Selected Silver Book Highlights
and Update on Related Activities
Gary Ginsberg
Toxicologist
Connecticut Dept of Public Health
Challenges to Risk Assessment 25 Years
After the Red Book
Precautionary Principle
The 2 Silos
Multiple Chemicals
Cost Benefit
Human Health Risk Assessment
Emerging
Technologies
The dioxin syndrome
SCIENCE AND DECISIONS:
ADVANCING RISK ASSESSMENT
National Research Council
Committee on Improving Risk Analysis Approaches Used by EPA
Board on Environmental Studies and Toxicology
Report Overview
• Make RA more solutions oriented, decision-based
• Uncertainty and Variability
• Unification of cancer and non-cancer RA
– New definition of RfD
– Low dose linear approach for non-carcinogens
– Variability into cancer RA
• Defaults
– Hidden default of zero risk for chems with little data
• Community risk –combined effects of diff
stressors
Risk Policy Report, Vol. 17, No. 37 - September 14, 2010
Guest Perspective
The NRC Silver Book: The Case for Improving Non-Cancer
Risk Assessment
Gary Ginsberg, Connecticut Dept of Public Health, Hartford CT, Jonathan Levy,
Harvard School of Public Health, CambridgeMA, A. John Bailer, Miami University,
Oxford OH and Lauren Zeise, California EPA, Oakland CA
What’s Broken in Risk Assessment?
Two silos – Cancer vs. Non-Cancer
The Fix?
One Unifying Framework
Will it make regulation more complex?
Will it make regulations too stringent?
Is it consistent with the underlying science?
Factors that Contribute to Risk
Community Factors
Housing
Medical Care
Education
Stress
Host Factors
Genetics, Age
Lifestyle, Disease
Cumulative Risk
Disease??
Chemical Exp
Air, water, soil,
consumer prod, food
Do the 2 silos help RA address variability and uncertainty?
tidy constructs, functional but imperfect representations
Threshold vs Non-Threshold
• Easy to define “safe”
dose – RfD or RfC
• Uncertainty factors
• Yes / No answer
• Not useful for
cost/benefit
• No “safe” dose
• No uncertainty factors
• Risk is a continuum to
very low dose
• Probability of risk
useful for cost/benefit
PM10 and Mortality in 20 US Cities
Daniels, et al. Amer J Epi 2000
Unified Approach for
Cancer and Non-Cancer
• Not based upon precaution
• Not making everything a carcinogen
• Not blowing up the RfD
=================================
• Organize around likelihood for threshold
– MOA
– Interaction with background dx, exposure
– Variability and vulnerability in pop
• Create a probability of risk for all endpoints
– Where practical
• Create framework better for cumulative risk
Conceptual Model 1
Linear at the Population Level
•
•
•
•
Threshold exists at individual level
Threshold different for different people
As increase dose, recruit in the more resistant
At population level, no threshold
–
–
–
–
–
Even tiny doses have some finite risk
More likely if already a significant background risk
Intersection with aging or disease process
Interaction with similarly acting agents
Interaction with unique host vulnerability factors
• Possibility for a separate assessment for subgroups
• Examples: PM, mercury, lead, ozone, arsenic
• Analytical approach – linear slope at low dose that can
be shallower than at high dose
Liver Spongiosis in Male Rats in Response
to p-Dioxane
(Yam azaki, et al., 1994)
0.9
Liver Pathology
0.8
0.7
0.6
0.5
0.4
0.3
0.2
0.1
0
0
100
200
300
400
500
600
700
1,4-Dioxane Dose
Spongiosis in Female Rats
Liver Pathology
1
0.8
0.6
0.4
0.2
0
0
200
400
1,4-Dioxane Dose
600
800
What Happens at Low Dose??
100
90
80
Response
70
60
50
Series1
40
30
20
10
0
0
5
10
15
Dose
20
25
30
Threshold??
RfD
100
90
80
Response
70
60
50
40
30
20
10
0
0
5
10
15
Dose
20
25
30
No Threshold?? - Cancer
RfD
100
90
80
General Pop Affected
Response
70
60
50
40
30
20
Vulnerable Affected
10
0
0
5
10
15
Dose
20
25
30
Conceptual Model 2
Threshold Both Individual & Pop
• Chemical MOA not mutagenic
– Toxicity can be quenched
• Defense, repair, homeostatic mechs
• Chemical not adding to background exp or dx
• Alachlor-induced hemolytic anemia
• Xenon-induced asphyxia
– Exposure levels below threshold across pop
• Background exposures, conditions, risk factors not boosting
the dose response
• Analyze by distributions for each uncertainty
– Probability for harm
Conceptual Model 3
Linear at Individual Level
• Mutagens
– Probability of DNA damage, oncogene activation even
at low dose
• Ozone?
– Probability that some percentage of dose effective
even at very low doses within range of human
exposure
• High / low dose slopes not necessarily the same
• Methods suggested for bringing variability into
linear low dose assessment
Redefined RfD
•
•
•
•
Not a bright line yes/no answer
Dose assoc with some probability of harm
Defines the risk at a given reference dose
E.G., May be defined as
– Dose that with 95% confidence confers 1 in
1000 risk for a particular endpoint
• Acceptable level of risk could depend upon
severity of endpoint
Upstream Biomarkers and
Thresholds
• Often a continuous variable
– Birth wt, sperm count, pulmonary fn, blood
pressure, IQ points, TH levels, omics
– Can be concerned about slight shifts in the
distribution even within the normal range
– Threshold? Possibly not if key biomarker
• Threshold applies if you can document a dose in
the population below which there is no shift in the
underlying distribution
– Including susceptible pops
Threshold on Pop Level More
Likely if:
• The pop distribution lies far from the clinical
disease point
– Vulnerable subpopulations don’t exist
– Background dx rate is low or rare
– Chemical doesn’t contribute to something already in
the pop
• There are no interacting background exposures
– Examples of chem interaction
•
•
•
•
•
Ozone and PM, other irritants and oxidants
Mercury – organic and inorganic
Pesticides
Phthalates
Interactions at ER and AhR
Linear Low Dose Concepts
• Increasing risk with increasing dose
• Not necessarily the same slope as at high dose
– Possible to have curvilinear high dose and linear low
dose
• Not all agents should be modeled linear low
dose
– Interaction with background dx or aging process?
– Interaction with other chemical exposures?
So What if there is a threshold at
some very low dose …. If….
• Within the range of common human exposure can’t
demonstrate a threshold
–
–
–
–
–
–
–
–
–
PM
Ozone
Hg
Pb
Phthalates
PCBs
Perchlorate
BPA
Arsenic
• Implication: low enviro levels of these agents cause or
contribute via interaction with other factors
Toxic Chemicals May Interact
with Disease Process
Carcinogens 
PM 
Mercury 
Pb 
TCE 
Arsenic 
Early Estrogens 
Ozone 
Cancer
CardioPulm Dx
CV disease
Attention Deficit
Autoimmune Dx
Diabetes
Obesity
Asthma
Benign?? / Contributory?? / Causative??
Application of Silver Book
Concepts for Low Dose Linear
• Pick low hanging fruit (e.g. Pb, Hg, Ozone)
– Develop a low dose slope from which the
protectiveness of RfD can be explored
– How different is high dose and low dose slope?
– Is it better to address as separate subpops
• Explore other low dose slope approaches
–
–
–
–
Actual Epi data
Cancer model – POD to zero linear
Background additivity model
Variability (signal to noise model)
Exploring Interaction with
Background Dx/Aging Processes
• Collaborate with medical researchers
– What are key upsteam events and risk biomarkers for
diabetes, ht dx, kidney dx, hypothyroid, cancer, etc.
– Does the chemical hit these events?
• Epi evidence of chemical ↑ing dx risk
• Animal models of the human dx to test dose
response?
– TCE and mouse autoimmune model
• Griffin et al 2000; Blossom et al 2007
Variability Model to Address
Signal-to-Noise Issues
• Variability in large pop can create linear low dose
• Variability across a small pop can bury it
• Is there a low dose slope buried the noise?
– Signal to noise crossover dose not yet reached but
doesn’t mean no signal
• Develop plausible bound on this low dose slope
– Statistical limit – e.g., 95% LCL on control response to
95% UCL on low dose response
• Constrained by high dose slope
• Variability by itself won’t create linear low dose
Plot Showing Plausible Bound on Low Dose Slope
100
Response
80
60
40
20
0
0
20
40
60
Dose
80
100
120
Variability Burying Low Dose Slope
in Small Pops
• Animal evidence – Festing et al.
– Strain diffs in CAP hematological response
– Outbred strains insensitive
• Human evidence – Huang and Ghio 2009
– Summary of controlled PM studies in
cardiopulmonary patients
• Diminished responses relative to healthy subjects
• Too much variability in the small groups tested
– Impaired and variable baseline values makes it difficult to see
an effect
– May need to focus on a narrower subpop- more severe patients
CD-1
Festing et al
2001
Background Additivity Model
• If there is background rate of toxic effect (e.g.
dx, oxidative stress, ER binding)
– Convert background to chem “Effective” dose
– If 10 mg/kg/d causes 30% increase in urinary Beta2microglobulin excretion
– And 60 yr old men have 10% increase, then the
starting dose in a 60 yr old man is 5 mg/kg/d
– If exposure in drinking water is at 0.1 mg/kg/d his
overall dose is 5.1 mg/kg/d
– Risk @ 5.1 mg/kg/d – Risk 5.0 mg/kg/d = Risk
associated with drinking water at 0.1 mg/kg/d
Dose Response for Metal Effects on Renal Tubular Function
% Increase in Urinary MicroG
70
60
50
40
30
20
10
0
0
5
10
15
Dose (mg/kg/d)
20
25
Hattis et al. 2009 – Analysis of TCDD Low Dose Risk
Plot of Observed Rat Cholangiocarcinoma
Incidence and Central Estimate of Hill Model
Fit to the Data Vs Human Equivalent Dose
F r action D evel op in g C h olan gi ocarc in om a
(B il e Du c t C an ce r)
0.5
Obs Fract Bile Duct Ca in R ats
Hill Mod Fit--Central Est of N
(25/53)
0.4
0.3
0.2
0.1
(4/49)
(0/49, 48 and 46)
(1/50)
0.0
0
2
4
6
8
10
12
14
16
Human Equivalent Dose (ng/kg-day)
[From Simple Body Weight^(-1/4) Projection]
18
20
Hattis et al. 2009 – Analysis of TCDD Low Dose Risk
Effect of an Interacting Background on the Incremental Risks of Various Lifetime Dose Rates of TCDD
1% of US Background Rate of Cholangiosarcoma Assumed Relevant (1.9E-05)
ng/kg-day
continuous
TCDD dose
equivalent TCDD dose
including backround
(ng/kg-day)
Total Risk incl bkgd
for central (50h
%tile) estimate of n
Incremental
risk after
Subtracting
bkgd
Slope-incremental risk/
ng/kg-day from
previous dose
1.00E-05
0.36896
1.8770E-05
1.10E-09
0.0001
0.36905
1.8783E-05
1.39E-08
1.42E-04
0.001
0.36995
1.891E-05
1.42E-07
1.42E-04
0.01
0.379
2.02E-05
1.45E-06
1.46E-04
0.1
0.469
3.66E-05
1.79E-05
1.82E-04
1
1.37
7.26E-04
7.07E-04
7.66E-04
10
10.37
1.71E-01
1.71E-01
1.89E-02
Combined Exposures
• How many kids in top 10th % for Pb, As, Hg,
PCBs, Perchlorate
– Theoretically 0.15 (0.001%) of population or 1 in
100,000
– Biomonitoring with a purpose
• Similar considerations for anti-thyroid agents,
pesticides, endocrine disruptors
• House dust a key source for children
– Phthalates
– PFOA/PFOS
– Lead
Useful Case Studies
• Ozone and airway response
• Methyl and Inorganic Mercury
– Fish and amalgam exposure
• Arsenic and neurodevelopment
• Binding to estrogen receptor
Ozone and Low Dose Linearity
• Direct effects – some % O3 escapes
antioxidant defenses even at low dose
– 1 ppt = 11 trillion molecules /hr
• Collateral effects – quenched O3 ↓s
antioxidant defenses even at low dose
• Additive to background asthma
– Endogenous ozone poss during inflammation
• Large variability in threshold in population
% Effective Dose Related to Ozone
Antioxidant Capacity in URT
• Greater at high dose than low
– Overcome antioxidant defenses
– Still could have linear low dose slope
• Some ozone at low dose can escape defenses
• Define here as % O3 escaping URT lining fluid
• Baseline, low breathing rate– 17.5%
• Ultman et al.
– Measured OZAC in nasal lavage – 2 subjects
• Generate ozone in cuvette
• Dye bleached – color change correlates with O3
• If antioxidants present – less color change
• Approx 2 mM OZAC in undiluted nasal lining fluid
Ultman et al.
•
•
•
•
•
•
•
OZAC depletion by O3 leads to greater ED
Exposed subjects to 0.36 ppm x 30 min
Went from 17.5 to 28.8% ED
Approx 37% depletion of OZAC
Should have been 100% in 8.9 mins
OZAC repletion must be occurring
Run simple one compartment model to
backfit OZAC repletion rate
Inspired Ozone
(0.36 ppm)
URT
2 mM OZAC
Nasal Lining Fluid
Ozone loss = K*CO3*COZAC
OZAC depletion = Ozone loss
OZAC repletion = backfit
10 micron
0.16 ml
LRT
Effective Dose
Percent Effective Dose At Two Different Ozone Concentrations
2.90E-01
Fraction ED
2.70E-01
2.50E-01
2.30E-01
0.36 ppm
2.10E-01
0.05 ppm
1.90E-01
1.70E-01
1.50E-01
0
10
20
30
40
Time (sec)
50
60
70
P e rc e n t E ffe c tiv e D o s e a s a F u n c tio n o f O z o n e A ir
C o n c e n tra tio n a n d O ZA C L e v e l
100
90
% E ffe c tiv e D o s e
80
70
60
50
B as eline O ZA C
40
50% O ZA C
30
20
10
0
10
1000
100
O zo n e (p p b )
10000
Bell et al. EHP 114: 532-536, 2006
Ozone and Mortality Data from 96 Cities
Brown et al. EHP 2008
Shore et al. J Appl Phys 95: 938-945 2003
Mercury: Why Carp on Cavities?
•
•
•
•
•
Some fish contain methylHg of concern
Effects partially balanced by O-3 FAs
Dental amalgam: lower dose of Hg (Hg°)
Some epi suggests amalgam safe
More brain Hg in those with amalgams
– Below a toxicity threshold?
– Dentists – 100x > exposure than gen public
• neuro effects occur
• How do the amalgam data fit within Silver Book
models?
Neonatal Rat Behavioral Response to
Prenatal Hg Exposure (Fredriksson et al. 1996). Dashed lines represent
range of human brain Hg (Eggleston et al. 1987)
35
Behavioral Response
30
25
20
Swimming Latency
15
Rearing Activity
10
5
0
0
5
10
Brain Hg (ng/g)
15
20
Wasserman et al. 2004 Children’s Arsenic Water Exposure
and Intellectual Function in Bangladesh
Wasserman et al. 2004
Estrogen Sensitive Diseases
Background chemical and dx additivity
• Breast Cancer and Endometriosis
– High background, estrogen driven
• Pop not doing a good job coping w/E within
context of obesity, stress, other carcinogens
• Each E dose on pop level plausibly adds
some risk
• Xenoestrogens may act differently than
natural hormone
• Xenoestrogens early in life may lead to
accentuated response to later estrogen
• Estrogens late life – breast cancer and HRT
Conclusions
• Numerous examples of non-cancer linear D-R
– Across large populations’
– Interaction with dx or other chemicals
– Key focus may be Pb, As, Hg and developmental
neurotoxicity
• Approaches to estimate low dose effects
–
–
–
–
Slope from Epi studies
Default carcinogen approach
Background additivity
Variability (Signal/Noise) model
Conclusions
• Risk managers still use RfD in same way –
just defined differently
– critical to cost-benefit analysis for non-cancer
• Not all chemicals will have linear low dose
effects
– Important to document those as well
– Can still create probabilistic version of RfD
• Need to put variability into Conceptual
Model 3
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Selected Silver Book Highlights and Update on Related Activities