Public Stakeholder Workshop
to Inform EPA's Upcoming IRIS
Toxicological Review
of Inorganic Arsenic
SESSION 3:
Dose-Response
Tuesday, January 8 &
Wednesday, January 9
RTP, North Carolina
Hosted by EPA's National Center
for Environmental Assessment
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Dose-Response
Weihsueh Chiu
Dose-Response Assessment =
Smoothly Meshii^Three Components
Public Stakeholder Workshop to Inform EPA's Upcoming IRIS Toxicological Review of Inorganic Arsenic
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Dose-Response Challenges for iAs
Modeling & i
Extrapolation
Approaches
8, nntrwitc
Multiple risk management
objectives.
Multiple methods used for
risk characterization.
Multiple exposure
pathways & scenarios.
Need to address
susceptibility
Dose-Response Challenges for iAs
Both experimental and
epidemiologic studies.
Multiple different
measures of exposure.
Multiple health effects and
endpoints.
Multiple covariates that
may affect susceptibility.
Public Stakeholder Workshop to Inform EPA's Upcoming IRIS Toxicological Review of Inorganic Arsenic
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Dose-Response Challenges for iAs
Modeling & i
Extrapolation
Approaches
& Outputs -
• Multiple analyses of
diverse datasets.
• Multiple extrapolations
necessary to meet risk
management needs.
• Schedule may limit
development of new
pproaches.
Public Stakeholder Workshop to Inform EPA's Upcoming IRIS Toxicological Review of Inorganic Arsenic
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IS ARSENIC AN
APHRODISIAC?
The sociochemistry of an element
William R Cullen
Royal Society of Chemistry
arsenous acid
As(lll)
arsenic acid
As(V)
OH
i
HO—As—OH
HO—As—OH
in
monomethylarsonic acid O
MM A II
CH,-As-OH
I
dimethylarsinIc acid
DMA
arsenobetaine
AsB
OH
0
ii
CH,—As—OH
1
CH,
CH.—.
tetramethylarsonium ion
TETRA
ch3 9"
1 . A>
As-—' u
I
CH3
CH,
CH —
As—CH,
in,
"SIMPLE" ARSENICALS IN THE
ENVIRONMENT
Public Stakeholder Workshop to Inform EPA's Upcoming IRIS Toxicological Review of Inorganic Arsenic
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Arsenosugars AsS Thioarseriosugars thioAsS
0 As=S replaces As=0
R
cL
H OH
R= V Y^OH AsS-OH thioAsS-OH
jH
ASS-P04 thioAsS-P04
OH OH OH
SO,H AsS-S03
AsS-S04
thioAsS-S03
thioAsS-S04
OH 0
OH
1 CH3® 11
2e
1
HO-As: CH3— As — OH
> ch3
— As:
OH OH
OH
0
OH
CH3® II 2e
1
CH®
^ CH3—As—OH >
CH3—As: -
-v
ch3
ch3
0
II 2e
* •
CH3-As-CH3 > ch3-
As — CH3
X
-o
ch3
Challenger
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^-SH
SH
t
MeAs V
As V
As III
S- adenosylhomocysteine
S- adenosylmethionine
Lipoic acid
Reductive Elimination
,SR
Me3As(V)0 + 2RSH -> Me3As(V) -> Me3As(lll) + RS-SR
^SR
Reglinski 1984
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Identifying Factors Relevant to
Dose Response
3.1 What types of exposure could contribute to the
aggregate dose, and in what ways might this
impact how an iAs dose-response
characterization is used/applied? How can we
estimate impact of drinking water exposure
alone vs. aggregate exposure on possible effects
of iAs exposure?
Lead Discussants: Karen Bradham, Bill Mendez
3.1. What types of exposure could contribute to
the aggregate dose, and in what ways might this
impact how an iAs dose-response
characterization is used/applied? How can we
estimate impact of drinking water exposure
alone vs. aggregate exposure on possible effects
of iAs exposure?
Bill Mendez
Inorganic Arsenic Public Stakeholder
Workshop
January 8-9, 2013
Public Stakeholder Workshop to Inform EPA's Upcoming IRIS Toxicological Review of Inorganic Arsenic
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What types of exposure could contribute to
the aggregate dose...
• Dietary
- Food contaminated by arsenic from soil/water
• Contaminated Soil/Dust
- Inhalation
- Ingestion
• Severity of As contamination varies widely
- Many natural and man-made sources
...and in what ways might this impact how an iAs
dose-response characterization is used/applied?
• Adverse effects in epidemiological studies usually
reported as function of single exposure medium
concentration
• Failure to account for other exposures can:
- Bias magnitude/form of dose-response
- Effect statistical significance of relationship
- Serious problem if "background" dose is a
large fraction of dose from primary exposure
medium
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How can we estimate impact of drinking water
exposure alone vs. aggregate exposure on
possible effects of iAs exposure?
• Need to account for water and non-water As
- Estimate As dose from primary medium exposures and
exposure factors
- Add in dose from other media
• Varies by region, proximity to point sources
- Consider speciation, bioavailability, absorption
- Fit dose- (instead of exposure-) response model to
estimate risk
• Need to consider As intake of target
population (U.S. general public)
• Account for uncertainty through sensitivity
analysis, simulation
Speciation/Bioavailability Assessment for Rice
Arsenic Species in Uncooked Rice (Juhasz et al. 2006)
800
¦ Inorganic As
¦ "Organic" As
¦ "Other" As
mi
•ft
^ ^ ^ J?
\*> if ^
Lull
/ * Z ~ / J? ^ f f S *
* J? J?
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1,300
1,170
1,040
Dietary and Water Arsenic Intake,
Bangladeshi Women (Kile et al. 2007)
¦ Drinking water As
~ Dietary As
cc
¦a 780
CO
c
(0
a
390
260
Ml Jill
<1 | 1-10 | 11-50 | 51-100 | 101-200 | >200
Tube well As concentration (ng/L)
Public Stakeholder Workshop to Inform EPA's Upcoming IRIS Toxicological Review of Inorganic Arsenic
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Bioavailability of Inorganic Arsenic
Workshop on the Integrated Risk Information System (IRIS)
Toxicological Review of Inorganic Arsenic
Presented by Dr, Karen Bradham
U.S. Environmental Protection Agency
Office of Research and Development
Potential for exposure to toxicants at contaminated sites
U.S. Population within Four-Mile Buffers of Superfund Sites
| Supedund
I with 4-mie buffet
Center for Icvsciturkmil Eirdi
Seimcc InfwmMKXi Network
2QQ0 U.S Consul
Sf 1 Population
High 4OT&8
Sourc** EPA 2008 A AT5DR 1 (AvalWM* »t
M MtpjVsedac.cwsin .colurrfiModu.Wifctpop MfW>,
U.S. Census. ESRI
Public Stakeholder Workshop to Inform EPA's Upcoming IRIS Toxicological Review of Inorganic Arsenic
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2007 Priority list of hazardous substances
1 ARSENIC
2 LEAD
3 MERCURY
4 VINYL CHLORIDE
5 POLYCHLORINATED BIPHENYLS
6 BENZENE
7 CADMIUM
8 POLYCYCLIC AROMATIC HYDROCARBONS
9 BENZO(A)PYRENE
10 BENZO(B)FLUORANTHENE
FREQUENTLY OCCURRING AT NFL SITES
TOXICITY POTENTIAL FOR HUMAN EXPOSURE
Arsenic exposure at contaminated sites
• Oral ingestion of soil and dust-"risk driver" for
human exposure
• Conventional methods or default values - do not
adequately address metal bioavailability under site
conditions
• Bioavailability of metals in soils and dusts vary
depending on the mineralogy and physicochemical
properties
• Default assumption for assessing risk from arsenic in
soil is that the bioavailability of arsenic in soil is the
same as the bioavailability of arsenic dissolved in
water
• Recent bioavailability studies conducted in animal
models show that the bioavailability of arsenic in soil
is typically less than that of highly water soluble
forms of arsenic (< 100%)
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EPA bioavailability guidance
• EPA's Technical Review Workgroup, Bioavailability Committee
recently conducted a review of all available in vivo estimates of soil
arsenic bioavailability
• 103 studies identified, most studies had values < 60% and studies
were performed to support remedial investigations and risk
assessments of specific sites
• OSWER Directive 9200.1-113:
- Based upon evaluation of current data sets of arsenic bioavailability, the
upper percentile of the data set results in a default value of 60%
- The default value for arsenic in soils should only be used if site-specific
assessments for arsenic are not feasible
• EPA's Guidance for Evaluating the Bioavailability of Metals in Soils
for Use in Human Health Risk Assessment
http://www.epa.aov/superfund/bioavailabilitv/auidance.htm
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What kinds of dose-response characterization may
be needed (e.g., reference value, incremental
change in risk with dose, probabilistic risk at dose)
for aggregate (e.g., urine, blood) and source-
specific (e.g., food, water) dose metrics?
Weihsueh Chiu
3.3 Dose-Response Characterization:
What kind(s) of output needed (for each endpoint)?
What can the
available
data
support?
Dose-Response
Outputs
Point of departure
(e.g., Benchmark Dose)
Exposure limit (e.g.,
RfD)
Changes in continuous
parameters as a
function of exposure
Incidence of effects as
a function of exposure
Risk
Characterization
Method
Different
regulatory
programs
have
different risk
management
needs
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3.3 Dose-Response Characterization:
What metric(s) of dose/exposure are needed?
What can the
available data
support?
v y
Can we
extrapolate
across dose
metrics?
Source specific
Exposure
Durations
&
Life stages
Depends on
how each
regulatory
program
estimates
exposure.
Public Stakeholder Workshop to Inform EPA's Upcoming IRIS Toxicological Review of Inorganic Arsenic
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Approaches to Dose-Response Analysis
3.4 What kinds of approaches are available to
analyze dose-response data (e.g., statistical
models, non-parametric approaches)?
Lead Discussants: Weihsueh Chiu, Jeff Gift
What kinds of approaches are available to
analyze dose-response data (e.g., statistical
models, non-parametric approaches)?
Multitumor Analysis
Jeff Gift
(January 9, 2013)
Benchmark Dose Training California Environmental Protection Agency Training Workshop 128
Public Stakeholder Workshop to Inform EPA's Upcoming IRIS Toxicological Review of Inorganic Arsenic
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3ERA
Analyzing Dose-response Data
0.8
0.6
0.4
0.2
BMDL
BMD
50
100
dose
150
200
14:40 01/25 2007
Benchmark Dose Training California Environmental Protection Agency Training Workshop
Possible Topics
IMDL?
Parajfrflric or semi para metric modeling?
Prob^^fetic risk?
Mod^^n certainty?
OtheQ^rametric models?
Othe^^thods for estimating BMDLs for an endpoint
Multitumor analysis
Benchmark Dose Training California Environmental Protection Agency Training Workshop
Public Stakeholder Workshop to Inform EPA's Upcoming IRIS Toxicological Review of Inorganic Arsenic
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3ERA
Multiple Tumor Analysis
• Basing unit risk on one tumor type may
underestimate cancer risk of a chemical that
induces neoplasia at multiple sites (NRC, 1994).
• Tumor selection and derivation of confidence
limits are key aspects of a multitumor analysis.
• Tumors must be independent of one another.
MOA for arsenic related tumor formation not known
No reason to assume tumors not formed independently
• Low risk tumors combined with high risk tumors
For example, though skin cancer is rarely fatal and less
potent than internal tumors, its risk can theoretically be
accounted for in a combined risk analysis.
Selection of Tumors
Public Stakeholder Workshop to Inform EPA's Upcoming IRIS Toxicological Review of Inorganic Arsenic
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Confidence Limits for Combined
Risk
LED (BMDL) from BEIR IV model is not 95% LB on dose.
"Normally distributed uncertainties" assumption used to get
LB for combined risk has been criticized in peer review.
Two alternative approaches are used in the EPR draft of
the EPA 1,4-Dioxane assessment:
Markov Chain Monte Caro (MCMC)/Bayesian computational
approach (Kopylev et al., 2009)
BMDS multitumor (MS_combo) profile likelihood approach
(epa.gov/ncea/bmds)
These approaches were well received by peer reviewers,
but do not account for time/age dependency.
Benchmark Dose Training California Environmental Protection Agency Training Workshop
uose-response modeling results
for male rat tumors that inhaled
1,4-dioxane for 2 years
Rat Exposure Human Equivalent
Tumor Type
Nasal squamous cell
carcinoma
Hepatocellular adenoma
or carcinoma
Renal cell carcinoma
Peritoneal mesothelioma
Mammary gland
fibroadenoma
Zymbal gland adenoma
Subcutis fibroma
Bayesian Total Tumor Analysis
BMDS Multitumor (MS_Combo)
Multistage
Model
Degree
1
Inhalation
(ppm)
(mg/m3)
Unit
Risk
BMC10
BMCL10
BMC10
BMCL10
(M9/m3)-1
1107
629.9
712.3
405.3
2.5 x 10"7
252.8
182.3
162.7
117.3
o
X
LO
CO
1355
1016
872
653.7
o
X
LO
82.21
64.38
52.89
41.42
2.4 x 10"6
1635
703.0
1052
452.4
2.2 x 10"7
1355
1016
872
653.7
o
X
LO
141.8
81.91
91.21
52.70
(D
X
O
CT>
39.2
31.4
25.2
20.2
5.0 x 10"6
40.5
32.3
26.1
20.8
4.8 x 10"6
Benchmark Dose Training California Environmental Protection Agency Training Workshop
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3ERA
Questions
• Should all "confirmed" As-related tumors (lung, bladder,
kidney and skin) be included in a multitumor analysis?
• If so, how should lower risk cancers (e.g., skin) be
"weighted" relative to the other cancer risks?
• Should a time dependent multitumor modeling approach
be developed that can calculate more defensible
confidence limits?
• Most recent approach proposed for As:
"Upper confidence limits on the combined cancer risks
can be calculated based in the assumption that the
uncertainties in the two CSFs are both normally
distributed. If this is the case, the 95% upper bound, U,
for the combined cancer potency can be calculated as:
U = (m1 + m2) + (u 1 - m 1) + (u2 ~ m2) (Equation 5-5)
where mi and ui, i = 1,2, are respectively mean and 95%
upper bound cancer potency for the two tumor types."
Confidence Limits for Combined
Risk
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Approaches to Dose-Response Analysis
3.5 What are factors (e.g., toxicokinetics,
bioavailability, water consumption rates,
background exposure, susceptibility) that can
impact the dose response analysis, and how
could these factors be transparently accounted
for?
Lead Discussants: William Cullen, Hisham El-Masri
* 3*
What are factors (e.g., toxicokinetics,
bioavailability, water consumption rates,
background exposure, susceptibility) that can
impact the dose-response analysis, and how
could these factors be transparently
accounted for?
Hisham El-Masri
Public Stakeholder Workshop to Inform EPA's Upcoming IRIS Toxicological Review of Inorganic Arsenic
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Exposure-Dose-Response Paradigm
Exposure
Nonavailability
Interna! Dose
\
Biologically Effective Dose
\
Early Biological Effects
\
Susceptibility
Altered Function/Structure
Susceptibility
\
Clinical Disease
Modified from Schuite, 1989 Prognostic Significance
Public Stakeholder Workshop to Inform EPA's Upcoming IRIS Toxicological Review of Inorganic Arsenic
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IAsv
a)
iAs
~
Induce chromosomal aberrati
(4), genetic instability (5).
Induce alterations in methylation
patterns (6).
Generate reactive oxygen
species (7) and 8-oxo-dG
adducts (8).
Interfere with DNA repair (9).
Induce p53 (10) and cell
proliferation (11).
Mouse carcinogen (12) and co-
carcinogen (13).
Inhibit DNA repair (9). Non
turnorigenie to mice and rats
(14).
MMAsv
0)
MMAs'"
\ ra .
DMAs1'
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).
Methylation
Reduction '
Induce DNA damage (19) and 8-oxo-
dG adducts (20).
Induce p53 (10) and cell proliferation
(11).
Rat bladder carcinogen (20) and rat
bladder tumor promoter (21).
(2)
Induces 8-oxo-dG adducts (17).
Rat liver carcinogen (24).
/
DMAs
III
Induce chromosomal aberrations and DNA
breaks (15,22)
Generate reactive oxygen species (16)
Inhibit DNA repair (9)
Induce p53 (10) and cell proliferation (23).
(2)
TMAsv
(3)
TMAsH")
Accumulation of Arsenicals Varies
Significantly Across Tissues
IAs
MMA
DMA
O 4 -
O
Blood Liver Lung Kidney Bladder Skin
Tissue
Female C57BI6 Mice -12 week drinking water exposure to As(V)
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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
Factors Impact on Dose-Response
• TK
— Linkage to a hypothesized mode of action to identify key
target tissue dosimetry
• HUMAN VS ANIMAL
• Epidemiological studies (human only)
— Exposure (speciation of organic vs inorganic As, DMA,
M MA..etc.)
• Is As dose-response a mixture problem?
— Susceptible populations
— Can biomarkers (e.g. levels of DMA in urine) be
misleading?
• Need of appropriate target tissue levels (blood levels of affected
population at minimum)
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Approaches to Dose-Response Analysis
3.6 EPA has traditionally addressed uncertainty in
modeling dose-response data by using a
statistical lower confidence bound on the
benchmark dose. What other approaches are
available to address and transparently convey
the impact of uncertainty on the dose-
response analysis?
Lead Discussants: Bill Mendez, Warner North
3.6
"EPA has traditionally addressed
uncertainty in modeling dose-response
data by using a statistical lower
confidence bound on the benchmark dose
(BMD). What other approaches are
available to address and transparently
convey the impact of uncertainty on the
dose-response analysis?"
D. Warner North
Public Stakeholder Workshop to Inform EPA's Upcoming IRIS Toxicological Review of Inorganic Arsenic
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Comments for the Arsenic/IRIS Workshop Jan 8-9, 2013
D. Warner North
President and Principal Scientist, NorthWorks, Inc.
E-mail: northworks@mindsprinq.com
Web: www.northworks.net
3.6:
"EPA has traditionally addressed
uncertainty in modeling dose-response
data by using a statistical lower
confidence bound on the benchmark dose
(BMD). What other approaches are
available to address and transparently
convey the impact of uncertainty on the
dose-response analysis?"
Section 1.3 North comments
• Inorganic arsenic is unusual as an IRIS entry.
• Not all risk assessments should be done the
same way - for IRIS, or in other contexts.
(Reference: the NAS "Color" books)
• Focus should be on health risk at potential
low-dose human exposure.
- Is the health risk potentially significant?
- If so, want quantitative estimate(s) and uncertainty
disclosure. Key aspect: dose-response relationship,
built upon evidence from epidemiology, mode of
action/toxicology investigations.
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Section 1.3 North comments, 2
• For important cases/IRIS entries, may want more than
a review of published papers.
- Convene a gathering of the best experts for
discussion and debate. Publish the proceedings.
- Frame the problem first. What is included?
- Want a transparent process, understandable by
stakeholders.
- Don't preclude evidence: Assemble it, then evaluate
it.
Reference: Public Participation in Environmental
Assessment and Decision Making, National Academy
Press, 2008, See esp. Chapter 6, "Practice:
Integrating Science," particularly page 141 on defaults
and guidelines. (Page 141 to be handed out). On web
at www.nap.edu/catalogue.php7record id=12434.
Dose-Response with uncertainty
Goal: Summarize the information available,
disclosing uncertainty.
Consider three levels : (response y,
given dose x)
1. A range (e.g., zero to a plausible upper
bound)
2. A probability distribution over the range
3. A model that computes a probability
distribution based on inputs, permitting
sensitivity analysis to these inputs.
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Section 3.6: D-R with uncertainty
Goal: Summarize the information available,
disclosing uncertainty.
Consider three levels : (response y
given dose x)
1. A range (e.g., zero to a plausible upper
bound - old pre-BMD, EPA cancer risk
number-see page 141 handout)
2. A probability distribution over the range
3. A model that computes a probability
distribution based on inputs, permitting
sensitivity analysis to these inputs.
Section 3.6: D-R with Uncertainty
Goal: Summarize the information available,
disclosing uncertainty.
Consider three levels : (response y
given dose x)
1. A range (e.g., zero to a plausible upper
bound)
2. A probability distribution over the range
3. A model that computes a probability
distribution based on inputs, permitting
sensitivity analysis to these inputs.
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Example for Level 3:
(ecological risk, introduced
species)
Reference:
D. W. North, "Limitations, definitions, principles, and
methods of risk analysis, Rev. sci. tech. Off. Int.
Epiz.14(4), 913-923, 1995
Available at: http://www.northworks.net/limitations.pdf
Microbes
Do we infect Mars with
terrestrial microbes by landing
a spacecraft on its surface?
(Decision facing NASA in 1972 for
billion-dollar Project Viking Mission,
first surface landing)
Public Stakeholder Workshop to Inform EPA's Upcoming IRIS Toxicological Review of Inorganic Arsenic
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Planetary Environmental Protection
(an example of risk assessment
with almost no "data")
Mars from Viking Orbiter Viking Lander, 1976
Mission Contamination Model
BIO-BURDEN
RELEASE
TRANSPORT
REPRODUCTION
External
ImpJarw
RESISTANCE
l ANDl.NO MOOe
Covered
USABLE WATCH
Reach
Usable
TO
ENVIRONMENT
RtCONI AMfcNAT IOM
REiEASt
Efotion
Water
Growth
Mated
MECHANISM
MECHANISM
•IIOEXPSHIMENT
Encap-
sulated
RELEASE
Vibration
TRANSPORT
NUTRIENTS
LETHALITY
m
PROBABILITY \
" )
, CON T AMI NAT SON /
Bioexperlment Contamination
Mission scenarios, fate and transport of microbes on lander
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Bio-burden Submodel
(Microbes on Spacecraft)
Input Elements:
• Pre-sterilization burden by location
• Sensitivity to sterilization
• Sterilization regime
• Recontamination
• Inflight mortality or proliferation
• Contamination and subsequent
amplification in biology experiment:
(probability = 10"6)
Outputs:
• Bio-burden estimates by Viking Project
• Expected number of Viable Terrestrial
Organisms (VTOs), by location on
spacecraft
BIO-BURDEN
SiHStflVt
11
External
VARIABLES
• EltXlMl.
16
Covered
WKaiWjUM
Mo-buf
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Transport Submodel
(How Many Get to "Usable Water"?)
Input Elements:
• Expected number of VTOs released, by
mechanism
• Lethality of UV radiation, in normal
atmosphere and dust storm (probability that
a VTO survives transit)
• Extent of usable water
- Probability it exists anywhere
- Portion of surface covered
Output
• Expected number of VTOs that will reach
usable water
0.045
Implan-
tation
14.9
Erosion
0.11
Vibration
TRANSPORT
sensitive
VARIABLES
~ Availability ol
usable v*a\tt
» Drgr«c ol UV
shielding
i
1.2 x 10"3!
Beach
Usable !
Water
~
Reproduction Submodel
(Can at Least One Do It?)
Input Elements:
• Fraction of VTOs that are
facultatively anaerobic and
psychrophilic: (0.05)
• Probability that nutrients needed for
reproduction will be present in the
water microenvironment (0.10)
Output:
• Expected number of VTOs that
reproduce at least once, defined as
"contamination"
HE PRODUCT ION
SENSITIVE
1.2 * Iff3
VARIABLES
Reach
6 x 10"
Usable
• PllCafll
Water
ptyctirophilic lod
Growth
* AvmubiJiiv ol
mm will ncciiH'T
1 oi microtowl jrdnMh
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Mission Contamination Model
Results
BIO-BURDEN RELEASE TRANSPORT REPRODUCTION
Mission Contamination Model
Marginal
Sensitivity Analysis
Probability of Contamination
Contamination Model Variables
Values
Units: = 10**-6
Extreme
Intermed.
Intermed.
Extreme
Nominal
5.9
Low
Low
NOMINAL
High
High
Low
High
Bio-Burden Variables
1. bio External
2.2
5.5
11
22
55
5
10.7
2. bio Covered
3.2
8
16
32
80
3.1
20.2
3. bio Encapsulated
4,000
10,000
20,000
40,000
100,000
5
10.4
Release Variables
1. rel Hard Landing Probability
0.0004
0.001
0.002
0.004
0.01
5.2
9.6
3. rel Newly Exposed/Hard, Encaps
0.0001
0.0002
0.001
0.005
0.01
5.4
10.9
4. rel Implanted, Soft
0.0001
0.0002
0.001
0.005
0.01
5.7
8.7
6. rel VTO/Vibration
0.001
0.002
0.01
0.05
0.01
5.4
11.1
9. rel VTO/Erosion, Encaps
0.00001
0.00002
0.0001
0.0005
0.001
5.4
10.9
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Mission Contamination Model
Marginal Sensitivity Analysis -2
Probability of Contamination
Contamination Model Variables
Values
Units:
= 10 **-6
Extreme
Intermed.
Intermed.
Extreme
Nominal: 5.9
Low
Low
NOMINAL
High
High
Low
High
Transport Variables
1 tra Survive Transit
0.001
0.002
0.01
0.05
0.1
2.2
45.2
2tra Find Water
0.0005
0.001
0.005
0.025
0.05
1.5
49.9
4 tra Water Deposition
0.00005
0.0001
0.0005
0.0025
0.005
5
15.2
5 tra Stay Lodged
0.1
0.2
0.5
0.8
0.9
5.5
10
Reproduction Variables
1 rep Psychrophilic, Anaerobic
0.005
0.01
0.05
0.1
0.25
0.6
29.6
2 rep Availability of Nutrients
0.01
0.02
0.1
0.2
0.5
0.6
29.6
Results
• Contamination probability 6 x10"6, well below
mission limit of 10 4, insensitive to model
assumptions and input data
• Simple explanation for why number is low: UV
flux through thin atmosphere kills microbes
• NASA Scientific Advisory Committee (Carl
Sagan, Joshua Lederberg, et al.) persuaded of
acceptable safety
• Mission flew, Viking Lander successful
• Mid-course correction eliminated, Orbiter life
extended to one year
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National Academy of Sciences,
Viewpoint -1992
"... it is the unanimous opinion of the task group that
terrestrial organisms have almost no chance of
multiplying on the surface of Mars and in fact have little
chance of surviving for long periods of time, especially if
they are exposed to wind and to UV radiation."
— Space Studies Board, National Research
Council, Biological Contamination of Mars, 1992,
page 49: http://www.naD.edu/cataloci.DhD7recorcl id=12305
Retrospective on this Case Example
• Planetary Quarantine since Viking Landing (1976):
- Not a major concern, perhaps excepting Mars
sample return. (Possibly new observations might
change the concern level.)
• Example of Quantitative Risk Analysis built on highly
judgmental information
• Accepted by scientific leaders and Agency managers; no
public concern or controversy regarding risk assessment
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A Key Conceptual Aspect for Levels 2 and 3
• Probability assessments are summaries of
information and subject to change:
- "Necessarist" viewpoint; Ref: E. T. Jaynes, Probability Theory:
The Logic of Science, Cambridge Univ. Press, 2003.
(Endorsement in Nassim Taleb, The Black Swan, 2007)
- Usual statistics viewpoint: probabilities are frequencies in data
obtained in independent, identical experimental trials.
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Extrapolation approaches
3.7 What kinds of extrapolations are needed (e.g.,
interspecies, exposure route, human variability,
low-dose/effect)?
Lead Discussants: Bill Mendez, Warner North
3.7. What kinds of extrapolations (e.g.,
high-low dose/effect, exposure route,
human variability, interspecies) are
needed?
Bill Mendez
Inorganic Arsenic Public
Stakeholder Workshop
January 8-9, 2013
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High to low exposure/dose
• Context: High-quality epidemiological studies are
available for many endpoints
• Often address primarily or exclusively high
exposures
• Tradition: identify point of departure (POD),
extrapolate to low doses (linear, UFs)
• Feasibility of model-based extrapolation
- Need to account for covariates
- Uncertainty about model form, confidence
bounds, at low exposures
- Use in vivo, in vitro toxicity, metabolism data
to support extrapolation?
exposure route
• Epidemiology available for inhalation and
ingestion exposures
• Dosimetry for cross-pathway (ingestion/
inhalation) extrapolation is not well-established
• Portal-of-entry effects can be significant
• Consensus(?) is that cross-pathway estimation of
PODs for risk assessment is not advisable
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human variability
• Exposure/dose-response
- Important covariates (diet, smoking,
exposure to other stressors) not always
available
- Sensitive developmental windows
- Hard to generalize relationships between
genetic/metabolic variations and risks
• Risk extrapolation to U.S. population
- Differences in mortality, background
disease rates, background As exposures,
smoking, etc.
interspecies
• In vivo tests could, in theory, be used to estimate
endpoints for risk assessment
- Derive POD (NOAEL, BMDL) in range of data (curve
fitting)
- Then ??
• BMD models generally do not have a mechanistic basis
• Apply CSAF (based on PBPK modeling) to account for
pharmacokinetic differences
• Pharmacodynamic differences are highly species- and
endpoint-dependent, and generally not well-characterized
• Use animal tests as back-stop for PODs derived from
epidemiology?
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in vitro, "omics" data
• Role of in vitro data? (We have lots)
-Quantitative risk assessment
- Safety assessment
-Inform low-dose extrapolation
• Questions
- How to simulate transport, metabolism
- Identification of key pathways/events
- Modeling of complex causal networks
over time
-Assessment of uncertainty
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Extrapolation approaches
3.8 What approaches are available for such
extrapolations (e.g., PBPK modeling, uncertainty
factors, probabilistic factors, linear/non-linear
dose-response)?
Lead Discussants: Hisham El-Masri, Jeff Gift
What approaches are available for
such extrapolations (e.g., PBPK
modeling, uncertainty factors,
probabilistic factors, linear/non-
linear dose-response)?
Hisham El-Masri
Public Stakeholder Workshop to Inform EPA's Upcoming IRIS Toxicological Review of Inorganic Arsenic
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Gastrointestinal
T ract
Skin arid Lungs
Liver
ABSORPTION
Bile
Blood and Lymph
Circulation
Metabolism
y
Metabolites
i
Storage
Kidney
Lung
Organs and Bones
Fatty Tissues
Feces
Urine
Extracellular
Fluids
DISTRIBUTION/
METABOLISM
Expired Air
ELIMINATION
PBPK Models: Internal Dosimetry
• Defines the relationship between external concentration and
an internal measure of (biologically effective) exposure in both
experimental animals and humans
* Use of PBPK Models can account for:
— Interspecies differences in ADME
— Nonlinear uptake, metabolism, clearance
— Toxicity associated with products of metabolism rather than
parent chemical only
— Tissue interactions (e.g. GSH depletion, induction of
clearance/repair, receptor occupancy)
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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.
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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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 (Aslll)
or organoarsenical pesticides.
• The overall model consists of interconnected individual PBPK models
forAsv, Aslll, 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.
Model Evaluation
Q9
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TctelAS
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TdciAB
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/
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0 0 o 0 0 J 0 0
0 r
<
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0
o
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rine (un
o
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3ERA
What approaches are available for such
extrapolations (e.g., PBPK modeling,
uncertainty factors, probabilistic factors,
linear/non-linear dose-response)?
Low Dose Linear/Nonlinear Approaches
Jeff Gift
(January 9, 2013)
Benchmark Dose Training California Environmental Protection Agency Training Workshop
EPA 2005 Cancer Guidelines
Low dose linear - "slope is greater than zero at a dose of zero"
(curvature is possible near observed data).
Low dose nonlinear - "slope is zero at (and perhaps above) a
dose of zero."
"It is the Agency's long-standing science policy position that use
of the linear low-dose extrapolation approach provides adequate
public health conservatism in the absence of chemical-specific
data indicating differential early-life sensitivity or when the mode
of action is not mutagenic."
Benchmark Dose Training California Environmental Protection Agency Training Workshop
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3ERA
EPA 2005 Cancer Guidelines
Linear Approach
• MOA known, evidence for direct mutagenic activity
• MOA known, background doses of As (or other agents
with a common MOA) near levels associated with key
precursor events.
• MOA Unknown
Nonlinear Approach
• MOA known, evidence for nonlinearity at low doses (e.g.,
key precursor events with well defined nonlinear dose-
response relationships) and no evidence for mutagenic or
other activity consistent with linearity at low doses.
Linear vs NonLinear Approach
BMDL BMD
0
50
100
150
200
dose
14:40 01/25 2007
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SEPA
EGBE MOA,
hemangiosarcomas
in mice (Gift, 2005)
Benchmark Dose Training California Environmental Protection Agency Training Workshop
Arsenic History
Following the commendations of NRC (2001), EPA
(2010) used the linear approach because of "remaining
uncertainties regarding the ultimate carcinogenic
metabolites and whether mixtures of toxic metabolites
interact at the site(s) of action."
SAB (2007) concurred, indicating that:
As has the potential for a highly complex mode of action.
What is known about PK/PD properties of As not sufficient
to support a specific nonlinear dose-response relationship.
Agreed with NRC (2001) recommendation for linear dose-
response analysis of southwestern Taiwan population.
Benchmark Dose Training California Environmental Protection Agency Training Workshop
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Questions/Considerations
Population Data
• Is population data useful forjudging low dose shape
given what is know about individual predispositions (e.g.,
from smoking, diet, genetic variants, bimethylation)?
MOA
• Does MOA evidence exists for nonlinearity of Arsenic
cancer dose-responses (e.g., key nonlinear events)?
Arsenic's Low Dose Anticancer Activity
• Is arsenic's therapeutic effect on certain cancers (e.g.,
leukemia) relevant to its ability to initiate the subject (e.g.,
lung and bladder) cancers at low doses?
Benchmark Dose Training California Environmental Protection Agency Training Workshop
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Extrapolation approaches
3.9 EPA has traditionally addressed uncertainty via
the application of uncertainty factors. What
other approaches may be available to address
and transparently convey the impact
of uncertainty on these extrapolations?
Lead Discussants: Ken Cantor, Weihsueh Chiu
3.9 Uncertainties in Extrapolation
Weihsueh Chiu
Public Stakeholder Workshop to Inform EPA's Upcoming IRIS Toxicological Review of Inorganic Arsenic
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• Each extrapolation has associated uncertainty.
• EPA is moving towards separating out the "adjustment"
component from the "uncertainty" component.
• For example, for interspecies extrapolation of oral
doses:
— BWS/4-scaling is considered a "central estimate" cross-
species adjustment.
— A remaining 10,/2-fold "Uncertainty Factor" addresses
residual uncertainty.
— Could also combine these probabilistically based on
analyses of multiple chemicals & endpoints.
What options are available for the
extrapolations needed for iAs?
• Uncertainties
— Across dose metrics
— In variability in susceptibility across population (including
lifestages)
— From precursor markers to disease endpoints
— Below detectable effect levels
• Data (or lack thereof) to support different approaches
— Fixed factors
— Sensitivity analyses
— Probabilistic factors
— Other approaches?
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Extrapolations to derive a RfD are
performed at a fixed level of response
Are there empirical data for iAs to support
these extrapolations?
Human
RfD
Dose
(log scale)
Disaggregating TK & TD requires an
internal dose metric
Is such data available for iAs endpoints?
TOXICO DYNAMICS
TOXICOKINETICS
Human ..
Human
(sensitive) , .. »
* ' (median)
Human (sensitive) Human (median)
/ / —
/ /
LU
CO
/ /
/ / ¦¦¦ "aT
/ /
O
O.
/ / -=
/ i p w
/ / ^ s
/ / o
/ /
i/>
LU
0£
/ / to ~
/ / °
(M m
/ /
DOSE METRIC
EXTERNAL DOSE
(log scale)
(log scale)
Public Stakeholder Workshop to Inform EPA's Upcoming IRIS Toxicological Review of Inorganic Arsenic
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"Low dose extrapolation" is a bit of a misnomer,
and is also related to uncertainty
• In the Benchmark Dose approach, we are fixing
the response level (usually in the observable
range), and estimating the associated dose.
— In the range of observation, sensitivity to the model
form generally less.
• Extrapolation is to lower response levels.
— As the response level decreases below the observable
range, the uncertainty increases dramatically,
especially due to the assumed model form.
— Linear extrapolation from the POD serves to define a
bound of the uncertainty range.
Alternative approaches to "low-
response extrapolation"
• Both avoid the "linear/non-linear" (false) dichotomy.
• Quantitatively characterize the extrapolation uncertainty
— Acknowledges multiple dose-response shapes consistent with
the data.
— Should incorporate both parameter and model uncertainty.
• Use a dose-response characterization approach that does
not require "low-response extrapolation."
— Even for cancer, could fix the response at "X% increased risk"
and derive an exposure limit that "protects" sensitive individuals
from that level of risk.
— But not useful for addressing some risk management needs
(e.g., estimating overall population incidence).
— For precursor endpoints, may shift debate to (false) dichotomy
of "adaptive" versus "adverse."
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