Biologically-based Dose-response Model Research Articles

Updating the biologically based dose-response model for the nasal carcinogenicity of inhaled formaldehyde in the F344 rat.

Chronic inhalation of formaldehyde by F344 rats causes nasal squamous cell carcinoma (SCC). This outcome is well-characterized: including dose-response and time course data for SCC, mechanistic endpoints, and nasal dosimetry. Conolly et al. (Toxicol. Sci. 75, 432-447, 2003) used these resources to develop a biologically based dose-response (BBDR) model for SCC in F344 rats. This model, scaled up to humans, has informed dose-response conclusions reached by several international regulatory agencies. However, USEPA concluded that uncertainties precluded its use for cancer risk assessment. Here, we describe an updated BBDR model that addresses uncertainties through refined dosimetry modeling, revised analysis of labeling index data, and an extended dataset where both inhaled (exogenous) and endogenous formaldehyde (exogF, endoF) form DNA adducts. Further, since Conolly et al. (ibid) was published, it has become clear that, when controls from all F344 inhalation bioassays are considered, accounting for over 4000 rats, at most one nasal SCC occurred. This low spontaneous incidence constrains possible contribution of endoF to the formation of nasal SCC via DNA reactivity. Further, since both exogF and endoF form DNA adducts, this constraint also applies to exogF. The revised BBDR model therefore drives SCC formation through the cytotoxicity of high concentration exogF. An option for direct mutagenicity associated with DNA adducts is retained to allow estimation of an upper bound on adduct mutagenicity consistent with the lack of a spontaneous SCC incidence. These updates represent an iterative refinement of the 2003 model, incorporating new data and insights to reduce identified model uncertainties.

An evaluation of the USEPA Proposed Approaches for applying a biologically based dose-response model in a risk assessment for perchlorate in drinking water

The United States Environmental Protection Agency's (USEPA) 2017 report, “Draft Report: Proposed Approaches to Inform the Derivation of a Maximum Contaminant Level Goal for Perchlorate in Drinking Water”, proposes novel approaches for deriving a Maximum Contaminant Level Goal (MCLG) for perchlorate using a biologically-based dose-response (BBDR) model. The USEPA (2017) BBDR model extends previously peer-reviewed perchlorate models to describe the relationship between perchlorate exposure and thyroid hormone levels during early pregnancy. Our evaluation focuses on two key elements of the USEPA (2017) report: the plausibility of BBDR model revisions to describe control of thyroid hormone production in early pregnancy and the basis for linking BBDR model results to neurodevelopmental outcomes. While the USEPA (2017) BBDR model represents a valuable research tool, the lack of supporting data for many of the model assumptions and parameters calls into question the fitness of the extended BBDR model to support quantitative analyses for regulatory decisions on perchlorate in drinking water. Until more data can be developed to address uncertainties in the current BBDR model, USEPA should continue to rely on the RfD recommended by the NAS (USEPA, 2005) when considering further regulatory action.

Evaluation of the risk of perchlorate exposure in a population of late-gestation pregnant women in the United States: Application of probabilistic biologically-based dose response modeling

The risk of ubiquitous perchlorate exposure and the dose-response on thyroid hormone levels in pregnant women in the United States (U.S.) have yet to be characterized. In the current work, we integrated a previously developed perchlorate submodel into a recently developed population-based pregnancy model to predict reductions in maternal serum free thyroxine (fT4) levels for late-gestation pregnant women in the U.S. Our findings indicated no significant difference in geometric mean estimates of fT4 when perchlorate exposure from food only was compared to no perchlorate exposure. The reduction in maternal fT4 levels reached statistical significance when an added contribution from drinking water (i.e., 15μg/L, 20μg/L, or 24.5μg/L) was assumed in addition to the 90th percentile of food intake for pregnant women (0.198μg/kg/day). We determined that a daily intake of 0.45 to 0.50μg/kg/day of perchlorate was necessary to produce results that were significantly different than those obtained from no perchlorate exposure. Adjusting for this food intake dose, the relative source contribution of perchlorate from drinking water (or other non-dietary sources) was estimated to range from 0.25–0.3μg/kg/day. Assuming a drinking water intake rate of 0.033L/kg/day, the drinking water concentration allowance for perchlorate equates to 7.6–9.2μg/L. In summary, we have demonstrated the utility of a probabilistic biologically-based dose-response model for perchlorate risk assessment in a sensitive life-stage at a population level; however, there is a need for continued monitoring in regions of the U.S. where perchlorate exposure may be higher.

Estimation of iodine nutrition and thyroid function status in late-gestation pregnant women in the United States: Development and application of a population-based pregnancy model

Previously, a deterministic biologically-based dose-response (BBDR) pregnancy model was developed to evaluate moderate thyroid axis disturbances with and without thyroid-active chemical exposure in a near-term pregnant woman and fetus. In the current study, the existing BBDR model was adapted to include a wider functional range of iodine nutrition, including more severe iodine deficiency conditions, and to incorporate empirically the effects of homeostatic mechanisms. The extended model was further developed into a population-based model and was constructed using a Monte Carlo-based probabilistic framework. In order to characterize total (T4) and free (fT4) thyroxine levels for a given iodine status at the population-level, the distribution of iodine intake for late-gestation pregnant women in the U.S was reconstructed using various reverse dosimetry methods and available biomonitoring data. The range of median (mean) iodine intake values resulting from three different methods of reverse dosimetry tested was 196.5–219.9μg of iodine/day (228.2–392.9μg of iodine/day). There was minimal variation in model-predicted maternal serum T4 and ft4 thyroxine levels from use of the three reconstructed distributions of iodine intake; the range of geometric mean for T4 and fT4, was 138–151.7nmol/L and 7.9–8.7pmol/L, respectively. The average value of the ratio of the 97.5th percentile to the 2.5th percentile equaled 3.1 and agreed well with similar estimates from recent observations in third-trimester pregnant women in the U.S. In addition, the reconstructed distributions of iodine intake allowed us to estimate nutrient inadequacy for late-gestation pregnant women in the U.S. via the probability approach. The prevalence of iodine inadequacy for third-trimester pregnant women in the U.S. was estimated to be between 21% and 44%. Taken together, the current work provides an improved tool for evaluating iodine nutritional status and the corresponding thyroid function status in pregnant women in the U.S. This model enables future assessments of the relevant risk of thyroid hormone level perturbations due to exposure to thyroid-active chemicals at the population-level.

Marginal iodide deficiency and thyroid function: Dose–response analysis for quantitative pharmacokinetic modeling

Severe iodine deficiency (ID) results in adverse health outcomes and remains a benchmark for understanding the effects of developmental hypothyroidism. The implications of marginal ID, however, remain less well known. The current study examined the relationship between graded levels of ID in rats and serum thyroid hormones, thyroid iodine content, and urinary iodide excretion. The goals of this study were to provide parametric and dose–response information for development of a quantitative model of the thyroid axis. Female Long Evans rats were fed casein-based diets containing varying iodine (I) concentrations for 8 weeks. Diets were created by adding 975, 200, 125, 25, or 0 μg/kg I to the base diet (∼25 μg I/kg chow) to produce 5 nominal I levels, ranging from excess (basal + added I, Treatment 1: 1000 μg I/kg chow) to deficient (Treatment 5: 25 μg I/kg chow). Food intake and body weight were monitored throughout and on 2 consecutive days each week over the 8-week exposure period, animals were placed in metabolism cages to capture urine. Food, water intake, and body weight gain did not differ among treatment groups. Serum T4 was dose-dependently reduced relative to Treatment 1 with significant declines (19 and 48%) at the two lowest I groups, and no significant changes in serum T3 or TSH were detected. Increases in thyroid weight and decreases in thyroidal and urinary iodide content were observed as a function of decreasing I in the diet. Data were compared with predictions from a recently published biologically based dose–response (BBDR) model for ID. Relative to model predictions, female Long Evans rats under the conditions of this study appeared more resilient to low I intake. These results challenge existing models and provide essential information for development of quantitative BBDR models for ID during pregnancy and lactation.

Development of a Quantitative Model Incorporating Key Events in a Hepatotoxic Mode of Action to Predict Tumor Incidence

Biologically based dose-response (BBDR) modeling of environmental pollutants can be utilized to inform the mode of action (MOA) by which compounds elicit adverse health effects. Chemicals that produce tumors are typically labeled as either genotoxic or nongenotoxic. Though both the genotoxic and the nongenotoxic MOA may be operative as a function of dose, it is important to note that the label informs but does not define a MOA. One commonly proposed MOA for nongenotoxic carcinogens is characterized by the key events cytotoxicity and regenerative proliferation. The increased division rate associated with such proliferation can cause an increase in the probability of mutations, which may result in tumor formation. We included these steps in a generalized computational pharmacodynamic (PD) model incorporating cytotoxicity as a MOA for three carcinogens (chloroform, CHCl(3); carbon tetrachloride, CCL(4); and N,N-dimethylformamide, DMF). For each compound, the BBDR model is composed of a chemical-specific physiologically based pharmacokinetic model linked to a PD model of cytotoxicity and cellular proliferation. The rate of proliferation is then linked to a clonal growth model to predict tumor incidences. Comparisons of the BBDR simulations and parameterizations across chemicals suggested that significant variation among the models for the three chemicals arises in a few parameters expected to be chemical specific (such as metabolism and cellular injury rate constants). Optimization of model parameters to tumor data for CCL(4) and DMF resulted in similar estimates for all parameters related to cytotoxicity and tumor incidences. However, optimization of the CHCl(3) data resulted in a higher estimate for one parameter (BD) related to death of initiated cells. This implies that additional steps beyond cytotoxicity leading to induced cellular proliferation can be quantitatively different among chemicals that share cytotoxicity as a hypothesized carcinogenic MOA.

What Role for Biologically Based Dose–Response Models in Estimating Low-Dose Risk?

BackgroundBiologically based dose–response (BBDR) models can incorporate data on biological processes at the cellular and molecular level to link external exposure to an adverse effect.ObjectivesOur goal was to examine the utility of BBDR models in estimating low-dose risk.MethodsWe reviewed the utility of BBDR models in risk assessment.ResultsBBDR models have been used profitably to evaluate proposed mechanisms of toxicity and identify data gaps. However, these models have not improved the reliability of quantitative predictions of low-dose human risk. In this commentary we identify serious impediments to developing BBDR models for this purpose. BBDR models do not eliminate the need for empirical modeling of the relationship between dose and effect, but only move it from the whole organism to a lower level of biological organization. However, in doing this, BBDR models introduce significant new sources of uncertainty. Quantitative inferences are limited by inter- and intraindividual heterogeneity that cannot be eliminated with available or reasonably anticipated experimental techniques. BBDR modeling does not avoid uncertainties in the mechanisms of toxicity relevant to low-level human exposures. Although implementation of BBDR models for low-dose risk estimation have thus far been limited mainly to cancer modeled using a two-stage clonal expansion framework, these problems are expected to be present in all attempts at BBDR modeling.ConclusionsThe problems discussed here appear so intractable that we conclude that BBDR models are unlikely to be fruitful in reducing uncertainty in quantitative estimates of human risk from low-level exposures in the foreseeable future. Use of in vitro data from recent advances in molecular toxicology in BBDR models is not likely to remove these problems and will introduce new issues regarding extrapolation of data from in vitro systems.

Arsenic-Induced Carcinogenesis—Oxidative Stress as a Possible Mode of Action and Future Research Needs for More Biologically Based Risk Assessment

Exposure to inorganic arsenic (iAs) induces cancer in human lungs, urinary bladder, skin, kidney, and liver, with the majority of deaths from lung and bladder cancer. To date, cancer risk assessments for iAs have not relied on mechanistic data, as we have lacked sufficient understanding of arsenic's pharmacokinetics and mode(s) of carcinogenic action (MOA). Furthermore, while there are vast amounts of toxicological data on iAs, relatively little of it has been collected using experimental designs that efficiently support development of biologically based dose-response (BBDR) models and subsequently risk assessment. This review outlines an efficient approach to the development of a BBDR model for iAs that would reduce uncertainties in its cancer risk assessment. This BBDR-based approach is illustrated by using oxidative stress as the carcinogenic MOA for iAs but would be generically applicable to other MOAs. Six major research needs that will facilitate BBDR model development for arsenic-induced cancer are (1) MOA research, which is needed to reduce the uncertainty in risk assessment; (2) development and integration of the pharmacodynamic component (MOA) of the BBDR model; (3) dose-response and extrapolation model selection; (4) the determination of internal human speciated arsenical concentrations to improve physiologically based pharmacokinetic (PBPK) models; (5) animal models of arsenic carcinogenesis; and (6) the determination of the low dose human relationship for death from cancer, particularly in lungs and urinary bladder. The major parts of the BBDR model are arsenic exposure, a physiologically based pharmacokinetic model, reactive species, antioxidant defenses, oxidative stress, cytotoxicity, growth factors, transcription factors, DNA damage, chromosome damage, cell proliferation, mutation accumulation, and cancer. The BBDR model will need to be developed concurrently with data collection so that model uncertainties can be identified and addressed through an iterative process of targeted additional research.

Research toward the development of a biologically based dose response assessment for inorganic arsenic carcinogenicity: A progress report

Cancer risk assessments for inorganic arsenic have been based on human epidemiological data, assuming a linear dose response below the range of observation of tumors. Part of the reason for the continued use of the linear approach in arsenic risk assessments is the lack of an adequate biologically based dose response (BBDR) model that could provide a quantitative basis for an alternative nonlinear approach. This paper describes elements of an ongoing collaborative research effort between the CIIT Centers for Health Research, the U.S. Environmental Protection Agency, ENVIRON International, and EPRI to develop BBDR modeling approaches that could be used to inform a nonlinear cancer dose response assessment for inorganic arsenic. These efforts are focused on: (1) the refinement of physiologically based pharmacokinetic (PBPK) models of the kinetics of inorganic arsenic and its metabolites in the mouse and human; (2) the investigation of mathematical solutions for multi-stage cancer models involving multiple pathways of cell transformation; (3) the review and evaluation of the literature on the dose response for the genomic effects of arsenic; and (4) the collection of data on the dose response for genomic changes in the urinary bladder (a human target tissue for arsenic carcinogenesis) associated with in vivo drinking water exposures in the mouse as well as in vitro exposures of both mouse and human cells. An approach is proposed for conducting a biologically based margin of exposure risk assessment for inorganic arsenic using the in vitro dose response for the expression of genes associated with the obligatory precursor events for arsenic tumorigenesis.

Organophosphates and Outdoor Air Concentrations

Organophosphates and Outdoor Air Concentrations

Organophosphates and Outdoor Air Concentrations

Harnly et al. (2005) suggested that measured air concentrations of organophosphate insecticides may pose a particular concern for children’s health. However, when considering the current scientific weight of evidence, their conclusions cannot be supported for two reasons: first, they did not demonstrate a particular concern for children based on their results, and second, they cited incomplete and inappropriate literature to support the notion that recent toxicologic and epidemiologic studies indicate a health concern. Harnly et al. (2005) did not conduct a risk assessment to demonstrate a concern for children. Rather, they detected pesticides in air concentrations in agricultural areas and then suggested there may be a concern for children because of recent toxicologic and epidemiologic studies. Their observed median exposures of all three active ingredients (chlorpyrifos, diazinon, and malathion) were all low and well within established regulatory limits. A risk assessment approach would have been quite useful. For chlorpyrifos, Harnly et al. (2005) detected a 20-day median concentration in air of 0.000033 mg/m3. A tier-1 risk assessment assuming an air concentration of chlorpyrifos at 0.000033 mg/m3, the mean body weight of a 1- to 2-year-old child of 12.3 kg, a child inhalation rate of 6.8 m3/day, and 24-hr outdoor respiration results in a chlorpyrifos inhalation exposure of 0.0000182 mg/kg/day. Margins of exposure (MOE) would be 5,495 [acute inhalation no observed adverse effect level (NOAEL) = 0.1 mg/kg/day], 27,473 (acute NOAEL = 0.5 mg/kg/day), and 1,648 (chronic NOAEL = 0.03 mg/kg/day). All MOEs are greater than the U.S. Environmental Protection Agency (EPA) target MOE of 1,000 for infants, children, and females 13–50 years of age (U.S. EPA 2002). More problematic is that Harnly et al. (2005) stated that Recent cellular, animal, and human evidence of toxicity, particularly in newborns, supports the public health concern indicated by initial risk estimates. The authors did not provide a sufficiently thorough review of the literature relevant to risk assessment to support or refute their statement. In the case of chlorpyrifos, this statement cannot be supported by the available evidence. The principal problem with Harnly et al.’s approach is not unique to their article. Appropriate risk assessment requires appropriate data, and, as simple as this relationship sounds, it is often ignored. Harnly et al. (2005) cited toxicity and epidemiologic studies, but these particular studies are not appropriate for use in risk assessment. This problem has become so pervasive that Conolly et al. (1999) clarified the basic features of toxicology studies that are and are not appropriate for use in risk assessment. Harnly et al. (2005) should not have included the findings of Qiao et al. (2002) because the high doses, subcutaneous route of administration, and carrier were inappropriate for toxicologic risk assessment (Conolly et al. 1999; Zhao et al. 2005). Indeed, Slotkin (2004), a coauthor of Qiao, has written that there is little academic interest in relevant routes of exposure or pharmacokinetics. He stated that Practical issues that are critical to standardized testing are de-emphasized, such as pharmacokinetics/toxicokinetics, the matching of routes of exposure to those of humans in industrial, agricultural or domestic settings, or the development of biologically-based dose response models of established hazards. In that sense, the academic approach is entirely deficient in those attributes that are necessary components of the application of research findings to regulatory science. Harnly et al. (2005) cited Eskenazi et al. (1999) as a source of concern for adverse consequences of organophosphate exposure. To be complete, Eskenazi et al. (2004) provide more information. They stated, “We failed to demonstrate an adverse relationship between fetal growth and any measure of in utero organophosphate pesticide exposure.” An association was found for a couple of variables and decreased gestational duration, but the conclusion was that these potential pesticide effects appear to have “little clinical impact at the population level.” Finally, air concentrations have been shown to translate poorly into systemic exposure. Hore et al. (2005) showed that children in houses treated with chlorpyrifos had no detectable increase in urinary 3,5,6-trichloropyridinol (TCP), whereas median peak ambient air chlorpyrifos increased > 10-fold (median of 14 ng/m3 pretreatment, 196 ng/m3 on day of treatment). If a 10-fold increase in air chlorpyrifos does not cause a detectable increase in urinary TCP, then the 1-fold background air cannot be contributing measurably to the children’s background levels of urinary TCP.

An integrated pharmacokinetic and pharmacodynamic study of arsenite action2. Heme oxygenase induction in mice

Heme oxygenase (HO) is the rate-limiting enzyme in heme degradation and its activity has a significant impact on intracellular heme pools. Rat studies indicate that HO induction is a sensitive, dose-dependent response to arsenite (As(III)) exposure in both liver and kidney. The objective of this study was to evaluate the relationship of HO induction to administered As(III) dose, and concentrations of inorganic arsenic (iAs) in tissues and urine. Levels of iAs, mono- (MMA) and dimethylated arsenic (DMA) as well as HO activity were determined in liver, lung and kidney over time in female B6C3F1 mice given a single oral dose of 0, 1, 10, 30 or 100 micromol/kg As(III). Increased HO activity was a time and dose-dependent response in liver and kidney, but not in lung. Activity peaked in the 4-6 h time range in liver and kidney with the responsiveness in liver being approximately 2- to 3-fold greater than kidney. The lowest observed effect levels (LOELs) in this study for HO induction are 30 and 100 micromol/kg, respectively, in liver and kidney. The predominant form of arsenic (As) was iAs in liver at all doses, whereas DMA was the predominant form of As in kidney at all doses. Three- to four-fold higher levels of iAs were achieved in liver compared to kidney. MMA was the least abundant form of As in liver and kidney, never exceeding more than 20% of the total As present. The concentration of iAs in tissue or urine demonstrated the strongest correlation with HO activity in both liver and kidney. Results of this study suggest that HO induction is a biomarker of effect that is specific for tissue iAs because a high, but nontoxic, acute dose of DMA (5220 micromol/kg) did not induce HO in mice. Thus, HO induction has potential for use as a biomarker of effect for inorganic arsenic exposure and may be used as an indicator response to further the development of a biologically-based dose response model for As.

High signal intensities on T1-weighted MRI as a biomarker of exposure to manganese.

Increased signal in T1-weighted images was observed in the experimental manganese (Mn) poisoning of the non-human primate and a patient with Mn neurointoxication. However, our study showed that the increased signals in magnetic resonance images (MRI) were highly prevalent (41.6%) in Mn-exposed workers. Especially 73.5% of the welders showed increased signal intensities. Blood Mn concentration correlated with pallidal index. These changes in MRI tend to disappear following the withdrawal from the source of Mn accumulation, despite permanent neurological damage. Thus increased signal intensities on a T1-weighted image reflect exposure to Mn, but not necessarily manganism. Our study also showed that the concentration of Mn required to produce increased signal intensities on MRI is much lower than the threshold necessary to result in overt clinical signs of manganism. Increased signal intensities in the globus pallidus were determined by Mn accumulation in the animal experiment. All these results strongly suggest that signal intensities in T1-weighted MRI reflect a target site dose in a biologically-based dose-response model. At which increase of signal intensity, the progression of manganism from Mn exposure occurs, however, remains to be solved.

Development of a biologically-based controlled growth and differentiation model for developmental toxicology.

Amathematical model is developed with a highly controlled birth and death process for precursor cells. This model is both biologically- and statistically-based. The controlled growth and differentiation (CGD) model limits the number of replications allowed in the development of a tissue or organ and thus, more closely reflects the presence of a true stem cell population. Leroux et al. (1996) presented a biologically-based dose-response model for developmental toxicology that was derived from a partial differential equation for the generating function. This formulation limits further expansion into more realistic models of mammalian development. The same formulae for the probability of a defect (a system of ordinary differential equations) can be derived through the Kolmogorov forward equations due to the nature of this Markov process. This modified approach is easily amenable to the expansion of more complicated models of the developmental process such as the one presented here. Comparisons between the Leroux et al. (1996) model and the controlled growth and differentiation (CGD) model as developed in this paper are also discussed.

Molecular circuits, biological switches, and nonlinear dose-response relationships.

Signaling motifs (nuclear transcriptional receptors, kinase/phosphatase cascades, G-coupled protein receptors, etc.) have composite dose-response behaviors in relation to concentrations of protein receptors and endogenous signaling molecules. "Molecular circuits" include the biological components and their interactions that comprise the workings of these signaling motifs. Many of these molecular circuits have nonlinear dose-response behaviors for endogenous ligands and for exogenous toxicants, acting as switches with "all-or-none" responses over a narrow range of concentration. In turn, these biological switches regulate large-scale cellular processes, e.g., commitment to cell division, cell differentiation, and phenotypic alterations. Biologically based dose-response (BBDR) models accounting for these biological switches would improve risk assessment for many nonlinear processes in toxicology. These BBDR models must account for normal control of the signaling motifs and for perturbations by toxic compounds. We describe several of these biological switches, current tools available for constructing BBDR models of these processes, and the potential value of these models in risk assessment.

Rate and capacity of hepatic microsomal ring-hydroxylation of phenol to hydroquinone and catechol in rainbow trout ( Oncorhynchus mykiss)

Rainbow trout ( Oncorhynchus mykiss) liver microsomes were used to study the rate of ring-hydroxylation of phenol at 11 and 25 °C by directly measuring the production of two potentially toxic metabolites, hydroquinone (HQ) and catechol (CAT). An HPLC method with integrated ultraviolet and electrochemical detection was used for metabolite identification and quantification at low (pmol) formation rates found in fish. The Michaelis–Menten saturation kinetics for the production of HQ and CAT over a range of phenol concentrations were determined at trout physiological pH. The apparent Km's for the production of HQ and CAT at 11 °C were 14±1 and 10±1 mM, respectively, with Vmax's of 552±71 and 161±15 pmol/min per mg protein. The kinetic parameters for HQ and CAT at 25 °C were 22±1 and 32±3 mM (Km) and 1752±175 and 940±73 pmol/min per mg protein (Vmax), respectively. The calculated increase in metabolic rate per 10 °C temperature rise ( Q 10) was 2.28 for HQ and 3.53 for CAT production. These experiments assess the potential for metabolic bioactivation in fish through direct quantification of putative reactive metabolites at the low, but toxicologically significant, chemical concentrations found in aquatic organisms. This work initiates a series of studies to compare activation pathway, rate, and capacity across fish species, providing a basis for development of biologically-based dose response models in diverse species.

The use of quantitative histological and molecular data for risk assessment and biologically based model development.

In organs with diverse cell populations, it is not uncommon for one type of cell to respond while others are spared. Even in an organ with common cell types, such as hepatocytes within the liver, the population of cells may respond with different sensitivities for injury or for biochemical responses to toxicants. In the liver, many tumor promoters induce cytochrome P450 enzymes and other proteins in centrilobular cells at much lower doses than required to cause induction in periportal cells. In addition, these induction responses appear to occur at the level of individual cells--a 50% response of the liver for induction does not represent 50% induction in all cells. Instead, half of the cells are fully induced and half are unaffected. Cells "switch" from one phenotypic state to another. Over the past 10 years, several attempts have been made to model these cellular switches and to understand their relevance for hepatic tumor promotion and risk assessment. The data used for analyzing these switches include responses of the entire liver (total induction), responses of individual cells in the liver (regional induction), and cellular responses such as proliferation and apoptosis. This brief overview describes the development of biologically based, dose-response (BBDR) models for protein induction and tumor promotion in liver by 2,3,7,8-tetrachlorodibenzo-p-dioxin (TCDD) with emphasis on the role of specific types of histological and molecular data in providing insights about mechanisms for cellular switches and their implications for tumor promotion. As the biological basis of these switches become unraveled and incorporated into the models, these BBDR models should eventually serve to improve risk assessments with a variety of liver tumor promoters with receptor-based modes of action.

Improving the Development and Use of Biologically Based Dose Response Models (BBDR) in Risk Assessment

Biologically based dose-response (BBDR) models predict health outcomes (response) resulting from the presence of a toxicant at a biological target (dose). The benefits of BBDR models are many, and research programs are increasingly focusing on mechanistic research to support model development; however, progress has been slow. Impediments to progress include the complexity of dose response modeling, the need for a multidisciplinary team and consistent funding support, and difficulty in identifying and extracting the needed data. Of immediate concern is the lack of transparency of published models to the supporting data and literature, difficulty in accessing model code and simulation conditions sufficient to allow independent replication of results, and absence of well-defined quality criteria. Suggestions are presented to improve the development and use of BBDR models in risk assessment and to address the above limitations. Examples from BBDR models for methylmercury neurotoxicity and 5-fluorouracil embryotoxicity are presented to illustrate the suggestions including what kinds of databases are needed to support model development and transparency, quality assurance for modeling, and how the internet can advance database development and collaboration within the biological modeling community.

Mode of action and tissue dosimetry in current and future risk assessments

Two fundamental concepts have emerged to organize contemporary approaches to chemical risk assessment – mode of action and tissue dosimetry. Mode of action specifies the nature of the interactions between the chemical and the body that lead to toxic responses and should, under optimal circumstances, also specify the form of the tissue dose that leads to these effects. This paper highlights recent development of biologically based dose response (BBDR) models for specific toxic endpoints that use knowledge on mode of action to specify measures of dose. These dose measures then are used to support low dose and interspecies extrapolations. We first focus on a series of dose response models developed for several compounds that produce nasal toxicity. These examples demonstrate a range of model structures from simple dosimetry models (methylmethacrylate) to linkage of dosimetry with specific biological processes involved in carcinogenesis (formaldehyde). Two BBDR models with dioxin illustrate the organization of biological and dosimetry information into specific testable hypotheses that could distinguish these different models and lead to a more uniform approach to risk assessment for this compound. A final section discusses the impact of molecular biology and the genomic revolution in relation to development of BBDR models for specific toxic endpoints.

Applications of mechanistic data in risk assessment: the past, present, and future.

Mechanistic data, when available, have long been considered in risk assessment, such as in the development of the nitrate RfD based on effects in a sensitive group (infants). Recent advances in biology and risk assessment methods have led to a tremendous increase in the use of mechanistic data in risk assessment. Toxicokinetic data can improve extrapolation from animals to humans and characterization of human variability. This is done by the development of improved tissue dosimetry, by the use of uncertainty factors based on chemical-specific data, and in the development of physiologically based pharmacokinetic (PBPK) models. The development of the boron RfD illustrates the use of chemical-specific data in the improved choice of uncertainty factors. The draft cancer guidelines of the U.S. Environmental Protection Agency emphasize the use of mode of action data. The first choice under the guidelines is to use a chemical-specific, biologically based dose-response (BBDR) model. In the absence of a BBDR model, mode of action data are used to determine whether low-dose extrapolation is done using a linear or nonlinear (margin of exposure) approach. Considerations involved in evaluating a hypothesized mode of action are illustrated using 1,3-dichloropropene, and use of a BBDR model is illustrated using formaldehyde. Recent developments in molecular biology, including transgenic animals, microarrays, and the characterization of genetic polymorphisms, have significant potential for improving risk assessments, although further methods development is needed. Overall, use of mechanistic data has significant potential for reducing the uncertainty in assessments, while at the same time highlighting the areas of uncertainty.