Is the cholesterol-perfluoroalkyl substance association confounded by dietary fiber intake?: a Bayesian analysis of NHANES data with adjustment for measurement error in fiber intake.

BACKGROUND: Serum concentrations of total cholesterol and related lipid measures have been associated with serum concentrations of per- and polyfluoroalkyl substances (PFAS) in humans, even among those with only background-level exposure to PFAS. Fiber is known to decrease serum cholesterol and a recent report based on National Health and Nutrition Examination Survey (NHANES) showed that PFAS and fiber are inversely associated. We hypothesized that confounding by dietary fiber may account for some of the association between cholesterol and PFAS. METHODS: We implemented a Bayesian correction for measurement error in estimated intake of dietary fiber to evaluate whether fiber confounds the cholesterol-PFAS association. The NHANES measure of diet, two 24-h recalls, allowed calculation of an estimate of the "true" long-term fiber intake for each subject. We fit models to the NHANES data on serum cholesterol and serum concentration of perfluorooctanoic acid (PFOA) and two other PFAS for 7,242 participants in NHANES. RESULTS: The Bayesian model, after adjustment for soluble fiber intake, suggested a decrease in the size of the coefficient for PFOA by 6.4% compared with the fiber-unadjusted model. CONCLUSIONS: The results indicated that the association of serum cholesterol with PFAS was not substantially confounded by fiber intake.

Bayesian hierarchical evaluation of dose-response for peanut allergy in clinical trial screening.

Risk-based labeling based on the minimal eliciting doses (EDs) in sensitized populations is a potential replacement for precautionary allergen labeling of food allergens. We estimated the dose-response distribution for peanut allergen using data from double-blind placebo-controlled food challenges (DBPCFCs) conducted in the US at multiple sites, testing a population believed to be similar to the general U.S. food allergic population. Our final (placebo-adjusted) dataset included 548 challenges of 481 subjects. Bayesian hierarchical analysis facilitated model fitting, and accounted for variability associated with various levels of data organization. The data are best described using a complex hierarchical structure that accounts for inter-individual variability and variability across study locations or substudies. Bayesian model averaging could simultaneously consider the fit of multiple models, but the Weibull model dominated so strongly that model averaging was not needed. The ED01 and ED05 (and 95% credible intervals) are 0.052 (0.021, 0.13) and 0.49 (0.22, 0.97) mg peanut protein, respectively. Accounting for challenges with severe reactions at the LOAEL, by using the dose prior to the LOAEL as the new LOAEL, the ED01 drops to 0.029 (0.014, 0.074) mg peanut protein. Our results could aid in establishing improved food labeling guidelines in the management of food allergies.

Impact of updated BMD modeling methods on perchlorate and chlorate assessments of human health hazard.

Several exposure limits for perchlorate have been developed based on an early key event, inhibition of radioactive iodide uptake (RAIU) by the thyroid. These assessments have used a variety of definitions of the point of departure. The current assessment revisited the modeling for inhibition of RAIU, using state of the science methods. Bayesian hierarchical modeling was used to account for the repeated measures on the same individuals in the key dataset, and the underlying Beta distribution used for the modeling correctly reflected the bounding of RAIU between 0 and 1. We defined the BMR as a point value of 8% RAIU (rather than a change in RAIU), based on descriptions in the medical literature that RAIU below this value is considered abnormal. Because a definition of the BMR based on the mean response would correspond to about 50% of the population with a response below the BMR at the benchmark dose, we used a hybrid definition of the BMR. That is, the BMD was defined as the dose at which it was estimated that there would be a 10% extra risk in the population of having RAIU of 8% or lower. The resulting point of departure based on the BMDL was 0.03 mg/kg-day.

Quantitative Risk Assessment: Developing a Bayesian Approach to Dichotomous Dose-Response Uncertainty.

Model averaging for dichotomous dose-response estimation is preferred to estimate the benchmark dose (BMD) from a single model, but challenges remain regarding implementing these methods for general analyses before model averaging is feasible to use in many risk assessment applications, and there is little work on Bayesian methods that include informative prior information for both the models and the parameters of the constituent models. This article introduces a novel approach that addresses many of the challenges seen while providing a fully Bayesian framework. Furthermore, in contrast to methods that use Monte Carlo Markov Chain, we approximate the posterior density using maximum a posteriori estimation. The approximation allows for an accurate and reproducible estimate while maintaining the speed of maximum likelihood, which is crucial in many applications such as processing massive high throughput data sets. We assess this method by applying it to empirical laboratory dose-response data and measuring the coverage of confidence limits for the BMD. We compare the coverage of this method to that of other approaches using the same set of models. Through the simulation study, the method is shown to be markedly superior to the traditional approach of selecting a single preferred model (e.g., from the U.S. EPA BMD software) for the analysis of dichotomous data and is comparable or superior to the other approaches.

Pharmacokinetic bias analysis of an association between clinical thyroid disease and two perfluoroalkyl substances.

Exposure to perfluorooctanoic acid (PFOA) and perfluorooctane sulfonate (PFOS) has been associated with the occurrence of thyroid disease in some epidemiologic studies. We hypothesized that in a specific epidemiologic study based on the National Health and Nutrition Examination Survey, the association of clinical thyroid disease with serum concentration of PFOA and PFOS was due to reverse causality. Thyroid hormone affects glomerular filtration, which in turn affects excretion of PFOA and PFOS. We evaluated this by linking a model of thyroid disease status over the lifetime to a physiologically based pharmacokinetic model of PFOA and PFOS. Using Monte Carlo methods, we simulated the target study population and analyzed the data using multivariable logistic regression. The target and simulated populations were similar with respect to age, estimated glomerular filtration rate, serum concentrations of PFOA and PFOS, and prevalence of clinical thyroid disease. The analysis showed little or no evidence of bias from the hypothesized mechanism. The largest bias was for the fourth quartile of PFOA in females, with an odds ratio of 0.93 (95% CI, 0.90, 0.97). The reported odds ratio of clinical thyroid disease for this group was 1.63 (1.07, 2.47), and if it were corrected for the bias would have been 1.74 (1.14, 2.65). Our results suggest that little of the reported association in the target study was due to reverse causality.

Quantitative bias analysis of the association between subclinical thyroid disease and two perfluoroalkyl substances in a single study.

Exposure to perfluorooctanoic acid (PFOA) and perfluorooctane sulfonate (PFOS) has been associated with the occurrence of thyroid disease in some epidemiologic studies. We hypothesized that in a specific epidemiologic study based on the National Health and Nutrition Examination Survey, the association of subclinical thyroid disease with serum concentration of PFOA and PFOS was due to reverse causality. Thyroid hormone affects glomerular filtration, which in turn affects excretion of PFOA and PFOS. We evaluated this by linking a model of thyroid disease status over the lifetime to physiologically based pharmacokinetic models of PFOA and PFOS. Using Monte Carlo methods, we simulated the target study population and analyzed the data using multivariable logistic regression. The target and simulated populations were similar with respect to age, estimated glomerular filtration rate, serum concentrations of PFOA and PFOS, and prevalence of subclinical thyroid disease. Our findings suggest that in the target study the associations with subclinical hypothyroidism were overstated and the results for subclinical hyperthyroidism were, in general, understated. For example, for subclinical hypothyroidism in men, the reported odds ratio per ln(PFOS) increase was 1.98 (95% CI 1.19-3.28), whereas in the simulated data the bias due to reverse causality gave an odds ratio of 1.19 (1.16-1.23). Our results provide evidence of bias due to reverse causality in a specific cross-sectional study of subclinical thyroid disease with exposure to PFOA and PFOS among adults.

A model of functional thyroid disease status over the lifetime.

Mathematical models of the natural history of disease can predict incidence rates based on prevalence data and support simulations of populations where thyroid function affects other aspects of physiology. We developed a Markov chain model of functional thyroid disease status over the lifetime. Subjects were in one of seven thyroid disease states at any given point in their lives [normal, subclinical hypothyroidism, overt hypothyroidism, treated thyroid disease (ever), subclinical hyperthyroidism, overt hyperthyroidism, and reverted to normal thyroid status]. We used a Bayesian approach to fitting model parameters. A priori probabilities of changing from each disease state to another per unit time were based on published data and summarized using meta-analysis, when possible. The probabilities of changing state were fitted to observed prevalence data based on the National Health and Nutrition Examination Survey 2007-2012. The fitted model provided a satisfactory fit to the observed prevalence data for each disease state, by sex and decade of age. For example, for males 50-59 years old, the observed prevalence of ever having treated thyroid disease was 4.4% and the predicted value was 4.6%. Comparing the incidence rates of treated disease predicted from our model with published values revealed that 82% were within a 4-fold difference. The differences seemed to be systematic and were consistent with expectation based on national iodine intakes. The model provided new and comprehensive estimates of functional thyroid disease incidence rates for the U.S. Because the model provides a reasonable fit to national prevalence data and predicts thyroid disease status over the lifetime, it is suitable for simulating populations, thereby making possible quantitative bias analyses of selected epidemiologic data reporting an association of thyroid disease with serum concentrations of environmental contaminants.

Benchmark dose (BMD) modeling: current practice, issues, and challenges.

Benchmark dose (BMD) modeling is now the state of the science for determining the point of departure for risk assessment. Key advantages include the fact that the modeling takes account of all of the data for a particular effect from a particular experiment, increased consistency, and better accounting for statistical uncertainties. Despite these strong advantages, disagreements remain as to several specific aspects of the modeling, including differences in the recommendations of the US Environmental Protection Agency (US EPA) and the European Food Safety Authority (EFSA). Differences exist in the choice of the benchmark response (BMR) for continuous data, the use of unrestricted models, and the mathematical models used; these can lead to differences in the final BMDL. It is important to take confidence in the model into account in choosing the BMDL, rather than simply choosing the lowest value. The field is moving in the direction of model averaging, which will avoid many of the challenges of choosing a single best model when the underlying biology does not suggest one, but additional research would be useful into methods of incorporating biological considerations into the weights used in the averaging. Additional research is also needed regarding the interplay between the BMR and the UF to ensure appropriate use for studies supporting a lower BMR than default values, such as for epidemiology data. Addressing these issues will aid in harmonizing methods and moving the field of risk assessment forward.

Derivation of an oral toxicity reference value for nickel.

Nickel (Ni) is in the earth's crust and can be found in environmental compartments such as water, soil, and air, as well as food. This paper presents an assessment of the oral nickel toxicity data in support of non-cancer health-based oral exposure limits or toxicity reference values (TRVs). This paper derives TRVs for three populations of interest: adults, toddlers, and people who have been dermally sensitized to nickel. The adult/lifetime TRV of 20 µg Ni/kg-day is based on post-implantation loss/perinatal mortality in a 2-generation reproductive study in rats. Several recent assessments by regulatory agencies have used the same study and endpoint, but the dose-response modeling conducted here was more appropriate for the study design. Toxicokinetic data from rats and humans indicate that the applied uncertainty factors are very conservative. Because the endpoint relates to fetal exposure and is not relevant to toddlers, a toddler TRV was derived based on decreased body weight in young rats; this TRV was also 20 µg Ni/kg-day. A separate TRV of 4 µg Ni/kg in addition to Ni in food was derived for protection of nickel-sensitized populations from flare-up of dermatitis, based on studies of single exposures in humans under conditions that maximize oral absorption.

Evaluation of Serum Lipid, Thyroid, and Hepatic Clinical Chemistries in Association With Serum Perfluorooctanesulfonate (PFOS) in Cynomolgus Monkeys After Oral Dosing With Potassium PFOS.

An oral dose study with perfluorooctanesulfonate (PFOS) was undertaken to identify potential associations between serum PFOS and changes in serum clinical chemistry parameters in purpose-bred young adult cynomolgus monkeys (Macaca fascicularis). In this study, control group (n = 6/sex) was sham-dosed with vehicle (0.5% Tween 20 and 5% ethanol in water), low-dose group (n = 6/sex) received 1 single K+PFOS dose (9 mg/kg), and high-dose group (n = 4-6/sex) received 3 separate K+ PFOS doses (11-17.2 mg/kg). Monkeys were given routine checkups and observed carefully for health problems on a daily basis. Scheduled blood samples were drawn from all monkeys prior to, during, and after K+PFOS administration for up to 1 year and they were analyzed for PFOS concentrations and clinical chemistry markers for coagulation, lipids, hepatic, renal, electrolytes, and thyroid-related hormones. No mortality occurred during the study. All the monkeys were healthy, gained weight, and were released back to the colony at the end of the study. The highest serum PFOS achieved was approximately 165 µg/ml. When compared with time-matched controls, administration of K+PFOS to monkeys did not result in any toxicologically meaningful or clinically relevant changes in serum clinical measurements for coagulation, lipids, hepatic, renal, electrolytes, and thyroid-related hormones. A slight reduction in serum cholesterol (primarily the high-density lipoprotein fraction), although not toxicologically significant, was observed. The corresponding lower-bound fifth percentile benchmark concentrations (BMCL1sd) were 74 and 76 µg/ml for male and female monkeys, respectively. Compared to the 2013-2014 geometric mean serum PFOS level of 4.99 ng/ml (0.00499 µg/ml) in US general population reported by CDC NHANES, this represents 4 orders of magnitude for margin of exposure.

Bayesian Hierarchical Structure for Quantifying Population Variability to Inform Probabilistic Health Risk Assessments.

Human variability is a very important factor considered in human health risk assessment for protecting sensitive populations from chemical exposure. Traditionally, to account for this variability, an interhuman uncertainty factor is applied to lower the exposure limit. However, using a fixed uncertainty factor rather than probabilistically accounting for human variability can hardly support probabilistic risk assessment advocated by a number of researchers; new methods are needed to probabilistically quantify human population variability. We propose a Bayesian hierarchical model to quantify variability among different populations. This approach jointly characterizes the distribution of risk at background exposure and the sensitivity of response to exposure, which are commonly represented by model parameters. We demonstrate, through both an application to real data and a simulation study, that using the proposed hierarchical structure adequately characterizes variability across different populations.

Meta-regression analysis of the effect of trans fatty acids on low-density lipoprotein cholesterol.

We conducted a meta-regression of controlled clinical trial data to investigate quantitatively the relationship between dietary intake of industrial trans fatty acids (iTFA) and increased low-density lipoprotein cholesterol (LDL-C). Previous regression analyses included insufficient data to determine the nature of the dose response in the low-dose region and have nonetheless assumed a linear relationship between iTFA intake and LDL-C levels. This work contributes to the previous work by 1) including additional studies examining low-dose intake (identified using an evidence mapping procedure); 2) investigating a range of curve shapes, including both linear and nonlinear models; and 3) using Bayesian meta-regression to combine results across trials. We found that, contrary to previous assumptions, the linear model does not acceptably fit the data, while the nonlinear, S-shaped Hill model fits the data well. Based on a conservative estimate of the degree of intra-individual variability in LDL-C (0.1 mmoL/L), as an estimate of a change in LDL-C that is not adverse, a change in iTFA intake of 2.2% of energy intake (%en) (corresponding to a total iTFA intake of 2.2-2.9%en) does not cause adverse effects on LDL-C. The iTFA intake associated with this change in LDL-C is substantially higher than the average iTFA intake (0.5%en).

The need for transparency and reproducibility in documenting values for regulatory decision making and evaluating causality: The example of formaldehyde.

Reproducibility and transparency in scientific reporting is paramount to advancing science and providing the foundation required for sound regulation. Recent examples demonstrate that pivotal scientific findings cannot be replicated, due to poor documentation or methodological bias, sparking debate across scientific and regulatory communities. However, there is general agreement that improvements in communicating and documenting research and risk assessment methods are needed. In the case of formaldehyde, the peer-review conducted by a National Academy of Sciences (NAS) Committee questioned the approaches used by the Integrated Risk Information System (IRIS) in developing draft unit risk values. Using the original data from the key study (Beane Freeman et al., 2009) and documentation provided in the draft IRIS profile, we attempted to duplicate the reported inhalation unit risk values and address the NAS Committee's questions regarding application of the appropriate dose-response model. Overall, documentation of the methods lacked sufficient detail to allow for replication of the unit risk estimates, specifically for Hodgkin lymphoma and leukemias, the key systemic endpoints selected by IRIS. The lack of apparent exposure-response relationships for selected endpoints raises the question whether quantitative analyses are appropriate for these endpoints, and if so, how results are to be interpreted.

Quantitative dose-response analysis of ethyl methanesulfonate genotoxicity in adult gpt-delta transgenic mice.

The assumption that mutagens have linear dose-responses recently has been challenged. In particular, ethyl methanesulfonate (EMS), a DNA-reactive mutagen and carcinogen, exhibited sublinear or thresholded dose-responses for LacZ mutation in transgenic Muta™Mouse and for micronucleus (MN) frequency in CD1 mice (Gocke E and Müller L [2009]: Mutat Res 678:101-107). In order to explore variables in establishing genotoxicity dose-responses, we characterized the genotoxicity of EMS using gene mutation assays anticipated to have lower spontaneous mutant frequencies (MFs) than Muta™Mouse. Male gpt-delta transgenic mice were treated daily for 28 days with 5 to 100 mg/kg EMS, and measurements were made on: (i) gpt MFs in liver, lung, bone marrow, kidney, small intestine, and spleen; and (ii) Pig-a MFs in peripheral blood reticulocytes (RETs) and total red blood cells. MN induction also was measured in peripheral blood RETs. These data were used to calculate Points of Departure (PoDs) for the dose responses, i.e., no-observed-genotoxic-effect-levels (NOGELs), lower confidence limits of threshold effect levels (Td-LCIs), and lower confidence limits of 10% benchmark response rates (BMDL10 s). Similar PoDs were calculated from the published EMS dose-responses for LacZ mutation and CD1 MN induction. Vehicle control gpt and Pig-a MFs were 13-40-fold lower than published vehicle control LacZ MFs. In general, the EMS genotoxicity dose-responses in gpt-delta mice had lower PoDs than those calculated from the Muta™Mouse and CD1 mouse data. Our results indicate that the magnitude and possibly the shape of mutagenicity dose responses differ between in vivo models, with lower PoDs generally detected by gene mutation assays with lower backgrounds.

Comparison of microarrays and RNA-seq for gene expression analyses of dose-response experiments.

Relative to microarrays, RNA-seq has been reported to offer higher precision estimates of transcript abundance, a greater dynamic range, and detection of novel transcripts. However, previous comparisons of the 2 technologies have not covered dose-response experiments that are relevant to toxicology. Male F344 rats were exposed for 13 weeks to 5 doses of bromobenzene, and liver gene expression was measured using both microarrays and RNA-seq. Multiple normalization methods were evaluated for each technology, and gene expression changes were statistically analyzed using both analysis of variance and benchmark dose (BMD). Fold-change values were highly correlated between the 2 technologies, whereas the -log p values showed lower correlation. RNA-seq detected fewer statistically significant genes at lower doses, but more significant genes based on fold change except when a negative binomial transformation was applied. Overlap in genes significant by both p value and fold change was approximately 30%-40%. Random sampling of the RNA-seq data showed an equivalent number of differentially expressed genes compared with microarrays at ~5 million reads. Quantitative RT-PCR of differentially expressed genes uniquely identified by each technology showed a high degree of confirmation when both fold change and p value were considered. The mean dose-response expression of each gene was highly correlated between technologies, whereas estimates of sample variability and gene-based BMD values showed lower correlation. Differences in BMD estimates and statistical significance may be due, in part, to differences in the dynamic range of each technology and the degree to which normalization corrects genes at either end of the scale.

Temporal concordance between apical and transcriptional points of departure for chemical risk assessment.

The number of legacy chemicals without toxicity reference values combined with the rate of new chemical development is overwhelming the capacity of the traditional risk assessment paradigm. More efficient approaches are needed to quantitatively estimate chemical risks. In this study, rats were dosed orally with multiple doses of six chemicals for 5 days and 2, 4, and 13 weeks. Target organs were analyzed for traditional histological and organ weight changes and transcriptional changes using microarrays. Histological and organ weight changes in this study and the tumor incidences in the original cancer bioassays were analyzed using benchmark dose (BMD) methods to identify noncancer and cancer points of departure. The dose-response changes in gene expression were also analyzed using BMD methods and the responses grouped based on signaling pathways. A comparison of transcriptional BMD values for the most sensitive pathway with BMD values for the noncancer and cancer apical endpoints showed a high degree of correlation at all time points. When the analysis included data from an earlier study with eight additional chemicals, transcriptional BMD values for the most sensitive pathway were significantly correlated with noncancer (r = 0.827, p = 0.0031) and cancer-related (r = 0.940, p = 0.0002) BMD values at 13 weeks. The average ratio of apical-to-transcriptional BMD values was less than two, suggesting that for the current chemicals, transcriptional perturbation did not occur at significantly lower doses than apical responses. Based on our results, we propose a practical framework for application of transcriptomic data to chemical risk assessment.

Integrating pathway-based transcriptomic data into quantitative chemical risk assessment: a five chemical case study.

The traditional approach for performing a chemical risk assessment is time and resource intensive leading to a limited number of published assessments on which to base human health decisions. In comparison, most contaminated sites contain chemicals without published reference values or cancer slope factors that are not considered quantitatively in the overall hazard index calculation. The integration of transcriptomic technology into the risk assessment process may provide an efficient means to evaluate quantitatively the health risks associated with data poor chemicals. In a previous study, female B6C3F1 mice were exposed to multiple concentrations of five chemicals that were positive for lung and/or liver tumor formation in a two-year rodent cancer bioassay. The mice were exposed for a period of 13 weeks and the target tissues were analyzed for traditional histological and organ weight changes and transcriptional changes using microarrays. In this study, the dose-response changes in gene expression were analyzed using a benchmark dose (BMD) approach and the responses grouped based on pathways. A comparison of the transcriptional BMD values with those for the traditional non-cancer and cancer apical endpoints showed a high degree of correlation for specific pathways. Many of the correlated pathways have been implicated in non-cancer and cancer disease pathogenesis. The results demonstrate that transcriptomic changes in pathways can be used to estimate non-cancer and cancer points-of-departure for use in quantitative risk assessments and have identified potential toxicity pathways involved in chemically induced mouse lung and liver responses.

Application of transcriptional benchmark dose values in quantitative cancer and noncancer risk assessment.

The traditional approach for estimating noncancer and cancer reference values in quantitative chemical risk assessment is time and resource intensive. The extent and nature of the studies required under the traditional approach has limited the number of chemicals with published risk assessments. In this study, female mice were exposed for 13 weeks to multiple concentrations of five chemicals that were positive in a 2-year cancer bioassay. Traditional histological and organ weight changes were evaluated, and gene expression microarray analysis was performed on the target tissues. The histological, organ weight changes, and the original tumor incidences in the original cancer bioassay were analyzed using standard benchmark dose (BMD) methods to identify noncancer and cancer points of departure, respectively. The dose-related changes in gene expression were also analyzed using a BMD approach and the responses grouped based on cellular biological processes. A comparison of the transcriptional BMD values with those for the traditional noncancer and cancer apical endpoints showed a high degree of correlation for specific cellular biological processes. For chemicals with human exposure data, the transcriptional BMD values were also used to calculate a margin of exposure. The margins of exposure ranged from 1900 to 54,000. Both the correlation between the BMD values for the transcriptional and apical endpoints and the margin of exposure analysis suggest that transcriptional BMD values may be used as potential points of departure for noncancer and cancer risk assessment.

Human chemosensory perception of methyl isothiocyanate: chemesthesis and odor.

An unpublished laboratory study by Russell and Rush (1996) showed that human subjects sense the presence of methyl isothiocyanate (MITC) via the eyes at concentrations as low as hundreds of ppb in air, with dependence upon duration of exposure. The longer the stimulation, the lower the concentrations sensed. Application of benchmark concentration (BMC10) modeling indicated a best estimate of 330 ppb by the end of 4h. With a confidence limit (BMCL) applied, the level dropped to 220 ppb, when employing a probit model. Receptors known as TRPA1 ion channels present in trigeminal and associated peripheral afferent nerves have shown particular sensitivity to isothiocyanates. Sensitivity to these electrophiles, which occur naturally in plants (e.g., capers and mustard greens), most likely derives from a mechanism of reversible covalent bonding. Such sensing can provide warning of potential damage rather than actual damage itself. Based upon its reputation as a lachrymator, Russell and Rush assumed that the eyes would sense MITC, before the upper airways, so gathered no data from the airways, except for odor. Field results from spills and results of acute exposures to animals covered in Dourson et al. (2010) add pertinent information on the matter.

Dose response assessment for effects of acute exposure to methyl isothiocyanate (MITC).

The preplant fumigants, metam-sodium, metam-potassium, and dazomet undergo decomposition to the biocide methyl isothiocyanate (MITC) in moist soils. Since MITC vapor can migrate from its site of application, we developed an estimate of health protective concentrations for airborne exposures to MITC that prevents effects among bystanders near treated agricultural fields. Our findings show that, at concentrations of environmental relevance, MITC most likely acts via stimulation of the trigeminal nerve, which mediates sensory irritation in the eyes and nose. Several lines of evidence support the conclusion that sensory irritation of the eyes is the most sensitive effect relevant for health risk assessment arising from short-term MITC exposures. The outcome of a clinical study that included sensitive individuals and measured multiple ocular responses to irritation (e.g., perceived irritation, tearing, and blinking of the eyes) is consistent with this proposed mode of action, as are experimental animal data. Databases and studies by the California Department of Pesticide Regulation (CDPR) show that, in accidental exposures, human eye irritation is consistently the most sensitive endpoint at low-modeled acute exposure and is often the most sensitive endpoint from acute exposures of unknown, but likely higher, concentrations. Based upon benchmark concentration lower limits from the clinical study and consideration of uncertainties, health protective concentrations of MITC were estimated as 0.2 ppm for 4h of exposure and 0.8 ppm for 14-min of exposure.