Regional hepatic CYP1A1 and CYP1A2 induction with 2,3,7,8-tetrachlorodibenzo-p-dioxin evaluated with a multicompartment geometric model of hepatic zonation

How single-cell transcriptomics provides insight on hepatic responses to TCDD

The prototypical aryl hydrocarbon receptor (AHR) ligand, 2,3,7,8-tetrachlorodibenzo-p-dioxin (TCDD), has been a valuable model for investigating toxicant-associated fatty liver disease (TAFLD). TCDD induces dose-dependent hepatic lipid accumulation, followed by the development of inflammatory foci and eventual progression to fibrosis in mice. Previously, bulk approaches and in vitro examination of different cell types were relied upon to study the mechanisms underlying TCDD-induced liver pathologies. However, the advent of single-cell transcriptomic technologies, such as single-nuclei RNA sequencing (snRNAseq) and spatial transcriptomics (STx), has provided new insights into the responses of hepatic cell types to TCDD exposure. This review explores the application of these single-cell transcriptomic technologies and highlights their contributions towards unraveling the cell-specific mechanisms mediating the hepatic responses to TCDD.

Single-cell transcriptomics shows dose-dependent disruption of hepatic zonation by TCDD in mice

2,3,7,8-Tetrachlorodibenzo-p-dioxin (TCDD) dose-dependently induces the development of hepatic fat accumulation and inflammation with fibrosis in mice initially in the portal region. Conversely, differential gene and protein expression is first detected in the central region. To further investigate cell-specific and spatially resolved dose-dependent changes in gene expression elicited by TCDD, single-nuclei RNA sequencing and spatial transcriptomics were used for livers of male mice gavaged with TCDD every 4 days for 28 days. The proportion of 11 cell (sub)types across 131 613 nuclei dose-dependently changed with 68% of all portal and central hepatocyte nuclei in control mice being overtaken by macrophages following TCDD treatment. We identified 368 (portal fibroblasts) to 1339 (macrophages) differentially expressed genes. Spatial analyses revealed initial loss of portal identity that eventually spanned the entire liver lobule with increasing dose. Induction of R-spondin 3 (Rspo3) and pericentral Apc, suggested dysregulation of the Wnt/β-catenin signaling cascade in zonally resolved steatosis. Collectively, the integrated results suggest disruption of zonation contributes to the pattern of TCDD-elicited NAFLD pathologies.

A Physiologically Based Pharmacokinetic (PBPK) Modeling Framework for Mixtures of Dioxin-like Compounds

Humans are exposed to persistent organic pollutants, such as dioxin-like compounds (DLCs), as mixtures. Understanding and predicting the toxicokinetics and thus internal burden of major constituents of a DLC mixture is important for assessing their contributions to health risks. PBPK models, including dioxin models, traditionally focus on one or a small number of compounds; developing new or extending existing models for mixtures often requires tedious, error-prone coding work. This lack of efficiency to scale up for multi-compound exposures is a major technical barrier toward large-scale mixture PBPK simulations. Congeners in the DLC family, including 2,3,7,8-tetrachlorodibenzo-p-dioxin (TCDD), share similar albeit quantitatively different toxicokinetic and toxicodynamic properties. Taking advantage of these similarities, here we reported the development of a human PBPK modeling framework for DLC mixtures that can flexibly accommodate an arbitrary number of congeners. Adapted from existing TCDD models, our mixture model contains the blood and three diffusion-limited compartments-liver, fat, and rest of the body. Depending on the number of congeners in a mixture, varying-length vectors of ordinary differential equations (ODEs) are automatically generated to track the tissue concentrations of the congeners. Shared ODEs are used to account for common variables, including the aryl hydrocarbon receptor (AHR) and CYP1A2, to which the congeners compete for binding. Binary and multi-congener mixture simulations showed that the AHR-mediated cross-induction of CYP1A2 accelerates the sequestration and metabolism of DLC congeners, resulting in consistently lower tissue burdens than in single exposure, except for the liver. Using dietary intake data to simulate lifetime exposures to DLC mixtures, the model demonstrated that the relative contributions of individual congeners to blood or tissue toxic equivalency (TEQ) values are markedly different than those to intake TEQ. In summary, we developed a mixture PBPK modeling framework for DLCs that may be utilized upon further improvement as a quantitative tool to estimate tissue dosimetry and health risks of DLC mixtures.

A Negative Feedback Loop and Transcription Factor Cooperation Regulate Zonal Gene Induction by 2, 3, 7, 8-Tetrachlorodibenzo-p-Dioxin in the Mouse Liver

The cytochrome P450 (Cyp) proteins Cyp1A1 and Cyp1A2 are strongly induced in the mouse liver by the potent environmental toxicant 2, 3, 7, 8‐tetrachlorodibenzo‐p‐dioxin (TCDD), acting through the aryl hydrocarbon receptor (AHR). The induction of Cyp1A1 is localized within the centrilobular regions of the mouse liver at low doses of TCDD, progressing to pan‐lobular induction at higher doses. Even without chemical perturbation, metabolic functions and associated genes are basally zonated in the liver lobule along the central‐to‐portal axis. To investigate the mechanistic basis of spatially restricted gene induction by TCDD, we have developed a multiscale computational model of the mouse liver lobule with single‐cell resolution. The spatial location of individual hepatocytes in the model was calibrated from previously published high‐resolution images. A systems biology model of the network of biochemical signaling pathways underlying Cyp1A1 and Cyp1A2 induction was then incorporated into each hepatocyte in the model. Model simulations showed that a negative feedback loop formed by binding of the induced Cyp1A2 protein to TCDD, together with cooperative gene induction by the β‐catenin/AHR/TCDD transcription factor complex and β‐catenin, help produce the spatially localized induction pattern of Cyp1A1. Although endogenous WNT regulates the metabolic zonation of many genes, it was not a driver of zonal Cyp1A1 induction in our model. Conclusion: In this work, we used data‐driven computational modeling to identify the mechanistic basis of zonally restricted gene expression induced by the potent and persistent environmental pollutant TCDD. The multiscale model and derived results clarify the mechanisms of dose‐dependent hepatic gene induction responses to TCDD. Additionally, this work contributes to our broader understanding of spatial gene regulation along the liver lobule.

Defining the Contribution of CYP1A1 and CYP1A2 to Drug Metabolism Using Humanized CYP1A1/1A2 and Cyp1a1/Cyp1a2 Knockout Mice

Cytochrome P450s CYP1A1 and CYP1A2 can metabolize a broad range of foreign compounds and drugs. However, these enzymes have significantly overlapping substrate specificities. To establish their relative contribution to drug metabolism in vivo, we used a combination of mice humanized for CYP1A1 and CYP1A2 together with mice nulled at the Cyp1a1 and Cyp1a2 gene loci. CYP1A2 was constitutively expressed in the liver, and both proteins were highly inducible by 2,3,7,8-tetrachlorodibenzodioxin (TCDD) in a number of tissues, including the liver, lung, kidney, and small intestine. Using the differential inhibition of the human enzymes by quinidine, we developed a method to distinguish the relative contribution of CYP1A1 or CYP1A2 in the metabolism of drugs and foreign compounds. Both enzymes made a significant contribution to the hepatic metabolism of the probe compounds 7-methoxy and 7-ehthoxyresorufin in microsomal fractions from animals treated with TCDD. This enzyme kinetic approach allows modeling of the CYP1A1, CYP1A2, and non-CYP1A contribution to the metabolism of any substrate at any substrate, inhibitor, or enzyme concentration and, as a consequence, can be integrated into a physiologically based pharmacokinetics model. The validity of the model can then be tested in humanized mice in vivo. SIGNIFICANCE STATEMENT: Human CYP1A1 and CYP1A2 are important in defining the efficacy and toxicity/carcinogenicity of drugs and foreign compounds. In light of differences in substrate specificity and sensitivity to inhibitors, it is of central importance to understand their relative role in foreign compound metabolism. To address this issue, we have generated mice humanized or nulled at the Cyp1a gene locus and, through the use of these mouse lines and selective inhibitors, developed an enzyme kinetic-based model to enable more accurate prediction of the fate of new chemicals in humans and which can be validated in vivo using mice humanized for cytochrome P450-mediated metabolism.

Integrated zebrafish-based tests as an investigation strategy for water quality assessment

Water pollution risks to human health and the environment are emerging as serious concerns in the European Union and worldwide. With the aim to achieve good ecological and chemical status of all European water bodies, the "European Water Framework Directive" (WFD) was enacted. With the framework, bioanalytical techniques have been recognized as an important aspect. However, there are limitations to the application of bioassays directly for water quality assessment. Such approaches often fail to identify pollutants of concern, since the defined priority and monitored pollutants often fail to explain the observed toxicity. In this study, we integrated an effect-based risk assessment with a zebrafish-based investigation strategy to evaluate water sample extracts and fractions collected from the Danube. Four tiered bioassays were implemented, namely RNA-level gene expression assay, protein-level ethoxyresorufin-O-deethylase (EROD) assay, cell-level micronucleus assay and organism-level fish embryo test (FET). The results show that teratogenicity and lethality during embryonic development might be induced by molecular or cellular damages mediated by the aryl hydrocarbon receptor (AhR) -mediated activity, estrogenic activity and genotoxic activity. With the combination of high-throughput fractionation, this effect-based strategy elucidated the major responsible mixtures of each specific toxic response. In particularly, the most toxic mixture in faction F4, covering a log Kow range from 2.83 to 3.42, was composed by 12 chemicals, which were then evaluated as a designed mixture. Our study applied tiered bioassays with zebrafish to avoid interspecies differences and highlights effect-based approaches to address toxic mixtures in water samples. This strategy can be applied for large throughput screenings to support the main toxic compounds identification in water quality assessment.

Multiscale modelling approaches for assessing cosmetic ingredients safety

The European Union's ban on animal testing for cosmetic ingredients and products has generated a strong momentum for the development of in silico and in vitro alternative methods. One of the focus of the COSMOS project was ab initio prediction of kinetics and toxic effects through multiscale pharmacokinetic modeling and in vitro data integration. In our experience, mathematical or computer modeling and in vitro experiments are complementary. We present here a summary of the main models and results obtained within the framework of the project on these topics. A first section presents our work at the organelle and cellular level. We then go toward modeling cell levels effects (monitored continuously), multiscale physiologically based pharmacokinetic and effect models, and route to route extrapolation. We follow with a short presentation of the automated KNIME workflows developed for dissemination and easy use of the models. We end with a discussion of two challenges to the field: our limited ability to deal with massive data and complex computations.

The adverse outcome pathway for rodent liver tumor promotion by sustained activation of the aryl hydrocarbon receptor

An Adverse Outcome Pathway (AOP) represents the existing knowledge of a biological pathway leading from initial molecular interactions of a toxicant and progressing through a series of key events (KEs), culminating with an apical adverse outcome (AO) that has to be of regulatory relevance. An AOP based on the mode of action (MOA) of rodent liver tumor promotion by dioxin-like compounds (DLCs) has been developed and the weight of evidence (WoE) of key event relationships (KERs) evaluated using evolved Bradford Hill considerations. Dioxins and DLCs are potent aryl hydrocarbon receptor (AHR) ligands that cause a range of species-specific adverse outcomes. The occurrence of KEs is necessary for inducing downstream biological responses and KEs may occur at the molecular, cellular, tissue and organ levels. The common convention is that an AOP begins with the toxicant interaction with a biological response element; for this AOP, this initial event is binding of a DLC ligand to the AHR. Data from mechanistic studies, lifetime bioassays and approximately thirty initiation-promotion studies have established dioxin and DLCs as rat liver tumor promoters. Such studies clearly show that sustained AHR activation, weeks or months in duration, is necessary to induce rodent liver tumor promotion--hence, sustained AHR activation is deemed the molecular initiating event (MIE). After this MIE, subsequent KEs are 1) changes in cellular growth homeostasis likely associated with expression changes in a number of genes and observed as development of hepatic foci and decreases in apoptosis within foci; 2) extensive liver toxicity observed as the constellation of effects called toxic hepatopathy; 3) cellular proliferation and hyperplasia in several hepatic cell types. This progression of KEs culminates in the AO, the development of hepatocellular adenomas and carcinomas and cholangiolar carcinomas. A rich data set provides both qualitative and quantitative knowledge of the progression of this AOP through KEs and the KERs. Thus, the WoE for this AOP is judged to be strong. Species-specific effects of dioxins and DLCs are well known--humans are less responsive than rodents and rodent species differ in sensitivity between strains. Consequently, application of this AOP to evaluate potential human health risks must take these differences into account.

Proposing a scientific confidence framework to help support the application of adverse outcome pathways for regulatory purposes

An adverse outcome pathway (AOP) describes the causal linkage between initial molecular events and an adverse outcome at individual or population levels. Whilst there has been considerable momentum in AOP development, far less attention has been paid to how AOPs might be practically applied for different regulatory purposes. This paper proposes a scientific confidence framework (SCF) for evaluating and applying a given AOP for different regulatory purposes ranging from prioritizing chemicals for further evaluation, to hazard prediction, and ultimately, risk assessment. The framework is illustrated using three different AOPs for several typical regulatory applications. The AOPs chosen are ones that have been recently developed and/or published, namely those for estrogenic effects, skin sensitisation, and rodent liver tumor promotion. The examples confirm how critical the data-richness of an AOP is for driving its practical application. In terms of performing risk assessment, human dosimetry methods are necessary to inform meaningful comparisons with human exposures; dosimetry is applied to effect levels based on non-testing approaches and in vitro data. Such a comparison is presented in the form of an exposure:activity ratio (EAR) to interpret biological activity in the context of exposure and to provide a basis for product stewardship and regulatory decision making.

Dephosphorylation of Sp1 at Ser-59 by protein phosphatase 2A (PP2A) is required for induction of CYP1A1 transcription after treatment with 2,3,7,8-tetrachlorodibenzo-p-dioxin or omeprazole

The aryl hydrocarbon receptor (AhR) is a transcription factor that is activated by either 2,3,7,8-tetrachlorodibenzo-p-dioxin (TCDD) or omeprazole (OP). Activated AhR can induce CYP1A1 transcription by binding to the xenobiotic responsive element (XRE). However, the mechanism of activation of the CYP1A1 promoter region is poorly understood. Previous reports showed that Sp1 could bind to a GC-rich region near the CYP1A1 promoter. This study sought to clarify the function of Sp1 in CYP1A1 transcription. Phosphorylation of Sp1 at Ser-59 (pSer-59) was previously reported to be closely related to transcriptional regulation. We used a site-specific phospho-antibody to show that treatment with TCDD or OP drastically reduced the level of pSer-59 in Sp1 from HepG2 cells. This reduction was too much, we hypothesized that the reduced phosphorylation level resulted from activation of phosphatase activity. Given that pSer-59 is dephosphorylated by PP2A, we examined the effect of a PP2A inhibitor, okadaic acid (OA), on pSer-59 and transcription of CYP1A1. The results showed that OA blocked dephosphorylation of Ser-59 and drastically inhibited transcription of CYP1A1. Similar results were obtained after knockdown of PP2A. Treatment with OA had no effect on the expression of AhR, its nuclear translocation, or its ability to bind to the XRE. Furthermore, dephosphorylation of Sp1 at Ser-59 was not affected by knockdown of AhR. These results indicate that the signals from TCDD or OP caused PP2A-mediated dephosphorylation of Sp1 at Ser-59 and induced CYP1A1 transcription. This signaling pathway was independent of the AhR-mediated pathway.

Recent advances in 2D and 3D in vitro systems using primary hepatocytes, alternative hepatocyte sources and non-parenchymal liver cells and their use in investigating mechanisms of hepatotoxicity, cell signaling and ADME

This review encompasses the most important advances in liver functions and hepatotoxicity and analyzes which mechanisms can be studied in vitro. In a complex architecture of nested, zonated lobules, the liver consists of approximately 80 % hepatocytes and 20 % non-parenchymal cells, the latter being involved in a secondary phase that may dramatically aggravate the initial damage. Hepatotoxicity, as well as hepatic metabolism, is controlled by a set of nuclear receptors (including PXR, CAR, HNF-4α, FXR, LXR, SHP, VDR and PPAR) and signaling pathways. When isolating liver cells, some pathways are activated, e.g., the RAS/MEK/ERK pathway, whereas others are silenced (e.g. HNF-4α), resulting in up- and downregulation of hundreds of genes. An understanding of these changes is crucial for a correct interpretation of in vitro data. The possibilities and limitations of the most useful liver in vitro systems are summarized, including three-dimensional culture techniques, co-cultures with non-parenchymal cells, hepatospheres, precision cut liver slices and the isolated perfused liver. Also discussed is how closely hepatoma, stem cell and iPS cell-derived hepatocyte-like-cells resemble real hepatocytes. Finally, a summary is given of the state of the art of liver in vitro and mathematical modeling systems that are currently used in the pharmaceutical industry with an emphasis on drug metabolism, prediction of clearance, drug interaction, transporter studies and hepatotoxicity. One key message is that despite our enthusiasm for in vitro systems, we must never lose sight of the in vivo situation. Although hepatocytes have been isolated for decades, the hunt for relevant alternative systems has only just begun.

Bayesian Analysis of a Lipid-Based Physiologically Based Toxicokinetic Model for a Mixture of PCBs in Rats

A lipid-based physiologically based toxicokinetic (PBTK) model has been developed for a mixture of six polychlorinated biphenyls (PCBs) in rats. The aim of this study was to apply population Bayesian analysis to a lipid PBTK model, while incorporating an internal exposure-response model linking enzyme induction and metabolic rate. Lipid-based physiologically based toxicokinetic models are a subset of PBTK models that can simulate concentrations of highly lipophilic compounds in tissue lipids, without the need for partition coefficients. A hierarchical treatment of population metabolic parameters and a CYP450 induction model were incorporated into the lipid-based PBTK framework, and Markov-Chain Monte Carlo was applied to in vivo data. A mass balance of CYP1A and CYP2B in the liver was necessary to model PCB metabolism at high doses. The linked PBTK/induction model remained on a lipid basis and was capable of modeling PCB concentrations in multiple tissues for all dose levels and dose profiles.

A mechanistic modeling framework for predicting metabolic interactions in complex mixtures

BACKGROUND

Computational modeling of the absorption, distribution, metabolism, and excretion of chemicals is now theoretically able to describe metabolic interactions in realistic mixtures of tens to hundreds of substances. That framework awaits validation.

OBJECTIVES

Our objectives were to a) evaluate the conditions of application of such a framework, b) confront the predictions of a physiologically integrated model of benzene, toluene, ethylbenzene, and m-xylene (BTEX) interactions with observed kinetics data on these substances in mixtures and, c) assess whether improving the mechanistic description has the potential to lead to better predictions of interactions.

METHODS

We developed three joint models of BTEX toxicokinetics and metabolism and calibrated them using Markov chain Monte Carlo simulations and single-substance exposure data. We then checked their predictive capabilities for metabolic interactions by comparison with mixture kinetic data.

RESULTS

The simplest joint model (BTEX interacting competitively for cytochrome P450 2E1 access) gives qualitatively correct and quantitatively acceptable predictions (with at most 50% deviations from the data). More complex models with two pathways or back-competition with metabolites have the potential to further improve predictions for BTEX mixtures.

CONCLUSIONS

A systems biology approach to large-scale prediction of metabolic interactions is advantageous on several counts and technically feasible. However, ways to obtain the required parameters need to be further explored.

Physiologically-based pharmacokinetic modeling for absorption, transport, metabolism and excretion

The seminal paper on the liver physiologically-based pharmacokinetic (PBPK) model by Rowland et al. (J Pharmacokinet Biopharm 1:123-136, 1973) that described the influence of blood flow, intrinsic clearance, and binding on hepatic clearance had inspired further development of PBPK modeling of the liver, kidney and intestine as well as whole body. Shortly thereafter, a series of papers from Pang and Rowland compared the well-stirred and parallel-tube liver models and sparked further development on clearance concepts in the liver, including those described by the dispersion model. From 2005 onwards, several seminal papers by Rodgers and Rowland, in their recognition of the binding of molecules to tissue acidic and neutral phospholipids, improved the methodology in providing estimates of the tissue-to-plasma coefficient and rendering easy calculation of these hard-to-get constants. The improvement has strongly consolidated the basic premise on PBPK modeling and simulations and these basics have allowed scientists to focus on other important variables: membrane barriers, and transporter and enzyme and their heterogeneities that further impact drug disposition. In particular, the PBPK models have delved into sequential metabolism and futile cycling to illustrate how transporters and enzymes could affect the metabolism of drugs and metabolites. PBPK models that are especially pertinent to metabolite kinetics are being utilized in drug studies and risk assessment. These types of PBPK modeling reveal differences in kinetics between the formed vs. preformed metabolite, showing special considerations for membrane barriers, and the influence of competing pathways and competing organs.

Computational experiments reveal plausible mechanisms for changing patterns of hepatic zonation of xenobiotic clearance and hepatotoxicity

No concrete, causal, mechanistic theory is available to explain how different hepatic zonation patterns of P450 isozyme levels and hepatotoxicity emerge following dosing with different compounds. We used the synthetic method of modeling and simulation to discover, explore, and experimentally challenge concrete mechanisms that show how and why biomimetic zonation patterns can emerge and change within agent-based analogues, expecting that those mechanisms may have counterparts in rats. Mobile objects map to compounds. One analogue represents a cross-section through a lobule. It is comprised of 460 identical, quasi-autonomous functional units called sinusoidal segments (SSs). SSs detect and respond to compound-generated response signals and the local level of an endogenous gradient. Each SS adapts by using those signals to adjust (or not) the probability that it will clear a detected compound during the next simulation cycle. The adjustment decision is based on the value of a biomimetic algorithm that is based on an assumed, evolution imposed, genetic mandate that normal hepatocytes resist increasing the cost of their actions. The algorithm estimates the long-term, discounted cost to a given SS of continuing to use its current clearance effort. Upon compound exposure, lobular analogues developed a variety of clearance and hepatotoxicity patterns that were strikingly similar to those reported in the literature. A degree of quantitative validation was achieved against data on hepatic zonation of CYP1A2 mRNA expression caused by three different doses of TCDD (2,3,7,8-tetracholorodibenzo-p-dioxone).

A bistable switch underlying B-cell differentiation and its disruption by the environmental contaminant 2,3,7,8-tetrachlorodibenzo-p-dioxin

The differentiation of B cells into antibody-secreting plasma cells upon antigen stimulation, a crucial step in the humoral immune response, is disrupted by 2,3,7,8-tetrachlorodibenzo-p-dioxin (TCDD). Several key regulatory proteins in the B-cell transcriptional network have been identified, with two coupled mutually repressive feedback loops among the three transcription factors B-cell lymphoma 6 (Bcl-6), B lymphocyte-induced maturation protein 1(Blimp-1), and paired box 5 (Pax5) forming the core of the network. However, the precise mechanisms underlying B-cell differentiation and its disruption by TCDD are not fully understood. Here we show with a computational systems biology model that coupling of the two feedback loops at the Blimp-1 node, through parallel inhibition of Blimp-1 gene activation by Bcl-6 and repression of Blimp-1 gene deactivation by Pax5, can generate a bistable switch capable of directing B cells to differentiate into plasma cells. We also use bifurcation analysis to propose that TCDD may suppress the B-cell to plasma cell differentiation process by raising the threshold dose of antigens such as lipopolysaccharide required to trigger the bistable switch. Our model further predicts that high doses of TCDD may render the switch reversible, thus causing plasma cells to lose immune function and dedifferentiate to a B cell-like state. The immunotoxic implications of these predictions are twofold. First, TCDD and related compounds would disrupt the initiation of the humoral immune response by reducing the proportion of B cells that respond to antigen and differentiate into antibody-secreting plasma cells. Second, TCDD may also disrupt the maintenance of the immune response by depleting the pool of available plasma cells through dedifferentiation.

Safety testing of metabolites: Expectations and outcomes

Metabolites arising from chemical entities, old or new, are often mediators of toxicity. Frequently, metabolites are investigated in test animals, with the expectation that the resultant toxicity or activity will mimic the exposure of their formed counterparts. This communication described observations that showed discrepant kinetics between formed and preformed metabolites in the liver, intestine, and kidney, major drug removal organs. Differences in the observed areas under the curve (AUCs) or the extraction ratios (Es) of formed and preformed metabolites in the liver had been attributed to zonal, enzyme heterogeneity, membrane barriers, or transporters. Preformed and formed metabolite also differed in their handling by the kidney; only the preformed and not the formed metabolite would be filtered. In the intestine, differences in the absorption of the precursor and the metabolite and the flow pattern in the intestine would bring about discrepancy in the time-courses of the formed vs. preformed metabolites. Analytical solutions of the AUCs of the metabolites and extraction ratios, based on physiological modeling of the liver, kidney, and intestine, showed that the AUC of the preformed, administered metabolite was dependent only on metabolite parameters, whereas the AUC of the formed metabolite was modulated additionally by the metabolic, secretory and intestinal absorptive intrinsic clearances of the precursor drug. Hence, administration of the synthetic metabolite would not reflect the toxicity associated with the metabolite formed via bioactivation. However, data on preformed metabolite may be used for simultaneous fitting by a combined model of drug and metabolite. Such a strategy is shown to be successful in risk assessment of environmental chemicals. Upon refinement of the resultant model with data on metabolite transport and handling by modeling and simulations, the resultant model would be more robust to provide improved predictions on metabolite toxicity pursuant to drug administration.

Time dependencies in perfluorooctylacids disposition in rat and monkeys: a kinetic analysis

Perfluorooctanoate (PFOA) and perfluorooctanesulfonate (PFOS) are surfactants that have been used for various industrial and consumer applications. The widespread exposure and persistence of PFOA and PFOS in humans have caused these chemicals to be the subject of intense kinetic and toxicity studies. To identify the biological determinants of the species different in elimination observed in kinetic studies, we incorporated time-dependent descriptions for free fraction in plasma and for volume of distribution into an earlier pharmacokinetic model to simulate the time course behaviors of PFOA and PFOS in monkeys and rats. The structurally similar model for monkeys and rats also allows for examination of the complex kinetics observed in animal studies. A higher estimated liver:blood partition coefficient in the rat and additional binding in rat liver suggest that PFOS retention in liver occurs in rats but not in monkeys. Higher liver:blood partition coefficient and renal filtration suggest that PFOS is retained longer in tissues compared to PFOA. A much lower renal resorption may explain the fast elimination of PFOA from plasma observed in female compared to male rats. Understanding these cross-species, cross-compound, and cross-gender difference is an important step in the future development of a human model for these compounds.

Evaluation of physiologically based pharmacokinetic models for use in risk assessment

Physiologically based pharmacokinetic (PBPK) models are sophisticated dosimetry models that offer great flexibility in modeling exposure scenarios for which there are limited data. This is particularly of relevance to assessing human exposure to environmental toxicants, which often requires a number of extrapolations across species, route, or dose levels. The continued development of PBPK models ensures that regulatory agencies will increasingly experience the need to evaluate available models for their application in risk assessment. To date, there are few published criteria or well-defined standards for evaluating these models. Herein, important considerations for evaluating such models are described. The evaluation of PBPK models intended for risk assessment applications should include a consideration of: model purpose, model structure, mathematical representation, parameter estimation, computer implementation, predictive capacity and statistical analyses. Model purpose and structure require qualitative checks on the biological plausibility of a model. Mathematical representation, parameter estimation, computer implementation involve an assessment of the coding of the model, as well as the selection and justification of the physical, physicochemical and biochemical parameters chosen to represent a biological organism. Finally, the predictive capacity and sensitivity, variability and uncertainty of the model are analysed so that the applicability of a model for risk assessment can be determined. Published in 2007 by John Wiley & Sons, Ltd.

Use of a physiologically based pharmacokinetic model for rats to study the influence of body fat mass and induction of CYP1A2 on the pharmacokinetics of TCDD

2,3,7,8-Tetrachlorodibenzo-p-dioxin (TCDD) is a highly lipophilic chemical that distributes into adipose tissue, especially at low doses. However, at high doses TCDD sequesters in liver because it induces cytochrome P450 1A2 (CYP1A2) that binds TCDD. A physiologically based pharmacokinetic (PBPK) model was developed that included an inducible elimination rate of TCDD in the Sprague-Dawley rat. Objectives of this work were to characterize the influence of induction of CYP1A2 and adipose tissue mass fraction on the terminal elimination half-life (t1/2) of TCDD using this PBPK model. When the model assumes a fixed elimination of TCDD, t1/2 increases with dose, due to hepatic sequestration. Because experimental data indicate that the t1/2 of TCDD decreases with dose, the model was modified to include an inducible elimination rate. The PBPK model was then used to compare the t1/2 after an increase of adipose tissue mass fraction from 6.9 to 70%. The model suggests that at low exposures, increasing adipose tissue mass increases the terminal t1/2. However, at higher exposures, as CYP1A2 is induced, the relationship between adipose tissue mass and t1/2 reaches a plateau. This demonstrates that an inducible elimination rate is needed in a PBPK model in order to describe the pharmacokinetics of TCDD. At low exposures these models are more sensitive to parameters related to partitioning into adipose tissue.

Estimated cancer risk of dioxins to humans using a bioassay and physiologically based pharmacokinetic model

The health risk of dioxins and dioxin-like compounds to humans was analyzed quantitatively using experimental data and mathematical models. To quantify the toxicity of a mixture of three dioxin congeners, we calculated the new relative potencies (REPs) for 2,3,7,8-tetrachlorodibenzo-p-dioxin (TCDD), 1,2,3,7,8-pentachlorodibenzo-p-dioxin (PeCDD), and 2,3,4,7,8- pentachlorodibenzofuran (PeCDF), focusing on their tumor promotion activity. We applied a liver foci formation assay to female SD rats after repeated oral administration of dioxins. The REP of dioxin for a rat was determined using dioxin concentration and the number of the foci in rat liver. A physiologically based pharmacokinetic model (PBPK model) was used for interspecies extrapolation targeting on dioxin concentration in liver. Toxic dose for human was determined by back-estimation with a human PBPK model, assuming that the same concentration in the target tissue may cause the same level of effect in rats and humans, and the REP for human was determined by the toxic dose obtained. The calculated REPs for TCDD, PeCDD, and PeCDF were 1.0, 0.34, and 0.05 for rats, respectively, and the REPs for humans were almost the same as those for rats. These values were different from the toxic equivalency factors (TEFs) presented previously (Van den Berg, M., Birnbaum, L., Bosveld, A.T.C., Brunstrom, B., Cook, P., Feeley, M., Giesy, J.P., Hanberg, A., Hasegawa, R., Kennedy, S.W., Kubiak, T., Larsen, J.C., Rolaf van Leeuwen, F.X., Liem, A.K.D., Nolt, C., Peterson, R.E., Poellinger. L., Safe, S., Schrenk, D., Tillitt, D, Tysklind, M., Younes, M., Waern, F., Zacharewski, T., 1998. Toxic equivalency factors (TEFs) for PCBs, PCDDs, PCDFs for humans and wildlife. Environ. Health Perspect. 106, 775-792). The relative risk of excess liver cancer for Japanese people in general was 1.7-6.5 x 10(-7) by TCDD only, and 2.9-11 x 10(-7) by the three dioxins at the present level of contamination.

Improving predictive modeling in pediatric drug development: pharmacokinetics, pharmacodynamics, and mechanistic modeling

A workshop was conducted on November 18-19, 2004, to address the issue of improving predictive models for drug delivery to developing humans. Although considerable progress has been made for adult humans, large gaps remain for predicting pharmacokinetic/pharmacodynamic (PK/PD) outcome in children because most adult models have not been tested during development. The goals of the meeting included a description of when, during development, infants/children become adult-like in handling drugs. The issue of incorporating the most recent advances into the predictive models was also addressed: both the use of imaging approaches and genomic information were considered. Disease state, as exemplified by obesity, was addressed as a modifier of drug pharmacokinetics and pharmacodynamics during development. Issues addressed in this workshop should be considered in the development of new predictive and mechanistic models of drug kinetics and dynamics in the developing human.

Induction of oxidative stress responses by dioxin and other ligands of the aryl hydrocarbon receptor

TCDD and other polyhalogenated aromatic hydrocarbon ligands of the aryl hydrocarbon receptor (AHR) have been classically considered as non-genotoxic compounds because they fail to be directly mutagenic in either bacteria or most in vitro assay systems. They do so in spite of having repeatedly been linked to oxidative stress and to mutagenic and carcinogenic outcomes. Oxidative stress, on the other hand, has been used as a marker for the toxicity of dioxin and its congeners. We have focused this review on the connection between oxidative stress induction and the toxic effects of fetal and adult dioxin exposure, with emphasis on the large species difference in sensitivity to this agent. We examine the roles that the dioxin-inducible cytochromes P450s play in the cellular and toxicological consequences of dioxin exposure with emphasis on oxidative stress involvement. Many components of the health consequences resulting from dioxin exposure may be attributable to epigenetic mechanisms arising from prolonged reactive oxygen generation.

Carcinogenic risks of dioxin: Mechanistic considerations

Dioxins and dioxin-like chemicals demonstrate high affinity binding to the aryl hydrocarbon receptor (AhR), a ligand activated transcription factor, which mediates most, if not all, of the toxic responses of these agents. Since dioxins are not directly genotoxic their carcinogenic effect is likely the result of their tumor promoting activity produced by activation of the AhR. For the purpose of risk assessment extrapolation from effects in the observable high dose range to background dietary exposure is necessary. In the present review, we discuss various aspects of low-dose-response of receptor-mediated processes in general, including threshold phenomena with regard to tumor promotion during multi-stage carcinogenesis. In this connection the reversibility of tumor promotion plays an important role but this may not be valid for dioxins due to their long half life. The relevance of cytochrome P 4501 A-induction as biomarker for prediction of carcinogenic effects of dioxins at low doses is considered. Dioxins may act in concert with endogenous ligands of the AhR, an effect which becomes particularly relevant at low toxicant concentrations. At present, however, the nature and role of these postulated ligands are unknown. Furthermore, it is unclear whether dioxins produce synergistic tumor promotional effects with non-dioxin-like chemicals to which humans are also exposed. Dioxins and, e.g., non-dioxin-like PCBs act through different receptors and there is, albeit yet limited, experimental evidence from experimental studies to suggest that they may act on different target cell populations within the same target organ. From the available data the existence of a (physiological) threshold of effects cannot be proven and may not even exist. For regulatory purposes the application of a so called "practical threshold" for the carcinogenic effect of dioxins is proposed. Further mechanistic studies should be conducted to get insight into the dose-response characteristics of relevant events of dioxin-like and non-dioxin-like agents and into the consequences of potential interactions between both group of compounds during carcinogenesis.

New directions in incidence-dose modeling

Many cellular responses are quantal; that is, they either take place or they do not. Examples of "either-or" responses include cell replication, differentiation and apoptosis. Surprisingly, induction of suites of genes and coordinated phenotypic changes in cells are also often quantal, where embedded molecular circuitry creates on-off switches. Mechanistic incidence-dose (ID) models need to account for the quantal characteristics of cellular switches that contribute, in turn, to dose thresholds and to the incidence of biological responses in individuals. Interdisciplinary systems biology approaches create mechanistic ID models based on: (i) detailed knowledge of the cellular circuitry controlling signal transduction; (ii) evolving biological modeling tools describing cellular circuits and their perturbations by chemicals and (iii) high throughput, high coverage "omic" screens for examining cell signaling pathways and biological responses. These interdisciplinary approaches should produce novel, quantitative ID models for biological responses and greatly improve the biological basis of safety and risk assessments.

Framework for evaluation of physiologically-based pharmacokinetic models for use in safety or risk assessment

Proposed applications of increasingly sophisticated biologically-based computational models, such as physiologically-based pharmacokinetic models, raise the issue of how to evaluate whether the models are adequate for proposed uses, including safety or risk assessment. A six-step process for model evaluation is described. It relies on multidisciplinary expertise to address the biological, toxicological, mathematical, statistical, and risk assessment aspects of the modeling and its application. The first step is to have a clear definition of the purpose(s) of the model in the particular assessment; this provides critical perspectives on all subsequent steps. The second step is to evaluate the biological characterization described by the model structure based on the intended uses of the model and available information on the compound being modeled or related compounds. The next two steps review the mathematical equations used to describe the biology and their implementation in an appropriate computer program. At this point, the values selected for the model parameters (i.e., model calibration) must be evaluated. Thus, the fifth step is a combination of evaluating the model parameterization and calibration against data and evaluating the uncertainty in the model outputs. The final step is to evaluate specialized analyses that were done using the model, such as modeling of population distributions of parameters leading to population estimates for model outcomes or inclusion of early pharmacodynamic events. The process also helps to define the kinds of documentation that would be needed for a model to facilitate its evaluation and implementation.

Induction of CYP1A1 in primary rat hepatocytes by 3,3',4,4',5-pentachlorobiphenyl: evidence for a switch circuit element

In vivo induction of CYP1A1 in hepatocytes by aryl hydrocarbon receptor agonists is heterogeneous. Using immunohistochemistry, cells appear to be either induced or not induced as if the response of an individual cell is better represented as a switch. We have examined induction of CYP1A1 in vitro in primary rat hepatocytes to distinguish the responses of populations of cells and responses of individual cells. Cells were treated with various concentrations of the aryl hydrocarbon receptor agonist, 3,3',4,4',5-pentachlorobiphenyl. Concentration-response and time-course responses were determined for the population of cells by Western blotting for CYP1A1 protein and by real-time RT-PCR for CYP1A1 mRNA. Individual cell responses were visualized by immunocytochemistry (ICC) for protein and by in situ hybridization (ISH) for mRNA. CYP1A1 mRNA was quantified by frequency distribution analysis of grains observed on the ISH slides. Population responses showed time- and concentration-related increases in induction. Single cell responses appeared as all-or-none in the field, with cells appearing to be induced and others appearing to be not induced. Even at the highest concentrations (2.5 x 10(-7) M), some hepatocytes remained unresponsive. Distribution frequencies of single cell induction were more consistent with a switch with variable levels of induction in cells depending on treatment concentration. Combined with the reports from in vivo studies, our results support a switch with rheostat behavior for individual hepatocytes. Mechanistic studies in liver cell lines that are confirmed to exhibit switch-like induction of single cells will be necessary to assess the molecular pathways of this circuit element.

Mechanistic approaches for mixture risk assessments-present capabilities with simple mixtures and future directions

Mechanistic studies with simple mixtures have provided insights into the nature of interactions among chemicals that lead to non-additive effects and have elucidated the exposure conditions under which interactions are likely to occur. This paper discusses studies on four mixtures: (1) 1,1-dichloroethylene and trichloroethylene, (2) carbon tetrachloride and Kepone, (3) hexane and methyl-n-butylketone, and (4) coplanar and non-coplanar polychlorinated biphenyls. These mechanistic studies show that interactions should be described at the level of target tissue dose and are best categorized as either pharmacokinetic (PK) or pharmacodynamic (PD) interactions. In PK interactions the presence of a second chemical alters the kinetics such that a unit of administered dose no longer produces a unit of dose at the target tissue. In PD interactions, the presence of other compounds alters the PDs such that a unit tissue dose no longer produces a unit of response. Physiologically based pharmacokinetic (PBPK) models for mixtures have become important tools for predicting conditions under which interactions are likely to alter the assumption of additivity and have permitted calculation of interaction thresholds with more confidence. New cumulative risk assessment approaches have provided opportunities to classify compounds on the basis of similar chemistry-based modes of action (cholinesterase inhibitors) or similar physiological modes of action (diverse chemicals that alter a common biological outcome, such as defeminization of the developing nervous system). The latter examples present challenges for expanding our risk assessment paradigm to focus on the biology of responses more than on the kinetics of the xenobiotics. Some of the future advances in mixture research will depend on progress in systems biology, a discipline that integrates information across multiple level of biological organization producing PD models of normal function and assessing conditions under which exposures to chemicals lead to the perturbations sufficiently great to produce toxicity and disease. We describe briefly the elements of a systems biology approach for assessing the interactions between various PCB congeners.

Uncoupling-mediated generation of reactive oxygen by halogenated aromatic hydrocarbons in mouse liver microsomes

Studying liver microsomes from 2,3,7,8-tetrachlorodibenzo-p-dioxin (TCDD)-induced or vehicle-treated (noninduced) mice, we evaluated the in vitro effects of added chemicals on the production of reactive oxygen due to substrate/P450-mediated uncoupling. The catalase-inhibited NADPH-dependent H(2)O(2) production (luminol assay) was lower in induced than noninduced microsomes. The effects of adding chemicals (2.5 microM) in vitro could be divided into three categories: Group 1, highly halogenated and coplanar compounds that increased H(2)O(2) production at least 5-fold in induced, but not in noninduced, microsomes; Group 2, non-coplanar halogenated biphenyls that did not affect H(2)O(2) production; Group 3, minimally halogenated biphenyls and benzo[a]pyrene that decreased H(2)O(2) production. Molar consumption of NADPH and O(2) and molar H(2)O(2) production (o-dianisidine oxidation) revealed that Group 1 compounds mostly increased, Group 2 had no effect, and Group 3 decreased the H(2)O(2)/O(2) and H(2)O(2)/NADPH ratios. Microsomal lipid peroxidation (thiobarbituric acid-reactive substances) was proportional to H(2)O(2) production. Although TCDD induction decreased microsomal production of H(2)O(2), addition of Group 1 compounds to TCDD-induced microsomes in vitro stimulated the second-electron reduction of cytochrome P450 and subsequent release of H(2)O(2) production. This pathway is likely to contribute to the oxidative stress response and associated toxicity produced by many of these environmental chemicals.

Evaluation of the potential impact of pharmacokinetic differences on tissue dosimetry in offspring during pregnancy and lactation

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.

Toxicokinetic modeling and its applications in chemical risk assessment

In recent years physiologically based pharmacokinetic (PBPK) modeling has found frequent application in risk assessments where PBPK models serve as important adjuncts to studies on modes of action of xenobiotics. In this regard, studies on mode of action provide insight into both the sites/mechanisms of action and the form of the xenobiotic associated with toxic responses. Validated PBPK models permit calculation of tissue doses of xenobiotics and metabolites for a variety of conditions, i.e. at low-doses, in different animal species, and in different members of a human population. In this manner, these PBPK models support the low-dose and interspecies extrapolations that are important components of current risk assessment methodologies. PBPK models are sometimes referred to as physiological toxicokinetic (PT) models to emphasize their application with compounds causing toxic responses. Pharmacokinetic (PK) modeling in general has a rich history. Data-based PK compartmental models were developed in the 1930's when only primitive tools were available for solving sets of differential equations. These models were expanded in the 1960's and 1970's to accommodate new observations on dose-dependent elimination and flow-limited metabolism. The application of clearance concepts brought many new insights about the disposition of drugs in the body. In the 1970's PBPK/PT models were developed to evaluate metabolism of volatile compounds of occupational importance, and, for the first time, dose-dependent processes in toxicology were included in PBPK models in order to assess the conditions under which saturation of metabolic and elimination processes lead to non-linear dose response relationships. In the 1980's insights from chemical engineers and occupational toxicology were combined to develop PBPK/PT models to support risk assessment with methylene chloride and other solvents. The 1990's witnessed explosive growth in risk assessment applications of PBPK/PT models and in applying sensitivity and variability methods to evaluate model performance. Some of the compounds examined in detail include butadiene, styrene, glycol ethers, dioxins and organic esters/aids. This paper outlines the history of PBPK/PT modeling, emphasizes more recent applications of PBPK/TK models in health risk assessment, and discusses the risk assessment perspective provided by modern uses of these modeling approaches.

Toxicokinetics and physiologically based toxicokinetics in toxicology and risk assessment

Toxicokinetics is the study of kinetics of absorption, distribution, metabolism, and excretion of a xenobiotic under the conditions of toxicity evaluation. Conventional toxicokinetics uses the hypothetical compartments, and the model is composed of rate equations that describe the time course of drug and chemical disposition. The utility of toxicokinetics in toxicity evaluation and interpretation of animal toxicology data is emerging as an important tool in product discovery and development. With implementation of the International Conference on Harmonization (ICH) guidelines on systemic exposure and dose selection, toxicokinetics have been integrated in routine toxicity evaluations. Although traditional compartmental/noncompartmental models are generally adequate for assessing systemic exposure, they are unable to the predict time course of drug disposition in target tissues and often fail to relate systemic drug levels to a biological response. Physiologically based toxicokinetic (PB-TK) models address this deficiency of traditional compartmental models. PB-TK models are the kinetic models of the uptake and disposition of chemicals based on rates of biochemical reactions, physiological and anatomical characteristics. These models, when developed appropriately, can predict target organ drug distribution in different species under variety of conditions. This minireview discusses the basic principles, and applications of traditional compartmental toxicokinetic and physiologically based toxicokinetics (PB-TK) models in drug development and risk assessment. Special emphasis will be placed on discussion related to interpretation of the ICH guidelines related to toxicokinetics and the utility of toxicokinetics data in dose selection for toxicity and carcinogenicity studies. The utility of PB-TK models in risk assessment of methylene chloride, vinyl chloride, retinoic acid, dioxin, and inhaled organic esters is discussed.

Impact of physiologically based pharmacokinetic modeling on benchmark dose calculations for TCDD-induced biochemical responses

In risk assessment, noncancer risk is currently estimated using a no observed adverse effect level (NOAEL) from an experimental dose-response study, divided by uncertainty factors, to estimate a presumably safe level of human exposure. A benchmark dose approach, in which an effective dose (ED) resulting in a specified percentage increase over background for effects is estimated by empirical modeling, has been proposed as a replacement for the NOAEL methodology. The aim of this analysis is to compare methods for estimation of body burden resulting in a 1 or 10% maximum increase over background (BB(01) or BB(10)) for biochemical responses following exposure to 2,3,7,8-tetrachlorodibenzo-p-dioxin (TCDD) in female Sprague-Dawley rats. In one method, an ED resulting in a prespecified increase in response over background was estimated using average daily doses and an empirical Hill model. The ED was then converted to an equivalent body burden by a simple kinetic model assuming steady-state conditions, half-life of TCDD in the rat, and 100% absorption of TCDD. Alternatively, a mechanistic physiologically based pharmacokinetic (PBPK) model of TCDD in the rat was used to predict body burdens for administered doses. These PBPK-modeled body burdens were then used directly by the Hill model to calculate a BB(01) or BB(10). In general, the body burden values derived from EDs were within five-fold of BB(01) or BB(10) calculated from the PBPK model. BB(01) and BB(10) values from both methods were within two orders of magnitude of current human general population exposure to all dioxin-like compounds.

Induction of cellular oxidative stress by aryl hydrocarbon receptor activation

The aryl hydrocarbon receptor (AHR) has long been associated with the induction of a battery of genes involved in the metabolism of foreign and endogenous compounds. Depending on experimental conditions, AHR can mediate either activation or amelioration of chemical toxicity. For the past decade, evidence has mounted that AHR is associated with a cellular oxidative stress response that must be considered when evaluating the mechanism of action of xenobiotics capable of activating AHR, or capable of metabolic activation by enzymes encoded by genes under control of AHR. In this review, we have evaluated the diverse mechanisms by which AHR generates an oxidative stress response, including inflammation, antioxidant and prooxidant enzymes and cytochrome P450. A review of the regulation of Ahr transcription and functional polymorphisms especially related to oxidative stress is also included. We have carefully avoided placing a value judgment on the degree of toxicity produced by such a response, in view of the realization that an oxidative response is involved in many normal physiological processes. Since the interface between physiological, adaptive and toxicological responses elicited by the AHR-mediated oxidative stress response is not clearly defined, it behooves the researcher to evaluate both toxicological and physiological features of the response.

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.

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.

Physiological modeling of a proposed mechanism of enzyme induction by TCDD

A physiological model was previously constructed to facilitate extrapolation of surrogates for the effects of 2,3,7,8-tetrachlorodibenzo-p-dioxin (TCDD) in rat liver to doses comparable to human environmental exposures. The model included induction of P450 isozymes and suggested the presence of multiple binding sites with different affinities for the TCDD-liganded Ah receptor at CYP1A1 dioxin responsive elements. The model also indicated that protein synthesis on the mRNA template exhibited saturation kinetics with respect to message levels. In the present work the earlier model was revised to include the increased proteolysis of the Ah receptor on binding TCDD, more realistic representations of gene transcription and mRNA translation, and different stability for each mRNA. The revised model includes multiple TCDD-liganded Ah receptor binding sites for CYP1A1 and CYP1B1 genes, a lag of 0.2 day for production of mRNA and induced proteins, and stabilization of mRNA by a poly(A) tail. The model reproduced the transient depletion of the Ah receptor subsequent to binding ligand and the dose-response of the receptor in rats treated with biweekly oral doses of TCDD in corn oil. The model reproduced tissue TCDD concentrations observed for several dosing scenarios. Such robustness indicates the utility of the model in estimating internal dose. The model also reproduced the observed dose-response patterns for mRNA and protein for CYP1A1, CYP1A2, and CYP1B1 after repeated dosing. Neither of the two dissociation constants for the Ah receptor bound to the CYP1B1 gene is negligible, supporting the assumption of multiple response elements for this gene. The poorer induction of CYP1B1 was predicted to be due to lower affinity of the dioxin responsive elements for binding the liganded Ah receptor, suggesting the involvement of other regulatory factors, and a shorter poly(A) tail on CYP1B1 mRNA, leading to a shorter lifetime. Saturation in the kinetics of protein synthesis was linked to the limited number of ribosomes that could bind to each message molecule, resulting in fewer ribosomes bound per message at higher doses. Predicted induction at low doses was found to vary widely with the assumptions used in the construction of a model. More detailed descriptions of biological processes might provide more reliable predictions of enzyme induction.

Mechanistic aspects--the dioxin (aryl hydrocarbon) receptor

The Ah receptor mediates the toxicological responses of 2,3,7,8-TCDD and related compounds. Receptor-deficient animals were shown to be resistant to the toxic effects of dioxin, although there is also evidence for the existence of a receptor-independent pathway for dioxin-induced toxicity. In the cytosol the receptor is present in a non-activated ligand binding conformation. Association with Arnt in the nucleus turns the receptor complex into a ligand activated form. The physiological role of the receptor is not yet understood; however, the conservation of the receptor in a wide range of animal species (including humans) suggests a fundamental role in cellular physiology.

Risk ranges for various endpoints following exposure to 2,3,7,8-TCDD

Conducting a dose-response analysis for an environmental contaminant requires a careful evaluation of most of the available data focusing on both the magnitude of the effect and the possible ranges of dose-response shapes which fit the data. This paper calculates potency values (1% response exposures) for human and animal data on cancer, non-cancer and biological effect endpoints for TCDD and finds that a reasonable estimate for 1% excess cancer would be between 1 and 50 pg/kg/day. The paper also evaluates the adequacy of linear and non-linear models for fitting these data and concludes that the assumption of a threshold dose-response is not fully supported by these data.

Hazard assessment of chemical carcinogens: the impact of hormesis

The recent report of reductions in the number and area of preneoplastic hepatic lesions in response to low doses of the tumor promoter phenobarbital provides important new support for the existence of hormetic responses to carcinogens. The presence of hormetic responses to carcinogenic agents and the corollary that beneficial doses of these compounds can be determined have several implications for the bioassay and hazard assessment of carcinogens as well as for public policy regulating exposure to these agents. To be adequately sensitive to detect and quantify hormetic or other non-linear dose-response functions, current study designs must be modified to include lower doses and sufficiently large numbers of animals. Short- or medium-term animal studies are a cost-effective means of addressing these needs and have been used recently to describe a classical hormetic response to the non-genotoxic carcinogen phenobarbital. These basic changes should be supported by a continuing emphasis on mechanistic research and the development of biologically based quantitative models of toxicant action. Linking these models with physiologically based pharmacokinetic model descriptions of target dose holds the greatest promise for improving the description of the dose-response curve at low doses. These approaches are generally encouraged by the USEPA in the form of The 1996 Proposed Carcinogen Risk Assessment Guidelines. However, there remain substantial questions regarding integration of the concept of hormesis into hazard testing and public policy that require careful consideration. Herein, we explore the issues that surround testing for hormetic responses and the implications for public policy.

Implications of hormesis on the bioassay and hazard assessment of chemical carcinogens

Hormesis has been defined as a dose-response relationship which depicts improvement in some endpoint (increased metabolic rates, reduction in tumor incidence, etc.) at low doses of a toxic compound followed by a decline in the endpoint at higher doses. The existence of hormetic responses to carcinogenic agents has several implications for the bioassay and hazard assessment of carcinogens. To be capable of detecting and statistically testing for hormetic or other nonlinear dose-response functions, current study designs must be modified to include lower doses and sufficiently large numbers of animals. In addition, improved statistical methods for testing nonlinear dose-response relationships will have to be developed. Research integrating physiologically-based pharmacokinetic model descriptions of target dose with mechanistic data holds the greatest promise for improving the description of the dose-response curve at low doses. The 1996 Proposed Carcinogen Risk Assessment Guidelines encourage the use of mechanistic data to improve the descriptions of the dose-response curve at low doses, but do not distinguish between the types of nonlinear dose-response curves. Should this refined approach lead to substantial support for hormesis in carcinogenic processes, future guidelines will need to provide guidance on establishing safe doses and communicating the results to the public.

Hepatic foci in rats after diethylnitrosamine initiation and 2,3,7,8-tetrachlorodibenzo-p-dioxin promotion: evaluation of a quantitative two-cell model and of CYP 1A1/1A2 as a dosimeter

2,3,7,8-Tetrachlorodibenzo-p-dioxin (TCDD) is a potent hepatic tumor promoter in female rats. We used a quantitative, stochastic initiation-promotion model based on R. B. Conolly and J. S. Kimbell (Toxicol. Appl. Pharmacol. 124, 284-295, 1994) to analyze initiation-promotion results from a previously published study (H. C. Pitot et al., Carcinogenesis 8, 1491-1499, 1987) within the context of a negative selection model of tumor promotion. In this model, two types of initiated cells (called A and B cells) are produced by DEN initiation. Visually excellent correspondence between model predictions and data (i.e., foci/cm3 liver and percentage of liver occupied by foci) are obtained when TCDD is described as having dose-responsive effects on division and death (apoptotic) rates of these two cell types. For A cells, both the division and the death rates increase while the difference between division and apoptotic rates decreases. For B cells, the difference between division and apoptotic rates increases, primarily due to a decrease in the apoptotic rate. We also linked these alterations in cell kinetics to a pharmacokinetic model for TCDD incorporating a five subcompartment model of the liver acinus with induction of CYP1A1 and 1A2 genes in the subcompartments. Alterations in A cell kinetics correlate with effects of TCDD in the region most sensitive to induction (subcompartment 5-centrilobular region); B cell dynamics correlate with induction in subcompartments 3-5 (centrilobular and mid-zonal regions). In summary, these modeling exercises show that (1) the two-cell model, without presuming effects of TCDD on the mutation rate of normal hepatocytes, reproduces the data of Pitot et al. (1987) and (2) induction of CYP1A1/1A2 in different regions of the hepatic acinus can be used as a general correlate of these presumed changes in cell growth kinetics.