Editor's Highlight: Computational Modeling of Plasma Vitellogenin Alterations in Response to Aromatase Inhibition in Fathead Minnows

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;43:1406-1422. Published 2024. This article is a U.S. Government work and is in the public domain in the USA.

Case Study in 21st-Century Ecotoxicology: Using In Vitro Aromatase Inhibition Data to Predict Reproductive Outcomes in Fish In Vivo

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.

Case Study in 21st Century Ecotoxicology: Using In Vitro Aromatase Inhibition Data to Predict Short-Term In Vivo Responses in Adult Female Fish

The present study evaluated whether in vitro measures of aromatase inhibition as inputs into a quantitative adverse outcome pathway (qAOP) construct could effectively predict in vivo effects on 17β-estradiol (E2) and vitellogenin (VTG) concentrations in female fathead minnows. Five chemicals identified as aromatase inhibitors in mammalian-based ToxCast assays were screened for their ability to inhibit fathead minnow aromatase in vitro. Female fathead minnows were then exposed to 3 of those chemicals: letrozole, epoxiconazole, and imazalil in concentration-response (5 concentrations plus control) for 24 h. Consistent with AOP-based expectations, all 3 chemicals caused significant reductions in plasma E2 and hepatic VTG transcription. Characteristic compensatory upregulation of aromatase and follicle-stimulating hormone receptor (fshr) transcripts in the ovary were observed for letrozole but not for the other 2 compounds. Considering the overall patterns of concentration-response and temporal concordance among endpoints, data from the in vivo experiments strengthen confidence in the qualitative relationships outlined by the AOP. Quantitatively, the qAOP model provided predictions that fell within the standard error of measured data for letrozole but not for imazalil and epoxiconazole. However, the inclusion of measured plasma concentrations of the test chemicals as inputs improved model predictions, with all predictions falling within the range of measured values. Results highlight both the utility and limitations of the qAOP and its potential use in 21st century ecotoxicology. Environ Toxicol Chem 2021;40:1155-1170. © 2020 SETAC. This article has been contributed to by US Government employees and their work is in the public domain in the USA.

Anti-masculinization induced by aromatase inhibitors in adult female zebrafish

Background

Early sex differentiation genes of zebrafish remain an unsolved mystery due to the difficulty to distinguish the sex of juvenile zebrafish. However, aromatase inhibitors (AIs) could direct juvenile zebrafish sex differentiation to male and even induce ovary-to-testis reversal in adult zebrafish.

Results

In order to determine the transcriptomic changes of sex differentiation in juvenile zebrafish and early sex-reversal in adult zebrafish, we sequenced the transcriptomes of juvenile and adult zebrafish treated with AI exemestane (EM) for 32 days, when juvenile zebrafish sex differentiation finished. EM treatment in females up-regulated the expression of genes involved in estrogen metabolic process, female gamete generation and oogenesis, including gsdf, macf1a and paqr5a, while down-regulated the expression of vitellogenin (vtg) genes, including vtg6, vtg2, vtg4, and vtg7 due to the lower level of Estradiol (E2). Furthermore, EM-juveniles showed up-regulation in genes related to cell death and apoptosis, such as bcl2l16 and anax1c, while the control-juveniles exhibited up-regulation of genes involved in positive regulation of reproductive process and oocyte differentiation such as zar1 and zpcx. Moreover, EM-females showed higher enrichment than control females in genes involved in VEGF signaling pathway, glycosaminoglycan degradation, hedgehog signaling pathway, GnRH signaling pathway and steroid hormone biosynthesis.

Conclusions

Our study shows anti-masculinization in EM-treated adult females but not in EM-treated juveniles. This may be responsible for the lower sex plasticity in adults than juveniles.

Quantitative Response-Response Relationships Linking Aromatase Inhibition to Decreased Fecundity are Conserved Across Three Fishes with Asynchronous Oocyte Development

Quantitative adverse outcome pathways (qAOPs) describe quantitative response-response relationships that can predict the probability or severity of an adverse outcome for a given magnitude of chemical interaction with a molecular initiating event. However, the taxonomic domain of applicability for these predictions is largely untested. The present study began defining this applicability for a previously described qAOP for aromatase inhibition leading to decreased fecundity developed using data from fathead minnow (Pimephales promelas). This qAOP includes quantitative response-response relationships describing plasma 17β-estradiol (E2) as a function of plasma fadrozole, plasma vitellogenin (VTG) as a function of plasma E2, and fecundity as a function of plasma VTG. These quantitative response-response relationships simulated plasma E2, plasma VTG, and fecundity measured in female zebrafish (Danio rerio) exposed to fadrozole for 21 days but not these responses measured in female Japanese medaka (Oryzias latipes). However, Japanese medaka had different basal levels of plasma E2, plasma VTG, and fecundity. Normalizing basal levels of each measurement to equal those of female fathead minnow enabled the relationships to accurately simulate plasma E2, plasma VTG, and fecundity measured in female Japanese medaka. This suggests that these quantitative response-response relationships are conserved across these three fishes when considering relative change rather than absolute measurements. The present study represents an early step toward defining the appropriate taxonomic domain of applicability and extending the regulatory applications of this qAOP.

Building and Applying Quantitative Adverse Outcome Pathway Models for Chemical Hazard and Risk Assessment

An important goal in toxicology is the development of new ways to increase the speed, accuracy, and applicability of chemical hazard and risk assessment approaches. A promising route is the integration of in vitro assays with biological pathway information. We examined how the adverse outcome pathway (AOP) framework can be used to develop pathway-based quantitative models useful for regulatory chemical safety assessment. By using AOPs as initial conceptual models and the AOP knowledge base as a source of data on key event relationships, different methods can be applied to develop computational quantitative AOP models (qAOPs) relevant for decision making. A qAOP model may not necessarily have the same structure as the AOP it is based on. Useful AOP modeling methods range from statistical, Bayesian networks, regression, and ordinary differential equations to individual-based models and should be chosen according to the questions being asked and the data available. We discuss the need for toxicokinetic models to provide linkages between exposure and qAOPs, to extrapolate from in vitro to in vivo, and to extrapolate across species. Finally, we identify best practices for modeling and model building and the necessity for transparent and comprehensive documentation to gain confidence in the use of qAOP models and ultimately their use in regulatory applications. Environ Toxicol Chem 2019;38:1850-1865. © 2019 The Authors. Environmental Toxicology and Chemistry published by Wiley Periodicals, Inc. on behalf of 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.

Adverse outcome pathways: a concise introduction for toxicologists

Adverse outcome pathways (AOPs) are designed to provide a clear-cut mechanistic representation of critical toxicological effects that propagate over different layers of biological organization from the initial interaction of a chemical with a molecular target to an adverse outcome at the individual or population level. Adverse outcome pathways are currently gaining momentum, especially in view of their many potential applications as pragmatic tools in the fields of human toxicology, ecotoxicology, and risk assessment. A number of guidance documents, issued by the Organization for Economic Cooperation and Development, as well as landmark papers, outlining best practices to develop, assess and use AOPs, have been published in the last few years. The present paper provides a synopsis of the main principles related to the AOP framework for the toxicologist less familiar with this area, followed by two case studies relevant for human toxicology and ecotoxicology.

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.