x>EPA
United States
Environmental Protection
Agency
Office of Research and
Development
Washington, DC 20460
EPA/600/8-90/064
November 1988
Technical Support
Document on Risk
Assessment of Chemical
Mixtures
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EPA/600/8-90/064
November 1988
Technical Support Document on Risk
Assessment of Chemical Mixtures
Environmental Criteria and Assessment Office
Office of Health and Environmental Assessment
Office of Research and Development
U.S. Environmental Protection Agency
Cincinnati, Ohio 45268
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DISCLAIMER
This document has been reviewed In accordance with U.S. Environmental
Protection Agency Policy and approved for publication. Mention of trade
names or commercial products does not constitute endorsement or recommenda-
tion for use.
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PREFACE
The preparation of the mixtures Technical Support Document (TSD) was
recommended 1n 1985 by the U,S, EPA Science Advisory Board (SAB) panel that
reviewed the Agency's mixtures guidelines. Following completion of the
external review draft 1n December, 1987, the TSD was reviewed by both Agency
and external experts In the field of chemical mixtures risk assessment.
Among the external reviewers were Ron Wyzga (EPRI), who was a member of the
original SAB review panel for the mixtures guidelines, and Richard Cothern,
who Is currently a member of the SAB.
Unique sections of the TSD Include: an overview of available toxiclty
data on complex mixtures and binary exposures (ch. 2) and mechanisms of
interaction (ch. 3), an estimate of the maximum synergistic effect observed
for environmental chemicals (ch. 2), an evaluation of quantitative methods
(statistics and models) that have been used In characterizing interactions
(ch. 4), a summary of the U.S. EPA's Interaction data base (appendix A),
recommendations for revisions to the existing mixtures guidelines (ch. 5)
and recommendations for future research relevant to risk assessment (ch.
6). The two most significant conclusions in this
available literature is extremely inadequate for
extent of synergism expected from environmental
document are 1) that the
use in quantifying the
exposures, and 2) that
validation of in vitro and short-term in vivo studies seems to offer the
most promise for Improving risk assessments of complex mixtures.
The first draft of this document was prepared by Syracuse Research
Corporation under contract no. 68-C8-0004 with chapters contributed by the
Department of Environmental Health of the University of Cincinnati under
cooperative agreement no. CR-813569-01-0, and by staff of the Agency's
Environmental Criteria and Assessment Office In Cincinnati. The literature
search perfomed is current as of August, 1988.
ill
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TABLE OF CONTENTS
Page
1. INTRODUCTION AND BACKGROUND 1-1
1.1. THE CHEMICAL MIXTURE GUIDELINES 1-1
1.2. EXAMPLES OF THE U.S. EPA CHEMICAL MIXTURES RISK
ASSESSMENT ACTIVITIES 1-3
1.3. DEFINITIONS USED IN THIS DOCUMENT 1-8
1.4. OVERVIEW OF THIS DOCUMENT 1-10
2. TYPES OF INFORMATION AVAILABLE 2-1
2.1. OVERVIEW 2-1
2.2. COMPLEX MIXTURES 2-4
2.2.1. Overview 2-4
2.2.2. Ep1dem1olog1c Studies 2-4
2.2.3. Whole Animal Bloassays 2-5
2.2.4. In vitro Studies and Other Screening Tests. ... 2-8
2.3. MIXTURES OF CHEMICAL CLASSES 2-13
2.4. SIMPLE MIXTURES, COMPONENTS AND TOXIC INTERACTIONS .... 2-14
2.4.1. Overview 2-14
2.4.2. Measurements of Toxicant Interactions ...... 2-16
2.4.3. The U.S. EPA Data Base on Toxic Interactions. . . 2-18
2.5; INTERACTIONS OF CARCINOGENS WITH OTHER COMPOUNDS . . . . . 2-18
2.5.1. Promoters and Cocarclnogens 2-18
2.5.2. Inhibitors and Masking 2-21
2.6 QUANTIFICATION OF INTERACTIONS 2-23
3. AVAILABLE INFORMATION ON INTERACTION MECHANISMS 3-1
3.1. OVERVIEW 3-1
3.2. CHEMICAL INTERACTIONS 3-4
3.3. PHARMACOKINETIC-BASED INTERACTIONS 3-5
3.3.1. Effects on Absorption 3-5
3.3.2. Effects on Distribution 3-7
3.3.3. Effects on Excretion 3-8
3.3.4. Effects on Metabolism 3-8
3.3.5. Interactions at Receptor Sites or Critical
Cellular Targets 3-9
3.3.6. Promotion and Co-carc1nogen1c1ty 3-11
3.3.7. Interactions and Developmental Toxlclty ..... 3-15
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TABLE OF CONTENTS (cont.)
Page
4. MATHEMATICAL MODELS AND STATISTICAL TECHNIQUES 4-1
4.1. INTRODUCTION 4-1
4.2. DOSE ADDITION 4-2
4.3. RESPONSE ADDITION . 4-5
4.4. GENERALIZED LINEAR MODELS. . 4-9
4.5. RESPONSE SURFACE MODELS 4-13
4.6. SUMMARY OF INTERACTION DATA BASE 4-14
4.6.1. Description of the Mixtures Data Base Sample. . . 4-9
4.7. CRITICAL ASSESSMENT EXAMPLE 4-20
4.7.1. Experimental Conditions 4-21
4.7.2. Discussion of Design 4-22
4.7.3. Discussion of Results 4-23
SUMMARY 4-25
5. DISCUSSION AND REASSESSMENT OF THE GUIDELINES 5-1
5.1. OVERVIEW 5-1
5.2. COMPLEX MIXTURES 5-4
5.3. MIXTURES OF CHEMICAL CLASSES 5-9
5.4. SIMPLE MIXTURES, COMPONENTS AND TOXIC INTERACTIONS .... 5-12
5.5. MIXTURES OF CARCINOGENS WITH OTHER COMPOUNDS ....... 5-15
6. RESEARCH NEEDS . 6-1
7. REFERENCES 7-1
APPENDIX A: Agency Data Base on Mixture Toxlclty A-l
APPENDIX B: Diesel Exhaust Emissions and "Sufficient Similarity" ... B-l
APPENDIX C: Analysis of the Sample Studies from
the Interaction Data Base C-l
APPENDIX D: References . D-l
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No.
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3-1
3-2
4-1
4-2
LIST OF TABLES
Title
Page
Summary of Interaction Data Base 2-19
Chemical and Biological Bases of Toxicant Interactions. . . . 3-2
Mechanisms of Promotion and Co-carcinogenicity . . 3-13
Survey of interaction Studies Methodologies .... 4-15
Combined Results for CaDTPA and DMSA Inhibition of
Cd Toxicity 4-24
VII
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1. INTRODUCTION AND BACKGROUND
This technical support document Is a supplement to the U.S. Environ-
mental Protection Agency's Guidelines for the .Health Risk Assessment of
Chemical Mixtures published on September 24, 1986 (U.S. EPA, 1986a, 1987a).
This document was developed 1n response to a recommendation of the Science
Advisory Board (SAB). It discusses available toxlclty and Interaction
Information useful in assessing human health risks from mixtures. In
addition, applicable mathematical models and statistical techniques are
reviewed and research needs are identified. The results of the above
information are discussed along with implications for the current guidelines.
1.1. THE CHEMICAL MIXTURE GUIDELINES
The mixtures guidelines are Intended to guide Agency analysis of infor-
mation relating to health effects data on chemical mixtures in line with the
policies and procedures established in the statutes administered by the U.S.
EPA. They were developed as part of an interoffice guidelines development
program under the auspices of the Office of Health and Environmental
Assessment (OHEA) in the Agency's Office of Research and Development. They
reflect Agency consideration of public and SAB comments on the Proposed
Guidelines for the Health Risk Assessment of Chemical Mixtures published
January 9, 1985 (50 FR 1170).
These guidelines set forth the principles and procedures to guide U.S.
EPA scientists in the conduct of Agency risk assessments, and to inform
Agency decision makers and the public about these procedures. In
particular, the guidelines emphasize that risk assessments will be conducted
on a case-by-case basis, giving full consideration to all relevant
scientific information. This case-by-case approach means that Agency
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experts review the scientific information on each chemical mixture and use
the most scientifically appropriate interpretation to assess risk. The
guidelines also stress that this information will be fully presented in
Agency risk assessment documents, and that Agency scientists will identify
the strengths and weaknesses of each assessment by describing uncertainties,
assumptions and limitations, as well as the scientific basis and rationale
for each assessment.
Finally, the guidelines are formulated in part to bridge gaps in risk
assessment methodology and data. By identifying these gaps and the Import-
ance of the missing information to the risk assessment process, the U.S. EPA
wishes to encourage research and analysis that will lead to new risk
assessment methods and data.
Work on the guidelines began in January 1984. Draft guidelines were
developed by an Agency working group composed of expert scientists from
throughout the U.S. EPA. The draft was peer-reviewed by expert scientists
in the fields of toxicology, pharmacokinetics, and statistics from
universities, environmental groups, industry, labor, and other governmental
agencies. They were then proposed for public comment. On November 9, 1984,
the Administrator directed U.S. EPA offices to use the proposed guidelines
In performing risk assessments until final guidelines become available.
After the close of the public comment period, Agency staff prepared
summaries of the comments, analyses of the major issues presented by the
commentors, and preliminary Agency responses to those comments. These
analyses were presented to review panels of the SAB. The guidelines were
revised, where appropriate, consistent with the SAB recommendations.
The SAB made several comments and recommendations. Among the recommen-
dations was that the U.S. EPA should develop a separate technical support
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document for the mixtures guidelines. The SAB pointed out that the
scientific and technical background from which these Guidelines must draw
their validity Is so broad and varied that It cannot reasonably be
synthesized within the framework of a brief set of guidelines. The SAB also
Identified the need for a technical support document because of the limited
knowledge on Interactions of chemicals 1n biological systems and commented
that progress In Improving risk assessment will be particularly dependent
upon progress In the science of Interactions. The Identification of
research needs was an additional SAB concern to be addressed in this support
document.
1.2. EXAMPLES OF THE U.S. EPA CHEMICAL MIXTURES RISK ASSESSMENT ACTIVITIES
U.S. EPA personnel were directed by the Administrator to use the
guidelines when assessing the human health risks from mixtures of
chemicals. They are to be used In developing regulations under the various
statutes for pollutants that are mixtures, such as dlesel exhaust, coke oven
emissions, gasoline and gasoline vapors. Another major use Is In assessing
the health risks at hazardous waste sites where large numbers of chemicals
are frequently encountered.
Many of the statutes that govern U.S. EPA activities suggest a single
chemical approach to the regulation of toxic chemicals. For example, the
Clean Air Act, Clean Water Act and Safe Drinking Water Act generally
instruct the U.S. EPA to protect public health and the environment through
regulation of specific sources of pollution or establishment of standards
and allowable levels for specific contaminants. In general, when developing
regulations to implement these Acts, the U.S. EPA considers the human health
hazards of single chemicals.
Some statutes mention chemical mixtures, but generally in combination
with the term chemical substance, as 1n "chemical substance or mixture."
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These statutory discussions do not provide one with a clear definition. For
example, the Toxic Substances Control Act (TSCA) defines the term "mixture"
as follows:
"The term 'mixture1 means any combination of two or more chemical
substances If the combination does not occur 1n nature and 1s not,
In whole or in part, the result of a chemical reaction; except that
such term does Include any combination which occurs, 1n whole or In
part, as a result of a chemical reaction 1f none of the chemical
substances comprising the combination 1s a new chemical substance
and 1f the combination could have been manufactured for commercial
purposes without a chemical reaction at the time the chemical
substances comprising the combination were combined." (TSCA, sec. 3)
Other mixture-related terms are also not clearly defined 1n the
statutes. The term 'hazardous waste1 1s defined under the Resource
Conservation and Recovery Act (RCRA) as a solid waste, or combination of
wastes that pose a substantial hazard to human health or the environment
when Improperly managed. Hazardous wastes encountered at Inactive or
abandoned facilities or from emergency spill situations are covered under
provisions of the Comprehensive Emergency Response, Compensation and
Liability Act (CERCLA) and the Superfund Amendments and Reauthorlzatlon Act
of 1986 (SARA). CERCLA1s definition of hazardous substance Includes
substances and mixtures as defined under a variety of other environmental
Acts. For the purposes of this technical support document, definitions for
different types of mixtures and mixture interactions are presented later in
this chapter.
Perhaps the greatest use of the mixtures guidelines In the U.S. EPA is
in assessing human health risk at Superfund sites. These sites generally
contain dozens of chemicals In varying concentrations. The Office of
Emergency and Remedial Response (OERR) utilizes the risk assessment
guidelines, and particularly the mixtures and exposure guidelines In
analyzing public health impacts of remedial alternatives at Superfund
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hazardous waste sites. OERR's approach is outlined in the Stiperfund Public
Health Evaluation Manual (U.S. EPA, 1986c). The manual covers two
elements: baseline evaluations and analysis of remedial alternatives. OERR
is currently revising this manual to ensure that it is consistent with the
final risk assessment guidelines.
The OERR approach for mixtures is perhaps the most structured of the
Agency mixture approaches, involving five specific steps for determining
human health risk:
1. Selection of Indicator Chemicals
2. Estimation of Exposure Point Concentrations of Individual Chemicals
3. Estimation of Chemical Intakes
4. Toxicity Assessments
5. Risk Characterization for the Site
An assumption in this process is that there are no data on the specific
mixture of concern, or a similar mixture.
The first step is to select a workable number of indicator chemicals.
When the number of chemicals found at a site is determined to be too large
to work with (>10-15), a scoring system is used to develop a list of
indicator chemicals on which to base the assessment. The scoring system
considers toxicity information, site concentration data and environmental
mobility. Use of professional judgment is encouraged to add or delete
chemicals to the list. Indicator scores are used only for relative ranking
among the chemicals present and have no meaning outside of the context of
the individual chemical selection process. From the indicator scores a
smaller, more manageable list of chemicals is selected. .
In the second step of this process, baseline environmental concentra-
tions of individual chemicals are estimated using monitoring data and
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modeling to estimate when and how human exposures will take place. The
Superfund Exposure Assessment Manual describes various chemical fate and
transport models that may be used for this step.
The estimation of the amount of human exposure to the selected contami-
nants Is the next step. Concentrations estimated in step two are used to
calculate separate intakes for each chemical in each environmental medium:
air, groundwater, surface water, fish and soil. These are summed, resulting
in total oral exposure and total inhalation exposure. Subchronlc and
chronic durations are calculated separately. In some cases intake calcula-
tions may be based on personal air monitors and body burden data for exposed
Individuals. Site-specific considerations, such as nonstandard intake
values, are considered as appropriate.
In step four, the toxicity information is identified that will be used
with results of the exposure assessment in the risk characterization.
Toxicity values for chronic and subchronic exposures to noncarcinogens, and
carcinogenic potency factors for potential carcinogens are located in avail-
able Agency sources. Toxicity data may be developed when necessary.
Teratogenic chemicals are listed separately.
The final step involves a comparison between estimated exposures and
toxicity values or potency factors. For the noncarcinogenic chemicals, a
hazard index is calculated (see Section 5.4) for all chemicals for each
medium of exposure. Separate hazard indices, by critical effect, are recom-
mended when the overall hazard index exceeds unity. The mixture guidelines
suggest consideration of all types of effects from a particular chemical,
not just the "critical effect," i.e., the effect seen at the lowest dose.
Critical effect information is readily available in U.S. EPA documentation,
while data on other effects may sometimes be more difficult to obtain. For
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potential carcinogens, response addition for Independently-acting chemicals
at low doses 1s the approach recommended. The manual further assumes that
cancer risks are additive across all exposure routes.
Following these five steps, It Is recommended that the risk assessor
determine the validity of the Initial list of Indicator chemicals. In
addition, a written summary of all the significant uncertainties 1s recom-
mended as part of the risk characterization step. Assumptions were to have
been noted along the way for each step. These public health evaluations are
used to develop performance goals and analyses of risks for remedial action
alternatives.
Two other approaches for chemical mixtures, relative potency and toxic
equivalency factors, have been considered and utilized by U.S. EPA risk
assessors and are discussed In Chapter 5 of this document. Briefly, a
relative potency method for carcinogenic mixtures is based on the assumption
that the ratio of the two potencies Is constant, whether It is based on
comparisons between human studies, jm vivo assays or jm vitro assays. The
results of human studies are correlated with those of in \nyo assays, and
results of j_n vivo bioassays are correlated with the results of jm vitro
bioassays. The human potency of a poorly-studied mixture can then be
estimated from its \n vivo (or in vitro) potency multiplied by the potency
ratios of a well-studied, similar mixture. The toxic equivalency factor
approach has been adopted by U.S. EPA as an interim procedure for estimating
risks associated with exposure to chlorinated dioxins and dibenzofurans
(U.S. EPA, 1987c). This method relies on in vitro and in vivo data to
estimate "toxic equivalency factors" for the various congeners in the
mixture. These factors then express the inferred toxicity or cancer risk of
poorly studied congeners in terms of the toxicity of a well-studied
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congener, and can be used in an additive model to estimate toxlclty of a
mixture of these congeners.
Many of U.S. EPA's regional offices are routinely using the guidelines,
with Superfund activities being the primary application. In addition, at
least one region is applying the guidelines in the NPDES permitting program,
by using additivity when the pollutants have the same mechanism of action.
There are currently programs underway in the U.S. EPA to Implement the risk
assessment guidelines in all appropriate Agency activities. It will take
some time before they are being fully applied in all U.S. EPA operations.
1.3. DEFINITIONS USED IN THIS DOCUMENT
Consistent and clear terminology is critical in the discussion of chem-
ical mixtures risk assessment. Many different definitions have been offered
for the terms used with toxicity of chemical mixtures, and most of these are
discussed in the body of this document. Except for these historical discus-
sions, the definitions below are used in this document. These definitions
are oriented toward their use in risk assessment. For example, the
definition of a mixture actually describes "mixed exposures." From a
toxlcologlc standpoint, however, the joint exposures are similar to the
single exposure (perhaps time-varying) that would result if the chemicals
were physically combined into a true chemical mixture. The following
definitions are generally consistent with those found in the literature:
Mixture: Any set of two or more chemical substances, regardless of
their sources, that may jointly contribute to toxicity in the
target population.
Simple
Mixture:
A mixture containing two or more identifiable components, but
few enough that the mixture toxicity can be adequately
characterized by a combination of the component toxicities.
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Complex
Mixture:
Similar
Mixtures:
Chemical
Classes:
Interaction:
Synergism:
Antagonism:
Potentiation;
Inhibition:
A mixture containing so many components that any estimation of
its toxicity based on its component toxicities contains too
much uncertainty and error to be useful. The chemical compo-
sition may vary unpredictably over time or with different
conditions under which the mixture is produced. Complex
mixture components may be generated simultaneously as
by-products from a single source or process, intentionally
produced as a commercial product, or may co-exist because of
disposal practices. Risk assessments of complex mixtures are
preferably based on toxicity and exposure data on the
complete mixture. Gasoline is an example.
Mixtures having the same components but in slightly different
ratios, or having most components in nearly the same ratios
with only a f<*w different (more or fewer) components, and
displaying similar types and degrees of toxicity. Diesel
exhausts from different engines are an example of similar
mixtures (Appendix B).
Groups of compounds that are similar in chemical structure and
biological activity, and which frequently occur together in
the environment, usually because they are generated by the
same commercial process. The composition of these mixtures
is often well controlled, so that the mixture can be treated
as a single chemical. Polychlorinated biphenyls (PCBs) are
an example.
The circumstance in which exposure to two or more chemicals
results in a qualitatively or quantitatively altered biolog-
ical response relative to that predicted from the actions of
the components administered separately. The multiple chem-
ical exposures may be simultaneous or sequential in time and
the altered response may be greater or smaller in magnitude
(adapted from NRC, 1980). For quantitative evaluations, the
"no-interaction" prediction is based on dose or response
addition, as appropriate.
A response to a mixture of toxic chemicals that is greater
than that suggested by the component toxicities.
A response to a mixture of toxic chemicals that is less than
that suggested by the component toxicities.
A special case of synergism in which one substance does not
have a toxic effect on a certain organ or system, but when
added to another chemical it makes the latter much more toxic.
I
A special case of antagonism in which one substance does not
have a toxic effect on a certain organ or system, but when
added to a toxic chemical it makes the latter less toxic.
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Masking: The situation In which the toxic effect of one chemical 1s
not displayed because of functionally competing effects from
the other chemical. The most striking example 1s when the
carcinogenic activity of the mixture Is not observed at the
experimental doses, because of more obvious toxic signs,
particularly mortality, Induced by other toxic components.
1.4. OVERVIEW OF THIS DOCUMENT
The main body of this report discusses the Information available on
chemical mixtures, the mechanisms by which chemicals Interact, and the
mathematical models used to describe toxicant Interactions. After a brief
Initial description of the terminology used to describe toxicant Inter-
actions, Chapter 2 discusses the nature of the available Information on
three general categories of mixtures: complex mixtures, mixtures composed
of a single class of chemicals and simple mixtures. This section 1s
Intended to Illustrate the differences between the types of Information that
are available on the various categories of mixtures but Is not Intended to
be a compendium of all available Information on all mixtures. Emphasis Is
placed on the description of the tests used to assess the toxlclty of the
mixture as well as the available methods and feasibility of these methods
for quantitatively measuring Interactions of the components 1n the mixture.
This chapter concludes with discussions of additional topics: Interactions
of carcinogens with other compounds, some results from the Agency's data
base on mixtures and quantitative measures of Interactions.
A discussion of mechanisms of toxicant Interactions Is presented 1n
Chapter 3. This section discusses the ways In which compounds may Interact:
direct chemical-chemical reactions that result 1n the formation of a
different chemical species as well as the biological bases of toxicant
Interactions such as effects on absorption, distribution, metabolism, excre-
tion and receptor site affinity. This Is followed In Chapter 4 by a review
of the mathematical models and statistical procedures used to assess toxic
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Interactions, including dose addition, response addition, generalized linear
models, and response surface models. This section concludes With a critical
review of statistical methods used in research articles that are covered 1n
the Agency's mixtures data base.
Chapter 5 reassesses the guidelines in terms of the information summa-
rized in the previous chapters. Following the organization of Chapter 2,
which is in turn dictated by the different types of information available on
the various chemical classes, this chapter separately discusses complex
mixtures, similar mixtures and simple mixtures. For- complex mixtures,
emphasis remains on in yjivo bioassays, the applicability of which can be
extended by the concept of sufficient similarity, as illustrated in
Appendix B. Recognizing the highly variable nature of some complex mixtures
as well as the difficulty and expense of obtaining good in viyp. bioassays,
the relative potency method, the "toxic equivalency factor" method and
analogous methods based on In vitro assays, are more strongly endorsed than
in the original guidelines. A limitation of dose addition Is also
discussed, primarily related to limitations of risk assessment of single
compounds. -
This document concludes with a brief outline of research needed to
improve or validate the risk assessment procedures for mixtures. Because
the reassessment of the guidelines relies heavily on the use . of In yUro
tests, emphasis is placed on the validation of such tests using whole animal
assays. -• - i
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2. TYPES OF INFORMATION AVAILABLE
2.1. OVERVIEW
This chapter summarizes the kinds of Information available on various
categories of mixtures; namely, complex mixtures, chemical classes and
simple mixtures. Also covered 1s the nature and utility of Information
available on the Interactions of carcinogens with other compounds Including
discussions of promotion, cocardnogenldty, Inhibition and masking. The
focus of this chapter 1s on the usefulness as well as the limitations of
available data on mixtures; for risk assessment. This 1s not Intended to
provide a comprehensive summary of all available Information on these topics
but 1s based on the Information Included 1n the computerized data base,
which Is described 1n Section 2.4.3., Chapter 4 and Appendix A.
Given the quality and quantity of the available data on chemical Inter-
actions, few generalizations can be made concerning the likelihood, nature
or magnitude of Interactions. Most Interactions that have been quantified
are within a factor of 10 of the expected activity based on the assumption
of dose addition. The limited available Information suggest;; that at least
some interactions may have thresholds and that addltlvlty may be a plausible
assumption at low levels of environmental exposure. This supposition 1s
reenforced by mechanistic considerations discussed in Chapter 3. It must be
emphasized, however, that these generalizations are based on very limited
data.
The information available on complex mixtures is fundamentally different
in design and focus from that on simple mixtures. Studies on complex
mixtures generally are designed to characterize the toxic properties or
potency of the mixture as an entity. In this respect, the design and
conduct of such studies do not differ greatly from studies on single
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compounds. As a consequence, the great majority of the bloassays on complex
mixtures are not useful for assessing potential Interactions of components
In the mixtures. In some cases, however, sample collection or concentration
of complex mixtures prior to a bloassay may cause changes 1n the composition
of the mixture, which could limit the applicability of the study In risk
assessment. This factor, however, 1s not greatly different from problems
that can be encountered In the preparation and purification of a single
compound prior to bloassay.
Studies available on simple mixtures are generally restricted to binary
combinations and are usually designed to measure the magnitude of the Inter-
action among the components In the mixture. The study design generally
Includes a control group, one or more groups of subjects exposed to each
component of the mixture at one or more dose levels, and one or more groups
exposed to one or more doses of all components at equal ratios. The Inter-
action Is generally reported as the ratio of the observed response to a
response predicted by the assumption of dose add1t1v1ty (discussed 1n
Chapter 4).
Studies on chemical classes are generally similar to those on complex
mixtures. For Instance, most of the available information on mixtures of
polychlorinated blphenyls (PCBs) comes from bloassays on commercial mixtures
of these substances, and no quantitative measures have been attempted of the
individual components as to their concentration or biological activity. A
significant amount of information is available on individual components of
many complex mixtures and chemical classes, but such studies are not
directly useful In quantifying interaction.
The restriction to binary mixtures of bloassays that attempt to quantify
mixture Interaction, and the virtual absence of bloassays on complex
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mixtures or mixture classes that attempt to define such Interactions, Is
attributable to the nature of the experimental design that 1<> necessary for
quantifying Interactions. Note the example given by Clayson (1984):
"... If It was wished to examine the Interactions of just 10 chem-
icals In pairs It would Involve conducting 45 separate bloassays
plus a further 10 for the single chemicals. If 1t was deemed
necessary to study these pairs of chemicals In just 5 different
ratios 1t would be necessary to undertake 255 separate bloassays.
As there are estimated to be 1n excess of 25,000 chemicals produced
commercially in significant quantities, examination even in pairs
becomes quite impracticable with about 313 million tests 1f only
one ratio Is used or 1.57 American billion tests [sic] If 5 dif-
ferent ratios were employed."
The difficulties in obtaining quantitative measures on toxicant interactions
are exacerbated by the fact that many of the studies on binary mixtures that
purport to quantify toxicant interactions are Improperly designed and the
reported results are either unlnterpretable or are difficult to compare
among different studies.
Studies on the interactions of carcinogens with other compounds share
many of the same difficulties and limitations as those discussed above. A
substantial body of data, however, has accumulated which suggests that some
compounds may markedly modify the carcinogenic potency of other compounds.
Although the early investigations focused on dermal applications and
enhancement of skin tumor response, more recent studies indicate that such
interactions may be relatively common and affect cancer induction at other
sites. Conversely, some agents are known to Inhibit the carclnogenicity of
other compounds. The inhibitory activity of some materials can vary as a
function of time of application in relation to the carcinogen as well as the
tumor site.
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2.2. COMPLEX MIXTURES
2.2.1. Overview. Some classes of chemical mixtures, such as automotive
emissions and coke oven emissions, are composed of hundreds of components
produced by a single process or set of related processes. Some of the
components may be grouped Into similar classes while others may not have any
apparent structural or toxlcologlc similarity to other elements of the
mixture. While toxlcologic data may be available on some of the mixture
components or classes of components, the characterization of the toxidty of
other agents 1n the mixture may be incomplete or nonexistent. In addition,
the chemical composition of such mixtures may vary over time or as a
function of changes in conditions (e.g., temperature or pressure) under
which the mixtures are generated. For example, it has been demonstrated
that malfunctioning fuel injection systems in diesel engine cars can cause
increased mutagenicity and benzo[a]pyrene emissions (Zweidinger, 1982). As
is the case for data on Individual toxic agents, the quality and quantity of
data on complex mixtures varies markedly among different mixtures. Few
generalizations can be made concerning the nature of the available data or
the applicability of these data for use in risk assessment.
2.2.2. Epldemiologic Studies. In a few Instances, human exposures to
complex mixtures have been sufficiently high that direct human data are
available for quantifying risks from exposure to the mixtures or processes
generating the mixtures. This has most often been the case for mixtures
that Induced cancer. For instance, a substantial body of epidemiologic
literature is available on the carcinogenic potency of cigarette smoke and
of coke oven emissions. Such epidemiologic investigations, while sometimes
allowing for quantifying of risk from exposure to the complex mixture,
seldom provide information on the nature, magnitude or significance of
2-4
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Interactions among the components 1n the mixture. Some Interactions
Involving exposure to complex mixtures that have been quantified Include
those between cigarette smoke and asbestos (Hammond and Sellkoff, 1973;
Hammond et al., 1979; and Sellkoff et al., 1968), cigarette smoke and radia-
tion exposure (Lundln et al., 1969), as well as cigarette smoke and vitamin
A (Dayal, 1980). Even these examples, however, which are the best studied
examples providing human data on Interactions Involving complex mixtures, do
not quantify Interactions among components In the complex mixture but rather
measure Interactions between the complex mixture and another agent.
In 1981, a WHO committee on health effects of combined exposures 1n the
work,environment concluded the following: "The dearth of sound epldemlolog-
1cal studies to date and the potential Importance of at least some of the
possible Interactions between occupational and nonoccupatlonal environmental
factors attest to the need for more work 1n this field" (WHO, 1981). The
more recent literature (e.g., Kopfler and Craun, 1986; WHO, 1983) has not
substantially Improved the prospect of developing human data on complex
mixtures that will be useful In quantifying component Interactions. Given
the difficulties 1n assessing and designing studies to measure Interactions
1n simple binary mixtures [as discussed In general 1n Section 2.4.1. and
discussed specifically In terms of epldemlologlc studies by Andelman and
Barnett (1986)], human data on complex mixtures are likely to remain most
useful for risk assessments on the complex mixture Itself but will seldom If
ever be adequate for the quantitative assessment of Interactions among
components within the mixture. i
2.2.3. Whole Animal Bloassays. For most groups of highly complex
mixtures, data on whole animal bloassays that are directly useful for risk
assessment are not available. Lewtas (1985), for example, has reviewed the
2-5
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available data on combustion emissions from diesel engines, gasoline
engines, and energy combustion sources (wood stoves, oil furnaces, and
utility power plants). For the gasoline and diesel engines, the most
comprehensive jm vivo data are from mouse skin, tumor initiation studies,
which are usually not directly used in risk assessment to estimate carcino-
genic potency in humans. While several iji vivo studies have examined the
carcinogenic and systemic effects of diesel exhaust, the data base,
Including epidemiologic data, in general, is extremely limited (NAS, 1981).
For the energy combustion sources, no in vivo studies are available. A
large body of data, however, is available on these and other mixtures using
a variety of in vitro test systems. This information is discussed in
Section 2.2.4. below, and the potential use of these data in quantitative
risk assessment of mixtures Is discussed in Chapter 5.
Although data from animal studies are available .for the few complex
mixtures that have been identified as human carcinogens, long-term in vivo
animal bloassays on complex mixtures have tended to follow rather than lead
ep1dem1olog1c investigations and have focussed on complex mixtures such as
polycycllc aromatic hydrocarbons (PAH), coke oven emission, and diesel
exhaust (as discussed in Appendix B) for which data on human effects or
human exposures suggested a potential hazard. The paucity of whole animal
bloassay data on complex mixtures is illustrated by the compilation of
cancer risk assessments currently on the U.S. EPA's Integrated Risk
Information System (IRIS). Of the 95 risk assessments currently on IRIS one
is for a technical grade mixture of hexachlorocyclohexane isomers, one is
for a binary mixture of hexachloro-p-dioxins and one is for mixtures of
xylene Isomers. Only two assessments, nickel refinery dust and creosote,
are for complex mixtures. The assessment of one of these mixtures, nickel
2-6
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refinery dust, Is based on epidemiologic data rather than animal bioassay
!
data (U.S. EPA, .19875). Similarly, although the International Agency for
Research on Cancer has identified several industrial processes that Involve
exposure to complex mixtures; and which are causally 'associated with cancer
in humans based on epidemiologic studies, no complex mixtures have been
designated as carcinogens based solely on the results of animal bioassays
(IARC, 1982). i
As is the case for epidemlologic investigations, long-term whole animal
i
bioassays on complex mixtures can be useful for risk assessments on the
complex mixture itself but are not, and from a practical perspective cannot,
be designed for the quantitative measurement of interactions among compo-
nents within the mixtures. The practical difficulties In making such
measurements for complex mixtures are an extension of those discussed for
binary mixtures In Section 2.4.1. In addition, because of the variability
of complex mixtures over time or with different conditions in the generation
of the mixture, the few bioassays that are available on complex mixtures are
i
not necessarily applicable to all exposures to the complex mixture. This is
illustrated in Appendix B for diesel exhaust.
Several short-term jn. vivo assays for carcinogenic activity such as the
mouse skin initiation/promotion assay (Pereira, 1982a; Slaga et al., 1982),
rat liver focus bioassay (Herren-Freund and Pereira, 1986; Pereira, 1982b),
and strain A mouse lung tumor bioassay (Haronpot et al., 1986; Stoner and
Shimkin, 1982) have been developed for assessing the effects of mixtures.
Such studies are normally not used as the sole basis for a quantitative risk
assessment because of the relatively short periods of exposure and the
endpoints that are measured. Nonetheless, because these studies can be
conducted more rapidly and less expensively than standard chronic bioassays,
2-7
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they can be applied In qualitative or quantitative assessments of
Interactions. Such short-term \jn vivo tests more closely approximate the
chronic in vivo assays that are normally used In risk assessments and thus
may have more Intuitive appeal than _1n. vitro assays. Nonetheless,
comparative analyses between the results of such short-term In vivo assays
with other short-term assays (Perelra and Stoner, 1985) or long-term in vivo
bloassays (Herren-Freund and Perelra, 1986) do not clearly Indicate the such
assays are superior to some of the in vitro assays discussed below. The
short-term in vivo assays that have been developed to date focus only on
sceenlng tests for carcinogenic activity. Research articles describing
comparable tests for measuring Interactions 1n the Induction of chronic
toxic effects have not been located.
2.2.4. In. vitro Studies and Other Screening Tests. Certain aspects of the
toxlclty of complex environmental mixtures have been evaluated extensively
using In vitro assays and other screening tests. Four types of assays have
been most often used: mlcroblal mutagenldty, cell culture, embryo bio-
assays and plant cytogenetlcs. The endpolnts assessed In these assays are
one or more of genotoxidty, cytotoxlclty, embryotoxlclty and Impaired
development. Although the utility of many of these assays 1n quantitatively
or qualitatively assessing the in vivo biological activity of single
compounds or complex mixtures has not been extensively validated (as
discussed 1n Section 5.1.), these j.n. vitro assays are currently the only
practical approach to obtaining detailed Information on the biological
activity of complex mixtures, particularly In site-specific and
process-specific assessments.
The Salmonella hlstldlne auxotroph reversion assay (Ames et al., 1975)
has been the most widely used procedure for detection of mutagenldty of
complex mixtures. Numerous environmental mixtures, as entitles or after
2-8
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fractlonatlon, have been tested In this assay: coal-llqulflcatlon and
gasification products (Epler et al., 1978; Rao et al., 1980; Schoeny et al.,
1981; Houk and Claxton, 1986), automotive and dlesel exhaust (Hulslngh et
al., 1978; Claxton and Kohan, 1980), crude shale oil (Epler et al., 1978),
drinking water (Chrlswell et al., 1978; Loper and Lang, 1978), cigarette
smoke (Kourl et al., 1978), Industrial effluents (Commoner !et al., 1978;
Douglas et al., 1983; HcGeorge et al., 1984), urban ambient air partlculate
and extracts (Commoner et al., 1978; Butler et al., 1984), sludge (Houk and
Claxton, 1986) and waste-amended soil (Donnelly et al., 1983). Mutagenlc
activity of mixtures has also been assessed In a forward mutation In
Salmonella typhlmurlum using 8-azaguan1ne resistance for selection.
I
Automotive exhaust (Claxton and Kohan, 1980), oil shale and water samples
(Whong et al., 1983) and coal liquefaction products (Schoeny et al., 1986)
have produced positive results in this assay.
Fractlonatlon, or separation of the mixture Into chemically-related or
distinct constituents, has been utilized to define constituents In a mixture
more clearly and to determine which compounds are responsible for mutagenlc
activity. Fractlonatlon procedures have also been used to concentrate
materials and to reduce toxlclty of whole mixtures, thus making them more
amenable to assay. Extraction methods (e.g., acid/base, polar/nonpolar),
however, may lead to chemical reactions that could alter the components of
the mixture, thereby affecting the toxlclty.
The application of reconstruction assays can be useful In assessing the
effects of fractlonatlon procedures or In uncovering Interactions among the
fractions. Thllly et al. (1983), for example, Identified the relative
abundance of constituents In partlculates from kerosene combustion (kerosene
soot). The mutagenlc contribution of the 14 most abundant compounds was
2-9
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determined 1n the Salmonella forward mutation assay. When these chemicals
were combined 1n appropriate proportions to approximate the "pure soot," the
mutagenldty of the reconstituted kerosene soot was equivalent to the
original soot extract, demonstrating the concentration dependence of
mutagenldty for the mixture, that Is, addltlvlty. By contrast, a similar
study of fractionated coal hydrogenation materials in which the sum of the
mutagenic activities of organic extracts was compared with the activity from
the whole sample and with a reconstituted whole sample, Indicated a depar-
ture from addltivity for some mixtures (Schoeny et al., 1986).
DNA repair-deficient strains of Bacillus subtil Is (Donnelly et al.,
1983) and Escherichia coll (Rossman et al., 1984) have been used to detect
alterations in DNA induced by wood-preserving waste and by urban air
partlculates, respectively. Assays for reverse mutation in Saccharomyces
cerevislae (yeast) (Douglas et al., 1983) and assays detecting dominant or
recessive lethals in Paramecium tetraurelia (SmUh-Sonneborn et al., 1983)
have been less frequently used.
Embryo culture assays have been developed to examine potential embryo-
lethality, malformation and growth/developmental alterations induced by
Individual substances and complex environmental mixtures. Dumont et al.
(1983) developed the Frog Embryo Teratogeniclty Assay: Xenopus or FETAX.
Coal and shale-derived synfuels (Dumont et al., 1983) and mine water
discharge (Dawson et al., 1985) have caused one or more of embryolethality,
gross malformation or embryotoxicHy in frog embryos exposed in vitro.
Other embryo assays using the rat (Klein et al., 1983) and sea urchin (Hose,
1985) are being developed and validated for application to mixtures.
Environmental mixtures have been evaluated in cell culture assays as to
their potential mutagenldty at specific loci, as well as for their capacity
2-10
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I
to Induce sister chromatid exchange or chromosomal aberrations. Cytotox-
iclty has also been evaluated as measured by effects on cellular growth and
division, and on morphological, cytochemical and biochemical alterations.
For example, Chinese hamster ovary (CHO) cells have been used in determina-
tions of the ability of complex environmental mixtures to produce cytotox-
icity and mutagenicity at the hypoxanthine guanine phosphoribosyl trans-
ferase locus (Hsie et al., 1978). Subfractions of crude synthetic oil (Hsie
et al., 1978), coal gasification condensate tar (Cunningham et al., 1984),
oil and coal fly ash (Chescheir et al., 1980; Li et al., 1983), textile mill
effluents (Waters et al., 1978), diesel engine exhaust (Chescheir et al.,
1980; Li et al., 1983), retort process water from crude shale oil (Strnlste
et al., 1983), as well as coke oven emissions, roofing tar and cigarette
smoke condensate (Li et al., 1983), have produced positive cytotoxic and
mutagenic responses in this assay.
I
Other endpoirits measured in CHO cells in response to complex environ-
mental mixtures have included mutagenicity at the Naf~Kf-dependent
ATPase locus using ouabain resistance for selection, sister chromatid
exchange and chromosomal aberrations. Diesel exhaust particle extract (Li
et al., 1983) and pulp and paper mill effluents (Douglas et al., 1983) were
genotoxic In these assays.
Cell types such as alveolar macrophages or epithelial tissue, which
would be directly exposed to environmental agents, have been used to
evaluate toxicity of mixtures. In the pulmonary alveolar macrophage assays,
viability, phagocytic ability, specific enzyme activities and ATP levels are
the endpolnts most often evaluated. The toxicity of jn vitro exposure to
various fly ash particles (Waters et al., 1978; Aranyi et al., 1980; Humford
and Lewtas, 1983), liquid textile mill effluents (Waters et al., 1978) and
2-11
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smelter dust (Aranyi et al., 1980) was assessed using rabbit alveolar macro-
phages. Fisher et al. (1983) used bovine alveolar macrophages for the
analysis of coal and oil fly ash. Unscheduled DNA synthesis, an indication
of DNA damage, was induced in organ cultures of hamster trachea! epithelium
exposed to coal-fired fly ash, diesel fuel exhaust and cigarette smoke
condensate (Schiff et al., 1983).
Other less frequently used cell culture assays have been applied to
environmental mixtures. The BALB/c-313 cell transformation assay showed
enhanced toxicity of drinking water organic concentrate fractions (Loper and
Lang, 1978). The rainbow trout gonadal tissue (RTG-2) assay, wherein
anaphase aberrations resulting from jji vitro exposure are determined, showed
genotoxiclty of marine sediment samples (Kocan and Powell, 1984).
Ijn vitro plant assays have been used to evaluate various environmental
mixtures. Plants, like animals, are eukaryotlc organisms and may have the
ability to convert chemical compounds to biologically active species. The
most widely used higher plant for testing genetic toxicity has been Trades-
cantia. Tradescantia plant systems are especially useful for in situ
environmental air exposure and the testing of gaseous agents. The induction
of somatic mutation at a particular locus is measured in the Tradescantia
stamen hair system as a phenotypic change in pigmentation in mature flowers
following exposure of the developing floral tissue (Schairer et al., 1978,
1983). Tradescantia exposed jn situ for 10 days to ambient air pollution 1n
several cities in the United States have shown positive results for mutagen-
Iclty in this assay (Schairer et al., 1978, 1983). In the Tradescantia
mlcronucleus test, early prophase I meiotic pollen mother cells of Trades-
cantia plant cuttings are exposed and the frequency of mlcronuclei (chromo-
somal fragments) determined in the tetrads following meiosis (Ma et al.,
1980, 1983; Plewa, 1984). Sewage sludge from several cities (Plewa, 1984),
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shallow well water samples and deep well water containing 226Ra, as well
as combustion products of diesel and dlesel/soybean oil fueled engine
exhaust fumes (Ma et al., 1980, 1983, 1984} were genotoxic In this assay.
The barley root tip cytogenetic system involves scoring barley (Hordeum
vulqare) root tip cells in germinating seeds at anaphase for detectable
aberrations following treatment of the seed (Constantin et al., 1980). Fly
ash-aqueous extracts and arsenic-contaminated groundwater have produced
positive results 1n this assay. The Arabidopsis thaliana assay (Redei,
1980) and the Soybean Spot Test using Glyclne max {Vig, 1980), while not yet
applied to complex environmental mixtures, detect phenotyplc alterations in
the embryo or mature plant indicative of mutational events
result of exposure of the seed.
2.3. MIXTURES OF CHEMICAL CLASSES
A mixture of a class of chemicals refers to a group of
are structurally and biologically similar and which usually
occurring as a
compounds that
occur together
in the environment because they are produced by the same process. Mixtures
of chemical classes, like the complex mixtures, may contain tens or hundreds
of components. Also, as with the complex mixtures, the composition of
i
similar mixtures may vary over time because of environmental partitioning or
different conditions of generation, use and release. Examples of mixtures
of chemical classes include the chlorinated dioxins, chlorinated dibenzo-
furans, chlorinated naphthalenes and chlorinated biphenyls.
As with the complex mixtures, the amount of data available on mixtures
of chemical classes varies markedly, but the types of data are similar:
human data (generally data from accidental exposures), animal bloassay data,
and data from in vitro assays. The relative amounts of the various types of
data are dependent on the levels and nature of human exposure to the
2-13
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mixtures, the perceived levels of hazards associated with exposure to each
mixture, and certain practical considerations that are associated with some
of the more common simple mixtures.
For Instance, PCBs have been commercially produced as several groups of
similar mixtures varying In the average degree (percent by weight) of
chlorlnatlon (U.S. EPA, 1984). For the more commercially significant PCB
mixtures, such as Aroclor 1254 (54% Cl) and 1262 (62% Cl), whole animal
bloassays for carcinogenic effects are available on the mixture and have
been used directly to estimate cancer potency. The chlorinated dioxins,
however, have never been used as a commercial product but have occurred as
contaminants in commercial products or as combustion by-products (U.S. EPA,
1985). Consequently, there is no "typical" dioxin mixture, and whole animal
bloassays have been-conducted only on certain individual dioxins (such as
2,3,7,8-tetrachlorod1benzo-p_-dioxin) or on simple mixtures of hexachlori-
nated dioxins, which are difficult to separate chemically. Given technical
problems associated with the synthesis and purification of large quantities
of chlorinated dioxins as well as the undesirability of synthesizing large
quantities of them, it is not likely that many more whole animal bloassays
will or should be conducted on this class of chemicals. Much research,
however, has been and continues to be conducted using Vn vitro bioassays to
facilitate a better understanding of structure-activity relationships and
mechanisms of action of chlorinated dioxins as well as many other classes of
simple mixtures. These data have been recently reviewed (Kociba and Cabey,
1985) and their application to risk assessment is an active topic In the
literature (U.S. EPA, 1987c; Eadon et al., 1986) and is discussed further in
Chapter 5.
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2.4. SIMPLE MIXTURES, COMPONENTS AND TOXIC INTERACTIONS
2.4.1. Overview. The great majority of studies In which attempts have
been made to assess toxic Interactions quantitatively have used simple
binary mixtures. Only a few studies (Gullino et a!., 1956; Hermens et a!.,
1985a,b) have used mixtures of over 10 compounds. In such studies of
relatively simple mixtures,, approaches to the analysis of toxicant inter-
actions used by most toxicologists have been based on the assumption of dose
addition using simple experimental designs involving a control group, groups
exposed separately to each compound at multiple doses so that the LOC s or
50
ED50i! can be estiimated» and groups exposed at multiple doses to one
mixture of all compounds in fixed proportions. The degree and nature of the
toxic interaction is then expressed as the ratio (or some transformation of
the ratio) of the observed LD5Q or ED5Q of the mixture to the LQ Q or
ED50 exPected from tne assumption of dose addition. This can be referred
to as the ratio of interaction (R.I.) and expressed as
R.I. = ED5Q(Obs)/ED50(Exp) Equation 2-1
A ratio of Interaction greater than one is associated with antagonism in
that the observed ED5Q is greater than expected (i.e., less toxic) based
on the assumption of dose additlvity and the measured ED5Qs ' of the compo-
nents in the mixture. Conversely, a ratio of less than one is associated
with synerglsm.
,!
As discussed by Berenbaum (1981, 1985a) and detailed in Chapter 4, the
difficulty in demonstrating significant interaction based on studies using
i
single ratios of interaction is primarily one of experimental design. Since
the ratio of interaction is dependent on the proportions of the components
i
in the mixture, a test has the best chance of demonstrating significant
interaction if the mixture giving maximum interaction is selected. If the
2-15
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combination of toxicants being tested 1s assumed to evidence a pattern of
symmetric Interaction, a mixture of equltoxlc doses would be the best selec-
tion. Even with this simplifying and not necessarily valid assumption,
however, tests based on single ratios of Interaction will not yield signifi-
cant results unless the magnitude of the interaction is substantial and the
experimental variability is minimal.
2.4.2. Measurements of Toxicant Interactions. Keplinger and Deichmann
(1967) used the ratio of interaction to measure the joint action of various
pesticides in mice. In this study, only one mixture of each combination was
used, and significant interaction was arbitrarily defined as ratios of <0.57
for synergism and >1.75 for antagonism. Smyth et al. (1969, 1970} used a
slightly modified expression of the ratio of interaction, which resulted in
estimates that resembled the shape of a normal distribution. Significant
interaction was then defined as those ratios of observed to predicted
LD5Qs in rats that were beyond 1.96 standard deviations from the mean
ratio. In studies on the joint action of pesticides in houseflies, Sun and
Johnson (1960) defined the cotoxicity coefficient as the ratio of inter-
action multiplied by 100. Significant interaction was estimated in this
study by taking repeated measurements and determining if the 95% confidence
Interval of the cotoxicity coefficients included 100. They reported a high
degree of synergism for a mixture of 8-(dimethoxyphosphinyloxy) N,N-di-
methylcrotonamide and sesamex while methylparathion and sesamex were antago-
nistic. Wolfenbarger (1973) used cotoxicity coefficients to estimate the
joint action of toxaphene-DDT mixtures in insects. Although different
combinations of each mixture were used and cotoxicity coefficients were
derived for each combination, no attempt was made to derive coefficents of
interaction, as defined and discussed in Section 4.3., which could be used
2-16
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to characterize the direction and magnitude of the Interaction for all
combinations of the mixtures.
All of the above approaches are severely limited by their reliance on a
single Interactive ratio. As discussed by Hewlett (1969), the ratio of
interaction is characteristic only of a particular combination of compounds.
In other words, the estimated value of the ratio of interaction will vary
depending on the proportions of the toxicants present In the mixture.
Another limitation in the use of ratios of Interaction is encountered in
attempts to demonstrate statistical significance. The method used by Sun
and Johnson (I960), based on repeated measurements of the ratio of inter-
action, may be the least objectionable; however, because of the dependence
of the ratio of interaction on the proportion of the components in the
mixture, the estimate of interaction is valid only for the particular
mixture tested and has no merit in assessing the overall interaction charac-
teristic of the combination being tested. This limitation may be particu-
larly misleading for those compounds that evidence asymmetric interaction,
as discussed in Chapter 4. The approach adopted by Kepiinger and Deichmann
(1967) 1s totally arbitrary and makes no attempt to establish a criterion
for statistical significance. The method of Smyth et al. (1969, 1970) is
based on arbitrary selection of test chemicals that influence the criteria
for interaction. The other methods that use 95% confidence intervals of the
LD5Qs of the mixture and individual components (Marking and Dawson, 1975;
Wolfenbarger, 1973) are overly sensitive to both endogenous and exogenous
variance. Marking and Dawson (1975) recognized the difficulty with
exogenous variance in stating that "well planned toxicity tests which result
in narrow confidence Intervals are most useful 1n the assignment of the
effects of chemical mixtures." If endogenous variation is high (that is,
2-17
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the slope of the log dose-probH response line is low), however, even
well-designed toxldty tests may yield 95% confidence Intervals that
preclude the detection of Interaction.
2.4.3. The U.S. EPA Data Base on Toxic Interactions. The Interaction data
base was constructed to determine the nature and extent of Information on
component Interactions. Host of the entries are for studies on two chemical
Interactions, but a few consider combinations of two mixtures. The data
base currently covers roughly 600 chemicals. Host of the studies evaluate
the Interactions based on mortality following acute exposure. Host of the
studies investigate the influence of one chemical on the toxlcity of another
(I.e., potentiation or inhibition), where the first is administered at a
nontoxic dose (Table 2-1). The statistical methods used in these studies
are discussed In Chapter 4. Details of the data base are given in
Appendix A.
2.5. INTERACTIONS OF CARCINOGENS WITH OTHER COHPOUNDS
2.5.1. Promoters and Cocardnogens. Only 13 years after Bauer (1928)
proposed the somatic cell mutation theory of cancer, Rous and Kidd (1941)
and Berenblum (1941a) proposed that some forms of chemically induced cancers
Involved a two step process. Berenblum1s (1941b) report on the enhancement
of benzo(a)pyrene Induced carcinogenicity by extracts of Croton tiqlium. a
complex mixture, was the first example of one chemical enhancing the
carcinogenic activity of another. With improvements in chemical techniques
for fractlonation and isolation, the active components of Croton resin have
been identified (Hecker, 1968; Van Duuren, 1969). Since 1941, over 30 such
agents, Including all extracts or derivatives from Croton oil, have been
Identified (Van Duuren, 1976; Pitot and Sirica, 1980; Fujiki et al., 1979).
The best known of these is TPA (12-o-tetradecanoyl-phorbol-13-acetate).
2-18
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TABLE 2-1
Summary of Interaction Data Base
Category
Duration
Interaction
Type
acute
subchronlc
chronic
lifetime
synergism
potentiation
antagonism
inhibition
additivity
no apparent interaction
masking
chemical synergism
unable to assess
Percentage
of Studies*
73
11
8.4
0.29
I
2.8
29
1.7
31
3.7
I 25
; 0.59
0.13
5.6
*Representing a total of 587 chemicals
2-19
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The extensive and complex literature on promotion and cocardnogeniclty
has been recently reviewed by Bohrman (1983), Clayson (1984), Driver and
McLean (1986). For purposes of this document, promoters will be defined as
agents which, when applied after but not before an initiator, act to enhance
the carcinogenicity of the initiating agent. Cocarcinogens are taken to be
agents that may enhance carcinogenicity when applied before or at the same
time as the initiator. The definitions of cocarcinogens and promoters are,
thus, operational and depend largely on the design of the experiments In
which they are found to have an effect. It is likely that cocarcinogens and
promoters may have some mechanisms of action in common, as well as some
unique modes of enhancing a carcinogenic response.
As discussed by Van Duuren (1976), all promoters can probably display at
least some tumorigenlc activity in the absence of a known initiator. This
Is to be expected "... if one assumes that in any group of animals there
will be some that have latent tumor cells, either by earlier exposure to an
external initiating agent or by spontaneous conversion of normal cells into
latent tumor cells... If this explanation is accepted, the question about
'pure' promoting agents should be obsolete." While this may be true within
the context of interpreting the results of an initiation-promotion assay,
the distinction between promoters and initiators could have a significant
Impact on risk assessment. Because it is generally accepted that Initiation
1s a nonthreshold (genotoxic) phenomenon and promotion is probably a
threshold (epigenetic) phenomenon, the distinction between "pure" promoters
and those promoters that may also be weak initiators may be crucial to the
selection of high- to low-dose extrapolation models, as discussed further by
Clayton (1984) and in Section 3.3.6. and 5.5.
While most of the studies on chemical promotion involve dermal or oral
exposure, Nettesheim et al. (1981) documented several factors enhancing
2-20
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carcinogenesis in the respiratory tract; at least some of these were attri-
butable to initiation-promotion processes (e.g., promotion of urethane-
induced pulmonary tumors in mice by phorbol esters or butylated hydrqxy-
toluene). In addition to the skin and respiratory tract, Initiation-
promotion has also been observed In the liver (2-AAF- or DMN-phenobarbital),
bladder (N-methyl-N-nitrosourea-sodium saccharin or cyclamate), gastrointes-
tinal tract (DMBA-TPA, dimethylhydrazlne-phenobarbital), and mammary glands
(DMBA-estrogens or phorbol esters), as detailed in an extensive review by
Bohrman (1983). Some epidemiologic data are suggestive of a two-stage
Initiation-promotion process in humans, although the evidence is scanty
(Hakaraa, 1971; Berenblum, 1979). Thus, promotion may be a very common
phenomenon that occurs among many chemicals and affects most species.
2.5.2,. Inhibitors and Masking. Some compounds, such as butylated
hydroxytoluene (BHT) and other antloxidants, have been shown to decrease the
development of tumors when administered before the administration of known
carcinogens (Ito et al., 1985; King and McCay, 1983; NRC, 1980). In
addition, a compound that causes an increase in the mortality rate could
result in a decreased cumulative Incidence of late appearing tumors because
of competing risks.
In the case of compounds that apparently decrease carcinogenic response
through a "protective" mechanism, the nature of the protective mechanisms
and the dose-response relationship of the protective effect have not been
clearly defined. In addition, some of the compounds that display a
"protective" effect under one set of circumstances may, in fact, enhance the
carcinogenic response under different conditions of exposure. For Instance,
BHT reduces carcinogenic responses when administered before some carcino-
gens but enhances carcinogenic responses when administered after other
2-21
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carcinogens. The protective effect Is attributed to the antloxldant proper-
ties of BHT and the enhancement to production of a metabolite of BHT with
promoting activity. Any attempt to predict the Interaction of BHT with a
specific carcinogen Is complicated because BHT Is known to Inhibit the
mutagenlc activity of benzo[a]pyrene but to enhance the mutagenlc activity
of aflatoxin Bl 1n the Salmonella reverse mutation assay (Malklnson, 1983).
The sequence of exposure Is an Important variable for other compounds as
well. Both phenobarbltal and cloflbrate, for example, enhance carcinogenic
response when administered subsequent to an Initiator. When administered
concurrently with an Initiator, however, phenobarbltal Inhibits tumor forma-
tion whereas cloflbrate enhances tumor formation (Moch1zuk1 et a!., 1983).
In addition to variations 1n the effects of dose schedule on carcinogenic
Interaction, the nature of the Interaction may also vary with the site of
action. For example, Anderson et al. (1983) have noted that PCBs (Aroclor
1254) Inhibit the development of lung tumors but enhance the development of
liver tumors In mice Initiated with N-nltrosodlmethylamlne.
As with the quantifying of cocarcinogenlclty and promotion, a consis-
tent and predlcable pattern of Interaction has not yet emerged 1n the
assessment of compounds that inhibit carclnogenldty (Schulte-Herman, 1985;
Williams, 1984). Until such a pattern does emerge, It Is not likely that
studies such as those described above will be used to modify quantitative
risk assessments for chemical mixtures.
Conversely, both inhibition and masking may be significant in the inter-
pretation of cancer bioassay data on mixtures. For instance, Raabe (1987)
has recently presented an analysis of the dose-time-response relationships
of plutonium-239 in causing deaths from pneumonitis and lung cancer In
beagle dogs. Deaths from pneumonitis tended to occur at higher doses and
earlier in life than deaths from cancer, thus masking the carcinogenic
2-22
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activity seen at lower doses. Similar patterns have been seen In the
results of many cancer bloassays on single compounds In which early
mortality from causes other than cancer confounded the Interpretation of the
results. For bloassays on mixtures of compounds, the results; of masking of
carclnogenlcity because of early mortality could be particularly significant
1f the mixture contains known carcinogens. For example, human exposure to
the mixture at concentrations below the toxic threshold could result In a
significant Increase In the risk of cancer that would not be reflected In
the animal bloassay. No data, however, were located that specifically
address this Issue In the published literature.
2.6. QUANTIFICATION OF INTERACTIONS '
The practical or quantitative significance of toxic Interactions at
environmental levels of exposure Is difficult to assess. As discussed In
previous sections of this report and detailed further In Chapter 4, most
published studies on Interactions are not designed to quantify the magnitude
of the interaction but focus primarily on qualitatively characterizing the
nature of the interaction. In addition, quantitative measurements of inter-
actions can only be made In reference to a non-interactive mathematical
model, several of which are discussed in Chapter 4 and by NAS (1988a).
Thus, the interpretation of the data in determining whether Interactions
•
occur can be highly model dependent. The available models also assume that
the Interaction among the compounds in the mixture is consistent over the
entire range of relevant dose levels. An important consequence of this
assumption is that the interaction 1s assumed to have no threshold. Few
data are available for assessing the validity of this assumption.
The majority of studies that allow for any quantitative estimate of
I
interaction involve acute exposures in which death or some other severe
endpoint Is measured. In such studies (Smyth et a!., 1970; Hermens et al.,
2-23 !
-------
1985a,b), Interactions are expressed as the ratio of the observed LD5Q to
the expected LD5Q based on the assumption of dose addltlvlty. As
discussed In Section 2.2., this Is often referred to as the ratio of
Interaction. Most reported ratios of interaction do not exceed a factor of
2; the largest reported variation 1s a factor of 5 for an equivolume mixture
of morpholine and toluene in the study by Smyth et al. (1970). Given the
variations Inherent in the conduct of bioassays, the significance of these
variations from additivlty is unclear. Few data are available regarding the
variation of Interactions among bioassays conducted by the same investi-
gators (Sun and Johnson, 1960), and no interlaboratory studies have been
conducted. Another source of uncertainty In assessing the Implications of
these ratios of interaction is that the nature and magnitude of inter-
actions for severe toxic effects may not be the same as those for less
severe effects. Furthermore, interactions that occur at high doses may not
occur in the low-dose region. For example, the work of Plaa et al. (1982)
on the well-documented potentiation of carbon tetrachloride-induced hepato-
toxidty by acetone suggests that threshold concentrations exist below which
an enhancement of toxicity may not occur. As discussed 1n Chapter 3, many
of the biologic mechanisms by which interactions occur are also likely to be
threshold phenomena.
As with acute bioassays of simple binary mixtures, very few studies on
promotion or interaction were located that allow for the quantification of
the interaction. One exception is the study summarized by Pfeiffer (1977)
on interactions of carcinogenic and noncarclnogenic PAHs. This study, which
Involved 3000 mice, demonstrated both enhancement and Inhibition of
carcinogenic activity. Measured in terms of the observed proportion of
responders versus the expected proportion of responders, variations from
addltlvlty ranged up to a factor of approximately 3. Most other studies
2-24
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using experimental animals Involve far less elaborate experimental designs:
ethanol and vinyl chloride (Radlke et a!., 1981); cyclopenteno[cd]pyrene and
benzo[a]pyrene (Cavallerl et al., 1983); and diethylnitrosamine and
phenobarbHone or alcohol (Driver and McLean, 1986). These generally
discuss or provide data that suggest variations from additivity, based on
comparing the observed vs. the expected proportion of responders, by less
than a factor of 10. Because observed response rates In most of these
bloassays are over 10% and must be less than 100%, this observation may have
more to do with the design and limitations of most bloassays than with the
quantitative significance of Interactions. No quantitative reviews of
cocarclnogenlc activity or promotion efficiency have been encountered 1n the
literature that attempt a systematic and consistent analysis of the
available but diverse animal data in order to estimate the significance and
'
magnitude of these phenomena for risk assessment.
Epldemiologic studies on mixtures, as discussed In Section 2.2.2., focus
on measuring relative risk associated with exposure to a complex mixture.
Occasionally, Interactions can be quantified between exposures to two
complex mixtures or one complex mixture and another compound or agent. As
with measurements of interactions from other types of studies,; any quantita-
tive estimate of interaction must be made with reference to a non-inter-
active model. For example,, one of the most studied examples is the inter-
action between occupational exposure to asbestos fibers and cigarette
smoking (Hammond and Selikoff, 1973; Hammond et al., 1979; Selikoff et al.,
1968, 1980). In the study by Hammond et al. (1979), relative risks of about
I
5, 11, and 53 were noted for nonsmokers with occupational exposures to
asbestos, smokers with no occupational exposure to asbestos, and smokers
with occupational exposure to asbestos, respectively. As discussed in the
i
mixture guidelines, this can be interpreted as evidence for a substantial
2-25
-------
Interaction (synerglstlc) between cigarette smoking and asbestos exposure 1f
an additive risk model is assumed or as an Indication of no Interaction If a
multiplicative risk model 1s assumed.
More recently, Steenland and Thun (1986) have reviewed the measurement
of Interactions In epldemlologlc studies Including a reappraisal of the data
on cigarette smoking and exposures to asbestos, radon daughters, arsenic or
chloromethyl ethers. As discussed by Steenland and Thun (1986), synerglstlc
departures from an additive risk model have Important public health conse-
quences In that eliminating exposure to one agent can result, In a greater
reduction In risk than 1f no synerglstlc Interaction occurred. The multi-
plicative risk model, on the other hand, 1s used 1n characterizing the
etiology of a disease by determining 1f one risk, factor modifies the effect
of another risk factor. Of the epldemlologlc studies reviewed by Steenland
and Thun (1986), the Hammond et al. (1979) study showed the greatest devia-
tion, by a factor of about 3.5, from risk addltlvlty. Other deviations from
risk addltlvlty ranged from a factor of about 2 for smoking and radon or
arsenic to 0.2 for smoking and chloromethyl ethers. In no Instance did the
observed relative risk for smoking and the other agent exceed the relative
risk predicted by the multiplicative risk model. The recent reanalysls of
the combined effects of cigarette smoking and exposure to radon daughters In
the BIER IV report (NAS, 1988b) also noted evidence for a multiplicative or
a "submultlpHcatlve" model (I.e., the risk was greater than that predicted
by the additive risk model but less than predicted by the multiplicative
risk model) for uranium miners, although some support was found for a supra-
multiplicative model.
2-26
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3. AVAILABLE INFORMATION ON INTERACTION MECHANISMS
3.1. OVERVIEW
This chapter summarizes Information on the chemical and biological
I
mechanisms by which compounds Interact. Such mechanisms Include chemical-
chemical interactions, pharmacokinetic effects and interactions at receptor
site:; and other critical cellular targets* For the most part, effects of
different types (lethality, narcosis, enzyme induction, reproductive
effects) or effects at different sites involve a common set of mechanisms.
The phenomena of promotion and cocarcinogenicity have been extensively
studied in a distinct body of literature and may involve a complex and
as-yet-not-fully understood series of mechanisms, which are discussed at the
end of this section.
I
As stated in the mixtures guidelines, toxicant interactions may be based
on any of the processes that are significant to the toxicologic expression
of a single compound: absorption, distribution, metabolism, excretion and
activity at cellular site(s). In addition, compounds may interact
chemically, causing a change in the biological effect or they may Interact
by causing different effects at different receptor sites. Of greatest
practical importance is that most of these mechanisms are saturable as are
most kinetic processes for single compounds. Consequently, many of the
interactions observed at high doses may not be significant in the low-dose
region.
Table 3-1, which summarizes these general modes of interaction along
with some examples, was prepared using a modification of the basic scheme
proposed by Veldstra (1956). As detailed in an extensive review by WHO
(1981), "... the available evidence from In vitro and animal experiments and
from human observations has shown that a limited number of mechanisms seem
3-1
-------
TABLE 3-1
Chemical and Biological Bases of Toxicant Interactions
(See text for discussion, additional examples and references)
Examples
Bases of Interaction Synerglsm or Potent1at1on
Antagonism
Chemical
Biological
Absorption
Distribution
formation of nltrosamines
from nitrites and amines
neurotoxldty of EPN
(o-ethyl o-r-n1trophenyl
phenylphosphorothloate)
enhanced by aliphatic
hexacarbons due In part
to Increased skin absorp-
tion (Abou-Donla et a].,
(1985)
Increased lead levels
brain after treatment
with dlthiocarbamate/
thluram derivatives
(Oskarsson and L1nd,
1985)
In
dimethyl hydrazine
reacts jm vivo with
pyridoxal phosphate
(vitamin B6) to form
a hydrazone, thus
rapidly depleting
tissue stores of this
enzymatic cofactor
(Cornish, 1969)
dietary zinc inhibits
lead toxldty 1n part
by decreasing the
percent dietary lead
absorbed (Cerklewski
and Forbes, 1976)
the mechanisms by which
selenium protects
against cadmium toxlc-
Ity Include decreasing
the concentration of
cadmium in Hver and
kidney and the redis-
tribution of cadmium In
the testes from the
low-to-high molecular
weight Cd-binding
proteins (Chen et al.,
1975)
3-2
-------
TABLE 3-1 (cont.)
Examples
Bases of Interaction Synerglsm or Potentiatlon
Antagonism
Excretion
Metabolism
decreased renal excretion
of penicillin when co-
administered with pro-
benecld
organophosphorous com-
pounds (profenfos, sul-
profos, DEF) potentiate
the toxldty of fenval-
erate and malathlon by
Inhibiting esterase which
detoxifies many pyreth-
rold Insecticides (Gaughan
et al., 1980)
Interaction at
Receptor Sites
(Receptor Antagonism)
no Information available
Interaction Among no Information available
Receptor Sites
(Functional Antagonism)
Interaction at DNA no information available
arsenic antagonizes the
effects of selenium in
part by enhancing the
biliary excretion of
selenium (Levander and
Argrett, 1969)
selenium inhibits 2-
acetylaminofluorene-
induced hepatic damage
and liver tumor inci-
dence in part by
shifting metabolism
toward detoxification
(ring hydroxylation)
relative to metabolic
activation (N-hydroxy--
lation) (Marshall
et al., 1979)
blocking of acetyl-
choline receptor sites
by atropine after
poisoning with organo-
phosphates
interaction of hlsta-
mine and norepinephrine
on vasodllation and
blood pressure
i
Induction of DNA repair
by exposure to
alkylating agents
3-3
-------
to account for the majority of the Important known biological Interactions."
In other words, the basic mechanisms by which toxicants interact as detailed
by Veldstra (1956) are based on classic pharmacologic principles that have
not changed substantially over the past 30 years. While most of the best
studied examples of the mechanisms of compound interactions are still from
the pharmacologic literature on therapeutic drugs or abused substances such
as ethanol (Seitz, 1985; Puurunen et al., 1983), an increasing number of
examples are available showing similar mechanisms for compounds of occupa-
tional and environmental concern.
3.2. CHEMICAL INTERACTIONS
Many cases of direct chemical-chemical Interactions lead to a decrease
1n toxlcologlc activity, and this is one of the common principles of anti-
dotal treatment. Examples Include the use of chelating agents to complex
with metal ions, the inactlvation of heparin by binding to protamine, and
the use of ammonia orally as an antidote to the ingestion of formaldehyde
through the formation of hexamethylenetetramine (Goldstein et a!., 1974).
This class of reactions has been referred to as chemical antagonism by
Klaassen and Doull (1980).
Chemical reactions that lead to greater than additive effects have been
less frequently documented. One example that has received considerable
attention is the formation, in the stomach, of nitrosamines from nitrites
and amines, which result in an increase in both toxic and carcinogenic
effects (Welsburger and Williams, 1980; U.S. EPA, 1986a). Other examples
Include the formation of arsine and stibine from ores containing arsenic and
antimony, respectively, which come into contact with strong acids in the
stomach. Thus, while antagonism may be the most widely recognized result of
3-4
-------
this mechanism for toxicant Interaction In the classic pharmacologlc litera-
ture, synerglsm or potentlatlon also occur and may be as significant In the
environment.
3.3. PHARMACOKINETIC-BASEI) INTERACTIONS
Many examples of toxicant Interactions are based on alterations In
patterns of absorption, distribution, excretion or metabolism of one or more
compounds 1n the mixture. Several reviews of these factors in the assess-
ment of multiple chemical exposures are available (Anderson and Clewell,
1984; Plaa and Hewitt, 1981; WHO, 1981; Wlthey, 1981). All of these kinds
of Interactions essentially alter the bioavailability of thejtoxic agent(s)
to the cellular slte(s) without qualitatively affecting the toxicant-
receptor site Interaction. ;
3.3.1. Effects on Absorption. Most kinds of Interactions based on alter-
ations In absorption involve vehicle effects, the chemical formation of
poorly absorbed conjugates or complexes, or decreases in gastrointestinal
motility. Examples of such effects have been noted for oral and dermal
i
exposures. j
i
For example, the dermal toxicity of TCOD adsorbed on charcoal is
considerably less than that of TCDD solubllized in a lipophilic medium.
This is presumably due to the reduced availability of the charcoal-adsorbed
TCDD for absorption by biological systems (Polger and Schlutter, 1980).
Conversely, dimethyl sulfoxide, a commonly used vehicle 1n dermal toxlcity
studies, is known to facilitate the absorption of many organic compounds
i
across the skin, thus causing apparent potentiatlon when compared with less
lipophilic vehicles (Goldstein et a!., 1974). A similar mechanism appears
to be involved 1n the enhancement of the neurotoxlclty of o-ethyl-o-4-n1tro-
phenyl phenylphosphonothioate by various aliphatic hydrocarbons when applied
j
derraally to hens (Abou-Donia et a!., 1985). i
!
!
3-5
-------
The acute oral toxicity of many compounds Is substantially affected by
the vehicle used, and many of these effects are probably due to differences
1n rate of absorption. For example, clloqulnol administered orally Is able
to complex with many metals, facilitating their absorption, and has been
Implicated In an outbreak of heavy metal-Induced subacute myelo-optlc neuro-
pathy In Japan (Tjalve, 1984). By contrast there are examples of compounds
that form poorly absorbed complexes after oral administration such as tetra-
cycllne and calcium carbonate, as well as cholestyramlne and cholesterol
(Goldstein et al., 1974). Some compounds given orally, such as codeine,
morphine, atroplne and chloroqulne, decrease the rate of gastric emptying,
thus decreasing the rate of absorption of orally administered compounds.
For the most part, such Interactions usually lead to decreases In effects,
because of the slower rate of absorption rather than Increases In effects
because of more complete absorption (Levlne, 1973).
As discussed by Wlthey (1981) and confirmed In the literature reviewed
for this report, there are no examples of toxlcologlcally significant
changes In absorption associated with the Inhalation of mixtures. Murphy
(1964) reported Increased carboxyhemoglobln levels In mice and rats exposed
to an ozone-CO mixture compared with CO alone. The exact mechanism of this
response, however, has yet to be determined. Anderson and Clewell (1984),
In their review of pharmacoklnetic Interactions and Inhalation modeling,
cite several examples of Interactions based on effects on metabolism but
none based on absorption. It has been hypothesized, however, that one
mechanism by which particulates such as ferric oxide serve as respiratory
cocarclnogens for benzo[a]pyrene (B[a]P) Is by Increasing residence time In
the lung and, thus, allowing for more complete absorption of the compound.
Alternatively, If absorbed on particles, the B[a]P 1s taken up by macro-
phages that have been shown to be capable of metabolizing B[a]P to a
3-6
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proximate carcinogen. In addition it has been shown that cocarcinogenic
particles facilitate uptake of the adsorbed chemical carcinogen across
phospholipid bilayer membranes (Lakowitz and Hylden, 1978; Leikowitz et a!.,
1980).
3.3.2. Effects on Distribution. Distribution can play a role in compound
Interactions if a more active agent is displaced from an inactive site to a
primary receptor site by a less active or inactive agent. One of the best
documented examples of this kind of activity is the displacement of anti-
coagulants from plasma proteins by compounds such as barbiturates, anal-
gesics, antibiotics or diuretics (Goldstein et al., 1974). Similarly,
tri-o-tolyl phosphate decreases the binding of paraoxon to nonvital tissue
in rat liver and plasma, consequently increasing the toxicity of paraoxon in
rats (Lauwerys and Murphy, 1969). Since body fat represents a major
nonvital storage site for many lipophilic xenobiotics, it may be anticipated
that compounds that cause fat mobilization could result in similar poten-
tiating effects (WHhey, 1981).
Recently, Oskarsson and Lind (1985) demonstrated that dithiocarbamates
and tetramethylthiuram disulflde may complex with lead and selectively
increase the accumulation of lead in the brain. While the toxicologic
significance of this interaction has not yet been demonstrated it can be
reasonably presumed that this effect on distribution is likely to lead to a
synergistic effect on the CNS effects of lead. A related mechanism was
proposed by Larsson et al. (1976) for the teratogenic effect of maneb, which
is antagonized by zinc acetate, suggesting that the teratogenic activity of
maneb is due to the binding of zinc, causing embryonic zinc deficiency.
Host, examples cited above, however, result in greater than additive effects
— synergism or potentiation.
3-7
-------
3.3.3. Effects on Excretion. Most examples of excretion as a basis for
toxicant Interaction Involve compounds that are eliminated through the
kidneys. For Instance, probenedd or carinamide both competitively Inhibit
the elimination of penicillin, thus prolonging or potentiating its desirable
therapeutic effect. Similarly, phenylbutazone inhibits the renal excretion
of hydroxyhexamide, which can cause undesirably prolonged hypoglycemia. If
a toxicant Is eliminated through the kidneys, a stimulation of renal
elimination can cause an antagonistic effect, as is seen with the
coadmlnistration of phenobarbital and sodium bicarbonate in which the
increased urine alkalinity induced by the bicarbonate ion increases the
excretion of phenobarbital (Goldstein et al., 1974).
A less direct effect on renal elimination has been suggested by
Herschberg and Sierles (1983) for the substantial potentiation of the
toxlclty of lithium, which is eliminated through the kidneys, by
indomethacin. These investigators suggest that the potentiation is due to
the inhibition of prostaglandin synthesis by Indomethacin, which in turn
causes vasoconstriction and a decrease in the renal excretion of lithium.
As summarized by WHO (1981), several drugs and other chemicals are also
able to compete for biliary excretion. Yamada et al. (1986) have demon-
strated that quinidine has a marked inhibitory effect on the presystemic
elimination of ajmaline by the liver when both compounds are administered
concurrently to rats; similar observations have been noted in humans.
3.3.4. Effects on Metabolism. Altered patterns of compound metabolism
have been shown to be the bases of many toxicant interactions (Anderson and
Clewell, 1984; WHO, 1981). A major enzyme system involved in such inter-
actions is liver microsomal mixed-function oxidases (MFO), which are
Involved 1n the activation or detoxification of a wide variety of compounds.
3-8
-------
Both the types (e.g., different forms of cytochrome P-450) and levels of
metabolic enzymes can be induced by agents such as phenobarbHal, and enzyme
activity can be inhibited by agents such as piperonyl butoxide (Goldstein et
al., 1974). Thus, depending on whether or not the toxicant is activated or
detoxified, inducers or inhibitors of this enzyme system may cause syner-
gistic/potentiating effects; or antagonistic effects (Freeman and Hayes,
1985; -Leonard et al., 1985). Toxicant interactions involving the MFO can be
i
complex and depend on both dose and duration of exposure, with some
compounds causing an initial inhibition of enzyme activity followed by a
marked induction of activity (WHO, 1981). Although liver microsomal MFO are
the most commonly studied enzymes involved in toxicant Interactions, MFO in
other tissues may also play an important role in toxicant Interactions as
may other enzyme systems, such as alcohol and aldehyde dehydrogenases,
monarnine and diamine oxidases, dehydrochlorinases, azo and nit.ro reductases,
hydrolases and enzyme systems involved in conjugation reactions. For
instance, ethanol serves as an antagonist of the toxic effects of methanol
by acting as a competitive inhibitor of alcohol dehydrogenase, thus
suppressing the formation of formaldehyde and formic acid from methanol
(Goldstein et al., 1974). !
3.3.!). Interactions at Receptor Sites or Critical Cellular ;Targets. All
of the biological modes of toxicant interactions discussed above -•- absorp-
tion,, distribution, excretion and metabolism -- are essentially disposi-
tional, affecting the amourit(s) of toxicant(s) reaching the primary recep-
tor(
-------
1nterat1ons. The antagonistic nature of Interactions that occur at the same
receptor site was discussed by Veldstra (1956):
...we may say that the effect of a combined action of two compounds
at the same site of primary action will not result in a synergism,
but will, generally, even be unfavorable. The competition for the
receptor will usually decrease the frequency of the best inter-
actions, and with decreasing intrinsic activity of one of the
components the combined action will more and more take the form of
a competitive antagonism.
Examples of such interaction Include the antagonistic effects of oxygen on
carbon monoxide, atroplne on cholinesterase inhibitors and naloxone on
morphine (Goldstein et a!., 1974). The antagonistic consequences of this
kind of toxicant Interaction are so consistent that it has been termed
"receptor antagonism" by Klaassen and Doull (1980) and "pharmacological
antagonism" by Levine (1973). While it seems conceivable that one compound
could increase the intrinsic activity of another compound by modifying the
receptor site -- analogous to the effect of modulators on regulatory enzymes
-- such Interactions have not been demonstrated.
Interactions of agents among receptor sites are also thought to result
primarily in antagonistic effects and has been referred to as "functional
antagonism" by both Klaassen and Doull (1980) and Levine (1973). This kind
of Interaction 1s most commonly defined as two or more compounds acting on
different receptor sites and causing opposite effects on the same physio-
logical function. Examples Include the opposite effects of hlstidlne and
noreplnephrlne on vasodilation and blood pressure and the antlconvulslve
effects of barbiturates on many compounds that cause convulsions. Neverthe-
less, that Interactions among receptor sites uniformly result in an antago-
nistic response is not certain, particularly when the receptor sites act on
3-10
-------
different physiological systems. The rationale for this statement was
presented by Veldstra (1956):
i
The sites of action for two compounds having the same type of
activity may be different. This is the case when the effect can be
caused either by a direct stimulation or by the annihilation of an
inhibition. In both cases, the combination of two compounds,
linked in parallel or in series, as it were, may well result in a
synergistlc effect. When the components of a combination possess
different sites of action and different types of activity, no
plausible prediction about the possibility of synergism can be
made, unless their mode of action is well known.
i
While examples of such Interactions have not been well characterized in
the literature, the potentlation of carbon tetrachlorlde by chlordecone may
,i
be at least partly mediated by an inhibition of hepatocellular repair
(Lockard et a!., 1983). j
Another possible illustration of Veldstra's argument is presented in the
work of Alstott et al. (1973), who examined the acute lethal effects of
combinations of 1-methylxanthine and ethanol on mice, and noted two basic
kinds of effects: kidney dysfunction and increased respiratory rate and
depth. In animals exposed to mixtures in which the ratio of 1-methyl-
xanthine to ethanol was relatively high, antagonism of acute lethal toxicity
was observed; however, in mixtures in which the same ratio !was relatively
low, a synergism of acute lethal toxicity was observed. This indicates that
i -
in cases where toxicants interact at more than one cellular site, the nature
of the Interaction can be either antagonistic or synergistic. The compli-
cating factor of the "asymmetric" pattern of interaction observed by Alstott
et al. (1973) is discussed in greater detail in Chapter 4. |
3.3.6. Promotion and Cocarcinogenicity. Mechanistic studies on promo-
tion and cocarcinogenicity have been active areas of research over the past
decade. The extensive literature has been the subject of several comprehen-
sive general reviews (Slaga, 1984; Williams, 1984; Yamasaki,, 1984) as well
3-11 j .
-------
as reviews that have focussed on specific topics such as hepatocarcino-
genesls (Pltot et al., 1982; Schulte-Hermann, 1985), the Inhibition of
cellular communication by promoters (Trosko et al., 1983), the induction of
superoxlde anions by promoters (Troll and Wiesner, 1985), and the binding of
promoters to protein kinase C in cell membranes (Hecker, 1985).
Various investigators have used different but generally overlapping
mechanistic schemes to categorize the types of information on promotion and
cocarclnogenicity. Table 3-2 summarizes the mechanisms based on the
approach taken by Williams (1984), who also provides many specific examples.
As mentioned earlier in this chapter, one possible mechanism of cocarcino-
genesls is to increase cellular exposure to an initiating substance.
Particulate ferric oxide can serve as an effective vehicle for delivery of
an adsorbed carcinogen, such as benzo[a]pyrene to the target organ, namely
lung. The particles are subject to phagocytosis by pulmonary alveolar
macrophages, which can elute the benzo[a]pyrene (Autrup et al., 1979),
transport the compound to a distant site or metabolize it. Likewise
solvents may also serve as cocarcinogens by increasing efficiency of
carcinogen delivery.
Agents may serve as cocarcinogens by affecting the metabolism of a
procarcinogen such that a more active metabolite or that a greater quantity
of reactive metabolites is maintained in the cell. This can be accomplished
by Induction of metabolic enzyme systems as described previously or by
depletion of or competition with detoxification systems. An example of a
compound with this latter activity is diethyl maleate, which is known to
deplete liver of glutathione, a cellular nucleophile. Depletion of gluta-
thione Increases hepatotoxicity (and presumably the potential for hepatocar-
cinogenicity) of aflatoxin Bl (MgBodile et al., 1975).
3-12
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TABLE 3-2 j
Mechanisms of Promotion and Co-carcinogenicity*
Co-carc1nogenes1s:
i
1. Increased uptake of carcinogen \
2. Increased proportion of carcinogen activation
[
3. Depletion of competing nucleophiles
4. Inhibition of the rate or fidelity of DNA repair |
5. Enhancement of the conversion of DNA lesions to permanent
alterations
Promotion: }
1. Enhancement of expression of neoplastic phenotype
Inhibition of differentiation
2. Stimulation of cell proliferation
- - 1-
Cytotoxlclty
Hormone Effects
3. Cell membrane effects [
I
Induction of proteases [
Inhibition of Intercellular communication
[
4. Immunosuppression j
*Source: Williams, 1984 i
3-13
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Another possible mechanism of cocarclnogenesls takes place at the level
of DNA damage. It Is known that certain compounds can act as co-mutagens In
In vitro systems: norharman for aniline and benzo[e]pyrene for benzo[a]-
pyrene. These Interactions could take place at any of a number of steps In
the mutagenlc process, Including enhancement of mutagenlc metabolite produc-
tion. It 1s known, however, that DNA that 1s being actively transcribed Is
more susceptible to damage than Is "resting" DNA. It seems plausible then
that some agents could enhance Initiation by making the DNA more susceptible
to damage, for example, by holding It 1n a single stranded configuration, or
by Increasing transcription.
Interference with error-free DNA repair Is another way In which a
cocarclnogen could work. Induction of an error-prone repair system by
DNA-damag1ng agents is a well-documented phenomenon In EscheMchla coll. In
mammalian cells, certain systems, such as that responsible for repair of
alkylation damage, also appear to be inducible (Swenberg et a!., 1982).
There 1s, however, no evidence as yet of an error-prone repair system that
could be turned on by either a DNA-damaging or a co-mutagenic agent. It has
been reported that some agents reduce the rate of DNA synthesis, including
repair synthesis. Such a reduction in rate of repair could have the effect
of increasing the number of permanent DNA alterations or mutations
{Williams, 1984). It has been reported that a compound, 3-aminobenzene,
which inhibits the activity of the poly(ADP-ribose) polymerase specific to
DNA repair enhances the formation of liver foci initiated by another
compound (Takahaski et a!., 1982).
The classic two-stage initiation-promotion sequence proposed by early
Investigators (Berenblum, 1941a,b) is more likely to reflect experimental
design constraints than two simple discrete mechanistic stages. Slaga
(1984) has described two separate stages in promotion in which the initiated
3-14
-------
cell develops to a benign tumor, as well as two stages of progression. The
first stage of progression Is that In which a benign tumor develops Into a
malignant tumor, while In the second stage the malignant tumor metastaslzes;
each of these stages may Involve different mechanisms of Interactions.
As mentioned In Chapter 2, the practical significance of the distinction
between tumor Initiation and tumor promotion Is that the former Is commonly
regarded as having no threshold while the later 1s often thought to display
a threshold below which no tumor promotion will occur (Driver and McLean,
1986). This view, however, has been challenged by Yamasaki (1984), who
claimed that the data are not adequate to determine If promotion evidences a
true dose-threshold. Rather, 1t was suggested that because at least some
stages of promotion are reversible, promoters display a dose-schedule
threshold (I.e., the dosing schedule Is of greater Importance than the total
administered dose) that Is different from that of InHators,--or complete
carcinogens. ;
i
The Implications of mechanisms of promotion for risk assessment are
further complicated by the fact that some compounds can interact with
promoters to Increase or diminish promoting efficiency (Schulte-Hermann,
1985; Slaga, 1984; Williams, 1984). For example, Sleight (1985) has
reported that 3,3',4,4',5,5'-hexabromob1phenyl enhances the promoting
efficiency of 2,2',4,4',5,5'-hexabromoblphenyl and that this may explain why
commercial mixtures of polybromlnated blphenyls have a greater promoting
ability than any of the Individual congeners. ]
3.3.7,. Interactions and Developmental Toxiclty. Developmental toxldty 1s
Indicated by many different types of endpolnts Including death, structural
abnormality, altered growth and functional deficits (U.S. EPA, 1986b).
3-15
-------
These various endpolnts are likely to arise as a consequence of any of a
number of cellular processes Including mutations, membrane changes, changes
1n gene expression, or other events leading to cell death. There Is
potential for Interaction to occur at any of these processes that would be
manifested as Increases or reduction 1n developmental measurements. It Is
generally assumed that there are dose thresholds for developmental effects
based on the rationale that the embryo has some capacity for repair "of
damage or replacement of dead cells. Interactions could have the effect of
raising or lowering this threshold as for other systemic effects.
3-16
-------
4. MATHEMATICAL MODELS AND STATISTICAL TECHNIQUES
4.1. INTRODUCTION
Thrs chapter presents a review and evaluation of some representative
statistical methods for the assessment of toxic responses to mixtures.
There are four different classes of methods, described as Follows: dose
addition, response addition, generalized linear models and response surface
models. The theoretical framework for each class is discussed, and varia-
tions within each class are described. Some recently proposed methods for
use in analysis of mixtures data are also presented, along with an evalua-
tion of applications of statistical methods in the mixtures literature.
Interaction is defined statistically as the effect of two or more treat-
ments applied jointly that cannot be predicted from the average responses of
the separate factors. This concept of dependence of the effect of one
factor on the level of another factor is a fundamental scientific idea.
When interaction is present, the result of two or more factors applied
jointly may result in either positive or negative deviations from the
expected result for each factor taken one at a time. As noted in Chapter 1
and Appendix A, when a large positive deviation is present, the common
i
biological terminology used is synergism. When a negative deviation Is
present, antagonism is said to be present. In the special case where a
deviation occurs when the two factors are applied together, but one factor
by itself has no effect, the positive deviation is called potentiatlon, and
the negative deviation is called inhibition.
The above definitions are contingent on how the expected (or "no-
interaction") effects are defined (Berenbaum, 1985a). There are two general
classes of models for joint action that assume no interaction. These
classes describe either dose addition or response addition.
4-1
-------
4.2. DOSE ADDITION
Dose addition, or simple similar action (Finney, 1971), assumes that the
compounds in a mixture act as if they are dilutions or concentrations of
each other. For example, in a binary mixture, a dose containing z, units
of compound 1 and z~ units of compound 2 would, under dose addition,
behave exactly as a dose of (z1+pz2) units, of compound 1 alone, where p
1s the potency of compound 2 relative to compound 1. In particular, assume
the two compounds have parallel regression lines of probits on log doses as
follows:
YI = a.j + 3 logZ (4-1)
Y2 = a2 + 13 logZ (4-2)
where Z is the dose. Simple similar action is said to occur when the
response to a mixture containing amounts Z, and Z2 units of compounds 1
and 2, respectively, has a response probit of the form
Y = a] + 13 log(Z14-pZ2) (4-3)
Alternatively, if the mixture is a total dose Z of the two compounds in
proportions f1 and f2 then the mixture has a response probit of the form:
Y = a-j + 13 Iog(f^pf2) + 13 logZ (4-4)
Note that the assumption of parallelism is implicit in the formulation of
this model (4-1 and 4-2).
One method for testing for dose additivity Is to assess the adequacy of
fit of the model (4-4) rewritten as
Y = a3 + 6 logZ (4-5)
Alternatively, when several mixtures of different proportionate concentra-
tions are to be tested, several different estimates of p can be obtained.
4-2
-------
The sum of squares between observed and predicted response from equation 4-4
i
can then be minimized with respect to a, B and p, and an overall estimate
of p is found. Testing for dose additivity is then done by comparing this
sum of squares against one where values of p were estimated separately for
each dose series. j
Finney (1971) has also proposed the -following model to be used for
assessment of Interaction:
Y = a + 13 1og(f1+f2p + K(f.,f2p)0-5) + B logZ (4_6)
where a, B, p are as deflined before, Z is the sum of Z, |and Z~, and K
is the coefficient of interaction. A positive value of K Indicates
synergism; a zero value, simple additivity; and a negative value, antagonism.
This model assumes a constant Interaction throughout the entire range of
proportions of individual components. In order to allow for a less
restrictive assumption, Durkin (1981) made the following modification:
Y = <* * B log(f1+f2p+(K1f1+K2f2)(f1f2p)°-5) + B logZ (4-7)
The properties of this model, however, have yet to be critically evaluated.
Durkin (1981) also proposes several statistical methods for testing for
j
departures from simple additivity. For example, the model for symmetrical
interactive action (Finney1;; model from equation 4-6): i
-p)0-5] (4-8)
where !„ is the observed LC5Q for the mixture and Z, is the LC,-0 for
compound 1, can be fit using weighted linear regression analysis. Similarly,
the model for asymmetrical interactive action (Durkin1 s model from equation
j
4-7),
(1/Z3) = (l/Z1)[f14-f2p+(K1f1^K2f2)(f1f2p)0-5] j (4-9)
4-3
-------
can also be fit by similar means. The hypothesis that
Kfp0'5)/!., = 0
(4-10)
or
0.5,
(K1f1+K2f2)(pu'3)/Z1 = 0 (4-11)
can then be tested. If the relevant hypothesis 1s not rejected, then the
data are consistent with simple addltlvity. Let SSE] denote the sum of
squares error for the simple addltlvity model, and SSE2 the sum of squares
error for the relevant Interactive model. Define then
where: $3 = (SSE-j - SSE2)/(M - g)
S2 = SSE2/(n - (M + 1))
where n 1s the number of measurements, g is the number of parameters 1n the
model for simple similar action and H is the number of parameters in the
Interactive model. Thus, this F statistic has M-g and n-(M+l) degrees of
freedom. Again, the properties of this method have not been rigorously
evaluated.
Another method proposed for testing for simple additivlty is to divide
the observed LC5Q of the mixture by the LC5Q predicted from simple
additlvity (Durkin, 1981). This method is to be used when LC,. estimates
3\J
of components and mixtures vary substantially, especially those from experi-
ments conducted at different times, thus obscuring trends to nonlinearity.
Under the null hypothesis of simple similar action the observed LC™ of
DU
the mixture will equal the LC5Q predicted from equation 4-5. Explicitly
= cj>1 -t-
= 1
(4-12)
where 1 and 2 can be considered the proportions of the mixture
toxicity attributable to compounds 1 and 2, respectively. Under a
4-4
-------
hypothesis of Interaction such as given by equations 4-8 or 4-9, then
(4-13)
or
Z3pred
(4-14)
This heuristic method does not have set rules for determination of statlstl-
j
cal significance and the method has not been rigorously evaluated. Several
variations on this approach have been discussed 1n Section 2.4.J
i
The dose addition model can be extended beyond two substances. The
mathematics of such models, however, are even more complicated, and data
-I
requirements for fitting these models Increase substantially as well.
Therefore, using current Information, these models can be of practical use
only with mixtures of relatively few component chemicals.
Plackett and Hewlett (1952) criticize the dose addition i model on two
points. First, the parameter K Is Inadmissible for certain values of Z,
and . !„. Second, this model assumes that p, the potency of compound 2
i
relative to compound 1, Is fixed and constant for all organisms under study,
a condition that they feel 1s unnecessarily restrictive.
4.3. RESPONSE ADDITION ; - •
Response addition models were first proposed by Bliss (1939). In the
original representation for "Independent joint action" of the two chemicals,
Bliss (1939) assumed that the two chemicals acted on different physiologic
systems. This assumption can be generalized to functional Independence of
the two separate effects, even 1f they are on the same organ system. Define
the following:
4-5
-------
D, = dose of chemical 1
PI m proportion of animals responding to D_
and similarly for chemical 2. Bliss (1939) noted that the proportion of
animals that respond to the mixture depends not only on P, and P? but
also on the correlation between the two distributions of Individual
tolerances to the chemicals. If there 1s parallelism In the susceptibility
to the two chemicals so that the correlation 1s 1, I.e., If the ordering of
the animal sensitivities to chemical 1 1s the same as the ordering for
chemical 2, then the most toxic chemical will elicit the response first.
The mixture response 1s then:
P = max(P1,P2)
(4-15)
As noted 1n the U.S. EPA (1986a) mixture guidelines, 1f the tolerance
correlation is -1, I.e., if the animal least sensitive to chemical 1 is most
sensitive to chemical 2, and so on throughout the range of sensitivity, then
P = min(P1i-P2, 1)
(4-16)
For P<1, equation 4-16 is the simplest response addition formula:
P ' Pl * P2
(4-17)
Other tolerance correlations give P values between these extremes. If the
correlation is 0, then one obtains the familiar model for statistical
independence:
4-6
-------
P = Pl * P2 ' (P1P2)
Hewlett and Plackett (1964) discuss a different class of models based on
combining responses of two chemicals. Instead of counting
(4-18)
the number of
animals responding, they model the number of tissue receptors that are
affected by the chemicals. Their fundamental assumption Is that the tissue
.
damage can be described by chemical complexlng of the tissue receptor with
i
the administered chemical. The manner of competition of chemical molecules
for these tissue receptors; is assumed to be described by laws of mass
I
action, so that key model parameters are the chemicals' dissociation
constants for complexes. Their model also assumes that a quanta! response
occurs only when an underlying graded response, E, exceeds some critical
threshold, E . The model assumes that the joint action of two compounds
\f
Is the result of competition for the same set of receptors. ! Using Hewlett
and Plackett 's (1964) notation, let m, and m- be the reciprocals of the
dissociation of the receptor-compound complexes for compounds 1 and 2,
respectively, and let u, amd w^ be the respective molar
concentrations of these compounds at their respective sites of action. The
graded response to compounds 1 and 2 Is
o-) (4-19)
,,
-,-1
where
1,2,
A. is the intrinsic activity of compound i, and [r] is the total molar
concentration of receptors. Let u. .
[-1
-------
When compound 1 1s an agonist and compound 2 Is an antagonist (Case 1),
quanta! response occurs when
to-, /(I
i
(4-20)
If w^ Is log-normally distributed In the population of Individuals
considered then the model for the normal equivalent deviate of response Is
Y = e + e'log (o.j/0
(4-21)
If the relation between the acting concentration of compound 1, u., and
the administered amount, Z., 1s assumed to be
i -
then equation 4-21 becomes
(4-22)
Y =
a-, + B1 log(Z.,/(l
(4-23)
If both compounds are agonists both elicit the same maximum response (Case 2)
then quanta! response occurs when
,u,
(4-24)
where K 1s the critical graded response for compound 1 alone and for
compound 2 alone. Thus quanta! response occurs when
i i
(0,7(0, + (o-Ao,, > 1 (4-25)
4-8
-------
Let
. Then the nonresponse proportion is
q =
< 1}
(4-26)
which can be evaluated If w-, and o>2 have a blvariate normal
distribution. If the maximum response attainable by compound 1 1s greater
than that attainable by compound 2 (Case 3), then quanta! response occurs If
n^ -i- n2o>2)/(l +• m-jo^ + m2&>2) > n^o-j/O + m^
(4-27)
As n2-»0, Case 1 results. As (n2/m2)-^(n1/m-1 ), Case 2 results.
Otherwise, subjecting this model to the same derivations as for Case 1
results 1n "a model which has doubtful practical value on account of the
number of parameters Involved" (Hewlett and Plackett, 1964).
The parameter q Is evaluated by integrating the blvariate normal density
function over the appropriate region. Subsequent analysis of dose-mortality
data then uses the log-dose-probit response line, which is curvilinear under
independent action of the two compounds and 1s skewed upward as response
increases and the correlation coefficient between the action tolerances for
the two compounds decreases. Bliss (1939) notes that curvilinearity of a
i
dose-response curve is difficult to test in experimental data, and Ourkin
(1981) attributes the paucity of studies with examples of response addition
to this difficulty.
Similar to the dose addition models, the response addition models can be
easily generalized to more than two chemicals. The complexity of such
models and the accompanying extreme data requirements, however, make such
models of little practical use.
4-9
-------
4.4. GENERALIZED LINEAR MODELS
For the ordinary linear model, "Interaction" Is taken by statisticians
to mean a departure from response additivity assuming a normal distribution
of the response variable. For generalized linear models (e.g., logistic,
log-linear, log-probit and multistage models), interaction is taken to mean
a departure from additivity for a transformation of the response variable.
For Instance, In the log-linear model
= m
b, t d.,, 1=0,1, j=0,l
(4-28)
where a0-b0-d00-d10=d01-0.
and where p.. denotes the proportion
responding in the group receiving dose level 1 of compound 1 and dose level
j of compound 2, the d,, term describes the presence and extent of
Interaction between compounds 1 and 2. Similarly, in the logistic model
m m + a1 + b^ + d^, 1=0,1, j=0,l
(4-29)
ll
descr1bes
where p^ is as before, a0=b(fdoo=d10=d01=0' and
the Interaction.
Although the examples of generalized linear models given above are
applicable only to experiments with simple binary mixtures, these models can
be extended to experimentation with three or more compounds. The difficulty
in doing so is not in the mathematics, but rather time and expense Incurred
in the conduct of appropriately designed factorial experiments.
Use of fractional factorial designs can be used more economically, but
still can be lengthy if whole animal lifetime studies are conducted.
4-10
-------
Moreover, fractional designs also assume that one or more higher order
interactions are zero, when information on all interactions may be the
object of the exercise.
i
Nonlinear terms can also be incorporated into generalized linear models
•
and the Box-Tidwell fitting technique can be applied to obtain parameter
estimates (McCullagh and Nelder, 1983). In particular, if c|(X;e) is the
covarlate of interest where e is unknown, the expansion of g(X;0) about an
initial value eQ is obtained to derive the linear approximation,
g(X;e) - g(X;e0) * (e-e0)[ag/ae]0=eo (4_3Q)
Therefore, if the model contains a nonlinear term of the form :
Bg(X;e)
then replace it by two linear terms of the form
IJu •«• yv
where u = g(X;eQ)
v = [ag/ae]e=e i
Y = B(e-e0) |
The estimation procedure for e is then iterative as follows:
1. Fit the generalized linear model with covariates u and v
2. Obtain e, = e0+Y/B as; the improved estimate [
3. Iterate to convergence
McCuTlagh and Nelder (1983) noted that this technique is highly useful and
probably under-used, but cautioned that this method is not appropriate for
the inclusion of many nonlinear terms since the estimates of these
parameters will have large sample variances and will usually be highly
correlated with the linear parameters and possibly with each other.
4-11 ;
-------
Elashoff et al. (1987) describe a modification of the proportional
hazards model to allow for the Incorporation of competing risk for death to
evaluate interactions between two chemicals in a 2x2 carcinogeniclty experi-
ment. For the analysis of tumor incidence data, they test for Interaction
using the additivity index (Wahrendorf et al., 1981) as follows:
(4-31)
10
where qQQ Is the background probability of not developing a tumor, q
and qQ1 are the probabilities of developing a tumor when compounds 1 and 2
are administered alone, respectively, and q,, is the probability of not
developing a tumor when compounds 1 and 2 are administered concurrently. If
I>0, synergy is said to have occurred, and if I<0 then antagonism is said to
have occurred.
The time-to-death data is important to consider in addition to the tumor
Incidence data when lethal nontumorigenic toxicity in the doubly exposed
group relative to the singly exposed groups is excessive since It can cause
a negative bias in I. Therefore, they used the proportional hazard model
Pr(survival without tumor at T years for treatment ij) =
exp[-jj(h'.j(t) + hQO(t)) dt], (4-32)
where h1 represents the incremental force of mortality due to treatment. To
test for interaction they use the null model
h'10(t) + h'Q1(t) - h'n(t) = 0
(4-33)
4-12
-------
A test statistic developed by Korn and Liu (1983), which uses a Mantel-
Haenszel approach, 1s then used to test for no Interaction with respect to
time to death.
Generalized linear models have also been proposed for multi-effect data
on the complete mixture. The responses are graded (nonquantal) and the
overall toxlclty of the mixture 1s assigned to a severity category
(Hertzberg, 1987). A link function transforms the response frequencies for
each dose In each severity category, and the transformed response for these
ordered categories Is then regressed on a linear function of|dose (duration
i
could also be Included as a covarlate). For example, 1f effects are
l
categorized as "none," "mild," "moderate" and "severe," and 1f "mild"
effects were considered tolerable, then one could determine the risk of
"moderate or severe" effects for a given mixture dose. The model 1s similar
to those discussed previously. For example, for the logistic link function,
the counterpart to equation 4-29 1s
- b*[ log(D) - log(D)
(4-34)
where D now denotes the dose of the complete mixture, the overbar denotes
the mean of log(D), and j denotes the severity category., The response
I
I
variable G 1s a function of the mixture dose D and represents the cumulative
response frequency at dose D, I.e., the organisms responding at severity
level j or less. If P. Is the proportion of animals responding to dose D
K
at severity level k, then the transformed response Is
(4-35)
4-13
-------
The probabilistic risk estimate from such a model is obtained by inverting
the link function, to give the risk of an effect worse than category j,
p(J>j) = 1 - expCF^D}] / (1 t
(4-36)
where F represents the right-hand side of equation 4-34.
A mixture of chemicals is likely to Induce several different kinds of
effects 1n different organs. Applying the previously discussed response
models to each kind of effect, even if data were available on the complete
mixture, would generate several dose-response curves, and would require some
statistical combination algorithm to address the multiplicity of effects.
The recasting of the risk problem using severity categories is mathematic-
ally simpler, and also avoids the difficult issue of correlation of specific
toxic effects across species. The risk assessor then evaluates only the
risk of general systemic toxicity, e.g., the risk of unacceptable effects.
This procedure also allows the toxicologist to assign multiple effects to a
higher severity category. For example, "mild" effects in several diverse
organs and tissues would be deemed "moderate" and unacceptable when
considered as a composite toxic response.
4.5. RESPONSE SURFACE MODELS
When a 2x2 factorial design is used to study the interaction of two com-
pounds, no information is gained about how the response changes with changes
in the magnitude of exposure to both compounds 1 and 2. If the dose ranges
for these compounds are more completely studied, however, the economic
requirements increase as well. For instance, with only three nonzero doses
of each compound in a binary mixture, 16 treatment groups must be studied if
all possible combinations of the two compounds are used. An alternative
4-14
-------
approach Is motivated by conceptualizing the response to the joint exposure
as forming a surface over the experimental plane with peaks and valleys.
Designs that maximize or minimize this surface by sequential exploration are
called response surface models. They are most frequently used 1n Industrial
experimentation where the response can be measured quickly and where a small
number of factors are to be combined. Thus, their utility for the study of
mixtures of even moderate complexity or for use 1n long-term toxldty
studies Is questionable.
4.6. SUMMARY OF INTERACTION DATA BASE
i
A survey of the statistical methods utilized In studies pertaining to
mixtures was conducted using those papers Included In the U.S. EPA Inter-
action data base (U.S. EPA, 1988) as well as papers retrieved: subsequent to
the construction of the data base. A total of 462 relevant references were
i
Included In this survey, which also examined the type of mixture studied
(binary, simple, or complex), whether the study was descriptive or mecha-
nistic In Its approach, and whether the mixture Included carcinogenic
i
compounds. A relevant reference was considered one In which both methods
and data were presented, I.e., abstracts and reviews were not Included. Of
the 331 references contained In the Interaction data base, 307 were con-
sidered relevant. An additional 155 studies were also Included In this
survey (Table 4-1).
A summary of the types of studies examined and the statistics used In
each Is presented In Table 4-1. Individual columns are used for those
papers found In the data base and those not Included so that an exclusive
analysis of the, data base can be made separately. The first group of cate-
gories pertains to the general characteristics of each Individual study.
4-15
-------
TABLE 4-1
Survey of Interaction Studies Methodologies*
Data Base
Other
Total
Percent
Number of studies
307 155
Nature of Individual Studies
462
Binary mixture
Simple mixture
Complex mixture
Descriptive
Mechanistic
Noncardnogen
Carcinogen
Statistical
Student's t-Test
No Statistics
Statistics Not Specified
Analysis of Variance
Ch1 -Square Test
Neumann-Keuls Test
Mann-Whitney U Test
Wilcoxon Test
Duncan's Multiple Range Test
Fisher Exact Test
Tukey's Test
Dunnet's Test
Kruskall-WalUs Test
Least Significant Difference
Finney Additivity Formula
F Test
Scheffe's Test
2-Sample Rank Test
Fisher-Yates Test
Mantel-Haenszel Procedure
294
24
17
276
61
261
46
Breakdown of
85
85
71
34
17
13
7
3
9
4
3
3
3
3
1
2
1
1
0
0
150
16
7
136
50
118
37
Individual
52
35
35
16
12
3
5
7
1
4
3
2
2
1
3
1
2
0
1
1
444
40
24
412
111
379
83
Studies
158
119
106
52
29
16
12
10
10
8
6
5
5
4
4
3
3
1
1
1
96.1
8.7
5.2
89.2
24.0
82.0
18.0
34.2
25.8
22.9
11.3
6.3
3.5
2.6
2.2
2.2
1.7
T . 3
1 1
1*1
1 .1
0.9
0.9
0.6
0.6
0.2
0.2
0.2
*Refer to text for explanation of individual categories.
4-16
-------
Interactions result from a binary mixture (two constituents), a simple
mixture (more than two but less than dozens of Identifiable components), or
a complex mixture (dozens or more constituents, many of which are unidenti-
fied or present 1n low concentration). Several studies used more than one
type of mixture Involving, In most cases, the effect of one compound on the
Interaction of two other compounds. In other Instances, the Interaction
between two single components, e.g., carbon tetrachloride and phenobarbltal
(binary mixture), as well as the Interaction between a single compound and a
mixture of compounds, e.g., carbon tetrachlorlde and PCBs (complex mixture),
would be Investigated 1n the same study. The total number of evaluations 1s
then much larger than the number of references, although It Is obvious that
an overwhelming number of evaluations pertain only to binary mixtures.
These studies are also segregated as to whether they analyze an Inter-
action mechanistically, descriptively or both. A descriptive study 1s one
that only looks at one or more toxic endpolnt(s) to characterize the
magnitude of the Interaction without examining the underlying cause(s) for
the Interaction. Such endpoints commonly Include LD5Q values, serum
enzyme levels, and sleeping times. Mechanistic studies, on the other hand,
attempt to quantify changes in the absorption, distribution, metabolism,
i
excretion, receptor binding, or physical characteristics of a compound.
Examples of mechanistic endpoints Include urinary metabolite profile,
Intestinal absorption, hepatic enzyme activities, and tissue distribution.
Several studies incorporate both approaches by attempting to correlate a
change in toxicity with the biological or chemical bases of the Interaction.
For example, several studies have examined the effects of certain enzyme
inducers such as phenobarbital, 3-methylcholanthrene, or PCBs with a change
4-17
-------
In hepatotoxldty Induced by carbon tetrachlorlde. Table 4-1 Indicates that
61 studies (412 descriptive + 111 mechanistic - 462 total) utilized both
strategies.
Finally, the number of studies involving carcinogenic endpoints was
determined. A carcinogen study is defined as one in which a determination
of tumor frequency, latency or incidence is made. Studies in which known
carcinogens were used but were not of sufficient duration for tumor
formation were included in the noncarcinogen category. Unlike the other
categorizations, a study was classified as either carcinogen or noncarcino-
gen but not both.
The use of statistical methods as specifically stated in either the
methods section, in tables or figures, or in the text was tabulated for each
study. As reflected 1n Table 4-1, the most widely used procedure is the
Student t-test, which was utilized in over one-third of the studies. This
test was frequently used 1n conjunction with other methods such as analysis
of variance (ANOVA). Most often, however, the t-test was the only method
employed. A noteworthy finding was that one-quarter of the studies 1n the
survey contained no reference to any statistical procedures. In addition,
nearly 23% of the studies did not specify the type of statistical tests
used. In these cases, either the p values were given in the text or in the
footnotes to tables or figures without explanation or the use of statistics
was referenced to another source. In one study, the authors stated that
"statistical comparisons were made by standard procedures" (Cerklewski and
Forbes, 1976). Table 4-1 indicates that 83% of those studies examined
either used no statistics, did not specify the statistical methodology or
used Student's t-test.
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The other statistical tests employed in these studies are also listed in
Table 4-1. Because many studies used more than one procedure, the total
number of individual tests is greater than the total number of studies In
!
the survey. Nearly 37% of the studies used a method other than or in
addition to the Student t-test. No attempt will be made here to define or
characterize each method nor critically assess the appropriateness of these
tests for interaction studies except for the use of Finney's (1971) equation
(equation 4-8 with p=0) for joint toxic action. Four studies used this
additivity model to calculate the predicted LD5Q values for a number of
i
binary mixtures. Ratios of predicted to observed LD5Qs were calculated
and a determination was made as to the significance of the deviance from
additivity. Keplinger and Deichmann (1967) determined the acute toxicity
Induced by combinations of two and three pesticides and reported that while
most of the combinations induced essentially additive effects in mice and
rats, there were cases of less than or more than additivity. Pairs of 27
industrial chemicals tested for joint toxic Interaction demonstrated that
the additive model reasonably predicted the toxiclties of a majority of
these binary mixtures (Smyth et al., 1969). Departures from additivity were
reported by Withey and Hall (1975) who investigated the joint toxic action
of perchloroethylene with benzene or toluene and by Freeman and Hayes (1985)
i
who observed the potentiation of acute acetonitrile toxicity by acetone.
A handful of other studies has also attempted to quantify toxic inter-
actions in terms of deviation from an additive response, j An undefined
i
additive model was employed by Woolverton and Balster (1981) to Investigate
the effects of combined ethanol and 1,1,1-trichloroethane exposure.
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Wysocka-Paruszewska et al. (1980) used the coefficient of combined action,
defined as "the ratio of the calculated LDcn on the basis of LD,. of a
bu 50
single compound to the experimental LD50" to evaluate the toxicity of
thiuram in combination with several other pesticides. Derr et al. (1970)
used a response addition approach in which the mean heart or body weights
for individual treatment groups (minus control values) were added to
calculate the expected combined response to cobalt (cobaltous chloride) and
ethanol exposures. The observed and calculated weights were then compared
using a Student t-test. The effects of prophylactic protection against
cyanide intoxication were evaluated using potency ratios defined as the
LDgo of KCN with antagonist(s) divided by the LD5Q of KCN without
antagonlst(s) (Way and Burrows, 1976). The results of the above studies
were varied in that additive, potentiated and antagonistic effects were
observed depending on the mixture components and concentrations.
4.6.1. Description of the Mixtures Data Base Sample. The use of statis-
tics in the U.S. EPA mixtures data base has been described in the previous
section. A 10% random sample of papers from the U.S. EPA mixtures data base
was taken to review the quality of experimental design, use of statistics
and ensuing conclusions. The sample was stratified by classification of
type of statistics used; there were 32 papers assessed. A detailed critique
of these papers is contained in Appendix C. It is important to note that if
an investigator used a poor experimental design or inappropriate statistical
analyses, the conclusions regarding the interaction are suspect. Unfortu-
nately, it is impossible to determine if the conclusions are correct without
access to the raw data for re-analysis.
In summary, there was no use of statistics in 8 studies, the statistics
used were not specified in 7, no statistics were given in 2 abstracts, and
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no quantitative data were given 1n 1 paper. Of the remaining papers, the
ones that described their statistical methods, the methods used were
Inappropriate In 9 and there was no baseline control 1n 4 papers. In one
paper, the design and use of statistics were appropriate with the conclusion
justified. !
4.7. CRITICAL ASSESSMENT EXAMPLE ;
As a further assessment of the quality of statistical analysis in the
mixtures literature, one paper was selected for Intensive scrutiny. The
j
study by Eybl et al. (1984) was chosen because of Its detailed descriptions
I
of the toxlcologlc and statistical methods employed.
Eybl et al. (1984) Investigated the Influence of several chelating
agents on the acute toxlclty of cadmium (Cd). As will be shown in the
following discussion, the experimental design and the statistical methods
i
i
used were Inappropriate for characterizing the Interaction for risk
assessment purposes, and were in fact inadequate for some of the authors'
goals as well. Eybl et all. (1984) examined effects on mice and rats; only
the mouse experiments are discussed here. Characteristics | common to the
I
mouse test series were as follows:
species: male mice (SPF, Velaz Prague), 20.22 g body weight
route: i.v. (single injection)
chemicals: Cd with any of six chelatlng agents or combinations
endpolnt: survival rate at 10 days
4.7,1. Experimental Conditions. The first series studied; the effect of
single chelatlng agents on survival of mice injected with CdCl2. The
conditions were as follows:
Groups:
20 mice per exposure group
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Exposure:
Statistical method:
toxicant- CdCl2»2.5H20 single subcutaneous
Injection 20 mg/kg; Inhibiting agent- single
1ntraper1toneal Injection at a molar ratio of
25:1 (chelator:CdCl2)
unspecified, probably Fisher's exact test
(Fisher's exact, Chl-square, t-test all
mentioned 1n Methods section)
The conditions for the second, third and fourth series were similar to
those of the first series.. The second series Included three dose levels
(molar ratio of 1:1, 2:1, 5:1). The third series used one dose level (molar
ratio 5:1) but two treatment sequences (simultaneous vs. 2 hours after the
Cd Injection). The fourth series used one dose level for single chelator
effects and a different dose level for effects of two chelators together.
For example, ZnDTPA and DMSA were tested Individually at a molar ratio of
2:1, while the combination ZnDTPA+DMSA was tested at a ratio of 1:1:1.
4.7.2. Discussion of Design. This first series seems to have been
Intended only to screen for the most effective Inhibitors (antidotes) of Cd
toxldty. Cadmium Is always administered at the same dose, and each of the
chelators 1s administered at only one dose level. Consequently, no dose-
related Interaction can be determined. The authors apparently assume that
the data are similar to data on treatment regimens for a disease, where here
the disease is Cd toxlcity and the treatment is one of the chelators. The
"disease-treatment" interpretation, however, requires the assumption that Cd
lethality occurs only at 20 mg/kg or more, and that the administered
chelator levels are the standard antidote dosages. None of these assump-
tions has been demonstrated in this paper. Consequently, any conclusions
are then specific to the doses used.
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No models were presented by the authors as a means of estimating the
"expected" response from the Cd-chelator combination. Models of the inter-
action between a chelator aind Cd cannot be applied to the data as presented
since results are not given for a control group (no CD, no chelator), nor
for exposure to a chelator alone (no Cd). A key unstated assumption is that
all the chelators are administered at nontoxic doses.
The statistical test used is not stated, but can be assumed to be the
Chi-square or Fisher's exact test. These tests are consistent with the
interpretation of the experiment as if it were the treatment; of a disease.
As further confirmation, use of Fisher's .exact test in recalculating the
significance levels showed agreement with Eybl's published values (Eybl et
a!., 1984, Table 1) except for the group 4 to group 6 comparison, which
should show p=0.02, I.e., it should be footnoted by an asterisk to denote
p<0.05, not p<0.01. ;
i
The preceding comments; also apply to the other test series. In
,
addition, the doses (molar ratios) used in the second and third series were
the same for all chelators, regardless of each one's inhibitory effective-
ness. The doses used in the fourth series were selected to provide the same
number of moles of mixed chelators as used in each individual test. No
model has been located that uses such a dose selection in a mixture study,
and the authors do not provide any justification for these doses.
4.7.3. Discussion of Results. The reported results for all four series
include the survival fraction (n/20) and significance level (percent) of
various differences in survival rate. Several comparisons are made in the
first, second and fourth test series with no adjustment for multiple
comparisons. The Importance of the multiple comparison problem is easily
demonstrated with.the first series. Note that two comparisons are reported
4-23
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between chelators, suggesting that all chelator survival rates may have been
compared with one another but only a few comparisons were reported. There
are 6!/(2! x 4!) = 15 such palrwlse comparisons In addition to the six
comparisons between each chelator and Cd alone. At a decision significance
of 0.05, one of the 21 comparisons can turn out to appear significant
through random chance alone. So one of the six significant findings could
be circumstantial and not due to actual differences in inhibition. If the
decision rule is to require significance of at least 0.05, then the
chelators showing survival increase at a significance of 0.01 or lower would
probably be significant after the multiple comparison adjustment. The
finding that was significant at 0.05 but not 0.01 is suspect. In the fourth
series, the multiple comparison issue is not as strong, since only six
possible comparisons could be made; the reported significance levels,
however, are still inaccurate.
In addition to compensating for multiple comparisons, the analysis
should have used survival time (when the animals died) instead of the end
survival fraction. In addition to using more data 1n the statistical
analysis, comparing survival curves would have also provided more informa-
tion for studies on the mechanism and pharmacokinetics of inhibition by the
chelators.
Use of a single dose level in the first series Is justified for screen-
ing purposes. The analysis of the second series should have combined the
dose levels, instead of merely reporting pairwise comparisons. For example,
if the different series are assumed to be comparable and the groups com-
bined, then the dose-response data appear as in Table 4-2. The dose selec-
tion for the fourth series could then have been made according to some
interaction model so that the response to the combined chelators could be
4-24
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TABLE 4-2 ;
Combined Results for CaOTPA and DMSA Inhibition of Cd Toxicity3
Chelator
Doseb
Survival
Surviving/Total
CaDTPA
DMSA
1
1
2
5
5
25
1
2
5
60.0
60.6
80.0
81.8
86.7
85.0
3.0
35.0
100.0
1 12/20
i 20/33
16/20
27/33
1 13/15
17/20
1/33
7/20
33/33
aSource: Eybl et a!., 1984
bDose is molar ratio of chelator:Cd with Cd administered as CdCl2»2.5H20 at
20 mg/kg ;
4-25
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predicted. For example, for a response addition model where Independence of
action 1s assumed, the doses used In Individual testing would be duplicated
In the mixed test (1f the molar ratio of chelator:Cd of 2:1 was used for
each single chelator test, then the mixed exposure ratio of chelator:
chelator:Cd should be 2:2:1). For a dose addition model where similarity of
action 1s assumed, the mixed exposure would use doses scaled according to
potency, where the summed scaled dose of the mixture would have been
previously tested for one of the single chelators. Instead, since the dose
selection was not justified by the authors, and since no predictive model
was presented, the authors' conclusion that "the additive effect of these
two chelatlng agents was demonstrated" 1s false. In general, the conclu-
sions throughout this paper are much weaker than they could have been had
adequate design and analysis been Implemented.
4.8. SUMMARY
In summary, statistical methods that have been used for assessing Inter-
actions among components 1n chemical mixtures have been examined. This
review Indicates that proper experimental design Is Infrequently utilized,
and that statistical techniques are rarely chosen appropriate to the experi-
mental data. In particular, current techniques for Investigating the
presence and extent of Interactions in complex mixtures are Inadequate,
Impractical or Impossible to apply. At best, practical design and analysis
techniques can be applied to characterize Interactions in the experimental
dose ranges only among constituents of simple mixtures.
4-26
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5. DISCUSSION AND REASSESSMENT OF THE GUIDELINES
5.1. OVERVIEW j
i
This chapter reviews and reevaluates the current Agency guidelines on
mixtures based on the Agency's experience In applying these guidelines as
well as considerations of new information that has been obtained and new
approaches that have been proposed since the guidelines were developed.
Revisions suggested in this chapter along with other comments received by
•
the Agency will be considered for future incorporation into the guidelines.
Based on the mechanistic considerations summarized in Chapter 3, toxic
interactions may modify significantly the toxic and carcinogenic potency of
environmental contaminants. The types of information available for quanti-
tatively assessing the magnitude of such Interactions as reviewed 1n Chapter
2., however, are not extensive. While appropriate mathematical models and
statistical techniques are available to quantify some simple binary inter-
actions, these methods cannot be extended to complex mixtures because the
data requirements of such extensions lead to experimental designs that are
impractical. In addition, mathematical models for quantifying promotion and
cocardnogenlc efficiency that could be used to systematically assess and
compare the quantitative significance of these phenomena have not been
developed. Those quantitative estimates of compound interactions that can
be made suggest that most interactions are within a factor of 10 of those
that would be predicted based on the assumption of no Interaction. The data
on which this generalization is based, however, are limited.
The preferred approach presented in the guidelines for conducting risk
assessments on mixtures Is to use Vn vivo toxicity data on the mixture
itself based on the route of exposure and duration period of concern. This
5-1
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remains the preferred approach, as long as certain factors such as masking
of toxic or carcinogenic effects are considered. Nonetheless, this approach
will not be practical In most cases because adequate toxidty data are
available on only a few complex mixtures. While the concept of "sufficient
similarity" may be able to extend this approach somewhat, this approach will
stm be restricted to a few well-studied groups of complex mixtures (see
Appendix B).
The use of an assumption of dose or response addltlvlty as the basis for
risk assessments on mixtures remains a useful, and 1n many cases the only
practical, approach. Some mechanistic considerations suggest that
addltlvlty may be a plausible assumption 1n the low-dose region because
thresholds for many types of Interactions are expected to exist. In
addition, many acute bloassays on binary or simple mixtures suggest that the
dose addltlvlty often adequately accounts for mixture toxlcity based on
gross toxic endpolnts. Nonetheless, the credibility of this approach dimin-
ishes as the number of components 1n the mixture Increases because for many
mixtures the toxlcity and perhaps the Identity of all components are not
known.
Alternatives to any of the above approaches are being developed and
explored by the Agency and other groups to more fully utilize the extensive
In. vitro and short-term In vivo data on many mixtures. Two such alterna-
tives, the "comparative potency approach" and the "toxic equivalency
factor," were not discussed 1n the guidelines.
The "comparative potency approach" attempts to'calibrate the Ijn vitro
potency of groups of complex mixtures to the limited jm vivo potency
estimates of these mixtures. Once a relationship between in vitro and In
vivo potency has been demonstrated, the results of jin vitro assays on other
5-2
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related complex mixtures can be estimated. As discussed below 1n Section
5.3.,, this approach has been applied to the carcinogenic potency of combus-
1
tlon emissions and can be regarded as a more formal and quantitative exten-
sion of "sufficient similarity." As with the direct application of suffi-
cient similarity, care must be taken to ensure that the approach Is applied
only to mixtures that are likely to exert effects by the same mode of action.
The "toxic equivalency factor" method Involves estimating the potency of
less well studied components In a mixture relative to the potency of better
studied components, using data from comparable types of in vitro and 1_n vivo
assays. So far, this method has been used only to estimate the toxlclty of
j
mixtures of chlorinated dloxlns and dlbenzofurans (a group of similar
compounds) by using the considerable data on the Ui vitro activity of these
compounds. The toxiclty of the mixture 1s then estimated by summing the
products of the equivalency factors and concentrations of the components 1n
the mixture. An estimate of the In vivo potency of the mixture can be made
by multiplying this sum of the products by the ^n vivo, potency of the refer-
ence compound, I.e., the compound that served as the basis for estimating
the toxic equivalency factors (2,3,7,8-TCOD In the case of mixtures of
i
chlorinated dloxins). This approach can thus be regarded as an extension of
the assumption of dose addltlvlty and like dose add1t1v1ty must be
restricted to compounds that act by the same mechanism.
Both of the above approaches are likely to prove useful as alternatives
or bases for comparison with risk assessments using the hazard index based
on dose or response additiv'ity as given in the guidelines. As with any type
i
of analysis based on jji vitro data, confidence in these methods will vary
with the degree to which the 1_n vitro analyses have been validated as
predictors of in vivo responses. >
5-3
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None of the above considerations fundamentally alter the basic approach
recommended In the original guidelines. All of these considerations do
reinforce the underlying principle of the guidelines: "No single approach
can be recommended to risk assessments for multiple chemical exposures.
Given the complexity of this issue and the relative paucity of empirical
data from which sound generalizations can be constructed, emphasis must be
placed on flexibility, judgment, and a clear articulation of the assumptions
and limitations in any risk assessment that is developed."
5.2. COMPLEX MIXTURES
For complex mixtures, It is not likely that toxic or carcinogenic inter-
actions will or can be quantified using the mathematical constructs given in
Chapter 4. As discussed in Chapter 4 and illustrated in Section 2.4., the
types of experimental designs that are required for meaningfully quantifying
interactions for single pairs of chemicals are prohibitively complex for the
routine assessment of chronic effects. For mixtures containing tens or
hundreds of chemicals, the proportions of which can vary over time or among
sources of generation, elaborate bioassays for quantifying interactions
among components are impractical.
The guidelines currently recommend using data on the mixture or a
"sufficiently similar" mixture for the risk assessment. In general terms,
the determination of sufficient similarity should consider the chemical
composition of the mixture, any variation in the chemical composition, as
well as the toxlcologic properties of the mixture components and fractions.
The criteria for determining "sufficient similarity" are intentionally vague
and are likely to vary depending on the nature and quality of the available
data, the toxlcologic endpoint, and the extent of human exposure. A case
study applying the concept of "sufficient similarity" is given in Appendix
B. Using this approach, a risk assessment can be conducted if the mixture
5-4
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on which adequate toxlcologlc data are available is judged sufficiently
similar to the mixture for which a risk assessment is desired. For certain
classes of complex mixtures on which human or animal data are available on a
relevant route of exposure and are adequate for conducting a quantitative
risk assessment (e.g., coke oven emissions), the assessment of "sufficient
similarity" should be a useful approach.
For many other classes of complex mixtures, however, such In vivo data
are not available or if available are not by a route of exposure likely to
occur in the environment. As currently written, the guidelines suggest, in
the absence of "sufficient similarity," that an additivlty assumption be
used for similar-acting components after assessing whether data are suffi-
cient for quantifying any component interactions. In practice, this will
normally lead to an additivlty assumption. If the mixture contains many
i
chemicals, it is also likely that adequate toxicity data will not be avail-
i
able on some of the components. Furthermore, for some highly complex or
highly variable mixtures, not all of the chemical components may be known.
The Agency recognizes that as the number of components increases and as the
number of components lacking adequate toxicity data increases, confidence 1n
the risk assessment diminishes. !
The use of a comparative potency method may sometimes be preferable to a
simple additivity assumption in cases where the criteria for sufficient
similarity are not met. This method, as applied to carcinogens^ was
presented by Albert et al. (1983) and was further refined by Lewtas (1985).
The underlying assumption is that relative potencies among j_n vivo and In
vitro bioassays are constant:
RP-, = kRP2 (5-1)
I
where RP, and RP~ are the relative potencies of a compound or mixture In
.
bioassays 1 and 2, respectively, and k is a constant. It is also assumed
5-5
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that a single number 1s sufficient to characterize the response In each
assay and that species show parallel response within an assay. Using these
assumptions, the results of 1_n vivo mixture bloassays from which quantita-
tive risk assessments can be made are correlated with the quantitative
results of in_ vitro bloassays. This correlation can be used as a "calibra-
tion curve" to estimate the jji vivo rate of response of similar compounds or
mixtures when only quantitative In vitro results are available. Using this
approach, Albert et al. (1983) reported that estimates of comparative
potency for coke oven emissions, roofing tar and cigarette smoke based on
several in 'vitro bloassays (Salmonella mutagenlclty assay, L5178Y mouse
lymphoma cell mutagenlclty assay and a sister chromatld exchange assay) were
within a factor of <2 of estimates of comparative potency based on
epldemlologlc data for lung cancer. Using additional data from mouse skin
tumor Initiation studies, Albert et al. (1983) proposed unit lung cancer
risks for dlesel and gasoline engine exhaust partlculates based on the
relative potencies of these partlculates In In vitro assays. Lewtas (1985)
extended this analysis to Include emissions from various energy combustion
sources.
As discussed by both Albert et al. (1983) and Lewtas (1985), the
relative potency approach makes several assumptions concerning mechanisms of
action and dose-response relationships among the various types of Iji vivo
and _1n_ vitro bloassays that are used. These assumptions and the corre-
sponding uncertainties must be weighed against the assumption of and uncer-
tainties 1n dose or response addition. The relative potency approach Is
attractive because data on the mixture of concern can be generated
relatively quickly and Inexpensively. In addition, given the Increasing
amount of data available on the effects of mixtures in in vitro tests, as
5-6
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discussed In Section 2.2., and the dearth of information on the magnitude of
toxic interactions In vivo, the relative potency method offers one approach
to the problem of complex mixtures that is amenable to experimental testing
and validation.
The use of the relative potency method or other approaches based on In
vitro or short-term in vivo bioassays seems to be potentially useful for
assessing the biologic activity of complex mixtures. Only limited data,
however, are available for supporting the quantitative correlation of i_n
vitro and j[n vivo relative potencies and the data that are available suggest
that the correlation between biological activity in the jjn vitro assay and
the In vivo assay will not be uniform for all types of mixtures. For
Instance, Salmonella are known to be particularly sensitive to the mutagenlc
effects of nitropyrene by virtue of the organism's endogenous nitroarene
reductase (Mermelstein et a!., 1981). A comparative potencyjjudgment of a
nitropyrene-containing mixture based solely on Salmonella mutation data
would likely overestimate eukaryotic mutagenic or tumorlgenic activity.
An empirical approach to selecting the most appropriate i_n vitro assay
for applying the relative potency approach could be based on the use of a
battery of screening tests, including in vitro assays and short-term in yivo
assays (NAS, 1988a). The quality of the correlation in biological activity
between the screening tests; and the known in vivo relative potencies of a
related group of complex mixtures could then serve as a guide in determining
the most appropriate assays for applying the relative potency method to
other related complex mixtures. The scientific validity of applying the
j
relative potency method based solely on empirical correlations is question-
able, however, particularly when multiple pair-wise comparisons are made
among several in. vivo and in vitro assays. An alternative to multiple
5-7
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pair-wise comparisons has been proposed by DuMouchel and Harris (1983) using
Bayeslan statistical methods to combine the results of multiple jm vivo and
!n vitro assays. Nonetheless, confidence 1n the use of any In vitro or
short-term i_n vivo assay for estimating environmental risk will depend on
the extent to which the assay reflects the mechanism of action and
pharmacoklnetlcs of the mixture. For many in vitro assays, which provide
only an exogenous activating system, this confidence may be limited.
Furthermore, 1n many Instances, the dose-response curves within in vivo
or in. vitro assays for even pure chemicals are not linear over a wide range
of concentrations or doses. Consequently, a single meaningful "potency"
term will not be appropriate for comparing arrays of nonlinear curves. If
the "potency" Is expressed as an estimate of single slope parameters taken
from the mid-range or linear portion of the dose-concentration curve of the
in vitro bioassay and such values are correlated with linearized potency
terms from in. vivo bioassays with relatively few dose groups and small
numbers of animals per group, the errors associated with the estimated
potencies are likely to be high and the significance of any correlation
questionable.
Notwithstanding these limitations and concerns, the use of the compara-
tive potency method or some analogous approach based on in vitro or short-
term in. vivo tests may be the only practical method for assessing risks
posed by complex mixtures on which adequate long-term in vivo studies are
not available. The extent to which the use of such an approach can be
considered scientifically valid or simply the application of a risk manage-
ment decision scheme Is likely to vary depending on the quality of the
correlations in biological activity and the degree to which a clear associa-
tion can be made between mechanisms of action in the screening assays and in
5-8
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the In vivo effect Induced by the mixture. Depending On the number of
compounds In the mixture of concern and the adequacy of the toxlcologic data
i
on these compounds, 1t may be most reasonable to use both the comparative
\
potency method as well as the assumption of dose or response additivHy to
gauge the variability between the two methods and better express the uncer-
tainty 1n the risk assessment. i
This approach has generally been applied only to carcinogenic effects.
An application to noncancer health effects could be reasonably made 1f the
mechanisms of action were similar between the effect of concern and the jn.
vitro or short-term In vivo bloassays proposed and If data were adequate for
assessing the constancy and the correlation In potencies between the short-
term and long-term assays.
5.3. MIXTURES OF CHEMICAL CLASSES
As discussed In Section 2.2., mixtures of chemical classes differ from
complex mixtures In that the compounds in the former category are struc-
turally and toxlcologically related. Some types of mixtures of chemical
classes are produced and used as a mixture following a reasonably consistent
and well-defined procedure. Examples of such mixtures Include the various
commercial polychlorinated and polybrominated blphenyls, toxaphene and
chlorinated naphthalene. Other types of such mixtures are chemically and
toxicologically related compounds that are usually found together In the
environment but can vary substantially in the proportions of the components
depending on the source of the mixture. Examples of these latter mixtures
include polychlorinated and polybrominated dioxlns and dibenzofurans. This
distinction between these two mixture types Is Intended to reflect the
different types of data that are available or might reasonably be obtained
on mixtures of chemical classes.
5-9
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Because some mixtures are reasonably consistent- and limited in the
diversity of their composition, data are available on their different
commercial formulations (e.g., Aroclor 1264). When data are not available
on a specific formulation, the formulation lacking data may often be suffi-
ciently similar to a formulation for which data are available so that a risk
assessment can be conducted by analogy. For such mixtures, It thus seems
reasonable to continue to conduct risk assessments using toxicity data on
the mixture as the preferred approach. Nonetheless, data may sometimes
suggest that differential rates of environmental decay or environmental
partitioning of the mixture components may lead to human exposures to a
mixture that is not representative of the mixture on which the risk assess-
ment was originally based. In such cases, quantitative structure activity
relationships or approaches based on the relative potency method discussed
above may have merit. Such modifications to the current approach have not
been conducted as yet by the Agency and examples of such approaches have not
been encountered in the literature. If such approaches are used, their
validity will be dependent, as with the relative potency approach, on the
degree to which the approach can be validated with in vivo data.
Other mixtures, such as the chlorinated dioxins and dibenzofurans,
require a different approach since "typical" formulations or compositions do
not exist and thus the multiple chronic bioassays may be not be feasible.
The Agency has proposed an interim procedure for estimating risks associated
with exposure to chlorinated dioxins and dibenzofurans (U.S. EPA, 1987c). A
similar approach has been used by the New York State Department of Health
(Eadon et a!., 1986). As with the relative potency approach, these methods
rely on in vitro or ;acute in vivo data. Rather than using such data to
assess the toxicity of the mixture of concern, however, these approaches
5-10
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estimate "toxic equivalency factors" for the various congeners In the
mixture based on acute or In vitro data and validate the relationship with
the available data on chronic or subchronlc toxicity. The toxic equivalency
factors can then be used to assess the hazard posed by exposure to any
combination of the congeners in any ratio. To do this, the concentration of
each component in the mixture is multiplied by the toxic equivalency factor
of that component. This product expresses the concentration of the compo-
nent as an equivalent concentration of the reference compound. The equiva-
lent concentrations for all components are then added. This total repre-
sents an estimate of exposure to the mixture 1n terms of the reference
compound. This transformed exposure estimate is then multiplied by the
potency of the reference compound (2,3,7,8-TCDD in the case of the
chlorinated dioxins) to obtain an overall estimate of risk. Depending on
- i
the quality of the monitoring data and exposure assessment, U.S. EPA (1987c)
' . - •> " - ' J*
also provides recommendations for modifying the risk assessment. As
f - • ; , i
reviewed by U.S. EPA (1987c), several other countries and organizations have
adopted similar approaches for the chlorinated dioxins.
The relative potency approach and the toxic equivalency approach are
• ! /
similar in that both use types of data to assess and quantify the toxicity
of mixtures that are not often used to quantify the risk from exposure to
single chemicals (I.e., acute data, data from atypical routes of environ-
mental exposure and jjjn vitro data). They differ, however, in that the toxic
t
equivalency approach rests explicitly on the assumption of dose or response
additivity; this method should be applied only to compounds that have the
same mode of action or act Independently, and does not account for any
potential interactions. If significant interactions do occur in the
5-11
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mixture, as appears to be the case with the promotion efficiency of poly-
bromlnated blphenyls (Sleight, 1985), the toxic equivalency approach could
result 1n risk assessments that are misleading.
The relative potency approach, while not explicitly based on simple
similar action, assumes a linear nonthreshold response as 1t 1s applied to
carcinogens by Albert et al. (1983). In that the relative potency method,
however, Is conducted on mixtures and validated using In. vivo data on
mixtures, the possibility to account for Interactions Is not excluded. A
combination of the relative potency and toxic equivalency approaches could
Improve confidence in risk assessments of similar mixtures and mixtures of
chemical classes.
In applying either the relative potency or the toxic equivalency factor
methods, care must be taken to ensure that the compounds are not only chem-
ically but also biologically similar. Taking an example from Hehlman and
Witz (1986), a mixture of ketones containing methyl-n-butyl ketone and
methyl isobutyl ketone would be similar only superficially because methyl-
n-butyl ketone, unlike methyl isobutyl ketone, is a potent peripheral
neuropathic agent. The failure to account for the neurotoxic potency of
methyl-n-butyl ketone, which is toxicologically more similar to n-hexane and
2,5-hexadione than to other ketones, could lead to an erroneous risk assess-
ment. While this type of potential error can occur in dealing with single
chemicals with an incompTe.te data base (e.g., lack of a teratogenicity
\
study), the potential for this type of error is higher when dealing with
mixtures and using data that are normally considered inadequate for con-
ducting risk assessments on single compounds.
5.4. SIMPLE MIXTURES, COMPONENTS AND TOXIC INTERACTIONS
In the guidelines for mixtures, the Agency has proposed using addltivity
assumptions when data are not available on the mixture of concern or a
5-12
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reasonably similar mixture, and when the components are mechanistically
similar or Independent. For toxic agents with thresholds, a Hazard Index
(HI) Is recommended based on the assumption of dose additivity, and can be
expressed as follows: •
HI = E1/AL1 + E2/AL2 + ... + En/AI_n (5-2)
where E Is the level of exposure and AL Is the acceptable level of exposure.
The reference dose (RfD), an estimate (with uncertainty spanning perhaps an
order of magnitude) of a dally exposure to the human population (Including
sensitive subgroups) that 1s likely to be without an appreciable risk of
deleterious effects during a lifetime, Is recommended for use as the "accep-
table level" (AL) 1n order to standardize Agency risk assessments. Since HI
Is dlmenslonless, use of the RfD means that exposure (E) must then be
presented In similar units as dally intake (mg/kg/day). For carcinogens,
the recommended equation Is based on a simple addition of risks. At low
risk levels, this equation simplifies to
P =
(5-3)
where P is the expected response, D is the dose (level of exposure) and B is
an estimate of response rate (usually a plausible upper bound called a slope
factor). In the low-dose region where responses are linear, equation 5-3 is
considered to be a reasonable approximation. At higher levels of risk,
nonlinearity and competing risks would need to be considered. In addition,
the guidelines also suggest some simple interactive models by which nonaddi-
tive joint action could be considered, while recognizing that adequate data
for using such models will usually not be available.
Since the publication of the guidelines, the literature on joint action
has not suggested any fundamental revisions to the above approach.
Berenbaum (1985a) has suggested a general approach estimate of expected
5-13
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responses under the assumption of addHlvlty. Seller and Scott (1987) Illus-
trated a method for partitioning attributable risks under either the assump-
tion of addltlvity or using data adequate for quantifying Interactions. The
available data base on the magnitude of toxic Interactions for environmental
contaminants has not, however, changed substantially. In most cases, an
estimate of risk for exposure to a chemical mixture will be based on an
addHlvlty assumption, except In those cases where, chronic mixture data or
an appropriate surrogate approach (e.g., relative potency) are available.
The addltlvity assumptions presented In equations 5-2 and 5-3 do, none-
theless, have serious shortcomings. As applied to toxicants, equation 5-2
Implies that as the acceptable level 1s approached or exceeded, the level of
concern Increases linearly (e.g., an HI of 50 1s of twice as much concern as
an HI of 25) and In the same manner for all mixtures. As the mixtures
guidelines note, these Implications are Incorrect. RfDs (the values
recommended for use as acceptable levels) do not have equal accuracy or
precision, and are not based on the same severity of toxic effect.
Moreover, slopes of dose-response curves In excess of the RfO 1n theory are
expected to differ widely. The determinations of accuracy, precision or
slope are exceedingly difficult because of the general lack of toxlclty
data. Severity of endpolnt, however, 1s often known. For example, with
fluoride and selenium 1t Is known that relatively narrow excursions above or
below the RfD can cause severe adverse effects through toxlclty or
deficiency, respectively. Among other compounds, the margins of safety or
error are thought to vary because of differences 1n the quality of the
available data or the relationships of dose and time of exposure to the
Incidence, severity or intensity of effects. Some of these sources of
variability and uncertainty have been discussed in the literature (Crump,
1984; Dourson and Stara, 1983; Lu, 1985; Rulis, 1987), but approaches to
5-14
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quantifying these differences among chemicals have not been adopted for
single compounds, and this inhibits modification and improvement of the
current approach for the assessment of mixtures of systemic toxicants.
For carcinogens, equation 5-3 may be overly conservative because upper
bounds rather than estimates of expected risk are added. This limitation is
I
recognized but a practical alternative has not been proposed. As discussed
in the Agency's guidelines for carcinogens, upper bounds on risk are used
because of the substantial uncertainties involved in high- to low-dose and
species-to-species extrapolation. Conversely, as discussed by Berenbaum
(1985b), synergistic interactions between carcinogens may result in dose-
response curves that are steeper in the low-response region than in the
experimentally observable region. In such cases, the assumptions of
linearity and additivity could underestimate risk. This can also be the
I
case in heterogeneous responding populations (Margosches et a!., 1981).
Mechanisms for low-dose synergism have not been proposed; in fact, Thorslund
and Charnley (1987) show that under the multistage theory, experimentally
determined synergism will not significantly differ from the low-dose risk
'
estimate based on additivity. [
5.5. MIXTURES OF CARCINOGENS WITH OTHER COMPOUNDS
'[
The enhancement (by promotion or cocarcinogenicity), inhibition or
masking of the carcinogenic activity of known or unidentified carcinogens in
complex mixtures is only briefly discussed in the Agency's guidelines on
mixtures. While potentially of great practical importance (Reif, 1984), few
specific proposals have been made to assess and quantify such interactions.
Even with all the work that has been done on tumor promoters and cocar-
cinogens, much of which is summarized by Lucier and Hook (1983), systematic
5-15
-------
and predictive relationships for expressing and measuring enhancement have
not yet emerged. Given the complexities of promotion/cocarcinogenicity, It
Is not surprising that no clear approach for incorporating these concepts
into a risk assessment methodology has been recommended. While some
approaches to low-dose extrapolation have been recommended which consider
the effect of promoters on the initiator dose-response relationship (Burns
et a!., 1983), no dose-response models that consider variations in both
doses of the initiator and doses of the promoter have been proposed or
applied to complex mixtures. As discussed by Stara et al. (1983), several
questions must be answered before such applications are likely to be made:
How specific and consistent are Initiator-promoter interactions?
Does the promoting efficiency of a compound vary with initiating
agents and, If so, does this variation follow a consistent or
predictable pattern?
How does exposure to multiple promoting agents affect the promoting
efficiency of the individual promoters? If addltlvHy is a reason-
able assumption, which type of additivlty might be expected based
on what we know about the mechanism of promotion?
How does promoting efficiency vary with the duration of exposure to
the initiator and the promoter?
Is there any validity in using promotion data from one route of
administration to predict promoting activity from another route of
exposure?
These questions remain largely unanswered. Until answers or reasonable
assumptions are proposed, progress in directly applying promotion/cocarcino-
genlcity data to quantitatively modifying risk assessments for mixtures is
likely to be minimal.
A similar situation exists with compounds that cause an apparent inhibi-
tion of or protection from chemically-induced carcinogenicity. As reviewed
by NRC (1980), very few examples of this type of interaction have been noted
and the nature of the interaction can vary with the time course of exposure.
5-16
-------
More recently, in reviewing the literature on tumor promotion of the liver,
Hermann (1985) cites a few additional studies showing a decrease of preneo-
i
plastic liver foci after prolonged treatment with some anti-oxidants or
hypolipidemic compounds and suggests that such "anti-promoters" may have
potential in the control of cancer. While such a prospect is encouraging,
the data currently available are not sufficient for quantifying the dose and
time relationships for tumor inhibition. Until such data become available,
the presence of tumor inhibitors in mixtures are not likely to be used in
quantitatively modifying the risk assessment unless they are incorporated in
a comparative potency assessment. !
The problem of masking of the carcinogenic activity of some components
in a mixture that is due to simple dilution or to the toxic but noncarcino-
genic activity of other components in the mixture is less difficult to
address than either enhancement or inhibition of carcinogenicity. One
component of this problem simply is to account for competing risks. As In
the example cited in Section 2.5. from Raabe (1987), this problem is not
unique to mixtures. The statistical methods for accounting for competing
risks in animal bioassays are available in the literature (Altschuler, 1970;
Hoel, 1972; Peto et al., 1972; Peto, 1974) and are Incorporated into some
commercially available statistical programs for the analysis of cancer
bioassay data (e.g., MULTI-WEIB by Howe and Crump, n.d.). In other
Instances, an adequate chronic study showing no carcinogenic activity may be
available on a mixture that contains known carcinogens. While the guide-
lines state that data on the mixture of concern are preferred, to additivity
assumptions based on the known activity of the components in the mixture,
the analysis of such a "negative" bioassay must consider whether a carcino-
genic response would have been expected given the doses and numbers of
5-17
-------
experimental animals used. As with masking due to toxlclty, masking due to
dilution 1s not unique to mixtures but Is essentially Identical to evalu-
ating the significance of negative and positive results from different
bloassays of a single compound.
5-18
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6. RESEARCH NEEDS
For complex mixtures, similar mixtures and mixtures of chemical classes,
the kinds of research needs vary depending on the specific approach to be
taken in developing the risk assessment. For instance, U.S. EPA (1987c)
proposed the following research needs for better validating the toxic
equivalency factor approach for chlorinated dioxins and dibenzofurans:
1. Validation and completion of in vitro test data.
2. Investigation of the relationships between short-term in. vivo
and in vitro tests and the chronic toxic endpoints of concern
(I.e., carcinogenlcity, reproductive toxicity, immunotoxlcity
and other significant human health effects). i
.
3. Additional data on pharmacodynamics and metabolite toxicity.
'
4. Development of additional short-term assays which can test the
mechanistic hypotheses underlying the toxic equivalency factor
approach.
i
Since this approach may also prove useful for other classes of compounds,
such as the bromlnated dioxins and dibenzofurans, comparable studies on
these classes of compounds might also be added to the above list.
I
Research needs for the comparative potency method are somewhat similar.
Currently, the relationship is validated by comparing the in vitro and in.
vivo relative potencies of relatively few mixture classes. Confidence in
this method could be improved if the basis for the comparison was broadened
to include not only relative potency estimates from human studies but also
potency estimates from animal bioassays. In addition, a more extensive
comparison including not only data on mixtures but also data on Individual
i
compounds would help to strengthen this approach.
Both the toxic equivalency factor and comparative potency methods are
generally applied only to carcinogens. While the in vitro tests on which
6-1
-------
these methods are currently based are probably only appropriate for carcino-
gens, other short-term assays have been developed for other endpolnts (e.g.,
teratogenlclty and cytotoxlcity) that may be applicable to the assessment of
the noncardnogenlc toxlclty of mixtures. Given the diversity of mixtures
1n the environment, the validation of a battery of short-term assays to
assess the systemic effects of mixtures could serve as a valuable adjunct to
the addltlvlty assumption.
In Implementing this research, the validation of screening tests must be
recognized as a complex process. As discussed with respect to several kinds
of assays (Brown et al., 1979; Purchase et al., 1976; Rlnkus and Legator,
1979, 1980; Rosenkranz and Po1r1er, 1979; Suglmura et al., 1976) validations
require not only careful criteria for assessing false positive and false
negatives but also a consideration of the class of compounds used to
validate the assay and the limitations that this may Impose on the
usefulness of the assay for other classes of chemicals. In addition, the
proposal to use any screening test Is greatly supported by the demonstration
that the mechanisms of action are similar for the toxic effect of concern
and the response observed 1n the screening test. Depending on use and
consistency of the screening test, greater attention may need to be given to
the statistical analyses of the assay results (Gart et al., 1979; Frome and
DuFrain, 1986) so that the errors and uncertainty In any analysis can be
more explicitly Identified.
As noted In Chapter 5, the use of the addltlvlty assumption 1s somewhat
restricted by the approach currently used for risk assessment of single
systemic toxicants. While an Improvement of this situation appears to be
more a matter of analysis than the generation of additional data, 1t 1s an
area that must be addressed If an Improvement In the application of the
assumption of addltlvlty 1s to be made.
6-2
-------
As discussed In Section 2.6. and Chapter 4, several nonlnteractlve
models can be applied to the diverse kinds of quantitative Interaction data
that are available. In addition, appropriate statistical methods have not
been applied to much of the data that are available, and the limitations of
some of the available information preclude the application of any quantita-
tive model. Consequently, no generalizations can be made on the quantitative
significance of interactions at normal environmental levels. This problem
could be at least partially addressed by a detailed reanalysis of the
available data by applying a variety of noninteractive models to derive
quantitative interactive coefficents. .'
In conducting risk assessments for single compounds, both carcinogens
and systemic toxicants, the Agency uses conservative but plausible assump-
tions concerning extrapolations from high to low doses and species to
species, and concerning modeling of time-to-effects data. Concern has been
expressed both within the Agency and by other elements of the scientific
community that the use of dose or response additivity combining such conser-
vative risk assessments for individual chemicals could lead to implausibly
conservative risk estimates for complex mixtures. This limitation in the
use of an additivity assumption is one of the reasons that the Agency
prefers using data oh, the mixture of concern or a sufficiently similar
mixture and has used the relative potency method or toxic equivalency factor
approach for complex mixtures. Nonetheless, additivity assumptions will be
used for many risk assessments on mixtures, and the need to develop alterna-
tive risk assessment procedures or testing strategies is recognized. The
Agency is currently reviewing the recent recommendations of NAS (1988a)
"\
along with other approaches. '
6-3
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-------
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study of the chemopreventlon of cancer. Acta Pharmacol Toxicol. 55(2):
107-124.
7-25
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Slaga, T.J., S.M. Fisher, L.L. Triplett and S. Nesnow. 1982. Comparison of
complete carcinogenesis and tumor initiation in mouse skin: Tumor initia-
tion-promotion a reliable short term assay. J. Am. Coll. Toxicol. 1(1):
83-89.
Sleight, S. 1985. Effects of PCBs and related compounds on hepatocardno-
genesls in rats and mice. Environ. Health Perspect. 60: 35-39.
Smlth-Sonneborn, J., E.A. McCann and R.A. Palizzi. 1983. Bioassays of oil
shale process waters in Paramecium and Salmonella. Ir»: Short-term Bioassays
In the Analysis of Complex Environmental Mixtures III, Waters, Sandhu,
Lewtas, Claxton, Chernoff and Nesnow, Ed. Plenum Press, New York.
p. 197-210.
Smyth, H.F., C.S. Weil, O.S. West and C.P. Carpenter. 1969. An exploration
of joint toxic action: I. Twenty-seven industrial chemicals intubated in
rats in all possible pairs. Toxicol. Appl. Pharmacol. 14: 340-347.
Smyth, H.F., C.S. Weil, G.S. West and C.P. Carpenter. 1970. An exploration
of joint toxic action: II. Equitoxic versus equivolume mixtures. Toxicol.
Appl. Pharmacol. 17: 498-503.
Stara, J.F., D. Hukerjee, R. McGaughy, P. Durkin and M.L. Dourson. 1983.
The current use of studies on promoters and cocarcinogens In quantitative
risk assessment. Environ. Health Perspect. 50: 359-368.
7-26
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Steenland, K. and M. Thun. 1986. Interaction between tobacco smoking and
occupational exposures In the causation of lung cancer. 3. Occup. Med.
28(2): 110-118. !
Stoner, G.D. and M.B. Shlmkln. 1982. Strain A mouse lung tumor bloassay.
J. Am. Coll. Toxlcol. 1(1): 143-169.
Strnlste, G.F., J.M. Blngham, W.D. Spall, G.W. Nlckols, R.t. Oklnaka and
D.J.-C. Chen. 1983. Fractlonatlon of an oil shale retort process water:
Isolation of photoactive genotoxlc components. In.: Short-term Bloassays 1n
the Analysis of Complex Environmental Mixtures III, Waters, Sandhu, Lewtas,
Claxton, Chernoff and Nesnow, Ed. Plenum Press, New York. p. 139-152.
Suglrnura, T., S. Sato, M. Nagao, et al. 1976. Fundamentals 1n Cancer
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Sun, Y.P. and E.R. Johnson. 1960, Analysis of joint action of Insecticides
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i
Swenberg, J.A., M.A. Bedell, K.C. Billings, D.R. Umbenhauer and A.E. Pegg.
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Takahashl S., T. Ohnlshl, A. Denda and Y. Konlshl. 1982. Enhancing effect
of 3-amlnobenzamlde on Induction of y-glutamyl transpeptldase positive
foci In rat Hver. Chem-Blol. Interact. 39: 363-368.
7-27 '•
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Thllly, W.6., 0. Longweel and B.A. Andon. 1983. General approach to
biological analysis of 9 complex mixtures. Environ. Health. Perspect. 48:
129-136.
Thorslund, T. and G. Charnley. 1987. Use of the multistage model to
predict the carcinogenic response associated with time-dependent exposures
to multiple agents. In: Current Assessment of Combined Toxicant Effects.
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EPA 230-03-87-027.
Tjalve, H. 1984. The aetiology of SMON may Involve an Interaction between
cHoqulnol and environmental metals. Med. Hypotheses. 15(3): 293-299.
Troll, W. and R. Wlesner. 1985. The role of oxygen radicals as a possible
mechanism of tumor promotion. Ann. Rev. Pharmacol. Toxlcol. 25: 509-520.
Trosko, O.E., C.C. Chang and A. Medcalf. 1983. Mechanisms of tumor promo-
tion: Potential role of Intercellular communication. Cancer Invest. 1(6):
511-526.
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7-28
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U.S. EPA. 1985. Health Assessment Document for Polychlorlnated Dlbenzo-p-
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i
p. 3-1 to 3-16.
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(CDDs and DCFs). Risk Assessment Forum, Washington, DC. EPA 625/3-87/012.
7-29
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7-30
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I
Weisburger, H.O. and G.M. Williams. 1980. Chemical carcinogens. In.:
i
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i
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j
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j
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i
!
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i
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7-31
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macoklnetlc Interaction between ajmaline and quinidlne in rats. J. Pharma-
coblo-Dyn. 9: 347-351.
Yamasakl, H. 1984. Tumor promotion — Mechanisms and implication to risk
estimation. Acta. Pharmacol. Toxicol. 55(2): 89-106.
Zweldlnger, B.B. 1982. Emission factors from diesel and gasoline powered
vehicles: Correlation with the Ames test. In: Toxicological Effects of
Emissions from Diesel Engines, J. Lewtas, Ed. Elsevier Science Publishing
Co., The Netherlands, p. 83-96.
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APPENDIX A
AGENCY DATA BASE ON MIXTURE TOXICITY
A-l
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OVERVIEW
The toxic Interaction data base contains Information obtained from
literature searches of all published studies on Interactions between toxic
chemicals (U.S. EPA, 1988). The goal Is to be complete, not merely repre-
sentative, so that analysis of the data, e.g., for trends across chemical
classes, can be performed If desired. This version does not contain
extensive quantitative data. This constraint Is consistent with the Agency
Guidelines for the Health Risk Assessment of Chemical Mixtures (U.S. EPA,
1986), which do not recommend any quantitative method for Including
Interaction data Into a risk assessment. As a result, the current version
of the data base 1s most useful for a qualitative evaluation of the
potential types of toxic Interaction between two environmental chemicals.
The data base package contains a User's Guide, diskettes (IBM PC
compatible), and a table for Interpreting the CASSI codes (CAS, 1980) for
the reference citations. The data base Is In dBASE III Plus format. The
access programs are compiled dBASE programs, and can be run without the need
for dBASE III Plus. The data base 1s available from the Risk Assessment
Contacts In each of the U.S. EPA's Regional Offices.
A-2
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DATA BASE STRUCTURE
i
The data base Includes 13 data fields, which are described In detail In
the next section. The structure 1s as follows:
Field Name
Type
Width
CAS-ONE
CHPD-ONE
CAS-TWO
CMPD-TWO
RTE-EXP
SPECIES
SEQUENCE
DUR-EXP
SITE
EFFECTS
INTERACT
AUTHOR
REFERENCE
Character
Character
Character
Character
Character
Character
Character
Character
Character
Character
Character
Character
Character
12
30
12
30
7
7
8
7
7
10
7
30
26
Description
CAS No. of first chemical
First chemical name
CAS No. of second chemical
Second chemical name
Exposure route \
Animal species !
Treatment sequence
Exposure duration
Site of adverse effects
Type of adverse effects
Type of Interaction
First two authors (or et al.)
Reference code, volume:page
An example of Input format and corresponding on-screen computer display
Is Illustrated In Figure 1.
DESCRIPTION OF DESIRED FIELDS |
Compounds
Each compound Is listed as either Compound I or Compound II.
CAS Numbers
CAS numbers corresponding to the above mentioned compounds are Included.
Routes of Exposure (codes provided - Table 1)
i
The routes of administration are specified for each compound and are
listed 1n order, I.e., Compound I first and Compound II second. For
example, In the study by Short et al. (1977), vinylldene chloride was given
via Inhalation while d1sulf1ram was administered orally. In Figure 1, this
Is Illustrated as follows:
IHL; ORL
A-3
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CAS No.: 75-35-4
CAS No.: 97-77-8
a) Input Data Sheet
Compound I: Vlnylldene chloride
Compound II: D1sulf1ram
Route of Exposure: IHL; ORL
Species: HUS
Treatment Regimen: II; I and SIM
Duration: ACU
Site: WBY
Effects: HOR(I)
Qualitative Assessment: INH
Reference: Short, R.D., Winston, J.M., Minor, J.L., Hong, C., Selfter, 0.,
and Lee, C. 1977. Toxlclty of vlnylldene chloride In mice and rats and Us
alteration by various treatments. 0. Toxlcol. Environ. Health. 3: 913-921.
b) Corresponding On-Screen Display
75-35-4 VINYLIDENE CHLORIDE
97-77-8 DISULFIRAH
Route: IHL;ORL
Site: WBY
Species:
Effects:
Author: SHORT,RD ETAL
HUS
HOR(I)
Sequence: II;I&SIM Duration: ACU
Interaction: INH
Ref: JTEHD6 (1977) 3:913-21
FIGURE 1
Example of Interaction Data Showing Original Coded Data
and On-Screen Representation
A-4
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TABLE 1
Route of Exposure
GAV
IAT
IAL
IBR
ICE
ICV
IDR
IDU
IHL
IMP
IMS
IPC
IPL
IPR
- gavage
- Intraarterial
- Intraaural
- 1ntrabronch1al
- Intracerebral
- 1ntracerv1cal
- Intradermal
- Intraduodenal
- Inhalation
- Implant
- Intramuscular
- Intraplacental
- Intrapleural
- Intraperltoneal
IRN
ISC
ISP
ITR
IVG
IVN
OCU
ORL
PAR
REC
SCU
SKN
UNR
- Intrarenal
- Intrascapular
- Intrasplnal
- Intratracheal
- Intravaglnal
- Intravenous
- ocular
- oral (dietary)
- parenteral
- rectal
- subcutaneous
- skin ;
- unreported
i
1
A-5
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When the same exposure route 1s used for both compounds, the route Is listed
only once. For example, 1f lead and zinc were both administered orally, the
Input would read
ORL
Species (codes provided - Table 2)
The species utilized In the study of Interest
Treatment Sequence
This field specifies whether the compounds of Interest were administered
simultaneously or sequentially. If administration was sequential, the order
of administration Is specified by the number of the compound. In Figure 1,
"treatment regimen" Indicates that dlsulflram was administered before (II;
I) and simultaneously with (SIM) vlnylldene chloride. If the two compounds
had been administered concurrently, the format would read
SIM
Duration of Study
The duration of the study of Interest 1s classified as either acute,
subchronlc, chronic or lifetime where
acute = <14 days (ACU)
subchronlc = >14 days but not <90 days (SCH)
chronic = >90 days (CHR)
lifetime = lifetime (LIF)
Sites (codes provided In Table 3)
The site or sites affected by the compound of Interest are entered In
this field. In Figure 1, the observed endpolnt was decreased survival,
which Is considered a whole body effect. Thus, the site of the effect Is
coded as WBY. Duration 1s defined as the period between the beginning of
A-6
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TABLE 2
Species
CAT - cat
CTL - cattle
CHD - clilld
DOG - adult dog
DOM - domestic animals (goat, sheep, horse)
GRB - gerbll ;.
GPG - guinea pig
HAH - hamster
HMN - human
INF - Infant
MKY - monkey i
MUS - mouse
PIG - pig ;
i
RBT - rabbit j
RAT - rat ;
SQL - squirrel
A-7
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TABLE 3
Site/Organ Affected
ADR
BID
BHR
BRN
BRS
CAR
CER
CNS
COL
CVS
EAR
EHB
END
EYE
FAT
FET
GEN
GIT
HED
HRT
KDN
LIH
LNG
- adrenals
- blood
- bone marrow
- brain
- breast
- carcass
- cervix
- central nervous system
- colon
- cardiovascular system
- ears
- embryo
- endocrine
- eyes/ocular
- fatty tissue
- fetus
- genitals (external)
- gastrointestinal tract
- head
- heart
- kidney
- limbs
- lung
LVR
LYM
MMB
HSK
HTH
NSL
OVR
PAN
PLC
PNS
PUL
RBC
SEN
SKN
SOI
SPL
TES
THM
THR
UNS
UTS
WBY
- liver
- lymphocyte
- mucous membrane
- musculoskeletal
- mouth
- nasal passageways
- ovary
- pancreas
- placenta
- peripheral nervous system
- pulmonary system
- red blood cells
- gen. sensory
- skin
- site of Injection
- spleen
- testes
- thymus
- thyroid
- unspecified
- uterus
- whole body
A-8
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treatment and the time when the endpolnt assay Is conducted. In teratology
studies, exposure during gestation 1s considered chronic to the life of the
fetus.
Effects (codes provided In Table 4) !
i
The effects observed at the above-mentioned site or sites. In cases
where only one compound produces an effect (potentlatlon, no apparent
Interaction, Inhibition), the compound number Is placed In parentheses after
the code for effect. I
For example, In Figure 1, the effect of Interest Is a vlnylldene
chloride-Induced Increase In mortality (MOR). Thus the "effects" field reads
MOR(I)
In cases where both compounds cause an effect at a given site (antago-
nism, addl-tlvHy, synerglsm) or opposite effects at a given site (masking),
the Interacting compounds are not listed 1n parentheses after the effect.
Type of Interaction
In an attempt to characterize toxicant Interactions, a scheme of
classification (see Figure 2 for an outline) has been devised to distinguish
between the various types of Interactions encountered 1n the existing
literature. The scheme Is as follows:
Both Compounds I and II Produce a Given Effect at a Given Site.
i
1) Additive - The magnitude of the effect observed In the
presence of both compounds 1s not quantitatively greater or
less than the sum of effects produced by each compound alone.
For example, both aldrln and aramlte cause Increased mortality
when administered Individually to mice. When administered
together, the observed mortality Is equal to the sum of
mortalities observed for each compound administered
Individually (simple response addition). :j
A-9
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TABLE 4
Nature of Effect
ABS - absorption altered
ALR - allergic responses
(I.e., hypersensltlvlty)
COR - corrosive effects
(burns, desquamatlon)
DDP - drug dependence
DE6 - degenerative changes
DEP - depression of function
DIS - distribution altered
ELI - elimination altered
ENZ - enzyme activity altered
EXC - excretion altered
FUN - functional Impairment
HEM - hematologlc changes
HMR - hemorrhage
IRR - Irritation
MET - metabolism altered
MOR - mortality
MUT - mutagenlc
NBH - neurobehavloral effects
NEO - neoplastlc
NPY - neuropathy
OCC - ocular effects
PIG - pigmentation changes
PRO - prollferatlve changes
REP - reproductive effects
RET - retention altered
STI - stimulation of function
SUR - survival/viability altered
TEM - temperature changes
TER - teratogenlc
UNS - unspecified effects
WGT - weight altered
A-10
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A. Both compounds produce a given effect at a given site or sites
1. Additive (ADD)
2. Antagonism (ANT)
3. Synerglsm (SYN) ;
B. Only one compound produces a given effect at a given site or sites
1. Inhibition (INH) [
2. No Apparent Influence (NAI)
3. Potentlatlon (POT)
C. Neither compound alone produces a given effect but when placed together,
an effect Is seen - Chemical Synerglsm (CSV)
!
D. Compounds I and II produce opposite effects at the same site or sites -
Masking (MSK)
E. Unable to assess (UTA)
FIGURE 2
Types of Interaction with Codes
A-ll
-------
2) Antagonism - The magnitude of the effect In the presence of
both compounds I and II Is less than would be expected 1n the
case of addltlvlty. For example, both 2,4-D butyl and 2,4,5-T
butyl produce teratogenlc effects and fetal mortality when
administered alone; however, the effects seen when both
compounds are administered together are less severe than those
seen when either 2,4-D butyl or 2,4,5-T butyl Is administered
alone, and hence, less than expected under the addltlvlty
assumption.
3) Synerglsm - The effect seen In the presence of both compounds
1s quantitatively greater than would be expected In the case
of addltlvlty. For example both PCB and vlnylldlne fluoride
cause an alteration In enzyme activity In the Hver when
administered Individually. When administered together, the
effect Is quantitatively greater than would be expected In the
case of addltlvlty.
Only One Compound Produces a Given Effect at a Given Site.
The following three classifications are special cases of the three
discussed previously: addition, antagonism and synerglsm.
1) No Apparent Influence - A noneffectlve compound, II, does not
modify ostensibly the effect produced by compound I. For
example, acrylamlde-promoted neuropathy Is unaffected by the
co-administration of cortlsol. Cortlsol alone has no effect
upon the peripheral nervous system. Thus, cortlsol has no
apparent Influence on the acrylamlde-promoted neuropathy.
A-12
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2) Inhibition - The noneffectlve compound, II, quantitatively
Inhibits the effect produced by Compound I. An tsxample of
inhibition Is presented in Figure 1. Vinylidene chloride
(compound I) caused . an Increase In mortality, which was
inhibited by co-administration of disulflram (compound II).
When administered alone, dlsulfiram had no effect on the
survival rate; thus, the Interaction was classified as Inhibi-
tion rather than "antagonism" or "masking."
3) Potentiatlon - The noneffectlve compound, II, enhances the
magnitude of the effect produced by compound I. An example of
potentlatlon is vinylidene chloride-promoted degenerative
changes in the liver, which are enhanced quantitatively by the
co-administration of acetone. Under the conditions of the
experiment, acetone alone has no effect upon the liver.
i-
Masking
The assessment of "masking" is reserved for the instance when compounds
I arid II produce opposite effects at the same site or sHes and either
diminish or override the effects of each other. For example, zinc alone has
been shown to cause an Increase in S-aminolevul1n1c acid dehydratase
activity (ALA-D) In red blood cells, while ethanol alone causes a decrease
In ALA-D In red blood cells. Co-administration results in a rise in ALA-D
quantitatively similar to that observed when zinc was administered by
itself. Thus, on the Input sheet, "Effects" would read "ENZ" and qualita-
tive assessment would read "MSK."
Unable to Assess
This Is used for studies that are poorly designed or insufficiently
detailed to discern the nature of the Interaction.
A-13
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Reference
The Input data sheets contain the complete reference (see Figure 1 for
an example). The data base Includes only the first two authors (second
author 1s "et al." 1f more than two authors), year, reference code and
volume:page numbers.
GENERAL COMMENTS
In most cases, Identical data generated by the same laboratory but
reported In more than one reference were not repeated In the data base. In
addition, results reported In the text without accompanying data were not
used because an adequate evaluation of the Interaction could not be made.
This data base Is only concerned with effects resulting from excess expo-
sures, e.g., studies examining the consequences of feeding diets deficient
1n an essential nutrient were not Included.
In general, because of a widespread lack of adequate statistical
methodology In the studies reviewed, assessing the qualitative relationships
between compounds was often difficult. In many cases, It was left to the
judgment of the reviewer whether an Interaction existed at all and, 1f so,
how to classify It according to the scheme presented above. It should be
emphasized that this data base should be used only as a tool to direct the
user to the literature currently available regarding toxicant Interactions.
A-14
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APPENDIX B
DIESEL EXHAUST EMISSIONS AND "SUFFICIENT SIMILARITY"
B-l
-------
DIESEL EXHAUST EMISSIONS AND "SUFFICIENT SIMILARITY"
An Important concept 1n the Guidelines for the Health Risk Assessment of
Chemical Mixtures (U.S. EPA, 1986) Is the use of data on similar mixtures
for a risk assessment on the mixture of concern. This procedure Is
predicated on the determination of "sufficient similarity" between the
mixtures. In brief, If health effects data on a similar mixture are
available, 1t must be determined 1f the mixture on which there are data Is
or 1s not sufficiently similar to the mixture of concern to allow a risk
assessment. This determination should Include consideration of the
component proportions of the mixtures as well as any toxlcologlc or
pharmacoklnetlc data on the components or the mixtures that would assist In
assessing the significance of any chemical differences between the similar
mixture and the mixture of concern. The determination of "sufficient
similarity" should be made on a case-by-case basis In light of the
uncertainties associated with using data on a dissimilar mixture and with
using other approaches such as addltlvlty. (For further Information
concerning the applicability of the sufficient similarity approach, refer to
the guidelines.)
Diesel engine exhaust represents a family of complex mixtures that are
generated with varying compositions depending on different temporal,
emission source, or operating condition characteristics. Because dlesel
engine emissions were expected to make a significant contribution to urban
pollution, the U.S. EPA Instituted a major research program aimed at
quantifying the potential health and environmental Impacts of dlesel-powered
light-duty vehicles (U.S. EPA, 1979). The purpose of this exercise Is to
use these data to determine whether dlesel emissions from different
B-2
-------
sources are sufficiently similar to warrant their use for the purposes of
predicting the health effects of unknown dlesel emissions as outlined In the
guidelines. I
The U.S. EPA research program was designed to determine the relative
mutagenlc and carcinogenic potency of extractable organlcs; from dlesel
partlculate emissions compared with particle-bound organlcs from other
environmental emissions (gasoline engines, cigarette smoke condensate, and
i
coke oven and roofing tar emissions) (Lewtas et a!., 1981). The mobile
source samples selected for this study Included a heavy-duty Caterpillar
3304 dlesel engine, three light-duty dlesel passenger car engines (Datsun
Nissan 220C, Oldsmoblle 350, and Volkswagen turbocharged Rabbit), and a
gasoline catalyst Mustang II. All dlesel engines were operated on the same
lot of No. 2 dlesel fuel. In addition, all vehicles (except for the
Caterpillar) were operated on a chassis dynamometer under Identical
conditions using the highway fuel economy test cycle (HWFET). Particle
samples from all engines were collected with a dilution tunnel In which the
hot exhaust was diluted, cooled, and filtered through Pallflex T filters.
All samples were extracted by a Soxhlet apparatus with dlchloromethane,
which was removed by evaporation under dry nitrogen.
The test matrix consisted of the following bloassays: reverse mutation
In Salmonella typhlmurlum; sister chromatld exchange (SCE) In Chinese
hamster ovary (CHO) cells; gene mutation In L5178Y mouse lymphoma cells and
BALB/c 3T3 (3T3); viral enhancement of transformation In Syrian hamster
embryo (SHE) cells; oncogenic transformation In 3T3 cells; and skin tumor
Initiation In SENCAR mice. Where possible, these bloassays were conducted
such that a positive dose-response relationship was observed over at least
i
three doses above spontaneous levels. Comparative potency rankings of the
.8-3
-------
samples were determined based on the Initial linear slope of the response
curve. Where dose-response data were not obtained, the lowest effective
dose (LOEL) tested was determined.
A wide range of activity was observed 1n S^. typhlmurlum strains TA98 and
TA100 (Table B-l). The majority of the activity associated with the dlesel
samples was direct acting while the addition of a mammalian activation
system Increased the activity of the gasoline engine sample (Claxton, 1981).
All of the emission samples gave positive mutagenlc responses both In
the presence and absence of metabolic activation using the criteria of the
L5178Y mouse lymphoma thymldlne klnase (TK) locus forward mutation assay.
The dlesel engine emissions were more cytotoxlc In the absence of metabolic
activation while cytotoxldty Increased In the presence of activation with
the Mustang emissions. Among the dlesel engines, the Nissan emission sample
was the most cytotoxlc while the Caterpillar sample was the least cytotoxlc
with a potency below that of the gasoline engine (Mitchell et a!., 1981).
Curren et al. (1981) assayed the Caterpillar, Nissan, and Oldsmoblle
dlesel samples, and the Mustang gasoline sample 1n the BALB/c 3T3 muta-
genesls assay. Although several Individual doses of the dlesel extracts
Induced a significant increase in ouabain-reslstant mutants, none of the
samples yielded a dose-dependent Increase in mutation frequency. Based on a
determination of mutation frequency for the dose ranges tested, both the
Nissan and Mustang samples were significantly mutagenic both with and
without metabolic activation while the Caterpillar and Qldsmobile samples
were not significantly different from controls.
Definitive conclusions concerning the DNA-damaging capabilities of
dlesel emissions as measured by the SCE test are difficult to reach given
B-4
-------
• TABLE B-l
Specific Activities at TOO yg of Organic Material*
In Salmonella typhlmurium Strains TA98 and TAiOO
Sample
Caterpillar
Nissan
Oldsmobile
VW
Mustang
+S9
59.3
1367.1
318.7
297.5
341.9
TA98
-S9
Diesel
65.9
1225.2
614.8
399.2
Gasoline
137.8
TAIOO
tS9
115.2
881.7
169.9
426.0
228.0
-S9
167.8
1270.1
247.5
641.6
-
196.5
*Source: Claxton, 1981
B-5
-------
that the results are based on single experiments. However, It Is signifi-
cant that an observed Increase In SCE frequencies In CHO cells following
exposure to all except the Oldsmoblle sample In the absence of activation
Indicates that these samples contain one or more components that are
direct-acting chromosome-damaging agents. Although the significance of the
differences among these dlesel and gasoline emission samples cannot be
Inferred from the data, this test gave the following qualitative comparative
potency ranking: Nissan > Rabbit, Mustang » Caterpillar, Oldsmoblle
(Mitchell et al., 1981).
Two assays, one measuring morphologic transformation In 3T3 cells and
the other measuring viral enhancement of transformation In SHE cells, were
used to observe the effects of gasoline and dlesel emissions on oncogenlc
transformation. As with the 3T3 mutation assay, dose-related, responses 1n
transformation frequency 1n 3T3 cells were not observed for any of the
samples (Caterpillar, Nissan, OldsmobUe, and Mustang). Both the Nissan and
Mustang samples Induced a significant number of transformed foci In the
absence of metabolic activation while only emissions from the Mustang had a
transformation frequency significantly greater than that of controls In the
presence of metabolic activation (Curren et al., 1981).
In the viral enhancement assay, the Nissan appeared to be the most
potent followed by the Rabbit and Mustang, which were equlpotent, and the
OldsmobUe and Caterpillar according to the lowest effective concentration
tested that Induced significant enhancement (Casto et al., 1981). However,
1f the data from three separate experiments were combined to determine the
slope of the pooled dose-response curve for each sample, the comparative
potency ranking would be: Nissan, Mustang > Rabbit > Oldsmoblle'(Caterpillar
1s considered negative). Because the variation 1n response between the
B-6
-------
three experiments was significant (r value as low as 0.18), each
experiment was analyzed separately and the experiment resulting 1n the
2
highest r was used to determine the following potency ranking: Nissan >
Rabbit > Oldsmoblle, Mustang (Lewtas, 1983). Despite these variations, the
ranking for light-duty dlesel engine samples remains fairly constant: Nissan
> Rabbit > Oldsmoblle.
In the skin tumor Initiation assay In SENCAR mice, the four dlesel
samples varied significantly 1n the tumorlgenlc responses they produced,
ranging In activity from 0 to 5.7 paplllomas/mouse (Nesnow et al., 1982).
i
Paplllomas were produced'1n .all samples except for the Caterpillar. Excess
tumor multiplicity activities 1n paplllomas per mouse at 1 mg of extract
were calculated as follows: Nissan - 0.59, Oldsmoblle - 0.31, Rabbit - 0.24,
i
and Mustang - 0.17 (Albert et al., 1983). These data Indicate that only the
i
Nissan extract can be considered a strong tumor Initiator, with activity
similar to that of roofing tar. !
A comparison of these test systems reveals that, 1n general, there Is a
consistency 1n the comparative potency of these extracts wHth the Nissan
sample the most active and the Caterpillar sample the least potent In all
j
bloassays. The main Issue, however, Is whether these data demonstrate
sufficient biological similarity among the different samples to warrant
their use In predicting the effects of other dlesel mixtures. Based on
these data, results from heavy-duty dlesel engine (Caterpillar) emissions
would severely underestimate the effects of a light-duty dlesel engine and
i
should not be used for that purpose. Within the light duty class of dlesel
engines, there appears to be reasonably close agreement between the
Oldsmoblle and Rabbit engines while the Nissan Is considerably more potent.
•
Because of, the Nissan data, It would not be prudent to assume that all
B-7
-------
light-duty dlesel engine emissions are sufficiently similar as to their
biological effects.
The available Information on components of the four dlesel and one
gasoline emission samples Indicates a wide range of organic extractable
material (Table B-2). Benzo(a)pyrene (BaP) content per mg extract also
varied considerably, from 0.0002 to 0.11% (Lewtas et a!., 1981). Nesnow et
al. (1982) state that the tumor data from the SENCAR mouse skin tumor
Initiation assay cannot be explained solely by BaP content since there Is no
significant relationship between tumor Incidence and BaP content In each
complex mixture (Including dlesel and gasoline engine, roofing tar, and coke
oven emissions). They estimate that BaP accounts for only 20-30% of the
activity seen and that other constituents must be contributing toward the
tumorlgenlc activity. Whether this contribution Is through Interaction or
direct component activity cannot be determined from the available data.
It 1s evident that the available component data does not meet the
sufficient similarity criteria at least In the case of BaP content for the
Nissan and Mustang samples. If BaP can account for.no more than 30% of the
tumorlgenlc activity of the mixture, It Is apparent that additional
component Information Is necessary before the Issue of sufficient
constituent similarity can be accurately evaluated.
B-8
-------
TABLE B-2 ''
Results of Extraction and Benzo(a)pyrene Analysis*
Benzo(a)Dvrene
Extractable
Matter nq BaP riq BaP
Sample Source percent mg extract mg partlculate
Diesel CAT 26-27 2
NISSAN 4-8 1173
OLDS 12-17 2
VW RAB 18 26
Gasoline MUSTANG 39-43 103
*Source: Lewtas et al., 1981
0.5
96.2
0.4
4.6
44.1
B-9
-------
-------
APPENDIX C i
ANALYSIS OF THE SAMPLE STUDIES FROM THE INTERACTION DATA BASE
C-l
-------
Thirty-two studies were selected from the U.S. EPA Interaction data base
(see Appendix A) for detailed evaluation of the statistical methods that
were employed In determining the type of toxic Interaction. The evaluation
Included the appropriateness of the statistical method used and the correct-
ness of Interpretation of the statistical results. The 10% random sample
was stratified by the type of statistics used. The following text Is the
evaluation of each study.
Carlson (1973) pretreated rats with either phenobarbltal (PB), 3-methyl-
cholanthrene (3-MC), saline or corn oil vehicle, then exposed them to air,
1,1,1-trlchloroethane or 1,1,2-trlchloroethane. Endpolnts assessed were
Hver and body weights, serum glutamlc oxaloacetlc transamlnase (S60T),
serum glutamlc pyruvlc transamlnase (SGPT) and liver glucose-6-phosphatase.
Analysis was by 2-way analysis of variance to assess differences between
pretreatments, Inhalation treatments and the Interactions between the two.
The analysis was appropriate. No differences were found In liver or body
weights, and 1t was concluded that 3-MC did not potentiate the hepato-
toxldty of the trlchloroethanes, but PB did and enhancement was greater
with 1,1,2-trlchloroethane than with 1,1,1-trlchloroethane.
Short et al. (1977) exposed mice and rats to continuously Inhaled air or
1,1-dlchloroethylene (VDC), then to one of dlsulflram, dlethyldHhlocarba-
mlde (DDC), thlram, aptelne, methlonlne, N-acetylcystelne, SKF 525-A,
cobaltous chloride, phenoxybenzamlne, propanolol, Vitamin C or to Vitamin £.
Endpolnts assessed were death, organ damage as assessed by serum enzymes and
hlstopathology, changes In Hver and kidney, and radioactivity In protein.
Statistical methods used were calculation of the LCcn and LTcn for VDC
bU bu
for assessment of survival, and the 2 sample rank test and Fisher's exact
test for the other endpolnts. No methods were used to control for multiple
comparisons. Survival analysis would have been more appropriate to use. No
%
C-2
-------
negative control group was present. Repeated measures analysis should have
been used to assess the changes across time In SGOT and SGPT. They con-
cluded that dlsulflram reduced the severity of the lethal, hepatotoxlc and
renal effects of VDC In mice, that DDC and thlram protect; mice from the
lethal effects of VDC, and that the dlthlocarbamates protected against the
toxlclty of VDC. . ;
Castro et al. (1974) exposed rats to SKF 525-A, Sch 5705, Sch. 570.6, Sch
5712, CFT 1201, Lilly 18947, DPEA, promethlzlne or vehicle control, then to
Endpolnts assessed were! ethylmorphlne
N-demethylase activity (EM-ase), cytochrome P-450 activity, peroxldatlon of
CC1,, or olive on vehicle.
4
liver mlcrosomal llplds, CC1. concentration In liver, protein concentra-
tion, and NADP-llnked Isocltrlc dehydrogenase (ICD) activity Mn plasma. In
examination of the time course of CCl^ concentration, they used Student's
t-test and the Mann-Whltney-U test for comparison at each time point. In
this situation the groups were CC1. alone vs. CC1. and other compound,
«t HI
sometimes varying the level of CC14. To examine the effects on EM-ase,
ICD and P-450, they made comparisons via 2~way ANOVA. To examine the time
course of llpld peroxldatlon and body temperature, they used a 2-way ANOVA
at each time point, whereas a repeated measures analysis would have been
correct. They concluded that "although these compounds tested are known to
Inhibit cytochrome P-450 dependent drug-metabolizing enzymes In liver
mlcrosomes, they apparently do not evoke their protective effects by slowing
the elimination of CC1.." These conclusions are appropriate In light of
the methods used.
Andrews et al. (1977) examined the effects of toluene
disposition and hemopoletle toxlclty of [H3]benzene, particularly red cell
59Fe Incorporation as a measure of erythropolesls. Two
C-3
on metabolism,
2x2 factorial
-------
experiments were conducted on mice, with the administration of 0 or 880
mg/kg benzene and 0 or 1720 mg/kg toluene as one experiment, and the admin-
istration of 0 or 440 mg/kg benzene and 0 or 1720 mg/kg toluene as the
other. Endpolnts assessed were benzene metabolites In urine, expressed as
percent administered dose and as benzene equivalents, percent 59Fe utili-
zation, exhaled [3H]benzene, and levels of [3H]benzene 1n liver, spleen,
epldldymal fat pads, blood, and bone marrow. Although the experiments were
conducted as 2x2 factorials, making the use of 2-way ANOVA appropriate,
Student's t-tests were In fact used. Furthermore, the time course of
accumulation of [3H]benzene In tissues was analyzed by t-tests at each time
point, whereas repeated measures analyses were appropriate. The authors
concluded that toluene reduced the level of urinary metabolites of benzene
and also reduced the benzene-Induced Inhibition of erythrocyte S9Fe
uptake. These conclusions are consistent with the results of the statis-
tical methods used, but may not be valid due to the Increased likelihood of
false positives with these methods.
Friedman and Eaton (1978) studied the effects of an Inhibitor of mixed
function oxldase (MFO) activity, plperonyl butoxide (PB), on methylmercury
(MM) toxlclty. Rats were fed diets containing either 0, 20 or 40 ppm
methylmercury, and either 0, 0.5 or 1.0% PB. Endpolnts assessed were weight
gain and mortality. No statistical methods and no dose-response models were
used. The authors conclude that "PB synergises MM poisoning in a dose-
dependent fashion."
Blanclflorl et al. (1967) examined the effects of estrogen on the
pathway 'through which chemical carcinogens exert their action. Both
ovarlectomlzed and intact mice were given estrone at 0, 500 or 1000 tig/9.
drinking water, and the mice were administered either nothing, or one of
C-4
-------
9,10-dimethyl-l,2-benzanthracene (DMBA), 1,2:5,6-dibenzanthracene (DBA),
20-methylcholanthrene (MC) or 3,4-benzopyrene (BP) at 0.5% In almond oil,
twice weekly until 8 weeks of age. Endpoints assessed were mammary
carcinoma, survival, gastric tumor, ovarian tumor, leukemia, and lung
tumor. Although no statistics were used, the authors conclude that
"administration of oestrone Increased the Incidence of mammary carcinomas In
both Intact and ovarlectomlsed mice when DBA or MC were the carcinogens;
only a minimal effect was obtained with BP and the result with DMBA was
equivocal," "squamous carcinomas of the forestomach occurred when the
carcinogen was BP or DMBA,," and "DBA with oestrone Induced ovarian tumours."
Cone and Netteshelm (1973) Investigated the effects of high levels of
vitamin A on the toxlclty of 3-methylcholanthrene (MCA) In the respiratory
tract epithelium of the rat. All animals received vitamin A, either In
doses of 17, 87 or 1740 yg/week, and either 0 or 5 mg MCA. The endpolnt
assessed was respiratory tract tumor. Although no statistics were given,
the authors concluded that vitamin A has an Inhibitory effect on the
i
development of respiratory tract tumors.
Daoud and Griffin (1980) Investigated the effect of retlnoic acid,
butylated hydroxytoluene (BHT), selenium (Se) and sorbic add on azo-dye
I
hepatocarcinogenesls 1n the rat. All animals received a; diet containing
0.05% 3'-methyl-4-dimethylam1noazobenzene (3'-MeDAB), and either nothing,
0.05% BHT, 1 or 2% sorbic acid, 0.02% retlnoic acid or 2 or 4 ppm Se, but no
i
combinations or the latter four compounds. The endpoint assessed was liver
carcinoma. Although no statistics were given, the authors conclude that
I
protection was "afforded the animals given the azo compound by the dietary
supplementation with either retlnoic add or BHT."
C-5
-------
Schlede et al. (1970) examined the stimulatory effect of benzo(a)pyrene
(BaP) and phenobarbltal pretreatment on the biliary excretion of BaP metabo-
lites In the rat. Animals were pretreated with either BaP, phenobarbltal or
vehicle, then received either 10 or 300 vg of 14C-labeled BaP
(BaP-14C). The endpolnt assessed was the rate of excretion of metabolites
of BaP-14C Into bile. Although no statistics were given, the authors
conclude that "pretreatment of rats with BaP or phenobarbltal prior to the
l.v. Injection of 10 or 300 VS of BaP-14C enhances the rate of excretion
of metabolites of BaP-14C Into the bile."
Ito et al. (1973) examined the effect of polychlorlnated blphenyls
(PCBs) on tumorlgenesls by benzene hexachlorlde (BHC) 1n mouse liver.
Animals received diets containing 0, 100, 250 or 500 ppm PCBs, alone or 1n
conjunction with 0, 100 or 250 ppm of a, 13 or Y-BHC. Endpolnts assessed
were hlstopathology of the liver, liver weight and body weight, No
statistics were given. The authors conclude that "PCBs themselves Induced
hepatic neoplasms In mice and also promoted the Induction of tumors by
a-BHC and B-BHC."
Hagos et al. (1974) describe the effect of cadmium pretreatment on the
nephrotoxlc action and kidney uptake of mercury 1n male and female rats.
Animals were pretreated with either 0 or 2x2.46 mg/kg CdCK, then treated
with 0, 0.5, 1.0 or 1.5 mg/kg HgCl2. Endpolnts assessed were vg Kg2*
In kidneys/100 g bw, and severity of tubule damage. No statistics were
given. The authors state that there was a "significant sex difference
observed 1n effect of Cd2* pretreatment on the uptake of Hg2* by the
kidneys" and a "protective effect of Cd pretreatment against tubular damage
caused by mercury."
C-6
-------
Moxon and DuBois (1939) Investigated the Influence of arsenic and other
i
elements on the toxlclty of selenlferous grains In the rat,; Twelve groups
of animals were given diets containing selenlferous wheat and 11 of these
I
groups were given 5 ppm of one of the following elements In drinking water:
W, F, Mo, As, Cr, V, Cd, Zn, Co, U, N1. A thirteenth group received a diet
containing selenium-free wheat. The elements In drinking water were not
given In conjunction with a diet containing selenium-free wheat. Endpolnts
assessed were survival and Se content In liver. No statistics were given.
A further experiment was then conducted since 1t appeared from the Initial
experiment that "tungsten and arsenic, especially the latter, reduced
selenium toxlclty in some manner." Animals were fed diets containing either
i
selenium-free or selenlferous wheat, then drinking water containing either
nothing, 2.5 ppm W or 2.5 ppm As. Endpolnts assessed were survival and
liver damage, and again ino statistics were given. The authors concluded
that F, Mo, Cr, Cd, Zn, Co, N1 and U given with Se caused an Increase In
mortality, that W seemed to reduce the mortality rate of rats with Se, and
As prevented Se poisoning symptoms, although 1t did not prevent liver damage
appreciably.
Thlnd and Blery (1974) Investigated the antagonism of renal anglographlc
effects of cadmium by zinc In the dog. In one group, animals received
Intrarenal doses of cadmium acetate (2-20 mg) and zinc chloride (2-20 mg).
The animals 1n a second group received a series of arterlograms for the
following exposure sequence: control, vasoactlve drug (anglotensln,
eplnephrlne, noreplnephrlne), cadmium acetate + vasoactlve drug, vasoactlve
drug, zinc chloride + cadmium acetate + vasoactlve drug; this sequence was
then repeated In each dog with two different vasoactlve drugs. No
quantitative data were -presented; Instead, the authors presented the radio-
graphs from the anglograms;. The authors conclude that "pretreatment of the
I
C-7
-------
for Injected p-xylene and the LC5Q for Inhaled p-xylene were
renal vasculature with zinc Ions 1n the present study effectively blocked
the acute Inhibitory effects of cadmium Ions In the kidney of normal dogs."
Drew and Fouts (1974) studied the effects of pretreatment with either
phenobarbltal (PB), 3-methylcholanthrene (3-MC), or chlorpromazlne (CPZ), on
p-xylene toxiclty In rats. Animals were subsequently either exposed to
p-xylene vapors or were Injected with p-xylene. The authors allude to the
presence of control groups, but the nature of these groups 1s not stated.
The LDc
3
calculated for the control groups and the pretreatment groups. This work
was presented In abstract. No data were given, and the statistical methods
used were not specified. The authors conclude that the pretreatments raise
the LCgo of Inhaled p-xylene, whereas only 3-MC Increases the LD5Q of
Injected p-xylene.
Dletz (1980) Investigated the roles of 2~butanol, 2-butanone and
2,3-butaned1ol In potentiating CC14 hepatotoxlclty. Rats were pretreated
with various dosages (unspecified) of one of these three compounds, 'then
In addition, some animals were
pretreated with pyrazole. Endpolnts assessed were SGPT, glucose-6-phos-
phatase activity and trlglycerlde concentration. This work was presented 1n
abstract form. No data were given, and the statistical methods used were
not specified. The authors concluded that the capability of 2-butanol and
2-butanone to potentiate CC1. hepatotoxlclty Is due to their further
metabolism to 3-hydroxy-2-butanone and 2,3-butanedlol.
Bhargava and Way (1974) examined the effect of l-phenyl-3-(2-th1azolyl)-
2-th1ourea (PTT) on morphine analgesia, tolerance and physical dependence In
the mouse. Animals previously rendered tolerant to morphine were pretreated
with PTT or vehicle, and brain uptake of noreplnephMne, dopamlne, copper,
were administered one dose of CC1..
C-8
-------
serotonin, acetylchollne, choline and morphine were assessed, The statisti-
cal methods used were not specified. The authors state that the analgesic
effect of morphine was potentiated by PIT, but this effect was not corre-
lated with changes 1n brain levels of noreplnephrine, dopamine, copper,
serotonin, acetylchollne and choline.
Gupta and Gupta {1977} Investigated the effect of the insecticide endo-
sulfan on pentobarbltone sleep time and concentration of pentobarbltone In
blood and brain In rats. Animals received 0, 1, 2.5 or 5 irng/kg endosulfan
for 7 or 15 days, then all animals received 50 mg/kg pentobarbltone. The
statistical methods used were not specified. The higher doses of endosulfan
(2.5 and 5 mg/kg) were associated with Increased Induction time and
decreased sleep time. There was no change In pentobarbltone concentrations
1n blood and brain.
i
Gunn et al. (1968) studied compounds that potentially were protective
against cadmium toxlcity. Mice received CdCl2 (doses | ranged between
0.0055 and 0.0664 mM/kg) In conjunction with control, an arolno add (either
alanlne, arginine, asparaglne, cystelne, glyclne, Isoleuclne, lyslne,
methlonlne, proline, serlne, threonlne, vallne, leuclne or phenylalanlne),
2,3-d1mercaptopropanol (BAL), selenium dioxide or zinc acetate. Endpoints
were death at 7 days, and percent Cd uptake 1n various organs (testes,
kidneys, heart, lungs, pancreas, spleen and gastrointestinal tract). The
statistical methods used were not specified. The authors found that cadmium
destroyed the testis, and, while this destruction was prevented by cystelne,
that lethality was increased by cystelne. Moreover, BAL, selenium and zinc
also protected the testis from cadmium, but did not affect levels of kidney
cadmium nor toxlcity of cadmium. Furthermore, none of the other amino acids
was protective against cadmium damage of the testis or increased cadmium
toxicity.
C-9
-------
Jernlgan and Harbison (1982) Investigated the role of 2,5-hexanedlone
(2,5-HD) In halocarbon hepatotoxlclty. Mice were pretreated with corn oil.
2,5-HD or phenobarbHal sodium (PB), then subsequently received one of the
following halocarbons: CDC13,
, CC14. trlchloroethylene (TCE),
1,1,2-trlchloroethane (TRI), or perchloroethylene (PERC). Endpolnts
assessed were hepatic cytochrome P-450, NADPH cytochrome c reductase,
aniline hydroxylatlon, p-nltroanlsole 0-demethylatlon and amlnopyrlne
N-demethylatlon, and serum alanlne amlno transferase activity. Statistical
comparisons were made using analysis of variance. Because of the absence of
a control group for the halocarbons, and the authors' continual testing by
comparison back to the corn oil pretreatment group, this paper Is
Insufficient to assess the potentlatlon of any of these halocarbons by
2,5-HD. The authors conclude that ketone potentlatlon of CHCl^-lnduced
O
hepatotoxlclty was demonstrated 1n mice and that pretreatment with 2,5-HD
can potentiate the hepatotoxlclty of other halocarbons.
Snyder et al. (1981) studied the effect of ethanol 1ngest1on on hemato-
toxldty of Inhaled benzene 1n mice. The Inhalatlon-lngestlon groups were
as follows: air + water, air + 5% ethanol, air +• 15% ethanol, 300 ppm
benzene + water, 300 ppm benzene + 5% ethanol, 300 ppm benzene + 15% ethanol.
Endpolnts assessed were body weight and blood counts. The statistical
methods used were not stated. The authors find that the "results indicate a
true potenttatlon of the toxic effects of benzene by ethanol."
Csallany and Ayaz (1978b) assessed the effects of N02 and vitamin E in
mice. Animals were exposed first to either filtered air or 0.5 ppm NO or
1.0 ppm N02, then to 0, 30 or 300 ppm vitamin E or 30 ppm N.N'-dlphenyl-p-
phenylenedlamlne (DPPD). Endpolnts studied were body weight, tissue
weights, llpofusdn pigment (LFP) concentration In tissues, and survival
C-10
-------
rates. The statistical methods used were not specified. The authors
i.
conclude that continuous low level NO exposures do not result In higher
concentrations of tissue organic solvent soluble LFP, but N02 does have an
overall detrimental effect on animals, as measured by lowered whole-body
| :
weights and survival rates.
El-hawarl (1978) examined the potentlatlon of dlbromoethane (EOB) toxlc-
Hy by dlsulflram (DS), thlram, dlethyldlthlocarbamate and carbon dlsulflde
1n mice. Animals were pretreated with either DS, thlram, 'dlethyldlthlo-
carbamate or CS , then treated with EOB. Only one dose of the various
-i
pretreatment compounds was administered. It was unclear what control groups
were present. Endpolnts assessed were SGPT, SGOT, blood urea nitrogen (BUN)
levels and survival. The report Is In abstract form. No statistical
j
methods are stated. The authors conclude that pretreatment with any of
these compounds enhances EDO toxldty.
Agarwal et al. (1983) studied the Interactions of CBr4 and chlordecone
(CD) 1n rats. Animals were fed diets containing either 0 or 10 ppm CD, then
!
were Injected with either 0, 25, 50, 100 or 125 vs. CBr4« Endpolnts
assessed were urine parameters, Including volume, osmolallty, blood,
protein, glucose; p-amlnohlppurate (PAH) and tetraethylammonlum (TEA) levels
1n the renal cortex; SGOT and SGPT levels. Since the authors felt that
CBr. was acting like a nephrotoxln, a second experiment was undertaken 1n
which animals were fed diets containing either 0 or 10 ppm CD, then were
i
administered either vehicle or 54 yS. CC14 or 75, 125 or 175 mg CBr4.
Endpolnts measured were PAH and TEA levels In renal cortical slices. In
both experiments, statistical methods were not specified, although
comparisons were made to control groups. The authors concluded that
chlordecone did not modify rerial slice response, and that CD does not
potentiate CBr4 hepatotoxlclty. i
C-ll I
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Berlin and Lewander (1965) Investigated the effect of 2,3-dlmercapto-
propanol (BAL) on brain uptake of mercury 1n mice given mercuric chloride.
In the acute experiment, animals were given 0.5 mg/kg Hg, then either 0 or
0.3 mg/kg BAL. Endpolnts measured were tissue Hg concentration. In the
chronic experiment, animals were given 1 mg/kg 203HgCl and either 0 or
2 mg/kg bw BAL for 16 days. Again, the endpolnts assessed were Hg
concentrations In tissue. There were no negative control groups (no BAL, no
mercuric chloride). The statistical methods used were not specified. The
authors concluded that BAL does not affect Hg elimination.
deFerreyra et al. (1983) assessed the potentlatlon of CC1. necrosis by
cystelne and tryptophan, both alone and together, In the rat. Experimental
groups were as follows: control, CC14, cystelne, tryptophan, cystelne +•
CCl^, tryptophan + CC14, and cystelne + tryptophan + CC1.. Endpolnts
measured were ICO levels and degree of liver necrosis. Although the authors
state that a 2-way analysis of variance was used, H 1s unclear what groups
were compared. All other comparisons were to the control group, using
unspecified statistical methods. This experiment Is missing one experi-
mental group (cystelne + tryptophan); otherwise a 3-way analysis of variance
would have been the correct procedure. The authors conclude that adminis-
tration of cystelne but not tryptophan decreased ICD, and when both cystelne
and tryptophan were given together, a "marked protective effect Is observed."
Agarwal and Mehendale (1984) studied the potentlatlon of CC1 hepato-
toxlclty by chlordecone (CD) In ovarlectomlzed rats. Animals were either
sham operated or ovarlectomlzed, then fed diets containing either 0. or 10
ppm CD, then received either 25 ^ CC14 or vehicle. Endpolnts measured
were S6PT, SGOT, Isodtrlc dehydrogenase (ICD) and ornlthlne carbamyl
transferase (OCT) activity. The authors used student's t-test and one-way
C-12
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analysis of variance, making comparisons to controls, : although 3-way
analysis of variance would have been the correct method to Investigate the
I
Interplay of these compounds. They also measured biliary excretion of
phenolphthaleln glucuronlde (PG) over time, which would have been correctly
analyzed by a repeated measures analysts. The authors conclude that "CD
Induced potentlatlon of CC14 hepatotoxlclty In ovarlectomlzed rats was not
significantly enhanced as compared to earlier observations In Intact
females."
Klnnamon and Bunce (1965) examined the effects of copper, molybdenum and
zinc on 6SZn tissue distribution and excretion In the rat. There were
eight experimental groups consisting of the combinations of 0 or 100 mg/kg
.
Cu, 0 or 800 mg/kg Ho and 0 or 5000 mg/kg Zn In feed. Eridpolnts assessed
I
were body weight, weight gain, feed consumption, percent Zn retention In
tissues and percent Zn excretion In urine. Comparisons were made to
i
controls, using t-tests, although a 3-way analysis of variance would have
been the correct procedure. The authors concluded that "Zn, not Mo or Cu,
I
significantly Influences tissue distribution and excretion of tracer Zn."
Jaeger and Murphy (1973) studied the effects of 1,1-dlchloroethylene
(1,1-DCE), cortlcosterone or acroleln on barbltuate action 1n the rat.
Animals were pretreated with either 0 or 400 mg/kg 1,1-DCE,! then were given
either pentobarbltal (PB) or hexobarbltal (HB). Endpolnts assessed were
sleep time, barbltuate concentration In serum and brain, and serum cortlco-
i
sterone concentration. A second and third experiment were conducted 1n
which pretreatment was either 0 or 25 mg/kg cortlcosterone or 3 mg/kg
acroleln, assessing the same endpolnts. Statistical techniques employed
Included t-test, analysis of variance and regression, although It 1s Impos-
sible to tell which technique (t-test or analysis of variance) was used to
make certain comparisons. There was no negative (untreated) control 1n any
.
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experiment. The authors concluded that both 1,1-DCE and cortlcosterone
alter PB-1nduced, but not HB-lnduced, sleep time, and that acroleln
Increases both PB and HB sleep time.
Hasumura et al. (1974) Investigated the effect of chronic ethanol
consumption on CC14 hepatotoxlclty In the rat. Experimental animals were
fed diets consisting of ethanol (36% of total calories), and control animals
were pair-fed diets In which ethanol had been Isocalorlcally replaced by
carbohydrate. Animals then received either 0.5 mil/kg CC1. or mineral
oil. Endpolnts measured were serum ornlthlne carbamyl transferase (SOCT),
S6PT, blUrubln, total llpld, cytochrome P-450, amlnopyrine N-demethylase
activity, and glucose-6-phosphatase activity. Paired Student's t-test was
used to compare ethanol to pair-fed control, whereas a randomized complete
block design would have been the correct procedure to make this comparison,
allowing for multiple comparisons. The authors concluded that "chronic
ethanol administration to rats potentiates CC1. hepatotoxlclty," although
they did not use methods that would allow for the assessment of Interactions.
Harbison and Becker (1971) examined the effect of treatment with pheno-
barbltal (PB) or SKF 525A on dlphenylhydantoln (DPH) disposition on pregnant
mice. All animals received 100 mg/kg DPH, after pretreatment.. with either
control, PB or SKF 525A. Endpolnts measured were DPH metabolism In plasma,
placenta, fetus, amnlotlc fluid, liver, brain, fat, and muscle over time.
They used Student's t-test to compare each pretreatment group with DPH alone
at each time point, although a repeated measures analysis would have been
the correct procedure. They concluded that pretreatment with PB enhanced
the metabolism of DPH, with decreased plasma DPH and DPH-1nduced terato-
genldty and in utero deaths, while pretreatment with SKF 525A decreased
metabolism of DPH, with Increased plasma DPH and DPH-lnduced teratogenlclty
and 1n utero deaths.
C-14
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Csallany and Ayaz (1978a) Investigated the effects of Intermittent N02
exposure and vitamin E In rats. Animals were fed either vitamin E deficient
(0 ppm), normal (30 ppm), or high (300 ppm) diets, then were exposed to
either air or 15 ppm NO for either 5 or 18 weeks. Endpoints assessed
Included methemoglobin levels, histopathology, Upofuscin pigment
concentration in tissue and fatty acid component in lung tissue. Student's
t-test was used to make comparisons between the treatment groups, although
It was not always clear which groups were being compared. Analysis of
variance techniques would have been correct. The authors concluded that
"Intermittent NO- exposure, under the described conditions, did not cause
ultimate changes of the biochemical parameters measured." ;
Derr et al. (1970) examined the synergism between cobalt and ethanol on
rat. growth rate. Water, allowed ad libitum, was replaced with, either 0 or
10% ethanol, and either 0 or 1 mg Co/10 ma. H20. Endpoints measured were
body weight, hematocrit, heart weight, heart Zn, and heart-to-body weight
ratio. Student's t-test was used to compare the various groups, and an
additive model was used to calculate an expected body weight for the ethanol
i
•H Co group, which was then compared with the observed body weight for that
group by Student's t-test. A two-way analysis of variance would have been
the correct procedure. The additive model that was used added the weight
deviations from the control in order to predict the weight deviation of the
two chemicals combined. The authors did not provide any biological justifi-
caton for such a model. Even a simple method using relative potencies would
be better justified. The authors' conclusion was that ethanol and cobalt
have a synerglstic effect.
C-15
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APPENDIX D
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**U.S. GOVIiRNMJENT PRINTING OFFICE: 19 9 0- 7 * 8 - 1 5 yi 0 * >t 5
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