Scott M. Arnold

The use of biomonitoring data in exposure and human health risk assessment: benzene case study

Abstract A framework of “Common Criteria” (i.e. a series of questions) has been developed to inform the use and evaluation of biomonitoring data in the context of human exposure and risk assessment. The data-rich chemical benzene was selected for use in a case study to assess whether refinement of the Common Criteria framework was necessary, and to gain additional perspective on approaches for integrating biomonitoring data into a risk-based context. The available data for benzene satisfied most of the Common Criteria and allowed for a risk-based evaluation of the benzene biomonitoring data. In general, biomarker (blood benzene, urinary benzene and urinary S-phenylmercapturic acid) central tendency (i.e. mean, median and geometric mean) concentrations for non-smokers are at or below the predicted blood or urine concentrations that would correspond to exposure at the US Environmental Protection Agency reference concentration (30 µg/m3), but greater than blood or urine concentrations relating to the air concentration at the 1 × 10−5 excess cancer risk (2.9 µg/m3). Smokers clearly have higher levels of benzene exposure, and biomarker levels of benzene for non-smokers are generally consistent with ambient air monitoring results. While some biomarkers of benzene are specific indicators of exposure, the interpretation of benzene biomonitoring levels in a health-risk context are complicated by issues associated with short half-lives and gaps in knowledge regarding the relationship between the biomarkers and subsequent toxic effects.

Risk assessment for consumer exposure to toluene diisocyanate (TDI) derived from polyurethane flexible foam.

Polyurethanes (PU) are polymers made from diisocyanates and polyols for a variety of consumer products. It has been suggested that PU foam may contain trace amounts of residual toluene diisocyanate (TDI) monomers and present a health risk. To address this concern, the exposure scenario and health risks posed by sleeping on a PU foam mattress were evaluated. Toxicity benchmarks for key non-cancer endpoints (i.e., irritation, sensitization, respiratory tract effects) were determined by dividing points of departure by uncertainty factors. The cancer benchmark was derived using the USEPA Benchmark Dose Software. Results of previous migration and emission data of TDI from PU foam were combined with conservative exposure factors to calculate upper-bound dermal and inhalation exposures to TDI as well as a lifetime average daily dose to TDI from dermal exposure. For each non-cancer endpoint, the toxicity benchmark was divided by the calculated exposure to determine the margin of safety (MOS), which ranged from 200 (respiratory tract) to 3×10(6) (irritation). Although available data indicate TDI is not carcinogenic, a theoretical excess cancer risk (1×10(-7)) was calculated. We conclude from this assessment that sleeping on a PU foam mattress does not pose TDI-related health risks to consumers.

A reanalysis of the evidence for increased efficiency in benzene metabolism at airborne exposure levels below 3 p.p.m.

An analysis of monitoring data on workers in Tianjin, China, reported a 9-fold increase in the production of benzene metabolites per unit exposure as air concentrations declined from 88.9 to 0.03 p.p.m. The increase is attributed to an enhanced efficiency of benzene metabolism at lower air concentrations. This finding, however, is not consistent with other studies demonstrating that adsorbed benzene is almost completely metabolized at airborne levels ranging from <1 to 70 p.p.m. In this article (i) the modeling performed in Kim et al. is repeated and the model predictions are reproduced; (ii) the impacts of technical issues in the corrections for background levels of metabolites, accounting for biases in the regression modeling, and the uncertainties introduced by the use of a calibration model to estimate benzene air levels for certain workers are evaluated and (iii) alternative methods of correcting for background levels of metabolites are examined. The new analysis indicates that findings of increased production are probably smaller and are highly uncertain, 4.8 fold [0.1-18] (mean and [95% confidence limits]). Defining background levels as either the levels in all workers with no occupational exposures or in workers with predicted air levels of <0.03 p.p.m. results in estimates of 2.4 fold [<0.1-15] and 3.3 fold [<0.1-19] increases, respectively. Based on this reanalysis, the Tianjin data appear to be too uncertain to support any conclusions of a change in the efficiency of benzene metabolism with variations in exposure.

Development of PK- and PBPK-based modeling tools for derivation of biomonitoring guidance values

There are numerous programs ongoing to analyze environmental exposure of humans to xenobiotic chemicals via biomonitoring measurements (e.g.: EU ESBIO, COPHES; US CDC NHANES; Canadian Health Measures Survey). The goal of these projects is to determine relative trends in exposure to chemicals, across time and subpopulations. Due to the lack of data, there is often little information correlating biomarker concentrations with exposure levels and durations. As a result, it can be difficult to utilize biomonitoring data to evaluate if exposures adhere to or exceed hazard/exposure criteria such as the Derived No-Effect Level values under the EU REACH program, or Reference Dose/Concentration values of the US EPA. A tiered approach of simple, arithmetic pharmacokinetic (PK) models, as well as more standardized mean-value, physiologically-based (PBPK) models, have therefore been developed to estimate exposures from biomonitoring results. Both model types utilize a user-friendly Excel spreadsheet interface. QSPR estimations of chemical-specific parameters have been included, as well as accommodation of variations in urine production. Validation of each models structure by simulations of published datasets and the impact of assumptions of major model parameters will be presented.

Derivation of human Biomonitoring Guidance Values for chlorpyrifos using a physiologically based pharmacokinetic and pharmacodynamic model of cholinesterase inhibition

A number of biomonitoring surveys have been performed for chlorpyrifos (CPF) and its metabolite (3,5,6-trichloro-2-pyridinol, TCPy); however, there is no available guidance on how to interpret these data in a health risk assessment context. To address this gap, Biomonitoring Guidance Values (BGVs) are developed using a physiologically based pharmacokinetic and pharmacodynamic (PBPK/PD) model. The PBPK/PD model is used to predict the impact of age and human variability on the relationship between an early marker of cholinesterase (ChE) inhibition in the peripheral and central nervous systems [10% red blood cell (RBC) ChE inhibition] and levels of systemic biomarkers. Since the PBPK/PD model characterizes variation of sensitivity to CPF in humans, interspecies and intraspecies uncertainty factors are not needed. Derived BGVs represent the concentration of blood CPF and urinary TCPy associated with 95% of the population having less than or equal to 10% RBC ChE inhibition. Blood BGV values for CPF in adults and infants are 6100 ng/L and 4200 ng/L, respectively. Urinary TCPy BGVs for adults and infants are 2100 μg/L and 520 μg/L, respectively. The reported biomonitoring data are more than 150-fold lower than the BGVs suggesting that current US population exposures to CPF are well below levels associated with any adverse health effect.

Evaluation of Potential Exposure to Formaldehyde Air Emissions from a Washing Machine Using the IAQX Model

ABSTRACT Consumers may be exposed to formaldehyde during the use of liquid laundry detergent containing a preservative. The primary objective of this analysis was to present an approach to predict formaldehyde air emissions from a washing machine and the subsequent vapor concentrations in the laundry room air using the U.S. Environmental Protection Agencys (USEPAs) Simulation Tool Kit for Indoor Air Quality and Inhalation Exposure, referred to as the IAQX model. A second objective was to identify key model input parameters for formaldehyde. This analysis recommends use of the IAQX emission Model 52 because it provided the best estimates by correlating the formaldehyde evaporation to the Henrys law constant and to the overall gas-phase mass transfer coefficient that was based on washing machine experimental results. The mass balance estimated that 99.7% of the initial formaldehyde mass in the washing machine was discharged down the drain with the wash water and the rest of the formaldehyde was emitted to the air from the top loading washing machine and the hot air clothes dryer. The predicted formaldehyde exposures were acceptable and much lower than the USEPA proposed targets for non-cancer effects and cancer risk.

Quantitation of 4,4′-methylene diphenyl diisocyanate human serum albumin adducts

4,4′-Methylene diphenyl diisocyanate (herein 4,4′-MDI) is used in the production of polyurethane foams, elastomers, coatings, adhesives and the like for a wide range of commercial products. Occupational exposure to MDI levels above current airborne exposure limits can elicit immune mediated hypersensitivity reactions such as occupational asthma in sensitive individuals. To accurately determine exposure, there has been increasing interest in developing analytical methods to measure internal biomarkers of exposure to MDI. Previous investigators have reported methodologies for measuring MDI diamine metabolites and MDI-Lysine (4,4′-MDI-Lys) adducts. The purpose of this study was to develop and validate an ultra performance liquid chromatography isotope dilution tandem mass spectrometry (UPLC-ID/MS/MS) quantitation method via a signature peptide approach to enable biomonitoring of 4,4′-MDI adducted to human serum albumin (HSA) in plasma. A murine, anti-4,4′-MDI monoclonal IgM antibody was bound to magnetic beads and utilized for enrichment of the MDI adducted HSA. Following enrichment, trypsin digestion was performed to generate the expected 414 site (primary site of adduction) 4,4′-MDI-adducted HSA signature peptide that was quantified by UPLC-ID/MS/MS. An Agilent 6530 UPLC/quadrupole time of flight MS (QTOF) system was utilized for intact adducted protein analysis and an Agilent 6490 UPLC/MS/MS system operated in multiple reaction monitoring (MRM) mode was utilized for quantification of the adducted signature peptide biomarker both for in chemico and worker serum samples. Worker serum samples were initially screened utilizing the previously developed 4,4′-MDI-Lys amino acid method and results showed that 12 samples were identified as quantifiable for 4,4′-MDI-Lys adducts. The signature peptide adduct approach was applied to the 12 worker samples identified as quantifiable for 4,4′-MDI-Lys adducts. Results indicated no positive results were obtained above the quantification limit by the signature peptide approach. If the 414 site of lysine adduction accounted for 100% of the 4,4′-MDI adductions in the signature peptide adduct approach, the three highest quantifiable samples by the 4,4′-MDI-Lys method should have at least been detectable by the signature peptide method. Results show that although the 4,4′-MDI signature peptide approach is more selective, it is 18 times less sensitive than the 4,4′-MDI-Lys method, thus limiting the ability to detect adduct levels relative to the 4,4′-MDI-Lys amino acid method.

Letter to the editor in response to 'Low-dose metabolism of benzene in humans: science and obfuscation' rappaport et al. (2013)

In the paper ‘Low-dose metabolism of benzene in humans: science and obfuscation’ (1), Dr Rappaport and his coauthors vigorously defend the findings of the 2006 publications (2,3) against the points raised in our 2012 publication ‘A reanalysis of the evidence for increased efficiency in benzene metabolism at airborne exposure levels below 3 p.p.m.’ (4). The work leading to Price et al. was driven by the contradiction of the Kim et al. findings of 9to 11-fold enhancement of metabolism of benzene at low doses and multiple published reports of lung clearance (5–7) that suggest only a 2-fold change in metabolism with dose. As shown below, this contradiction is confirmed by the information presented in Rappaport et al. We support the right of Dr Rappaport and his coauthors to question the technical merits of our work or any work in the public domain and to defend Kim et al. (2,3), but we strongly object to the authors’ wording in the title and in the paper’s concluding paragraph. Although the analyses in the Price et al. paper may have been complex, they were clearly presented and subjected to the peer review process. Furthermore, we published the raw data from the study so that experts could replicate the analyses and come to their own conclusions, which is something Kim et al. (2,3) did not do. We included factors in our reanalysis that favor a finding of increased metabolism at low doses as well as others that do not. Thus, we clarified and made transparent legitimate technical issues with the analyses of Kim et al. (2,3) and did not ‘obfuscate’ the original findings. It is unfortunate that Rappaport et al. attempted to disparage our analyses with allegations regarding both our motivations and our professional ethics in expressing our concerns with the Kim et al. data sets and analyses. After reviewing Rappaport et al., we still contend that (i) methodological errors were made in Kim et al. (2,3), (ii) the uncertainties in the predictions of the dose-specific metabolites are too large to determine changes in benzene metabolism at air levels below 0.2 p.p.m. and (iii) biases related to the definition of background are the likely cause of the finding of enhanced metabolism at low doses. As the following text demonstrates, Rappaport et al. have not refuted the first two findings and have confirmed the third finding.

The Use of Henry's Law to Estimate Formaldehyde Inhalation Exposure Concentrations

ABSTRACT This article derives a recommended range of partial vapor pressures of formaldehyde (FA) above dilute solutions (0.01% to 0.1%) that are useful for determining inhalation exposure concentrations for risk assessment evaluation of professional and consumer products containing FA and FA-releasing biocides. Independently derived experimental datasets of the relationship between dilute aqueous FA concentrations and vapor phase concentrations were evaluated. These datasets were consistent with each other and Henrys Law principles. A range of partial vapor pressures, 0.00077 mmHg to 0.0074 mmHg, corresponding to solution concentrations ranging from 0.01% to 0.10% for application temperatures of 25°C and 0.0016 mmHg to 0.016 mmHg for 35°C is proposed. This approach is applicable to a wide range of aqueous-based consumer and professional applications.