Integration of Pharmacokinetic PK and Pharmacodynamic PD Modeling of Arsenic to Inform the Risk Asse

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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

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Disclaimer !

• This presentation does not necessarily reflect
  EPA policy. Mention of trade names or commercial
  products does not constitute endorsement or
  recommendation for use.
• This work is a work in progress!

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Exposure-Dose-Response Paradigm
Exposure
bioavailability
Internal Dose
Biologically Effective Dose
Early Biological Effects
Altered Function/Structure
Clinical Disease
Prognostic Significance
Modified from Schulte, 1989
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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

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TMAs(-III)
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Accumulation of Arsenicals Varies Significantly
Across Tissues
Female C57Bl6 Mice - 12 week drinking water
exposure to As(V)
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Role of PBPK and BBDR Models
INTERNAL DOSE AT TARGET (e.g., TISSUE, ORGAN)
RESPONSE
APPLIED DOSE
BBDR MODEL
PBPK MODEL
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• Biological Response
• (chemicals 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).

• 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).

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Biological Hypothesis
Physiological Biochemical Parameters
PBPK Model
Model Simulations (tissue levels)
Model-Designed Experiments
Disagree

Experimental Data
Model Evaluation
Agree
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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

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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
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An ExampleDMAV-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

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DMAV Metabolism (2007)
DMTAV
DMAV
DMAIII
DMTAIII
TMAO
TMA
TMASV
Adair et al., 2007
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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?

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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

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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.

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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.

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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.
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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

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Noncompetitive inhibition
GSH
AS3MT
GSH
AsV
AsIII
MMAIII
DMAV
MMAV
Reduction
AS3MT
Reduction
GSH
oxidation
Reduction
DMAIII
oxidation
oxidation
Noncompetitive inhibition
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Table 3. An example of some of the biochemical
Parameters
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Utility of Urine Data
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Model Calibration (DMA Dose)
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Model Calibration (MMA Dose)
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Model Calibration (As Dose)
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Model Evaluation
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Conclusions
Table 3. Biochemical Parameters Values

• 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.

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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

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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)
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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

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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

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From tissue dose to toxic response
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Biological mechanisms determine dose-response
Tissue dose
Tissue interaction
Exposure
Tissue interaction
Sequence of events (MoA)
Cancer
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Early
Intermediate
Late
Organism
Tissue
Cellular
Molecular
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Reduce uncertainty by describing the system more
accurately
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Arsenical Exposure
Tissue Dose (PBPK modeling)
ROS
- SH reactivity
D DNA methylationenzymes
D DNA repairenzymes
protein oxidation
lipid oxidation
DNA damage
D chromosome copy number
altered DNA methylation
Change in cell phenotype
D cell cycle / apoptosis
Genomic instability (chromosome damage/ mutation
accumulation)
cell proliferation
Cancer self sufficiency in growth signals,
evading apoptosis, insensitivity to anti-growth
signals, limitless replicative potential
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Arsenical Exposure
Tissue Dose (PBPK modeling)
ROS
- SH reactivity
D DNA methylationenzymes
D DNA repairenzymes
protein oxidation
lipid oxidation
DNA damage
D chromosome copy number
altered DNA methylation
Change in cell phenotype
D cell cycle / apoptosis
Genomic instability (chromosome damage/ mutation
accumulation)
cell proliferation
Cancer self sufficiency in growth signals,
evading apoptosis, insensitivity to anti-growth
signals, limitless replicative potential
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Overall dose-response and time-course is built up
from the key event relationships
(dosimetry)
Dose-response and time-course
Regulatory endpoint
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Arsenical Exposure
Tissue Dose (PBPK modeling)
ROS
- SH reactivity
D DNA methylationenzymes
D DNA repairenzymes
protein oxidation
lipid oxidation
DNA damage
D chromosome copy number
altered DNA methylation
Change in cell phenotype
D cell cycle / apoptosis
Genomic instability (chromosome damage/ mutation
accumulation)
cell proliferation
Cancer self sufficiency in growth signals,
evading apoptosis, insensitivity to anti-growth
signals, limitless replicative potential
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Arsenical Exposure
Tissue Dose (PBPK modeling)
ROS
- SH reactivity
Dose-response and time-course for each key
event!!!!
D DNA methylationenzymes
D DNA repairenzymes
protein oxidation
lipid oxidation
DNA damage
D chromosome copy number
altered DNA methylation
Change in cell phenotype
D cell cycle / apoptosis
Genomic instability (chromosome damage/ mutation
accumulation)
cell proliferation
Cancer self sufficiency in growth signals,
evading apoptosis, insensitivity to anti-growth
signals, limitless replicative potential
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Arsenic dosimetry
Lung dose
Bladder dose
MOAbladder
MOAskin
MOAlung
Bladder cancer
Skin cancer
Lung cancer
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Available data
Epi cancer dose-response
Lab animal in vivo dose-response time-course
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Relevance to model development
Epi cancer dose-response
Very!
Informs MOA, but generally lacking dose-response
and time course. Also relevance issues (i.e.,
transformed cell lines).
Lab animal in vivo dose-response time-course
Very!
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• 85 ppm in drinking water
• 1 applied dose

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• 15 ppm in drinking water
• 1 applied dose
• human relevance?

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As(III) causes oxidative DNA damage
Concentration (?M)
Incubation time (hr)
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As(III) causes oxidative DNA damage
Ke Jian Jim Liu, Ph.D. College of
Pharmacy University of New Mexico Health Sciences
Center
Concentration (?M)
Incubation time (hr)
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As(III) causes oxidative DNA damage
Ke Jian Jim Liu, Ph.D. College of
Pharmacy University of New Mexico Health Sciences
Center
HaCaT human keratinocyte transformed cell line
Concentration (?M)
Incubation time (hr)
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Formaldehyde Dose-time response surface for
regenerative cellular proliferation in nasal
epithelium of the F344 rat.
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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

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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

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End
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What is the Bottom Line?

• Utilizing only exposure measures in dose-response
  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

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Collaboration and ConsultationTeamwork!

• 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

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Many possibilities for the actual dose-response
Response
Dose
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Choose the model that minimizes uncertainty