Computational model of the fathead minnow hypothalamic-pituitary-gonadal axis: Incorporating protein synthesis in improving predictability of responses to endocrine active chemicals

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.

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

In vertebrates, conversion of testosterone into 17β-estradiol (E2) is catalyzed by cytochrome P450 (CYP) 19A aromatase. An important role of E2 in oviparous vertebrates such as fish is stimulation of hepatic synthesis of the glycolipoprotein vitellogenin (VTG), an egg yolk precursor essential to oocyte development and larval survival. In fathead minnows (FHMs) (Pimephales promelas) exposed to the aromatase inhibitor fadrozole, plasma VTG levels do not change in concert with plasma E2 levels. Specifically, while plasma VTG and E2 levels both drop quickly when aromatase is first inhibited, the recovery of plasma VTG upon cessation of aromatase inhibition is substantially delayed relative to the recovery of plasma E2. We modified an existing computational model of the FHM hypothalamic-pituitary-gonadal axis to evaluate alternative hypotheses that might explain this delay. In the first hypothesis, a feedback loop involving active transport of VTG from the blood into the ovary is used. The activity of the transporter is negatively regulated by ovarian VTG. In the second hypothesis, a type 1 coherent feed-forward loop is implemented in the liver. This loop has 2 arms, both requiring E2 activation. The first arm describes direct, canonical E2-driven transcriptional induction of VTG, and the second describes an E2-driven intermediate transcriptional regulator that is also required for VTG synthesis. Both hypotheses accurately described the observed VTG dynamics. This result could be used to guide design of laboratory experiments intended to determine if either of the motifs, or perhaps even both of them, actually do control VTG dynamics in FHMs exposed to aromatase inhibitors.