MECHANISTIC MODEL OF STEROIDOGENESIS IN FISH OVARIES
TO 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,
Program, Department of Statistics, North Carolina State University, Raleigh, NC, USA
3Mid-Continent Ecology Division, U.S. EPA, Duluth, MN, USA
2Biomathematics
ABSTRACT
OVARIAN STEROIDOGENESIS MODEL
ASSESSMENT OF MODEL FIT
Sex 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.
Metabolic Pathway with Inhibition by Fadrozole
Baseline (medium only) Study
EFFECTS OF ENDOCRINE ACTIVE
COMPOUNDS ON HPG AXIS
High sensitivity for T
High sensitivity for E2
*
High dose-dependent
sensitivity for E2
*
*
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).
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.
4
x
10
1
Dynamic Mass Balances
Fadrozole Study
Net metabolic rate
Ovary:
Medium:
IN VITRO STEROIDOGENESIS
EXPERIMENTS WITH OVARY EXPLANTS
•
•
•
•
Dissect fish ovary
•
•
•
•
Incubate ovary in medium
supplemented with cholesterol
dCx,med
dt
 Px ,ovy  U x,ovy  I x,ovy  S x,ovy
0.8
Net uptake rate
 Sx,ovy
0.6
Vovy  ovary volume
Vmed  medium volume
Cx ,ovy  concentration of substrate x in ovary
Cx ,med  concentration of substrate x in medium
Px ,ovy  production rate of substrate x in ovary
0.4
0.2
U x ,ovy  utilization rate of substrate x in ovary
I x ,ovy  import rate of substrate x into ovary
S x ,ovy  secretion rate of substrate x from ovary
0
First-order enzyme kinetics and transport rates
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.
Competitive enzyme inhibition by fadrozole
STEADY-STATE ANALYSIS
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):
CT,med  t  
Collect medium at multiple time
points over 31.5 hr
RELATIVE SENSITIVITY ANALYSIS
k0 k9 k10  k16  k15 CFAD,med  k17  k15 CFAD,med  t
High sensitivity for T
High sensitivity for E2
D1D2
*
where: D1  k9 k16  k9 k15 CFAD ,med  k11 k16  k18 k16  k18 k15C FAD ,med
PARAMETER ESTIMATION
Cost function: J (k ) 
nd
 C
6
d 1 i 1
where:
d ,i
T,med
 
2
d
d ,i
d
 CT,med (ti ; CFAD,med
, k )  CE2,med
 CE2,med (ti ; CFAD,med
,k)

2
d ,i
T,med
C
= measured testosterone conc. in medium for dth FAD dose at ith time
CT,med = model-predicted testosterone conc. in medium
d ,i
= measured estradiol conc. in medium for dth FAD dose at ith time
CE2,med
CE2,med = model-predicted estradiol conc. in medium
d
= measured fadrozole conc. in medium for dth FAD dose
CFAD,med
Fathead minnows
Applied an iterative optimization algorithm. Simultaneously estimated parameters
using data from baseline and fadrozole-exposure studies
Estimated Parameters
R2 = 0.98
Ovary Uptake of Cholesterol and Fadrozole
pg ml-1 hr-1
k0
15401.470
k15
R2 = 0.94
•
•
High dose-dependent
sensitivity for E2
D2  k10 k17  k10 k15 CFAD,med  k12 k17
CFAD,med  fadrozole conc. in medium
Measure medium concentrations of
testosterone (T) and estradiol (E2)
using radioimmunoassay
Small fish culture facility
dt
Vmed
where:
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
dCx ,ovy
Vovy
0.0015
Secretion of Testosterone and Estradiol
k10
1726.553 hr-1
Partition coefficient
(dimensionless)
k18
149.301
hr-1
k19
102.171
hr-1
First-order Enzyme Kinetics with Inhibition by Fadrozole
hr-1
* Literature values from
0.509
k9
fish experiments
hr-1
5.8*
k11
k12
3.2*
hr-1
k13
356.217
hr-1
Good evidence steroidogenic pathway is operating at steady-state during experiments
k16
8143.017
pg ml-1
Steady-state assumption reduces model complexity
k17
4671.198
pg ml-1

FAD inhibition
constants
* *
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.
•
•
•
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
DISCLAIMER
This work was reviewed by the U.S. EPA and approved for publication
but does not necessarily reflect Agency policy.