MECHANISTIC MODEL OF STEROIDOGENESIS IN FISH OVARIESTO PREDICT BIOCHEMICAL RESPONSE TO ENDOCRINE ACTIVE CHEMICALS

Michael S. Breen,1 Miyuki Breen,2 Daniel L. Villeneuve,3 Gerald T. Ankley,3 Rory B. Conolly1

1National Center for Computational Toxicology, U.S. EPA, Research Triangle Park, NC, USA, 2Biomathematics Program, Department of Statistics, North Carolina State University, Raleigh, NC, USA

3Mid-Continent Ecology Division, U.S. EPA, Duluth, MN, USA

ABSTRACTSex steroids, which have an important role in a wide range of physiological and pathological processes, are synthesized primarily in the gonads and adrenal glands through a series of enzyme‑mediated reactions. The activity of steroidogenic enzymes can be altered by a variety of endocrine active compounds (EAC), some of which are therapeutics and others that are environmental contaminants. A steady‑state computational model of the intraovarian metabolic network was developed to predict the synthesis and secretion of testosterone (T) and estradiol (E2), and their responses to EAC. Model predictions were compared to data from an in vitro steroidogenesis assay with ovary explants from a small fish model, the fathead minnow. Model parameters were estimated using an iterative optimization algorithm. Model‑predicted concentrations of T and E2 closely correspond to the time‑course data from baseline (control) experiments, and dose‑response data from experiments with the EAC, fadrozole. A sensitivity analysis of the model parameters identified specific transport and metabolic processes that most influence the concentrations of T and E2, which included uptake of cholesterol into the ovary, secretion of androstenedione (AD) from the ovary, and conversions of AD to T, and AD to estrone. The sensitivity analysis also indicated the E1 pathway as the preferred pathway for E2 synthesis, as compared to the T pathway. Our study demonstrates the feasibility of using the steroidogenesis model to predict T and E2 concentrations, in vitro, while reducing model complexity with a steady‑state assumption. This capability could be useful to help define mechanisms of action for poorly characterized chemicals in support of predictive environmental risk assessments.

EFFECTS OF ENDOCRINE ACTIVE COMPOUNDS ON HPG AXIS

Feedback control system of hypothalamus-pituitary-gonadal (HPG) axis regulates synthesis and secretion of sex steroid hormones (estradiol (E2), testosterone (T)) by release of gonadotropin releasing hormone (GnRH) from hypothalamus, and luteinizing hormone (LH) and follicle stimulationg hormone (FSH) from pituitary

OVARIAN STEROIDOGENESIS MODEL

Mathematical model based on in vitro experimental design with two compartments: culture medium and ovary tissue. Transport pathways include ovary uptake of cholesterol (CHOL) and fadrozole (FAD; endocrine active compound), and secretion of androstenedione (AD), estrone (E1), testosterone (T) and estradiol (E2). Metabolic pathway includes conversion of CHOL into T and E2 with specific enzyme inhibition by FAD. For steady-state model, T and E2 medium concentrations are independent of 9 processes (black arrows), and dependent on 11 processes (white arrows).

IN VITRO STEROIDOGENESIS EXPERIMENTS WITH OVARY EXPLANTS

• Dissect fish ovary

• Incubate ovary in medium

supplemented with cholesterol

• Collect medium at multiple time

points over 31.5 hr

• Measure medium concentrations of

testosterone (T) and estradiol (E2)

using radioimmunoassay

Small fish culture facility Fathead minnows

• Good evidence steroidogenic pathway is operating at steady-state during experiments

• Steady-state assumption reduces model complexity

R2 = 0.94

R2 = 0.98

Dynamic Mass Balances

PARAMETER ESTIMATION

ASSESSMENT OF MODEL FITBaseline (medium only) Study

Fadrozole Study

CONCLUSION• Steroidogenesis model can predict testosterone and estradiol concentrations,

in vitro, while reducing model complexity with a steady-state assumption

• Sensitivity analysis indicated E1 pathway as preferred pathway for E2 synthesis

• This capability could be useful for predictive environmental risk assessments, and screening drug candidates based on steroidogenic effects in early phase of drug development

DISCLAIMERThis work was reviewed by the U.S. EPA and approved for publication but does not necessarily reflect Agency policy.

RELATIVE SENSITIVITY ANALYSIS

Cost function:

Applied an iterative optimization algorithm. Simultaneously estimated parameters

using data from baseline and fadrozole-exposure studies

STEADY-STATE ANALYSIS

Relative sensitivities for model outputs, testosterone (a) and estradiol (b) in medium, are plotted as function of 11 model parameters for control (no fadrozole) and the lowest, middle, and highest fadrozole concentrations. Negative values indicate an inverse relationship between a parameter change and resulting model output change; positive values indicate a direct relationship. Magnitudes indicate degree to which changes in parameter values lead to changes in model outputs; percentage change of model output for a given percentage change of parameter.

Comparison of model‑predicted and dose‑response data after a 14.5 hr incubation of ovary explants with fadrozole. Model-predicted testosterone and estradiol concentrations in the medium were plotted as a function of fadrozole concentration, and compared with mean concentrations measured from on control and five fadrozole concentrations.

Comparison of model‑predictions with time‑course data from baseline experiments. Model‑predicted concentrations of testosterone and estradiol in the medium were plotted as a function of time, and compared with mean concentrations measured at six time points.

= measured testosterone conc. in medium for dth FAD dose at ith time

= model-predicted testosterone conc. in medium

= measured estradiol conc. in medium for dth FAD dose at ith time

= model-predicted estradiol conc. in medium

= measured fadrozole conc. in medium for dth FAD dose

where: ,T,medd iC

• Set differential equations to zero to yield algebraic equations

• Determined analytical solution for medium concentrations of testosterone, CT,med,

and estradiol, CE2,med (not shown):

Metabolic Pathway with Inhibition by Fadrozole

Estimated ParametersOvary Uptake of Cholesterol and Fadrozole Secretion of Testosterone and Estradiol

First-order Enzyme Kinetics with Inhibition by Fadrozole

k0

k15

k10

k18

k19

k9

k11

k12

k13

k16

k17

15401.470

0.0015

pg ml-1 hr-1

Partition coefficient (dimensionless)

1726.553

149.301

102.171

hr-1

hr-1

hr-1

0.509

5.8*

3.2*

356.217

8143.017

4671.198

hr-1

hr-1

hr-1

hr-1

pg ml-1

pg ml-1 FAD inhibition

constants

,ovyovy ,ovy ,ovy ,ovy ,ovy

xx x x x

dCV P U I S

dt

x,medmed x,ovy

dCV S

dt

• First-order enzyme kinetics and transport rates

• Competitive enzyme inhibition by fadrozole

where:

Ovary:

Medium:

ovy ovary volumeV

med medium volumeV

,ovy concentration of substrate in ovaryxC x

,med concentration of substrate in mediumxC x

,ovy production rate of substrate in ovaryxP x

,ovy utilization rate of substrate in ovaryxU x

,ovy import rate of substrate into ovaryxI x

,ovy secretion rate of substrate from ovaryxS x

Net uptake rate

Net metabolic rate

0 9 10 16 15 , 17 15 ,

T,med1 2

FAD med FAD medk k k k k C k k C tC t

D D

1 9 16 9 15 , 11 16 18 16 18 15 ,FAD med FAD medD k k k k C k k k k k k C

2 10 17 10 15 , 12 17FAD medD k k k k C k k where:

6 2 2, ,

T,med T,med FAD,med E2,med E2,med FAD,med1 1

( ) ( ; , ) ( ; , )dn

d i d d i di i

d i

J k C C t C k C C t C k

FAD,meddC

,E2,medd iC

T,medC

E2,medC

* Literature values from fish experiments

x 104

1

0.8

0.6

0.4

0.2

0

FAD,med fadrozole conc. in mediumC

**

*

High sensitivity for T

High sensitivity for E2

High dose-dependent sensitivity for E2

* *

*

High sensitivity for T

High sensitivity for E2

High dose-dependent sensitivity for E2