Biological Variability and
Improving Environmental
Decision Making
Weihsueh A. Chiu, PhD
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NRC Variability Workshop
Disclaimer: The views expressed in this presentation are those of the author, and do not
necessarily represent the views or policies of the U.S. Environmental Protection Agency.
Office of Research and Development
National Center for Environmental Assessment
April 18, 2012
Outline
• Variability data at different levels of biological
organization
organization.
• Environmental decision making context and needs.
• Incorporating new variability data
–Integrate into current dose-response assessment
paradigm.
–Contribute to extensions of current approaches for
improving option discrimination.
–Informing
Informing new approaches to dose
dose-response.
response
1
Outline
• Variability data at different levels of biological
organization
organization.
• Environmental decision making context and needs.
• Incorporating new variability data
–Integrate into current dose-response assessment
paradigm.
–Contribute to extensions of current approaches for
improving option discrimination.
–Informing
Informing new approaches to dose
dose-response.
response
2
Data on variability is across
different levels of biological
g
organization
Molecule
Cell
Tissue/Organ
System
Individual
• Data at higher levels of biological organization recruit
y
more sources of variability.
3
Molecular/biochemical reactivity
assay
Chemical
concentration
Molecule
Cell
Tissue/Organ
System
Individual
Molecule
• Target enzyme affinity
differences
4
Rate of reaction
Cell-based bioactivity
y assay
y
Molecule
Chemical
concentration
Bioactivity
Cell
Tissue/Organ
System
Individual
Molecule
• Target enzyme affinity
differences
5
+
Cell
• Target enzyme
expression differences
• Intra-cellular network
differences
Tissue-based assay
y
Molecule
Cell
Chemical
concentration
Functional
output
Tissue/Organ
System
Individual
Molecule
• Target enzyme affinity
differences
6
+
Cell
• Target enzyme
expression differences
• Intra-cellular network
differences
+
Tissue/Organ
• Inter-cellular network
differences
• Distribution of cell
types
• Microdosimetry
In vivo assay
y - biomarker
Molecule
Cell
Tissue/Organ
In vivo dose
Biomarker
change
System
Individual
Molecule
• Target enzyme affinity
differences
7
+
Cell
• Target enzyme
expression differences
• Intra-cellular network
differences
+
Tissue/Organ
• Inter-cellular network
differences
• Distribution of cell
types
• Microdosimetry
+
System
• Systemic PK (external
dose to tissue dose)
• Systemic PD (tissue
dose eliciting system
response)
In vivo assay
y – clinical endpoint
p
Molecule
Cell
Tissue/Organ
System
In vivo dose
Molecule
• Target enzyme affinity
differences
8
+
Probability of
clinical endpoint
Individual
Cell
• Target enzyme
expression differences
• Intra-cellular network
differences
+
Tissue/Organ
• Inter-cellular network
differences
• Distribution of cell
types
• Microdosimetry
+
System
• Systemic PK (external
dose to tissue dose)
• Systemic PD (tissue
dose eliciting system
response)
+
Individual
• Homeostatic set-point
• Lability
• Baseline risk of
endpoint
Outline
• Variability data at different levels of biological
organization
organization.
• Environmental decision making context and
needs.
• Incorporating new variability data
–Integrate into current dose-response assessment
paradigm.
di
–Contribute to extensions of current approaches for
improving option discrimination.
discrimination
–Informing new approaches to dose-response.
9
Variability in the context of
environmental decision making.
making
Environmental risk
management
options/
interventions
Environmental
Chemical
Exposure
Biological
g
Variability
Differential
Susceptibility
in Outcome
Population Health
Co seque ces
Consequences
10
Environmental decision making
needs are context-dependent.
context dependent
• Available risk management options.
• Regulatory
R
l t
authority.
th it
• Available data.
• Available time/resources/expertise to conduct
analyses or collect additional data.
• Stakeholder involvement/concerns.
11
Decision makers generally have
“tiered”
tiered” needs.
needs
1.
2
2.
3.
4
4.
5.
•
12
Is there a problem?
I the
Is
th problem
bl
bi or small?
big
ll?
What are each option’s quantified benefits?
What is each option’s
option s robustness to uncertainties?
What is each option’s apportionment of benefits?
Human variability is integral to all tiers – but can be
addressed at different levels of detail.
Outline
• Variability data at different levels of biological
organization
organization.
• Environmental decision making context and needs.
• Incorporating new variability data
–Integrate into current dose-response
assessment paradigm.
–Contribute to extensions of current approaches for
improving option discrimination.
–Informing
Informing new approaches to dose
dose-response.
response
13
1. Dose-response in test
assay/system
• Select a benchmark
response (BMR) level
• Calculate the
benchmark dose 95%
lower limit (BMDL)
• Result: 95% lower limit
of animal dose eliciting
a response=BMR.
• Preferred as a
replacement for the
NOAEL
14
RA
Animal
Response
DA
BMR
BMDL
Animal dose
2. Derive Human Equivalent
Dose (HED) for median individual
RA
• Empirical approaches
– Divide
Di id b
by 10 or 3 or 1
– Allometric scaling
• Biologically-based approaches
– Inhalation g
gas or p
particle regional
g
dosimetry
– PBPK model
– PD model
Animal
Response
DA
BMR
RH
BMDL
Animal dose
Human
Response
• Result: Lower confidence limit
(unspecified percentile) of dose
at which the median individual
will have a response=BMR.
• For linear cancer – stop here
and extrapolate to derive oral
cancer slope factor (CSF):
CSF=BMR/HED50
15
DH
BMR
HED50
Human dose
3. Derive HED for “sensitive”
individual
• Empirical approaches
– Divide byy 10 or 3 or 1
• Biologically-based
approaches
– PBPK model
– PD model
d l
• Result: Lower confidence
limit (unspecified
percentile) of dose at which
the “sensitive” individual will
have a response=BMR.
• For non-cancer and
“threshold” cancer, oral
Reference Dose
(RfD)=HEDsens.
RA
Animal
Response
RH
BMDL
Animal dose
Human
Response
DH
BMR
HED50
BMR
16
DA
BMR
HEDsens
Human dose
DH
Human dose
How can new data on variability
contribute to the current paradigm?
RA
• Primary value is for
deriving the HED for a
“sensitive” individual.
–Support a default
empirical
ii lh
human
variability factor.
–Derive chemical- or
endpoint-specific
d i t
ifi h
human
variability factors.
–Support development of
biologically based
biologically-based
models that incorporate
human variability.
Animal
Response
RH
BMDL
Animal dose
Human
Response
DH
BMR
HED50
BMR
17
DA
BMR
HEDsens
Human dose
DH
Human dose
Outline
• Variability data at different levels of biological
organization
organization.
• Environmental decision making context and needs.
• Incorporating new variability data
–Integrate into current dose-response assessment
paradigm.
–Contribute to extensions of current approaches
for improving option discrimination.
–Informing
Informing new approaches to dose
dose-response.
response
18
Improving option discrimination I:
Comparing across endpoints
• Decision
D i i makers
k
often
ft need
d tto
–Compare alternatives
–Cumulate
Cumulate across chemicals
• Difficult to interpret
p comparison
p
and cumulation of
RfDs and RfCs because they are based on different
endpoints and effect levels.
19
Example:
p Interpreting
p
g BMRs
• BMRs often based on convention or anchoring to NOAELs,
rather than on consistent biological or statistical criteria
criteria.
For example:
–BMR=5% often used for decrease in neonatal weight in
rodent
d t studies.
t di
–What are the consequences of a 5% decrease in birth
weight across the population?
–Equivalent to effect of active smoking during pregnancy!
• Key data gaps that could be informed by human variability
data:
–Biological significance of current BMR conventions.
–Consensus as to “equivalent” BMRs across endpoints.
20
Improving option discrimination II:
Unified Probabilistic Approach
• Limitations of current approaches
– RfD and RfC are treated as “bright
bright lines”
lines
– Oral slope factor and inhalation unit risk do not explicitly
account for human variability
– Artificial separation
p
between cancer and non-cancer effects
• Benefits of replacing with a unified probabilistic approach.
– Explicit as to
• Level of effect (BMR)
• Definition of “sensitive” individual (specific percentile of
population)
• Characterization of uncertainty
y (specific
( p
p
percentile
confidence limit)
– Common approach for cancer and non-cancer effects
• Issue of BMR interpretation still needs to be addressed.
21
Equivalent probabilistic
approach for RfD
• Select a BMR level
• Ca
Calculate
cu ate tthe
e BMD
distribution.
• Derive HED distribution for
median individual.
• Derive HED distribution for
a sensitive individual (e.g.,
99th percentile).
– Informed by probabilistic
human variability data.
• Result: uncertainty
distribution for dose at
which
hi h a 99th percentile
il
individual will have a
response=BMR.
RA
Animal
Response
RH
BMDL
Animal dose
Human
Response
DH
BMR
RH
Human
Response
BMR
22
DA
BMR
HED99
HED50
Human dose
DH
Human dose
Outline
• Variability data at different levels of biological
organization
organization.
• Environmental decision making context and needs.
• Incorporating new variability data
–Integrate into current dose-response assessment
paradigm.
–Contribute to extensions of current approaches for
improving option discrimination.
–Informing
Informing new approaches to dose
dose-response.
response
23
Informing new approaches:
Using high throughput data
• Many chemicals with little or no toxicity data – and no
regulatory values.
• How will new data on human variability inform “high
throughput” chemical assessments?
24
Example risk characterization based
on in vitro testing (NRC
(NRC, 2007)
25
Apply the same principles as
current dose-response
p
assessment paradigm.
Variability
V
i bilit iin
concentration C* eliciting
a perturbation of
magnitude
M*.
g
Variability in dose D*
corresponding to
concentration C*
(compound
or metabolite))
(
p
26
Variability in magnitude of
perturbation M*
considered “unlikely”
unlikely to
activate toxicity
y pathway.
p
y
Interindividual variability in in
vitro concentration-response
• For a given magnitude
of perturbation M
M*,,
what is the biological
variability in C*?
–Benchmark
Benchmark dose
modeling of in vitro
pathway perturbation
data.
• Data
D t needed
d d tto probe:
b
–Different genetics.
–Different background
states.
• E.g., presence/absence of
heat stress.
27
M
Magnitude
of
perturbation
C
M*
C*
In vitro
concentration
t ti
Interindividual variability in
equivalent in vivo dose
M
• For a concentration C*,
what
h t is
i the
th biological
bi l i l
variability in human dose
D*?
Magnitude
of
perturbation
C
– Combined in-vitro-to-in
in vitro to in vivo Target cell/
and interindividual variability
tissue
D*
adjustments for
concentration
toxicokinetics.
• Data needed to probe:
– Different genetics.
– Different life stages.
– Different background
g
states.
• E.g., presence/absence of liver
disease affecting metabolism
enzyme expression.
28
C*
C
M*
C*
In vitro
concentration
t ti
D
In vivo dose/exposure
Interindividual variability in M*
“unlikely”
unlikely to activate pathway
M
• For a magnitude of
perturbation
t b ti M*,
M* what
h t iis th
the
biological variability in
pathway activation A*?
Magnitude
of
perturbation
C
– Interindividual toxicodynamics Target
& susceptibility, such as
cell/tissue
• Polymorphisms leading to lower
concentration
adaptive response capability.
• Depleted
p
functional reserve.
• Pre-existing background
perturbations.
• Data need to probe:
– Different genetics
genetics.
– Different life stages.
– Different background states
C
M*
C*
D*
D
C*
In vivo
dose/exposure
A
Activity of
pathway
• E.g., presence/absence of
nutritional deficiency that perturbs
pathway.
29
In vitro
concentration
t ti
M
A*
M*
Magnitude of
perturbation
Probabilistic approach required
for better integration
• Variability in multiple variables
–Most progress so far on
• Reverse dosimetry using PBPK modeling.
• Genetic variability using genetically defined cell lines,
rodent strains,
strains and genotyped human populations
populations.
–Little progress on
• Incorporating lifestage variability.
• Background stressors and disease states.
• Multiple exposures.
• Can address additional challenges, such as
–Uncertainty in each estimate (easier).
–Possibility of correlations (harder).
30
Parallels between current
paradigm
p
g and TT21C-based
approach
Current dose-response assessment
paradigm
Dose-response assessment using
high throughput data
•
Dose-response analysis of test
animal data.
•
Dose-response analysis of
perturbations of toxicity pathways.
•
Deriving the HED for a “sensitive”
individual.
•
Variability in concentration
eliciting a specified perturbation.
Variability in in vivo dose
corresponding to in vitro
concentration.
•
31
•
Impact of variability on
interpretation
p
of BMRs.
•
Variability in perturbation
“unlikely”
y to activate p
pathway.
y
•
Probabilistic approach can
enhance scientific rigor, and
utility and transparency to the
decision maker.
•
Probabilistic approach can
enhance scientific rigor, and
utility and transparency to the
decision maker.
Conclusions
• In order to appropriately incorporate variability data, need to
understand:
– Different biological level(s) of organization that are probed.
– Environmental decision making has different tiers of needs.
• Progress
g
can be made through:
g
– Integrating variability data into estimating the human equivalent
dose for sensitive individuals when deriving RfD, RfC, CSF, or
IUR.
– Contributing to interpretation of benchmark response levels in
the context of human variability.
– Developing distributional human variability data for use in
probabilistic assessments.
assessments
– Using the same principles for dose-response assessments
based on high throughput/toxicity pathway perturbation data.
32
Acknowledgments
g
Thanks to many colleagues for useful discussions and
comments on human variability,
variability including:
Frederic Bois
Ila Cote
L
Lynn
Fl
Flowers
Gary Ginsberg
Kate Guyton
D l H
Dale
Hattis
tti
Dan Krewski
Greg Paoli
Ivan Rusyn
Wout Slob
Paul White
Lauren Zeise
33