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

Abstract

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 concentration-response 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.

Figure 1

(A) Conceptual steroidogenesis model for control and MET‑exposed H295R cells. The model consists of two compartments: culture medium and H295R cells. Cellular uptake of CHOL from medium is depicted by the broad gray arrow labeled with the StAR protein. Reversible steroid transport between medium and cells is depicted by bidirectional thin gray arrows. Irreversible metabolic reactions in the cells are depicted by arrows, with each pattern representing a unique enzyme. Enzymes are labeled next to reactions they catalyze: CYP450 side‑chain‑cleavage (CYP11A), CYP450c17‑α‑hydroxylase (CYP17H), CYP450c17,20‑lyase (CYP17L), 3‑β‑hydroxydehydrogenase type 2 (3βHSD2), 17β‑hydroxydehydrogenase type 1 (17βHSD1), CYP450 aromatase (CYP19), CYP450 21-α-hydroxylase (CYP21A), CYP450 11‑β‑hydroxylase type 1 (CYP11B1), and aldosterone synthase (CYP11B2). Steroids are PREG, HPREG, DHEA, PROG, HPROG, DIONE, T, E₁, E₂, DCORTICO, CORTICO, ALDO, DCORT, and CORT. The EAC MET is shown as enzyme inhibitor of CYP11B1. (B) A graphical representation of the parameters for the mathematical H295R steroidogenesis model, which consists of first‑order rate constants for CHOL uptake into the cells, k₁, and for each metabolic process, k₂–k₁₈. For the quasi‑equilibrium analysis, the equilibrium constants are q₁₉–q₃₂. Partition coefficient for MET is q₄₀. Enzyme inhibition constants for MET are k₄₁ and k₄₂ for CORTICO and CORT pathways, respectively.

Figure 2

Model evaluation of metabolic and transport pathways for control study. Model-predicted concentrations in medium were plotted as a function of time and compared with concentrations (mean ± SD) measured at five sampling times for steroids: ALDO, E₂, and T (A); PROG, HPROG, and DHEA (B); HPREG, DIONE, and E₁ (C); CORTICO and DCORTICO (D); and PREG, CORT, and DCORT (E).

Figure 3

Model evaluation of metabolic and transport pathways for control and two MET concentrations (1 µM and 10 µM). Model‑predicted concentrations in medium were plotted as a function of time and compared with concentrations (mean ± SD) measured at five sampling times for steroids: ALDO (A), CORTICO (B), CORT (C), DCORTICO (D), and DCORT (E). For controls, model‑predicted and measured steroid concentrations are the same as shown in Figure 2.

Figure 4

Relative sensitivities for model‑predicted steroids ALDO (A) and CORT (B), plotted as a function of the 35 model parameters (k₁–k₁₈, q₁₉–q₃₂, k₄₀–k₄₂) for control and two MET concentrations (1 and 10 µM). Each bar represents the L2 norm of the relative sensitivities across time (0–80 hr) and indicates the degree to which changes in parameter values lead to changes in model outputs. Odd- and even-numbered parameters are shown in A and B, respectively.