Transcript Slide 1 - Alliance for Risk Assessment

Issues in Harmonizing Methods for
Risk Assessment
Kenny S. Crump
Louisiana Tech University
[email protected]
1
NRC (2008) Recommendations for
Harmonizing Risk Assessment
• Providing quantitative low-dose-extrapolated risk estimates not
only for cancer, as is currently done, but for all types of health
effects;
• Basing the quantitative approach not on the type of toxic effect
(whether cancer or not), but on consideration of the perceived
individual dose responses, the nature of human variability and
how the toxic substance interacts with background processes
that contribute to background toxicity;
• Proposing linear extrapolation not be restricted to carcinogenic
responses but applied to some non-carcinogenic responses as
well; and
• Providing not just a single estimate of risk, but a probabilistic
description.
2
NRC (2008) Recommendations for
Harmonizing Risk Assessment
Proposed quantitatively estimating low-dose risk in all cases:
• Model 1 (threshold dose response on individual level, linear on
population level);
• Model 2 (threshold response on individual level, nonlinear on
population level);
• Model 3 (linear response on individual level, linear on
population level).
Determining which model is appropriate involves
understanding whether the toxicological mechanisms are
independent of background exposures and processes,
or whether they augment background processes.
3
NRC (2008) Recommendations for
Harmonizing Risk Assessment
Proposed quantitatively estimating low-dose risk in all cases:
• Model 1 (threshold dose response on individual level, linear on
population level);
• Model 2 (threshold response on individual level, nonlinear on
population level);
• Model 3 (linear response on individual level, linear on
population level).
However, there are conceptual and operational difficulties
with the nonlinear approach (Model 2):
Crump KS, Chiu WA, Subramaniam RP. (March 2010) Issues in
using human variability distributions to estimate low-dose risk.
Environmental Health Perspectives 118(3): 387-393.
4
The Non-Linear Approach Utilizes Human
Variability Distribution (HVD) Modeling
1. Individual sensitivities to a toxic response are determined by
various pharmacokinetic and pharmacodynamic parameters.
Log-normal distributions are estimated from data on these
parameters.
2. These distributions are combined into an overall log-normal
distribution for the product of the individual parameters by
adding their variances, which assumes independence.
3. This log-normal distribution is transferred to the dose axis by
centering it at a point of departure (POD) dose usually
estimated from animal data.
4. The resulting log-normal distribution (median from animal data
and log-variance from HVD modeling) is used to quantify lowdose risk.
5
Difficulties with HVD Modeling
• The theoretical basis for the log-normal assumption
is not supportable.
– No phenomenological support for assumption that
factors affecting human variability act
multiplicatively and independently.
6
A Simple Example
• The tolerance distribution for an adverse response is a
log-normal function of serum concentration of a toxin.
• The half-life of the serum concentration has a log-normal
distribution.
Then (Crump et al., EHP, March 2010) ,
• These do not operate multiplicatively or independently.
• The risk from exposure to a constant dose rate, D, is
which is not what is predicted by HVD modeling:
,
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Difficulties with HVD Modeling
• The theoretical basis for the log-normal assumption
is not supportable.
– No phenomenological support for assumption that
factors affecting human variability act
multiplicatively and independently.
– Other distributions fit the existing data as well as
the log-normal but predict very different risks.
8
Distributions other than the log-normal can
describe data equally well**
Number of times the log-normal, gamma or shifted log-gamma (eX-1
where X has a gamma distribution) provided the best fit* to data on
variability in pharmacokinetic parameters:
Log-Normal
Gamma
Log-Gamma
38
77
83
(19%)
(39%)
(42%)
*Based on Akaike AIC criterion
** from Data Base Files 1-4 downloaded from
http://www2.clarku.edu/faculty/dhattis
These distributions have very different low-dose extrapolated risks.
9
Gamma and Log-Normal Distributions Can Fit Data Equally Well
But Diverge at Low Doses
10
Difficulties with HVD Modeling
• The theoretical basis for the log-normal assumption
is not supportable.
– No phenomenological support for assumption that
factors affecting human variability act
multiplicatively and independently.
– Other distributions fit the existing data as well as
the log-normal but predict vastly different risks
– Even if the Central Limit Theorem basis for
asymptotic log-normality were valid, predictions of
low risks (e.g., ≤ 10-3) could still be seriously in
error.
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Comparison of exact risks expressed as the product of
between 5 and 80 log-gamma or reciprocal log-gamma
variables with the risks predicted by the Central Limit
Theorem. Agree in observable range
(risk >= 0.10)
Diverge at low doses
Product of 10 Reciprocal
Log-Gammas with shape
α=2
12
Although these problems are illustrated
using the example methodology in the
Science and Decision report that utilize a
log-normal distribution, similar problems
will be present with any particular
distribution.
13
Threshold determination must refer to an endpoint
Biochemical
Effect
Cellular
Effect
Apical
Effect
Exposure
THRESHOLD IN:
1. Exposure doesn’t result in
any biochemical perturbation
•Biochemical
•Cellular
•Apical
2. Exposure causes some
biochemical perturbation, but
doesn’t cause a cellular response
•Cellular
•Apical
3. Exposure that causes
biochemical perturbation that
results in a cellular response but
does not increase apical risk
•Apical
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As illustrated in the next few slides, it is not
possible to have enough data to
distinguish between a low-dose linear
response and a threshold response.
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Threshold versus Low-Dose Linear
Threshold model
Response ==>
Low-Dose Linear
model
0
0
Dose ==>
Red curve is linear at low-dose.
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Threshold versus Low-Dose Linear
Threshold model
Response ==>
Low-Dose Linear
model
0
0
Dose ==>
Red Curve is still linear at low-dose.
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Threshold versus Low-Dose Linear
Threshold model
Response ==>
Low-Dose Linear
model
0
0
Dose ==>
Red Curve is still linear at low-dose.
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Threshold versus Low-Dose Linear
Threshold model
Response ==>
Low-Dose Linear
model
0
0
Dose ==>
Red Curve is still linear at low-dose.
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Threshold versus Low-Dose Linear
Threshold model
Response ==>
Low-Dose Linear
model
0
0
Dose ==>
Red Curve is still linear at low-dose..
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Threshold versus Low-Dose Linear
Response ==>
Threshold model
Low-Dose Linear model
0
0
Dose ==>
Blue curve exhibits a threshold.
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Threshold versus Low-Dose Linear
Response ==>
Threshold model
Low-Dose Linear model
0
0
Dose ==>
Blue curve still exhibits a threshold.
22
Threshold versus Low-Dose Linear
Response ==>
Threshold model
Low-Dose Linear model
0
0
Dose ==>
Blue curve still exhibits a threshold.
23
Threshold versus Low-Dose Linear
Response ==>
Threshold model
Low-Dose Linear model
0
0
Dose ==>
Blue curve still exhibits a threshold.
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Threshold versus Low-Dose Linear
Response ==>
Threshold model
Low-Dose Linear model
0
0
Dose ==>
Blue curve still exhibits a threshold. (And the two
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curves predict very different low-dose risks.)
What about statistical methods for setting
lower bounds for thresholds?
As illustrated in the next few slides, such
statistical methods are based on highly
specific and often implausible
assumptions.
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Model Dependence of Threshold Estimates
Example:
Response
Data Point with 95% Confidence Interval
0
0
Dose =>
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Model Dependence of Threshold Estimates
Data Point with 95% Confidence Interval
Response
Hockey stick model
0
0
Threshold
Estimate = 4
Dose =>
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Model Dependence of Threshold Estimates
Data Point with 95% Confidence Interval
Hockey stick model
Response
Hockey stick model with 95% LB on
threshold
0
0
Lower bound
on Threshold =3.2
Threshold
Estimate = 4
Dose =>
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Model Dependence of Threshold Estimates
Response
Data Point with 95% Confidence Interval
Hockey stick model
Curvalinear model
0
0
Threshold
Estimate = 4
Dose =>
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Model Dependence of Threshold Estimates
Response
Data Point with 95% Confidence Interval
Hockey stick model
Hockey stick model with 95% LB on threshold
Curvalinear model with 95% LB on threshold
0
0
Curvalinear
lower bound
on Threshold =0
(no threshold)
Hockey stick
lower bound
on Threshold =3.2
Threshold
Estimate = 4
Dose =>
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• Even if a population threshold exists, it
cannot be bounded away from zero (i.e.,
no threshold) without making unverifiable
assumptions about the shape of the dose
response.
• Likewise, a non-linear, non-threshold lowdose response cannot provide lower
bounds for low-dose risk different from
those provided by a low-dose linear model
without making unverifiable assumptions.
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Consequently,
Whenever low-dose risk is estimated, upper bounds on risk
should generally allow for the possibility of low-dose
linearity, e.g., Models 1 or 3 from the Science and
Decisions report.
• low-dose linearity is generally difficult to completely rule
out (e.g., any amount of additivity to background will lead
to low-dose linearity).
• Without strong and likely unverifiable distributional
assumptions (e.g., log-normal), upper bounds from
threshold and non-linear models will still reflect low-dose
linearity. (There are threshold and low-dose non-linear
models arbitrarily close to any low-dose linear model and
vice-versa.)
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Interest in the threshold concept is stimulated by
the current approach to risk assessment that
involves two incompatible paths:
1. If the response is thought to be linear at low dose,
low dose risk is estimated by linear extrapolation
below a point of departure (POD).
2. If the response is thought to be threshold or sublinear, safety or uncertainty factors are applied to
a POD (POD-safety factor approach) and low
dose risk is not estimated. The threshold also is
not estimated, but only used in a qualitative
sense.
34
2007 and 2008 NRC Committees’ Recommendations
Need To Be Harmonized
NRC 2008 Science and
Decisions
• estimate risk and provide
uncertainty bounds.
• No population thresholds.
NRC 2007 Toxicity Testing
in 21st Century
• No estimates of apical risk.
• Estimate thresholds (exposures
that will not result in biologically
significant perturbations).
Harmonized Approach for In Vivo or In Vitro Data
Apply POD-safety factor approach and use scientific judgment in
setting of safety factors.
• Better reflects the true nature of our knowledge about low-dose
risks, which is mainly qualitative.
• Does not need to assume a threshold, but safety factors should
reflect toxicological judgment on dose response below POD.
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• Safety factors should account for severity of disease.
Appendix
36
Science has been described as formulating
falsifiable hypotheses and testing these
hypotheses using observational data.
None of the following statements are falsifiable:
“The dose response for chemical X has a threshold.”
“The dose response for chemical X does not have a threshold.”
“The dose response for chemical X is low-dose linear.”
“The dose response for chemical X is not low-dose linear.”
“Dose Y of chemical X has an effect on response Z.”
Although the statement ,
“Dose Y of chemical X has no effect on response Z.”
is in principle falsifiable, in practice it often is not falsifiable at very low
doses that may be of interest in risk assessment.
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Example of linear dose response resulting
from additivity to background
– inactivation of acetylcholinesterase
molecules by an organophosphorus
pesticide
(adapted from Figure 3.3 of Rhomberg, L. R. (2004). Mechanistic
considerations in the harmonization of dose-response methodology: the role of
redundancy at different levels of biological organization. In Risk Analysis and
Society: An Interdisciplinary Characterization of the Field, McDaniels TL, Small
MJ, eds. New York: Cambridge University Press.)
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Collective function (% of needed)
Inactivation of acetylcholinesterase molecules
individual level
140%
120%
100%
80%
60%
40%
20%
0%
0
# active molecules => # needed
for normal
function
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Inactivation of acetylcholinesterase molecules
Population Level
Cumulative distribution
100%
80%
60%
40%
Original distribution
20%
0%
0
# needed
for normal
function
# active molecules =>
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Inactivation of acetylcholinesterase molecules
Population Level
Cumulative distribution
100%
80%
60%
40%
Original distribution
20%
Distribution after
exposure
0%
0
# needed
for normal
function
# active molecules =>
distribution after exposure defined by law of mass action
(# bound molecules ~ dose of organophosphorus pesticide,
proportionality constant determined by binding affinities)
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Inactivation of acetylcholinesterase molecules
Population level response is linear at low-dose.
Cumulative distribution
100%
80%
60%
δP ~ δU
40%
Original distribution
δU ~ dose
20%
0%
0
# needed
for normal
function
Distribution after
exposure
# active molecules =>
δU = [change in # active molecules] ~ Dose
(law of mass action – Rhomberg 2004)
δP = [change in proportion with # active molecules
below cutoff for normal function] ~ δU ~ Dose
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