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To reduce the use of intact animals for chemical safety testing, while ensuring protection of ecosystems and human health, there is a demand for new approach methodologies (NAMs) that provide relevant scientific information at a quality equivalent to or better than traditional approaches. The present case study examined whether bioactivity and associated potency measured in an in vitro screening assay for aromatase inhibition could be used together with an adverse outcome pathway (AOP) and mechanistically based computational models to predict previously uncharacterized in vivo effects. Model simulations were used to inform designs of 60-h and 10-21-day in vivo exposures of adult fathead minnows (Pimephales promelas) to three or four test concentrations of the in vitro aromatase inhibitor imazalil ranging from 0.12 to 260 µg/L water. Consistent with an AOP linking aromatase inhibition to reproductive impairment in fish, exposure to the fungicide resulted in significant reductions in ex vivo production of 17β-estradiol (E2) by ovary tissue (≥165 µg imazalil/L), plasma E2 concentrations (≥74 µg imazalil/L), vitellogenin (Vtg) messenger RNA expression (≥165 µg imazalil/L), Vtg plasma concentrations (≥74 µg imazalil/L), uptake of Vtg into oocytes (≥260 µg imazalil/L), and overall reproductive output in terms of cumulative fecundity, number of spawning events, and eggs per spawning event (≥24 µg imazalil/L). Despite many potential sources of uncertainty in potency and efficacy estimates based on model simulations, observed magnitudes of apical effects were quite consistent with model predictions, and in vivo potency was within an order of magnitude of that predicted based on in vitro relative potency. Overall, our study suggests that NAMs and AOP-based approaches can support meaningful reduction and refinement of animal testing. Environ Toxicol Chem 2023;42:100-116. © 2022 SETAC. This article has been contributed to by U.S. Government employees and their work is in the public domain in the USA., (© 2022 SETAC. This article has been contributed to by U.S. Government employees and their work is in the public domain in the USA.)

An adverse outcome pathway (AOP) is a sequence of key events from a molecular-level initiating event and an ensuing cascade of steps to an adverse outcome with population-level significance. To implement a predictive strategy for ecotoxicology, the multiscale nature of an AOP requires computational models to link salient processes (e.g., in chemical uptake, toxicokinetics, toxicodynamics, and population dynamics). A case study with domoic acid was used to demonstrate strategies and enable generic recommendations for developing computational models in an effort to move toward a toxicity testing paradigm focused on toxicity pathway perturbations applicable to ecological risk assessment. Domoic acid, an algal toxin with adverse effects on both wildlife and humans, is a potent agonist for kainate receptors (ionotropic glutamate receptors whose activation leads to the influx of Na(+) and Ca²(+)). Increased Ca²(+) concentrations result in neuronal excitotoxicity and cell death, primarily in the hippocampus, which produces seizures, impairs learning and memory, and alters behavior in some species. Altered neuronal Ca²(+) is a key process in domoic acid toxicity, which can be evaluated in vitro. Furthermore, results of these assays would be amenable to mechanistic modeling for identifying domoic acid concentrations and Ca²(+) perturbations that are normal, adaptive, or clearly toxic. In vitro assays with outputs amenable to measurement in exposed populations can link in vitro to in vivo conditions, and toxicokinetic information will aid in linking in vitro results to the individual organism. Development of an AOP required an iterative process with three important outcomes: a critically reviewed, stressor-specific AOP; identification of key processes suitable for evaluation with in vitro assays; and strategies for model development., (© 2010 SETAC.)

Recently, researchers have begun looking at changes in gene expression in the fathead minnow (Pimephales promelas) after contaminant exposure as a way to develop biomarkers of exposure and effects. However, the bulk of this research has been conducted on adults, with few studies focusing on early life stages. Expression of selected genes important in growth, development, and reproduction in teleosts was quantified by quantitative polymerase chain reaction during different developmental time periods (from 0 to 28 d postfertilization [dpf]). Over the developmental period studied, there was a significant up-regulation of growth hormone mRNA and no significant changes in the expression of insulin-like growth factor 1. Thyroid hormone receptors A and B were detected in 4 dpf embryos and their expression stayed relatively constant. The variation in cytochrome P45019A mRNA expression was large during the first week of development, returning to 0 dpf expression levels thereafter. Estrogen receptor 2B was up-regulated during the first three weeks postfertilization, returning to prehatch values by 28 dpf. Expression of hydroxysteroid dehydrogenase 3B and steroidogenic acute regulatory protein increased after the third or fourth week postfertilization, respectively. Vitellogenin exhibited a large degree of variation within time points, especially after day 15, and a significant up-regulation for this gene was observed at 7 and 10 dpf. Knowledge of the normal changes in gene expression during embryo and larval development will allow for better experimental design and selection of suitable biomarkers when testing the potential toxicological effects of contaminants in this model fish species.

Bioaccumulation models are used to describe chemical uptake and clearances by organisms. Averaged input parameter values are traditionally used and yield point estimates of model outputs. Hence, the uncertainty and variability of model predictions are ignored. Probabilistic modeling approaches, such as Monte Carlo simulation and the Bayesian method, have been recommended by the U.S. Environmental Protection Agency to provide a quantitative description of the degree of uncertainty and/or variability in risk estimates in ecological hazards and human health effects. In this study, a Bayesian analysis was conducted to account for the combined uncertainty and variability of model parameters in a crayfish bioaccumulation model. After a 5-d exposure in the LaBranche Wetlands (LA, USA), crayfish were analyzed for polycyclic aromatic hydrocarbon concentrations and lipid fractions. The posterior distribution of model parameters were derived from the joint posterior parameter distributions using a Markov chain Monte Carlo approach and the experimental data. The results were then used to predict the distribution of chrysene concentration versus time in the crayfish to compare the predicted ranges at the different study sites.