Linking mechanistic toxicology to population models in forecasting recovery from chemical stress: A case study from Jackfish Bay, Ontario, Canada

Projection of Interspecific Competition (PIC) Matrices: A Conceptual Framework for Inclusion in Population Risk Assessments

Accounting for intraspecific and interspecific competition when assessing the effects of chemical and nonchemical stressors is an important uncertainty in ecological risk assessments. We developed novel projection of interspecific competition (PIC) matrices that allow for analysis of population dynamics of two or more species exposed to a given stressor(s) that compete for shared resources within a landscape. We demonstrate the application of PIC matrices to investigate the population dynamics of two hypothetical fish species that compete with one another and have differences in net reproductive rate and intrinsic rate of population increase. Population status predictions were made under scenarios that included exposure to a chemical stressor that reduced fecundity for one or both species. The results of our simulations demonstrated that measures obtained from the life table and Leslie matrix of an organism, including net reproductive rate and intrinsic rate of increase, can result in erroneous conclusions of population status and viability in the absence of a consideration of resource limitation and interspecific competition. This modeling approach can be used in conjunction with field monitoring efforts and/or laboratory testing to link effects due to stressors to possible outcomes within an ecosystem. In addition, PIC matrices could be combined with adverse outcome pathways to allow for ecosystem projection based on taxonomic conservation of molecular targets of chemicals to predict the likelihood of relative cross-species susceptibility. Overall, the present study shows how PIC matrices can integrate effects across the life cycles of multiple species, provide a linkage between endpoints observed in individual and population-level responses, and project outcomes at the community level for multiple generations for multiple species that compete for limited resources. Environ Toxicol Chem 2024;00:1-17. Published 2024. This article is a U.S. Government work and is in the public domain in the USA.

Assessing Contaminants of Emerging Concern in the Great Lakes Ecosystem: A Decade of Method Development and Practical Application

Assessing the ecological risk of contaminants in the field typically involves consideration of a complex mixture of compounds which may or may not be detected via instrumental analyses. Further, there are insufficient data to predict the potential biological effects of many detected compounds, leading to their being characterized as contaminants of emerging concern (CECs). Over the past several years, advances in chemistry, toxicology, and bioinformatics have resulted in a variety of concepts and tools that can enhance the pragmatic assessment of the ecological risk of CECs. The present Focus article describes a 10+- year multiagency effort supported through the U.S. Great Lakes Restoration Initiative to assess the occurrence and implications of CECs in the North American Great Lakes. State-of-the-science methods and models were used to evaluate more than 700 sites in about approximately 200 tributaries across lakes Ontario, Erie, Huron, Michigan, and Superior, sometimes on multiple occasions. Studies featured measurement of up to 500 different target analytes in different environmental matrices, coupled with evaluation of biological effects in resident species, animals from in situ and laboratory exposures, and in vitro systems. Experimental taxa included birds, fish, and a variety of invertebrates, and measured endpoints ranged from molecular to apical responses. Data were integrated and evaluated using a diversity of curated knowledgebases and models with the goal of producing actionable insights for risk assessors and managers charged with evaluating and mitigating the effects of CECs in the Great Lakes. This overview is based on research and data captured in approximately about 90 peer-reviewed journal articles and reports, including approximately about 30 appearing in a virtual issue comprised of highlighted papers published in Environmental Toxicology and Chemistry or Integrated Environmental Assessment and Management. Environ Toxicol Chem 2023;42:2506-2518. © 2023 SETAC. This article has been contributed to by U.S. Government employees and their work is in the public domain in the USA.

A Multidimensional Matrix Model for Predicting the Effects of Male-Biased Sex Ratios on Fish Populations

Laboratory experiments have established that exposure to certain endocrine-active substances prior to and/or during the period of sexual differentiation can lead to skewed sex ratios in fish. However, the potential long-term population impact of biased sex ratio depends on multiple factors including the life history of the species and whether the ratio is male or female-biased. In the present study, we describe a novel multidimensional, density-dependent matrix model that analyzes age class-structure of both males and females over time, allowing for the quantitative evaluation of the effects of biased sex ratio on population status. This approach can be used in conjunction with field monitoring efforts and/or laboratory testing to link effects on sex ratio due to chemical and/or nonchemical stressors to adverse outcomes in whole organisms and populations. For demonstration purposes, we applied the model to evaluate population trajectories for fathead minnow (Pimephales promelas) exposed to prochloraz, an aromatase inhibitor, during sexual differentiation. The model also was used to explore the population impact in a more realistic exposure scenario in which both adult and early life stages of fish are exposed concurrently to prochloraz, which, in addition to altering sex ratio during development, can decrease vitellogenin and egg production in adult females. For each exposure scenario, the model was used to analyze total population size, numbers of females and of males, and sex specific recruitment of the F1 generation. The present study illustrates the utility of multidimensional matrix population models for ecological risk assessment in terms of integrating effects across a population of an organism even when chemical effects on individuals are manifested via different pathways depending on life stage. Environ Toxicol Chem 2022;41:1066-1077. Published 2022. This article is a U.S. Government work and is in the public domain in the USA.

A 30-Year Study of Impacts, Recovery, and Development of Critical Effect Sizes for Endocrine Disruption in White Sucker (Catostomus commersonii) Exposed to Bleached-Kraft Pulp Mill Effluent at Jackfish Bay, Ontario, Canada

Jackfish Bay is an isolated bay on the north shore of Lake Superior, Canada that has received effluent from a large bleached-kraft pulp mill since the 1940s. Studies conducted in the late 1980s found evidence of reductions in sex steroid hormone levels in multiple fish species living in the Bay, and increased growth, condition and relative liver weights, with a reduction in internal fat storage, reduced gonadal sizes, delayed sexual maturation, and altered levels of circulating sex steroid hormones in white sucker (Catostomus commersonii). These early studies provided some of the first pieces of evidence of endocrine disruption in wild animals. Studies on white sucker have continued at Jackfish Bay, monitoring fish health after the installation of secondary waste treatment (1989), changes in the pulp bleaching process (1990s), during facility maintenance shutdowns and during a series of facility closures associated with changing ownership (2000s), and were carried through to 2019 resulting in a 30-year study of fish health impacts, endocrine disruption, chemical exposure, and ecosystem recovery. The objective of the present study was to summarize and understand more than 75 physiological, endocrine, chemical and whole organism endpoints that have been studied providing important context for the complexity of endocrine responses, species differences, and challenges with extrapolation. Differences in body size, liver size, gonad size and condition persist, although changes in liver and gonad indices are much smaller than in the early years. Population modeling of the initial reproductive alterations predicted a 30% reduction in the population size, however with improvements over the last couple of decades those population impacts improved considerably. Reflection on these 30 years of detailed studies, on environmental conditions, physiological, and whole organism endpoints, gives insight into the complexity of endocrine responses to environmental change and mitigation.

Quantification of an Adverse Outcome Pathway Network by Bayesian Regression and Bayesian Network Modeling

The adverse outcome pathway (AOP) framework has gained international recognition as a systematic approach linking mechanistic processes to toxicity endpoints. Nevertheless, successful implementation into risk assessments is still limited by the lack of quantitative AOP models (qAOPs) and assessment of uncertainties. The few published qAOP models so far are typically based on data-demanding systems biology models. Here, we propose a less data-demanding approach for quantification of AOPs and AOP networks, based on regression modeling and Bayesian networks (BNs). We demonstrate this approach with the proposed AOP #245, "Uncoupling of photophosphorylation leading to reduced ATP production associated growth inhibition," using a small experimental data set from exposure of Lemna minor to the pesticide 3,5-dichlorophenol. The AOP-BN reflects the network structure of AOP #245 containing 2 molecular initiating events (MIEs), 3 key events (KEs), and 1 adverse outcome (AO). First, for each dose-response and response-response (KE) relationship, we quantify the causal relationship by Bayesian regression modeling. The regression models correspond to dose-response functions commonly applied in ecotoxicology. Secondly, we apply the fitted regression models with associated uncertainty to simulate 10 000 response values along the predictor gradient. Thirdly, we use the simulated values to parameterize the conditional probability tables of the BN model. The quantified AOP-BN model can be run in several directions: 1) prognostic inference, run forward from the stressor node to predict the AO level; 2) diagnostic inference, run backward from the AO node; and 3) omnidirectionally, run from the intermediate MIEs and/or KEs. Internal validation shows that the AOP-BN can obtain a high accuracy rate, when run is from intermediate nodes and when a low resolution is acceptable for the AO. Although the performance of this AOP-BN is limited by the small data set, our study demonstrates a proof-of-concept: the combined use of Bayesian regression modeling and Bayesian network modeling for quantifying AOPs. Integr Environ Assess Manag 2021;17:147-164. © 2020 The Authors. Integrated Environmental Assessment and Management published by Wiley Periodicals LLC on behalf of Society of Environmental Toxicology & Chemistry (SETAC).

A multidimensional density dependent matrix population model for assessing risk of stressors to fish populations

Modeling exposure and recovery of fish and wildlife populations after stressor mitigation serves as a basis for evaluating remediation success. Herein, we develop a novel multidimensional density dependent matrix population model that analyzes both size-structure and age class-structure simultaneously. This modeling approach emphasizes application in conjunction with field monitoring efforts (e.g., through effects-based monitoring programs) and/or laboratory analysis to link effects due to stressors to outcomes in populations. We applied the model to investigate Atlantic killifish (Fundulus heteroclitus) exposed to 2,3,7,8-tetrachlorodibenzo-p-dioxin with effects on fertility and survival rates. The Atlantic killifish is an important and well-studied model organism for understanding the effects of pollutants and other stressors in estuarine and marine ecosystems. For each exposure concentration, the corresponding plots of total population size, population size structure, and age structure over time were generated. The present study serves as an example of how a multidimensional matrix population model can integrate effects across the life cycle, provide a linkage between endpoints observed in the individual and ecological risk to the population as a whole, and project outcomes for multiple generations.

Identifying adverse outcome pathways (AOP) for Amsterdam city fish by integrated field monitoring

The European City Fish project aimed to develop a generic methodology for ecological risk assessment for urban rivers. Since traditional methods only consider a small fraction of substances present in the water cycle, biological effect monitoring is required for a more reliable assessment of the pollution status. A major challenge for environmental risk assessment (ERA) is the application of adverse outcome pathways (AOP), i.e. the linking of pollutant exposure via early molecular and biochemical changes to physiological effects and, ultimately, effects on populations and ecosystems. We investigated the linkage between responses at these different levels. Many AOP aspects were investigated, from external and internal exposure to different classes of micropollutants, via molecular key events (MKE) the impacts on organs and organisms (fish physiology), to changes in the population dynamics of fish. Risk assessment procedures were evaluated by comparing environmental quality standards, bioassay responses, biomarkers in caged and feral fish, and the impact on fish populations. Although no complete AOP was observed, indirect relationships linking pollutant exposure via MKE to impaired locomotion were demonstrated at the most polluted site near a landfill for chemical waste. The pathway indicated that several upstream key events requiring energy for stress responses and toxic defence are likely to converge at a single common MKE: increased metabolic demands. Both fish biomarkers and the bioanalytical SIMONI strategy are valuable indicators for micropollutant risks to fish communities.

Integration of Chlorpyrifos Acetylcholinesterase Inhibition, Water Temperature, and Dissolved Oxygen Concentration into a Regional Scale Multiple Stressor Risk Assessment Estimating Risk to Chinook Salmon

We estimated the risk to populations of Chinook salmon (Oncorhynchus tshawytscha) due to chlorpyrifos (CH), water temperature (WT), and dissolved oxygen concentration (DO) in 4 watersheds in Washington State, USA. The watersheds included the Nooksack and Skagit Rivers in the Northern Puget Sound, the Cedar River in the Seattle-Tacoma corridor, and the Yakima River, a tributary of the Columbia River. The Bayesian network relative risk model (BN-RRM) was used to conduct this ecological risk assessment and was modified to contain an acetylcholinesterase (AChE) inhibition pathway parameterized using data from CH toxicity data sets. The completed BN-RRM estimated risk at a population scale to Chinook salmon employing classical matrix modeling runs up to 50-y timeframes. There were 3 primary conclusions drawn from the model-building process and the risk calculations. First, the incorporation of an AChE inhibition pathway and the output from a population model can be combined with environmental factors in a quantitative fashion. Second, the probability of not meeting the management goal of no loss to the population ranges from 65% to 85%. Environmental conditions contributed to a larger proportion of the risk compared to CH. Third, the sensitivity analysis describing the influence of the variables on the predicted risk varied depending on seasonal conditions. In the summer, WT and DO were more influential than CH. In the winter, when the seasonal conditions are more benign, CH was the driver. Fourth, in order to reach the management goal, we calculated the conditions that would increase juvenile survival, adult survival, and a reduction in toxicological effects. The same process in this example should be applicable to the inclusion of multiple pesticides and to more descriptive population models such as those describing metapopulations. Integr Environ Assess Manag 2019;00:1-15. © 2019 SETAC.

How do we take the pulse of an aquatic ecosystem? Current and historical approaches to measuring ecosystem integrity

Global environmental monitoring has indicated that the structure and function of some aquatic ecosystems has been significantly altered by human activities. There are many potential causes for these changes; however, one major concern is the increasing release of anthropogenic contaminants into aquatic environments. Although toxicological responses of individual organisms are typically well characterized, few studies have focused on characterizing toxicity at the ecosystem level. In fact, because of their scale and complexity, changes in ecosystem integrity are rarely considered in assessments of risks to ecosystems. This work attempts to move the conversation forward by defining integrity of ecosystems, reviewing current and historical approaches to measuring ecosystem integrity status (e.g., structural and functional measurements), and highlighting methods that could significantly contribute to the field of ecosystem toxicology (e.g., keystone species, environmental energetics, ecotoxicological modeling, and adverse outcome pathways [AOPs]). Through a critical analysis of current and historical methodologies, the present study offers a comprehensive, conceptual framework for the assessment of risks of contaminant exposure for whole ecosystems and proposes steps to facilitate better diagnoses of the integrity of aquatic systems. Environ Toxicol Chem 2019;38:289-301. © 2018 SETAC.

In-silico approach for drug induced liver injury prediction: Recent advances

Drug induced liver injury (DILI) is the prime cause of liver disfunction which may lead to mild non-specific symptoms to more severe signs like hepatitis, cholestasis, cirrhosis and jaundice. Not only the prescription medications, but the consumption of herbs and health supplements have also been reported to cause these adverse reactions resulting into high mortality rates and post marketing withdrawal of drugs. Due to the continuously increasing DILI incidences in recent years, robust prediction methods with high accuracy, specificity and sensitivity are of priority. Bioinformatics is the emerging field of science that has been used in the past few years to explore the mechanisms of DILI. The major emphasis of this review is the recent advances of in silico tools for the diagnostic and therapeutic interventions of DILI. These tools have been developed and widely used in the past few years for the prediction of pathways induced from both hepatotoxic as well as hepatoprotective Chinese drugs and for the identification of DILI specific biomarkers for prognostic purpose. In addition to this, advanced machine learning models have been developed for the classification of drugs into DILI causing and non-DILI causing. Moreover, development of 3 class models over 2 class offers better understanding of multi-class DILI risks and at the same time providing authentic prediction of toxicity during drug designing before clinical trials.

The Adverse Outcome Pathway: A Multifaceted Framework Supporting 21st Century Toxicology

The adverse outcome pathway (AOP) framework serves as a knowledge assembly, interpretation, and communication tool designed to support the translation of pathway-specific mechanistic data into responses relevant to assessing and managing risks of chemicals to human health and the environment. As such, AOPs facilitate the use of data streams often not employed by risk assessors, including information from in silico models, in vitro assays and short-term in vivo tests with molecular/biochemical endpoints. This translational capability can increase the capacity and efficiency of safety assessments both for single chemicals and chemical mixtures. Our mini-review describes the conceptual basis of the AOP framework and aspects of its current status relative to use by toxicologists and risk assessors, including four illustrative applications of the framework to diverse assessment scenarios.

Evaluating the potential of effluent extracts from pulp and paper mills in Canada, Brazil, and New Zealand to affect fish reproduction: Estrogenic effects in fish

The authors examined the potential of pulp mill effluent from pulp-producing countries (Canada, Brazil, New Zealand) to affect fish reproduction. Specifically, the estrogenic effects in juvenile rainbow trout (Oncorhynchus mykiss) pulse-exposed to 11 different mill effluent extracts (intraperitoneal injections of solid-phase extraction-dichloromethane nonpolar fraction). The results indicated that effluent extracts were estrogenic in juvenile trout irrespective of the gender, as reflected by increasing level of plasma vitellogenin (VTG; Brazil > New Zealand > Canada). Despite the high variability observed among mills, differences in VTG levels were related to the type of mill process (kraft > elementary chlorine-free kraft > thermomechanical pulping). Moreover, effluent treatments did not appear to significantly decrease VTG induction. A consistent estrogenic effect was observed in those mills that process a combination of feedstocks (softwood and hardwood), with the highest increase in VTG related to eucalyptus feedstock. The results demonstrate significant estrogenic effects of pulp mill effluents on chronically exposed juvenile trout, suggesting that in vivo metabolic activation of precursors is necessary to cause the observed increases in VTG levels. This molecular estrogenic response provides a useful starting point for predicting population-level impacts through the adverse outcome pathway methodology. Environ Toxicol Chem 2017;36:1547-1555. © 2016 SETAC.

Quantitative Adverse Outcome Pathways and Their Application to Predictive Toxicology

A quantitative adverse outcome pathway (qAOP) consists of one or more biologically based, computational models describing key event relationships linking a molecular initiating event (MIE) to an adverse outcome. A qAOP provides quantitative, dose-response, and time-course predictions that can support regulatory decision-making. Herein we describe several facets of qAOPs, including (a) motivation for development, (b) technical considerations, (c) evaluation of confidence, and (d) potential applications. The qAOP used as an illustrative example for these points describes the linkage between inhibition of cytochrome P450 19A aromatase (the MIE) and population-level decreases in the fathead minnow (FHM; Pimephales promelas). The qAOP consists of three linked computational models for the following: (a) the hypothalamic-pitutitary-gonadal axis in female FHMs, where aromatase inhibition decreases the conversion of testosterone to 17β-estradiol (E2), thereby reducing E2-dependent vitellogenin (VTG; egg yolk protein precursor) synthesis, (b) VTG-dependent egg development and spawning (fecundity), and (c) fecundity-dependent population trajectory. While development of the example qAOP was based on experiments with FHMs exposed to the aromatase inhibitor fadrozole, we also show how a toxic equivalence (TEQ) calculation allows use of the qAOP to predict effects of another, untested aromatase inhibitor, iprodione. While qAOP development can be resource-intensive, the quantitative predictions obtained, and TEQ-based application to multiple chemicals, may be sufficient to justify the cost for some applications in regulatory decision-making.

How Adverse Outcome Pathways Can Aid the Development and Use of Computational Prediction Models for Regulatory Toxicology

Efforts are underway to transform regulatory toxicology and chemical safety assessment from a largely empirical science based on direct observation of apical toxicity outcomes in whole organism toxicity tests to a predictive one in which outcomes and risk are inferred from accumulated mechanistic understanding. The adverse outcome pathway (AOP) framework provides a systematic approach for organizing knowledge that may support such inference. Likewise, computational models of biological systems at various scales provide another means and platform to integrate current biological understanding to facilitate inference and extrapolation. We argue that the systematic organization of knowledge into AOP frameworks can inform and help direct the design and development of computational prediction models that can further enhance the utility of mechanistic and in silico data for chemical safety assessment. This concept was explored as part of a workshop on AOP-Informed Predictive Modeling Approaches for Regulatory Toxicology held September 24-25, 2015. Examples of AOP-informed model development and its application to the assessment of chemicals for skin sensitization and multiple modes of endocrine disruption are provided. The role of problem formulation, not only as a critical phase of risk assessment, but also as guide for both AOP and complementary model development is described. Finally, a proposal for actively engaging the modeling community in AOP-informed computational model development is made. The contents serve as a vision for how AOPs can be leveraged to facilitate development of computational prediction models needed to support the next generation of chemical safety assessment.

Linking field-based metabolomics and chemical analyses to prioritize contaminants of emerging concern in the Great Lakes basin

The ability to focus on the most biologically relevant contaminants affecting aquatic ecosystems can be challenging because toxicity-assessment programs have not kept pace with the growing number of contaminants requiring testing. Because it has proven effective at assessing the biological impacts of potentially toxic contaminants, profiling of endogenous metabolites (metabolomics) may help screen out contaminants with a lower likelihood of eliciting biological impacts, thereby prioritizing the most biologically important contaminants. The authors present results from a study that utilized cage-deployed fathead minnows (Pimephales promelas) at 18 sites across the Great Lakes basin. They measured water temperature and contaminant concentrations in water samples (132 contaminants targeted, 86 detected) and used ¹ H-nuclear magnetic resonance spectroscopy to measure endogenous metabolites in polar extracts of livers. They used partial least-squares regression to compare relative abundances of endogenous metabolites with contaminant concentrations and temperature. The results indicated that profiles of endogenous polar metabolites covaried with at most 49 contaminants. The authors identified up to 52% of detected contaminants as not significantly covarying with changes in endogenous metabolites, suggesting they likely were not eliciting measurable impacts at these sites. This represents a first step in screening for the biological relevance of detected contaminants by shortening lists of contaminants potentially affecting these sites. Such information may allow risk assessors to prioritize contaminants and focus toxicity testing on the most biologically relevant contaminants. Environ Toxicol Chem 2016;35:2493-2502. Published 2016 Wiley Periodicals Inc. on behalf of SETAC. This article is a US Government work and, as such, is in the public domain in the United States of America.