Effects of bisphenol analogues on steroidogenic gene expression and hormone synthesis in H295R cells.

This paper presents a mathematical model of the HPA axis. The HPA axis consists of the hypothalamus, the pituitary and the adrenal glands in which the three hormones CRH, ACTH and cortisol interact through receptor dynamics. Furthermore, it has been suggested that receptors in the hippocampus have an influence on the axis. A model is presented with three coupled, non-linear differential equations, with the hormones CRH, ACTH and cortisol as variables. The model includes the known features of the HPA axis, and includes the effects from the hippocampus through its impact on CRH in the hypothalamus. The model is investigated both analytically and numerically for oscillating solutions, related to the ultradian rhythm seen in data, and for multiple fixed points related to hypercortisolemic and hypocortisolemic depression. The existence of an attracting trapping region guarantees that solution curves stay non-negative and bounded, which can be interpreted as a mathematical formulation of homeostasis. No oscillating solutions are present when using physiologically reasonable parameter values. This indicates that the ultradian rhythm originate from different mechanisms. Using physiologically reasonable parameters, the system has a unique fixed point, and the system is globally stable. Therefore, solutions converge to the fixed point for all initial conditions. This is in agreement with cortisol levels returning to normal, after periods of mild stress, in healthy individuals. Perturbing parameters lead to a bifurcation, where two additional fixed points emerge. Thus, the system changes from having a unique stable fixed point into having three fixed points. Of the three fixed points, two are stable and one is unstable. Further investigations show that solutions converge to one of the two stable fixed points depending on the initial conditions. This could explain why healthy people becoming depressed usually fall into one of two groups: a hypercortisolemic depressive group or a hypocortisolemic depressive group.

Mathematical modeling of the hypothalamic–pituitary–adrenal gland (HPA) axis, including hippocampal mechanisms

Mixture effects of imidazole fungicides on cortisol and aldosterone secretion in human adrenocortical H295R cells.

The recently OECD validated H295R steroidogenesis assay provides an in vitro alternative to evaluate the potential interference of exogenous compounds with endogenous steroid hormone synthesis. Currently, this assay is used for a simple negative-positive screening of compounds using testosterone and estradiol levels as end points, measured with specific enzyme immunoassays (EIAs) or targeted liquid chromatography (LC) and gas chromatography (GC)-mass spectrometry (MS) methods. However, recent developments in LC-MS and bioinformatics allow for more comprehensive approaches to evaluate changes in steroid profiles. In the current work, the H295R cell model was combined with a metabolomics approach to monitor changes in metabolite profiles in both a targeted and untargeted way. H295R cells were exposed for 48 h to model compounds, i.e., forskolin, abiraterone, prochloraz, ketoconazole, trilostane, formestane, aminoglutethimide, fadrozole, etomidate, and metyrapone, known to affect steroidogenesis. After exposure, the levels of 9 natural steroids were determined by a quantitative targeted GC-MS/MS method and compared to a metabolomics method using Ultra Performance Liquid Chromatography-Time-of-Flight-Mass Spectrometry (UPLC-ToF-MS). Like the EIAs, both methods were suited for negative-positive screening, but the MS methods also generated specific fingerprints, allowing chemical class prediction of the compound under investigation. Although the targeted GC-MS/MS was more sensitive, which was an advantage regarding analysis of the estrogens 17β-estradiol and estrone, the untargeted UPLC-ToF-MS was able to evaluate effects on the synthesis of the corticosteroids. Moreover, untargeted comparison of the aligned chemical profiles allowed identification of all m/z-values that are differential between exposed and nonexposed H295R cells. In conclusion, application of a comprehensive metabolite profiling methodology not only provides a tool to screen compounds for steroidogenic modulating properties, but also allows chemical class prediction. As such, steroid profiling methodologies in conjunction with the H295R assay can contribute to the prioritization of chemicals for additional safety testing.

Screening for modulatory effects on steroidogenesis using the human H295R adrenocortical cell line: a metabolomics approach.

Steroid profiling in H295R cells to identify chemicals potentially disrupting the production of adrenal steroids.

Background: An in vitro steroidogenesis assay using the human adrenocortical carcinoma cell line H295R is being evaluated as a possible screening assay to detect and assess the impact of endocrine-active chemicals (EACs) capable of altering steroid biosynthesis. Data interpretation and their quantitative use in human and ecological risk assessments can be enhanced with mechanistic computational models to help define mechanisms of action and improve understanding of intracellular concentrationresponse behavior.

Objectives: The goal of this study was to develop a mechanistic computational model of the metabolic network of adrenal steroidogenesis to estimate the synthesis and secretion of adrenal steroids in human H295R cells and their biochemical response to steroidogenesis-disrupting EAC.

Methods: We developed a deterministic model that describes the biosynthetic pathways for the conversion of cholesterol to adrenal steroids and the kinetics for enzyme inhibition by metryrapone (MET), a model EAC. Using a nonlinear parameter estimation method, the model was fitted to the measurements from an in vitro steroidogenesis assay using H295R cells.

Results: Model-predicted steroid concentrations in cells and culture medium corresponded well to the time-course measurements from control and MET-exposed cells. A sensitivity analysis indicated the parameter uncertainties and identified transport and metabolic processes that most influenced the concentrations of primary adrenal steroids, aldosterone and cortisol.

Conclusions: Our study demonstrates the feasibility of using a computational model of steroidogenesis to estimate steroid concentrations in vitro. This capability could be useful to help define mechanisms of action for poorly characterized chemicals and mixtures in support of predictive hazard and risk assessments with EACs.

Computational model of steroidogenesis in human H295R cells to predict biochemical response to endocrine-active chemicals: model development for metyrapone.

Mechanistic Computational Model of Steroidogenesis in H295R Cells: Role of Oxysterols and Cell Proliferation to Improve Predictability of Biochemical Response to Endocrine Active Chemical—Metyrapone

Adrenal toxicity is one of the major concerns in drug development. To quantitatively understand the effect of endocrine-active compounds on adrenal steroidogenesis and to assess the human adrenal toxicity of novel pharmaceutical drugs, we developed a mathematical model of steroidogenesis in human adrenocortical carcinoma NCI-H295R cells. The model includes cellular proliferation, intracellular cholesterol translocation, diffusional transport of steroids, and metabolic pathways of adrenal steroidogenesis, which serially involve steroidogenic proteins and enzymes such as StAR, CYP11A1, CYP17A1, HSD3B2, CYP21A2, CYP11B1, CYP11B2, HSD17B3, and CYP19A1. It was reconstructed in an experimental dynamics of cholesterol and 14 steroids from an in vitro steroidogenesis assay using NCI-H295R cells. Results of dynamic sensitivity analysis suggested that HSD3B2 plays the most important role in the metabolic balance of adrenal steroidogenesis. Based on differential metabolic profiling of 12 steroid hormones and 11 adrenal toxic compounds, we could estimate which steroidogenic enzymes were affected in this mathematical model. In terms of adrenal steroidogenic inhibitors, the predicted action sites were approximately matched to reported target enzymes. Thus, our computer-aided system based on systems biological approach may be useful to understand the mechanism of action of endocrine-active compounds and to assess the human adrenal toxicity of novel pharmaceutical drugs.

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Estimation of the Mechanism of Adrenal Action of Endocrine-Disrupting Compounds Using a Computational Model of Adrenal Steroidogenesis in NCI-H295R Cells

Makoto Yamazaki

Papers

Computational model of steroidogenesis in human H295R cells to predict biochemical response to endocrine-active chemicals: model development for metyrapone.

Mechanistic Computational Model of Steroidogenesis in H295R Cells: Role of Oxysterols and Cell Proliferation to Improve Predictability of Biochemical Response to Endocrine Active Chemical—Metyrapone

Estimation of the Mechanism of Adrenal Action of Endocrine-Disrupting Compounds Using a Computational Model of Adrenal Steroidogenesis in NCI-H295R Cells