Integration of Pharmacokinetic (PK) and
Pharmacodynamic (PD) Modeling of Arsenic
to Inform the Risk Assessment Process
Elaina M. Kenyon
Hisham A. El-Masri
Rory B. Conolly
U.S. EPA, ORD
Disclaimer !
1) This presentation does not necessarily
reflect EPA policy. Mention of trade names
or commercial products does not constitute
endorsement or recommendation for use.
2) This work is a work in progress!
Exposure-Dose-Response Paradigm
Exposure
Susceptibility
bioavailability
Internal Dose
Biologically Effective Dose
Early Biological Effects
Altered Function/Structure
Susceptibility
Modified from Schulte, 1989
Clinical Disease
Prognostic Significance
What Makes Arsenic Unique?
• Pancarcinogenic in humans, whereas
rodents are much less responsive
• Large cross-species differences in
metabolism
• Tissue-specific differences in metabolite
accumulation
• Toxicity most likely mediated by metabolism
• Known variations in metabolism due to age
and ethnicity in humans
• Polymorphisms identified in AS3MT, the
principal As metabolizing enzyme
IAsV
Methylation
(1)
(1)
Induce chromosomal aberrations
(4), genetic instability (5).
Induce alterations in methylation
patterns (6).
Reduction
Inhibit DNA repair (9). Non
tumorigenic to mice and rats
(14).
Induce DNA damage (19) and 8-oxodG adducts (20).
MMAsV
Induce p53 (10) and cell proliferation
(11).
(1)
Generate reactive oxygen
species (7) and 8-oxo-dG
adducts (8).
Interfere with DNA repair (9).
Rat bladder carcinogen (20) and rat
bladder tumor promoter (21).
(1)
Induce p53 (10) and cell
proliferation (11).
Mouse carcinogen (12) and cocarcinogen (13).
DMAsV
Induces 8-oxo-dG adducts (17).
(2)
Induce chromosomal aberrations
and DNA breaks (15).
Generate reactive oxygen species
(16) and 8-oxo-dG adducts (17).
Induce cell proliferation (18).
Inhibit DNA repair (9).
Rat liver carcinogen (24).
(2)
Induce chromosomal aberrations and DNA
breaks (15,22)
Generate reactive oxygen species (16)
TMAsV
(3)
Inhibit DNA repair (9)
Induce p53 (10) and cell proliferation (23).
TMAs(-III)
Arsenical Concentration (g/g)
Accumulation of Arsenicals Varies
Significantly Across Tissues
9
IAs
MMA
DMA
8
4
3
2
1
0
Blood
Liver
Lung
Kidney Bladder
Skin
Tissue
Female C57Bl6 Mice - 12 week drinking water exposure to As(V)
Role of PBPK and BBDR Models
APPLIED DOSE
INTERNAL DOSE AT TARGET
(e.g., TISSUE, ORGAN)
1
RESPONSE
1
82m
81m
82m
0.1
PBPK MODEL
0.1
BBDR MODEL
80r
75k
76k
75k
77k
78k
83c
81m
79k
83c
83c
Chemical Disposition
(bodies effect on the chemical)
Information to Develop the PBPK Model
• Target site (s) (organ, tissue, cell).
• Chemical specific ADME rates.
• Species specific parameter values (tissue
volumes, blood flow rates.
• Which internal dose metric to use (based on
mode of action).
Biological Response
(chemical’s effect on the body)
Information to Develop BBDR Model
• Target site.
• Adverse effect (what constitutes a significant
deviation from normal).
• Mode of Action (i.e., key events leading to an
effect).
• Best measure of effect (s).
Biological Hypothesis
Physiological
Biochemical
Parameters
PBPK Model
Disagree
Model
Evaluation
{
Agree
Model Simulations
(tissue levels)
Experimental
Data
Model-Designed
Experiments
PK/PD Model Utility in Risk Assessment?
• Relate Exposure to target tissue dose of parent
chemical or metabolite(s)
 Tissue dose is related to injury
 Predictions at different exposure levels
• Relate tissue dose between species
 Animals to humans
• Biologically based model to address variability
and uncertainty
 Exposure variability
 Physiological and biochemical variability
• Experimental design to test hypotheses
Key Question
Given that arsenic toxicity is most likely
mediated by metabolism, what are the
implications of interspecies differences in
metabolism and tissue accumulation?
Use the model to assess the relationship
between measures of arsenical dose to target
tissue and toxic outcomes across species
An Example
DMAV-Induced Bladder Cancer
• Putative mode of action is cytotoxicity
and regenerative cell proliferation
• Rat bladder urothelium is highly
responsive by several endpoints
• Mouse is almost non-responsive
(some evidence of cytotoxicity)
• DMAV metabolism (2000)
 DMAV ￫ DMAIII ￫ TMAO
DMAV Metabolism (2007)
DMTAV
DMAV
DMAIII
DMTAIII
TMAO
TMA
TMASV
Adair et al., 2007
What makes the rat different?
• Much longer t1/2 (weeks) compared to
mice (days) or humans
• Binding of DMAIII to rat hemoglobin
creates large storage depot
• Metabolism more extensive
• Pharmacodynamics – is rat
urothelium intrinsically more
sensitive?
Use the PBPK Model to Evaluate the Basis
for Interspecies Differences in Response
• Incorporate PK features that account for
known interspecies differences in ADME
 Hemoglobin binding
 Metabolism
• Simulate long-term exposure scenarios
• Assess relationship between measures of
internal dose and differences in response
among species
Previous As PBPK Models
Yu (1999) model:
• Partition coefficients were solely determined using a
child poisoning case. This study provided total arsenic
levels only. There was no information in poisoning
study that would help the researchers to determine the
partition coefficients for arsenic and its metabolites
(MMA and DMA) as was published and referenced in
the Yu (1999) publication.
• Yu (1999) stated in their publication that they used the
child poisoning study to determine metabolic
parameters such as Vmax and Km. The child
poisoning study did not have any information that can
lead to these estimates.
• Yu (1999) model simulations were not tested against
data.
Previous As PBPK Models
Mann et al. (1996) model:
• The modeling effort for the humans was based on
modification of an earlier one that was
established for rabbits and hamsters. Both
models did not include descriptions of current
knowledge about metabolism of arsenic (such
as the inhibition effects of Arsenic and MMA).
• The model calibration relied heavily on “global”
optimization of parameters such as partition
coefficients, first order oral absorption constant,
methylation rate constants, oxidation and
reduction constants. All of these parameters were
optimized using urine data. “Global” optimization
would yield a set of unidentifiable parameters.
Development of a Human
PBPK Model for Arsenic
El-Masri, H. and Kenyon, E.M. 2007. Development
of a Human Physiologically-Based
Pharmacokinetic (PBPK) Model for Inorganic
Arsenic and its Mono- and Di-methylated
Metabolites. Journal of Pharmacokinetics and
Pharmacodynamics, epub.
As Human PBPK Model
• A physiologically-based pharmacokinetic (PBPK) model was
developed to estimate levels of arsenic and its metabolites in human
tissues and urine after oral exposure to arsenate (AsV), arsenite (AsIII)
or organoarsenical pesticides.
• The overall model consists of interconnected individual PBPK models
for Asv, AsIII, monomethylarsenic acid (MMAv), and, dimethylarsenic
acid (DMAv).
• Metabolism of inorganic arsenic in liver was described as a series of
reduction and oxidative methylation steps incorporating the inhibitory
influence of metabolites on methylation.
• Unique aspects of this model development effort are that it addresses
parameter sensitivity and identifiably, utilizes human data whenever
possible and incorporates new data on arsenic methylation
Liver
Blood
Blood
Blood
Liver
GI
GI
Kidney
Lung
Liver
Liver
GI
Kidney
Lung
Lung
Kidney
GI
Blood
Lung
Kidney
Muscle
Muscle
Muscle
Muscle
Brain
Brain
Brain
Brain
Skin
Skin
Skin
Skin
Heart
Heart
Heart
Heart
Noncompetitive inhibition
AsV
GSH
Reduction
GSH
AsIII
AS3MT
MMAV
Reduction
AS3MT
MMAIII
DMAV
Noncompetitive inhibition
DMAIII
oxidation
oxidation
GSH
Reduction
oxidation
Table 3. An example of some of the biochemical Parameters
Parameter
Value (units)
Method of Estimation
Ka (Asv)
0.003 (min-1)
Optimized for blood data from mice dosed
orally AsV
Ka (AsIII)
0.004 (min-1)
Optimized for blood data from mice dosed
orally AsIII
0.01 (min-1)
Optimized using Buchet et al. (1981a)
0.25 (unitless)
Calculated from Aposhian et al. (2004) data
Vmax (AsIII→MMA)
5.3x 10-7 (mole/min)
Optimized using Buchet et al. (1981a) data
Km (AsIII→MMA)
3 x 10-6(M)
Zakharyan et al. (1999)
Vmax (AsIII→DMA)
2 x 10-6 (mole/min)
Optimized using Buchet et al. (1981a) data
Km (AsIII→DMA)
3 x 10-6(M)
Zakharyan et al. (1999)
Kinh (noncompetitive inhibition)
4 x 10-5(M)
Recalculated using Zakharyan et al.(1999)
0.07 (min-1)
Optimized using Buchet et al. (1981a) data
Oral Absorption
Reduction of AsV
Kred
Oxidation of AsIII
Kox
Methylation of AsIII*
Urine Excretion Const
Kurine/As
Utility of Urine Data
7
7
4*k
0.1*k
0.5*k
6
Cummulative As in urine (umole As)
2*k
5
4
3
0.5*k
2
(a)
1
0
0
0.1*k
1000
2000
5
4
3
2*k
4*k
2
(b)
1
3000
Time (min)
4000
5000
6
0
0
6000
1000
2000
0.5*v
0.1*v
5
Cummulative As in urine (umole As)
Cummulative As in Urine (umole As)
6
4*v
4
2*v
3
2
1
0
(c)
0
1000
2000
3000
Time (min)
4000
5000
6000
3000
Time (min)
4000
5000
6000
Model Calibration (DMA Dose)
Cumulative DMA in urine (umole As)
6
5
4
DMA
3
2
1
0
0
1000
2000
3000
Time (min)
4000
5000
6000
Model Calibration (MMA Dose)
Cumulative MMA and DMA in urine (umole As)
8
7
MMA
6
5
4
3
DMA
2
1
0
0
1000
2000
3000
Time (min)
4000
5000
6000
Model Calibration (As Dose)
3
Cummulative As in urine (umole As)
2.5
DMA
2
1.5
As
1
0.5
MMA
0
0
1000
2000
3000
Time (min)
4000
5000
6000
Model Evaluation
1
1
(a)
(b)
0.8
0.7
0.6
DMA
0.5
0.4
0.3
As
0.2
0.1
0
Total As
0.9
Total As
Cummulative As in urine (umole As)
Cummulative As in urine (umole As)
0.9
0.8
0.7
0.6
DMA
0.5
0.4
As
0.3
0.2
0.1
0
1000
2000
3000
Time (min)
4000
MMA
5000
MMA
6000
0
0
1000
2000
3000
Time (min)
4000
5000
6000
Arsenical Concentration (g/g creatinine)
400
300
Data - Minimum
Data - Maximum
Data - Geometric Mean
Model Prediction
200
100
0
As(V)
As(III)
MMA(V) MMA(III) DMA(V) DMA(III) Total As
Conclusions
•
The current As Human PBPK model was developed to include complex
metabolic pathways consistent with recent experimental observations of the
interrelations between arsenic and its metabolites.
•
Model parameterization was largely based on up-to-date in vitro studies,
and optimization of parameters that are only sensitive to the shape of the
urinary excretion curve.
•
The current model was calibrated and evaluated using human urine data
obtained from several sources
•
The current model can be used to assess the relationship between target
tissue dose of arsenic metabolites (including MMAIII, DMAIII or both) and
response in conjunction with BBDR.
•
Because the model describes physiological and biochemical processes, it
can be used to quantitatively assess kinetic variability such as ones related
to polymorphisms in human arsenic metabolizing enzymes.
What is the Utility of the Human
Arsenic Model Now and in the Future?
• Assess the impact of human variability in arsenic
metabolism
• Evaluate assumptions used in default risk
analysis methods against experimental data
• Linking with Exposure Models (multi-media,
multi-pathway)
• Examine the role of kinetics in cross-species
extrapolation
• Essential to Link with BBDR models for multiple
arsenicals and modes of action
Key Question:
What are the implications of
polymorphisms and age-dependent
variation in arsenic metabolism?
Use the Model to Estimate the Impact of
Variability in Human Metabolic Profiles (and its
relationship to disease outcome measures)
What is Needed?
• Physiological parameter distributions
(literature)
• Biochemical parameter distributions
(e.g. methylation rate constants)
• Human data collected at the level of
the individual subject, especially
exposure and urinary metabolite
profiles
Advantages of this Approach
• Incorporate and consider data from a variety of
sources
 in vitro metabolism studies (human hepatocytes)
 Genetic association studies
 Epidemiologic investigations
• Assess the impact of variability in sensitive
parameters on model predictions
• Identify key uncertainties in model
parameterization
From tissue dose to toxic
response
Biological mechanisms
determine dose-response
Exposure
Tissue
dose
Tissue
interaction
Tissue
interaction
Sequence of
events (MoA)
Cancer
Early
Intermediate
Organism
Lung
Tissue
Venous
Residual
Kur
Kidney
Bladder
Vmax,
Km
Liver
Ka
Kb
GI Tract
Cellular
Molecular
Arterial
Skin
Late
Reduce uncertainty by describing the
system more accurately
(a)
U pper
boun d
Range of
uncertainty
un d
o
b
r
e
w
Lo
Risk
(b)
Information
Arsenical Exposure
Tissue Dose (PBPK modeling)
ROS
lipid
oxidation
Change in cell
phenotype
protein
oxidation
D cell cycle /
apoptosis
cell proliferation
- SH reactivity
D DNA
repair
enzymes
DNA
damage
D DNA
methylation
enzymes
D chromosome
copy number
altered DNA
methylation
Genomic instability (chromosome
damage/ mutation accumulation)
Cancer: self sufficiency in growth signals, evading apoptosis, insensitivity to antigrowth signals, limitless replicative potential
Arsenical Exposure
Tissue Dose (PBPK modeling)
ROS
lipid
oxidation
Change in cell
phenotype
protein
oxidation
D cell cycle /
apoptosis
cell proliferation
- SH reactivity
D DNA
repair
enzymes
DNA
damage
D DNA
methylation
enzymes
D chromosome
copy number
altered DNA
methylation
Genomic instability (chromosome
damage/ mutation accumulation)
Cancer: self sufficiency in growth signals, evading apoptosis, insensitivity to antigrowth signals, limitless replicative potential
Overall dose-response and time-course is
built up from the key event relationships
(dosimetry)
Regulatory
endpoint
Arsenical Exposure
Tissue Dose (PBPK modeling)
ROS
lipid
oxidation
Change in cell
phenotype
protein
oxidation
D cell cycle /
apoptosis
cell proliferation
- SH reactivity
D DNA
repair
enzymes
DNA
damage
D DNA
methylation
enzymes
D chromosome
copy number
altered DNA
methylation
Genomic instability (chromosome
damage/ mutation accumulation)
Cancer: self sufficiency in growth signals, evading apoptosis, insensitivity to antigrowth signals, limitless replicative potential
Arsenical Exposure
Tissue Dose (PBPK modeling)
ROS
lipid
oxidation
protein
oxidation
- SH reactivity
D DNA
repair
enzymes
D DNA
methylation
enzymes
Dose-response and time-course
for each key event!!!!
Change in cell
phenotype
D cell cycle /
apoptosis
cell proliferation
DNA
damage
D chromosome
copy number
altered DNA
methylation
Genomic instability (chromosome
damage/ mutation accumulation)
Cancer: self sufficiency in growth signals, evading apoptosis, insensitivity to antigrowth signals, limitless replicative potential
Arsenic dosimetry
Skin
dose
Lung
dose
MOAbladder
MOAskin
MOAlung
Bladder
cancer
Skin cancer
Lung cancer
Bladder
dose
Available data
Epi cancer
dose-response
Lab animal in vivo
dose-response
& time-course
In vitro studies of MOA
Relevance to model development
Epi cancer
dose-response
Very!
Lab animal in vivo
dose-response
& time-course
Very!
In vitro studies of MOA
Informs MOA, but
generally lacking doseresponse and time course.
Also relevance issues (i.e.,
transformed cell lines).
•85 ppm in drinking water
•1 applied dose
•15 ppm in drinking water
•1 applied dose
•human relevance?
As(III) causes oxidative DNA
damage
7
6
*
5
*
4
3
*
2
4
3
*
2
1
1
0
*
5
8-OHdG/106dG
8-OHdG/106 dG
6
0
0
5
10
20
Concentration (M)
30
0
4
8
16
Incubation time (hr)
24
As(III) causes oxidative DNA
damage
7
5
4
Ke Jian
“Jim” Liu,
*
*
5 Ph.D.
College of Pharmacy
4
*
University of New Mexico Health Sciences Center
8-OHdG/106dG
8-OHdG/106 dG
6
6
3
*
2
*
2
1
1
0
3
0
0
5
10
20
Concentration (M)
30
0
4
8
16
Incubation time (hr)
24
As(III) causes oxidative
DNA damage
7
5
4
Ke Jian
“Jim” Liu,
*
*
5 Ph.D.
College of Pharmacy
4
*
University of New Mexico Health Sciences Center
3
*
2
3
*
2
1
1
0
8-OHdG/106dG
8-OHdG/106 dG
6
6
0
5
HaCaT human keratinocyte
0 line
transformed
cell
10
20
30
0
4
Concentration (M)
8
16
Incubation time (hr)
24
300
Formaldehyde
200
200
150
150
100
100
4
4
1
5
4
50
50
3
2p 2
pm
1
5
4
0.7 3 2
pp
m
1
5
4
A5
A4
A3
A2
A1
2
B5
B4
B3
B2
B1
3
6p
pm
C5
C4
C3
C2
C1
54
D5
D4
D3
D2
D1
F5
F4
F3
F2
F1
5
10 3 2 1
pp
m
E5
E4
E3
E2
E1
5
15 3 2
pp 1
m
con 3
tro
l
2
0.57 0.14
6. 1.29
0. 1 4
sure
of expo
n
o
i
t
a
r
Du
)
(weeks
0. 5 7
1 78.
7 8 . 00
13.
52. 26.
1 3 . 00
2 6 . 00
5 2 . 00
1 .2 9
6 . 00
0
Labeling index
Dose-time
response
surface for
regenerative
cellular
proliferation in
nasal epithelium
of the F344
rat.
250
Considerations for experimental
design
• Dose-dependence of key events
 Lower dose effects of greater interest
• Time courses of key events
 Classify early vs late events
• If data are obtained in vitro then need
an accurate method for extrapolation to
in vivo
Final thoughts
• BBDR model is data-based.
 Accuracy of predictions as good as the quality
and completeness of the data used in developing
the model
• Model describes the in vivo situation
• Important extrapolations that can be
informed by data
 In vitro  in vivo
 Lab animal  human
 Hi  low dose
End
What is the Bottom Line?
• Utilizing only exposure measures in doseresponse modeling can be misleading
• The PBPK model can be used to assess
the impact of variability in metabolism at
the population level
• A functional PBPK model is essential for
linking with response (BBDR) models
• PBPK and BBDR models provide a
framework for planning and design of
studies utilizing animal models or human
populations
Collaboration and Consultation
Teamwork!
•
•
•
•
•
•
•
•
•
Harvey Clewell (Hamner)
Stephen Edwards (NCCT)
Marina Evans (NHEERL)
Michael F. Hughes (NHEERL)
David Thomas (NHEERL)
Jan Yager (EPRI)
ECD Researchers (NHEERL)
NCEA
Office of Water
Response
Many possibilities for the
actual dose-response
Dose
Choose the model that
minimizes uncertainty
(Mechanism-based approach)
(Policy-based approach)
(a)
U pper
boun d
RfC
Range of
uncertainty
oun d
Low er b
Risk
(b)
Risk
Information
U pper
boun d
Range of
uncertainty
Risk
oun d
Low er b
Range of
uncertainty