. This paper provides guidance for evaluating key aspects of study design and conduct that must be considered when judging the reliability and adequacy of reported laboratory bioaccumulation data. Key criteria identified for judging study reliability include 1) clear specification of test substance and fish species investigated, 2) analysis of test substance in both fish tissue and exposure medium, 3) no significant adverse effects on exposed test fish, and 4) a reported test BCF that reflects steady-state conditions with unambiguous units. This guidance is then applied to 2 data-rich chemicals (anthracene and 2,3,7,8-tetrachlorodibenzo-p-dioxin) to illustrate the critical need for applying a systematic data quality assessment process. Use of these guidelines will foster development of more accurate QSPR models, improve the performance and reporting of future laboratory studies, and strengthen the technical basis for bioaccumulation assessment in chemicals management.

A QSAR study of the toxicity of amines to the fathead minnow.

Simple and multiple linear regressions were applied to the development of fish toxicity QSAR models for the 96-h LC50 to the fathead minnow, Pimephales promelas. The data on unbranched saturated primary alkylamines as well as the complete data set were well-fitted to linear QSAR models using log P or the valence first-order connectivity index (1XV) as descriptors. Although adding data on other subclasses of amines in this data set yield acceptable QSARs, only the tertiary amine subclass provided a poor fit with both of these descriptors. The amines include both acyclic and cyclic derivatives, either with no additional functional groups, or with the hydroxyl, keto, methoxy, and propargyl moieties. The molecular mechanism for fish toxicity of these amines as well as the outliers in the study were investigated. Based upon the calculated log P value of -1.40, tripropargylamine has an apparent excess toxicity of 84 times; in contrast, the measured shake-flask log P for this compound was subsequently found to be 1.26, giving a predicted LC50 consistent with the observed value. An upward curvature of the QSAR plot for the most hydrophilic compounds suggests a shift in mechanism for the lowest members of the series.