Download PERSPECTIVE Rationale and Procedures for Using the Tissue

Human and Ecological Risk Assessment, 12: 1018–1073, 2006
ISSN: 1080-7039 print / 1549-7680 online
DOI: 10.1080/10807030600801535
PERSPECTIVE
Rationale and Procedures for Using the
Tissue-Residue Approach for Toxicity Assessment
and Determination of Tissue, Water, and Sediment
Quality Guidelines for Aquatic Organisms
James Meador
National Marine Fisheries Service, National Oceanic and Atmospheric
Administration, Seattle, Washington, USA
ABSTRACT
This article describes a set of procedures for developing tissue, water, and sediment quality guidelines for the protection of aquatic life by using the tissue-residue
approach (TRA) for toxicity assessment. The TRA, which includes aspects of the
Critical Body Residue (CBR) approach, associates tissue concentrations of chemicals with adverse biological effects in a dose-response fashion that can be used to
determine CBRs. These CBRs can then be used to develop tissue quality guidelines
(TQGs), which may be translated into water or sediment guidelines with bioaccumulation factors. Not all toxicants are amenable to this type of analysis; however, some
appear to exhibit relatively consistent results that can likely be applied in a regulatory framework. By examining tissue residues, variations in toxicokinetics (temporal
aspects of accumulation, biotransformation, and internal distribution) are greatly
reduced allowing a greater focus on toxicodynamics (action and potency) of the toxicants. The strongest feature of this approach is causality; hence, guidelines based
on tissue concentrations are based on data demonstrating a causal relationship between the acquired dose and the biological effect. Because the TRA has utility for
assessing the toxicity of contaminant mixtures, an approach is presented here using
toxic unit values that can be used to assess the likelihood of observing toxic effects
based on tissue residues.
Key Words:
tissue residue toxicity, CBR, mixtures, guidelines.
Received 8 March 2005; revised manuscript accepted 21 August 2005.
This article not subject to United States copyright law.
Address correspondence to James Meador, Ecotoxicology and Environmental Fish Health
Program, Northwest Fisheries Science Center, National Marine Fisheries Service, National
Oceanic and Atmospheric Administration, 2725 Montlake Blvd. East, Seattle, WA 98112, USA.
E-mail: [email protected]
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INTRODUCTION
Assessing the toxicity response as a function of whole-body or specific organ tissue
concentrations has been employed for many years, but only recently has emerged
as an important area of research in aquatic environmental toxicology (Friant and
Henry 1985; McCarty 1986; McCarty and Mackay 1993). For the majority of chemicals, uptake of the toxicant across external membranes or from ingestion is required
for a toxic response; however, accumulated residues may not be toxic because they
are below toxic thresholds. The justification for the tissue-residue approach is that
environmental exposure concentrations (sediment, water, diet) are often proportional to bioaccumulated concentrations. A corollary to this is that the whole-body
tissue concentration of the accumulated toxicant is proportional to that concentration at the site of toxic action, for example the receptor (McCarty and Mackay 1993),
which is generally a specific chemical moeity or active site in a biochemical pathway. Because we rarely have information that allows determination of the toxicant
concentration at the receptor, we use whole-body concentrations as a surrogate for
the toxic dose, just as external concentrations (e.g., water and sediment) are used
as surrogates for the toxic dose. This is not to imply that surrogates always maintain
proportionality in a dose response fashion. Indeed, for some toxicants the response
may be all or none above or below a given threshold concentration. For a given external concentration that produces a toxic response, there is generally a whole-body
concentration associated with that response. The critical residue associated with the
receptor is some proportion of the total, whole-body concentration, which may or
may not be easily related to the environmental exposure concentration.
In a recent review article, Barron et al. (2002) questioned the utility of the Critical
Body Residue (CBR) approach for assessing toxicant effects in aquatic biota. The
conclusion of this review was that large variability existed among species and toxicants
when tissue concentrations were used as the dose metric and that variability was
not reduced over that observed for external exposure concentrations. As Barron
et al. (2002) point out, “CBRs” have been promoted as consistent across different
chemicals, species, and exposure conditions. A rebuttal to the key points made
by Barron et al. (2002) acknowledges that the tissue-residue approach has been
misapplied and that many important aspects have been ignored (Landrum and
Meador 2002). The review article and the rebuttal letter highlight the importance
of a detailed examination of the data for each compound and emphasize careful
selection of compounds based on mechanism of action when considering mixtures
or broad classifications. More importantly, several factors (see list in next section)
must be considered before an accurate assessment can be made. An additional note
here is the term “CBR.” Although the CBR has often been used synonymously with
the lethal residue for 50% mortality (LR50 ), in this article the CBR will be used in
its original sense to denote any one of a number of response metrics, including the
LR50 , lowest observed effect residue (LOER), and others.
The main advantage of the tissue residue approach (TRA) for toxicity assessment
is that the tissue concentrations observed for toxicity responses will generally be less
variable than those responses expressed as a function of an exposure concentration
(water, sediment, or diet) because of the reduced toxicokinetic variability. For many
compounds, critical body residues, such as the lethal residue (LR50 , LR10 ) or LOER,
for a given toxicant will exhibit relatively low variability among species. For those that
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are more variable, selection of a low percentile ranking from a species sensitivity distribution (SSD; a type of cumulative density function, CDF) based on tissue residues
would be the most appropriate method for determining the CBR for protecting the
most sensitive species. Perhaps the strongest point to be made for the TRA approach
is that for many toxicants a relatively consistent CBR will be observed over a wide
range of species that will allow characterization of the toxic response for these species
and many similar aquatic species. Even for those toxicants where the SSD is needed
to define the CBR, once a sufficient number of species are tested, the distribution is
known and additional species will not likely change the results. This feature provides
a considerable advantage because only a relatively small number of species across
diverse taxa are needed to define the tissue concentration–biological response relationship that can be used to protect the majority of aquatic species. Once the
relationship is characterized, testing large numbers of species will be unnecessary.
Environmental exposures via water, sediment, or diet produce highly variable
dose-response curves across species and exposure periods. As an example, toxicity
due to water exposure of chlorophenols (CPs) shows a three order of magnitude
range in response over different compounds and high variability among species for
a given chlorophenol (Figure 1). Most of this variability among species is due to
species-specific differences in their uptake and elimination kinetics (toxicokinetics,
Figure 1.
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Water exposure LC50 values in nmol/mL for several chlorophenol compounds. Data shown for several species (Carassius auratus, Salmo trutta,
Lumbriculus reticulatus, and Oncorhynchus mykiss). Data from Kishino and
Kobayashi (1995, 1996), Hattula et al. (1981), Kukkonen (2002), and
Hodson (1988).
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see Appendix A) and the bioavailability of each compound, which is a function of
compound hydrophobicity and the proportion that is ionized. Toxicodynamics may
explain some the variability (e.g., pentachlorophenol versus other CPs); however,
this factor is often not as important as the toxicokinetic rates for a given compound
or group of compounds exhibiting the same mechanism of action.
When tissue residues are examined as a function of the toxic response, the variability is greatly reduced because the toxicokinetics and bioavailability of that compound
are accounted for in the tissue-residue determinations. This important feature reduces the variability likely to occur between different exposure conditions. As an
example, the steady-state LC50 based on water exposure of tributyltin (TBT) for the
amphipods Rhepoxynius abronius and Eohaustorius washingtonianus differs by a factor
of 112. In terms of the whole-body tissue residue associated with mortality (LR50 ),
there is no statistical difference between these two species (Meador 1997).
One appealing aspect of the TRA for toxicity assessment is that toxicants can be
grouped generally by mode of action as outlined in McCarty and Mackay (1993).
In their review, eight major modes of toxic action are highlighted for organic compounds with estimated CBRs for both acute and chronic responses. The data presented by McCarty and Mackay (1993) show variability in the range of one to two
orders of magnitude for a given mode of action, but 8 orders of magnitude difference from the least to most toxic groups of compounds. The relatively low variability
for a given mode of action facilitates predictions for compounds within that group.
These modes constitute one framework that has been developed for aquatic organisms and is different than that devised for mammals (McCarty 2002). It should be
noted that many compounds or their metabolites, (e.g., PAHs) are mutagenic, which
is a mode of action not generally considered for aquatic species.
Exceptions
One notable exception for the tissue-residue approach is the lack of a doseresponse relationship for those compounds that do not bioaccumulate but cause
a toxic response. There are many definitions for bioaccumulation, ranging from
simple uptake of substances from the environment to an accumulation over time
(e.g., increase) or retention. Many substances will occur in biological tissues, but once
the source is removed they may be eliminated very quickly. For some hydrophobic
compounds that would normally increase in tissue this can occur due to metabolic
breakdown and rapid elimination. For other compounds that are not hydrophobic
or associated with a specific receptor, their presence may be observed in the tissue’s
aqueous fraction, including plasma, but will be eliminated very quickly once the
source is removed. This category would include compounds that are strongly acidic
or basic, irritants, compounds that act at the surface (e.g., some metals on the gill or
sensory structures), and possibly ammonia, cyanide, and others. Chemicals that fall
into the aforementioned categories are likely not amenable to the TRA for toxicity
assessment.
A number of compounds are substantially metabolized making it difficult to determine a realistic CBR. For example, polycyclic aromatic hydrocarbons (PAHs) can
cause adverse effects in fish even though measured tissue concentrations are extremely low (Johnson et al. 2002). In some species (mostly invertebrates) that do
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not extensively metabolize PAHs, a dose-response relationship can be established
(Lee et al. 2002a; Schuler et al. 2004). Future work linking biological responses to
biomarkers of exposure or specific metabolites may be valuable in defining a CBR for
some of these compounds. For example, the determination of fluorescent aromatic
compounds (FACs) in bile as a biomarker for PAH exposure (Krahn et al. 1992) may
be a useful way to link exposure and response metrics. Also, for compounds such
as PAHs, the throughput of a compound (e.g., µg of toxicant/gram organism/day)
may be a more important factor than the actual whole-body tissue concentration or
the amount present at the receptor and should be considered as a dose metric.
The temporal component for the dose-response relationship is also important. A
CBR is not a good surrogate for some compounds when exposure and response are
separated by long periods of time (e.g., mutagenicity) such that the measured dose
does not reflect the dose that led to the response.
Metals
A dose-response relationship may be observed for many organic compounds; however, some metals may not exhibit such a correlation. High concentration of aqueous
metal can interact at the gill surface and cause toxicity without increasing total tissue concentrations by destroying the tissue or causing excessive mucous production
that will lead to reduced uptake of oxygen and suffocation. At lower concentrations,
metals will bioaccumulate; however, many factors, such as metallothionein (MT) induction, formation of detoxified granules, specific tissue affinity, and homeostatic
regulation can preclude a whole-body, dose-response relationship. Metallothionein,
an inducible protein, can sequester free metals and render them non-toxic. Induction of MT proteins is a common physiological response in many aquatic species due
to exposure to several metals including cadmium, copper, mercury, nickel, silver, and
zinc.
For many species, homeostatic regulation of internal metal concentrations maintain near constant levels in the face of variable environmental concentrations. For
those species that are considered regulators, homeostatic control of metal concentrations in tissue will likely be exceeded at some exposure concentration, resulting
in increases in whole-body concentrations that may exhibit a dose-response relationship. According to Rainbow and Dallinger (1993), regulation of metals at the
whole-body level is not common in invertebrates and is somewhat restricted to essential elements (e.g., copper and zinc). These authors also note that some species that
are considered net accumulators will exhibit regulation at the tissue level, which may
affect the concentration at the site of action and the assumption that whole-body
residues are a surrogate for concentrations at the site of action.
There are several studies showing a dose-response relationship between accumulated metals and biological responses. Some examples include; copper and growth
impairment in Oncorhynchus mykiss (rainbow trout) (Hansen et al. 2002), silver and
reproductive effects in a Potamocorbula amurensis (bivalve) (Brown et al. 2003), reproductive effects in a Macoma balthica (bivalve) from elevated copper and silver
(Hornberger et al. 2000), growth effects in O. mykiss from elevated arsenic (Hansen
et al. 2004), and reproductive impairment in Acartia spp. (copepod) from silver
(Hook and Fisher 2001). Additionally, Norwood et al. (2003) reviewed the literature
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on dose-response studies using the freshwater amphipod Hyalella azteca and provided
a table listing lethal residue (LR25 ) values for seven metals. As more residue-effect
data become available for various metals, attempts to develop CBRs will undoubtedly
uncover some useful relationships. Even for those metals that are regulated and held
constant by some species, there may be a CBR that can be associated with biological
responses once a homeostatic tissue threshold has been exceeded. Additionally, in
some cases considering the amount of metal found in various biological compartments (e.g., cytosol) (Vijver et al. 2004) may lead to improvement in the predictive
relationship between accumulated metal and toxic response.
Guidelines
Once the CBR is determined for a given compound, a tissue quality guideline
(TQG) can be developed for the protection of all aquatic species (usually invertebrates and fish). In general, CBRs are endpoint-specific values that characterize
the toxicity response often with some species-specific variability. TQGs are developed from a statistical evaluation of all available endpoint-specific CBRs for a given
toxicant that can be used to protect a high percentage of all species.
A critical feature of this approach is that the CBR is based on a causal relationship
between the whole-body tissue concentrations and the biological response, which
provides a defensible approach for toxicity assessment and development of guidelines or criteria for contaminants. This TQG may be used to develop water or sediment guidelines, which will introduce additional uncertainty, but may be more
amenable to regulatory enforcement and consistent with environmental cleanup
goals and management. Another advantage of developing sediment and water guidelines from TQG values is that a field assessment may find only tolerant species that
exhibit reduced uptake or elevated elimination kinetics or those species that have
adapted to toxicant exposure. By characterizing CBR values in laboratory studies
and determining water and sediment guidelines with an upper percentile of bioaccumulation factors, those areas that are potentially toxic to sensitive species can be
identified.
Currently, guidelines to protect aquatic species from adverse effects due to chemical exposure are based on water and sediment concentrations, which have been determined from low percentile rankings of biological responses (e.g., 5th percentile)
from toxicity bioassays or impaired populations from the field. The USEPA’s Ambient Water Quality Criteria (WQC) (Stephan et al. 1985) provides a framework for
determining water concentrations that are intended to protect against adverse effects in 95% of the species most of the time. The data used to produce these WQC
are based on a causal relationship between individual compounds and species from
a variety of taxa.
Over the last few years sediment quality guidelines (SQGs) have been promulgated by various researchers and agencies that attempt to relate biological responses
from a limited set of standard, invertebrate bioassay species that are exposed to
whole, field-contaminated sediment (Barrick et al. 1989; Long and Morgan 1990;
Long et al. 1998). Some of these SQGs also include sediment bioassays with invertebrates conducted with added (spiked) toxicants and field-based responses for benthic communities. Protection against adverse effects for fish has not been considered
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in the development of SQGs. In general, these SQGs are produced for individual
compounds but are based on the response to complex mixtures of contaminants.
Because of this feature, these SQGs are not based on a causal relationship between
individual toxicants and adverse biological responses, nor are they able to characterize risk from multiple contaminants because each bioassay result is based on the
unique combination of compounds found in the sediment. A few recent critiques
have questioned the rationale for such SQG values and their lack of a causal relationship (O’Connor 1999; Borgmann 2003; Lee and Jones-Lee 2004). Many of these
also conclude that SQGs are poor predictors of toxicity. A recent analysis (Word
et al. 2005) has determined that these empirical (correlative) SQGs may have some
utility in predicting sediment toxicity, indicating that they may be useful for screening level assessments; however, the lack of causality is problematic and may not be
scientifically defensible for some assessments.
Another methodology to determine SQGs has been provided by Di Toro et al.
(2000) and Di Toro and McGrath (2000). These SQG values have been developed
for non-specific toxicants (narcosis) using the target lipid model and equilibrium
partitioning coefficients that are determined with quantitative-structure activity relationships (QSARs). This method allows for mixture assessment because of the general additivity of compounds at narcotic concentrations. The methods presented
here in this review differ from the approach of Di Toro et al. (2000) and Di Toro and
McGrath (2000) in that measured values are generally used to determine CBRs and
bioaccumulation factors and all modes/mechanisms of action are considered. The
two approaches are similar in that both are based on causal relationships between
exposure and response and in the determination of toxic units. Both approaches
will produce similar results for some groups of compounds.
The intent of this review is to examine the rationale and recommend procedures
for determining critical body residues for various toxicants. An additional goal was
to explore how these CBRs may be used to develop guidelines for tissue, water, and
sediment that could be used in a regulatory framework to protect aquatic species
from adverse effects. Based on our current understanding, most of the chemicals that
will be appropriate for this approach are hydrophobic organic compounds, some
organometallics, and a few metals. This review has been limited to fish and aquatic
invertebrates; however, the principles of CBR determination may be extended to
other taxa. Separate analyses for aquatic-dependent wildlife (e.g., birds and mammals) may be required for similar assessments and will likely involve assessment of
target organs or blood and more detailed analysis of bioaccumulation (e.g., foodweb
interactions). Because this subject is very large and complex, this review was not
intended to be comprehensive. It was however, written to introduce and evaluate
concepts and to examine their assumptions, to generate discussion on how best to
employ the tissue-residue approach for assessing toxicity, and how to protect species
with a relatively high degree of success and a defensible scientific justification.
APPROACH FOR TISSUE QUALITY GUIDELINES
Calculation of the Endpoint-Specific Critical Body Residue
The first step in calculating a CBR is to determine if enough high quality data are
available to develop a value. Selection criteria for all data should be decided before a
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critical examination of each study is conducted. Among the many factors to consider,
the more important ones are (1). Analytical chemistry quality control/assurance,
(2). Use of radio labeled compounds, (3). The presence of other contaminants, (4).
The variability in environmental conditions, (5). Appropriate units (ppm versus
µmol/g, dry or wet weight concentrations), (6). The length of time allowed for the
response to develop, (7). Route of exposure (e.g., injection, water, diet), (8). The
determination of whole-body lipid content, and (9). Inappropriate statistical analysis
(e.g., spatial or temporal pseudoreplication). A discussion on some of these points
and additional concerns are presented in the following sections.
Controlling factors
Mode/mechanism of toxic action. The terms “mode of toxic action” and “mechanism of toxic action” are often inconsistent and frequently used interchangeably to
describe different processes. Mode of action was defined by Rand et al. (1995) as
a common set of physiological and behavioral signs that characterize a type of adverse biological response. This term can be divided into two categories: specific and
non-specific. Non-specific toxicity is generally referred to as baseline toxicity or the
narcosis mode of action. Compounds that interact with a biochemical pathway by
binding with specific components of that pathway are said to act by a specific mode
of toxic action. Several definitions for mechanism of action have been proposed;
however, in many applications it refers to the biochemical target or specific biochemical pathway affected. More precise definitions have recently been proposed
for both mode and mechanism of action (Borgert et al. 2004).
In this review, mode of action will be used as general descriptor for an impaired
function, such as uncoupling of oxidative phosphorylation, narcosis, or acetylcholine
esterase inhibition, which is consistent with previous use (e.g., McCarty and Mackay
1993; Rand et al. 1995). In general, mechanism of action will be used in general
terms to denote just the molecular target or biochemical pathway affected, which is
information that is required to determine if toxicants are likely to be dose additive.
Toxicants are often classified by their mode of action, which in many cases is
a definition that is probably too coarse for grouping and mixture assessment. For
example, there are many compounds that uncouple oxidative phosphosphorylation
(a mode of action), but do so by different biochemical mechanisms. In theory,
only those compounds that act by a common biochemical mechanism should be
considered for dose addition; however, response addition may be appropriate for
compounds grouped by mode. This likely holds for the modes of action mentioned
here and in other papers (McCarty and Mackay 1993), which are only a subset of
the total number that are likely to be identified. For example, additional modes of
action that should be considered include octopamine mimics, acetylcholine receptor
agonists/antagonists, insect hormone mimics, estrogen agonists/antagonists, and
ion channel (sodium, chloride, calcium, and potassium) activators and blockers, to
name a few.
As discussed elsewhere in this article, compounds that exhibit the same biochemical mechanism of action will likely be amenable for toxicity assessment by dose
addition. In general, narcosis and polar narcosis are considered non-specific modes
of action and the exact biochemical mechanism is unknown (for a review see van
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Wezel and Opperhuizen 1995). Even though narcosis is considered a mode of action, most compounds at narcotic concentrations exhibit dose addition (Di Toro
and McGrath 2000), which is a characteristic of toxicants with a specific mechanism
of action.
Accurate classification of the mode and mechanism of action is crucial, especially
when considering mixtures. Many compounds exhibit multiple modes of action
that are often dose dependent. One example is tributyltin, which acts as an inhibitor
of oxidative phosphorylation in one dose range and as an endocrine disruptor at
lower concentrations (see Fent 1996 for a review on organotin effects). At high tissue concentration (2–8 µmol/g) a large number of organic compounds (including
PAHs, polychlorinated biphenyls; PCBs; and organic solvents) can act by the narcosis mode of action (McCarty and Mackay 1993). At lower concentrations these
chemicals can elicit myriad responses (e.g., reduced growth, impaired reproduction,
developmental abnormalities, altered behavior, tumor formation, and suppressed
immune system) by multiple mechanisms of action. Of course, the duration of exposure is a critical factor for the manifestation of these varied responses. Additionally,
some compounds may exhibit different modes and mechanisms of action for different species, especially when receptor specific toxicity is involved. Changes in the
receptor or the absence of a specific receptor in a species will undoubtedly cause differences in the CBR. For many toxicants we have only limited knowledge about the
mechanisms of toxicity. Future work in toxicogenomics and proteonomics (the study
of the effects of toxicants on gene and protein expression, respectively) will likely
be an indispensable tool for determining biochemical mechanisms by highlighting
altered molecular pathways. Toxicogenomic data in combination with whole animal
bioassays will be important in assuring that a common mechanism is responsible for
the observed adverse response to toxicant exposure.
Temporal factors. Time is a critical variable in characterizing the response metric
(e.g., LR50 ) and should always be considered. It is generally more important for acute
exposure because of slow partition dynamics, redistribution kinetics among various
tissues and biochemical receptors, and temporal lag or overshoot for the response.
Even though CBRs are generally considered time independent, recent research has
shown that the tissue concentrations associated with a response metric can decrease
over time (e.g., Chaisuksant et al. 1997; Lee et al. 2002a,b; Landrum et al. 2004).
Other studies have found no differences in the CBR over time (Donkin et al. 1989;
van Wezel et al. 1995) and in one case the CBR increased with time (van Wezel et al.
1995). Comparisons between tests should be made using an equivalent exposure
duration, unless it can be demonstrated that tissue concentrations associated with
effects are time independent. It should be noted that the CBR values in McCarty and
Mackay (1993) for acute and chronic toxicity generally refer to lethal and sublethal
responses when tissue and exposure concentrations were at approximate steady state
and do not imply a temporal feature for a given endpoint (e.g., growth reduction)
(see Appendix A). The exception is for lethality, which often is defined in terms of
exposure time (acute, ≤96 hours and chronic, > 96 hours exposure).
Many factors may explain the results for studies that demonstrate a declining CBR
over time. Recent work has shown that changes in the rate of biotransformation
or the rate of damage repair (Lee et al. 2002a,b) can lead to a reduction in the
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CBR after an acute exposure. Another temporal factor concerns the mechanism
and mode of toxic action, which may change for a given toxicant with differing
durations of exposure. For example, mortality may result after a few days due to one
mode of action such as narcosis, but additional organisms may die weeks later due to
another mode or mechanism of action, such as a weakened immune system. Careful
consideration of the causal mechanism of the adverse response must be included in
any toxicity assessment.
Lipids. Lipid normalization may be useful for comparing responses among species
on a body-residue basis for hydrophobic toxicants; however, if the compound is not
lipid soluble (e.g., log10 Kow <2) then normalization may have little utility. Several
studies have demonstrated that lipid content of the organism is important for interpreting the toxic response (Hickie et al. 1989; Lassiter and Hallam 1990; Meador
1993; van Wezel et al. 1995). In theory, storage lipids may sequester toxic compounds
from the site of toxic action, thus organisms with higher lipid content may require a
higher total tissue residue to exhibit toxicity. This is likely true for non-specific acting
compounds (narcosis) as well as those that interact with a receptor (e.g., specific enzyme). For all mechanisms of action, lipid normalization may improve comparisons
between studies because storage lipid will likely reduce the amount of chemical that
is circulating in plasma and the amount that will reach the site of toxic action (e.g.,
a specific enzyme or cell membrane).
In some cases, assessing the toxic response with lipid normalized tissue concentrations may be inaccurate because of the inter- and intra-specific variability in lipid
content (Lassiter and Hallam 1990) and lipid classes (e.g., triglycerides, phospholipids, wax esters). Because membrane lipids are the probable target for the narcosis
mode of action and these are not likely to change over seasons or life-stage in the
same fashion as storage lipids, uniform normalization without consideration of the
potential differences in target and storage lipid may lead to inaccuracy in toxicity
assessment. Additionally, one study has suggested that if the relationship between
the biological response and lipid content is not isometric, normalization by the ratio
approach may produce erroneous results (Hebert and Keenleyside 1995).
Lipid normalization of toxicity data could provide additional information and reduce variability, but accuracy and validity are currently limited by the lack of detailed
knowledge in several areas. In addition to the factors mentioned earlier, differences
in character, toxicant-specific storage capacity, and perfusion differences between
target and non-target (storage) lipid compartments within an organism are also crucial. Additionally, the nature of temporal and exposure concentration differences
on the distribution of toxicants between the various target and non-target compartments will also be an important consideration. A review of many of the factors
mentioned can be found in Elskus et al. (2005).
Species differences. For those toxicants that exert their toxicity through a specific
receptor, it is generally recognized that the response dose for a particular toxicant
may be species-specific, depending on the characteristics of the receptor and toxicant. This is well known in mammalian toxicology and is likely true for aquatic
species as well. In some cases, however, these observations are based on administered dose (e.g., µg toxicant/g body weight/day) and do not consider interspecific
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differences in metabolism and internal redistribution of the toxicant, which will
confound the assessment of potency. For those toxicants that exhibit variable potency among species, an SSD is the most appropriate method for selecting the tissue
residue that will protect most or all species from adverse effects.
Metabolism. Metabolism and the resulting metabolites must be considered. In
many cases, the extent of biotransformation of contaminants has not been examined and in most cases the role of metabolites in the toxicity of compounds is usually
not known. In some cases, the metabolite is more toxic than the parent compound,
which has been demonstrated for organophosphates (oxon form) and some mutagenic polycyclic aromatic hydrocarbons (e.g., benzo[a]pyrene). In many cases, the
metabolites are generally considered equally or less toxic, especially for those acting
by narcosis. For those toxicants where the metabolites are considerably less toxic
than the parent compound, the degree of metabolic conversion would have little effect on the CBR value for the parent compound. Additionally, in those cases where
the metabolites are considered equitoxic as the parent compound (e.g., Di Toro
and McGrath 2000), any change in metabolism will have little effect on the overall
toxic response; however, both parent compound and metabolites would have to be
quantified to be useful for CBR determination. Appropriate use of the tissue residue
approach must allow for exclusion or inclusion of the metabolites as contributing
toxicants.
Additionally, many toxicity studies use radiolabeled compounds in bioaccumulation and toxicity experiments. Great care must be used when considering these studies in development of CBRs because of the potential for metabolites or conjugates
to skew the results. Techniques, such as thin layer chromatography or high pressure
liquid chromatography, can be used to separate and quantify parent compound and
metabolites or conjugates and therefore help account for the contribution of the
metabolites. For a more complete accountability of the role of specific metabolites,
studies with biotransformation inhibitors would add important information.
Environmental stressors. Additional environmental stressors are expected to affect
the observed toxicity. For example, variable salinity, temperature, hydrogen ion activity (pH), and oxygen content not only affect the amount bioaccumulated, which
is accounted for when using tissue residue as the dose metric, but may also affect the
physiology and sensitivity of the organisms producing variable results. By expressing the toxicity on a tissue-residue basis, bioavailability issues can be separated from
physiological impacts on the toxic response. Even with the TRA approach, these environmental variables must be considered to avoid confounding the interpretation.
Response metrics. There are various ways to calculate the statistic of interest to characterize the tissue residue associated with effects. For example, many studies report
tissue concentrations only for dead individuals, whereas others analyze toxicants
only in surviving organisms. For the determination of the LR50 , which is a statistic
for the population response, all individuals (alive and dead) should be included.
One way to calculate the LR50 would be to determine tissue concentrations in each
replicate generating a mean value, then calculate the LR50 in the same fashion as
the LC50 using tissue concentrations instead of ambient exposure concentrations.
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Figure 2.
Tributyltin bioconcentration factors (BCF), LC50 , and LR50 values over
time. Observed time-matched BCF and LC50 values for the amphipod
Eohaustorius washingtonianus. Two observed values for BCF shown for
days 10 and 16. Additional values for BCF were determined with uptake (k1 ) and elimination (k2 ) rate constants. LR50 values from 5 experiments with E. washingtonianus and E. estuarius. Mean (sd) LR50 values
for Days 10, 16, and 30 were 45.8 (11.8) n = 3, 26 µg/g n = 1, and
54 µg/g dry weight n = 1. Data from Meador (1997) and unpublished
results.
This approach is important for comparisons between the LC50 , BCF, and LR50 (e.g.,
Figures 2 and 3). The LOER would also be determined in the same fashion with all
individuals being analyzed. Ideally, dead organisms are removed daily and frozen
for analysis at a later date. Once the test has ended and all individuals are collected,
analytical determinations can be performed on all individuals in the replicate. Of
course, for some species, dead individuals deteriorate quickly or are not found, complicating the determinations. For these species, all individuals that are found should
be included in the analytical determinations and the type of CBR described in the
results section.
Recent papers (Landrum et al. 2004, 2005) provide methodology and rationale
for calculating LR50 and mean lethal residues (MLR50 ) values with either all dead
or surviving individuals in toxicity tests. Landrum et al. (2005) point out that for
many toxicants, the LR50 values using either all dead or all live organisms are very
similar for the amphipod Hyalella azteca. This approach is important for application
of CBR and TQG values developed from laboratory studies to tissue concentrations
observed in field-collected organisms. Another important observation for lethality
studies is the declining LR50 for some toxicants during acute exposures (Landrum
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Figure 3.
Relationship between observed bioconcentration factors, LC50 , and LR50
values for several species. Data from Meador (2000) for tributyltin.
BCF and LR50 values based on dry-weight concentrations and are timematched.
et al. 2005). For some compounds, this downward trend in the LR50 is steep for the
first few days, but lessens considerably for exposures greater than 4 to 7 days. Based
on these studies, it is recommended that laboratory toxicity tests that attempt to
characterized an acute CBR should be conducted for a sufficient period of time to
assure a steady-state value (e.g., 7–10 days), which is a departure from the standard
time period of ≤ 96 hours.
The typical approach for experimentally defining a concentration-response relationship is to test chemicals at several water, diet, or sediment concentrations and
calculate the concentration at which the response of interest is observed. As such,
the curve representing the relationship is not perfectly known, but rather is interpolated between actual measurement points and sometimes extrapolated beyond
the areas of measurement, if the relationship is well defined. For the TRA, tissue
concentrations can be substituted in place of the exposure concentrations and doseresponse curves produced. The lethal residue (LRp ) and effective residue (ERp )
values (p is the proportion, as a percent, of individuals responding) are determined
directly with the dose-response curve and are a good statistical representation of
the response, especially when a low proportion (e.g., ER10 ) of the population is
considered.
Several methods are available for generating LRp or ERp point estimates or actual regression equations (Scholze et al. 2001). In general, lethality data (LRp )
are binomial and ERp data are continuous because the effective residue is usually associated with such responses as growth impairment, reproductive abnormalities, and other continuous parameters. Techniques to determine LRp and ERp values have been published using Generalized Linear Models (GLM) (e.g., Kerr and
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Tissue-Residue Approach for Toxicity Assessment
Meador 1996; Bailer and Oris 1997). These methods are excellent for determining
robust estimations of low dose responses (e.g., LR05 or ER01 ), which are important
for response additivity when organisms are exposed to contaminant mixtures (see
section on mixtures). Of course, these regression techniques provide point estimates below actual measured response values and should be viewed cautiously.
Considering the range of values within the 95th percentile confidence band for
the response and bootstrapping techniques using estimated distribution parameters
will help reduce uncertainty, especially for low-level point estimates (Scholze et al.
2001).
One potential problem in calculating the dose-response curve is hormesis, which
is a situation where the biological response is not monotonic over concentration.
Hormesis has been defined as low-dose stimulation followed by high-dose inhibition (e.g., a “U” or “J” shaped dose-response curve). Calabrese and Baldwin (2003)
provide an excellent review of hormesis citing many examples of its occurrence and
recommendations for incorporating this phenomenon into risk assessment. Statistical analysis for a hormetic response includes the use of GLMs, which have been
addressed by Bailer and Oris (1997, 1998). Using GLM to characterize the doseresponse curve is advantageous because it allows the accommodation of hormesis,
providing viable and defensible LRp or ERp estimates (which is equivalent to the relative inhibition [RIp ] value). It should be noted that enhanced biological responses
(e.g., increased growth or reproductive output) due to low dose stimulation are not
necessarily beneficial for populations. The life-history parameters for many species
depend on a predictable sequence of events and alterations may affect the timing
of maturation and reproduction, change predator/prey relationships, or affect population abundance, all of which could be disadvantageous for the species at the
population and community levels of organization.
Unfortunately, in many cases a regression equation linking concentrations with
a biological effect is not possible because of too few treatments, responses in only
one or two treatments, or inconsistent responses. For these, and other reasons, researchers often calculate the “no observed effect residue” (NOER) and LOER for
exposure concentrations, which are determined by ANOVA. These values (LOER
and NOER) are often information-poor because they are dependent on the quantal nature of allocating exposure concentrations. In many studies, the experimental design consists of only two or three concentrations that differ by up to an order of magnitude, leading to large gaps between the NOER and LOER values.
Because of this, LOER values should be viewed with caution because they may
overestimate (=less toxic) the true LOER value. Additionally, NOER values are inherently less useful as indicators of biological effects than LOER values and their
inclusion in calculations for guidelines or thresholds should be carefully considered. Based on the well-known statistical saying, “the absence of evidence is not
evidence for absence,” NOER values do not imply that a given toxicant concentration will not cause a biological response. In the absence of a dose-response regression, one approach would be to determine the LOER for a toxicant and apply a safety or uncertainty factor (UF) (Chapman et al. 1998; Duke and Taggart
2000) to reduce the value to a predicted no-effect level. The magnitude of the
UF that is selected is often not well defined or supported and is usually a policy
decision.
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Developing a Tissue Quality Guideline
Once the studies are screened, an appropriate range of species, not unlike that
required for the USEPA’s water quality criteria (Stephan et al. 1985) would be needed
to develop a TQG that would be considered representative of all species, or a designated subset, if desired. In general, the CBR is endpoint specific including such responses as mortality, growth impairment, reproductive impairment/abnormalities,
alteration in behavior, and immunosupression. Although these endpoints are valuable for assessing population responses, other endpoints may also be selected for
determining a threshold CBR to protect against adverse biological responses at the
individual and population levels. It is also important to consider the mechanism of
action when selecting data. For example, it is well known that dioxins are very toxic
to vertebrates and much less so to invertebrates as a consequence of receptor specificity and binding (van den Berg et al. 1998). Even among vertebrates, toxicity can
be substantially different for major taxa such as mammals and fish (van den Berg
et al. 1998). For these compounds, reasonable CBRs may be obtained by grouping
by the appropriate taxa (e.g., fish).
Once all available studies have been examined, those that are deemed acceptable
are used to compile a list of values (e.g., LR50 or ER10 values). From this list the
basic statistics, such as mean, variance, and confidence intervals are produced with
the appropriate algorithms. It is very common for environmental concentrations,
such as tissue residues to be lognormally distributed. Calculating the arithmetic
mean and variance for such data can be biased. For lognormally distributed data,
a minimum variance unbiased (MVU) estimator should be used to estimate these
statistics, especially for dataset with a coefficient of variation (CV) exceeding 120%
(Gilbert 1987).
CV =
sd
∗100
mean
(1)
Algorithms for computing the mean, variance, and confidence interval for the
mean can be found in Gilbert (1987). Data for each compound are expected to
be variable among species and tests. Each study should be examined carefully to
determine if the results are supported by the experimental design, chemical analyses,
and the quality assurance measures. As mentioned earlier, lipid normalization for
some hydrophobic compounds may reduce variability. If sufficient lipid data are
available (i.e ., majority of studies considered) then the dose-response values should
be lipid normalized and analyzed for variability. If the variance for the mean TQG
is reduced, then the lipid normalized value may be considered.
There are many considerations for estimating a protective concentration from a
dataset of toxicity values. One popular approach is to generate an SSD and calculate a
low percentile value that would then be used to protect all similar species represented
in the observed data. The SSD can be strictly an empirical CDF or a line may be
fitted to the data based on the known distribution (e.g., lognormal) using the mean
and standard deviation for the data set. Several statistical programs are capable
of distribution fitting and performing goodness of fit tests between the observed
data and the expected distribution. In many cases the data will follow a lognormal,
log-logistic, or Gompertz distribution (Newman et al. 2000), especially for ambient
exposure concentrations. For tissue-residue toxicity data, many of the datasets are
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Tissue-Residue Approach for Toxicity Assessment
expected to follow a normal distribution because of the expected uniformity across
species. For example, the LR50 data for chlorophenols and tributyltin, as well as the
TBT growth CBRs presented in this review are normally distributed.
In recent years, the hazardous concentration percentile (HCp ; p = percentile)
has become the standard way to select a concentration for protection. In many applications, the HC05 , or that concentration representing the 5th percentile of the SSD
is selected to protect the 100 - p percentile of all species. Often, the lower 95th percentile confidence interval for the HC05 point estimate is selected as the concentration for protection (Newman et al. 2000; Wheeler et al. 2002). The HCp for protection
may be simply the 5th percentile of the observed data or an interpolated value from
a fitted distribution. One major drawback to the latter approach is the selection of
the best distribution to fit the data. Goodness of fit tests (e.g., Kolmogorov-Smirnoff;
K-S) can be used to test the appropriateness of the chosen distribution. If the K-S
test fails, alternate methods are recommended. Recently, bootstrap techniques have
been applied to data sets to determine HCp values (Newman et al. 2000). The main
advantage of bootstrapping is that it is applied to the raw data and is non-parametric
(no distribution is assumed); however, relatively large datasets are required (e.g.,
>20 values are needed to determine the HC05 ).
Ideally, specific CBRs (e.g., growth or mortality) should be determined for all
species; however, a more tailored approach may be desired for a specific situation,
such as the protection of an endangered species. If few data exist for a given compound and endpoint or several endpoints are summarized together, then using
an SSD for selection of a low percentile value is warranted. This is advantageous
because it allows for selection of the most sensitive responses in addition to protecting the most sensitive individuals. Using several sublethal endpoints to characterize
a contaminant-specific CBR is not unlike that used to determine a chronic WQC
(Stephan et al. 1985).
Selecting a single value for a given CBR or a low value (e.g., 5th percentile)
from a SSD for generating the TQG is somewhat subjective. If the CBR data are
lognormally distributed an SSD is likely the best approach for selecting a TQG;
however, for normally distributed datasets with low variability, the mean value may
be acceptable. In addition to the approaches described earlier, other metrics, such
as those proposed by Beckvar et al. (2005) should be considered. In many cases,
the selected value is a policy decision guided by environmental statutes. TQGs for
superfund sites under the Comprehensive Environmental Response, Compensation,
and Liability Act (CERCLA) will likely be different from those chosen for threatened
and endangered species. Selection of the TQG is therefore dependent on the goals
of the risk assessment. In many cases, the TQG will be selected from those CBR
values that are considered critical for continuation of the species or population and
exhibit the lowest tissue concentration.
Sources of tissue-residue toxicity data
Databases and literature. Two major databases are available that contain toxicity
and tissue residue data. Jarvinen and Ankley (1999) provide an extensive review
of tissue values associated with biological responses for many contaminants. Similarly, the Environmental Residue-Effects Database (ERED) (Bridges and Lutz 1999)
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is an Internet-based database providing similar information that can be queried
and specific analysis downloaded. This database is maintained by the U.S. Army
Corps of Engineers (ACOE) at http://www.wes.army.mil/el/ered/. Additionally,
the database ECOTOX (USEPA 2002) maintained by the U.S. Environmental Protection Agency (USEPA) contains tens of thousands of records of water-exposure
toxicity metrics and bioaccumulation data that may useful for determining water
and sediment guidelines (see next section).
A number of compound and class-specific studies have been published recently
that use the tissue-residue approach to determine CBRs and propose tissue quality
guidelines for the protection of aquatic species. These include PAHs and other compounds at narcotic concentrations (Di Toro et al. 2000), chlorophenols (Kukkonnen
2002), PCBs for salmonids (Meador et al. 2002a), tributyltin (Meador et al. 2002b),
mercury and DDT for fish (Beckvar et al. 2005), and dioxins for fish (Steevens et al.
2005). These studies represent a diversity of approaches, including one study that
used the SSD approach for only one group of species (salmonids) over all biological
responses (Meador et al. 2002a). Until other compounds can be analyzed in detail
and CBRs developed, the screening tissue levels proposed by Shephard (1997) using
the USEPA’s water quality criteria and bioconcentration factors should be considered. These values are available for a large number of compounds and can be used to
assess effects based on measured tissue concentrations in organisms from the field.
Data mining, modeling, and QSARs. For most compounds, the BCF and resulting
LC50 are generally highly predictable by knowing the uptake and elimination kinetics for these compounds. For many chemicals, the common link between the BCF
and the compound’s response metric (e.g., LC50 ) is the tissue concentration (CBR)
associated with the response.
In some cases, species-specific, tissue-residue toxicity values can be determined
when the only data available are exposure-based toxicity values (e .g., LC50 ) and bioaccumulation factors (BCF, BAFs, or BSAFs). For toxicants that exhibit reversible toxicity, the whole-body concentration is generally proportional to the water-exposure
concentration and can be linked by the equation:
LR50 = BCF∗ LC50
(2)
The LC50 , which is based on an exposure concentration, is often directly proportional
to the toxicity value based on tissue concentrations. This same relationship can also
be used to determine response metrics based on tissue residues using the sediment
LC50 (or any other response metric) and BAF or BSAF values. Because bioaccumulation is a function of the uptake and elimination kinetics for a toxicant, this equation
is likely not appropriate for compounds exhibiting non-reversible accumulation.
This equation holds for any CBR (e.g., LOER, ER25 ) and BCF. For example:
ER25 = BCF∗ EC25
(3)
An important requirement for both these equations is that the BCF and exposurebased response metric (LCp or ECp ) are at steady state or time matched, such that they
represent an equal time period for exposure (e.g., 96-hour LC50 and 96-hour BCF).
Additionally, Landrum et al. (1992) and McCarty et al. (1992) discuss toxicokinetic
approaches for determining bioaccumulation and CBRs.
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It is interesting to note that when the exposure-based response metric (e.g., LC50 ,
LC10 , or EC10 ) and bioaccumulation factor (e.g., BCF or BAF) are plotted over time
for a given species, the theoretical feature of time independence for the endpointspecific CBR is demonstrated. For example, it is well known that the LC50 will decrease
and BCF will increase over time until they reach steady state. In theory, the CBR
(e.g., LR50 ) will be constant over this time period because of the inverse relationship
between these two parameters (equations 2 and 3). This relationship is exemplified
by the data plotted in Figure 2 showing the declining LC50 , increasing BCF, and
relatively constant LR50 .
As hypothesized by McCarty (1991), the bioconcentration factor and LC50 (as plotted over the octanol-water partition coefficient; Kow ) are inversely correlated and
related by a common factor, the lethal tissue residue (LR50 ). This QSAR between
the LC50 , BCF, and LRSO as a function of the Kow has been confirmed with measured values for compounds at concentrations high enough to act non-specifically
(as narcotics) (Donkin et al. 1989; McKim and Schmieder 1990), which may not be
applicable for many specific-acting compounds. These QSARs for bioaccumulation
may not be valid for those compounds that fail to exhibit tissue accumulation according to equilibrium partition theory, which may be caused by specific interaction
or non-lipid controlling factors.
Because the pH will affect the Kow of ionizable organic compounds and the amount
bioaccumulated, a correction is often needed (Kaiser and Valdmanis 1982; Saarikoski
et al. 1986; McCarty et al. 1993). For example, a log10 Kow of approximately 5.0 is reported for pentachlorophenol (PCP), an uncoupler of oxidative phosphorylation.
Many Kow values are determined for the un-ionized compound and often do not
reflect the conditions generally found in toxicity tests. According to Kaiser and
Valdmanis (1982), the log10 Kow for PCP is approximately 3.2 (±0.1) in the pH range
that is common for aquatic toxicity tests (pH 7.2–8.4). This observation is supported
by Saarikoski et al. (1986) who demonstrated a strong inverse correlation between increasing pH (pH 6 to 9) and decreasing uptake for several chlorophenols, including
PCP.
According to the BCF QSAR published by McCarty (1986) and the lower Kow expected for PCP in circumneutral water, the predicted QSAR based wet-weight BCF
for PCP is approximately 73 (≈365 dry weight) in aquatic species that do not metabolize this compound. A large number of BCF values reported in the ECOTOX
database (USEPA 2002) for PCP exceed this QSAR value (median BCF = 120; 75th
percentile BCF = 600 wet wt.). This QSAR is based on a relationship that was developed for fish with an average lipid content of approximately 4.6% wet weight. If
a lower lipid value was used, such as that found in many invertebrates (1–2% wet
weight), the QSAR would predict a much lower BCF.
The same discrepancy was also shown for TBT, a polar and ionizable cation.
Meador (2000) discovered that the bioconcentration factors for tributyltin greatly
exceed the predicted values based on the commonly used BCF QSAR (McCarty 1986)
using a Kow determined at pH 8. The pKa for TBT is 6.25 and as pH increases, the proportion of un-ionized compound increases. The expected dry-weight BCF for TBT
in species that do not metabolize this compound is predicted to be approximately
5,800 (= 3.76 in log10 units) (or 1,160 as wet weight). As seen in Figure 3, most of
the dry-weight BCF values for TBT far exceed 5,800, even for species that extensively
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J. Meador
biotransform TBT and for many invertebrates with whole-body lipid levels in the
1–2% wet weight range. Although the QSAR approach may be useful for predicting
toxicity and bioaccumulation for some specific-acting compounds, each compound
should be assessed independently. Based on these data, the conclusion here is that
not all hydrophobic compounds will bioaccumulate according to EqP QSARs, indicating that compound hydrophobicity may be less important than a possible specific
interaction.
Even though the QSAR approach may not be applicable for predicting CBRs for
some specific-acting compounds, the relationship between the BCF, the response
metric (e .g., LC50 ), and CBR may hold for a given compound or group of related
compounds over species (Figure 3). This relationship can also be expressed as a
regression equation where the slope coefficient is the CBR for the plotted data (e.g.,
response metric versus 1/BLF). An example of this approach is shown in Meador
(2000) for mortality and growth impairment due to TBT exposure. For many nonspecific and specific-acting compounds, a CBR (e.g., LOER or LR10 ) can be determined in this same fashion with the BCF and corresponding water exposure value
(e.g., LOEC or LC10 ), which must be time-matched for exposure. This relationship is
also applicable to ionizable organics because the errors due to ionization cancel out
preserving the relationship between the toxicity metric and BCF (McCarty 1986).
Mixtures
An important advantage of the TRA is the ability to assess mixtures of chemicals
when common mechanisms and modes of action can be determined. Mixture toxicity studies based on tissue residues are expected to be less complicated than those
with external exposure concentrations because the variability observed among compounds in bioavailability, bioaccumulation, multiple dose-dependent mechanisms,
and metabolic conversion is reduced. Assessing mixtures with water and sediment
concentrations is difficult because the adverse biological response is dependent on
the tissue concentration, which is a function of the uptake and elimination kinetics
of the compounds in the mixture. Because these kinetic rates can vary greatly among
compounds in a mixture for a given species, determination of the compound’s contribution to the overall response will vary with its bioaccumulation. Additionally, mixture toxicity assessment from ambient exposure concentrations can be confounded
by differences in time to steady state from the various compounds in the mixture,
whereas chronic CBRs are generally time independent. There are many excellent
overviews of mixture toxicity including Könemann (1981), Cassee et al. (1998), Altenburger et al. (2000), and Warne (2003); however, none of these addresses tissue
residues.
While the final contaminant-specific TQG results from the selection of the most
appropriate CBR determined from literature values (e.g., the lowest value or the most
environmentally relevant), toxic mixtures are assessed with individual CBR values
to determine the summed contribution of each contaminant to the toxic response.
The TQG is appropriate for a chemical by chemical assessment of toxicity; however,
contaminants in the environment usually occur in mixtures and determination of
potential toxicity is more relevant when the combined potency of all toxicants is
considered. The tissue-residue approach is relatively direct for single compounds;
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Tissue-Residue Approach for Toxicity Assessment
Table 1.
Non-interactive toxicants.
Dose additive
Compounds behave as dilutions of each
other
Toxic contribution possible from individual
chemicals present below their no effect
level (e.g., NOER or ER01 )
Mixture toxicity determined by adding
proportional dose
Similar mechanism of action
Response additive
Compounds do not behave as dilutions of
each other
No contribution to mixture effect from
individual chemicals present below their
no effect level (e.g., NOER or ER01 )
Mixture toxicity determined by adding
proportional responses
Dissimilar mechanism of action
Non interaction = not synergistic or antagonistic. After Warne (2003).
however, when considering the combined effect of multiple contaminants, factors
such as additivity and interaction need to be considered. In some cases, a single
compound TRA will determine that a location is contaminated. For those situations where many compounds exist at concentrations expected to cause low-level
responses, an assessment of the total suite of compounds is necessary for a more
complete determination of potential adverse effects.
Toxicants in a mixture are either interactive or non-interactive and can act at
the same receptor site (similar joint action) or at different sites (dissimilar joint
action) (Warne 2003). Mixtures that are considered interactive contain one or more
components that affect the mechanism of action of one or more other components
in the mixture such that their toxicodynamics or toxicokinetics cause a synergistic
(greater than additive) or antagonistic (less than additive) biological response. Noninteractive compounds, by definition, do not affect the physiological activity of other
compounds in the mixture and can act at the same receptor (dose addition) or at
different sites (response addition) (Table 1).
There are two models of non-interaction, Loewe additivity and Bliss independence, which are discussed in a recent review of mixture toxicity (Borgert et al.
2001). In general, Loewe additivity is used to describe compounds that act by dose
(or concentration) addition and Bliss independence is generally used to characterize response addition, which is also known as “independent action.” Cassee et al.
(1998) provide an overview of statistical methods for determining if a mixture is acting by dose- or response-addition and Dawson and Wilke (1991) give an example of
each using isobolograms to characterize these responses. Another paper (Backhaus
et al. 2000), also provides a compelling framework for distinguishing response addition from dose addition. Because information regarding the mechanism of action is
frequently not known, many researchers consider the dose-addition model to be a
conservative approach for assessing mixture toxicity (Borgert et al. 2001); however,
others consider Bliss independence to be the more appropriate non-interaction
model for mixtures of dissimilar-acting compounds (Backhaus et al. 2000). Dose addition usually predicts higher mixture toxicity than response addition; therefore the
dose addition model will be the conservative choice for environmental protection.
It is difficult to predict the toxicity of mixtures with interactive components and
such a discussion is beyond the scope of this review. Toxicity predictions for mixtures with interactive components are highly dependent on mixture composition,
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the mechanism of action, the species being tested, as well as the dose of each component. Such relationships are usually detected by experimentation with multiple
ratios of the different chemicals. For example, Sorensen (1991) describes several
interactive responses between different metals and other well-known examples have
been described for pesticides (Murphy 1986). Even though there are several examples of interactive effects for compounds in exposure experiments (mostly water
exposure), it is not known if the same interactive effects would also occur when considered in terms of tissue residues. Borgert et al. (2001) provide a thorough review of
the criteria needed to conduct and assess chemical interaction studies. Many studies of multiple compounds have found that non-interactive additivity occurs more
frequently than interaction. As noted by Warne (2003), several review papers have
examined mixture toxicity and found that 70 to 80% of all mixtures are considered additive in producing a toxic response, indicating that additivity is a reasonable
assumption for most mixtures.
The mechanism of action is crucial when addressing additivity, which will determine if the compounds are dose additive or response additive. Organotins and
halophenols (nitro- and chlorophenols) are examples of compounds with the same
mode of action (uncouplers of oxidative phosphorylation), but act by different biochemical mechanisms. Organotins are generally strong inhibitors of ATPase in cell
membrane (Na+ /K+ and Ca+ ATPase) and have been shown to affect mitochondrial proton flux by acting on the Fo sector (membrane protein complex) of ATP
synthase (Matsuno-Yagi and Hatefi 1993; Hunziker et al. 2002). Substituted phenols
have been shown to bind to the quinone reductase site Qi (Escher et al. 1997). Based
on these different mechanisms, it is unlikely that organotins and halophenols are
dose additive; however, no studies have been conducted that test this hypothesis.
Compounds that act on different biochemical pathways within the same mode of
action are predicted not to be dose additive, but may be response additive.
Pesticides that inhibit cholinesterase (ChE) leading to high levels of acetylcholine
are another good example. Organophosphate insecticides have been shown to bind
and phosphorylate the active site of the cholinesterase enzyme. Carbamates, another
group of ChE inhibitors, are known to add a carbamyl moiety to the seryl hydroxyl
group in the active site of the same enzyme (Murphy 1986). Even though these
two groups of pesticides achieve ChE inhibition by different means, they affect the
same binding site of this enzyme. Based on this, a hypothesis of dose addition may
be warranted, although see Borgert et al. (2004). Testing various mixture ratios
and plotting as an isobologram is one way to test this hypothesis. Additionally, if a
reduction of cholinesterase activity occurred at external exposure concentrations
or tissue residues below the NOEC or NOER for each ChE inhibitor in a mixture
belonging to the different classes, then dose additivity would likely be appropriate
for summarizing total mixture toxicity.
Many studies have examined the toxicity of metal mixtures. The majority of those
studies have been conducted as water or sediment exposures, which are difficult to
interpret because the bioavailable form of most metals is dependent on many external factors such as pH, dissolved organic carbon, redox state, and alkalinity. Slight
changes in any of these physical/chemical parameters will likely affect the toxicity
of the metals in a mixture leading to different results. Additionally, the toxicokinetic
differences expected for the various species studied will also have a major effect on
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the results. A review of 68 such studies is presented by Norwood et al. (2003), which
indicates that about 43% of the interactions were less than additive, 27% were additive, and 30% were more than additive. As CBRs for metals are developed they can be
incorporated into the framework discussed later for assessing mixtures. Additionally,
for those TRA assessments where CBRs are generated for only one species or group of
similar species, the toxicity of metal residues may be addressed as response-additive
toxicants and included in determination of the toxic unit (TU) value.
In general, the toxic unit value is simply the ratio of a tissue concentration to a specific response metric, (e.g., LR50 , LOER, or ER10 ) for a given toxicant and represents
the fractional contribution of that toxicant to the overall response. The response
metric for a given TU should be the same (e.g., LR50 , LR10 ) and consistent for the
endpoint (i.e ., if mortality, then all LRp values, if growth, then all ERp values; p being
the proportion responding). The sum of toxic units is the additive result of all toxic
unit values resulting in a proportion that can be used to predict the probability of
an endpoint- and metric-specific response. A value above 1.0 indicates that the toxic
response (e.g., EC25 growth reduction) is likely and a value below 1.0 would indicate
a reduced probability of toxicity from the mixture for that endpoint and response.
Dose-additive compounds
For non-interactive, dose-additive compounds, a toxic unit approach can be used
to characterize the toxicity of mixtures. For these dose-additive compounds, a toxic
effect may be observed even though each measured tissue concentration is below
its individual no effect level. In theory, adding the fractional contribution of each
similarly acting component will lead to an observable toxic effect because each
compound would act in concert and add to the effective concentration producing a
response. Of course, this depends on the total contribution leading to a TU in the
range of detectable responses.
The following equation describes the general equation for total toxicity due to
dose-additive compounds:
Toxic units (TUda ) =
n
[tissue]i
i=1
CBRi
(4)
where TUda is the mechanism-, endpoint- and metric-specific toxic unit value for
dose-additive compounds; [tissue]i is the measured tissue concentration for each
compound, and CBR is the chosen response (e.g., growth impairment ER25 for
chlorophenol uncoupling of oxidative phosphorylation). All concentrations expressed on a molar basis.
Ideally, separate TUda values would be developed for several endpoints, such as
mortality, growth impairment, immunosuppression, various reproductive abnormalities, and behavioral alterations at a given response level (e.g., LR50 or ER25 ). As noted
by Borgert et al. (2004) compounds with the same apparent biochemical activity may
not be dose additive if some of the features of the mechanism of action are different
(e.g., target organ specificity or rates of tissue distribution). For these similarly acting
compounds, toxicity testing would be warranted to examine the potential for dose
additivity when based on tissue concentrations.
Hum. Ecol. Risk Assess. Vol. 12, No. 6, 2006
1039
J. Meador
A good example of dose additivity is that for dioxin-like compounds. For dioxins, PCBs, and PAHs, toxic equivalent factors (TEFs) are used to produce a summed
toxic equivalent concentration (TEQ) by considering the fractional addition of each
component (van den Berg et al. 1998; Barron et al. 2004b). The TEQ value is a tissue
concentration (e.g., pmol/g) from the mixture of compounds that can be related
to the dose-response curve produced for 2,3,7,8 tetrachlorodibenzodioxin (TCDD),
the most potent compound for this mechanism of action. The TEFs are specific
for a mechanism of action, such as those biochemical responses resulting from aryl
hydrocarbon receptor (AhR) interaction. Some TEFs are for a given response, such
as early life-stage mortality in fish, which is not a mechanism of action and therefore
may not be dose additive. It should be noted that these toxicants (dioxins, PCBs, and
PAHs) may have many CBRs due to multiple mechanisms of action, including some
that are considered non-dioxin like (Giesy and Kannan 1998; Barron et al. 2004a).
Additionally, even the dioxin-like biological responses may be due to multiple mechanisms of action, which are undefined in many cases.
The TEQ equation is not unlike the TUda equation expressed earlier. The basic
equation for generating a TEQ is:
TEQ =
(PCDDi ∗ TEFi ) + (PCDFi ∗ TEFi ) + (PCBi ∗ TEFi ) + (PAHi ∗ TEFi )
(5)
for all polychlorinated dibenzodioxins (PCDDs), polychlorinated dichlorodibenzofurans (PCDF), PCBs, and PAHs exhibiting the same mechanism of action and a
TEF value relating individual compound potency to that of 2,3,7,8 TCDD. The tissue concentration of each compound is multiplied by its respective TEF and the
resulting value added to all others to determine the TEQ (the summed tissue concentration). TEF values for dioxin-like compounds are somewhat different for major
taxa and separate values have been developed for humans, fish, and birds (van den
Berg et al. 1998). Because the TEFs are based on tissue concentrations and bioaccumulation is so highly variable among compounds and species, it is not appropriate
to multiply these by water or sediment concentrations to produce a TEQ because
bioaccumulation is not implicitly considered.
For dose-additive mixtures that contain compounds with known differences in
toxicity, a scaling factor (e.g., TEF) is not needed because the CBR for each compound will reflect its potency for the mixture. For example, the data presented
later indicate that among the chlorophenols (CPs), pentachlorophenol (PCP) is
the most toxic when based on tissue residues. The CBR for PCP is lower than that
for all other CPs; hence the term for PCP in equation 4 will be larger for a given tissue concentration and will contribute in proportion to its toxic potential. As another
example, organophosphates have been shown to exhibit different toxic potencies,
which would allow for a TEF or TUda approach. The TEFs for ChE inhibition in
mammals have been summarized in NRC (1993) for several organophosphate insecticides using chlorpyrifos as the reference standard, which provides data for dose
additivity assessment for this group. Of course these TEFs may not be applicable to
aquatic species; however, these data do indicate the possibility of dose additivity for
lower taxa.
For the narcosis mode of action, dose additivity is highly probable, which means
that the toxic unit approach (TUda ) is appropriate. As indicated by several authors
(Broderius et al. 1995; Di Toro and McGrath 2000) a large number of compounds
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Hum. Ecol. Risk Assess. Vol. 12, No. 6, 2006
Tissue-Residue Approach for Toxicity Assessment
including halogenated and non-halogenated aliphatic and aromatic hydrocarbons,
nitriles, amines, alcohols, ethers, and ketones can act by narcosis. All of these compounds may be added together in the TUda equation. The acute, whole-body lethal
CBR is fairly well defined for narcosis and occurs at approximately 2–8 µmol/g (wet
weight), with many chronic (mostly sublethal) responses occurring at CBRs that are
about 10-fold lower in concentration (McCarty and Mackay 1993). It should be noted
that this value was determined for fish and is based on an average lipid content of
approximately 5% (wet weight) (McCarty 1986), which may require an adjustment
for species with higher or lower lipid content.
Even though many compounds can elicit a narcotic response at high concentration, these same chemicals can act to produce different biological responses by other
mechanisms of action when present at lower concentrations such as reproductive
effects, growth alteration, enzyme inhibition, tumor formation, metabolic disorders,
and organ abnormalities (Cornish 1980; Hwang et al. 2004). Even though all PAH
congeners may be considered additive for the narcosis mode of action (Di Toro et al.
2000), the biological responses for PAHs are quite varied (Payne et al. 2003; Barron
et al. 2004a; Incardona et al. 2004). Consequently, a narcosis CBR for total PAHs will
be quite different than the CBRs for PAHs when describing toxic effects for early lifestage development, reproductive alterations, reduced growth, immunosuppression,
behavioral effects, or mutagenicity, which are all biological responses attributed to
PAH exposure.
Non-interactive, dose-additive toxicants are likely to include all the narcosis acting
compounds mentioned earlier when present at high concentration, chlorophenols
(reviewed in this article), trialkyltins, pyrethroids, and possibly pesticides that act on
cholinesterase (organophosphate insecticides and carbamates). Phenylureas and triazines may also be dose additive because they have the same site of molecular action
(the D1 protein of photosystem II). This characterization may also apply to dioxins
and related compounds (PCBs and some PAHs) that act by the same mechanism of
action. Of course all these compounds likely exhibit multiple mechanisms of action,
therefore assurances that the CBR is due to a common mechanism and biological
response is necessary when applying dose additivity. As mentioned earlier, toxicogenomics may be a useful tool to assure that the same mechanism of action is being
considered in such assessments.
Borgert et al. (2004) state that the available data are usually insufficient to support
dose additivity for compounds below their effects level without additional evaluation.
These authors maintain that many factors besides the biochemical mechanism need
to be considered before compounds can be accurately classified as possessing the
same mechanism of action. Some of the factors listed by Borgert et al. (2004), such
as kinetics and metabolism, are important when considering ambient exposure (e.g.,
water); however, the importance of these particular factors are reduced when tissue
residues are considered for toxicity assessment.
Even without such detailed information, dose additivity has been observed and
may be a viable scheme for some toxicants and a reasonable first approximation
for others. For example, Merino-Garcia et al. (2003) reported dose-additivity with
the isobologram method for Daphnia magna exposed to two amines. Another study
demonstrated that several compounds purported to be uncouplers of oxidative phosphorylation were dose additive when tested on fathead minnows (Pimephales promelas)
Hum. Ecol. Risk Assess. Vol. 12, No. 6, 2006
1041
J. Meador
in binary mixtures (7 different ratios) with 2,4 dinitrophenol (DNP) a classic uncoupler (Broderius et al. 1995). These included other halogenated phenols, several
substituted phenols, two amines, and one pyridine. Because these mixtures were
tested at concentrations below effects levels (NOECs), the results would imply that
several types of uncouplers can be considered dose additive. These data are also
supported by the results of Altenburger et al. (2000) who demonstrated dose additivity for a mixture of 16 phenol-type uncouplers. Another example is from Silva
et al. (2002) who demonstrated dose additivity with estrogenic compounds. They
added these compounds at concentrations well below their NOEC and found effects at response levels far higher than those predicted with response addition. As
described earlier, dioxins are also considered a good example of dose additivity. If
possible, detailed information as proscribed by Borgert et al. (2004) or experimental
evidence should be obtained for compounds or classes of compounds to provide assurance that the dose-additivity model is appropriate. If such data are not available,
the toxicity assessment can proceed as long as the potential limitations are explicit.
One additional caveat here concerns the response metric. Even though the effect
level may be considered below the NOER (or NOECs), the selected concentration
may have produced a low-level response (e.g., 5%), which would not be statistically
detectable with an NOEC-type evaluation. When conducting dose-additive mixture
studies, a regression approach (as discussed earlier) is recommended as the best way
to ensure that mixture components are present at concentrations low enough not
to cause adverse effects (e.g., below the ER05 or ER01 ).
Response-additive compounds
Many toxicants can produce similar effects (e.g., growth impairment) by the same
mode of action (e.g., disruption of the energy cycle). Those compounds that produce
a similar response, but do so by different mechanisms of action may be treated as
response additive. The main difference between dose additivity and response additivity is that components of a dose-additive mixture will contribute to the biological
response at tissue concentrations below any effect level, whereas response-additive
compounds will not.
In theory, several chemicals acting by disparate mechanisms of action should not
produce any measurable response if they occur at concentrations well below the level
needed to cause a response. The general equation for response-additive mixtures,
which is based on probability theory for independent events, is:
Rmix = 1 −
n
(1 − Ri )
(6)
i=1
where Rmix is the total fractional response (scaled from 0–1) and Ri is the fraction
response for each compound. The maximum value is 1.0, because this is considered the highest absolute response (e.g., 100%) for any endpoint. For a mixture
of compounds where the responses are low (e.g., 5%), the cross product values are
insignificant and the result is similar to that obtained for simple addition (e.g., R1 =
0.05 + R2 = 0.05 + R3 = 0.05 produces an expected response of 0.15 [15%] whereas
equation 6 = 0.143 or 14.3%). In general, for those multi-contaminant mixtures
where the sum of responses are low (e.g., below 25%), the prediction from equation
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Hum. Ecol. Risk Assess. Vol. 12, No. 6, 2006
Tissue-Residue Approach for Toxicity Assessment
6 is very similar to simple addition of all individual responses. It should be noted that
this equation holds only if there is no correlation in organismal susceptibility to the
various compounds. Modifications to this equation are required if negative or positive correlation in susceptibility are expected (Cassee et al. 1998). Also, dose-response
data for each component is required for determining the expected response and
only those values known or predicted to cause a response are included.
For the present application, the TU approach was selected for response additive compounds because simple addition of the responses is a good representation
of the total response, especially when all components are present at low response
levels. Toxic units were determined by considering the response expected for each
component of the mixture.
Toxic units (TUra ) =
n
Ri
(7)
i=1
where TUra is the endpoint-specific toxic unit value for response-additive compounds. Ri is the fractional response expected for chemical i based on the measured tissue concentration, which is determined from an existing dose-response
relationship. For example, a tissue residue of 0.04 µmol/g for PCP, may cause a 25%
inhibition in growth, which would translate into an R1 = 0.25. This value would be
added to the response values measured for all other compounds with toxicity data for
the given endpoint that are considered response additive. For this application, the
endpoints must be the same (e.g., growth impairment), but not necessarily the mode
or mechanism of action. Even though the TUra can add up to values over 1 (100%),
the TU approach is useful for determining the amount of “excess” toxicity expected
from accumulated toxicants and for gauging the probability that the response will
occur. The TUra is different from the TUda in that the TUra is not response-metric
specific. This is due to the fact that only positive responses can be considered. Because the proportional responses are summed, the final TUra is an indication of the
likelihood of the adverse effect occurring. For example, a TUra of 2 would indicate
a higher probability of growth impairment from a mixture than a TUra of 0.5.
There are several studies on mixtures that examined the relationship between
ambient exposure concentrations and biological responses and report results that
are consistent with response addition. No such studies were found that were based
on tissue concentrations. For example, a study by Dawson and Wilke (1991) provide a convincing argument for response addition, which is based on an analysis
with isobolic diagrams. In another study Könemann (1981) reported that a mixture of 9 chemicals (potassium, copper, decamethrin, triphenyltin, trichlorophenol,
trichlorobenzene, dichloroxylene, dichloroaniline, and hexachlorobutadiene), with
likely disparate mechanisms of action, produced toxicity (mortality) consistent with
response addition. Two studies examined very low response levels (EC01 = 1% response). Backhaus et al. (2000) provide compelling evidence for response addition
from a mixture of 14 dissimilarly acting compounds testing both the EC50 and EC01
values. Another interesting study found that a mixture of 16 compounds with dissimilar mechanisms of action at their respective EC01 toxicity concentrations produced
an overall toxicity response of 18.4% in algae (Faust et al. 2003). These last two
studies provide a solid demonstration of response additivity at concentrations that
Hum. Ecol. Risk Assess. Vol. 12, No. 6, 2006
1043
J. Meador
would be considered far below the purported NOEC level (i.e ., the level of statistical
significance) in a standard toxicity assay.
Summed toxicity
Mixtures of chemicals can be complex and contain compounds that are both
dose additive and response additive, in addition to being interactive. The question
now is how to deal with myriad tissue concentrations for a given individual and
determine if the sum total is likely to be toxic. When assessing toxicity with the
tissue-residue approach for species exposed to complex mixtures in the field or
from laboratory toxicity assays, considering the contribution of all compounds seems
daunting. Ideally, it would be advantageous to know the mechanism of action for
each compound; however, for most compounds this information is not available.
One approach is to consider that all compounds act by non-specific toxicity (e.g.,
baseline toxicity or the narcosis mode of action). A very large number of organic
compounds act by narcosis at high concentration, so this approach is not unreasonable. For this assumption the TUda equation would be used to produce the final
TU for all compounds, as described earlier. Because all non-specific acting toxicants
are expected to be dose additive, each compound would be included in the calculation of the TU value. Procedurally, calculation of the summed toxic units would be
relatively easy. Without specific CBR data for these compounds, an acute lethality
CBR of 5 µmol/g (wet wt.) is selected (mean of 2–8 µmol/g range for the narcosis
mode of action) for comparison against all measured tissue concentrations. A CBR
for sublethal effects would be set at 0.5 µmol/g with the acute to chronic ratio of
10 (McCarty and Mackay 1993). The acute lethality CBR assumes 5% lipid for the
organism; however, a lipid adjustment may be required. To extend this value from a
low effect value (LOER) to a no-effect value (NOER), a safety factor (uncertainty factor) of approximately 10 may be applied as discussed by Chapman et al. (1998) and
Duke and Taggart (2000). This would produce a NOER CBR of 0.05 µmol/g for use
in equation 4. Although this approach may have some utility for the large number of
non-specific acting compounds, the result may not be protective against all adverse
biological effects because many of these organic compounds act by other mechanisms of action at much lower doses. Given these limitations, this approach, while
likely an underestimation of potential toxicity, is a reasonable first approximation
for toxicity from exposure to a mixture of compounds acting by narcosis.
With more specific information, a second option would be to assume that all
compounds are response additive. The TUra equation (equation 7) would then be
appropriate for this assumption and each toxicant would be considered for the final
TU. A measured tissue residue for each chemical would be compared to its respective
endpoint-specific CBR and all Ri components would be added together for the final
TU value, which would be used to consider the likelihood of toxicity for a given
response.
A third, and more realistic, option is a combined assessment with dose-additive
and response-additive components. For those dose-additive compounds that can be
identified in a sample, a TUda would be developed for each mechanism of action. For
all other compounds, a TUra is determined. Because each value for the TUda and TUra
equations are based on the same endpoint (e.g., mortality, growth reduction, reproductive impairment, immunosuppression), they can be added together to produce
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Hum. Ecol. Risk Assess. Vol. 12, No. 6, 2006
Tissue-Residue Approach for Toxicity Assessment
a final endpoint-specific TUsum value for the sample. This would be accomplished
with the following equation:
Sum toxic units (TUsum ) = TUra +
n
TUdai
(8)
i=1
where TUsum is the endpoint-specific and mechanism-independent toxic unit value,
TUdai are the individual TUda values determined with equation 4, and TUra is the
value from equation 7. From equation 4, the TU value for each group of toxicants
with the same mechanism of action would be calculated. For example, organotins
could be one group (TUda1 ), chlorophenols another (TUda2 ), and narcosis a third
group, and so on for all groups where a specific CBR has been defined (e.g., mortality, growth, or immunosuppression). These would be summed and added to the
TUra value produced for all other compounds (see equation 7) of unknown mechanism that would be assumed to be response additive. The same assumptions for
each component as described earlier for equations 4 and 7 would apply. Of course,
each toxicant is counted only once (either dose or response additive) and all toxic
unit values (equations 4, 7, and 8) are calculated for one biological endpoint. As
with equation 7, the TU value would be a relative value that would indicate an increased likelihood of toxicity as values increased. A key determination for any risk
assessment is the selection of the most appropriate response metric(s) to be used
for environmental protection.
Another consideration for the toxic unit is the measurement scale. A TU value
can be determined for each individual organism (as with TEQs) or for a mean tissue
concentration from one sample or population. Determination of individual TUs
would be preferred because of the expected variability in contaminant concentrations. If individual TU values are determined, statistical analysis on the distribution
of values can be conducted to determine percentiles for use in a probabilistic risk
assessment. For example, if the 25th percentile TU is approximately 1.0, then it can
be concluded that 75 percent of the individuals are likely to exhibit adverse effects
because the TUs for this group are greater than 1.0. Of course, the TU values chosen
for protecting species and populations will be driven by statute-specific goals.
It should be noted that these simple models described here do not account for the
additional toxicity that is likely to occur from multiple adverse biological responses.
Developing such an index that would account for all potential responses would require a substantial effort with experimental support. If these simple, experimentally
validated toxic unit approaches are used, far more protection will be afforded species
than the current chemical by chemical approach now used for many ecological risk
assessments.
Examples of Tissue Quality Guidelines
Two examples of the process for developing TQGs are presented. The first is for
tributyltin, which is a summary of previous publications detailing the CBR values
for this compound. The other example is for chlorophenols, which examines the
available data for this class of compounds. The TQGs for tributyltin are used in
a subsequent section of this article to demonstrate the development of water and
sediment quality guidelines.
Hum. Ecol. Risk Assess. Vol. 12, No. 6, 2006
1045
J. Meador
Tributyltin
Tributyltin is an organotin, which is a class of very toxic compounds that operate by several modes and mechanisms of action. Triorganotins (e.g., tributyltin,
triphenyltin, trimethyltin, tripropyltin), and some of the disubstituted organotins,
are known inhibitors of cellular energy metabolism (Aldridge et al. 1977; Hunziker
et al. 2002), act as endocrine disruptors (Matthiessen and Gibbs 1998), and are
immunotoxic for humans and aquatic species (Bouchard et al. 1999; De Santiago
and Aguilar-Santelises 1999; Békri and Pelletier 2004). There is very little toxicity
information for the monosubstituted organotins; however, one study found that two
of these (monobutyltin and monophenyltin) were several times more toxic to sulfate reducing bacteria (Desulfovibrio sp.) than the di- or tri-substituted organotins
(Lascourréges et al. 2000).
Immunotoxicity of organotins may result in atrophy of the thymus, reduction of
kidney macrophages, and changes to the spleen (Fent 1996). One of the biochemical
mechanisms promoting immunotoxicity is likely related to the disruption of calcium
homeostasis (Chow et al. 1992). The human health reference dose for TBT is based
on immunotoxicity (USEPA 1997; Cardwell et al. 1999) and there is evidence that
dibutyltin is also immunotoxic (Fent 1996; O’Halloran et al. 1998). Bouchard et al.
(1999) and O’Halloran et al. (1998) concluded that dibutyltin (DBT) was a more
potent immunotoxicant than TBT, which had important implications for assessing
the toxicity of DBT tissue concentrations.
Organotins are also endocrine disruptors causing imposex in meso- and neogastropods (Matthiessen and Gibbs 1998), which is the manifestation of secondary
male sexual characteristics in female gastropods. The lowest effects concentrations
reported for TBT involve the imposex endpoint, which is the primary driver for the
USEPA WQC (USEPA 2003b). A recent paper has provided compelling evidence
for the mechanism of action that is responsible for imposex in gastropod snails
(Nishikawa et al. 2004). In addition to demonstrating that TBT strongly binds the
retinoid X receptor, the authors were able to induce imposex in the rock shell (Thais
clavigera) within a one-month period by injecting individuals with 9-cis retinoic acid.
As mentioned earlier, organotins are generally strong inhibitors of ATPase in the
cell membrane. Organotins can also inhibit primary proton pumps, induce hydroxide/anion exchange, and create a hydroxide uniport (Hunziker et al. 2002), all which
affect energy production. Aldridge et al. (1977) found that dibutyltin inhibited ATP
synthesis, but was approximately 3 times less potent than TBT for this mechanism
of action when based on tissue concentrations. When considering organotin tissue
residues, DBT is particularly important because of its similar potency to TBT and because high concentrations of DBT are often found in tissue due to TBT metabolism,
which occurs by sequential debutylation (tributyltin -> dibutyltin -> monobutyltin
->Sn).
Water exposure to tributyltin produces LC50 values ranging over two orders of
magnitude among species (approx 0.5 to 200 ng/ml). The same variability is observed for sublethal effects from a long-term exposure (0.005 to 5 ng/ml) (Cardwell
and Meador 1989; Meador 2000; USEPA 2003b). Most of the studies report responses
based on mortality, growth impairment, and reproductive abnormalities with a few reporting effects on abnormal development, alterations in behavior, and physiological
1046
Hum. Ecol. Risk Assess. Vol. 12, No. 6, 2006
Tissue-Residue Approach for Toxicity Assessment
dysfunction. Bioaccumulation of tributyltin is highly variable in aquatic organisms
and can range almost three orders of magnitude (Table 2).
There are very few studies concerning whole animal toxicity of dibutyltin and
monobutyltin; however, those that have been conducted conclude that these compounds are more than 50 times less toxic than TBT (Lytle et al. 2003) when based
on exposure concentrations. The much reduced whole-animal toxicity for DBT is
likely due to lower bioaccumulation factors because of its lower hydrophobicity (Kow )
(Tsuda et al. 1988). As stated earlier, DBT is very toxic when tissue residues are considered.
Summary CBR data for mortality, growth impairment, and population sterility due
to imposex are listed in Table 3 for TBT, which were used recently for proposed tissue
and sediment quality guidelines (Meador 2000; Meador et al. 2002b). The results are
striking for these endpoints and the relatively low variance in these datasets justifies
the calculation of a single and consistent CBR for each endpoint.
Under the assessment scheme for mixtures presented earlier for tissue concentrations, the triorganotins are likely dose additive, especially for the mechanism of
action concerning inhibition of energy production (mortality and growth) and possibly for immunotoxicity, although few data are available for the latter response.
One comparative study found very similar lethal body burdens for tributyltin, triphenyltin, and tri-c-hexyltin, although tri-n-hexyltin was about 10 times more toxic (Tas
1993). When based on tissue residues (or exposure concentrations) there may be
differences in toxic potency between the organotins, even within a series (i.e ., the
trisubstituted organotins). These differences in potency should be addressed with
separate CBRs, if so warranted, before applying a dose-additive model. For many
of the biological responses, some of the disubstituted organotins may also work by
the same mechanism as the trisubstituted forms; however, each compound will have
to be carefully evaluated. Based on the available data, tri- and dibutyltin are likely
dose additive for immunotoxicity and responses due to energy uncoupling. Because
dibutyltin may be additive for some of these endpoints, tissue concentrations of both
TBT and DBT should be considered for the mortality and growth CBR values, which
may lead to reductions in some of the observed variability. There are not enough data
to determine if monobutyltin, is dose additive for any of the mechanisms discussed
earlier.
For tributyltin, the TQG would likely be selected from the growth or imposex
CBR, depending on the application. For example, at a USEPA superfund site, the
data for growth impairment was the primary driver for the TBT TQG primarily
because imposex was considered only when tissue concentrations were high enough
to cause substantial sterilization and because it was concluded that suitable habitat
for stenoglossan snails was not available (USEPA 2003a).
Chlorophenols
Chlorophenols are common contaminants often found in pulp mill effluent
(Kringstad and Lindström 1984). Studies that relate tissue concentrations and toxicity responses (lethal and sublethal) for chlorophenols were assembled to produce
LR50 and sublethal LOER values. Experiments that determined the LD50 by intraperitoneal injection were not used because the tissue-residue approach uses the acquired
Hum. Ecol. Risk Assess. Vol. 12, No. 6, 2006
1047
1048
95,000
50,000
46,750
42,000
31,400
28,466
21,650
21,600
21,120
18,000
14,500
13,940
13,000
10,000
9,880
7,500
2,690
1,850
1,200
1,000
Type
L
L
L
L
L
F
L
L
L
L
L
L
L
L
L
L
L
L
L
L
gastropod
fish
bivalve
polychaete
amphipod
bivalve
amphipod
bivalve
fish
fish
fish
polychaete
amphipod
fish
fish
fish
fish
amphipod
fish
daphnid
Taxa
0.5
1.0
1.0
1.1
1.5
1.7
2.2
2.2
2.3
3.0
3.3
3.4
3.7
4.8
4.9
6.4
18
26
40
48
LC50
water
16.1
30.7
32.8
36.6
48.9
53.9
70.9
71.1
72.7
85.3
106
110
118
154
155
205
571
830
1280
1536
LC50
sedoc
0.03
0.06
0.07
0.08
0.10
0.11
0.14
0.14
0.15
0.18
0.22
0.23
0.25
0.32
0.32
0.43
1.2
1.7
2.7
3.2
LOEC
growth
water
1.1
2.0
2.2
2.4
3.3
3.6
4.7
4.7
4.8
5.7
7.0
7.3
7.9
10.2
10.4
13.6
38.0
55.3
85.3
102
LOEC
growth
sedoc
0.003
0.006
0.007
0.008
0.010
0.011
0.015
0.015
0.015
0.018
0.022
0.023
0.025
0.032
0.030
0.043
0.12
0.17
0.27
0.32
LOEC
repro
water
0.11
0.21
0.22
0.24
0.33
0.36
0.47
0.47
0.48
0.57
0.71
0.73
0.79
1.0
1.0
1.4
3.8
5.5
8.5
10.2
LOEC
repro
sedoc
1
2
3
4
5
6
5
7
5
11
8
5
9
10
14
11
13
5
15
9
Ref
Predicted water (ng/mL) and sediment (µg/g OC) response metrics (LC50 and LOEC) for each endpoint. Based on equations 2 and 10 in
text using measured (obs) dry-weight BCF values, Koc , and the CBR for each endpoint (see Table 3). Koc = 32,000 (Meador 2000). sedoc =
organic-carbon normalized sediment concentrations. References 1. Bryan et al. 1987, 2. Yamada and Takayanagi 1992, 3. Guolan and Yong
1995, 4. Moore et al. 1991, 5. Meador 1997, 6. Batley et al. 1989, 7. Gomez-Ariza et al. 1999, 8. Martin et al. 1989, 9. Borgmann et al. 1996, 10.
Fent and Looser 1995, 11. Tsuda et al. 1990a, 12. Tsuda et al. 1992, 13. Fent 1991, 14. Tsuda et al. 1991, 15. Tsuda et al. 1990b. Type is L for
laboratory and F for field study.
Obs
BCF
Predicted water and sediment toxicity values for tributyltin using measured BCF and CBR for a given endpoint.
Nucella lapillus
Pagrus major
Mytilus edulis
Neanthes arenaceodentata
Eohaustorius estuarius
Crassotrea gigas
E. washingtonianus
Venerupis decussata
Platichthys stellatus
Gnathopogon caerulescens
Oncorhynchus mykiss
Armandia brevis
Hyalella azteca
Thymallus thymallus
Carassius auratus
Cyprinus carpio
Phoxinus phoxinus
Rhepoxynius abronius
Lebistes reticulatus
Daphnia magna
Species
Table 2.
Tissue-Residue Approach for Toxicity Assessment
Table 3.
Critical body residues (CBRs) for tributyltin.
Response
Mortality
Growth impairment
Imposex
CBR (nmol/g)
Range
167
10.3
1.1
59–255
2.8–21.7
0.35–2.1
Std
CV
62
5.9
0.57
37
57
52
CBR (µg/g)
48
3.2
0.32
n
11
11
8
CBR values in nmol/g dry weight and equivalent µg/g value. One µg/g = 3.45 nmol/g.
Mortality CBR is based on LR50 , the growth impairment CBR is based on the LOER for
growth effects, and the imposex CBR is based on the threshold for population sterilization.
Range shows minimum and maximum values for n studies. CV is the coefficient of variation
in %. Data from Meador (2000) and Meador et al. (2002b).
dose, not the administered dose. Because the rate of metabolism is high for this
compound in some species, only those studies that measured the parent compound
were included. As an example, Haque and Ebing (1988) found very high levels
of PCP metabolism by earthworms, Allolobophora caliginosa. Additionally, Tachikawa
et al. (1991) found that more than 50% of the total PCP in fish tissue occurred as
sulfate and glucuronide metabolites, which was similar to that reported by Kukkonen
and Oikari (1988) for Daphnia magna. Studies that determined tissue residues with
14
C labeled chlorophenols were not used due to the uncertainty of metabolites and
their role in causing toxicity. Most studies using radiolabeled chlorophenols did not
distinguish between parent compound and metabolites. An exception was made for
one study (Makela and Oikari 1990) that used 14 C labeled PCP, because the authors
demonstrated a very low rate of metabolic conversion of this compound for the
species studied (freshwater mussel Anodonta anatina).
A compilation of the CBR data for chlorophenols (CPs) is shown in Tables 4, 5, and
6. The 19 values (all wet weights) for a range of chlorophenols in Table 4 are normally
distributed (p = 0.4, Lilliefors test) and indicate a relatively consistent mean (sd)
acute lethal CBR (LR50 ) of 1.1 (0.7) µmol/g (wet weight), even though there is a
slight trend of higher toxicity with increasing chlorination. A separate analysis of the
LR50 data for PCP is shown in Table 5. The mean CBR for the chlorophenols in Table
4 (1.1 µmol/g) is statistically larger than the mean CBR (0.34 µmol/g) for PCP ( p <
0.0002; t-test for unequal variances) (Table 5). Because the variability for the CP and
PCP lethality data is relatively low, selection of one value (the mean) for these CBRs is
reasonable. Based on the mean values it is concluded that the toxic potential for PCP
is approximately a factor of three higher than that for all other chlorophenols. Of
course, this conclusion does not take into account the vastly different ratios between
ionized and un-ionized CPs that would be found at physiological pH (Tables 4 and
5), which may be an important factor once the compound enters the body.
Instead of using the mean value, all data may be considered as a distribution
(Figure 4) and an acceptably low value may be selected for protection of all sensitive
species. When the SSD for each group is examined (Figure 4), it appears that the
HC05 (5th percentile) for the chlorophenols (0.034 µmol/g) may be smaller than
that for PCP, which is due to the very low 2,3,5 trichlorophenol value (0.03 µmol/g)
observed for the salmonid Salmo trutta. Unfortunately, there were too few data points
for the lethal and sublethal PCP datasets to estimate an HC05 .
Hum. Ecol. Risk Assess. Vol. 12, No. 6, 2006
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J. Meador
Table 4.
Critical Body Residues for several chlorophenols. Lethal values (LR50 ).
Log10 Ion/
Chlorophenol Kow Un-ion
2 CP
2 CP
3 CP
4 CP
4 CP
2,3 DCP
2,4 DCP
2,4 DCP
2,5 DCP
2,6 DCP
3,5 DCP
2,3,5 TCP
2,4,5 TCP
2,4,5 TCP
2,4,6 TCP
2,4,6 TCP
2,3,4,6 TeCP
2,3,4,6 TeCP
2,3,4,6 TeCP
2.29
0.16
2.64
2.53
0.04
0.02
3.26
3.20
0.79
0.50
3.26
2.92
3.60
4.20
4.02
1.58
7.94
0.25
2.51
3.98
3.67
32
4.24
200
LR50
(µmol/g) sd
Species
Carassius auratus
Pimephales promelas
Carassius auratus
Carassius auratus
Pimephales promelas
Carassius auratus
Carassius auratus
Salmo trutta
Carassius auratus
Lumbriculus variegatus
Carassius auratus
Salmo trutta
Lumbriculus variegatus
Carassius auratus
Carassius auratus
Lumbriculus variegatus
Salmo trutta
Lumbriculus variegatus
Carassius auratus
1.2
1.7
3.0
2.1
1.4
1.6
1.6
0.11
0.88
1.5
0.91
0.03
0.58
0.47
0.99
0.74
0.91
0.93
0.31
LR50 Time
n (µg/g) (hrs) Ref
0.33 6
10∗
1
1.8 9
10∗
0.04 6
0.08 8
0.04 5
0.09 4
0.34 2
0.08 6
0.03 5
0.06 2
0.13 6
0.03 6
0.11 2
0.45 5
0.03 2
0.02 9
158
212
378
267
179
263
259
18
143
240
148
6
116
94
196
145
210
216
71
25, 5
<6
5
25, 5
<7
25, 5
25, 5
24
5
24–48
5
24
24–48
25,5
25,5
24–48
24
24–48
25,5
1, 4
5
4
1, 4
5
1, 4
1, 4
3
4
2
4
3
2
1,4
1,4
2
3
2
1,4
Whole body values LR50 values in µmol/g and equivalent µg/g. CP is chlorophenol, DCP,
TCP, and TeCP are di-, tri-, and tetrachlorophenol. Ion/un-ion is the ratio of ionized to
un-ionized compound based on pH of 7.5 and pKa values. Log10 Kow and pKa values from
Kishino and Kobayashi (1996). Time indicates exposure period. Studies 1, 4, and 5 analyzed
only dead individuals, study 2 analyzed only surviving individuals, and study 3 analyzed all
fish tissues. N is number of replicates for each study or total for multiple studies (∗ denotes
individuals). Species mean value is given for compounds with multiple references. Overall
mean and standard deviation for all species is 1.1 (0.7) µmol/g, standard error of the mean
is 0.16. The lower and upper 95% confidence limit for the mean is 0.75 and 1.4 µmol/g.
References: 1. Kobayashi et al. (1979), 2. Kukkonen (2002), 3. Hattula et al. (1981), 4.
Kishino and Kobayashi (1995), 5. van Wezel et al. (1995). All concentrations as wet weight.
Mean values from study 4 based on mortality ranging from 35–65% in replicate exposures.
Salmonids such as rainbow trout (Oncorhynchus mykiss) and brown trout (Salmo
trutta) appear to be one of the more sensitive taxa to chlorophenols. Some of the
lowest LOER and LR50 values for these compounds occurred for the salmonids
(Tables 4 and 5). Even though one of the means was relatively high (e.g., PCP LR50
for S. trutta), the high standard deviation indicated inconsistent results; therefore
these data should be viewed cautiously. Based on the salmonid values, this taxa
warrants special consideration for any tissue, water, or sediment guideline developed
for chlorophenols, hence using the SSD approach (as opposed to a constant value)
may be prudent for protecting this sensitive taxa.
This analysis for chlorophenols is supported by recent studies demonstrating
additive toxicity for phenolic compounds (Escher et al., 2001; Kukkonen 2002) based
on water exposure concentrations and tissue residues. Based on these data and
the mechanism of action, additivity is a reasonable assumption for toxicity from
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Tissue-Residue Approach for Toxicity Assessment
Table 5.
Critical Body Residues for pentachlorophenol. Lethal values (LR50 ).
Species
Type
Oncorhynchus mykiss
Chironomus riparius
Lumbriculus variegatus
Pimephales promelas
Carassius auratus
Pimephales promelas
Pisidium amnicum
Salmo trutta
LR50 (µmol/g) sd
Fish
Insect
Oligochaete
Fish
Fish
Fish
Clam
Fish
0.11
0.18
0.30
0.30
0.35
0.37
0.39
0.75
n LR50 (µg/g) Time (hrs) Ref
0.03 5
0.05 2
0.17 3
0.02 11
0.03 12
0.03 4
0.08 4
0.41 5
29
49
81
80
93
98
104
200
144
24–48
24–48
144
5, 25
96
50–672
24
3
1
1
7∗
2,6
5
8∗
4
Whole body values LR50 values in µmol/g and equivalent µg/g. N is number of replicates
for each species, which are usually different exposure times and conditions from the same
study. Mean and standard deviation for all values (n = 6) is 0.34 (0.22) µmol/g, standard
error of the mean is 0.09 µmol/g. The lower and upper 95% confidence interval of the
mean is 0.17 and 0.52 µmol/g. Time in hours is the exposure time or time to death.
References: 1. Kukkonen (2002), 2. Kishino and Kobayashi (1995), 3. van den Huevel et al.
(1991), 4. Hattula et al. (1981), 5. Spehar et al. (1985), 6. Kobayashi et al. (1979), 7. Hickie
et al. (1995), 8. Heinonen et al. (2001). All concentrations as wet weight. ∗ CBR determined
with 14 C PCP and not included in statistics. The ratio of ionized to un-ionized PCP at pH 7.5
is 631 (see Table 4) and the log10 Kow is 5.02; however at pH 7.5 the Kow is reported to be
lower (see text). Log10 Kow and pKa values from Kishino and Kobayashi (1996).
bioaccumulated chlorophenols. Hence, a simple toxic-unit approach (TUda ) would
be valuable in protecting aquatic species from chlorophenol exposure and related
compounds that are commonly encountered in the environment.
Although the CP dataset for mortality is based on only four species, it does include 12 compounds, therefore it may be sufficient for use until more species are
Table 6.
Critical Body Residues for pentachlorophenol. Sublethal values
(LOER).
Species
Mercenaria mercenaria
Glycera dibranchiata
Mytilus edulis
Anodonta anatina
Micropterus salmoides
Micropterus salmoides
Micropterus salmoides
Pimephales promelas
Type
LOER
(µmol/g)
Clam
Polychaete
Mussel
Mussel
Fish
Fish
Fish
Fish
0.002
0.006
0.009
0.012
0.036
0.040
0.040
0.148
sd
LOER
n (µg/g)
0.0002 10
0.003 10
0.018
0.005
0.005
0.074
5
4
4
3
0.5
1.5
2.3
3.1
9.6
10.8
10.8
39.5
Response
Ref
Immunological
Immunological
Metabolic
Behavior
Prey consumption
Condition factor
Growth
Growth
1
5
3
4
2
2
2
6
LOER is the lowest observed effects tissue residue in µmol/g and equivalent µg/g. N is
number of replicates for each value, which are usually different exposure times and
conditions from the same study. The overall mean (sd) for all endpoints is 0.038 (0.02)
µmol/g using the MVU estimator (Gilbert 1987) (see text). References: 1. Anderson et al.
(1981), 2. Mathers et al. (1985), 3. Wang et al. (1992), 4. Makela and Oikari (1990), 5.
Anderson et al. (1984), and 6. Spehar et al. (1985). All concentrations as wet weight.
Hum. Ecol. Risk Assess. Vol. 12, No. 6, 2006
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J. Meador
Figure 4.
Chlorophenols species sensitivity distribution. Values for pentachlorophenol and several other chlorophenol compounds (see Tables
4–6 for data). The 5th percentile value for the chlorophenols (CPs) is
0.034 µmol/g. Values as wet weights.
tested. If the mortality CBR is selected, the approach used earlier for compounds
at narcotic concentrations could be applied, using an acute to chronic ratio of 10
and an additional safety factor of 10 to protect species at the no effect tissue residue.
Using this approach would produce a TQG value of 0.01 µmol/g for CPs and 0.003
µmol/g for PCP (all wet weight).
Other chlorinated phenolic compounds. Many chlorinated phenolic compounds
(CPCs) are formed as byproducts of the bleaching process and can be found in
the effluents of pulp and paper mills. Other common CPCs, especially those associated with pulp mill effluent, include catechols, guaiacols, and syringols. Substituted
catechols are mono-ortho-hydroxy phenols, substituted guaiacols are mono-orthomethoxy phenols, and substituted syringols are di-ortho-methoxy phenols all with
additional chlorines as indicated by the nomenclature.
In general, many phenols and anisoles exert their toxic effect by blocking the
Qi quinone site in the electron transfer chain (Escher et al. 1997), although Escher
and Schwarzenbach (2002) conclude that monochloro- and methylated phenols
act strictly as baseline (narcotic) toxicants. These authors examined the activity
of 21 substituted phenols and concluded that all were specific inhibitors of the
Qi site and therefore likely fit in the dose-additive model (Escher et al. 1997).
Because chlorophenol, and many of the phenolic compounds in general, act by
a common biochemical mechanism of action (Escher et al. 1996), other related
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Hum. Ecol. Risk Assess. Vol. 12, No. 6, 2006
Tissue-Residue Approach for Toxicity Assessment
compounds should be considered when generating CBRs and toxic unit values
for mixtures. Exposure toxicity studies by Hattula et al. (1981) and Kovacs et al.
(1993) demonstrated similar toxicity by substituted chlorinated phenolics (catechols, guaiacols, and syringols) as that exhibited by related chlorophenols providing a solid justification for considering these compounds in the dose-additive TU
approach. In addition to CPCs, there are several hydrophobic ionogenic organic
compounds (HIOC) that can potentially disrupt energy production in membranes
(Escher et al. 2001) that should be considered with this group in the dose-additive
model.
WATER AND SEDIMENT QUALITY GUIDELINES
Although guidelines based on tissue residues are more ecologically relevant and
preferred, the development of water or sediment values may be desirable for many
situations and statutory requirements primarily because these media are considered
easier to manage and cleanup. Conversion of a tissue quality guideline (TQG) to a
water (WQG) or sediment quality guideline (SQG) with bioaccumulation factors is a
reasonable exercise that maintains the causality feature. For the following discussion
the more general term “guideline” will be used because “criteria” implies a regulatory
value, which is applied under statutory authority. The procedures presented here for
WQGs and SQGs are for single-compound assessments and do not account for the
potentially additive contribution expected from multiple contaminants, as described
earlier for mixtures.
Once a protective tissue residue has been selected, a protective water or sediment concentration (guideline) may be developed by equating the TQG for a given
toxicant to a WQC or SQG with bioaccumulation factors. Ideally, representative
bioaccumulation values from several species should be obtained to produce a cumulative density function. From this empirical CDF, a percentile value (e.g., 95th)
can be selected to ensure that the most sensitive species are protected. Because the
bioaccumulation factor is controlled by the uptake and elimination kinetics and
these are variable among species and conditions, a high percentile value is desirable
to account for this variability and to produce a guideline that will be protective for
most species under a variety of environmental conditions. Alternatively, each measured bioaccumulation factor from the CDF may be used in equations (9–12) to
generate an SSD of water or sediment values (e.g., Figure 5) that can then be used
to select the desired level for protection. For some applications, site-specific bioaccumulation factors may be appropriate for characterizing the risk from water or
sediment exposure. This is especially important for metals because of the large variability observed among sites due to differences in metal speciation and bioavailable
fractions.
Tissue concentrations approaching those that are known to cause adverse effects
can alter the toxicokinetics and toxicodynamics of the toxicant leading to lower BCF
or BSAF values (e.g., Landrum et al. 1994; Meador and Rice 2001). Therefore, a conservative approach would be to use only bioaccumulation factors from low exposure
concentrations. Additionally, caution should be used when selecting the bioaccumulation factor to use for conversion. Using lab derived bioconcentration factors
Hum. Ecol. Risk Assess. Vol. 12, No. 6, 2006
1053
J. Meador
Figure 5.
Tributyltin species sensitivity distribution for organic-carbon normalized
sediment concentrations. Determined with critical body residues and
observed bioaccumulation factors using equations 10 (BCFs) and 11
(BSAFs) in text. BSAF values from Meador et al. (2002b) and Koc from
Meador (2000). Values determined for the endpoints mortality, growth
impairment, and reproductive impairment (see Table 2 and text for data
and details). The 5th percentile for each of the three sedoc endpoints
based on data generated with equation 10 is as follows: mortality (16.2
µg/g OC), growth impairment (1.08 µg/g OC), and reproductive impairment (0.11 µg/g OC).
to determine WQG or SQG is likely appropriate only for those compounds that are
mainly accumulated from the aqueous phase. Using lab BCFs for compounds that exhibit a strong dietary accumulation component will underestimate bioaccumulation
in the field leading to underprotective SQGs and WQGs.
If lipid values are available, the TQG in each equation would be expressed as a lipid
normalized concentration (e.g., µg toxicant/g lipid). To complete the equation, the
bioconcentration factors would also need to be expressed as lipid normalized values
(BCFlip ). The one exception is for equation 11 because the bioaccumulation factor
(BSAF) is already lipid normalized. If the TQG is expressed as a lipid normalized
value then the flip term would be dropped from this equation. In many cases the
whole-body lipid content for a species can be determined from literature values,
which may help in the interpretation. While some variability will exist for a species,
mean literature values may be useful for determining mean BCFlip values.
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Hum. Ecol. Risk Assess. Vol. 12, No. 6, 2006
Tissue-Residue Approach for Toxicity Assessment
Water Quality Guideline
The conversion of a TQG to a WQG can be accomplished with the equation:
[Water] =
TQG
= WQG
BCF
(9)
where [Water] is a water concentration, TQG is the CBR-based tissue concentration
selected for protecting species, and BCF is a high percentile value from the CDF
for all species or each measured value that can be used to produce a distribution of
predicted water concentrations. The BCF can also be modeled with QSAR equations,
if the compound of interest is known to exhibit bioaccumulation that is consistent
with EqP theory.
Selection of a high percentile BCF (e.g., 95th percentile) for equation 9 may
be appropriate for protection of most species. In theory, a WQG produced by this
equation with a TQG is inherently similar to the standard approach used by the
USEPA to develop the Water Quality Criteria with water exposure-response data. If
enough data are available, the two values are likely to be similar. For example, using
the imposex CBR of 0.32 ppm from Table 2 and the 95th percentile BCF of 72,550
determined from Table 2, equation 9 produces a WQG of 4 ng/L for TBT, which
is relatively close to USEPA’s chronic water quality criteria of 7.4 ng/L for seawater
(USEPA 2003b), which is based on this endpoint.
Implicit in selection of the 5th percentile LC50 or LOEC for the USEPA’s WQC
is the assumption that those species in this percentile range represent the most
sensitive species. For many compounds with relatively constant CBRs, the most sensitive species will generally exhibit the highest BCF and lowest LC50 (or LOEC). For
those compounds without a constant CBR, the most sensitive species will exhibit the
highest BCFs and lowest CBRs, as determined with the SSDs.
As discussed earlier, the bioavailability of many ionizable organic compounds is
affected by pH (e.g., chlorophenols and organotins). This characteristic will likely
produce differences in bioaccumulation factors between species found in marine
waters and those from circumneutral or acidic waters. For these compounds, the
CDFs of bioaccumulation factors for species from different systems will need to be
examined in a separate analysis and tested for differences.
One major difference between the TQG approach for determining a WQC and
that using LC50 or LOEC values is the temporal component. A CDF of all BCFs will
likely include acute and chronic BCF values. Because the BCF will increase over time
until steady state is reached, a range of time-specific values will be included in the
CDF. If enough data are available, the chances are that the distribution of BCFs will
include those from the highest bioaccumulators and those at steady state, which
when used in equation 9, should be considered as the chronic WQC. Alternatively,
one could select only steady-state BCFs and use the high percentile value from this
CDF in equation 9 to determine the chronic WQG. If an acute WQG is desired, then
all of the BCFs selected should be in the range considered appropriate for acute
exposure (e.g., 48 to 96 hours). Although some of these short-term BCFs will be at
steady state, the acute definition will be satisfied for the determination of the WQG.
Hum. Ecol. Risk Assess. Vol. 12, No. 6, 2006
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J. Meador
Sediment Quality Guideline
Two methods to convert the TQG to a SQG using bioaccumulation factors are
presented here. One uses the bioconcentration factor coupled with the sedimentwater partition coefficient for a given compound. The other uses the BSAF to convert
a TQG to an equivalent SQG.
Bioconcentration approach
This approach is described for hydrophobic organic compounds although it may
be modified for metals, which are addressed at the end of this section. The first step
is to compile all available bioconcentration factors and determine if freshwater and
marine species should be combined or considered separately. The organic-carbon
normalized sediment-water partition coefficient (Koc ) for a compound can be obtained from empirical measurements or modeled (see Mackay et al. 1992). These
coefficients can also be affected by pH, which is a factor that should be considered.
This approach should be reserved for those compounds that are known to accumulate primarily from aqueous uptake.
For many neutral hydrophobic compounds the Koc can be predicted with the
octanol-water partition coefficient (Kow ), which is a good predictor of sediment–
water partitioning for many compounds. Several authors have developed QSAR
equations that predict Koc values from the Kow for various hydrophobic compounds
(Karickhoff et al. 1979; Means et al. 1980; Karickhoff 1981; Di Toro et al. 1991; Di Toro
and McGrath 2000). These studies show the Koc to range from 0.4∗Kow to 1.0∗Kow .
Recent work has shown that soot carbon can increase sediment–water partition coefficients (Burgess et al. 2003) and should be considered when predicting Koc values.
For each selected TQG, an appropriate Koc in combination with a high percentile
BCF or each measured value can be applied in the following equation to produce
an SQG or a distribution of sedoc values that can be used to select the SQG. The
equation is:
[sedoc ] = Koc ∗
TQG
= SQG
BCF
(10)
where sedoc is the organic-carbon normalized sediment concentration, TQG is the
tissue concentration selected for protecting species, Koc is the organic-carbon normalized sediment–water partition coefficient, and BCF is a high percentile value
from the empirical CDF for all species or each measured value. An example for TBT
is shown in Table 2. If appropriate, BCF values can also be modeled by QSAR analysis or determined by toxicokinetic parameters for the compound of interest. If lipid
normalized TQG and BCF values are used, then the equation must be modified to
reflect these data.
BSAF approach
The BSAF approach is preferred because this bioaccumulation factor generally
exhibits much less variability than values for the BCF and Koc and is likely more
reflective of all sources for accumulation. Because tissue concentrations are normalized to lipid, and sediment concentrations are normalized to organic carbon, BSAFs
will achieve a theoretical value of approximately 1 (Di Toro et al. 1991; Di Toro and
McGrath 2000), which is based on equilibrium partitioning (EqP) between all phases.
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Tissue-Residue Approach for Toxicity Assessment
McFarland and Clarke (1986) proposed a value of 1.7; however, several laboratory
and field studies report higher values in the range of 3 to 5 for several compounds
(Boese and Lee 1992; Tracey and Hansen 1996; Wong et al. 2001). These higher values are in the 70th to 95th percentile range reported by Tracey and Hansen (1996)
for PAHs, PCBs, and pesticides. Many factors, including metabolism, soot carbon,
and insufficient time to steady state for partitioning between phases, will lead to
tissue residues less than the theoretical maximum. If no BSAF data are available and
the organic compound is known to behave according to EqP partitioning, a default
value of 4 would be reasonable to represent worst-case bioaccumulation for many
species. This is based on the several studies cited earlier showing a high number
of values in this range. One point to keep in mind is that the BSAF value is not
used to assess bioaccumulation strictly from sediment sources, but is an indication
of contaminant uptake from all sources in the entire foodweb. Additionally, BSAF
values are a first approximation of bioaccumulation potential and do not account
for biomagnification that occurs for some compounds (Di Toro et al. 1991).
The sediment quality guideline can be determined by:
[sedoc ] =
TQG
= SQG
BSAF ∗ flip
(11)
where sedoc is the organic carbon normalized sediment concentration, TQG is the
tissue quality guideline selected for protecting species, flip is the mean or high percentile value for all species considered, and BSAF refers to measured values, modeled
EqP values, or a high percentile value from a CDF of measured values for a large
number of species.
For those organic compounds and metals that do not behave according to EqP
and are not hydrophobic (e.g., log10 Kow < 2), a standard bioaccumulation factor
that is selected from a high percentile (e.g., 95th) of all BAF values could be used to
convert the TQG to a SQG with the equation:
[sediment] =
[TQG]
= SQG
BAF
(12)
This approach was applied by Meador et al. (2002b) to TBT, which does not bioaccumulate according to EqP because tissue lipid is not an important factor (Meador
2000). In that case the BAFoc was used to determine the SQG because organic carbon
was shown to be an important factor for TBT bioavailability in marine waters. The
equation is:
[sedoc ] =
[TQG]
= SQG
BAFoc
(13)
where BAFoc is tissue concentration/sedoc and is selected from a distribution of such
values determined for several species. For those compounds that exhibit bioaccumulation that is independent of lipid content, equation 11 may work in some cases (see
later example for TBT) when sediment organic carbon is a major factor for bioavailability and lipid content is not highly variable among species. For these cases, a
simple conversion between the BSAF and BAFoc can be determined.
Converting a TQG for metals to a SQG would likely be far more complicated than
the same exercise for organic compounds. Considering all the potential ligands that
can form complexes with metals (e.g., acid volatile sulfides [AVS], organic carbon,
and iron and manganese hydroxides), predicting bioaccumulation would be more
Hum. Ecol. Risk Assess. Vol. 12, No. 6, 2006
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complicated. This would entail a detailed analyses of the sediment geochemistry at
all potential sites where SQGs would be utilized in addition to the development of
predictive models incorporating all potential ligands as a function of several environmental parameters, including pH and redox state. Using AVS to predict water
concentrations may be a viable approach that could be used in a modified version
of equation 10; however, this would assume that water uptake is the predominate
route of exposure and AVS is the only ligand controlling metal bioavailability. Several studies have indicated that dietary uptake of metals is an important pathway for
exposure and bioaccumulation (Ankley 1996; Lee et al. 2000; Griscom and Fisher
2004). Recently, Luoma and Rainbow (2005) proposed a biodynamic model to assess metal bioaccumulation. With information on uptake and elimination kinetics,
assimilation efficiency, and concentrations in water and food, fairly accurate predictions of bioaccumulation for diverse metals and species can be obtained. This model
could be used to determine species- and site-specific bioaccumulation factors when
such information is available.
Site-specific bioaccumulation factors may be preferred in some assessments or as
a check against the literature-derived values. For such site-specific assessments, relatively large datasets would be needed to account for spatial and temporally variability
and inherent species differences. Site-specific BSAF and BAF values are very useful
for small benthic invertebrate species that have limited mobility because relatively
low variability is expected, assuming conditions are relatively similar and steady state
has occurred. Higher variability is expected for more mobile species, especially fish;
however, one large-scale review of BSAF values found that variability can be relatively
low for some groups and species of fish (Wong et al. 2001). Even if steady-state BSAFs
are not attained for a given species, these normalized bioaccumulation factors can
be informative and indicate the potential for bioaccumulation. Although fish bioaccumulation factors may be variable, sediment values may be generated if reliable
site-specific values can be determined or a distribution can be produced. Increasing
sample sizes for mobile species will reduce the variance about the mean leading to
a more reliable estimate of bioaccumulation for the focal species.
Examples of Sediment Quality Guidelines
Tributyltin
The following is an example of developing an SQG for tributyltin, which appears
to follow organic carbon normalized sediment-water partitioning in marine waters
(Meador 2000). The mean log10 Koc for TBT is 4.5, a value that is similar to its log10
Kow of 4.4, which was determined in seawater (pH ≈ 8). TBT is ionizable and the pKa
value of 6.25 indicates that as pH falls below 8, which is typical in many freshwater
systems, the proportion of ionized TBT increases leading to a likely reduction in
bioavailability (Meador 2000). Sediment–water partitioning for TBT in freshwater
may be similar to that in marine waters as long as pH is comparable (Figure 1b in
Meador 2000).
Using the Koc of 32,000 from Meador (2000), the measured BCFs from Table 2,
and equation 10, sedoc distribution curves were produced for three different biological endpoints (mortality, growth, and reproduction) (Figure 5). Even though the
reproductive CBR is based on the imposex endpoint, a few studies have demonstrated
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Tissue-Residue Approach for Toxicity Assessment
reproductive impairment in species other than gastropods at comparable low water
exposure concentrations (Johansen and Mohlenberg 1987; Nakayama et al. 2004;
Shimasaki et al. 2003). Also shown in this plot are the sedoc curves produced using
equation 11, which is based on the distribution of BSAF values observed for several
species (data not shown, see Meador et al. 2002b). Lipid content (flip ) was set to 7%
dry weight, which is based on an average value for several invertebrate species (Boese
and Lee 1992; Higgs et al. 1995; Meador 1997).
Two related approaches were used to determine the HC05 for each dataset in
Figure 5 that was generated with equation 10. The BCF dataset was used because it
was the larger of the two. Each set of sedoc data (mortality, growth, reproduction)
was examined and the best fit distribution was determined. A Kolmogorov-Smirnoff
test indicated that each of these CDFs were lognormally distributed (p > 0.05). For
each fitted distribution, a population mean and S.D. were estimated, which were
then used to determine the HC05 (5th percentile) and its 95% confidence interval
(CI). A Monte Carlo simulation was used to generate 500 datasets each with a sample
size of 50 using the population mean and S.D. determined for each sedoc dataset.
The 5th percentile was determined for each of the 500 datasets and analyzed as a
group. The median quantile of the 500 5th percentile values was determined as the
midpoint value. The 95% CI for the median HC05 was determined with an algorithm
for non-parametric confidence limits (Gilbert 1987). Additionally, the HC05 was also
calculated using only the mean and S.D. estimated for the observed datasets in
Figure 5 and the equations for determining quantiles for a lognormal distribution
(Gilbert 1987). For each paired sedoc dataset, the HC05 values determined using both
methods were within 2–5% of each other. The 5th percentile and lower 95% CI (in
parentheses) for each of the sedoc values (based on BCFs and each respective CBR)
is as follows: mortality (16.2 (15.6) µg/g OC), growth impairment (1.08 (1.01) µg/g
OC), and imposex (0.111 (0.106) µg/g OC).
The relative closeness of the BCF and BSAF generated sedoc curves for TBT is
likely due to the predominantly aqueous route of uptake (Meador et al. 1997), even
though this compound does not follow EqP predictions for bioaccumulation. For
those compounds that bioaccumulate according to EqP theory, the route of uptake
may be inconsequential when equilibrium is attained for all phases (Di Toro et al.
1991), therefore either the BCF of BSAF approach for determining the sedoc may
produce similar curves. Because far more BCF values are usually available for myriad
species for a given compound and the two approaches are likely to provide very similar results, using the BCF method to produce an SQG may be acceptable for EqP
compounds until additional BSAFs are available. It is possible that BCF and BSAF generated sedoc curves will be different for those compounds that do not exhibit bioaccumulation consistent with EqP theory and are predominately accumulated from
dietary sources, although this hypothesis remains to be tested. For these compounds,
comparing SQGs determined with both approaches presented earlier (equations 10
and 11) may provide insight regarding the most important routes for uptake.
Non-specific acting compounds
The following is offered as a simple example for soft-bodied benthic invertebrates
exposed to compounds at narcotic concentrations using the assumptions presented
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earlier and from the literature. From the analyses in the section on “Summed Toxicity,” a LOER CBR of 0.5 µmol/g was calculated for narcosis mode of action. Using
an approximate wet-weight to dry-weight conversion factor of 5 that has been determined for many invertebrate species (Higgs et al. 1995; Meador 1997) this LOER
CBR equates to a dry-weight tissue value of 2.5 µmol/g. Using equation 11, a TQG
of 2.5 µmol/g, a BSAF of 4 (maximum EqP value), and an average dry-weight lipid
content for benthic invertebrates of 7% (flip = 0.07), the proposed LOER SQG would
equal 8.9 µmol/g organic carbon (OC). For organisms with a higher lipid content
(e.g., fish flip = 0.25 dry wt.), this SQG would be approximately 2.5 µmol/g OC. For
compounds with an average molecular weight (MW) of 200 daltons (e.g., PAHs), the
8.9 µmol/g OC SQG would equal 1,786 µg/g OC. Translating this into a dry-weight
concentration for sediment containing 1% total organic carbon would lead to an
LOEC SQG of 18 µg/g dry weight.
These values are very close to the SQG values (4–11 µmol/g OC) determined by
Di Toro and McGrath (2000) for the narcosis mode of action, which is based on the
target lipid model and equilibrium partitioning for water concentrations. Their final
chronic tissue values were derived from the 5th percentile estimate of the lethality
CBRs and application of an acute to chronic ratio of 5.1, and is therefore an LOER.
The LOER is an effect concentration that will need to be reduced to a no-effect
concentration for some regulatory applications. Determining an SQG based on the
NOER is preferred to one using the LOER due to the chronic exposure expected
for species at contaminated sites. The WQC, which are based on LOEC values, uses
time as a factor to protect against adverse effects from acute and chronic exposure
to contaminated water (Stephan et al. 1985). These temporal factors for WQC are
acceptable because it is far easier to assess and manage exposure from contaminated
water than chronic exposure to bedded sediment. Based on the review of safety
factors provided by Chapman et al. (1998) and Duke and Taggart (2000), a value of
10 would be reasonable for reducing the LOER based SQG to one that would be
closer to a NOER SQG. This application would result in a “no-effect” SQG value of
0.89 µmol/g OC, or 1.8 µg/g for compounds with an average MW of 200 daltons
found in a 1% TOC sediment.
All the aforementioned values (e.g., lipid content, molecular weight, the wet to
dry conversion, and the TQG) exhibit variability that will produce alternate values.
The variability for each of these parameters can be used to produce a distribution of
SQG values that can then be used to determine a reasonable level for protection. As
measured values become available, they can be added where appropriate, especially
for site-specific assessments. Because these assumptions should hold for all nonspecific acting compounds, this SQG can be considered a total concentration for all
compounds in this group. Of course this is just one example for the narcosis mode
of action. Results for other specific mechanisms of action and those compounds
that exhibit bioaccumulation factors higher than those predicted by EqP theory are
expected to produce lower SQG values.
DISCUSSION AND CONCLUSIONS
This article has presented the rationale and procedures for developing tissue
quality guidelines and for converting these to water or sediment guidelines for a
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Tissue-Residue Approach for Toxicity Assessment
variety of contaminants of concern. The main advantages of the TRA for toxicity
assessment and conversion of such values to WQGs and SQGs include (1). Relatively
low variability in critical body residues among all aquatic species for many contaminants, (2). Greater reliance on toxicodynamics (toxic potential) for characterizing
biological responses and elimination of the often large variability observed for toxicokinetics, (3). A causal relationship between tissue residues and adverse biological
effects that provides a scientifically defensible approach for generating tissue, water,
or sediment guidelines, and (4). An approach that is consistent with an ecological
risk assessment (ERA) framework (USEPA 1998).
The tissue-residue approach is a potentially powerful tool because it is the most
direct way for linking various lines of evidence from laboratory tests and field monitoring. It should be noted that in many cases tissue concentrations considered
adverse may not be found in organisms collected from the field because sensitive
individuals and species would likely have been eliminated or severely impacted. This
is one reason why development of WQG or SQG values from TQGs may be advantageous. Due to the potential for contaminants at a site to elicit adverse effects in
many of the sensitive species, tissue values measured at impacted sites will likely be
measured mainly in tolerant species, which is an important consideration for any
risk assessment. Field comparisons of measured tissue concentrations with a TQG
would likely be useful for assessing potential impacts from low-level exposure and
for evaluating those biological endpoints with long latent development periods (e.g.,
reproductive effects). One study that has applied this approach to field monitoring
found impaired growth in mussels due to tissue residues of TBT that were consistent
with CBR values determined in laboratory studies (Salazar and Salazar 1998).
Even if the data for generating TQGs are somewhat variable (e.g., Table 4) a
difference of two- to threefold is considered relatively small. Given such variability,
this approach provides a causally based value that can be evaluated for uncertainty
and applied to hazard assessments accordingly. For those CBRs that are highly variable, a species sensitivity distribution is strongly recommended with selection of low
percentile value to protect the highest percentage of species. If a WQG or SQG
is desired, the equations presented earlier utilizing the TQG and bioaccumulation
factors will produce a scientifically defensible value. Converting the TQG to either
a WQG or SQG with bioaccumulation factors may appear counterintuitive because
variability is being introduced into the determination. However, by protecting most
of the species with bioaccumulation factors at the upper end of the distribution (e.g.,
95th percentile), a defensible WQG or SQG value can be developed that maintains
the feature of causality.
As mentioned in the Introduction, a powerful advantage of the TRA approach
is that the tissue concentration–biological response relationship can likely be
established with a relatively small number of species from diverse phyla. Once characterized, either as a consistent CBR or as a low percentile value from a SSD for
a given endpoint, additional testing of species will likely not change the relationship allowing a greater degree of confidence that the majority of aquatic species
can be protected. This concept would also extend to WQGs and SQGs that use a
CBR and bioaccumulation factors. Because the bioaccumulation factors are selected
from a CDF, which is also the case for some CBRs, it is reasonable to assume that
the distribution of values captures the variability expressed across a wide range of
Hum. Ecol. Risk Assess. Vol. 12, No. 6, 2006
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J. Meador
taxa and thus are appropriate for developing guidelines to protect all similar aquatic
species.
As presented by several authors (McCarty 1986; McKim and Schmieder 1990) the
QSAR approach for predicting CBRs may be viable for some compounds and modes
of action, but not all. One example can be found in this article using chlorophenols
and tributyltin. Both are considered uncouplers and according to QSAR analysis
should exhibit similar CBRs. A comparison of the lethal CBRs for these two compounds indicates that TBT is approximately 33 times more toxic than the chlorophenols (Tables 3 and 4), even though the Kow values are similar. Additionally, the CBR
for the two sublethal endpoints for TBT are 15 and 150 times lower than that for the
lethality CBR, which is substantially more than the generalized 10-fold difference
expected for many compounds (McCarty and Mackay 1993). This may be the rule
rather than the exception for specific-acting toxicants.
One major advantage of the TRA for toxicity assessment is the inclusion of mixtures. Regulatory application based on single chemical guidelines or criteria cannot
capture the risk from toxicant exposure that species experience from the majority of
contaminated environments. While synergistic and antagonistic interactions would
be difficult to quantify, most studies show that additivity is very common and therefore is an appropriate a priori model for toxicity assessment. Based on the concepts,
equations, and examples presented here, reasonable values for mixtures can be produced that will likely be far more protective for species than the single chemical
approach now utilized in most applications.
Unfortunately, the CBR data are relatively sparse; however, there are several recent
publications quantifying CBRs for a number of compounds that can be used to
develop toxic unit values. Until more empirical CBR data are generated, modeled
values such as that described by Shephard (1997) can be used in such assessments
where specific data are lacking. In addition to the procedures described here, the
target lipid model described by Di Toro et al. (2000) can be used to determine
tissue effect concentrations for the narcosis mode of action. As an a priori approach,
simple response addition has been shown to occur for diverse compounds and can
be included in a risk assessment as data become available. Of course, when doseaddition data are available for compounds exhibiting the same mechanism of action,
these should be included in the assessment and applied in combination with the toxic
unit values for response addition.
When analyzing a number of species for their biological responses based on tissue concentrations, unusual studies need to be examined in detail and repeated.
For example, the results for the brown trout (Salmo trutta) in Table 4 indicate that
this species may be particularly sensitive to some of these chlorophenols. Additionally, salmonids were also found to be the most sensitive taxa when tissue residues of
dioxins (Steevens et al. 2005) and DDT (Beckvar et al. 2005) were used for toxicity
assessment. In any regulatory application, allowances for these types of results must
be made, just as the EPA allows for special consideration of ecologically and economically important species in the generation of water quality criteria (Stephan
et al. 1985). In general, species in the family Salmonidae are expected to exhibit
similar levels of toxic response for a given exposure concentration. For this reason,
results with the standard test salmonid, rainbow trout (Oncorhynchus mykiss) may
adequately represent other species in the salmonidae, especially those considered
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Tissue-Residue Approach for Toxicity Assessment
threatened and endangered. For example, Sappington et al. (2001) found that rainbow trout were similar in their toxic response to water borne exposures of carbaryl,
copper, nonylphenol, pentachlorophenol, and permethrin to that observed for three
trout species (apache, greenback cutthroat, and the Lahontan cutthroat; O. apache,
O. clarki stomias, andO. clarki henshawi) that are protected under the Endangered
Species Act. Of course, protecting individuals does not guarantee protection for the
population. Uncertainty is introduced when species are tested for only one part of
their life cycle or from substituting similar species with disparate life-history characteristics. Therefore, the effects of a toxicant on different phases of a species life
cycle are key to a complete understanding of contaminant impacts to the population
(Spromberg and Meador 2005).
ACKNOWLEDGEMENTS
This manuscript has improved greatly from the critical reviews provided by Peter
Landrum, Lynn McCarty, and Michael Salazar as part of the author-directed peerreview process for HERA. Their interaction in this review process help fortify a
number of key points in this paper. I also thank Tracy Collier and Nathaniel Scholz
of the NWFSC for their insightful comments on various phases of the manuscript.
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APPENDIX A
Definitions
Acquired dose—That amount of a toxicant accumulated by the organism. This dose
can be determined by direct observation, QSAR, or uptake and elimination rates
(toxicokinetics). The acquired dose is used to determine adverse effects (e.g.,
LR50 ). This is distinguished from the LD50 , which can be based on the acquired
dose or administered dose.
Acute and Chronic—These are two terms with multiple and often confused definitions that generally refer to the temporal aspect for exposure and biological response (see Rand 1995). Acute is generally reserved for short-term (≤96
hours) exposures and the lethal response. In many cases chronic effects refer
to long-term responses (all lethal and sublethal) that develop after 96 hours. In
other situations, chronic is used to denote only sublethal effects. For the U.S.
EPA WQC, chronic is generally concerned with life-cycle and early life-stage toxicity tests (Stephan et al. 1985). Where appropriate, these terms will be used
only when referring to the duration of exposure. The nature and severity of the
response will be treated separately (e.g., lethal and sublethal).
Administered dose—The amount of a toxicant that is delivered to the organism.
Expressed as µg or µmol toxicant/gram body weight/day or as single dose µg/g
or µmol/g. Usually involves introduction by feeding, injection, gavage, or bolus.
This value is used to determine adverse effects (e.g., LD50 ). The administered
dose can be different from the actual tissue concentration (acquired dose) that
is associated with the biological response due to factors such as metabolism.
BAF—Bioaccumulation factor
[tissue]
BAF =
[sediment]
BCF—Bioconcentration factor
BCF =
[tissue]
[water]
Primarily used in lab studies where water exposure can be separated from all
other media. In field studies, BAF is more appropriate.
BSAF—Biota-sediment bioaccumulation factor
BSAF =
[tissue]
flip
[sediment]
foc
where flip and foc are the fraction of lipid in organisms and the fraction of organic
carbon in sediment. The numerator and denominator must be consistent in
terms of dry or wet weight values. Even though sediment is the only media in the
equation, bioaccumulation is assumed to be from all sources (water, sediment,
and prey).
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CBR—Critical body residue. Any one of several statistics that describe an adverse
biological response (e.g., LR50 , ER10 , LOER) that is associated with a tissue concentration expressed in molar units.
CDF—Cumulative distribution function. The cumulative distribution function describes the probability distribution of a random variable X. The CDF can be
based on empirical data (ordered observations; i.e., i = 1−n where Fn (x) = i/n)
or population parameters and a known distribution (e.g., mean and standard
deviation for data that follow a normal distribution) (D’Agostino 1986).
ERp —Effective tissue residue. Effective meaning sublethal; p represents the proportion responding. For example, a growth inhibition of 10% would be an ER10 .
Determined with a dose-response curve.
Koc —Organic-carbon normalized sediment-water partition coefficient
Koc =
[sediment]/foc
[water]
where foc is the fraction of organic carbon in sediment.
LOER and LOEC—Lowest observed effect residue (tissue concentration) or ambient concentration (usually water or sediment). Lowest statistically significant
treatment concentration causing an adverse effect. Determined by Analysis of
Variance (ANOVA). Used for sublethal effects in this review.
LRp —A statistic that estimates a population parameter and is based on the acquired
dose. P is the proportion of the population responding, (e.g., LR50 for 50%
mortality). Determined with a dose-response curve using the acquired dose.
Not the same as the LD50 (see definition for acquired dose).
NOER or NOEC—No observed effect residue or ambient concentration (usually
water or sediment). Highest treatment concentration that is statistically indistinguishable from the control. Determined by ANOVA.
QSAR—Quantitative-structure activity relationship. An approach used to relate biological activity (e.g., bioaccumulation or toxicity) with physical-chemical features
of a compound (e.g., octanol-water partition coefficient; Kow ).
SSD—Species sensitivity distribution. A CDF showing the relative response of species
to a given toxicant.
Toxicodynamics—The interaction of toxicants with their receptor(s) and the magnitude of the resulting response, which can be characterized as toxic potency
(see Rand 1995).
Toxicokinetics—A general term referring to the quantitative assessment of toxicant
behavior for the processes of uptake (absorption), elimination, internal distribution, and biotransformation. In this paper, the term elimination is used to
describe all loss processes of the parent compound including biotransformation, passive diffusive loss, and excretion (Meador et al. 1995).
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