equivalent concentration or dose at the target site. 14. Figure 2: An .... may be induced by a set of key events that are required for the conversion of a ...... PSD. 0.01. Estimated. Plasma-RBCs Partition Coefficients. AsV. PPR5. 0.2. Mann et al.
The development of novel quantitative methods in toxicology for human risk assessment
Pamela Robinan Gentry
METH - can be snorted, injected, smoked or swallowed. One of the most addictive drugs out there. increases the heart rate increases the blood pressure ★ increased risk of stroke ★ mind and mood changes, can feel anxious ★ chronic fatigue ★ paranoid or delusional thinking ★ hallucinations and mood disorders ★ can also result in kidney, liver and lung damage ★ ★
Prescription and OTC drugs - usually taken orally. Side effects and abuse of the drugs can result in: poor concentration ★ tremors disorientation ★ convulsions ★ apathy, confusion ★ lack of energy ★ addiction ★ panic attacks ★ anxiety ★ psychosis, headache ★ hostility and aggression ★ Irregular heartbeat, numbness of fingers and toes ★ respiratory depression ★ Insomnia ★ dizziness ★ restlessness ★ slurred speech ★ increased heart rate and breathing ★ excessive sweating ★ heart attacks, coma, death ★ nausea and/or vomiting, or diarrhea ★ ★
PHENCYCLIDINE (PCP) - (Angle Dust)- comes in tablet, capsule, or white crystal-like powder. Was developed as an anesthetic but sometimes causes hallucinations, so it was taken off the market. People often lace joints with angle dust. Increase heart rate and blood pressure Flushing, sweating, dizziness and numbness ★ Speeds up body functions, and may also act as a depressant, pain killer, anesthetic or hallucinogenic drug ★ Slowing of body movements, blurred or double vision ★ Dulled sense of touch and pain ★ “Spacing out” of time ★ Signs of paranoia ★ Flashbacks ★ Drowsiness, convulsions and coma (effects of large doses) ★ Death from repeated convulsions, heart and lung failure or ruptured blood vessels in the brain ★ Feeling of superiority, being invincible, power ★ This drug creates a very aggressive, hostile and violent driver with very little patience and no fear of death ★ Auditory and visual hallucinations, impaired coordination and dulled senses ★ Time seems to slow down, convulsions, coma and/or death ★ ★
ALCOHOL - IMPAIRED JUNDGMENT, FALSE SENSE OF SECURITY Effects: ★ Initial stimulation followed by depressed nervous system ★ Impaired short term memory ★ Slowed reaction time, impaired motor skills ★ Inability to concentrate ★ Brain and nervous system and liver damage ★ Blurred and/or double vision
The development of novel quantitative methods in toxicology for human risk assessment
Pamela Robinan Gentry
The development of novel quantitative methods in toxicology for human risk assessment
De ontwikkeling van nieuwe kwantitatieve methoden in de toxicologie voor de humane risicoschatting (met een samenvatting in het Nederlands)
PROEFSCHRIFT
ter verkrijging van de graad van doctor aan di Universiteit Utrecht op gezag van de rector magnificus, prof.dr. J.C. Stoof, ingevolge het besluit van het college voor promoties in het openbaar te verdedigen op maandag 1 december 2008 des middags te 4.15 uur
door
Pamela Robinan Gentry
geboren op 11 juli 1964, te Crossett, Arkansas, Verenigde Staten van Amerika
Promotoren:
Prof. dr. M. van den Berg Prof. dr. B.J. Blaauboer
Co-promotor:
Dr. Harvey J. Clewell, III, The Hamner Institutes for Health Sciences
This thesis was partly accomplished with financial support from the American Chemistry Council, 1300 Wilson Blvd., Arlington, VA 22209, USA, and the Electric Power Research Institute, 3420 Hillview Avenue, Palo Alto, California 94304, USA.
The development of novel quantitative methods in toxicology for human risk assessment / Pamela Robinan Gentry - Utrecht University, Faculty of Veterinary Medicine, IRAS, 2008 ISBN 978-90-393-4893-2 The studies described in Chapters 2 through 9 were performed at ENVIRON International Corporation, currently in Monroe, LA, USA. The financial support for these studies is acknowledged separately in each chapter.
Contents
Chapter 1:
Introduction
Part I:
Use of Biokinetic Modeling and Genomics Information to Inform a Dose-Response Assessment for Arsenic
33
Physiologically Based Pharmacokinetic Modeling of Arsenic in the Mouse. (Gentry, P.R., Covington, T.R., Mann, S., Shipp, A.M., Yager, J.W., and Clewell, H.J. Journal of Environmental Toxicology and Environmental Health, Part A, 2004, 67:43-71)
35
Comparison of Tissue Dosimetry in the Mouse Following Chronic Exposure to Arsenic Compounds. (Gentry, P.R, Covington, T.R, Lawrence, G., McDonald, T., Snow, E.T., Germolec, D., Moser, G., Yager, J.W., and Clewell, H.J. Journal of Environmental Toxicology and Environmental Health, Part A, 2005, 68:329-351)
71
Analysis of Genomic Dose-Response Information on Arsenic to Inform Key Events in a Mode of Action for Carcinogenicity. (Gentry, P.R, McDonald, T.B., Sullivan, D.W., Shipp, A.M., Yager, J.W., and Clewell, H.J. Environmental and Molecular Mutagenesis, 2008, accepted)
97
Chapter 2:
Chapter 3:
Chapter 4:
Part II: Chapter 5:
9
Use of Biokinetic Modeling to Identify Critical Periods of Exposure during the Perinatal Period
139
Evaluation of the Potential Impact of Pharmacokinetic Differences on Tissue Dosimetry in Offspring during Pregnancy and Lactation. (Gentry, P.R., Covington, T.R, and Clewell, H.J. Regulatory Toxicology and Pharmacology, 2003, 38:1-16)
141
Chapter 6:
Part III:
Chapter 7:
Chapter 8:
Part IV: Chapter 9:
Chapter 10:
Data for Physiologically-Based Pharmacokinetic Modeling in Neonatal Animals: Physiological Parameters in Mice and Sprague-Dawley Rats. (Gentry, P.R., Haber, L.T., McDonald, T.B., Zhao, Q., Covington, T., Nance, P., Clewell, H.J., Lipscomb, J.C., Barton, H.A. Journal of Children’s Health, 2004, 2(3-4):363-411)
181
Using Biokinetic Modeling in Combination with the Family Approach in the Determination of Reference Doses/ Concentrations
255
Application of a Physiologically-Based Pharmacokinetic Model for Isopropanol in the Derivation of an RfD/RfC. (Gentry, P.R., Covington, T.R., Andersen, M.A., and Clewell, H.J. Regulatory Toxicology and Pharmacology, 2002, 36:51-68)
257
Application of a Physiologically-Based Pharmacokinetic Model for Reference Dose and Reference Concentration Estimation for Acetone. (Gentry, P.R., Covington, T.R., Clewell, H.J., and Andersen, M.A. Journal of Environmental Toxicology and Environmental Health, Part A, 2003, 66:2209-2225)
297
Using Biokinetic Modeling and In Vitro Polymorphism Data in Risk Assessment
319
An Approach for the Quantitative Consideration of Genetic Polymorphism Data in Chemical Risk Assessment: Examples with Warfarin and Parathion. (Gentry, P.R., Hack, C.E., Haber, L., Maier, A., and Clewell, H.J. Toxicological Sciences, 2002, 70:120-139)
321
Discussion
377
Samenvatting in het Nederlands
395
Dankwoord
401
Curriculum vitae
403
Chapter 1
The Development of Novel Quantitative Methods in Toxicology for Human Risk Assessment: Introduction P. Robinan Gentry
ENVIRON International Corporation Monroe, LA, USA
9
The current approach to human health risk assessment is an integrated approach requiring a significant number of studies conducted in experimental animals. The results from these animal studies are usually used to determine the potential for adverse effects in humans resulting from chemical exposure. The risk assessment process is initiated with a critical review of the available toxicity studies for a chemical (Hazard Assessment) (Figure 1). Hazard Identification
Is there a hazard?
No
Stop
Yes
Assessment of Current or Future Exposures
Dose-Response Assessment
Risk Characterization
Figure 1: Paradigm for a Human Risk Assessment The types of studies considered typically include toxicity studies ranging from acute to chronic in duration, biokinetic 1 studies, and mechanistic studies. One goal of the critical review is the integration of the available quantitative and 1
In the following chapters, the use of the term “pharmacokinetics” is used to describe the timecourse of chemicals in biological systems. It has also been used to describe these processes for pharmaceuticals. The term “toxicokinetics” is also used frequently in the published literature to describe these processes specifically for “toxic” compounds. However, it should be recognized that all compounds may be toxic at high enough doses. To avoid any distinction between compounds, the term “biokinetic” is used in the introduction and discussion of this thesis to describe the biological processes associated with the disposition of xenobiotic compounds in the body.
10
qualitative data to quantify the potential for adverse effects in humans from a selected compound (Dose-Response Assessment). Another goal of the Hazard Assessment is to develop qualitatively hypotheses related to the mode of action for the identified adverse effects. The hypothesis regarding the potential mode of action for a compound becomes critical in the determination of the quantitative methods that will be applied in risk assessment. Currently, the number of experimental animal studies needed to conduct an adequate hazard assessment is large. Therefore, there are pressures, both regulatory and ethical, to decrease the number of studies that are conducted in whole animals. The vision for the future is to shift from the use of whole animals to the use of in vitro testing to evaluate the potential for adverse effects in humans (NRC 2007). This desired change in toxicity testing has resulted in an increase in new testing approaches in vitro that capitalize on recent advances in genomics and systems-biology research. The ultimate goal of this new research is to find high throughput screening methods that will replace animal testing in the determination of the potential toxicity of compounds in humans. Large integrated reviews of the available literature for compounds that are currently data rich are needed to identify lessons learned to accelerate the progression to in vitro testing. These efforts will decrease the need for additional testing for ongoing risk assessments and eventually decrease the number and types of studies needed for future risk assessments. This new vision of toxicity testing (NRC 2007), with a focus on the development of computational models combined with in vitro screens, has the potential to decrease animal testing. However, before this new vision can be applied, novel computational approaches must be developed or current approaches extended to address many of the critical issues involved in the process of quantitative human health risk assessment. One approach that presents multiple applications in this new vision of risk assessment is physiologically based biokinetic (PBBK) modeling (Clewell and Clewell 2008; Thompson et al. 2008). The use of these models, in combination with the appropriate data, provides the risk assessor the ability to extrapolate from concentrations in the in vitro setting to concentrations at the target tissue of concern in the human. The development and application of PBBK models and the use of in vitro data in the risk assessment process has increased dramatically in the last decade. PBBK modeling has grown from a limited use in the characterization of general biokinetics or toxicodynamics of a compound to use in quantifying the impact of 11
critical issues in toxicology or risk assessment. Critical issues that have been quantitatively addressed with modeling include the impact of human variability on the determination of acceptable exposure concentrations (Allen et al. 2007) and the identification of critical windows of exposure over lifestages (Clewell et al. 2004). In vitro data are also being used to as investigative tools to provide information regarding mechanisms of toxicity at the cellular level and biological changes at the genomic level. This extension of the application of biokinetic models and in vitro data has been in response to recent scientific and policy initiatives. These initiatives have increased the need to quantify the risk of adverse effects in sensitive subpopulations, such as children or individuals with kinetic polymorphisms. The goal of the research presented in this thesis is three fold. One goal is to demonstrate an approach to integrate all of the currently available toxicological information on a compound quantitatively to adequately address the potential for adverse effects from chemical exposure. In making use of all of the available information in a quantitative manner, the result will be estimates of potential risk or acceptable concentration that will be biologically sound. The second goal is to address critical issues that could significantly impact dose-response estimates (i.e., children, polymorphisms). Dealing quantitatively with these issues will decrease the potential for adverse effects in the general population resulting from chemical exposure. The third goal is to further extend the development of novel quantitative methods towards the ultimate aim of decreasing the need for animal research. Critical Issues in Quantitative Risk Assessment Identification of Target Tissue Dose Evaluation of data in the hazard assessment can generally be divided into two categories: biokinetics, or the processes by which a chemical is absorbed, metabolized, distributed, and excreted, and toxicodynamics, the action of a compound resulting in significant biological perturbations or adverse effects. In evaluating the biokinetics for a compound for determination of the toxic moiety, it is critical that the most sensitive target organ be identified. This is typically achieved by evaluating the results of multiple animal studies conducted for multiple durations of exposure, but relying heavily upon studies conducted for chronic duration. 12
The eventual vision for the identification of target tissues with the anticipated decrease in animal testing is to use the results from in vitro testing in human cell lines to evaluate biologically significant perturbations in toxicity pathways (NRC 2007). This approach is currently being achieved with pharmaceutical compounds, using a screening approach for multiple chemicals under conditions that model key biologic mechanisms. In the case of environmental compounds, current toxicology studies conducted in animals for hazard identification are high-dose toxicity studies generally not characteristic of low-dose exposure expected in humans. Characterization of the potential human exposures prior to the initiation of experimental testing will facilitate the shift from high-dose exposure in the animals to in vitro screens in human cells. This combined with additional research on biological perturbations in key toxicity pathways will allow the achievement of the use of in vitro testing to identify target tissues. Once the target tissue has been identified, a dose-response assessment is conducted to quantify the potential for adverse effects in the target organ at exposure levels of interest (USEPA 2005). The default approach in risk assessment to characterize “exposure” or “dose” is to rely upon the external exposure concentration and the corresponding responses to characterize the doseresponse for potential adverse effects resulting from chemical exposure. However, the dose that would be most associated with any potential adverse effect would be the amount (or concentration) of chemical at the target tissue site. Addressing this critical issue of the estimation of a target tissue dose associated with various external exposure concentrations can be achieved through the development and application of biokinetic models. The ultimate aim of incorporating biokinetic modeling in risk assessment is to provide a measure of dose which better represents the “biologically effective dose” or the dose which is causally related to the toxic outcome (Clewell et al. 2002). Physiologically based biokinetic (PBBK) models have proven to be a useful tool for integrating the existing toxicological and biokinetic data for a particular chemical to provide an alternative to traditional methods for the estimation of acceptable levels (Barton and Clewell, 2000; Clewell et al., 1995). The important feature of these types of models is that they rely largely on the actual physiology of the organism (Figure 2), rather than being an empirical exercise, such as classical compartmental modeling. In addition to the application for the determination of target tissue dose, the inherent capabilities of PBBK modeling are particularly advantageous for cross-species, as well as cross-route, 13
extrapolation. For cross-species extrapolation, the physiological and biochemical parameters in the model can be changed from those for the test species to those that are appropriate for humans to provide a biologically meaningful human equivalent concentration or dose at the target site.
Parent Chemical URT Alveolar
Region
Metabolite Surface Skin
Alveolar Region
Fat
Fat
Rapid
Rapid
Slow
Slow
Brain
Brain
Liver
Liver VMaxC, KM
Duodenum
Stomach
VMax1C, KM1
PDose
Figure 2: An example of a PBBK model. Moreover, PBBK models can also be used to predict the target tissue dose associated with an animal toxicity study conducted by one route, enabling the prediction of the equivalent human exposure by another route that would result in the same target tissue dose (see Chapter 8). Current default approaches for noncancer Reference Dose (RfD) or Reference Concentration (RfC) derivation 14
provide no guidelines for the use of toxicity data from a route of exposure other than the human exposure route of concern. Thus, for example, in performing an inhalation risk assessment for a chemical, data from animal studies performed by the oral route could not be included in the quantitative dose-response calculations. Except in the case of exposure-route-specific, portal-of-entry effects, the use of PBBK modeling makes it possible to use data from different routes in a quantitative risk assessment (Gerrity and Henry, 1990). The USEPA has shown increasing interest in the use of PBBK modeling to perform direct comparisons of toxicity studies across routes of exposure (Foureman and Clewell, 1999). For example, the published RfC for vinyl chloride is based on an oral study (USEPA, 2000a). In addition, the current USEPA RfC dosimetry guidelines (USEPA, 1994) indicate that the preferred methodology for cross-species extrapolation is the use of a validated PBBK model. The ultimate aim of using PBBK modeling in risk assessment is to provide a measure of dose that better represents the “biologically effective dose:” the dose that causally relates to the toxic outcome. The improved dose metric can then be used in place of traditional dose metrics (such as administered dose) in the risk assessment calculations (Clewell and Andersen, 1985), provided that the appropriate data related to species differences are available. The incorporation of this novel quantitative approach into the risk assessment process provides a useful tool for not only integration of existing data, but also an opportunity to decrease the number of toxicity studies required. An example of the use of this approach to decrease the need for additional testing is provided in Chapter 8 of this thesis. In evaluating the database for acetone, the USEPA considered the database deficient due to a lack of reproductive/developmental studies and chronic studies. Through the application of PBBK modeling and using data available for isopropanol (whose major metabolite is acetone), it was demonstrated that no additional testing was needed to determine the potential for acetone to cause these types of effects. These valuable tools can be used to determine target tissue dose, which is a critical issue in risk assessment, as well as other issues in risk assessment (i.e., animal-tohuman extrapolation, subchronic-to-chronic exposure extrapolation, high-dose to low-dose extrapolation).
15
Determination of Sensitive Subpopulations In general, the consideration of human variability in risk assessment has been semi-quantitative. Typically, in noncancer assessment a default factor has been applied to adjust for variability in both biokinetics and toxicodynamics across individuals (USEPA 1994). In cancer assessment, this adjustment is not typically made, rather the assumption is made that the endpoints relied upon and dose-response models used are sufficiently conservative that sensitive subpopulations are protected. Sources of conservatism include (1) dose–response assessments for cancer generally rely on chronic animal bioassays at high doses (where metabolism may be saturated), so toxicokinetics and other parameters may not be representative of those at low doses; (2) the default use of linear extrapolation or the assumption that there is no “zero” risk dose or concentration (USEPA, 1999); and (3) the default use of the most sensitive species, strain, and sex, unless there is evidence that the data are not applicable to humans (Haber et al. 2002). Observed variability in response to chemical exposure may be attributed to several factors including lifestyle (e.g., eating patterns) exposure to multiple chemicals, and genetic differences. All of these have the potential to be addressed in risk assessment using quantitative methods such as PBBK models. There is increased recognition that there is a genetic basis for variability (e.g., genetic polymorphisms) in xenobiotic response, but the quantitative impact of these genetic factors has not been well characterized. This deficiency has resulted in an increased need for quantitative approaches to characterize target tissue dose in potentially sensitive subpopulations. There may be wide ranges in metabolic activity resulting from a polymorphism in genes encoding enzymes associated with metabolism. This variation in activity might lead to large differences in tissue dose arising from similar exposures. This conclusion is supported by epidemiological comparisons of cancer risk between populations with the wild-type and variant alleles that show an increased risk (or decreased risk) among populations harboring different alleles (Uematsu et al. 1991). It is further supported by the observed variability in blood or tissue levels of pharmaceuticals in patients receiving similar administered doses (Furuya et al. 1995). On the other hand, a genetic polymorphism may have minimal or no impact on toxicity. Some genetic polymorphisms may not affect the resulting amino acid sequence (e.g. they may be in a noncoding region of the gene, or in the coding region without altering the encoded amino acid), and so not affect 16
enzyme activity. Conversely, polymorphisms in the regulatory region of a gene may affect gene expression or mRNA stability, and thereby modify the total level of enzyme activity in a tissue, without directly modifying the protein. Other polymorphisms may affect enzyme activity, but the effect may be insignificant at environmental exposure levels, perhaps because other enzymes can carry out the same reaction, or the kinetics of the secondary enzymes are not rate limiting. Other genetic and environmental factors may also affect the enzyme level and activity. Overall, the key question for evaluating the effects of polymorphisms is how this affects the inter-individual variability in the tissue dose of an active agent resulting from a given administered dose of the parent compound. For risk assessment scientists, this question is critical in deriving “safe” or subthreshold dose estimates that are protective for a highly variable human population. Many of the genes that give rise to enzymes that metabolize environmentally relevant toxicants and pharmaceuticals are polymorphic. The critical issue that then needs to be addressed as part of a risk assessment is whether or the not these polymorphisms result in some individuals being more susceptible to adverse responses resulting from chemical exposure. The available quantitative information on polymorphisms is largely from in vitro experiments. There is a need to use novel approaches, such as biokinetic modeling and Monte Carlo analysis, to investigate the potential impact of polymorphisms on target tissue dose. Children are also generally recognized as a potential sensitive subpopulation. Recently, there has been increased focus on developing methods for assessing exposure to children. These include the Voluntary Children’s Chemical Evaluation Program (VCCEP), which represents the most significant children’s health assessment activity undertaken thus far by the USEPA since passage of the Children’s Health Protection Act by the U.S. Congress in 1997 and the establishment of the Office of Children’s Health Protection within USEPA in 1998. As mentioned previously, in evaluating the differences between children and adults, these differences can also be grouped into two broad categories: biokinetic factors, or those factors that influence the target tissue dose for a given external exposure, and biodynamic factors, or those factors that influence the target tissue response at a given target tissue dose. Even at the same level of exposure, because of the heterogeneity of the human population, it is generally expected that there will be a broad range of observed susceptibilities to the 17
biological effects of exposure to chemicals or drugs. Often it is possible to distinguish specific classes of individuals, such as infants or the elderly, who appear to be more susceptible to an adverse effect following chemical exposure. Another application of PBBK models is to provide a quantitative structure for determining the effect of various age- and gender-specific factors on the relationship between the external (environmental) exposure and the internal (biologically effective) target tissue exposure. In particular, PBBK models can be used to determine the impact of differences not only due to normal variation in enzyme activities within the general population (kinetically susceptible populations), but also due to differences in metabolism across age groups, such as children versus adults (Clewell et al. 2004). PBBK models can also be applied to quantitatively describe internal tissue dose metrics during pregnancy in the fetus, as well as the mother (Clewell et al. 2007; Gentry et al. 2003). For the perinatal period, exposure to the fetus is dependent on placental transfer, while for the neonate significant exposure may occur via ingestion of breast milk. Estimation of exposure during this period is complex, since maternal and fetal, as well as nursing infant, exposure must be considered together. An added dimension when considering the relevant internal dose metric for perinatal exposure is the timing of that exposure relative to the stage of development of the potential target organ or system. In utero exposure to either the parent chemical or metabolite will be influenced by the physicochemical properties of the exogenous chemical. These properties will govern its placental transfer to the fetus and accumulation in the fetus. High molecular weight chemicals, including highly lipophilic chemicals, are generally thought to be less efficiently transferred across the placenta, but once in the fetus may be more persistent, thereby providing a sustained target tissue dose. Highly lipophilic compounds are also more readily concentrated in and transferred with breast milk during lactation, providing a potentially important route of postnatal exposure. Exposure to the developing fetus/infant with time will change even if the maternal exposure remains constant. This is due to changes in maternal kinetic parameters (e.g., body weight, volume of distribution) and fetal/infant metabolic capabilities to either detoxify or activate a chemical and clear those chemicals as metabolic and physiological systems develop. Consequently, perinatal exposure cannot be readily estimated from blood levels of parent chemical and/or metabolite in either a child or nonpregnant woman.
18
The expansion and application of PBBK models to characterize potential fetal exposure as well as transfer to milk for potential lactational exposure to children, offers a useful quantitative approach for identifying critical windows of exposure during the perinatal period. These models can also be adapted, as has demonstrated by Clewell et al. (2004), to characterize age- or sex- related changes in physiological or biokinetic parameters in growing children or aging adults that may represent the opportunity for increased susceptibility to chemical exposure. Changes in Gene or Protein Expression With the completion of the Human Genome project and evolving capabilities and technologies in the area of genomic research, there has been a significant increase in information related to the consequences of chemical exposure on gene expression. While these types of information are currently being used qualitatively to make decisions, the challenge remains to be able to incorporate genomic information quantitatively into the risk/safety assessment process. The main challenge facing scientists in the regulatory arena is not only attempting to identify a genomic signature or threshold associated with chemical exposures, but to also correlate this information with a phenotypic threshold, whether that is at the level of a protein, cell, tissue or behavior. While the data in this area continues to grow, we are still in the early stages of determining what changes in gene or protein expression represent an adverse effect. The latest USEPA Guidelines for Carcinogen Risk Assessment (USEPA 2005) provide a framework for the consideration of mechanistic data, such as changes in gene or protein expression, in the cancer risk assessment process. In the case of cancer assessment, the general assumption is that the tumor of interest may be induced by a set of key events that are required for the conversion of a normal cell into a transformed one and ultimately to produce a malignant phenotype (Preston 2007). The key events for a particular chemical that result in an adverse effect are described by the mode-of-action for that chemical. It is likely that multiple modes of actions are operating for any particular chemical. Given the possibility that multiple key events may be involved in any adverse effect, assessment of the potential effects on genes at the whole genome level using microarray techniques provides valuable quantitative information. The results of these assays provide a wealth of information that may be critical in the assessment of potential adverse effects. Genomics data at the DNA, mRNA and protein levels have already been informative in the identification of specific genetic and phenotypic alterations associated with tumor formation. This 19
approach can lead to the development of informative biomarkers of response. The use of genomic approaches is just entering the realm of risk assessment; there remains considerable groundwork to be laid before a clearly defined role for these data can be established. The challenge in incorporating this type of information in risk assessment is whether to rely upon single gene changes or changes in groups of genes within a functional category. Ultimately, changes in categories of genes would be most representative of a significant change that will most likely result in the observation of an adverse effect in the whole organism. Biological systems in general are robust with compensatory mechanisms in place to hold the system in homeostasis. What is yet to be determined is how many genes and at what level they need to be affected to overwhelm the system, resulting in biological perturbations. In addition, another issue that should be considered is whether the observed changes in categories of genes following relatively short-term administration of chemicals will be representative of changes likely observed following longer-term administration. The approach developed by Thomas et al. (2007) in the quantitative evaluation of formaldehyde microarray results represents a significant step forward in the use of genomic information in the risk assessment process. This approach allows for the quantitative estimation of risk, using an approach (Benchmark Dose modeling) currently used in risk assessment in combination with a comprehensive survey of molecular and cellular changes associated with chemical exposure, and provides the ability to identify doses at which cellular processes are altered. In this approach, gene expression changes in the rat nasal epithelium following acute formaldehyde exposure were first analyzed using standard statistical methods (e.g., ANOVA) to identify genes that showed significant dose-response behavior. These genes were then categorized, based on their associated gene ontology (GO) classification and evaluated using Benchmark Dose modeling. The results were used to identify doses at which individual cellular processes were altered. This approach is also being applied for other compounds, such as arsenic (Clewell et al. 2007). This combination of standard dose-response models with the results of microarray data represents a novel quantitative approach in the use of changes in gene or protein expression for risk assessment. An alternative approach to generating microarray data is to rely upon the wealth of studies that have been conducted to date on the impact of chemical 20
exposure on selected genes. Because of the decreasing cost in evaluating the impact of a chemical on either isolated genes or arrays of genes, the amount of research in this area has grown exponentially in recent years. However, limited work is being done to integrate this large body of information and determine its potential relevance to risk assessment. One chapter of this thesis provides an approach for this type of analyses of existing literature in the case of arsenic (Gentry et al. 2008). Overall, the current analysis provided evidence of dosedependence of the effects of arsenic compounds on various genes or proteins from concentrations of 0.005 to up to 1000 µM. The available in vitro gene expression data, together with information on the metabolism and protein binding of arsenic compounds, supports a mode of action for inorganic arsenic carcinogenicity. This mode of action involves the superposition of highly-specific direct interactions with critical proteins such as those involved in DNA repair, overlaid against a background of chemical stress, including proteotoxicity and depletion of nonprotein sulfhydryls. Therefore, the results of this approach provided relevant information, not only from a quantitative dose-response perspective, but also from a qualitative perspective as it relates to the development of a mode of action for carcinogenicity of a compound. While both of these approaches represent an attempt to consider changes in gene or protein expression as part of the risk assessment process, there still remain uncertainties as to how these types of analyses should be interpreted with respect to the whole organisms. However, the development of approaches such as that reported by Thomas et al. (2007) are a step in the direction of what is needed to determine what the growing, and possibly overwhelming, information on genomics and proteomics means from a risk assessment perspective. In addition, these approaches are needed to provide interpretation of the increasing amount of in vitro data collected and the pressure to decrease the number of whole animal studies required in risk assessment. Consideration of Metabolically Similar Compounds (Family Approach) In considering the current paradigm for dose-response assessment, which includes exposure, internal dose metric determination, potential mode of action, and response, it appears that there may be opportunities to decrease the number of experimental animal tests when metabolically-related chemicals can be considered in combination. A novel approach to decrease the toxicity testing needed, referred to as the family approach, was suggested by Barton et al. (2000). One example of its application provided by Barton et al. (2000) would be in the 21
case where individuals were exposed two chemicals, chemicals A and B. Traditionally in risk assessment, exposure limits would be developed for both chemicals based on the standard battery of studies for each compound. However, if chemical B were a metabolite of chemical A, the study in which animals were exposed to chemical A would result in internal exposures to both chemicals A and B, thus allowing for the determination of hazards associated with both chemicals and the ability, with PBBK modeling, to determine acceptable exposures to both chemicals. This approach was named the family approach in reference to its application to a family of metabolically related chemicals. The family approach to risk assessment makes use of all of the quantitative data for a group of metabolically-related compounds to decrease the need for additional research. It allows for the use of toxicity testing for one compound within the group, if metabolically similar, to potentially fill data gaps for other members of the group. It also allows information from one compound to address the need for uncertainty adjustments in the risk assessment of a metabolically-related compound. This approach is demonstrated in Chapter 8 with the use of information from toxicity testing for isopropanol to inform the risk assessment for acetone. This type of approach is currently being considered by the USEPA for “formaldehyde-generators”, a group of metabolically-similar compounds which includes formaldehyde, methyl tert-butyl ether (MTBE), methanol, and aspartame. Overview of This Thesis This thesis is comprised of a number of chapters representing individually published studies that were conducted to incorporate novel approaches into the risk assessment process. These novel approaches were all developed to attempt to address the specific issues in risk assessment discussed previously. The chapters are divided into four sections based on the application of different approaches. While each section includes an element of biokinetic modeling, these modeling approaches have been extended to consider a specific issue or combined with in vitro data which can expand the quantitative information to characterize potential adverse effects. The first section describes three studies that were performed to refine the dose-response assessment on the potential carcinogenicity of arsenic compounds. Incorporation of biokinetic modeling and the combined use of in vivo and in vitro information were used in these initial investigations towards the development of a 22
potential mode of action for arsenic carcinogenicity and to determine the need for additional toxicity testing. Exposure of human populations to high concentrations of inorganic arsenic in drinking water has been associated with a variety of adverse health effects, including both cancer and non-cancer endpoints (Clewell et al. 1998; Tsai et al. 1998; Golub et al. 1998; Bernstam and Nriagu 2000). An unusual feature of the toxicity of arsenic is that although human exposure has repeatedly been associated with increased incidences of cancer, inorganic arsenic has generally not been observed to cause tumors in standard laboratory animal test protocols. Of the cancer bioassays that have been conducted in animals, only one positive study has been reported in mice by Ng et al. (1999). The remaining studies conducted in mice are negative for carcinogenicity. Chapter 2 describes the development of a biokinetic mouse model for arsenic which was used, based on the available strain-specific data in the mouse, in the evaluation of the potential role of metabolism and tissue dosimetry in the disparate results observed in the cancer bioassays for various strains of mice. The development of this model was based largely on previously published acute kinetic studies conducted in the mouse. Chapter 3 of the first section provides the results of the extension of the arsenic mouse PBBK model to investigate if the differences in response in mouse strains are related to duration of exposure. In the Moser et al. (2000) and Mass (1998) studies, which were conducted in similar strains to Ng et al. (1999), the incidence of tumors was not increased despite the administration of doses of arsenic approximately 40- to 400-fold higher than the dose administered in the Ng et al. (1999) study. However, while comparable strains of mice were used by Moser et al. (2000) and Mass (1998) the durations of exposure in these studies were 26 and 51 weeks respectively, considerably less than the duration of exposure (112 weeks) in the Ng et al. (1999) study. The only study where the duration of exposure was comparable to the duration of exposure in the Ng et al. (1999) study was the study by Kanisawa and Schroeder (1967), where groups of CD mice received arsenic in the drinking water and diet for 132 weeks. In addition to the longer duration of exposure, Kanisawa and Schroeder (1967) exposed the mice to a higher dose (0.38 mg/kg/day). In that study, the tumor incidence in the arsenic-treated mice was statistically significantly decreased, when compared with the incidence in the control mice. The reason for the different results reported in these two studies is unclear; however, one possible explanation is that different strains of mice (C57Bl/6J and Swiss-derived CD) were used in the studies. The PBBK model was used, along with biokinetic data from two chronic studies in which arsenic species were measured in tissues for 23
C57Bl/6N mice (Klein et al. 2002), and TgAc mice, to determine whether tissue dosimetry could provide insights into the apparently disparate bioassay results discussed above. Chapter 4 provides the results of a study which examines the apparent inconsistencies between animal and human data in regards to the carcinogenic response to arsenic and attempts to integrate the available information on mode of action to inform the debate as to whether there is evidence of a “threshold” or highly nonlinear dose-response at environmentally relevant drinking water concentrations of arsenic compounds. The mode of action for inorganic arsenic or its metabolites in the production of these cancers must be explored and expressed quantitatively. Several modes of action for arsenic carcinogenesis have been proposed (Clewell et al. 1999; Hughes et al. 2007; Kitchin 2001; Kligerman and Tennant 2007; Schoen et al. 2004); however, many of these proposed modes of action lack sufficient supporting data. The common element for these proposed modes of action is that they are the likely consequence of dosedependent transitions in gene or protein expression resulting in a biological cascade from adaptive to proliferative to apoptotic responses, similar to the hierarchy described by Nel et al. (2006) for environmental particulates. A comprehensive literature search was conducted to identify research results that focus on gene or protein expression changes following exposures to inorganic arsenic compounds over a range of concentrations. The goal was to organize the changes in gene or protein expression observed as it relates to arsenic concentration to identify dose-dependent transitions in gene or protein expression that may then inform mode of action for the potential carcinogenicity of arsenic. The second section of the thesis describes two studies that were performed to provide information related to the identification of critical periods of exposure during the perinatal period in humans and experimental animals. Over the past decade, there have been an increasing number of attempts to quantitatively describe differences across lifestages (i.e., children versus young adults versus the elderly) that may impact susceptibility to adverse effects following chemical exposure. Chapter 5 presents the results of a study in which a PBBK model was used to evaluate internal dose metrics during the perinatal period in an attempt to develop a methodology to assess, based on chemical/physical properties of a chemical, biokinetic changes that may impact susceptibility of the fetus or neonate to adverse effects following chemical exposure. Six chemical classes were targeted to provide a variety of physicochemical properties (volatility, lipophilicity, water solubility), and 24
surrogate chemicals were selected to represent each class (isopropanol, vinyl chloride, methylene chloride, perchloroethylene, nicotine, and TCDD), based on the availability of pharmacokinetic information. This case study demonstrates how a human PBPK model can be used to assess the critical period of exposure (from a biokinetic perspective) during development, as a function of chemical/physical properties. Because this type of modeling approach is increasing in the assessment of lifestage-specific dosimetry, the second chapter of this section (Chapter 6) provides the results of a critical review of the available kinetic literature for mice and rats to collect physiological parameters to aid in the development age-specific biokinetic models in experimental animals. The third section of the thesis provides the results from two studies in which PBBK modeling was applied in the determination of Reference Doses (RfDs) and Reference Concentrations (RfCs). RfDs/RfCs are exposure levels calculated by the U.S. Environmental Protection Agency (USEPA) to quantify the health risk associated with chronic chemical exposures (USEPA 1988, 1994) (Figure 3). These levels serve as a basis for making risk management decisions to protect exposed populations from adverse health effects resulting from exposure to environmental chemicals. An RfD is defined as an estimate (with uncertainty spanning one or more orders of magnitude) of a daily oral exposure to human populations (including sensitive subpopulations) that is likely to be without an appreciable risk of deleterious effects over the course of a lifetime. An RfC is defined similarly, but is associated with daily inhalation exposure to human populations. RfDs/RfCs are generally derived from a No-Observed-Adverse Effect Level (NOAEL), which is defined as the highest experimentally determined dose or concentration in humans or animals that is without adverse biological effect. An alternative to a NOAEL for the basis of an RfD/RfC is the Benchmark Dose (BMD), which is an estimate of a dose or concentration that corresponds to a predetermined response level that is estimated using a doseresponse model. The NOAEL or BMD is then adjusted by the application of uncertainty factors (UFs) that reflect limitations in the various types of data used and potential uncertainty in extrapolation to humans. The process of deriving RfDs/RfCs necessarily relies on a number of assumptions, estimates, and judgments. When deriving RfD/RfCs from animal data, several uncertainties of extrapolation must be addressed: from the animal species to humans, from high dose to low dose, from one exposure route to 25
Reference Dose =
NOAEL or LOAEL Uncertainty Factors
X LowestLowest-ObservedObservedAdverseAdverse-Effect Level (LOAEL)
% Response
Reference Dose (RfD)
Threshold NoNo-ObservedObservedAdverseAdverse-Effect Level (NOAEL)
X X
X X Average Daily Dose (mg/kg(mg/kg-day)
Uncertainty Factors Short-duration to long
up to 10
Animal to human
up to 10
LOAEL to NOAEL
up to 10
Human Variability
up to 10
Inadequate database
up to 10
Figure 3: Derivation of a Reference Dose (RfD)/Reference Concentration (RfC)
another, and from one exposure time-frame to another (e.g., 6 hours per day to continuous). Another challenge may involve the comparison of the results of available toxicity studies, because different critical studies for consideration in a risk assessment may have been performed by different routes of administration (e.g., oral or inhalation). Chapter 7 describes the use of a validated PBPK model for isopropanol and its principal metabolite, acetone, in the rat and human (Clewell et al., 2001) to perform route-to-route and cross-species dosimetry in support of the derivation of RfD and RfC values for isopropanol based on the results from toxicity studies in animals. Chapter 8 extends the application of this model by demonstrating the use of the validated acetone component of the PBBK model for isopropanol in the development of both an RfD and RfC for acetone. For the estimation of the RfD 26
for acetone, two approaches were used. The first approach involved the use of the available oral dataset for acetone, using the most appropriate internal dose metric associated with the adverse effect of concern. In addition, the model was also used to evaluate the appropriateness of uncertainty factors that would typically be applied in standard risk assessments. An uncertainty factor has been considered in the estimation of the proposed RfD for acetone due to a lack of a complete dataset for acetone; however, the database for IPA, whose major metabolite is acetone, is considered complete. The PBPK model, validated for both acetone and IPA, was therefore used, in combination with the available studies for IPA, in the application of a family approach to address the necessity of this UF in this risk assessment for acetone. The second approach involved the use of an inhalation developmental study for acetone in the derivation of an oral RfD, using the validated PBBK model. The fourth section of this thesis (Chapter 9) provides a study which was conducted to develop an optimal approach for evaluating the variability in tissue dose resulting from polymorphisms in genes that encode enzymes important for xenobiotic metabolism. The ultimate goal is to incorporate information about genetic polymorphisms into the derivation of Chemical Specific Adjustment Factors (CSAFs) (IPCS 2005), and thereby enhance noncancer risk assessment by facilitating the movement from default uncertainty factor approaches to datainformed, biologically-based methods. Two case studies are presented that were conducted for warfarin and parathion, in which the combination of PBPK modeling and Monte Carlo analysis is used to develop a quantitative estimate of the impact of genetic polymorphisms on tissue doses, and hence internal dose metrics for risk assessment. These two case study chemicals provided a useful comparison because they are metabolized by two very different metabolic pathways, involving polymorphisms in two unrelated metabolic enzymes, with different biological implications resulting from the presence of the polymorphisms. These analyses evaluated the impact of different choices of key input data on resulting distributions of tissue doses, and downstream implications for noncancer dose-response assessment when different approaches to calculating CSAFs are used. References Allen, BC, Hack, CE, and Clewell, HJ. (2007). Use of Markov Chain Monte Carlo analysis with a physiologically-based pharmacokinetic model of 27
methylmercury to estimate exposures in US women of childbearing age. Risk Analysis 27(4):947-959. Barton, HA and Clewell, HJ, III. (2000). Evaluating noncancer effects of trichloroethylene: dosimetry, mode of action, and risk assessment. Environmental Health Perspectives 108(suppl 2): 323-334. Barton, HA, Deisinger, PJ, English, JC, Gearhart, JM, Faber, WD, Tyler, TR, Banton, MI, Teegaurden, J, and Andersen, ME. (2000). Family approach for estimating reference concentrations/doses for series of related organic chemicals. Toxicological Sciences 54(1): 251-261. Bernstam, L and Nriagu, J. (2000). Molecular Aspects of Arsenic Stress. Journal of Toxicology and Environmental Health (Part B) 3(4): 293-322. Clewell, HJ, III, and Andersen, ME. (1985). Risk assessment extrapolations and physiological modeling. Toxicology and Industrial Health 1(4): 111-131 Clewell, RA, and Clewell, HJ, III. (2008). Development and specification of physiologically based pharmacokinetic models for use in risk assessment. Regulatory Toxicology and Pharmacology 50(1):129-143. Clewell, HJ, III, Gentry, PR, Gearhart, J, Allen, B, and Andersen, ME. (1995). Considering pharmacokinetic and mechanistic information in cancer risk assessments for environmental contaminants: examples with vinyl chloride and trichloroethylene. Chemosphere 31(1): 2561-2578. Clewell, HJ, III, Barton, H, Gentry, PR, Shipp, AM, Yager, JW, and Andersen, ME. (1998). Requirements for a Biologically-Realistic Arsenic Risk Assessment. International Journal of Toxicology 18(2): 131-147. Clewell HJ, III, Gentry PR, Barton H, Shipp AM, Yager JW, Andersen ME. (1999). Requirements for a biologically realistic cancer risk assessment for inorganic arsenic. International Journal of Cancer 18(2):131-147. Clewell, HJ, III, Gentry, PR, Gearhart, JM, Covington, TR, Banton, MI, and Andersen, ME. (2001). Development of a physiologically based pharmacokinetic model of isopropanol and its metabolite acetone. Toxicological Sciences 63(2): 160-172. 28
Clewell HJ, III, Teeguarden J, McDonald TB, Sarangapani R, Lawrence G, Covington TR, Gentry R, and Shipp AM. (2002). Review and Evaluation of the Potential Impact of Age and Gender-Specific Pharmacokinetic Differences on Tissue Dosimetry. Critical Reviews in Toxicology 32(5): 329-389. Clewell, HJ, III, Gentry, PR, Covington, TR, Sarangapani, R, and Teeguarden, J. (2004). Evaluation of the potential impact of age- and gender- specific pharmacokinetic differences in tissue dosimetry. Toxicological Sciences 79(2): 381-393 Clewell, HJ, III, Thomas, R, Kenyon, E, Hughes, M, and Yager, J. (2007). Gene Expression Dose-Response in the Mouse Bladder Following Exposure to Arsenate in Drinking Water. 46th Annual Meeting and ToxExpo, Charlotte, North Carolina, March. Toxicological Sciences 96(1): 21. Clewell, RA, Merrill, EA, Gearhart, JM, Robinson, PJ, Sterner, TR, Mattie, DR, and Clewell, HJ, III. (2007). Perchlorate and radioiodide kinetics across life stages in the human: using PBPK models to predict dosimetry and thyroid inhibition and sensitive subpopulations based on developmental stage. Journal of Toxicology and Environmental Health (Part A) 70(5): 408-428. Foureman, G, and Clewell, HJ, III. (1999). Route-to-route extrapolation with a physiologically based pharmacokinetic (PBPK) model for cumene. Toxicologist 48, 395. Furuya, H, Fernandez-Salguero, P, Gregory, W, Taber, H, Steward, A, Gonzalez, F and Idle, J. (1995). Genetic polymorphism of CYP2C9 and its effect on warfarin maintenance dose requirement in patients undergoing anticoagulation therapy. Pharmacogenetics 5(6): 389-392. Gentry, PR, Covington, TR, and Clewell, HJ, III. (2003). Evaluation of the potential impact of pharmacokinetic differences on tissue dosimetry in offspring during pregnancy and lactation. Regulatory Toxicology and Pharmacology 38(1): 1-16. Gentry, PR, McDonald, TB, Sullivan, DW, Shipp, AM, Yager, JW, and Clewell, HJ, III. (2008). Analysis of Genomic Dose-response information on Arsenic to 29
inform Key Events in a Mode of Action for Carcinogenicity. Environmental and Molecular Mutagenicity (Accepted). Gerrity, T, and Henry, C. (1990). Principles of Route-to-Route Extrapolation for Risk Assessment. Elsevier Science, New York. Golub, MS, Macintosh, MS, and Baumrind, N. (1998). Developmental and Reproductive Toxicity of Inorganic Arsenic: Animal Studies and Human Concerns. Journal of Toxicology and Environmental Health (Part B) 1(3): 199241. Haber, LT, Maier, A, Gentry, PR, Clewell, HJ, III, and Dourson, ML (2002). Genetic polymorphisms in assessing inter-individual variability in delivered dose. Regulatory Toxicology and Pharmacology 35(2 pt 1): 177-197. Hughes MF, Kenyon EM, and Kitchin KT. (2007). Research approaches to address uncertainties in the risk assessment of arsenic in drinking water. Toxicology and Applied Pharmacology 222(3): 399-404. IPCS (International Programme on Chemical Safety). (2005). Guidance document for the use of data in development of chemical-specific adjustment factors (CSAF) for interspecies differences and human variability in dose/concentration response assessment. World Health Organization, Geneva. Kanisawa, M and Schroeder, HA. (1967). Life term studies on the effects of arsenic, germanium, tin, and vanadium on spontaneous tumors in mice. Cancer Research 27(7): 1192-1195. Klein, CB, Snow, L, and Bosland, MC. (2002). Low dose arsenate in drinking water prevents skin tumor formation in a DMBA/TPA dependent mouse model. Fifth International Conference on Arsenic Exposure and Health Effects, San Diego, CA, p. 45. Kligerman AD and Tennant AH. (2007). Insights into the carcinogenic mode of action of arsenic. Toxicology and Applied Pharmacology 222(3): 281-288. Mass, M (1998). Wild Type and p53 Deficient Mice Exposed to Arsenical Compounds. EPA Study Number 97-05. 30
Moser, G, Goldsworthy, T, and Tice, R (2000). Sodium Arsenite Studies in p53 +/- Mice. Denver, CO, AWWA Research Foundation and the American Water Works Association. Nel A, Xia T, Madler L, and Li N. (2006). Toxic potential of materials at the nanolevel. Science 311(5761): 622-627 Ng, JC, Seawright, AA, Qi, L, Garnett, CM, Cirswell, B, and Moore, MR. (1999). Tumours in mice induced by exposure to sodium arsenate in drinking water. In: Chappell, WR, Abernathy, CO, and Calderon, RL, eds. Arsenic Exposure and Health Effects. Elsevier Science p. 217-223. NRC (National Research Council). (2007). Toxicity testing in the 21st Century: A vision and A Strategy. Washington, DC: The National Academies Press. Preston, RJ. (2007). Epigenetic processes and cancer risk assessment. Mutation Research 616(1-2): 7-10. Schoen A, Beck B, Sharma R, and Dube E. (2004). Arsenic toxicity at low doses: epidemiological and mode of action considerations. Toxicology and Applied Pharmacology 198(3): 253-267. Thomas, RS, Allen, BC, Nong, A, Yang, L, Bermudez, E, Clewell, HJ, III, and Andersen, ME. (2007). A method to integrate benchmark dose estimates with genomic data to assess the functional effects of chemical exposure. Toxicological Sciences 98(1): 240-248. Thompson, CM, Sonawane, B, Barton, HA, DeWoskin, RS, Lipscomb, JC, Schlosser, P, Chiu, WA, Krishnan, K. (2008). Approaches for applications of physiologically based pharmacokinetic models in risk assessment. Journal of Toxicology and Environmental Health (Part B) 11(7):519-547. Tsai, SM, Wang, TM, and Ko, YC. (1998). Cancer Mortality Trends in a Blackfoot Disease Endemic Community of Taiwan Following Water Source Replacement. Journal of Toxicology and Environmental Health (Part A) 55(6): 389-404. Uematsu, F, Kikuchi, H, Motomiya, M, Abe, T, Sagami, I, Ohmachi, T, Wakui, A, Kanamaru, R and Watanabe, M. (1991). Association between restriction 31
fragment length polymorphism of the human cytochrome P450IIE1 gene and susceptibility to lung cancer. Japanese Journal of Cancer Research 82, 254-256. USEPA (U.S. Environmental Protection Agency). (1988). General Quantitative Risk Assessment Guidelines for Noncancer Health Effects. ECAO-CIN-538, Prepared for Risk Assessment Forum by the Technical Panel on Risk Assessment Guidelines for Noncancer Health Effects, October 1988. USEPA (U.S. Environmental Protection Agency). (1994). Methods for Derivation of Inhalation Reference Concentrations and Application of Inhalation Dosimetry. EPA/600/8-90/066F. Office of Health and Environmental Assessment, Washington, D.C. USEPA (U.S. Environmental Protection Agency). (2000). Integrated Risk Information Service (IRIS), vinyl chloride. Cincinnati, OH. USEPA (U.S. Environmental Protection Agency). (2005). Guidelines for Carcinogen Risk Assessment. EPA/630/P-03/001F. Risk Assessment Forum, U.S. Environmental Protection Agency. [online]. Available: http://www.epa.gov/iris/cancer032505.pdf.
32
Part I
Use of Biokinetic Modeling and Genomics Information to Inform a Dose-Response Assessment for Arsenic
33
34
Chapter 2
Physiologically Based Pharmacokinetic Modeling of Arsenic in the Mouse P. Robinan Gentry, Tammie R. Covington, Sabine Mann, Annette M. Shipp, Janice W. Yager and Harvey J. Clewell, III
ENVIRON International Corporation, Ruston, LA, USA
Journal of Environmental Toxicology and Environmental Health, Part A, 2004, 67:43-71. 35
Abstract A remarkable feature of the carcinogenicity of inorganic arsenic is that while human exposures to high concentrations of inorganic arsenic in drinking water are associated with increases in skin, lung, and bladder cancer, inorganic arsenic has not typically caused tumors in standard laboratory animal test protocols. Inorganic arsenic administered for periods of up to 2 years to various strains of laboratory mice, including the Swiss CD-1, Swiss CR:NIH(S), C57Bl/6p53(+/-), and C57Bl/6p53(+/+) has not resulted in significant increases in tumor incidence. However, Ng et al. (1999) have reported a 40% tumor incidence in C57Bl/6J mice exposed to arsenic in their drinking water throughout their lifetime, with no tumors reported in controls. In order to investigate the potential role of tissue dosimetry in differential susceptibility to arsenic carcinogenicity, a physiologically based pharmacokinetic (PBPK) model for inorganic arsenic in the rat, hamster, monkey, and human (Mann et al. 1996a, b) was extended to describe the kinetics in the mouse. The PBPK model was parameterized in the mouse using published data from acute exposures of B6C3F1 mice to arsenate, arsenite, monomethylarsonic acid (MMA) and dimethylarsinic acid (DMA). and validated using data from acute exposures of C57Black mice. Predictions of the acute model were then compared with data from chronic exposures. There was no evidence of changes in the apparent volume of distribution or in the tissue-plasma concentration ratios between acute and chronic exposure that might support the possibility of inducible arsenite efflux. The PBPK model was also used to project tissue dosimetry in the C57Bl/6J study, in comparison with tissue levels in studies having shorter duration but higher arsenic treatment concentrations. The model evaluation indicates that pharmacokinetic factors do not provide an explanation for the difference in outcomes across the various mouse bioassays. Other possible explanations may relate to strain-specific differences, or to the different durations of dosing in each of the mouse studies, given the evidence that inorganic arsenic is likely to be active in the later stages of the carcinogenic process. Introduction Exposure of human populations to high concentrations of inorganic arsenic in drinking water has been associated with a variety of adverse health effects, including both cancer and non-cancer endpoints (Clewell et al. 1998; Tsai et al. 1998; Golub et al. 1998; Bernstam and Nriagu 2000). In addition, occupational exposure to arsenic-laden dust has been associated with an increased incidence of lung cancer (Lubin et al. 2000; Chen and Lin 1994). An unusual 36
feature of the toxicity of arsenic is that although human exposure has repeatedly been associated with increased incidence of cancer, inorganic arsenic has generally not been observed to cause tumors in standard laboratory animal test protocols. Available pharmacokinetic data demonstrate significant species differences in the methylation of inorganic arsenic, which until recently has generally been thought of as the major detoxification pathway (Mann et al. 1996a; Hsueh et al. 1998). Gender-related differences in methylation have also been reported in humans, with results in women indicating a higher capacity for methylation of MMA (Hsueh et al. 1998). It has been suggested that these species differences in metabolism may be related to the observed differences in susceptibility to arsenic carcinogenicity. In order to investigate this possibility, a physiologically based pharmacokinetic (PBPK) model was developed for inorganic arsenic in the rabbit and hamster (Mann et al. 1996a). These species were selected because of their similarity to humans with regard to the methylation of arsenic. This model was then extended to provide a description of the kinetics of arsenic metabolites in humans following oral or inhalation exposure to inorganic arsenic (Mann et al. 1996b). Unfortunately, while the rabbit and hamster appear to be the most pharmacokinetically similar species to humans, based on urinary excretion profiles, cancer bioassays have typically been performed in the rodent. In particular, several bioassays have been conducted in which inorganic arsenic was administered in the drinking water to various strains of laboratory mice (Table 1), including the Swiss CD-1, Swiss CR:NIH(S), C57Bl/6J, C57Bl/6p53(+/-), and C57Bl/6p53(+/+) strains (Kanisawa and Schroeder 1967; Mass 1998; Ng et al. 1999; Moser et al. 2000; Waalkes et al. 2000). No significant increases in tumor incidence were reported in studies conducted by Kanisawa and Schroeder (1967), Mass (1998), Moser et al. (2000), or Waalkes et al. (2000). However, Ng et al. (1999) reported a 40% tumor incidence in arsenic-treated C57Bl/6J mice, with no tumors reported in controls. Using the available pharmacokinetic data, an initial mouse PBPK model was developed based on the Mann et al. (1996a) model. The Mann et al. (1996a) rabbit and hamster model was extended to describe the kinetics of inorganic arsenic and its methylated metabolites in the mouse. The purpose of this model was to investigate, based on the available strain-specific data, the potential role of 37
metabolism and tissue dosimetry in the disparate results observed in the cancer bioassays for various strains of mice. Methods The PBPK model (Figure 1) used for this work is based on the published model for the rabbit and hamster (Mann et al. 1996a). Physiological parameters for the mouse were obtained from the literature (Brown et al. 1997). For several of the parameters in the model, including permeability coefficients, percentage pore area, urinary filtration ratio, and urinary secretion ratio, values previously used for the hamster were assumed to be applicable to the mouse (Table 2). Tissue volumes and blood flows were determined based on body weight using the same methods as those used for the hamster in Mann et al. (1996a). The urinary volume of excretion was scaled to the mouse based on a value of 3.5 mL/day reported for a 350 gram rat (Baker et al. 1979). The fecal excretion rate was calculated from the gut lumen volume and gut lumen transit time reported for the mouse (King et al. 1983). All other parameters in the mouse model were fit using kinetic data from B6C3F1 mice following acute dosing with DMA, MMA, arsenite, and arsenate (Hughes et al. 1994, 1999; Kenyon et al. 1997; Hughes and Kenyon 1998). Due to the large number of parameters and data sets involved, the identification of these parameters was accomplished through a stepwise process, beginning with the final metabolite, DMA, and working backwards through the metabolic pathway (Figure 1b). At each point, the parameters previously estimated were initially held fixed, and only the new parameters were varied. In this way there was sufficient information content in the data used at each step to separately identify each of the parameters varied at that step. After this initial parameterization, only small re-adjustments of parameters were necessary to provide the best simultaneous fit to all of the kinetic data sets. The first step was to adjust the relevant parameters for DMA in order to simulate percent of the administered dose of 14C-DMA excreted in the urine of female B6C3F1 mice following single IV doses of 8.04 or 804 µmoles DMA per kg BW (Hughes and Kenyon 1998). In this study, urine was collected at 1, 2, 4, 8, 12, and 24 hours following administration. Feces were also collected at 24 hours post administration. Animals were sacrificed 24 hours following exposure and tissues removed and analyzed for radioactivity. Partition coefficients for DMA in the model were adjusted from the hamster values until the model 38
C57Bl/6 p53 (+/-) C57Bl/6 p53 (+/-); p53 (+/+) C57Bl/6 p53 (+/-); p53 (+/+) Swiss CR:NIH(S) C57Bl/6J
Swiss CD-1
Strain
1.24
Sodium arsenate
27b
331a 0.0202 (i.v.) 0.07
7.7b
66a
NA
Sodium arsenate
10 males
2.9
0.38
Dose (mg As/kg/d)
50
8.66
Water Conc. (mg/L)
Sodium arsenate
Sodium arsenite
10 males
25 males 25 females 90 females
Sodium arsenite
Sodium arsenic and inorganic As
Test Substance
25 males
# animals/ arsenic dose group 108 total (males and females)
112
20c
51
51
26
132
Dose Duration (weeks)
Table 1: Summary of Arsenic Mouse Studies1
+
-
-
-
-
-
Results (+ or -)
Waalkes et al. (2000) Ng et al. (1999)
Mass (1998)
Mass (1998)
Moser et al. (2000)
Kanisawa and Schroeder (1967)
Reference
- No statistically significant increase in tumor incidence. + Author reports statistically significant increase in tumor incidence. 1 Arsenic was administered in drinking water in all studies except for the study conducted by Waalkes et al. (2000) where the material was administered via intravenous injection. a Values for compound concentrations in drinking water in this study (mg/L) are time weighted average b Drinking water arsenic concentrations were converted to doses in milligrams per kilograms per day (mg/kg/day). Doses were converted using an average drinking water consumption rate of 6 milliliters per day (mL/day) and an average body weight of 0.03 kg for the mouse. c Animals were given one injection per week for 20 weeks and necropsied at 96 weeks.
39
Intravenous injection (µg As/kg BW)
Intratracheal instillation (µg As/kg BW)
Oral dose (µg As/kg BW)
Naso-pharynx (NP)
Tracheo-bronchial (TB)
Pulmonary (P)
Plasma RBCs
Liver
GI Tract
Skin
Lungs
Other
Kidneys
Keratin
Urine
Feces
(a)
Drinking Water or Inhalation
kOx As(V)
VmMMA As(III)
kRed
VmDMA MMA
KmMMA
Urine
KmDMA
DMA
(b)
Figure 1: PBPK Model for Arsenic (this diagram is repeated four times, one for each arsenic species in Figure 1b) (Reproduced from Mann et al. (1996a), with permission)
40
Table 2: Parameters Used in the Mouse Model Parameter Body weight (kg) Blood to plasma ratio Tissue Partition Coefficients for AsV Kidney
Value
Reference
BW
0.03
Study specific
BP
0.696
Mann et al., 1996a
PK5
100.0
Estimated
Liver
PL5
0.5
Estimated
Lungs
PLu5
0.5
Estimated
Others
PO5
1.25
Estimated
PS5
0.0167
Estimated
Skin Tissue Partition Coefficients for AsIII Kidney
PK3
50.0
Estimated
Liver
PL3
100.0
Estimated
Lungs
PLu3
0.5
Estimated
Others Skin Tissue Partition Coefficients for MMA Kidney
PO3
5.0
Estimated
PS3
1.0
Estimated
PKM
0.001
Estimated
Liver
PLM
1.0
Estimated
Lungs
PLuM
0.01
Estimated
Others
POM
0.01
Estimated
PSM
0.01
Estimated
Skin Tissue Partition Coefficients for DMA Kidney
PKD
0.001
Estimated
Liver
PLD
0.1
Estimated
Lungs
PLuD
0.01
Estimated
Others
POD
0.01
Estimated
PSD
0.01
Estimated
PPR5
0.2
Mann et al., 1996a
AsIII
PPR3
1.5
Mann et al., 1996a
MMA
PPRM
0.2
Mann et al., 1996a
PPRD
0.2
Mann et al., 1996a
PermK5
21.0
Mann et al., 1996a
Skin Plasma-RBCs Partition Coefficients AsV
DMA Permeability Coefficients for AsV (cm/hr) Kidney Liver
PermL5
20.0
Mann et al., 1996a
Lungs
PermLu5
23.0
Mann et al., 1996a
Others
PermO5
23.0
Mann et al., 1996a
Skin
PermS5
23.0
Mann et al., 1996a
41
Parameter Permeability Coefficients for AsIII (cm/hr) Kidney
Value PermK3
Reference
8.0
Mann et al., 1996a
Liver
PermL3
8.0
Mann et al., 1996a
Lungs
PermLu3
17.0
Mann et al., 1996a
Others Skin Permeability Coefficients for MMA (cm/hr) Kidney
PermO3
17.0
Mann et al., 1996a
PermS3
17.0
Mann et al., 1996a
PermKM
12.0
Mann et al., 1996a
Liver
PermLM
11.0
Mann et al., 1996a
Lungs
PermLuM
9.0
Mann et al., 1996a
Others
PermOM
9.0
Mann et al., 1996a
PermSM
9.0
Mann et al., 1996a
PermKD
12.0
Mann et al., 1996a
Liver
PermLD
11.0
Mann et al., 1996a
Lungs
PermLuD
9.0
Mann et al., 1996a
Skin Permeability Coefficients for DMA (cm/hr) Kidney
Others Skin Percentage of Pore Area (%) Kidney
PermOD
9.0
Mann et al., 1996a
PermSD
9.0
Mann et al., 1996a
PAK
0.06
Mann et al., 1996a
Liver
PAL
0.02
Mann et al., 1996a
Lungs
PALu
0.1
Mann et al., 1996a
Others
PAO
0.1
Mann et al., 1996a
PAS
0.1
Mann et al., 1996a
Ratio
0.75
Mann et al., 1996a
SR
0.2
Mann et al., 1996a
LitreC
42.3
Scaled from rat value
Skin Urinary Excretion Filtration ratio for AsV Secretion ratio for AsIII Urinary volume excretion (mL/day/kg¾) GI Tract-Plasma Absorption Rates (mL/hr) AsV
KAP5
5.0
Estimated
AsIII
KAP3
5.0
Estimated
MMA
KAPM
0.2
Estimated
DMA
KAPD
0.3
Estimated
Lung-Plasma Absorption Rates (/hr/cm2) AsV AsIII Biliary Excretion Rates (mL/hr) AsV
42
BP5
1.0E-7
Mann et al., 1996a
BP3
0.01
Mann et al., 1996a
KBB5
3.0
Estimated
AsIII
KBB3
3.0
Estimated
MMA
KBBM
3.0
Estimated
DMA
KBBD
1.0
Estimated
Parameter Lung Mechanical Clearance Rates (/hr) Naso-pharyngeal to GI tract
Value
Reference
GNP
0.07083
Mann et al., 1996a
GTB
0.14583
Mann et al., 1996a
KKer
2.0
Mann et al., 1996a
Reduc
10.0
Estimated
Oxidation rate of AsIII to AsV
Oxi
40.0
Estimated
Kidney reduction rate
KReduc
1.0
Estimated
0.5
Estimated
700.0
Estimated
50.0
Estimated
600.0
Estimated
1.0
Estimated
100000.0
Estimated
10000.0
Estimated
2.0
Estimated
0.333
Estimated
Tracheo-bronchial to GI tract Binding Rate (mL/hr) Keratine AsIII binding rate Oxido-Reduction Rates (mL/hr) Reduction rate of AsV to AsIII
Urine reduction rate UReduc Metabolism Parameters Maximum rate of metabolism of AsIII to VMMMA MMA (nmole/mL/hr) Affinity constant for metabolism of KMMMA AsIII to MMA (nmole/mL) Maximum rate of metabolism of VMDMA MMA to DMA (nmole/mL/hr) Affinity constant for metabolism of KMDMA MMA to DMA (nmole/mL) Affinity constant for uncompetitive inhibition between MMA and DMA KMI (nmole/mL) Co-Locality Parameters for Methylation of AsIII and MMA (mL/hr) Transfer between liver compartments for ClMA3 AsIII Transfer between liver compartments for ClMMMA MMA Fraction of liver containing enzymes Frac Clearance Rates (mL/hr) Clearance of AsV and AsIII GFR
1.0
Estimated
Clearance of MMA
GFRMMA
1.0
Estimated
Clearance of DMA
GFRDMA
1.25
Estimated
Fecal excretion rate
KF
0.2
King et al., 1983
reproduced the experimental values in the corresponding mouse tissue compartment. Absorption rate, biliary excretion rate, and urinary clearance were adjusted to provide an acceptable fit of the model to the urinary and fecal data. The next step was to hold the DMA parameters fixed and adjust the parameters for MMA based on urinary excretion data on MMA and DMA following IV administration of MMA in the mouse (Hughes and Kenyon 1998). In this study, female B6C3F1 mice were administered a single IV injection of either 4.84 or 484 µmoles of MMA per kg BW. As with DMA, tissue partition coefficients, absorption rate, biliary excretion rate, and urinary clearance of MMA 43
were adjusted from the hamster values to reproduce the reported data. At the same time, the metabolism parameters describing the methylation of MMA to DMA were also adjusted to reproduce the data on the production of DMA. Since this study provided additional kinetic data on DMA clearance, minor readjustments to the DMA parameters were then made as needed to obtain the best simultaneous fit to both sets of data (DMA administration and MMA administration). The parameters for arsenite and arsenate were then fit using urinary data in B6C3F1 mice following administration of a single oral dose of either chemical (Kenyon et al. 1997; Hughes et al. 1999). In the study reported by Hughes et al. (1999), female B6C3F1 mice received a single oral dose of 0.5 or 5.0 mg per kg BW of radiolabelled arsenic as either arsenate or arsenite. Disposition of the radiolabelled arsenic was assessed by whole-body counting, and analysis of urine, feces, and tissues 24 hours after dosing. The Kenyon et al. (1997) study gave B6C3F1 mice a single oral dose of 5 mg of arsenic as arsenate per kg BW. Hamster parameter values from Mann et al. (1996a) were adjusted to obtain the best fit to the data. Since data on MMA and DMA were also available in these studies, the parameters for MMA and DMA were also fine-tuned as needed to obtain the best fit to all of the data available. Because the equations for each chemical species in the model were interconnected, adjustments in parameters for one chemical usually required some adjustments to parameters for the other chemicals; therefore, the process of fitting all of the parameters required several iterations. In comparing the kinetics of MMA and DMA following administration of MMA and administration of inorganic arsenic, it was observed that one set of kinetic parameters would not adequately describe the formation of DMA from MMA under both situations. Data presented by Zakharyan et al. (1995) indicate that although arsenite methyltransferase and MMA methyltransferase have different substrates, the two enzyme activities co-elute and there is no evidence to suggest that they are on different protein molecules. Co-locality of the two methyltransferases would imply that, following administration of arsenite, MMA produced by the methyltransferase might be more readily metabolized to DMA, because of its proximity to the second enzyme site. Moreover, in vitro studies of hepatocytes (Easterling et al. 2002) found that the rate constant for transport of MMA was slow compared to those for other arsenic species. A limitation in cellular uptake/efflux would also result in a greater availability of MMA generated in situ as compared to exogenously administered MMA. Therefore, a 44
simple description of “co-locality” was added to the model. This description is not meant to suggest a specific mechanism for the uptake and metabolism of MMA to DMA, but rather represents an attempt to describe the greater apparent availability for metabolism of MMA produced in the vicinity of the enzyme as compared to MMA introduced extracellularly. To represent the effect of “co-locality”, the description of the liver was divided into two compartments (Figure 2), one representing a region of the liver in the vicinity of the metabolizing enzyme and the second representing the rest of the liver. Transport parameters for both arsenite and MMA were then estimated based on the differential production of DMA from MMA following the administration of MMA or arsenite. Inhibition by arsenite of the formation of DMA from MMA was also included in the description of metabolism, based on an analysis provided by Kenyon et al. (2001) and Easterling et al. (2002). In this study, a preliminary mechanistically-based liver cell model was developed. The intent of the model was to describe methylation and protein binding of arsenite, MMA and DMA at the subcellular level in the cytosol. As part of the model development, inhibition of the metabolism of MMA by arsenite was described. Three different types of inhibition (competitive, uncompetitive, and noncompetitive) were incorporated into the model and evaluated to determine the mechanism most consistent with the available data. The description of uncompetitive inhibition was the only inhibition mechanism that was consistent with the time-course data and increasing lag in the detection of DMA. Therefore, a description of uncompetitive inhibition of MMA methylation by arsenite was added to the mouse model:
dALA3M ⎛ VMMMA • ALA3M ⎞ = [ClMA3 • (CVLA3NM − CVLA3M )] − ⎜ ⎟ dt ⎝ KMMMA + CLA3M ⎠ ⎛ ⎜ dALMMAM VMDMA • ALMMAM ⎛ VMMMA • ALA3M ⎞ ⎜ = [ClMMMA • (CVLMMANM − CVLMMAM )] + ⎜ ⎟−⎜ dt ⎛ ⎝ KMMMA + CLA3M ⎠ ⎜ ⎛ CVLA3M ⎜ KMDMA + ⎜⎜ CLMMAM • ⎜⎝1 + KMI ⎝ ⎝
where: ALA3M = Amount of arsenite in metabolizing liver (nmoles) ALMMAM = Amount of MMA in metabolizing liver (nmoles) ClMA3 = Transport parameter for arsenite (mL/hr) 45
⎞ ⎟ ⎟ ⎟ ⎞⎞ ⎟ ⎟ ⎟⎟ ⎟ ⎠⎠ ⎠
ClMMMA CLA3M CLMMAM CVLA3M
= Transport parameter for MMA (mL/hr) = Concentration of arsenite in metabolizing liver (nmole/mL) = Concentration of MMA in metabolizing liver (nmole/mL) = Venous blood concentration of arsenite in the metabolizing liver (nmoles/mL) CVLA3NM = Venous blood concentration of arsenite in the non-metabolizing liver (nmoles/mL) CVLMMAM = Venous blood concentration of MMA in the metabolizing liver (nmoles/mL) CVLMMANM= Venous blood concentration of MMA in the non-metabolizing liver (nmoles/mL) KMI = Inhibition constant for uncompetitive inhibition of metabolism of MMA to DMA (nmole/mL) KMDMA = Affinity constant for metabolism of MMA to DMA (nmole/mL) KMMMA = Affinity constant for metabolism of arsenite to MMA (nmole/mL) VMDMA = Michaelis-Menten rate constant for metabolism of MMA to DMA (nmole/mL/hr) VMMMA = Michaelis-Menten rate constant for metabolism of arsenite to MMA (nmole/mL/hr)
Non-metabolizing Region of Liver
liver blood flow
ClMMMA
Metabolizing Region of Liver
liver blood flow
KBBM
Figure 2. Description of Co-locality in the Liver for Metabolism of MMA to DMA
46
Once adequate simulation of the available data in the B6C3F1 mouse was completed, the model was validated using data reported by Hughes et al. (1999) for C57BL/6N mice. As with the B6C3F1 mice, these mice were given a single oral dose of 0.5 or 5.0 mg of arsenic as either arsenate or arsenite per kg BW. The parameter estimation and validation of the mouse PBPK model relied upon the results of experiments in mice following acute (single) exposures to arsenic. In vitro experiments conducted in Chinese hamster V79 cells suggest significant changes in cellular efflux (partitioning) of arsenite following acute versus chronic exposure (Wang et al. 1996). Therefore, an additional simulation of chronic repeated exposure was conducted with the validated model to evaluate the evidence for changes in the apparent volume of distribution or in the tissueplasma concentration ratios between acute and chronic exposure that would support the existence of an inducible arsenite efflux pump. Model predictions of total inorganic arsenic, arsenate, arsenite, MMA, and DMA following drinking water exposure to 0.005% sodium arsenite were compared to the results obtained by Moser et al. (2000). Moser et al. (2000) conducted a six-month carcinogenicity study in C57Bl/6 mice, in which the animals were administered 0.005% sodium arsenite in water. Urine was collected on the last day of treatment, with blood and liver tissue samples obtained at terminal sacrifice. These samples were flash frozen in liquid nitrogen and stored at approximately 70ºC. Speciation of the arsenic in urine, blood, and liver tissue was conducted in a separate analysis. Prior to analysis, the tissue samples were digested in hydrochloric acid or sodium hydroxide at 80ºC for 16 hours. An aliquot of the digestate was then placed in a reaction vessel with 6M hydrochloride acid. Four percent sodium borohydride (NaBH4) was then added to convert inorganic arsenic, MMA, and DMA to volatile arsines. The arsines were purged from the sample onto a cooled glass trap packed with 15% OV-3 on Chromasorb®. Levels of total inorganic arsenic, arsenite, MMA, and DMA in µg/ml were then quantified by hybrid generation coupled with atomic absorption spectrometry. Arsenate levels were calculated by subtracting levels of arsenite from total inorganic arsenic.
47
Results
Model Parameterization: Pharmacokinetics in the B6C3F1 Mouse – Acute Exposure Following a single oral exposure to DMA (8.04 or 804 µmole/kg), the majority of the administered dose was found in the feces or the carcass (Figure 3) (Hughes and Kenyon 1998). The data on percent dose excreted in the feces was 100
Total Radioactivity (% Dose)
484 µmole/kg MMA
Data Simulation
10
1
0.1
0.01
0.001 Feces
Liver
Kidneys
Lungs
Carcass
100
Total Radioactivity (% Dose)
4.84 µmole/kg MMA
Data Simulation
10
1
0.1
0.01
0.001 Feces
Liver
Kidneys
Lungs
Carcass
Figure 3. Total radioactivity in various tissues 24 hours after a single oral dose of DMA to female B6C3F1 mice. Data from Hughes and Kenyon (1998) (mean + 2SEM; N=3)
48
used to adjust biliary excretion. The tissue:blood partition coefficients for DMA were varied until the model results for total radioactivity in the liver, kidneys, lungs, and carcass were consistent with the reported radioactivity distributed to the tissues 24 hours after dosing. These partition coefficients should not be interpreted with the classical definition of a partition coefficient in terms of thermodynamic equilibrium. They are simply empirical values used to adjust for the relative concentrations in the tissue versus the blood in the model, and may reflect protein binding as well as active transport. The time course for the urinary excretion of DMA was used to estimate the parameter defined as glomerular filtration rate (GFR) in the mouse (Table 2), the only parameter impacting the urinary excretion of DMA in the model (Figure 4). Although this parameter is referred to as the GFR in the Mann et al. (1996) model, its value reflects total urinary clearance, including tubular secretion and therefore is not necessarily within the range of typical GFR values reported in the literature.
100
DMA Excreted in Urine (% Dose)
80
60
40
20 8.04 µmole/kg DMA 804 µmole/kg DMA 0 0
5
10
15
20
25
Hours
Figure 4. Amount of DMA excreted in urine following a single oral dose of DMA to female B6C3F1 mice. Data from Hughes and Kenyon (1998) (mean ± 2SEM; N=9).
49
Once the DMA parameters were estimated, they were held fixed and the partition coefficients adjusted for MMA to simulate the total radioactivity measured in various tissues following administration of 4.84 or 484 µmole/kg MMA (Figure 5). The model was then able to reproduce the urinary excretion of both MMA and DMA (Figure 6). The GFR that provided the best fit to the urinary excretion of MMA (1.0 mL/hr) was only slightly lower than the GFR used for DMA (1.25 mL/hr). 100
Total Radioactivity (% Dose)
484 µmole/kg MMA
Data Simulation
10
1
0.1
0.01
0.001 Feces
Liver
Kidneys
Lungs
Carcass
100
Total Radioactivity (% Dose)
4.84 µmole/kg MMA
Data Simulation
10
1
0.1
0.01
0.001 Feces
Liver
Kidneys
Lungs
Carcass
Figure 5. Total radioactivity in various tissues 24 hours after a single oral dose of MMA to female B6C3F1 mice. Data from Hughes and Kenyon (1998) (mean + 2SEM; N=3).
50
MMA Excreted in Urine (% Dose)
100 80 60 40 20
4.84 µmole/kg MMA 484 µmole/kg MMA
0 0
5
10
15
20
25
15
20
25
Hours
DMA Excreted in Urine (% Dose)
12 4.84 µmole/kg MMA 484 µmole/kg MMA
10 8 6 4 2 0 0
5
10 Hours
Figure 6. Amount of MMA or DMA excreted in urine following a single oral dose of MMA to female B6C3F1 mice. Data from Hughes and Kenyon (1998) (mean ± 2SEM; N=9).
Similar data were available following administration of arsenite and arsenate (0.5 or 5 mg As/kg as arsenite or arsenate) in the study reported by Hughes et al. (1999). These data sets were used to identify the parameters related to the oxidation and reduction of arsenate and arsenite, as well as the methylation of arsenite. Following exposure to arsenite (AsIII), the model may somewhat overestimate the percent dose of arsenate (AsV) excreted in the urine (Figure 7), since no arsenate was detected in the study. However, use of the same parameters 51
results in a reasonable estimate of the percent dose excreted as arsenate in the urine following administration of arsenate (Figure 8). Simulations of the distribution of total radioactivity in the tissues 24 hours after dosing were also successfully conducted (Figures 9 and 10).
Percent Dose Excreted in Urine
100
10
5.0 mg As/kg as [73As]arsenite Data Simulation
1
0.1
0.01
**
0.001 AsV
Percent Dose Excreted in Urine
100
10
AsIII
MMA
DMA
0.5 mg As/kg as [73As]arsenite Data Simulation
1
0.1
0.01
**
0.001 AsV
AsIII
MMA
DMA
Figure 7. Amount excreted in urine 24 hours after a single oral dose of [73As]arsenite to female B6C3F1 mice. Data from Hughes et al. (1999) (mean + 2SEM; N=5). ** Detection Limit
52
Model Validation: Pharmacokinetics in the C57BL/6N Mouse – Acute Exposure The model, as parameterized using the data obtained from B6C3F1 mice, was used to predict the kinetics of arsenic in C57BL/6N mice. Figure 11 compares the model-predicted values of the whole body clearance of arsenate to the measured values in the both the B6C3F1 and C57BL/6N mouse (Hughes et al. 1999) following a single oral dose of arsenate. The model adequately predicted the kinetics of arsenate in the two strains of mice reported by Hughes et al. (1999), but did not predict the same difference in kinetics as that observed
Percent Dose Excreted in Urine
100
5.0 mg As/kg as [73As]arsenate Data Simulation
10
1
0.1
0.01
0.001 AsV
Percent Dose Excreted in Urine
100
AsIII
MMA
DMA
0.5 mg As/kg as [73As]arsenate Data Simulation
10
1
0.1
0.01
0.001 AsV
AsIII
MMA
DMA
Figure 8. Amount excreted in urine 24 hours after a single oral dose of [73As]arsenate to female B6C3F1 mice. Data from Hughes et al. (1999) (mean + 2SEM; N=4 for low dose; N=5 for high dose).
53
100
73
Total Radioactivity (% Dose)
5.0 mg As/kg as [ As]arsenite 10
Data Simulation
1
0.1
0.01
0.001 Urine
Feces
Liver
Kidneys
100
Lungs
Skin
73
Total Radioactivity (% Dose)
0.5 mg As/kg as [ As]arsenite 10
Carcass
Blood (%dose/g)
Data Simulation
1
0.1
0.01
0.001 Urine
Feces
Liver
Kidneys
Lungs
Skin
Carcass
Blood (%dose/g)
Figure 9. Total radioactivity in urine, feces, and various tissues 24 hours after a single oral dose of [73As]arsenite to female B6C3F1 mice. Data from Hughes et al. (1999) (mean + 2SEM; N=5).
following the two administered doses. This failure may be attributed to the lack of consideration of methylation of AsIII or AsV in the mouse intestine. In an in vitro study conducted by Hall et al. (1997), up to 40% methylation of AsIII or AsV was reported following incubation with mouse intestinal cecal microflora at concentrations of 0.1 µM of either substrate. Over the concentration range of 0.1 to 10 µM, there was a linear increase in the production of MMA and DMA, with evidence of saturation or inhibition of methylation at 100 µM of either substrate. 54
This may explain the dose-related differences in the data collected by Hughes et al. (1999). 100
73
Total Radioactivity (% Dose)
5.0 mg As/kg as [ As]arsenate
Data Simulation
10
1
0.1
0.01
0.001 Urine
Feces
Liver
Kidneys
100
Lungs
Skin
73
0.5 mg As/kg as [ As]arsenate
Carcass
Blood (%dose/g)
Data
Total Radioactivity (% Dose)
Simulation 10
1
0.1
0.01
0.001 Urine
Feces
Liver
Kidneys
Lungs
Skin
Carcass
Blood (%dose/g)
Figure 10. Total radioactivity in urine, feces, and various tissues 24 hours after a single oral dose of [73As]arsenate to female B6C3F1 mice. Data from Hughes et al. (1999) (mean + 2SEM; N=4 for urinary and fecal data; N=5 for remainder of data).
The ability of the model to predict the kinetics in the both the B6C3F1 and C57BL/6N mouse using the same metabolic parameters in the model reflects a lack of strain differences in the metabolism or disposition of arsenic in these two strains of mice. The parameters estimated from the B6C3F1 data for simulations 55
also provided reasonably accurate predictions of the distribution of total radioactivity (Figure 12) and percent dose excreted in the urine (Figure 13) 24 hours after dosing in the C57BL/6N mouse as reported by Hughes et al. (1999). Similar results were also observed following a single oral dose of arsenite (Figures 14, 15 and 16).
Whole-body Clearance (% Dose)
100 0.5 mg As/kg as arsenate 80
5.0 mg As/kg as arsenate
60 B6C3F1mice
40 20 0 0
5
10
15
20
25
Hours
Whole-body Clearance (% Dose)
100 0.5 mg As/kg as arsenate 5.0 mg As/kg as arsenate
80 60
C57BL/6N mice
40 20 0 0
5
10
15
20
25
Hours
Figure 11. Whole-body clearance following a single oral dose of [73As]arsenate to either female B6C3F1 or female C57BL/6N mice. Data from Hughes et al. (1999) (mean ± 2SEM; N=5).
56
Total Radioactivity (% Dose)
100
5.0 mg As/kg as [73As]arsenate
Data Simulation
10
1
0.1
0.01
0.001 Urine
Feces
Total Radioactivity (% Dose)
100
Liver
Kidneys
Lungs
Skin
0.5 mg As/kg as [73As]arsenate
Carcass
Blood (%dose/g)
Data Simulation
10
1
0.1
0.01
0.001 Urine
Feces
Liver
Kidneys
Lungs
Skin
Carcass
Blood (%dose/g)
Figure 12. Total radioactivity in urine, feces, and various tissues 24 hours after a single oral dose of [73As]arsenate to female C57BL/6N mice. Data from Hughes et al. (1999) (mean + 2SEM; N=5).
Pharmacokinetics in the C57BL/6N Mouse – Chronic Exposure Predictions of the acute model were compared with the measured concentrations of arsenic species in blood, liver, and urine in the C57BL/6N mouse following chronic exposure to arsenite in drinking water (Table 3). The average 24-hour urine concentrations predicted by the acute model compare very well with the experimental data, as shown on the right side of Figure 17. 57
Percent Dose Excreted in Urine
100
5.0 mg As/kg as [73As]arsenate Data
10
Simulation 1
0.1
0.01
0.001 AsV
Percent Dose Excreted in Urine
100
AsIII
MMA
DMA
0.5 mg As/kg as [73As]arsenate Data
10
Simulation 1
0.1
0.01
0.001 AsV
AsIII
MMA
DMA
Figure 13. Amount excreted in urine 24 hours after a single oral dose of [73As]arsenate to female C57BL/6N mice. Data from Hughes et al. (1999) (mean + 2SEM; N=5).
Comparison of the model predictions with terminal data obtained for blood and liver concentrations is slightly more complicated. Due to the fact that the dosing is via drinking water ingestion, the concentrations in the blood and liver would, of course, be expected to vary over time during the day, depending on when a particular animal ingests water. In the model, drinking water ingestion 58
is simulated with a simple diurnal pattern, but the actual ingestion pattern of a particular animal is uncertain. To accommodate this uncertainty, the highest and lowest concentrations predicted by the model for the last day of the exposure are represented by the lightly shaded area in the second of each pair of bars. This lightly shaded range can be compared with the top of the left bar, which represents the experimentally measured concentration. In general, the mouse model, using parameters based on the pharmacokinetics of arsenic following Whole-body Clearance (% Dose)
100 0.5 mg As/kg as arsenite 80
5.0 mg As/kg as arsenite
60 B6C3F1 mice
40 20 0 0
5
10
15
20
25
Hours
Whole-body Clearance (% Dose)
100 0.5 mg As/kg as arsenite 5.0 mg As/kg as arsenite
80 60
C57BL/6N mice
40 20 0 0
5
10
15
20
25
Hours
Figure 14. Whole-body clearance following a single oral dose of [73As]arsenite to either female B6C3F1 or female C57BL/6N mice. Data from Hughes et al. (1999) (mean ± 2SEM; N=5 for B6C3F1 mice; N=3 for C57BL/6N mice).
59
acute exposure, adequately describes the pharmacokinetics of arsenic following this chronic exposure (Figure 17). However, the slightly higher predictions in the blood and lower predictions of arsenic species in the liver by the model could be suggestive of induction of arsenic efflux. However, additional blood and tissue time course data at early time points following both acute and chronic exposure as needed to evaluate the possibility of induction of arsenic efflux.
Total Radioactivity (% Dose)
100
5.0 mg As/kg as [73As]arsenite
Data Simulation
10
1
0.1
0.01
0.001 Urine
Feces
Total Radioactivity (% Dose)
100
Liver
Kidneys
Lungs
Skin
0.5 mg As/kg as [73As]arsenite
Carcass
Blood (%dose/g)
Data Simulation
10
1
0.1
0.01
0.001 Urine
Feces
Liver
Kidneys
Lungs
Skin
Carcass
Blood (%dose/g)
Figure 15. Total radioactivity in urine, feces, and various tissues 24 hours after a single oral dose of [73As]arsenite to female C57BL/6N mice. Data from Hughes et al. (1999) (mean + 2SEM; N=5 for low dose; N=5 for high dose for urinary and fecal data and N=3 for remainder of high dose data).
60
Percent Dose Excreted in Urine
100
10
73
5.0 mg As/kg as [ As]arsenite Data Simulation
1
0.1
0.01
**
0.001 AsV
Percent Dose Excreted in Urine
100
10
AsIII
MMA
DMA
73
0.5 mg As/kg as [ As]arsenite Data Simulation
1
0.1
0.01
**
0.001 AsV
AsIII
MMA
DMA
Figure 16. Amount excreted in urine 24 hours after a single oral dose of [73As]arsenite to female C57BL/6N mice. Data from Hughes et al. (1999) (mean + 2SEM; N=5).
Discussion
The mouse model presented here adequately describes both the acute and chronic pharmacokinetics of arsenic in two strains of mice and provides an initial PBPK model for arsenic in the mouse. This effort also highlighted the limited strain-specific pharmacokinetic information in the mouse. The collection of 61
Table 3. Levels of Arsenic Species Measured in the Blood, Liver and Urine of Mice Exposed to 0.005% Sodium Arsenite in the Drinking Water for 26 Weeks
Concentration in Blood (µg/g wet weight) Total Inorganic As+3 As+5 Total MMA As 0.0303 0.0206 0.0097 0.000847 * 0.0406 0.0255 0.0152 0.0013 * 0.0608 0.0410 0.0198 0.0012 * 0.0483 0.0242 0.0241 0.0014 * 0.0528 0.0333 0.0195 0.0013 * 0.0499 0.0179 0.0321 0.00117 * 0.0468 0.0193 0.0275 0.00117 * Concentration in Liver (µg/g wet weight) Total Inorganic As+3 As+5 Total MMA As 0.0211 0.0176 0.00353 0.000372 * 0.0201 0.0149 0.00521 0.000730 * 0.0127 0.00613 0.00658 0.000524 * 0.139 0.121 0.0178 0.00583 0.0255 0.0202 0.00530 0.000513 * 0.0407 0.0346 0.00612 0.000439 * 0.0360 0.0299 0.00610 0.000575 * Total Inorganic As 60.7 * 120 * 262 * 171 * 61.7 * 60.5
Concentration in Urine (µg/L) As+3 As+5 Total MMA 29.1 64.7 18.4 20.0 10.3 19.4
60.7 * 120 * 262 * 171 * 61.7 * 41.0
140 186 473 444 131 184
Total DMA 0.0204 0.0177 0.0524 0.00164 * 0.113 0.0142 0.0137 Total DMA 0.0765 0.0472 0.0533 0.320 0.0325 0.258 0.0847 Total DMA 5469 12948 36017 22649 12177 21045
* Amounts in samples were below the detection limit, so the value presented is the detection limit (µ) divided by the sample size (g).
62
100,000.
10,000.
Blood
Liver
Urine
Concentration (µg/mL)
1,000.
100. 10.
1. 0.1
0.01 0.001
In or ga ni c As V As II M I M A D M A In or ga ni c As V As II M I M A D M A In or ga ni c As V As II M I M A D M A
0.0001
Urinary Data Mean ± 2 SEM
Simulation (Average) Simulation (Range)
Figure 17. Arsenic in blood, liver or urine following chronic exposure of C57BL/6N mice to 2.9 mg As/kg/day in drinking water (mean ± 2SEM; N=7 for blood inorganic As, As(V), and As(III); N=6 for blood DMA; N=7 for all liver except MMA where N=1; N=1 for urine for inorganic As and As(V); N=6 for urine for As(III), MMA, and DMA).
63
additional strain-specific information should be possible based on the newer analytical techniques discussed previously. In addition, before a carcinogenic risk assessment could be conducted, additional quantitative information is needed to address recent issues associated with the selection of the relevant dose metric for arsenic based on the potential mode of action. These include the estimation of the relative concentrations of trivalent versus pentavalent methylated arsenicals, metabolism in target tissues other than the liver, and tissue-specific disposition. Until recently, the methylation of arsenic was viewed as a detoxification pathway, with the likely causal agent of arsenic-induced carcinogenicity being arsenite. However, results of recent studies have expanded this view to consider the potential contribution of the other trivalent species (i.e., trivalent MMA and DMA). The mouse PBPK model described here only considers the pentavalent MMA and DMA. Recently, the technology has been developed to enable the detection of different valance states of MMA and DMA (DelRazo et al. 2000; Le et al. 2000; Sampayo-Reyes et al. 2000); therefore, information on tissue concentrations of the trivalent chemical forms is now becoming available. Results of studies conducted in vitro with human epidermal keratinocytes indicate relative toxicities of arsenite>trivalent MMA>trivalent DMA>pentavalent DMA>pentavalent MMA>arsenate (Vega et al. 2001). The trivalent arsenicals also induce an increase in cell proliferation and stimulate the secretion of growthpromoting cytokines and tumor necrosis factor-alpha at low concentrations (0.001 to 0.01 µM), while at higher concentrations (>0.5 µM) cell proliferation is inhibited. Results from epidemiological studies also provide evidence that trivalent MMA may be a potential contributor to arsenic-induced carcinogenicity (Bernstam and Nriagu 2000). Individuals with a higher percentage of MMA, compared to matched controls, had a higher odds ratio of developing skin cancer than individuals having a lower percentage (Yu et al. 2000). The fact that humans excrete more MMA than any other species, may be a factor in their apparently higher sensitivity to arsenic-induced carcinogenesis (Kitchin 2001). Trivalent DMA has also been suggested as a potential contributor to arsenic carcinogenesis, based on results of carcinogenicity studies with DMA in laboratory animals and its potential to directly interact with DNA far more easily that arsenite (Kitchin 2001). However, additional experimentation is needed to quantify tissue 64
concentrations of trivalent methylated arsenicals in vivo before these species could be included in the PBPK model. The mouse PBPK model described here also does not consider methylation or MMA reduction in tissues other than the liver. In a study conducted by Healy et al. (1998), arsenite methyltransferase activities were measured in the liver, testis, kidney, and lung of mice. The order of specific activity was testes>kidney>liver>lung, with the specific activity of the testes 3.6 times greater than that of the liver. A recent human model by Yu (1999) considered extrahepatic metabolism, including methylation of arsenite in both the liver and the kidney, as well as reduction of arsenate by GSH in every perfused tissue included in the model (large intestine, skin, fat, muscle, kidney, liver, and lung). The mouse model includes a description of uncompetitive inhibition for the metabolic pathway of MMA to DMA, based on in vitro work conducted by Kenyon et al. (2001). However, in fitting the in vivo data in the mouse (Hughes et al. 1999), affinity constant for this pathway had to be set to a very large value (KMI=100000) to achieve an acceptable fit. This would indicate that at the exposure concentrations administered in the in vivo studies, uncompetitive inhibition may not be a critical factor in the metabolism of MMA to DMA. Since the same model parameters described the disposition of arsenic species following both acute and chronic exposure, these results provide no evidence of the induction of methylation between acute and chronic exposure to arsenite. This is consistent with the results reported by Healy et al. (1998) in which mice were given arsenate in drinking water (25 or 2500 µg As/L) for 32 or 91 days. Arsenite methyltransferase activities of the liver, testis, kidney and lung were not significantly increased in animals receiving either concentration of arsenic for 32 or 91 days, compared to controls. These preliminary results using the mouse PBPK model suggest that pharmacokinetic factors alone cannot explain the difference in outcomes across the various mouse bioassays (Table 1). Further possible explanations may relate to strain-specific differences, and the different durations of dosing employed in each of the mouse studies. In conclusion, a PBPK arsenic mouse model has been validated following acute oral exposure to arsenic. Comparison of the model-predicted urinary, blood and liver concentrations of multiple arsenic species following chronic exposure to that measured in a bioassay with C57BL/6N mice indicates that this model has the 65
capability to adequately predict tissue concentrations following chronic exposure to arsenic without adjustments to the pharmacokinetic parameters determined following acute exposure. However, the results of the PBPK model indicate that pharmacokinetic differences may not explain the strain-specific differences observed in the carcinogenicity of arsenic in the mouse. Acknowledgements
This project was funded by the Electric Power Research Institute, but the conclusions and analyses are those of the authors. The authors would like to gratefully acknowledge the samples provided by Dr. Ray Tice and the analyses of these samples by Dr. Eric Crecelius to determine the levels of arsenic species in mice following chronic exposure in drinking water. References
Baker, H., J. Lindsey and S. Weisbroth (1979). The Laboratory Rat: Volume I Biology and Diseases. San Diego, CA, Academic Press, Inc. Bernstam, L. and J. Nriagu (2000). Molecular Aspects of Arsenic Stress. Journal of Toxciology and Environmental Health, Part B 3:293-322. Brown, R., M. Delp, S. Lindstedt, L. Rhomberg and R. Beliles (1997). "Physiological Parameter Values for Physiologically Based Pharmacokinetic Models." Toxicology and Industrial Health 13(4): 407-484. Chen, C. and Lin L. (1994). Human carcinogenicity and atherogenicity induced by chronic exposure to inorganic arsenic. In: Arsenic in the environment. Part II: Human health and ecosystem effects, ed. J. O. Nriagu, 109-131. New York: John Wiley & Sons. Clewell, H., H. Barton, P. Gentry, A. Shipp, J. Yager and M. Andersen (1998). "Requirements for a Biologically-Realistic Arsenic Risk Assessment." Intl J Toxicol 18(2): 131-147. DelRazo, L., M. Styblo and D. Thomas (2000). Determination of trivalent methylated arsenic species in water, cultured rat hepatocytes, and human urine. 4th International Conference on Arsenic Exposure and Health Effects, Poster 10. 66
Easterling, M., Styblo, M., Evans, M. and E. Kenyon (2002). Pharmacokinetic Modeling of Arsenite Uptake and Metabolism in Hepatocytes – Mechanistic Insights and Implications for Further Experiments. Journal of Pharmacokinetics and Pharmacodynamics 29(3):207-233. Golub, M., Macintosh, M. and N. Baumrind (1998). Developmental and Reproductive Toxicity of Inorganic Arsenic: Animal Studies and Human Concerns. Journal of Toxicology and Environmental Health, Part B 1:199-241. Hall, L., George, S., Kohan, M., Styblo, M. and D. Thomas (1997). In vitro methylation of inorganic arsenic in mouse intestinal cecum. Toxicology and Applied Pharmacology 147(1):101-109. Healy, S., E. Casarez, F. Ayala-Fierro and H. Aposhian (1998). "Enzymatic methylation of arsenic compounds. V. Arsenite methyltransferase activity in tissues of mice." Toxicology and Applied Pharmacology 148(1): 65-70. Hsueh, Y., Huany, Y., Huang, C., Wu, W., Chen, H., Yang, M., Lue, L., and C. Chen (1998). Urinary Levels of Hughes, M. and E. Kenyon (1998). "Dose-dependent effects on the disposition of monomethylarsonic acid and dimethylarsenic acid in the mouse after intravenous administration." Journal of Toxicology and Environmental Health, Part A 53: 95112. Hughes, M., E. Kenyon, B. Edwards, C. Mitchell and D. Thomas (1999). "Straindependent disposition of inorganic arsenic in the mouse." Toxicology 137: 95108. Hughes, M., M. Menache and D. Thompson (1994). "Dose-dependent disposition of sodium arsenate in mice following acute oral exposure." Fundamental and Applied Toxicology 22: 80-89. Kanisawa, M. and H. Schroeder (1967). "Life term studies on the effects of arsenic, germanium, tin, and vanadium on spontaneous tumors in mice." Cancer Research 27: 1192-1195.
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Kenyon, E., M. Fea, M. Styblo and M. Evans (2001). "Application of Modelling Techniques to the Planning of In Vitro Arsenic Kinetic Studies." ATLA 29: 1533. Kenyon, E., M. Hughes and O. Levander (1997). "Influence of dietary selenium on the disposition of arsenate in the female B6C3F1 mouse." Journal of Toxicology and Environmental Health 51: 279-299. King, F., R. Dedrick, J. Collins, H. Matthews and L. Birnbaum (1983). "Physiological Model for the pharmacokinetics of 2,3,7,8-tetrachlorodibenzofuran in several species." Toxicology and Applied Pharmacology 67: 392-400. Kitchin, K. (2001). "Recent Advances in Arsenic Carcinogenesis: Modes of Action, Animal Model Systems, and Methylated Arsenic Metabolites." Toxicology and Applied Pharmacology 172(3): 249-261. Le, X., X. Lu, M. Ma, W. Cullen, H. Aposhian and B. Zheng (2000). "Speciation of key arsenic metabolic intermediates in human urine." Anal. Chem. 72: 51725177. Lubin, J., Pottern, L., Stone, B. and J. Fraumeni (2000). Respiratory cancer in a cohort of copper smelter workers: results from more than 50 years of follow-up. American Journal of Epidemiology 151(6):554-565. Mann, S., P. Droz and M. Vahter (1996a). "A Physiologically Based Pharmacokinetic Model for Arsenic Exposure: I. Development in Hamsters and Rabbits." Toxicology and Applied Pharmacology 136: 8-22. Mann, S., P. Droz and M. Vahter (1996b). "A Physiologically Based Pharmaokinetic Model for Arsenic Exposure: II. Validation and Application in Humans." Toxicology and Applied Pharmacology 140: 471-486. Mass, M. (1998). Wild Type and p53 Deficient Mice Exposed to Arsenical Compounds. EPA Study Number 97-05. Moser, G., T. Goldsworthy and R. Tice (2000). Sodium Arsenite Studies in p53 +/- Mice. Denver, CO, AWWA Research Foundation and the American Water Works Association. 68
Ng, J., S. Seawright, L. Wi, C. Garnett, B. Cirswell and M. Moore (1999). Tumours in mice induced by exposure to sodium arsenate in drinking water. Arsenic Exposure and Health Effects. W. Chappell, C. Abernathy and R. Calderon, Elsevier Science: 217-223. Sampayo-Reyes, A., R. Zakharyan, S. Healy and H. Aposhian (2000). "Monomethylarsonic acid reductase and monomethyarsonous acid in hamster tissue." Chem. Res. Toxicol. 13: 1181-1186. Tsai, S., Wang, T. and Y Ko (1998). Cancer Mortality Trends in a Blackfoot Disease Endemic Community of Taiwan Following Water Source Replacement. Journal of Toxicology and Environmental Health, Part A 55:389-404. Vega, L., M. Styblo, R. Patterson, W. Cullen, C. Wang and D. Germolec (2001). "Differential effects of trivalent and pentavalent arsenicals on cell proliferation and cytokine secretion in normal human epidermal keratinocytes." Toxicology and Applied Pharmacology 172(3): 225-232. Waalkes, M., L. Keefer and B. Diwan (2000). "Induction of proliferative lesions of the uterus, testes, and liver in Swiss mice given repeated injections of sodium arsenate: possible estrogenic mode of action." Toxicology and Applied Pharmacology 166: 24-35. Wang, Z., S. Dey, B. Rosen and T. Rossman (1996). "Efflux-mediated resistance to arsenicals in arsenic-resistant and -hypersensitive Chinese hamster cells." Toxicology and Applied Pharmacology 137: 112-119. Yu, D. (1999). "A pharmacokinetic modeling of inorganic arsenic: a short-term oral exposure model for humans." Chemosphere 39(15): 2737-2747. Yu, R., K. Hsu, C. Chen and J. Froines (2000). "Arsenic methylation capacity and skin cancer." Cancer Epidemiol. Biomarkers 9(11): 1259-1262. Zakharyan, R., Y. Wu, G. Bogdan and H. Aposhian (1995). "Enzymatic Methylation of Arsenic Compounds: Assay, Partial Purification, and Properties of Arsenite Methyltransferase and Monomethylarsonic Acid Methyltransferase of Rabbit Liver." Chem. Res. Toxicol. 8: 1029-1038.
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Chapter 3
Comparison of Tissue Dosimetry in the Mouse Following Chronic Exposure to Arsenic Compounds P. Robinan Gentry, Tammie R. Covington, Greg Lawrence, Tracy McDonald, Elizabeth T. Snow, Dori Germolec, Glenda Moser, Janice W. Yager and Harvey J. Clewell, III
ENVIRON International Corporation, Ruston, LA, USA
Journal of Environmental Toxicology and Environmental Health, Part A, 2005, 68:329-351 71
Abstract Several chronic bioassays have been conducted in multiple strains of mice in which various concentrations of arsenate or arsenite were administered in the drinking water without a tumorigenic effect. However, one study (Ng et al. 1999) has reported a significant increase in tumor incidence in C57Bl/6J mice exposed to arsenic in their drinking water throughout their lifetime, with no tumors reported in controls. A physiologically-based pharmacokinetic model for arsenic in the mouse has previously been developed (Gentry et al. 2004) to investigate potential differences in tissue dosimetry of arsenic species across various strains of mice. Initial results indicated no significant differences in blood, liver or urine dosimetry in B6C3F1 and C57Bl/6 mice for acute or subchronic exposure. The current work was conducted to compare model-predicted estimates of tissue dosimetry to additional kinetic information from the (C57Bl/6xCBA)F1 and TgAc mouse. The results from the current modeling indicate that the pharmacokinetic parameters derived based on information in the B6C3F1 mouse adequately describe the measured concentrations in the blood/plasma, liver, and urine of both the (C57Bl/6xCBA)F1 and TgAc mouse, providing further support that the differences in response observed in the chronic bioassays are not related to strain-specific differences in pharmacokinetics. One significant finding was that no increases in skin or lung concentrations of arsenic species in the (C57Bl/6xCBA)F1 strain were observed following administration of low concentrations (0.2 or 2 mg/L) of arsenate in the drinking water, even though differences in response in the skin were reported. These data suggest that pharmacodynamic changes may be observed following exposure to arsenic compounds without an observable change in tissue dosimetry. These results provided further indirect support for the existence of inducible arsenic efflux in these tissues. Introduction Several bioassays have been conducted in which arsenic was administered to various strains of laboratory mice, including the Swiss CD-1, Swiss CR:NIH(S), C57Bl/6J, C57Bl/6p53(+/-), and C57Bl/6p53(+/+) strains (Kanisawa and Schroeder 1967; Mass, 1998; Moser et al. 2000; Ng et al. 1999; Waalkes et al. 2000). No significant increase in the incidence of any tumor type was reported in most of these studies (Kanisawa and Schroeder (1967), Mass (1998), Moser et al. (2000), or Waalkes et al. (2000)) (Table 1). However, in the study 72
conducted by Ng et al. (1999) in which arsenic was administered at 0.5 ppm in drinking water for 112 weeks, a 40% incidence of total tumors was reported in arsenic-treated C57Bl/6J mice, with no tumors reported in the controls (Table 2). In the Moser et al. (2000) and Mass (1998) studies, which were conducted in similar strains 1 to Ng et al. (1999), the incidence of tumors was not increased despite the administration of doses of arsenic approximately 40- to 400-fold higher than the dose administered in the Ng et al. (1999) study. In the Ng et al. (1999) study, significant increases in the incidence of lung, liver and gastrointestinal tract tumors were reported following microscopic examination. However, in the Moser et al. (2000) study, the only tissues examined microscopically were the liver, skin and bladder, while Mass (1998) examined only the lung and liver. Thus, the only tissue that was examined in all three studies was the liver, with an increased tumor incidence reported in the Ng et al. (1999) study, but not in the Moser et al. (2000) or Mass (1998) studies. An increase in the incidence of lung tumors was reported by Ng et al. (1999), but not by Mass (1998). The gastrointestinal tract was not examined microscopically by either Moser et al. (1999) or Mass (1998). Therefore, the potential effects of arsenic on the gastrointestinal tract could not be compared across the studies. Nevertheless, the results of these studies suggest that the potential carcinogenic effects of arsenic were not related to the magnitude of the doses administered. While the daily doses that were administered in the Ng et al. (1999) study were substantially less than the doses administered by Moser et al. (2000) and Mass (1998), the durations of exposure in the Moser et al. (2000) and Mass (1998) studies were 26 and 51 weeks respectively, considerably less than the duration of exposure (112 weeks) in the Ng et al. (1999) study. Based on these results, it might be concluded that the duration of arsenic exposure could explain the different results obtained in these studies. The only study where the duration of exposure was comparable to the duration of exposure in the Ng et al. (199) study was the study by Kanisawa and Schroeder (1967), where groups of CD mice received arsenic in the drinking water (93% of exposure) and diet (7% of exposure) for 132 weeks. In addition to the longer duration of exposure, Kanisawa and Schroeder (1967) exposed the 1
These studies (Moser et al. 2000; Mass 1998) employed heterozygous p53-deficient (p53(+/-)) mice derived from C57Bl/6 parents. In addition, in the Mass (1998) study, wild type C57Bl/6 p53 (+/+) mice were used.
73
C57Bl/6 p53 (+/-) C57Bl/6 p53 (+/-); p53 (+/+) C57Bl/6 p53 (+/-); p53 (+/+) Swiss CR:NIH(S) C57Bl/6J
Swiss CD-1
Strain
1.24
Sodium arsenate
27b
331a 0.0202 (i.v.) 0.07
7.7b
66a
NA
Sodium arsenate
10 males
2.9
0.38
Dose (mg As/kg/d)
50
8.66
Water Conc. (mg/L)
Sodium arsenate
Sodium arsenite
10 males
25 males 25 females 90 females
Sodium arsenite
Sodium arsenic and inorganic As
Test Substance
25 males
# animals/ arsenic dose group 108 total (males and females)
112
20c
51
51
26
132
Dose Duration (weeks)
Table 1: Summary of Arsenic Mouse Studies1
+
-
-
-
-
-
Results (+ or -)
Waalkes et al. (2000) Ng et al. (1999)
Mass (1998)
Mass (1998)
Moser et al. (2000)
Kanisawa and Schroeder (1967)
Reference
- No statistically significant increase in tumor incidence. + Author reports statistically significant increase in tumor incidence. 1 Arsenic was administered in drinking water in all studies except for the study conducted by Waalkes et al. (2000) where the material was administered via intravenous injection. a Values for compound concentrations in drinking water in this study (mg/L) are time weighted average b Drinking water arsenic concentrations were converted to doses in milligrams per kilograms per day (mg/kg/day). Doses were converted using an average drinking water consumption rate of 6 milliliters per day (mL/day) and an average body weight of 0.03 kg for the mouse. c Animals were given one injection per week for 20 weeks and necropsied at 96 weeks.
74
Table 2 Tumor Incidence in Ng et al. (1999) Organ System Lung Gastrointestinal Tract Liver Spleen Reproductive Skin Bone Eye
Control
Treated (0.5 mg As/L)
0 0 0 0 0 0 0 0
16/90 (17.5)* 13/90 (14.4)* 7/90 (7.8)* 3/90 (3.3) 3/90 (3.3) 3/90 (3.3) 2/90 (2.2) 1/90 (1.1)
Values in parentheses represent percent of animals with tumors * Statistically significantly different from controls using Fisher=s exact test (p45 mg/L). No information is provided on potential changes in genomics in the low concentration region near the MCL (0.01 mg/L). Simeonova et al. (2002; 2000) conducted in vivo studies, potentially relevant to the current analysis, to investigate the ability of arsenic to alter the activity of selected genes or proteins in the mouse bladder. In these studies, C57BL/6 male mice were administered 50 or 100 ppm sodium arsenite in drinking water for up to 16 weeks and bladder tissue evaluated to determine whether inorganic arsenic administration induced selected proteins critical to the epidermal growth factor receptor (EGFR)-extracellular signal-regulated protein kinase (ERK) pathway. This pathway is important is mediating gene expression related to regulation of cellular growth. The studies demonstrated induction of phosphorylated EGFR and ERK in the bladder tissue of mice, as well as AP-1 transactivation, which is a regulator of cell growth. This is informative for the mode of action, however, only a single concentration was administered in each study, providing limited information on the dose-dependent transitions for these two genes following administration of lower concentrations of arsenic. Because of the lack of information for gene or protein expression changes following 111
administration of low concentrations in in vivo studies, these studies were not analyzed further for this assessment. It is important to note, however, that the changes in gene or protein expression observed in these studies is consistent with the changes observed in in vitro studies at high concentrations. Discussion The combined concentration- response information from primary cells (Figure 1) provides clear evidence of the concentration dependence of arsenic effects on the expression of various genes or proteins from concentrations of 0.005 up to 100 µM. At low concentrations (below 0.1 μM), the observed responses appear to be consistent with the cell entering an adaptive state (Hamadeh et al. 2002; Hu et al. 2002; Snow et al. 2005; Yager and Wiencke). Although some changes, such as a decrease in DNA damage-associated gene (DDB2), relate to the maintenance of DNA integrity, the general down-regulation of genes associated with cell repair control and apoptosis suggests that there is no impairment of the cell’s ability to preserve the integrity of its DNA. It is important to note that there is no evidence of the induction of any genes, such as CDC25C, associated with cell cycle checkpoints. At arsenite concentrations between 0.1 and 10 μM, increased expression of genes or proteins associated with cell cycle control (e.g., CDC25C and p21) are noted, possibly indicating cellular recognition of unrepaired DNA damage. In addition, evidence of cytotoxicity was observed at the upper end of this concentration range (approximately 5 μM and greater) in most cell lines exposed to arsenite for up to 24 hours. The changes in gene or protein expression related to p21 and p53 are important in terms of cell cycle control. p53 protein, when modified by phosphorylation and acetylation, becomes an active transcription factor that transactivates several key proteins regulating DNA repair processes and cellular proliferation (Vogt and Rossman 2001). This protein is important in the induction of p21, which initiates a cascade of events leading to subsequent G1 arrest of the cell to provide more opportunity for DNA repair. Changes in isoforms of cyclin-dependant kinase (i.e., CDC25A, CDC25B) were also observed following administration of arsenite concentrations between 0.1 and 10 μM. An increase in expression of the C isoform of this kinase was observed, which is predominantly expressed in G2 and M phases of the cell cycle to regulate entry of cells into M phase. Slight degradation in the isoform CDC25B, which is also related to the regulation of G2/M, was observed at similar 112
concentrations of arsenite, while a dose-dependent increase was observed in the isoform CDC25A. The major function of the isoform CDC25A is related to the regulation of the G1/S phase transition. However, this isoform has also been postulated to be an oncogene because of its ability to cooperate with c-Myc and Ras to promote carcinogenic transformation (Chen et al. 2002b). Changes in expression of genes or proteins associated with apoptosis (e.g., NF-κB) are also noted at these concentrations that may reflect a cellular response to toxicity. At the higher end of this concentration range (approximately 1 to10 μM), there is increased expression of genes or proteins associated with proliferative signaling (e.g., VEGF, ERK), possibly related to the chemical stresses and cytotoxicity of arsenic exerting pressure for cell division, together with inhibition of DNA ligase I, impacting repair of damage to DNA. At the highest concentrations tested (>10 μM), increasing toxicity is reflected in evidence of cell cycle stasis and apoptotic responses. As noted previously, the ability of arsenicals to induce apoptosis is critical to the effectiveness of high-dose arsenic as an antineoplastic agent. While sensitivity is noted for selected tumor-derived cell lines (Mahieux et al. 2001), administration at high doses (up to 20 mg/day) has been demonstrated to be effective in the treatment of acute promyelocytic leukemia and multiple myeloma (Berenson and Yeh 2006; Zheng et al. 2005). Overall, the available gene/protein dose-response information for inorganic arsenic is consistent with the observed effects in humans under different exposure conditions. As mentioned in the introduction, carcinogenic effects observed have been observed at drinking water concentrations that are relatively high (on the order of 600 μg/l, resulting in a daily dose on the order of 1 to 2 mg/day). This is consistent with the observation of changes in genes/proteins (i.e., Jun, JNK3) that could be related to carcinogenesis at higher concentrations in in vitro studies. A significant result of this analysis is the general consistency in response, not only across cell lines (primary and immortalized) as previously mentioned, but also across different tissue types and species. Consistent concentrationspecific changes in gene or protein expression within a specific functional category (e.g., proliferation) were observed for multiple tissues across multiple species. For example, in the primary cell lines administered approximately 1.0 μM, increases in the expression of multiple genes (i.e., VEGF, p70, Myc, ERK) 113
were noted (Figure 1) that were all associated with proliferation. These changes were noted in human umbilical vein endothelium (Kao et al. 2003), rat cardiomyocites (Wang and Proud 1997), rat myoblasts (Shimizu et al. 1998) and rat lung epithelium (Lau et al. 2004b). While the magnitude of change may vary, the relative consistency in the change in gene expression with concentration is striking. This consistency is important for the application of the results of this analysis in the development of an MOA for inorganic arsenic carcinogenicity and suggests that the relatively greater sensitivity of humans to the carcinogenicity of inorganic arsenic may not be due to differences in tissue response. Recent analyses have suggested similar dose-response curves for cancer and noncancer effects of arsenic (Clewell and Crump 2005). This is consistent with the evidence from the current analysis of induction of vascular endothelial growth factor (VEGF), which is relevant to noncancer vascular proliferative effects of arsenic, and inhibition of DNA Ligase I in the same concentration range (1 to10 μM). Both of these endpoints have been proposed as pivotal in assessing potential nonlinearities in the noncancer and cancer responses for arsenic. The threshold for carcinogenic effects has been proposed to be roughly an order of magnitude below the concentrations at which tumors are observed (Clewell 2001; Snow et al. 2005). The reversal of genomic responses in the current analysis at concentrations below 0.1 μM is consistent with this evidence. The observed dominance of apoptotic responses at higher concentrations (>50 μM) is also consistent with the effectiveness of arsenicals as chemotherapeutic agents. Overall, the current analysis suggests dose-dependence of the effects of arsenic compounds on various genes or proteins from concentrations of 0.005 to up to 1000 µM. The available in vitro gene expression data, together with information on the metabolism and protein binding of arsenic compounds, supports a mode of action for inorganic arsenic carcinogenicity involving the superposition of highly-specific direct interactions with critical proteins such as those involved in DNA repair, overlaid against a background of chemical stress, including proteotoxicity and depletion of non-protein sulfhydryls. It has been suggested that the dose-response component of cancer risk assessment could be based on quantitation of molecular endpoints, or “bioindicators” of response, selected on the basis of their association with obligatory precursor events for tumorigenesis (Preston 2002). Based on the existing data on the cellular effects of arsenic, it appears that the key events in arsenic carcinogenesis include DNA repair inhibition under conditions of oxidative stress, inflammation, and 114
proliferative signaling, leading to a situation in which the cell is no longer able to maintain the integrity of its DNA prior to division (Figure 3). Data Gaps and Ongoing Research Inferences derived from this analysis using genomic data have been used to identify potential data gaps and to contribute to the design of a gene expression dose-response study that compares in vivo and in vitro genomic responses in the mouse bladder with in vitro responses in human bladder cells (Clewell et al. 2007). The results of the current analyses highlight the limited availability of parallel information across species and tissue to support the cross-species comparisons needed for the human risk assessment. Based on the results of this investigation, an initial in vivo study has been completed that investigated the genomic alterations in bladder epithelial cells of mice following exposure to inorganic arsenic (Clewell et al. 2007). Female C57Bl/6 mice (sex and strain used in the most recent inorganic arsenic animal bioassays) were administered arsenate in drinking water for 14 days at concentration of 0, 0.05, or 50 mg/l. Gene expression microarray measurements were conducted on bladder tissues from animals in each dose group. The results suggest up-regulation of genes related to DNA repair, cell cycle control, proteotoxicity, and apoptosis in the high dose group, while down-regulation of genes related to cell growth, adhesion differentiation were also observed. A follow-up study has recently been completed in which groups of mice were administered multiple concentrations of arsenate over a 12-week period. The results of this study will further characterize gene expression changes in the bladder of mice as a function of dose and time. Additional in vitro studies are also planned for both mouse and human bladder epithelial cells. In these studies, the cells will be exposed in vitro at concentrations equivalent to those achieved in the mouse in vivo genomic studies. Comparison of the in vitro and in vivo results in the mouse will provide a test of the ability of in vitro data to predict in vivo genomic responses to arsenic exposure. The mouse-human comparison will provide data on species differences in the genomic dose-response to inorganic arsenic exposure and will be of value for understanding the lack of an animal model for the human bladder carcinogenicity of inorganic arsenic. The quantitative genomic data from the current analysis in combination with the results from ongoing research in both mouse and human cells will be used to inform a proposed risk assessment 115
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Part II
Use of Biokinetic Modeling to Identify Critical Periods of Exposure during the Perinatal Period
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Chapter 5
Evaluation of the Potential Impact of Pharmacokinetic Differences on Tissue Dosimetry in Offspring during Pregnancy and Lactation P. Robinan Gentry, Tammie R. Covington, and Harvey J. Clewell, III ENVIRON International Corporation, Ruston, LA, USA
Regulatory Toxicology and Pharmacology, 2003, 38:1-16 141
Abstract In recent years efforts have increased to develop a framework for assessing differences, both pharmacokinetic and pharmacodynamic, between children and adults for purposes of assessing risk of adverse effects following chemical exposure. The specific goal of this study was to demonstrate an approach for using PBPK modeling to compare maternal and fetal/neonatal blood and tissue dose metrics during pregnancy and lactation. Six chemical classes were targeted to provide a variety of physicochemical properties (volatility, lipophilicity, water solubility), and surrogate chemicals were selected to represent each class (isopropanol, vinyl chloride, methylene chloride, perchloroethylene, nicotine, and TCDD), based on the availability of pharmacokinetic information. These chemicals were also selected to provide different pharmacokinetic characteristics, including metabolic production of stable or reactive intermediates in the liver and competing pathways for metabolism. Changes in dosimetry during pregnancy predicted by the modeling were mainly attributable to the development of enzymatic pathways in the fetus or to changes in tissue composition in the mother and fetus during pregnancy. In general, blood concentrations were lower in the neonate during the lactation period than in the fetus during gestation. This postnatal decrease varied from only a slight change (for TCDD) to approximately four orders of magnitude (for vinyl chloride). As compared to maternal exposure, fetal/neonatal exposures ranged from approximately twice as great (for TCDD) to several orders of magnitude lower (for isopropanol). The results of this study are in general agreement with the analyses of data on pharmaceutical chemicals, which have suggested that the largest difference in pharmacokinetics observed between children and adults is for the perinatal period. The most important factor appears to be the potential for decreased clearance of toxic chemicals in the perinatal period due to immature metabolic enzyme systems, although this same factor can also reduce the risk from reactive metabolites during the same period. Introduction Over the past decade, there have been an increasing number of attempts to quantitatively describe differences across lifestages (i.e., children versus young adults versus the elderly) that may impact susceptibility to adverse effects following chemical exposure. Recently, there has been even greater focus on developing methods for assessing exposure to children with the announcement of 142
the Voluntary Children’s Chemical Evaluation Program (VCCEP), which represents the most significant children’s health assessment activity undertaken thus far by the Agency since passage of the Children’s Health Protection Act by the U.S. Congress in 1997 and the establishment of the Office of Children’s Health Protection within USEPA in 1998. In evaluating the differences between children and adults, these differences can be grouped into two broad categories: pharmacokinetic factors, or those factors that influence the target tissue dose for a given external exposure, and pharmacodynamic factors, or those factors that influence the target tissue response at a given target tissue dose. In recent risk assessments, information on the pharmacokinetics of a particular chemical or class of chemicals has sometimes been used to provide a more accurate basis for cross-species extrapolation. In particular, physiologically-based pharmacokinetic (PBPK) models have been incorporated into chemical risk assessments to help make key pharmacokinetic factors in the process more explicit, and provide a means for estimating the significance of these factors in the final risk estimates (Andersen et al. 1987; Clewell et al. 2001). PBPK modeling can be used in risk assessment when extrapolating across species or dosing patterns (high to low dose exposure, dosing duration or pattern, and route of exposure) to provide quantitative estimates of the relevant internal tissue dose or dose metric for the target population. Even at the same level of exposure, because of the heterogeneity of the human population, it is generally expected that there will be a broad range of observed susceptibilities to the biological effects of exposure to chemicals or drugs. Often it is possible to distinguish specific classes of individuals, such as infants or the elderly, who appear to be more susceptible to a specific effect. A PBPK model can provide a quantitative structure for determining the effect of various age- and gender-specific factors on the relationship between the external (environmental) exposure and the internal (biologically effective) target tissue exposure. In particular, PBPK models can be used to determine the impact of differences in key metabolic enzymes not only due to normal variation in enzyme activities within the general population, but also due to differences in metabolism across age groups, such as children versus adults. PBPK models can also be applied to quantitatively describe internal tissue dose metrics during pregnancy in the fetus, as well as the mother. For the perinatal period, exposure to the fetus is dependent on placental transfer, while for the neonate significant exposure may occur via ingestion of 143
breast milk. Estimation of exposure during this period is complex, since maternal and fetal, as well as nursing infant, exposure must be considered together. An added dimension when considering the relevant internal dose metric for perinatal exposure is the timing of that exposure relative to the stage of development of the potential target organ or system. In utero exposure to either the parent chemical or metabolite will be influenced by the physicochemical properties of the exogenous chemical, which will govern its placental transfer to the fetus and accumulation in the fetus. High molecular weight chemicals, including highly lipophilic chemicals, are generally thought to be less efficiently transferred across the placenta, but once in the fetus may be more persistent, thereby providing a sustained target tissue dose. Highly lipophilic compounds are also more readily concentrated in and transferred with breast milk during lactation, providing a potentially important route of postnatal exposure. Exposure to the developing fetus/infant with time will change even if the maternal exposure remains constant. This is due to changes in maternal kinetic parameters (e.g., body weight, volume of distribution) and fetal/infant metabolic capabilities to either detoxify or activate a chemical and clear those chemicals as metabolic and physiological systems develop. Consequently, perinatal exposure cannot be readily estimated from blood levels of parent chemical and/or metabolite in either a child or nonpregnant woman. Several PBPK models have been developed in an attempt to assess chemical exposure of the offspring of laboratory animals via transplacental or lactational transfer to environmentally relevant chemicals, such as DDE (You et al. 1999), a persistent metabolite of DDT, PCBs (Lee et al. 2002), tetrachloroethylene (Byczkowski and Fisher 1994), methanol (Ward et al. 1997), and methylmercury (Gray 1995). Human PBPK models have also been developed to assess exposure to the fetus following maternal exposure (Clewell et al. 1999; Young 1998; Luecke et al. 1994; Gentry et al. 2002) and to the child from lactational transfer (Clewell and Gearhart 2002; Byczkowski and Fisher 1995). However, little work has been conducted to compare changes in internal dose metrics over time for these various chemicals across pregnancy and lactation. Prior to this analysis, an investigation was conducted to provide a comprehensive review of the information available in humans to address the 144
impact of age- and gender-specific physiological, biochemical, and pharmacokinetic differences in individual risk (Clewell et al. 2002). The focus of this analysis was to review the available quantitative information and organize, from a pharmacokinetic perspective, the key factors that are likely to have a significant impact on susceptibility, as it relates to estimates of target tissue concentrations. Based on the results of this review, an attempt was made to develop a predictive pharmacokinetic framework that could be used to characterize the effect of age and gender differences on tissue dosimetry for a chemical or class of chemicals. Using the available data, three case studies have now been conducted that demonstrate practical methods for quantitatively incorporating information on age- and gender-specific pharmacokinetic differences in risk assessments for chemicals. These consist of a “lifestage” study, which is an initial attempt to provide a predictive pharmacokinetic framework that could be used to characterize the effect of age and gender differences on tissue dosimetry (Clewell et al. 2003b) for a chemical or class of chemicals, a study focused on the perinatal period, and a study focused on ageand gender-specific inhalation dosimetry (Sarangapani et al. 2003). The focus of the study presented here is specifically on the perinatal period, and is an attempt to develop a methodology to assess, based on chemical/physical properties of a chemical, pharmacokinetic changes that may impact susceptibility of the fetus or neonate to adverse effects following chemical exposure. This case study demonstrates how a human PBPK model can be used to assess the critical period of exposure (from a pharmacokinetic perspective) during development, as a function of chemical/physical properties. Methods Selection of Chemical Class Surrogates The specific goal of this study was to use a PBPK model to predict both maternal and fetal/infant blood and tissue dose metrics during pregnancy and lactation for various chemical classes. Six chemical classes were targeted, with surrogate chemicals selected to represent each class. The surrogates were selected based on the availability of pharmacokinetic information critical to conducting the case study, such as metabolic parameters and partition coefficients. Examples were conducted for two non- or semi-volatile classes and four volatile classes (Table 1). The four volatile chemicals were selected based on increasing 145
lipophilicity: isopropanol, vinyl chloride, methylene chloride, and perchloroethylene. For the nonvolatile classes, one example was for a highly lipophilic, nonvolatile, of which TCDD served as the surrogate. Nicotine was selected as a representative for water soluble, semi-volatile chemicals. Table 1 Surrogate Chemicals and Important Characteristics Compound
Physicochemical Characteristics
Isopropanol
water soluble/volatile
Vinyl Chloride
Lipophilic/volatile
Methylene Chloride
Lipophilic/volatile
Percloroethylene
Lipophilic/ volatile
TCDD Nicotine
Lipophilic/nonvolatile Water soluble/nonvolatile
Active Compound Parent, Circulating Metabolite Reactive Metabolite Parent, Reactive Metabolite Parent, Circulating Metabolite Parent Parent
The chemicals selected to serve as surrogates for the volatile classes represented not only differences in physicochemical properties, but differences in pharmacokinetic characteristics as well, including metabolic production of stable or reactive intermediates in the liver and competing pathways for metabolism. Of the selected volatile surrogates, vinyl chloride has a single metabolic pathway, while methylene chloride has two competing metabolic pathways. Isopropanol and perchloroethylene produce stable circulating metabolites, whereas the metabolites of vinyl chloride and methylene chloride are highly reactive and are unlikely to escape the tissue in which they are produced. Model Structure To evaluate the potential for an enhanced toxic response in the fetus or neonate, PBPK models were developed based on an existing model (Gentry et al. 2002), which included changes in the pregnant female and the developing fetus. By including the available quantitative information on the development of metabolic pathways in the fetus and the neonate, specifically the cytochrome 146
P450 metabolic enzymes, sustained tissue doses of the relevant toxic agent for a class of chemicals with certain physicochemical properties at a given time point during development could be estimated across the entire pregnancy and lactational period. The pregnancy model was a modification of a previously published pregnancy model for isopropanol (IPA) (Gentry et al. 2002), and the lactation model was an extension of this pregnancy model. The models were coded using the Advanced Continuous Simulation Language (ACSL, Aegis Technologies Group, Inc., Huntsville, AL). Because the pregnancy model has been documented in a previous publication (Gentry et al. 2002), only the changes to the model necessary to describe the development of metabolic pathways in the fetus or to describe exposure to the infant via lactation will be discussed. Additions to the Pregnancy Model Because the previously published pregnancy model (Gentry et al. 2002) had only a single compartment for the fetus, compartments were added to the model for fetal liver, fetal blood, and remaining fetal tissue (Figure 1). Equations were added to allow for changes in the fetal volumes of blood and liver, fetal cardiac output, and fetal liver blood flow and to describe the kinetics in the new compartments. Additional model parameters are provided in Table 2. For simplicity, it was assumed that the tissue volumes remain a constant fraction of fetal body weight (Equations A1 and A2 – see Appendix A) and that the fractional volumes were the same as for a newborn. The fractional volumes were obtained from the International Commission on Radiological Protection (ICRP 1975). The volume of the remaining fetus was defined to be the total fetal weight minus the weight of the fetal blood and liver compartments. Fetal cardiac output was defined as a function of fetal blood volume (Clewell et al. 1999) (Equation A3). Liver blood flow was defined as a fraction of fetal cardiac output, and it was assumed that the fractional blood flow to the fetal liver was the same as to the maternal liver (Equation A4). The blood flow to the remaining fetus was defined as the total cardiac output minus the blood flow to the fetal liver. The added fetal tissue compartments were described with flow-limited kinetics, similar to the corresponding maternal compartments in the pregnancy model, and used the same partitions as were used for the maternal compartments. The placental compartment was modified in order to describe diffusion-limited 147
Parent Chemical QP URT Mucous QAlv Alveolar
QC
QC
Metabolite QAlv
Surface P Skin
Alveolar
QC
QFat
Fat
Mammary
Mammary
QPla
Placenta
PAF
QPla
Fetus QUtr
Uterus
Uterus
QLiv
QUtr QLiv
Liver
Liver VMaxC,KM
RAO
Duodenum
QMam
PAF
Fetus
kAD
QBrn
Brain
QMam
Placenta
QSlw
Slow
QBrn
Brain
QRap
Rapid
QSlw
Slow
QFat
Fat
QRap
Rapid
QC
QSkn
VMax1C, KM1
kAS kTSD
Stomach
PDose
kTD
(a) Placenta
QPla
PAF Fetal Blood
Fetal Liver
Fetal Body
QFet
QLivFet
QRemFet
(b) Figure 1. (a) PBPK pregnancy model showing the systemic tissues. (b) PBPK fetal model showing the systemic tissues. Abbreviations are defined in Appendix A.
148
Table 2 Pregnancy Model Parameters Parameter
Definition Fetal blood QFetC flow Fetal blood VBldFetC volume Fetal liver VLivFetC volume
Units L/hr per kg fetal blood volume Fraction of fetal weight Fraction of fetal weight
Value 54.0 0.085 0.04
Reference Clewell et al. (1999) Clewell et al. (1999) ICRP (1975)
kinetics between the placenta and the fetal blood compartments instead of between the placenta and the total fetus (Equation A5). For the surrogate chemicals, quantitative information on the age-related changes in the relevant metabolic pathways (CYP2E1, CYP1A2, and alcohol dehydrogenase (ADH)) was available (Vieira et al. 1996; Sonnier and Cresteil, 1998; and Pikkarainen and Räihä, 1967). However, only metabolism via ADH was modeled in the fetus as the available data for the other metabolic pathways indicated no fetal metabolic activity. The age-related changes in ADH were averaged across fetal age as needed (Pikkarainen and Räihä, 1967), and were divided by the adult value to get fractional activities. Linear interpolation between the last prenatal value and the first post-natal value was used to estimate a value for ADH at birth. These fractional activities (Table 3) were used to calculate age-specific metabolism rates for the fetus as functions of maternal metabolism rate (mg/hr), the maternal liver volume (kg), the fetal age-specific liver volume (kg), and the linearly interpolated fractional activity (Equations A6 and A7). Lactation Model In order to simulate the changes during lactation to the mother and the resulting exposure via breast milk to the infant, the pregnancy model was modified (Figure 2) in a similar fashion to a published rodent lactation model (Fisher et al. 1990). Parameters added to this human model were primarily obtained from ICRP (1975). Modifications included the deletion of all equations
149
Table 3 Fetal Enzyme Activity for ADH (fraction of adult value)1 Months 0 2.3 3.0 5.0 9.0
Relative ADH Activity 0.0 0.0 0.084 0.1638 0.23182
1
Based on data provided in Pikkarainen and Räihä (1967). Interpolated based on prenatal and postnatal values reported in Pikkarainen and Räihä (1967).
2
and parameters pertaining to the placenta and uterus, the addition of parameters and equations to simulate lactation, and the addition of compartments for the infant. Several changes were made to the maternal portion of the pregnancy model for the simulation of lactation. Initial volumes and blood flows for all tissues were defined as a fraction of pre-pregnancy body weight and cardiac output, respectively, plus any increase due to physiological changes during pregnancy. Initial maternal body weight (Table 4) and cardiac output were defined to be the pre-pregnancy values plus the increases during pregnancy due to increases in the fat and mammary tissue volumes. As in the pregnancy model, the volumes and blood flows for fat and mammary tissue were allowed to change over time. Due to lack of data on decreases in fat tissue volume during lactation, the tissue volume for fat was modeled as a linear decrease over 6 months to the pre-pregnancy value. Increases in mammary tissue volume during lactation, compared to tissue volume prior to lactation, are based on information from ICRP (1975). It was assumed that maximum increases in mammary tissue volume would be achieved approximately two weeks postpartum and sustained throughout lactation, based on data provided by Casey et al. (1986) on milk production. Both the volume of fat and the volume of mammary tissue were described to be functions of pre-pregnancy fractional tissue weight, pre-pregnancy body weight in kg, and fractional change in tissue volume (Equation A8). However, fat volume was modeled as a linear decrease to pre-pregnancy values, 150
while mammary tissue volume was modeled as a linear increase in volume until two weeks postpartum, at which time the estimated peak mammary tissue volume was sustained throughout lactation. The values for body weight and cardiac output during lactation were defined to be the post-pregnancy values plus any changes due to decreases in fat tissue volume or increases in mammary tissue volume. The maternal ventilation rates were recalculated during lactation using the current body weight. A milk compartment was also added to the maternal model to describe the amount of chemical partitioned into the milk and the amount of milk suckled. The residual milk volume was based on a value of 0.125 L provided in Fisher et al. (1997), and the milk production rate of 0.03229 L/hr was obtained from the Food and Nutrition Board (1987). It was assumed that the rate of suckling was equivalent to the rate of milk production, and the suckling rate was increased linearly from 0 at birth to 0.03229 L/hr at 8 days post-partum (Casey et al. 1986). An equation for the partition coefficient for milk, based on Shelley et al. (1989), was used in the equation for the mammary tissue compartment to describe the partitioning of a chemical into the milk (Equation A9). The infant portion of the model was defined similarly to the maternal portion of the model, with a few exceptions. The mammary and milk compartments were not included in the infant portion of the model, and infantspecific physiological parameters were used (Table 4). Due to a lack of data, the alveolar ventilation rate, cardiac output, fractional blood flows, urinary clearance rate, and upper respiratory tract uptake rate used for the infant were the same as those used for the non-pregnant mother, but all of these values were scaled allometrically using the current infant body weight. The fractional blood flow to rapidly perfused tissues for the infant was defined as the sum of the fractional maternal blood flow to rapidly perfused tissues and non-pregnant mammary tissue. The fractional tissue volumes for the infant were obtained from ICRP (1975) with the exception of those for mucous and rapidly and slowly perfused tissues. The non-pregnant maternal value for mucous was used for the infant. The fractional tissue volumes for rapidly perfused tissues were calculated as the total fractional maternal rapidly perfused tissue volume minus the infant brain and liver fractional volumes. The fractional volume for slowly perfused tissue in the infant was estimated, based on the total maternal fractional volume for slowly perfused
151
Parent Chemical QP URTMucus QAlv Alveolar
QC
QC
Metabolite QAlv
Surface P Skin
QC
QSkn
QFat
Fat
Mammary
QMam
Mammary
Milk
QLiv
to infant stomach QLiv
Liver
Liver VMaxC, KM
RAO
Duodenum
QMam
PMilk
Milk
kAD
QBrn
Brain
PMilk
to infant
QSlw
Slow
QBrn
Brain
QRap
Rapid
QSlw
Slow
QC
QFat
Fat
QRap
Rapid
Alveolar
VMax1C, KM1
kAS kTSD
Stomach
PDose
kTD
Figure 2a. PBPK lactation model showing the systemic tissues. Abbreviations are defined in Appendix B.
152
Parent Chemical QP URT Mucous QAlv Alveolar
QC
QC
Metabolite QAlv
Surface QC
P
Alveolar
Skin
QFat
Fat
Fat
QRap
Rapid
Rapid
QSlw
Slow
Slow
QBrn
Brain
Brain
QLiv
QFat
QRap
QSlw
QBrn
QLiv
Liver
Liver
VMaxC, KM
RAO kAD
QC
QSkn
VMax1C, KM1
kAS Milk
Duodenum
kTSD
Stomach
Milk
kTD
Figure 2b. PBPK infant model showing the systemic tissues. Abbreviations are defined in Appendix B.
153
Table 4 Lactation Model Parameters Parameter
Definition
Value
Reference
Body Weights (kg) BWPre BirthBW
Maternal body weight before pregnancy Infant birth weight
67.77
Clewell et al. (1999)
3.375
Clewell et al. (2001a)
Infant Cardiac Output and Pulmonary Ventilation Rates (L/hr/kg0.75) QCBabC
Cardiac output
12.89
Same as maternal value
QPBabC
Pulmonary ventilation
27.75
Same as maternal value
Maternal Blood Flows (fraction of maternal cardiac output) QRapC
Rapidly perfused tissues
0.392 a
Clewell et al. (2001a)
Infant Blood Flows (fraction of infant cardiac output) QBrnBabC
Brain
0.114
QFatBabC
Fat
0.05
QLivBabC
Liver
0.227
QRapBabC
Rapidly perfused
0.358 b
Adjusted maternal value
c
Adjusted maternal value
QSlwBabC
Slowly perfused
0.251
Same as maternal value Male value from Brown et al. (1997) Same as maternal value
Maternal Tissue Volumes (fraction of maternal pre-pregnancy body weight) Rapidly perfused 0.1058 a Peak mammary tissue VMamCPeak 0.02 volume during lactation Infant Tissue Volumes (fraction of infant body weight) VRapC
Clewell et al. (2001a) ICRP (1975)
VBrnBabC
Brain
0.10
ICRP (1975)
VFatBabC
Fat
0.12
ICRP (1975)
VLivBabC
Liver
0.04
ICRP (1975)
VMucBabC
Mucous
0.0001
Same as maternal value
VRapBabC
Rapidly perfused
0.018
Adjusted maternal value
VSlwBabC
Slowly perfused
0.503
Adjusted maternal value
Lactation Parameters
a
VMilk
Residual milk volume (L)
0.6320
Shelley et al. (1989)
kMilkC
Milk production rate (L/hr)
0.03229
Food and Nutrition Board (1987)
Rapidly perfused value from pregnancy model plus uterine value from the pregnancy model. Maternal rapidly perfused blood flow value plus maternal mammary blood flow value. c Maternal slowly perfused blood flow value plus difference between maternal fat blood flow value and infant fat blood flow value. b
154
tissue minus the infant fat fractional volume. The fractional volume for infant alveolar blood was allowed to change over time, and was modeled based on data from ICRP (1975) (Table 5). It was assumed that alveolar blood volume is 10% of the total blood volume. Data on monthly infant body weights for birth to age 3 years were obtained from USEPA (1997). In order to adequately describe the age-related changes in infant body weight, two separate equations were needed, and the equations were fit to the data such that the curve was continuous (Figure 3 and Equation A10). The infant tissue volumes were also continually recalculated using the current infant body weight. Initial concentrations in the tissue compartments as a result of exposure during pregnancy were set by using the fetal concentration at birth and the partitions for the different compartments (Equation A11). Initial amounts in both maternal and infant tissues were defined as the initial tissue concentration times the tissue volume.
15
Body Weight (kg)
13
11
9
7
5
3 0
6
12
18
24
30
36
Months
Figure 3. Changes in infant body weight from birth to 36 months, based on data provided in USEPA (1997). The squares represent the data points reported in USEPA (1997), and the line represents multiple equations used to fit the data.
155
Table 5 Infant Alveolar Blood Volume (fraction of body weight)
Months 0 0.00548 3.0 7.0
Fractional Alveolar Blood Volume 0.00879 0.01071 0.00879 0.00879
The same data on the age-related changes in the relevant metabolic pathways (CYP2E1, CYP1A2, and ADH) for the specific chemicals selected for the case study that were gathered for the pregnancy model were used in the lactation model (Vieira et al. 1996; Sonnier and Cresteil, 1998; and Pikkarainen and Räihä, 1967). There was no information on age-related differences for the cytochrome 2D pathway (relevant for nicotine metabolism); however, information was available for CYP2C (Treluyer et al. 1997); therefore, these differences were used as a surrogate for the changes in the CYP2D activity used for nicotine. The data on age-related changes in CYP2E1, CYP1A2, and CYP2C activity were obtained from figures that provided the amount of protein at each age. These values were then divided by the corresponding adult value to get a fraction. The age-related changes in ADH were averaged across age as needed, and were then divided by the adult value to get fractional activities. These fractional activities were used to calculate age-specific metabolism rates (Table 6). Metabolism by all enzyme systems was initiated at zero at birth, with the exception of ADH as mentioned for the pregnancy model. Linear interpolation between the last prenatal value and the first postnatal value was used to estimate a value for ADH at birth. Age-specific metabolism rates were calculated using the maternal metabolism rate (mg/hr), the maternal liver volume (kg), the age-specific infant liver volume (kg), and the appropriate linearly interpolated fractional activity (Equations A12 and A13). The infant portion of the model was not coded to allow for oral dosing or drinking water exposure; instead the amount of milk suckled from the mother was input into the infant stomach compartment as a zero-order rate.
156
Table 6 Age-Related Enzyme Activity (fraction of adult value)1
Months 0 0.0164 0.1281 0.1314 0.5552 0.5881 1.15 1.9877 2.0041 5.2238 5.4538 7
0.2378
Relative Activity 1A2 2C 0 0 0.0105 0.0382 0.0199 0.2209 0.3150 0.0356
0.3163
0.1245
2E1 0 0.1009 0.1429
ADH 0.2318
0.2513 0.3860 0.3221 0.3579 0.3680
0.2458 0.2519
0.3262
0.2651
1
Fractional values estimated based on data provided in Vieira et al. (1996), Sonnier and Cresteil (1998), and Treluyer et al. (1997)
For TCDD, an additional analysis was conducted to evaluate infant blood concentrations in breast-fed versus bottle-fed infants. For the bottle fed scenario, an infant model was run assuming exposure at the same rate (ng/kg/day) as the maternal scenario, rather than exposure via lactation. Model Simulations for Surrogate Chemicals The simulations conducted for this case study evaluated potential exposure to the fetus and neonate following exposure of the mother to various classes of chemicals. The expanded models were run to simulate exposure to different chemicals, representative of classes of chemicals, to the fetus during pregnancy and to the infant during lactation. For the purposes of this comparison, a unit exposure of 1 µg/kg/day was assumed, with the exception of TCDD. For TCDD, a unit exposure of 1 ng/kg/day was assumed. For each chemical, the model simulations consisted of three runs. First, the previously published adult model 157
(Clewell et al. 2001a) was run using the chemical specific partition coefficients and metabolism parameters to simulate continuous drinking water exposure to 1 mg/kg/day of the chemical until steady-state had been reached. The steady-state tissue concentrations were input into the pregnancy model as starting values, and the pregnancy model was run for 274 days to simulate continuous drinking water exposure in the mother to 1 mg/kg/day of the chemical. The tissue concentrations at the end of the pregnancy run were output, along with the dose metrics, and were input as starting values for the lactation model. The lactation model was run using the chemical specific partitions and metabolism parameters to simulate continuous drinking water exposure in the mother to 1 mg/kg/day of the chemical and the resulting exposure in breast milk to the infant during the first 6 months of life. The dose metrics of concern were output at the end of each trimester of pregnancy and at 1, 3, and 6 months of age. The partition coefficients, upper respiratory tract uptake rate, and urinary clearance rates from a previously published adult human model for each of the surrogate chemicals were used when available (Table 7). In addition, adult metabolic parameters reported in the literature or used in previously developed models were combined with the age-related information on the activity of the relevant metabolic enzymes to estimate the age-specific metabolic capacity of each surrogate chemical. However, the resulting age-dependent models were not directly validated against data for the surrogate chemicals. Volatiles The chemical-specific parameters for isopropanol (IPA) and its metabolite, acetone, were taken from Clewell et al. (2001a). Oxidative metabolism of IPA in the fetus was modeled using available age-specific data for ADH to estimate fractional adult values IPA (Table 6). For the lactation model, the adult oxidative metabolism values reported for IPA and acetone were adjusted to age-specific values using age-specific information on ADH (for IPA metabolism) and CYP2E1 (for acetone metabolism). The dose metrics estimated for IPA were fetal and infant arterial blood concentrations of both IPA and acetone. The chemical-specific parameters for vinyl chloride were taken from Clewell et al. (2001b). No fetal metabolism was modeled, because there is no quantitative information suggesting the development of CYP2E1 in utero. 158
KFC1
Methylene Chlorideb 12.9 12.9 0.93 9.1 2.9 0.78 0.93 0.93 0.78 0.93 5.0 0.4 1.5 0.0 0.0 ---------0.0 --
Isopropanola 848.0 848.0 1.33 0.32 1.16 1.3 1.25 1.25 1.3 1.25 300.0 10.0 0.0 0.0 11.0 260.0 0.69 0.44 0.58 0.7 0.69 0.69 0.7 0.69 3.5 10.0
0.0 --
1.0e+7 1.5 0.5 2.0 1.5 1.5 1.5 1.5 1.5
0.431 0.0
114.19
0.0
1.0e+7 1.0e+7 3.0 1.0 9.0 2.5 3.0 3.0 2.5 3.0
Nicotinec
Table 7: Chemical Specific Parameters
0.0 --
0.1 0.1 0.1 0.1 0.1 0.1 0.1 0.1 0.1
0.0 0.0
0.28 7.7 0.0
11.58 11.58 5.06 125.2 5.27 6.11 5.06 5.06 6.11 5.06
Perchloroethylened
0.0 --
----------
0.0 0.0
0.0 1.0 0.0618
1.0e+7 1.0e+7 3.35 187.0 4.6 4.48 3.35 3.35 4.48 3.35
TCDDe
a
First-order metabolism rate (kg0.25/hr) 0.0 0.0 5.762 0.0 0.0 Clewell et al. 2001a; bAndersen et al. 1987; cRobinson et al. 1992; dGearhart et al. 1993; eAndersen et al. 1997; fClewell et al. 2001b
Parameter Parent Chemical Partition Coefficients PB Blood/air PMuc Mucous/air PBrn Brain/blood PFat Fat/blood PLiv Liver/blood PMam Mammary tissue/blood PPla Placenta/blood PRap Rapidly perfused tissue/blood PSlw Slowly perfused tissue/blood PUtr Uterus/blood Parent Chemical Metabolism Parameters VMaxC Maximum reaction rate (mg/hr/kg0.75) KM Affinity constant (mg/L) KFC First-order metabolism rate (kg0.25/hr) Parent Chemical Uptake and Clearance Parameters ClUrC Urinary clearance rate (L/hr/kg0.75) kUrtC Upper respiratory tract uptake (L/hr/kg0.75) Metabolite Partition Coefficients PB1 Blood/air PBrn1 Brain/blood PFat1 Fat/blood PLiv1 Liver/blood PMam1 Mammary tissue/blood PPla1 Placenta/blood PRap1 Rapidly perfused tissue/blood PSlw1 Slowly perfused tissue/blood PUtr1 Uterus/blood Metabolite Metabolism Parameters VMaxC1 Maximum reaction rate (mg/hr/kg0.75) KM1 Affinity constant (mg/L)
159
0.0
0.0 --
----------
0.0 0.0
4.0 0.1 0.0
1.16 1.16 1.45 20.7 1.45 0.83 1.45 1.45 0.83 1.45
Vinyl Chloridef
Oxidative metabolism for the mother and lactating infant was modeled using data for CYP2E1 (Vieira et al. 1996). Dose metrics relevant for comparisons were the fetal and infant arterial blood concentrations of vinyl chloride and the infant rate of metabolism of vinyl chloride per kg of liver volume, as a measure of tissue exposure to reactive metabolites (Clewell et al. 2001b). The chemical-specific parameters for methylene chloride were taken from Andersen et al. (1987). As with vinyl chloride, oxidative metabolism of methylene chloride in the fetus was not modeled due to the lack of any data suggesting activity of the CYP2E1 pathway during gestation. First-order metabolism of methylene chloride also occurs via the glutathione-S-transferase (GST) pathway; however, only limited quantitative information is available on the development of this enzymatic pathway. Based on the limited data, the trend associated with the development of this pathway (Pacifici et al. 1981; Mendrala et al. 1993) is similar to that observed for ADH. Therefore, age-related information on the metabolic capacity for ADH was used as a surrogate for GST. For the lactation model, the adult oxidative metabolism of methylene chloride was modeled using age-specific information on CYP2E1 (oxidative) and ADH (first-order). Dose metrics output for the methylene chloride runs were the fetal and infant arterial blood concentrations of the chemical, the infant rate of oxidative metabolism of methylene chloride per kg of liver volume, and the fetal and infant rates of first-order metabolism per kg of liver volume. The chemical-specific parameters for perchloroethylene and its metabolite, trichloroacetic acid, were taken from Gearhart et al. (1993). Since the available PBPK model for perchloroethylene used only a single-compartment model for the metabolite, the tissue:blood partitions in the pregnancy and lactation models for the metabolite were set to be equal to the fractional volume of distribution from the perchloroethylene model. Metabolism in the fetus was not modeled for perchloroethylene due to the lack of information suggesting the development of the relevant metabolic pathway during gestation; however, oxidative metabolism in the mother and lactating infant was modeled using available data for CYP2E1 (Vieira et al. 1996). The existing model for perchloroethylene (Gearhart et al. 1993) described the amount of TCA produced as only 60% of the total metabolized amount of perchloroethylene; therefore, the three models were parameterized such that 60% of the metabolized perchloroethylene was assumed to form TCA. Dose metrics output for perchloroethylene were the fetal and infant arterial blood concentrations of both the parent chemical and the metabolite TCA, 160
the proposed toxic moiety for effects observed following perchloroethylene exposure. Non- or Semi-Volatiles Partition coefficients for TCDD were obtained from Murphy et al. (1995), and the adult metabolism parameters used were reported in Andersen et al. (1997). Because TCDD is a nonvolatile chemical, the pregnancy and lactations models were run with a sufficiently large blood/air partition coefficient to prevent elimination via the respiratory tract. Fetal metabolism was not modeled due to a lack of information suggesting that the relevant pathway develops in utero, and maternal and infant first-order metabolism were modeled using data for CYP1A2. The first (pre-pregnancy) simulation for TCDD was run for five years of exposure rather than until steady-state was reached because, after simulation of five years, the model still had not completely reached steady-state, reflecting the long timeframe for bioaccumulation of TCDD. The dose metrics output for TCDD were the fetal and infant arterial blood concentrations of TCDD. For nicotine, the pregnancy and lactation models were run using the partition coefficients and the adult metabolism parameters from Robinson et al. (1992). The metabolism parameters reported for this model were absolute clearances (L/hr) and were therefore converted to body-weight adjusted kinetic constants (kg¼/hr) using female body and liver weights as needed. As with TCDD, the model was run with a very large blood/air partition coefficient to prevent elimination via exhalation because of the low volatility of nicotine. Firstorder metabolism of nicotine is via CYP2D, but, due to the lack of age-related data on CYP2D, metabolism was modeled using data on CYP2C. The same was true for the metabolite cotinine, which is also metabolized via the CYP2D pathway. The model was parameterized such that 80% of nicotine was metabolized to cotinine based upon experimental evidence (Robinson et al. 1992). No fetal metabolism was modeled, but maternal and infant metabolism were modeled using the age-specific data for CYP2D. Urinary clearance of nicotine was modeled using the adult urinary clearance values from Robinson et al. (1992) which had to be converted to 1st order rate constants for use in the three models. For the infant, the urinary clearance rate for nicotine was scaled by the current body weight, which may result in an overestimate of urinary clearance of the stable metabolites due to the development of enzymatic capabilities during the first few weeks of life. The dose metrics output for nicotine were the fetal and infant arterial blood concentrations of nicotine and cotinine. 161
Results and Discussion The time course for isopropanol and its metabolite acetone during gestation is shown in Figure 4. Initially, both isopropanol and acetone concentrations in the fetus are determined by the maternal circulating concentrations. However, near the end of the first trimester the development of fetal ADH metabolism results in a sharp drop in the fetal concentration of isopropanol. Acetone concentrations continue to be dominated by maternal
Fetal Blood Concentration of Isopropanol (mg/L)
5.0E-6
2.0E-4
4.0E-6 1.5E-4 3.0E-6 1.0E-4 2.0E-6 5.0E-5
Isopropanol
1.0E-6
Fetal Blood Concentration of Acetone (mg/L)
2.5E-4
6.0E-6
Acetone 0.0E+0
0.0E+0 0
1
2
3
4
5
6
7
8
9
Fetal Age (months)
Figure 4. Fetal blood concentrations of isopropanol and its metabolite acetone as a function of fetal age following maternal oral exposure to 1 µg/kg/day isopropanol. levels because the enzyme system responsible for its metabolism, CYP2E1, does not develop prenatally. For the same reason, blood concentrations of both vinyl chloride and methylene chloride are relatively constant during gestation (Figures 5 and 6), while the production of metabolites from methylene chloride by the first-order pathway increases sharply with the prenatal development of 162
glutathione transferase activity (Figure 6). The less pronounced changes in concentration seen with perchloroethylene, TCDD, and nicotine (Figures 7-9) result from changes in tissue composition in both the mother and fetus during pregnancy. During lactation, the concentrations of isopropanol and its metabolite acetone in the infant blood (Figure 10) followed the estimated intake of the parent chemical from breast milk (Figure 11). Similar results were predicted for the other volatile chemicals (vinyl chloride, methylene chloride and perchloroethylene) (Figures12, 13, 14). The rapid decline in concentration at birth with these volatile chemicals reflects the transition from the fetal exposure to that of the nursing infant (Figures 10, 12, 13, 14). A strong relationship between infant concentrations and amount of chemical transferred in the milk was also observed with the more water soluble compounds, nicotine and cotinine (Figures 15 and 16), although the more complex time course for cotinine suggests a dependence on other changes in the infant. With TCDD, only a slight decline in blood concentrations was observed in the transition from pregnancy to exposure via lactation (Figure 17), due to the slow elimination of TCDD. When TCDD blood levels in breast-fed infants are compared to bottlefed infants (Figure 18), a greater decrease in blood concentration is observed in the bottle-fed infants by six months of age, even when the bottle-fed infants are assumed to be exposed to the same daily intake of TCDD (in mg/kg/day) as the mother. For all of the chemical classes evaluated, a decrease in blood concentrations of the parent chemical was observed during the lactation period, compared to during pregnancy (Table 8). This decrease varied from only a slight change (i.e., TCDD) to approximately four orders of magnitude (i.e., vinyl chloride). With the exception of cotinine, metabolite exposure during lactation was also lower than during gestation. These differences primarily reflect the lower exposure rate through lactation compared to placental exposure, although maturation of metabolic systems also plays a role in some cases (e.g., cotinine). For reactive metabolites (e.g., for vinyl chloride and methylene chloride) there is essentially no fetal exposure, but increasing exposure during lactation can occur as the responsible enzyme systems (e.g., CYP2E1) develop in the infant.
163
Fetal Blood Concentration of Vinyl Chloride (mg/L)
8.0E-7
6.0E-7
4.0E-7
2.0E-7
0.0E+0 0
1
2
3
4
5
6
7
8
9
Fetal Age (months)
6.0E-6
6.0E-8
5.0E-6
5.0E-8
4.0E-6
4.0E-8
3.0E-6
3.0E-8
Blood concentration First-order metabolism rate
2.0E-6
2.0E-8
1.0E-6
1.0E-8
0.0E+0
Fetal First-Order Metab. Rate of Methylene Chloride / kg Liver
Fetal Blood Concentration of Methylene Chloride (mg/L)
Figure 5. Fetal blood concentrations of vinyl chloride as a function of fetal age following maternal oral exposure to 1 µg/kg/day vinyl chloride
0.0E+0 0
1
2
3
4
5
6
7
8
9
Fetal Age (months)
Figure 6. Fetal blood concentrations of methylene chloride and the fetal 1st order rate of metabolite production per kg of liver as a function of fetal age following maternal oral exposure to 1 µg/kg/day methylene chloride.
164
1.5E-9
3.0E-4 1.0E-9
2.0E-4
5.0E-10 1.0E-4
Fetal Blood Concentration of TCA (mg/L)
Fetal Blood Concentration of Perchloroethylene (mg/L)
4.0E-4
Perchloroethylene TCA 0.0E+0
0.0E+0 0
1
2
3
4
5
6
7
8
9
Fetal Age (months)
Figure 7. Fetal blood concentrations of perchloroethylene and its metabolite TCA as a function of fetal age following maternal oral exposure to 1 µg/kg/day perchloroethylene.
Fetal Blood Concentration of TCDD (mg/L)
1.0E-4
8.0E-5
6.0E-5
4.0E-5
2.0E-5
0.0E+0 0
1
2
3
4
5
6
7
8
9
Fetal Age (months)
Figure 8. Fetal blood concentrations of TCDD as a function of fetal age following maternal oral exposure to 1 ng/kg/day TCDD.
165
1.2E-3
1.0E-3
8.0E-5
8.0E-4 6.0E-5 6.0E-4 4.0E-5 4.0E-4 2.0E-5
Fetal Blood Concentration of Cotinine (mg/L)
Fetal Blood Concentration of Nicotine (mg/L)
1.0E-4
2.0E-4
Nicotine Cotinine 0.0E+0
0.0E+0 0
1
2
3
4
5
6
7
8
9
Fetal Age (months)
Figure 9. Fetal blood concentrations of nicotine and its metabolite cotinine as a function of fetal age following maternal oral exposure to 1 µg/kg/day nicotine. 1.0E-8
4.0E-5 Isopropanol
7.5E-9
3.0E-5
5.0E-9
2.0E-5
2.5E-9
1.0E-5
0.0E+0
Infant Blood Concentration of Acetone (mg/L)
Infant Blood Concentration of Isopropanol (mg/L)
Acetone
0.0E+0 0
1
2
3
4
5
6
Age (months)
Figure 10. Infant blood concentrations of isopropanol and its metabolite acetone as a function of age following exposure via lactation to isopropanol.
166
1.0E-6
8.0E-5 Isopropanol Acetone
8.0E-7
6.0E-7 4.0E-5 4.0E-7
Amount of Acetone Suckled (mg/kg/day)
Amount of Isopropanol Suckled (mg/kg/day)
6.0E-5
2.0E-5 2.0E-7
0.0E+0
0.0E+0 0
1
2
3
4
5
6
Age (months)
Figure 11. Amount of isopropanol and its metabolite acetone in milk suckled by the infant as a function of age following maternal oral exposure at 1 µg/kg/day. 3.0E-10
8.0E-8 Blood concentration
Infant Blood Concentration of Vinyl Chloride (mg/L)
6.0E-8 2.0E-10
1.5E-10
4.0E-8
1.0E-10 2.0E-8 5.0E-11
0.0E+0
Infant Oxidative Metabolism Rate of Vinyl Chloride / kg Liver
Metabolism rate
2.5E-10
0.0E+0 0
1
2
3
4
5
6
Age (months)
Figure 12. Infant blood concentrations of vinyl chloride and the infant oxidative rate of metabolite production per kg of liver as a function of age following exposure via lactation to vinyl chloride
167
5.0E-8
1.0E-6 Blood concentration
4.0E-8
8.0E-7
3.0E-8
6.0E-7
2.0E-8
4.0E-7
1.0E-8
2.0E-7
0.0E+0
Infant Total Metabolism Rate of Methylene Chloride / kg Liver
Infant Blood Concentration of Methylene Chloride (mg/L)
Metabolism rate
0.0E+0 0
1
2
3
4
5
6
Age (months)
Figure 13. Infant blood concentrations of methylene chloride and the infant total metabolism rate per kg of liver as a function of age following exposure via lactation to methylene chloride. 5.0E-5
5.0E-10 Perchloroethylene 4.0E-10
3.0E-5
3.0E-10
2.0E-5
2.0E-10
1.0E-5
1.0E-10
0.0E+0
Infant Blood Concentration of TCA (mg/L)
Infant Blood Concentration of Perchloroethylene (mg/L)
TCA 4.0E-5
0.0E+0 0
1
2
3
4
5
6
Age (months)
Figure 14. Infant blood concentrations of perchloroethylene and its metabolite TCA as a function of age following exposure via lactation to perchloroethylene.
168
3.0E-6
2.0E-3 Nicotine
Infant Blood Concentration of Nicotine (mg/L)
1.5E-3 2.0E-6
1.5E-6
1.0E-3
1.0E-6 5.0E-4
Infant Blood Concentration of Cotinine (mg/L)
Cotinine
2.5E-6
5.0E-7
0.0E+0
0.0E+0 0
1
2
3
4
5
6
Age (months)
Figure 15. Infant blood concentrations of nicotine and its metabolite cotinine as a function of age following exposure via lactation to nicotine. 8.0E-6
2.0E-4 Nicotine Cotinine 1.6E-4
1.2E-4 4.0E-6 8.0E-5
Amount of Cotinine Suckled (mg/kg/day)
Amount of Nicotine Suckled (mg/kg/day)
6.0E-6
2.0E-6 4.0E-5
0.0E+0
0.0E+0 0
1
2
3
4
5
6
Age (months)
Figure 16. Amount of nicotine and its metabolite cotinine in milk suckled by the infant as a function of age following maternal oral exposure at 1 µg/kg/day to nicotine.
169
7.0E-5
Infant Blood Concentration of TCDD (mg/L)
6.0E-5 5.0E-5 4.0E-5 3.0E-5 2.0E-5 1.0E-5 0.0E+0 0
1
2
3
4
5
6
Age (months)
Figure 17. Infant blood concentrations of TCDD as a function of age following exposure via lactation to TCDD. 7.0E-5
Infant Blood Concentration of TCDD (mg/L)
6.0E-5 5.0E-5 4.0E-5 3.0E-5 2.0E-5 Breast-fed
1.0E-5
Bottle-fed 0.0E+0 0
1
2
3
4
5
6
Age (months)
Figure 18. Blood concentrations of TCDD as a function of age in breast-fed versus bottle-fed infants.
170
Chemical
2.24e-4 1.27e-9
Parent Metabolite
Perchloroethylene
7.31e-5 8.02e-4
Parent Metabolite
Nicotine
6.48e-5
Parent
TCDD
5.06e-6
Parent
Methylene Chloride
5.77e-7
1.73e-4
Metabolite Parent
1.91e-7
Parent
1st Trimester
8.96e-4
8.34e-5
6.27e-5
1.26e-9
2.50e-4
5.44e-6
6.08e-7
1.89e-4
7.08e-8
2nd Trimester
9.79e-4
9.29e-5
6.47e-5
1.25e-9
2.78e-4
5.58e-6
6.31e-7
2.01e-4
5.19e-8
Birth
Fetal Concentrations During Pregnancy
1.81e-3
6.99e-7
5.38e-5
1.89e-11
4.99e-7
1.30e-8
7.12e-11
2.16e-5
7.92e-9
1 month
9.60e-4
4.88e-7
4.78e-5
1.99e-11
3.90e-7
7.99e-9
4.35e-11
1.41e-5
5.47e-9
3 months
5.72e-4
3.52e-7
4.65e-5
1.86e-11
3.09e-7
5.45e-9
2.99e-11
9.88e-6
3.81e-9
6 months
Infant Concentrations During Lactation
Table 8 Average Blood Concentrations During Pregnancy and Lactation
Vinyl Chloride
Isopropanol
171
6.50e-4
3.30e-5
2.64e-5
1.28e-8
7.28e-5
6.18e-6
4.43e-7
2.81e-4
4.20e-6
Pregnancy
6.41e-4
3.29e-5
2.82e-5
1.29e-8
7.33e-5
6.17e-6
4.43e-7
2.79e-4
4.18e-6
Lactation
Maternal Concentrations
A comparison with similar dose metric calculations for the mother (Table 8, last two columns) shows that the perinatal exposures are never more than a factor of three greater than the maternal exposure, and are often much lower. The results for TCDD are consistent with the generally held principle that perinatal exposure is of more concern for highly lipophilic chemicals; however, relatively high perinatal exposure is also predicted for the water soluble compounds nicotine (during gestation) and cotinine (during lactation). Several other analyses have been conducted or are ongoing to evaluate the pharmacokinetic differences between children and adults and how these differences should be considered in risk assessment (Renwick et al. 2000; Hattis et al. 2000; Pelekis et al. 2001). These studies are partly in response to the recommendation of an additional uncertainty factor of 10 in the Food Quality Protection Act of 1996 to take into account pre- and postnatal toxicity and the completeness of the database for exposure and toxicity in children. Some of the work (Renwick et al. 2000; Hattis et al. 2000) has relied upon the available pharmacokinetic data for pharmaceuticals, which are more readily available than similar data for environmentally relevant chemicals. However, there have been concerns regarding the applicability of results for pharmaceuticals to environmentally relevant chemicals. The results of the present analysis for environmental contaminants are in general agreement with the findings of quantitative analyses of data on pharmaceutical chemicals (Hattis et al. 2000; Renwick et al. 2000), which have suggested that the largest difference in pharmacokinetics observed between children and adults is for the early postnatal period. These differences, which are reflected in a slower clearance of the chemicals in the infant, appear to be related primarily to the immaturity of the metabolic enzyme systems responsible for clearance of these chemicals from the body. However, these enzyme systems mature rapidly, resulting in smaller differences in pharmacokinetics as compared to adults (generally on the order of a factor of 2 or 3) by the time children reach 6 months of age. However, depending on the mode of action of the chemical, the parent chemical may not be the relevant dose metric. For example, in the case of exposures to vinyl chloride, the observed chronic toxicity and carcinogenicity is produced by a reactive metabolite. The analyses performed in this study suggest that variations in metabolite exposure can be more significant than the corresponding variations in parent chemical exposure. Fortunately, in many, 172
though not all, of the other cases where metabolite concentrations are highly variable across age, the variation puts the infant/child at lower risk than the adult (e.g., methylene chloride and perchloroethylene), based on differences in pharmacokinetics. Conclusions The results of the current analyses provide some insight into potential differences in pharmacokinetics between children and adults and how these differences may result in differences in internal dose metrics following chemical exposure. The age range of greatest concern is clearly the perinatal period. The most important factor appears to be the potential for decreased clearance of toxic chemicals in the perinatal period due to immature metabolic enzyme systems, although this same factor can also reduce the risk from reactive metabolites during the same period. Fortunately, when large differences in dosimetry are limited to the early childhood period, their influence on the lifetime average daily dose, the focus of cancer risk assessment and many chronic non-cancer effects, is limited because those changes occur during a relatively small portion of the lifetime. However, exposure during childhood may present a window of susceptibility (from a pharmacokinetic perspective) for short-term effects, due to immaturity of the various clearance systems. References Andersen M, Birnbaum L, Barton H, Eklund C. 1997. Regional hepatic CYP1A1 and CYP1A2 induction with 2,3,7,8-tetrachlorodibenzo-p-dioxin evaluated with a multicompartment geometric model of hepatic zonation. Toxicology and Applied Pharmacology 144(1):145-155. Andersen M, Clewell H, Gargas M, Smith F, and Reitz R. 1987. Physiologically based pharmacokinetics and the risk assessment process for methylene chloride. Toxicology and Applied Pharmacology 87(2):185-205. Casey CE, Neifert MR, Seacat JM, and Neville MC. 1986. Nutrient Intake by Breast-fed Infants During the First Five Days After Birth. Am J Dis Child 140:933-936.
173
Clewell HJ and Andersen ME. 1996. Use of physiologically-based pharmacokinetic modeling to investigate individual versus population risk. Toxicology 111:315-329. Clewell, HJ, Gearhart, JM, Gentry, PR, Covington, TR, VanLandingham, CB, Crump, KS, and Shipp, AM. 1999. Evaluation of the uncertainty in an oral Reference Dose for methylmercury due to interindividual variability in pharmacokinetics. Risk Anal 19:541-552. Clewell HJ, Gentry PR, Gearhart JM, Covington TR, Banton MI, and Andersen ME. 2001a. Development of a physiologically based pharmacokinetic model of isopropanol and its metabolite acetone. Toxicol. Sci. 63:160-172. Clewell HJ, Gentry PR, Gearhart JM, Allen BC,and Andersen ME. 2001b. Comparison of cancer risk estimates for vinyl chloride using animal and human data with a PBPK model. Science of the Total Environment 274(1-3):37-66. Clewell H, Teeguarden J, McDonald T, Sarangapani R, Lawrence G, Covington T, Gentry R, and Shipp A. 2002. Review and Evaluation of the Potential Impact of Age and Gender-Specific Pharmacokinetic Differences on Tissue Dosimetry. Critical Reviews in Toxicology 32(5):329-389. Clewell H. J., Gentry, R., Covington T., Sarangapani R., and Teeguarden J. 2003. Evaluation of the Potential Impact of Age- and Gender-Specific Pharmacokinetic Differences on Tissue Dosimetry. Toxicological Sciences (submitted). Fisher J, Whittaker T, Taylor D, Clewell H, and Andersen M. 1990. Physiologically based pharmacokinetic modeling of the lactating rat and nursing pup: a multiroute exposure model for trichloroethylene and its metabolite, trichloroacetic acid. Toxicology and Applied Pharmacology 102(3):497-513. Fisher J, Mahle D, Bankston L, Greene R, and Gearhart J. 1997. Lactational Transfer of Volatile Chemicals in Breast Milk. American Industrial Hygiene Association Journal 58:425-431. Food and Nutrition Board. 1987. Nutrition Recommendations for the Breastfeeding Woman and her Infant. Committee on Nutritional Status During Pregnancy and Lactation. Nutrition Status During Lactation Report, National 174
Academy Press, 1991. www.mcg.edu/PedsOnL/ForHealthProf/PedNutrition/brstfeed.html. Gearhart JM, Mahle DA, Greene RJ, Seckel CS, Flemming CD, Fisher JW, Clewell HJ. 1993. Variability of physiologically-based pharmacokinetic (PB-PK) model parameters and their effects on PB-PK model predictions in a risk assessment for perchloroethylene. Toxicol Lett 68:131-144. Gentry PR, Covington TR, Andersen ME, and Clewell HJ. 2002. Application of a physiologically-based pharmacokinetic model for isopropanol in the derivation of an RfD/RfC. Regulatory Toxicology and Pharmacology 36:51-68. Hattis D, Russ A, Banati P, Kozlak M, Goble R, and Ginsberg, G. 2000. Development of a Comparative Child/Adult Pharmacokinetic Database based upon the Therapeutic Drug Literature. Draft report. Developed for NCEA/ORD, Washington, D.C. International Commission on Radiological Protection (ICRP). 1975. Report of the Task Group on Reference Man. Pergamon Press, Oxford. ICRP Publication 23. Mendrala AL, Langvardt PW, Nitschke KD, Quast JF and Nolan RJ. 1993. In vitro kinetics of styrene and styrene oxide metabolism in rat, mouse, and human. Arch. Toxicol. 67:18-27. Murphy J, Janszen D, and Gargas M. 1995. An in vitro method for determination of tissue partition coefficients of non-volatile chemicals such as 2,3,7,8tetrachlorodibenzo-p-dioxin and estradiol. Journal of Applied Toxicology 15(2):147-152. Pacifici GM, Boobis AR, Brodie MJ, McManus ME and Davies DS. 1981. Tissue and species differences in enzymes of epoxide metabolism. Xenobiotica 11:73-79. Pelekis M, Gephart L, and Lerman S. 2001. Physiological-Model-Based Derivation of the Adult and Child Pharmacokinetic Intraspecies Uncertainty Factors for Volatile Organic Compounds. Regulatory Toxicology and Pharmacology 33:12-20. 175
Pikkarainen PH and Räihä NCR. 1967. Development of alcohol dehydrogenase activity in the human liver. Pediatric Res. 1:165-168. Renwick A, Dorne J, and Walton K. 2000. An Analysis of the Need for an Additional Uncertainty Factor for Infants and Children. Regulatory Toxicology and Pharmacology 31:286-296. Robinson DE, Balter NJ, and Schwartz SL. 1992. A physiologically based pharmacokinetic model for nicotine and cotinine in man. Journal of Pharmacokinetics and Biopharmaceutics 20:591-609. Sarangapani R, Gentry PR, Covington TR, and Clewell HJ. 2003. Evaluation of the Potential Impact of Age- and Gender-Specific Lung Morphology and Ventilation Rate on the Dosimetry of Vapors. Inhalation Toxicology: (submitted). Shelley M, Andersen M, and Fisher J. 1989. A Risk Assessment Approach for Nursing Infants Exposed to Volatile Organics through the Mother’s Occupational Inhalation Exposure. Appl. Ind. Hyg. 4(1):21-26. Sonnier M and Cresteil T. 1998. Delayed ontogenesis of CYP1A2 in the human liver. Eur. J. Biochem. 251:893-898. Treluyer JM, Gueret G, Cheron G, Sonnier M, and Cresteil T. 1997. Developmental expression of CYP2C and CYP2C-dependent activities in the human liver: in-vivo/in-vitro correlation and inducibility. Pharmacogenetics 7:441-452. U. S. Environmental Protection Agency (USEPA). 1997. Exposure Factors Handbook Volume I: General Factors. Office of Research and Development, Washington, D.C., EPA/600/P-95/002Fa. Vieira I, Sonnier M, and Cresteil T. 1996. Developmental expression of CYP2E1 in the human liver. Hypermethylation control of gene expression during the neonatal period. Eur. J. Biochem. 238:476-483.
176
Appendix A Pregnancy Equations VBldFet = VBldFetC • VFet
(A1)
where VBldFet is the fetal blood volume in L, VBldFetC is the fetal blood volume as a fraction of body weight, and VFet is the fetal body weight in kg. VLivFet = VLivFetC • VFet
(A2)
where VLivFet is the fetal liver volume in L, VLivFetC is the fetal liver volume as a fraction of body weight, and VFet is the fetal body weight in kg. QFet = QFetC • VBldFet (A3) where QFet is the fetal cardiac output in L/hr, QFetC is the fetal cardiac output in L/hr/kg blood, and VBldFet is the fetal blood volume in kg.
QLivFet = QLivC • QFet
(A4)
where QLivFet is the fetal liver blood flow in L/hr, QLivC is the maternal liver blood flow as a fraction of cardiac output, and QFet is the fetal cardiac output in L/hr. dABldFet = (QRemFet • CVBodFet ) + (QLivFetC • CVLivFet ) − (QFet • CBldFet ) + (PAF • (CPla − CBldFet )) dt
(A5)
where ABldFet is the amount of chemical in fetal blood in mg; QRemFet and QLivFet are the fetal blood flows to the remaining fetus and the fetal liver, respectively, in L/hr; CVBodFet and CVLivFet are the fetal venous blood concentrations of the chemical in the remaining fetus and fetal liver, respectively, in mg/L; QFet is the fetal cardiac output in L/hr; CBldFet is the fetal blood concentration of the chemical in mg/L; PAF is the permeability-area factor for diffusion through the placenta in L/hr; and CPla is the blood concentration in the placenta in mg/L. 177
⎛ VMaxC • BWPre 0.75 ⎞ ⎟⎟ • VLivFet • FracMet [Months ] , (A6) VMaxFet = ⎜⎜ VLiv ⎠ ⎝ where VMaxFet is the fetal rate of oxidative metabolism in mg/hr, VMaxC is the maternal body-weight adjusted rate of oxidative metabolism in mg/hr/kg¾, BWPre is the maternal pre-pregnancy body weight in kg, VLiv is the maternal liver volume in L, VLivFet is the fetal liver volume in L, and FracMet(Months) is the table function that gives enzyme activity as a fraction of the adult level.
KFC ⎛ ⎞ KFFet = ⎜ ⎟ • VLivFet • FracMet1[Months ] , 0.25 ⎝ BWPre • VLiv ⎠
(A7)
where KFFet is the fetal rate constant for 1st order metabolism in /hr, KFC is the maternal body-weight adjusted rate constant for 1st order metabolism in kg¼/hr, BWPre is the maternal pre-pregnancy body weight in kg, VLiv is the maternal liver volume in L, VLivFet is the fetal liver volume in L, and FracMet1(Months) is the table function that gives enzyme activity as a fraction of the adult level. Lactation Equations ⎧⎛ ⎞ Months ⎪⎜ ((VTissueC • BWPre) − VTissuePreg ) • ⎟ + VTissuePreg , if Months ≤ Period of Change VTissue = ⎨⎜⎝ Period of Change ⎟⎠ ⎪⎩VTissueC • BWPre, if Months > Period of Change
(A8)
where Tissue is either fat or mammary tissue, VTissue is the volume of the specified tissue in L; VTissueC is the pre-pregnancy tissue volume for fat as a fraction of pre-pregnancy body weight or the peak tissue volume for mammary tissue as a fraction of pre-pregnancy body weight; BWPre is the pre-pregnancy body weight in kg; VTissuePreg is the tissue volume at the end of pregnancy in L; and Period of Change is the time period, in months, over which the decrease or increase in tissue volume is occurring.
178
PMilk:Blood = (0.04 • PFat:Blood ) + (0.96 • (1.0) )
(A9)
where PMilk:Blood is the partition coefficient between the milk and blood for a surrogate chemical and PFat:Blood is the partition coefficient for a surrogate chemical. ⎧⎛ ⎛ Hours ⎞ ⎞ ⎪⎜ 7.669 • tanh ⎜ ⎟ ⎟ + 3.375, if Months < 11 BabBW = ⎨⎝ ⎝ 6810.212 ⎠ ⎠ ⎪ ((0.006486 • Hours ) • 43.202 )0.5 , if Months ≥ 11 ⎩
(A10)
where BabBW is the infant body weight in kg, Hours is the age in hours, and Months is the age in months. ⎛ PTissue ⎞ ICTissueBab = ⎜ ⎟ • ICFet ⎝ PSlw ⎠
(A11)
where Tissue is blood, brain, fat, liver, mucous, rapidly perfused tissues, or slowly perfused tissues; ICTissueBab is the initial concentration of chemical in the specified tissue in mg/L; PTissue is the tissue to blood partition coefficient for the specified tissue (for blood this value is 1.0); PSlw is the slowly perfused tissues to blood partition coefficient, which is considered to be representative of partitioning of chemicals into total fetal tissue; ICFet is the fetal concentration of the chemical in mg/L at the end of the pregnancy run based on the total amount of the chemical in each compartment combined. ⎛ VMaxC • BWPre 0.75 ⎞ ⎟⎟ • VLivBab • FracMet [Months ] , VMaxBab = ⎜⎜ VLiv ⎠ ⎝
(A12)
where VMaxBab is the infant rate of oxidative metabolism in mg/hr, VMaxC is the maternal body-weight adjusted rate of oxidative metabolism in mg/hr/kg¾, BWPre is the maternal pre-pregnancy body weight in kg, VLiv is the maternal liver volume in L, VLivBab is the infant liver volume in L, and FracMet(Months) is the table function that gives enzyme activity as a fraction of the adult level. 179
KFC ⎛ ⎞ KFBab = ⎜ ⎟ • VLivBab • FracMet1[Months ] , 0.25 ⎝ BWPre • VLiv ⎠
(A13)
where KFBab is the infant rate constant for 1st order metabolism in /hr, KFC is the maternal body-weight adjusted rate constant for 1st order metabolism in kg¼/hr, BWPre is the maternal pre-pregnancy body weight in kg, VLiv is the maternal liver volume in L, VLivBab is the infant liver volume in L, and FracMet1(Months) is the table function that gives enzyme activity as a fraction of the adult level.
180
Chapter 6
Data for Physiologically-Based Pharmacokinetic Modeling in Neonatal Animals: Physiological Parameters in Mice and Sprague-Dawley Rats P. Robinan Gentry, Lynne T. Haber, Tracy B. McDonald, Qiyu Zhao, Tammie Covington, Patricia Nance, Harvey J. Clewell, III, John C. Lipscomb, and Hugh A. Barton ENVIRON International Corporation, Ruston, LA, USA
Journal of Children’s Health, 2004, 2(3-4):363-411 181
Abstract Recent scientific and policy initiatives have resulted in increased interest in risk to fetuses, infants, and children and consideration of how such risks should be evaluated. A useful way of addressing this issue is to use physiologically-based pharmacokinetic (PBPK) models to compare the tissue dose that children and adults receive for a given amount of a chemical ingested or inhaled. The response in children and adults for a given tissue dose can also be compared. To aid in the development of age-specific PBPK models for experimental animals, we have collected information on physiological parameters in neonates and young animals, through 60 days of age. Our effort focussed on generic physiological values, such as tissue weight (termed tissue volume in the context of PBPK modeling), intake (alveolar ventilation, food intake, water intake) and flows (blood flows to tissues, bile flow, creatinine clearance, and glomerular filtration rate). To date, parameters for Sprague Dawley rats and mice of multiple strains have been collected and evaluated for data gaps and patterns. Using this database, we found that food intake in neonates does scale with approximately bw3/4. The database is available on request from the corresponding author. Introduction Growing attention to children in risk assessments for chemicals is motivated in part by the growing awareness that children, infants, and fetuses may react to chemical exposures differently from adults. For example, the neurological, immunological, and digestive systems of children are still developing, and thus can be more sensitive to chemical damage. These differences can lead to impaired fetal or child development at doses that do not induce adverse effects in adults. Other systems may be fully developed, but may be more sensitive, due to the more rapid cell division associated with the growing organism. In addition, children undergo substantial hormonal changes as they enter sexual maturity. Chemical toxicokinetics (absorption, distribution, metabolism, and excretion) can also differ between adults and children, particularly for children under one year of age (reviewed by Scheuplein et al. 2002; Renwick 1998; NAS 1993; Clewell et al. 2002, Ginsberg et al. 2002, 2004; Price et al. 2003; Hattis et al. 2003). For example, gastric dynamics and enzyme levels differ from adult human values, but generally reach adult levels by the first year of life or earlier. Distribution can exhibit age-specific differences, due to such factors as changes in total body fat and extracellular water. Both chemical biotransformation (Phase I) 182
and conjugation (Phase II) metabolism systems are generally immature at birth, though infants may have alternative metabolic pathways (e.g. neonatal-specific enzyme forms). The neonate also has a higher relative liver mass and lower glomerular filtration rate (GFR), leading to differences in elimination. When expressed per unit body weight, infants and children also consume more food and water, and breathe more air than adults. These differences in intake rates and in toxicokinetics can lead to age-related differences between adults and children in the relationship between ingested dose and tissue dose, or between the concentration in food, water, or air, and the resulting tissue dose. Physiologically-based pharmacokinetic (PBPK) models can be used to estimate the relationship between the amount of a chemical ingested or the concentration in air, and the tissue dose of the agent (e.g., parent chemical or a toxic metabolite) causing the toxic effect (Clewell and Andersen 1994). Expressing the dose in terms of the dose metric (a measure of internal dose tightly linked to the endpoint of interest) allows one to address mechanistic questions for dose response. Expressing the dose-response relationship in terms of target tissue dose also allows one to determine whether age-related differences are due to differences in the relationship between ingested/inhaled amount and tissue dose (i.e., toxicokinetics), or due to differences in how the tissue responds (toxicodynamics). It is possible that clearly separating toxicokinetics and toxicodynamics may help clarify or address some age-related differences that are currently attributed to differences in toxicodynamics. PBPK models of young animals and children address the question of whether children of a specific age receive a higher dose than adults for a given chemical exposure. PBPK models of young animals can be used to assist in the extrapolation of toxicity data from young experimental animals to humans. Such extrapolations depend both upon the correspondence across species of the pharmacokinetics and the correspondence of the window of susceptibility for the effect. To estimate tissue dose in young animals, information on age-related changes in the parameters used for PBPK modeling is needed. The absorption, distribution, metabolism and elimination of potentially toxic chemicals within the environment can change significantly with changes in physiological parameters, such as the size, composition, and perfusion of organs and tissues (Altman and Dittmer 1971; Davies and Morris 1993). Relatively few PBPK models of developing animals are available. The models published to date have usually investigated only one age group (besides adults) or included only limited agerelated changes. For example, Rao et al. (1995) developed a PBPK model of 183
fluoride in developing rats and humans using age-dependent body weight, but otherwise assuming that cardiac output for various tissues is independent of age. O’Flaherty (1991a, 1991b) developed a PBPK model for bone-seeking elements that included age-related changes in the bone and skeleton, but not in richly perfused tissues. Evans et al. (2002) developed a model that evaluated chloroform dose in adolescent, adult, and senescent rats, using the age-related physiological parameters from Delp et al. (1998). Pelekis et al. (2001) estimated the adult/child variability in tissue dose following exposure to volatile organic compounds, using child-specific parameters for tissue volume and blood flows for the 10-kg child; other ages were not included in the model. Clewell et al. (2004) developed a “life-stage” PBPK model that incorporates age- and gender-specific pharmacokinetic information in the human related to physiological and biochemical processes that affect tissue dosimetry. It was developed based on quantitative information identified in Clewell et al. (2002). Models for pregnancy and lactation are also available (O’Flaherty et al. 1992; Clarke et al. 1993; Welsch et al. 1995; Gray 1995; Gentry et al. 2003; Byczkowski and Lipscomb 2001; Byczkowski and Fisher 1994). Notable recent work focuses on describing perchlorate exposures and its effects on iodine kinetics at different lifestages in rats (Clewell et al. 2003a, b). Reviews documenting and collating information on age-related changes in physiological parameters in animals are limited. Fuller and Geils (1972) developed an equation relating age and brain weight, or age and body weight, in mice. In an early publication, Donaldson (1924) compiled data on the albino and Norway rat, and developed equations relating organ weights to body weight. Goedbloed (1972) compiled data on postnatal body weight for a number of strains of mice, but did not collect tissue volume data. Bartlett and Areson (1977) developed equations relating oxygen consumption and other parameters not considered in this report (lung volume and respiratory surface area) to body weight for newborn animals for a variety of species; age-dependent changes were not evaluated. More extensive data however, is needed on age-related changes in physiological parameters for a variety of strains and species of experimental animals. To aid in the development of more age-specific data, we have collected information on physiological parameters in neonates and young animals, through 60 days of age. This effort focussed on basic underlying physiological values, such as tissue weight (termed tissue volume in the context of PBPK modeling), intake (alveolar ventilation, food intake, water intake) and flows (blood flows to 184
tissues, bile flow, creatinine clearance, and glomerular filtration rate). Metabolic and distribution parameters were not collected; such work would need to be done on an enzyme or chemical-specific basis. Approaches such as those developed by Poulin and Krishnan (1996) expressing tissue:air partition coefficients as a function of the tissue solubility of the chemical and its saturable vapor concentration could be extended to address age-dependent changes in body composition and distribution. This report presents data on tissue volumes, flows, and intake for neonatal and young mice and Sprague Dawley rats. Future work may address other strains of rat, for which data collection was begun. This effort is modeled after a similar collection of physiological parameter values to support PBPK models for adult animals (ILSI 1994; Brown et al. 1997). Few studies were specifically designed to measure age-related changes in these parameters. Instead, data are scattered throughout the literature, with much of the data collected from control groups in studies evaluating toxic effects of chemicals. Time series data were preferred, to aid in consistency of the data. The ultimate goals of this work are to: (1) provide a state-of-the-science compilation of PBPK parameter values for developing animals; (2) use actual age-specific and strain-specific data, to reflect agespecific differences in growth, in lieu of default algorithms; and (3) identify data gaps for further development of age-specific PBPK models. This effort compliments other research efforts to compile similar data for humans or other test animals. Methods Literature search and screening Literature searches were conducted for age-related changes in (1) volumes of organ/tissue compartment, (2) rate of blood flow to organs/tissues (including cardiac output), and (3) intake (including alveolar ventilation and food/water consumption). A separate set of search terms was used to identify relevant citations for each major class. The search strategy for tissue volume specified liver, kidney, lung, heart, brain, and lipid. Other tissues identified in the literature search and in retrieved articles were added to the database, but comprehensive literature searches were not conducted for these other tissues. In particular, bone, blood, muscle, and skin were not included in the search terms. Due to the enormous volume of literature available for each major class, multiple databases were searched for review articles only. The multiple databases used for this 185
search included: Medline, Toxline, Biosis, NTIS, Embase, and Current Contents. The web-based search engines, PubMed, Toxline, and DART, were searched for complete references and not limited to review articles. We also reviewed the web site for Charles River Laboratories, a major animal supplier, and directly corresponded with their personnel to see if they had unpublished data on food or water intake for neonates. No such data were available. An initial screening identified approximately 1014 articles on tissue volume, 93 articles on intake, and 118 on blood flow (which was expanded to include glomerular filtration rate). Based on these numbers, all intake and flow articles were retrieved. To keep the retrieval to a manageable size, and in light of the very large number of studies that evaluated brain weight, studies that only evaluated brain weight were not retrieved. More than 850 articles were retrieved and underwent further screening. Of these, approximately 375 were not useful for entry into the database. This was often because, although the abstract suggested the study would have useful data, no age-specific data were available for the parameters of interest. Other studies were not useful because only data for animals >60 days old were available, or no body weight data were available for the age of interest. The availability of body weight data was critical, since algorithms that might be developed from this database would likely tie the parameters of interest to body weight and/or age. Data for animals 61-90 days were entered in the database only if the study also contained data for animals
