IVIVE: Facilitating the Use of In Vitro Toxicity Data in Risk Assessment and Decision Making.

During the past few decades, the science of toxicology has been undergoing a transformation from observational to predictive science. New approach methodologies (NAMs), including in vitro assays, in silico models, read-across, and in vitro to in vivo extrapolation (IVIVE), are being developed to reduce, refine, or replace whole animal testing, encouraging the judicious use of time and resources. Some of these methods have advanced past the exploratory research stage and are beginning to gain acceptance for the risk assessment of chemicals. A review of the recent literature reveals a burst of IVIVE publications over the past decade. In this review, we propose operational definitions for IVIVE, present literature examples for several common toxicity endpoints, and highlight their implications in decision-making processes across various federal agencies, as well as international organizations, including those in the European Union (EU). The current challenges and future needs are also summarized for IVIVE. In addition to refining and reducing the number of animals in traditional toxicity testing protocols and being used for prioritizing chemical testing, the goal to use IVIVE to facilitate the replacement of animal models can be achieved through their continued evolution and development, including a strategic plan to qualify IVIVE methods for regulatory acceptance.

A Systematic Review of Published Physiologically-based Kinetic Models and an Assessment of their Chemical Space Coverage.

Across multiple sectors, including food, cosmetics and pharmaceutical industries, there is a need to predict the potential effects of xenobiotics. These effects are determined by the intrinsic ability of the substance, or its derivatives, to interact with the biological system, and its concentration-time profile at the target site. Physiologically-based kinetic (PBK) models can predict organ-level concentration-time profiles, however, the models are time and resource intensive to generate de novo. Read-across is an approach used to reduce or replace animal testing, wherein information from a data-rich chemical is used to make predictions for a data-poor chemical. The recent increase in published PBK models presents the opportunity to use a read-across approach for PBK modelling, that is, to use PBK model information from one chemical to inform the development or evaluation of a PBK model for a similar chemical. Essential to this process, is identifying the chemicals for which a PBK model already exists. Herein, the results of a systematic review of existing PBK models, compliant with the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) format, are presented. Model information, including species, sex, life-stage, route of administration, software platform used and the availability of model equations, was captured for 7541 PBK models. Chemical information (identifiers and physico-chemical properties) has also been recorded for 1150 unique chemicals associated with these models. This PBK model data set has been made readily accessible, as a Microsoft Excel® spreadsheet, providing a valuable resource for those developing, using or evaluating PBK models in industry, academia and the regulatory sectors.

Opportunities and challenges related to saturation of toxicokinetic processes: Implications for risk assessment.

Top dose selection for repeated dose animal studies has generally focused on identification of apical endpoints, use of the limit dose, or determination of a maximum tolerated dose (MTD). The intent is to optimize the ability of toxicity tests performed in a small number of animals to detect effects for hazard identification. An alternative approach, the kinetically derived maximum dose (KMD), has been proposed as a mechanism to integrate toxicokinetic (TK) data into the dose selection process. The approach refers to the dose above which the systemic exposures depart from being proportional to external doses. This non-linear external-internal dose relationship arises from saturation or limitation of TK process(es), such as absorption or metabolism. The importance of TK information is widely acknowledged when assessing human health risks arising from exposures to environmental chemicals, as TK determines the amount of chemical at potential sites of toxicological responses. However, there have been differing opinions and interpretations within the scientific and regulatory communities related to the validity and application of the KMD concept. A multi-stakeholder working group, led by the Health and Environmental Sciences Institute (HESI), was formed to provide an opportunity for impacted stakeholders to address commonly raised scientific and technical issues related to this topic and, more specifically, a weight of evidence approach is recommended to inform design and dose selection for repeated dose animal studies. Commonly raised challenges related to the use of TK data for dose selection are discussed, recommendations are provided, and illustrative case examples are provided to address these challenges or refute misconceptions.

PBPK model reporting template for chemical risk assessment applications.

Physiologically-based pharmacokinetic (PBPK) modeling analysis does not stand on its own for regulatory purposes but is a robust tool to support drug/chemical safety assessment. While the development of PBPK models have grown steadily since their emergence, only a handful of models have been accepted to support regulatory purposes due to obstacles such as the lack of a standardized template for reporting PBPK analysis. Here, we expand the existing guidances designed for pharmaceutical applications by recommending additional elements that are relevant to environmental chemicals. This harmonized reporting template can be adopted and customized by public health agencies receiving PBPK model submission, and it can also serve as general guidance for submitting PBPK-related studies for publication in journals or other modeling sharing purposes. The current effort represents one of several ongoing collaborations among the PBPK modeling and risk assessment communities to promote, when appropriate, incorporating PBPK modeling to characterize the influence of pharmacokinetics on safety decisions made by regulatory agencies.

Application of a combined aggregate exposure pathway and adverse outcome pathway (AEP-AOP) approach to inform a cumulative risk assessment: A case study with phthalates.

Advancements in measurement and modeling capabilities are providing unprecedented access to estimates of chemical exposure and bioactivity. With this influx of new data, there is a need for frameworks that help organize and disseminate information on chemical hazard and exposure in a manner that is accessible and transparent. A case study approach was used to demonstrate integration of the Adverse Outcome Pathway (AOP) and Aggregate Exposure Pathway (AEP) frameworks to support cumulative risk assessment of co-exposure to two phthalate esters that are ubiquitous in the environment and that are associated with disruption of male sexual development in the rat: di(2-ethylhexyl) phthalate (DEHP) and di-n-butyl phthalate (DnBP). A putative AOP was developed to guide selection of an in vitro assay for derivation of bioactivity values for DEHP and DnBP and their metabolites. AEPs for DEHP and DnBP were used to extract key exposure data as inputs for a physiologically based pharmacokinetic (PBPK) model to predict internal metabolite concentrations. These metabolite concentrations were then combined using in vitro-based relative potency factors for comparison with an internal dose metric, resulting in an estimated margin of safety of ~13,000. This case study provides an adaptable workflow for integrating exposure and toxicity data by coupling AEP and AOP frameworks and using in vitro and in silico methodologies for cumulative risk assessment.

A weight of evidence approach to investigate potential common mechanisms in pesticide groups to support cumulative risk assessment: A case study with dinitroaniline pesticides.

In 2016, the United States Environmental Protection Agency's (EPA) Office of Pesticide Programs published guidelines for establishing candidate common mechanism groups (CMGs) for cumulative risk assessment (CRA) weight-of-evidence-based screenings. A candidate CMG is a group of chemicals that may share similar structure, apical endpoints, and/or mechanistic data that suggest the potential for a common mechanism of toxicity among them. Here, a weight-of-evidence approach is presented to establish candidacy of a CMG for a group of nine dinitroaniline pesticides. This approach involves review of available in vivo toxicity information and literature to determine mode of action, along with analyses of in vitro toxicity data and chemical structure. Despite structural similarity among some dinitroanilines and some shared target organs identified through toxicity observed in in vivo studies, there were no consistencies among groups, suggesting lack of a common mechanism when all analyses are considered together. For example, two structurally similar compounds with thyroid/liver in vivo effects were not found active in any Toxicity Forecaster (ToxCast) in vitro assays. The weight-of-evidence is insufficient to support the testable hypothesis that dinitroanilines could form a CMG, and highlights the importance of establishing a consensus among multiple lines of evidence prior to CRA.

Aggregate Exposure Pathways in Support of Risk Assessment.

Over time, risk assessment has shifted from establishing relationships between exposure to a single chemical and a resulting adverse health outcome, to evaluation of multiple chemicals and disease outcomes simultaneously. As a result, there is an increasing need to better understand the complex mechanisms that influence risk of chemical and non-chemical stressors, beginning at their source and ending at a biological endpoint relevant to human or ecosystem health risk assessment. Just as the Adverse Outcome Pathway (AOP) framework has emerged as a means of providing insight into mechanism-based toxicity, the exposure science community has seen the recent introduction of the Aggregate Exposure Pathway (AEP) framework. AEPs aid in making exposure data applicable to the FAIR (i.e., findable, accessible, interoperable, and reusable) principle, especially by (1) organizing continuous flow of disjointed exposure information;(2) identifying data gaps, to focus resources on acquiring the most relevant data; (3) optimizing use and repurposing of existing exposure data; and (4) facilitating interoperability among predictive models. Herein, we discuss integration of the AOP and AEP frameworks and how such integration can improve confidence in both traditional and cumulative risk assessment approaches.

Investigating the state of physiologically based kinetic modelling practices and challenges associated with gaining regulatory acceptance of model applications.

Physiologically based kinetic (PBK) models are used widely throughout a number of working sectors, including academia and industry, to provide insight into the dosimetry related to observed adverse health effects in humans and other species. Use of these models has increased over the last several decades, especially in conjunction with emerging alternative methods to animal testing, such as in vitro studies and data-driven in silico quantitative-structure-activity-relationship (QSAR) predictions. Experimental information derived from these new approach methods can be used as input for model parameters and allows for increased confidence in models for chemicals that did not have in vivo data for model calibration. Despite significant advancements in good modelling practice (GMP) for model development and evaluation, there remains some reluctance among regulatory agencies to use such models during the risk assessment process. Here, the results of a survey disseminated to the modelling community are presented in order to inform the frequency of use and applications of PBK models in science and regulatory submission. Additionally, the survey was designed to identify a network of investigators involved in PBK modelling and knowledgeable of GMP so that they might be contacted in the future for peer review of PBK models, especially in regards to vetting the models to such a degree as to gain a greater acceptance for regulatory purposes.

Conceptual Framework To Extend Life Cycle Assessment Using Near-Field Human Exposure Modeling and High-Throughput Tools for Chemicals.

Life Cycle Assessment (LCA) is a decision-making tool that accounts for multiple impacts across the life cycle of a product or service. This paper presents a conceptual framework to integrate human health impact assessment with risk screening approaches to extend LCA to include near-field chemical sources (e.g., those originating from consumer products and building materials) that have traditionally been excluded from LCA. A new generation of rapid human exposure modeling and high-throughput toxicity testing is transforming chemical risk prioritization and provides an opportunity for integration of screening-level risk assessment (RA) with LCA. The combined LCA and RA approach considers environmental impacts of products alongside risks to human health, which is consistent with regulatory frameworks addressing RA within a sustainability mindset. A case study is presented to juxtapose LCA and risk screening approaches for a chemical used in a consumer product. The case study demonstrates how these new risk screening tools can be used to inform toxicity impact estimates in LCA and highlights needs for future research. The framework provides a basis for developing tools and methods to support decision making on the use of chemicals in products.

Reconstructing exposures from biomarkers using exposure-pharmacokinetic modeling--A case study with carbaryl.

Sources of uncertainty involved in exposure reconstruction for short half-life chemicals were characterized using computational models that link external exposures to biomarkers. Using carbaryl as an example, an exposure model, the Cumulative and Aggregate Risk Evaluation System (CARES), was used to generate time-concentration profiles for 500 virtual individuals exposed to carbaryl. These exposure profiles were used as inputs into a physiologically based pharmacokinetic (PBPK) model to predict urinary biomarker concentrations. These matching dietary intake levels and biomarker concentrations were used to (1) compare three reverse dosimetry approaches based on their ability to predict the central tendency of the intake dose distribution; and (2) identify parameters necessary for a more accurate exposure reconstruction. This study illustrates the trade-offs between using non-iterative reverse dosimetry methods that are fast, less precise and iterative methods that are slow, more precise. This study also intimates the necessity of including urine flow rate and elapsed time between last dose and urine sampling as part of the biomarker sampling collection for better interpretation of urinary biomarker data of short biological half-life chemicals. Resolution of these critical data gaps can allow exposure reconstruction methods to better predict population-level intake doses from large biomonitoring studies.

Uses of NHANES Biomarker Data for Chemical Risk Assessment: Trends, Challenges, and Opportunities.

BACKGROUND: Each year, the U.S. NHANES measures hundreds of chemical biomarkers in samples from thousands of study participants. These biomarker measurements are used to establish population reference ranges, track exposure trends, identify population subsets with elevated exposures, and prioritize research needs. There is now interest in further utilizing the NHANES data to inform chemical risk assessments. OBJECTIVES: This article highlights a) the extent to which U.S. NHANES chemical biomarker data have been evaluated, b) groups of chemicals that have been studied, c) data analysis approaches and challenges, and d) opportunities for using these data to inform risk assessments. METHODS: A literature search (1999-2013) was performed to identify publications in which U.S. NHANES data were reported. Manual curation identified only the subset of publications that clearly utilized chemical biomarker data. This subset was evaluated for chemical groupings, data analysis approaches, and overall trends. RESULTS: A small percentage of the sampled NHANES-related publications reported on chemical biomarkers (8% yearly average). Of 11 chemical groups, metals/metalloids were most frequently evaluated (49%), followed by pesticides (9%) and environmental phenols (7%). Studies of multiple chemical groups were also common (8%). Publications linking chemical biomarkers to health metrics have increased dramatically in recent years. New studies are addressing challenges related to NHANES data interpretation in health risk contexts. CONCLUSIONS: This article demonstrates growing use of NHANES chemical biomarker data in studies that can impact risk assessments. Best practices for analysis and interpretation must be defined and adopted to allow the full potential of NHANES to be realized.

Improving the risk assessment of lipophilic persistent environmental chemicals in breast milk.

Lipophilic persistent environmental chemicals (LPECs) have the potential to accumulate within a woman's body lipids over the course of many years prior to pregnancy, to partition into human milk, and to transfer to infants upon breastfeeding. As a result of this accumulation and partitioning, a breastfeeding infant's intake of these LPECs may be much greater than his/her mother's average daily exposure. Because the developmental period sets the stage for lifelong health, it is important to be able to accurately assess chemical exposures in early life. In many cases, current human health risk assessment methods do not account for differences between maternal and infant exposures to LPECs or for lifestage-specific effects of exposure to these chemicals. Because of their persistence and accumulation in body lipids and partitioning into breast milk, LPECs present unique challenges for each component of the human health risk assessment process, including hazard identification, dose-response assessment, and exposure assessment. Specific biological modeling approaches are available to support both dose-response and exposure assessment for lactational exposures to LPECs. Yet, lack of data limits the application of these approaches. The goal of this review is to outline the available approaches and to identify key issues that, if addressed, could improve efforts to apply these approaches to risk assessment of lactational exposure to these chemicals.

A new method for generating distributions of biomonitoring equivalents to support exposure assessment and prioritization.

Biomonitoring data are now available for hundreds of chemicals through state and national health surveys. Exposure guidance values also exist for many of these chemicals. Several methods are frequently used to evaluate biomarker data with respect to a guidance value. The "biomonitoring equivalent" (BE) approach estimates a single biomarker concentration (called the BE) that corresponds to a guidance value (e.g., Maximum Contaminant Level, Reference Dose, etc.), which can then be compared with measured biomarker data. The resulting "hazard quotient" estimates (HQ=biomarker concentration/BE) can then be used to prioritize chemicals for follow-up examinations. This approach is used exclusively for population-level assessments, and works best when the central tendency of measurement data is considered. Complementary approaches are therefore needed for assessing individual biomarker levels, particularly those that fall within the upper percentiles of measurement distributions. In this case study, probabilistic models were first used to generate distributions of BEs for perchlorate based on the point-of-departure (POD) of 7µg/kg/day. These distributions reflect possible biomarker concentrations in a hypothetical population where all individuals are exposed at the POD. A statistical analysis was then performed to evaluate urinary perchlorate measurements from adults in the 2001 to 2002 National Health and Nutrition Examination Survey (NHANES). Each NHANES adult was assumed to have experienced repeated exposure at the POD, and their biomarker concentration was interpreted probabilistically with respect to a BE distribution. The HQ based on the geometric mean (GM) urinary perchlorate concentration was estimated to be much lower than unity (HQ≈0.07). This result suggests that the average NHANES adult was exposed to perchlorate at a level well below the POD. Regarding individuals, at least a 99.8% probability was calculated for all but two NHANES adults that a higher biomarker concentration would have been observed compared to what was actually measured if the daily dietary exposure had been at the POD. This is strong evidence that individual perchlorate exposures in the 2001-2002 NHANES adult population were likely well below the POD. This case study demonstrates that the "stochastic BE approach" provides useful quantitative metrics, in addition to HQ estimates, for comparison across chemicals. This methodology should be considered when evaluating biomarker measurements against exposure guidance values, and when examining chemicals that have been identified as needing follow-up investigation based on existing HQ estimates.

Using a physiologically based pharmacokinetic model to link urinary biomarker concentrations to dietary exposure of perchlorate.

Exposure to perchlorate is widespread in the United States and many studies have attempted to character the perchlorate exposure by estimating the average daily intakes of perchlorate. These approaches provided population-based estimates, but did not provide individual-level exposure estimates. Until recently, exposure activity database such as CSFII, TDS and NHANES become available and provide opportunities to evaluate the individual-level exposure to chemical using exposure surveillance dataset. In this study, we use perchlorate as an example to investigate the usefulness of urinary biomarker data for predicting exposures at the individual level. Specifically, two analyses were conducted: (1) using data from a controlled human study to examine the ability of a physiologically based pharmacokinetic (PBPK) model to predict perchlorate concentrations in single-spot and cumulative urine samples; and (2) using biomarker data from a population-based study and a PBPK model to demonstrate the challenges in linking urinary biomarker concentrations to intake doses for individuals. Results showed that the modeling approach was able to characterize the distribution of biomarker concentrations at the population level, but predicting the exposure-biomarker relationship for individuals was much more difficult. The type of information needed to reduce the uncertainty in estimating intake doses, for individuals, based on biomarker measurements is discussed.

Reconstructing human exposures using biomarkers and other "clues".

Biomonitoring is the process by which biomarkers are measured in human tissues and specimens to evaluate exposures. Given the growing number of population-based biomonitoring surveys, there is now an escalated interest in using biomarker data to reconstruct exposures for supporting risk assessment and risk management. While detection of biomarkers is de facto evidence of exposure and absorption, biomarker data cannot be used to reconstruct exposure unless other information is available to establish the external exposure-biomarker concentration relationship. In this review, the process of using biomarker data and other information to reconstruct human exposures is examined. Information that is essential to the exposure reconstruction process includes (1) the type of biomarker based on its origin (e.g., endogenous vs. exogenous), (2) the purpose/design of the biomonitoring study (e.g., occupational monitoring), (3) exposure information (including product/chemical use scenarios and reasons for expected contact, the physicochemical properties of the chemical and nature of the residues, and likely exposure scenarios), and (4) an understanding of the biological system and mechanisms of clearance. This review also presents the use of exposure modeling, pharmacokinetic modeling, and molecular modeling to assist in integrating these various types of information.

Evaluating pharmacokinetic and pharmacodynamic interactions with computational models in supporting cumulative risk assessment.

Simultaneous or sequential exposure to multiple chemicals may cause interactions in the pharmacokinetics (PK) and/or pharmacodynamics (PD) of the individual chemicals. Such interactions can cause modification of the internal or target dose/response of one chemical in the mixture by other chemical(s), resulting in a change in the toxicity from that predicted from the summation of the effects of the single chemicals using dose additivity. In such cases, conducting quantitative cumulative risk assessment for chemicals present as a mixture is difficult. The uncertainties that arise from PK interactions can be addressed by developing physiologically based pharmacokinetic (PBPK) models to describe the disposition of chemical mixtures. Further, PK models can be developed to describe mechanisms of action and tissue responses. In this article, PBPK/PD modeling efforts conducted to investigate chemical interactions at the PK and PD levels are reviewed to demonstrate the use of this predictive modeling framework in assessing health risks associated with exposures to complex chemical mixtures.

Bayesian analysis of a rat formaldehyde DNA-protein cross-link model.

As the initial effort in a multi-step uncertainty analysis of a biologically based cancer model for formaldehyde, a Markov chain Monte Carlo (MCMC) analysis was performed for a compartmental model that predicts DNA-protein cross-links (DPX) produced by formaldehyde exposure. The Bayesian approach represented by the MCMC analysis integrates existing knowledge of the model parameters with observed, formaldehyde-DPX-specific data, providing a statistically sound basis for estimating model output uncertainty. Uncertainty and variability were evaluated through a hierarchical structure, where interindividual variability was considered for all model parameters and that variability was assumed to be uncertain on population levels. The uncertainty of the population mean and that of the population variance were significantly reduced through the MCMC analysis. Our investigation highlights several issues that must be dealt with in many real-world analyses (e.g., issues of parameters' nonidentifiability due to limited data) while demonstrating the feasibility of conducting a comprehensive quantitative uncertainty evaluation. The current analysis can be viewed as a case study, for a relatively simple model, illustrating some of the constraints that analysts will face when applying Bayesian approaches to biologically or physiologically based models of increasing complexity.

Bayesian calibration of a physiologically based pharmacokinetic/pharmacodynamic model of carbaryl cholinesterase inhibition.

Carbaryl, an N-methyl carbamate (NMC), is a common insecticide that reversibly inhibits neuronal cholinesterase activity. The objective of this work was to use a hierarchical Bayesian approach to estimate the parameters in a physiologically based pharmacokinetic and pharmacodynamic (PBPK/PD) model from experimental measurements of carbaryl in rats. A PBPK/PD model was developed to describe the tissue dosimetry of carbaryl and its metabolites (1-naphthol and "other hydroxylated metabolites") and subsequently to predict the carbaryl-induced inhibition of cholinesterase activity, in particular in the brain and blood. In support of the model parameterization, kinetic tracer studies were undertaken to determine total radioactive tissue levels of carbaryl and metabolites in rats exposed by oral or intravenous routes at doses ranging from 0.8 to 9.2 mg/kg body weight. Inhibition of cholinesterase activity in blood and brain was also measured from the exposed rats. Markov Chain Monte Carlo (MCMC) calibration of the rat model parameters was implemented using prior information from literature for physiological parameter distributions together with kinetic and inhibition data on carbaryl. The posterior estimates of the parameters displayed at most a twofold deviation from the mean. Monte Carlo simulations of the PBPK/PD model with the posterior distribution estimates predicted a 95% credible interval of tissue doses for carbaryl and 1-naphthol within the range of observed data. Similar prediction results were achieved for cholinesterase inhibition by carbaryl. This initial model will be used to determine the experimental studies that may provide the highest added value for model refinement. The Bayesian PBPK/PD modeling approach developed here will serve as a prototype for developing mechanism-based risk models for the other NMCs.

Bayesian estimation of pharmacokinetic and pharmacodynamic parameters in a mode-of-action-based cancer risk assessment for chloroform.

Chloroform is a carcinogen in rodents and its carcinogenicity is secondary to events associated with cytotoxicity and regenerative cell proliferation. In this study, a physiologically based pharmacokinetic/pharmacodynamic (PBPK/PD) model that links the processes of chloroform metabolism, reparable cell damage, cell death, and regenerative cellular proliferation was developed to support a new cancer dose-response assessment for chloroform. Model parameters were estimated using Markov Chain Monte Carlo (MCMC) analysis in a two-step approach: (1) metabolism parameters for male and female mice and rats were estimated against available closed chamber gas uptake data; and (2) PD parameters for each of the four rodent groups were estimated from hepatic and renal labeling index data following inhalation exposures. Subsequently, the resulting rodent PD parameters together with literature values for human age-dependent physiological and metabolism parameters were used to scale up the rodent model to a human model. The human model was used to predict exposure conditions under which chloroform-mediated cytolethality is expected to occur in liver and kidney of adults and children. Using the human model, inhalation Reference Concentrations (RfCs) and oral Reference Doses (RfDs) were derived using an uncertainty factor of 10. Based on liver and kidney dose metrics, the respective RfCs were 0.9 and 0.09 ppm; and the respective RfDs were 0.4 and 3 mg/kg/day.

Development of a screening approach to interpret human biomonitoring data on volatile organic compounds: reverse dosimetry on biomonitoring data for trichloroethylene.

A screening approach is developed for volatile organic compounds (VOCs) to estimate exposures that correspond to levels measured in fluids and/or tissues in human biomonitoring studies. The approach makes use of a generic physiologically-based pharmacokinetic (PBPK) model coupled with exposure pattern characterization, Monte Carlo analysis, and quantitative structure property relationships (QSPRs). QSPRs are used for VOCs with minimal data to develop chemical-specific parameters needed for the PBPK model. The PBPK model is capable of simulating VOC kinetics following multiple routes of exposure, such as oral exposure via water ingestion and inhalation exposure during shower events. Using published human biomonitoring data of trichloroethylene (TCE), the generic model is evaluated to determine how well it estimates TCE concentrations in blood based on the known drinking water concentrations. In addition, Monte Carlo analysis is conducted to characterize the impact of the following factors: (1) uncertainties in the QSPR-estimated chemical-specific parameters; (2) variability in physiological parameters; and (3) variability in exposure patterns. The results indicate that uncertainty in chemical-specific parameters makes only a minor contribution to the overall variability and uncertainty in the predicted TCE concentrations in blood. The model is used in a reverse dosimetry approach to derive estimates of TCE concentrations in drinking water based on given measurements of TCE in blood, for comparison to the U.S. EPA's Maximum Contaminant Level in drinking water. This example demonstrates how a reverse dosimetry approach can be used to facilitate interpretation of human biomonitoring data in a health risk context by deriving external exposures that are consistent with a biomonitoring data set, thereby permitting comparison with health-based exposure guidelines.