Considerations for refining the risk assessment process for formaldehyde: Results from an interdisciplinary workshop.

Anticipating the need to evaluate and integrate scientific evidence to inform new risk assessments or to update existing risk assessments, the Formaldehyde Panel of the American Chemistry Council (ACC), in collaboration with the University of North Carolina, convened a workshop: "Understanding Potential Human Health Cancer Risk - From Data Integration to Risk Evaluation" in October 2017. Twenty-four (24) invited-experts participated with expertise in epidemiology, toxicology, science integration and risk evaluation. Including members of the organizing committee, there were 29 participants. The meeting included eleven presentations encompassing an introduction and three sessions: (1) "integrating the formaldehyde science on nasal/nasopharyngeal carcinogenicity and potential for causality"; (2) "integrating the formaldehyde science on lymphohematopoietic cancer and potential for causality; and, (3) "formaldehyde research-data suitable for risk assessment". Here we describe key points from the presentations on epidemiology, toxicology and mechanistic studies that should inform decisions about the potential carcinogenicity of formaldehyde in humans and the discussions about approaches for structuring an integrated, comprehensive risk assessment for formaldehyde. We also note challenges expected when attempting to reconcile divergent results observed from research conducted within and across different scientific disciplines - especially toxicology and epidemiology - and in integrating diverse, multi-disciplinary mechanistic evidence.

Peak Exposures in Epidemiologic Studies and Cancer Risks: Considerations for Regulatory Risk Assessment.

We review approaches for characterizing "peak" exposures in epidemiologic studies and methods for incorporating peak exposure metrics in dose-response assessments that contribute to risk assessment. The focus was on potential etiologic relations between environmental chemical exposures and cancer risks. We searched the epidemiologic literature on environmental chemicals classified as carcinogens in which cancer risks were described in relation to "peak" exposures. These articles were evaluated to identify some of the challenges associated with defining and describing cancer risks in relation to peak exposures. We found that definitions of peak exposure varied considerably across studies. Of nine chemical agents included in our review of peak exposure, six had epidemiologic data used by the U.S. Environmental Protection Agency (US EPA) in dose-response assessments to derive inhalation unit risk values. These were benzene, formaldehyde, styrene, trichloroethylene, acrylonitrile, and ethylene oxide. All derived unit risks relied on cumulative exposure for dose-response estimation and none, to our knowledge, considered peak exposure metrics. This is not surprising, given the historical linear no-threshold default model (generally based on cumulative exposure) used in regulatory risk assessments. With newly proposed US EPA rule language, fuller consideration of alternative exposure and dose-response metrics will be supported. "Peak" exposure has not been consistently defined and rarely has been evaluated in epidemiologic studies of cancer risks. We recommend developing uniform definitions of "peak" exposure to facilitate fuller evaluation of dose response for environmental chemicals and cancer risks, especially where mechanistic understanding indicates that the dose response is unlikely linear and that short-term high-intensity exposures increase risk.

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

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

Incorporation of in vitro metabolism data and physiologically based pharmacokinetic modeling in a risk assessment for chloroprene.

Objective: To develop a physiologically based pharmacokinetic (PBPK) model for chloroprene in the mouse, rat and human, relying only on in vitro data to estimate tissue metabolism rates and partitioning, and to apply the model to calculate an inhalation unit risk (IUR) for chloroprene.Materials and methods: Female B6C3F1 mice were the most sensitive species/gender for lung tumors in the 2-year bioassay conducted with chloroprene. The PBPK model included tissue metabolism rate constants for chloroprene estimated from results of in vitro gas uptake studies using liver and lung microsomes. To assess the validity of the PBPK model, a 6-hr, nose-only chloroprene inhalation study was conducted with female B6C3F1 mice in which both chloroprene blood concentrations and ventilation rates were measured. The PBPK model was then used to predict dose measures - amounts of chloroprene metabolized in lungs per unit time - in mice and humans.Results: The mouse PBPK model accurately predicted in vivo pharmacokinetic data from the 6-hr, nose-only chloroprene inhalation study. The PBPK model was used to conduct a cancer risk assessment based on metabolism of chloroprene to reactive epoxides in the lung, the target tissue in mice. The IUR was over100-fold lower than the IUR from the EPA Integrated Risk Information System (IRIS), which was based on inhaled chloroprene concentration. The different result from the PBPK model risk assessment arises from use of the more relevant tissue dose metric, amount metabolized, rather than inhaled concentrationDiscussion and conclusions: The revised chloroprene PBPK model is based on the best available science, including new test animal in vivo validation, updated literature review and a Markov-Chain Monte Carlo analysis to assess parameter uncertainty. Relying on both mouse and human metabolism data also provides an important advancement in the use of quantitative in vitro to in vivo extrapolation (QIVIVE). Inclusion of the best available science is especially important when deriving a toxicity value based on species extrapolation for the potential carcinogenicity of a reactive metabolite.

Quantitative risk assessment of tobacco products: A potentially useful component of substantial equivalence evaluations.

Quantitative risk assessment (QRA), a scientific, evidence-based analytical process that combines chemical and biological data to quantify the probability and potential impact of some defined risk, is used by regulatory agencies for decision-making. Thus, in tobacco product regulation, specifically in substantial equivalence (SE) evaluations, QRA can provide a useful, practical, and efficient approach to address questions that might arise regarding human health risk and potential influence on public health. In SE reporting, when differences in product characteristics may necessitate the determination of whether a new product raises different questions of public health, the results from QRA are a valuable metric. An approach for QRA in this context is discussed, which is modeled after the methodology for assessment of constituent mixtures by the US Environmental Protection Agency for environmental Superfund site assessment. Given the intent in both cases is an assessment of the public health impact resulting from the totality of exposure to a mixture of constituents, the application is appropriate. Although some uncertainties in the information incorporated may exist, relying on the most appropriate of the available data increases the confidence and decreases the uncertainty in the risk characterization using this data-driven methodology.

Evaluation of triclosan in Minnesota lakes and rivers: Part II - human health risk assessment.

Triclosan, an antimicrobial compound found in consumer products, has been detected in low concentrations in Minnesota municipal wastewater treatment plant (WWTP) effluent. This assessment evaluates potential health risks for exposure of adults and children to triclosan in Minnesota surface water, sediments, and fish. Potential exposures via fish consumption are considered for recreational or subsistence-level consumers. This assessment uses two chronic oral toxicity benchmarks, which bracket other available toxicity values. The first benchmark is a lower bound on a benchmark dose associated with a 10% risk (BMDL10) of 47mg per kilogram per day (mg/kg-day) for kidney effects in hamsters. This value was identified as the most sensitive endpoint and species in a review by Rodricks et al. (2010) and is used herein to derive an estimated reference dose (RfD(Rodricks)) of 0.47mg/kg-day. The second benchmark is a reference dose (RfD) of 0.047mg/kg-day derived from a no observed adverse effect level (NOAEL) of 10mg/kg-day for hepatic and hematopoietic effects in mice (Minnesota Department of Health [MDH] 2014). Based on conservative assumptions regarding human exposures to triclosan, calculated risk estimates are far below levels of concern. These estimates are likely to overestimate risks for potential receptors, particularly because sample locations were generally biased towards known discharges (i.e., WWTP effluent).

A tissue dose-based comparative exposure assessment of manganese using physiologically based pharmacokinetic modeling-The importance of homeostatic control for an essential metal.

A physiologically-based pharmacokinetic (PBPK) model (Schroeter et al., 2011) was applied to simulate target tissue manganese (Mn) concentrations following occupational and environmental exposures. These estimates of target tissue Mn concentrations were compared to determine margins of safety (MOS) and to evaluate the biological relevance of applying safety factors to derive acceptable Mn air concentrations. Mn blood concentrations measured in occupational studies permitted verification of the human PBPK models, increasing confidence in the resulting estimates. Mn exposure was determined based on measured ambient air Mn concentrations and dietary data in Canada and the United States (US). Incorporating dietary and inhalation exposures into the models indicated that increases in target tissue concentrations above endogenous levels only begin to occur when humans are exposed to levels of Mn in ambient air (i.e. >10µg/m3) that are far higher than those currently measured in Canada or the US. A MOS greater than three orders of magnitude was observed, indicating that current Mn air concentrations are far below concentrations that would be required to produce the target tissue Mn concentrations associated with subclinical neurological effects. This application of PBPK modeling for an essential element clearly demonstrates that the conventional application of default factors to "convert" an occupational exposure to an equivalent continuous environmental exposure, followed by the application of safety factors, is not appropriate in the case of Mn. PBPK modeling demonstrates that the relationship between ambient Mn exposures and dose-to-target tissue is not linear due to normal tissue background levels and homeostatic controls.

The impact of recent advances in research on arsenic cancer risk assessment.

Scientific debate surrounds the regulatory approach for evaluating carcinogenic risk of arsenic compounds. The arsenic ambient water quality criteria (AWQC), based on the assumption of a linear mode of action for skin cancer risk, results in an allowable limit of 0.018ppb in ambient waters; the drinking water Maximum Contaminant Level (MCL) was determined using a similar linear approach. Integration of results from recent studies investigating arsenic's mode of action provide the basis for a change in the approach for conducting an arsenic cancer risk assessment. Results provide support for a concentration demonstrating a dose-dependent transition in response from those representing adaptive changes to those that may be key events in the development of cancer endpoints. While additional information is needed, integration of current research results provides insight for a new quantitative cancer risk assessment methodology as an alternative toxicologically-based dose response (BBDR) cancer modeling. Integration of the new experimental results, combined with epidemiological evidence, support a dose-dependent transition concentration of approximately 0.1µM arsenic. Some uncertainties remain; additional information from chronic in vitro studies underway is needed. Results to date also provide initial insight into variability in population response at low arsenic exposures.

Challenges in the application of quantitative approaches in risk assessment: a case study with di-(2-ethylhexyl)phthalate.

The constantly evolving science of risk assessment is currently faced with many challenges, not only from the interpretation of the volume of data being generated with new innovative technologies, but also in attempting to quantitatively incorporate this information into understanding potential risk of adverse events in human populations. The objective of the case study described was to use the more recent data for di-(2-ethylhexyl)phthalate (DEHP) to investigate the impact of innovative quantitative approaches on the risk assessment of a compound, specifically as it can be used to move towards the new vision of risk assessment involving the integration of the available toxicological data to understand underlying biological processes. What emerged were several outcomes that demonstrated clearly the importance of the integration of the toxicological data, specifically to understand the biological processes being impacted, because standard statistical modeling approaches may not be adequate to describe the dose-response relationships observed. Alternative approaches demonstrate that a definitive mode of action is not needed to justify the shape of the low-dose region or a threshold, when the integration of the available data assist risk assessors in understanding the shape of the dose-response curve for both noncancer and cancer endpoints. Many of the challenges described as part of this case study would likely be encountered with compounds other than DEHP, especially other receptor-mediated compounds or compounds that "perturb" biological pathways, such as endocrine disruptors. This case study also highlights the importance of communication between risk assessors and the research community to focus on the generation of data most relevant for assessing the potential for chemicals to impact biological systems in the human.

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

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

Revised assessment of cancer risk to dichloromethane II. Application of probabilistic methods to cancer risk determinations.

An updated PBPK model of methylene chloride (DCM, dichloromethane) carcinogenicity in mice was recently published using Bayesian statistical methods (Marino et al., 2006). In this work, this model was applied to humans, as recommended by Sweeney et al.(2004). Physiological parameters for input into the MCMC analysis were selected from multiple sources reflecting, in each case, the source that was considered to represent the most current scientific evidence for each parameter. Metabolic data for individual subjects from five human studies were combined into a single data set and population values derived using MCSim. These population values were used for calibration of the human model. The PBPK model using the calibrated metabolic parameters was used to perform a cancer risk assessment for DCM, using the same tumor incidence and exposure concentration data relied upon in the current IRIS entry. Unit risks, i.e., the risk of cancer from exposure to 1 microg/m3 over a lifetime, for DCM were estimated using the calibrated human model. The results indicate skewed distributions for liver and lung tumor risks, alone or in combination, with a mean unit risk (per microg/m3) of 1.05 x 10(-9), considering both liver and lung tumors. Adding the distribution of genetic polymorphisms for metabolism to the ultimate carcinogen, the unit risks range from 0 (which is expected given that approximately 20% of the US population is estimated to be nonconjugators) up to a unit risk of 2.70 x 10(-9) at the 95th percentile. The median, or 50th percentile, is 9.33 x 10(-10), which is approximately a factor of 500 lower than the current EPA unit risk of 4.7 x 10(-7) using a previous PBPK model. These values represent the best estimates to date for DCM cancer risk because all available human data sets were used, and a probabilistic methodology was followed.

Revised assessment of cancer risk to dichloromethane: part I Bayesian PBPK and dose-response modeling in mice.

The current USEPA cancer risk assessment for dichloromethane (DCM) is based on deterministic physiologically based pharmacokinetic (PBPK) modeling involving comparative metabolism of DCM by the GST pathway in the lung and liver of humans and mice. Recent advances in PBPK modeling include probabilistic methods and, in particular, Bayesian inference to quantitatively address variability and uncertainty separately. Although Bayesian analysis of human PBPK models has been published, no such efforts have been reported specifically addressing the mouse, apart from results included in the OSHA final rule on DCM. Certain aspects of the OSHA model, however, are not consistent with current approaches or with the USEPA's current DCM cancer risk assessment. Therefore, Bayesian analysis of the mouse PBPK model and dose-response modeling was undertaken to support development of an improved cancer risk assessment for DCM. A hierarchical population model was developed and prior parameter distributions were selected to reflect parameter values that were considered the most appropriate and best available. Bayesian modeling was conducted using MCSim, a publicly available software program for Markov Chain Monte Carlo analysis. Mean posterior values from the calibrated model were used to develop internal dose metrics, i.e., mg DCM metabolized by the GST pathway/L tissue/day in the lung and liver using exposure concentrations and results from the NTP mouse bioassay, consistent with the approach used by the USEPA for its current DCM cancer risk assessment. Internal dose metrics were 3- to 4-fold higher than those that support the current USEPA IRIS assessment. A decrease of similar magnitude was also noted in dose-response modeling results. These results show that the Bayesian PBPK model in the mouse provides an improved basis for a cancer risk assessment of DCM.

Evaluation of physiologically based pharmacokinetic models in risk assessment: an example with perchloroethylene.

One of the more problematic aspects of the application of physiologically based pharmacokinetic (PBPK) models in risk assessment is the question of whether the model has been adequately validated to provide confidence in the dose metrics calculated with it. A number of PBPK models have been developed for perchloroethylene (PCE), differing primarily in the parameters estimated for metabolism. All of the models provide reasonably accurate simulations of selected kinetic data for PCE in mice and humans and could thus be considered to be "validated" to some extent. However, quantitative estimates of PCE cancer risk are critically dependent on the prediction of the rate of metabolism at low environmental exposures. Recent data on the urinary excretion of trichloroacetic acid (TCA), the major metabolite of PCE, for human subjects exposed to lower concentrations than those used in previous studies, make it possible to compare the high- to low-dose extrapolation capability of the various published human models. The model of Gearhart et al., which is the only model to include a description of TCA kinetics, provided the closest predictions of the urinary excretion observed in these low-concentration exposures. Other models overestimated metabolite excretion in this study by 5- to 15-fold. A systematic discrepancy between model predictions and experimental data for the time course of the urinary excretion of TCA suggested a contribution from TCA formed by metabolism of PCE in the kidney and excreted directly into the urine. A modification of the model of Gearhart et al. to include metabolism of PCE to TCA in the kidney at 10% of the capacity of the liver, with direct excretion of the TCA formed in the kidney into the urine, markedly improved agreement with the experimental time-course data, without altering predictions of liver metabolism. This case study with PCE demonstrates the danger of relying on parent chemical kinetic data to validate a model that will be used for the prediction of metabolism.

An approach for the quantitative consideration of genetic polymorphism data in chemical risk assessment: examples with warfarin and parathion.

In recent years, a great deal of research has been conducted to identify genetic polymorphisms. One focus has been to characterize variability in metabolic enzyme systems that could impact internal doses of pharmaceuticals or environmental pollutants. Methods are needed for using this metabolic information to estimate the resulting variability in tissue doses associated with chemical exposure. We demonstrate here the use of physiologically based pharmacokinetic (PBPK) modeling in combination with Monte Carlo analysis to incorporate information on polymorphisms into the analysis of toxicokinetic variability. Warfarin and parathion were used as case studies to demonstrate this approach. Our results suggest that polymorphisms in the PON1 gene, that give rise to allelic variants of paraoxonase, which is involved in the metabolism of paraoxon (a metabolite of parathion), make only a minor contribution to the overall variability in paraoxon tissue dose, while polymorphisms in the CYP2C9 gene, which gives rise to allelic variants of the major metabolic enzyme for warfarin, account for a significant portion of the overall variability in (S)-warfarin tissue dose. These analyses were used to estimate chemical-specific adjustment factors (CSAFs) for the human variability in toxicokinetics for both parathion and warfarin. Implications of alternatives in the calculation of CSAFs are explored. Key decision points for applying the PBPK-Monte Carlo approach to evaluate toxicokinetic variability for other chemicals are also discussed.