Colloquium on Approaches to Quantifying
Health Risks for
Threshold or Nonlinear Effects at Low Dose
September 28, 2000
Final Summary Report
Submitted to:
Risk Assessment Forum
U.S. Environmental Protection Agency
Washington, DC
Submitted by:
The CDM Group, Inc.
Chevy Chase, MD
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63020001
Contents
Introduction 1
Welcome 1
Background on Risk Assessment 1
Background on Environmental Economics 3
Discussion 5
Dose-Response-Based Distributional Analysis of Threshold Effects 6
Discussion 9
Characterizing Risks Above the Reference Dose 10
Discussion 14
Expected Values of Population Dose-Response Relationships 15
Discussion 18
Risk-Based Reference Doses 19
Use of Categorical Regression to Characterize Risk Above the RfD 20
Discussion 24
Risks Between the LOAEL and the RfD/RfC: A Minimalist's Approach 24
Discussion 26
General Discussion 28
Multiple Endpoints 29
Other Considerations 32
Suggestions for Moving Ahead 32
Adjournment 34
Appendices
Appendix A: External Participants
Appendix B: Participant List
Appendix C: Agenda
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Contents (continued)
Appendices D-L: Presentation Overheads—
D. Vanessa V. Vu
E. Al McGartland
F. Sandra Baird and Lorenz Rhomberg
G. Paul S. Price
H. Dale Hattis
I. David Gaylor and Ralph Kodell
J. Lynne Haber and Michael Dourson
K. Reisha Putzrath
L. Kenny Crump
in
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Introduction
The Colloquium on Approaches to Quantifying Health Risks for Threshold or Nonlinear Effects
at Low Dose took place September 28, 2000, at the Omni Shoreham Hotel in Washington, DC.
The meeting was held to explore approaches to characterizing variability and uncertainty in
RfDs/RfCs and to provide a probabilistic framework for estimating risks associated with
exposures above the RfD in order to assist Environmental Protection Agency (EPA) economists
in valuing health benefits associated with environmental regulations.
Outside experts made presentations on distributional analysis of threshold effect based on dose
response; characterizing risks above the reference dose; expected values of population
dose-response relationships; risk-based reference doses; use of categorical regression to
characterize risk above the RfD; and risks between the LOAEL and the RfD. A discussion
period followed the presentations, focusing on a number of questions submitted by participants.
Vanessa Vu, National Center for Environmental Assessment (NCEA) Assistant Director, and Al
McGartland, National Center for Environmental Economics (NCEE) Director, served as
colloquium chairs. External presenters are listed in Appendix A, and colloquium participants are
listed in Appendix B. The colloquium agenda is provided as Appendix C.
Welcome
Bill Wood, Executive Director of EPA's Risk Assessment Forum, welcomed everyone to the
colloquium. He noted that NCEA had asked the Forum to organize the colloquium to explore
issues surrounding quantification of risk above the RfD. In addition, he said the purpose was
different from similar events in the past that focused simply on risk assessment issues. The
colloquium resulted from a partnership between NCEA and NCEE and included risk assessors,
economists, and outside experts from academia, consulting firms, State environmental programs,
and others. He asked participants to submit one or two questions to help focus the afternoon's
discussion.
Background on Risk Assessment
Vanessa Vu, NCEA Assistant Director, provided background on the need for the colloquium,
focusing on how risk assessment methods can be improved to enable better benefit valuation and
analysis. Her presentation slides appear at the end of this report as Appendix D. She explained
the structure of the meeting. The objective was to explore possible approaches for quantifying
risk below the point of departure, especially for those substances with biological thresholds or
nonlinear dose-response curves at low dose.
Dr. Vu noted that EPA has many efforts underway to improve risk assessment methods for
mixtures, aggregate risk of single contaminants, and cumulative risk of multiple stressors. EPA
is also trying to harmonize the approach to all human health endpoints by moving away from the
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terms "cancer/noncancer effects." To do so requires improving the understanding of mode of
action and its incorporation into risk assessment.
For most (although not all) of the chemicals EPA is concerned with, it is necessary to take
available human data or toxicology data from animal studies and extrapolate to exposures of
interest. The Agency is also developing approaches to consider different exposure scenarios,
including chronic, acute, and episodic exposure, as well as duration and temporal response, and
to better characterize risk among sensitive subpopulations.
EPA currently has two approaches for characterizing risk. In the first, probabilistic estimates are
used with carcinogens. The second approach, described in the revised draft cancer guidelines,
uses margin of exposure for carcinogens with a nonlinear dose-response curve. The Agency
assumes there is a biological threshold for reference dose, reference concentration, and margin of
exposure for noncancerous health effects.
Dr. Vu showed a graph depicting a hypothetical dose response for a noncancer health endpoint.
She explained that the Agency has used the 95 percent lower confidence limit to derive a
benchmark for extrapolation to low dose because of considerations of statistical variability of the
animals studied as well as the design of studies.
For effects believed to operate through a biological threshold, without knowing the
dose-response curve from the observed range, the Agency's typical procedure is to apply
uncertainty factors to account for extrapolation from animal data and across human populations
in order to determine a reference dose or reference concentration. The day's speakers were to talk
about this issue.
Dr. Vu noted that some EPA offices, such as the Office of Pesticides, use a margin of exposure
approach for all noncancer effects. That approach takes any point of departure, such as an
LED 10 or one based on a NOAEL or LOAEL, accounts for potential estimates of human
exposure, and determines the adequacy of the margin of exposure.
She showed another graph depicting a typical cancer dose-response curve based on observed
data. When there is not enough information to know the mode of action or the shape of the
dose-response curve, the Agency uses a linear default. If there is adequate mode of action
information to suggest the shape of the dose-response curve but not to prove it, the Agency uses
the margin of exposure approach, or an RfD-like approach when there are data to demonstrate a
biological threshold mechanism.
Just as with the reference dose, a margin of exposure approach can be used when the anticipated
human exposure level is known and can be compared with the LED 10 to determine the adequacy
of the margin of exposure.
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Dr. Vu noted that while improving risk assessment methods is important as a way to improve
risk characterization, it is also essential to providing health benefit analysts with adequate
information to characterize benefits. She described the information economists need from risk
assessors in order to perform cost-benefit analysis: a full characterization of the range of health
effects potentially associated with contaminants, including the nature of a specific effect, the
severity, onset, and duration. Economists also need to know who is potentially affected,
specifying age, health status, income, and so on. And most important, they need to know how
many people actually are at risk. The RfD/RfC and margin of exposure methods do not provide a
quantitative estimate of risk below the point of departure, whether that would be a benchmark
dose, or NOAEL/LOAEL, or LED 10 for cancer. This is the focus of the colloquium.
In addition, the RfD/RfC addresses only critical effects from chronic exposure, not all the
toxicological health effects as in the IRIS database, so they do not capture all the health benefits
associated with a risk management action. The critical effects need to be related to adverse
human health outcomes (liver disease versus liver weight change). Furthermore, in cancer
assessment, more mechanistic information is used to emphasize reliance on precursor
nonneoplastic response, rather than tumor data, and that should be reflected.
Emerging issues for future consideration include how more subtle effects can be valued as
research provides new information on biomarker effects and susceptibility and mode of action at
the cellular and molecular levels.
The overall goal for the colloquium is to explore possible approaches for quantifying risk below
the POD, Vu concluded, specifically for effects presumed to have biological thresholds or for
which mode of action information indicates that the dose-response curve at doses below
observation could be nonlinear. Success means identifying some viable methods and approaches
for the Agency to continue to develop for near-term use. She noted that EPA scientists would
meet the following day to continue the discussion.
Background on Environmental Economics
Al McGartland, NCEE Director, described the mission of is center within the new Office of
Policy, Economics and Innovation. His presentation slides appear at the end of this report as
Appendix E. He welcomed all the participants, thanked those who put the colloquium together,
and said he was optimistic about its chances for success. Even during the planning there was
progress toward better understanding between economists and risk assessors. He called the
colloquium a major milestone in bridging the gap between what economists need and what risk
assessors are comfortable providing, and he invited people to participate in the work ahead.
Dr. McGartland briefly described the economic analysis guidelines due to be released, the
paradigm behind benefit-cost analysis, and economists' methods and information needs. He also
discussed as an example a case currently before the Supreme Court, in which the American
Truckers Association has asked that EPA use benefit-cost analysis to set air quality standards.
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Dr. McGartland described some history behind cost-benefit analysis for regulations. President
Clinton's Executive Order 12866 is the current mandate. It requires a cost-benefit analysis of all
major rules that have an effect on the economy of SI00 million or more or that the Office of
Management and Budget (OMB) believes are significant. In addition, the "Thompson language"
is attached to the budget bill every year and requires OMB to report to Congress on the benefits
and costs of all regulations. Each year OMB and EPA debate over how to capture the benefits
that are not quantified, whether as zero or in some other way. In some cases, OMB assigns a
value. The Safe Drinking Water Act and small business legislation in the Unfunded Mandates
Reform Act require cost-benefit analysis, as do several bills currently pending on Capitol Hill.
Dr. McGartland emphasized that cost-benefit analysis merely provides a single input into the
decisionmaking process but does not provide answers for what regulation or what option one
should choose. Other considerations also need to be taken into account.
He explained Adam Smith's concept of "the invisible hand," which squeezes inefficient users of
scarce resources out of the competitive market. He gave the example of a bad restaurant being
driven out of business and a better restaurant taking its place. He said Smith was mostly correct,
except with regard to market failures such as pollution. For example, clean air is a public good,
not something that can be bought or sold. Cost-benefit analysis simulates how a private market
would treat something like clean air. To do that, economists try to assess people's willingness to
pay for a commodity such as clean air and determine whether the private market, if it could,
would provide that good. Courts recognize the notion of that willingness to pay as a foundation
for environmental damages, as in the Exxon Valdez oil spill.
Economists use two general approaches. The most frequently used is the damage function
approach, which takes an interdisciplinary look at changes in emissions, exposure, environmental
quality, and quantified risk reduction, then assigns a unit value per avoided effect. Another
approach uses more indirect methods to value environmental improvements described in more
general terms (for example, cleanup of the Exxon Valdez spill) without detailed enumeration of
the specific improvements.
To get at unit values for reduced health effects, economists use several methodologies. The
easiest, but least satisfactory, is cost-of-illness, which calculates the total cost of a disease,
including hospital admissions, medical insurance, and so on. That cost is recognized as a lower
bound but because it is a very hard number it can be very useful. Averting behavior teases out of
people's observed behavior their willingness to pay. The hedonics approach takes market data
and teases out the value of an attribute of an environment-related commodity. For example,
regression techniques can be used to determine how much of the value of a home derives from
the clean environment in which it is located. Wage-risk studies provide information on the wage
premiums required for workers to assume risks on the job. To determine stated preferences
through surveys, economists work with cognitive psychologists and scientists to establish the
commodity to be valued, such as less exposure to toxics and therefore fewer health effects or
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avoiding an environmental disaster like the Exxon Valdez. spill. This approach is the most
controversial but offers a way to get at values that are otherwise hard to capture.
In valuing a human life, economists look at a statistical human life but prefer to consider the
value of small changes in the risk of mortality. Dr. McGartland mentioned the challenge in
valuing the air toxics program as required by section 812 of the Clean Air Act because many
variables are unknown. One approach is to try to value the safety EPA is providing the public
through its air regulations. That makes the commodity of concern a broader notion of safety,
rather than the risk of dying. Another approach is to think about the value of exceeding some
threshold. He noted that without an ability to develop dose-response curves and quantify cases, it
could become impossible to value anything.
Dr. McGartland discussed the two studies of Benefits and Costs of the Clean Air Act, required by
section 812 of the act, which used a damage function approach to investigate many health effects
that had relatively rich databases in the epidemiology literature. The first step was to quantify the
cases after determining the change in emissions resulting from EPA regulations. That yielded a
large number of cases to value. The numbers are controversial because they are so large, but the
analysis showed that the Clean Air Act of 1970 prevented 23,000 premature mortalities annually,
20,000 chronic bronchitis cases, and similar numbers of hospitalizations for respiratory and
cardiovascular disease. Then economists scoured the literature and determined a mean value for
those health effects, in 1990 dollars, of $4.8 million, based on a fairly standardized approach for
valuing risk reductions for mortality. Based on the 1990 amendments to the act, looking
prospectively at the year 2010, the benefit in premature mortality alone is $100,000 million.
Looking back to 1970, the ratio of benefits to cost was something on the order of 42 to 1, he said.
It was somewhat less for the 1990 amendments but was still large.
The same methodology was used in preparing the economic analysis of the revised National
Ambient Air Quality Standards for particulate matter and ozone. However, providing changes in
margins of exposure would not mean the same thing to the public or economists who are charged
with doing the cost-benefit work. Approaches are required to meet everyone's needs, grounded
in the best science, but that can inform decisionmakers in the most realistic way possible about
the trade-offs of different regulatory options.
Dr. McGartland concluded with the caveat that unquantified values are often counted as a zero.
That complicates efforts to engage the public.
Discussion
One participant asked about the uncertainties associated with McGartland's numbers. He replied
that they are very uncertain. The report to Congress included a whole chapter of Monte Carlo
analysis. He noted there is a debate with OMB over what assumptions should go into a lower
bound of the total benefits numbers. Often there can be overlap between choosing which
epidemiological study to use and doing regressions, so great care must be taken in adding up
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different kinds of categories to get a total benefit number. But, he added, the costs were always
below even the lower bound of benefits.
Dose-Response-Based Distributional Analysis of Threshold Effects
Sandy Baird of The Baird Group and Lorenz Rhomberg of Gradient Corporation presented their
findings. Their slides appear at the end of this report as Appendix F. Dr. Baird spoke first about
methods developed collaboratively between toxicologists, risk analysts, and biostatisticians with
a focus on the path from exposure to estimating the number of cases and providing a probabilistic
estimate of those cases.
She said informed decisionmaking is complicated when it is based on RfDs. Uncertainty in the
RfD is unknown; therefore, the protection from any particular RfD value is both unknown and
inconsistent due to inconsistent data that go into the RfD. Decisions about levels of
conservatism are actually risk management decisions, but they get intertwined with risk
assessment during the process of creating an RfD and assigning uncertainty factors for each of
the areas of extrapolation. At the end of the process it is unclear how much uncertainty there is.
And there is no estimate of the risks of exposure at levels greater than the RfD.
Current models, the NOAEL and the benchmark dose, have the same underlying model. But
many developmental studies show a 0 to 4.5 percent risk of a response rate at the level of the
NOAEL, so the assumption that the NOAEL actually represents a dose where there is no effect is
somewhat weak. Study quality also has a big impact on the ability to observe effects. Both of
these methods apply a series of uncertainty factors. Dr. Baird and Dr. Rhomberg's talk focused
only on the factors for animal-to-human extrapolation and accounting for sensitive human
populations. Remaining factors representing subchronic to chronic, LOAEL to NOAEL, and
data deficiency factors are related to not having as much data as one would like and can be
accounted for probabilistically.
Dr. Baird showed a slide of three parallel dose-response curves depicting NOAELs from
experimental animals, extrapolation using uncertainty factors to depict average humans, and a
second extrapolation to depict sensitive humans. Dr. Baird and Dr. Rhomberg's model does not
require the typical assumption that the NOAEL is below the population threshold after applying
the uncertainty factors.
Dr. Baird built a dose-response model using the proposed methodology. It begins with animal
experimental data in a dose-response curve. Uncertainty in the model is characterized as a full
distribution that is carried through the model, unlike a benchmark dose where the lower
confidence limit is the focus. The second step involves scaling, or centering, using whatever
available methodology provides the most chemical-specific information to estimate the human
equivalent concentration. The team chose the ED50 as the point of comparison between the
animal and the human dose-response curves. In the third step there is a broadening of the
distribution to account for the animal-to-human adjustment factor (AFA), adding in the
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uncertainty in the accuracy of the scaling adjustment. Because variability is reduced as much as
possible in animal experiments, the curve is flattened to depict greater heterogeneity in humans.
Since the curve now represents the distribution of human population thresholds, and depicts the
uncertainty about the dose associated with levels of risk, a risk manager can decide what human
population to protect (such as the 1/100 more sensitive individual) as well as how confident to be
in the dose estimate. Finally, Dr. Baird provided the supporting equation (see Appendix F, p.
10).
Dr. Rhomberg described in more detail the theoretical and empirical evidence underlying the
method, provided a brief case study of ethylene oxide that was recently completed using the
framework, and summarized benefits of the strategy.
He walked backward through the methodology because the overheads were difficult to read. The
goal of this methodology is a human dose-response curve showing proportions of the population
expected to respond at different doses, based on the idea that each individual has a threshold and
that as doses increase they exceed that threshold in an increasing percentage of the population.
To express the uncertainty posed by extrapolating from an animal study, the dose-response curve
also shows distributions around particular percentiles. The team chose to center the curve on the
ED50 median effective dose as the most reliable point. That curve was achieved through
centering, a theory of cross-species extrapolation that allows for the best available estimate of an
equivalent toxicity between humans and animals, acknowledging the uncertainty in that process
as well as from chemical to chemical. Backing up to the test animal, Dr. Rhomberg noted the
animal dose-response curve is on a probit scale so it is really a matter of a probit curve being fit
to the animal data. The experimental uncertainty of fitting the model is therefore represented,
and carried through the whole process rather than just using a lower bound. He said the
methodology tries to identify and account for all the elements of uncertainty that come from
extrapolating, then tries to fill them with empirical distributions of how those extrapolation
factors actually vary.
Dr. Rhomberg explained that instead of uncertainty factors (UFs), the methodology uses AFA
and AFH, or human heterogeneity adjustment factor. AFA provides a central estimate of where a
human response should be, given the animal ED50 response. That is separate from the
characterization of uncertainty around that central estimate. AFH empirical data (developed and
presented by Dale Hattis later in the colloquium) is used to show the variation among humans in
sensitivity to toxicity.
Animal-to-human extrapolation can use various methodologies, such as allometric, RfC, and
chemical specific, that are not dictated by the framework. But even the best extrapolation
methodologies have uncertainties that must be taken into account.
Another question is how to characterize empirically the variation in scaling from one chemical to
another. The team used various estimates based on distributions of relative potency. More
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familiar in the cancer area, the concept considers the distributions of ratios such as potencies
from epidemiological studies that are compared to animal studies. The availability of such ratios
for noncancer endpoints is more limited. A study by Dr. Baird et al. (1996) of pesticide NOAEL
ratios showed they tend to be lognormal distributions with a range of geometric standard
deviations of 4.1 to 4.9. Rhomberg did a paper more recently with Scott Wolf on LD50 values,
based on a larger database of more than 4,000 chemicals for some species-to-species
comparisons, and found variations toward the lower end of the 2.5 to 6 range, mostly on the
order of a GSD of 3. There are about 50 antineoplastic agents for which there are actual human
data for maximum tolerated dose in a cancer therapeutic setting, compared with similar kinds of
endpoints in animals over a series of several animals and humans, and those give GSDs in the
same range (2.6 to 3.7).
Dr. Rhomberg showed one example of LD50 ratios for both guinea pigs and rabbits over about
3,000 chemicals. He noted the lognormal distribution is "more peaky" than most. He suggested
the effect can be generalized, regardless of what species are used.
On human heterogeneity, the idea is that there is a tolerance distribution in the human population.
Rather than using dose-response data from animals, which tend to be very heterogeneous due to
study conditions, the methodology uses general human data on variation and sensitivity over lots
of chemicals. In fact, while humans are slightly more variable, it is not as much as would be
expected. That concept is described by a log-probit with a GSD that is derived from Dr. Hattis'
empirical data.
Dr. Rhomberg also presented a case study applying the methodology to two developmental and
two reproductive studies on ethylene oxide. The endpoints were postimplantation loss, a quantal
endpoint, and fetal body weight, a continuous endpoint. The first step was to apply whatever
advanced statistical dose-response modeling was available. That step captured some problems
such as intralitter correlations and correlations among endpoints and covariants, like litter size, to
characterize the full distribution of uncertainties and bring that through the population. Finally,
sensitivity analysis demonstrated that controlling for litter size do not make much difference,
while different GSD values associated with cross-species extrapolation do make a significant
difference. Sensitivity analysis can suggest research strategies to focus on places where
sharpening up information on a particular element would be the most beneficial.
He showed a graph of the results (see Appendix F, p. 17), depicting a fairly wide uncertainty
distribution around the 0.1 percentile of the human sensitivity distributions, or the 1/1,000 risk
level in humans. The mean was about 700 ppb.
To be very sure of the characterization of that percentile, one could choose an appropriate lower
bound, such as 95 percent, and find the associated value, basically the ED01. This can be done
for any percentile of distribution, so in cases where there are exposures at various levels in a
population, uncertainties could be characterized for various dose levels and the associated
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expected percentile. Therefore the method does allow for projections of a number of cases above
or below the RfD with a full uncertainty characterization.
Issues that were not addressed included:
• Severity of effect
• Defining adverse effect
• Concordance of endpoints across species
But Dr. Rhomberg summarized the following benefits of the approach:
• Provides a distribution of the probability of a health impact occurring
• Estimates risks to specified sensitive subpopulations
• Quantitatively characterizes uncertainty in the risk estimates
• Determines the level of protection at the end of the process
• Estimates risk above and below the RfD
• Provides a framework for each component of extrapolation
• Allows for updating of components with chemical-specific data
Allows for identification of components that contribute the greatest uncertainties so that
resources can be allocated to reduce those uncertainties
Dr. Rhomberg concluded that the team believes the approach maximized the use of the available
data in a framework that makes assumptions very transparent. It provides estimates of risk and
uncertainty in those risks for sensitive human populations. He believes it is well suited for
cost-benefit analysis that assesses the number of cases affected by a change in exposure.
Discussion
One participant asked whether the selection of the ED50 assumes that the log dose-response
curves are parallel. Dr. Rhomberg replied that part of the purpose in choosing the ED50 is to
avoid making that assumption. If they are not parallel, the point at which the comparison is made
matters. The team selected the ED50 as the most appropriate point.
The participant asked whether the team had done a sensitivity analysis on the effects of choosing
such a high effect level where variations would be expected to be smaller. She inquired whether
the method introduces more uncertainty because of the extrapolation over several orders of
magnitude. Dr. Rhomberg replied that the team has looked at the ED 10 as an alternative. The
magnitude of the effect depends on two things: how different the slopes are and how different
from the ED50 the point is at which the comparison is made. Because the method is based on
empirical distributions of relative toxicities, which are going to be near the middle of the
distributions, he said the choice should not be too far from the ED50. He also noted that while
humans have a broader distribution and shallower slope than animals, the difference is not as
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great as might be expected because experimental animals were found to be more varied than
expected.
Another participant asked whether two dose-response curves having the same ED50s and using
allometric scaling under the methodology would result in the same answer.
Dr. Rhomberg answered yes, adding that there is a tradeoff regarding whether to start with
animal dose-response curves or to take data directly on interindividual variability in humans.
The former requires an assumption of interindividual variability; the latter is not specific to the
endpoint and chemical of concern. The team chose to use general human heterogeneity. One
alternative might be to expand the animal slope using a human flattening factor, but the team
decided there was no real basis for doing that. However, it would be possible to apply a
correction factor to the general steepness of animal dose-response curves to obtain a general
steepness for human dose-response curves. He noted that while the method makes a general
accounting of all the sources of uncertainty, it does not yet address the fact that, from chemical to
chemical, the degree of human heterogeneity probably varies. That can be captured in a
distribution based on Dr. Hattis' data, but it has not yet been done.
The participant inquired whether using the ED50 instead of the ED 10 loses some of the
heterogeneity of the animal data.
Dr. Baird replied that the heterogeneity in the animal population is carried through in the
distribution of the stochastic experimental uncertainty. A better-quality experiment provides a
narrower distribution on that stochastic uncertainty. So, using two chemicals, one with a better
quality study than the other, would show a narrow distribution of the uncertainty in that estimate
that would be carried through to the end.
Dr. Hattis remarked that his presentation would show there tends to be more variability for less
severe effects, and for some kinds of organ systems. So to the degree that the effects of concern
can be subcategorized, there can be some clues to adjust the human dose-response relationship
accordingly. Dr. Rhomberg added that the team tried to design an approach that provides a
natural place for adding refinements as they are developed.
Characterizing Risks Above the Reference Dose
Paul Price of Ogden Environmental and Energy Services reported on work done by his firm, in
which the reference dose was put into the dose-response framework and the traditional
uncertainty factors used in deriving the reference dose inside that framework were defined. He
also proposed two approaches for defining risks above the RfD and discussed the implications
for assessing carcinogenic risks. His presentation slides appear at the end of this report as
Appendix G.
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The current approach for noncarcinogenic risk assessment calls for setting a permitted dose, but
that is never associated with a likelihood of response for doses either above or below it. As a
result, use of the risk tools that are associated with this permitted doseuthe risk cup, the MOE,
and the hazard quotientuprovide no guidance for the benefits associated with reduction. His
presentation was the result of work done by his firm in collaboration with EPA and TERA under
a cooperative research and development agreement to investigate the uncertainty in the variation
in the RfD and in assessments of noncancer risk. The work resulted in four publications and has
been used to develop quantitative estimates of noncancer risks for both PCBs and mercury.
Mr. Price noted that in his approach the reference dose is a technical finding rather than the
product of a political or social process. Before talking about risks above the RfD, it is important
to establish a framework that clearly states variations versus uncertainties. He suggested that
framework would be absolutely consistent with Drs. Baird and Rhomberg's. Variation must be
stated in terms of differences in the relative sensitivity of individuals, usually a fraction of the
population that will respond at a certain dose (i.e., the dose-response curve) or the amount of
interindividual variation that contributes to sensitivity differences. In contrast, uncertainty comes
from either measurement error, or to represent the extrapolation from the animal model to
human. He suggested that toxicological criteria such as the RfDs are best understood by stating
they only have uncertainty.
Mr. Price's approach postulates that each individual has a threshold and that the distribution of
the thresholds goes to zero, allowing for discussion of a population threshold. He also addressed
the implications of a situation where there is no population threshold. He noted that the
differences between individual thresholds and population thresholds are clearly a matter of
variation.
Uncertainty comes in because the distribution of individuals' thresholds cannot be measured.
Therefore it must be either modeled mathematically or based upon animal surrogates or other
indirect methods for determining the shape of the curve in humans and whether it has a
population threshold. That results in a true but unknown dose-response curve with confidence
limits around the estimate. The lower confidence limit of the dose that causes a zero response is
the lower confidence of the population threshold. That falls on the curve of the upper confidence
limit of the response rate.
To provide a basis for the mathematical approaches for calculating risks above the RfD, Mr.
Price defined the RfD as an estimate of the lower confidence limit on the estimate of population
thresholds in humans rather than as an estimate of the population threshold. In some cases where
the RfD does not correspond to zero risk, it is actually the lower confidence limit of some finite
but very low level of risk.
He suggested that taking the standard equation for the reference dose of the NOAEL, dividing it
by the various uncertainty factors, and replacing them with distributions does not result in the
distribution of the RfD. Rather, the result is the confidence function for the estimate of either the
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population threshold or the estimate of the dose that causes a very low level of risk. At some
point on the lower confidence level of this distribution is the actual RfD. By substituting
distributions for each of the terms in the RfD equation, the result is not uncertainty in the RfD
but uncertainty in the population threshold or some EDR, where R is a very small number, and
the RfD is best understood as a lower value taken off this distribution.
Mr. Price divided the traditional uncertainty factors into three categories. The primary factors, of
inter- and intraspecies uncertainty factors, must be used to go from a NOAEL in an ideal data set
for animals to the RfD. The secondary factors are all the other factors that are necessary to try to
estimate that NOAEL in an appropriate data set and reflect data limitations. The third category is
all the modifying factors, such as FQPA, which are adjustments in the reference dose that reflect
other concerns.
He suggested that the intrafactor is best understood as another type of interspecies uncertainty
factor. The classic interspecies uncertainty factor refers to the average differences between the
sensitivity of two species. Starting at the ED50 provides a useful way of separating the
differences between an average member of each of two species that can be well understood by
pharmacokinetics or readily measured and the differences in the slopes or the dispersion around
that mean between two species. He proposed understanding the interspecies factors in terms of
differences between the average sensitivity of two species, typically the animal and the human,
and the intraspecies as reflecting differences between the dispersion or slope of the dose-response
curve in the animal model as compared with the human model.
Mr. Price showed a graph depicting that concept, proposing that the UFA be understood as either
a scaling or a simple displacement of the dose-response curve in the animal to arrive at a
dose-response curve in humans showing a log dose response. The sensitive individual factor is
used to change the shape of the dose-response curve, leading to a flatter dose response in
humans. It also leads to an estimate of the RfD that is a function of moving from some sort of
NOAEL or benchmark that is a measure of the low response rate of the animal divided by the
UFA or UFH to come up with an estimate of the RfD. Noting that the approach is rather
simplistic, he suggested it provides a firm basis for estimating risks above the RfD and a firm
starting point to talk about traditional complexities such as the true shape of the dose-response
curve in humans, uncertainty in estimates, and variation.
He then proposed two approaches. The first used basic algebra, based on the prior assumptions.
The second, assuming the shape of the dose-response curve in animals has no particular
relevance to humans, took from the animal study only some measure of the dispersion, or very
general description of the dose-response curve.
Under the first approach, inter- and intraindividual uncertainty factors move the center through a
simple displacement and by changing the slope. Simple algebra produces an equation,
EDRh = (ED50a/UFA)(EDRa/ED50a)(l-(log UFH/log(EDOa/ED50a)),
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which relates the estimate of the dose causing a response R in humans as a function of the ED50
in animals (ED50a), the size of the interspecies uncertainty factor, the dose causing a response R
in animals and the intraspecies uncertainty factor or UFH, as well as the estimate of a dose
causing zero or extremely small response. The equation comes from the requirement for one
scaling factor to move the entire curve over and another associated with intraspecies to move the
lower portion of the curve over by an amount equal to the uncertainty factor.
This equation would be used by putting in a mathematical distribution of an animal curve to map
over the shape of the curve. He showed an example using factors of 10 for inter- and
intraspecies. At the 50 percent response level, the curve moves over by a factor of 10, while at
the estimated threshold, it moves over by a factor of 100. In between, the move is a linear
function of the difference in response between zero and 50 percent. While very simple, the
approach builds on the existing definitions of the uncertainty factors. The equation allows
inclusion of a distribution for the uncertainty factors that reflect estimates of the threshold or the
ED50 that can be carried through to determine uncertainty confidence limits, the uncertainty
around the predictions of the dose response in humans. A major drawback, however, is that it
requires the assumption that the shape of the dose-response curve in the animal is relevant for
humans. The approach does require an estimate of the threshold in the test species, which can be
derived either from a mathematical consideration, such as some policy that starts with the
benchmark dose and divides by some factor, or based on pharmacokinetic or other mechanistic
arguments. To do a meaningful Monte Carlo version, he said, the selected values must be
correlated.
A more minimalist model takes a couple of points on the animal dose-response curve and simply
draws a straight line between them, using the same mathematical assumptions to extrapolate
those data points over to humans. That model has the advantage of not requiring the assumption
that a dose-response curve in animals predicts that in humans. Taking data in the animal species,
Mr. Price showed the actual curve that may truly exist or may be predicted from models. Then,
the value of the ED50 and the RfD are moved over, generating a "hockey stick equation" that is
used to predict risk where the doses below the population threshold give zero response and the
doses above it just follow a straight line from the estimate of the population response up to the
estimate of the ED50.
The equations for doing this are very simple, based on available data (NOAEL, the ED50, the
uncertainty factors). Mr. Price's team published a paper applying the method to four compounds
for which data were available for estimating the NOAEL and the ED50. The paper used a
distribution for the uncertainty factors to estimate the median values of the dose response and the
upper confidence limit. Mr. Price showed a slide illustrating the results for the median values,
with hazard indices from zero up to 10 times the hazard index of 10 (corresponding to a dose 10
times the RfD). The model gives a best estimate prediction that no risk occurs until the dose
increases 10-fold, then increases proportionately after that. At 50 times the RfD, estimates of
risk go anywhere from about 8 percent to about 30 percent of the population responding. He
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showed another slide of the same data showing the upper confidence limits. The risks at 10
times the RfD were therefore not zero as the earlier slide showed, but about 4 percent to 15
percent, reflecting differences in the magnitude of the uncertainty factors.
For cancer risk determination, Mr. Price noted that either of the two approaches can be used to
estimate response. Obvious modifications include replacing the interspecies factors traditionally
used in noncancer assessments with assumptions that come from the cancer tradition of
extrapolation based on body weight to the two-thirds power, rather than a factor of 10. He
suggested that Dr. Hattis' data on the traditional variability may be a better basis for developing
estimates of the magnitude of the intra-individual factor. And the approach is amenable to
calculating uncertainty using the Monte Carlo model.
In contrast to the Baird and Rhomberg approach, Mr. Price's approach requires choosing a dose
believed to be associated with the threshold and the confidence limits around that dose.
In summary, the approach provides a basis for beginning to talk about deriving quantitative
estimates. While intended to reflect the existing RfD framework and traditional uncertainty
factors, it can be used to look at other issues. It is not limited to noncancer, but can be applied to
any endpoint where a population threshold is believed to exist above zero. And it allows
separate modeling of variation uncertainty. He concluded that using all the equations and forcing
the threshold to go to zero produces something that looks very much like the existing cancer
quantitative risk numbers.
Discussion
One participant asked whether using the ED50 would work as well for asymmetrical distributions
as for ones where the median and mean are the same. Mr. Price said the analogy would still hold,
but the question gets into difficulties in describing multimodal measures of dispersion, rather
than the simple lognormal distribution described by GSD. The complexity of that result requires
many more data to suggest how humans actually vary and how sensitive subpopulations differ
from typical individuals. But although the math would be more complex, he said, it would not be
difficult to do inside the framework.
Another participant asked whether, in the approaches presented by both teams, the population
distributions account for an effect that might be specific to some segment of the population, such
as a developmental effect. Mr. Price said the reference dose approach tries to establish a dose
that protects the entire population, and thus considers endpoints that may affect only a portion of
the population. Addressing this issue comes not in the toxicology portion but in the exposure
portion of the benefits analysis, where the goal should be to describe the distribution of doses
received by the portion of the population to which the RfD applies. It also suggests that benefits
might be missed by only looking at reproductive effects of dose among women in child-bearing
years. So it may be desirable to have separate dose-response curves for the general population to
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investigate the benefits associated with reducing their exposures as well. That would have to be
done by starting over from scratch with the toxicological database, not just with the RfD.
Dr. Rhomberg concurred, stating that in his team's case the effect was aimed at a particular
portion of the population, assuming that is taken into account in the exposure analysis and in
identifying the number of people who are subject to those risks. It is more difficult to start
identifying that population based on some sensitivity, and not just qualitatively, because then the
calculations of variation and sensitivity are confounded with the definition of the population at
risk.
He noted that the example raises an issue usually considered under sensitive endpoints. Sensitive
endpoints can be identified under the traditional RfC/RfD framework, and as long as everybody
is protected from that, they are protected from everything else. But it is not so clear in thinking
about a risk above the RfD. Moreover, many of the endpoints are graded by severity. Dr.
Rhomberg said that without further modification the team's method really only predicts a low
grade of severity and does not allow for a higher severity among people who are particularly
sensitive.
The discussant made the point that doing benefits analysis may require looking at something
broader than the typical RfD.
Mr. Price noted that as the critical factor on the RfD increases to an ever-larger fraction of the
population, there is potential for other noncritical effects that occur at higher doses but are of far
greater concern, such as frank effect levels. So in looking at analysis showing only a 20 percent
chance of suppression of liver weight, there may be a 3 percent chance of a truly frank effect, the
avoidance of which would be associated with a great economic benefit. Approaches that start
with the RfD and the critical factor would have to be modified to address that.
One discussant noted that an advantage of the Baird-Rhomberg approach was in recognizing that
there can be some residual risk at the RfD. She suggested that perhaps Mr. Price's approach
could be extended to model some residual risk at and below the RfD. Mr. Price agreed, saying
that comment captured one of the main critiques of the published paper. He said allowing for a
finite level of risk at the reference dose would not be hard to introduce into the equation; in fact
the paper offers a method for doing that. But he said his approach does not accomplish that as
elegantly as that proposed by Dr. Baird and Dr. Rhomberg.
Expected Values of Population Dose-Response Relationships
Dale Hattis of Clark University spoke about human risk. His presentation slides appear at the
end of this report as Appendix H. Many different parameters have been measured in humans that
are helpful for quantifying risk as a function of dose. The database of pharmacokinetic and
pharmacodynamic effects includes mostly pharmaceutical data, with very few environmental
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data. The parameters measured cover different portions of the pathway from external exposure
to internal response.
There are a number of different routes to causation and quantification of health effects. Cases
where direct human epidemiological observations can be made do not require as much risk
assessment, just direct measurement tools and controls for confounders. Indirect projections also
can be made of possible effects as a function of changes in various intermediate parameters,
assuming that whatever is causing those changes is having a parallel impact on quantal effects
like mortality. This is a relatively underdeveloped area.
Dr. Hattis looked at population distributions of individual susceptibility to effects that occur as a
result of overwhelming homeostatic systems. He divided the pathway from external exposure to
internal response, identifying variabilities in
contact rate that can affect the projection, such as a distribution of water or food
consumption,
• variability in uptake or absorption, per unit intake or contact rate,
• general systemic availability, net of first pass elimination,
• dilution via distribution volume, and
• systemic elimination or clearance.
Those variabilities are part of pharmacokinetics. Pharmacodynamics includes
• how much an internal physiological parameter (e.g., FEV1) is changed per unit of internal
concentration, and
• how much change in that physiological parameter is required to achieve some effect of
concern.
The database currently contains 443 data sets, each measuring variability in a particular relevant
pharmacokinetic or pharmacodynamic parameter for a particular chemical. The bulk of the
database provides pharmacokinetic data, such as half-lives and volumes of distribution. There is
now also a respectable body of pharmacodynamic information, about 89 data sets. While the
database has been expanding to include some data from children under 12, there are not as many
data of as high quality for children.
For the distributions of human inter-individual variability of different parameters, Dr. Hattis
assumed lognormality and showed some plots to support that assumption. He combined the
variability from multiple causal steps by adding together the lognormal variances associated with
each step. Different chemicals have different amounts of variability. Plotting the measures of
human lognormal variability for particular parameter types indicates that the log(GSD)'s
themselves are approximately lognormally distributed. But there is still a question about how
much of the differences seen in individual variability observations is due chemical-to-chemical
variation and how much is due to measurement error, which tends to inflate the observed
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variability. That must be deflated to get as realistic a measure as possible to obtain a central
estimate of the amount of variation and therefore the amount of real uncertainty in the slope of
the population dose-response relationship in people.
The basic methodology for assessing expected values, or arithmetic mean risks averaged over the
relevant uncertainties and capturing uncertainty as fairly as possible, requires an explicit
treatment of uncertainty. This is because these distributions tend to be highly skewed. And the
expected value or mean of the distribution is generally larger than the median, so fairly capturing
uncertainties is important.
Dr. Hattis' process uses the human database to make a central estimate of overall lognormal
variability from the observed variances associated with various causal steps. Depending on the
kind of exposure data it starts with, the route of exposure, the effect, and the degree of severity,
the variabilities associated with those causal steps are added together. Then, based on the
available observations, one can determine the lognormal uncertainty in the log(GSD)'s
themselves and reduce the inflating influence of statistical sampling error on the observed spread
of those values. Next, Dr. Hattis sampled repeatedly from the assessed lognormal distribution of
log GSDs. (GSD is the standard deviation of the logs to base 10 of the individual parameter
values, or the log to the base 10 of the geometric standard deviation.) He calculated the
arithmetic average of risk for people exposed at various fractions of a human ED05 level,
although with some extra standard deviations it can easily be done from an ED50. It can be done
in a Monte Carlo format or as a simple spreadsheet. Dr. Hattis said that the surprising bottom
line is that he can summarize the expected value as simple power law functions, basically as
graphs on log-log plots that look relatively straight but have different slopes depending on the
variability and the spread of the log GSD variability observations that are chosen to represent the
uncertainties.
The approach does not address the underrepresentation of children, the elderly, and sick people in
the studies, which is likely to result in some understatement of variability. On the other hand,
some measurement errors in the primary variability observations act in the opposite direction.
That can be analyzed, but Dr. Hattis said he has not yet done so. Another difficulty lies in the
fact that the drugs studied might not perfectly represent the environmental or occupational
chemicals of interest to EPA.
Dr. Hattis explained the meaning of the log GSD numbers relative to the 10-fold baseline
assumption. Using a 3.3 standard deviation range from a 5th percentile to a 95th percentile, the
number of standard deviations is similar to what is needed to go from an ED05 to an ED 10-5. In
that range, the 10-fold baseline corresponds to a log GSD of about 0.3. Below 0.3, the 10-fold
factor allows for a projection from a low or no effect level to an incidence of effect that is
potentially socially tolerable on an RfD basis. Going much beyond that would require a larger
reduction of dose than the 10-fold factor provides in order to achieve a comparable reduction in
expected risk.
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Dr. Hattis showed a slide depicting a set of raw, unweighted pharmacokinetic results in the
general range of 0.1 to 0.2, with upper 90 percent confidence levels going up to the order of 0.3.
Most of the time, pharmacokinetics alone fall within the 0.3 level. But the pharmacodynamic
portion of the database is usually larger. That can result in variabilities of very substantial
amounts, such as in cases like local contact site parameter change, where the central value is
around 0.6. Systemic effects from external administration are often less than that, with central
values on the order of 0.2, but with 90th percentile values in the range of 0.6-0.8, in rare cases
approaching 1.
He showed a few actual distributions as examples. One was a probit plot with a Log(GSD) on
the order of one, of the fraction of people who suffer a particular kind of skin hypersensitivity as
a function of log of the chromium concentration applied on the skin. Another was a plot of the
distribution of concentrations of the chemical methacholine that cause a 20 percent decrease in
FEV1 in smokers with mild to moderate air flow obstruction. The data set included 5,000 people
and provided a fairly good lognormal distribution for the four different concentration points on
the curve. Another big data set for histamine showed the same sort of distributions, but a log
GSD on the order of 0.6. He also showed a plot of aggregate log GSD distributions, basically log
GSDs for pharmacological half-lives in adults and children, that was basically lognormal.
With pharmacokinetics, Dr. Hattis concluded, there is not too much overall variability. Results
may not be perfectly lognormal, but the approach is feasible for trying to fairly capture some of
the uncertainty.
Discussion
One participant inquired how widely applicable the approach is, whether it also works with more
subtle effects. Dr. Hattis replied that it works when individual thresholds for a response can be
defined, noting that some individuals will always be more sensitive. It can be used on more
subtle effects and with background. But a population threshold should not be expected if there is
background dose.
Another discussant asked whether the less severe, reversible effects that were measured, such as
moderate nose irritation, eye irritation, and skin rash, were subjectively reported. Dr. Hattis
noted that this is a worry and reporting error variability needs to be teased out.
One participant pointed out that phase two drug trials routinely include genetic screening, which
would narrow the variability found. He asked whether data are available to determine the
impact. Dr. Hattis noted that the Food and Drug Administration drug database is extensive and
completely secret. He said a cooperative effort between EPA and FDA, providing adequate
privacy protection, could be of great value.
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Risk-Based Reference Doses
Dave Gaylor of Sciences International presented work on risk-based reference doses done in
collaboration with Dr. Ralph Kodell at the National Center for Toxicological Research and FDA.
His presentation slides appear at the end of this report as Appendix I.
Current noncancer safety assessments can be based on a NOAEL or LOAEL, but do not
characterize the risk. He suggested starting instead with the benchmark dose, which provides an
estimate of the risk.
The median of the overall distribution of the product uncertainty factors can be found using the
individual median of the individual subcomponents. The overall standard deviation then is just
simply the square root of the sum of the variances associated with each of the uncertainty factors.
Dr. Gaylor showed a formula illustrating how to translate estimates of the median values and
standard deviations for effects into percentiles. He referred to a 1983 paper by Dourson and
Stara, reviewing published literature on a few dozen chemicals having results for more than one
strain of a species. They used that paper to obtain a measure of interindividual variability to
estimate variation in a human population. The ratio of the individual values for a strain
compared with the overall median provides a measure of interindividual uncertainty.
Using an uncertainty factor of 10 for human variability covered about 92 percent of the chemicals
in the database. To cover 95 percent required a factor of 15. Dr. Gaylor does not advocate
changing the default value because additional conservatism results when several uncertainty
factors are multiplied together. But with a single uncertainty factor, 10 generally is not large
enough to give a high confidence of covering a large percentage of the chemicals. Calculated
from Dourson and Stara's set, the standard deviation for the random variable, the uncertainty
factor for human variability log base e, is 1.64. In terms of base 10, that is a standard deviation
of about 0.7.
Starting with a benchmark dose of 10 percent and assuming a lognormal distribution for human
variability, it is possible to plot various specified levels of risk, such as 1/10, 1/100, or 1/1,000.
Stated another way, given a reference dose, one can determine the number of standard deviations
from the benchmark dose and estimate what the risk is. This gives a procedure for estimating
risk at the reference dose or for setting a reference dose associated with a specified level of risk.
Two things are required to do this: a benchmark dose as a point of departure (Dr. Gaylor started
from an ED 10), and an estimate of the standard deviation. Dr. Gaylor used a standard deviation
base e for variability among humans of 1.7 (a middle ground value) in cases where nothing is
known about what kind of variation to expect for a particular endpoint or a class of chemical.
On Dr. Gaylor's chart, the risk of 1/10 was right at the benchmark dose of 10 percent,
corresponding to 1.28 standard deviations below the median. A risk of 1/10,000 was 3.72
standard deviations below the median or about a factor of 63 below the benchmark dose for 10.
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Ordinarily starting with a benchmark dose with a 10 percent adverse effect level, and applying a
factor of 10 for going from an effect level to a low level and another factor of 10 for human
variability, results in a factor of 100. At a risk level of 1/10,000, a factor of 60 would be
sufficient if this estimate of standard deviation is appropriate. A risk of 1/1,000,000 requires a
factor of 365 below the benchmark dose.
To provide for other uncertainty, another factor of 10 can be added for extrapolating from
animals to humans, and another to extrapolate from subchronic data to chronic effects. It is
possible to increase the confidence of this risk estimate by including additional uncertainty
factors or starting from a lower confidence limit on the ED 10.
Taking a benchmark dose of 10 percent and dividing it by 100, with this estimate of variability,
gives an estimated risk of about 3/100,000. It is based only on some estimate of the standard
deviation of variability expected among individuals. Given information about a particular
endpoint, such as mortality, and estimates of standard deviation, this procedure could be
improved by using that standard deviation. With information about a certain class of chemicals,
a more specific estimate of the standard deviation could be used.
Dr. Gaylor noted that all the presentations rely on a log probit and assume a lognormal
distribution to obtain an estimate of risk that can be observed in an animal study, whether at the
10 or 50 percent level. He noted that the procedure is basically what Mantel and Bryan published
in 1961 for cancer data. Looking at cancer data, they suggested starting with a point of departure
(they did not call it a benchmark dose) of a 1 percent risk level. They used a slope of 1 probit
over a factor of 10 that is shallow and conservative. Dr. Gaylor stated that the standard deviation
slope of 1.35 over a factor of 10 he used is not much different. The same approach can be taken
now, i.e., pick a shallow slope to be conservative based on the best estimate for a particular
endpoint or class of chemicals.
To estimate risk at reference doses requires starting with a specified benchmark dose rather than
LOAELs or NOAELs. But it does not require extrapolating a dose response below a benchmark
dose. It does require an estimate of the standard deviation for interindividual variation. With
these approaches, risk can be estimated at a reference dose, above it, or below it. Or, dose can be
calculated based on a specified risk for a certain endpoint. Confidence limits are derived from
those on the benchmark dose. Other uncertainty factors can be introduced. Hence, it is possible
to estimate risk at reference doses, or estimate a dose associated with a risk given an estimate of
human variability.
Use of Categorical Regression to Characterize Risk Above the RfD
Lynne Haber of TERA presented categorical regression as a method for calculating risk above
the RfD. Her presentation slides appear at the end of this report as Appendix J. The method was
developed at EPA's NCEA-Cincinnati office.
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In categorical regression, the toxicologist makes a judgment for each dose as to what the severity
level is. Several different severity ratings have been used, such as no effects, minimal, moderate,
and extreme. Some of the morning presentations did not address different severities. Categorical
regression can include multiple studies and model incidence data, continuous data (such as the
mean), as well as qualitative data, such as observations of liver necrosis at a given dose level.
A dose-response curve is fit to the data using the different severity levels. Earlier studies used a
logistic regression. Current modeling abilities allow other mathematical forms to be fit also.
The toxicologist can evaluate (or stratify) the data separately by the endpoint and by the species
to see whether data from different endpoints or species can appropriately be combined or whether
there are differences that need to be taken into account.
The results of the modeling are judged graphically as well as by the data quality. Several
statistical tests can be used.
There are several advantages to categorical regression. The data requirements are much less
rigorous than those for benchmark dose modeling, so categorical regression can be applied when
the data are not sufficient to calculate an ED 10. The modeling approach also addresses an
increasing severity of effect with increasing dose. It is possible to combine different studies
using this analytical technique, and it is also possible to take duration into account as one of the
parameters used in the modeling.
Limitations include the need to account for animal-to-human extrapolation and a loss of
variability information in categorizing a group based on the mean response. But there are
methods to take that into account.
Dr. Haber provided an example showing data from clinical studies of aldicarb exposure with
human volunteers. As dose increased, the severity effect increased, as did the percentage of
people affected. The frank effect level was defined based on nausea, vomiting, and
lightheadedness, different from what is typically thought of as a frank effect. The data were
modeled on a graph of exposure versus probability of response at that dose. As the dose
increased, the response was higher. After a certain level, the probability of an adverse effect
decreased because the probability of a frank effect increased.
Another slide showed the calculated probabilities of the adverse or frank effect or higher. As
expected, there was minimal or no effect predicted at the best estimate of the RfD. The upper
confidence limit was 10-5. Based strictly on curve fitting, Dr. Haber pointed out predicted
probabilities and upper confidence limits of having the probability of an effect based on the data.
She noted some advantages and strengths of this example. Human data were used so there was
no extrapolation from animals. The data did show several no effect levels and no adverse effect
levels, so there was not much extrapolation below the data. A weakness was small group sizes,
which raises questions about how to account for sensitive populations. There are two
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possibilities: (1) that the sensitive populations would be part of the same dose-response curve so
that the dose response for those sampled adequately characterized the entire population, or (2) to
have a separate curve of the sensitive population that differs qualitatively from the people who
are studied. That case requires some sort of uncertainty factor to predict the risk in the sensitive
population. But to predict the overall general population risk, one needs also to know what
percentage of the total population is composed of that sensitive group.
Dr. Haber showed a second example comparing pesticides. That work was done to assist a risk
manager in prioritizing when the risk cup is exceeded for several different pesticides. She said
prioritization and the relative risk between the different chemicals is as much an issue as
predicting the absolute risk. The work was done with data from animal studies and included both
incidence and continuous data (where dose groups were categorized by severity of effect). The
plot is of the log of the dose over the RfD, so if the dose is normalized to the RfD for the
chemical, the plot represents the probability that the dose level is an adverse effect level. The
graph showed three cholinesterase inhibitors, modeling the same effect for three chemicals that
act via the same mode of action. For disulfoton, the lower risk is predicted at a given multiple of
the RfD. This chemical had a larger uncertainty factor because the RfD was calculated from a
LOAEL instead of a NOAEL, but calculating a benchmark dose could help refine the analysis.
But it does give some sense of the relative risk in that the first two chemicals have very similar
risks at a given dose relative to the RfD.
• to*
The plot shows a probability of having an adverse effect, but could also be viewed as equivalent
to a population risk, assuming 100 percent are affected if the dose is an adverse effect level.
There are a number of issues in translating the relative risk of different chemicals to the expected
human population risk at the dose relative to the RfD. The slope of the animal dose-response
curve is expected to be steeper than the slope of the human dose-response curve. The doses that
were modeled used a body weight to the two-thirds adjustment, so there has been a toxicokinetic
adjustment in the dose-response curve. However, there still are toxicodynamic differences
between humans and animals that were not taken into account, and the slope of the dose-response
curve reflects the variability in response of the subject population. Because animals have much
less variability than humans the slope for human data would be shallower.
Dr. Haber showed a slide depicting a model for two chemicals with two different modes of
action: EPTC, with a very steep dose-response curve, and lindane, with a much shallower curve.
Unlike with the cholinesterase inhibitors, there is quite a difference between predictions for the
two chemicals. In the low dose range, the prediction for lindane is higher than that for EPTC,
whereas in the higher dose range the predicted risk is lower.
When extrapolating from animals to humans, there are several issues to take into account in
using categorical regression to predict the low-dose risk. One is sensitive populations. Does the
dose-response curve include the sensitive populations if human data are available? If
extrapolating from animal data, how are differences in the shape of the dose-response curve
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between animals and humans accounted for? How do we account for the use of uncertainty
factors? These questions were not addressed by the study but are becoming more of a concern.
Another is model dependence, which increases with the distance from the range of the data. This
has not been considered in great detail, but that is also a concern in extrapolating to lower doses.
Do we force the model to go to zero at the RfD because toxicologists may expect that the RfD is
a subthreshold dose, or do we just let it go mathematically wherever it goes because the RfD can
be considered as an expected probability of response? Other issues include what data are chosen
for modeling, what are the criteria for excluding studies due to low quality, what are the rules for
assigning severity categories, what are the rules for combining studies, what constitutes an
acceptable model, and how are results interpreted.
Other advantages are inclusion of all the useful data in the quantitative analysis, the possibility of
meta-analysis, ability to take duration into account, and providing a consistent basis for
calculating the risk above the RfD. Overall, categorical regression modeling is not as well
developed for calculating the risk above the RfD as some of the other methods presented, but it
has utility.
Dr. Haber addressed several more general issues raised in the earlier talks. She noted that intra-
and interspecies factors can be used to characterize variability or variation, which can be a known
quantity, although there may be uncertainty involved. LOAEL to NOAEL extrapolation can be
dealt with given the data for a benchmark dose. But the subchronic to chronic and database
uncertainty factors address uncertainty. And the proper numbers for those uncertainty factors
cannot be known, only estimated (otherwise they are no longer uncertainty factors). In contrast,
uncertainty factors that address variation can be broken down into, for instance, toxicokinetic and
toxicodynamic aspects. There is a movement under way to use compound-specific data to
replace the default uncertainty factors. The data may be specific to the chemical, the class of
chemical, or the mode of action. It is important to consider, as the chemical specific-data
become available to enhance various methods of analysis, how such data would be used in
evaluating the predicted variability in response for a given chemical.
She also noted that not all uncertainty factors are equal. Drs. Baird and Rhomberg talked about
using an entire database to characterize the uncertainty factor, while Mr. Price uses first
principles about the sort of data that go into an uncertainty factor. At present, none of these
approaches take into account explicitly the fact that the year of the assessment affects what is
meant by a given uncertainty factor. During the early 1980s, an uncertainty factor of 10 was the
default. Now, it means experienced risk assessors have made a judgment that the data are
insufficient to reduce or modify the default. In the future, 10 may be a true chemical-specific
adjustment factor. That meaning will affect the appropriate distribution to be used for that
uncertainty factor. How that is taken into account will be important as compound-specific
adjustment factors become available.
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Discussion
One participant asked what effect was considered in the aldicarb study. Dr. Haber replied that all
the test subject data were evaluated and categorized by severity. Most of the categorization was
based on plasma cholinesterase inhibition, and alternative analyses were done based on whether
certain levels of inhibition were considered adverse. The frank effects included clinical effects
such as nausea and lightheadedness.
The participant asked whether an uncertainty factor was used for childhood sensitivity. Dr.
Haber said the initial RfD was done with a basic uncertainty factor of 10. For the extrapolation
down to low doses, at the time the work was done (in the mid-1990s), it was assumed that the
dose response for humans adequately characterized the dose response for the whole population,
so no other adjustment was made. That assumption could be looked at more carefully. There are
many ways in which the work could be enhanced.
Risks Between the LOAEL and the RfD/RfC: A Minimalist's Approach
Resha Putzrath of Georgetown Risk Group began with a suggestion that models should be made
simple, but noted that Mr. Price's analysis was more minimalist than her own. Her presentation
slides appear at the end of this report as Appendix K.
She proposed three possible questions to be answered. Do we like the current RfD/RfC method
at all? She suggested a strong case could be made for starting afresh with all the data,
information, and models produced over the last 25 years rather than trying to improve various
uncertainty factors or estimates of NOAELs, LOAELs, or thresholds. Assuming use of RfD/RfC
methods, how can they be expanded to the risk of exposures above the RfD? How can the
current point estimates be made more amenable for combination and comparison with data that
tend to have ranges and distributions? She suggested it is hard to answer all three questions at
the same time.
Assuming some confidence in the RfD/RfC method and looking at what can be done above it,
Dr. Putzrath urged thinking about risk estimates between the RfD/RfC and NOAEL and between
NOAEL and LOAEL. She also suggested looking at carcinogens with curvilinear low-dose
dose-response curves.
Dr. Putzrath mentioned problems she has had with margin of exposure. She noted there are at
least two distinct definitions of margin of exposure within EPA. One is used for noncancer risk
assessments that assume a threshold but absolutely nothing about the shape of the dose-response
curve. The second is, for carcinogens, assessments that assume there is no threshold and the
dose-response curves are highly curvilinear. Although the definitions in terms of a point of
departure over an expected exposure are the same, the mathematical implications are quite
different and should not be described using the same term. In addition, she said, the inherent
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initial assumption with margin of exposure is that it is somehow linearly proportional to risk, but
that can be proven false.
Taking carcinogens, Dr. Putzrath said her minimalistic solution assumes the dose-response
curves are not curvilinear. Claiming that they are requires a lot of data, which provides a
dose-response curve that can be used to calculate an upper and lower bound. Without a lot of
data, or assuming the lower response regions differ from the known dose-response curve, she
suggested the method is not critical if one is not using the dose-response curve. But new
methods often focus only on the upper bound of the risk curve, and that creates a problem in
trying to make the true dose-response curve and its upper bound continuous functions that
intersect at the origin. More importantly, she said, the evaluations of upper bound risks deal with
individual chemicals. That focus ignores a maximum likelihood curve (best estimate of what is
actually going on), which is required in order to combine evaluations and recalculate upper
bounds for mixtures. That is a problem with the 1996 Proposed Guidelines for Carcinogen Risk
Assessment.
It is important to note that, while analyses can be done for generic distributions,
chemical-specific distributions, groups of chemicals, or same methods of action, they may not be
terribly useful. She proposed instead looking at whether the data can be used to answer the
questions simply.
Assuming existing RfD/RfC methods have value, the questions become: Is the dose-response
curve, in the low dose and low response region, shallow or steep? How close is the NOAEL to
the RfD? Is the exposure of interest closer to the RfD or the NOAEL or the LOAEL? That is
one measure of the accuracy that is needed. The bottom line is, what are the consequences of a
wrong answer or suboptimal decision? How likely are we to over- or underestimate the risk?
Slightly underestimating a fairly minor effect is not the same thing as slightly underestimating a
lethal effect. She also noted there are opportunity costs of misallocating resources.
Dr. Putzrath suggested a simplest case. Taking a relatively steep dose-response curve and an
RfD that is a distance from the NOAEL (or LED10 or LEDx), the decision about how much
accuracy is required should differ depending upon whether the exposure is e, e-prime, or
e-doubleprime. In particular, if RfDs are meaningful and there is no regulatory concern below
that level, then being slightly above an RfD that is very distant from a NOAEL on a very steep
dose-response curve is not that much different from being at or below the RfD, from a decision
point of view, given the uncertainty factors that generated the RfD. But being at e-prime is quite
different because it is near the NOAEL. The consequences of having the effect seen at the
LOAEL at either e-prime or e-doubleprime are relatively the same, given the uncertainties. So
the issue becomes whether one can live with either the upper bound or the lower bound of the
effect around the LOAEL.
Looking at how to combine this information with other data, the range might be sufficient to
indicate whether the costs or benefits of a decision can be determined. If not, a more complex
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analysis is required, unless the range is narrow enough or the effect minor enough. That is the
easy decision. With the same NOAEL and LOAEL, but an RfD that is closer, there is less
uncertainty and therefore the first exposure is much closer to the NOAEL. The consequences of
being at the exposure e are then different. What is likely to happen and the likelihood of being
wrong are different. Dr. Putzrath discussed a third case with a shallow dose-response curve,
where the change of exposure does not make much difference in the response. Again, the
question is whether the anticipated response is acceptable. She noted the approach is much
different from the quantitative analysis she usually does, but suggested that "quick and dirty
qualitative analyses" can sometimes answer the important question, which is whether the result is
acceptable and cost of dealing with an effect worthwhile.
Dr. Putzrath mentioned several general issues similar to those raised by Dr. Haber. When
thinking about improving uncertainty factors, it is very important to remember they are not all the
same. For variability, distributional analysis has a lot of value. Distributional analyses can be
done with interspecies, either chemical-specific or generic, and there are biologically based
models. That might be more useful than a generic method. With LOAEL to NOAEL, there are
some potential problems, and using chemical-specific data will likely give more information than
a generic distributional analysis. With missing or poor quality data, additional safety factors can
be used but cannot truly replace good data.
Some of the methods discussed may effectively do away with thresholds. If that is the goal, it
should be explicit. There have been heated discussions about whether thresholds exist and, if
they do, how to estimate them. If there is a policy choice to eliminate thresholds from the
analyses, that can be done. But it should not be done by a mathematical or statistical blurring of
methods.
Discussion
One participant noted there had been little consideration of risk as a function of biology and
exposure. Advanced tools now exist for studying that and can give estimates of risks with less
uncertainty than estimates based on default approaches. Dr. Putzrath replied that this would
involve starting over, rather than improving existing methods.
Dr. Rhomberg said it is very important to use biological knowledge and advanced methods for
characterizing dose responses. Distinguishing between adjustments and uncertainty about those
adjustments is important to enhance the "centering" step of his approach. There remains a
question about how to extrapolate from animal to human even after answering questions about
variability and uncertainty.
The participant agreed that uncertainties remain even with biologically based models. But the
advantage is in seeing clearly what the major sources of uncertainty are rather than leaving them
hidden in the statistics. He advised care in refining a 20-year-old method.
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Kenny Crump wrote an equation on a transparency to illustrate the use of lognormal distribution
for extrapolating from higher to lower dose (see Appendix L). He noted that products of
lognormal distributions are also lognormal. Other distributions could describe the same data
with different implications at the tails, so it is important to look at the distribution assumptions
very carefully.
Crump further noted the key is not risk itself but incremental risk over background. He said that
this is an important distinction with important implications. He suggested the distributional
approach might not be appropriate to apply to the entire population, but only to the population
that would not get the disease except for the exposure. That segment of the population would
likely be characterized by shallower slopes than would the entire population.
Another question is how to incorporate background. The proposed approaches start with an
exposure. The "transfer function" is pharmacokinetic information that gives an internal dose. It
can be represented in the model by saying, if it is greater than the threshold dose obtained from
the pharmacodynamic data, there would be a response (see Appendix L). And if the threshold
(internal) dose from pharmacodynamic data has a lognormal distribution, and the transfer relating
external to internal dose from pharmacokinetic data has a lognormal distribution, the ratio would
have a lognormal distribution. But the threshold dose cannot have a lognormal distribution
because whenever there is background response, it must have mass at zero. How that mass at
zero is incorporated makes a large difference in the low-dose risk result.
Dr. Crump noted that the standard log normal model (assumed by Dr. Hattis, Dr. Gaylor and
others) is very flat at low dose. All the derivatives are zero at zero. But depending on how
background is incorporated, it can become linear very quickly at low dose. So the argument of
additivity to background should not be ruled out. He suggested that this model is just as
consistent with the data as any other, so model uncertainty must be kept in mind. He
recommended that any approach have a wide enough range so that linearity is included in the
range that is a possible risk. He suggested looking at the large data set on PM and mortality.
Those data appear linear down to the lowest observable doses. He advised taking some of these
approaches, positing a level that results in a 5 percent mortality, and seeing how the approaches
compare with the responses measured in the PM.
Dr. Hattis agreed with Dr. Crump that other distributions likely can be found to describe the data
as well or better. Lognormal is justified not just by statistical convenience. There is also an
expectation, given the many different features of people (such as chemical absorption rates and
internal half-lives) that each affect susceptibility in a multiplicative way, that things will
approach lognormality. The individual distributions do not have to be lognormal, they just have
to interact more or less multiplicatively. Model error can never be eliminated as a concern,
particularly projecting down to very low doses and risks.
Dr. Hattis said that he investigated that issue with the pharmacokinetic data. He said about 2,700
pharmacokinetic data points line up in terms of comparing a z score, suggesting they are
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basically lognormal. A concentration in the upper right corner indicates a slight excess of values
that would suggest a somewhat greater risk than would be projected from the lognormal in the
data. Thus, for the larger data sets there could be some mixed distribution character. That means
the data should be described not just with one lognormal distribution, but with two or perhaps
more. That is an appropriate adjustment, rather than inventing another distribution. It makes a
more complicated model, but is probably faithful to the mechanistic idea that the
pharmacokinetic differences among people arise from many small factors each acting in a
basically multiplicative way. Looking at all kinds of pharmacokinetic data, such as mixing
volume distributions, half-lives, and so on, Dr. Hattis said his plots indicate a slight deviation
from normality, which suggests slightly fatter tails at the high end than would be suggested by a
lognormal distribution. He agreed that detailed modeling would be appropriate in cases of doses
slightly above background.
Dr. Rhomberg agreed that additivity to background is important. In his team's methodology, they
tried to distinguish where there are distributions one wants to know and specify. The team used
lognormal distributions, but there are other approaches. He noted that although the same
problem is found in cancer risk assessment of low doses and divergence among the tails of
distributions, it will not be to the same degree.
As for Dr. Hattis' remark about lognormal being appropriate in terms of a number of factors that
can be acting in a multiplicative way, Dr. Rhomberg noted the number of those factors is limited.
So although the theoretical lognormal distribution goes all the way down to zero, the actual
distribution from multiplying factors is limited by the number of factors. Thus somewhere in the
tail of the distribution, it stops being really meaningful. Dr. Rhomberg's team chose 1/1,000 as
that point where it may not longer be real.
Dr. Crump reiterated his belief that the assumption of lognormal distribution needs to be
investigated. Having a finite number of factors means not having an exact lognormal
distribution, but only an approximation. To get that approximation requires assumptions that are
not plausible, such as that different factors in the same individual are independent. He also
suggested that cost-benefit analyses would want to characterize the total risk, going down to
minute exposures, which means going below the assumed lower limit of 1/1,000. There could be
cases where the real impact of the analysis comes at the risk of 1/1,000,000 but where a lot of
people are exposed at that level.
General Discussion
Dr. Wood read the following questions submitted by colloquium participants:
How do we take into account multiple effects from multiple data sets for use in economic
analysis?
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• What is the willingness to pay question to be addressed? We are not asking what are you
willing to pay so that a rat will not have a 10 percent chance of liver damage. We need
this question defined for the human population.
Dr. Putzrath reiterated her question about whether thresholds are believed to exist. She said the
question of low dose and how to do cost-benefit analysis in part depends on whether there is a
value below which no effect is expected.
One participant said the discussions and presentations so far only addressed part of the problem.
She restated Dr. Vu's colloquium objectives: to provide economists with full range of health
effects associated with a chemical exposure; to define severity, onset, and duration for those
effects; to identify the characteristics of the people that may be most susceptible to those effects;
and to estimate the number of people at risk.
She suggested risks right at the reference dose may not be the right subject. With exposures 50
times the reference dose, the concern would be not only about a particular effect but some of the
other effects seen in animal studies that were not used to derive the reference dose. Some of
those issues need to be looked at. And, she said, to get at willingness to pay, economists need
not just risk numbers but some advice on what kinds of effects to expect and the severity in the
sensitive subpopulations.
Another discussant echoed that frustration. He suggested that Dr. Vu's objectives were the ideal
data for economists, but that they might be satisfied with the same dose-specific probability for
noncancer threshold effects that is available for cancer. The discussion identified more than
enough tools to do that. He added that many things apply regardless of whether the effect is
cancer or noncancer, including variation in the population, variations in extrapolating from
animals to humans, and pharmacokinetics. But he said economists really want the slope factor
for use in estimating the probability of an effect at different doses. He suggested, as an example,
that economists would like to know the probability of health effects in a population living near a
stack both before and after a scrubber is installed, in order to quantify the incremental impact.
The question therefore becomes, why are there no dose-specific probabilities for noncancer and
nonlinear threshold kinds of effects?
Multiple Endpoints
One participant replied that the problem with noncancer has nothing to do with threshold and
nonlinearity or the lack of statistical methods, but is in increasing the dimensionality of the effect
and in the lack of data concordance from animal species to humans. It is not a single noncancer
effect, but potentially four or five effects covering a whole spectrum of target organs and levels
of severity. Methods are lacking in the multivariate case. Cancer is very simple because the goal
is total avoidance of any kind of cancer, ignoring the difference between lethal and nonlethal
cancers. But for noncancer effects it becomes difficult to look at the risk of many different kinds
of things. And there is a huge difference in costs for hospitalization for different types of
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respiratory effects, for example. Pharmacokinetics provides a good handle on some aspects of
differences among species, chemicals, and duration, but does not provide any risk numbers. It is
still necessary to relate tissue exposure levels to toxicity, so dynamics come into play. That is
where there is not enough understanding of the connection between animals and humans.
Another participant responded that pharmacokinetics helps explain the linkage between exposure
and developing an adverse health effect. The discussion must include what science will provide
the most accurate risk assessment, including pharmacokinetics and pharmacodynamics, to feed
into an economic analysis.
Dr. Rhomberg emphasized the importance of looking at multiple effects. His team's method
looks at only one effect at a time. One alternative is, in assessing impacts on a human
population, to look simultaneously at all of the endpoints for which there is information, not just
project from a sensitive endpoint. But there may be questions about whether each one is relevant
to humans. Another problem is that the same endpoint might be measured in several different
experiments, maybe in different species, with some different results. The traditional approach is
to take the most sensitive, but that does not help in understanding uncertainty. One could
consider how to use data showing that different doses cause liver toxicity in mice, rats, and
hamsters to illuminate the distribution of differences among species. So it will be essential to
look at several different endpoints, not just different degrees of severity of one endpoint. But that
might require treating them as though they were separate endpoints and doing several parallel
analyses in the absence of a method to model such a multidimensional response.
Dr. Vu refocused the discussion about multiple effects. She noted that the reference dose was
developed to identify a dose that would cover all endpoints considered to be generally safe.
Cancer is treated separately as a means of simplification. Recognizing that contaminants or
mixtures could pose a myriad of effects, is it possible to look at disease outcomes, then see what
dose-response information is available to predict the risk for various health endpoints, rather than
lumping all noncancer into one box and looking only at critical effects? That would complicate
risk assessment but would facilitate benefits analysis. If a health outcome is identified for effects
thought to have biological thresholds in individuals and populations, can the available dose
responses be incorporated using some of these models to give a probabilistic estimate?
One discussant suggested that if more than one effect contributes in a material way, they should
be treated as separate problems, maybe correlated to some degree, but with different end effects,
severities of effect, background risks, and interindividual variabilities. The aggregate effect and
benefits would be the sum of those different effects.
Dr. McGartland commented that although it would be ideal to have the dose response for an RfD
for noncancer effects, the draft cancer guidelines are moving toward an RfD approach for cancer,
at least when the data permit a nonlinear threshold. That is a step backward to an economist
because it eliminates the ability to quantify the benefits for cancer. He noted that mathematical
models discussed could be applied to dose-response information at least from the RfD or
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something similarly defined, like an MOE, that would, from a threshold for cancer, be a floor on
which to go forward.
Another discussant emphasized that in considering multiple endpoints, the probabilistic
procedures and analyses be kept very clear and as simple as possible. The benchmark dose
procedures are complicated and hard for people to learn. Adding analysis for multiple endpoints
will require a commitment from management of money and time, unless it is kept very simple.
Another participant pointed out that, ordinarily, dose-response information is available only for
some of the endpoints of interest. The ones that are not quantified cannot be used by economists
or considered by those reading their analyses. But looking at this as a multidimensional problem
can provide economists a tool for incorporating the fact that certain endpoints have not been
considered. It still may be necessary to look at endpoints individually as well, as is done with
different cancer endpoints.
Dr. Don Barnes, Science Advisory Board (SAB) Staff Director, summarized the colloquium's
progress so far. People generally have a comfortable feeling about the various methods
presented. The difficulty comes in looking at the implications of their use, such as considering
additivity to background and looking at multiple endpoints. In addition, economists will be
challenged to determine how to value precursor effects and elicit preferences from people who do
not understand what precursor effects are.
Another discussant pointed out that while the discussion has focused on cancer versus noncancer
risk assessment, the Agency is trying to harmonize those approaches. From a biologist's
perspective, there is no reason to develop separate methodologies for cancer and noncancer.
Both include multiplicities of endpoints.
He added that the complexity is a pressing issue. Biologists are just beginning to understand the
mechanism of action for cancers at the molecular level. Obtaining accurate risk assessment that
reflects that biology will require complex research procedures and complicated models. To the
extent that is not described accurately, there will be significant uncertainties in risk assessments
and cost-benefit analyses. The complexity is embedded in the biology that underlies the risk.
That is a long-range problem but one that can be approached rationally.
Dr. Hattis remarked that although his analysis was complex, the bottom line was relatively
simple, in terms of power law relationships for the expected value. He said caveats on the form
of the distribution are worth noting. But he suggested there is some utility in a generic analysis
that can be done for appreciable numbers of chemicals and effects while the more accurate
methodologies are being developed for cases with a richer database.
Another participant said the RfD/RfC-type approach has the advantage of being simple and
looking at one thing. But in going above the RfD and looking at multiple endpoints, the analysis
is going to change every time the exposure of interest changes, sometimes in very small
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increments. With thresholds and dose-response curves, regardless of the equations and
regulations, the risk will not be in a linear proportion to exposure. Even the simplest
dose-response curve can get into an analysis that is complex enough for one exposure of interest.
When it is 2-fold or 10-fold higher, everything could change. And that will be very complex, not
just to calculate but also to communicate. One scenario might deal with liver effects, while
another deals with lung cancer, and those have completely different cost structures. So it is
important to think about how to do it and what to say about it.
Other Considerations
A discussant mentioned another concern with concordance of effects across species. Because
most of the data are in animal species and the goal is preventing all adverse effects, the specific
effect was not so critical. But trying to value a specific effect observed in animals, not knowing
whether that is the response of interest in people, goes beyond any of the methodologies
discussed. That issue needs to be raised in a bigger context of the type of research done for risk
assessment.
Any guidance to economists should deal with exposure considerations, another discussant urged,
to reflect chemicals with different routes of exposures for humans, sensitive subpopulations that
may or may not be exposed, and human activity patterns that affect exposure. But one recalled
that there may not be a correlation with the exposure parameters in describing distributions for
RfDs or thresholds. He noted that Mr. Price found that situation in a risk assessment he did of
PCBs where he distributed the human interindividual adjustment factor.
One participant raised an additional caveat, a concern about how well sensitive populations have
been studied. The distributions can become bumpy and speculative and many of the data are
from small data sets. The issue of background is also really important and needs to be tackled in
general in trying to characterize nonlinear effects, not just for benefits analysis.
Dr. Vu summarized the discussion, noting that the next day's meeting would focus on developing
an agenda for how to address these issues, identifying available tools and methods, and getting
economists and risk assessors to work together to address some of these issues. She asked the
group for suggestions. Although research into biological risk assessment models is further
enhancing the understanding of the mechanism, EPA must use the standard toxicological
information that is available. Approaches and methods can be developed to address some of the
questions of economists, and can also improve the ability to characterize risk to inform risk
management decisions.
•*•&*•
Suggestions for Moving Ahead
One discussant suggested doing a case study or a series of case studies. Another suggested
following the Air office's example of starting with the economists' need and working backwards
to see how many human data are really available, using clinical studies, hospital studies, epi
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studies, and occupational health investigations. Identifying different health endpoints, and
finding environmental exposures that might be linked to a health effect, can identify what
information is required to give economists what they need. It might also identify the kind of
animal studies needed to supplement that information base.
Dr. Rhomberg reminded the group of the saying, "the perfect is the enemy of the good." Doing it
exactly right may not get to the goal, so the question becomes, what is practical to try to do? He
advised working within a structure that, to the extent possible, does not bind you to a particular
choice that is made now for expediency. Begin to think about multiple endpoints, but consider
how often that would be a critical thing to worry about. He noted that his team's sensitivity
analysis found some assumptions were really important to try to improve, whereas others were
too small to be a factor affecting satisfaction with the approach. That kind of sensitivity analysis
can help prioritize.
Dr. Crump suggested that a study of model uncertainty could be done fairly simply using existing
data to identify what models are consistent with the data and what risks they would indicate.
That would be similar to sensitivity analysis but with a slightly different focus.
Dr. Haber mentioned precursors and biomarkers, hot areas of research that can be used to
extrapolate the dose-response curve to lower dose levels and to give information about what is
going on at environmentally relevant exposure levels. Those precursor effects may be
measurable in human populations that are exposed. If they can be measured quantitatively, this
can assist in quantifying the real risk.
One discussant pointed out that, if the goal is to do better risk assessments for use by the
economists, it is important to know how much accuracy they need. That is different from doing
better risk assessments for the purposes of better risk assessments. A crude range that
encompasses a fair amount of sensitivity, identifying which parameters are important, might be
much more useful than better refinements of distributions or other parameters of models,
especially when branching out to multiple endpoints. One type of risk analysis can get in the
range between 1/1,000 and 1/10,000; an entirely different type is needed if the difference
between 1/10,000 and 2/10,000 matters to the economist. Where refinements are made depends
on what they need.
Another suggested the idea of giving economists a worst case so they can determine whether
there is any economic impact, or a best case to begin looking at an overall range, rather than
trying to begin with perfect knowledge.
Another discussant noted that whatever methodology EPA chooses will have an impact on all the
State EPA offices. She suggested that incremental changes are easier to adapt to, particularly
given the growing complexity of risk assessment methodologies and the need to communicate
the answers to the public.
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One participant suggested that there might be a benefit in focusing more on the qualitative
descriptive aspects of the chemicals, rather than just on the quantitative ones. That might help
economists in the risk characterization portion. She inquired whether there are certain categories
of outcome with which they are particularly concerned. It is possible to describe qualitatively the
likelihood that a particular chemical exposure would cause a certain type of outcome.
She added that there is a lot of room to improve understanding of biology, biomarkers of
exposure, and precursors. But very carefully done studies in humans are needed before they can
be used in a truly predictive way. This may be the time to really start assessing how and where
the money goes for the studies. It is possible to leverage that investment, such as doing exposure
assessment work in the context of an ongoing study.
Dr. Hattis supported that view, saying that his data were not designed for the purpose for which
he used them. But to really know about all of the interactions with different subpopulations
according to age, gender, and illness categories, it would be helpful to actually collect new data
deliberately designed with stratified random samples that can measure some of these parameters
and response functions in a more deliberate way.
One participant supported the idea of using biological and chemical-specific data wherever
possible in evaluating variability and uncertainty, but added that the level of detail is not always
there to do biological modeling. So in developing methods, it is important to look at how to treat
the large uncertainties of an incomplete database.
Another said that, having looked at all the issues, the goal is still attainable. Case studies might
help, as could a chart of information for the economists that includes factors such as the major
effects of concern; information on mode of action; some kind of likelihood that the mode of
action might be active in humans; what the expected contact rate would be with segments of the
population, as well as a sense of the toxicity; what the concentrations might be in a medium and
what kind of contact with that medium would be expected. With that chart and the risk numbers,
economists could determine how many cases of some kind of effect would be seen. That
package would have to include a translation into human health conditions, given an animal effect
and what is known about mode of action, pharmacokinetics, even chemical structure and effects
for similar chemicals. That could be made into guidance on whether the effects would be
expected in humans, what is the real exposure, and what is the real likelihood of toxicity in a
human. It is not as simple as providing a slope factor that can be applied to the whole population
of the United States. That is how the really large numbers come about.
Adjournment
Dr. McGartland noted that he remains optimistic. There are some models with which to begin
making headway, at least for single effects, toward meaningful cost-benefit analysis. It is
important to put these benefits in context, even if they cannot be valued, and cases or symptoms
mean a lot more than some of the other measures. He thanked participants from outside EPA for
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their presentations and comments. Over the long run, he said, he could see lobbying for more
joint research on the issues.
In the short run, Dr. McGartland said that he was intrigued with the case studies approach,
perhaps starting with single effects and going on from there, and perhaps even coming up with
some guidelines for standardizing the approach, with appropriate caveats about model selection,
and so on, and what they mean for the benefit estimates. But to do that, a lot more work is
required. One or two case studies will not be sufficient to provide the necessary sense of
robustness and comfort with the cost-benefit analyses and risk assessments that go into the public
domain. The ideal for economists would be a continuous curve that allows for talking about
margins. But they can work with far less.
His hope for day two is to talk about more concrete steps in both the short run and the longer run.
He said there would be an opportunity for colloquium participants to join in further work. He
concluded by noting that this was the first meeting where economists and risk assessors have
tried to solve a common problem. He called the chart idea intriguing and a good way to move
forward and help economists articulate the kinds of information risk assessors can provide. He
said the work will pay dividends quickly.
Dr. Wood closed the meeting by thanking the participants.
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Appendix A
External Participants
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Colloquium on Approaches to Quantifying Health Risks for
Threshold or Nonlinear Effects at Low Dose
September 28,2000
External Participants
Sandra Baird
The Baird Group
36 Duffield Road
Auburndale, MA 02466-1004
(617) 527-9868 (v)
(617) 527 4235 (f)
sbaird@world.std.com
Kenny Crump
ICF Consulting
602 East Georgia Avenue
Ruston, LA 71270
(318) 242 5019 (v)
(318) 255 4960 (f)
kcrump @ icfconsulting.com
Dave Gay lor
Sciences International
13815 Abinger Court
Little Rock, Arkansas 72212
(501) 228-9773 (v)
(501)228-7010(f)
dgaylor@sciences.com
Lynne Haber
Toxicology Excellence for
Risk Assessment (TERA)
1757 Chase Avenue
Cincinnati, OH 45223
(513) 5427475 Ext. 17 (v)
(513) 542-7487 (f)
haber@tera.org
Dale Hattis
Center for Technology, Environment and
Development
Clark University
950 Main Street
Worcester, MA 01610
(508) 751-4603 (v)
(508) 751-4600 (f)
dhattis@aol.com
Paul Price
Ogden Environmental and Energy Services
15 Franklin Street
Portland, ME 04107
(207) 879-4222 (v)
(207) 879-4223 (f)
psprice@oees.com
Reisha Putzrath
Georgetown Risk Group
3223 N Street N.W.
Washington D.C. 20007'
(202) 337-8103 (v)
(202)342-2110(0
rmputzrath @ mindspring.com
Lorenz Rhomberg
Gradient Corporation
238 Main Street
Cambridge, MA 02142
(617) 395-5000 (v)
(617) 395-5001 (0
lrhomberg@gradientcorp.com
-------
Appendix B
Participant List
-------
Colloquium on Approaches to Quantifying Health Risks for
Threshold or Nonlinear Effects at Low Doses
September 28, 2000
Participant List
Rebecca Allen
Jihad Alsadek
Dan Axelrad
Sandra Baird
Don Bames
Steven Bayard
Nancy Beck
Robert Bellies
Dave Bennett
John Bennett
Lynne Blake-Hedges
Tracey Bone
Ethel Brandt
Marilyn Brower
Susan Carillo
David Chen
Jim Cogliano
Gary Cole
Rory Conolly
Marion Coply
Kenny Crump
Linda Cullen
Vicki Dellarco
Chris Dockins
Julie Du
Gary Foureman
Dave Gaylor
Jeff Gift
Lynne Haber
Trish Hall
Dale Hattis
Rick Hertzberg
-------
Richard Hill
Lee Hoffman
Jennifer Janoit
Barnes Johnson
Mark Johnson
Jin Kim
Gary Kimmel
Steve Knott
Arnie Kuzmak
Elizabeth Margosches
Alec McBride
Al McGartland
Robert McGaughy
Patricia Murphy
Deirdre Murphy
Onyemaechi Nweke
Ed Ohanian
Marian Olsen
Dan Olson
Nicole Owens
Fred Parham
Resha Putzrath
Lorenz Rhomberg
Paul Rice
Rita Schoeny
Jean Schuman
Jennifer Seed
R. Woodrow Setzer
Nathalie Simon
Ted Simon
Judy Strickland
Linda Teuschler
Vanessa Vu
Pauline Wagner
Ann Watkins
David Widawsky
Diane Wong
-------
Bill Wood
Tracey Woodruff
-------
Appendix C
Agenda
-------
r/EPA
United States
Environmental Protection Agency
Risk Assessment Forum
Colloquium on Approaches to Quantifying Health Risks for
Threshold or Nonlinear Effects at Low Dose
Omni Shoreham Hotel
2500 Calvert Street N.W.
Washington D.C. 20004
September 28, 2000
Agenda
Colloquium Co-Chairs: Al McGartland and Vanessa Vu
8:30AM Registration
9:OOAM Welcome
Bill Wood, EPA Risk Assessment Forum
9:05AM Perspectives: A Risk Assessor's Point of View
Vanessa Vu, National Center for Environmental Assessment
9:25AM Perspectives: An Economist's Point of View
Al McGartland, National Center for Environmental Economics -
9:45AM Dose-Response Based Distributional Analysis of Threshold Effects
Lorenz Rhomberg, Gradient Corporation
Sandra Baird, The Baird Group
10:1 SAM Characterizing Risks Above the Reference Dose
Paul Price, Ogden Environmental and Energy Services
10:35AM Expected Values of Population Dose Response Relationships Inferred
from Data on Human Interindividual Variability in PK and PD Parameters
Dale Hattis, Clark University
10:55AM
BREAK
11:10AM Interindividual Sensitivity
Dave Gay/or, Sciences International
11:30AM Use of the Categorical Regression Methodology to Characterize the Risk
Above the RfD
Lynne Haber, Toxicology Excellence for Risk Assessment (TERA)
-------
11:50AM Risks Between the LOAEL and the RfD/RfC: A Minimalist's Approach
Resha Putzrath, Georgetown Risk Group
12:10PM LUNCH (on your own)
1:15PM Facilitated Roundtable Discussion
Moderator: Bill Wood
(BREAK 3:00-3:15PM)
4:30PM Concluding Comments and Next Steps
Vanessa Vu and Al McGartland
5:OOPM ADJOURN
-------
Appendix D
Presentation Overheads
Vanessa T. Vu
National Center for Environmental Assessment
Office of Research and Development
U.S. Environmental Protection Agency
-------
pW:i'l
i'tttfnk-3
Risk Assessment Forum
National Center for Environmental
Economics
September 28, 2000
Vanessa T. Vu, Ph.D.
National Center for Environmental Assessment
Office of Research and Development
U.S. Environmental Protection Agency
-------
•}i"u;,!1,.".t,H
!« ]' '1 i i • *'"
Outline
Background
4^ Issues in human health risk assessment &
valuation of health benefits
a Objectives and Structure of Colloquium
-------
* < >',t!l 'S '
IW.W'
i\ "
' I f1s
' ._»,!,' L. ',
Current Efforts in Improving
Risk Assessment
a Harmonized and integrated approaches
for all health endpoints
Emphasis of mode of action (MOA)
Two-step dose response assessment
Chronic and less-than lifetime exposures
D Fuller characterization of risks
4 Probabilistic estimates, RfD/C, margin of
exposure (MOE)
4 Susceptible populations
-------
II
f< A .V'S
ia'l' •»,-
vi
fc. / ••
,:s t < i,^
J.^,/ fe1 J
1 ft. >«f|%r
?f. ^/.Ji
Benchmark Dose" Approach to Pose
Response Analysis for Noncancer Endpoints
Cf)
c
'•5
c
o
a
0)
CD
E
c
("BMR")
0.1
95% Lower
Confidence Limit
\
Mathematical
Model Fitted to
Experimental
Data ( • ) in
Observable Range
UF l-ED10
ED
10
RfC
("BMD")
"Dose" (ppm)
-------
-w to.
,t< ' »
Dose Response Assessment
Q
fc-4
O
o
6
c
o
(X
8
oi
10%
0%
nnvironiiKntal
Exposure levels
of Inlcrest
Empirical
Range of
Obscrvalion
Riingo of
Extrapolation
ED,
rio
— MOG
Nuitlincar itcfaul
Dose
-------
A* }'-, .,• i ',(!».
1 ' • I ' I "
Health Benefit Analysis
Information Needs
Characterization of a full range of health
effects potentially associated with a
contaminant(s)
D Nature of specific effects- e.g. severity,
onset, duration
Characteristics of people potentially
affected- e.g. age, health status
Estimation of number of people at risk
-------
ten*
Issues Related to Valuation of
Health Benefit Analysis
Near-term
+ RfD/C & MOE methods do not provide
quantitative estimates of risk below POD
RfD/C focuses only on critical effect from
chronic exposure
Critical effects need to be related to adverse
human health outcomes
MOE for cancer based on precursor effects
-------
VV ',,.•(!
Issues Related to Valuation of
Health Benefit Analysis
Emerging Issues
+ Increased use of biomarkers of effect and
susceptibility
4 Valuation of more subtle effects
, 1
/ ' V
?", i-1, <
i-ir^r ' S"t. f | f S ^ '
v i'1*4**1'^1'.'
-------
Colloquium Objectives
%;f^>,'l;,vll -•• tJ
' ' i
Q Explore possible approaches for
quantifying risks below POD
Biological thresholds
Nonlinear dose-response curve at low dose
•' ,"'
i ft , I
•, "'
!;. ,.', 4-
*"*\ . 1 '<;
a,
-------
I I,
t. I
Colloquium Structure
Overview of approaches to health benefit
analysis
Presentations of available approaches
and methods to quantify risks below POD
Roundtable discussion
4 Identify tools and methods for near-term use
-------
Appendix E
Presentation Overheads
Al McGartland
National Center for Environmental Economics
Office of Policy, Economics and Innovation
U.S. Environmental Protection Agency
-------
Benefits Analysis at EPA
Al McGartland
Director
National Center for Environmental Economics
Office of Policy, Economics and Innovation
-------
Outline
Economic Analysis Guidelines
Background: Benefit-Cost Analysis
Benefits Analysis Methods
Criteria Air Pollutant Examples
-------
Guidelines for Preparing
Economic Analyses
EPA's Science Advisory Board, comprising
leading environmental economists from major
universities and research institutions, reviewed the
Guidelines throughout its development for
accuracy in both economic theory and practice.
In their final report, the Board gave the Guidelines
an overall rating of "excellent," saying, they
"succeed in reflecting methods and practices that
enjoy widespread acceptance in the environmental
economics profession."
-------
Growing Demand for Economic
Analysis at EPA
Executive Orders and legislation
increasingly require economic analysis of
Agency rules:
- E.G. 12866
- Thompson Language
- Safe Drinking Water Act
- SBREFA
- UMRA
- Proposed Regulatory Reform bills
-------
Multiple Decision Criteria
Consistent with Economics
Factors in Decision Making Process
• Ethics
- Distributive Justice
- Environmental Justice
• Sustainability
• Political Concerns
• Legal Consistency
• Institutional Feasibility
• Technical Feasibility
• Enforceability
• Efficiency (Benefits/Costs)
-------
Why use Benefit-Cost Analysis?
• Benefit-Cost Analysis attempts to simulate
a private market test on the production of
public goods (e.g., environmental
protection)
-------
Private Market Test
• Private Markets allocate resources to efficient uses
automatically
• If a manufacturer cannot sell its output for more than
it costs to produce, it goes out of business.
- This manufacturer was an "inefficient" user of society's
scarce resources.
- The cost of the resources used in production was greater
than the value of goods produced.
- Discipline of the private market forces this inefficiency out
of the system and rewards efficient users of resources -
i.e., those who create net positive value or net benefits.
7
-------
Approaches to Benefits Analysis
• Damage Function Approach: estimate
reduced incidence of adverse effects,
multiply by estimated value per case
avoided
1 Indirect Approach: value an environmental
improvement understood in general terms
(e.g., Exxon Valdez damages assessment)
8
-------
Steps in Benefits Analysis
Emissions
Environmental
Concentrations
Effects
Exposure
Benefits
$
-------
Implementing the Damage
Function Approach: Valuation
Methods
Cost-of-Illness
Averting Behaviors
Hedonics (e.g., wage-risk tradeoffs)
Stated Preference (survey methods)
10
-------
Alternative Benefits Methods
Potential approaches to valuing human
health effects without using damage
function approach:
- Value exceedances of the RfD/RfC
- Value "peace of mind" or hypothetical insurance policy
Potential problem with these approaches:
how to express the values in terms of
marginal changes
-------
Valuation Examples—Criteria
Pollutants
The benefits estimates for criteria air
pollutants are the most extensive that EPA
has produced
Dose-response functions for many effects
- able to quantify reduced incidence of: mortality,
bronchitis, asthma, hospital admissions, respiratory
symptoms, etc.
Conducted under Section 812 of the CAA--
which mandated extensive SAB review
12
-------
Health Benefits from Air
Pollution Control (Selected Effects)
Health Effects Annual Cases
Avoided (2010)
Premature Mortality 23,000
Chronic Bronchitis 20,000
Hospitalizations
-Respiratory 22,000
-Cardiovascular 42,000
13
-------
Benefits-Unit Values
Health Effect
Mean Value per Type of
Case Avoided Valuation Study
(1990 $)
Mortality
Chronic
Bronchitis
Hospitalization
-Respiratory
-Cardiovascular
$4,800,000 Wage-risk, stated
preference
$ 260,000 Stated preference
$
$
6,900
9,500
Cost of Illness
Cost of Illness
14
-------
Results of Human Health
Benefits Valuation, 2010
Health Effects Monetary Benefits
(in millions 1990$)
Premature Mortality $ 100,000
Chronic Bronchitis $ 5,600
Hospitalizations
-Respiratory $ 130
-Cardiovascular $ 390
Other Health Effects $ 2,000
15
-------
Conclusions
Demands for economic analysis at EPA are
increasing
Dose-response functions are a critical input
to quantification of benefits
- Alternate benefits methods a possibility
Effects without dose-response functions are
unquantified in benefit-cost analysis, and
thus perceived by some as "not counting"
16
-------
Appendix F
Presentation Overheads
Sandra Baird
The Baird Group
and
Lorenz Rhomberg
Gradient Corporation
-------
V
Dose-Response Based
Distributional Analysis of
Threshold Effects
Sandra J.S. Baird
Lorenz Rhomberg
Collaborators:
John S. Evans, Paige Williams,
Andrew Wilson
-------
Overview
• Why move from existing approach
• Mental models
• Framework for distributional
approach
• Underlying theoretical and
empirical support
• Case study: Ethylene Oxide
• Benefits of Distributional D-R
approach
9/28/00
-------
Why Change?
Uncertainty in RfD is unknown
Protection at RfD is unknown and
inconsistent
Risk assessment and risk
management are intertwined
Risk from exposures greater than
RfD is unknown
Prevents informed decision making
9/28/00
-------
Current Mental Model
RfD =
NOAEL
UFA * UFH * UFS * UFL * MF * D
\
PT =
NOAEL
AFA * APR * AFs * AFL * AFD * MF
9/28/00
-------
Current Mental Model
o
I
o
CO
o
03
O
"*.
O
eg
o
Q
O
9/28/00
dose
-------
Dose-Response Based
Mental Model
oa
,C
'5
B
.a
H
CL
•u
"*
I
99.9
99
90
80
70
60
30
20
10
0.1
Animal Experiment
Stochastic Uncertainty
Test
Animal
Log Dose
9/28/00
-------
Dose-Response Based
Mental Model
1
41
'5
CL
I
99.9
99
90
80
70
60
30
20
10
0.1
Scaled
Animal
Test
Animal
ED50
Log Dose
9/28/00
7
-------
Dose-Response Based
Mental Model
oa
'§
41
'5
8
B
,8
•K
•a
a
fi
•u
99.9
99
90
80
70
60
30
20
10
0.1
Scaled
Animal
AFA * stochastic uncertainty
ED50
Test
Animal
Log Dose
9/28/00
8
-------
Dose-Response Based
Mental Model
'§
•V
'5
•a
Q.
•u
•ft
I
99.9
99
90
80
70
60
30
20
10
0.1
Scaled
Animal
AFn
Test
Animal
ED001 human ED001 animal ED50
Log Dose
9/28/00
9
-------
Framework for
Distributional Approach
ED
AFA
RfD « ED
J
AFH
RfD is determined by choosing level of population risk, y,
and by choosing level of confidence from the uncertainty
distribution of the dose estimated to yield y response.
9/28/00 10
-------
Theoretical and
Empirical Support
• Animal to Human - AFA
Scaling ("Centering")
Estimates of uncertainty
• Human Heterogeneity - AFH
Probit slope
Estimates of human
population variability
9/28/00 11
-------
Dose Metrics: Scaling
Allometric - BWb
RfC - HEC
Chemical specific - PBPK
12
9/28/00
-------
Estimates of
Uncertainty In Scaling
Source Reference Range of GSDs
Pesticide Baird et al., 1996 4.1 - 4.9
NOAELs
LD50s Rhomberg & Wolff, 2.5 - 6
1999
Antineoplastic Schmidt et al., 2.6 - 3.7
Agent MTDs 1997
9/28/00 13
-------
Uncertainty in Scaling
0.0
-3
Guinea Pig: Rabbit
Distribution of Ratio of Oral LD-50
9/28/00
14
-------
Human Heterogeneity
Based on tolerance distribution
Assumes humans are more variable
in response thresholds than test
animals
Variability described by log-probit
slope of dose-response curve (GSD)
Slopes derived from human data
(Hattis et al., 1999)
9/28/00 15
-------
Case Study:
Ethylene Oxide
• Studies: 2 Developmental
2 Reproduction
• Endpoints: post implantation loss,
fetal/pup body weight
• Advanced statistical d-r models
• Empirical estimates of
uncertainty
• Sensitivity analysis
9/28/00 16
-------
Case Study:
Ethylene Oxide
700
600
500 -
6-
9
I
400 •
300 •
200 -
100 •
Comparison of EDOOlli Distributions for Fetal Death for the
Average During Exposure Period Dose Metric
— — '0111111 and Neeper-Hnidley FO
Chun and Nccpcr-Brndlcy Fl
••"" "Spellings FO
1000
2000
Dose (ppb)
.1000
"1000
9/28/00
17
-------
Dose-Response Based
Mental Model
99.9
•B 99
•V
'5
.2
•«
a
«
Vi
i
90
80
70
40
30
20
10
0.1
Scaled
Animal
Test
Animal
Human
/;, A
ED001 human ED001 animal ED50
Log Dose
9/28/00
18
-------
Ethylene Oxide:
Results Summary
RfC
LED10a/30 ED10a LED10a EDOOIh LEDOOIh
Endpoint (ppb) (ppb) (ppb) (ppb) (ppb)
5%BodyWt. 180 7,000 5,400 290 46
Reduction
(Devel.)
Fetal Death 200 6,800 6,000 700 120
(Repro.)
9/28/00 19
-------
Sensitivity Analysis:
Model
Comparison of Model Effect: Logit vs. One-hit
FO Gen: Fetal Death
1400
1200-
1000-
£ .800
p
E 600-
400-
200-
J
n
• Logit
D One-hit
Ihiu.. .
Io|>lU Populnliun Threshold Vnlu<
9/28/00
20
-------
Sensitivity Analysis:
Covariate
Comparison of Covariate Effect: Controlling vs. Not Controlling
FO Gen: Fetal Death
1400
1200
1000
£- 800
p
£ 600
400-
200-
• Controlling for Covariate
DNot Controlling for Covariate
h
lo|;l() Population Tlircsliold Vnlu<
9/28/00
21
-------
Sensitivity Analysis:
AF
1800
1600-
1400-
1200
1000-J
600
400
200
0
Comparison of AFah Effect: AFah 1.5 vs. AFah 6
FO Gen: Fetal Death
n n
• AFahGSD=1.5
nAFahGSD=6
n n
n n —
** s* s* & & *> & & ?' j* *•>
log 10 Population Threshold Vali
9/28/00
22
-------
Sensitivity Analysis
AF
H
2500
Comparison of AFhh Effect: AFhh 2 vs. AFIih 3
FO Gen: Fetal Death
2000
1500
1000
500-
„ nJ1J1
• AFhhGSD=2
DAFhhGSD=3
lllli . . -
.> .> ^ ,^ K> ,.^ ,> •"> .> .> '!
-O V N N -V -V •.* •> •>
InglO Popuhitinn Tlircslinld Vnhi'
9/28/00
23
-------
Issues not Currently
Addressed Quantitatively
• Severity of effect
• Defining adverse effect
• Concordance of endpoints across
species
9/28/00 24
-------
Benefits of D-R Based
Probabilistic Method
• Distribution of probability of a
health impact occurring
• Risk to specified sensitive
population is estimated
• Uncertainty in risk estimate is
quantitatively characterized
• Level of protection is determined at
end of process
• Estimate risk above and below RfD
9/28/00 25
-------
Benefits of D-R Based
Probabilistic Method
• Provides a framework for each
component of extrapolation
• Components can be updated with
chemical specific data
• Components contributing the
greatest uncertainty can be
identified and resources allocated
to reduce uncertainty
9/28/00 26
-------
Summary
Maximizes use of available data
Assumptions are more transparent
Estimates risk and uncertainty in
risk for specified sensitive human
population
Can be incorporated into benefit -
cost analysis
9/28/00 27
-------
References
Baird, S.J.S., Cohen, J.T., Graham, J.D., Shlyakhter, A.I., and Evans, J.S. (1996) Noncancer
risk assessment: A probabilistic alternative to current practice//»/nn/i and Ecological Risk
Assessment 2(1) ,79-102.
Baird, S.J.S., Slob, W., and Jarabek, A.M. (2000) Probabilistic noncancer risk estimation. In
preparation.
Brand, K.P., Rhomberg, L., and Evans, J.S. (1999) Estimating noncancer uncertainty factors:
are ratios of NOAELs informative? Risk Analysis, 19(2) , 295-308.
Chun, J.S., and Neeper-Bradley, T.L. (1993) Two-generation reproduction study of inhaled
ethylene oxide vapor in CD rats. Bushy Run Research Center, Export, PA. Study sponsored by
ARC Chemical Division (Balchem), PRAXAIR, Inc. and Union Carbide Chemicals and Plastics
Company, Inc.
Evans, J.S., and Baird, S.J.S. (1998) Accounting for missing data in noncancer risk assessment.
Human and Ecological Risk Assessment 4(2) , 291-317.
Evans, J.S., Rhomberg, L.R., Williams, P.L., Wilson, A.W., and Baird, S.J.S. (2000)
Reproductive and developmental risks from ethylene oxide: A probabilistic characterization of
possible regulatory thresholds. Submitted.
Hattis, D., Banati, P., and Goble, R. (1999) Distributions of individual susceptibility among
humans for toxic effects. How much protection does the traditional tenfold factor provide for wh;
fraction of which kinds of chemicals and effects?A;?//<7/.y of the New York Academy of Sciences,
895 ,286-316.
9/28/00 28
-------
Renwick, A.G., and Lazarus, N.R. (1998) Human variability and noncancer risk assessment -
an analysis of the default uncertainty factor. Regulatory Toxicology and Pharmacology, 27, 3-20.
Rhomberg, L.R., and Wolff, S.K. (1998) Empirical scaling of single oral lethal doses across
mammalian species based on a large database. Risk Analysis, 18(6) , 741-753.
Ryan, L. M. (1992) The use of generalized estimating equations for risk assessment in
developmental tonicity .Risk Analysis, 12,439-447.
Schmidt, C.W., Gillis, C.A., Keenan, R.E., and Price, P.S. (1997) Characterizing inter-
chemical variation in the interspecics uncertainty factor (Up. Fundamental and Applied
Toxicology, Supplement - The Toxicologist, 36(1, Fart 2) , 208.
Snellings, W.M., Maronpot, R.R., Zelenak, J.P., and Laffoon, C.P. (1982a) Teratology study
in Fischer 344 rats exposed to ethylene oxide by inhalation. Toxicology and Applied
Pharmacology, 64 , 476-481.
Swarlout, J.C., Price, P.S., Dourson, M.L., Carlson-Lynch, H.L., and Keenan, R.E. (1998) A
probabilistic framework for the reference dose (probabilistic \tSU).Risk Analysis, 18(3) ,271-
282.
U.S. EPA (1992) Draft report: A cross-species scaling factor for carcinogen risk assessment
based on equivalence of mg/kg^/day. Federal Register 57(109), 24152-24173, June 5, 1992.
U.S. EPA (1994) Methods for Derivation of Inhalation Reference Concentration and Applicatior
of Inhalation Dosimetry, EPA/600/8-90/066, Office of Research and Development, U.S. EPA,
Washington, DC.
9/28/00 29
-------
Appendix G
Presentation Overheads
Paul S. Price
Ogden Environmental and Energy Services
-------
Characterizing Risks Above
the Reference Dose
Presentation at the
Colloquium on Approaches to Quantifying Health Risks
for Threshold or Nonlinear Effects
Paul S. Price, M.S.
Washington DC
September 28, 2000
OGDEN
ENVIRONMENTAL AND ENERGY SERVICES
Westford, MA and Portland, ME
OGDEN
-------
Topics
> Background
> Placing the RfD into a dose response
framework
> Defining the "uncertainty" in the RfD
> Placing the inter- and intra- species
uncertainty into a dose response framework
> Assessing risks above the RfD -two
approaches
> Implications for assessing carcinogenic risks
OGDEN
-------
Background
> Current system for noncancer risk consists of
+ A methodology for setting a "permitted dose"
+ Several closely related systems for comparing estimated
dose to the "permitted dose" (HQ, MOE, Risk Cup, etc.)
> In contrast to the characterization of cancer risks no
estimate of the risk (likelihood of response)
associated with any dose or combination of doses
> No guidance on the meaning of doses in excess of the
"permitted dose"
> Provides no guidance for the determination of
benefits from reduction
OGDEN
-------
Project Background
> 1993-1998 Cooperative Research and
Development Agreement between EPA and
private industry
> Goal: To characterize the uncertainty and
variation in the assessment of noncancer risk
> Four publications: Swartout et al. 1998, Price
et al. 1997 , Carlson Lynch et al. 1999, and
Price et al. (Submitted-2000)
> Approach for quantitative non-cancer risks
has been applied to PCBs and mercury OGDEN
-------
What is the RfD:
> A policy finding:
+ The result of a consensus between appropriate
experts after the review of a data base
+ A product of a political process
> A scientific finding:
+ A sub-threshold dose
i
+ A dose that is without appreciable risk
> The second position is assumed to be true
OGDEN
-------
Framework
> It is important to understand how uncertainty
and variation are defined in any proposed
framework
> Variation refers to the level of protection
+ Fraction of the population or
+ Amount of interindividual variation
> Uncertainty comes from measurement error
and interspecies extrapolation
> Toxicological criteria such as the RfD's only
have uncertainty
OGDEN
-------
Dose Response Curve in Humans
(Variation in individual's thresholds)
en
rt
o
PH
c/i
-------
Uncertainty
It is rarely possible to directly determine
dose-response rates for adverse effects in
humans
Use of mathematical models, animal
surrogates, or measurements of non-adverse
effects can provide a basis for estimating the
curve
> Such estimates are subject to uncertainty
OGDEN
-------
-------
Proposed Definition of the RfD
> Not an estimate of the population threshold
of a compound in humans
> An estimate of the lower confidence limit of
the threshold in humans
OGDEN
-------
Reference Dose (RfD) in the Context of the
Uncertainty in the Human Dose Response Curve
en
C
O
Upper Confidence
Limit or Response
RfD
Dose
OGDEN
-------
Monte Carlo Modeling and the RfD
NOAEL
RfD
UF, x UF
H
NOAEL
UF
A
x UF
H
OGDEN
-------
The Uncertainty in the Population Threshold
and the RfD
-------
The Uncertainty in the Population
Threshold
NOAEL
Population
Threshold
UF
A
x UF
H
The RfD is some lower percentile of the population
threshold generated by the equation. OGDEN
-------
Understanding Uncertainty
Factors
> Uncertainty factors can be divided into three groups
+ Primary factors (inter- and intra- species uncertainty)
+ Secondary factors (sub-chronic to chronic, LOAEL to
NOAEL, database)
+ FQPA and modifying factors
> Both inter- and intra- uncertainty factors can be
defined in terms of the difference between the test
i
species and humans
+ Interspecies reflects the difference between the average
sensitivity of the two species
+ Intraspecies reflects the fact that the animal model will
have less inter-individual variation than humans OGDEN
-------
The Interspecies Factor and its Impact on
the Extrapolation of An Animal Dose
Response To Humans
Human
o>
o
o>
C/3
Animal
Log Dose
-------
The Intraspecies Factor and its Impact on
the Extrapolation of An Animal Dose
Response To Humans
o>
o
d,
Cfl
O)
tfUUI
Log Dose
-------
Characterizing Risks Above the RfD
> Two approaches can be defined
> The first is the general approach
+ Based on the proposed definitions of the RfD and the
primary uncertainty factors
+ This approach presumes that the the shape of the dose
response curve in animals is relevant to the prediction
of the dose response curve in humans
> The second approach (minimalistic) assumes that
only the estimate of the ED50 and NOAEL are
relevant OGDEN
-------
Characterizing Risks Above the RfD
> Using the above definition of the inter and intra individual
uncertainty factors it is possible to derive an equation that
maps the observed dose response in animals to humans
> By simple algebra the relationship between the dose causing
a response rate "r" in humans EDRa and the dose causing a
response rate "r" in humans EDRh is given by the equation:
EDRh = (ED50a/UFA)
OGDEN
-------
Example Extrapolation of An Animal Dose
Response To Humans
0.1
Animal Dose Response
Human Dose Response
10
Dose
100
1000
-------
Characterizing Risks Above the
RfD General Model
> The approach assumes that the dose response curve in
animals is relevant to humans
> Can be used in simple Monte Carlo models of uncertainty in
the value of EDRh
4- Uncertainty in the size of the uncertainty factor required for the
compound
+ Uncertainty in the value of EDRh (benchmark data)
> Requires an estimate of the threshold (ED0a in the test
animals
> Requires explicit modeling of the correlation between the
values of ED0a and EDRh (A topic for future research) OGDEN
-------
Characterizing Risks Above the
RfD (Minimal Model)
> Three concepts:
+ View the RfD as a conservative estimate of the population
threshold
4- Use the interspecies uncertainty factor to estimate the ED50h in
humans from the ED50a in animals
+ Assume a linear response between the RfD and the ED50
> The result is a hockey stick model of dose response
> Has the advantage of not requiring the assumption that the
specific shape of the animal's dose response curve is a model
of humans
OGDEN
22
-------
The RfD and the ED 50
c/}
a
o
D-,
to
Dose
OGDEN
23
-------
Dose Response Equation
> The equation for the "hockey stick equation requires
estimates of the doses associated with the:
+ Population threshold
ED
50
The RfD can be used to estimate the population
threshold or the uncertainty distribution can be used
> The ED50 for humans can be estimated based on the
ED50 in the test animals
OGDEN
-------
Dose Response Equation
Risk(d) = 0.5(d*UFH*UFA - NOAELa)/(EDSOil*UFH - NOAELa)
Since a response cannot be greater than 1.0 or less than 0,
the dose response relationship must be truncated such that:
Risk(d) =0 if d < NOAELa/(UFH*UFA)
Risk(d) = 0.5(d*UFH*UFA - (NOAELa)/(EDSOa*UFH - NOAELa)
Risk(d) =1 if d > 2ED50a -(NOAELa/ (UFH*UFA))
OGDEN
-------
0.5
0.45
Unbiased Estimate of Response Rate
•—Alachlor
HCB
Paraquat
•— PCP
20
30
50
60
70
80
90
00
Hazard Indices
OGDEN
-------
Conservative Estimates of Dose
Response
Alachlor
HCB
Paraquat
PCP
0246
0 12 14 16 18 20 22 24 26 28 30 32 34 36 38 40 42 44 46 48 50
Hazard Indices
OGDEN
-------
Implications for Cancer Risk
Determinations
> Either approach can be used to derive estimates of response
below the dose associated with an observable response in
animals
> Can use the interspecies assumptions from the cancer tradition
+ Replace the factor of 10 with a body weight based value
> Can use data on interindividual variability
+ Replace the intra individual factor with more complex functions
> Can incorporate quantitative data on uncertainty
> Approach requires some method for deriving an estimate of the
threshold of carcinogenic effects
OGDEN
-------
Summary
> The approach allows a quantitative estimate of risk for
doses above the threshold
> Based on the establishment of a framework for the
RfD and/or other criteria
> The framework is not the only possible interpretation
of regulatory standards
> The approach is not limited to non-cancer
> Allows the separate modeling of variation and
uncertainty
> Requires a quantitative estimate of the thresholdPGDEN
29
-------
Appendix H
Presentation Overheads
Dale Hattis
Center for Technology, Environment and Development
Clark University
-------
Expected Values of Population Dose
Response Relationships
Background—Routes to Quantitative Assessment Depending
on Fundamental Causal Processes and Available Types of
Information
Effects Caused by Individual Threshold
Processes—Horneostatic System Overwhelming
Population Dose Response Is Determined by the
Population Distribution of Individual Thresholds
Data Base of Human Interindividual Variability in
Pharmacokinetic and Pharmacodynamic Parameters
Observations Primarily from Pharmaceuticals
Parameters Measured Cover Various Portions of the
Pathway from External Exposure to Effect
Analysis to Derive "Expected Value" Risks is Based on
Observations and Assumptions About Representativeness of
the Current Database, and About Distributional Forms
For individual sensitivities (among people)
For overall degrees of variability (among chemicals,
controlling for types of effects and route of exposure)
-------
Routes to Causation and Quantification of
Health Effects
Direct Epidemiological Observations of Excess Health
Outcomes of Concern in Relation to Exposure, After Control
for Confounders
Projections Based on Changes in Intermediate Parameters
Related to End Effects of Ultimate Concern (currently
underdeveloped assessment methodology)
Excess Infant Mortality in Relation to Birth Weight Changes
Decreased Male Fertility in Relation to Sperm
Count/Quality Changes
Increased Cardiovascular Mortality in Relation to
Changes in Cardiovascular Risk Factors (FEV1, Blood
Pressure, Heart Rate Variability, Serum Fibrinogen)
Projections Based on Population Distributions of Individual
Susceptibility to Effects Caused by Overwhelming
Homeostatic Systems (this talk)
Projections Based on Incremental Addition to Stochastic
Background Mutation Processes (e.g. Primary Genetic
Mechanisms of Carcinogenesis)
-------
Data Base of Human Intel-individual
Variability in Pharmacokinetic and
Pharmacodynamic Parameters
Observations Primarily from Pharmaceuticals
Data Sets Selected Provide Individual Data for at Least 5
Reasonably Healthy People
Parameters Measured Cover Various Portions of the
Pathway from External Exposure to Effect
Current Data Base has 443 Total Data Groups (Each
Yielding a Variability Observation)
11 Contact Rate (2 for children)
343 Pharmacokinetic (71 include children)
89 with Pharmacodynamic (and often also
pharmacokinetic) Information (6 include children)
Variability is Predominantly Lognormal—Expressed as
Log(GSD) —the standard deviation of the Logs of the
primary data points
Within Specific Data Types, Distributions of the Log(GSD)'s
Themselves Are Reasonably Close to Lognormal.
-------
Challenges for Modeling Human Variability
in Susceptibility
Diverse Data Types-each provides information about
interindividual variability for a portion of the pathway from
exposure to effect
-Characterize each data type with "dummy" (0,1)
variables to represent the presence or absence of
variability due to each step in the causal pathway
Form of the Distribution(s) of Human Interindividual
Variability for Different Parameters
-Assume lognormality
-Combine variability from multiple causal steps by
adding together lognormal variances
Differences Among Chemicals in Amounts of Variability-
Distinguishing the Spread of Variability Estimates Due to
Measurement Errors from the Real Spread of Variability
Among Chemicals
—Assess the spread of model predictions from observed
variability for statistically stronger vs weaker
observations
-------
Components That Can Contribute to the
Intel-individual Variability in Different
Measured Parameters
• Contact Rate (Breathing rates/body weight; fish
consumption/body weight)
• Uptake or Absorption (mg/kg)/Intake or Contact Rate
• General Systemic Availability Net of First Pass
Elimination
• Dilution via Distribution Volume
• Systemic Elimination/Clearance or Half Life
• Active Site Availability/General Systemic Availability
• Physiological Parameter Change/Active Site Availability
• Functional Reserve Capacity—Change in Baseline
Physiological Parameter Needed to Pass a Criterion of
Abnormal Function
-------
Basic Methodology for Assessing Expected
Values for the Incidence of Individual
Threshold Responses as a Function of Dose
1. Use the human database to make central estimates of
overall lognormal variability [as a log(GSD)] from the
observed variances associated with various causal
steps—depending on the route of exposure, the type of
effect, and the severity of the response to be modeled.
2. Determine the lognormal uncertainty in log(GSD)'s
estimated from the model for the largest data
sets—reducing the inflating influence of statistical
sampling errors on the observed spread of log(GSD)
estimates for individual cases.
3. Sample repeatedly from the assessed lognormal
distribution of log(GSD) values, and calculate arithmetic
average of risks for people exposed at various fractions of
the dose causing a 5% incidence of effect in humans (for
model calculations this is done in a simple Excel
spreadsheet, without the need for a formal Monte Carlo
simulation model).
4. Summarize the results as simple power-law functions.
-------
Additional Challenges-Not Addressed in the
Current Analysis
Unrepresentativeness of the Populations Studied
-Children, Elderly, and Sick Likely to be Underrepresented
(although some appreciable children's data is included in the
current data base) (leads to some understatement of likely
variability)
Inclusion of Measurement Errors in the Basic Observations
as Part of Apparent Variability (leads to some overstatement
of likely variability)
Possible Unrepresentativeness of the Chemicals Studied
-too many "problem chemicals" and "problem responses"
with more variability than might be seen by agencies dealing
with usual drugs, food additives?
-------
A Scale For Understanding Lognormal Variability-Fold
Differences Between Particular Percentiles of Lognormal
Distributions
LoglO
(GSD)
0.1
0.2
0.3
0.4
0.5
0.6
0.7
0.8
0.9
1
1.1
1.2
Probit slope
[l/Log10
(GSD)]
10
5
3.33
2.5
2
1.67
1.43
1.25
1.11
1.0
0.91
0.83
Geometric
standard
deviation
1.26
1.58
2.0
2.5
3.2
4.0
5.0
6.3
7.9
10.0
12.6
15.8
5%-95% Range
(3.3 standard
deviations)
2.1 fold
4.5 fold
10 fold
21 fold
44 fold
94 fold
200 fold
430 fold
910 fold
1,900 fold
4,200 fold
8,900 fold
l%-99% Range
(4.6 standard
deviations)
2.9 fold
8.5 fold
25 fold
73 fold
2 10 fold
620 fold
1800 fold
5,300 fold
15,000 fold
45,000 fold
130,000 fold
380,000 fold
-------
Summary of Unweighted Log(GSD) Variability Observations for Different
Types of Uptake and Pharmacokinetic Parameters
Parameter Type
Blood concentration for
toxicant
Body weight (adults only)
Contact rate/body weight
Volume of Distribution/body
weight
Volume of Distribution with
no control for body weight
Cmax/(dose/body weight)
Cniax/dose with no control
for body weight
Hi mi nation Half -Life or
Clearance/Body Weight
Clearance with no control for
body weight
AUC/(dose/body weight)
AUC/dose with no control for
body weight
Total uptake and
phamiacokinetic observations
Oral
.3221
(3)
.295-.351
.299
(2)
.227-393
.156
(28)
.067-362
.160
(12)
.074-374
.169
(35)
.084-341
.200
(24)
.102-391
(106)
IV
.121
(3)
.062-.237
.150
(2)
.110-.204
.125
(14)
.075-. 209
.140
(5)
.OSO-.246
(24)
Inhaled
.090
(3)
..059-.137
.071
(1)
.252
(1)
.149
(1)
.354
(2)
.169-.742
(6)
Other
Routes
.168
(1)
.176
(2)
.113-.273
.227
(4)
.167-307
.139
(5)
.061-317
.257
(4)
.202-327
(16)
All Routes +
Route-
Nonspecific
.322
(3)
.295-351
.086
(2)
.065-.113
.149
(6)
..066-336
.124
(49)
.058-.284
.109
(5)
.070.170
.150
(34)
.067-337
.175
(19)
.090-339
.129
(136)
.068-.248
.137
(5)
.076-.248
.154
(55)
.078-301
.202
(35)
.104-391
(354)
Ranges are approximate 10th and 90th percentiles of the individual data sets in each category.
-------
Summary of Unweighted Log(GSD) Variability Observations for Different
Types of Pharmacodynamic Parameters
Local (Contact Site)
Parameter
Change/ Ex tenial
Exposure or Dose
Local (Contact Site)
Response/External
Exposure or Dose
Physiological Parameter
Change/Internal
Concentration After
Systemic Delivery
Physiological Parameter
Change/ External Systemic
Dose
Response/Blood Level or
Internal Concentration
After Systemic Delivery
Response/ External Dose
(IV or Oral Admin)
Without Large Dosimetric
Uncertainty
Response/External Dose
With Large Dosimetric
Uncertainty (e.g.
workplace epidemiology)
Total Observations
Including
Pharmacodynamic
Variability
GI Tract
.325
(1-
stomach
pH)
(1)
Nervous
System
.259
(6)
.200-337
.235
(1)
.247
(ID
.109-.561
Oral .527 (2)
IV .359 (3)
Inhl .051 (2)
(26)
Resp.
System
Acute .655
(17)
369-1.16
Chronic
.279
(1)
.475
(7)
.208-1.087
1.33
(1-talc
ing disease)
(25)
Cardiovascular
Renal System +
Receptor-Based
Effects
.175
(13)
.072-.425
.276
(1)
.297
(5)
.108-.815
.266
(1)
.684
(3)
.430-1.09
(23)
Other (e.g.,
eye. skin
irritation)
.433
(8)
..227-. 825
.536
(4)
.330-.869
(Immune)
.060
(Immune)
.502
(cataracts)
(14)
All Effects
Acute .655
(17)
.369-1.16
Chronic
.279
(1)
.443
(16)
.221-.887
.235
(23)
.098-. 566
.232
(2)
.170-317
.250
(18)
.097-.644
.233
(8)
.065-.836
.807
(4)
.456-1.43
(89)
-------
STATISTICAL UNCERTAINTY IN THE
ESTIMATES OF INTERINDIVIDUAL
VARIABILITY
Weight of Each Variability Observation = I/Variance of
Log[log(GSD)]
Continuous Parameters-Empirical Formula Derived from
Standard Statistical/Sampling Error Variance of Normally-
Distributed Data
Weight = 10.6 N -10.33,
where N = the number of people studied
Quantal Response Parameters—Variance Derived from 10
Points of Likelihood Distribution (5th - 95th % confidences
levels) Fit Using Haas Spreadsheet System
-------
Comparison of 2700 Pharmacokinetic Data Points with
Expectations Under Lornormal and Normal Distributions
A. Lognormal Comparison
o
HH
O
N
I
o
2 -
0 -
-2 -
-4
= 6.22e-5 + 1.016x RA2 = 0.941
-3 -2 -1
Ordinal Z-Score
-------
B. Normal Comparison
0}
t-(
C
o
in
N
f-H
o
53
4-
2-
0 -
-2-
-4
y = .00024 + 0.982x RA2 = 0.882
-3
i
-2
i
2
Ordinal Z-Score
-------
•=1
3
w
I"
o •*•
n
0 -
-1 -
-2 -
-3
-2
Log Probit Plot of the Percentage of 102 Tested
People Who Gave Positive Skin Patch Tests for
Chromium (VI)--Data of Nethercott et al. (1994)
y = - 0.528 + 1.03x RA2 = 0.996
(54)
(32)
(10)
^ ^ Error bars are approximte ± 1 SD from a
binomial distribution based only on
counting error
Number of observed positive responses in paren
tieses
-1 0
Log(Cr Cone. ^g/cm2)
-------
o
s
I
I
•^_»
be
o
Lognormal Plot of the Distribution of PC20
Methacholine Response thresholds in 5623
Smokers with Mild to Moderate Airflow
Obstruction-Data of Tashkin et al., 1996
y = 0.980 + 0.624x RA2 = 0.990
Z-Score
-------
Lognormal Plot of the Distribution of PC10 Histamine
Response Thresholds in 1892 Randomly Selected Adults from
Two Dutch Communitites-Data of Rijcken et al. (1987)
d
o
K
1 -
y= 1.6224+0.60022X RA2 = 0.991
-3
-------
o
*—I
"EC
O
Lognormal Plots of Log(GSD) Variability
Observations for Elimination Half Lives
for Groups Inducing Children (<12 years)
vs Groups Including Only Adults
0.0
-0.2 -
-0.4-
-0.6 -
-0.8 -
-1.0 -
-1.2 -
-1.4-
-1.6
y= -0.773+ 0.198x RA2 = 0.953 D ChildTl/2
y = - 0.931 + 0.214x RA2 = 0.984 A AduItTl/2
n
= 99
-3
Z-%
core
-------
Lognormal Plot of Log(GSD) Variability
Observations for Oral Cmax Values
Q
OT
GO
o
o
_J
M
re
y= - 0.808 + 0.293x RA2 = 0.991
N = 28
-2.5 -2.0 -1.5 -1.0 -0.5 0.0 0.5 1.0 1.5 2.0 2.5
-1.0 -
-1.2 -
-1.4
-------
Lognormal Plot of Log(GSD) Variability
Observations for Oral AUC Values
-0.2
-0.4 -
o
u
f
•a
»H
o
u
-0.8 -
-1.0 -
-1.2 -
-1.4
y= -0.772 + 0.242x RA2= 0.981
N = 35
0
Z-Score
-------
Lognormal Plot of Log(GSD) Variability
Observations for All Systemic Parameter
Changes In Relation to Internal Doses
0.0
o>
Vt
O
a
• ^i
"bo
•£ 00
B.,3
-0.5 -
y = - 0.628 + 0.300x RA2 = 0.952
-i.o -
-2
-------
Lognormal Plog of Log(GSD) Variability
Observations for Acute Changes in Lung
Function in Response to External Exposure
0.2
03
w
O
Q
w
o.o -
-0.2 -
C5
o o
•J J
-0.4 -
-0.6
y= -0.184 + 0.201X
N=17
-2
-1
Z-Score
-------
o
o
o
-J
0)
en
O
Q
p-
M
S3
Lognormal Plot of Log(GSD) Variability
for All Systemic Responses in Relation
to Internal Doses or Blood Levels
0.0
-0.2 -
-0.4 -
-o.e -
« -0.8 -
-1.0 -
3 -1.2 -
<
-1.4
y = - 0.583 + 0.324x RA2 = 0.946
N = 17
-2
-1
o
Z-Score
-------
Lognormal Plot of Log(GSD) Variability
Observations for All Systemic Parameter
Changes In Relation to Internal Doses
0.0
03
W
o
"CJ
-0.5 -
a
-1.0 -
-1.5
y =- 0.628 + O.SOOx RA2 = 0.952 /a
N = 23
-1
0 1
Z-Score
-------
Examples of Tentative "Severity" Categorizations by Type
of Effect
Responses Rated as "Mild Reversible"
Olfactory cognition—air concentrations needed to produce 3 levels of smell perception
Nasal Dryness
Throat Irritation
Nose irritation—slight or moderate
Pulmonary discomfort—"slight" and "moderate" or more
Eye irritation—External air concentration causing 4 levels
Skin hypersensitivity to chromium (VI)
Skin hypersensitivity—lowest dilution of allergen needed to cause a 2mm diameter wheel
Skin irritation response to sodium laurel sulfate applied via skin patch
Eye irritation—slight or moderate and above
Paresthesia/blood level
Achievement of a specific degree of cardiac blood flow (unblocking of a clot) following
an infarction in relation to the 2-90 minute AUC of a tissue plasminogen activator
Skin Rash in relation to plasma concentration
"Adequate" sedation/drowsiness
Analgesia from dental pain (not taking medication at 3 and 6 hours after procedure)
Suppression of coughing (2 levels) on intubation
Creation of conditions for intubation (2 levels~"excellent" and "good")
-------
Responses Rated as "Moderate-Severe Reversible" or Irreversible
"Significant" hearing loss/one dose of cisplatin
Haloperidol toxicity (minimum of 4 other signs plus, in some cases seizures, catatonia,
mental confusion) in relation to maximum blood level
Disarthria/blood level
Hearing defects/blood level
Visual effects/blood level
Anxiety/blood cholinesterase
Psychomotor depression/blood cholinesterase
Unusual dreams/blood cholinesterase
High 62M urinary excretion vs occupational blood cone X time
Digoxin toxicity in relation to serum digoxin concentration
Cataracts in relation to TNT hemoglobin adducts
Dose-limiting toxicity including malaise, neurotoxicity, pericardial effusion and
coagulopathy
End tidal concentration for anesthesia (not moving in response to stimulus)
Neutropenia (2 levels)
Pneumoconiosis (2 levels) in relation to cumulative talc air exposure
Responses Rated as "Severe" and/or Irreversible
Ataxia/blood level
Deaths/blood level
Deaths/red blood cell cholinesterase inhibition "hits"
-------
Model Estimates of Human Intel-individual Variability
Partitioned Among Various Causal Steps
A. Pharmacokinetic Steps
Central Estimate
Log(GSD)
Total Number of Variability Data Sets Included 443
Oral Contact Rate (e.g. tap water/kg BW) 0.262
Inhalation Contact Rate (breathing rate/kg BW) 0.091
Other Contact Rate 0.168
Oral Uptake or Absorption(mg/kg)/Intake or 0.000
Contact Rate
Inhalation Fraction Absorbed 0.000
Other Route Fraction Absorbed 0.000
Oral Systemic Availability Net of Local Metabolism 0.124
or First Pass Liver Elimination
Systemic Availability After Absorption by Inhalation 0.147
or Other Route
Body weight correction 0.086
Dilution via Distribution Volume/BW 0.088
(Adults only) Systemic Elimination Half Life or 0.136
Clearance/BW
(Children included) Systemic Elimination Half Life 0.171
or Clearance/BW
-------
B. Pharmacodynamic Steps
Central Estimate
Log(GSD)
Active Site Availability/General Systemic 0.084
Availability
Non-Immune Physiological Parameter 0.230
Change/Active Site Availability
Immune Physiological Parameter Change/Active Site 0.568
Availability
Reversible Non-Immune Mild Functional Reserve 0.452
Capacity-Change in Baseline Physiological
Parameter Needed to Pass a Criterion of Abnormal
Function
Non-Immune Moderate Reversible or Irreversible 0.202
Functional Reserve Capacity
Non-Immune Severe and Irreversible Functional 0.000
Reserve Capacity
Reversible Immune Functional Reserve Capacity 0.510
-------
Example—Central Estimates of Summary Overall Log(GSD's) for Various
Ingested Systemic Toxicants
Route and Type of Response Log(GSD)
Ingested Systemic Chronic Toxicant-mild reversible 0.621
nonimmune effects
Systemic Chronic Toxicant—moderate reversible or 0.471
irreversible nonimmune effects
Systemic Chronic Toxicant—severe irreversible effects 0.426
Chronic toxicity from an orally administered drug 0.563
with perfect compliance (no contact rate variabity)—
mild reversible non-immune effects
Chronic toxicity from an orally administered drug 0.392
with perfect compliance—moderate reversible or
irreversible non-immune effects
Chronic toxicity from an orally administered drug 0.336
with perfect compliance—severe and irreversible non-
immune effects
Acute toxicity from an orally administered drug with 0.536
perfect compliance (no contact rate or elimination
rate variability)—Mild reversible non-immune effects
Acute toxicity from an orally administered drug with 0.352
perfect compliance -Moderate reversible or
irreversible non-immune effects
Acute toxicity from an orally administered drug with 0.289
perfect compliance—Severe irreversible non-immune
effects
-------
ASSESSING THE SPREAD OF VARIABILITY VALUES
AMONG CHEMICALS AFTER CONTROL FOR TYPE
OF TOXICITY AND ROUTE OF EXPOSURE
Funnel Plots-Show Tendency for Reduced Model Prediction
Error for Stronger Data Points
Ideally, With Increasing Statistical Power, Measurement
Error Becomes Small Relative to Real Variation Among
Chemicals in Interindividual Variability in Susceptibility
-------
bo
o
•d
S
•n
CD
OJ
•d
o
3
i
•d
o
to
O
"Funnel Plot" for Pharmacokinetic
Interindividual Variability Observations
?5 i
Q
-1
1.5 2.0 2.5 3.0 3.5 4.0 4.5
Log (Statistical Weight = 1/variancc)
-------
13
3
u
0)
0)
13
O
I
tJ
01
in
"Funnel Plot" for Pharmacodynamic
Interindividual Variability Observations
i—i 1
*~x
Q
in
bo
o
o -
-1 -
a
a
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Log(Statistical Weight = I/variance)
-------
Prediction Error and Log(Statistical Weight) for
Pharmacokinetic Interindividual Variability Observations
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-------
Prediction Error and Log(Statistical Weight) for
Pharmacodynamic Intel-individual Variability Observations
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-------
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0.26
0.24
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0.18
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Strongest 20% of datasets
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for Inclusion in the Average
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N
CU
Ui
cu
-------
Incidence of Effects Projected At Various Fractions of a Human ED05 Dose
Fraction of EDO5
Type of Agent Central StdDevof 1 .31 0.1 .032 .01
Estimate Log[log(GSD)]
of
Log(GSD)
Orally administred 0.563 0.12 5.0E-02 6.3E-03 7.0E-04 8.0E-05 9.4E-06
drug, mild chronic
effect
0.14 5.0E-02 6.5E-03 8.4E-04 1.2E-04 1.8E-05
0.18 5.0E-02 7.0E-03 1.2E-03 2.4E-04 5.5E-05
Orally
administered drug
with severe
chronic effect
0.336
0.12
0.14
0.18
5.0E-02 1.4E-03 3.8E-05 l.OE-06 2.1E-08
5.0E-02 1.6E-03 6.2E-05 2.6E-06 9.8E-08
5.0E-02 2.1E-03 1.4E-04 1.2E-05 1.1E-06
Orally administred
drug, mild acute
effect
0.536
Orally
administered drug
with severe acute
effect
0.289
0.12
0.14
0.18
0.12
0.14
0.18
5.0E-02 5.6E-03 5.6E-04 5.8E-05 6.1E-06
5.0E-02 5.8E-03 6.9E-04 9.0E-05 1.2E-05
5.0E-02 6.3E-03 l.OE-03 1.9E-04 4.1E-05
5.0E-02 7.9E-04 1.2E-05 1.6E-07 1.4E-09
5.0E-02 9.3E-04 2.2E-05 5.4E-07 9.7E-09
5.0E-02 1.3E-03 6.4E-05 3.8E-06 2.0E-07
-------
o
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Log Log Plots of Model Projections of the
Mean Risk of Toxicant Exposures at Various
Fractions of an ED05 Dose or Exposure Level
y= - 1.34-1- 1.66x RA2 = 1.00
y = -l.33-i-2.15x RA2 = 1.00
y = -l.20-i-3.18x
Direct (xintaa-Site Responses
* Acute C ral Toxicant. (Drug)
B Inhaled Syst Acute Neurotox
-2. -1
Log(Fract of ED05 dose)
-------
Power Law Plots Illustrating the Effect of Different Assumptions
About the True Cheinical-to-Chemical Variability of Log(GSD)'s
--Chronic Toxicity for an Orally Administered Drug with IVIild Effects
J3 3
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-2 -
-3 -
-5 -
-6 -
D Oral Dtug Mild Effect Var .18
* Oral Dmg Mild Effect Var .14
" Oral Drug Mild Effect Var .12
log(Fraction of ED05 Dose)
-------
Power Law Plots Illustrating the Model-Estimated Implications
of Different Degrees of Severity for Risk As a Function
of Dose—Acute Toxicity for an Orally Administered Drug
Oral Drug Mild Ac Eft Var .14
Oral Drug Mod Ac Eff Var .14
Oral Drug Scv Ac Eff Var . 14
y= - 1.34+ 1.78x RA2 = 1.000
y= -1.27+2.80X RA2 = 0.999
y = - 1.13 + 3.58x RA2 = 0.996
-2 -1
logCFraetioii of ED05 Dose)
-------
Appendix I
Presentation Overheads
David W. Gaylor
Sciences International, Inc.
and
Ralph L. Kodell
National Center for Toxicological Research
U.S. Food and Drug Administration
-------
Sep 18 CO C'.:5Sp David tl. Gaoler, PhD. 501-223-7010 p.2
Risk-based Reference Doses
David W.Gaylor*
Sciences teafe/, Inc.
Jtadria, VA 22314
and
Ralph LKodell
Nafaal Center lor Toro/og/cal Research
U.S. Food and Drug Admm/sfrafibn
Jefferaon,AR 72073
David W.Gayior,Ph.D.
Sciences International, Inc.
13315Ab!nger Court
Little Reck, AR 72212
Ph: 501-223-9773
dga7lor@sciences.com
* This work was done when at the National Center fcr lexicological Research.
-------
Sep 18 00 01:59p David U. Gaylor, PhD. 501-233-7010 p.3
Reference doses (RfDs) for toxic substances are established to confine human
exposures to only nontoxic or minimally toxic levels. Typically RfDs are calculated by
dividing no observed adverse effect levels (NOAELs) or low observed adverse effect
levels (LOAELs) by a series of uncertainty factors. Among these uncertainty factors is
one for interindh/idual sensitivity, typically assigned a value of 10. If information is
available on interindividual sensitivity, this default factor can be replaced with a factor
expected to provide protection for a specified proportion of a population. To illustrate
the procedure, examination of published databases suggests a standard deviation of
the logarithm (base e) of individual sensitivity to be on the order of 1.7, i.e., a factor of
5.5. Using this information in combination with an RfD based on a benchmark dose
associated with a specified level of risk, the risk at the RfD can be estimated. For
example, a benchmark dose associated with a risk of 10% divided by 60 is expected to
limit risk at the RfD to about 1 in 10,000. This would replace an RfD having an unknown
risk based on the LOAEL divided by 100.
-------
Sep 18 00 02:00P David U. Gaylor, PhD. 501-228-7010 p.4
Current Noncancer
Safety Assessment
Based on NOAEL:
ADI (RfD) = NOAEL/UA«UH«US «UC «M
UA = uncertainty of animal to human extrapolation
UH = sensitive individuals
Us = extrapolation from subchronic data to chronic effects
Uc = additional sensitivity of children
M = modifying factor
Based on LOAEL:
UL = ratio of LOAEL to NOAEL
Default Values: UA = UH = Us = Uc = UL = 10
Worst Case: ADI (RfD) = LOAEL/ 10,000 (EPA, 1991)
-------
Benchmark Dose
Propose a benchmark dose estimated to
produce an excess disease incidence of
10%(ED10).
With typical bioassays, this is near the
lowest incidence that can be estimated
with adequate precision and tends to be
near the lowest observed adverse effect
level (LOAEL).
Use a lower confidence limit on the
benchmark dose (LED10) to account for
experimental variation (EPA, 1996).
-------
Confidence Associated
with the Product of
Uncertainty Factors
Consider the U's as independent
random variables.
U = UAxUnxULxUs
= I nUA + AiUH + AiUL + drills
Approximately normally distributed.
Products of default values of 10 provide
approximately 99% coverage.
5
-------
Statistical Distribution of U
- AiUA + AiUH + MJS +
Mean (median) of
L
= f nUA
Standard deviation of
4-
-------
Estimated Percentiles
UA=1
UH=1
Percentile for the normally distributed AiU is
estimated by
U - exp
= Us x UL x exp (ZS^nU)
Z = 1 .645 for 95th percentile
Z = 2.327 for 99th percentile
i
-------
Nonrandom Variables
If the RfD is based on human
data:
UA=landStoUA=0
If the RfD is based on the
NOAEL:
vJ T — JL dllvJ. i3/;._TT \J
If the RfD is based on chronic
exposure data:
Us = 1 and S,nUs = 0
-------
Intraspecies Variation (UH)
UH = Individual/median
UH = 10 at 92nd percentile
UH = 15 at 95th percentile
Dourson and Stara (1983)
-------
ep 18 DO 02:Olp David U. Gaylor, PhD. 501-228-701O
Table I. Estimated doses for specified levels of risk assuming a lognormal distribution
with a standard deviation of 1.7 (log base e) for intraspecies variation.
Risk
1 in 10
1 in 100
1 in 1000
1 in 10,000
1 in 100,000
1 in 1,000,000
Standard deviations
below the median
1.28
2.33
3.09
3.72
4.26
4.75
Factor below
the median
8.8
52.5
191
558
1400
3210
Factor below
the BMDa
1.0
6
22
63
159
365
10
-------
Risk at the RfD(RfC) Based on the BMDio
BMD / ( UL = 10 ) x ( UH= 10 ) = BMD / 100
Estimated risk is 3 in 100,000
Assuming intraspecies variability standard deviation is 1.7
( log base e ) ,i.e., a factor of 5.5
-------
SUMMARY
The proposed method does not require extrapolating a dose
response curve below an estimable benchmark dose.
Replaces uncertainty factors for LOAEL to NOAEL and
intraspecies variation with a probabilistic approach.
Assumes a log normal distribution of intraspecies variability and
requires an estimate of the standard deviation.
Provides estimates of the risk at the RfD(RfC) or at any specific
exposure level.
Can calculate an RfD(RfC) with a specified level of risk.
Can obtain confidence limit by using the lower confidence limit
on the BMD.
Can use in conjunction with uncertainty factors for extrapolation
from animals to humans and from subchronic to chronic exposures.
Risk estimates can be improved with estimates of intraspecies
variability for a specific chemical, class of chemicals, and/or
specific biological effects.
\ 2-
-------
References
1. Barnes, D.G. and M.L: Dourson. Reference dose (RfD): Description and use in
health risk assessments. Regulatory Toxicol. Pharmacol. 8: 471-486 (1988).
2. Gaylor, D.W. and Slikker, W., Jr. Risk assessment for neurotoxic effects.
NeuroToxicotogy 11: 211-218 (1990).
3. Dourson, M.L., S.P. Felter, and D. Robinson. Evolution of science-based
uncertainty factors in noncancer risk assessment. Regulatory Toxicol. Pharmacol.
24: 108-120(1996).
4. Dourson, M.L. and J.F. Stara. Regulatory history and experimental support of
uncertainty (safety) factors. Regulatory Toxicol. Pharmacol. 3: 224-238 (1983).
5. Hattis, D. Strategies for assessing human variability in susceptibility and using
variability to infer human risks. In: Characterizing Human Variability in the Risk
Assessment Process, D. Neumann (ed.). ILSI Press, Washington, D.C. (1988).
6. International Programme on Chemical Safety. Environmental Health Criteria No.
170: Assessing Human Health Risks of chemicals: Derivation of Guidance Values
for Health-Based Exposure Limits. World Health Organization, Geneva (1994).
7. Allen, B.C., Kavlock, R.J., Kimmel, C.A., and Faustman, E.M. Dose-response
assessment for developmental toxicity. Fundamental Appl. Toxicol. 23: 487-495
(1994).
8. Gayior, D.W. and Kodell, R. L. Percentiles of the product of uncertainty factors for
establishing probabilistic reference doses. Risk Analysis 20: 245-250 (2000).
-------
Appendix J
Presentation Overheads
Lynne Haber
Toxicology Excellence for Risk Assessment (TERA)
and
Michael Dourson
TERA
-------
Use of Categorical Regression to
Clicartctetize Ri^cAbove the
RJD
Lynne Haber and
Michael Dourson
Toxicology Excellence for Risk
Assessment (TERA)
Toxicology Excellence for Risk Assessment
-------
Definitions of R/D
An RfD is an estimate (with uncertainty spanning perhaps an order of
magnitude) of a daily exposure to the human population (including
sensitive subgroups) that is likely to be without an appreciable risk of
deleterious effects during a lifetime.
RfD Definition Regression model
"is likely to be" P(*) > 0.95
"without appreciable risk" r < 10~:
"deleterious effect" severity =
AEL
New RfD Definition
P ( r < 10-2 at dose 0.95
where r = P (severity >AEL)
Hence for Risk Assessment
-------
Categorical Regression
Toxicologist judgment
» Each dose assigned severity level
0 = no effects observed
1 = minimal effects
2 = moderate - severe adverse effects
3 = extreme or lethal effects
» Can include multiple studies, incidence, mean, or qualitative
data
» Can evaluate separately (stratify) by endpoint, by species,
etc.
Mathematical analysis
Results judged by data quality, statistics, graphics
Toxicology Excellence for Risk Assessment
-------
Adtonteges andLinttifaons of
Categorical Regression
Advantages
» All useful data can be categorized and included in
quantitative analysis
» Can apply when data inadequate for calculating ED 10
» Accounts for severity of effect
» Meta-analysis possible
» Can take duration into account
Limitations
» Animal to human extrapolation
» Loss of information
Toxicology Excellence for Ride Assessment
-------
Frequency of Categories of Effects Associated
with Aldicarb Exposure in Humans
Dose
0
0.01
0.025
0.025
0.050
0.050
0.075
0.10
Group
Size
22
8
12
4
12
4
4
4
Frequency of Responders
within Category
NOAEL
22
8
8
0
1
0
0
0
AEL
0
0
4
4
11
4
4
2
PEL
0
0
0
0
0
0
0
2
Adapted from Dourson et al. (1997)
Toxicology Excellence for Risk Assessment
-------
DOUBSON ET AL.
0.001 0.01
Exposure (mg/kg-d)
0.1
-------
Probability of an Adverse or
Frank Effect - Aldicarb
Dose
0.001
(RfD)
0.003
0.01
0.015
0.02
0.025
0.03
P(AE or
FE)
--
—
0.0014
0.03
0.14
0.44
0.79
Upper
95% CL
0.00001
0.0007
0.04
0.17
0.36
0.67
0.93
Adapted from Dourson et al. (1997)
Toxicology Excellence for Risk Assessment
-------
0.8
0.6
Probability
0.4 -
0.2 -
TEtJSCHLBR ET AL.
Legend
• Fanainipbni
~~ Diuzirjoa
"" Disulfolnn
I)o«-Rn)
1 2
Lon (naso/RfD)
FIO. I. PrcJicfed probabilities of adveruo or fnmk effects in humana afl«r oral exposure to three pesticides. Threu-cntegory regression
unclcl. Doses scaled U) Kunuiu dosea ba.<>ed on equivalence oftltody weight)1".
-------
Probability
-1
0
1 2 3
Log (Dose/RfD)
i
6
-------
TEUKCH1.EII ET AL.
0.8 -
Probability
Legend
Limhne
F.PTC
0.6 -
0.4
0.2 -
-4
-2
—1—=—i r
1 2 3
Log (Dose/RfD)
Toxicology Expedience for Risk Assessment
-------
Sensitive populations
How account for use of UFs
Model dependence
Force model to go through 0 at RfD?
Choice of data to model
Rules for assigning severity categories
Rules for combining studies
Rules for model acceptance
Policy regarding interpretation
Hence for Risk Assessment
-------
Advantages of Categorical Regression
All useful data can be categorized and
included in quantitative analysis
Can apply when data inadequate for
calculating ED 10
Accounts for severity of effect
Meta-analysis possible
Can take duration into account
Consistent basis for calculating risk above
RfD
Toxicology Excellence for Risk Assessment
-------
Urcertcdnty Factors
RfD - NOAEL/UF
Variability/Variation (CSAF)
Interspecies -tk and td
Intraspecies - tk and td
Uncertainty
LOAEL/NOAEL
Subchronic to chronic
Database
-------
Issues for Use of Other
Approaches
Not all UFs are equal
Year of assessment affects what a UF of 10 means
» Initially - default
» Now - judgement that data are insufficient to
reduce
» Future - may be CSAF
Distributions for UFs need to reflect data supporting
UF
What use for distributions when use CSAFs?
Toxicology Excellence for Risk Assessment
-------
Appendix K
Presentation Overheads
Reisha Putzrath
Georgetown Risk Group
-------
We should make our models
as simple as possible, but no
simpler.
You can't get out of a problem
by using the same thinking it
took to create the problem.
- Albert Einstein
-------
What Is the Issue to Be Resolved?
• Replace the RfD/RfC method
and assumptions, e.g., with
distributional analyses
• Use RfD/RfC, but expand
method to evaluate exposures
above these levels
• Make the current (or alternative)
method more amenable to
combine with other information
-------
Assume Current Methods, but
Estimate Risks:
• Above the RfD and
between RfD/RfC and
NOAEL or LEDX
between NOAEL or LEDX
and LOAEL or EDX
A.
• Carcinogens with curvilinear
dose-response curves
• Margins of exposure?
-------
Curvilinear Dose-response
Curves for Carcinogens
• Must have data to reject the default.
Use these data to estimate dose-
response curve and its upper bound.
• If have reason to believe dose-
response curve is not the same at
exposure of interest, but no data, use
policy decisions rather than
mathematics.
• The true dose-response curve and its
upper bound can not both be
continuous functions through the
origin (Putzrath 2000).
-------
Use a Tiered Approach, as
Recommended by NAS
• Tier between bright line and
distributional analyses: look at
RfD/RfC, NOAEL, LOAEL, and
exposure
• Generic distributions
• Distributions for similar
chemicals
-------
Characterize the Low-dose, Dose-
response Curve
• Is the dose-response curve
shallow or steep?
• How close to the NOAEL is the
RfD?
• Is the exposure closer to the RfD,
the NOAEL, or the LOAEL, i.e.,
how accurately must the curve
and risk be estimated?
-------
LOAEEX x
x
RfDx
x x/
-------
LOAELX / x
TV *
RfDx
X ./ X/
'
E? E1
-------
RfD
O
NOAEL
O
LOAEL
O
O
O
O
-------
Uncertainty Factors Differ:
• Human variability: Distributions can
characterize variability better than
point estimates.
• Interspecies: Distribution or
biologically based model?
• LOAEL to NOAEL: Distribution or
chemical-specific estimate of dose-
response curve?
• Missing or poor quality data: use
surrogates or ?????
-------
Thresholds
• Policy for non-cancer endpoints
is that one exists.
- Different distributions for
cancer and non-cancer?
- Which distributions have
limits?
• How should the threshold be
estimated?
• Will alternative methods
eliminate thresholds, and what
are the implications?
-------
Appendix L
Presentation Overheads
Kenny Crump
ICF Consulting
-------
Equations produced by Kenny Crump:
Use of lognormal distribution for extrapolating from higher to lower dose
P(e) = N((Lne-aV.)
e = exposure
• = Ln (standard deviation)
a = set so that risk at BMD = 5 percent
N = normal distribution
Transfer function
eT do
P(e) =Pr(eT>do)
= Pr (e > do/T)
eT = internal dose
T = transfer relating external dose to internal dose (determined
from pharmokinetic data)
Do = threshold dose (internal exposure) (determined from
pharmacodynamic data)
------- |